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(knitr)
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) %>% kable()| Subject | Cond | Exper | Inifile | Date | Time | Game1Angry1 | Game1Angry2 | Game1Angry3 | Game1AngryFriends | Game1AngryStrangers | Game1CalmFriends | Game1CalmStrangers | Game1ExcitedFriends | Game1ExcitedStrangers | Game1Exciting1 | Game1Exciting2 | Game1Exciting3 | Game1Intro | Game1Neutral1 | Game1Neutral2 | Game1Neutral3 | Game2Angry1 | Game2Angry2 | Game2Angry3 | Game2AngryFriends | Game2AngryStrangers | Game2CalmFriends | Game2CalmStrangers | Game2ExcitedFriends | Game2ExcitedStrangers | Game2Exciting1 | Game2Exciting2 | Game2Exciting3 | Game2Intro | Game2Neutral1 | Game2Neutral2 | Game2Neutral3 | Game3Angry1 | Game3Angry2 | Game3Angry3 | Game3AngryFriends | Game3AngryStrangers | Game3CalmFriends | Game3CalmStrangers | Game3ExcitedFriends | Game3ExcitedStrangers | Game3Exciting1 | Game3Exciting2 | Game3Exciting3 | Game3Intro | Game3Neutral1 | Game3Neutral2 | Game3Neutral3 | Game4Angry1 | Game4Angry2 | Game4Angry3 | Game4AngryFriends | Game4AngryStrangers | Game4CalmFriends | Game4CalmStrangers | Game4ExcitedFriends | Game4ExcitedStrangers | Game4Exciting1 | Game4Exciting2 | Game4Exciting3 | Game4Intro | Game4Neutral1 | Game4Neutral2 | Game4Neutral3 | MusicSelectionEnd | MusicSelectionInstrx | RecallSelectionEnd | RecallSelectionInstrx | Subject2 | Cond2 | Exper_A | Inifile_A | Date_A | Time_A | DescribeMusic | HowActiveAngry1 | HowActiveAngry2 | HowActiveAngry3 | HowActiveExciting1 | HowActiveExciting2 | HowActiveExciting3 | HowActiveNeutral1 | HowActiveNeutral2 | HowActiveNeutral3 | HowAngryAngry1 | HowAngryAngry2 | HowAngryAngry3 | HowAngryExciting1 | HowAngryExciting2 | HowAngryExciting3 | HowAngryNeutral1 | HowAngryNeutral2 | HowAngryNeutral3 | HowExcitedAngry1 | HowExcitedAngry2 | HowExcitedAngry3 | HowExcitedExciting1 | HowExcitedExciting2 | HowExcitedExciting3 | HowExcitedNeutral1 | HowExcitedNeutral2 | HowExcitedNeutral3 | HowPleasantAngry1 | HowPleasantAngry2 | HowPleasantAngry3 | HowPleasantExciting1 | HowPleasantExciting2 | HowPleasantExciting3 | HowPleasantNeutral1 | HowPleasantNeutral2 | HowPleasantNeutral3 | MusicRatingEnd | MusicRatingInstrx | WhichGames | aboutyou | age | distractions | endinstructions | ethnicity | overlooking | race | sex | whatabout | year | Subject3 | DDNoMusicLevel | DDNoMusicScore | DDMusicLevel | DDMusicScore | SOFNoMusicEnemies | SOFNoMusicFriendlies | SOFNoMusicTime | SOFMusicEnemies | SOFMusicFriendlies | SOFMusicTime | GameComments | DoNotUseVideoGamePerformanceData | ConfrontationalAngryMusicScore | ConfrontationalExcitingMusicScore | ConfrontationalNeutralMusicScore | ConfrontationalAngryRecallScore | ConfrontationalExcitingRecallScore | ConfrontationalNeutralRecallScore | NonconfrontationalAngryMusicScore | NonconfrontationalExcitingMusicScore | NonconfrontationalNeutralMusicScore | NonconfrontationalAngryRecallScore | NonconfrontationalExcitingRecallScore | NonconfrontationalNeutralRecallScore | ConfrontationalAngerScore | ConfrontationalExcitingScore | ConfrontationalNeutralScore | NonconfrontationalAngerScore | NonconfrontationalExcitingScore | NonconfrontationalNeutralScore | Usable | DoNotUse | ProblemDetails | DinerDashWithMusicScore | DinerDashWithoutMusicScore | MusicCondition | ZDinerDashWithMusicScore | ZDinerDashWithoutMusicScore | ZSOFNoMusicEnemies | ZSOFMusicEnemies | DinerDashDifferenceScore | SOFDifferenceScore | PleasantScoreForAngryMusic | PleasantScoreForExcitingMusic | PleasantScoreForNeutralMusic | AngryScoreForAngryMusic | AngryScoreForExcitingMusic | AngryScoreForNeutralMusic | ExcitedScoreForExcitingMusic | ExcitedScoreForNeutralMusic | ActiveScoreForExcitingMusic | ActiveScoreForNeutralMusic | ExcitedScoreForAngryMusic | ActiveScoreForAngryMusic |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | C:151Part1.exp | default.mlp | 13642819200 | 40781 | 6 | 6 | 5 | 2 | 5 | 2 | 2 | 1 | 2 | 3 | 2 | 6 | ok | 2 | 4 | 4 | 6 | 4 | 6 | 3 | 6 | 1 | 2 | 1 | 1 | 3 | 2 | 4 | ok | 1 | 3 | 1 | 2 | 2 | 3 | 3 | 2 | 7 | 6 | 6 | 5 | 2 | 2 | 3 | ok | 5 | 6 | 5 | 2 | 2 | 2 | 2 | 2 | 5 | 5 | 7 | 4 | 5 | 5 | 2 | ok | 1 | 5 | 2 | ok | ok | ok | ok | 1 | 2 | C:151Part2.exp | default.mlp | 13642819200 | 43151 | 2 | 4 | 4 | 4 | 5 | 4 | 5 | 2 | 2 | 2 | 5 | 4 | 4 | 3 | 4 | 3 | 2 | 2 | 1 | 4 | 3 | 3 | 4 | 4 | 4 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | 5 | 4 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 1 | 3 | 0 | 3 | 830 | 22 | 2 | 24360 | 19 | 0 | 23340 | NA | 5.500000 | 3.333333 | 2.500000 | 3.75 | 1.25 | 2.00 | 2.166667 | 3.166667 | 4.000000 | 2.50 | 5.25 | 6.25 | 4.8 | 2.5 | 2.3 | 2.3 | 4.0 | 4.9 | 1 | NA | 5830 | 5000 | Exciting | -0.0733328 | 0.2692740 | 0.7501199 | -0.2020329 | -0.3426068 | -0.9521528 | 1.333333 | 1.666667 | 4.666667 | 4.333333 | 3.333333 | 1.666667 | 4.000000 | 2.000000 | 4.666667 | 2.000000 | 3.333333 | 4.000000 | ||
| 2 | 3 | C:151Part1.exp | default.mlp | 13642819200 | 50753 | 7 | 7 | 7 | 7 | 7 | 6 | 6 | 6 | 6 | 5 | 3 | 2 | ok | 1 | 1 | 1 | 7 | 6 | 7 | 6 | 7 | 2 | 3 | 5 | 5 | 5 | 2 | 1 | ok | 1 | 1 | 2 | 6 | 3 | 5 | 3 | 2 | 6 | 5 | 6 | 5 | 4 | 3 | 3 | ok | 2 | 1 | 5 | 2 | 5 | 2 | 4 | 4 | 2 | 4 | 3 | 4 | 1 | 2 | 6 | ok | 5 | 5 | 2 | ok | ok | ok | ok | 2 | 3 | C:151Part2.exp | default.mlp | 13642819200 | 53012 | 3 | 5 | 5 | 5 | 5 | 2 | 4 | 2 | 2 | 1 | 5 | 5 | 5 | 4 | 3 | 3 | 2 | 1 | 1 | 5 | 5 | 5 | 4 | 2 | 4 | 3 | 2 | 1 | 1 | 2 | 1 | 1 | 4 | 3 | 4 | 4 | 4 | ok | ok | ok | ok | 20 | ok | ok | 2 | ok | 2 | 2 | ok | 2 | 2 | 3 | 20 | 3 | 2930 | 18 | 1 | 23580 | 18 | 2 | 22500 | NA | 6.833333 | 3.000000 | 1.166667 | 7.00 | 5.75 | 5.25 | 3.833333 | 3.166667 | 3.333333 | 3.00 | 5.25 | 5.25 | 6.9 | 4.1 | 2.8 | 3.5 | 4.0 | 4.1 | 0 | 1 | Female participant (this is a males only study) | 7930 | 5020 | Neutral | NA | NA | NA | NA | NA | NA | 1.333333 | 2.666667 | 4.000000 | 5.000000 | 3.333333 | 1.333333 | 3.333333 | 2.000000 | 3.666667 | 1.666667 | 5.000000 | 5.000000 | |
