Tidyverse课程目录
Chapter 1. 数据整形
Chapter 2. 数据可视化
Chapter 3. 分组和概括
Chapter 4. 可视化类型
Chapter 3. 分组和概括
用summarize进行描述性统计
summarize的功能就是对某个变量根据指定(比方说平均数，中位数)就行概述。举个例子，我们要看一下
lifeExp的中位数。
# Summarize to find the median life expectancy
gapminder %>%
summarize(medianLifeExp=median(lifeExp))
# A tibble: 1 x 1
medianLifeExp
<dbl>
1 60.7
接下来结合一下之前学到的filter，统计分析一下year为1957的数据里的lifeExp的中位数。
# Filter for 1957 then summarize the median life expectancy
gapminder %>%
filter(year==1957)%>%
summarize(medianLifeExp=median(lifeExp))
# A tibble: 1 x 1
medianLifeExp
<dbl>
1 48.4
当然也可以同时统计两个变量。比方说用max()查看最大值。
# Filter for 1957 then summarize the median life expectancy and the maximum GDP per capita
gapminder %>%
filter(year==1957)%>%
summarize(medianLifeExp=median(lifeExp),maxGdpPercap=max(gdpPercap))
# A tibble: 1 x 2
medianLifeExp maxGdpPercap
<dbl> <dbl>
1 48.4 113523.
用group_by进行分组描述性统计
在
summarize之前用group_by的话可以实现根据某个变量的种类进行分类描述性统计。比方说，根据下面的代码寻找每一年的lifeExp的中位数和gdpPercap的最大值。
# Find median life expectancy and maximum GDP per capita in each year
gapminder %>%
group_by(year) %>%
summarize(medianLifeExp=median(lifeExp),maxGdpPercap=max(gdpPercap))
# A tibble: 12 x 3
year medianLifeExp maxGdpPercap
* <int> <dbl> <dbl>
1 1952 45.1 108382.
2 1957 48.4 113523.
3 1962 50.9 95458.
4 1967 53.8 80895.
5 1972 56.5 109348.
6 1977 59.7 59265.
7 1982 62.4 33693.
8 1987 65.8 31541.
9 1992 67.7 34933.
10 1997 69.4 41283.
11 2002 70.8 44684.
12 2007 71.9 49357.
结合filter，这次我们需要寻找year为1957数据里每个continent里的lifeExp的中位数和gdpPercap的最大值。
gapminder %>%
filter(year==1957) %>%
group_by(continent) %>%
summarize(medianLifeExp=median(lifeExp),maxGdpPercap=max(gdpPercap))
当然，group_by里的变量也可以是多个，比方说
# Find median life expectancy and maximum GDP per capita in each continent/year combination
gapminder %>%
group_by(continent,year) %>%
summarize(medianLifeExp=median(lifeExp),maxGdpPercap=max(gdpPercap))
`summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
# A tibble: 60 x 4
# Groups: continent [5]
continent year medianLifeExp maxGdpPercap
<fct> <int> <dbl> <dbl>
1 Africa 1952 38.8 4725.
2 Africa 1957 40.6 5487.
3 Africa 1962 42.6 6757.
4 Africa 1967 44.7 18773.
5 Africa 1972 47.0 21011.
6 Africa 1977 49.3 21951.
7 Africa 1982 50.8 17364.
8 Africa 1987 51.6 11864.
9 Africa 1992 52.4 13522.
10 Africa 1997 52.8 14723.
# … with 50 more rows
描述性统计的可视化
先根据year总结出每年lifeExp的中位数和gdpPercap的最大值。然后用ggplot2对其进行可视化，此处加入了expand_limits(y = 0)这条指令，这是为了让y轴包含0值。
by_year <- gapminder %>%
group_by(year) %>%
summarize(medianLifeExp = median(lifeExp),
maxGdpPercap = max(gdpPercap))
# Create a scatter plot showing the change in medianLifeExp over time
ggplot(by_year,aes(x=year,y=medianLifeExp))+
geom_point()+
expand_limits(y = 0)
接下来画一个稍微复杂的图，此处会用到Chapter.2数据可视化的知识。首先根据year和continent将数据进行组化，并且计算gdpPercap的中位数。然后将数据可视化，横轴是year，纵轴是medianGdpPercap。并且根据continent进行上色。
# Summarize medianGdpPercap within each continent within each year: by_year_continent
by_year_continent <- gapminder %>%
group_by(year,continent) %>%
summarize(medianGdpPercap = median(gdpPercap))
# Plot the change in medianGdpPercap in each continent over time
ggplot(by_year_continent,aes(x=year,y=medianGdpPercap,color=continent)) +
geom_point()+
expand_limits(y=0)
还可以可视化两个变量的描述性统计的关联。比方说根据下面的代码可以对2007年的gdpPercap的中位数和lifeExp的中位数进行可视化，并根据continent给图形上色。
# Summarize the median GDP and median life expectancy per continent in 2007
by_continent_2007<- gapminder %>%
filter(year==2007) %>%
group_by(continent) %>%
summarize(medianGdpPercap=median(gdpPercap),medianLifeExp=median(lifeExp))
# Use a scatter plot to compare the median GDP and median life expectancy
ggplot(by_continent_2007,aes(x=medianGdpPercap,y=medianLifeExp,color=continent))+
geom_point()+
expand_limits(y = 0)
网友评论