http://datacamp-community-prod.s3.amazonaws.com/c1fae72f-d2d7-4646-9dce-dd0f8fb5c5e8

This tidyverse cheat sheet for beginners will help you to find your way around the well-known packages dplyr and ggplot2!

Tidyverse Cheat Sheet For Beginners

https://www.datacamp.com/community/podcast/data-science-pharmaceuticals

What are the biggest challenges in Pharmaceuticals that data science can help to solve?

What are the biggest challenges in Pharmaceuticals that data science can help to solve?

Pharmaceuticals and Data Science

https://www.datacamp.com/community/blog/pharmaceuticals-data-science

https://www.datacamp.com/community/news/video-tidyverse-exploratory-analysis-4cvm3bp8akn

In this video tutorial, you'll be given a brief introduction to the concept of the tidyverse and then walk through an exploratory analysis.

Video: Tidyverse Exploratory Analysis

https://www.datacamp.com/community/news/lego-mosaics-from-photos-using-r-and-the-tidyverse-3zyf90qr8u

Instructions for turning any photo into a LEGO mosaic using R and the tidyverse.

LEGO mosaics from photos using R & the tidyverse

https://www.datacamp.com/community/news/getting-started-with-the-tidyverse-tutorial-hf369yzqzar

Start analyzing titanic data with R and the tidyverse: learn how to filter, arrange, summarise, mutate and visualize your data with dplyr and ggplot2!

Getting Started with the Tidyverse: Tutorial

https://www.datacamp.com/community/news/tidy-sentiment-analysis-in-r-7kcg2vmpxpd

Learn how to perform tidy sentiment analysis in R on Prince's songs, sentiment over time, song level sentiment, the impact of bigrams, and much more!

Tidy Sentiment Analysis in R

https://www.datacamp.com/community/news/web-scraping-in-r-rvest-tutorial-43z6wf5u86

A short case study that shows how to webscrape using rvest. Trustpilot reviews for individual organisations are scraped and exploratively analysed for fraudulent activity.

