Group Outliers-QS analysis
wendy wong
Sunday, May 17, 2015
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#set working directory
setwd("~/Data Science course/QS assignment")
read.table(file="Outliers1.csv",header=TRUE,sep=",")## Date Steps Distance floors Asleep Gender
## 1 12/03/2015 5266 4 0 324 Male
## 2 12/03/2015 14100 0 0 0 Male
## 3 12/03/2015 7532 4 9 0 Male
## 4 12/03/2015 11797 5 30 316 Female
## 5 12/03/2015 2801 1 0 728 Male
## 6 13/03/2015 4873 3 0 365 Male
## 7 13/03/2015 14635 0 0 0 Male
## 8 13/03/2015 25265 12 203 0 Male
## 9 13/03/2015 9630 4 33 284 Female
## 10 13/03/2015 1887 1 0 534 Male
## 11 14/03/2015 14103 10 0 480 Male
## 12 14/03/2015 19329 0 0 0 Male
## 13 14/03/2015 4436 2 3 0 Male
## 14 14/03/2015 7283 3 20 386 Female
## 15 14/03/2015 6778 3 0 684 Male
## 16 15/03/2015 7015 5 0 502 Male
## 17 15/03/2015 19387 0 0 0 Male
## 18 15/03/2015 3865 2 5 0 Male
## 19 15/03/2015 2741 1 3 358 Female
## 20 15/03/2015 4876 2 0 0 Male
## 21 16/03/2015 5996 4 0 405 Male
## 22 16/03/2015 8250 0 0 0 Male
## 23 16/03/2015 5476 3 10 0 Male
## 24 16/03/2015 6013 2 12 387 Female
## 25 16/03/2015 4541 2 0 828 Male
## 26 17/03/2015 5184 4 0 390 Male
## 27 17/03/2015 5654 0 0 0 Male
## 28 17/03/2015 4606 2 5 0 Male
## 29 17/03/2015 9402 4 19 338 Female
## 30 17/03/2015 3162 2 0 783 Male
## 31 18/03/2015 2734 2 0 369 Male
## 32 18/03/2015 2660 0 0 0 Male
## 33 18/03/2015 9548 5 6 0 Male
## 34 18/03/2015 10563 4 18 323 Female
## 35 18/03/2015 3424 2 0 702 Male
## 36 19/03/2015 3448 2 0 392 Male
## 37 19/03/2015 9564 0 0 0 Male
## 38 19/03/2015 4848 2 2 0 Male
## 39 19/03/2015 13147 5 20 382 Female
## 40 19/03/2015 6224 3 0 701 Male
## 41 20/03/2015 4873 3 0 450 Male
## 42 20/03/2015 12977 0 0 0 Male
## 43 20/03/2015 3832 2 3 0 Male
## 44 20/03/2015 11291 5 22 417 Female
## 45 20/03/2015 3573 2 0 733 Male
## 46 21/03/2015 9519 7 0 510 Male
## 47 21/03/2015 6981 0 0 0 Male
## 48 21/03/2015 5893 3 10 0 Male
## 49 21/03/2015 6350 3 15 462 Female
## 50 21/03/2015 2239 1 0 662 Male
## 51 22/03/2015 1454 1 0 450 Male
## 52 22/03/2015 0 0 0 0 Male
## 53 22/03/2015 12044 6 25 0 Male
## 54 22/03/2015 7874 3 12 485 Female
## 55 22/03/2015 3320 2 0 0 Male
## 56 23/03/2015 3787 3 0 348 Male
## 57 23/03/2015 0 0 0 0 Male
## 58 23/03/2015 5491 3 7 0 Male
## 59 23/03/2015 10853 4 25 371 Female
## 60 23/03/2015 2148 1 0 698 Male
## 61 24/03/2015 5184 4 0 382 Male
## 62 24/03/2015 9943 0 0 0 Male
## 63 24/03/2015 7357 3 5 0 Male
## 64 24/03/2015 11220 5 23 355 Female
## 65 24/03/2015 1438 1 0 0 Male
## 66 25/03/2015 5329 4 0 390 Male
