README.md
In rossellhayes/leafpeepr: Prepare Raking Targets from Census Microdata
leafpeepr 
‘I’m sorry, leaf peeping? Is that something we do now?’ – President
Jed Bartlet, The West Wing
leafpeepr prepares data for weighting with raking packages like
autumn. It creates
weighting targets from census microdata. It can also recode values and
collapse values into an other category in both census data and survey
data.
Installation
# install.packages("remotes")
remotes::install_github("rossellhayes/leafpeepr")
Usage
Let’s get an example dataset of census microdata ready to be used as
targets for raking.
library(leafpeepr)
acs_nh
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 124 2 57 1 0 36 8
#> 2 18 2 35 1 0 33 10
#> 3 104 2 15 1 0 33 3
#> 4 213 2 31 1 0 25 10
#> 5 182 1 52 1 0 410 10
#> # ... with 1.378e+04 more rows
Recoding
leaf_recode() recodes columns using a data frame as a map.
acs_sex_codes
#> # A tibble: 2 x 2
#> code SEX
#> <dbl> <chr>
#> 1 1 Male
#> 2 2 Female
leaf_recode(acs_nh, acs_sex_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 124 Female 57 1 0 36 8
#> 2 18 Female 35 1 0 33 10
#> 3 104 Female 15 1 0 33 3
#> 4 213 Female 31 1 0 25 10
#> 5 182 Male 52 1 0 410 10
#> # ... with 1.378e+04 more rows
The recoding map can also use formulas.
acs_bpl_codes
#> # A tibble: 2 x 2
#> code BPL
#> <chr> <chr>
#> 1 ~ . < 120 United States
#> 2 ~ . >= 120 Another country
leaf_recode(acs_nh, acs_bpl_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 124 2 57 1 0 United States 8
#> 2 18 2 35 1 0 United States 10
#> 3 104 2 15 1 0 United States 3
#> 4 213 2 31 1 0 United States 10
#> 5 182 1 52 1 0 Another country 10
#> # ... with 1.378e+04 more rows
Or a combination of values and formulas.
acs_educ_codes
#> # A tibble: 4 x 2
#> code EDUC
#> <chr> <chr>
#> 1 ~ . %in% 0:5 Non-high school graduate
#> 2 6 High school graduate
#> 3 ~ . %in% 7:9 Some college
#> 4 ~ . %in% 10:11 College graduate
leaf_recode(acs_nh, acs_educ_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 124 2 57 1 0 36 Some college
#> 2 18 2 35 1 0 33 College graduate
#> 3 104 2 15 1 0 33 Non-high school graduate
#> 4 213 2 31 1 0 25 College graduate
#> 5 182 1 52 1 0 410 College graduate
#> # ... with 1.378e+04 more rows
You can recode multiple columns at once using wide or long data frames.
acs_codes
#> # A tibble: 8 x 12
#> code SEX code RACE code HISPAN code EDUC code BPL code AGE
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 Male 1 White 0 Not Hi~ ~ . ~ Non-h~ ~ . ~ Unit~ ~ . ~ 17 o~
#> 2 2 Female 2 Black ~ . %~ Hispan~ 6 High ~ ~ . ~ Anot~ ~ . ~ 18-23
#> 3 NA <NA> 3 AIAN 9 <NA> ~ . ~ Some ~ <NA> <NA> ~ . ~ 24-29
#> 4 NA <NA> ~ . %~ AAPI <NA> <NA> ~ . ~ Colle~ <NA> <NA> ~ . ~ 30-39
#> 5 NA <NA> 7 Other~ <NA> <NA> <NA> <NA> <NA> <NA> ~ . ~ 40-49
#> # ... with 3 more rows
leaf_recode(acs_nh, acs_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not Hispan~ United States Some college
#> 2 18 Female 30-39 White Not Hispan~ United States College graduate
#> 3 104 Female 17 or young~ White Not Hispan~ United States Non-high school gra~
#> 4 213 Female 30-39 White Not Hispan~ United States College graduate
#> 5 182 Male 50-59 White Not Hispan~ Another coun~ College graduate
#> # ... with 1.378e+04 more rows
acs_codes_long
#> # A tibble: 25 x 3
#> variable code value
#> <chr> <chr> <chr>
#> 1 SEX 1 Male
#> 2 SEX 2 Female
#> 3 RACE 1 White
#> 4 RACE 2 Black
#> 5 RACE 3 AIAN
#> # ... with 20 more rows
leaf_recode(acs_nh, acs_codes_long)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not Hispan~ United States Some college
#> 2 18 Female 30-39 White Not Hispan~ United States College graduate
#> 3 104 Female 17 or young~ White Not Hispan~ United States Non-high school gra~
#> 4 213 Female 30-39 White Not Hispan~ United States College graduate
#> 5 182 Male 50-59 White Not Hispan~ Another coun~ College graduate
#> # ... with 1.378e+04 more rows
Creating interaction variables
leaf_interact() creates an interaction between two variables.
