Hi,
I am very new to R and I've been trying to work through the R book to gain a
better idea of the code (which is also completely new to me).
Initially I imputed my data from a text file and that seemed to work ok, but
I'm trying to examine linear relationships between gdist and gair, gdist and
gsub, m6dist and m6air, etc.
This didn't work and I think it might have something to do with the n/a's in
my dataset.
> habitat
gdist gair gsub m6dist m6air m6sub m7dist m7air m7sub m8dist m8air m8sub
1 20 8 14 -0.5 24 19 7 12.1 16.1 2.5 12 12
2 4 13 15 -0.1 24.5 24.5 0.1 11.4 15.1 2 14 16
3 30 12.6 16.4 -3 25 26 2.5 9.7 12.8 0.1 11.5 14
4 40 12.6 17.9 1 n/a n/a 0.1 8.1 15.2 2 16 20
5 40 2 1.8 1 n/a n/a 0.7 10.2 24.1 2 16 19
6 10 13 31 1.5 n/a n/a n/a 20 n/a 2 17 20
7 0.1 19.1 27.9 1 24.5 26 0.1 20.6 22.4 6 17 21.5
8 1 23.4 33.1 0.25 25 24.5 2 22.4 24.1 1.5 17 18
9 7 23.5 30.5 -1 29.7 29 0.1 27.8 24.2 3 11 12
10 9 23.5 25.4 2 n/a n/a 4 29.3 24.2 6 13 14
11 2 23.5 23 0.05 28.5 26 1 29.7 26.6 2 15 15
12 1 23.6 23.4 0.3 22.2 24.8 0.1 20.6 22.6 2 15 21
13 1.5 24 26.2 0.1 23.7 23.2 0.1 20.9 26.6 4 17.5 17
14 6 19.4 23.4 0.05 24.5 27.6 1 21.1 25.5 5 18 22
15 0.5 19.6 32.7 2.5 26.4 n/a 2 12.1 16.4 2 19 26
16 5 20.2 23.4 -12 22.4 26.1 2 14.4 16.6 1 n/a n/a
17 10 23.1 24.1 0.2 23.6 24.3 0.1 14.4 17.7 4 9 12
18 6 17 19 -10 23.6 21.5 1 16.2 16.9 0.1 10 12
19 6 17 19 60 n/a n/a 10 13.3 24.3 3 8 12
20 2 19 21 60 n/a n/a 7 19.5 23.9 3 9 13
21 2 19 21 2 17.3 17.3 2 21.1 25.5 2 10 15
22 2 20 23 2 17.3 17.3 3 21.5 21.4 4 11 16.5
23 3 20 23 2 22.5 24.1 1.5 17.6 21.7 0.1 12 15
24 1 8.1 8.6 2 22.5 24.5 10 17.7 23 8 15 21
25 2.5 8.4 9.6 3 n/a n/a 1 22.3 26.8 2 8 14
26 15 11.5 12.1 20 n/a n/a -1 27.3 26.6 1 15 14
27 -0.5 13.6 9.3 5 n/a n/a 1 27.4 31.3 3 15 12
28 4 13.9 16.6 7 n/a n/a 1 23.2 30.1 0.1 13 16
29 1 14.7 17.7 1.5 n/a n/a 3 18.9 31.4 3 16 21
30 5 14.9 23.3 0.2 23.3 25.3 3 18.9 29.7 0.1 16 18
31 6 14.9 19.1 2.5 n/a n/a 5 19 24.8 8 13.5 16
32 2.5 14.9 21.6 3 n/a n/a 4 19 20.5 3 20 23
33 8 15.4 14.6 4 13.3 12.8 0.3 20.5 25.8 1 20 18
34 0.2 16.3 16.2 3.5 14.5 15.7 8 20.6 28 1 21 23
35 7 17.4 19.4 2 16 15.7 8 22.3 23 1 21 25
36 12 18.7 21.1 0.5 14.5 13.5 8 22.3 21.6 2 12 14
37 1 18.8 18.9 n/a n/a n/a 7 22.3 23.4 3 13.5 24
38 1.5 19 21.7 n/a n/a n/a 7 14.5 18.6 3 14 27
39 1.5 19 19.3 n/a n/a n/a 7 15 18.6 0.3 14 21
40 1 19.4 21 n/a n/a n/a 0.1 17.3 21 0.01 15 16
41 0.3 19 17.9 n/a n/a n/a 10 18 26.3 0.01 16 14