## 67 25/03/2015 22123 0 0 0 Male
## 68 25/03/2015 3189 2 3 0 Male
## 69 25/03/2015 11429 5 19 314 Female
## 70 25/03/2015 4387 2 0 929 Male
## 71 26/03/2015 4404 3 0 402 Male
## 72 26/03/2015 15334 0 0 0 Male
## 73 26/03/2015 6436 3 6 0 Male
## 74 26/03/2015 10503 5 15 290 Female
## 75 26/03/2015 4923 2 0 782 Male
## 76 27/03/2015 14349 10 0 450 Male
## 77 27/03/2015 3694 0 0 0 Male
## 78 27/03/2015 5800 3 6 0 Male
## 79 27/03/2015 11474 5 17 296 Female
## 80 27/03/2015 6691 3 0 836 Male
## 81 28/03/2015 12444 9 0 420 Male
## 82 28/03/2015 17840 0 0 0 Male
## 83 28/03/2015 4982 2 3 0 Male
## 84 28/03/2015 7562 3 14 376 Female
## 85 28/03/2015 419 0 0 0 Male
## 86 29/03/2015 1454 1 0 435 Male
## 87 29/03/2015 852 0 0 0 Male
## 88 29/03/2015 13477 6 7 0 Male
## 89 29/03/2015 1996 1 0 350 Female
## 90 29/03/2015 0 0 0 0 Male
## 91 30/03/2015 3787 3 0 450 Male
## 92 30/03/2015 7822 0 0 0 Male
## 93 30/03/2015 8362 4 10 0 Male
## 94 30/03/2015 14370 6 24 294 Female
## 95 30/03/2015 4889 2 0 0 Male
## 96 31/03/2015 3065 2 0 420 Male
## 97 31/03/2015 NA 0 0 0 Male
## 98 31/03/2015 11946 6 1 0 Male
## 99 31/03/2015 7975 3 12 388 Female
## 100 31/03/2015 6921 3 0 663 Male
## 101 1/04/2015 3701 3 0 390 Male
## 102 1/04/2015 NA 0 0 0 Male
## 103 1/04/2015 12122 6 18 0 Male
## 104 1/04/2015 9942 4 20 212 Female
## 105 1/04/2015 5107 3 0 770 Male
## 106 2/04/2015 4304 3 0 376 Male
## 107 2/04/2015 NA 0 0 0 Male
## 108 2/04/2015 12773 6 16 0 Male
## 109 2/04/2015 10736 4 18 283 Female
## 110 2/04/2015 0 0 0 0 Male
## 111 3/04/2015 2418 2 0 417 Male
## 112 3/04/2015 NA 0 0 0 Male
## 113 3/04/2015 11090 5 17 0 Male
## 114 3/04/2015 5196 2 8 452 Female
## 115 3/04/2015 2067 1 0 0 Male
## 116 4/04/2015 9145 6 0 418 Male
## 117 4/04/2015 NA 0 0 0 Male
## 118 4/04/2015 9098 4 5 0 Male
## 119 4/04/2015 9880 4 33 392 Female
## 120 4/04/2015 586 0 0 917 Male
## 121 5/04/2015 1454 1 0 504 Male
## 122 5/04/2015 NA 0 0 0 Male
## 123 5/04/2015 7199 3 4 0 Male
## 124 5/04/2015 10508 4 17 530 Female
## 125 5/04/2015 3204 2 0 0 Male
## 126 6/04/2015 2871 2 0 410 Male
## 127 6/04/2015 NA 0 0 0 Male
## 128 6/04/2015 5330 3 5 0 Male
## 129 6/04/2015 4229 2 4 497 Female
## 130 6/04/2015 0 0 0 955 Male
## 131 7/04/2015 3065 2 0 395 Male
## 132 7/04/2015 NA 0 0 0 Male
## 133 7/04/2015 7436 4 7 0 Male
## 134 7/04/2015 9813 4 9 305 Female
## 135 7/04/2015 7319 4 0 0 Male
## 136 8/04/2015 3758 3 0 392 Male
## 137 8/04/2015 NA 0 0 0 Male
## 138 8/04/2015 10591 5 21 0 Male
## 139 8/04/2015 9241 4 21 280 Female
## 140 8/04/2015 2022 1 0 691 Male
## 141 9/04/2015 5138 