Filtering for significant countries
Not all slopes are significant, and you can use the p-value to guess which are and which are not.
However, when you have lots of p-values, like one for each country, you run into the problem of multiple hypothesis testing, where you have to set a stricter threshold. The p.adjust() function is a simple way to correct for this, where p.adjust(p.value) on a vector of p-values returns a set that you can trust.
Here you'll add two steps to process the slope_terms dataset: use a mutate to create the new, adjusted p-value column, and filter to filter for those below a .05 threshold.
This exercise is part of the course
Case Study: Exploratory Data Analysis in R
Exercise instructions
Use the p.adjust() function to adjust the p.value column, saving the result into a new p.adjusted column. Then, filter for cases where p.adjusted is less than .05.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Filter for only the slope terms
slope_terms <- country_coefficients %>%
filter(term == "year")
# Add p.adjusted column, then filter