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gnoblet 2025-02-09 17:19:54 +01:00
parent 5beec7fb90
commit 7f56642954
68 changed files with 1380 additions and 953 deletions
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1
.gitignore vendored
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@ -4,3 +4,4 @@
.httr-oauth
.DS_Store
R/test.R
inst/doc

12
DESCRIPTION
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@ -1,7 +1,7 @@
Package: visualizeR
Type: Package
Title: What a color! What a viz!
Version: 0.8.9000
Version: 1.0
Authors@R: c(
person(
'Noblet', 'Guillaume',
@ -32,11 +32,13 @@ Imports:
viridisLite,
waffle,
stringr,
checkmate,
data.table
checkmate
Suggests:
knitr,
rmarkdown,
roxygen2,
sf,
tmap
rio,
testthat (>= 3.0.0),
vdiffr
VignetteBuilder: knitr
Config/testthat/edition: 3

162
R/bar.R
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@ -3,11 +3,13 @@
#' @inheritParams bar
#'
#' @export
hbar <- function(...) bar(flip = TRUE, theme_fun = theme_bar(flip = TRUE, add_text = FALSE), ...)
hbar <- function(..., flip = TRUE, add_text = FALSE, theme_fun = theme_bar(flip = flip, add_text = add_text)) {
bar(flip = flip, add_text = add_text, theme_fun = theme_fun, ...)
}
#' Simple bar chart
#'
#' [bar()] is a simple bar chart with some customization allowed, in particular the `theme_fun` argument for theming. [hbar()] uses [bar()] with sane defaults for a horizontal bar chart.
#' `bar()` is a simple bar chart with some customization allowed, in particular the `theme_fun` argument for theming. `hbar()` uses `bar()` with sane defaults for a horizontal bar chart.
#'
#' @param df A data frame.
#' @param x A quoted numeric column.
@ -18,7 +20,10 @@ hbar <- function(...) bar(flip = TRUE, theme_fun = theme_bar(flip = TRUE, add_te
#' @param x_rm_na Remove NAs in x?
#' @param y_rm_na Remove NAs in y?
#' @param group_rm_na Remove NAs in group?
#' @param facet_rm_na Remove NAs in facet?
#' @param y_expand Multiplier to expand the y axis.
#' @param add_color Add a color to bars (if no grouping).
#' @param add_color_guide Should a legend be added?
#' @param flip TRUE or FALSE (default). Default to TRUE or horizontal bar plot.
#' @param wrap Should x-labels be wrapped? Number of characters.
#' @param position Should the chart be stacked? Default to "dodge". Can take "dodge" and "stack".
@ -36,11 +41,13 @@ hbar <- function(...) bar(flip = TRUE, theme_fun = theme_bar(flip = TRUE, add_te
#' @param add_text_font_face Text font_face.
#' @param add_text_threshold_display Minimum value to add the text label.
#' @param add_text_suffix If percent is FALSE, should we add a suffix to the text label?
#' @param add_text_expand_limit Default to adding 10% on top of the bar.
#' @param add_text_expand_limit Default to adding 10\% on top of the bar.
#' @param add_text_round Round the text label.
#' @param theme_fun Whatever theme function. For no custom theme, use theme_fun = NULL.
#'
#' @inheritParams reorder
#' @inheritParams reorder_by
#'
#' @importFrom rlang `:=`
#'
#' @export
bar <- function(
@ -54,7 +61,9 @@ bar <- function(
y_rm_na = TRUE,
group_rm_na = TRUE,
facet_rm_na = TRUE,
y_expand = 0.1,
add_color = color("cat_5_main_1"),
add_color_guide = TRUE,
flip = FALSE,
wrap = NULL,
position = "dodge",
@ -65,24 +74,24 @@ bar <- function(
title = NULL,
subtitle = NULL,
caption = NULL,
width = 0.7,
width = 0.8,
add_text = FALSE,
add_text_size = 4,
add_text_size = 4.5,
add_text_color = color("dark_grey"),
add_text_font_face = "plain",
add_text_font_face = "bold",
add_text_threshold_display = 0.05,
add_text_suffix = "%",
add_text_expand_limit = 1.2,
add_text_round = 1,
theme_fun = theme_bar(
flip = FALSE,
add_text = FALSE,
axis_text_x_angle = 45,
axis_text_x_vjust = 1,
axis_text_x_hjust = 1
flip = flip,
add_text = add_text,
axis_text_x_angle = 0,
axis_text_x_vjust = 0.5,
axis_text_x_hjust = 0.5
),
scale_fill_fun = scale_fill_impact_discrete,
scale_color_fun = scale_color_impact_discrete
scale_fill_fun = scale_fill_visualizer_discrete(),
scale_color_fun = scale_color_visualizer_discrete()
){
@ -105,10 +114,26 @@ if (group != "") checkmate::assert_choice(group, colnames(df))
checkmate::assert_logical(x_rm_na, len = 1)
checkmate::assert_logical(y_rm_na, len = 1)
checkmate::assert_logical(group_rm_na, len = 1)
checkmate::assert_logical(facet_rm_na, len = 1)
# flip is a logical scalar
checkmate::assert_logical(flip, len = 1)
# wrap is a numeric scalar or NULL
if (!is.null(wrap)) checkmate::assert_numeric(wrap, len = 1, null.ok = TRUE)
# alpha is a numeric scalar between 0 and 1
checkmate::assert_numeric(alpha, lower = 0, upper = 1, len = 1)
# add_text is a logical scalar
checkmate::assert_logical(add_text, len = 1)
# add_text_size is a numeric scalar
checkmate::assert_numeric(add_text_size, len = 1)
# add_text_font_face is a character scalar in bold plain or italic
checkmate::assert_choice(add_text_font_face, c("bold", "plain", "italic"))
# add_text_threshold_display is a numeric scalar
checkmate::assert_numeric(add_text_threshold_display, len = 1)
@ -121,8 +146,7 @@ checkmate::assert_numeric(add_text_expand_limit, len = 1)
# add_text_round is a numeric scalar
checkmate::assert_numeric(add_text_round, len = 1)
# Check if numeric and character
# x and y are numeric or character
if (class(df[[y]]) %notin% c("integer", "numeric")) rlang::abort(paste0(y, " must be numeric."))
if (!any(class(df[[x]]) %in% c("character", "factor"))) rlang::abort(paste0(x, " must be character or factor"))
@ -131,22 +155,36 @@ if (position %notin% c("stack", "dodge")) rlang::abort("Position should be eithe
#----- Data wrangling
# want to use df as a data.table
if (!checkmate::test_data_table(df)) {
rlang::warn("Converting df to data.table.")
data.table::setDT(df)
# facets over group
if (group != "" && facet != "" && group == facet) {
rlang::warn("'group' and 'facet' are the same identical.")
}
# Remove NAs using data.table
if (x_rm_na) df[, (x) := na.omit(get(x))]
if (y_rm_na) df[, (y) := na.omit(get(y))]
if (group != "" && group_rm_na) df[, (group) := na.omit(get(group))]
# remove NAs using base R
if (x_rm_na) df <- df[!(is.na(df[[x]])),]
if (y_rm_na) df <- df[!(is.na(df[[y]])),]
if (group != "" && group_rm_na) df <- df[!(is.na(df[[group]])),]
if (facet != "" && facet_rm_na) df <- df[!(is.na(df[[facet]])),]
# Reorder
dir_order = ifelse(flip, 1, -1)
df <- reorder(df, x, y, group, order, dir_order)
# Prepare aes
# reorder
dir_order <- if(flip && order %in% c("x", "grouped_x")) {
-1
} else if (!flip && order %in% c("x", "grouped_x")) {
1
} else if (flip) {
1
} else {
-1
}
group_order <- if (group != "" || (group == "" && facet == "")) {
group
} else if (group == "" && facet != "") {
facet
}
df <- reorder_by(df = df, x = x, y = y, group = group_order, order = order, dir_order = dir_order)
# prepare aes
if(group != "") {
g <- ggplot2::ggplot(
@ -170,7 +208,7 @@ if(group != "") {
)
}
# Add title, subtitle, caption, x_title, y_title
# add title, subtitle, caption, x_title, y_title
g <- g + ggplot2::labs(
title = title,
subtitle = subtitle,
@ -181,14 +219,19 @@ g <- g + ggplot2::labs(
fill = group_title
)
# Width
# width
width <- width
dodge_width <- width
#Facets
# facets
if (facet != "") {
g <- g + ggforce::facet_row(facet, scales = "free_x", space = "free")
if (flip) {
g <- g + ggplot2::facet_grid(rows = ggplot2::vars(!!rlang::sym(facet)), scales = "free", space = "free_y")
} else {
g <- g + ggplot2::facet_grid(cols = ggplot2::vars(!!rlang::sym(facet)), scales = "free", space = "free_x")
}
}
# Guides for legend
# g <- g + ggplot2::guides(
@ -206,7 +249,7 @@ if (facet != "") {
# direction = "horizontal")
# )
# Should the graph use position_fill?
# should the graph use position_fill?
if(group != "") {
if (position == "stack"){
@ -260,35 +303,53 @@ if(group != "") {
}
}
# Wrap labels on the x scale?
# wrap labels on the x scale?
if (!is.null(wrap)) {
g <- g + ggplot2::scale_x_discrete(labels = scales::label_wrap(wrap))
}
# Because a text legend should always be horizontal, especially for an horizontal bar graph
# because a text legend should always be horizontal, especially for an horizontal bar graph
if (flip) g <- g + ggplot2::coord_flip()
# Add text to bars
if (flip) hjust_flip <- -0.5 else hjust_flip <- 0.5
if (flip) vjust_flip <- 0.5 else vjust_flip <- -0.5
# Function for interaction
interaction_f <- function(group, facet, data) {
if (group == "" && facet == "") {
return(NULL)
} else if (group != "" && facet != "") {
return(interaction(data[[group]], data[[facet]]))
} else if (group != "") {
return(data[[group]])
} else if (facet != "") {
return(data[[facet]])
} else {
return(NULL)
}
}
# Add text labels
# add text labels
if (add_text & position == "dodge") {
df <- dplyr::mutate(df, "y_threshold" := ifelse(!!rlang::sym(y) >= add_text_threshold_display, !!rlang::sym(y), NA ))
# Expand limits
# expand limits
g <- g + ggplot2::geom_blank(
data = df,
ggplot2::aes(x = !!rlang::sym(x), y = !!rlang::sym(y) * add_text_expand_limit, group = !!rlang::sym(group))
ggplot2::aes(
x = !!rlang::sym(x),
y = !!rlang::sym(y) * add_text_expand_limit,
group = interaction_f(group, facet, df)
)
)
g <- g + ggplot2::geom_text(
data = df,
ggplot2::aes(
label = ifelse(is.na(!!rlang::sym("y_threshold")), NA, paste0(round(!!rlang::sym("y_threshold"), add_text_round), add_text_suffix)),
group = !!rlang::sym(group)),
group = interaction_f(group, facet, df)),
hjust = hjust_flip,
vjust = vjust_flip,
color = add_text_color,
@ -304,12 +365,16 @@ if (add_text & position == "dodge") {
g <- g + ggplot2::geom_text(
data = df,
ggplot2::aes(
label = ifelse(is.na(!!rlang::sym("y_threshold")), NA, paste0(round(!!rlang::sym("y_threshold"), add_text_round), add_text_suffix)),
group = !!rlang::sym(group)),
label = ifelse(is.na(!!rlang::sym("y_threshold")), NA,
paste0(round(!!rlang::sym("y_threshold"), add_text_round), add_text_suffix)),
group = interaction_f(group, facet, df)
),
hjust = hjust_flip,
vjust = vjust_flip,
color = add_text_color,
fontface = add_text_font_face,
size = add_text_size,
position = ggplot2::position_stack(vjust = 0.5)
position = ggplot2::position_dodge2(width = dodge_width)
)
}
@ -318,7 +383,7 @@ if (add_text & position == "dodge") {
g <- g +
ggplot2::scale_y_continuous(
# start at 0
expand = c(0, 0),
expand = ggplot2::expansion(mult = c(0, y_expand)),
# remove trailing 0 and choose accuracy of y labels
labels = scales::label_number(
accuracy = 0.1,
@ -327,9 +392,16 @@ if (add_text & position == "dodge") {
decimal.mark = "."),
)
# Remove guides for legend if !add_color_guide
if (!add_color_guide) g <- g + ggplot2::guides(fill = "none", color = "none")
# Add theme fun
if (!is.null(theme_fun)) g <- g + theme_fun
# Add scale fun
if (!is.null(scale_fill_fun)) g <- g + scale_fill_fun
if (!is.null(scale_color_fun)) g <- g + scale_color_fun
return(g)
}

