英文:
Replace values of a variable with the most frequent value, or the highest value in R
问题
无法找到此问题的示例：
我想要将分类变量（V1）的值替换为每个ID的最频繁出现值，新变量为（V2）。如果V1中没有最频繁的值，我想用最高的值来替换。
这是我的数据示例：
my_data <- data.frame(ID = c("2", "2", "2", "2" ,"4", "4", "4", "4"),
V1 = c("2", "1", "2", "1", "3", "1", "4", "3"))
这是我希望它看起来的样子：
非常感谢任何帮助！
英文:
I can't seem to find an example of this problem:
I would like to replace the values of a categorical variable (V1) with the most frequently occuring value per ID for new variable (V2). If there is no most frequent value in V1, I would like to replace with the highest value.
Here is an example of my data:
my_data <- data.frame(ID = c("2", "2", "2", "2" ,"4", "4", "4", "4"),
V1 = c("2", "1", "2", "1", "3", "1", "4", "3"))
This is what I would like it to look like:
Any help hugely appreciated!!
答案1
得分: 2
Output
ID V1 V2
1 2 2 2
2 2 1 2
3 2 2 2
4 2 1 2
5 4 3 3
6 4 1 3
7 4 4 3
8 4 3 3
9 5 6 7
10 5 7 7
英文:
One approach is to sort or arrange by the frequencies of each value n and the V1 value secondarily. Then, for each ID, take the highest value of n first, followed by V1, and join back to original data (I modified data with two extra rows for example where there is no highest value for a given ID).
library(dplyr) # v1.1.0
my_data %>%
count(ID, V1) %>%
arrange(ID, desc(n), desc(V1)) %>%
slice(1, .by = ID) %>%
rename(V2 = V1) %>%
right_join(my_data, multiple = "all") %>%
select(ID, V1, V2)
Output
ID V1 V2
1 2 2 2
2 2 1 2
3 2 2 2
4 2 1 2
5 4 3 3
6 4 1 3
7 4 4 3
8 4 3 3
9 5 6 7
10 5 7 7
答案2
得分: 1
更新（添加了max()并移除了1）：
我们可以这样做：使用add_count：
library(dplyr)
df %>%
group_by(ID) %>%
add_count(V1) %>%
mutate(V2 = max(V1[n == max(n)])) %>%
ungroup() %>%
select(-n)
ID V1 V2
<chr> <chr> <chr>
1 2 2 2
2 2 1 2
3 2 2 2
4 2 1 2
5 4 3 3
6 4 1 3
7 4 4 3
8 4 3 3
英文:
Update(added max() and removed 1):
We could do it this way: Using add_count:
library(dplyr)
df %>%
group_by(ID) %>%
add_count(V1) %>%
mutate(V2 = max(V1[n==max(n)])) %>%
ungroup() %>%
select(-n)
ID V1 V2
<chr> <chr> <chr>
1 2 2 2
2 2 1 2
3 2 2 2
4 2 1 2
5 4 3 3
6 4 1 3
7 4 4 3
8 4 3 3
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