Program 8

Author

Mohammed_Maqsood_1NT23IS127_Sec_C

Quarto

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Program 1

Develop an r program to quickly explore a given dataset including categorical analysis using the group_by command,and visualize the findings using ggplot2 features.

Step1: Load necessary libraries

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 (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(dplyr)

Step 2: Load the dataset

#Load dataset
data <- mtcars

# Convert 'cyl' to a factor for categorical analysis
data$cyl <- as.factor(data$cyl)

Step 3: Group by categorical variables

# Summarize average mpg by cylinder category
summary_data <- data %>%
  group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg), .groups = 'drop')

# Display summary
print(summary_data)

# A tibble: 3 × 2
  cyl   avg_mpg
  <fct>   <dbl>
1 4        26.7
2 6        19.7
3 8        15.1

Step 4: Visualizing the findings

# Create a bar plot using ggplot2
ggplot(summary_data, aes(x = cyl, y = avg_mpg, fill = cyl)) +
  geom_bar(stat = "identity") +
  labs(title = "Average MPG by Cylinder count",
       x = "Number of Cylinders",
       Y = "Average MPG") +
  theme_minimal()

Program 2

Step 1: Load necessary libraries

# Load necessary libraries
library(ggplot2)
library(dplyr)

Step 2: Load the Dataset

Explanation:

The iris dataset contains 150 samples of iris flowers categorized into three species: setosa, versicolor, and virginica.
Each sample has sepal and petal measurements.

head(data) displays the first few rows.

# Load the iris dataset
data <- iris

# Display first few rows
head(data)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Step 3: Create a Scatter Plot

X-Axis (Sepal.Length)

Represents the length of the flower’s sepal.

Y-Axis (Sepal.Width)

Represents the width of the flower’s sepal.

Color (Species)

Differentiates three species using distinct colors

Customization

geom_point(size = 3, alpha = 0.7): Increases the size of points and makes them slightly transparent.
labs(): Adds a title and axis labels.
theme_minimal(): Uses a clean background for readability

theme(legend.position = "top"): Moves the legend to the top.

# Create a scatter plot using ggplot2
ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.7) +  # Increase point size & transparency
  labs(title = "Scatter Plot of Sepal Dimensions",
       x = "Sepal Length",
       y = "Sepal Width",
       color = "Species") +  # Legend title
  theme_minimal() +  # Clean layout
  theme(legend.position = "top")  # Move legend to the top

Program 3

Implement an R function to generate a line graph depicting the trend of a time-series dataset, with separate lines for each group, utilizing ggplot2’s group aesthetic.

Introduction

This document demonstrates how to create a time-series line graph using the built-in AirPassengers dataset in R.

The dataset contains monthly airline passenger counts from 1949 to 1960. We will use ggplot2 to visualize trends, with separate lines for each year.

Step 1: Load necessary libraries

library(ggplot2)
library(dplyr)
library(tidyr)

Step 2Load the Built-in `AirPassengers` Dataset

The AirPassengers dataset is a time series object in R.

We first convert it into a dataframe to use it with ggplot2.

Date: Represents the month and year (from January 1949 to December 1960).
Passengers: Monthly airline passenger counts.
Year: Extracted year from the date column, which will be used to group the data

# Convert time-series data to a dataframe
data <- data.frame(
  Date = seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)),
  Passengers = as.numeric(AirPassengers),
  Year = as.factor(format(seq(as.Date("1949-01-01"), by = "month", length.out = length(AirPassengers)), "%Y"))
)

# Display first few rows
head(data, n=20)

         Date Passengers Year
1  1949-01-01        112 1949
2  1949-02-01        118 1949
3  1949-03-01        132 1949
4  1949-04-01        129 1949
5  1949-05-01        121 1949
6  1949-06-01        135 1949
7  1949-07-01        148 1949
8  1949-08-01        148 1949
9  1949-09-01        136 1949
10 1949-10-01        119 1949
11 1949-11-01        104 1949
12 1949-12-01        118 1949
13 1950-01-01        115 1950
14 1950-02-01        126 1950
15 1950-03-01        141 1950
16 1950-04-01        135 1950
17 1950-05-01        125 1950
18 1950-06-01        149 1950
19 1950-07-01        170 1950
20 1950-08-01        170 1950

Step 3: Define a Function for Time-Series Line Graph

We define a function to create a time-series line graph where:

The x-axis represents time (Date).
The y-axis represents the number of passengers (Passengers).
Each year has a separate line to compare trends.