| 3 | 1 | C:151Part1.exp | default.mlp | 13642819200 | 54540 | 6 | 5 | 7 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 4 | ok | 1 | 2 | 3 | 5 | 3 | 6 | 3 | 3 | 3 | 3 | 3 | 3 | 2 | 5 | 2 | ok | 4 | 3 | 1 | 2 | 2 | 3 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 6 | 2 | ok | 2 | 3 | 3 | 5 | 2 | 2 | 4 | 5 | 2 | 4 | 4 | 5 | 7 | 4 | 5 | ok | 3 | 2 | 4 | ok | ok | ok | ok | 3 | 1 | C:151Part2.exp | default.mlp | 13642819200 | 57041 | 2 | 4 | 4 | 4 | 2 | 1 | 3 | 1 | 2 | 1 | 4 | 4 | 4 | 3 | 1 | 3 | 1 | 1 | 2 | 3 | 3 | 2 | 2 | 2 | 3 | 2 | 1 | 2 | 2 | 2 | 4 | 2 | 2 | 2 | 2 | 2 | 1 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 3 | 2 | 1250 | 3 | 370 | 15 | 0 | 15300 | 23 | 1 | 24300 | NA | 5.333333 | 3.000000 | 2.333333 | 2.25 | 2.25 | 2.25 | 2.666667 | 4.500000 | 2.833333 | 4.25 | 4.25 | 3.25 | 4.1 | 2.7 | 2.3 | 3.3 | 4.4 | 3.0 | 1 | NA | 5370 | 1250 | Anger | -0.7334425 | -2.8616517 | -0.1401958 | 0.3183548 | 2.1282092 | 0.4585506 | 2.666667 | 2.000000 | 1.666667 | 4.000000 | 2.333333 | 1.333333 | 2.333333 | 1.666667 | 2.000000 | 1.333333 | 2.666667 | 4.000000 | ||
| 4 | 4 | C:151Part1.exp | default.mlp | 13642905600 | 34952 | 4 | 1 | 1 | 6 | 6 | 2 | 1 | 3 | 4 | 5 | 4 | 5 | ok | 1 | 2 | 2 | 6 | 2 | 6 | 3 | 6 | 1 | 1 | 2 | 4 | 3 | 2 | 2 | ok | 1 | 1 | 3 | 2 | 1 | 6 | 5 | 4 | 2 | 2 | 5 | 6 | 3 | 1 | 3 | ok | 2 | 2 | 6 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 4 | 4 | 6 | 6 | 6 | ok | 4 | 5 | 2 | ok | ok | ok | ok | 4 | 4 | C:151Part2.exp | default.mlp | 13642905600 | 37630 | 3 | 5 | 3 | 3 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 2 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 1 | 3 | 4 | 3 | 5 | 2 | 2 | 1 | 1 | 1 | 3 | 4 | 4 | 3 | 2 | 4 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 4 | 3 | 1742 | 3 | 1921 | 3 | 0 | 5280 | 19 | 0 | 16860 | Participant died, restart | 1 | 3.333333 | 3.500000 | 1.666667 | 6.00 | 3.50 | 1.50 | 2.166667 | 4.166667 | 3.500000 | 3.75 | 5.00 | 2.00 | 4.4 | 3.5 | 1.6 | 2.8 | 4.5 | 2.9 | 1 | NA | 6921 | 6742 | Anger | 1.4922750 | 1.7236934 | -1.6664514 | -0.2020329 | -0.2314183 | 1.4644185 | 1.666667 | 3.666667 | 3.666667 | 2.666667 | 1.000000 | 1.333333 | 4.000000 | 1.666667 | 5.000000 | 1.666667 | 2.666667 | 3.666667 | |
| 5 | 5 | C:151Part1.exp | default.mlp | 13642905600 | 49095 | 6 | 6 | 7 | 6 | 6 | 2 | 2 | 5 | 5 | 1 | 3 | 2 | ok | 3 | 2 | 4 | 5 | 6 | 6 | 5 | 6 | 1 | 1 | 4 | 4 | 1 | 2 | 2 | ok | 4 | 4 | 5 | 3 | 5 | 6 | 1 | 3 | 5 | 5 | 6 | 5 | 3 | 1 | 3 | ok | 2 | 4 | 5 | 3 | 4 | 3 | 2 | 3 | 5 | 5 | 5 | 6 | 1 | 5 | 5 | ok | 4 | 2 | 5 | ok | ok | ok | ok | 5 | 5 | C:151Part2.exp | default.mlp | 13642905600 | 51434 | 2 | 5 | 4 | 5 | 3 | 3 | 3 | 2 | 1 | 1 | 2 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 4 | 4 | 5 | 3 | 3 | 3 | 2 | 1 | 3 | 4 | 3 | 2 | 1 | 1 | 2 | 1 | 1 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 5 | 3 | 60 | 3 | 1750 | 18 | 2 | 19140 | 23 | 3 | 20820 | Error in game towards the end of time | 1 | 6.000000 | 1.833333 | 3.666667 | 6.00 | 4.75 | 1.75 | 4.000000 | 3.000000 | 3.666667 | 2.00 | 5.75 | 5.00 | 6.0 | 3.0 | 2.9 | 3.2 | 4.1 | 4.2 | 1 | NA | 6750 | 5060 | Exciting | 1.2468865 | 0.3193688 | 0.2413681 | 0.3183548 | 0.9275176 | 0.0769867 | 3.000000 | 1.333333 | 2.333333 | 2.333333 | 1.666667 | 1.000000 | 3.000000 | 2.000000 | 3.000000 | 1.333333 | 4.333333 | 4.666667 | |
