dplyr
dplyr是tidyverse数据科学包中的重要成员，提供了许多数据操控(data manipulation)的语法，用于快速处理数据。
Verbs 语法
在dplyr包中，提供了一致的动词语法，帮助解决最常见的数据操作:
filter(): 根据值筛选数据arrange(): 数据排序select()和rename(): 根据名字选择变量mutate()和transmute(): 修改数据summarise(): 数据摘要sample_n()和sample_frac(): 随机选择样本
filter()
library(nycflights13)dim(flights)
## [1] 336776 19
flights
## # A tibble: 336,776 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 1 517 515 2## 2 2013 1 1 533 529 4## 3 2013 1 1 542 540 2## 4 2013 1 1 544 545 -1## 5 2013 1 1 554 600 -6## 6 2013 1 1 554 558 -4## 7 2013 1 1 555 600 -5## 8 2013 1 1 557 600 -3## 9 2013 1 1 557 600 -3## 10 2013 1 1 558 600 -2## # ... with 336,766 more rows, and 13 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
根据条件筛选行：
filter(flights, month == 1, day == 1)
## # A tibble: 842 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 1 517 515 2## 2 2013 1 1 533 529 4## 3 2013 1 1 542 540 2## 4 2013 1 1 544 545 -1## 5 2013 1 1 554 600 -6## 6 2013 1 1 554 558 -4## 7 2013 1 1 555 600 -5## 8 2013 1 1 557 600 -3## 9 2013 1 1 557 600 -3## 10 2013 1 1 558 600 -2## # ... with 832 more rows, and 13 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
这等价于flights[flights$month == 1 & flights$day == 1, ]
arrange()
数据排序
arrange(flights, year, month, day)
## # A tibble: 336,776 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 1 517 515 2## 2 2013 1 1 533 529 4## 3 2013 1 1 542 540 2## 4 2013 1 1 544 545 -1## 5 2013 1 1 554 600 -6## 6 2013 1 1 554 558 -4## 7 2013 1 1 555 600 -5## 8 2013 1 1 557 600 -3## 9 2013 1 1 557 600 -3## 10 2013 1 1 558 600 -2## # ... with 336,766 more rows, and 13 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
降序排列
arrange(flights, desc(arr_delay))
## # A tibble: 336,776 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 9 641 900 1301## 2 2013 6 15 1432 1935 1137## 3 2013 1 10 1121 1635 1126## 4 2013 9 20 1139 1845 1014## 5 2013 7 22 845 1600 1005## 6 2013 4 10 1100 1900 960## 7 2013 3 17 2321 810 911## 8 2013 7 22 2257 759 898## 9 2013 12 5 756 1700 896## 10 2013 5 3 1133 2055 878## # ... with 336,766 more rows, and 13 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
select()
变量筛选
# Select columns by nameselect(flights, year, month, day)
## # A tibble: 336,776 x 3## year month day## <int> <int> <int>## 1 2013 1 1## 2 2013 1 1## 3 2013 1 1## 4 2013 1 1## 5 2013 1 1## 6 2013 1 1## 7 2013 1 1## 8 2013 1 1## 9 2013 1 1## 10 2013 1 1## # ... with 336,766 more rows
# Select all columns between year and day (inclusive)select(flights, year:day)
## # A tibble: 336,776 x 3## year month day## <int> <int> <int>## 1 2013 1 1## 2 2013 1 1## 3 2013 1 1## 4 2013 1 1## 5 2013 1 1## 6 2013 1 1## 7 2013 1 1## 8 2013 1 1## 9 2013 1 1## 10 2013 1 1## # ... with 336,766 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## <int> <int> <dbl> <int> <int>## 1 517 515 2 830 819## 2 533 529 4 850 830## 3 542 540 2 923 850## 4 544 545 -1 1004 1022## 5 554 600 -6 812 837## 6 554 558 -4 740 728## 7 555 600 -5 913 854## 8 557 600 -3 709 723## 9 557 600 -3 838 846## 10 558 600 -2 753 745## # ... with 336,766 more rows, and 11 more variables:## # arr_delay <dbl>, carrier <chr>, flight <int>,## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
利用select和其他辅助函数进行组合，进行更多类型的操作
