R/plots.R
In xavier-gutierrez/validation_figures: Restructures Rgcam Project Files and Plots Validation Figures
# lineplot() --------------------------------------------------------------
#' Aggregate Line Plot
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @export
lineplot <- function(list.q, query, reg = "Global") {
# construct ttle
ttle <- paste0(query, " (", reg, ")")
# message
print(paste0("Plotting ", ttle))
# grab query data from restructured project data, subset by region
df <- filter(list.q[[query]], region == reg)
# get aggregate value, conditional on presence of "ghg" column
if("ghg" %in% names(df)) {
df <- group_by(df, Units, scenario, forcing, branch, year, ghg)
} else {
df <- group_by(df, Units, scenario, forcing, branch, year)
}
df <- df %>%
summarise(value = sum(value)) %>%
ungroup()
# basic aggregate line plot
p <- ggplot(df, aes(x = year, y = value)) +
ggtitle(ttle) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(forcing ~ scenario) +
ylab(unique(df$Units))
# linetype conditional on presence of "GHG"
if ("ghg" %in% names(df)) {
p <- p + geom_line(aes(col = branch, linetype = ghg))
} else {
p <- p + geom_line(aes(col = branch))
}
# display plot
return(list(p, ttle))
}
# barchart() --------------------------------------------------------------
#' Stacked Barchart
#'
#' This is the generic that dispatches different methods (below) according to class of list.q
#'
#' This plotting function outputs a *stacked* barchart. Queries which can be plotted using this function
#' need to have at least one more column than the base set of columns. The base set of columns is
#' units, scenario, forcing, branch, year, region, value. Technology and sector are common columns
#' which contain the keys which make a *stacked* barchart possible. constructPlot() is used in each method,
#' and requires a column that indicates the levels that each get their own color in the "stack", as
#' indicated in the plot's legend.
#'
#' Because barcharts can only be plotted for one forcing target, all methods require some dataPrep(),
#' which is found in utils.R. This function simply grabs the query data and filters it according to
#' user-input region and forcing target. Without any input, region defaults to "Global". dataPrep()
#' outputs this filtered query data as well as a string that combines query, region, and forcing. This
#' string is later used as the plot title.
#'
#' The final image is a grid of barcharts, where columns distinguish scenario-runs and rows distinguish
#' which branch of model development the scenarios were run on.
#'
#' barchart methods output a list whose first entry is the ggplot object, and the second entry
#' is the plot title. This is meant to facilitate programatically saving image files to a directory.
#'
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @export
barchart <- function(list.q, query, frc, reg = "Global") {
# check presence of query in list.q
if (! query %in% names(list.q)) error("Query not found in list.queries")
params <- dataPrep(list.q, query, reg, frc)
df <- params[[1]] # query data filtered by region and forcing target
ttle <- params[[2]] # plot title "Query Name (Region) (Forcing)"
# produce 3rd row of barcharts: Model.Minus.Int
if (query == "Primary energy with CCS (Direct Equivalent)" |
query == "Electricity generation by technology (inc solar roofs)" |
query == "Ag Production by Crop Type" |
query == "Average Yield" |
query == "Land Allocation") {
df <- Master.Minus.Development(df, query)
}
#specific barchart method
UseMethod("barchart", list.q)
}
# barchart.electricity() --------------------------------------------------
#' Barchart method for class=electricity
#'
#' Not all electricity queries in classes.csv can be plotted as a stacked barchart. The queries
#' supported by this method are:
#' Primary energy with CCS (Direct Equivalent)
#' Electricity generation by technology (inc solar roofs)
#'
#' Both barcharts display three rows: Master Branch, Development Branch, and Master.Minus.Development.
#' The third row is calculated using Master.Minus.Development(), found in utils.R.
#'
#' Both barcharts display three rows: Master Branch, Development Branch, and Master.Minus.Development.
