Last updated on 2026-03-05 01:50:16 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.4.0 | 8.21 | 340.47 | 348.68 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.4.0 | 5.41 | 209.45 | 214.86 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.4.0 | 19.00 | 558.48 | 577.48 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.4.0 | 16.00 | 546.98 | 562.98 | ERROR | |
| r-devel-macos-arm64 | 0.4.0 | 2.00 | 80.00 | 82.00 | OK | |
| r-devel-windows-x86_64 | 0.4.0 | 14.00 | 344.00 | 358.00 | OK | |
| r-patched-linux-x86_64 | 0.4.0 | 11.46 | 323.83 | 335.29 | OK | |
| r-release-linux-x86_64 | 0.4.0 | 8.40 | 322.88 | 331.28 | OK | |
| r-release-macos-arm64 | 0.4.0 | 2.00 | 80.00 | 82.00 | OK | |
| r-release-macos-x86_64 | 0.4.0 | 7.00 | 506.00 | 513.00 | OK | |
| r-release-windows-x86_64 | 0.4.0 | 13.00 | 348.00 | 361.00 | OK | |
| r-oldrel-macos-arm64 | 0.4.0 | 2.00 | 82.00 | 84.00 | NOTE | |
| r-oldrel-macos-x86_64 | 0.4.0 | 7.00 | 423.00 | 430.00 | NOTE | |
| r-oldrel-windows-x86_64 | 0.4.0 | 17.00 | 430.00 | 447.00 | NOTE |
Version: 0.4.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [69s/91s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(evanverse)
>
> test_check("evanverse")
x RDS file not found: '/tmp/RtmppsA49q/file290ee974c3a74a.rds'
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'demo_palette' (Type: qualitative, 2 colors)
v Saved RDS: /tmp/RtmppsA49q/file290ee9405aa141.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 1
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Failed to parse JSON: /tmp/RtmppsA49q/palettes_290ee9222829c8/qualitative/invalid.json
v Saved RDS: /tmp/RtmppsA49q/file290ee965fc2d8e.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Missing fields (colors) in: /tmp/RtmppsA49q/palettes_290ee9a7bdf0e/qualitative/incomplete.json
v Saved RDS: /tmp/RtmppsA49q/file290ee94046de5f.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'seq_pal' (Type: sequential, 2 colors)
v Added 'div_pal' (Type: diverging, 2 colors)
v Saved RDS: /tmp/RtmppsA49q/file290ee914477ac1.rds
-- Compilation Summary --
i Sequential: 1
i Diverging: 1
i Qualitative: 0
v All palettes compiled successfully!
i Converting symbols to UPPERCASE (human standard)
i Creating standardized column: symbol_upper
i Creating standardized column: symbol_lower
i Converting symbols to UPPERCASE (human standard)
i Converting symbols to UPPERCASE (human standard)
i Directory created: /tmp/RtmppsA49q/palette_test_290ee929eaa06a/qualitative
v Palette saved: /tmp/RtmppsA49q/palette_test_290ee929eaa06a/qualitative/testset.json
i Directory created: /tmp/RtmppsA49q/reuse_test_290ee919efab1f/diverging
v Palette saved: /tmp/RtmppsA49q/reuse_test_290ee919efab1f/diverging/reused.json
! Palette already exists: /tmp/RtmppsA49q/reuse_test_290ee919efab1f/diverging/reused.json
i Directory created: /tmp/RtmppsA49q/log_test_290ee917968f53/qualitative
v Palette saved: /tmp/RtmppsA49q/log_test_290ee917968f53/qualitative/logtest.json
Saving _problems/test-download_gene_ref-29.R
Saving _problems/test-download_geo_data-30.R
Saving _problems/test-download_geo_data-94.R
Saving _problems/test-download_geo_data-131.R
Saving _problems/test-download_geo_data-162.R
Saving _problems/test-download_geo_data-193.R
Saving _problems/test-download_geo_data-220.R
Saving _problems/test-download_geo_data-263.R
-- Directory Tree: /tmp/RtmppsA49q/file290ee96c303cea --------------------------
+-- test.txt
-- Directory Tree: /tmp/RtmppsA49q/file290ee96f4cd831 --------------------------
+-- file1.txt
+-- subdir
+-- file2.txt
-- Directory Tree: /tmp/RtmppsA49q/file290ee955ac413c --------------------------
+-- level1
-- Directory Tree: /tmp/RtmppsA49q/file290ee955ac413c --------------------------
+-- level1
+-- level2
+-- deep_file.txt
-- Directory Tree: /tmp/RtmppsA49q/file290ee92d745222 --------------------------
+-- example.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee92d745222/logs/tree/test.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee96c4c3560 --------------------------
+-- sample.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee96c4c3560/logs/content_test.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee94afe75 ----------------------------
+-- test.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee94afe75/logs/file_tree_20260304_155212.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee91a18ad47 --------------------------
+-- file1.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee9fe70522/overwrite_test.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee91a18ad47 --------------------------
+-- file1.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee9fe70522/overwrite_test.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee942052b3e --------------------------
+-- file1.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee958bbb64a/append_test.log
-- Directory Tree: /tmp/RtmppsA49q/file290ee942052b3e --------------------------
+-- file1.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee958bbb64a/append_test.log
x Directory does not exist: nonexistent/directory/path
-- Directory Tree: /tmp/RtmppsA49q/file290ee97622fec ---------------------------
-- Directory Tree: /tmp/RtmppsA49q/file290ee917c2e4b2 --------------------------
+-- test.txt
v File tree log saved to: /tmp/RtmppsA49q/file290ee917c2e4b2/new/log/path/test.log
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
Saving _problems/test-gmt2df-15.R
Saving _problems/test-gmt2df-29.R
Saving _problems/test-gmt2df-89.R
Saving _problems/test-gmt2list-15.R
Saving _problems/test-gmt2list-27.R
Saving _problems/test-gmt2list-88.R
Saving _problems/test-gmt2list-110.R
i #FF8000 -> RGB: c(255, 128, 0)
i #00FF00 -> RGB: c(0, 255, 0)
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'group': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Class mismatch: integer vs numeric
x Values differ at 1 position(s), e.g., index 3: 3 vs 4
-- Objects are NOT identical ---------------------------------------------------
x Values differ at 1 position(s), e.g., index 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Values differ at 1 cell(s), e.g., [2,2]: 4 vs 5
-- Objects are NOT identical ---------------------------------------------------
x Values differ in column 'y' at 1 row(s), e.g., row 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Names differ: a, b vs b, a
x Values differ at 2 position(s), e.g., index 1: 1 vs 2
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x, y vs y, x
-- Objects are NOT identical ---------------------------------------------------
x Dimnames differ: (r1|r2; c1|c2) vs (r1|r2; cX|c2)
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 0x0 vs 1x0
-- Objects are NOT identical ---------------------------------------------------
x Length mismatch: 2 vs 3
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 2x2 vs 1x4
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x vs y
x Unsupported type for 'a': list
x Unsupported type for 'b': function
! Large n (25) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
! Large n (171) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.westlake.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.westlake.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
-- Configuring bioc mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
-- Configuring all mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://cloud.r-project.org>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://bioconductor.org>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
v Installed: stats
v Installed: cli
v Installed: ggplot2
v Installed: stats
v Installed: utils
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 0
! No exported names matched keyword: "zzzzzz"
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i a had 26 duplicates, now de-duplicated.
i b had 22 duplicates, now de-duplicated.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick ANOVA Results --
i Method: anova
v Significant group differences (p < 0.001)
-- Descriptive Statistics
-- Post-hoc Summary (tukey)
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick ANOVA Summary --
-- Omnibus Test
i Effect sizes: eta_squared 0.448, omega_squared 0.430
-- Descriptive Statistics
-- Normality Checks (Shapiro-Wilk)
A: n = 25, p = 0.5812
B: n = 25, p = 0.8166
C: n = 25, p = 0.9928
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality (Levene)
Levene's test: p = 0.9887
Equal variance: TRUE
-- Post-hoc Comparisons (tukey)
Analysis performed: 2026-03-04 15:52:39
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
A: n = 25, p = 0.581
B: n = 25, p = 0.817
C: n = 25, p = 0.993
v Variances appear equal (Levene's test, p = 0.989)
-- Omnibus Test --
v Completed classical one-way ANOVA (p = 0.0000)
i Applied Tukey HSD post-hoc comparisons.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
i var1 converted to factor with 1 level.
i var2 converted to factor with 2 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Removed 10 rows with missing values.
i var1 converted to factor with 3 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Pearson residuals not available. Using grouped bar chart.
-- Data Preparation --
i Automatically selected 6 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
i Automatically selected 5 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
-- Data Preparation --
i Automatically selected 1 numeric column.
-- Data Preparation --
-- Data Preparation --
-- Data Preparation --
i Automatically selected 3 numeric columns.
-- Computing Correlations --
i Found 0 significant correlations out of 3 tests
-- Creating Heatmap --
v Analysis complete!
-- Quick Correlation Analysis Results --
i Method: pearson
i Variables: 5
i Significant pairs: 1
-- Top 5 Significant Correlations
Use `summary()` for detailed results.
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: none
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on unadjusted p-values
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-12
Analysis performed: 2026-03-04 15:52:59
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: bonferroni
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on adjusted p-values (method: bonferroni)
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-11
Analysis performed: 2026-03-04 15:52:59
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick t-test Results --
i Method: t.test
v Significant difference (p < 0.001)
-- Descriptive Statistics
Use `summary()` for detailed results.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick t-test Summary --
-- Test Method
Method used: t.test
Paired: FALSE
Alternative: two.sided
Equal variance: TRUE
-- Test Result
-- Descriptive Statistics
-- Normality Tests (Shapiro-Wilk)
Control: n = 25, p = 0.5812
Treatment: n = 25, p = 0.8166
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality Test (Levene)
Levene's test: p = 0.9863
Equal variances: TRUE
Analysis performed: 2026-03-04 15:53:10
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
Control: n = 25, p = 0.581
Treatment: n = 25, p = 0.817
v Variances appear equal (Levene's test, p = 0.986)
v Using Student's t-test (equal variances assumed)
-- Statistical Test --
v Significant difference detected (p < 0.001)
-- Creating Visualization --
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Analysis complete!
