covid19-philadelphia

KO | EN

gitlite — search

#hacktoberfest #ai #llm #go #golang #machine-learning #rust #macos #windows #cli #awesome-list #pytorch #security #ios

covid19-philadelphia

(★ 13)

De-identified, aggregate datasets showing COVID-19 cases, hospitalizations, deaths and vaccinations by date, zip, or age/sex/race as made available by the City of Philadelphia through its Open Data Program.

covid19-philadelphia 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

covid19-philadelphia
================

### About this repo

De-identified, aggregate datasets showing COVID-19 cases,
hospitalizations, deaths and vaccinations by date, zip, or age/sex/race
as made available by the City of Philadelphia through its Open Data
Program.

The data in this repository is a history of daily snapshots made
available here:
<https://www.opendataphilly.org/showcase/philadelphia-covid-19-information>

The following datasets have been collected since 6/4/2020:

- COVID Cases by Age
- COVID Cases by Date
- COVID Cases by Sex
- COVID Cases by Zipcode
- COVID Deaths by Date
- COVID Deaths by Sex/Age
- COVID Deaths by Zipcode

These have been collected since 8/14/2020:

- COVID Cases by Race
- COVID Deaths by Race
- COVID Hospitalizations by Age
- COVID Hospitalizations by Date
- COVID Hospitalizations by Race
- COVID Hospitalizations by Sex
- COVID Hospitalizations by Zipcode

These have been collected since 3/21/2021:

- COVID Vaccinations Total
- COVID Vaccinations by Age
- COVID Vaccinations by Race
- COVID Vaccinations by Sex
- COVID Vaccinations by Zipcode

Data reporting changes:

- Starting on September 29, 2020, COVID Cases by Date are reported by
  test specimen collection date; prior to this date, these were reported
  by test result date. This repository keeps the entire history with
  test result date showing null and test specimen collection date
  populated after this change.
- Beginning May 3, 2021, the Health Department is reporting testing data
  twice weekly around 1:30 p.m. every Monday and Thursday. This
  repository will continue to run daily updates with the latest
  information posted by 2:00 p.m.

Questions about these datasets? Visit the OpenDataPhilly Discussion
Group: <https://groups.google.com/forum/#!forum/opendataphilly>

For terms of use:
<https://www.opendataphilly.org/organization/about/city-of-philadelphia>

#### No more updates will be published after 6/4/2023. Users are encouraged to clone the repo and set up daily cron jobs to collect data on their own beyond this date.

------------------------------------------------------------------------

### How to use

To get started reading and analyzing the data:

- Clone this repo
- If you use R/RStudio, open a new project based on an existing folder,
  pointing to the local copy of the repo
- Setup the following environment variable in \~/.Renviron, completing
  /\<…\>/ based on the repo location in your local folder structure:

COVID19PHILLY_DIR='~/<...>/ambientpointcorp/covid19-philadelphia/'

- Set up cron jobs in your local machine with the cronR or taskscheduleR
  package:
  <https://cran.r-project.org/web/packages/cronR/vignettes/cronR.html>

library(cronR)
    library(stringr)

# Daily cron job to fetch cases
    cmd_cases <- cron_rscript(rscript = str_c(Sys.getenv("COVID19PHILLY_DIR"),
                                              "philadelphia_covid19_cases_cron.R"))
    cron_add(command = cmd_cases, frequency = 'daily', at='2PM', id = 'covid19_cases')

# Daily cron job to fetch deaths
    cmd_deaths <- cron_rscript(rscript = str_c(Sys.getenv("COVID19PHILLY_DIR"),
                                               "philadelphia_covid19_deaths_cron.R"))
    cron_add(command = cmd_deaths, frequency = 'daily', at='2PM', id = 'covid19_deaths')

# Daily cron job to fetch hospitalizations
    cmd_hosp <- cron_rscript(rscript = str_c(Sys.getenv("COVID19PHILLY_DIR"),
                                      "philadelphia_covid19_hospitalizations_cron.R"))
    cron_add(command = cmd_hosp, frequency = 'daily', at='2PM', id = 'covid19_hospitalizations')

# Daily cron job to fetch vaccinations
    cmd_vaccs <- cron_rscript(rscript = str_c(Sys.getenv("COVID19PHILLY_DIR"),
                                          "philadelphia_covid19_vaccinations_cron.R"))
    cron_add(command = cmd_vaccs, frequency = 'daily', at='2PM', id = 'covid19_vaccinations')

