https://www.datacamp.com/community/news/dec-2017-new-r-package-picks-top-40-mv75jfop3on

A top 40 of the new R packages of December 2017 by R Views. Eight of the 129 new packages that stuck to CRAN in December fall under Computational Methods, a category they have only recently begun using. All of them made it into the list of the “Top 40” picks. 



Dec 2017: New R package picks top 40 

<p>One of the first DataCamp Donates partners is LaunchCode, a nonprofit offering free tech education and job placement opportunities, headquartered in St. Louis, MO. DataCamp Donates is our initiative to donate Premium DataCamp subscriptions to those who need a leg up to start a career in STEM in today’s harsh economy. Eight months later, we’ve donated over 21,000 subscriptions all over the globe, and we are witnessing how DataCamp has equipped real people in local communities to achieve their career goals.</p>
<p>“DataCamp fits directly into the mission we have here at LaunchCode—to provide free education for anyone looking to learn how to program regardless of what experience you have prior to taking a LaunchCode course,” says John Woolbright, Community Engagement Manager at LaunchCode. “When we started our partnership, we had next to no support for people who wanted to learn Data Science in Kansas City. Since then, I have been able to transition our students from web development into data science to get a more broad learning experience.”</p>
<h2 id="achieving-high-engagement-and-adoption-on-datacamp">Achieving high engagement and adoption on DataCamp</h2>
<p>The partnership was an instant hit with many of their learners. “I was excited to have a new avenue for personal and professional growth,” says Forest Roberts.</p>
<p>Other learners like Jen Murphy locked onto specific courses. “Introduction to Data Visualization with Seaborn explains concepts with clear videos. The hands-on learning is built in such a way that each question required more of me as a learner. By the last step, I was providing almost all of the required code on my own.”</p>
<p>The excitement generated by becoming a DataCamp Donates partner has led to extremely high adoption and engagement levels. “Our students have truly enjoyed DataCamp and find that time spent with the software is exceptionally valuable,” says John Woolbright. The numbers don’t lie—LaunchCode learners have completed an average of four DataCamp courses each (in addition to LaunchCode’s own computer science and programming courses).</p>
<h2 id="datacamp-builds-skills-for-job-readiness">DataCamp builds skills for job readiness</h2>
<p><center><a href="https://www.launchcode.org"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615918722/Megan_Cullins_iuxzhf.png" /></a><br></p>
<p>Some members of the LaunchCode community have even landed jobs with skills gained from DataCamp Donates partnership. Megan Cullins was in her ninth year working at an office furniture company when she first started learning with LaunchCode and DataCamp. She completed 50 DataCamp courses and accumulated 322,000 XP to expand her Python skills. “After a couple of months of learning, I was confident enough to interview and eventually landed a job at a data analytics company! I didn&#39;t know any Python before this class, but because of DataCamp I was able to honestly tell an interviewer that I did have experience, which helped me get a job with their company.”</p>
<p>John wasn’t surprised Megan was able to secure a position. It’s the speed at which she and others achieved job readiness that was so impressive. “We started by rolling out 25 seats for Kansas City in September. Onboarding students was a breeze. By the end of the year, four of the top DataCamp learners got jobs in data science with no previous experience—just three months of learning on DataCamp.”</p>
<p>The correlation between time spent learning on DataCamp and job readiness is not lost on Megan. “I feel that my access to DataCamp absolutely helped me get a job,” she says. “The lessons I learned on DataCamp are applied every day on my job, and I love it!”</p>
<p><em>To learn more about DataCamp Donates, read our recent <a href="https://www.datacamp.com/community/blog/datacamp-donates-50-partners">blog post</a> announcing 20,000 licenses donated to nonprofits as well as the <a href="https://support.datacamp.com/hc/en-us/articles/360051596574">DataCamp Donates FAQ</a>. To apply on behalf of an organization, email <a href="mailto:donate@datacamp.com">donate@datacamp.com</a>. If you live in the greater Kansas City and St. Louis areas and would like free data science education or you want to support LaunchCode’s efforts directly, visit <a href="https://www.launchcode.org">www.launchcode.org</a>.</em></p>


https://www.datacamp.com/community/blog/dcdonates-launchcode

Our partnership with LaunchCode is getting real people real jobs.

