Lists (aka recursive vectors) are the main data structure in R. Since lists are omnipresent (data.frames are a special sybtype) having a deeper understanding of them will make for a more enjoyable data analysis and helps avoid bugs. Solutions are available here. Exercise 1 Create a list called x with two elements; two vectors of […]

# data manipulation

## Functional Programming With Purrr: Exercises (Part 2)

One of R`s cool features is functional programming. It makes development much easier and the code you write shorter and less prone to errors. There are a few tool kits for functional programming in R (with famous base`s apply functions family among them). In this set of exercises,you will familiarize yourself with basic functions from […]

## Functional Programming With Purrr: Exercises (Part 1)

One of R`s cool features is functional programming. It makes development much easier and the code you write shorter and less prone to errors. There are few tool kits for functional programming in R (with famous apply functions family among them). In this set of exercises, you will familiarize yourself with basic functions from the purrr […]

## Working With Factors: Exercises

Factor data type in R can be very painful to use, especially for beginners. Fortunately, like everything else, there are packages for working with factors. One of the packages is forcats by RStudio. In this set, you will have an opportunity to exercise it. Answers to the exercises are available here. Please, do all exercises […]

## Basic Time-Series Hydro-logical Data Analysis:Exercises (Part 1)

Time series dataset is a well-recognized data type for modeling and forecasting purposes. However, the application in R may vary depending on the research areas. In this exercise set, we will explore basic analysis for time series hydrological data in R . We will also introduce the hydroTSM package; a useful package in hydrology. Hydro TSM is […]

## How to tidy up your data set with tidyr

INTRODUCTION In general data analysis includes four parts: Data collection, Data manipulation, Data visualization and Data Insights. The tidyr package is one of the most useful packages for the second category of data manipulation as tidy data is the number one factor for a succesfull analysis. Tidy data means that every column stands for a […]

## Dplyr Basic Functions – Exercises

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” […]

## Protected: Bonus: Basic Data Exploration in Base R Exercises

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## How to use basic dplyr functions

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” […]

## 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 […]