4 0 407 Male
## 142 9/04/2015 NA 0 0 0 Male
## 143 9/04/2015 8499 4 10 0 Male
## 144 9/04/2015 9499 4 17 310 Female
## 145 9/04/2015 0 0 0 955 Male
## 146 10/04/2015 6774 5 0 356 Male
## 147 10/04/2015 NA 0 0 0 Male
## 148 10/04/2015 6828 3 7 0 Male
## 149 10/04/2015 12115 5 24 286 Female
## 150 10/04/2015 3750 2 0 0 Male
## 151 11/04/2015 6869 5 0 420 Male
## 152 11/04/2015 NA 0 0 0 Male
## 153 11/04/2015 4933 2 3 0 Male
## 154 11/04/2015 4790 2 3 360 Female
## 155 11/04/2015 5820 3 0 0 Male
## 156 12/04/2015 5676 4 0 418 Male
## 157 12/04/2015 NA 0 0 0 Male
## 158 12/04/2015 2452 1 3 0 Male
## 159 12/04/2015 4703 2 4 281 Female
## 160 12/04/2015 0 0 0 0 Male
## 161 13/04/2015 5018 4 0 411 Male
## 162 13/04/2015 NA 0 0 0 Male
## 163 13/04/2015 6038 3 4 0 Male
## 164 13/04/2015 10115 4 21 321 Female
## 165 13/04/2015 0 0 0 955 Male
## 166 14/04/2015 3933 3 0 390 Male
## 167 14/04/2015 NA 0 0 0 Male
## 168 14/04/2015 9090 4 9 0 Male
## 169 14/04/2015 11612 5 26 328 Female
## 170 14/04/2015 3458 2 0 0 Male
## 171 15/04/2015 3758 3 0 440 Male
## 172 15/04/2015 NA 0 0 0 Male
## 173 15/04/2015 8404 4 5 0 Male
## 174 15/04/2015 11355 5 23 301 Female
## 175 15/04/2015 2538 1 0 946 Male
## 176 16/04/2015 5138 4 0 416 Male
## 177 16/04/2015 NA 0 0 0 Male
## 178 16/04/2015 9669 5 10 0 Male
## 179 16/04/2015 13878 6 29 290 Female
## 180 16/04/2015 0 0 0 955 Male
## 181 17/04/2015 6774 5 0 411 Male
## 182 17/04/2015 NA 0 0 0 Male
## 183 17/04/2015 10703 5 4 0 Male
## 184 17/04/2015 13436 5 28 310 Female
## 185 17/04/2015 0 0 0 0 Male
## 186 18/04/2015 6869 5 0 451 Male
## 187 18/04/2015 NA 0 0 0 Male
## 188 18/04/2015 6565 3 4 0 Male
## 189 18/04/2015 5701 2 13 493 Female
## 190 18/04/2015 0 0 0 0 Male
## 191 19/04/2015 5676 4 0 425 Male
## 192 19/04/2015 NA 0 0 0 Male
## 193 19/04/2015 3251 2 3 0 Male
## 194 19/04/2015 5679 2 8 490 Female
## 195 19/04/2015 0 0 0 0 Male
## 196 20/04/2015 5018 4 0 424 Male
## 197 20/04/2015 NA 0 0 0 Male
## 198 20/04/2015 4960 2 8 0 Male
## 199 20/04/2015 6985 3 21 419 Female
## 200 20/04/2015 0 0 0 0 Male
## 201 21/04/2015 473 0 0 442 Male
## 202 21/04/2015 NA 0 0 0 Male
## 203 21/04/2015 4906 2 9 0 Male
## 204 21/04/2015 9401 4 51 365 Female
## 205 21/04/2015 1780 1 0 0 Male
## 206 22/04/2015 5708 4 0 420 Male
## 207 22/04/2015 NA 0 0 0 Male
## 208 22/04/2015 5395 3 6 0 Male
## 209 22/04/2015 9378 4 23 328 Female
## 210 22/04/2015 2684 1 0 955 Malegroup <-read.csv("Outliers1.csv")
attach(group)
#remove missing values
na.rm=TRUE
#find structure of data
str(group)## 'data.frame': 210 obs. of 6 variables:
