R: Data visualization: ggplot

# (for personal learning, anyone can take it as a reference)

1. Introduction

it has four layers: 1) data 2) aesthetics 3) geometries 4) theme

aes() 

aes = aesthetics

-- color, size, shape

library(ggplot2)
ggplot2(mtcars, aes(wt, mpg, color =disp, size = disp)) +
    geom_point()

geom_*()

geom = geometries

2. Aesthetics

  2.1 visible aesthetics

notes: shape and size can be only used for categorical data

e.g.

​​​​ggplot(mtcars, aes(wt, mpg, color = fcyl)) +
  # Set the shape and size of the points
  geom_point(shape = 1, size = 4)

str(diamonds)
ggplot(diamonds, aes(carat, price, color = clarity)) +
    geom_point(alpha = 0.4) +
    geom_smooth()

geom_point(): adds points (as in a scatter plot), 可以在里面添加内容, alpha = * (* ranges 0-1)

geom_smooth(): adds a smooth trend curve3

e.g.

# Establish the base layer
plt_mpg_vs_wt <- ggplot(mtcars, aes(x=wt, y=mpg)) 
# Map fcyl to size
plt_mpg_vs_wt +
  geom_point(aes(size = fcyl))
  

e.g.

# Base layer
plt_mpg_vs_wt <- ggplot(mtcars, aes(wt, mpg))
# Use text layer and map fcyl to label
plt_mpg_vs_wt +
  geom_text(aes(label = fcyl))

geom_text(aes(label=***)): 直接不显示point，而是显示值

2.2  position, scale, lab, color, lim, etc functions

position

--- identity, dodge, stack, fill, jitter, jitterdodge, nudge

1) position = “identify” (default)

2) position = "jitter"

we can first define "*** <- position_jitter()" (arugment), then "position = ***" as a function

3) position = "fill"

the y-axis will be filled

scale functions

scale_x_*(), scale_y_*()

scale_color_*()

scale_fill_*()

scale_linetype_*()

scale_size_*()

......

lab, color

添加标签

  labs(x = "Number of Cylinders", y = "Count")

设置填充颜色和位置 

palette <- c(automatic = "#377EB8", manual = "#E41A1C")
# Set the position
ggplot(mtcars, aes(fcyl, fill = fam)) +
  geom_bar(position = "dodge") +
  labs(x = "Number of Cylinders", y = "Count")
  scale_fill_manual("Transmission", values = palette)

make univariate plot

add a fake axis by mapping x/y to zero

ggplot(mtcars, aes(mpg, 0)) +
  geom_jitter()

设置图片上下限

for example, set the limit of the y-axis to (-2, 2)

+ ylim(-2, 2)

2.3 Form follows function

1) Function

- primary

• accurate and efficient representations

- secondary

• visually appealing, beautiful plots 

2) Guiding principles

- never

• misrepresent or obscure data
• confuse viewers with complexity

- always

• consider the audience and purpose of every plot

2.4 The best choices for aesthetics

• efficient: provides a faster overview than numeric summaries
• accurate: minimizes information loss

for continuous variable

for categorical variables

color is always a good choice for categorical data

3. Geometries

3.1 overview

48 geometries

3.2 scatter plot

3.3 bar plot

geom_histogram():

geom_bar(): count the number of cases at each x position

geom_col(): plot actual values

3.4 line plot

linetype aesthetic

size aesthetic

color aesthetic

 Fill aesthetic with geom_area()

Using position = "fill"

geom_ribbon(）

force the y-min to be 0

4. Themes 

4.1 themes from scratch

the theme layer controls all the non-data ink on the plot

three types:

arguments of theme layer function:

1) text elements

 

2)  line elements

3) rect elements

 4) element_blank()

to remove any items

例题：

T1. position

---- remove the legend

plt_prop_unemployed_over_time +
  theme(legend. Position = "none")

---  Update the plot to position the legend at the bottom of the plot

plt_prop_unemployed_over_time +
  theme(legend. Position = "bottom")

--- Position the legend inside the plot, with x-position 0.6 and y-position 0.1.

plt_prop_unemployed_over_time +
  theme(legend. Position = c(0.6, 0.1))

