Package 'mipplot' reference manual

Title:	An Open-Source Tool for Visualization of Climate Mitigation Scenarios
Description:	Generic functions to produce area/bar/box/line plots of data following IAMC (Integrated Assessment Modeling Consortium) submission format.
Authors:	Diego Silva Herran [aut], Jiayang WANG [aut], Masahiro SUGIYAMA [aut], Hiroto SHIRAKI [aut], Akimitsu Inoue [ctr, cre]
Maintainer:	Akimitsu Inoue <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.1
Built:	2024-12-08 07:18:07 UTC
Source:	CRAN

Add credit text to plots

Description

Add credit text to a list of ggplot2 plot objects

Usage

add_credit_to_list_of_plot(list_of_plot)
add_credit_to_list_of_plot(list_of_plot)

Arguments

list_of_plot

list of ggplot2 plot objects

Value

list of modified ggplot2 plot objects

Add credit text to a plot

Description

Add credit text and project URL to a ggplot2 plot object

Usage

add_credit_to_plot(plot_object)
add_credit_to_plot(plot_object)

Arguments

plot_object

ggplot2 plot object

Value

modified ggplot2 plot object

Sample Dataset

Description

A sample dataset of IAMC format

Usage

ar5_db_sample_data
ar5_db_sample_data

Format

A tibble data.table with 25240 rows and 7 variables:

model: model, categorical
scenario: scenario, categorical
region: region, ASIA, OECD90 or World
variable: the name of simulated variable that changes over time
unit: unit of a variable
period: year
value: the value of a variable

Source

https://tntcat.iiasa.ac.at/AR5DB/dsd?Action=htmlpage&page=about

Sample Rule Table

Description

A sample rule table

Usage

ar5_db_sample_rule_table
ar5_db_sample_rule_table

Format

A data frame of additivity rule.

Rule_ID: rule id
Left_side: name of left-side variable
Right_side: name of right-side variable
Color_code: hex color code

change column data type in data-set

Description

change column data type in data-set to be able to be treated as an IAMC data-set.

Usage

change_data_types_of_iamc_dataframe(iamc_data)
change_data_types_of_iamc_dataframe(iamc_data)

Arguments

iamc_data

data frame which has columns 'model', 'scenario', 'region', 'variable', 'period', 'unit'

Value

converted data-frame.

check if the dataset has required fields of IAMC dataset

Description

if dataset has all required fields, then returns TRUE

Usage

check_column_availability(iamc_data)
check_column_availability(iamc_data)

Arguments

iamc_data

IAMC data frame

Value

boolean flag

check if the format of given data is valid as an IAMC dataset.

Description

check if the format of given data is valid as an IAMC dataset.

Usage

check_format_of_iamc_dataframe(iamc_data)
check_format_of_iamc_dataframe(iamc_data)

Arguments

iamc_data

IAMC dataset in dataframe format

Value

TRUE if it is valid

correct data format of given IAMC data table

Description

Dataset in IAMC format rule is not rigid. This function corrects data types of columns in the dataset. If necessary columns is missing, it throws exception. Output object of this function is as follows:

type: data.table columns: model: factor scenario: factor region: factor variable: factor unit: factor period: double value: double

Usage

correct_format_of_iamc_dataframe(iamc_data)
correct_format_of_iamc_dataframe(iamc_data)

Arguments

iamc_data

IAMC dataset described above

Value

modified dataframe

generate code to reproduce area plot

Description

This function is called in the mipplot_interactive_area() and provides R code to reproduce the currently drawn plot. This function cannot be used out of reactive expression in Shiny.

Usage

generate_code_to_plot_area(
  input,
  name_of_input_data_variable,
  name_of_input_rule_table_variable
)
generate_code_to_plot_area(
  input,
  name_of_input_data_variable,
  name_of_input_rule_table_variable
)

Arguments

`input`	This is the same as the input argument in the shiny:ui().
`name_of_input_data_variable`	A string such as "ar5_sample_data".
`name_of_input_rule_table_variable`	A string such as "ar5_sample_rule".

Value

A string representing the R code for rerun.

generate code to reproduce bar plot

Description

This function is called in the mipplot_interactive_bar() and provides R code to reproduce the currently drawn plot. This function cannot be used out of reactive expression in Shiny.

