class: monash-bg-blue center middle hide-slide-number <div class="bg-black white" style="width:45%;right:0;bottom:0;padding-left:5px;border: solid 4px white;margin: auto;"> <i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. See here for <a href=part2-session2.pdf>PDF <i class="fas fa-file-pdf"></i></a>. </div> <br> .white[Push the **right arrow key** to see the next slide.] --- count: false background-image: url(images/d2bg5.jpg) background-size: cover class: hide-slide-number title-slide <div class="grid-row" style="grid: 1fr / 2fr;"> .item.center[ # <span style="text-shadow: 2px 2px 30px white;">Data Visualization with R <br> Workshop Part 2</span> <!-- ## <span style="color:yellow;text-shadow: 2px 2px 30px black;">Making maps</span> --> ] .center.shade_black.animated.bounceInUp.slower[ <br><br> ## Making maps <br> Presented by Di Cook Department of Econometrics and Business Statistics <img src="images/monash-one-line-reversed.png" style="width:500px"><br> <span><i class="fas fa-envelope faa-float animated "></i></span> dicook@monash.edu <span><i class="fab fa-twitter faa-float animated faa-fast "></i></span> @statsgen .bottom_abs.width100.bg-black[ 22nd Feb 2022 | methods@manchester | Zoom ] ] </div> --- class: font_smaller # <img src="images/1920px-World_Health_Organization_Logo.svg.png" width="50px" style="vertical-align: middle;"> Tuberculosis incidence The TB data is from the [WHO]( https://www.who.int/tb/country/data/download/en/). ``` ## # A tibble: 40,800 × 5 ## country year age_group sex count ## <chr> <dbl> <fct> <chr> <dbl> ## 1 Afghanistan 1997 15-24 m 10 ## 2 Afghanistan 1997 25-34 m 6 ## 3 Afghanistan 1997 35-44 m 3 ## 4 Afghanistan 1997 45-54 m 5 ## 5 Afghanistan 1997 55-64 m 2 ## 6 Afghanistan 1997 65- m 0 ## 7 Afghanistan 1997 15-24 f 38 ## 8 Afghanistan 1997 25-34 f 36 ## 9 Afghanistan 1997 35-44 f 14 ## 10 Afghanistan 1997 45-54 f 8 ## # … with 40,790 more rows ``` --- class: question <br><br><br><br> What is a choropleth map? -- <br> Why use a choropleth map? -- --- # How do we get a map? 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of Wales Island","Prince of Wales Island","Prince of Wales Island","Prince of Wales Island","Prince of Wales Island","Prince of Wales Island","Prince of Wales Island","Moa Island","Moa Island","Moa Island","Moa Island","Moa Island","Moa Island","Moa Island","Mulgrave Island","Mulgrave Island","Mulgrave Island","Mulgrave Island","Mulgrave Island","Mulgrave Island"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>long<\/th>\n <th>lat<\/th>\n <th>group<\/th>\n <th>order<\/th>\n <th>region<\/th>\n <th>subregion<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script> ] --- # Maps are basically groups of connected dots .left-code[ <br> <br> These are the points, defining the country boundary for Australia <br> <br> .font_small[ ```r oz <- world_map %>% filter(region == "Australia") ggplot(oz, aes(x = long, y = lat)) + geom_point() + * coord_map() ``` ] ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-4-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots .left-code[ <br> <br> Connect the dots <br> <br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = group)) + geom_point() + * geom_line() + coord_map() ``` ] <br> <span class=" faa-tada animated " style=" color:#D93F00; display: -moz-inline-stack; display: inline-block; transform: rotate(0deg);">What happened?</span> ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-5-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots .left-code[ <br> <br> Connect the dots <br> <br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = group)) + #geom_point() + * geom_path() + coord_map() ``` ] ] .right-plot[ <br> <br> <img src="images/day2-session2/unnamed-chunk-6-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ This map doesn't have states and territory connections, and also sub-regions is not uniquely defining islands. <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, * group = subregion)) + geom_path() + coord_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-7-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ We can also plot the map using `geom_polygon`, and fill with colour. <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, group = group)) + * geom_polygon() + coord_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-8-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Maps