covid19sf
The covid19sf package provides a daily summary of the covid19 cases in San Francisco. The package includes the following datasets:
-
covid19sf_age- Cases summarized by age group -
covid19sf_gender- Confirmed cases summarized by gender -
covid19sf_geo- Confirmed cases and deaths summarized by geography -
covid19sf_homeless- Confirmed cases by homelessness -
covid19sf_hospital- Hospital capacity data -
covid19sf_hospitalizations- Hospitalizations data -
covid19sf_housing- Alternative housing sites -
covid19sf_summary- Cases summarized by date, transmission and case disposition -
covid19sf_test_loc- Testing locations -
covid19sf_tests- Daily number of tests -
covid19sf_vaccine_demo- Summary of vaccine doses given to San Franciscans by demographics groups (age and race) -
covid19sf_vaccine_demo_ts- Time series view of vaccine doses given to San Franciscans by demographics groups (age and race) -
covid19sf_vaccine_geo- COVID-19 vaccines given to San Franciscans by geography -
covid19sf_population- COVID-19 cases by population characteristics over time -
covid19sf_demo[deprecated] - Cases summarized by date, transmission and case disposition
Data soucre: San Francisco, Department of Public Health - Population Health Division through the San Francisco Opne Data protal website
Installation
# install.packages("devtools")
devtools::install_github("RamiKrispin/covid19sf")Usage
The ccovid19sf package provides different views for the covid19 cases in San Francisco. That includes case distribution by age, gender, race, etc. The following examples demonstrate some of the data use cases.
library(covid19sf)Cases distribution by age
The covid19sf_age provides a daily summary of the cumulative positive cases by age group:
data(covid19sf_age)
head(covid19sf_age)
#> specimen_collection_date age_group new_confirmed_cases
#> 1 2020-03-13 25-29 3
#> 2 2020-03-14 25-29 2
#> 3 2020-03-15 25-29 1
#> 4 2020-03-16 25-29 1
#> 5 2020-03-17 25-29 0
#> 6 2020-03-18 25-29 3
#> cumulative_confirmed_cases last_updated
#> 1 7 2021-11-01 16:00:03
#> 2 9 2021-11-01 16:00:03
#> 3 10 2021-11-01 16:00:03
#> 4 11 2021-11-01 16:00:03
#> 5 11 2021-11-01 16:00:03
#> 6 14 2021-11-01 16:00:03The following box-plot shows the distribution of the positive cases by age group:
library(plotly)
covid19sf_age$age_group <- factor(covid19sf_age$age_group,
levels = c("0-4", "5-10",
"11-13", "14-17",
"18-20", "21-24",
"25-29",
"30-39", "40-49",
"50-59", "60-69",
"70-79", "80+"))
plot_ly(covid19sf_age,
color = ~ age_group,
y = ~ new_confirmed_cases,
boxpoints = "all",
jitter = 0.3,
pointpos = -1.8,
type = "box" ) %>%
layout(title = "Distribution of Daily New Covid Cases in San Francisco by Age Group",
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.9, y = 0.9))
Here is the overall distribution of cases by age group as of 2021-10-28:
library(dplyr)
library(plotly)
covid19sf_age %>%
filter(specimen_collection_date == max(specimen_collection_date)) %>%
plot_ly(values = ~ cumulative_confirmed_cases,
labels = ~ age_group,
type = "pie",
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste("Age Group:", age_group, "<br>",
"Total:", cumulative_confirmed_cases)) %>%
layout(title = "Total Cases Distribution by Age Group")
Geospatial visualiztion
The package provides several geo-spatial dataset:
-
covid19sf_vaccine_geo- COVID-19 vaccines given to San Franciscans by geography -
covid19sf_geo- Confirmed cases and deaths summarized by geography -
covid19sf_test_loc- Testing locations
Those three datasets are sf objects, ready to use. For example,
plotting the COVID19 vaccination data by geography:
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
data(covid19sf_vaccine_geo)
str(covid19sf_vaccine_geo)
#> Classes 'sf' and 'data.frame': 40 obs. of 9 variables:
#> $ id : chr "Castro/Upper Market" "Nob Hill" "Oceanview/Merced/Ingleside" "Outer Richmond" ...
#> $ area_type : chr "Analysis Neighborhood" "Analysis Neighborhood" "Analysis Neighborhood" "Analysis Neighborhood" ...
#> $ count_vaccinated_by_dph : num 1399 1795 4949 2692 17446 ...
#> $ count_vaccinated : num 18867 20246 23474 37192 37526 ...
#> $ count_series_completed : num 17459 18373 22067 34939 34765 ...
#> $ acs_population : num 22502 26445 27530 45921 37917 ...
#> $ percent_pop_series_completed: num 0.776 0.695 0.802 0.761 0.917 ...
