From 2669b0a47591e7b90f46bf511a4d14c97b337ded Mon Sep 17 00:00:00 2001 From: "Steven Paul Sanderson II, MPH" Date: Wed, 30 Oct 2024 22:04:34 -0400 Subject: [PATCH] new post --- .../index/execute-results/html.json | 15 + docs/posts/2024-10-31/index.html | 1217 +++++++++++++++++ docs/search.json | 98 ++ docs/sitemap.xml | 4 + posts/2024-10-31/index.qmd | 307 +++++ posts/2024-10-31/todays_post.png | Bin 0 -> 1583882 bytes site_libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting-dark.css | 189 +++ .../quarto-syntax-highlighting.css | 205 +++ site_libs/quarto-html/quarto.js | 908 ++++++++++++ site_libs/quarto-html/tippy.css | 1 + site_libs/quarto-html/tippy.umd.min.js | 2 + 12 files changed, 2952 insertions(+) create mode 100644 _freeze/posts/2024-10-31/index/execute-results/html.json create mode 100644 docs/posts/2024-10-31/index.html create mode 100644 posts/2024-10-31/index.qmd create mode 100644 posts/2024-10-31/todays_post.png create mode 100644 site_libs/quarto-html/popper.min.js create mode 100644 site_libs/quarto-html/quarto-syntax-highlighting-dark.css create mode 100644 site_libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 site_libs/quarto-html/quarto.js create mode 100644 site_libs/quarto-html/tippy.css create mode 100644 site_libs/quarto-html/tippy.umd.min.js diff --git a/_freeze/posts/2024-10-31/index/execute-results/html.json b/_freeze/posts/2024-10-31/index/execute-results/html.json new file mode 100644 index 00000000..937d553d --- /dev/null +++ b/_freeze/posts/2024-10-31/index/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "b086db37813ed127b86ddc4104ab35b3", + "result": { + "engine": "knitr", + "markdown": "---\ntitle: \"How to Use 'OR' Operator in R: A Comprehensive Guide for Beginners\"\nauthor: \"Steven P. Sanderson II, MPH\"\ndate: \"2024-10-31\"\ncategories: [code, rtip, operations]\ntoc: TRUE\ndescription: \"Learn how to effectively use the OR operator in R programming with practical examples. Master boolean logic and conditional filtering for better data manipulation.\"\nkeywords: [Programming, R logical operators, Boolean operations in R, R conditional statements, R vector filtering, R data frame filtering, OR operator syntax, R programming operators, R boolean logic, R conditional filtering, R data manipulation, OR operator in R, R logical operators, R programming operators, R boolean operations, R conditional statements, R vector filtering, R data frame filtering, Single pipe vs double pipe R, R logical operations examples, Boolean logic R programming, how to use OR operator in R with data frames, difference between | and || operators in R, filtering data in R using OR operator, R programming OR operator with NA values, combining AND and OR operators in R programming]\ndraft: TRUE\n---\n\n\n\n# Introduction\n\nThe OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis.\n\n# Understanding OR Operators in R\n\n## Types of OR Operators\n\nR provides two distinct OR operators (source: DataMentor):\n\n- `|`: Element-wise OR operator\n- `||`: Logical OR operator\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Basic syntax comparison\nx <- c(TRUE, FALSE)\ny <- c(FALSE, TRUE)\n\n# Element-wise OR\nx | y # Returns: TRUE TRUE\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE TRUE\n```\n\n\n:::\n\n```{.r .cell-code}\n# Logical OR (only first elements)\nx[1] || y[1] # Returns: TRUE\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n\n```{.r .cell-code}\nx[2] || y[2]\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE\n```\n\n\n:::\n:::\n\n\n\n## Comparison Table: | vs ||\n\n```r\n|--------------------|------------------|-------------------|\n| Feature | Single | (|) | Double || (||) |\n|--------------------|------------------|-------------------|\n| Vector Operation | Yes | No |\n| Short-circuit | No | Yes |\n| Performance | Slower | Faster |\n| Use Case | Vectors/Arrays | Single values |\n|--------------------|------------------|-------------------|\n```\n\n# Working with Numeric Values\n\n## Basic Numeric Examples\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Example from Statistics Globe\nnumbers <- c(2, 5, 8, 12, 15)\nresult <- numbers < 5 | numbers > 10\nprint(result) # Returns: TRUE FALSE FALSE TRUE TRUE\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE FALSE FALSE TRUE TRUE\n```\n\n\n:::\n:::\n\n\n\n## Real-World Application with mtcars Dataset\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Example from R-bloggers\ndata(mtcars)\n# Find cars with high MPG or low weight\nefficient_cars <- mtcars[mtcars$mpg > 25 | mtcars$wt < 2.5, ]\nprint(head(efficient_cars))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n