Lab 5 - COVID data Part 2
Alison Fowler
9/28/2020
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Read in data
# March 13
Confirmed_State_3_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-13-2020.csv")) %>%
rename(Country_Region = "Country/Region", Province_State = "Province/State") %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## `Province/State` = col_character(),
## `Country/Region` = col_character(),
## `Last Update` = col_datetime(format = ""),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Latitude = col_double(),
## Longitude = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_3_13)## tibble [53 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:53] "US" "US" "US" "US" ...
## $ Confirmed : num [1:53] 5 1 9 6 282 49 11 4 46 10 ...
## - attr(*, "groups")= tibble [53 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:53]
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## ..- attr(*, ".drop")= logi TRUE# Sept 13
Confirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_9_13)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 138755 6268 208512 70219 761728 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
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## ..- attr(*, ".drop")= logi TRUE# Notice from the above `str’ calls that there are 53 states in the table on 3/13/2020 and
# 58 states in the table on 9/13/2020... Let's see what the differences are
setdiff(Confirmed_State_9_13$Province_State, Confirmed_State_3_13$Province_State)## [1] "Guam" "Northern Mariana Islands"
## [3] "Puerto Rico" "Recovered"
## [5] "Virgin Islands"# There are 4 US territories and a field you may have notice last week: “Recovered”.
# We don’t need the “Recovered” row so let’s filter it from the data set
Confirmed_State_9_13 <- Confirmed_State_9_13 %>%
filter(Province_State != "Recovered") # Since all of the states and territories in 9/13/2020 are found in 3/13/2020 a left_join or full_join can
# be used to merge the tables
Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State"))# look at the new joined dataframe
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 5 US 138755
## 2 Alaska US 1 US 6268
## 3 Arizona US 9 US 208512
## 4 Arkansas US 6 US 70219
## 5 California US 282 US 761728
## 6 Colorado US 49 US 61293tail(Confirmed_State_3_13_9_13_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Wyoming US 1 US 4346
## 2 Guam <NA> NA US 1863
## 3 Northern Mariana Is… <NA> NA US 60
## 4 Puerto Rico <NA> NA US 37380
## 5 Virgin Islands <NA> NA US 1220# look to see what cells are NA
which(is.na(Confirmed_State_3_13_9_13_joined))## [1] 111 112 113 114 168 169 170 171# let's turn the NAs to 0 and rename the columns
Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_3_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_3_13_2020 = 0))
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_3_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 5 138755
## 2 Alaska 1 6268
## 3 Arizona 9 208512
## 4 Arkansas 6 70219
## 5 California 282 761728
## 6 Colorado 49 61293# Switching between wide and long table formats
Confirmed_State_3_13_9_13_joined_long <- Confirmed_State_3_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")# to adjust figure size {r, fig.width = 5, fig.height = 10}
ggplot(Confirmed_State_3_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date)) 
# Working with the time series data
# load the most recent time series data for confirmed cases
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "time_series_covid19_confirmed_global.csv")
time_series_confirmed <- read_csv("time_series_covid19_confirmed_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.head(time_series_confirmed)## # A tibble: 6 x 258
## Province_State Country_Region Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antigua and B… 17.1 -61.8 0 0 0
## # … with 251 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
## # `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>, `2/23/20` <dbl>, `2/24/20` <dbl>,
## # `2/25/20` <dbl>, `2/26/20` <dbl>, `2/27/20` <dbl>, `2/28/20` <dbl>,
## # `2/29/20` <dbl>, `3/1/20` <dbl>, `3/2/20` <dbl>, `3/3/20` <dbl>,
## # `3/4/20` <dbl>, `3/5/20` <dbl>, `3/6/20` <dbl>, `3/7/20` <dbl>,
## # `3/8/20` <dbl>, `3/9/20` <dbl>, `3/10/20` <dbl>, `3/11/20` <dbl>,
## # `3/12/20` <dbl>, `3/13/20` <dbl>, `3/14/20` <dbl>, `3/15/20` <dbl>,
## # `3/16/20` <dbl>, `3/17/20` <dbl>, `3/18/20` <dbl>, `3/19/20` <dbl>,
## # `3/20/20` <dbl>, `3/21/20` <dbl>, `3/22/20` <dbl>, `3/23/20` <dbl>,
## # `3/24/20` <dbl>, `3/25/20` <dbl>, `3/26/20` <dbl>, `3/27/20` <dbl>,
## # `3/28/20` <dbl>, `3/29/20` <dbl>, `3/30/20` <dbl>, `3/31/20` <dbl>,
## # `4/1/20` <dbl>, `4/2/20` <dbl>, `4/3/20` <dbl>, `4/4/20` <dbl>,
## # `4/5/20` <dbl>, `4/6/20` <dbl>, `4/7/20` <dbl>, `4/8/20` <dbl>,
## # `4/9/20` <dbl>, `4/10/20` <dbl>, `4/11/20` <dbl>, `4/12/20` <dbl>,
## # `4/13/20` <dbl>, `4/14/20` <dbl>, `4/15/20` <dbl>, `4/16/20` <dbl>,
## # `4/17/20` <dbl>, `4/18/20` <dbl>, `4/19/20` <dbl>, `4/20/20` <dbl>,
## # `4/21/20` <dbl>, `4/22/20` <dbl>, `4/23/20` <dbl>, `4/24/20` <dbl>,
## # `4/25/20` <dbl>, `4/26/20` <dbl>, `4/27/20` <dbl>, `4/28/20` <dbl>,
## # `4/29/20` <dbl>, `4/30/20` <dbl>, `5/1/20` <dbl>, `5/2/20` <dbl>,
## # `5/3/20` <dbl>, …time_series_confirmed_long <- time_series_confirmed %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
head(time_series_confirmed_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "time_series_covid19_deaths_global.csv")
