Data Visualisation

👨‍💻 Eugene Hickey @ Atlantic Technological University 👨‍💻

• eugene.hickey@tudublin.ie
• @eugene100hickey
• github.com/eugene100hickey
• www.fizzics.ie

Graphics Key Feature of R

• Graphics are important, overlooked, and inconsistent

  • the last mile of data analysis

• Need to tell a story

• Can be misleading, almost always by accident

• Choice of colours / fonts

• Keep it simple - reduce amount of ink

• Increasing number of options for showcasing your data

Kernel of graphics in R is ggplot

• ggplot is easy to make publication-ready

• easier to make sequence of visualisations

• fits in nicely with the rest of the tidyverse

Lots of addin packages for ggplot

gg.gap, ggalignment, ggallin, ggalluvial, ggalt, ggamma, gganimate, ggarchery, ggasym, ggbeeswarm, ggblanket, ggborderline, ggbrain, ggbreak, ggBubbles, ggbuildr, ggbump, ggchangepoint, ggcharts, ggChernoff, ggcleveland, ggcorrplot, ggcorset, ggcoverage, ggdag, ggdark, ggDCA, ggdemetra, ggdendro, ggdensity, ggdist, ggdmc, ggDoE, ggDoubleHeat, gge, ggeasy, ggedit, ggeffects, ggenealogy, ggESDA, ggetho, ggExtra, ggfan, ggfittext, ggfocus, ggforce, ggformula, ggfortify, ggfun, ggfx, gggap, gggenes, ggghost, gggibbous, gggrid, ggh4x, gghalfnorm, gghalves, gghdr, ggheatmap, gghighlight, gghilbertstrings, ggHoriPlot, ggimage, ggimg, gginference, gginnards, ggip, ggiraph, ggiraphExtra, ggisotonic, ggjoy, gglasso, gglgbtq, gglm, gglorenz, ggm, ggmap, ggmapinset, ggmatplot, ggmcmc, ggmice, ggmix, ggmosaic, ggmotif, ggmr, ggmuller, ggmulti, ggnetwork, ggnewscale, ggnormalviolin, ggnuplot, ggOceanMaps, ggokabeito, ggpackets, ggpage, ggparallel, ggparliament, ggparty, ggpath, ggpattern, ggpcp, ggperiodic, ggpie, ggplate, ggplot.multistats, ggplot2, ggplot2movies, ggplotAssist, ggplotgui, ggplotify, ggplotlyExtra, ggpmisc, ggPMX, ggpointdensity, ggpointless, ggpol, ggpolar, ggpolypath, ggpp, ggprism, ggpubr, ggpval, ggQC, ggQQunif, ggquickeda, ggquiver, ggrain, ggRandomForests, ggraph, ggraptR, ggrasp, ggrastr, ggredist, ggrepel, ggResidpanel, ggridges, ggrisk, ggroups, ggsci, ggseas, ggsector, ggseg, ggseg3d, ggseqlogo, ggseqplot, ggshadow, ggside, ggsignif, ggsn, ggsoccer, ggsolvencyii, ggsom, ggspatial, ggspectra, ggstance, ggstar, ggstats, ggstatsplot, ggstream, ggstudent, ggsurvey, ggsurvfit, ggswissmaps, ggtea, ggtern, ggtext, ggThemeAssist, ggthemes, ggtikz, ggTimeSeries, ggtrace, ggtrendline, ggupset, ggvenn, ggVennDiagram, ggversa, ggvis, ggvoronoi, ggwordcloud, ggx

Basic Picture of ggplot

Three Features of a Plot

• aesthetics
  • values that each individual observation (row) has
  • will be different for each observation
• attributes
  • values that are shared between all points
  • decide to make everything mint green
• layers
  • each visualisation is built sequentially
  • add features in layers, one on top of the last
  • examples: add a plot title, change an axis scale….

ggplot Runthrough

penguins %>% drop_na()

# A tibble: 333 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           36.7          19.3               193        3450
 5 Adelie  Torgersen           39.3          20.6               190        3650
 6 Adelie  Torgersen           38.9          17.8               181        3625
 7 Adelie  Torgersen           39.2          19.6               195        4675
 8 Adelie  Torgersen           41.1          17.6               182        3200
 9 Adelie  Torgersen           38.6          21.2               191        3800
10 Adelie  Torgersen           34.6          21.1               198        4400
# ℹ 323 more rows
# ℹ 2 more variables: sex <fct>, year <int>

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot()

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20))

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70"))

