The RaceCounts Package contains user-defined functions for analysis we do repeatedly when prepping an indicator script. For example, calculating an index of disparity or a quadrant value.
You can install the development version of RaceCounts like so:
#remotes::install_github("catalystcalifornia/RC")
library(RC)
This is a personal package we use for Race Counts indicator prep. It contains 10 helpful functions that allows us to be more efficient with our code. These functions are:
- count_values: Calculate the number of raced rate values.*
- calc_best: Calculate the best rate.
- calc_diff: Calculate the difference from the best.
- calc_avg_diff: Calculate average difference from the best.
- calc_s_var: Calculate sample variance of differences from the best.
- calc_p_var: Calculate population variance of differences from the best.
- calc_id: Calculate index of disparity.
- calc_state_z: Calculate state disparity z-scores.
- calc_z: Calculate county disparity and performance z-scores.
- calc_ranks: Calculate disparity and performance ranks and quadrants.
To use the following RC Functions, 'd' will need the following columns at minimum:
- geoid and total and raced "_rate" and total and raced "_raw" (following RC naming conventions) columns. If you use a rate calc function, you will need _pop and _raw columns as well.
- Any pop screens or data reliability screens (like CV) should be complete before you get to RC Functions.
- Your dataframe must also contain both county and state (eventually city where available) data.
- Your final table export(s) must have either county_id/county_name, state_id/state_name, or city_id/city_name fields.
- Your final table export(s) should contain cv values if you used a cv screen, same for _pop columns if you used a pop screen.
Suppose we have these values:
d <- data.frame (geoid = c("06037", "06001", "06113", "06101", "06091"),
geoname = c("Los Angeles", "Alameda", "Yolo", "Sutter", "Sierra"),
total_rate = c("35.70", "70.03", "23.99", "65.93", "29.55"),
aian_rate = c("44.9", "45", "48.99", "77", "86.55"),
black_rate = c("65.70", "40.03", "42.99", "54.93", "82.55"),
latino_rate = c("77.70", "48.04", "40.99", "82.93", "92.55"),
nh_white_rate = c("23.70", "50.03", "45.99", "23.93", "12.55"),
asian_rate = c(NA, "80.03", "2.99", "2.93", "32.55"))
| county_id | county_name | total_rate | aian_rate | black_rate | latino_rate | nh_white_rate | asian_rate |
|---|---|---|---|---|---|---|---|
| 06037 | Los Angeles | 35.70 | 44.9 | 65.70 | 77.70 | 23.70 | NA |
| 06001 | Alameda | 70.03 | 45 | 40.03 | 48.04 | 50.03 | 80.03 |
| 06113 | Yolo | 28.99 | 48.99 | 42.99 | 40.99 | 45.99 | 2.99 |
| 06101 | Sutter | 65.93 | 77 | 54.93 | 82.93 | 23.93 | 2.93 |
| 06091 | Sierra | 29.55 | 86.55 | 82.55 | 92.55 | 12.55 | 32.55 |
Now let's use one of the functions. For example, let's calculate the number of raced rate values per county.
d$asbest = 'max' #YOU MUST UPDATE THIS FIELD AS NECESSARY: assign 'min' or 'max'
d <- count_values(d) #calculate number of "_rate" values
We should now have this:
| county_id | county_name | total_rate | aian_rate | black_rate | latino_rate | nh_white_rate | asian_rate | values_count |
|---|---|---|---|---|---|---|---|---|
| 06037 | Los Angeles | 35.70 | 44.9 | 65.70 | 77.70 | 23.70 | 0 | 4 |
| 06001 | Alameda | 70.03 | 45 | 40.03 | 48.04 | 50.03 | 80.03 | 5 |
| 06113 | Yolo | 28.99 | 48.99 | 42.99 | 40.99 | 45.99 | 2.99 | 5 |
| 06101 | Sutter | 65.93 | 77 | 54.93 | 82.93 | 23.93 | 2.93 | 5 |
| 06091 | Sierra | 29.55 | 86.55 | 82.55 | 92.55 | 12.55 | 32.55 | 5 |
Research and Data Analyst Team - rda[at]catalystcalifornia.org
Click here to view the RACE COUNTS Github Repo
To cite RACE COUNTS, please use the following:
Catalyst California; RACE COUNTS, racecounts.org, [current year].