For this week’s homework, let’s work on mapping the covid-19 data. You have two choices of data source. The first is the coronavirus data we have already loaded.
library(coronavirus)
head(coronavirus)## date province country lat long type cases uid iso2 iso3
## 1 2020-01-22 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## 2 2020-01-23 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## 3 2020-01-24 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## 4 2020-01-25 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## 5 2020-01-26 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## 6 2020-01-27 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
## code3 combined_key population continent_name continent_code
## 1 124 Alberta, Canada 4413146 North America NA
## 2 124 Alberta, Canada 4413146 North America NA
## 3 124 Alberta, Canada 4413146 North America NA
## 4 124 Alberta, Canada 4413146 North America NA
## 5 124 Alberta, Canada 4413146 North America NA
## 6 124 Alberta, Canada 4413146 North America NAThe second is a newer dataset. It harvests data that is from the New York Times. It is focused solely on the US. To install it, you’ll need to do the following
#if you don't have it already
install.packages("devtools")
#install the library from github
devtools::install_github("covid19R/covid19nytimes")library(covid19nytimes)
covid_states <- refresh_covid19nytimes_states()
head(covid_states)## # A tibble: 6 × 7
## date location location_type location_code location_code_type data_type
## <date> <chr> <chr> <chr> <chr> <chr>
## 1 2023-03-23 Alabama state 01 fips_code cases_to…
## 2 2023-03-23 Alabama state 01 fips_code deaths_t…
## 3 2023-03-23 Alaska state 02 fips_code cases_to…
## 4 2023-03-23 Alaska state 02 fips_code deaths_t…
## 5 2023-03-23 American … state 60 fips_code cases_to…
## 6 2023-03-23 American … state 60 fips_code deaths_t…
## # ℹ 1 more variable: value <dbl>covid_counties <- refresh_covid19nytimes_counties()
head(covid_counties)## # A tibble: 6 × 7
## date location location_type location_code location_code_type data_type
## <date> <chr> <chr> <chr> <chr> <chr>
## 1 2022-05-13 Abbeville… county 45001 fips_code cases_to…
## 2 2022-05-13 Abbeville… county 45001 fips_code deaths_t…
## 3 2022-05-13 Acadia,Lo… county 22001 fips_code cases_to…
## 4 2022-05-13 Acadia,Lo… county 22001 fips_code deaths_t…
## 5 2022-05-13 Accomack,… county 51001 fips_code cases_to…
## 6 2022-05-13 Accomack,… county 51001 fips_code deaths_t…
## # ℹ 1 more variable: value <dbl>Now, you have three data sets to choose from! Countries, states, or counties. Remember, with the coronavirus data, you have to do some dplyr::summarizing to get it down to countries, though!
OK, so, we need world, US state, and US county maps - depending on which of the three datasets you chose
library(sf)
# The world
library(rnaturalearth)
world_map <- ne_countries()
# US States
# install if you don't have USAboundaries loaded
# install.packages("remotes")
# remotes::install_github("ropensci/USAboundaries")
# remotes::install_github("ropensci/USAboundariesData")
library(USAboundaries)
us_states <- us_states()
# US Counties
us_counties <- us_counties() |> janitor::clean_names()Armed with this, let’s make some maps!
Filter or summarize your data to just what you are interested in, in terms of space.
For example
library(dplyr)
florida_covid <- covid_counties |>
filter(stringr::str_detect(location, "[Ff]lorida"))
florida_map <- us_counties |>
filter(state_name == "Florida")What type or types of data from that dataset are you interested in? Why? Filter the dataset to that data type only.
What do you want to learn from this slice of the data? Formulate a question and write it out here.
Filter and manipulate the data so that it is in a format to be used to answer the question.
Join the covid data with spatial data to build a map.
Create a map from this data! Make it awesome!
What do you learn from the map you made?
This static map is, I’m sure, great. Load up tmap
and make it dynamic! Is there anything different you can learn from this
form of visualization?
Last… it’s time to start thinking about your final project. Name the one or more data sources you are interested in exploring. Tell us a bit about what is in them.