Using mutate and filter

[beniam]

I am trying to sort my data into multiple columns, based on other column values.

The code I am using to get one group is:
df_grouped <- df %>%
group_by(region) %>%
filter(age > 10, age < 15) %>%
summarise(population10-15 = sum(population))

What I want is to get multiple groups:
df_grouped <- df %>%
group_by(region) %>%
mutate(population1 = filter(age > 0, age < 5)) %>%
mutate(population2 = filter(age >= 5, age < 10)) %>%
mutate(population3 = filter(age > =10, age < 15)) %>%
summarise(population = sum(population))

Hope this makes sense, my DF is a list of multiple regions with age (0-55) and population(total number)
ie
region age population
a - 1 - 183
a - 2 - 218
b - 1 - 153
b - 2 - 128
c - 1 - 67
c - 2 - 35
(but 500 regions and age going from 0-55)

I also tried case_when, but couldn't get this to work.

Thanks,
Ben

[rdr]

Might be helpful to show an example of what you'd like to get given the data set you describe.

[ChrisL]

Something like this would do I think:

n <- 500
X <- dplyr::tibble(
  age = sample(60, n, replace = TRUE),
  region = sample(LETTERS[1:5], n, replace = TRUE),
  population = sample(20, n, replace = TRUE)
)
breaks <- c(seq(0,80, by = 5))
labels <- paste("population", seq_len(length(breaks) - 1))

X %>% 
  dplyr::mutate(bracket = cut(age, breaks = breaks, labels = labels, right=TRUE)) %>% 
  dplyr::group_by(bracket, region) %>% 
  dplyr::summarise(population = sum(population)) %>% 
  tidyr::pivot_wider(names_from="bracket", values_from="population", values_fill = 0)

[beniam]

Thanks, I do need to think about this further, this is my first week of R, and I am still figuring a lot out.

What I was thinking was:
group - age group - population
a - (0-5) - 450
a - (5-10) - 389
a- (10-15) - 482
b - (0-5) - 217
b - (5-10) - 624
b - (10-15) - 537

But thinking about this more, I am thinking:
group - pop0-5 - pop(5-10 - pop (10-15
a - 450 - 389 - 482
b - 217 - 624 - 537

Regards,
Ben