Marginal distributions vs. bivariate associations

Marginal Distributions Bivariate Associations
Description The distribution of each variable individually (i.e., without reference to the values of the other variables). Describing the association between two numeric variables.
Visually Plot each variable individually.

You could use, for example, geom_density() for a density plot, geom_boxplot() for a boxplot or geom_histogram() for a histogram to comment on and/or examine:
  • The shape of the distribution. Look at the shape, centre and spread of the distribution. Is it symmetric or skewed? Is it unimodal or bimodal?
  • Identify any unusual observations. Do you notice any extreme observations (i.e., outliers)?
Plot associations among two variables.

You could use, for example, geom_point() for a scatterplot to comment on and/or examine:
  • The direction of the association indicates whether there is a positive or negative association
  • The form of association refers to whether the relationship between the variables can be summarized well with a straight line or some more complicated pattern
  • The strength of association entails how closely the points fall to a recognizable pattern such as a line
  • Unusual observations that do not fit the pattern of the rest of the observations and which are worth examining in more detail
Numerically    Compute and report summary statistics e.g., mean, standard deviation, median, min, max, etc.

You could, for example, calculate summary statistics such as the mean (mean()) and standard deviation (sd()), etc. within summarize() 		Compute and report the correlation coefficient.

You can use the cor() function to calculate this