## $ Date : Factor w/ 42 levels "1/04/2015","10/04/2015",..: 4 4 4 4 4 6 6 6 6 6 ...
## $ Steps : int 5266 14100 7532 11797 2801 4873 14635 25265 9630 1887 ...
## $ Distance: int 4 0 4 5 1 3 0 12 4 1 ...
## $ floors : int 0 0 9 30 0 0 0 203 33 0 ...
## $ Asleep : int 324 0 0 316 728 365 0 0 284 534 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 2 2 2 1 2 ...summary(group)## Date Steps Distance floors
## 1/04/2015 : 5 Min. : 0 Min. : 0.00 Min. : 0.0
## 10/04/2015: 5 1st Qu.: 3516 1st Qu.: 0.00 1st Qu.: 0.0
## 11/04/2015: 5 Median : 5676 Median : 2.00 Median : 0.0
## 12/03/2015: 5 Mean : 6585 Mean : 2.51 Mean : 6.1
## 12/04/2015: 5 3rd Qu.: 9509 3rd Qu.: 4.00 3rd Qu.: 7.0
## 13/03/2015: 5 Max. :25265 Max. :12.00 Max. :203.0
## (Other) :180 NA's :23
## Asleep Gender
## Min. : 0.0 Female: 42
## 1st Qu.: 0.0 Male :168
## Median :280.5
## Mean :242.1
## 3rd Qu.:416.8
## Max. :955.0
## #recode factor variable for Gender
library(car)## Warning: package 'car' was built under R version 3.1.3group$Sex <-recode(group$Gender,"'Male'='1';'Female'='0'")
#creates a new dummy variable that is numerical
group## Date Steps Distance floors Asleep Gender Sex
## 1 12/03/2015 5266 4 0 324 Male 1
## 2 12/03/2015 14100 0 0 0 Male 1
## 3 12/03/2015 7532 4 9 0 Male 1
## 4 12/03/2015 11797 5 30 316 Female 0
## 5 12/03/2015 2801 1 0 728 Male 1
## 6 13/03/2015 4873 3 0 365 Male 1
## 7 13/03/2015 14635 0 0 0 Male 1
## 8 13/03/2015 25265 12 203 0 Male 1
## 9 13/03/2015 9630 4 33 284 Female 0
## 10 13/03/2015 1887 1 0 534 Male 1
## 11 14/03/2015 14103 10 0 480 Male 1
## 12 14/03/2015 19329 0 0 0 Male 1
## 13 14/03/2015 4436 2 3 0 Male 1
## 14 14/03/2015 7283 3 20 386 Female 0
## 15 14/03/2015 6778 3 0 684 Male 1
## 16 15/03/2015 7015 5 0 502 Male 1
## 17 15/03/2015 19387 0 0 0 Male 1
## 18 15/03/2015 3865 2 5 0 Male 1
## 19 15/03/2015 2741 1 3 358 Female 0
## 20 15/03/2015 4876 2 0 0 Male 1
## 21 16/03/2015 5996 4 0 405 Male 1
## 22 16/03/2015 8250 0 0 0 Male 1
## 23 16/03/2015 5476 3 10 0 Male 1
## 24 16/03/2015 6013 2 12 387 Female 0
## 25 16/03/2015 4541 2 0 828 Male 1
## 26 17/03/2015 5184 4 0 390 Male 1
## 27 17/03/2015 5654 0 0 0 Male 1
## 28 17/03/2015 4606 2 5 0 Male 1
## 29 17/03/2015 9402 4 19 338 Female 0
## 30 17/03/2015 3162 2 0 783 Male 1
## 31 18/03/2015 2734 2 0 369 Male 1
## 32 18/03/2015 2660 0 0 0 Male 1
## 33 18/03/2015 9548 5 6 0 Male 1
## 34 18/03/2015 10563 4 18 323 Female 0
## 35 18/03/2015 3424 2 0 702 Male 1
## 36 19/03/2015 3448 2 0 