Program-8

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.

Step 1: 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

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

Step 3: Group by categorical variables

summary_data <- data %>%
  group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg), .groups = 'drop')

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

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

Write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different groups, using ggplot2.

Step 1: Load necessary libraries

library(ggplot2)
library(dplyr)

Step 2: Load the dataset

data<-iris
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

table(data$Species)

    setosa versicolor  virginica 
        50         50         50 

Step 3: Create a Scatter Plot

ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.7) + 
  labs(title = "Scatter Plot of Sepal Dimensions",
       x = "Sepal Length",
       y = "Sepal Width",
       color = "Species") +  
  theme_minimal() +  
  theme(legend.position = "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.

Step 1: Load necessary libraries

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

Step 2: Load the Built-in AirPassengers dataset

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"))
)

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

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) +  
    geom_point(size = 2) +   
    labs(title = title,
         x = "Year",
         y = "Number of Passengers",
         color = "Year") +  
    theme_minimal() +
    theme(legend.position = "top")
}

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

Step 1: Load libraries

library(ggplot2)

Step 2: Load the dataset

data <- mtcars
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

Step 3: Convert numeric data to categorical

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

Step 4: Creating a Bar Graph

ggplot(data, aes(x=cyl,fill=gear)) +
  geom_bar(position = "dodge")+
labs(title = "Frequency of cylinders grouped by gear type",
     x="Number of cylinders",
     y="Count",
     fill = "Gears")+
  theme_minimal()

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

str(iris)

'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)

  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 Histogram With Group Wise Density curves

Step 3.1: initialize the ggplot with aesthetic mappings

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

Step 3.2: Add Histogram Layer

 p <- p + geom_histogram(aes(y= ..density..),
 alpha = 0.4,
 position = "identity",
 bins = 30)
p

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

Step 3.3: Add Density Curve Layer

p<-p +
  geom_density(aes(colour = Species),
  size=1.2)
p

Step 3.4: Add Labels and Theme

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

p

Step 5: Display 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

library(ggplot2)

Step 2: Explore the Inbuilt Dataset

str(iris)

'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)

  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

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

Step 3.2: Add Box Plot Layer

p <- p + geom_boxplot()
p

Step 3.3: Add Labels and Theme

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

Step 3.4: Display the Plot

p

Program-7

Develop a function in R to plot a function curve based on a mathematical equation provided as input ,with different curve styles for each group ,using ggplot2.

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  2.449294e-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)
34   -5.45214276  7.386346e-01 sin(x)
35   -5.42695965  7.553746e-01 sin(x)
36   -5.40177655  7.716357e-01 sin(x)
37   -5.37659344  7.874074e-01 sin(x)
38   -5.35141033  8.026798e-01 sin(x)
39   -5.32622722  8.174432e-01 sin(x)
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)
44   -5.20031169  8.833086e-01 sin(x)
45   -5.17512858  8.948329e-01 sin(x)
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)
96   -3.89079010  6.810513e-01 sin(x)
97   -3.86560699  6.623974e-01 sin(x)
98   -3.84042389  6.433233e-01 sin(x)
99   -3.81524078  6.238413e-01 sin(x)
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)
141  -2.75755027 -3.746715e-01 sin(x)
142  -2.73236716 -3.978989e-01 sin(x)
143  -2.70718405 -4.208740e-01 sin(x)
144  -2.68200094 -4.435822e-01 sin(x)
145  -2.65681784 -4.660091e-01 sin(x)
146  -2.63163473 -4.881405e-01 sin(x)
147  -2.60645162 -5.099624e-01 sin(x)
148  -2.58126851 -5.314608e-01 sin(x)
149  -2.55608541 -5.526222e-01 sin(x)
150  -2.53090230 -5.734332e-01 sin(x)
151  -2.50571919 -5.938805e-01 sin(x)
152  -2.48053608 -6.139512e-01 sin(x)
153  -2.45535298 -6.336326e-01 sin(x)
154  -2.43016987 -6.529121e-01 sin(x)
155  -2.40498676 -6.717776e-01 sin(x)
156  -2.37980365 -6.902171e-01 sin(x)
157  -2.35462055 -7.082189e-01 sin(x)
158  -2.32943744 -7.257715e-01 sin(x)
159  -2.30425433 -7.428639e-01 sin(x)
160  -2.27907122 -7.594852e-01 sin(x)
161  -2.25388812 -7.756249e-01 sin(x)
162  -2.22870501 -7.912727e-01 sin(x)
163  -2.20352190 -8.064188e-01 sin(x)
164  -2.17833879 -8.210534e-01 sin(x)
165  -2.15315569 -8.351673e-01 sin(x)
166  -2.12797258 -8.487517e-01 sin(x)
167  -2.10278947 -8.617978e-01 sin(x)
168  -2.07760636 -8.742973e-01 sin(x)
169  -2.05242326 -8.862425e-01 sin(x)
170  -2.02724015 -8.976256e-01 sin(x)
171  -2.00205704 -9.084395e-01 sin(x)
172  -1.97687393 -9.186773e-01 sin(x)
173  -1.95169083 -9.283325e-01 sin(x)
174  -1.92650772 -9.373990e-01 sin(x)
175  -1.90132461 -9.458710e-01 sin(x)
176  -1.87614150 -9.537432e-01 sin(x)
177  -1.85095840 -9.610106e-01 sin(x)
178  -1.82577529 -9.676686e-01 sin(x)
179  -1.80059218 -9.737129e-01 sin(x)
180  -1.77540907 -9.791397e-01 sin(x)
181  -1.75022597 -9.839456e-01 sin(x)
182  -1.72504286 -9.881276e-01 sin(x)
183  -1.69985975 -9.916829e-01 sin(x)
184  -1.67467664 -9.946093e-01 sin(x)
185  -1.64949354 -9.969050e-01 sin(x)
186  -1.62431043 -9.985685e-01 sin(x)
187  -1.59912732 -9.995987e-01 sin(x)
188  -1.57394422 -9.999950e-01 sin(x)
189  -1.54876111 -9.997572e-01 sin(x)
190  -1.52357800 -9.988854e-01 sin(x)
191  -1.49839489 -9.973802e-01 sin(x)
192  -1.47321179 -9.952424e-01 sin(x)
193  -1.44802868 -9.924735e-01 sin(x)
194  -1.42284557 -9.890752e-01 sin(x)
195  -1.39766246 -9.850497e-01 sin(x)
196  -1.37247936 -9.803996e-01 sin(x)
197  -1.34729625 -9.751277e-01 sin(x)
198  -1.32211314 -9.692374e-01 sin(x)
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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