How a Nonprofit Leveraged DataCamp to Get Learners Employed, Fast | DataCamp

How a Nonprofit Leveraged DataCamp to Get Learners Employed, Fast

<center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center><br>
<p>From powering self-driving cars to conquering the game of Go, organizations have dominated headlines by making previously impossible tasks possible with data science. It is therefore easy to overlook the hidden data science revolution, which is that companies are increasingly making what is currently possible widespread. In other words, more companies are striving for stronger organizational data fluency.
An organization’s path to data fluency depends on five key pillars, according to Ramnath Vaidyanathan, the VP of Product Research at DataCamp: Infrastructure, People, Tools, Organization and Processes (IPTOP). In a <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">webinar last month</a>, he shared a data fluency framework that any organization can use to advance in data maturity from data reactive to data scaling, data progressive, and data fluent.</p>
<h2 id="to-move-from-data-reactivity-build-a-data-culture">To Move from Data Reactivity, Build a Data Culture</h2>
<p>A data reactive company is at the lowest rung of the data maturity ladder. Such a company rarely accesses, uses, and presents data, contributing to the absence of a data culture. It uses disparate legacy tools to collect data in silos and perform ad-hoc data processes. Further, it does not invest in a data upskilling strategy to build data talent internally and in data infrastructure.
A company can progress in its organizational data literacy from data reactive to data scaling by building its data culture and data infrastructure. To inseminate such a data culture, the company should strongly consider pursuing low-hanging fruits that allow stakeholders to see the value of data literacy. These include proof-of-concept dashboards and analytics projects like customer churn modelling. The company can also begin developing its data architecture and data collection processes.</p>
<h2 id="depart-from-data-scaling-with-the-proper-infrastructure-and-organizational-model">Depart from Data Scaling with the Proper Infrastructure and Organizational Model</h2>
<p>Unlike a data reactive company, a data scaling company has some form of data strategy and culture. But its shortcomings are still apparent. It has very few employees with the necessary skills and access to analyze, report, and present data confidently, resulting in a weak data culture. Moreover, it lacks centralized data storage and teams, resulting in highly siloed datasets and data processes.</p>
<p>Considering these shortcomings, a data scaling company must improve its organizational data literacy. To do so, it must enable data access by working on its data infrastructure and by building centralized data storage. Such data infrastructure allows high-quality data to be stored in the cloud and easily accessed by all stakeholders. With appropriate data storage and access, employees are empowered to develop data quality standards. For example, DataCamp stores its data in Amazon Redshift, a cloud-based centralized data warehouse, and manages data pipelines using Apache Airflow. This marks a departure from siloed data that is difficult to reconcile and paves the way to a single source of truth.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985155/fivetrain_whyweo.png" /></a></p>
<h5 id="a-simplified-view-of-datacamp-s-centralized-data-storage">A simplified view of DataCamp’s centralized data storage</h5>
<p>A data scaling company must also develop a clear organizational model for data scientists as part of its data maturation. Some companies choose to develop a centralized model characterized by a data science team, which acts as a <a href="https://hbr.org/2019/01/how-to-set-up-an-ai-center-of-excellence">center of excellence</a>, that handles requests from various functions like finance, marketing and engineering. Others decide to adopt a decentralized model, where data scientists are embedded within functions and handle the data needs of that function exclusively. A company should adopt the model that best fits its data and business needs.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985029/Model_jgxxzz.png" /></a><br></p>
<h2 id="transition-from-data-progressive-with-data-tools-and-processes">Transition from Data Progressive with Data Tools and Processes</h2>
