### Import Libraries and Setup Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_pbmc.ipynb

Imports necessary Python libraries and creates a directory for example data if it doesn't exist. Sets a random seed for reproducibility.

```python
import os
import pandas as pd
import numpy as np
from scipy.io import mmread
import scipy.sparse as sp
import scanpy as sc
from cnmf import cNMF

if not os.path.exists('example_PBMC'):
    os.mkdir('example_PBMC')
    
np.random.seed(14)
```

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

### Install and Load Package

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Installs a package if not already installed and then loads it. Use this for any package not included in base R.

```r
if (!require(package_name)) {
  install.packages("package_name")
}
library(package_name)
```

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

### Import Libraries and Setup

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_batcheffectcorrect_BaronEtAl.ipynb

Imports necessary Python libraries for data manipulation, plotting, and scNMF analysis. Sets up a random seed for reproducibility and creates a directory for example data if it doesn't exist.

```python
%matplotlib inline

import os
import pandas as pd
import numpy as np
from scipy.io import mmread
import scipy.sparse as sp
import matplotlib.pyplot as plt
from IPython.display import Image
import scanpy as sc
from cnmf import cNMF, Preprocess

import seaborn as sns

if not os.path.exists('example_islets'):
    os.mkdir('example_islets')
    
np.random.seed(14)

```

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

### List Files in cNMF Example Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_simulation.ipynb

Use this command to list all files within the example cNMF directory. This helps in understanding the structure and content of the simulation data.

```bash
! ls simulated_example_data/example_cNMF
```

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

### Install and Load R Packages

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Provides the standard R commands for installing a package from CRAN and then loading it into the current session for use.

```r
install.packages("package_name")
library(package_name)
```

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

### Download and Extract Example Data in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Downloads a tar.gz file containing the pbmc3k example dataset and extracts it to a specified directory. Ensures the directory exists before downloading.

```r
data_url <- 'https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz'
data_dir = './R_Example_Data/'

if (!dir.exists(data_dir)) {
  dir.create(data_dir, recursive = TRUE)
}

# Path where the tar.gz file will be saved
file_path <- file.path(data_dir, 'pbmc3k_filtered_gene_bc_matrices.tar.gz')

# Download the file
download.file(data_url, file_path, mode='wb')
```

```r
extract_command <- sprintf("tar -xzf %s -C %s", shQuote(file_path), shQuote(data_dir))
system(extract_command)

rm_command <- sprintf("rm %s", file_path)
system(rm_command)
```

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

### Download and Extract Example Data

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_batcheffectcorrect_BaronEtAl.ipynb

Downloads a compressed archive containing example islet data and extracts it. This step is necessary to obtain the data for subsequent analysis.

```bash
! wget https://storage.googleapis.com/sabeti-public/dkotliar/data/example_islets.tar.gz -O example_islets/example_islets.tar.gz
! tar -xzf example_islets/example_islets.tar.gz

```

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

### Import Libraries and Setup

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_pbmc_example_data.ipynb

Imports necessary libraries for data analysis and sets up the output directory and random seed.

```python
%matplotlib inline

import os
import pandas as pd
import numpy as np
from scipy.io import mmread
import scipy.sparse as sp
import matplotlib.pyplot as plt
from IPython.display import Image
import scanpy as sc
from cnmf import cNMF

if not os.path.exists('example_PBMC'):
    os.mkdir('example_PBMC')
    
np.random.seed(14)
```

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

### Download and Extract Example Data

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_simulation.ipynb

Downloads a compressed archive of example data from a specified URL and extracts its contents. The downloaded archive is removed after extraction.

```shell
! wget -O ./example_simulated_data.tar.gz https://storage.googleapis.com/sabeti-public/dkotliar/cNMF/example_data_20191024.tar.gz
! tar -zxvf ./example_simulated_data.tar.gz && rm ./example_simulated_data.tar.gz
```

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

### For Loop Example

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates a basic for loop for iterating over a sequence.

```r
for (i in 1:5) {
  print(paste("Iteration:", i))
}
```

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

### Getting Help

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Accesses the documentation for a specific function.

```r
?function_name
help(function_name)
```

