Data visualization with ggplot2

Cork R-User's Group - September 16th, 2015

Darren L Dahly

























































ggplot2 works with data frames

   library(ggplot2)
   head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Object class matters

   lapply(iris, class)

## $Sepal.Length
## [1] "numeric"
## 
## $Sepal.Width
## [1] "numeric"
## 
## $Petal.Length
## [1] "numeric"
## 
## $Petal.Width
## [1] "numeric"
## 
## $Species
## [1] "factor"

Minimal example

   ggplot(iris, aes(x = Sepal.Width)) +
   geom_bar()

plot of chunk first plot

 
   plot <- ggplot(iris, aes(x = Sepal.Width)) 
   plot <- plot + geom_bar()  

   plot

plot of chunk as object 2

Data

   ggplot(iris[1:10, ], aes(x = Sepal.Width)) +
   geom_bar()

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   ggplot(subset(iris, Species == "setosa"), aes(x = Sepal.Width)) +
   geom_bar()

plot of chunk subset data

 
   library(dplyr)

   iris %>%
   filter(Species == "virginica") %>%

   ggplot(aes(x = Sepal.Width)) +
   geom_bar()

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Class matters

  iris %>%
  mutate(Sepal.Width = factor(round(Sepal.Width, 0))) %>%

  ggplot(aes(x = Sepal.Width)) +
  geom_bar()

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  iris %>%
  mutate(Sepal.Width = round(Sepal.Width, 0)) %>%

  ggplot(aes(x = Sepal.Width)) +
  geom_bar()

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aes - Aesthetic Mapping

x-axis

y-axis

color/fill

shape/linetype

size

alpha

faceting

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point()

plot of chunk iris x y

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point()

plot of chunk iris color

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, size = Species)) +
  geom_point()

plot of chunk iris size

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, shape = Species)) +
  geom_point()

plot of chunk iris shape

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, 
                   shape = Species, color = Sepal.Length, size = Sepal.Width)) +
  geom_point()

plot of chunk iris max

 
  ggplot(mpg, aes(x = cty, y = hwy, shape = drv, color = class, size = cyl)) +
  geom_point()

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  ggplot(mpg, aes(x = cty, y = hwy, shape = drv, color = class)) +
  geom_point() +
  facet_wrap(~cyl)

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Layers

  ggplot(mpg, aes(x = cty, y = hwy, color = cyl)) +
  geom_point()

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  ggplot(mpg, aes(x = cty, y = hwy, color = cyl)) +
  geom_point(color = "red")

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  ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point(aes(color = cyl))

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  ggplot(mpg, aes(x = cty, y = hwy, color = cyl)) +
  geom_point() +
  geom_point(data = mpg[1:10, ], color = "red")

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Geometries

Scatter plots

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point()

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_jitter()

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_jitter(size = 3.5, alpha = 0.5)

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_jitter(size = 3.5, alpha = 0.5)

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_jitter(size = 3.5, alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE)

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_jitter(size = 3.5, alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE, color = "black")

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_jitter(size = 3.5, alpha = 0.5) +
  geom_smooth(method = "lm", se = FALSE) +
  geom_smooth(method = "lm", se = FALSE, color = "black")

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  ggplot(mpg, aes(x = drv, y = class)) +
  geom_jitter(alpha = 0.5, position = position_jitter(width = 0.2, 
                                                      height = 0.2))

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  ggplot(mpg, aes(x = drv, y = class, color = year)) +
  geom_jitter(position = position_jitter(width = 0.2, height = 0.2))

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  ggplot(mpg, aes(x = drv, y = class, color = factor(year))) +
  geom_jitter(position = position_jitter(width = 0.2, height = 0.2))

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Boxplots

 
  ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot()

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  ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_jitter() +
  geom_boxplot()

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  ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_jitter(position = position_jitter(width = 0.2, height = 0.1)) +
  geom_boxplot(alpha = 0.5)

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  ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species)) +
  geom_jitter(position = position_jitter(width = 0.2, height = 0.1), 
              outlier.shape = NA) +
  geom_boxplot(alpha = 0.5)

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  ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species, 
                   fill = Species)) +
  geom_jitter(position = position_jitter(width = 0.2, height = 0.1)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA)

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  ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species, 
                   fill = Species)) +
  geom_violin(alpha = 0.5)