Usage

generate_code_to_plot_bar(
  input,
  name_of_input_data_variable,
  name_of_input_rule_table_variable
)
generate_code_to_plot_bar(
  input,
  name_of_input_data_variable,
  name_of_input_rule_table_variable
)

Arguments

`input`	This is the same as the input argument in the shiny:ui().
`name_of_input_data_variable`	A string such as "ar5_sample_data".
`name_of_input_rule_table_variable`	A string such as "ar5_sample_rule".

Value

A string representing the R code for rerun.

generate code to reproduce line plot

Description

from 'input' argument generally used in reactive context in Shiny, this function generates R code to reproduce current plot. This function could not used out of reactive expression in Shiny.

Usage

generate_code_to_plot_line(input, name_of_iamc_data_variable = "D")
generate_code_to_plot_line(input, name_of_iamc_data_variable = "D")

Arguments

`input`	it is same as the argument of shiny::ui() this function accesses following attributes: - model - period - variable - scenario - region
`name_of_iamc_data_variable`	name of IAMC data variable

Value

R code

Get name list of models in IAMC formatted data frame

Description

select name of models from the column "model" then make unique it. output is character vector such as, c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4" )

Usage

get_model_name_list(D)
get_model_name_list(D)

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.

Value

A list of strings representing model names

Get name list of scenarios in IAMC formatted data frame

Description

select name of scenarios from the column "scenario" then make unique it. output is character vector such as, c("EMF27-450-Conv", "EMF27-450-FullTech", "EMF27-450-NoCCS", "EMF27-450-NucOff")

Usage

get_scenario_name_list(D)
get_scenario_name_list(D)

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.

Value

A list of strings representing scenario names

Get expression of vector of string in string format

Description

To evaluate expression, get string of expression

Usage

get_string_expression_of_vector_of_strings(vector_of_strings)
get_string_expression_of_vector_of_strings(vector_of_strings)

Arguments

vector_of_strings

vector of strings, such as c("A", "B")

Value

An R code representing character vector

Get variable-group-name list

Description

variable-group is a combination of one LHS and one or more RHS. this function outputs the list of names of variable-group in given rule-table. the format of return value is "LHS|RHS1,RHS2,RHS3,...".

Usage

get_variable_group_name_list(rule_table)
get_variable_group_name_list(rule_table)

Arguments

rule_table

A rule table

Value

variable group name

Examples

get_variable_group_name_list(ar5_db_sample_rule_table)
get_variable_group_name_list(ar5_db_sample_rule_table)

Get variable name list in given variable-group

Description

Scan rule-table and extract variable names in given variable-group.

Usage

get_variable_name_list_in_variable_group(group_name)
get_variable_name_list_in_variable_group(group_name)

Arguments

group_name

variable-group-name

Value

A list of strings representing variable names

Examples

get_variable_name_list_in_variable_group(
  "Final Energy|Industry,Residential and Commercial,Transportation")
get_variable_name_list_in_variable_group(
  "Final Energy|Industry,Residential and Commercial,Transportation")

mipplot

Description

Package contains generic functions to produce area/bar/box/line plots of data following IAMC submission format.

check additivity of rules and data

Description

This function is used for debugging a rule table and data-set. An input is a rule table and a data-set, the outputs are some area plots showing the divergence between the left-side variable and the sum of the right-side variables.

Usage

mipplot_additivity_check(D, R, max_n_plots = Inf, plot_all = FALSE)
mipplot_additivity_check(D, R, max_n_plots = Inf, plot_all = FALSE)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce area plots.
`R`	A dataframe of data aggregation rules (meta data).
`max_n_plots`	The maximum number of output plots.
`plot_all`	set FALSE to plot only inconsistent combinations

Value

A list of area plots.

Examples


if (interactive()) {
  mipplot_additivity_check(
    ar5_db_sample_data, ar5_db_sample_rule_table, max_n_plots = 10)
  }

if (interactive()) {
  mipplot_additivity_check(
    ar5_db_sample_data, ar5_db_sample_rule_table, max_n_plots = 10)
  }

Additivity check using bar plot

Description

This function is used for debugging a rule table and data-set. An input is a rule table and a data-set, the outputs are some bar plots showing the divergence between the left-side variable and the sum of the right-side variables.

