R语言第二章数据处理⑥dplyr包(1)列选取

目录

R语言第二章数据处理①选择列
R语言第二章数据处理②选择行
R语言第二章数据处理③删除重复数据
R语言第二章数据处理④数据框排序和重命名
R语言第二章数据处理⑤数据框列的转化和计算
R语言第二章数据处理⑥dplyr包(1)列选取

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注意:所有代码都将作为管道的一部分呈现,即使它们中的任何一个都不是完整的管道。 在某些情况下,我添加了一个glimpse()语句,允许您查看输出tibble中选择的列,而不必每次都打印所有数据。

数据集

library(tidyverse)

#built-in R dataset 
glimpse(msleep)

## Observations: 83
## Variables: 11
## $ name         <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Grea...
## $ genus        <chr> "Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bo...
## $ vore         <chr> "carni", "omni", "herbi", "omni", "herbi", "herbi...
## $ order        <chr> "Carnivora", "Primates", "Rodentia", "Soricomorph...
## $ conservation <chr> "lc", NA, "nt", "lc", "domesticated", NA, "vu", N...
## $ sleep_total  <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1...
## $ sleep_rem    <dbl> NA, 1.8, 2.4, 2.3, 0.7, 2.2, 1.4, NA, 2.9, NA, 0....
## $ sleep_cycle  <dbl> NA, NA, NA, 0.1333333, 0.6666667, 0.7666667, 0.38...
## $ awake        <dbl> 11.9, 7.0, 9.6, 9.1, 20.0, 9.6, 15.3, 17.0, 13.9,...
## $ brainwt      <dbl> NA, 0.01550, NA, 0.00029, 0.42300, NA, NA, NA, 0....
## $ bodywt       <dbl> 50.000, 0.480, 1.350, 0.019, 600.000, 3.850, 20.4...

选取列

选取列:基础部分

如果目的是选择其中几列,只需在select语句中添加列的名称即可。 添加它们的顺序将决定它们在output中的显示顺序。

msleep %>%
  select(name, genus, sleep_total, awake) %>%
  glimpse()

## Observations: 83
## Variables: 4
## $ name        <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Great...
## $ genus       <chr> "Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bos...
## $ sleep_total <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1,...
## $ awake       <dbl> 11.9, 7.0, 9.6, 9.1, 20.0, 9.6, 15.3, 17.0, 13.9, ...

如果你想添加很多列,可以通过使用:提高工作效率,取消选择甚至取消选择列并重新添加它来进行选择。同时可以请使用start_col:end_col语法选择某些列:

msleep %>%
  select(name:order, sleep_total:sleep_cycle) %>%
  glimpse

## Observations: 83
## Variables: 7
## $ name        <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Great...
## $ genus       <chr> "Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bos...
## $ vore        <chr> "carni", "omni", "herbi", "omni", "herbi", "herbi"...
## $ order       <chr> "Carnivora", "Primates", "Rodentia", "Soricomorpha...
## $ sleep_total <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1,...
## $ sleep_rem   <dbl> NA, 1.8, 2.4, 2.3, 0.7, 2.2, 1.4, NA, 2.9, NA, 0.6...
## $ sleep_cycle <dbl> NA, NA, NA, 0.1333333, 0.6666667, 0.7666667, 0.383...

另一种方法是通过在列名称前添加减号来取消选择列。 还可以通过此操作取消选择某些列。

msleep %>% 
  select(-conservation, -(sleep_total:awake)) %>%
  glimpse

## Observations: 83
## Variables: 6
## $ name    <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Greater s...
## $ genus   <chr> "Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bos", "...
## $ vore    <chr> "carni", "omni", "herbi", "omni", "herbi", "herbi", "c...
## $ order   <chr> "Carnivora", "Primates", "Rodentia", "Soricomorpha", "...
## $ brainwt <dbl> NA, 0.01550, NA, 0.00029, 0.42300, NA, NA, NA, 0.07000...
## $ bodywt  <dbl> 50.000, 0.480, 1.350, 0.019, 600.000, 3.850, 20.490, 0...

