We can create faceted plots and combine multiple visualizations. Faceted Plot Combining Plots You can also use the patchwork package to combine multiple plots:
Time Series Analysis
Let’s analyze a simple time series dataset using the forecast package. Step 1: Install and Load Forecast If you don’t have forecast installed, you can install it: Then, load the library: Step 2: Create a Time Series Dataset We’ll create a sample time series dataset: Step 3: Decompose the Time Series Step 4: Forecasting
Machine Learning with Caret
We’ll demonstrate a basic machine learning workflow using the caret package for building a predictive model. Step 1: Install and Load Caret If you don’t have caret installed, you can do so with: Then, load the library: Step 2: Create a Sample Dataset We’ll use the same dataset but add a binary outcome variable to predict: Step 3: Split the Dataset Split the data into training and testing sets: Step 4: Train a Model We’ll train a simple logistic regression model: Step 5: Make Predictions Use the model to make predictions on the test set:
Hypothesis Testing
We can perform a t-test to compare the means of weight between two age groups. Step 1: Create Age Groups Step 2: Conduct a t-test
What is vector recycling in R?
If we try to perform some operation on two R vectors with different lengths, the R interpreter detects under the hood the shorter one, recycles its items in the same order until the lengths of the two vectors match, and only then performs the necessary operation on these vectors. Before starting vector recycling, though, the R interpreter throws a warning message about the initial mismatch of the vectors’ lengths. For example, if we try to run the following addition: The second vector, due to the vector recycling, will actually be converted into c(1, 2, 3, 1, 2). Hence, the final result of this operation will be c(2, 4, 6, 5, 7). While sometimes vector recycling can be beneficial (e.g., when we expect the cyclicity of values in the vectors), more often, it’s inappropriate and misleading. Hence, we should be careful and mind the vectors’ lengths before performing operations on them.
Advanced Visualization with ggplot2
Let’s create more complex visualizations, such as a boxplot and a density plot. Boxplot Density Plot
Data Manipulation with dplyr
In this example, we’ll use the dplyr package for data manipulation. We’ll filter, summarize, and arrange data. Step 1: Install and Load dplyr If you don’t have dplyr installed yet, you can install it with: Then, load the library: Step 2: Create a Sample Dataset We’ll continue using the previous dataset or create a new one: Step 3: Data Manipulation
What types of data plots can be created in R?
Being data visualization one of the strong sides of the R programming languages, we can create all types of data plots in R: The skill track Data Visualization with R will help you broaden your horizons in the field of R graphics. If you prefer to learn data visualization in R in a broader context, explore a thorough and beginner-friendly career track Data Scientist with R.
Statistical Analysis
We can perform a linear regression analysis to understand the relationship between height and weight.
Data Visualization
Using the ggplot2 package, we can create a scatter plot to visualize the relationship between height and weight.