### Install robvis Package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Install the robvis package from GitHub. Ensure you have devtools installed.

```r
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("mcguinlu/robvis")
```

--------------------------------

### Load example data

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Load the example dataset included with the robvis package. This dataset can be used to follow along with the package's tutorials and examples.

```r
data(robvis)
```

--------------------------------

### Install robvis package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Install the robvis package from GitHub. This is the first step before using any of the package's functionalities.

```r
remotes::install_github("mcguinlu/robvis")
```

--------------------------------

### Load Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Load the built-in example dataset 'robvis_data' for demonstration purposes. This dataset contains sample robotic vision data.

```r
data(robvis_data)
```

--------------------------------

### Install robvis Package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Install the robvis package from Bioconductor. This is the first step before using any of its functionalities.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# BiocManager::install(version = "3.18") # Uncomment to set BiocManager version

BiocManager::install("robvis")
```

--------------------------------

### Structure of data_rob1 Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Illustrates the structure of the `data_rob1` example dataset, a generic example for ROB1 assessments. This dataset uses full domain names instead of abbreviated codes like D1, D2.

```r
# Structure: 9 obs. of 9 variables
# Columns are full domain names instead of D1, D2, etc.:
# Study
# Random.sequence.generation
```

--------------------------------

### Minimal robvis Example

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/README.md

Demonstrates the basic usage of robvis by creating a summary plot and a traffic light plot using built-in example data, and saving the summary plot to a file.

```r
library(robvis)

# Use built-in example data for ROB2.0
data <- data_rob2

# Create summary plot
summary_plot <- rob_summary(data, tool = "ROB2")
plot(summary_plot)

# Create traffic light plot
traffic_plot <- rob_traffic_light(data, tool = "ROB2")
plot(traffic_plot)

# Save to file
rob_save(summary_plot, file = "rob_summary.png")
```

--------------------------------

### Install robvis package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Install the robvis package from Bioconductor. This is the first step before using any of the package's functionalities.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("robvis")
```

--------------------------------

### Install robvis package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Install the robvis package from GitHub using the devtools package. This is the primary method for obtaining the latest version.

```r
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

# Install robvis from GitHub
devtools::install_github("mcguinlu/robvis")
```

--------------------------------

### Load robvis and robyn

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Load the robvis package and the robyn object for visualization. Ensure robyn is installed.

```r
library(robvis)
library(robyn)
robyn_object <- robyn_object
```

--------------------------------

### Structure of data_quips Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Shows the structure of the `data_quips` example dataset for QUIPS assessments of prognostic factor studies. It contains 6 domains, Study, and Overall columns.

```r
# Structure: 12 obs. of 8 variables
# Domains (D1-D6):
# D1: Bias due to participation
# D2: Bias due to attrition
# D3: Bias due to prognostic factor measurement
# D4: Bias due to outcome measurement
# D5: Bias due to confounding
# D6: Bias in statistical analysis and reporting
```

--------------------------------

### Load Example RNA-Seq Data

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Load the example RNA-Seq dataset included with the robvis package. This dataset can be used for practicing the package's functions.

```r
data(robvis.data)
```

--------------------------------

### Structure of data_quadas Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Presents the structure of the `data_quadas` example dataset for QUADAS-2 assessments of diagnostic accuracy studies. It includes 4 domains plus Study and Overall columns.

```r
# Structure: 12 obs. of 6 variables
# Domains (D1-D4):
# D1: Patient selection
# D2: Index test
# D3: Reference standard
# D4: Flow and timing
```

--------------------------------

### Load robvis package

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Before using robvis, ensure the package is installed and loaded into your R session. This is a standard first step for any R package.

```r
library(robvis)
```

--------------------------------

### Install robvis Development Version

Source: https://github.com/mcguinlu/robvis/blob/main/README.md

Install the development version of the robvis package from GitHub using the devtools package. This version includes new functionality and bug fixes.

```r
install.packages("devtools")
devtools::install_github("mcguinlu/robvis")
```

--------------------------------

### Structure of data_rob2_cluster Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Provides the structure for the `data_rob2_cluster` example dataset, a variant of ROB2.0 for cluster-randomized trials. It highlights the inclusion of an additional domain (D1b) related to timing.

```r
# Structure: 9 studies with 8 columns (includes D1b)
# D1: Bias arising from the randomization process
# D1b: Bias from timing of identification and recruitment
# D2-D5: Same as standard ROB2.0
```

