Course overview 📋

Contents

Psychology	Technology
Bad viz	ggplot2
Better viz	ggplot2
Best viz	ggplot2
Interactive viz	Shiny, plotly

Objectives

Design visualizations that communicate the intended message clearly, build data visualizations using the ggplot2 library in R, build interactive dashboards, pitch the results of a data visualization project to a client.

r4ds.had.co.nz

socviz.co

Bad viz

When	What
14 Sep, 09:00 – 16 Sep, 12:00	Self-study Read: Chapter 3 (r4ds.had.co.nz). Chapter 1, 3 (socviz.co). Watch: Basics of Data Vis (canvas.uva.nl).
16 Sep, 12:00 – 16 Sep, 15:00	Tutorial Bad viz, self-study Q&A.
16 Sep, 15:00 – 16 Sep, 18:00	Assignment 1 (Part 1) Improve a visualization, then make it ugly.
19 Sep, 09:00 – 23 Sep, 18:00	Assignment 1 (Part 1 & 2) Improve a visualization, then make it ugly. Reproduce (and improve) a single visualization.

Better viz

When	What
27 Sep, 15:00 – 27 Sep, 18:00	Tutorial Better viz, assignment 1 feedback, final project introduction, Oefenweb Q&A.
28 Sep, 09:00 – 30 Sep, 18:00	Assignment 2 Draft a visualization for the final project. Reproduce (and improve) a combination of visualizations.

Best viz

When	What
28 Sep, 09:00 – 30 Sep, 12:00	Self-study Read: Chapter 3.7, 4.3, 5.3, 5.5, 5.6, 6.0, 6.1, 6.2, 8.1, 8.2, 8.3 (socviz.co) Watch: Themes, Recycling Themes, Trend Lines / Stats, Color Scheme, Multiple Plots, Facets, Saving Plots (canvas.uva.nl)
30 Sep, 12:00 – 30 Sep, 15:00	Tutorial Best viz, self-study Q&A.
28 Sep, 09:00 – 30 Sep, 18:00	Assignment 2 Draft a visualization for the final project. Reproduce (and improve) a combination of visualizations.

Interactive viz (Abe)

When	What
05 Oct, 09:00 – 07 Oct, 12:00	Self-study Watch: Shiny Files (canvas.uva.nl)
07 Oct, 12:00 – 07 Oct, 15:00	Tutorial Interactive viz.
07 Oct, 15:00 – 07 Oct, 18:00	Assignment 3 Improve the final project visualization, add plotly. Reproduce (and improve) a Shiny app.

Project

When	What
10 Oct, 09 :00 – 14 Oct, 18:00	Final project Submit final project files.

Project

1. Create a strong visualization
2. Write a report (one-pager)
   1. Introduction: what did you do and why?
   2. Results / discussion
   3. Reflection:
3. Pitch your results (one-slider)

Grading

Project

• 2 pts. pitch
• 2 pts. aesthetic quality of visualization
• 2 pts. communicative quality of visualization
• 2 pts. one-page report
• 1 pt. creativity / complexity
• 1 pt. code efficiency / styling

Final grade

• 30% data visualization
  • 20% assignment 1
  • 20% assignment 2
  • 20% assignment 3
  • 40% project

Awards

Study tip top ten

1. Live and breathe the flipped classroom.
2. Consult the ggplot2 website.
3. Consult the ggplot2 book.
4. Print the ggplot2 cheatsheet.
5. This is not a presentation: use the code, follow the links.
6. Sketch before you code.
7. Help and challenge each other.
8. Data science is serious. Have some fun.
9. Play. Break the rules.

Source: Martin Telefont

Self-study Q&A

Code

# remotes::install_github("hadley/emo")
library("emo")
qna <- emo::ji("raising_hand")
htmltools::h1(qna)

🙋

Bad viz 💩 speed date

Worst graph

Source: Karl Broman

Say cheese

Source: Tyler Vigen

Correlation does not imply causation. Spurious correlations.

Data is ugly

Source: reddit.com/r/dataisugly

This is marketing. https://towardsdatascience.com/lessons-from-how-to-lie-with-statistics-57060c0d2f19

Data is beautiful

Source: reddit.com/r/dataisbeautiful

Correct for country population or number of schools?