<p>The next milestone in the company’s path of data fluency is the state of data progressive. In a data progressive company, every team has at least one data fluent employee who can analyze, report, and present their data. However, there is still room for improvement in terms of data quality and discoverability as a result of its maturing data infrastructure. Also, while the company’s employees have some level of data literacy, its data is almost exclusively used for reporting and is therefore underutilized. The value of data is further suppressed by the lack of data democratization contributed by the difficulty in accessing data tools and data processes.</p>
<p>To advance from the state of data progressiveness to data fluency, an organization should consider developing its data infrastructure, people, tools, organization, and processes simultaneously in accordance with the IPTOP data fluency framework.</p>
<p>To transition to data fluency, a company can reinforce its data culture by developing and investing in appropriate upskilling strategies for its people. This involves continuous data education that caters to the <a href="https://www.datacamp.com/community/blog/eight-personas">different data personas</a> and the business goals of the company. For instance, <a href="https://hbr.org/2016/10/atts-talent-overhaul">AT&amp;T&#39;s intensive talent overhaul</a> saw it investing $1B to upskill 140,000 employees over 10 years. Companies stand to benefit from a stronger data culture by incentivizing data-driven behaviors and rewarding data evangelists who actively promote the company’s data culture.</p>
<p>A company can also build internal data products that operationalize data processes as part of its data literacy program. These data products can be used to improve data quality standards and monitor business goals. For instance, DataCamp has <a href="https://www.datacamp.com/open-source">internal frameworks</a> that allow stakeholders to create presentation-ready visualizations of business metrics with minimal code.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615985174/image3_yiwxjc.png" /></a></p>
<h5 id="datacamp-dashboards-developed-easily-with-internal-frameworks">DataCamp dashboards developed easily with internal frameworks</h5>
<p>As a company progresses in organizational data literacy, it tends to move toward a hybrid model, where data scientists belong not only in the centralized data team but also in a functional team. This allows the data science team to build and share data tools that align with functional business goals.</p>
<p>Achieving data fluency requires organizations to make data an integral part of their processes. Such processes reduce the friction of extracting value from data and democratize data within the organization. For instance, <a href="https://medium.com/airbnb-engineering/data-quality-at-airbnb-e582465f3ef7">AirBnb’s Data Quality Initiative</a> defines and enforces the quality of datasets across the company, which in turn builds stakeholder trust in the data and strengthens the data culture. Netflix makes it seamless for stakeholders to create effective notebooks <a href="https://netflixtechblog.com/notebook-innovation-591ee3221233">by customizing notebook templates to different target personas</a>.</p>
<h2 id="become-data-fluent-with-the-iptop-framework">Become Data Fluent with the IPTOP Framework</h2>
<p>Finally, data fluency is a company’s ideal state of data maturity. In a data-fluent company, every employee has the skills to access and understand the data they need to do their jobs. They are empowered by reliable data infrastructure and tools that make data-driven decision-making seamless. Better yet, the company has data processes and organization models that enable cross-departmental collaboration to scale efficiently.</p>
<p>Clearly, an organization’s path to data fluency is not linear. It requires a clear data strategy and a strong data literacy framework. The chart below summarizes the steps an organization should take to move its data literacy from data reactive to data fluent using the IPTOP framework.</p>
<p><center><a href="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615989330/blueprint_data_fluency_przpge.png"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615989330/blueprint_data_fluency_przpge.png" /></a></p>
<p>For more on the path to scaling data maturity, <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">tune into our webinar</a>.</p>
<br><center><a target="_blank" href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1624651228/schedule_a_demo_bfhlx1.png" /></a></center>


