vignettes/intro_coronavirus_dataset.Rmd
intro_coronavirus_dataset.RmdThe coronavirus dataset provides a snapshot of the daily confirmed, recovered, and death cases of the Coronavirus (the 2019 Novel Coronavirus COVID-19) by geographic location (i.e., country/province). Let’s load the dataset from the coronavirus package:
The dataset has the following fields:
date - The date of the summaryprovince - The province or state, when applicablecountry - The country or region nameLat - Latitude pointLong - Longitude pointtype - the type of case (i.e., confirmed, death)cases - the number of daily cases (corresponding to the case type)We can use the head and str functions to see the structure of the dataset:
head(coronavirus)
#> date province country lat long type cases
#> 1 2020-01-22 Afghanistan 33.93911 67.70995 confirmed 0
#> 2 2020-01-23 Afghanistan 33.93911 67.70995 confirmed 0
#> 3 2020-01-24 Afghanistan 33.93911 67.70995 confirmed 0
#> 4 2020-01-25 Afghanistan 33.93911 67.70995 confirmed 0
#> 5 2020-01-26 Afghanistan 33.93911 67.70995 confirmed 0
#> 6 2020-01-27 Afghanistan 33.93911 67.70995 confirmed 0
str(coronavirus)
#> 'data.frame': 161710 obs. of 7 variables:
#> $ date : Date, format: "2020-01-22" "2020-01-23" ...
#> $ province: chr "" "" "" "" ...
#> $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
#> $ lat : num 33.9 33.9 33.9 33.9 33.9 ...
#> $ long : num 67.7 67.7 67.7 67.7 67.7 ...
#> $ type : chr "confirmed" "confirmed" "confirmed" "confirmed" ...
#> $ cases : int 0 0 0 0 0 0 0 0 0 0 ...We will use the dplyr and tidyr packages to query, transform, reshape, and keep the data tidy, the plotly package to plot the data and the DT package to view it:
Let’s start with summarizing the total number of cases by type as of 2020-08-14 and then plot it:
total_cases <- coronavirus %>%
group_by(type) %>%
summarise(cases = sum(cases)) %>%
mutate(type = factor(type, levels = c("confirmed", "death", "recovered")))
total_cases
#> # A tibble: 3 x 2
#> type cases
#> <fct> <int>
#> 1 confirmed 21159730
#> 2 death 764683
#> 3 recovered 13276831You can use those numbers to derive the current worldwide death rate (precentage):
round(100 * total_cases$cases[2] / total_cases$cases[1], 2)
#> [1] 3.61Likewise, you can derive the recovery rate:
round(100 * total_cases$cases[3] / total_cases$cases[1], 2)
#> [1] 62.75The total active cases are the difference between the total confirmed cases and the total recovered and death cases:
The following plot presents the cases (active, recovered, and death) distribution over time:
coronavirus %>%
group_by(type, date) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(date) %>%
mutate(active = confirmed - death - recovered) %>%
mutate(active_total = cumsum(active),
recovered_total = cumsum(recovered),
death_total = cumsum(death)) %>%
plot_ly(x = ~ date,
y = ~ active_total,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace(y = ~ death_total,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered_total,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Distribution of Covid19 Cases Worldwide",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))The next table provides an overview of the ten countries with the highest confirmed cases. We will use the datatable function from the DT package to view the table:
confirmed_country <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
mutate(perc = total_cases / sum(total_cases)) %>%
arrange(-total_cases)
confirmed_country %>%
head(10) %>%
datatable(rownames = FALSE,
colnames = c("Country", "Cases", "Perc of Total")) %>%
formatPercentage("perc", 2)The next plot summarize the distribution of confrimed cases by country:
conf_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases) %>%
mutate(parents = "Confirmed") %>%
ungroup()
plot_ly(data = conf_df,
type= "treemap",
values = ~total_cases,
labels= ~ country,
parents= ~parents,
domain = list(column=0),
name = "Confirmed",
textinfo="label+value+percent parent")Similarly, we can use the pivot_wider function from the tidyr package (in addition to the dplyr functions we used above) to get an overview of the three types of cases (confirmed, recovered, and death). We then will use it to derive the recovery and death rate by country. As for most of the countries, there is not enough information about the results of the confirmed cases, we will filter the data for countries with at least 25 confirmed cases and above:
coronavirus %>%
filter(country != "Others") %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(- confirmed) %>%
filter(confirmed >= 25) %>%
mutate(death_rate = death / confirmed) %>%
datatable(rownames = FALSE,
colnames = c("Country", "Confirmed","Death", "Death Rate")) %>%
formatPercentage("death_rate", 2) Note that it will be misleading to make any conclusion about the recovery and death rate. As there is no detail information about:
The following plot describes the overall distribution of the total confirmed cases in China by province:
coronavirus %>%
filter(country == "China",
type == "confirmed") %>%
group_by(province, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(- confirmed) %>%
plot_ly(labels = ~ province,
values = ~confirmed,
type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~ paste(province, "<br />",
"Number of confirmed cases: ", confirmed, sep = "")) %>%
layout(title = "Total China Confirmed Cases Dist. by Province")