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Create summary statistics for rural & urban classification.

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Nick Hepler 2024-09-30 14:25:54 -04:00
parent f4f9329e70
commit 279e612635
1 changed files with 35 additions and 1 deletions
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36
analysis.Rmd
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@ -103,7 +103,9 @@ This figure shows the distribution of alternative fueling station locations by u
```{r}
charger_by_UR20_summary <- dataset %>%
group_by(UR20) %>%
summarise(`Total Chargers` = sum(`Total Chargers`))
summarise(`Total Chargers` = sum(`Total Chargers`),
`Total Level 2 Chargers` = sum(`EV Level2 EVSE Num`, na.rm = TRUE),
`Total DC Fast Chargers` = sum(`EV DC Fast Count`, na.rm = TRUE),)
```
A new data frame is created to summarize the Total Chargers based on urban-rural classification.
@ -122,6 +124,38 @@ ggplot(charger_by_UR20_summary, aes(x = UR20, y = `Total Chargers`)) +
This figure shows the total number of electric vehicle chargers based on urban-rural classification.
### Rural-Classified Alternate Fuel Station Summary Statistics
```{r}
rural_charger_summary <- dataset %>%
filter(UR20 == 'Rural') %>%
summarise(
Count = n(),
Minimum = min(`Total Chargers`),
`First Quartile` = quantile(`Total Chargers`, 0.25),
Mean = mean(`Total Chargers`),
Median = median(`Total Chargers`),
`Third Quartile` = quantile(`Total Chargers`, 0.75),
Maximum = max(`Total Chargers`),
) %>%
print()
```
### Urban-Classified Alternate Fuel Station Summary Statistics
```{r}
urban_charger_summary <- dataset %>%
filter(UR20 == 'Urban') %>%
summarise(
Count = n(),
Minimum = min(`Total Chargers`),
`First Quartile` = quantile(`Total Chargers`, 0.25),
Mean = mean(`Total Chargers`),
Median = median(`Total Chargers`),
`Third Quartile` = quantile(`Total Chargers`, 0.75),
Maximum = max(`Total Chargers`),
) %>%
print()
```
```{r}
charger_by_city_summary <- dataset %>%
group_by(City) %>%