INTRODUCTION The dplyr is an R-package that is used for transformation and summarization of tabular data with rows and columns. It includes a set of functions that filter rows, select specific columns, re-order rows, adds new columns and summarizes data. Moreover, dplyr contains a useful function to perform another common task, which is the “split-apply-combine” […]
data manipulation
dplyr Non-Standard Evaluation Exercises
dplyr is a great package for interactive data wrangling and exploration. One of key aspects that makes it so great is that it uses non-standard evaluation so a user does not have to repeat data frame name and quote names all the time. On the other hand this feature makes programming with dplyr a non-trivial […]
dplyr basics: More smooth data exploration
Anywhere you look at R code these days, dplyr seems to be there – indeed data indicate that its popularity is growing relative to many common R packages. Influential data scientists have recommended that beginners start “from scratch with the dplyr package for manipulating a data frame” leaving for later standard R subsetting and loops. […]
R with remote databases Exercises (Part-2)
This is common case when working with data that your source is a remote database. Usual ways to cope this when using R is either to load all the data into R or to perform the heaviest joins and aggregations with SQL before loading the data. Both of them have cons: the former one is […]
Data wrangling : Cleansing – Regular expressions (3/3)
Data wrangling is the process of importing, cleaning, and transforming raw data into actionable information for analysis. It is a time-consuming process that is estimated to take about 60-80% of analysts’ time. In this series, we will go through this process. It will be a brief series with the goal of crafting the reader’s skills […]
R with remote databases Exercises (Part-1)
This is common case when working with data that your source is a remote database. Usual ways to cope this when using R is either to load all the data into R or to perform the heaviest joins and aggregations with SQL before loading the data. Both of them have cons: the former one is […]
Beyond the basics of data.table: Smooth data exploration
This exercise set provides practice using the fast and concise data.table package. If you are new to the syntax it is recommended that you start by solving the set on the basics of data.table before attempting this one. We will use data on used cars (Toyota Corollas) on sale during 2004 in the Netherlands. There […]
Data wrangling : Cleansing – Regular expressions (2/3)
Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be a brief series with goal to craft the reader’s skills on […]
Protected: Bonus: Dummify your Data Exercises
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Big Data analytics with RevoScaleR Exercises
In this set of exercise , you will explore how to handle bigdata with RevoscaleR package from Microsoft R (previously Revolution Analytics).It comes with Microsoft R client . You can get it from here . get the Credit card fraud data set from revolutionanalytics and lets get started Answers to the exercises are available here.Please […]