My bad

Source: Savi et al. (2021)

Give me a break

library("RXKCD")
RXKCD::getXKCD(which = "2031")

Wrong, wronger, wrongst. We won’t focus on the obvious examples.

Guiding principles 🫶

Table versus plot

Code

library("gt")
data("pizzaplace")
pizza_top <- pizzaplace %>%
  dplyr::mutate(size = factor(size, levels = c("S", "M", "L"))) %>%
  dplyr::count(name, type, size, price, sort = TRUE) %>%
  dplyr::top_n(n = 5)
pizza_top %>%
  gt::gt() %>%
  gt::tab_header(title = "Pizza Top 5", subtitle = "2015") %>%
  gt::fmt_currency(columns = price, currency = "USD") %>%
  gt::tab_source_note(source_note = gt::md("Source: [pizzaplace dataset](https://gt.rstudio.com/articles/gt-datasets.html#pizzaplace)")) %>%
  gt::opt_stylize(style = 6)

Pizza Top 5
2015
name	type	size	price	n
big_meat	classic	S	$12.00	1914
thai_ckn	chicken	L	$20.75	1410
five_cheese	veggie	L	$18.50	1409
four_cheese	veggie	L	$17.95	1316
classic_dlx	classic	M	$16.00	1181
Source: pizzaplace dataset

Code

library("ggplot2")
pizza_top %>%
  ggplot2::ggplot(aes(x = reorder(name, n, decreasing = TRUE), y = n)) +
  ggplot2::geom_point(aes(color = type, size = size)) +
  ggplot2::geom_text(aes(label = price), nudge_y = -30) +
  ggplot2::labs(title = "Pizza Top 5", subtitle = "2015", x = "name")

Table is the basis for every visualization. But table is for looking up specific information, visualization is for seeing patterns in data. Here, the pattern or story is unclear, so table would be fine probably. Find vs. reveal. Also, viz can easily capture more information (also include size in seasonal plot).

Table versus plot

Code

library("gt")
pizza_season <- pizzaplace %>%
  dplyr::mutate(month = lubridate::month(date, label = TRUE)) %>%
  dplyr::group_by(month) %>%
  dplyr::count(type)
pizza_season %>%
  tidyr::pivot_wider(names_from = month, values_from = n) %>%
  gt::gt() %>%
  gt::tab_header(title = "Pizza Season", subtitle = "2015") %>%
  gt::tab_source_note(source_note = gt::md("Source: [pizzaplace dataset](https://gt.rstudio.com/articles/gt-datasets.html#pizzaplace)")) %>%
  gt::opt_stylize(style = 6)

Pizza Season
2015
type	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
chicken	913	875	994	924	939	910	963	934	900	832	981	885
classic	1257	1178	1236	1253	1324	1199	1331	1283	1202	1181	1262	1182
supreme	1044	964	991	1013	1045	1040	1041	991	877	998	1050	933
veggie	1018	944	1040	961	1020	958	1057	960	911	872	973	935
Source: pizzaplace dataset

Code

library("ggplot2")
library("lubridate")
fig_season_1 <- pizza_season %>%
  ggplot2::ggplot(aes(x = month, y = n, group = type)) +
  ggplot2::geom_line(aes(linetype = type)) +
  ggplot2::labs(title = "Pizza Season", subtitle = "2015")
fig_season_1

fig_season_2 <- pizza_season %>%
  ggplot2::ggplot(aes(x = month, y = n, group = type)) +
  ggplot2::geom_bar(aes(fill = type), stat = "identity") +
  ggplot2::labs(title = "Pizza Season", subtitle = "2015")
fig_season_2

Visualization allows you to represent the same data differently.

