Disaggregate in the context of a time series

Question

I have a dataset that I want to visualize overall and disaggregated by a few different variables. I created a flexdashboard with a toy shiny app to select the type of disaggrega

Answer 1

This is a good place to make a function, to shorten your code and make it less prone to error.

http://r4ds.had.co.nz/functions.html

A complicating bit is that programming with dplyr often requires wading into a framework called tidyeval, which is very powerful but can be intimidating. https://dplyr.tidyverse.org/articles/programming.html

(Here's an alternative approach that sidesteps tidyeval: https://cran.r-project.org/web/packages/seplyr/vignettes/using_seplyr.html)

In your scenario, it's possible to avoid these challenges entirely by doing a bit of manipulation before and after your function. It's not as elegant, but works.

BTW, I can't guarantee it'll work since you didn't share a verifiable reprex (e.g. including a sample of data with the same form as yours), but it worked with the fake data I made up. (See bottom.) Sorry, I missed the chunk where your sample data was provided.

prep_dat <- function(filtered_dat, col_name = "total") {
  filtered_dat %>%
    mutate(new = 1) %>%
    arrange(date) %>%
  # time series analysis
  tibbletime::as_tbl_time(index = date) %>% # convert to tibble time object
    select(date, new) %>%
    tibbletime::collapse_by("1 week", side = "start", clean = TRUE) %>%
    group_by(date) %>%
    mutate(total = sum(new, na.rm = TRUE)) %>%
    distinct(date, .keep_all = TRUE) %>%
    ungroup() %>%
    # expand matrix to include weeks without data
    complete(
      date = seq(date[1], date[length(date)], by = "1 week"),
      fill = list(total = 0)
    )
}

Then you could call it with your filtered data and the name of the total column. This fragment should be able to replace the ~20 lines you're currently using:

males <- prep_dat(dat_fake %>% 
  filter(sex == "male")) %>% 
  rename("total_m" = "total")

---

Fake data that I tested on:

dat_fake <- tibble(
  date = as.Date("2018-01-01") + runif(500, 0, 100),
  new  = runif(500, 0, 100),
  sex  = sample(c("male", "female"), 
                500, replace = TRUE),
  lang = sample(c("english", "french", "spanish", "portuguese", "tagalog"), 
                500, replace = TRUE)
)