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+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
+  <head>
+    <title>Reproducible Data Visualizations Using R and Binder</title>
+    <meta charset="utf-8" />
+    <link href="libs/remark-css/default.css" rel="stylesheet" />
+    <link href="libs/remark-css/default-fonts.css" rel="stylesheet" />
+    <script src="libs/htmlwidgets/htmlwidgets.js"></script>
+    <script src="libs/jquery/jquery.min.js"></script>
+    <link href="libs/leaflet/leaflet.css" rel="stylesheet" />
+    <script src="libs/leaflet/leaflet.js"></script>
+    <link href="libs/leafletfix/leafletfix.css" rel="stylesheet" />
+    <script src="libs/Proj4Leaflet/proj4-compressed.js"></script>
+    <script src="libs/Proj4Leaflet/proj4leaflet.js"></script>
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+    <script src="libs/leaflet-binding/leaflet.js"></script>
+  </head>
+  <body>
+    <textarea id="source">
+class: center, middle, title-slide
+
+# Reproducible Data Visualizations Using R and Binder
+### </p>
+Andrew Stewart<br>University of Manchester<br>Twitter: <span class="citation">@ajstewart_lang</span>
+</p>
+<p>Oli Clark<br>Manchester Metropolitan University<br>Twitter: <span class="citation">@psytecholi</span></p>
+### (updated: 2019-07-07)
+
+---
+
+
+
+
+
+
+class: center
+
+# The following is a fully reproducible talk (just add my accent)
+
+All slides and R code used to generate these slides available [here](https://github.com/ajstewartlang/SIPS_presentation)
+
+&lt;br style=“line-height:1;”&gt;
+
+&lt;img src="images/ssi.png" width="45%" /&gt;
+
+&lt;br style=“line-height:1;”&gt;
+
+Reproducibility and The Turing Way
+
+https://the-turing-way.netlify.com/
+
+.footnote[
+Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan), [**knitr**](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com).
+]
+---
+class: center, middle
+
+&lt;img src="images/plan.gif" width="150%" /&gt;
+
+---
+# Open and Reproducible Research
+
+Fully open and reproducible research requires:
+
+--
+
+&lt;b&gt;Shared data&lt;/b&gt; - we need to share our data with others.
+
+--
+
+&lt;b&gt;Shared code&lt;/b&gt; - we need to share our code with others. We need to ensure it's well
+documented so that it is usable in conjunction with our data.
+
+--
+
+&lt;b&gt;Shared computational environment&lt;/b&gt; - we need to share our computational 
+environment with others. This includes our analysis software, the specific
+packages we used, plus any system-level dependencies.
+
+---
+# Why do we need a shared computational environment?
+
+Quite often analysis code ‘breaks’ - often in one of two ways:
+
+--
+
+Code that worked previously now doesn’t - maybe a function in an R package was 
+updated (e.g., lsmeans became emmeans so old code using lsmeans wouldn’t now run).
+
+--
+
+Code that worked previously still works - but produces a slightly different 
+result or now throws a warning where it didn’t previousy (e.g., convergence/
+singular fit warnings in lme4 version 1.1-19 vs. version 1.1-20).
+
+--
+
+Arguably, sharing only your data and code means you are not engaging in fully 
+reproducible research practices.
+
+---
+.center[
+.middle[
+&lt;img src="images/peng.png" width="100%" /&gt;
+]
+]
+---
+# Make your Visualizations Reproducible
+"&lt;i&gt;(visualizations)&lt;/i&gt; should be autogenerated as part of the data analysis 
+pipeline (which should also be automated), and they should come out of the 
+pipeline ready to be sent to the printer...
+
+...the moment you manually edit a figure, your final figure becomes 
+irreproducible. A third party cannot generate the exact same figure you did."