42 0.2 19 17.9 n/a n/a n/a 10 18.1 24.9 0.25 16 25
43 0.2 21.5 18.4 n/a n/a n/a 2.5 19 21.1 2 15 18
44 1 22.1 22.3 n/a n/a n/a 2 19.5 21.1 2 18 18
45 2 22.5 20.6 n/a n/a n/a 1 24.1 27.7 -1 22 25
46 10 n/a n/a n/a n/a n/a 0.5 14.7 18.1 -1 23 22
47 10 21.1 25.8 n/a n/a n/a 15 16.4 20.3 3 23 30
48 30 n/a n/a n/a n/a n/a 15 16.4 20.3 0.15 30 24
49 10 n/a n/a n/a n/a n/a 16 16.4 23.2 4 23 23
50 10 n/a n/a n/a n/a n/a 8 18.2 22.5 3 23 24
51 15 14.4 20.2 n/a n/a n/a 10 18.2 24.5 0.1 26 29
52 3 12.7 19.7 n/a n/a n/a 8 18.7 22.5 0.2 20 21
53 5 14 14.7 n/a n/a n/a 3 19 24.1 1.5 21 21
54 1 16.9 17.9 n/a n/a n/a 4 20.7 26.2 1.5 23 23
55 2 17 17.9 n/a n/a n/a 3.5 17 18.8 0.05 24 24
56 0.5 11.2 11.7 n/a n/a n/a 3 17.4 20.4 2 26 26
57 0 12.7 14.7 n/a n/a n/a 1.5 19.4 21.2 n/a n/a n/a
58 0 14.2 20 n/a n/a n/a 5 n/a n/a 10 22 23
59 1.5 14.2 16.8 n/a n/a n/a 5 20.8 22.3 3 25 25
60 10 16.1 2 n/a n/a n/a 7 20.9 27.2 2 25 25
61 3.5 14.8 17 n/a n/a n/a 4 21 20.5 4 21 23
62 0.1 16.6 14.8 n/a n/a n/a 4 22.3 21.7 15 28 26
63 -0.1 17.1 26.9 n/a n/a n/a 8 22.3 27.3 2 23 22
64 -2 17.7 27.1 n/a n/a n/a 2 22.8 23.2 3 22 25
65 1.5 18.9 20.3 n/a n/a n/a 6 25.5 24.3 2 25 27
66 3 19.7 23.3 n/a n/a n/a 5 n/a n/a 0.1 26 27
67 -0.3 20.4 23.4 n/a n/a n/a 7 n/a n/a 0.5 28 36
68 0.3 23.3 33.6 n/a n/a n/a 7 n/a n/a 3 27 29
69 0 20.8 25.4 n/a n/a n/a 6 n/a n/a 1.5 23 23
70 0.7 22 26.6 n/a n/a n/a 4 n/a n/a 2 23 23
71 2 22.4 25.8 n/a n/a n/a 4 23.1 21.8 2 24 25
72 0 23.4 26.6 n/a n/a n/a 0.05 23.2 24.4 2 24 25
73 5 19.4 24.1 n/a n/a n/a 0.1 25.3 28.4 0.2 24 24
74 8 19.6 27.1 n/a n/a n/a 0.5 25.4 25.4 -0.1 24 n/a
75 5 19.6 27 n/a n/a n/a 10 n/a n/a 2 18 19
76 1 19.7 29.8 n/a n/a n/a -3 22.4 22.4 15 19 20
77 8 20.6 37.6 n/a n/a n/a -2 22.8 21.6 4 17 19
78 15 21 23.7 n/a n/a n/a -1 23.1 23.4 4 30 24
79 2 24.6 25.3 n/a n/a n/a -3 23.1 24.1 n/a 26 n/a
80 3.5 25.2 26.9 n/a n/a n/a -3.5 24.5 20.5 1 28 n/a
81 5 17.8 22.8 n/a n/a n/a 2.5 25.4 31.9 n/a 28 n/a
82 15 20 24.6 n/a n/a n/a 7 19.6 20.4 2 29 n/a
83 3 21.1 24.3 n/a n/a n/a -3 23.1 27.1 1 24 n/a
84 5 17.2 19.5 n/a n/a n/a 3 23.8 28.4 1 25 n/a
85 7 17.2 18 n/a n/a n/a 0.5 24.4 25.2 0.75 25 n/a
86 -0.3 23.8 24.5 n/a n/a n/a -1.5 25.2 23.9 2 25 n/a
87 0.2 25.9 26.5 n/a n/a n/a -2 29.5 25.2 1 24 28
88 -3 20.4 24 n/a n/a n/a 6 29.8 33.6 5 18 21
89 -5 24.9 23.7 n/a n/a n/a 8 25.2 26.4 15 23 24
90 0.5 26.6 27 n/a n/a n/a 0.05 26 29.7 3 24 27
