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, unionpm2
write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different group,using ggplot2.
Step1: 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 categorical in the data set).
step 2:Load the dataset (iris)
we use the built-in data set iris.
what this data set contains:
- Each row is one flower sample (an observation).
- There are 150 total observation. -The column
Speciesis a categorical variable with 3 groups:setosaversicolorvirginica
- The column
sepal.LengthandSepal.Widthare numeric measurement that we will plot.
data <- irisstep:3
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 setosatail(data) Sepal.Length Sepal.Width Petal.Length Petal.Width Species
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 virginicanames(data)[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" 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
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 ...dim(data)[1] 150 5data$Sepal.Length [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
[19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
[37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
[55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
[73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
[91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
[109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
[127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
[145] 6.7 6.7 6.3 6.5 6.2 5.9typeof(data$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 5.9data[2][] Sepal.Width
1 3.5
2 3.0
3 3.2
4 3.1
5 3.6
6 3.9
7 3.4
8 3.4
9 2.9
10 3.1
11 3.7
12 3.4
13 3.0
14 3.0
15 4.0
16 4.4
17 3.9
18 3.5
19 3.8
20 3.8
21 3.4
22 3.7
23 3.6
24 3.3
25 3.4
26 3.0
27 3.4
28 3.5
29 3.4
30 3.2
31 3.1
32 3.4
33 4.1
34 4.2
35 3.1
36 3.2
37 3.5
38 3.6
39 3.0
40 3.4
41 3.5
42 2.3
43 3.2
44 3.5
45 3.8
46 3.0
47 3.8
48 3.2
49 3.7
50 3.3
51 3.2
52 3.2
53 3.1
54 2.3
55 2.8
56 2.8
57 3.3
58 2.4
59 2.9
60 2.7
61 2.0
62 3.0
63 2.2
64 2.9
65 2.9
66 3.1
67 3.0
68 2.7
69 2.2
70 2.5
71 3.2
72 2.8
73 2.5
74 2.8
75 2.9
76 3.0
77 2.8
78 3.0
79 2.9
80 2.6
81 2.4
82 2.4
83 2.7
84 2.7
85 3.0
86 3.4
87 3.1
88 2.3
89 3.0
90 2.5
91 2.6
92 3.0
93 2.6
94 2.3
95 2.7
96 3.0
97 2.9
98 2.9
99 2.5
100 2.8
101 3.3
102 2.7
103 3.0
104 2.9
105 3.0
106 3.0
107 2.5
108 2.9
109 2.5
110 3.6
111 3.2
112 2.7
113 3.0
114 2.5
115 2.8
116 3.2
117 3.0
118 3.8
119 2.6
120 2.2
121 3.2
122 2.8
123 2.8
124 2.7
125 3.3
126 3.2
127 2.8
128 3.0
129 2.8
130 3.0
131 2.8
132 3.8
133 2.8
134 2.8
135 2.6
136 3.0
137 3.4
138 3.1
139 3.0
140 3.1
141 3.1
142 3.1
143 2.7
144 3.2
145 3.3
146 3.0
147 2.5
148 3.0
149 3.4
150 3.0data[][5] Species
1 setosa
2 setosa
3 setosa
4 setosa
5 setosa
6 setosa
7 setosa
8 setosa
9 setosa
10 setosa
11 setosa
12 setosa
13 setosa
14 setosa
15 setosa
16 setosa
17 setosa
18 setosa
19 setosa
20 setosa
21 setosa
22 setosa
23 setosa
24 setosa
25 setosa
26 setosa
27 setosa
28 setosa
29 setosa
30 setosa
31 setosa
32 setosa
33 setosa
34 setosa
35 setosa
36 setosa
37 setosa
38 setosa
39 setosa
40 setosa
41 setosa
42 setosa
43 setosa
44 setosa
45 setosa
46 setosa
47 setosa
48 setosa
49 setosa
50 setosa
51 versicolor
52 versicolor
53 versicolor
54 versicolor
55 versicolor
56 versicolor
57 versicolor
58 versicolor
59 versicolor
60 versicolor
61 versicolor
62 versicolor
63 versicolor
64 versicolor
65 versicolor
66 versicolor
67 versicolor
68 versicolor
69 versicolor
70 versicolor
71 versicolor
72 versicolor
73 versicolor
74 versicolor
75 versicolor
76 versicolor
77 versicolor
78 versicolor
79 versicolor
80 versicolor
81 versicolor
82 versicolor
83 versicolor
84 versicolor
85 versicolor
86 versicolor
87 versicolor
