+ - 0:00:00
Notes for current slide

Welcome to the workshop on ggplot.

Where we'll show you how to create impressive data visualisations.

Notes for next slide

NHS Workshop
Introduction to ggplot

colours and facets

Eugene Hickey
January 21st 2021

Graphic by Elaine Hickey

Welcome to the workshop on ggplot.

Where we'll show you how to create impressive data visualisations.

intro-ggplot-nhs — Eugene Hickey

Choice of Colours in R


We'll also discuss faceting.

  • 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

intro-ggplot-nhs — Eugene Hickey

Types of Colour Scales

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

  • band of colours, increasingly intense
  • go from low to high

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

intro-ggplot-nhs — Eugene Hickey

Getting Colours in R

  • some really great packages
    • RColorBrewer
      • excellent, 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")
    • rtist
      • lifts principle colours from paintings

intro-ggplot-nhs — Eugene Hickey

library(rtist)
par(mfrow = c(3, 5))
map(names(rtist_palettes), function(x) print(rtist_palette(x)))

intro-ggplot-nhs — Eugene Hickey

  • 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

intro-ggplot-nhs — Eugene Hickey

intro-ggplot-nhs — Eugene Hickey

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".... (see htmlcolors])
  • by rgb values: (249, 67, 64), (64, 249, 249), (57, 14, 30), (216, 191, 209)....
    • note, rgb() function takes these as a fraction from 0-1
  • 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)....

intro-ggplot-nhs — Eugene Hickey

Investigating Colours in R

  • the following code shows the first "N" colours in R where N is set to 10 here:
N <- 10
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())

intro-ggplot-nhs — Eugene Hickey

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"

intro-ggplot-nhs — Eugene Hickey

  • colourPicker() from the colourpicker package
    • colourPicker(numCols = 4), opens up shiny app, returns colours

  • col2rgb(), also col2hex() from the gplots (not ggplot2) 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)

  • colorfindr takes an image and identifies major colours

intro-ggplot-nhs — Eugene Hickey

## # A tibble: 6 x 3
##   col_hex col_freq col_share
##   <chr>      <int>     <dbl>
## 1 #F9C6CB     1511   0.0300 
## 2 #7183C1      508   0.0101 
## 3 #268EB3      259   0.00515
## 4 #AFB7DE      244   0.00485
## 5 #6F81BF       99   0.00197
## 6 #FAC7CC       99   0.00197

intro-ggplot-nhs — Eugene Hickey

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

intro-ggplot-nhs — Eugene Hickey

intro-ggplot-nhs — Eugene Hickey

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

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_brewer(type = 'qual')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_brewer(type = 'div')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_viridis_d(option = 'magma')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('rtist::warhol')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('MapPalettes::irish_flag')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('yarrr::southpark')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('dutchmasters::pearl_earring')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('dutchmasters::view_of_Delft')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('ghibli::KikiLight')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('LaCroixColoR::Coconut')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('LaCroixColoR::PassionFruit')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('ggsci::lanonc_lancet')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('ggsci::default_uchicago')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  group_by(continent, year) %>%
  summarise(average_fertility =
              mean(fertility, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x = year,
             y = average_fertility,
             col = continent)) +
  geom_line(size = 2) +
  scale_color_paletteer_d('ggsci::uniform_startrek')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  ggplot(aes(x = life_expectancy,
             fill = ..x..)) +
  geom_histogram() +
  scale_fill_continuous(type = 'viridis')

intro-ggplot-nhs — Eugene Hickey

dslabs::gapminder %>%
  ggplot(aes(x = life_expectancy,
             fill = ..x..)) +
  geom_histogram() +
  scale_fill_continuous(type = 'viridis') +
  scale_fill_gradient2(low = "darkgreen",
                       mid = "white",
                       high = "firebrick4",
                       midpoint = 65)

intro-ggplot-nhs — Eugene Hickey

faceting

  • Faceting means producing multiple panels of a plot
  • Splits a plot into several versions based on a categorical variable
  • functions facet_wrap() and facet_grid()
  • useful when lots of data in different subsets
  • important to keep axis scales the same

