This is the homepage for the {bertheme} R package https://github.com/Bureau-for-Economic-Research/bertheme.
Bureau for Economic Research ggplot2 theme. Theme based on the plots in reporting.
Install from GitHub.
remotes::install_github("Bureau-for-Economic-Research/bertheme")[[1]]
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packageVersion("bertheme")[1] '0.1.1'
suppressMessages(library(dplyr))
library(bertheme)
library(scales)
library(ggplot2)
p <- economics_long %>%
filter(variable %in% c("psavert", "uempmed")) %>%
filter(date > "2002-01-01") %>%
ggplot(., aes(x = date, y = value, color = variable, lty = rev(variable))) +
geom_line(lwd = 1)
p +
scale_colour_ber("core") +
scale_y_continuous(labels = comma) +
labs(
title = "Main Title",
subtitle = "Source: South Africa Reserve Bank",
y = "Numbers (#)",
x = "",
caption = "Source: Quaterly Bulletin, South Africa Reserve Bank, 2016"
) +
guides(linetype = "none")+
geom_event(start = "2008-01-01", end = "2009-01-01", # Add for event highlight
label = "",
alpha = 0.1) +
theme_ber() 
p +
scale_colour_ber("core") +
scale_y_continuous(labels = comma) +
labs(
title = "Main Title",
subtitle = "Source: South Africa Reserve Bank",
y = "Numbers (#)",
x = "",
caption = "Source: Quaterly Bulletin, South Africa Reserve Bank, 2016"
) +
guides(linetype = "none") +
geom_reference(xdate = as.Date('2015-04-01'), # Add to add a reference line
yintercept = 17.6,
label = "Average\nUnemployed",
angle = 0,
hjust = 0.75,
vjust = 0.5,
label_color = "black") +
geom_event(start = "2008-01-01", end = "2009-01-01",
label = "",
alpha = 0.1) +
theme_ber() 
p <- ggplot() +
geom_area(data = economics, aes(x = date, y = uempmed, fill = "PCI"), alpha = 0.5) +
geom_line(data = economics, aes(x = date, y = psavert, color = "CCI")) +
geom_smooth(data = economics, aes(x = date, y = psavert, color = "CCI")) +
scale_fill_manual(values = bertheme_data()$core[1], name = "Legend") +
scale_color_manual(values = bertheme_data()$core[2], name = "Legend") +
guides(fill = guide_legend(override.aes = list(linetype = 1, color = bertheme_data()$core[1])),
color = guide_legend(override.aes = list(linetype = 1)))
p +
scale_y_continuous(labels = comma) +
labs(
title = "Main Title",
y = "Numbers (#)",
x = "",
caption = "Source: Quaterly Bulletin, South Africa Reserve Bank, 2016"
) +
guides(linetype = "none")+
geom_event(start = "2008-01-01", end = "2009-01-01",
label = "",
alpha = 0.25) +
theme_ber() `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Create Data
data <- data.frame(
group = LETTERS[1:5],
value = c(13,7,9,21,2)
)
# Compute the position of labels
data <- data %>%
arrange(desc(group)) %>%
mutate(prop = value / sum(data$value) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
# Basic piechart
ggplot(data, aes(x = "", y = prop, fill = group)) +
geom_bar(stat = "identity", width = 1, color = "white") +
coord_polar("y", start = 0) +
theme_void() +
theme(legend.position = "none") +
geom_text(aes(y = ypos, label = group), color = "white", size = 6) +
scale_fill_ber(palette = "core") 
# library
library(ggplot2)
# create a dataset
specie <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition <- rep(c("normal" , "stress" , "nitrogen") , 4)
value <- abs(rnorm(12 , 0 , 15))
data <- data.frame(specie, condition, value)
# Small multiple
ggplot(data, aes(fill = condition, y = value, x = specie)) +
geom_bar(position = "stack", stat = "identity") +
scale_fill_ber(palette = "core") +
theme_ber() +
labs(
y = "",
x = "",
title = "This is main heading")
# Build dataset with different distributions
data <- data.frame(
type = c( rep("variable 1", 1000), rep("variable 2", 1000), rep("variable 3", 1000)),
value = c( rnorm(1000), rnorm(1000, mean = 4), rnorm(1000, mean = 6, sd = 4) )
)
# Represent it
data %>%
ggplot( aes(x = value, fill = type)) +
geom_histogram( color = "#e9ecef", alpha = 0.6, position = 'identity') +
scale_fill_ber(palette = "darks") +
theme_ber() +
labs(
fill = "",
y = "",
x = "")`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Loading required packages
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ tibble 3.2.1 ✔ purrr 1.0.1
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.0.2 ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ readr::col_factor() masks scales::col_factor()
✖ purrr::discard() masks scales::discard()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(ggrepel)
library(bertheme)
# Loading data
df <- read_csv("https://raw.githubusercontent.com/GDS-ODSSS/unhcr-dataviz-platform/master/data/change_over_time/slope.csv")Rows: 22 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): country_origin
dbl (2): year, refugee_number
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Plot
ggplot(df, aes(
x = year,
y = refugee_number,
group = country_origin
)) +
geom_line(
linewidth = 0.75,
color = bertheme_data()$core[1]
) +
geom_text_repel(
data = df |> filter(year == 2016),
aes(label = paste(
country_origin,
if_else(refugee_number > 1e6,
paste0(round(refugee_number / 1e6, 1), "M"),
paste0(round(refugee_number / 1e3, 1), "k")
)
)),
size = 8 / .pt,
hjust = 1,
direction = "y",
nudge_x = -0.3
) +
geom_text_repel(
data = df |> filter(year == 2021),
aes(label = paste(
country_origin,
if_else(refugee_number > 1e6,
paste0(round(refugee_number / 1e6, 1), "M"),
paste0(round(refugee_number / 1e3, 1), "k")
)
)),
size = 8 / .pt,
hjust = 0,
direction = "y",
nudge_x = 0.3
) +
geom_point(
size = 2.5,
color = bertheme_data()$core[2]
) +
labs(
title = "Evolution of refugee population in East and\nHorn of Africa region | 2016 vs 2021",
caption = "Source: UNHCR Refugee Data Finder\n© UNHCR, The UN Refugee Agency"
) +
scale_x_continuous(
breaks = c(2016, 2021),
limits = c(2013, 2024)
) +
scale_y_continuous(limits = c(-2e5, NA)) +
theme_void() +
theme(panel.grid.major.x = element_line(colour = "grey", linewidth = 1),
axis.text.x = element_text(face = "bold", color="grey",
size = 14))
library(bertheme)
library(colorspace)
demoplot(bertheme_data()[['core']], type = "lines")
demoplot(bertheme_data()[['core']], type = "mosaic")
demoplot(bertheme_data()[['core']], type = "map")
demoplot(bertheme_data()[['core']], type = "scatter")
demoplot(bertheme_data()[['core']], type = "perspective")
demoplot(bertheme_data()[['core']], type = "spine")
demoplot(bertheme_data()[['core']], type = "heatmap")
demoplot(bertheme_data()[['core']], type = "pie")