class: center, middle, inverse, title-slide # Data Visualization Workshop ## DataFEWSion Graduate Traineeship ### <span style='color:yellow'>Anabelle Laurent ### March 02, 2021 --- ### But first... <iframe src="https://app.sli.do/event/vid6lkpl/embed/polls/fcc19b45-618f-4550-8732-b5929884cc0d" width="300" height="400"></iframe> --- ### Why is Data Visualization important? 🤔 <iframe src="https://app.sli.do/event/vid6lkpl/embed/polls/f67942d8-85e6-442b-9c1c-e17269635c10" width="300" height="400"></iframe> --- ### Why is Data Visualization important? - Universal way to communicate information - Provides clear and effective message - Find patterns, trends, spot extreme values - Make data memorable - Maintain the audience's interest --- Who is your audience? <iframe src="https://app.sli.do/event/vid6lkpl/embed/polls/c3c918fa-e88e-418e-9f57-d982285a6bd2" width="300" height="400"></iframe> --- ### What about you? - What is your audience? + scientist 👩‍🔬 + students 👨‍🎓 + industry + general audience - Which support? + peer-reviewed paper 🗞 + oral presentations 💬 + website, blog, etc. --- ### What make a good visualization? 🤔 No online poll, let's talk! 😄 --- ### What make a good visualization? - Reveals a **trend** or **relationship** between variables - Always have at minimum a **caption**, **axis**, **scales** and **symbols** - Distinct and legible symbols (i.e., use contrast) - Caption should convey as much information as possible - No noise: keep information at minimum - the **correct graph type** based on the kind of data to be presented --- ### Disclaimer This workshop does not provide code but all the plots were made using R Studio (see last slides for more details) <center><img src="images/ggplot2_masterpiece.png" style="width: 70%" /> </center> [Artwork by @allison_horst](https://github.com/allisonhorst/stats-illustrations) --- # Visualizing quantity --- ### Visualizing quantity : bar plot .right-column[ <!-- --> ] -- .left-column[ What's wrong with this plot? ] --- ### Visualizing quantity : bar plot .right-column[ <!-- --> ] .left-column[ - Avoid abbreviations - Precise axis title + unit - Make it more attractive ] --- ### Visualizing quantity : bar plot .right-column[ <!-- --> ] -- .left-column[ For long x-axis labels, flip the the axis ] --- ### Visualizing quantity: bar plot .right-column[ <!-- --> ] .left-column[ - Order the categories by ascending or descending values - Keep categories naturally ordered like age group - For long labels: flip the axis ] --- ### Visualizing quantity : grouped bar plot .right-column[ <!-- --> ] .left-column[ Useful to draw bars within each group according to another other categorical variable ] --- ### What's wrong with this plot? .right-column[ <!-- --> ] -- .left-column[ - bars are too long - Can be impractical sometimes ] --- ### Don't do that! ⏹ .right-column[ <!-- --> ] -- .left-column[ - Bars charts start at zero. Indeed, the bar length is proportional to the amount displayed. - **dot plot** is a better option ] --- ### Visualizing quantity: dot plot <!-- --> --- ### Visualizing quantity: dot plot .right-column[ <!-- --> ] -- .left-column[ - Bars charts or dot plot: **the order matters** - Here, you don't deliver a clear message ] --- ### Visualizing quantity : lollipop plot .right-column[ <!-- --> ] .left-column[ - Database: On-time data for all flights that departed NYC - Lollipop plots are an alternative for simple barchart ] --- # Visualizing distribution <center><img src="images/histogram.png" style="width: 70%" /> </center> [Artwork by @allison_horst](https://github.com/allisonhorst/stats-illustrations) --- ### Visualizing distribution : histograms .left-right[ <!-- --> ] .left-column[ Histogram are useful for plotting the distribution of a single quantitative variable ] --- ### Visualizing distribution : histograms .right-column[ <!