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats   ### cats dataset from MASS
##     Sex Bwt  Hwt
## 1     F 2.0  7.0
## 2     F 2.0  7.4
## 3     F 2.0  9.5
## 4     F 2.1  7.2
## 5     F 2.1  7.3
## 6     F 2.1  7.6
## 7     F 2.1  8.1
## 8     F 2.1  8.2
## 9     F 2.1  8.3
## 10    F 2.1  8.5
## 11    F 2.1  8.7
## 12    F 2.1  9.8
## 13    F 2.2  7.1
## 14    F 2.2  8.7
## 15    F 2.2  9.1
## 16    F 2.2  9.7
## 17    F 2.2 10.9
## 18    F 2.2 11.0
## 19    F 2.3  7.3
## 20    F 2.3  7.9
## 21    F 2.3  8.4
## 22    F 2.3  9.0
## 23    F 2.3  9.0
## 24    F 2.3  9.5
## 25    F 2.3  9.6
## 26    F 2.3  9.7
## 27    F 2.3 10.1
## 28    F 2.3 10.1
## 29    F 2.3 10.6
## 30    F 2.3 11.2
## 31    F 2.4  6.3
## 32    F 2.4  8.7
## 33    F 2.4  8.8
## 34    F 2.4 10.2
## 35    F 2.5  9.0
## 36    F 2.5 10.9
## 37    F 2.6  8.7
## 38    F 2.6 10.1
## 39    F 2.6 10.1
## 40    F 2.7  8.5
## 41    F 2.7 10.2
## 42    F 2.7 10.8
## 43    F 2.9  9.9
## 44    F 2.9 10.1
## 45    F 2.9 10.1
## 46    F 3.0 10.6
## 47    F 3.0 13.0
## 48    M 2.0  6.5
## 49    M 2.0  6.5
## 50    M 2.1 10.1
## 51    M 2.2  7.2
## 52    M 2.2  7.6
## 53    M 2.2  7.9
## 54    M 2.2  8.5
## 55    M 2.2  9.1
## 56    M 2.2  9.6
## 57    M 2.2  9.6
## 58    M 2.2 10.7
## 59    M 2.3  9.6
## 60    M 2.4  7.3
## 61    M 2.4  7.9
## 62    M 2.4  7.9
## 63    M 2.4  9.1
## 64    M 2.4  9.3
## 65    M 2.5  7.9
## 66    M 2.5  8.6
## 67    M 2.5  8.8
## 68    M 2.5  8.8
## 69    M 2.5  9.3
## 70    M 2.5 11.0
## 71    M 2.5 12.7
## 72    M 2.5 12.7
## 73    M 2.6  7.7
## 74    M 2.6  8.3
## 75    M 2.6  9.4
## 76    M 2.6  9.4
## 77    M 2.6 10.5
## 78    M 2.6 11.5
## 79    M 2.7  8.0
## 80    M 2.7  9.0
## 81    M 2.7  9.6
## 82    M 2.7  9.6
## 83    M 2.7  9.8
## 84    M 2.7 10.4
## 85    M 2.7 11.1
## 86    M 2.7 12.0
## 87    M 2.7 12.5
## 88    M 2.8  9.1
## 89    M 2.8 10.0
## 90    M 2.8 10.2
## 91    M 2.8 11.4
## 92    M 2.8 12.0
## 93    M 2.8 13.3
## 94    M 2.8 13.5
## 95    M 2.9  9.4
## 96    M 2.9 10.1
## 97    M 2.9 10.6
## 98    M 2.9 11.3
## 99    M 2.9 11.8
## 100   M 3.0 10.0
## 101   M 3.0 10.4
## 102   M 3.0 10.6
## 103   M 3.0 11.6
## 104   M 3.0 12.2
## 105   M 3.0 12.4
## 106   M 3.0 12.7
## 107   M 3.0 13.3
## 108   M 3.0 13.8
## 109   M 3.1  9.9
## 110   M 3.1 11.5
## 111   M 3.1 12.1
## 112   M 3.1 12.5
## 113   M 3.1 13.0
## 114   M 3.1 14.3
## 115   M 3.2 11.6
## 116   M 3.2 11.9
## 117   M 3.2 12.3
## 118   M 3.2 13.0
## 119   M 3.2 13.5
## 120   M 3.2 13.6
## 121   M 3.3 11.5
## 122   M 3.3 12.0
## 123   M 3.3 14.1
## 124   M 3.3 14.9
## 125   M 3.3 15.4
## 126   M 3.4 11.2
## 127   M 3.4 12.2
## 128   M 3.4 12.4
## 129   M 3.4 12.8
## 130   M 3.4 14.4
## 131   M 3.5 11.7
## 132   M 3.5 12.9
## 133   M 3.5 15.6
## 134   M 3.5 15.7
## 135   M 3.5 17.2
## 136   M 3.6 11.8
## 137   M 3.6 13.3
## 138   M 3.6 14.8
## 139   M 3.6 15.0
## 140   M 3.7 11.0
## 141   M 3.8 14.8
## 142   M 3.8 16.8
## 143   M 3.9 14.4
## 144   M 3.9 20.5

