https://www.datacamp.com/community/news/teaching-data-science-to-high-schoolers-4mn0vqanyzo

Teaching Data Science to High Schoolers

https://www.datacamp.com/community/news/which-world-leaders-are-twitter-bots-bjp2gko4tk

Using R to investigate world leaders twitter timelines.

Which World Leaders Are Twitter Bots?

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

<p>In the past several months, the data science and AI worlds have been abuzz with the arrival of GPT-3, OpenAI’s newest language model. For many, the model represents a significant leap in the ability of one algorithm to reason with human language across a variety of tasks.</p>
<p>Developers testing GPT-3 have provided many intriguing use cases. Examples of automatic code generation based on plain English prompt, answering medical questions, and legal language translation have ignited the imaginations of many data scientists thinking about the next generation of AI-powered software.</p>
<p><center><a href="https://twitter.com/sharifshameem/status/1284807152603820032?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590183/Screen_Shot_2020-11-05_at_10.28.53_AM_eejh4q.png" /></a></center><br></p>
<p><center><a href="https://twitter.com/QasimMunye/status/1278750809094750211?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590335/Screen_Shot_2020-11-05_at_10.32.11_AM_xqgdjo.png" /></a></center><br></p>
<p><center><a href="https://twitter.com/f_j_j_/status/1283349995144359937?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590534/Screen_Shot_2020-11-05_at_10.35.27_AM_kgfk88.png" /></a></center><br></p>
<p>While much of the value of machine learning at an organizational level is in low-hanging fruit like predicting churn, simple sales forecasts, and customer segmentation—it’s useful to consider what the <a href="https://openai.com/blog/openai-api/">commercialization of GPT-3</a> means for the future. It has the potential to transform how we think about and operationalize artificial intelligence.</p>
<h2 id="defining-artificial-intelligence-machine-learning-and-deep-learning">Defining artificial intelligence, machine learning, and deep learning</h2>
<p>The business world and media are overloaded with buzz terms like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Let’s quickly define these terms before delving into how GPT-3 works.</p>
<p>Andrew Ng, co-founder of Google Brain and former Chief Scientist at Baidu, describes artificial intelligence as a “huge set of tools for making computers behave intelligently.” This encompasses explicitly programmed software like calculators, as well as ML applications like recommendation systems and self-driving cars.</p>
<p>Machine learning is the “field of study that gives computers the ability to learn without being explicitly programmed,” according to Arthur Samuel, pioneer in artificial intelligence and computer gaming. There are generally two types of machine learning algorithms. The first is supervised learning, where algorithms learn the patterns between existing data (inputs) and labels (outputs) and predict the output on unseen data, like whether a new customer will churn based on historical churn data. The second is unsupervised learning, where algorithms discover general patterns in the data and cluster different data points that are similar to each other, as in segmenting customers based on common patterns of behavior.</p>
<p>Deep learning is a form of machine learning based on multi-layered artificial neural networks, which are loosely inspired by biological neural networks in the brain. They can be both supervised and unsupervised and are largely responsible for the last decade’s high-profile ML use cases, like image recognition and sentiment analysis. Deep learning models vary in architecture, ranging from simple to complex based on the number of layers and nodes in the neural network architecture. The more complex a model is, the more parameters it has. If you want to learn more about how deep learning models are built, check out DataCamp’s <a href="https://www.datacamp.com/tracks/deep-learning-in-python">deep learning skill track</a>.</p>
<p>For a more in-depth exploration of these topics, read our e-book, <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">The Definitive Guide to Machine Learning for Business Leaders</a>.</p>
<h2 id="how-gpt-3-works">How GPT-3 works</h2>
<p>So where does GPT-3 intersect with artificial intelligence, machine learning, and deep learning? The acronym GPT refers to “generative pre-trained transformer”—an unsupervised deep learning algorithm that is usually pre-trained on a large amount of unlabeled text. It’s fine-tuned and trained on a large task-specific labeled dataset (e.g., translation of English to French), and is then tasked with inferring the most likely set of outputs (French translation) given a specific set of inputs (English words). You can think of this as a highly sophisticated form of autocomplete for a variety of different language tasks.</p>
<p>GPT-3 is the third iteration of this model, and while it does not innovate on the architecture of its predecessors, it’s pre-trained on extremely large datasets comprising a large portion of the internet, including the <a href="https://commoncrawl.org/the-data/">Common Crawl</a> dataset, and includes many more layers in its network architecture. This makes GPT-3 the most complex language model ever conceived, with 175 billion parameters in its network architecture. This is ten times more parameters than the most complex model prior to GPT-3’s release, <a href="https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/">Turing-NLG by Microsoft</a>, and 117 times more complex than GPT-2.</p>
