跟着这个问题和这个问题,我想知道在一个数据集中汇总分类变量的最佳选项。
我有一个类似于以下的数据集
# A tibble: 10 <U+00D7> 4
empstat_couple nssec7_couple3 nchild07 age_couple
<chr> <fctr> <fctr> <dbl>
1 Neo-Trad Lower Managerial 1child 39
2 Neo-Trad Higher Managerial 1child 31
3 Neo-Trad Manual and Routine 1child 33
4 Trad Higher Managerial 1child 43
前三个变量是分类的(字符或因子),最后一个是数值型的。
我想要的是类似于(输出)这样的东西。
var n p
1: Neo-Trad 6 0.6
2: OtherArrangment 2 0.2
3: Trad 2 0.2
4: Higher Managerial 4 0.4
5: Lower Managerial 5 0.5
6: Manual and Routine 1 0.1
7: 1child 9 0.9
8: 2children 1 0.1
对于数字变量,我不确定如何将其有意义地添加到总结中。
我想最基本的方式是:
library(dplyr)
library(data.table)
a = count(dt, empstat_couple) %>% mutate(p = n / sum(n))
b = count(dt, nssec7_couple3) %>% mutate(p = n / sum(n))
c = count(dt, nchild07) %>% mutate(p = n / sum(n))
rbindlist(list(a,b,c))
我想知道是否存在summarise_each
解决方案?
这个不起作用。
dt %>% summarise_each(funs(count))
使用apply
,我可以得出以下结果:
apply(dt, 2, as.data.frame(table)) %>% rbindlist()
但这并不好。有任何建议吗?数据。
dt = structure(list(empstat_couple = c("Neo-Trad", "Neo-Trad", "Neo-Trad",
"Trad", "OtherArrangment", "Neo-Trad", "Trad", "OtherArrangment",
"Neo-Trad", "Neo-Trad"), nssec7_couple3 = structure(c(2L, 1L,
4L, 1L, 2L, 2L, 1L, 2L, 1L, 2L), .Label = c("Higher Managerial",
"Lower Managerial", "Intermediate", "Manual and Routine"), class = "factor"),
nchild07 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L), .Label = c("1child", "2children", ">2children"), class = "factor"),
age_couple = c(39, 31, 33, 43, 32, 28, 28, 40, 33, 26), hldid = 1:10), .Names = c("empstat_couple",
"nssec7_couple3", "nchild07", "age_couple", "hldid"), row.names = c(NA,
-10L), class = "data.frame")