```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load your packages 
```{r}
library(tidyverse)
library(brms)
library(bmlm)
library(bayesplot)
library(tidybayes)
```

# Load your data 

We are going to work again with the PANDA dataset that you have now seen for a few weeks. However, in this challenge we are going to be combining cleaning, reformatting, and modeling from the past 4-weeks, so this should be a challenge! 

Load the data from the SIPPS website: https://columbia-sipps.github.io/workshops/coding_advanced/. As a reminder, these data include a variable for ID and gender, and then assess self-worth (SPPA_SWORTH), communication quality with mothers (MPACS), and generalized anxiety (SCARED_GAD) every six months at 6 different time-points. 

```{r}
d <- read.csv('https://columbia-sipps.github.io/workshop_files/coding-adv-04/mlm-data-cleaning.csv')
```

# Research question 

As opposed to the week when we explored whether/how self-worth changed over time, today we are going to look at the association between communication quality with mothers and self-worth. Specifically, our question is **at timepoints when adolescents reported better communication quality with their mothers (as compared to their own average communication quality across the 6 timepoints) did they concurrently report better/higher self-worth?** Remember, that this is a within-person (as oppossed to a between-person) research question. 

# Subset the data

Keep ID, MPCAS variables and SWORTH variables in the dataset
**hint** use the contains function for MPACS and SWORTH
```{r}

```

# Rename the variables

Make the variable names easier to understand
```{r}

```

# Transform the data to long format

Here, you need to do this separately for self-worth and communication. That is, use the pivot_longer function like we did in the past for time and self-worth, and then create another long dataset for communication. **hint: include include a command for names_prefix to make the time labels the same (and numeric) across the two dataframes you are creating**

You will then need to merge the two dataframes using full_join by both id and time 
```{r}
#self-worth long dataset


#communication long dataset


#merge the two dataframes (using full_join)
#use select to keep the variables we want (id, time, self-worth and communication)

```

# Clean your long format data

Here, we want you to do 4 times 
>- filter out the NA responses for self_worth (use !is.na)
>- filter out the NA responses for communication 

```{r}

```

# Rescale time 

Make it so that timepoint 1 is recoded tp be 0, so that time ranges from 0 to 5. 
```{r}

```

# Put communication and self-worth on a 0 to 10 scale 

Self worth was previously on a 1 to 4 scale 
Communication was on a 20 to 100 scale 

There are numerous ways to do this, but one way is to implement this equation: (max new - min new)/(max old - min old) * (variable - max old) + max new 
```{r}

```

# Parse within-subject communication from between-subject communication 

**hint, use the isolate function** 
```{r}

```

# Run a bayesian model with random intercept and random slope 

Self-worth should be predicted by time and within-subject communication quality. There should be a random intercept, as well as a random slope for within0subject communication quality. 
```{r}

```

# Write a few sentences to interpret the fixed effects for time and communication


# Write a couple sentences to interpret and the random effect of communication quality and quantify heterogeneity 
**hint, when quantifying the heterogeneity interval and where 95% of the slopes from the population fall, use the formula: fixed effect +/- (plus or minus) 2 times sd of random effects** 
 

# Visualize the fixed and random effects 
Plot a spaghetti plot 
```{r}

```


# Visualize the heterogeneity interval
Make sure to visualize where the fixed effect is as well. 

```{r}

```
