library(ggplot2)program15
Compilation of programs from 9 to 14
Create multiple histograms using ggplot2::facet_wrap() to visualize how a variable is distributed across different groups in a built-in R data set
Step 1: Load the necessary library
Step 2: Load the data set
data(iris)
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 setosaStep 3: Create histogram of sepal length
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(binwidth = 0.3, fill = "skyblue", color = "black") +
facet_wrap(~ Species) +
labs(
title = "Distribution of Sepal Length by Species",
x = "Sepal Length (cm)",
y = "Frequency"
) +
theme_minimal()
10.Develop an R program to draw a density curve representing the probability density of a continuous variable, with separate curves for each group , using ggplot2
Step 1: Load required library
library(ggplot2)Step 2: Define the function
plot_density_by_group <- function(data, continuous_var, group_var, fill_colors = NULL) {
# Check if the specified columns exist
if (!(continuous_var %in% names(data)) || !(group_var %in% names(data))) {
stop("Invalid column names. Make sure both variables exist in the dataset.")
}
# Create the ggplot object
p <- ggplot(data, aes_string(x = continuous_var, color = group_var, fill = group_var)) +
geom_density(alpha = 0.4) +
labs(title = paste("Density Plot of", continuous_var, "by", group_var),
x = continuous_var,
y = "Density") +
theme_minimal()
# Apply custom fill colors if provided
if (!is.null(fill_colors)) {
p <- p + scale_fill_manual(values = fill_colors) +
scale_color_manual(values = fill_colors)
}
# Return the plot
return(p)
}Step 3-Load Dataset
data(iris)
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 setosastep 4-Call the Function with Example
# Basic usage
plot_density_by_group(iris, "Sepal.Length", "Species")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.
Step 5: Example with Custom Colors
# Define custom colors
custom_colors <- c("setosa" = "steelblue",
"versicolor" = "forestgreen",
"virginica" = "darkorange")
# Plot with custom colors
plot_density_by_group(iris, "Petal.Length", "Species", fill_colors = custom_colors)
11. To generate a basic box plot using ggplot2, enhanced with notches and outliners , and grouped by a categorical variable using an in-built dataset in R
Step 1 : Load the library
library(ggplot2)Step 2: Load and explore the dataset
data(iris)
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 setosastr(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 ...Step 3: Create a box plot
ggplot(iris, aes(x=Species, y=Sepal.Length)) + geom_boxplot(notch = TRUE) + geom_boxplot(outlier.color="red") + geom_boxplot(fill="yellow",outlier.shape = 16) + labs(title = "Distribution of Species",
x=" Species",
y="Sepal Length") + theme_minimal()
12. Develop a script in R to create a violin plot displaying the distribution of a continous variable with a separate violins for each group using ggplot2.
Step1- load the library
library(ggplot2)Step2- Create the violin plot
violin_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin(trim = FALSE) + # Show full distribution without trimming tails
geom_boxplot(width = 0.1, fill = "white") + # Add a boxplot inside violins
labs(title = "Distribution of Sepal Length by Species",
x = "Species",
y = "Sepal Length") +
theme_minimal() +
theme(legend.position = "none")Step3- Display the plot
print(violin_plot)
Write an R program to create multiple dot plots or group data , comparing the distributions of variables across different categories, using ggplot2’s position_dodge function
Step1: Load the library
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionStep2: Explore the data-set
data(ToothGrowth)
head(ToothGrowth) len supp dose
1 4.2 VC 0.5
2 11.5 VC 0.5
3 7.3 VC 0.5
4 5.8 VC 0.5
5 6.4 VC 0.5
6 10.0 VC 0.5str(ToothGrowth)'data.frame': 60 obs. of 3 variables:
$ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
$ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
$ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...p=ToothGrowthStep 3: Create a dot plot
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
ToothGrowth$dose [1] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1 1 1 1 1 1 1 1 1
