---
title: "Data Transformation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Data Transformation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE
)
```

```{r setup}
library(mariposa)
library(dplyr)
data(survey_data)
```

## Overview

Before analyzing survey data, you often need to transform variables --- recode responses, create dummy variables, standardize scales, or compute row-wise indices. mariposa provides a set of functions that handle these tasks with a clean, survey-oriented syntax.

| Function | Purpose |
|----------|---------|
| `rec()` | Recode values using a string syntax |
| `to_dummy()` | Create dummy (0/1) variables from categorical columns |
| `std()` | Z-standardize variables (4 methods) |
| `center()` | Mean-center variables (grand-mean or group-mean) |
| `row_means()` | Compute row-wise means across items |
| `row_sums()` | Compute row-wise sums across items |
| `row_count()` | Count specific values per row |
| `pomps()` | Transform to Percent of Maximum Possible Scores (0--100) |

## Recoding with rec()

`rec()` uses a concise string syntax to recode variables. It works inside `mutate()` and supports ranges, reverse-coding, and inline value labels.

### Simple Recoding

Collapse categories by mapping old values to new values:

```{r}
survey_data <- rec(survey_data, age,
  rules = "18:29=1 [Young]; 30:49=2 [Middle]; 50:99=3 [Older]",
  suffix = "_group", as_factor = TRUE
)

frequency(survey_data, age_group)
```

The bracket notation (`[Young]`) automatically creates value labels on the recoded variable. The `suffix` argument creates a new column (here `age_group`) instead of overwriting the original.

### Reverse-Coding

Reverse the direction of a Likert scale. Useful when some items in a scale are negatively worded:

```{r}
survey_data <- rec(survey_data, trust_government,
  rules = "rev", suffix = "_rev"
)

# Original: 1=low trust ... 5=high trust
# Reversed: 1=high trust ... 5=low trust
head(data.frame(
  original = survey_data$trust_government,
  reversed = survey_data$trust_government_rev
))
```

### Dichotomizing

Split a variable at its median into two groups:

```{r}
survey_data <- rec(survey_data, income,
  rules = "dicho", suffix = "_dicho"
)

frequency(survey_data, income_dicho)
```

Other split options:

```{r}
# Split at the mean
survey_data <- rec(survey_data, income,
  rules = "mean", suffix = "_mean_split"
)

# Split at a fixed cut-point
survey_data <- rec(survey_data, income,
  rules = "dicho(3000)", suffix = "_custom"
)
```

### Keeping and Copying Values

Use `copy` to keep values that are not explicitly recoded, and `else` for a catch-all:

```{r}
survey_data <- rec(survey_data, education,
  rules = "1:2=1 [Lower]; else=2 [Higher]",
  suffix = "_binary", as_factor = TRUE
)

frequency(survey_data, education_binary)
```

## Dummy Coding with to_dummy()

`to_dummy()` creates binary (0/1) indicator variables from categorical columns --- also known as one-hot encoding.

### Basic Usage

```{r}
# Create dummy variables for region
dummies <- to_dummy(survey_data, region, append = FALSE)
head(dummies)
```

### Label-Based Column Names

Use `suffix = "label"` to name columns after the value labels instead of numeric codes:

```{r}
dummies <- to_dummy(survey_data, gender, suffix = "label", append = FALSE)
head(dummies)
```

### Reference Category (n-1 Coding)

For regression analysis, you typically need n-1 dummies (one category omitted as reference):

```{r}
dummies <- to_dummy(survey_data, education, ref = 1, append = FALSE)
head(dummies)
```

### Adding to Existing Data

By default, dummy columns are appended to the original data:

```{r, eval = FALSE}
survey_data <- to_dummy(survey_data, gender, suffix = "label")
# Adds gender_Male, gender_Female to the data frame
```

## Standardization with std()

`std()` z-standardizes variables so they have mean 0 and standard deviation 1. This is useful for comparing variables on different scales.

### Basic Standardization

```{r}
survey_data <- survey_data %>%
  std(age, income)

# Check: mean ≈ 0, sd ≈ 1
survey_data %>%
  describe(age, income, show = c("mean", "sd"))
```

### Standardization Methods

`std()` supports four methods:

```{r}
# Default: divide by SD
survey_data_methods <- survey_data %>%
  std(life_satisfaction, method = "sd", suffix = "_sd") %>%
  std(life_satisfaction, method = "2sd", suffix = "_2sd") %>%
  std(life_satisfaction, method = "mad", suffix = "_mad")

survey_data_methods %>%
  describe(life_satisfaction_sd, life_satisfaction_2sd, life_satisfaction_mad,
           show = c("mean", "sd"))
```

- **`"sd"`** (default): Classic z-standardization ($\frac{x - \bar{x}}{SD}$)
- **`"2sd"`**: Gelman's (2008) recommendation --- divides by 2 SD, making coefficients comparable to untransformed binary predictors
- **`"mad"`**: Robust standardization using median and MAD (resistant to outliers)
- **`"gmd"`**: Standardization using the Gini Mean Difference

### Weighted Standardization

```{r}
survey_data <- survey_data %>%
  std(income, weights = sampling_weight, suffix = "_wstd")

survey_data %>%
  describe(income_wstd, show = c("mean", "sd"))
```

### Group-Wise Standardization

Standardize within subgroups:

```{r}
survey_data <- survey_data %>%
  group_by(region) %>%
  std(income, suffix = "_gstd") %>%
  ungroup()
```

## Centering with center()

`center()` subtracts the mean from each value, shifting the distribution so the mean is zero while preserving the original scale.

### Grand-Mean Centering

