load("C:\\Users\\user\\Documents\\U of Waterloo\\Courses\\2014a_PSYCH670_Data Visualization\\R files\\sampDat.dat")
summary(subS)
## pos orient judgori rt
## left :14662 Min. : 0.01 Min. :-110 Min. :0.102
## right:14714 1st Qu.: 45.11 1st Qu.: 47 1st Qu.:0.929
## Median : 90.38 Median : 90 Median :1.215
## Mean : 90.21 Mean : 89 Mean :1.343
## 3rd Qu.:135.45 3rd Qu.: 132 3rd Qu.:1.615
## Max. :180.00 Max. : 315 Max. :4.942
##
## angdiff validcue blocknum trialnum
## Min. :-89.89 FALSE : 0 Min. :1.00 Min. : 1.0
## 1st Qu.: -8.69 TRUE : 0 1st Qu.:1.00 1st Qu.: 38.0
## Median : -0.06 invalid: 5908 Median :2.00 Median : 76.0
## Mean : 0.16 valid :23468 Mean :2.42 Mean : 75.6
## 3rd Qu.: 8.89 3rd Qu.:3.00 3rd Qu.:113.0
## Max. : 89.89 Max. :5.00 Max. :150.0
##
## userid tilt orientToVert angdiff_TV
## 701 : 750 l:14735 Min. : 0.0 Min. :-89.89
## 704 : 750 r:14641 1st Qu.:22.7 1st Qu.:-11.68
## 705 : 750 Median :45.2 Median : -2.50
## 715L : 750 Mean :45.0 Mean : -3.62
## 717L : 750 3rd Qu.:67.5 3rd Qu.: 5.96
## 718L : 750 Max. :90.0 Max. : 89.89
## (Other):24876
## angdiff_TV_MC
## Min. :-104.65
## 1st Qu.: -8.16
## Median : 0.00
## Mean : -0.44
## 3rd Qu.: 8.03
## Max. : 130.31
##
attach(subS)
sss <- subset(subS, grepl("exp", userid))
attach(sss)
## The following objects are masked from subS:
##
## angdiff, angdiff_TV, angdiff_TV_MC, blocknum, judgori, orient,
## orientToVert, pos, rt, tilt, trialnum, userid, validcue
aggregate(sss$angdiff, list(sss$trialnum), mean)
## Group.1 x
## 1 1 -2.670398
## 2 2 -5.124995
## 3 3 2.647381
## 4 4 0.832403
## 5 5 -3.441631
## 6 6 5.461729
## 7 7 -4.156179
## 8 8 -0.643324
## 9 9 -3.210095
## 10 10 -8.192300
## 11 11 -3.222406
## 12 12 -3.866651
## 13 13 6.166466
## 14 14 -1.505699
## 15 15 2.407629
## 16 16 -6.481469
## 17 17 0.388247
## 18 18 -1.223829
## 19 19 -5.772757
## 20 20 -6.933442
## 21 21 -1.157801
## 22 22 -3.086077
## 23 23 -2.743384
## 24 24 -0.650478
## 25 25 -2.100910
## 26 26 -0.448781
## 27 27 2.569314
## 28 28 -3.574442
## 29 29 -4.434958
## 30 30 5.066296
## 31 31 -1.075828
## 32 32 4.881443
## 33 33 2.039234
## 34 34 -0.554684
## 35 35 -6.696518
## 36 36 -5.359474
## 37 37 -1.124459
## 38 38 -1.490703
## 39 39 -1.352312
## 40 40 -3.672581
## 41 41 1.578817
## 42 42 -0.002027
## 43 43 4.209206
## 44 44 2.906927
## 45 45 1.686794
## 46 46 -1.633090
## 47 47 -1.806894
## 48 48 -1.808613
## 49 49 0.426521
## 50 50 1.779458
## 51 51 -2.781542
## 52 52 6.097033
## 53 53 0.866697
## 54 54 1.987467
## 55 55 -0.237049
## 56 56 -0.915178
## 57 57 5.306105
## 58 58 -0.631636
## 59 59 -0.858855
## 60 60 -1.486899
## 61 61 7.175333
## 62 62 2.748632
## 63 63 0.787155
## 64 64 0.014780
## 65 65 -3.828708
## 66 66 3.333454
## 67 67 2.129087
## 68 68 7.539733
## 69 69 1.913520
## 70 70 -2.296308
## 71 71 -0.757699
## 72 72 2.866162
## 73 73 1.054805
## 74 74 -3.589325
## 75 75 0.299681
## 76 76 3.008128
## 77 77 4.315755
## 78 78 -3.582272
## 79 79 -1.132524
## 80 80 1.140614
## 81 81 0.163047
## 82 82 1.048168
## 83 83 -3.797248
## 84 84 3.121168
## 85 85 2.062459
## 86 86 1.109950
## 87 87 -1.284351
## 88 88 1.316607
## 89 89 -1.380916
## 90 90 -0.777615
## 91 91 -4.882464
## 92 92 -6.715901
## 93 93 1.906358
## 94 94 -1.806107
## 95 95 5.267178
## 96 96 -3.817129
## 97 97 1.564589
## 98 98 -7.348951
## 99 99 1.721656
## 100 100 -6.648906
## 101 101 -0.136319
## 102 102 0.380423
## 103 103 0.831317
## 104 104 7.885080
## 105 105 -0.698950
## 106 106 0.922815
## 107 107 3.902420
## 108 108 -9.749621
## 109 109 -6.350129
## 110 110 0.021219
## 111 111 -2.776406
## 112 112 1.818551
## 113 113 0.092698
## 114 114 -1.680790
## 115 115 -1.561477
## 116 116 -2.658477
## 117 117 -2.039022
## 118 118 3.467404
## 119 119 7.397940
## 120 120 -0.260429
## 121 121 5.527710
## 122 122 1.652656
## 123 123 3.983540
## 124 124 -0.236345
## 125 125 -1.229429
## 126 126 -0.858935
## 127 127 8.172214
## 128 128 -3.405212
## 129 129 2.426962
## 130 130 -0.989612
## 131 131 -0.015726
## 132 132 0.607751
## 133 133 -6.660906
## 134 134 -4.883076
## 135 135 -0.952799
## 136 136 -1.683391
## 137 137 2.455502
## 138 138 2.522643
## 139 139 2.466422
## 140 140 8.883960
## 141 141 0.615883
## 142 142 2.329115
## 143 143 -2.477037
## 144 144 0.794649
## 145 145 -0.043735
## 146 146 -1.094312
## 147 147 0.093204
## 148 148 -0.165426
## 149 149 -5.050404
## 150 150 -1.192389
library(ggplot2)
attach(sss)
## The following objects are masked from sss (position 4):
##
## angdiff, angdiff_TV, angdiff_TV_MC, blocknum, judgori, orient,
## orientToVert, pos, rt, tilt, trialnum, userid, validcue
## The following objects are masked from subS:
##
## angdiff, angdiff_TV, angdiff_TV_MC, blocknum, judgori, orient,
## orientToVert, pos, rt, tilt, trialnum, userid, validcue
z <- aggregate(sss$angdiff, list(sss$trialnum), mean)
attach(z)
ggplot(data = z, aes(x = Group.1, y = x)) + geom_smooth() + labs(x = "Trials",
y = "Average AngDiff")
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.