acs_nh_recoded <- leaf_recode(acs_nh, acs_codes) %>%
janitor::clean_names() #Make column names nicer to look at
leaf_interact(acs_nh_recoded, race, hispan)
#> # A tibble: 13,780 x 8
#> perwt sex age race hispan bpl educ race_x_hispan
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not His~ United S~ Some college White x Not His~
#> 2 18 Female 30-39 White Not His~ United S~ College grad~ White x Not His~
#> 3 104 Female 17 or yo~ White Not His~ United S~ Non-high sch~ White x Not His~
#> 4 213 Female 30-39 White Not His~ United S~ College grad~ White x Not His~
#> 5 182 Male 50-59 White Not His~ Another ~ College grad~ White x Not His~
#> # ... with 1.378e+04 more rows
leaf_interactions() creates multiple interactions at once using a
list.
leaf_interactions(acs_nh_recoded, c("race", "educ"), c("sex", "age"))
#> # A tibble: 13,780 x 9
#> perwt sex age race hispan bpl educ race_x_educ sex_x_age
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not His~ Unite~ Some co~ White x Some ~ Female x 5~
#> 2 18 Female 30-39 White Not His~ Unite~ College~ White x Colle~ Female x 3~
#> 3 104 Female 17 or ~ White Not His~ Unite~ Non-hig~ White x Non-h~ Female x 1~
#> 4 213 Female 30-39 White Not His~ Unite~ College~ White x Colle~ Female x 3~
#> 5 182 Male 50-59 White Not His~ Anoth~ College~ White x Colle~ Male x 50-~
#> # ... with 1.378e+04 more rows
leaf_interact_all() creates interactions between one variable and all
other variables.
leaf_interact_all(acs_nh_recoded, sex, except = perwt)
#> # A tibble: 13,780 x 12
#> perwt sex age race hispan bpl educ age_x_sex race_x_sex hispan_x_sex
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Fema~ 50-59 White Not H~ Unit~ Some~ 50-59 x ~ White x F~ Not Hispani~
#> 2 18 Fema~ 30-39 White Not H~ Unit~ Coll~ 30-39 x ~ White x F~ Not Hispani~
#> 3 104 Fema~ 17 o~ White Not H~ Unit~ Non-~ 17 or yo~ White x F~ Not Hispani~
#> 4 213 Fema~ 30-39 White Not H~ Unit~ Coll~ 30-39 x ~ White x F~ Not Hispani~
#> 5 182 Male 50-59 White Not H~ Anot~ Coll~ 50-59 x ~ White x M~ Not Hispani~
#> # ... with 1.378e+04 more rows, and 2 more variables: bpl_x_sex <chr>,
#> # educ_x_sex <chr>
Generating a target data frame
Once our data is recoded, leaf_peep() prepares it to be used as
weighting targets in autumn::harvest()
acs_nh_interacted <- leaf_interactions(
acs_nh_recoded, c("race", "educ"), c("sex", "age")
)
leaf_peep(acs_nh_interacted, weight_col = perwt)
#> # A tibble: 64 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 sex Female 0.502
#> 2 sex Male 0.498
#> 3 age 17 or younger 0.183
#> 4 age 18-23 0.0825
#> 5 age 24-29 0.0729
#> # ... with 59 more rows
Collapsing categories
leaf_other() recategorizes levels into an other category if their
proportion is below a certain cutoff.
