http://datacamp-community-prod.s3.amazonaws.com/72771032-0653-4d87-a798-4a83879e99c8

Tackle time series in R with this xts (Extensible Time Series) cheat sheet! 

xts Cheat Sheet: Time Series in R

http://datacamp-community-prod.s3.amazonaws.com/21fdc814-3f08-4aa9-90fa-247eedefd655

This Jupyter Notebook cheat sheet will help you to find your way around the well-known Jupyter Notebook App, a subproject of Project Jupyter.

Jupyter Notebook Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Jupyter_Notebook_Cheat_Sheet.pdf

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<p>In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators.&nbsp;</p>

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators. 

Operationalizing Machine Learning with MLOps

https://www.datacamp.com/community/blog/operationalizing-machine-learning-with-mlops

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<p>In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.</p>

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.

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Amen Ra Mashariki deep dives into the state of data literacy in government agencies, the use cases he worked on to make the City of New York smarter, and more!

Creating Smart Cities With Data Science

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Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.

The Credibility Crisis in Data Science (with Skipper Seabold)

https://www.datacamp.com/community/news/choosing-r-or-python-for-data-analysis-an-infographic-rhcw8x3m5wa

Wondering whether you should use R or Python for your next data analysis post? Check our infographic "Data Science Wars: R vs Python".

Choosing R or Python for Data Analysis? An Infographic

<section>
<p>Even though the <code>data.frame</code> object is one of the core objects to hold data in R, you&#39;ll find that it&#39;s not really efficient when you&#39;re working with time series data. You&#39;ll find yourself wanting a more flexible time series class in R that offers a variety of methods to manipulate your data.</p>

<p>&nbsp;<code>xts</code> &nbsp;or the Extensible Time Series&nbsp;is one of such packages that offers such a time series object. It&#39;s a powerful R package&nbsp;that&nbsp;provides an extensible time series class, enabling uniform handling of many R time series classes by extending <code>zoo</code>, which is the package that is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.</p>

<p><code><tt>xts</tt></code> has a lot to offer to make your time series analysis fast and mistake free, but it can take some time to get used to it.&nbsp;</p>

<p>This <code>xts</code> cheat sheet provides you not only with an overview of the <code>xts</code> object, how to create and inspect them, but also goes deeper into how you can manipulate time series with <code>xts</code>: you&#39;ll see how to replace and update values, how to select, index and subset your objects, how to handle missing values and how to perform arithmetic operations.</p>

<p>Click on the button below to see (and download) the xts cheat sheet:&nbsp;</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/72771032-0653-4d87-a798-4a83879e99c8" target="_blank"><img alt="" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838439/xts_Cheat_Sheet-_Time_Series_in_R_nbiik0.png" style="width: 800px; height: 551px;" /></a></p>

<p><strong>(Click above to download a printable version or read the online version below.)&nbsp;</strong></p>

<p>This cheat sheet will help you to get yourself up to speed in no time!&nbsp;</p>

<div id="scoped-content">
<style type="text/css">:target:before { content:""; display:block; height:150px; margin:-150px 0 0; } h3 {font-weight:normal; } h4 { 	font-weight: lighter; }
</style>
<h2>R For Data Science Cheat Sheet: xts</h2>

<p>eXtensible Time Series (xts) is a powerful package that provides an extensible time series class, enabling uniform handling of many R time series classes by extending zoo.</p>

<p>Load the package as follows:</p>

<pre>
<code>library(xts)</code></pre>

<h3>Xts Objects</h3>

<p>xts objects have three main components:</p>

<ul>
	<li>coredata: always a matrix for xts objects</li>
	<li>index: vector of any <code>Date</code>, <code>POSIXct</code>, <code>chron</code>, <code>yearmon</code>, <code>yearqtr</code>, or <code>DateTime</code> classes</li>
	<li>xtsAttributes: arbitrary attributes</li>
</ul>

