感谢到目前为止我在这里获得的所有支持。非常感谢!
我正在尝试将值标签添加到 R 变量。
例如，我目前在 SPSS 中有这个:
VARIABLE LABELS band_age_3 'Three-way banded age group'.
VALUE LABELS band_age_3
1 '1-29'
2 '30-49'
3 '50-59'
FREQUENCIES band_age_3.
这是不是最好的方法:
2019data$band_age_3 <- factor(2019data$band_age_3,
levels = c(1,2,3),
labels = c("1-29", "30-49", "50-59"))
与所有与 R 相关的事物一样，往往有多种做事方式，我想知道这是否是最合适的。
干杯。
最佳答案
要在 R 中以 SPSS 风格管理变量元数据，您可以使用 expss 包。
使用可重现的例子:
df <- data.frame(band_age_3 = c(1, 3, 1, 2, 3),
sex = c(1, 2, 2, 1, 1),
weight = c(50, 20, 30, 40, 5))
library(expss)
library(labelled)
=======================================================================
# Setting variable metadata
=======================================================================
# Set variable labels
var_lab(df$band_age_3) <- "Three-way banded age group"
var_lab(df$sex) <- "Gender"
# Get variable labels
var_lab(df$band_age_3)
[1] "Three-way banded age group"
var_lab(df$sex)
[1] "Gender"
# Set value labels
val_lab(df$band_age_3) <- make_labels("1 1-29
2 30-49
3 50-59")
val_lab(df$sex) <- make_labels("1 Men
2 Women")
# Get variable labels
val_lab(df$band_age_3)
1-29 30-49 50-59
1 2 3
val_lab(df$sex)
Men Women
1 2
=======================================================================
Frequencies and crosstabulations
=======================================================================
# Frequencies
=================================
# Unweighted
fre(df$band_age_3)
| Three-way banded age group | Count | Valid percent | Percent | Responses, % | Cumulative responses, % |
| -------------------------- | ----- | ------------- | ------- | ------------ | ----------------------- |
| 1-29 | 2 | 40 | 40 | 40 | 40 |
| 30-49 | 1 | 20 | 20 | 20 | 60 |
| 50-59 | 2 | 40 | 40 | 40 | 100 |
| #Total | 5 | 100 | 100 | 100 | |
| <NA> | 0 | | 0 | | |
# Weighted
fre(df$band_age_3, weight = df$weight)
| Three-way banded age group | Count | Valid percent | Percent | Responses, % | Cumulative responses, % |
| -------------------------- | ----- | ------------- | ------- | ------------ | ----------------------- |
| 1-29 | 80 | 55.2 | 55.2 | 55.2 | 55.2 |
| 30-49 | 40 | 27.6 | 27.6 | 27.6 | 82.8 |
| 50-59 | 25 | 17.2 | 17.2 | 17.2 | 100.0 |
| #Total | 145 | 100.0 | 100.0 | 100.0 | |
| <NA> | 0 | | 0.0 | | |
# Crosstabs
=================================
# Weighted
# Count
cro(df$band_age_3, list(total(),df$sex), weight = df$weight, total_statistic = "w_cases")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ----------------- | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 80 | 50 | 30 |
| | 30-49 | 40 | 40 | |
| | 50-59 | 25 | 5 | 20 |
| | #Total wtd. cases | 145 | 95 | 50 |
# Row percentages
cro_rpct(df$band_age_3, list(total(),df$sex), weight = df$weight, total_statistic = "w_rpct")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ---------------- | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 100 | 62.5 | 37.5 |
| | 30-49 | 100 | 100.0 | |
| | 50-59 | 100 | 20.0 | 80.0 |
| | #Total wtd. rpct | 100 | 65.5 | 34.5
# Column percentages
cro_cpct(df$band_age_3, list(total(),df$sex), weight = df$weight, total_statistic = "w_cpct")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ---------------- | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 55.2 | 52.6 | 60 |
| | 30-49 | 27.6 | 42.1 | |
| | 50-59 | 17.2 | 5.3 | 40 |
| | #Total wtd. cpct | 100.0 | 100.0 | 1
# Total percentages
cro_tpct(df$band_age_3, list(total(),df$sex), weight = df$weight, total_statistic = "w_tpct")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ---------------- | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 55.2 | 34.5 | 20.7 |
| | 30-49 | 27.6 | 27.6 | |
| | 50-59 | 17.2 | 3.4 | 13.8 |
| | #Total wtd. tpct | 100.0 | 65.5
# Unweighted
# Count
cro(df$band_age_3, list(total(),df$sex), total_statistic = "u_cases")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ------------ | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 2 | 1 | 1 |
| | 30-49 | 1 | 1 | |
| | 50-59 | 2 | 1 | 1 |
| | #Total cases | 5 | 3 | 2 |
# Row percentages
cro_rpct(df$band_age_3, list(total(),df$sex), total_statistic = "u_cases")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ------------ | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 100 | 50 | 50 |
| | 30-49 | 100 | 100 | |
| | 50-59 | 100 | 50 | 50 |
| | #Total cases | 5 | 3 | 2 |
# Column percentages
cro_cpct(df$band_age_3, list(total(),df$sex), total_statistic = "u_cases")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ------------ | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 40 | 33.3 | 50 |
| | 30-49 | 20 | 33.3 | |
| | 50-59 | 40 | 33.3 | 50 |
| | #Total cases | 5 | 3.0 | 2 |
# Total percentages
cro_tpct(df$band_age_3, list(total(),df$sex), total_statistic = "u_cases")
| | | #Total | Gender | |
| | | | Men | Women |
| -------------------------- | ------------ | ------ | ------ | ----- |
| Three-way banded age group | 1-29 | 40 | 20 | 20 |
| | 30-49 | 20 | 20 | |
| | 50-59 | 40 | 20 | 20 |
| | #Total cases | 5 | 3 |
关于r - R 中的值标签？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/60253192/