使用dplyr tidyr保留汇总表中输入变量和因子水平的顺序

Question

我喜欢这么简单,dplyr并且tidyr创建了一个包含多个预测变量和结果变量的汇总表.令我难过的一件事是在输出表中保留/定义预测变量的顺序及其因子水平的最后一步.

我已经提出了各种解决方案(下面),其中涉及使用mutate手动创建一个因子变量,该变量将预测变量和预测变量值(例如"gender_female")与所需输出顺序中的级别相结合.但是如果有很多变量,我的解决方案有点长,我想知道是否有更好的方法？

library(dplyr)
library(tidyr)
levels_eth <- c("Maori", "Pacific", "Asian", "Other", "European", "Unknown")
levels_gnd <- c("Female", "Male", "Unknown")

set.seed(1234)

dat <- data.frame(
  gender    = factor(sample(levels_gnd, 100, replace = TRUE), levels = levels_gnd),
  ethnicity = factor(sample(levels_eth, 100, replace = TRUE), levels = levels_eth),
  outcome1  = sample(c(TRUE, FALSE), 100, replace = TRUE),
  outcome2  = sample(c(TRUE, FALSE), 100, replace = TRUE)
)

dat %>% 
  gather(key = outcome, value = outcome_value, contains("outcome")) %>%
  gather(key = predictor, value = pred_value, gender, ethnicity) %>%
  # Statement below creates variable for ordering output
  mutate(
    pred_ord = factor(interaction(predictor, addNA(pred_value), sep = "_"),
                      levels = c(paste("gender", levels(addNA(dat$gender)), sep = "_"),
                                 paste("ethnicity", levels(addNA(dat$ethnicity)), sep = "_")))
  ) %>%
  group_by(pred_ord, outcome) %>%
  summarise(n = sum(outcome_value, na.rm = TRUE)) %>%
  ungroup() %>%
  spread(key = outcome, value = n) %>%
  separate(pred_ord, c("Predictor", "Pred_value"))

Source: local data frame [9 x 4]

  Predictor Pred_value outcome1 outcome2
      (chr)      (chr)    (int)    (int)
1    gender     Female       25       27
2    gender       Male       11       10
3    gender    Unknown       12       15
4 ethnicity      Maori       10        9
5 ethnicity    Pacific        7        7
6 ethnicity      Asian        6       12
7 ethnicity      Other       10        9
8 ethnicity   European        5        4
9 ethnicity    Unknown       10       11
Warning message:
attributes are not identical across measure variables; they will be dropped 

上表是正确的,因为Predictor和Predictor值都不是按字母顺序排序的.

编辑

根据要求,如果使用默认排序(按字母顺序排列),则会生成此内容.有意义的是,当组合因子时,它们被转换为字符变量并且所有属性都被删除.

dat %>% 
  gather(key = outcome, value = outcome_value, contains("outcome")) %>%
  gather(key = predictor, value = pred_value, gender, ethnicity) %>%
  group_by(predictor, pred_value, outcome) %>%
  summarise(n = sum(outcome_value, na.rm = TRUE)) %>%
  spread(key = outcome, value = n)

Source: local data frame [9 x 4]

  predictor pred_value outcome1 outcome2
      (chr)      (chr)    (int)    (int)
1 ethnicity      Asian        6       12
2 ethnicity   European        5        4
3 ethnicity      Maori       10        9
4 ethnicity      Other       10        9
5 ethnicity    Pacific        7        7
6 ethnicity    Unknown       10       11
7    gender     Female       25       27
8    gender       Male       11       10
9    gender    Unknown       12       15
Warning message:
attributes are not identical across measure variables; they will be dropped 

Answer 1

如果你希望你的数据是这样排列的因素,你需要将它们转换回因子,作为gather对字符的强制(它警告你).您可以使用gather的factor_key参数来照顾predictor,但你需要组装的水平pred_value,因为它现在结合两种因素从原来的.简化一下:

library(tidyr)
library(dplyr)

dat %>% 
    gather(key = predictor, value = pred_value, gender, ethnicity, factor_key = TRUE) %>%
    group_by(predictor, pred_value) %>% 
    summarise_all(sum) %>%
    ungroup() %>% 
    mutate(pred_value = factor(pred_value, levels = unique(c(levels_eth, levels_gnd), 
                                                           fromLast = TRUE))) %>% 
    arrange(predictor, pred_value)

## # A tibble: 9 × 4
##   predictor pred_value outcome1 outcome2
##      <fctr>     <fctr>    <int>    <int>
## 1    gender     Female       25       27
## 2    gender       Male       11       10
## 3    gender    Unknown       12       15
## 4 ethnicity      Maori       10        9
## 5 ethnicity    Pacific        7        7
## 6 ethnicity      Asian        6       12
## 7 ethnicity      Other       10        9
## 8 ethnicity   European        5        4
## 9 ethnicity    Unknown       10       11

请注意,您需要使用uniquewith fromLast = TRUE将重复的"未知"值排列到正确位置的单个事件中; union会提前说的.

Answer 2

您可以以更加简洁和有效的方式来执行此操作，而无需使用特殊程序包：

rbind(aggregate(dat[,colnames(dat) %in% c("outcome1", "outcome2")], 
                by = list(dat$gender), sum),
      aggregate(dat[,colnames(dat) %in% c("outcome1", "outcome2")], 
                by = list(dat$ethnicity), sum))

它以一种简单直接的方式聚合了多个预测变量和结果变量，并且还避免了必须创建作为您提到的复杂解决方案一部分的变量。

   Group.1 outcome1 outcome2
1   Female       25       27
2     Male       11       10
3  Unknown       12       15
4    Maori       10        9
5  Pacific        7        7
6    Asian        6       12
7    Other       10        9
8 European        5        4
9  Unknown       10       11

如果要重命名以上各列，只需将其分配给一个对象（例如mytable <-）并重命名它们（即colnames(mytable) <- c("Pred_value", "outcome1", "outcome2")）。apply如果要输入的变量太多，也可以使用进行扩展。