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Reading Excel file --
i Sheets in '/tmp/RtmppsA49q/file290ee9257100da.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmppsA49q/file290ee9257100da.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmppsA49q/file290ee944f3bd42.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmppsA49q/file290ee944f3bd42.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmppsA49q/file290ee93572db93.xlsx': Sheet1
v Successfully read sheet 1 from '/tmp/RtmppsA49q/file290ee93572db93.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmppsA49q/file290ee92eefc3b2.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmppsA49q/file290ee92eefc3b2.xlsx'.
Path: '/tmp/RtmppsA49q/file290ee93b8843db.csv'
Separator: "," | Encoding: "UTF-8"
OpenMP version (_OPENMP) 201511
omp_get_num_procs() 64
R_DATATABLE_NUM_PROCS_PERCENT unset (default 50)
R_DATATABLE_NUM_THREADS unset
R_DATATABLE_THROTTLE unset (default 1024)
omp_get_thread_limit() 3
omp_get_max_threads() 3
OMP_THREAD_LIMIT 3
OMP_NUM_THREADS 3
RestoreAfterFork true
data.table is using 3 threads with throttle==1024. See ?setDTthreads.
freadR.c has been passed a filename: /tmp/RtmppsA49q/file290ee93b8843db.csv
[01] Check arguments
Using 3 threads (omp_get_max_threads()=3, nth=3)
NAstrings = [<<NA>>]
None of the NAstrings look like numbers.
show progress = 1
0/1 column will be read as integer
Y/N column will be read as character
[02] Opening the file
Opening file /tmp/RtmppsA49q/file290ee93b8843db.csv
File opened, size = 235 bytes.
Memory mapped ok
[03] Detect and skip BOM
[04] Arrange mmap to be \0 terminated
\n has been found in the input (counts: 0 \r by themselves vs 6 [\r]*\n) and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
[05] Skipping initial rows if needed
Positioned on line 1 starting: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
[06] Detect separator, quoting rule, and ncolumns
Using supplied sep ','
sep=',' with 6 lines of 11 fields using quote rule 0
Detected 11 columns on line 1. This line is either column names or first data row. Line starts as: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
Quote rule picked = 0
fill=false and the most number of columns found is 11
[07] Detect column types, dec, good nrow estimate and whether first row is column names
'header' changed by user from 'auto' to true
sep=',' so dec set to '.'
Number of sampling jump points = 1 because (234 bytes from row 1 to eof) / (2 * 234 jump0size) == 0
Type codes (jump 000) : 97779997777 Quote rule 0
All rows were sampled since file is small so we know nrow=5 exactly
[08] Assign column names
[09] Apply user overrides on column types
After 0 type and 0 drop user overrides : 97779997777
[10] Allocate memory for the datatable
Allocating 11 column slots (11 - 0 dropped) with 5 rows
[11] Read the data
jumps=[0..1), chunk_size=1048576, total_size=189
Read 5 rows x 11 columns from 235 bytes file in 00:00.000 wall clock time
[12] Finalizing the datatable
Type counts:
7 : int32 '7'
4 : float64 '9'
=============================
0.000s ( 28%) Memory map 0.000GiB file
0.000s ( 41%) sep=',' ncol=11 and header detection
0.000s ( 8%) Column type detection using 5 sample rows
0.000s ( 12%) Allocation of 5 rows x 11 cols (0.000GiB) of which 5 (100%) rows used
0.000s ( 11%) Reading 1 chunks (0 swept) of 1.000MiB (each chunk 5 rows) using 1 threads
+ 0.000s ( 3%) Parse to row-major thread buffers (grown 0 times)
+ 0.000s ( 1%) Transpose
+ 0.000s ( 8%) Waiting
0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions
0.000s Total
v File loaded successfully (5 rows x 11 cols)
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- Usage Examples --------------------------------------------------------------
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- droplevels
`droplevels(df)` removes unused factor levels from a data frame or factor.
-- modifyList
`modifyList(x, y)` merges two lists; elements in `y` overwrite those in `x`.
-- do.call
`do.call(fun, args)` calls a function with arguments in a list: `do.call(plot, list(x = 1:10))`.
-- sprintf
`sprintf("Hello, %s!", name)` formats strings with `%s`, `%d`, etc.
-- scRNAseq
`scRNAseq` (Bioconductor) provides scRNA-seq datasets, e.g., `ZeiselBrainData()`.
-- basename
`basename(path)` extracts the filename from a full path. See also `dirname()`.
-- here
`here::here("data", "raw", "sample1.rds")` builds a path from project root.
-- stopifnot
`stopifnot(cond1, cond2, ...)` throws if any condition is FALSE.
-- object.size
`object.size(x)` estimates memory size; use `format()` to pretty-print.
-- slice
`slice(df, 1:3)` selects rows by position; see `slice_head()`, `slice_tail()`, `slice_max()`.
-- unzip
`unzip("file.zip", exdir = "dir")` extracts ZIP archives.
-- gunzip
`R.utils::gunzip("file.csv.gz", remove = FALSE)` decompresses .gz files.
-- untar
`untar("file.tar.gz", exdir = "dir")` extracts .tar or .tar.gz archives.
-- NoLegend
`NoLegend()` removes legends from ggplot2/Seurat plots.
-- RotatedAxis
`RotatedAxis()` rotates x-axis text for readability in dot plots.
-- guides
`guides(fill = "none")` customizes or removes legends (with `scale_*`).
-- log2
`log2(x)` base-2 logarithm (often for fold change).
-- log
`log(x, base = exp(1))` natural log by default; set `base = 10` or `2` for others.
-- log10
`log10(x)` base-10 logarithm (orders of magnitude).
-- round
`round(x, digits = 0)` rounds; use `signif()` for significant digits.
-- floor
`floor(x)` greatest integer <= x (e.g., `floor(2.8)` -> 2).
-- ceiling
`ceiling(x)` smallest integer >= x (e.g., `ceiling(2.1)` -> 3).
-- cut
`cut(x, breaks)` bins numeric vector; `breaks = 3` or custom; `labels = FALSE` for group indices.
-- cumsum
`cumsum(x)` cumulative sum.
-- cumprod
`cumprod(x)` cumulative product.
-- cummin
`cummin(x)` running minimum.
-- cummax
`cummax(x)` running maximum.
-- row_number
`row_number(x)` order rank (ties broken arbitrarily).
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
-- cume_dist
`cume_dist(x)` cumulative proportion of values <= x.
-- str_view
`stringr::str_view(string, pattern)` highlights regex matches; `str_view_all()` for all.
-- str_c
`stringr::str_c(...)` concatenates; use `sep`/`collapse` as needed.
-- str_glue
`glue::glue("Hello, {name}!")` inline expressions with `{}`.
-- str_flatten
`stringr::str_flatten(x, collapse = ", ")` join a character vector.
-- str_length
`stringr::str_length(x)` string lengths.
-- str_sub
`stringr::str_sub(x, start, end)` extract/replace substrings (supports negative indices).
-- today
`lubridate::today()` current Date (no time).
-- now
`lubridate::now()` current POSIXct date-time.
-- Sys.timezone
`Sys.timezone()` system time zone name.
-- skimr
`skimr::skim(df)` compact, readable data summaries.
-- par
`par(mfrow = c(m, n))` split plotting area (e.g., 2x2).
-- layout
`layout(matrix, widths, heights)` flexible plot arrangement.
-- datatable
`DT::datatable(data)` interactive table (search/filter/sort/paginate).
-- windowsFonts
`windowsFonts()` register system fonts (Windows).
-- sign
`sign(x)` returns -1/0/1 for negative/zero/positive.
-- reactable
`reactable::reactable(data)` modern interactive table.
-- trimws
`trimws(x)` removes leading and trailing whitespace.
-- cranlogs
`cranlogs::cran_downloads('pkgname', from = 'last-month')` gets CRAN download stats; use `'last-week'`, `'last-day'`, or specific dates.
-- dlstats
`dlstats::cran_stats('pkgname')` shows CRAN download trends with plots; supports Bioconductor via `source = 'bioc'`.
-- Available Keywords ----------------------------------------------------------
`glimpse, read_excel, droplevels, modifyList, do.call, sprintf, scRNAseq,
basename, here, stopifnot, object.size, slice, unzip, gunzip, untar, NoLegend,
RotatedAxis, guides, log2, log, log10, round, floor, ceiling, cut, cumsum,
cumprod, cummin, cummax, row_number, min_rank, dense_rank, percent_rank,
cume_dist, str_view, str_c, str_glue, str_flatten, str_length, str_sub, today,
now, Sys.timezone, skimr, par, layout, datatable, windowsFonts, sign,
reactable, trimws, cranlogs, dlstats`
x No match found for keyword: "notakeyword"
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
v Palette removed from qualitative: '/tmp/RtmppsA49q/palettes_test_290ee94f649ef4/qualitative/test_palette.json'
! Palette not found in any type: nonexistent
v Palette removed from diverging: '/tmp/RtmppsA49q/palettes_test_290ee957e0fb67/diverging/test_palette.json'
v Palette removed from qualitative: '/tmp/RtmppsA49q/custom_palettes_290ee911ac39a5/qualitative/custom_test.json'
v RGB: c(255, 128, 0) -> HEX: #FF8000
v RGB: c(0, 0, 0) -> HEX: #000000
v RGB: c(255, 255, 255) -> HEX: #FFFFFF
v Converted 3 RGB values to HEX.
