The mipplot
package is an R
package for visualizing data in the IAMC template format.
This vignette introduces the basic features of the mipplot
package.
This vignette uses a subset of the SR1.5 data. This data is included in the mipplot
package and does not need to be downloaded separately.
Load the mipplot
and tidyverse
packages.
library(mipplot)
library(tidyverse)
The SR1.5 subset data that comes with the mipplot
package can be accessed under the name mipplot::sr15_sample_data
. Let’s take a look at what’s inside.
sr15_sample_data
#> # A tibble: 396,425 x 7
#> model scenario region variable unit period value
#> <fct> <fct> <fct> <fct> <fct> <int> <dbl>
#> 1 AIM/CGE 2.0 SSP1-19 World Emissions|CO2 Mt CO2/yr 2000 NA
#> 2 AIM/CGE 2.0 SSP1-19 World Final Energy EJ/yr 2000 NA
#> 3 AIM/CGE 2.0 SSP1-19 World Population million 2000 NA
#> 4 AIM/CGE 2.0 SSP1-19 World Price|Carbon US$2010/t C~ 2000 NA
#> 5 AIM/CGE 2.0 SSP1-19 World Primary Energy EJ/yr 2000 NA
#> 6 AIM/CGE 2.0 SSP1-19 World Primary Energy|Biomass EJ/yr 2000 NA
#> 7 AIM/CGE 2.0 SSP1-19 World Primary Energy|Coal EJ/yr 2000 NA
#> 8 AIM/CGE 2.0 SSP1-19 World Primary Energy|Fossil EJ/yr 2000 NA
#> 9 AIM/CGE 2.0 SSP1-19 World Primary Energy|Gas EJ/yr 2000 NA
#> 10 AIM/CGE 2.0 SSP1-19 World Primary Energy|Geother~ EJ/yr 2000 NA
#> # ... with 396,415 more rows
The data is in tibble
format. It contains the following columns: model
, scenario
, region
, variable
, unit
, period
, and value
.
In addition, to read another data from a file, do the following.
iamc_data <- mipplot_read_iamc("path_to_data.csv")
Check the list of models included in the data.
sr15_sample_data$model %>% levels
#> [1] "AIM/CGE 2.0" "GCAM 4.2" "IMAGE 3.0.1"
#> [4] "MESSAGE-GLOBIOM 1.0" "MESSAGEix-GLOBIOM 1.0" "REMIND-MAgPIE 1.5"
#> [7] "WITCH-GLOBIOM 3.1"
Check the list of scenarios.
sr15_sample_data$scenario %>% levels
#> [1] "SSP1-19" "SSP1-26" "SSP1-34" "SSP1-45"
#> [5] "SSP1-Baseline" "SSP2-19" "SSP2-26" "SSP2-34"
#> [9] "SSP2-45" "SSP2-60" "SSP2-Baseline" "SSP3-34"
#> [13] "SSP3-45" "SSP3-60" "SSP3-Baseline" "SSP4-26"
#> [17] "SSP4-34" "SSP4-45" "SSP4-Baseline" "SSP5-26"
#> [21] "SSP5-34" "SSP5-45" "SSP5-60" "SSP5-Baseline"
#> [25] "SSP4-60" "SSP5-19" "LowEnergyDemand" "SSP4-19"
Check the list of regions.
sr15_sample_data$region %>% levels
#> [1] "World"
Let’s check the list of variables.
sr15_sample_data %>% select(variable, unit) %>% distinct
#> # A tibble: 35 x 2
#> variable unit
#> <fct> <fct>
#> 1 Emissions|CO2 Mt CO2/yr
#> 2 Final Energy EJ/yr
#> 3 Population million
#> 4 Price|Carbon US$2010/t CO2
#> 5 Primary Energy EJ/yr
#> 6 Primary Energy|Biomass EJ/yr
#> 7 Primary Energy|Coal EJ/yr
#> 8 Primary Energy|Fossil EJ/yr
#> 9 Primary Energy|Gas EJ/yr
#> 10 Primary Energy|Geothermal EJ/yr
#> # ... with 25 more rows
This section explains how to filter data, using the example of extracting only models that contain SSP2
scenarios.
sr15_sample_data %>%
filter(str_detect(scenario, "^SSP2")) %>%
select(model, scenario) %>% distinct
#> # A tibble: 34 x 2
#> model scenario
#> <fct> <fct>
#> 1 AIM/CGE 2.0 SSP2-19
#> 2 AIM/CGE 2.0 SSP2-26
#> 3 AIM/CGE 2.0 SSP2-34
#> 4 AIM/CGE 2.0 SSP2-45
#> 5 AIM/CGE 2.0 SSP2-60
#> 6 AIM/CGE 2.0 SSP2-Baseline
#> 7 GCAM 4.2 SSP2-19
#> 8 GCAM 4.2 SSP2-26
#> 9 GCAM 4.2 SSP2-34
#> 10 GCAM 4.2 SSP2-45
#> # ... with 24 more rows
The "^SSP2"
given to the str_detect
function is called a regular expression. The "^"
stands for the beginning of a sentence, and the regular expression “^SSP2”" means to check if the string starts with “SSP2”.
distinct
is a function that removes duplicate data.
