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 libraries

We load two libraries:

• ggplot2 is used to build plots layer-by-layer (we will use it to create the scatter plot).
• dplyr provides functions for exploring and summarizing data (we will use it to understand the categories in the dataset).

library(ggplot2)
library(dplyr)

Warning: package 'dplyr' was built under R version 4.5.2

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

---

Step 2: Load the dataset (iris)

We use the built-in dataset iris.

What this dataset contains:

• Each row is one flower sample (an observation).
• There are 150 total observations.
• The column Species is a categorical variable with 3 groups:
  • setosa
  • versicolor
  • virginica
• The columns Sepal.Length and Sepal.Width are numeric measurements that we will plot.

data <- iris
head(data,10)

   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
7           4.6         3.4          1.4         0.3  setosa
8           5.0         3.4          1.5         0.2  setosa
9           4.4         2.9          1.4         0.2  setosa
10          4.9         3.1          1.5         0.1  setosa

tail(data, 10)

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
141          6.7         3.1          5.6         2.4 virginica
142          6.9         3.1          5.1         2.3 virginica
143          5.8         2.7          5.1         1.9 virginica
144          6.8         3.2          5.9         2.3 virginica
145          6.7         3.3          5.7         2.5 virginica
146          6.7         3.0          5.2         2.3 virginica
147          6.3         2.5          5.0         1.9 virginica
148          6.5         3.0          5.2         2.0 virginica
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica

str(data)

'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 ...

summary(data)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50  
                
                
                

names(data)

[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"     

dim(data)

[1] 150   5

data[1]

    Sepal.Length
1            5.1
2            4.9
3            4.7
4            4.6
5            5.0
6            5.4
7            4.6
8            5.0
9            4.4
10           4.9
11           5.4
12           4.8
13           4.8
14           4.3
15           5.8
16           5.7
17           5.4
18           5.1
19           5.7
20           5.1
21           5.4
22           5.1
23           4.6
24           5.1
25           4.8
26           5.0
27           5.0
28           5.2
29           5.2
30           4.7
31           4.8
32           5.4
33           5.2
34           5.5
35           4.9
36           5.0
37           5.5
38           4.9
39           4.4
40           5.1
41           5.0
42           4.5
43           4.4
44           5.0
45           5.1
46           4.8
47           5.1
48           4.6
49           5.3
50           5.0
51           7.0
52           6.4
53           6.9
54           5.5
55           6.5
56           5.7
57           6.3
58           4.9
59           6.6
60           5.2
61           5.0
62           5.9
63           6.0
64           6.1
65           5.6
66           6.7
67           5.6
68           5.8
69           6.2
70           5.6
71           5.9
72           6.1
73           6.3
74           6.1
75           6.4
76           6.6
77           6.8
78           6.7
79           6.0
80           5.7
81           5.5
82           5.5
83           5.8
84           6.0
85           5.4
86           6.0
87           6.7
88           6.3
89           5.6
90           5.5
91           5.5
92           6.1
93           5.8
94           5.0
95           5.6
96           5.7
97           5.7
98           6.2
99           5.1
100          5.7
101          6.3
102          5.8
103          7.1
104          6.3
105          6.5
106          7.6
107          4.9
108          7.3
109          6.7
110          7.2
111          6.5
112          6.4
113          6.8
114          5.7
115          5.8
116          6.4
117          6.5
118          7.7
119          7.7
120          6.0
121          6.9
122          5.6
123          7.7
124          6.3
125          6.7
126          7.2
127          6.2
128          6.1
129          6.4
130          7.2
131          7.4
132          7.9
133          6.4
134          6.3
135          6.1
136          7.7
137          6.3
138          6.4
139          6.0
140          6.9
141          6.7
142          6.9
143          5.8
144          6.8
145          6.7
146          6.7
147          6.3
148          6.5
149          6.2
150          5.9

data$Sepal.Length

  [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
 [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
 [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
 [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
 [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
 [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
[109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
[127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
[145] 6.7 6.7 6.3 6.5 6.2 5.9

typeof(data$Sepal.Length)

[1] "double"

typeof(data[1])

[1] "list"

data[][1]