<p>Most importantly, GPT-3 benefits from few-shot learning, where the pre-trained model doesn’t have to be fine-tuned with large labeled training data for a specific language task. It’s instead just given a task description—translate English words to French—and a few examples of inputs mapped to outputs. Coupled with an easy to use plug-and-play interface, GPT-3 largely eliminates barriers to entry and allows non-experts to produce meaningful results on different language tasks.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598020446/gpt3-1_gxwhsa.png" /></a></center><br></p>
<h2 id="why-gpt-3-is-important">Why GPT-3 is important</h2>
<p>With just a few examples and task descriptions, GPT-3 rivals fine-tuned language models that have been trained on task-specific training data for a variety of language tasks. GPT-3 also exhibits some form of success on tasks that require reasoning, like arithmetic, which are not necessarily language tasks. For example, GPT-3 exhibited 100% accuracy on two-digit addition and subtraction after it was fed a few examples of addition and subtraction. Less complex models with fewer parameters have not been able to break the 60% accuracy ceiling on these tasks. While GPT-3 falters on more complex forms of arithmetic, this implies that more complex models may have the ability to generalize outside of the domain they were trained on.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598020446/gpt3-2_tiy26v.png" /></a></center><br></p>
<p>Interestingly, this suggests further gains can be achieved from purely increasing the dataset and model sizes. Currently, it doesn’t seem that the model’s aggregate performance across different tasks is plateauing at 175B parameters. Assuming the same increase in parameter scaling from GPT-2 to GPT-3, one can only wonder how model performance would scale if GPT-4 had 117 times more parameters than GPT-3.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598020447/gpt3-3_krvb14.png" /></a></center><br></p>
<p>While it’s currently being calibrated in a private beta release, packaging GPT-3 in a plug-and-play API means it may be utilized at scale as soon as it’s out of private beta. As AI Researcher Shreya Shankar pointed out, an important challenge will be serving this API efficiently and easily for organizations to use.</p>
<p><center><a href="https://twitter.com/sh_reya/status/1284545976892403714?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590711/Screen_Shot_2020-11-05_at_10.37.57_AM_jbn8p8.png" /></a></center><br></p>
<h2 id="what-this-means-for-the-future">What this means for the future</h2>
<p>New technologies often follow Gartner’s hype cycle—in fact, OpenAI CEO Sam Altman has already <a href="https://twitter.com/sama/status/1284922296348454913">sounded the hype alarm bells about GPT-3</a>.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1598020581/group_3x_ugeoch.png" /></a></center><br></p>
<p>However, the use cases coming out of GPT-3 developers shed light on the type of AI-powered applications we can expect in the medium to long term. Potential applications include tools that will help designers prototype easily, streamline data analysis, enable more robust research, automate content generation for content marketers, and more.</p>
<p><center><a href="https://twitter.com/dhvanilp/status/1286452207513038848?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590877/Screen_Shot_2020-11-05_at_10.41.03_AM_kuc7gd.png" /></a></center><br></p>
<p><center><a href="https://twitter.com/FaraazNishtar/status/1285934622891667457?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604590984/Screen_Shot_2020-11-05_at_10.42.50_AM_yjls8y.png" /></a></center><br></p>
<p><center><a href="https://twitter.com/dhvanilp/status/1295661122079453184?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604591100/Screen_Shot_2020-11-05_at_10.44.43_AM_cn7ph9.png" /></a></center><br></p>
<p><center><a href="https://twitter.com/pavtalk/status/1285410751092416513?ref_src=twsrc%5Etfw"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604591198/Screen_Shot_2020-11-05_at_10.45.59_AM_flapcz.png" /></a></center><br></p>
<p>Additionally, packaging the model in an easy plug-and-play interface could change the dynamics of how AI is instrumentalized across the organization. For example, this could disincentivize organizations from developing their own in-house models, and allow less technical experts to build solutions using GPT-3.</p>
<p>Finally, when thinking about deploying AI systems at scale, you must be aware of the capacity to spread harm through bias. As many researchers noted while testing out GPT-3, it’s relatively easy to generate harmful outputs that reinforce stereotypes and biases based on neutral inputs.</p>
<p><center><a href="https://twitter.com/an_open_mind/status/1285940858290933767?s=20"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604591295/Screen_Shot_2020-11-05_at_10.47.54_AM_wgqfxn.png" /></a></center><br></p>
<p>Like any machine learning algorithm <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">being deployed at scale</a>, GPT-3 requires serious scrutiny and monitoring over potential harm.</p>
<p><center><a href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1585330745/demobanner_hg8ffe.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/gpt3