3
R/color.R
View file

@ -25,8 +25,11 @@ color <- function(..., unname = TRUE) {
# Defined colors
colors <- c(
white = "#FFFFFF"
, lighter_grey = "#F5F5F5"
, light_grey = "#E3E3E3"
, dark_grey = "#464647"
, light_blue_grey = "#B3C6D1"
, grey = "#71716F"
, black = "#000000"
, cat_2_yellow_1 = "#ffc20a"
, cat_2_yellow_2 = "#0c7bdc"

189
R/dumbbell.R Normal file
View file

@ -0,0 +1,189 @@
#' Make dumbbell chart.
#'
#' @param df A data frame.
#' @param col A numeric column.
#' @param group_x The grouping column on the x-axis; only two groups.
#' @param group_y The grouping column on the y-axis.
#' @param point_size Point size.
#' @param point_alpha Point alpha.
#' @param segment_size Segment size.
#' @param segment_color Segment color.
#' @param group_x_title X-group and legend title.
#' @param group_y_title Y-axis and group title.
#' @param x_title X-axis title.
#' @param title Title.
#' @param subtitle Subtitle.
#' @param caption Caption.
#' @param line_to_y_axis TRUE or FALSE; add a line connected points and Y-axis.
#' @param line_to_y_axis_type Line to Y-axis type.
#' @param line_to_y_axis_width Line to Y-axis width.
#' @param line_to_y_axis_color Line to Y-axis color.
#' @param add_text TRUE or FALSE; add text at the points.
#' @param add_text_vjust Vertical adjustment.
#' @param add_text_size Text size.
#' @param add_text_color Text color.
#' @param theme_fun A ggplot2 theme, default to `theme_dumbbell()`
#' @param scale_fill_fun A ggplot2 scale_fill function, default to `scale_fill_visualizer_discrete()`
#' @param scale_color_fun A ggplot2 scale_color function, default to `scale_color_visualizer_discrete()`
#'
#' @return A dumbbell chart.
#' @export
#'
dumbbell <- function(df,
col,
group_x,
group_y,
point_size = 5,
point_alpha = 1,
segment_size = 2.5,
segment_color = color("light_blue_grey"),
group_x_title = NULL,
group_y_title = NULL,
x_title = NULL,
title = NULL,
subtitle = NULL,
caption = NULL,
line_to_y_axis = FALSE,
line_to_y_axis_type = 3,
line_to_y_axis_width = 0.5,
line_to_y_axis_color = color("dark_grey"),
add_text = FALSE,
add_text_vjust = 2,
add_text_size = 3.5,
add_text_color = color("dark_grey"),
theme_fun = theme_dumbbell(),
scale_fill_fun = scale_fill_visualizer_discrete(),
scale_color_fun = scale_color_visualizer_discrete()){
#------ Checks
# df is a data frame
checkmate::assert_data_frame(df)
# col, group_x, group_y are character
checkmate::assert_character(col, len = 1)
checkmate::assert_character(group_x, len = 1)
checkmate::assert_character(group_y, len = 1)
# col, group_x, group_y are columns in df
checkmate::assert_choice(col, colnames(df))
checkmate::assert_choice(group_x, colnames(df))
checkmate::assert_choice(group_y, colnames(df))
# Check numeric/logical values
checkmate::assert_numeric(point_size, len = 1)
checkmate::assert_numeric(point_alpha, lower = 0, upper = 1, len = 1)
checkmate::assert_numeric(segment_size, len = 1)
checkmate::assert_logical(line_to_y_axis, len = 1)
checkmate::assert_numeric(line_to_y_axis_type, len = 1)
checkmate::assert_numeric(line_to_y_axis_width, len = 1)
checkmate::assert_logical(add_text, len = 1)
checkmate::assert_numeric(add_text_vjust, len = 1)
checkmate::assert_numeric(add_text_size, len = 1)
# Get group keys
group_x_keys <- df |>
dplyr::group_by(!!rlang::sym(group_x)) |>
dplyr::group_keys() |>
dplyr::pull()
# Check if only two groups
if (length(group_x_keys) > 2) rlang::abort("Cannot draw a dumbbell plot for `group_x` with more than 2 groups")
# Pivot long data
df_pivot <- df |>
tidyr::pivot_wider(
id_cols = c(!!rlang::sym(group_y)),
values_from = !!rlang::sym(col),
names_from = !!rlang::sym(group_x)
)
df_pivot <- df_pivot |>
dplyr::rowwise() |>
dplyr::mutate(
min = min(!!rlang::sym(group_x_keys[[1]]), !!rlang::sym(group_x_keys[[2]]), na.rm = T),
max = max(!!rlang::sym(group_x_keys[[1]]), !!rlang::sym(group_x_keys[[2]]), na.rm = T)) |>
dplyr::ungroup() |>
dplyr::mutate(diff = max - min)
g <- ggplot2::ggplot(df_pivot)
# Add line
if(line_to_y_axis) {
xend <- min(dplyr::pull(df, !!rlang::sym(col)))
g <- g +
ggplot2::geom_segment(
ggplot2::aes(
x = min,
y = !!rlang::sym(group_y),
yend = !!rlang::sym(group_y)),
xend = xend,
linetype = line_to_y_axis_type,
linewidth = line_to_y_axis_width,
color = line_to_y_axis_color)
}
# Add segment
g <- g +
ggplot2::geom_segment(
ggplot2::aes(
x = !!rlang::sym(group_x_keys[[1]]),
y = !!rlang::sym(group_y),
xend = !!rlang::sym(group_x_keys[[2]]),
yend = !!rlang::sym(group_y)),
linewidth = segment_size,
color = segment_color
)
# Add points
g <- g +
ggplot2::geom_point(
data = df,
ggplot2::aes(
x = !!rlang::sym(col),
y = !!rlang::sym(group_y),
color = !!rlang::sym(group_x),
fill = !!rlang::sym(group_x)
),
size = point_size,
alpha = point_alpha
)
# Add title, subtitle, caption, x_title, y_title
g <- g + ggplot2::labs(
title = title,
subtitle = subtitle,
caption = caption,
x = x_title,
y = group_y_title,
color = group_x_title,
fill = group_x_title
)
# Add stat labels to points
if(add_text) g <- g +
ggrepel::geom_text_repel(
data = df,
ggplot2::aes(
x = !!rlang::sym(col),
y = !!rlang::sym(group_y),
label = !!rlang::sym(col)
),
vjust = add_text_vjust,
size = add_text_size,
color = add_text_color
)
# Add theme
g <- g + theme_fun
# Add scale fun
if (!is.null(scale_fill_fun)) g <- g + scale_fill_fun
if (!is.null(scale_color_fun)) g <- g + scale_color_fun
return(g)
}

139
R/point.R
View file

@ -1,11 +1,18 @@
#' @title Simple point chart
#' @title Simple scatterplot
#'
#' @param df A data frame.
#' @param x A numeric column.
#' @param y Another numeric column.
#' @param group Some grouping categorical column, e.g. administrative areas or population groups.
#' @param add_color Add a color to bars (if no grouping).
#' @param flip TRUE or FALSE. Default to TRUE or horizontal bar plot.
#' @param x A quoted numeric column.
#' @param y A quoted numeric column.
#' @param group Some quoted grouping categorical column, e.g. administrative areas or population groups.
#' @param facet Some quoted grouping categorical column.
#' @param facet_scales Character. Either "free" (default) or "fixed" for facet scales.
#' @param x_rm_na Remove NAs in x?
#' @param y_rm_na Remove NAs in y?
#' @param group_rm_na Remove NAs in group?
#' @param facet_rm_na Remove NAs in facet?
#' @param add_color Add a color to points (if no grouping).
#' @param add_color_guide Should a legend be added?
#' @param flip TRUE or FALSE.
#' @param alpha Fill transparency.
#' @param size Point size.
#' @param x_title The x scale title. Default to NULL.
@ -14,18 +21,90 @@
#' @param title Plot title. Default to NULL.
#' @param subtitle Plot subtitle. Default to NULL.
#' @param caption Plot caption. Default to NULL.
#' @param theme_fun Whatever theme. Default to theme_reach(). NULL if no theming needed.
#' @param scale_impact Use the package custom scales for fill and color.
#' @param theme_fun Whatever theme. Default to theme_point(). NULL if no theming needed.
#'
#' @inheritParams scale_color_impact_discrete
#' @inheritParams scale_color_visualizer_discrete
#'
#' @export
point <- function(df, x, y, group = "", add_color = color("branding_reach_red"), flip = TRUE, alpha = 1, size = 2, x_title = NULL, y_title = NULL, group_title = NULL, title = NULL, subtitle = NULL, caption = NULL, theme_fun = theme_reach(grid_major_y = TRUE), palette = "cat_5_ibm", scale_impact = TRUE, direction = 1, reverse_guide = TRUE) {
# # Check if numeric and character
if (!any(c("numeric", "integer") %in% class(df[[x]]))) rlang::abort(paste0(x, " must be numeric."))
if (!any(c("numeric", "integer") %in% class(df[[y]]))) rlang::abort(paste0(x, " must be numeric."))
point <- function(
df,
x,
y,
group = "",
facet = "",
facet_scales = "free",
x_rm_na = TRUE,
y_rm_na = TRUE,
group_rm_na = TRUE,
facet_rm_na = TRUE,
add_color = color("cat_5_main_1"),
add_color_guide = TRUE,
flip = TRUE,
alpha = 1,
size = 2,
x_title = NULL,
y_title = NULL,
group_title = NULL,
title = NULL,
subtitle = NULL,
caption = NULL,
theme_fun = theme_point(),
scale_fill_fun = scale_fill_visualizer_discrete(),
scale_color_fun = scale_color_visualizer_discrete()
) {
# Mapping
#------ Checks
# df is a data frame
checkmate::assert_data_frame(df)
# x and y and group are character
checkmate::assert_character(x, len = 1)
checkmate::assert_character(y, len = 1)
checkmate::assert_character(group, len = 1)
# x and y are columns in df
checkmate::assert_choice(x, colnames(df))
checkmate::assert_choice(y, colnames(df))
if (group != "") checkmate::assert_choice(group, colnames(df))
# x_rm_na, y_rm_na and group_rm_na are logical scalar
checkmate::assert_logical(x_rm_na, len = 1)
checkmate::assert_logical(y_rm_na, len = 1)
checkmate::assert_logical(group_rm_na, len = 1)
checkmate::assert_logical(facet_rm_na, len = 1)
# facet_scales is a character scalar in c("free", "fixed")
checkmate::assert_choice(facet_scales, c("free", "fixed"))
# flip is a logical scalar
checkmate::assert_logical(flip, len = 1)
# alpha is a numeric scalar between 0 and 1
checkmate::assert_numeric(alpha, lower = 0, upper = 1, len = 1)
# size is a numeric scalar
checkmate::assert_numeric(size, len = 1)
# x and y are numeric
if (!any(c("numeric", "integer") %in% class(df[[x]]))) rlang::abort(paste0(x, " must be numeric."))
if (!any(c("numeric", "integer") %in% class(df[[y]]))) rlang::abort(paste0(y, " must be numeric."))
#----- Data wrangling
# facets over group
if (group != "" && facet != "" && group == facet) {
rlang::warn("'group' and 'facet' are the same identical.")
}
# remove NAs using base R
if (x_rm_na) df <- df[!(is.na(df[[x]])),]
if (y_rm_na) df <- df[!(is.na(df[[y]])),]
if (group != "" && group_rm_na) df <- df[!(is.na(df[[group]])),]
if (facet != "" && facet_rm_na) df <- df[!(is.na(df[[facet]])),]
# prepare aes
if (group != "") {
g <- ggplot2::ggplot(
df,
@ -46,8 +125,7 @@ point <- function(df, x, y, group = "", add_color = color("branding_reach_red"),
)
}
# Add title, subtitle, caption, x_title, y_title
# add title, subtitle, caption, x_title, y_title
g <- g + ggplot2::labs(
title = title,
subtitle = subtitle,
@ -58,6 +136,24 @@ point <- function(df, x, y, group = "", add_color = color("branding_reach_red"),
fill = group_title
)
# facets
# facets
if (facet != "") {
if (flip) {
g <- g + ggplot2::facet_grid(
rows = ggplot2::vars(!!rlang::sym(facet)),
scales = facet_scales,
space = if(facet_scales == "free") "free_y" else "fixed"
)
} else {
g <- g + ggplot2::facet_grid(
cols = ggplot2::vars(!!rlang::sym(facet)),
scales = facet_scales,
space = if(facet_scales == "free") "free_x" else "fixed"
)
}
}
# Should the graph use position_fill?
if (group != "") {
g <- g + ggplot2::geom_point(
@ -76,15 +172,16 @@ point <- function(df, x, y, group = "", add_color = color("branding_reach_red"),
g <- g + ggplot2::coord_flip()
}
# Add theme
g <- g + theme_fun
# Remove guides for legend if !add_color_guide
if (!add_color_guide) g <- g + ggplot2::guides(fill = "none", color = "none")
# Add theme
if (!is.null(theme_fun)) g <- g + theme_fun
# Add scale
if (scale_impact) g <- g + scale_fill_impact_discrete(palette, direction, reverse_guide) + scale_color_impact_discrete(palette, direction, reverse_guide)
# Add scale fun
if (!is.null(scale_fill_fun)) g <- g + scale_fill_fun
if (!is.null(scale_color_fun)) g <- g + scale_color_fun
return(g)
}

