tests/testthat/gallery.R
In olihawkins/clplot: Plotting tools for the House of Commons Library
### Functions to create a gallery of examples using the theme and scales
# Imports ---------------------------------------------------------------------
library(magrittr)
# Constants -------------------------------------------------------------------
DATASET_DIR <- "datasets"
DATASET_CON_FILE <- file.path(DATASET_DIR, "constituencies.csv")
DATASET_STOCKS_FILE <- file.path(DATASET_DIR, "stocks.csv")
GALLERY_DIR <- "gallery"
GALLERY_TEST_FILE <- file.path(GALLERY_DIR, "test.svg")
GALLERY_SCATTER_MPG_NAME <- file.path("scatter-mpg")
GALLERY_SCATTER_CONS_TYPE_NAME <- file.path("scatter-cons-type")
GALLERY_SCATTER_CONS_FACET_NAME <- file.path("scatter-cons-facet")
GALLERY_BAR_CONS_TYPE_NAME <- file.path("bar-cons-type")
GALLERY_BAR_CONS_REGION_NAME <- file.path("bar-cons-region")
GALLERY_LINE_STOCKS_NAME <- file.path("line-stocks")
SVG_WIDTH <- 8
SVG_HEIGHT <- 5
# Build gallery ---------------------------------------------------------------
build_example <- function(example_name, example_func) {
svgfile <- file.path(GALLERY_DIR, stringr::str_glue("{example_name}.svg"))
svg(filename = svgfile, width = SVG_WIDTH, height = SVG_HEIGHT)
print(example_func())
dev.off()
}
build_gallery <- function() {
examples <- list()
examples[[GALLERY_SCATTER_MPG_NAME]] = example_scatter_mpg
examples[[GALLERY_SCATTER_CONS_TYPE_NAME]] = example_scatter_cons_type
examples[[GALLERY_SCATTER_CONS_FACET_NAME]] = example_scatter_cons_facet
examples[[GALLERY_BAR_CONS_TYPE_NAME]] = example_bar_cons_type
examples[[GALLERY_BAR_CONS_REGION_NAME]] = example_bar_cons_region
examples[[GALLERY_LINE_STOCKS_NAME]] = example_line_stocks
elements <- purrr::map_chr(names(examples), function(example_name) {
build_example(example_name, examples[[example_name]])
element <- stringr::str_glue(
"<p>
<img src=\"{example_name}.svg\" height=\"500\" width=\"800\" />
</p>")
})
elements <- stringr::str_c(elements, collapse = "")
html <- stringr::str_glue(
"<html><head></head><body>{elements}</body></html>")
htmlfile <- file.path(GALLERY_DIR, stringr::str_glue("gallery.html"))
readr::write_file(html, htmlfile)
}
# Compare examples ------------------------------------------------------------
compare_examples <- function(example_name, example_func) {
svg(
filename = GALLERY_TEST_FILE,
width = SVG_WIDTH,
height = SVG_HEIGHT)
print(example_func())
dev.off()
expname <- file.path(GALLERY_DIR, stringr::str_glue("{example_name}.svg"))
exp <- readr::read_file(expname)
obs <- readr::read_file(GALLERY_TEST_FILE)
exp <- stringr::str_replace_all(exp, "<g id=\"surface[0-9]{1,}\">", "")
obs <- stringr::str_replace_all(obs, "<g id=\"surface[0-9]{1,}\">", "")
exp == obs
}
# Examples --------------------------------------------------------------------
example_scatter_mpg <- function() {
mpg <- ggplot2::mpg
p <- ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, color = class)) +
ggplot2::geom_point() +
ggplot2::labs(
x = "Engine displacement (litres)",
y = "Highway mileage (mpg)",
title = "Bigger engines are more fuel efficient",
subtitle = "Highway mileage by engine displacement",
caption = "@commonslibrary",
colour = "Vehicle type") +
ggplot2::xlim(0, 8) +
ggplot2::ylim(10, 50) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_commonslib()
}
example_scatter_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types)
p <- ggplot2::ggplot(cs, ggplot2::aes(
median_age, turnout, color = type)) +
ggplot2::geom_point() +
ggplot2::labs(
x = "Median age",
y = "Turnout",
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017",
color = "Settlement class",
caption = "@commonslibrary") +
ggplot2::scale_x_continuous(
limits = c(25, 55)) +
ggplot2::scale_y_continuous(
limits = c(0.5, 0.8),
label = scales::percent_format(accuracy = 1)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib() +
scale_color_commonslib()
}
example_scatter_cons_facet <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types)
p <- ggplot2::ggplot(cs, ggplot2::aes(
median_age, turnout, color = type)) +
ggplot2::geom_point() +
ggplot2::facet_wrap(~ type) +
ggplot2::labs(
x = "Median age",
y = "Turnout",
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017",
color = "Settlement class",
caption = "@commonslibrary") +
ggplot2::scale_x_continuous(
limits = c(25, 55)) +
ggplot2::scale_y_continuous(
limits = c(0.5, 0.85),
label = scales::percent_format(accuracy = 1)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_commonslib() +
ggplot2::theme(legend.position = "None")
}
