adding vignettes

.gitignore

@@ -2,3 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
+inst/doc

DESCRIPTION

@@ -15,5 +15,8 @@ Imports:
 RoxygenNote: 7.3.2
 Encoding: UTF-8
 Suggests: 
+    knitr,
+    rmarkdown,
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
+VignetteBuilder: knitr

R/visualize_airport_delay_dplyr.R

@@ -8,29 +8,23 @@
 #' @import dplyr
 #' @import ggplot2
 #' @import nycflights13
-#' @param dep_delay' dep_delay
-#' @param origin origin
-#' @param lon lon
-#' @param lat lat
-#' @param mean_delay mean_dealy
 #' @examples
 #' # Run the function to visualize mean delays by airport:
 #' visualize_airport_delays()
 #' @name visualize_airport_delays
 #' @export
-library(dplyr)
 
 visualize_airport_delays <- function() {
   # Join flights and airports datasets to include latitude and longitude
   airport_delays <- nycflights13::flights %>%
-    dplyr::filter(!is.na(dep_delay)) %>%  # Exclude rows where departure delay is NA
-    dplyr::group_by(origin) %>%  # Group by airport
-    dplyr::summarize(mean_delay = mean(dep_delay, na.rm = TRUE)) %>%
-    dplyr::left_join(nycflights13::airports, by = c("origin" = "faa"))  # Join with airports for lat/lon data
+    dplyr::filter(!is.na(.data$dep_delay)) %>%
+    dplyr::group_by(.data$origin) %>%
+    dplyr::summarize(mean_delay = mean(.data$dep_delay, na.rm = TRUE)) %>%
+    dplyr::left_join(nycflights13::airports, by = c("origin" = "faa"))
 
   # Create the ggplot2 plot
-  ggplot2::ggplot(airport_delays, ggplot2::aes(x = lon, y = lat)) +
-    ggplot2::geom_point(ggplot2::aes(size = mean_delay, color = mean_delay), alpha = 0.9) +
+  ggplot2::ggplot(airport_delays, ggplot2::aes(x = .data$lon, y = .data$lat)) +
+    ggplot2::geom_point(ggplot2::aes(size = .data$mean_delay, color = .data$mean_delay), alpha = 0.9) +
     ggplot2::scale_color_gradient(low = "blue", high = "red") +
     ggplot2::labs(
       title = "Mean Flight Delay by Airport",
@@ -42,6 +36,4 @@ visualize_airport_delays <- function() {
     ggplot2::theme_minimal()
 }
 
-# Call the function
-visualize_airport_delays()
-
+# visualize_airport_delays()

man/group5labbonus-package.Rd

@@ -35,8 +35,10 @@ For more information, see \url{https://en.wikipedia.org/wiki/Ridge_regression}
 }
 \examples{
 
-
+%% for ridge model
 data(iris)
 model <- ridgereg$new(Sepal.Length ~ Sepal.Width + Petal.Length, data = iris,lambda = 0.1)
+%% for visualze of deplays
+visualize_airport_delays()
 %%  ~~ Optional simple examples of the most important functions ~~
 }

man/visualize_airport_delays.Rd

@@ -3,16 +3,8 @@
 \name{visualize_airport_delays}
 \alias{visualize_airport_delays}
 \title{Visualize Mean Flight Delay by Airport}
-\arguments{
-\item{dep_delay'}{dep_delay}
-
-\item{origin}{origin}
-
-\item{lon}{lon}
-
-\item{lat}{lat}
-
-\item{mean_delay}{mean_dealy}
+\usage{
+visualize_airport_delays()
 }
 \value{
 A ggplot2 object representing the visualization of mean flight delays by airport.

vignettes/.gitignore

+*.html
+*.R

vignettes/group5labbonus.Rmd

+---
+title: "group5labbonus"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{group5labbonus}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(group5labbonus)
+```
+
+## Introduction
+This vignette illustrates how to use lab4 package to perform linear  and  ridge progression models and to perform various special functions like print(), plot(), resid(), pred(),coef() and summary().
+
+## DataSet
+For testing purpose, here we are using iris dataset, containing measurements of features of flower iris. To load data use the following:
+```{r}
+data(iris)
+```
+
+## Invoking linreg function
+This Function will take two parameters namely formula and data. Type of formula is an expression specifying the relationship between dependent and independent variables, whereas data is the dataset(iris in this case). 
+```{r}
+  linreg_mod <- linreg$new(Petal.Length~Sepal.Width+Sepal.Length, data=iris)
+```
+
+## Print Function
+Printing the coefficients and coefficients name of the model generated using linreg function.This can be invoked by the following 
+```{r}
+linreg_mod$print()
+```
+
+## Plot Function
+Plotting for residuals against Fitted Values and for Scale-Locations against sqaure root of standardized residuals.
+```{r}
+plots <- linreg_mod$plot()
+print(plots[[1]])
+print(plots[[2]])
+```
+
+## Resid Function
+Resid Function is to display the residuals of the model as a vector.
+```{r}
+# Get residuals
+residuals <- linreg_mod$resid()
+print(residuals)
+```
+
+## pred Function
+pred Function is to display the predicted values of the model.
+```{r}
+# Get predicted values
+predictions <- linreg_mod$pred()
+print(predictions)
+```
+
+## Summary Function
+Summary function is to display coefficients along with standard error, t-values, p-values with significance.
+```{r}
+linreg_mod$summary()
+```
+
+## Invoking Ridge function
+To create a ridge regression model, specify a formula, a data frame, and a lambda value. In this case, we’ll set lambda to 0.1.
+```{r}
+data(iris)
+
+# Create a ridge regression model with lambda = 0.1
+mod <- ridgereg$new(formula = Sepal.Length ~ Sepal.Width + Petal.Length, data = iris, lambda = 0.1)
+```
+
+## Display the Model Coefficients
+To view the model coefficients, use the show() method.
+```{r}
+mod$show()
+```
+
+## Extract model coefficients
+```{r}
+coefficients <- mod$coef()
+print(coefficients)
+```
+
+## visualize airport delays
+This function visualizes the mean flight delay at each origin airport in the nycflights13 dataset.It calculates the mean departure delay for each airport, joins this data with airport locations,and creates a scatter plot showing mean delay as point size and color on a map of airport locations.
+```{r}
+visualize_airport_delays()
+```
+