+
+.pull-left[
+.center[
+&lt;img src="images/wilke.png" width="200" /&gt;
+]
+]
+
+.pull-right[
+&lt;i&gt;Claus Wilke, Fundamentals of Data Visualization (2019). Page xiii.&lt;/i&gt;
+]
+
+---
+class: center, middle
+
+&lt;img src="images/wilke_tweet.png" width="783" /&gt;
+
+---
+# A workflow for reproducible science in the Tidyverse
+
+.center[
+&lt;img src="images/tidyverse.png" width="90%" /&gt;
+]
+---
+# A workflow for reproducible science in the Tidyverse
+
+&lt;img src="images/tidyflow.png" width="1200" /&gt;
+
+https://www.tidyverse.org
+
+---
+class: center, middle
+# Why Data Visualization is Important
+---
+class: center, middle
+# Anscombe's Quartert
+---
+# Plot 1
+.pull-left[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-9-1.png)&lt;!-- --&gt;
+]
+
+.pull-right[
+
+```
+## [1] "Mean of X is: 9"
+```
+
+```
+## [1] "SD of X is: 3.32"
+```
+
+```
+## [1] "Mean of Y is: 7.5"
+```
+
+```
+## [1] "SD of Y is: 2.03"
+```
+]
+
+```
+## [1] "Pearson's r is 0.82"
+```
+
+---
+# Plot 2
+.pull-left[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-12-1.png)&lt;!-- --&gt;
+]
+.pull-right[
+
+```
+## [1] "Mean of X is: 9"
+```
+
+```
+## [1] "SD of X is: 3.32"
+```
+
+```
+## [1] "Mean of Y is: 7.5"
+```
+
+```
+## [1] "SD of Y is: 2.03"
+```
+]
+
+```
+## [1] "Pearson's r is 0.82"
+```
+
+---
+# Plot 3
+.pull-left[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-15-1.png)&lt;!-- --&gt;
+]
+.pull-right[
+
+```
+## [1] "Mean of X is: 9"
+```
+
+```
+## [1] "SD of X is: 3.32"
+```
+
+```
+## [1] "Mean of Y is: 7.5"
+```
+
+```
+## [1] "SD of Y is: 2.03"
+```
+]
+
+```
+## [1] "Pearson's r is 0.82"
+```
+
+---
+# Plot 4
+.pull-left[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-18-1.png)&lt;!-- --&gt;
+]
+.pull-right[
+
+```
+## [1] "Mean of X is: 9"
+```
+
+```
+## [1] "SD of X is: 3.32"
+```
+
+```
+## [1] "Mean of Y is: 7.5"
+```
+
+```
+## [1] "SD of Y is: 2.03"
+```
+]
+
+```
+## [1] "Pearson's r is 0.82"
+```
+
+---
+# Plots Based on Aggregated Data Can Mislead…
+
+
+
+.center[
+
+```r
+ggplot(data1, aes(x = Group, y = RT)) + geom_boxplot() 
+```
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-22-1.png)&lt;!-- --&gt;
+]
+---
+# But look more closely at the actual data…
+
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-23-1.png)&lt;!-- --&gt;
+]
+---
+# The distribution of data matters
+
+The data on the previous slide are clearly bimodal with no data point near the 
+mean.  Distribution shape matters and we need to capture that in our data 
+visualizations.
+
+If we only plotted and reported information related to aggregated data, we 
+wouldn't be being honest about what our data look like.
+---
+# Reasons for visualizing data
+
+--
+
+For yourself - once you have collected your data, you should visualize it 
+before you build any statistical models - does the data look (roughly) as 
+expected with the right number of data points?
+
+--
+
+For others - when you present your work in a talk, on a poster, or in a 
+published paper you want the viewer to be able to quickly and unambiguously 
+extract the intended meaning from your visualization.  
+
+--
+
+Just as the reproducibilty of statistical models is important in the context 
+of engaging in open and reproducible science, so too is the reproducibilty of 
+data visualizations.
+
+
+---
+
+# ggplot2
+
+The ggplot2 package is part of the Tidyverse and is based on the layered Grammar of Graphics (Wickham, 2010):
+
+https://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf
+
+Start with defining your data and aesthetics of the plot, before adding geometric objects (geoms), information about labelling, faceting etc.
