PRDV420: Introduction to R Programming

Now let's try a quick practice exercise creating variables. This exercise does not count toward your grade. It is just for practice!

This practice aims to make you more familiar with the string format and operations we often need to match or subset strings while preparing data for analysis. Try to solve these string operations tasks. This exercise does not count toward your grade. It is just for practice!

In these exercises, you will practice operations with factors needed for implementing the analysis of variance (ANOVA) analysis or drawing a boxplot. Also, try applying the function table(X) to some factor X in your R environment – it is the function that quickly counts the number of occurrences of each element in X. These exercises do not count toward your grade. They are just for practice!

The type of your data in R can be changed. Sometimes some other function you apply automatically changes the type internally, while the data object you supplied remains unaffected. For example, if x is a character object, lm(y ~ x) will treat x as a factor; x will remain the type character in the R environment. In other cases, to count the total or proportion of certain instances using a logical vector LV, you can apply sum(LV) or mean(LV) knowing that the logical values TRUE and FALSE will be treated as 1 and 0 by these functions. Please pay attention to these coercion rules.

This exercise shows how easy it is to work with vectors in R and modify and reorganize the data in vectors. In the exercise, you will create and manipulate a vector, then save elements 5-10 of your vector as a new (separate) vector. This exercise does not count toward your grade. It is just for practice!

The video demonstrates the differences between matrices and arrays and how these objects' elements can be accessed or subsetted. 

These exercises test your knowledge of creating, accessing, and manipulating arrays and matrices. These exercises do not count toward your grade. They are just for practice!

Lists are used to hold elements of different sizes and types, such as outputs of a regression model fit or results of a statistical test. However, if we restrict list elements to vectors of the same length, we can get a data.frame. The data.frame structure is in-between a matrix (data.frame has columns and rows and can be indexed as a matrix) and a list (each column in a matrix is a list element and can be indexed accordingly). The data.frame structure is convenient for holding typical spreadsheet data, where each column can be of a different type, for example, Date (Date class), Location (character type), and Temperature (numeric type).

We'll practice with the data frame format, which is the usual format for storing information on different variables. We'll practice the extensions of this format later. Use the cats data frame to solve these challenges

cats <- data.frame(coat = c("calico", "black", "tabby"),
                    weight = c(2.1, 5.0, 3.2),
                    likes_string = c(1, 0, 1))

The tibble format belongs to the family of packages "tidyverse" and attempts to make operations with data.frame-like structures more user-friendly. The tidyverse conveniently aggregates several popular packages, such as ggplot2 for plotting and dplyr for data manipulation. You can convert a data.frame to tibble and back if needed.

Try working with tibbles in these exercises. Remember, tibbles are just another form of the data.frame format. Do you find tibbles more convenient to use? This exercise does not count toward your grade. It is just for practice!

This is the final practice of data frame formats. Now you should be familiar with the three main ones: data.frame, data.table, and tibble. Repeat the practice tasks for tibbles now using the data.table format instead. This exercise does not count toward your grade. It is just for practice!