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE) +
  labs(title = "Chinstraps have Short Flippers",
       subtitle = "{.black Adelie}, {.blue Chinstrap}, and {.#B0B0B0 Gentoo} penguins",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)",
       caption = "@Data from Palmer Penguins")

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE) +
  labs(title = "Chinstraps have Short Flippers",
       subtitle = "{.black Adelie}, {.blue Chinstrap}, and {.#B0B0B0 Gentoo} penguins",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)",
       caption = "@Data from Palmer Penguins") +
  theme(text = element_text(family = "Ink Free", size = 32))

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE) +
  labs(title = "Chinstraps have Short Flippers",
       subtitle = "{.black Adelie}, {.blue Chinstrap}, and {.#B0B0B0 Gentoo} penguins",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)",
       caption = "@Data from Palmer Penguins") +
  theme(text = element_text(family = "Ink Free", size = 32)) +
  theme(plot.subtitle = element_marquee(width = 1))

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE) +
  labs(title = "Chinstraps have Short Flippers",
       subtitle = "{.black Adelie}, {.blue Chinstrap}, and {.#B0B0B0 Gentoo} penguins",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)",
       caption = "@Data from Palmer Penguins") +
  theme(text = element_text(family = "Ink Free", size = 32)) +
  theme(plot.subtitle = element_marquee(width = 1)) +
  facet_grid(~sex)

ggplot Runthrough

penguins %>% drop_na() %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  scale_x_continuous(breaks = seq(170, 230, by = 20)) +
  aes(y = bill_length_mm) +
  geom_point(size = 3, show.legend = F) +
  aes(colour = species) +
  scale_color_manual(values = c("black", "blue", "grey70")) +
  ggalt::geom_encircle(size = 5, show.legend = FALSE) +
  labs(title = "Chinstraps have Short Flippers",
       subtitle = "{.black Adelie}, {.blue Chinstrap}, and {.#B0B0B0 Gentoo} penguins",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)",
       caption = "@Data from Palmer Penguins") +
  theme(text = element_text(family = "Ink Free", size = 32)) +
  theme(plot.subtitle = element_marquee(width = 1)) +
  facet_grid(~sex)

Graphics can be Fun

Picturing Data Different Ways with ggplot

We’re going to set out some of the options for looking at data

these depend on what kind of data you have

and what you want to investigate

Lots of these come from Top 50 Visualizations in R

• Show the data

• Use ink sparingly

• Title should tell the story

• Don’t try to show too much

• Start with grey

Visualising Amounts

• Visualising Proportions

• Visualising Distributions

• Visualising Relationships

• Visualising Time Series

• Visualising Groups

• Visualising Networks

• Visualising Spatial Data

Items in red we’ll cover this today. In blue will have to wait for a future workshop.

Visualising Amounts

• barplot

• dot plot

• lollipop plot

Bar Plots

diamonds

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
# ℹ 53,930 more rows

Bar Plots

diamonds %>%
  ggplot(aes(cut))

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1")

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds")

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse")

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip()

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip() +
  theme_clean()

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip() +
  theme_clean() +
  theme(text = element_text(size = 40))

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip() +
  theme_clean() +
  theme(text = element_text(size = 40)) +
  theme(axis.text.x = element_blank())

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip() +
  theme_clean() +
  theme(text = element_text(size = 40)) +
  theme(axis.text.x = element_blank()) +
  theme(axis.title = element_blank())

Bar Plots

diamonds %>%
  ggplot(aes(cut)) +
  geom_bar(fill = "dodgerblue1") +
  ggtitle("Proportion of Cuts of Diamonds") +
  labs(caption = "@Data tidyverse") +
  coord_flip() +
  theme_clean() +
  theme(text = element_text(size = 40)) +
  theme(axis.text.x = element_blank()) +
  theme(axis.title = element_blank()) +
  theme(title = element_text(face = "bold"))

boxoffice_date <- Sys.Date()-7
movies <- my_boxoffice(boxoffice_date) |>  
  mutate(gross = gross / 1e6,
         movie_name = movie,
         movie = abbreviate(movie))
sf <- stamp("Sunday, 8th January, 1999")
boxoffice_date_string <- sf(boxoffice_date)

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross))

# A tibble: 10 × 4
   movie      weekly  gross movie_name                                  
   <fct>       <dbl>  <dbl> <chr>                                       
 1 MKII     16801022  16.8  Mortal Kombat II                            
 2 TDWP2     9888491 112.   The Devil Wears Prada 2                     
 3 Mchl      8795396 213.   Michael                                     
 4 BEHMHaST  4520209   4.52 Billie Eilish: Hit Me Hard and Soft—The Tour
 5 ThSD      4010418   4.01 The Sheep Detectives                        
 6 PrHM      1561640 323.   Project Hail Mary                           
 7 TSMGM     1553750 407.   The Super Mario Galaxy Movie                
 8 Hokm      1056476  10.2  Hokum                                       
 9 DpWt       226343   3.12 Deep Water                                  
10 AnmF       183704   4.52 Animal Farm                                 