Usage

mipplot_additivity_check_bar(
  D,
  R,
  target_scenarios,
  target_rule_ids = 4,
  show_all_scenarios = FALSE,
  show_all_rule_ids = FALSE,
  debug = FALSE
)
mipplot_additivity_check_bar(
  D,
  R,
  target_scenarios,
  target_rule_ids = 4,
  show_all_scenarios = FALSE,
  show_all_rule_ids = FALSE,
  debug = FALSE
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce area plots.
`R`	A dataframe of data aggregation rules (meta data).
`target_scenarios`	A character vector of scenario names
`target_rule_ids`	A list of rule id.
`show_all_scenarios`	Set TRUE to show all scenarios.
`show_all_rule_ids`	Set TRUE to show all rules.
`debug`	Set TRUE if show intermediate dataframe using View function.

Value

A list of bar plots.

Examples

mipplot_additivity_check_bar(
   ar5_db_sample_data, ar5_db_sample_rule_table,
   target_scenarios = c("EMF27-450-Conv", "EMF27-Base-NucOff"))
mipplot_additivity_check_bar(
   ar5_db_sample_data, ar5_db_sample_rule_table,
   target_scenarios = c("EMF27-450-Conv", "EMF27-Base-NucOff"))

Area plot from IAMC data

Description

Area plots using right-hand-side values of target additivity rule. The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions (two in this version).

Usage

mipplot_area(
  D,
  R,
  region = levels(D$region),
  scenario = levels(D$scenario),
  facet_x = NULL,
  facet_y = NULL,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  fontsize = 20,
  color_code_specify = TRUE,
  one_hundred_percent_stacked = FALSE,
  axis_year_text_angle = 0,
  language = "en"
)
mipplot_area(
  D,
  R,
  region = levels(D$region),
  scenario = levels(D$scenario),
  facet_x = NULL,
  facet_y = NULL,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  fontsize = 20,
  color_code_specify = TRUE,
  one_hundred_percent_stacked = FALSE,
  axis_year_text_angle = 0,
  language = "en"
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce area plots.
`R`	A dataframe of data aggregation rules (meta data).
`region`	A list of regions.
`scenario`	A list of scenario.
`facet_x`	facet_x
`facet_y`	facet_y
`PRINT_OUT`	set TRUE to generate PDF file.
`DEBUG`	set TRUE to show debug messages.
`fontsize`	font size of text.
`color_code_specify`	set FALSE if you apply default color palette.
`one_hundred_percent_stacked`	set TRUE if you want a graph of 100% stacked, set this to TRUE.
`axis_year_text_angle`	text angle of x axis
`language`	A string of language. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

A list of area plots.

Examples


library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(2005 <= period) %>%
filter(period <= 2100)
mipplot_area(data_subset, ar5_db_sample_rule_table,
region = c("ASIA"),
scenario = c("EMF27-450-Conv"),
one_hundred_percent_stacked = FALSE,
axis_year_text_angle = 0,
language = 'en')

library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(2005 <= period) %>%
filter(period <= 2100)
mipplot_area(data_subset, ar5_db_sample_rule_table,
region = c("ASIA"),
scenario = c("EMF27-450-Conv"),
one_hundred_percent_stacked = FALSE,
axis_year_text_angle = 0,
language = 'en')

Complementation of color scheme

Description

fill colors automatically

Usage

mipplot_autofill_color(rule_table_without_colors)
mipplot_autofill_color(rule_table_without_colors)

Arguments

rule_table_without_colors

Incomplete color specification rule table. It dosen't contain "Color_code" column.

Value

Complete color specification rule table. It is containing "Color_code" column. However, if color complementation can not be performed automatically, the return value is an incomplete color specification.

Bar plot from IAMC data

Description

Bar plots using right-hand-side values of target additivity rule. The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions.