How GPT-3 expands the world of possibilities for language tasks—and why it will pave the way for designers to prototype more easily, streamline work for data analysts, enable more robust research, and automate content generation.

GPT-3 and the Next Generation of AI-Powered Services

<p><center><a href="https://www.datacamp.com/courses/generalized-linear-models-in-r"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537988231/Generalized_Linear_Models_Banner_tpe5rq.png" /></a></p>
<p>Here is the course <a href="https://www.datacamp.com/courses/generalized-linear-models-in-r">link</a>.</p>
<h3 id="course-description">Course Description</h3>
<p>Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.</p>
<h3 id="chapter-1-glms-an-extension-of-your-regression-toolbox-free-">Chapter 1: GLMs, an extension of your regression toolbox (Free)</h3>
<p>This chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.</p>
<h3 id="chapter-2-logistic-regression">Chapter 2: Logistic Regression</h3>
<p>This chapter covers running a logistic regression and examining the model outputs.</p>
<p><center><a href="https://www.datacamp.com/courses/generalized-linear-models-in-r"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1537988489/Screen_Shot_2018-09-25_at_11.47.53_AM_wmg7dh.png" /></a></p>
<h3 id="chapter-3-interpreting-and-visualizing-glms">Chapter 3: Interpreting and visualizing GLMs</h3>
<p>This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.</p>
<h3 id="chapter-4-multiple-regression-with-glms">Chapter 4: Multiple regression with GLMs</h3>
<p>In this chapter, you will learn how to do multiple regression with GLMs in R.</p>
<h3 id="prerequisites">Prerequisites</h3>
<ul>
<li><a href="https://www.datacamp.com/courses/multiple-and-logistic-regression">Multiple and Logistic Regression</a></li>
</ul>


https://www.datacamp.com/community/blog/course-generalized-linear-models-r

New Course: Generalized Linear Models in R

<p>Data visualization is an essential skill for data scientists. It combines statistics and design in meaningful and appropriate ways. On the one hand, data visualization is a form of graphical data analysis, emphasizing accurate representation, and data interpretation. On the other hand, data visualization relies on good design choices to make our plots attractive and aid both the understanding and communication of results. On top of that, there is an element of creativity, since data visualization is a form of visual communication at its heart.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_400/v1601047315/Screen_Shot_2020-09-25_at_11.19.09_AM_mufbol.png" /></center>

<p>It&#39;s important to understand the distinction between exploratory and explanatory visualizations. Exploratory visualizations are easily-generated, data-heavy, and intended for a small specialist audience, such as yourself and your colleagues - their primary purpose is graphical data analysis. Explanatory visualizations are labor-intensive, data-specific, and intended for a broader audience, e.g., in publications or presentations - they are part of the communications process. As a data scientist, it&#39;s essential that you can quickly explore data, but you&#39;ll also be tasked with explaining your results to stake-holders. Good design begins with thinking about the audience - and sometimes that just means ourselves.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601047315/Screen_Shot_2020-09-25_at_11.19.25_AM_xcki8p.png" /></center>