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10)

# A tibble: 10 × 4
   movie      weekly  gross movie_name                                  
   <fct>       <dbl>  <dbl> <chr>                                       
 1 MKII     16801022  16.8  Mortal Kombat II                            
 2 TDWP2     9888491 112.   The Devil Wears Prada 2                     
 3 Mchl      8795396 213.   Michael                                     
 4 BEHMHaST  4520209   4.52 Billie Eilish: Hit Me Hard and Soft—The Tour
 5 ThSD      4010418   4.01 The Sheep Detectives                        
 6 PrHM      1561640 323.   Project Hail Mary                           
 7 TSMGM     1553750 407.   The Super Mario Galaxy Movie                
 8 Hokm      1056476  10.2  Hokum                                       
 9 DpWt       226343   3.12 Deep Water                                  
10 AnmF       183704   4.52 Animal Farm                                 

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross))

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4")

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4") +
  theme_clean()

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4") +
  theme_clean() +
  scale_y_continuous(breaks = scales::breaks_extended(8),
                     labels = scales::label_dollar(scale = 1))

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4") +
  theme_clean() +
  scale_y_continuous(breaks = scales::breaks_extended(8),
                     labels = scales::label_dollar(scale = 1)) +
  labs(title = glue::glue("Box Office {boxoffice_date_string}"),
       caption = "@Data from BoxOfficeMojo",
       y = "Gross (Million$)")

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4") +
  theme_clean() +
  scale_y_continuous(breaks = scales::breaks_extended(8),
                     labels = scales::label_dollar(scale = 1)) +
  labs(title = glue::glue("Box Office {boxoffice_date_string}"),
       caption = "@Data from BoxOfficeMojo",
       y = "Gross (Million$)") +
  coord_flip()

Movie Barplot

movies %>% mutate(movie = fct_reorder(movie, gross)) %>%
  slice_head(n=10) %>%
  ggplot(aes(movie, gross)) +
  geom_col(fill = "firebrick4") +
  theme_clean() +
  scale_y_continuous(breaks = scales::breaks_extended(8),
                     labels = scales::label_dollar(scale = 1)) +
  labs(title = glue::glue("Box Office {boxoffice_date_string}"),
       caption = "@Data from BoxOfficeMojo",
       y = "Gross (Million$)") +
  coord_flip() +
  theme(axis.title.y = element_blank())

Movie Title	Abbreviated Title
Mortal Kombat II	MKII
The Devil Wears Prada 2	TDWP2
Michael	Mchl
Billie Eilish: Hit Me Hard and Soft—The Tour	BEHMHaST
The Sheep Detectives	ThSD
Project Hail Mary	PrHM
The Super Mario Galaxy Movie	TSMGM
Hokum	Hokm
Deep Water	DpWt
Animal Farm	AnmF

Column Plot

penguins %>%
  group_by(species) %>%
  summarise(body_mass = mean(body_mass_g, na.rm = T)) %>%
  ggplot(aes(species, body_mass, xend = species, yend = body_mass)) +
  theme_clean() +
  coord_flip() +
  labs(caption = "@PalmerPenguins",
       y = "Body Mass (g)",
       x = "") +
  ylim(c(0, 6000)) +
  geom_col(fill = "firebrick4") +
  labs(x = "")

Column Plot

penguins %>%
  group_by(species) %>%
  summarise(body_mass = mean(body_mass_g, na.rm = T)) %>%
  ggplot(aes(species, body_mass, xend = species, yend = body_mass)) +
  theme_clean() +
  coord_flip() +
  labs(caption = "@PalmerPenguins",
       y = "Body Mass (g)",
       x = "") +
  ylim(c(0, 6000)) +
  geom_point(colour = "firebrick4", size = 4) +
  labs(x = "")

Column Plot

penguins %>%
  group_by(species) %>%
  summarise(body_mass = mean(body_mass_g, na.rm = T)) %>%
  ggplot(aes(species, body_mass, xend = species, yend = body_mass)) +
  theme_clean() +
  coord_flip() +
  labs(caption = "@PalmerPenguins",
       y = "Body Mass (g)",
       x = "") +
  ylim(c(0, 6000)) +
  geom_segment(linewidth = 2, colour = "firebrick4", y = 0) + geom_point(colour = "firebrick4", size = 4) +
  labs(x = "")

