Dropping levels
The contingency table from the last exercise revealed that there are some levels that have very low counts. To simplify the analysis, it often helps to drop such levels.
In R, this requires two steps: first filtering out any rows with the levels that have very low counts, then removing these levels from the factor variable with droplevels(). This is because the droplevels() function would keep levels that have just 1 or 2 counts; it only drops levels that don't exist in a dataset.
This is a part of the course
“Exploratory Data Analysis in R”
Exercise instructions
The contingency table from the last exercise is available in your workspace as tab.
- Load the
dplyrpackage. - Print
tabto find out which level ofalignhas the fewest total entries. - Use
filter()to filter out all rows ofcomicswith that level, then drop the unused level withdroplevels(). Save the simplified dataset ascomics_filtered.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load dplyr
___
# Print tab
___
# Remove align level
comics_filtered <- ___ %>%
___(align != ___) %>%
___()
# See the result
comics_filteredThis exercise is part of the course
Exploratory Data Analysis in R
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.
Exercise 1: Exploring categorical dataExercise 2: Bar chart expectationsExercise 3: Contingency table reviewExercise 4: Dropping levelsExercise 5: Side-by-side bar chartsExercise 6: Bar chart interpretationExercise 7: Counts vs. proportionsExercise 8: Conditional proportionsExercise 9: Counts vs. proportions (2)Exercise 10: Distribution of one variableExercise 11: Marginal bar chartExercise 12: Conditional bar chartExercise 13: Improve pie chartWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.