Download and import data

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library(readODS)
library(here)
library(tidyverse)

path <- here("data")

df_sheets <- ods_sheets(paste0(path, "/EMData.ods"))

df_sheets
## [1] "Contents"    "England"     "Age_Group"   "deprivation" "Ethnicity"  
## [6] "region"      "utla"        "all_cause"   "pod"
df_region <- read_ods(paste0(path, "/EMData.ods"), sheet = "region")

df_region %>%
  janitor::clean_names() %>%
  mutate(covid_per = round(100 * deaths_with_covid_19_on_the_death_certificate / registered_deaths, 2)) %>%
  group_by(population_subgroup) %>%
  summarise(mean = mean(covid_per), 
            median = median(covid_per), 
            sd = sd(covid_per), 
            q = quantile(covid_per, probs = c(0.1, 0.25, .75, .9), names = TRUE)) %>% 
  mutate(quant = c(0.1, 0.25, 0.75, 0.9)) %>%
  pivot_wider(names_from =  "quant", values_from = "q") %>%
  mutate_if(is.numeric, round, 1) %>%
  knitr::kable()
population_subgroup mean median sd 0.1 0.25 0.75 0.9
East Midlands 12.6 7.9 11.9 1.6 2.7 21.6 31.1
East of England 11.4 6.0 13.4 1.0 2.4 15.1 32.5
England 12.3 7.0 12.3 1.6 3.3 20.0 33.3
London 14.3 8.2 15.8 2.2 3.9 18.5 45.7
North East 12.5 8.1 11.6 1.0 2.9 20.9 31.3
North West 13.8 8.2 12.2 2.6 4.6 23.6 33.7
South East 11.1 5.3 13.1 1.5 2.6 14.9 32.6
South West 7.6 4.5 8.9 0.5 1.2 10.2 20.4
West Midlands 12.8 7.2 12.8 1.5 3.4 22.1 34.1
Yorkshire & Humber 12.7 8.3 11.2 1.6 3.8 23.1 31.2

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