R:使用tidyr清理结构缺失和冗余数据的数据表 [英] R: use tidyr to clean-up data table with structural missing and redundant data

问题描述

仍在尝试使用 tidyr 包.如果一个数据集有这样的冗余行:

Still trying to get my hands on tidyrpackages. If one has a data set with redundant rows like this:

require(dplyr)
require(tidyr)
data <-
      data.frame(
        v1 = c("ID1", NA, "ID2", NA),
        v2 = c("x", NA, "xx", NA),
        v3 = c(NA, "z", NA, "zz"),
        v4 = c(22, 22, 6, 6),
        v5 = c(5, 5, 9, 9)) %>%
      tbl_df()

> data
Source: local data frame [4 x 5]

   v1 v2 v3 v4 v5
1 ID1  x NA 22  5
2  NA NA  z 22  5
3 ID2 xx NA  6  9
4  NA NA zz  6  9

由于 id 变量 v1- v3 被拆分为具有许多 NA 的冗余行(因此也重复了两次测量)，因此我们希望得到这样的结果下面:

Since the id variables v1- v3 is split into redundant rows with many NAs (and therefore the two measurements are also repeated) one would like to get something like this below:

    v1  v2  v3  v4  v5
1   ID1 x   z   22  5
2   ID2 xx  zz  6   9

使用 tidyr 获取此信息的一般方法是什么?我觉得它可以使用 gather() 来完成，但是怎么做?

What would be a general way of getting this using tidyr ? I feel it could be done using gather() but how ?

推荐答案

一种方法是这样的.使用 zoo 包中的 na.locf()，我替换了 v1 中的 NA.然后，我使用变量对数据进行分组.我再次使用 na.locf() 来处理 v3.最后，我删除了 v2 中带有 NA 的行.

One way would be like this. Using na.locf() from the zoo package, I replaced NAs in v1. Then, I grouped the data using the variable. I employed na.locf() one more time to take care of v3. Finally, I removed rows with NAs in v2.

library(zoo)
library(dplyr)

mutate(data, v1 = na.locf(v1)) %>%
group_by(v1) %>%
mutate(v3 = na.locf(v3, fromLast = TRUE)) %>%
filter(complete.cases(v2)) %>%
ungroup

#   v1 v2 v3 v4 v5
#1 ID1  x  z 22  5
#2 ID2 xx zz  6  9

这篇关于R:使用tidyr清理结构缺失和冗余数据的数据表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！