Usage

mipplot_bar(
  D,
  R,
  region = levels(D$region),
  xby = "scenario",
  target_year = levels(as.factor(D$period)),
  facet_x = NULL,
  facet_y = NULL,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  fontsize = 20,
  color_code_specify = TRUE,
  one_hundred_percent_stacked = FALSE,
  axis_scenario_text_angle = 0,
  language = "en"
)
mipplot_bar(
  D,
  R,
  region = levels(D$region),
  xby = "scenario",
  target_year = levels(as.factor(D$period)),
  facet_x = NULL,
  facet_y = NULL,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  fontsize = 20,
  color_code_specify = TRUE,
  one_hundred_percent_stacked = FALSE,
  axis_scenario_text_angle = 0,
  language = "en"
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce plots.
`R`	A dataframe of data aggregation rules (meta data).
`region`	A list of region.
`xby`	name of axis. the default setting is "scenario".
`target_year`	target year.
`facet_x`	facet_x
`facet_y`	facet_y
`PRINT_OUT`	set TRUE to generate A PDF file.
`DEBUG`	set TRUE to show debug messages.
`fontsize`	size of font in the output plot.
`color_code_specify`	set FALSE if you apply default color palette.
`one_hundred_percent_stacked`	set TRUE if you want a graph of 100% stacked, set this to TRUE.
`axis_scenario_text_angle`	text angle of x axis
`language`	A string of language. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

A list of bar plots.

Examples


library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(scenario %in% c("EMF27-450-Conv", "EMF27-450-FullTech"))

mipplot_bar(data_subset, ar5_db_sample_rule_table,
region = c("ASIA"),
target_year = 2005,
one_hundred_percent_stacked = FALSE,
axis_scenario_text_angle = 0,
language = 'en')

library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(scenario %in% c("EMF27-450-Conv", "EMF27-450-FullTech"))

mipplot_bar(data_subset, ar5_db_sample_rule_table,
region = c("ASIA"),
target_year = 2005,
one_hundred_percent_stacked = FALSE,
axis_scenario_text_angle = 0,
language = 'en')

Box plot from IAMC data

Description

The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions

Usage

mipplot_box(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  target_year = levels(as.factor(D$period)),
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  language = "en"
)
mipplot_box(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  target_year = levels(as.factor(D$period)),
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  language = "en"
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce plots.
`region`	A list of regions.
`variable`	A list of variables.
`target_year`	target year.
`PRINT_OUT`	set TRUE to generate PDF file.
`DEBUG`	set TRUE to show debug messages.
`language`	A string of language. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

A list of box plots.

Examples


library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(period == 2100) %>% filter(region == "OECD90")
mipplot_box(data_subset)

library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(period == 2100) %>% filter(region == "OECD90")
mipplot_box(data_subset)

Default color palette.

Description

Default color palette.

Usage

mipplot_default_color_palette
mipplot_default_color_palette

Format

A default color palette object, which maps variable name (such as "Land Use") to hex color code.

Manual coloring

Description

Generate mapper from name of variable to name of color

Usage

mipplot_generate_color_mapper(raw_table, category_separator = "\\|")
mipplot_generate_color_mapper(raw_table, category_separator = "\\|")

Arguments

`raw_table`	rule table which includes "Color_code" column.
`category_separator`	regular expression for separating right-hand-side variable name into categories. For example: separator should be "\\|" for "Secondary Energy\|Electricity\|Coal"

Value

named list of named string vectors. for example,

result = list( "Emissions|CO2" = c( "Fossil Fuels and Industry" = "#17202a", "Land Use" = "#008000", ...), "Emissions|CO2|Fossil Fuels and Industry" = c( "Energy Demand" = "#444444", ... ),...

A function to launch interactive plot using Shiny

Description

A function to launch interactive plot for additivity check.

Usage

mipplot_interactive_additivity_check_bar(D, R, debug = FALSE)
mipplot_interactive_additivity_check_bar(D, R, debug = FALSE)

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.
`R`	A table with additivity rules.
`debug`	Set TRUE if table view is required.

Value

No return value, called for side effects

Examples


if (interactive()) {
mipplot_interactive_additivity_check_bar(ar5_db_sample_data, ar5_db_sample_rule_table)
}

if (interactive()) {
mipplot_interactive_additivity_check_bar(ar5_db_sample_data, ar5_db_sample_rule_table)
}

A function to launch interactive plotting session on Shiny

Description

Provides gui to set plotting parameter for area plot.