Anscombe’s quartet

Code

library("datasauRus")
datasaurus_dozen %>% 
  ggplot2::ggplot(aes(x = x, y = y, color = dataset)) +
  ggplot2::geom_point() +
  ggplot2::theme_void() +
  ggplot2::theme(legend.position = "none", text = element_text(size = 30)) +
  ggplot2::facet_wrap(~dataset, ncol = 3)

Source: Wikipedia

Source: Francis Anscombe

Exploratory versus explanatory

Source: Natalia Kiseleva

Source: Scott McCloud

Exploratory in the sense of data exploration. Exploratory in the sense of visualization exploration (as in previous example, where the same data can be plotted in several ways). In both cases, you can create ugly plots.

We focus on explanatory data viz (also bridge to next). Beautiful plots!

All you need is love

First impression. Love at first sight?

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted. Prince(ss) Charming?

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, …

Rough times. Till death do us apart?

Resizing, color blindness, color accuracy, font embedding, ...

An intuitive step-by-step way to evaluate visualizations.

Better viz 📈 speed date

Contemporary statistical graphics

Source: socviz.co

Instead, we’ll look at contemporary statistical graphics and look at what’s good, and what’s not.

Either ... raw data: distributions

Source: Savi et al. (2021)

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

“These ‘raincloud plots’ can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy.”

Either ... raw data: responses

Source: Abe Hofman

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Let Abe explain.

Either ... raw data: set intersections

Source: Savi (2021)

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Or ... stat. model: 160 coefficients

Source: Savi et al. (2021)

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Exploratory. Four conditions and three ‘Oefenweb player types’. How to spot patterns in 160 regression coefficients?

Or ... stat. model: 320 Brier scores

Source: Savi et al. (2021)

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Systematic error tracing model. Evaluation of different model choices (window, edge weights) and parameters (beta). Brier scores evaluate how well predicted probabilities match the observed proportions. Much like a table/visualization hybrid!

Data-to-ink ratio?

Source: NYT

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Chart junk?

Source: NYT

First impression.

Data-to-ink ratio, chart junk, memorable, readability, ...

Getting acquainted.

Scales, aspect ratios, caption, interpretability, attributes (color, shapes, size, etc), suitability, misleading, cherry-picking, 3d, ...

Give me a break

library("RXKCD")
RXKCD::getXKCD(which = "833")

Best viz 🔥 attributes

Captions

Title Descriptive or declarative

Methods Keep it brief

Results If not (fully captured) in title

Definitions Colors, line types, error bars, etc.

Data source If external

Source: sketch.es

Typography

Learn everything about typography.

Find inspiration and recommendations.

Choose a font for data visualizations.

Pick good font combinations.

Or just use arial or helvetica.

Code

library("showtext")
sysfonts::font_add_google("Press Start 2P", "2P")
showtext::showtext_auto()
fig_season_2 +
  ggplot2::theme(text = element_text(family = "2P", size = 20))

Themes

Code

library("cowplot")
fig_season_1 +
  cowplot::theme_cowplot()