<h2>Creating xts Objects</h2>

<pre>
<code>xts1 &lt;- xts(x=1:10, order.by=Sys.Date()-1:10)
data &lt;- rnorm(5)
dates &lt;- seq(as.Date("2017-05-01"), length=5, by="days")
xts2 &lt;- xts(x=data, order.by=dates)
xts3 &lt;- xts(x=rnorm(10), order.by=as.POSIXct(Sys.Date()+1:10), born=as.POSIXct("1899-05-08")
xts4 &lt;- xts(x=1:10, order.by=Sys.Date()+1:10)</code></pre>

<h3>Convert To And From xts</h3>

<pre>
<code>data(AirPassengers)
xts5 &lt;- as.xts(AirPassengers)</code></pre>

<h3>Import From Files</h3>

<pre>
<code>dat &lt;- read.csv(tmp_file)
xts(dat, order.by=as.Date(rownames(dat),"%m/%d/%Y"))
dat_zoo &lt;- read.zoo(tmp_file, index.column=0, sep=",", format="%m/%d/%Y")
dat_zoo &lt;- read.zoo(tmp,sep=",",FUN=as.yearmon)
dat_xts &lt;- as.xts(dat_zoo)</code></pre>

<h2>Inspect Your Data</h2>

<p>Extract core data of objects</p>

<pre>
<code>core_data &lt;- coredata(xts2)</code></pre>

<p>Extract index from objects</p>

<pre>
<code>index(xts1)</code></pre>

<h3>Class Attributes</h3>

<p>Get index class</p>

<pre>
<code>indexClass(xts2)</code></pre>

<p>Replacing index class</p>

<pre>
<code>indexClass(convertIndex(xts,'POSIXct'))</code></pre>

<p>Get index class</p>

<pre>
<code>indexTZ(xts5)</code></pre>

<p>Change format of time display</p>

<pre>
<code>indexFormat(xts5) &lt;- "%Y-%m-%d"</code></pre>

<h3>Time Zones</h3>

<p>Change the time zone</p>

<pre>
<code>tzone(xts1) &lt;- "Asia/Hong_Kong"</code></pre>

<p>Extract the current time zone</p>

<pre>
<code>tzone(xts1)</code></pre>

<h3>Periods, Periodicity &amp; Timestamps</h3>

<p>Estimate frequency of observations</p>

<pre>
<code>periodicity(xts5)</code></pre>

<p>Convert xts5 to yearly OHLC</p>

<pre>
<code>to.yearly(xts5)</code></pre>

<p>Convert <code>xts3</code> to monthly OHLC</p>

<pre>
<code>to.monthly(xts3)</code></pre>

<p>Convert <code>xts5</code> to quarterly OHLC</p>

<pre>
<code>to.quarterly(xts5)</code></pre>

<p>Convert to quarterly OHLC</p>

<pre>
<code>to.period(xts5,period="quarters")</code></pre>

<p>Convert to yearly OHLC</p>

<pre>
<code>to.period(xts5,period="years")</code></pre>

<p>Count the months in <code>xts5</code></p>

<pre>
<code>nmonths(xts5)</code></pre>

<p>Count the quarters in <code>xts5</code></p>

<pre>
<code>nquarters(xts5)</code></pre>

<p>Count the years in <code>xts5</code></p>

<pre>
<code>nyears(xts5)</code></pre>

<p>Make index unique</p>

<pre>
<code>make.index.unique(xts3,eps=1e-4)</code></pre>

<p>Remove duplicate times</p>

<pre>
<code>make.index.unique(xts3,drop=TRUE)</code></pre>

<p>Round index time to the next <code>n</code> seconds</p>

<pre>
<code>align.time(xts3, n=3600)</code></pre>

<h3>Other Useful Functions</h3>

<p>Extract raw numeric index of <code><tt>xts1</tt></code></p>

<pre>
<code>.index(xts4)</code></pre>

<p>value of weekday&nbsp;in index of <code>xts3</code></p>

<pre>
<code>.indexwday(xts3)</code></pre>

<p>Value of hour in index of <code>xts3</code></p>

