Aggregated sum through time using dplyr and the tidyverse [duplicate] - r

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How to calculate cumulative sum? [duplicate]
(1 answer)
Closed 2 years ago.
I'd like to compute aggregated values in a column through time (with "year" being another column in my data.
I know how to do this easily in base R using a loop, but I feel there must be a way to do the same with dplyr using summarise in combination with something else. I would like to learn that, so I can integrate if in my code better.
I've made a toy example for the same case. Consider this data where we have the number of questions asked in Stackoverflow by someone each year.
> library(tidyverse)
> data <- tribble(~year, ~questions,
2015, 1,
2016, 3,
2016, 2,
2017, 2,
2018, 3,
2018, 7,
2019, 10,
2020, 21)
> data
# A tibble: 6 x 2
year questions
<dbl> <dbl>
1 2015 1
2 2016 3
3 2016 2
4 2017 2
5 2018 3
6 2018 7
7 2019 10
8 2020 21
The following loop will do what I want
> for (i in 1:length(data$year)){
+ data$agg_questions[i] <- sum(data$questions[data$year <= data$year[I]])
+ }
> data
# A tibble: 6 x 3
year questions agg_questions
<dbl> <dbl> <dbl>
1 2015 1 1
2 2016 5 6
3 2017 2 8
4 2018 10 18
5 2019 10 28
6 2020 21 49
And, of course, I'm looking for a way that allows me not to use the loop. Not something like this:
> for (i in 1:length(data$year)){
+ data$agg_questions2[i] <- data %>%
+ filter(year <= data$year[i]) %>%
+ pull(questions) %>%
+ sum()
+ }
> data
# A tibble: 6 x 4
year questions agg_questions agg_questions2
<dbl> <dbl> <dbl> <dbl>
1 2015 1 1 1
2 2016 5 6 6
3 2017 2 8 8
4 2018 10 18 18
5 2019 10 28 28
6 2020 21 49 49
I know if possible to use [] to subset inside the summarise() and mutate() functions, but I've always struggled with that. Thanks!
Is that possible?
EDIT
After reading the first answers, I realised that I had simplified the example too much. I've edited the example data by adding several rows for the same year to make it look more like what I want (and, I think, complicates just using cumsum())

Answer 1

You can do this by using summarise and sum to create a year totals column and mutate with cumsum to create a column that provides cumulative sums over the years.
library(dplyr)
data <- tribble(~year, ~questions,
2015, 1,
2016, 3,
2016, 2,
2017, 2,
2018, 3,
2018, 7,
2019, 10,
2020, 21)
data %>%
group_by(year) %>%
summarise(year_total = sum(questions)) %>%
mutate(cum_over_years = cumsum(year_total))
#> # A tibble: 6 x 3
#> year year_total cum_over_years
#> <dbl> <dbl> <dbl>
#> 1 2015 1 1
#> 2 2016 5 6
#> 3 2017 2 8
#> 4 2018 10 18
#> 5 2019 10 28
#> 6 2020 21 49

Answer 2

library(tibble)
data <- tribble(~year, ~questions,
2015, 1,
2016, 3,
2017, 2,
2018, 3,
2019, 10,
2020, 21)
In base R:
data <- as.data.frame(data)
data$agg_questions <- cumsum(data$questions)
> data
year questions agg_questions
1 2015 1 1
2 2016 3 4
3 2017 2 6
4 2018 3 9
5 2019 10 19
6 2020 21 40
In data.table:
library(data.table)
data <- as.data.table(data)
data[, agg_questions := cumsum(questions)]
> data
year questions agg_questions
1: 2015 1 1
2: 2016 3 4
3: 2017 2 6
4: 2018 3 9
5: 2019 10 19
6: 2020 21 40