R中分组计算的新变量
New variable from grouped calculation in R
我有一个数据集:
library(dplyr)
my_df <- data.frame(day = c(1,1,1,2,2,2,3,3,3), age = c(18, 18, 18, 25, 18, 35, 76, 76, 15))
my_df
# day age
# 1 1 18
# 2 1 18
# 3 1 18
# 4 2 25
# 5 2 18
# 6 2 35
# 7 3 76
# 8 3 76
# 9 3 15
对于每一行,我想知道给定日期值的频率和年龄百分比。例如,我可以用 dplyr 链来计算:
my_df %>%
group_by(day, age) %>%
summarize(n=n()) %>%
group_by(day) %>%
mutate(pct = n/sum(n))
# day age n pct
# 1 1 18 3 1
# 2 2 18 1 0.333
# 3 2 25 1 0.333
# 4 2 35 1 0.333
# 5 3 15 1 0.333
# 6 3 76 2 0.667
如何将 n 个值的值添加回我的原始 df?期望的输出:
# day age n
# 1 1 18 3
# 2 1 18 3
# 3 1 18 3
# 4 2 25 1
# 5 2 18 1
# 6 2 35 1
# 7 3 76 2
# 8 3 76 2
# 9 3 15 1
我会将其存储为变量,如下所示:
my_helper_df <- my_df %>%
group_by(day, age) %>%
summarize(n=n()) %>%
group_by(day) %>%
mutate(pct = n/sum(n))
然后left_join
到原来的df
,这样:
final_df <- dplyr::left_join(df, my_helper_df, by = c("day", "age"))
对于您想要的输出,我们可以使用 add_count()
library(dplyr)
my_df %>%
add_count(day, age)
day age n
1 1 18 3
2 1 18 3
3 1 18 3
4 2 25 1
5 2 18 1
6 2 35 1
7 3 76 2
8 3 76 2
9 3 15 1
我有一个数据集:
library(dplyr)
my_df <- data.frame(day = c(1,1,1,2,2,2,3,3,3), age = c(18, 18, 18, 25, 18, 35, 76, 76, 15))
my_df
# day age
# 1 1 18
# 2 1 18
# 3 1 18
# 4 2 25
# 5 2 18
# 6 2 35
# 7 3 76
# 8 3 76
# 9 3 15
对于每一行,我想知道给定日期值的频率和年龄百分比。例如,我可以用 dplyr 链来计算:
my_df %>%
group_by(day, age) %>%
summarize(n=n()) %>%
group_by(day) %>%
mutate(pct = n/sum(n))
# day age n pct
# 1 1 18 3 1
# 2 2 18 1 0.333
# 3 2 25 1 0.333
# 4 2 35 1 0.333
# 5 3 15 1 0.333
# 6 3 76 2 0.667
如何将 n 个值的值添加回我的原始 df?期望的输出:
# day age n
# 1 1 18 3
# 2 1 18 3
# 3 1 18 3
# 4 2 25 1
# 5 2 18 1
# 6 2 35 1
# 7 3 76 2
# 8 3 76 2
# 9 3 15 1
我会将其存储为变量,如下所示:
my_helper_df <- my_df %>%
group_by(day, age) %>%
summarize(n=n()) %>%
group_by(day) %>%
mutate(pct = n/sum(n))
然后left_join
到原来的df
,这样:
final_df <- dplyr::left_join(df, my_helper_df, by = c("day", "age"))
对于您想要的输出,我们可以使用 add_count()
library(dplyr)
my_df %>%
add_count(day, age)
day age n
1 1 18 3
2 1 18 3
3 1 18 3
4 2 25 1
5 2 18 1
6 2 35 1
7 3 76 2
8 3 76 2
9 3 15 1