通过收集多个列来整理数据集？

Question

我想通过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

成为这样:

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(),但我只能让它与一个列一起使用并且我不断收到此警告:

警告消息:测量变量的属性不相同; 他们将被丢弃

有任何想法吗？

Answer 1

随着melt从data.table(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

或者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

注意:

对于该tidyr方法,convert=TRUE用于转换time_taken回numeric,因为当gather使用qID列编辑时它被强制转换为字符.

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))

Answer 2

在基础R中,您可以使用强大的功能reshape在一行语句中将数据从宽格式转换为长格式:

   reshape(dx,direction="long",
        varying=list(grep("qID",colnames(dx)),
                     grep("time_taken",colnames(dx))),
        v.names=c("qID","time_taken"))

     age gender     education previous_comp_exp tutorial_time time  qID time_taken id
1.1  18   Male Undergraduate      casual_gamer      62.17926    1 sor9   39.61206  1
2.1  24   Male Undergraduate      casual_gamer      85.01288    1 sor9   50.92343  2
1.2  18   Male Undergraduate      casual_gamer      62.17926    2 sor8   19.48920  1
2.2  24   Male Undergraduate      casual_gamer      85.01288    2 sor8   16.15616  2