Web Scraping in R: rvest Tutorial

<center><a href="https://www.datacamp.com/tracks/tidyverse-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537311012/Tidyverse_Fundamentals_Skill_Track_Banner_zrtfol.png" /></a>
Here is the track <a href="https://www.datacamp.com/tracks/tidyverse-fundamentals">link</a>.
<h3 id="track-details">Track Details</h3>
In this track, you’ll learn the skills needed to get you up and running with data science in R using the tidyverse. The tidyverse is a collection of R packages that share a common design philosophy and are designed to seamlessly work together, all with novices to data science professionals equally benefiting. You’ll begin by hopping into data wrangling and data visualization with the gapminder dataset in the <a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a> course. Next, in <a href="https://www.datacamp.com/courses/working-with-data-in-the-tidyverse">Working with Data in the Tidyverse</a>, you’ll learn about “tidy data” and see how to get data into tidy format with fun datasets from the British reality television series “Great British Bake Off” allowing you to see what’s cooking behind the scenes of so many R data analyses. After that, you’ll ease into general modeling concepts via regression using tidyverse principles in <a href="https://www.datacamp.com/courses/modeling-with-data-in-the-tidyverse">Modeling with Data in the Tidyverse</a>. There you’ll explore Seattle housing prices and how different variables can be used to explore patterns in these prices.
Throughout these first three courses, you’ll use the dplyr, ggplot2, and tidyr packages that serve as the powerhouses of the tidyverse allowing you to see the power of readable code. In <a href="https://www.datacamp.com/courses/communicating-with-data-in-the-tidyverse">Communicating with Data in the Tidyverse</a>, you’ll learn how to further customize your ggplot2 graphics and use R Markdown to write reproducible reports while working with data from the International Labour Organization in Europe. The track closes with <a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse">Categorical Data in the Tidyverse</a> that explores ways to handle the sometimes tricky concept of factors in data science with R using datasets from Kaggle’s Data Science and Machine Learning Survey and FiveThirtyEight.com.
The goal of the track is for you to gain experience using the tools and techniques of the whole data science pipeline made famous by Hadley Wickham and Garrett Grolemund as shown below. You’ll gain exposure to each component of this pipeline from a variety of different perspectives in this track. We look forward to seeing you in the track!
<center><a href="https://www.datacamp.com/tracks/tidyverse-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537312074/Screen_Shot_2018-09-18_at_7.06.55_PM_bqq54o.png" /></a>
<h3 id="introduction-to-the-tidyverse">Introduction to the Tidyverse</h3>
This is an introduction to the programming language R, focused on a powerful set of tools known as the &quot;tidyverse&quot;. In the course, you&#39;ll learn the intertwined processes of data manipulation and visualization through the tools dplyr and ggplot2. You&#39;ll learn to manipulate data by filtering, sorting and summarizing a real dataset of historical country data in order to answer exploratory questions. You&#39;ll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data analysis.
This course was even approved by Hadley himself:
<a href="https://twitter.com/hadleywickham/status/928806877269434368"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537322555/Screen_Shot_2018-09-18_at_10.01.37_PM_jzfbgg.png" /></a>
<h3 id="working-with-data-in-the-tidyverse">Working with Data in the Tidyverse</h3>
In this course, you&#39;ll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people&#39;s data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn&#39;t mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you&#39;ll work with data from a popular television baking competition called &quot;The Great British Bake Off.&quot;
<center><a href="https://www.datacamp.com/tracks/tidyverse-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537322122/Screen_Shot_2018-09-11_at_4.32.39_PM_uguvin.png" /></a>
<h3 id="modeling-with-data-in-the-tidyverse">Modeling with Data in the Tidyverse</h3>
In this course, you will learn to model with data. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. Such models can be used for both explanatory purposes, e.g. &quot;Does knowing professors&#39; ages help explain their teaching evaluation scores?&quot;, and predictive purposes, e.g., &quot;How well can we predict a house&#39;s price based on its size and condition?&quot; You will leverage your tidyverse skills to construct and interpret such models. This course centers around the use of linear regression, one of the most commonly-used and easy to understand approaches to modeling. Such modeling and thinking is used in a wide variety of fields, including statistics, causal inference, machine learning, and artificial intelligence.
<center><a href="https://www.datacamp.com/tracks/tidyverse-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537322122/Screen_Shot_2018-09-11_at_4.36.32_PM_scvavy.png" /></a>
<h3 id="communicating-with-data-in-the-tidyverse">Communicating with Data in the Tidyverse</h3>
They say that a picture is worth a thousand words. Indeed, successfully promoting your data analysis is not only a matter of accurate and effective graphics, but also of aesthetics and uniqueness. This course teaches you how to leverage the power of ggplot2 themes for producing publication-quality graphics that stick out from the mass of boilerplate plots out there. It shows you how to tweak and get the most out of ggplot2 in order to produce unconventional plots that draw attention on social media. In the end, you will combine that knowledge to produce a slick and custom-styled report with RMarkdown and CSS – all of that within the powerful tidyverse.
<h3 id="categorical-data-in-the-tidyverse">Categorical Data in the Tidyverse</h3>
As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. This type of data is qualitative and can be ordinal if they have an order to them, or categorical/nominal, if they don&#39;t. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real-world datasets, such as the FiveThirtyEight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!

https://www.datacamp.com/community/blog/skill-track-tidyverse-r

Learn about our new R skill track: Tidyverse Fundamentals with R.