Function Inputs

data – The dataset containing time-series data.
x_col – The column representing time (Date).
y_col – The column representing values (Passengers).
group_col – The categorical variable for grouping (Year).
title – Custom plot title.

Features of the Line Graph

Group-based Visualization:

Each year has a distinct line color.
The group aesthetic ensures lines are drawn separately for each year.

geom_line(size = 1.2)

Adds a smooth line for trend analysis.

geom_point(size = 2)

Highlights individual data points.

theme_minimal() & theme(legend.position = “top”)

Enhances readability with a clean layout.

Moves legend to the top for better visualization.

# Function to plot time-series trend
plot_time_series <- function(data, x_col, y_col, group_col, title="Air Passenger Trends") {
  ggplot(data, aes_string(x = x_col, y = y_col, color = group_col, group = group_col)) +
    geom_line(size = 1.2) +  # Line graph
    geom_point(size = 2) +   # Add points for clarity
    labs(title = title,
         x = "Year",
         y = "Number of Passengers",
         color = "Year") +  # Legend title
    theme_minimal() +
    theme(legend.position = "top")
}

# Call the function
plot_time_series(data, "Date", "Passengers", "Year", "Trend of Airline Passengers Over Time")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Program 4

Develop a script in R to produce a bar graph displaying the frequency distribution of categorical data in a given dataset, grouped by a specific variable, using ggplot2.

# Load necessary libraries
library(ggplot2)

Step 1: Load the Dataset

We use the built-in mtcars dataset, which contains information about different car models.

# Load dataset
data <- mtcars

# Display first few rows
head(data)

                   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

Explanation

The mtcars dataset includes various car specifications.
We will analyze the number of cylinders (cyl) and group by the number of gears (gear).

Step 2: Convert Numeric Data to Categorical

Since cyl (number of cylinders) and gear (number of gears) are numerical, we convert them into factors.

data$cyl <- as.factor(data$cyl) 
data$gear <- as.factor(data$gear)

Why Convert to Factors?

ggplot2 treats factors as categories, making it easy to group and visualize.

Step 3: Create a Bar Graph

We now create a bar plot to show the frequency distribution of cyl, grouped by gear.

# Create a bar graph
ggplot(data, aes(x = cyl, fill = gear)) +
  geom_bar(position = "dodge") +  # Grouped bar chart
  labs(title = "Frequency of Cylinders Grouped by Gear Type",
       x = "Number of Cylinders",
       y = "Count",
       fill = "Gears") +  # Legend title
  theme_minimal()

Explanation of the Plot

X-Axis (cyl)

Displays cylinder categories (4, 6, 8 cylinders).

Y-Axis (Frequency Count)

Represents the number of cars in each category.

Color Fill (gear)

Differentiates cars based on number of gears (3, 4, 5 gears).

Grouped Bars (position = "dodge")

Ensures bars are side by side instead of stacked.

Minimal Theme (theme_minimal())

Provides a clean and readable layout.

Program 5

Implement an R program to create a histogram illustrating the distribution of a continuous variable, with overlays of density curves for each group, using ggplot2.

Step 1: Load Required Library

library(ggplot2)

Step 2: Explore the Inbuilt Dataset

# Use the built-in 'iris' dataset
# 'Petal.Length' is a continuous variable
# 'Species' is a categorical grouping variable

str(iris)  # Shows the structure of the dataset

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Step 3: Create Histogram with Group-wise Density Curves

Step 3.1: Initialize the ggplot with aesthetic mappings

# Start ggplot with iris dataset
# Map Petal.Length to x-axis and fill by Species (grouping variable)

p <- ggplot(data = iris, aes(x = Petal.Length, fill = Species))
p

Explanation:

This initializes the plot and tells ggplot to map:

Petal.Length (continuous variable) to the x-axis

Species (categorical) to fill aesthetic to distinguish groups

Step 3.2: Add Histogram Layer

# Add histogram with density scaling

p <- p + geom_histogram(aes(y = ..density..),
         alpha = 0.4, # Set transparency
         position = "identity",# Overlap histograms
         bins = 30)            # Number of bins
p

Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.