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

### R: Rcpp Integration Example

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates basic integration with Rcpp for C++ code execution within R.

```r
# Example Rcpp function (assuming it's defined elsewhere)
# result <- cpp_function(input_data)
```

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

### Install Optional Dependencies for Batch Correction

Source: https://github.com/dylkot/cnmf/blob/main/README.md

Install harmonypy and scikit-misc if you plan to use the batch correction preprocessing feature of cNMF.

```bash
pip install harmonypy
pip install scikit-misc
```

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

### Move example data directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Moves the 'example_data' directory to 'simulated_example_data' using the 'mv' command. This is a shell command for file system operations.

```shell
! mv example_data simulated_example_data 
```

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

### Remove Example Data Files

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Removes specific files ('geneparams.npz' and 'cellparams.npz') from the './example_data/' directory. Use with caution.

```bash
! rm ./example_data/geneparams.npz
! rm ./example_data/cellparams.npz
```

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

### AJAX Setup for Script Content Type

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Sets up AJAX to accept and process JavaScript content, including defining converters for script-to-JSON conversion.

```javascript
S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\\b(?:java|ecma)script\\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")})
```

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

### Data Manipulation with dplyr

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates data filtering and selection using the dplyr package. Ensure dplyr is installed and loaded.

```r
library(dplyr)
filtered_data <- data %>% filter(x > 10) %>% select(y)
```

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

### Import Libraries for CNMF Analysis

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Imports necessary libraries for data manipulation, visualization, and CNMF analysis. Ensure these libraries are installed before running.

```python
%matplotlib inline
import sys
sys.path.append('..')
from cnmf import load_df_from_npz, get_highvar_genes
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn import preprocessing
from bhtsne import tsne
import palettable
import matplotlib.gridspec as gridspec
```

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

### Create output directory for simulation

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Create a directory named 'example_data' to store the simulation output. This ensures organized storage of generated data.

```bash
! mkdir ./example_data
```

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

### List Files in Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Lists the contents of the './example_data/' directory. Useful for verifying file creation or deletion.

```bash
! ls ./example_data/
```

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

### R Vignette Examples

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

A collection of R code snippets extracted from the vignette. These examples showcase various R functionalities and data manipulations.

```R
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
```

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

### Install cNMF with pip

Source: https://github.com/dylkot/cnmf/blob/main/README.md

Install the cNMF package using pip. Ensure you have Python 3.7 or higher and compatible versions of scikit-learn, scanpy, and AnnData.

```bash
pip install cnmf
```

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

### List Files in PBMC Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_pbmc.ipynb

Lists the contents of the ./example_PBMC directory, showing input data and CNMF output directories.

```bash
! ls ./example_PBMC
```

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

### Display cNMF Help Options

Source: https://github.com/dylkot/cnmf/blob/main/Stepwise_Guide.md

Run this command to see all available command-line options for cNMF.

```bash
cnmf --help
```

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

### Create a Scatter Plot

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Generates a scatter plot using ggplot2. Requires the ggplot2 package to be installed and loaded.

```r
library(ggplot2)
ggplot(data, aes(x = x, y = y)) + geom_point()
```

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

### Create Tar Archive of PBMC Results

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_pbmc.ipynb

Creates a gzipped tar archive of all files within the ./example_PBMC directory. This is useful for backing up or transferring results.

```bash
! tar -czvf example_pbmc_results_20250301.tar.gz ./example_PBMC/*
```

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

### Getting Element Position

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Calculates the position of the first element in the set of matched elements relative to its offset parent.

```javascript
S.fn.position=function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}}
```

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

### List Files in PBMC cNMF Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_pbmc.ipynb

Lists the contents of the ./example_PBMC/pbmc_cNMF directory, showing generated CNMF output files.

```bash
! ls ./example_PBMC/pbmc_cNMF
```

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

### Getting Element Offset

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Calculates the offset (position) of the first element in the set of matched elements relative to the document.

```javascript
S.fn.offset=function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0}
```

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

### Set up plotting environment

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_simulated_example_data.ipynb

Ensures plots are displayed inline within the notebook.

```python
%matplotlib inline
```

