Introduction to R

Hello World!

print("Hello World!")

print("Hello World!", quote = FALSE)

print(paste("Hello","World","!"))

[1] "Hello World!"
[1] Hello World!
[1] "Hello World !"

---

Using R interactively

$ R - start the R program

> q() - quit the R program

> help("topic") - help facility similar to the man facility of unix

> ?"topic" - help facility similar to the man facility of unix

> source("file.R") - execute commands stored in file.R

---

Installing packages

> install.packages("package_name")

---

Loading packages

> require(package_name)

> library(package_name)

With pacman

> library(pacman)

> pacman::p_load(package_name)

You can use pacman to install packages

---

Unload packages

> detach(package_name)

With pacman

> p_unload(package_name)

> p_unload(all)

Native package can't be loaded or unloaded with pacman

Pacman is a package, you should install it first

---

Playing with iris dataset

iris

A data.frame: 150 × 5

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
<dbl>	<dbl>	<dbl>	<dbl>	<fct>
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa
4.6	3.4	1.4	0.3	setosa
5.0	3.4	1.5	0.2	setosa
4.4	2.9	1.4	0.2	setosa
4.9	3.1	1.5	0.1	setosa
5.4	3.7	1.5	0.2	setosa
4.8	3.4	1.6	0.2	setosa
4.8	3.0	1.4	0.1	setosa
4.3	3.0	1.1	0.1	setosa
5.8	4.0	1.2	0.2	setosa
5.7	4.4	1.5	0.4	setosa
5.4	3.9	1.3	0.4	setosa
5.1	3.5	1.4	0.3	setosa
5.7	3.8	1.7	0.3	setosa
5.1	3.8	1.5	0.3	setosa
5.4	3.4	1.7	0.2	setosa
5.1	3.7	1.5	0.4	setosa
4.6	3.6	1.0	0.2	setosa
5.1	3.3	1.7	0.5	setosa
4.8	3.4	1.9	0.2	setosa
5.0	3.0	1.6	0.2	setosa
5.0	3.4	1.6	0.4	setosa
5.2	3.5	1.5	0.2	setosa
5.2	3.4	1.4	0.2	setosa
4.7	3.2	1.6	0.2	setosa
⋮	⋮	⋮	⋮	⋮
6.9	3.2	5.7	2.3	virginica
5.6	2.8	4.9	2.0	virginica
7.7	2.8	6.7	2.0	virginica
6.3	2.7	4.9	1.8	virginica
6.7	3.3	5.7	2.1	virginica
7.2	3.2	6.0	1.8	virginica
6.2	2.8	4.8	1.8	virginica
6.1	3.0	4.9	1.8	virginica
6.4	2.8	5.6	2.1	virginica
7.2	3.0	5.8	1.6	virginica
7.4	2.8	6.1	1.9	virginica
7.9	3.8	6.4	2.0	virginica
6.4	2.8	5.6	2.2	virginica
6.3	2.8	5.1	1.5	virginica
6.1	2.6	5.6	1.4	virginica
7.7	3.0	6.1	2.3	virginica
6.3	3.4	5.6	2.4	virginica
6.4	3.1	5.5	1.8	virginica
6.0	3.0	4.8	1.8	virginica
6.9	3.1	5.4	2.1	virginica
6.7	3.1	5.6	2.4	virginica
6.9	3.1	5.1	2.3	virginica
5.8	2.7	5.1	1.9	virginica
6.8	3.2	5.9	2.3	virginica
6.7	3.3	5.7	2.5	virginica
6.7	3.0	5.2	2.3	virginica
6.3	2.5	5.0	1.9	virginica
6.5	3.0	5.2	2.0	virginica
6.2	3.4	5.4	2.3	virginica
5.9	3.0	5.1	1.8	virginica

Ploting iris dataset

plot(iris)

Select Sepal.Length

x <- iris[,1]
x

1. 5.1
2. 4.9
3. 4.7
4. 4.6
5. 5
6. 5.4
7. 4.6
8. 5
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
27. 5
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
37. 5.5
38. 4.9
39. 4.4
40. 5.1
41. 5
42. 4.5
43. 4.4
44. 5
45. 5.1
46. 4.8
47. 5.1
48. 4.6
49. 5.3
50. 5
51. 7
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
62. 5.9
63. 6
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
80. 5.7
81. 5.5
82. 5.5
83. 5.8
84. 6
85. 5.4
86. 6
87. 6.7
88. 6.3
89. 5.6
90. 5.5
91. 5.5
92. 6.1
93. 5.8
94. 5
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
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
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

summary(x)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.300   5.100   5.800   5.843   6.400   7.900 

Discretize dataset by frequency

require(arules)
discretize(x)

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

Levels:

1. '[4.3,5.4)'
2. '[5.4,6.3)'
3. '[6.3,7.9]'

To use discretize you neet to import the arules package

> install.packages(arules)

> require(arules)

table(discretize(x))

[4.3,5.4) [5.4,6.3) [6.3,7.9] 
       46        53        51 

discretize(x, breaks = 10)

