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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
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<title>R Setup and Data Import</title>
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<div class="topics">
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<div id="section-introduction-to-r-for-applied-epidemiology-and-public-health" class="section level2">
<h2>Introduction to R for Applied Epidemiology and Public Health</h2>
<p><img src="images/moz-banner.png" width="100%" /></p>
<div id="section-welcome" class="section level4">
<h4>Welcome</h4>
<p>Welcome to the course “Introduction to R for applied epidemiologists”, offered for free by <a href="www.appliedepi.org">Applied Epi</a> - a non-profit organisation that offers open-source tools, training, and support to frontline public health practitioners.</p>
<p>This interactive tutorial focuses on <strong>setting up R for the first time, an introduction to basic R coding, and how to import your dataset into R</strong>.</p>
<p>If you are already comfortable with the above skills, consider taking instead our tutorials for R in applied epidemiology, on [data cleaning], [data visualisation], or [tidy data best practices].</p>
<p>This tutorial draws from chapters of our free <a href="https://epirhandbook.com/en/">Epidemiologist R handbook</a> such as <a href="https://epirhandbook.com/en/r-basics.html">R Basics</a> and <a href="https://www.epirhandbook.com/en/import-and-export.html">Import and Export</a>. The Epi R Handbook has over 50 chapters, has helped over 110,000 people learn R, and is also available for offline use.</p>
</div>
<div id="section-target-audience" class="section level4">
<h4>Target Audience</h4>
<p>This course is designed with the following objectives:</p>
<ul>
<li>To be friendly to people who have never used a programming language before</li>
<li>To teach R emphasizing examples, datasets, and challenges commonly faced by applied epidemiologists</li>
<li>To be modular - so that you can skip to section most relevant to you</li>
</ul>
<p>We expect that you know how to do … TO DO</p>
</div>
<div id="section-other-languages" class="section level4">
<h4>Other languages</h4>
<p>This course is available…</p>
</div>
<div id="section-offline-online" class="section level4">
<h4>Offline / Online</h4>
<p>You can access this tutorial offline by downloading our R package … If viewing offline, you can view the videos by doing …</p>
</div>
<div id="section-learning-goals" class="section level4">
<h4>Learning goals</h4>
<p>In this tutorial you will:</p>
<ul>
<li>Become familiar with typing code “commands”</li>
<li>“Importing” (loading) a dataset into R to conduct cleaning and other modifications</li>
<li>View a dataset in R</li>
<li>Review and access columns in a dataset</li>
<li>Key operators and functions</li>
<li>Piping and pipe operators</li>
<li>Errors and warnings</li>
</ul>
</div>
<div id="section-data-consent" class="section level4">
<h4>Data consent</h4>
<p>This tutorial is anonymously collecting your entries, for purposes of improvement… by continuing you consent to this collection and use.</p>
</div>
<div id="section-who-made-this-course" class="section level4">
<h4>Who made this course</h4>
<p>This course is designed by Applied Epi … epidemiologists with decades of ground-level experience in outbreak response and local public health work.</p>
<p><img src="images/hex-sidebyside.png" width="50%" /></p>
</div>
</div>
<div id="section-data-used-in-this-tutorial" class="section level2">
<h2>Data used in this tutorial</h2>
<p>In this tutorial we will use the following datasets. Please take a few minutes to review the structure and content of each dataset before continuing.</p>
<p>Use the arrows on the right to scroll through hidden columns. Note that these are “raw” (messy) datasets that mimic problems commonly found in real-life epidemiological datasets.</p>
<div id="section-a-linelist-of-cases-in-a-fictional-not-real-ebola-outbreak" class="section level3">
<h3><strong>A “linelist” of cases in a fictional (not real) Ebola outbreak</strong>*</h3>
<p>A “linelist” is a term used in applied epidemiology to refer to a table that contains key information about each case or suspect case in an outbreak. Each row represents one case, and the columns contain variables such as age, sex, date of symptom onset, outcomes, etc.</p>
<p>This dataset contains 6611 rows and 28 columns. Below are the first 5 rows:</p>
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</div>
<div id="section-aggregated-data-from-malaria-surveillance-in-a-fictional-country" class="section level3">
<h3><strong>Aggregated data from malaria surveillance in a fictional country</strong></h3>
<p>Aggregated data in epidemiology usually means a table of counts for each facility, or district, etc. Sometimes, the counts can also be per day, week, or month.</p>
<p>In this dataset, each facility reported <em>daily</em> case counts of rapid-test (RDT)-confirmed malaria. Thus, each row represents the number of cases for a specific facility on a specific day.</p>
<p>This dataset contains 3038 rows and 10 columns. Below are the first 5 rows:</p>
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</div>
</div>
<div id="section-tidy-data" class="section level3">
