r - 用R中的向量列表替换小标题列表中的指定列
问题描述
我正在分解一个数据文件，然后在它的组件上运行一个函数，然后尝试将它拼凑在一起，用新值替换指定的变量。函数如下，用于统计一个数据文件特定组成部分的c个数，如果只有1个，则用另一个c替换下一个最小值，保证至少有2个数据文件的任何单个组件。
secondary_supp <- function(x){
if(sum(x == "c") == 1){
replace(x, which.min(x), "c")
}
else{x}
}
目前我开始使用以下方法分解数据文件：
exploded_file <- data %>%
group_by(year,area,treatment_a,treatment_b) %>%
nest()
# selecting the list of tibbles
components <- exploded_file[5][[1]]
# running the function on the components
measure_a_supped <- lapply(components, function(x){secondary_supp(x[['measure_a']])})
正是在这一点上，我需要用这些新的“measure_a_supped”值替换列表中每个小标题的“measure_a”变量的原始值。
我假设我可以用另一个 tibbles 列表替换 'exploded_file' 中的 tibbles 列，然后知道我可以取消嵌套和取消组合以返回完整的数据文件。我被困在如何用我创建的向量列表 (measure_a_supped) 替换值，然后返回到小标题列表，让我用更新后的值返回原始数据表的结构.
如果需要在以下位置运行上述代码行的示例数据：
library(tidyverse)
raw_data = read.table(text="year level area location treatment_a treatment_b measure_a
2015 national NULL NULL Total Total 563
2015 national NULL NULL group_a Total 370
2015 national NULL NULL group_b Total 193
2015 location area_a location_a Total Total 119
2015 location area_a location_a group_a Total 93
2015 location area_a location_a group_b Total 26
2015 location area_a location_b Total Total 163
2015 location area_a location_b group_a Total 92
2015 location area_a location_b group_b Total 71
2015 area area_a NULL Total Total 282
2015 area area_a NULL group_a Total 185
2015 area area_a NULL group_b Total 97
2015 location area_b location_c Total Total 143
2015 location area_b location_c group_a Total 88
2015 location area_b location_c group_b Total 55
2015 location area_b location_d Total Total 138
2015 location area_b location_d group_a Total 97
2015 location area_b location_d group_b Total 41
2015 area area_b NULL Total Total 281
2015 area area_b NULL group_a Total 185
2015 area area_b NULL group_b Total 96
2015 national NULL NULL Total group_1 389
2015 national NULL NULL group_a group_1 295
2015 national NULL NULL group_b group_1 94
2015 location area_a location_a Total group_1 80
2015 location area_a location_a group_a group_1 64
2015 location area_a location_a group_b group_1 16
2015 location area_a location_b Total group_1 88
2015 location area_a location_b group_a group_1 72
2015 location area_a location_b group_b group_1 16
2015 area area_a NULL Total group_1 168
2015 area area_a NULL group_a group_1 136
2015 area area_a NULL group_b group_1 32
2015 location area_b location_c Total group_1 125
2015 location area_b location_c group_a group_1 75
2015 location area_b location_c group_b group_1 50
2015 location area_b location_d Total group_1 96
2015 location area_b location_d group_a group_1 84
2015 location area_b location_d group_b group_1 12
2015 area area_b NULL Total group_1 221
2015 area area_b NULL group_a group_1 159
2015 area area_b NULL group_b group_1 62
2015 national NULL NULL Total group_2 426
2015 national NULL NULL group_a group_2 212
2015 national NULL NULL group_b group_2 214
2015 location area_a location_a Total group_2 72
2015 location area_a location_a group_a group_2 71
2015 location area_a location_a group_b group_2 1
2015 location area_a location_b Total group_2 135
2015 location area_a location_b group_a group_2 80
2015 location area_a location_b group_b group_2 55
2015 area area_a NULL Total group_2 207
2015 area area_a NULL group_a group_2 151
2015 area area_a NULL group_b group_2 56
2015 location area_b location_c Total group_2 125
2015 location area_b location_c group_a group_2 30
2015 location area_b location_c group_b group_2 95
2015 location area_b location_d Total group_2 94
2015 location area_b location_d group_a group_2 31
2015 location area_b location_d group_b group_2 63
2015 area area_b NULL Total group_2 219
2015 area area_b NULL group_a group_2 61
