Last updated: 2020-07-27
Checks: 1 1
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the HTML.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 2649bd4. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use
wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files: Ignored: .Rhistory Ignored: .Rproj.user/ Unstaged changes: Modified: analysis/about.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (
analysis/about.Rmd) and HTML (
docs/about.html) files. If you’ve configured a remote Git repository (see
?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
|html||abd9c55||Damon Bayer||2020-07-27||Update HTML|
|Rmd||01f6052||vnminin||2020-07-27||edits of About text|
|html||01f6052||vnminin||2020-07-27||edits of About text|
|html||84ab7e1||vnminin||2020-07-27||july 27 ca data update|
|html||bab42bc||vnminin||2020-07-26||triggering an update with latest data|
|Rmd||32001f7||igoldsteinh||2020-07-24||hopefully commented methods section|
|html||f08c6cc||Damon Bayer||2020-07-24||Watermark & Legend Fix|
|html||eb680ce||vnminin||2020-07-24||republishing to update plots|
|Rmd||f07587a||vnminin||2020-07-23||all plots start on the same date; moved subtitle to title|
|html||f07587a||vnminin||2020-07-23||all plots start on the same date; moved subtitle to title|
|html||9aa359e||vnminin||2020-07-23||shortenting title for friendlier mobile version|
|html||c14824c||Damon Bayer||2020-07-23||Remove Home text|
|Rmd||4d981d1||vnminin||2020-07-22||started editing about|
|html||4d981d1||vnminin||2020-07-22||started editing about|
|html||b4fb3ae||Damon Bayer||2020-07-21||update 2020-07-21|
|html||c95cee3||Damon Bayer||2020-07-20||Hide Workflowr buttons|
|html||5369804||Damon Bayer||2020-07-19||Update 2020-07-19|
|html||65c7195||Damon Bayer||2020-07-16||theme change|
|html||66110a1||Damon Bayer||2020-07-14||Initial Proof of Concept|
|Rmd||6151dc8||Damon Bayer||2020-07-14||Start workflowr project.|
This is an effort to provide timely and understandable COVID-19 data visualizations to the public in Orange County, California. The effort is coordinated by the UC Irvine COVID Awareness Group, consisting of students and faculty from the following units at UC Irvine.
Our figures present 7 day moving averages. This means that, for cases on July 7th, for example, we report the average number of cases per million people during the period July 1st to July 7th. On July 8th, we report the average number of cases per million people during the period July 2nd to July 8th, etc. We use moving averages as opposed to raw data (such as the actual number of cases per million people on July 7th) because cases can increase or decrease dramatically on a particular day, making it difficult to see trends in the time series of interest. Moving averages help highlight these trends more clearly.
In order to calculate, for example, cases per million people, we take the number of cases reported in a county for a day, divide by the 2019 census population for that county and then multiply by 1 million. We do this because different counties have very different population sizes (Los Angeles County has approximately 10 million while Orange County has approximately 3 million) and we want to provide numbers which are comparable across counties.
The number of daily new cases depend not only on the number of infected individuals in the population, but also on the number of tests performed. It would be much better to plot positivity ratio (positive cases divided by the total number of tests) instead of cases. Unfortunately, the number of Covid-19 diagnostic tests performed in a day is not available at a county level from the California Open Data Portal at this time. LA and OC counties do have testing data available (LA COVID-19 data, OC COVID-19 Data), but we prefer to use one data source for now.