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index.Rmd
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---
title: "Getting familiar with ggplot2 extensions"
subtitle: "Some of my most frequently used extensions"
author: "<br><br>Ashirwad Barnwal "
date: "<br>18-Feb-2021<br><br>"
output:
xaringan::moon_reader:
css: ["default", "kunoichi", "ninjutsu", "assets/custom.css"]
nature:
ratio: "16:9"
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
lib_dir: libs
---
```{r before-slides, include = FALSE}
# Prettify slide appearance
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
fig.height = 6,
out.width = "100%",
comment = " ",
dpi = 300
)
xaringanExtra::use_panelset()
xaringanExtra::use_share_again()
# Add metadata
library(metathis)
meta() %>%
meta_name("github-repo" = "ashirwad/isu-graphics-ggext") %>%
meta_social(
title = "Getting familiar with ggplot2 extensions",
description = paste(
"Some of my most frequently used ggplot2 extensions.",
"Presented at ISU Graphics Group meeting in Spring 2021."
),
url = "https://isugg21-ggext.netlify.app",
image = "https://isugg21-ggext.netlify.app/social-media-card.png",
image_alt = paste(
"Title slide of Getting familiar with ggplot2 extensions:",
"Some of my most frequently used ggplot2 extensions,",
"presented at ISU Graphics Group meeting in Spring 2021 by Ashirwad Barnwal"
),
og_type = "website",
og_author = "Ashirwad Barnwal",
twitter_card_type = "summary_large_image"
)
```
```{r setup, include = FALSE}
## Load packages
# Path and data manipulation
library(conflicted)
library(here)
library(glue)
library(tidyverse)
conflict_prefer("filter", "dplyr")
# ggplot2 extensions and helpers
library(gghighlight)
library(ggrepel)
library(ggpubr)
library(patchwork)
library(ggalt)
library(ggridges)
library(countrycode)
library(ggimage)
library(ggthemes)
library(ggdark)
library(viridisLite)
# Miscellaneous
library(flipbookr)
library(emo)
library(default)
library(knitr)
default(include_url) <- list(height = "600px")
## Import & process data
midwest <- c(
"Illinois", "Indiana", "Iowa", "Kansas", "Michigan", "Minnesota", "Missouri",
"Nebraska", "North Dakota", "Ohio", "South Dakota", "Wisconsin"
)
fatal_crash_smry_by_state <- read_csv(
here("data", "usa-fatal-crash-totals-2018.csv")
) %>%
filter(state %in% midwest) %>%
rename(
death_rate_vmt = deaths_per_100m_vmt, death_rate_pop = deaths_per_100k_pop
) %>%
mutate(death_rate_vmt_above_mean = if_else(
death_rate_vmt > mean(death_rate_vmt), "Yes", "No"),
death_rate_vmt_z = death_rate_vmt
) %>%
mutate(across(death_rate_vmt_z, ~ round(as.vector(scale(.x)), 2)))
fatal_crash_monthly_counts <- read_csv(
here("data", "usa-fatal-crash-count-by-month.csv")
) %>%
rename(crash_year = Crash_Year) %>%
pivot_longer(
-crash_year, names_to = "crash_month", values_to = "fatal_crash_freq"
)
states_with_alcohol_problem <- read_csv(
here("data", "states-with-alcohol-problem-in-2018.csv")
)
```
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[Welcome!]
]
]]