<h3>Tidy Data</h3>
<p>We highly recommend doing our tutorial on Tidy Data in Applied Epidemiology. Collecting, formatting, and preparing your dataset <em>before</em> importing it into R is a critical step!</p>
<p>LINK</p>
<p>VIDEO TEASER</p>
</div>
</div>
<div id="section-why-r" class="section level2">
<h2>Why R?</h2>
<p>R is a free, open-source analytical tool, used by many disciplines, that has existed for over 25 years! Although Applied Epi did not invent R, we do believe that R is a perfect vehicle to transform epidemiology to be more more advanced, efficient, standardised, accessible, and equitable.</p>
<p>Widespread adoption of R can transform epidemiology.</p>
<p>“A one-stop shop… the epidemiologist greatest companion.” - Epidemiologist, Nigeria CDC</p>
<p>“It is not easy to weave together so much, so succinctly, and so incredibly clearly.” - Epidemiologist, US CDC (this one might be interpreted to say that R is not easy?)</p>
<p>“Holy smokes! Boom! I can’t get enough of it!” - WHO African Region Epidemiologist, Brazzaville</p>
<div id="section-efficiency" class="section level3">
<h3>Efficiency</h3>
<p>‘With R I can do anything. What took 10 hours in Excel, I do in less than 1 hour. Also it is easier to detect if there is a mistake.’ For this Haitian epidemiologist, using our free Epi R Handbook to transition routine analyses to R meant more flexibility, confidence, and time for systemic change.</p>
<p>Implementing R skills into your daily workflow as an epidemiologist will allow you to increase your work capacity and reduce response time to rapidly evolving public health scenarios.</p>
</div>
<div id="section-flexibility" class="section level3">
<h3>Flexibility</h3>
<p>R can interact with many common data collection tools, websites, and software. You can also automate R in integration with other tools, for example, to produce surveillance reports and email them out.</p>
<p><img src="images/whyR/interoperability.png" width="50%" /></p>
</div>
<div id="section-popularity" class="section level3">
<h3>Popularity</h3>
<p>The below graph shows the number of publications citing R, SPSS, SAS, and STATA from 2015 to 2019. As the only free software tool shown here, R is the future of analytic software.</p>
<p><img src="images/whyR/r_popularity.png" width="50%" /></p>
</div>
<div id="section-learning-curve" class="section level3">
<h3>Learning Curve</h3>
<p><em>R is achievable</em></p>
<p>Over its 25 years of existence, R has evolved to become a relatively easy coding language to learn, with the advent of a friendly user interface (RStudio) and code syntax (the tidyverse). Moreover, vibrant support communities of R users, such as R-Ladies, exist all over the world. Its evolution has made R much easier to use in the past 5 years.</p>
<p>There is an initial learning curve, but the more R you know then the easier it is to advance.</p>
<p><img src="images/whyR/difficulty_plot-1.png" width="50%" /></p>
</div>
<div id="section-user-community" class="section level3">
<h3>User community</h3>
<p><em>R is not just a new tool, but a new culture advancing equity</em></p>
<p>As an open-source language, R gives end-users creative agency and decentralizes innovation. An R user in any part of the world can write code to produce a tool, share it, have it be verified by the community, and watch it be adopted widely. This empowerment of end-users is paramount to a more equitable epidemiological community.</p>
<p>Likewise, there is robust user support online such as question and answer forums on StackOverflow or R-Bloggers, events facilitated by groups like R-Ladies, and widely used learning resources on Coursera and Youtube.</p>
<p><img src="images/whyR/user_community.png" width="50%" /></p>
</div>
<div id="section-data-visualization" class="section level3">
<h3>Data visualization</h3>
<p>One thing R is known for is data visualization. It is capable of producing detailed tables, maps, and epi-curves. With a bit more advanced knowledge you can visualize contact tracing, phylogenetics, and publication-ready plots.</p>
<p><img src="images/whyR/visualization.png" width="50%" /></p>
</div>
</div>
<div id="section-setup-r-and-rstudio" class="section level2">
<h2>Setup R and RStudio</h2>
<div id="section-install-r" class="section level3">
<h3>Install R</h3>
<div id="section-r-on-a-usb-drive" class="section level4">
<h4>R on a USB drive</h4>
</div>
</div>
<div id="section-install-rstudio" class="section level3">
<h3>Install RStudio</h3>
</div>
<div id="section-considerations-for-shared-drives" class="section level3">
<h3>Considerations for Shared Drives</h3>
</div>
</div>
<div id="section-working-in-an-r-project" class="section level2">
<h2>Working in an R project</h2>
<p><a href="https://www.epirhandbook.com/en/r-projects.html" class="uri">https://www.epirhandbook.com/en/r-projects.html</a> Talk about R projects here…</p>
<p>SHORT VIDEO SUMMARISING TRIAD OF RPROJECTS/HERE/IMPORT</p>
<div id="section-create-an-r-project" class="section level3">
<h3>Create an R project</h3>
<p><img src="images/rprojects/rproject_create1.gif" width="100%" /></p>
</div>
<div id="section-review-your-r-project" class="section level3">
<h3>Review your R project</h3>
<p>Now, we can see the new folder that we created (/example), and within this folder is a new “R project” file (example.Rproj).</p>
