In this assignment we’ll learn about dplyr and tidyr, two packages from the tidyverse that allow elegant and easily understandable data tidying and manipulation. We’ll do this by working through the steps of loading an actual dataset, tidying it up, and carrying out some basic analyses.
The dataset we’re using comes from the OSF Reproduciblity project replication of a study by Maya Tamir, Christopher Mitchell, and our very own James Gross (“Hedonic and Instrumental Motives in Anger Regulation,” Tamir, Mitchell, and Gross, Psychological Science, 2008). You can find the replication report here, and the original paper here. The replication tests two hypotheses from the original paper:
Rating hypothesis: Participants will prefer listening to angry music (or recalling an anger-inducing experience) before playing a confrontational (violent) game, but will prefer listening to exciting or neutral music (or recalling a calm experience) before a neutral game. This is assessed through preference ratings where the participants read a description of a game, and then are asked to rate on a likert scale.
Performance hypothesis: Subjects would perform better after listening to angry music on a confrontational game (not one of the ones described in the materials for the previous hypothesis, to avoid contamination), but would perform better on a non-confrontational game (again, not described in the materials for hypothesis 1) after listening to non-angry music. This is computed by having the subjects play without music for 5 minutes, and then after/with music for 5 minutes, and comparing change scores depending on the music type.
First, let’s load the libraries we’re going to use.
library(foreign) # for reading spss formatted data
library(tidyr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(stringr) # useful for some string manipulation
library(ggplot2)d = read.spss("data/Tamiretal2008ReplicationData.sav", to.data.frame=T)Take a look at the data structure:
head(d) Subject Cond Exper
1 1 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
2 2 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
3 3 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
4 4 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
5 5 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
6 6 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
Inifile Date Time Game1Angry1 Game1Angry2 Game1Angry3
1 default.mlp 13642819200 40781 6 6 5
2 default.mlp 13642819200 50753 7 7 7
3 default.mlp 13642819200 54540 6 5 7
4 default.mlp 13642905600 34952 4 1 1
5 default.mlp 13642905600 49095 6 6 7
6 default.mlp 13642905600 59714 5 5 6
Game1AngryFriends Game1AngryStrangers Game1CalmFriends Game1CalmStrangers
1 2 5 2 2
2 7 7 6 6
3 2 2 2 2
4 6 6 2 1
5 6 6 2 2
6 3 4 5 4
Game1ExcitedFriends Game1ExcitedStrangers Game1Exciting1 Game1Exciting2
1 1 2 3 2
2 6 6 5 3
3 2 2 2 3
4 3 4 5 4
5 5 5 1 3
6 6 4 3 2
Game1Exciting3 Game1Intro Game1Neutral1 Game1Neutral2 Game1Neutral3
1 6 ok 2 4 4
2 2 ok 1 1 1
3 4 ok 1 2 3
4 5 ok 1 2 2
5 2 ok 3 2 4
6 4 ok 2 2 4
Game2Angry1 Game2Angry2 Game2Angry3 Game2AngryFriends Game2AngryStrangers
1 6 4 6 3 6
2 7 6 7 6 7
3 5 3 6 3 3
4 6 2 6 3 6
5 5 6 6 5 6
6 6 5 6 3 5
Game2CalmFriends Game2CalmStrangers Game2ExcitedFriends Game2ExcitedStrangers
1 1 2 1 1
2 2 3 5 5
3 3 3 3 3
4 1 1 2 4
5 1 1 4 4
6 3 2 5 4
Game2Exciting1 Game2Exciting2 Game2Exciting3 Game2Intro Game2Neutral1
1 3 2 4 ok 1
2 5 2 1 ok 1
3 2 5 2 ok 4
4 3 2 2 ok 1
5 1 2 2 ok 4
6 2 2 3 ok 2
Game2Neutral2 Game2Neutral3 Game3Angry1 Game3Angry2 Game3Angry3
1 3 1 2 2 3
2 1 2 6 3 5
3 3 1 2 2 3
4 1 3 2 1 6
5 4 5 3 5 6
6 3 4 2 2 5
Game3AngryFriends Game3AngryStrangers Game3CalmFriends Game3CalmStrangers
1 3 2 7 6
2 3 2 6 5
3 4 4 3 3
4 5 4 2 2
5 1 3 5 5
6 1 1 4 3
Game3ExcitedFriends Game3ExcitedStrangers Game3Exciting1 Game3Exciting2
1 6 5 2 2
2 6 5 4 3
3 4 4 3 6
4 5 6 3 1
5 6 5 3 1
6 4 2 1 2
Game3Exciting3 Game3Intro Game3Neutral1 Game3Neutral2 Game3Neutral3
1 3 ok 5 6 5
2 3 ok 2 1 5
3 2 ok 2 3 3
4 3 ok 2 2 6
5 3 ok 2 4 5
6 2 ok 5 4 4
Game4Angry1 Game4Angry2 Game4Angry3 Game4AngryFriends Game4AngryStrangers
1 2 2 2 2 2
2 2 5 2 4 4
3 5 2 2 4 5
4 1 1 2 1 1
5 3 4 3 2 3
6 2 3 3 1 2
Game4CalmFriends Game4CalmStrangers Game4ExcitedFriends Game4ExcitedStrangers
1 5 5 7 4
2 2 4 3 4
3 2 4 4 5
4 2 2 4 4
5 5 5 5 6
6 4 4 5 4
Game4Exciting1 Game4Exciting2 Game4Exciting3 Game4Intro Game4Neutral1
1 5 5 2 ok 1
2 1 2 6 ok 5
3 7 4 5 ok 3
4 6 6 6 ok 4
5 1 5 5 ok 4
6 2 4 3 ok 3
Game4Neutral2 Game4Neutral3 MusicSelectionEnd MusicSelectionInstrx
1 5 2 ok ok
2 5 2 ok ok
3 2 4 ok ok
4 5 2 ok ok
5 2 5 ok ok
6 5 5 ok ok
RecallSelectionEnd RecallSelectionInstrx Subject2 Cond2
1 ok ok 1 2
2 ok ok 2 3
3 ok ok 3 1
4 ok ok 4 4
5 ok ok 5 5
6 ok ok 6 6
Exper_A Inifile_A
1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
Date_A Time_A DescribeMusic HowActiveAngry1 HowActiveAngry2
1 13642819200 43151 2 4 4
2 13642819200 53012 3 5 5
3 13642819200 57041 2 4 4
4 13642905600 37630 3 5 3
5 13642905600 51434 2 5 4
6 13642905600 62320 3 3 3
HowActiveAngry3 HowActiveExciting1 HowActiveExciting2 HowActiveExciting3
1 4 5 4 5
2 5 5 2 4
3 4 2 1 3
4 3 5 5 5
5 5 3 3 3
6 2 3 3 4
HowActiveNeutral1 HowActiveNeutral2 HowActiveNeutral3 HowAngryAngry1
1 2 2 2 5
2 2 2 1 5
3 1 2 1 4
4 2 2 1 3
5 2 1 1 2
6 1 2 1 2
HowAngryAngry2 HowAngryAngry3 HowAngryExciting1 HowAngryExciting2
1 4 4 3 4
2 5 5 4 3
3 4 4 3 1
4 2 3 1 1
5 2 3 2 2
6 2 2 2 1
HowAngryExciting3 HowAngryNeutral1 HowAngryNeutral2 HowAngryNeutral3
1 3 2 2 1
2 3 2 1 1
3 3 1 1 2
4 1 2 1 1
5 1 1 1 1
6 1 1 1 1
HowExcitedAngry1 HowExcitedAngry2 HowExcitedAngry3 HowExcitedExciting1
1 4 3 3 4
2 5 5 5 4
3 3 3 2 2
4 4 1 3 4
5 4 4 5 3
6 5 2 3 3
HowExcitedExciting2 HowExcitedExciting3 HowExcitedNeutral1 HowExcitedNeutral2
1 4 4 2 2
2 2 4 3 2
3 2 3 2 1
4 3 5 2 2
5 3 3 2 1
6 2 4 1 1
HowExcitedNeutral3 HowPleasantAngry1 HowPleasantAngry2 HowPleasantAngry3
1 2 1 2 1
2 1 1 2 1
3 2 2 2 4
4 1 1 1 3
5 3 4 3 2
6 2 2 2 3
HowPleasantExciting1 HowPleasantExciting2 HowPleasantExciting3
1 2 2 1
2 1 4 3
3 2 2 2
4 4 4 3
5 1 1 2
6 3 3 4
HowPleasantNeutral1 HowPleasantNeutral2 HowPleasantNeutral3 MusicRatingEnd
1 5 4 5 ok
2 4 4 4 ok
3 2 2 1 ok
4 2 4 5 ok
5 1 1 5 ok
6 3 3 4 ok
MusicRatingInstrx WhichGames aboutyou age distractions endinstructions
1 ok ok ok 18 ok ok
2 ok ok ok 20 ok ok
3 ok ok ok 18 ok ok
4 ok ok ok 18 ok ok
5 ok ok ok 18 ok ok
6 ok ok ok 19 ok ok
ethnicity overlooking race sex whatabout year Subject3 DDNoMusicLevel
1 2 ok 2 1 ok 1 1 3
2 2 ok 2 2 ok 2 2 3
3 2 ok 2 1 ok 1 3 2
4 2 ok 2 1 ok 1 4 3
5 2 ok 2 1 ok 1 5 3
6 2 ok 2 1 ok 1 6 3
DDNoMusicScore DDMusicLevel DDMusicScore SOFNoMusicEnemies
1 0 3 830 22
2 20 3 2930 18
3 1250 3 370 15
4 1742 3 1921 3
5 60 3 1750 18
6 840 3 1380 23
SOFNoMusicFriendlies SOFNoMusicTime SOFMusicEnemies SOFMusicFriendlies
1 2 24360 19 0
2 1 23580 18 2
3 0 15300 23 1
4 0 5280 19 0
5 2 19140 23 3
6 1 23220 24 0
SOFMusicTime GameComments
1 23340
2 22500
3 24300
4 16860 Participant died, restart
5 20820 Error in game towards the end of time
6 23400
DoNotUseVideoGamePerformanceData ConfrontationalAngryMusicScore
1 NA 5.500000
2 NA 6.833333
3 NA 5.333333
4 1 3.333333
5 1 6.000000
6 NA 5.500000
ConfrontationalExcitingMusicScore ConfrontationalNeutralMusicScore
1 3.333333 2.500000
2 3.000000 1.166667
3 3.000000 2.333333
4 3.500000 1.666667
5 1.833333 3.666667
6 2.666667 2.833333
ConfrontationalAngryRecallScore ConfrontationalExcitingRecallScore
1 3.75 1.25
2 7.00 5.75
3 2.25 2.25
4 6.00 3.50
5 6.00 4.75
6 3.75 5.00
ConfrontationalNeutralRecallScore NonconfrontationalAngryMusicScore
1 2.00 2.166667
2 5.25 3.833333
3 2.25 2.666667
4 1.50 2.166667
5 1.75 4.000000
6 4.00 2.833333
NonconfrontationalExcitingMusicScore NonconfrontationalNeutralMusicScore
1 3.166667 4.000000
2 3.166667 3.333333
3 4.500000 2.833333
4 4.166667 3.500000
5 3.000000 3.666667
6 2.333333 4.333333
NonconfrontationalAngryRecallScore NonconfrontationalExcitingRecallScore
1 2.50 5.25
2 3.00 5.25
3 4.25 4.25
4 3.75 5.00
5 2.00 5.75
6 1.25 3.50
NonconfrontationalNeutralRecallScore ConfrontationalAngerScore
1 6.25 4.8
2 5.25 6.9
3 3.25 4.1
4 2.00 4.4
5 5.00 6.0
6 3.75 4.8
ConfrontationalExcitingScore ConfrontationalNeutralScore
1 2.5 2.3
2 4.1 2.8
3 2.7 2.3
4 3.5 1.6
5 3.0 2.9
6 3.6 3.3
NonconfrontationalAngerScore NonconfrontationalExcitingScore
1 2.3 4.0
2 3.5 4.0
3 3.3 4.4
4 2.8 4.5
5 3.2 4.1
6 2.2 2.8
NonconfrontationalNeutralScore Usable DoNotUse
1 4.9 1 NA
2 4.1 0 1
3 3.0 1 NA
4 2.9 1 NA
5 4.2 1 NA
6 4.1 1 NA
ProblemDetails
1
2 Female participant (this is a males only study)
3
4
5
6
DinerDashWithMusicScore DinerDashWithoutMusicScore MusicCondition
1 5830 5000 Exciting
2 7930 5020 Neutral
3 5370 1250 Anger
4 6921 6742 Anger
5 6750 5060 Exciting
6 6380 5840 Neutral
ZDinerDashWithMusicScore ZDinerDashWithoutMusicScore ZSOFNoMusicEnemies
1 -0.07333283 0.2692740 0.7501199
2 NA NA NA
3 -0.73344247 -2.8616517 -0.1401958
4 1.49227504 1.7236934 -1.6664514
5 1.24688645 0.3193688 0.2413681
6 0.71592870 0.9706014 0.8773079
ZSOFMusicEnemies DinerDashDifferenceScore SOFDifferenceScore
1 -0.2020329 -0.3426068 -0.95215278
2 NA NA NA
3 0.3183548 2.1282092 0.45855062
4 -0.2020329 -0.2314183 1.46441854
5 0.3183548 0.9275176 0.07698673
6 0.4484517 -0.2546727 -0.42885618
PleasantScoreForAngryMusic PleasantScoreForExcitingMusic
1 1.333333 1.666667
2 1.333333 2.666667
3 2.666667 2.000000
4 1.666667 3.666667
5 3.000000 1.333333
6 2.333333 3.333333
PleasantScoreForNeutralMusic AngryScoreForAngryMusic
1 4.666667 4.333333
2 4.000000 5.000000
3 1.666667 4.000000
4 3.666667 2.666667
5 2.333333 2.333333
6 3.333333 2.000000
AngryScoreForExcitingMusic AngryScoreForNeutralMusic
1 3.333333 1.666667
2 3.333333 1.333333
3 2.333333 1.333333
4 1.000000 1.333333
5 1.666667 1.000000
6 1.333333 1.000000
ExcitedScoreForExcitingMusic ExcitedScoreForNeutralMusic
1 4.000000 2.000000
2 3.333333 2.000000
3 2.333333 1.666667
4 4.000000 1.666667
5 3.000000 2.000000
6 3.000000 1.333333
ActiveScoreForExcitingMusic ActiveScoreForNeutralMusic
1 4.666667 2.000000
2 3.666667 1.666667
3 2.000000 1.333333
4 5.000000 1.666667
5 3.000000 1.333333
6 3.333333 1.333333
ExcitedScoreForAngryMusic ActiveScoreForAngryMusic
1 3.333333 4.000000
2 5.000000 5.000000
3 2.666667 4.000000
4 2.666667 3.666667
5 4.333333 4.666667
6 3.333333 2.666667This data is what we call wide form – each subject is a single row, and the columns represent different observations. This is a somewhat inconvenient way of representing the data, for example if we wanted to do the same operation to each likert rating (for example normalize it to be in the range 0-1), we’d have to do it on each of the 40 or so rating columns. To avoid this, our eventual goal will be to convert the data into long form, where each row is a single observation.
