D Appendix D Coronavirus
An example of an R Notebook, rendered at 2022-11-30 23:41:17 JST
This version uses
tidyversepackages for importing and transforming data.
library(tidyverse)
library(DT)D.1 Introduction
The following site of Johns Hopkins University is famous:
In this article, we study a coronavirus data collected by Johns Hopkins University called “JHU Covid-19 global time series data”. Since the original data requires reshaping, we use a data provided by RamiKrispin in the following site.
See also the R package coronavirus at
- https://CRAN.R-project.org/package=coronavirus
- For installation: `install.packages(“coronavirus”)
- To attach:
library(coronavirus)
We can directly download and read the data from:
It is updated daily.
In this note, we use the original JHU data and transform it using dplyr in the form similar to the Krispin’s.
D.2 Exploration of Data with Base R
D.2.1 Download and read csv (comma separated value) file
coronavirus <- read.csv("https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv")D.2.2 Summaries and structures of the data
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 <NA>str(coronavirus)## 'data.frame': 888636 obs. of 15 variables:
## $ date : chr "2020-01-22" "2020-01-23" "2020-01-24" "2020-01-25" ...
## $ province : chr "Alberta" "Alberta" "Alberta" "Alberta" ...
## $ country : chr "Canada" "Canada" "Canada" "Canada" ...
## $ lat : num 53.9 53.9 53.9 53.9 53.9 ...
## $ long : num -117 -117 -117 -117 -117 ...
## $ type : chr "confirmed" "confirmed" "confirmed" "confirmed" ...
## $ cases : int 0 0 0 0 0 0 0 0 0 0 ...
## $ uid : int 12401 12401 12401 12401 12401 12401 12401 12401 12401 12401 ...
## $ iso2 : chr "CA" "CA" "CA" "CA" ...
## $ iso3 : chr "CAN" "CAN" "CAN" "CAN" ...
## $ code3 : int 124 124 124 124 124 124 124 124 124 124 ...
## $ combined_key : chr "Alberta, Canada" "Alberta, Canada" "Alberta, Canada" "Alberta, Canada" ...
## $ population : num 4413146 4413146 4413146 4413146 4413146 ...
## $ continent_name: chr "North America" "North America" "North America" "North America" ...
## $ continent_code: chr NA NA NA NA ...Since the first column consists of date in character format, we change it to Date.
coronavirus$date <- as.Date(coronavirus$date)
str(coronavirus)## 'data.frame': 888636 obs. of 15 variables:
## $ date : Date, format: "2020-01-22" "2020-01-23" ...
## $ province : chr "Alberta" "Alberta" "Alberta" "Alberta" ...
## $ country : chr "Canada" "Canada" "Canada" "Canada" ...
## $ lat : num 53.9 53.9 53.9 53.9 53.9 ...
## $ long : num -117 -117 -117 -117 -117 ...
## $ type : chr "confirmed" "confirmed" "confirmed" "confirmed" ...
## $ cases : int 0 0 0 0 0 0 0 0 0 0 ...
## $ uid : int 12401 12401 12401 12401 12401 12401 12401 12401 12401 12401 ...
## $ iso2 : chr "CA" "CA" "CA" "CA" ...
## $ iso3 : chr "CAN" "CAN" "CAN" "CAN" ...
## $ code3 : int 124 124 124 124 124 124 124 124 124 124 ...
## $ combined_key : chr "Alberta, Canada" "Alberta, Canada" "Alberta, Canada" "Alberta, Canada" ...
## $ population : num 4413146 4413146 4413146 4413146 4413146 ...
## $ continent_name: chr "North America" "North America" "North America" "North America" ...
## $ continent_code: chr NA NA NA NA ...Now the first column is recognized as date. The range of dates covered can be seen by the following.
range(coronavirus$date)## [1] "2020-01-22" "2022-11-29"Let us check the countries by unique function. We choose a country from this list later.
head(unique(coronavirus$country), 10)## [1] "Canada" "United Kingdom" "China" "Netherlands"
## [5] "Australia" "New Zealand" "Denmark" "France"
## [9] "Afghanistan" "Albania"Now check the type column similarly.
unique(coronavirus$type)## [1] "confirmed" "death" "recovery"D.2.3 Set a Country
As a test we choose “Japan” as a country. Please check the country list above.
We apply a filter country == COUNTRY to the country column.
COUNTRY <- "Japan"
df0 <- coronavirus[coronavirus$country == COUNTRY,]Let us check the subset of our data and see if the population column changes over time.
head(df0)## date province country lat long type cases uid iso2
## 183569 2020-01-22 <NA> Japan 36.20482 138.2529 confirmed 2 392 JP
## 183570 2020-01-23 <NA> Japan 36.20482 138.2529 confirmed 0 392 JP
## 183571 2020-01-24 <NA> Japan 36.20482 138.2529 confirmed 0 392 JP
## 183572 2020-01-25 <NA> Japan 36.20482 138.2529 confirmed 0 392 JP
## 183573 2020-01-26 <NA> Japan 36.20482 138.2529 confirmed 2 392 JP
## 183574 2020-01-27 <NA> Japan 36.20482 138.2529 confirmed 0 392 JP
## iso3 code3 combined_key population continent_name continent_code
## 183569 JPN 392 Japan 126476458 Asia AS
## 183570 JPN 392 Japan 126476458 Asia AS
## 183571 JPN 392 Japan 126476458 Asia AS
## 183572 JPN 392 Japan 126476458 Asia AS
## 183573 JPN 392 Japan 126476458 Asia AS
## 183574 JPN 392 Japan 126476458 Asia AStail(df0)## date province country lat long type cases uid iso2
## 771815 2022-11-24 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## 771816 2022-11-25 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## 771817 2022-11-26 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## 771818 2022-11-27 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## 771819 2022-11-28 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## 771820 2022-11-29 <NA> Japan 36.20482 138.2529 recovery 0 392 JP
## iso3 code3 combined_key population continent_name continent_code
## 771815 JPN 392 Japan 126476458 Asia AS
## 771816 JPN 392 Japan 126476458 Asia AS
## 771817 JPN 392 Japan 126476458 Asia AS
## 771818 JPN 392 Japan 126476458 Asia AS
## 771819 JPN 392 Japan 126476458 Asia AS
## 771820 JPN 392 Japan 126476458 Asia ASSince the population on the first day and the last day are equal, you can set one as the population of the country.
(pop <- df0$population[1])## [1] 126476458We need only the first, the sixth, the seventh and the thirteenth column, namely, “date”, “type”, “cases”, “population”, we create a new data frame called df by the following.
df <- df0[c(1,6,7,13)]
str(df)## 'data.frame': 3129 obs. of 4 variables:
## $ date : Date, format: "2020-01-22" "2020-01-23" ...
## $ type : chr "confirmed" "confirmed" "confirmed" "confirmed" ...
## $ cases : int 2 0 0 0 2 0 3 0 4 4 ...
## $ population: num 1.26e+08 1.26e+08 1.26e+08 1.26e+08 1.26e+08 ...head(df)## date type cases population
## 183569 2020-01-22 confirmed 2 126476458
## 183570 2020-01-23 confirmed 0 126476458
## 183571 2020-01-24 confirmed 0 126476458
## 183572 2020-01-25 confirmed 0 126476458
## 183573 2020-01-26 confirmed 2 126476458
## 183574 2020-01-27 confirmed 0 126476458Alternatively, df0[c(1,6,7,13)] can be replaced by df0[c("date", "type", "cases", "population")].
head(df0[c("date", "type", "cases", "population")])## date type cases population
## 183569 2020-01-22 confirmed 2 126476458
## 183570 2020-01-23 confirmed 0 126476458
## 183571 2020-01-24 confirmed 0 126476458
## 183572 2020-01-25 confirmed 0 126476458
## 183573 2020-01-26 confirmed 2 126476458
## 183574 2020-01-27 confirmed 0 126476458D.2.4 Types: “confirmed” “death” and “recovery”
Let us check each type as follows.
df_confirmed <- df[df$type == "confirmed",]
df_death <- df[df$type == "death",]
df_recovery <- df[df$data_type == "recovery",]
head(df_confirmed)## date type cases population
## 183569 2020-01-22 confirmed 2 126476458
## 183570 2020-01-23 confirmed 0 126476458
## 183571 2020-01-24 confirmed 0 126476458
## 183572 2020-01-25 confirmed 0 126476458
## 183573 2020-01-26 confirmed 2 126476458
## 183574 2020-01-27 confirmed 0 126476458head(df_death)## date type cases population
## 484996 2020-01-22 death 0 126476458
## 484997 2020-01-23 death 0 126476458
## 484998 2020-01-24 death 0 126476458
## 484999 2020-01-25 death 0 126476458
## 485000 2020-01-26 death 0 126476458
## 485001 2020-01-27 death 0 126476458head(df_recovery)## [1] date type cases population
## <0 行> (または長さ 0 の row.names)Notice that “recovery” data is empty.
D.2.6 Scatter Plot and Correlation for Two Numerial Data
plot(df_confirmed$cases, df_death$cases, type = "p")
cor(df_confirmed$cases, df_death$cases)## [1] 0.716229D.2.6.1 Extra
It is better to add the title of the graph and the labels of x-axis and y-axis.
Here we used paste("Comfirmed Cases in",COUNTRY) to automatically paste the chosen country name.
plot(df_confirmed$date, df_confirmed$cases, type = "h",
main = paste("Comfirmed Cases in",COUNTRY),
xlab = "Date", ylab = "Number of Cases")
D.2.7 In Addition Set a Period
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
df_date <- df[df$date >=start_date & df$date <= end_date,]Apply the same operations on this subset.
D.2.7.1 Setting types
df_date_confirmed <- df_date[df_date$type == "confirmed",]
df_date_death <- df_date[df_date$type == "death",]
df_date_recovery <- df_date[df_date$data_type == "recovery",]
head(df_date_confirmed)## date type cases population
## 184095 2021-07-01 confirmed 1754 126476458
## 184096 2021-07-02 confirmed 1775 126476458
## 184097 2021-07-03 confirmed 1878 126476458
## 184098 2021-07-04 confirmed 1485 126476458
## 184099 2021-07-05 confirmed 1029 126476458
## 184100 2021-07-06 confirmed 1668 126476458head(df_date_death)## date type cases population
## 485522 2021-07-01 death 24 126476458
## 485523 2021-07-02 death 25 126476458
## 485524 2021-07-03 death 9 126476458
## 485525 2021-07-04 death 6 126476458
## 485526 2021-07-05 death 19 126476458
## 485527 2021-07-06 death 22 126476458head(df_date_recovery)## [1] date type cases population
## <0 行> (または長さ 0 の row.names)D.2.8 Histograms
plot(df_date_confirmed$date, df_date_confirmed$cases, type = "h")
plot(df_date_death$date, df_date_death$cases, type = "h")
# plot(df_date_recovered$date, df_date_recovered$cases, type = "h") # no data for recovery
D.2.9 List Observations and Questions for Further Exploration
- Q0. Change the values of the location and the period and see the outcomes.
- Q1. What is the correlation between df_confirmed\(cases and df_death\)cases?
- Q2. Do we have a larger correlation value if we shift the dates to implement the time-lag?
- Q3. Do you have any other questions to explore?
D.3 Selection of Several Countries with ggplot2
Let us choose “US”, “Germany”, “India”, “South Africa”, “Korea, South” and “Japan”
Check whether province part is valid in these countries.
df0 <- coronavirus[coronavirus$country %in% c("US", "Germany", "India", "South Africa","Korea, South", "Japan"),]
unique(df0$province)## [1] NAWe keep the country name.
df <- df0[c(1,3,6,7,13)]
str(df)## 'data.frame': 18774 obs. of 5 variables:
## $ date : Date, format: "2020-01-22" "2020-01-23" ...
## $ country : chr "Germany" "Germany" "Germany" "Germany" ...
## $ type : chr "confirmed" "confirmed" "confirmed" "confirmed" ...
## $ cases : int 0 0 0 0 0 1 3 0 0 1 ...
## $ population: num 83783945 83783945 83783945 83783945 83783945 ...head(df)## date country type cases population
## 161666 2020-01-22 Germany confirmed 0 83783945
## 161667 2020-01-23 Germany confirmed 0 83783945
## 161668 2020-01-24 Germany confirmed 0 83783945
## 161669 2020-01-25 Germany confirmed 0 83783945
## 161670 2020-01-26 Germany confirmed 0 83783945
## 161671 2020-01-27 Germany confirmed 1 83783945D.3.1 Set types
df_confirmed <- df[df$type == "confirmed",]
df_death <- df[df$type == "death",]
df_recovery <- df[df$data_type == "recovery",]
head(df_confirmed)## date country type cases population
## 161666 2020-01-22 Germany confirmed 0 83783945
## 161667 2020-01-23 Germany confirmed 0 83783945
## 161668 2020-01-24 Germany confirmed 0 83783945
## 161669 2020-01-25 Germany confirmed 0 83783945
## 161670 2020-01-26 Germany confirmed 0 83783945
## 161671 2020-01-27 Germany confirmed 1 83783945head(df_death)## date country type cases population
## 463093 2020-01-22 Germany death 0 83783945
## 463094 2020-01-23 Germany death 0 83783945
## 463095 2020-01-24 Germany death 0 83783945
## 463096 2020-01-25 Germany death 0 83783945
## 463097 2020-01-26 Germany death 0 83783945
## 463098 2020-01-27 Germany death 0 83783945head(df_recovery)## [1] date country type cases population
## <0 行> (または長さ 0 の row.names)unique(df_confirmed$population)## [1] 83783945 1380004385 126476458 51269183 59308690 329466283D.3.2 Visualization using ggplot2 package
library(ggplot2)ggplot(df_confirmed) +
geom_line(aes(x = date, y = cases, color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases", title = "Number of Confirmed Cases")
ggplot(df_confirmed) +
geom_line(aes(x = date, y = cases, color = country)) +
facet_wrap(vars(country)) +
labs(x = "Date", y = "Number of Confirmed Cases", title = "Number of Confirmed Cases")
ggplot(df_confirmed) +
geom_boxplot(aes(x = country, y = cases)) +
labs(x = "Date", y = "Number of Confirmed Cases", title = "Number of Confirmed Cases")
ggplot(df_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000", title = "Number of Confirmed Cases per 100,000")
ggplot(df_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
facet_wrap(vars(country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000", title = "Number of Confirmed Cases per 100,000")
ggplot(df_confirmed) +
geom_boxplot(aes(x = country, y = (cases*100000)/population)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000", title = "Number of Confirmed Cases per 100,000")
ggplot(df_confirmed) +
geom_boxplot(aes(x = country, y = (cases*100000)/population)) +
scale_y_continuous(trans='log10') +
labs(x = "Date", y = "Number of Confirmed Cases in log10 per 100,000", title = "Number of Confirmed Cases in log10 Scale per 100,000")## Warning in self$trans$transform(x): 計算結果が NaN になりました## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 265 rows containing non-finite values (`stat_boxplot()`).
ggplot(df_death) +
geom_line(aes(x = date, y = cases, color = country)) +
labs(x = "Date", y = "Number of Deaths", title = "Number of Deaths")
ggplot(df_death) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
labs(x = "Date", y = "Number of Deaths per 100,000", title = "Number of Deaths per 100,000")
ggplot(df_death) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
facet_wrap(vars(country)) +
labs(x = "Date", y = "Number of Deaths per 100,000", title = "Number of Deaths per 100,000")
ggplot(df_death) +
geom_boxplot(aes(x = country, y = (cases*100000)/population)) +
scale_y_continuous(trans='log10') +
labs(x = "Date", y = "Number of Deaths in log10 per 100,000", title = "Number of Deaths in log10 Scale per 100,000")## Warning in self$trans$transform(x): 計算結果が NaN になりました## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 635 rows containing non-finite values (`stat_boxplot()`).
D.3.3 Setting a Period
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
df_date <- df[df$date >=start_date & df$date <= end_date,]
df_date_confirmed <- df_date[df_date$type == "confirmed",]
df_date_death <- df_date[df_date$type == "death",]ggplot(df_date_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country))
ggplot(df_date_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
facet_wrap(vars(country))
start_date <- as.Date("2021-11-20")
end_date <- Sys.Date()
df_date <- df[df$date >=start_date & df$date <= end_date & df$country %in% c("Germany", "South Africa", "US"),]
df_date_confirmed <- df_date[df_date$type == "confirmed",]
df_date_death <- df_date[df_date$type == "death",]ggplot(df_date_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country))
The number of deaths in 100,000.
ggplot(df_date_death) +
geom_line(aes(x = date, y = (cases*100000/population), color = country))
D.4 Importing and Transforming Data with readr and dplyr in tidyverse Packages
D.4.1 Review
- Attaching neccessary packages
- Importing data
- Glimpsing data with
head(),str()and changing types of a column, i.e.,characters to date - Selecting columns of data
- Filtering rows of data
- Mutating data
- Visualizing data by
ggplot()
library(ggplot2)
coronavirus <- read.csv("https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv")
coronavirus$date <- as.Date(coronavirus$date)
df <- coronavirus[c(1,3,6,7,13)]
COUNTRIES <- c("US", "Germany", "India", "South Africa","Korea, South", "Japan")
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
df0 <- coronavirus[df$country %in% COUNTRIES,]
df1 <- df0[df0$date >=start_date & df0$date <= end_date,]
df1_confirmed <- df1[df1$type == "confirmed",]
ggplot(df1_confirmed) +
geom_line(aes(x = date, y = (cases*100000/population), color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000", title = "Number of Confirmed Cases per 100,000")
### library: Loading/Attaching Packages
To use packages,
- Install packages by
install.packages()only once, e.g.,install.packages("tidyverse"). - Load and attach packages by
library()at each session, e.g.,library(tidyverse).
For library(), see https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/library.
library(tidyverse)D.4.2 Importing data by readr in tidyverse
The goal of readr is to provide a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes. If you are new to readr, the best place to start is the data import chapter in R for data science.
To accurately read a rectangular dataset with readr you combine two pieces: a function that parses the overall file, and a column specification. The column specification describes how each column should be converted from a character vector to the most appropriate data type, and in most cases it’s not necessary because readr will guess it for you automatically.
readr supports seven file formats with seven read_ functions:
- read_csv(): comma separated (CSV) files
- read_tsv(): tab separated files
- read_delim(): general delimited files
- read_fwf(): fixed width files
- read_table(): tabular files where columns are separated by white-space.