70
R/reorder.R → R/reorder_by.R
View file

@ -1,5 +1,4 @@
#' Reorder a Data Frame Factoring Column x
#' Reorder a Data Frame
#'
#' @param df A data frame to be reordered.
#' @param x A character scalar specifying the column to be reordered.
@ -22,21 +21,16 @@
#' @examples
#' # Example usage
#' df <- data.frame(col1 = c("b", "a", "c"), col2 = c(10, 25, 3))
#' reorder(df, "col1", "col2")
#' reorder_by(df, "col1", "col2")
#'
#' @export
reorder <- function(df, x, y, group = "", order = "y", dir_order = 1){
reorder_by <- function(df, x, y, group = "", order = "y", dir_order = 1){
#------ Checks
# df is a data frame
checkmate::assert_data_frame(df)
# df is data.table, if not convert
if (!checkmate::test_data_table(df)) {
rlang::warn("Converting df to data.table.")
data.table::setDT(df)
}
# x and y are character scalar and in df
checkmate::assert_character(x, len = 1)
checkmate::assert_character(y, len = 1)
@ -53,52 +47,44 @@ reorder <- function(df, x, y, group = "", order = "y", dir_order = 1){
# dir_order is 1 or -1 (numeric scalar)
checkmate::assert_subset(dir_order, c(1, -1))
#------ Reorder
# droplevels first
if (is.factor(df[[x]])) {
df[, (x) := droplevels(get(x))]
df[[x]] <- droplevels(df[[x]])
}
# reording options
if (order == "y") {
data.table::setorderv(df, y, order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
} else if (order == "grouped" && group == "") {
rlang::warn("Group is empty. Ordering by y only.")
data.table::setorderv(df, y, order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
# Order by values of y
df <- df[order(df[[y]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
} else if (order == "grouped_y" && group != "") {
data.table::setorderv(df, c(group, y), order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
# Order by group first, then by values of y
df <- df[order(df[[group]], df[[y]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
} else if (order == "grouped_y" && group == "") {
# Fallback to ordering by y if group is empty
rlang::warn("Group is empty. Ordering by y only.")
df <- df[order(df[[y]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
} else if (order == "x") {
data.table::setorderv(df, x, order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
# Order alphabetically by x
df <- df[order(df[[x]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
} else if (order == "grouped_x" && group != "") {
data.table::setorderv(df, c(group, x), order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
# Order by group first, then alphabetically by x
df <- df[order(df[[group]], df[[x]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
} else if (order == "grouped_x" && group == "") {
# Fallback to ordering by x if group is empty
rlang::warn("Group is empty. Ordering by x only.")
data.table::setorderv(df, x, order = dir_order)
df[, (x) := forcats::fct_inorder(get(x))]
df <- df[order(df[[x]] * dir_order), ]
df[[x]] <- forcats::fct_inorder(df[[x]])
}
# Reset row names
rownames(df) <- NULL
return(df)
}

32
R/scale.R
View file

@ -1,35 +1,3 @@
#' One scale for all
#'
#' This function is based on [palette()]. If palette is NULL, the used palette will be magma from gpplot2's viridis scale constructors.
#'
#' @inheritParams palette_gen
#'
#' @param reverse_guide Boolean indicating whether the guide should be reversed.
#' @param ... Additional arguments passed to [ggplot2::discrete_scale()] if discrete or [ggplot2::scale_fill_gradient()] if continuous.
#'
#' @export
scale_visualizer_discrete <- function(palette = "cat_5_main", direction = 1, reverse_guide = TRUE, title_position = NULL, ...) {
s <- scale_color_visualizer_discrete(palette, direction, reverse_guide, ...) +
scale_fill_visualizer_discrete(palette, direction, reverse_guide, ...)
return(s)
}
#' @rdname scale_visualizer_dicscrete
#'
#' @export
scale_visualizer_continuous <- function(palette = "seq_5_main", direction = 1, reverse_guide = TRUE, title_position = NULL, ...) {
s <- scale_color_visualizer_continuous(palette, direction, reverse_guide, ...) +
scale_fill_visualizer_continuous(palette, direction, reverse_guide, ...)
return(s)
}
#' Scale constructors for fill and colors
#'
#' This function is based on [palette()]. If palette is NULL, the used palette will be magma from gpplot2's viridis scale constructors.

45
R/theme_bar.R
View file

@ -2,42 +2,64 @@
#'
#' @return A custom theme object.
#'
#' @rdname theme_default
#'
#' @export
theme_bar <- function(flip = TRUE, add_text = FALSE, axis_text_x_angle = 0, axis_text_x_vjust = 0.5, axis_text_x_hjust = 0.5) {
# If add_text is TRUE, flip is FALSE
if (!flip && !add_text){
par_axis_text_font_face <- "plain"
par_axis_x <- TRUE
par_axis_y <- TRUE
par_axis_line_y <- FALSE
par_axis_ticks_y <- FALSE
par_axis_ticks_y <- TRUE
par_axis_text_y <- TRUE
par_axis_line_x <- TRUE
par_axis_ticks_x <- TRUE
par_axis_text_x <- TRUE
par_grid_major_y <- TRUE
par_grid_major_x <- FALSE
par_grid_minor_y <- TRUE
par_grid_minor_x <- FALSE
} else if (flip && !add_text){
par_axis_text_font_face <- "plain"
par_axis_x <- TRUE
par_axis_y <- TRUE
par_axis_line_y <- TRUE
par_axis_ticks_y <- TRUE
par_axis_text_y <- TRUE
par_axis_line_x <- FALSE
par_axis_ticks_x <- FALSE
par_axis_ticks_x <- TRUE
par_axis_text_x <- TRUE
par_grid_major_y <- FALSE
par_grid_major_x <- TRUE
par_grid_minor_y <- FALSE
par_grid_minor_x <- TRUE
} else if (!flip && add_text){
par_axis_text_font_face <- "bold"
par_axis_x <- TRUE
par_axis_y <- TRUE
par_axis_line_y <- FALSE
par_axis_ticks_y <- FALSE
par_axis_line_x <- TRUE
par_axis_text_y <- FALSE
par_axis_line_x <- FALSE
par_axis_ticks_x <- TRUE
par_axis_text_x <- TRUE
par_grid_major_y <- FALSE
par_grid_major_x <- FALSE
par_grid_minor_y <- FALSE
par_grid_minor_x <- FALSE
} else if (flip && add_text){
par_axis_line_y <- TRUE
par_axis_text_font_face <- "bold"
par_axis_x <- TRUE
par_axis_y <- TRUE
par_axis_line_y <- FALSE
par_axis_ticks_y <- TRUE
par_axis_text_y <- TRUE
par_axis_line_x <- FALSE
par_axis_ticks_x <- FALSE
par_axis_text_x <- FALSE
par_grid_major_y <- FALSE
par_grid_major_x <- FALSE
par_grid_minor_y <- FALSE
@ -46,14 +68,19 @@ theme_bar <- function(flip = TRUE, add_text = FALSE, axis_text_x_angle = 0, axis
# Theme
t <- theme_default(
grid_major_y = par_grid_major_y
, axis_line_y = par_axis_line_y
, axis_ticks_y = par_axis_ticks_y
, axis_ticks_x = par_axis_ticks_x
, axis_line_x = par_axis_line_x
axis_text_font_face = par_axis_text_font_face
, axis_x = par_axis_x
, axis_y = par_axis_y
, grid_major_y = par_grid_major_y
, grid_major_x = par_grid_major_x
, grid_minor_y = par_grid_minor_y
, grid_minor_x = par_grid_minor_x
, axis_text_y = par_axis_text_y
, axis_line_y = par_axis_line_y
, axis_ticks_y = par_axis_ticks_y
, axis_text_x = par_axis_text_x
, axis_line_x = par_axis_line_x
, axis_ticks_x = par_axis_ticks_x
, axis_text_x_angle = axis_text_x_angle
, axis_text_x_vjust = axis_text_x_vjust
, axis_text_x_hjust = axis_text_x_hjust

49
R/theme.R → R/theme_default.R
View file

@ -86,15 +86,16 @@ theme_default <- function(
legend_title_size = 13,
legend_title_color = color("dark_grey"),
legend_title_font_face = "plain",
legend_title_font_family = "Carlito",
legend_text_size = 13,
legend_text_color = color("dark_grey"),
legend_text_font_face = "plain",
facet_title_size = 13,
facet_title_color = color("dark_grey"),
facet_title_font_face = "bold",
facet_title_font_family = "Carlito",
facet_title_position = "bottom",
facet_background_color = color("light_grey"),
legend_text_font_family = "Carlito",
facet_size = 14,
facet_color = color("dark_grey"),
facet_font_face = "bold",
facet_font_family = "Carlito",
facet_bg_color = color("lighter_grey"),
axis_x = TRUE,
axis_y = TRUE,
axis_text_x = TRUE,
@ -182,20 +183,18 @@ theme_default <- function(
color = caption_color,
margin = ggplot2::margin(t = 5)
),
# legend.title = ggplot2::element_text(
# size = legend_title_size,
# face = legend_title_font_face,
# family = font_family,
# color = legend_title_color
# #, vjust = 0.5
# ),
# legend.text = ggplot2::element_text(
# size = legend_text_size,
# face = legend_text_font_face,
# family = font_family,
# color = legend_text_color
# # #, hjust = 0.5
# # ),
legend.title = ggplot2::element_text(
size = legend_title_size,
face = legend_title_font_face,
family = legend_title_font_family,
color = legend_title_color
),
legend.text = ggplot2::element_text(
size = legend_text_size,
face = legend_text_font_face,
family = legend_text_font_family,
color = legend_text_color
),
axis.text.x = ggplot2::element_text(
angle = axis_text_x_angle,
vjust = axis_text_x_vjust,
@ -365,13 +364,13 @@ theme_default <- function(
# Add facet title text size
theme <- theme + ggplot2::theme(
strip.text = ggplot2::element_text(
size = facet_title_size,
family = facet_title_font_family,
face = facet_title_font_face,
color = facet_title_color
size = facet_size,
family = facet_font_family,
face = facet_font_face,
color = facet_color
),
strip.background = ggplot2::element_rect(
fill = facet_background_color,
fill = facet_bg_color,
linewidth = 0
)
)