--------------------------------

### Structure of data_robins_e Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Outlines the structure of the `data_robins_e` example dataset for ROBINS-E assessments of non-randomized exposure studies. It comprises 7 domains, Study, and Overall columns.

```r
# Structure: 10 obs. of 9 variables (7 domains + Study + Overall)
# Domains (D1-D7):
# D1: Bias due to confounding
# D2: Bias arising from measurement of the exposure
# D3: Bias in selection of participants into the study
# D4: Bias due to post-exposure interventions
# D5: Bias due to missing data
# D6: Bias arising from measurement of the outcome
# D7: Bias in selection of the reported result
```

--------------------------------

### AJAX Helper Functions (get, post)

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Provides convenience methods for making GET and POST requests. These functions simplify common AJAX scenarios by abstracting away some of the default configurations.

```JavaScript
S.getJSON=function(e,t,n){return S.get(e,t,n,"json")},S.getScript=function(e,t){return S.get(e,void 0,t,"script")}},S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}})
```

--------------------------------

### Structure of data_rob2 Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Displays the structure of the `data_rob2` example dataset, which is used for ROB2.0 assessments of randomized controlled trials. It shows the number of observations, variables, and the data types of each column.

```r
# Structure
str(data_rob2)
# 'data.frame': 9 obs. of 7 variables:
#  $ Study  : chr "Study 1" "Study 2" ...
#  $ D1     : chr "Low" "Low" ...
#  $ D2     : chr "Low" "Low" ...
#  $ D3     : chr "Low" "Low" ...
#  $ D4     : chr "Low" "Low" ...
#  $ D5     : chr "Low" "Low" ...
#  $ Overall: chr "Low" "Low" ...

# Domains
# D1: Bias arising from the randomization process
# D2: Bias due to deviations from intended interventions
# D3: Bias due to missing outcome data
# D4: Bias in measurement of the outcome
# D5: Bias in selection of the reported result
```

--------------------------------

### Create a color scheme from a palette name

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

This example shows how to create a color scheme by specifying a predefined palette name. Robvis supports various standard palettes, simplifying the process of applying well-established color sets.

```R
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
colour("ESIAESIAESIAESIAESIAESIAESIAESIA")
```

--------------------------------

### Structure of data_robins_i Example Dataset

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Details the structure of the `data_robins_i` example dataset, used for ROBINS-I assessments of non-randomized intervention studies. It includes 7 domains plus Study and Overall columns.

```r
# Structure: 10 obs. of 9 variables (7 domains + Study + Overall)
# Domains (D1-D7):
# D1: Bias due to confounding
# D2: Bias due to selection of participants
# D3: Bias in classification of interventions
# D4: Bias due to deviations from intended interventions
# D5: Bias due to missing data
# D6: Bias in measurement of outcomes
# D7: Bias in selection of the reported result
```

--------------------------------

### rob_dummy()

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/README.md

Generates example risk-of-bias dataframes for testing and demonstration purposes.

```APIDOC
## rob_dummy()

### Description
Generates example risk-of-bias dataframes for testing and demonstration purposes.

### Method
`rob_dummy(n, tool = "ROB2", study = TRUE)`

### Parameters
- **n** (numeric) - The number of studies to generate data for.
- **tool** (string) - The risk-of-bias assessment tool to generate data for. Defaults to "ROB2".
- **study** (boolean) - Whether to include study identifiers. Defaults to TRUE.

### Response
- Returns a data.frame with dummy risk-of-bias assessment data.
```

--------------------------------

### Initialize Google Analytics Data Layer and gtag

Source: https://github.com/mcguinlu/robvis/blob/main/robvisapp/google-analytics.html

Sets up the Google Analytics data layer and initializes the gtag function for sending data. This is a standard setup for Google Analytics.

```javascript
window.dataLayer = window.dataLayer || [];
function gtag(){
  dataLayer.push(arguments);
}

gtag('js', new Date());
gtag('config', 'UA-139874649-1');
```

--------------------------------

### Basic rob_summary() Usage with ROB2.0 Data

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/api-reference/rob_summary.md

Demonstrates basic usage of the rob_summary function with the ROB2 tool using built-in example data. The resulting plot can be displayed using the plot() function.

```r
library(robvis)

# Using built-in example data
summary_plot <- rob_summary(data = data_rob2, tool = "ROB2")
plot(summary_plot)
```

--------------------------------

### Basic Workflow for Appending Weights

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/api-reference/rob_append_weights.md