- read_log(): web log files
coronavirus_tv <- read_csv("https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv")## Rows: 888636 Columns: 15
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): province, country, type, iso2, iso3, combined_key, continent_name,...
## dbl (6): lat, long, cases, uid, code3, population
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.coronavirus_tv## # A tibble: 888,636 × 15
## date province country lat long type cases uid iso2 iso3 code3
## <date> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 2020-01-22 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 2 2020-01-23 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 3 2020-01-24 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 4 2020-01-25 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 5 2020-01-26 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 6 2020-01-27 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 7 2020-01-28 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 8 2020-01-29 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 9 2020-01-30 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## 10 2020-01-31 Alberta Canada 53.9 -117. confir… 0 12401 CA CAN 124
## # … with 888,626 more rows, and 4 more variables: combined_key <chr>,
## # population <dbl>, continent_name <chr>, continent_code <chr># print(coronavirus_tv) # same as above- Data is in
tibble, see https://tibble.tidyverse.org. - See thatthe first column is already recognized as date.
glimpseis similar tostrand is like a transposed version ofprint()
glimpse(coronavirus_tv)## Rows: 888,636
## Columns: 15
## $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-0…
## $ province <chr> "Alberta", "Alberta", "Alberta", "Alberta", "Alberta", …
## $ country <chr> "Canada", "Canada", "Canada", "Canada", "Canada", "Cana…
## $ lat <dbl> 53.9333, 53.9333, 53.9333, 53.9333, 53.9333, 53.9333, 5…
## $ long <dbl> -116.5765, -116.5765, -116.5765, -116.5765, -116.5765, …
## $ type <chr> "confirmed", "confirmed", "confirmed", "confirmed", "co…
## $ cases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ uid <dbl> 12401, 12401, 12401, 12401, 12401, 12401, 12401, 12401,…
## $ iso2 <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "…
## $ iso3 <chr> "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "CAN",…
## $ code3 <dbl> 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, …
## $ combined_key <chr> "Alberta, Canada", "Alberta, Canada", "Alberta, Canada"…
## $ population <dbl> 4413146, 4413146, 4413146, 4413146, 4413146, 4413146, 4…
## $ continent_name <chr> "North America", "North America", "North America", "Nor…
## $ continent_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…download.file()andwrite_csv()
# Don't run repeatedly
DLURL <- "https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv"
DLDATE <- paste0("coronavirus", Sys.Date(), ".csv")
download.file(DLURL, destfile = DLDATE)# Don't run repeatedly
WRITEDATE <- paste0("covid19_", Sys.Date(), ".csv")
write_csv(coronavirus, WRITEDATE)D.4.3 Transforming data by dplyr in tidyverse
dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:
- mutate() adds new variables that are functions of existing variables
- select() picks variables based on their names.
- filter() picks cases based on their values.
- summarise() reduces multiple values down to a single summary.
- arrange() changes the ordering of the rows.
These all combine naturally with group_by() which allows you to perform any operation “by group”. You can learn more about them in vignette(“dplyr”). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table").
If you are new to dplyr, the best place to start is the data transformation chapter in R for data science.
D.4.3.1 slice(): Subset rows using their positions
slice() lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:
- slice_head() and slice_tail() select the first or last rows.
- slice_sample() randomly selects rows.
- slice_min() and slice_max() select rows with highest or lowest values of a variable.
The following is similar to head() but it does much more.
slice(coronavirus_tv, 6)## # A tibble: 1 × 15
## date province country lat long type cases uid iso2 iso3 code3
## <date> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 2020-01-27 Alberta Canada 53.9 -117. confirm… 0 12401 CA CAN 124
## # … with 4 more variables: combined_key <chr>, population <dbl>,
## # continent_name <chr>, continent_code <chr>D.4.3.2 select() Subset columns using their names and types
Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric to select variables based on their properties.
| Helper Function | Use | Example |
|---|---|---|
| - | Columns except | select(babynames, -prop) |
| : | Columns between (inclusive) | select(babynames, year:n) |
| contains() | Columns that contains a string | select(babynames, contains(“n”)) |
| ends_with() | Columns that ends with a string | select(babynames, ends_with(“n”)) |
| matches() | Columns that matches a regex | select(babynames, matches(“n”)) |
| num_range() | Columns with a numerical suffix in the range | Not applicable with babynames |
| one_of() | Columns whose name appear in the given set | select(babynames, one_of(c(“sex”, “gender”))) |
| starts_with() | Columns that starts with a string | select(babynames, starts_with(“n”)) |
df_tv <- select(coronavirus_tv, c(date, country, type, cases, population))
df_tv## # A tibble: 888,636 × 5
## date country type cases population
## <date> <chr> <chr> <dbl> <dbl>
## 1 2020-01-22 Canada confirmed 0 4413146
## 2 2020-01-23 Canada confirmed 0 4413146
## 3 2020-01-24 Canada confirmed 0 4413146
## 4 2020-01-25 Canada confirmed 0 4413146
## 5 2020-01-26 Canada confirmed 0 4413146
## 6 2020-01-27 Canada confirmed 0 4413146
## 7 2020-01-28 Canada confirmed 0 4413146
## 8 2020-01-29 Canada confirmed 0 4413146
## 9 2020-01-30 Canada confirmed 0 4413146
## 10 2020-01-31 Canada confirmed 0 4413146
## # … with 888,626 more rowsidentical(df_tv, select(coronavirus_tv, date, country, type, cases, population))## [1] TRUEidentical(df_tv, select(coronavirus_tv, 1, 3, 6, 7, 13))## [1] TRUEidentical(df_tv, select(coronavirus_tv, "date", "country", "type", "cases", "population"))## [1] TRUED.4.3.3 filter() Subset rows using column values
The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [.
| Logical operator | tests | Example |
|---|---|---|
| > | Is x greater than y? | x > y |
| >= | Is x greater than or equal to y? | x >= y |
| < | Is x less than y? | x < y |
| <= | Is x less than or equal to y? | x <= y |
| == | Is x equal to y? | x == y |
| != | Is x not equal to y? | x != y |
| is.na() | Is x an NA? | is.na(x) |
| !is.na() | Is x not an NA? | !is.na(x) |
COUNTRIES <- c("US", "Germany", "India", "South Africa","Korea, South", "Japan")
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date() df_tv0 <- filter(df_tv, country %in% COUNTRIES)
df_tv1 <- filter(df_tv0, date >=start_date & df_tv0$date <= end_date)
df_tv1_confirmed <- filter(df_tv1, type == "confirmed")identical(df_tv1_confirmed,
filter(df_tv, (country %in% COUNTRIES) &
(date >=start_date & date <= end_date) &
(type == "confirmed")))## [1] TRUE- Advanced method using piping
df_tv %>% filter(country %in% COUNTRIES) %>%
filter(date >=start_date & df_tv0$date <= end_date) %>%
filter(type == "confirmed") %>%
identical(df_tv1_confirmed)## [1] TRUED.4.3.4 mutate(): Create, modify, and delete columns
mutate() adds new variables and preserves existing ones; transmute() adds new variables and drops existing ones. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to NULL.
df_tv1_confirmed_pp <- mutate(df_tv1_confirmed, confirmed_pp = cases*100000/population)D.4.3.5 ggplot(): Plotting
ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
ggplot(df_tv1_confirmed_pp) +
geom_line(aes(x = date, y = confirmed_pp, color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = "Number of Confirmed Cases per 100,000")
D.4.3.6 Summary
library(tidyverse)
coronavirus_tv <- read_csv("https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv")## Rows: 888636 Columns: 15
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): province, country, type, iso2, iso3, combined_key, continent_name,...
## dbl (6): lat, long, cases, uid, code3, population
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.COUNTRIES <- c("US", "Germany", "India", "South Africa","Korea, South", "Japan")
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
df_tv <- select(coronavirus_tv, c(date, country, type, cases, population))
df_tv0 <- filter(df_tv, country %in% COUNTRIES)
df_tv1 <- filter(df_tv0, date >=start_date & df_tv0$date <= end_date)
df_tv1_confirmed <- filter(df_tv1, type == "confirmed")
df_tv1_confirmed_pp <- mutate(df_tv1_confirmed, confirmed_pp = cases*100000/population)
ggplot(df_tv1_confirmed_pp) +
geom_line(aes(x = date, y = confirmed_pp, color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = "Number of Confirmed Cases per 100,000")
D.4.3.7 Pipes
After importing data and setting parameters; COUNTRIES, start_date and end_date, we can simplify the code block as follows.
coronavirus_tv %>%
select(date, country, type, cases, population) %>%
filter(country %in% COUNTRIES) %>%
filter(date >=start_date & df_tv0$date <= end_date) %>%
filter(type == "confirmed") %>%
mutate(confirmed_pp = cases*100000/population) %>%
ggplot() +
geom_line(aes(x = date, y = confirmed_pp, color = country)) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = "Number of Confirmed Cases per 100,000")
D.5 Data of Johns Hopkins Universiy and World Bank
D.5.1 Importing Raw Data
We import the original Johns Hopkins Github data.
- COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/CSSEGISandData/COVID-19
- We use time series data
# IMPORT RAW DATA: Johns Hopkins Github data
confirmedraw <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")## Rows: 289 Columns: 1047
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1045): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(confirmedraw)## Rows: 289
## Columns: 1,047
## $ `Province/State` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Australian Capit…
## $ `Country/Region` <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Ango…
## $ Lat <dbl> 33.93911, 41.15330, 28.03390, 42.50630, -11.20270, -7…
## $ Long <dbl> 67.709953, 20.168300, 1.659600, 1.521800, 17.873900, …
## $ `1/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/24/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/25/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/26/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0,…
## $ `1/27/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0,…
## $ `1/28/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0,…
## $ `1/29/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 0, 0, 1, 0, 0,…
## $ `1/30/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 0, 0, 2, 0, 0,…
## $ `1/31/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 3, 0, 0,…
## $ `2/1/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 1, 0, 4, 0, 0,…
## $ `2/2/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 4, 0, 0,…
## $ `2/3/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 2, 0, 4, 0, 0,…
## $ `2/4/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 2, 0, 4, 0, 0,…
## $ `2/5/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 3, 2, 0, 4, 0, 0,…
## $ `2/6/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 2, 0, 4, 0, 0,…
## $ `2/7/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/8/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/9/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/10/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/11/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/12/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/13/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/14/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/15/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/16/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/17/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/18/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/19/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/20/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/21/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/22/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/23/20` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/24/20` <dbl> 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 0,…
## $ `2/25/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 2,…
## $ `2/26/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 1,…
## $ `2/27/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 1,…
## $ `2/28/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 5, 2, 0, 4, 0, 1,…
## $ `2/29/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 9, 3, 0, 7, 2, 3,…
## $ `3/1/20` <dbl> 5, 0, 1, 0, 0, 0, 0, 0, 1, 0, 6, 0, 9, 3, 0, 7, 2, 7,…
## $ `3/2/20` <dbl> 5, 0, 3, 1, 0, 0, 0, 0, 1, 0, 6, 0, 9, 3, 1, 9, 2, 8,…
## $ `3/3/20` <dbl> 5, 0, 5, 1, 0, 0, 0, 1, 1, 0, 13, 0, 11, 3, 1, 9, 2, …
## $ `3/4/20` <dbl> 5, 0, 12, 1, 0, 0, 0, 1, 1, 0, 22, 1, 11, 5, 1, 10, 2…
## $ `3/5/20` <dbl> 5, 0, 12, 1, 0, 0, 0, 1, 1, 0, 22, 1, 13, 5, 1, 10, 3…
## $ `3/6/20` <dbl> 5, 0, 17, 1, 0, 0, 0, 2, 1, 0, 26, 0, 13, 7, 1, 10, 3…
## $ `3/7/20` <dbl> 