甚至可以取消所有列,然后重新添加其中某列。下面的示例代码取消选择从name到awake的所有列,但重新添加列’conservation’,即使它是取消选择的列的一部分。 但这只适用于在同一select()语句中。

msleep %>%
  select(-(name:awake), conservation) %>%
  glimpse

## Observations: 83
## Variables: 3
## $ brainwt      <dbl> NA, 0.01550, NA, 0.00029, 0.42300, NA, NA, NA, 0....
## $ bodywt       <dbl> 50.000, 0.480, 1.350, 0.019, 600.000, 3.850, 20.4...
## $ conservation <chr> "lc", NA, "nt", "lc", "domesticated", NA, "vu", N...

根据列名特点选择列

如果你有很多具有类似列名的列,你可以通过在select语句中添加starts_with()ends_with()contains()来使用匹配。

msleep %>%
  select(name, starts_with("sleep")) %>%
  glimpse

## Observations: 83
## Variables: 4
## $ name        <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Great...
## $ sleep_total <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1,...
## $ sleep_rem   <dbl> NA, 1.8, 2.4, 2.3, 0.7, 2.2, 1.4, NA, 2.9, NA, 0.6...
## $ sleep_cycle <dbl> NA, NA, NA, 0.1333333, 0.6666667, 0.7666667, 0.383...

msleep %>%
  select(contains("eep"), ends_with("wt")) %>%
  glimpse

## Observations: 83
## Variables: 5
## $ sleep_total <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1,...
## $ sleep_rem   <dbl> NA, 1.8, 2.4, 2.3, 0.7, 2.2, 1.4, NA, 2.9, NA, 0.6...
## $ sleep_cycle <dbl> NA, NA, NA, 0.1333333, 0.6666667, 0.7666667, 0.383...
## $ brainwt     <dbl> NA, 0.01550, NA, 0.00029, 0.42300, NA, NA, NA, 0.0...
## $ bodywt      <dbl> 50.000, 0.480, 1.350, 0.019, 600.000, 3.850, 20.49...

根据正则表达式选择列

以上的辅助函数都是使用精确的模式匹配。 如果你有列名模式并不精确相同,你可以在matches()中使用任何正则表达式。下面的示例代码将添加任何包含“o”的列,后跟一个或多个其他字母,以及“er”。

#selecting based on regex
msleep %>%
  select(matches("o.+er")) %>%
  glimpse

## Observations: 83
## Variables: 2
## $ order        <chr> "Carnivora", "Primates", "Rodentia", "Soricomorph...
## $ conservation <chr> "lc", NA, "nt", "lc", "domesticated", NA, "vu", N...

根据预先确定的列名选择列

还有另一个选项可以避免连续重新输入列名:one_of()。 您可以预先设置列名,然后在select()语句中通过将它们包装在one_of()中或使用!!运算符来引用它们。

classification <- c("name", "genus", "vore", "order", "conservation")

msleep %>%
  select(!!classification)

## # A tibble: 83 x 5
##    name                       genus       vore  order        conservation
##    <chr>                      <chr>       <chr> <chr>        <chr>       
##  1 Cheetah                    Acinonyx    carni Carnivora    lc          
##  2 Owl monkey                 Aotus       omni  Primates     <NA>        
##  3 Mountain beaver            Aplodontia  herbi Rodentia     nt          
##  4 Greater short-tailed shrew Blarina     omni  Soricomorpha lc          
##  5 Cow                        Bos         herbi Artiodactyla domesticated
##  6 Three-toed sloth           Bradypus    herbi Pilosa       <NA>        
##  7 Northern fur seal          Callorhinus carni Carnivora    vu          
##  8 Vesper mouse               Calomys     <NA>  Rodentia     <NA>        
##  9 Dog                        Canis       carni Carnivora    domesticated
## 10 Roe deer                   Capreolus   herbi Artiodactyla lc          
## # ... with 73 more rows

    原文作者:左手柳叶刀右手小鼠标
    原文地址: https://www.jianshu.com/p/4b37580a6c31
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
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