i RGB: c(255, 128, 0) -> HEX: #FF8000
i RGB: c(0, 255, 0) -> HEX: #00FF00
i RGB: c(0, 0, 255) -> HEX: #0000FF
v RGB: c(0, 1, 255) -> HEX: #0001FF
x An error occurred: non-numeric argument to mathematical function
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
i Square test started at 2026-03-04 15:53:29
v Square test completed in 0.000 seconds
i Silent task started at 2026-03-04 15:53:29
v Silent task completed in 0.000 seconds
v Excel file written to '/tmp/RtmppsA49q/file290ee959812685.xlsx'
v Excel file written to '/tmp/RtmppsA49q/file290ee9196c3dec.xlsx'
v Excel file written to '/tmp/RtmppsA49q/file290ee910a8dbdd.xlsx'
! File already exists and will be overwritten: '/tmp/RtmppsA49q/file290ee910a8dbdd.xlsx'
v Excel file written to '/tmp/RtmppsA49q/file290ee910a8dbdd.xlsx'
v Excel file written to '/tmp/RtmppsA49q/test-write-290ee97acc9c30_2026-03-04.xlsx'
[ FAIL 17 | WARN 0 | SKIP 70 | PASS 2065 ]
══ Skipped tests (70) ══════════════════════════════════════════════════════════
• GSEABase not available (2): 'test-gmt2df.R:95:3', 'test-gmt2list.R:94:3'
• On CRAN (56): 'test-download_batch.R:19:3', 'test-download_batch.R:39:3',
'test-download_url.R:206:3', 'test-download_url.R:238:3',
'test-download_url.R:266:3', 'test-download_url.R:296:3',
'test-download_url.R:319:3', 'test-download_url.R:342:3',
'test-download_url.R:365:3', 'test-file_info.R:12:3',
'test-file_info.R:26:3', 'test-file_info.R:48:3', 'test-file_info.R:59:3',
'test-file_info.R:71:3', 'test-pkg.R:389:3', 'test-pkg.R:528:3',
'test-pkg.R:536:3', 'test-pkg.R:546:3', 'test-pkg.R:561:3',
'test-plot_forest.R:61:3', 'test-plot_forest.R:77:3',
'test-plot_forest.R:96:3', 'test-plot_forest.R:113:3',
'test-plot_forest.R:134:3', 'test-plot_forest.R:149:3',
'test-plot_forest.R:164:3', 'test-plot_forest.R:183:3',
'test-plot_forest.R:200:3', 'test-plot_forest.R:217:3',
'test-plot_forest.R:235:3', 'test-plot_forest.R:253:3',
'test-plot_forest.R:275:3', 'test-plot_forest.R:292:3',
'test-plot_forest.R:310:3', 'test-plot_forest.R:327:3',
'test-plot_forest.R:350:3', 'test-plot_forest.R:368:3',
'test-plot_forest.R:389:3', 'test-plot_forest.R:410:3',
'test-plot_forest.R:427:3', 'test-plot_forest.R:444:3',
'test-plot_forest.R:475:3', 'test-plot_forest.R:493:3',
'test-plot_forest.R:515:3', 'test-plot_forest.R:532:3',
'test-plot_forest.R:553:3', 'test-plot_forest.R:570:3',
'test-plot_forest.R:594:3', 'test-plot_forest.R:611:3',
'test-plot_forest.R:632:3', 'test-plot_forest.R:662:3',
'test-plot_forest.R:696:3', 'test-plot_forest.R:730:3',
'test-plot_forest.R:756:3', 'test-plot_forest.R:772:3',
'test-plot_forest.R:789:3'
• {biomaRt} is not installed (12): 'test-download_gene_ref.R:65:3',
'test-download_gene_ref.R:108:3', 'test-download_gene_ref.R:149:3',
'test-download_gene_ref.R:173:3', 'test-download_gene_ref.R:197:3',
'test-download_gene_ref.R:217:3', 'test-download_gene_ref.R:236:3',
'test-download_gene_ref.R:263:3', 'test-download_gene_ref.R:291:3',
'test-download_gene_ref.R:319:3', 'test-download_gene_ref.R:338:3',
'test-download_gene_ref.R:358:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-download_gene_ref.R:26:3'): download_gene_ref() validates species parameter ──
Error in `download_gene_ref(species = "invalid")`: Please install 'biomaRt' via BiocManager::install('biomaRt').
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_gene_ref.R:26:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_gene_ref(species = "invalid")
8. └─cli::cli_abort("Please install 'biomaRt' via BiocManager::install('biomaRt').")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:27:3'): download_geo_data() validates gse_id parameter ──
Error in `download_geo_data("invalid", dest_dir = temp_dir)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:27:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("invalid", dest_dir = temp_dir)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:91:3'): download_geo_data() validates dest_dir parameter ──
Error in `download_geo_data("GSE12345", dest_dir = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:91:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:128:3'): download_geo_data() validates overwrite parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:128:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:159:3'): download_geo_data() validates log parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:159:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:190:3'): download_geo_data() validates log_file parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:190:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:217:3'): download_geo_data() validates retries parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:217:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:260:3'): download_geo_data() validates timeout parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:260:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-gmt2df.R:15:3'): gmt2df() parses valid GMT file correctly ──────
Error in `gmt2df(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2df().
Backtrace:
▆
1. └─evanverse::gmt2df(gmt_file, verbose = FALSE) at test-gmt2df.R:15:3
2. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2df().")
3. └─rlang::abort(...)
── Error ('test-gmt2df.R:29:3'): gmt2df() returns correct data structure ───────
Error in `gmt2df(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2df().
Backtrace:
▆
1. └─evanverse::gmt2df(gmt_file, verbose = FALSE) at test-gmt2df.R:29:3
2. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2df().")
3. └─rlang::abort(...)
── Error ('test-gmt2df.R:48:3'): gmt2df() handles verbose parameter correctly ──
Error in `gmt2df(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2df().
Backtrace:
▆
1. ├─testthat::expect_silent(gmt2df(gmt_file, verbose = FALSE)) at test-gmt2df.R:48:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. └─evanverse::gmt2df(gmt_file, verbose = FALSE)
10. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2df().")
11. └─rlang::abort(...)
── Error ('test-gmt2df.R:89:3'): gmt2df() handles invalid GMT file format ──────
Error in `gmt2df(temp_file)`: Package GSEABase required. Please install it to use gmt2df().
Backtrace:
▆
1. ├─testthat::expect_error(gmt2df(temp_file), "Failed to parse GMT file") at test-gmt2df.R:89:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::gmt2df(temp_file)
8. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2df().")
9. └─rlang::abort(...)
── Error ('test-gmt2list.R:15:3'): gmt2list() parses valid GMT file correctly ──
Error in `gmt2list(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2list().
Backtrace:
▆
1. └─evanverse::gmt2list(gmt_file, verbose = FALSE) at test-gmt2list.R:15:3
2. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2list().")
3. └─rlang::abort(...)
── Error ('test-gmt2list.R:27:3'): gmt2list() returns correct data structure ───
Error in `gmt2list(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2list().
Backtrace:
▆
1. └─evanverse::gmt2list(gmt_file, verbose = FALSE) at test-gmt2list.R:27:3
2. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2list().")
3. └─rlang::abort(...)
── Error ('test-gmt2list.R:47:3'): gmt2list() handles verbose parameter correctly ──
Error in `gmt2list(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2list().
Backtrace:
▆
1. ├─testthat::expect_silent(gmt2list(gmt_file, verbose = FALSE)) at test-gmt2list.R:47:3
2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise)
3. │ ├─testthat (local) .capture(...)
4. │ │ ├─withr::with_output_sink(...)
5. │ │ │ └─base::force(code)
6. │ │ ├─base::withCallingHandlers(...)
7. │ │ └─base::withVisible(code)
8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
9. └─evanverse::gmt2list(gmt_file, verbose = FALSE)
10. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2list().")
11. └─rlang::abort(...)
── Error ('test-gmt2list.R:88:3'): gmt2list() handles invalid GMT file format ──
Error in `gmt2list(temp_file)`: Package GSEABase required. Please install it to use gmt2list().
Backtrace:
▆
1. ├─testthat::expect_error(gmt2list(temp_file), "Failed to parse GMT file") at test-gmt2list.R:88:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::gmt2list(temp_file)
8. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2list().")
9. └─rlang::abort(...)
── Error ('test-gmt2list.R:110:3'): gmt2list() produces consistent results with gmt2df() ──
Error in `gmt2list(gmt_file, verbose = FALSE)`: Package GSEABase required. Please install it to use gmt2list().
Backtrace:
▆
1. └─evanverse::gmt2list(gmt_file, verbose = FALSE) at test-gmt2list.R:110:3
2. └─cli::cli_abort("Package {.pkg GSEABase} required. Please install it to use gmt2list().")
3. └─rlang::abort(...)
[ FAIL 17 | WARN 0 | SKIP 70 | PASS 2065 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.4.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [3m/12m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(evanverse)
>
> test_check("evanverse")
x RDS file not found: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6173dff2.rds'
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'demo_palette' (Type: qualitative, 2 colors)
v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7ddd602c.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 1
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Failed to parse JSON: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_982bf2d98810d/qualitative/invalid.json
v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6261d60a.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Missing fields (colors) in: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_982bf20681186/qualitative/incomplete.json
v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf2717149c.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'seq_pal' (Type: sequential, 2 colors)
v Added 'div_pal' (Type: diverging, 2 colors)
v Saved RDS: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf2bb5e5.rds
-- Compilation Summary --
i Sequential: 1
i Diverging: 1
i Qualitative: 0
v All palettes compiled successfully!
