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hi and welcome back we've given you a big picture overview of R and R studio now we'll turn our Focus to the actual programming and coding you'll do using R Studio I went pretty far in my career not knowing programming before it became clear I needed to learn it getting to know R was such a valuable learning experience it took some time and I reached out to more experien to our users with lots of questions and eventually it all came together for me being open to learning new skills is such an important part of your career and now I'm able to help you learn some new skills too I'll start by sharing the fundamentals of programming using R in R Studio earlier we explained how R is like the engine of a car and our Studios like the accelerator steering wheel and dashboard allinone getting to know fundamentals will help you keep your r car running smoothly these fundamentals are both alike and different from the other analysis platforms you've come to know well spreadsheets and SQL then we'll move into coding in our studio we'll discuss the Syntax for performing calculations and the standards and naming conventions for all code we'll also explore the r tool known as a pipe which you'll use to make a sequence of code easier to work with and read then we'll check out our packages while these packages won't be delivered to your door they are delivered by the r Community these packages contain reusable functions and more and are usually built by users for users like yourself we'll get to know a collection of packages called the Tidy verse you'll learn how to the Tidy verse so you can start using it in R Studio we'll also work with some of the more popular tidyverse packages like ggplot 2 for visualization you'll be able to carry over what you've learned about R Studio to the next part of the program where you'll start working with data as we explained earlier for this program we'll use the in browser version of R Studio our studio Cloud but our studio is also available to be down downloaded so let's get going see you soon anytime you're learning a new skill from cooking to driving to dancing you should always start with the fundamentals programming with r is no different to build this Foundation you'll get familiar with the basic concepts of R including functions comments variables data types vectors and and pipes some of these terms might sound familiar for example we've come across functions in spreadsheets and SQL as a quick refresher functions are a body of reusable code used to perform specific tasks in R functions begin with function names like print or paste and are usually followed by one or more arguments in parentheses an argument is that a function in R needs in order to run here's a simple function in action feel free to join in and try it yourself in R Studio using your cloud account check out the reading for more details on how to get started you can pause the video anytime you need to we'll open R Studio Cloud to get started we'll start our function in the console with the function name print this function name will return whatever we include in the Valu in parentheses we'll type an open parenthesis followed by a quotation mark both the Clos parenthesis and end quote automatically pop up because our studio recognizes this syntax now we just have to add the text string we'll type coding in R then we'll press enter success the code Returns the words coding in R if you want to to find out more about the print function or any function all you have to do is type A question mark the function name and a set of parentheses this returns a page in the help window which helps you learn more about the functions you're working with keep in mind that functions are case sensitive so typing print with a capital P brings back an error message functions are great but it can be pretty timec consuming to type out lots of values to save time we can use variables to represent the values this lets us call out the values anytime we need to with just the variable earlier we learned about variables in SQL a variable is a representation of a value in r that can be stored for use later during programming variables can also be called objects as a data analyst you'll find variables are very useful when programming for example if you want to filter a data set just assign a variable to the function you use to filter the data that way all you have to do is use that variable to filter the data later when naming a variable in R you can use a short phrase a variable name should start with a letter and can also contain numbers and underscores so the variable five penguin wouldn't work well because it starts with a number also just like functions variable names are case sensitive using all lowercase letters is good practice whenever possible now before we get to coding a variable let's add a comment comments are helpful when you want to describe or explain what's going on in your code use them as much as possible so that you and everyone can understand the reasoning behind it comment should be used to make an R script more readable a comment shouldn't be treated as code so we'll put a hashtag in front of it then we'll add our comment here's an example of a variable now let's go ahead with our example it makes sense to use a variable name to connect to what the variable is representing so we'll type the variable name first uncore variable then after the variable name we'll type a less than sign followed by a dash this is the assignment operator it assigns the value to the variable it looks like an arrow which makes sense since it's pointing from the value to the variable there are other assignment operators that work too but it's always good to stick with just one type in your code next we'll add the value that our variable will represent we'll use the text this is my variable if we type the variable and hit run it'll return the value that the variable represents this is a very basic way of using a variable you'll learn more ways of using variables in your code soon for now let's assign a variable to a different data type numeric we'll name this second variable and type our assignment operator we'll give it the numeric value 12.5 the environment pane in the upper right part of our workspace now shows both