class: center, middle, hide-logo <style type="text/css"> pre { background: #F8F8F8; max-width: 100%; overflow-x: scroll; } </style> <style type="text/css"> .scroll-output { height: 80%; overflow-y: scroll; } </style> <img src="GraphicsSlides/Logo RUG hell.png" width="50%" /> # Data Visualisation ### Canadian Wind Turbines ##### Author/Presenter: Mathias Steilen ##### Last updated: _2022-09-20 20:08:50_ --- class: hide-logo, center, middle <img src="GraphicsSlides/Wind Turbines.jpg" width="100%" /> --- # Goals for today's session <br> <br> <br> <img src="GraphicsSlides/DataViz_Meeting_Process.png" width="100%" /> --- ### Data and Libraries ```r wind <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-27/wind-turbine.csv') ``` ``` ## Rows: 6698 Columns: 15 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (8): province_territory, project_name, turbine_identifier, turbine_numbe... ## dbl (7): objectid, total_project_capacity_mw, turbine_rated_capacity_k_w, ro... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` ```r library(tidyverse) library(broom) ``` --- ### The Data ```r glimpse(wind) ``` ``` ## 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", "WW… ## $ 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, 1… ## $ 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", "Ves… ## $ model <chr> "AN 150/30", "V44/600", "V44/600", "V44/600… ## $ commissioning_date <chr> "1993", "1997", "1998", "1998", "1998", "20… ## $ 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,… ``` --- ### `commissioning_date` The column is formatted as a character because of combinations of multiple years, but should really be a factor. For simplicity, let's just parse the first value that occurs to a new `year` column. ```r wind %>% filter(str_detect(commissioning_date, "/")) %>% count(commissioning_date, sort = T) ``` ``` ## # A tibble: 10 × 2 ## commissioning_date n ## <chr> <int> ## 1 2014/2015 207 ## 2 2013/2014 154 ## 3 2011/2012 141 ## 4 2006/2008 133 ## 5 2005/2006/2012 73 ## 6 2000/2001 59 ## 7 2004/2005 47 ## 8 2006/2007 35 ## 9 2001/2003 16 ## 10 2002/2006 3 ``` --- ### `commissioning_date` ```r wind <- wind %>% mutate(year = parse_number(commissioning_date) %>% as.factor) wind %>% count(year) ``` ``` ## # A tibble: 25 × 2 ## year n ## <fct> <int> ## 1 1993 2 ## 2 1995 1 ## 3 1997 1 ## 4 1998 3 ## 5 1999 132 ## 6 2000 61 ## 7 2001 66 ## 8 2002 11 ## 9 2003 116 ## 10 2004 121 ## # … with 15 more rows ``` --- ### Missing Values .pull-left[ ```r colMeans(is.na(wind)) %>% tidy() %>% rename(missing = x) %>% mutate(names = fct_reorder(names, missing)) %>% ggplot(aes(missing, names)) + geom_col(fill = "#2A3950") + labs(title = "Missing Values", y = NULL) + scale_x_continuous(labels = scales::percent_format(), limits = c(0,1)) + theme_light() + theme(plot.title = element_text(face = "bold", size = 14), panel.grid.major.y = element_blank()) ``` ] .pull-right[ <!-- --> ] --- ### Looks clean, let's collect some questions! ```r wind %>% glimpse() ``` ``` ## Rows: 6,698 ## Columns: 16 ## $ 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", "WW… ## $ 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, 1… ## $ 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", "Ves… ## $ model <chr> "AN 150/30", "V44/600", "V44/600", "V44/600… ## $ commissioning_date <chr> "1993", "1997", "1998", "1998", "1998", "20… ## $ 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,… ## $ year <fct> 1993, 1997, 1998, 1998, 1998, 2000, 2001, 2… ``` --- ### Asking Questions Take a moment to get a feel for the data set and write down questions that seem interesting to you. Examples might be: - Did wind turbines get taller with time? - How are projects distributed across Canada? Tip: Use functions like `count()`, `group_by()`, `summarise()`, `select()` #### 🕒 10 min .center[ <img src="GraphicsSlides/count_meme.png" width="40%" /> ] --- ### Answering Questions by Visualising Have a look at five questions and pick your team according to your greatest interest. Spend 30 min on creating one or multiple charts with your team. If your time is done before the time is up, feel free to join other teams. #### 🕒 30 min .center[ <img src="GraphicsSlides/teamwork.png" width="50%" /> ] --- ### Discussion Let's walk through your charts and discuss them. Come to the front with your computer or send the code to our email address. #### 🕒 15 min (3 min per group) --- ### Example Question: How are wind energy projects distributed across Canada? .center[ <img src="Canadian-Wind-Turbines_files/figure-html/unnamed-chunk-17-1.png" width="80%" /> ] --- #### _Example Question (Credit: Mathias Steilen)_ .scroll-output[ ```r canada <- map_data("world", region = "canada") projects <- wind %>% group_by(project_name) %>% summarise(across(c(total_project_capacity_mw, latitude, longitude, year), last)) %>% ungroup() %>% mutate(year = as.numeric(as.character(year)), period = cut(year, seq(1990, 2020, 10), dig.lab = 5)) ggplot() + geom_polygon(data = canada, aes(x = long, y = lat, group = group), color = "grey50", fill = "gray95", alpha = 0.3, size = 0.25) + geom_point(data = projects, aes(x = longitude, y = latitude, size = total_project_capacity_mw, colour = period), alpha = 0.25) + scale_size_continuous(range = c(0.5, 3), labels = scales::comma_format(suffix = " MW")) + labs(title = "Wind Energy Projects In Canada", subtitle = "Each dot constitutes one completed wind farm with size representing production capacity", y = NULL, x = NULL, size = "Project Capacity:", colour = "Period of Construction:") + coord_map() + theme_minimal() + theme(panel.background = element_blank(), panel.grid = element_blank(), axis.ticks = element_blank(), axis.line = element_blank(), axis.text = element_blank(), plot.title = element_text(size = 12, face="bold", colour="black"), plot.subtitle = element_text(face = "italic", colour = "gray50")) + scale_colour_manual(values = c("firebrick", "darkorange", "dodgerblue4", "dodgerblue")) ``` ] --- # That's it for today! For further questions, feel free to reach out to us. Make sure to stay updated on our socials and via our website where all resources and dates are also published. We are looking forward to welcome you to the next event! [Data Source](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-10-27/readme.md) <br> .center[ <img src="GraphicsSlides/Logo RUG hell.png" width="60%" /> **[Website](https://rusergroup-sg.ch/) | [Instagram](https://www.instagram.com/rusergroupstgallen/?hl=en) | [Twitter](https://twitter.com/rusergroupsg)** ] --- class: middle, inverse, hide-logo # Thank you for attending! <em style="color:#404040">The material provided in this presentation including any information, tools, features, content and any images incorporated in the presentation, is solely for your lawful, personal, private use. 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