i Converting symbols to UPPERCASE (human standard)
i Creating standardized column: symbol_upper
i Creating standardized column: symbol_lower
i Converting symbols to UPPERCASE (human standard)
i Converting symbols to UPPERCASE (human standard)
i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palette_test_982bf223b0f85/qualitative
v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palette_test_982bf223b0f85/qualitative/testset.json
i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging
v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging/reused.json
! Palette already exists: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/reuse_test_982bfb2029d8/diverging/reused.json
i Directory created: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/log_test_982bf3ba92981/qualitative
v Palette saved: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/log_test_982bf3ba92981/qualitative/logtest.json
Saving _problems/test-download_geo_data-30.R
Saving _problems/test-download_geo_data-94.R
Saving _problems/test-download_geo_data-131.R
Saving _problems/test-download_geo_data-162.R
Saving _problems/test-download_geo_data-193.R
Saving _problems/test-download_geo_data-220.R
Saving _problems/test-download_geo_data-263.R
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf69c7cdc1 ----
+-- test.txt
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf59a06e02 ----
+-- file1.txt
+-- subdir
+-- file2.txt
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1324b699 ----
+-- level1
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1324b699 ----
+-- level1
+-- level2
+-- deep_file.txt
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf10201e03 ----
+-- example.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf10201e03/logs/tree/test.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf16886e3d ----
+-- sample.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf16886e3d/logs/content_test.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6a5face6 ----
+-- test.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf6a5face6/logs/file_tree_20260304_194341.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf792b3ba1 ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b6b6d9/overwrite_test.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf792b3ba1 ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b6b6d9/overwrite_test.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf3894f0ea ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf71676207/append_test.log
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf3894f0ea ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf71676207/append_test.log
x Directory does not exist: nonexistent/directory/path
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7b2da8e2 ----
-- Directory Tree: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf8a4ea2d -----
+-- test.txt
v File tree log saved to: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf8a4ea2d/new/log/path/test.log
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
i #FF8000 -> RGB: c(255, 128, 0)
i #00FF00 -> RGB: c(0, 255, 0)
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'group': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Class mismatch: integer vs numeric
x Values differ at 1 position(s), e.g., index 3: 3 vs 4
-- Objects are NOT identical ---------------------------------------------------
x Values differ at 1 position(s), e.g., index 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Values differ at 1 cell(s), e.g., [2,2]: 4 vs 5
-- Objects are NOT identical ---------------------------------------------------
x Values differ in column 'y' at 1 row(s), e.g., row 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Names differ: a, b vs b, a
x Values differ at 2 position(s), e.g., index 1: 1 vs 2
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x, y vs y, x
-- Objects are NOT identical ---------------------------------------------------
x Dimnames differ: (r1|r2; c1|c2) vs (r1|r2; cX|c2)
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 0x0 vs 1x0
-- Objects are NOT identical ---------------------------------------------------
x Length mismatch: 2 vs 3
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 2x2 vs 1x4
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x vs y
x Unsupported type for 'a': list
x Unsupported type for 'b': function
! Large n (25) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
! Large n (171) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.westlake.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.westlake.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
-- Configuring bioc mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
-- Configuring all mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://cloud.r-project.org>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://bioconductor.org>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
v Installed: stats
v Installed: cli
v Installed: ggplot2
v Installed: stats
v Installed: utils
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 0
! No exported names matched keyword: "zzzzzz"
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i a had 26 duplicates, now de-duplicated.
i b had 22 duplicates, now de-duplicated.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick ANOVA Results --
i Method: anova
v Significant group differences (p < 0.001)
-- Descriptive Statistics
-- Post-hoc Summary (tukey)
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick ANOVA Summary --
-- Omnibus Test
i Effect sizes: eta_squared 0.448, omega_squared 0.430
-- Descriptive Statistics
-- Normality Checks (Shapiro-Wilk)
A: n = 25, p = 0.5812
B: n = 25, p = 0.8166
C: n = 25, p = 0.9928
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality (Levene)
Levene's test: p = 0.9887
Equal variance: TRUE
-- Post-hoc Comparisons (tukey)
Analysis performed: 2026-03-04 19:47:54
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
A: n = 25, p = 0.581
B: n = 25, p = 0.817
C: n = 25, p = 0.993
v Variances appear equal (Levene's test, p = 0.989)
-- Omnibus Test --
v Completed classical one-way ANOVA (p = 0.0000)
i Applied Tukey HSD post-hoc comparisons.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
i var1 converted to factor with 1 level.
i var2 converted to factor with 2 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Removed 10 rows with missing values.
i var1 converted to factor with 3 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Pearson residuals not available. Using grouped bar chart.
-- Data Preparation --
i Automatically selected 6 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
i Automatically selected 5 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
-- Data Preparation --
i Automatically selected 1 numeric column.
-- Data Preparation --
-- Data Preparation --
-- Data Preparation --
i Automatically selected 3 numeric columns.
-- Computing Correlations --
i Found 0 significant correlations out of 3 tests
-- Creating Heatmap --
v Analysis complete!
-- Quick Correlation Analysis Results --
i Method: pearson
i Variables: 5
i Significant pairs: 1
-- Top 5 Significant Correlations
Use `summary()` for detailed results.
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: none
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on unadjusted p-values
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-12
Analysis performed: 2026-03-04 19:50:32
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: bonferroni
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on adjusted p-values (method: bonferroni)
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-11
Analysis performed: 2026-03-04 19:50:35
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick t-test Results --
i Method: t.test
v Significant difference (p < 0.001)
-- Descriptive Statistics
Use `summary()` for detailed results.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick t-test Summary --
-- Test Method
Method used: t.test
Paired: FALSE
Alternative: two.sided
Equal variance: TRUE
-- Test Result
-- Descriptive Statistics
-- Normality Tests (Shapiro-Wilk)
Control: n = 25, p = 0.5812
Treatment: n = 25, p = 0.8166
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality Test (Levene)
Levene's test: p = 0.9863
Equal variances: TRUE
Analysis performed: 2026-03-04 19:51:42
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
Control: n = 25, p = 0.581
Treatment: n = 25, p = 0.817
v Variances appear equal (Levene's test, p = 0.986)
v Using Student's t-test (equal variances assumed)
-- Statistical Test --
v Significant difference detected (p < 0.001)
-- Creating Visualization --
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Analysis complete!
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Reading Excel file --
i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1a04a8a2.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf1a04a8a2.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf379022f3.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf379022f3.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf130f833b.xlsx': Sheet1
v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf130f833b.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf5fc367ed.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf5fc367ed.xlsx'.
Path: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv'
Separator: "," | Encoding: "UTF-8"
OpenMP version (_OPENMP) 202011
omp_get_num_procs() 24
R_DATATABLE_NUM_PROCS_PERCENT unset (default 50)
R_DATATABLE_NUM_THREADS unset
R_DATATABLE_THROTTLE unset (default 1024)
omp_get_thread_limit() 2
omp_get_max_threads() 24
OMP_THREAD_LIMIT 2
OMP_NUM_THREADS unset
RestoreAfterFork true
data.table is using 2 threads with throttle==1024. See ?setDTthreads.
freadR.c has been passed a filename: /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv
[01] Check arguments
Using 2 threads (omp_get_max_threads()=24, nth=2)
NAstrings = [<<NA>>]
None of the NAstrings look like numbers.
show progress = 1
0/1 column will be read as integer
Y/N column will be read as character
[02] Opening the file
Opening file /tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf7a200709.csv
File opened, size = 235 bytes.
Memory mapped ok
[03] Detect and skip BOM
[04] Arrange mmap to be \0 terminated
\n has been found in the input (counts: 0 \r by themselves vs 6 [\r]*\n) and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
[05] Skipping initial rows if needed
Positioned on line 1 starting: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
[06] Detect separator, quoting rule, and ncolumns
Using supplied sep ','
sep=',' with 6 lines of 11 fields using quote rule 0
Detected 11 columns on line 1. This line is either column names or first data row. Line starts as: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
Quote rule picked = 0
fill=false and the most number of columns found is 11
[07] Detect column types, dec, good nrow estimate and whether first row is column names
'header' changed by user from 'auto' to true
sep=',' so dec set to '.'
Number of sampling jump points = 1 because (234 bytes from row 1 to eof) / (2 * 234 jump0size) == 0
Type codes (jump 000) : 97779997777 Quote rule 0
All rows were sampled since file is small so we know nrow=5 exactly
[08] Assign column names
[09] Apply user overrides on column types
After 0 type and 0 drop user overrides : 97779997777
[10] Allocate memory for the datatable
Allocating 11 column slots (11 - 0 dropped) with 5 rows
[11] Read the data
jumps=[0..1), chunk_size=1048576, total_size=189
Read 5 rows x 11 columns from 235 bytes file in 00:00.000 wall clock time
[12] Finalizing the datatable
Type counts:
7 : int32 '7'
4 : float64 '9'
=============================
0.000s ( 31%) Memory map 0.000GiB file
0.000s ( 47%) sep=',' ncol=11 and header detection
0.000s ( 6%) Column type detection using 5 sample rows
0.000s ( 4%) Allocation of 5 rows x 11 cols (0.000GiB) of which 5 (100%) rows used
0.000s ( 13%) Reading 1 chunks (0 swept) of 1.000MiB (each chunk 5 rows) using 1 threads
+ 0.000s ( 2%) Parse to row-major thread buffers (grown 0 times)
+ 0.000s ( 0%) Transpose
+ 0.000s ( 10%) Waiting
0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions
0.000s Total
v File loaded successfully (5 rows x 11 cols)
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- Usage Examples --------------------------------------------------------------
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- droplevels
`droplevels(df)` removes unused factor levels from a data frame or factor.
-- modifyList
`modifyList(x, y)` merges two lists; elements in `y` overwrite those in `x`.
-- do.call
`do.call(fun, args)` calls a function with arguments in a list: `do.call(plot, list(x = 1:10))`.
-- sprintf
`sprintf("Hello, %s!", name)` formats strings with `%s`, `%d`, etc.
-- scRNAseq
`scRNAseq` (Bioconductor) provides scRNA-seq datasets, e.g., `ZeiselBrainData()`.
-- basename
`basename(path)` extracts the filename from a full path. See also `dirname()`.
-- here
`here::here("data", "raw", "sample1.rds")` builds a path from project root.
-- stopifnot
`stopifnot(cond1, cond2, ...)` throws if any condition is FALSE.
-- object.size
`object.size(x)` estimates memory size; use `format()` to pretty-print.
-- slice
`slice(df, 1:3)` selects rows by position; see `slice_head()`, `slice_tail()`, `slice_max()`.
-- unzip
`unzip("file.zip", exdir = "dir")` extracts ZIP archives.
-- gunzip
`R.utils::gunzip("file.csv.gz", remove = FALSE)` decompresses .gz files.