#' The third row is calculated using master.minus.int(), found in utils.R.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.electricity <- function(list.q, query, frc, reg = "Global") {
if (query == "Primary energy with CCS (Direct Equivalent)") {
# Primary energy colors including CCS
PAL_pri_ene <- c(
"a oil" = "#d01c2a",
"a oil CCS" = "#f7988f",
"b natural gas" = "#25a9e0",
"b natural gas CCS" = "#84e7f9",
"c coal" = "#a0237c",
"c coal CCS" = "#dab4c7",
"d biomass" = "#00931d",
"d biomass CCS" = "#88c892",
"e nuclear" = "#ef8e27",
"f hydro" = "#fdfa28",
"g wind" = "#3d86f9",
"h solar" = "#fdd67b",
"i geothermal" = "#ad440c",
"j traditional biomass" = "#11d081",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, fuel, color_scheme=PAL_pri_ene)
return(list(p, ttle))
} else if (query == "Electricity generation by technology (inc solar roofs)") {
# Color scheme for electricity generation by aggregate fuel
elec_tech_colors <- c( "a Coal" = "#a0237c",
"b Coal w/CCS" = "#dab4c7",
"c Gas" = "#25a9e0",
"d Gas w/CCS" = "#84e7f9",
"e Oil" = "#d01c2a",
"f Oil w/CCS" = "#f7988f",
"g Biomass" = "#00931d",
"h Biomass w/CCS" = "#88c892",
"i Nuclear" = "#ef8e27",
"j Geothermal" = "#ad440c",
"k Hydro" = "#fdfa28",
"l Wind" = "#3d86f9",
"m Solar" = "#fdd67b",
"n CHP" = "#507fab",
"o Battery" = "#92a75d",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, agg.tech, color_scheme=elec_tech_colors)
return(list(p, ttle))
} else if (query == "Transportation final energy by fuel" |
query == "Building final energy by fuel" |
query == "Industry final energy by fuel") {
enduse_fuel_numbered <- c( "1 liquids" = "#d01c2a",
"2 gas" = "#25a9e0",
"3 coal" = "#a0237c",
"4 biomass" = "#00931d",
"5 electricity" = "#ef8e27",
"6 hydrogen" = "#fffbac",
"7 trad biomass" = "#11d081",
"8 district heat" = "#b14d38",
"feedstocks" = "#ff7467",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, input, color_scheme=enduse_fuel_numbered)
return(list(p, ttle))
} else if (query == "Cogeneration by region") {
p <- constructPlot(df, ttle, output)
return(list(p, ttle))
} else if (query == "Building floorspace") {
p <- constructPlot(df, ttle, building)
return(list(p, ttle))
} else {
warning(paste0("No barchart method found for query :", ttle))
}
}
# barchart.water() --------------------------------------------------------
#' Barchart method for class=water
#'
#' This method currently supports all queries specified in the water class.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.water <- function(list.q, query, frc, reg = "Global") {
p <- constructPlot(df, ttle, sector)
return(list(p, ttle))
}
# barchart.land() ---------------------------------------------------------
#' Barchart method for class=land
#'
#' This method currently supports all queries specified in the land class.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.land <- function(list.q, query, frc, reg = "Global") {
if (query == "Ag Production by Crop Type" |
query == "Average Yield" |
query == "Land Allocation") {
# Color scheme for land type
PAL_landuse<- c(
"biomass" = "#00f629",
"crops" = "#ffd125",
"desert" = "#a7a7a7",
"forest" = "#41a67a",
"grass" = "#ffd081",
"pasture" = "#aacf22",
"shrub" = "#b14d00",
"tundra" = "#938e15",
"urban" = "#000000")
# don't need to run an aggregation b/c ggplot sums all values in the same land_type
p <- constructPlot(df, ttle, land_type, color_scheme=PAL_landuse)
return(list(p, ttle))
} else if (query == "Water Management Shares") {
p <- constructPlot(df, ttle, water)
return(list(p, ttle))
} else if (query == "Fertilizer Tech Shares") {
p <- constructPlot(df, ttle, fertilizer)
return(list(p, ttle))
} else {
warning(paste0("No barchart method found for query :", ttle))
}
}
# barchart.refining() -----------------------------------------------------
#' Barchart method for class=refining
#'
#' This method currently supports all queries specified in the land class except
#' the calculated query "Refined liquids use by technology and aggregate end-use".