Explanation:

aes(y = ..density..) normalizes the histogram to density

alpha = 0.4 makes bars semi-transparent so overlaps are visible

position = "identity" lets different group histograms stack on top

bins = 30 controls histogram resolution

Step 3.3: Add Density Curve Layer

# Overlay density curves for each group

p <- p + 
  geom_density(aes(color = Species), # Line color by group
  size = 1.2)# Line thickness
p

Explanation: This overlays smooth density curves for each species using color. The aes(color = Species) ensures each curve is colored by group.

Step 3.4: Add Labels and Theme

# Add title and axis labels, and apply clean theme

p <- p + labs(
title = "Distribution of Petal Length with Group-wise Density Curves", 
x = "Petal Length", 
y = "Density")+ 
theme_minimal()

p

Explanation:

labs() adds a title and axis labels

theme_minimal() applies a clean, modern plot style

Step 3.5: Display the Plot

# Finally, render the plot
p

Program 6

Write an R script to construct a box plot showcasing the distribution of a continuous variable, grouped by a categorical variable, using ggplot2’s fill aesthetic.

Step 1: Load Required Library# Load ggplot2 package for visualization

# Load ggplot2 package for visualization
library(ggplot2)

Step 2: Explore the Inbuilt Dataset

# Use the built-in 'iris' dataset
# 'Petal.Width' is a continuous variable
# 'Species' is a categorical grouping variable

str(iris)  # View structure of the dataset

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

head(iris) # View sample data

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Step 3: Construct Box Plot with Grouping

Step 3.1: Initialize ggplot with Aesthetic Mappings

# Initialize ggplot with data and aesthetic mappings

p <- ggplot(data = iris, aes(x = Species, y = Petal.Width, fill = Species))

Step 3.2: Add Box Plot Layer

# Add the box plot layer

p <- p + geom_boxplot()

Step 3.3: Add Labels and Theme

# Add title and labels and use a minimal theme

p <- p + labs(title = "Box Plot of Petal Width by Species",
              x = "Species",
              y = "Petal Width") +
         theme_minimal()

Step 3.4: Display the Plot

# Render the final plot
p

Program 7

Step 1: Load Required Library

library(ggplot2)

Step 2: Create data for the function

x<-seq(-2*pi,2*pi,length.out= 500)

y1<-sin(x)
y2<-cos(x)
df<- data.frame(x=rep(x,2),
                y=c(y1,y2),
                group= rep(c("sin(x)","cos(x)"),each =length(x))
)
df