-- untar
`untar("file.tar.gz", exdir = "dir")` extracts .tar or .tar.gz archives.
-- NoLegend
`NoLegend()` removes legends from ggplot2/Seurat plots.
-- RotatedAxis
`RotatedAxis()` rotates x-axis text for readability in dot plots.
-- guides
`guides(fill = "none")` customizes or removes legends (with `scale_*`).
-- log2
`log2(x)` base-2 logarithm (often for fold change).
-- log
`log(x, base = exp(1))` natural log by default; set `base = 10` or `2` for others.
-- log10
`log10(x)` base-10 logarithm (orders of magnitude).
-- round
`round(x, digits = 0)` rounds; use `signif()` for significant digits.
-- floor
`floor(x)` greatest integer <= x (e.g., `floor(2.8)` -> 2).
-- ceiling
`ceiling(x)` smallest integer >= x (e.g., `ceiling(2.1)` -> 3).
-- cut
`cut(x, breaks)` bins numeric vector; `breaks = 3` or custom; `labels = FALSE` for group indices.
-- cumsum
`cumsum(x)` cumulative sum.
-- cumprod
`cumprod(x)` cumulative product.
-- cummin
`cummin(x)` running minimum.
-- cummax
`cummax(x)` running maximum.
-- row_number
`row_number(x)` order rank (ties broken arbitrarily).
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
-- cume_dist
`cume_dist(x)` cumulative proportion of values <= x.
-- str_view
`stringr::str_view(string, pattern)` highlights regex matches; `str_view_all()` for all.
-- str_c
`stringr::str_c(...)` concatenates; use `sep`/`collapse` as needed.
-- str_glue
`glue::glue("Hello, {name}!")` inline expressions with `{}`.
-- str_flatten
`stringr::str_flatten(x, collapse = ", ")` join a character vector.
-- str_length
`stringr::str_length(x)` string lengths.
-- str_sub
`stringr::str_sub(x, start, end)` extract/replace substrings (supports negative indices).
-- today
`lubridate::today()` current Date (no time).
-- now
`lubridate::now()` current POSIXct date-time.
-- Sys.timezone
`Sys.timezone()` system time zone name.
-- skimr
`skimr::skim(df)` compact, readable data summaries.
-- par
`par(mfrow = c(m, n))` split plotting area (e.g., 2x2).
-- layout
`layout(matrix, widths, heights)` flexible plot arrangement.
-- datatable
`DT::datatable(data)` interactive table (search/filter/sort/paginate).
-- windowsFonts
`windowsFonts()` register system fonts (Windows).
-- sign
`sign(x)` returns -1/0/1 for negative/zero/positive.
-- reactable
`reactable::reactable(data)` modern interactive table.
-- trimws
`trimws(x)` removes leading and trailing whitespace.
-- cranlogs
`cranlogs::cran_downloads('pkgname', from = 'last-month')` gets CRAN download stats; use `'last-week'`, `'last-day'`, or specific dates.
-- dlstats
`dlstats::cran_stats('pkgname')` shows CRAN download trends with plots; supports Bioconductor via `source = 'bioc'`.
-- Available Keywords ----------------------------------------------------------
`glimpse, read_excel, droplevels, modifyList, do.call, sprintf, scRNAseq,
basename, here, stopifnot, object.size, slice, unzip, gunzip, untar, NoLegend,
RotatedAxis, guides, log2, log, log10, round, floor, ceiling, cut, cumsum,
cumprod, cummin, cummax, row_number, min_rank, dense_rank, percent_rank,
cume_dist, str_view, str_c, str_glue, str_flatten, str_length, str_sub, today,
now, Sys.timezone, skimr, par, layout, datatable, windowsFonts, sign,
reactable, trimws, cranlogs, dlstats`
x No match found for keyword: "notakeyword"
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
v Palette removed from qualitative: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_test_982bf41596a56/qualitative/test_palette.json'
! Palette not found in any type: nonexistent
v Palette removed from diverging: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/palettes_test_982bf13083d9b/diverging/test_palette.json'
v Palette removed from qualitative: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/custom_palettes_982bfa8e89c6/qualitative/custom_test.json'
v RGB: c(255, 128, 0) -> HEX: #FF8000
v RGB: c(0, 0, 0) -> HEX: #000000
v RGB: c(255, 255, 255) -> HEX: #FFFFFF
v Converted 3 RGB values to HEX.
i RGB: c(255, 128, 0) -> HEX: #FF8000
i RGB: c(0, 255, 0) -> HEX: #00FF00
i RGB: c(0, 0, 255) -> HEX: #0000FF
v RGB: c(0, 1, 255) -> HEX: #0001FF
x An error occurred: non-numeric argument to mathematical function
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
i Square test started at 2026-03-04 19:53:18
v Square test completed in 0.000 seconds
i Silent task started at 2026-03-04 19:53:18
v Silent task completed in 0.001 seconds
v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30958dff.xlsx'
v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf73bddaa1.xlsx'
v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
! File already exists and will be overwritten: '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/file982bf30f7af20.xlsx'
v Excel file written to '/tmp/RtmpwJX7Ju/working_dir/RtmpWg8ssj/test-write-982bf35b58fdb_2026-03-04.xlsx'
[ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
══ Skipped tests (68) ══════════════════════════════════════════════════════════
• On CRAN (68): 'test-download_batch.R:19:3', 'test-download_batch.R:39:3',
'test-download_gene_ref.R:66:3', 'test-download_gene_ref.R:109:3',
'test-download_gene_ref.R:150:3', 'test-download_gene_ref.R:174:3',
'test-download_gene_ref.R:198:3', 'test-download_gene_ref.R:218:3',
'test-download_gene_ref.R:237:3', 'test-download_gene_ref.R:264:3',
'test-download_gene_ref.R:292:3', 'test-download_gene_ref.R:320:3',
'test-download_gene_ref.R:339:3', 'test-download_gene_ref.R:359:3',
'test-download_url.R:206:3', 'test-download_url.R:238:3',
'test-download_url.R:266:3', 'test-download_url.R:296:3',
'test-download_url.R:319:3', 'test-download_url.R:342:3',
'test-download_url.R:365:3', 'test-file_info.R:12:3',
'test-file_info.R:26:3', 'test-file_info.R:48:3', 'test-file_info.R:59:3',
'test-file_info.R:71:3', 'test-pkg.R:389:3', 'test-pkg.R:528:3',
'test-pkg.R:536:3', 'test-pkg.R:546:3', 'test-pkg.R:561:3',
'test-plot_forest.R:61:3', 'test-plot_forest.R:77:3',
'test-plot_forest.R:96:3', 'test-plot_forest.R:113:3',
'test-plot_forest.R:134:3', 'test-plot_forest.R:149:3',
'test-plot_forest.R:164:3', 'test-plot_forest.R:183:3',
'test-plot_forest.R:200:3', 'test-plot_forest.R:217:3',
'test-plot_forest.R:235:3', 'test-plot_forest.R:253:3',
'test-plot_forest.R:275:3', 'test-plot_forest.R:292:3',
'test-plot_forest.R:310:3', 'test-plot_forest.R:327:3',
'test-plot_forest.R:350:3', 'test-plot_forest.R:368:3',
'test-plot_forest.R:389:3', 'test-plot_forest.R:410:3',
'test-plot_forest.R:427:3', 'test-plot_forest.R:444:3',
'test-plot_forest.R:475:3', 'test-plot_forest.R:493:3',
'test-plot_forest.R:515:3', 'test-plot_forest.R:532:3',
'test-plot_forest.R:553:3', 'test-plot_forest.R:570:3',
'test-plot_forest.R:594:3', 'test-plot_forest.R:611:3',
'test-plot_forest.R:632:3', 'test-plot_forest.R:662:3',
'test-plot_forest.R:696:3', 'test-plot_forest.R:730:3',
'test-plot_forest.R:756:3', 'test-plot_forest.R:772:3',
'test-plot_forest.R:789:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-download_geo_data.R:27:3'): download_geo_data() validates gse_id parameter ──
Error in `download_geo_data("invalid", dest_dir = temp_dir)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:27:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("invalid", dest_dir = temp_dir)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:91:3'): download_geo_data() validates dest_dir parameter ──
Error in `download_geo_data("GSE12345", dest_dir = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:91:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:128:3'): download_geo_data() validates overwrite parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:128:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:159:3'): download_geo_data() validates log parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:159:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:190:3'): download_geo_data() validates log_file parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:190:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:217:3'): download_geo_data() validates retries parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:217:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:260:3'): download_geo_data() validates timeout parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:260:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
[ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.4.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [193s/521s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(evanverse)
>
> test_check("evanverse")
x RDS file not found: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4018e1d53c.rds'
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'demo_palette' (Type: qualitative, 2 colors)
v Saved RDS: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40688ba0aa.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 1
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Failed to parse JSON: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palettes_7fc404e3258e2/qualitative/invalid.json
v Saved RDS: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403e72bc6d.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
! Missing fields (colors) in: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palettes_7fc4054bd430e/qualitative/incomplete.json
v Saved RDS: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4057d93c3a.rds
-- Compilation Summary --
i Sequential: 0
i Diverging: 0
i Qualitative: 0
v All palettes compiled successfully!