Code

# download.file("https://github.com/ipython/xkcd-font/raw/master/xkcd-script/font/xkcd-script.ttf", destfile = "xkcd-script.ttf")  # download xkcd Script font
# system("open xkcd-script.ttf", wait = FALSE)  # open and install xkcd Script font on MacOS
# download.file("http://simonsoftware.se/other/xkcd.ttf", destfile = "xkcd.ttf")  # download xkcd font
# system("open xkcd.ttf", wait = FALSE)  # open and install xkcd font on MacOS
library("showtext")
library("xkcd")
sysfonts::font_add(family = "xkcd Script", regular = "xkcd-script.ttf")
showtext::showtext_auto()
xrange <- range(as.numeric(pizza_season$month))
yrange <- range(pizza_season$n)
ratioxy <- diff(xrange) / diff(yrange)
x <- 3
y <- 1180
scale <- 60
n <- 1
mapman <- ggplot2::aes(x, y, scale, ratioxy, angleofspine, anglerighthumerus, anglelefthumerus,
              anglerightradius, angleleftradius, anglerightleg, angleleftleg, angleofneck)
dataman <- tibble::tibble(x = x, y = y,
                          scale = scale,
                          ratioxy = ratioxy,
                          angleofspine = runif(n, -pi/2-pi/10, -pi/2+pi/10),
                          anglerighthumerus = runif(n, -pi/6-pi/10, -pi/6+pi/10),
                          anglelefthumerus = runif(n, pi+pi/6-pi/10, pi+pi/6+pi/10),
                          anglerightradius =  runif(n, -pi/4, pi/4),
                          angleleftradius =  runif(n, pi-pi/4, pi+pi/4),
                          anglerightleg = runif(n, 3*pi/2+pi/12, 3*pi/2+pi/12+pi/10),
                          angleleftleg = runif(n, 3*pi/2-pi/12-pi/10, 3*pi/2-pi/12),
                          angleofneck = runif(n, -pi/2-pi/10, -pi/2+pi/10))
datatalk <- tibble::tibble(xbegin = 4, ybegin = 1185, xend = 5, yend = 1215)
pizza_season %>%
  ggplot2::ggplot() +
  ggplot2::geom_smooth(aes(x = month, y = n, group = type, linetype = type),
                       color = "black", se = FALSE) +
  ggplot2::labs(title = "Pizza Season", subtitle = "2015") +
  ggplot2::theme_minimal() +
  ggplot2::theme(text = element_text(family = "xkcd Script", size = 30)) +
  ggplot2::annotate("text", x = 6.5, y = 1215, label = "The figures don't go\noff the charts, so\nwhat's the plot of this graph?", family="xkcd Script") +
  xkcd::xkcdaxis(xrange, yrange) +
  xkcd::xkcdman(mapman, dataman) +
  xkcd::xkcdline(aes(x = xbegin, y = ybegin, xend = xend, yend = yend),
           datatalk, xjitteramount = 0.4)

Color

Code

fig_season_2 +
  ggplot2::scale_fill_viridis_d()

Code

fig_season_2 +
  ggplot2::scale_fill_brewer(type = "qual")

Best viz 🥊 rough times

Color blindness

Code

# remotes::install_github("clauswilke/colorblindr")
library("colorblindr")
colorblindr::cvd_grid(fig_season_2)

Code

library("MetBrewer")
MetBrewer::colorblind_palettes

 [1] "Archambault" "Cassatt1"    "Cassatt2"    "Demuth"      "Derain"     
 [6] "Egypt"       "Greek"       "Hiroshige"   "Hokusai2"    "Hokusai3"   
[11] "Ingres"      "Isfahan1"    "Isfahan2"    "Java"        "Johnson"    
[16] "Kandinsky"   "Morgenstern" "OKeeffe1"    "OKeeffe2"    "Pillement"  
[21] "Tam"         "Troy"        "VanGogh3"    "Veronese"   

Code

fig_season_2 + ggplot2::scale_fill_manual(values = MetBrewer::met.brewer("VanGogh3", n = 4))

Color accuracy

Print-proof, monitor/beamer-proof, colorblind-proof?

• Use grayscale (but color-coding is superior)
• Use a secondary aesthetic for color
• Use a viridis color map

Source: benq.com

Source: Savi et al. (2018)

File format/size

• File size: email attachment, webpage/image load time, compilation time
• File format: resizing vector vs. bitmap/raster. For bitmap images, set the plot resolution: dpi = c(“retina”, “print”, “screen”)

ggplot2::ggsave("awesome_plot.png",
                width = 5,
                height = 5,
                units = "cm",
                dpi = "retina")

Source: clauswilke.com

Font embedding

Vector images pick the closest font available (if the actual font is not available on the recipients computer). You can embed fonts into the vector image.

• showtext converts characters into polygons. Embedding is no longer required, but text is no longer searchable.
• extrafont supports font embedding, but supports fewer fonts.

Adobe Acrobat (paid version) can be used to manually embed fonts in a PDF.

What’s next 🔮

Continue learning

RTFM: ggplot2 Book

Viz types and examples: From Data to Viz, The R Graph Gallery, clauswilke.com

Extensions: ggplot2 Extensions Gallery

Books: Fundamentals of Data Visualization

NYT: What’s going on in this graph?

