R、 使用字符对象为“因子”列添加标签

我有一个data.frame，其中的列需要标记

df <- structure(list(q1 = c("5", "6", "5", "5", "7", "5", "5", "5", 
"5", "6", "5", "6", "6", "6", "7", "6", "5", "6", "5", "6", "6", 
"5", "7", "5", "6", "6", "5", "6", "6", "5", "5", "5", "5", "5", 
"5", "5", "4", "5", "5", "4", "4", "5", "4", "4", "5", "4", "5", 
"5", "4", "5"), q2 = c("2", "2", "1", "1", "2", "1", "1", "2", 
"1", "1", "1", "2", "1", "1", "2", "1", "2", "1", "2", "2", "2", 
"1", "2", "2", "2", "2", "2", "1", "1", "2", "2", "2", "2", "2", 
"2", "1", "2", "1", "1", "1", "1", "2", "1", "1", "1", "1", "2", 
"2", "1", "2"), q3 = c("3", "3", "3", "3", "3", "3", "3", "3", 
"3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", 
"3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", 
"3", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2")), row.names = c(NA, -50L), class = c("tbl_df", 
"tbl", "data.frame"), na.action = structure(c(`71` = 71L, `78` = 78L, 
`96` = 96L, `250` = 250L, `393` = 393L, `488` = 488L, `644` = 644L, 
`847` = 847L, `862` = 862L, `1083` = 1083L, `1120` = 1120L, `1149` = 1149L, 
`1322` = 1322L, `1357` = 1357L), class = "omit"))

df
输出

      q1     q2       q3    
 [1,] "16 y" "Male"   "11th"
 [2,] "17 y" "Male"   "11th"
 [3,] "16 y" "Female" "11th"
 [4,] "16 y" "Female" "11th"
 [5,] "18 y" "Male"   "11th"
 [6,] "16 y" "Female" "11th"
 [7,] "16 y" "Female" "11th"
 [8,] "16 y" "Male"   "11th"
 [9,] "16 y" "Female" "11th"
[10,] "17 y" "Female" "11th"
[11,] "16 y" "Female" "11th"
[12,] "17 y" "Male"   "11th"
[13,] "17 y" "Female" "11th"
[14,] "17 y" "Female" "11th"
[15,] "18 y" "Male"   "11th"
[16,] "17 y" "Female" "11th"
[17,] "16 y" "Male"   "11th"
[18,] "17 y" "Female" "11th"
[19,] "16 y" "Male"   "11th"
[20,] "17 y" "Male"   "11th"
[21,] "17 y" "Male"   "11th"
[22,] "16 y" "Female" "11th"
[23,] "18 y" "Male"   "11th"
[24,] "16 y" "Male"   "11th"
[25,] "17 y" "Male"   "11th"
[26,] "17 y" "Male"   "11th"
[27,] "16 y" "Male"   "11th"
[28,] "17 y" "Female" "11th"
[29,] "17 y" "Female" "11th"
[30,] "16 y" "Male"   "11th"
[31,] "16 y" "Male"   "11th"
[32,] "16 y" "Male"   "11th"
[33,] "16 y" "Male"   "11th"
[34,] "16 y" "Male"   "11th"
[35,] "16 y" "Male"   "11th"
[36,] "16 y" "Female" "10th"
[37,] "15 y" "Male"   "10th"
[38,] "16 y" "Female" "10th"
[39,] "16 y" "Female" "10th"
[40,] "15 y" "Female" "10th"
[41,] "15 y" "Female" "10th"
[42,] "16 y" "Male"   "10th"
[43,] "15 y" "Female" "10th"
[44,] "15 y" "Female" "10th"
[45,] "16 y" "Female" "10th"
[46,] "15 y" "Female" "10th"
[47,] "16 y" "Male"   "10th"
[48,] "16 y" "Male"   "10th"
[49,] "15 y" "Female" "10th"
[50,] "16 y" "Male"   "10th"

