class: center, middle, inverse, title-slide .title[ # Advanced Data Visualization ] .subtitle[ ## Going Deeper with R ] --- layout: true <div class="dk-footer"> <span> <a href="https://rfortherestofus.com/" target="_blank">R for the Rest of Us </a> </span> </div> --- class: center, middle, dk-section-title background-image:url("images/jbm-covid-plot.jpeg") # Best Practices in Data Visualization in R --- class: center, middle  .small[ Source: [Financial Times, March 11, 2020](https://twitter.com/jburnmurdoch/status/1237737352879112194) ] --- class: center, middle, dk-section-title background-image:url("images/highlight.jpg") ## Highlight --- class: center, middle  --- class: center, middle, dk-section-title background-image:url("images/declutter.jpg") ## Declutter --- class: center, middle  --- class: center, middle, dk-section-title background-image:url("images/explain.jpg") ## Explain --- class: center, middle  --- class: center, middle, dk-section-title background-image:url("images/sparkle.jpg") ## Sparkle --- class: center, middle  --- class: center, middle, dk-section-title background-image:url("images/messy.jpg") # Tidy Data --- ## Can We Plot Untidy Data? ```r german_speakers_numeric <- read_excel(path = "data-raw/german-and-french-speakers.xlsx", sheet = "German speakers", na = "-") %>% clean_names() ``` -- ```r german_speakers_numeric ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> 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Charter School","2":"4593","3":"Medford SD 549C","4":"2048","5":"0.25000000","6":"2018-2019"},{"1":"Forest Grove Community School","2":"4595","3":"Forest Grove SD 15","4":"2241","5":"0.54545455","6":"2018-2019"},{"1":"Gresham Arthur Academy","2":"4601","3":"Gresham-Barlow SD 10J","4":"2183","5":"0.68965517","6":"2018-2019"},{"1":"St Helens Arthur Academy","2":"4602","3":"St Helens SD 502","4":"1948","5":"0.55172414","6":"2018-2019"},{"1":"The Cottonwood School of Civics and Science","2":"4604","3":"Oregon Department of Education","4":"2336","5":"0.29166667","6":"2018-2019"},{"1":"Inavale Community Partners dba Muddy Creek Charter School","2":"4637","3":"Corvallis SD 509J","4":"1901","5":"0.82352941","6":"2018-2019"},{"1":"Sue Buel Elementary","2":"4639","3":"McMinnville SD 40","4":"2256","5":"0.44285714","6":"2018-2019"},{"1":"Creative Science School","2":"4640","3":"Portland SD 1J","4":"2180","5":"0.37500000","6":"2018-2019"},{"1":"Free Orchards Elementary School","2":"4641","3":"Hillsboro SD 1J","4":"2239","5":"0.40425532","6":"2018-2019"},{"1":"Lincoln Street Elementary School","2":"4642","3":"Hillsboro SD 1J","4":"2239","5":"0.20967742","6":"2018-2019"},{"1":"Quatama Elementary School","2":"4643","3":"Hillsboro SD 1J","4":"2239","5":"0.68493151","6":"2018-2019"},{"1":"Ponderosa Elementary","2":"4646","3":"Bend-LaPine Administrative SD 1","4":"1976","5":"0.49484536","6":"2018-2019"},{"1":"Summit Learning Charter","2":"4670","3":"Estacada SD 108","4":"1930","5":"0.37500000","6":"2018-2019"},{"1":"Bonny Slope Elementary School","2":"4671","3":"Beaverton SD 48J","4":"2243","5":"0.77678571","6":"2018-2019"},{"1":"William E Miller Elementary","2":"4680","3":"Bend-LaPine Administrative SD 1","4":"1976","5":"0.82644628","6":"2018-2019"},{"1":"Oregon Virtual Academy","2":"4690","3":"North Bend SD 13","4":"1966","5":"0.31521739","6":"2018-2019"},{"1":"Silvies River Charter School","2":"4702","3":"Frenchglen SD 16","4":"2020","5":"0.25925926","6":"2018-2019"},{"1":"Rosedale Elementary School","2":"4703","3":"Hillsboro SD 1J","4":"2239","5":"0.44642857","6":"2018-2019"},{"1":"Springville K-8 School","2":"4712","3":"Beaverton SD 48J","4":"2243","5":"0.84466019","6":"2018-2019"},{"1":"Scouters Mountain Elementary","2":"4713","3":"North Clackamas SD 12","4":"1924","5":"0.56842105","6":"2018-2019"},{"1":"Verne A Duncan Elementary School","2":"4714","3":"North Clackamas SD 12","4":"1924","5":"0.44565217","6":"2018-2019"},{"1":"The Ivy School","2":"4720","3":"Oregon Department of Education","4":"2336","5":"0.60000000","6":"2018-2019"},{"1":"Baker Web Academy","2":"4728","3":"Baker SD 5J","4":"1894","5":"0.28767123","6":"2018-2019"},{"1":"Edy Ridge Elementary School","2":"4730","3":"Sherwood SD 88J","4":"2244","5":"0.80208333","6":"2018-2019"},{"1":"Camas Ridge Community Elementary","2":"4739","3":"Eugene SD 4J","4":"2082","5":"0.42857143","6":"2018-2019"},{"1":"Timber Ridge School","2":"4744","3":"Greater Albany Public SD 8J","4":"2100","5":"0.53061224","6":"2018-2019"},{"1":"Molalla River Academy","2":"4745","3":"Molalla River SD 35","4":"1925","5":"0.25000000","6":"2018-2019"},{"1":"The Community Roots School","2":"4746","3":"Silver Falls SD 4J","4":"2138","5":"0.46666667","6":"2018-2019"},{"1":"Ardenwald Elementary School","2":"4762","3":"North Clackamas SD 12","4":"1924","5":"0.45762712","6":"2018-2019"},{"1":"Happy