删除几列中的重复值，但保留行 [英] Remove duplicate values across a few columns but keep rows

问题描述

我有一个看起来像这样的数据框:

I have a dataframe that looks like this:

dat <- data.frame(id=1:6,
                  z_1=c(100,290,38,129,0,290),
                  z_2=c(20,0,0,0,0,290),
                  z_3=c(0,0,38,0,0,98),
                  z_4=c(0,0,38,127,38,78),
                  z_5=c(23,0,25,0,0,98),
                  z_6=c(100,0,25,127,0,9))

日期

  id z_1 z_2 z_3 z_4 z_5 z_6
1  1 100 20  0   0   23  100
2  2 290  0  0   0   0   0
3  3  38  0  38  38  25  25
4  4 129  0  0   127 0   127
5  5   0  0  0   38  0   0
6  6 290 290 98  78  98  9

我想删除每行中 z_x 的重复值，用 0 或 NA 替换所有重复项，但保留行&完整的列(即不删除任何列).这里的0不算作重复项，它们是缺失值.列中的重复值是可以的.我理想的输出如下所示:

I want to remove duplicate values of z_x across each row, replacing any duplicates with either a 0 or NA, but leaving the rows & columns intact (ie not dropping any). The 0s here do not count as duplicates, they are missing values. Duplicate values within a column are ok. My ideal output would look like this:

   id z_1 z_2 z_3 z_4 z_5 z_6
1  1  100 20  0   0   23  0
2  2  290 0   0   0   0   0
3  3  38  0   0   0   25  0
4  4  129 0   0   127 0   0
5  5   0  0   0   38  0   0
6  6  290 0   98  78  0   9

我真的不在乎 z_x 中的值是以什么顺序出现的，因此，如果它们四处移动，也很好.有没有一种有效的方式来做到这一点，最好是采用某种整洁的方式?我知道我可以旋转更长的时间并删除重复的行，但是我的数据集非常大，我正在寻找一种无需旋转的方法.

I don't really care what order the values within the z_xs appear in, so it's fine if they get moved around. Is there an efficient way to do this, preferably in some tidyverse way? I know I can pivot longer and drop duplicate rows, but my dataset is very large and I'm looking for a way to do this without pivoting.

推荐答案

使用 apply 的Base R方法:

Base R way using apply :

cols <- grep('z_\\d+', names(dat))
dat[cols] <- t(apply(dat[cols], 1, function(x)  replace(x, duplicated(x), 0)))

#  id z_1 z_2 z_3 z_4 z_5 z_6
#1  1 100  20   0   0  23   0
#2  2 290   0   0   0   0   0
#3  3  38   0   0   0  25   0
#4  4 129   0   0 127   0   0
#5  5   0   0   0  38   0   0
#6  6 290   0  98  78   0   9

无需重塑的

tidyverse 方法可以使用 pmap 来完成:

tidyverse way without reshaping can be done using pmap :

library(tidyverse)

dat %>%
  mutate(result = pmap(select(., matches('z_\\d+')), ~{
    x <- c(...)
    replace(x, duplicated(x), 0)
    })) %>%
  select(id, result) %>%
  unnest_wider(result)

由于@thelatemail执行的测试表明，重塑是比按行处理数据更好的选择.

Since tests performed by @thelatemail suggests reshaping is a better option than handling the data rowwise you might want to consider it.

dat %>%
  pivot_longer(cols = matches('z_\\d+')) %>%
  group_by(id) %>%
  mutate(value = replace(value, duplicated(value), 0)) %>%
  pivot_wider()

这篇关于删除几列中的重复值，但保留行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！