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

### Load and Inspect Data in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates loading a dataset into R and performing initial inspection. Ensure the file path is correct for your environment.

```r
library(dplyr)
library(readr)

# Load the dataset
data <- read_csv("path/to/your/data.csv")

# Inspect the data structure and first few rows
str(data)
head(data)
```

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

### Initialize Table Visibility

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Sets the initial opacity of the table to 0 if horizontal space is not yet provided by the host. It then renders the table and sets up a retry mechanism for fitting columns if space is initially insufficient.

```javascript
if (tableDiv.clientWidth <= 0) {
  tableDiv.style.opacity = "0";
}
me.render();

function retryFit() {
  if (tableDiv.clientWidth <= 0) {
    setTimeout(retryFit, 100);
  } else {
    me.render();
    triggerOnChange();
  }
}

if (tableDiv.clientWidth <= 0) {
  retryFit();
}
```

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

### Create a Scatter Plot in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Generates a scatter plot using ggplot2. Requires the ggplot2 package to be installed and loaded.

```r
library(ggplot2)
ggplot(data, aes(x = column1, y = column2)) +
  geom_point()
```

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

### Initialize CNMF Object with Different Algorithm

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates initializing a CNMF object using a different algorithm ('nmfstd'). This might be suitable for specific data characteristics or analysis goals.

```R
cnmf_obj_nmfstd <- CNMF(data, k=5, alg='nmfstd', n_iter=1000, seed=123)
```

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

### Create a Bar Plot

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Creates a bar plot from summarized data. This example assumes 'category' and 'count' columns exist.

```r
barplot(data$count, names.arg = data$category)
```

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

### CNMF with Different Initialization Methods

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Compares CNMF results obtained using different initialization strategies.

```python
cnmf_init1 = CNMF(params, initialization='method1')
```

```python
cnmf_init2 = CNMF(params, initialization='method2')
```

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

### Getting Element Offset Parent

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Finds the closest positioned ancestor element for the first element in the set of matched elements.

```javascript
S.fn.offsetParent=function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}
```

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

### Get DataFrame Columns

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_simulated_example_data.ipynb

Retrieves the column names of a pandas DataFrame. Useful for verifying merged data and understanding available features.

```python
tsne_and_usages.columns
```

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

### List Files in Simulated Data Directory

Source: https://github.com/dylkot/cnmf/blob/main/Extras/prepare_unittest_simulation.ipynb

Use this command to list all files in the main simulated data directory. This provides an overview of all available simulation assets.

```bash
! ls simulated_example_data
```

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

### Display scsim help message

Source: https://github.com/dylkot/cnmf/blob/main/Extras/simulate_example_data.ipynb

Display the help message for the run_scsim.py script to understand available simulation parameters and their descriptions. This is useful for configuring simulation runs.

```bash
! python ../../scsim/run_scsim.py -h
```

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

### Using Rcpp for C++ Integration

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Example of integrating C++ code into R using Rcpp for performance improvements. Requires Rcpp package.

```r
library(Rcpp)

# Define C++ code as a string
cxx_code <- "
SEXP add_cpp(SEXP x, SEXP y) {
    return Rf_ScalarReal(Rcpp::as<double>(x) + Rcpp::as<double>(y));
}
"

# Compile and load the C++ function
compiled_add <- cxxfunction(signature("x=numeric(1), y=numeric(1)"),
                            plugin = "Rcpp",
                            body = cxx_code)

# Use the compiled C++ function in R
result <- compiled_add(x = 5, y = 3)
print(result)
```

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

### Highlight.js Initialization

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Configures and initializes Highlight.js for syntax highlighting on the page. It sets the available languages and then triggers the highlighting process.

```javascript
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() {
hljs.initHighlighting();
}, 0);
}
}
```

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

### Create a Network Graph with Layouts

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Visualizes network data using different layout algorithms for better representation. Requires 'igraph'.

```r
library(igraph)
graph <- graph_from_data_frame(d = edges_data)
plot(graph, layout = layout_with_fr)
```

