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Plot
# 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)
## 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)
>