of our variables and their values there are other data types in R like logical date and date time R has a few options for dealing with these data types we explore them later with functions comments variables and data types you've got a good foundation for working with r we'll revisit these throughout this program and show you how they're used in different ways during analysis let's finish up with two more fundamental concepts vectors and pipes simply put a vector is a group of data elements of the same type stored in a sequence in R you can make make a vector using the combined function in R this function is just the letter c followed by the values you want in your vector inside parentheses all right let's create a vector imagine this Vector is for a measurement data that we need to analyze we'll start our code with the variable vcore one to assign to the vector then we'll type C and the open parenthesis then we'll type our list of numbers separated by commas we'll then close our parentheses and press enter this time when we type our variable and press enter it returns our Vector we can use this Vector anywhere in our analysis with only its variable name vect underscore 1 the values in the vector will automatically be applied to our analysis that brings us to the last of our fundamentals pipes a pipe is a tool in R for expressing a sequence of multiple operations a pipe is represented by a percentage sign followed by a greater than sign and another percentage sign it's used to apply the output of one function into another function function pipes can make your code easier to read and understand for example this pipe filters and sorts the data later we'll learn how each part of the pipe works so there they are the Super Six fundamentals functions comments variables data types vectors and pipes they all work together as a foundation for using R it's a lot to take in so feel free to watch any of these videos again if you need a refresher when you're ready there's so much more to know about R and R studio so let's get to it we've shown you how your work as a data analyst can be done in different ways using different tools that's true in this program and it'll be just as true when you start your job operations and calculations are two concepts we've checked out before coming up we'll go back to them and learn how to use operators in R for a range of tasks including calculations an operator is one of the key components of a calculation when we first talked about operators we defined them as a symbol that names the type of operation or calculation to be performed in a formula the same is true when we use operators in R code so let's check out some of these oper operators in R imagine we've got our hands on some eCommerce sales data that we need to analyze we'll learn how to use operators to complete calculations on the sales data and for some other tasks too throughout our analysis we'll use variables that R will store so that we can reference them whenever we need to we'll use assignment operators which we worked with earlier to do this assignment operators are used to assign Val values to variables and vectors so if we've got a bunch of sales figures that we want to include in a vector we can use an assignment operator to assign them to a variable here's an example now whenever we want to use these sales figures we just type the variable we assigned next let's check out arithmetic operators these operators are used to complete math calculations and they might seem familiar plus signs do addition on variables and minus signs do subtraction we use an ASC to perform multiplication and a slash performs division there's other arithmetic operators too but these are enough to get you started let's try a calculation for our sales data in our studio feel free to follow along on your own as we go through these steps we'll complete our work in a script to make sure our calculations are saved as an analyst developing code and R you'll spend most of your time in scripts when you save a script you'll have a complete record of your work you'll use the console mostly to show the results of your programming also even though we're not doing a deep analysis here it's still a good idea to save our work for easy access later if we need it first let's add a comment after after the hashtag we'll typee our first calculations we'll start by assigning sales figures from the first two quarters of the year to variables before we complete our first calculation we'll assign it to a new variable midore sales then we'll add our quarter figures using the plus sign as our addition operator let's run it and get the total of our sales data when we run code in a script the return shows up in the console this totals now assigned to the midyear uncore sales variable we can check this by typing in midyear unor sales into the console and hitting enter you may notice that calculations in R work in a similar way to calculations in spreadsheets and SQL it's helpful to make connections across the tools that you're working with let's do one more calculation using our total sales from the first two quarters represented by midyear uncore sales we'll multiply it by two to get a general idea of total sales for the year we'll use an asterisk as our arithmatic operator you'll find there's other ways to perform these types of calculations but these are great examples of how the operators work both for calculations and other operations for now let's save our script so that we can use these same variables again if we need to do more work in our sales data just like in other formats we simply click save as and then type a file name an R file extension is automatically applied to our file name we'll close our script when we're ready for more sales data analysis we can open it again using the file menu there are other categories of operators that you'll learn about later but knowing how assignment and arithmetic operators help you program calculations is is a good place to start we're definitely moving forward in R and R Studio let's keep it rolling by learning more about pipes another great tool in R earlier we introduced something called pipes a pipe is a tool in r that helps make your code more efficient and easier to read and understand in this video we'll explore pipes in more detail