Stat 492 (Data Management and Data Wrangling Final Project)

The following project below was Part One of my Submission for My Data Wrangling and Data Management Class during my Final Semester at Rutgers University. The following packages were used in the creation of Part One: tidyverse, rvest, httr, jsonlite, leaflet, and broom.

For the first stage of the final project, I aim to make an interactive map in R using brewery coordinates from the Open Brewery API.

In this chunk, I connected to the API, limited my search to just microbreweries, and filtered out all values that did not have a latitude and a longitude. I used a map to create a list of all 84 pages of the API

breweries_list <- 1:84 %>% map(function(page) {
  url <- paste0("https://api.openbrewerydb.org/breweries?by_type=micro",
         "&per_page=50&page=",
         page)
  
  url %>%
    fromJSON()%>%
    filter(!is.na(latitude), !is.na(longitude))
  
})

In this chunk, I combined all of the elements in the list into a single dataframe consisting of all microbreweries with latitudes and longitutdes. I also converted latitude and longitude to numeric. breweries_data_30 was 30 micobreweries I used to test the initial leaflet map

breweries_data <- as.data.frame(do.call(rbind, breweries_list))

breweries_data <- breweries_data %>%
  mutate(latitude = as.numeric(latitude),
         longitude = as.numeric(longitude))

breweries_data_30 <- breweries_data %>% head(n=30) %>%
  select(name, latitude, longitude)

In this chunk I create the map using the leaflet package. It is interactive.

map <- leaflet(breweries_data) %>%
  addTiles() %>%
  addMarkers(~breweries_data$longitude, ~breweries_data$latitude,
             popup = breweries_data$name,
             clusterOptions = markerClusterOptions())
map

This Concludes Part One of the Project.