Basic histogram with alpha(for transparency of graph).

library(ggplot2)
data(mpg)
ggplot(mpg, aes(x=hwy)) + geom_histogram(bins = 20, fill="red", alpha=0.5)

# Histogram with manufacturer using facet_grid.

data(mpg)
ggplot(mpg, aes(x=manufacturer)) + geom_bar(aes(fill=factor(cyl)))

## Use position=“dodge” to change the stacking of graph.

Scatter plot on ‘txhousing’.

data("txhousing")
head(txhousing)

## # A tibble: 6 x 9
##   city     year month sales   volume median listings inventory  date
##   <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
## 1 Abilene  2000     1    72  5380000  71400      701       6.3 2000 
## 2 Abilene  2000     2    98  6505000  58700      746       6.6 2000.
## 3 Abilene  2000     3   130  9285000  58100      784       6.8 2000.
## 4 Abilene  2000     4    98  9730000  68600      785       6.9 2000.
## 5 Abilene  2000     5   141 10590000  67300      794       6.8 2000.
## 6 Abilene  2000     6   156 13910000  66900      780       6.6 2000.

ggplot(txhousing, aes(x=sales, y=volume))+geom_point(aes(color="pink", alpha=0.4))

## Warning: Removed 568 rows containing missing values (geom_point).

Adding a smooth fit line to graph.

ggplot(txhousing, aes(x=sales, y=volume))+geom_point(aes(color="pink", alpha=0.4))+geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 568 rows containing non-finite values (stat_smooth).

## Warning: Removed 568 rows containing missing values (geom_point).

Can add inbuild themes.

ggplot(txhousing, aes(x=sales, y=volume))+geom_point(aes(color="pink", alpha=0.3)) + theme_dark()

## Warning: Removed 568 rows containing missing values (geom_point).

To add extra themes install ggthemes.

library(ggthemes)
ggplot(txhousing, aes(x=sales, y=volume))+geom_point(aes(color="pink", alpha=0.4))+geom_smooth()+theme_economist()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 568 rows containing non-finite values (stat_smooth).

## Warning: Removed 568 rows containing missing values (geom_point).

For theme wall street journal.

ggplot(txhousing, aes(x=sales, y=volume))+geom_point(aes(color="pink", alpha=0.4))+geom_smooth()+theme_wsj()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 568 rows containing non-finite values (stat_smooth).

## Warning: Removed 568 rows containing missing values (geom_point).

Without using coordinates.

ggplot(mpg, aes(x=displ, y=hwy))+geom_point()

With coordinates.

ggplot(mpg, aes(x=displ, y=hwy))+geom_point()+coord_cartesian(xlim = c(1,5), ylim = c(10,35))

## Now check the x-axis & y-axis, they are set to the limits.

Using Facet(Use to make graph on each factor component separately.

ggplot(mpg, aes(x=displ, y=hwy))+geom_point()+facet_grid(.~cyl)

For 2d facets.

ggplot(mpg, aes(x=displ, y=hwy))+geom_point()+facet_grid(drv~cyl)

## This facet syntax works as (y~x), means teh variable before telda(~) will be shown on y-axis & the variable after telda(~) will be shown on x-axis.

Example of using statictics.

data(mtcars)
ggplot(mtcars, aes(x=mpg, y=hp))+geom_point()+stat_smooth(level = 0.95)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## The shaded region showing the 95% confidence interval around the values.