Data: Statistics Netherlands or data()

#30DayChartChallenge

Source: data-to-viz.com

Get inspiration

Podcasts: Data Stories, Explore Explain, Data Viz Today

Blogs: FlowingData

Journals: Nightingale

Comics: Matt-Heun Hong, Martin Telefont, Natalia Kiseleva

Generative art: Clause O. Wilke, Danielle Navarro, Thomas Lin Pedersen

Awards: Information is Beautiful

Interactive visualizations: R Psychologist

Explorable explanations: Nicky Case, Setosa

Source: Danielle Navarro

Interactive viz

shiny builds interactive web apps

ggplotly converts ggplot2 figures into interactive figures

gganimate includes animated elements to ggplot2 figures

htmlwidgets creates JavaScript visualization widgets

flexdashboard creates interactive dashboards

ggvis (dormant)

Give me a break

library("RXKCD")
RXKCD::getXKCD(which = "2476")

Is visualization required for interpretation? If so, is it sufficient?

Colophon

Part of the Behavioural Data Science Toolbox course, M.S. Behavioural Data Science, University of Amsterdam, the Netherlands.

Created with Quarto (Revealjs) and generated on October 28, 2022.

Reproducibility receipt

Session information

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.1 (2022-06-23)
 os       macOS Ventura 13.0
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Europe/Amsterdam
 date     2022-10-28
 pandoc   2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version    date (UTC) lib source
 assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.0)
 backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)
 base64enc       0.1-3      2015-07-28 [1] CRAN (R 4.2.0)
 broom           1.0.0      2022-07-01 [1] CRAN (R 4.2.0)
 bslib           0.4.0      2022-07-16 [1] CRAN (R 4.2.0)
 cachem          1.0.6      2021-08-19 [1] CRAN (R 4.2.0)
 cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
 checkmate       2.1.0      2022-04-21 [1] CRAN (R 4.2.0)
 cli             3.3.0      2022-04-25 [1] CRAN (R 4.2.0)
 clipr           0.8.0      2022-02-22 [1] CRAN (R 4.2.0)
 cluster         2.1.3      2022-03-28 [1] CRAN (R 4.2.1)
 colorblindr   * 0.1.0      2022-08-17 [1] Github (clauswilke/colorblindr@e6730be)
 colorspace    * 2.1-0      2022-07-09 [1] R-Forge (R 4.2.1)
 commonmark      1.8.0      2022-03-09 [1] CRAN (R 4.2.0)
 cowplot       * 1.1.1      2022-08-17 [1] Github (wilkelab/cowplot@555c9ae)
 crayon          1.5.1      2022-03-26 [1] CRAN (R 4.2.0)
 curl            4.3.2      2021-06-23 [1] CRAN (R 4.2.0)
 data.table      1.14.2     2021-09-27 [1] CRAN (R 4.2.0)
 datasauRus    * 0.1.6      2022-05-04 [1] CRAN (R 4.2.0)
 DBI             1.1.3      2022-06-18 [1] CRAN (R 4.2.0)
 dbplyr          2.2.1      2022-06-27 [1] CRAN (R 4.2.0)
 deldir          1.0-6      2021-10-23 [1] CRAN (R 4.2.0)
 desc            1.4.1      2022-03-06 [1] CRAN (R 4.2.0)
 details       * 0.3.0      2022-03-27 [1] CRAN (R 4.2.0)
 digest          0.6.29     2021-12-01 [1] CRAN (R 4.2.0)
 dplyr         * 1.0.9      2022-04-28 [1] CRAN (R 4.2.0)
 ellipsis        0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
 emo           * 0.0.0.9000 2022-09-06 [1] Github (hadley/emo@3f03b11)
 evaluate        0.16       2022-08-09 [1] CRAN (R 4.2.0)
