R Final Project

#Problem 1- describing the datset and choosing variables

data<- read.csv
View(mtcars)
data<-mtcars
class(data)

## [1] "data.frame"

str(data)

## '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 ...

I chose mtcars as the dataset and have selected two variables-wt,mpg,hp and cyl. This dataset consists of different variables related to a wide variety of car models.

#Problem 2-Static visualization of ggplot 2 using parametres

library("ggplot2")
ggplot(data = data)+
  geom_point(mapping=aes(x=wt, y=mpg, alpha=wt),col="Red")

ggplot(data=data)+
  geom_point(aes(x=wt,y=mpg,col=hp))

N<-ggplot(mtcars, aes(x=wt, y=mpg))
N + geom_point()

# a regression line
N +  
  geom_point() + 
  geom_smooth(method="lm", col="Red") +
  ggtitle("weight Vs Miles per gallon", 
          subtitle="From Mtcars Data") + 
          xlab("wt") + ylab("mpg")

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

#the regression line shows a negative correlation between the weight of cars and miles per gallon.This negative correlation between weight and mpg is expected since heavier cars generally consume more fuel

#Problem 3- Creating an interactive chart using plotly

library(ggplot2)
library(plotly)

## Warning: package 'plotly' was built under R version 4.4.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

gg <- ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(aes(col=cyl), size=1.5) +  
  geom_smooth(method="scatter", col="Steelblue") + 
  coord_cartesian(xlim=c(0, 8), ylim=c(0, 50)) + 
  labs(title="Interactive Scatter plot of Weight Vs Mpg", subtitle="From mtcars dataset", y="mpg", x="wt",   
caption="Mtcars Demographics")
gg

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

## Warning: Computation failed in `stat_smooth()`.
## Caused by error in `get()`:
## ! object 'scatter' of mode 'function' was not found

library(plotly)
ggplotly(gg)

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

## Warning: Computation failed in `stat_smooth()`.
## Caused by error in `get()`:
## ! object 'scatter' of mode 'function' was not found

#The interactive scatter plot visualizes the relationship between weight of cars and miles per gallon (mpg) in the mtcars dataset. The color of each point corresponds to the number of cylinders in the car. we have weight of cars in the x axis and mpg in the y axis. As the weight of the car increases, the miles per gallon tends to decrease. This negative correlation between weight and mpg is expected since heavier cars generally consume more fuel.on the other hand, Cars with fewer cylinders (indicated by different colors in the legend) tend to have higher miles per gallon compared to cars with more cylinders. This trend is evident as the colors shift from light blue (fewer cylinders) to dark blue (more cylinders).

#problem 4- creating an animation using gganimate

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gganimate)

## Warning: package 'gganimate' was built under R version 4.4.3

mtcars

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

cars<-mtcars %>% 
  ggplot(aes(wt, cyl, color=mpg)) +
  geom_point() +
  labs(title = 'cyl: {frame_time}', x = 'wt', y = 'mpg') +
  
  transition_time(cyl) +
  ease_aes('linear')+
  theme_minimal()

cars

###Saving your animation

anim_save("car_Weights_over_cylinder.gif", cars)

#For a video
a <- animate(cars, fps=5, renderer = av_renderer())

anim_save("Car_Weights_over_mpg.mp4", a, path = 'C:/Rwork' )