mpg cyl disp hp drat wt qsec vs am gear carb\nDatsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\nFiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1\nHonda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2\nToyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1\nToyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1\nFiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1\n```\n\n\n:::\n:::\n\n\n\n# Advanced Applications\n\n## Using OR with dplyr (source: DataCamp)\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n\nmtcars %>%\n filter(mpg > 25 | wt < 2.5) %>%\n select(mpg, wt)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n mpg wt\nDatsun 710 22.8 2.320\nFiat 128 32.4 2.200\nHonda Civic 30.4 1.615\nToyota Corolla 33.9 1.835\nToyota Corona 21.5 2.465\nFiat X1-9 27.3 1.935\nPorsche 914-2 26.0 2.140\nLotus Europa 30.4 1.513\n```\n\n\n:::\n:::\n\n\n\n## Performance Optimization Tips\n\nAccording to Statistics Globe, consider these performance best practices:\n\n1. Use `||` for single conditions in if statements\n2. Place more likely conditions first when using `||`\n3. Use vectorized operations with `|` for large datasets\n\n```r\n# Efficient code example\nif(nrow(df) > 1000 || any(is.na(df))) {\n # Process large or incomplete datasets\n}\n```\n\n# Common Pitfalls and Solutions\n\n## Handling NA Values\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Example from GeeksforGeeks\nx <- c(TRUE, FALSE, NA)\ny <- c(FALSE, FALSE, TRUE)\n\n# Standard OR operation\nx | y # Returns: TRUE FALSE NA\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE FALSE TRUE\n```\n\n\n:::\n\n```{.r .cell-code}\n# Handling NAs explicitly\nx | y | is.na(x) # Returns: TRUE FALSE TRUE\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE FALSE TRUE\n```\n\n\n:::\n:::\n\n\n\n## Vector Recycling Issues\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Potential issue\nvec1 <- c(TRUE, FALSE, TRUE)\nvec2 <- c(FALSE)\nresult <- vec1 | vec2 # Recycling occurs\n\n# Better approach\nvec2 <- rep(FALSE, length(vec1))\nresult <- vec1 | vec2\nprint(result)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE FALSE TRUE\n```\n\n\n:::\n:::\n\n\n\n# Your Turn! Real-World Practice Problems\n\n## Problem 1: Data Analysis Challenge\n\nUsing the built-in `iris` dataset, find all flowers that meet either of these conditions:\n- Sepal length greater than 6.5\n- Petal width greater than 1.8\n\n```r\n# Your code here\n```\n\nSolution:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# From DataCamp's practical examples\ndata(iris)\nselected_flowers <- iris[iris$Sepal.Length > 6.5 | iris$Petal.Width > 1.8, ]\nprint(head(selected_flowers))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n51 7.0 3.2 4.7 1.4 versicolor\n53 6.9 3.1 4.9 1.5 versicolor\n59 6.6 2.9 4.6 1.3 versicolor\n66 6.7 3.1 4.4 1.4 versicolor\n76 6.6 3.0 4.4 1.4 versicolor\n77 6.8 2.8 4.8 1.4 versicolor\n```\n\n\n:::\n:::\n\n\n\n## Problem 2: Customer Analysis\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Create sample customer data\ncustomers <- data.frame(\n age = c(25, 35, 42, 19, 55),\n purchase = c(150, 450, 200, 100, 300),\n loyal = c(TRUE, TRUE, FALSE, FALSE, TRUE)\n)\n\n# Find high-value or loyal customers\n# Your code here\n```\n:::\n\n\n\nSolution:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvaluable_customers <- customers[customers$purchase > 250 | customers$loyal == TRUE, ]\nprint(valuable_customers)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n age purchase loyal\n1 25 150 TRUE\n2 35 450 TRUE\n5 55 300 TRUE\n```\n\n\n:::\n:::\n\n\n\n# Integration with Popular R Packages\n\n## Using OR with dplyr and tidyverse\n\nFrom R-bloggers' advanced examples:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\n\nmtcars %>%\n filter(mpg > 20 | hp > 200) %>%\n arrange(desc(mpg)) %>%\n select(mpg, hp) %>%\n head(5)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n mpg hp\nToyota Corolla 33.9 65\nFiat 128 32.4 66\nHonda Civic 30.4 52\nLotus Europa 30.4 113\nFiat X1-9 27.3 66\n```\n\n\n:::\n:::\n\n\n\n## OR Operations in data.table\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(data.table)\n\ndt <- as.data.table(mtcars)\nresult <- dt[mpg > 20 | hp > 200]\nprint(result)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n mpg cyl disp hp drat wt qsec vs am gear carb\n \n 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4\n 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4\n 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\n 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1\n 