+
+Each plot can be built up gradually, layer by layer like the following:
+
+---
+
+
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-25-1.png)&lt;!-- --&gt;
+]
+
+.bottom[
+
+```r
+ggplot(data_long, aes(x = Condition, y = RT)) +
+  geom_jitter(alpha = .25, position = position_jitter(0.05))
+```
+]
+
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-27-1.png)&lt;!-- --&gt;
+]
+
+.bottom[
+
+```r
+ggplot(data_long, aes(x = Condition, y = RT)) +
+  geom_jitter(alpha = .25, position = position_jitter(0.05)) +
+  stat_summary(fun.data = "mean_cl_boot", colour = "black", 
+               size = 1) 
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-29-1.png)&lt;!-- --&gt;
+]
+
+.bottom[
+
+```r
+ggplot(data_long, aes(x = Condition, y = RT)) +
+  geom_jitter(alpha = .25, position = position_jitter(0.05)) +
+  stat_summary(fun.data = "mean_cl_boot", colour = "black", 
+               size = 1) +
+  geom_violin(aes(fill = Condition), alpha = .2)
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-31-1.png)&lt;!-- --&gt;
+]
+
+.bottom[
+
+```r
+ggplot(data_long, aes(x = Condition, y = RT)) +
+  geom_jitter(alpha = .25, position = position_jitter(0.05)) +
+  stat_summary(fun.data = "mean_cl_boot", colour = "black", 
+               size = 1) +
+  geom_violin(aes(fill = Condition), alpha = .2) +
+  guides(fill = FALSE)
+```
+]
+
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-33-1.png)&lt;!-- --&gt;
+]
+
+.bottom[
+
+```r
+ggplot(data_long, aes(x = Condition, y = RT)) +
+  geom_jitter(alpha = .25, position = position_jitter(0.05)) +
+  stat_summary(fun.data = "mean_cl_boot", colour = "black", 
+               size = 1) +
+  geom_violin(aes(fill = Condition), alpha = .2) +
+  guides(fill = FALSE) + 
+  coord_flip()
+```
+]
+
+---
+# Violin Plots
+
+These are Violin Plots - these are an example of an RDI plot as they capture the Raw data, information about the Distribution, and some Inferential statistics (e.g., Confidence Intervals).
+
+We can modify other characteristics of the plot such as the colour palette we're using, the orientation, and we can also add some labels:
+
+
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-36-1.png)&lt;!-- --&gt;
+---
+# Raincloud Plots
+![](SIPS_presentation_files/figure-html/unnamed-chunk-37-1.png)&lt;!-- --&gt;
+
+.footnote[Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization [version 1; peer review: 2 approved]. Wellcome Open Res 2019, 4:63
+https://doi.org/10.12688/wellcomeopenres.15191.1
+]
+
+---
+# Using Different Themes
+
+You can change the ggplot theme to a number of built in ones (or define your own.)  
+
+On the next page you'll see the same plot with (a) the Economist theme, (b) 
+the fivethirtyeight theme, (c) the Tufte theme, and (d) the solarized theme. 
+Below is the plot with the default theme.
+
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-38-1.png)&lt;!-- --&gt;
+]
+
+---
+class: center, middle
+![](SIPS_presentation_files/figure-html/unnamed-chunk-39-1.png)&lt;!-- --&gt;
+
+---
+class: center, middle
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-40-1.png)&lt;!-- --&gt;
+
+---
+class: center, middle
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-41-1.png)&lt;!-- --&gt;
+
+---
+class: center, middle
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-42-1.png)&lt;!-- --&gt;
+---
+# The BBC Cookbook
+
+The BBC (like many other organisations such as the FT) use R and ggplot to generate their data They have even published their own style guide and code for their BBC data visualization theme.
+
+https://bbc.github.io/rcookbook/
+.center[
+&lt;img src="images/bbc.png" width="80%" /&gt;
+]
+
+---
+# World Happiness Data
+
+
+We can have a look at the World Happiness dataset that measures Happiness (called Life Ladder) and a bunch of other things (e.g., GDP) over countries over time.
