我有一个数据集,其中某些条目出现多次,有时一个条目中的数据在另一个条目中丢失,有时完全丢失数据,有时数据冲突:
# A tibble: 9 x 4
ID name age fsm
<chr> <chr> <dbl> <dbl>
1 0001 Peter 13 NA
2 0001 NA 13 1
3 0001 Barry 13 1
4 0002 Jane 13 1
5 0002 Jane NA 1
6 0003 Billy 12 0
7 0003 Billy 12 1
8 0004 Sally 12 NA
9 0004 Sally 12 NA
我想合并缺失数据的条目(例如 0001 和 0002),维护冲突数据 (0003) 并维护双倍 NA 的数据 (0004)。要生成这个:
# A tibble: 9 x 4
ID name age fsm
<chr> <chr> <dbl> <dbl>
1 0001 Peter 13 1
2 0001 Barry 13 1
3 0002 Jane 13 1
4 0003 Billy 12 0
5 0003 Billy 12 1
6 0004 Sally 12 NA
在此基础上: how to combine repeated rows with missing fields R
我写了以下内容:
tmp %>%
group_by(ID) %>%
summarise(across(everything(), ~ ifelse(length(na.omit(.x)) == 0, NA, na.omit(.x)))) %>%
distinct()
但是它丢失了复式输入,0003
ID name age fsm
<chr> <chr> <dbl> <dbl>
1 0001 Peter 13 1
2 0002 Jane 13 1
3 0003 Billy 12 0
4 0004 Sally 12 NA
数据作为输出:
structure(list(ID = c("0001", "0001", "0001", "0002", "0002",
"0003", "0003", "0004", "0004"), name = c("Peter", NA, "Barry",
"Jane", "Jane", "Billy", "Billy", "Sally", "Sally"), age = c(13,
13, 13, 13, NA, 12, 12, 12, 12), fsm = c(NA, 1, 1, 1, 1, 0, 1,
NA, NA)), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -9L), spec = structure(list(cols = list(
ID = structure(list(), class = c("collector_character", "collector"
)), name = structure(list(), class = c("collector_character",
"collector")), age = structure(list(), class = c("collector_double",
"collector")), fsm = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))
最佳答案
您正在寻找这种解决方案吗?
library(tidyverse)
df %>%
group_by(ID, name, age, fsm) %>%
mutate(dupe = n()>1) %>%
group_by(ID) %>%
dplyr::summarise(across(everything(), ~ ifelse(length(na.omit(.x)) == 0 & dupe == TRUE, NA, na.omit(.x)))) %>%
distinct() %>%
select(-dupe)
输出:
ID name age fsm
<chr> <chr> <dbl> <dbl>
1 0001 Peter 13 1
2 0001 Barry 13 1
3 0002 Jane 13 1
4 0003 Billy 12 0
5 0003 Billy 12 1
6 0004 Sally 12 NA
关于r - dplyr 函数用于合并重复数据、删除缺失数据并维护冲突数据,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/67055389/