For now, take a look at the column names to get a better idea of what all is in the dataset.
colnames(d) [1] "Subject"
[2] "Cond"
[3] "Exper"
[4] "Inifile"
[5] "Date"
[6] "Time"
[7] "Game1Angry1"
[8] "Game1Angry2"
[9] "Game1Angry3"
[10] "Game1AngryFriends"
[11] "Game1AngryStrangers"
[12] "Game1CalmFriends"
[13] "Game1CalmStrangers"
[14] "Game1ExcitedFriends"
[15] "Game1ExcitedStrangers"
[16] "Game1Exciting1"
[17] "Game1Exciting2"
[18] "Game1Exciting3"
[19] "Game1Intro"
[20] "Game1Neutral1"
[21] "Game1Neutral2"
[22] "Game1Neutral3"
[23] "Game2Angry1"
[24] "Game2Angry2"
[25] "Game2Angry3"
[26] "Game2AngryFriends"
[27] "Game2AngryStrangers"
[28] "Game2CalmFriends"
[29] "Game2CalmStrangers"
[30] "Game2ExcitedFriends"
[31] "Game2ExcitedStrangers"
[32] "Game2Exciting1"
[33] "Game2Exciting2"
[34] "Game2Exciting3"
[35] "Game2Intro"
[36] "Game2Neutral1"
[37] "Game2Neutral2"
[38] "Game2Neutral3"
[39] "Game3Angry1"
[40] "Game3Angry2"
[41] "Game3Angry3"
[42] "Game3AngryFriends"
[43] "Game3AngryStrangers"
[44] "Game3CalmFriends"
[45] "Game3CalmStrangers"
[46] "Game3ExcitedFriends"
[47] "Game3ExcitedStrangers"
[48] "Game3Exciting1"
[49] "Game3Exciting2"
[50] "Game3Exciting3"
[51] "Game3Intro"
[52] "Game3Neutral1"
[53] "Game3Neutral2"
[54] "Game3Neutral3"
[55] "Game4Angry1"
[56] "Game4Angry2"
[57] "Game4Angry3"
[58] "Game4AngryFriends"
[59] "Game4AngryStrangers"
[60] "Game4CalmFriends"
[61] "Game4CalmStrangers"
[62] "Game4ExcitedFriends"
[63] "Game4ExcitedStrangers"
[64] "Game4Exciting1"
[65] "Game4Exciting2"
[66] "Game4Exciting3"
[67] "Game4Intro"
[68] "Game4Neutral1"
[69] "Game4Neutral2"
[70] "Game4Neutral3"
[71] "MusicSelectionEnd"
[72] "MusicSelectionInstrx"
[73] "RecallSelectionEnd"
[74] "RecallSelectionInstrx"
[75] "Subject2"
[76] "Cond2"
[77] "Exper_A"
[78] "Inifile_A"
[79] "Date_A"
[80] "Time_A"
[81] "DescribeMusic"
[82] "HowActiveAngry1"
[83] "HowActiveAngry2"
[84] "HowActiveAngry3"
[85] "HowActiveExciting1"
[86] "HowActiveExciting2"
[87] "HowActiveExciting3"
[88] "HowActiveNeutral1"
[89] "HowActiveNeutral2"
[90] "HowActiveNeutral3"
[91] "HowAngryAngry1"
[92] "HowAngryAngry2"
[93] "HowAngryAngry3"
[94] "HowAngryExciting1"
[95] "HowAngryExciting2"
[96] "HowAngryExciting3"
[97] "HowAngryNeutral1"
[98] "HowAngryNeutral2"
[99] "HowAngryNeutral3"
[100] "HowExcitedAngry1"
[101] "HowExcitedAngry2"
[102] "HowExcitedAngry3"
[103] "HowExcitedExciting1"
[104] "HowExcitedExciting2"
[105] "HowExcitedExciting3"
[106] "HowExcitedNeutral1"
[107] "HowExcitedNeutral2"
[108] "HowExcitedNeutral3"
[109] "HowPleasantAngry1"
[110] "HowPleasantAngry2"
[111] "HowPleasantAngry3"
[112] "HowPleasantExciting1"
[113] "HowPleasantExciting2"
[114] "HowPleasantExciting3"
[115] "HowPleasantNeutral1"
[116] "HowPleasantNeutral2"
[117] "HowPleasantNeutral3"
[118] "MusicRatingEnd"
[119] "MusicRatingInstrx"
[120] "WhichGames"
[121] "aboutyou"
[122] "age"
[123] "distractions"
[124] "endinstructions"
[125] "ethnicity"
[126] "overlooking"
[127] "race"
[128] "sex"
[129] "whatabout"
[130] "year"
[131] "Subject3"
[132] "DDNoMusicLevel"
[133] "DDNoMusicScore"
[134] "DDMusicLevel"
[135] "DDMusicScore"
[136] "SOFNoMusicEnemies"
[137] "SOFNoMusicFriendlies"
[138] "SOFNoMusicTime"
[139] "SOFMusicEnemies"
[140] "SOFMusicFriendlies"
[141] "SOFMusicTime"
[142] "GameComments"
[143] "DoNotUseVideoGamePerformanceData"
[144] "ConfrontationalAngryMusicScore"
[145] "ConfrontationalExcitingMusicScore"
[146] "ConfrontationalNeutralMusicScore"
[147] "ConfrontationalAngryRecallScore"
[148] "ConfrontationalExcitingRecallScore"
[149] "ConfrontationalNeutralRecallScore"
[150] "NonconfrontationalAngryMusicScore"
[151] "NonconfrontationalExcitingMusicScore"
[152] "NonconfrontationalNeutralMusicScore"
[153] "NonconfrontationalAngryRecallScore"
[154] "NonconfrontationalExcitingRecallScore"
[155] "NonconfrontationalNeutralRecallScore"
[156] "ConfrontationalAngerScore"
[157] "ConfrontationalExcitingScore"
[158] "ConfrontationalNeutralScore"
[159] "NonconfrontationalAngerScore"
[160] "NonconfrontationalExcitingScore"
[161] "NonconfrontationalNeutralScore"
[162] "Usable"
[163] "DoNotUse"
[164] "ProblemDetails"
[165] "DinerDashWithMusicScore"
[166] "DinerDashWithoutMusicScore"
[167] "MusicCondition"
[168] "ZDinerDashWithMusicScore"
[169] "ZDinerDashWithoutMusicScore"
[170] "ZSOFNoMusicEnemies"
[171] "ZSOFMusicEnemies"
[172] "DinerDashDifferenceScore"
[173] "SOFDifferenceScore"
[174] "PleasantScoreForAngryMusic"
[175] "PleasantScoreForExcitingMusic"
[176] "PleasantScoreForNeutralMusic"
[177] "AngryScoreForAngryMusic"
[178] "AngryScoreForExcitingMusic"
[179] "AngryScoreForNeutralMusic"
[180] "ExcitedScoreForExcitingMusic"
[181] "ExcitedScoreForNeutralMusic"
[182] "ActiveScoreForExcitingMusic"
[183] "ActiveScoreForNeutralMusic"
[184] "ExcitedScoreForAngryMusic"
[185] "ActiveScoreForAngryMusic" And see if you can figure out what range the likert scores are in. What’s the highest number on the likert scale, and what’s the lowest? (Hint, d$Game1Angry1 is one of the likert rating columns, and you may want to use unique or range or hist)
unique(d$Game1Angry1)[1] 6 7 4 5 3 2 1 NAHighest number: [7] Lowest number: [1]
First, we’ll get rid of rows and columns of the data that we don’t need.
First, we need to filter out any rows that should be excluded. According to the report, there are two exclusions:
“exclude data from participant 2 and participant 23 participant 2 is female, and this is a males only study participant 23 was set up on part 2 of the study (the music ratings) twice and never did part 1”
You can see participant 23’s data and the fact that they did not do part 1 by looking at the last rows of the dataframe:
tail(d) Subject Cond Exper
86 87 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
87 88 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
88 89 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
89 90 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
90 23 NA
91 23 NA
Inifile Date Time Game1Angry1 Game1Angry2 Game1Angry3
86 default.mlp 13644633600 40065 1 3 4
87 default.mlp 13644633600 51237 7 7 5
88 default.mlp 13644633600 54293 7 6 6
89 default.mlp 13644633600 58190 5 5 5
90 NA NA NA NA NA
91 NA NA NA NA NA
Game1AngryFriends Game1AngryStrangers Game1CalmFriends Game1CalmStrangers
86 6 7 1 1
87 4 1 4 4
88 7 5 3 2
89 7 7 1 1
90 NA NA NA NA
91 NA NA NA NA
Game1ExcitedFriends Game1ExcitedStrangers Game1Exciting1 Game1Exciting2
86 1 1 1 1
87 7 4 7 7
88 7 6 3 5
89 4 1 1 1
90 NA NA NA NA
91 NA NA NA NA
Game1Exciting3 Game1Intro Game1Neutral1 Game1Neutral2 Game1Neutral3
86 1 ok 2 2 3
87 6 ok 2 1 1
88 2 ok 1 2 1
89 1 ok 1 1 6
90 NA NA NA NA
91 NA NA NA NA
Game2Angry1 Game2Angry2 Game2Angry3 Game2AngryFriends Game2AngryStrangers
86 5 5 7 1 7
87 7 7 4 1 1
88 6 4 6 7 2
89 5 1 7 7 7
90 NA NA NA NA NA
91 NA NA NA NA NA
Game2CalmFriends Game2CalmStrangers Game2ExcitedFriends
86 4 4 2
87 5 6 7
88 3 1 7
89 1 1 1
90 NA NA NA
91 NA NA NA
Game2ExcitedStrangers Game2Exciting1 Game2Exciting2 Game2Exciting3
86 2 5 1 1
87 4 7 1 1
88 5 1 3 1
89 4 3 2 2
90 NA NA NA NA
91 NA NA NA NA
Game2Intro Game2Neutral1 Game2Neutral2 Game2Neutral3 Game3Angry1 Game3Angry2
86 ok 1 1 1 5 3
87 ok 1 1 1 2 1
88 ok 1 2 2 2 4
89 ok 1 3 1 1 1
90 NA NA NA NA NA
91 NA NA NA NA NA
Game3Angry3 Game3AngryFriends Game3AngryStrangers Game3CalmFriends
86 6 1 2 5
87 7 1 1 7
88 4 1 1 6
89 5 2 2 7
90 NA NA NA NA
91 NA NA NA NA
Game3CalmStrangers Game3ExcitedFriends Game3ExcitedStrangers Game3Exciting1
86 6 4 2 1
87 2 7 3 2
88 4 3 6 5
89 6 7 7 2
90 NA NA NA NA
91 NA NA NA NA
Game3Exciting2 Game3Exciting3 Game3Intro Game3Neutral1 Game3Neutral2
86 1 1 ok 5 1
87 1 1 ok 4 6
88 5 6 ok 4 1
89 1 1 ok 4 4
90 NA NA NA NA
91 NA NA NA NA
Game3Neutral3 Game4Angry1 Game4Angry2 Game4Angry3 Game4AngryFriends
86 2 3 1 4 1
87 2 2 1 7 3
88 6 1 1 1 1
89 7 1 3 1 3
90 NA NA NA NA NA
91 NA NA NA NA NA
Game4AngryStrangers Game4CalmFriends Game4CalmStrangers Game4ExcitedFriends
86 1 7 7 7
87 4 2 6 7
88 1 7 5 7
89 3 5 4 7
90 NA NA NA NA
91 NA NA NA NA
Game4ExcitedStrangers Game4Exciting1 Game4Exciting2 Game4Exciting3
86 7 2 5 5
87 7 4 1 2
88 5 5 4 7
89 7 2 4 5
90 NA NA NA NA
91 NA NA NA NA
Game4Intro Game4Neutral1 Game4Neutral2 Game4Neutral3 MusicSelectionEnd
86 ok 5 5 4 ok
87 ok 5 3 1 ok
88 ok 5 5 3 ok
89 ok 1 2 5 ok
90 NA NA NA
91 NA NA NA
MusicSelectionInstrx RecallSelectionEnd RecallSelectionInstrx Subject2 Cond2
86 ok ok ok 87 1
87 ok ok ok 88 6
88 ok ok ok 89 2
89 ok ok ok 90 3
90 23 1
91 23 1
Exper_A Inifile_A
86 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
87 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
88 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
89 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
90 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
91 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
Date_A Time_A DescribeMusic HowActiveAngry1 HowActiveAngry2
86 13644633600 42314 2 5 5
87 13644633600 53402 2 5 5
88 13644633600 56552 2 5 3
89 13644633600 60558 2 5 5
90 13643078400 61329 2 4 5
91 13643078400 63502 2 4 3
HowActiveAngry3 HowActiveExciting1 HowActiveExciting2 HowActiveExciting3
86 4 5 5 5
87 5 5 5 5
88 4 4 5 5
89 3 5 5 5
90 5 3 3 3
91 5 4 3 5
HowActiveNeutral1 HowActiveNeutral2 HowActiveNeutral3 HowAngryAngry1
86 1 1 1 3
87 2 2 1 5
88 1 2 1 5
89 1 1 1 5
90 3 4 3 3
91 4 4 2 2
HowAngryAngry2 HowAngryAngry3 HowAngryExciting1 HowAngryExciting2
86 5 1 1 1
87 5 1 3 1
88 5 4 2 3
89 5 3 3 1
90 3 2 3 2
91 3 2 3 3
HowAngryExciting3 HowAngryNeutral1 HowAngryNeutral2 HowAngryNeutral3
86 1 1 5 1
87 2 1 1 1
88 1 1 1 1
89 1 1 1 1
90 2 2 2 2
91 1 2 2 1
HowExcitedAngry1 HowExcitedAngry2 HowExcitedAngry3 HowExcitedExciting1
86 4 4 4 3
87 5 5 5 5
88 5 5 4 3
89 5 5 5 4
90 4 4 5 5
91 3 3 3 3
HowExcitedExciting2 HowExcitedExciting3 HowExcitedNeutral1
86 4 4 1
87 5 5 1
88 4 5 2
89 5 4 1
90 5 3 3
91 5 4 3
HowExcitedNeutral2 HowExcitedNeutral3 HowPleasantAngry1 HowPleasantAngry2
86 2 1 3 3
87 5 5 1 1
88 2 1 3 3
89 1 2 2 1
90 4 4 1 1
91 4 3 2 2
HowPleasantAngry3 HowPleasantExciting1 HowPleasantExciting2
86 4 2 4
87 5 5 5
88 2 3 3
89 3 1 5
90 1 1 2
91 1 2 5
HowPleasantExciting3 HowPleasantNeutral1 HowPleasantNeutral2
86 3 3 3
87 2 5 5
88 5 4 4
89 2 4 4
90 1 3 3
91 3 5 5
HowPleasantNeutral3 MusicRatingEnd MusicRatingInstrx WhichGames aboutyou age
86 2 ok ok ok ok 20
87 5 ok ok ok ok 18
88 5 ok ok ok ok 18
89 5 ok ok ok ok 18
90 3 ok ok ok ok 20
91 1 ok ok ok ok 20
distractions endinstructions ethnicity overlooking race sex whatabout year
86 ok ok 2 ok 2 1 ok 2
87 ok ok 2 ok 1 1 ok 1
88 ok ok 2 ok 2 1 ok 1
89 ok ok 2 ok 2 1 ok 1
90 ok ok 2 ok 1 1 ok 2
91 ok ok 2 ok 1 1 ok 2
Subject3 DDNoMusicLevel DDNoMusicScore DDMusicLevel DDMusicScore
86 87 3 0 3 170
87 88 3 0 3 866
88 89 2 3280 3 820
89 90 2 3040 3 0
90 23 2 3990 3 750
91 23 NA NA NA NA
SOFNoMusicEnemies SOFNoMusicFriendlies SOFNoMusicTime SOFMusicEnemies
86 15 0 13140 25
87 24 0 23460 27
88 7 0 8880 31
89 22 2 28440 26
90 9 2 19260 18
91 NA NA NA NA
SOFMusicFriendlies SOFMusicTime GameComments
86 1 23160 Participant died, restart
87 0 22380
88 0 23100
89 0 25500
90 2 24120
91 NA NA
DoNotUseVideoGamePerformanceData ConfrontationalAngryMusicScore
86 1 4.166667
87 NA 6.166667
88 1 5.833333
89 NA 4.666667
90 NA NA
91 NA NA
ConfrontationalExcitingMusicScore ConfrontationalNeutralMusicScore
86 1.666667 1.666667
87 4.833333 1.166667
88 2.500000 1.500000
89 1.666667 2.166667
90 NA NA
91 NA NA
ConfrontationalAngryRecallScore ConfrontationalExcitingRecallScore
86 6.50 1.25
87 2.50 5.50
88 5.25 6.25
89 7.00 3.25
90 NA NA
91 NA NA
ConfrontationalNeutralRecallScore NonconfrontationalAngryMusicScore
86 1.75 3.666667
87 4.50 3.333333
88 2.25 2.166667
89 1.00 2.000000
90 NA NA
91 NA NA
NonconfrontationalExcitingMusicScore NonconfrontationalNeutralMusicScore
86 2.500000 3.666667
87 1.833333 3.500000
88 5.333333 4.000000
89 2.500000 3.833333
90 NA NA
91 NA NA
NonconfrontationalAngryRecallScore NonconfrontationalExcitingRecallScore
86 1.25 4.25
87 1.75 6.00
88 1.00 4.25
89 2.25 7.00
90 NA NA
91 NA NA
NonconfrontationalNeutralRecallScore ConfrontationalAngerScore
86 5.75 5.1
87 5.50 4.7
88 5.25 5.6
89 6.00 5.6
90 NA NA
91 NA NA
ConfrontationalExcitingScore ConfrontationalNeutralScore
86 1.5 1.7
87 5.1 2.5
88 4.0 1.8
89 2.3 1.7
90 NA NA
91 NA NA
NonconfrontationalAngerScore NonconfrontationalExcitingScore
86 2.7 3.2
87 2.7 3.5
88 1.7 4.9
89 2.1 4.3
90 NA NA
91 NA NA
NonconfrontationalNeutralScore Usable DoNotUse
86 4.5 1 NA
87 4.3 1 NA
88 4.5 1 NA
89 4.7 1 NA
90 NA 0 1
91 NA 0 1
ProblemDetails
86
87
88
89
90 Participant 23 was set up on part 2 of the survey when he was supposed to be set up on part 1; he did part 2 twice; data should be excluded entirely
91 Participant 23 was set up on part 2 of the survey when he was supposed to be set up on part 1; he did part 2 twice; data should be excluded entirely
DinerDashWithMusicScore DinerDashWithoutMusicScore MusicCondition
86 5170 5000 Anger
87 5866 5000 Neutral
88 5820 3280 Exciting
89 5000 3040 Neutral
90 5750 3990 <NA>
91 NA NA <NA>
ZDinerDashWithMusicScore ZDinerDashWithoutMusicScore ZSOFNoMusicEnemies
86 -1.02044667 0.2692740 -0.1401958
87 -0.02167208 0.2692740 1.0044959
88 -0.08768304 -1.1667773 -1.1576995
89 -1.26440023 -1.3671565 0.7501199
90 -0.18813451 -0.5739887 -0.9033236
91 NA NA NA
ZSOFMusicEnemies DinerDashDifferenceScore SOFDifferenceScore
86 0.5785486 -1.2897207 0.71874445
87 0.8387424 -0.2909461 -0.16575340
88 1.3591301 1.0790942 2.51682964
89 0.7086455 0.1027563 -0.04147439
90 -0.3321298 0.3858541 0.57119384
91 NA NA NA
PleasantScoreForAngryMusic PleasantScoreForExcitingMusic
86 3.333333 3.000000
87 2.333333 4.000000
88 2.666667 3.666667
89 2.000000 2.666667
90 1.000000 1.333333
91 1.666667 3.333333
PleasantScoreForNeutralMusic AngryScoreForAngryMusic
86 2.666667 3.000000
87 5.000000 3.666667
88 4.333333 4.666667
89 4.333333 4.333333
90 3.000000 2.666667
91 3.666667 2.333333
AngryScoreForExcitingMusic AngryScoreForNeutralMusic
86 1.000000 2.333333
87 2.000000 1.000000
88 2.000000 1.000000
89 1.666667 1.000000
90 2.333333 2.000000
91 2.333333 1.666667
ExcitedScoreForExcitingMusic ExcitedScoreForNeutralMusic
86 3.666667 1.333333
87 5.000000 3.666667
88 4.000000 1.666667
89 4.333333 1.333333
90 4.333333 3.666667
91 4.000000 3.333333
ActiveScoreForExcitingMusic ActiveScoreForNeutralMusic
86 5.000000 1.000000
87 5.000000 1.666667
88 4.666667 1.333333
89 5.000000 1.000000
90 3.000000 3.333333
91 4.000000 3.333333
ExcitedScoreForAngryMusic ActiveScoreForAngryMusic
86 4.000000 4.666667
87 5.000000 5.000000
88 4.666667 4.000000
89 5.000000 4.333333
90 4.333333 4.666667
91 3.000000 4.000000Notice that participant 23 has missing values for part 1.