as a quick reminder a pipe is a tool in R for expressing a sequence of multiple operations in other words it takes the output of one statement and makes it the input of the next statement so instead of typing out functions contained inside other functions you could use the pipe operator to do the same work in programming we describe this as nested nested describes code that performs a particular function and is contained within code that performs a broader function you can think of a pipe as a way to code the phrase and then say you've got sales data and you need to find the mean or average you can create a pipe by calling up the data and then grouping the data and then summarizing the group data using a mean function let's let's check out an example first we'll open our studio then we'll start a new script so we can save our work we'll save it as tooth growth exploration we'll use the tooth growth data set which is already installed in R this data set contains data about the effect of vitamin C on the growth of teeth in guinea pigs it's a well-known data set that'll help us learn about how pipes work to load any data set already installed we use the data function we then add the name of the data set tooth growth now that the data is loaded we can check it out with the view function notice how view begins with the capital V it's a good reminder that functions and variables are case sensitive in r in a script we use the Run button to run our code the return usually shows up in the console but with view a new tab appears in the script showing the contents of the data set now let's say we need to filter and sort this data to organize it for analysis without pipes we could do this either by nesting commands or by creating a sequence of data frames we'll chat more about data frame soon let's start by filtering the data set note that we'll want to First and load the correct filter function which comes as part of a package installing a package may take a few moments this function comes as part of the dly r package we'll assign a name to the new data set and then apply the filter function this filters the data so that we only see rows where the dose of vitamin C is exactly 0.5 this includes both types of vitamin C used in a study orange juice or OJ in our data set and acorbic acid or VC next we'll sort it with the arrange function we'll include the name of the the filter data set followed by the column name we want to sort by in this case Len which stands for length of tooth when we run this the return appears in the console the data is arranged in ascending order by Len the return only shows rows where the dose amount is .5 so the data has been filtered and sorted based on our code let's try another way to get the same return we'll use a nested function which is a function that is completely contained within another function here's the nested function for filtering and sorting this data set notice that the filter function from our previous code is the nested function with tested functions we read from the inside out the code filters the data first then it arranges or sorts it now let's run this we tweaked the code but we get the same result now we'll use a pipe as a quick reminder the operator used to call out a pipe is a percentage sign followed by a greater than sign and another percentage sign you can also use keyboard shortcuts to insert pipe operators contrl shift M for PCs and Chromebooks and command shift M for Max we'll start this pipe by assigning it to a variable then we'll type the name of the data set we're pulling data from tooth growth we'll use our keyboard shortcut to add the pipe operator after that now now we can press enter to go to the next line our studio automatically indents the next line recognizing that it's part of the pipe next we'll filter the data we don't have to call out the data set inside parentheses like we did in earlier examples because we started our pipe with it the pipe automatically applies the data set to each step all right let's finish up our pipe on a new line with the arrange function and sort the data since this is our last line of code we don't need a pipe operator finally click run and Presto we get the same return as our other methods our pipe is set up to call the data set and then filter the data set and then sort the data set all three methods work but you can see how pipes help make your programming more efficient and less cluttered this means fewer chances for mistakes and better readability for anyone looking at your code and because of the structure of a pipe we can easily add to or change the code without having to start over so let's do that building on our example let's say we also wanted to compute the average tooth length or Len for each of the two supplements used in the study orange juice or OJ and aorc acid or VC we'll replace the arrange function with the group by function this will group our results by the two supplements so we type sub in the parentheses and add a pipe we're adding a pipe this time because we have another line of code to add so we Group by and then we summarize our argument which comes after the function summarize looks pretty complex but it basically tells our what to do with missing values and to make sure the data is grouped the right way when we add the summarized function now we'll run our new pipe and get the average length of tooth when the dose is equal to.5 for each of our supplements nice now there's a couple of things to remember when using pipes first it's important to add the pipe operator at the end of each line of the piped operation except the last one another goodp rule of thumb is to check your code after you've programmed your pipe remember our studio automatically indents lines of code that are part of a pipe if a line in your code isn't indented it probably hasn't been added to the pipe that could lead to an error statement then you can revisit the piped operation to check for parts of your code to fix with the other methods we showed you it'd be more of a challenge to find the messy Parts another reason to use pipes when you can pipes or piping and the functions that are part of the piping process are building blocks for putting together analyses in r in upcoming videos you'll learn how you can use these building blocks to clean