Usage

mipplot_interactive_area(D, R, language = "en")
mipplot_interactive_area(D, R, language = "en")

Arguments

`D`	A dataframe of IAMC data in tibble format to produce area plots.
`R`	A dataframe of data aggregation rules (meta data).
`language`	A string of language for initial plot. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

No return value, called for side effects

Examples


if (interactive()) {
mipplot_interactive_area(ar5_db_sample_data, ar5_db_sample_rule_table)
}

if (interactive()) {
mipplot_interactive_area(ar5_db_sample_data, ar5_db_sample_rule_table)
}

A function to launch interactive plot using Shiny

Description

A function to launch interactive bar plot using right-hand-side values of target additivity rule. The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions

Usage

mipplot_interactive_bar(D, R, language = "en")
mipplot_interactive_bar(D, R, language = "en")

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.
`R`	A table with additivity rules.
`language`	A string of language for initial plot. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

No return value, called for side effects

Examples


if (interactive()) {
mipplot_interactive_bar(ar5_db_sample_data, ar5_db_sample_rule_table)
}

if (interactive()) {
mipplot_interactive_bar(ar5_db_sample_data, ar5_db_sample_rule_table)
}

A function to launch interactive plot using Shiny

Description

A function to launch interactive line plot. The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions

Usage

mipplot_interactive_line(D, language = "en")
mipplot_interactive_line(D, language = "en")

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.
`language`	A string of language for initial plot. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".

Value

No return value, called for side effects

Examples


if (interactive()) {
mipplot_interactive_line(ar5_db_sample_data)
}

if (interactive()) {
mipplot_interactive_line(ar5_db_sample_data)
}

A function to launch interactive plot using Shiny

Description

A function to launch interactive plot using Shiny

Usage

mipplot_interactive_plot_line(D, R)
mipplot_interactive_plot_line(D, R)

Arguments

`D`	A quitte format dataframe of IAMC data to produce graph.
`R`	A table with additivity rules.

Value

No return value, called for side effects

Examples


if (interactive()) {
mipplot_interactive_plot_line(ar5_db_sample_data, ar5_db_sample_rule_table)
}

if (interactive()) {
mipplot_interactive_plot_line(ar5_db_sample_data, ar5_db_sample_rule_table)
}

Line plot from IAMC data

Description

The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions

Usage

mipplot_line(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  colorby = "scenario",
  linetypeby = "model",
  shapeby = "model",
  scenario = levels(D$scenario),
  facet_x = NULL,
  facet_y = NULL,
  legend = TRUE,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  axis_year_text_angle = 0,
  language = "en",
  max_scenarios = 15,
  max_models = 15
)
mipplot_line(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  colorby = "scenario",
  linetypeby = "model",
  shapeby = "model",
  scenario = levels(D$scenario),
  facet_x = NULL,
  facet_y = NULL,
  legend = TRUE,
  PRINT_OUT = FALSE,
  DEBUG = TRUE,
  axis_year_text_angle = 0,
  language = "en",
  max_scenarios = 15,
  max_models = 15
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce plots.
`region`	A list of regions.
`variable`	A list of variables.
`colorby`	an axis for color setting.
`linetypeby`	an axis for line type setting.
`shapeby`	an axis for shape setting.
`scenario`	A list of scenarios.
`facet_x`	facet_x
`facet_y`	facet_y
`legend`	set TRUE to plot legend. default is TRUE.
`PRINT_OUT`	set TRUE to generate PDF files.
`DEBUG`	set TRUE to show debug messages.
`axis_year_text_angle`	text angle of x axis
`language`	A string of language. Possible values are "en", "jp", "es", "zh-cn", "zh-tw". The default value is "en".
`max_scenarios`	Maximum number of scenarios to be shown. If legend is FALSE, this option is .
`max_models`	Maximum number of models to be shown. If legend is FALSE, this option is

Value

A list of line plots.

Examples


library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter( model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4") ) %>%
filter(2005 <= period) %>%
filter(period <= 2100)
mipplot_line(
data_subset,
variable = c("Emissions|CO2"),
scenario = c("EMF27-450-Conv", "EMF27-450-FullTech", "EMF27-450-NoCCS"),
region = c("ASIA"),
legend = TRUE,
axis_year_text_angle = 0,
language = 'en')

library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter( model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4") ) %>%
filter(2005 <= period) %>%
filter(period <= 2100)
mipplot_line(
data_subset,
variable = c("Emissions|CO2"),
scenario = c("EMF27-450-Conv", "EMF27-450-FullTech", "EMF27-450-NoCCS"),
region = c("ASIA"),
legend = TRUE,
axis_year_text_angle = 0,
language = 'en')

Point plot from IAMC data

Description

The function arguments include the input dataframe, labels for the plot/axes/legend, and faceting dimensions

