So far we've covered reading data from a large number of sources, whether it's from different file types like the XML or JSON files, or whether it's different sources like HDFI, or MySQL. While I've covered a few of the highlights, I think that there's a large number of other places where you might wanna be able to get data from, and I have by no means comprehensively covered all of the different R packages that can be used to access those data. Roger has a really nice advertising video on how there's an R package for everything, kind of modeled on the Apple, there's an app for everything, Here I'm gonna briefly review a few useful packages I know about, but you should by no means consider this a comprehensive list. In general, I found the best way to find out if an R package exists is to Google data storage mechanism, followed by R package. You just time R, sometimes I haven't been able to find it with the search. So for example, I might type, my SQL R package, and that usually comes up with the very first taking, the most interesting package. I found that almost every kind of data type that you might wanna have access to, as long as its relatively common, there's a R package for accessing the data. If you wanna interact directly with files, we've seen how to use URL in getting data lectures so far, but you can also use the File function to access a connection to a particular file that's already localized on your computer. You could also use gzfile and bzfile to access zipped files that might be laying on your computer. And then, if you do question mark connections, you can learn a lot more about opening connections to files, both locally and remotely. The foreign package is very useful if you work with people who use other programming languages or other statistical programming languages. So, it loads data from files for Minitab, S, SAS, SPSS and a bunch of others. So, the basic functions read like read and then .foo, where foo is the extension for the particular file from the particular programming language. So, for example it's dta for Stata, and it it's an Octave file it's .octave. And if it's SPSS it's .spss. And then you can read .xport files from SAS, for example. If you want to see a lot more about this you can go read about the Foreign package in the help file which is pretty self explanatory, and I've been able to read most files from other programming languages. There's also a large number of other database packages, so for example PostgreSQL, I've provided DB-compliant database connection from R. So, here's a tutorial, actually at this first link. And then the second link is the help file. There's also the RODBC, which provides interfaces for multiple databases including PostgreQL, MySQL, Microsoft Access, and SQLite. There's a tutorial here that's very nice as well, and then a help file that will give you, walk you through most of the functions. If you use MongoDb, there's actually a very nice package called RMongo, and there's also another package, RMongoDb, which can be used to interface with MongoDb and extract data from that database. Again, you're gonna be sending queries very similar to the way you were doing with the R MySQL package, you're gonna actually send queries to the database using the database's own syntax. So, if you're gonna use the R package, you have to learn just a little bit about the syntax for the database to be able to extract data. You can also read other things that aren't necessarily data in the traditional format in terms of text files and so forth. You can read directly images with jpeg, or readbitmap, png. These are all, png, these are all functions that allow you to access image files that load them into R directly and manipulate them. The EBI image package in bioconductor is also very nice for analyzing and manipulating image data. You can read GIS data, so this is geographic information systems data. You can read it with a variety of different packages that will allow you to access and play with different kinds of GIS data that are exported by proprietary and not proprietary software. Another cool thing that you can read is directly from mp3. So you can use either the tuneR or the seewave packages to actually read musical data, and do analysis. There's a lot of nice musical processing software in R that you can use to analyze those data, and come up with interesting cool new analysis, as well. So like I said, this is just a list of the packages that I thought of off the top of my head, but whatever your application is, you can just Google the R package that will allow you to access the data. There's always an R package for that.
