library(dplyr)
library(leaflet)
pokeStops <- read.csv("pokemon.csv")
head(pokeStops)
##   cell_id encounter_id spawn_id pokemon_type_id   latitude longitude
## 1       0            0        0              21  -0.257622  52.51879
## 2       0            0        0              48 -76.536133  38.69669
## 3       0            0        0              13 -76.537127  38.69688
## 4       0            0        0              13 -76.536043  38.69652
## 5       0            0        0              48 -76.536585  38.69682
## 6       0            0        0              21 -76.535682  38.69672
##   despawn_time_ms scan_time_ms
## 1       146903878    146903855
## 2       146901961    146901931
## 3       146901956    146901931
## 4       146901935    146901931
## 5       146901976    146901931
## 6       146901979    146901931

Pokestops in Orlando, Florida

First we select only valuable variables. In this case latitude and longitude. These are needed to determine where Florida is in respect to the world map.

pokeStops <- select(pokeStops, latitude, longitude)

Then boundries are set around the state of Florida. Latitude must be within 27° N and 30° N. Longitude must be within 99° W and 100° W to pinpoint it on the Orlando metropolitan area.

pokeStopsFlorida <- filter(pokeStops, latitude > "27", latitude < "30")
pokeStopsFlorida <- filter(pokeStopsFlorida, longitude > "-100", longitude < "-99")

Due to massive data size, a sample of 50,000 is selected from data.

set.seed(137)
pokeStopsFlorida <- sample_n(pokeStopsFlorida, 50000, replace = TRUE)
head(pokeStopsFlorida)
##         latitude longitude
## 695150   28.6266  -81.4000
## 441740   28.5206  -81.4651
## 978684   28.6478  -81.2325
## 817492   28.5378  -81.3508
## 390724   28.6238  -81.2438
## 1025174  28.5414  -81.3720

The following leaflet app shows 50,000 pokestops located throughout the Orlando, Florida metropolitan area.