[20] 1 2 2 2 2 2 2 2 2 2 2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[39] 0.5 0.5 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[58] 2 2 2
Levels: 0.5 1 2ggplot(ToothGrowth, aes(x=dose, y=len, color=supp)) 
ggplot(ToothGrowth, aes(x=dose, y=len, color=supp))+
geom_dotplot(
binaxis = 'y', #used to group many entries into separate bins, x means group vertically , y means group horizontally
stackdir = 'center', #which direction to stack the dots
position = position_dodge(width = 0.8),
dotsize = 0.6,
binwidth = 1.5
) + labs(title = "Dotplot of Tooth Length and Dosage",
x="Dose",
y="Len")+ theme_minimal()
Create a R program to calculate and visualize co relation matrix for a given data set with color coded cells the strength and direction of co relations , using ggplot2 geom_tile function
Step 1 : Load the libraries
library(ggplot2)
library(dplyr)Step 2: Explore the dataset
data(mtcars)
head(mtcars, n=10) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4cor_matrix <- cor(mtcars)
cor_df <- as.data.frame(as.table(cor_matrix))
cor_df Var1 Var2 Freq
1 mpg mpg 1.00000000
2 cyl mpg -0.85216196
3 disp mpg -0.84755138
4 hp mpg -0.77616837
5 drat mpg 0.68117191
6 wt mpg -0.86765938
7 qsec mpg 0.41868403
8 vs mpg 0.66403892
9 am mpg 0.59983243
10 gear mpg 0.48028476
11 carb mpg -0.55092507
12 mpg cyl -0.85216196
13 cyl cyl 1.00000000
14 disp cyl 0.90203287
15 hp cyl 0.83244745
16 drat cyl -0.69993811
17 wt cyl 0.78249579
18 qsec cyl -0.59124207
19 vs cyl -0.81081180
20 am cyl -0.52260705
21 gear cyl -0.49268660
22 carb cyl 0.52698829
23 mpg disp -0.84755138
24 cyl disp 0.90203287
25 disp disp 1.00000000
26 hp disp 0.79094859
27 drat disp -0.71021393
28 wt disp 0.88797992
29 qsec disp -0.43369788
30 vs disp -0.71041589
31 am disp -0.59122704
32 gear disp -0.55556920
33 carb disp 0.39497686
34 mpg hp -0.77616837
35 cyl hp 0.83244745
36 disp hp 0.79094859
37 hp hp 1.00000000
38 drat hp -0.44875912
39 wt hp 0.65874789
40 qsec hp -0.70822339
41 vs hp -0.72309674
42 am hp -0.24320426
43 gear hp -0.12570426
44 carb hp 0.74981247
45 mpg drat 0.68117191
46 cyl drat -0.69993811
47 disp drat -0.71021393
48 hp drat -0.44875912
49 drat drat 1.00000000
50 wt drat -0.71244065
51 qsec drat 0.09120476
52 vs drat 0.44027846
53 am drat 0.71271113
54 gear drat 0.69961013
55 carb drat -0.09078980
56 mpg wt -0.86765938
57 cyl wt 0.78249579
58 disp wt 0.88797992
59 hp wt 0.65874789
60 drat wt -0.71244065
61 wt wt 1.00000000
62 qsec wt -0.17471588
63 vs wt -0.55491568
64 am wt -0.69249526
65 gear wt -0.58328700
66 carb wt 0.42760594
67 mpg qsec 0.41868403
68 cyl qsec -0.59124207
69 disp qsec -0.43369788
70 hp qsec -0.70822339
71 drat qsec 0.09120476
72 wt qsec -0.17471588
73 qsec qsec 1.00000000
74 vs qsec 0.74453544
75 am qsec -0.22986086
76 gear qsec -0.21268223
77 carb qsec -0.65624923
78 mpg vs 0.66403892
79 cyl vs -0.81081180
80 disp vs -0.71041589
81 hp vs -0.72309674
82 drat vs 0.44027846
83 wt vs -0.55491568
84 qsec vs 0.74453544
85 vs vs 1.00000000
86 am vs 0.16834512
87 gear vs 0.20602335
88 carb vs -0.56960714
89 mpg am 0.59983243
90 cyl am -0.52260705
91 disp am -0.59122704
92 hp am -0.24320426
93 drat am 0.71271113
94 wt am -0.69249526
95 qsec am -0.22986086
96 vs am 0.16834512
97 am am 1.00000000
98 gear am 0.79405876
99 carb am 0.05753435
100 mpg gear 0.48028476
101 cyl gear -0.49268660
102 disp gear -0.55556920
103 hp gear -0.12570426
104 drat gear 0.69961013
105 wt gear -0.58328700
106 qsec gear -0.21268223
107 vs gear 0.20602335
108 am gear 0.79405876
109 gear gear 1.00000000
110 carb gear 0.27407284
111 mpg carb -0.55092507
112 cyl carb 0.52698829
113 disp carb 0.39497686
114 hp carb 0.74981247
115 drat carb -0.09078980
116 wt carb 0.42760594
117 qsec carb -0.65624923
118 vs carb -0.56960714
119 am carb 0.05753435
120 gear carb 0.27407284
121 carb carb 1.00000000Step 3: Visualize the graph
ggplot(cor_df, aes(x=Var1, y=Var2, fill = Freq)) + geom_tile(color = "white") +
scale_fill_gradient2(
low="blue", mid="white", high="red", midpoint = 0, limit= c(-1,1),name="Corelation"
) + geom_text(aes(label = round(Freq, 2)), size =3) + theme_minimal()+
labs(title="Corelation matrix(mtcars)", x="", y="")+ theme(axis.text.x = element_text(angle = 45, hjust=1))