setwd("~/Google Drive/PhD/Projects/Personal projects and well-being/data")
library(car)
library(psych)
library(tidyverse)
library(readr)
library(dplyr)
library(corrplot)
library(ggplot2)
library(GGally)
library(ggpubr)
library(Hmisc)
library(PerformanceAnalytics)
library(jmv)
library(stargazer)
library(plyr)
#read data in
pp <- read.csv("personal.projects.cleaned.v3.csv")
#descriptives
descriptives(pp, vars = vars(bpns, autonomy, competence, relatedness), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────────
## bpns autonomy competence relatedness
## ────────────────────────────────────────────────────────────────
## N 191 191 191 191
## Missing 0 0 0 0
## Mean 5.042757 5.450262 5.078534 4.599476
## Median 5.166667 5.500000 5.500000 5.000000
## Minimum 1.500000 2.000000 1.000000 1.000000
## Maximum 7.000000 7.000000 7.000000 7.000000
## ────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(passion, vlq, zest, engagement, mhc), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────────────────────────
## passion vlq zest engagement mhc
## ─────────────────────────────────────────────────────────────────────────
## N 191 190 191 191 191
## Missing 0 1 0 0 0
## Mean 4.193063 52.68466 4.387625 3.195201 3.888859
## Median 4.500000 51.13889 4.416667 3.166667 4.071429
## Minimum 1.375000 12.90000 1.916667 1.000000 1.384615
## Maximum 5.000000 98.00000 6.416667 5.000000 6.000000
## ─────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(as.5f, as.6f, aps.standards), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────
## as.5f as.6f aps.standards
## ────────────────────────────────────────────────────
## N 191 191 191
## Missing 0 0 0
## Mean 4.755516 4.405448 41.71204
## Median 4.777778 4.375000 42
## Minimum 3.111111 2.625000 23
## Maximum 6.111111 5.875000 49
## ────────────────────────────────────────────────────
#tabulated counts of mental health category and perfectionism style
xtabs(~ mh+perf, data=pp, na.action=na.exclude)
## perf
## mh adaptive maladaptive non-perfectionist
## flourishing 2 19 18
## languishing 0 2 0
## moderate 24 63 63
# well-being variables for correlations
pp.wb<- pp[,c("as.5f", "as.6f", "autonomy", "competence", "relatedness", "mhc", "aps.standards", "passion", "vlq", "zest", "engagement")]
#calculating correlations and CIs
cor1 <- cor.mtest(pp.wb, use="pairwise.complete.obs", conf.level = 0.95)
cor1
## $p
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 0.565858341 1.343457e-05 2.590771e-04 9.653685e-02
## [2,] 5.658583e-01 0.000000000 9.889010e-01 5.172522e-01 4.741342e-01
## [3,] 1.343457e-05 0.988901048 0.000000e+00 1.018359e-22 4.622112e-08
## [4,] 2.590771e-04 0.517252194 1.018359e-22 0.000000e+00 1.997229e-11
## [5,] 9.653685e-02 0.474134233 4.622112e-08 1.997229e-11 0.000000e+00
## [6,] 2.687097e-04 0.732980567 4.494337e-16 6.915654e-17 2.485119e-16
## [7,] 4.140706e-02 0.916479428 5.165033e-03 2.632195e-07 5.061281e-02
## [8,] 1.040594e-03 0.909560239 1.640097e-08 5.205198e-08 2.995544e-07
## [9,] 4.440177e-04 0.723248449 6.275581e-07 1.408462e-08 8.760185e-12
## [10,] 2.265509e-02 0.007866747 1.338143e-02 1.244400e-02 2.629393e-02
## [11,] 8.949248e-01 0.006364637 9.118677e-02 5.760085e-02 7.285191e-02
## [,6] [,7] [,8] [,9] [,10]
## [1,] 2.687097e-04 4.140706e-02 1.040594e-03 4.440177e-04 2.265509e-02
## [2,] 7.329806e-01 9.164794e-01 9.095602e-01 7.232484e-01 7.866747e-03
## [3,] 4.494337e-16 5.165033e-03 1.640097e-08 6.275581e-07 1.338143e-02
## [4,] 6.915654e-17 2.632195e-07 5.205198e-08 1.408462e-08 1.244400e-02
## [5,] 2.485119e-16 5.061281e-02 2.995544e-07 8.760185e-12 