 extrafont     * 0.18       2022-04-12 [1] CRAN (R 4.2.0)
 extrafontdb     1.0        2012-06-11 [1] CRAN (R 4.2.0)
 fansi           1.0.3      2022-03-24 [1] CRAN (R 4.2.0)
 farver          2.1.1      2022-07-06 [1] CRAN (R 4.2.0)
 fastmap         1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
 forcats       * 0.5.1      2021-01-27 [1] CRAN (R 4.2.0)
 foreign         0.8-82     2022-01-16 [1] CRAN (R 4.2.1)
 Formula         1.2-4      2020-10-16 [1] CRAN (R 4.2.0)
 fs              1.5.2      2021-12-08 [1] CRAN (R 4.2.0)
 gargle          1.2.0      2021-07-02 [1] CRAN (R 4.2.0)
 generics        0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
 ggfun           0.0.7      2022-08-31 [1] CRAN (R 4.2.0)
 ggimage         0.3.1      2022-04-25 [1] CRAN (R 4.2.0)
 ggplot2       * 3.3.6      2022-05-03 [1] CRAN (R 4.2.0)
 ggplotify       0.1.0      2021-09-02 [1] CRAN (R 4.2.0)
 glue            1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
 googledrive     2.0.0      2021-07-08 [1] CRAN (R 4.2.0)
 googlesheets4   1.0.1      2022-08-13 [1] CRAN (R 4.2.0)
 gridExtra       2.3        2017-09-09 [1] CRAN (R 4.2.0)
 gridGraphics    0.5-1      2020-12-13 [1] CRAN (R 4.2.0)
 gt            * 0.7.0      2022-08-25 [1] CRAN (R 4.2.0)
 gtable          0.3.0      2019-03-25 [1] CRAN (R 4.2.0)
 haven           2.5.0      2022-04-15 [1] CRAN (R 4.2.0)
 hexbin          1.28.2     2021-01-08 [1] CRAN (R 4.2.0)
 hexSticker    * 0.4.9      2020-12-05 [1] CRAN (R 4.2.0)
 Hmisc           4.7-1      2022-08-15 [1] CRAN (R 4.2.0)
 hms             1.1.1      2021-09-26 [1] CRAN (R 4.2.0)
 htmlTable       2.4.1      2022-07-07 [1] CRAN (R 4.2.0)
 htmltools       0.5.3      2022-07-18 [1] CRAN (R 4.2.0)
 htmlwidgets     1.5.4      2021-09-08 [1] CRAN (R 4.2.0)
 httpuv          1.6.5      2022-01-05 [1] CRAN (R 4.2.0)
 httr            1.4.3      2022-05-04 [1] CRAN (R 4.2.0)
 interp          1.1-3      2022-07-13 [1] CRAN (R 4.2.0)
 jpeg            0.1-9      2021-07-24 [1] CRAN (R 4.2.0)
 jquerylib       0.1.4      2021-04-26 [1] CRAN (R 4.2.0)
 jsonlite        1.8.0      2022-02-22 [1] CRAN (R 4.2.0)
 knitr           1.39       2022-04-26 [1] CRAN (R 4.2.0)
 labeling        0.4.2      2020-10-20 [1] CRAN (R 4.2.0)
 later           1.3.0      2021-08-18 [1] CRAN (R 4.2.0)
 lattice         0.20-45    2021-09-22 [1] CRAN (R 4.2.1)
 latticeExtra    0.6-30     2022-07-04 [1] CRAN (R 4.2.0)
 lifecycle       1.0.1      2021-09-24 [1] CRAN (R 4.2.0)
 lubridate     * 1.8.0      2021-10-07 [1] CRAN (R 4.2.0)
 magick          2.7.3      2021-08-18 [1] CRAN (R 4.2.0)
 magrittr        2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
 Matrix          1.4-1      2022-03-23 [1] CRAN (R 4.2.1)
 MetBrewer     * 0.2.0      2022-03-21 [1] CRAN (R 4.2.0)
 mgcv            1.8-40     2022-03-29 [1] CRAN (R 4.2.1)
 mime            0.12       2021-09-28 [1] CRAN (R 4.2.0)
 modelr          0.1.8      2020-05-19 [1] CRAN (R 4.2.0)
 munsell         0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
 nlme            3.1-157    2022-03-25 [1] CRAN (R 4.2.1)
 nnet            7.3-17     2022-01-16 [1] CRAN (R 4.2.1)
 pillar          1.8.0      2022-07-18 [1] CRAN (R 4.2.0)
 pkgconfig       2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
