tweaking vignettes

R/calculate_custom_geographies.R

@@ -433,7 +433,8 @@ calculate_custom_geographies = function(
           paste0(definition, " [Percentage recalculated from summed components.]"),
         aggregation_strategy == "weighted_average" ~
           paste0(definition, " [Aggregated via population-weighted average using ", weight_variable, ".]"),
-        TRUE ~ definition))
+        TRUE ~ definition)) %>%
+    dplyr::select(calculated_variable, variable_type, definition, dplyr::everything())
 
   attr(result, "codebook") = updated_codebook
 

R/compile_acs_data.R

@@ -332,7 +332,8 @@ this function returns.")}
   })})
 
   ## attach the codebook and resolved tables as attributes to the returned dataset
-  attr(df_cvs, "codebook") = codebook
+  attr(df_cvs, "codebook") = codebook %>%
+    dplyr::select(calculated_variable, variable_type, definition, dplyr::everything())
   attr(df_cvs, "resolved_tables") = resolved_tables
 
   if (isTRUE(spatial)) { df_cvs = sf::st_as_sf(df_cvs) }

README.Rmd

@@ -37,8 +37,11 @@ the American Community Survey (ACS).
 
 With a single function call, you get:
 
--   Access to hundreds of standardized variables, such as percentages, in addition
-    to the raw count variables used to produce them.
+-   Access to hundreds of standardized variables, such as percentages and
+    the raw count variables used to produce them.
+
+-   Margins of error and coefficients of variation for all
+    variables--those direct from the API as well as derived variables.
 
 -   Meaningful, consistent variable names.
 
@@ -50,23 +53,23 @@ With a single function call, you get:
 -   Supplemental measures, such as population density, that aren't
     available from the ACS.
 
--   Built-in quality checks to help ensure that calculated variables are
-    accurate. Plus some good, old-fashioned manual QC.
+-   Built-in quality checks to help ensure that calculated variables 
+    and measures of error are accurate. Plus some good, old-fashioned manual QC.
+    That said--use at your own risk. We cannot and do not guarantee there aren't bugs.
+
 
--   Margins of error and coefficients of variation for all
-    variables--those direct from the API as well as derived variables.
 
 # Installation
 
 Install the development version of `urbnindicators` from
 [GitHub](https://github.com/) with:
 
 ```r
-install.packages("renv")
+# install.packages("renv")
 renv::install("UI-Research/urbnindicators")
 ```
 
-You'll need a Census API key
+You'll want a Census API key
 ([request one here](https://api.census.gov/data/key_signup.html)).
 Set it once with:
 
@@ -95,8 +98,11 @@ library(urbnindicators)
 ## Discover Available Data
 
 ```{r, warning = FALSE, message = FALSE}
-list_tables()
-list_variables()
+list_tables() |> head(10)
+```
+
+```{r, warning = FALSE, message = FALSE}
+list_variables() |> head(10)
 ```
 
 ## Obtain Data
@@ -112,7 +118,7 @@ df = compile_acs_data(
   geography = "county",
   states = "NJ")
 
-glimpse(df)
+glimpse(df) |> head(10)
 ```
 
 ## Visualize Data
@@ -136,8 +142,9 @@ plot_data = df %>%
     data_source_year = factor(data_source_year))
 
 state_averages = plot_data %>%
-  group_by(data_source_year) %>%
-  summarize(mean_pct = mean(race_personofcolor_percent)) %>%
+  summarize(
+    .by = data_source_year,
+    mean_pct = mean(race_personofcolor_percent)) %>%
   arrange(data_source_year) %>%
   pull(mean_pct)
 
@@ -156,9 +163,7 @@ dumbbell_data = plot_data %>%
     names_from = data_source_year,
     values_from = race_personofcolor_percent,
     names_prefix = "year_")
-```
 
-```{r, warning = FALSE, message = FALSE}
 ggplot() +
   geom_segment(
     data = dumbbell_data,
@@ -210,7 +215,7 @@ ggplot() +
     title = "All NJ Counties Experienced Racial Diversification from 2019 to 2024",
     subtitle = paste0("Share of population who are people of color, by county, 2019-2024
 Confidence intervals are presented around each point but are extremely small"),
-    x = "County",
+    x = "",
     y = "Share of population who are people of color") +
   scale_x_discrete(expand = expansion(mult = c(.03, .04))) +
   scale_y_continuous(
@@ -223,8 +228,7 @@ Confidence intervals are presented around each point but are extremely small"),
 
 # Learn More
 
-A growing number of vignettes aim to support users in effectively using
-this package. These vignettes include:
+Check out the vignettes for additional details:
 
 -   A package overview to help users [**Get
     Started**](articles/urbnindicators.html).
@@ -241,6 +245,10 @@ this package. These vignettes include:
     Error**](articles/quantified-survey-error.html) can improve
     inference making.
 
+-   You can re-create your indicators and their measures of error
+    for [**Custom Geographies**](articles/custom-geographies.html).
+    Neighborhoods? Unincorporated counties? Start here.
+
 # Credits
 
 This package is built on top of and enormously indebted to

README.md

@@ -21,8 +21,11 @@ the American Community Survey (ACS).
 
 With a single function call, you get:
 
-- Access to hundreds of standardized variables, such as percentages, in
-  addition to the raw count variables used to produce them.
+- Access to hundreds of standardized variables, such as percentages and
+  the raw count variables used to produce them.
+
+- Margins of error and coefficients of variation for all variables–those
+  direct from the API as well as derived variables.
 
 - Meaningful, consistent variable names.
 
@@ -34,23 +37,22 @@ With a single function call, you get:
 - Supplemental measures, such as population density, that aren’t
   available from the ACS.
 
-- Built-in quality checks to help ensure that calculated variables are
-  accurate. Plus some good, old-fashioned manual QC.
-
-- Margins of error and coefficients of variation for all variables–those
-  direct from the API as well as derived variables.
+- Built-in quality checks to help ensure that calculated variables and
+  measures of error are accurate. Plus some good, old-fashioned manual
+  QC. That said–use at your own risk. We cannot and do not guarantee
+  there aren’t bugs.
 
 # Installation
 
 Install the development version of `urbnindicators` from
 [GitHub](https://github.com/) with:
 