13
R/theme_dumbbell.R Normal file
View file

@ -0,0 +1,13 @@
#' @title Dumbbell Theme
#' @description Theme for dumbbell charts based on theme_default.
#'
#' @rdname theme_default
#'
#' @export
theme_dumbbell <- function() {
theme_default(
axis_line_x = TRUE,
grid_)
}

32
R/theme_point.R Normal file
View file

@ -0,0 +1,32 @@
#' Custom Theme for Point Charts
#'
#' @param flip Logical. Whether the plot is flipped (horizonal).
#' @param axis_text_x_angle Angle for x-axis text.
#' @param axis_text_x_vjust Vertical justification for x-axis text.
#' @param axis_text_x_hjust Horizontal justification for x-axis text.
#'
#' @rdname theme_default
#'
#' @return A custom theme object.
#'
#' @export
theme_point <- function(
) {
t <- theme_default(
axis_text_font_face = "plain",
axis_x = TRUE,
axis_y = TRUE,
grid_major_y = TRUE,
grid_major_x = TRUE,
grid_minor_y = FALSE,
grid_minor_x = FALSE,
axis_text_x = TRUE,
axis_line_x = TRUE,
axis_ticks_x = TRUE,
axis_text_x_angle = 0,
axis_text_x_vjust = 0.5,
axis_text_x_hjust = 0
)
return(t)
}

49
R/theme_visualizer_bar.R
View file

@ -1,49 +0,0 @@
#' Dynamic Theme for ggplot2
#'
#' A dynamic theme that adjusts axis text styles based on whether the plot is flipped.
#'
#' This function dynamically applies different axis text styles depending on
#' the coordinate system of the plot. If the plot is flipped (e.g., using
#' `coord_flip()`), the x-axis and y-axis text styles are adjusted accordingly.
#'
#' @return A ggproto object that applies a dynamic theme to a ggplot2 plot.
#' @examples
#' library(ggplot2)
#'
#' # Example with a regular plot
#' p <- ggplot(mpg, aes(displ, hwy)) +
#' geom_col()
#'
#' # Add the dynamic theme
#' p + theme_visualizer_bar()
#'
#' # Add the dynamic theme with a flipped coordinate system
#' p + theme_visualizer_bar() + coord_flip()
#'
#' @export
theme_visualizer_bar <- function() {
out <- theme_grey()
class(out) <- c("ThemeVisualizerBar", class(out))
#structure(list(), class = c("ThemeVisualizerBar", "theme", "gg"))
return(out)
}
ggplot_add.theme_visualizer_bar <- function(object, p, object_name) {
# Check if the plot is flipped
is_flipped <- inherits(p$coordinates, "CoordFlip")
if (!is_flipped) {
object <- object +
theme_minimal()
} else {
object <- object +
theme(
panel.grid.major = ggplot2::element_line(color = "blue")
)
}
return(object)
}

6
R/visualizeR-package.R Normal file
View file

@ -0,0 +1,6 @@
#' @keywords internal
"_PACKAGE"
## usethis namespace: start
## usethis namespace: end
NULL