8, 0, 17, 1, 0, 0, 0, 8, 1, 0, 28, 0, 13, 7, 1, 11, 3…
## $ `3/8/20` <dbl> 8, 0, 19, 1, 0, 0, 0, 12, 1, 0, 38, 0, 15, 7, 2, 11, …
## $ `3/9/20` <dbl> 8, 2, 20, 1, 0, 0, 0, 12, 1, 0, 48, 0, 15, 7, 2, 15, …
## $ `3/10/20` <dbl> 8, 10, 20, 1, 0, 0, 0, 17, 1, 0, 55, 1, 18, 7, 2, 18,…
## $ `3/11/20` <dbl> 11, 12, 20, 1, 0, 0, 0, 19, 1, 0, 65, 1, 20, 9, 3, 21…
## $ `3/12/20` <dbl> 11, 23, 24, 1, 0, 0, 0, 19, 4, 0, 65, 1, 20, 9, 3, 21…
## $ `3/13/20` <dbl> 11, 33, 26, 1, 0, 0, 1, 31, 8, 1, 92, 1, 35, 16, 5, 3…
## $ `3/14/20` <dbl> 14, 38, 37, 1, 0, 0, 1, 34, 18, 1, 112, 1, 46, 19, 5,…
## $ `3/15/20` <dbl> 20, 42, 48, 1, 0, 0, 1, 45, 26, 1, 134, 1, 61, 20, 6,…
## $ `3/16/20` <dbl> 25, 51, 54, 2, 0, 0, 1, 56, 52, 2, 171, 1, 68, 29, 7,…
## $ `3/17/20` <dbl> 26, 55, 60, 39, 0, 0, 1, 68, 78, 2, 210, 1, 78, 29, 7…
## $ `3/18/20` <dbl> 26, 59, 74, 39, 0, 0, 1, 79, 84, 3, 267, 1, 94, 37, 1…
## $ `3/19/20` <dbl> 26, 64, 87, 53, 0, 0, 1, 97, 115, 4, 307, 1, 144, 42,…
## $ `3/20/20` <dbl> 24, 70, 90, 75, 1, 0, 1, 128, 136, 6, 353, 3, 184, 50…
## $ `3/21/20` <dbl> 24, 76, 139, 88, 2, 0, 1, 158, 160, 9, 436, 3, 221, 6…
## $ `3/22/20` <dbl> 34, 89, 201, 113, 2, 0, 1, 266, 194, 19, 669, 5, 259,…
## $ `3/23/20` <dbl> 40, 104, 230, 133, 3, 0, 3, 301, 235, 32, 669, 5, 319…
## $ `3/24/20` <dbl> 42, 123, 264, 164, 3, 0, 3, 387, 249, 39, 818, 6, 397…
## $ `3/25/20` <dbl> 74, 146, 302, 188, 3, 0, 3, 387, 265, 39, 1029, 6, 44…
## $ `3/26/20` <dbl> 80, 174, 367, 224, 4, 0, 7, 502, 290, 53, 1219, 12, 4…
## $ `3/27/20` <dbl> 91, 186, 409, 267, 4, 0, 7, 589, 329, 62, 1405, 12, 5…
## $ `3/28/20` <dbl> 106, 197, 454, 308, 5, 0, 7, 690, 407, 71, 1617, 15, …
## $ `3/29/20` <dbl> 114, 212, 511, 334, 7, 0, 7, 745, 424, 77, 1791, 15, …
## $ `3/30/20` <dbl> 114, 223, 584, 370, 7, 0, 7, 820, 482, 78, 2032, 15, …
## $ `3/31/20` <dbl> 166, 243, 716, 376, 7, 0, 7, 1054, 532, 80, 2032, 17,…
## $ `4/1/20` <dbl> 192, 259, 847, 390, 8, 0, 7, 1054, 571, 84, 2182, 19,…
## $ `4/2/20` <dbl> 235, 277, 986, 428, 8, 0, 9, 1133, 663, 87, 2298, 21,…
## $ `4/3/20` <dbl> 269, 304, 1171, 439, 8, 0, 15, 1265, 736, 91, 2389, 2…
## $ `4/4/20` <dbl> 270, 333, 1251, 466, 10, 0, 15, 1451, 770, 93, 2493, …
## $ `4/5/20` <dbl> 299, 361, 1320, 501, 14, 0, 15, 1451, 822, 96, 2580, …
## $ `4/6/20` <dbl> 337, 377, 1423, 525, 16, 0, 15, 1554, 833, 96, 2637, …
## $ `4/7/20` <dbl> 367, 383, 1468, 545, 17, 0, 19, 1628, 853, 96, 2686, …
## $ `4/8/20` <dbl> 423, 400, 1572, 564, 19, 0, 19, 1715, 881, 99, 2734, …
## $ `4/9/20` <dbl> 444, 409, 1666, 583, 19, 0, 19, 1795, 921, 100, 2773,…
## $ `4/10/20` <dbl> 521, 416, 1761, 601, 19, 0, 19, 1975, 937, 103, 2822,…
## $ `4/11/20` <dbl> 521, 433, 1825, 601, 19, 0, 21, 1975, 967, 103, 2857,…
## $ `4/12/20` <dbl> 555, 446, 1914, 638, 19, 0, 21, 2142, 1013, 103, 2857…
## $ `4/13/20` <dbl> 607, 467, 1983, 646, 19, 0, 23, 2208, 1039, 102, 2863…
## $ `4/14/20` <dbl> 665, 475, 2070, 659, 19, 0, 23, 2277, 1067, 103, 2870…
## $ `4/15/20` <dbl> 770, 494, 2160, 673, 19, 0, 23, 2443, 1111, 103, 2886…
## $ `4/16/20` <dbl> 794, 518, 2268, 673, 19, 0, 23, 2571, 1159, 103, 2897…
## $ `4/17/20` <dbl> 845, 539, 2418, 696, 19, 0, 23, 2669, 1201, 103, 2926…
## $ `4/18/20` <dbl> 908, 548, 2534, 704, 24, 0, 23, 2758, 1248, 103, 2936…
## $ `4/19/20` <dbl> 933, 562, 2629, 713, 24, 0, 23, 2839, 1291, 103, 2957…
## $ `4/20/20` <dbl> 996, 584, 2718, 717, 24, 0, 23, 2941, 1339, 104, 2963…
## $ `4/21/20` <dbl> 1026, 609, 2811, 717, 24, 0, 23, 3031, 1401, 104, 296…
## $ `4/22/20` <dbl> 1092, 634, 2910, 723, 25, 0, 24, 3144, 1473, 104, 297…
## $ `4/23/20` <dbl> 1176, 663, 3007, 723, 25, 0, 24, 3435, 1523, 104, 297…
## $ `4/24/20` <dbl> 1226, 678, 3127, 731, 25, 0, 24, 3607, 1596, 105, 298…
## $ `4/25/20` <dbl> 1330, 712, 3256, 738, 25, 0, 24, 3780, 1677, 106, 299…
## $ `4/26/20` <dbl> 1463, 726, 3382, 738, 26, 0, 24, 3892, 1746, 106, 300…
## $ `4/27/20` <dbl> 1531, 736, 3517, 743, 27, 0, 24, 4003, 1808, 106, 300…
## $ `4/28/20` <dbl> 1703, 750, 3649, 743, 27, 0, 24, 4127, 1867, 106, 301…
## $ `4/29/20` <dbl> 1827, 766, 3848, 743, 27, 0, 24, 4285, 1932, 106, 301…
## $ `4/30/20` <dbl> 1827, 773, 4006, 745, 27, 0, 24, 4428, 2066, 106, 302…
## $ `5/1/20` <dbl> 2171, 782, 4154, 745, 30, 0, 25, 4532, 2148, 106, 303…
## $ `5/2/20` <dbl> 2469, 789, 4295, 747, 35, 0, 25, 4681, 2273, 106, 303…
## $ `5/3/20` <dbl> 2469, 795, 4474, 748, 35, 0, 25, 4783, 2386, 106, 303…
## $ `5/4/20` <dbl> 2469, 803, 4648, 750, 35, 0, 25, 4887, 2507, 107, 303…
## $ `5/5/20` <dbl> 2469, 820, 4838, 751, 36, 0, 25, 5020, 2619, 107, 304…
## $ `5/6/20` <dbl> 3224, 832, 4997, 751, 36, 0, 25, 5208, 2782, 107, 304…
## $ `5/7/20` <dbl> 3392, 842, 5182, 752, 36, 0, 25, 5371, 2884, 107, 304…
## $ `5/8/20` <dbl> 3563, 850, 5369, 752, 43, 0, 25, 5611, 3029, 107, 305…
## $ `5/9/20` <dbl> 3563, 856, 5558, 754, 43, 0, 25, 5776, 3175, 107, 305…
## $ `5/10/20` <dbl> 4402, 868, 5723, 755, 45, 0, 25, 6034, 3313, 107, 305…
## $ `5/11/20` <dbl> 4664, 872, 5891, 755, 45, 0, 25, 6278, 3392, 107, 305…
## $ `5/12/20` <dbl> 4967, 876, 6067, 758, 45, 0, 25, 6563, 3538, 107, 305…
## $ `5/13/20` <dbl> 4967, 880, 6253, 760, 45, 0, 25, 6879, 3718, 107, 306…
## $ `5/14/20` <dbl> 5339, 898, 6442, 761, 48, 0, 25, 7134, 3860, 107, 307…
## $ `5/15/20` <dbl> 6053, 916, 6629, 761, 48, 0, 25, 7479, 4044, 107, 307…
## $ `5/16/20` <dbl> 6402, 933, 6821, 761, 48, 0, 25, 7805, 4283, 107, 307…
## $ `5/17/20` <dbl> 6635, 946, 7019, 761, 48, 0, 25, 8068, 4472, 107, 307…
## $ `5/18/20` <dbl> 7072, 948, 7201, 761, 50, 0, 25, 8371, 4823, 107, 307…
## $ `5/19/20` <dbl> 7655, 949, 7377, 761, 52, 0, 25, 8809, 5041, 107, 308…
## $ `5/20/20` <dbl> 8145, 964, 7542, 762, 52, 0, 25, 9283, 5271, 107, 308…
## $ `5/21/20` <dbl> 8676, 969, 7728, 762, 58, 0, 25, 9931, 5606, 107, 308…
## $ `5/22/20` <dbl> 9216, 981, 7918, 762, 60, 0, 25, 10649, 5928, 107, 30…
## $ `5/23/20` <dbl> 9952, 989, 8113, 762, 61, 0, 25, 11353, 6302, 107, 30…
## $ `5/24/20` <dbl> 10668, 998, 8306, 762, 69, 0, 25, 12076, 6661, 107, 3…
## $ `5/25/20` <dbl> 11180, 1004, 8503, 763, 70, 0, 25, 12628, 7113, 107, …
## $ `5/26/20` <dbl> 11917, 1029, 8697, 763, 70, 0, 25, 13228, 7402, 107, …
## $ `5/27/20` <dbl> 12465, 1050, 8857, 763, 71, 0, 25, 13933, 7774, 107, …
## $ `5/28/20` <dbl> 13102, 1076, 8997, 763, 74, 0, 25, 14702, 8216, 107, …
## $ `5/29/20` <dbl> 13745, 1099, 9134, 764, 81, 0, 25, 15419, 8676, 107, …
## $ `5/30/20` <dbl> 14529, 1122, 9267, 764, 84, 0, 25, 16214, 8927, 107, …
## $ `5/31/20` <dbl> 15180, 1137, 9394, 764, 86, 0, 26, 16851, 9282, 107, …
## $ `6/1/20` <dbl> 15836, 1143, 9513, 765, 86, 0, 26, 17415, 9492, 107, …
## $ `6/2/20` <dbl> 16578, 1164, 9626, 844, 86, 0, 26, 18319, 10009, 107,…
## $ `6/3/20` <dbl> 17353, 1184, 9733, 851, 86, 0, 26, 19268, 10524, 107,…
## $ `6/4/20` <dbl> 17977, 1197, 9831, 852, 86, 0, 26, 20197, 11221, 107,…
## $ `6/5/20` <dbl> 19055, 1212, 9935, 852, 86, 0, 26, 21037, 11817, 107,…
## $ `6/6/20` <dbl> 19637, 1232, 10050, 852, 88, 0, 26, 22020, 12364, 108…
## $ `6/7/20` <dbl> 20428, 1246, 10154, 852, 91, 0, 26, 22794, 13130, 108…
## $ `6/8/20` <dbl> 21003, 1263, 10265, 852, 92, 0, 26, 23620, 13325, 108…
## $ `6/9/20` <dbl> 21308, 1299, 10382, 852, 96, 0, 26, 24761, 13675, 108…
## $ `6/10/20` <dbl> 22228, 1341, 10484, 852, 113, 0, 26, 25987, 14103, 10…
## $ `6/11/20` <dbl> 22976, 1385, 10589, 852, 118, 0, 26, 27373, 14669, 10…
## $ `6/12/20` <dbl> 23632, 1416, 10698, 853, 130, 0, 26, 28764, 15281, 10…
## $ `6/13/20` <dbl> 24188, 1464, 10810, 853, 138, 0, 26, 30295, 16004, 10…
## $ `6/14/20` <dbl> 24852, 1521, 10919, 853, 140, 0, 26, 31577, 16667, 10…
## $ `6/15/20` <dbl> 25613, 1590, 11031, 853, 142, 0, 26, 32785, 17064, 10…
## $ `6/16/20` <dbl> 25719, 1672, 11147, 854, 148, 0, 26, 34159, 17489, 10…
## $ `6/17/20` <dbl> 26960, 1722, 11268, 854, 155, 0, 26, 35552, 18033, 10…
## $ `6/18/20` <dbl> 27423, 1788, 11385, 855, 166, 0, 26, 37510, 18698, 10…
## $ `6/19/20` <dbl> 27964, 1838, 11504, 855, 172, 0, 26, 39570, 19157, 10…
## $ `6/20/20` <dbl> 28383, 1891, 11631, 855, 176, 0, 26, 41204, 19708, 10…
## $ `6/21/20` <dbl> 28919, 1962, 11771, 855, 183, 0, 26, 42785, 20268, 10…
## $ `6/22/20` <dbl> 29229, 1995, 11920, 855, 186, 0, 26, 44931, 20588, 10…
## $ `6/23/20` <dbl> 29567, 2047, 12076, 855, 189, 0, 26, 47203, 21006, 10…
## $ `6/24/20` <dbl> 29726, 2114, 12248, 855, 197, 0, 26, 49851, 21717, 10…
## $ `6/25/20` <dbl> 30261, 2192, 12445, 855, 212, 0, 65, 52457, 22488, 10…
## $ `6/26/20` <dbl> 30346, 2269, 12685, 855, 212, 0, 65, 55343, 23247, 10…
## $ `6/27/20` <dbl> 30702, 2330, 12968, 855, 259, 0, 65, 57744, 23909, 10…
## $ `6/28/20` <dbl> 31053, 2402, 13273, 855, 267, 0, 69, 59933, 24645, 10…
## $ `6/29/20` <dbl> 31324, 2466, 13571, 855, 276, 0, 69, 62268, 25127, 10…
## $ `6/30/20` <dbl> 31445, 2535, 13907, 855, 284, 0, 69, 64530, 25542, 10…
## $ `7/1/20` <dbl> 31848, 2580, 14272, 855, 291, 0, 69, 67197, 26065, 10…
## $ `7/2/20` <dbl> 32108, 2662, 14657, 855, 315, 0, 69, 69941, 26658, 10…
## $ `7/3/20` <dbl> 32410, 2752, 15070, 855, 328, 0, 68, 72786, 27320, 10…
## $ `7/4/20` <dbl> 32758, 2819, 15500, 855, 346, 0, 68, 75376, 27900, 10…
## $ `7/5/20` <dbl> 33037, 2893, 15941, 855, 346, 0, 68, 77815, 28606, 10…
## $ `7/6/20` <dbl> 33150, 2964, 16404, 855, 346, 0, 70, 80447, 28936, 10…
## $ `7/7/20` <dbl> 33470, 3038, 16879, 855, 386, 0, 70, 83426, 29285, 11…
## $ `7/8/20` <dbl> 33680, 3106, 17348, 855, 386, 0, 70, 87030, 29820, 11…
## $ `7/9/20` <dbl> 33739, 3188, 17808, 855, 396, 0, 73, 90693, 30346, 11…
## $ `7/10/20` <dbl> 34280, 3278, 18242, 855, 458, 0, 74, 94060, 30903, 11…
## $ `7/11/20` <dbl> 34437, 3371, 18712, 855, 462, 0, 74, 97509, 31392, 11…
## $ `7/12/20` <dbl> 34537, 3454, 19195, 855, 506, 0, 74, 100166, 31969, 1…
## $ `7/13/20` <dbl> 34541, 3571, 19689, 858, 525, 0, 74, 103265, 32151, 1…
## $ `7/14/20` <dbl> 34826, 3667, 20216, 861, 541, 0, 74, 106910, 32490, 1…
## $ `7/15/20` <dbl> 35026, 3752, 20770, 862, 576, 0, 74, 111146, 33005, 1…
## $ `7/16/20` <dbl> 35156, 3851, 21355, 877, 607, 0, 74, 114783, 33559, 1…
## $ `7/17/20` <dbl> 35315, 3906, 21948, 880, 638, 0, 76, 119301, 34001, 1…
## $ `7/18/20` <dbl> 35375, 4008, 22549, 880, 687, 0, 76, 122524, 34462, 1…
## $ `7/19/20` <dbl> 35561, 4090, 23084, 880, 705, 0, 76, 126755, 34877, 1…
## $ `7/20/20` <dbl> 35595, 4171, 23691, 884, 749, 0, 76, 130774, 34981, 1…
## $ `7/21/20` <dbl> 35701, 4290, 24278, 884, 779, 0, 76, 136118, 35254, 1…
## $ `7/22/20` <dbl> 35813, 4358, 24872, 889, 812, 0, 76, 141900, 35693, 1…
## $ `7/23/20` <dbl> 36001, 4466, 25484, 889, 851, 0, 76, 148027, 36162, 1…
## $ `7/24/20` <dbl> 36067, 4570, 26159, 897, 880, 0, 82, 153520, 36613, 1…
## $ `7/25/20` <dbl> 36122, 4637, 26764, 897, 916, 0, 82, 158334, 36996, 1…
## $ `7/26/20` <dbl> 36243, 4763, 27357, 897, 932, 0, 82, 162526, 37317, 1…
## $ `7/27/20` <dbl> 36349, 4880, 27973, 907, 950, 0, 86, 167416, 37390, 1…
## $ `7/28/20` <dbl> 36454, 4997, 28615, 907, 1000, 0, 86, 173355, 37629, …
## $ `7/29/20` <dbl> 36557, 5105, 29229, 918, 1078, 0, 91, 178996, 37937, …
## $ `7/30/20` <dbl> 36628, 5197, 29831, 922, 1109, 0, 91, 185373, 38196, …
## $ `7/31/20` <dbl> 36628, 5276, 30394, 925, 1148, 0, 91, 191302, 38550, …
## $ `8/1/20` <dbl> 36796, 5396, 30950, 925, 1164, 0, 91, 196543, 38841, …
## $ `8/2/20` <dbl> 36796, 5519, 31465, 925, 1199, 0, 91, 201919, 39050, …
## $ `8/3/20` <dbl> 36796, 5620, 31972, 937, 1280, 0, 92, 206743, 39102, …
## $ `8/4/20` <dbl> 36833, 5750, 32504, 939, 1344, 0, 92, 213535, 39298, …
## $ `8/5/20` <dbl> 36915, 5889, 33055, 939, 1395, 0, 92, 220682, 39586, …
## $ `8/6/20` <dbl> 36982, 6016, 33626, 944, 1483, 0, 92, 228195, 39819, …
## $ `8/7/20` <dbl> 37023, 6151, 34155, 955, 1538, 0, 92, 235677, 39985, …
## $ `8/8/20` <dbl> 37101, 6275, 34693, 955, 1572, 0, 92, 241811, 40185, …
## $ `8/9/20` <dbl> 37140, 6411, 35160, 955, 1672, 0, 92, 246499, 40410, …
## $ `8/10/20` <dbl> 37140, 6536, 35712, 963, 1679, 0, 92, 253868, 40433, …
## $ `8/11/20` <dbl> 37355, 6676, 36204, 963, 1735, 0, 92, 260911, 40593, …
## $ `8/12/20` <dbl> 37431, 6817, 36699, 977, 1762, 0, 92, 268574, 40794, …
## $ `8/13/20` <dbl> 37510, 6971, 37187, 981, 1815, 0, 92, 276072, 41023, …
## $ `8/14/20` <dbl> 37517, 7117, 37664, 989, 1852, 0, 93, 282437, 41299, …
## $ `8/15/20` <dbl> 37637, 7260, 38133, 989, 1879, 0, 93, 289100, 41495, …
## $ `8/16/20` <dbl> 37682, 7380, 38583, 989, 1906, 0, 93, 294569, 41663, …
## $ `8/17/20` <dbl> 37682, 7499, 39025, 1005, 1935, 0, 93, 299126, 41701,…
## $ `8/18/20` <dbl> 37685, 7654, 39444, 1005, 1966, 0, 93, 305966, 41846,…
## $ `8/19/20` <dbl> 37685, 7812, 39847, 1024, 2015, 0, 94, 312659, 42056,…
## $ `8/20/20` <dbl> 37845, 7967, 40258, 1024, 2044, 0, 94, 320884, 42319,…
## $ `8/21/20` <dbl> 37942, 8119, 40667, 1045, 2068, 0, 94, 329043, 42477,…
## $ `8/22/20` <dbl> 37980, 8275, 41068, 1045, 2134, 0, 94, 336802, 42616,…
## $ `8/23/20` <dbl> 38039, 8427, 41460, 1045, 2171, 0, 94, 342154, 42792,…
## $ `8/24/20` <dbl> 38085, 8605, 41858, 1060, 2222, 0, 94, 350867, 42825,…
## $ `8/25/20` <dbl> 38156, 8759, 42228, 1060, 2283, 0, 94, 359638, 42936,…
## $ `8/26/20` <dbl> 38199, 8927, 42619, 1098, 2332, 0, 94, 370188, 43067,…
## $ `8/27/20` <dbl> 38215, 9083, 43016, 1098, 2415, 0, 94, 380292, 43270,…
## $ `8/28/20` <dbl> 38226, 9195, 43403, 1124, 2471, 0, 94, 392009, 43451,…
## $ `8/29/20` <dbl> 38229, 9279, 43781, 1124, 2551, 0, 94, 401239, 43626,…
## $ `8/30/20` <dbl> 38229, 