R Tutorial: Plotting a single variable

Key Takeaways

in the previous lesson you learned how to convert your static ggplot2 plots in interactive plotly charts not all ggplot objects can be converted to plot Li objects and sometimes you want more control over how your graphics are rendered in this lesson we'll explore how to create histograms and bar charts using plot Li as first examples of univariate graphics we'll explore the distribution of the wine types and phenols using the wine data set from the previous lesson to explore the distribution of wine type a categorical variable we use a bar chart displaying the number of wines of each type there are three fundamental parts to a plot li graphic first we have the data set here we calculate a frequency table giving the number of wines of each type using the count command we then pass this summarized data set to the plot underscore ly command which creates our base layer similar to the GG plot function the second piece is the mapping of the variables in the data set to aesthetics in the graph here we specified the mappings using Tilda's x equals tilde type y equals tilde n telling the plot which variable defines each aesthetic third we specified the plot type by adding a trace similar to how we added geometry in ggplot2 to create a bar chart we add the pipe operator percent greater than percent after the plot underscore ly base layer and specify ad underscore bars with only three wine types our bar chart was easy enough to read however with more categories bar charts can become difficult to read unless the bars are sorted for example we may wish to rearrange the bars in descending order to do this we use the FC t underscore reorder command found in the for cats package to sort the bars in descending order we add a single line of code to our data plot pipeline mutate creates a new variable type an FC T underscore reorder reorders the levels of type by the values in n to organize the levels in descending order we add the argument dot des c equals true to explore the distribution of phenols a numeric variable we use a histogram displaying the number of wines with phenols falling into equal width bins we again need to specify three parts first we pipe the wine data set into the plot underscore ly command next we specify x equals tilde phenols indicating that phenols should be plotted on the x-axis finally we add the histogram trace using the add underscore histogram command notice that we do not need to specify a variable for the y-axis here since plotly calculates the frequency for each bin in the background whenever you create a histogram it's important to explore different thinning schemes since bins that are too wide may mask interesting features of the data and bins that are too small provide little insight there are two ways to adjust the binning scheme and plot Lee the first is to change the number of bins displayed by adding the n bins X argument to the add underscore histogram command here we specify that 10 bins should be displayed the second way to change the binning is to specify the exact values for the bins here we specify X bins equals lists parentheses start equals 0.8 end equals 4 and size equals 0.25 resulting in bins of with 0.25 spanning from point 8 to 4 if you specify the exact values for the bins be sure to look at the summary of the variable first so that you choose a logical start and end value