---
class: split-40 white
.column.bg-main1[.content.vmiddle.center[
# Overview
<br><br>
### ggplot2 alone = `r ji("flexed_biceps")`
### ggplot2 + extensions = `r ji("flexed_biceps")` `r ji("flexed_biceps")`
<br>
### In this talk, I will introduce some of my .yellow[most frequently used] ggplot2 extensions.
<br>
### Let's get started!
]]
.column[.content.vmiddle.center[
```{r ggext-gallery, echo = FALSE}
include_url("https://exts.ggplot2.tidyverse.org/gallery/")
```
]]
---
# .purple[Data description]
.panelset[
.panel[.panel-name[Fatal crash summary by state, 2018]
```{r fatal-crash-smry-by-state}
# Only midwest
glimpse(fatal_crash_smry_by_state)
```
.center[Data source: [Insurance Institute for Highway Safety (IIHS)](https://www.iihs.org/topics/fatality-statistics/detail/state-by-state#fatal-crash-totals)]
]
.panel[.panel-name[Fatal crash monthly counts, 2010-19]
```{r fatal-crash-monthly-counts}
# Across the United States
glimpse(fatal_crash_monthly_counts)
```
.center[Data source: [Fatality Analysis Reporting System (FARS)](https://cdan.dot.gov/query)]
]
.panel[.panel-name[States with alcohol problem, 2017-18]
```{r states-with-alcohol-problem}
# Across the United States
glimpse(states_with_alcohol_problem)
```
.center[Data source: [Traffic safety facts (Table 7)](https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826)]
]
]
---
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[gghighlight]
]
]]
---
class: middle center bg-main1
.font5[Example 1]
---
```{r gghigh-demo1, include = FALSE}
ggplot(fatal_crash_smry_by_state) +
aes(x = death_rate_vmt, y = state) +
geom_point() +
aes(
y = fct_reorder(
state, death_rate_vmt
)
) +
geom_segment(
aes(
x = 0, y = state,
xend = death_rate_vmt,
yend = state
)
) +
labs(
x = paste(
"Deaths per 100 million",
"vehicle miles traveled"
)
) +
labs(y = "Midwestern state") +
labs(
title = paste(
"Motor vehicle crash death rate",
"per midwestern state, 2018"
)
) +
labs(caption = "Data source: IIHS") +
gghighlight(state == "Iowa")
```
`r chunk_reveal("gghigh-demo1")`
---
class: middle center bg-main1
.font5[Example 2]
---
```{r gghigh-demo2, include = FALSE}
ggplot(fatal_crash_monthly_counts) +
aes(
x = crash_month,
y = fatal_crash_freq
) +
aes(group = crash_year) +
geom_line() +
scale_x_discrete(limits = month.abb) +
aes(color = factor(crash_year)) +
labs(x = "Crash month") +
labs(y = "Fatal crash frequency") +
labs(color = "Crash year") +
labs(
title = paste(
"Motor vehicle fatal crash trend",
"in the U.S. by month, 2010–2019"
)
) +
labs(caption = "Data source: FARS") +
gghighlight(
max(fatal_crash_freq) > 3000
)
```
`r chunk_reveal("gghigh-demo2")`
---
class: split-40 white
.column.bg-main1[.content.vmiddle.center[
## Check [.yellow[gghighlight website]](https://yutannihilation.github.io/gghighlight/index.html) to learn more
## `r ji("point_right")`
]]
.column[.content.vmiddle[
```{r gghigh-website, echo = FALSE}
include_url("https://yutannihilation.github.io/gghighlight/index.html")
```
]]
---
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[ggrepel]
]
]]
---
class: middle center bg-main1
.font5[Example 1]
---
```{r ggrepel-demo1, include = FALSE}
ggplot(fatal_crash_smry_by_state) +
aes(
x = death_rate_pop,
y = death_rate_vmt
) +
geom_point() ->
ggrepel_baseplot
ggrepel_baseplot +
geom_text(aes(label = state)) ->
ggtext_ugly
ggrepel_baseplot +
geom_text_repel(
aes(label = state), seed = 123
) ->
ggtext_pretty
ggrepel_baseplot +
geom_label(aes(label = state)) ->