Valley Elementary School","2":"4763","3":"North Clackamas SD 12","4":"1924","5":"0.69444444","6":"2018-2019"},{"1":"Lewelling Elementary School","2":"4764","3":"North Clackamas SD 12","4":"1924","5":"0.28813559","6":"2018-2019"},{"1":"Linwood Elementary School","2":"4765","3":"North Clackamas SD 12","4":"1924","5":"0.14285714","6":"2018-2019"},{"1":"Oregon Trail Elementary School","2":"4766","3":"North Clackamas SD 12","4":"1924","5":"0.42857143","6":"2018-2019"},{"1":"Rosland Elementary","2":"4793","3":"Bend-LaPine Administrative SD 1","4":"1976","5":"0.54545455","6":"2018-2019"},{"1":"Arco Iris Spanish Immersion School","2":"4805","3":"Beaverton SD 48J","4":"2243","5":"0.54166667","6":"2018-2019"},{"1":"Oregon Trail Academy","2":"4820","3":"Oregon Trail SD 46","4":"1926","5":"0.89285714","6":"2018-2019"},{"1":"Logos Public Charter School","2":"4821","3":"Medford SD 549C","4":"2048","5":"0.34615385","6":"2018-2019"},{"1":"Rockwood Preparatory Academy","2":"4822","3":"Reynolds SD 7","4":"2182","5":"0.31707317","6":"2018-2019"},{"1":"Sunny Wolf Charter School","2":"4823","3":"Three Rivers/Josephine County SD","4":"2055","5":"0.65000000","6":"2018-2019"},{"1":"Eagle Charter School","2":"4850","3":"Oregon Department of Education","4":"2336","5":"0.41666667","6":"2018-2019"},{"1":"Kalapuya Elementary School","2":"4858","3":"Salem-Keizer SD 24J","4":"2142","5":"0.72093023","6":"2018-2019"},{"1":"Hope Chinese Charter School","2":"4867","3":"Beaverton SD 48J","4":"2243","5":"0.82692308","6":"2018-2019"},{"1":"Lowrie Primary School","2":"5056","3":"West Linn-Wilsonville SD 3J","4":"1922","5":"0.58823529","6":"2018-2019"},{"1":"Trillium Creek Primary School","2":"5057","3":"West Linn-Wilsonville SD 3J","4":"1922","5":"0.75675676","6":"2018-2019"},{"1":"Two Rivers Dos Rios Elementary","2":"5059","3":"Springfield SD 19","4":"2083","5":"0.22972973","6":"2018-2019"},{"1":"Le Monde French Immersion Public Charter School","2":"5060","3":"Portland SD 1J","4":"2180","5":"0.84126984","6":"2018-2019"},{"1":"Cesar E Chavez Elem","2":"5064","3":"Salem-Keizer SD 24J","4":"2142","5":"0.31111111","6":"2018-2019"},{"1":"Battle Creek Elementary School","2":"5066","3":"Salem-Keizer SD 24J","4":"2142","5":"0.38317757","6":"2018-2019"},{"1":"Kids Unlimited Academy","2":"5205","3":"Medford SD 549C","4":"2048","5":"0.46000000","6":"2018-2019"},{"1":"Kairos PDX","2":"5218","3":"Portland SD 1J","4":"2180","5":"0.08000000","6":"2018-2019"},{"1":"Crater Lake Charter Academy","2":"5251","3":"Eagle Point SD 9","4":"2043","5":"0.20000000","6":"2018-2019"},{"1":"Mountain View Academy","2":"5252","3":"Lowell SD 71","4":"2092","5":"0.75000000","6":"2018-2019"},{"1":"Silver Rail Elementary School","2":"5293","3":"Bend-LaPine Administrative SD 1","4":"1976","5":"0.39726027","6":"2018-2019"},{"1":"Dallas Community Charter","2":"5298","3":"Dallas SD 2","4":"2190","5":"0.22727273","6":"2018-2019"},{"1":"Barnes Butte Elementary","2":"5302","3":"Crook County SD","4":"1970","5":"0.32989691","6":"2018-2019"},{"1":"Bend International School","2":"5309","3":"Bend-LaPine Administrative SD 1","4":"1976","5":"0.87500000","6":"2018-2019"},{"1":"Bridge Charter Academy","2":"5349","3":"Lowell SD 71","4":"2092","5":"0.09090909","6":"2018-2019"},{"1":"Sato Elementary School","2":"5382","3":"Beaverton SD 48J","4":"2243","5":"0.86407767","6":"2018-2019"},{"1":"Rose City Park","2":"5427","3":"Portland SD 1J","4":"2180","5":"0.63218391","6":"2018-2019"},{"1":"Gib Olinger Elementary School","2":"5434","3":"Milton-Freewater Unified SD 7","4":"2205","5":"0.33571429","6":"2018-2019"},{"1":"Cascade Virtual Academy","2":"5440","3":"Mitchell SD 55","4":"2249","5":"0.14285714","6":"2018-2019"},{"1":"Oregon Family School","2":"5446","3":"Harney County SD 4","4":"2015","5":"0.11764706","6":"2018-2019"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[5],"max":[5]},"pages":{}}} </script> </div> --- ## Pipe Data Into ggplot ```r third_grade_math_proficiency %>% filter(year == "2018-2019") %>% filter(district == "Portland SD 1J") ``` -- <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> {"columns":[{"label":["school"],"name":[1],"type":["chr"],"align":["left"]},{"label":["school_id"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["district"],"name":[3],"type":["chr"],"align":["left"]},{"label":["district_id"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["percent_proficient"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["year"],"name":[6],"type":["chr"],"align":["left"]}],"data":[{"1":"Abernethy Elementary School","2":"822","3":"Portland SD 1J","4":"2180","5":"0.70652174","6":"2018-2019"},{"1":"Ainsworth Elementary School","2":"823","3":"Portland SD 1J","4":"2180","5":"0.80434783","6":"2018-2019"},{"1":"Alameda Elementary School","2":"824","3":"Portland SD 