are basically groups of connected dots <br> <br> .left-code[ Using a .monash-orange2[map theme] makes the result look more map-like <br><br> .font_small[ ```r ggplot(oz, aes(x = long, y = lat, group = group)) + geom_polygon() + coord_map() + * theme_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-10-1.png" width="110%" style="display: block; margin: auto;" /> ] --- # Tips for mapping .top-color-box[For data analysis, maps are a set of points, connected correctly to generate polygons.] -- .color-box[Note: It is important when converting spatial objects from a mapping software to a data analysis project is .monash-blue2["thinning" the map] to make it smaller and efficient to work with. Both the [rmapshaper](https://cran.r-project.org/web/packages/rmapshaper/index.html) package and `st_simplify` in the [sf](https://r-spatial.github.io/sf/) have tools to thin the number of points defining a polygon, while respecting the shape, and adjacent boundaries.] --- class: transition middle # Let's make a choropleth map of tuberculosis --- # Pre-process the data Aggregate counts across sex and age group for 2012 <br> .font_small[ ```r tb_2012 <- tb %>% filter(year == 2012) %>% rename(region = country) %>% group_by(region) %>% summarise(count = sum(count)) ggplot(tb_2012, aes(map_id = region)) + * geom_map(aes(fill = count), map = world_map, * color="grey70", size = 0.1, na.rm = TRUE) + expand_limits(x = world_map$long, y = world_map$lat) + scale_fill_viridis("Count") + theme_map() ``` ] --- <img src="images/day2-session2/unnamed-chunk-11-1.png" width="100%" style="display: block; margin: auto;" /> -- .corner-box[What happened to the USA? UK?] --- # Check the name matching .font_small[ ```r wm_names <- world_map %>% select(region) %>% distinct() tb_names <- tb %>% filter(year == 2012) %>% select(country) %>% distinct() *tb_miss_from_wm <- anti_join(tb_names, wm_names, * by=c("country" = "region")) *wm_miss_from_tb <- anti_join(wm_names, tb_names, * by=c("region" = "country")) ``` ] --- ```r DT::datatable(tb_miss_from_wm, width = 1150, height = 100) ``` <div id="htmlwidget-bc2e61b42d2003ad4191" style="width:1150px;height:100px;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-bc2e61b42d2003ad4191">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33"],["Antigua and Barbuda","Bolivia (Plurinational State of)","British Virgin Islands","Brunei Darussalam","Cabo Verde","China, Hong Kong SAR","China, Macao SAR","Congo","Côte d'Ivoire","Curaçao","Czechia","Democratic People's Republic of Korea","Eswatini","Iran (Islamic Republic of)","Lao People's Democratic Republic","Micronesia (Federated States of)","Republic of Korea","Republic of Moldova","Russian Federation","Saint Kitts and Nevis","Saint Vincent and the Grenadines","Sint Maarten (Dutch part)","Syrian Arab Republic","Tokelau","Trinidad and Tobago","Tuvalu","United Kingdom of Great Britain and Northern Ireland","United Republic of Tanzania","United States of America","Venezuela (Bolivarian Republic of)","Viet Nam","Wallis and Futuna Islands","West Bank and Gaza Strip"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>country<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script> --- ```r DT::datatable(wm_miss_from_tb, width = 1150, height = 100) ``` <div id="htmlwidget-76f26022eb183ce57ae8" style="width:1150px;height:100px;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-76f26022eb183ce57ae8">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60","61","62","63","64","65","66","67","68","69","70"],["Antarctica","French Southern and Antarctic Lands","Antigua","Barbuda","Saint Barthelemy","Bolivia","Brunei","Ivory Coast","Republic of Congo","Cape Verde","Curacao","Czech Republic","Canary Islands","Falkland Islands","Reunion","Mayotte","French Guiana","Martinique","Guadeloupe","Faroe Islands","Micronesia","UK","Guernsey","Heard Island","Isle of Man","Cocos Islands","Christmas Island","Chagos Archipelago","Iran","Jersey","Siachen Glacier","Nevis","Saint Kitts","South Korea","Kosovo","Laos","Liechtenstein","Saint Martin","Moldova","Norfolk Island","Bonaire","Sint Eustatius","Saba","Pitcairn Islands","North Korea","Madeira Islands","Azores","Palestine","Russia","Western Sahara","South Sandwich Islands","South Georgia","Saint Helena","Ascension Island","Saint Pierre and