The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Live Code Execution with typeRun

library(typeR)

Introduction

typeRun() is an enhanced version of typeR() that not only simulates typing animation but also executes your R code in real-time. This makes it perfect for:

Basic Usage

Simple Script Execution

The most basic usage is to run an R script with typing animation and live execution:

# Create a simple script
cat("# Data Analysis Demo
x <- 1:10
mean(x)
sum(x)
", file = "demo.R")

# Type and execute it
typeRun("demo.R")

What you’ll see:

typeR::typeRun demo
typeR::typeRun demo

Comparison: typeR vs typeRun

# typeR: Just displays, doesn't execute
typeR("demo.R")
# Output: Shows the code typing out (no results)

# typeRun: Types AND executes
typeRun("demo.R")
# Output: Shows code typing + execution results

Customizing the Experience

Control Typing Speed

# Slower typing for dramatic effect
typeRun("demo.R", delay = 0.1, jitter = 0.02)

# Faster typing for quick demos
typeRun("demo.R", delay = 0.02, jitter = 0.005)

Parameters:

Limit Output Length

For long outputs, control how much is displayed:

cat("
# Long vector
long_vec <- 1:1000
long_vec

# Large data frame
big_df <- mtcars[rep(1:32, 10), ]
big_df
", file = "long_output.R")

# Show only first 5 elements/rows
typeRun("long_output.R", max_print = 5)

Working with Models

Linear Models

cat("
# Linear regression
model <- lm(mpg ~ hp + wt, data = mtcars)
summary(model)
", file = "model_demo.R")

typeRun("model_demo.R")

What happens:

GLM and Other Models

typeRun() handles all model types intelligently:

cat("
# Logistic regression
glm_model <- glm(am ~ hp + wt, data = mtcars, family = binomial)
summary(glm_model)

# Poisson regression
pois_model <- glm(carb ~ mpg, data = mtcars, family = poisson)
summary(pois_model)
", file = "glm_demo.R")

typeRun("glm_demo.R")

Statistical Tests

The typeRun() function properly displays statistical test results using their formatted print methods:

library(typeR)

# Create a test script
cat('x <- rnorm(50, mean = 20, sd = 3)
tt <- t.test(x, alternative = "two.sided", conf.level = 0.95)
tt
', file = "test_ttest.R")

# Run it
typeRun("test_ttest.R", delay = 0.01)

This will display the t-test results in their proper formatted output:

    One Sample t-test

data:  x
t = 45.185, df = 49, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 18.68753 20.42711
sample estimates:
mean of x 
 19.55732

The same applies to other statistical objects like anova(), aov(), lm(), etc.

Interactive Control

Pause and Resume

During execution, you have full control:

  1. Press ESC (or Ctrl+C) to pause
  2. You’ll see: Enter choice (1 resume or 2 stop):
  3. Enter 1 to continue from where you paused
  4. Enter 2 to stop completely
typeRun("long_script.R")
# Press ESC while running
# Enter 1 to resume
# Or enter 2 to stop

This is perfect for:

Working with R Markdown and Quarto

R Markdown Files

typeRun() intelligently handles .Rmd files:

# Create an R Markdown example
typeRun("report.Rmd")

Behavior:

Quarto Documents

Works identically with .qmd files:

typeRun("analysis.qmd")

Advanced Features

Custom Evaluation Environment

Run code in an isolated environment to keep your workspace clean:

# Create custom environment
my_env <- new.env()

# Run in that environment
typeRun("demo.R", envir = my_env)

# Check what was created
ls(my_env)
ls()  # Your global environment is unchanged

Suppressing Library Messages

Package loading messages are automatically suppressed:

cat("
library(ggplot2)  # No startup message shown
library(dplyr)    # Clean output

# But code works normally
mtcars %>% head()
", file = "packages.R")

typeRun("packages.R")
# Shows only the actual results, not package messages

Output Handling Details

What Executes Silently

These don’t produce output (cleaner demos):

cat("
# Assignments - silent
x <- 1:10
y <- mean(x)