1. [5,5.27)
2. [4.8,5)
3. [4.3,4.8)
4. [4.3,4.8)
5. [5,5.27)
6. [5.27,5.6)
7. [4.3,4.8)
8. [5,5.27)
9. [4.3,4.8)
10. [4.8,5)
11. [5.27,5.6)
12. [4.8,5)
13. [4.8,5)
14. [4.3,4.8)
15. [5.8,6.1)
16. [5.6,5.8)
17. [5.27,5.6)
18. [5,5.27)
19. [5.6,5.8)
20. [5,5.27)
21. [5.27,5.6)
22. [5,5.27)
23. [4.3,4.8)
24. [5,5.27)
25. [4.8,5)
26. [5,5.27)
27. [5,5.27)
28. [5,5.27)
29. [5,5.27)
30. [4.3,4.8)
31. [4.8,5)
32. [5.27,5.6)
33. [5,5.27)
34. [5.27,5.6)
35. [4.8,5)
36. [5,5.27)
37. [5.27,5.6)
38. [4.8,5)
39. [4.3,4.8)
40. [5,5.27)
41. [5,5.27)
42. [4.3,4.8)
43. [4.3,4.8)
44. [5,5.27)
45. [5,5.27)
46. [4.8,5)
47. [5,5.27)
48. [4.3,4.8)
49. [5.27,5.6)
50. [5,5.27)
51. [6.9,7.9]
52. [6.3,6.52)
53. [6.9,7.9]
54. [5.27,5.6)
55. [6.3,6.52)
56. [5.6,5.8)
57. [6.3,6.52)
58. [4.8,5)
59. [6.52,6.9)
60. [5,5.27)
61. [5,5.27)
62. [5.8,6.1)
63. [5.8,6.1)
64. [6.1,6.3)
65. [5.6,5.8)
66. [6.52,6.9)
67. [5.6,5.8)
68. [5.8,6.1)
69. [6.1,6.3)
70. [5.6,5.8)
71. [5.8,6.1)
72. [6.1,6.3)
73. [6.3,6.52)
74. [6.1,6.3)
75. [6.3,6.52)
76. [6.52,6.9)
77. [6.52,6.9)
78. [6.52,6.9)
79. [5.8,6.1)
80. [5.6,5.8)
81. [5.27,5.6)
82. [5.27,5.6)
83. [5.8,6.1)
84. [5.8,6.1)
85. [5.27,5.6)
86. [5.8,6.1)
87. [6.52,6.9)
88. [6.3,6.52)
89. [5.6,5.8)
90. [5.27,5.6)
91. [5.27,5.6)
92. [6.1,6.3)
93. [5.8,6.1)
94. [5,5.27)
95. [5.6,5.8)
96. [5.6,5.8)
97. [5.6,5.8)
98. [6.1,6.3)
99. [5,5.27)
100. [5.6,5.8)
101. [6.3,6.52)
102. [5.8,6.1)
103. [6.9,7.9]
104. [6.3,6.52)
105. [6.3,6.52)
106. [6.9,7.9]
107. [4.8,5)
108. [6.9,7.9]
109. [6.52,6.9)
110. [6.9,7.9]
111. [6.3,6.52)
112. [6.3,6.52)
113. [6.52,6.9)
114. [5.6,5.8)
115. [5.8,6.1)
116. [6.3,6.52)
117. [6.3,6.52)
118. [6.9,7.9]
119. [6.9,7.9]
120. [5.8,6.1)
121. [6.9,7.9]
122. [5.6,5.8)
123. [6.9,7.9]
124. [6.3,6.52)
125. [6.52,6.9)
126. [6.9,7.9]
127. [6.1,6.3)
128. [6.1,6.3)
129. [6.3,6.52)
130. [6.9,7.9]
131. [6.9,7.9]
132. [6.9,7.9]
133. [6.3,6.52)
134. [6.3,6.52)
135. [6.1,6.3)
136. [6.9,7.9]
137. [6.3,6.52)
138. [6.3,6.52)
139. [5.8,6.1)
140. [6.9,7.9]
141. [6.52,6.9)
142. [6.9,7.9]
143. [5.8,6.1)
144. [6.52,6.9)
145. [6.52,6.9)
146. [6.52,6.9)
147. [6.3,6.52)
148. [6.3,6.52)
149. [6.1,6.3)
150. [5.8,6.1)

Levels:

1. '[4.3,4.8)'
2. '[4.8,5)'
3. '[5,5.27)'
4. '[5.27,5.6)'
5. '[5.6,5.8)'
6. '[5.8,6.1)'
7. '[6.1,6.3)'
8. '[6.3,6.52)'
9. '[6.52,6.9)'
10. '[6.9,7.9]'

table(discretize(x, breaks = 10))

 [4.3,4.8)    [4.8,5)   [5,5.27) [5.27,5.6)  [5.6,5.8)  [5.8,6.1)  [6.1,6.3) 
        11         11         23         14         14         16         10 
[6.3,6.52) [6.52,6.9)  [6.9,7.9] 
        21         13         17 

Discretize dataset by intervals

discretize(x, method = "interval", breaks = 3)

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

Levels:

