从连续变量创建虚拟分位数变量

Question

这是我正在处理的数据：

x <- getURL("https://raw.githubusercontent.com/dothemathonthatone/maps/master/testmain.csv")
    data <- read.csv(text = x)

我想为 year_hh_inc 中的顶部、中间和底部三分之一的值创建一个虚拟变量。我的 id 列 reg_schl 中的每个值都可能有多个 year_hh_inc 的值，因此虚拟变量需要在 reg_schl 上分组。我希望能够在每个唯一 reg_schl 中区分 year_hh_inc 中的值。

到目前为止，我有以下内容作为 Sotos 的解决方案发布在下面：

data %>% 
 group_by(reg_schl) %>%
 mutate(category = cut(year_hh_inc, breaks = (quantile(year_hh_inc, c(0, 1 / 3, 2 / 3, 1), na.rm = TRUE)), labels = c("low", "middle", "high"), include.lowest = TRUE), vals = 1) %>% 
 pivot_wider(names_from = category, values_from = vals, values_fill = list(vals = 0))

效果很好。

我也用过Allan提供的这个解决方案：

cut_by_id <- function(x)
{
  x$category <- cut(x$year_hh_inc, quantile(x$year_hh_inc, c(0,1/3,2/3,1), na.rm = TRUE), 
                    labels = c("low","middle","high"), include.lowest = TRUE)
  return(x)
}

data <- do.call(rbind, lapply(split(data, data$id), cut_by_id))

Answer 1

您可以使用 split - lapply - rbind 范式：

cut_by_id <- function(x)
{
  x$category <- cut(x$inc, quantile(x$inc, c(0,1/3,2/3,1), na.rm = TRUE), 
                    labels = c("low","middle","high"), include.lowest = TRUE)
  return(x)
}

data <- do.call(rbind, lapply(split(data, data$id), cut_by_id))

data
#>      id   inc fee fert fee_per_inc category
#> 1.1   1 11000 125 0.15 0.011363636      low
#> 1.2   1 15000 150 0.12 0.010000000      low
#> 1.3   1 17000 175 0.22 0.010294118   middle
#> 1.4   1 19000 200 0.13 0.010526316     high
#> 1.5   1 21000 225 0.12 0.010714286     high
#> 2.6   2 13000  55 0.11 0.004230769      low
#> 2.7   2 16000  75 0.09 0.004687500      low
#> 2.8   2 19000  85 0.23 0.004473684   middle
#> 2.9   2 21000  95 0.05 0.004523810     high
#> 2.10  2 25000 105 0.01 0.004200000     high
#> 3.11  3 18000  75 0.25 0.004166667      low
#> 3.12  3 21000  85 0.03 0.004047619      low
#> 3.13  3 23000  95 0.05 0.004130435   middle
#> 3.14  3 27000 105 0.15 0.003888889     high
#> 3.15  3 30000 115 0.25 0.003833333     high

box  <- boxplot(data$inc ~ data$category, col = 3:5)

^{由 reprex package (v0.3.0)}

于 2020-02-26 创建

Answer 2

我们可以根据分位数创建您的因子变量并传播这些值，即

library(dplyr)
library(tidyr)

data %>% 
 group_by(id) %>%
 mutate(category = cut(inc, breaks = (quantile(inc, c(0, 1 / 3, 2 / 3, 1), na.rm = TRUE)), labels = c("low", "middle", "high"), include.lowest = TRUE), vals = 1) %>% 
 pivot_wider(names_from = category, values_from = vals, values_fill = list(vals = 0))

这给出了，

# A tibble: 15 x 8
# Groups:   id [3]
      id   inc   fee  fert fee_per_inc   low middle  high
   <dbl> <dbl> <dbl> <dbl>       <dbl> <dbl>  <dbl> <dbl>
 1     1 11000   125  0.15     0.0114      1      0     0
 2     1 15000   150  0.12     0.01        1      0     0
 3     1 17000   175  0.22     0.0103      0      1     0
 4     1 19000   200  0.13     0.0105      0      0     1
 5     1 21000   225  0.12     0.0107      0      0     1
 6     2 13000    55  0.11     0.00423     1      0     0
 7     2 16000    75  0.09     0.00469     1      0     0
 8     2 19000    85  0.23     0.00447     0      1     0
 9     2 21000    95  0.05     0.00452     0      0     1
10     2 25000   105  0.01     0.0042      0      0     1
11     3 18000    75  0.25     0.00417     1      0     0
12     3 21000    85  0.03     0.00405     1      0     0
13     3 23000    95  0.05     0.00413     0      1     0
14     3 27000   105  0.15     0.00389     0      0     1
15     3 30000   115  0.25     0.00383     0      0     1

注意我在 cut 中添加了参数 include.lowest = TRUE 以捕获第一个标签中的最低值 (low)

从连续变量创建虚拟分位数变量

creating dummy quantile variable from continuous variable

r

continuous

dummy-variable