example_bar_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types, ordered = FALSE)
p <- ggplot2::ggplot(cs, ggplot2::aes(type, fill = type)) +
ggplot2::geom_bar() +
ggplot2::labs(
x = "Settlement class",
y= "Number of constituencies",
title = "Around half of constituencies are towns",
subtitle = "Constituencies by settlement class, Great Britain",
caption = "@commonslibrary") +
ggplot2::scale_x_discrete(
expand = ggplot2::expansion(add = c(0.5, 0.5))) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h") +
scale_fill_commonslib(palette = "green", guide = "none")
}
example_bar_cons_region <- function() {
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs$region <- factor(cs$region)
cs$region <- forcats::fct_rev(reorder(cs$region, cs$region, length))
p <- ggplot2::ggplot(cs, ggplot2::aes(region, fill = region)) +
ggplot2::geom_bar() +
ggplot2::labs(
x = "Country or region",
y = "Number of constituencies",
title = "Countries and regions vary in representation",
subtitle = "Constituencies by country or region, United Kingdom",
caption = "@commonslibrary") +
ggplot2::scale_x_discrete(
expand = ggplot2::expansion(add = c(0.5, 0.5))) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h") +
scale_fill_commonslib(guide = "none")
}
example_line_stocks <- function() {
stocks <- suppressMessages(readr::read_csv(DATASET_STOCKS_FILE))
codes <- c("AMZN", "AAPL", "GOOG", "MSFT", "ORCL", "IBM")
stocks$date <- lubridate::dmy(stocks$date)
stocks <- cltools::get_indices(stocks)
stocks <- tidyr::gather(stocks, stock, value, -date)
stocks$stock <- factor(stocks$stock, levels = codes)
p <- ggplot2::ggplot(stocks,
ggplot2::aes(date, value, color = stock)) +
ggplot2::geom_line() +
ggplot2::labs(
x = NULL,
y = "Price index",
title = "Amazon has done pretty well lately",
subtitle = "Change in the value of tech stocks during the last decade (baseline = 100)",
color = NULL,
caption = "@commonslibrary") +
ggplot2::scale_y_continuous(
label = scales::comma,
limits = c(0, 2700)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "b", grid = "h") +
scale_color_commonslib()
}
olihawkins/clplot documentation built on March 22, 2020, 8:17 p.m.
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### Functions to create a gallery of examples using the theme and scales
# Imports ---------------------------------------------------------------------
library(magrittr)
# Constants -------------------------------------------------------------------
DATASET_DIR <- "datasets"
DATASET_CON_FILE <- file.path(DATASET_DIR, "constituencies.csv")
DATASET_STOCKS_FILE <- file.path(DATASET_DIR, "stocks.csv")
GALLERY_DIR <- "gallery"
GALLERY_TEST_FILE <- file.path(GALLERY_DIR, "test.svg")
GALLERY_SCATTER_MPG_NAME <- file.path("scatter-mpg")
GALLERY_SCATTER_CONS_TYPE_NAME <- file.path("scatter-cons-type")
GALLERY_SCATTER_CONS_FACET_NAME <- file.path("scatter-cons-facet")
GALLERY_BAR_CONS_TYPE_NAME <- file.path("bar-cons-type")
GALLERY_BAR_CONS_REGION_NAME <- file.path("bar-cons-region")
GALLERY_LINE_STOCKS_NAME <- file.path("line-stocks")
SVG_WIDTH <- 8
SVG_HEIGHT <- 5
# Build gallery ---------------------------------------------------------------
build_example <- function(example_name, example_func) {
svgfile <- file.path(GALLERY_DIR, stringr::str_glue("{example_name}.svg"))
svg(filename = svgfile, width = SVG_WIDTH, height = SVG_HEIGHT)
print(example_func())
dev.off()
}
build_gallery <- function() {
examples <- list()
examples[[GALLERY_SCATTER_MPG_NAME]] = example_scatter_mpg
examples[[GALLERY_SCATTER_CONS_TYPE_NAME]] = example_scatter_cons_type
examples[[GALLERY_SCATTER_CONS_FACET_NAME]] = example_scatter_cons_facet
examples[[GALLERY_BAR_CONS_TYPE_NAME]] = example_bar_cons_type
examples[[GALLERY_BAR_CONS_REGION_NAME]] = example_bar_cons_region
examples[[GALLERY_LINE_STOCKS_NAME]] = example_line_stocks
elements <- purrr::map_chr(names(examples), function(example_name) {
build_example(example_name, examples[[example_name]])
element <- stringr::str_glue(
"<p>
<img src=\"{example_name}.svg\" height=\"500\" width=\"800\" />
</p>")
})
elements <- stringr::str_c(elements, collapse = "")
html <- stringr::str_glue(
"<html><head></head><body>{elements}</body></html>")
htmlfile <- file.path(GALLERY_DIR, stringr::str_glue("gallery.html"))
readr::write_file(html, htmlfile)
}
# Compare examples ------------------------------------------------------------
compare_examples <- function(example_name, example_func) {
svg(
filename = GALLERY_TEST_FILE,
width = SVG_WIDTH,
height = SVG_HEIGHT)
print(example_func())
dev.off()
expname <- file.path(GALLERY_DIR, stringr::str_glue("{example_name}.svg"))