Visualising Distributions

• histograms

• density plots

• boxplot

• violin plot

• ridge plots

basketball <- read_csv("https://raw.githubusercontent.com/eugene100hickey/ATU-2023/main/week-05/data/basketball.csv")

Histogram

basketball

# A tibble: 3,366 × 8
   name            year_start year_end position height weight birth_date college
   <chr>                <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>      <chr>  
 1 Kareem Abdul-J…       1970     1989 C          218.  102.  April 16,… Univer…
 2 Mahmoud Abdul-…       1991     2001 G          185.   73.5 March 9, … Louisi…
 3 Tariq Abdul-Wa…       1998     2003 F          198.  101.  November … San Jo…
 4 Shareef Abdur-…       1997     2008 F          206.  102.  December … Univer…
 5 Tom Abernethy         1977     1981 F          201.   99.8 May 6, 19… Indian…
 6 Forest Able           1957     1957 G          190.   81.6 July 27, … Wester…
 7 John Abramovic        1947     1948 F          190.   88.5 February … Salem …
 8 Alex Acker            2006     2009 G          196.   83.9 January 2… Pepper…
 9 Don Ackerman          1954     1954 G          183.   83.0 September… Long I…
10 Bud Acton             1968     1968 F          198.   95.3 January 1… Hillsd…
# ℹ 3,356 more rows

Histogram

basketball %>%
  ggplot(aes(weight))

Histogram

basketball %>%
  ggplot(aes(weight)) +
  geom_histogram(fill = "firebrick4",
                 bins = 50)

Histogram

basketball %>%
  ggplot(aes(weight)) +
  geom_histogram(fill = "firebrick4",
                 bins = 50) +
  labs(x = "weight (kg)",
       y = "",
       caption = "@Data from Kaggle",
       title = "Weight of NBA Players")

Histogram

basketball

# A tibble: 3,366 × 8
   name            year_start year_end position height weight birth_date college
   <chr>                <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>      <chr>  
 1 Kareem Abdul-J…       1970     1989 C          218.  102.  April 16,… Univer…
 2 Mahmoud Abdul-…       1991     2001 G          185.   73.5 March 9, … Louisi…
 3 Tariq Abdul-Wa…       1998     2003 F          198.  101.  November … San Jo…
 4 Shareef Abdur-…       1997     2008 F          206.  102.  December … Univer…
 5 Tom Abernethy         1977     1981 F          201.   99.8 May 6, 19… Indian…
 6 Forest Able           1957     1957 G          190.   81.6 July 27, … Wester…
 7 John Abramovic        1947     1948 F          190.   88.5 February … Salem …
 8 Alex Acker            2006     2009 G          196.   83.9 January 2… Pepper…
 9 Don Ackerman          1954     1954 G          183.   83.0 September… Long I…
10 Bud Acton             1968     1968 F          198.   95.3 January 1… Hillsd…
# ℹ 3,356 more rows

Histogram

basketball %>%
  ggplot(aes(weight,
             fill = position))

Histogram

basketball %>%
  ggplot(aes(weight,
             fill = position)) +
  geom_histogram(bins = 20,
                 position = "dodge")

Histogram

basketball %>%
  ggplot(aes(weight,
             fill = position)) +
  geom_histogram(bins = 20,
                 position = "dodge") +
  labs(x = "weight (kg)",
       y = "",
       caption = "@Data from Kaggle",
       title = "Weight of NBA Players by\nPosition")

Density Plot

basketball

# A tibble: 3,366 × 8
   name            year_start year_end position height weight birth_date college
   <chr>                <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>      <chr>  
 1 Kareem Abdul-J…       1970     1989 C          218.  102.  April 16,… Univer…
 2 Mahmoud Abdul-…       1991     2001 G          185.   73.5 March 9, … Louisi…
 3 Tariq Abdul-Wa…       1998     2003 F          198.  101.  November … San Jo…
 4 Shareef Abdur-…       1997     2008 F          206.  102.  December … Univer…
 5 Tom Abernethy         1977     1981 F          201.   99.8 May 6, 19… Indian…
 6 Forest Able           1957     1957 G          190.   81.6 July 27, … Wester…
 7 John Abramovic        1947     1948 F          190.   88.5 February … Salem …
 8 Alex Acker            2006     2009 G          196.   83.9 January 2… Pepper…
 9 Don Ackerman          1954     1954 G          183.   83.0 September… Long I…
10 Bud Acton             1968     1968 F          198.   95.3 January 1… Hillsd…
# ℹ 3,356 more rows

