I have a thousand-row table of the type below and need to calculate the sum and mean of a continuous "count" variable for every categorical "df" varible.
I have attempted to solve this through table() function, but since I am a using continuous variable, I can't work myself towards a solution.
df count
1 a 5
2 f 3
3 g 8
4 l 2
5 a 10
6 s 4
7 l 6
8 s 8
9 a 2
10 g 1
if I am not mistaken, you are looking for the following code
library(dplyr)
daf %>%
group_by(df) %>%
summarise(Sum = sum(count), Count = n()) %>%
ungroup() %>%
arrange(df)
"daf" is the data set that I am working on.
Enjoy R programming!!!
Maybe this would help you out ,
> df3 <- aggregate(count ~ df , df, mean)
> df3
df count
1 a 5.666667
2 f 3.000000
3 g 4.500000
4 l 4.000000
5 s 6.000000
> df2 <- aggregate(count ~ df , df, sum)
> df2
df count
1 a 17
2 f 3
3 g 9
4 l 8
5 s 12
Simple aggregate functions can do it . Count in df3 is the mean and count in df2 is the sum .
This isn't an especially unique question, but the suggested duplicated questions only ask for a single summary statistic. As this is a simple problem to solve in dplyr I thought I'd throw this in.
dframe <- data.frame(df = c("a", "f", "g", "l", "a", "s", "l", "s", "a", "g"), count = c(5, 3, 8, 2, 10, 4, 6, 8, 2, 1))
dframe
df count
1 a 5
2 f 3
3 g 8
4 l 2
5 a 10
6 s 4
7 l 6
8 s 8
9 a 2
10 g 1
library(dplyr)
dframe %>% group_by(df) %>% summarise(sum = sum(count), mean = mean(count))
Source: local data frame [5 x 3]
df sum mean
(fctr) (dbl) (dbl)
1 a 17 5.666667
2 f 3 3.000000
3 g 9 4.500000
4 l 8 4.000000
5 s 12 6.000000
You can see that summarise() allows you to calculate whatever, and however many, summary statistics for each group that you like.
