Aravind Hebbali aravindhebbali

## sort-best-subset.md

      
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              Last active Mar 8, 2022
            
              
                Sort best subset selection by AIC
              
          
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    # load packages
library(olsrr)
#> 
#> Attaching package: 'olsrr'
#> The following object is masked from 'package:datasets':
#> 
#>     rivers

# regression

  
## viz_facet.R
# Case Study 1: combine 4 plots in 2 rows and 2 columns
# store the current parameter settings in init
init <- par(no.readonly=TRUE)

# specify that 4 graphs to be combined and filled by rows
par(mfrow = c(2, 2))

# specify the graphs to be combined
plot(mtcars$mpg)
plot(mtcars$disp, mtcars$mpg)

## viz_annotations.R
# text inside plot
plot(mtcars$disp, mtcars$mpg)
text(x = 340, y = 30, labels = 'Sample Text')

# color
plot(mtcars$disp, mtcars$mpg)
text(x = 340, y = 30, labels = 'Sample Text', col = 'red')

# font
plot(mtcars$disp, mtcars$mpg)

## viz_legend.R
# data
year <- seq(2010, 2014, 1)
india <- c(10.3, 6.6, 5.6, 6.6, 7.2)
china <- c(10.6, 9.5, 7.8, 7.7, 7.3)
russia <- c(4.5, 4.3, 3.5, 1.3, 0.7)
brazil <- c(7.5, 3.9, 1.9, 3.0, 0.1)
s_africa <- c(3.2, 3.2, 2.2, 2.2, 1.5)
gdp <- data.frame(year, india, china, russia, brazil, s_africa, stringsAsFactors = FALSE)
gdp

## vis_hist.R
# distributions
# normal distribution
hist(rbeta(10000, 5, 5), ann = FALSE, col = 'blue')

# skewed distributions
hist(rbeta(10000, 2, 5), ann = FALSE, col = 'blue')
hist(rbeta(10000, 5, 2), ann = FALSE, col = 'blue')

# histogram
# store the results of hist function

## viz_box.R
# basic plot
boxplot(mtcars$mpg)

# horizontal box plot
boxplot(mtcars$mpg, horizontal = TRUE)

# color
boxplot(mtcars$mpg, col = 'blue')

# border color

## viz_bar.R
# using plot function
plot(as.factor(mtcars$cyl))

# using bar plot function
barplot(table(mtcars$cyl))

# tabulated data
cyl_freq <- table(mtcars$cyl)
cyl_freq

## viz_line.R
# data
head(AirPassengers)

# plot
data <- head(AirPassengers)
plot(data, type = 'l')
plot(data, type = 'b')
plot(data, type = 'o')
plot(data, type = 'c')

## viz_scatter.R
# scatter plot
plot(mtcars$disp, mtcars$mpg)

# add title and axis labels
plot(mtcars$disp, mtcars$mpg,
     main = 'Displacement vs Miles Per Gallon',
     xlab = 'Displacement', ylab = 'Miles Per Gallon')

# point shape
plot(mtcars$disp, mtcars$mpg, pch = 6)

## viz_title_axis_labels.R
# title
plot(mtcars$disp, mtcars$mpg,
     main = 'Displacement vs Miles Per Gallon')

# subtitle
plot(mtcars$disp, mtcars$mpg,
     main = 'Displacement vs Miles Per Gallon',
     sub = 'Mileage is inversely related to Displacement')

# axis labels
	# Case Study 1: combine 4 plots in 2 rows and 2 columns
	# store the current parameter settings in init
	init <- par(no.readonly=TRUE)

	# specify that 4 graphs to be combined and filled by rows
	par(mfrow = c(2, 2))

	# specify the graphs to be combined
	plot(mtcars$mpg)
	plot(mtcars$disp, mtcars$mpg)
	# text inside plot
	plot(mtcars$disp, mtcars$mpg)
	text(x = 340, y = 30, labels = 'Sample Text')

	# color
	plot(mtcars$disp, mtcars$mpg)
	text(x = 340, y = 30, labels = 'Sample Text', col = 'red')

	# font
	plot(mtcars$disp, mtcars$mpg)
	# data
	year <- seq(2010, 2014, 1)
	india <- c(10.3, 6.6, 5.6, 6.6, 7.2)
	china <- c(10.6, 9.5, 7.8, 7.7, 7.3)
	russia <- c(4.5, 4.3, 3.5, 1.3, 0.7)
	brazil <- c(7.5, 3.9, 1.9, 3.0, 0.1)
	s_africa <- c(3.2, 3.2, 2.2, 2.2, 1.5)
	gdp <- data.frame(year, india, china, russia, brazil, s_africa, stringsAsFactors = FALSE)
	gdp
	# distributions
	# normal distribution
	hist(rbeta(10000, 5, 5), ann = FALSE, col = 'blue')

	# skewed distributions
	hist(rbeta(10000, 2, 5), ann = FALSE, col = 'blue')
	hist(rbeta(10000, 5, 2), ann = FALSE, col = 'blue')

	# histogram
	# store the results of hist function
	# basic plot
	boxplot(mtcars$mpg)

	# horizontal box plot
	boxplot(mtcars$mpg, horizontal = TRUE)

	# color
	boxplot(mtcars$mpg, col = 'blue')

	# border color
	# using plot function
	plot(as.factor(mtcars$cyl))

	# using bar plot function
	barplot(table(mtcars$cyl))

	# tabulated data
	cyl_freq <- table(mtcars$cyl)
	cyl_freq
	# data
	head(AirPassengers)

	# plot
	data <- head(AirPassengers)
	plot(data, type = 'l')
	plot(data, type = 'b')
	plot(data, type = 'o')
	plot(data, type = 'c')
	# scatter plot
	plot(mtcars$disp, mtcars$mpg)

	# add title and axis labels
	plot(mtcars$disp, mtcars$mpg,
	main = 'Displacement vs Miles Per Gallon',
	xlab = 'Displacement', ylab = 'Miles Per Gallon')

	# point shape
	plot(mtcars$disp, mtcars$mpg, pch = 6)
	# title
	plot(mtcars$disp, mtcars$mpg,
	main = 'Displacement vs Miles Per Gallon')

	# subtitle
	plot(mtcars$disp, mtcars$mpg,
	main = 'Displacement vs Miles Per Gallon',
	sub = 'Mileage is inversely related to Displacement')

	# axis labels