08-lpa-plot.Rmd

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE,
                      message = FALSE) #Here, I have made it so that when you knit your .rmd, warnings and messages will not show up in the html markdown. 
```

# Item Mean Plots

------------------------------------------------------------------------

*Example: PISA Student Data*

1.  The first example closely follows the vignette used to demonstrate the [tidyLPA](https://data-edu.github.io/tidyLPA/articles/Introduction_to_tidyLPA.html) package (Rosenberg, 2019).

-   This model utilizes the `PISA` data collected in the U.S. in 2015. To learn more about this data [see here](http://www.oecd.org/pisa/data/).
-   To access the 2015 US `PISA` data & documentation in R use the following code:

Variables:

`broad_interest`

:   composite measure of students' self reported broad interest

`enjoyment`

:   composite measure of students' self reported enjoyment

`instrumental_mot`

:   composite measure of students' self reported instrumental motivation

`self_efficacy`

:   composite measure of students' self reported self efficacy

```{r, eval = FALSE}
#devtools::install_github("jrosen48/pisaUSA15")
#library(pisaUSA15)
```

------------------------------------------------------------------------

## Load packages

```{r}
library(naniar)
library(tidyverse)
library(haven)
library(glue)
library(MplusAutomation)
library(here)
library(janitor)
library(gt)
library(tidyLPA)
library(pisaUSA15)
library(cowplot)
library(filesstrings)
library(patchwork)
library(RcppAlgos)
```

------------------------------------------------------------------------

## Prepare Data

```{r, eval=TRUE}

pisa <- pisaUSA15[1:500,] %>%
  dplyr::select(broad_interest, enjoyment, instrumental_mot, self_efficacy)

```

------------------------------------------------------------------------

## Descriptive Statistics

Quick Summary

```{r}
summary(pisa)
```


Mean Table

```{r}
ds <- pisa %>% 
  pivot_longer(broad_interest:self_efficacy, names_to = "variable") %>% 
  group_by(variable) %>% 
  summarise(mean = mean(value, na.rm = TRUE),
            sd = sd(value, na.rm = TRUE)) 

ds %>% 
  gt () %>% 
  tab_header(title = md("**Descriptive Summary**")) %>%
  cols_label(
    variable = "Variable",
    mean = md("M"),
    sd = md("SD")
  ) %>%
  fmt_number(c(2:3),
             decimals = 2) %>% 
  cols_align(
    align = "center",
    columns = mean
  ) 
```

Histograms

```{r out.width="90%"}
data_long <- pisa %>%
  pivot_longer(broad_interest:self_efficacy, names_to = "variable")

ggplot(data_long, aes(x = value)) +
  geom_histogram(binwidth = .3, fill = "#69b3a2", color = "black") +
  facet_wrap(~ variable, scales = "free_x") +
  labs(title = "Histograms of Variables", x = "Value", y = "Frequency") +
  theme_cowplot()
```

------------------------------------------------------------------------

## Visualization

### Latent Profile Plot

```{r}
source(here("functions", "plot_lpa.R"))

# Read in models
output_enum <- readModels(here("lpa", "tidyLPA"), quiet = TRUE)

plot_lpa(model_name = output_enum$model_3_class_4.out)
```

Save figure

```{r, eval = FALSE}
ggsave(here("figures", "model3_profile4.png"), dpi = "retina", bg = "white", height=5, width=8, units="in")
```

------------------------------------------------------------------------

### Plots Means and Variances

```{r}
plotMixtures(output_enum$model_3_class_4.out, ci = 0.95, bw = FALSE) + 
  labs(title = "Model 3: Equal Variances, Equal Covariances")
```

------------------------------------------------------------------------

### Plot comparison

We can also plot the comparisons and look at the error bars.

NOTE: The `plotMixtures()` function is used for plotting LPA models only (i.e., means & variances)

```{r, fig.width= 10}
a <- plotMixtures(output_enum$model_2_class_3.out,
  ci = 0.95, bw = FALSE) 

b <- plotMixtures(output_enum$model_4_class_3.out,
  ci = 0.95, bw = FALSE) 

a + labs(title = "Model 2") +
    theme(plot.title = element_text(size = 12)) +
b + labs(title = "Model 4") +
    theme(plot.title = element_text(size = 12))
```

------------------------------------------------------------------------

<div style="text-align: center;"><img src="images/ucsb_logo.png" width="75%" /></div>