5: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4\n 6: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2\n 7: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2\n 8: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4\n 9: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4\n10: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4\n11: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1\n12: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2\n13: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1\n14: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1\n15: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4\n16: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1\n17: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2\n18: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2\n19: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4\n20: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8\n21: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2\n mpg cyl disp hp drat wt qsec vs am gear carb\n```\n\n\n:::\n:::\n\n\n\n# Quick Takeaways\n\nBased on Statistics Globe's expert analysis:\n\n1. Use `|` for vectorized operations across entire datasets\n2. Implement `||` for single logical comparisons in control structures\n3. Consider NA handling in logical operations\n4. Leverage package-specific implementations for better performance\n5. Always test with small datasets first\n\n# Enhanced Troubleshooting Guide\n\n## Common Issues and Solutions\n\nFrom GeeksforGeeks and DataMentor:\n\n1. Vector Length Mismatch\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Problem\nx <- c(TRUE, FALSE)\ny <- c(TRUE, FALSE, TRUE) # Different length\n\n# Solution\n# Ensure equal lengths\nlength(y) <- length(x)\n```\n:::\n\n\n\n2. NA Handling\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Problem\ndata <- c(1, NA, 3, 4)\nresult <- data > 2 | data < 2 # Contains NA\nprint(result)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE NA TRUE TRUE\n```\n\n\n:::\n\n```{.r .cell-code}\n# Solution\nresult <- data > 2 | data < 2 | is.na(data)\nprint(result)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] TRUE TRUE TRUE TRUE\n```\n\n\n:::\n:::\n\n\n\n# FAQs\n\n**Q: How does OR operator performance compare in large datasets?**\n\nAccording to DataCamp, vectorized operations with `|` are more efficient for large datasets, while `||` is faster for single conditions.\n\n**Q: Can I use OR operators with factor variables?**\n\nYes, but convert factors to character or numeric first for reliable results (Statistics Globe).\n\n**Q: How do OR operators work with different data types?**\n\nR coerces values to logical before applying OR operations. See type conversion rules in R documentation.\n\n**Q: What's the best practice for complex conditions?**\n\nR-bloggers recommends using parentheses and breaking complex conditions into smaller, readable chunks.\n\n**Q: How do I optimize OR operations in data.table?**\n\ndata.table provides optimized methods for logical operations within its syntax.\n\n# References\n\n1. [DataMentor: \"R Operators Guide\"](https://www.datamentor.io/r-programming/operator/)\n\n2. [GeeksforGeeks: \"R Programming Logical Operators\"](https://www.geeksforgeeks.org/r-operators/?ref=header_outind#logical-operators)\n\n# Engage!\n\nShare your OR operator experiences or questions in the comments below! Follow us for more R programming tutorials and tips.\n\nFor hands-on practice, try our example code in RStudio and experiment with different conditions. Join our R programming community to discuss more advanced techniques and best practices.\n\n------------------------------------------------------------------------\n\nHappy Coding! 🚀\n\n------------------------------------------------------------------------\n\n*You can connect with me at any one of the below*:\n\n*Telegram Channel here*: \n\n*LinkedIn Network here*: \n\n*Mastadon Social here*: [https://mstdn.social/\\@stevensanderson](https://mstdn.social/@stevensanderson)\n\n*RStats Network here*: [https://rstats.me/\\@spsanderson](https://rstats.me/@spsanderson)\n\n*GitHub Network here*: \n\n------------------------------------------------------------------------\n\n\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/docs/posts/2024-10-31/index.html b/docs/posts/2024-10-31/index.html new file mode 100644 index 00000000..9e505c52 --- /dev/null +++ b/docs/posts/2024-10-31/index.html @@ -0,0 +1,1217 @@ + + + + + + + + + + + + + +How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners – Steve's Data Tips and Tricks + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
Draft
+ +
+ +
+
+
+