time_series_deaths <- read_csv("time_series_covid19_deaths_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0# Joining the time series tables
# first need to create the common column (key), which will be a column where we combine province, country, and date
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
head(time_series_confirmed_long)## # A tibble: 6 x 7
## Key Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/22/… 0
## 2 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/23/… 0
## 3 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/24/… 0
## 4 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/25/… 0
## 5 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/26/… 0
## 6 NA.Afghanistan.1/2… <NA> Afghanistan 33.9 67.7 1/27/… 0# do the same thing for deaths
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
head(time_series_deaths_long)## # A tibble: 6 x 2
## Key Deaths
## <chr> <dbl>
## 1 NA.Afghanistan.1/22/20 0
## 2 NA.Afghanistan.1/23/20 0
## 3 NA.Afghanistan.1/24/20 0
## 4 NA.Afghanistan.1/25/20 0
## 5 NA.Afghanistan.1/26/20 0
## 6 NA.Afghanistan.1/27/20 0# now join the tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 0which(is.na(time_series_long_joined$Confirmed))## integer(0)which(is.na(time_series_long_joined$Deaths))## integer(0)no NAs - great
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, uniontime_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# create a column using pivot_long
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
head(time_series_long_joined_counts)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Report_Type Counts
## <chr> <chr> <dbl> <dbl> <date> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 Confirmed 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-22 Deaths 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-23 Confirmed 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-23 Deaths 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-24 Confirmed 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-24 Deaths 0# Making graphs from the time series data
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
# some other countries
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Japan", "Korea, South",
"Italy","Spain", "US")) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")
# several countries on the same graph
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")
# cases and deaths on the same graph
time_series_long_joined_counts %>%
group_by(Country_Region, Report_Type, Date) %>%
summarise(Counts = sum(Counts)) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = log2(Counts), fill = Report_Type, color = Report_Type)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Cases")## `summarise()` regrouping output by 'Country_Region', 'Report_Type' (override with `.groups` argument)
Exercises
Finish the visualization tutorial: Lab 5A
Revise the above example for joining tables using 6/13/2020 and 9/13/2020. Plot the data as a bar plot. Make sure your graph looks nice in the report by adjusting the height in the R code chunk header.
# Read in the June data
june <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/06-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(june)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 24601 653 34660 12095 150018 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
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## ..- attr(*, ".drop")= logi TRUEsept <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(sept)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 138755 6268 208512 70219 761728 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
## .. ..$ : int 1
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## ..- attr(*, ".drop")= logi TRUE# Join the two dataframes
june_sept_joined <- full_join(june, sept, by = c("Province_State"))
str(june_sept_joined)## tibble [58 × 5] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State : chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region.x: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed.x : num [1:58] 24601 653 34660 12095 150018 ...
