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How to find the groupwise cumulative mean in R?
To find the groupwise cumulative mean, we can use cummean function of dplyr package.
For example, if we have a data frame called df that contains a categorical column say Group and a numerical column say Response then the groupwise cumulative mean can be found by using the command given below −
df%>%group_by(Group)%>%mutate(CM=cummean(Response))
Example 1
Following snippet creates a sample data frame −
Group<-sample(LETTERS[1:4],20,replace=TRUE) Score<-rpois(20,50) df1<-data.frame(Group,Score) df1
Output
The following dataframe is created −
Group Score 1 B 48 2 D 54 3 B 44 4 C 50 5 B 50 6 C 48 7 A 58 8 B 44 9 B 44 10 C 51 11 C 50 12 B 48 13 B 63 14 D 43 15 C 53 16 C 52 17 C 59 18 B 46 19 A 51 20 C 58
In order to load the dplyr package and find the groupwise cumulative mean for Score in df1, add the following code to the above snippet −
library(dplyr) df1 %>% group_by(Group) %>% mutate(Cum_Mean = cummean(Score)) # A tibble: 20 x 3 # Groups: Group [4]
Output
If you execute all the above given snippets as a single program, it generates the following output −
Group Score Cum_Mean <chr> <int> <dbl> 1 B 48 48 2 D 54 54 3 B 44 46 4 C 50 50 5 B 50 47.3 6 C 48 49 7 A 58 58 8 B 44 46.5 9 B 44 46 10 C 51 49.7 11 C 50 49.8 12 B 48 46.3 13 B 63 48.7 14 D 43 48.5 15 C 53 50.4 16 C 52 50.7 17 C 59 51.9 18 B 46 48.4 19 A 51 54.5 20 C 58 52.6
Example 2
Following snippet creates a sample data frame −
Department<-sample(c("Finance","Marketing","HR"),20,replace=TRUE)
Salary<-sample(20000:50000,20)
df2<-data.frame(Department,Salary)
df2The following dataframe is created −
Department Salary 1 HR 20179 2 Finance 30870 3 Marketing 48467 4 HR 22429 5 Marketing 26829 6 Finance 34267 7 HR 27498 8 Marketing 38346 9 Finance 31771 10 HR 30438 11 HR 26913 12 HR 30034 13 Finance 26277 14 HR 43676 15 HR 45958 16 Finance 37371 17 HR 41023 18 HR 34838 19 Finance 44879 20 HR 20964
To find the groupwise cumulative mean for Salary in df2, add the following code to the above snippet −
df2 %>% group_by(Department) %>% mutate(Cum_Mean=cummean(Salary)) # A tibble: 20 x 3 # Groups: Department [3]
Output
If you execute all the above given snippets as a single program, it generates the following output −
Department Salary Cum_Mean <chr> <int> <dbl> 1 HR 20179 20179 2 Finance 30870 30870 3 Marketing 48467 48467 4 HR 22429 21304 5 Marketing 26829 37648 6 Finance 34267 32568. 7 HR 27498 23369. 8 Marketing 38346 37881. 9 Finance 31771 32303. 10 HR 30438 25136 11 HR 26913 25491. 12 HR 30034 26248. 13 Finance 26277 30796. 14 HR 43676 28738. 15 HR 45958 30891. 16 Finance 37371 32111. 17 HR 41023 32016. 18 HR 34838 32299. 19 Finance 44879 34239. 20 HR 20964 31268.
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