将跨数据帧的共享数据列中的多个值重新编码/替换为单个值 [英] recode/replace multiple values in a shared data column to a single value across data frames
问题描述
我希望我不会错过它，但是我一直无法找到解决该问题的可行方法. 我有一组带有共享列的数据框.这些列包含多个变化的转录错误，对于多个值，其中一些是共享的，而其他则不共享. 我想用所有数据帧中的正确值(good_values)替换/重新编码转录错误(bad_values).
I hope I haven't missed it, but I haven't been able to find a working solution to this problem. I have a set of data frames with a shared column. These columns contain multiple and varying transcription errors, some of which are shared, others not, for multiple values. I would like replace/recode the transcription errors (bad_values) with the correct values (good_values) across all data frames.
我已经尝试在数据框，bad_values和good_values列表之间嵌套map*()函数系列，以实现此目的.这是一个示例:
I have tried nesting the map*() family of functions across lists of data frames, bad_values, and good_values to do this, among other things. Here is an example:
df1 = data.frame(grp = c("a1","a.","a.",rep("b",7)), measure = rnorm(10))
df2 = data.frame(grp = c(rep("as", 3), "b2",rep("a",22)), measure = rnorm(26))
df3 = data.frame(grp = c(rep("b-",3),rep("bq",2),"a", rep("a.", 3)), measure = 1:9)
df_list = list(df1, df2, df3)
bad_values = list(c("a1","a.","as"), c("b2","b-","bq"))
good_values = list("a", "b")
dfs = map(df_list, function(x) {
x %>% mutate(grp = plyr::mapvalues(grp, bad_values, rep(good_values,length(bad_values))))
})
我不一定希望能超越一个好坏值对.但是，我认为在此范围内嵌套另一个对map*()的调用可能会起作用:
Which I didn't necessarily expect to work beyond a single good-bad value pair. However, I thought nesting another call to map*() within this might work:
dfs = map(df_list, function(x) {
x %>% mutate(grp = map2(bad_values, good_values, function(x,y) {
recode(grp, bad_values = good_values)})
})
我尝试了许多其他方法，但都没有奏效.
I have tried a number of other approaches, none of which have worked.
最终，我想从一组有错误的数据帧开始，如下所示:
Ultimately, I would like to go from a set of data frames with errors, as here:
[[1]]
grp measure
1 a1 0.5582253
2 a. 0.3400904
3 a. -0.2200824
4 b -0.7287385
5 b -0.2128275
6 b 1.9030766
[[2]]
grp measure
1 as 1.6148772
2 as 0.1090853
3 as -1.3714180
4 b2 -0.1606979
5 a 1.1726395
6 a -0.3201150
[[3]]
grp measure
1 b- 1
2 b- 2
3 b- 3
4 bq 4
5 bq 5
6 a 6
对于固定"数据帧的列表，例如:
To a list of 'fixed' data frames, as such:
[[1]]
grp measure
1 a -0.7671052
2 a 0.1781247
3 a -0.7565773
4 b -0.3606900
5 b 1.9264804
6 b 0.9506608
[[2]]
grp measure
1 a 1.45036125
2 a -2.16715639
3 a 0.80105611
4 b 0.24216723
5 a 1.33089426
6 a -0.08388404
[[3]]
grp measure
1 b 1
2 b 2
3 b 3
4 b 4
5 b 5
6 a 6
任何帮助将不胜感激
推荐答案
以下是将tidyverse与recode_factor结合使用的选项.当有多个要更改的元素时，创建键/val元素的list并使用recode_factor进行匹配并将值更改为新的levels
Here is an option using tidyverse with recode_factor. When there are multiple elements to be changed, create a list of key/val elements and use recode_factor to match and change the values to new levels
library(tidyverse)
keyval <- setNames(rep(good_values, lengths(bad_values)), unlist(bad_values))
out <- map(df_list, ~ .x %>%
mutate(grp = recode_factor(grp, !!! keyval)))
-输出
out
#[[1]]
# grp measure
#1 a -1.63295876
#2 a 0.03859976
#3 a -0.46541610
#4 b -0.72356671
#5 b -1.11552841
#6 b 0.99352861
#....
#[[2]]
# grp measure
#1 a 1.26536789
#2 a -0.48189740
#3 a 0.23041056
#4 b -1.01324689
#5 a -1.41586086
#6 a 0.59026463
#....
#[[3]]
# grp measure
#1 b 1
#2 b 2
#3 b 3
#4 b 4
#5 b 5
#6 a 6
#....
注意:这不会更改初始数据集列的class
NOTE: This doesn't change the class of the initial dataset column
str(out)
#List of 3
# $ :'data.frame': 10 obs. of 2 variables:
# ..$ grp : Factor w/ 2 levels "a","b": 1 1 1 2 2 2 2 2 2 2
# ..$ measure: num [1:10] -1.633 0.0386 -0.4654 -0.7236 -1.1155 ...
# $ :'data.frame': 26 obs. of 2 variables:
# ..$ grp : Factor w/ 2 levels "a","b": 1 1 1 2 1 1 1 1 1 1 ...
# ..$ measure: num [1:26] 1.265 -0.482 0.23 -1.013 -1.416 ...
# $ :'data.frame': 9 obs. of 2 variables:
# ..$ grp : Factor w/ 2 levels "a","b": 2 2 2 2 2 1 1 1 1
# ..$ measure: int [1:9] 1 2 3 4 5 6 7 8 9
一旦我们有一个键值对list，它也可以在base R函数中使用
Once we have a keyval pair list, this can be also used in base R functions
out1 <- lapply(df_list, transform, grp = unlist(keyval[grp]))
这篇关于将跨数据帧的共享数据列中的多个值重新编码/替换为单个值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！
