First steps with the mipplot package
U-Tokyo mipplot team
2021-05-17
Summary
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.
Data
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 library
Load the mipplot and tidyverse packages.
library(mipplot)
library(tidyverse)load sample data
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$2010/t C~ 2000 NA
#> 5 AIM/CGE 2.0 SSP1-19 World Primary Energy EJ/yr 2000 NA
#> 6 AIM/CGE 2.0 SSP1-19 World Primary Energy|Biomass EJ/yr 2000 NA
#> 7 AIM/CGE 2.0 SSP1-19 World Primary Energy|Coal EJ/yr 2000 NA
#> 8 AIM/CGE 2.0 SSP1-19 World Primary Energy|Fossil EJ/yr 2000 NA
#> 9 AIM/CGE 2.0 SSP1-19 World Primary Energy|Gas EJ/yr 2000 NA
#> 10 AIM/CGE 2.0 SSP1-19 World Primary Energy|Geother~ EJ/yr 2000 NA
#> # ... with 396,415 more rowsThe data is in tibble format. It contains the following columns: model, scenario, region, variable, unit, period, and value.
In addition, to read another data from a file, do the following.
iamc_data <- mipplot_read_iamc("path_to_data.csv")Check the list of models included in the data.
sr15_sample_data$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 rowsData Filtering
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 rowsThe "^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|WindVisualization with interactive tool
Let’s use the interactive interface of mipplot to filter and visualize the data.
line plot
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]]
bar plot
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]]
area plot
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]]
Output in another language
So far, we have plotted in English, but in addition to this, we can plot in Chinese (Simplified), Chinese (Traditional), Japanese, and Spanish.
All plots so far have specified language = "en" (English), so replace this with, for example, the following:
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 = 'jp')[[1]] # <--------------- We changed hereThe following table shows the languages and language codes that can be set.
| language | language code |
|---|---|
| English | en |
| Chinese (Simplified) | zh-cn |
| Chinese (Traditional) | zh-tw |
| Japanese | jp |
| Spanish | es |
Output to PDF file
To output an image to a PDF file, do the following:
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)
# Save plot to `graph` variable
graph <- mipplot_area(data_subset, ar5_db_sample_rule_table,
region = c("World"),
scenario = c("SSP3-Baseline"),
one_hundred_percent_stacked = FALSE,
language = 'zh-cn')[[1]]
# Give `graph` to `mipplot_print_pdf`
# mipplot_print_pdf(graph)
This will bring up a dialog box to save the file. You can save the file as a PDF file.