<h2 id="scatter-plot">Scatter Plot</h2>
<p>Below, we have a dataset that contains the average brain and body weights of 62 mammals.</p>
<pre><code>MASS::mammals
</code></pre><pre><code>                              body   brain
Arctic fox                   3.385   44.50
Owl monkey                   0.480   15.50
Mountain beaver              1.350    8.10
Cow                        465.000  423.00
Grey wolf                   36.330  119.50
Goat                        27.660  115.00
Roe deer                    14.830   98.20
...
Pig                        192.000  180.00
Echidna                      3.000   25.00
Brazilian tapir            160.000  169.00
Tenrec                       0.900    2.60
Phalanger                    1.620   11.40
Tree shrew                   0.104    2.50
Red fox                      4.235   50.40
</code></pre><p>To understand the relationship here, the most obvious first step is to make a scatter plot, like the one shown below:</p>
<pre><code class="lang-r">ggplot(mammals, aes(x = body, y = brain)) +
  geom_point()
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_400/v1601047315/Screen_Shot_2020-09-25_at_11.19.41_AM_psxeu9.png" /></center>

<p>Two mammals, the African and the Asian Elephants have both very large brain and body weights, leading to a positive skew on both axes.</p>
<h2 id="linear-model">Linear Model</h2>
<p>Now, if we were to apply a linear model, it would be a poor choice since a few extreme values have a large influence.</p>
<pre><code class="lang-r">ggplot(mammals, aes(x = body, y = brain)) +
  geom_point(alpha = 0.6) +
  stat_smooth(
    method = &quot;lm&quot;,
    color = &quot;red&quot;,
    se = FALSE
  )
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_400/v1601047315/Screen_Shot_2020-09-25_at_11.19.52_AM_sduhm8.png" /></center>

<p>Applying a log transformation of both variables allows for a better fit.</p>
<pre><code>ggplot(mammals, aes(x = body, y = brain)) +
  geom_point(alpha = 0.6) +
  coord_fixed() +
  scale_x_log10() +
  scale_y_log10() +
  stat_smooth(
    method = &quot;lm&quot;,
    color = &quot;#C42126&quot;,
    se = FALSE,
    size = 1
  )
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_400/v1601047315/Screen_Shot_2020-09-25_at_11.20.10_AM_mkqjpw.png" /></center>

<p>So, although we began with a rough exploratory plot, it informed us about our data and led us to a meaningful result.</p>
<h3 id="anscombe-s-plots">Anscombe&#39;s Plots</h3>
<p>When we imagine a linear model, as presented on this anonymous plot, we imagine that we are describing data that looks something like this.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_400/v1601047784/Screen_Shot_2020-09-25_at_11.29.23_AM_n4rynf.png" /></center>

<p>But this same model could be describing a very different set of data, such as a parabolic relationship, which calls for a different model.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best,w_400/v1601047791/Screen_Shot_2020-09-25_at_11.29.30_AM_yondtv.png" /></center>

<p>Or data in which an extreme value has a large effect. which becomes clear when the outlier is removed.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best,w_400/v1601048085/Screen_Shot_2020-09-25_at_11.34.30_AM_y0cqcq.png" /></center>

<p>And sometimes, the model may be describing a relationship where, in fact, there is none at all because some extreme values may be incorrect.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best,w_400/v1601048272/Screen_Shot_2020-09-25_at_11.37.23_AM_fiuxww.png" /></center>

<p>If we relied solely on the numerical output without plotting our data, we&#39;d have missed distinct and interesting underlying trends.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601048272/Screen_Shot_2020-09-25_at_11.37.38_AM_ohqkck.png" /></center>