| 6 | 6 | C:151Part1.exp | default.mlp | 13642905600 | 59714 | 5 | 5 | 6 | 3 | 4 | 5 | 4 | 6 | 4 | 3 | 2 | 4 | ok | 2 | 2 | 4 | 6 | 5 | 6 | 3 | 5 | 3 | 2 | 5 | 4 | 2 | 2 | 3 | ok | 2 | 3 | 4 | 2 | 2 | 5 | 1 | 1 | 4 | 3 | 4 | 2 | 1 | 2 | 2 | ok | 5 | 4 | 4 | 2 | 3 | 3 | 1 | 2 | 4 | 4 | 5 | 4 | 2 | 4 | 3 | ok | 3 | 5 | 5 | ok | ok | ok | ok | 6 | 6 | C:151Part2.exp | default.mlp | 13642905600 | 62320 | 3 | 3 | 3 | 2 | 3 | 3 | 4 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 5 | 2 | 3 | 3 | 2 | 4 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 3 | 4 | 3 | 3 | 4 | ok | ok | ok | ok | 19 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 6 | 3 | 840 | 3 | 1380 | 23 | 1 | 23220 | 24 | 0 | 23400 | NA | 5.500000 | 2.666667 | 2.833333 | 3.75 | 5.00 | 4.00 | 2.833333 | 2.333333 | 4.333333 | 1.25 | 3.50 | 3.75 | 4.8 | 3.6 | 3.3 | 2.2 | 2.8 | 4.1 | 1 | NA | 6380 | 5840 | Neutral | 0.7159287 | 0.9706014 | 0.8773079 | 0.4484517 | -0.2546727 | -0.4288562 | 2.333333 | 3.333333 | 3.333333 | 2.000000 | 1.333333 | 1.000000 | 3.000000 | 1.333333 | 3.333333 | 1.333333 | 3.333333 | 2.666667 |
This 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$Game1Angry1 is one of the likert rating columns, and you may want to use unique or range or hist)
## your code here
min(d$Game1Angry1, na.rm = T)[1] 1max(d$Game1Angry1, na.rm = T)[1] 7Highest 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) %>% kable()| Subject | Cond | Exper | Inifile | Date | Time | Game1Angry1 | Game1Angry2 | Game1Angry3 | Game1AngryFriends | Game1AngryStrangers | Game1CalmFriends | Game1CalmStrangers | Game1ExcitedFriends | Game1ExcitedStrangers | Game1Exciting1 | Game1Exciting2 | Game1Exciting3 | Game1Intro | Game1Neutral1 | Game1Neutral2 | Game1Neutral3 | Game2Angry1 | Game2Angry2 | Game2Angry3 | Game2AngryFriends | Game2AngryStrangers | Game2CalmFriends | Game2CalmStrangers | Game2ExcitedFriends | Game2ExcitedStrangers | Game2Exciting1 | Game2Exciting2 | Game2Exciting3 | Game2Intro | Game2Neutral1 | Game2Neutral2 | Game2Neutral3 | Game3Angry1 | Game3Angry2 | Game3Angry3 | Game3AngryFriends | Game3AngryStrangers | Game3CalmFriends | Game3CalmStrangers | Game3ExcitedFriends | Game3ExcitedStrangers | Game3Exciting1 | Game3Exciting2 | Game3Exciting3 | Game3Intro | Game3Neutral1 | Game3Neutral2 | Game3Neutral3 | Game4Angry1 | Game4Angry2 | Game4Angry3 | Game4AngryFriends | Game4AngryStrangers | Game4CalmFriends | Game4CalmStrangers | Game4ExcitedFriends | Game4ExcitedStrangers | Game4Exciting1 | Game4Exciting2 | Game4Exciting3 | Game4Intro | Game4Neutral1 | Game4Neutral2 | Game4Neutral3 | MusicSelectionEnd | MusicSelectionInstrx | RecallSelectionEnd | RecallSelectionInstrx | Subject2 | Cond2 | Exper_A | Inifile_A | Date_A | Time_A | DescribeMusic | HowActiveAngry1 | HowActiveAngry2 | HowActiveAngry3 | HowActiveExciting1 | HowActiveExciting2 | HowActiveExciting3 | HowActiveNeutral1 | HowActiveNeutral2 | HowActiveNeutral3 | HowAngryAngry1 | HowAngryAngry2 | HowAngryAngry3 | HowAngryExciting1 | HowAngryExciting2 | HowAngryExciting3 | HowAngryNeutral1 | HowAngryNeutral2 | HowAngryNeutral3 | HowExcitedAngry1 | HowExcitedAngry2 | HowExcitedAngry3 | HowExcitedExciting1 | HowExcitedExciting2 | HowExcitedExciting3 | HowExcitedNeutral1 | HowExcitedNeutral2 | HowExcitedNeutral3 | HowPleasantAngry1 | HowPleasantAngry2 | HowPleasantAngry3 | HowPleasantExciting1 | HowPleasantExciting2 | HowPleasantExciting3 | HowPleasantNeutral1 | HowPleasantNeutral2 | HowPleasantNeutral3 | MusicRatingEnd | MusicRatingInstrx | WhichGames | aboutyou | age | distractions | endinstructions | ethnicity | overlooking | race | sex | whatabout | year | Subject3 | DDNoMusicLevel | DDNoMusicScore | DDMusicLevel | DDMusicScore | SOFNoMusicEnemies | SOFNoMusicFriendlies | SOFNoMusicTime | SOFMusicEnemies | SOFMusicFriendlies | SOFMusicTime | GameComments | DoNotUseVideoGamePerformanceData | ConfrontationalAngryMusicScore | ConfrontationalExcitingMusicScore | ConfrontationalNeutralMusicScore | ConfrontationalAngryRecallScore | ConfrontationalExcitingRecallScore | ConfrontationalNeutralRecallScore | NonconfrontationalAngryMusicScore | NonconfrontationalExcitingMusicScore | NonconfrontationalNeutralMusicScore | NonconfrontationalAngryRecallScore | NonconfrontationalExcitingRecallScore | NonconfrontationalNeutralRecallScore | ConfrontationalAngerScore | ConfrontationalExcitingScore | ConfrontationalNeutralScore | NonconfrontationalAngerScore | NonconfrontationalExcitingScore | NonconfrontationalNeutralScore | Usable | DoNotUse | ProblemDetails | DinerDashWithMusicScore | DinerDashWithoutMusicScore | MusicCondition | ZDinerDashWithMusicScore | ZDinerDashWithoutMusicScore | ZSOFNoMusicEnemies | ZSOFMusicEnemies | DinerDashDifferenceScore | SOFDifferenceScore | PleasantScoreForAngryMusic | PleasantScoreForExcitingMusic | PleasantScoreForNeutralMusic | AngryScoreForAngryMusic | AngryScoreForExcitingMusic | AngryScoreForNeutralMusic | ExcitedScoreForExcitingMusic | ExcitedScoreForNeutralMusic | ActiveScoreForExcitingMusic | ActiveScoreForNeutralMusic | ExcitedScoreForAngryMusic | ActiveScoreForAngryMusic | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 86 | 87 | 1 | C:151Part1.exp | default.mlp | 13644633600 | 40065 | 1 | 3 | 4 | 6 | 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ok | 2 | 2 | 3 | 5 | 5 | 7 | 1 | 7 | 4 | 4 | 2 | 2 | 5 | 1 | 1 | ok | 1 | 1 | 1 | 5 | 3 | 6 | 1 | 2 | 5 | 6 | 4 | 2 | 1 | 1 | 1 | ok | 5 | 1 | 2 | 3 | 1 | 4 | 1 | 1 | 7 | 7 | 7 | 7 | 2 | 5 | 5 | ok | 5 | 5 | 4 | ok | ok | ok | ok | 87 | 1 | C:151Part2.exp | default.mlp | 13644633600 | 42314 | 2 | 5 | 5 | 4 | 5 | 5 | 5 | 1 | 1 | 1 | 3 | 5 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 4 | 4 | 4 | 3 | 4 | 4 | 1 | 2 | 1 | 3 | 3 | 4 | 2 | 4 | 3 | 3 | 3 | 2 | ok | ok | ok | ok | 20 | ok | ok | 2 | ok | 2 | 1 | ok | 2 | 87 | 3 | 0 | 3 | 170 | 15 | 0 | 13140 | 25 | 1 | 23160 | Participant died, restart | 1 | 4.166667 | 1.666667 | 1.666667 | 6.50 | 1.25 | 1.75 | 3.666667 | 2.500000 | 3.666667 | 1.25 | 4.25 | 5.75 | 5.1 | 1.5 | 1.7 | 2.7 | 3.2 | 4.5 | 1 | NA | 5170 | 5000 | Anger | -1.0204467 | 0.2692740 | -0.1401958 | 0.5785486 | -1.2897207 | 0.7187444 | 3.333333 | 3.000000 | 2.666667 | 3.000000 | 1.000000 | 2.333333 | 3.666667 | 1.333333 | 5.000000 | 1.000000 | 4.000000 | 4.666667 | |