252
README.Rmd
View file

@ -20,16 +20,15 @@ desc <- read.dcf("DESCRIPTION")
desc <- setNames(as.list(desc), colnames(desc))
```
# `r desc$Package` <img src="man/figures/logo.png" align="right" alt="" width="120"/>
# `r desc$Package` <img src="man/figures/logo.png" align="right" width="120"/>
> `r desc$Title`
`visualizeR` proposes some utils to get REACH and AGORA colors, ready-to-go color palettes, and a few visualization functions (horizontal hist graph for instance).
`visualizeR` proposes some utils to sane colors, ready-to-go color palettes, and a few visualization functions.
## Installation
You can install the last version of visualizeR from
[GitHub](https://github.com/) with:
You can install the last version of visualizeR from [GitHub](https://github.com/) with:
```{r, eval = FALSE}
# install.packages("devtools")
@ -38,44 +37,32 @@ devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE)
## Roadmap
Roadmap is as follows:
Roadmap is as follows: - [ ] Full revamp \## Request
- [X] Add IMPACT's colors
- [X] Add all color palettes from the internal documentation
- [ ] There remains to be added more-than-7-color palettes and black color palettes
- [X] Add new types of visualization (e.g. dumbbell plot, lollipop plot, etc.)
- [X] Use examples
- [ ] Add some ease-map functions
- [ ] Add some interactive functions (maps and graphs)
- [ ] Consolidate and make errors transparent
## Request
Please, do not hesitate to pull request any new viz or colors or color palettes, or to email request any change (guillaume.noblet@reach-initiative.org or gnoblet@zaclys.net).
Please, do not hesitate to pull request any new viz or colors or color palettes, or to email request any change ([gnoblet\@zaclys.net](mailto:gnoblet@zaclys.net){.email}).
## Colors
Color palettes for REACH, AGORA and IMPACT are available. Functions to access colors and palettes are `cols_initiative()` or `pal_initiative()`. For now, the initiative with the most colors and color palettes is REACH. Feel free to pull requests new AGORA and IMPACT colors.
Functions to access colors and palettes are `color()` or `palette()`. Feel free to pull request new colors.
```{r example-colors, eval = TRUE}
library(visualizeR)
# Get all saved REACH colors, named
cols_reach(unnamed = F)[1:10]
# Get all saved colors, named
color(unname = F)[1:10]
# Extract a color palette as hexadecimal codes and reversed
pal_reach(palette = "main", reversed = TRUE, color_ramp_palette = FALSE)
palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)
# Get all color palettes names
pal_reach(show_palettes = T)
palette(show_palettes = TRUE)
```
## Charts
### Example 1: Bar chart, already REACH themed
### Example 1: Bar chart
```{r example-bar-chart, out.width = "65%", eval = TRUE}
library(visualizeR)
library(palmerpenguins)
library(dplyr)
@ -87,33 +74,41 @@ df <- penguins |>
) |>
ungroup()
df_island <- penguins |>
group_by(island) |>
summarize(
mean_bl = mean(bill_length_mm, na.rm = T),
mean_fl = mean(flipper_length_mm, na.rm = T)
) |>
ungroup()
# Simple bar chart by group with some alpha transparency
bar(df, island, mean_bl, species, percent = FALSE, alpha = 0.6, x_title = "Mean of bill length")
bar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
# Using another color palette through `theme_reach()` and changing scale to percent
bar(df, island, mean_bl, species, percent = TRUE, theme = theme_reach(palette = "artichoke_3"))
# Flipped / Horizontal
hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
# Not flipped, with text added, group_title, no y-axis and no bold for legend
bar(df, island, mean_bl, species, group_title = "Species", flip = FALSE, add_text = TRUE, add_text_suffix = "%", percent = FALSE, theme = theme_reach(text_font_face = "plain", axis_y = FALSE))
# Facetted
bar(df, "island", "mean_bl", "species", facet = "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species", add_color_guide = FALSE)
# Flipped, with text, smaller width
hbar(df = df_island, x = "island", y = "mean_bl", group = "island", title = "Mean of bill length by island", add_text = T, width = 0.6, add_text_suffix = "mm", add_text_expand_limit = 1.3, add_color_guide = FALSE)
```
### Example 2: Point chart, already REACH themed
At this stage, `point_reach()` only supports categorical grouping colors with the `group` arg.
### Example 2: Scatterplot
```{r example-point-chart, out.width = "65%", eval = TRUE}
# Simple point chart
point(penguins, bill_length_mm, flipper_length_mm)
# Simple scatterplot
point(penguins, "bill_length_mm", "flipper_length_mm")
# Point chart with grouping colors, greater dot size, some transparency, reversed color palette
point(penguins, bill_length_mm, flipper_length_mm, island, alpha = 0.6, size = 3, theme = theme_reach(reverse = TRUE))
# Scatterplot with grouping colors, greater dot size, some transparency
point(penguins, "bill_length_mm", "flipper_length_mm", "island", group_title = "Island", alpha = 0.6, size = 3, title = "Bill vs. flipper length", , add_color_guide = FALSE)
# Using another color palettes
point(penguins, bill_length_mm, flipper_length_mm, island, size = 1.5, x_title = "Bill", y_title = "Flipper", title = "Length (mm)", theme = theme_reach(palette = "artichoke_3", text_font_face = , grid_major_x = TRUE, title_position_to_plot = FALSE))
# Facetted scatterplot by island
point(penguins, "bill_length_mm", "flipper_length_mm", "species", "island", "fixed", group_title = "Species", title = "Bill vs. flipper length by species and island", add_color_guide = FALSE)
```
### Example 3: Dumbbell plot, REACH themed
### Example 3: Dumbbell plot
Remember to ensure that your data are in the long format and you only have two groups on the x-axis; for instance, IDP and returnee and no NA values.
@ -126,32 +121,22 @@ df <- tibble::tibble(
) |>
dplyr::mutate(stat = round(stat, 0))
# Example, adding a parameter to `theme_reach()` passed on `ggplot2::theme()` to align legend title
dumbbell(df,
stat,
setting,
admin1,
title = "% of HHs that reported open defecation as sanitation facility",
group_y_title = "Admin 1",
group_x_title = "Setting",
theme = theme_reach(
legend_position = "bottom",
legend_direction = "horizontal",
legend_title_font_face = "bold",
palette = "primary",
title_position_to_plot = FALSE,
legend.title.align = 0.5
)
) +
# Change legend title position (could be included as part of the function)
ggplot2::guides(
color = ggplot2::guide_legend(title.position = "left"),
fill = ggplot2::guide_legend(title.position = "left")
)
# dumbbell(
# df,
# "stat",
# "setting",
# "admin1",
# title = "% of HHs that reported open defecation as sanitation facility",
# group_y_title = "Admin 1",
# group_x_title = "Setting"
# )
```
### Example 4: donut chart, REACH themed (to used once, not twice)
### Example 4: donut chart
```{r example-donut-plot, out.width = "65%", warning = FALSE}
# Some summarized data: % of HHs by displacement status
df <- tibble::tibble(
@ -160,28 +145,27 @@ df <- tibble::tibble(
)
# Donut
donut(df,
status,
percentage,
hole_size = 3,
add_text_suffix = "%",
add_text_color = cols_reach("dk_grey"),
add_text_treshold_display = 5,
x_title = "Displacement status",
title = "% of HHs by displacement status",
theme = theme_reach(legend_reverse = TRUE)
)
# donut(df,
# status,
# percentage,
# hole_size = 3,
# add_text_suffix = "%",
# add_text_color = color("dark_grey"),
# add_text_treshold_display = 5,
# x_title = "Displacement status",
# title = "% of HHs by displacement status"
# )
```
### Example 5: Waffle chart
### Example 5: waffle chart
```{r example-waffle-plot, out.width = "65%", warning = FALSE}
#
waffle(df, status, percentage, x_title = "A caption", title = "A title", subtitle = "A subtitle")
# waffle(df, status, percentage, x_title = "A caption", title = "A title", subtitle = "A subtitle")
```
### Example 6: Alluvial chart
### Example 6: alluvial chart, REACH themed
```{r example-alluvial-plot, out.width = "65%", warning = FALSE}
# Some summarized data: % of HHs by self-reported status of displacement in 2021 and in 2022
df <- tibble::tibble(
@ -197,23 +181,20 @@ df <- tibble::tibble(
# Alluvial, here the group is the status for 2021
alluvial(df,
status_from,
status_to,
percentage,
status_from,
from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
alpha = 0.8,
group_title = "Status for 2021",
title = "% of HHs by self-reported status from 2021 to 2022",
theme = theme_reach(
axis_y = FALSE,
legend_position = "none"
)
)
# alluvial(df,
# status_from,
# status_to,
# percentage,
# status_from,
# from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
# alpha = 0.8,
# group_title = "Status for 2021",
# title = "% of HHs by self-reported status from 2021 to 2022"
# )
```
### Example 7: lollipop chart
### Example 7: Lollipop chart
```{r example-lollipop-chart, out.width = "65%", warning = FALSE}
library(tidyr)
# Prepare long data
@ -223,80 +204,15 @@ df <- tibble::tibble(
) |>
dplyr::mutate(stat = round(stat, 0))
# Make lollipop plot, REACH themed, vertical with 45 degrees angle X-labels
lollipop(df,
admin1,
stat,
arrange = FALSE,
add_text = FALSE,
flip = FALSE,
y_title = "% of HHs",
x_title = "Admin 1",
title = "% of HHs that reported having received a humanitarian assistance",
theme = theme_reach(
axis_text_x_angle = 45,
grid_major_y = TRUE,
grid_major_y_size = 0.2,
grid_major_x = TRUE,
grid_minor_y = TRUE
)
)
# Horizontal, greater point size, arranged by value, no grid, and text labels added
lollipop(df,
admin1,
stat,
arrange = TRUE,
point_size = 10,
point_color = cols_reach("main_beige"),
segment_size = 2,
add_text = TRUE,
add_text_suffix = "%",
y_title = "% of HHs",
x_title = "Admin 1",
title = "% of HHs that reported having received a humanitarian assistance in the 12 months prior to the assessment",
theme = theme_reach(title_position_to_plot = FALSE)
)
# Make lollipop plot, vertical with 45 degrees angle X-labels
# lollipop(df,
# admin1,
# stat,
# arrange = FALSE,
# add_text = FALSE,
# flip = FALSE,
# y_title = "% of HHs",
# x_title = "Admin 1",
# title = "% of HHs that reported having received a humanitarian assistance"
# )
```
## Maps
```{r example-map, out.width = "50%"}
# Add indicator layer
# - based on "pretty" classes and title "Proportion (%)"
# - buffer to add a 10% around the bounding box
map <- add_indicator_layer(
indicator_admin1,
opn_dfc,
buffer = 0.1
) +
# Layout - some defaults - add the map title
add_layout("% of HH that reported open defecation as sanitation facility") +
# Admin boundaries as list of shape files (lines) and colors, line widths and labels as vectors
add_admin_boundaries(
lines = list(line_admin1, border_admin0, frontier_admin0),
colors = cols_reach("main_lt_grey", "dk_grey", "black"),
lwds = c(0.5, 2, 3),
labels = c("Department", "Country", "Dominican Rep. frontier"),
title = "Administrative boundaries"
) +
# Add text labels - centered on admin 1 centroids
add_admin_labels(centroid_admin1, ADM1_FR_UPPER) +
# Add a compass
add_compass() +
# Add a scale bar
add_scale_bar() +
# Add credits
add_credits("Admin. boundaries. : CNIGS \nCoord. system: GCS WGS 1984")
```
```{r map-save, eval = TRUE, include = FALSE, echo = TRUE}
tmap::tmap_save(map,
"man/figures/README-example-map.png",
height = 4.5,
width = 6
)
```
![Once exported with `tmap::tmap_save()`.](man/figures/README-example-map.png)

303
README.md
View file

@ -1,13 +1,12 @@
<!-- README.md is generated from README.Rmd. Please edit that file -->
# visualizeR <img src="man/figures/logo.png" align="right" alt="" width="120"/>
# visualizeR <img src="man/figures/logo.png" align="right" width="120"/>
> What a color\! What a viz\!
> What a color! What a viz!
`visualizeR` proposes some utils to get REACH and AGORA colors,
ready-to-go color palettes, and a few visualization functions
(horizontal hist graph for instance).
`visualizeR` proposes some utils to sane colors, ready-to-go color
palettes, and a few visualization functions.
## Installation
@ -21,66 +20,47 @@ devtools::install_github("gnoblet/visualizeR", build_vignettes = TRUE)
## Roadmap
Roadmap is as follows:
- \[X\] Add IMPACT’s colors
- \[X\] Add all color palettes from the internal documentation
- \[ \] There remains to be added more-than-7-color palettes and black
color palettes
- \[X\] Add new types of visualization (e.g. dumbbell plot, lollipop
plot, etc.)
- \[X\] Use examples
- \[ \] Add some ease-map functions
- \[ \] Add some interactive functions (maps and graphs)
- \[ \] Consolidate and make errors transparent
## Request
Roadmap is as follows: - \[ \] Full revamp \## Request
Please, do not hesitate to pull request any new viz or colors or color
palettes, or to email request any change
(<guillaume.noblet@reach-initiative.org> or <gnoblet@zaclys.net>).
palettes, or to email request any change (<gnoblet@zaclys.net>).
## Colors
Color palettes for REACH, AGORA and IMPACT are available. Functions to
access colors and palettes are `cols_initiative()` or
`pal_initiative()`. For now, the initiative with the most colors and
color palettes is REACH. Feel free to pull requests new AGORA and IMPACT
colors.
Functions to access colors and palettes are `color()` or `palette()`.
Feel free to pull request new colors.
``` r
library(visualizeR)
# Get all saved REACH colors, named
cols_reach(unnamed = F)[1:10]
#> white black main_grey main_red main_lt_grey main_beige
#> "#FFFFFF" "#000000" "#58585A" "#EE5859" "#C7C8CA" "#D2CBB8"
#> iroise_1 iroise_2 iroise_3 iroise_4
#> "#DFECEF" "#B1D7E0" "#699DA3" "#236A7A"
# Get all saved colors, named
color(unname = F)[1:10]
#> white lighter_grey light_grey dark_grey black
#> "#FFFFFF" "#F5F5F5" "#E3E3E3" "#464647" "#000000"
#> cat_2_yellow_1 cat_2_yellow_2 cat_2_light_1 cat_2_light_2 cat_2_green_1
#> "#ffc20a" "#0c7bdc" "#fefe62" "#d35fb7" "#1aff1a"
# Extract a color palette as hexadecimal codes and reversed
pal_reach(palette = "main", reversed = TRUE, color_ramp_palette = FALSE)
#> [1] "#58585A" "#EE5859" "#C7C8CA" "#D2CBB8"
palette(palette = "cat_5_main", reversed = TRUE, color_ramp_palette = FALSE)
#> [1] "#083d77" "#4ecdc4" "#f4c095" "#b47eb3" "#ffd5ff"
# Get all color palettes names
pal_reach(show_palettes = T)
#> [1] "main" "primary" "secondary" "two_dots"
#> [5] "two_dots_flashy" "red_main" "red_main_5" "red_alt"
#> [9] "red_alt_5" "iroise" "iroise_5" "discrete_6"
#> [13] "red_2" "red_3" "red_4" "red_5"
#> [17] "red_6" "red_7" "green_2" "green_3"
#> [21] "green_4" "green_5" "green_6" "green_7"
#> [25] "artichoke_2" "artichoke_3" "artichoke_4" "artichoke_5"
#> [29] "artichoke_6" "artichoke_7" "blue_2" "blue_3"
#> [33] "blue_4" "blue_5" "blue_6" "blue_7"
palette(show_palettes = TRUE)
#> [1] "cat_2_yellow" "cat_2_light"
#> [3] "cat_2_green" "cat_2_blue"
#> [5] "cat_5_main" "cat_5_ibm"
#> [7] "cat_3_aquamarine" "cat_3_tol_high_contrast"
#> [9] "cat_8_tol_adapted" "cat_3_custom_1"
#> [11] "cat_4_custom_1" "cat_5_custom_1"
#> [13] "cat_6_custom_1" "div_5_orange_blue"
#> [15] "div_5_green_purple"
```
## Charts
### Example 1: Bar chart, already REACH themed
### Example 1: Bar chart
``` r
library(visualizeR)
library(palmerpenguins)
library(dplyr)
@ -88,61 +68,74 @@ df <- penguins |>
group_by(island, species) |>
summarize(
mean_bl = mean(bill_length_mm, na.rm = T),
mean_fl = mean(flipper_length_mm, na.rm = T)) |>
mean_fl = mean(flipper_length_mm, na.rm = T)
) |>
ungroup()
df_island <- penguins |>
group_by(island) |>
summarize(
mean_bl = mean(bill_length_mm, na.rm = T),
mean_fl = mean(flipper_length_mm, na.rm = T)
) |>
ungroup()
# Simple bar chart by group with some alpha transparency
bar(df, island, mean_bl, species, percent = FALSE, alpha = 0.6, x_title = "Mean of bill length")
bar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
```
<img src="man/figures/README-example-bar-chart-1.png" width="65%" />
``` r
# Using another color palette through `theme_reach()` and changing scale to percent
bar(df, island,mean_bl, species, percent = TRUE, theme = theme_reach(palette = "artichoke_3"))
# Flipped / Horizontal
hbar(df, "island", "mean_bl", "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species")
```
<img src="man/figures/README-example-bar-chart-2.png" width="65%" />
``` r
# Not flipped, with text added, group_title, no y-axis and no bold for legend
bar(df, island, mean_bl, species, group_title = "Species", flip = FALSE, add_text = TRUE, add_text_suffix = "%", percent = FALSE, theme = theme_reach(text_font_face = "plain", axis_y = FALSE))
# Facetted
bar(df, "island", "mean_bl", "species", facet = "species", x_title = "Mean of bill length", title = "Mean of bill length by island and species", add_color_guide = FALSE)
```
<img src="man/figures/README-example-bar-chart-3.png" width="65%" />
### Example 2: Point chart, already REACH themed
At this stage, `point_reach()` only supports categorical grouping colors
with the `group` arg.
``` r
# Simple point chart
point(penguins, bill_length_mm, flipper_length_mm)
# Flipped, with text, smaller width
hbar(df = df_island, x = "island", y = "mean_bl", group = "island", title = "Mean of bill length by island", add_text = T, width = 0.6, add_text_suffix = "mm", add_text_expand_limit = 1.3, add_color_guide = FALSE)
```
<img src="man/figures/README-example-bar-chart-4.png" width="65%" />
### Example 2: Scatterplot
``` r
# Simple scatterplot
point(penguins, "bill_length_mm", "flipper_length_mm")
```
<img src="man/figures/README-example-point-chart-1.png" width="65%" />
``` r
# Point chart with grouping colors, greater dot size, some transparency, reversed color palette
point(penguins, bill_length_mm, flipper_length_mm, island, alpha = 0.6, size = 3, theme = theme_reach(reverse = TRUE))
# Scatterplot with grouping colors, greater dot size, some transparency
point(penguins, "bill_length_mm", "flipper_length_mm", "island", group_title = "Island", alpha = 0.6, size = 3, title = "Bill vs. flipper length", , add_color_guide = FALSE)
```
<img src="man/figures/README-example-point-chart-2.png" width="65%" />
``` r
# Using another color palettes
point(penguins, bill_length_mm, flipper_length_mm, island, size = 1.5, x_title = "Bill", y_title = "Flipper", title = "Length (mm)", theme = theme_reach(palette = "artichoke_3", text_font_face = , grid_major_x = TRUE, title_position_to_plot = FALSE))
# Facetted scatterplot by island
point(penguins, "bill_length_mm", "flipper_length_mm", "species", "island", "fixed", group_title = "Species", title = "Bill vs. flipper length by species and island", add_color_guide = FALSE)
```
<img src="man/figures/README-example-point-chart-3.png" width="65%" />
### Example 3: Dumbbell plot, REACH themed
### Example 3: Dumbbell plot
Remember to ensure that your data are in the long format and you only
have two groups on the x-axis; for instance, IDP and returnee and no NA
@ -157,34 +150,23 @@ df <- tibble::tibble(
) |>
dplyr::mutate(stat = round(stat, 0))
# Example, adding a parameter to `theme_reach()` passed on `ggplot2::theme()` to align legend title
dumbbell(df,
stat,
setting,
admin1,
title = "% of HHs that reported open defecation as sanitation facility",
group_y_title = "Admin 1",
group_x_title = "Setting",
theme = theme_reach(legend_position = "bottom",
legend_direction = "horizontal",
legend_title_font_face = "bold",
palette = "primary",
title_position_to_plot = FALSE,
legend.title.align = 0.5)) +
# Change legend title position (could be included as part of the function)
ggplot2::guides(
color = ggplot2::guide_legend(title.position = "left"),
fill = ggplot2::guide_legend(title.position = "left")
)
# dumbbell(
# df,
# "stat",
# "setting",
# "admin1",
# title = "% of HHs that reported open defecation as sanitation facility",
# group_y_title = "Admin 1",
# group_x_title = "Setting"
# )
```
<img src="man/figures/README-example-dumbbell-plot-1.png" width="65%" />
### Example 4: donut chart, REACH themed (to used once, not twice)
### Example 4: donut chart
``` r
# Some summarized data: % of HHs by displacement status
df <- tibble::tibble(
status = c("Displaced", "Non displaced", "Returnee", "Don't know/Prefer not to say"),
@ -192,139 +174,74 @@ df <- tibble::tibble(
)
# Donut
donut(df,
status,
percentage,
hole_size = 3,
add_text_suffix = "%",
add_text_color = cols_reach("dk_grey"),
add_text_treshold_display = 5,
x_title = "Displacement status",
title = "% of HHs by displacement status",
theme = theme_reach(legend_reverse = TRUE))
# donut(df,
# status,
# percentage,
# hole_size = 3,
# add_text_suffix = "%",
# add_text_color = color("dark_grey"),
# add_text_treshold_display = 5,
# x_title = "Displacement status",
# title = "% of HHs by displacement status"
# )
```
<img src="man/figures/README-example-donut-plot-1.png" width="65%" />
### Example 5: waffle chart
### Example 5: Waffle chart
``` r
#
waffle(df, status, percentage, x_title = "A caption", title = "A title", subtitle = "A subtitle")
# waffle(df, status, percentage, x_title = "A caption", title = "A title", subtitle = "A subtitle")
```
<img src="man/figures/README-example-waffle-plot-1.png" width="65%" />
### Example 6: alluvial chart, REACH themed
### Example 6: Alluvial chart
``` r
# Some summarized data: % of HHs by self-reported status of displacement in 2021 and in 2022
df <- tibble::tibble(
status_from = c(rep("Displaced", 4),
status_from = c(
rep("Displaced", 4),
rep("Non displaced", 4),
rep("Returnee", 4),
rep("Dnk/Pnts", 4)),
rep("Dnk/Pnts", 4)
),
status_to = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts", "Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
percentage = c(20, 8, 18, 1, 12, 21, 0, 2, 0, 3, 12, 1, 0, 0, 1, 1)
)
# Alluvial, here the group is the status for 2021
alluvial(df,
status_from,
status_to,
percentage,
status_from,
from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
alpha = 0.8,
group_title = "Status for 2021",
title = "% of HHs by self-reported status from 2021 to 2022",
theme = theme_reach(
axis_y = FALSE,
legend_position = "none"))
# alluvial(df,
# status_from,
# status_to,
# percentage,
# status_from,
# from_levels = c("Displaced", "Non displaced", "Returnee", "Dnk/Pnts"),
# alpha = 0.8,
# group_title = "Status for 2021",
# title = "% of HHs by self-reported status from 2021 to 2022"
# )
```
<img src="man/figures/README-example-alluvial-plot-1.png" width="65%" />
### Example 7: lollipop chart
### Example 7: Lollipop chart
``` r
library(tidyr)
# Prepare long data
df <- tibble::tibble(
admin1 = replicate(15, sample(letters, 8)) |> t() |> as.data.frame() |> unite("admin1", sep = "") |> dplyr::pull(admin1),
stat = rnorm(15, mean = 50, sd = 15)) |>
stat = rnorm(15, mean = 50, sd = 15)
) |>
dplyr::mutate(stat = round(stat, 0))
# Make lollipop plot, REACH themed, vertical with 45 degrees angle X-labels
lollipop(df,
admin1,
stat,
arrange = FALSE,
add_text = FALSE,
flip = FALSE,
y_title = "% of HHs",
x_title = "Admin 1",
title = "% of HHs that reported having received a humanitarian assistance",
theme = theme_reach(axis_text_x_angle = 45,
grid_major_y = TRUE,
grid_major_y_size = 0.2,
grid_major_x = TRUE,
grid_minor_y = TRUE))
# Make lollipop plot, vertical with 45 degrees angle X-labels
# lollipop(df,
# admin1,
# stat,
# arrange = FALSE,
# add_text = FALSE,
# flip = FALSE,
# y_title = "% of HHs",
# x_title = "Admin 1",
# title = "% of HHs that reported having received a humanitarian assistance"
# )
```
<img src="man/figures/README-example-lollipop-chart-1.png" width="65%" />
``` r
# Horizontal, greater point size, arranged by value, no grid, and text labels added
lollipop(df,
admin1,
stat,
arrange = TRUE,
point_size = 10,
point_color = cols_reach("main_beige"),
segment_size = 2,
add_text = TRUE,
add_text_suffix = "%",
y_title = "% of HHs",
x_title = "Admin 1",
title = "% of HHs that reported having received a humanitarian assistance in the 12 months prior to the assessment",
theme = theme_reach(title_position_to_plot = FALSE))
```
<img src="man/figures/README-example-lollipop-chart-2.png" width="65%" />
## Maps
``` r
# Add indicator layer
# - based on "pretty" classes and title "Proportion (%)"
# - buffer to add a 10% around the bounding box
map <- add_indicator_layer(
indicator_admin1,
opn_dfc,
buffer = 0.1) +
# Layout - some defaults - add the map title
add_layout("% of HH that reported open defecation as sanitation facility") +
# Admin boundaries as list of shape files (lines) and colors, line widths and labels as vectors
add_admin_boundaries(
lines = list(line_admin1, border_admin0, frontier_admin0),
colors = cols_reach("main_lt_grey", "dk_grey", "black"),
lwds = c(0.5, 2, 3),
labels = c("Department", "Country", "Dominican Rep. frontier"),
title = "Administrative boundaries") +
# Add text labels - centered on admin 1 centroids
add_admin_labels(centroid_admin1, ADM1_FR_UPPER) +
# Add a compass
add_compass() +
# Add a scale bar
add_scale_bar() +
# Add credits
add_credits("Admin. boundaries. : CNIGS \nCoord. system: GCS WGS 1984")
```
![Once exported with
`tmap::tmap_save()`.](man/figures/README-example-map.png)