## $ Country_Region.y: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed.y : num [1:58] 138755 6268 208512 70219 761728 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
## .. ..$ : int 1
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## ..- attr(*, ".drop")= logi TRUE# look at the new joined dataframe
head(june_sept_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 24601 US 138755
## 2 Alaska US 653 US 6268
## 3 Arizona US 34660 US 208512
## 4 Arkansas US 12095 US 70219
## 5 California US 150018 US 761728
## 6 Colorado US 29002 US 61293tail(june_sept_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Virginia US 53869 US 133742
## 2 Washington US 25538 US 79826
## 3 West Virginia US 2274 US 12705
## 4 Wisconsin US 22518 US 89185
## 5 Wyoming US 1050 US 4346# look to see what cells are NA
which(is.na(june_sept_joined))## integer(0)# rename the columns
june_sept_joined <- full_join(june, sept, by = c("Province_State")) %>%
rename(Confirmed_June = "Confirmed.x", Confirmed_Sept = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y)
head(june_sept_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_June Confirmed_Sept
## <chr> <dbl> <dbl>
## 1 Alabama 24601 138755
## 2 Alaska 653 6268
## 3 Arizona 34660 208512
## 4 Arkansas 12095 70219
## 5 California 150018 761728
## 6 Colorado 29002 61293# pivot the data
june_sept_joined_long <- june_sept_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")
head(june_sept_joined_long)## # A tibble: 6 x 3
## # Groups: Province_State [3]
## Province_State Date Confirmed
## <chr> <chr> <dbl>
## 1 Alabama Confirmed_June 24601
## 2 Alabama Confirmed_Sept 138755
## 3 Alaska Confirmed_June 653
## 4 Alaska Confirmed_Sept 6268
## 5 Arizona Confirmed_June 34660
## 6 Arizona Confirmed_Sept 208512ggplot(june_sept_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date)) 
- Add more informative x and y labels and a title to the above graph in Ex2.
ggplot(june_sept_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date)) +
labs(x = "Confirmed Cases", y = "State/Province")
- Using the time series data. Make a plot of the total number of confirmed deaths per day worldwide.
# load time series death data again
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data_raw/time_series_covid19_deaths_global.csv")
time_series_deaths <- read_csv("data_raw/time_series_covid19_deaths_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.head(time_series_deaths)## # A tibble: 6 x 258
## Province_State Country_Region Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antigua and B… 17.1 -61.8 0 0 0
## # … with 251 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
## # `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>, `2/23/20` <dbl>, `2/24/20` <dbl>,
## # `2/25/20` <dbl>, `2/26/20` <dbl>, `2/27/20` <dbl>, `2/28/20` <dbl>,
## # `2/29/20` <dbl>, `3/1/20` <dbl>, `3/2/20` <dbl>, `3/3/20` <dbl>,
## # `3/4/20` <dbl>, `3/5/20` <dbl>, `3/6/20` <dbl>, `3/7/20` <dbl>,
## # `3/8/20` <dbl>, `3/9/20` <dbl>, `3/10/20` <dbl>, `3/11/20` <dbl>,
## # `3/12/20` <dbl>, `3/13/20` <dbl>, `3/14/20` <dbl>, `3/15/20` <dbl>,
## # `3/16/20` <dbl>, `3/17/20` <dbl>, `3/18/20` <dbl>, `3/19/20` <dbl>,
## # `3/20/20` <dbl>, `3/21/20` <dbl>, `3/22/20` <dbl>, `3/23/20` <dbl>,
## # `3/24/20` <dbl>, `3/25/20` <dbl>, `3/26/20` <dbl>, `3/27/20` <dbl>,
## # `3/28/20` <dbl>, `3/29/20` <dbl>, `3/30/20` <dbl>, `3/31/20` <dbl>,
## # `4/1/20` <dbl>, `4/2/20` <dbl>, `4/3/20` <dbl>, `4/4/20` <dbl>,
## # `4/5/20` <dbl>, `4/6/20` <dbl>, `4/7/20` <dbl>, `4/8/20` <dbl>,
## # `4/9/20` <dbl>, `4/10/20` <dbl>, `4/11/20` <dbl>, `4/12/20` <dbl>,
## # `4/13/20` <dbl>, `4/14/20` <dbl>, `4/15/20` <dbl>, `4/16/20` <dbl>,
## # `4/17/20` <dbl>, `4/18/20` <dbl>, `4/19/20` <dbl>, `4/20/20` <dbl>,
## # `4/21/20` <dbl>, `4/22/20` <dbl>, `4/23/20` <dbl>, `4/24/20` <dbl>,
## # `4/25/20` <dbl>, `4/26/20` <dbl>, `4/27/20` <dbl>, `4/28/20` <dbl>,
## # `4/29/20` <dbl>, `4/30/20` <dbl>, `5/1/20` <dbl>, `5/2/20` <dbl>,
## # `5/3/20` <dbl>, …time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0# reformat date
library(lubridate)
time_series_deaths_long$Date <- mdy(time_series_deaths_long$Date)
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <date> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-23 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-24 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-25 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-26 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-27 0time_series_deaths_long %>%
group_by(Date) %>%
summarize(Total_Deaths = sum(Deaths)) %>%
ggplot(aes(x = Date, y = Total_Deaths)) +
geom_line() +
ggtitle("Worldwide COVID-19 Deaths")## `summarise()` ungrouping output (override with `.groups` argument)
- Use Mutate to make a new column with deaths/confirmed cases (Hint: this might be best done after joining the tables, but before pivoting longer).