 ``` r
-install.packages("renv")
+# install.packages("renv")
 renv::install("UI-Research/urbnindicators")
 ```
 
-You’ll need a Census API key ([request one
+You’ll want a Census API key ([request one
 here](https://api.census.gov/data/key_signup.html)). Set it once with:
 
 ``` r
@@ -65,26 +67,16 @@ updates–check to ensure you have the most recent version installed!
 ## Discover Available Data
 
 ``` r
-list_tables()
-#>  [1] "age"                          "computing_devices"           
-#>  [3] "cost_burden"                  "disability"                  
-#>  [5] "educational_attainment"       "employment"                  
-#>  [7] "gini"                         "health_insurance"            
-#>  [9] "household_size"               "income_quintiles"            
-#> [11] "internet"                     "language"                    
-#> [13] "median_household_income"      "median_housing_cost"         
-#> [15] "median_income_by_tenure"      "mortgage_status"             
-#> [17] "nativity"                     "occupants_per_room"          
-#> [19] "population_density"           "poverty"                     
-#> [21] "public_assistance"            "race"                        
-#> [23] "school_enrollment"            "sex"                         
-#> [25] "snap"                         "tenure"                      
-#> [27] "tenure_by_housing_costs"      "tenure_by_units_in_structure"
-#> [29] "total_population"             "transportation_to_work"      
-#> [31] "travel_time_to_work"          "units_in_structure"          
-#> [33] "vehicles_available"           "year_structure_built"
-list_variables()
-#> # A tibble: 861 × 2
+list_tables() |> head(10)
+#>  [1] "age"                    "computing_devices"      "cost_burden"           
+#>  [4] "disability"             "educational_attainment" "employment"            
+#>  [7] "gini"                   "health_insurance"       "household_size"        
+#> [10] "income_quintiles"
+```
+
+``` r
+list_variables() |> head(10)
+#> # A tibble: 10 × 2
 #>    variable                                table            
 #>    <chr>                                   <chr>            
 #>  1 total_population_universe               total_population 
@@ -96,8 +88,7 @@ list_variables()
 #>  7 snap_received_percent                   snap             
 #>  8 household_income_quintile_upper_limit_1 income_quintiles 
 #>  9 household_income_quintile_upper_limit_2 income_quintiles 
-#> 10 household_income_quintile_upper_limit_3 income_quintiles 
-#> # ℹ 851 more rows
+#> 10 household_income_quintile_upper_limit_3 income_quintiles
 ```
 