<p>We can see that data visualization is rooted in statistics and graphical data analysis, but it&#39;s also a creative process that involves some amount of trial and error.</p>
<h2 id="interactive-example">Interactive Example</h2>
<p>In the following example, you will first Load the <code>ggplot2</code> package using <a href="http://www.rdocumentation.org/packages/base/functions/library">library()</a>. Then, you will use <a href="http://www.rdocumentation.org/packages/utils/functions/str">str()</a> to explore the structure of the <code>mtcars</code> dataset.</p>
<p>Finally, you will visualize the ggplot and try to understand what ggplot does with the data.</p>
<p>You will use the <code>mtcars</code> dataset contains information on 32 cars from a 1973 issue of Motor Trend magazine. This dataset is small, intuitive, and contains a variety of continuous and categorical variables.</p>
<pre><code class="lang-python"># Load the ggplot2 package
library(ggplot2)

# Explore the mtcars data frame with str()
str(mtcars)

# Execute the following command
p &lt;- ggplot(mtcars, aes(cyl, mpg)) +
  geom_point()
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<pre><code>data.frame&#39;:    32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601049048/ggplot2_au2hht.png" /></center>

<p><a href="https://campus.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2/introduction-b4b7f9b2-ec8d-49a0-b2a9-9abd707dd710?ex=3">Try it for yourself</a>.</p>
<p>To learn more about data visualization with ggplot, please see this video from our course <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2/introduction-c90b4341-2fc9-4d0e-9c57-8d74ae7fae5f?ex=1">Introduction to Data Visualization with ggplot2</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/K8Al2CEd_6k?start=17" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2">Introduction to Data Visualization with ggplot2</a> course by Rick Scavetta.</p>


https://www.datacamp.com/community/tutorials/graphics-with-ggplot2

(Tutorial) Graphics with ggplot2

Graphics with ggplot2

<center>
<a target=“_blank” href="https://www.datacamp.com/learn/r/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1604679611/BANNER_mrsctv.png" /></a>
</center><br>
<p>R is one of the main languages used for data science today. As such, it is natural that any beginner may want to know how to get started with this powerful language regardless of the operating system running on a computer. Thus, this tutorial will address this by covering the installation process of R on Windows 10, Mac OSX, and Ubuntu Linux.</p>
<p>Furthermore, it will also go over the installation of RStudio, which is an IDE (Integrated Development Environment) that makes R easier to use as well as how to install R packages such as dplyr or ggplot2.</p>
<h2 id="installing-r-on-windows-10">Installing R on Windows 10</h2>
<p>Installing R on Windows 10 is very straightforward. The easiest way is to install it through CRAN, which stands for The Comprehensive R Archive Network. Just visit the <a href="https://cran.r-project.org/">CRAN downloads</a> page and follow the links as shown in the video below:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946484/ezgif.com-resize_zzbzbg.gif" /></center>

<p>Once the download is finished, you will obtain a file named &quot;R-3.6.3-win.exe&quot; or similar depending on the version of R that you download. The links shown in the video above will take you to the most recent version. To finish installing R on your computer, all that is left to do is to run the .exe file. Most of the time, you will likely want to go with the defaults, so click the button &#39;Next&#39; until the process is complete, as shown in the video below. Note that, even though I do not do so, you can add  desktop or quick start shortcuts during the process.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946566/ezgif.com-resize_1_pnz4tu.gif" /></center>

<h3 id="installing-rstudio">Installing RStudio</h3>
<p>Once R is installed, you can proceed to install the RStudio IDE to have a much-improved environment to work in your R scripts. It includes a console that supports direct code execution and tools for plotting and keeping track of your variables in the workspace, among other features. The installation process is very straightforward, as well. Simply go to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and follow the video below:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946691/ezgif.com-video-to-gif_6_pz7f09_x4spfr.gif" /></center>