New Skill Track: Tidyverse Fundamentals with R

<center><a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse"><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1536757071/Categorical_Data_in_the_Tidyverse_Banner_juqigp.png" /></a>
Here is the course <a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse">link</a>.
<h3 id="course-description">Course Description</h3>
As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. This type of data is qualitative and can be ordinal, if they have an order to them, or categorical/nominal, if they don&#39;t. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real world datasets, such as the fivethirtyeight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!
<h3 id="chapter-1-introduction-to-factor-variables-free-">Chapter 1: Introduction to Factor Variables (Free)</h3>
In this chapter, you’ll learn all about qualitative variables. You’ll discover the difference between categorical and ordinal variables, how R represents them, and how to inspect them to find the number and names of the levels. Finally, you’ll find how forcats, a tidyverse package, can improve your plots by letting you quickly reorder variables by their frequency.
<center><a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse"><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1536757142/Screen_Shot_2018-09-11_at_4.51.15_PM_wvjqbf.png" /></a>
<h3 id="chapter-2-manipulating-factor-variables">Chapter 2: Manipulating Factor Variables</h3>
You’ll continue to dive into the forcats package, learning how to change the order and names of levels and even collapse them into one another.
<h3 id="chapter-3-creating-factor-variables">Chapter 3: Creating Factor Variables</h3>
Having gotten a good grasp of forcats, you’ll expand out to the rest of the tidyverse, learning and reviewing functions from dplyr, tidyr, and stringr. You’ll refine graphs with ggplot2 by changing axes to percentage scales, editing the layout of the text, and more.
<center><a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse"><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1536757313/Screen_Shot_2018-09-12_at_9.01.16_AM_nene2s.png" /></a>
<h3 id="chapter-4-case-study-on-flight-etiquette">Chapter 4: Case Study on Flight Etiquette</h3>
In this final chapter, you’ll take all that you’ve learned and apply it in a case study. You’ll learn more about working with strings and summarizing data, then replicate a publication quality plot 538 plot.
<h3 id="prerequisites">Prerequisites</h3>
<ul>
<li><a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a></li>
<li><a href="https://www.datacamp.com/courses/working-with-data-in-the-tidyverse">Working with Data in the Tidyverse</a></li>
</ul>

https://www.datacamp.com/community/blog/course-categorial-data-tidyverse

New Course: Categorical Data in the Tidyverse

<h2>Getting Started With Plotly</h2>This chapter will introduce you to plotly and how you can use R and plotly together to create stunning data visualizations.<h2>Getting Fancy With Plotly</h2>In this chapter you will bring your plotly skills to the next level. Learn how to use plotly to create heatmaps and 3D surface plots, a choropleth map, and how to add slides. There is even a short meet and greet with ggplotly, the interactive sister of ggplot2.

https://www.datacamp.com/community/open-courses/plotly-tutorial-plotly-and-r

This Plotly tutorial will show you how you can use plotly to easily create stunning data visualizations with R. 

Plotly Tutorial: Plotly and R

If you would like to learn more about the Tidyverse, take Emily Robinson&#39;s <a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse">Categorical Data in the Tidyverse</a> course.
While most R programmers have heard of ggplot2 and dplyr, many are unfamiliar with the breadth of the tidyverse and the variety of problems it can solve. In this video, Emily Robinson, a data scientist at DataCamp, will give a brief introduction to the concept of the tidyverse and then walk through an exploratory analysis of the 2017 Kaggle Data Science and Machine Learning Survey. You’ll see how functions like purrr’s map_if, forcat’s fct_reorder, dplyr’s select_if, and tidyr’s separate_rows all work together to create some great plots and interesting insights!
Whether you’ve been an R programmer for years but never used the tidyverse or are just starting on your R journey, you’ll probably find some new tricks. If you want to learn more, check out our <a href="https://www.datacamp.com/tracks/tidyverse-fundamentals">Tidyverse Fundamental track</a>.
<ul>
<li><a href="https://www.kaggle.com/kaggle/kaggle-survey-2017">Kaggle Dataset</a></li>
</ul>
<iframe width="560" height="315" src="https://www.youtube.com/embed/uG3igAGX7UE" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

