使用 tidyverse mutate 删除 NA 值

Question

我的数据框包含无法被 na.omit() 检测到的条目 'n/a'。我知道 tidyverse 包中的 mutate 函数能够将 'n/a' 条目转换为适当的空值并被删除。这是我尝试过的但发生错误：替换错误（值，值==“n/a”，NA）：找不到对象'value'。提前致谢！

> head(data)
  customer_id gender past_3_years_bike_related_purchases   DOB
1           1      F                                  93 19644
2           2   Male                                  81 29571
3           5 Female                                  56 28258
4           8   Male                                  31 22735
5           9 Female                                  97 26733
6          12   Male                                  58 34536
  job_industry_category    wealth_segment owns_car tenure           state
1                Health     Mass Customer      Yes     11 New South Wales
2    Financial Services     Mass Customer      Yes     16 New South Wales
3                   n/a Affluent Customer      Yes      8 New South Wales
4                   n/a     Mass Customer       No      7 New South Wales
5           Argiculture Affluent Customer      Yes      8 New South Wales
6         Manufacturing     Mass Customer       No      8             QLD

data %>% 
  mutate(value = replace(value, value == "n/a", NA)) %>% 
  drop_na()

Answer 1

您需要使用要检测“n/a”值的列名。

library(dplyr)
library(tidyr)

data %>% 
  mutate(value = replace(job_industry_category, 
                         job_industry_category == "n/a", NA)) %>% 
  drop_na()

您也可以在不将这些值转换为实际值的情况下执行此操作 NA。

data %>% filter(job_industry_category != "n/a")

#Base R :

subset(data, job_industry_category != "n/a")

Answer 2

当使用 na.strings() 参数将数据读入 R 时，n/a 值也可以转换为适用于 na.omit() 的值。

例如，如果我们从原始 post 中获取数据并将其转换为管道分隔值文件，我们可以使用 na.strings() 将 n/a 作为缺失值包含在内使用 read.csv()，然后使用 na.omit() 对数据进行子集化。

textData <- "customer_id|gender|past_3_years_bike_related_purchases|DOB|job_industry_category|wealth_segment|owns_car|tenure|state
 1|Female| 93|19644|Health|Mass Customer|Yes|11|New South Wales
 2|Male| 81|29571|Financial Services|Mass Customer|Yes|16|New South Wales
 5|Female| 56|28258|n/a|Affluent Customer|Yes|8|New South Wales
 8|Male| 31|22735|n/a|Mass Customer| No|7|New South Wales
 9|Female| 97|26733|Argiculture|Affluent Customer|Yes| 8|New South Wales
12|Male| 58|34536|Manufacturing|Mass Customer| No| 8|QLD"

data <- read.csv(text = textData,
                 header = TRUE,
                 na.strings = c("n/a","na"),
                 sep="|")

data

> data
  customer_id gender past_3_years_bike_related_purchases   DOB job_industry_category
1           1 Female                                  93 19644                Health
2           2   Male                                  81 29571    Financial Services
3           5 Female                                  56 28258                  <NA>
4           8   Male                                  31 22735                  <NA>
5           9 Female                                  97 26733           Argiculture
6          12   Male                                  58 34536         Manufacturing
     wealth_segment owns_car tenure           state
1     Mass Customer      Yes     11 New South Wales
2     Mass Customer      Yes     16 New South Wales
3 Affluent Customer      Yes      8 New South Wales
4     Mass Customer       No      7 New South Wales
5 Affluent Customer      Yes      8 New South Wales
6     Mass Customer       No      8             QLD

正如我们从输出中看到的，第 3 行和第 4 行现在有 <NA> for job_industry_category。

# now omit missing values
na.omit(data)

...现在具有 <NA> 值的行已从数据框中删除。

> na.omit(data)
  customer_id gender past_3_years_bike_related_purchases   DOB job_industry_category
1           1 Female                                  93 19644                Health
2           2   Male                                  81 29571    Financial Services
5           9 Female                                  97 26733           Argiculture
6          12   Male                                  58 34536         Manufacturing
     wealth_segment owns_car tenure           state
1     Mass Customer      Yes     11 New South Wales
2     Mass Customer      Yes     16 New South Wales
5 Affluent Customer      Yes      8 New South Wales
6     Mass Customer       No      8             QLD

Answer 3

我们可以使用na_if将元素转换为NA并使用drop_na

library(dplyr)
library(tidyr)
data %>% 
   mutate(value = na_if(job_industry_category, "n/a")) %>%
   drop_na()

Answer 4

一个data.table选项

> setDT(df)[!"n/a", on = .(job_industry_category)]
   customer_id gender past_3_years_bike_related_purchases   DOB
1:           1 Female                                  93 19644
2:           2   Male                                  81 29571
3:           9 Female                                  97 26733
4:          12   Male                                  58 34536
   job_industry_category    wealth_segment owns_car tenure           state
1:                Health     Mass Customer      Yes     11 New South Wales
2:    Financial Services     Mass Customer      Yes     16 New South Wales
3:           Argiculture Affluent Customer      Yes      8 New South Wales
4:         Manufacturing     Mass Customer       No      8             QLD

数据

> dput(df)
structure(list(customer_id = c(1L, 2L, 5L, 8L, 9L, 12L), gender = c("Female",
"Male", "Female", "Male", "Female", "Male"), past_3_years_bike_related_purchases = c(93L,
81L, 56L, 31L, 97L, 58L), DOB = c(19644L, 29571L, 28258L, 22735L,
26733L, 34536L), job_industry_category = c("Health", "Financial Services",
"n/a", "n/a", "Argiculture", "Manufacturing"), wealth_segment = c("Mass Customer",
"Mass Customer", "Affluent Customer", "Mass Customer", "Affluent Customer",
"Mass Customer"), owns_car = c("Yes", "Yes", "Yes", " No", "Yes",
" No"), tenure = c(11L, 16L, 8L, 7L, 8L, 8L), state = c("New South Wales",
"New South Wales", "New South Wales", "New South Wales", "New South Wales",
"QLD")), class = "data.frame", row.names = c(NA, -6L))

使用 tidyverse mutate 删除 NA 值

Remove NA values with tidyverse mutate

r

missing-data