Meta-Analysis using R-Project-based Metafor, and Online calculator (R-Project-Shiny-based)

This online calculator has been developed using R-Project (4.4.1), RStudio 2025.05.0 Build 496. It is made using packages, including shiny, shinydashboard, metafor, readr, DT, ggplot2, plotly, broom, and rmarkdown..

Check my other online advanced statistical tools here:

1. Structural Equation Modeling using Lavaan package in R - https://jeepakistan.blogspot.com/2025/07/structural-equation-modeling-sem-using.html

2. Meta-analysis using Odds Ratio (OR) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/06/meta-analysis-using-odds-ratio-or-in.html

3. Meta-analysis using Cohen's d in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-cohens-d-in.html

4. Meta-analysis using Correlation (r) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-correlation-r-in.html

Day 8: 30-days to learn rgl, plotly, and gganimate - Creating Animated Plots with gganimate and Exporting Frames to Plotly

 

Step 1: Set Up Your Environment

1. Install Required Packages: Ensure you have the following libraries installed:

install.packages(c("gganimate", "ggplot2", "plotly"))

2. Load Libraries:

library(gganimate)

library(ggplot2)

library(plotly)

---

Step 2: Prepare Your Dataset

1. Use a time-based dataset or create one for practice. For example:

data <- data.frame(

  year = rep(2000:2010, each = 3),

  category = rep(c("A", "B", "C"), times = 11),

  value = runif(33, 10, 100)

)

2. Preview your dataset:

head(data)

---

Step 3: Create a Static ggplot

1. Start by plotting the data without animation:

p <- ggplot(data, aes(x = year, y = value, color = category)) +

  geom_line(aes(group = category)) +

  theme_minimal() +

  labs(title = "Value Trends Over Time", x = "Year", y = "Value")

2. Print the plot to ensure correctness:

print(p)

---

Step 4: Animate the Plot with gganimate

1. Add animation to visualize trends over time:

animated_plot <- p +

  transition_time(year) +

  ease_aes('linear') +

  labs(subtitle = "Year: {frame_time}")

2. Render the animation in your RStudio viewer:

animate(animated_plot, nframes = 100, fps = 10)

3. Save the animation as a GIF (optional):

anim_save("animated_plot.gif", animation = animated_plot)

Step 5: Export Frames to Plotly

Here’s a corrected version of how to export frames to Plotly:

---

1. Add a Frame Column: Add a frame column to the dataset for use in plot_ly():

data$frame <- data$year

2. Create a Plotly Animation: Use plot_ly() to build the animated visualization. Explicitly set the trace type to "scatter" and specify the mode:

plotly_animation <- plot_ly(

  data = data,

  x = ~year,

  y = ~value,

  color = ~category,

  frame = ~frame,

  type = "scatter",

  mode = "lines+markers"

) %>%

  layout(

    title = "Interactive Animation with Plotly",

    xaxis = list(title = "Year"),

    yaxis = list(title = "Value")

  )

3. Render the Plotly Animation: Display the interactive animated plot:

plotly_animation

Day 7: 30-days to learn rgl, plotly, and gganimate - Combining gganimate and Plotly for added interactivity

 

Step 1: Install Required Libraries

Ensure you have the necessary libraries installed and loaded:

install.packages("ggplot2")

install.packages("gganimate")

install.packages("plotly")

library(ggplot2)

library(gganimate)

library(plotly)

---

Step 2: Create an Animated Plot with gganimate

We’ll first create a simple animated scatter plot showing the movement of points over time.

1. Prepare the Data:

# Sample data for animation

set.seed(42)

data <- data.frame(

  x = rnorm(50),

  y = rnorm(50),

  time = rep(1:5, each = 10),

  group = rep(letters[1:10], times = 5)

)

2. Create the Animated Plot:

# Animated scatter plot with gganimate

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point(size = 4) +

  labs(title = 'Time: {frame_time}', x = 'X-Axis', y = 'Y-Axis') +

  transition_time(time) +

  ease_aes('linear')

# Save the animation

anim <- animate(p, nframes = 100, fps = 10, renderer = gifski_renderer())

---

Step 3: Export Animation Frames

Convert the gganimate frames into individual images that can be used in plotly.