gglabel_ugly
ggrepel_baseplot +
geom_label_repel(
aes(label = state), seed = 123
) ->
gglabel_pretty
```
`r chunk_reveal("ggrepel-demo1")`
---
class: middle center bg-main1
.font5[Example 2]
---
```{r ggrepel-demo2, include = FALSE}
ggrepel_baseplot +
geom_label_repel(
aes(label = state),
seed = 123,
nudge_x = 0.2, #BREAK2
box.padding = 1, #BREAK3
segment.curvature = -0.2, #BREAK4
segment.ncp = 5, #BREAK5
segment.angle = 30 #BREAK6
) +
geom_smooth(
method = "lm", se = FALSE
) +
labs(
x = "Deaths per 100k population"
) +
labs(
y = "Deaths per 100 million VMT"
) +
labs(
title = paste(
"Relationship between motor",
"vehicle crash death rates",
"for midwest, 2018"
)
) +
labs(caption = "Data source: IIHS")
```
`r chunk_reveal("ggrepel-demo2", break_type = "non_seq")`
---
class: split-40 white
.column.bg-main1[.content.vmiddle.center[
## Check [.yellow[ggrepel website]](https://ggrepel.slowkow.com/index.html) to learn more
## `r ji("point_right")`
]]
.column[.content.vmiddle[
```{r ggrepel-website, echo = FALSE}
include_url("https://ggrepel.slowkow.com/index.html")
```
]]
---
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[ggpubr]
]
]]
---
class: middle center bg-main1
.font5[Example 1a]
---
```{r ggpubr-demo1, include = FALSE}
lol_chart <- ggdotchart(
data = fatal_crash_smry_by_state,
x = "state",
y = "death_rate_vmt",
sorting = "descending", #BREAK2
add = "segment", #BREAK3
add.params = list(size = 1), #BREAK4
dot.size = 9, #BREAK5
rotate = TRUE, #BREAK6
color = "death_rate_vmt_above_mean", #BREAK7
label = "death_rate_vmt", #BREAK8
font.label = list( #BREAK8
color = "white", size = 9, #BREAK8
vjust = 0.5 #BREAK8
), #BREAK8
legend.title = "Value above mean", #BREAK9
title = paste( #BREAK9
"Motor vehicle crash death rate", #BREAK9
"per midwestern state, 2018" #BREAK9
), #BREAK9
xlab = "Midwestern state", #BREAK9
ylab = "Deaths per 100 million VMT", #BREAK9
caption = "Data source: IIHS" #BREAK9
)
```
`r chunk_reveal("ggpubr-demo1", break_type = "non_seq", left_assign = TRUE)`
---
class: middle center bg-main1
.font5[Example 1b]
---
```{r ggpubr-demo2, include = FALSE}
dev_chart <- ggdotchart(
data = fatal_crash_smry_by_state,
x = "state", y = "death_rate_vmt_z",
sorting = "descending", #BREAK2
add = "segment", #BREAK3
add.params = list(size = 2), #BREAK4
dot.size = 9, rotate = TRUE, #BREAK5
color = "death_rate_vmt_above_mean", #BREAK6
label = "death_rate_vmt_z", #BREAK7
font.label = list( #BREAK8
color = "white", size = 9, #BREAK8
vjust = 0.5 #BREAK8
), #BREAK8
legend.title = "Value above mean", #BREAK9
title = paste( #BREAK9
"Motor vehicle crash death", #BREAK9
"rate per midwestern state, 2018" #BREAK9
), #BREAK9
xlab = "Midwestern state", #BREAK9
ylab = paste( #BREAK9
"Deaths per 100 million VMT", #BREAK9
"(normalized)" #BREAK9
) #BREAK9
) +
geom_hline(
yintercept = 0,
linetype = 2, color = "lightgrey" #BREAK9
)
```
`r chunk_reveal("ggpubr-demo2", break_type = "non_seq", left_assign = TRUE)`
---
class: middle center bg-main1
.font5[Example 2]
---
```{r ggpubr-demo3, include = FALSE}
fatal_crash_monthly_counts %>%
filter(
crash_year %in% c(2015:2019)
) %>%
ggviolin(
x = "crash_year",
y = "fatal_crash_freq",
fill = "crash_year", #BREAK2
palette = plasma( #BREAK3
5, begin = 0.4, end = 0.8 #BREAK3
), #BREAK3
add = "boxplot", #BREAK4
add.params = list(fill = "white"), #BREAK5
title = paste( #BREAK6
"U.S. fatal traffic crash", #BREAK6
"distribution by month, 2015–19" #BREAK6
), #BREAK6
legend.title = "Crash year", #BREAK7
