r - 使用 dplyr tidyr 在汇总表中保留输入变量和因子水平的顺序

Question

我爱多么容易dplyr和 tidyr已经创建了一个带有多个预测变量和结果变量的汇总表。让我难倒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。

我想出了一个解决方案(如下)，其中涉及使用 mutate手动制作一个因子变量，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，我的解决方案有点啰嗦，不知道有没有更好的方法？

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列的。

编辑

根据要求，这就是使用默认排序(按字母顺序)时产生的结果。这是有道理的，当这些因素组合在一起时，它们会被转换为一个字符变量，并且所有属性都将被删除。

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

如果您希望您的数据是这样排列的因子，则需要将它们转换回因子，如 gather强制字符(它警告你)。您可以使用 gather的 factor_key要处理的参数 predictor ，但您需要为 pred_value 组装关卡因为它现在结合了原始的两个因素。稍微简化一下:

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 unique与 fromLast = TRUE将重复的“未知”值排列在正确位置的单个事件中； union会早点放。