https://www.datacamp.com/community/blog/achieving-organizational-data-fluency

The blueprint for ramping your organization’s data maturity from data reactive to data scaling, data progressive, and data fluent.

Achieving Organizational Data Fluency with IPTOP | DataCamp

Achieving Organizational Data Fluency with IPTOP

https://www.datacamp.com/community/tutorials/r-packages-guide

An introduction to R packages based on 11 of the most frequently asked user questions. 

Find an introduction to R packages. Based on the 11 most  frequently asked questions about R. See how you can download & install R packages today!

R Packages: How to Download & Install Packages?

R Packages: A Beginner's Guide 

<h2 id="the-problem">The problem</h2>
<p>pkgdown is an R package that can create a beautifully looking website for your own R package. Built and maintained by Hadley Wickham and his gang of prolific contributors, this package can parse the documentation files and vignettes for your package and builds a website from them with a single command: <code>build_site()</code>. <a href="https://datacamp.github.io/testwhat/">This is what such a pkgdown-generated website looks like in action</a>.</p>
<p>The html files that pkgdown generated are stored in a <code>docs</code> folder. If your source code is hosted on GitHub, you just have to commit this folder to GitHub, navigate to the Settings panel of your GitHub repo and enable GitHub pages to host the <code>docs</code> folder at <code>https://&lt;name_or_org&gt;.github.io/&lt;package_name&gt;</code>. It&#39;s remarkably easy and a great first step. In fact, this is how the <a href="http://pkgdown.r-lib.org/index.html">pkgdown-built website</a> for pkgdown itself is hosted.</p>
<p>Although it&#39;s an elegant flow, there are some issues with this approach. First, you&#39;re committing files that were automatically generated even though the source required to build them is already stored in the package. In general, it&#39;s not good practice to commit automatically generated files to your repo. What if you update your documentation, and commit the changes without rerendering the pkgdown website locally? Your repo files will be out of sync, and the pkgdown website will not reflect the latest changes. Second, there is no easy way to control when you release your documentation. Maybe you want to work off of the master branch, but you don&#39;t want to update the docs until you&#39;ve done a CRAN release and corresponding GitHub release. With the ad-hoc approach of committing the <code>docs</code> folder, this would be tedious.</p>
<h2 id="the-solution">The solution</h2>
<p>There&#39;s a quick fix for these concerns though, and that is to use Travis CI. Travis CI is a continuous integartion tool that is free for open-source projects. When configured properly, Travis will pick up on any changes you make to your repo. For R packages, Travis is typically used to automatically run the battery of unit tests and check if the package builds on several previous versions of R, among other things. But that&#39;s not all; Travis is also capable of doing deployments. In this case, I&#39;ll show you how you can set up Travis so it automatically builds the pkgdown website for you, and commits the web files to the <code>gh-pages</code> branch, which is then subsequently used by GitHub to host your package website. To see how it&#39;s set up for a R package in production check out the <a href="https://github.com/datacamp/testwhat">testwhat package on GitHub</a>, which we use at DataCamp to grade student submissions and give useful feedback. In this tutorial, I will set up pkgdown for the <a href="https://github.com/datacamp/tutorial"><code>tutorial</code> package</a>, another one of DataCamp&#39;s open-source projects to make your blogs interactive.</p>
<h2 id="the-steps">The steps</h2>
<ol>
<li>Go to <a href="https://travis-ci.org">https://travis-ci.org</a> and link your GitHub account.</li>
<li><p>On your <a href="https://travis-ci.org/profile/">Travis CI profile page</a>, enable Travis for the project repo that you want to build the documentation for. The next time you push a change to your GitHub project, Travis will be notified and will try to build your project. More on that later.</p>
<p><center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530890141/cd-package-docs-activate-repo_r2kt0p.png" /></p>
</li>
<li><p>In the <code>DESCRIPTION</code> file of your R package, add <code>pkgdown</code> to the <code>Suggests</code> list of packages. This ensures that when travis builds/installs your package, it will also install <code>pkgdown</code> so we can use it for building the website.</p>
</li>
<li>In the <code>.gitignore</code> file, make sure that the entire <code>docs</code> folder is ignored by git: add the line <code>docs/*</code>.</li>
<li><p>Add a file with the name <code>.travis.yml</code> to your repo&#39;s root folder, with the following content:</p>
<pre><code>language: r
cache: packages

after_success:
  - Rscript -e &#39;pkgdown::build_site()&#39;