The researchers have made a column called DoNotUse based on their exclusion criteria. Use this column to filter the dataframe! (hint: this is a little trickier than it might be because of how R treats NA values. You may want to use ?unique to check values in the column and check out ?is.na.)
unique(d$DoNotUse)[1] NA 1filtered_d = d %>%
filter(is.na(DoNotUse),
is.na(DoNotUseVideoGamePerformanceData)) # your code here: exclude subjects that are marked as "DoNotUse" and "DoNotUseVideoGamePerformanceData
filtered_d Subject Cond Exper
1 1 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
2 3 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
3 6 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
4 9 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
5 10 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
6 11 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
7 12 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
8 16 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
9 18 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
10 20 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
11 21 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
12 22 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
13 24 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
14 25 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
15 26 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
16 35 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
17 36 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
18 38 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
19 41 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
20 42 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
21 43 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
22 44 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
23 45 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
24 47 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
25 49 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
26 52 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
27 53 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
28 54 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
29 57 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
30 58 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
31 59 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
32 60 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
33 67 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
34 68 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
35 69 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
36 70 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
37 71 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
38 73 1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
39 74 2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
40 81 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
41 82 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
42 83 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
43 84 4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
44 85 5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
45 86 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
46 88 6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
47 90 3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part1.exp
Inifile Date Time Game1Angry1 Game1Angry2 Game1Angry3
1 default.mlp 13642819200 40781 6 6 5
2 default.mlp 13642819200 54540 6 5 7
3 default.mlp 13642905600 59714 5 5 6
4 default.mlp 13642992000 36299 4 1 4
5 default.mlp 13642992000 47504 7 5 7
6 default.mlp 13642992000 50951 3 4 3
7 default.mlp 13642992000 50998 5 6 1
8 default.mlp 13642992000 62005 4 5 5
9 default.mlp 13643078400 47311 7 7 5
10 default.mlp 13643078400 50811 5 3 7
11 default.mlp 13643078400 53975 4 1 4
12 default.mlp 13643078400 54359 4 2 2
13 default.mlp 13643164800 33135 5 5 4
14 default.mlp 13643164800 37160 4 7 6
15 default.mlp 13643164800 36800 6 2 6
16 default.mlp 13643424000 40163 2 2 3
17 default.mlp 13643424000 50744 6 5 5
18 default.mlp 13643596800 32883 1 5 4
19 default.mlp 13643596800 54412 6 4 6
20 default.mlp 13643596800 50805 6 6 7
21 default.mlp 13643596800 54392 5 6 7
22 default.mlp 13643596800 57907 5 6 5
23 default.mlp 13643596800 58055 5 5 6
24 default.mlp 13643596800 61653 4 2 5
25 default.mlp 13643683200 51896 5 6 5
26 default.mlp 13643683200 61688 5 3 4
27 default.mlp 13643683200 61526 7 6 6
28 default.mlp 13643769600 33015 3 6 7
29 default.mlp 13643769600 51257 3 3 4
30 default.mlp 13643769600 54100 5 1 7
31 default.mlp 13644028800 39776 7 7 7
32 default.mlp 13644028800 50941 6 7 5
33 default.mlp 13644115200 60094 1 1 1
34 default.mlp 13644115200 60278 6 7 6
35 default.mlp 13644201600 36667 7 7 7
36 default.mlp 13644201600 47428 5 5 7
37 default.mlp 13644201600 54401 6 6 1
38 default.mlp 13644201600 58109 3 1 4
39 default.mlp 13644201600 58319 4 5 7
40 default.mlp 13644374400 50929 5 5 7
41 default.mlp 13644374400 58020 6 6 5
42 default.mlp 13644374400 54775 7 6 4
43 default.mlp 13644374400 62243 7 7 7
44 default.mlp 13644374400 61618 2 5 4
45 default.mlp 13644633600 33161 4 3 6
46 default.mlp 13644633600 51237 7 7 5
47 default.mlp 13644633600 58190 5 5 5
Game1AngryFriends Game1AngryStrangers Game1CalmFriends Game1CalmStrangers
1 2 5 2 2
2 2 2 2 2
3 3 4 5 4
4 7 7 4 5
5 6 6 2 2
6 3 5 1 2
7 7 7 3 3
8 4 7 2 1
9 5 6 2 3
10 6 7 2 2
11 1 1 1 1
12 7 7 2 2
13 3 5 1 1
14 4 5 2 2
15 1 1 7 7
16 5 6 4 4
17 6 5 2 2
18 1 1 6 5
19 3 7 1 2
20 5 7 2 2
21 5 7 1 1
22 3 4 5 3
23 5 5 3 3
24 4 4 4 4
25 4 5 4 4
26 2 3 5 4
27 2 4 4 3
28 6 6 3 3
29 1 4 1 1
30 2 1 4 4
31 5 7 2 2
32 6 7 1 2
33 1 1 3 3
34 4 7 1 4
35 6 4 3 2
36 6 6 1 1
37 6 4 3 1
38 2 7 2 2
39 2 7 4 2
40 6 5 4 5
41 5 6 3 1
42 1 6 2 1
43 4 7 1 1
44 1 1 1 1
45 2 3 2 2
46 4 1 4 4
47 7 7 1 1
Game1ExcitedFriends Game1ExcitedStrangers Game1Exciting1 Game1Exciting2
1 1 2 3 2
2 2 2 2 3
3 6 4 3 2
4 3 2 3 4
5 5 5 6 3
6 2 3 1 5
7 4 5 4 4
8 1 3 3 1
9 3 3 5 5
10 4 4 2 1
11 2 2 5 5
12 2 4 2 1
13 1 2 4 1
14 5 6 2 4
15 5 5 7 4
16 2 1 1 5
17 4 4 3 2
18 4 4 1 4
19 4 6 1 3
20 6 4 4 3
21 2 2 3 2
22 5 5 3 4
23 4 4 5 2
24 4 4 1 1
25 5 5 4 3
26 5 5 4 2
27 6 5 6 2
28 4 4 1 1
29 1 2 2 2
30 3 6 1 1
31 4 4 4 3
32 5 4 3 3
33 4 3 1 4
34 3 5 3 3
35 5 3 5 5
36 1 1 1 1
37 5 6 3 5
38 2 2 5 5
39 7 4 1 1
40 5 3 1 2
41 5 4 2 1
42 7 7 6 1
43 1 3 3 1
44 1 1 1 1
45 5 5 3 4
46 7 4 7 7
47 4 1 1 1
Game1Exciting3 Game1Intro Game1Neutral1 Game1Neutral2 Game1Neutral3
1 6 ok 2 4 4
2 4 ok 1 2 3
3 4 ok 2 2 4
4 2 ok 2 1 1
5 4 ok 1 2 1
6 1 ok 1 1 2
7 5 ok 1 1 1
8 3 ok 1 1 2
9 3 ok 2 3 1
10 2 ok 1 1 1
11 4 ok 1 1 2
12 2 ok 1 1 1
13 4 ok 1 1 1
14 6 ok 1 2 2
15 7 ok 1 4 3
16 1 ok 2 3 5
17 2 ok 2 2 1
18 2 ok 3 6 3
19 3 ok 2 3 4
20 5 ok 2 3 1
21 3 ok 1 1 1
22 3 ok 3 2 2
23 6 ok 3 4 2
24 1 ok 1 7 7
25 4 ok 3 3 3
26 2 ok 2 3 3
27 7 ok 3 1 1
28 1 ok 1 1 1
29 2 ok 2 2 2
30 1 ok 1 1 1
31 2 ok 2 5 3
32 4 ok 3 2 1
33 3 ok 3 4 1
34 4 ok 2 2 2
35 2 ok 1 3 2
36 1 ok 1 1 1
37 1 ok 1 1 1
38 3 ok 3 3 2
39 1 ok 1 1 1
40 3 ok 1 3 4
41 4 ok 1 1 1
42 1 ok 1 1 1
43 5 ok 1 1 2
44 3 ok 2 1 2
45 2 ok 3 3 2
46 6 ok 2 1 1
47 1 ok 1 1 6
Game2Angry1 Game2Angry2 Game2Angry3 Game2AngryFriends Game2AngryStrangers
1 6 4 6 3 6
2 5 3 6 3 3
3 6 5 6 3 5
4 1 1 2 1 7
5 6 5 6 6 7
6 6 5 4 5 6
7 7 7 4 6 7
8 1 4 6 3 6
9 7 7 6 4 6
10 4 4 7 6 7
11 4 2 4 1 2
12 4 5 5 7 7
13 4 4 3 2 5
14 1 6 5 7 6
15 7 4 5 1 1
16 4 5 4 4 4
17 5 4 4 4 5
18 1 1 7 1 1
19 6 6 5 4 6
20 6 6 6 5 7
21 3 2 7 5 7
22 5 5 5 6 4
23 6 5 4 6 5
24 3 2 5 4 4
25 7 6 4 5 5
26 5 2 5 3 3
27 6 6 6 2 6
28 5 6 3 5 6
29 4 4 3 2 3
30 6 5 6 1 1
31 7 7 7 6 6
32 5 5 4 5 5
33 4 1 3 4 1
34 5 5 6 4 7
35 5 7 7 7 5
36 5 7 1 6 7
37 4 6 4 7 5
38 4 3 4 5 6
39 4 7 2 3 7
40 3 4 7 4 6
41 4 5 6 5 6
42 7 7 5 6 7
43 7 7 7 1 6
44 1 1 3 1 1
45 4 4 6 2 5
46 7 7 4 1 1
47 5 1 7 7 7
Game2CalmFriends Game2CalmStrangers Game2ExcitedFriends
1 1 2 1
2 3 3 3
3 3 2 5
4 7 1 4
5 1 2 4
6 2 2 4
7 1 3 5
8 1 2 3
9 2 4 5
10 2 2 4
11 1 1 1
12 2 2 4
13 1 1 1
14 2 2 5
15 7 5 5
16 2 3 2
17 1 2 6
18 6 3 3
19 2 2 3
20 4 2 3
21 2 1 2
22 5 4 4
23 3 4 5
24 4 4 4
25 4 4 4
26 4 3 4
27 4 2 5
28 3 2 2
29 1 1 1
30 3 1 2
31 2 2 3
32 2 1 7
33 3 3 3
34 1 3 2
35 3 2 6
36 1 1 1
37 3 1 5
38 3 3 3
39 4 4 6
40 4 5 3
41 2 1 4
42 1 1 1
43 1 1 1
44 1 1 1
45 1 1 5
46 5 6 7
47 1 1 1
Game2ExcitedStrangers Game2Exciting1 Game2Exciting2 Game2Exciting3
1 1 3 2 4
2 3 2 5 2
3 4 2 2 3
4 1 1 1 1
5 3 7 3 5
6 4 1 1 1
7 5 6 4 4
8 4 2 1 2
9 4 2 1 1
10 4 1 1 1
11 1 4 3 2
12 2 6 1 1
13 3 1 2 5
14 2 2 4 3
15 6 1 1 1
16 6 4 7 1
17 1 2 2 2
18 6 1 1 2
19 5 1 4 4
20 3 2 1 2
21 3 1 3 1
22 3 6 5 4
23 5 3 3 4
24 4 1 2 1
25 5 2 2 1
26 4 2 3 2
27 3 2 2 3
28 3 1 1 1
29 1 3 2 3
30 1 1 2 1
31 4 3 3 2
32 6 2 2 2
33 3 3 2 1
34 5 4 4 3
35 5 3 3 2
36 5 1 1 1
37 5 3 1 3
38 2 4 6 3
39 7 3 1 4
40 5 1 4 4
41 5 2 2 2
42 2 3 1 1
43 1 2 1 2
44 1 1 1 1
45 5 1 2 3
46 4 7 1 1
47 4 3 2 2
Game2Intro Game2Neutral1 Game2Neutral2 Game2Neutral3 Game3Angry1 Game3Angry2
1 ok 1 3 1 2 2
2 ok 4 3 1 2 2
3 ok 2 3 4 2 2
4 ok 1 1 1 1 1
5 ok 1 3 1 6 4
6 ok 1 1 1 1 1
7 ok 1 1 1 5 1
8 ok 1 1 4 1 2
9 ok 1 1 1 2 2
10 ok 1 1 1 4 2
11 ok 1 1 1 1 1
12 ok 1 1 1 4 3
13 ok 1 1 1 1 2
14 ok 1 2 1 1 4
15 ok 1 5 3 3 1
16 ok 1 1 1 3 2
17 ok 1 2 1 4 1
18 ok 2 4 1 1 1
19 ok 1 2 5 6 6
20 ok 1 2 3 4 2
21 ok 1 1 1 1 3
22 ok 3 3 3 4 3
23 ok 3 4 2 2 2
24 ok 1 3 6 4 2
25 ok 4 2 2 3 2
26 ok 2 3 2 3 4
27 ok 3 1 1 3 1
28 ok 1 2 2 1 1
29 ok 2 2 2 2 2
30 ok 1 1 3 4 1
