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Below you’ll find the complete code used to create the ggplot2 graphs in my talk The Good, the Bad and the Ugly: how (not) to visualize data at this year’s data2day conference. You can find the German slides here:
You can also find a German blog article accompanying my talk on codecentric’s blog.
If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free – one slot available per weekday):
If you have been enjoying my content and would like to help me be able to create more, please consider sending me a donation at . Thank you! 🙂
library(tidyverse) library(ggExtra) library(ragg) library(ggalluvial) library(treemapify) library(ggalt) library(palmerpenguins)
Dataset
head(penguins) ## # A tibble: 6 x 8 ## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex ## <fct> <fct> <dbl> <dbl> <int> <int> <fct> ## 1 Adelie Torge… 39.1 18.7 181 3750 male ## 2 Adelie Torge… 39.5 17.4 186 3800 fema… ## 3 Adelie Torge… 40.3 18 195 3250 fema… ## 4 Adelie Torge… NA NA NA NA <NA> ## 5 Adelie Torge… 36.7 19.3 193 3450 fema… ## 6 Adelie Torge… 39.3 20.6 190 3650 male ## # … with 1 more variable: year <int> #head(penguins_raw)
Colors
# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(plotrix)
sliceValues <- rep(10, 8) # each slice value=10 for proportionate slices
(
p <- pie3D(sliceValues,
explode=0,
theta = 1.2,
col = cbp1,
labels = cbp1,
labelcex = 0.9,
shade = 0.6,
main = "Colorblind\nfriendly palette")
)
## [1] 0.3926991 1.1780972 1.9634954 2.7488936 3.5342917 4.3196899 5.1050881 ## [8] 5.8904862 ggplot <- function(...) ggplot2::ggplot(...) + scale_color_manual(values = cbp1) + scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales theme_bw()
Main diagram types
https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Pointcharts
penguins %>%
remove_missing() %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm)) +
geom_jitter(alpha = 0.5) +
facet_wrap(vars(species), ncol = 3) +
scale_x_reverse() +
scale_y_reverse() +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
size = "body mass (g)",
title = "Scatterplot",
subtitle = "Penguins bill v. flipper length by species",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
penguins %>%
remove_missing() %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm,
color = species, shape = species)) +
geom_point(alpha = 0.7) +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
title = "Scatterplot",
subtitle = "Penguins bill v. flipper length by species",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
- Jitter with smoothing line
penguins %>%
remove_missing() %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm,
color = species, shape = species)) +
geom_jitter(alpha = 0.5) +
geom_smooth(method = "loess", se = TRUE) +
facet_wrap(vars(species), nrow = 3) +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
title = "Scatterplot with smoothing line",
subtitle = "Penguins bill v. flipper length by species with loess smoothing line",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
penguins %>%
remove_missing() %>%
filter(species == "Adelie") %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "loess", se = TRUE) +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
title = "Scatterplot with smoothing line",
subtitle = "Penguins bill v. flipper length by species with\nloess smoothing line, histogram & density distribution",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
#(ggMarginal(p, type = "densigram", fill = "transparent"))
Bubblecharts
penguins %>%
remove_missing() %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm,
color = species, shape = species, size = body_mass_g)) +
geom_point(alpha = 0.5) +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
title = "Bubble plot",
size = "body mass (g)",
subtitle = "Penguins bill v. flipper length by species;\nsize indicates body mass in grams",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Linecharts
penguins %>%
remove_missing() %>%
filter(species == "Adelie") %>%
ggplot(aes(x = bill_length_mm, y = flipper_length_mm,
color = sex)) +
geom_line() +
geom_point() +
labs(x = "Bill length (mm)",
y = "Flipper length (mm)",
title = "Line plot",
subtitle = "Penguins bill v. flipper length by species and sex",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Correlation plots / heatmaps
mat <- penguins %>%
remove_missing() %>%
select(bill_depth_mm, bill_length_mm, body_mass_g, flipper_length_mm)
cormat <- round(cor(mat), 2)
cormat[upper.tri(cormat)] <- NA
cormat <- cormat %>%
as_data_frame() %>%
mutate(x = colnames(mat)) %>%
gather(key = "y", value = "value", bill_depth_mm:flipper_length_mm)
cormat %>%
remove_missing() %>%
