houseData.r

url_Data <- readLines("https://raw.githubusercontent.com/issactoast/Machine-Learning-with-R/master/Data/house.txt") %>% paste(collapse = "\n")
houseData <- read.table(text = url_Data, header = FALSE, sep = ":")
houseData
for (i in 1:dim(houseData)[2]){
houseData[,i] <- gsub(paste("",i), "", houseData[,i])
}
houseData <- houseData %>% unlist() %>% as.numeric() %>% matrix(ncol = 14)
colnames(houseData) <- c("MEDV", "CRIM", "ZN", "INDUS",
                        "CHAS", "NOX", "RM", "AGE",
                        "DIS", "RAD", "TAX", "PTRATIO",
                        "B", "LSTAT")
 
################
library(tidyverse)
houseData2 <- read_csv("https://raw.githubusercontent.com/issactoast/Machine-Learning-with-R/master/Data/house.txt", col_names = F)
houseData2 = houseData2 %>% separate(col = X1, into = letters[1:14], sep = " [0-9]{1,2}:")
houseData2 = houseData2 %>% map_df(as.numeric) %>% as.matrix
colnames(houseData2) <- c("MEDV", "CRIM", "ZN", "INDUS",
                        "CHAS", "NOX", "RM", "AGE",
                        "DIS", "RAD", "TAX", "PTRATIO",
                        "B", "LSTAT")
 
 
identical(houseData, houseData2)
# > identical(houseData, houseData2)
# [1] TRUE


################
library(tidyverse)
houseData3 = read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data", col_names = F)
houseData3 = houseData3 %>% separate(col = X1, into = letters[1:14], sep = "[[:space:]]+")
houseData3 = houseData3 %>% map_df(as.numeric) %>% as.matrix
colnames(houseData3) <- c("MEDV", "CRIM", "ZN", "INDUS",
                         "CHAS", "NOX", "RM", "AGE",
                         "DIS", "RAD", "TAX", "PTRATIO",
                         "B", "LSTAT")
 
identical(houseData, houseData3)
# > identical(houseData, houseData3)
# [1] FALSE