https://www.datacamp.com/community/tutorials/tidyverse-exploratory-analysis

Tidyverse Exploratory Analysis

<h2 id="when-scottish-soccer-meets-the-tidyverse">When Scottish soccer meets the tidyverse</h2>
I collated some data on my local soccer team which we can use to practice some data reshaping techniques using tools from the tidyverse.
Once we&#39;ve reshaped the data, we&#39;re going to plot every goal, from every match of their 2017/2018 league campaign.
The aim is to create a plot where each facet shows a timeline of goals scored and conceded.
We&#39;ll use tidyr, dplyr and ggplot2 to create the main graphic, along with some plots looking at trends in the time of goals scored and conceded.
Optionally, we can animate the final graphic using the in development &quot;gganimate&quot; package.
This idea is inspired by (but in no way meant to be a direct replica of) the work of data visualization guru Andy Kirk, who visualized Liverpool FC&#39;s season a a couple of years ago (<a href="http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/">http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/</a>).
That season finished in a European final for the Reds.
My team is Inverness Caledonian Thistle FC, based in the Scottish Highlands.
Last year they aimed to gain promotion back up to the Scottish Premier League, having been relegated the season before.
Let&#39;s see if it was a rollercoaster season at the Tulloch Caledonian Stadium.
The raw data is stored in an excel workbook, so in addition to loading the tidyverse package, we&#39;ll use the readxl package to import our data.
Let&#39;s load the packages we need, and read in the data.
<pre><code class="lang-{r}">library(readxl)
library(tidyverse)
scores &lt;- read_excel(&quot;InvernessResults2017.xlsx&quot;,sheet = &quot;scores&quot;)
</code></pre>
Here is a <a href="https://s3.amazonaws.com/assets.datacamp.com/blog_assets/InvernessResults2017.xlsx">link</a> to the spreadsheet data.
Let&#39;s take a look at the data.
<pre><code class="lang-{r}">glimpse(scores)
</code></pre>
The &#39;Outcome&#39; column tells us whether Inverness Won, Lost or the match was a Draw.
We have columns for the &#39;Home&#39; team score, and the &#39;Away&#39; team score, and a column indicating whether Inverness were Home or Away.
Usually teams alternate, but sometimes there is a run of home games, and vice versa.
When we make the plot, we&#39;ll always want the Inverness score to appear first, so we&#39;ll need to figure out a way to make the order of the Result column consistent.
We can see the name of the opposing teams:
<pre><code class="lang-{r}">unique(scores$Opponent)
</code></pre>
There are 10 teams in the league, and each team plays 4 games against the others ( 2 at home, 2 away), so 36 games in all.
We have a column for each team&#39;s score, and additional columns are showing the time of each individual goal.
For example, if Inverness score 3 goals, then columns Inv_Goal_1,
Inv_Goal_2, and Inv_Goal_3 will have entries, while columns Inv_Goal4 and Inv_Goal5 will remain as NA.
We&#39;ll definitely want to condense those columns. 
Before we do that, we&#39;ll create a helper dataframe for plotting purposes.
We can use this to join to our scores data frame later on.
<pre><code class="lang-{r}">team &lt;- c(&#39;Brechin&#39;,&#39;Dumbarton&#39;,&#39;Dundee_United&#39;,&#39;Dunfermline&#39;,&#39;Falkirk&#39;,&#39;Greenock_Morton&#39;,&#39;Inverness&#39;,&#39;Livingston&#39;,&#39;Queen_Of_The_South&#39;,&#39;St_Mirren&#39;)
colors &lt;- c(&#39;#E3001B&#39;,&#39;#F8BE02&#39;,&#39;#C6631D&#39;,&#39;#161616&#39;,&#39;midnightblue&#39;,&#39;#316891&#39;,
 &#39;#0355AF&#39;,&#39;#C19B24&#39;,&#39;#093C71&#39;,&#39;#000000&#39;)


team_df &lt;- tibble(team,colors)