<p><img src="images/rprojects/rproject_folder.png" width="50%" /></p>
<p>**It is important that you conduct all your work and changes within the R project. When you sit down to begin working, remember to open the R project by opening the .Rproj file. This will open an RStudio window.</p>
</div>
<div id="section-opening-your-r-project" class="section level3">
<h3>Opening your R project</h3>
<p>**It is important that you conduct all your work and changes within the R project. When you sit down to begin working, remember to open the R project by opening the .Rproj file. This will open an RStudio window.</p>
</div>
<div id="section-considerations-for-shared-drives-1" class="section level3">
<h3>Considerations for Shared Drives</h3>
<p>Reference Epi R Handbook page on shared drives Permissions to install, admin rights Storing R projects in shared drive folders</p>
</div>
</div>
<div id="section-what-is-programming" class="section level2">
<h2>What is programming?</h2>
<div id="section-what-does-it-mean-that-r-is-a-statistical-programming-language" class="section level3">
<h3>What does it mean that R is a statistical programming language?</h3>
</div>
<div id="section-commands-as-instructions" class="section level3">
<h3>Commands as instructions</h3>
</div>
<div id="section-scripts-are-a-list-of-instructions" class="section level3">
<h3>Scripts are a list of instructions</h3>
<div id="section-design-of-a-typical-script-in-public-health" class="section level4">
<h4>Design of a typical script in public health</h4>
<p>Header with objective, authors, contact info, date, etc. Load packages Load data Clean data Perform analyses Display results as plots, tables, maps, etc.</p>
<p>All of the above can be done all in R, and can be transformed into a report via R Markdown.</p>
</div>
</div>
<div id="section-reproducibility" class="section level3">
<h3>Reproducibility</h3>
<p>(get quote or video from someone about how reproducibility helped them in collaboration)</p>
</div>
<div id="section-collaboration" class="section level3">
<h3>Collaboration</h3>
<p>(get quote or video from someone about how reproducibility helped them in collaboration)</p>
</div>
<div id="section-compare-to-excel-analysis" class="section level3">
<h3>Compare to Excel analysis</h3>
</div>
<div id="section-r-has-become-more-easy-to-learn" class="section level3">
<h3>R has become more easy to learn</h3>
<p>Tidyverse, Epi R Handbook, other materials</p>
</div>
</div>
<div id="section-r-functions" class="section level2">
<h2>R functions</h2>
<p>About R functions at very basic level.</p>
<p>import() or pload()</p>
<div id="section-packages" class="section level3">
<h3>Install and Load R packages</h3>
<p>PERHAPS WE JUST HAVE THEM RUN THIS CODE AND EXPLAIN IT LATER? NEED TO INTRODUCE FUNCTIONS FIRST, AND IMPORT() IS A GREAT FUNCTION TO TEACH THAT (BETTER THAN LIBRARY()). OR WE HAVE A FIRST SECTION THAT INTRODUCES FUNCTION LIKE sqrt() or age_pyramid() like in the handbook.</p>
<p>In order to practice basic R functions, we will use the <code>linelist</code> fictional ebola outbreak data set you met in the EpiRHandbook.</p>
<p>To use basic functions with public health data, the tidyverse metapackage is very useful. Tidyverse loads the dplyr, ggplot2, and other packages that are useful in epi data analysis.</p>
<p>We’ve preloaded the below packages for now. Installation and loading of these packages is described on the EpiRHandbook <a href="https://epirhandbook.com/en/suggested-packages-1.html">Suggested Packages</a> page.</p>
<p>In this assignment we’ll work with 3 R packages, let’s load them!</p>
<div class="tutorial-exercise" data-label="load-package" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>___() # load the rio package
___() # load the here package
___() # load the tidyverse megapackage</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":true,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="load-package-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>library(rio)
library(here)
library(tidyverse)</code></pre>
</div>
</div>
</div>
<div id="section-r-packages" class="section level2">
<h2>R packages</h2>
<p>Recommended packages</p>
<p>See epirhandbook LINK for recommended packages</p>
<p>Masking and use of the <code>::</code> operator</p>
</div>
<div id="section-import" class="section level2">
<h2>Importing data</h2>
<div id="section-import-root" class="section level3">
<h3>Save your data in the R project</h3>
<p>In order to import data into R, you must tell R where to access the dataset file on your computer (e.g. a specific folder, or on a shared drive).</p>
<p>This can be surprisingly difficult (e.g. data are stored on shared drives). However, by saving the data <em>within our R project</em> the whole process becomes much easier.</p>
<p>Below is a screenshot of our R project’s files, as viewed in a typical file explorer. See the dataset <em>linelist_raw.xlsx</em> that is saved in the same folder as the <em>example.Rproj</em> file. This is the “root” folder of the R project, meaning to access this folder from the R project file you do not need to click into any sub-directories (sub-folders).</p>
<p><img src="images/importing_data/import_folder_raw_root.png" width="50%" /></p>
</div>
<div id="section-import-data-from-the-r-projects-root-folder" class="section level3">
