In this post we look at How to Use data.table package in R language. This package is used in the data analysis and it can do all the tasks that data.frame can do in the r language.
You can create data table and store the data structure in a tabular form using this package. I have covered some of the interesting data package previously which includes a coverage of the dataset called the Iris dataset in R.
I also covered few of the brief package updates on the Torch package. This package is in the Machine Learning. So I thought i should also cover the data analysis packages like data.table package. It would be cool to try out this package today.
I recommend you to download the RStudio IDE for R Language. It is a good IDE for you to try out and also you can use it for various operations which can make things a bit easier with the data handling and analysis. So for this tutorial I have used it.
I have created a video to give you an overview on how to use the data.table Package for the data science work. You should give this one a try.
First thing we are going to be doing is installing the package - data.table. After we install the package we are going to be making use of creating the tabular data structure. It's a good way for you to properly put out the data table.
Use the below code for installing the package.
install.packages(data.table)
You can use the package using the library.
library(data.table)
Now that we have the data table package imported you can use the below code for creating the data table structure. Check the image below for the code.
Lot of interesting things you can do with the prebuilt and default packages and also you can use it for further data analysis. You can try one of the example for say IRIS.
library(data.table)
DT = as.data.table(iris)
You can also take a look at the data.table cheetsheet for you to try out on the data.
You can find more cool tutorials from the package documentation as well here.
This package is pretty basic but has a lot of use cases. Like you can use it for most of the data analysis and the data cleanup usage work. I have seen many people making use of this package for the design and also extend it with tidyverse package.
I feel like covering the tidyverse package as it can help me with the design and the work of the content. However it seems like the other packages which I have not covered yet needs to be covered too. So I would be covering them on the basis of the use case. Hopefully you would find them useful.
In upcoming tutorials, I'd be covering most of the data and the plot related packages which you would find useful if you are making use of it for the data science work. Also those who like using the data visualization libraries would appreciate upcoming tutorials.
I hope that you are enjoying some of my R language and data science tutorials. I hope that some of my coverage on this topic would be helpful to you.
If you happen to like this content, do give me feedback over there and that would help me improve my efforts in near future.