1J","4":"2180","5":"0.83050847","6":"2018-2019"},{"1":"Arleta Elementary School","2":"826","3":"Portland SD 1J","4":"2180","5":"0.42307692","6":"2018-2019"},{"1":"Astor Elementary School","2":"827","3":"Portland SD 1J","4":"2180","5":"0.59523810","6":"2018-2019"},{"1":"Atkinson Elementary School","2":"828","3":"Portland SD 1J","4":"2180","5":"0.50793651","6":"2018-2019"},{"1":"Rosa Parks Elementary School","2":"829","3":"Portland SD 1J","4":"2180","5":"0.14893617","6":"2018-2019"},{"1":"Beach Elementary School","2":"830","3":"Portland SD 1J","4":"2180","5":"0.56521739","6":"2018-2019"},{"1":"Boise-Eliot Elementary School","2":"833","3":"Portland SD 1J","4":"2180","5":"0.17500000","6":"2018-2019"},{"1":"Bridger Elementary School","2":"834","3":"Portland SD 1J","4":"2180","5":"0.60317460","6":"2018-2019"},{"1":"Bridlemile Elementary School","2":"835","3":"Portland SD 1J","4":"2180","5":"0.68292683","6":"2018-2019"},{"1":"Buckman Elementary School","2":"837","3":"Portland SD 1J","4":"2180","5":"0.56410256","6":"2018-2019"},{"1":"Capitol Hill Elementary School","2":"838","3":"Portland SD 1J","4":"2180","5":"0.66250000","6":"2018-2019"},{"1":"Chapman Elementary School","2":"839","3":"Portland SD 1J","4":"2180","5":"0.69736842","6":"2018-2019"},{"1":"Chief Joseph Elementary School","2":"840","3":"Portland SD 1J","4":"2180","5":"0.50877193","6":"2018-2019"},{"1":"Cesar Chavez K-8 School","2":"841","3":"Portland SD 1J","4":"2180","5":"0.26000000","6":"2018-2019"},{"1":"Harrison Park School","2":"842","3":"Portland SD 1J","4":"2180","5":"0.16438356","6":"2018-2019"},{"1":"Creston Elementary School","2":"843","3":"Portland SD 1J","4":"2180","5":"0.55263158","6":"2018-2019"},{"1":"Duniway Elementary School","2":"844","3":"Portland SD 1J","4":"2180","5":"0.70588235","6":"2018-2019"},{"1":"Faubion Elementary School","2":"847","3":"Portland SD 1J","4":"2180","5":"0.21052632","6":"2018-2019"},{"1":"Glencoe Elementary School","2":"850","3":"Portland SD 1J","4":"2180","5":"0.69512195","6":"2018-2019"},{"1":"Grout Elementary School","2":"854","3":"Portland SD 1J","4":"2180","5":"0.48333333","6":"2018-2019"},{"1":"Hayhurst Elementary School","2":"855","3":"Portland SD 1J","4":"2180","5":"0.67021277","6":"2018-2019"},{"1":"Beverly Cleary School","2":"857","3":"Portland SD 1J","4":"2180","5":"0.68055556","6":"2018-2019"},{"1":"Irvington Elementary School","2":"861","3":"Portland SD 1J","4":"2180","5":"0.64285714","6":"2018-2019"},{"1":"James John Elementary School","2":"862","3":"Portland SD 1J","4":"2180","5":"0.41860465","6":"2018-2019"},{"1":"Kelly Elementary School","2":"864","3":"Portland SD 1J","4":"2180","5":"0.23376623","6":"2018-2019"},{"1":"Martin Luther King Jr. School","2":"866","3":"Portland SD 1J","4":"2180","5":"0.09090909","6":"2018-2019"},{"1":"Laurelhurst Elementary School","2":"868","3":"Portland SD 1J","4":"2180","5":"0.68000000","6":"2018-2019"},{"1":"Lee Elementary School","2":"869","3":"Portland SD 1J","4":"2180","5":"0.22222222","6":"2018-2019"},{"1":"Lent Elementary School","2":"870","3":"Portland SD 1J","4":"2180","5":"0.11111111","6":"2018-2019"},{"1":"Lewis Elementary School","2":"871","3":"Portland SD 1J","4":"2180","5":"0.54687500","6":"2018-2019"},{"1":"Llewellyn Elementary School","2":"872","3":"Portland SD 1J","4":"2180","5":"0.79268293","6":"2018-2019"},{"1":"Maplewood Elementary School","2":"873","3":"Portland SD 1J","4":"2180","5":"0.43750000","6":"2018-2019"},{"1":"Marysville Elementary School","2":"875","3":"Portland SD 1J","4":"2180","5":"0.25000000","6":"2018-2019"},{"1":"Peninsula Elementary School","2":"879","3":"Portland SD 1J","4":"2180","5":"0.27272727","6":"2018-2019"},{"1":"Richmond Elementary School","2":"883","3":"Portland SD 1J","4":"2180","5":"0.77884615","6":"2018-2019"},{"1":"Rigler Elementary School","2":"884","3":"Portland SD 1J","4":"2180","5":"0.06666667","6":"2018-2019"},{"1":"Sabin Elementary School","2":"886","3":"Portland SD 1J","4":"2180","5":"0.61971831","6":"2018-2019"},{"1":"Scott Elementary School","2":"887","3":"Portland SD 1J","4":"2180","5":"0.27027027","6":"2018-2019"},{"1":"Sitton Elementary School","2":"889","3":"Portland SD 1J","4":"2180","5":"0.14545455","6":"2018-2019"},{"1":"Skyline Elementary School","2":"890","3":"Portland SD 1J","4":"2180","5":"0.50000000","6":"2018-2019"},{"1":"Stephenson Elementary School","2":"892","3":"Portland SD 1J","4":"2180","5":"0.80000000","6":"2018-2019"},{"1":"Sunnyside Environmental School","2":"893","3":"Portland SD 1J","4":"2180","5":"0.39655172","6":"2018-2019"},{"1":"Vernon Elementary School","2":"895","3":"Portland SD 1J","4":"2180","5":"0.52380952","6":"2018-2019"},{"1":"Vestal Elementary School","2":"896","3":"Portland SD 1J","4":"2180","5":"0.64285714","6":"2018-2019"},{"1":"Whitman Elementary School","2":"900","3":"Portland SD 1J","4":"2180","5":"0.37500000","6":"2018-2019"},{"1":"Woodlawn