<h3>2) Import data from the R project’s “root” folder</h3>
<p>To import a dataset into R, we run a command. This command will use the function <code>import()</code>.</p>
<pre class="r"><code>import("linelist_raw.xlsx")</code></pre>
<p>If you run this command from RStudio (either in the R console, or from an R script), R will search in the root folder of the R project for the file <em>linelist_raw.xlsx</em>, and will print the dataset into your R console.</p>
<p>In the next step, we show a slight adjustment to this command that will <em>save</em> the dataset as an object in R, so that you can actually modify and analyse it.</p>
<p>IMAGE OF DATA IMPORTED BUT NOT SAVED</p>
</div>
<div id="section-saving-an-imported-dataset-as-an-r-object" class="section level3">
<h3>Saving an imported dataset as an R object</h3>
<p>Most times, you import a dataset into R in order to modify, transform, and analyse it. To achieve this, we must modify the <code>import()</code> command slightly so that the dataset is saved as an R <em>“object”</em>.</p>
<pre class="r"><code>linelist_raw <- import("linelist_raw.xlsx")</code></pre>
<p>IMAGE OF DATASET SAVED AS R OBJECT</p>
<p>As described in the EpiRHandbook, an object exists when you have assigned it a value (see the assignment section below). When it is assigned a value, the object appears in the Environment (see the upper right pane of RStudio). It can then be operated upon, manipulated, changed, and re-defined.</p>
<p>In applied epidemiology), you will most often encounter objects that are data frames and vectors. The data we are using is classified as a data frame.</p>
<p>The cleaned linelist data object would have the name of the file <code>linelist_cleaned.xlsx</code> unless assigned a different name.</p>
<p>We want to give the data a simpler name for easier recall, so we will use the <code><-</code> operator to assign the object name <code>linelist</code>.</p>
<p>See below how the name <code>linelist</code> is assigned to the data brought in following the standard order:</p>
<p><strong>object_name <- value</strong> (or process/calculation that produce a value)</p>
</div>
<div id="section-import-data-from-a-subfolder" class="section level3">
<h3>Import data from a subfolder</h3>
<p>Best practices suggest that you organise your R project with sub-folders. Typically, you will create a folder for “data”, in which you will store your datasets.</p>
<p>To import data from a sub-folder, the <code>import()</code> command should be modified so that it correctly tells R where to search for this file. This is done using the here() function.</p>
<pre class="r"><code>linelist_raw <- import(here("data", "linelist_raw.xlsx")) # import data and save as named object</code></pre>
<p>A more efficient approach for a quick check is to use the <code>head()</code> function to produce the first few rows of the data instead of the entire dataset.</p>
<div data-pagedtable="false">
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</div>
</div>
<div id="section-exercise-1" class="section level3">
<h3>Exercise 1</h3>
<p><em>Now it’s your turn. Here’s a simple exercise with an empty code chunk provided for entering the answer.</em></p>
<p>Write the R code required to read in the original data object <code>linelist_cleaned.rds</code> and call it <code>linelist</code>.</p>
<div class="tutorial-exercise" data-label="import-data" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
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</div>
<div class="tutorial-exercise-support" data-label="import-data-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist <- rio::import("data", "linelist_cleaned.rds")</code></pre>
</div>
<div class="tutorial-exercise" data-label="exercise1" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
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</div>
<div class="tutorial-exercise-support" data-label="exercise1-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist <- import("linelist_cleaned.rds")</code></pre>
</div>
<div class="tutorial-exercise-support" data-label="exercise1-hint" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>"Use the function import() from the rio package, and put the full name of the file (including extension) in quotation marks"</code></pre>
</div>
<p>Now type code that will print the first 10 lines of data in the <code>linelist</code> data frame.</p>
<div class="tutorial-exercise" data-label="exercise2-head" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
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</div>
<div class="tutorial-exercise-support" data-label="exercise2-head-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>head(linelist)</code></pre>
</div>
</div>
</div>
<div id="section-object-classes" class="section level2">
<h2>Object Classes</h2>
<p>All the objects stored in R have a class which tells R how to handle the object. There are many possible classes. While working with epidemiology data sets, object classes will most likely vary the most within columns (or variables).</p>
<p>You can test the class of an object by providing its name to the function <code>class()</code>.</p>
<div id="section-exercise-2" class="section level3">
<h3>Exercise 2</h3>
<p>Enter and run the code to check the class of <code>linelist</code>.</p>