rm(list = c(&quot;colors&quot;,&quot;team&quot;))
</code></pre>
The colors have been chosen as the best match (without being too obsessive over it) to the primary color in each team&#39;s playing kit.
<h2 id="time-to-tidy">Time to tidy</h2>
OK, enough preamble, let&#39;s see how my team did...
First of all, the column names in our data frame all start with capitals.
That can be problematic so let&#39;s change them to lower case:
<pre><code class="lang-{r}">colnames(scores) &lt;- colnames(scores) %&gt;% str_to_lower()
</code></pre>
That&#39;s better. Now let&#39;s tackle all those columns that show the time of each goal, using tidyr&#39;s gather function.
First, we select the date column, all the columns that start with &quot;inv&quot; plus those starting with &quot;opp&quot;. We&#39;ll also select the &#39;gameid&#39; column for use in calculating future variables.
Take a look at the &#39;gather&#39; call. We&#39;re taking all the goal columns, and we&#39;re transposing them into one long column, named &quot;goal&quot; ( defined by the &#39;key&#39; argument). Alongside this, we&#39;re creating a new long column called &#39;time&#39;, where all the values for the times of goal are scored.
All the other columns that we selected are also stored in the &#39;long&#39; format.
<pre><code class="lang-{r}">data &lt;- scores %&gt;%
 select(date, starts_with(&quot;inv&quot;),starts_with(&quot;opp&quot;), gameid) %&gt;%
 gather(inv_goal_1,inv_goal_2,inv_goal_3,
 inv_goal_4,inv_goal_5,opp_goal_1,opp_goal_2,opp_goal_3,
 opp_goal_4,opp_goal_5, key = &quot;goal&quot;,value = &quot;time&quot;)
</code></pre>
Let&#39;s take a look at this new dataframe:
<pre><code class="lang-{r}">str(data)
</code></pre>
Our &#39;scores&#39; dataframe was 36 rows long and 20 columns wide.
Now our &#39;gathered&#39; dataframe is 360 rows in length but only 8 columns wide.
We&#39;ll add in one more column, for use when faceting the plot later on.
<pre><code class="lang-{r}">data &lt;- data %&gt;%
mutate( result = paste(inverness_score,&quot;-&quot;,opponent_score))
</code></pre>
We need to add a team column in there, plus we also need to get a cumulative count of each goal by team, by match.
<pre><code class="lang-{r}">plot_data &lt;- data %&gt;%
 select(date,opponent,gameid,goal,time,result,inverness_status) %&gt;%
 mutate(team = if_else(str_sub(goal,1,1) == &quot;i&quot;,&quot;inverness&quot;,tolower(opponent))) %&gt;%
 group_by(gameid,team) %&gt;%
 arrange(time) %&gt;%
 mutate(count = 1) %&gt;%
 ungroup() %&gt;%
 group_by(gameid,team) %&gt;%
 mutate(goalcount = cumsum(count)) %&gt;%
 ungroup() %&gt;%
 select(-count)
</code></pre>
We&#39;re going to use geom_rect() for each goal.
This requires minimum and maximum values along both the x and y-axis.
We&#39;ll use the lag and lead functions from dplyr to create these values.
For example, the second goal scored will have a minimum value equal to the time of the first goal, and the max will be the actual time of the second goal.
Meanwhile, the minimum y value will be 1, and the maximum will be 2.
It&#39;ll become clearer when we produce the plot!
<pre><code class="lang-{r}">plot_data &lt;- plot_data %&gt;%
 group_by(gameid) %&gt;%
 arrange(gameid,time) %&gt;%
 mutate(lag_time = lag(time),
 lead_time = lead(time)) %&gt;%
 ungroup()

plot_data$lag_time[is.na(plot_data$lag_time)] &lt;- 0

plot_data$lead_time[is.na(plot_data$lead_time)] &lt;- 90
</code></pre>
Those last 2 commands replace NA&#39;s in the lag time and lead time with 0 and 90 respectively. Now we select the columns we want in the desired order.
<pre><code class="lang-{r}">plot_data &lt;- plot_data %&gt;%
 select(gameid, date,team, result,opponent,goalcount,lag_time, time, lead_time, inverness_status)
</code></pre>
We want to ensure we keep the rows for the games that ended in a scoreless 0-0 draw. Otherwise, those results will be dropped from the final plot.
<pre><code class="lang-{r}">goalless &lt;- filter(plot_data, result == &quot;0 - 0&quot;)
</code></pre>
However, we don&#39;t need the extraneous rows from the other games where there is no &#39;time&#39; value.
So we&#39;ll filter them out and then combine the two dataframes, keeping the plot_data name.
<pre><code class="lang-{r}">scored &lt;- filter(plot_data, result != &quot;0 - 0&quot;) %&gt;%
 filter(!is.na(time))

plot_data &lt;- bind_rows(goalless,scored)

