Introduction

This is a sample data blog, where in I will be working with one of the most famous & processed dataset in Data analytics & ML community mtcars. The comments & workings would be around the dataset, hence it is advisable to go through the dataset once.

mtcars dataset –> 32 rows & 11 columns, column names as below:

[, 1]   mpg     Miles/(US) gallon
[, 2]   cyl     Number of cylinders
[, 3]   disp    Displacement (cu.in.)
[, 4]   hp      Gross horsepower
[, 5]   drat    Rear axle ratio
[, 6]   wt      Weight (lb/1000)
[, 7]   qsec    1/4 mile time
[, 8]   vs      Engine (0 = V-shaped, 1 = straight)
[, 9]   am      Transmission (0 = automatic, 1 = manual)
[,10]   gear    Number of forward gears
[,11]   carb    Number of carburetors

mtcars dataset
Download data set here
Read about the variables here

Break the Syntax

This is an important section, if you want to just skim through what I have done & not getting details of things.

Anything written in red is the question which I will answer during the post

#Anything written under this grey box is R Code & its output 

Anything written like this is the comment on output produced

Anything written like this is hyperlinked for further readings

Anything written like this is to just get your attention

Anything left are the Images or output produced (I am sure you can identify them :) For example refer plot 1

Figure 1: A fancy pie chart.

library(tidyverse)
library(broom)
library(knitr)
library(printr)

dim(mtcars)

[1] 32 11

kable(head(mtcars),align = 'c')

str(mtcars)

'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

summary(mtcars)

Data Visualization

Let’s say we want to analyse what is the relationship between mpg(miles per gallon) & hp (horse power) in different cyl (cylinder types)

mtcars %>% 
  ggplot(aes(x=mpg, y=hp, color=as.factor(cyl)))+
  geom_point()+
  labs(title="Relationship between mpg & hp",
       subtitle = "We can observe that as number of cylinders increases so does mpg but horse power decreases",
       x="Miles Per Gallon",
       y="Gross Horse Power ",
       color="Number of cylinders")

Linear Regression

This would be the last section for this intro post. Lets quickly see that if we have to predict mpg using all other variables which variable would be most important feature in our prediction. While there are many alogrithms to do this, we would be using linear regression in this post.

head(mtcars)

linear <- lm(mpg~. , data=mtcars)

tidy(summary(linear))

Above, we can see that wt is the most important predictor as its p-value is the lowest. (Well, its not so simple, but we'll call off at this point this as of now)