Density Plot

basketball %>%
  ggplot(aes(weight,
             col = position))

Density Plot

basketball %>%
  ggplot(aes(weight,
             col = position)) +
  stat_density(geom = "line",
               position = "identity")

Density Plot

basketball %>%
  ggplot(aes(weight,
             col = position)) +
  stat_density(geom = "line",
               position = "identity") +
  labs(x = "weight (kg)",
       y = "",
       caption = "@Data from Kaggle",
       title = "Weight of NBA Players by\nPosition")

Density Plot

basketball %>%
  ggplot(aes(weight,
             col = position)) +
  stat_density(geom = "line",
               position = "identity") +
  labs(x = "weight (kg)",
       y = "",
       caption = "@Data from Kaggle",
       title = "Weight of NBA Players by\nPosition") +
  geom_rug()

Boxplot

basketball

# A tibble: 3,366 × 8
   name            year_start year_end position height weight birth_date college
   <chr>                <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>      <chr>  
 1 Kareem Abdul-J…       1970     1989 C          218.  102.  April 16,… Univer…
 2 Mahmoud Abdul-…       1991     2001 G          185.   73.5 March 9, … Louisi…
 3 Tariq Abdul-Wa…       1998     2003 F          198.  101.  November … San Jo…
 4 Shareef Abdur-…       1997     2008 F          206.  102.  December … Univer…
 5 Tom Abernethy         1977     1981 F          201.   99.8 May 6, 19… Indian…
 6 Forest Able           1957     1957 G          190.   81.6 July 27, … Wester…
 7 John Abramovic        1947     1948 F          190.   88.5 February … Salem …
 8 Alex Acker            2006     2009 G          196.   83.9 January 2… Pepper…
 9 Don Ackerman          1954     1954 G          183.   83.0 September… Long I…
10 Bud Acton             1968     1968 F          198.   95.3 January 1… Hillsd…
# ℹ 3,356 more rows

Boxplot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position))

Boxplot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_boxplot(show.legend = F)

Boxplot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_boxplot(show.legend = F) +
   labs(y = "weight (kg)",
        x = "position",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by\nPosition")

Boxplot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_boxplot(show.legend = F) +
   labs(y = "weight (kg)",
        x = "position",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by\nPosition") +
  geom_jitter(size = 0.4,
              alpha = 0.2,
              show.legend = F)

Violin Plot

basketball

# A tibble: 3,366 × 8
   name            year_start year_end position height weight birth_date college
   <chr>                <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>      <chr>  
 1 Kareem Abdul-J…       1970     1989 C          218.  102.  April 16,… Univer…
 2 Mahmoud Abdul-…       1991     2001 G          185.   73.5 March 9, … Louisi…
 3 Tariq Abdul-Wa…       1998     2003 F          198.  101.  November … San Jo…
 4 Shareef Abdur-…       1997     2008 F          206.  102.  December … Univer…
 5 Tom Abernethy         1977     1981 F          201.   99.8 May 6, 19… Indian…
 6 Forest Able           1957     1957 G          190.   81.6 July 27, … Wester…
 7 John Abramovic        1947     1948 F          190.   88.5 February … Salem …
 8 Alex Acker            2006     2009 G          196.   83.9 January 2… Pepper…
 9 Don Ackerman          1954     1954 G          183.   83.0 September… Long I…
10 Bud Acton             1968     1968 F          198.   95.3 January 1… Hillsd…
# ℹ 3,356 more rows

Violin Plot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position))

Violin Plot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_violin(show.legend = F)

Violin Plot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_violin(show.legend = F) +
   labs(x = "position",
        y = "weight (kg)",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by\nPosition")

Violin Plot

basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_violin(show.legend = F) +
   labs(x = "position",
        y = "weight (kg)",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by\nPosition") +
   geom_jitter(size = 0.4,
               alpha = 0.2,
               show.legend = F)

Ridge Plot

gapminder::gapminder

# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Ridge Plot

gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year)))

Ridge Plot

gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
  geom_density_ridges(fill = "firebrick4",
                      colour = "firebrick4",
                      alpha = 0.4)

Ridge Plot

gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
  geom_density_ridges(fill = "firebrick4",
                      colour = "firebrick4",
                      alpha = 0.4) +
  theme_ridges()

Ridge Plot

gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
  geom_density_ridges(fill = "firebrick4",
                      colour = "firebrick4",
                      alpha = 0.4) +
  theme_ridges() +
  labs(x = "Life Expectancy (years)",
       y = "",
       caption = "@Data Gapminder (WHO)")