+
+.center[
+
+```r
+vis_dat(happy_data)
+```
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-45-1.png)&lt;!-- --&gt;
+]
+---
+.center[
+
+```r
+vis_miss(happy_data)
+```
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-46-1.png)&lt;!-- --&gt;
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-47-1.png)&lt;!-- --&gt;
+]
+.bottom[
+
+```r
+happy_data %&gt;% 
+  ggplot(aes(x = `Life Ladder`)) + 
+  geom_histogram() +
+  labs(x = "Happiness Index Score", y = "Number of Instances", 
+       title = "Histogram of Happiness Scores") 
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-49-1.png)&lt;!-- --&gt;
+]
+.bottom[
+
+```r
+happy_data %&gt;% 
+  filter(!is.na(`Life Ladder`) &amp; year == 2016) %&gt;%
+  mutate(score = `Life Ladder`) %&gt;%
+  mutate(country = reorder(country, score)) %&gt;%
+  top_n(20) %&gt;%
+  ggplot(aes(x = score, y = country)) + 
+  geom_point() +
+  labs(x = "Happiness Index Score", y = "Country", 
+       title = "Top 20 Happiest Countries in 2016") +
+  theme_tufte(base_size = 15) 
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-51-1.png)&lt;!-- --&gt;
+]
+.bottom[
+
+```r
+country_list &lt;- c("United Kingdom", "France", "Germany", 
+                  "Italy", "Norway", "United States")
+happy_data %&gt;% 
+  filter(country %in% country_list) %&gt;%
+  filter(!is.na(`Social support`)) %&gt;%
+  mutate(score = `Social support`) %&gt;%
+  mutate(country = reorder(country, score)) %&gt;%
+  ggplot(aes(y = score, x = country, fill = country)) + 
+  geom_boxplot(width = .5) +
+  labs(y = "Boxplot of Social support", x = "Country", 
+       title = "Social support") +  
+  guides(fill = FALSE) + 
+  coord_flip() + theme_tufte(base_size = 15)
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-53-1.png)&lt;!-- --&gt;
+]
+.bottom[
+
+```r
+happy_data %&gt;% 
+  filter(country %in% country_list) %&gt;%
+  mutate(score = `Life Ladder`) %&gt;%
+  mutate(country = reorder(country, score)) %&gt;%
+  ggplot(aes(x = country, y = score)) + 
+  geom_boxplot() +
+  labs(x = "Country", y = "Happiness Index Score", 
+       title = "Average Happiness Index for 6 Countries") +
+  coord_flip() + theme_fivethirtyeight(base_size = 15)  
+```
+]
+---
+.top[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-55-1.png)&lt;!-- --&gt;
+]
+.bottom[
+
+```r
+country_list &lt;- c("United Kingdom", "France", "Germany", 
+                  "Italy", "Norway", "United States")
+happy_data %&gt;%
+  filter(country %in% country_list) %&gt;%
+  group_by(year) %&gt;%
+  filter(!is.na(`Life Ladder`)) %&gt;%
+  ggplot(aes(x = year, y = `Life Ladder`)) +
+  geom_line() +
+  facet_wrap(~ country) +
+  labs(x = "Year", y = "Happiness index", 
+       title = "Happiness Index over Time for 6 Countries") +
+  theme_fivethirtyeight(base_size = 15) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+```
+]
+---
+# Visualising Data from Twitter
+
+Scraping Twitter using the rtweet() package for everyone's favourite progressive 
+Swedish death metal band, Opeth! 🤘  
+
+&lt;p&gt;
+
+
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-58-1.png)&lt;!-- --&gt;
+]
+---
+class: centre
+
+# Geospatial Plotting of Tweets
+
+
+
+.center[
+<div id="htmlwidget-af31fcf2b1bdbba72aec" style="width:576px;height:432px;" class="leaflet html-widget"></div>
+<script type="application/json" data-for="htmlwidget-af31fcf2b1bdbba72aec">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"&copy; <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a 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+]
+
+---
+class: middle, center
+# Teaching with Animations
+
+---
+# Data Simulations and Data Visualisation - Hockey Game Simulation
+
+Imagine a hockey game where we know that Team A scores exactly 1 goal for sure and Team B takes 20 shots, each with a 5.5% chance of going in.