9380, 44146, 1124, 2624, 0, 94, 408426, 43750,…
## $ `8/31/20` <dbl> 38248, 9513, 44494, 1176, 2654, 0, 94, 417735, 43781,…
## $ `9/1/20` <dbl> 38282, 9606, 44833, 1184, 2729, 0, 94, 428239, 43878,…
## $ `9/2/20` <dbl> 38329, 9728, 45158, 1199, 2777, 0, 94, 439172, 44075,…
## $ `9/3/20` <dbl> 38374, 9844, 45469, 1199, 2805, 0, 95, 451198, 44271,…
## $ `9/4/20` <dbl> 38374, 9967, 45773, 1215, 2876, 0, 95, 461882, 44461,…
## $ `9/5/20` <dbl> 38390, 10102, 46071, 1215, 2935, 0, 95, 471806, 44649…
## $ `9/6/20` <dbl> 38484, 10255, 46364, 1215, 2965, 0, 95, 478792, 44783…
## $ `9/7/20` <dbl> 38580, 10406, 46653, 1261, 2981, 0, 95, 488007, 44845…
## $ `9/8/20` <dbl> 38606, 10553, 46938, 1261, 3033, 0, 95, 500034, 44953…
## $ `9/9/20` <dbl> 38630, 10704, 47216, 1301, 3092, 0, 95, 512293, 45152…
## $ `9/10/20` <dbl> 38658, 10860, 47488, 1301, 3217, 0, 95, 524198, 45326…
## $ `9/11/20` <dbl> 38692, 11021, 47752, 1344, 3279, 0, 95, 535705, 45503…
## $ `9/12/20` <dbl> 38727, 11185, 48007, 1344, 3335, 0, 95, 546481, 45675…
## $ `9/13/20` <dbl> 38802, 11353, 48254, 1344, 3388, 0, 95, 555537, 45862…
## $ `9/14/20` <dbl> 38858, 11520, 48496, 1438, 3439, 0, 95, 565446, 45969…
## $ `9/15/20` <dbl> 38901, 11672, 48734, 1438, 3569, 0, 95, 577338, 46119…
## $ `9/16/20` <dbl> 38941, 11816, 48966, 1483, 3675, 0, 95, 589012, 46376…
## $ `9/17/20` <dbl> 38958, 11948, 49194, 1483, 3789, 0, 95, 601713, 46671…
## $ `9/18/20` <dbl> 38969, 12073, 49413, 1564, 3848, 0, 95, 613658, 46910…
## $ `9/19/20` <dbl> 39005, 12226, 49623, 1564, 3901, 0, 96, 622934, 47154…
## $ `9/20/20` <dbl> 39130, 12385, 49826, 1564, 3991, 0, 96, 631365, 47431…
## $ `9/21/20` <dbl> 39160, 12535, 50023, 1681, 4117, 0, 96, 640147, 47552…
## $ `9/22/20` <dbl> 39182, 12666, 50214, 1681, 4236, 0, 96, 652174, 47667…
## $ `9/23/20` <dbl> 39231, 12787, 50400, 1753, 4363, 0, 97, 664799, 47877…
## $ `9/24/20` <dbl> 39256, 12921, 50579, 1753, 4475, 0, 97, 678266, 48251…
## $ `9/25/20` <dbl> 39272, 13045, 50754, 1836, 4590, 0, 98, 691235, 48643…
## $ `9/26/20` <dbl> 39278, 13153, 50914, 1836, 4672, 0, 98, 702484, 49072…
## $ `9/27/20` <dbl> 39313, 13259, 51067, 1836, 4718, 0, 101, 711325, 4940…
## $ `9/28/20` <dbl> 39325, 13391, 51213, 1966, 4797, 0, 101, 723132, 4957…
## $ `9/29/20` <dbl> 39340, 13518, 51368, 1966, 4905, 0, 101, 736609, 4990…
## $ `9/30/20` <dbl> 39354, 13649, 51530, 2050, 4972, 0, 101, 751001, 5035…
## $ `10/1/20` <dbl> 39371, 13806, 51690, 2050, 5114, 0, 101, 765002, 5085…
## $ `10/2/20` <dbl> 39376, 13965, 51847, 2110, 5211, 0, 106, 779689, 5138…
## $ `10/3/20` <dbl> 39383, 14117, 51995, 2110, 5370, 0, 107, 790818, 5192…
## $ `10/4/20` <dbl> 39427, 14266, 52136, 2110, 5402, 0, 107, 798486, 5249…
## $ `10/5/20` <dbl> 39508, 14410, 52270, 2370, 5530, 0, 107, 809728, 5267…
## $ `10/6/20` <dbl> 39572, 14568, 52399, 2370, 5725, 0, 107, 824468, 5308…
## $ `10/7/20` <dbl> 39634, 14730, 52520, 2568, 5725, 0, 108, 840915, 5375…
## $ `10/8/20` <dbl> 39702, 14899, 52658, 2568, 5958, 0, 111, 856369, 5447…
## $ `10/9/20` <dbl> 39779, 15066, 52804, 2696, 6031, 0, 111, 871468, 5508…
## $ `10/10/20` <dbl> 39789, 15231, 52940, 2696, 6246, 0, 111, 883882, 5573…
## $ `10/11/20` <dbl> 39885, 15399, 53072, 2696, 6366, 0, 111, 894206, 5645…
## $ `10/12/20` <dbl> 39956, 15570, 53325, 2995, 6488, 0, 111, 903730, 5682…
## $ `10/13/20` <dbl> 40014, 15752, 53399, 2995, 6680, 0, 111, 917035, 5756…
## $ `10/14/20` <dbl> 40080, 15955, 53584, 3190, 6846, 0, 112, 931967, 5862…
## $ `10/15/20` <dbl> 40112, 16212, 53777, 3190, 7096, 0, 112, 949063, 5999…
## $ `10/16/20` <dbl> 40159, 16501, 53998, 3377, 7222, 0, 112, 965609, 6146…
## $ `10/17/20` <dbl> 40227, 16774, 54203, 3377, 7462, 0, 119, 979119, 6300…
## $ `10/18/20` <dbl> 40286, 17055, 54402, 3377, 7622, 0, 119, 989680, 6469…
## $ `10/19/20` <dbl> 40373, 17350, 54616, 3623, 7829, 0, 119, 1002662, 654…
## $ `10/20/20` <dbl> 40461, 17651, 54829, 3623, 8049, 0, 119, 1018999, 666…
## $ `10/21/20` <dbl> 40461, 17948, 55081, 3811, 8338, 0, 122, 1037325, 685…
## $ `10/22/20` <dbl> 40510, 18250, 55357, 3811, 8582, 0, 122, 1053650, 708…
## $ `10/23/20` <dbl> 40626, 18556, 55630, 4038, 8829, 0, 122, 1069368, 733…
## $ `10/24/20` <dbl> 40687, 18858, 55880, 4038, 9026, 0, 124, 1081336, 755…
## $ `10/25/20` <dbl> 40768, 19157, 56143, 4038, 9381, 0, 124, 1090589, 778…
## $ `10/26/20` <dbl> 40833, 19445, 56419, 4325, 9644, 0, 124, 1102301, 788…
## $ `10/27/20` <dbl> 40937, 19729, 56706, 4410, 9871, 0, 124, 1116609, 804…
## $ `10/28/20` <dbl> 41032, 20040, 57026, 4517, 10074, 0, 124, 1130533, 82…
## $ `10/29/20` <dbl> 41145, 20315, 57332, 4567, 10269, 0, 124, 1143800, 85…
## $ `10/30/20` <dbl> 41268, 20634, 57651, 4665, 10558, 0, 127, 1157179, 87…
## $ `10/31/20` <dbl> 41334, 20875, 57942, 4756, 10805, 0, 128, 1166924, 89…
## $ `11/1/20` <dbl> 41425, 21202, 58272, 4825, 11035, 0, 128, 1173533, 92…
## $ `11/2/20` <dbl> 41501, 21523, 58574, 4888, 11228, 0, 128, 1183131, 93…
## $ `11/3/20` <dbl> 41633, 21904, 58979, 4910, 11577, 0, 128, 1195276, 94…
## $ `11/4/20` <dbl> 41728, 22300, 59527, 5045, 11813, 0, 130, 1205928, 97…
## $ `11/5/20` <dbl> 41814, 22721, 60169, 5135, 12102, 0, 130, 1217028, 99…
## $ `11/6/20` <dbl> 41935, 23210, 60800, 5135, 12223, 0, 130, 1228814, 10…
## $ `11/7/20` <dbl> 41975, 23705, 61381, 5319, 12335, 0, 131, 1236851, 10…
## $ `11/8/20` <dbl> 42033, 24206, 62051, 5383, 12433, 0, 131, 1242182, 10…
## $ `11/9/20` <dbl> 42159, 24731, 62693, 5437, 12680, 0, 131, 1250499, 10…
## $ `11/10/20` <dbl> 42297, 25294, 63446, 5477, 12816, 0, 131, 1262476, 10…
## $ `11/11/20` <dbl> 42463, 25801, 64257, 5567, 12953, 0, 131, 1273356, 11…
## $ `11/12/20` <dbl> 42609, 26211, 65108, 5616, 13053, 0, 131, 1284519, 11…
## $ `11/13/20` <dbl> 42795, 26701, 65975, 5725, 13228, 0, 133, 1296378, 11…
## $ `11/14/20` <dbl> 42969, 27233, 66819, 5725, 13374, 0, 134, 1304846, 11…
## $ `11/15/20` <dbl> 43035, 27830, 67679, 5872, 13451, 0, 134, 1310491, 11…
## $ `11/16/20` <dbl> 43240, 28432, 68589, 5914, 13615, 0, 134, 1318384, 11…
## $ `11/17/20` <dbl> 43403, 29126, 69591, 5951, 13818, 0, 134, 1329005, 11…
## $ `11/18/20` <dbl> 43628, 29837, 70629, 6018, 13922, 0, 139, 1339337, 12…
## $ `11/19/20` <dbl> 43851, 30623, 71652, 6066, 14134, 0, 139, 1349434, 12…
## $ `11/20/20` <dbl> 44228, 31459, 72755, 6142, 14267, 0, 139, 1359042, 12…
## $ `11/21/20` <dbl> 44443, 32196, 73774, 6207, 14413, 0, 139, 1366182, 12…
## $ `11/22/20` <dbl> 44503, 32761, 74862, 6256, 14493, 0, 139, 1370366, 12…
## $ `11/23/20` <dbl> 44706, 33556, 75867, 6304, 14634, 0, 139, 1374631, 12…
## $ `11/24/20` <dbl> 44988, 34300, 77000, 6351, 14742, 0, 139, 1381795, 12…
## $ `11/25/20` <dbl> 45278, 34944, 78025, 6428, 14821, 0, 140, 1390388, 12…
## $ `11/26/20` <dbl> 45490, 35600, 79110, 6534, 14920, 0, 141, 1399431, 13…
## $ `11/27/20` <dbl> 45716, 36245, 80168, 6610, 15008, 0, 141, 1407277, 13…
## $ `11/28/20` <dbl> 45839, 36790, 81212, 6610, 15087, 0, 141, 1413375, 13…
## $ `11/29/20` <dbl> 45966, 37625, 82221, 6712, 15103, 0, 141, 1418807, 13…
## $ `11/30/20` <dbl> 46215, 38182, 83199, 6745, 15139, 0, 141, 1424533, 13…
## $ `12/1/20` <dbl> 46498, 39014, 84152, 6790, 15251, 0, 142, 1432570, 13…
## $ `12/2/20` <dbl> 46717, 39719, 85084, 6842, 15319, 0, 144, 1440103, 13…
## $ `12/3/20` <dbl> 46980, 40501, 85927, 6904, 15361, 0, 144, 1447732, 13…
## $ `12/4/20` <dbl> 47258, 41302, 86730, 6955, 15493, 0, 144, 1454631, 13…
## $ `12/5/20` <dbl> 47388, 42148, 87502, 7005, 15536, 0, 144, 1459832, 14…
## $ `12/6/20` <dbl> 47641, 42988, 88252, 7050, 15591, 0, 144, 1463110, 14…
## $ `12/7/20` <dbl> 47901, 43683, 88825, 7084, 15648, 0, 146, 1466309, 14…
## $ `12/8/20` <dbl> 48136, 44436, 89416, 7127, 15729, 0, 146, 1469919, 14…
## $ `12/9/20` <dbl> 48366, 45188, 90014, 7162, 15804, 0, 146, 1475222, 14…
## $ `12/10/20` <dbl> 48540, 46061, 90579, 7190, 15925, 0, 146, 1482216, 14…
## $ `12/11/20` <dbl> 48753, 46863, 91121, 7236, 16061, 0, 147, 1489328, 14…
## $ `12/12/20` <dbl> 48826, 47742, 91638, 7288, 16161, 0, 148, 1494602, 14…
## $ `12/13/20` <dbl> 48952, 48530, 92102, 7338, 16188, 0, 148, 1498160, 14…
## $ `12/14/20` <dbl> 49273, 49191, 92597, 7382, 16277, 0, 148, 1503222, 14…
## $ `12/15/20` <dbl> 49484, 50000, 93065, 7382, 16362, 0, 148, 1510203, 14…
## $ `12/16/20` <dbl> 49703, 50637, 93507, 7446, 16407, 0, 151, 1517046, 15…
## $ `12/17/20` <dbl> 49927, 51424, 93933, 7466, 16484, 0, 151, 1524372, 15…
## $ `12/18/20` <dbl> 50202, 52004, 94371, 7519, 16562, 0, 152, 1531374, 15…
## $ `12/19/20` <dbl> 50456, 52542, 94781, 7560, 16626, 0, 152, 1537169, 15…
## $ `12/20/20` <dbl> 50536, 53003, 95203, 7577, 16644, 0, 153, 1541285, 15…
## $ `12/21/20` <dbl> 50678, 53425, 95659, 7602, 16686, 0, 153, 1547138, 15…
## $ `12/22/20` <dbl> 50888, 53814, 96069, 7633, 16802, 0, 153, 1555279, 15…
## $ `12/23/20` <dbl> 51070, 54317, 96549, 7669, 16931, 0, 154, 1563865, 15…
## $ `12/24/20` <dbl> 51357, 54827, 97007, 7699, 17029, 0, 154, 1563865, 15…
## $ `12/25/20` <dbl> 51595, 55380, 97441, 7756, 17099, 0, 155, 1574554, 15…
## $ `12/26/20` <dbl> 51764, 55755, 97857, 7806, 17149, 0, 155, 1578267, 15…
## $ `12/27/20` <dbl> 51848, 56254, 98249, 7821, 17240, 0, 155, 1583297, 15…
## $ `12/28/20` <dbl> 52007, 56572, 98631, 7875, 17296, 0, 158, 1590513, 15…
## $ `12/29/20` <dbl> 52147, 57146, 98988, 7919, 17371, 0, 158, 1602163, 15…
## $ `12/30/20` <dbl> 52330, 57727, 99311, 7983, 17433, 0, 158, 1613928, 15…
## $ `12/31/20` <dbl> 52330, 58316, 99610, 8049, 17553, 0, 159, 1625514, 15…
## $ `1/1/21` <dbl> 52513, 58316, 99897, 8117, 17568, 0, 159, 1629594, 15…
## $ `1/2/21` <dbl> 52586, 58991, 100159, 8166, 17608, 0, 159, 1634834, 1…
## $ `1/3/21` <dbl> 52709, 59438, 100408, 8192, 17642, 0, 160, 1640718, 1…
## $ `1/4/21` <dbl> 52909, 59623, 100645, 8249, 17684, 0, 160, 1648940, 1…
## $ `1/5/21` <dbl> 53011, 60283, 100873, 8308, 17756, 0, 160, 1662730, 1…
## $ `1/6/21` <dbl> 53105, 61008, 101120, 8348, 17864, 0, 163, 1676171, 1…
## $ `1/7/21` <dbl> 53207, 61705, 101382, 8348, 17974, 0, 163, 1690006, 1…
## $ `1/8/21` <dbl> 53332, 62378, 101657, 8489, 18066, 0, 167, 1703352, 1…
## $ `1/9/21` <dbl> 53400, 63033, 101913, 8586, 18156, 0, 169, 1714409, 1…
## $ `1/10/21` <dbl> 53489, 63595, 102144, 8586, 18193, 0, 176, 1722217, 1…
## $ `1/11/21` <dbl> 53538, 63971, 102369, 8586, 18254, 0, 176, 1730921, 1…
## $ `1/12/21` <dbl> 53584, 64627, 102641, 8682, 18343, 0, 176, 1744704, 1…
## $ `1/13/21` <dbl> 53690, 65334, 102860, 8818, 18425, 0, 176, 1757429, 1…
## $ `1/14/21` <dbl> 53775, 65994, 103127, 8868, 18613, 0, 184, 1770715, 1…
## $ `1/15/21` <dbl> 53831, 66635, 103381, 8946, 18679, 0, 184, 1783047, 1…
## $ `1/16/21` <dbl> 53938, 67216, 103611, 9038, 18765, 0, 187, 1791979, 1…
## $ `1/17/21` <dbl> 53984, 67690, 103833, 9083, 18875, 0, 189, 1799243, 1…
## $ `1/18/21` <dbl> 54062, 67982, 104092, 9083, 18926, 0, 189, 1807428, 1…
## $ `1/19/21` <dbl> 54141, 68568, 104341, 9194, 19011, 0, 190, 1819569, 1…
## $ `1/20/21` <dbl> 54278, 69238, 104606, 9308, 19093, 0, 190, 1831681, 1…
## $ `1/21/21` <dbl> 54403, 69916, 104852, 9379, 19177, 0, 192, 1843077, 1…
## $ `1/22/21` <dbl> 54483, 70655, 105124, 9416, 19269, 0, 195, 1853830, 1…
## $ `1/23/21` <dbl> 54559, 71441, 105369, 9499, 19367, 0, 195, 1862192, 1…
## $ `1/24/21` <dbl> 54595, 72274, 105596, 9549, 19399, 0, 198, 1867223, 1…
## $ `1/25/21` <dbl> 54672, 72812, 105854, 9596, 19476, 0, 201, 1874801, 1…
## $ `1/26/21` <dbl> 54750, 73691, 106097, 9638, 19553, 0, 201, 1885210, 1…
## $ `1/27/21` <dbl> 54854, 74567, 106359, 9716, 19580, 0, 215, 1896053, 1…
## $ `1/28/21` <dbl> 54891, 75454, 106610, 9779, 19672, 0, 215, 1905524, 1…
## $ `1/29/21` <dbl> 54939, 76350, 106887, 9837, 19723, 0, 218, 1915362, 1…
## $ `1/30/21` <dbl> 55008, 77251, 107122, 9885, 19782, 0, 218, 1922264, 1…
## $ `1/31/21` <dbl> 55023, 78127, 107339, 9937, 19796, 0, 234, 1927239, 1…
## $ `2/1/21` <dbl> 55059, 78992, 107578, 9972, 19829, 0, 234, 1933853, 1…
## $ `2/2/21` <dbl> 55121, 79934, 107841, 10017, 19900, 0, 249, 1943548, …
## $ `2/3/21` <dbl> 55174, 80941, 108116, 10070, 19937, 0, 249, 1952744, …
## $ `2/4/21` <dbl> 55231, 81993, 108381, 10137, 19996, 0, 268, 1961635, …
## $ `2/5/21` <dbl> 55265, 83082, 108629, 10172, 20030, 0, 277, 1970009, …
## $ `2/6/21` <dbl> 55330, 84212, 108629, 10206, 20062, 0, 288, 1976689, …
## $ `2/7/21` <dbl> 55335, 85336, 109088, 10251, 20086, 0, 299, 1980347, …
## $ `2/8/21` <dbl> 55359, 86289, 109313, 10275, 20112, 0, 316, 1985501, …
## $ `2/9/21` <dbl> 55384, 87528, 109559, 10312, 20163, 0, 316, 1993295, …
## $ `2/10/21` <dbl> 55402, 88671, 109782, 10352, 20210, 0, 350, 2001034, …
## $ `2/11/21` <dbl> 55420, 89776, 110049, 10391, 20261, 0, 381, 2008345, …
## $ `2/12/21` <dbl> 55445, 90835, 110303, 10427, 20294, 0, 419, 2015496, …
## $ `2/13/21` <dbl> 55473, 91987, 110513, 10463, 20329, 0, 427, 2021553, …
## $ `2/14/21` <dbl> 55492, 93075, 110711, 10503, 20366, 0, 427, 2025798, …
## $ `2/15/21` <dbl> 55514, 93850, 110894, 10538, 20381, 0, 443, 2029057, …
## $ `2/16/21` <dbl> 55518, 94651, 111069, 10555, 20389, 0, 443, 2033060, …
## $ `2/17/21` <dbl> 55540, 95726, 111247, 10583, 20400, 0, 525, 2039124, …
## $ `2/18/21` <dbl> 55557, 96838, 111418, 10610, 20452, 0, 548, 2046795, …
## $ `2/19/21` <dbl> 55575, 97909, 111600, 10645, 20478, 0, 548, 2054681, …
## $ `2/20/21` <dbl> 55580, 99062, 111764, 10672, 20499, 0, 598, 2060625, …
## $ `2/21/21` <dbl> 55604, 