I have a data of 199 test subjects (125 exposed subjects & 74 unexposed subjects). Each subject has 3 different points attached to them. I need to:
- Split the data into 2 groups (the exposed group & the unexposed group).
- Find the average of the 3 points for each subject
- Find the average of the averages for both groups
The data:
...1 babyStatus oneMin fiveMin tenMin
1 1 ZIKV-exposed 6 7 9
2 2 ZIKV-exposed 4 8 8
3 3 ZIKV-exposed 4 9 8
4 4 ZIKV-exposed 8 9 7
5 5 ZIKV-exposed 7 7 7
6 6 ZIKV-exposed 10 10 7
7 7 ZIKV-exposed 9 6 7
8 8 ZIKV-exposed 8 7 6
9 9 ZIKV-exposed 4 6 8
10 10 ZIKV-exposed 1 7 9
11 11 ZIKV-exposed 4 8 7
12 12 ZIKV-exposed 2 6 10
13 13 ZIKV-exposed 5 9 7
14 14 ZIKV-exposed 7 6 7
15 15 ZIKV-exposed 6 8 7
16 16 ZIKV-exposed 2 10 9
17 17 ZIKV-exposed 8 5 7
18 18 ZIKV-exposed 7 8 10
19 19 ZIKV-exposed 8 7 8
20 20 ZIKV-exposed 8 9 8
21 21 ZIKV-exposed 9 8 10
22 22 ZIKV-exposed 4 10 8
23 23 ZIKV-exposed 9 6 6
24 24 ZIKV-exposed 5 8 8
25 25 ZIKV-exposed 6 10 8
26 26 ZIKV-exposed 3 8 8
27 27 ZIKV-exposed 10 10 10
28 28 ZIKV-exposed 3 7 7
29 29 ZIKV-exposed 6 9 8
30 30 ZIKV-exposed 9 6 7
31 31 ZIKV-exposed 7 6 8
32 32 ZIKV-exposed 7 6 8
33 33 ZIKV-exposed 7 9 9
34 34 ZIKV-exposed 4 7 9
35 35 ZIKV-exposed 3 7 6
36 36 ZIKV-exposed 2 6 8
37 37 ZIKV-exposed 3 8 8
38 38 ZIKV-exposed 2 9 10
39 39 ZIKV-exposed 1 9 6
40 40 ZIKV-exposed 7 7 6
41 41 ZIKV-exposed 5 8 8
42 42 ZIKV-exposed 3 9 9
43 43 ZIKV-exposed 9 9 7
44 44 ZIKV-exposed 8 9 7
45 45 ZIKV-exposed 5 6 9
46 46 ZIKV-exposed 4 8 7
47 47 ZIKV-exposed 3 5 7
48 48 ZIKV-exposed 5 10 6
49 49 ZIKV-exposed 3 7 6
50 50 ZIKV-exposed 6 9 10
51 51 ZIKV-exposed 4 9 9
52 52 ZIKV-exposed 3 8 9
53 53 ZIKV-exposed 8 7 8
54 54 ZIKV-exposed 4 10 9
55 55 ZIKV-exposed 8 6 8
56 56 ZIKV-exposed 8 8 8
57 57 ZIKV-exposed 5 6 9
58 58 ZIKV-exposed 1 5 8
59 59 ZIKV-exposed 4 8 8
60 60 ZIKV-exposed 2 7 8
61 61 ZIKV-exposed 3 8 7
62 62 ZIKV-exposed 9 7 10
63 63 ZIKV-exposed 6 6 6
64 64 ZIKV-exposed 8 8 9
65 65 ZIKV-exposed 6 7 7
66 66 ZIKV-exposed 3 10 8
67 67 ZIKV-exposed 9 7 7
68 68 ZIKV-exposed 2 10 6
69 69 ZIKV-exposed 10 5 8
70 70 ZIKV-exposed 8 9 7
71 71 ZIKV-exposed 5 10 8
72 72 ZIKV-exposed 3 7 7
73 73 ZIKV-exposed 1 8 7