rm(list = c(&quot;goalless&quot;, &quot;scored&quot;))
</code></pre>
Now finally, we&#39;re ready to plot.
You might want to maximize your plot window for this.
<pre><code class="lang-{r}">p &lt;- ggplot(plot_data,aes(time,goalcount, group = opponent, fill = team)) +
 geom_rect(aes(xmin = lag_time,xmax = time, ymin = (goalcount - 1), ymax = goalcount)) +
 geom_text(aes(x = 25,y = 4.5,label = result , size = 0.5)) +
theme_void() +
 scale_fill_manual(values = team_df$colors) +
 facet_wrap(date + inverness_status ~ opponent, ncol = 9) +
 ggtitle(label = &quot;Inverness Caledonian Thistle Goals Scored / Conceded - Scottish Championship 2017/2018&quot;) +
 labs(x = NULL, y = NULL) +
 theme(legend.position = &quot;none&quot;)
print(p)
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534957961/01-SeasonStatic_a8kc2f.png" />
And there you go, a timeline for goals scored in each match.
While we have this data, we may as well look to see if there are any patterns in when the team scores or concedes. Because each match lasts 90 minutes (excluding time added on for stoppages), we can look at goals scored in 15-minute intervals.
<pre><code class="lang-{r}">plot_data$cut &lt;- cut(plot_data$time, seq(0,90,by = 15))

inv_scored &lt;- plot_data %&gt;%
 filter(team == &quot;inverness&quot;, !is.na(time))

 ggplot(inv_scored,aes(goalcount,time, group = goalcount)) +
 geom_boxplot(width = 0.2) +
 geom_point() +
 ggtitle(label = &quot; When Inverness goals 1 to 5 are scored - by minute of match&quot;) +
 scale_y_continuous(breaks = seq(0, 90, by = 15)) +
 theme_bw()
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534957961/02-TimeofGoalsBoxplot_wga4fd.png" />
It looks like the team often scores 1 or 2 goals within the first 30 minutes (This does not correlate with my memory of the matches though). 
Were any of the opposing team particularly susceptible to letting goals in early on in matches?
<pre><code class="lang-{r}">opposing_colors &lt;- team_df %&gt;% filter(team != &quot;Inverness&quot;)

ggplot(inv_scored,aes(goalcount,time , group = goalcount,fill = opponent)) +
 geom_boxplot(width = 0.2) +
 ggtitle(label = &quot;Inverness goals scored by opponent and minute of match&quot;) +
 scale_y_continuous(breaks = seq(0, 90, by = 15)) +
 facet_wrap(~ opponent) +
 theme_bw() +
 theme(legend.position = &quot;bottom&quot;) +
 scale_fill_manual(values = opposing_colors$colors)
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534957961/03-TimeofGoalsSoredByTeamBoxplot_cfslcg.png" />
So it looks like the team scored early against Brechin, Dumbarton, and Livingston.
And of course we can do the same for goals conceded:
<pre><code class="lang-{r}">ggplot(opposition_score,aes(goalcount,time, group = goalcount)) +
 geom_boxplot(width = 0.2) +
 geom_point() +
 ggtitle(label = &quot;Inverness goals conceded by minute of match&quot;) +
 scale_y_continuous(breaks = seq(0, 90, by = 15)) +
 theme_bw()
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534957961/04-TimeofGoalsConcededBoxplot_yyjvnc.png" />
Well, this is interesting because it appears that if Inverness concede 2 or more goals, it generally tends to happen in the second half of the match.
Is there a statistical difference between the times of goals scored and conceded?
We can use ggplot2 to help.
<pre><code class="lang-{r}">comparison &lt;- plot_data %&gt;%
 select(team, goalcount, time) %&gt;%
 filter(!is.na(time)) %&gt;%
 mutate(team_type = if_else(str_sub(team,1,1) == &quot;i&quot;,&quot;Inverness&quot;,&quot;Opponent&quot;))