xlab = "", ylab = "" #BREAK8
) +
theme_economist() #BREAK9
```
`r chunk_reveal("ggpubr-demo3", break_type = "non_seq")`
---
# .purple[Case for violin plots]
> The data in each category is shifting overtime, as can clearly be seen in the "Raw" data view, yet the boxplots remain static. Violin plots are a good method for presenting the distribution of a dataset with more detail than is available with a traditional boxplot. This is not to say that using a boxplot is never appropriate, but if you are going to use a boxplot, it is important to make sure the underlying data is distrubuted in a way that important information is not hidden. – **Justin Matejka & George Fitzmaurice**
```{r violin-vs-boxplot, echo = FALSE}
include_graphics("https://d2f99xq7vri1nk.cloudfront.net/BoxViolinSmaller.gif")
```
.center[
Source: [.blue[Autodesk research]](https://www.autodesk.com/research/publications/same-stats-different-graphs)
]
---
class: split-40 white
.column.bg-main1[.content.vmiddle.center[
## Check [.yellow[ggpubr website]](https://rpkgs.datanovia.com/ggpubr/index.html) to learn more
## `r ji("point_right")`
]]
.column[.content.vmiddle[
```{r ggpubr-website, echo = FALSE}
include_url("https://rpkgs.datanovia.com/ggpubr/index.html")
```
]]
---
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[patchwork]
]
]]
---
class: middle center bg-main1
.font5[Example 1]
---
```{r ggpatch-demo0, include = FALSE}
# Create plots for demo
ggplot() ->
p1
ggplot() +
theme_bw() ->
p2
```
`r chunk_reveal("ggpatch-demo0")`
---
```{r ggpatch-demo1, include = FALSE}
# Fill a grid
p1 +
p2 +
p1 +
p2 ->
ggpatch_grid
# Place side-by-side
p1 |
p2 |
p1 |
p2 ->
ggpatch_juxta
# Place on top of each other
p1 /
p2 /
p1 /
p2 ->
ggpatch_updown
# Hybrids
(p1 / p2) |
(p1 / p2) ->
ggpatch_hybrid1
(p1 | p2) /
(p1 | p2) ->
ggpatch_hybrid2
```
`r chunk_reveal("ggpatch-demo1")`
---
```{r ggpatch-demo2, include = FALSE}
(p1 | p2) / p1 ->
ggpatch_hybrid3
p1 | p2 |
p1 / p2 ->
ggpatch_hybrid4
# Asterisk operator
ggpatch_hybrid3 * theme_dark() ->
ggpatch_star1
ggpatch_hybrid4 * theme_dark() ->
ggpatch_star2
# Ampersand operator
ggpatch_hybrid3 & theme_dark() ->
ggpatch_amp1
ggpatch_hybrid4 & theme_dark() ->
ggpatch_amp2
# Annotation
ggpatch_amp2 +
plot_annotation(tag_levels = "A") ->
ggpatch_annot1
ggpatch_amp2[[3]] <- ggpatch_amp2[[3]] +
plot_layout(tag_level = "new")
ggpatch_amp2 +
plot_annotation(
tag_levels = c("A", "1")
)
```
`r chunk_reveal("ggpatch-demo2")`
---
class: middle center bg-main1
.font5[Example 2]
---
```{r ggpatch-demo3, include = FALSE}
(lol_chart / dev_chart) ->
ggpatch_final
ggpatch_final[[1]] +
labs(title = NULL, caption = NULL) ->
ggpatch_final[[1]]
ggpatch_final[[2]] +
labs(title = NULL) ->
ggpatch_final[[2]]
ggpatch_final +
plot_layout(guides = "collect") +
plot_annotation(
title = paste( #BREAK2
"Motor vehicle crash death rate", #BREAK2
"per midwestern state, 2018" #BREAK2
), #BREAK2
caption = "Data source: IIHS", #BREAK3
theme = theme_pubr(), #BREAK4
tag_levels = "A", #BREAK5
tag_prefix = "Fig. " #BREAK6
) &
theme( #BREAK7
plot.tag = element_text(size = 10) #BREAK7
) #BREAK7
```
`r chunk_reveal("ggpatch-demo3")`
---
`r chunk_reveal("ggpatch-demo3", chunk_options = "fig.height = 7", break_type = "non_seq")`
---
class: split-40 white
.column.bg-main1[.content.vmiddle.center[
## Check [.yellow[patchwork website]](https://patchwork.data-imaginist.com/index.html) to learn more
## `r ji("point_right")`
]]
.column[.content.vmiddle[
