【Udacity】3,7,6，第三个定性变量利用比率思考问题

一、利用比率思考问题
比如回答，女性好友数量是男性好友数量的多少倍
二、将长格式数据转换为宽格式数据

使用 R 进行的数据整理
宽格式和长格式之间的转换
融合数据框

> pf.fc_by_age_gender1 <- pf%>%
+     filter(!is.na(gender))%>%
+     group_by(age,gender)%>%
+     summarise(mean_friend_count=mean(friend_count),
+               median_friend_count = median(friend_count),
+               n=n())%>%
+     arrange(age)
> head(pf.fc_by_age_gender)
# A tibble: 6 x 5
    age gender mean_friend_count median_friend_count     n
  <int> <fct>              <dbl>               <dbl> <int>
1    13 female              259.               148.    193
2    13 male                102.                55.0   291
3    14 female              362.               224.    847
4    14 male                164.                92.5  1078
5    15 female              539.               276.   1139
6    15 male                201.               106.   1478

• 使用tidyr进行整理

install.packages("tidyr")
library("tidyr")
> spread(subset(pf.fc_by_age_gender, select = c('gender', 'age', 'median_friend_count')), gender, median_friend_count)
# A tibble: 101 x 3
     age female  male
   <int>  <dbl> <dbl>
 1    13   148.  55.0
 2    14   224.  92.5
 3    15   276. 106. 
 4    16   258. 136. 
 5    17   246. 125. 
 6    18   243. 122. 
 7    19   229. 130. 
 8    20   190. 112. 
 9    21   158. 108. 
10    22   124.  97.0
# ... with 91 more rows

• 使用reshape2进行整理
  reshape2 入门

install.packages('reshape2')
library(reshape2)
> pf.fc_by_age_gender.wide <- dcast(pf.fc_by_age_gender, age ~ gender,value.var="median_friend_count")
> head(pf.fc_by_age_gender.wide)
  age female  male
1  13  148.0  55.0
2  14  224.0  92.5
3  15  276.0 106.5
4  16  258.5 136.0
5  17  245.5 125.0
6  18  243.0 122.0

三、比例图
绘制比例图，并添加y=1的基准线

ggplot(aes(x=age,y=female/male),data=pf.fc_by_age_gender.wide)+
  geom_line()+
  geom_hline(yintercept = 1,alpha=0.3,linetype=2)