Elementary School","2":"902","3":"Portland SD 1J","4":"2180","5":"0.46666667","6":"2018-2019"},{"1":"Woodmere Elementary School","2":"903","3":"Portland SD 1J","4":"2180","5":"0.21276596","6":"2018-2019"},{"1":"Woodstock Elementary School","2":"904","3":"Portland SD 1J","4":"2180","5":"0.69620253","6":"2018-2019"},{"1":"Metropolitan Learning Center","2":"916","3":"Portland SD 1J","4":"2180","5":"0.42857143","6":"2018-2019"},{"1":"Markham Elementary School","2":"1278","3":"Portland SD 1J","4":"2180","5":"0.71428571","6":"2018-2019"},{"1":"Rieke Elementary School","2":"1299","3":"Portland SD 1J","4":"2180","5":"0.75438596","6":"2018-2019"},{"1":"Winterhaven School","2":"1364","3":"Portland SD 1J","4":"2180","5":"0.36363636","6":"2018-2019"},{"1":"Forest Park Elementary School","2":"2413","3":"Portland SD 1J","4":"2180","5":"0.69642857","6":"2018-2019"},{"1":"Opal School of the Portland Children's Museum","2":"3451","3":"Portland SD 1J","4":"2180","5":"0.28571429","6":"2018-2019"},{"1":"Trillium","2":"3616","3":"Portland SD 1J","4":"2180","5":"0.28571429","6":"2018-2019"},{"1":"Emerson School","2":"3991","3":"Portland SD 1J","4":"2180","5":"0.52173913","6":"2018-2019"},{"1":"Portland Arthur Academy Charter School","2":"4400","3":"Portland SD 1J","4":"2180","5":"0.42857143","6":"2018-2019"},{"1":"Portland Village School","2":"4534","3":"Portland SD 1J","4":"2180","5":"0.56410256","6":"2018-2019"},{"1":"Creative Science School","2":"4640","3":"Portland SD 1J","4":"2180","5":"0.37500000","6":"2018-2019"},{"1":"Le Monde French Immersion Public Charter School","2":"5060","3":"Portland SD 1J","4":"2180","5":"0.84126984","6":"2018-2019"},{"1":"Kairos PDX","2":"5218","3":"Portland SD 1J","4":"2180","5":"0.08000000","6":"2018-2019"},{"1":"Rose City Park","2":"5427","3":"Portland SD 1J","4":"2180","5":"0.63218391","6":"2018-2019"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[5],"max":[5]},"pages":{}}} </script> </div> --- ## Pipe Data Into ggplot ```r third_grade_math_proficiency %>% filter(year == "2018-2019") %>% filter(district == "Portland SD 1J") %>% * ggplot(aes(x = school, y = percent_proficient)) + * geom_col() + * coord_flip() ``` --- ## Pipe Data Into ggplot <!-- --> --- class: inverse ## Your Turn 1. Create a new RMarkdown document -- 1. Create a data frame called `enrollment_by_race_ethnicity` by reading in your race/ethnicity data from the data wrangling and analysis section using the `read_rds()` function -- 1. Pipe your data into a bar chart that shows the breakdown of race/ethnicity among students in Beaverton SD 48J in 2018-2019 ??? ```r enrollment_by_race_ethnicity <- read_rds("../data/enrollment_by_race_ethnicity.rds") ``` --- class: center, middle, dk-section-title background-image:url("images/highlight.jpg") # Highlight --- class: center, middle, dk-section-title background-image:url("images/goya-cans.jpg") ## Reorder Plots to Highlight Findings --- ### Reorder Plots to Highlight Findings ```r third_grade_math_proficiency %>% filter(year == "2018-2019") %>% filter(district == "Portland SD 1J") %>% * ggplot(aes(x = reorder(school, percent_proficient), y = percent_proficient)) + geom_col() + coord_flip() ``` ??? Example: https://yougov.co.uk/topics/politics/articles-reports/2020/04/09/covid-19-healthcare-workers-fear-policies-risk-inf --- ### Reorder Plots to Highlight Findings <!-- --> --- ### Reorder Plots to Highlight Findings ```r third_grade_math_proficiency %>% filter(year == "2018-2019") %>% filter(district == "Portland SD 1J") %>% * mutate(school = fct_reorder(school, percent_proficient)) %>% ggplot(aes(x = school, y = percent_proficient)) + geom_col() + coord_flip() ``` --- ### Reorder Plots to Highlight Findings <!-- --> --- class: inverse ### Your Turn Make a bar chart that shows race/ethnicity in Beaverton SD 48J. As before, filter your data to only include 2018-2019 data and only include Beaverton SD 48J. Then, do the following: -- 1. Using the `reorder()` function, make a bar chart that shows the percent of race/ethnicity groups in descending order -- 1. Make the same bar chart using `mutate()` and `fct_reorder()` to reorder the race/ethnicity groups ??? ```r enrollment_by_race_ethnicity %>% filter(year == "2018-2019") %>% filter(district == "Beaverton SD 48J") %>% mutate(race_ethnicity = fct_reorder(race_ethnicity, percent_of_total_enrollment)) %>% ggplot(aes(percent_of_total_enrollment, race_ethnicity)) + geom_col() ``` --- class: center, middle, dk-section-title background-image:url("images/lines.jpg") ## Line Charts --- ### Line Charts ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient)) + * geom_line() ``` --- ### Line Charts <!-- --> --- ### Line Charts ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, * group = school)) + geom_line() ``` --- ### Line Charts <!