# Data Wrangling & Visualization

#### Bernardo Lares

#### 2026-08-27

## Install and Load

Install `lares` from CRAN or get the development version from GitHub. Then, load the package:

    library(lares)

## Quick Start with Built-in Data

We’ll use the Titanic dataset included in `lares`:

    data(dft)
    head(dft, 3)
    #>   PassengerId Survived Pclass    Sex Age SibSp Parch           Ticket    Fare
    #> 1           1    FALSE      3   male  22     1     0        A/5 21171  7.2500
    #> 2           2     TRUE      1 female  38     1     0         PC 17599 71.2833
    #> 3           3     TRUE      3 female  26     0     0 STON/O2. 3101282  7.9250
    #>   Cabin Embarked
    #> 1              S
    #> 2   C85        C
    #> 3              S

## Frequency Analysis

### Basic Frequencies

The `freqs()` function provides quick frequency tables with percentages and cumulative values:

    # How many survived?
    freqs(dft, Survived)
    #> # A tibble: 2 × 5
    #>   Survived     n     p order  pcum
    #>   <lgl>    <int> <dbl> <int> <dbl>
    #> 1 FALSE      549  61.6     1  61.6
    #> 2 TRUE       342  38.4     2 100

### Multi-variable Frequencies

    # Survival by passenger class
    freqs(dft, Pclass, Survived)
    #> # A tibble: 6 × 6
    #>   Pclass Survived     n     p order  pcum
    #>   <fct>  <lgl>    <int> <dbl> <int> <dbl>
    #> 1 3      FALSE      372 41.8      1  41.8
    #> 2 1      TRUE       136 15.3      2  57.0
    #> 3 3      TRUE       119 13.4      3  70.4
    #> 4 2      FALSE       97 10.9      4  81.3
    #> 5 2      TRUE        87  9.76     5  91.0
    #> 6 1      FALSE       80  8.98     6 100

### Visual Frequencies

    # Visualize survival by class
    freqs(dft, Pclass, Survived, plot = TRUE)

### Dataframe-wide Frequencies

Analyze all variables at once:

    freqs_df(dft, plot = TRUE, top = 10)

## Correlation Analysis

### Correlation Matrix

Get correlations between all variables (automatically handles categorical variables):

    # Correlation matrix of numeric variables
    cors <- corr(dft[, 2:5], method = "pearson")
    head(cors, 3)
    #>                     Age Survived_TRUE  Sex_male  Pclass_1  Pclass_2  Pclass_3
    #> Age            1.000000     -0.077221  0.093254  0.348941  0.006954 -0.312271
    #> Survived_TRUE -0.077221      1.000000 -0.543351  0.285904  0.093349 -0.322308
    #> Sex_male       0.093254     -0.543351  1.000000 -0.098013 -0.064746  0.137143

### Correlate One Variable with All Others

    # Which variables correlate most with Survival?
    corr_var(dft, Survived, top = 10)

### Cross-Correlations

Find the strongest correlations across the entire dataset:

    # Top cross-correlations
    corr_cross(dft[, 2:6], top = 8)

## Data Transformation

### Categorical Reduction

Reduce categories in high-cardinality variables:

    # Reduce ticket categories (keep top 5, group rest as "other")
    dft_reduced <- categ_reducer(dft, Ticket, top = 5)
    freqs(dft_reduced, Ticket, top = 10)
    #> # A tibble: 6 × 5
    #>   Ticket       n     p order  pcum
    #>   <chr>    <int> <dbl> <int> <dbl>
    #> 1 other      858 96.3      1  96.3
    #> 2 1601         7  0.79     2  97.1
    #> 3 347082       7  0.79     3  97.9
    #> 4 CA. 2343     7  0.79     4  98.7
    #> 5 3101295      6  0.67     5  99.3
    #> 6 347088       6  0.67     6 100.

