R package:[ggplot2]

using ggplot2 to draw point,line,histogram ect

packages
Author

Tony Duan

Published

July 8, 2023

Code 
library(tidyverse)
library(ggplot2)
library(gapminder)
library(ggpubr)
library(ggthemr)
Code 
data001=gapminder
head(data001)
# A tibble: 6 × 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.
Code 
data002= data001 %>% filter(year==1997)

1 dot chart

ggplot(data,aex(x=,y=,color=))+geom_xxxx()

Code 
ggplot(data002, aes(lifeExp, gdpPercap, colour = continent)) + 
  geom_point()

2 line chart

Code 
data002= data001 %>% group_by(continent,year) %>% summarise(pop=sum(pop))
Code 
ggplot(data002, aes(year, pop, colour = continent)) + 
  geom_line()+ scale_y_continuous(labels = scales::label_number_si())

3 bar chart

Code 
data002= data001 %>% filter(year==1997) %>% group_by(continent) %>% summarise(pop=sum(pop))
Code 
ggplot(data002, aes(x=continent, y=pop)) +
  geom_bar(stat="identity")+scale_y_continuous(labels = scales::comma)

4 histogram

Code 
data002= data001 %>% filter(year==1997,continent=='Asia')
Code 
ggplot(data002, aes(gdpPercap)) + 
  geom_histogram()

multiple histogram

Code 
data002= data001 %>% filter(year==1997)
ggplot(data002, aes(gdpPercap,fill=continent,colour=continent)) + 
  geom_histogram(alpha = 0.5, position = "identity")

5 box plot

Code 
data002= data001 %>% filter(year==1997)
Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()

Code 
data002 %>% filter(continent=='Americas') %>% arrange(desc(gdpPercap)) %>% head()
# A tibble: 6 × 6
  country       continent  year lifeExp       pop gdpPercap
  <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
1 United States Americas   1997    76.8 272911760    35767.
2 Canada        Americas   1997    78.6  30305843    28955.
3 Puerto Rico   Americas   1997    74.9   3759430    16999.
4 Argentina     Americas   1997    73.3  36203463    10967.
5 Venezuela     Americas   1997    72.1  22374398    10165.
6 Chile         Americas   1997    75.8  14599929    10118.

6 title,subtitle,caption

6.1 add title,subtitle,caption

Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()+
  labs(title = "gdpPercap",  subtitle = "by continent",caption = "*Data from gapminder")

6.2 Put title and subtitle into the center

Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()+
  labs(title = "gdpPercap",  subtitle = "by continent",caption = "*Data from gapminder")+
  theme(plot.title=element_text(hjust=0.5),plot.subtitle=element_text(hjust=0.5))

6.3 add x and y axis labels

Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()+
  labs(title = "gdpPercap",  subtitle = "by continent",caption = "*Data from gapminder"
       , x ="each continent", y = "GDP Per cap")

6.4 remove x and y axis labels

Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()+
  labs(title = "gdpPercap",  subtitle = "by continent",caption = "*Data from gapminder"
       , x ="", y = "")

7 save ggplot

Code 
ggplot(data002, aes(x=continent, y=gdpPercap)) + 
  geom_boxplot()+
  labs(title = "gdpPercap",  subtitle = "by continent",caption = "*Data from gapminder"
       , x ="", y = "")

Code 
ggsave("ggplot.png",width = 20, height = 20, units = "cm")

8 themes

准备数据

Code 
gapminder_data=gapminder 
gapminder_data_cn_us_2007=gapminder_data %>%  filter(country %in% c('China','United States')) %>% filter(year==year)
gapminder_data_cn_2007=gapminder_data %>%  filter(country %in% c('China')) %>% filter(year==year)
gapminder_data_2007=gapminder_data %>% filter(year==2007)

gapminder_data_cn=gapminder_data %>%  filter(country %in% c('China')) %>% filter(year==year)
gapminder_data_us=gapminder_data %>%  filter(country %in% c('United States')) %>% filter(year==year)
gapminder_data_cn_us=gapminder_data %>%  filter(country %in% c('China','United States')) %>% filter(year==year)
Show the code 
# theme_bw
theme_bw=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_bw 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_bw()+
  theme(text = element_text(family='Kai'))

# theme_classic
theme_classic=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_classic 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_classic()+
  theme(text = element_text(family='Kai'))

# theme_dark
theme_dark=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_dark 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_dark()+
  theme(text = element_text(family='Kai'))

# theme_void
theme_void=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_void 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_void()+
  theme(text = element_text(family='Kai'))

ggarrange(theme_bw,theme_classic,theme_dark,theme_void)

Show the code 
library(ggthemes)

#theme_base
theme_base=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_base 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_base()+
  theme(text = element_text(family='Kai'))

#theme_calc 
theme_calc=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_calc 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_calc()+scale_fill_calc()+
  theme(text = element_text(family='Kai'))

# theme_economist
theme_economist=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_economist 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_economist()+scale_fill_economist()+
  theme(text = element_text(family='Kai'))

# theme_gdocs
theme_gdocs=ggplot(data=gapminder_data_cn_us, aes(x=year, y=gdpPercap,color=country)) +
  geom_line()+
  geom_point()+
  labs(
    title = "theme_gdocs 主题",
    subtitle = "1952年到2007年",
    caption = "数据来源: Gapminder dataset",
  )+
  ylab("人均GPD")+
  xlab("年")+
  theme_gdocs()+scale_fill_gdocs()+
  theme(text = element_text(family='Kai'))


ggarrange(theme_base,theme_calc,theme_economist,theme_gdocs)

9 Reference

https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf