You can see that this function is really great when you want to know more about the data that you are working with. Now you know how to see more information about the data frame. But the magic of data frames lies in the amazing capabilities and functionality that they offer, so let's see this in more detail.
Data frames are like matrices, so you can access individual values using two indices surrounded by square brackets and separated by a comma to indicate which rows and which columns you would like to include in the result, like this:. Variables of type "factor" have "levels" that represent the different categories or values that they can take.
We can also use this syntax to access a range of rows and columns to get a portion of the original matrix, like this:. For example, if we want to get the age and number of siblings of the third, fourth, and fifth student in the list, we would use:.
Note that these indices are inclusive, so the third and fifth elements are included in the example above when we write If we want to get all the rows or columns, we simply omit the interval and include the comma, like this:. You can filter the rows of a data frame to get a portion of the matrix that meets certain conditions.
For this, we use this syntax, passing the condition as the first element within square brackets, then a comma, and finally leaving the second element empty. You can modify individual values of a data frame, add columns, add rows, and remove them. Let's see how you can do this!
For example, if we want to change the value that is currently at row 4 and column 6, denoted in blue right here:. You need to add this argument to the first line of code that reads the CSV file and then make changes to it. When you assign the value Null to a column, that column is removed from the data frame automatically. To remove rows from a data frame, you can use indices and ranges. For example, to remove the first row of a data frame:. The [-1,] takes a portion of the data frame that doesn't include the first row.
Then, this portion is assigned to the same variable. I really hope that you liked my article and found it helpful. Step 3 Finally, it is time to install xlsx. Once again, you can use conda to do it:. Exporting data to different software is as simple as importing them. First of all, import the library. Last but not least, R is equipped with fantastic libraries to interact with the cloud computing services. Note : This part of the tutorial assumes you have an account with Google and Dropbox.
You need to install the library googledrive to access the function allowing to interact with Google Drive. The library is not yet available at Anaconda. You can install it with the code below in the console. For non-conda user, installing a library is easy, you can use the function install. It is worth to see it in action. Google successfully uploaded the file located locally on the Drive.
Google assigned an ID to each file in the drive. Upload a file from Google Drive with the ID is convenient. If you know the file name, you can get its ID as follow:. Note : Depending on your internet connection and the size of your Drive, it takes times. You stored the ID in the variable x.
The file is stored in your working directory. It also explains how to import this data to R or RStudio. Please accept YouTube cookies to play this video. By accepting you will be accessing content from YouTube, a service provided by an external third party.
YouTube privacy policy. Accept YouTube Content. In addition, you might also want to have a look at the other R tutorials on this website. This article explained how to download data from the internet with the download. I case you have any further questions, let me know in the comments. I think the code shown in this tutorial is still useful, because it creates a copy of the data on your computer. Storing the data in an excel sheet is the most common practice in many companies.
In the majority of firms, people are storing data as comma-separated-values CSV , as the process is easier than creating normal spreadsheets. Being the most popular and powerful statistical analysis programming language, R offers specific functions to read data into organized data frames from a CSV file. The first thing in this process is to getting and setting up the working directory. You need to choose the working path of the CSV file.
Here you can check the default working directory using getwd function and you can also change the directory using the function setwd.
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