<div class="tutorial-exercise" data-label="exercise2a" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
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</div>
<div class="tutorial-exercise-support" data-label="exercise2a-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>class(linelist)</code></pre>
</div>
<p>Note: you can reference a specific column within a dataset using the <code>$</code> notation to separate the name of the dataset and the name of the column. It’s good practice to do this while getting to know key columns in your data.</p>
<p>Now, enter the code to check the class of the outcome column in <code>linelist</code>.</p>
<div class="tutorial-exercise" data-label="exercise2b" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="exercise2b-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>class(linelist$outcome)</code></pre>
</div>
<p>While we’re at it, why don’t we check the class of the temp column?</p>
<div class="tutorial-exercise" data-label="exercise2c" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="exercise2c-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>class(linelist$temp)</code></pre>
</div>
<p>It’s likely at some point you will need to change the class of a column in your dataset, but we will go over that in a later lesson.</p>
</div>
</div>
<div id="section-indexing" class="section level2">
<h2>Indexing</h2>
<p>You may need to view parts of objects, also called “indexing”, which is often done using the square brackets <code>[ ]</code>. Using <code>$</code> on a dataframe to access a column is also a type of indexing, which you used in the previous exercise.</p>
<p>Using square brackets with the <code>summary()</code> function is one helpful way to use indexing. See below for examples from the handbook.</p>
<p>Let’s say we want a summary of ebola case temperatures from our data <code>linelist</code>:</p>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 35.20 38.20 38.80 38.56 39.20 40.80 149</code></pre>
<p>If we just want to know what the <strong>maximum</strong> temperature of an ebola case from our data is:</p>
<pre><code>## Max.
## 40.8</code></pre>
<p>It can also be done by the element title using double brackets:</p>
<pre><code>## [1] 40.8</code></pre>
<p>Note: Character elements need to be entered with quotations</p>
<div id="section-exercise-3" class="section level3">
<h3>Exercise 3</h3>
<p>You try. Enter the code to index the median case temperature from <code>linelist</code>.</p>
<div class="tutorial-exercise" data-label="exercise3a" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="exercise3a-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>summary(linelist$temp)[3]</code></pre>
</div>
<div id="section-exercise3a-hint">
<p><strong>Hint:</strong> the median is the third element in the <code>summary()</code> output.</p>
</div>
<p>Once you’ve indexed the median ebola case temperature, enter the same line again below. Except this time use double brackets <code>[[ ]]</code> to index. Let’s see what changes in the output.</p>
<div class="tutorial-exercise" data-label="exercise3b" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="exercise3b-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>summary(linelist$temp)[[3]]</code></pre>
</div>
<p>Brackets also work on data frames to view specific rows and columns. You can do this using the syntax <code>dataframe[rows, columns]</code>.</p>
Below are some examples from the handbook:
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</script>
</div>
<p>How would you index <code>linelist</code> to show the age, gender, and hospital columns of cases below the age of 18? Enter the code based on this criteria.</p>
<div class="tutorial-exercise" data-label="exercise3c" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="exercise3c-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist[linelist$age < 18 , c("age", "gender", "hospital")]</code></pre>
</div>
</div>
</div>
<div id="section-piping" class="section level2">
<h2>Piping</h2>
<p>There are different ways of writing code. One popular methods of writing R code is using “pipes”… Epi R Handbook explanation.</p>
<div id="section-index-with-dplyr" class="section level3">
<h3>Index with <strong>dplyr</strong></h3>
<p>In the handbook you saw dataframe indexing using the package <code>dplyr</code> and its functions: * <code>filter()</code> with the logical statement <code>row_number()</code>for rows, * <code>select()</code> for columns, and * <code>head()</code>.</p>
<p>Fill in the blank with the correct code for filtering and indexing your data in the next exercises.</p>
View the <strong>first two rows</strong> of linelist using <code>head()</code>
<div class="tutorial-exercise" data-label="blank1" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist ____ head(2)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="blank1-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist %>% head(2)</code></pre>
</div>
Show only row 100 of linelist
<div class="tutorial-exercise" data-label="blank2" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist ____ filter(____)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="blank2-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist %>% filter(row_number == 100)</code></pre>
</div>
View only the date_onset, outcome, and age columns of rows 15 to 25.