74 74 ZIKV-exposed 3 6 6
75 75 ZIKV-exposed 2 5 7
76 76 ZIKV-exposed 6 9 6
77 77 ZIKV-exposed 2 7 10
78 78 ZIKV-exposed 3 7 7
79 79 ZIKV-exposed 5 9 10
80 80 ZIKV-exposed 2 9 6
81 81 ZIKV-exposed 10 6 7
82 82 ZIKV-exposed 2 9 10
83 83 ZIKV-exposed 2 5 9
84 84 ZIKV-exposed 4 6 7
85 85 ZIKV-exposed 6 8 9
86 86 ZIKV-exposed 5 8 9
87 87 ZIKV-exposed 6 9 8
88 88 ZIKV-exposed 7 7 10
89 89 ZIKV-exposed 8 9 9
90 90 ZIKV-exposed 8 6 7
91 91 ZIKV-exposed 6 6 7
92 92 ZIKV-exposed 5 6 9
93 93 ZIKV-exposed 6 6 8
94 94 ZIKV-exposed 8 7 10
95 95 ZIKV-exposed 5 9 6
96 96 ZIKV-exposed 9 7 10
97 97 ZIKV-exposed 10 6 7
98 98 ZIKV-exposed 1 8 9
99 99 ZIKV-exposed 7 7 9
100 100 ZIKV-exposed 3 8 7
101 101 ZIKV-exposed 3 7 7
102 102 ZIKV-exposed 7 6 8
103 103 ZIKV-exposed 4 5 9
104 104 ZIKV-exposed 4 8 6
105 105 ZIKV-exposed 4 7 7
106 106 ZIKV-exposed 3 8 6
107 107 ZIKV-exposed 9 9 7
108 108 ZIKV-exposed 4 5 9
109 109 ZIKV-exposed 4 6 10
110 110 ZIKV-exposed 9 5 8
111 111 ZIKV-exposed 6 10 6
112 112 ZIKV-exposed 1 9 7
113 113 ZIKV-exposed 8 6 8
114 114 ZIKV-exposed 8 9 6
115 115 ZIKV-exposed 2 9 7
116 116 ZIKV-exposed 7 7 7
117 117 ZIKV-exposed 6 7 6
118 118 ZIKV-exposed 2 6 10
119 119 ZIKV-exposed 6 8 7
120 120 ZIKV-exposed 2 7 8
121 121 ZIKV-exposed 5 10 8
122 122 ZIKV-exposed 5 7 9
123 123 ZIKV-exposed 5 9 10
124 124 ZIKV-exposed 10 8 8
125 125 ZIKV-exposed 9 8 9
126 126 ZIKV-unexposed 8 9 10
127 127 ZIKV-unexposed 9 10 10
128 128 ZIKV-unexposed 7 10 10
129 129 ZIKV-unexposed 7 10 9
130 130 ZIKV-unexposed 10 10 9
131 131 ZIKV-unexposed 9 10 9
132 132 ZIKV-unexposed 9 10 9
133 133 ZIKV-unexposed 7 10 10
134 134 ZIKV-unexposed 8 10 9
135 135 ZIKV-unexposed 8 10 9
136 136 ZIKV-unexposed 9 9 9
137 137 ZIKV-unexposed 10 10 10
138 138 ZIKV-unexposed 7 10 10
139 139 ZIKV-unexposed 10 9 10
140 140 ZIKV-unexposed 7 9 10
141 141 ZIKV-unexposed 10 9 9
142 142 ZIKV-unexposed 6 10 9
143 143 ZIKV-unexposed 10 10 9
144 144 ZIKV-unexposed 7 10 9
145 145 ZIKV-unexposed 6 9 9
146 146 ZIKV-unexposed 6 10 9
147 147 ZIKV-unexposed 6 10 9
148 148 ZIKV-unexposed 7 9 9
149 149 ZIKV-unexposed 6 10 9
150 150 ZIKV-unexposed 9 9 9
151 151 ZIKV-unexposed 9 10 9
152 152 ZIKV-unexposed 8 10 10
153 153 ZIKV-unexposed 8 10 9