# lets look at the data set we want, which we made a bit ago
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <date> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 0 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-23 0 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-24 0 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-25 0 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-26 0 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-27 0 0# change the date format
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)## Warning: All formats failed to parse. No formats found.# now mutate to add column
mortality<-time_series_long_joined %>%
mutate(rate = Deaths/Confirmed)
head(mortality)## # A tibble: 6 x 8
## Province_State Country_Region Lat Long Date Confirmed Deaths rate
## <chr> <chr> <dbl> <dbl> <date> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN
## 2 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN
## 3 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN
## 4 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN
## 5 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN
## 6 <NA> Afghanistan 33.9 67.7 NA 0 0 NaN- Plot US deaths/confirmed cases per day. For some reason my code works - it produces th plot that I want - but it runs into an error when I knit.
#mortality %>%
# replace_na(list(rate = 0)) %>%
# filter(Country_Region == "US") %>%
# ggplot(aes(x = Date, y = rate)) +
# geom_line() +
# ggtitle("Mortality rate in the US (deaths per positive case)")- Make a single graph with from the times series data with the 10 countries with the hightest death total.
# first find the total deaths of the top ten countries
top_deaths<-time_series_deaths_long %>%
group_by(Country_Region) %>%
summarize(total_deaths = sum(Deaths)) %>%
arrange(desc(total_deaths)) %>%
slice_head(n=10)## `summarise()` ungrouping output (override with `.groups` argument)countries <- top_deaths$Country_Regiontime_series_deaths_long %>%
filter(Country_Region == countries) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_line(lwd = .7) +
ggtitle("COVID-19 Deaths in Top 10 Countries") ## Warning in Country_Region == countries: longer object length is not a multiple
## of shorter object length
I guess UK and France were reporting numbers once a week?
- Using Facet_Wraps make a graph from the times series data with the 10 countries with the hightest death total. Format the table to look nice in the report by changing the number of rows in your graph and/or adjusting the height in the R code chunk header.
time_series_deaths_long %>%
filter(Country_Region == countries) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")## Warning in Country_Region == countries: longer object length is not a multiple
## of shorter object length
- Using Facet_Wraps make a graph from the times series data for confirmed cases for all US states and territories. Format the table to look nice in the report by changing the number of rows in your graph and/or adjusting the height in the R code chunk header.
NEED TO FIND THIS DATA
Currently, the only time series data I have downloaded has all of US together (no states). The states data I have is cumulative total cases/deaths, not time series.
#data %>%
# filter(Country_Region == "US") %>%
# ggplot(aes(x = Date, y = Confirmed)) +
# geom_point() +
# geom_line() +
# ggtitle("COVID-19 Cases") +
# facet_wrap(~Province_State, ncol=2, scales="free_y")- Apply a theme other than the one used in the Data Carpentries tutorial to the graph in Ex8.
time_series_deaths_long %>%
filter(Country_Region == countries) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2) +
theme_classic()## Warning in Country_Region == countries: longer object length is not a multiple
## of shorter object length