Miquelon","Swaziland","Sint Maarten","Syria","Trinidad","Tobago","Taiwan","Tanzania","USA","Vatican","Grenadines","Saint Vincent","Venezuela","Virgin Islands","Vietnam","Wallis and Futuna"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>region<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"order":[],"autoWidth":false,"orderClasses":false,"columnDefs":[{"orderable":false,"targets":0}]}},"evals":[],"jsHooks":[]}</script> --- .top-color-box[Countries often have different names across data sets. Ideally one could use isocodes (e.g. AUS, AU) but this is not common practice.] -- .code-box[.font_small[ ```r tb_fixed <- tb %>% mutate(region=recode(country, "United States of America" = "USA", "United Kingdom of Great Britain and Northern Ireland" = "UK", "Russian Federation" = "Russia", "Viet Nam" = "Vietnam", "Venezuela (Bolivarian Republic of)" = "Venezuela", "Bolivia (Plurinational State of)" = "Bolivia", "Czechia" = "Czech Republic", "Iran (Islamic Republic of)" = "Iran", "Iran (Islamic Republic of)" = "Laos", "Democratic People's Republic of Korea" = "North Korea", "Republic of Korea" = "South Korea", "United Republic of Tanzania" = "Tanzania", "Congo" = "Republic of Congo")) ``` ] ] --- <br><br> 😄 <span class=" faa-bounce animated " style=" display: -moz-inline-stack; display: inline-block; transform: rotate(0deg);">Try again!</span> .font_small[ ```r *tb_2012 <- tb_fixed %>% filter(year == 2012) %>% group_by(region) %>% summarise(count = sum(count)) ggplot(tb_2012, aes(map_id = region)) + * geom_map(aes(fill = count), map = world_map, * color = "grey70", size = 0.1, na.rm = TRUE) + expand_limits(x = world_map$long, y = world_map$lat) + scale_fill_viridis("Count") + theme_map() ``` ] --- <img src="images/day2-session2/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" /> --- # Counts are typically skewed .left-code[ .font_small[ ```r ggplot(tb_2012, aes(x = count)) + geom_histogram() ``` ] <br> <br> Symmetrising count, helps visual perception of a choropleth map. <br> <br> <br> .font_small[ ```r ggplot(tb_2012, aes(x = count)) + geom_histogram() + * scale_x_log10() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-17-1.png" width="60%" style="display: block; margin: auto;" /> <img src="images/day2-session2/unnamed-chunk-18-1.png" width="60%" style="display: block; margin: auto;" /> ] --- # Choropleth on log scale <br><br> .font_small[ ```r *tb_2012_map <- world_map %>% left_join(tb_2012) ggplot(tb_2012_map, aes(x = long, y = lat, group=group)) + geom_polygon(aes(fill = count), color="grey70", size = 0.1, na.rm = TRUE) + expand_limits(x = world_map$long*1.1, y = world_map$lat*1.1) + * scale_fill_viridis("Count", trans = "log10") + theme_map() ``` ] <br> <br> .corner-box[Note: `geom_polygon()` can be used instead of `geom_map()`. Also `geom_sf()` works similarly with `sf` spatial polygons. ] --- <img src="images/day2-session2/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" /> --- class: transition middle # Choropleth maps can be misleading --- class: middle .info-box.pad10[The population is not likely to be uniformly distributed across space. Big spatial areas may have few individuals, and high density areas are likely to be very small spatially. Choropleth maps can mislead the reader about the distribution of the statistic relative to a population. Think about using a .blue[cartogram], which transforms the spatial polygons to represent the population whilst keeping faithful to geographic proximity.<br><br> ] -- <center> .blue["Land doesn't vote, people do"] </center> --- <!-- - Read the LGA data from `ozmaps` package. - This has LGAs for all of Australia. - Need to filter out Victoria LGAs, avoiding LGAs from other states with same name, and make the names match covid data names. This is done using a regex expression removing () state and LGA type text strings--> # COVID incidence in Victoria 2020 .pull-left[ <img src="images/day2-session2/covid-choropleth-1.png" width="100%" style="display: block; margin: auto;" /> ] <!