-- --> --- class: inverse ### Your Turn Make a line chart that shows the growth in the Hispanic/Latino population in school districts from 2017-2018 to 2018-2019 ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line() ``` --- class: center, middle, dk-section-title background-image:url("images/color.jpg") ## Use Color to Highlight Findings --- class: center, middle  .small[ Source: [Financial Times, March 11, 2020](https://twitter.com/jburnmurdoch/status/1237737352879112194) ] --- ### Use Color to Highlight Findings <!-- --> --- ### Use Color to Highlight Findings ```r highlight_school <- third_grade_math_proficiency %>% filter(school == "Vestal Elementary School") ``` -- ```r highlight_school ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> {"columns":[{"label":["school"],"name":[1],"type":["chr"],"align":["left"]},{"label":["school_id"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["district"],"name":[3],"type":["chr"],"align":["left"]},{"label":["district_id"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["percent_proficient"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["year"],"name":[6],"type":["chr"],"align":["left"]}],"data":[{"1":"Vestal Elementary School","2":"896","3":"Portland SD 1J","4":"2180","5":"0.2820513","6":"2017-2018"},{"1":"Vestal Elementary School","2":"896","3":"Portland SD 1J","4":"2180","5":"0.6428571","6":"2018-2019"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[5],"max":[5]},"pages":{}}} </script> </div> --- ### Use Color to Highlight Findings ```r rru_orange <- "#FF7400" rru_gray <- "#C3C3C3" third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + * geom_line(color = rru_gray) + * geom_line(data = highlight_school, * inherit.aes = TRUE, * color = rru_orange) ``` --- ### Use Color to Highlight Findings <!-- --> --- class: inverse ### Your Turn 1. Identify one school district that has had a lot of growth in its Hispanic/Latino population from 2017-2018 to 2018-2019 -- 1. Create a new data frame called `highlight_district` and only include this district in it -- 1. Use the `highlight_district` data frame to create a new `geom_line()` layer on top of the other data -- 1. Make sure this new layer is a bright color and all other layers are some type of light gray ??? ```r highlight_district <- enrollment_by_race_ethnicity %>% filter(district == "Douglas ESD") %>% filter(race_ethnicity == "Hispanic/Latino") enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") ``` <!-- --> --- class: center, middle, dk-section-title background-image:url("images/declutter.jpg") # Declutter --- ## Remove the Default Gray Background -- <!-- --> --- ## Remove the Default Gray Background ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + * theme_minimal() ``` --- ## Remove the Default Gray Background <!-- --> --- ## Remove Axis Titles --  .small[ Source: [`ggplot2` documentation](https://ggplot2.tidyverse.org/reference/theme.html) ] --- ## Remove Axis Titles ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + theme_minimal() + * theme(axis.title = element_blank()) ``` --- ## Remove Axis Titles <!-- --> --- ## Remove or Minimize Grid Lines -- ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + theme_minimal() + theme(axis.title = element_blank(), * panel.grid.minor = element_blank()) ``` --- ## Remove or Minimize Grid Lines <!-- --> --- ## Work with Existing Themes -- ```r *library(hrbrthemes) third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + * theme_ipsum(axis_title_size = 0, * grid = "XY") ``` --- ## Work with Existing Themes <!-- --> ??? - Link to themes RRU blog post: https://rfortherestofus.com/2019/08/themes-to-improve-your-ggplot-figures/ - https://github.com/jmcastagnetto/ggplot2_themes_in_github/ --- class: inverse ## Your Turn Use some combination of: -- - Complete themes: e.g. `theme_minimal()` or `theme_ipsum()` from `hrbrthemes` -- - The `theme()` function -- Do the following: -- 1. Remove gray background -- 1. Remove axis titles -- 1. Remove or minimize grid lines ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") ``` --- class: center, middle, dk-section-title background-image:url("images/explain.jpg") # Explain --- class: center, middle  --- class: center, middle, dk-section-title background-image:url("images/scales.jpg") ## Use the `scales` Package for Nicely Formatted Values --- ### `scales` ```r highlight_school ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> {"columns":[{"label":["school"],"name":[1],"type":["chr"],"align":["left"]},{"label":["school_id"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["district"],"name":[3],"type":["chr"],"align":["left"]},{"label":["district_id"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["percent_proficient"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["year"],"name":[6],"type":["chr"],"align":["left"]}],"data":[{"1":"Vestal