2.629393e-02
## [6,] 0.000000e+00 2.964637e-03 4.334679e-11 1.240068e-15 7.655631e-03
## [7,] 2.964637e-03 0.000000e+00 5.155935e-06 4.554646e-02 1.165403e-01
## [8,] 4.334679e-11 5.155935e-06 0.000000e+00 4.479783e-06 5.635796e-05
## [9,] 1.240068e-15 4.554646e-02 4.479783e-06 0.000000e+00 4.926631e-03
## [10,] 7.655631e-03 1.165403e-01 5.635796e-05 4.926631e-03 0.000000e+00
## [11,] 6.372302e-01 1.470294e-01 1.135053e-02 2.075489e-01 7.246950e-03
## [,11]
## [1,] 0.894924797
## [2,] 0.006364637
## [3,] 0.091186767
## [4,] 0.057600851
## [5,] 0.072851906
## [6,] 0.637230172
## [7,] 0.147029425
## [8,] 0.011350534
## [9,] 0.207548917
## [10,] 0.007246950
## [11,] 0.000000000
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.000000000 -0.10077632 0.17494175 0.12405975 -0.0217500047
## [2,] -0.100776324 1.00000000 -0.14098642 -0.09547725 -0.0905511361
## [3,] 0.174941751 -0.14098642 1.00000000 0.53867588 0.2546603820
## [4,] 0.124059751 -0.09547725 0.53867588 1.00000000 0.3410320836
## [5,] -0.021750005 -0.09055114 0.25466038 0.34103208 1.0000000000
## [6,] 0.123375066 -0.16623794 0.43511368 0.44943359 0.4397054355
## [7,] 0.005870901 -0.14945538 0.06137056 0.23210928 -0.0003295592
## [8,] 0.096737873 -0.15007654 0.26744748 0.25316476 0.2303741052
## [9,] 0.114092815 -0.16759339 0.22086754 0.26999936 0.3500547892
## [10,] 0.023438590 0.05119212 0.03767948 0.03957441 0.0192347005
## [11,] -0.132540814 0.05637193 -0.26002182 -0.27425571 -0.2671386957
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.12337507 0.0058709010 0.09673787 0.114092815 0.02343859
## [2,] -0.16623794 -0.1494553799 -0.15007654 -0.167593391 0.05119212
## [3,] 0.43511368 0.0613705576 0.26744748 0.220867536 0.03767948
## [4,] 0.44943359 0.2321092829 0.25316476 0.269999360 0.03957441
## [5,] 0.43970544 -0.0003295592 0.23037411 0.350054789 0.01923470
## [6,] 1.00000000 0.0741852333 0.33323338 0.428182717 0.05186347
## [7,] 0.07418523 1.0000000000 0.18978890 0.002960371 -0.02850030
## [8,] 0.33323338 0.1897889032 1.00000000 0.192426183 0.15133855
## [9,] 0.42818272 0.0029603710 0.19242618 1.000000000 0.06265629
## [10,] 0.05186347 -0.0285003001 0.15133855 0.062656291 1.00000000
## [11,] -0.17546124 -0.2436608232 -0.31660237 -0.231177680 0.05321163
## [,11]
## [1,] -0.13254081
## [2,] 0.05637193
## [3,] -0.26002182
## [4,] -0.27425571
## [5,] -0.26713870
## [6,] -0.17546124
## [7,] -0.24366082
## [8,] -0.31660237
## [9,] -0.23117768
## [10,] 0.05321163
## [11,] 1.00000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.1826962 0.43224162 0.388975466 0.25816063 0.3883852
## [2,] 0.1826962 1.0000000 0.14297199 0.187863093 0.19265233 0.1175490
## [3,] 0.4322416 0.1429720 1.00000000 0.710492864 0.49772714 0.6363868
## [4,] 0.3889755 0.1878631 0.71049286 1.000000000 0.56568214 0.6468599
## [5,] 0.2581606 0.1926523 0.49772714 0.565682141 1.00000000 0.6397531
## [6,] 0.3883852 0.1175490 0.63638675 0.646859904 0.63975313 1.0000000
## [7,] 0.2837549 0.1344871 0.33401256 0.479479243 0.27804366 0.3454007
## [8,] 0.3652506 0.1338631 0.50797948 0.496523543 0.47806625 0.5596691
## [9,] 0.3810140 0.1169268 0.47089637 0.510581983 0.57312208 0.6317498
## [10,] 0.2998302 0.3249101 0.31274711 0.314458233 0.29599767 0.3255121
## [11,] 0.1513921 0.3295487 0.01975575 0.004430199 0.01210945 0.1081694
## [,7] [,8] [,9] [,10] [,11]
## [1,] 0.28375492 0.36525057 0.3810140 0.2998302 0.151392129
## [2,] 0.13448708 0.13386310 0.1169268 0.3249101 0.329548703
## [3,] 0.33401256 0.50797948 0.4708964 0.3127471 0.019755746
## [4,] 0.47947924 0.49652354 0.5105820 0.3144582 0.004430199
## [5,] 0.27804366 0.47806625 0.5731221 0.2959977 0.012109451
## [6,] 0.34540067 0.55966913 0.6317498 0.3255121 0.108169386
## [7,] 1.00000000 0.44464711 0.2817794 0.2518453 0.037210609
## [8,] 0.44464711 1.00000000 0.4474513 0.4123175 -0.041951667
## [9,] 0.28177941 0.44745126 1.0000000 0.3358362 0.051175496
## [10,] 0.25184531 0.41231750 0.3358362 1.0000000 0.326720183
## [11,] 0.03721061 -0.04195167 0.0511755 0.3267202 1.000000000
#correlation Matrix