How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners

+
+
+ Learn how to effectively use the OR operator in R programming with practical examples. Master boolean logic and conditional filtering for better data manipulation. +
+
+
+
code
+
rtip
+
operations
+
+
+
+ + +
+ +
+
Author
+
+

Steven P. Sanderson II, MPH

+
+
+ +
+
Published
+
+

October 31, 2024

+
+
+ + +
+ + +
+
+
Keywords
+

Programming, R logical operators, Boolean operations in R, R conditional statements, R vector filtering, R data frame filtering, OR operator syntax, R programming operators, R boolean logic, R conditional filtering, R data manipulation, OR operator in R, R logical operators, R programming operators, R boolean operations, R conditional statements, R vector filtering, R data frame filtering, Single pipe vs double pipe R, R logical operations examples, Boolean logic R programming, how to use OR operator in R with data frames, difference between | and || operators in R, filtering data in R using OR operator, R programming OR operator with NA values, combining AND and OR operators in R programming

+
+
+ +
+ + + + +
+ + + + + +
+

Introduction

+

The OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis.

+
+
+

Understanding OR Operators in R

+
+

Types of OR Operators

+

R provides two distinct OR operators (source: DataMentor):

+
    +
  • |: Element-wise OR operator
  • +
  • ||: Logical OR operator
  • +
+
+
# Basic syntax comparison
+x <- c(TRUE, FALSE)
+y <- c(FALSE, TRUE)
+
+# Element-wise OR
+x | y    # Returns: TRUE TRUE
+
+
[1] TRUE TRUE
+
+
# Logical OR (only first elements)
+x[1] || y[1]   # Returns: TRUE
+
+
[1] TRUE
+
+
x[2] || y[2]
+
+
[1] TRUE
+
+
+
+
+

Comparison Table: | vs ||

+
|--------------------|------------------|-------------------|
+| Feature            | Single | (|)     | Double || (||)   |
+|--------------------|------------------|-------------------|
+| Vector Operation   | Yes              | No               |
+| Short-circuit      | No               | Yes              |
+| Performance        | Slower           | Faster           |
+| Use Case           | Vectors/Arrays   | Single values    |
+|--------------------|------------------|-------------------|
+
+
+
+

Working with Numeric Values

+
+

Basic Numeric Examples

+
+
# Example from Statistics Globe
+numbers <- c(2, 5, 8, 12, 15)
+result <- numbers < 5 | numbers > 10
+print(result)  # Returns: TRUE FALSE FALSE TRUE TRUE
+
+
[1]  TRUE FALSE FALSE  TRUE  TRUE
+
+
+
+
+

Real-World Application with mtcars Dataset

+
+
# Example from R-bloggers
+data(mtcars)
+# Find cars with high MPG or low weight
+efficient_cars <- mtcars[mtcars$mpg > 25 | mtcars$wt < 2.5, ]
+print(head(efficient_cars))
+
+
                mpg cyl  disp hp drat    wt  qsec vs am gear carb
+Datsun 710     22.8   4 108.0 93 3.85 2.320 18.61  1  1    4    1
+Fiat 128       32.4   4  78.7 66 4.08 2.200 19.47  1  1    4    1
+Honda Civic    30.4   4  75.7 52 4.93 1.615 18.52  1  1    4    2
+Toyota Corolla 33.9   4  71.1 65 4.22 1.835 19.90  1  1    4    1
+Toyota Corona  21.5   4 120.1 97 3.70 2.465 20.01  1  0    3    1
+Fiat X1-9      27.3   4  79.0 66 4.08 1.935 18.90  1  1    4    1
+
+
+
+
+
+

Advanced Applications

+
+

Using OR with dplyr (source: DataCamp)

+
+
library(dplyr)
+
+mtcars %>%
+  filter(mpg > 25 | wt < 2.5) %>%
+  select(mpg, wt)
+
+
                mpg    wt
+Datsun 710     22.8 2.320
+Fiat 128       32.4 2.200
+Honda Civic    30.4 1.615
+Toyota Corolla 33.9 1.835
+Toyota Corona  21.5 2.465
+Fiat X1-9      27.3 1.935
+Porsche 914-2  26.0 2.140
+Lotus Europa   30.4 1.513
+
+
+
+
+