acs_nh_targets <- leaf_peep(acs_nh_interacted, weight_col = perwt)
dplyr::arrange(acs_nh_targets, proportion)
#> # A tibble: 64 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race_x_educ AIAN x Non-high school graduate 0.000280
#> 2 race_x_educ AIAN x High school graduate 0.000469
#> 3 race_x_educ AIAN x Some college 0.000626
#> 4 race_x_educ Other race x Some college 0.000830
#> 5 race_x_educ AIAN x College graduate 0.000881
#> 6 race_x_educ Other race x College graduate 0.00110
#> 7 race_x_educ Other race x High school graduate 0.00158
#> 8 race AIAN 0.00226
#> 9 race_x_educ Other race x Non-high school graduate 0.00256
#> 10 race_x_educ Black x College graduate 0.00261
#> # ... with 54 more rows
leaf_other(acs_nh_targets, 0.01) %>%
dplyr::arrange(proportion)
#> # A tibble: 44 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race_x_educ AAPI x College graduate 0.0127
#> 2 race Mixed race 0.0208
#> 3 race Other 0.0229
#> 4 race AAPI 0.0311
#> 5 sex_x_age Female x 24-29 0.0358
#> 6 sex_x_age Male x 24-29 0.0371
#> 7 hispan Hispanic 0.0386
#> 8 sex_x_age Male x 18-23 0.0402
#> 9 sex_x_age Female x 18-23 0.0423
#> 10 sex_x_age Male x 70 or older 0.0524
#> # ... with 34 more rows
If the other category would itself be under the cutoff proportion, the
next smallest level is added to the other category. To avoid this, set
inclusive = FALSE.
leaf_other(acs_nh_targets, 0.01, inclusive = FALSE) %>%
dplyr::arrange(proportion)
#> # A tibble: 45 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race Other 0.00833
#> 2 race_x_educ AAPI x College graduate 0.0127
#> 3 race Black 0.0146
#> 4 race Mixed race 0.0208
#> 5 race AAPI 0.0311
#> 6 sex_x_age Female x 24-29 0.0358
#> 7 sex_x_age Male x 24-29 0.0371
#> 8 hispan Hispanic 0.0386
#> 9 sex_x_age Male x 18-23 0.0402
#> 10 sex_x_age Female x 18-23 0.0423
#> # ... with 35 more rows
Credits
Hex sticker font is Source Code
Pro by
Adobe.
Image adapted from
publicdomainvectors.org
and Twemoji by
Twitter.
Please note that leafpeepr is released with a Contributor Code of
Conduct.
rossellhayes/leafpeepr documentation built on Feb. 29, 2020, 12:48 a.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.
leafpeepr
‘I’m sorry, leaf peeping? Is that something we do now?’ – President Jed Bartlet, The West Wing
leafpeepr prepares data for weighting with raking packages like autumn. It creates weighting targets from census microdata. It can also recode values and collapse values into an other category in both census data and survey data.
Installation
# install.packages("remotes")
remotes::install_github("rossellhayes/leafpeepr")
Usage
Let’s get an example dataset of census microdata ready to be used as targets for raking.
library(leafpeepr)
acs_nh
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 124 2 57 1 0 36 8
#> 2 18 2 35 1 0 33 10
#> 3 104 2 15 1 0 33 3
#> 4 213 2 31 1 0 25 10
#> 5 182 1 52 1 0 410 10
#> # ... with 1.378e+04 more rows
Recoding
leaf_recode() recodes columns using a data frame as a map.
acs_sex_codes
#> # A tibble: 2 x 2
#> code SEX
#> <dbl> <chr>
#> 1 1 Male
#> 2 2 Female
leaf_recode(acs_nh, acs_sex_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 124 Female 57 1 0 36 8
#> 2 18 Female 35 1 0 33 10
#> 3 104 Female 15 1 0 33 3
#> 4 213 Female 31 1 0 25 10
#> 5 182 Male 52 1 0 410 10
#> # ... with 1.378e+04 more rows
The recoding map can also use formulas.
acs_bpl_codes
#> # A tibble: 2 x 2
#> code BPL
#> <chr> <chr>
#> 1 ~ . < 120 United States
#> 2 ~ . >= 120 Another country
leaf_recode(acs_nh, acs_bpl_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 124 2 57 1 0 United States 8
#> 2 18 2 35 1 0 United States 10
#> 3 104 2 15 1 0 United States 3
#> 4 213 2 31 1 0 United States 10
#> 5 182 1 52 1 0 Another country 10
#> # ... with 1.378e+04 more rows
Or a combination of values and formulas.