-- - Incorporate population data to make cartogram - Population from https://www.planning.vic.gov.au/land-use-and-population-research/victoria-in-future/tab-pages/victoria-in-future-data-tables - Polygons are transformed so that area matches, as best possible, to the population --> .pull-right[ <img src="images/day2-session2/vic-cartogram-1.png" width="100%" style="display: block; margin: auto;" /> ] .corner-box[The .white[cartogram] package can be used to transform the polygons. Other alternative include hexagon binning with .white[sugarbag], and spatial facets with .white[geofacet].] --- class: transition middle # Point data overlaid on a map --- # Where are the platypi? <img src="https://upload.wikimedia.org/wikipedia/commons/7/7e/Platypus-sketch.jpg"> --- .left-code[ .font_small[ ```r load(here::here("data/platypus.rda")) p <- ggplot(platypus) + geom_point(aes(x = Longitude, y = Latitude), alpha = 0.1) p ``` <img src="images/day2-session2/unnamed-chunk-22-1.png" width="100%" style="display: block; margin: auto;" /> ] ] .right-plot[ <br> <br> .font_small[ ```r p + coord_map() ``` <img src="images/day2-session2/unnamed-chunk-23-1.png" width="100%" style="display: block; margin: auto;" /> ] ] --- # Extract Open Street Map using `ggmap` <br><br> Download and save the map, so that you don't need to do multiple downloads. <br><br> .font_small[ ```r oz_bbox <- c(112.9, # min long -45, # min lat 159, # max long -10) # max lat *oz_map <- get_map(location = oz_bbox, source = "osm") save(oz_map, file=here::here("data/oz_map.rda")) ``` ] --- # Platypus occurrences across Australia <br><br> .left-code[ .font_small[ ```r load(here::here("data/oz_map.rda")) ggmap(oz_map) + geom_point(data = platypus, aes(x = Longitude, y = Latitude), alpha = 0.1, colour = "orange") + theme_map() ``` ] ] .right-plot[ <img src="images/day2-session2/unnamed-chunk-26-1.png" width="100%" style="display: block; margin: auto;" /> ] --- class: exercise middle hide-slide-number <i class="fas fa-users"></i> # <i class="fas fa-code"></i> Open `part2-exercise-02.Rmd` <center> <div class="countdown clock" id="timer_6214a328" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">15</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> </center> --- # Resources These are sites with lots of useful information about making maps in R: <br> - https://www.littlemissdata.com/blog/maps - https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html - https://www.paulamoraga.com/book-geospatial/sec-spatialdataandCRS.html - https://rspatialdata.github.io - Thematic maps with [tmap](https://r-tmap.github.io/tmap/) - Spatial polygons with [sf](https://r-spatial.github.io/sf/) --- class: font_smaller background-color: #e5e5e5 # Session Information .scroll-350[ ```r devtools::session_info() ``` ``` ## 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sass 0.4.0 2021-05-12 [1] CRAN (R 4.1.0) ## scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0) ## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.1) ## sf * 1.0-6 2022-02-04 [1] CRAN (R 4.1.1) ## sp 1.4-6 2021-11-14 [1] CRAN (R 4.1.1) ## stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.1) ## stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.1.1) ## testthat 3.1.2 2022-01-20 [1] CRAN (R 4.1.1) ## tibble * 3.1.6 2021-11-07 [1] CRAN (R 4.1.1) ## tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.1.1) ## tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.0) ## tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.0) ## tzdb 0.2.0 2021-10-27 [1] CRAN (R 4.1.1) ## units 0.8-0 2022-02-05 [1] CRAN (R 4.1.1) ## usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.1) ## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0) ## vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0) ## viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.1.1) ## viridisLite * 0.4.0 2021-04-13 [1] CRAN (R 4.1.0) ## vroom 1.5.7 2021-11-30 [1] CRAN (R 4.1.1) ## whisker 0.4 2019-08-28 [1] CRAN (R 4.1.0) ## withr 2.4.3 2021-11-30 [1] CRAN (R 4.1.1) ## xaringan 0.22 2021-06-23 [1] CRAN (R 4.1.0) ## xaringanExtra 0.5.5 2022-02-16 [1] Github (gadenbuie/xaringanExtra@ee5092d) ## xfun 0.29 2021-12-14 [1] CRAN (R 4.1.1) ## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.1.1) ## yaml 2.2.2 2022-01-25 [1] CRAN (R 4.1.1) ## ## [1] /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library ## ## ────────────────────────────────────────────────────────────────────────────── ``` ] These slides are licensed under <br><center><a href="https://creativecommons.org/licenses/by-sa/3.0/au/"><img src="images/cc.svg" style="height:2em;"/><img src="images/by.svg" style="height:2em;"/><img src="images/sa.svg" style="height:2em;"/></a></center>