添加一个包含 NAs 和 Mean 计数的列

Question

我有一个数据框，我需要向其中添加另一列，显示该行所有其他列中的 NA 计数以及非 NA 值的平均值。我认为可以在dplyr中完成。

> df1 <- data.frame(a = 1:5, b = c(1,2,NA,4,NA), c = c(NA,2,3,NA,NA))
> df1
  a  b  c
1 1  1 NA
2 2  2  2
3 3 NA  3
4 4  4 NA
5 5 NA NA

我想改变另一列计算该行中 NA 的数量，另一列显示该行中所有非 NA 值的平均值。

Answer 1

你可以试试这个：

#Find the row mean and add it to a new column in the dataframe
df1$Mean <- rowMeans(df1, na.rm = TRUE)

#Find the count of NA and add it to a new column in the dataframe
df1$CountNa <- rowSums(apply(is.na(df1), 2, as.numeric))

Answer 2

library(dplyr)

count_na <- function(x) sum(is.na(x))    

df1 %>%
  mutate(means = rowMeans(., na.rm = T),
         count_na = apply(., 1, count_na))

#### ANSWER FOR RADEK ####
elected_cols <- c('b', 'c')

df1 %>%
  mutate(means = rowMeans(.[elected_cols], na.rm = T),
         count_na = apply(.[elected_cols], 1, count_na))

Answer 3

如此处所述

df1 <- data.frame(a = 1:5, b = c(1,2,NA,4,NA), c = c(NA,2,3,NA,NA))

df1 %>%
  mutate(means = rowMeans(., na.rm = T),
         count_na = rowSums(is.na(.)))

处理选定的列（此处的示例适用于列 a 和列 c）：

df1 %>%
  mutate(means = rowMeans(., na.rm = T),
       count_na = rowSums(is.na(select(.,one_of(c('a','c'))))))

Answer 4

我最近遇到了这个问题的变体，我需要计算完整值的百分比，但针对特定变量（不是所有变量）。这是一种对我有用的方法。

df1 %>% 
  # create dummy variables representing if the observation is missing ----
  # can modify here for specific variables ----
  mutate_all(list(dummy = is.na)) %>% 
  # compute a row wise sum of missing ----
  rowwise() %>% 
  mutate(
    # number of missing observations ----
    n_miss = sum(c_across(matches("_dummy"))),
    # percent of observations that are complete (non-missing) ----
    pct_complete = 1 - mean(c_across(matches("_dummy")))
  ) %>% 
  # remove grouping from rowwise ---- 
  ungroup() %>% 
  # remove dummy variables ----
  dplyr::select(-matches("dummy"))

添加一个包含 NAs 和 Mean 计数的列

Add a column with count of NAs and Mean

r

na

dplyr