The attach() function alters the R environment search path by making dataframe variables into global variables. If incorrectly scripted, the attach() function might create symantic errors. To prevent this possibility, detach() is needed to reset the dataframe objects in the search path. The transform() function allows for transformation of dataframe objects. The within() function creates […]

# Exercises

## Cross Tabulation with Xtabs exercises

The xtabs() function creates contingency tables in frequency-weighted format. Use xtabs() when you want to numerically study the distribution of one categorical variable, or the relationship between two categorical variables. Categorical variables are also called “factor” variables in R. Using a formula interface, xtabs() can create a contingency table, (also a “sparse matrix”), from cross-classifying […]

## Lattice exercises – part 1

In the exercises below we will use the lattice package. First, we have to install this package with install.packages(“lattice”) and then we will call it library(lattice) . The Lattice package permits us to create univariate, bivariate and trivariate plots. For this set of exercises we will see univariate and bivariate plots. We will use a […]

## Complex Tables – Exercises

The ftable() function combines Cross-Tabulation with the ability to format , or “flatten”, contingency tables of 3 or more dimensions. The resulting tables contain the combined counts of the categorical variables, (also factor variables in R), that are then arranged as a matrix, whose rows and columns correspond to the original data’s rows and columns. […]

## Data Exploration with Tables exercises

The table() function is intended for use during the Data Exploration phase of Data Analysis. The table() function performs categorical tabulation of data. In the R programming language, “categorical” variables are also called “factor” variables. The tabulation of data categories allows for Cross-Validation of data. Thereby, finding possible flaws within a dataset, or possible flaws […]

## Merging Dataframes Exercises

When combining separate dataframes, (in the R programming language), into a single dataframe, using the cbind() function usually requires use of the “Match()” function. To simulate the database joining functionality in SQL, the “Merge()” function in R accomplishes dataframe merging with the following protocols; “Inner Join” where the left table has matching rows from one, […]

## 3D plotting exercises

In this set of exercises we will introduce the concept of 3D plotting. Specifically, we will use these commands:image(), contour() and persp(). For these exercises, you need to have a basic understanding of R objects and functions, in particular some knowledge about matrix . This set is the fourth set of exercises is a series […]

## Graphics parameters exercises

In the exercises below we practice how to personalize graphics parameters, how to produce different plots at the same time and how to save a plot in a file. We will use commands such as par and jpeg. We will use the mtcars dataset, provided by R Cran (we can upload the dataset by typing […]

## Lets Begin with something sample

The following R-exercises constitute the first set in a series of posts aimed to review fundamental probability and (bio)statistics concepts while learning to use R. Today we will focus on generating random numbers from some of the built-in distributions in R as well as using the sample() function to obtain random samples from a given […]

## Data table exercises: keys and subsetting

The data.table package is a popular R package that facilitates fast selections, aggregations and joins on large data sets. It is well-documented through several vignettes, and even has its own interactive course, offered by Datacamp. For those who want to build some mileage practising the use of data.table, there’s good news! In the coming weeks, […]