-- Compiling Color Palettes (JSON -> RDS) --------------------------------------
v Added 'seq_pal' (Type: sequential, 2 colors)
v Added 'div_pal' (Type: diverging, 2 colors)
v Saved RDS: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403b995573.rds
-- Compilation Summary --
i Sequential: 1
i Diverging: 1
i Qualitative: 0
v All palettes compiled successfully!
i Converting symbols to UPPERCASE (human standard)
i Creating standardized column: symbol_upper
i Creating standardized column: symbol_lower
i Converting symbols to UPPERCASE (human standard)
i Converting symbols to UPPERCASE (human standard)
i Directory created: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palette_test_7fc407b4d952a/qualitative
v Palette saved: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palette_test_7fc407b4d952a/qualitative/testset.json
i Directory created: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/reuse_test_7fc40375038a7/diverging
v Palette saved: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/reuse_test_7fc40375038a7/diverging/reused.json
! Palette already exists: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/reuse_test_7fc40375038a7/diverging/reused.json
i Directory created: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/log_test_7fc40101f7f4f/qualitative
v Palette saved: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/log_test_7fc40101f7f4f/qualitative/logtest.json
Saving _problems/test-download_geo_data-30.R
Saving _problems/test-download_geo_data-94.R
Saving _problems/test-download_geo_data-131.R
Saving _problems/test-download_geo_data-162.R
Saving _problems/test-download_geo_data-193.R
Saving _problems/test-download_geo_data-220.R
Saving _problems/test-download_geo_data-263.R
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4078f20c90 ----
+-- test.txt
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc401ab993a6 ----
+-- file1.txt
+-- subdir
+-- file2.txt
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403fb6f95b ----
+-- level1
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403fb6f95b ----
+-- level1
+-- level2
+-- deep_file.txt
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403d364901 ----
+-- example.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403d364901/logs/tree/test.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc402869a122 ----
+-- sample.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc402869a122/logs/content_test.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403a7a33e3 ----
+-- test.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403a7a33e3/logs/file_tree_20260304_192120.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4061382fc8 ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc406eb8c376/overwrite_test.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4061382fc8 ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc406eb8c376/overwrite_test.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc407201cd8e ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40a99d7af/append_test.log
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc407201cd8e ----
+-- file1.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40a99d7af/append_test.log
x Directory does not exist: nonexistent/directory/path
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc404121549b ----
-- Directory Tree: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40191c4bb2 ----
+-- test.txt
v File tree log saved to: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40191c4bb2/new/log/path/test.log
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
i #FF8000 -> RGB: c(255, 128, 0)
i #00FF00 -> RGB: c(0, 255, 0)
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
i Mapping completed for 'group': 1 unmatched value(s) assigned to default.
i Mapping completed for 'id': 1 unmatched value(s) assigned to default.
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Class mismatch: integer vs numeric
x Values differ at 1 position(s), e.g., index 3: 3 vs 4
-- Objects are NOT identical ---------------------------------------------------
x Values differ at 1 position(s), e.g., index 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Type mismatch: integer vs double
x Values differ at 1 cell(s), e.g., [2,2]: 4 vs 5
-- Objects are NOT identical ---------------------------------------------------
x Values differ in column 'y' at 1 row(s), e.g., row 2: b vs c
-- Objects are NOT identical ---------------------------------------------------
x Names differ: a, b vs b, a
x Values differ at 2 position(s), e.g., index 1: 1 vs 2
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x, y vs y, x
-- Objects are NOT identical ---------------------------------------------------
x Dimnames differ: (r1|r2; c1|c2) vs (r1|r2; cX|c2)
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 0x0 vs 1x0
-- Objects are NOT identical ---------------------------------------------------
x Length mismatch: 2 vs 3
-- Objects are NOT identical ---------------------------------------------------
x Dimension mismatch: 2x2 vs 1x4
-- Objects are NOT identical ---------------------------------------------------
x Column names differ: x vs y
x Unsupported type for 'a': list
x Unsupported type for 'b': function
! Large n (25) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
! Large n (171) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision.
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.westlake.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://mirrors.westlake.edu.cn/bioconductor>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.ustc.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.ustc.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
-- Configuring bioc mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring all mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
v Bioconductor mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/bioconductor>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
-- Configuring cran mirror --
v CRAN mirror set to: <https://mirrors.tuna.tsinghua.edu.cn/CRAN>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
-- Configuring all mirror --
-- Configuring cran mirror --
v CRAN mirror set to: <https://cloud.r-project.org>
i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
-- Configuring bioc mirror --
v Bioconductor mirror set to: <https://bioconductor.org>
i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju"
i View current settings: `getOption('repos')` & `getOption('BioC_mirror')`
v Installed: stats
v Installed: cli
v Installed: ggplot2
v Installed: stats
v Installed: utils
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 465
.MFclass
.checkMFClasses
.getXlevels
.lm.fit
.nknots.smspl
.preformat.ts
.vcov.aliased
AIC
ARMAacf
ARMAtoMA
BIC
Box.test
C
D
DF2formula
Gamma
HoltWinters
IQR
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
NLSstAsymptotic
NLSstClosestX
NLSstLfAsymptote
NLSstRtAsymptote
PP.test
Pair
SSD
SSasymp
SSasympOff
SSasympOrig
SSbiexp
SSfol
SSfpl
SSgompertz
SSlogis
SSmicmen
SSweibull
StructTS
TukeyHSD
acf
acf2AR
add.scope
add1
addmargins
aggregate
aggregate.data.frame
aggregate.ts
alias
anova
ansari.test
aov
approx
approxfun
ar
ar.burg
ar.mle
ar.ols
ar.yw
arima
arima.sim
arima0
arima0.diag
as.dendrogram
as.dist
as.formula
as.hclust
as.stepfun
as.ts
asOneSidedFormula
ave
bandwidth.kernel
bartlett.test
binom.test
binomial
biplot
bw.SJ
bw.bcv
bw.nrd
bw.nrd0
bw.ucv
cancor
case.names
ccf
chisq.test
cmdscale
coef
coefficients
complete.cases
confint
confint.default
confint.lm
constrOptim
contr.SAS
contr.helmert
contr.poly
contr.sum
contr.treatment
contrasts
contrasts<-
convolve
cooks.distance
cophenetic
cor
cor.test
cov
cov.wt
cov2cor
covratio
cpgram
cutree
cycle
dbeta
dbinom
dcauchy
dchisq
decompose
delete.response
deltat
dendrapply
density
density.default
deriv
deriv3
deviance
dexp
df
df.kernel
df.residual
dfbeta
dfbetas
dffits
dgamma
dgeom
dhyper
diffinv
dist
dlnorm
dlogis
dmultinom
dnbinom
dnorm
dpois
drop.scope
drop.terms
drop1
dsignrank
dt
dummy.coef
dummy.coef.lm
dunif
dweibull
dwilcox
ecdf
eff.aovlist
effects
embed
end
estVar
expand.model.frame
extractAIC
factanal
factor.scope
family
fft
filter
fisher.test
fitted
fitted.values
fivenum
fligner.test
formula
free1way
frequency
friedman.test
ftable
gaussian
getCall
getInitial
get_all_vars
glm
glm.control
glm.fit
hasTsp
hat
hatvalues
hclust
heatmap
influence
influence.measures
integrate
interaction.plot
inverse.gaussian
is.empty.model
is.leaf
is.mts
is.stepfun
is.ts
is.tskernel
isoreg
kernapply
kernel
kmeans
knots
kruskal.test
ks.test
ksmooth
lag
lag.plot
line
lm
lm.fit
lm.influence
lm.wfit
loadings
loess
loess.control
loess.smooth
logLik
loglin
lowess
ls.diag
ls.print
lsfit
mad
mahalanobis
make.link
makeARIMA
makepredictcall
manova
mantelhaen.test
mauchly.test
mcnemar.test
median
median.default
medpolish
model.extract
model.frame
model.frame.default
model.matrix
model.matrix.default
model.matrix.lm
model.offset
model.response
model.tables
model.weights
monthplot
mood.test
mvfft
na.action
na.contiguous
na.exclude
na.fail
na.omit
na.pass
napredict
naprint
naresid
nextn
nlm
nlminb
nls
nls.control
nobs
numericDeriv
offset
oneway.test
optim
optimHess
optimise
optimize
order.dendrogram
p.adjust
p.adjust.methods
pacf
pairwise.prop.test
pairwise.t.test
pairwise.table
pairwise.wilcox.test
pbeta
pbinom
pbirthday
pcauchy
pchisq
pexp
pf
pgamma
pgeom
phyper
plclust
plnorm
plogis
plot.ecdf
plot.spec.coherency
plot.spec.phase
plot.stepfun
plot.ts
pnbinom
pnorm
poisson
poisson.test
poly
polym
power
power.anova.test
power.free1way.test
power.prop.test
power.t.test
ppoints
ppois
ppplot
ppr
prcomp
predict
predict.glm
predict.lm
preplot
princomp
printCoefmat
profile
proj
promax
prop.test
prop.trend.test
psignrank
psmirnov
pt
ptukey
punif
pweibull
pwilcox
qbeta
qbinom
qbirthday
qcauchy
qchisq
qexp
qf
qgamma
qgeom
qhyper
qlnorm
qlogis
qnbinom
qnorm
qpois
qqline
qqnorm
qqplot
qr.influence
qsignrank
qsmirnov
qt
qtukey
quade.test
quantile
quasi
quasibinomial
quasipoisson
qunif
qweibull
qwilcox
r2dtable
rWishart
rbeta
rbinom
rcauchy
rchisq
read.ftable
rect.hclust
reformulate
relevel
reorder
replications
reshape
resid
residuals
residuals.glm
residuals.lm
rexp
rf
rfree1way
rgamma
rgeom
rhyper
rlnorm
rlogis
rmultinom
rnbinom
rnorm
rpois
rsignrank
rsmirnov
rstandard
rstudent
rt
runif
runmed
rweibull
rwilcox
scatter.smooth
screeplot
sd
se.contrast
selfStart
setNames
shapiro.test
sigma
simulate
smooth
smooth.spline
smoothEnds
sortedXyData
spec.ar
spec.pgram
spec.taper
spectrum
spline
splinefun
splinefunH
start
stat.anova
step
stepfun
stl
summary.aov
summary.glm
summary.lm
summary.manova
summary.stepfun
supsmu
symnum
t.test
termplot
terms
terms.formula
time
toeplitz
toeplitz2
ts
ts.intersect
ts.plot
ts.union
tsSmooth
tsdiag
tsp
tsp<-
uniroot
update
update.default
update.formula
var
var.test
variable.names
varimax
vcov
weighted.mean
weighted.residuals
weights
wilcox.test
window
window<-
write.ftable
xtabs
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 24
.lm.fit
KalmanForecast
KalmanLike
KalmanRun
KalmanSmooth
confint.lm
contr.helmert
dummy.coef.lm
glm
glm.control
glm.fit
lm
lm.fit
lm.influence
lm.wfit
model.matrix.lm
nlm
nlminb
predict.glm
predict.lm
residuals.glm
residuals.lm
summary.glm
summary.lm
-- Package: stats --
i Matched exported names: 0
! No exported names matched keyword: "zzzzzz"
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i s1 had 75 duplicates, now de-duplicated.