31 ok 2 4 1 5 5
32 ok 1 1 1 3 3
33 ok 6 6 2 1 1
34 ok 2 3 2 6 4
35 ok 1 2 1 1 2
36 ok 1 1 1 1 1
37 ok 2 1 1 1 1
38 ok 2 2 2 5 2
39 ok 1 1 1 2 2
40 ok 2 3 6 3 3
41 ok 1 2 4 6 6
42 ok 1 1 1 7 3
43 ok 1 1 2 3 1
44 ok 1 1 1 3 4
45 ok 2 2 1 3 3
46 ok 1 1 1 2 1
47 ok 1 3 1 1 1
Game3Angry3 Game3AngryFriends Game3AngryStrangers Game3CalmFriends
1 3 3 2 7
2 3 4 4 3
3 5 1 1 4
4 5 4 3 6
5 7 2 2 4
6 5 2 2 4
7 3 3 2 7
8 4 2 1 6
9 7 4 3 5
10 2 2 2 5
11 3 1 1 4
12 6 1 1 4
13 5 1 1 2
14 7 3 5 5
15 7 1 1 7
16 5 1 1 7
17 6 2 3 5
18 3 1 1 6
19 5 5 3 6
20 6 1 7 5
21 7 1 2 5
22 7 3 4 5
23 3 3 3 5
24 5 4 4 4
25 6 3 3 5
26 6 2 2 5
27 5 2 3 6
28 3 1 1 4
29 4 2 1 5
30 7 2 3 2
31 7 1 2 5
32 6 1 1 4
33 5 1 1 2
34 6 4 4 5
35 7 1 1 5
36 5 1 1 3
37 7 2 1 7
38 7 1 1 4
39 5 1 2 5
40 7 4 4 6
41 7 4 3 3
42 5 1 1 7
43 7 1 1 7
44 5 2 2 2
45 6 2 2 4
46 7 1 1 7
47 5 2 2 7
Game3CalmStrangers Game3ExcitedFriends Game3ExcitedStrangers Game3Exciting1
1 6 6 5 2
2 3 4 4 3
3 3 4 2 1
4 1 6 3 1
5 3 5 4 4
6 3 6 6 1
7 6 4 6 5
8 4 5 4 2
9 5 4 3 1
10 5 4 5 3
11 4 4 4 4
12 4 7 7 4
13 2 5 3 3
14 2 4 4 5
15 7 5 5 2
16 7 7 7 1
17 5 5 5 4
18 5 4 4 1
19 4 7 4 1
20 1 6 4 3
21 4 6 6 5
22 4 5 5 3
23 5 4 4 3
24 4 4 4 2
25 5 6 5 2
26 5 5 6 3
27 5 6 6 4
28 5 6 6 1
29 4 4 2 2
30 2 1 1 5
31 4 7 5 2
32 5 4 4 2
33 2 5 6 1
34 6 6 6 4
35 4 3 2 6
36 3 7 5 1
37 6 5 2 1
38 5 5 6 4
39 5 7 7 2
40 4 4 5 1
41 2 6 5 3
42 7 5 7 3
43 7 5 1 1
44 3 4 2 1
45 4 5 5 2
46 2 7 3 2
47 6 7 7 2
Game3Exciting2 Game3Exciting3 Game3Intro Game3Neutral1 Game3Neutral2
1 2 3 ok 5 6
2 6 2 ok 2 3
3 2 2 ok 5 4
4 2 2 ok 1 3
5 4 2 ok 2 2
6 1 1 ok 2 1
7 3 7 ok 2 2
8 1 3 ok 1 2
9 1 1 ok 3 2
10 4 3 ok 3 4
11 1 5 ok 1 1
12 1 4 ok 2 1
13 2 2 ok 2 2
14 5 2 ok 6 6
15 2 2 ok 6 5
16 3 3 ok 3 1
17 2 2 ok 3 5
18 1 1 ok 4 4
19 1 2 ok 4 3
20 5 6 ok 2 2
21 2 5 ok 4 5
22 2 2 ok 3 1
23 3 3 ok 5 5
24 2 1 ok 2 6
25 2 1 ok 3 2
26 5 4 ok 4 6
27 4 6 ok 6 5
28 3 1 ok 5 6
29 2 4 ok 4 3
30 1 1 ok 1 1
31 2 3 ok 3 5
32 3 4 ok 2 1
33 3 1 ok 2 4
34 3 3 ok 2 2
35 2 6 ok 1 7
36 1 1 ok 4 5
37 3 1 ok 6 6
38 4 3 ok 2 2
39 1 4 ok 1 2
40 1 3 ok 1 2
41 2 2 ok 2 1
42 2 1 ok 4 1
43 1 1 ok 3 1
44 2 3 ok 1 1
45 4 2 ok 3 1
46 1 1 ok 4 6
47 1 1 ok 4 4
Game3Neutral3 Game4Angry1 Game4Angry2 Game4Angry3 Game4AngryFriends
1 5 2 2 2 2
2 3 5 2 2 4
3 4 2 3 3 1
4 1 7 7 1 2
5 1 3 5 5 2
6 4 1 5 1 1
7 1 1 2 1 1
8 6 1 2 3 2
9 6 6 5 3 3
10 4 4 3 3 1
11 4 1 1 2 1
12 1 2 4 1 1
13 4 1 1 2 1
14 7 1 2 3 4
15 6 3 1 3 1
16 4 5 2 4 2
17 5 4 4 2 2
18 6 1 1 2 1
19 6 5 4 4 4
20 5 3 2 2 2
21 1 1 1 4 1
22 3 4 5 6 3
23 6 4 4 3 5
24 6 6 2 3 4
25 5 2 3 3 3
26 5 2 1 2 2
27 3 3 1 2 2
28 5 2 1 3 1
29 5 2 2 5 1
30 2 4 2 6 1
31 5 3 3 3 1
32 4 2 5 2 1
33 4 1 1 5 1
34 4 4 5 4 4
35 4 3 3 5 3
36 3 1 1 1 1
37 3 1 1 1 3
38 2 5 5 6 1
39 3 3 2 5 1
40 5 2 1 5 4
41 1 2 2 1 2
42 1 4 1 3 1
43 7 2 1 1 1
44 2 1 3 3 2
45 1 3 3 2 2
46 2 2 1 7 3
47 7 1 3 1 3
Game4AngryStrangers Game4CalmFriends Game4CalmStrangers Game4ExcitedFriends
1 2 5 5 7
2 5 2 4 4
3 2 4 4 5
4 1 1 1 4
5 2 1 1 6
6 4 3 4 3
7 2 4 6 7
8 5 5 4 7
9 3 4 3 7
10 1 5 4 3
11 1 4 5 5
12 1 7 4 7
13 1 3 4 5
14 5 3 2 5
15 1 7 6 3
16 1 2 2 7
17 2 5 5 6
18 1 4 5 3
19 2 6 5 7
20 1 3 5 7
21 1 5 4 7
22 4 4 5 4
23 5 4 4 4
24 4 4 4 4
25 2 4 4 5
26 2 5 4 5
27 2 5 4 7
28 1 3 2 6
29 2 4 3 6
30 1 1 1 7
31 2 3 1 7
32 1 5 4 6
33 1 6 3 6
34 4 4 4 7
35 1 6 2 6
36 1 1 1 7
37 1 6 5 3
38 1 1 1 7
39 1 4 4 7
40 4 6 5 7
41 1 4 3 5
42 1 6 5 7
43 1 1 1 7
44 2 3 2 4
45 3 5 3 5
46 4 2 6 7
47 3 5 4 7
Game4ExcitedStrangers Game4Exciting1 Game4Exciting2 Game4Exciting3
1 4 5 5 2
2 5 7 4 5
3 4 2 4 3
4 1 4 7 3
5 3 5 6 4
6 6 1 4 5
7 7 3 4 6
8 4 2 2 4
9 5 2 2 2
10 6 2 4 5
11 4 4 3 2
12 7 6 3 1
13 4 1 3 2
14 2 1 5 4
15 2 3 5 5
16 2 3 7 6
17 5 4 5 5
18 5 1 2 1
19 5 2 2 5
20 6 4 6 6
21 5 6 3 3
22 5 1 4 2
23 3 6 5 6
24 4 3 1 3
25 5 4 4 6
26 5 5 6 5
27 7 4 7 5
28 7 3 6 7
29 3 3 6 3
30 6 3 3 3
31 6 5 5 5
32 5 3 7 5
33 3 1 2 1
34 5 4 4 5
35 6 4 2 4
36 7 7 7 6
37 4 1 1 1
38 7 6 7 5
39 7 5 2 4
40 7 1 4 6
41 4 5 7 5
42 7 3 6 7
43 7 7 7 7
44 4 1 4 1
45 4 3 5 5
46 7 4 1 2
47 7 2 4 5
Game4Intro Game4Neutral1 Game4Neutral2 Game4Neutral3 MusicSelectionEnd
1 ok 1 5 2 ok
2 ok 3 2 4 ok
3 ok 3 5 5 ok
4 ok 6 2 2 ok
5 ok 2 4 2 ok
6 ok 1 5 2 ok
7 ok 4 5 1 ok
8 ok 2 3 4 ok
9 ok 1 2 1 ok
10 ok 4 5 3 ok
11 ok 2 1 2 ok
12 ok 5 1 2 ok
13 ok 5 3 5 ok
14 ok 7 7 5 ok
15 ok 6 6 7 ok
16 ok 4 5 3 ok
17 ok 4 3 3 ok
18 ok 6 5 4 ok
19 ok 3 2 6 ok
20 ok 4 1 1 ok
21 ok 5 2 2 ok
22 ok 2 5 4 ok
23 ok 5 3 2 ok
24 ok 1 5 4 ok
25 ok 5 3 3 ok
26 ok 4 5 4 ok
27 ok 6 1 1 ok
28 ok 6 3 2 ok
29 ok 5 5 4 ok
30 ok 5 1 1 ok
31 ok 4 4 4 ok
32 ok 6 4 1 ok
33 ok 2 3 4 ok
34 ok 3 2 2 ok
35 ok 6 3 1 ok
36 ok 4 4 4 ok
37 ok 5 6 5 ok
38 ok 2 2 2 ok
39 ok 4 2 1 ok
40 ok 7 5 3 ok
41 ok 6 6 4 ok
42 ok 1 1 1 ok
43 ok 3 3 2 ok
44 ok 1 1 4 ok
45 ok 3 4 1 ok
46 ok 5 3 1 ok
47 ok 1 2 5 ok
MusicSelectionInstrx RecallSelectionEnd RecallSelectionInstrx Subject2 Cond2
1 ok ok ok 1 2
2 ok ok ok 3 1
3 ok ok ok 6 6
4 ok ok ok 9 5
5 ok ok ok 10 1
6 ok ok ok 11 2
7 ok ok ok 12 4
8 ok ok ok 16 3
9 ok ok ok 18 1
10 ok ok ok 20 3
11 ok ok ok 21 6
12 ok ok ok 22 5
13 ok ok ok 24 2
14 ok ok ok 25 2
15 ok ok ok 26 1
16 ok ok ok 35 2
17 ok ok ok 36 6
18 ok ok ok 38 6
19 ok ok ok 41 5
20 ok ok ok 42 2
21 ok ok ok 43 6
22 ok ok ok 44 2
23 ok ok ok 45 3
24 ok ok ok 47 4
25 ok ok ok 49 5
26 ok ok ok 52 1
27 ok ok ok 53 6
28 ok ok ok 54 2
29 ok ok ok 57 2
30 ok ok ok 58 5
31 ok ok ok 59 1
32 ok ok ok 60 6
33 ok ok ok 67 4
34 ok ok ok 68 3
35 ok ok ok 69 6
36 ok ok ok 70 2
37 ok ok ok 71 5
38 ok ok ok 73 1
39 ok ok ok 74 2
40 ok ok ok 81 5
41 ok ok ok 82 3
42 ok ok ok 83 4
43 ok ok ok 84 4
44 ok ok ok 85 5
45 ok ok ok 86 3
46 ok ok ok 88 6
47 ok ok ok 90 3
Exper_A Inifile_A
1 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
2 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
3 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
4 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
5 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
6 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
7 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
8 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
9 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
10 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
11 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
12 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
13 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
14 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
15 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
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17 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
18 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
19 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
20 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
21 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
22 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
23 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
24 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
25 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
26 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
27 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
28 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
29 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
30 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
31 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
32 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
33 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
34 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
35 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
36 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
37 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
38 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
39 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
40 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
41 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
42 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
43 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
44 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
45 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
46 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
47 C:\\Users\\msplab\\Desktop\\Study 151\\Study151Part2.exp default.mlp
Date_A Time_A DescribeMusic HowActiveAngry1 HowActiveAngry2
1 13642819200 43151 2 4 4
2 13642819200 57041 2 4 4
3 13642905600 62320 3 3 3
4 13642992000 38665 2 3 3
5 13642992000 49847 1 5 4
6 13642992000 53401 2 5 4
7 13642992000 53299 3 5 5
8 13642992000 64661 3 2 5
9 13643078400 49818 2 5 5
10 13643078400 53056 3 4 4
11 13643078400 56544 3 3 3
12 13643078400 56687 2 4 3
13 13643164800 35368 2 5 4
14 13643164800 39515 2 3 5
15 13643164800 39052 3 5 4
16 13643424000 42463 2 4 3
17 13643424000 53140 3 3 3
18 13643596800 35111 3 4 4
19 13643596800 56639 2 5 5
20 13643596800 52998 2 4 4
21 13643596800 56744 3 5 5
22 13643596800 60083 2 3 4
23 13643596800 60410 2 5 5
24 13643596800 65196 1 3 3
25 13643683200 54140 2 5 5
26 13643683200 64173 2 3 2
27 13643683200 63747 3 4 5
28 13643769600 35193 2 5 4
29 13643769600 53640 2 2 2
30 13643769600 57171 2 4 4
31 13644028800 42012 2 4 5
32 13644028800 53179 2 5 5
33 13644115200 62294 1 1 1
34 13644115200 62661 3 4 3
35 13644201600 39038 3 4 5
36 13644201600 49568 2 4 5
37 13644201600 56662 2 5 5
38 13644201600 60432 1 4 4