Performance Optimization Tips

+

According to Statistics Globe, consider these performance best practices:

+
    +
  1. Use || for single conditions in if statements
  2. +
  3. Place more likely conditions first when using ||
  4. +
  5. Use vectorized operations with | for large datasets
  6. +
+
# Efficient code example
+if(nrow(df) > 1000 || any(is.na(df))) {
+  # Process large or incomplete datasets
+}
+
+
+
+

Common Pitfalls and Solutions

+
+

Handling NA Values

+
+
# Example from GeeksforGeeks
+x <- c(TRUE, FALSE, NA)
+y <- c(FALSE, FALSE, TRUE)
+
+# Standard OR operation
+x | y  # Returns: TRUE FALSE NA
+
+
[1]  TRUE FALSE  TRUE
+
+
# Handling NAs explicitly
+x | y | is.na(x)  # Returns: TRUE FALSE TRUE
+
+
[1]  TRUE FALSE  TRUE
+
+
+
+
+

Vector Recycling Issues

+
+
# Potential issue
+vec1 <- c(TRUE, FALSE, TRUE)
+vec2 <- c(FALSE)
+result <- vec1 | vec2  # Recycling occurs
+
+# Better approach
+vec2 <- rep(FALSE, length(vec1))
+result <- vec1 | vec2
+print(result)
+
+
[1]  TRUE FALSE  TRUE
+
+
+
+
+
+

Your Turn! Real-World Practice Problems

+
+

Problem 1: Data Analysis Challenge

+

Using the built-in iris dataset, find all flowers that meet either of these conditions: - Sepal length greater than 6.5 - Petal width greater than 1.8

+
# Your code here
+

Solution:

+
+
# From DataCamp's practical examples
+data(iris)
+selected_flowers <- iris[iris$Sepal.Length > 6.5 | iris$Petal.Width > 1.8, ]
+print(head(selected_flowers))
+
+
   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
+51          7.0         3.2          4.7         1.4 versicolor
+53          6.9         3.1          4.9         1.5 versicolor
+59          6.6         2.9          4.6         1.3 versicolor
+66          6.7         3.1          4.4         1.4 versicolor
+76          6.6         3.0          4.4         1.4 versicolor
+77          6.8         2.8          4.8         1.4 versicolor
+
+
+
+
+

Problem 2: Customer Analysis

+
+
# Create sample customer data
+customers <- data.frame(
+    age = c(25, 35, 42, 19, 55),
+    purchase = c(150, 450, 200, 100, 300),
+    loyal = c(TRUE, TRUE, FALSE, FALSE, TRUE)
+)
+
+# Find high-value or loyal customers
+# Your code here
+
+

Solution:

+
+
valuable_customers <- customers[customers$purchase > 250 | customers$loyal == TRUE, ]
+print(valuable_customers)
+
+
  age purchase loyal
+1  25      150  TRUE
+2  35      450  TRUE
+5  55      300  TRUE
+
+
+
+
+ +
+

Quick Takeaways

+

Based on Statistics Globe’s expert analysis:

+
    +
  1. Use | for vectorized operations across entire datasets
  2. +
  3. Implement || for single logical comparisons in control structures
  4. +
  5. Consider NA handling in logical operations
  6. +
  7. Leverage package-specific implementations for better performance
  8. +
  9. Always test with small datasets first
  10. +
+
+
+

Enhanced Troubleshooting Guide

+
+

Common Issues and Solutions

+

From GeeksforGeeks and DataMentor:

+
    +
  1. Vector Length Mismatch
  2. +
+
+
# Problem
+x <- c(TRUE, FALSE)
+y <- c(TRUE, FALSE, TRUE)  # Different length
+
+# Solution
+# Ensure equal lengths
+length(y) <- length(x)
+
+
    +
  1. NA Handling
  2. +
+
+
# Problem
+data <- c(1, NA, 3, 4)
+result <- data > 2 | data < 2  # Contains NA
+print(result)
+
+
[1] TRUE   NA TRUE TRUE
+
+
# Solution
+result <- data > 2 | data < 2 | is.na(data)
+print(result)
+
+
[1] TRUE TRUE TRUE TRUE
+
+
+
+
+
+

FAQs

+

Q: How does OR operator performance compare in large datasets?

+

According to DataCamp, vectorized operations with | are more efficient for large datasets, while || is faster for single conditions.

+

Q: Can I use OR operators with factor variables?