2015 area area_b NULL group_b group_2 158
2014 national NULL NULL Total Total 470
2014 national NULL NULL group_a Total 156
2014 national NULL NULL group_b Total 314
2014 location area_a location_a Total Total 117
2014 location area_a location_a group_a Total 28
2014 location area_a location_a group_b Total 89
2014 location area_a location_b Total Total 125
2014 location area_a location_b group_a Total 27
2014 location area_a location_b group_b Total 98
2014 area area_a NULL Total Total 242
2014 area area_a NULL group_a Total 55
2014 area area_a NULL group_b Total 187
2014 location area_b location_c Total Total 71
2014 location area_b location_c group_a Total 25
2014 location area_b location_c group_b Total 46
2014 location area_b location_d Total Total 157
2014 location area_b location_d group_a Total 76
2014 location area_b location_d group_b Total 81
2014 area area_b NULL Total Total 228
2014 area area_b NULL group_a Total 101
2014 area area_b NULL group_b Total 127
2014 national NULL NULL Total group_1 502
2014 national NULL NULL group_a group_1 210
2014 national NULL NULL group_b group_1 292
2014 location area_a location_a Total group_1 179
2014 location area_a location_a group_a group_1 84
2014 location area_a location_a group_b group_1 95
2014 location area_a location_b Total group_1 100
2014 location area_a location_b group_a group_1 49
2014 location area_a location_b group_b group_1 51
2014 area area_a NULL Total group_1 279
2014 area area_a NULL group_a group_1 133
2014 area area_a NULL group_b group_1 146
2014 location area_b location_c Total group_1 107
2014 location area_b location_c group_a group_1 35
2014 location area_b location_c group_b group_1 72
2014 location area_b location_d Total group_1 116
2014 location area_b location_d group_a group_1 42
2014 location area_b location_d group_b group_1 74
2014 area area_b NULL Total group_1 223
2014 area area_b NULL group_a group_1 77
2014 area area_b NULL group_b group_1 146
2014 national NULL NULL Total group_2 478
2014 national NULL NULL group_a group_2 241
2014 national NULL NULL group_b group_2 237
2014 location area_a location_a Total group_2 69
2014 location area_a location_a group_a group_2 56
2014 location area_a location_a group_b group_2 13
2014 location area_a location_b Total group_2 139
2014 location area_a location_b group_a group_2 52
2014 location area_a location_b group_b group_2 87
2014 area area_a NULL Total group_2 208
2014 area area_a NULL group_a group_2 108
2014 area area_a NULL group_b group_2 100
2014 location area_b location_c Total group_2 149
2014 location area_b location_c group_a group_2 50
2014 location area_b location_c group_b group_2 99
2014 location area_b location_d Total group_2 121
2014 location area_b location_d group_a group_2 83
2014 location area_b location_d group_b group_2 38
2014 area area_b NULL Total group_2 270
2014 area area_b NULL group_a group_2 133
2014 area area_b NULL group_b group_2 137
",header=TRUE)
data <- raw_data
data$measure_a <- replace(data[["measure_a"]],data[["measure_a"]]<40,"c")
解决方案
我们可以使用mutate_if来选择我们应用到的列，然后使用withlag的replace下一个值"area_c""area_c"
library(dplyr)
library(purrr)
#Create one group which includes "area_c" for testing
raw_data$area <- as.character(raw_data$area)
raw_data[1,"area"] <- "area_c"
#Use `group_split` instead of nest()
lst <- raw_data %>% group_split(year, level)
#Test with one element from lst
lst[[6]] %>%
mutate_if(is.factor, as.character) %>%
mutate_if(.,~sum(grepl("c",.))==1,~replace(.,lag(.)=="area_c","c"))
#Use map_df if you need the output as df
map(lst, ~.x %>%
mutate_if(is.factor, as.character) %>%
mutate_if(., ~sum(grepl("c",.))==1, ~replace(.,lag(.)=="area_c","area_c")))
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