这个问题在这里已经有了答案:
Reshaping multiple sets of measurement columns (wide format) into single columns (long format)
(7 个回答)
3年前关闭。
我想通过以这种方式操作数据来整理数据集:
age gender education previous_comp_exp tutorial_time qID.1 time_taken.1 qID.2 time_taken.2
18 Male Undergraduate casual gamer 62.17926 sor9 39.61206 sor8 19.4892
24 Male Undergraduate casual gamer 85.01288 sor9 50.92343 sor8 16.15616
变成这样:
age gender education previous_comp_exp tutorial_time qID time_taken
18 Male Undergraduate casual gamer 62.17926 sor9 39.61206
18 Male Undergraduate casual gamer 62.17926 sor8 19.4892
24 Male Undergraduate casual gamer 85.01288 sor9 50.92343
24 Male Undergraduate casual gamer 85.01288 sor8 16.15616
我已经尝试过
gather() ，但我只能让它与一列一起工作，并且我不断收到此警告:有任何想法吗？
最佳答案
来自 melt 的 data.table(参见 ?patterns):
library(data.table)
melt(setDT(df), measure = patterns("^qID", "^time_taken"),
value.name = c("qID", "time_taken"))
结果:
age gender education previous_comp_exp tutorial_time variable qID time_taken
1: 18 Male Undergraduate casual_gamer 62.17926 1 sor9 39.61206
2: 24 Male Undergraduate casual_gamer 85.01288 1 sor9 50.92343
3: 18 Male Undergraduate casual_gamer 62.17926 2 sor8 19.48920
4: 24 Male Undergraduate casual_gamer 85.01288 2 sor8 16.15616
或使用
tidyr :library(dplyr)
library(tidyr)
df %>%
gather(variable, value, qID.1:time_taken.2) %>%
mutate(variable = sub("\\.\\d$", "", variable)) %>%
group_by(variable) %>%
mutate(ID = row_number()) %>%
spread(variable, value, convert = TRUE) %>%
select(-ID)
结果:
# A tibble: 4 x 7
age gender education previous_comp_exp tutorial_time qID time_taken
<int> <fctr> <fctr> <fctr> <dbl> <chr> <dbl>
1 18 Male Undergraduate casual_gamer 62.17926 sor9 39.61206
2 18 Male Undergraduate casual_gamer 62.17926 sor8 19.48920
3 24 Male Undergraduate casual_gamer 85.01288 sor9 50.92343
4 24 Male Undergraduate casual_gamer 85.01288 sor8 16.15616
注:
对于
tidyr 方法， convert=TRUE 用于将 time_taken 转换回 numeric ，因为它在 10x23132313231351323153135 列时被强制转换为字符数据:
df = structure(list(age = c(18L, 24L), gender = structure(c(1L, 1L
), .Label = "Male", class = "factor"), education = structure(c(1L,
1L), .Label = "Undergraduate", class = "factor"), previous_comp_exp = structure(c(1L,
1L), .Label = "casual_gamer", class = "factor"), tutorial_time = c(62.17926,
85.01288), qID.1 = structure(c(1L, 1L), .Label = "sor9", class = "factor"),
time_taken.1 = c(39.61206, 50.92343), qID.2 = structure(c(1L,
1L), .Label = "sor8", class = "factor"), time_taken.2 = c(19.4892,
16.15616)), .Names = c("age", "gender", "education", "previous_comp_exp",
"tutorial_time", "qID.1", "time_taken.1", "qID.2", "time_taken.2"
), class = "data.frame", row.names = c(NA, -2L))
关于r - 通过收集多列来整理数据集？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/47272371/