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Regular Expressions Exercises – Part 1

October 30, 2016
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Regular Expressions Exercises – Part 1

A common task performed during data preparation or data analysis is the manipulation of strings. Regular expressions are meant to assist in such and similar tasks. A regular expression is a pattern that describes a set of strings. Regular expressions can range from simple patterns (such as finding a single number) thru complex ones (such

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Creating Sample Datasets – Exercises

October 7, 2016
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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

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

October 4, 2016
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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

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Dates and Times – Simple and Easy with lubridate exercises (part 2)

August 29, 2016
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Dates and Times – Simple and Easy with lubridate exercises (part 2)

This is the second part in the series teaching the “lubridate” package. As a short recap from the previous part, I mentioned that date/date_time formats are easily parced using the ymd set of functions (for example, dmy, ymd_h, etc). I also explained that arithmetic calculations are performed using the days, months, years, etc. functions. In

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Dates and Times – Simple and Easy with lubridate exercises (part 1)

August 15, 2016
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Dates and Times – Simple and Easy with lubridate exercises (part 1)

As in any programming language, handling date and time variables can be quite frustrating, since, for example, there is no one single format for dates, there are different time zones and there are issues such as daylight saving time. Base R provides several packages for handling date and time variables, but they require mastering cumbersome

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