39 13644201600 60888 2 4 2
40 13644374400 53231 2 4 4
41 13644374400 60664 3 4 4
42 13644374400 57215 2 5 3
43 13644374400 64189 2 5 3
44 13644374400 63914 2 4 3
45 13644633600 35448 3 4 5
46 13644633600 53402 2 5 5
47 13644633600 60558 2 5 5
HowActiveAngry3 HowActiveExciting1 HowActiveExciting2 HowActiveExciting3
1 4 5 4 5
2 4 2 1 3
3 2 3 3 4
4 5 5 4 1
5 3 4 3 5
6 2 3 3 3
7 4 4 3 4
8 5 4 4 3
9 4 5 5 5
10 5 3 3 4
11 3 5 5 5
12 2 5 3 3
13 2 5 4 5
14 4 5 5 4
15 2 4 5 5
16 2 4 5 5
17 4 3 4 4
18 4 5 5 5
19 5 5 5 4
20 2 4 5 5
21 4 4 4 5
22 4 1 3 1
23 4 4 4 5
24 4 4 4 4
25 4 5 5 5
26 4 3 4 5
27 4 4 4 5
28 4 5 5 5
29 1 3 4 3
30 5 5 4 4
31 4 4 4 4
32 3 5 3 3
33 2 1 3 1
34 4 4 4 4
35 4 4 4 5
36 3 5 5 5
37 2 3 5 5
38 4 5 5 5
39 4 5 5 5
40 4 4 5 3
41 3 4 4 5
42 1 4 4 5
43 1 5 4 5
44 3 2 4 4
45 4 4 4 4
46 5 5 5 5
47 3 5 5 5
HowActiveNeutral1 HowActiveNeutral2 HowActiveNeutral3 HowAngryAngry1
1 2 2 2 5
2 1 2 1 4
3 1 2 1 2
4 1 3 2 1
5 2 1 1 3
6 2 1 2 3
7 1 2 1 5
8 2 1 1 4
9 3 2 2 4
10 1 1 1 4
11 2 1 1 3
12 1 1 1 2
13 3 1 1 4
14 1 2 1 5
15 1 1 1 1
16 3 1 1 1
17 2 2 1 4
18 3 2 2 5
19 1 1 2 3
20 2 2 1 5
21 2 1 1 3
22 2 1 2 4
23 3 2 1 4
24 2 2 2 4
25 1 1 2 3
26 1 1 1 3
27 3 1 1 3
28 1 5 1 3
29 2 1 1 3
30 2 2 1 5
31 2 2 1 3
32 4 4 2 5
33 4 2 4 1
34 1 2 3 4
35 2 1 1 5
36 1 1 1 4
37 1 1 1 5
38 2 2 2 3
39 4 1 1 1
40 2 1 3 3
41 3 1 1 4
42 2 2 1 4
43 2 1 1 1
44 3 2 1 2
45 1 2 1 4
46 2 2 1 5
47 1 1 1 5
HowAngryAngry2 HowAngryAngry3 HowAngryExciting1 HowAngryExciting2
1 4 4 3 4
2 4 4 3 1
3 2 2 2 1
4 1 1 1 1
5 4 2 2 1
6 4 2 2 1
7 5 3 2 2
8 5 4 2 2
9 3 3 1 1
10 4 4 1 2
11 3 2 1 1
12 3 3 1 1
13 4 1 2 2
14 5 3 4 1
15 1 1 1 1
16 2 1 2 2
17 3 3 1 2
18 5 3 2 2
19 2 4 4 2
20 4 5 2 4
21 4 2 1 1
22 2 2 4 3
23 4 3 2 3
24 5 2 1 1
25 4 4 4 4
26 3 1 1 1
27 4 4 3 2
28 3 2 1 1
29 3 1 2 2
30 5 4 1 1
31 4 3 3 2
32 5 3 1 2
33 1 2 1 3
34 5 4 2 3
35 5 2 1 1
36 5 3 3 1
37 5 2 2 2
38 4 1 1 1
39 3 1 1 1
40 5 2 4 3
41 4 2 2 1
42 5 1 1 1
43 3 1 1 1
44 3 3 2 2
45 5 4 3 2
46 5 1 3 1
47 5 3 3 1
HowAngryExciting3 HowAngryNeutral1 HowAngryNeutral2 HowAngryNeutral3
1 3 2 2 1
2 3 1 1 2
3 1 1 1 1
4 1 1 1 1
5 1 1 2 1
6 1 1 1 1
7 1 1 1 1
8 1 1 1 1
9 1 1 1 1
10 2 1 1 1
11 1 1 1 1
12 1 1 1 1
13 1 1 1 1
14 1 1 1 1
15 1 1 1 1
16 1 2 1 1
17 2 1 1 1
18 2 1 1 1
19 3 2 1 1
20 4 1 2 1
21 1 1 1 1
22 3 1 3 1
23 5 1 1 1
24 1 1 1 1
25 4 1 2 1
26 1 1 1 1
27 3 2 1 1
28 1 1 1 1
29 2 1 1 1
30 1 1 1 1
31 2 1 1 1
32 2 2 1 1
33 1 4 4 4
34 2 2 2 1
35 1 1 1 1
36 1 1 1 1
37 3 1 1 1
38 1 1 1 1
39 1 1 1 1
40 3 4 2 1
41 2 2 1 1
42 1 1 1 1
43 1 1 1 1
44 2 2 2 2
45 1 1 1 1
46 2 1 1 1
47 1 1 1 1
HowExcitedAngry1 HowExcitedAngry2 HowExcitedAngry3 HowExcitedExciting1
1 4 3 3 4
2 3 3 2 2
3 5 2 3 3
4 5 5 5 5
5 4 5 4 4
6 4 4 2 3
7 5 4 3 5
8 2 5 5 4
9 5 5 4 4
10 5 4 5 2
11 3 1 2 4
12 4 3 2 5
13 5 5 2 4
14 3 5 4 4
15 5 4 3 4
16 4 5 3 5
17 2 4 4 3
18 4 4 4 5
19 4 5 5 3
20 5 5 5 3
21 4 4 4 4
22 4 3 5 4
23 4 4 3 4
24 1 1 3 2
25 5 5 4 4
26 3 1 3 2
27 4 5 5 5
28 4 4 4 4
29 2 1 2 3
30 3 4 4 4
31 4 5 4 3
32 4 4 3 3
33 1 1 1 1
34 3 1 2 1
35 5 4 4 3
36 4 3 4 5
37 1 2 1 4
38 4 3 5 4
39 5 3 3 5
40 2 2 3 4
41 4 2 5 4
42 5 4 3 3
43 4 5 2 2
44 2 4 3 1
45 5 5 5 4
46 5 5 5 5
47 5 5 5 4
HowExcitedExciting2 HowExcitedExciting3 HowExcitedNeutral1
1 4 4 2
2 2 3 2
3 2 4 1
4 5 5 1
5 3 2 2
6 3 3 2
7 4 5 2
8 3 3 1
9 5 4 2
10 1 2 1
11 4 5 1
12 3 3 1
13 4 5 2
14 5 5 1
15 5 4 1
16 5 5 1
17 4 2 1
18 5 5 2
19 3 3 1
20 4 5 2
21 4 4 1
22 4 2 2
23 3 4 3
24 3 3 1
25 3 5 2
26 2 3 1
27 4 5 4
28 4 4 1
29 3 3 1
30 3 3 2
31 3 3 1
32 5 4 3
33 3 1 3
34 2 2 3
35 4 3 1
36 5 4 1
37 4 5 1
38 4 4 2
39 4 5 2
40 4 5 2
41 4 4 1
42 4 5 1
43 1 3 1
44 3 3 2
45 4 4 1
46 5 5 1
47 5 4 1
HowExcitedNeutral2 HowExcitedNeutral3 HowPleasantAngry1 HowPleasantAngry2
1 2 2 1 2
2 1 2 2 2
3 1 2 2 2
4 2 2 1 1
5 2 1 3 2
6 1 1 1 1
7 1 1 2 1
8 1 1 1 2
9 1 1 4 2
10 1 1 2 1
11 1 1 1 1
12 1 1 3 2
13 1 1 1 1
14 1 1 2 1
15 1 1 2 1
16 2 1 4 4
17 1 1 1 2
18 2 2 4 1
19 1 2 4 3
20 1 2 2 3
21 3 1 1 1
22 2 2 3 4
23 2 2 2 1
24 1 1 2 2
25 1 1 2 2
26 1 1 2 1
27 2 1 2 1
28 1 2 2 1
29 1 1 2 2
30 2 1 1 1
31 1 1 3 3
32 2 2 2 1
33 4 3 1 1
34 3 5 2 1
35 1 1 1 1
36 1 1 1 1
37 1 1 1 1
38 1 2 2 1
39 2 1 5 2
40 3 4 3 2
41 2 1 2 1
42 1 1 5 1
43 1 3 1 1
44 2 2 2 2
45 1 1 2 2
46 5 5 1 1
47 1 2 2 1
HowPleasantAngry3 HowPleasantExciting1 HowPleasantExciting2
1 1 2 2
2 4 2 2
3 3 3 3
4 5 4 3
5 2 2 4
6 2 1 3
7 3 1 2
8 1 1 1
9 3 2 4
10 3 2 1
11 2 3 3
12 2 3 2
13 2 1 2
14 2 1 5
15 3 3 3
16 2 3 5
17 4 4 3
18 4 2 3
19 3 1 3
20 2 3 5
21 1 1 1
22 4 4 3
23 3 3 3
24 4 3 3
25 2 4 1
26 3 2 3
27 2 3 3
28 2 4 4
29 2 2 2
30 2 3 2
31 4 2 3
32 3 4 4
33 3 1 4
34 1 1 1
35 3 2 3
36 5 2 4
37 3 1 3
38 2 4 5
39 3 5 4
40 3 1 2
41 2 3 3
42 2 5 3
43 3 1 2
44 3 1 1
45 2 1 2
46 5 5 5
47 3 1 5
HowPleasantExciting3 HowPleasantNeutral1 HowPleasantNeutral2
1 1 5 4
2 2 2 2
3 4 3 3
4 1 2 3
5 4 3 3
6 2 2 2
7 4 4 3
8 1 3 4
9 1 4 4
10 1 4 4
11 3 4 5
12 1 3 3
13 2 3 4
14 5 3 1
15 3 5 4
16 3 5 3
17 4 2 3
18 5 5 5
19 3 2 3
20 4 3 3
21 1 3 4
22 2 2 2
23 3 5 4
24 3 3 5
25 4 5 4
26 3 4 3
27 3 4 5
28 4 3 3
29 3 4 4
30 3 3 1
31 3 5 5
32 5 3 3
33 2 3 2
34 2 2 2
35 4 5 5
36 2 5 5
37 3 2 5
38 5 3 3
39 5 3 1
40 2 5 4
41 3 4 4
42 4 3 1
43 2 5 5
44 1 3 4
45 3 4 5
46 2 5 5
47 2 4 4
HowPleasantNeutral3 MusicRatingEnd MusicRatingInstrx WhichGames aboutyou age
1 5 ok ok ok ok 18
2 1 ok ok ok ok 18
3 4 ok ok ok ok 19
4 5 ok ok ok ok 19
5 4 ok ok ok ok 19
6 3 ok ok ok ok 19
7 5 ok ok ok ok 19
8 4 ok ok ok ok 18
9 4 ok ok ok ok 17
10 5 ok ok ok ok 18
11 5 ok ok ok ok 22
12 3 ok ok ok ok 19
13 4 ok ok ok ok 18
14 3 ok ok ok ok 19
15 5 ok ok ok ok 18
16 5 ok ok ok ok 20
17 4 ok ok ok ok 20
18 5 ok ok ok ok 20
19 5 ok ok ok ok 19
20 4 ok ok ok ok 19
21 4 ok ok ok ok 18
22 2 ok ok ok ok 19
23 5 ok ok ok ok 19
24 5 ok ok ok ok 20
25 4 ok ok ok ok 21
26 4 ok ok ok ok 19
27 4 ok ok ok ok 19
28 5 ok ok ok ok 19
29 3 ok ok ok ok 19
30 4 ok ok ok ok 21
31 4 ok ok ok ok 19
32 5 ok ok ok ok 18
33 4 ok ok ok ok 18
34 5 ok ok ok ok 19
35 5 ok ok ok ok 22
36 5 ok ok ok ok 22
37 5 ok ok ok ok 21
38 2 ok ok ok ok 19
39 3 ok ok ok ok 19
40 5 ok ok ok ok 20
41 5 ok ok ok ok 19
42 1 ok ok ok ok 19
43 4 ok ok ok ok 18
44 4 ok ok ok ok 19
45 4 ok ok ok ok 19
46 5 ok ok ok ok 18
47 5 ok ok ok ok 18
distractions endinstructions ethnicity overlooking race sex whatabout year
1 ok ok 2 ok 2 1 ok 1
2 ok ok 2 ok 2 1 ok 1
3 ok ok 2 ok 2 1 ok 1
4 ok ok 2 ok 3 1 ok 2
5 ok ok 2 ok 2 1 ok 1
6 ok ok 2 ok 2 1 ok 1
7 ok ok 2 ok 2 1 ok 2
8 ok ok 2 ok 2 1 ok 1
9 ok ok 1 ok 5 1 ok 1
10 ok ok 2 ok 2 1 ok 1
11 ok ok 2 ok 2 1 ok 4
12 ok ok 2 ok 2 1 ok 1
13 ok ok 2 ok 2 1 ok 1
14 ok ok 2 ok 2 1 ok 2
15 ok ok 2 ok 2 1 ok 1
16 ok ok 2 ok 3 1 ok 2
17 ok ok 2 ok 3 1 ok 1
18 ok ok 2 ok 2 1 ok 1
19 ok ok 2 ok 2 1 ok 1
20 ok ok 2 ok 2 1 ok 1
21 ok ok 2 ok 2 1 ok 1
22 ok ok 2 ok 3 1 ok 2
23 ok ok 2 ok 2 1 ok 2
24 ok ok 2 ok 3 1 ok 2
25 ok ok 1 ok 6 1 ok 4
26 ok ok 2 ok 2 1 ok 1
27 ok ok 2 ok 3 1 ok 2
28 ok ok 2 ok 2 1 ok 1
29 ok ok 2 ok 2 1 ok 2
30 ok ok 2 ok 3 1 ok 3
31 ok ok 2 ok 2 1 ok 1
32 ok ok 1 ok 5 1 ok 1
33 ok ok 2 ok 2 1 ok 1
34 ok ok 2 ok 2 1 ok 1
35 ok ok 2 ok 2 1 ok 4
36 ok ok 2 ok 2 1 ok 4
37 ok ok 2 ok 2 1 ok 4
38 ok ok 2 ok 6 1 ok 2
39 ok ok 2 ok 2 1 ok 1
40 ok ok 2 ok 2 1 ok 2
41 ok ok 2 ok 2 1 ok 1
42 ok ok 2 ok 2 1 ok 1
43 ok ok 2 ok 2 1 ok 1
44 ok ok 2 ok 2 1 ok 1
45 ok ok 2 ok 2 1 ok 1
46 ok ok 2 ok 1 1 ok 1
47 ok ok 2 ok 2 1 ok 1
Subject3 DDNoMusicLevel DDNoMusicScore DDMusicLevel DDMusicScore
1 1 3 0 3 830
2 3 2 1250 3 370
3 6 3 840 3 1380
4 9 3 1460 3 1440
5 10 2 2990 3 480
6 11 3 90 3 1210
7 12 3 490 3 1270
8 16 3 200 3 1810
9 18 3 140 3 1160
10 20 3 170 3 1650
11 21 2 3000 3 1500
12 22 3 0 3 770
13 24 3 60 3 950
14 25 3 0 3 550
15 26 3 20 3 1560
16 35 3 0 3 1010
17 36 3 60 3 730
18 38 3 840 3 2430
19 41 2 4189 3 790
20 42 3 90 3 1410
21 43 3 0 3 2310
22 44 3 170 3 1460
23 45 3 60 3 1820
24 47 4 0 4 100
25 49 3 370 3 920
26 52 3 0 3 990
27 53 3 530 3 670
28 54 2 3140 3 0
29 57 2 2870 3 620
30 58 3 150 3 1080
31 59 3 0 3 830
32 60 3 490 3 1200
33 67 2 1210 3 170
34 68 2 2960 3 650
35 69 3 170 3 600
36 70 3 860 3 900
37 71 3 0 3 660
38 73 3 1180 3 1480
39 74 2 3160 3 430
40 81 3 70 3 670
41 82 3 60 3 960
42 83 3 60 3 630
43 84 2 2870 2 3290
44 85 3 1000 3 1150
45 86 3 0 3 940
46 88 3 0 3 866
47 90 2 3040 3 0
SOFNoMusicEnemies SOFNoMusicFriendlies SOFNoMusicTime SOFMusicEnemies
1 22 2 24360 19
2 15 0 15300 23
3 23 1 23220 24
4 18 2 22800 16
5 16 2 19680 24
6 19 2 19260 26
7 23 1 23280 23
8 24 0 16200 26
9 19 1 22860 23
10 21 1 22320 23
11 10 0 15240 14
12 21 0 23820 23
13 16 1 24000 15
14 25 0 22920 27
15 24 2 22920 24
16 25 1 21900 23
17 18 1 23280 24
18 24 1 21660 26
19 15 0 23460 25
20 19 1 22260 23
21 27 2 22080 30
22 24 1 18000 22
23 13 0 23340 23
24 27 1 25380 28
25 17 1 19440 24
26 23 0 23880 23
27 25 0 24540 24
28 24 0 23700 23
29 24 0 23040 24
30 22 0 22020 26
31 23 2 23460 23
32 18 1 23040 26
33 19 1 21600 27
34 21 1 22680 19
35 24 0 26760 25
36 16 0 19080 28
37 19 1 23280 26
38 27 0 21480 36
39 24 2 24180 24
40 21 0 23220 27
41 20 1 17520 28
42 17 1 18180 26
43 7 0 17100 19
44 25 2 24420 30
45 19 2 24480 18
46 24 0 23460 27
47 22 2 28440 26
SOFMusicFriendlies SOFMusicTime GameComments
1 0 23340
2 1 24300
3 0 23400
4 2 23940
5 0 23100
6 0 18960
7 0 22980
8 1 24480
9 0 22440
10 1 23160
11 0 23280
12 1 23340
13 0 23760
14 0 23520
15 1 22560
16 1 23460
17 2 23640
18 1 21360
19 0 15000
20 1 21060
21 1 21480
22 1 15900
23 0 24060
24 0 18240
25 0 23220
26 1 22740
27 1 22020
28 0 22560
