Our Required Library:

Head of the data set.

head(mpg)
## # A tibble: 6 x 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa~
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa~
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa~
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa~
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa~
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa~
names(mpg)
##  [1] "manufacturer" "model"        "displ"        "year"         "cyl"         
##  [6] "trans"        "drv"          "cty"          "hwy"          "fl"          
## [11] "class"

scatter plot between hwy and dspl

plot1<-mpg %>% 
  ggplot(aes(displ,hwy))+geom_point(aes(col=class))+geom_smooth(se=F)+labs(title = "Fuel efficiency generally decreases with engine size",subtitle = "Created by Md Sojbul Islam",caption = "Scatterplot")+theme(plot.title = element_text(hjust = 0.5))+theme_classic()

Itearactive graph

ggplotly(plot1)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

New labels with more informative visalization.

plot1<-mpg %>% 
  ggplot(aes(displ,hwy))+geom_point(aes(col=class))+geom_smooth(se=F)+labs(title = "Fuel efficiency generally decreases with engine size",subtitle = "Created by Md Sojbul Islam",x="Engine displamnet",y="Highway fuel economey", col="car type",caption = "Scatterplot")+theme(plot.title = element_text(hjust = 0.5))+theme_classic()
plot1
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Matchmatical euation visualize:

df <- tibble(
  x = runif(10),
  y = runif(10)
)

mt<-ggplot(df, aes(x, y)) +
  geom_point(size=2) +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )+geom_line(col="blue")
  
mt

best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

la<-ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), data = best_in_class)


ggplotly(la)