+
+Which team would you rather be?
+
+(nothing additional happens if you tie.)
+
+
+```r
+set.seed(1234)
+
+team_b_goals &lt;- NULL
+
+for(i in 1:10000) {
+score &lt;- sum(sample(c(1, 0), size = 20, replace = TRUE, 
+                    prob = c(0.055, 1-.055)))
+team_b_goals &lt;- c(team_b_goals, score)}
+
+team_a_goals &lt;- rep(1, 10000)
+
+all_games &lt;- as_tibble(cbind(team_a_goals, team_b_goals))
+```
+
+---
+
+
+
+```
+## [1] "Games where Team A scores more goals than Team B:  3145"
+```
+
+```
+## [1] "Games where Team A scores fewer goals than Team B:  3050"
+```
+
+```
+## [1] "Games where there is a tie :  3805"
+```
+
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-64-1.gif)&lt;!-- --&gt;
+]
+---
+class: middle, center
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-65-1.gif)&lt;!-- --&gt;
+
+---
+# Sample size of 25 when population r = .5
+
+.center[
+
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-67-1.gif)&lt;!-- --&gt;
+]
+
+---
+# Sample size of 250 when population r = .5
+
+.center[
+
+
+![](SIPS_presentation_files/figure-html/unnamed-chunk-69-1.gif)&lt;!-- --&gt;
+]
+
+---
+# Binder for fully reproducible computational environments
+
+On the previous slides, we saw a number of example visualizations and 
+associated code.  Open code is great, but we also need to ensure we share our
+computational environment so our code can be re-run (and produce the same 
+output).
+
+We need to capture the versions of the different R packages we're using (plus 
+their dependencies). 
+
+May sound trivial but trying running some old R code and be amazed at how many 
+things now don’t work as they once did!  
+
+Many R packages have introduced 'breaking changes' when they have been updated. 
+
+
+---
+# Binder, BinderHub, and repro2docker
+
+Binder is powered by BinderHub, which is an open-source tool that deploys the 
+Binder service in the cloud.
+
+Binder works by pulling a repository that you set up on GitHub into a 
+Docker container.  Docker packages your data, code and all its dependencies in 
+the form called a docker container to ensure that your application works 
+seamlessly in any environment.
+
+.center[
+&lt;img src="images/docker.png" width="50%" /&gt;
+]
+---
+class: middle, center
+
+&lt;img src="images/binder1.png" width="80%" /&gt;
+
+![](images/thinking.gif)&lt;!-- --&gt;
+
+---
+class: middle, center
+
+# But you don't need to worry about any of that!
+
+---
+# Step 1 - Set up a GitHub account
+
+&lt;img src="images/binder2.png" width="1365" /&gt;
+
+---
+# Step 2 - Create a new public repository
+
+&lt;img src="images/binder3.png" width="1255" /&gt;
+
+---
+# Step 3 - Upload your R script and data and make your first “Commit”
+
+&lt;img src="images/binder4.png" width="1276" /&gt;
+
+---
+# Step 3 - Upload your R script and data and make your first “Commit”
+
+We need two other files at this point - one is called “runtime.txt” and contains 
+the date of R and its associated packages that you want to simulate.
+
+The other is called “install.R” and contains the list of R packages that need to 
+be installed in order for your script to run. 
+
+To create a new file select “Create new file”.
+
+&lt;img src="images/binder5.png" width="603" /&gt;
+
+---
+
+&lt;img src="images/binder6.png" width="1252" /&gt;
+
+---
+# Step 5 - Now we need to link our repo to Binder (mybinder.org)
+
+&lt;img src="images/binder7.png" width="1308" /&gt;
+
+---
+&lt;img src="images/binder8.png" width="1187" /&gt;
+
+Paste the first bit of code into your GitHub repo README.md - you’ll then be 
+able to click on the ‘launch binder’ button in your repository to launch the 
+actual binder once it has been built - makes it easy for others to go from you 
+GitHub repo to your code running in Binder.
