根据R中的其他变量有条件填充缺失的数据

发布于 2025-02-11 20:51:19 字数 677 浏览 2 评论 0原文

很抱歉添加屏幕截图，我从 https://www.kaggle.com/datasets/datasets/rikdifos/rikdifos/rikdifos/credit-card-card-card-approval-proval-proval-proval-proval-prediction < /a>

有人可以通知我填写职业列的NA值的方法吗？我创建一个新的变量来确定申请人是否有效，如果在IS_Working列中相同的观察结果为零，并且将其他NA填充为零，并且将NA值填充。

df <- data.frame (occupation  = c("NA","NA","Drivers","Accountants","NA","Drivers","Laborers","Cleaning staff","Drivers","Drivers"),
                  is_working = c("1","0","1","1","1","1","1","1","1","1")
                  )

原文

enter image description here

sorry for adding the screenshot, I download data from https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction

Can someone inform me about the way to fill those NA values that the occupation column has? I create a new variable to determine whether an applicant is working or not and I want to fill NA values as zero if the same observation is zero in is_working column and left the others NA.

df <- data.frame (occupation  = c("NA","NA","Drivers","Accountants","NA","Drivers","Laborers","Cleaning staff","Drivers","Drivers"),
                  is_working = c("1","0","1","1","1","1","1","1","1","1")
                  )

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一笔一画续写前缘 2025-02-18 20:51:19

library(dplyr)
df %>%
  mutate(
    # change string "NA" to missing values NA
    occupation = ifelse(occupation == "NA", NA, occupation),
    # replace NAs where is_working is 0 with 0
    occupation = ifelse(is.na(occupation) & is_working == 0, "0", occupation)
  )
#        occupation is_working
# 1            <NA>          1
# 2               0          0
# 3         Drivers          1
# 4     Accountants          1
# 5            <NA>          1
# 6         Drivers          1
# 7        Laborers          1
# 8  Cleaning staff          1
# 9         Drivers          1
# 10        Drivers          1

library(dplyr)
df %>%
  mutate(
    # change string "NA" to missing values NA
    occupation = ifelse(occupation == "NA", NA, occupation),
    # replace NAs where is_working is 0 with 0
    occupation = ifelse(is.na(occupation) & is_working == 0, "0", occupation)
  )
#        occupation is_working
# 1            <NA>          1
# 2               0          0
# 3         Drivers          1
# 4     Accountants          1
# 5            <NA>          1
# 6         Drivers          1
# 7        Laborers          1
# 8  Cleaning staff          1
# 9         Drivers          1
# 10        Drivers          1

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