calculating mean by keeping all variable in the dataset in r - r

Question

I am trying to calculate the mean of time by keeping all the variables in the final dataset within dplyr package.
Here how my sample dataset looks like:
library(dplyr)
id <- c(1,1,1,1, 2,2,2,2, 3,3,3,3, 4,4,4,4)
gender <- c(1,1,1,1, 2,2,2,2, 2,2,2,2, 1,1,1,1)
item.id <-c(1,1,1,2, 1,1,2,2, 1,2,3,4, 1,2,2,3)
sequence<-c(1,2,3,1, 1,2,1,2, 1,1,1,1, 1,1,2,1)
time <- c(5,6,7,1, 2,3,4,9, 1,2,3,9, 5,6,7,8)
data <- data.frame(id, gender, item.id, sequence, time)
> data
id gender item.id sequence time
1 1 1 1 1 5
2 1 1 1 2 6
3 1 1 1 3 7
4 1 1 2 1 1
5 2 2 1 1 2
6 2 2 1 2 3
7 2 2 2 1 4
8 2 2 2 2 9
9 3 2 1 1 1
10 3 2 2 1 2
11 3 2 3 1 3
12 3 2 4 1 9
13 4 1 1 1 5
14 4 1 2 1 6
15 4 1 2 2 7
16 4 1 3 1 8
id for student id, gender for gender, item.id for the question ids students take, sequence is the sequence number of attempts to solve the question because students might return back to questions and try to answer again, and time is the time spent on each trial.
When calculating the mean of the time, I need to follow three steps:
(a) students have multiple trials for each question. I need to calculate the mean of the time for each item having multiple trials.
(b) then calculate the overall mean of the time for each id. For example, for id=1, I have two items, the first item has 3 trials and the second item has 1 trial. First I need to aggregate the time for the first item by (5+6+7)/3=6, so id=1 has item1 time 6 and item2 time 1. Second, taking 6 and 1 and calculating the mean for this student (6+1)/2=3.5.
(c) Lastly, I would like to keep all the variables in the dataset.
data <- data %>%
group_by(id) %>%
select(id, gender, item.id, sequence, time) %>%
summarize(mean.time = mean(time))
I got this but obviously this is only aggregating the mean by not taking into account of the within mean for each trial and this also does not keep all the variables:
> data
# A tibble: 4 x 2
id mean.time
<dbl> <dbl>
1 1 4.75
2 2 4.5
3 3 3.75
4 4 6.5
I thought select() was going to keep all variables.
The final dataset should look like this below:
> data
id gender item.id sequence time mean.time
1 1 1 1 1 5 3.5
2 1 1 1 2 6 3.5
3 1 1 1 3 7 3.5
4 1 1 2 1 1 3.5
5 2 2 1 1 2 4.5
6 2 2 1 2 3 4.5
7 2 2 2 1 4 4.5
8 2 2 2 2 5 4.5
9 3 2 1 1 1 3.75
10 3 2 2 1 2 3.75
11 3 2 3 1 3 3.75
12 3 2 4 1 9 3.75
13 4 1 1 1 5 6.5
14 4 1 2 1 6 6.5
15 4 1 2 2 7 6.5
16 4 1 3 1 8 6.5
I used dplyr but open any other solutions.
Thanks in advance!

Answer 1

We can use mutate instead of summarise as summarise returns a summarised output of 1 row per each group, while mutate creates a new column in the dataset
...
%>%
mutate(mean.time = mean(time))
If wee want to get the mean of mean, then first group by 'id', 'item.id', get the mean, and then grouped by 'id', get the mean of unique elements
data %>%
group_by(id, item.id) %>%
mutate(mean.time = mean(time)) %>%
group_by(id) %>%
mutate(mean.time = mean(unique(mean.time)))
# A tibble: 16 x 6
# Groups: id [4]
# id gender item.id sequence time mean.time
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 1 1 1 5 3.5
# 2 1 1 1 2 6 3.5
# 3 1 1 1 3 7 3.5
# 4 1 1 2 1 1 3.5
# 5 2 2 1 1 2 4.5
# 6 2 2 1 2 3 4.5
# 7 2 2 2 1 4 4.5
# 8 2 2 2 2 9 4.5
# 9 3 2 1 1 1 3.75
#10 3 2 2 1 2 3.75
#11 3 2 3 1 3 3.75
#12 3 2 4 1 9 3.75
#13 4 1 1 1 5 6.5
#14 4 1 2 1 6 6.5
#15 4 1 2 2 7 6.5
#16 4 1 3 1 8 6.5
Or instead of creating a second group by, we can do a match to get the first position of 'item.id', extract the 'mean.time' and get the mean
data %>%
group_by(id, item.id) %>%
mutate(mean.time = mean(time),
mean.time = mean(mean.time[match(unique(item.id), item.id)]))
Or use summarise and then do a left_join
data %>%
group_by(id, item.id) %>%
summarise(mean.time = mean(time)) %>%
group_by(id) %>%
summarise(mean.time = mean(mean.time)) %>%
right_join(data)