交叉制表多个回答问题 [英] Cross tabulate multiple response questions
问题描述
我需要通过分组变量交叉列出多个响应（存储为一组变量）。我的调查问题是：您有以下哪些水果？然后，给来自地理区域1或区域2的受访者一个带有 1.橙色，2。芒果...的列表，根据是（1）或否（0）问题得出的数据为：
I need to cross tabulate multiple responses (stored as a set of variables) by a grouping variable. My survey question is: "Which of the following fruits have you had?" The respondent from either geographical Area 1 or Area 2 is then given a list with "1. Orange, 2. Mango, ..." and the resulting data from the yes (1) or no (0) questions is:
set.seed(1)
df <- data.frame(area=rep(c('Area 1','Area 2'), each=6),
var_orange=sample(0:1, 12, T),
var_banana=sample(0:1, 12, T),
var_melon=sample(0:1, 12, T),
var_mango=sample(0:1, 12, T))
area var_orange var_banana var_melon var_mango
1 Area 1 0 1 0 1
2 Area 1 0 0 0 0
3 Area 1 1 1 0 1
4 Area 1 1 0 0 0
5 Area 1 0 1 1 1
6 Area 1 1 1 0 1
7 Area 2 1 0 0 1
8 Area 2 1 1 1 1
9 Area 2 1 1 0 1
10 Area 2 0 0 0 1
11 Area 2 0 1 1 0
12 Area 2 0 0 1 0
我想获得一个在Stata中生成的摘要输出：
I would like to get an summary output like this generated in Stata:
| area
| Area 1 Area 2 | Total
------------+------------------------+-----------
var_orange | 50.00 50.00 | 50.00
var_banana | 66.67 50.00 | 58.33
var_melon | 16.67 50.00 | 33.33
var_mango | 66.67 66.67 | 66.67
------------+------------------------+-----------
Total | 200.00 216.67 | 208.33
我发现了一个相关的发布，并带有多频函数，该函数可对我的数据进行单向汇总：
I found a related post with a multfreqtable function which gives a one-way summary for my data:
multfreqtable = function(data, question.prefix) {
z = length(question.prefix)
temp = vector("list", z)
for (i in 1:z) {
a = grep(question.prefix[i], names(data))
b = sum(data[, a] != 0)
d = colSums(data[, a] )
e = sum(rowSums(data[,a]) !=0)
f = as.numeric(c(d, b))
temp[[i]] = data.frame(question = c(sub(question.prefix[i],
"", names(d)), "Total"),
freq = f,
percent_response = (f/b)*100,
percent_cases = round((f/e)*100, 2))
names(temp)[i] = question.prefix[i]
}
temp
}
multfreqtable(df, "var_")
$var_
question freq percent_response percent_cases
1 orange 6 24 54.55
2 banana 7 28 63.64
3 melon 4 16 36.36
4 mango 8 32 72.73
5 Total 25 100 227.27
但是我对双向总结感兴趣。
But I am interested in a two-way summary.
我可以按照建议使用 dplyr 发布并获取：
I could use dplyr as suggested in a post and get:
df %>%
summarise(orange_pct=round(sum(var_orange,na.rm=TRUE)*100/n(),2),
banana_pct=round(sum(var_banana,na.rm=TRUE)*100/n(),2),
melon_pct=round(sum(var_melon,na.rm=TRUE)*100/n(),2),
mango_pct=round(sum(var_mango,na.rm=TRUE)*100/n(),2))
orange_pct banana_pct melon_pct mango_pct
1 50 58.33 33.33 66.67
但是我需要整洁的表输出和边际列频率。
But I need a neater table output with marginal column frequencies.
推荐答案
您可以先使用 dplyr 计算值，然后将其放入在表中使用例如 knitr :: kable 。
You could first calculate the values using dplyr, then put them in a table using e.g. knitr::kable.
library(dplyr)
library(knitr)
set.seed(1)
df <- data.frame(area = rep(c('Area 1','Area 2'), each = 6),
var_orange = sample(0:1, 12, T),
var_banana = sample(0:1, 12, T),
var_melon = sample(0:1, 12, T),
var_mango = sample(0:1, 12, T))
t1 <- df %>% group_by(area) %>% summarise_each(funs(mean))
t2 <- df %>% summarise_each(funs(mean))
kable(rbind(t1, t2))
：
|area | var_orange| var_banana| var_melon| var_mango|
|:------|----------:|----------:|---------:|---------:|
|Area 1 | 0.5| 0.6666667| 0.1666667| 0.6666667|
|Area 2 | 0.5| 0.5000000| 0.5000000| 0.6666667|
|NA | 0.5| 0.5833333| 0.3333333| 0.6666667|
进一步完善输出以模仿Stata的输出，
To further polish the output to mimick that from Stata:
polished <- 100 * rbind(t1, t2) %>% # Use percentages
select(-area) %>% # Drop "area"
mutate(Total = rowSums(.[])) %>% # Add Total
as.matrix %>% t
kable(polished, digits = 2, col.names = c("Area 1", "Area 2", "Total"))
最终结果将是：
| | Area 1| Area 2| Total|
|:----------|------:|------:|------:|
|var_orange | 50.00| 50.00| 50.00|
|var_banana | 66.67| 50.00| 58.33|
|var_melon | 16.67| 50.00| 33.33|
|var_mango | 66.67| 66.67| 66.67|
|Total | 200.00| 216.67| 208.33|
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