```{r patchwork-website, echo = FALSE}
include_url("https://patchwork.data-imaginist.com/index.html")
```
]]
---
layout: false
class: bg-main3 split-30 hide-slide-number
.column[
]
.column.slide-in-right[.content.vmiddle[
.sliderbox.shade_main.pad1[
.font5[ggalt]
]
]]
---
class: middle center bg-main1
.font5[Example 1]
.font2[Heavily based on [.yellow[this blog post]](https://rud.is/b/2016/04/17/ggplot2-exercising-with-ggalt-dumbbells/)]
---
```{r ggalt-demo1a, include = FALSE}
rename(
states_with_alcohol_problem,
prop_2017 = alcohol_deaths_prop_2017,
prop_2018 = alcohol_deaths_prop_2018
) %>%
mutate(
diff = round(
prop_2018 - prop_2017, 2
),
diff_pretty = paste0(
"+", diff * 100
),
lab_2017 = ifelse(
state == "Montana",
paste0(
round(prop_2017 * 100), "%"
),
paste0(round(prop_2017 * 100))
),
lab_2018 = ifelse(
state == "Montana",
paste0(
round(prop_2018 * 100), "%"
),
paste0(round(prop_2018 * 100))
)
) ->
ggdumb_df
```
`r chunk_reveal("ggalt-demo1a")`
---
```{r ggalt-demo1b, include = FALSE}
ggdumb_df %>%
ggplot() +
aes(
y = state, x = prop_2017,
xend = prop_2018
) +
geom_dumbbell(
size = 1.5,
color = "#b2b2b2",
size_x = 3,
colour_x = "#9fb059",
size_xend = 3,
colour_xend = "#edae52"
) +
aes(y = fct_reorder(state, diff)) +
geom_text(
data = filter(
ggdumb_df, state == "Montana"
),
aes(
x = prop_2017, y = state,
label = "Year 2017"
),
color = "#9fb059", size = 3,
vjust = -2, fontface = "bold"
) ->
ggdumb_part1
```
`r chunk_reveal("ggalt-demo1b")`
---
```{r ggalt-demo1c, include = FALSE}
ggdumb_part1 +
geom_text(
data = filter(
ggdumb_df, state == "Montana"
),
aes(
x = prop_2018, y = state,
label = "Year 2018"
),
color = "#edae52", size = 3,
vjust = -2, fontface = "bold"
) +
scale_y_discrete(
expand = expansion(0, 1.2)
) +
geom_text(
aes(x = prop_2017, y = state,
label = lab_2017),
color = "#9fb059", size = 2.75,
vjust = 2.5
) +
geom_text(
aes(x = prop_2018, y = state,
label = lab_2018),
color = "#edae52", size = 2.75,
vjust = 2.5
) ->
ggdumb_part2
```
`r chunk_reveal("ggalt-demo1c")`
---
```{r ggalt-demo1d, include = FALSE}
ggdumb_part2 +
geom_rect(
aes(
xmin = 0.55, xmax = 0.675,
ymin = -Inf, ymax = Inf
),
fill = "#efefe3"
) +
geom_text(
aes(
y = state, x = 0.6125,
label = diff_pretty,
),
fontface = "bold", size = 3
) +
geom_text(
data = filter(
ggdumb_df, state == "Montana"
),
aes(
x = 0.6125, y = state,
label = "DIFF"
),
color = "#7a7d7e", size = 3.1,
vjust = -2, fontface = "bold"
) ->
ggdumb_part3
```
`r chunk_reveal("ggalt-demo1d")`
---
```{r ggalt-demo1e, include = FALSE}
ggdumb_part3 +
scale_x_continuous(
expand = c(0, 0),
limits = c(0, 0.675)
) +
labs(
x = NULL, y = NULL,
title = paste(
"States with alcohol problem",
"based on 2017-18 comparison"
),
subtitle = paste(
"Alcohol-impaired-driving",
"fatalities (% of total)"
),
caption = "Data source: FARS"
) +
theme_bw() +
theme(
panel.grid.major = element_blank()
) +
theme(
panel.grid.minor = element_blank()
) +
theme(
panel.border = element_blank()
) ->
ggdumb_part4
```
`r chunk_reveal("ggalt-demo1e")`
---
```{r ggalt-demo1f, include = FALSE}
ggdumb_part4 +
theme(axis.ticks = element_blank()) +
theme(axis.text.x = element_blank()) +
theme(
plot.title = element_text(
face = "bold"
)
) +
theme(
plot.subtitle = element_text(
face = "italic", size = 9,
margin = margin(b = 12)
)
) +
theme(
plot.caption = element_text(
size = 7, margin = margin(t = 12),
color = "#7a7d7e"
)
) +
geom_segment(
aes(
y = state, yend = state,
x = 0, xend = 0.5
),
color = "#b2b2b2", size = 0.15
)
```