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@ -1 +0,0 @@
PROJCS["WGS_1984_UTM_Zone_18N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-75.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["m",1.0]]

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@ -1 +0,0 @@
GEOGCS["GCS_unknown",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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@ -1,20 +0,0 @@
#------ Border - admin 0
border_admin0 <- sf::st_read("data-raw/border_admin0.shp")
usethis::use_data(border_admin0, overwrite = TRUE)
#------ Frontier - admin 0
frontier_admin0 <- sf::st_read("data-raw/frontier_admin0.shp")
usethis::use_data(frontier_admin0, overwrite = TRUE)
#------ Line - admin 1
line_admin1 <- sf::st_read("data-raw/line_admin1.shp")
usethis::use_data(line_admin1, overwrite = TRUE)
#------ Centroid - admin 1
centroid_admin1 <- sf::st_read("data-raw/centroid_admin1.shp") |>
dplyr::rename(ADM1_FR_UPPER = ADM1_FR_)
usethis::use_data(centroid_admin1, overwrite = TRUE)
#------ Indicator polygon - admin 1
indicator_admin1 <- sf::st_read("data-raw/indicator_admin1.shp")
usethis::use_data(indicator_admin1, overwrite = TRUE)

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@ -1,16 +1,32 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bar.R
\name{bar}
\name{hbar}
\alias{hbar}
\alias{bar}
\title{Simple bar chart}
\usage{
hbar(
...,
flip = TRUE,
add_text = FALSE,
theme_fun = theme_bar(flip = flip, add_text = add_text)
)
bar(
df,
x,
y,
group = "",
add_color = color("dark_grey"),
flip = TRUE,
facet = "",
order = "none",
x_rm_na = TRUE,
y_rm_na = TRUE,
group_rm_na = TRUE,
facet_rm_na = TRUE,
y_expand = 0.1,
add_color = color("cat_5_main_1"),
add_color_guide = TRUE,
flip = FALSE,
wrap = NULL,
position = "dodge",
alpha = 1,
@ -20,18 +36,28 @@ bar(
title = NULL,
subtitle = NULL,
caption = NULL,
width = 0.5,
add_text = TRUE,
add_text_size = 5,
width = 0.8,
add_text = FALSE,
add_text_size = 4.5,
add_text_color = color("dark_grey"),
add_text_font_face = "plain",
add_text_font_face = "bold",
add_text_threshold_display = 0.05,
add_text_suffix = "\%",
add_text_expand_limit = 1.2,
add_text_round = 1
add_text_round = 1,
theme_fun = theme_bar(flip = flip, add_text = add_text, axis_text_x_angle = 0,
axis_text_x_vjust = 0.5, axis_text_x_hjust = 0.5),
scale_fill_fun = scale_fill_visualizer_discrete(),
scale_color_fun = scale_color_visualizer_discrete()
)
}
\arguments{
\item{flip}{TRUE or FALSE (default). Default to TRUE or horizontal bar plot.}
\item{add_text}{TRUE or FALSE. Add values as text.}
\item{theme_fun}{Whatever theme function. For no custom theme, use theme_fun = NULL.}
\item{df}{A data frame.}
\item{x}{A quoted numeric column.}
@ -40,9 +66,23 @@ bar(
\item{group}{Some quoted grouping categorical column, e.g. administrative areas or population groups.}
\item{facet}{Some quoted grouping categorical column, e.g. administrative areas or population groups.}
\item{order}{Should bars be ordered? "none" if no, "y" if yes based on y, "grouped" if yes based on y and group.}
\item{x_rm_na}{Remove NAs in x?}
\item{y_rm_na}{Remove NAs in y?}
\item{group_rm_na}{Remove NAs in group?}
\item{facet_rm_na}{Remove NAs in facet?}
\item{y_expand}{Multiplier to expand the y axis.}
\item{add_color}{Add a color to bars (if no grouping).}
\item{flip}{TRUE or FALSE. Default to TRUE or horizontal bar plot.}
\item{add_color_guide}{Should a legend be added?}
\item{wrap}{Should x-labels be wrapped? Number of characters.}
@ -64,8 +104,6 @@ bar(
\item{width}{Bar width.}
\item{add_text}{TRUE or FALSE. Add values as text.}
\item{add_text_size}{Text size.}
\item{add_text_color}{Text color.}
@ -76,14 +114,10 @@ bar(
\item{add_text_suffix}{If percent is FALSE, should we add a suffix to the text label?}
\item{add_text_expand_limit}{Default to adding 10% on top of the bar.}
\item{add_text_expand_limit}{Default to adding 10\% on top of the bar.}
\item{add_text_round}{Round the text label.}
\item{theme_fun}{Whatever theme function. For no custom theme, use theme_fun = NULL.}
\item{scale_impact}{Use the package custom scales for fill and color.}
}
\description{
Simple bar chart
`bar()` is a simple bar chart with some customization allowed, in particular the `theme_fun` argument for theming. `hbar()` uses `bar()` with sane defaults for a horizontal bar chart.
}