29 0 23100
30 1 22080
31 0 23280
32 1 22980
33 0 22320
34 2 16380
35 1 23280
36 1 18840
37 0 23280
38 0 24300
39 0 24540
40 0 22980
41 0 22860
42 2 19080
43 0 19800
44 0 23460
45 1 23640
46 0 22380
47 0 25500
DoNotUseVideoGamePerformanceData ConfrontationalAngryMusicScore
1 NA 5.500000
2 NA 5.333333
3 NA 5.500000
4 NA 2.166667
5 NA 6.000000
6 NA 4.166667
7 NA 5.000000
8 NA 4.166667
9 NA 6.500000
10 NA 5.000000
11 NA 3.166667
12 NA 3.666667
13 NA 4.166667
14 NA 4.833333
15 NA 5.000000
16 NA 3.333333
17 NA 4.833333
18 NA 3.166667
19 NA 5.500000
20 NA 6.166667
21 NA 5.000000
22 NA 5.166667
23 NA 5.166667
24 NA 3.500000
25 NA 5.500000
26 NA 4.000000
27 NA 6.166667
28 NA 5.000000
29 NA 3.500000
30 NA 5.000000
31 NA 7.000000
32 NA 5.333333
33 NA 1.833333
34 NA 5.833333
35 NA 6.666667
36 NA 5.000000
37 NA 4.500000
38 NA 3.166667
39 NA 4.833333
40 NA 5.166667
41 NA 5.333333
42 NA 6.000000
43 NA 7.000000
44 NA 2.666667
45 NA 4.500000
46 NA 6.166667
47 NA 4.666667
ConfrontationalExcitingMusicScore ConfrontationalNeutralMusicScore
1 3.333333 2.500000
2 3.000000 2.333333
3 2.666667 2.833333
4 2.000000 1.166667
5 4.666667 1.500000
6 1.666667 1.166667
7 4.500000 1.000000
8 2.000000 1.666667
9 2.833333 1.500000
10 1.333333 1.000000
11 3.833333 1.166667
12 2.166667 1.000000
13 2.833333 1.000000
14 3.500000 1.500000
15 3.500000 2.833333
16 3.166667 2.166667
17 2.166667 1.500000
18 1.833333 3.166667
19 2.666667 2.833333
20 2.833333 2.000000
21 2.166667 1.000000
22 4.166667 2.666667
23 3.833333 3.000000
24 1.166667 4.166667
25 2.666667 2.833333
26 2.500000 2.500000
27 3.666667 1.666667
28 1.000000 1.333333
29 2.333333 2.000000
30 1.166667 1.333333
31 2.833333 2.833333
32 2.666667 1.500000
33 2.333333 3.666667
34 3.500000 2.166667
35 3.333333 1.666667
36 1.000000 1.000000
37 2.666667 1.166667
38 4.333333 2.333333
39 1.833333 1.000000
40 2.500000 3.166667
41 2.166667 1.666667
42 2.166667 1.000000
43 2.333333 1.333333
44 1.333333 1.333333
45 2.500000 2.166667
46 4.833333 1.166667
47 1.666667 2.166667
ConfrontationalAngryRecallScore ConfrontationalExcitingRecallScore
1 3.75 1.25
2 2.25 2.25
3 3.75 5.00
4 7.00 2.25
5 6.25 4.50
6 4.25 2.75
7 7.00 4.50
8 5.25 2.25
9 5.50 3.25
10 6.50 4.00
11 1.25 1.75
12 7.00 2.50
13 4.00 1.75
14 4.75 4.50
15 1.00 5.25
16 5.00 2.75
17 5.50 3.25
18 1.00 4.50
19 4.75 4.75
20 6.00 4.75
21 6.00 2.25
22 3.50 4.50
23 5.00 4.25
24 4.00 4.00
25 4.50 5.00
26 2.50 4.75
27 3.50 5.00
28 6.00 3.75
29 2.25 1.25
30 1.50 3.25
31 5.75 4.00
32 6.00 5.00
33 1.00 3.50
34 5.50 4.00
35 5.25 4.50
36 6.25 2.00
37 5.25 5.25
38 4.25 2.00
39 4.50 6.25
40 5.75 4.50
41 5.50 4.75
42 3.75 5.75
43 5.25 1.50
44 1.00 1.00
45 3.00 5.00
46 2.50 5.50
47 7.00 3.25
ConfrontationalNeutralRecallScore NonconfrontationalAngryMusicScore
1 2.00 2.166667
2 2.25 2.666667
3 4.00 2.833333
4 3.50 3.666667
5 2.00 5.000000
6 1.50 2.333333
7 3.00 2.166667
8 1.75 2.166667
9 2.75 4.166667
10 2.00 3.000000
11 1.00 1.500000
12 2.00 3.333333
13 1.00 2.000000
14 2.00 3.000000
15 6.50 3.000000
16 3.75 3.500000
17 2.00 3.500000
18 5.00 1.500000
19 1.50 5.000000
20 2.00 3.166667
21 1.00 2.833333
22 4.25 4.833333
23 3.25 3.000000
24 4.00 3.666667
25 4.00 3.166667
26 4.25 3.000000
27 3.25 2.500000
28 2.75 1.833333
29 1.00 2.833333
30 3.25 4.000000
31 2.00 4.333333
32 1.25 3.500000
33 3.00 2.333333
34 2.25 4.833333
35 2.50 3.500000
36 1.00 1.666667
37 2.00 2.000000
38 2.25 5.000000
39 3.50 3.166667
40 4.50 3.500000
41 2.00 4.000000
42 1.50 3.833333
43 1.00 2.500000
44 1.00 3.166667
45 1.75 3.333333
46 4.50 3.333333
47 1.00 2.000000
NonconfrontationalExcitingMusicScore NonconfrontationalNeutralMusicScore
1 3.166667 4.000000
2 4.500000 2.833333
3 2.333333 4.333333
4 3.166667 2.500000
5 4.166667 2.166667
6 2.166667 2.500000
7 4.666667 2.500000
8 2.333333 3.000000
9 1.500000 2.500000
10 3.500000 3.833333
11 3.166667 1.833333
12 3.166667 2.000000
13 2.166667 3.500000
14 3.666667 6.333333
15 3.166667 6.000000
16 3.833333 3.333333
17 3.666667 3.833333
18 1.166667 4.833333
19 2.166667 4.000000
20 5.000000 2.500000
21 4.000000 3.166667
22 2.333333 3.000000
23 4.333333 4.333333
24 2.000000 4.000000
25 3.166667 3.500000
26 4.666667 4.666667
27 5.000000 3.666667
28 3.500000 4.500000
29 3.333333 4.333333
30 2.666667 1.833333
31 3.666667 4.166667
32 4.000000 3.000000
33 1.500000 3.166667
34 3.833333 2.500000
35 4.000000 3.666667
36 3.833333 4.000000
37 1.333333 5.166667
38 4.833333 2.000000
39 3.000000 2.166667
40 2.666667 3.833333
41 4.000000 3.333333
42 3.666667 1.500000
43 4.000000 3.166667
44 2.000000 1.666667
45 3.500000 2.166667
46 1.833333 3.500000
47 2.500000 3.833333
NonconfrontationalAngryRecallScore NonconfrontationalExcitingRecallScore
1 2.50 5.25
2 4.25 4.25
3 1.25 3.50
4 3.00 4.00
5 2.00 4.25
6 2.50 6.00
7 2.50 5.25
8 2.50 4.50
9 3.50 4.00
10 1.75 4.75
11 1.00 4.00
12 1.00 7.00
13 1.00 4.25
14 4.00 3.50
15 1.00 4.25
16 1.00 5.75
17 2.25 5.00
18 1.00 4.25
19 3.75 5.75
20 2.50 5.50
21 1.25 5.75
22 3.50 5.00
23 3.50 3.75
24 4.00 4.00
25 2.75 5.50
26 2.00 5.25
27 2.25 6.25
28 1.00 6.25
29 1.75 3.25
30 2.00 2.25
31 1.50 6.25
32 1.00 4.25
33 1.00 4.75
34 4.00 5.75
35 1.00 3.50
36 1.00 6.50
37 1.50 4.00
38 1.00 5.75
39 1.25 7.00
40 4.00 5.00
41 3.00 5.25
42 1.00 6.00
43 1.00 4.50
44 2.00 3.50
45 2.25 4.75
46 1.75 6.00
47 2.25 7.00
NonconfrontationalNeutralRecallScore ConfrontationalAngerScore
1 6.25 4.8
2 3.25 4.1
3 3.75 4.8
4 3.50 4.1
5 3.00 6.1
6 3.75 4.2
7 6.50 5.8
8 5.00 4.6
9 4.50 6.1
10 4.75 5.6
11 4.25 2.4
12 4.00 5.0
13 2.50 4.1
14 3.50 4.8
15 6.75 3.4
16 5.75 4.0
17 5.00 5.1
18 5.50 2.3
19 5.25 5.2
20 4.00 6.1
21 4.50 5.4
22 4.75 4.5
23 4.75 5.1
24 4.00 3.7
25 4.75 5.1
26 4.75 3.4
27 5.25 5.1
28 3.75 5.4
29 4.25 3.0
30 1.75 3.6
31 3.75 6.5
32 4.25 5.6
33 2.25 1.5
34 5.00 5.7
35 4.00 6.1
36 2.50 5.5
37 6.25 4.8
38 3.50 3.6
39 4.75 4.7
40 5.25 5.4
41 2.75 5.4
42 6.50 5.1
43 5.50 6.3
44 2.25 2.0
45 3.75 3.9
46 5.50 4.7
47 6.00 5.6
ConfrontationalExcitingScore ConfrontationalNeutralScore
1 2.5 2.3
2 2.7 2.3
3 3.6 3.3
4 2.1 2.1
5 4.6 1.7
6 2.1 1.3
7 4.5 1.8
8 2.1 1.7
9 3.0 2.0
10 2.4 1.4
11 3.0 1.1
12 2.3 1.4
13 2.4 1.0
14 3.9 1.7
15 4.2 4.3
16 3.0 2.8
17 2.6 1.7
18 2.9 3.9
19 3.5 2.3
20 3.6 2.0
21 2.2 1.0
22 4.3 3.3
23 4.0 3.1
24 2.3 4.1
25 3.6 3.3
26 3.4 3.2
27 4.2 2.3
28 2.1 1.9
29 1.9 1.6
30 2.0 2.1
31 3.3 2.5
32 3.6 1.4
33 2.8 3.4
34 3.7 2.2
35 3.8 2.0
36 1.4 1.0
37 3.7 1.5
38 3.4 2.3
39 3.6 2.0
40 3.3 3.7
41 3.2 1.8
42 3.6 1.2
43 2.0 1.2
44 1.2 1.2
45 3.5 2.0
46 5.1 2.5
47 2.3 1.7
NonconfrontationalAngerScore NonconfrontationalExcitingScore
1 2.3 4.0
2 3.3 4.4
3 2.2 2.8
4 3.4 3.5
5 3.8 4.2
6 2.4 3.7
7 2.3 4.9
8 2.3 3.2
9 3.9 2.5
10 2.5 4.0
11 1.3 3.5
12 2.4 4.7
13 1.6 3.0
14 3.4 3.6
15 2.2 3.6
16 2.5 4.6
17 3.0 4.2
18 1.3 2.4
19 4.5 3.6
20 2.9 5.2
21 2.2 4.7
22 4.3 3.4
23 3.2 4.1
24 3.8 2.8
25 3.0 4.1
26 2.6 4.9
27 2.4 5.5
28 1.5 4.6
29 2.4 3.3
30 3.2 2.5
31 3.2 4.7
32 2.5 4.1
33 1.8 2.8
34 4.5 4.6
35 2.5 3.8
36 1.4 4.9
37 1.8 2.4
38 3.4 5.2
39 2.4 4.6
40 3.7 3.6
41 3.6 4.5
42 2.7 4.6
43 1.9 4.2
44 2.7 2.6
45 2.9 4.0
46 2.7 3.5
47 2.1 4.3
NonconfrontationalNeutralScore Usable DoNotUse
1 4.9 1 NA
2 3.0 1 NA
3 4.1 1 NA
4 2.9 1 NA
5 2.5 1 NA
6 3.0 1 NA
7 4.1 1 NA
8 3.8 1 NA
9 3.3 1 NA
10 4.2 1 NA
11 2.8 1 NA
12 2.8 1 NA
13 3.1 1 NA
14 5.2 1 NA
15 6.3 1 NA
16 4.3 1 NA
17 4.3 1 NA
18 5.1 1 NA
19 4.5 1 NA
20 3.1 1 NA
21 3.7 1 NA
22 3.7 1 NA
23 4.5 1 NA
24 4.0 1 NA
25 4.0 1 NA
26 4.7 1 NA
27 4.3 1 NA
28 4.2 1 NA
29 4.3 1 NA
30 1.8 1 NA
31 4.0 1 NA
32 3.5 1 NA
33 2.8 1 NA
34 3.5 1 NA
35 3.8 1 NA
36 3.4 1 NA
37 5.6 1 NA
38 2.6 1 NA
39 3.2 1 NA
40 4.4 1 NA
41 3.1 1 NA
42 3.5 1 NA
43 4.1 1 NA
44 1.9 1 NA
45 2.8 1 NA
46 4.3 1 NA
47 4.7 1 NA
ProblemDetails
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DinerDashWithMusicScore DinerDashWithoutMusicScore MusicCondition
1 5830 5000 Exciting
2 5370 1250 Anger
3 6380 5840 Neutral
4 6440 6460 Exciting
5 5480 2990 Anger
6 6210 5090 Exciting
7 6270 5490 Anger
8 6810 5200 Neutral
9 6160 5140 Anger
10 6650 5170 Neutral
11 6500 3000 Neutral
12 5770 5000 Exciting
13 5950 5060 Exciting
14 5550 5000 Exciting
15 6560 5020 Anger
16 6010 5000 Exciting
17 5730 5060 Neutral
18 7430 5840 Neutral
19 5790 4189 Exciting
20 6410 5090 Exciting
21 7310 5000 Neutral
22 6460 5170 Exciting
23 6820 5060 Neutral
24 5100 5000 Anger
25 5920 5370 Exciting
26 5990 5000 Anger
27 5670 5530 Neutral
28 5000 3140 Exciting
29 5620 2870 Exciting
30 6080 5150 Exciting
31 5830 5000 Anger
32 6200 5490 Neutral
33 5170 1210 Anger
34 5650 2960 Neutral
35 5600 5170 Neutral
36 5900 5860 Exciting
37 5660 5000 Exciting
38 6480 6180 Anger
39 5430 3160 Exciting
40 5670 5070 Exciting
41 5960 5060 Neutral
42 5630 5060 Anger
43 3290 2870 Anger
44 6150 6000 Exciting
45 5940 5000 Neutral
46 5866 5000 Neutral
47 5000 3040 Neutral
ZDinerDashWithMusicScore ZDinerDashWithoutMusicScore ZSOFNoMusicEnemies
1 -0.07333283 0.2692740 0.75011992
2 -0.73344247 -2.8616517 -0.14019582
3 0.71592870 0.9706014 0.87730789
4 0.80202995 1.4882478 0.24136807
5 -0.57559017 -1.4089022 -0.01300786
6 0.47197513 0.3444162 0.36855603
7 0.55807639 0.6783816 0.87730789
8 1.33298771 0.4362567 1.00449585
9 0.40022408 0.3861619 0.36855603
10 1.10338436 0.4112093 0.62293196
11 0.88813121 -1.4005530 -0.77613564
12 -0.15943409 0.2692740 0.62293196
13 0.09886968 0.3193688 -0.01300786
14 -0.47513870 0.2692740 1.13168382
15 0.97423247 0.2859723 1.00449585
16 0.18497094 0.2692740 1.13168382
17 -0.21683493 0.3193688 0.24136807