Elementary School","2":"896","3":"Portland SD 1J","4":"2180","5":"0.2820513","6":"2017-2018"},{"1":"Vestal Elementary School","2":"896","3":"Portland SD 1J","4":"2180","5":"0.6428571","6":"2018-2019"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[5],"max":[5]},"pages":{}}} </script> </div> --- ### `scales` ```r library(scales) highlight_school <- third_grade_math_proficiency %>% filter(school == "Vestal Elementary School") %>% mutate(percent_proficient_display = percent(percent_proficient, accuracy = 1)) ``` -- ```r highlight_school %>% select(school, percent_proficient, percent_proficient_display) ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> {"columns":[{"label":["school"],"name":[1],"type":["chr"],"align":["left"]},{"label":["percent_proficient"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["percent_proficient_display"],"name":[3],"type":["chr"],"align":["left"]}],"data":[{"1":"Vestal Elementary School","2":"0.2820513","3":"28%"},{"1":"Vestal Elementary School","2":"0.6428571","3":"64%"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[5],"max":[5]},"pages":{}}} </script> </div> --- class: inverse ### Your Turn 1. Make a new variable called `percent_display` that shows the `percent_of_total_enrollment` variable as a nicely formatted percent (rounded to the nearest whole number) -- 1. Make sure you save this as `highlight_district` (i.e. don't just display the result) ??? ```r highlight_district <- highlight_district %>% mutate(percent_display = percent(percent_of_total_enrollment, accuracy = 1)) ``` --- class: center, middle, dk-section-title background-image:url("images/card-catalog.jpg") ## Use Direct Labeling --- class: center, middle  --- ### Use Direct Labeling ```r rru_gray <- "#d9d9d9" third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + * geom_text(data = highlight_school, * inherit.aes = TRUE, * aes(label = percent_proficient_display), * color = rru_orange, * nudge_x = c(-0.06, 0.06)) + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), panel.grid.minor = element_blank()) ``` --- ### Use Direct Labeling <!-- --> --- ### Use Direct Labeling ```r highlight_school <- third_grade_math_proficiency %>% filter(school == "Vestal Elementary School") %>% mutate(percent_proficient_display = percent(percent_proficient, accuracy = 1)) %>% * mutate(percent_proficient_display = case_when( * year == "2018-2019" ~ str_glue("{percent_proficient_display} of students are proficient"), * TRUE ~ percent_proficient_display )) ``` --- ### Use Direct Labeling ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), panel.grid.minor = element_blank()) ``` --- ### Use Direct Labeling <!-- --> --- class: inverse ### Your Turn Add text to display the percentage of Hispanic/Latino students in Douglas ESD in 2017-2018 and 2018-2019 ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), panel.grid.minor = element_blank()) ``` --- class: center, middle, dk-section-title background-image:url("images/wise.jpg") ## Use Axis Text Wisely --- ### Use Axis Text Wisely <!-- --> --- ### Use Axis Text Wisely ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + * scale_y_continuous(labels = percent_format(), limits = c(0, 1)) + theme_ipsum(axis_title_size = 0, grid = "XY") ``` --- ### Use Axis Text Wisely <!-- --> --- class: inverse ### Your Turn Make your y axis labels show up as nicely formatted percents using the `percent_format()` function. ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) ``` --- class: center, middle, dk-section-title background-image:url("images/coronavirus-billboard.jpg") ## Use Titles to Highlight Findings --- class: center, middle  --- ### Use Titles to Highlight Findings .small[ ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + scale_y_continuous(labels = percent_format(accuracy = 1)) + * labs(title = "Vestal Elementary School showed large gains in\nthird grade math proficiency scores from 2017-2018 to 2018-2019") + scale_y_continuous(labels = percent_format(), limits = c(0, 1)) + theme_ipsum(axis_title_size = 0, grid = "XY") ``` ] --- ### Use Titles to Highlight Findings <!-- --> --- class: inverse ### Your Turn Add a title to highlight your main finding ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) + labs(title = "Douglas ESD saw a steep increase in its\nLatino student population in the last two years") ``` --- class: center, middle, dk-section-title background-image:url("images/colors.jpg") ## Use Color in Titles to Highlight Findings --- ### Use Color in Titles to Highlight Findings .small[ ```r library(ggtext) third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + * labs(title = "<span style = 'color: #FF7400;'>Vestal Elementary School</span> showed large gains in<br>third grade math proficiency scores from 2017-2018 to 2018-2019") + scale_y_continuous(labels = percent_format(), limits = c(0, 1)) + theme_ipsum(axis_title_size = 0, grid = "XY") + * theme(plot.title = element_markdown()) ``` ] --- ### Use Color in Titles to Highlight Findings <!