How to download data files r

The base R function download. Apply download. Have a look at the folder that you have specified as file destination. You should find the downloaded data in csv format:. Figure 2: Downloaded csv File in Folder on Computer. Note: R allows for the download of any file format you want. In the previous example, we have downloaded a csv file. Furthermore, it is possible to download files from a sharepoint or a web application such as shiny.

Do you need further guidance for the downloading of files from the web? Both users will use the library xlsx to create an Excel file. The slight difference comes from the installation of the library. Indeed, the library xlsx uses Java to create the file. Java needs to be installed if not present in your machine for Data R export to Excel. If you are a Windows user, you can install the library directly with conda to export dataframe to excel R:.

Once the library installed, you can use the function write. A new Excel workbook is created in the working directory for R export to Excel data.

Step 2 You need to install rjava in R. We recommended you to install R and Rstudio with Anaconda. Anaconda manages the dependencies between libraries. In this sense, Anaconda will handle the intricacies of rJava installation. First of all, you need to update conda and then install the library. You can copy and paste the next two lines of code in the terminal.

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. Good—this is what I want. That means each row in ExOffndrs has two links associated with their name. The stringr package can help me wrangle this long vector into the ExOffndrs tibble.

Finally, add the first portion of the url used above. Now I want the offender information links so I omit the links with last in the pattern. Some of the url s end in. Test a few of these out in the browser:. 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.