README.md
In MatthiasUckert/Rrace: Predict race (also gender) form first name/last name pairings
Introduction
This packages standardizes the output for the R packages
wru,
predictrace,
and includes a python script and virtual environment setup for the
python library
ethnicolr.
Codes
library(rRace); library(reticulate); library(tidyverse); library(ggthemes)
#> -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
#> v dplyr 1.1.2 v readr 2.1.4
#> v forcats 1.0.0 v stringr 1.5.0
#> v ggplot2 3.4.2 v tibble 3.2.1
#> v lubridate 1.9.2 v tidyr 1.3.0
#> v purrr 1.0.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
We test the package with a build-in table “name_table” which includes
some random names.
tab_names <- name_table
head(tab_names)
#> # A tibble: 6 x 8
#> id first_name last_name gen age state county tract
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 144 Wynetta Palma female 40 AR 067 480500
#> 2 231 Bobbie Garcia female 41 FL <NA> <NA>
#> 3 449 Rebecca Beeson female 51 IA 043 070100
#> 4 465 Georgie Head female 35 KS 115 489500
#> 5 470 Joyce Langdon female 40 KY 029 020400
#> 6 682 Mary Guthrie female 44 KY 107 970200
Prediction based on predictrace
tab_prr <- race_prr(tab_names)
head(tab_prr)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian pblack
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> PRR-0~ 0.0042 0.266
#> 2 1 Michael POWELL male 47 AL <NA> <NA> PRR-1~ 0.0159 0.0214
#> 3 2 Cody Maupin male 35 AL 007 0100~ PRR-0~ 0.0053 0.0581
#> 4 2 Cody Maupin male 35 AL 007 0100~ PRR-1~ 0.0101 0.0121
#> 5 3 Kevin Neff male 48 AL 081 0421~ PRR-0~ 0.006 0.0092
#> 6 3 Kevin Neff male 48 AL 081 0421~ PRR-1~ 0.0324 0.0284
#> # i 4 more variables: phispa <dbl>, pwhite <dbl>, pother <dbl>, race <chr>
Prediction based on wru
First we have to download census data
download_census(
.key = "YOUR KEY",
.geo = "county",
.use_age = TRUE,
.use_gen = TRUE,
.dir = "_debug_data/census/",
.workers = 10,
.retry = 10,
.progress = TRUE,
.states = unique(tab_names$state)
)
#> All State-Age-Gender Census Data already Downloaded
tab_wru <- race_wru(
.tab = tab_names,
.use_geo = TRUE,
.use_age = TRUE,
.use_gen = TRUE,
.census_dir = "_debug_data/census/",
.census_geo = "county"
)
head(tab_wru)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-0~ 0.0046
#> 2 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 3 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 4 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 5 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 6 2 Cody Maupin male 35 AL 007 010003 WRU-0-1-0~ 0.00610
#> # i 5 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # race <chr>
Prediction based on ethnicolr
Python Setup
ToDO: Explain the Setup
.py_path <- filter(conda_list(), name == "env-ethnicolr")[[2]]
Sys.setenv(RETICULATE_PYTHON = .py_path)
reticulate::py_discover_config()
#> python: E:/Programs/anaconda202205/envs/env-ethnicolr/python.exe
#> libpython: E:/Programs/anaconda202205/envs/env-ethnicolr/python37.dll
#> pythonhome: E:/Programs/anaconda202205/envs/env-ethnicolr
#> version: 3.7.13 (default, Mar 28 2022, 08:03:21) [MSC v.1916 64 bit (AMD64)]
#> Architecture: 64bit
#> numpy: E:/Programs/anaconda202205/envs/env-ethnicolr/Lib/site-packages/numpy
#> numpy_version: 1.21.6
#>
#> NOTE: Python version was forced by RETICULATE_PYTHON
Load Function
The ethnicolr function is stored as a system file, you can easily load
it with the following command.
.path_py <- system.file("extdata/script_ethnicolr.py", package = "rRace")
reticulate::source_python(.path_py)
You can safely ignore the error about tensorflow (if present). It only
tells you that you don’t have a GPU setup on your machine.
