Summarizing PCA in R

As we saw in the video, there was a categorical variable (position) in our data that seemed to identify itself with clusters in the first two principal components. Even when scaling the data, these two PCs still explain a great deal of variation in the data. What if we looked at only one position at a time?

Cet exercice fait partie du cours

Linear Algebra for Data Science in R

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Instructions

Perform the same analysis as in the previous exercise, but only use the subset of the data where position equals "WR" (wide receiver):

Use the scale() function to scale the 5th through the 12th columns of combine_WR data. Name this data frame B and show some of the values using head().
Use prcomp() to perform principal component analysis on the data and summarize this analysis using summary().

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Subset combine only to "WR"
combine_WR <- subset(combine, position == "WR")

# Scale columns 5-12 of combine_WR
B <- ___(___[, ___])

# Print the first 6 rows of the data
___

# Summarize the principal component analysis
___(___)

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