91 -0.8 27.3 25.4 n/a n/a n/a 20 23.4 26.3 1.5 25 n/a
92 2 24 25.8 n/a n/a n/a 1 23.7 22.7 1 18 22
93 -0.1 26 28 n/a n/a n/a -0.01 24.8 27.2 10 21 23
94 1 26 35 n/a n/a n/a 1 25 25.8 15 21 23
95 0 25 21.5 n/a n/a n/a 1.5 25.1 25.9 4 22 20
96 -3 26.9 25.9 n/a n/a n/a 2 25.3 26.6 n/a n/a n/a
97 1.5 24.1 30.4 n/a n/a n/a 2 25.6 25.5 n/a n/a n/a
98 1 24.1 24.8 n/a n/a n/a 1.5 25.8 28.5 n/a n/a n/a
99 10 26.5 28.9 n/a n/a n/a 2 25.9 28 n/a n/a n/a
100 -0.7 27.5 27.6 n/a n/a n/a 5 29.2 24.2 n/a n/a n/a
101 -3 28.1 17.6 n/a n/a n/a 1.5 n/a n/a n/a n/a n/a
102 1 29.7 28.3 n/a n/a n/a 2 n/a n/a n/a n/a n/a
103 2 24 25.8 n/a n/a n/a 2 n/a n/a n/a n/a n/a
104 30 28 29 n/a n/a n/a 2 n/a n/a n/a n/a n/a
105 17 32 36 n/a n/a n/a 1 n/a n/a n/a n/a n/a
106 8 19.1 23.2 n/a n/a n/a 0.01 30.2 30.4 n/a n/a n/a
107 5 19.1 23.1 n/a n/a n/a -3 31.6 35.7 n/a n/a n/a
108 -3 23.7 25.4 n/a n/a n/a 0.01 27.5 25.1 n/a n/a n/a
109 -2.5 24.1 25.1 n/a n/a n/a -0.02 28.6 31.5 n/a n/a n/a
110 -2 24.4 26.9 n/a n/a n/a 1 28.6 30.9 n/a n/a n/a
111 -4 24.6 26.3 n/a n/a n/a 8 30.3 29.7 n/a n/a n/a
112 0.7 21.3 24.7 n/a n/a n/a -3 26.7 28.4 n/a n/a n/a
113 -3 21.6 27.6 n/a n/a n/a 4 28.8 28.7 n/a n/a n/a
114 -2 21 23 n/a n/a n/a 0.5 31.2 31.8 n/a n/a n/a
115 -0.1 24 20 n/a n/a n/a 8 32.3 38.7 n/a n/a n/a
116 3 26 21 n/a n/a n/a 0.1 26.4 27 n/a n/a n/a
117 -0.2 27 24 n/a n/a n/a -2 21.4 25.8 n/a n/a n/a
118 1 28 28 n/a n/a n/a 3 22.3 25.8 n/a n/a n/a
119 0.1 24.1 23.1 n/a n/a n/a 7 23 24.1 n/a n/a n/a
120 3.5 24.5 25 n/a n/a n/a 0.2 24.5 27.1 n/a n/a n/a
121 0.1 24.6 25.7 n/a n/a n/a 3 25.2 24.1 n/a n/a n/a
122 3 28 24 n/a n/a n/a -0.5 25.8 28.3 n/a n/a n/a
123 5 29 28 n/a n/a n/a 0.2 25.8 27.8 n/a n/a n/a
124 -2 n/a n/a n/a n/a n/a 10 26.3 23.3 n/a n/a n/a
125 1.5 n/a n/a n/a n/a n/a 20 26.5 24 n/a n/a n/a
126 3 n/a n/a n/a n/a n/a 3 26.5 24.3 n/a n/a n/a
127 -0.2 26 24 n/a n/a n/a 3 n/a 27.7 n/a n/a n/a
128 -0.1 26 22 n/a n/a n/a 2 23.3 n/a n/a n/a n/a
129 3 19 22 n/a n/a n/a 8 23.9 25.9 n/a n/a n/a
130 2 21 25 n/a n/a n/a -0.05 24.4 26.7 n/a n/a n/a
131 1 15 15 n/a n/a n/a -0.1 24.8 25.1 n/a n/a n/a
132 6 16 18 n/a n/a n/a -0.01 26.2 26.2 n/a n/a n/a
133 6 18 19 n/a n/a n/a 0.01 26.2 27.6 n/a n/a n/a
134 -0.2 16 19 n/a n/a n/a 12 27 26.4 n/a n/a n/a
135 2 17 18.5 n/a n/a n/a 0.1 27.6 28.8 n/a n/a n/a
136 0.1 17.5 16.5 n/a n/a n/a -1.2 21.1 22.2 n/a n/a n/a
137 1.5 18 17 n/a n/a n/a -2 21.1 22.4 n/a n/a n/a