18 2.22270071 0.9706014 1.00449585
19 -0.13073367 -0.4078409 -0.14019582
20 0.75897932 0.3444162 0.36855603
21 2.05049819 0.2692740 1.38605975
22 0.83073037 0.4112093 1.00449585
23 1.34733792 0.3193688 -0.39457175
24 -1.12089813 0.2692740 1.38605975
25 0.05581905 0.5781920 0.11418010
26 0.15627052 0.2692740 0.87730789
27 -0.30293619 0.7117782 1.13168382
28 -1.26440023 -1.2836652 1.00449585
29 -0.37468723 -1.5090918 1.00449585
30 0.28542241 0.3945110 0.75011992
31 -0.07333283 0.2692740 0.87730789
32 0.45762492 0.6783816 0.24136807
33 -1.02044667 -2.8950483 0.36855603
34 -0.33163661 -1.4339496 0.62293196
35 -0.40338765 0.4112093 1.00449585
36 0.02711863 0.9872996 -0.01300786
37 -0.31728640 0.2692740 0.36855603
38 0.85943079 1.2544720 1.38605975
39 -0.64734122 -1.2669669 1.00449585
40 -0.30293619 0.3277180 0.62293196
41 0.11321989 0.3193688 0.49574400
42 -0.36033702 0.3193688 0.11418010
43 -3.71828607 -1.5090918 -1.15769954
44 0.38587388 1.1041875 1.13168382
45 0.08451947 0.2692740 0.36855603
46 -0.02167208 0.2692740 1.00449585
47 -1.26440023 -1.3671565 0.75011992
ZSOFMusicEnemies DinerDashDifferenceScore SOFDifferenceScore
1 -0.2020329 -0.34260684 -0.95215278
2 0.3183548 2.12820924 0.45855062
3 0.4484517 -0.25467267 -0.42885618
4 -0.5923236 -0.68621780 -0.83369166
5 0.4484517 0.83331201 0.46145957
6 0.7086455 0.12755891 0.34008950
7 0.3183548 -0.12030525 -0.55895309
8 0.7086455 0.89673100 -0.29585032
9 0.3183548 0.01406218 -0.05020123
10 0.3183548 0.69217505 -0.30457716
11 -0.8525174 2.28868426 -0.07638177
12 0.3183548 -0.42870810 -0.30457716
13 -0.7224205 -0.22049914 -0.70941264
14 0.8387424 -0.74441271 -0.29294137
15 0.4484517 0.68826019 -0.55604414
16 0.3183548 -0.08430307 -0.81332902
17 0.4484517 -0.53620375 0.20708364
18 0.7086455 1.25209934 -0.29585032
19 0.5785486 0.27710719 0.71874445
20 0.3183548 0.41456310 -0.05020123
21 1.2290332 1.78122418 -0.15702656
22 0.1882579 0.41952107 -0.81623797
23 0.3183548 1.02796910 0.71292655
24 0.9688394 -1.39017214 -0.41722038
25 0.4484517 -0.52237296 0.33427161
26 0.3183548 -0.11300349 -0.55895309
27 0.4484517 -1.01471436 -0.68323211
28 0.3183548 0.01926492 -0.68614105
29 0.4484517 1.13440457 -0.55604414
30 0.7086455 -0.10908863 -0.04147439
31 0.3183548 -0.34260684 -0.55895309
32 0.7086455 -0.22075671 0.46727747
33 0.8387424 1.87460159 0.47018642
34 -0.2020329 1.10231298 -0.82496481
35 0.5785486 -0.81459696 -0.42594723
36 0.9688394 -0.96018101 0.98184722
37 0.7086455 -0.58656040 0.34008950
38 2.0096147 -0.39504118 0.62355492
39 0.4484517 0.61962566 -0.55604414
40 0.8387424 -0.63065414 0.21581049
41 0.9688394 -0.20614893 0.47309537
42 0.7086455 -0.67970584 0.59446543
43 -0.2020329 -2.20919427 0.95566668
44 1.2290332 -0.71831366 0.09734937
45 -0.3321298 -0.18475453 -0.70068580
46 0.8387424 -0.29094609 -0.16575340
47 0.7086455 0.10275627 -0.04147439
PleasantScoreForAngryMusic PleasantScoreForExcitingMusic
1 1.333333 1.666667
2 2.666667 2.000000
3 2.333333 3.333333
4 2.333333 2.666667
5 2.333333 3.333333
6 1.333333 2.000000
7 2.000000 2.333333
8 1.333333 1.000000
9 3.000000 2.333333
10 2.000000 1.333333
11 1.333333 3.000000
12 2.333333 2.000000
13 1.333333 1.666667
14 1.666667 3.666667
15 2.000000 3.000000
16 3.333333 3.666667
17 2.333333 3.666667
18 3.000000 3.333333
19 3.333333 2.333333
20 2.333333 4.000000
21 1.000000 1.000000
22 3.666667 3.000000
23 2.000000 3.000000
24 2.666667 3.000000
25 2.000000 3.000000
26 2.000000 2.666667
27 1.666667 3.000000
28 1.666667 4.000000
29 2.000000 2.333333
30 1.333333 2.666667
31 3.333333 2.666667
32 2.000000 4.333333
33 1.666667 2.333333
34 1.333333 1.333333
35 1.666667 3.000000
36 2.333333 2.666667
37 1.666667 2.333333
38 1.666667 4.666667
39 3.333333 4.666667
40 2.666667 1.666667
41 1.666667 3.000000
42 2.666667 4.000000
43 1.666667 1.666667
44 2.333333 1.000000
45 2.000000 2.000000
46 2.333333 4.000000
47 2.000000 2.666667
PleasantScoreForNeutralMusic AngryScoreForAngryMusic
1 4.666667 4.333333
2 1.666667 4.000000
3 3.333333 2.000000
4 3.333333 1.000000
5 3.333333 3.000000
6 2.333333 3.000000
7 4.000000 4.333333
8 3.666667 4.333333
9 4.000000 3.333333
10 4.333333 4.000000
11 4.666667 2.666667
12 3.000000 2.666667
13 3.666667 3.000000
14 2.333333 4.333333
15 4.666667 1.000000
16 4.333333 1.333333
17 3.000000 3.333333
18 5.000000 4.333333
19 3.333333 3.000000
20 3.333333 4.666667
21 3.666667 3.000000
22 2.000000 2.666667
23 4.666667 3.666667
24 4.333333 3.666667
25 4.333333 3.666667
26 3.666667 2.333333
27 4.333333 3.666667
28 3.666667 2.666667
29 3.666667 2.333333
30 2.666667 4.666667
31 4.666667 3.333333
32 3.666667 4.333333
33 3.000000 1.333333
34 3.000000 4.333333
35 5.000000 4.000000
36 5.000000 4.000000
37 4.000000 4.000000
38 2.666667 2.666667
39 2.333333 1.666667
40 4.666667 3.333333
41 4.333333 3.333333
42 1.666667 3.333333
43 4.666667 1.666667
44 3.666667 2.666667
45 4.333333 4.333333
46 5.000000 3.666667
47 4.333333 4.333333
AngryScoreForExcitingMusic AngryScoreForNeutralMusic
1 3.333333 1.666667
2 2.333333 1.333333
3 1.333333 1.000000
4 1.000000 1.000000
5 1.333333 1.333333
6 1.333333 1.000000
7 1.666667 1.000000
8 1.666667 1.000000
9 1.000000 1.000000
10 1.666667 1.000000
11 1.000000 1.000000
12 1.000000 1.000000
13 1.666667 1.000000
14 2.000000 1.000000
15 1.000000 1.000000
16 1.666667 1.333333
17 1.666667 1.000000
18 2.000000 1.000000
19 3.000000 1.333333
20 3.333333 1.333333
21 1.000000 1.000000
22 3.333333 1.666667
23 3.333333 1.000000
24 1.000000 1.000000
25 4.000000 1.333333
26 1.000000 1.000000
27 2.666667 1.333333
28 1.000000 1.000000
29 2.000000 1.000000
30 1.000000 1.000000
31 2.333333 1.000000
32 1.666667 1.333333
33 1.666667 4.000000
34 2.333333 1.666667
35 1.000000 1.000000
36 1.666667 1.000000
37 2.333333 1.000000
38 1.000000 1.000000
39 1.000000 1.000000
40 3.333333 2.333333
41 1.666667 1.333333
42 1.000000 1.000000
43 1.000000 1.000000
44 2.000000 2.000000
45 2.000000 1.000000
46 2.000000 1.000000
47 1.666667 1.000000
ExcitedScoreForExcitingMusic ExcitedScoreForNeutralMusic
1 4.000000 2.000000
2 2.333333 1.666667
3 3.000000 1.333333
4 5.000000 1.666667
5 3.000000 1.666667
6 3.000000 1.333333
7 4.666667 1.333333
8 3.333333 1.000000
9 4.333333 1.333333
10 1.666667 1.000000
11 4.333333 1.000000
12 3.666667 1.000000
13 4.333333 1.333333
14 4.666667 1.000000
15 4.333333 1.000000
16 5.000000 1.333333
17 3.000000 1.000000
18 5.000000 2.000000
19 3.000000 1.333333
20 4.000000 1.666667
21 4.000000 1.666667
22 3.333333 2.000000
23 3.666667 2.333333
24 2.666667 1.000000
25 4.000000 1.333333
26 2.333333 1.000000
27 4.666667 2.333333
28 4.000000 1.333333
29 3.000000 1.000000
30 3.333333 1.666667
31 3.000000 1.000000
32 4.000000 2.333333
33 1.666667 3.333333
34 1.666667 3.666667
35 3.333333 1.000000
36 4.666667 1.000000
37 4.333333 1.000000
38 4.000000 1.666667
39 4.666667 1.666667
40 4.333333 3.000000
41 4.000000 1.333333
42 4.000000 1.000000
43 2.000000 1.666667
44 2.333333 2.000000
45 4.000000 1.000000
46 5.000000 3.666667
47 4.333333 1.333333
ActiveScoreForExcitingMusic ActiveScoreForNeutralMusic
1 4.666667 2.000000
2 2.000000 1.333333
3 3.333333 1.333333
4 3.333333 2.000000
5 4.000000 1.333333
6 3.000000 1.666667
7 3.666667 1.333333
8 3.666667 1.333333
9 5.000000 2.333333
10 3.333333 1.000000
11 5.000000 1.333333
12 3.666667 1.000000
13 4.666667 1.666667
14 4.666667 1.333333
15 4.666667 1.000000
16 4.666667 1.666667
17 3.666667 1.666667
18 5.000000 2.333333
19 4.666667 1.333333
20 4.666667 1.666667
21 4.333333 1.333333
22 1.666667 1.666667
23 4.333333 2.000000
24 4.000000 2.000000
25 5.000000 1.333333
26 4.000000 1.000000
27 4.333333 1.666667
28 5.000000 2.333333
29 3.333333 1.333333
30 4.333333 1.666667
31 4.000000 1.666667
32 3.666667 3.333333
33 1.666667 3.333333
34 4.000000 2.000000
35 4.333333 1.333333
36 5.000000 1.000000
37 4.333333 1.000000
38 5.000000 2.000000
39 5.000000 2.000000
40 4.000000 2.000000
41 4.333333 1.666667
42 4.333333 1.666667
43 4.666667 1.333333
44 3.333333 2.000000
45 4.000000 1.333333
46 5.000000 1.666667
47 5.000000 1.000000
ExcitedScoreForAngryMusic ActiveScoreForAngryMusic
1 3.333333 4.000000
2 2.666667 4.000000
3 3.333333 2.666667
4 5.000000 3.666667
5 4.333333 4.000000
6 3.333333 3.666667
7 4.000000 4.666667
8 4.000000 4.000000
9 4.666667 4.666667
10 4.666667 4.333333
11 2.000000 3.000000
12 3.000000 3.000000
13 4.000000 3.666667
14 4.000000 4.000000
15 4.000000 3.666667
16 4.000000 3.000000
17 3.333333 3.333333
18 4.000000 4.000000
19 4.666667 5.000000
20 5.000000 3.333333
21 4.000000 4.666667
22 4.000000 3.666667
23 3.666667 4.666667
24 1.666667 3.333333
25 4.666667 4.666667
26 2.333333 3.000000
27 4.666667 4.333333
28 4.000000 4.333333
29 1.666667 1.666667
30 3.666667 4.333333
31 4.333333 4.333333
32 3.666667 4.333333
33 1.000000 1.333333
34 2.000000 3.666667
35 4.333333 4.333333
36 3.666667 4.000000
37 1.333333 4.000000
38 4.000000 4.000000
39 3.666667 3.333333
40 2.333333 4.000000
41 3.666667 3.666667
42 4.000000 3.000000
43 3.666667 3.000000
44 3.000000 3.333333
45 5.000000 4.333333
46 5.000000 5.000000
47 5.000000 4.333333It’s good practice to assign a new variable name (in this case filtered_d) to a data frame when you change it in an important way, or apply a code chunk that shouldn’t be run twice. This helps prevent you seeing different results when you run your code in chunks (and might run one multiple times, or skip it, etc.) vs. knit the document.