| 87 | 88 | 6 | C:151Part1.exp | default.mlp | 13644633600 | 51237 | 7 | 7 | 5 | 4 | 1 | 4 | 4 | 7 | 4 | 7 | 7 | 6 | ok | 2 | 1 | 1 | 7 | 7 | 4 | 1 | 1 | 5 | 6 | 7 | 4 | 7 | 1 | 1 | ok | 1 | 1 | 1 | 2 | 1 | 7 | 1 | 1 | 7 | 2 | 7 | 3 | 2 | 1 | 1 | ok | 4 | 6 | 2 | 2 | 1 | 7 | 3 | 4 | 2 | 6 | 7 | 7 | 4 | 1 | 2 | ok | 5 | 3 | 1 | ok | ok | ok | ok | 88 | 6 | C:151Part2.exp | default.mlp | 13644633600 | 53402 | 2 | 5 | 5 | 5 | 5 | 5 | 5 | 2 | 2 | 1 | 5 | 5 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 5 | 1 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 2 | 5 | 5 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 1 | 1 | ok | 1 | 88 | 3 | 0 | 3 | 866 | 24 | 0 | 23460 | 27 | 0 | 22380 | NA | 6.166667 | 4.833333 | 1.166667 | 2.50 | 5.50 | 4.50 | 3.333333 | 1.833333 | 3.500000 | 1.75 | 6.00 | 5.50 | 4.7 | 5.1 | 2.5 | 2.7 | 3.5 | 4.3 | 1 | NA | 5866 | 5000 | Neutral | -0.0216721 | 0.2692740 | 1.0044959 | 0.8387424 | -0.2909461 | -0.1657534 | 2.333333 | 4.000000 | 5.000000 | 3.666667 | 2.000000 | 1.000000 | 5.000000 | 3.666667 | 5.000000 | 1.666667 | 5.000000 | 5.000000 | ||
| 88 | 89 | 2 | C:151Part1.exp | default.mlp | 13644633600 | 54293 | 7 | 6 | 6 | 7 | 5 | 3 | 2 | 7 | 6 | 3 | 5 | 2 | ok | 1 | 2 | 1 | 6 | 4 | 6 | 7 | 2 | 3 | 1 | 7 | 5 | 1 | 3 | 1 | ok | 1 | 2 | 2 | 2 | 4 | 4 | 1 | 1 | 6 | 4 | 3 | 6 | 5 | 5 | 6 | ok | 4 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 7 | 5 | 7 | 5 | 5 | 4 | 7 | ok | 5 | 5 | 3 | ok | ok | ok | ok | 89 | 2 | C:151Part2.exp | default.mlp | 13644633600 | 56552 | 2 | 5 | 3 | 4 | 4 | 5 | 5 | 1 | 2 | 1 | 5 | 5 | 4 | 2 | 3 | 1 | 1 | 1 | 1 | 5 | 5 | 4 | 3 | 4 | 5 | 2 | 2 | 1 | 3 | 3 | 2 | 3 | 3 | 5 | 4 | 4 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 89 | 2 | 3280 | 3 | 820 | 7 | 0 | 8880 | 31 | 0 | 23100 | 1 | 5.833333 | 2.500000 | 1.500000 | 5.25 | 6.25 | 2.25 | 2.166667 | 5.333333 | 4.000000 | 1.00 | 4.25 | 5.25 | 5.6 | 4.0 | 1.8 | 1.7 | 4.9 | 4.5 | 1 | NA | 5820 | 3280 | Exciting | -0.0876830 | -1.1667773 | -1.1576995 | 1.3591301 | 1.0790942 | 2.5168296 | 2.666667 | 3.666667 | 4.333333 | 4.666667 | 2.000000 | 1.000000 | 4.000000 | 1.666667 | 4.666667 | 1.333333 | 4.666667 | 4.000000 | ||
| 89 | 90 | 3 | C:151Part1.exp | default.mlp | 13644633600 | 58190 | 5 | 5 | 5 | 7 | 7 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | ok | 1 | 1 | 6 | 5 | 1 | 7 | 7 | 7 | 1 | 1 | 1 | 4 | 3 | 2 | 2 | ok | 1 | 3 | 1 | 1 | 1 | 5 | 2 | 2 | 7 | 6 | 7 | 7 | 2 | 1 | 1 | ok | 4 | 4 | 7 | 1 | 3 | 1 | 3 | 3 | 5 | 4 | 7 | 7 | 2 | 4 | 5 | ok | 1 | 2 | 5 | ok | ok | ok | ok | 90 | 3 | C:151Part2.exp | default.mlp | 13644633600 | 60558 | 2 | 5 | 5 | 3 | 5 | 5 | 5 | 1 | 1 | 1 | 5 | 5 | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 4 | 5 | 4 | 1 | 1 | 2 | 2 | 1 | 