As a reference the complete function (not necessary to execute it)
import os
import pandas as pd
import ethnicolr
def race_eth(tab, methods = ["CEL", "FLF", "FLL"]):
# CEL: CENSUS DATA, prediction on LAST NAME ------------------------------------------
if "CEL" in methods:
df1 = ethnicolr.pred_census_ln(df=tab, namecol="last_name")
cols = {'api': 'pasian', 'black': 'pblack', 'hispanic': 'phispa', 'white': 'pwhite'}
df1.rename(columns=cols, inplace=True)
df1["method"]="CEL-1-0-0-0-0-N"
else:
df1=pd.DataFrame()
# FLF: FLORIDA DATA, prediction on FULL NAME -----------------------------------------
if "FLF" in methods:
df2 = ethnicolr.pred_fl_reg_name(df=tab, lname_col="last_name", fname_col="first_name")
cols={'asian': 'pasian', 'nh_black': 'pblack', 'hispanic': 'phispa', 'nh_white': 'pwhite'}
df2.rename(columns=cols, inplace=True)
df2["method"]="FLF-1-1-0-0-0-N"
else:
df2=pd.DataFrame()
# FLL: FLORIDA DATA, prediction on LAST NAME -----------------------------------------
if "FLL" in methods:
df3 = ethnicolr.pred_fl_reg_ln(df=tab, namecol="last_name")
cols={'asian': 'pasian', 'nh_black': 'pblack', 'hispanic': 'phispa', 'nh_white': 'pwhite'}
df3.rename(columns=cols, inplace=True)
df3["method"]="FLL-1-1-0-0-0-N"
else:
df3=pd.DataFrame()
# combine dataframes to single dataframe ----------------------------------------------------
df_out=pd.concat([df1, df2, df3], ignore_index=True)
df_out["pother"]=0
# select and reorder columns ----------------------------------------------------------------
cols_old=list(tab.columns.values)
cols_old.remove("__name")
cols_new=["method", "pasian", "pblack", "phispa", "pwhite", "pother", "race"]
df_out=df_out[cols_old+cols_new]
# recode race values ------------------------------------------------------------------------
race_old=["hispanic", "nh_white", "nh_black"]
race_new=["hispa", "white", "black"]
df_out["race"]=df_out["race"].replace(race_old, race_new)
# return output -----------------------------------------------------------------------------
return df_out
Again, you can safely ignore the error about tensorflow (if present).
Prediction (Ethnicolr)
tab_eth <- as_tibble(race_eth(tab_names))
head(tab_eth)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian
#> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 144 Wynetta Palma female 40 AR 067 480500 CEL-1-0-0~ 0.0159
#> 2 231 Bobbie Garcia female 41 FL NA NA CEL-1-0-0~ 0.0134
#> 3 449 Rebecca Beeson female 51 IA 043 070100 CEL-1-0-0~ 0.00443
#> 4 465 Georgie Head female 35 KS 115 489500 CEL-1-0-0~ 0.00751
#> 5 470 Joyce Langdon female 40 KY 029 020400 CEL-1-0-0~ 0.0110
#> 6 682 Mary Guthrie female 44 KY 107 970200 CEL-1-0-0~ 0.00269
#> # i 5 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # race <chr>
Combine Predictions
tab_race <- race_predict(tab_prr, tab_wru, tab_eth)
head(tab_race)
#> # A tibble: 6 x 19
#> id first_name last_name gen age state county tract method pasian
#> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> PRR-1-0-0-0~ 0.0159
#> 2 1 Michael Powell male 47 AL NA NA FLF-1-1-0-0~ 0.00217
#> 3 1 Michael Powell male 47 AL NA NA CEL-1-0-0-0~ 0.00506
#> 4 1 Michael POWELL male 47 AL <NA> <NA> PRR-0-1-0-0~ 0.0042
#> 5 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-0-0~ 0.0046
#> 6 1 Michael Powell male 47 AL NA NA FLL-1-1-0-0~ 0.00940
#> # i 9 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # hp <dbl>, gd <dbl>, race <chr>, rank_hp <int>, rank_gd <int>
Output
Race based on Highest Prob.
tab_race %>%
filter(rank_hp == 1) %>%
count(race) %>%
ggplot(aes(race, n, fill = race)) +
geom_col() +
scale_fill_brewer(palette = "PuBu") +
ggthemes::geom_rangeframe(color = "black") +
ggthemes::theme_tufte()
Race based on Guess Diff.
tab_race %>%
filter(rank_gd == 1) %>%
count(race) %>%
ggplot(aes(race, n, fill = race)) +
geom_col() +
scale_fill_brewer(palette = "PuBu") +
ggthemes::geom_rangeframe(color = "black") +
ggthemes::theme_tufte()
MatthiasUckert/Rrace documentation built on Sept. 8, 2023, 3:26 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Introduction
This packages standardizes the output for the R packages wru, predictrace, and includes a python script and virtual environment setup for the python library ethnicolr.
