In this tutorial, you will be introduced to the command line. We have selected a set of commands we think will be useful in general to a wide range of audience. We have created a RStudio Cloud Project to ensure that all readers are using the same environment while going through the tutorial. Our goal was to ensure that after completing this tutorial, readers should be able to use the shell for version control, managing cloud services (like deploying your own shiny server etc.
Introduction In a previous post, we had briefly looked at connecting to databases from R and using dplyr for querying data. In this new expanded post, we will focus on the following: connect to & explore database read & write data use RStudio SQL script & knitr SQL engine query data using dplyr visualize data with dbplot modeling data with modeldb & tidypredict explore RStudio connections pane handling credentials Resources Below are the links to all the resources related to this post:
Introduction In a previous post, we had introduced our R package rfm but did not go into the conceptual details of RFM analysis. In this post, we will explore RFM in much more depth and work through a case study as well. RFM (Recency, Frequency & Monetary) analysis is a behavior based technique used to segment customers by examining their transaction history such as:
Motivation There are several wonderful tools for retrieving information about R packages, some of which are listed below: cranlogs, dlstats and packageRank for R package download stats pkgsearch and packagefinder for searching CRAN R packages crandb provides API for programatically accessing meta-data cchecks for CRAN check results We have used some or all of these to track/monitor our own R packages available on CRAN. Over time, we wanted to have a single interface which would retrieve information from different places including:
Introduction In this post, we will learn about using regular expressions in R. While it is aimed at absolute beginners, we hope experienced users will find it useful as well. The post is broadly divided into 3 sections. In the first section, we will introduce the pattern matching functions such as grep, grepl etc. in base R as we will be using them in the rest of the post. Once the reader is comfortable with the above mentioned pattern matching functions, we will learn about regular expressions while exploring the names of R packages by probing the following:
Introduction Ever wondered why items are displayed in a particular way in retail/online stores. Why certain items are suggested to you based on what you have added to the cart? Blame it on market basket analysis or association rule mining. Resources Below are the links to all the resources related to this post: Slides Code & Data RStudio Cloud What? Market basket analysis uses association rule mining under the hood to identify products frequently bought together.
We had published a web scraping tutorial a couple of days back and it had received a good response from the #rstats community. While we thank you for that, we made a mistake in choosing one of the case study as pointed out by @hrbrmstr in this tweet: Whomever runs "R Squared Academy" needs to _really_ learn more about web scraping. https://t.co/jOQRAxFVro clearly prohibits the activity in their recent blog post and puts #rstats users in harm's way.
Introduction Are you trying to compare price of products across websites? Are you trying to monitor price changes every hour? Or planning to do some text mining or sentiment analysis on reviews of products or services? If yes, how would you do that? How do you get the details available on the website into a format in which you can analyse it? Can you copy/paste the data from their website?
We are excited and happy to share a set of shiny apps built for interactive data analysis and teaching at Rsquared Academy. The apps are part of our R packages and presently cover the following topics: Descriptive Statistics Probability Distributions Hypothesis Testing Linear Regression Logistic Regression RFM Analysis Data Visualization We would suggest that you explore the apps using sample data sets available within the app before using your own data set so that you get comfortable with the user interface.
We are happy to introduce the vistributions package, a set of tools for visually exploring probability distributions. Installation # Install release version from CRAN install.packages("vistributions") # Install development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/vistributions") Shiny App vistributions includes a shiny app which can be launched using vdist_launch_app() or try the live version here. Read on to learn more about the features of vistributions, or see the vistributions website for detailed documentation on using the package.