在心理学中,下面介绍的这种数据集很常见
我想group
所有年龄(变量 =quest
),而不是对所有尺度进行分组(com_a4_1:com_a4_6
; and gm_a4_1:gm_a4_6
等),然后对数据应用可靠性函数(psych::alpha
).
我成功创建了这个语法
d %>%
select(quest,contains("_a4_")) %>% #get the data
group_by(quest) %>% #group by all age interval
do(alpha(.)$total)
但是,我无法使用秤的项目“子”嵌套。
据我想象,我将不得不旋转我的数据,然后分组或嵌套。但是,我目前还没有取得任何成功。我的预期结果类似于下图。有“两个嵌套结果”。第一个结果按比例分组(例如:com_a4_1:com_a4_6
),第二个按年龄分组(quest
)
下面是假数据和代码
library(psych)
library(tidyverse)
d %>%
select(quest,contains("_a4_")) %>% #get the data
group_by(quest) %>% #group by all age interval
do(alpha(.)$total)
d <-structure(list(quest = c(6, 4, 2, 4, 2, 6, 2, 4, 2, 2, 4, 2,
6, 4, 4, 2, 2, 4, 2, 6, 2, 2, 4, 6, 6, 4, 4, 4, 2, 6, 4, 2, 6,
4, 6, 2, 2, 4, 6, 4, 2), com_a4_1 = c(10, 0, 10, 10, 5, 10, 5,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10, 0, 10,
10, 10, 10, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10,
10, 10), com_a4_2 = c(10, 10, 5, 10, 10, 5, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 5, 10, 10, 10, 10, 5,
10, 10, 10, 5, 0, 10, 10, 10, 10, 0, 10, 10, 10, 10), com_a4_3 = c(10,
5, 0, 5, 10, 5, 5, 10, 10, 10, 10, 10, 5, 5, 10, 10, 5, 10, 10,
10, 10, 5, 5, 10, 10, 5, 5, 10, 10, 10, 10, 5, 10, 10, 10, 10,
0, 10, 5, 10, 10), com_a4_4 = c(10, 0, 0, 10, 5, 10, 10, 10,
10, 5, 5, 10, 10, 5, 10, 10, 5, 10, 10, 10, 10, 5, 10, 10, 10,
10, 0, 10, 5, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10),
com_a4_5 = c(10, 0, 0, 5, 0, 10, 5, 10, 10, 5, 10, 10, 0,
10, 10, 10, 0, 10, 5, 10, 0, 0, 10, 0, 10, 10, 10, 10, 5,
0, 10, 5, 5, 10, 10, 10, 0, 10, 10, 10, 10), com_a4_6 = c(5,
10, 0, 10, 10, 5, 10, 10, 10, 0, 10, 10, 5, 10, 10, 10, 10,
10, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 5, 10,
5, 10, 5, 10, 0, 10, 5, 10, 10), gm_a4_1 = c(10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10), gm_a4_2 = c(10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 5, 5, 10, 10, 10, 0, 10, 10,
5, 10, 10, 5, 10, 10, 10, 10), gm_a4_3 = c(10, 10, 10, 10,
10, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 0, 0, 10, 10, 10, 0, 10, 10, 10,
10, 10, 5, 10, 10, 10, 10), gm_a4_4 = c(0, 5, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 5,
10, 10, 10, 10, 10, 0, 0, 10, 10, 10, 0, 10, 5, 5, 5, 10,
10, 10, 10, 10, 10), gm_a4_5 = c(10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10, 10, 10, 5, 10,
5, 10, 10, 10, 10), gm_a4_6 = c(0, 10, 5, 5, 10, 5, 5, 10,
10, 5, 10, 10, 0, 10, 10, 10, 5, 10, 5, 10, 10, 10, 10, 0,
10, 10, 10, 10, 10, 0, 10, 10, 10, 10, 0, 10, 0, 10, 10,
10, 10), fm_a4_1 = c(10, 5, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 5, 0, 10, 10, 0, 5,
10, 10, 10, 10, 5, 5, 10, 10, 5, 5, 10, 10, 10, 10, 10),