#> $ last_updated : POSIXct, format: "2021-12-13 12:45:07" "2021-12-13 12:45:09" ...
#> $ geometry :sfc_MULTIPOLYGON of length 40; first list element: List of 1
#> ..$ :List of 1
#> .. ..$ : num [1:183, 1:2] -122 -122 -122 -122 -122 ...
#> ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
#> - attr(*, "sf_column")= chr "geometry"
#> - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA
#> ..- attr(*, "names")= chr [1:8] "id" "area_type" "count_vaccinated_by_dph" "count_vaccinated" ...
df <- covid19sf_vaccine_geo %>% filter(area_type == "Analysis Neighborhood") %>%
dplyr::mutate(perc_complated = percent_pop_series_completed * 100)We will plot the object Using the sf package:
plot(df[, c("perc_complated", "geometry")],
main = "San Francisco - Percentage of Fully Vaccinated Population by Geo",
key.pos = 1, axes = TRUE, key.width = lcm(1.2), key.length = 1.0)
More examples available on this vignette.
Tests results distribution
The covid19sf_tests provides a daily summary of the daily number of tests and their results (positive, negative, and indeterminate):
data(covid19sf_tests)
head(covid19sf_tests)
#> specimen_collection_date tests pos pct neg indeterminate
#> 1 2020-03-01 2 0 0.00000000 2 0
#> 2 2020-03-03 8 2 0.25000000 6 0
#> 3 2020-03-04 12 0 0.00000000 12 0
#> 4 2020-03-06 21 1 0.04761905 20 0
#> 5 2020-03-07 23 7 0.30434783 16 0
#> 6 2020-03-08 12 3 0.25000000 9 0
#> data_loaded_at last_updated
#> 1 2021-12-12T09:00:08.013 2021-12-11 16:35:02
#> 2 2021-12-12T09:00:10.082 2021-12-11 16:35:02
#> 3 2021-12-12T09:00:10.087 2021-12-11 16:35:02
#> 4 2021-12-12T09:00:10.090 2021-12-11 16:35:02
#> 5 2021-12-12T09:00:10.093 2021-12-11 16:35:02
#> 6 2021-12-12T09:00:10.095 2021-12-11 16:35:02The plot below shows the daily distribution of the results of the tests:
covid19sf_tests %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ pos,
name = "Positive",
type = 'scatter',
mode = 'none',
stackgroup = 'one',
fillcolor = "red") %>%
plotly::add_trace(y = ~ neg, name = "Negative", fillcolor = "green") %>%
plotly::add_trace(y = ~ indeterminate, name = "Indeterminate", fillcolor = "gray") %>%
plotly::layout(title = "Tests Results Distribution",
yaxis = list(title = "Tests Count"),
xaxis = list(title = "Source: San Francisco Department of Public Health"),
legend = list(x = 0.1, y = 0.9))
Cases distribution by race ethnicity
The covid19sf_population dataset provides a daily summary of the COVID19 positive cases by race and ethnicity:
data(covid19sf_population)
head(covid19sf_population)
#> specimen_collection_date characteristic_type characteristic_group
#> 1 2020-03-03 Age Group 0-4
#> 2 2020-03-03 Age Group 5-11
#> 3 2020-03-03 Age Group 12-17
#> 4 2020-03-03 Age Group 18-20
#> 5 2020-03-03 Age Group 21-24
#> 6 2020-03-03 Age Group 25-29
#> characteristic_group_sort_order new_cases cumulative_cases
#> 1 1 NA NA
#> 2 2 NA NA
#> 3 3 NA NA
#> 4 4 NA NA
#> 5 5 NA NA
#> 6 6 NA NA
#> population_estimate last_updated
#> 1 39353 2021-12-16 07:00:11
#> 2 44153 2021-12-16 07:00:15
#> 3 34664 2021-12-16 07:00:11
#> 4 20407 2021-12-16 07:00:11
#> 5 39944 2021-12-16 07:00:14
#> 6 100792 2021-12-16 07:00:42Below is a plot of the cumulative positive cases by race and ethnicity:
covid19sf_population %>%
filter(characteristic_type == "Race/Ethnicity") %>%
dplyr::arrange(specimen_collection_date) %>%
plotly::plot_ly(x = ~ specimen_collection_date,
y = ~ cumulative_cases,
# name = 'Cases',
type = 'scatter',
mode = 'none',
color = ~characteristic_group,
stackgroup = 'one') %>%
layout(title = "Total Cases Dist. by Race and Ethnicity",
legend = list(x = 0.05, y = 0.9),
yaxis = list(title = "Number of Cases", tickformat = ".0f"),
xaxis = list(title = "Source: San Francisco Department of Public Health"))