Demonstrates the complete process from preparing meta-analysis data to visualizing a weighted ROB plot. Ensure the 'robvis', 'metafor', and 'metadat' libraries are loaded.

```r
library(robvis)
library(metafor)
library(metadat)

# Step 1: Prepare data and calculate effect sizes
dat <- metadat::dat.bcg[c(1:9), ]
dat <- metafor::escalc(
  measure = "RR",
  ai = tpos,
  bi = tneg,
  ci = cpos,
  di = cneg,
  data = dat,
  slab = paste(author, year)
)

# Step 2: Fit meta-analysis model
res <- metafor::rma(yi, vi, data = dat)

# Step 3: Create risk-of-bias dataset (7 columns: Study + 5 domains + Overall)
data_rob2$Study <- paste(dat$author, dat$year)
rob_data_subset <- data_rob2[1:9, 1:7]

# Step 4: Append weights from meta-analysis
rob_weighted_data <- rob_append_weights(rob_data_subset, res)

# Step 5: Create weighted barplot
summary_plot <- rob_summary(rob_weighted_data, tool = "ROB2", weighted = TRUE)
plot(summary_plot)
```

--------------------------------

### Creating a data.frame from scratch

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

This snippet shows how to manually create a data.frame for ROB assessments, specifying study names and judgments for different domains.

```r
rob_data <- data.frame(
  Study = c("Author 2020", "Author 2021"),
  D1 = c("Low", "High"),
  D2 = c("Low", "Some concerns"),
  D3 = c("Low", "Low"),
  D4 = c("Low", "Low"),
  D5 = c("Low", "High"),
  Overall = c("Low", "High"),
  stringsAsFactors = FALSE
)
```

--------------------------------

### Install robvis CRAN Version

Source: https://github.com/mcguinlu/robvis/blob/main/README.md

Install the stable version of the robvis package from CRAN. Use this command if you prefer the version available on the Comprehensive R Archive Network.

```r
install.packages("robvis")
```

--------------------------------

### Use Predefined Color Scheme

Source: https://github.com/mcguinlu/robvis/blob/main/README.md

Demonstrates how to use the 'colourblind' predefined color scheme in the rob_summary function.

```r
rob_summary(data = data_rob2,
            tool = "ROB2",
            colour = "colourblind")
```

--------------------------------

### Load Libraries and Prepare Data

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Loads necessary libraries (robvis, metafor, dplyr) and prepares the BCG dataset for meta-analysis, combining it with dummy ROB2 data.

```R
library(robvis)
library(metafor)
library(dplyr)

# Define your studies, using the BCG dataset included in the metadat package
dat_bcg <- metadat::dat.bcg

glimpse(dat_bcg)

# Create some example data for ROB2 using rob_dummy(), and add it to the BCG
# data.
# We don't need a "Study" column for this example, so we set `study = FALSE`

dat_rob <- rob_dummy(13, 
                     "ROB2",
                     study = FALSE)

dat_analysis <- cbind(dat_bcg, dat_rob)

glimpse(dat_analysis)
```

--------------------------------

### Basic Data Import and Visualization

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Demonstrates how to load data into robvis and create a basic forest plot. Ensure your data is in the correct format.

```r
library(robvis)

data("robvis_data")

robvis(robvis_data, x = "riskofbias", y = "comparison", colour = "overallrisk",
         legend.title = "Overall Risk", x.title = "Risk of Bias Assessment",
         y.title = "Comparison", colour.title = "Overall Risk",
         plot.title = "Forest Plot of Risk of Bias",
         legend.position = "right", x.rotate = 45, y.rotate = 0,
         text.size = 3.5, point.size = 2.5, line.size = 0.5,
         x.range = c(0, 1), y.range = c(0, 1),
         legend.bottom = FALSE, legend.right = TRUE,
         plot.type = "forestplot")
```

--------------------------------

### Use Dummy Data for Visualization Testing

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/api-reference/rob_dummy.md

Demonstrates generating dummy data for the QUADAS-2 tool and then using it to create and plot summary and traffic light visualizations. This is ideal for validating visualization functions.

```r
# Generate and immediately visualize
dummy <- rob_dummy(n = 20, tool = "QUADAS-2")

# Create summary plot
summary_plot <- rob_summary(dummy, tool = "QUADAS-2")
plot(summary_plot)

# Create traffic light plot
traffic_plot <- rob_traffic_light(dummy, tool = "QUADAS-2")
plot(traffic_plot)
```

--------------------------------

### Creating a Sample Metadata Data Frame

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Construct a sample metadata data frame that can be used with `robvis` functions. This data frame should contain columns for sample IDs and any grouping or condition variables.

```r
sample_metadata <- data.frame(
  sample_id = c("Sample1", "Sample2", "Sample3", "Sample4"),
  group = c("A", "A", "B", "B"),
  condition = c("Control", "Treated", "Control", "Treated")
)
```

--------------------------------

### Build Tabsets

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Initializes tabset functionality for the page. This is typically called when the document is ready.

```javascript
$(document).ready(function () {
  window.buildTabsets("TOC");
});
```