acs_educ_codes
#> # A tibble: 4 x 2
#> code EDUC
#> <chr> <chr>
#> 1 ~ . %in% 0:5 Non-high school graduate
#> 2 6 High school graduate
#> 3 ~ . %in% 7:9 Some college
#> 4 ~ . %in% 10:11 College graduate
leaf_recode(acs_nh, acs_educ_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 124 2 57 1 0 36 Some college
#> 2 18 2 35 1 0 33 College graduate
#> 3 104 2 15 1 0 33 Non-high school graduate
#> 4 213 2 31 1 0 25 College graduate
#> 5 182 1 52 1 0 410 College graduate
#> # ... with 1.378e+04 more rows
You can recode multiple columns at once using wide or long data frames.
acs_codes
#> # A tibble: 8 x 12
#> code SEX code RACE code HISPAN code EDUC code BPL code AGE
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 Male 1 White 0 Not Hi~ ~ . ~ Non-h~ ~ . ~ Unit~ ~ . ~ 17 o~
#> 2 2 Female 2 Black ~ . %~ Hispan~ 6 High ~ ~ . ~ Anot~ ~ . ~ 18-23
#> 3 NA <NA> 3 AIAN 9 <NA> ~ . ~ Some ~ <NA> <NA> ~ . ~ 24-29
#> 4 NA <NA> ~ . %~ AAPI <NA> <NA> ~ . ~ Colle~ <NA> <NA> ~ . ~ 30-39
#> 5 NA <NA> 7 Other~ <NA> <NA> <NA> <NA> <NA> <NA> ~ . ~ 40-49
#> # ... with 3 more rows
leaf_recode(acs_nh, acs_codes)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not Hispan~ United States Some college
#> 2 18 Female 30-39 White Not Hispan~ United States College graduate
#> 3 104 Female 17 or young~ White Not Hispan~ United States Non-high school gra~
#> 4 213 Female 30-39 White Not Hispan~ United States College graduate
#> 5 182 Male 50-59 White Not Hispan~ Another coun~ College graduate
#> # ... with 1.378e+04 more rows
acs_codes_long
#> # A tibble: 25 x 3
#> variable code value
#> <chr> <chr> <chr>
#> 1 SEX 1 Male
#> 2 SEX 2 Female
#> 3 RACE 1 White
#> 4 RACE 2 Black
#> 5 RACE 3 AIAN
#> # ... with 20 more rows
leaf_recode(acs_nh, acs_codes_long)
#> # A tibble: 13,780 x 7
#> PERWT SEX AGE RACE HISPAN BPL EDUC
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not Hispan~ United States Some college
#> 2 18 Female 30-39 White Not Hispan~ United States College graduate
#> 3 104 Female 17 or young~ White Not Hispan~ United States Non-high school gra~
#> 4 213 Female 30-39 White Not Hispan~ United States College graduate
#> 5 182 Male 50-59 White Not Hispan~ Another coun~ College graduate
#> # ... with 1.378e+04 more rows
Creating interaction variables
leaf_interact() creates an interaction between two variables.
acs_nh_recoded <- leaf_recode(acs_nh, acs_codes) %>%
janitor::clean_names() #Make column names nicer to look at
leaf_interact(acs_nh_recoded, race, hispan)
#> # A tibble: 13,780 x 8
#> perwt sex age race hispan bpl educ race_x_hispan
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not His~ United S~ Some college White x Not His~
#> 2 18 Female 30-39 White Not His~ United S~ College grad~ White x Not His~
#> 3 104 Female 17 or yo~ White Not His~ United S~ Non-high sch~ White x Not His~
#> 4 213 Female 30-39 White Not His~ United S~ College grad~ White x Not His~
#> 5 182 Male 50-59 White Not His~ Another ~ College grad~ White x Not His~
#> # ... with 1.378e+04 more rows
leaf_interactions() creates multiple interactions at once using a
list.
leaf_interactions(acs_nh_recoded, c("race", "educ"), c("sex", "age"))
#> # A tibble: 13,780 x 9
#> perwt sex age race hispan bpl educ race_x_educ sex_x_age
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Female 50-59 White Not His~ Unite~ Some co~ White x Some ~ Female x 5~
#> 2 18 Female 30-39 White Not His~ Unite~ College~ White x Colle~ Female x 3~
#> 3 104 Female 17 or ~ White Not His~ Unite~ Non-hig~ White x Non-h~ Female x 1~
#> 4 213 Female 30-39 White Not His~ Unite~ College~ White x Colle~ Female x 3~
#> 5 182 Male 50-59 White Not His~ Anoth~ College~ White x Colle~ Male x 50-~
#> # ... with 1.378e+04 more rows
leaf_interact_all() creates interactions between one variable and all
other variables.