transform and analyze your data for now feel free to take your time reviewing and maybe even practicing with the functions operations and other elements in R and R Studio that we've already covered I have to say getting a package delivered to you is one of life Simple Pleasures doesn't matter if it's a surprise package or something you ordered yourself it's exciting to open your package to discover what's inside no wonder those unboxing videos on YouTube are so popular well R has a different kind of package that R users can open these packages are units of reproducible R code and they make it easier to keep track of code they're created by members of the r Community to keep track of the r functions that they write and reuse these community members might then make the packages available to other users it's one of the great things about being part of this community packages in R include reusable R functions and documentation about the functions including how to use them they also contain sample data sets and tests for checking your code to make sure it does what you want it to do by default art includes a set of packages called Bas r that are available to use an R Studio when you start your first programming session there's also recommended packages that are loaded but not installed before using functions from one of these packages you'd have to load it with a library command like Library boot for example let's find out which packages we already have in our studio we'll work in our console instead of a script for now because we're practicing and don't need to save this code for later to check out our packages we'll just run the command installed. packages and there's our list let's focus on the package and priority columns the package column gives the name of the package like cluster or Graphics the priority column tells us what's needed to use functions from the pack package if you come across the word base in the priority column then the package is already installed and loaded you can use all of the functions of that package as soon as you open our studio if you find the word recommended then the package is installed but not loaded you'll also notice a list of packages in the bottom right part of our workspace this list includes a brief description of of each package to load class and other uninstalled packages we'll need to use the library function followed by the name of the package and now the class package has a check next to it so it's been successfully loaded for use if you want to learn even more about your loaded packages you can click on their names in the packages tab this opens the help Tab and shows topics related to the package you selected you can also use the help function in your programming to call up the help Tab while the pre-installed packages give you tons of useful functions there's even more packages that'll further expand your programming abilities you can find thousands of R packages just by doing an online search one of the most commonly used sources of packages is cran cran stands for comprehensive R archive Network it's an online archive with r packages source code manuals and documentation when you start working with r you'll be able to do your own searches to find packages in crayon or elsewhere it's almost always easier to just search with your favorite search engine though so packages are a pretty big part of using R they give you most of what you need to complete your programming throughout the data analysis process who knows you you might even turn your own code into packages for others to use up next we'll keep unpacking our packages as we discussed earlier packages are a big part of what makes R so great packages offer a helpful combination of code reusable R functions descriptive documentation tests for checking operability and Sample data sets and for lots of data analysts at the top of the list of useful packages is tidy verse tidy verse is actually a collection of packages in r with a common design Philosophy for data manipulation exploration and visualization using tidyverse can help you work your way through pretty much the entire data analysis process the packages in tidyverse work together naturally I started learning about tidy verse when I was working on a survey project it felt like I was stepping into a more advanced zone of R I understood the basics but now I was finding out how the Tidy verse improves on the basics that's when I got even more excited about working in R I realized that the more I put into learning about the Tidy verse the more I'd get out of it on top of that the community support for tidy verse is strong too it's one of the reasons why tidyverse is considered a key part of programming for most our users the principles associated with tidy verse which you'll learn both here and at your job have been widely adopted by the r Community you'll find lots of tutorials and examples related to the tidyverse online that show you these principles and how they're applied to data analytics okay let's the Tidy verse you can follow along on your own using your R Studio cloud account check out the reading for more details earlier you learned how to find base r packages using the function installed packages to packages like the Tidy verse that aren't in Bas R we'll use the packages function as we discussed earlier this function calls the Tidy verse and other packages from cray let's talk about why cray was created since packages not in base R are mostly made by our users people need a reli able way to check and validate submitted code cran makes sure any R content open to the public meets the required quality standards so if it's sourced through cran you can feel good that the package is authentic and valid another major source of packages and other AR content is GitHub now we'll get back to installing the Tidy verse we'll first type . packages then between the parentheses we'll type tidy verse in quotes the quotes aren't always necessary but best practice is to use quotes to make sure that we're accurate we'll press enter and wait for our studio to tidy verse when we click on our packages tab we come across a lot of new packages on the list that's tidy verse you might have noticed that none of the packages are checked off we need to load them first before we can use them but