```{r}
survey_data <- survey_data %>%
  center(age, income, suffix = "_c")

survey_data %>%
  describe(age_c, income_c, show = c("mean", "sd", "min", "max"))
```

### Group-Mean Centering

Center within groups --- each observation is expressed as a deviation from its group mean:

```{r}
survey_data <- survey_data %>%
  group_by(region) %>%
  center(income, suffix = "_gc") %>%
  ungroup()

# Group means are now zero within each region
survey_data %>%
  group_by(region) %>%
  describe(income_gc, show = c("mean", "sd"))
```

### Weighted Centering

```{r}
survey_data <- survey_data %>%
  center(age, weights = sampling_weight, suffix = "_wc")
```

## Row Operations

Row operations compute values *across columns* for each respondent --- essential for creating scale scores from multiple survey items.

### Row Means

`row_means()` computes the arithmetic mean across selected variables for each row:

```{r}
survey_data <- survey_data %>%
  mutate(m_trust = row_means(., trust_government, trust_media, trust_science))

survey_data %>%
  describe(m_trust)
```

#### Using tidyselect

```{r}
survey_data <- survey_data %>%
  mutate(m_trust2 = row_means(., starts_with("trust")))
```

#### Using pick()

The `pick()` function works with both `%>%` and `|>`:

```{r}
survey_data <- survey_data %>%
  mutate(m_trust3 = row_means(
    pick(trust_government, trust_media, trust_science)
  ))
```

#### Minimum Valid Items

Require a minimum number of non-missing items per row. This matches SPSS `MEAN.2()` syntax:

```{r}
survey_data <- survey_data %>%
  mutate(m_trust_strict = row_means(
    ., trust_government, trust_media, trust_science,
    min_valid = 2
  ))
```

If a respondent answered fewer than 2 of the 3 items, they receive `NA` instead of a potentially unreliable score.

### Row Sums

`row_sums()` works like `row_means()` but returns the total:

```{r}
survey_data <- survey_data %>%
  mutate(trust_total = row_sums(., trust_government, trust_media, trust_science))

survey_data %>%
  describe(trust_total)
```

### Row Count

`row_count()` counts how many times a specific value appears in each row:

```{r}
# How many trust items did each person rate as 5 (highest)?
survey_data <- survey_data %>%
  mutate(n_high_trust = row_count(
    ., trust_government, trust_media, trust_science,
    count = 5
  ))

frequency(survey_data, n_high_trust)
```

## POMPS Transformation

`pomps()` transforms scores to a Percent of Maximum Possible Scores scale (0--100). This makes scores from different scales directly comparable:

```{r}
survey_data <- survey_data %>%
  mutate(trust_pomps = pomps(m_trust, scale_min = 1, scale_max = 5))

survey_data %>%
  describe(trust_pomps)
```

A score of 0 means the respondent chose the minimum on every item; 100 means the maximum on every item.

Always specify `scale_min` and `scale_max` based on the theoretical scale range, not the observed range. This ensures scores are comparable across samples.

```{r}
# Transform multiple variables at once
survey_data <- survey_data %>%
  mutate(across(
    c(trust_government, trust_media, trust_science),
    ~ pomps(.x, scale_min = 1, scale_max = 5),
    .names = "{.col}_pomps"
  ))
```

## Complete Example

A typical data transformation workflow before analysis:

```{r}
data(survey_data)  # fresh copy

# 1. Recode: create age groups and reverse-code an item
survey_data <- rec(survey_data, age,
  rules = "18:29=1 [Young]; 30:49=2 [Middle]; 50:99=3 [Older]",
  suffix = "_group", as_factor = TRUE)
survey_data <- rec(survey_data, trust_government,
  rules = "rev", suffix = "_rev")

# 2. Create scale score
survey_data <- survey_data %>%
  mutate(m_trust = row_means(., trust_government, trust_media, trust_science,
                             min_valid = 2))

# 3. Standardize for regression
survey_data <- survey_data %>%
  std(age, income, suffix = "_z")

# 4. Use in analysis
survey_data %>%
  t_test(m_trust, group = gender, weights = sampling_weight)

survey_data %>%
  linear_regression(life_satisfaction ~ age_z + income_z + m_trust,
                    weights = sampling_weight)
```

## Practical Tips

1. **Use `rec()` for survey-specific recoding.** The string syntax is more readable than nested `ifelse()` or `case_when()` for typical survey transformations.

2. **Always specify `min_valid` for `row_means()`.** Without it, a respondent who answered only 1 out of 10 items gets a scale score based on a single response. Setting `min_valid` to half the number of items is a common rule of thumb.

3. **Center variables before regression.** Centering makes the intercept interpretable as the expected value at the mean of all predictors. Group-mean centering separates within-group and between-group effects.

4. **Use `std(method = "2sd")` for mixed models.** Gelman (2008) recommends dividing by 2 SD so that standardized coefficients for continuous predictors are comparable to those for binary predictors.

5. **Always use theoretical min/max in `pomps()`.** Using observed min/max makes scores sample-dependent and non-comparable.

## Summary

1. `rec()` recodes variables with a concise string syntax (ranges, reverse, dichotomize, inline labels)
2. `to_dummy()` creates binary indicator variables for regression
3. `std()` z-standardizes with four methods (sd, 2sd, mad, gmd) and weight/group support
4. `center()` mean-centers variables (grand-mean or group-mean)
5. `row_means()`, `row_sums()`, `row_count()` compute values across columns per row
6. `pomps()` transforms scores to a comparable 0--100 scale

## Next Steps

- Explore your transformed data --- see `vignette("descriptive-statistics")`
- Build and validate scales --- see `vignette("scale-analysis")`
- Test group differences --- see `vignette("hypothesis-testing")`
- Predict outcomes with regression --- see `vignette("regression-analysis")`