CUNY Borough of Manhattan Community College Data in Science Questions

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TUTO MUS BE FAMILIAR WITH RSTUDIO

INTRO

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.

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library(coronavirus)
head(coronavirus)
##   Province.State Country.Region      Lat     Long       date cases      type
## 1                         Japan 35.67620 139.6503 2020-01-22     2 confirmed
## 2                   South Korea 37.56650 126.9780 2020-01-22     1 confirmed
## 3                      Thailand 13.75630 100.5018 2020-01-22     2 confirmed
## 4          Anhui Mainland China 31.82571 117.2264 2020-01-22     1 confirmed
## 5        Beijing Mainland China 40.18238 116.4142 2020-01-22    14 confirmed
## 6      Chongqing Mainland China 30.05718 107.8740 2020-01-22     6 confirmed

The 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 x 7
##   date       location location_type location_standa… location_standa… data_type
##   <date>     <chr>    <chr>         <chr>            <chr>            <chr>    
## 1 2020-01-21 Washing… state         53               fips_code        cases_to…
## 2 2020-01-21 Washing… state         53               fips_code        deaths_t…
## 3 2020-01-22 Washing… state         53               fips_code        cases_to…
## 4 2020-01-22 Washing… state         53               fips_code        deaths_t…
## 5 2020-01-23 Washing… state         53               fips_code        cases_to…
## 6 2020-01-23 Washing… state         53               fips_code        deaths_t…
## # … with 1 more variable: value <dbl>
covid_counties <- refresh_covid19nytimes_counties()

head(covid_counties)
## # A tibble: 6 x 7
##   date       location location_type location_standa… location_standa… data_type
##   <date>     <chr>    <chr>         <chr>            <chr>            <chr>    
## 1 2020-01-21 Snohomi… county_state  53061            fips_code        cases_to…
## 2 2020-01-21 Snohomi… county_state  53061            fips_code        deaths_t…
## 3 2020-01-22 Snohomi… county_state  53061            fips_code        cases_to…
## 4 2020-01-22 Snohomi… county_state  53061            fips_code        deaths_t…
## 5 2020-01-23 Snohomi… county_state  53061            fips_code        cases_to…
## 6 2020-01-23 Snohomi… county_state  53061            fips_code        deaths_t…
## # … with 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!

Maps to use for this assignment

OK, so, we need world, US state, and US county maps – depending on which of the three datasets you chose

library(sf)
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
#The world
library(rnaturalearth)
world_map <- ne_countries()

#US States
library(USAboundaries)
us_states <- us_states()

#US Counties
us_counties <- us_counties()

Armed with this, let’s make some maps!

QUESTIONS

  1. Which data set – or aspect of a single data set, are you most interested in? Sort through the datasets. What is there? Is it the world? A single country? Multiple contries? All states? Counties in one state?

Filter or summarize your data to just what you are interested in, in terms of space.

For example

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
florida_covid <- covid_counties %>%
  filter(stringr::str_detect(location, "[Ff]lorida"))

florida_map <- us_counties %>%
  filter(state_name == "Florida")
  1. What type or types of data from that dataset are you interested in? Why? Filter the dataset to that data type only.
  2. What do you want to learn from this slice of the data? Formulate a question and write it out here.
  3. Filter and manipulate the data so that it is in a format to be used to answer the question.
  4. Join the covid data with spatial data to build a map.
  5. Create a map from this data! Make it awesome!
  6. What do you learn from the map you made?
  7. This static map is, I’m sure, great. Load up tmapand make it dynamic! Is there anything different you can learn from this form of visualization?
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