--------------------------------

### Basic robvis plot

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/Introduction_to_robvis.html

Generates a standard risk of bias plot. Ensure the 'robvis' package is installed and loaded.

```R
library(robvis)
robvis(systematic_review = "robvis_data.csv",
         legend_position = "bottom",
         colors = c("#4E79A7", "#F28E2B", "#E15759", "#76B7B2", "#59A14F", "#EDC948", "#B07AA1", "#FF9DA7", "#9C755F", "#868686"))
```

--------------------------------

### Get CSS Property Value

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Retrieves the computed value of a CSS property for an element. It handles different property names and units.

```javascript
S.css=function(e,t,n,r){
  var i,o,a,s=X(t);
  return Xe.test(t)||
  (t=ze(s)),
  (a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&
  (i=a.get(e,!0,n)),
  void 0===i&&
  (i=We(e,t,r)),
  "normal"===i&&
  t in Ge&&
  (i=Ge[t]),
  ""===n||
  n?(
  (o=parseFloat(i),!0===n||
  isFinite(o)?o||0:
  i)):
  i
}
```

--------------------------------

### Configure Generic Tool Parameters

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/configuration.md

Use this snippet to customize domain labels and titles when the 'Generic' tool is selected. Ensure the number of domain shortcodes matches the number of domains in your data.

```r
rob_traffic_light(
  data = custom_data,
  tool = "Generic",
  domain_shortcodes = c("Design", "Methods", "Analysis"),
  x_title = "Methodological Domain",
  y_title = "First Author and Year",
  judgement_title = "Risk Assessment",
  judgement_labels = c(
    c = "Critical Concern",
    h = "High Concern",
    s = "Moderate Concern",
    l = "Low Concern",
    n = "Insufficient Data",
    x = "Not Applicable"
  )
)
```

--------------------------------

### Get Element Dimension with Box Sizing

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Retrieves the computed dimension (height or width) of an element, correctly accounting for box-sizing, padding, and borders.

```javascript
function Je(e,t,n){
  var r=Re(e),
  i=(!y.boxSizingReliable()||
  n)&&"border-box"===S.css(e,"boxSizing",!1,r),
  o=i;
  a=We(e,t,r);
  s="offset"+t[0].toUpperCase()+t.slice(1);
  if(Pe.test(a)){
    if(!n)return a;
    a="auto"
  }
  return (!y.boxSizingReliable()&&i||
  !y.reliableTrDimensions()&&
  A(e,"tr")||
  "auto"===a||
  !parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&
  (e.getClientRects().length&&
  (i="border-box"===S.css(e,"boxSizing",!1,r),
  (o=s in e)&&(a=e[s])))
  ;
  (a=parseFloat(a)||0)+Qe(e,t,n||
  (i?"border":"content"),o,r,a)+"px"
}
```

--------------------------------

### Meta-Analysis with Different Models

Source: https://github.com/mcguinlu/robvis/blob/main/vignettes/articles/metafor.html

Demonstrates fitting meta-analysis models using different estimation methods.

```r
res.ml <- rma(yi, vi, method = "ML", data = dat.bcg)
res.reml <- rma(yi, vi, method = "REML", data = dat.bcg)
res.dl <- rma(yi, vi, method = "DL", data = dat.bcg)
res.pm <- rma(yi, vi, method = "PM", data = dat.bcg)
res.ie <- rma(yi, vi, method = "IE", data = dat.bcg)
res.is <- rma(yi, vi, method = "IS", data = dat.bcg)
```

--------------------------------

### Preparing weighted data with metafor

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

This pattern demonstrates appending weights from a meta-analysis result (rma object) to the ROB data and verifying that the weights sum to approximately 100.

```r
# Append weights from meta-analysis
library(metafor)
res <- metafor::rma(yi, vi, data = dat)
rob_weighted <- rob_append_weights(rob_data, res)

# Verify weights sum to 100
sum(rob_weighted$Weight)  # Should be approximately 100
```

--------------------------------

### Type Checking for rma class

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

Robvis performs implicit type validation. This example shows a check to ensure a result object is of class 'rma'.

```r
# Function expects rma class
if (!("rma" %in% class(res))) {
  stop("Result objects need to be of class 'meta'")
}
```

--------------------------------

### Type Checking for numeric column

Source: https://github.com/mcguinlu/robvis/blob/main/_autodocs/types.md

This example demonstrates a check to ensure a specific column in a data frame is numeric, which is required for operations like weight calculations.

```r
# Function expects numeric column for weights
if (is.numeric(data[2, ncol(data)]) == FALSE) {
  stop("Error. The final column does not seem to contain numeric values")
}
```