 plyr            1.8.7      2022-03-24 [1] CRAN (R 4.2.0)
 png             0.1-7      2013-12-03 [1] CRAN (R 4.2.0)
 promises        1.2.0.1    2021-02-11 [1] CRAN (R 4.2.0)
 purrr         * 0.3.4      2020-04-17 [1] CRAN (R 4.2.0)
 R6              2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
 ragg            1.2.2      2022-02-21 [1] CRAN (R 4.2.0)
 RColorBrewer    1.1-3      2022-04-03 [1] CRAN (R 4.2.0)
 Rcpp            1.0.9      2022-07-08 [1] CRAN (R 4.2.0)
 readr         * 2.1.2      2022-01-30 [1] CRAN (R 4.2.0)
 readxl          1.4.0      2022-03-28 [1] CRAN (R 4.2.0)
 reprex          2.0.2      2022-08-17 [1] CRAN (R 4.2.1)
 rfishdraw     * 0.1.0      2021-09-08 [1] CRAN (R 4.2.0)
 RJSONIO         1.3-1.6    2021-09-16 [1] CRAN (R 4.2.0)
 rlang           1.0.5      2022-08-31 [1] CRAN (R 4.2.0)
 rmarkdown       2.14       2022-04-25 [1] CRAN (R 4.2.0)
 rpart           4.1.16     2022-01-24 [1] CRAN (R 4.2.1)
 rprojroot       2.0.3      2022-04-02 [1] CRAN (R 4.2.0)
 rstudioapi      0.13       2020-11-12 [1] CRAN (R 4.2.0)
 Rttf2pt1        1.3.10     2022-02-07 [1] CRAN (R 4.2.0)
 rvest           1.0.2      2021-10-16 [1] CRAN (R 4.2.0)
 RXKCD         * 1.9.2      2020-02-24 [1] CRAN (R 4.2.0)
 sass            0.4.2      2022-07-16 [1] CRAN (R 4.2.0)
 scales          1.2.0      2022-04-13 [1] CRAN (R 4.2.0)
 sessioninfo   * 1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
 shiny           1.7.2      2022-07-19 [1] CRAN (R 4.2.0)
 showtext      * 0.9-5      2022-02-09 [1] CRAN (R 4.2.0)
 showtextdb    * 3.0        2020-06-04 [1] CRAN (R 4.2.0)
 stringi         1.7.8      2022-07-11 [1] CRAN (R 4.2.0)
 stringr       * 1.4.1      2022-08-20 [1] CRAN (R 4.2.0)
 survival        3.3-1      2022-03-03 [1] CRAN (R 4.2.1)
 sysfonts      * 0.8.8      2022-03-13 [1] CRAN (R 4.2.0)
 systemfonts     1.0.4      2022-02-11 [1] CRAN (R 4.2.0)
 textshaping     0.3.6      2021-10-13 [1] CRAN (R 4.2.0)
 tibble        * 3.1.8      2022-07-22 [1] CRAN (R 4.2.0)
 tidyr         * 1.2.0      2022-02-01 [1] CRAN (R 4.2.0)
 tidyselect      1.1.2      2022-02-21 [1] CRAN (R 4.2.0)
 tidyverse     * 1.3.2      2022-07-18 [1] CRAN (R 4.2.0)
 timevis       * 2.0.0      2021-12-20 [1] CRAN (R 4.2.0)
 tzdb            0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
 utf8            1.2.2      2021-07-24 [1] CRAN (R 4.2.0)
 vctrs           0.4.1      2022-04-13 [1] CRAN (R 4.2.0)
 viridisLite     0.4.0      2021-04-13 [1] CRAN (R 4.2.0)
 withr           2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
 xfun            0.32       2022-08-10 [1] CRAN (R 4.2.0)
 xkcd          * 0.0.6      2018-07-11 [1] CRAN (R 4.2.0)
 xml2            1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
 xtable          1.8-4      2019-04-21 [1] CRAN (R 4.2.0)
 yaml            2.3.5      2022-02-21 [1] CRAN (R 4.2.0)
 yulab.utils     0.0.5      2022-06-30 [1] CRAN (R 4.2.0)

 [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library

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License

Data Visualization by Alexander Savi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. An Open Educational Resource. Approved for Free Cultural Works.