1. '[4.3,5.5)'
2. '[5.5,6.7)'
3. '[6.7,7.9]'

table(discretize(x, method = "interval", breaks = 3))

[4.3,5.5) [5.5,6.7) [6.7,7.9] 
       52        70        28 

Discretize each numeric column

irisDisc <- discretizeDF(iris)
head(irisDisc)

A data.frame: 6 × 5

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	<fct>	<fct>	<fct>	<fct>	<fct>
1	[4.3,5.4)	[3.2,4.4]	[1,2.63)	[0.1,0.867)	setosa
2	[4.3,5.4)	[2.9,3.2)	[1,2.63)	[0.1,0.867)	setosa
3	[4.3,5.4)	[3.2,4.4]	[1,2.63)	[0.1,0.867)	setosa
4	[4.3,5.4)	[2.9,3.2)	[1,2.63)	[0.1,0.867)	setosa
5	[4.3,5.4)	[3.2,4.4]	[1,2.63)	[0.1,0.867)	setosa
6	[5.4,6.3)	[3.2,4.4]	[1,2.63)	[0.1,0.867)	setosa

Discretize each numeric column with labels

irisDisc <- discretizeDF(iris, default = list(method = "interval", breaks = 2, labels = c("small", "large")))
head(irisDisc)

A data.frame: 6 × 5

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	<fct>	<fct>	<fct>	<fct>	<fct>
1	small	large	small	small	setosa
2	small	small	small	small	setosa
3	small	large	small	small	setosa
4	small	small	small	small	setosa
5	small	large	small	small	setosa
6	small	large	small	small	setosa

irisDiscB <- discretizeDF(iris, methods = list(
Petal.Length = list(method = "frequency", breaks = 3, labels = c("short", "medium", "long")),
Petal.Width = list(method = "frequency", breaks = 2, labels = c("narrow", "wide"))
))
head(irisDiscB)

A data.frame: 6 × 5

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
	<fct>	<fct>	<fct>	<fct>	<fct>
1	[4.3,5.4)	[3.2,4.4]	short	narrow	setosa
2	[4.3,5.4)	[2.9,3.2)	short	narrow	setosa
3	[4.3,5.4)	[3.2,4.4]	short	narrow	setosa
4	[4.3,5.4)	[2.9,3.2)	short	narrow	setosa
5	[4.3,5.4)	[3.2,4.4]	short	narrow	setosa
6	[5.4,6.3)	[3.2,4.4]	short	narrow	setosa

Standardization

set.seed(1234) # for reproducibility
x <- rnorm(10) # standard normal
x

1. -1.20706574938542
2. 0.27742924211066
3. 1.08444117668306
4. -2.34569770262935
5. 0.42912468881105
6. 0.506055892157574
7. -0.574739960134649
8. -0.546631855784187
9. -0.564451999093283
10. -0.890037829044104

To use standardize you neet to import the robustHD package

> install.packages(arules)

> require(arules)

require(robustHD)
standardize(x) # mean and sd

1. -0.827393701468607
2. 0.66338111723447
3. 1.47380693702179
4. -1.97084238495393
5. 0.815718277917611
6. 0.892974921677615
7. -0.192392998043136
8. -0.164165988583901
9. -0.182061516061638
10. -0.509024664740275

summary(x)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-2.3457 -0.8112 -0.5555 -0.3832  0.3912  1.0844 

summary(standardize(x))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-1.9708 -0.4299 -0.1731  0.0000  0.7776  1.4738 

sd(x)

0.995787541399137

sd(standardize(x))

1

Basic graphics

Categorical variable

plot(iris$Species)

Quantitative variable

plot(iris$Petal.Length)

Cat x quant

plot(iris$Species, iris$Petal.Width)

Quant pair

plot(iris$Petal.Length, iris$Petal.Width)

Entire data frame

plot(iris)

Plot options

plot(iris$Petal.Length, iris$Petal.Width,
col = "#cc0000",
pch = 19,
main = "Iris: Petal Length vs. Petal Width",
xlab = "Petal Length",
ylab = "Petal Width"
)

Plot formulas

plot(cos, 0, 2*pi)

plot(exp, 1, 5)

plot(dnorm, -3, +3)

Bar charsts

barplot(mtcars$gear)

We need a table with frequencies for each category.

barplot(table(mtcars$gear))

Histograms

hist(iris$Sepal.Length)

# Put graphs in 3 rows and 1 column
par(mfrow = c(3,1))

# Histograms for setosa
hist(iris$Petal.Width [iris$Species == "setosa"],
xlim = c(0,3),
breaks = 9,
main = "Petal Width for Setosa",
xlab = "",
col = "red"
)

# Histograms for versicolor
hist(iris$Petal.Width [iris$Species == "versicolor"],
xlim = c(0,3),
breaks = 9,
main = "Petal Width for Versicolor",
xlab = "",
col = "green"
)

# Histograms for virginica
hist(iris$Petal.Width [iris$Species == "virginica"],
xlim = c(0,3),
breaks = 9,
main = "Petal Width for Virginica",
xlab = "",
col = "blue"
)

Sources

• R-intro.pdf
• R Programming Tutorial - Learn the Basics of Statistical Computing