-- --> ] .left-column[ Try different bin widths for best visual appearance. - Small bin width -> peaky and busy histogram - Large bin width -> features might disappear ] --- ### Visualizing distribution : density plot <!-- --> --- ### Visualizing distribution : density plot .right-column[ <!-- --> ] .left-column[ Try different bandwidths for best visual appearance - Small bandwidth -> peaky and busy density - Large bandwidth -> smooth feature and might look like a gaussian ] --- ### Visualizing multiple distributions <!-- --> --- ### Visualizing multiple distributions .right-column[ <!-- --> ] .left-column[ - The peaks of the density plot are where there is the highest concentration of points - For several distributions, density plots work better than histograms. ] --- ### Visualizing multiple distributions <!-- --> --- ### Visualizing multiple distributions: ridgeline plot <!-- --> --- ### Visualizing multiple distributions: ridgeline plot .right-column[ <!-- --> ] .left-column[ Ridgeline plot shows the distribution of a numeric value for several groups (at least 5-6 groups) or when they overlap each other. ] --- ### Visualizing distributions: boxplot <center><img src="images/read_boxplot.jpeg" style="width: 100%" /> </center> A boxplot can summarize the distribution of a numeric variable for several groups --- ### Visualizing distributions: boxplot .right-column[ <!-- --> ] .left-column[ Boxplot does not tell about the number of observations. ] --- ### Visualizing distributions: boxplot with jitter .right-column[ <!-- --> ] .left-column[ Boxplots with jitter tell about: - the distribution of the data - if the groups are balanced or unbalanced in terms of observations. ] --- ### Visualizing distributions: boxplot with jitter .right-column[ <!-- --> ] .left-column[ No overlapping facilitates the visual appearence of the plot ] --- ### Visualizing distributions: violin plot .right-column[ <!-- --> ] .left-column[ - Violins are equivalent to density estimate - They are useful to represent bimodal data. ] --- ### Your turn 👨‍💻 Create one visual using one of these types of graphics: - bar chart - histograms - density plot - boxplot - violin plot --- ### Your turn 👩‍💻 Choose the dataset of your choices: - Titanic - Nutritional and marketing information on US Cereals -- |Class |Sex |Age |Survived | Freq| |:-----|:------|:-----|:--------|----:| |1st |Male |Child |No | 0| |2nd |Male |Child |No | 0| |3rd |Male |Child |No | 35| |Crew |Male |Child |No | 0| |1st |Female |Child |No | 0| ``` ## 'data.frame': 32 obs. of 5 variables: ## $ Class : Factor w/ 4 levels "1st","2nd","3rd",..: 1 2 3 4 1 2 3 4 1 2 ... ## $ Sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 2 2 2 1 1 ... ## $ Age : Factor w/ 2 levels "Child","Adult": 1 1 1 1 1 1 1 1 2 2 ... ## $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ... ## $ Freq : num 0 0 35 0 0 0 17 0 118 154 ... ``` Freq = the number of observations --- ### Your turn 👨‍💻 Choose the dataset of your choices: - Titanic - Nutritional and marketing information on US Cereals -- Choose the dataset of your choices | |mfr | calories| protein| fat| sugars| shelf| |:-------------------------|:---|--------:|-------:|---:|------:|-----:| |100% Bran |N | 212.1| 12.1| 3| 18.2| 3| |All-Bran |K | 212.1| 12.1| 3| 15.2| 3| |All-Bran with Extra Fiber |K | 100.0| 8.0| 0| 0.0| 3| ``` ## 'data.frame': 65 obs. of 11 variables: ## $ mfr : Factor w/ 6 levels "G","K","N","P",..: 3 2 2 1 2 1 6 4 5 1 ... ## $ calories : num 212 212 100 147 110 ... ## $ protein : num 12.12 12.12 8 2.67 2 ... ## $ fat : num 3.03 3.03 0 2.67 0 ... ## $ sodium : num 394 788 280 240 125 ... ## $ fibre : num 30.3 27.3 28 2 1 ... ## $ carbo : num 15.2 21.2 16 14 11 ... ## $ sugars : num 18.2 15.2 0 13.3 14 ... ## $ shelf : int 3 3 3 1 2 3 1 3 2 1 ... ## $ potassium: num 848.5 969.7 660 93.3 30 ... ## $ vitamins : Factor w/ 3 levels "100%","enriched",..: 2 2 2 2 2 2 2 2 2 2 ... ``` --- ### Your turn 👨‍💻 ```r remotes::install_github("dreamRs/esquisse") library(MASS) library(esquisse) titanic <- as.data.frame(Titanic) ?Titanic # more details about the dataset ?UScereal # more details about the dataset esquisse::esquisser(titanic,viewer="browser") esquisse::esquisser(UScereal,viewer="browser") ``` --- # Visualizing associations among quantitative variables --- ### Relationship between 2 numeric variables: scatterplot <!-- --> --- ### Relationship between 2 numeric variables: scatterplot + linear fit <!-- --> --- ##### Relationship between 2 numeric variables: scatterplot + quadratic fit <!-- --> ⚠️ Linear fit is widely used but it is not always the best fit, try quadratic fit too. --- ### Relationship between 2 numeric variables: scatterplot <!-- --> --- ### Multi-panel plots <!-- --> Split a single plot using one variable with many levels --- ### Multi-panel plots <!-- --> Split a single plot using the combinations of two discrete variables. --- ### Multi-panel plots <!-- --> ⚠️ different scales can lead to misinterpretation --- ### Bubble plot A bublle plot is a scatterplot with 3 numerical variables <!-- --> --- ### Your turn 👨‍💻 - Create one visual using scatter plot or bubble plot - Use a data set from TidyTuesday --- ### Your turn 👩‍💻 |species |island | bill_length_mm| bill_depth_mm| flipper_length_mm| |:-------|:---------|--------------:|-------------:|-----------------:| |Adelie |Torgersen | 39.1| 18.7| 181| |Adelie |Torgersen | 39.5| 17.4| 186| |Adelie |Torgersen | 40.3| 18.0| 195| |Adelie |Torgersen | NA| NA| NA| |Adelie |Torgersen | 36.7| 19.3| 193| <center><img src="images/bill_penguin.png" style="width: 50%" /> </center> --- ### Your turn 👨‍💻 |species |island | body_mass_g|sex | year| |:-------|:---------|-----------:|:------|----:| |Adelie |Torgersen | 3750|male | 2007| |Adelie |Torgersen | 3800|female | 2007| |Adelie |Torgersen | 3250|female | 2007| |Adelie |Torgersen | NA|NA | 2007| |Adelie |Torgersen | 3450|female | 2007| <center><img src="images/penguins_drawing.png" style="width: 50%" /> </center> --- ### Your turn 👩‍💻 ```r penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv') esquisse::esquisser(penguins,viewer="browser") ``` 🐧See this [link for more details about the penguins dataset](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-07-28/readme.md) --- # Visualizing time series --- ### Visualizing time series Example with a NASA dataset: atmospheric measurements across a grid of locations in Central America (Murrell, 2010) <center><img src="images/grid_nasa.png" style="width: 50%" /> </center> --- ### Visualizing time series Overview of the data ``` ## time y x lat long date cloudhigh cloudlow cloudmid ozone ## 1 1 1 1 -21.2 -113.8000 1995-01-01 0.5 31.0 2.0 260 ## 2 1 1 2 -21.2 -111.2957 1995-01-01 1.5 31.5 2.5 260 ## 3 1 1 3 -21.2 -108.7913 1995-01-01 1.5 32.5 3.5 260 ## 4 1 1 4 -21.2 -106.2870 1995-01-01 1.0 39.0 4.0 258 ## 5 1 1 5 -21.2 -103.7826 1995-01-01 0.5 48.0 4.5 258 ## 6 1 1 6 -21.2 -101.2783 1995-01-01 0.0 50.0 2.5 258 ## pressure surftemp temperature id day month year ## 1 1000 297.4 296.9 1-1 0 1 1995 ## 2 1000 297.4 296.5 2-1 0 1 1995 ## 3 1000 297.4 296.0 3-1 0 1 1995 ## 4 1000 296.9 296.5 4-1 0 1 1995 ## 5 1000 296.5 295.5 5-1 0 1 1995 ## 6 1000 296.5 295.0 6-1 0 1 1995 ``` --- ### Visualizing time series Let's pick one location (x=1 & y=1) and focus on surface temperature (Kelvin) ``` ## time x y surftemp day month year ## 1 1 1 1 297.4 0 1 1995 ## 2 2 1 1 298.7 31 2 1995 ## 3 3 1 1 298.3 59 3 1995 ## 4 4 1 1 298.7 90 4 1995 ## 5 5 1 1 298.3 120 5 1995 ## 6 6 1 1 295.0 151 6 1995 ``` --- ### Visualizing time series <!