Krystal Won | Dashboard Project

Krystal Won - Workforce Training Providers in the US/README.md

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+This is the 'Dashboard Project' for MUSA 6110 (Fall 2024)

Krystal Won - Workforce Training Providers in the US/config.js

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+
+var config = {
+    style: "mapbox://styles/mapbox/dark-v11", 
+    accessToken: "pk.eyJ1IjoiZGFsaXZhbnBpY2Fzc29udW0yIiwiYSI6ImNtMTg0MGwzcTBxZnoybHB3dGsxOGs3N3MifQ.ubimGmdKCe9GNuSwkk5T6g",
+    markerColor: "#3FB1CE", 
+    theme: "light", 
+    use3dTerrain: false, 
+    mapSettings: {
+        center: [-107.13038, 40.22634], 
+        zoom: 3.74, 
+        pitch: 0, 
+        bearing: 0 
+    }
+};
+
+mapboxgl.accessToken = config.accessToken;
+
+var map = new mapboxgl.Map({
+    container: 'map', 
+    style: config.style, 
+    center: config.mapSettings.center, 
+    zoom: config.mapSettings.zoom, 
+    pitch: config.mapSettings.pitch, 
+    bearing: config.mapSettings.bearing 
+});

Krystal Won - Workforce Training Providers in the US/data/01-data cleaning.Rmd

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+---
+title: "01-data cleaning"
+author: "Krystal Won"
+date: "2024-10-23"
+output: html_document
+---
+
+```{r}
+library(readr)
+library(tidycensus)
+library(dplyr) 
+```
+
+
+```{r setup}
+acs_data <- read_csv("/Users/k.w./Desktop/acs_data_final.csv")
+
+acs_data <- acs_data %>% 
+  select(-AQPKE001, -AQPKE022, -AQPKE023, -AQPKE024, -AQPKE025, -`...4`) %>% 
+  mutate(`Unemployment rate` = paste0(`Unemployment rate`, "%")) %>% 
+  mutate(`Per Capita Personal Income` = ifelse(`Per Capita Personal Income` >= 1000000, paste0(round(`Per Capita Personal Income` / 1000000, 1), "M"), paste0(round(`Per Capita Personal Income` / 1000, 1), "k")))
+
+
+write_csv(acs_data, "/Users/k.w./Documents/MUSA 6110/dashboard project/data/acs_data_cleaned.csv")
+```
+
+
+```{r}
+
+workforce_data <- read_csv("/Users/k.w./Documents/MUSA 6110/dashboard project/data/workforce_training_ori.csv")
+
+workforce_data <- workforce_data %>%
+  mutate(
+    `Organization Type` = case_when(
+      (in_ipeds == TRUE & in_rapids == TRUE) |
+      (in_ipeds == TRUE & in_tpr == TRUE) |
+      (in_ipeds == TRUE & in_irs == TRUE) |
+      (in_rapids == TRUE & in_tpr == TRUE) |
+      (in_rapids == TRUE & in_irs == TRUE) |
+      (in_tpr == TRUE & in_irs == TRUE) ~ "Multiple types",
+
+      # Higher education institution
+      in_ipeds == TRUE ~ "Higher education institution",
+
+      # Registered apprenticeship
+      in_rapids == TRUE ~ "Registered apprenticeship",
+
+      # WIOA-eligible
+      in_tpr == TRUE ~ "WIOA-eligible",
+
+      # Non-profit organization
+      in_irs == TRUE ~ "Non-profit organization",
+
+      # If none of the above conditions are met, leave the type as NA
+      TRUE ~ NA_character_
+    ),
+
+    `Subtype` = case_when(
+      # Highest degree certificate
+      org_subtype_ipeds == "Nondegree-granting, sub-baccalaureate" ~ "Highest degree certificate",
+
+      # Highest degree associate’s
+      org_subtype_ipeds == "Degree-granting, Associate's and certificates" ~ "Highest degree associate’s",
+
+      # Highest degree bachelor’s+
+      org_subtype_ipeds == "Degree-granting, not primarily baccalaureate or above" ~ "Highest degree bachelor’s+",
+
+      # Other higher education institution
+      org_subtype_ipeds == 0 & org_subtype_irs == "Community or Junior Colleges" ~ "Other higher education institution",
+      org_subtype_ipeds == 0 & org_subtype_tpr == "Higher Ed*" ~ "Other higher education institution",
+      org_subtype_ipeds == 0 & org_subtype_rapids == "Community College/University" ~ "Other higher education institution",
+
+      # Private for-profit
+      org_subtype_tpr == "Private for profit" ~ "Private for-profit",
+
+      # Apprenticeship sponsor / labor/union
+      org_subtype_rapids == "Apprenticeship –Labor/Union" ~ "Apprenticeship sponsor / labor/union",
+
