refactor: IPD 2021 script update (#3)

* ipd review

* refactor: changed low_pop_tracts vector

* refactor: edited ipd script

* refactor: added fields to ipd output

* style: removed excess code

* refactor: renamed vars to match previous exports

* added summary tables and file exports

* chore: deleted python folder

* fix: environment variables

* fix: moe calculations

- removes the racial minority estimated MOE, which needs to be replaced by variance replicates

* fix: adds back variance replicates for MOE calc of RM indicator

* fix: add input settings at top of script

* fix: remove white population vars as unused

* chore: documentation

---------

Co-authored-by: Michael Ruane <mruane@dvrpc.org>

Author: brcarney

.gitignore

@@ -1,5 +1,5 @@
 # environment variables
-.env 
+.env
 .Renviron
 
 # Output files
@@ -46,3 +46,4 @@ rsconnect/
 /metadocumentation
 /compare_2015
 /compare_2016
+/data

README.md

@@ -4,13 +4,13 @@ This project automates DVRPC's Indicators of Potential Disadvantage (IPD) analys
 
 ## Getting the Code and Software
 
-1. Clone the repository. 
+1. Clone the repository.
 2. Download and install R from https://www.r-project.org/
 3. Download and install R Studio from https://www.rstudio.com/products/rstudio/#Desktop
 
-## Installing Package Dependencies 
+## Installing Package Dependencies
 
-The R script has the following dependencies: 
+The R script has the following dependencies:
 
 - plyr
 - here
@@ -22,32 +22,33 @@ The R script has the following dependencies:
 - dplyr
 - descr
 
-If you have not previously installed the dependencies, you will need to do so. If you try to run the script without installing the packages, you will get an error message like 
+If you have not previously installed the dependencies, you will need to do so. If you try to run the script without installing the packages, you will get an error message like
 `Error in library (name_of_package) : there is no package called 'name_of_package'`.
 
-Install each package from R Studio's console (typically at the bottom of the screen in R Studio) with the command  `install.packages('name_of_package')` (include the quotation marks). 
+Install each package from R Studio's console (typically at the bottom of the screen in R Studio) with the command `install.packages('name_of_package')` (include the quotation marks).
 
 ## Updating the Script for a New 5-Year Dataset
 
 If you are running the code against a newly released 5-year ACS dataset, do the following:
 
-1. Make a copy of the latest .R file (e.g. script-2019.R) and rename it for the year you are working on. (This is to ensure that any schema changes for a particular 5-year dataset are kept with the code for that set.)
-2. Adjust the value for the `ipd_year` variable (to be the end year of the dataset).
+1. Update the `ipd_year` in the `ipd.r` to be the end year of the dataset.
+2. Update the `output_dir` with the output location for the files.
 3. Verify the field names (listed under the `# Fields` section). Follow the link provided to check the schema for that dataset.
 
 ## Running the Code
 
-1. Open RStudio. 
+1. Open RStudio.
 2. Open the R file (File -> Open File)
-3. Run the code by clicking the Source button or Ctrl+A followed by Ctrl+Enter. 
- 
-If you see an error about packages not being installed, see [Installing Package Dependencies](#installing-package-dependencies) above. 
+3. Run the code by clicking the Source button or Ctrl+A followed by Ctrl+Enter.
+
+If you see an error about packages not being installed, see [Installing Package Dependencies](#installing-package-dependencies) above.
 
 Please provide your own API Key (this is required for the `tidycensus` package, not the Census API), you may get another one [here](https://api.census.gov/data/key_signup.html).
 
-### Outputs 
+### Outputs
 
 After the code has finished, outputs are saved in the /outputs subdirectory of where you cloned the repository on your local machine, including:
+
 - ipd.csv: tract-level statistics and scores for IPD's nine indicators
 - ipd.shp: spatial version of ipd.csv
 - breaks_by_indicator.csv: bin breaks by indicator
@@ -56,5 +57,6 @@ After the code has finished, outputs are saved in the /outputs subdirectory of w
 - mean_by_county.csv: population-weighted county means by indicator
 
 ## Additional Information
-- [documentation/discussion.pdf](https://github.com/dvrpc/ipd/blob/master/documentation/discussion.pdf) shows the essential math required to compute IPD scores. 
+
+- [documentation/discussion.pdf](https://github.com/dvrpc/ipd/blob/master/documentation/discussion.pdf) shows the essential math required to compute IPD scores.
 - [documentation/script_reference.pdf](https://github.com/dvrpc/ipd/blob/master/documentation/script_reference.pdf) is a companion document to the script and explains the way the script downloads data and implements IPD analysis, code chunk by code chunk.

documentation/variables.csv

@@ -21,7 +21,7 @@ older_adults_universe,S0101_C01_001,S0101_C01_001,S0101_C01_001,S0101_C01_001,S0
 older_adults_count,DP05_0025E,DP05_0025E,DP05_0025E,S0101_C01_030,S0101_C01_030,S0101_C01_030,S0101_C01_030
 older_adults_percent,S0101_C02_028,S0101_C02_028,S0101_C02_030,S0101_C02_030,S0101_C02_030,S0101_C02_030,S0101_C02_030
 racial_minority_universe,B02001_001,B02001_001,B02001_001,B02001_001,B02001_001,B02001_001,B02001_001
-racial_minority_count,B02001_002,B02001_002,B02001_002,B02001_002,B02001_002,B02001_002,B02001_002
+racial_minority_count,B02001_002,B02001_002,B02001_002,B02001_002,B02001_002,B02001_002,B02001_003...B02001_008
 racial_minority_percent,NA,NA,NA,NA,NA,NA,NA
 youth_universe,B03002_001,B03002_001,B03002_001,B03002_001,B03002_001,B03002_001,B03002_001
 youth_count,B09001_001,B09001_001,B09001_001,B09001_001,B09001_001,B09001_001,B09001_001