<div class="tutorial-exercise" data-label="blank3" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist ____ filter(_____ %in% _____) %>% _____(date_onset, outcome, age)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support" data-label="blank3-solution" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>linelist %>% filter(row_number() %in% 15:25) %>% select(date_onset, outcome, age)</code></pre>
</div>
<p>Note: For indexing lists, refer back to the handbook for examples and resources. Remember that the linelist data you are working with is classified as a dataframe.</p>
</div>
</div>
<div id="section-transition-to-r" class="section level2">
<h2>Transition to R</h2>
<p>From another statistical language <a href="https://www.epirhandbook.com/en/transition-to-r.html" class="uri">https://www.epirhandbook.com/en/transition-to-r.html</a></p>
</div>
<div id="section-template-exercises" class="section level2">
<h2>TEMPLATE Exercises</h2>
<div id="section-exercise-with-code" class="section level3">
<h3>Exercise with Code</h3>
<p><em>Here’s an exercise with some prepopulated code as well as <code>exercise.lines = 5</code> to provide a bit more initial room to work.</em></p>
<p>Now write a function that adds any two numbers and then call it:</p>
<div class="tutorial-exercise" data-label="add-function" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="5">
<pre class="text"><code>add <- function() {
}</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
</div>
<div id="section-exercise-with-hint" class="section level3">
<h3>Exercise with Hint</h3>
<p><em>Here’s an exercise where the chunk is pre-evaulated via the <code>exercise.eval</code> option (so the user can see the default output we’d like them to customize). We also add a “hint” to the correct solution via the chunk immediate below labeled <code>print-limit-hint</code>.</em></p>
<p>Modify the following code to limit the number of rows printed to 5:</p>
<div class="tutorial-exercise" data-label="print-limit" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>mtcars</code></pre>
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<div class="tutorial-exercise-support" data-label="print-limit-hint" data-completion="1" data-diagnostics="1" data-startover="1" data-lines="0">
<pre class="text"><code>head(mtcars)</code></pre>
</div>
</div>
<div id="section-quiz" class="section level3">
<h3>Quiz</h3>
<p><em>You can include any number of single or multiple choice questions as a quiz. Use the <code>question</code> function to define a question and the <code>quiz</code> function for grouping multiple questions together.</em></p>
<p>Some questions to verify that you understand the purposes of various base and recommended R packages:</p>
<p><div class="panel-heading tutorial-quiz-title"><span data-i18n="text.quiz">Quiz</span></div></p>
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# load packages ----------------------------------------------------------------
library(learnr)
library(gradethis)
library(tidyverse)
library(here)
library(rio)
library(basket) # not sure if we need this
library(etude) # helper functions for gradethis
# set options for exercises and checking ---------------------------------------
gradethis_setup()
learnr::tutorial_options(exercise.timelimit = 60)
#exercise.checker = gradethis::grade_learnr)
# alternatively, submitr::null_code_checker
# event recorder ---------------------------------------------------------------
# see https://github.com/dtkaplan/submitr/blob/master/R/make_a_recorder.R
tutorial_options(exercise.eval = FALSE) # pre-evaluate exercises
vfun <- submitr::make_basic_validator(NULL, "hello") #basket::check_valid
new_recorder <- function(tutorial_id, tutorial_version, user_id, event, data) {
cat(
tutorial_id,
" (v", tutorial_version, "); ",
format(Sys.time(), "%Y-%M%-%D %H:%M:%S %Z"), "; ",
user_id, "; ",
event, "; ",
data$label, "; ",
data$answers, "; ",
data$code, "; ",
data$correct, "\n", sep = "",
file = here::here("event_records", "learnr_basics.txt"),
append = TRUE)
}
options(tutorial.event_recorder = new_recorder)
# hide non-exercise code chunks ------------------------------------------------
knitr::opts_chunk$set(echo = FALSE)
# data prep --------------------------------------------------------------------
linelist_raw <- rio::import(here::here("data", "linelist_raw.xlsx"))
linelist <- rio::import(here::here("data", "linelist_cleaned.rds"))
malaria_counts <- rio::import(here::here("data", "malaria_facility_count_data.rds"))
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<script type="application/shiny-prerendered" data-context="server">
learnr:::register_http_handlers(session, metadata = NULL)
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learnr:::prepare_tutorial_state(session)
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learnr:::i18n_observe_tutorial_language(input, session)
</script>
