用dplyr操作数据课程目录

Chapter1. 数据变形
Chapter2. 数据统计
Chapter3. 数据选择和变形
Chapter4. 实战演练

本章节前半部分内容在之前的<Tidyverse>有出现过一些，重复的内容就不详细讲解了，简单带过。但是后半部分出现了一些新的内容会稍微详细的说明一下。希望能对大家有所帮助。

用select()选择变量

用select()选择变量，并用arrange()根据某变量进行排序。

counties %>%
  # Select state, county, population, and industry-related columns
  select(state,county,population,professional,service,office,construction,production) %>% 
  # Arrange service in descending order 
  arrange(desc(service))
# A tibble: 3,138 x 8
   state   county population professional service office construction production
   <chr>   <chr>       <dbl>        <dbl>   <dbl>  <dbl>        <dbl>      <dbl>
 1 Missis~ Tunica      10477         23.9    36.6   21.5          3.5       14.5
 2 Texas   Kinney       3577         30      36.5   11.6         20.5        1.3
 3 Texas   Kenedy        565         24.9    34.1   20.5         20.5        0  
 4 New Yo~ Bronx     1428357         24.3    33.3   24.2          7.1       11  
 5 Texas   Brooks       7221         19.6    32.4   25.3         11.1       11.5
 6 Colora~ Fremo~      46809         26.6    32.2   22.8         10.7        7.6
 7 Texas   Culbe~       2296         20.1    32.2   24.2         15.7        7.8
 8 Califo~ Del N~      27788         33.9    31.5   18.8          8.9        6.8
 9 Minnes~ Mahno~       5496         26.8    31.5   18.7         13.1        9.9
10 Virgin~ Lanca~      11129         30.3    31.2   22.8          8.1        7.6
# ... with 3,128 more rows

用filter()对数据进行筛选。

counties %>%
  # Select the state, county, population, and those ending with "work"
  select(state,county,population,ends_with("work")) %>% 
  # Filter for counties that have at least 50% of people engaged in public work
  filter( public_work >= 50) 
# A tibble: 7 x 6
  state      county              population private_work public_work family_work
  <chr>      <chr>                    <dbl>        <dbl>       <dbl>       <dbl>
1 Alaska     Lake and Peninsula~       1474         42.2        51.6         0.2
2 Alaska     Yukon-Koyukuk Cens~       5644         33.3        61.7         0  
3 California Lassen                   32645         42.6        50.5         0.1
4 Hawaii     Kalawao                     85         25          64.1         0  
5 North Dak~ Sioux                     4380         32.9        56.8         0.1
6 South Dak~ Todd                      9942         34.4        55           0.8
7 Wisconsin  Menominee                 4451         36.8        59.1         0.4

select()的其他用法

当数据变量很多的时候，手动一个一个输入变量明显会降低神产销率。select()支持批量性的选择变量。

counties %>%
  select(state, county, drive:work_at_home)

• contains包含xx的变量
• starts_with以xx开始的变量
• ends_with以xx结尾的变量
  举个例子
  

也可以用select()删除某个变量

用rename()给变量重新命名

rename()是第一次出现，用法可以参照下面的代码。

counties %>%
  count(state)
# A tibble: 50 x 2
   state           n
   <chr>       <int>
 1 Alabama        67
 2 Alaska         28
 3 Arizona        15
 4 Arkansas       75
 5 California     58
 6 Colorado       64
 7 Connecticut     8
 8 Delaware        3
 9 Florida        67
10 Georgia       159
# ... with 40 more rows
  
# Rename the n column to num_counties
counties %>%
  count(state) %>% 
  rename(num_counties=n)
# A tibble: 50 x 2
   state       num_counties
   <chr>              <int>
 1 Alabama               67
 2 Alaska                28
 3 Arizona               15
 4 Arkansas              75
 5 California            58
 6 Colorado              64
 7 Connecticut            8
 8 Delaware               3
 9 Florida               67
10 Georgia              159
# ... with 40 more rows

也可以不用rename()直接简单粗暴点。

# Select state, county, and poverty as poverty_rate
counties %>%
  select(state,county,poverty_rate=poverty)
# A tibble: 3,138 x 3
   state   county   poverty_rate
   <chr>   <chr>           <dbl>
 1 Alabama Autauga          12.9
 2 Alabama Baldwin          13.4
 3 Alabama Barbour          26.7
 4 Alabama Bibb             16.8
 5 Alabama Blount           16.7
 6 Alabama Bullock          24.6
 7 Alabama Butler           25.4
 8 Alabama Calhoun          20.5
 9 Alabama Chambers         21.6
10 Alabama Cherokee         19.2
# ... with 3,128 more rows

用transmute()变换和产生新的变量

transmute()的特点

• 选择变量&转换变量
• 产生的新变量会替换之前的变量

比方说我们要根据population/land_area来产生新的变量density。用transmute就不需要先select()再mutate()了。

counties %>%
  # Keep the state, county, and populations columns, and add a density column
  transmute(state,county,population,density=population/land_area) %>% 
  # Filter for counties with a population greater than one million 
  filter(population > 1000000) %>% 
  # Sort density in ascending order 
  arrange(density)
# A tibble: 41 x 4
   state      county         population density
   <chr>      <chr>               <dbl>   <dbl>
 1 California San Bernardino    2094769    104.
 2 Nevada     Clark             2035572    258.
 3 California Riverside         2298032    319.
 4 Arizona    Maricopa          4018143    437.
 5 Florida    Palm Beach        1378806    700.
 6 California San Diego         3223096    766.
 7 Washington King              2045756    967.
 8 Texas      Travis            1121645   1133.
 9 Florida    Hillsborough      1302884   1277.
10 Florida    Orange            1229039   1360.
# ... with 31 more rows

语法总结