# Plots - execute but don't clutter console
plot(x, y)
hist(x)

# Package loading - no startup messages
library(stats)
", file = "silent.R")

typeRun("silent.R")

What Shows Output

cat("
# Direct values
1:10          # Shows the vector

# Function calls
mean(1:10)    # Shows: [1] 5.5

# Print statements
print('Hello') # Shows: [1] 'Hello'

# Model summaries
summary(lm(mpg ~ hp, data = mtcars))  # Shows full summary
", file = "visible.R")

typeRun("visible.R")

Practical Examples

Teaching Linear Regression

cat("
# Load data
data(mtcars)
head(mtcars, 3)

# Visualize relationship
plot(mtcars$hp, mtcars$mpg,
     xlab = 'Horsepower',
     ylab = 'Miles per Gallon',
     main = 'MPG vs Horsepower')

# Fit model
model <- lm(mpg ~ hp, data = mtcars)
summary(model)

# Add regression line
abline(model, col = 'red', lwd = 2)

# Predictions
new_data <- data.frame(hp = c(100, 150, 200))
predict(model, new_data)
", file = "teaching_demo.R")

typeRun("teaching_demo.R", delay = 0.08)

Data Analysis Workflow

cat("
# 1. Load and explore
data <- iris
str(data)

# 2. Summary statistics
summary(data)

# 3. Visualization
boxplot(Sepal.Length ~ Species, data = data,
        main = 'Sepal Length by Species',
        col = c('lightblue', 'lightgreen', 'pink'))

# 4. Statistical test
aov_result <- aov(Sepal.Length ~ Species, data = data)
summary(aov_result)

# 5. Post-hoc test
TukeyHSD(aov_result)
", file = "analysis_demo.R")

typeRun("analysis_demo.R", delay = 0.06, max_print = 8)

GLM Demonstration

cat("
# Binary outcome: Manual transmission (am)
# Predictors: HP and weight

# Fit logistic regression
logit_model <- glm(am ~ hp + wt, 
                   data = mtcars, 
                   family = binomial(link = 'logit'))

# Model summary
summary(logit_model)

# Odds ratios
exp(coef(logit_model))

# Predicted probabilities
mtcars$pred_prob <- predict(logit_model, type = 'response')
head(mtcars[, c('am', 'hp', 'wt', 'pred_prob')])
", file = "glm_example.R")

typeRun("glm_example.R", max_print = 6)

Tips and Best Practices

1. Prepare Your Script

Before presenting:

# Test your script normally first
source("demo.R")

# Then test with typeRun
typeRun("demo.R")

2. Timing Considerations

# For 5-minute demos: ~0.05 delay
typeRun("demo.R", delay = 0.05)

# For dramatic reveals: ~0.1 delay
typeRun("demo.R", delay = 0.1)

# For quick reviews: ~0.02 delay
typeRun("demo.R", delay = 0.02)

3. Output Management

# Lots of output? Limit it
typeRun("demo.R", max_print = 5)

# Models in script? They'll show nicely
# Plots in script? They'll render silently

4. Keep Scripts Focused

# Good: One concept per script
typeRun("01_data_loading.R")
typeRun("02_visualization.R")
typeRun("03_modeling.R")

# Less good: One giant script
typeRun("everything.R")  # Too long, hard to control

Troubleshooting

Script Not Found

# Error: The file does not exist
typeRun("missing.R")

# Solution: Check file path
file.exists("demo.R")
list.files(pattern = "\\.R$")

Unexpected Output

# If output looks wrong, test the script normally first
source("demo.R")

# Then try with typeRun
typeRun("demo.R")

Want to Stop Mid-Execution

# Press ESC, then enter 2
# Or press Ctrl+C (on some systems)

Comparison Table

Feature typeR() typeRun()
Typing animation ✅ ✅
Code execution ❌ ✅
Shows output ❌ ✅
Interactive pause/resume ❌ ✅
Output truncation ❌ ✅
Custom environment ❌ ✅
Model summary handling N/A ✅
Library message suppression N/A ✅

See Also

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.