Summary of Distributions

• hugely important

• great way to explore your data / introduce it to others

• make sure you show you data when possible

  • use geom_rug()
  • use geom_jitter()
  • if lots of points, then use alpha to mute them

Visualising Relationships

• scatter plots

  • encircling
  • jittering
  • using colour / size / shape
  • fitting lines
  • histograms and boxplots on the axes (and geom_rug())

• line plots

• correlation

Encircle

dslabs::stars

                star magnitude  temp type
1                Sun       4.8  5840    G
2            SiriusA       1.4  9620    A
3            Canopus      -3.1  7400    F
4           Arcturus      -0.4  4590    K
5     AlphaCentauriA       4.3  5840    G
6               Vega       0.5  9900    A
7            Capella      -0.6  5150    G
8              Rigel      -7.2 12140    B
9           ProcyonA       2.6  6580    F
10        Betelgeuse      -5.7  3200    M
11           Achemar      -2.4 20500    B
12             Hadar      -5.3 25500    B
13            Altair       2.2  8060    A
14         Aldebaran      -0.8  4130    K
15             Spica      -3.4 25500    B
16           Antares      -5.2  3340    M
17         Fomalhaut       2.0  9060    A
18            Pollux       1.0  4900    K
19             Deneb      -7.2  9340    A
20        BetaCrucis      -4.7 28000    B
21           Regulus      -0.8 13260    B
22             Acrux      -4.0 28000    B
23            Adhara      -5.2 23000    B
24            Shaula      -3.4 25500    B
25         Bellatrix      -4.3 23000    B
26            Castor       1.2  9620    A
27            Gacrux      -0.5  3750    M
28      BetaCentauri      -5.1 25500    B
29    AlphaCentauriB       5.8  4730    K
30           AlNa'ir      -1.1 15550    B
31       Miaplacidus      -0.6  9300    A
32            Elnath      -1.6 12400    B
33           Alnilam      -6.2 26950    B
34            Mirfak      -4.6  7700    F
35           Alnitak      -5.9 33600    O
36             Dubhe       0.2  4900    K
37            Alioth       0.4  9900    A
38           Peacock      -2.3 20500    B
39     KausAustralis      -0.3 11000    B
40      ThetaScorpii      -5.6  7400    F
41             Atria      -0.1  4590    K
42            Alkaid      -1.7 20500    B
43      AlphaCrucisB      -3.3 20500    B
44             Avior      -2.1  4900    K
45 DeltaCanisMajoris      -8.0  6100    F
46            Alhena       0.0  9900    A
47        Menkalinan       0.6  9340    A
48           Polaris      -4.6  6100    F
49            Mirzam      -4.8 25500    B
50   DeltaVulpeculae       0.6  9900    A
51  *ProximaCentauri      15.5  2670    M
52   *AlphaCentauriB       5.8  4900    K
53     Barnard'sStar      13.2  2800    M
54           Wolf359      16.7  2670    M
55           HD93735      10.5  3200    M
56           *L726-8      15.5  2670    M
57           *UVCeti      16.0  2670    M
58          *SiriusA       1.4  9620    A
59          *SiriusB      11.2 14800   DA
60           Ross154      13.1  2800    M
61           Ross248      14.8  2670    M
62    EpsilonEridani       6.1  4590    K
63           Ross128      13.5  2800    M
64            L789-6      14.5  2670    M
65     *GXAndromedae      10.4  3340    M
66     *GQAndromedae      13.4  2670    M
67       EpsilonIndi       7.0  4130    K
68         *61CygniA       7.6  4130    K
69         *61CygniB       8.4  3870    K
70      *Struve2398A      11.2  3070    M
71      *Struve2398B      11.9  2940    M
72           TauCeti       5.7  5150    G
73         *ProcyonA       2.6  6600    F
74         *ProcyonB      13.0  9700   DF
75      Lacaille9352       9.6  3340    M
76            G51-I5      17.0  2500    M
77            YZCeti      14.1  2670    M
78         BD+051668      11.9  2800    M
79      Lacaille8760       8.7  3340    K
80      KapteynsStar      10.9  3480    M
81        *Kruger60A      11.9  2940    M
82        *Kruger60B      13.3  2670    M
83         BD-124523      12.1  2940    M
84          Ross614A      13.1  2800    M
85          Wolf424A      15.0  2670    M
86   vanMaanen'sStar      14.2 13000   DB
87         TZArietis      14.0  2800    M
88          HD225213      10.3  3200    M
89            Altair       2.2  8060    A
90          ADLeonis      11.0  2940    M
91       *40EridaniA       6.0  4900    K
92       *40EridaniB      11.1 10000   DA
93       *40EridaniC      12.8  2940    M
94      *70OphiuchiA       5.8  4950    K
95      *70OphiuchiB       7.5  3870    K
96        EVLacertae      11.7  2800    M