3 | 1 | 5 | 2 | 4 | 4 | 5 | ok | ok | ok | ok | 18 | ok | ok | 2 | ok | 2 | 1 | ok | 1 | 90 | 2 | 3040 | 3 | 0 | 22 | 2 | 28440 | 26 | 0 | 25500 | NA | 4.666667 | 1.666667 | 2.166667 | 7.00 | 3.25 | 1.00 | 2.000000 | 2.500000 | 3.833333 | 2.25 | 7.00 | 6.00 | 5.6 | 2.3 | 1.7 | 2.1 | 4.3 | 4.7 | 1 | NA | 5000 | 3040 | Neutral | -1.2644002 | -1.3671565 | 0.7501199 | 0.7086455 | 0.1027563 | -0.0414744 | 2.000000 | 2.666667 | 4.333333 | 4.333333 | 1.666667 | 1.000000 | 4.333333 | 1.333333 | 5.000000 | 1.000000 | 5.000000 | 4.333333 | ||
| 90 | 23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 23 | 1 | C:151Part2.exp | default.mlp | 13643078400 | 61329 | 2 | 4 | 5 | 5 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 5 | 5 | 5 | 3 | 3 | 4 | 4 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 3 | 3 | ok | ok | ok | ok | 20 | ok | ok | 2 | ok | 1 | 1 | ok | 2 | 23 | 2 | 3990 | 3 | 750 | 9 | 2 | 19260 | 18 | 2 | 24120 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 1 | 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 | 5750 | 3990 | NA | -0.1881345 | -0.5739887 | -0.9033236 | -0.3321298 | 0.3858541 | 0.5711938 | 1.000000 | 1.333333 | 3.000000 | 2.666667 | 2.333333 | 2.000000 | 4.333333 | 3.666667 | 3.000000 | 3.333333 | 4.333333 | 4.666667 | |||||||||||
| 91 | 23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 23 | 1 | C:151Part2.exp | default.mlp | 13643078400 | 63502 | 2 | 4 | 3 | 5 | 4 | 3 | 5 | 4 | 4 | 2 | 2 | 3 | 2 | 3 | 3 | 1 | 2 | 2 | 1 | 3 | 3 | 3 | 3 | 5 | 4 | 3 | 4 | 3 | 2 | 2 | 1 | 2 | 5 | 3 | 5 | 5 | 1 | ok | ok | ok | ok | 20 | ok | ok | 2 | ok | 1 | 1 | ok | 2 | 23 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 1 | 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 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1.666667 | 3.333333 | 3.666667 | 2.333333 | 2.333333 | 1.666667 | 4.000000 | 3.333333 | 4.000000 | 3.333333 | 3.000000 | 4.000000 |
Notice 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.)
filtered_d = d %>%
filter(is.na(DoNotUse)) # your code here: exclude subjects that are marked as "DoNotUse"It’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(-contains("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 DinerDashWithMusicScore DinerDashWithoutMusicScore
1 1 2 5830 5000
2 3 1 5370 1250
SOFMusicEnemies SOFNoMusicEnemies
1 19 22
2 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: 8 × 4
Subject Cond Measurement Value
<dbl> <dbl> <chr> <dbl>
1 1 2 DinerDashWithMusicScore 5830
2 1 2 DinerDashWithoutMusicScore 5000
3 1 2 SOFMusicEnemies 19
4 1 2 SOFNoMusicEnemies 22
5 3 1 DinerDashWithMusicScore 5370
6 3 1 DinerDashWithoutMusicScore 1250
7 3 1 SOFMusicEnemies 23
8 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 DinerDashWithMusicScore 5830
2 1 2 DinerDashWithoutMusicScore 5000
3 1 2 SOFMusicEnemies 19
4 1 2 SOFNoMusicEnemies 22
5 3 1 DinerDashWithMusicScore 5370
6 3 1 DinerDashWithoutMusicScore 1250And 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),## Your code here
)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 5288.