deploy:
  provider: pages
  skip-cleanup: true
  github-token: $GITHUB_PAT
  keep-history: true
  local-dir: docs
  on:
    branch: master
</code></pre>
<p>This configuration file is very short, but it&#39;s doing a lot of different things. Jeroen Ooms and Jim Hester are maintaining a default Travis build configuration for R packages that does a lot of things for you out of the box. A Travis config file with only the <code>language: r</code> tag would already build, test and check your package for inconsistencies. Let&#39;s go over the other fields:</p>
<ul>
<li><code>cache: packages</code> tells Travis to cache the package installs between builds. This will significantly speed up your package build time if you have some package dependencies.</li>
<li><code>after_success</code> tells Travis which steps to take when the <code>R CMD CHECK</code> step has succeeded. In our case, we&#39;re telling Travis to build the <code>pkgdown</code> website, which will create a <code>docs</code> folder on Travis&#39;s servers.</li>
<li>Finally, <code>deploy</code> asks Travis to go ahead and upload the files in the <code>docs</code> folder (<code>local-dir</code>) to GitHub pages, as specified through <code>provider: pages</code>. The <code>on</code> field tells Travis to do this deployment step if the change that triggered a build happened on the <code>master</code> branch.</li>
</ul>
<p>For a full overview of the settings, you can visit <a href="https://docs.travis-ci.com/user/deployment/pages/">this help article</a>.    We do not have to specify the GitHub target branch where the docs have to be pushed to, as it defaults to <code>gh-pages</code>.</p>
</li>
<li><p>Notice that the <code>deploy</code> step also features a <code>github-token</code> field, that takes an environment variable. Travis needs this key to make changes to the <code>gh-pages</code> branch. To get these credentials and make sure Travis can find them:</p>
<ul>
<li>Go to your GitHub profile settings and create a new personal access token (PAT) under the Developer Settings tab. Give it a meaningful description, and make sure to generate a PAT that has either the <code>public_repo</code> (for public packages) or <code>repo</code> (for private packages) scope.</li>
</ul>
<p><center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530890141/cd-package-docs-get-pat_fqdwd7.png" /></p>
<ul>
<li>Copy the PAT and head over to the Travis repository settings, where you can specify environment variables. Make sure to name the environment variable <code>GITHUB_PAT</code>.</li>
</ul>
<p><center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530890141/cd-package-docs-set-pat_owuad4.png" /></p>
</li>
<li><p>The build should be good to go now! Commit the changes to your repo (<code>DESCRIPTION</code> and <code>.travis.yml</code>) to the <code>master</code> branch of your GitHub repo with a meaningful message.</p>
</li>
<li><p>Travis will be notified and get to work: it builds the package, checks it, and if these steps are successful, it will build the <code>pkgdown</code> website and upload it to <code>gh-pages</code>.</p>
<p><center><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530890141/cd-package-docs-branches_p8pbpb.png" /></p>
</li>
<li><p>GitHub notices that a <code>gh-pages</code> branch has been created, and immediately hosts it at <code>https://&lt;name_or_org&gt;.github.io/&lt;package_name&gt;</code>. In our case, that is <a href="https://datacamp.github.io/tutorial">https://datacamp.github.io/tutorial</a>. Have a look. What a beauty! Without any additional configuration, pkgdown has built a website with the GitHub README as the homepage, a full overview of all exported functions, and vignettes under the Articles section.</p>
</li>
</ol>
<p>From now on, every time you update the <code>master</code> branch of your package and the package checks pass, your documentation website will be updated automatically. You no longer have to worry about keeping the generated files in sync with your actual in-package documentation and vignettes. You can also easily tweak the deployment process so it only builds the documentation whenever you make a GitHub release. Along the way, you got continuous integration for your R package for free: the next time you make a change, Travis will notify you they broke any tests or checks.</p>
<p>Happy packaging!</p>
<h2 id="references">References</h2>
<ul>
<li>pkgdown: <a href="https://github.com/r-lib/pkgdown">https://github.com/r-lib/pkgdown</a>, <a href="https://pkgdown.r-lib.org/">https://pkgdown.r-lib.org/</a>.</li>
<li>testwhat: <a href="https://github.com/datacamp/testwhat">https://github.com/datacamp/testwhat</a>, <a href="https://datacamp.github.io/testwhat">https://datacamp.github.io/testwhat</a>.</li>
<li>tutorial: <a href="https://github.com/datacamp/tutorial">https://github.com/datacamp/tutorial</a>, <a href="https://datacamp.github.io/tutorial">https://datacamp.github.io/tutorial</a>.</li>
<li>Full docs on building an R project with Travis CI: <a href="https://docs.travis-ci.com/user/languages/r/">https://docs.travis-ci.com/user/languages/r/</a>.</li>
<li>Interested in getting involved with the internals of <code>testwhat</code>? We&#39;re hiring a software engineer to further improve our automated checking and feedback generation and establish DataCamp as the leading player in the field. Check out our <a href="https://boards.greenhouse.io/datacamp/jobs/1230320">job listing</a>!</li>
</ul>


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Follow these simple steps to enable continuous deployment of your package documentation.

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