-- --> - Without the dots you emphasize on the general trend and not on the individual observation - A plot with line + dots is called a line graph --- ### Visualizing time series <!-- --> --- ### Display change between two time periods: dummbbell chart <center><img src="images/dummbbell_chart.png" style="width: 80%" /> </center> Source: Rob Kabacoff (2020) --- # Color & Symbols --- ### Color to distinguish <!-- --> 🐧 --- ### Color to highlight <!-- --> Something wrong?🤷 --- ### Color & shape to highlight <!-- --> Alternative 1 --- ### Color & shape to highlight <!-- --> Alternative 2 --- # Tell a story with your data 📖 --- ### Tell a story with your data Before data visualizatio, you must: - Know your audience - Know the level of data detail expected - Give enough context - Ask yourself: What do I want my audience know/remember with the data I am presenting? --- ### Tell a story with your data .right-column[ <center><img src="images/2020_08_14_penguins.png" style="width: 80%" /> </center> ] .left-column[ Don't be repetitive but be consistent (theme, color scheme, font size etc.) ] --- ### Tell a story with your data Guide your audience by point out specific values <center><img src="images/2019_10_08_powerlifting.png" style="width: 80%" /> </center> --- ### Tell a story with your data Guide your audience by pointing out specific values <center><img src="images/2020_foodconsumption.png" style="width: 70%" /> </center> --- ### Tell a story with your data Customize your plot using highlighting <!-- --> --- ### Tell a story with your data Customize your plot using highlighting + text <!-- --> --- ### Interactive graphics with ggplotly <div id="htmlwidget-71ddb8b35514e2e95ecd" style="width:720px;height:504px;" class="plotly html-widget"></div> <script type="application/json" data-for="htmlwidget-71ddb8b35514e2e95ecd">{"x":{"data":[{"x":[2014,5581,691,278,9270,2602,1107,1463,3820,6223,1056,1328,824,1045,1545,2042,1217,759,620,4797,470,1712,1043,1271,7093,1704,943,926,863,430,1441,863,12057,883,641,706,3633,1803,415,4811,1569,753,12570,579,13206,10957,4513,7670,986,12154,2082,1598],"y":[46.9,71.3,52.9,46.5,49.3,58.6,52.5,54.1,71.2,72.3,51.5,60,42.1,59.4,48.3,50.4,52.3,48.3,56.9,42.7,43.5,63.1,54.5,42.4,73.9,50.7,56,48.2,46.2,49.6,56.7,42.6,74,58.4,58,44.7,55.3,64.2,45.7,52.9,42.6,59.4,50.7,46.4,56.7,72.8,39.6,76.4,65.2,51.6,54.8,65.5],"text":["Country: Nigeria<br />Population (M): 135.03<br />Life Expectancy: 46.9<br />Gdp per capita: 2014","Country: Egypt<br />Population (M): 80.26<br />Life Expectancy: 71.3<br />Gdp per capita: 5581","Country: Ethiopia<br />Population (M): 76.51<br />Life Expectancy: 52.9<br />Gdp per capita: 691","Country: Congo, Dem. 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Korea, Rep.<br />Population (M): 49.04<br />Life Expectancy: 78.6<br />Gdp per capita: 23348","Country: Myanmar<br />Population (M): 47.76<br />Life Expectancy: 62.1<br />Gdp per capita: 944","Country: Afghanistan<br />Population (M): 31.89<br />Life Expectancy: 43.8<br />Gdp per capita: 975","Country: Nepal<br />Population (M): 28.9<br />Life Expectancy: 63.8<br />Gdp per capita: 1091","Country: Saudi Arabia<br />Population (M): 27.6<br />Life Expectancy: 72.8<br />Gdp per capita: 21655","Country: Iraq<br />Population (M): 27.5<br />Life Expectancy: 59.5<br />Gdp per capita: 4471","Country: Malaysia<br />Population (M): 24.82<br />Life Expectancy: 74.2<br />Gdp per capita: 12452","Country: Korea, Dem. Rep.<br />Population (M): 23.3<br />Life Expectancy: 67.3<br />Gdp per capita: 1593","Country: Taiwan<br />Population (M): 23.17<br />Life Expectancy: 78.4<br />Gdp per capita: 28718","Country: Yemen, Rep.