+
+---
+# Once you click ‘Launch’…
+
+&lt;img src="images/binder9.png" width="1163" /&gt;
+
+---
+class: center, middle
+
+&lt;img src="images/obi.gif" width="150%" /&gt;
+
+---
+class: middle
+
+If Binder can find an image that you’ve built previously, it will simply launch 
+that.
+
+If you’ve made changes to your GitHub repo, it will rebuild the Docker image and 
+create a new Binder.
+
+And then...
+
+.center[
+&lt;img src="images/han.gif" width="50%" /&gt;
+]
+---
+# Ta da!
+.center[
+&lt;img src="images/binder10.png" width="1152" height="50%" /&gt;
+]
+
+---
+# A few key things...
+
+Installing the entire Tidyverse in a Binder can take a long time - better to 
+install only the packages you use (e.g., ggplot2, dplyr, readr etc.) - this 
+will also ensure the packages are consistent with the date in your runtime.txt 
+file.
+
+Even with just a couple of packages it can take ~15 minutes or so for your 
+Binder to be built.
+
+Some R packages need system-level packages to also be installed - you can do 
+that via an additional apt.txt file which lists those packages - this is used 
+by apt-install to install those packages from the Ubuntu apt repository.
+
+At the moment, you can’t change the version of R that runs on Binder (currently 
+set to 3.4.4.) via the runtime.txt file so need to go down the dockerfile route 
+using the holepunch package - link to additional slides at the end of our 
+[hackmd.io](https://hackmd.io/wlp5EX3ATK2ZCqjnx9DKLg?view) doc.
+
+You can close your laptop if Binder is taking too long - the image and your 
+Binder will continue to be built in the Cloud.  And it’s always a good excuse 
+for another coffee…
+
+---
+# For Ultimate Reproducibility
+
+Make sure you have updated all your packages before you run your script.
+
+Build your Binder and specify the day you ran your analysis in the runtime.txt 
+file.
+
+Patience (young Skywalker) while your Binder builds…
+
+---
+# Now for your data visualization backwards coding challenge...
+
+Ideally in groups (assuming a range of skill levels), generate R code that will
+re-create each of the following visualizations.  The data are SNCF train data 
+from a TidyTuesday.  All instructions for loading the data and the workshop are 
+in the hackmd.io document below.
+
+https://hackmd.io/wlp5EX3ATK2ZCqjnx9DKLg?view
+
+After you have re-created each visualization, create a GitHub repo of your 
+script, a runtime.txt file, and an install.R file.  Then create a Binder from 
+your repo.  While your Binder is building, move onto the next visualization 
+challenge.
+
+At the end of the workshop, you should each have 6 R scripts, 6 new GitHub 
+repositories, and a Binder associated with each of the 6 repositories.
+---
+# Visualization 1
+
+
+
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-85-1.png)&lt;!-- --&gt;
+]
+]
+---
+# Visualization 2
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-86-1.png)&lt;!-- --&gt;
+]
+]
+---
+# Visualization 3
+
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-87-1.png)&lt;!-- --&gt;
+]
+]
+---
+# Visualization 4
+
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-88-1.png)&lt;!-- --&gt;
+]
+]
+---
+# Visualization 5
+
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-89-1.png)&lt;!-- --&gt;
+]
+]
+---
+# Visualization 6
+
+.middle[
+.center[
+![](SIPS_presentation_files/figure-html/unnamed-chunk-90-1.gif)&lt;!-- --&gt;
+]
+]
+---
+# For the animated plot (Visualization 6)...
+
+In addition to the runtime.txt and install.R files, you will also need to create
+an apt.txt file as the gganimate package requires a system level installation
+of magick.  Create your apt.txt file in your GitHub repo as you would the 
+runtime.txt file, and add the following lines:
+
+
+```r
+libmagick++-dev
+cargo
+```
+
+Your install.R file will probably also need to include the following:
+
+
+```r
+install.packages("gganimate")
+install.packages("ggimage")
+install.packages("gifski")
+install.packages("png")
+```
+
+But for now...
+---
+class: center, middle
+&lt;img src="images/pooh.gif" width="75%" /&gt;
+    </textarea>
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