392 Male 1
## 37 19/03/2015 9564 0 0 0 Male 1
## 38 19/03/2015 4848 2 2 0 Male 1
## 39 19/03/2015 13147 5 20 382 Female 0
## 40 19/03/2015 6224 3 0 701 Male 1
## 41 20/03/2015 4873 3 0 450 Male 1
## 42 20/03/2015 12977 0 0 0 Male 1
## 43 20/03/2015 3832 2 3 0 Male 1
## 44 20/03/2015 11291 5 22 417 Female 0
## 45 20/03/2015 3573 2 0 733 Male 1
## 46 21/03/2015 9519 7 0 510 Male 1
## 47 21/03/2015 6981 0 0 0 Male 1
## 48 21/03/2015 5893 3 10 0 Male 1
## 49 21/03/2015 6350 3 15 462 Female 0
## 50 21/03/2015 2239 1 0 662 Male 1
## 51 22/03/2015 1454 1 0 450 Male 1
## 52 22/03/2015 0 0 0 0 Male 1
## 53 22/03/2015 12044 6 25 0 Male 1
## 54 22/03/2015 7874 3 12 485 Female 0
## 55 22/03/2015 3320 2 0 0 Male 1
## 56 23/03/2015 3787 3 0 348 Male 1
## 57 23/03/2015 0 0 0 0 Male 1
## 58 23/03/2015 5491 3 7 0 Male 1
## 59 23/03/2015 10853 4 25 371 Female 0
## 60 23/03/2015 2148 1 0 698 Male 1
## 61 24/03/2015 5184 4 0 382 Male 1
## 62 24/03/2015 9943 0 0 0 Male 1
## 63 24/03/2015 7357 3 5 0 Male 1
## 64 24/03/2015 11220 5 23 355 Female 0
## 65 24/03/2015 1438 1 0 0 Male 1
## 66 25/03/2015 5329 4 0 390 Male 1
## 67 25/03/2015 22123 0 0 0 Male 1
## 68 25/03/2015 3189 2 3 0 Male 1
## 69 25/03/2015 11429 5 19 314 Female 0
## 70 25/03/2015 4387 2 0 929 Male 1
## 71 26/03/2015 4404 3 0 402 Male 1
## 72 26/03/2015 15334 0 0 0 Male 1
## 73 26/03/2015 6436 3 6 0 Male 1
## 74 26/03/2015 10503 5 15 290 Female 0
## 75 26/03/2015 4923 2 0 782 Male 1
## 76 27/03/2015 14349 10 0 450 Male 1
## 77 27/03/2015 3694 0 0 0 Male 1
## 78 27/03/2015 5800 3 6 0 Male 1
## 79 27/03/2015 11474 5 17 296 Female 0
## 80 27/03/2015 6691 3 0 836 Male 1
## 81 28/03/2015 12444 9 0 420 Male 1
## 82 28/03/2015 17840 0 0 0 Male 1
## 83 28/03/2015 4982 2 3 0 Male 1
## 84 28/03/2015 7562 3 14 376 Female 0
## 85 28/03/2015 419 0 0 0 Male 1
## 86 29/03/2015 1454 1 0 435 Male 1
## 87 29/03/2015 852 0 0 0 Male 1
## 88 29/03/2015 13477 6 7 0 Male 1
## 89 29/03/2015 1996 1 0 350 Female 0
## 90 29/03/2015 0 0 0 0 Male 1
## 91 30/03/2015 3787 3 0 450 Male 1
## 92 30/03/2015 7822 0 0 0 Male 1
## 93 30/03/2015 8362 4 10 0 Male 1
## 94 30/03/2015 14370 6 24 294 Female 0
## 95 30/03/2015 4889 2 0 0 Male 1
## 96 31/03/2015 3065 2 0 420 Male 1
## 97 31/03/2015 NA 0 0 0 Male 1
## 98 31/03/2015 11946 6 1 0 Male 1
## 99 31/03/2015 7975 3 12 388 Female 0
## 100 31/03/2015 6921 3 0 663 Male 1
## 101 1/04/2015 3701 3 0 390 Male 1
## 102 1/04/2015 NA 0 0 0 Male 1
## 103 1/04/2015 12122 6 18 0 Male 1
## 104 