# Install packages if not already install.packages("ggplot2") install.packages("gridExtra") library(ggplot2) library(gridExtra) # Create sample plots representing dashboard icons/metrics plot1 <- ggplot(mtcars, aes(x=hp)) + geom_histogram(fill="steelblue", color="white") + labs(title="Horsepower Distribution") plot2 <- ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point(color="darkgreen") + labs(title="MPG vs Weight") plot3 <- ggplot(mtcars, aes(x=factor(cyl))) + geom_bar(fill="orange") + labs(title="Cylinder Count") # Combine in a dashboard-style layout grid.arrange(plot1, plot2, plot3, ncol=2)

## Machine Learning Algorithm (Linear Regression) ## COde # Install required packages if not already installed install.packages("tidyverse") # Load the library library(tidyverse) # Load the dataset data(mtcars) # View the first few rows head(mtcars) # Fit a linear regression model model <- lm(mpg ~ wt, data = mtcars) # Summary of the model summary(model) # Visualize the regression line ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(color = "blue") + geom_smooth(method = "lm", color = "red") + labs(title = "Linear Regression: MPG vs Weight", x = "Weight of Car", y = "Miles Per Gallon") summary(cars$speed) summary(pressure) plot(pressure) ### Ouptput > # Load the library > 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 ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ✔ lubridate 1.9.4 ✔ tidyr 1.3.1 ✔ purrr 1.0.4 ── Conflicts ─────────────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() ℹ Use the conflicted package to force all conflicts to become errors Warning messages: 1: package ‘tidyverse’ was built under R version 4.4.3 2: package ‘ggplot2’ was built under R version 4.4.3 > # Load the dataset > data(mtcars) > # View the first few rows > head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 > # Fit a linear regression model > model <- lm(mpg ~ wt, data = mtcars) > # Summary of the model > summary(model) Call: lm(formula = mpg ~ wt, data = mtcars) Residuals: Min 1Q Median 3Q Max -4.5432 -2.3647 -0.1252 1.4096 6.8727 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 37.2851 1.8776 19.858 < 2e-16 *** wt -5.3445 0.5591 -9.559 1.29e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.046 on 30 degrees of freedom Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446 F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10 > # Visualize the regression line > ggplot(mtcars, aes(x = wt, y = mpg)) + + geom_point(color = "blue") + + geom_smooth(method = "lm", color = "red") + + labs(title = "Linear Regression: MPG vs Weight", + x = "Weight of Car", + y = "Miles Per Gallon") `geom_smooth()` using formula = 'y ~ x' > summary(cars$speed) Min. 1st Qu. Median Mean 3rd Qu. Max. 4.0 12.0 15.0 15.4 19.0 25.0 > summary(pressure) temperature pressure Min. : 0 Min. : 0.0002 1st Qu.: 90 1st Qu.: 0.1800 Median :180 Median : 8.8000 Mean :180 Mean :124.3367 3rd Qu.:270 3rd Qu.:126.5000 Max. :360 Max. :806.0000 > plot(pressure) >