corrplot(cor(pp.wb, use="pairwise.complete.obs"), method="number", type="upper",
addCoef.col = "orchid4", tl.col="hotpink3", tl.srt=45, p.mat = cor1$p,
sig.level = 0.05, insig = "blank", diag=FALSE)
ggpairs(pp.wb) + theme_classic()
#distributions of scale scores by level of mental health
boxplot(passion ~ mh, data = pp, xlab = "mental health", ylab = "passion",
plot = TRUE, outlier.tagging = TRUE)
boxplot(zest ~ mh, data = pp, xlab = "mental health", ylab = "zest",
plot = TRUE, outlier.tagging = TRUE)
boxplot(vlq ~ mh, data = pp, xlab = "mental health", ylab = "valued living",
plot = TRUE, outlier.tagging = TRUE)
boxplot(engagement ~ mh, data = pp, xlab = "mental health", ylab = "engagement",
plot = TRUE, outlier.tagging = TRUE)
boxplot(autonomy ~ mh, data = pp, xlab = "mental health", ylab = "autonomy satisfaction",
plot = TRUE, outlier.tagging = TRUE)
boxplot(competence ~ mh, data = pp, xlab = "mental health", ylab = "competence satisfaction",
plot = TRUE, outlier.tagging = TRUE)
boxplot(relatedness ~ mh, data = pp, xlab = "mental health", ylab = "relatedness satisfaction",
plot = TRUE, outlier.tagging = TRUE)
boxplot(bpns ~ mh, data = pp, xlab = "mental health", ylab = "overall psych need satisfaction",
plot = TRUE, outlier.tagging = TRUE)
#demographic descriptives
pp$gender <- as.factor(pp$gender)
pp$ethnicity <- as.factor(pp$ethnicity)
pp$student <- as.factor(pp$student)
pp$gender <- factor(pp$gender, levels = c(1,2,4),
labels = c("female", "male", "other identity"))
pp$ethnicity <- factor(pp$ethnicity, levels = c(1,2,3,4,5,6),
labels = c("white", "black", "asian", "indigenous", "latin", "other"))
pp$student <- factor(pp$student, levels = c(1,2),
labels = c("full time", "part time"))
descriptives(pp, age)
##
## DESCRIPTIVES
##
## Descriptives
## ───────────────────────
## age
## ───────────────────────
## N 177
## Missing 14
## Mean 20.71186
## Median 20
## Minimum 17
## Maximum 33
## ───────────────────────
eth = table(pp$ethnicity)
eth
##
## white black asian indigenous latin other
## 119 6 46 0 0 0
student = table(pp$student)
student
##
## full time part time
## 186 5
gender = table(pp$gender)
gender
##
## female male other identity
## 168 22 1
#distributions of scale scores by type of perfectionism
boxplot(passion ~ perf, data = pp, xlab = "perfectionism", ylab = "passion",
plot = TRUE)
boxplot(zest ~ perf, data = pp, xlab = "perfectionism", ylab = "zest",
plot = TRUE)
boxplot(vlq ~ perf, data = pp, xlab = "perfectionism", ylab = "valued living",
plot = TRUE)
boxplot(engagement ~ perf, data = pp, xlab = "perfectionism", ylab = "engagement",
plot = TRUE)
boxplot(autonomy ~ perf, data = pp, xlab = "perfectionism", ylab = "autonomy satisfaction",
plot = TRUE)
boxplot(competence ~ perf, data = pp, xlab = "perfectionism", ylab = "competence satisfaction",
plot = TRUE)
boxplot(relatedness ~ perf, data = pp, xlab = "perfectionism", ylab = "relatedness satisfaction",
plot = TRUE)
boxplot(bpns ~ perf, data = pp, xlab = "perfectionism", ylab = "overall psych need satisfaction",
plot = TRUE)
boxplot(mhc ~ perf, data = pp, xlab = "perfectionism", ylab = "mental health level",
plot = TRUE)