#' This query could be stacked by either the technology or sector column.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.refining <- function(list.q, query, frc, reg = "Global") {
if (query == "Regional oil production by fuel" |
query == "Oil Type Shares" ) {
p <- constructPlot(df, ttle, technology)
return(list(p,ttle))
} else if (query == "Refined liquids production by technology") {
p <- constructPlot(df, ttle, subsector)
return(list(p, ttle))
} else if (query == "Refined liquids use by aggregate end-use") {
p <- constructPlot(df, ttle, sector)
return(list(p, ttle))
} else {
warning(paste0("no barchart method found for query :", query))
}
}
# constructPlot() ---------------------------------------------------------
#' Stacked barchart plotting function
#'
#' Each barchart method calls this function to construct its plot.
#'
#' This function is just a wrapper around code using the ggplot2 package. The input
#' specifies aesthetic mappings such as title, fill, and an optional color scheme.
#'
#' @param df data.frame, query data
#' @param ttle string, "Query Name (region) (forcing)"
#' @param keysCol unquoted column name, holds keys to use for ggplot fill parameter
#' @param color_scheme named vector, GCAM color scheme
#' @importFrom rlang quo_text
#' @import ggplot2
constructPlot <- function(df, ttle, keysCol, color_scheme = NULL) {
# convert input column to quosure
keysCol <- enquo(keysCol)
print(paste0("Plot: ", ttle))
p <- ggplot(df, aes(x = year, y = value)) +
ggtitle(ttle) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(branch ~ scenario, scales="free") + # to better show Master.Minus.Int row
ylab(unique(df$Units)) +
geom_bar(aes_string(fill = rlang::quo_text(keysCol)), stat = "identity") # convert quosure to string. TODO: use aes_(fill=keysCol) instead?
if (!is.null(color_scheme)) {
p <- p +
scale_fill_manual(values=color_scheme)
}
p
}
# End ---------------------------------------------------------------------
xavier-gutierrez/validation_figures documentation built on May 24, 2019, 9:58 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# lineplot() --------------------------------------------------------------
#' Aggregate Line Plot
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @export
lineplot <- function(list.q, query, reg = "Global") {
# construct ttle
ttle <- paste0(query, " (", reg, ")")
# message
print(paste0("Plotting ", ttle))
# grab query data from restructured project data, subset by region
df <- filter(list.q[[query]], region == reg)
# get aggregate value, conditional on presence of "ghg" column
if("ghg" %in% names(df)) {
df <- group_by(df, Units, scenario, forcing, branch, year, ghg)
} else {
df <- group_by(df, Units, scenario, forcing, branch, year)
}
df <- df %>%
summarise(value = sum(value)) %>%
ungroup()
# basic aggregate line plot
p <- ggplot(df, aes(x = year, y = value)) +
ggtitle(ttle) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(forcing ~ scenario) +
ylab(unique(df$Units))
# linetype conditional on presence of "GHG"
if ("ghg" %in% names(df)) {
p <- p + geom_line(aes(col = branch, linetype = ghg))
} else {
p <- p + geom_line(aes(col = branch))
}
# display plot
return(list(p, ttle))
}
# barchart() --------------------------------------------------------------
#' Stacked Barchart
#'
#' This is the generic that dispatches different methods (below) according to class of list.q
#'
#' This plotting function outputs a *stacked* barchart. Queries which can be plotted using this function
#' need to have at least one more column than the base set of columns. The base set of columns is
#' units, scenario, forcing, branch, year, region, value. Technology and sector are common columns
#' which contain the keys which make a *stacked* barchart possible. constructPlot() is used in each method,
#' and requires a column that indicates the levels that each get their own color in the "stack", as
#' indicated in the plot's legend.
#'
#' Because barcharts can only be plotted for one forcing target, all methods require some dataPrep(),
#' which is found in utils.R. This function simply grabs the query data and filters it according to
#' user-input region and forcing target. Without any input, region defaults to "Global". dataPrep()
#' outputs this filtered query data as well as a string that combines query, region, and forcing. This
#' string is later used as the plot title.
