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Wrangle covid-19 data for rolling averages
#=====================================================================#
# This is code to create: 38-rolling-averages-wrangle.R
# Authored by and feedback to: @mjfrigaard
# MIT License
# Version: 1.1
#=====================================================================#
# Download files into COVID-19/ folder from:
# https://github.com/CSSEGISandData/COVID-19
# packages ----------------------------------------------------------------
library(zoo) # moving averages
library(tidyverse) # all tidyverse packages
library(skimr) # summaries
library(hrbrthemes) # themes for graphs
library(socviz) # %nin%
library(openintro) #
library(geofacet) #
library(usmap) # lat and long
library(socviz) # for %nin%
library(ggmap) # mapping
# create list of .csv files:
csse_csv_files <- fs::dir_ls(path = ".drafts/data/jhsph/COVID-19/csse_covid_19_data/csse_covid_19_daily_reports_us",
glob = "*.csv")
# csse_csv_files
JHCovid19DataRaw <- csse_csv_files %>%
purrr::map_df(.f = read_csv, .id = "file", col_types = cols()) %>%
janitor::clean_names(case = "snake")
# I only need cases by state, so I'll also convert the `last_update` column to a
# `date` variable with some help from lubridate:
# https://lubridate.tidyverse.org/,
# Then I will create new `month_abbr` and `day` variables, rename province_state
# to state, and remove the variables I won't be using
JHCovid19 <- JHCovid19DataRaw %>%
dplyr::mutate(date = lubridate::as_date(last_update),
month_abbr = lubridate::month(date, label = TRUE,
abbr = TRUE),
# day
day = lubridate::day(date)) %>%
dplyr::select(-c(file, country_region, uid, iso3))
# Next I'll get the state abbreviations by creating a crosswalk table and
# joining these with the `JHCovid19NewDeaths` dataset.
StateCrosswalk <- tibble::tibble(state = state.name) %>%
# stick this to the abbreviations
dplyr::bind_cols(tibble::tibble(state_abbr = state.abb)) %>%
# bind this to District of Columbia
dplyr::bind_rows(tibble(state = "District of Columbia",
state_abbr = "DC")) %>%
dplyr::rename(province_state = state)
# head(StateCrosswalk)
# Now I join the `state_abbr` column to `JHCovid19` and remove the non-states
# in the `JHCovid19States` dataset.
JHCovid19States <- JHCovid19 %>%
# join these two together
dplyr::inner_join(x = .,
y = StateCrosswalk,
by = "province_state")
# remove non-states
JHCovid19States <- JHCovid19States %>%
dplyr::filter(province_state %nin% c("American Samoa", "Diamond Princess",
"Grand Princess", "Guam",
"Northern Mariana Islands",
"Puerto Rico", "Virgin Islands"))
JHCovid19States <- JHCovid19States %>%
dplyr::rename(state = province_state)
# export these data
write_csv(as.data.frame(JHCovid19States), paste0("drafts/data/jhsph/",
base::noquote(lubridate::today()),
"-JHCovid19States.csv"))
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