In evpatora/hermitage: Create versatile palettes inspired by colors of art collection of L'ermitage/Hermitage museum in St. Petersburg, Russia
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.retina = 3,
fig.width = 10,
fig.height = 7
)
Installation
install.packages("devtools")
devtools::install_github("evpatora/hermitage")
library(hermitage)
library(tidyverse)
library(magrittr)
Overview of palettes
names_of_palettes <- names(hermitage_palettes)
lengths_of_palletes <- map(hermitage_palettes,~length(.x))
# palettes' details
map(names_of_palettes, ~hermitage_palette(.x, type = "discrete"))
# palettes's colours
walk2(names_of_palettes, lengths_of_palletes, ~pie(rep(1, length(hermitage_palette(.x, type = "discrete"))), labels = "", col = hermitage_palette(.x, type = "discrete") , main = paste0(.x, " | n = ", .y), clockwise = T, border = "white", family = "Varela Round"))
Use cases
# install.packages("palmerpenguins")
library(palmerpenguins)
ggplot(data = penguins, aes(x = species, y = flipper_length_mm, fill = species, color = species)) +
geom_violin() +
theme_light(base_size = 12, base_family = "Varela Round") +
theme(panel.grid = element_blank()) +
scale_fill_manual(values = hermitage_palette("parsons_2")) +
scale_color_manual(values = hermitage_palette("parsons_2")) +
labs(title = "Flipper length by penguin species",
caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
col <- hermitage_palette("magdalene_titian")
set.seed(34567)
values <- sample(x = col, size = 3)
ggplot(data = penguins, aes(x = bill_length_mm, y = body_mass_g, fill = species, color = species, group = species)) +
geom_point(size = 3, alpha = 0.8) +
theme_light(base_size = 12, base_family = "Varela Round") +
theme(panel.grid = element_blank()) +
scale_fill_manual(values = values) +
scale_color_manual(values = values) +
labs(title = "Correlation between bill length and body mass by penguin species",
caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
col <- hermitage_palette("faberge")
set.seed(34567)
values <- sample(x = col, size = 3)
ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, fill = species, color = species, group = species)) +
geom_point(size = 3, alpha = 0.8) +
theme_light(base_size = 12, base_family = "Varela Round") +
theme(panel.grid = element_blank()) +
scale_fill_manual(values = values) +
scale_color_manual(values = values) +
labs(title = "Correlation between bill length and bill depth by penguin species",
caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
col <- hermitage_palette("judith")
ggplot(data = penguins, aes(x = bill_length_mm, fill = species, group = species)) +
geom_histogram(alpha = 0.6, bins = 50) +
theme_light(base_size = 12, base_family = "Varela Round") +
scale_fill_manual(values = col) +
labs(title = "Bill length by penguin species",
caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
col <- hermitage_palette("harmony")
ggplot(data = penguins, aes(x = bill_length_mm, y = flipper_length_mm,
fill = species, color = species, group = species)) +
geom_point(size = 3, alpha = 0.7) +
geom_smooth(alpha = 0.6, method = "glm") +
theme_light(base_size = 12, base_family = "Varela Round") +
theme(panel.grid = element_blank()) +
scale_fill_manual(values = col) +
scale_color_manual(values = col) +
labs(title = "Correlation between bill length and flipper length by penguin species",
caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
gdp %>%
filter(Entity %in% c("Sub-Sahara Africa", "East Asia", "Middle East", "South and South-East Asia",
"Latin America", "Western Offshoots", "Eastern Europe", "Western Europe", "World")) %>%
group_by(Entity) %>%
mutate(label = case_when(
row_number() == n() ~ Entity,
T ~ NA_character_
)) %>%
ungroup() %>%
ggplot(aes(x = Year, y = `GDP per capita`, color = Entity)) +
geom_path() +
theme_light(base_size = 12, base_family = "Varela Round") +
expand_limits(y = 0) +
scale_x_continuous(breaks = c(seq(1820, 2018, 25))) +
scale_y_continuous(labels = scales::dollar_format())+
scale_color_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) +
theme(plot.caption = element_text(hjust = 0, size = 10),
legend.position = "bottom",
panel.spacing = unit(0.8, "cm")) +
ggrepel::geom_label_repel(aes(label = label), na.rm = TRUE, nudge_x = 1,
direction = "y", segment.size = 0.1, segment.colour = "black", show.legend = F, inherit.aes = T) +
labs(title = "Global economic inequality", caption = "Source | Our World in Data | https://ourworldindata.org/global-economic-inequality-introduction\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
plot_area <- gdp %>%
filter(Entity %in% c("Sub-Sahara Africa", "East Asia", "Middle East", "South and South-East Asia",