1/04/2015 9942 4 20 212 Female 0
## 105 1/04/2015 5107 3 0 770 Male 1
## 106 2/04/2015 4304 3 0 376 Male 1
## 107 2/04/2015 NA 0 0 0 Male 1
## 108 2/04/2015 12773 6 16 0 Male 1
## 109 2/04/2015 10736 4 18 283 Female 0
## 110 2/04/2015 0 0 0 0 Male 1
## 111 3/04/2015 2418 2 0 417 Male 1
## 112 3/04/2015 NA 0 0 0 Male 1
## 113 3/04/2015 11090 5 17 0 Male 1
## 114 3/04/2015 5196 2 8 452 Female 0
## 115 3/04/2015 2067 1 0 0 Male 1
## 116 4/04/2015 9145 6 0 418 Male 1
## 117 4/04/2015 NA 0 0 0 Male 1
## 118 4/04/2015 9098 4 5 0 Male 1
## 119 4/04/2015 9880 4 33 392 Female 0
## 120 4/04/2015 586 0 0 917 Male 1
## 121 5/04/2015 1454 1 0 504 Male 1
## 122 5/04/2015 NA 0 0 0 Male 1
## 123 5/04/2015 7199 3 4 0 Male 1
## 124 5/04/2015 10508 4 17 530 Female 0
## 125 5/04/2015 3204 2 0 0 Male 1
## 126 6/04/2015 2871 2 0 410 Male 1
## 127 6/04/2015 NA 0 0 0 Male 1
## 128 6/04/2015 5330 3 5 0 Male 1
## 129 6/04/2015 4229 2 4 497 Female 0
## 130 6/04/2015 0 0 0 955 Male 1
## 131 7/04/2015 3065 2 0 395 Male 1
## 132 7/04/2015 NA 0 0 0 Male 1
## 133 7/04/2015 7436 4 7 0 Male 1
## 134 7/04/2015 9813 4 9 305 Female 0
## 135 7/04/2015 7319 4 0 0 Male 1
## 136 8/04/2015 3758 3 0 392 Male 1
## 137 8/04/2015 NA 0 0 0 Male 1
## 138 8/04/2015 10591 5 21 0 Male 1
## 139 8/04/2015 9241 4 21 280 Female 0
## 140 8/04/2015 2022 1 0 691 Male 1
## 141 9/04/2015 5138 4 0 407 Male 1
## 142 9/04/2015 NA 0 0 0 Male 1
## 143 9/04/2015 8499 4 10 0 Male 1
## 144 9/04/2015 9499 4 17 310 Female 0
## 145 9/04/2015 0 0 0 955 Male 1
## 146 10/04/2015 6774 5 0 356 Male 1
## 147 10/04/2015 NA 0 0 0 Male 1
## 148 10/04/2015 6828 3 7 0 Male 1
## 149 10/04/2015 12115 5 24 286 Female 0
## 150 10/04/2015 3750 2 0 0 Male 1
## 151 11/04/2015 6869 5 0 420 Male 1
## 152 11/04/2015 NA 0 0 0 Male 1
## 153 11/04/2015 4933 2 3 0 Male 1
## 154 11/04/2015 4790 2 3 360 Female 0
## 155 11/04/2015 5820 3 0 0 Male 1
## 156 12/04/2015 5676 4 0 418 Male 1
## 157 12/04/2015 NA 0 0 0 Male 1
## 158 12/04/2015 2452 1 3 0 Male 1
## 159 12/04/2015 4703 2 4 281 Female 0
## 160 12/04/2015 0 0 0 0 Male 1
## 161 13/04/2015 5018 4 0 411 Male 1
## 162 13/04/2015 NA 0 0 0 Male 1
## 163 13/04/2015 6038 3 4 0 Male 1
## 164 13/04/2015 10115 4 21 321 Female 0
## 165 13/04/2015 0 0 0 955 Male 1
## 166 14/04/2015 3933 3 0 390 Male 1
## 167 14/04/2015 NA 0 0 0 Male 1
## 168 14/04/2015 9090 4 9 0 Male 1
## 169 14/04/2015 11612 5 26 328 Female 0
## 170 14/04/2015 3458 2 0 0 Male 1
## 171 15/04/2015 3758 3 0 440 Male 1
## 172 