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, unionProgram 2
Step 1: Load Libraries
We load two libraries:
ggplot2is used to build plots layer-by-layer(we will use it to create the scatter plot).dplyrprovides functoins for exploring and summarizing data ( we will use it to understand the categories in the dataset).
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 observation.
- The column
Speciesis a categorical variable with 3 groups:setosaversicolorvirginica
- The columns
Sepal.LengthandSepal.Widthare numeric measurements that we will plot.
data<-irishead(data<-iris,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 setosatail(data<-iris) 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 virginicastr(data<-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 ...names(data<-iris)[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" summary(data<-iris) 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
dim(data<-iris)[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"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 5.9typeof(data[1])[1] "list"data[150,5][1] virginica
Levels: setosa versicolor virginicadata$Species [1] setosa setosa setosa setosa setosa setosa
[7] setosa setosa setosa setosa setosa setosa
[13] setosa setosa setosa setosa setosa setosa
[19] setosa setosa setosa setosa setosa setosa
[25] setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa
[37] setosa setosa setosa setosa setosa setosa
[43] setosa setosa setosa setosa setosa setosa
[49] setosa setosa versicolor versicolor versicolor versicolor
[55] versicolor versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor versicolor
[67] versicolor versicolor versicolor versicolor versicolor versicolor
[73] versicolor versicolor versicolor versicolor versicolor versicolor
[79] versicolor versicolor versicolor versicolor versicolor versicolor
[85] versicolor versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor versicolor
[97] versicolor versicolor versicolor versicolor virginica virginica
[103] virginica virginica virginica virginica virginica virginica
[109] virginica virginica virginica virginica virginica virginica
[115] virginica virginica virginica virginica virginica virginica
[121] virginica virginica virginica virginica virginica virginica
[127] virginica virginica virginica virginica virginica virginica
[133] virginica virginica virginica virginica virginica virginica
[139] virginica virginica virginica virginica virginica virginica
[145] virginica virginica virginica virginica virginica virginica
Levels: setosa versicolor virginicadata[1:5,1:3] Sepal.Length Sepal.Width Petal.Length
1 5.1 3.5 1.4
2 4.9 3.0 1.4
3 4.7 3.2 1.3
4 4.6 3.1 1.5
5 5.0 3.6 1.4table(data$Species)
setosa versicolor virginica
50 50 50 Step 3: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 4: Add a categorical grouping using color=species
Now we include the categorical variable:
color= Speciestells 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 5: 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 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 6:Add informative labels(title,axes,legend)
Good plots should clearly communicate what the viewer is seeing.
lab() adds:
titlefor the plot headingxandyaxis labelscolorlegend title(so the legend has a meaninful 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 7: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")