 ## Obtain Data
@@ -113,7 +104,7 @@ df = compile_acs_data(
   geography = "county",
   states = "NJ")
 
-glimpse(df)
+glimpse(df) |> head(10)
 #> Rows: 21
 #> Columns: 175
 #> $ data_source_year                                         <dbl> 2024, 2024, 2…
@@ -291,6 +282,26 @@ glimpse(df)
 #> $ race_hispanic_twoormore_includingotherrace_percent_M     <dbl> 0.0073, 0.003…
 #> $ race_hispanic_twoormore_excludingotherrace_percent_M     <dbl> 0.0017, 0.001…
 #> $ race_personofcolor_percent_M                             <dbl> 0.0023, 0.001…
+#> # A tibble: 10 × 175
+#>    data_source_year GEOID NAME              total_population_uni…¹ race_universe
+#>               <dbl> <chr> <chr>                              <dbl>         <dbl>
+#>  1             2024 34001 Atlantic County,…                 276270        276270
+#>  2             2024 34003 Bergen County, N…                 962316        962316
+#>  3             2024 34005 Burlington Count…                 467805        467805
+#>  4             2024 34007 Camden County, N…                 527257        527257
+#>  5             2024 34009 Cape May County,…                  94941         94941
+#>  6             2024 34011 Cumberland Count…                 153305        153305
+#>  7             2024 34013 Essex County, Ne…                 863002        863002
+#>  8             2024 34015 Gloucester Count…                 306954        306954
+#>  9             2024 34017 Hudson County, N…                 718323        718323
+#> 10             2024 34019 Hunterdon County…                 130160        130160
+#> # ℹ abbreviated name: ¹​total_population_universe
+#> # ℹ 170 more variables: race_nonhispanic_allraces <dbl>,
+#> #   race_nonhispanic_white_alone <dbl>, race_nonhispanic_black_alone <dbl>,
+#> #   race_nonhispanic_aian_alone <dbl>, race_nonhispanic_asian_alone <dbl>,
+#> #   race_nonhispanic_nhpi_alone <dbl>, race_nonhispanic_otherrace_alone <dbl>,
+#> #   race_nonhispanic_twoormore <dbl>,
+#> #   race_nonhispanic_twoormore_includingotherrace <dbl>, …
 ```
 
 ## Visualize Data
@@ -314,8 +325,9 @@ plot_data = df %>%
     data_source_year = factor(data_source_year))
 
 state_averages = plot_data %>%
-  group_by(data_source_year) %>%
-  summarize(mean_pct = mean(race_personofcolor_percent)) %>%
+  summarize(
+    .by = data_source_year,
+    mean_pct = mean(race_personofcolor_percent)) %>%
   arrange(data_source_year) %>%
   pull(mean_pct)
 
@@ -334,9 +346,7 @@ dumbbell_data = plot_data %>%
     names_from = data_source_year,
     values_from = race_personofcolor_percent,
     names_prefix = "year_")
-```
 
-``` r
 ggplot() +
   geom_segment(
     data = dumbbell_data,
@@ -388,7 +398,7 @@ ggplot() +
     title = "All NJ Counties Experienced Racial Diversification from 2019 to 2024",
     subtitle = paste0("Share of population who are people of color, by county, 2019-2024
 Confidence intervals are presented around each point but are extremely small"),
-    x = "County",
+    x = "",
     y = "Share of population who are people of color") +
   scale_x_discrete(expand = expansion(mult = c(.03, .04))) +
   scale_y_continuous(
@@ -403,8 +413,7 @@ Confidence intervals are presented around each point but are extremely small"),
 
 # Learn More
 
-A growing number of vignettes aim to support users in effectively using
-this package. These vignettes include:
+Check out the vignettes for additional details:
 
 - A package overview to help users [**Get
   Started**](articles/urbnindicators.html).
@@ -421,6 +430,10 @@ this package. These vignettes include:
   Error**](articles/quantified-survey-error.html) can improve inference
   making.
 
+- You can re-create your indicators and their measures of error for
+  [**Custom Geographies**](articles/custom-geographies.html).
+  Neighborhoods? Unincorporated counties? Start here.
+
 # Credits
 
 This package is built on top of and enormously indebted to

vignettes/.gitignore

@@ -1,2 +1,5 @@
 *.html
 *.R
+
+/.quarto/
+**/*.quarto_ipynb

vignettes/codebook.Rmd

@@ -17,19 +17,21 @@ knitr::opts_chunk$set(
 
 ```{r setup, echo = FALSE}
 library(urbnindicators)
+library(dplyr)
+library(reactable)
 
-df = compile_acs_data(year = 2023, geography = "us")
+df = compile_acs_data(year = 2024, geography = "us")
 codebook = attr(df, "codebook")
 
-reactable::reactable(
+reactable(
   codebook %>% 
-    dplyr::rename(
+    transmute(
       Variable = calculated_variable, 
       Type = variable_type, 
       Definition = definition), 
   columns = list(
-    Variable = reactable::colDef(minWidth = 200), 
-    Type = reactable::colDef(minWidth = 50),
-    Type = reactable::colDef(minWidth = 100)),
+    Variable = colDef(minWidth = 200), 
+    Type = colDef(minWidth = 50),
+    Definition = colDef(minWidth = 100)),
   filterable = TRUE)
 ```