Introduction

In 2016, USGS, LBNL, and AWEA collaborated to develop USWTDB for a more comprensehive and accurate wind turbine data set. As of Janurary 2019, the USWTDB contains more than 58,000 turbines constructed since 1980 in atleast 43 states including Puerto Rice and Guam.The data was released to the public in April 2018 and recently updated in April 2019.

U.S. Wind Turbine Facts

  • The average US home uses 867 kWh per month, and the average wind turbine generates energy in 94 minutes to power an average US home for one month.

  • On average, 3,000 turbines have been built in the U.S. each year since 2005.

Data Preparation

The original dataset is provided by the United States Geological Survey in zip folder. For more information on the data click here. The data will download as a zip folder and the code will download the file called uswtdb_v2_0_20190417.csv

Import Data

url<-'https://eerscmap.usgs.gov/uswtdb/assets/data/uswtdbCSV.zip'
tmp<-tempfile()
download.file(url,tmp)
unzip(tmp)
unlink(tmp)
usgs<-read.csv('uswtdb_v2_0_20190417.csv')

Structure of dataset

str(usgs)
## 'data.frame':    59338 obs. of  24 variables:
##  $ case_id   : int  3073326 3072687 3073388 3072701 3073382 3002329 3073341 3011054 3073432 3003585 ...
##  $ faa_ors   : Factor w/ 52001 levels "","02-000669",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ faa_asn   : Factor w/ 51801 levels "","1987-AGL-900-OE",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ usgs_pr_id: int  4970 5041 5814 5043 5810 5830 5787 5837 4978 5832 ...
##  $ t_state   : Factor w/ 45 levels "AK","AR","AZ",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ t_county  : Factor w/ 531 levels "Adair County",..: 248 248 248 248 248 248 248 248 248 248 ...
##  $ t_fips    : int  6029 6029 6029 6029 6029 6029 6029 6029 6029 6029 ...
##  $ p_name    : Factor w/ 1518 levels "251 Wind","30 MW Iowa DG Portfolio",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ p_year    : int  1987 1987 1987 1987 1987 1987 1987 1987 1987 1987 ...
##  $ p_tnum    : int  194 194 194 194 194 194 194 194 194 194 ...
##  $ p_cap     : num  18.4 18.4 18.4 18.4 18.4 ...
##  $ t_manu    : Factor w/ 69 levels "","AAER","Acciona",..: 63 63 63 63 63 63 63 63 63 63 ...
##  $ t_model   : Factor w/ 266 levels "","1.3-65","1.5_74",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ t_cap     : int  95 95 95 95 95 95 95 95 95 95 ...
##  $ t_hh      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ t_rd      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ t_rsa     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ t_ttlh    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ t_conf_atr: int  2 2 2 2 2 2 2 2 2 2 ...
##  $ t_conf_loc: int  3 3 3 3 3 3 3 3 3 3 ...
##  $ t_img_date: Factor w/ 633 levels "","1/1/2006",..: 393 393 393 393 393 393 393 393 393 393 ...
##  $ t_img_srce: Factor w/ 4 levels "Bing Maps Aerial",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ xlong     : num  -118 -118 -118 -118 -118 ...
##  $ ylat      : num  35.1 35.1 35.1 35.1 35.1 ...

For this project, the focus will be on case id, county and state, year activated, manufacturer, and coordinates. This dataset does not need any data elimination as it containts the coordinates for each Wind Turbine.

Creating Leaflet Visual

First and foremost download and read in the leaflet package.

Interactive Layer Display

An interactive map will be created with two map layers. For more map style options click here.

usgs %>% 
leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery, group="World Imagery") %>% 
addProviderTiles(providers$Esri.WorldStreetMap, group= "Street View") %>%
addLayersControl( baseGroups= c("World Imagery", "Street View"), options= layersControlOptions(collapsed=FALSE))

Mapping Clusters

For a better visual of the Wind Turbines a clusters of markers will be used. You can zoom in to each cluster, which will separate to see the individual markers with addMarkers(clusterOptions = markerClusterOptions()) . A popup will be added to each marker containing: case id, location, year activated, and manufacturer. Click on any marker for more information on the Wind Turbines. The code below will be added to the Interactive Layer Display Map using %>% as a single line of code.

addAwesomeMarkers(lng= ~xlong, lat=~ylat, popup= ~paste("ID:",dat$case_id,"<br>","Location:",usgs$t_county,",",usgs$t_state,"<br>","Year Activated:",usgs$p_year,"<br>","Manufacturer:", usgs$t_manu), clusterOptions=markerClusterOptions())