i s2 had 94 duplicates, now de-duplicated.
i a had 26 duplicates, now de-duplicated.
i b had 22 duplicates, now de-duplicated.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick ANOVA Results --
i Method: anova
v Significant group differences (p < 0.001)
-- Descriptive Statistics
-- Post-hoc Summary (tukey)
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick ANOVA Summary --
-- Omnibus Test
i Effect sizes: eta_squared 0.448, omega_squared 0.430
-- Descriptive Statistics
-- Normality Checks (Shapiro-Wilk)
A: n = 25, p = 0.5812
B: n = 25, p = 0.8166
C: n = 25, p = 0.9928
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality (Levene)
Levene's test: p = 0.9887
Equal variance: TRUE
-- Post-hoc Comparisons (tukey)
Analysis performed: 2026-03-04 19:24:24
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
A: n = 25, p = 0.581
B: n = 25, p = 0.817
C: n = 25, p = 0.993
v Variances appear equal (Levene's test, p = 0.989)
-- Omnibus Test --
v Completed classical one-way ANOVA (p = 0.0000)
i Applied Tukey HSD post-hoc comparisons.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
i var1 converted to factor with 1 level.
i var2 converted to factor with 2 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Removed 10 rows with missing values.
i var1 converted to factor with 3 levels.
i var2 converted to factor with 2 levels.
! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors.
! Pearson residuals not available. Using grouped bar chart.
-- Data Preparation --
i Automatically selected 6 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
i Automatically selected 5 numeric columns.
-- Computing Correlations --
i Found 1 significant correlation out of 10 tests
-- Creating Heatmap --
v Analysis complete!
-- Data Preparation --
-- Data Preparation --
i Automatically selected 1 numeric column.
-- Data Preparation --
-- Data Preparation --
-- Data Preparation --
i Automatically selected 3 numeric columns.
-- Computing Correlations --
i Found 0 significant correlations out of 3 tests
-- Creating Heatmap --
v Analysis complete!
-- Quick Correlation Analysis Results --
i Method: pearson
i Variables: 5
i Significant pairs: 1
-- Top 5 Significant Correlations
Use `summary()` for detailed results.
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: none
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on unadjusted p-values
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-12
Analysis performed: 2026-03-04 19:26:11
-- Detailed Correlation Analysis Summary --
-- Analysis Parameters
Correlation method: pearson
Missing value handling: pairwise.complete.obs
P-value adjustment: bonferroni
Number of variables: 5
-- Descriptive Statistics
variable n mean sd median min max
var1 50 10.068807 1.851740 9.854719 6.066766 14.33791
var2 50 15.439225 2.716342 15.457737 8.072493 21.56200
var3 50 18.984398 3.957336 18.780520 11.787011 28.40044
var4 50 25.194034 4.654875 24.616200 18.445992 41.20520
var5 50 8.046525 1.578258 8.027126 4.130347 12.53476
-- Correlation Summary
Min correlation: -0.156
Max correlation: 0.81
Mean |correlation|: 0.144
-- Significant Correlations
i Significant pairs are based on adjusted p-values (method: bonferroni)
Significant pairs: 1 out of 10 tests
All significant pairs:
var1 var2 correlation p_value
var1 var5 0.8100595 1.041235e-11
Analysis performed: 2026-03-04 19:26:12
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
! Could not load palette "qual_bold". Using defaults.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Quick t-test Results --
i Method: t.test
v Significant difference (p < 0.001)
-- Descriptive Statistics
Use `summary()` for detailed results.
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Detailed Quick t-test Summary --
-- Test Method
Method used: t.test
Paired: FALSE
Alternative: two.sided
Equal variance: TRUE
-- Test Result
-- Descriptive Statistics
-- Normality Tests (Shapiro-Wilk)
Control: n = 25, p = 0.5812
Treatment: n = 25, p = 0.8166
i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test.
-- Variance Equality Test (Levene)
Levene's test: p = 0.9863
Equal variances: TRUE
Analysis performed: 2026-03-04 19:27:01
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Automatic Method Selection --
i Checking normality for each group...
v Data appears normal (Shapiro-Wilk p >= 0.05).
Control: n = 25, p = 0.581
Treatment: n = 25, p = 0.817
v Variances appear equal (Levene's test, p = 0.986)
v Using Student's t-test (equal variances assumed)
-- Statistical Test --
v Significant difference detected (p < 0.001)
-- Creating Visualization --
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Analysis complete!
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
v Loaded palette "qual_vivid" ("qualitative"), 9 colors
-- Reading Excel file --
i Sheets in '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403c376794.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc403c376794.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc406723beda.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc406723beda.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4068f7c52d.xlsx': Sheet1
v Successfully read sheet 1 from '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4068f7c52d.xlsx'.
-- Reading Excel file --
i Sheets in '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4035788050.xlsx': Sheet1
v Column names cleaned with janitor::clean_names().
v Successfully read sheet 1 from '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4035788050.xlsx'.
Path: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40a18ff53.csv'
Separator: "," | Encoding: "UTF-8"
OpenMP version (_OPENMP) 201511
omp_get_num_procs() 24
R_DATATABLE_NUM_PROCS_PERCENT unset (default 50)
R_DATATABLE_NUM_THREADS unset
R_DATATABLE_THROTTLE unset (default 1024)
omp_get_thread_limit() 2
omp_get_max_threads() 24
OMP_THREAD_LIMIT 2
OMP_NUM_THREADS unset
RestoreAfterFork true
data.table is using 2 threads with throttle==1024. See ?setDTthreads.
freadR.c has been passed a filename: /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40a18ff53.csv
[01] Check arguments
Using 2 threads (omp_get_max_threads()=24, nth=2)
NAstrings = [<<NA>>]
None of the NAstrings look like numbers.
show progress = 1
0/1 column will be read as integer
Y/N column will be read as character
[02] Opening the file
Opening file /tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc40a18ff53.csv
File opened, size = 235 bytes.
Memory mapped ok
[03] Detect and skip BOM
[04] Arrange mmap to be \0 terminated
\n has been found in the input (counts: 0 \r by themselves vs 6 [\r]*\n) and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
[05] Skipping initial rows if needed
Positioned on line 1 starting: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
[06] Detect separator, quoting rule, and ncolumns
Using supplied sep ','
sep=',' with 6 lines of 11 fields using quote rule 0
Detected 11 columns on line 1. This line is either column names or first data row. Line starts as: <<mpg,cyl,disp,hp,drat,wt,qsec,v>>
Quote rule picked = 0
fill=false and the most number of columns found is 11
[07] Detect column types, dec, good nrow estimate and whether first row is column names
'header' changed by user from 'auto' to true
sep=',' so dec set to '.'
Number of sampling jump points = 1 because (234 bytes from row 1 to eof) / (2 * 234 jump0size) == 0
Type codes (jump 000) : 97779997777 Quote rule 0
All rows were sampled since file is small so we know nrow=5 exactly
[08] Assign column names
[09] Apply user overrides on column types
After 0 type and 0 drop user overrides : 97779997777
[10] Allocate memory for the datatable
Allocating 11 column slots (11 - 0 dropped) with 5 rows
[11] Read the data
jumps=[0..1), chunk_size=1048576, total_size=189
Read 5 rows x 11 columns from 235 bytes file in 00:00.000 wall clock time
[12] Finalizing the datatable
Type counts:
7 : int32 '7'
4 : float64 '9'
=============================
0.000s ( 32%) Memory map 0.000GiB file
0.000s ( 50%) sep=',' ncol=11 and header detection
0.000s ( 5%) Column type detection using 5 sample rows
0.000s ( 4%) Allocation of 5 rows x 11 cols (0.000GiB) of which 5 (100%) rows used
0.000s ( 9%) Reading 1 chunks (0 swept) of 1.000MiB (each chunk 5 rows) using 1 threads
+ 0.000s ( 2%) Parse to row-major thread buffers (grown 0 times)
+ 0.000s ( 0%) Transpose
+ 0.000s ( 7%) Waiting
0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions
0.000s Total
v File loaded successfully (5 rows x 11 cols)
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- Usage Examples --------------------------------------------------------------
-- glimpse
`glimpse(df)` from dplyr/tibble gives a compact overview.
-- read_excel
`readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc.
-- droplevels
`droplevels(df)` removes unused factor levels from a data frame or factor.
-- modifyList
`modifyList(x, y)` merges two lists; elements in `y` overwrite those in `x`.
-- do.call
`do.call(fun, args)` calls a function with arguments in a list: `do.call(plot, list(x = 1:10))`.
-- sprintf
`sprintf("Hello, %s!", name)` formats strings with `%s`, `%d`, etc.
-- scRNAseq
`scRNAseq` (Bioconductor) provides scRNA-seq datasets, e.g., `ZeiselBrainData()`.
-- basename
`basename(path)` extracts the filename from a full path. See also `dirname()`.
-- here
`here::here("data", "raw", "sample1.rds")` builds a path from project root.
-- stopifnot
`stopifnot(cond1, cond2, ...)` throws if any condition is FALSE.
-- object.size
`object.size(x)` estimates memory size; use `format()` to pretty-print.
-- slice
`slice(df, 1:3)` selects rows by position; see `slice_head()`, `slice_tail()`, `slice_max()`.
-- unzip
`unzip("file.zip", exdir = "dir")` extracts ZIP archives.
-- gunzip
`R.utils::gunzip("file.csv.gz", remove = FALSE)` decompresses .gz files.
-- untar
`untar("file.tar.gz", exdir = "dir")` extracts .tar or .tar.gz archives.
-- NoLegend
`NoLegend()` removes legends from ggplot2/Seurat plots.
-- RotatedAxis
`RotatedAxis()` rotates x-axis text for readability in dot plots.