<pre>
<code>.indexhour(xts3)</code></pre>

<p>Extract first observation of <code>xts3</code>&nbsp;</p>

<pre>
<code>start(xts3)</code></pre>

<p>Extract last observation of <code>xts4</code>&nbsp;</p>

<pre>
<code>end(xts4)</code></pre>

<p>Display structure of <code>xts3</code>&nbsp;</p>

<pre>
<code>str(xts3) </code></pre>

<p>Extract raw numeric index of <code>xts1</code></p>

<pre>
<code>time(xts1)</code></pre>

<p>First part of <code>xts2</code></p>

<pre>
<code>head(xts2)</code></pre>

<p>Last part of <code>xts2</code>&nbsp;</p>

<pre>
<code>tail(xts2)</code></pre>

<p>&nbsp;</p>

<h2>Export <code>xts</code> Objects</h2>

<pre>
<code>data_xts &lt;- as.xts(matrix)
tmp &lt;- tempfile()
write.zoo(data_xts,sep=",",file=tmp)</code></pre>

<h2>Replace &amp; Update</h2>

<p>Replace values in <code>xts2</code> on dates with <code><tt>0</tt></code>&nbsp;</p>

<pre>
<code>xts2[dates] &lt;- 0 </code></pre>

<p>Replace dates from 1961 with <code>NA</code>&nbsp;</p>

<pre>
<code>xts5["1961"] &lt;- NA</code></pre>

<p>Replace the value at 1 specific index with <code>NA</code></p>

<pre>
<code>xts2["2016-05-02"] &lt;- NA </code></pre>

<p>&nbsp;&nbsp;</p>

<h2>Applying Functions</h2>

<p>Locate endpoints by time&nbsp;</p>

<pre>
<code>ep1 &lt;- endpoints(xts4,on="weeks",k=2)  
ep2 &lt;- endpoints(xts5,on="years") </code></pre>

<p>Calculate the yearly mean&nbsp;</p>

<pre>
<code>period.apply(xts5,INDEX=ep2,FUN=mean)</code></pre>

<p>Split <code>xts5</code> by year</p>

<pre>
<code>xts5_yearly &lt;- split(xts5,f="years")</code></pre>

<p>Create a list of yearly means&nbsp; &nbsp;</p>

<pre>
<code>lapply(xts5_yearly,FUN=mean) </code></pre>

<p>Find the last observation in each year in <code>xts5</code></p>

<pre>
<code>do.call(rbind,  lapply(split(xts5,"years"), function(w) last(w,n="1 month"))) </code></pre>

<p>Calculate cumulative annual passengers</p>

<pre>
<code>do.call(rbind,  lapply(split(xts5,"years"),  cumsum)) </code></pre>

<p>Apply standard deviation&nbsp;to rolling margins of <code>xts5</code></p>

<pre>
<code>rollapply(xts5, 3, sd)  </code></pre>

<h2>Selecting, Subsetting &amp; Indexing</h2>

<h3>Select</h3>

<pre>
<code>mar55 &lt;- xts5["1955-03"] #Get value for March 1955</code></pre>

<h3>Subset</h3>

<p>Get all data from 1954&nbsp;</p>

<pre>
<code>xts5_1954 &lt;- xts5["1954"] </code></pre>

<p>Extract data from Jan to March &lsquo;54&nbsp;</p>

<pre>
<code>xts5_janmarch &lt;- xts5["1954/1954-03"]</code></pre>

<p>Get all data until March &lsquo;54&nbsp;&nbsp;</p>

<pre>
<code>xts5_janmarch &lt;- xts5["/1954-03"] </code></pre>

<p>Subset <code>xts4</code> using <code>ep2</code>&nbsp;</p>

<pre>
<code>xts4[ep1] </code></pre>

<h3><code>first()</code> and <code>last()</code></h3>

<pre>
<code>first(xts4,'1 week') #Extract first 1 week
first(last(xts4,'1 week'),'3 days') #Get first 3 days of the last week of data</code></pre>