<script type="application/shiny-prerendered" data-context="server">
session$onSessionEnded(function() {
learnr:::event_trigger(session, "session_stop")
})
</script>
<script type="application/shiny-prerendered" data-context="server">
# see https://rdrr.io/github/dtkaplan/submitr/f/vignettes/using.Rmd
options(tutorial.storage = "none")
vfun <- submitr::make_basic_validator(NULL, "hello") #basket::check_valid
storage_actions <- submitr::record_local("./minimal_submissions.csv")
submitr::shiny_logic(input, output, session, vfun,
storage_actions)
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-load-package-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-load-package-code-editor`)), session)
output$`tutorial-exercise-load-package-output` <- renderUI({
`tutorial-exercise-load-package-result`()
})
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"linelist <- rio::import(here::here(\"data\", \"linelist_cleaned.rds\"))",
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<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-import-data-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-import-data-code-editor`)), session)
output$`tutorial-exercise-import-data-output` <- renderUI({
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})
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`tutorial-exercise-exercise1-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-exercise1-code-editor`)), session)
output$`tutorial-exercise-exercise1-output` <- renderUI({
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dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ...) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
exercise.eval = FALSE, label = "exercise1", exercise = TRUE,
code = "", out.width.px = 624, out.height.px = 384, params.src = "exercise1, exercise=TRUE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r"), class = "tutorial_exercise"))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-exercise2-head-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-exercise2-head-code-editor`)), session)
output$`tutorial-exercise-exercise2-head-output` <- renderUI({
`tutorial-exercise-exercise2-head-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(global_setup = structure(c("# load packages ----------------------------------------------------------------",
"library(learnr)", "library(gradethis)", "library(tidyverse)",
"library(here)", "library(rio)", "library(basket) # not sure if we need this",
"library(etude) # helper functions for gradethis", "", "# set options for exercises and checking ---------------------------------------",
"gradethis_setup()", "", "learnr::tutorial_options(exercise.timelimit = 60) ",
" #exercise.checker = gradethis::grade_learnr) ", " # alternatively, submitr::null_code_checker",
"", "", "# event recorder ---------------------------------------------------------------",
"# see https://github.com/dtkaplan/submitr/blob/master/R/make_a_recorder.R",
"tutorial_options(exercise.eval = FALSE) # pre-evaluate exercises",
"", "vfun <- submitr::make_basic_validator(NULL, \"hello\") #basket::check_valid",
"", "new_recorder <- function(tutorial_id, tutorial_version, user_id, event, data) {",
" cat(", " tutorial_id, ", " \" (v\", tutorial_version, \"); \",",
" format(Sys.time(), \"%Y-%M%-%D %H:%M:%S %Z\"), \"; \",",
" user_id, \"; \",", " event, \"; \",", " data$label, \"; \",",
" data$answers, \"; \",", " data$code, \"; \",", " data$correct, \"\\n\", sep = \"\",",
" ", " file = here::here(\"event_records\", \"learnr_basics.txt\"),",
" append = TRUE)", "}", "", "options(tutorial.event_recorder = new_recorder)",
"", "", "# hide non-exercise code chunks ------------------------------------------------",
"knitr::opts_chunk$set(echo = FALSE)", "", "", "# data prep --------------------------------------------------------------------",
"linelist_raw <- rio::import(here::here(\"data\", \"linelist_raw.xlsx\"))",
"linelist <- rio::import(here::here(\"data\", \"linelist_cleaned.rds\"))",
"malaria_counts <- rio::import(here::here(\"data\", \"malaria_facility_count_data.rds\"))"
), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL,
chunks = list(list(label = "exercise2-head", code = "", opts = list(
label = "\"exercise2-head\"", exercise = "TRUE"), engine = "r")),
code_check = NULL, error_check = NULL, check = NULL, solution = structure("head(linelist)", chunk_opts = list(
label = "exercise2-head-solution")), options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "setup_import_cache/html/", cache.vars = NULL,
cache.lazy = TRUE, dependson = NULL, autodep = FALSE,
cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis",
fig.align = "default", fig.path = "setup_import_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ...) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
exercise.eval = FALSE, label = "exercise2-head", exercise = TRUE,
