Data transformations The datasets: (1) NYC Subway Transit (2) BLM (Bureau of Land Management) uranium mines (CO,UT) (3) EPA Air Pollutants (Vertical tidy) Consider other set

  1. Cleaning Data set 1: NYC Subway Transit: Entrances and Exits
library(tidyr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(leaflet)
t <- read.csv("https://raw.githubusercontent.com/jconno/NYC-Subway-Transit/main/NYC_Transit_Subway_Entrance_And_Exit_Data.csv")
names(t)
##  [1] "Division"           "Line"               "Station.Name"      
##  [4] "Station.Latitude"   "Station.Longitude"  "Route1"            
##  [7] "Route2"             "Route3"             "Route4"            
## [10] "Route5"             "Route6"             "Route7"            
## [13] "Route8"             "Route9"             "Route10"           
## [16] "Route11"            "Entrance.Type"      "Entry"             
## [19] "Exit.Only"          "Vending"            "Staffing"          
## [22] "Staff.Hours"        "ADA"                "ADA.Notes"         
## [25] "Free.Crossover"     "North.South.Street" "East.West.Street"  
## [28] "Corner"             "Entrance.Latitude"  "Entrance.Longitude"
## [31] "Station.Location"   "Entrance.Location"

New dataset will omit: Division, Line, Exit Only, Staff Hours, ADA Notes, North South Street, East West Street, Corner, Entrance Latitude, Entrance Longitude, Station Location

Entrances and Exits INCLUDES: Station.Name, Routes 1-11, Entrance.Type, Vending, Staffing, Entrance.Location

stns <- t %>% select(Station.Name, Corner, Route1, Route2, Route3, Route4, Route5, Route6, Route7, Route8, Route9, Route10, Route11, Entrance.Type, Vending, Staffing, Station.Latitude, Station.Longitude)
#Combining all Route columns into 1:

stns <- unite(stns, Routes, Route1, Route2, Route3, Route4, Route5, Route6, Route7, Route8, Route9, Route10, Route11, sep = " " )

Station Helpfulness: Variables: Station.Name, ADA, Vending, Staffing, Staff.Hours, Free.Crossover, Corner

helpful <- t %>% select(Station.Name, Staffing, Vending, ADA, Entrance.Type, Exit.Only, Free.Crossover)
head(helpful)
##   Station.Name Staffing Vending   ADA Entrance.Type Exit.Only Free.Crossover
## 1      25th St     FULL     YES FALSE         Stair                    FALSE
## 2      25th St     NONE     YES FALSE         Stair                    FALSE
## 3      36th St     FULL     YES FALSE         Stair                     TRUE
## 4      36th St     FULL     YES FALSE         Stair                     TRUE
## 5      36th St     FULL     YES FALSE         Stair                     TRUE
## 6      45th St     FULL     YES FALSE         Stair                     TRUE

Station Staffing

table(helpful$Staffing) 
## 
##   FULL   NONE   PART Spc Ev 
##   1119    700     45      4

Free Crossover/Transfer

table(helpful$Free.Crossover)
## 
## FALSE  TRUE 
##   420  1448

Exit Only

table(helpful$Exit.Only)
## 
##       Yes 
## 1812   56

ADA Access

table(helpful$ADA)
## 
## FALSE  TRUE 
##  1400   468

Vending Machines Available

table(helpful$Vending)
## 
##   NO  YES 
##  183 1685

Interactive Informative Map

pop_up <- paste(stns$Station.Name, "||", stns$Corner, "||", stns$Routes, ",", stns$Entrance.Type)
leaflet() %>%
  addTiles() %>%
  addCircleMarkers(lat = stns$Station.Latitude,  lng = stns$Station.Longitude, popup = pop_up, color = "purple", stroke = FALSE, fillOpacity = 0.2)