-- guides
`guides(fill = "none")` customizes or removes legends (with `scale_*`).
-- log2
`log2(x)` base-2 logarithm (often for fold change).
-- log
`log(x, base = exp(1))` natural log by default; set `base = 10` or `2` for others.
-- log10
`log10(x)` base-10 logarithm (orders of magnitude).
-- round
`round(x, digits = 0)` rounds; use `signif()` for significant digits.
-- floor
`floor(x)` greatest integer <= x (e.g., `floor(2.8)` -> 2).
-- ceiling
`ceiling(x)` smallest integer >= x (e.g., `ceiling(2.1)` -> 3).
-- cut
`cut(x, breaks)` bins numeric vector; `breaks = 3` or custom; `labels = FALSE` for group indices.
-- cumsum
`cumsum(x)` cumulative sum.
-- cumprod
`cumprod(x)` cumulative product.
-- cummin
`cummin(x)` running minimum.
-- cummax
`cummax(x)` running maximum.
-- row_number
`row_number(x)` order rank (ties broken arbitrarily).
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
-- cume_dist
`cume_dist(x)` cumulative proportion of values <= x.
-- str_view
`stringr::str_view(string, pattern)` highlights regex matches; `str_view_all()` for all.
-- str_c
`stringr::str_c(...)` concatenates; use `sep`/`collapse` as needed.
-- str_glue
`glue::glue("Hello, {name}!")` inline expressions with `{}`.
-- str_flatten
`stringr::str_flatten(x, collapse = ", ")` join a character vector.
-- str_length
`stringr::str_length(x)` string lengths.
-- str_sub
`stringr::str_sub(x, start, end)` extract/replace substrings (supports negative indices).
-- today
`lubridate::today()` current Date (no time).
-- now
`lubridate::now()` current POSIXct date-time.
-- Sys.timezone
`Sys.timezone()` system time zone name.
-- skimr
`skimr::skim(df)` compact, readable data summaries.
-- par
`par(mfrow = c(m, n))` split plotting area (e.g., 2x2).
-- layout
`layout(matrix, widths, heights)` flexible plot arrangement.
-- datatable
`DT::datatable(data)` interactive table (search/filter/sort/paginate).
-- windowsFonts
`windowsFonts()` register system fonts (Windows).
-- sign
`sign(x)` returns -1/0/1 for negative/zero/positive.
-- reactable
`reactable::reactable(data)` modern interactive table.
-- trimws
`trimws(x)` removes leading and trailing whitespace.
-- cranlogs
`cranlogs::cran_downloads('pkgname', from = 'last-month')` gets CRAN download stats; use `'last-week'`, `'last-day'`, or specific dates.
-- dlstats
`dlstats::cran_stats('pkgname')` shows CRAN download trends with plots; supports Bioconductor via `source = 'bioc'`.
-- Available Keywords ----------------------------------------------------------
`glimpse, read_excel, droplevels, modifyList, do.call, sprintf, scRNAseq,
basename, here, stopifnot, object.size, slice, unzip, gunzip, untar, NoLegend,
RotatedAxis, guides, log2, log, log10, round, floor, ceiling, cut, cumsum,
cumprod, cummin, cummax, row_number, min_rank, dense_rank, percent_rank,
cume_dist, str_view, str_c, str_glue, str_flatten, str_length, str_sub, today,
now, Sys.timezone, skimr, par, layout, datatable, windowsFonts, sign,
reactable, trimws, cranlogs, dlstats`
x No match found for keyword: "notakeyword"
-- min_rank
`min_rank(x)` ties get the same minimum rank.
-- dense_rank
`dense_rank(x)` like `min_rank()` but without gaps.
-- percent_rank
`percent_rank(x)` relative rank in [0,1], normalized by n-1.
v Palette removed from qualitative: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palettes_test_7fc4025071042/qualitative/test_palette.json'
! Palette not found in any type: nonexistent
v Palette removed from diverging: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/palettes_test_7fc40bba5c11/diverging/test_palette.json'
v Palette removed from qualitative: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/custom_palettes_7fc40750397f3/qualitative/custom_test.json'
v RGB: c(255, 128, 0) -> HEX: #FF8000
v RGB: c(0, 0, 0) -> HEX: #000000
v RGB: c(255, 255, 255) -> HEX: #FFFFFF
v Converted 3 RGB values to HEX.
i RGB: c(255, 128, 0) -> HEX: #FF8000
i RGB: c(0, 255, 0) -> HEX: #00FF00
i RGB: c(0, 0, 255) -> HEX: #0000FF
v RGB: c(0, 1, 255) -> HEX: #0001FF
x An error occurred: non-numeric argument to mathematical function
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Statistical Power Analysis --------------------------------------------------
i Test: t.test (two.sample)
i Effect size: 0.500
i Sample size: 30 (per group)
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
x Statistical Power: "47.79%" (Very Low)
This study has only 47.8% power, meaning there is a 47.8% chance of detecting a
true effect of size 0.50. This is considered very low power.
i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
-- Sample Size Estimation ------------------------------------------------------
i Test: t.test (two.sample)
i Target power: 0.80
i Effect size: 0.500
i Significance level (alpha): 0.050
i Alternative: two.sided
-- Result --
v Sample size per group: 64
v Total sample size: 128
To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects
per group (128 total).
i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations.
i Square test started at 2026-03-04 19:28:31
v Square test completed in 0.000 seconds
i Silent task started at 2026-03-04 19:28:31
v Silent task completed in 0.000 seconds
v Excel file written to '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4067dcaab2.xlsx'
v Excel file written to '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc4056b60ea6.xlsx'
v Excel file written to '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc401f4d8db.xlsx'
! File already exists and will be overwritten: '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc401f4d8db.xlsx'
v Excel file written to '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/file7fc401f4d8db.xlsx'
v Excel file written to '/tmp/RtmpZ2qh8S/working_dir/RtmpL2JrBh/test-write-7fc40347d3e83_2026-03-04.xlsx'
[ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
══ Skipped tests (68) ══════════════════════════════════════════════════════════
• On CRAN (68): 'test-download_batch.R:19:3', 'test-download_batch.R:39:3',
'test-download_gene_ref.R:66:3', 'test-download_gene_ref.R:109:3',
'test-download_gene_ref.R:150:3', 'test-download_gene_ref.R:174:3',
'test-download_gene_ref.R:198:3', 'test-download_gene_ref.R:218:3',
'test-download_gene_ref.R:237:3', 'test-download_gene_ref.R:264:3',
'test-download_gene_ref.R:292:3', 'test-download_gene_ref.R:320:3',
'test-download_gene_ref.R:339:3', 'test-download_gene_ref.R:359:3',
'test-download_url.R:206:3', 'test-download_url.R:238:3',
'test-download_url.R:266:3', 'test-download_url.R:296:3',
'test-download_url.R:319:3', 'test-download_url.R:342:3',
'test-download_url.R:365:3', 'test-file_info.R:12:3',
'test-file_info.R:26:3', 'test-file_info.R:48:3', 'test-file_info.R:59:3',
'test-file_info.R:71:3', 'test-pkg.R:389:3', 'test-pkg.R:528:3',
'test-pkg.R:536:3', 'test-pkg.R:546:3', 'test-pkg.R:561:3',
'test-plot_forest.R:61:3', 'test-plot_forest.R:77:3',
'test-plot_forest.R:96:3', 'test-plot_forest.R:113:3',
'test-plot_forest.R:134:3', 'test-plot_forest.R:149:3',
'test-plot_forest.R:164:3', 'test-plot_forest.R:183:3',
'test-plot_forest.R:200:3', 'test-plot_forest.R:217:3',
'test-plot_forest.R:235:3', 'test-plot_forest.R:253:3',
'test-plot_forest.R:275:3', 'test-plot_forest.R:292:3',
'test-plot_forest.R:310:3', 'test-plot_forest.R:327:3',
'test-plot_forest.R:350:3', 'test-plot_forest.R:368:3',
'test-plot_forest.R:389:3', 'test-plot_forest.R:410:3',
'test-plot_forest.R:427:3', 'test-plot_forest.R:444:3',
'test-plot_forest.R:475:3', 'test-plot_forest.R:493:3',
'test-plot_forest.R:515:3', 'test-plot_forest.R:532:3',
'test-plot_forest.R:553:3', 'test-plot_forest.R:570:3',
'test-plot_forest.R:594:3', 'test-plot_forest.R:611:3',
'test-plot_forest.R:632:3', 'test-plot_forest.R:662:3',
'test-plot_forest.R:696:3', 'test-plot_forest.R:730:3',
'test-plot_forest.R:756:3', 'test-plot_forest.R:772:3',
'test-plot_forest.R:789:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-download_geo_data.R:27:3'): download_geo_data() validates gse_id parameter ──
Error in `download_geo_data("invalid", dest_dir = temp_dir)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:27:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("invalid", dest_dir = temp_dir)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:91:3'): download_geo_data() validates dest_dir parameter ──
Error in `download_geo_data("GSE12345", dest_dir = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:91:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:128:3'): download_geo_data() validates overwrite parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:128:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, overwrite = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:159:3'): download_geo_data() validates log parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:159:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log = "yes")
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:190:3'): download_geo_data() validates log_file parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:190:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, log_file = 123)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:217:3'): download_geo_data() validates retries parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:217:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, retries = -1)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
── Error ('test-download_geo_data.R:260:3'): download_geo_data() validates timeout parameter ──
Error in `download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)`: GEOquery package is required. Please install it with: BiocManager::install('GEOquery')
Backtrace:
▆
1. ├─testthat::expect_error(...) at test-download_geo_data.R:260:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. └─evanverse::download_geo_data("GSE12345", dest_dir = temp_dir, timeout = 0)
8. └─cli::cli_abort("GEOquery package is required. Please install it with: BiocManager::install('GEOquery')")
9. └─rlang::abort(...)
[ FAIL 7 | WARN 0 | SKIP 68 | PASS 2100 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.4.0
Check: installed package size
Result: NOTE
installed size is 8.8Mb
sub-directories of 1Mb or more:
doc 7.5Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64