leaf_interact_all(acs_nh_recoded, sex, except = perwt)
#> # A tibble: 13,780 x 12
#> perwt sex age race hispan bpl educ age_x_sex race_x_sex hispan_x_sex
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 124 Fema~ 50-59 White Not H~ Unit~ Some~ 50-59 x ~ White x F~ Not Hispani~
#> 2 18 Fema~ 30-39 White Not H~ Unit~ Coll~ 30-39 x ~ White x F~ Not Hispani~
#> 3 104 Fema~ 17 o~ White Not H~ Unit~ Non-~ 17 or yo~ White x F~ Not Hispani~
#> 4 213 Fema~ 30-39 White Not H~ Unit~ Coll~ 30-39 x ~ White x F~ Not Hispani~
#> 5 182 Male 50-59 White Not H~ Anot~ Coll~ 50-59 x ~ White x M~ Not Hispani~
#> # ... with 1.378e+04 more rows, and 2 more variables: bpl_x_sex <chr>,
#> # educ_x_sex <chr>
Generating a target data frame
Once our data is recoded, leaf_peep() prepares it to be used as
weighting targets in autumn::harvest()
acs_nh_interacted <- leaf_interactions(
acs_nh_recoded, c("race", "educ"), c("sex", "age")
)
leaf_peep(acs_nh_interacted, weight_col = perwt)
#> # A tibble: 64 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 sex Female 0.502
#> 2 sex Male 0.498
#> 3 age 17 or younger 0.183
#> 4 age 18-23 0.0825
#> 5 age 24-29 0.0729
#> # ... with 59 more rows
Collapsing categories
leaf_other() recategorizes levels into an other category if their
proportion is below a certain cutoff.
acs_nh_targets <- leaf_peep(acs_nh_interacted, weight_col = perwt)
dplyr::arrange(acs_nh_targets, proportion)
#> # A tibble: 64 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race_x_educ AIAN x Non-high school graduate 0.000280
#> 2 race_x_educ AIAN x High school graduate 0.000469
#> 3 race_x_educ AIAN x Some college 0.000626
#> 4 race_x_educ Other race x Some college 0.000830
#> 5 race_x_educ AIAN x College graduate 0.000881
#> 6 race_x_educ Other race x College graduate 0.00110
#> 7 race_x_educ Other race x High school graduate 0.00158
#> 8 race AIAN 0.00226
#> 9 race_x_educ Other race x Non-high school graduate 0.00256
#> 10 race_x_educ Black x College graduate 0.00261
#> # ... with 54 more rows
leaf_other(acs_nh_targets, 0.01) %>%
dplyr::arrange(proportion)
#> # A tibble: 44 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race_x_educ AAPI x College graduate 0.0127
#> 2 race Mixed race 0.0208
#> 3 race Other 0.0229
#> 4 race AAPI 0.0311
#> 5 sex_x_age Female x 24-29 0.0358
#> 6 sex_x_age Male x 24-29 0.0371
#> 7 hispan Hispanic 0.0386
#> 8 sex_x_age Male x 18-23 0.0402
#> 9 sex_x_age Female x 18-23 0.0423
#> 10 sex_x_age Male x 70 or older 0.0524
#> # ... with 34 more rows
If the other category would itself be under the cutoff proportion, the
next smallest level is added to the other category. To avoid this, set
inclusive = FALSE.
leaf_other(acs_nh_targets, 0.01, inclusive = FALSE) %>%
dplyr::arrange(proportion)
#> # A tibble: 45 x 3
#> variable level proportion
#> <chr> <chr> <dbl>
#> 1 race Other 0.00833
#> 2 race_x_educ AAPI x College graduate 0.0127
#> 3 race Black 0.0146
#> 4 race Mixed race 0.0208
#> 5 race AAPI 0.0311
#> 6 sex_x_age Female x 24-29 0.0358
#> 7 sex_x_age Male x 24-29 0.0371
#> 8 hispan Hispanic 0.0386
#> 9 sex_x_age Male x 18-23 0.0402
#> 10 sex_x_age Female x 18-23 0.0423
#> # ... with 35 more rows
Credits
Hex sticker font is Source Code Pro by Adobe.
Image adapted from publicdomainvectors.org and Twemoji by Twitter.
Please note that leafpeepr is released with a Contributor Code of Conduct.
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.
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