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

### Construct cNMF Prepare Command

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Constructs the command-line string for the cNMF 'prepare' step, specifying output directory, run name, input matrix, and NMF parameters. This command is then executed.

```R
runname = "example_cNMF"
cmd = paste("cnmf prepare --output-dir", data_dir,
            "--name", runname,
            "-c", paste0(filtered_dir, 'matrix.mtx'),
            "--max-nmf-iter 2000", 
            "-k 5 6 7 8 9 10 --n-iter 20", sep=" ")
print(cmd)
```

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

### Data Visualization with ggplot2 in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Provides an example of creating a scatter plot using ggplot2. Customize aesthetics and geoms for different plot types.

```r
library(ggplot2)

# Create a scatter plot
ggplot(data, aes(x = x_column, y = y_column)) +
  geom_point() +
  labs(title = "Scatter Plot Example", x = "X Axis Label", y = "Y Axis Label")
```

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

### Scrolling Element Position

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Gets or sets the scroll position of each element in the set of matched elements. Supports both horizontal (scrollLeft) and vertical (scrollTop) scrolling.

```javascript
S.each({"scrollLeft":"pageXOffset","scrollTop":"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn\[t\]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r\[i\]:e\[t\];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e\[t\]=n},t,e,arguments.leng})
```

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

### Combine Factorizations using Command Line

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_pbmc_example_data.ipynb

Combine individual factorization results using the cNMF command-line interface. This prepares the data for consensus estimation.

```bash
cmd = 'cnmf combine --output-dir example_PBMC/cNMF --name pbmc_cNMF'
print('Command line - combine step: %s' % cmd)
!{cmd}
```

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

### Load R Libraries for Data Handling

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Loads necessary R packages for data manipulation and Seurat object handling. Ensure these packages are installed before running.

```R
suppressPackageStartupMessages({
    library(data.table)
    library(Matrix)
    library(Seurat)
})
```

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

### Creating a Shiny App (Server)

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Defines the server logic for a Shiny application.

```r
library(shiny)

server <- function(input, output) {
  output$distPlot <- renderPlot({
    x    <- faithful[, 2]
    bins <- seq(min(x), max(x), length.out = input$bins + 1)
    hist(x, breaks = bins, col = 'skyblue', border = 'white', 
         xlab = 'Waiting time to next eruption (in mins)',
         main = 'Histogram of waiting times')
  })
}
```

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

### Task Execution Output

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_pbmc_example_data.ipynb

This output shows the progress of tasks being executed by worker 0. It indicates the start of each task, useful for monitoring the progress of distributed computations.

```text
[Worker 0]. Starting task 0.
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```

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

### Parameter Tuning for CNMF

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Demonstrates how to adjust parameters for optimal CNMF performance. This often involves iterative testing.

```python
params.set('parameter_name', value)
```

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

### Execute cNMF Prepare Step

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Executes the cNMF 'prepare' command constructed previously using the system() function. This step normalizes the data and prepares it for factorization.

```R
system(cmd)
```

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

### Create a Scatter Plot with ggplot2 in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Generates a scatter plot using ggplot2 to visualize the relationship between two variables. Requires the ggplot2 library to be installed and loaded.

```r
library(ggplot2)
ggplot(data, aes(x = var1, y = var2)) +
  geom_point()
```

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

### Creating Bar Plots with ggplot2

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Illustrates how to create a bar plot using the ggplot2 package to visualize categorical data. This requires the ggplot2 package to be installed and loaded.

```r
library(ggplot2)

# Sample data frame for plotting
plot_data <- data.frame(
  Category = c("A", "B", "C", "D"),
  Value = c(23, 45, 56, 12)
)

# Create a bar plot
ggplot(plot_data, aes(x = Category, y = Value)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Bar Plot of Categories", x = "Category", y = "Value") +
  theme_minimal()

```

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

### Initialize and Resize Paged Tables

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Initializes all paged tables on the page and sets up a listener to resize them on window resize events. Ensures tables are responsive.

