# Reading the CSV file using base R Function
USDAData<-read.csv("path/gtrU6OP5tzjZ7owE8Rdw.csv")

# Now that we have the data into R Environment let's look at the dimentions of the data
dim(USDAData)
## [1] 7058 16
# We can see that we have 16 Variables in our dataset and 7058 observations

# Now we will have a glimpse of the structure of the data
str(USDAData)
## 'data.frame': 7058 obs. of 16 variables:
## $ ID       : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
## $ Description : Factor w/ 7054 levels "ABALONE,MIXED SPECIES,RAW",..: 1303 1302 1298 2303 2304 2305 2306 2307 2308 2309 ...
## $ Calories : int 717 717 876 353 371 334 300 376 403 387 ...
## $ Protein : num 0.85 0.85 0.28 21.4 23.24 ...
## $ TotalFat : num 81.1 81.1 99.5 28.7 29.7 ...
## $ Carbohydrate: num 0.06 0.06 0 2.34 2.79 0.45 0.46 3.06 1.28 4.78 ...
## $ Sodium    : int 714 827 2 1395 560 629 842 690 621 700 ...
## $ SaturatedFat: num 51.4 50.5 61.9 18.7 18.8 ...
## $ Cholesterol : int 215 219 256 75 94 100 72 93 105 103 ...
## $ Sugar    : num 0.06 0.06 0 0.5 0.51 0.45 0.46 NA 0.52 NA ...
## $ Calcium : int 24 24 4 528 674 184 388 673 721 643 ...
## $ Iron    : num 0.02 0.16 0 0.31 0.43 0.5 0.33 0.64 0.68 0.21 ...
## $ Potassium : int 24 26 5 256 136 152 187 93 98 95 ...
## $ VitaminC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VitaminE : num 2.32 2.32 2.8 0.25 0.26 0.24 0.21 NA 0.29 NA ...
## $ VitaminD : num 1.5 1.5 1.8 0.5 0.5 0.5 0.4 NA 0.6 NA ...
# From the above output we can see that only one variable is categorical i.e. Description resta ll are numeric

# 1.
# To see which column have maximum NA value, and other descriptives we will look at the summary
summary(USDAData)
##    ID
## Min. : 1001
## 1st Qu.: 8387
## Median :13294
## Mean :14260
## 3rd Qu.:18337
## Max. :93600

A polling company is interested in predicting the result of a poten...