"Latin America", "Western Offshoots", "Eastern Europe", "Western Europe", "World")) %>%
group_by(Entity) %>%
mutate(label = case_when(
row_number() == n() ~ Entity,
T ~ NA_character_
)) %>%
ungroup() %>%
ggplot(aes(x = Year, y = `GDP per capita`, color = Entity, fill = Entity)) +
geom_area() +
theme_light(base_size = 12, base_family = "Varela Round") +
expand_limits(y = 0) +
scale_x_continuous(breaks = c(seq(1820, 2018, 25))) +
scale_y_continuous(labels = scales::dollar_format())+
scale_color_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) +
guides(color = "none") +
theme(plot.caption = element_text(hjust = 0, size = 10),
legend.position = "right",
panel.spacing = unit(0.8, "cm"),
panel.grid = element_blank()) +
labs(title = "Global economic inequality",
caption = "Source | Our World in Data | https://ourworldindata.org/global-economic-inequality-introduction\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
plot_area
plot_area +
scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "parsons_2", type = "discrete"))
plot_area +
scale_color_manual(values = hermitage_palette(name = "battista_cima", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "battista_cima", type = "discrete"))
plot_area +
scale_color_manual(values = hermitage_palette(name = "kunstnefarver", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "kunstnefarver", type = "discrete"))
plot_area +
scale_color_manual(values = hermitage_palette(name = "water_paint", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "water_paint", type = "discrete"))
col <- hermitage_palette("parsons_1")
set.seed(3256789)
values <- sample(x = col, size = 9)
plot_area +
scale_color_manual(values = values) +
scale_fill_manual(values = values)
edu %>%
ggplot(aes(x = Year, y = `Primary adjusted enrolment ratio (Lee-Lee (2016))`, color = Entity)) +
geom_path() +
facet_wrap(~ Entity, labeller = labeller(Entity = label_wrap_gen(10))) +
theme_minimal(base_size = 8, base_family = "Varela Round") +
expand_limits(y = 0) +
scale_x_continuous(breaks = c(seq(1820, 2010, 75))) +
scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) +
theme(plot.caption = element_text(hjust = 0, size = 10),
legend.position = "none",
panel.spacing = unit(0.8, "cm"),
panel.grid = element_blank()
) +
labs(title = "Proportion of children attending primary school: from 1820 to 2010",
caption = "Source | Lee and Lee (2016), Human capital in the long run. Journal of Development Economics & Our World in Data\nhttps://ourworldindata.org/children-not-in-school\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
edu %>%
ggplot(aes(x = Year, y = `Primary adjusted enrolment ratio (Lee-Lee (2016))`, color = Entity, fill = Entity)) +
geom_area() +
facet_wrap(~ Entity, labeller = labeller(Entity = label_wrap_gen(10))) +
geom_vline(xintercept = 2005, color = "#99598b", linetype = 6) +
theme_void(base_size = 8, base_family = "Varela Round") +
expand_limits(y = 0) +
scale_x_continuous(breaks = c(seq(1820, 2010, 75))) +
scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) +
scale_fill_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) +
theme(plot.caption = element_text(hjust = 0, size = 10),
legend.position = "none",
panel.spacing = unit(0.8, "cm")
) +
labs(title = "Proportion of children attending primary school: from 1820 to 2010",
caption = "Vertical line indicates year 2005\nSource | Lee and Lee (2016) & Our World in Data | https://ourworldindata.org/children-not-in-school\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
# remotes::install_github("wilkelab/practicalgg")
data(texas_income, package = "practicalgg")
col_1 <- hermitage_palette(n = 1e3, name = "hermitage_1", type = "continuous")
col_2 <- hermitage_palette(n = 1e3, name = "hermitage_2", type = "continuous")
col_3 <- hermitage_palette(n = 1e3, name = "delft_ware", type = "continuous")
col_4 <- hermitage_palette(n = 1e3, name = "faberge", type = "continuous")
plot <- ggplot(texas_income, aes(fill = estimate)) +
geom_sf(color = "white") +
coord_sf(xlim = c(538250, 2125629), crs = 3083) +
cowplot::theme_map(12, font_family = "Varela Round") +
theme(
legend.title.align = 0.5,
legend.text.align = 0.5,
legend.justification = c(0, 0),
legend.position = c(0.02, 0.1)
) +
labs(caption = "code inspired by https://clauswilke.com/blog/2020/09/08/a-blogdown-post-for-the-ages/")
plot + scale_fill_gradientn(colors = col_1)
plot + scale_fill_gradientn(colors = col_2)
plot + scale_fill_gradientn(colors = col_3)
plot + scale_fill_gradientn(colors = col_4)