<p>Once the download is complete, you will get a file named &quot;RStudio-1.2.5033.exe&quot; or similar. Again this will be dependent on the version. To complete the installation, it is as easy as before. Just run the previously mentioned .exe file with the default settings by clicking &#39;Next&#39;, and wait until the installation finishes. Bear in mind that RStudio requires that R is installed beforehand.</p>
<h3 id="installing-packages-in-r">Installing Packages in R</h3>
<p>Now you have base R installed on your system and a nice IDE to begin your R programming journey. However, base R is rather limited in the things that it can do, which is why we have R packages such as dplyr for enhanced data-wrangling capabilities or ggplot2 for improved data visualizations. There are two simple ways to install R packages using RStudio. The first is to execute the following line of code in the console:</p>
<p><strong><code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code></strong></p>
<p>The second is shown in the video below. It is an easy-to-use graphical interface built into RStudio from which you can search and download any R package available on CRAN.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583946948/ezgif.com-resize_3_usjbvk.gif" /></center>

<h2 id="installing-r-on-mac-osx">Installing R on Mac OSX</h2>
<p> Installing R on Mac OS is similar to Windows. Once again, The easiest way is to install it through CRAN by going to the <a href="https://cran.r-project.org/">CRAN downloads</a> page and following the links as shown in the video below:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583947086/ezgif.com-resize_4_pmdxfv.gif" /></center>

<p>The next step is to click on the &quot;R-3.6.2.pkg&quot; (or newer version) file to begin the installation. You can leave the default options as is just like for Windows.</p>
<h3 id="installing-rstudio-and-r-packages">Installing RStudio and R packages</h3>
<p>This process is essentially the same as in Windows. To download RStudio, go to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and get the .dmg for Mac OS, as shown in the image below. Remember to keep default installation options.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583354177/Img1_a2bfpu.png" /></center>

<p>Once you open RStudio, installing packages is the same as with Windows. You can use either <strong><code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code></strong> in the console or go ahead and use the graphical interface shown in the video under the installing packages in R subsection of this tutorial.</p>
<h2 id="installing-r-on-ubuntu-19-04-18-04-16-04">Installing R on Ubuntu 19.04/18.04/16.04</h2>
<p>Installing R on Ubuntu maybe a little bit more tricky for those unused to working in the command line. However, it is perhaps just as easy as with Windows or Mac OS. Before you start, make sure to have root access in order to use sudo.</p>
<p>As it is common, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands:</p>
<p><strong><code>sudo apt update</code></strong></p>
<p><strong><code>sudo apt -y upgrade</code></strong></p>
<p>After that, all that you have to do is run the following in the command line to install base R.</p>
<p><strong><code>sudo apt -y install r-base</code></strong></p>
<h3 id="installing-rstudio-and-r-packages">Installing RStudio and R Packages</h3>
<p>Once base R is installed, you can go ahead and install RStudio. For that we are going to head over again to the <a href="https://rstudio.com/products/rstudio/download/#download">RStudio downloads page</a> and download the .deb file for our Ubuntu version as shown in the image below:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583354178/Img2_q3uwjt.png" /></center>

<p>Once you have the .deb file, all that is left is to navigate to your downloads folder using <strong><code>cd Downloads</code></strong> in the command line and then run the following command to begin the installation process:</p>
<p><strong><code>sudo dpkg -i rstudio-1.2.5033-amd64.deb</code></strong></p>
<p>You may encounter some dependency problems that may cause your first try to install RStudio to fail, but this has an easy fix. Just run the following command and try again:</p>
<p><strong><code>sudo apt -f install</code></strong></p>
<p>When the process finishes, you will have an RStudio shortcut in your Ubuntu app list, but you will also be able to start RStudio by typing <strong>rstudio</strong> in the command line.</p>
<p>Once you open RStudio, installing packages can be done in the exact same manner as with Windows or Mac OS. Either by typing <strong><code>install.packages(c(&quot;dplyr&quot;,&quot;ggplot2&quot;))</code></strong> in the console or using the graphical interface shown in the video under the installing packages in R subsection of this tutorial</p>
<h2 id="conclusion">Conclusion</h2>
<p>I hope that this tutorial will help those of you eager to dive into the world of R programming regardless of your operating system choice. If you are looking to start learning R as such after installing it, please refer to the <a href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a> course, which will guide you through the basics of R programming. Keep learning; the sky is the limit.</p>


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