Load Map data
Load the geographical information including the latitude and longitude
for the states in USA.
states_boundaries <- map_data("state")
The dataset contains 15537 rows describing the geographical boundaries
each state (except Alaska and Hawaii) in USA. The long,
lat and region are the columns of interest to
us. long and lat describe the longitude and
latitudes respectively of the boundaries of the states, while
region contains the names of each state.
head(states_boundaries)
#> long lat group order region subregion
#> 1 -87.46201 30.38968 1 1 alabama <NA>
#> 2 -87.48493 30.37249 1 2 alabama <NA>
#> 3 -87.52503 30.37249 1 3 alabama <NA>
#> 4 -87.53076 30.33239 1 4 alabama <NA>
#> 5 -87.57087 30.32665 1 5 alabama <NA>
#> 6 -87.58806 30.32665 1 6 alabama <NA>
Create fake elections results each state in
states_boundaries data
set.seed(123)
# Get names of state names from map data
votes_data <- data.frame('State' = tolower(state.name))
# Simulate percentage of votes received in each state by the Democratic, Republic and other parties
votes_data <- votes_data %>%
mutate('Democratic' = round(runif(50, 1, 100)),
'Republic' = round(runif(50, 1, (100 - Democratic))),
'Other' = 100 - Democratic - Republic)
# Add the latitude and longitude of the geographical centers of the states to place the pies
votes_data <- votes_data %>%
mutate('pie_lat' = state.center$y,
'pie_long' = state.center$x)
# Filter out any states that weren't present in the map_data
votes_data <- votes_data %>% filter(State %in% unique(states_boundaries$region))
The dataset contains 48 rows describing the percentage of votes
different parties got in the respective state. State
describes the state name, Democaratic,
Republic and Other describe the percent of
votes the parties got in the state. pie_lat and
pie_long describe the geographical centre of each state
(this is where the pie will be placed on the plot).
head(votes_data)
#> State Democratic Republic Other pie_lat pie_long
#> 1 alabama 29 4 67 32.5901 -86.7509
#> 2 arizona 41 47 12 34.2192 -111.6250
#> 3 arkansas 88 2 10 34.7336 -92.2992
#> 4 california 94 4 2 36.5341 -119.7730
#> 5 colorado 6 20 74 38.6777 -105.5130
#> 6 connecticut 53 7 40 41.5928 -72.3573
