[英]Tidy Data: Rename columns, get non-NA column names, then gather
我整理的数据非常难看,需要帮助! 我的数据现在看起来像什么:
countries <- c("Austria", "Belgium", "Croatia")
df <- tibble("age" = c(28,42,19, 67),
"1_recreate_1"=c(NA,15,NA,NA),
"1_recreate_2"=c(NA,10,NA,NA),
"1_recreate_3"=c(NA,8,NA,NA),
"1_recreate_4"=c(NA,4,NA,NA),
"1_fairness" = c(NA, 7, NA, NA),
"1_confidence" = c(NA, 5, NA, NA),
"2_recreate_1"=c(29,NA,NA,30),
"2_recreate_2"=c(20,NA,NA,24),
"2_recreate_3"=c(15,NA,NA,15),
"2_recreate_4"=c(11,NA,NA,9),
"2_fairness" = c(4, NA, NA, 1),
"2_confidence" = c(5, NA, NA, 4),
"3_recreate_1"=c(NA,NA,50,NA),
"3_recreate_2"=c(NA,NA,40,NA),
"3_recreate_3"=c(NA,NA,30,NA),
"3_recreate_4"=c(NA,NA,20,NA),
"3_fairness" = c(NA, NA, 2, NA),
"3_confidence" = c(NA, NA, 2, NA),
"overall" = c(3,3,2,5))
我需要它们在最后看起来像什么(对它进行硬编码):
df <- tibble(age = rep(c(28,42,19,67), each=4),
country = rep(c("Belgium", "Austria", "Croatia", "Belgium"), each=4),
recreate = rep(1:4, times=4),
fairness = rep(c(4,7,2,1), each=4),
confidence = rep(c(5,5,2,4), each=4),
allocation = c(29, 20, 15, 11,
15, 10, 8, 4,
50, 40, 30, 20,
30, 24, 15, 9),
overall = rep(c(3,3,2,5), each=4))
到达那里的步骤(我认为!):
1.使用我的国家/地区列表替换这些列的起始编号。
字符串开头的数字是countries
的索引。 换句话说, 16_recreate_1
将对应于vector country中的第16 countries
。 我认为以下代码可以工作(尽管不确定是否完全正确):
for(i in length(countries):1){
colnames(df) <- str_replace(colnames(df), paste0(i,"_"), paste0(countries[i],"_"))
}
2.通过获取每一行不是NA的列名来创建一个名为“ country”的新变量。
我尝试了使用which.max
和names
大量实验,但无法完全发挥作用。
3.创建新变量( recreate_1
... recreate_4
),以获取每行的[country_name]_recreate_1
... [country_name]_recreate_4
值,无论该人所在国家/地区是否为非NA。
也许rowSums
是做到这一点的方法?
4.使数据变长而不是变宽我认为这将需要gather
,但是我不确定如何仅从变量country
和recreate_1
... recreate_4
进行收集。
很抱歉,这是如此复杂。 Tidyverse解决方案是首选,但任何帮助是极大的赞赏!
library(dplyr)
library(tidyr)
df %>% mutate(rid=row_number()) %>%
gather(key,val,-c(age,overall,rid, matches('recreate'))) %>% mutate(country=sub('(^\\d)_.*','\\1',key),country=countries[as.numeric(country)]) %>%
filter(!is.na(val)) %>% mutate(key=sub('(^\\d\\_)(.*)','\\2',key)) %>%
spread(key,val) %>% gather(key = recreate,value = allocation,-c(rid,age,overall,Country,confidence,fairness)) %>%
filter(!is.na(allocation)) %>% mutate(recreate=sub('.*_(\\d$)','\\1',recreate))
此处(^\\\\d)_.*
表示获取第一个数字,而.*_(\\\\d$)
表示获取最后一个数字。
某种不同的tidyverse
可能性可能是:
df %>%
gather(variable, allocation, na.rm = TRUE) %>%
separate(variable, c("ID", "variable", "recreate"), convert = TRUE) %>%
left_join(data.frame(countries) %>%
mutate(country = countries,
ID = seq_along(countries)) %>%
select(-countries), by = c("ID" = "ID")) %>%
select(-variable, -ID)
recreate allocation country
<int> <dbl> <fct>
1 1 15 Austria
2 2 10 Austria
3 3 8 Austria
4 4 4 Austria
5 1 29 Belgium
6 1 30 Belgium
7 2 20 Belgium
8 2 24 Belgium
9 3 15 Belgium
10 3 15 Belgium
11 4 11 Belgium
12 4 9 Belgium
13 1 50 Croatia
14 2 40 Croatia
15 3 30 Croatia
16 4 20 Croatia
在这里,它首先将数据从宽格式转换为长格式,并用NA删除行。 其次,它将变量名称分为三列。 第三,它将国家/地区的向量转换为df,并为每个国家/地区分配一个唯一的ID。 最后,它将两者合并,并删除冗余变量。
已编辑问题的解决方案:
df %>%
select(matches("(recreate)")) %>%
rowid_to_column() %>%
gather(var, allocation, -rowid, na.rm = TRUE) %>%
separate(var, c("ID", "var", "recreate"), convert = TRUE) %>%
select(-var) %>%
left_join(data.frame(countries) %>%
mutate(country = countries,
ID = seq_along(countries)) %>%
select(-countries), by = c("ID" = "ID")) %>%
left_join(df %>%
select(-matches("(recreate)")) %>%
rowid_to_column() %>%
gather(var, val, -rowid, na.rm = TRUE) %>%
mutate(var = gsub("[^[:alpha:]]", "", var)) %>%
spread(var, val), by = c("rowid" = "rowid")) %>%
select(-rowid, -ID)
recreate allocation country age confidence fairness overall
<int> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 1 15 Austria 42 5 7 3
2 2 10 Austria 42 5 7 3
3 3 8 Austria 42 5 7 3
4 4 4 Austria 42 5 7 3
5 1 29 Belgium 28 5 4 3
6 1 30 Belgium 67 4 1 5
7 2 20 Belgium 28 5 4 3
8 2 24 Belgium 67 4 1 5
9 3 15 Belgium 28 5 4 3
10 3 15 Belgium 67 4 1 5
11 4 11 Belgium 28 5 4 3
12 4 9 Belgium 67 4 1 5
13 1 50 Croatia 19 2 2 2
14 2 40 Croatia 19 2 2 2
15 3 30 Croatia 19 2 2 2
16 4 20 Croatia 19 2 2 2
首先,在这里选择包含recreate
的列,并添加具有行ID的列。 其次,它遵循原始解决方案中的步骤。 第三,它选择不包含recreate
的列,执行从宽到长的数据转换,从列名中删除数字,然后将数据转换回原始的宽格式。 最后,它将两个行ID结合在一起,并删除冗余变量。
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