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt))

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point()

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point() +
  geom_smooth(aes(col = Sex), show.legend = F, se = F)

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point() +
  geom_smooth(aes(col = Sex), show.legend = F, se = F) +
  facet_grid(~Sex, labeller = labeller(Sex = sex))

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point() +
  geom_smooth(aes(col = Sex), show.legend = F, se = F) +
  facet_grid(~Sex, labeller = labeller(Sex = sex)) +
  labs(x = "Bodyweight (kg)", y = "Heart Weight (g)")

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point() +
  geom_smooth(aes(col = Sex), show.legend = F, se = F) +
  facet_grid(~Sex, labeller = labeller(Sex = sex)) +
  labs(x = "Bodyweight (kg)", y = "Heart Weight (g)") +
  theme_minimal()

intro-ggplot-nhs — Eugene Hickey

sex <- c("Female", "Male")
names(sex) <- c("F", "M")
cats %>%  ### cats dataset from MASS
  ggplot(aes(Bwt, Hwt)) +
  geom_point() +
  geom_smooth(aes(col = Sex), show.legend = F, se = F) +
  facet_grid(~Sex, labeller = labeller(Sex = sex)) +
  labs(x = "Bodyweight (kg)", y = "Heart Weight (g)") +
  theme_minimal() +
  theme(strip.background = element_blank(),
        text = element_text(size = 32,
                            family = "Ink Free"))

intro-ggplot-nhs — Eugene Hickey

movielens %>%  ### movielens dataset from dslabs
  mutate(genres = str_replace(genres, pattern = "\\|.*", "")) %>%
  filter(genres != "(no genres listed)") %>%
  ggplot(aes(rating)) +
  stat_density(position = "identity", geom = "line", adjust = 3)

intro-ggplot-nhs — Eugene Hickey

movielens %>%  ### movielens dataset from dslabs
  mutate(genres = str_replace(genres, pattern = "\\|.*", "")) %>%
  filter(genres != "(no genres listed)") %>%
  ggplot(aes(rating)) +
  stat_density(position = "identity", geom = "line", adjust = 3) +
  facet_wrap(.~genres, strip.position = "bottom", nrow = 3)

intro-ggplot-nhs — Eugene Hickey

movielens %>%  ### movielens dataset from dslabs
  mutate(genres = str_replace(genres, pattern = "\\|.*", "")) %>%
  filter(genres != "(no genres listed)") %>%
  ggplot(aes(rating)) +
  stat_density(position = "identity", geom = "line", adjust = 3) +
  facet_wrap(.~genres, strip.position = "bottom", nrow = 3) +
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