输出

      q1     q2       q3    
 [1,] "16 y" "Male"   "11th"
 [2,] "17 y" "Male"   "11th"
 [3,] "16 y" "Female" "11th"
 [4,] "16 y" "Female" "11th"
 [5,] "18 y" "Male"   "11th"
 [6,] "16 y" "Female" "11th"
 [7,] "16 y" "Female" "11th"
 [8,] "16 y" "Male"   "11th"
 [9,] "16 y" "Female" "11th"
[10,] "17 y" "Female" "11th"
[11,] "16 y" "Female" "11th"
[12,] "17 y" "Male"   "11th"
[13,] "17 y" "Female" "11th"
[14,] "17 y" "Female" "11th"
[15,] "18 y" "Male"   "11th"
[16,] "17 y" "Female" "11th"
[17,] "16 y" "Male"   "11th"
[18,] "17 y" "Female" "11th"
[19,] "16 y" "Male"   "11th"
[20,] "17 y" "Male"   "11th"
[21,] "17 y" "Male"   "11th"
[22,] "16 y" "Female" "11th"
[23,] "18 y" "Male"   "11th"
[24,] "16 y" "Male"   "11th"
[25,] "17 y" "Male"   "11th"
[26,] "17 y" "Male"   "11th"
[27,] "16 y" "Male"   "11th"
[28,] "17 y" "Female" "11th"
[29,] "17 y" "Female" "11th"
[30,] "16 y" "Male"   "11th"
[31,] "16 y" "Male"   "11th"
[32,] "16 y" "Male"   "11th"
[33,] "16 y" "Male"   "11th"
[34,] "16 y" "Male"   "11th"
[35,] "16 y" "Male"   "11th"
[36,] "16 y" "Female" "10th"
[37,] "15 y" "Male"   "10th"
[38,] "16 y" "Female" "10th"
[39,] "16 y" "Female" "10th"
[40,] "15 y" "Female" "10th"
[41,] "15 y" "Female" "10th"
[42,] "16 y" "Male"   "10th"
[43,] "15 y" "Female" "10th"
[44,] "15 y" "Female" "10th"
[45,] "16 y" "Female" "10th"
[46,] "15 y" "Female" "10th"
[47,] "16 y" "Male"   "10th"
[48,] "16 y" "Male"   "10th"
[49,] "15 y" "Female" "10th"
[50,] "16 y" "Male"   "10th"

我们可以使用“将所有标签放入列表”并使用
因子更改列中的值

df[] <- Map(function(x, y) factor(x, labels = y[1:length(unique(x))]),
            df,mget(ls(pattern = "Q\\d+_Label")))
df
# A tibble: 50 x 3
#   q1    q2     q3   
#   <fct> <fct>  <fct>
# 1 13 y  Male   10th 
# 2 14 y  Male   10th 
# 3 13 y  Female 10th 
# 4 13 y  Female 10th 
# 5 15 y  Male   10th 
# 6 13 y  Female 10th 
# 7 13 y  Female 10th 
# 8 13 y  Male   10th 
# 9 13 y  Female 10th 
#10 14 y  Female 10th 
# … with 40 more rows

df[]我们可以使用将所有标签放在一个列表中，并使用
因子更改列中的值

df[] <- Map(function(x, y) factor(x, labels = y[1:length(unique(x))]),
            df,mget(ls(pattern = "Q\\d+_Label")))
df
# A tibble: 50 x 3
#   q1    q2     q3   
#   <fct> <fct>  <fct>
# 1 13 y  Male   10th 
# 2 14 y  Male   10th 
# 3 13 y  Female 10th 
# 4 13 y  Female 10th 
# 5 15 y  Male   10th 
# 6 13 y  Female 10th 
# 7 13 y  Female 10th 
# 8 13 y  Male   10th 
# 9 13 y  Female 10th 
#10 14 y  Female 10th 
# … with 40 more rows

df[]您能提供一个预期最终结果的示例吗？您能提供一个预期最终结果的示例吗？此解决方案有效。最终结果是一个矩阵。我将添加as.data.frame（）。非常感谢您的帮助。此解决方案有效。最终结果是一个矩阵。我将添加as.data.frame（）。非常感谢你的帮助。
df[] <- Map(function(x, y) factor(x, labels = y[1:length(unique(x))]),
            df,mget(ls(pattern = "Q\\d+_Label")))
df
# A tibble: 50 x 3
#   q1    q2     q3   
#   <fct> <fct>  <fct>
# 1 13 y  Male   10th 
# 2 14 y  Male   10th 
# 3 13 y  Female 10th 
# 4 13 y  Female 10th 
# 5 15 y  Male   10th 
# 6 13 y  Female 10th 
# 7 13 y  Female 10th 
# 8 13 y  Male   10th 
# 9 13 y  Female 10th 
#10 14 y  Female 10th 
# … with 40 more rows