This lab exercise is due 23:59 Monday 10 May (NZST).

  • You should submit an R file (i.e. file extension .R) containing R code that assigns the appropriate values to the appropriate symbols.
  • Your R file will be executed in order and checked against the values that have been assigned to the symbols using an automatic grading system. Marks will be fully deducted for non-identical results.
  • Intermediate steps to achieve the final results will NOT be checked.
  • Each question is worth 0.2 points.
  • You should submit your R file on Canvas.
  • Late assignments are NOT accepted unless prior arrangement for medical/compassionate reasons.

In this lab exercise, you are going to work on the alternative visualisation of “Flattening the curve” for the publication purpose, using covid19-daily-cases.csv that contains cumulative daily confirmed cases between 2020-03-01 and 2020-05-31 around the world. You shall use the following code snippet (and include them upfront in your R file) for this lab session:

library(scales)
library(tidyverse)
selected <- c("Australia", "Brazil", "Canada", "China", "France", "Germany", 
  "India", "Israel", "Italy", "Japan", "Korea, South", "New Zealand",
  "Spain", "Sweden", "United Kingdom", "US")
covid19 <- read_csv("data/covid19-daily-cases.csv") %>% 
  filter(country_region %in% selected)
covid19

#> # A tibble: 1,452 x 3
#>    country_region date       confirmed
#>    <chr>          <date>         <dbl>
#>  1 Australia      2020-03-01        27
#>  2 Australia      2020-03-02        30
#>  3 Australia      2020-03-03        39
#>  4 Australia      2020-03-04        52
#>  5 Australia      2020-03-05        55
#>  6 Australia      2020-03-06        60
#>  7 Australia      2020-03-07        63
#>  8 Australia      2020-03-08        76
#>  9 Australia      2020-03-09        91
#> 10 Australia      2020-03-10       107
#> # … with 1,442 more rows

Suppose that you have created an Rproj for this course. You need to download covid19-daily-cases.csv here, to data/ under your Rproj folder.

  • You’re required to use relative file paths data/covid19-daily-cases.csv to import these data. NO marks will be given to this lab for using URL links or different file paths.

Question 1

Substitute "Korea, South" with "South Korea" in country_region, and then add a new column new_cases that computes new confirmed cases in the past week for each region.

You should end up with a tibble called covid19_cases.

HINTS
  1. You may find one of {dplyr}’s vector functions useful.

covid19_cases

#> # A tibble: 1,452 x 4
#>    country_region date       confirmed new_cases
#>    <chr>          <date>         <dbl>     <dbl>
#>  1 Australia      2020-03-01        27        NA
#>  2 Australia      2020-03-02        30        NA
#>  3 Australia      2020-03-03        39        NA
#>  4 Australia      2020-03-04        52        NA
#>  5 Australia      2020-03-05        55        NA
#>  6 Australia      2020-03-06        60        NA
#>  7 Australia      2020-03-07        63        NA
#>  8 Australia      2020-03-08        76        49
#>  9 Australia      2020-03-09        91        61
#> 10 Australia      2020-03-10       107        68
#> # … with 1,442 more rows

mean(covid19_cases$new_cases, na.rm = TRUE)

#> [1] 18871.79

Question 2

Rename country_region to country_region_bg.

You should end up with a tibble called covid19_bg.

covid19_bg

#> # A tibble: 1,452 x 4
#>    country_region_bg date       confirmed new_cases
#>    <chr>             <date>         <dbl>     <dbl>
#>  1 Australia         2020-03-01        27        NA
#>  2 Australia         2020-03-02        30        NA
#>  3 Australia         2020-03-03        39        NA
#>  4 Australia         2020-03-04        52        NA
#>  5 Australia         2020-03-05        55        NA
#>  6 Australia         2020-03-06        60        NA
#>  7 Australia         2020-03-07        63        NA
#>  8 Australia         2020-03-08        76        49
#>  9 Australia         2020-03-09        91        61
#> 10 Australia         2020-03-10       107        68
#> # … with 1,442 more rows

Question 3

Create a line chart of new_cases vs confirmed for each region.

You should end up with a ggplot called p1, with

  • lines: colour = "gray80" and size = 0.4,
  • label_number_si() to axis labels and logarithmic scales applied.
p1

Question 4

Generate a small multiples of p1 by regions ordered by their confirmed cases on 2020-05-31 from high to low, and highlight each region with colour = "firebrick" and size = 0.8 for each panel.

You should end up with a ggplot called p2.

p2

Question 5

Add the following titles and labels to p2:

  • x: "Total Confirmed Cases",
  • y: "New Confirmed Cases (in the Past Week)",
  • title: "Trajectory of World COVID-19 Confirmed Cases",
  • subtitle: "Data as of 2020-05-31",
  • caption: "Data: John Hopkins University, CSSE"

You should end up with a ggplot called p3, with

  • the minimal theme,
  • the plot title in bold,
  • setting the axis text font size to be 8.
p3

Question4fun (NO marks)

Polish p3 by:

  • adding the point at the end of each highlighted line,
  • placing each region labels at the top left position of each panel,
  • removing facet texts.