               x             y  group
1    -6.28318531  2.449213e-16 sin(x)
2    -6.25800220  2.518045e-02 sin(x)
3    -6.23281909  5.034492e-02 sin(x)
4    -6.20763598  7.547747e-02 sin(x)
5    -6.18245288  1.005622e-01 sin(x)
6    -6.15726977  1.255831e-01 sin(x)
7    -6.13208666  1.505244e-01 sin(x)
8    -6.10690356  1.753702e-01 sin(x)
9    -6.08172045  2.001048e-01 sin(x)
10   -6.05653734  2.247125e-01 sin(x)
11   -6.03135423  2.491777e-01 sin(x)
12   -6.00617113  2.734849e-01 sin(x)
13   -5.98098802  2.976186e-01 sin(x)
14   -5.95580491  3.215637e-01 sin(x)
15   -5.93062180  3.453048e-01 sin(x)
16   -5.90543870  3.688269e-01 sin(x)
17   -5.88025559  3.921151e-01 sin(x)
18   -5.85507248  4.151547e-01 sin(x)
19   -5.82988937  4.379310e-01 sin(x)
20   -5.80470627  4.604296e-01 sin(x)
21   -5.77952316  4.826362e-01 sin(x)
22   -5.75434005  5.045367e-01 sin(x)
23   -5.72915694  5.261173e-01 sin(x)
24   -5.70397384  5.473642e-01 sin(x)
25   -5.67879073  5.682640e-01 sin(x)
26   -5.65360762  5.888035e-01 sin(x)
27   -5.62842451  6.089695e-01 sin(x)
28   -5.60324141  6.287494e-01 sin(x)
29   -5.57805830  6.481306e-01 sin(x)
30   -5.55287519  6.671007e-01 sin(x)
31   -5.52769208  6.856478e-01 sin(x)
32   -5.50250898  7.037601e-01 sin(x)
33   -5.47732587  7.214261e-01 sin(x)
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36   -5.40177655  7.716357e-01 sin(x)
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38   -5.35141033  8.026798e-01 sin(x)
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40   -5.30104412  8.316882e-01 sin(x)
41   -5.27586101  8.454057e-01 sin(x)
42   -5.25067790  8.585872e-01 sin(x)
43   -5.22549479  8.712241e-01 sin(x)
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46   -5.14994547  9.057897e-01 sin(x)
47   -5.12476236  9.161722e-01 sin(x)
48   -5.09957926  9.259736e-01 sin(x)
49   -5.07439615  9.351879e-01 sin(x)
50   -5.04921304  9.438090e-01 sin(x)
51   -5.02402993  9.518317e-01 sin(x)
52   -4.99884683  9.592507e-01 sin(x)
53   -4.97366372  9.660615e-01 sin(x)
54   -4.94848061  9.722596e-01 sin(x)
55   -4.92329751  9.778411e-01 sin(x)
56   -4.89811440  9.828026e-01 sin(x)
57   -4.87293129  9.871407e-01 sin(x)
58   -4.84774818  9.908529e-01 sin(x)
59   -4.82256508  9.939368e-01 sin(x)
60   -4.79738197  9.963903e-01 sin(x)
61   -4.77219886  9.982119e-01 sin(x)
62   -4.74701575  9.994006e-01 sin(x)
63   -4.72183265  9.999554e-01 sin(x)
64   -4.69664954  9.998761e-01 sin(x)
65   -4.67146643  9.991628e-01 sin(x)
66   -4.64628332  9.978158e-01 sin(x)
67   -4.62110022  9.958361e-01 sin(x)
68   -4.59591711  9.932248e-01 sin(x)
69   -4.57073400  9.899837e-01 sin(x)
70   -4.54555089  9.861148e-01 sin(x)
71   -4.52036779  9.816205e-01 sin(x)
72   -4.49518468  9.765037e-01 sin(x)
73   -4.47000157  9.707677e-01 sin(x)
74   -4.44481846  9.644161e-01 sin(x)
75   -4.41963536  9.574528e-01 sin(x)
76   -4.39445225  9.498824e-01 sin(x)
77   -4.36926914  9.417097e-01 sin(x)
78   -4.34408603  9.329397e-01 sin(x)
79   -4.31890293  9.235781e-01 sin(x)
80   -4.29371982  9.136308e-01 sin(x)
81   -4.26853671  9.031041e-01 sin(x)
82   -4.24335360  8.920047e-01 sin(x)
83   -4.21817050  8.803397e-01 sin(x)
84   -4.19298739  8.681164e-01 sin(x)
85   -4.16780428  8.553425e-01 sin(x)
86   -4.14262117  8.420263e-01 sin(x)
87   -4.11743807  8.281760e-01 sin(x)
88   -4.09225496  8.138006e-01 sin(x)
89   -4.06707185  7.989091e-01 sin(x)
90   -4.04188874  7.835109e-01 sin(x)
91   -4.01670564  7.676159e-01 sin(x)
92   -3.99152253  7.512341e-01 sin(x)
93   -3.96633942  7.343759e-01 sin(x)