2 TRUE 18.3This 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()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(ConfrontationalGame, Emotion) %>%
summarise(MeanRating = mean(Rating, na.rm=T))`summarise()` has grouped output by 'ConfrontationalGame'. You can override
using the `.groups` argument.Let’s take a look at the result:
rating_summary_d# A tibble: 6 × 3
# Groups: ConfrontationalGame [2]
ConfrontationalGame Emotion MeanRating
<lgl> <chr> <dbl>
1 FALSE Angry 2.72
2 FALSE Exciting 3.97
3 FALSE Neutral 3.68
4 TRUE Angry 4.68
5 TRUE Exciting 3.05
6 TRUE Neutral 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"))) %>% # first, tweak the variable so our code is easier to read.
select(-c("Score", "Measurement")) %>% # 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_d# A tibble: 176 × 7
Subject Cond ConfrontationalGame MusicCondition PostMusic[,1] PreMusic[,1]
<dbl> <dbl> <lgl> <fct> <dbl> <dbl>
1 1 2 FALSE Exciting -0.0751 0.262
2 1 2 TRUE Exciting -0.205 0.739
3 3 1 FALSE Anger -0.732 -2.86
4 3 1 TRUE Anger 0.313 -0.150
5 4 4 FALSE Anger 1.48 1.71
6 4 4 TRUE Anger -0.205 -1.68
7 5 5 FALSE Exciting 1.24 0.311
8 5 5 TRUE Exciting 0.313 0.231
9 6 6 FALSE Neutral 0.710 0.960
10 6 6 TRUE Neutral 0.442 0.866
# ℹ 166 more rows
# ℹ 1 more variable: ImprovementScore <dbl[,1]>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_d %>%
group_by(ConfrontationalGame, MusicCondition) %>%
summarise(MeanImprovementScore = mean(ImprovementScore, na.rm = T),
SEMImprovementScore = sd(ImprovementScore, na.rm = T)/n())`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 × 4
# Groups: ConfrontationalGame [2]
ConfrontationalGame MusicCondition MeanImprovementScore SEMImprovementScore
<lgl> <fct> <dbl> <dbl>
1 FALSE Anger -0.179 0.0478
2 FALSE Exciting -0.0182 0.0256
3 FALSE Neutral 0.114 0.0290
4 TRUE Anger 0.0612 0.0319
5 TRUE Exciting 0.169 0.0332
6 TRUE Neutral -0.225 0.0350and plot it!
ggplot(performance_diff_summary_d, aes(x=ConfrontationalGame, y=MeanImprovementScore, fill=MusicCondition)) +
geom_bar(position="dodge", stat="identity") +
geom_errorbar(aes(x = ConfrontationalGame,
ymin = MeanImprovementScore - SEMImprovementScore,
ymax = MeanImprovementScore + SEMImprovementScore),
position = "dodge") +
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_d)
summary(performance_model)
Call:
lm(formula = ImprovementScore ~ ConfrontationalGame * MusicCondition,
data = performance_diff_d)
Residuals:
Min 1Q Median 3Q Max
-3.5402 -0.6284 -0.0744 0.6253 2.8550
Coefficients:
Estimate Std. Error t value
(Intercept) -0.1786 0.1895 -0.942
ConfrontationalGameTRUE 0.2398 0.2760 0.869
MusicConditionExciting 0.1603 0.2657 0.603
MusicConditionNeutral 0.2926 0.2657 1.101
ConfrontationalGameTRUE:MusicConditionExciting -0.0530 0.3815 -0.139
ConfrontationalGameTRUE:MusicConditionNeutral -0.5786 0.3800 -1.523
Pr(>|t|)
(Intercept) 0.347
ConfrontationalGameTRUE 0.386
MusicConditionExciting 0.547
MusicConditionNeutral 0.272
ConfrontationalGameTRUE:MusicConditionExciting 0.890
ConfrontationalGameTRUE:MusicConditionNeutral 0.130
Residual standard error: 1.003 on 164 degrees of freedom
(6 observations deleted due to missingness)
Multiple R-squared: 0.02179, Adjusted R-squared: -0.008033
F-statistic: 0.7306 on 5 and 164 DF, p-value: 0.6014