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Line plots

 
  ggplot(economics, aes(x = date, y = pop)) +
  geom_line()

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  ggplot(Orange, aes(x = age, y = circumference)) +
  geom_line()

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  ggplot(Orange, aes(x = age, y = circumference, group = Tree)) +
  geom_line()

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  ggplot(Orange, aes(x = age, y = circumference, group = Tree, color = Tree)) +
  geom_line()

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  ggplot(Orange, aes(x = age, y = circumference, group = Tree, 
                     linetype = Tree, color = Tree)) +
  geom_line()

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  ggplot(Orange, aes(x = age, y = circumference, group = Tree, 
                     linetype = Tree, color = Tree)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

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  ggplot(Orange, aes(x = age, y = circumference, group = Tree, 
                     linetype = Tree, color = Tree)) +
  geom_point() +
  geom_smooth(se = FALSE)

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Bar plots

 
  ggplot(mpg, aes(x = factor(cyl))) +
  geom_bar()

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  ggplot(mpg, aes(x = factor(cyl))) +
  geom_bar() +
  coord_flip()

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  mpg %>%
  group_by(cyl) %>%
  summarize(total = n()) %>%
  mutate(cyl = factor(cyl)) %>%
  mutate(cyl = reorder(cyl, total, max)) 

## Source: local data frame [4 x 2]
## 
##   cyl total
## 1   4    81
## 2   5     4
## 3   6    79
## 4   8    70

  mpg %>%
  group_by(cyl) %>%
  summarize(total = n()) %>%
  mutate(cyl = factor(cyl)) %>%
  mutate(cyl = reorder(cyl, total, max)) %>%

  ggplot(aes(x = cyl, y = total)) +
  geom_bar(stat = "identity") +
  coord_flip()

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  ggplot(mpg, aes(x = interaction(factor(cyl), class))) +
  geom_bar() +
  coord_flip()

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  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar()

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  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar(position = "dodge")

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  mpg %>%
  group_by(cyl, class) %>%   
  summarize(subtotal = n()) %>%
  mutate(total = max(cumsum(subtotal))) %>%
  mutate(prop = subtotal/total) %>%
  head()

## Source: local data frame [6 x 5]
## Groups: cyl
## 
##   cyl      class subtotal total       prop
## 1   4    compact       32    81 0.39506173
## 2   4    midsize       16    81 0.19753086
## 3   4    minivan        1    81 0.01234568
## 4   4     pickup        3    81 0.03703704
## 5   4 subcompact       21    81 0.25925926
## 6   4        suv        8    81 0.09876543

  mpg %>%
  group_by(cyl, class) %>%   
  summarize(subtotal = n()) %>%
  mutate(total = max(cumsum(subtotal))) %>%
  mutate(prop = subtotal/total) %>%  

  ggplot(aes(x = factor(cyl), fill = class, y = prop)) +
  geom_bar(position = "stack", stat = "identity")

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  ggplot(mpg, aes(x = factor(cyl), y = cty)) +
  geom_bar(stat = "identity")

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  mpg %>%
  group_by(cyl) %>%
  summarize(meancty = mean(cty)) 

## Source: local data frame [4 x 2]
## 
##   cyl  meancty
## 1   4 21.01235
## 2   5 20.50000
## 3   6 16.21519
## 4   8 12.57143

  mpg %>%
  group_by(cyl) %>%
  summarize(meancty = mean(cty)) %>%

  ggplot(aes(x = factor(cyl), y = meancty)) +
  geom_bar(stat = "identity")

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Heat maps

 
  data_frame("ID" = c(1:50), 
             "A"  = sample(c(1:5), 50, replace = T),
             "B"  = sample(c(1:5), 50, replace = T),
             "C"  = sample(c(1:5), 50, replace = T),
             "D"  = sample(c(1:5), 50, replace = T),
             "E"  = sample(c(1:5), 50, replace = T),
             "G"  = sample(c(1:5), 50, replace = T)) %>% head()

