R save to file

If all that stuff takes several minutes or even longer , you might not want to do it everytime you are working with the data. I will use some fairly but not very large dataset from the car package.

The dataset is called MplsStops and holds information about stops made by the Minneapolis Police Department in Of course, you can access this dataset by installing and loading the car package and typing MplsStops. However, I want to simulate a more typical workflow here.

Namely, loading a dataset from your disk I will load it over the WWW. The dataset is also available from GitHub:. Just for the sake of simulating a real workflow, I will do some very light data manipulation. As I wrote above: Saving the current state of your dataset in R makes sense when all the preparations take a lot of time. There are two ways of doing this:.

By default, the parameter compress of the save function is turned on. That means that the resulting file will use less space on your disk. However, if it is a really huge dataset, it could take longer to load it later because R first has to extract the file again.

So, if you want to save space , then leave it as it is. Then, the object is available in your workspace with its old name. Here, the new variable will also have the name data.

With save You can also save several objects in one file. Now, if you do load "data. Rdata" , you will have two more objects in your workspace, namely data and data2. This is the second option of saving R objects.

Now, you have another R object in your workspace which is an exact copy of data. The compress parameter is also available for readRDS. Actually, I like saveRDS better - for one specific reason that you might not have noticed in the calls above. When we use load , we do not assign the result of the loading process to a variable because the original names of the objects are used. When we use readRDS , we have to assign the result of the reading process to a variable.

This might mean more typing but it also has the advantage that you can choose a new name for the variable to integrate it in into the rest of the new script more smoothly. The only advantage of save really is that you can save several objects into one file - but in the end it might be better to have one file for one object. This might be more clearly organized. Whenever you are not so who will work with the data later on and whether these people are all using R, you might want to export your dataset as a CSV file.

Also, if you provide a dataset on some website e. You can think of write. If you will be loading more than one data file, you can change the default factoring behavior at the global level with:. This will ensure that all strings will be read as strings, not as factors, until you end your R session, or rechange the global default by running:. R also comes with some prepackaged short cuts for read.

The first shortcut, read. These are files where each cell is separated by a tab. These functions tell R that the data uses a comma instead of a period to denote decimal places.

One type of plain-text file defies the pattern by using its layout to separate data cells. Each row is placed in its own line as with other plain-text files , and then each column begins at a specific number of characters from the lefthand side of the document.

To achieve this, an arbitrary number of character spaces is added to the end of each entry to correctly position the next entry. These documents are known as fixed-width files and usually end with the extension. In each row, the suit entry begins exactly 10 characters from the start of the line. Fixed-width files look nice to human eyes but no better than a tab-delimited file ; however, they can be difficult to work with. Perhaps because of this, R comes with a function for reading fixed-width files, but no function for saving them.

You can read fixed-width files into R with the function read. The function takes the same arguments as read. If the aforementioned fixed-width royal flush data was saved as poker. Many data files are made available on the Internet at their own web address. If you are connected to the Internet, you can open these files straight into R with read. Just make sure that the web address links directly to the file and not to a web page that links to the file.

Once your data is in R, you can save it to any file format that R supports. The three basic write functions appear in Table D. Use write. The first argument of each function is the R object that contains your data set. The file argument is the file name including extension that you wish to give the saved data. By default, each function will save your data into your working directory. However, you can supply a file path to the file argument.

R will oblige by saving the file at the end of the file path. For example, you can save the hypothetical poker data frame to a subdirectory named data within your working directory with the command:. Keep in mind that write. Each folder in the file path must exist before you try to save a file with it. The row. You might have noticed that R automatically names each row in a data frame with a number.

For example, each row in our poker data frame appears with a number next to it:. These row numbers are helpful, but can quickly accumulate if you start saving them. R will add a new set of numbers by default each time you read the file back in. Avoid this by always setting row. To compress a plain-text file, surround the file name or file path with the function bzfile , gzfile , or xzfile.

Each of these functions will compress the output with a different type of compression format, shown in Table D. For example, you could read a compressed file named poker. R provides two file formats of its own for storing data,. RDS and. For example, if the royal flush data was saved as poker. RDS , you could open it with:. The R objects in your RData file will be loaded into your R session with their original names. RData files can contain multiple R objects, so loading one may read in multiple objects.

If worse comes to worst, you can keep an eye on the environment pane in RStudio as you load an RData file. It displays all of the objects that you have created or loaded during your R session.

Another useful trick is to put parentheses around your load command like so, load "poker. This will cause R to print out the names of each object it loads from the file.

If your file is in your working directory, the file path will be the file name. RData files can store multiple R objects at once, but RDS files are the better choice because they foster reproducible code. To save data as an RData object, use the save function. In each case, the first argument should be the name of the R object you wish to save.

You should then include a file argument that has the file name or file path you want to save the data set to. For example, if you have three R objects, a , b , and c , you could save them all in the same RData file and then reload them in another R session:. However, if you forget the names of your objects or give your file to someone else to use, it will be difficult to determine what was in the file—even after you or they load it.

The user interface for RDS files is much more clear. You can save only one object per file, and whoever loads it can decide what they want to call their new data. Saving your data as an R file offers some advantages over saving your data as a plain-text file. R automatically compresses the file and will also save any R-related metadata associated with your object.

This can be handy if your data contains factors, dates and times, or class attributes. On the other hand, R files cannot be read by many other programs, which makes them inefficient for sharing. Microsoft Excel is a popular spreadsheet program that has become almost industry standard in the business world. There is a good chance that you will need to work with an Excel spreadsheet in R at least once in your career. You can read spreadsheets into R and also save R data as a spreadsheet in a variety of ways.

The best method for moving data from Excel to R is to export the spreadsheet from Excel as a. Not only will R be able to read the text file, so will any other data analysis software.

Text files are the lingua franca of data storage. The "Environment" tab also has a diskette save icon, which will save R objects. Ronaldo Urtiz Explainer. How do you stop an R session?

Go the the processes tab. Select the R session process rsession. Hit Ctrl-z to move R to the background. Run ps and find the process id. Run kill [id] to kill the process if it is still not stopping use kill -KILL [id]. Erondina Sacaluga Explainer. How do I use Setwd in R? Godstime Baladron Pundit. How do I update R version? Here is how to use it. Step 1: load installr. Lahcen Salavessa Pundit. Select File. Input parameters.

Column Selection. Preview and Import. Yackeline Davidovich Pundit. What is R data file? The RData format usually with extension. Arane Olderdissen Teacher. How do you calculate mean in R? It is calculated by taking the sum of the values and dividing with the number of values in a data series. The function mean is used to calculate this in R. Maliki Iozzino Teacher.

How do I import Excel data into R? On Windows system. Vasudeva Andreichin Teacher. How do I make a histogram in R? How to Make a Histogram with Basic R. Since histograms require some data to be plotted in the first place, you do well importing a dataset or using one that is built into R. Want To Go Further? Francesca Villela Teacher. What is an R function? In R , a function is an object so the R interpreter is able to pass control to the function , along with arguments that may be necessary for the function to accomplish the actions.

The function in turn performs its task and returns control to the interpreter as well as any result which may be stored in other objects. Radya Heindorf Reviewer.