The functions pastem corresponds to string interpolations similarly to Stata and Julia.
R name <- "Bob" height <- 72 units <- "inches" weight <- 230 pastem("My record indicates your height is $height $(units).") #> [1] "My record indicates your height is 72 inches" pastem("Your body mass index is $(round(703*weight/height^2,1))") #> [1] "Your body mass index is 31.2" a <- "ght" pastem("My record indicates you are $(hei$a) inches tall") #> [1] "My record indicates you are 72 inches tall" The option pattern allows to use a different pattern. R pastem("My record indicates you are #height inches tall", pattern = "#") #> [1] "My record indicates you are 72 inches tall"
If you choose a common pattern, the option parenthesis.only = TRUE allows to replace only expressions enclosed in parenthesis. Note that only the first dot is replaced in the following string:
R pastem("You are .(height) inches tall.This is below average", pattern = ".", parenthesis.only = TRUE) #> [1] "You are 72 inches tall.This is below average."
The function quotem implements expression interpolations. The function evalm is a wrapper for eval(quotem()): it corresponds to Julia @eval and to Stata natural macros (although restricted to name substitutions).
R library(statar) library(data.table) N <- 100 DT <- data.table( id = sample(5, N, TRUE), v1 = sample(5, N, TRUE), v2 = sample(1e6, N, TRUE) ) newvar <- quote(temp) myvar <- quote(v1) byvar <- c("id", "v1") quotem(DT[, list(`$newvar` = mean(`$myvar`)), by = `$byvar`]) #> DT[, list(temp = mean(v1)), by = c("id", "v1")] evalm(DT[, list(`$newvar` = mean(`$myvar`)), by = `$byvar`]) #> id v1 temp #> 1: 2 4 4 #> 2: 5 5 5 #> 3: 4 5 5 #> 4: 1 3 3 #> 5: 5 2 2 #> 6: 3 4 4 #> 7: 3 5 5 #> 8: 3 1 1
Note that names starting with the pattern $ must be enclosed in backquotes to be considered valid names in R.