R语言dplyr包介绍 introduction to dplyr
使用dplyr包可以让我们更方便地操作R中的数据框
- 示例数据集
- 选择行：filter, slice函数
- 对行排序：arrange函数
- 选择列：select, rename函数
- 选择列：distinct函数
- 生成新列：mutate, transform, transmute函数
- 生成列数据的统计量：summarise函数
- 随机选取行：sample_n, sample_frac函数
- 分组操作：结合group_by函数
- 链接Chaining：利用操作符%>%
示例数据集 nycflights
library(nycflights13)
dim(flights)
#> [1] 336776 19
head(flights)
#> # A tibble: 6 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 2 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour filter, slice函数
filter(data，conditions）
根据基于列名的条件，选取符合的行，返回数据框
filter(flights, month == 1, day == 1)
#> # A tibble: 842 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 838 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour slice(data, …)
根据行的位置来选择行
slice(flights, 1:10)
#> # A tibble: 10 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 6 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour arrange函数
arrange(data, column names)
按照给定的column的顺序对行进行排列，然后返回排列后的数据框
如果要按照某列的降序来排列，采用desc（column names）函数
arrange(flights, year, month, day)
#> # A tibble: 336,776 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 336,772 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour select, rename函数
select(data, column names)
返回指定列组成的数据框
# Select columns by name
select(flights, year, month, day)
#> # A tibble: 336,776 x 3
#> year month day
#>
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> ... with 336,772 more rows
# Select all columns between year and day (inclusive)
select(flights, year:day)
#> # A tibble: 336,776 x 3
#> year month day
#>
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> ... with 336,772 more rows
# Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
#> # A tibble: 336,776 x 16
#> dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
#>
#> 1 517 515 2 830 819 11
#> 2 533 529 4 850 830 20
#> 3 542 540 2 923 850 33
#> 4 544 545 -1 1004 1022 -18
#> ... with 336,772 more rows, and 10 more variables: carrier ,
#> flight , tailnum , origin , dest , air_time ,
#> distance , hour , minute , time_hour select(data, new names = column names)
给指定的列重命名，返回包含指定列的数据框
rename(data, new names = column names)
给指定的列重命名，返回包含所有列的数据框
select(flights, tail_num = tailnum)
#> # A tibble: 336,776 x 1
#> tail_num
#>
#> 1 N14228
#> 2 N24211
#> 3 N619AA
#> 4 N804JB
#> ... with 336,772 more rows
rename(flights, tail_num = tailnum)
#> # A tibble: 336,776 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 336,772 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tail_num ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour distinct函数
distinct(data, column names)
返回由某几列组成的数据框，速度快
distinct(flights, tailnum)
#> # A tibble: 4,044 x 1
#> tailnum
#>
#> 1 N14228
#> 2 N24211
#> 3 N619AA
#> 4 N804JB
#> ... with 4,040 more rows
distinct(flights, origin, dest)
#> # A tibble: 224 x 2
#> origin dest
#>
#> 1 EWR IAH
#> 2 LGA IAH
#> 3 JFK MIA
#> 4 JFK BQN
#> ... with 220 more rows
mutate, transform, transmute函数
mutate(data, expressions of new columns)
通过原有的列的计算得到新的列，将新的列与原来的数据框合并，返回新的数据框
mutate(flights,
gain = arr_delay - dep_delay,
speed = distance / air_time * 60)
#> # A tibble: 336,776 x 21
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 336,772 more rows, and 14 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour transform(data, expression of new columns)
与mutate功能相同，但不能通过刚新生成的列来生成更新的列
mutate(flights,
gain = arr_delay - dep_delay,
gain_per_hour = gain / (air_time / 60)
)
#> # A tibble: 336,776 x 21
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> ... with 336,772 more rows, and 14 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour transmute(data, expressions of new columns)
通过原有的列的计算得到新的列，返回仅包含新列的数据框
transmute(flights,
gain = arr_delay - dep_delay,
gain_per_hour = gain / (air_time / 60)
)
#> # A tibble: 336,776 x 2
#> gain gain_per_hour
#>
#> 1 9 2.378855
#> 2 16 4.229075
#> 3 31 11.625000
#> 4 -17 -5.573770
#> ... with 336,772 more rows
summarise函数
summarise(data, expressions)
epressions是多个多元函数f(x1,x2,….xn)赋值给变量的表达式,每个表达式中，某一列的数据传入多元函数得到结果，最终返回由这些表达式得到的结果组成的数据框
summarise(flights,
delay = mean(dep_delay, na.rm = TRUE))
#> # A tibble: 1 x 1
#> delay
#>
#> 1 12.63907
sample_n, sample_frac函数
sample_n(data, size, replace = FALSE, weight = NULL, .env = parent.frame())
sample_frac(data, size = 1, replace = FALSE, weight = NULL, .env = parent.frame())
随机挑选行，sample_frac是通过抽取总体的某一百分比的样本来确定样本个数
sample_n(flights, 10)
#> # A tibble: 10 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#>
#> 1 2013 7 8 2205 2019 106 103
#> 2 2013 9 12 1602 1545 17 NA
#> 3 2013 11 4 1459 1459 0 1642
#> 4 2013 10 25 1354 1350 4 1534
#> ... with 6 more rows, and 12 more variables: sched_arr_time ,
#> arr_delay , carrier , flight , tailnum ,
#> origin , dest , air_time , distance , hour ,
#> minute , time_hour group_by函数
group_by(.data, …, add = FALSE)
group_by_(.data, …, .dots, add = FALSE)
返回一个分组后的数据框
| 参数 | |
|---|---|
| .data | 一个tbl类型数据 |
| … | 用于分组的列。可以传入某一确定的列，有时可以传入某个函数 |
按照tailnum对总的数据分类，再计算某几列的统计量，得到各个类别下各个统计量的值
by_tailnum <- group_by(flights, tailnum)
delay <- summarise(by_tailnum,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE))
delay <- filter(delay, count > 20, dist < 2000)
# Interestingly, the average delay is only slightly related to the
# average distance flown by a plane.
ggplot(delay, aes(dist, delay)) +
geom_point(aes(size = count), alpha = 1/2) +
geom_smooth() +
scale_size_area()
操作符%>%
当嵌套使用group_by时，可以用%>%进行简化书写
x %>% f(y) 等价于 f(x, y)
以下三种书写方式等价：
1.生成中间变量
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)
2.嵌套写法
filter(
summarise(
select(
group_by(flights, year, month, day),
arr_delay, dep_delay
),
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
),
arr > 30 | dep > 30
)
#> Adding missing grouping variables: `year`, `month`, `day`
#> Source: local data frame [49 x 5]
#> Groups: year, month [11]
#>
#> # A tibble: 49 x 5
#> year month day arr dep
#>
#> 1 2013 1 16 34.24736 24.61287
#> 2 2013 1 31 32.60285 28.65836
#> 3 2013 2 11 36.29009 39.07360
#> 4 2013 2 27 31.25249 37.76327
#> ... with 45 more rows
3.%>%
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)
https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html