15/04/2015 NA 0 0 0 Male 1
## 173 15/04/2015 8404 4 5 0 Male 1
## 174 15/04/2015 11355 5 23 301 Female 0
## 175 15/04/2015 2538 1 0 946 Male 1
## 176 16/04/2015 5138 4 0 416 Male 1
## 177 16/04/2015 NA 0 0 0 Male 1
## 178 16/04/2015 9669 5 10 0 Male 1
## 179 16/04/2015 13878 6 29 290 Female 0
## 180 16/04/2015 0 0 0 955 Male 1
## 181 17/04/2015 6774 5 0 411 Male 1
## 182 17/04/2015 NA 0 0 0 Male 1
## 183 17/04/2015 10703 5 4 0 Male 1
## 184 17/04/2015 13436 5 28 310 Female 0
## 185 17/04/2015 0 0 0 0 Male 1
## 186 18/04/2015 6869 5 0 451 Male 1
## 187 18/04/2015 NA 0 0 0 Male 1
## 188 18/04/2015 6565 3 4 0 Male 1
## 189 18/04/2015 5701 2 13 493 Female 0
## 190 18/04/2015 0 0 0 0 Male 1
## 191 19/04/2015 5676 4 0 425 Male 1
## 192 19/04/2015 NA 0 0 0 Male 1
## 193 19/04/2015 3251 2 3 0 Male 1
## 194 19/04/2015 5679 2 8 490 Female 0
## 195 19/04/2015 0 0 0 0 Male 1
## 196 20/04/2015 5018 4 0 424 Male 1
## 197 20/04/2015 NA 0 0 0 Male 1
## 198 20/04/2015 4960 2 8 0 Male 1
## 199 20/04/2015 6985 3 21 419 Female 0
## 200 20/04/2015 0 0 0 0 Male 1
## 201 21/04/2015 473 0 0 442 Male 1
## 202 21/04/2015 NA 0 0 0 Male 1
## 203 21/04/2015 4906 2 9 0 Male 1
## 204 21/04/2015 9401 4 51 365 Female 0
## 205 21/04/2015 1780 1 0 0 Male 1
## 206 22/04/2015 5708 4 0 420 Male 1
## 207 22/04/2015 NA 0 0 0 Male 1
## 208 22/04/2015 5395 3 6 0 Male 1
## 209 22/04/2015 9378 4 23 328 Female 0
## 210 22/04/2015 2684 1 0 955 Male 1is.factor(Gender)## [1] TRUEsummary(group)## Date Steps Distance floors
## 1/04/2015 : 5 Min. : 0 Min. : 0.00 Min. : 0.0
## 10/04/2015: 5 1st Qu.: 3516 1st Qu.: 0.00 1st Qu.: 0.0
## 11/04/2015: 5 Median : 5676 Median : 2.00 Median : 0.0
## 12/03/2015: 5 Mean : 6585 Mean : 2.51 Mean : 6.1
## 12/04/2015: 5 3rd Qu.: 9509 3rd Qu.: 4.00 3rd Qu.: 7.0
## 13/03/2015: 5 Max. :25265 Max. :12.00 Max. :203.0
## (Other) :180 NA's :23
## Asleep Gender Sex
## Min. : 0.0 Female: 42 0: 42
## 1st Qu.: 0.0 Male :168 1:168
## Median :280.5
## Mean :242.1
## 3rd Qu.:416.8
## Max. :955.0
## plot(group)
# plot specific variables
plot(group$Distance,group$Steps)
hist(group$Distance)
hist(group$Steps)
hist(group$floors)
hist(group$Asleep)
boxplot(group$Distance,group$Steps)
# Check Correlation between distance and steps
cor(group$Distance,group$Steps)## [1] NAmodel1<-glm(as.factor(Gender)~Steps+Distance+floors+Asleep,family="binomial")
model1##