138 -1 18 17 n/a n/a n/a 0.5 21.4 25.4 n/a n/a n/a
139 8 18 18.5 n/a n/a n/a 1 22.6 24.4 n/a n/a n/a
140 1.5 19 18.5 n/a n/a n/a 1 25.1 31.4 n/a n/a n/a
141 5 19 21 n/a n/a n/a 2 25.2 25 n/a n/a n/a
142 10 19 20 n/a n/a n/a 0.5 25.2 30.2 n/a n/a n/a
143 8 19 21 n/a n/a n/a 5 22.3 23.5 n/a n/a n/a
144 6 19 18 n/a n/a n/a 0.1 24.1 23.4 n/a n/a n/a
145 0 20 20 n/a n/a n/a 1.5 24.1 24 n/a n/a n/a
146 0.3 12 13 n/a n/a n/a 1 25.2 27.9 n/a n/a n/a
147 2.5 13 12.5 n/a n/a n/a 5 25.2 27.6 n/a n/a n/a
148 2 14 16 n/a n/a n/a 1 25.2 29.1 n/a n/a n/a
149 40 14 12 n/a n/a n/a -1.5 26.5 27 n/a n/a n/a
150 30 15 16 n/a n/a n/a n/a n/a n/a n/a n/a n/a
151 40 15.5 16 n/a n/a n/a 0.01 n/a n/a n/a n/a n/a
152 50 18 12.5 n/a n/a n/a -0.02 n/a n/a n/a n/a n/a
153 n/a n/a n/a n/a n/a n/a 0.05 n/a n/a n/a n/a n/a
154 40 14 21 n/a n/a n/a -1 n/a n/a n/a n/a n/a
155 n/a n/a n/a n/a n/a n/a 0.05 n/a n/a n/a n/a n/a
156 n/a n/a n/a n/a n/a n/a -10 n/a n/a n/a n/a n/a
157 n/a n/a n/a n/a n/a n/a 0.1 19.3 19.8 n/a n/a n/a
158 n/a n/a n/a n/a n/a n/a 0.5 21 26.2 n/a n/a n/a
159 n/a n/a n/a n/a n/a n/a 1 n/a n/a n/a n/a n/a
160 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a
161 n/a n/a n/a n/a n/a n/a 0.15 22.8 23.3 n/a n/a n/a
162 n/a n/a n/a n/a n/a n/a 1 24.3 26.5 n/a n/a n/a
163 n/a n/a n/a n/a n/a n/a 2 24.4 24.6 n/a n/a n/a
164 n/a n/a n/a n/a n/a n/a 3 15 18.5 n/a n/a n/a
165 n/a n/a n/a n/a n/a n/a 4 n/a n/a n/a n/a n/a
166 n/a n/a n/a n/a n/a n/a 15 n/a n/a n/a n/a n/a
167 n/a n/a n/a n/a n/a n/a 4 n/a n/a n/a n/a n/a
168 n/a n/a n/a n/a n/a n/a 0.3 n/a n/a n/a n/a n/a
169 n/a n/a n/a n/a n/a n/a 1.5 n/a n/a n/a n/a n/a
170 n/a n/a n/a n/a n/a n/a 0 n/a n/a n/a n/a n/a
171 n/a n/a n/a n/a n/a n/a 3 n/a n/a n/a n/a n/a
172 n/a n/a n/a n/a n/a n/a 0.1 17 18 n/a n/a n/a
173 n/a n/a n/a n/a n/a n/a 0.2 17.5 18 n/a n/a n/a
174 n/a n/a n/a n/a n/a n/a 5 20 21 n/a n/a n/a
175 n/a n/a n/a n/a n/a n/a 1 10 12 n/a n/a n/a
176 n/a n/a n/a n/a n/a n/a 2 13.5 12 n/a n/a n/a
177 n/a n/a n/a n/a n/a n/a 2 12 12 n/a n/a n/a
178 n/a n/a n/a n/a n/a n/a 2.5 13 15 n/a n/a n/a
179 n/a n/a n/a n/a n/a n/a 10 12.5 14 n/a n/a n/a
I had to give up on this data set, because I wasn't sure how to fix the
problem, so I've been creating separate text files for all the combinations
I'm interested in without the extra n/a's. This is really time consuming,
and I know there is probably a simpler way I just don't know what it is!