evpatora/hermitage documentation built on Dec. 23, 2021, 11:15 p.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.retina = 3, fig.width = 10, fig.height = 7 )
Installation
install.packages("devtools") devtools::install_github("evpatora/hermitage")
library(hermitage) library(tidyverse) library(magrittr)
Overview of palettes
names_of_palettes <- names(hermitage_palettes) lengths_of_palletes <- map(hermitage_palettes,~length(.x)) # palettes' details map(names_of_palettes, ~hermitage_palette(.x, type = "discrete")) # palettes's colours walk2(names_of_palettes, lengths_of_palletes, ~pie(rep(1, length(hermitage_palette(.x, type = "discrete"))), labels = "", col = hermitage_palette(.x, type = "discrete") , main = paste0(.x, " | n = ", .y), clockwise = T, border = "white", family = "Varela Round"))
Use cases
# install.packages("palmerpenguins") library(palmerpenguins) ggplot(data = penguins, aes(x = species, y = flipper_length_mm, fill = species, color = species)) + geom_violin() + theme_light(base_size = 12, base_family = "Varela Round") + theme(panel.grid = element_blank()) + scale_fill_manual(values = hermitage_palette("parsons_2")) + scale_color_manual(values = hermitage_palette("parsons_2")) + labs(title = "Flipper length by penguin species", caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") col <- hermitage_palette("magdalene_titian") set.seed(34567) values <- sample(x = col, size = 3) ggplot(data = penguins, aes(x = bill_length_mm, y = body_mass_g, fill = species, color = species, group = species)) + geom_point(size = 3, alpha = 0.8) + theme_light(base_size = 12, base_family = "Varela Round") + theme(panel.grid = element_blank()) + scale_fill_manual(values = values) + scale_color_manual(values = values) + labs(title = "Correlation between bill length and body mass by penguin species", caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") col <- hermitage_palette("faberge") set.seed(34567) values <- sample(x = col, size = 3) ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm, fill = species, color = species, group = species)) + geom_point(size = 3, alpha = 0.8) + theme_light(base_size = 12, base_family = "Varela Round") + theme(panel.grid = element_blank()) + scale_fill_manual(values = values) + scale_color_manual(values = values) + labs(title = "Correlation between bill length and bill depth by penguin species", caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") col <- hermitage_palette("judith") ggplot(data = penguins, aes(x = bill_length_mm, fill = species, group = species)) + geom_histogram(alpha = 0.6, bins = 50) + theme_light(base_size = 12, base_family = "Varela Round") + scale_fill_manual(values = col) + labs(title = "Bill length by penguin species", caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") col <- hermitage_palette("harmony") ggplot(data = penguins, aes(x = bill_length_mm, y = flipper_length_mm, fill = species, color = species, group = species)) + geom_point(size = 3, alpha = 0.7) + geom_smooth(alpha = 0.6, method = "glm") + theme_light(base_size = 12, base_family = "Varela Round") + theme(panel.grid = element_blank()) + scale_fill_manual(values = col) + scale_color_manual(values = col) + labs(title = "Correlation between bill length and flipper length by penguin species", caption = "Source | palmerpenguins| https://allisonhorst.github.io/palmerpenguins/\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
gdp %>% filter(Entity %in% c("Sub-Sahara Africa", "East Asia", "Middle East", "South and South-East Asia", "Latin America", "Western Offshoots", "Eastern Europe", "Western Europe", "World")) %>% group_by(Entity) %>% mutate(label = case_when( row_number() == n() ~ Entity, T ~ NA_character_ )) %>% ungroup() %>% ggplot(aes(x = Year, y = `GDP per capita`, color = Entity)) + geom_path() + theme_light(base_size = 12, base_family = "Varela Round") + expand_limits(y = 0) + scale_x_continuous(breaks = c(seq(1820, 2018, 25))) + scale_y_continuous(labels = scales::dollar_format())+ scale_color_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) + theme(plot.caption = element_text(hjust = 0, size = 10), legend.position = "bottom", panel.spacing = unit(0.8, "cm")) + ggrepel::geom_label_repel(aes(label = label), na.rm = TRUE, nudge_x = 1, direction = "y", segment.size = 0.1, segment.colour = "black", show.legend = F, inherit.aes = T) + labs(title = "Global economic inequality", caption = "Source | Our World in Data | https://ourworldindata.org/global-economic-inequality-introduction\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") plot_area <- gdp %>% filter(Entity %in% c("Sub-Sahara Africa", "East Asia", "Middle East", "South and South-East