R语言扩展包dplyr——数据清洗和整理
该包主要用于数据清洗和整理，coursera课程链接：Getting and Cleaning Data
也可以载入swirl包，加载课Getting and Cleaning Data跟着学习。
如下：
library(swirl)
install_from_swirl("Getting and Cleaning Data")
swirl()
此文主要是参考R自带的简介：Introduce to dplyr
1、示范数据
library(nycflights13)
dim(flights)
[1] 336776 16
head(flights, 3)
Source: local data frame [3 x 16]
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227
3 2013 1 1 542 2 923 33 AA N619AA 1141 JFK MIA 160
Variables not shown: distance (dbl), hour (dbl), minute (dbl)
2、将过长的数据整理成友好的tbl_df数据
flights_df <- tbl_df(flights)
flights_df
3、筛选filter()
filter(flights_df, month == 1, day == 1)
Source: local data frame [842 x 16]
year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight origin dest air_time
1 2013 1 1 517 2 830 11 UA N14228 1545 EWR IAH 227
2 2013 1 1 533 4 850 20 UA N24211 1714 LGA IAH 227
筛选出month=1和day=1的数据
同样效果的，
flights_df[flights_dfday == 1, ]
4、选出几行数据slice()
slice(flights_df, 1:10)
5、排列arrange()
arrange(flights_df, year, month, day)
将flights_df数据按照year,month,day的升序排列。
降序
arrange(flights_df, year, desc(month), day)
R语言当中的自带函数
flights_df[order(flightsmonth, flights_df
arr_delay)), ]
6、选择select()
通过列名来选择所要的数据
select(flights_df, year, month, day)
选出三列数据
使用：符号
select(flights_df, year:day)
使用-来删除不要的列表
select(flights_df, -(year:day))
7、变形mutate()
产生新的列
mutate(flights_df,
gain = arr_delay - dep_delay,speed = distance / air_time * 60)
8、汇总summarize()
<pre name="code" class="html">> summarise(flights,
delay = mean(dep_delay, na.rm = TRUE)
求dep_delay的均值
9、随机选出样本
sample_n(flights_df, 10)
随机选出10个样本
sample_frac(flights_df, 0.01)
随机选出1%个样本
10、分组group_py()
by_tailnum <- group_by(flights, tailnum)
确定组别为tailnum，赋值为by_tailnum
delay <- summarise(by_tailnum,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE))
汇总flights里地tailnum组的分类数量，及其组别对应的distance和arr_delay的均值
delay <- filter(delay, count > 20, dist < 2000)
ggplot(delay, aes(dist, delay)) +
geom_point(aes(size = count), alpha = 1/2) +
geom_smooth() +
scale_size_area()
结果都需要通过赋值存储
a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
a3 <- summarise(a2,
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)
11、引入链接符%>%
使用时把数据名作为开头，然后依次对数据进行多步操作：
flights %>%
group_by(year, month, day) %>%
select(arr_delay, dep_delay) %>%
summarise(
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
) %>%
filter(arr > 30 | dep > 30)
前面都免去了数据名
若想要进行更多地了解这个包，可以参考其自带的说明书（60页）：dplyr
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版权声明：本文为CSDN博主「Eunice_33」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u011253874/article/details/43022835
