To stay on top of R in the news, we’re sharing some stories related to R published last week. Interactive Data Visualization Using the Shiny App!(Saurav Kaushik) This is one of the best tutorials on Shiny that I have seen, and anything that says interactive next to data visualization really should get the top story […]

## Network Analysis Part 3 Exercises

This is the third set of exercises on networks in which we practice the functions for graph structure, using package igraph. The first and second part are available here: Part 1 Part 2 If you don’t have package already installed, install it using the following code: install.packages(“igraph”) and load it into the session using the […]

## Descriptive Analytics – Part 0 : data exploration

Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”. This is the first set of exercise of a series of exercises that aims to provide a descriptive analytics solution to the ‘2008’ data set from here. Download it and save it as a csv file. This […]

## Two Way ANOVA in R Exercises

One way analysis of variance helps us understand the relationship between one continuous dependent variable and one categorical independent variable. When we have one continuous dependent variable and more than one independent categorical variable we cannot use one way ANOVA. When we have two independent categorical variable we need to use two way ANOVA. When […]

## The R-Studio Founder, Debate Language and Who is the most Active Data Scientist?

To stay on top of R in the news, we’re sharing some stories related to R published last week. A great interview with JJ Allaire, creator of RStudio.(Joseph Rickert) The man who build RStudio now 13 years ago shares some insight on the company and his own motivation. Or was it a company? we are […]

## Optimize Data Exploration With Sapply() – Exercises

The apply() functions in R are a utilization of the Split-Apply-Combine strategy for Data Analysis, and are a faster alternative to writing loops. The sapply() function applies a function to individual values of a dataframe, and simplifies the output. Structure of the sapply() function: sapply(data, function, …) The dataframe used for these exercises: dataset1 <- […]

## Big Salaries, Recommendation Systems, and Where We’ll Be 5 Years from Now

To stay on top of R in the news, we’re sharing some stories related to R published last week. Why Data Science ‘Rock Stars’ Earn Big Salaries (Dennis McCafferty) Recent post and slide deck related to the 2016 Data Science Salary Survey (O’Reilly Media), with R mentioned as one of the high-demand programming languages (next […]

## How can we improve R-exercises?

Hey there! We’ve been sharing R exercise sets for about a year, and think this is a good moment to reflect and ask for your feedback. So here is your opportunity to have a say in where we take R-exercises next! We’d like to hone in a bit on the degree of difficulty of the […]

## Creating Sample Datasets – Exercises

Creating sample data is a common task performed in many different scenarios. R has several base functions that make the sampling process quite easy and fast. Below is an explanation of the main functions used in the current set of exercices: 1. set.seed() – Although R executes a random mechanism of sample creation, set.seed() function […]

## Dates and Times – Simple and Easy with lubridate Exercises (part 3)

Welcome to the third and last part of the “lubridate” exercises. If you missed Part 1 and 2 then please refer to the links below: Part 1 Part 2 In this part, I’ll cover the following topics: 1. Durations (exact spans of time) 2. Periods (relative spans of time) 3. Rounding dates As always, in […]