Chapter 4 Data Visuallization II

9월 17일 목요일, 202AIE17 송채은

1. Annotations 주석

1) Adding Text Annotations

annotations are extra contextual information

head(faithful)

##   eruptions waiting
## 1     3.600      79
## 2     1.800      54
## 3     3.333      74
## 4     2.283      62
## 5     4.533      85
## 6     2.883      55

library(ggplot2)

p <- ggplot(faithful, aes(eruptions, waiting)) + geom_point()

p

annotate() function can be used to add any type of geometric object

p +
  annotate("text", x = 3, y = 48, label = "Group 1") +
  annotate("text", x = 4.5, y = 66, label = "Group 2")

2) Adding Lines

library(gcookbook)

head(heightweight)

##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0

hw_plot <- ggplot(heightweight, aes(ageYear, heightIn, color = sex)) +
  geom_point()

hw_plot

geom_hline(yintercept = y) adds horizontal line at y

hw_plot +
  geom_hline(yintercept = 60)

geom_vline(xintercept = x) adds horizontal line at x

hw_plot +
  geom_vline(xintercept = 14)

hw_plot +
  geom_hline(yintercept = 60) +
  geom_vline(xintercept = 14)

geom_abline(intercept = i, slope = s) adds horizontal line with y = i + s*x

hw_plot +
  geom_abline(intercept = 37.4, slope = 1.75)

mpg

## # A tibble: 234 x 11
##    manufacturer model      displ  year   cyl trans      drv     cty   hwy fl    class  
##    <chr>        <chr>      <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr>  
##  1 audi         a4           1.8  1999     4 auto(l5)   f        18    29 p     compact
##  2 audi         a4           1.8  1999     4 manual(m5) f        21    29 p     compact
##  3 audi         a4           2    2008     4 manual(m6) f        20    31 p     compact
##  4 audi         a4           2    2008     4 auto(av)   f        21    30 p     compact
##  5 audi         a4           2.8  1999     6 auto(l5)   f        16    26 p     compact
##  6 audi         a4           2.8  1999     6 manual(m5) f        18    26 p     compact
##  7 audi         a4           3.1  2008     6 auto(av)   f        18    27 p     compact
##  8 audi         a4 quattro   1.8  1999     4 manual(m5) 4        18    26 p     compact
##  9 audi         a4 quattro   1.8  1999     4 auto(l5)   4        16    25 p     compact
## 10 audi         a4 quattro   2    2008     4 manual(m6) 4        20    28 p     compact
## # ... with 224 more rows

2. Axes 축

1) Swapping X- and Y-Axes

ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot()

coord_flip() flips the axes

ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  coord_flip()

ggplot(mpg, aes(x = hwy, y = model)) +
  geom_boxplot()

2) Setting the Position of Tick Marks

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point()

“breaks” sets the tick mark

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  scale_x_continuous(breaks = c(2,4,6)) +
  scale_y_continuous(breaks = c(15, 25, 35, 45))

3) Changing the Text of Tick Labels

“labels” sets the tick labels

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  scale_x_continuous(breaks = c(2,4,6), labels = c("2 cylinders", "4 cylinders", "6 cylinders")) +
  scale_y_continuous(breaks = c(15, 25, 35, 45))

4) Changing the Appearance of Tick Labels

theme() can rotate your tick labels

ggplot(mpg, aes(model, hwy)) + geom_boxplot()

ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 30))

ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90))

3. Using Colors in Plots

1) Setting and Mapping the Colors of Objects

• setting : aesthetics to a constant
• mapping : aesthetics to a variable

setting : fix the value of aesthetics to a constant value

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(color = "blue") 

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

mapping : use different colors depending on the value of the variable

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(aes(color = drv)) 

2) Using a Different Palette for a Discrete Variable

To use different color scheme, color palettes are available from the RColorBrewer package

library(RColorBrewer)

display.brewer.all() generates available palette

display.brewer.all()

library(gcookbook)

hw_splot <- ggplot(heightweight, aes(ageYear, heightIn, color = sex)) +
  geom_point()

hw_splot

hw_splot + 
  scale_colour_brewer(palette = "Oranges")

hw_splot + 
  scale_colour_brewer(palette = "Set3")

3) Using a Manually Defined Palette for a Discrete Variable

scale_colour_manual() sets the values of color

hw_splot +
  scale_colour_manual(values = c("red", "blue"))

4) Using a Manuallly Defined Palette for a Continuous Variable

hw_plot2 <- ggplot(heightweight, aes(ageYear, heightIn, color = weightLb)) +
  geom_point(size = 3)

hw_plot2

scale_colour_gradient() sets the low and high values of a color gradient

hw_plot2 +
  scale_colour_gradient(low = "yellow", high = "orange")

4. Legends

pg_plot <- ggplot(PlantGrowth, aes(group, weight, fill = group)) +
  geom_boxplot()

pg_plot

Use labs() and set the value of fill, colour, shape, or whatever aesthetic is appropriate for the legend

pg_plot + 
  labs(fill = "Condition")

labs() sets the title, subtitle, caption, x-axis label, y-axis label, and the title of the legend

pg_plot + 
  labs(title = "Weight of Plants", 
       subtitle = "By Experimental Conditions", 
       caption = "source: PlantGrowth", 
       x = "Experimental Conditions", 
       y = "Weight (pounds)", 
       fill = "Condition")

pg_plot +
  theme(legend.position = "none")

pg_plot +
  theme(legend.position = "bottom")

pg_plot +
  theme(legend.position = "left")

pg_plot +
  theme(legend.position = c(.8, .3))

4.1 Exercise 3-1

head(heightweight)

##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0

ggplot(heightweight, aes(ageYear, heightIn, size = weightLb, color = sex)) +
  geom_point()

ggplot(heightweight, aes(ageYear, heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3)

ggplot(heightweight, aes(ageYear, heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3) +
  labs(title = "Height and weight of school children", 
       subtitle = "Height vs Weight", 
       caption = "Source: heightweight", 
       x = "Age (year)", 
       y = "Height (inches)", 
       size = "Weight (Lb)", 
       color = "Gender")

ggplot(heightweight, aes(x = ageYear, y = heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3) +
  labs(title = "Height and weight of school children", 
       subtitle = "Height vs Weight", 
       caption = "Source: heightweight", 
       x = "Age (year)", 
       y = "Height (inches)", 
       size = "Weight (Lb)", 
       color = "Gender") +
  theme_classic()

4.2 Exercise 3-2

head(heightweight)

##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0

geom_histogram() displays a histogram to display the distribution of a variable

ggplot(heightweight, aes(heightIn)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(heightweight, aes(weightLb, fill = sex)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(heightweight, aes(x = weightLb, fill = sex)) +
  geom_histogram(alpha = 0.4) + 
  scale_fill_manual(values = c("orange", "yellow"))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(heightweight, aes(x = weightLb, fill = sex)) +
  geom_histogram(alpha = 0.4) + 
  scale_fill_manual(values = c("orange", "yellow")) +
  labs(title = "A histogram of the weight of school children", 
       subtitle = "By gender", 
       x = "Weight (Lb)", 
       y = "Count", 
       fill = "Gender") + 
  theme_minimal()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.