code = "", out.width.px = 624, out.height.px = 384, params.src = "exercise2-head, exercise = TRUE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r"), class = "tutorial_exercise"))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-exercise2a-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-exercise2a-code-editor`)), session)
output$`tutorial-exercise-exercise2a-output` <- renderUI({
`tutorial-exercise-exercise2a-result`()
})
</script>
<script type="application/shiny-prerendered" data-context="server">
learnr:::store_exercise_cache(structure(list(global_setup = structure(c("# load packages ----------------------------------------------------------------",
"library(learnr)", "library(gradethis)", "library(tidyverse)",
"library(here)", "library(rio)", "library(basket) # not sure if we need this",
"library(etude) # helper functions for gradethis", "", "# set options for exercises and checking ---------------------------------------",
"gradethis_setup()", "", "learnr::tutorial_options(exercise.timelimit = 60) ",
" #exercise.checker = gradethis::grade_learnr) ", " # alternatively, submitr::null_code_checker",
"", "", "# event recorder ---------------------------------------------------------------",
"# see https://github.com/dtkaplan/submitr/blob/master/R/make_a_recorder.R",
"tutorial_options(exercise.eval = FALSE) # pre-evaluate exercises",
"", "vfun <- submitr::make_basic_validator(NULL, \"hello\") #basket::check_valid",
"", "new_recorder <- function(tutorial_id, tutorial_version, user_id, event, data) {",
" cat(", " tutorial_id, ", " \" (v\", tutorial_version, \"); \",",
" format(Sys.time(), \"%Y-%M%-%D %H:%M:%S %Z\"), \"; \",",
" user_id, \"; \",", " event, \"; \",", " data$label, \"; \",",
" data$answers, \"; \",", " data$code, \"; \",", " data$correct, \"\\n\", sep = \"\",",
" ", " file = here::here(\"event_records\", \"learnr_basics.txt\"),",
" append = TRUE)", "}", "", "options(tutorial.event_recorder = new_recorder)",
"", "", "# hide non-exercise code chunks ------------------------------------------------",
"knitr::opts_chunk$set(echo = FALSE)", "", "", "# data prep --------------------------------------------------------------------",
"linelist_raw <- rio::import(here::here(\"data\", \"linelist_raw.xlsx\"))",
"linelist <- rio::import(here::here(\"data\", \"linelist_cleaned.rds\"))",
"malaria_counts <- rio::import(here::here(\"data\", \"malaria_facility_count_data.rds\"))"
), chunk_opts = list(label = "setup", include = FALSE)), setup = NULL,
chunks = list(list(label = "exercise2a", code = "", opts = list(
label = "\"exercise2a\"", exercise = "TRUE"), engine = "r")),
code_check = NULL, error_check = NULL, check = NULL, solution = structure("class(linelist)", chunk_opts = list(
label = "exercise2a-solution")), options = list(eval = FALSE,
echo = TRUE, results = "markup", tidy = FALSE, tidy.opts = NULL,
collapse = FALSE, prompt = FALSE, comment = NA, highlight = FALSE,
size = "normalsize", background = "#F7F7F7", strip.white = TRUE,
cache = 0, cache.path = "setup_import_cache/html/", cache.vars = NULL,
cache.lazy = TRUE, dependson = NULL, autodep = FALSE,
cache.rebuild = FALSE, fig.keep = "high", fig.show = "asis",
fig.align = "default", fig.path = "setup_import_files/figure-html/",
dev = "png", dev.args = NULL, dpi = 192, fig.ext = "png",
fig.width = 6.5, fig.height = 4, fig.env = "figure",
fig.cap = NULL, fig.scap = NULL, fig.lp = "fig:", fig.subcap = NULL,
fig.pos = "", out.width = 624, out.height = NULL, out.extra = NULL,
fig.retina = 2, external = TRUE, sanitize = FALSE, interval = 1,
aniopts = "controls,loop", warning = TRUE, error = FALSE,
message = TRUE, render = NULL, ref.label = NULL, child = NULL,
engine = "r", split = FALSE, include = TRUE, purl = TRUE,
max.print = 1000, exercise.timelimit = 60, exercise.checker = "function (label = NULL, solution_code = NULL, user_code = NULL, \n check_code = NULL, envir_result = NULL, evaluate_result = NULL, \n envir_prep = NULL, last_value = NULL, stage = NULL, ...) \n{\n (utils::getFromNamespace(\"check_exercise\", \"gradethis\"))(label = label, \n solution_code = solution_code, user_code = user_code, \n check_code = check_code, envir_result = envir_result, \n evaluate_result = evaluate_result, envir_prep = envir_prep, \n last_value = last_value, stage = stage, ...)\n}",
exercise.error.check.code = "gradethis_error_checker()",
exercise.eval = FALSE, label = "exercise2a", exercise = TRUE,
code = "", out.width.px = 624, out.height.px = 384, params.src = "exercise2a, exercise=TRUE",
fig.num = 0L, exercise.df_print = "paged"), engine = "r"), class = "tutorial_exercise"))
</script>
<script type="application/shiny-prerendered" data-context="server">
`tutorial-exercise-exercise2b-result` <- learnr:::setup_exercise_handler(reactive(req(input$`tutorial-exercise-exercise2b-code-editor`)), session)
output$`tutorial-exercise-exercise2b-output` <- renderUI({
`tutorial-exercise-exercise2b-result`()
})
</script>