I managed to run a lm with just the data in a separate file for gdist and
gair and I have a few outliers. I've tried to remove them with g_dist_air2
<- update(g_dist_air, subset=(gair !=97)), but this doesn't seem to work.
> g_dist_temp
gdist gair
1 17.0 32.0
2 1.0 29.7
3 5.0 29.0
4 -3.0 28.1
5 30.0 28.0
6 1.0 28.0
7 3.0 28.0
8 -0.7 27.5
9 -0.8 27.3
10 -0.2 27.0
11 -3.0 26.9
12 0.5 26.6
13 10.0 26.5
14 -0.1 26.0
15 1.0 26.0
16 3.0 26.0
17 -0.2 26.0
18 -0.1 26.0
19 0.2 25.9
20 3.5 25.2
21 0.0 25.0
22 -5.0 24.9
23 2.0 24.6
24 -4.0 24.6
25 0.1 24.6
26 3.5 24.5
27 -2.0 24.4
28 1.5 24.1
29 1.0 24.1
30 -2.5 24.1
31 0.1 24.1
32 1.5 24.0
33 2.0 24.0
34 2.0 24.0
35 -0.1 24.0
36 -0.3 23.8
37 -3.0 23.7
38 1.0 23.6
39 7.0 23.5
40 9.0 23.5
41 2.0 23.5
42 1.0 23.4
43 0.0 23.4
44 0.3 23.3
45 10.0 23.1
46 2.0 22.5
47 2.0 22.4
48 1.0 22.1
49 0.7 22.0
50 -3.0 21.6
51 0.2 21.5
52 0.7 21.3
53 10.0 21.1
54 3.0 21.1
55 15.0 21.0
56 -2.0 21.0
57 2.0 21.0
58 0.0 20.8
59 8.0 20.6
60 -0.3 20.4
61 -3.0 20.4
62 5.0 20.2
63 2.0 20.0
64 3.0 20.0
65 15.0 20.0
66 0.0 20.0
67 3.0 19.7
68 1.0 19.7
69 0.5 19.6
70 8.0 19.6
71 5.0 19.6
72 6.0 19.4
73 1.0 19.4
74 5.0 19.4
75 0.1 19.1
76 8.0 19.1
77 5.0 19.1
78 2.0 19.0
79 2.0 19.0
80 1.5 19.0
81 1.5 19.0
82 0.3 19.0
83 0.2 19.0
84 3.0 19.0
85 1.5 19.0
86 5.0 19.0
87 10.0 19.0
88 8.0 19.0
89 6.0 19.0
90 1.5 18.9
91 1.0 18.8
92 12.0 18.7
93 6.0 18.0
94 1.5 18.0
95 -1.0 18.0
96 8.0 18.0
97 50.0 18.0
98 5.0 17.8
99 -2.0 17.7
100 0.1 17.5
101 7.0 17.4
102 5.0 17.2
103 7.0 17.2
104 -0.1 17.1
105 6.0 17.0
106 6.0 17.0
107 2.0 17.0
108 2.0 17.0
109 1.0 16.9
110 0.1 16.6
111 0.2 16.3
112 10.0 16.1
113 6.0 16.0
114 -0.2 16.0
115 40.0 15.5
116 8.0 15.4
117 1.0 15.0
118 30.0 15.0
119 5.0 14.9
120 6.0 14.9
121 2.5 14.9
122 3.5 14.8
123 1.0 14.7
124 15.0 14.4
125 0.0 14.2
126 1.5 14.2
127 5.0 14.0
128 2.0 14.0
129 40.0 14.0
130 40.0 14.0
131 4.0 13.9
132 -0.5 13.6
133 4.0 13.0
134 10.0 13.0
135 2.5 13.0
136 3.0 12.7
137 0.0 12.7
138 30.0 12.6
139 40.0 12.6
140 0.3 12.0
141 15.0 11.5
142 0.5 11.2
143 2.5 8.4
144 1.0 8.1
145 20.0 8.0
146 40.0 2.0
Are there any other ways to remove lines from data sets? Or is there
something wrong with my code?
Is there anyway to use my old data set with all the n/a's to look at
relationships between the variables? Ideally I want to add in more habitat
variables to this analysis, that will include some categorical data and more
n/a's since the data collection was not complete with every observation.
Any help is appreciated.
Tara
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