#'
#' The final image is a grid of barcharts, where columns distinguish scenario-runs and rows distinguish
#' which branch of model development the scenarios were run on.
#'
#' barchart methods output a list whose first entry is the ggplot object, and the second entry
#' is the plot title. This is meant to facilitate programatically saving image files to a directory.
#'
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @export
barchart <- function(list.q, query, frc, reg = "Global") {
# check presence of query in list.q
if (! query %in% names(list.q)) error("Query not found in list.queries")
params <- dataPrep(list.q, query, reg, frc)
df <- params[[1]] # query data filtered by region and forcing target
ttle <- params[[2]] # plot title "Query Name (Region) (Forcing)"
# produce 3rd row of barcharts: Model.Minus.Int
if (query == "Primary energy with CCS (Direct Equivalent)" |
query == "Electricity generation by technology (inc solar roofs)" |
query == "Ag Production by Crop Type" |
query == "Average Yield" |
query == "Land Allocation") {
df <- Master.Minus.Development(df, query)
}
#specific barchart method
UseMethod("barchart", list.q)
}
# barchart.electricity() --------------------------------------------------
#' Barchart method for class=electricity
#'
#' Not all electricity queries in classes.csv can be plotted as a stacked barchart. The queries
#' supported by this method are:
#' Primary energy with CCS (Direct Equivalent)
#' Electricity generation by technology (inc solar roofs)
#'
#' Both barcharts display three rows: Master Branch, Development Branch, and Master.Minus.Development.
#' The third row is calculated using Master.Minus.Development(), found in utils.R.
#'
#' Both barcharts display three rows: Master Branch, Development Branch, and Master.Minus.Development.
#' The third row is calculated using master.minus.int(), found in utils.R.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.electricity <- function(list.q, query, frc, reg = "Global") {
if (query == "Primary energy with CCS (Direct Equivalent)") {
# Primary energy colors including CCS
PAL_pri_ene <- c(
"a oil" = "#d01c2a",
"a oil CCS" = "#f7988f",
"b natural gas" = "#25a9e0",
"b natural gas CCS" = "#84e7f9",
"c coal" = "#a0237c",
"c coal CCS" = "#dab4c7",
"d biomass" = "#00931d",
"d biomass CCS" = "#88c892",
"e nuclear" = "#ef8e27",
"f hydro" = "#fdfa28",
"g wind" = "#3d86f9",
"h solar" = "#fdd67b",
"i geothermal" = "#ad440c",
"j traditional biomass" = "#11d081",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, fuel, color_scheme=PAL_pri_ene)
return(list(p, ttle))
} else if (query == "Electricity generation by technology (inc solar roofs)") {
# Color scheme for electricity generation by aggregate fuel
elec_tech_colors <- c( "a Coal" = "#a0237c",
"b Coal w/CCS" = "#dab4c7",
"c Gas" = "#25a9e0",
"d Gas w/CCS" = "#84e7f9",
"e Oil" = "#d01c2a",
"f Oil w/CCS" = "#f7988f",
"g Biomass" = "#00931d",
"h Biomass w/CCS" = "#88c892",
"i Nuclear" = "#ef8e27",
"j Geothermal" = "#ad440c",
"k Hydro" = "#fdfa28",
"l Wind" = "#3d86f9",
"m Solar" = "#fdd67b",
"n CHP" = "#507fab",
"o Battery" = "#92a75d",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, agg.tech, color_scheme=elec_tech_colors)
return(list(p, ttle))
} else if (query == "Transportation final energy by fuel" |
query == "Building final energy by fuel" |
query == "Industry final energy by fuel") {
enduse_fuel_numbered <- c( "1 liquids" = "#d01c2a",
"2 gas" = "#25a9e0",
"3 coal" = "#a0237c",
"4 biomass" = "#00931d",
"5 electricity" = "#ef8e27",
"6 hydrogen" = "#fffbac",
"7 trad biomass" = "#11d081",
"8 district heat" = "#b14d38",
"feedstocks" = "#ff7467",
"energy reduction" = "grey")
p <- constructPlot(df, ttle, input, color_scheme=enduse_fuel_numbered)
return(list(p, ttle))
} else if (query == "Cogeneration by region") {
p <- constructPlot(df, ttle, output)
return(list(p, ttle))
} else if (query == "Building floorspace") {
p <- constructPlot(df, ttle, building)
return(list(p, ttle))
} else {