fm_a4_2 = c(10, 10, 10, 10, 0, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10, 10, 5,
10, 10, 5, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10, 10), fm_a4_3 = c(0,
5, 10, 10, 5, 10, 5, 10, 10, 10, 10, 10, 5, 10, 5, 5, 5,
10, 10, 5, 0, 10, 5, 10, 5, 10, 10, 0, 10, 10, 5, 10, 10,
10, 0, 10, 0, 10, 10, 10, 10), fm_a4_4 = c(10, 5, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 5, 10, 10, 10, 5, 10, 10, 10, 0, 10, 10, 10,
10, 10, 0, 10, 10, 10, 10), fm_a4_5 = c(0, 5, 10, 10, 10,
0, 10, 10, 10, 10, 10, 10, 0, 10, 10, 5, 10, 10, 5, 0, 10,
10, 10, 10, 10, 10, 5, 10, 10, 0, 5, 10, 0, 10, 0, 5, 5,
5, 10, 10, 10), fm_a4_6 = c(10, 5, 5, 0, 0, 5, 10, 10, 10,
0, 10, 10, 5, 10, 10, 10, 0, 10, 0, 10, 10, 0, 10, 10, 5,
0, 0, 10, 10, 10, 0, 10, 10, 5, 5, 10, 0, 0, 10, 10, 5),
cg_a4_1 = c(10, 5, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 0, 10, 10, 10, 10, 5, 0,
10, 10, 10, 10, 5, 10, 10, 10, 10, 5, 5, 10, 10, 10), cg_a4_2 = c(5,
10, 10, 5, 10, 5, 10, 10, 10, 10, 10, 10, 5, 10, 10, 10,
10, 10, 10, 5, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10), cg_a4_3 = c(10,
10, 5, 10, 10, 10, 10, 10, 10, 5, 10, 10, 5, 10, 10, 10,
5, 10, 10, 10, 10, 0, 10, 10, 5, 10, 5, 10, 10, 10, 5, 10,
10, 10, 10, 10, 5, 10, 10, 10, 10), cg_a4_4 = c(10, 10, 0,
5, 5, 5, 10, 10, 10, 5, 10, 10, 0, 5, 10, 10, 5, 10, 10,
10, 10, 0, 5, 10, 10, 5, 0, 0, 10, 10, 0, 10, 0, 10, 10,
5, 0, 5, 5, 10, 10), cg_a4_5 = c(5, 0, 0, 5, 0, 10, 5, 10,
10, 0, 10, 10, 10, 10, 5, 10, 0, 10, 0, 10, 0, 0, 10, 10,
5, 10, 5, 10, 5, 5, 5, 0, 10, 10, 5, 10, 0, 10, 10, 10, 10
), cg_a4_6 = c(0, 0, 5, 10, 10, 10, 10, 10, 0, 10, 5, 10,
10, 10, 5, 10, 10, 10, 10, 10, 5, 10, 10, 10, 10, 5, 5, 10,
5, 10, 0, 10, 10, 5, 5, 10, 5, 10, 10, 10, 10), ps_a4_1 = c(10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 5, 5, 10, 5, 10, 10, 10, 10), ps_a4_2 = c(0, 10,
10, 10, 5, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 5, 10, 5, 10, 10, 10, 5, 10, 10, 10, 5, 0, 10, 10, 10,
5, 0, 10, 5, 10, 10, 10, 10), ps_a4_3 = c(10, 0, 10, 5, 5,
10, 5, 10, 10, 5, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
5, 10, 10, 10, 5, 10, 10, 10, 5, 10, 10, 10, 10, 5, 0, 5,
0, 10, 5, 10, 10), ps_a4_4 = c(10, 10, 10, 10, 5, 10, 5,
10, 10, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 10,
10, 10, 10, 10, 10, 10, 5, 10, 5, 10, 10, 10, 10, 5, 5, 10,
10, 10, 10), ps_a4_5 = c(5, 5, 10, 5, 10, 5, 10, 10, 0, 0,
10, 10, 5, 10, 10, 10, 10, 10, 0, 10, 5, 5, 5, 10, 0, 10,
5, 10, 5, 0, 10, 10, 10, 10, 0, 5, 0, 5, 10, 10, 5), ps_a4_6 = c(5,
5, 0, 5, 0, 10, 0, 10, 5, 5, 10, 10, 5, 10, 10, 10, 0, 10,
5, 10, 5, 0, 5, 10, 5, 10, 5, 0, 5, 10, 0, 0, 10, 5, 0, 5,
0, 10, 10, 10, 10)), row.names = c(NA, -41L), class = "data.frame")