T2: element_xxx

plt_prop_unemployed_over_time +
  theme(
    # For all rectangles, set the fill color to grey92
    rect = element_rect(fill = "grey92"),
    # For the legend key, turn off the outline
    legend.key = element_rect(color = NA)
    # Turn off axis ticks
    axis.ticks = element_blank(),
    # Turn off the panel grid
    panel.grid = element_blank()
    # Add major y-axis panel grid lines back
    panel.grid.major.y = element_line(
      # Set the color to white
      color = "white",
      # Set the size to 0.5
      size = 0.5,
      # Set the line type to dotted
      linetype = "dotted"
    ),
    # Set the axis text color to grey25
    axis.text = element_text(color = "grey25"),
    # Set the plot title font face to italic and font size to 16
   plot.title = element_text(size = 16, face = "italic")
  )

T3:  unit and margin

To set a single whitespace value, use unit(x, unit), where x is the amount and unit is the unit of measure.

Borders require you to set 4 positions, so use margin(top, right, bottom, left, unit). To remember the margin order, think TRouBLe.

for example:

• Give the axis tick length, axis.ticks.length, a unit of 2 "lines". 

plt_mpg_vs_wt_by_cyl +
  theme(
    # Set the axis tick length to 2 lines
    axis.ticks.length = unit(2, "lines")
  )

• Set the legend.margin to 20 points ("pt") on the top, 30 pts on the right, 40pts on the bottom, and 50pts on the left.

plt_mpg_vs_wt_by_cyl +
  theme(
    # Set the legend margin to (20, 30, 40, 50) points
    legend.margin = margin(20, 30, 40, 50,"pt")
  )

4.2 theme flexibility

define the theme -> reuse the theme

ways to use themes

• From scratch
• Theme layer object
• built-in themes (packages: ggplot2 or ggthemes)
• update / set default theme

using built-in themes

theme_*()

z +
    theme_classic()

library(ggthemes)
z +
    theme_tufte()

the tufte theme removes all non-data ink and sets the font to a serif typeface 

合并主题

theme_tufte_recession <- theme_tufte() + theme_recession

将主题设成默认(无需+theme了) 

theme_set(theme_tufte_recession)

4.3 effective explanatory plots

use intuitive and attractive geoms / add text labels to plot / remove non-data ink / add threshold line / add informative text

For example:

T1: using geoms to explanatory plot

初步设置

# Add a geom_segment() layer
ggplot(gm2007, aes(x = lifeExp, y = country, color = lifeExp)) +
  geom_point(size = 4) +
  geom_segment(aes(xend = 30, yend = country), size = 2)

继续美化 

( 1.添加label 2.设置scales, 3. 添加title和caption）

# Set the color scale
palette <- brewer.pal(5, "RdYlBu")[-(2:4)]

ggplot(gm2007, aes(x = lifeExp, y = country, color = lifeExp)) +
  geom_point(size = 4) +
  geom_segment(aes(xend = 30, yend = country), size = 2) +
  geom_text(aes(label = round(lifeExp,1)), color = "white", size = 1.5) +
  scale_x_continuous("", expand = c(0,0), limits = c(30,90), position = "top") +
  scale_color_gradientn(colors = palette) +
  labs(title = "Highest and lowest life expectancies, 2007", caption = "Source: gapminder")

T2: using annotate() for embellishment

1) add a vertical line

# Add a vertical line
plt_country_vs_lifeExp +
  step_1_themes +
  geom_vline(xintercept = global_mean, color = "grey40", linetype = 3)

2) add a "text" geom as an annotation

plt_country_vs_lifeExp +
  step_1_themes +
  geom_vline(xintercept = global_mean, color = "grey40", linetype = 3) +
  annotate(
    "text",
    x = x_start, y = y_start,
    label = "The\nglobal\naverage",
    vjust = 1, size = 3, color = "grey40"
  )

3) annotate the plot with an arrow connecting your text to the line

plt_country_vs_lifeExp +  
  step_1_themes +
  geom_vline(xintercept = global_mean, color = "grey40", linetype = 3) +
  step_3_annotation +
  annotate(
    "curve",
    x = x_start, y = y_start,
    xend = x_end, yend = y_end,
    arrow = arrow(length = unit(0.2, "cm"), type = "closed"),
    color = "grey40"
  )