100246, 111917, 10699, 20519, 0, 598, 2064334,…
## $ `2/22/21` <dbl> 55617, 101285, 112094, 10712, 20548, 0, 614, 2069751,…
## $ `2/23/21` <dbl> 55646, 102306, 112279, 10739, 20584, 0, 636, 2077228,…
## $ `2/24/21` <dbl> 55664, 103327, 112461, 10775, 20640, 0, 646, 2085411,…
## $ `2/25/21` <dbl> 55680, 104313, 112622, 10799, 20695, 0, 701, 2093645,…
## $ `2/26/21` <dbl> 55696, 105229, 112805, 10822, 20759, 0, 701, 2098728,…
## $ `2/27/21` <dbl> 55707, 106215, 112960, 10849, 20782, 0, 726, 2104197,…
## $ `2/28/21` <dbl> 55714, 107167, 113092, 10866, 20807, 0, 730, 2107365,…
## $ `3/1/21` <dbl> 55733, 107931, 113255, 10889, 20854, 0, 769, 2112023,…
## $ `3/2/21` <dbl> 55759, 108823, 113430, 10908, 20882, 0, 769, 2118676,…
## $ `3/3/21` <dbl> 55770, 109674, 113593, 10948, 20923, 0, 769, 2126531,…
## $ `3/4/21` <dbl> 55775, 110521, 113761, 10976, 20981, 0, 813, 2133963,…
## $ `3/5/21` <dbl> 55827, 111301, 113948, 10998, 21026, 0, 813, 2141854,…
## $ `3/6/21` <dbl> 55840, 112078, 114104, 11019, 21055, 0, 813, 2146714,…
## $ `3/7/21` <dbl> 55847, 112897, 114234, 11042, 21086, 0, 848, 2149636,…
## $ `3/8/21` <dbl> 55876, 113580, 114382, 11069, 21108, 0, 848, 2154694,…
## $ `3/9/21` <dbl> 55876, 114209, 114543, 11089, 21114, 0, 862, 2162001,…
## $ `3/10/21` <dbl> 55894, 114840, 114681, 11130, 21161, 0, 882, 2169694,…
## $ `3/11/21` <dbl> 55917, 115442, 114851, 11130, 21205, 0, 882, 2177898,…
## $ `3/12/21` <dbl> 55959, 116123, 115008, 11199, 21265, 0, 945, 2185747,…
## $ `3/13/21` <dbl> 55959, 116821, 115143, 11228, 21323, 0, 962, 2192025,…
## $ `3/14/21` <dbl> 55985, 117474, 115265, 11266, 21380, 0, 963, 2195722,…
## $ `3/15/21` <dbl> 55985, 118017, 115410, 11289, 21407, 0, 963, 2201886,…
## $ `3/16/21` <dbl> 55995, 118492, 115540, 11319, 21446, 0, 992, 2210121,…
## $ `3/17/21` <dbl> 56016, 118938, 115688, 11360, 21489, 0, 992, 2218425,…
## $ `3/18/21` <dbl> 56044, 119528, 115842, 11393, 21558, 0, 1008, 2226753…
## $ `3/19/21` <dbl> 56069, 120022, 115970, 11431, 21642, 0, 1011, 2234913…
## $ `3/20/21` <dbl> 56093, 120541, 116066, 11481, 21696, 0, 1033, 2241739…
## $ `3/21/21` <dbl> 56103, 121200, 116157, 11517, 21733, 0, 1033, 2245771…
## $ `3/22/21` <dbl> 56153, 121544, 116255, 11545, 21757, 0, 1072, 2252172…
## $ `3/23/21` <dbl> 56177, 121847, 116349, 11591, 21774, 0, 1080, 2261577…
## $ `3/24/21` <dbl> 56192, 122295, 116438, 11638, 21836, 0, 1080, 2269877…
## $ `3/25/21` <dbl> 56226, 122767, 116543, 11687, 21914, 0, 1103, 2278115…
## $ `3/26/21` <dbl> 56254, 123216, 116657, 11732, 21961, 0, 1122, 2291051…
## $ `3/27/21` <dbl> 56290, 123641, 116750, 11809, 22031, 0, 1122, 2301389…
## $ `3/28/21` <dbl> 56294, 124134, 116836, 11850, 22063, 0, 1128, 2308597…
## $ `3/29/21` <dbl> 56322, 124419, 116946, 11888, 22132, 0, 1136, 2322611…
## $ `3/30/21` <dbl> 56384, 124723, 117061, 11944, 22182, 0, 1136, 2332765…
## $ `3/31/21` <dbl> 56454, 125157, 117192, 12010, 22311, 0, 1136, 2348821…
## $ `4/1/21` <dbl> 56517, 125506, 117304, 12053, 22399, 0, 1147, 2363251…
## $ `4/2/21` <dbl> 56572, 125842, 117429, 12115, 22467, 0, 1152, 2373153…
## $ `4/3/21` <dbl> 56595, 126183, 117524, 12174, 22579, 0, 1170, 2383537…
## $ `4/4/21` <dbl> 56676, 126531, 117622, 12231, 22631, 0, 1170, 2393492…
## $ `4/5/21` <dbl> 56717, 126795, 117739, 12286, 22717, 0, 1173, 2407159…
## $ `4/6/21` <dbl> 56779, 126936, 117879, 12328, 22885, 0, 1173, 2428029…
## $ `4/7/21` <dbl> 56873, 127192, 118004, 12363, 23010, 0, 1177, 2450068…
## $ `4/8/21` <dbl> 56943, 127509, 118116, 12409, 23108, 0, 1180, 2473751…
## $ `4/9/21` <dbl> 57019, 127795, 118251, 12456, 23242, 0, 1182, 2497881…
## $ `4/10/21` <dbl> 57144, 128155, 118378, 12497, 23331, 0, 1197, 2517300…
## $ `4/11/21` <dbl> 57160, 128393, 118516, 12545, 23457, 0, 1198, 2532562…
## $ `4/12/21` <dbl> 57242, 128518, 118645, 12581, 23549, 0, 1198, 2551999…
## $ `4/13/21` <dbl> 57364, 128752, 118799, 12614, 23697, 0, 1201, 2579000…
## $ `4/14/21` <dbl> 57492, 128959, 118975, 12641, 23841, 0, 1201, 2604157…
## $ `4/15/21` <dbl> 57534, 129128, 119142, 12641, 23951, 0, 1209, 2629156…
## $ `4/16/21` <dbl> 57612, 129307, 119323, 12712, 24122, 0, 1213, 2658628…
## $ `4/17/21` <dbl> 57721, 129456, 119486, 12771, 24300, 0, 1216, 2677747…
## $ `4/18/21` <dbl> 57793, 129594, 119642, 12805, 24389, 0, 1216, 2694014…
## $ `4/19/21` <dbl> 57898, 129694, 119805, 12805, 24518, 0, 1217, 2714475…
## $ `4/20/21` <dbl> 58037, 129842, 119992, 12874, 24661, 0, 1217, 2743620…
## $ `4/21/21` <dbl> 58214, 129980, 120174, 12917, 24883, 0, 1217, 2769552…
## $ `4/22/21` <dbl> 58312, 130114, 120363, 12942, 25051, 0, 1217, 2796768…
## $ `4/23/21` <dbl> 58542, 130270, 120562, 13007, 25279, 0, 1222, 2824652…
## $ `4/24/21` <dbl> 58730, 130409, 120736, 13024, 25492, 0, 1227, 2845872…
## $ `4/25/21` <dbl> 58843, 130537, 120922, 13060, 25609, 0, 1227, 2860884…
## $ `4/26/21` <dbl> 59015, 130606, 121112, 13083, 25710, 0, 1228, 2879677…
## $ `4/27/21` <dbl> 59225, 130736, 121344, 13121, 25942, 0, 1232, 2905172…
## $ `4/28/21` <dbl> 59370, 130859, 121580, 13148, 26168, 0, 1232, 2928890…
## $ `4/29/21` <dbl> 59576, 130977, 121866, 13198, 26431, 0, 1232, 2954943…
## $ `4/30/21` <dbl> 59745, 131085, 122108, 13232, 26652, 0, 1232, 2977363…
## $ `5/1/21` <dbl> 59939, 131185, 122311, 13232, 26815, 0, 1232, 2993865…
## $ `5/2/21` <dbl> 60122, 131238, 122522, 13282, 26993, 0, 1232, 3005259…
## $ `5/3/21` <dbl> 60300, 131276, 122717, 13295, 27133, 0, 1232, 3021179…
## $ `5/4/21` <dbl> 60563, 131327, 122999, 13316, 27284, 0, 1232, 3047417…
## $ `5/5/21` <dbl> 60797, 131419, 123272, 13340, 27529, 0, 1232, 3071496…
## $ `5/6/21` <dbl> 61162, 131510, 123473, 13363, 27921, 0, 1232, 3095582…
## $ `5/7/21` <dbl> 61455, 131577, 123692, 13390, 28201, 0, 1232, 3118134…
## $ `5/8/21` <dbl> 61755, 131666, 123900, 13406, 28477, 0, 1232, 3136158…
## $ `5/9/21` <dbl> 61842, 131723, 124104, 13423, 28740, 0, 1231, 3147740…
## $ `5/10/21` <dbl> 62063, 131753, 124288, 13429, 28875, 0, 1237, 3165121…
## $ `5/11/21` <dbl> 62403, 131803, 124483, 13447, 29146, 0, 1238, 3191097…
## $ `5/12/21` <dbl> 62718, 131845, 124682, 13470, 29405, 0, 1240, 3215572…
## $ `5/13/21` <dbl> 63045, 131890, 124889, 13470, 29695, 0, 1240, 3242103…
## $ `5/14/21` <dbl> 63355, 131939, 125059, 13510, 30030, 0, 1240, 3269466…
## $ `5/15/21` <dbl> 63412, 131978, 125194, 13510, 30354, 0, 1241, 3290935…
## $ `5/16/21` <dbl> 63484, 132015, 125311, 13510, 30637, 0, 1241, 3307285…
## $ `5/17/21` <dbl> 63598, 132032, 125485, 13555, 30787, 0, 1251, 3335965…
## $ `5/18/21` <dbl> 63819, 132071, 125693, 13569, 31045, 0, 1251, 3371508…
## $ `5/19/21` <dbl> 64122, 132095, 125896, 13569, 31438, 0, 1252, 3411160…
## $ `5/20/21` <dbl> 64575, 132118, 126156, 13569, 31661, 0, 1255, 3447044…
## $ `5/21/21` <dbl> 65080, 132153, 126434, 13569, 31909, 0, 1255, 3482512…
## $ `5/22/21` <dbl> 65486, 132176, 126651, 13569, 32149, 0, 1257, 3514683…
## $ `5/23/21` <dbl> 65728, 132209, 126860, 13569, 32441, 0, 1257, 3539484…
## $ `5/24/21` <dbl> 66275, 132215, 127107, 13569, 32623, 0, 1258, 3562135…
## $ `5/25/21` <dbl> 66903, 132229, 127361, 13664, 32933, 0, 1258, 3586736…
## $ `5/26/21` <dbl> 67743, 132244, 127646, 13671, 33338, 0, 1258, 3622135…
## $ `5/27/21` <dbl> 68366, 132264, 127926, 13682, 33607, 0, 1258, 3663215…
## $ `5/28/21` <dbl> 69130, 132285, 128198, 13693, 33944, 0, 1259, 3702422…
## $ `5/29/21` <dbl> 70111, 132297, 128456, 13693, 34180, 0, 1259, 3732263…
## $ `5/30/21` <dbl> 70761, 132309, 128725, 13693, 34366, 0, 1259, 3753609…
## $ `5/31/21` <dbl> 71838, 132315, 128913, 13727, 34551, 0, 1260, 3781784…
## $ `6/1/21` <dbl> 72977, 132337, 129218, 13729, 34752, 0, 1260, 3817139…
## $ `6/2/21` <dbl> 74026, 132351, 129640, 13744, 34960, 0, 1262, 3852156…
## $ `6/3/21` <dbl> 75119, 132360, 129976, 13752, 35140, 0, 1262, 3884447…
## $ `6/4/21` <dbl> 76628, 132372, 130361, 13758, 35307, 0, 1263, 3915397…
## $ `6/5/21` <dbl> 77963, 132374, 130681, 13758, 35594, 0, 1263, 3939024…
## $ `6/6/21` <dbl> 79224, 132379, 130958, 13758, 35772, 0, 1263, 3955439…
## $ `6/7/21` <dbl> 80841, 132384, 131283, 13777, 35854, 0, 1263, 3977634…
## $ `6/8/21` <dbl> 82326, 132397, 131647, 13781, 36004, 0, 1263, 4008771…
## $ `6/9/21` <dbl> 84050, 132415, 132034, 13791, 36115, 0, 1263, 4038528…
## $ `6/10/21` <dbl> 85892, 132426, 132355, 13805, 36325, 0, 1263, 4066156…
## $ `6/11/21` <dbl> 87716, 132437, 132727, 13813, 36455, 0, 1263, 4093090…
## $ `6/12/21` <dbl> 88740, 132449, 133070, 13813, 36600, 0, 1263, 4111147…
## $ `6/13/21` <dbl> 89861, 132459, 133388, 13813, 36705, 0, 1263, 4124190…
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## $ `12/9/21` <dbl> 157585, 202863, 212224, 19272, 65346, 0, 4151, 535086…
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## $ `12/15/21` <dbl> 157665, 204627, 213533, 20136, 65565, 11, 4177, 53713…
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## $ `12/21/21` <dbl> 157816, 206273, 215145, 21062, 66566, 11, 4201, 54043…
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## $ `12/24/21` <dbl> 157887, 207221, 216098, 21730, 70221, 11, 4216, 54452…
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## $ `12/26/21` <dbl> 157951, 207709, 216637, 21730, 71752, 11, 4236, 54600…
## $ `12/27/21` <dbl> 157967, 207709, 216930, 22332, 71752, 11, 4259, 54803…
## $ `12/28/21` <dbl> 157998, 208352, 217265, 22540, 76787, 11, 4259, 55142…
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## $ `12/30/21` <dbl> 158056, 208899, 218037, 23122, 79871, 11, 4283, 56067…
## $ `12/31/21` <dbl> 158084, 210224, 218432, 23740, 81593, 11, 4283, 56544…
## $ `1/1/22` <dbl> 158107, 210224, 218818, 23740, 82398, 11, 4283, 56744…
## $ `1/2/22` <dbl> 158189, 210885, 219159, 23740, 82920, 11, 4283, 56949…
## $ `1/3/22` <dbl> 158183, 210885, 219532, 24502, 83764, 11, 4283, 57393…
## $ `1/4/22` <dbl> 158205, 212021, 219953, 24802, 84666, 11, 4486, 58205…
## $ `1/5/22` <dbl> 158245, 212021, 220415, 25289, 86636, 11, 4486, 59156…
## $ `1/6/22` <dbl> 158275, 213257, 220825, 25289, 87625, 11, 4715, 60253…
## $ `1/7/22` <dbl> 158300, 214905, 221316, 26408, 88775, 11, 4715, 61358…
## $ `1/8/22` <dbl> 158309, 214905, 221742, 26408, 89251, 11, 4844, 62375…
## $ `1/9/22` <dbl> 158381, 219694, 222157, 26408, 89718, 11, 5058, 63108…
## $ `1/10/22` <dbl> 158394, 220487, 222639, 27983, 90316, 11, 5058, 63991…
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## $ `1/12/22` <dbl> 158511, 224569, 223806, 28899, 91907, 11, 5214, 66647…
## $ `1/13/22` <dbl> 158602, 226598, 224383, 28899, 92581, 11, 5214, 67931…
## $ `1/14/22` <dbl> 158639, 228777, 224979, 29888, 93302, 11, 5246, 69329…
## $ `1/15/22` <dbl> 158678, 230940, 225484, 29888, 93524, 11, 5321, 70296…
## $ `1/16/22` <dbl> 158717, 232637, 226057, 29888, 93694, 11, 5321, 70948…
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## $ `2/4/22` <dbl> 165358, 263172, 256806, 36599, 98340, 11, 6732, 85553…
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## $ `6/26/22` <dbl> 182149, 279077, 266038, 43774, 99761, 11, 8625, 93671…
## $ `6/27/22` <dbl> 182228, 279077, 266049, 43774, 99761, 11, 8625, 93671…
## $ `6/28/22` <dbl> 182324, 279167, 266062, 43774, 101320, 11, 8625, 9367…
## $ `6/29/22` <dbl> 182403, 280298, 266073, 43774, 101320, 11, 8625, 9367…
## $ `6/30/22` <dbl> 182528, 280851, 266087, 43774, 101320, 11, 8641, 9367…
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## $ `7/4/22` <dbl> 182793, 282690, 266173, 44177, 101320, 11, 8665, 9394…
## $ `7/5/22` <dbl> 182793, 282690, 266173, 44177, 101320, 11, 8665, 9394…
## $ `7/6/22` <dbl> 182979, 283811, 266181, 44671, 101320, 11, 8668, 9394…
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## $ `7/8/22` <dbl> 183221, 285731, 266228, 44671, 101320, 11, 8686, 9394…