warning(paste0("No barchart method found for query :", ttle))
}
}
# barchart.water() --------------------------------------------------------
#' Barchart method for class=water
#'
#' This method currently supports all queries specified in the water class.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.water <- function(list.q, query, frc, reg = "Global") {
p <- constructPlot(df, ttle, sector)
return(list(p, ttle))
}
# barchart.land() ---------------------------------------------------------
#' Barchart method for class=land
#'
#' This method currently supports all queries specified in the land class.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.land <- function(list.q, query, frc, reg = "Global") {
if (query == "Ag Production by Crop Type" |
query == "Average Yield" |
query == "Land Allocation") {
# Color scheme for land type
PAL_landuse<- c(
"biomass" = "#00f629",
"crops" = "#ffd125",
"desert" = "#a7a7a7",
"forest" = "#41a67a",
"grass" = "#ffd081",
"pasture" = "#aacf22",
"shrub" = "#b14d00",
"tundra" = "#938e15",
"urban" = "#000000")
# don't need to run an aggregation b/c ggplot sums all values in the same land_type
p <- constructPlot(df, ttle, land_type, color_scheme=PAL_landuse)
return(list(p, ttle))
} else if (query == "Water Management Shares") {
p <- constructPlot(df, ttle, water)
return(list(p, ttle))
} else if (query == "Fertilizer Tech Shares") {
p <- constructPlot(df, ttle, fertilizer)
return(list(p, ttle))
} else {
warning(paste0("No barchart method found for query :", ttle))
}
}
# barchart.refining() -----------------------------------------------------
#' Barchart method for class=refining
#'
#' This method currently supports all queries specified in the land class except
#' the calculated query "Refined liquids use by technology and aggregate end-use".
#' This query could be stacked by either the technology or sector column.
#'
#' @param list.q list of queries, each query a single data.frame
#' @param query string, query must be in list.q
#' @param reg string, one of 32 GCAM regions or Global. Defaults to Global
#' @param frc string, one of forcing targets in list.q[[query]]
#' @export
barchart.refining <- function(list.q, query, frc, reg = "Global") {
if (query == "Regional oil production by fuel" |
query == "Oil Type Shares" ) {
p <- constructPlot(df, ttle, technology)
return(list(p,ttle))
} else if (query == "Refined liquids production by technology") {
p <- constructPlot(df, ttle, subsector)
return(list(p, ttle))
} else if (query == "Refined liquids use by aggregate end-use") {
p <- constructPlot(df, ttle, sector)
return(list(p, ttle))
} else {
warning(paste0("no barchart method found for query :", query))
}
}
# constructPlot() ---------------------------------------------------------
#' Stacked barchart plotting function
#'
#' Each barchart method calls this function to construct its plot.
#'
#' This function is just a wrapper around code using the ggplot2 package. The input
#' specifies aesthetic mappings such as title, fill, and an optional color scheme.
#'
#' @param df data.frame, query data
#' @param ttle string, "Query Name (region) (forcing)"
#' @param keysCol unquoted column name, holds keys to use for ggplot fill parameter
#' @param color_scheme named vector, GCAM color scheme
#' @importFrom rlang quo_text
#' @import ggplot2
constructPlot <- function(df, ttle, keysCol, color_scheme = NULL) {
# convert input column to quosure
keysCol <- enquo(keysCol)
print(paste0("Plot: ", ttle))
p <- ggplot(df, aes(x = year, y = value)) +
ggtitle(ttle) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(branch ~ scenario, scales="free") + # to better show Master.Minus.Int row
ylab(unique(df$Units)) +
geom_bar(aes_string(fill = rlang::quo_text(keysCol)), stat = "identity") # convert quosure to string. TODO: use aes_(fill=keysCol) instead?
if (!is.null(color_scheme)) {
p <- p +
scale_fill_manual(values=color_scheme)
}
p
}
# End ---------------------------------------------------------------------
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