The dataset contains a bunch of columns we don’t care about:
To get rid of these, we’ll use the select function to take only the columns we need.
filtered_d = filtered_d %>%
select(c("Subject", "Cond"), # Generally important columns for both hypotheses
contains("Game"), # we want all the game columns for hypothesis 1
-contains("Intro"), -c("WhichGames", "GameComments"), # except these
starts_with("DinerDashWith"), c("SOFMusicEnemies", "SOFNoMusicEnemies")) # These columns are for hypothesis 2Even better, let’s split this into separate data frames for hypothesis 1 and hypothesis 2, since they are different types of experiments with different measurements, and therefore different analyses that will need to be performed. Now that we’ve cleaned up the data, this is pretty easy to do! We’ll just drop the columns that are for the other hypothesis. The select function lets us choose which columns to remove (instead of which to keep) by putting a minus sign in front of them. First, let’s create a dataset for the rating hypothesis by getting rid of the game performance columns:
rating_hyp_d = filtered_d %>%
filter(is.na(DoNotUseVideoGamePerformanceData)) %>% # first, let's get rid of the subjects who did so poorly on one game that their data is unusable
select(-DoNotUseVideoGamePerformanceData, # now get rid of that column
-starts_with("DinerDash"), # and the other columns we don't need
-starts_with("SOF"))Now you try! Fill in the selection criteria to get rid of the “Game” columns, which we don’t need for the performance hypothesis. (It’s simpler than the code block above, because you don’t need to do a filter first, only a select.)
performance_hyp_d = filtered_d %>%
select(-starts_with("Game")) # your code here: remove the columns containing "Game" in the nameNow we want to convert the data to long form, to make the rest of our manipulations easier. To do this, we can use pivot_longer on the target columns. This will take many columns, and change the column names into entries in a “key” column, while the values that were in the original column will be turned into entries in a “value” column. It’s easiest to see with an example:
tiny_demo_d = head(performance_hyp_d, 2) # get just the first two subjects performance data, for a demoFirst, take a look at the original wide-form data:
tiny_demo_d Subject Cond DoNotUseVideoGamePerformanceData DinerDashWithMusicScore
1 1 2 NA 5830
2 3 1 NA 5370
DinerDashWithoutMusicScore SOFMusicEnemies SOFNoMusicEnemies
1 5000 19 22
2 1250 23 15Now, take a look at the long-form version:
tiny_demo_d %>% pivot_longer(cols=-c("Subject", "Cond"), # this tells it to transform all columns *except* these ones
names_to='Measurement',
values_to='Value')# A tibble: 10 × 4
Subject Cond Measurement Value
<dbl> <dbl> <chr> <dbl>
1 1 2 DoNotUseVideoGamePerformanceData NA
2 1 2 DinerDashWithMusicScore 5830
3 1 2 DinerDashWithoutMusicScore 5000
4 1 2 SOFMusicEnemies 19
5 1 2 SOFNoMusicEnemies 22
6 3 1 DoNotUseVideoGamePerformanceData NA
7 3 1 DinerDashWithMusicScore 5370
8 3 1 DinerDashWithoutMusicScore 1250
9 3 1 SOFMusicEnemies 23
10 3 1 SOFNoMusicEnemies 15See how the columns have been converted into rows (except for the two we excluded), and the dataset has gone from wide to long?
Now let’s actually convert the performance dataset
performance_hyp_long_d = performance_hyp_d %>%
pivot_longer(cols=-c("Subject", "Cond"),
names_to='Measurement',
values_to='Score')
head(performance_hyp_long_d)# A tibble: 6 × 4
Subject Cond Measurement Score
<dbl> <dbl> <chr> <dbl>
1 1 2 DoNotUseVideoGamePerformanceData NA
2 1 2 DinerDashWithMusicScore 5830
3 1 2 DinerDashWithoutMusicScore 5000
4 1 2 SOFMusicEnemies 19
5 1 2 SOFNoMusicEnemies 22
6 3 1 DoNotUseVideoGamePerformanceData NAAnd you can convert the rating dataset! (Call the “Key” column “Measurement” and call the “Value” column “Rating”, so that the code below will work)
rating_hyp_long_d = rating_hyp_d %>%
pivot_longer(cols=-c("Subject", "Cond"),
names_to = "Measurement",
values_to = "Rating")
head(rating_hyp_long_d)# A tibble: 6 × 4
Subject Cond Measurement Rating
<dbl> <dbl> <chr> <dbl>
1 1 2 Game1Angry1 6
2 1 2 Game1Angry2 6
3 1 2 Game1Angry3 5
4 1 2 Game1AngryFriends 2
5 1 2 Game1AngryStrangers 5
6 1 2 Game1CalmFriends 2The measurement column in each dataset now contains a bunch of different types of information. Really, we would like these to be separate columns. For example, we could have one column telling you which video-game it is, and one telling you whether there was music. Tidyverse contains some handy features for splitting columns, but unfortunately the measurement names here are not well suited to it (if the different types of information were always the same length, or were separated by a symbol like “.” or “_”, it would be easy). Thus we’ll have to do a bit of manual testing. We can use the mutate function in dplyr to create new columns as functions of old ones (or alter existing columns). We’ll also use the grepl function, which lets us test whether a regular expression (a fancy type of search pattern) is contained in a column name. For most your purposes, you can probably just use grepl to search for strings, but there are some other quite useful functions in regular expressions, like the “or”” function (|) we use below.
performance_hyp_long_d = performance_hyp_long_d %>%
mutate(ConfrontationalGame = grepl("SOF", Measurement), # create a new variable that will say whether the measurement was of the game soldier of fortune (SOF).
WithMusic = !grepl("NoMusic|WithoutMusic", Measurement), # creates a new column named WithMusic, which is False if the measurement contains *either* "NoMusic" or "WithoutMusic"
MusicCondition = factor(ifelse(Cond > 3, Cond-3, Cond), levels = 1:3, labels = c("Anger", "Exciting", "Neutral"))) # Get rid of uninterpretable condition labelsNow you can help! For the rating dataset, write a test on a measurement name, using grepl or %in% to figure out whether it’s a recall or a music rating. Your new IsRecall column should be true if the measurement name contain either “Friends” or “Strangers”.
rating_hyp_long_d = rating_hyp_long_d %>%
mutate(
IsRecall = grepl("Friends|Strangers", Measurement)
)Here are a couple other useful ways of manipulating columns. (You won’t remember all the functions you see here now, but that’s okay. You can always reference this tutorial later if there’s something you need to figure out how to do.)
rating_hyp_long_d = rating_hyp_long_d %>%
mutate(
GameNumber = as.numeric(substr(rating_hyp_long_d$Measurement, 5, 5)),
ConfrontationalGame = GameNumber <= 2, # in a mutate, we can use a column we created (or changed) right away. Games 1 and 2 are confrontational, games 3 and 4 are not.
Emotion = str_extract(Measurement, "Angry|Neutral|Excited|Exciting|Calm"),
Emotion = ifelse(Emotion == "Excited", "Exciting", # this just gets rid of some annoying labeling choices
ifelse(Emotion == "Calm", "Neutral", Emotion))
) For the performance data, we need to do a little bit of manipulation of the columns in order to get to the performance measures the experimenters actually used. Because they want to compare changes in performance across games that have very different scoring systems, the easiest solution is to compare z-scores. The way they did this was to z-score performance before music, z-score performance after music, and then create a difference measure which is a difference of z-scores. (To my mind, this is actually not quite the correct way to analyze this data, but like the replication we will follow the original authors.)
We’ll add a new z-scored value column. However, we have to be careful! We want to z-score within groups of the rows, that are all the same type of measurement. For example, we want to z-score the “DinnerDashWithMusic” scores with respect to eachother, but not with respect to the scores from the other game, for example. We can use the group_by function to set groups, and then all the changes we apply will only occur within those groups until we ungroup the dataset.
To make this more concrete, let’s see how the group_by function can let us compute means within different groups, for example mean scores on the two different games.
performance_hyp_long_d %>%
group_by(ConfrontationalGame) %>%
summarize(AvgScore = mean(Score, na.rm=T)) # the na.rm tells R to ignore NA values# A tibble: 2 × 2
ConfrontationalGame AvgScore
<lgl> <dbl>
1 FALSE 5309.
2 TRUE 22.4This makes it clear why we can’t just z-score the games together! The scores are very different between games. So let’s z-score within groups (using the scale function):
performance_hyp_long_d = performance_hyp_long_d %>%
group_by(ConfrontationalGame, WithMusic) %>% # we're going to compute four sets of z-scores, one for the confrontational game without music, one for the confrontational game with, one for the nonconfrontational game without music, and one for the nonconfrontational game with
mutate(z_scored_performance = scale(Score)) %>%
ungroup() %>%
mutate(z_scored_performance = as.numeric(z_scored_performance))
class(performance_hyp_long_d$z_scored_performance)[1] "numeric"The rating hypothesis analysis also requires some grouped manipulation. The experimenters collected repeated measures on ratings in each emotion category and each music/recall category from each game. For this analysis, they averaged all the ratings over the following two variables: the given emotion and the game type, to produce a nice summary. Your job is to implement this, calling the new variable MeanRating, and save the summarized data in a new data frame called rating_summary_d. (Hint: use a group_by and a summarize.)
rating_summary_d = rating_hyp_long_d %>%
group_by(Emotion, ConfrontationalGame) %>%
summarize(MeanRating = mean(Rating))`summarise()` has grouped output by 'Emotion'. You can override using the
`.groups` argument.Let’s take a look at the result:
rating_summary_d# A tibble: 6 × 3
# Groups: Emotion [3]
Emotion ConfrontationalGame MeanRating
<chr> <lgl> <dbl>
1 Angry FALSE 2.72
2 Angry TRUE 4.68
3 Exciting FALSE 3.97
4 Exciting TRUE 3.05
5 Neutral FALSE 3.68
6 Neutral TRUE 2.16And a simple bar plot (don’t worry too much about what exactly this code is doing):
ggplot(rating_summary_d, aes(x=ConfrontationalGame, y=MeanRating, fill=Emotion)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_brewer(palette="Set1")
Up to reordering (and the fact that we didn’t compute error bars), this is a pretty decent replication of Fig. 1 from the original Tamir et al. paper. The ratings were highest for Angry in the confrontational game, and lowest for Angry in the non-confrontational game.
And the long form dataset makes it easy to run a linear model (don’t worry too much about this, we’ll talk more about it in 252).
model = lm(Rating ~ ConfrontationalGame * Emotion, rating_hyp_long_d)
summary(model)
Call:
lm(formula = Rating ~ ConfrontationalGame * Emotion, data = rating_hyp_long_d)
Residuals:
Min 1Q Median 3Q Max
-3.6787 -1.1553 -0.0468 1.3170 4.8447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.71702 0.07965 34.114 <2e-16
ConfrontationalGameTRUE 1.96170 0.11264 17.416 <2e-16
EmotionExciting 1.25319 0.11264 11.126 <2e-16
EmotionNeutral 0.96596 0.11264 8.576 <2e-16
ConfrontationalGameTRUE:EmotionExciting -2.88511 0.15929 -18.112 <2e-16
ConfrontationalGameTRUE:EmotionNeutral -3.48936 0.15929 -21.905 <2e-16
(Intercept) ***
ConfrontationalGameTRUE ***
EmotionExciting ***
EmotionNeutral ***
ConfrontationalGameTRUE:EmotionExciting ***
ConfrontationalGameTRUE:EmotionNeutral ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.727 on 2814 degrees of freedom
Multiple R-squared: 0.1896, Adjusted R-squared: 0.1882
F-statistic: 131.7 on 5 and 2814 DF, p-value: < 2.2e-16There are still a few more steps to go for the performance hypothesis. We need to take a difference score to see how people improved from before hearing the music to after, and then see if the improvement is larger if they heard music congruent with the type of game.
To compute the difference score, we have to make our data a bit wider. We now want to subtract the pre-music scores from the post-music scores, which is easiest to do if they are in two different columns. To do this we’ll use the pivot_wider function (which is more or less the opposite of pivot_longer)
performance_diff_d = performance_hyp_long_d %>%
mutate(WithMusic = factor(WithMusic, levels=c(F, T), labels=c("PreMusic", "PostMusic"))) %>%
select(-c("Score", "Measurement")) %>%
drop_na(z_scored_performance)
performance_diff_wide = performance_diff_d %>% # now we remove columns we don't need (bonus: leave them in and see if you can understand what goes wrong!) %>%
pivot_wider(names_from=WithMusic,
values_from=z_scored_performance) %>%
mutate(ImprovementScore=PostMusic-PreMusic)Let’s take a look at the end result:
performance_diff_wide# A tibble: 94 × 7
Subject Cond ConfrontationalGame MusicCondition PostMusic PreMusic
<dbl> <dbl> <lgl> <fct> <dbl> <dbl>
1 1 2 FALSE Exciting -0.150 0.265
2 1 2 TRUE Exciting -1.30 0.317
3 3 1 FALSE Anger -0.844 -2.91
4 3 1 TRUE Anger -0.283 -1.29
5 6 6 FALSE Neutral 0.679 0.975
6 6 6 TRUE Neutral -0.0272 0.546
7 9 5 FALSE Exciting 0.770 1.50
8 9 5 TRUE Exciting -2.07 -0.599
9 10 1 FALSE Anger -0.678 -1.44
10 10 1 TRUE Anger -0.0272 -1.06
# ℹ 84 more rows
# ℹ 1 more variable: ImprovementScore <dbl>If you don’t understand every step of that code (or any other dplyr code), it can be helpful to look at the result of running just the first line, then just the first two lines, and so on.
Now we’re finally to reproduce Fig. 2 from Tamir et al., we just need to get the mean differences within each game and each kind of music, and save them to a variable called MeanImprovementScore:
performance_diff_summary_d = performance_diff_wide %>%
group_by(ConfrontationalGame, MusicCondition) %>%
summarize(MeanImprovementScore = mean(ImprovementScore))`summarise()` has grouped output by 'ConfrontationalGame'. You can override
using the `.groups` argument.Let’s take a look at your result (if it has NA values, how can you fix it?):
performance_diff_summary_d# A tibble: 6 × 3
# Groups: ConfrontationalGame [2]
ConfrontationalGame MusicCondition MeanImprovementScore
<lgl> <fct> <dbl>
1 FALSE Anger -0.0552
2 FALSE Exciting -0.226
3 FALSE Neutral 0.310
4 TRUE Anger 0.348
5 TRUE Exciting -0.146
6 TRUE Neutral -0.0879and plot it!
ggplot(performance_diff_summary_d, aes(x=ConfrontationalGame, y=MeanImprovementScore, fill=MusicCondition)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_brewer(palette="Set1")
(Bonus: also calculate the SEM in the summary data, and then add errorbars to the plot with geom_errorbar!)
Not quite as exact a replication of the effect as Fig. 1. This concurs with the replication report, which says that the hypothesis 1 effect replicated, but hypothesis 2 did not. Here’s a model just for thoroughness (again, don’t worry too much about it):
performance_model = lm(ImprovementScore ~ ConfrontationalGame * MusicCondition, performance_diff_wide)
summary(performance_model)
Call:
lm(formula = ImprovementScore ~ ConfrontationalGame * MusicCondition,
data = performance_diff_wide)
Residuals:
Min 1Q Median 3Q Max
-2.3895 -0.6449 -0.1183 0.7093 2.1983
Coefficients:
Estimate Std. Error t value
(Intercept) -0.05522 0.27880 -0.198
ConfrontationalGameTRUE 0.40358 0.39428 1.024
MusicConditionExciting -0.17058 0.35612 -0.479
MusicConditionNeutral 0.36478 0.36882 0.989
ConfrontationalGameTRUE:MusicConditionExciting -0.32375 0.50363 -0.643
ConfrontationalGameTRUE:MusicConditionNeutral -0.80106 0.52158 -1.536
Pr(>|t|)
(Intercept) 0.843
ConfrontationalGameTRUE 0.309
MusicConditionExciting 0.633
MusicConditionNeutral 0.325
ConfrontationalGameTRUE:MusicConditionExciting 0.522
ConfrontationalGameTRUE:MusicConditionNeutral 0.128
Residual standard error: 0.9658 on 88 degrees of freedom
Multiple R-squared: 0.05223, Adjusted R-squared: -0.001621
F-statistic: 0.9699 on 5 and 88 DF, p-value: 0.4408