In this tutorial, we will learn to handle date & time in R. We will start off by learning how to get current date & time before moving on to understand how R handles date/time internally and the different classes such as Date & POSIXct/lt. We will spend some time exploring time zones, daylight savings and ISO 8001 standard for representing date/time. We will look at all the weird formats in which date/time come in real world and learn to parse them using conversion specifications.
We are excited to announce the nse2r package. NSE (National Stock Exchange) is the leading stock exchange of India, located in the city of Mumbai. While users can manually download data from NSE through a browser, importing this data into R becomes cumbersome. The nse2r R package implements the retrieval of data from NSE and aims to reduce the pre-processing steps needed in analyzing such data. nse2r is inspired by and a port of the Python package nsetools.
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?