<h3>Indexing</h3>

<p>Extract rows with the index of <code>xts3</code>&nbsp;</p>

<pre>
<code>xts2[index(xts3)] </code></pre>

<p>Extract rows using the vector <code>days</code>&nbsp;</p>

<pre>
<code>days &lt;- c("2017-05-03","2017-05-23") 
xts3[days] </code></pre>

<p>Extract rows using <code>days</code> as <code>POSIXct</code>&nbsp;</p>

<pre>
<code>xts2[as.POSIXct(days,tz="UTC")] </code></pre>

<p>Index of weekend days</p>

<pre>
<code>index&lt;-which(.indexwday(xts1)==0|.indexwday(xts1)==6) </code></pre>

<p>Extract weekend days of <code>xts1</code>&nbsp;</p>

<pre>
<code>xts1[index] </code></pre>

<h2>Missing Values</h2>

<p>Omit NA values in <code>xts5</code>&nbsp;</p>

<pre>
<code>na.omit(xts5) </code></pre>

<p>Fill missing values in <code>xts2</code>&nbsp;using last observation</p>

<pre>
<code>xts_last &lt;- na.locf(xts2) </code></pre>

<p>Fill missing values in <code>xts2</code> using next observation</p>

<pre>
<code>xts_last &lt;- na.locf(xts2,  fromLast=TRUE)</code></pre>

<p>Interpolate <code>NA</code>s</p>

<pre>
<code>na.approx(xts2)  </code></pre>

<h2>Arithmetic Operations</h2>

<h3><code>coredata()</code> or <code>as.numeric()</code></h3>

<pre>
<code>xts3 + as.numeric(xts2) #Addition
xts3 * as.numeric(xts4) #Multiplication
coredata(xts4) - xts3 #Subtraction
coredata(xts4) / xts3 #Division</code></pre>

<h3>Shifting Index Values</h3>

<p>Period-over-period differences&nbsp;</p>

<pre>
<code>xts5 - lag(xts5) </code></pre>

<p>Lagged differences</p>

<pre>
<code>diff(xts5,lag=12,differences=1) </code></pre>

<h3>Reindexing</h3>

<pre>
<code>xts1 + merge(xts2,index(xts1),fill=0) #Addition
xts1 - merge(xts2,index(xts1),fill=na.locf) #Subtraction</code></pre>

<h2>Merging</h2>

<pre>
<code>merge(xts2,xts1,join='inner') # Inner join of xts2 and xts1
merge(xts2,xts1,join='left',fill=0) #Left join of xts2 and xts1,
rbind(xts1, xts4)</code></pre>

<h2>Going Further</h2>

<p>Want to know more about <code>xts</code>? Check out DataCamp&#39;s <a href="https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo">Manipulating Time Series Data in R with </a><a href="https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo">xts</a><a href="https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo">&nbsp;&amp; zoo</a>&nbsp;course!&nbsp;</p>
</div>
</section>


https://www.datacamp.com/community/blog/r-xts-cheat-sheet

Get started on time series in R with this xts cheat sheet, with code examples. 

<h2>Introduction to Instrumental Variables</h2><p>This chapter will introduce you to Instrumental Variables (IV) analysis, a tricky but powerful method to find causality through indirect inference</p><h2>Instrumental Variables in Practice</h2><p>This chapter covers more details about Instrumental Variables Analysis, and allows you to practice instrumental variables for yourself</p><h2>Regression Discontinuity Design</h2><p>This chapter introduces you to Regression Discontinuity Design (RDD), which is often considered a more flexible relative of Instrumental Variables</p><h2>The Local Average Treatment Effect of IV</h2><p>This chapter explains the treatment effect we are estimating with the instrumental variables method - LATE, or the Local Average Treatment Effect</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-instrumental-variables-rdd

A look at using Instrumental Variables and Regression Discontinuity to estimate causal effects

Causal Inference with R - Instrumental Variables & RDD

<h2>Regressions 1: Introduction to Regression As Causality</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 2: Using Regression to Estimate Causal Effects</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 3: Introduction to Matching Methods</h2><p>This chapter will introduce you to using matching methods to find causal effects</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-regression

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. 

