Correlations PeMyCreP

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cat("\014")     # clean terminal



rm(list = ls()) # clean workspace
try(dev.off(), silent = TRUE) # close all plots
library(ggplot2)
library(GGally)

theme_set(
  theme_minimal()
)

p3_data   <- read.csv('P3_by_subject.csv')
# p3_data[p3_data$RUT == 1943801019, ]$RUT <- 194380119
# p3_data[p3_data$RUT == 167788139, ]$RUT <- 16788139
# p3_data[p3_data$RUT == 170508165, ]$RUT <- 17050816
names(p3_data)[names(p3_data) == "uv_mean"] <- "P3"
n400_data <- read.csv('N400_difference_by_subject.csv')
# n400_data[n400_data$RUT == 167788139, ]$RUT <- 16788139
# n400_data[n400_data$RUT == 170508165, ]$RUT <- 17050816
p10_data  <- read.csv('P10_DF_joined (copy).csv')
p10_data$RUT <- gsub('.', '', p10_data$id, fixed = TRUE)
p10_data$RUT <- gsub('-', '', p10_data$RUT)
p10_data$RUT <- gsub(' ', '', p10_data$RUT)
p17_data  <- read.csv('P17_DF_joined (copy).csv')
p17_data$RUT <- gsub('.', '', p17_data$id, fixed = TRUE)
p17_data$RUT <- gsub('-', '', p17_data$RUT)
p17_data$RUT <- gsub(' ', '', p17_data$RUT)
corr_data <- merge(p3_data[c('RUT', 'ERPset', 'num_id', 'vulnerability', 'belief', 'sex', 'P3')], n400_data[c('RUT', 'N400_diff_LeftFrontal', 'N400_diff_Central')], by = 'RUT', all = TRUE)
corr_data <- merge(corr_data, p10_data[c('RUT', 'SASS_DIRt', 'IBT_DIRt')], by = 'RUT', all.x = TRUE)
corr_data <- merge(corr_data, p17_data[c('RUT', 'AIM_TramoIngreso_DIRd')], by = 'RUT', all.x = TRUE)
corr_data[sapply(corr_data, is.character)] <- lapply(corr_data[sapply(corr_data, is.character)], as.factor)
write.csv(corr_data,  file.path('corr_data.csv'),  row.names = FALSE)

1 General description

options(width = 100)
summary(corr_data[c('RUT', 'ERPset', 'num_id', 'vulnerability', 'belief', 'sex', 'P3', 'N400_diff_LeftFrontal', 'N400_diff_Central', 'SASS_DIRt', 'IBT_DIRt', 'AIM_TramoIngreso_DIRd')])

        RUT                         ERPset       num_id           vulnerability        belief  
 13270311 : 1   S001VulCrF20957113_odd : 1   Min.   :  1.0   Invulnerable:65    Believer  :40  
 132947996: 1   S003nVulCrM18466555_odd: 1   1st Qu.: 47.5   Vulnerable  :13    Unbeliever:38  
 13683981 : 1   S006nVulCrM17923449_odd: 1   Median : 76.5                                     
 138291510: 1   S015nVulCrM18833005_odd: 1   Mean   : 72.1                                     
 13853986 : 1   S016VulCrF19423156_odd : 1   3rd Qu.:100.8                                     
 14467090 : 1   S018nVulCrF17671904_odd: 1   Max.   :120.0                                     
 (Other)  :72   (Other)                :72                                                     
     sex           P3         N400_diff_LeftFrontal N400_diff_Central    SASS_DIRt    
 Female:48   Min.   :-1.692   Min.   :-11.0315      Min.   :-8.42034   Min.   :24.00  
 Male  :30   1st Qu.: 2.352   1st Qu.: -1.9378      1st Qu.:-1.58816   1st Qu.:36.00  
             Median : 4.057   Median :  0.1340      Median :-0.07557   Median :41.50  
             Mean   : 4.081   Mean   : -0.1216      Mean   :-0.15826   Mean   :40.73  
             3rd Qu.: 5.203   3rd Qu.:  2.1211      3rd Qu.: 1.38196   3rd Qu.:45.00  
             Max.   : 9.750   Max.   :  7.0360      Max.   : 6.21919   Max.   :54.00  
                              NA's   :1             NA's   :1          NA's   :4      
    IBT_DIRt     AIM_TramoIngreso_DIRd
 Min.   : 9.00   Min.   :1.000        
 1st Qu.:17.00   1st Qu.:3.000        
 Median :23.50   Median :5.000        
 Mean   :23.76   Mean   :4.377        
 3rd Qu.:29.00   3rd Qu.:6.000        
 Max.   :44.00   Max.   :7.000        
 NA's   :4       NA's   :1            

1.1 Correlations

options(width = 100)
variables <- c('P3', 'N400_diff_LeftFrontal', 'N400_diff_Central', 'SASS_DIRt', 'IBT_DIRt', 'AIM_TramoIngreso_DIRd')
variables_pairs <- ggpairs(corr_data,
                       columns = variables,
                       aes(colour = belief, alpha = .25),
                       progress = FALSE,
                       lower = list(continuous = wrap("points")))
suppressWarnings(print(variables_pairs))

cor.test(corr_data$SASS_DIRt, corr_data$AIM_TramoIngreso_DIRd)

    Pearson's product-moment correlation

data:  corr_data$SASS_DIRt and corr_data$AIM_TramoIngreso_DIRd
t = 2.22, df = 72, p-value = 0.02957
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.02612297 0.45527836
sample estimates:
      cor 
0.2531109 

cor.test(corr_data$SASS_DIRt[corr_data$belief == 'Believer'], corr_data$AIM_TramoIngreso_DIRd[corr_data$belief == 'Believer'])

    Pearson's product-moment correlation

data:  corr_data$SASS_DIRt[corr_data$belief == "Believer"] and corr_data$AIM_TramoIngreso_DIRd[corr_data$belief == "Believer"]
t = 2.5379, df = 38, p-value = 0.01537
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.07850468 0.61882507
sample estimates:
      cor 
0.3807039 

cor.test(corr_data$SASS_DIRt[corr_data$belief == 'Unbeliever'], corr_data$AIM_TramoIngreso_DIRd[corr_data$belief == 'Unbeliever'])

    Pearson's product-moment correlation

data:  corr_data$SASS_DIRt[corr_data$belief == "Unbeliever"] and corr_data$AIM_TramoIngreso_DIRd[corr_data$belief == "Unbeliever"]
t = 0.4002, df = 32, p-value = 0.6917
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2741383  0.3992084
sample estimates:
       cor 
0.07056953