## Source: local data frame [6 x 7]
## 
##   ID A B C D E G
## 1  1 3 2 1 1 3 4
## 2  2 5 4 1 4 2 3
## 3  3 5 3 3 3 2 3
## 4  4 2 4 3 1 1 2
## 5  5 4 4 1 5 1 5
## 6  6 4 1 3 1 2 3

  library(tidyr) 

  data_frame("ID" = c(1:50), 
             "A"  = sample(c(1:5), 50, replace = T),
             "B"  = sample(c(1:5), 50, replace = T),
             "C"  = sample(c(1:5), 50, replace = T),
             "D"  = sample(c(1:5), 50, replace = T),
             "E"  = sample(c(1:5), 50, replace = T),
             "G"  = sample(c(1:5), 50, replace = T)) %>% 
  gather(question, value, A:G)  %>% 
  head()

## Source: local data frame [6 x 3]
## 
##   ID question value
## 1  1        A     1
## 2  2        A     1
## 3  3        A     1
## 4  4        A     5
## 5  5        A     3
## 6  6        A     4

  data_frame("ID" = c(1:50), 
             "A"  = sample(c(1:5), 50, replace = T),
             "B"  = sample(c(1:5), 50, replace = T),
             "C"  = sample(c(1:5), 50, replace = T),
             "D"  = sample(c(1:5), 50, replace = T),
             "E"  = sample(c(1:5), 50, replace = T),
             "G"  = sample(c(1:5), 50, replace = T)) %>% 
  gather(question, value, A:G) %>%
  mutate(value = factor(value, 
                        labels = c("Weekly", 
                                    "Monthly/Quarterly", 
                                    "Yearly",
                                    "Not yet", 
                                    "Not my job")))  %>%
  head()

## Source: local data frame [6 x 3]
## 
##   ID question             value
## 1  1        A            Yearly
## 2  2        A        Not my job
## 3  3        A            Yearly
## 4  4        A Monthly/Quarterly
## 5  5        A            Yearly
## 6  6        A        Not my job

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Scales

 
  library(RColorBrewer) 

  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_fill_brewer(palette = "Set1")

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  library(RColorBrewer) 

  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_fill_brewer(palette = "Blues")

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_brewer(palette = "Set1")

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_brewer(palette = "Reds")

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 brewer.pal(6, "Reds")

## [1] "#FEE5D9" "#FCBBA1" "#FC9272" "#FB6A4A" "#DE2D26" "#A50F15"

 brewer.pal(6, "Reds")[4:6]

## [1] "#FB6A4A" "#DE2D26" "#A50F15"

  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_manual(values = brewer.pal(6, "Reds")[4:6])

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_manual(guide = FALSE, values = brewer.pal(6, "Reds")[4:6])

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ColorBrewer

 
  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_x_continuous(breaks = c(2, 3, 4, 5))

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_x_continuous(limit = c(3, 4))

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  ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_x_reverse()

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  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_x_discrete(limits = c("4", "6", "8"))

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Labels

 
  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_x_discrete(limits = c("4", "6", "8")) +
  ggtitle("Title") +
  ylab("Number of observations") +
  xlab("Number of cylinders")

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Themes

  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_x_discrete(limits = c("4", "6", "8")) +
  ggtitle("Title") +
  ylab("Number of observations") +
  xlab("Number of cylinders") +
  theme_bw()

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  ggplot(mpg, aes(x = factor(cyl), fill = class)) +
  geom_bar() +
  scale_x_discrete(limits = c("4", "6", "8")) +
  ggtitle("Title") +
  ylab("Number of observations") +
  xlab("Number of cylinders") +
  theme_bw() +
  theme(panel.grid = element_blank(),
            panel.border = element_blank(),
            axis.text.x = element_text(angle = 45, hjust = 1))

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  ggplot(data, aes(x = question, y = as.factor(ID))) + 
  geom_tile(aes(fill = value)) +
  ylab("Each row is a person (n = 500)") +
  xlab("Survey Question") +
  scale_y_discrete(labels = "") +
  theme_bw() + 
  theme(text = element_text (color = "black", family = "serif"), 
        strip.background = element_blank(),
        panel.border = element_blank(), 
        panel.grid = element_blank(),
        axis.text.x = element_text (angle = 90), 
        axis.ticks.y = element_blank()) +
  scale_fill_brewer("", palette = "RdBu")

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Themes

Summary

  • Data
  • Map variables to visual properties (aesthetics)
  • Choose useful geometries
  • Take advantage of layers
  • Modify scales as needed
  • Add labels
  • Customize your theme