Codes
library(rRace); library(reticulate); library(tidyverse); library(ggthemes)
#> -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
#> v dplyr 1.1.2 v readr 2.1.4
#> v forcats 1.0.0 v stringr 1.5.0
#> v ggplot2 3.4.2 v tibble 3.2.1
#> v lubridate 1.9.2 v tidyr 1.3.0
#> v purrr 1.0.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
We test the package with a build-in table “name_table” which includes some random names.
tab_names <- name_table
head(tab_names)
#> # A tibble: 6 x 8
#> id first_name last_name gen age state county tract
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 144 Wynetta Palma female 40 AR 067 480500
#> 2 231 Bobbie Garcia female 41 FL <NA> <NA>
#> 3 449 Rebecca Beeson female 51 IA 043 070100
#> 4 465 Georgie Head female 35 KS 115 489500
#> 5 470 Joyce Langdon female 40 KY 029 020400
#> 6 682 Mary Guthrie female 44 KY 107 970200
Prediction based on predictrace
tab_prr <- race_prr(tab_names)
head(tab_prr)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian pblack
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> PRR-0~ 0.0042 0.266
#> 2 1 Michael POWELL male 47 AL <NA> <NA> PRR-1~ 0.0159 0.0214
#> 3 2 Cody Maupin male 35 AL 007 0100~ PRR-0~ 0.0053 0.0581
#> 4 2 Cody Maupin male 35 AL 007 0100~ PRR-1~ 0.0101 0.0121
#> 5 3 Kevin Neff male 48 AL 081 0421~ PRR-0~ 0.006 0.0092
#> 6 3 Kevin Neff male 48 AL 081 0421~ PRR-1~ 0.0324 0.0284
#> # i 4 more variables: phispa <dbl>, pwhite <dbl>, pother <dbl>, race <chr>
Prediction based on wru
First we have to download census data
download_census(
.key = "YOUR KEY",
.geo = "county",
.use_age = TRUE,
.use_gen = TRUE,
.dir = "_debug_data/census/",
.workers = 10,
.retry = 10,
.progress = TRUE,
.states = unique(tab_names$state)
)
#> All State-Age-Gender Census Data already Downloaded
tab_wru <- race_wru(
.tab = tab_names,
.use_geo = TRUE,
.use_age = TRUE,
.use_gen = TRUE,
.census_dir = "_debug_data/census/",
.census_geo = "county"
)
head(tab_wru)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian
#> <int> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-0~ 0.0046
#> 2 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 3 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 4 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 5 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-1~ NA
#> 6 2 Cody Maupin male 35 AL 007 010003 WRU-0-1-0~ 0.00610
#> # i 5 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # race <chr>
Prediction based on ethnicolr
Python Setup
ToDO: Explain the Setup
.py_path <- filter(conda_list(), name == "env-ethnicolr")[[2]]
Sys.setenv(RETICULATE_PYTHON = .py_path)
reticulate::py_discover_config()
#> python: E:/Programs/anaconda202205/envs/env-ethnicolr/python.exe
#> libpython: E:/Programs/anaconda202205/envs/env-ethnicolr/python37.dll
#> pythonhome: E:/Programs/anaconda202205/envs/env-ethnicolr
#> version: 3.7.13 (default, Mar 28 2022, 08:03:21) [MSC v.1916 64 bit (AMD64)]
#> Architecture: 64bit
#> numpy: E:/Programs/anaconda202205/envs/env-ethnicolr/Lib/site-packages/numpy
#> numpy_version: 1.21.6
#>
#> NOTE: Python version was forced by RETICULATE_PYTHON
Load Function
The ethnicolr function is stored as a system file, you can easily load it with the following command.
.path_py <- system.file("extdata/script_ethnicolr.py", package = "rRace")
reticulate::source_python(.path_py)
You can safely ignore the error about tensorflow (if present). It only tells you that you don’t have a GPU setup on your machine.
