Iss417

06_neighborhoods/Transportation/transit_cost_county.qmd

@@ -14,7 +14,7 @@ editor_options:
   chunk_output_type: console
 ---
 
-*2023-2024 Mobility Metrics update* 
+*2024-2025 Mobility Metrics update* 
 
 SUMMARY-LEVEL VALUES 
 
@@ -46,6 +46,7 @@ repository folder
 
 -   htaindex2015_data_counties.csv
 -   htaindex2019_data_counties.csv
+-   htaindex2020_data_counties.csv
 
 Import all the files (and/or combine into one file) with only the
 relevant variables and years
@@ -110,6 +111,36 @@ transportation_cost_county_2019 <- transport_county_2019 %>%
   select(state, county, blkgrps, population, households, t_80ami)
 ```
 
+### 2020
+
+```{r}
+transport_county_2020 <- read_csv(here::here("06_neighborhoods",
+                                             "Transportation", 
+                                             "data", 
+                                             "htaindex2020_data_counties.csv"))
+
+
+transport_county_2020 <- transport_county_2020 %>%
+  select(county, blkgrps, population, households, t_80ami)
+```
+
+create correct FIPS columns
+
+```{r}
+transport_county_2020 <- transport_county_2020 %>%
+  mutate(
+    state = substr(county, start = 2, stop = 3),
+    county = substr(county, start = 4, stop = 6)
+  )
+```
+
+Keep only variables of interest
+
+```{r}
+transportation_cost_county_2020 <- transport_county_2020 %>%
+  select(state, county, blkgrps, population, households, t_80ami)
+```
+
 
 Compare to our official county file to make sure we have all counties accounted for
 
@@ -125,9 +156,20 @@ counties_2015 <- counties %>%
 
 counties_2019 <- counties %>%
   filter(year == 2019)
+
+counties_2020 <- counties %>%
+  filter(year == 2020)
+```
+
+The 2015 and 2019 files have the same number of observations (3134, down from 3142 due to removing the 8 CT counties). 2020 file has 3,143 for due to the Alaska county split. Checking that's the case below:
+
+```{r}
+unique_to_2020 <- counties_2020 %>%
+  anti_join(counties_2015, by = c("county_name", "state"))
 ```
 
-All files have same number of observations (3142) so no merging needed to account for missings!
+But no data is MISSING, these represent accurate expectations based on each year, so no merging needed to account for missings.
+
 
 ## QC Checks
 
@@ -181,6 +223,7 @@ if (length(missing_indices) > 0) {
 
 1 missing value: Loving County, TX (48301 FIPS).
 
+
 County-Level Transportation Cost 2019
 
 ```{r}
@@ -225,7 +268,54 @@ if (length(missing_indices) > 0) {
 }
 ```
 
-No missing values for 2019.
+No missing values for 2020.
+
+County-Level Transportation Cost 2020
+
+```{r}
+ggplot(transportation_cost_county_2020, aes(x=t_80ami)) + geom_histogram(binwidth=10) + labs(y="number of counties", x="Annual Transit Cost for the Regional Moderate Income Household, 2020")
+```
+
+Look at summary stats
+```{r}
+summary(transportation_cost_county_2020$t_80ami) 
+```
+
+Examine outliers
+```{r}
+transportation_cost_county_2020_outliers <- transportation_cost_county_2020 %>% 
+  filter(t_80ami>100) 
+```
+
+No weird outliers
+
+Use stopifnot to check if all values in "transportation_cost_county_2020" are non-negative
+
+```{r}
+stopifnot(min(transportation_cost_county_2020$t_80ami, na.rm = TRUE) >= 0)
+```
+
+Good to go.
+
+Find indices of missing values for the "transit_cost_80ami" variable
+
+```{r}
+missing_indices <- which(is.na(transportation_cost_county_2020$t_80ami))
+```
+
+Print observations with missing values
+
+```{r}
+if (length(missing_indices) > 0) {
+  cat("Observations with missing values for transit_cost_80ami:\n")
+  print(transportation_cost_county_2020[missing_indices, , drop = FALSE])
+} else {
+  cat("No missing values for transportation_cost_county_2020\n")
+}
+```
+
+No missing values for 2020.
+
 
 ## Data quality marker
 
@@ -234,6 +324,7 @@ Determine data quality cutoffs based on number of observations (all at the HH le
 ```{r}
 summary(transportation_cost_county_2015$households) 
 summary(transportation_cost_county_2019$households) 
+summary(transportation_cost_county_2020$households) 
 ```
 
 We use a 30 HH cutoff for Data Quality 3 for the ACS variables, so for the sake of consistency, since none of these are less than 30 (all minimum values are at least 30 HHs), Data Quality can be 1 for all these observations BUT ALSO, rename all the metrics variables to what we had before (transit_trips & transit_cost), so we can name the quality variable appropriately
@@ -245,6 +336,9 @@ transportation_cost_county_2015 <- transportation_cost_county_2015 %>%
 transportation_cost_county_2019 <- transportation_cost_county_2019 %>% 
   rename(transit_cost = t_80ami) %>%
   mutate(transit_cost_quality = 1)
+transportation_cost_county_2020 <- transportation_cost_county_2020 %>% 
+  rename(transit_cost = t_80ami) %>%
+  mutate(transit_cost_quality = 1)
 ```
 
 ## Export files
@@ -267,12 +361,21 @@ transportation_cost_county_2019 <- transportation_cost_county_2019 %>%
   )
 ```
 
-Combine the two years into one overall files for both variables
+```{r}
+transportation_cost_county_2020 <- transportation_cost_county_2020 %>%
+  mutate(
+    year = 2020,
+    transit_cost = transit_cost/100
+  )
+```
+
+Combine the three years into one overall file for both variables
 
 ```{r}
-transit_cost_county <- rbind(transportation_cost_county_2015, transportation_cost_county_2019)
+transit_cost_county <- rbind(transportation_cost_county_2015, transportation_cost_county_2019, transportation_cost_county_2020)
 ```
 
+Combined file has 9427 observations, which is correct (3142+3142+3143)
 Keep variables of interest and order them appropriately also rename to correct var names
 
 ```{r}