-- --> --- class: inverse ### Your Turn Use color in your title to highlight your main finding. You'll need to: -- 1. Add HTML in the `labs()` function to add the title -- 1. Change the `plot.title` argument in the `theme()` function so that it interprets the HTML correctly ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), plot.title = element_markdown(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) ``` --- class: center, middle, dk-section-title background-image:url("images/ft-annotation-example.jpeg") ## Use Annotations to Explain --- class: center, middle  .small[ Source: [Financial Times](https://www.ft.com/content/662cedac-e4b2-4cbf-b257-664ea3ce250c) ] --- ### Use Annotations to Explain .small[ ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + scale_y_continuous(labels = percent_format(accuracy = 1)) + theme_ipsum(axis_title_size = 0, grid = "XY") + labs(title = "<span style = 'color: #FF7400'>Vestal Elementary School</span> showed large gains in<br>third grade math proficiency scores between 2017-2018 and 2018-2019") + theme(plot.title = element_markdown()) + * annotate("text", * x = 2.02, * y = .37, * label = "Gray lines repesent\nall schools in Portland", * color = "#A0A0A0", * hjust = 0) ``` ] ??? http://www.thefunctionalart.com/2020/01/an-example-of-how-to-annotate.html --- ### Use Annotations to Explain <!-- --> ??? https://github.com/MattCowgill/ggannotate --- class: inverse ### Your Turn Add an annotation somewhere on your chart to help the reader understand it better ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), plot.title = element_markdown(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) + annotate("text", x = 2.02, y = 0.77, * label = "Woodburn has the\nhighest percentage\nof any district", lineheight = 1, hjust = 0, size = 3, color = "gray") ``` --- class: center, middle, dk-section-title background-image:url("images/sparkle.jpg") # Make it Sparkle --- class: center, middle ## Don't Use Defaults  --- ### Don't Use Defaults <!-- --> --- ### Tweak Spacing Around Your Plot <!-- --> --- ### Tweak Spacing Around Your Plot .small[ ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + scale_y_continuous(label = percent_format(), limits = c(0, 1)) + theme_ipsum(axis_title_size = 0, grid = "XY") + labs(title = "<span style = 'color: #FF7400'>Vestal Elementary School</span> showed large gains in<br>third grade math proficiency scores between 2017-2018 and 2018-2019") + theme(plot.title = element_markdown()) + annotate("text", x = 2.02, y = .37, label = "Gray lines repesent\nall schools in Portland", color = "#A0A0A0", hjust = 0) + * scale_x_discrete(expand = expansion(add = c(0.1, 0.5))) ``` ] --- ### Tweak Spacing Around Your Plot <!-- --> --- class: inverse ### Your Turn Use the expand argument within the `scale_x_discrete()` function to remove any gaps in your plot ??? ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), plot.title = element_markdown(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) + annotate("text", x = 2.02, y = 0.77, * label = "Woodburn has the\nhighest percentage\nof any district", lineheight = 1, hjust = 0, size = 3, color = "gray") ``` --- class: center, middle, dk-section-title background-image:url("images/ggpomological.png") ## Customize Your Theme --- ### Make Your Own Custom Theme ```r theme_dk <- function() { theme_ipsum(axis_title_size = 0, grid = "XY") + theme(plot.title = element_markdown()) } ``` --- ### Make Your Own Custom Theme .small[ ```r third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, hjust = c(1.05, -0.05)) + scale_y_continuous(labels = percent_format(accuracy = 1)) + labs(title = "<span style = 'color: #FF7400;'>Vestal Elementary School</span> showed large gains in<br>third grade math proficiency scores from 2017-2018 to 2018-2019") + annotate("text", x = 2.02, y = 0.4, label = "Gray lines show other\nPortland Public Schools\nfor comparison", hjust = 0, size = 3, color = "#A0A0A0") + scale_x_discrete(expand = expansion(add = c(0.1, 0.5))) + * theme_dk() ``` ] ??? .small[ ```r enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), plot.title = element_markdown(), panel.grid.minor = element_blank()) + scale_y_continuous(labels = percent_format()) + annotate("text", x = 2.02, y = 0.77, label = "Woodburn has the\nhighest percentage\nof any district", lineheight = 1, hjust = 0, size = 3, color = "gray") + * theme_dk() ``` ] --- ### Make Your Own Custom Theme <!