英文:
Calculate deciles based on subgroup and apply to entire dataset
问题
我有一个具有以下列的数据集：
subgroup: [group1, group2]distribution: 连续变量
我想基于数据集的一个子组来计算十分位数：
df <- df %>%filter(subgroup == "group1") %>%mutate(decile = ntile(distribution, 10))
然后我想使用所得到的十分位数应用于整个数据集（不仅仅是group1）。
有没有办法可以做到这一点？
这是一个示例数据集：
df <- matrix(0, ncol=3, nrow=10000)df[,1] <- 1:10000df[,2] <- sample(c("group1","group2"), 10000, replace=TRUE)df[,3] <- rnorm(10000)df <- as.data.frame(df)colnames(df) <- c("id", "subgroup", "value")
我选择子组 group1 并基于列 value 计算十分位数：
df %>% filter(subgroup == 'group1') %>%mutate(decile = ntile(value, 10))
然后我想使用从 group1 获取的十分位数，并根据这些十分位数对 subgroup=='group2' 进行分类。
期望的输出是 df 中的第四列，其中每个观察都有一个介于1和10之间的单个值（即每个观察的十分位分类）。
英文:
I have a dataset with columns:
subgroup: [group1, group2]distribution: continuous variable
I want to calculate deciles based on a subgroup of the dataset:
df <- df %>%filter(subgroup == "group1") %>%mutate(decile = ntile(distribution, 10))
then I would like to use the obtained deciles and apply it to the entire dataset (i.e. not just group1).
is there a way to do this?
here's an example dataset
df <- matrix(0,ncol=3,nrow=10000)df[,1] <- 1:10000df[,2] <- sample(c("group1","group2"),10000,replace=T)df[,3] <- rnorm(10000)df <- as.data.frame(df)colnames(df) <- c("id", "subgroup","value")
I select the subgroup group1 and calculate deciles based on the column value
df %>% filter(subgroup == 'group1') %>%mutate(decile = ntile(value, 10))
then I would like to use the obtained deciles, and classify subgroup=='group2' based on the deciles obtained from 'group1'
the desired output would be a 4th column in df with a single value between 1 and 10 for each observation. (i.e. the decile classification for each observation)
答案1
得分: 0
以下是代码中需要翻译的部分：
We could use cut to divide the values into decile groups based on "group1".
library(dplyr)df |>mutate(decile = cut(value,quantile(value[subgroup == "group1"], seq(0, 1, 0.1)),labels = FALSE))
Output:
id subgroup value decile1 1 group1 0.674098613 82 2 group1 -2.881811886 13 3 group1 -0.377427063 44 4 group1 0.461585185 75 5 group1 0.460216469 76 6 group1 -1.374041767 17 7 group1 -0.945986918 28 8 group2 0.472525168 79 9 group2 0.418391193 710 10 group2 0.746413150 811 11 group2 0.175323464 612 12 group1 0.879160602 913 13 group1 0.469811384 714 14 group2 0.639019379 815 15 group1 -0.328276877 416 16 group1 -0.099512041 517 17 group1 -0.714642875 318 18 group1 -0.404702209 419 19 group1 -2.181077079 120 20 group2 -2.298182006 1
Data:
df$value <- as.numeric(df$value)
请注意，我已经将HTML编码中的"更改为正常的引号以便更好地理解代码和输出。
英文:
We could use cut to divide the values into decile groups based on "group1".
library(dplyr)df |>mutate(decile = cut(value,quantile(value[subgroup == "group1"], seq(0, 1, 0.1)),labels = FALSE))
Output:
id subgroup value decile1 1 group1 0.674098613 82 2 group1 -2.881811886 13 3 group1 -0.377427063 44 4 group1 0.461585185 75 5 group1 0.460216469 76 6 group1 -1.374041767 17 7 group1 -0.945986918 28 8 group2 0.472525168 79 9 group2 0.418391193 710 10 group2 0.746413150 811 11 group2 0.175323464 612 12 group1 0.879160602 913 13 group1 0.469811384 714 14 group2 0.639019379 815 15 group1 -0.328276877 416 16 group1 -0.099512041 517 17 group1 -0.714642875 318 18 group1 -0.404702209 419 19 group1 -2.181077079 120 20 group2 -2.298182006 1
Data:
df$value <- as.numeric(df$value)
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