<br />Population (M): 22.21<br />Life Expectancy: 62.7<br />Gdp per capita: 2281","Country: Sri Lanka<br />Population (M): 20.38<br />Life Expectancy: 72.4<br />Gdp per capita: 3970","Country: Syria<br />Population (M): 19.31<br />Life Expectancy: 74.1<br />Gdp per capita: 4185","Country: Cambodia<br />Population (M): 14.13<br />Life Expectancy: 59.7<br />Gdp per capita: 1714","Country: Hong Kong, China<br />Population (M): 6.98<br />Life Expectancy: 82.2<br />Gdp per capita: 39725","Country: Israel<br />Population (M): 6.43<br />Life Expectancy: 80.7<br />Gdp per capita: 25523","Country: Jordan<br />Population (M): 6.05<br />Life Expectancy: 72.5<br />Gdp per capita: 4519","Country: Singapore<br />Population (M): 4.55<br />Life Expectancy: 80<br />Gdp per capita: 47143","Country: 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Portugal<br />Population (M): 10.64<br />Life Expectancy: 78.1<br />Gdp per capita: 20510","Country: Belgium<br />Population (M): 10.39<br />Life Expectancy: 79.4<br />Gdp per capita: 33693","Country: Czech Republic<br />Population (M): 10.23<br />Life Expectancy: 76.5<br />Gdp per capita: 22833","Country: Serbia<br />Population (M): 10.15<br />Life Expectancy: 74<br />Gdp per capita: 9787","Country: Hungary<br />Population (M): 9.96<br />Life Expectancy: 73.3<br />Gdp per capita: 18009","Country: Sweden<br />Population (M): 9.03<br />Life Expectancy: 80.9<br />Gdp per capita: 33860","Country: Austria<br />Population (M): 8.2<br />Life Expectancy: 79.8<br />Gdp per capita: 36126","Country: Switzerland<br />Population (M): 7.55<br />Life Expectancy: 81.7<br />Gdp per capita: 37506","Country: Bulgaria<br />Population (M): 7.32<br />Life Expectancy: 73<br />Gdp per capita: 10681","Country: Denmark<br />Population (M): 5.47<br />Life Expectancy: 78.3<br />Gdp per capita: 35278","Country: 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id="htmlwidget-1916d4e2a1f5cac3e5c1" style="width:720px;height:432px;" class="dygraphs html-widget"></div> <script type="application/json" data-for="htmlwidget-1916d4e2a1f5cac3e5c1">{"x":{"attrs":{"labels":["month","mdeaths","fdeaths"],"legend":"auto","retainDateWindow":false,"axes":{"x":{"pixelsPerLabel":60}},"showRangeSelector":true,"rangeSelectorHeight":40,"rangeSelectorPlotFillColor":" #A7B1C4","rangeSelectorPlotStrokeColor":"#808FAB","interactionModel":"Dygraph.Interaction.defaultModel"},"scale":"monthly","annotations":[],"shadings":[],"events":[],"format":"date","data":[["1974-01-01T00:00:00.000Z","1974-02-01T00:00:00.000Z","1974-03-01T00:00:00.000Z","1974-04-01T00:00:00.000Z","1974-05-01T00:00:00.000Z","1974-06-01T00:00:00.000Z","1974-07-01T00:00:00.000Z","1974-08-01T00:00:00.000Z","1974-09-01T00:00:00.000Z","1974-10-01T00:00:00.000Z","1974-11-01T00:00:00.000Z","1974-12-01T00:00:00.000Z","1975-01-01T00:00:00.000Z","1975-02-01T00:00:00.000Z","1975-03-01T00:00:00.000Z","1975-04-01T00:00:00.000Z","1975-05-01T00:00:00.000Z","1975-06-01T00:00:00.000Z","1975-07-01T00:00:00.000Z","1975-08-01T00:00:00.000Z","1975-09-01T00:00:00.000Z","1975-10-01T00:00:00.000Z","1975-11-01T00:00:00.000Z","1975-12-01T00:00:00.000Z","1976-01-01T00:00:00.000Z","1976-02-01T00:00:00.000Z","1976-03-01T00:00:00.000Z","1976-04-01T00:00:00.000Z","1976-05-01T00:00:00.000Z","1976-06-01T00:00:00.000Z","1976-07-01T00:00:00.000Z","1976-08-01T00:00:00.000Z","1976-09-01T00:00:00.000Z","1976-10-01T00:00:00.000Z","1976-11-01T00:00:00.000Z","1976-12-01T00:00:00.000Z","1977-01-01T00:00:00.000Z","1977-02-01T00:00:00.000Z","1977-03-01T00:00:00.000Z","1977-04-01T00:00:00.000Z","1977-05-01T00:00:00.000Z","1977-06-01T00:00:00.000Z","1977-07-01T00:00:00.000Z","1977-08-01T00:00:00.000Z","1977-09-01T00:00:00.000Z","1977-10-01T00:00:00.000Z","1977-