    Sepal.Length
1            5.1
2            4.9
3            4.7
4            4.6
5            5.0
6            5.4
7            4.6
8            5.0
9            4.4
10           4.9
11           5.4
12           4.8
13           4.8
14           4.3
15           5.8
16           5.7
17           5.4
18           5.1
19           5.7
20           5.1
21           5.4
22           5.1
23           4.6
24           5.1
25           4.8
26           5.0
27           5.0
28           5.2
29           5.2
30           4.7
31           4.8
32           5.4
33           5.2
34           5.5
35           4.9
36           5.0
37           5.5
38           4.9
39           4.4
40           5.1
41           5.0
42           4.5
43           4.4
44           5.0
45           5.1
46           4.8
47           5.1
48           4.6
49           5.3
50           5.0
51           7.0
52           6.4
53           6.9
54           5.5
55           6.5
56           5.7
57           6.3
58           4.9
59           6.6
60           5.2
61           5.0
62           5.9
63           6.0
64           6.1
65           5.6
66           6.7
67           5.6
68           5.8
69           6.2
70           5.6
71           5.9
72           6.1
73           6.3
74           6.1
75           6.4
76           6.6
77           6.8
78           6.7
79           6.0
80           5.7
81           5.5
82           5.5
83           5.8
84           6.0
85           5.4
86           6.0
87           6.7
88           6.3
89           5.6
90           5.5
91           5.5
92           6.1
93           5.8
94           5.0
95           5.6
96           5.7
97           5.7
98           6.2
99           5.1
100          5.7
101          6.3
102          5.8
103          7.1
104          6.3
105          6.5
106          7.6
107          4.9
108          7.3
109          6.7
110          7.2
111          6.5
112          6.4
113          6.8
114          5.7
115          5.8
116          6.4
117          6.5
118          7.7
119          7.7
120          6.0
121          6.9
122          5.6
123          7.7
124          6.3
125          6.7
126          7.2
127          6.2
128          6.1
129          6.4
130          7.2
131          7.4
132          7.9
133          6.4
134          6.3
135          6.1
136          7.7
137          6.3
138          6.4
139          6.0
140          6.9
141          6.7
142          6.9
143          5.8
144          6.8
145          6.7
146          6.7
147          6.3
148          6.5
149          6.2
150          5.9

---

Step 3: Preview the dataset (see the first few rows)

We preview the data to understand:

• what columns exist,
• what values look like,
• whether the dataset was loaded correctly.

head(data, n = 10) displays the first 10 rows.

head(data, n = 10)

   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
7           4.6         3.4          1.4         0.3  setosa
8           5.0         3.4          1.5         0.2  setosa
9           4.4         2.9          1.4         0.2  setosa
10          4.9         3.1          1.5         0.1  setosa

data[1:5,1:3 ]

  Sepal.Length Sepal.Width Petal.Length
1          5.1         3.5          1.4
2          4.9         3.0          1.4
3          4.7         3.2          1.3
4          4.6         3.1          1.5
5          5.0         3.6          1.4

table(data$Species)

    setosa versicolor  virginica 
        50         50         50 

---

Step 4: Inspect the categorical variable (Species)

This is our categorical analysis step.

table(data$Species) counts how many observations belong to each species.

Why do this?

• It confirms that Species has the groups we expect.
• It helps us understand whether the data is balanced (equal counts per category).

table(data$Species)

    setosa versicolor  virginica 
        50         50         50 

---

Step 5: Create a basic scatter plot (no categories yet)

A scatter plot shows the relationship between two numeric variables.

Here we plot:

• X-axis: Sepal.Length
• Y-axis: Sepal.Width

Important point:

• Each dot represents one flower (one row in the dataset).

ggplot(data, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()

At this stage, all points are the same color, so we cannot see species-based grouping yet.

---

Step 6: Add categorical grouping using color = Species

Now we include the categorical variable:

• color = Species tells ggplot2 to assign a different color to each species.

What changes?

• The plot now visually separates the three species based on color.
• This is the main “categorical analysis” idea: we can see if different groups cluster differently.

ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point()

---

Step 7: Improve point visibility (size and transparency)

We adjust how points look:

• size = 3 makes each dot bigger, so it is easier to see.
• alpha = 0.7 makes dots slightly transparent, which helps when points overlap.

Why transparency helps:

• If many points overlap in the same region, transparency makes dense areas more visible.

ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.7)

---

Step 8: Add informative labels (title, axes, legend)

Good plots should clearly communicate what the viewer is seeing.

labs() adds:

• title for the plot heading
• x and y axis labels
• color legend title (so the legend has a meaningful name)

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"
  )

---

Step 9: Apply a clean theme and move the legend

Themes control the background, grids, and text styling.

• theme_minimal() removes heavy backgrounds and gives a clean look.
• theme(legend.position = "top") moves the legend above the plot.

Why move the legend?

• When the legend is at the top, it is often easier to notice and read, especially in presentations.

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")

---

Discussion Questions

1. Do you see clusters of points by species?
2. Which species appears most separated from the others?
3. What happens if you plot Petal.Length vs Petal.Width instead?
4. What changes if you remove alpha or increase size further?