## $ `7/9/22` <dbl> 183235, 286732, 266246, 44671, 101320, 11, 8686, 9394…
## $ `7/10/22` <dbl> 183265, 287984, 266257, 44671, 101320, 11, 8686, 9426…
## $ `7/11/22` <dbl> 183268, 288176, 266274, 44671, 101320, 11, 8686, 9426…
## $ `7/12/22` <dbl> 183272, 289391, 266303, 44671, 101320, 11, 8686, 9426…
## $ `7/13/22` <dbl> 183285, 290954, 266328, 44671, 101320, 11, 8686, 9426…
## $ `7/14/22` <dbl> 183358, 290954, 266356, 44671, 101600, 11, 8704, 9426…
## $ `7/15/22` <dbl> 183407, 293917, 266392, 44671, 101901, 11, 8704, 9426…
## $ `7/16/22` <dbl> 183445, 295243, 266424, 44671, 101901, 11, 8712, 9426…
## $ `7/17/22` <dbl> 183572, 296305, 266445, 44671, 101901, 11, 8712, 9465…
## $ `7/18/22` <dbl> 183687, 296732, 266487, 45061, 102209, 11, 8712, 9465…
## $ `7/19/22` <dbl> 183908, 298578, 266542, 45061, 102209, 11, 8712, 9465…
## $ `7/20/22` <dbl> 184038, 300058, 266591, 45061, 102209, 11, 8712, 9465…
## $ `7/21/22` <dbl> 184224, 301394, 266654, 45326, 102209, 11, 8712, 9465…
## $ `7/22/22` <dbl> 184360, 302767, 266700, 45326, 102301, 11, 8736, 9465…
## $ `7/23/22` <dbl> 184473, 303925, 266772, 45326, 102301, 11, 8736, 9465…
## $ `7/24/22` <dbl> 184587, 304890, 266839, 45326, 102301, 11, 8736, 9507…
## $ `7/25/22` <dbl> 184819, 305123, 266916, 45326, 102301, 11, 8736, 9507…
## $ `7/26/22` <dbl> 185086, 306789, 267010, 45326, 102301, 11, 8741, 9507…
## $ `7/27/22` <dbl> 185272, 308050, 267096, 45326, 102301, 11, 8741, 9507…
## $ `7/28/22` <dbl> 185393, 309278, 267194, 45508, 102301, 11, 8741, 9507…
## $ `7/29/22` <dbl> 185481, 310362, 267287, 45508, 102301, 11, 8741, 9507…
## $ `7/30/22` <dbl> 185552, 311381, 267374, 45508, 102301, 11, 8773, 9507…
## $ `7/31/22` <dbl> 185749, 312097, 267454, 45508, 102301, 11, 8773, 9560…
## $ `8/1/22` <dbl> 185930, 312375, 267546, 45508, 102301, 11, 8773, 9560…
## $ `8/2/22` <dbl> 186120, 313582, 267657, 45508, 102301, 11, 8773, 9560…
## $ `8/3/22` <dbl> 186393, 314561, 267777, 45793, 102301, 11, 8773, 9560…
## $ `8/4/22` <dbl> 186697, 315337, 267902, 45793, 102301, 11, 8773, 9560…
## $ `8/5/22` <dbl> 187037, 316145, 268033, 45793, 102636, 11, 8773, 9560…
## $ `8/6/22` <dbl> 187109, 316976, 268141, 45793, 102636, 11, 8773, 9560…
## $ `8/7/22` <dbl> 187442, 317514, 268254, 45793, 102636, 11, 8787, 9602…
## $ `8/8/22` <dbl> 187685, 317681, 268356, 45793, 102636, 11, 8809, 9602…
## $ `8/9/22` <dbl> 187966, 318638, 268478, 45793, 102636, 11, 8809, 9602…
## $ `8/10/22` <dbl> 188202, 319444, 268584, 45899, 102636, 11, 8809, 9602…
## $ `8/11/22` <dbl> 188506, 320086, 268718, 45899, 102636, 11, 8809, 9602…
## $ `8/12/22` <dbl> 188704, 320781, 268866, 45899, 102636, 11, 8820, 9602…
## $ `8/13/22` <dbl> 188820, 321345, 269008, 45899, 102636, 11, 8820, 9602…
## $ `8/14/22` <dbl> 189045, 321804, 269141, 45899, 102636, 11, 8820, 9633…
## $ `8/15/22` <dbl> 189343, 322125, 269269, 45899, 102636, 11, 8851, 9633…
## $ `8/16/22` <dbl> 189477, 322837, 269381, 45899, 102636, 11, 8851, 9633…
## $ `8/17/22` <dbl> 189710, 323282, 269473, 45975, 102636, 11, 8851, 9633…
## $ `8/18/22` <dbl> 190010, 323829, 269556, 45975, 102636, 11, 8895, 9633…
## $ `8/19/22` <dbl> 190254, 325241, 269650, 45975, 102636, 11, 8895, 9633…
## $ `8/20/22` <dbl> 190435, 325736, 269731, 45975, 102636, 11, 8895, 9633…
## $ `8/21/22` <dbl> 190643, 326077, 269805, 45975, 102636, 11, 8895, 9658…
## $ `8/22/22` <dbl> 191040, 326181, 269894, 45975, 102636, 11, 8895, 9658…
## $ `8/23/22` <dbl> 191247, 326787, 269971, 45975, 102636, 11, 8895, 9658…
## $ `8/24/22` <dbl> 191585, 327232, 270043, 46027, 102636, 11, 8949, 9658…
## $ `8/25/22` <dbl> 191967, 327607, 270097, 46027, 102636, 11, 8949, 9658…
## $ `8/26/22` <dbl> 191967, 327961, 270145, 46027, 102636, 11, 8949, 9658…
## $ `8/27/22` <dbl> 191967, 328299, 270175, 46027, 102636, 11, 8949, 9658…
## $ `8/28/22` <dbl> 192463, 328515, 270194, 46027, 102636, 11, 8949, 9678…
## $ `8/29/22` <dbl> 192906, 328571, 270235, 46027, 102636, 11, 8949, 9678…
## $ `8/30/22` <dbl> 193004, 329017, 270272, 46027, 102636, 11, 8974, 9678…
## $ `8/31/22` <dbl> 193250, 329352, 270304, 46027, 102636, 11, 8974, 9678…
## $ `9/1/22` <dbl> 193520, 329615, 270359, 46027, 102636, 11, 8974, 9678…
## $ `9/2/22` <dbl> 193520, 329862, 270405, 46027, 102636, 11, 8974, 9678…
## $ `9/3/22` <dbl> 193912, 330062, 270426, 46027, 102636, 11, 8974, 9678…
## $ `9/4/22` <dbl> 194163, 330193, 270443, 46027, 102636, 11, 8974, 9689…
## $ `9/5/22` <dbl> 194355, 330221, 270461, 46027, 102636, 11, 8974, 9689…
## $ `9/6/22` <dbl> 194614, 330283, 270476, 46027, 102636, 11, 8974, 9689…
## $ `9/7/22` <dbl> 195012, 330516, 270489, 46113, 102636, 11, 8974, 9689…
## $ `9/8/22` <dbl> 195298, 330687, 270507, 46113, 102636, 11, 8974, 9689…
## $ `9/9/22` <dbl> 195471, 330842, 270522, 46113, 103131, 11, 8974, 9689…
## $ `9/10/22` <dbl> 195631, 330948, 270532, 46113, 103131, 11, 8974, 9689…
## $ `9/11/22` <dbl> 195925, 331036, 270539, 46113, 103131, 11, 8974, 9697…
## $ `9/12/22` <dbl> 196182, 331053, 270551, 46113, 103131, 11, 8974, 9697…
## $ `9/13/22` <dbl> 196404, 331191, 270551, 46113, 103131, 11, 8974, 9697…
## $ `9/14/22` <dbl> 196751, 331295, 270570, 46147, 103131, 11, 9008, 9697…
## $ `9/15/22` <dbl> 196870, 331384, 270584, 46147, 103131, 11, 9008, 9697…
## $ `9/16/22` <dbl> 196992, 331459, 270599, 46147, 103131, 11, 9008, 9697…
## $ `9/17/22` <dbl> 197066, 331540, 270606, 46147, 103131, 11, 9008, 9697…
## $ `9/18/22` <dbl> 197240, 331583, 270609, 46147, 103131, 11, 9008, 9703…
## $ `9/19/22` <dbl> 197434, 331601, 270612, 46147, 103131, 11, 9008, 9703…
## $ `9/20/22` <dbl> 197608, 331715, 270612, 46147, 103131, 11, 9008, 9703…
## $ `9/21/22` <dbl> 197788, 331810, 270619, 46147, 103131, 11, 9008, 9703…
## $ `9/22/22` <dbl> 198023, 331861, 270625, 46147, 103131, 11, 9008, 9703…
## $ `9/23/22` <dbl> 198163, 331908, 270631, 46147, 103131, 11, 9008, 9703…
## $ `9/24/22` <dbl> 198244, 331953, 270637, 46147, 103131, 11, 9008, 9703…
## $ `9/25/22` <dbl> 198416, 331976, 270641, 46147, 103131, 11, 9008, 9708…
## $ `9/26/22` <dbl> 198543, 331987, 270649, 46147, 103131, 11, 9089, 9708…
## $ `9/27/22` <dbl> 198750, 332066, 270654, 46147, 103131, 11, 9089, 9708…
## $ `9/28/22` <dbl> 198876, 332129, 270662, 46227, 103131, 11, 9089, 9708…
## $ `9/29/22` <dbl> 199067, 332173, 270668, 46227, 103131, 11, 9098, 9708…
## $ `9/30/22` <dbl> 199188, 332221, 270673, 46227, 103131, 11, 9098, 9708…
## $ `10/1/22` <dbl> 199310, 332263, 270676, 46227, 103131, 11, 9098, 9708…
## $ `10/2/22` <dbl> 199386, 332285, 270679, 46227, 103131, 11, 9098, 9711…
## $ `10/3/22` <dbl> 199545, 332290, 270682, 46227, 103131, 11, 9098, 9711…
## $ `10/4/22` <dbl> 199690, 332337, 270690, 46227, 103131, 11, 9098, 9711…
## $ `10/5/22` <dbl> 199845, 332372, 270693, 46227, 103131, 11, 9098, 9711…
## $ `10/6/22` <dbl> 199994, 332410, 270697, 46275, 103131, 11, 9098, 9711…
## $ `10/7/22` <dbl> 200130, 332443, 270701, 46275, 103131, 11, 9098, 9711…
## $ `10/8/22` <dbl> 200202, 332472, 270701, 46275, 103131, 11, 9098, 9711…
## $ `10/9/22` <dbl> 200372, 332494, 270707, 46275, 103131, 11, 9098, 9713…
## $ `10/10/22` <dbl> 200469, 332503, 270713, 46275, 103131, 11, 9098, 9713…
## $ `10/11/22` <dbl> 200626, 332534, 270716, 46275, 103131, 11, 9098, 9713…
## $ `10/12/22` <dbl> 200729, 332555, 270722, 46366, 103131, 11, 9106, 9713…
## $ `10/13/22` <dbl> 200846, 332579, 270722, 46366, 103131, 11, 9106, 9713…
## $ `10/14/22` <dbl> 201014, 332598, 270734, 46366, 103131, 11, 9106, 9713…
## $ `10/15/22` <dbl> 201096, 332619, 270734, 46366, 103131, 11, 9106, 9713…
## $ `10/16/22` <dbl> 201212, 332638, 270740, 46366, 103131, 11, 9106, 9715…
## $ `10/17/22` <dbl> 201276, 332645, 270757, 46366, 103131, 11, 9106, 9715…
## $ `10/18/22` <dbl> 201503, 332673, 270766, 46366, 103131, 11, 9106, 9715…
## $ `10/19/22` <dbl> 201557, 332701, 270768, 46449, 103131, 11, 9106, 9715…
## $ `10/20/22` <dbl> 201750, 332719, 270769, 46449, 103131, 11, 9106, 9715…
## $ `10/21/22` <dbl> 201949, 332739, 270771, 46449, 103131, 11, 9106, 9715…
## $ `10/22/22` <dbl> 202026, 332754, 270771, 46449, 103131, 11, 9106, 9715…
## $ `10/23/22` <dbl> 202108, 332772, 270783, 46449, 103131, 11, 9106, 9717…
## $ `10/24/22` <dbl> 202199, 332776, 270788, 46449, 103131, 11, 9106, 9717…
## $ `10/25/22` <dbl> 202347, 332816, 270800, 46449, 103131, 11, 9106, 9717…
## $ `10/26/22` <dbl> 202509, 332847, 270810, 46535, 103131, 11, 9106, 9717…
## $ `10/27/22` <dbl> 202608, 332889, 270817, 46535, 103131, 11, 9106, 9717…
## $ `10/28/22` <dbl> 202756, 332911, 270826, 46535, 103131, 11, 9106, 9717…
## $ `10/29/22` <dbl> 202834, 332949, 270829, 46535, 103131, 11, 9106, 9717…
## $ `10/30/22` <dbl> 202966, 332966, 270836, 46535, 103131, 11, 9106, 9718…
## $ `10/31/22` <dbl> 203063, 332966, 270838, 46535, 103131, 11, 9106, 9718…
## $ `11/1/22` <dbl> 203167, 332969, 270839, 46535, 103131, 11, 9106, 9718…
## $ `11/2/22` <dbl> 203265, 332996, 270840, 46588, 103131, 11, 9106, 9718…
## $ `11/3/22` <dbl> 203395, 332996, 270847, 46588, 103131, 11, 9106, 9718…
## $ `11/4/22` <dbl> 203497, 333027, 270856, 46588, 103131, 11, 9106, 9718…
## $ `11/5/22` <dbl> 203574, 333046, 270862, 46588, 103131, 11, 9106, 9718…
## $ `11/6/22` <dbl> 203681, 333055, 270873, 46588, 103131, 11, 9106, 9720…
## $ `11/7/22` <dbl> 203829, 333058, 270881, 46588, 103131, 11, 9106, 9720…
## $ `11/8/22` <dbl> 203942, 333071, 270891, 46588, 103131, 11, 9106, 9720…
## $ `11/9/22` <dbl> 204094, 333088, 270906, 46664, 103131, 11, 9106, 9720…
## $ `11/10/22` <dbl> 204287, 333103, 270917, 46664, 103131, 11, 9106, 9720…
## $ `11/11/22` <dbl> 204392, 333125, 270924, 46664, 103131, 11, 9106, 9720…
## $ `11/12/22` <dbl> 204417, 333138, 270929, 46664, 103131, 11, 9106, 9720…
## $ `11/13/22` <dbl> 204510, 333156, 270939, 46664, 103131, 11, 9106, 9721…
## $ `11/14/22` <dbl> 204610, 333161, 270952, 46664, 103131, 11, 9106, 9721…
## $ `11/15/22` <dbl> 204724, 333197, 270969, 46664, 103131, 11, 9106, 9721…
## $ `11/16/22` <dbl> 204820, 333215, 270981, 46824, 103131, 11, 9106, 9721…
## $ `11/17/22` <dbl> 204982, 333233, 270996, 46824, 103131, 11, 9106, 9721…
## $ `11/18/22` <dbl> 205009, 333233, 270996, 46824, 103131, 11, 9106, 9721…
## $ `11/19/22` <dbl> 205039, 333246, 271011, 46824, 103131, 11, 9106, 9721…
## $ `11/20/22` <dbl> 205146, 333256, 271023, 46824, 103131, 11, 9106, 9723…
## $ `11/21/22` <dbl> 205229, 333257, 271028, 46824, 103131, 11, 9106, 9723…
## $ `11/22/22` <dbl> 205324, 333282, 271035, 46824, 103131, 11, 9106, 9723…
## $ `11/23/22` <dbl> 205391, 333293, 271041, 46824, 104491, 11, 9106, 9723…
## $ `11/24/22` <dbl> 205506, 333305, 271050, 46824, 104491, 11, 9106, 9723…
## $ `11/25/22` <dbl> 205541, 333316, 271057, 46824, 104491, 11, 9106, 9723…
## $ `11/26/22` <dbl> 205612, 333322, 271061, 46824, 104491, 11, 9106, 9723…
## $ `11/27/22` <dbl> 205612, 333330, 271061, 46824, 104491, 11, 9106, 9727…
## $ `11/28/22` <dbl> 205802, 333330, 271079, 46824, 104491, 11, 9106, 9727…
## $ `11/29/22` <dbl> 205830, 333338, 271082, 46824, 104491, 11, 9106, 9727…confirmedraw # %>% datatable() # Check latest date at the end of data as tibble## # A tibble: 289 × 1,047
## Provin…¹ Count…² Lat Long 1/22/…³ 1/23/…⁴ 1/24/…⁵ 1/25/…⁶ 1/26/…⁷ 1/27/…⁸
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghan… 33.9 67.7 0 0 0 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0 0 0 0
## 6 <NA> Antarc… -71.9 23.3 0 0 0 0 0 0
## 7 <NA> Antigu… 17.1 -61.8 0 0 0 0 0 0
## 8 <NA> Argent… -38.4 -63.6 0 0 0 0 0 0
## 9 <NA> Armenia 40.1 45.0 0 0 0 0 0 0
## 10 Austral… Austra… -35.5 149. 0 0 0 0 0 0
## # … with 279 more rows, 1,037 more variables: `1/28/20` <dbl>, `1/29/20` <dbl>,
## # `1/30/20` <dbl>, `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
## # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>,
## # `2/7/20` <dbl>, `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>,
## # `2/11/20` <dbl>, `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>,
## # `2/15/20` <dbl>, `2/16/20` <dbl>, `2/17/20` <dbl>, `2/18/20` <dbl>,
## # `2/19/20` <dbl>, `2/20/20` <dbl>, `2/21/20` <dbl>, `2/22/20` <dbl>, …deathsraw <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")## Rows: 289 Columns: 1047
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1045): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.deathsraw # %>% datatable()## # A tibble: 289 × 1,047
## Provin…¹ Count…² Lat Long 1/22/…³ 1/23/…⁴ 1/24/…⁵ 1/25/…⁶ 1/26/…⁷ 1/27/…⁸
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghan… 33.9 67.7 0 0 0 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0 0 0 0
## 6 <NA> Antarc… -71.9 23.3 0 0 0 0 0 0
## 7 <NA> Antigu… 17.1 -61.8 0 0 0 0 0 0
## 8 <NA> Argent… -38.4 -63.6 0 0 0 0 0 0
## 9 <NA> Armenia 40.1 45.0 0 0 0 0 0 0
## 10 Austral… Austra… -35.5 149. 0 0 0 0 0 0
## # … with 279 more rows, 1,037 more variables: `1/28/20` <dbl>, `1/29/20` <dbl>,
## # `1/30/20` <dbl>, `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
## # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>,
## # `2/7/20` <dbl>, `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>,
## # `2/11/20` <dbl>, `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>,
## # `2/15/20` <dbl>, `2/16/20` <dbl>, `2/17/20` <dbl>, `2/18/20` <dbl>,
## # `2/19/20` <dbl>, `2/20/20` <dbl>, `2/21/20` <dbl>, `2/22/20` <dbl>, …recoveredraw <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")## Rows: 274 Columns: 1047
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1045): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.recoveredraw # %>% datatable()## # A tibble: 274 × 1,047
## Provin…¹ Count…² Lat Long 1/22/…³ 1/23/…⁴ 1/24/…⁵ 1/25/…⁶ 1/26/…⁷ 1/27/…⁸
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghan… 33.9 67.7 0 0 0 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0 0 0 0
## 6 <NA> Antarc… -71.9 23.3 0 0 0 0 0 0
## 7 <NA> Antigu… 17.1 -61.8 0 0 0 0 0 0
## 8 <NA> Argent… -38.4 -63.6 0 0 0 0 0 0
## 9 <NA> Armenia 40.1 45.0 0 0 0 0 0 0
## 10 Austral… Austra… -35.5 149. 0 0 0 0 0 0
## # … with 264 more rows, 1,037 more variables: `1/28/20` <dbl>, `1/29/20` <dbl>,
## # `1/30/20` <dbl>, `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
## # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>,
## # `2/7/20` <dbl>, `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>,
## # `2/11/20` <dbl>, `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>,
## # `2/15/20` <dbl>, `2/16/20` <dbl>, `2/17/20` <dbl>, `2/18/20` <dbl>,
## # `2/19/20` <dbl>, `2/20/20` <dbl>, `2/21/20` <dbl>, `2/22/20` <dbl>, …# Note differences in the number of rows/columnsD.5.2 Tidying and Combining: To create country level and global combined data
D.5.2.1 Convert each data set from wide to long
confirmed <- confirmedraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = "Lat", long = "Long") %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="confirmed") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(confirmed = confirmed - lag(confirmed)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
group_by(country, province) %>%
arrange(province, date) Check the data.