+      # Apprenticeship – business association
+      org_subtype_rapids == "Apprenticeship – Business Association" ~ "Apprenticeship – business association",
+
+      # Apprenticeship sponsor / employer
+      org_subtype_rapids == "Apprenticeship – Employer" ~ "Apprenticeship sponsor / employer",
+
+      # Apprenticeship sponsor / intermediary
+      org_subtype_rapids == "Apprenticeship – Intermediary" ~ "Apprenticeship sponsor / intermediary",
+
+      # Apprenticeship sponsor / government
+      org_subtype_rapids %in% c("Apprenticeship – Federal Agency", "Apprenticeship – City/County Agency", "Apprenticeship – State Agency") ~ "Apprenticeship sponsor / government",
+
+      # Apprenticeship sponsor / workforce investment board
+      org_subtype_rapids == "Apprenticeship – Workforce Investment Board" ~ "Apprenticeship sponsor / workforce investment board",
+
+      # Apprenticeship sponsor / foundation
+      org_subtype_rapids == "Apprenticeship – Foundation" ~ "Apprenticeship sponsor / foundation",
+
+      # Apprenticeship sponsor / other
+      org_subtype_rapids %in% c("Apprenticeship – Other", "Apprenticeship – None", "Apprenticeship – Unknown") ~ "Apprenticeship sponsor / other",
+      org_subtype_tpr == "National Apprenticeship" ~ "Apprenticeship sponsor / other",
+
+      # Job training non-profit
+      org_subtype_irs %in% c("Vocational, Technical Schools", "Employment Procurement Assistance, Job Training", "Vocational Counseling, Guidance and Testing", "Vocational Training", "Vocational Rehabilitation", "Goodwill Industries", "Sheltered Remunerative Employment, Work Activity Center N.E.C.") ~ "Job training non-profit",
+      org_subtype_tpr == "private non-profit" ~ "Job training non-profit",
+      org_subtype_rapids == "community based organization" ~ "Job training non-profit",
+
+      # If no subtype matches, leave as NA
+      TRUE ~ NA_character_
+    )
+  ) %>%
+  select(
+    -org_subtype_tpr, -org_subtype_highest_degree_bachelors_plus, -org_subtype_highest_degree_associates,
+    -org_subtype_highest_degree_certificate, -org_subtype_other_higher_ed_institution, 
+    -org_subtype_private_for_profit, -org_subtype_apprenticeship_business_association, 
+    -org_subtype_apprenticeship_employer, -org_subtype_apprenticeship_intermediary, 
+    -org_subtype_apprenticeship_gov, -org_subtype_apprenticeship_wib, 
+    -org_subtype_apprenticeship_foundation, -org_subtype_apprenticeship_other, 
+    -org_subtype_apprenticeship_union_labor, -org_subtype_job_training_non_profits, -std_street, -std_city, -std_zip, -county, -metro, -in_ipeds, -in_rapids, -in_irs, -in_tpr, -org_subtype_ipeds, -org_subtype_rapids, -org_subtype_irs, -num_data_sources, -last_updated
+  )
+
+
+```
+
+
+```{r}
+state_name_lookup <- c(
+  "AL" = "Alabama", "AK" = "Alaska", "AZ" = "Arizona", "AR" = "Arkansas", "CA" = "California", 
+  "CO" = "Colorado", "CT" = "Connecticut", "DE" = "Delaware", "FL" = "Florida", "GA" = "Georgia",
+  "HI" = "Hawaii", "ID" = "Idaho", "IL" = "Illinois", "IN" = "Indiana", "IA" = "Iowa", 
+  "KS" = "Kansas", "KY" = "Kentucky", "LA" = "Louisiana", "ME" = "Maine", "MD" = "Maryland",
+  "MA" = "Massachusetts", "MI" = "Michigan", "MN" = "Minnesota", "MS" = "Mississippi", 
+  "MO" = "Missouri", "MT" = "Montana", "NE" = "Nebraska", "NV" = "Nevada", "NH" = "New Hampshire",
+  "NJ" = "New Jersey", "NM" = "New Mexico", "NY" = "New York", "NC" = "North Carolina", 
+  "ND" = "North Dakota", "OH" = "Ohio", "OK" = "Oklahoma", "OR" = "Oregon", "PA" = "Pennsylvania", 
+  "RI" = "Rhode Island", "SC" = "South Carolina", "SD" = "South Dakota", "TN" = "Tennessee", 
+  "TX" = "Texas", "UT" = "Utah", "VT" = "Vermont", "VA" = "Virginia", "WA" = "Washington", 
+  "WV" = "West Virginia", "WI" = "Wisconsin", "WY" = "Wyoming"
+)
+
+
+workforce_data <- workforce_data %>%
+  mutate(state = state_name_lookup[std_state])
+```
+
+
+```{r}
+write_csv(workforce_data, "/Users/k.w./Documents/MUSA 6110/dashboard project/data/workforce_training_cleaned.csv")
+```
+

