交叉两个不同的数据集以将一个数据集的结果变异和汇总到另一个数据集

Question

假设我有这个假数据框

这里还有这个“external_table”：

我想通过以下过程“同步”两个数据集以向 external_table 添加一个新列：

1. 访问 fake_ds
2. 对来自 external_table %>% itens_fator 的项目执行函数
3. 将结果变异到外部表中

我想要的输出（带有假结果）

这是脚本应该执行的伪函数：

fake_ds %>%  #get my ds
  mutate(cronbach_alpha = fake_ds %>% 
           select(external_table, itens_fator) %>% 
           alpha(.)$total[1]) #get variables from external table

如果此功能难以实现，我将向其他产生所需输出的方法开放。

我想留在 tidyverse。

重新创建我的数据的代码：

library(dplyr)
library(tidyr)

x <- paste0("y",seq(1:96)) #create X
y <- rep(0:5, 96*2) #create values
fake_ds <- data.frame(x,y) #dataframe
fake_ds %>% 
  pivot_wider(names_from = x, values_from=y, values_fn = {mean}) -> fake_ds
fake_ds <- fake_ds %>% slice(rep(1:n(), each = 50)) #replicate
fake_ds <- rbind(fake_ds, seq(1:96)) #add variability
    
#external table
external_table <- structure(list(
  name = c("X5", "X1", "X2", "X0", "X3", "X4"), 
  itens_fator = c("y1,y12,y59,y76,y78,y92,y93,y94,y96", 
                  "y5,y14,y15,y16,y17,y18,y20,y24,y40,y60,y62,y64,y75", 
                  "y10,y19,y32,y34,y36,y37,y47,y56,y58,y72,y80,y85", 
                  "y13,y30,y39,y53,y54,y55,y66,y73,y84,y91", 
                  "y42,y43,y45,y63,y69,y77,y87,y88", 
                  "y44,y49,y50,y68,y82,y89")), 
  row.names = c(NA, -6L), 
  groups = structure(list(name = c("X0", "X1", "X2", "X3", "X4", "X5"), 
                          .rows = structure(list(4L, 2L, 3L, 5L, 6L, 1L), 
                                            ptype = integer(0), 
                                            class = c("vctrs_list_of", 
                                                      "vctrs_vctr", "list"))), 
                     row.names = c(NA, -6L), 
                     class = c("tbl_df", "tbl", "data.frame"), 
                     .drop = TRUE), 
  class = c("grouped_df", "tbl_df", "tbl", "data.frame"))

Answer 1

正如我在 cmets 中提到的，您想要实现的目标是不可能的......要获得 cronbach 的 alpha，您需要做的就是使用 lapply 和 strsplit..

获取 cronbach 的 alphas

lapply(strsplit(external_table$itens_fator,","), function(x) fake_ds %>% 
           select(all_of(x)) %>% 
           alpha(.)%>% .$total %>% .$raw_alpha)

整洁的方式

external_table$itens_fator %>% strsplit(",") %>% purrr::map_dbl(function(x) fake_ds %>% 
           select(all_of(x)) %>% 
           psych::alpha(.)%>% .$total %>% .$raw_alpha)

数据

library(psych)
library(dplyr)
library(tidyr)

x <- paste0("y",seq(1:96)) #create X
y <- rep(0:5, 96*2) #create values
fake_ds <- data.frame(var=x,val=y) #dataframe
fake_ds %>% 
  pivot_wider(names_from = var, values_from=val, values_fn=list(val=mean)) -> fake_ds
fake_ds <- fake_ds %>% slice(rep(1:n(), each = 50)) #replicate
fake_ds <- rbind(fake_ds, seq(1:96)) #add variability
    
#external table
external_table <- structure(list(
  name = c("X5", "X1", "X2", "X0", "X3", "X4"), 
  itens_fator = c("y1,y12,y59,y76,y78,y92,y93,y94,y96", 
                  "y5,y14,y15,y16,y17,y18,y20,y24,y40,y60,y62,y64,y75", 
                  "y10,y19,y32,y34,y36,y37,y47,y56,y58,y72,y80,y85", 
                  "y13,y30,y39,y53,y54,y55,y66,y73,y84,y91", 
                  "y42,y43,y45,y63,y69,y77,y87,y88", 
                  "y44,y49,y50,y68,y82,y89")), 
  row.names = c(NA, -6L), 
  groups = structure(list(name = c("X0", "X1", "X2", "X3", "X4", "X5"), 
                          .rows = structure(list(4L, 2L, 3L, 5L, 6L, 1L), 
                                            ptype = integer(0), 
                                            class = c("vctrs_list_of", 
                                                      "vctrs_vctr", "list"))), 
                     row.names = c(NA, -6L), 
                     class = c("tbl_df", "tbl", "data.frame"), 
                     .drop = TRUE), 
  class = c("grouped_df", "tbl_df", "tbl", "data.frame"))