that's a mighty long list so let's just load the package named tidy verse for now using the library function the return shows that Not only was tidy verse loaded but eight other packages were too it also shows a list of conflicts conflicts happen when packages have functions with the same names as other functions basically the last package loaded is the one whose functions will be used so we'll stick with the tid Erse functions but it's important to note that these messages only appear once so as you get more used to R you'll be able to figure out if you want to use certain functions over others the loaded packages are ggplot 2 tibble tidy r read R per dly R string R and for cats these packages are the core of the Tidy verse because you'll use them in almost every analysis all of them work together to make your data analysis smooth and efficient with these packages tidy verse helps you do everything from importing and transforming data to exploring and visualizing it we'll check out this core packages soon and we'll use them even more as we continue working in R studio if you're working on your own in R you can check out some of the other packages too the packages available in tidy verse change a lot but you can always check for updates by running tidy verse underscore update open parentheses and close parentheses in your console you can then update the packages in a couple of ways if you use the update package packages function it'll update all of your packages that might take a while so if you just want to update one package you can use the packages function again with the package name as your argument in parenthesis you should update packages regularly to make sure you've got the latest version in your code conflict notifications are just one type of message that can show up in the console you might find warnings and error messages as well a quick search using the help tab will usually tell you what the message means and what if anything you'll need to do to address it coming up we'll keep moving through the Tidy verse you'll find out more about why tidy verse is such an integral part of R have you ever taken a tour of a famous landmark or an unfamiliar City it can be pretty exciting you get to learn all about the features of the landmark or city eventually you get to know them pretty well and you can share what you learned with others well we're here to take a different kind of tour a tour of the Tidy verse for this tour we won't be traveling anywhere special but we will help you learn about the exciting tidy verse features and once you know them a little better you can most definitely share what you learned with others for this tour we'll focus on the core packages of Tidy verse we discussed earlier ggplot 2 tidy R read r dly r tibble per string R and for cats we also learned how to and load them in R Studio once they're loaded you won't need to do anything else with their actual packages they'll do their thing as you program so what is their thing well it depends but there's four packages that are an essential part of the workflow for data analysts ggplot 2 dly R tidy R and RAR you'll most likely use these more often than the others ggplot 2 is used for data visualization specifically plots with ggplot 2 you can create a variety of data Vis by applying different visual properties to the data variable here's an example of ggplot 2 in action you'll have your own chance to use ggplot 2 later tidy R is a package used for data cleaning to make tidy data we covered tidy or clean data earlier but as a quick reminder it's data where every part of a data table or data frame is the right type in the right place tidy R works with wide and long data data to make sure this happens next we have read R which is used for importing data the most common function from read R is read uncore CSV this will import a CSV file into r a CSV file contains data separated by commas in a table format to accurately read a data set with read R you combine the function with a column specification the column specification describes how each column should be converted to the most appropriate data type it's good to keep in mind this isn't usually necessary because read R will figure it out for you automatically we'll come across redar functions as we continue to explore R now on to dly r dly r offers a consistent set of functions that help you complete some common data manipulation tasks for example the select function picks variables based on their names and the filter function finds cases where certain conditions are true and yes dly R is another package we'll get to later there's plenty to look forward to so that's the Fab Four of the Tidy verse they'll all make your programming in R more straightforward and efficient the other four packages are definitely useful too but you might not use them as often tibble works with data frames perr works with functions and vectors helping make your code easier to write and more expressive string R includes functions that make it easier to work with strings for cats provides tools that solve common problems with factors as a quick reminder factors store category data in R where the data values are limited and usually based on a finite group like country or year using the Tidy verse and its packages will help you fine-tune your analysis and besides tidy verse you also learned the fundamentals of R from variables to vectors and more you explored the different operators in R and saw how they can help you complete calculations you had the chance to check out pipe and how they can make your programming more efficient and you unpacked packages to find out how they're a big part of what you can do in R we've covered a lot of ground in just a few videos so this might be a good time for you to do a little review you can re-watch videos and revisit any other resources that can help you get an even better grasp of all the terms Concepts and processes that are part of R congratulations on finishing this video from the Google data analytics certificate access The Full Experience including job search help and start to earn the official certificate by clicking the icon or the link in the description watch the next video in the course by clicking here and subscribe to our channel for more from upcoming Google career certificates