Causal Inference with R - Regression

<p>The&nbsp;data.table R package provides an enhanced version of <code>data.frame</code> that allows you to do <a href="https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping" target="_blank">blazing fast</a> data manipulations. The <code>data.table</code> R package is being used in different fields such as finance and genomics&nbsp;and is especially useful for those of you that are working with large data sets (for example, 1GB to 100GB in RAM).</p>

<p>Although its typical syntax structure is not hard to master, it is unlike other things you might have seen in R. Hence the reason to create this cheat sheet. <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet" target="_blank" title="data.table tutorial">DataCamp</a>&rsquo;s <code>data.table</code> cheat sheet is a quick reference for doing data manipulations in R with the <code>data.table</code> R package and syntax</p>

<p>The cheat sheet will guide you from doing simple data manipulations using <code>data.table</code>&rsquo;s basic <code>i, j, by </code> syntax, to chaining expressions, to using the famous <code>set()</code>-family.</p>

<p>(To practice using the <code>data.table</code> package, try the free first chapter of this <a href="https://www.datacamp.com/courses/data-analysis-the-data-table-way/?utm_source=blog&amp;utm_medium=blog&amp;utm_content=cheat%20sheet%20blog&amp;utm_campaign=data.table%20cheat%20sheet">course</a>, which includes exercises in subsetting a <code>data.table</code> and manipulating data with <code>data.table</code>&#39;s&nbsp;<code>by</code>, <code>.N</code>, and <code>j</code> syntax. )</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/008e41e8-7526-4bbc-806b-8c828dfb81d4" target="_blank" title="data table cheat sheet"><img alt="data.table R package tutorial cheat sheet" class="aligncenter wp-image-437 size-medium" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839980/The_data.table_R_Package_Cheat_Sheet_fdbgnt.png" style="width: 800px; height: 551px;" /></a></p>


https://www.datacamp.com/community/tutorials/data-table-cheat-sheet

The data.table cheat sheet helps you master the syntax of this R package, and helps you to do data manipulations.

The data.table R Package Cheat Sheet

<p>Plot character or <em>pch</em> is the standard argument to set the character that will be plotted in a number of R functions.</p>
<p>Explanatory text can be added to a plot in several different forms, including axis labels, titles, legends, or a text added to the plot itself. Base graphics functions in R typically create axis labels by default, although these can be overridden through the argument <code>xlab()</code> that allows us to provide our own x-axis label and <code>ylab()</code> that allows us to provide our own y-axis label.</p>
<p>Some base graphics functions also provide default titles, but again, these can be overridden has we see here:</p>
<pre><code class="lang-python">library(MASS)
par(mfrow = c(1, 2))
plot(density(geyser$waiting))
plot(density(geyser$waiting), main = &quot;Estimated density: \n Old Faithful waiting times&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481962/pch_2_kukmll.png" /></center>


<p>The plot on the left uses the default title returned by the density function, which tells us that the plot was generated by this function using its default options and also gives the R specification for the variable whose density we are plotting. In the right-hand plot, this default title has been overridden by specifying the optional argument main. Note that by including the return character, backslash n, we are creating a two-line title in this character string.</p>
<h2 id="-text-function"><code>text()</code> Function</h2>
<p>Like the lines and points functions, text is a low-level graphics function that allows us to add explanatory text to the existing plot. To do this, we must specify the values for x and y, the coordinates on the plot where the text should appear, and labels, a character vector that specifies the text to be added.</p>
<pre><code class="lang-python">text(x, y, adj)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/c_scale,f_auto,q_auto:best,w_575/v1603481260/pch3_ubupgd.png" /></center>

<p>By default, the text added to the plot is centered at the specified x values, but the optional argument <code>adj</code> can be used to modify the alignment.</p>
<p>It is noted that the <code>adj</code> argument to the <code>text()</code> function determines the horizontal placement of the text, and it can take any value between 0 (left-justified text) and 1 (right-justified text). In fact, this argument can take values outside this range. That is, making this value negative causes the text to start to the right of the specified <code>x</code> position. Similarly, making <code>adj</code> greater than 1 causes the text to end to the left of the <code>x</code> position.</p>
<pre><code class="lang-python">library(MASS)
plot(UScereal$fibre)
text(5, 28, &quot;&lt;-- Outliers [left-justified text at (5, 28)]&quot;, adj = 0)
text(65, 23, &quot;[Right-justified text at (65, 23)]&quot;, adj = 1, col = &quot;red&quot;)
text(5, 28, &quot;[Centered text (default) at (31, 18)]&quot;, col = &quot;blue&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best,w_575/v1603481260/pch5_decn8p.png" /></center>