94   -3.94115631  7.170520e-01 sin(x)
95   -3.91597321  6.992734e-01 sin(x)
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98   -3.84042389  6.433233e-01 sin(x)
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100  -3.79005767  6.039637e-01 sin(x)
101  -3.76487456  5.837031e-01 sin(x)
102  -3.73969146  5.630723e-01 sin(x)
103  -3.71450835  5.420845e-01 sin(x)
104  -3.68932524  5.207529e-01 sin(x)
105  -3.66414213  4.990910e-01 sin(x)
106  -3.63895903  4.771127e-01 sin(x)
107  -3.61377592  4.548317e-01 sin(x)
108  -3.58859281  4.322624e-01 sin(x)
109  -3.56340970  4.094189e-01 sin(x)
110  -3.53822660  3.863158e-01 sin(x)
111  -3.51304349  3.629677e-01 sin(x)
112  -3.48786038  3.393894e-01 sin(x)
113  -3.46267727  3.155959e-01 sin(x)
114  -3.43749417  2.916023e-01 sin(x)
115  -3.41231106  2.674237e-01 sin(x)
116  -3.38712795  2.430756e-01 sin(x)
117  -3.36194484  2.185733e-01 sin(x)
118  -3.33676174  1.939324e-01 sin(x)
119  -3.31157863  1.691685e-01 sin(x)
120  -3.28639552  1.442974e-01 sin(x)
121  -3.26121241  1.193347e-01 sin(x)
122  -3.23602931  9.429635e-02 sin(x)
123  -3.21084620  6.919820e-02 sin(x)
124  -3.18566309  4.405617e-02 sin(x)
125  -3.16047998  1.888621e-02 sin(x)
126  -3.13529688 -6.295735e-03 sin(x)
127  -3.11011377 -3.147369e-02 sin(x)
128  -3.08493066 -5.663168e-02 sin(x)
129  -3.05974755 -8.175375e-02 sin(x)
130  -3.03456445 -1.068240e-01 sin(x)
131  -3.00938134 -1.318265e-01 sin(x)
132  -2.98419823 -1.567454e-01 sin(x)
133  -2.95901512 -1.815649e-01 sin(x)
134  -2.93383202 -2.062692e-01 sin(x)
135  -2.90864891 -2.308428e-01 sin(x)
136  -2.88346580 -2.552699e-01 sin(x)
137  -2.85828269 -2.795352e-01 sin(x)
138  -2.83309959 -3.036232e-01 sin(x)
139  -2.80791648 -3.275186e-01 sin(x)
140  -2.78273337 -3.512064e-01 sin(x)
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861   2.78273337 -9.362981e-01 cos(x)
862   2.80791648 -9.448447e-01 cos(x)
863   2.83309959 -9.527922e-01 cos(x)
864   2.85828269 -9.601354e-01 cos(x)
865   2.88346580 -9.668698e-01 cos(x)
866   2.90864891 -9.729911e-01 cos(x)
867   2.93383202 -9.784953e-01 cos(x)
868   2.95901512 -9.833790e-01 cos(x)
869   2.98419823 -9.876390e-01 cos(x)
870   3.00938134 -9.912728e-01 cos(x)
871   3.03456445 -9.942779e-01 cos(x)
872   3.05974755 -9.966526e-01 cos(x)
873   3.08493066 -9.983951e-01 cos(x)
874   3.11011377 -9.995046e-01 cos(x)
875   3.13529688 -9.999802e-01 cos(x)
876   3.16047998 -9.998216e-01 cos(x)
877   3.18566309 -9.990291e-01 cos(x)
878   3.21084620 -9.976029e-01 cos(x)
879   3.23602931 -9.955442e-01 cos(x)
880   3.26121241 -9.928541e-01 cos(x)
881   3.28639552 -9.895344e-01 cos(x)
882   3.31157863 -9.855871e-01 cos(x)
883   3.33676174 -9.810149e-01 cos(x)
884   3.36194484 -9.758205e-01 cos(x)
885   3.38712795 -9.700073e-01 cos(x)
886   3.41231106 -9.635790e-01 cos(x)
887   3.43749417 -9.565396e-01 cos(x)
888   3.46267727 -9.488937e-01 cos(x)
889   3.48786038 -9.406460e-01 cos(x)
890   3.51304349 -9.318017e-01 cos(x)
891   3.53822660 -9.223666e-01 cos(x)
892   3.56340970 -9.123465e-01 cos(x)
893   3.58859281 -9.017479e-01 cos(x)
894   3.61377592 -8.905774e-01 cos(x)
895   3.63895903 -8.788421e-01 cos(x)
896   3.66414213 -8.665496e-01 cos(x)
897   3.68932524 -8.537075e-01 cos(x)
898   3.71450835 -8.403240e-01 cos(x)
899   3.73969146 -8.264076e-01 cos(x)
900   3.76487456 -8.119672e-01 cos(x)
901   3.79005767 -7.970118e-01 cos(x)
902   3.81524078 -7.815510e-01 cos(x)
903   3.84042389 -7.655946e-01 cos(x)
904   3.86560699 -7.491527e-01 cos(x)
905   3.89079010 -7.322357e-01 cos(x)
906   3.91597321 -7.148543e-01 cos(x)
