Exercise

Variables inspection

Now that we've added several new variables to abaloneMod, the next set of exercises will explore the quality of the data using summary statistics and graphical visualization.

You will also filter() out cases (rows in the abaloneMod dataset) that have errors or illogical values using the dplyr::filter() function. For example, there are a few abalones that have a height of 0 mm which is incorrect due to possible typographical mistakes or measurement errors.

The abaloneMod dataset has been loaded for you along with the dplyr and ggplot2 packages. After filtering out cases, you will create a new modified copy of the dataset called abaloneKeep which will have the final cases kept for analysis in future lessons.

Instructions

100 XP
  • Get summary statistics for abalone heights.
  • Keep cases with heights greater than 0 and assign these cases to new dataframe abaloneKeep.
  • For abalones kept in abaloneKeep, make a histogram of heights which should all now be greater than 0.