exp <- readr::read_file(expname)
obs <- readr::read_file(GALLERY_TEST_FILE)
exp <- stringr::str_replace_all(exp, "<g id=\"surface[0-9]{1,}\">", "")
obs <- stringr::str_replace_all(obs, "<g id=\"surface[0-9]{1,}\">", "")
exp == obs
}
# Examples --------------------------------------------------------------------
example_scatter_mpg <- function() {
mpg <- ggplot2::mpg
p <- ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, color = class)) +
ggplot2::geom_point() +
ggplot2::labs(
x = "Engine displacement (litres)",
y = "Highway mileage (mpg)",
title = "Bigger engines are more fuel efficient",
subtitle = "Highway mileage by engine displacement",
caption = "@commonslibrary",
colour = "Vehicle type") +
ggplot2::xlim(0, 8) +
ggplot2::ylim(10, 50) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_commonslib()
}
example_scatter_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types)
p <- ggplot2::ggplot(cs, ggplot2::aes(
median_age, turnout, color = type)) +
ggplot2::geom_point() +
ggplot2::labs(
x = "Median age",
y = "Turnout",
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017",
color = "Settlement class",
caption = "@commonslibrary") +
ggplot2::scale_x_continuous(
limits = c(25, 55)) +
ggplot2::scale_y_continuous(
limits = c(0.5, 0.8),
label = scales::percent_format(accuracy = 1)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib() +
scale_color_commonslib()
}
example_scatter_cons_facet <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types)
p <- ggplot2::ggplot(cs, ggplot2::aes(
median_age, turnout, color = type)) +
ggplot2::geom_point() +
ggplot2::facet_wrap(~ type) +
ggplot2::labs(
x = "Median age",
y = "Turnout",
title = "Turnout was higher in older, less urban constituencies",
subtitle = "Constituencies by age, turnout and settlement class, 2017",
color = "Settlement class",
caption = "@commonslibrary") +
ggplot2::scale_x_continuous(
limits = c(25, 55)) +
ggplot2::scale_y_continuous(
limits = c(0.5, 0.85),
label = scales::percent_format(accuracy = 1)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "", grid = "hv") +
scale_color_commonslib() +
ggplot2::theme(legend.position = "None")
}
example_bar_cons_type <- function() {
types <- c("London", "Other city", "Large town",
"Medium town", "Small town", "Village")
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs <- cs %>% dplyr::filter(! is.na(classification))
cs$type <- factor(cs$classification, levels = types, ordered = FALSE)
p <- ggplot2::ggplot(cs, ggplot2::aes(type, fill = type)) +
ggplot2::geom_bar() +
ggplot2::labs(
x = "Settlement class",
y= "Number of constituencies",
title = "Around half of constituencies are towns",
subtitle = "Constituencies by settlement class, Great Britain",
caption = "@commonslibrary") +
ggplot2::scale_x_discrete(
expand = ggplot2::expansion(add = c(0.5, 0.5))) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h") +
scale_fill_commonslib(palette = "green", guide = "none")
}
example_bar_cons_region <- function() {
cs <- suppressMessages(readr::read_csv(DATASET_CON_FILE))
cs$region <- factor(cs$region)
cs$region <- forcats::fct_rev(reorder(cs$region, cs$region, length))
p <- ggplot2::ggplot(cs, ggplot2::aes(region, fill = region)) +
ggplot2::geom_bar() +
ggplot2::labs(
x = "Country or region",
y = "Number of constituencies",
title = "Countries and regions vary in representation",
subtitle = "Constituencies by country or region, United Kingdom",
caption = "@commonslibrary") +
ggplot2::scale_x_discrete(
expand = ggplot2::expansion(add = c(0.5, 0.5))) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
theme_commonslib(axes = "b", grid = "h") +
scale_fill_commonslib(guide = "none")
}
example_line_stocks <- function() {
stocks <- suppressMessages(readr::read_csv(DATASET_STOCKS_FILE))
codes <- c("AMZN", "AAPL", "GOOG", "MSFT", "ORCL", "IBM")
stocks$date <- lubridate::dmy(stocks$date)
stocks <- cltools::get_indices(stocks)
stocks <- tidyr::gather(stocks, stock, value, -date)
stocks$stock <- factor(stocks$stock, levels = codes)
p <- ggplot2::ggplot(stocks,
ggplot2::aes(date, value, color = stock)) +
ggplot2::geom_line() +
ggplot2::labs(
x = NULL,
y = "Price index",
title = "Amazon has done pretty well lately",
subtitle = "Change in the value of tech stocks during the last decade (baseline = 100)",
color = NULL,
caption = "@commonslibrary") +
ggplot2::scale_y_continuous(
label = scales::comma,
limits = c(0, 2700)) +
ggplot2::coord_cartesian(expand = FALSE) +
theme_commonslib(axes = "b", grid = "h") +
scale_color_commonslib()
}
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