intro-ggplot-nhs — Eugene Hickey

## snails dataset from the MASS library
snails
##    Species Exposure Rel.Hum Temp Deaths  N
## 1        A        1    60.0   10      0 20
## 2        A        1    60.0   15      0 20
## 3        A        1    60.0   20      0 20
## 4        A        1    65.8   10      0 20
## 5        A        1    65.8   15      0 20
## 6        A        1    65.8   20      0 20
## 7        A        1    70.5   10      0 20
## 8        A        1    70.5   15      0 20
## 9        A        1    70.5   20      0 20
## 10       A        1    75.8   10      0 20
## 11       A        1    75.8   15      0 20
## 12       A        1    75.8   20      0 20
## 13       A        2    60.0   10      0 20
## 14       A        2    60.0   15      1 20
## 15       A        2    60.0   20      1 20
## 16       A        2    65.8   10      0 20
## 17       A        2    65.8   15      1 20
## 18       A        2    65.8   20      0 20
## 19       A        2    70.5   10      0 20
## 20       A        2    70.5   15      0 20
## 21       A        2    70.5   20      0 20
## 22       A        2    75.8   10      0 20
## 23       A        2    75.8   15      0 20
## 24       A        2    75.8   20      0 20
## 25       A        3    60.0   10      1 20
## 26       A        3    60.0   15      4 20
## 27       A        3    60.0   20      5 20
## 28       A        3    65.8   10      0 20
## 29       A        3    65.8   15      2 20
## 30       A        3    65.8   20      4 20
## 31       A        3    70.5   10      0 20
## 32       A        3    70.5   15      2 20
## 33       A        3    70.5   20      3 20
## 34       A        3    75.8   10      0 20
## 35       A        3    75.8   15      1 20
## 36       A        3    75.8   20      2 20
## 37       A        4    60.0   10      7 20
## 38       A        4    60.0   15      7 20
## 39       A        4    60.0   20      7 20
## 40       A        4    65.8   10      4 20
## 41       A        4    65.8   15      4 20
## 42       A        4    65.8   20      7 20
## 43       A        4    70.5   10      3 20
## 44       A        4    70.5   15      3 20
## 45       A        4    70.5   20      5 20
## 46       A        4    75.8   10      2 20
## 47       A        4    75.8   15      3 20
## 48       A        4    75.8   20      3 20
## 49       B        1    60.0   10      0 20
## 50       B        1    60.0   15      0 20
## 51       B        1    60.0   20      0 20
## 52       B        1    65.8   10      0 20
## 53       B        1    65.8   15      0 20
## 54       B        1    65.8   20      0 20
## 55       B        1    70.5   10      0 20
## 56       B        1    70.5   15      0 20
## 57       B        1    70.5   20      0 20
## 58       B        1    75.8   10      0 20
## 59       B        1    75.8   15      0 20
## 60       B        1    75.8   20      0 20
## 61       B        2    60.0   10      0 20
## 62       B        2    60.0   15      3 20
## 63       B        2    60.0   20      2 20
## 64       B        2    65.8   10      0 20
## 65       B        2    65.8   15      2 20
## 66       B        2    65.8   20      1 20
## 67       B        2    70.5   10      0 20
## 68       B        2    70.5   15      0 20
## 69       B        2    70.5   20      1 20
## 70       B        2    75.8   10      1 20
## 71       B        2    75.8   15      0 20
## 72       B        2    75.8   20      1 20
## 73       B        3    60.0   10      7 20
## 74       B        3    60.0   15     11 20
## 75       B        3    60.0   20     11 20
## 76       B        3    65.8   10      4 20
## 77       B        3    65.8   15      5 20
## 78       B        3    65.8   20      9 20
## 79       B        3    70.5   10      2 20
## 80       B        3    70.5   15      4 20
## 81       B        3    70.5   20      6 20
## 82       B        3    75.8   10      2 20
## 83       B        3    75.8   15      3 20
## 84       B        3    75.8   20      5 20
## 85       B        4    60.0   10     12 20
## 86       B        4    60.0   15     14 20
## 87       B        4    60.0   20     16 20
## 88       B        4    65.8   10     10 20
## 89       B        4    65.8   15     12 20
## 90       B        4    65.8   20     12 20
## 91       B        4    70.5   10      5 20
## 92       B        4    70.5   15      7 20
## 93       B        4    70.5   20      9 20
## 94       B        4    75.8   10      4 20
## 95       B        4    75.8   15      5 20
## 96       B        4    75.8   20      7 20

intro-ggplot-nhs — Eugene Hickey

## snails dataset from the MASS library
snails %>%
  ggplot(aes(Exposure, Deaths, col = factor(Rel.Hum))) +
  geom_line() +
  geom_point() +
  theme(legend.title = element_blank())

intro-ggplot-nhs — Eugene Hickey

## snails dataset from the MASS library
snails %>%
  ggplot(aes(Exposure, Deaths, col = factor(Rel.Hum))) +
  geom_line() +
  geom_point() +
  theme(legend.title = element_blank()) +
  facet_grid(Species ~ Temp,
             scales = "free")

intro-ggplot-nhs — Eugene Hickey

intro-ggplot-nhs — Eugene Hickey

Choice of Colours in R


We'll also discuss faceting.

  • 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
Paused

Help

Keyboard shortcuts

↑, ←, Pg Up, k Go to previous slide
↓, →, Pg Dn, Space, j Go to next slide
Home Go to first slide
End Go to last slide
Number + Return Go to specific slide
b / m / f Toggle blackout / mirrored / fullscreen mode
c Clone slideshow
p Toggle presenter mode
t Restart the presentation timer
?, h Toggle this help
oTile View: Overview of Slides
Esc Back to slideshow