88 versicolor
89 versicolor
90 versicolor
91 versicolor
92 versicolor
93 versicolor
94 versicolor
95 versicolor
96 versicolor
97 versicolor
98 versicolor
99 versicolor
100 versicolor
101 virginica
102 virginica
103 virginica
104 virginica
105 virginica
106 virginica
107 virginica
108 virginica
109 virginica
110 virginica
111 virginica
112 virginica
113 virginica
114 virginica
115 virginica
116 virginica
117 virginica
118 virginica
119 virginica
120 virginica
121 virginica
122 virginica
123 virginica
124 virginica
125 virginica
126 virginica
127 virginica
128 virginica
129 virginica
130 virginica
131 virginica
132 virginica
133 virginica
134 virginica
135 virginica
136 virginica
137 virginica
138 virginica
139 virginica
140 virginica
141 virginica
142 virginica
143 virginica
144 virginica
145 virginica
146 virginica
147 virginica
148 virginica
149 virginica
150 virginicadata[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 1.4data[1,] Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosastep 4:
table(data$Species)
setosa versicolor virginica
50 50 50 data$Species [1] setosa setosa setosa setosa setosa setosa
[7] setosa setosa setosa setosa setosa setosa
[13] setosa setosa setosa setosa setosa setosa
[19] setosa setosa setosa setosa setosa setosa
[25] setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa
[37] setosa setosa setosa setosa setosa setosa
[43] setosa setosa setosa setosa setosa setosa
[49] setosa setosa versicolor versicolor versicolor versicolor
[55] versicolor versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor versicolor
[67] versicolor versicolor versicolor versicolor versicolor versicolor
[73] versicolor versicolor versicolor versicolor versicolor versicolor
[79] versicolor versicolor versicolor versicolor versicolor versicolor
[85] versicolor versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor versicolor
[97] versicolor versicolor versicolor versicolor virginica virginica
[103] virginica virginica virginica virginica virginica virginica
[109] virginica virginica virginica virginica virginica virginica
[115] virginica virginica virginica virginica virginica virginica
[121] virginica virginica virginica virginica virginica virginica
[127] virginica virginica virginica virginica virginica virginica
[133] virginica virginica virginica virginica virginica virginica
[139] virginica virginica virginica virginica virginica virginica
[145] virginica virginica virginica virginica virginica virginica
Levels: setosa versicolor virginicastep 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.Length
Important point:
- Each dot represents one flower (one row in the dataset) .
ggplot(data,aes(x =Sepal.Length, y = Sepal.Width)) +
geom_point()
step 6: Add categorical grouping using color = Species
color = Speciestells ggplot2 to assign a different color to each species.
what changes?
- The plot now visually separates the three species based on color. _ The 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 = 3makes each dot bigger, so it is easier to see. _alpha = 0.7makes dots slight transparent,which helps when points overlap.
why transparency helps:
- If may 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:
titlefor the plot headingxandyaxis labelscolorlegend 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 Length",
color = "Species"
)
)
step 9: Apply a clean theme and move the legend
Theme 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 it at the top,it is often easier to noties 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 Length",
color = "Species"
)+
theme_minimal()+
theme(legend.position = "top")
Discssion Questions
1.Do you see cluster 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?