| title-block-banner |
true |
| title |
MMDBER_Physics Education |
| format |
| html |
| toc |
toc-depth |
theme |
page-layout |
true |
4 |
Flatly |
full |
|
|
| editor |
visual |
| editor_options |
|
library(tidyverse)
library(haven)
library(glue)
library(MplusAutomation)
library(rhdf5)
library(here)
library(janitor)
library(gt)
library(semPlot)
library(reshape2)
library(cowplot)
library(filesstrings)
library(hrbrthemes)
library(poLCA)
library(naniar)
library(psych)
library(flextable)
library(officer)
conflicted::conflicts_prefer(flextable::compose)
options(scipen=999, digits=4)
support_data <- read.csv(here("k-means", "SocialSupport.csv"))
describe(support_data)
#> vars n mean sd median trimmed
#> ResponseId* 1 567 284.00 163.82 284.0 284.00
#> Family 2 567 0.65 0.48 1.0 0.69
#> PhysPeers 3 567 0.85 0.35 1.0 0.94
#> NonPhysPeers 4 567 0.73 0.44 1.0 0.79
#> HSPhysTeach 5 567 0.25 0.43 0.0 0.18
#> NonPhysFaculty 6 567 0.24 0.43 0.0 0.17
#> PhysFaculty 7 567 0.74 0.44 1.0 0.80
#> ResearchGrp 8 567 0.54 0.50 1.0 0.55
#> WiPGroup 9 567 0.51 0.50 1.0 0.51
#> GIPGroup 10 567 0.27 0.45 0.0 0.22
#> ReligOrg 11 567 0.08 0.27 0.0 0.00
#> OtherIdent 12 567 0.04 0.19 0.0 0.00
#> SocialComm 13 567 0.22 0.42 0.0 0.15
#> MentalHlth 14 567 0.21 0.41 0.0 0.14
#> Woman 15 567 0.88 0.32 1.0 0.98
#> Transgender 16 567 0.07 0.25 0.0 0.00
#> NonBinary 17 567 0.17 0.37 0.0 0.08
#> White 18 567 0.71 0.45 1.0 0.76
#> Asian 19 567 0.22 0.41 0.0 0.15
#> Hispanic 20 567 0.13 0.34 0.0 0.04
#> Black 21 567 0.05 0.22 0.0 0.00
#> MidEastern 22 567 0.03 0.17 0.0 0.00
#> NatAmerican 23 567 0.01 0.10 0.0 0.00
#> PacificIslander 24 567 0.01 0.10 0.0 0.00
#> InstitutionType* 25 567 4.41 0.69 5.0 4.50
#> AcademicYear* 26 567 4.20 1.62 5.0 4.29
#> Identity 27 563 2.54 0.84 2.5 2.59
#> mad min max range skew kurtosis se
#> ResponseId* 210.53 1 567 566 0.00 -1.21 6.88
#> Family 0.00 0 1 1 -0.62 -1.61 0.02
#> PhysPeers 0.00 0 1 1 -2.00 1.99 0.01
#> NonPhysPeers 0.00 0 1 1 -1.05 -0.89 0.02
#> HSPhysTeach 0.00 0 1 1 1.18 -0.60 0.02
#> NonPhysFaculty 0.00 0 1 1 1.24 -0.47 0.02
#> PhysFaculty 0.00 0 1 1 -1.10 -0.80 0.02
#> ResearchGrp 0.00 0 1 1 -0.15 -1.98 0.02
#> WiPGroup 0.00 0 1 1 -0.03 -2.00 0.02
#> GIPGroup 0.00 0 1 1 1.01 -0.97 0.02
#> ReligOrg 0.00 0 1 1 3.15 7.93 0.01
#> OtherIdent 0.00 0 1 1 4.76 20.73 0.01
#> SocialComm 0.00 0 1 1 1.33 -0.22 0.02
#> MentalHlth 0.00 0 1 1 1.41 -0.02 0.02
#> Woman 0.00 0 1 1 -2.39 3.70 0.01
#> Transgender 0.00 0 1 1 3.45 9.95 0.01
#> NonBinary 0.00 0 1 1 1.79 1.22 0.02
#> White 0.00 0 1 1 -0.93 -1.14 0.02
#> Asian 0.00 0 1 1 1.38 -0.09 0.02
#> Hispanic 0.00 0 1 1 2.14 2.60 0.01
#> Black 0.00 0 1 1 3.98 13.90 0.01
#> MidEastern 0.00 0 1 1 5.68 30.35 0.01
#> NatAmerican 0.00 0 1 1 9.54 89.18 0.00
#> PacificIslander 0.00 0 1 1 9.54 89.18 0.00
#> InstitutionType* 0.00 1 5 4 -1.25 2.22 0.03
#> AcademicYear* 1.48 1 6 5 -0.31 -1.40 0.07
#> Identity 0.74 0 4 4 -0.54 0.07 0.04
cor(support_data$Woman, support_data$Transgender)
#> [1] -0.4965
cor(support_data$Woman, support_data$NonBinary)
#> [1] -0.6959
cor(support_data$Transgender, support_data$NonBinary)
#> [1] 0.3547
conflicted::conflicts_prefer(dplyr::filter)
#> [conflicted] Will prefer dplyr::filter over any other
#> package.
nonbinary_subset <- support_data %>% filter(NonBinary == 1) #94 people
trans_subset <- support_data %>% filter(Transgender == 1)
nowoman_subset <- support_data %>% filter(Woman == 0) #66 people
nowoman_nonbinary <- nowoman_subset %>% filter(NonBinary == 1) #58 people
filtered_data <- support_data %>%
filter(Woman == 1 | NonBinary == 1 | Transgender == 1)
lca_7 <- lapply(1:7, function(k) {
lca_enum <- mplusObject(
TITLE = glue("{k}-Class"),
VARIABLE = glue(
"categorical = Family-MentalHlth;
usevar = Family-MentalHlth;
classes = c({k}); "),
ANALYSIS =
"estimator = mlr;
type = mixture;
starts = 200 100;
processors = 10;",
OUTPUT = "sampstat residual tech11 tech14 svalues;",
PLOT =
"type = plot3;
series = Family-MentalHlth(*);",
usevariables = colnames(support_data),
rdata = support_data)
lca_enum_fit <- mplusModeler(lca_enum,
dataout=glue("support.dat"),
modelout=glue("c{k}_support.inp") ,
check=TRUE, run = TRUE, hashfilename = FALSE)
})source(here("k-means", "function/extract_mplus_info.R"))
output_dir <- here("k-means", "enum_mplus")
output_files <- list.files(output_dir, pattern = "\\.out$", full.names = TRUE)
final_data <- map_dfr(output_files, extract_mplus_info_extended)
sample_size <- unique(final_data$Sample_Size)