ggplot(comparison,aes(team_type,time, group = team_type,fill = team_type)) +
geom_boxplot(width = 0.2, notch = TRUE) +
 ggtitle(label = &quot;Distribution of time of nth goal scored / conceded by minute of match&quot;) +
 theme_minimal() +
 scale_y_continuous(breaks = seq(0, 90, by = 15)) +
 theme(legend.position = &quot;bottom&quot;) +
 scale_fill_manual(values = c(&#39;#0355AF&#39;, &#39;#E3001B&#39;)) +
 facet_wrap(vars(goalcount), ncol = 3)
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534957961/05-NotchedBoxplot_zhkq2g.png" />
Although it might be hard to see, depending on your plot window, the notches on the boxplots for the second and third goals do not overlap. This suggests a statistical difference - Inverness is likely to score a second goal earlier than they concede one, and similarly for a third goal.
There is no overlap for goal 5 either, but because scoring or conceding 5 in a match is a rarity anyway, we can ignore that.
<h3 id="animating-the-plot">Animating the plot</h3>
Finally - although the goal timeline plot we created earlier doesn&#39;t quite have the same sophistication that Andy Kirk&#39;s original graphic did, we can still have some fun.
N.B - this is optional and will require installing the current version of gganimate from github.
If that is not possible, don&#39;t worry, as the final output will be shown below so you can see what happens.
Note also - it can take a few minutes to create this animation.
<pre><code class="lang-{r}"># gganimate is currently in development on github
# You will need to install devtools if you haven&#39;t already done so
# You will require to be using the latest version of R and may need to reinstall your existing packages.


# install.packages(&#39;devtools&#39;)
#devtools::install_github(&#39;thomasp85/gganimate&#39;)

library(gganimate)

#we already defined p as our original season plot
# but we will rebuild it here just in case you have removed it from your workspace
p &lt;- ggplot(plot_data,aes(time,goalcount, group = opponent, fill = team)) +
 geom_rect(aes(xmin = lag_time,xmax = time, ymin = (goalcount - 1), ymax = goalcount)) +
 geom_text(aes(x = 25,y = 4.5,label = result , size = 0.5)) +
theme_void() +
 scale_fill_manual(values = team_df$colors) +
 facet_wrap(date + inverness_status ~ opponent, ncol = 9) +
 ggtitle(label = &quot;Inverness Caledonian Thistle Goals Scored / Conceded - Scottish Championship 2017/2018&quot;) +
 labs(x = NULL, y = NULL) +
 theme(legend.position = &quot;none&quot;)
print(p)
</code></pre>
<pre><code class="lang-{r}"># now we take the previous plot, and animate it using the game id to render each faceted plot, in date order
# q &lt;- p + transition_states(gameid, transition_length = 1, state_length = 1) + shadow_mark(past = TRUE) # this ensures all previous plots are shown
# animate(q,width = 900, height = 750)
</code></pre>
<pre><code class="lang-{r}">#anim_save(&quot;game_by_game_season_plot.gif&quot;)
</code></pre>
<center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534860670/game_by_game_season_plot_vmk7f5.gif" />
After a terrible start to the season, which saw the team languishing in 9th place, they slowly began to gain momentum. There were several cup games in amongst the league games, during which they managed to rack up a club record for the number of games/ minutes played without conceding.
If the league had begun in January, they would have been champions.
As it was, a long unbeaten run towards the end of the season saw them winning the Irn Bru Cup (thanks to an extra-time winner), and they came agonizingly close to finishing in the promotion play-off places.
Ultimately, they were undone by Dunfermline, who matched their unbeaten run in the league, and crucially, in the last, &quot;must-win&quot; home game of the season, scored a 96th-minute equalizer to all but seal 4th place.
However, with the team now acclimatized to the Championship, some new signings, and at least 4 matches against their newly relegated local rivals, there are lots of things to look forward to in the new season.
If you would like to learn more about the Tidyverse, take the following DataCamp Courses:
<ul>
<li><a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a></li>
<li><a href="https://www.datacamp.com/courses/categorical-data-in-the-tidyverse">Categorical Data in the Tidyverse</a></li>
<li><a href="https://www.datacamp.com/courses/working-with-data-in-the-tidyverse">Working with Data in the Tidyverse</a></li>
<li><a href="https://www.datacamp.com/courses/communicating-with-data-in-the-tidyverse">Communicating with Data in the Tidyverse</a></li>
<li><a href="https://www.datacamp.com/courses/modeling-with-data-in-the-tidyverse">Modeling with Data in the Tidyverse</a></li>
</ul>

https://www.datacamp.com/community/tutorials/tidy-soccer-scores

In this tutorial, we'll use tidyr, dplyr, and ggplot2 to visualize a season of soccer scores, and investigate trends in the time of goals scored and conceded.

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