Fix mismatched race groups

R/load_data.R

@@ -16,7 +16,15 @@ load_data <- function() {
         .default = col_double()
       )
   ) %>%
-    prep_data()
+    prep_data() %>%
+    select(
+      -share_in_preschool_ub,
+      -share_in_preschool_lb,
+      -share_hs_degree_ub,
+      -share_hs_degree_lb,
+      -share_employed_ub,
+      -share_employed_ub
+    )
 
   data_years <- read_csv(
     here("mobility-metrics","00_mobility-metrics_longitudinal.csv"),
@@ -33,7 +41,15 @@ load_data <- function() {
       .default = col_double()
     )
   ) %>%
-    prep_data()
+    prep_data() %>%
+    select(
+      -share_in_preschool_ub,
+      -share_in_preschool_lb,
+      -share_hs_degree_ub,
+      -share_hs_degree_lb,
+      -share_employed_ub,
+      -share_employed_ub
+    )
 
   data_race_ethnicity <- read_csv(
     here("mobility-metrics", "01_mobility-metrics_race-ethnicity_longitudinal.csv"), 
@@ -47,7 +63,15 @@ load_data <- function() {
       .default = col_double()
     )
   ) %>%
-    prep_data()
+    prep_data() %>%
+    select(
+      -share_in_preschool_ub,
+      -share_in_preschool_lb,
+      -share_hs_degree_ub,
+      -share_hs_degree_lb,
+      -share_employed_ub,
+      -share_employed_ub
+    )
 
   data_race <- read_csv(
     here("mobility-metrics", "02_mobility-metrics_race_longitudinal.csv"), 

R/load_place_data.R

@@ -112,9 +112,6 @@ load_place_data <- function() {
     ) %>%
     prep_data(geography = "place")
 
-  data_digital <- read_csv(here("mobility-metrics", "07_digital-access_city_subgroup.csv")) %>%
-    prep_data(geography = "place") 
-
   data_pov_exp <- read_csv(here("mobility-metrics", "07_poverty-exposure_city_subgroup.csv")) %>%
     prep_data(geography = "place")
 
@@ -126,7 +123,6 @@ load_place_data <- function() {
     race_share = data_race_share,
     env = data_env,
     education_income = data_education_income,
-    digital = data_digital,
     pov_exp = data_pov_exp
   )
 

R/varlist.R

@@ -131,7 +131,6 @@ college_readiness <- list(
   ),
   detail_vars = c(
     "% HS degree" = "share_hs_degree", 
-    "% HS degree_ci" = "share_hs_degree_ci",
     "share_hs_degree_quality" = "share_hs_degree_quality"
   )
 )
@@ -142,7 +141,6 @@ emp_lst <- list(
   ),
   detail_vars = c(
     "Employment to population ratio" = "share_employed", 
-    "Employment to population ratio_ci" = "share_employed_ci",
     "share_employed_quality" = "share_employed_quality"
   )
 )