arrange(x, y) %>%
ggplot(aes(x = x, y = y, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name = "Pearson\nCorrelation") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
coord_fixed() +
labs(x = "",
y = "",
title = "Correlation heatmap",
subtitle = "Correlation btw. penguins' traits",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Barcharts
- per default: counts
penguins %>%
remove_missing() %>%
ggplot(aes(x = species,
fill = sex)) +
geom_bar() +
labs(x = "Species",
y = "Counts",
title = "Barchart",
subtitle = "Counts of male & female penguins per species in study",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
penguins %>%
remove_missing() %>%
ggplot(aes(x = species,
fill = sex)) +
geom_bar(position = 'dodge') +
labs(x = "Species",
y = "Counts",
title = "Barchart",
subtitle = "Counts of male & female penguins per species in study",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
- alternative: set y-values
penguins %>%
remove_missing() %>%
group_by(species, sex) %>%
summarise(mean_bmg = mean(body_mass_g),
sd_bmg = sd(body_mass_g)) %>%
ggplot(aes(x = species, y = mean_bmg,
fill = sex)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = mean_bmg - sd_bmg,
ymax = mean_bmg + sd_bmg),
width = 0.2,
position = position_dodge(0.9)) +
labs(x = "Species",
y = "Mean body mass (in g)",
title = "Barchart",
subtitle = "Mean body mass of male & female penguins per species\nwith standard deviation",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Boxplots
penguins %>%
remove_missing() %>%
ggplot(aes(x = species, y = body_mass_g,
fill = sex)) +
geom_boxplot() +
labs(x = "Species",
y = "Body mass (in g)",
title = "Boxplot",
subtitle = "Body mass of three penguin species per sex",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
- with points
penguins %>%
remove_missing() %>%
ggplot(aes(x = species, y = body_mass_g,
fill = sex, color = sex)) +
geom_boxplot(alpha = 0.5, notch = TRUE) +
geom_jitter(alpha = 0.5, position=position_jitter(0.3)) +
labs(x = "Species",
y = "Body mass (in g)",
title = "Boxplot with points (dotplot)",
subtitle = "Body mass of three penguin species per sex",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Violinplots
penguins %>%
remove_missing() %>%
ggplot(aes(x = species, y = body_mass_g,
fill = sex)) +
geom_violin(scale = "area") +
labs(x = "Species",
y = "Body mass (in g)",
title = "Violinplot",
subtitle = "Body mass of three penguin species per sex",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
- with dots (sina-plots)
penguins %>%
remove_missing() %>%
ggplot(aes(x = species, y = body_mass_g,
fill = sex, color = sex)) +
geom_dotplot(method = "dotdensity", alpha = 0.7,
binaxis = 'y', stackdir = 'center',
position = position_dodge(1)) +
labs(x = "Species",
y = "Body mass (in g)",
title = "Violinplot with points (dotplot)",
subtitle = "Body mass of three penguin species per sex",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Piecharts
penguins %>%
remove_missing() %>%
group_by(species, sex) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n),
percentage = freq * 100) %>%
ggplot(aes(x = "", y = percentage,
fill = sex)) +
facet_wrap(vars(species), nrow = 1) +
geom_bar(stat = "identity", alpha = 0.8) +
coord_polar("y", start = 0) +
labs(x = "",
y = "Percentage",
title = "Piechart",
subtitle = "Percentage of male v. female penguins per species in study",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Alluvial charts
as.data.frame(UCBAdmissions) %>%
ggplot(aes(y = Freq, axis1 = Gender, axis2 = Dept)) +
geom_alluvium(aes(fill = Admit), width = 1/12) +
geom_stratum(width = 1/12, fill = "black", color = "grey") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(limits = c("Gender", "Dept"), expand = c(.05, .05)) +
labs(x = "",
y = "Frequency",
title = "Alluvial chart",
subtitle = "UC Berkeley admissions and rejections, by sex and department",
caption = "Source: Bickel et al. (1975)\nSex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403.")
Treemaps
as.data.frame(UCBAdmissions) %>%
group_by(Admit, Gender) %>%
summarise(sum_freq = sum(Freq)) %>%
ggplot(aes(area = sum_freq, fill = sum_freq, label = Gender,
subgroup = Admit)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
"black", face = "italic", min.size = 0) +
geom_treemap_text(colour = "white", place = "centre", reflow = T) +
scale_fill_gradient2(low = "#999999", high = "#E69F00", mid = "white", midpoint = 1000, space = "Lab",
name = "Sum of\nfrequencies") +
labs(x = "",
y = "",
title = "Treemap",
subtitle = "UC Berkeley admissions and rejections by sex",
caption = "Source: Bickel et al. (1975)\nSex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403.")