85
man/dumbbell.Rd Normal file
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@ -0,0 +1,85 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dumbbell.R
\name{dumbbell}
\alias{dumbbell}
\title{Make dumbbell chart.}
\usage{
dumbbell(
df,
col,
group_x,
group_y,
point_size = 5,
point_alpha = 1,
segment_size = 2.5,
segment_color = cols_reach("main_lt_grey"),
group_x_title = NULL,
group_y_title = NULL,
x_title = NULL,
title = NULL,
subtitle = NULL,
caption = NULL,
line_to_y_axis = TRUE,
line_to_y_axis_type = 3,
line_to_y_axis_width = 0.5,
line_to_y_axis_color = cols_reach("main_grey"),
add_text = TRUE,
add_text_vjust = 2,
add_text_size = 3.5,
add_text_color = cols_reach("main_grey"),
theme = theme_reach(palette = "primary")
)
}
\arguments{
\item{df}{A data frame.}
\item{col}{A numeric column.}
\item{group_x}{The grouping column on the x-axis; only two groups.}
\item{group_y}{The grouping column on the y-axis.}
\item{point_size}{Point size.}
\item{point_alpha}{Point alpha.}
\item{segment_size}{Segment size.}
\item{segment_color}{Segment color.}
\item{group_x_title}{X-group and legend title.}
\item{group_y_title}{Y-axis and group title.}
\item{x_title}{X-axis title.}
\item{title}{Title.}
\item{subtitle}{Subtitle.}
\item{caption}{Caption.}
\item{line_to_y_axis}{TRUE or FALSE; add a line connected points and Y-axis.}
\item{line_to_y_axis_type}{Line to Y-axis type.}
\item{line_to_y_axis_width}{Line to Y-axis width.}
\item{line_to_y_axis_color}{Line to Y-axis color.}
\item{add_text}{TRUE or FALSE; add text at the points.}
\item{add_text_vjust}{Vertical adjustment.}
\item{add_text_size}{Text size.}
\item{add_text_color}{Text color.}
\item{theme}{A ggplot2 theme, default to `theme_reach()`}
}
\value{
A dumbbell chart.
}
\description{
Make dumbbell chart.
}

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49
man/point.Rd
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@ -2,14 +2,21 @@
% Please edit documentation in R/point.R
\name{point}
\alias{point}
\title{Simple point chart}
\title{Simple scatterplot}
\usage{
point(
df,
x,
y,
group = "",
add_color = color("branding_reach_red"),
facet = "",
facet_scales = "free",
x_rm_na = TRUE,
y_rm_na = TRUE,
group_rm_na = TRUE,
facet_rm_na = TRUE,
add_color = color("cat_5_main_1"),
add_color_guide = TRUE,
flip = TRUE,
alpha = 1,
size = 2,
@ -19,25 +26,37 @@ point(
title = NULL,
subtitle = NULL,
caption = NULL,
theme_fun = theme_reach(grid_major_y = TRUE),
palette = "cat_5_ibm",
scale_impact = TRUE,
direction = 1,
reverse_guide = TRUE
theme_fun = theme_point(),
scale_fill_fun = scale_fill_visualizer_discrete(),
scale_color_fun = scale_color_visualizer_discrete()
)
}
\arguments{
\item{df}{A data frame.}
\item{x}{A numeric column.}
\item{x}{A quoted numeric column.}
\item{y}{Another numeric column.}
\item{y}{A quoted numeric column.}
\item{group}{Some grouping categorical column, e.g. administrative areas or population groups.}
\item{group}{Some quoted grouping categorical column, e.g. administrative areas or population groups.}
\item{add_color}{Add a color to bars (if no grouping).}
\item{facet}{Some quoted grouping categorical column.}
\item{flip}{TRUE or FALSE. Default to TRUE or horizontal bar plot.}
\item{facet_scales}{Character. Either "free" (default) or "fixed" for facet scales.}
\item{x_rm_na}{Remove NAs in x?}
\item{y_rm_na}{Remove NAs in y?}
\item{group_rm_na}{Remove NAs in group?}
\item{facet_rm_na}{Remove NAs in facet?}
\item{add_color}{Add a color to points (if no grouping).}
\item{add_color_guide}{Should a legend be added?}
\item{flip}{TRUE or FALSE.}
\item{alpha}{Fill transparency.}
@ -55,10 +74,8 @@ point(
\item{caption}{Plot caption. Default to NULL.}
\item{theme_fun}{Whatever theme. Default to theme_reach(). NULL if no theming needed.}
\item{scale_impact}{Use the package custom scales for fill and color.}
\item{theme_fun}{Whatever theme. Default to theme_point(). NULL if no theming needed.}
}
\description{
Simple point chart
Simple scatterplot
}

42
man/reorder_by.Rd Normal file
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@ -0,0 +1,42 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reorder_by.R
\name{reorder_by}
\alias{reorder_by}
\title{Reorder a Data Frame}
\usage{
reorder_by(df, x, y, group = "", order = "y", dir_order = 1)
}
\arguments{
\item{df}{A data frame to be reordered.}
\item{x}{A character scalar specifying the column to be reordered.}
\item{y}{A character scalar specifying the column to order by if ordering by values.}
\item{group}{A character scalar specifying the grouping column (optional).}
\item{order}{A character scalar specifying the order type (one of "none", "y", "grouped"). See details.}
\item{dir_order}{A logical scalar specifying whether to flip the order.}
}
\value{
The reordered data frame.
}
\description{
Reorder a Data Frame
}
\details{
Ordering takes the following possible values:
* "none": No reordering.
* "y": Order by values of y.
* "grouped_y": Order by values of y and group.
* "x": Order alphabetically by x.
* "grouped_x": Order alphabetically by x and group.
}
\examples{
# Example usage
df <- data.frame(col1 = c("b", "a", "c"), col2 = c(10, 25, 3))
reorder_by(df, "col1", "col2")
}

4
man/scale_color_visualizer_discrete.Rd
View file

@ -11,6 +11,7 @@ scale_color_visualizer_discrete(
palette = "cat_5_main",
direction = 1,
reverse_guide = TRUE,
title_position = NULL,
...
)
@ -18,6 +19,7 @@ scale_fill_visualizer_discrete(
palette = "cat_5_main",
direction = 1,
reverse_guide = TRUE,
title_position = NULL,
...
)
@ -25,6 +27,7 @@ scale_fill_visualizer_continuous(
palette = "seq_5_main",
direction = 1,
reverse_guide = TRUE,
title_position = NULL,
...
)
@ -32,6 +35,7 @@ scale_color_visualizer_continuous(
palette = "seq_5_main",
direction = 1,
reverse_guide = TRUE,
title_position = NULL,
...
)
}

25
man/scale_visualizer_discrete.Rd
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@ -1,25 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scale.R
\name{scale_visualizer_discrete}
\alias{scale_visualizer_discrete}
\title{One scale for all}
\usage{
scale_visualizer_discrete(
palette = "cat_5_main",
direction = 1,
reverse_guide = TRUE,
...
)
}
\arguments{
\item{palette}{Palette name from [palette()].}
\item{direction}{1 or -1; should the order of colors be reversed?}
\item{reverse_guide}{Boolean indicating whether the guide should be reversed.}
\item{...}{Additional arguments passed to [ggplot2::discrete_scale()] if discrete or [ggplot2::scale_fill_gradient()] if continuous.}
}
\description{
This function is based on [palette()]. If palette is NULL, the used palette will be magma from gpplot2's viridis scale constructors.
}

20
man/theme_bar.Rd Normal file
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@ -0,0 +1,20 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_bar.R
\name{theme_bar}
\alias{theme_bar}
\title{Custom Theme for Bar Charts}
\usage{
theme_bar(
flip = TRUE,
add_text = FALSE,
axis_text_x_angle = 0,
axis_text_x_vjust = 0.5,
axis_text_x_hjust = 0.5
)
}
\value{
A custom theme object.
}
\description{
Custom Theme for Bar Charts
}

14
man/theme_custom.Rd
View file

@ -1,14 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_bar.R
\name{theme_custom}
\alias{theme_custom}
\title{Custom Theme}
\usage{
theme_custom()
}
\value{
A custom theme object.
}
\description{
Create a custom theme for ggplot2.
}

54
man/theme_visualizer_default.Rd → man/theme_default.Rd
View file

@ -1,21 +1,23 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme.R
\name{theme_visualizer_default}
\alias{theme_visualizer_default}
% Please edit documentation in R/theme_default.R
\name{theme_default}
\alias{theme_default}
\title{ggplot2 theme wrapper with fonts and colors}
\usage{
theme_visualizer_default(
font_family = "Carlito",
title_size = 14,
theme_default(
title_font_family = "Carlito",
title_size = 16,
title_color = color("dark_grey"),
title_font_face = "bold",
title_hjust = NULL,
title_position_to_plot = TRUE,
title_font_family = "Carlito",
subtitle_size = 13,
subtitle_font_face = "plain",
subtitle_font_family = "Carlito",
text_size = 12,
subtitle_size = 15,
subtitle_color = color("dark_grey"),
subtitle_font_face = "plain",
subtitle_hjust = NULL,
text_font_family = "Carlito",
text_size = 13,
text_color = color("dark_grey"),
text_font_face = "plain",
panel_background_color = "#FFFFFF",
@ -25,21 +27,29 @@ theme_visualizer_default(
legend_direction = "horizontal",
legend_justification = "center",
legend_reverse = TRUE,
legend_title_size = 12,
legend_title_size = 13,
legend_title_color = color("dark_grey"),
legend_title_font_face = "plain",
legend_text_size = 12,
legend_title_font_family = "Carlito",
legend_text_size = 13,
legend_text_color = color("dark_grey"),
legend_text_font_face = "plain",
legend_text_font_family = "Carlito",
facet_size = 14,
facet_color = color("dark_grey"),
facet_font_face = "bold",
facet_font_family = "Carlito",
facet_bg_color = color("lighter_grey"),
axis_x = TRUE,
axis_y = TRUE,
axis_text_x = TRUE,
axis_line_x = TRUE,
axis_ticks_x = TRUE,
axis_line_x = FALSE,
axis_ticks_x = FALSE,
axis_text_y = TRUE,
axis_line_y = TRUE,
axis_ticks_y = TRUE,
axis_text_size = 12,
axis_text_font_family = "Carlito",
axis_text_size = 13,
axis_text_color = color("dark_grey"),
axis_text_font_face = "plain",
axis_title_size = 15,
@ -53,19 +63,21 @@ theme_visualizer_default(
grid_major_color = color("dark_grey"),
grid_major_x_size = 0.1,
grid_major_y_size = 0.1,
grid_minor_x = FALSE,
grid_minor_x = TRUE,
grid_minor_y = FALSE,
grid_minor_color = color("dark_grey"),
grid_minor_x_size = 0.05,
grid_minor_y_size = 0.05,
caption_font_family = "Carlito",
caption_font_face = "plain",
caption_position_to_plot = TRUE,
caption_text_size = 10,
caption_text_color = color("dark_grey"),
caption_size = 11,
caption_color = color("dark_grey"),
...
)
}
\arguments{
\item{font_family}{The font family for all plot's texts. Default to "Segoe UI".}
\item{title_font_family}{Title font family. Default to "Roboto Condensed".}
\item{title_size}{The size of the legend title. Defaults to 11.}
@ -77,8 +89,6 @@ theme_visualizer_default(
\item{title_position_to_plot}{TRUE or FALSE. Positioning to plot or to panel?}
\item{title_font_family}{Title font family. Default to "Roboto Condensed".}
\item{text_size}{The size of all text other than the title, subtitle and caption. Defaults to 10.}
\item{text_color}{Text color.}
@ -169,7 +179,7 @@ theme_visualizer_default(
\item{...}{Additional arguments passed to [ggplot2::theme()].}
\item{p}{A ggplot2 object.}
\item{font_family}{The font family for all plot's texts. Default to "Segoe UI".}
}
\description{
Give some reach colors and fonts to a ggplot.