907   3.94115631 -6.970197e-01 cos(x)
908   3.96633942 -6.787430e-01 cos(x)
909   3.99152253 -6.600358e-01 cos(x)
910   4.01670564 -6.409101e-01 cos(x)
911   4.04188874 -6.213780e-01 cos(x)
912   4.06707185 -6.014518e-01 cos(x)
913   4.09225496 -5.811442e-01 cos(x)
914   4.11743807 -5.604681e-01 cos(x)
915   4.14262117 -5.394365e-01 cos(x)
916   4.16780428 -5.180629e-01 cos(x)
917   4.19298739 -4.963607e-01 cos(x)
918   4.21817050 -4.743438e-01 cos(x)
919   4.24335360 -4.520260e-01 cos(x)
920   4.26853671 -4.294216e-01 cos(x)
921   4.29371982 -4.065449e-01 cos(x)
922   4.31890293 -3.834104e-01 cos(x)
923   4.34408603 -3.600327e-01 cos(x)
924   4.36926914 -3.364267e-01 cos(x)
925   4.39445225 -3.126074e-01 cos(x)
926   4.41963536 -2.885898e-01 cos(x)
927   4.44481846 -2.643892e-01 cos(x)
928   4.47000157 -2.400209e-01 cos(x)
929   4.49518468 -2.155005e-01 cos(x)
930   4.52036779 -1.908433e-01 cos(x)
931   4.54555089 -1.660652e-01 cos(x)
932   4.57073400 -1.411817e-01 cos(x)
933   4.59591711 -1.162087e-01 cos(x)
934   4.62110022 -9.116202e-02 cos(x)
935   4.64628332 -6.605752e-02 cos(x)
936   4.67146643 -4.091113e-02 cos(x)
937   4.69664954 -1.573879e-02 cos(x)
938   4.72183265  9.443525e-03 cos(x)
939   4.74701575  3.461985e-02 cos(x)
940   4.77219886  5.977423e-02 cos(x)
941   4.79738197  8.489070e-02 cos(x)
942   4.82256508  1.099533e-01 cos(x)
943   4.84774818  1.349462e-01 cos(x)
944   4.87293129  1.598536e-01 cos(x)
945   4.89811440  1.846595e-01 cos(x)
946   4.92329751  2.093484e-01 cos(x)
947   4.94848061  2.339045e-01 cos(x)
948   4.97366372  2.583122e-01 cos(x)
949   4.99884683  2.825562e-01 cos(x)
950   5.02402993  3.066210e-01 cos(x)
951   5.04921304  3.304913e-01 cos(x)
952   5.07439615  3.541520e-01 cos(x)
953   5.09957926  3.775882e-01 cos(x)
954   5.12476236  4.007849e-01 cos(x)
955   5.14994547  4.237274e-01 cos(x)
956   5.17512858  4.464013e-01 cos(x)
957   5.20031169  4.687920e-01 cos(x)
958   5.22549479  4.908855e-01 cos(x)
959   5.25067790  5.126676e-01 cos(x)
960   5.27586101  5.341247e-01 cos(x)
961   5.30104412  5.552430e-01 cos(x)
962   5.32622722  5.760092e-01 cos(x)
963   5.35141033  5.964102e-01 cos(x)
964   5.37659344  6.164329e-01 cos(x)
965   5.40177655  6.360647e-01 cos(x)
966   5.42695965  6.552932e-01 cos(x)
967   5.45214276  6.741061e-01 cos(x)
968   5.47732587  6.924915e-01 cos(x)
969   5.50250898  7.104377e-01 cos(x)
970   5.52769208  7.279335e-01 cos(x)
971   5.55287519  7.449676e-01 cos(x)
972   5.57805830  7.615292e-01 cos(x)
973   5.60324141  7.776080e-01 cos(x)
974   5.62842451  7.931936e-01 cos(x)
975   5.65360762  8.082762e-01 cos(x)
976   5.67879073  8.228463e-01 cos(x)
977   5.70397384  8.368945e-01 cos(x)
978   5.72915694  8.504120e-01 cos(x)
979   5.75434005  8.633903e-01 cos(x)
980   5.77952316  8.758210e-01 cos(x)
981   5.80470627  8.876962e-01 cos(x)
982   5.82988937  8.990086e-01 cos(x)
983   5.85507248  9.097508e-01 cos(x)
984   5.88025559  9.199162e-01 cos(x)
985   5.90543870  9.294981e-01 cos(x)
986   5.93062180  9.384906e-01 cos(x)
987   5.95580491  9.468880e-01 cos(x)
988   5.98098802  9.546848e-01 cos(x)
989   6.00617113  9.618763e-01 cos(x)
990   6.03135423  9.684578e-01 cos(x)
991   6.05653734  9.744251e-01 cos(x)
992   6.08172045  9.797745e-01 cos(x)
993   6.10690356  9.845026e-01 cos(x)
994   6.13208666  9.886063e-01 cos(x)
995   6.15726977  9.920831e-01 cos(x)
996   6.18245288  9.949308e-01 cos(x)
997   6.20763598  9.971475e-01 cos(x)
998   6.23281909  9.987319e-01 cos(x)
999   6.25800220  9.996829e-01 cos(x)
1000  6.28318531  1.000000e+00 cos(x)