Dumbbell plots
penguins %>%
remove_missing() %>%
group_by(year, species, sex) %>%
summarise(mean_bmg = mean(body_mass_g)) %>%
mutate(species_sex = paste(species, sex, sep = "_"),
year = paste0("year_", year)) %>%
spread(year, mean_bmg) %>%
ggplot(aes(x = year_2007, xend = year_2009,
y = reorder(species_sex, year_2009))) +
geom_dumbbell(color = "#999999",
size_x = 3,
size_xend = 3,
#Note: there is no US:'color' for UK:'colour'
# in geom_dumbbel unlike standard geoms in ggplot()
colour_x = "#999999",
colour_xend = "#E69F00") +
labs(x = "Body mass (g)",
y = "Species & sex",
title = "Dumbbell plot",
subtitle = "Penguin's change in body mass from 2007 to 2009",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Slope charts
penguins %>%
remove_missing() %>%
group_by(year, species, sex) %>%
summarise(mean_bmg = mean(body_mass_g)) %>%
ggplot(aes(x = year, y = mean_bmg, group = sex,
color = sex)) +
facet_wrap(vars(species), nrow = 3) +
geom_line(alpha = 0.6, size = 2) +
geom_point(alpha = 1, size = 3) +
scale_x_continuous(breaks=c(2007, 2008, 2009)) +
labs(x = "Year",
y = "Body mass (g)",
color = "Sex",
title = "Slope chart",
subtitle = "Penguin's change in body mass from 2007 to 2009",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Stacked area charts
penguins %>%
remove_missing() %>%
group_by(year, species, sex) %>%
summarise(mean_bmg = mean(body_mass_g)) %>%
ggplot(aes(x = year, y = mean_bmg, fill = sex)) +
facet_wrap(vars(species), nrow = 3) +
geom_area(alpha = 0.6, size=.5, color = "white") +
scale_x_continuous(breaks=c(2007, 2008, 2009)) +
labs(x = "Year",
y = "Mean body mass (g)",
color = "Sex",
title = "Stacked area chart",
subtitle = "Penguin's change in body mass from 2007 to 2009",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Lolliplot chart
penguins %>%
remove_missing() %>%
group_by(year, species, sex) %>%
summarise(mean_bmg = mean(body_mass_g)) %>%
mutate(species_sex = paste(species, sex, sep = "_"),
year = paste0("year_", year)) %>%
spread(year, mean_bmg) %>%
ggplot() +
geom_segment(aes(x = reorder(species_sex, -year_2009), xend = reorder(species_sex, -year_2009),
y = 0, yend = year_2009),
color = "#999999", size = 1) +
geom_point(aes(x = reorder(species_sex, -year_2009), y = year_2009),
size = 4, color = "#E69F00") +
coord_flip() +
labs(x = "Species & sex",
y = "Body mass (g)",
title = "Lollipop chart",
subtitle = "Penguin's body mass in 2009",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Dendrograms
library(ggdendro) library(dendextend) penguins_hist <- penguins %>% filter(sex == "male") %>% select(species, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g) %>% group_by(species) %>% sample_n(10) %>% as.data.frame() rownames(penguins_hist) <- paste(penguins_hist$species, seq_len(nrow(penguins_hist)), sep = "_") penguins_hist <- penguins_hist %>% select(-species) %>% remove_missing() #hc <- hclust(dist(penguins_hist, method = "euclidean"), method = "ward.D2") #ggdendrogram(hc) # Create a dendrogram and plot it penguins_hist %>% scale %>% dist(method = "euclidean") %>% hclust(method = "ward.D2") %>% as.dendrogram ## 'dendrogram' with 2 branches and 30 members total, at height 11.94105
Waterfall charts
library(waterfall)
jaquith %>%
arrange(score) %>%
add_row(factor = "Total", score = sum(jaquith$score)) %>%
mutate(factor = factor(factor, levels = factor),
id = seq_along(score)) %>%
mutate(end = cumsum(score),
start = c(0, end[-length(end)]),
start = c(start[-length(start)], 0),
end = c(end[-length(end)], score[length(score)]),
gr_col = ifelse(factor == "Total", "Total", "Part")) %>%
ggplot(aes(x = factor, fill = gr_col)) +
geom_rect(aes(x = factor,
xmin = id - 0.45, xmax = id + 0.45,
ymin = end, ymax = start)) +
theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1),
legend.position = "none") +
labs(x = "",
y = "Amount",
title = "Waterfall chart",
subtitle = "Sample business-adjusted risk from Security Metrics",
caption = "Andrew Jaquith, Security Metrics: Replacing Fear, Uncertainty, and Doubt\n(Boston: Addison-Wesley Professional, 2007), 170-171.")
