Since 1872, the United States Congress has set aside 63 protected areas to be designated as “National Parks”. I have been fortunate enough to have been visiting these parks since childhood, and I am currently on a life-long mission to visit all current and future National Parks.
For this final R Markdown homework assignment, I chose to use Leaflet to map all the National Parks to show whether I have visited them or not, as well as use Plotly to show the percentage of parks I have visited as of May 2022.
I chose to source the National Park coordinates through Wikipedia, where the coordinates were available in a .kml file. I pulled in the .kml file with st_read() then saved it as an Excel file with write_xlsx for easier use (KML had spatial settings that I didn’t want to work with)
library(sf) library(writexl)
coord_kml <- st_read("doc.kml")## Reading layer `National parks' from data source
## `C:\Users\Garrett\Documents\R\HW5\doc.kml' using driver `KML'
## Simple feature collection with 63 features and 2 fields
## Geometry type: POINT
## Dimension: XYZ
## Bounding box: xmin: -170.68 ymin: -14.25 xmax: -64.73 ymax: 67.78
## z_range: zmin: 0 zmax: 0
## Geodetic CRS: WGS 84write_xlsx(coord_kml,"C:\\Users\\Garrett\\Documents\\R\\HW5\\NP_coords.xlsx")Additionally, I had a personal list in Google Sheets of National Parks and whether I had visited them or not. I converted this sheet to Excel and ensured that the park names were matching.
Files were then read in to R as coords and Visit_List.
library(readxl)
coords <- read_excel("NPCoords.xlsx")
head(coords)## # A tibble: 6 x 2
## Name geometry
## <chr> <chr>
## 1 Acadia -68.21,44.35,0
## 2 National Park of American Samoa -170.68,-14.25,0
## 3 Arches -109.57,38.68,0
## 4 Badlands -102.5,43.75,0
## 5 Big Bend -103.25,29.25,0
## 6 Biscayne -80.08,25.65,0Visit_List <- read_excel("NP_List.xlsx")
head(Visit_List)## # A tibble: 6 x 2
## Name Visited
## <chr> <chr>
## 1 Acadia No
## 2 National Park of American Samoa No
## 3 Arches Yes
## 4 Badlands No
## 5 Big Bend No
## 6 Biscayne NoSince the coordinates in coords$geography were in one column, I needed to separate them. I split them into their own columns as strings using str_split_fixed(), then reverted Longitude and Latitude back into numeric form with as.numeric()
library(tidyverse)
coords[c('Long', 'Lat', "delete")] <- str_split_fixed(coords$geometry, ',', 3)
coords$Long <- as.numeric(coords$Long)
coords$Lat <- as.numeric(coords$Lat)
head(coords)## # A tibble: 6 x 5
## Name geometry Long Lat delete
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Acadia -68.21,44.35,0 -68.2 44.4 0
## 2 National Park of American Samoa -170.68,-14.25,0 -171. -14.2 0
## 3 Arches -109.57,38.68,0 -110. 38.7 0
## 4 Badlands -102.5,43.75,0 -102. 43.8 0
## 5 Big Bend -103.25,29.25,0 -103. 29.2 0
## 6 Biscayne -80.08,25.65,0 -80.1 25.6 0Then, I felt it was best to get rid of the columns I no longer needed!
coords <- coords[c("Name", "Lat", "Long")]
head(coords)## # A tibble: 6 x 3
## Name Lat Long
## <chr> <dbl> <dbl>
## 1 Acadia 44.4 -68.2
## 2 National Park of American Samoa -14.2 -171.
## 3 Arches 38.7 -110.
## 4 Badlands 43.8 -102.
## 5 Big Bend 29.2 -103.
## 6 Biscayne 25.6 -80.1The last step of prepping was to merge coords and visit_list into the new NP_List using left_join()
NP_List <- left_join(coords, Visit_List, by="Name")
head(NP_List)## # A tibble: 6 x 4
## Name Lat Long Visited
## <chr> <dbl> <dbl> <chr>
## 1 Acadia 44.4 -68.2 No
## 2 National Park of American Samoa -14.2 -171. No
## 3 Arches 38.7 -110. Yes
## 4 Badlands 43.8 -102. No
## 5 Big Bend 29.2 -103. No
## 6 Biscayne 25.6 -80.1 NoKnowing that I wanted to plot these coordinates on the map using two colors, I consulted the this website to choose my colors to fit in with the National Park Service theme.
This code will assist me when I plot that circle markers in the map later
Color_Legend <- colorFactor(c("#99542C", "#2D4B1E"), domain = c("Yes", "No"))Next, it was time to build the map. Calling leaflet, I passed the NP_List dataframe into it, added circle markers to the map, and then added the legend.
addCircleMarkers required longitude, latitude, popup info (name of the park, and whether I visited or not), and then its colors and size.
addLegend utilized the same colors to match with the overall theme.
library(leaflet)
NP_map <- leaflet(NP_List) %>%
addTiles() %>%
addCircleMarkers(~Long, ~Lat, popup=paste(NP_List$Name,"<br>", "Visited: ", NP_List$Visited), weight=5, radius = 5, color = ~Color_Legend(Visited)) %>%
addLegend("bottomright", colors = c("#99542C", "#2D4B1E"), labels = c("No", "Yes"),
title = "National Parks Visited")
NP_mapThis homework assignment called for either adding markers to a map with Leaflet, or to create a plot with Plotly. I wanted to do both, so I thought I would also learn how to make a pie chart to show how many of the parks I have been to so far
The first step was to create a dataframe with the count of Yes and No from the NP_List$Visit column
Visit_Count <- as.data.frame(table(NP_List['Visited']))
Visit_Count## Var1 Freq
## 1 No 44
## 2 Yes 19With those numbers, its pretty straight forward to create the pie chart. Plot_ly() requires the dataframe, values, labels, and type. Since I wanted to keep the same colors, I also added those in.
library(plotly)
pie_chart <- plot_ly(Visit_Count, values = ~Freq, labels=~factor(Var1), marker=list(colors = c("#99542C", "#2D4B1E")), type="pie") %>%
layout(title = "Percentage of US National Parks Visited")
pie_chart#Conclusion
Creating map markers with leaflet and graphs with plotly in R is pretty quick! When I made the first map marker, I thought I was going to have to build a function or run iterative loops for all 63 points, but luckily Leaflet was able to take it all in without issue.
I hope you enjoyed the read!
- Garrett