Plot

For factors, a useful visualization is using the plot() function. This will show the counts of the individual values of the factor, e.g. “female”, “male” or “noun” and “verb” and allow you to see whether one group is more common in the data than the other. The plot below shows that the keyword analyzed is much more commonly preceded by a verb than by an adverb.

You can also use the built-in functions to visualize the dependencies between two variables. You will usually need to specify the predicting (or independent) variable and the predicted (or dependent) one.

To visualize the dependency of two numeric variables on each other (e.g. reading skill on speech rate), plot() can be used again. This time, however, you need to provide the function with two arguments – one to plot on the x-axis (the independent variable) and one for the y-axis (the dependent variable). On the example data, you can try plotting the score achieved in a reading test against the speech rate, by typing in plot(data$reading_skill, data$speech_rate), assuming you named the data frame “data.” This will yield the following picture, hinting that there might be a relation between the two variables: as one increases, so does the other.

Note, however, that the fact that two variables seem to be related does not mean that they are - there may always be a third variable which predicts both of them.