Krystal Won - Workforce Training Providers in the US/data/acs_data_cleaned.csv

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+State,Unemployment rate,Poverty rate,Population identified as POC,Total population,Population with less than Bachelor's degree,Per Capita Personal Income
+Alabama,2.5%,15.70%,35.50%,5.0M,27.21%,54k
+Alaska,4.2%,10.50%,42.30%,734.8k,30.75%,71.6k
+Arizona,3.8%,13.10%,48.20%,7.2M,31.80%,62.5k
+Arkansas,3.2%,16.20%,32.30%,3.0M,24.71%,57.6k
+California,4.3%,12.10%,66.30%,39.4M,35.86%,81.3k
+Colorado,3.1%,9.60%,35.00%,5.8M,43.73%,80.1k
+Connecticut,4.1%,10.10%,38.00%,3.6M,41.38%,89.9k
+Delaware,4.3%,11.10%,41.00%,993.6k,34.46%,66.8k
+District of Columbia,4.7%,15.10%,63.70%,670.6k,62.64%,106.8k
+Florida,3%,12.90%,49.20%,21.6M,32.33%,68.7k
+Georgia,3.1%,13.50%,50.20%,10.7M,33.63%,59.9k
+Hawaii,3.3%,9.60%,80.50%,1.5M,34.72%,66.2k
+Idaho,2.8%,11.00%,20.70%,1.9M,30.21%,59.4k
+Illinois,4.6%,11.80%,41.50%,12.8M,36.67%,72.2k
+Indiana,3.1%,12.30%,23.90%,6.8M,28.24%,61.2k
+Iowa,2.8%,11.10%,17.10%,3.2M,30.28%,62.8k
+Kansas,2.6%,11.60%,26.70%,2.9M,34.69%,66.1k
+Kentucky,4%,16.10%,17.50%,4.5M,26.47%,55.4k
+Louisiana,3.7%,18.70%,42.90%,4.6M,26.13%,58.5k
+Maine,2.8%,10.90%,9.50%,1.4M,34.12%,65.1k
+Maryland,3%,9.30%,52.80%,6.2M,42.22%,75.4k
+Massachusetts,3.7%,9.90%,32.70%,7.0M,45.91%,91k
+Michigan,4.1%,13.10%,27.10%,10.1M,31.14%,61.1k
+Minnesota,2.6%,9.30%,23.70%,5.7M,38.20%,72.6k
+Mississippi,3.8%,19.20%,44.30%,3.0M,23.88%,49.7k
+Missouri,2.6%,12.80%,23.10%,6.2M,31.19%,62.6k
+Montana,2.7%,12.40%,16.20%,1.1M,33.98%,65k
+Nebraska,2.2%,10.40%,24.10%,2.0M,33.46%,71.3k
+Nevada,5.2%,12.70%,55.60%,3.1M,26.55%,66.2k
+New Hampshire,2.3%,7.30%,13.30%,1.4M,39.05%,78.5k
+New Jersey,3.9%,9.70%,48.50%,9.2M,42.25%,82.1k
+New Mexico,4.1%,18.30%,65.30%,2.1M,29.07%,55.3k
+New York,4.3%,13.60%,47.00%,20.0M,38.79%,82.3k
+North Carolina,3.7%,13.30%,39.10%,10.5M,33.87%,61.8k
+North Dakota,2%,10.80%,17.20%,776.9k,31.43%,72k
+Ohio,4%,13.30%,23.70%,11.8M,30.37%,61.5k
+Oklahoma,3.1%,15.20%,37.20%,4.0M,27.27%,60.5k
+Oregon,3.9%,11.90%,28.20%,4.2M,35.48%,67.8k
+Pennsylvania,4.1%,11.80%,26.70%,13.0M,33.76%,68.5k
+Rhode Island,3.2%,11.20%,32.00%,1.1M,36.28%,67.6k
+South Carolina,3.2%,14.40%,37.20%,5.1M,30.64%,57.3k
+South Dakota,2%,12.30%,19.90%,890.3k,30.37%,72.5k
+Tennessee,3.4%,14.00%,27.90%,6.9M,29.71%,62.2k
+Texas,3.9%,13.90%,61.10%,29.2M,32.27%,66.3k
+Utah,2.4%,8.50%,24.40%,3.3M,36.13%,64.2k
+Vermont,2.3%,10.40%,9.60%,643.8k,41.72%,66.9k
+Virginia,2.8%,10.00%,41.10%,8.6M,40.97%,73.8k
+Washington,4.1%,9.90%,36.40%,7.7M,37.95%,80.9k
+West Virginia,3.9%,16.80%,9.60%,1.8M,22.71%,52.8k
+Wisconsin,2.9%,10.70%,20.90%,5.9M,32.02%,65k
+Wyoming,3.4%,10.70%,18.10%,577.9k,28.99%,82.1k