confirmed %>% filter(country == "Japan") %>% ggplot() + geom_line(aes(x = date, y = confirmed))
df_tv %>% filter(country == "Japan") %>% filter(type == "confirmed") %>% ggplot() + geom_line(aes(x = date, y = cases))
The dplyr::rename seems to have conflict with other rename function.
deaths <- deathsraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = Lat, long = Long) %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="death") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(death = death - lag(death)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
arrange(province, date) recovered <- recoveredraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = Lat, long = Long) %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="recovered") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(recovered = recovered - lag(recovered)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
arrange(province, date)D.5.2.2 Final data: combine all three
coronavirus_jhu <- full_join(confirmed, deaths) %>% full_join(recovered) %>%
pivot_longer(c(confirmed, death, recovered), names_to = "cases") %>%
arrange(cases, province, country, date)## Joining, by = c("date", "province", "country", "lat", "long")
## Joining, by = c("date", "province", "country", "lat", "long")coronavirus_jhu # %>% datatable()## # A tibble: 922,170 × 7
## # Groups: country, province [290]
## date province country lat long cases value
## <date> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 2020-01-23 Alberta Canada 53.9 -117. confirmed 0
## 2 2020-01-24 Alberta Canada 53.9 -117. confirmed 0
## 3 2020-01-25 Alberta Canada 53.9 -117. confirmed 0
## 4 2020-01-26 Alberta Canada 53.9 -117. confirmed 0
## 5 2020-01-27 Alberta Canada 53.9 -117. confirmed 0
## 6 2020-01-28 Alberta Canada 53.9 -117. confirmed 0
## 7 2020-01-29 Alberta Canada 53.9 -117. confirmed 0
## 8 2020-01-30 Alberta Canada 53.9 -117. confirmed 0
## 9 2020-01-31 Alberta Canada 53.9 -117. confirmed 0
## 10 2020-02-01 Alberta Canada 53.9 -117. confirmed 0
## # … with 922,160 more rowsD.5.3 Aggregated by Countries
The list of countries classified in provinces.
coronavirus_jhu %>% filter(!is.na(province)) %>% distinct(country)## # A tibble: 91 × 2
## # Groups: country, province [91]
## province country
## <chr> <chr>
## 1 Alberta Canada
## 2 Anguilla United Kingdom
## 3 Anhui China
## 4 Aruba Netherlands
## 5 Australian Capital Territory Australia
## 6 Beijing China
## 7 Bermuda United Kingdom
## 8 Bonaire, Sint Eustatius and Saba Netherlands
## 9 British Columbia Canada
## 10 British Virgin Islands United Kingdom
## # … with 81 more rowsCheck the data associated with provinces.
If we are only interested in coutries, the following is a possibility.
coronavirus_jhu_country <- coronavirus_jhu %>%
group_by(date, country, cases) %>%
summarize(value = sum(value)) %>%
arrange(cases, country, date)## `summarise()` has grouped output by 'date', 'country'. You can override using
## the `.groups` argument.coronavirus_jhu_country # %>% datatable()## # A tibble: 628,326 × 4
## # Groups: date, country [209,442]
## date country cases value
## <date> <chr> <chr> <dbl>
## 1 2020-01-23 Afghanistan confirmed 0
## 2 2020-01-24 Afghanistan confirmed 0
## 3 2020-01-25 Afghanistan confirmed 0
## 4 2020-01-26 Afghanistan confirmed 0
## 5 2020-01-27 Afghanistan confirmed 0
## 6 2020-01-28 Afghanistan confirmed 0
## 7 2020-01-29 Afghanistan confirmed 0
## 8 2020-01-30 Afghanistan confirmed 0
## 9 2020-01-31 Afghanistan confirmed 0
## 10 2020-02-01 Afghanistan confirmed 0
## # … with 628,316 more rowsD.5.4 Population of 2019
library(WDI)population <- WDI(
country = "all",
indicator = "SP.POP.TOTL",
start = 2019,
end = 2019,
extra = TRUE,
cache = NULL,
latest = NULL,
language = "en"
) %>%
select(country, iso2c, iso3c, region, income, population = SP.POP.TOTL)
population # %>% datatable()## country iso2c iso3c
## 1 Afghanistan AF AFG
## 2 Africa Eastern and Southern ZH AFE
## 3 Africa Western and Central ZI AFW
## 4 Albania AL ALB
## 5 Algeria DZ DZA
## 6 American Samoa AS ASM
## 7 Andorra AD AND
## 8 Angola AO AGO
## 9 Antigua and Barbuda AG ATG
## 10 Arab World 1A ARB
## 11 Argentina AR ARG
## 12 Armenia AM ARM
## 13 Aruba AW ABW
## 14 Australia AU AUS
## 15 Austria AT AUT
## 16 Azerbaijan AZ AZE
## 17 Bahamas, The BS BHS
## 18 Bahrain BH BHR
## 19 Bangladesh BD BGD
## 20 Barbados BB BRB
## 21 Belarus BY BLR
## 22 Belgium BE BEL
## 23 Belize BZ BLZ
## 24 Benin BJ BEN
## 25 Bermuda BM BMU
## 26 Bhutan BT BTN
## 27 Bolivia BO BOL
## 28 Bosnia and Herzegovina BA BIH
## 29 Botswana BW BWA
## 30 Brazil BR BRA
## 31 British Virgin Islands VG VGB
## 32 Brunei Darussalam BN BRN
## 33 Bulgaria BG BGR
## 34 Burkina Faso BF BFA
## 35 Burundi BI BDI
## 36 Cabo Verde CV CPV
## 37 Cambodia KH KHM
## 38 Cameroon CM CMR
## 39 Canada CA CAN
## 40 Caribbean small states S3 CSS
## 41 Cayman Islands KY CYM
## 42 Central African Republic CF CAF
## 43 Central Europe and the Baltics B8 CEB
## 44 Chad TD TCD
## 45 Channel Islands JG CHI
## 46 Chile CL CHL
## 47 China CN CHN
## 48 Colombia CO COL
## 49 Comoros KM COM
## 50 Congo, Dem. Rep. CD COD
## 51 Congo, Rep. CG COG
## 52 Costa Rica CR CRI
## 53 Cote d'Ivoire CI CIV
## 54 Croatia HR HRV
## 55 Cuba CU CUB
## 56 Curacao CW CUW
## 57 Cyprus CY CYP
## 58 Czechia CZ CZE
## 59 Denmark DK DNK
## 60 Djibouti DJ DJI
## 61 Dominica DM DMA
## 62 Dominican Republic DO DOM
## 63 Early-demographic dividend V2 EAR
## 64 East Asia & Pacific Z4 EAS
## 65 East Asia & Pacific (excluding high income) 4E EAP
## 66 East Asia & Pacific (IDA & IBRD countries) T4 TEA
## 67 Ecuador EC ECU
## 68 Egypt, Arab Rep. EG EGY
## 69 El Salvador SV SLV
## 70 Equatorial Guinea GQ GNQ
## 71 Eritrea ER ERI
## 72 Estonia EE EST
## 73 Eswatini SZ SWZ
## 74 Ethiopia ET ETH
## 75 Euro area XC EMU
## 76 Europe & Central Asia Z7 ECS
## 77 Europe & Central Asia (excluding high income) 7E ECA
## 78 Europe & Central Asia (IDA & IBRD countries) T7 TEC
## 79 European Union EU EUU
## 80 Faroe Islands FO FRO
## 81 Fiji FJ FJI
## 82 Finland FI FIN
## 83 Fragile and conflict affected situations F1 FCS
## 84 France FR FRA
## 85 French Polynesia PF PYF
## 86 Gabon GA GAB
## 87 Gambia, The GM GMB
## 88 Georgia GE GEO
## 89 Germany DE DEU
## 90 Ghana GH GHA
## 91 Gibraltar GI GIB
## 92 Greece GR GRC
## 93 Greenland GL GRL
## 94 Grenada GD GRD
## 95 Guam GU GUM
## 96 Guatemala GT GTM
## 97 Guinea GN GIN
## 98 Guinea-Bissau GW GNB
## 99 Guyana GY GUY
## 100 Haiti HT HTI
## 101 Heavily indebted poor countries (HIPC) XE HPC
## 102 High income XD
## 103 Honduras HN HND
## 104 Hong Kong SAR, China HK HKG
## 105 Hungary HU HUN
## 106 IBRD only XF IBD
## 107 Iceland IS ISL
## 108 IDA & IBRD total ZT IBT
## 109 IDA blend XH IDB
## 110 IDA only XI IDX
## 111 IDA total XG IDA
## 112 India IN IND
## 113 Indonesia ID IDN
## 114 Iran, Islamic Rep. IR IRN
## 115 Iraq IQ IRQ
## 116 Ireland IE IRL
## 117 Isle of Man IM IMN
## 118 Israel IL ISR
## 119 Italy IT ITA
## 120 Jamaica JM JAM
## 121 Japan JP JPN
## 122 Jordan JO JOR
## 123 Kazakhstan KZ KAZ
## 124 Kenya KE KEN
## 125 Kiribati KI KIR
## 126 Korea, Dem. People's Rep. KP PRK
## 127 Korea, Rep. KR KOR
## 128 Kosovo XK XKX
## 129 Kuwait KW KWT
## 130 Kyrgyz Republic KG KGZ
## 131 Lao PDR LA LAO
## 132 Late-demographic dividend V3 LTE
## 133 Latin America & Caribbean ZJ LCN
## 134 Latin America & Caribbean (excluding high income) XJ LAC
## 135 Latin America & the Caribbean (IDA & IBRD countries) T2 TLA
## 136 Latvia LV LVA
## 137 Least developed countries: UN classification XL LDC
## 138 Lebanon LB LBN
## 139 Lesotho LS LSO
## 140 Liberia LR LBR
## 141 Libya LY LBY
## 142 Liechtenstein LI LIE
## 143 Lithuania LT LTU
## 144 Low & middle income XO LMY
## 145 Low income XM
## 146 Lower middle income XN
## 147 Luxembourg LU LUX
## 148 Macao SAR, China MO MAC
## 149 Madagascar MG MDG
## 150 Malawi MW MWI
## 151 Malaysia MY MYS
## 152 Maldives MV MDV
## 153 Mali ML MLI
## 154 Malta MT MLT
## 155 Marshall Islands MH MHL
## 156 Mauritania MR MRT
## 157 Mauritius MU MUS
## 158 Mexico MX MEX
## 159 Micronesia, Fed. Sts. FM FSM
## 160 Middle East & North Africa ZQ MEA
## 161 Middle East & North Africa (excluding high income) XQ MNA
## 162 Middle East & North Africa (IDA & IBRD countries) T3 TMN
## 163 Middle income XP MIC
## 164 Moldova MD MDA
## 165 Monaco MC MCO
## 166 Mongolia MN MNG
## 167 Montenegro ME MNE
## 168 Morocco MA MAR
## 169 Mozambique MZ MOZ
## 170 Myanmar MM MMR
## 171 Namibia NA NAM
## 172 Nauru NR NRU
## 173 Nepal NP NPL
## 174 Netherlands NL NLD
## 175 New Caledonia NC NCL
## 176 New Zealand NZ NZL
## 177 Nicaragua NI NIC
## 178 Niger NE NER
## 179 Nigeria NG NGA
## 180 North America XU NAC
## 181 North Macedonia MK MKD
## 182 Northern Mariana Islands MP MNP
## 183 Norway NO NOR
## 184 Not classified XY
## 185 OECD members OE OED
## 186 Oman OM OMN
## 187 Other small states S4 OSS
## 188 Pacific island small states S2 PSS
## 189 Pakistan PK PAK
## 190 Palau PW PLW
## 191 Panama PA PAN
## 192 Papua New Guinea PG PNG
## 193 Paraguay PY PRY
## 194 Peru PE PER
## 195 Philippines PH PHL
## 196 Poland PL POL
## 197 Portugal PT PRT
## 198 Post-demographic dividend V4 PST
## 199 Pre-demographic dividend V1 PRE
## 200 Puerto Rico PR PRI
## 201 Qatar QA QAT
## 202 Romania RO ROU
## 203 Russian Federation RU RUS
## 204 Rwanda RW RWA
## 205 Samoa WS WSM
## 206 San Marino SM SMR
## 207 Sao Tome and Principe ST STP
## 208 Saudi Arabia SA SAU
## 209 Senegal SN SEN
## 210 Serbia RS SRB
## 211 Seychelles SC SYC
## 212 Sierra Leone SL SLE
## 213 Singapore SG SGP
## 214 Sint Maarten (Dutch part) SX SXM
## 215 Slovak Republic SK SVK
## 216 Slovenia SI SVN
## 217 Small states S1 SST
## 218 Solomon Islands SB SLB
## 219 Somalia SO SOM
## 220 South Africa ZA ZAF
## 221 South Asia 8S SAS
## 222 South Asia (IDA & IBRD) T5 TSA
## 223 South Sudan SS SSD
## 224 Spain ES ESP
## 225 Sri Lanka LK LKA
## 226 St. Kitts and Nevis KN KNA
## 227 St. Lucia LC LCA
## 228 St. Martin (French part) MF MAF
## 229 St. Vincent and the Grenadines VC VCT
## 230 Sub-Saharan Africa ZG SSF
## 231 Sub-Saharan Africa (excluding high income) ZF SSA
## 232 Sub-Saharan Africa (IDA & IBRD countries) T6 TSS
## 233 Sudan SD SDN
## 234 Suriname SR SUR
## 235 Sweden SE SWE
## 236 Switzerland CH CHE
## 237 Syrian Arab Republic SY SYR
## 238 Tajikistan TJ TJK
## 239 Tanzania TZ TZA
## 240 Thailand TH THA
## 241 Timor-Leste TL TLS
## 242 Togo TG TGO
## 243 Tonga TO TON
## 244 Trinidad and Tobago TT TTO
## 245 Tunisia TN TUN
## 246 Turkiye TR TUR
## 247 Turkmenistan TM TKM
## 248 Turks and Caicos Islands TC TCA
## 249 Tuvalu TV TUV
## 250 Uganda UG UGA
## 251 Ukraine UA UKR
## 252 United Arab Emirates AE ARE
## 253 United Kingdom GB GBR
## 254 United States US USA
## 255 Upper middle income XT
## 256 Uruguay UY URY
## 257 Uzbekistan UZ UZB
## 258 Vanuatu VU VUT
## 259 Venezuela, RB VE VEN
## 260 Vietnam VN VNM
## 261 Virgin Islands (U.S.) VI VIR
## 262 West Bank and Gaza PS PSE
## 263 World 1W WLD
## 264 Yemen, Rep. YE YEM
## 265 Zambia ZM ZMB
## 266 Zimbabwe ZW ZWE
## region income population
## 1 South Asia Low income 38041757
## 2 Aggregates Aggregates 660046272
## 3 Aggregates Aggregates 446911598
## 4 Europe & Central Asia Upper middle income 2854191
## 5 Middle East & North Africa Lower middle income 43053054
## 6 East Asia & Pacific Upper middle income 55312
## 7 Europe & Central Asia High income 77146
## 8 Sub-Saharan Africa Lower middle income 31825299
## 9 Latin America & Caribbean High income 97115
## 10 Aggregates Aggregates 427870273
## 11 Latin America & Caribbean Upper middle income 44938712
## 12 Europe & Central Asia Upper middle income 2957728
## 13 Latin America & Caribbean High income 106310
## 14 East Asia & Pacific High income 25365745
## 15 Europe & Central Asia High income 8879920
## 16 Europe & Central Asia Upper middle income 10024283
## 17 Latin America & Caribbean High income 389486
## 18 Middle East & North Africa High income 1641164
## 19 South Asia Lower middle income 163046173
## 20 Latin America & Caribbean High income 287021
## 21 Europe & Central Asia Upper middle income 9419758
## 22 Europe & Central Asia High income 11488980
## 23 Latin America & Caribbean Upper middle income 390351
## 24 Sub-Saharan Africa Lower middle income 11801151
## 25 North America High income 63911
## 26 South Asia Lower middle income 763094
## 27 Latin America & Caribbean Lower middle income 11513102
## 28 Europe & Central Asia Upper middle income 3300998
## 29 Sub-Saharan Africa Upper middle income 2303703
## 30 Latin America & Caribbean Upper middle income 211049519
## 31 Latin America & Caribbean High income 30033
## 32 East Asia & Pacific High income 433296
## 33 Europe & Central Asia Upper middle income 6975761
## 34 Sub-Saharan Africa Low income 20321383
## 35 Sub-Saharan Africa Low income 11530577
## 36 Sub-Saharan Africa Lower middle income 549936
## 37 East Asia & Pacific Lower middle income 16486542
## 38 Sub-Saharan Africa Lower middle income 25876387
## 39 North America High income 37601230
## 40 Aggregates Aggregates 7401389
## 41 Latin America & Caribbean High income 64948
## 42 Sub-Saharan Africa Low income 4745179
## 43 Aggregates Aggregates 102398494
## 44 Sub-Saharan Africa Low income 15946882
## 45 Europe & Central Asia High income 172264
## 46 Latin America & Caribbean High income 18952035
## 47 East Asia & Pacific Upper middle income 1407745000
## 48 Latin America & Caribbean Upper middle income 50339443
## 49 Sub-Saharan Africa Lower middle income 850891
## 50 Sub-Saharan Africa Low income 86790568
## 51 Sub-Saharan Africa Lower middle income 5380504
## 52 Latin America & Caribbean Upper middle income 5047561
## 53 Sub-Saharan Africa Lower middle income 25716554
## 54 Europe & Central Asia High income 4065253
## 55 Latin America & Caribbean Upper middle income 11333484
## 56 Latin America & Caribbean High income 157441
## 57 Europe & Central Asia High income 1198574
## 58 <NA> <NA> 10671870
## 59 Europe & Central Asia High income 5814422
## 60 Middle East & North Africa Lower middle income 973557
## 61 Latin America & Caribbean Upper middle income 71808
## 62 Latin America & Caribbean Upper middle income 10738957
## 63 Aggregates Aggregates 3290291029
## 64 Aggregates Aggregates 2351127942
## 65 Aggregates Aggregates 2103723076
## 66 Aggregates Aggregates 2078012370
## 67 Latin America & Caribbean Upper middle income 17373657
## 68 Middle East & North Africa Lower middle income 100388076
## 69 Latin America & Caribbean Lower middle income 6453550
## 70 Sub-Saharan Africa Upper middle income 1355982
## 71 Sub-Saharan Africa Low income NA
## 72 Europe & Central Asia High income 1326855
## 73 Sub-Saharan Africa Lower middle income 1148133
## 74 Sub-Saharan Africa Low income 112078727
## 75 Aggregates Aggregates 342283354
## 76 Aggregates Aggregates 920807612
## 77 Aggregates Aggregates 399386100
## 78 Aggregates Aggregates 460788476
## 79 Aggregates Aggregates 447197811
## 80 Europe & Central Asia High income 48677
## 81 East Asia & Pacific Upper middle income 889955
## 82 Europe & Central Asia High income 5521606
## 83 Aggregates Aggregates 940026046
## 84 Europe & Central Asia High income 67248926
## 85 East Asia & Pacific High income 279285
## 86 Sub-Saharan Africa Upper middle income 2172578
## 87 Sub-Saharan Africa Low income 2347696
## 88 Europe & Central Asia Upper middle income 3720161
## 89 Europe & Central Asia High income 83092962
## 90 Sub-Saharan Africa