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
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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
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ali*_*ire 11
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
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请注意,您需要使用unique
with 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.
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))
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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。
Run Code Online (Sandbox Code Playgroud)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。