## Call: glm(formula = as.factor(Gender) ~ Steps + Distance + floors +
## Asleep, family = "binomial")
##
## Coefficients:
## (Intercept) Steps Distance floors Asleep
## 3.7869716 -0.0001407 0.1091997 -0.1129707 -0.0030704
##
## Degrees of Freedom: 186 Total (i.e. Null); 182 Residual
## (23 observations deleted due to missingness)
## Null Deviance: 199.2
## Residual Deviance: 143.9 AIC: 153.9summary(model1)##
## Call:
## glm(formula = as.factor(Gender) ~ Steps + Distance + floors +
## Asleep, family = "binomial")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2876 0.2117 0.4018 0.5108 6.5409
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.7869716 0.7195981 5.263 1.42e-07 ***
## Steps -0.0001408 0.0000636 -2.213 0.026906 *
## Distance 0.1091997 0.1263732 0.864 0.387530
## floors -0.1129707 0.0267345 -4.226 2.38e-05 ***
## Asleep -0.0030704 0.0008892 -3.453 0.000555 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 199.22 on 186 degrees of freedom
## Residual deviance: 143.91 on 182 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: 153.91
##
## Number of Fisher Scoring iterations: 5#interpreting coefficients
exp(coef(model1))## (Intercept) Steps Distance floors Asleep
## 44.1225778 0.9998593 1.1153851 0.8931768 0.9969343plot(model1)
# compare and contrast with multiple linear regression model
model2<-lm(Steps~as.factor(Gender)+Distance+floors+Asleep)
model2##
## Call:
## lm(formula = Steps ~ as.factor(Gender) + Distance + floors +
## Asleep)
##
## Coefficients:
## (Intercept) as.factor(Gender)Male Distance
## 6210.091 -1814.751 909.932
## floors Asleep
## 53.521 -4.228summary(model2)##
## Call:
## lm(formula = Steps ~ as.factor(Gender) + Distance + floors +
## Asleep)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4395.3 -1664.8 -562.1 880.8 17727.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6210.0907 775.6688 8.006 1.36e-13 ***
## as.factor(Gender)Male -1814.7515 647.8022 -2.801 0.00564 **
## Distance 909.9315 136.5538 6.664 3.08e-10 ***
## floors 53.5209 17.7509 3.015 0.00294 **
## Asleep -4.2281 0.9219 -4.586 8.37e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3353 on 182 degrees of freedom
## (23 observations deleted due to missingness)
## Multiple R-squared: 0.4509, Adjusted R-squared: 0.4388
## F-statistic: 37.36 on 4 and 182 DF, p-value: < 2.2e-16plot(model2)
#interpreting coefficients
exp(coef(model2))## (Intercept) as.factor(Gender)Male Distance
## Inf 0.000000e+00 Inf
## floors Asleep
## 1.753117e+23 1.457952e-02You can also embed plots, for example:
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