<h3 id="fonts-orientations-and-other-text-features">Fonts, Orientations, and Other Text Features</h3>
<p>Below we see some of the ways the appearance of added text can be changed through optional arguments to the text function. By default, text is created horizontally across the page, but this can be changed with the <code>srt</code> argument, which specifies the angle of orientation with respect to the horizontal axis.</p>
<p>Next, the text in red angles upward as a result of specifying <code>srt</code> as the positive value 30 degrees, while the text in green angles downward by specifying <code>srt</code> equals -45.</p>
<p>We can also specify the color of the text with the <code>col</code> argument, and the red text shows that we can change the text size with the <code>cex</code> argument. By specifying <code>cex = 1.2</code>, we have specified the text to be 20% larger than normal.</p>
<p>The other text characteristic that has been modified in this example is the font, set by the <code>font</code> argument. The default value is <code>font = 1</code>, which specifies normal text, while <code>font = 2</code> specifies boldface, <code>font = 3</code> specifies italics, and <code>font = 4</code> specifies both boldface and italics.</p>
<pre><code class="lang-python">library(MASS)
plot(Boston$rad)

# &quot;Inner city&quot; with adjusted colour and rotation
text(350, 24, adj = 1, &quot;Inner city? --&gt;&quot;, srt = 30, font = 2, cex = 1.2, col = &quot;red&quot;)

# &quot;Suburbs&quot; with adjusted colour and rotation
text(100, 15, &quot;Suburbs? --&gt;&quot;, srt = -45, font = 3, col = &quot;green&quot;)

title(&quot;Text with varying orientations, fonts, sizes &amp; colors&quot;)
</code></pre>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481012/pch_1_qfa8ff.png" /></center>

<h2 id="adjusting-text-position-size-and-font">Adjusting Text Position, Size, and Font</h2>
<p>In the following example, you will:</p>
<ul>
<li>Create a plot of <code>MPG.city</code> vs. <code>Horsepower</code> from the <code>Cars93</code> data frame, with data represented as open circles (default <code>pch</code>).</li>
<li>Construct the variable <code>index3</code> using the <code>which()</code> function that identifies the row numbers containing all 3-cylinder cars.</li>
<li>Use the <code>points()</code> function to overlay solid circles, <code>pch = 16</code>, on top of all points in the plot that represent 3-cylinder cars.</li>
<li>Use the <code>text()</code> function with the <code>Make</code> variable as before to add labels to the right of the 3-cylinder cars, but now use <code>adj = 0.2</code> to move the labels further to the right, use the <code>cex</code> argument to increase the label size by 20 percent, and use the <code>font</code> argument to make the labels bold italic.</li>
</ul>
<pre><code class="lang-python"># Plot MPG.city vs. Horsepower as open circles
plot(Cars93$Horsepower, Cars93$MPG.city)

# Create index3, pointing to 3-cylinder cars
index3 &lt;- which(Cars93$Cylinders == 3)

# Highlight 3-cylinder cars as solid circles
points(Cars93$Horsepower[index3],
       Cars93$MPG.city[index3],
       pch = 16)

# Add car names, offset from points, with larger bold text
text(Cars93$Horsepower[index3],
     Cars93$MPG.city[index3],
     Cars93$Make[index3],
     adj = -0.2, cex = 1.2, font = 4)
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603481260/pch2_uumynj.png" /></center>

<p><a href="https://campus.datacamp.com/courses/data-visualization-in-r/adding-details-to-plots?ex=11">Try it for yourself</a>.</p>
<p>To learn more about adding text to plots, please see this video from our course <a href="https://campus.datacamp.com/courses/data-visualization-in-r/adding-details-to-plots?ex=9">Data Visualization in R</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/VY6X27P2uAQ" 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">Data Visualization in R</a> course by Ronald Pearson.</p>


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