Setup

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.6     ✓ dplyr   1.0.4
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Get Data

Go to Tidy Tuesday and get the wind turbine data from Oct. 27, 2020.

Answer

wind_turbine <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-27/wind-turbine.csv')
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   objectid = col_double(),
##   province_territory = col_character(),
##   project_name = col_character(),
##   total_project_capacity_mw = col_double(),
##   turbine_identifier = col_character(),
##   turbine_number_in_project = col_character(),
##   turbine_rated_capacity_k_w = col_double(),
##   rotor_diameter_m = col_double(),
##   hub_height_m = col_double(),
##   manufacturer = col_character(),
##   model = col_character(),
##   commissioning_date = col_character(),
##   latitude = col_double(),
##   longitude = col_double(),
##   notes = col_character()
## )

Basic Look

Run glimpse() on the dataframe.

Answer

glimpse(wind_turbine)
## Rows: 6,698
## Columns: 15
## $ objectid                   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
## $ province_territory         <chr> "Alberta", "Alberta", "Alberta", "Alberta"…
## $ project_name               <chr> "Optimist Wind Energy", "Castle River Wind…
## $ total_project_capacity_mw  <dbl> 0.90, 44.00, 3.78, 3.78, 3.78, 3.78, 19.50…
## $ turbine_identifier         <chr> "OWE1", "CRW1", "WWT1", "WWT2", "WWT3", "W…
## $ turbine_number_in_project  <chr> "1/2", "1/60", "1/6", "2/6", "3/6", "4/6",…
## $ turbine_rated_capacity_k_w <dbl> 150, 600, 600, 600, 600, 660, 1300, 1300, …
## $ rotor_diameter_m           <dbl> 23, 44, 44, 44, 44, 47, 60, 60, 60, 60, 60…
## $ hub_height_m               <dbl> 30, 40, 50, 50, 50, 50, 46, 46, 46, 46, 46…
## $ manufacturer               <chr> "Bonus", "Vestas", "Vestas", "Vestas", "Ve…
## $ model                      <chr> "AN 150/30", "V44/600", "V44/600", "V44/60…
## $ commissioning_date         <chr> "1993", "1997", "1998", "1998", "1998", "2…
## $ latitude                   <dbl> 49.52535, 49.51264, 49.23307, 49.23303, 49…
## $ longitude                  <dbl> -114.0548, -114.0357, -113.6549, -113.6302…
## $ notes                      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

Basic Changes

Use select to keep only the interesting variables. Make the date variable numeric. Make appropriate variables factors. Glimpse the resulting dataframe, which should be named “turbines”

turbines = wind_turbine %>% 
  select(objectid, province_territory, turbine_rated_capacity_k_w, rotor_diameter_m,
         hub_height_m, manufacturer, commissioning_date, latitude, longitude) %>% 
  mutate(province_territory = factor(province_territory),
         manufacturer = factor(manufacturer),
         commissioning_date = as.numeric(commissioning_date))  %>% 
        glimpse()
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
## Rows: 6,698
## Columns: 9
## $ objectid                   <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
## $ province_territory         <fct> Alberta, Alberta, Alberta, Alberta, Albert…
## $ turbine_rated_capacity_k_w <dbl> 150, 600, 600, 600, 600, 660, 1300, 1300, …
## $ rotor_diameter_m           <dbl> 23, 44, 44, 44, 44, 47, 60, 60, 60, 60, 60…
## $ hub_height_m               <dbl> 30, 40, 50, 50, 50, 50, 46, 46, 46, 46, 46…
## $ manufacturer               <fct> Bonus, Vestas, Vestas, Vestas, Vestas, Ves…
## $ commissioning_date         <dbl> 1993, 1997, 1998, 1998, 1998, 2000, 2001, …
## $ latitude                   <dbl> 49.52535, 49.51264, 49.23307, 49.23303, 49…
## $ longitude                  <dbl> -114.0548, -114.0357, -113.6549, -113.6302…

Run summary() on the dataframe turbines.

summary(turbines)
##     objectid           province_territory turbine_rated_capacity_k_w
##  Min.   :   1   Ontario         :2663     Min.   :  65              
##  1st Qu.:1675   Quebec          :1991     1st Qu.:1600              
##  Median :3350   Alberta         : 900     Median :1880              
##  Mean   :3350   Nova Scotia     : 310     Mean   :1967              
##  3rd Qu.:5024   British Columbia: 292     3rd Qu.:2300              
##  Max.   :6698   Saskatchewan    : 153     Max.   :3750              
##                 (Other)         : 389     NA's   :220               
##  rotor_diameter_m  hub_height_m       manufacturer  commissioning_date
##  Min.   : 15.00   Min.   : 24.50   Vestas   :1834   Min.   :1993      
##  1st Qu.: 80.00   1st Qu.: 80.00   GE       :1725   1st Qu.:2009      
##  Median : 90.00   Median : 80.00   Siemens  :1248   Median :2012      
##  Mean   : 88.62   Mean   : 83.34   Enercon  : 960   Mean   :2011      
##  3rd Qu.:100.00   3rd Qu.: 92.00   Senvion  : 643   3rd Qu.:2014      
##  Max.   :141.00   Max.   :132.00   NEG Micon: 132   Max.   :2019      
##                                    (Other)  : 156   NA's   :868       
##     latitude       longitude      
##  Min.   :42.00   Min.   :-135.23  
##  1st Qu.:43.98   1st Qu.: -84.41  
##  Median :46.67   Median : -80.67  
##  Mean   :46.76   Mean   : -83.03  
##  3rd Qu.:49.17   3rd Qu.: -67.85  
##  Max.   :64.49   Max.   : -52.97  
##

Questions

You should try to answer the following questions.

  1. How has the number of turbines installed per year increased over time.
  2. How has the amount of capacity installed per year inceased over time.
  3. Repeat the previous questions showing the differences among the provinces.
  4. Show how current installed capacity varies among the provinces. Put the province names on the y-axis.
  5. Show how current number of turbines varies among the provinces. Put the province names on the y-axis.