# Check scheduled jobs
    cron_njobs()
    cron_ls()

------------------------------------------------------------------------

### Read all the files within a folder as a historical dataset, for example:

``` r
library(tidyverse)

# Stack daily files within folder and keep distinct records
build_historical_dataset <- function(data_folder) {
  list.files(path = data_folder, full.names = TRUE) %>%
    map_dfr(read_csv) %>%
    distinct() # duplicates occur when there is an extract but no data update
}

# Cases by test result and reporting dates
cases_by_date <- build_historical_dataset("cases_by_date")
cases_by_date
```

## # A tibble: 285,444 × 6
    ##    result_date etl_timestamp       positive negative collection_date objectid
    ##    <date>      <dttm>                 <dbl>    <dbl> <date>             <dbl>
    ##  1 2020-03-27  2020-06-04 17:20:02      222      769 NA                    NA
    ##  2 2020-05-05  2020-06-04 17:20:02      362     1252 NA                    NA
    ##  3 2020-05-30  2020-06-04 17:20:02      126     1525 NA                    NA
    ##  4 2020-03-24  2020-06-04 17:20:02      115      477 NA                    NA
    ##  5 2020-04-26  2020-06-04 17:20:02      204      620 NA                    NA
    ##  6 2020-05-23  2020-06-04 17:20:02      192     1535 NA                    NA
    ##  7 2020-04-16  2020-06-04 17:20:02      524      772 NA                    NA
    ##  8 2020-05-01  2020-06-04 17:20:02      414     1104 NA                    NA
    ##  9 2020-05-20  2020-06-04 17:20:02      193     1697 NA                    NA
    ## 10 2020-04-24  2020-06-04 17:20:02      490     1147 NA                    NA
    ## # … with 285,434 more rows

``` r
# Cases by zip code and reporting date
cases_by_zipcode <- build_historical_dataset("cases_by_zipcode")
cases_by_zipcode
```

## # A tibble: 43,678 × 5
    ##    zip_code etl_timestamp         NEG   POS objectid
    ##       <dbl> <dttm>              <dbl> <dbl>    <dbl>
    ##  1    19122 2020-06-01 17:20:02  1018   245       NA
    ##  2    19101 2020-06-01 17:20:02    36    27       NA
    ##  3    19146 2020-06-01 17:20:02  2515   438       NA
    ##  4    19153 2020-06-01 17:20:02   610   197       NA
    ##  5    19136 2020-06-01 17:20:02  4456   894       NA
    ##  6    19143 2020-06-01 17:20:02  3805  1019       NA
    ##  7    19152 2020-06-01 17:20:02  1473   601       NA
    ##  8    19125 2020-06-01 17:20:02  1117   204       NA
    ##  9    19106 2020-06-01 17:20:02   589    55       NA
    ## 10    19132 2020-06-01 17:20:02  1720   573       NA
    ## # … with 43,668 more rows

### Analysis: COVID-19’s incidence and effective reproductive number in Philly

``` r
library(lubridate)
library(EpiEstim)

# Daily case count by test result date
incidence_data <- list.files(path = "cases_by_date", full.names = TRUE) %>% 
  last %>% 
  read_csv(col_types = cols(collection_date = col_character(),
                            etl_timestamp = col_skip(),
                            negative = col_integer(),
                            positive = col_integer())) %>% 
  filter(!is.na(positive) & (date(collection_date) >= date("2020-03-16"))) %>%
  mutate(dates = date(collection_date)) %>%
  arrange(dates) %>%
  mutate(positivity_rate = positive / (positive + negative)) %>%
  select(dates, positive, negative, positivity_rate) %>%
  filter(dates <= last(dates) - 3) # remove last 3 days considering lag in test results

# Plot incidence and effective reproductive number over time
# Serial interval mean and std estimates from: https://www.dhs.gov/publication/st-master-question-list-covid-19
res_parametric_si <- estimate_R(incidence_data %>% 
                                  select(dates, I = positive),
                                method="parametric_si",
                                config = make_config(list(mean_si = 5.29, std_si = 5.32)))
plot(res_parametric_si, legend = FALSE)
```

![](https://raw.githubusercontent.com/ambientpointcorp/covid19-philadelphia/master/README_files/figure-gfm/unnamed-chunk-2-1.png)

covid19-philadelphia

covid19-philadelphia 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

covid19-philadelphia

covid19-philadelphia 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README