Lower middle income 30417858
## 91 Europe & Central Asia High income 33706
## 92 Europe & Central Asia High income 10721582
## 93 Europe & Central Asia High income 56225
## 94 Latin America & Caribbean Upper middle income 112002
## 95 East Asia & Pacific High income 167295
## 96 Latin America & Caribbean Upper middle income 16604026
## 97 Sub-Saharan Africa Low income 12771246
## 98 Sub-Saharan Africa Low income 1920917
## 99 Latin America & Caribbean Upper middle income 782775
## 100 Latin America & Caribbean Lower middle income 11263079
## 101 Aggregates Aggregates 801708019
## 102 <NA> <NA> 1234830048
## 103 Latin America & Caribbean Lower middle income 9746115
## 104 East Asia & Pacific High income 7507900
## 105 Europe & Central Asia High income 9771141
## 106 Aggregates Aggregates 4826259460
## 107 Europe & Central Asia High income 360563
## 108 Aggregates Aggregates 6496952025
## 109 Aggregates Aggregates 561571929
## 110 Aggregates Aggregates 1109120636
## 111 Aggregates Aggregates 1670692565
## 112 South Asia Lower middle income 1366417756
## 113 East Asia & Pacific Lower middle income 270625567
## 114 Middle East & North Africa Lower middle income 82913893
## 115 Middle East & North Africa Upper middle income 39309789
## 116 Europe & Central Asia High income 4934340
## 117 Europe & Central Asia High income 84589
## 118 Middle East & North Africa High income 9054000
## 119 Europe & Central Asia High income 59729081
## 120 Latin America & Caribbean Upper middle income 2948277
## 121 East Asia & Pacific High income 126633000
## 122 Middle East & North Africa Upper middle income 10101697
## 123 Europe & Central Asia Upper middle income 18513673
## 124 Sub-Saharan Africa Lower middle income 52573967
## 125 East Asia & Pacific Lower middle income 117608
## 126 East Asia & Pacific Low income 25666158
## 127 East Asia & Pacific High income 51764822
## 128 Europe & Central Asia Upper middle income 1788878
## 129 Middle East & North Africa High income 4207077
## 130 Europe & Central Asia Lower middle income 6456200
## 131 East Asia & Pacific Lower middle income 7169456
## 132 Aggregates Aggregates 2308520840
## 133 <NA> <NA> 646431661
## 134 Aggregates Aggregates 585257302
## 135 Aggregates Aggregates 630644771
## 136 Europe & Central Asia High income 1913822
## 137 Aggregates Aggregates 1033088986
## 138 Middle East & North Africa Lower middle income 6855709
## 139 Sub-Saharan Africa Lower middle income 2125267
## 140 Sub-Saharan Africa Low income 4937374
## 141 Middle East & North Africa Upper middle income 6777453
## 142 Europe & Central Asia High income 38020
## 143 Europe & Central Asia High income 2794137
## 144 Aggregates Aggregates 6420460567
## 145 <NA> <NA> 665731857
## 146 <NA> <NA> 3274021191
## 147 Europe & Central Asia High income 620001
## 148 East Asia & Pacific High income 640446
## 149 Sub-Saharan Africa Low income 26969306
## 150 Sub-Saharan Africa Low income 18628749
## 151 East Asia & Pacific Upper middle income 31949789
## 152 South Asia Upper middle income 530957
## 153 Sub-Saharan Africa Low income 19658023
## 154 Middle East & North Africa High income 504062
## 155 East Asia & Pacific Upper middle income 58791
## 156 Sub-Saharan Africa Lower middle income 4525698
## 157 Sub-Saharan Africa Upper middle income 1265711
## 158 Latin America & Caribbean Upper middle income 127575529
## 159 East Asia & Pacific Lower middle income 113811
## 160 Aggregates Aggregates 456709496
## 161 Aggregates Aggregates 389457075
## 162 Aggregates Aggregates 384771769
## 163 Aggregates Aggregates 5754728710
## 164 Europe & Central Asia Upper middle income 2664974
## 165 Europe & Central Asia High income 38967
## 166 East Asia & Pacific Lower middle income 3225166
## 167 Europe & Central Asia Upper middle income 622028
## 168 Middle East & North Africa Lower middle income 36471766
## 169 Sub-Saharan Africa Low income 30366043
## 170 East Asia & Pacific Lower middle income 54045422
## 171 Sub-Saharan Africa Upper middle income 2494524
## 172 East Asia & Pacific High income 10764
## 173 South Asia Lower middle income 28608715
## 174 Europe & Central Asia High income 17344874
## 175 East Asia & Pacific High income 271300
## 176 East Asia & Pacific High income 4979200
## 177 Latin America & Caribbean Lower middle income 6545503
## 178 Sub-Saharan Africa Low income 23310719
## 179 Sub-Saharan Africa Lower middle income 200963603
## 180 Aggregates Aggregates 365995094
## 181 Europe & Central Asia Upper middle income 2076694
## 182 East Asia & Pacific High income 57213
## 183 Europe & Central Asia High income 5347896
## 184 <NA> <NA> NA
## 185 Aggregates Aggregates 1365274704
## 186 Middle East & North Africa High income 4974992
## 187 Aggregates Aggregates 31361216
## 188 Aggregates Aggregates 2491878
## 189 South Asia Lower middle income 216565317
## 190 East Asia & Pacific Upper middle income 18001
## 191 Latin America & Caribbean High income 4246440
## 192 East Asia & Pacific Lower middle income 8776119
## 193 Latin America & Caribbean Upper middle income 7044639
## 194 Latin America & Caribbean Upper middle income 32510462
## 195 East Asia & Pacific Lower middle income 108116622
## 196 Europe & Central Asia High income 37965475
## 197 Europe & Central Asia High income 10286263
## 198 Aggregates Aggregates 1113142897
## 199 Aggregates Aggregates 944902748
## 200 Latin America & Caribbean High income 3193694
## 201 Middle East & North Africa High income 2832071
## 202 Europe & Central Asia High income 19371648
## 203 Europe & Central Asia Upper middle income 144406261
## 204 Sub-Saharan Africa Low income 12626938
## 205 East Asia & Pacific Lower middle income 197093
## 206 Europe & Central Asia High income 33864
## 207 Sub-Saharan Africa Lower middle income 215048
## 208 Middle East & North Africa High income 34268529
## 209 Sub-Saharan Africa Lower middle income 16296362
## 210 Europe & Central Asia Upper middle income 6945235
## 211 Sub-Saharan Africa High income 97625
## 212 Sub-Saharan Africa Low income 7813207
## 213 East Asia & Pacific High income 5703569
## 214 Latin America & Caribbean High income 41608
## 215 Europe & Central Asia High income 5454147
## 216 Europe & Central Asia High income 2088385
## 217 Aggregates Aggregates 41254483
## 218 East Asia & Pacific Lower middle income 669821
## 219 Sub-Saharan Africa Low income 15442906
## 220 Sub-Saharan Africa Upper middle income 58558267
## 221 Aggregates Aggregates 1835776769
## 222 Aggregates Aggregates 1835776769
## 223 Sub-Saharan Africa Low income 11062114
## 224 Europe & Central Asia High income 47134837
## 225 South Asia Lower middle income 21803000
## 226 Latin America & Caribbean High income 52834
## 227 Latin America & Caribbean Upper middle income 182795
## 228 Latin America & Caribbean High income 38002
## 229 Latin America & Caribbean Upper middle income 110593
## 230 <NA> <NA> 1106957870
## 231 Aggregates Aggregates 1106860245
## 232 Aggregates Aggregates 1106957870
## 233 Sub-Saharan Africa Low income 42813237
## 234 Latin America & Caribbean Upper middle income 581363
## 235 Europe & Central Asia High income 10278887
## 236 Europe & Central Asia High income 8575280
## 237 Middle East & North Africa Low income 17070132
## 238 Europe & Central Asia Lower middle income 9321023
## 239 Sub-Saharan Africa Lower middle income 58005461
## 240 East Asia & Pacific Upper middle income 69625581
## 241 East Asia & Pacific Lower middle income 1293120
## 242 Sub-Saharan Africa Low income 8082359
## 243 East Asia & Pacific Upper middle income 104497
## 244 Latin America & Caribbean High income 1394969
## 245 Middle East & North Africa Lower middle income 11694721
## 246 Europe & Central Asia Upper middle income 83429607
## 247 Europe & Central Asia Upper middle income 5942094
## 248 Latin America & Caribbean High income 38194
## 249 East Asia & Pacific Upper middle income 11655
## 250 Sub-Saharan Africa Low income 44269587
## 251 Europe & Central Asia Lower middle income 44386203
## 252 Middle East & North Africa High income 9770526
## 253 Europe & Central Asia High income 66836327
## 254 North America High income 328329953
## 255 <NA> <NA> 2480707519
## 256 Latin America & Caribbean High income 3461731
## 257 Europe & Central Asia Lower middle income 33580350
## 258 East Asia & Pacific Lower middle income 299882
## 259 Latin America & Caribbean Not classified 28515829
## 260 East Asia & Pacific Lower middle income 96462108
## 261 Latin America & Caribbean High income 106669
## 262 Middle East & North Africa Lower middle income 4685306
## 263 Aggregates Aggregates 7683806444
## 264 Middle East & North Africa Low income 29161922
## 265 Sub-Saharan Africa Low income 17861034
## 266 Sub-Saharan Africa Lower middle income 14645473coronavirus_country <- coronavirus_jhu_country %>%
left_join(population)## Joining, by = "country"coronavirus_country # %>% datatable()## # A tibble: 628,326 × 9
## # Groups: date, country [209,442]
## date country cases value iso2c iso3c region income popul…¹
## <date> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 2020-01-23 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 2 2020-01-24 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 3 2020-01-25 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 4 2020-01-26 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 5 2020-01-27 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 6 2020-01-28 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 7 2020-01-29 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 8 2020-01-30 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 9 2020-01-31 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## 10 2020-02-01 Afghanistan confirmed 0 AF AFG South Asia Low in… 3.80e7
## # … with 628,316 more rows, and abbreviated variable name ¹populationsummary(coronavirus_country)## date country cases value
## Min. :2020-01-23 Length:628326 Length:628326 Min. :-30974748
## 1st Qu.:2020-10-09 Class :character Class :character 1st Qu.: 0
## Median :2021-06-26 Mode :character Mode :character Median : 0
## Mean :2021-06-26 Mean : 1046
## 3rd Qu.:2022-03-14 3rd Qu.: 54
## Max. :2022-11-29 Max. : 1355242
## NA's :15630
## iso2c iso3c region income
## Length:628326 Length:628326 Length:628326 Length:628326
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## population
## Min. :1.076e+04
## 1st Qu.:1.921e+06
## Median :8.828e+06
## Mean :3.869e+07
## 3rd Qu.:2.572e+07
## Max. :1.408e+09
## NA's :96906D.5.4.1 Region
coronavirus_country %>% group_by(region) %>%
summarize(n = n_distinct(country))## # A tibble: 8 × 2
## region n
## <chr> <int>
## 1 East Asia & Pacific 24
## 2 Europe & Central Asia 47
## 3 Latin America & Caribbean 28
## 4 Middle East & North Africa 17
## 5 North America 1
## 6 South Asia 8
## 7 Sub-Saharan Africa 45
## 8 <NA> 31coronavirus_country %>% filter(is.na(region)) %>% pull(country) %>% unique()## [1] "Antarctica" "Bahamas"
## [3] "Brunei" "Burma"
## [5] "Congo (Brazzaville)" "Congo (Kinshasa)"
## [7] "Czechia" "Diamond Princess"
## [9] "Egypt" "Gambia"
## [11] "Holy See" "Iran"
## [13] "Korea, North" "Korea, South"
## [15] "Kyrgyzstan" "Laos"
## [17] "Micronesia" "MS Zaandam"
## [19] "Russia" "Saint Kitts and Nevis"
## [21] "Saint Lucia" "Saint Vincent and the Grenadines"
## [23] "Slovakia" "Summer Olympics 2020"
## [25] "Syria" "Taiwan*"
## [27] "Turkey" "US"
## [29] "Venezuela" "Winter Olympics 2022"
## [31] "Yemen"coronavirus_country %>% drop_na() %>% group_by(region) %>%
summarize(n = n_distinct(country)) %>%
arrange(n) %>%
ggplot() +
geom_col(aes(y = reorder(region, n), x = n))
coronavirus_country %>% filter(cases == "confirmed") %>%
group_by(region, date) %>% summarize(confirmed = sum(value, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = date, y = confirmed, color = region)) +
labs(title = "Total Number of Confirmed Cases by Region")## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
coronavirus_country %>% filter(cases == "death") %>%
group_by(region, date) %>% summarize(death = sum(value, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = date, y = death, color = region)) +
labs(title = "Total Number of Deaths by Region")## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
D.5.4.2 Income
coronavirus_country %>% group_by(income) %>%
summarize(n = n_distinct(country))## # A tibble: 5 × 2
## income n
## <chr> <int>
## 1 High income 52
## 2 Low income 23
## 3 Lower middle income 47
## 4 Upper middle income 48
## 5 <NA> 31coronavirus_country %>% filter(is.na(income)) %>% pull(country) %>% unique()## [1] "Antarctica" "Bahamas"
## [3] "Brunei" "Burma"
## [5] "Congo (Brazzaville)" "Congo (Kinshasa)"
## [7] "Czechia" "Diamond Princess"
## [9] "Egypt" "Gambia"
## [11] "Holy See" "Iran"
## [13] "Korea, North" "Korea, South"
## [15] "Kyrgyzstan" "Laos"
## [17] "Micronesia" "MS Zaandam"
## [19] "Russia" "Saint Kitts and Nevis"
## [21] "Saint Lucia" "Saint Vincent and the Grenadines"
## [23] "Slovakia" "Summer Olympics 2020"
## [25] "Syria" "Taiwan*"
## [27] "Turkey" "US"
## [29] "Venezuela" "Winter Olympics 2022"
## [31] "Yemen"coronavirus_country %>% drop_na() %>% group_by(income) %>%
summarize(n = n_distinct(country)) %>%
arrange(n) %>%
ggplot() +
geom_col(aes(y = reorder(income, n), x = n))
coronavirus_country %>% filter(cases == "confirmed") %>%
group_by(income, date) %>% summarize(confirmed = sum(value, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = date, y = confirmed, color = income)) +
labs(title = "Total Number of Confirmed Cases by Income Level")## `summarise()` has grouped output by 'income'. You can override using the
## `.groups` argument.
coronavirus_country %>% filter(cases == "death") %>%
group_by(income, date) %>% summarize(death = sum(value, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = date, y = death, color = income)) +
labs(title = "Total Number of Deaths by Income Level")## `summarise()` has grouped output by 'income'. You can override using the
## `.groups` argument.
### Analysis Suggested by Rami Krispin
See https://github.com/RamiKrispin/coronavirus/
- Rami Krispin is the author of an R package
coronavirus
D.5.4.3 Summary of the total confrimed cases by country
coronavirus_country %>%
filter(cases == "confirmed") %>%
group_by(country) %>%
summarize(total_cases = sum(value)) %>%
arrange(desc(total_cases))## # A tibble: 201 × 2
## country total_cases
## <chr> <dbl>
## 1 US 98673987
## 2 India 44673567
## 3 France 37979248
## 4 Germany 36463485
## 5 Brazil 35227599
## 6 Korea, South 27098733
## 7 Japan 24676910
## 8 Italy 24260660
## 9 United Kingdom 24224763
## 10 Russia 21278433
## # … with 191 more rowsD.5.4.4 Summary of new cases during the past 24 hours by country and type
Date = 2022-11-28
coronavirus_country %>%
filter(date == Sys.Date() -2) %>%
select(country, cases, value) %>%
group_by(country, cases) %>%
summarize(total_cases = sum(value)) %>%
pivot_wider(names_from = cases,
values_from = total_cases) %>%
arrange(desc(confirmed))## Adding missing grouping variables: `date`
## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.## # A tibble: 201 × 4
## # Groups: country [201]
## country confirmed death recovered
## <chr> <dbl> <dbl> <dbl>
## 1 France 95382 132 0
## 2 Korea, South 71476 41 0
## 3 US 59717 280 0
## 4 Japan 49117 103 0
## 5 Germany 46552 162 0
## 6 Taiwan* 10651 41 0
## 7 Russia 4980 50 0
## 8 Australia 4149 19 0
## 9 Indonesia 3225 59 0
## 10 Belgium 3152 15 0
## # … with 191 more rows