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June 2, 2022 16:38
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Preparing the clinicalcommissioninggroupmidyearpopulationestimates data for plotting
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| library(tidyverse) | |
| library(httr) # to load from the web | |
| library(readxl) # to read in the file | |
| library(janitor) # to clean the spreadsheet | |
| # load data directly from the webpage --------------------------- | |
| tmp <- tempfile(fileext = ".xlsx") | |
| url <- GET(url = "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fclinicalcommissioninggroupmidyearpopulationestimates%2fmid2020sape23dt6a/sape23dt6amid2020ccg2021estimatesunformatted.xlsx", | |
| write_disk(tmp)) | |
| # tidy data --------------------------- | |
| # uses janitor package for removing empty rows, columns and making columns headers snake_case | |
| clean_data <- read_xlsx(tmp, sheet = 4, skip = 5) %>% | |
| remove_empty(c("rows", "cols")) %>% | |
| clean_names() %>% | |
| select(ccg_code:ccg_name, | |
| x5:x10) %>% | |
| pivot_longer(names_to = "population", | |
| cols = x5:x10) | |
| # To sum age groups 5 to 10 before plotting | |
| summed_ages <- clean_data %>% | |
| group_by(ccg_code, | |
| ccg_name) %>% | |
| summarise(total = sum(value)) |
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