Visualize Univariate Data – BOX plot in R
In R, a box plot is a useful tool for visualizing univariate data, or data that has only one variable. A box plot is a graph that uses boxes to represent the distribution of the data and to identify any potential outliers.
To create a box plot in R, you can use the boxplot() function. This function takes a vector of values as an argument and returns a box plot of the data. You can also customize the appearance of the plot by adding additional arguments to the function.
For example, if you have a variable called “var1” in your dataset, you can create a box plot of the data by using the command boxplot(var1)
You can also use the ggplot2 library in R to create a box plot. ggplot2 is a powerful data visualization package that provides a lot of customization options for creating box plots.
For example, if you have a variable called “var1” in your dataset, you can create a box plot of the data by using the command ggplot(data, aes(x=var1)) + geom_boxplot()
In summary, In R, a box plot is a useful tool for visualizing univariate data, or data that has only one variable. A box plot is a graph that uses boxes to represent the distribution of the data and to identify any potential outliers. To create a box plot in R, you can use the boxplot() function, which takes a vector of values as an argument and returns a box plot of the data. You can also customize the appearance of the plot by adding additional arguments to the function. Alternatively, you can use the ggplot2 library in R to create a box plot, which is a powerful data visualization package that provides a lot of customization options for creating box plots.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Visualize Univariate Data – BOX plot in R.
Visualize Univariate Data – BOX plot in R
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