mydata <- read.csv("study3data.cleaned.v2.csv")
jmv::descriptives(mydata, vars = vars(asi, perf.trait, perf.cog, perf.self, ac.se, ganx), missing = TRUE)
jmv::descriptives(mydata, vars = vars(fof,sars,achstriv, comp,flow,swl,ewb), missing = TRUE)
#looking at correlations of numeric variables
plot<- mydata[,c("age", "asi", "perf.trait", "perf.cog", "perf.self", "ac.se", "ganx", "fof", "sars", "achstriv",
"comp", "flow", "swl", "ewb")]
#Calculating correlations and CIs
cor1 <- cor.mtest(plot, use="pairwise.complete.obs", conf.level = 0.95)
#Labels for the subsequent plot
cor1b <- cor(plot, use="pairwise.complete.obs")
rownames(cor1b) <- c("Age",
"Anxiety sensitivity",
"Trait perfectionism",
"Perfectionistic cognitions",
"Perfectionistic self-presentation",
"Academic self-efficacy",
"General anxiety",
"Fear of failure",
"Statistics anxiety",
"Achievement striving",
"Competence",
"Flow",
"Life satisfaction",
"Eudaimonic well-being")
colnames(cor1b) <- c("Age",
"Anxiety sensitivity",
"Trait perfectionism",
"Perfectionistic cognitions",
"Perfectionistic self-presentation",
"Academic self-efficacy",
"General anxiety",
"Fear of failure",
"Statistics anxiety",
"Achievement striving",
"Competence",
"Flow",
"Life satisfaction",
"Eudaimonic well-being")
#Correlation Matrix
corrplot(cor1b, method="color", type="upper",
addCoef.col = "black", tl.col="black", tl.srt=40, p.mat = cor1$p, tl.cex = .55,
sig.level = 0.05, insig = "n", diag=FALSE, number.cex = 0.7,
col=colorRampPalette(c("#94AEBC", "#F5EABF", "#728393"))(50))