### Normalization

Normalize numeric variables to [0, 1] range:

    # Normalize age
    dft$Age_norm <- normalize(dft$Age)
    head(dft[, c("Age", "Age_norm")], 5)
    #>   Age Age_norm
    #> 1  22       NA
    #> 2  38       NA
    #> 3  26       NA
    #> 4  35       NA
    #> 5  35       NA

### One-Hot Encoding

Convert categorical variables to binary columns:

    # One-hot encode passenger class
    dft_encoded <- ohse(dft[, c("Pclass", "Survived")], limit = 5)
    colnames(dft_encoded)
    #> [1] "Survived_TRUE" "Pclass_1"      "Pclass_2"

## Date Manipulation

Create date features for time series analysis:

    # Create sample dates
    dates <- seq(as.Date("2024-01-01"), as.Date("2024-12-31"), by = "day")

    # Extract year-month
    ym <- year_month(dates[1:5])
    ym
    #> [1] "2024-01" "2024-01" "2024-01" "2024-01" "2024-01"

    # Extract year-quarter
    yq <- year_quarter(dates[1:5])
    yq
    #> [1] "2024-Q1" "2024-Q1" "2024-Q1" "2024-Q1" "2024-Q1"

    # Cut dates into quarters
    quarters <- date_cuts(dates[c(1, 100, 200, 300)], type = "Q")
    quarters
    #> [1] "Q1" "Q2" "Q3" "Q4"

## Visualization with theme_lares

### Custom ggplot2 Theme

`lares` includes a clean, professional theme:

    library(ggplot2)

    ggplot(dft, aes(x = Age, y = Fare * 1000, color = Survived)) +
      geom_point(alpha = 0.6) +
      labs(title = "Age vs Fare by Survival") +
      # Customize theme with several available options
      theme_lares(legend = "top", grid = "Yy", pal = 2, background = "#f2f2f2") +
      # Customize axis scales to look nicer
      scale_y_abbr()

### Distribution Plots

Visualize distributions quickly:

    # Analyze Fare distribution
    distr(dft, Fare, breaks = 20)

### Number Formatting

Format numbers for better readability:

    # Format large numbers
    formatNum(c(1234567, 987654.321), decimals = 2)
    #> [1] "1,234,567" "987,654.3"

    # Abbreviate numbers
    num_abbr(c(1500, 2500000, 1.5e9))
    #> [1] "1.5K" "2.5M" "1.5B"

    # Convert abbreviations back to numbers
    num_abbr(c("1.5K", "2.5M", "1.5B"), numeric = TRUE)
    #> [1] 1.5e+03 2.5e+06 1.5e+09

### Custom Scales

Use lares scales for better axis formatting:

    df_summary <- dft %>%
      group_by(Pclass) %>%
      summarize(avg_fare = mean(Fare, na.rm = TRUE), .groups = "drop")

    ggplot(df_summary, aes(x = factor(Pclass), y = avg_fare)) +
      geom_col(fill = "#00B1DA") +
      labs(title = "Average Fare by Class", x = "Class", y = NULL) +
      scale_y_dollar() + # Format as currency
      theme_lares()

## Text and Vector Utilities

### Vector to Text

Convert vectors to readable text:

    # Simple comma-separated
    vector2text(c("apple", "banana", "cherry"))
    #> [1] "'apple', 'banana', 'cherry'"

    # With "and" before last item
    vector2text(c("red", "green", "blue"), and = "and")
    #> [1] "red, green, and blue"

    # Shorter alias
    v2t(LETTERS[1:5])
    #> [1] "'A', 'B', 'C', 'D', 'E'"

## Putting It All Together

Here’s a complete analysis workflow:

    library(dplyr)

    # 1. Load and prepare data
    data(dft)

    # 2. Clean and transform
    dft_clean <- dft %>%
      mutate(Age_Group = cut(Age,
        breaks = c(0, 18, 35, 60, 100),
        labels = c("Child", "Young", "Adult", "Senior")
      ))

    # 3. Analyze frequencies
    freqs(dft_clean, Age_Group, Survived, plot = TRUE)

    # 4. Check correlations
    corr_var(dft_clean, Survived_TRUE, top = 8, max_pvalue = 0.05)

## Further Reading

### Package Resources

- Package documentation: https://laresbernardo.github.io/lares/
- GitHub repository: https://github.com/laresbernardo/lares
- Report issues: https://github.com/laresbernardo/lares/issues

### Blog Posts & Tutorials

- Find Insights with Ranked Cross-Correlations: DataScience+
- Visualize Monthly Income Distribution and Spend Curve: DataScience+
- All lares articles: Author page on DataScience+

## Next Steps

- Explore machine learning with `h2o_automl()` (see Machine Learning vignette)
- Learn about API integrations with ChatGPT and Gemini (see API Integrations vignette)
- Check individual function documentation: `?freqs`, `?corr`, `?theme_lares`