Step 3: Construct the graph for the function

Step 3.1:Initialize the ggplot Object

p<-ggplot(df, aes(x=x,y=y,color=group,linetype = group))
p

Step 3.2:Add the line Geometry

p<-p+geom_line(size=1.2)
p

Step 3.3 :Add plot Lables

p<-p+labs(title = "function Curves: sin(x) and cos(x)",
          x="x",
          y="y=f(x)",
          color="Function",
          linetype="Function")
p

Step 3.4: Display the Graph

p<-p+theme_minimal()
p

Quarto

Running Code

Program 1

Step1: Load necessary libraries

Step 2: Load the dataset

Step 3: Group by categorical variables

Step 4: Visualizing the findings

Program 2

Step 1: Load necessary libraries

Step 2: Load the Dataset

Step 3: Create a Scatter Plot

Program 3

Introduction

Step 1: Load necessary libraries

Step 2Load the Built-in AirPassengers Dataset

Step 3: Define a Function for Time-Series Line Graph

Program 4

Step 1: Load the Dataset

Explanation

Step 2: Convert Numeric Data to Categorical

Why Convert to Factors?

Step 3: Create a Bar Graph

Explanation of the Plot

Program 5

Step 1: Load Required Library

Step 2: Explore the Inbuilt Dataset

Step 3: Create Histogram with Group-wise Density Curves

Step 3.1: Initialize the ggplot with aesthetic mappings

Step 3.2: Add Histogram Layer

Explanation:

Step 3.3: Add Density Curve Layer

Step 3.4: Add Labels and Theme

Step 3.5: Display the Plot

Program 6

Step 1: Load Required Library# Load ggplot2 package for visualization

Step 2: Explore the Inbuilt Dataset

Step 3: Construct Box Plot with Grouping

Step 3.1: Initialize ggplot with Aesthetic Mappings

Step 3.2: Add Box Plot Layer

Step 3.3: Add Labels and Theme

Step 3.4: Display the Plot

Program 7

Step 1: Load Required Library

Step 2: Create data for the function

Step 3: Construct the graph for the function

Step 3.1:Initialize the ggplot Object

Step 3.2:Add the line Geometry

Step 3.3 :Add plot Lables

Step 3.4: Display the Graph

Step 2Load the Built-in `AirPassengers` Dataset