1. Save Frames:

anim_save("gganimate_frames.gif", animation = anim)

Alternatively, save the individual frames as PNG files:

frame_images <- animate(p, nframes = 100, fps = 10, renderer = file_renderer(dir = "frames", prefix = "frame", overwrite = TRUE))

---

Step 4: Integrate with plotly for Interactivity

1. Load Images into plotly: Use plotly to create an interactive slider for the frames.

library(magick)

# Load the GIF

frames_gif <- image_read("gganimate_frames.gif")

# Extract individual frames

frames_list <- lapply(seq_len(length(frames_gif)), function(i) {
  frame <- image_write(frames_gif[i], format = "png") # Convert frame to PNG format
  list(source = base64enc::dataURI(frame, mime = "image/png")) # Encode as Base64 URI
})

# Create the interactive plot

# Create the interactive plot using plotly
plotly_animation <- plot_ly() %>%
  layout(
    sliders = list(list(
      steps = lapply(seq_along(frames_list), function(i) {
        list(
          method = "restyle",
          args = list("images[0].source", frames_list[[i]]$source),
          label = paste("Frame", i)
        )
      })
    ))
  )

# Add the initial image
plotly_animation <- plotly_animation %>%
  add_image(
    source = frames_list[[1]]$source,
    x = 0, y = 0,
    xanchor = "center", yanchor = "middle"
  )

---

Step 5: Test the Integration

1. Run the above code to see your animated and interactive plot in the RStudio Viewer or browser.
2. Experiment with the hover, zoom, and pan features provided by plotly.

---

Step 6: Reflect and Experiment

• Experiment with more complex gganimate animations, such as using transition_states() or view_follow().
• Add custom interactivity in plotly, such as hover text and annotations.

N.B: The code may have some errors. 

Day 6: 30-days to learn rgl, plotly, and gganimate - Create an interactive 3D plot with rgl that uses various viewpoints or angles to simulate rotation

Step 1: Install and Load the rgl Package

Ensure that you have the rgl package installed and loaded in your R environment.

# Install rgl if not already installed

if (!require("rgl")) install.packages("rgl")

# Load the rgl package

library(rgl)

---

Step 2: Prepare a Dataset

For demonstration purposes, create or use a sample 3D dataset (e.g., random points in 3D space).

# Sample dataset with random 3D points

set.seed(123)

n <- 100

x <- rnorm(n)

y <- rnorm(n)

z <- rnorm(n)

colors <- rainbow(n)

---

Step 3: Create a Basic 3D Plot

Use plot3d() to create a basic 3D scatter plot.

# Create a 3D scatter plot

plot3d(x, y, z, col = colors, size = 5, type = 's', xlab = "X-axis", ylab = "Y-axis", zlab = "Z-axis")

---

Step 4: Enhance the Plot with Customization

Add customizations like grid lines, labels, and lighting for better visualization.

# Add grid lines

grid3d("x")

grid3d("y")

grid3d("z")

# Enhance lighting

light3d(specular = "white")

---

Step 5: Set Up Rotation Parameters

Define a sequence of angles to rotate the plot and simulate a continuous rotation.

# Define rotation parameters

angles <- seq(0, 360, by = 5) # 5-degree increments

---

Step 6: Rotate the Plot Programmatically

Use a loop to rotate the plot around a specific axis (e.g., the z-axis).

# Rotate around the z-axis

for (angle in angles) {

  view3d(theta = angle, phi = 30) # Rotate theta; phi is the vertical angle

  Sys.sleep(0.1)                 # Pause for 0.1 seconds for smooth transition

}

---

Step 7: Export the Visualization

Save the interactive 3D plot as an HTML widget or image file for sharing or further use.

# Save as an interactive HTML file

rglwidget() %>% htmlwidgets::saveWidget("3D_plot_rotation.html")

# Save as a static image (PNG)

rgl.snapshot("3D_plot_rotation.png")

---

Step 8: Test and Adjust

• Experiment with different rotation axes (x, y, or z) by modifying view3d() parameters.
• Adjust the speed of rotation using Sys.sleep().

---

Outcome

By the end of this exercise, you will have an interactive 3D plot that smoothly rotates, providing a dynamic visualization of the data. You can integrate this plot with tools like plotly or embed it into web presentations for a polished output.