Biplots
library(ggfortify)
penguins_prep <- penguins %>%
remove_missing() %>%
select(bill_length_mm:body_mass_g)
penguins_pca <- penguins_prep %>%
prcomp(scale. = TRUE)
penguins_km <- penguins_prep %>%
kmeans(3)
autoplot(penguins_pca,
data = penguins %>% remove_missing(),
colour = 'species',
shape = 'species',
loadings = TRUE,
loadings.colour = 'blue',
loadings.label = TRUE,
loadings.label.size = 3) +
scale_color_manual(values = cbp1) +
scale_fill_manual(values = cbp1) +
theme_bw() +
labs(
title = "Biplot PCA",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
autoplot(penguins_km,
data = penguins %>% remove_missing(),
colour = 'species',
shape = 'species',
frame = TRUE, frame.type = 'norm') +
scale_color_manual(values = cbp1) +
scale_fill_manual(values = cbp1) +
theme_bw() +
labs(
title = "Biplot k-Means clustering",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
Radar charts, aka star chart, aka spider plot
https://www.data-to-viz.com/caveat/spider.html
library(ggiraphExtra)
penguins %>%
remove_missing() %>%
select(-island, -year) %>%
ggRadar(aes(x = c(bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g),
group = species,
colour = sex, facet = sex),
rescale = TRUE,
size = 1, interactive = FALSE,
use.label = TRUE) +
scale_color_manual(values = cbp1) +
scale_fill_manual(values = cbp1) +
theme_bw() +
scale_y_discrete(breaks = NULL) + # don't show ticks
labs(
title = "Radar/spider/star chart",
subtitle = "Body mass of male & female penguins per species",
caption = "Source: https://github.com/allisonhorst/palmerpenguins")
devtools::session_info() ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.2 (2020-06-22) ## os macOS Catalina 10.15.7 ## system x86_64, darwin17.0 ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Europe/Berlin ## date 2020-10-20 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source ## ash 1.0-15 2015-09-01 [1] CRAN (R 4.0.2) ## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) ## backports 1.1.10 2020-09-15 [1] CRAN (R 4.0.2) ## blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.2) ## blogdown 0.20.1 2020-09-09 [1] Github (rstudio/blogdown@d96fe78) ## bookdown 0.20 2020-06-23 [1] CRAN (R 4.0.2) ## broom 0.7.0 2020-07-09 [1] CRAN (R 4.0.2) ## callr 3.4.4 2020-09-07 [1] CRAN (R 4.0.2) ## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.0) ## cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.0) ## colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0) ## crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0) ## DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.0) ## dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.2) ## dendextend * 1.14.0 2020-08-26 [1] CRAN (R 4.0.2) ## desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0) ## devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.2) ## digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.0) ## dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2) ## ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0) ## evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.1) ## extra 0.17 2014-12-08 [1] CRAN (R 4.0.2) ## extradb 1.0 2012-06-11 [1] CRAN (R 4.0.2) ## fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) ## farver 2.0.3 2020-01-16 [1] CRAN (R 4.0.0) ## fastmap 1.0.1 2019-10-08 [1] CRAN (R 4.0.0) ## forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.0) ## fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) ## gdtools 0.2.2 2020-04-03 [1] CRAN (R 4.0.2) ## generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.0) ## ggalluvial * 0.12.2 2020-08-30 [1] CRAN (R 4.0.2) ## ggalt * 0.4.0 2017-02-15 [1] CRAN (R 4.0.2) ## ggdendro * 0.1.22 2020-09-13 [1] CRAN (R 4.0.2) ## ggExtra * 0.9 2019-08-27 [1] CRAN (R 4.0.2) ## 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xfun 0.18 2020-09-29 [1] CRAN (R 4.0.2) ## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.0) ## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.0) ## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) ## ## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
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