Usage

mipplot_point(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  target_year = levels(as.factor(D$period)),
  colorby = "model",
  shapeby = "model",
  xby = "scenario",
  facetby = NULL,
  facet_x = NULL,
  facet_y = NULL,
  fontsize = 20,
  PRINT_OUT = FALSE,
  DEBUG = TRUE
)
mipplot_point(
  D,
  region = levels(D$region),
  variable = levels(D$variable),
  target_year = levels(as.factor(D$period)),
  colorby = "model",
  shapeby = "model",
  xby = "scenario",
  facetby = NULL,
  facet_x = NULL,
  facet_y = NULL,
  fontsize = 20,
  PRINT_OUT = FALSE,
  DEBUG = TRUE
)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce plots.
`region`	A list of regions.
`variable`	A list of variables.
`target_year`	A list of target years.
`colorby`	An axis for color setting.
`shapeby`	An axis for shape setting.
`xby`	An axis for x locating setting.
`facetby`	facetby.
`facet_x`	facet_x.
`facet_y`	facet_y.
`fontsize`	font size.
`PRINT_OUT`	set TRUE to generate PDF image.
`DEBUG`	set TRUE to show debug messages.

Value

A list of point plots.

Examples


library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(period == 2100) %>% filter(region == "OECD90")
mipplot_point(data_subset)

library(dplyr)
data_subset <- ar5_db_sample_data %>%
filter(variable == "Emissions|CO2|Land Use") %>%
filter(model %in% c("AIM-Enduse 12.1", "GCAM 3.0", "IMAGE 2.4")) %>%
filter(period == 2100) %>% filter(region == "OECD90")
mipplot_point(data_subset)

Print list of plots to pdf file

Description

This function plots a ggplot plots to PDF file.

Usage

mipplot_print_pdf(
  p_list1,
  filelabel = "",
  filename = tryCatch(file.choose(new = TRUE), error = function(e) {     NA })
)
mipplot_print_pdf(
  p_list1,
  filelabel = "",
  filename = tryCatch(file.choose(new = TRUE), error = function(e) {     NA })
)

Arguments

`p_list1`	A list of ggplot plot.
`filelabel`	A string of prefix of output filename.
`filename`	A string of filename. If it is given, filelabel is ignored.

Value

No return value, called for side effects

Examples


if (interactive()) {
p <- mipplot_area(ar5_db_sample_data, ar5_db_sample_rule_table,
             region = "World", scenario = "EMF27-450-FullTech")
mipplot_print_pdf(p)
}

if (interactive()) {
p <- mipplot_area(ar5_db_sample_data, ar5_db_sample_rule_table,
             region = "World", scenario = "EMF27-450-FullTech")
mipplot_print_pdf(p)
}

Read IAMC scenario input data.

Description

Read scenario input data (in IAMC format) as tibble format dataframe.

Usage

mipplot_read_iamc(
  filename = NULL,
  sep = ",",
  interactive = FALSE,
  DEBUG = TRUE
)
mipplot_read_iamc(
  filename = NULL,
  sep = ",",
  interactive = FALSE,
  DEBUG = TRUE
)

Arguments

`filename`	Path to a file containing scenario data in IAMC format.
`sep`	A character indicating the separator used in the input file.
`interactive`	open a dialog for selecting file if interactive=TRUE.
`DEBUG`	experimental.

Value

A dataframe in tibble format ("model, scenario, variable, unit, period, value")

Examples

## Not run: 
mipplot_read_iamc("filename")

## End(Not run)
## Not run: 
mipplot_read_iamc("filename")

## End(Not run)

Read file of rule table without ID number

Description

Read table of additivity rule and adds column with id number.

Usage

mipplot_read_ruletab(R_without_id)
mipplot_read_ruletab(R_without_id)

Arguments

R_without_id

Path to a file containing data of additivity rule.

Value

A dataframe of additivity rule ("ID, Left_side, Right_side")

Examples

## Not run: 
mipplot_read_ruletab("filename")

## End(Not run)
## Not run: 
mipplot_read_ruletab("filename")

## End(Not run)

Mutated table of SR15 Data

Description

Mutated Table using filtered variable from the rule table The function arguments include the input dataframes: The SR15 dataset and the Rule Table and returns a mutated table with variable, value, model, scenario, region, period

Usage

mipplot_return_table(D, R)
mipplot_return_table(D, R)

Arguments

`D`	A dataframe of IAMC data in tibble format to produce mutated table
`R`	A dataframe of data aggregation rules

Value

Mutated Table of model,scenario,region,variable,unit,period,value

Examples

mipplot_return_table(sr15_sample_data, sr15_sample_conversion_rule_table)
mipplot_return_table(sr15_sample_data, sr15_sample_conversion_rule_table)

variable SUBMISSION CHECK

Description

Verify whether data of variables included in list template have been submitted.