154 154 ZIKV-unexposed 7 10 9
155 155 ZIKV-unexposed 10 9 9
156 156 ZIKV-unexposed 8 10 10
157 157 ZIKV-unexposed 8 9 9
158 158 ZIKV-unexposed 10 10 9
159 159 ZIKV-unexposed 8 10 10
160 160 ZIKV-unexposed 8 10 9
161 161 ZIKV-unexposed 9 9 10
162 162 ZIKV-unexposed 7 10 10
163 163 ZIKV-unexposed 8 10 10
164 164 ZIKV-unexposed 10 9 9
165 165 ZIKV-unexposed 8 9 9
166 166 ZIKV-unexposed 7 9 10
167 167 ZIKV-unexposed 9 10 10
168 168 ZIKV-unexposed 8 10 10
169 169 ZIKV-unexposed 6 9 9
170 170 ZIKV-unexposed 8 10 10
171 171 ZIKV-unexposed 10 10 10
172 172 ZIKV-unexposed 7 10 9
173 173 ZIKV-unexposed 10 9 9
174 174 ZIKV-unexposed 7 10 10
175 175 ZIKV-unexposed 8 10 10
176 176 ZIKV-unexposed 9 9 10
177 177 ZIKV-unexposed 8 9 9
178 178 ZIKV-unexposed 7 9 9
179 179 ZIKV-unexposed 8 9 10
180 180 ZIKV-unexposed 8 9 10
181 181 ZIKV-unexposed 8 10 10
182 182 ZIKV-unexposed 7 10 9
183 183 ZIKV-unexposed 10 9 10
184 184 ZIKV-unexposed 10 10 9
185 185 ZIKV-unexposed 9 9 9
186 186 ZIKV-unexposed 9 10 9
187 187 ZIKV-unexposed 8 10 9
188 188 ZIKV-unexposed 8 9 10
189 189 ZIKV-unexposed 9 9 9
190 190 ZIKV-unexposed 9 9 10
191 191 ZIKV-unexposed 8 10 9
192 192 ZIKV-unexposed 10 9 9
193 193 ZIKV-unexposed 8 10 10
194 194 ZIKV-unexposed 6 10 9
195 195 ZIKV-unexposed 8 9 10
196 196 ZIKV-unexposed 7 10 9
197 197 ZIKV-unexposed 7 10 9
198 198 ZIKV-unexposed 7 10 9
199 199 ZIKV-unexposed 8 9 9
The final result I need:
ZIKV-exposed: 6.928000
ZIKV-unexposed: 9.040541
CodePudding user response：
library(dplyr)
mydf |>
rowwise() |>
mutate(avg = mean(c_across(ends_with("in")))) |>
group_by(babyStatus) |>
summarize(avg = mean(avg))
# A tibble: 2 × 2
babyStatus avg
<chr> <dbl>
1 ZIKV-exposed 6.93
2 ZIKV-unexposed 9.04
CodePudding user response：
mean_table <- your_df %>%
# create a table so you have the mean for each ZIKV
transmute(babyStatus, Mean = rowMeans(select(.,oneMin:tenMin))) %>%
# subdivide class ZIKV in exposed/unexposed
group_by(babyStatus) %>%
# you can calculate mean in 'Mean' column, since its class is numeric, for each ZIKV
summarise(across(where(is.numeric), ~ mean(.x)))
print(mean_table)
# A tibble: 2 × 2
babyStatus Mean
<chr> <dbl>
1 ZIKV-exposed 6.93
2 ZIKV-unexposed 9.04