AffectGrid - All words
Martin Kolnes
26 October 2015
library(dplyr)#funktsioonid: select, filter,
library(reshape2)#functions: dcast()
library(ggplot2)
library(plyr)#funktsioon ddply
library(htmlTable) #htmlTable()
library(psych)#describeBy
library(reshape2) #melt
library(plotly)#plotlyga ühenduse loomisekssetwd("C:/Users/Martin/Documents/Bitbucket/MIDIATAnalysis/AffectGrid")
data_subjects <- read.csv("subjects.csv", header=TRUE, sep=";")
data_All_Wtrials <- read.csv("subjects_trials.csv", header=TRUE, sep=",")
data_All_words <- read.csv("affect_results.csv", header=TRUE, sep=",")
data_trials <- read.csv("selectedWords.csv", header=TRUE, sep=",")
data_words <- read.csv("selectedWordsResults.csv", header=TRUE, sep=",")Words
levels(droplevels(filter(data_words, trial == "High")$stimuli))## [1] "äge" "äike" "erutus" "irvitus" "kihv"
## [6] "kirurg" "kiskja" "mõõk" "nõel" "nuga"
## [11] "purustaja" "raev" "süda" "süstal" "terav"
## [16] "torm" "tormine" "tugev" "tung" "võimas"levels(droplevels(filter(data_words, trial == "Low")$stimuli))## [1] "küla" "lihtne" "madal" "noogutus" "nööp" "padi"
## [7] "pilv" "pliiats" "põõsas" "rahulik" "sokk" "sujuv"
## [13] "sulg" "tava" "tigu" "tolm" "uinutaja" "ümar"
## [19] "uni" "unustus"levels(droplevels(filter(data_words, trial == "Positive")$stimuli))## [1] "energia" "hea" "ilus" "ime" "kaunis" "kirg" "lemmik"
## [8] "meeldiv" "mõnus" "päike" "parim" "rahu" "ravi" "selgus"
## [15] "sõber" "soojus" "unistus" "vabadus" "vaikus" "võit"levels(droplevels(filter(data_words, trial == "Negative")$stimuli))## [1] "ehmatus" "haigus" "halvem" "igav" "kaebus" "kaklus" "kaotus"
## [8] "kohutav" "kole" "mürk" "nohu" "nõrkus" "ohtlik" "paha"
## [15] "pettus" "pomm" "relv" "surve" "valus" "varas"VALIM
describe(data_subjects$age)## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 68 28.97 9.16 27 27.39 5.19 19 66 47 2.15 5.18 1.11summary(data_subjects$gender)## M N
## 19 49summary(data_subjects$education)## kesk- või kutseharidus kõrgharidus
## 5 63summary(data_subjects$language)## eesti vene
## 67 1summary(data_subjects$skill)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.794 6.000 7.000## Kasutatud andmestik - hinnangute keskmised
vtable <- describeBy(data_words[,8:9],data_words$trial)
vmat <- as.matrix(round((do.call(rbind.data.frame, vtable)), digits = 2))
htmlTable(vmat, caption="HINNANGUD",col.columns = c("none", "#F7F7F7"), css.cell = "padding-left: .7em; padding-right: .7em;", tfoot="† V - valents; I - intensiivsus; Z - standardiseeritud tulemustega arvutatud")| HINNANGUD | |||||||||||||
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| High.ZVkeskmine | 1 | 20 | -0.11 | 0.61 | -0.16 | -0.1 | 0.49 | -1.28 | 0.86 | 2.14 | -0.03 | -0.98 | 0.14 |
| High.ZIkeskmine | 2 | 20 | 0.17 | 0.39 | 0.16 | 0.17 | 0.51 | -0.44 | 0.82 | 1.26 | 0.03 | -1.3 | 0.09 |
| Low.ZVkeskmine | 1 | 20 | 0.24 | 0.47 | 0.2 | 0.24 | 0.32 | -0.8 | 1.06 | 1.85 | -0.04 | -0.4 | 0.1 |
| Low.ZIkeskmine | 2 | 20 | -0.57 | 0.32 | -0.56 | -0.58 | 0.41 | -1.02 | 0 | 1.02 | 0.33 | -1.21 | 0.07 |
| Negative.ZVkeskmine | 1 | 20 | -1.11 | 0.22 | -1.19 | -1.12 | 0.25 | -1.4 | -0.79 | 0.61 | 0.22 | -1.73 | 0.05 |
| Negative.ZIkeskmine | 2 | 20 | 0.06 | 0.36 | 0.14 | 0.09 | 0.38 | -0.77 | 0.52 | 1.29 | -0.61 | -0.76 | 0.08 |
| Positive.ZVkeskmine | 1 | 20 | 0.99 | 0.25 | 1.03 | 1.03 | 0.1 | 0.03 | 1.25 | 1.22 | -2.63 | 7.44 | 0.06 |
| Positive.ZIkeskmine | 2 | 20 | 0.48 | 0.34 | 0.47 | 0.48 | 0.41 | -0.11 | 1.08 | 1.19 | 0.05 | -1.03 | 0.08 |
| †V - valents; I - intensiivsus; Z - standardiseeritud tulemustega arvutatud | |||||||||||||
Graafikud
## Using stimuli, trial as id variables
ANOVA
VALENCE
# One Way ANOVA - valents
aov_val <- aov(Vkeskmine~trial, data = data_words)
summary(aov_val)## Df Sum Sq Mean Sq F value Pr(>F)
## trial 3 135963 45321 88.36 <2e-16 ***
## Residuals 76 38982 513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Millised grupid erinevad valnetsi poolest:
tkV <- TukeyHSD(aov_val)
tkV## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Vkeskmine ~ trial, data = data_words)
##
## $trial
## diff lwr upr p adj
## Low-High 19.56016 0.7475161 38.37281 0.0384372
## Negative-High -54.45746 -73.2701034 -35.64481 0.0000000
## Positive-High 60.06957 41.2569274 78.88222 0.0000000
## Negative-Low -74.01762 -92.8302640 -55.20497 0.0000000
## Positive-Low 40.50941 21.6967668 59.32206 0.0000015
## Positive-Negative 114.52703 95.7143862 133.33968 0.0000000plot(tkV, las = 1,cex.axis = 0.5)
AROUSAL
# One Way ANOVA - intensiivsus
aov_int<- aov(Ikeskmine~trial, data = data_words)
summary(aov_int)## Df Sum Sq Mean Sq F value Pr(>F)
## trial 3 8461 2820 32.06 1.69e-13 ***
## Residuals 76 6685 88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Millised grupid erinevad intensiivsuse poolest:
tkI <- TukeyHSD(aov_int)
tkI## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ikeskmine ~ trial, data = data_words)
##
## $trial
## diff lwr upr p adj
## Low-High -20.749006 -28.5396251 -12.958387 0.0000000
## Negative-High -3.416252 -11.2068714 4.374366 0.6587513
## Positive-High 7.242305 -0.5483139 15.032924 0.0778398
## Negative-Low 17.332754 9.5421348 25.123373 0.0000007
## Positive-Low 27.991311 20.2006923 35.781930 0.0000000
## Positive-Negative 10.658557 2.8679385 18.449176 0.0031701plot(tkI, las = 1,cex.axis = 0.5)
Tulemuste kuvamine