data/00_metrics-summary_county.csv

@@ -7,12 +7,12 @@ Social capital1,count_membership_associations_per_10k,count_membership_associati
 Social capital2,ratio_high_low_ses_fb_friends,ratio_high_low_ses_fb_friends_quality,3,none,Metric: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status (‘economic connectedness’),"Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)",,"This measures the interconnectivity, by location, between people from different economic backgrounds to estimate “economic connectedness.” Specifically, the metric is twice the average share of high-socioeconomic status (SES) friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-SES individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. A metric value of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-SES individuals’ friends being of high-SES.",,,2022
 Transportation access,index_transit_trips,index_transit_trips_quality,3,race_share,Metric: Transit trips index,"2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)",,"The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community.",<br><br>'Majority' means that at least 60% of residents in a census tract are members of the specified group.,,2016
 Transportation cost,transportation_cost,transportation_cost_quality,3,race_share,Metric: Transportation cost index,"2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)",,"Reflects local transportation costs as a share of renters' incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood.",<br><br>’Majority' means that at least 60% of residents in a census tract are members of the specified group.,,2016
-Access to preschool,share_in_preschool,share_in_preschool_quality,1,race_ethnicity,Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of a community's children aged three to four who are enrolled in nursery or preschool.,,,"2018, 2021"
+Access to preschool,share_in_preschool,share_in_preschool_quality,3,race_ethnicity,Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of a community's children aged three to four who are enrolled in nursery or preschool.,,,"2018, 2021"
 Effective public education,rate_learning,rate_learning_quality,1,"race_ethnicity, income",Metric: Average per grade change in English Language Arts achievement between third and eighth grades,"Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Year 2017-18)","Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974) (Time period: School Years 2016-17 & 2017-18)","The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. '1' indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average.","<br><br>Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills.","<br><br>Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. 'Low-income' means students are determined to be eligible for their schools' free and reduced-price meals under the National School Lunch Program.","2016, 2017"
 School economic diversity,"meps20_white, meps20_black, meps20_hispanic","meps20_white_quality, meps20_black_quality, meps20_hispanic_quality",3,none,"Metric: Share of students attending high-poverty schools, by student race/ethnicity ","National Center for Education Statistics Common Core of Data, SY 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time period: School Year 2018-19)","National Center for Education Statistics Common Core of Data, SY 2017-18 & 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time periods: School Years 2017-18 & 2018-19)",This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.,,,"2014, 2018"
-Preparation for college,share_hs_degree,share_hs_degree_quality,1,race_ethnicity,Metric: Share of 19- and 20-year-olds with a high school degree,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of 19- and 20-year-olds in a community who have a high school degree.,,,"2018, 2021"
+Preparation for college,share_hs_degree,share_hs_degree_quality,3,race_ethnicity,Metric: Share of 19- and 20-year-olds with a high school degree,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of 19- and 20-year-olds in a community who have a high school degree.,,,"2018, 2021"
 Digital access,share_digital_access,share_digital_access_quality,2,none,Metric: Share of people in households with broadband access in the home,US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021),,This metric represents the share of people in households with access to broadband in their home.,,,2021
-Employment opportunities,share_employed,share_employed_quality,1,race_ethnicity,Metric: Employment-to-population ratio for adults ages 25 to 54,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of adults between the ages of 25 and 54 in a given community who are employed.,,,"2018, 2021"
+Employment opportunities,share_employed,share_employed_quality,3,race_ethnicity,Metric: Employment-to-population ratio for adults ages 25 to 54,US Census Bureau’s 2021 5-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21),US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21),The share of adults between the ages of 25 and 54 in a given community who are employed.,,,"2018, 2021"
 Jobs paying a living wage,ratio_average_to_living_wage,ratio_average_to_living_wage_quality,3,none,Metric: Ratio of pay on an average job to the cost of living,"US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2021; Massachusetts Institute of Technology Living Wage Calculator, 2022. (Time period: 2021)","US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2018 & 2021; Massachusetts Institute of Technology Living Wage Calculator, 2018 & 2022. (Time period: 2018 & 2021)","What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.<br><br>For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.",,,"2018, 2021"
 Opportunities for income,pctl_income,pctl_income_quality,2,race_ethnicity,"Metric: Household income at the 20th, 50th, and 80th percentiles",US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021),US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Periods: 2014-18 & 2017-21),"To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.",,,"2018, 2021"
 Financial security,share_debt_col,share_debt_col_quality,1,race_share,Metric: Share with debt in collections,February 2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time period: February 2022),August 2018 and February 2022 credit bureau data from Urban Institute’s [Debt in America](https://apps.urban.org/features/debt-interactive-map/?type=overall&variable=totcoll) feature. (Time periods: August 2018 & February 2022),The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections.  ,"<br><br>For county-level August 2018 and February 2022 data, “majority” means that at least 60% of residents in a zip code are members of the specified population group.",,"2018, 2021"