```javascript
var PagedTableDoc;
(function (PagedTableDoc) {
    var allPagedTables = [];
    PagedTableDoc.initAll = function() {
        allPagedTables = [];
        var pagedTables = [].slice.call(document.querySelectorAll('["data-pagedtable"="false"],["data-pagedtable"=""]'));
        pagedTables.forEach(function(pagedTable, idx) {
            pagedTable.setAttribute("data-pagedtable", "true");
            pagedTable.setAttribute("pagedtable-page", 0);
            pagedTable.setAttribute("class", "pagedtable-wrapper");
            var pagedTableInstance = new PagedTable(pagedTable);
            pagedTableInstance.init();
            allPagedTables.push(pagedTableInstance);
        });
    };
    PagedTableDoc.resizeAll = function() {
        allPagedTables.forEach(function(pagedTable) {
            pagedTable.render();
        });
    };
    window.addEventListener("resize", PagedTableDoc.resizeAll);
    return PagedTableDoc;
})(PagedTableDoc || (PagedTableDoc = {}));
window.onload = function() {
    PagedTableDoc.initAll();
};
```

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

### Create a Bar Plot with ggplot2 in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Illustrates how to create a bar plot to compare categorical data. This example uses `geom_bar()` for counts or `geom_col()` for pre-summarized values.

```r
ggplot(data, aes(x = category_var)) +
  geom_bar()
```

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

### Preprocess Data for CNMF with Sex and Sample Covariates

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_batcheffectcorrect_BaronEtAl.ipynb

Initializes the Preprocess object and runs the preprocessing pipeline for CNMF. Includes 'Sex' and 'Sample' as harmony variables to correct for batch effects. Plots are generated and output is saved to a specified directory.

```python
p = Preprocess(random_seed=14)
(adata_c, adata_tp10k, hvgs) = p.preprocess_for_cnmf(adata, harmony_vars=['Sex', 'Sample'], n_top_rna_genes = 2000,
                                                     max_scaled_thresh = None, quantile_thresh = .9999, makeplots=True,
                                                    save_output_base='./example_islets/batchcorrect_example_sex')
```

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

### Initialize Table Components

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Initializes data, page, measurer, and columns objects. Sets up table elements and caches client width.

```javascript
var data = source.data;
var page = new Page(data, options);
var measurer = new Measurer(data, options);
var columns = new Columns(data, source.columns, options);
var table = null;
var tableDiv = null;
var header = null;
var footer = null;
var tbody = null;
// Caches pagedTable.clientWidth, specially for webkit
var cachedPagedTableClientWidth = null;
var onChangeCallbacks = [];
```

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

### Get Elements by Tag Name or Query Selector

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Retrieves elements from a given context, either by tag name or using querySelectorAll. It merges results and handles context.

```javascript
function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||""):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||""):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}
```

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

### Special Event Handling Setup

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Configures special event handling for a given event type on an element. It uses a proxy handler to manage event triggers and element states.

```javascript
function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}
```

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

### Data Transformation with dplyr in R

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Shows common data manipulation tasks such as filtering, selecting, arranging, mutating, and summarizing data using the dplyr package. Ensure dplyr is installed and loaded.

```r
library(dplyr)
filtered_data <- data %>%
  filter(condition) %>%
  select(col1, col2) %>%
  arrange(col1) %>%
  mutate(new_col = col1 * 2) %>%
  group_by(group_col) %>%
  summarise(mean_val = mean(value_col))
```

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

### Create a Calendar Heatmap

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Visualizes data over time on a calendar grid. Requires packages like 'calmap'.

```r
library(calmap)
calmap(data$date, data$value, start = "2023-01-01")
```

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

### Continued Task Execution Output

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/analyze_pbmc_example_data.ipynb

This output shows the continuation of task execution by worker 0. It indicates the start of subsequent tasks, useful for monitoring the progress of distributed computations after a potential interruption or completion of a batch.

```text
[Worker 0]. Starting task 113.
[Worker 0]. Starting task 114.
[Worker 0]. Starting task 115.
[Worker 0]. Starting task 116.
[Worker 0]. Starting task 117.
[Worker 0]. Starting task 118.
[Worker 0]. Starting task 119.
```

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

### Data Frame Creation

Source: https://github.com/dylkot/cnmf/blob/main/Tutorials/R_vignette.nb.html

Shows how to create a data frame in R.

```r
df <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(25, 30, 35),
  City = c("New York", "Los Angeles", "Chicago")
)
print(df)
```