Usage

mipplot_var_submission(D, V, na_name = "N/A")
mipplot_var_submission(D, V, na_name = "N/A")

Arguments

`D`	input data table
`V`	list of variables
`na_name`	string for N/A

Value

A dataframe representing variable availabilities.

Read IAMC scenario input data in Excel format

Description

Read scenario input data (in IAMC format) as tibble format dataframe from Excel

Usage

read_iamc_xlsx(file_path, sheet = 2)
read_iamc_xlsx(file_path, sheet = 2)

Arguments

`file_path`	Path to a file containing scenario data in IAMC format.
`sheet`	the index of sheet which contains records.

Value

A dataframe in tibble format ("model, scenario, variable, unit, period, value")

Examples

## Not run: 
read_iamc_xlsx("filename", sheet = 2)

## End(Not run)
## Not run: 
read_iamc_xlsx("filename", sheet = 2)

## End(Not run)

Split variable into positive and negative parts

Description

Generally, the range of the input value of stacked chart is greater than or equal to zero. This function splits variable into positive and negative parts in order to include negative values to stacked chart.

Usage

split_variable_into_positive_and_negative_parts(
  df_all,
  domain_column_name,
  variable_column_name,
  value_column_name,
  variable_name_converter = function(x) {     paste(x, "_negative", sep = "") },
  increment_of_domain_in_interpolation = 0.1
)
split_variable_into_positive_and_negative_parts(
  df_all,
  domain_column_name,
  variable_column_name,
  value_column_name,
  variable_name_converter = function(x) {     paste(x, "_negative", sep = "") },
  increment_of_domain_in_interpolation = 0.1
)

Arguments

`df_all`	input data frame
`domain_column_name`	domain column name, such as year
`variable_column_name`	variable column name, such as 'coal'
`value_column_name`	value column name, such as 'val'
`variable_name_converter`	function which convert original variable name into its negative part name
`increment_of_domain_in_interpolation`	step size for interpolation

Value

modified data frame

Sample Conversion Rule Table

Description

A sample conversion rule table for mipplot_return_table.

Usage

sr15_sample_conversion_rule_table
sr15_sample_conversion_rule_table

Format

An object of class data.frame with 37 rows and 6 columns.

Sample Dataset

Description

A sample dataset of IAMC format consist of a subset of IPCC special report (Global Warming of 1.5°C, 2018).

Usage

sr15_sample_data
sr15_sample_data

Format

A tibble data.table with 396425 rows and 7 variables:

model: model, categorical
scenario: scenario, categorical
region: region, ASIA, OECD90 or World
variable: the name of simulated variable that changes over time
unit: unit of a variable
period: year
value: the value of a variable

Source

https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/

Package 'mipplot'

Help Index

Add credit text to plots

Description

Usage

Arguments

Value

Add credit text to a plot

Description

Usage

Arguments

Value

Sample Dataset

Description

Usage

Format

Source

Sample Rule Table

Description

Usage

Format

change column data type in data-set

Description

Usage

Arguments

Value

check if the dataset has required fields of IAMC dataset

Description

Usage

Arguments

Value

check if the format of given data is valid as an IAMC dataset.

Description

Usage

Arguments

Value

correct data format of given IAMC data table

Description

Usage

Arguments

Value

generate code to reproduce area plot

Description

Usage

Arguments

Value

generate code to reproduce bar plot

Description

Usage

Arguments

Value

generate code to reproduce line plot

Description

Usage

Arguments

Value

Get name list of models in IAMC formatted data frame

Description

Usage

Arguments

Value

Get name list of scenarios in IAMC formatted data frame

Description

Usage

Arguments

Value

Get expression of vector of string in string format

Description

Usage

Arguments

Value

Get variable-group-name list

Description

Usage

Arguments

Value

Examples

Get variable name list in given variable-group

Description

Usage