Asia", "Latin America", "Western Offshoots", "Eastern Europe", "Western Europe", "World")) %>% group_by(Entity) %>% mutate(label = case_when( row_number() == n() ~ Entity, T ~ NA_character_ )) %>% ungroup() %>% ggplot(aes(x = Year, y = `GDP per capita`, color = Entity, fill = Entity)) + geom_area() + theme_light(base_size = 12, base_family = "Varela Round") + expand_limits(y = 0) + scale_x_continuous(breaks = c(seq(1820, 2018, 25))) + scale_y_continuous(labels = scales::dollar_format())+ scale_color_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "cottages_vincent", type = "discrete")) + guides(color = "none") + theme(plot.caption = element_text(hjust = 0, size = 10), legend.position = "right", panel.spacing = unit(0.8, "cm"), panel.grid = element_blank()) + labs(title = "Global economic inequality", caption = "Source | Our World in Data | https://ourworldindata.org/global-economic-inequality-introduction\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") plot_area plot_area + scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) plot_area + scale_color_manual(values = hermitage_palette(name = "battista_cima", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "battista_cima", type = "discrete")) plot_area + scale_color_manual(values = hermitage_palette(name = "kunstnefarver", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "kunstnefarver", type = "discrete")) plot_area + scale_color_manual(values = hermitage_palette(name = "water_paint", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "water_paint", type = "discrete")) col <- hermitage_palette("parsons_1") set.seed(3256789) values <- sample(x = col, size = 9) plot_area + scale_color_manual(values = values) + scale_fill_manual(values = values)
edu %>% ggplot(aes(x = Year, y = `Primary adjusted enrolment ratio (Lee-Lee (2016))`, color = Entity)) + geom_path() + facet_wrap(~ Entity, labeller = labeller(Entity = label_wrap_gen(10))) + theme_minimal(base_size = 8, base_family = "Varela Round") + expand_limits(y = 0) + scale_x_continuous(breaks = c(seq(1820, 2010, 75))) + scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) + theme(plot.caption = element_text(hjust = 0, size = 10), legend.position = "none", panel.spacing = unit(0.8, "cm"), panel.grid = element_blank() ) + labs(title = "Proportion of children attending primary school: from 1820 to 2010", caption = "Source | Lee and Lee (2016), Human capital in the long run. Journal of Development Economics & Our World in Data\nhttps://ourworldindata.org/children-not-in-school\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021") edu %>% ggplot(aes(x = Year, y = `Primary adjusted enrolment ratio (Lee-Lee (2016))`, color = Entity, fill = Entity)) + geom_area() + facet_wrap(~ Entity, labeller = labeller(Entity = label_wrap_gen(10))) + geom_vline(xintercept = 2005, color = "#99598b", linetype = 6) + theme_void(base_size = 8, base_family = "Varela Round") + expand_limits(y = 0) + scale_x_continuous(breaks = c(seq(1820, 2010, 75))) + scale_color_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) + scale_fill_manual(values = hermitage_palette(name = "parsons_2", type = "discrete")) + theme(plot.caption = element_text(hjust = 0, size = 10), legend.position = "none", panel.spacing = unit(0.8, "cm") ) + labs(title = "Proportion of children attending primary school: from 1820 to 2010", caption = "Vertical line indicates year 2005\nSource | Lee and Lee (2016) & Our World in Data | https://ourworldindata.org/children-not-in-school\nplot | Elena Dudukina | @evpatora\ncolors | Hermitage package 2021")
# remotes::install_github("wilkelab/practicalgg") data(texas_income, package = "practicalgg") col_1 <- hermitage_palette(n = 1e3, name = "hermitage_1", type = "continuous") col_2 <- hermitage_palette(n = 1e3, name = "hermitage_2", type = "continuous") col_3 <- hermitage_palette(n = 1e3, name = "delft_ware", type = "continuous") col_4 <- hermitage_palette(n = 1e3, name = "faberge", type = "continuous") plot <- ggplot(texas_income, aes(fill = estimate)) + geom_sf(color = "white") + coord_sf(xlim = c(538250, 2125629), crs = 3083) + cowplot::theme_map(12, font_family = "Varela Round") + theme( legend.title.align = 0.5, legend.text.align = 0.5, legend.justification = c(0, 0), legend.position = c(0.02, 0.1) ) + labs(caption = "code inspired by https://clauswilke.com/blog/2020/09/08/a-blogdown-post-for-the-ages/") plot + scale_fill_gradientn(colors = col_1) plot + scale_fill_gradientn(colors = col_2) plot + scale_fill_gradientn(colors = col_3) plot + scale_fill_gradientn(colors = col_4)
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