# 提取变量并重命名select(flights, tail_num = tailnum)
## # A tibble: 336,776 x 1## tail_num## <chr>## 1 N14228## 2 N24211## 3 N619AA## 4 N804JB## 5 N668DN## 6 N39463## 7 N516JB## 8 N829AS## 9 N593JB## 10 N3ALAA## # ... with 336,766 more rows
mutate()
基于已有变量，添加新变量
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## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 1 517 515 2## 2 2013 1 1 533 529 4## 3 2013 1 1 542 540 2## 4 2013 1 1 544 545 -1## 5 2013 1 1 554 600 -6## 6 2013 1 1 554 558 -4## 7 2013 1 1 555 600 -5## 8 2013 1 1 557 600 -3## 9 2013 1 1 557 600 -3## 10 2013 1 1 558 600 -2## # ... with 336,766 more rows, and 15 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>, gain <dbl>, speed <dbl>
并且，可以依据刚建立的新列进行操作
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## <int> <int> <int> <int> <int> <dbl>## 1 2013 1 1 517 515 2## 2 2013 1 1 533 529 4## 3 2013 1 1 542 540 2## 4 2013 1 1 544 545 -1## 5 2013 1 1 554 600 -6## 6 2013 1 1 554 558 -4## 7 2013 1 1 555 600 -5## 8 2013 1 1 557 600 -3## 9 2013 1 1 557 600 -3## 10 2013 1 1 558 600 -2## # ... with 336,766 more rows, and 15 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>, gain <dbl>, gain_per_hour <dbl>
若只想保留新列，可以使用transmute()
transmute(flights,gain = arr_delay - dep_delay,gain_per_hour = gain / (air_time / 60))
## # A tibble: 336,776 x 2## gain gain_per_hour## <dbl> <dbl>## 1 9 2.38## 2 16 4.23## 3 31 11.6## 4 -17 -5.57## 5 -19 -9.83## 6 16 6.4## 7 24 9.11## 8 -11 -12.5## 9 -5 -2.14## 10 10 4.35## # ... with 336,766 more rows
summarise()
根据要求提供数据概要
summarise(flights,delay = mean(dep_delay, na.rm = TRUE))
## # A tibble: 1 x 1## delay## <dbl>## 1 12.6
sample_n() 和 sample_frac()
随机选择变量
# 提取变量并重命名sample_n(flights, 10)
## # A tibble: 10 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 4 19 1948 1930 18## 2 2013 5 17 1328 1329 -1## 3 2013 6 27 732 600 92## 4 2013 7 17 1010 1014 -4## 5 2013 8 17 1241 1229 12## 6 2013 7 14 700 700 0## 7 2013 9 24 1015 1020 -5## 8 2013 11 14 1451 1455 -4## 9 2013 3 17 847 850 -3## 10 2013 11 9 1629 1630 -1## # ... with 13 more variables: arr_time <int>,## # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>,## # air_time <dbl>, distance <dbl>, hour <dbl>,## # minute <dbl>, time_hour <dttm>
# 按比例选择sample_frac(flights, 0.01)
## # A tibble: 3,368 x 19## year month day dep_time sched_dep_time dep_delay## <int> <int> <int> <int> <int> <dbl>## 1 2013 4 2 812 815 -3## 2 2013 8 6 542 545 -3## 3 2013 2 6 608 610 -2## 4 2013 10 5 1138 1145 -7## 5 2013 6 14 1515 1455 20## 6 2013 7 17 608 609 -1## 7 2013 10 2 1214 1223 -9## 8 2013 6 9 1739 1629 70## 9 2013 6 13 651 700 -9## 10 2013 10 1 2026 2029 -3## # ... with 3,358 more rows, and 13 more variables:## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,## # carrier <chr>, flight <int>, tailnum <chr>,## # origin <chr>, dest <chr>, air_time <dbl>,## # distance <dbl>, hour <dbl>, minute <dbl>,## # time_hour <dttm>
总结
dplyr动词语法的操作规律：
- 第一个参数是data.frame
- 第二个参数是要进行的操作
- 最后返回一个data.frame
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