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type))

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F)

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = dslabs::stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F)

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = dslabs::stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
  scale_x_log10()

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = dslabs::stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
  scale_x_log10() +
  annotate("text",
           x = c(15000, 5000),
           y = c(-4, 14),
           label = c("Type B Stars", "Faint Type M Stars"),
           col = c("blue", "olivedrab3"),
           family = "Ink Free",
           size = 4,
           fontface = 2)

Encircle

dslabs::stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = dslabs::stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
  scale_x_log10() +
  annotate("text",
           x = c(15000, 5000),
           y = c(-4, 14),
           label = c("Type B Stars", "Faint Type M Stars"),
           col = c("blue", "olivedrab3"),
           family = "Ink Free",
           size = 4,
           fontface = 2) +
  scale_color_viridis_d()

scatter <- HistData::Galton %>% 
  ggplot(aes(parent, child)) +
  geom_point()
jittered <- HistData::Galton %>% 
  ggplot(aes(parent, child)) + geom_jitter(width = 0.4, height = 0.4)
scatter + plot_spacer() + jittered 

Choice of Colours in R

• colours are very important

  • second only to position for perception

• can carry information

• also important to be visually pleasing

• worthwhile to make your figures aesthetically attractive

  • visualisations that are engaging are more effective

Types of Colour Scales

• qualitative

  • suite of colours that are easily distinguished
  • no heirarchy
  • caters for visual impairments

• sequential

  • band of colours that are increasingly intense
  • go from low to high

• diverging

  • suite of colours that go from minus to plus
  • contrasting colours at each end
  • something bland and neutral in the middle

Getting Colours in R

• some really great packages

  • RColorBrewer()

    • excellent package giving fine control over palette choice

  • viridis()

    • excels at palettes for vision-impaired readers

  • paletteer()

    • collection of palettes from various sources

  • wesanderson

    • names(wes_palettes) followed by wes_palette(“BottleRocket1”)

• more….

  • tvthemes()

    • not just colours, but layouts and fonts
    • everything from Game of Thrones to Spongebob (yes, really)

  • ggsci(), palettes for scientific publications (Lancet, AAAS, etc)

  • colorspace()

    • resources for picking colours
    • choose_color() and choose_palette()
    • can convert colours based on vision deficiencies
    • will convert from colour descriptions, e.g. hex2RGB()

• and a cheatsheet

Ways of Describing Colours

• by name: “red”, “cyan”, “violetred4”, “thistle”…..

  • get full list of 657 available in R from colors()

• by hex code: “#f49340”, “#40f9f9”, “#ee82ef”, “#d8bfd1”….

• by rgb values: (249, 67, 64), (64, 249, 249), (57, 14, 30), (216, 191, 209)….

• by hcl values: (53.24, 179.04, 12.17), (91.11, 72.10, 192.17), (32.36, 63.11, 349.86), (80.08, 20.79, 307.73)….

Investigating Colours in R

• the following code shows the first “N” colours in R where N is set to 20 here:

N <- 20
data.frame(col = colors()[1:N]) %>% 
  ggplot(aes(x = col, fill = col)) + 
  geom_bar(position = "stack", show.legend = F) + 
  coord_flip() + 
  theme_minimal() + 
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(), axis.text.y = element_blank())

Other Usful Functions

• show_col() from the scales package is super useful

  • e.g. show_col(“red”) or show_col(“#84a412”)

• rgb() will give a hex code for a fraction of red, green, blue

  • e.g. rgb(0.4, 0.2, 0.5) gives “#663380”

• colourPicker() from the colourpicker package

  • colourPicker(numCols = 4), opens up shiny app, returns colours

• col2rgb(), also col2hex() from the gplots package, and col2hcl from the jmw86069/jamba package

  • this last is on github, so you must install the package devtools then do install_github( jmw86069/jamba)

Some Websites and Tools

• coolors.co

  • will generate appropriate palettes

• colorpicker

• colorspace

• Chrome has an Eye Dropper tool

  • click on part of a webpage and it will tell you the colour

• Nice description of colurs from Stowers

Colours in ggplot()

• use for fill and for col aesthetics

• add the scale_fill… and scale_color… layers to control

• explore these by typing ?scale_fill and then TAB to see the range of options

Using Themes in R

• We’ll also discuss fonts (first).