-- --> --- class: inverse ### Your Turn 1. Make your own theme by combining elements of the `theme()` function and/or themes from other packages (e.g. `theme_ipsum()`) -- 1. Add this theme to your plot, removing any code that is now redundant ??? ```r theme_student <- function() { theme_ipsum(axis_title_size = 0) + theme(axis.title = element_blank(), plot.title = element_markdown(), panel.grid.minor = element_blank()) } enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_student() + scale_y_continuous(labels = percent_format()) + annotate("text", x = 2.02, y = 0.77, * label = "Woodburn has the\nhighest percentage\nof any district", lineheight = 1, hjust = 0, size = 3, color = "gray") ``` --- class: center, middle, dk-section-title background-image:url("images/fonts.jpg") ## Customize Your Fonts --- ### Import Custom Fonts ```r library(ragg) library(systemfonts) ``` --- ### See Your Fonts ```r library(systemfonts) system_fonts() ``` --- .small[ ```r theme_dk <- function() { * theme_minimal(base_family = "Inter") + theme(plot.title = element_markdown(face = "bold"), axis.title = element_blank(), panel.grid.minor = element_blank()) } third_grade_math_proficiency %>% filter(district == "Portland SD 1J") %>% ggplot(aes(x = year, y = percent_proficient, group = school)) + geom_line(color = rru_gray) + geom_line(data = highlight_school, inherit.aes = TRUE, color = rru_orange) + geom_text(data = highlight_school, inherit.aes = TRUE, aes(label = percent_proficient_display), color = rru_orange, * family = "Inter", hjust = c(1.05, -0.05)) + scale_y_continuous(label = percent_format(), limits = c(0, 1)) + labs(title = "<span style = 'color: #FF7400'>Vestal Elementary School</span> showed large gains in<br>third grade math proficiency scores between 2017-2018 and 2018-2019") + annotate("text", x = 2.02, y = .37, label = "Gray lines repesent\nall schools in Portland", color = "#A0A0A0", * family = "Inter", hjust = 0) + scale_x_discrete(expand = expansion(add = c(0.1, 0.5))) + theme_dk() ``` ] --- ### Use Custom Fonts in RStudio  --- ### Use Custom Fonts with `ggsave()` ```r ggsave("myplot.png", device = agg_png) ``` -- Make sure you load the `ragg` package first --- ### Use Custom Fonts when Knitting an RMarkdown File ```r knitr::opts_chunk$set(dev = "ragg_png") ``` --- class: inverse ### Your Turn -- 1. Install the `ragg` and `systemfonts` package -- 1. Change your settings in RStudio so you can render with AGG -- 1. Change all text in your last plot to use a custom font (you'll have to do this in a few different places) -- 1. Save your plot using `ggsave()` ??? ```r theme_student <- function() { theme_ipsum(axis_title_size = 0, base_family = "Inter") + theme(axis.title = element_blank(), plot.title = element_markdown(family = "Inter"), panel.grid.minor = element_blank()) } enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(year, percent_of_total_enrollment, group = district)) + geom_line(color = "gray", alpha = 0.5) + geom_line(data = highlight_district, inherit.aes = TRUE, color = "red") + geom_text(data = highlight_district, inherit.aes = TRUE, family = "Inter", aes(label = percent_display), color = "red", hjust = c(1.05, -0.05)) + labs(title = "<span style = 'color: red;'>Douglas ESD</span> saw a steep increase in its<br>Latino student population in the last two years") + theme_student() + scale_y_continuous(labels = percent_format()) + annotate("text", x = 2.02, y = 0.77, * label = "Woodburn has the\nhighest percentage\nof any district", lineheight = 1, family = "Inter", hjust = 0, size = 3, color = "gray") ``` --- class: center, middle, dk-section-title background-image:url("images/ggridges.png") ## Try New Plot Types --- .center[  ] --- ### `ggrepel` .center[ [](https://ggrepel.slowkow.com/) ] --- .center[  ] --- ### `gganimate` .center[ [](http://lenkiefer.com/2020/04/03/us-labor-market-update-april-2020/) ] --- ### `ggridges` [](https://wilkelab.org/ggridges) --- ### `ggalt` [](https://github.com/hrbrmstr/ggalt) --- ### `ggbeeswarm` [](https://github.com/eclarke/ggbeeswarm) --- ### `ggraph` [](https://ggraph.data-imaginist.com/index.html) --- ### `ggraph`  --- ### `ggraph`  --- ### `waffle` [](https://github.com/hrbrmstr/waffle) --- .center[ [](https://patchwork.data-imaginist.com/) ] --- ### `patchwork` [](https://twitter.com/littlemissdata/status/1229176433123168256) --- [](https://github.com/Z3tt/TidyTuesday#week-202004--spotify-songs-by-spotify-via-spotifyr) --- ### `shadowtext` [](https://twitter.com/dgkeyes/status/1237812892189093888) ??? TK: Add to end https://www.data-to-viz.com/ https://github.com/erikgahner/awesome-ggplot2 People to follow for Tidy Tuesday --- class: inverse ### Your Turn -- 1. Use one of the packages above to make a unique plot. For example, you might use dumbell plots in the `ggalt` package to show change in the Hispanic/Latino population from 2017-2018 to 2018-2019 for all districts. -- 1. When you finish your plot, email it to me at david@rfortherestofus.com! I'd love to see what you come up with.