Walkability

As part of the TidyTuesday, these figures display different levels of Bike and Walk Commutes based on American Community Survey data.

After some cleaning of the data, we can see that in New York and Vermont a decent percentage of communters walk to work. Interesting to see that Vermont commuters also bike to work more frequently.

#Modes of travling biking and walking ACS data
commute_mode <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-11-05/commute.csv")

## Rows: 3496 Columns: 9

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): city, state, city_size, mode, state_abb, state_region
## dbl (3): n, percent, moe

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

commute_mode$state <- recode(commute_mode$state, 
                       "Ca"= "California",
                       "Massachusett" = "Massachusetts")


#summarise data each state for percent biking and walking
commute_summary <- commute_mode %>%
  mutate(state = tolower(state)) %>%
  group_by(state, mode) %>%
  summarise(Percent = mean(percent))

## `summarise()` has grouped output by 'state'. You can override using the `.groups` argument.

#retrieve state geo data
states_map <- map_data("state")

#filter by Northeastern States
NE_states <- subset(states_map, region %in% c("connecticut", "massachusetts","maine", "new hampshire", 
                                          "new york", "rhode island", "vermont"))
#plot by new england states
commute_summary %>%
  ggplot(aes(map_id = state)) +
  geom_map(aes(fill=Percent), map = NE_states)+ #sets up map
  facet_wrap(vars(mode)) + #displays both bike and walk on same figure
  expand_limits(x= NE_states$long, y=NE_states$lat)+ #sets limits based on lat/long of states file
  coord_map("polyconic") +
  scale_fill_viridis(option = "D") +
  theme_void()+ #gets rid of xy grid
  labs(fill = "Percent of commuters", title= "The Northeast Loves Walking")+
  theme(legend.position="bottom", plot.title = element_text(hjust =0.5), 
        strip.text.x = element_text(size = 12))#changes text of mode

commute_summary

## # A tibble: 102 × 3
## # Groups:   state [51]
##    state      mode  Percent
##    <chr>      <chr>   <dbl>
##  1 alabama    Bike    0.235
##  2 alabama    Walk    1.33 
##  3 alaska     Bike    1.43 
##  4 alaska     Walk    5.03 
##  5 arizona    Bike    0.724
##  6 arizona    Walk    2.10 
##  7 arkansas   Bike    0.155
##  8 arkansas   Walk    2.05 
##  9 california Bike    0.983
## 10 california Walk    2.36 
## # … with 92 more rows

In contrast to Northeast, Southeast commuters do not seem to prefer biking or walking to work.

#filter by southeastern states
SE_states <- subset(states_map, region %in% c("alabama", "florida", "georgia", "kentucky", "mississippi", 
                                              "north carolina", "south carolina", "tennessee", "virginia"))

commute_summary %>%
  ggplot(aes(map_id = state)) +
  geom_map(aes(fill=Percent), map = SE_states)+
  facet_wrap(vars(mode)) +
  expand_limits(x= SE_states$long, y=SE_states$lat)+
  coord_map("polyconic") +
  scale_fill_viridis(option = "D") +
  theme_void()+
  labs(fill = "Percent of commuters", title= "The Southeast Hates Walking")+
  theme(legend.position="bottom", plot.title = element_text(hjust =0.5), 
        strip.text.x = element_text(size = 12))

To simplifiy the color scheme, I gropued the percentage of walking. It is surprising to see that West Virginia seems to be a big walking-to-work state.

#group dataset by % groups
commute_summary$Group[commute_summary$Percent <= 2] = "1"

## Warning: Unknown or uninitialised column: `Group`.

commute_summary$Group[commute_summary$Percent >= 2 & commute_summary$Percent <= 3] = "2"
commute_summary$Group[commute_summary$Percent >= 3 & commute_summary$Percent <= 4] = "3"
commute_summary$Group[commute_summary$Percent >= 4 & commute_summary$Percent <= 5] = "4"
commute_summary$Group[commute_summary$Percent >= 5 & commute_summary$Percent <= 6] = "5"
commute_summary$Group[commute_summary$Percent >= 6] = "6"

#change group category to Numeric so map can be colored with group #
commute_summary$Group <- as.numeric(commute_summary$Group)

#seperate 'mode' into bike and walk
commute_Walk <- dplyr::filter(commute_summary, mode == "Walk")

#Map of groups for % walking
commute_Walk %>%
  ggplot(aes(map_id = state)) +
  geom_map(aes(fill=Group), color="black", map = states_map)+
  expand_limits(x= states_map$long, y=states_map$lat)+
  coord_map("polyconic") +
  scale_fill_viridis(option = "D") +
  theme_void()+
  labs(fill = "Percent of commuters", title= "Walking to Work by State")+
  theme(legend.position="bottom", plot.title = element_text(hjust =0.5))