11-01T00:00:00.000Z","1977-12-01T00:00:00.000Z","1978-01-01T00:00:00.000Z","1978-02-01T00:00:00.000Z","1978-03-01T00:00:00.000Z","1978-04-01T00:00:00.000Z","1978-05-01T00:00:00.000Z","1978-06-01T00:00:00.000Z","1978-07-01T00:00:00.000Z","1978-08-01T00:00:00.000Z","1978-09-01T00:00:00.000Z","1978-10-01T00:00:00.000Z","1978-11-01T00:00:00.000Z","1978-12-01T00:00:00.000Z","1979-01-01T00:00:00.000Z","1979-02-01T00:00:00.000Z","1979-03-01T00:00:00.000Z","1979-04-01T00:00:00.000Z","1979-05-01T00:00:00.000Z","1979-06-01T00:00:00.000Z","1979-07-01T00:00:00.000Z","1979-08-01T00:00:00.000Z","1979-09-01T00:00:00.000Z","1979-10-01T00:00:00.000Z","1979-11-01T00:00:00.000Z","1979-12-01T00:00:00.000Z"],[2134,1863,1877,1877,1492,1249,1280,1131,1209,1492,1621,1846,2103,2137,2153,1833,1403,1288,1186,1133,1053,1347,1545,2066,2020,2750,2283,1479,1189,1160,1113,970,999,1208,1467,2059,2240,1634,1722,1801,1246,1162,1087,1013,959,1179,1229,1655,2019,2284,1942,1423,1340,1187,1098,1004,970,1140,1110,1812,2263,1820,1846,1531,1215,1075,1056,975,940,1081,1294,1341],[901,689,827,677,522,406,441,393,387,582,578,666,830,752,785,664,467,438,421,412,343,440,531,771,767,1141,896,532,447,420,376,330,357,445,546,764,862,660,663,643,502,392,411,348,387,385,411,638,796,853,737,546,530,446,431,362,387,430,425,679,821,785,727,612,478,429,405,379,393,411,487,574]]},"evals":["attrs.interactionModel"],"jsHooks":[]}</script> --- ### Your turn 👨‍💻 ```r library(dygraphs) lungDeaths <- cbind(ldeaths, mdeaths, fdeaths) # Ex 1 dygraph(lungDeaths, main = "Deaths from Lung Disease (UK)") %>% dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) #highlighting persist even after the mouse leaves the graph area. # Ex 2 dygraph(lungDeaths, main = "Deaths from Lung Disease (UK)") %>% dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) # stroke width of highlighted series # Ex 3 dygraph(ldeaths, main = "Deaths from Lung Disease (UK)") %>% dyOptions(fillGraph = TRUE, fillAlpha = 0.4) # fill in the area underneath the series # Ex 4 dygraph(ldeaths, main = "Deaths from Lung Disease (UK)") %>% dyOptions(drawPoints = TRUE, pointSize = 2) # display of the individual points in a series ``` --- ### Data visulization using interactive web-app One example [ISOFAST web-app](https://analytics.iasoybeans.com/cool-apps/ISOFAST/) --- ### R library used for this presentation ```r library(ggplot2) library(dplyr) library(tidyr) library(gapminder) library(gghighlight) library(ggrepel) library(dygraphs) library(plotly) ``` --- ### Ressources to go deeper into Data Viz - Website for [R colors and palettes](https://www.color-hex.com/) - [Claus Wilke's book](https://clauswilke.com/dataviz/index.html) - [Rob Kabacoff's book](https://rkabacoff.github.io/datavis/) - [Marie Döbler & Tim Großmann's book](https://www.barnesandnoble.com/w/the-data-visualization-workshop-second-edition-mario-d-bler/1136609407) Available online with ISU Library - [Cédric Scherer's blog](https://www.cedricscherer.com/top/dataviz/) - [From Data to Viz's website](https://www.data-to-viz.com/) - [dygraphs R package](https://rstudio.github.io/dygraphs/) - [Plotly R package](https://plotly.com/r/) - [Shiny tutorial](https://shiny.rstudio.com/tutorial/) - Check the hashtag **#tidytuesday** on twitter if you are looking for inspiration & R code. - [Shiny app about Tidy Tuesday tweets](https://nsgrantham.shinyapps.io/tidytuesdayrocks/) --- ### Accurate <center><img src="images/r_rollercoaster.png" style="width: 85%" /> </center> [Artwork by @allison_horst](https://github.com/allisonhorst/stats-illustrations) --- ### Thank you for your attention <center><img src="images/lastslide.jpg" style="width: 60%" /> </center> ✉️ my email: **alaurent@iastate.edu** Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).