The dataset below Universities.csv contains information on 1302 American colleges and universities offering an undergraduate program. For each university, there are 17 measurements that include continuous measurements (such as tuition and graduation rate) and categorical measurements (such as location by state and whether it is a private or a public school). You will use it to practice data mining in R

Remove all categorical variables. Then remove all records with missing numerical measurements from the dataset.

Conduct a principal components analysis on the cleaned data and comment on the results. Should the data be normalized? Discuss what characterizes the components you consider key.

Here are the codes

read.csv(file.choose(Universities))

Univ<-data.frame(Universities)

summary(Univ)

# (1) Remove all categorical variables.

# Then remove all records with missing numerical measurements from the data set.

# Remove all categorical variables

Univ <- Universities[-c(1:2)]

View(Univ)

# Remove all records with missing numerical measurements from the data set

sum(is.na(Univ))

complete.cases(Univ)

cleanUniversities<- Univ[complete.cases(Univ), ]

str(cleanUniversities)

View(cleanUniversities)

# (2) Conduct a principal components analysis on the cleaned data and comment on the results.

# Should the data be normalized? Discuss what characterizes the components you consider key

# Conduct a PCA

pcs <- prcomp(cleanUniversities)

Nothing showed under view.

pcs <- prcomp(cleanUniversities)

Error in svd(x, nu = 0, nv = k) : a dimension is zero

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