Next, let’s extract the list of variables under Secondary Energy|Electricity|.
“|” has a special meaning in regular expressions, so to search for it we must use something like “\|”. This is called escaping.
sr15_sample_data %>%
filter(str_detect(variable, "^Secondary Energy\\|Electricity\\|")) %>%
select(variable) %>%
distinct
#> # A tibble: 10 x 1
#> variable
#> <fct>
#> 1 Secondary Energy|Electricity|Biomass
#> 2 Secondary Energy|Electricity|Coal
#> 3 Secondary Energy|Electricity|Gas
#> 4 Secondary Energy|Electricity|Geothermal
#> 5 Secondary Energy|Electricity|Hydro
#> 6 Secondary Energy|Electricity|Non-Biomass Renewables
#> 7 Secondary Energy|Electricity|Nuclear
#> 8 Secondary Energy|Electricity|Oil
#> 9 Secondary Energy|Electricity|Solar
#> 10 Secondary Energy|Electricity|Wind
Let’s use the interactive interface of mipplot
to filter and visualize the data.
The first step is to perform a line plot, which can be done as follows:
mipplot_interactive_line(sr15_sample_data)
You will then see a screen like the one below.
Select the various conditions and plot the graph.
The R code for reproducing the graph will be displayed on the screen. You can copy and paste it to edit the plot as shown below.
data_subset <- sr15_sample_data %>%
filter( model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1") ) %>%
filter(2000 <= period) %>%
filter(period <= 2100)
mipplot_line(
data_subset,
variable = c("Emissions|CO2"),
scenario = c("SSP3-34", "SSP3-45", "SSP3-60", "SSP3-Baseline"),
region = c("World"),
legend = TRUE,
language = 'en')[[1]]
Next, we will create a bar plot. Since bar plot needs a rule table, we will use the rule ar5_db_sample_rule_table
for AR5.
mipplot_interactive_bar(sr15_sample_data, ar5_db_sample_rule_table)
On the screen you will see the R code to reproduce the plot. Copy and paste this to edit the plot as shown below.
data_subset <- sr15_sample_data %>%
filter(variable %in% c("Primary Energy", "Primary Energy|Coal", "Primary Energy|Gas", "Primary Energy|Oil", "Primary Energy|Nuclear", "Primary Energy|Hydro", "Primary Energy|Biomass", "Primary Energy|Solar", "Primary Energy|Wind", "Primary Energy|Geothermal", "Primary Energy|Ocean")) %>%
filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
filter(scenario %in% c("SSP3-34", "SSP3-45", "SSP3-60", "SSP3-Baseline"))
mipplot_bar(data_subset, ar5_db_sample_rule_table,
region = c("World"),
target_year = 2050,
one_hundred_percent_stacked = FALSE,
language = 'en')[[1]]
Since area plot also requires a rule, we will use the rule ar5_db_sample_rule_table
for AR5.
mipplot_interactive_area(sr15_sample_data, ar5_db_sample_rule_table)
On the screen you will see the R code to reproduce the plot. Copy and paste this to edit the plot as shown below.
data_subset <- sr15_sample_data %>%
filter(variable %in% c("Secondary Energy|Electricity", "Secondary Energy|Electricity|Coal", "Secondary Energy|Electricity|Gas", "Secondary Energy|Electricity|Oil", "Secondary Energy|Electricity|Nuclear", "Secondary Energy|Electricity|Hydro", "Secondary Energy|Electricity|Biomass", "Secondary Energy|Electricity|Solar", "Secondary Energy|Electricity|Wind", "Secondary Energy|Electricity|Geothermal", "Secondary Energy|Electricity|Ocean")) %>%
filter(model %in% c("AIM/CGE 2.0", "GCAM 4.2", "IMAGE 3.0.1")) %>%
filter(2000 <= period) %>%
filter(period <= 2100)
mipplot_area(data_subset, ar5_db_sample_rule_table,
region = c("World"),
scenario = c("SSP3-Baseline"),
one_hundred_percent_stacked = FALSE,
language = 'en')[[1]]