As a reference the complete function (not necessary to execute it)
import os
import pandas as pd
import ethnicolr
def race_eth(tab, methods = ["CEL", "FLF", "FLL"]):
# CEL: CENSUS DATA, prediction on LAST NAME ------------------------------------------
if "CEL" in methods:
df1 = ethnicolr.pred_census_ln(df=tab, namecol="last_name")
cols = {'api': 'pasian', 'black': 'pblack', 'hispanic': 'phispa', 'white': 'pwhite'}
df1.rename(columns=cols, inplace=True)
df1["method"]="CEL-1-0-0-0-0-N"
else:
df1=pd.DataFrame()
# FLF: FLORIDA DATA, prediction on FULL NAME -----------------------------------------
if "FLF" in methods:
df2 = ethnicolr.pred_fl_reg_name(df=tab, lname_col="last_name", fname_col="first_name")
cols={'asian': 'pasian', 'nh_black': 'pblack', 'hispanic': 'phispa', 'nh_white': 'pwhite'}
df2.rename(columns=cols, inplace=True)
df2["method"]="FLF-1-1-0-0-0-N"
else:
df2=pd.DataFrame()
# FLL: FLORIDA DATA, prediction on LAST NAME -----------------------------------------
if "FLL" in methods:
df3 = ethnicolr.pred_fl_reg_ln(df=tab, namecol="last_name")
cols={'asian': 'pasian', 'nh_black': 'pblack', 'hispanic': 'phispa', 'nh_white': 'pwhite'}
df3.rename(columns=cols, inplace=True)
df3["method"]="FLL-1-1-0-0-0-N"
else:
df3=pd.DataFrame()
# combine dataframes to single dataframe ----------------------------------------------------
df_out=pd.concat([df1, df2, df3], ignore_index=True)
df_out["pother"]=0
# select and reorder columns ----------------------------------------------------------------
cols_old=list(tab.columns.values)
cols_old.remove("__name")
cols_new=["method", "pasian", "pblack", "phispa", "pwhite", "pother", "race"]
df_out=df_out[cols_old+cols_new]
# recode race values ------------------------------------------------------------------------
race_old=["hispanic", "nh_white", "nh_black"]
race_new=["hispa", "white", "black"]
df_out["race"]=df_out["race"].replace(race_old, race_new)
# return output -----------------------------------------------------------------------------
return df_out
Again, you can safely ignore the error about tensorflow (if present).
Prediction (Ethnicolr)
tab_eth <- as_tibble(race_eth(tab_names))
head(tab_eth)
#> # A tibble: 6 x 15
#> id first_name last_name gen age state county tract method pasian
#> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 144 Wynetta Palma female 40 AR 067 480500 CEL-1-0-0~ 0.0159
#> 2 231 Bobbie Garcia female 41 FL NA NA CEL-1-0-0~ 0.0134
#> 3 449 Rebecca Beeson female 51 IA 043 070100 CEL-1-0-0~ 0.00443
#> 4 465 Georgie Head female 35 KS 115 489500 CEL-1-0-0~ 0.00751
#> 5 470 Joyce Langdon female 40 KY 029 020400 CEL-1-0-0~ 0.0110
#> 6 682 Mary Guthrie female 44 KY 107 970200 CEL-1-0-0~ 0.00269
#> # i 5 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # race <chr>
Combine Predictions
tab_race <- race_predict(tab_prr, tab_wru, tab_eth)
head(tab_race)
#> # A tibble: 6 x 19
#> id first_name last_name gen age state county tract method pasian
#> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 1 Michael POWELL male 47 AL <NA> <NA> PRR-1-0-0-0~ 0.0159
#> 2 1 Michael Powell male 47 AL NA NA FLF-1-1-0-0~ 0.00217
#> 3 1 Michael Powell male 47 AL NA NA CEL-1-0-0-0~ 0.00506
#> 4 1 Michael POWELL male 47 AL <NA> <NA> PRR-0-1-0-0~ 0.0042
#> 5 1 Michael POWELL male 47 AL <NA> <NA> WRU-0-1-0-0~ 0.0046
#> 6 1 Michael Powell male 47 AL NA NA FLL-1-1-0-0~ 0.00940
#> # i 9 more variables: pblack <dbl>, phispa <dbl>, pwhite <dbl>, pother <dbl>,
#> # hp <dbl>, gd <dbl>, race <chr>, rank_hp <int>, rank_gd <int>
Output
Race based on Highest Prob.
tab_race %>%
filter(rank_hp == 1) %>%
count(race) %>%
ggplot(aes(race, n, fill = race)) +
geom_col() +
scale_fill_brewer(palette = "PuBu") +
ggthemes::geom_rangeframe(color = "black") +
ggthemes::theme_tufte()
Race based on Guess Diff.
tab_race %>%
filter(rank_gd == 1) %>%
count(race) %>%
ggplot(aes(race, n, fill = race)) +
geom_col() +
scale_fill_brewer(palette = "PuBu") +
ggthemes::geom_rangeframe(color = "black") +
ggthemes::theme_tufte()
R Package Documentation
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