Nycflights13 Package Download [UPD]

Hello. I'm new to R and learning using "R for Data Science" online book -2 . In Section 5.1 on how to use dplyr packages, I am having a similar issue where the 'nycflights13' package is not found. Have updated, restarted, etc. but no luck. Any help is appreciated.

Another point of confusion with new R users is the notion of a package. R packages extend the functionality of R by providing additional functions, data, and documentation and can be downloaded for free from the internet. They are written by a world-wide community of R users. For example, among the many packages we will use in this book are the

Nycflights13 Package Download

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(Note that if you are working on an RStudio Server, you probably will not need to install your own packages as that has been already done for you. Still it is important that you know this process for later when you are not using the RStudio Server but rather your own installation of RStudio Desktop.)

Note: You only have to install a package once, unless you want to update an already installed package to the latest version. If you want to update a package to the latest version, then re-install it by repeating the above steps.

Note: You have to reload each package you want to use every time you open a new session of RStudio. This is a little annoying to get used to and will be your most common error as you begin. When you see an error such as

We will begin by exploring the flights data frame that is included in the nycflights13 package and getting an idea of its structure. Run the following in your code in your console: it loads in the flights dataset into your Console. Note depending on the size of your monitor, the output may vary slightly.

I am new to R and I am trying to get nycflights13 to download on my computer. I tried just downloading it via this command: > install.packages("nycflights13")....which downloads the package just fine.

I tried directly downloading the nycflights13 package directly online and setting it as my working directory but when I opened up the flights R file, it did not contain actual flight information just some general guidelines.

Note about RStudio Server or RStudio Cloud: If your instructor has provided you with a link and access to RStudio Server or RStudio Cloud, you might not need to install packages, as they might be preinstalled for you by your instructor. That being said, it is still a good idea to know this process for later on when you are not using RStudio Server or Cloud, but rather RStudio Desktop on your own computer.

An alternative but slightly less convenient way to install a package is by typing install.packages("ggplot2") in the console pane of RStudio and pressing Return/Enter on your keyboard. Note you must include the quotation marks around the name of the package.

Much like an app on your phone, you only have to install a package once. However, if you want to update a previously installed package to a newer version, you need to reinstall it by repeating the earlier steps.

The help file should pop up in the Help pane of RStudio. If you have questions about a function or data frame included in an R package, you should get in the habit of consulting the help file right away.

Another point of confusion with many new R users is the idea of an R package. R packages extend the functionality of R by providing additional functions, data, and documentation. They are written by a world-wide community of R users and can be downloaded for free from the internet. For example, among the many packages we will use in this book are the ggplot2 package for data visualization in Chapter 2, the dplyr package for data wrangling in Chapter 3, and the moderndive package that accompanies this book.

So R is like a new mobile phone: while it has a certain amount of features when you use it for the first time, it doesn't have everything. R packages are like the apps you can download onto your phone from Apple's App Store or Android's Google Play.

Note about RStudio Server: If your instructor has provided you with a link and access to RStudio Server, you probably will not need to install packages, as they have likely been pre-installed for you by your instructor. That being said, it is still a good idea to know this process for later on when you are not using RStudio Server, but rather RStudio Desktop on your own computer.

Slightly harder way: An alternative but slightly less convenient way to install a package is by typing install.packages("ggplot2") in the Console pane of RStudio and hitting enter. Note you must include the quotation marks.

Much like an app on your phone, you only have to install a package once. However, if you want to update an already installed package to a newer verions, you need to re-install it by repeating the above steps.

(LC2.1) Repeat the above installing steps, but for the dplyr, nycflights13, and knitr packages. This will install the earlier mentioned dplyr package, the nycflights13 package containing data on all domestic flights leaving a NYC airport in 2013, and the knitr package for writing reports in R.

Recall that after you've installed a package, you need to "load" it, in other words open it. We do this by using the library() command. For example, to load the ggplot2 package, run the following code in the Console pane. What do we mean by "run the following code"? Either type or copy & paste the following code into the Console pane and then hit the enter key.

If after running the above code, a blinking cursor returns next to the > "prompt" sign, it means you were successful and the ggplot2 package is now loaded and ready to use. If however, you get a red "error message" that reads...

One extremely common mistake new R users make when wanting to use particular packages is that they forget to "load" them first by using the library() command we just saw. Remember: you have to load each package you want to use every time you start RStudio. If you don't first "load" a package, but attempt to use one of its features, you'll see an error message similar to:

R is telling you that you are trying to use a function in a package that has not yet been "loaded." Almost all new users forget do this when starting out, and it is a little annoying to get used to. However, you'll remember with practice.

We'd all like to arrive at our destinations on time whenever possible. (Unless you secretly love hanging out at airports. If you are one of these people, pretend for the moment that you are very much anticipating being at your final destination.) Throughout this book, we're going to analyze data related to flights contained in the nycflights13 package (Wickham 2021). Specifically, this package contains five data sets saved in five separate data frames with information about all domestic flights departing from New York City in 2013. These include Newark Liberty International (EWR), John F. Kennedy International (JFK), and LaGuardia (LGA) airports:

We will begin by exploring the flights data frame that is included in the nycflights13 package and getting an idea of its structure. Run the following code in your console (either by typing it or cutting & pasting it): it loads in the flights dataset into your Console. Note depending on the size of your monitor, the output may vary slightly.

The second way to explore a data frame is using the glimpse() function included in the dplyr package. Thus, you can only use the glimpse() function after you've loaded the dplyr package. This function provides us with an alternative method for exploring a data frame:

The another way to explore the entirety of a data frame is using the kable() function from the knitr package. Let's explore the different carrier codes for all the airlines in our dataset two ways. Run both of these lines of code in your Console:

In this post, I walk through a toy example of building a reporting app from the flights data in the nycflights13 package to demonstrate how modules help scale basic Shiny skills. The recurring theme we will discuss are that modules help novice developers:

I also intentionally did not discuss more advanced features of modules or more formal and automated testing of them. These topics are covered in both Mastering Shiny and Engineering Production Grade Shiny Apps. These books also introduce ways to share modules via R packages and to organize them in Shiny app projects built with the excellent golem package (the usethis of Shiny apps).

In Subsection 1.2.1, we introduced the concept of a data frame in R: a rectangular spreadsheet-like representation of data where the rows correspond to observations and the columns correspond to variables describing each observation. In Section 1.4, we started exploring our first data frame: the flights data frame included in the nycflights13 package. In Chapter 2, we created visualizations based on the data included in flights and other data frames such as weather. In Chapter 3, we learned how to take existing data frames and transform/modify them to suit our ends.

Note that the read_csv() function included in the readr package is different than the read.csv() function that comes installed with R. While the difference in the names might seem trivial (an _ instead of a .), the read_csv() function is, in our opinion, easier to use since it can more easily read data off the web and generally imports data at a much faster speed. Furthermore, the read_csv() function included in the readr saves data frames as tibbles by default.

Recall that we saw in Subsection 1.4.3 that the observational unit for the flights data frame is an individual flight. In other words, the rows of the flights data frame refer to characteristics/measurements of individual flights. Also included in the nycflights13 package are other data frames with their rows representing different observational units (Wickham 2021): 2351a5e196