lapply(total2[c(2,4,6:53),drop=FALSE],as.numeric)

library(XML)
library(ggplot2)

# Grabs ADVANCED link and assigns df to ADVANCED table
advanced = "http://www.basketball-
reference.com/leagues/NBA_2017_advanced.html"
advanced.table = readHTMLTable(advanced, header=T, which=1,stringsAsFactors=F)

# Grabs REGULAR link and assigns df to REGULAR table
reg = "http://www.basketball-reference.com/leagues/NBA_2017_per_game.html"
reg.table = readHTMLTable(reg,header=T,which = 1,stringsAsFactors=F)

# Grab CONTRACTS link and assigns df to CONTRACTS table
contracts = 'http://www.basketball-reference.com/contracts/players.html'
contracts.table = readHTMLTable(contracts,header = T,which=1,stringsAsFactors=F)

# Puts them into tables
df <- advanced.table
rdf <- reg.table
cdf <- contracts.table

#Changes column name MP to MPG so no confusion later on
colnames(rdf)[8] <- "MPG"

# Drops rows with column labels
df <- df[!grepl('Player',df$Player),]
rdf <- rdf[!grepl('Player',rdf$Player),]
cdf <- cdf[!grepl('Player',cdf$Player),]
cdf <- cdf[!grepl('Salary',cdf$Player),]

# Creates list of duplicate col names and drops them from rdf and cdf
drops <- c("Pos","Age","Tm","G","Rk")
rdf <- rdf[ , !(names(rdf) %in% drops)]
cdf <- cdf[ , !(names(cdf) %in% drops)]

# Merges df and rdf. Then merge total with cdf to create total2
total <- merge(df, rdf,by=c("Player"))
total2 <- merge(total,cdf,by=c("Player"))

# Converts selected columns from character to numeric
total[c(2,4,6:53)] <- lapply(total[c(2,4,6:53),drop=FALSE],as.numeric)
total2[c(2,4,6:53)] <- lapply(total2[c(2,4,6:53),drop=FALSE],as.numeric)