• themes give fine control to the appearance of your plots

  • control over text size, fonts, text colour, etc
  • position of legends, titles, captions, etc
  • colours of backgrounds
  • delete unwanted features (like, say, tick marks on an axis)

• large number of preset themes

• several packages with neat bundle of useful themes

• and, of course, we can develop our own theme to have consistent graphics

Fonts

• we’ll discuss this first, as often themes require fonts which might not be present

• fonts are a whole world of their own

• see practicaltypography.com

• need to be in the system, load them into windows / mac / linux

  • only really works for true type fonts (.ttf)
  • go to folder where the R library lives and seek out fonts
  • e.g. R/x86_64-pc/3.6/tvthemes/fonts/SpongeBob
  • click on .ttf files to install

• then need to capture them in R

  • install package extrafont
  • run ttf_import() with path = folder from above
  • run fonts() to check available fonts
  • usually need to restart R (Session then Restart R)

• showtext package also useful

  • font_add(family = "Get Schwifty", regular = "fonts/get_schwifty.ttf")
  • showtext_auto()

• can also use google fonts (showtext::font_add_google("my_special_font"))

Complete Themes

• these set up ggplots with standard appearances

• can always adjust these, but do so in a layer after invoking the theme

• some defaults in ggplot2, see here

• you should experiment with these to see how they look

penguins %>% ggplot(aes(bill_length_mm, bill_depth_mm, col = species)) + 
  geom_point() +
  theme_classic()

penguins %>% ggplot(aes(bill_length_mm, bill_depth_mm, col = species)) + 
  geom_point() +
  theme_dark()

Complete Themes

• other package provide supplementary themes

  • ggthemes
  • ggthemr (see here)
  • tvthemes (see here)
  • hrbrthemes see here
  • firatheme (see here)
  • bbplot, themes from the BBC (see here)
  • ggtech themes from companies, e.g. Facebook. See here

• This website is pretty good on themes

• again, make sure you experiment with these

Fine Control Over Themes

• we can change any feature of a theme that we want
• type ?theme on the console to see available themes
• use element_&&&&& to replace theme
• example: theme(text = element_text(family = “Roboto Sans”))

penguins %>% ggplot(aes(bill_length_mm, bill_depth_mm, col = species)) + 
  geom_point() +
  theme(text = element_text(family = "Ink Free", size = 40, face = "bold"))

penguins %>% ggplot(aes(bill_length_mm, bill_depth_mm, col = species)) + 
  geom_point() +
  theme(legend.position = "bottom", axis.text.y = element_blank())

Workshop Week 5:

• you have a dataset with the counties of Ireland in one column and their populations in a second column. To produce a bar chart, should you use geom_col() or geom_bar()?

• make a bar chart of the number of counties in each of the five US midwest states. Use the midwest dataset from ggplot2

• make a bar chart of the number of each species of pengiun from the penguins dataset

• make a bar chart of the 12 Carnivora total sleep times from the msleep dataset in ggplot2

• make a lollipop plot of the 12 Primates total sleep times from the msleep dataset in ggplot2

• access a function we wrote from github (source("https://github.com/eugene100hickey/ATU-2026/raw/refs/heads/main/my_boxoffice.R")). Use the following commands to downloads box office receipts from this day last week:

boxoffice_date <- Sys.Date()-7
movies <- my_boxoffice(boxoffice_date) %>% 
     mutate(gross = gross / 1e3,
            movie_name = movie,
            movie = abbreviate(movie)) %>% 
     head()

Plot a pie chart of gross receipt for these top six films (see the R Graph Gallery)

Assignment - Week Five

You are tasked with reproducing the following figure:

Procedure

• You’ll need the tidyverse library and the dslabs library (install from CRAN by install.packages(“dslabs”))

• Get the dataset using data(“death_prob”)

• You’ll need to call ggplot setting data = death_prob

• There are three aesthetics; for age, prob, and sex

• Add the graph title, the axes labels, and add a caption

• The y axis should be plotted on a log scale

• There are also extra marks for improving the figure with your own ideas

• You can save your plot using ggsave(“my-first-assignment.png”) at the console or in your .R file, or by clicking Export in the plots pane of RStudio

Marking

• Correct call to ggplot to set up the figure framework (2 marks)

• Correct geom to insert the points (2 marks)

• Inserting the title, axes labels, and caption (2 marks)

• Making the y-axis on a log scale using scale_y_log10 (2 marks)

• Your improvement (2 marks)

Upload your work (the image and your code) to moodle at Week Five Assignment - death_prob. The deadline is midnight on Sunday 24th May 2026.