23
man/theme_point.Rd Normal file
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@ -0,0 +1,23 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_point.R
\name{theme_point}
\alias{theme_point}
\title{Custom Theme for Point Charts}
\usage{
theme_point()
}
\arguments{
\item{flip}{Logical. Whether the plot is flipped (horizonal).}
\item{axis_text_x_angle}{Angle for x-axis text.}
\item{axis_text_x_vjust}{Vertical justification for x-axis text.}
\item{axis_text_x_hjust}{Horizontal justification for x-axis text.}
}
\value{
A custom theme object.
}
\description{
Custom Theme for Point Charts
}

16
man/theme_visualizer.Rd
View file

@ -1,16 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_visualizer_bar.R
\docType{data}
\name{ThemeVisualizerBar}
\alias{ThemeVisualizerBar}
\title{ggplot2 theme for bar charts with sane defaults}
\format{
An object of class \code{ThemeVisualizerBar} (inherits from \code{ggproto}, \code{gg}) of length 1.
}
\usage{
ThemeVisualizerBar
}
\description{
ggplot2 theme for bar charts with sane defaults
}
\keyword{datasets}

33
man/theme_visualizer_bar.Rd
View file

@ -1,33 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_visualizer_bar.R
\name{theme_visualizer_bar}
\alias{theme_visualizer_bar}
\title{Dynamic Theme for ggplot2}
\usage{
theme_visualizer_bar()
}
\value{
A ggproto object that applies a dynamic theme to a ggplot2 plot.
}
\description{
A dynamic theme that adjusts axis text styles based on whether the plot is flipped.
}
\details{
This function dynamically applies different axis text styles depending on
the coordinate system of the plot. If the plot is flipped (e.g., using
`coord_flip()`), the x-axis and y-axis text styles are adjusted accordingly.
}
\examples{
library(ggplot2)
# Example with a regular plot
p <- ggplot(mpg, aes(displ, hwy)) +
geom_col()
# Add the dynamic theme
p + theme_visualizer_bar()
# Add the dynamic theme with a flipped coordinate system
p + theme_visualizer_bar() + coord_flip()
}

25
man/visualizeR-package.Rd Normal file
View file

@ -0,0 +1,25 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualizeR-package.R
\docType{package}
\name{visualizeR-package}
\alias{visualizeR}
\alias{visualizeR-package}
\title{visualizeR: What a color! What a viz!}
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
It basically provides colors as hex codes, color palettes, and some viz functions (graphs and maps).
}
\seealso{
Useful links:
\itemize{
\item \url{https://github.com/gnoblet/visualizeR}
\item \url{https://gnoblet.github.io/visualizeR/}
}
}
\author{
\strong{Maintainer}: Noblet Guillaume \email{gnoblet@zaclys.net}
}
\keyword{internal}

97
plot.svg
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72
test-example.R
View file

@ -27,8 +27,17 @@ library(visualizeR)
library(rio)
dat <- import("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/11_SevCatOneNumNestedOneObsPerGroup.csv")
dumbbell(
df,
"stat",
"setting",
"admin1",
title = "% of HHs that reported open defecation as sanitation facility",
group_y_title = "Admin 1",
group_x_title = "Setting"
)
library(dplyr)
library(ggplot2)
library(data.table)
# dat as a data.table if it4s not
if (!checkmate::test_data_table(dat)) {
@ -51,47 +60,38 @@ dat
# group_by(region) |>
# mutate(key = forcats::fct_reorder(key, value)) |>
dumbbell(
dat |> arrange(value) |> tail(50) |> mutate(
value = value/1000000,
key = ifelse(key == "Democratic Republic of the Congo", "DRC", key)) |>
filter(region %in% c("Europe", "Americas")),
"value",
"region",
"key",
title = "% of HHs that reported open defecation as sanitation facility",
group_y_title = "Admin 1",
group_x_title = "Setting", point_size = 3, line_to_y_axis = T
)
bar(
df = dat |> arrange(value) |> tail(20) |> mutate(
value = value/1000000,
key = ifelse(key == "Democratic Republic of the Congo", "DRC", key))
bar(
df,
key = ifelse(key == "Democratic Republic of the Congo", "DRC", key)),
x = "key",
y = "value",
group = "region",
group_title = "Region",
facet = "region",
order = "grouped_y",
title = "Population of Global Regions in Million"
) + scale_fill_visualizer_discrete(title_position = "top") + scale_color_visualizer_discrete()
hbar(
df,
x = "key",
y = "value",
group = "region",
group_title = "Region",
facet = "region",
order = "none",
order = "grouped",
x_rm_na = T,
y_rm_na = T,
group_rm_na = T,
title = "Population of Global Regions (in Million)"
) + scale_fill_visualizer_discrete(title_position = "left") + scale_color_visualizer_discrete()
ggplot2::ggsave(
"plot.svg",
gg
)
# ggplot2::theme(
# #legend.direction = "horizontal",
# legend.position = "top"
# )
#
#theme_bar(flip = F, axis_text_x_angle = 45) +
#scale_color_visualizer_discrete() +
#scale_fill_visualizer_discrete()
flip = F,
title = "Population of Global Regions in Million",
) +
theme_bar(flip = F, axis_text_x_angle = 45) +
scale_color_visualizer_discrete() +
scale_fill_visualizer_discrete()

12
tests/testthat.R Normal file
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@ -0,0 +1,12 @@
# # This file is part of the standard setup for testthat.
# # It is recommended that you do not modify it.
# #
# # Where should you do additional test configuration?
# # Learn more about the roles of various files in:
# # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# # * https://testthat.r-lib.org/articles/special-files.html
# library(testthat)
# library(visualizeR)
# test_check("visualizeR")

125
tests/testthat/test-bar.R Normal file
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@ -0,0 +1,125 @@
# testthat::test_that("bar() function handles various edge cases", {
# # Setup minimal test data
# test_df <- data.frame(
# category =c("A", "B", "C"),
# value = c(10, 20, 30),
# group = c("X", "X", "Y"),
# facet_var = c("F1", "F1", "F2")
# )
# # Test 1: Basic functionality with all parameters
# testthat::expect_s3_class({
# bar(test_df, x = "category", y = "value",
# position = "dodge", add_text = TRUE, flip = FALSE)
# }, "ggplot")
# # Test 2: Missing group parameter
# testthat::expect_s3_class({
# bar(test_df, x = "category", y = "value", facet = "facet_var")
# }, "ggplot")
# # Test 3: Missing facet parameter
# testthat::expect_s3_class({
# bar(test_df, x = "category", y = "value", group = "group")
# }, "ggplot")
# # Test 4: Identical group and facet
# testthat::expect_warning({
# bar(test_df, x = "category", y = "value", group = "facet_var", facet = "facet_var")
# }, "Using 'facet' for grouping")
# # Test 5: NA handling scenarios
# na_df <- data.table::data.table(
# category = c("A", "B", NA),
# value = c(10, NA, 30),
# group = c("X", NA, "Y")
# )
# # Test 5a: NA removal enabled
# testthat::expect_silent({
# bar(na_df, x = "category", y = "value", group = "group",
# x_rm_na = TRUE, y_rm_na = TRUE, group_rm_na = TRUE)
# })
# # Test 5b: NA removal disabled
# testthat::expect_warning({
# bar(na_df, x = "category", y = "value", group = "group",
# x_rm_na = FALSE, y_rm_na = FALSE, group_rm_na = FALSE)
# }, "Converting df to data.table")
# # Test 6: Ordering scenarios
# # Test 6a: Natural order
# testthat::expect_equal(
# levels(bar(test_df, x = "category", y = "value", order = "none")$data$category),
# c("A", "B", "C")
# )
# # Test 6b: Ordered by y
# testthat::expect_equal(
# levels(bar(test_df, x = "category", y = "value", order = "y")$data$category),
# c("A", "B", "C") # Should be ordered by value
# )
# # Test 7: Faceting edge cases
# # Test 7a: Single facet level
# single_facet <- data.table::data.table(
# category = c("A", "B"),
# value = c(10, 20),
# facet_var = c("F1", "F1")
# )
# testthat::expect_s3_class({
# bar(single_facet, x = "category", y = "value", facet = "facet_var")
# }, "ggplot")
# # Test 8: Text labeling thresholds
# small_values <- data.table::data.table(
# category = c("A", "B"),
# value = c(0.03, 0.04), # Below default 0.05 threshold
# group = c("X", "Y")
# )
# plot_data <- bar(small_values, x = "category", y = "value", group = "group", add_text = TRUE)
# testthat::expect_true(all(is.na(plot_data$layers[[2]]$data$y_threshold)))
# # Test 9: Invalid parameter combinations
# testthat::expect_error(
# bar(test_df, x = "value", y = "category"), # Reversed numeric/character
# "must be character or factor"
# )
# # Test 10: Facet/group interaction with insufficient data
# sparse_data <- data.table::data.table(
# category = "A",
# value = 10,
# group = "X",
# facet_var = "F1"
# )
# testthat::expect_s3_class({
# bar(sparse_data, x = "category", y = "value", group = "group", facet = "facet_var")
# }, "ggplot")
# })
# # Visual regression tests (requires vdiffr)
# testthat::test_that("Visual appearance remains consistent", {
# test_df <- data.table::data.table(
# category = factor(c("A", "B", "C")),
# value = c(10, 20, 30),
# group = c("X", "X", "Y"),
# facet_var = c("F1", "F1", "F2")
# )
# # Basic plot
# basic <- bar(test_df, x = "category", y = "value")
# vdiffr::expect_doppelganger("basic-bar", basic)
# # Grouped+dodged
# grouped <- bar(test_df, x = "category", y = "value", group = "group")
# vdiffr::expect_doppelganger("grouped-dodged-bar", grouped)
# # Faceted
# faceted <- bar(test_df, x = "category", y = "value", facet = "facet_var")
# vdiffr::expect_doppelganger("faceted-bar", faceted)
# # Stacked
# stacked <- bar(test_df, x = "category", y = "value", group = "group", position = "stack")
# vdiffr::expect_doppelganger("stacked-bar", stacked)
# })

4
vignettes/.gitignore vendored Normal file
View file

@ -0,0 +1,4 @@
*.html
*.R
/.quarto/

57
vignettes/bar_charts.Rmd Normal file
View file

@ -0,0 +1,57 @@
---
title: "Bar charts"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Bar charts}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
Let's start by importing some data and running some data wrangling:
```{r data-import}
library(rio)
library(data.table)
dat <- import("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/11_SevCatOneNumNestedOneObsPerGroup.csv", data.table = TRUE)
setDT(dat)
# in all character columns, tranform empty string to NA
vars_chr <- colnames(dat)[sapply(dat, is.character)]
dat[, (vars_chr) := lapply(.SD, function(x) fifelse(x == "", NA_character_, x)), .SDcols = vars_chr]
# in value, if -1 replace with NA
dat[, value := fifelse(value == -1, NA_real_, value)]
# remove lines where value is NA (in place)
dat <- dat[!is.na(value), ]
# kepp only top 20 values and divide data to get million units
df <- dat[
!is.na(value), ][
order(value, decreasing = TRUE), ][
1:20, ][
, value := value/1000000, ][
, key := ifelse(key == "Democratic Republic of the Congo", "DRC", key)]
```
Now, let's see the defaults for a horizontal bar diagram without any grouping and ordering values from highest to smallest:
```{r hbar}
library(visualizeR)
hbar(
df,
x = "key",
y = "value",
facet = "region",
order = "y",
title = "Top 20 countries by population (in Million)"
)
```
Moving on to a vertical bar chart, with country facets and groups