+

Yes, but convert factors to character or numeric first for reliable results (Statistics Globe).

+

Q: How do OR operators work with different data types?

+

R coerces values to logical before applying OR operations. See type conversion rules in R documentation.

+

Q: What’s the best practice for complex conditions?

+

R-bloggers recommends using parentheses and breaking complex conditions into smaller, readable chunks.

+

Q: How do I optimize OR operations in data.table?

+

data.table provides optimized methods for logical operations within its syntax.

+
+
+

References

+
    +
  1. DataMentor: “R Operators Guide”

  2. +
  3. GeeksforGeeks: “R Programming Logical Operators”

  4. +
+
+
+

Engage!

+

Share your OR operator experiences or questions in the comments below! Follow us for more R programming tutorials and tips.

+

For hands-on practice, try our example code in RStudio and experiment with different conditions. Join our R programming community to discuss more advanced techniques and best practices.

+
+

Happy Coding! 🚀

+
+

You can connect with me at any one of the below:

+

Telegram Channel here: https://t.me/steveondata

+

LinkedIn Network here: https://www.linkedin.com/in/spsanderson/

+

Mastadon Social here: https://mstdn.social/@stevensanderson

+

RStats Network here: https://rstats.me/@spsanderson

+

GitHub Network here: https://github.com/spsanderson

+
+ + + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/search.json b/docs/search.json index b95ef789..c0222971 100644 --- a/docs/search.json +++ b/docs/search.json @@ -12283,5 +12283,103 @@ "title": "Powering Up Your Variables with Assignments and Expressions in C", "section": "References", "text": "References\n\nC Programming: Absolute Beginners Guide, 3rd Edition\nhttps://www.geeksforgeeks.org/c-typecasting/\nhttps://www.geeksforgeeks.org/assignment-operators-in-c-c/\n\n\nHappy Coding! 🚀\n\n\n\nExample 1\n\n\n\n\n\nExample 2\n\n\n\n\n\nConstructing with C\n\n\n\nYou can connect with me at any one of the below:\nTelegram Channel here: https://t.me/steveondata\nLinkedIn Network here: https://www.linkedin.com/in/spsanderson/\nMastadon Social here: https://mstdn.social/@stevensanderson\nRStats Network here: https://rstats.me/@spsanderson\nGitHub Network here: https://github.com/spsanderson" + }, + { + "objectID": "posts/2024-10-31/index.html", + "href": "posts/2024-10-31/index.html", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "", + "text": "The OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis." + }, + { + "objectID": "posts/2024-10-31/index.html#types-of-or-operators", + "href": "posts/2024-10-31/index.html#types-of-or-operators", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Types of OR Operators", + "text": "Types of OR Operators\nR provides two distinct OR operators (source: DataMentor):\n\n|: Element-wise OR operator\n||: Logical OR operator\n\n\n# Basic syntax comparison\nx <- c(TRUE, FALSE)\ny <- c(FALSE, TRUE)\n\n# Element-wise OR\nx | y # Returns: TRUE TRUE\n\n[1] TRUE TRUE\n\n# Logical OR (only first elements)\nx[1] || y[1] # Returns: TRUE\n\n[1] TRUE\n\nx[2] || y[2]\n\n[1] TRUE" + }, + { + "objectID": "posts/2024-10-31/index.html#comparison-table-vs", + "href": "posts/2024-10-31/index.html#comparison-table-vs", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Comparison Table: | vs ||", + "text": "Comparison Table: | vs ||\n|--------------------|------------------|-------------------|\n| Feature | Single | (|) | Double || (||) |\n|--------------------|------------------|-------------------|\n| Vector Operation | Yes | No |\n| Short-circuit | No | Yes |\n| Performance | Slower | Faster |\n| Use Case | Vectors/Arrays | Single values |\n|--------------------|------------------|-------------------|" + }, + { + "objectID": "posts/2024-10-31/index.html#basic-numeric-examples", + "href": "posts/2024-10-31/index.html#basic-numeric-examples", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Basic Numeric Examples", + "text": "Basic Numeric Examples\n\n# Example from Statistics Globe\nnumbers <- c(2, 5, 8, 12, 15)\nresult <- numbers < 5 | numbers > 10\nprint(result) # Returns: TRUE FALSE FALSE TRUE TRUE\n\n[1] TRUE FALSE FALSE TRUE TRUE" + }, + { + "objectID": "posts/2024-10-31/index.html#real-world-application-with-mtcars-dataset", + "href": "posts/2024-10-31/index.html#real-world-application-with-mtcars-dataset", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Real-World Application with mtcars Dataset", + "text": "Real-World Application with mtcars Dataset\n\n# Example from R-bloggers\ndata(mtcars)\n# Find cars with high MPG or low weight\nefficient_cars <- mtcars[mtcars$mpg > 25 | mtcars$wt < 2.5, ]\nprint(head(efficient_cars))\n\n mpg cyl disp hp drat wt qsec vs am gear carb\nDatsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\nFiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1\nHonda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2\nToyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1\nToyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1\nFiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1" + }, + { + "objectID": "posts/2024-10-31/index.html#using-or-with-dplyr-source-datacamp", + "href": "posts/2024-10-31/index.html#using-or-with-dplyr-source-datacamp", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Using OR with dplyr (source: DataCamp)", + "text": "Using OR with dplyr (source: DataCamp)\n\nlibrary(dplyr)\n\nmtcars %>%\n filter(mpg > 25 | wt < 2.5) %>%\n select(mpg, wt)\n\n mpg wt\nDatsun 710 22.8 2.320\nFiat 128 32.4 2.200\nHonda Civic 30.4 1.615\nToyota Corolla 33.9 1.835\nToyota Corona 21.5 2.465\nFiat X1-9 27.3 1.935\nPorsche 914-2 26.0 2.140\nLotus Europa 30.4 1.513" + }, + { + "objectID": "posts/2024-10-31/index.html#performance-optimization-tips", + "href": "posts/2024-10-31/index.html#performance-optimization-tips", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Performance Optimization Tips", + "text": "Performance Optimization Tips\nAccording to Statistics Globe, consider these performance best practices:\n\nUse || for single conditions in if statements\nPlace more likely conditions first when using ||\nUse vectorized operations with | for large datasets\n\n# Efficient code example\nif(nrow(df) > 1000 || any(is.na(df))) {\n # Process large or incomplete datasets\n}" + }, + { + "objectID": "posts/2024-10-31/index.html#handling-na-values", + "href": "posts/2024-10-31/index.html#handling-na-values", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Handling NA Values", + "text": "Handling NA Values\n\n# Example from GeeksforGeeks\nx <- c(TRUE, FALSE, NA)\ny <- c(FALSE, FALSE, TRUE)\n\n# Standard OR operation\nx | y # Returns: TRUE FALSE NA\n\n[1] TRUE FALSE TRUE\n\n# Handling NAs explicitly\nx | y | is.na(x) # Returns: TRUE FALSE TRUE\n\n[1] TRUE FALSE TRUE" + }, + { + "objectID": "posts/2024-10-31/index.html#vector-recycling-issues", + "href": "posts/2024-10-31/index.html#vector-recycling-issues", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Vector Recycling Issues", + "text": "Vector Recycling Issues\n\n# Potential issue\nvec1 <- c(TRUE, FALSE, TRUE)\nvec2 <- c(FALSE)\nresult <- vec1 | vec2 # Recycling occurs\n\n# Better approach\nvec2 <- rep(FALSE, length(vec1))\nresult <- vec1 | vec2\nprint(result)\n\n[1] TRUE FALSE TRUE" + }, + { + "objectID": "posts/2024-10-31/index.html#problem-1-data-analysis-challenge", + "href": "posts/2024-10-31/index.html#problem-1-data-analysis-challenge", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Problem 1: Data Analysis Challenge", + "text": "Problem 1: Data Analysis Challenge\nUsing the built-in iris dataset, find all flowers that meet either of these conditions: - Sepal length greater than 6.5 - Petal width greater than 1.8\n# Your code here\nSolution:\n\n# From DataCamp's practical examples\ndata(iris)\nselected_flowers <- iris[iris$Sepal.Length > 6.5 | iris$Petal.Width > 1.8, ]\nprint(head(selected_flowers))\n\n Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n51 7.0 3.2 4.7 1.4 versicolor\n53 6.9 3.1 4.9 1.5 versicolor\n59 6.6 2.9 4.6 1.3 versicolor\n66 6.7 3.1 4.4 1.4 versicolor\n76 6.6 3.0 4.4 1.4 versicolor\n77 6.8 2.8 4.8 1.4 versicolor" + }, + { + "objectID": "posts/2024-10-31/index.html#problem-2-customer-analysis", + "href": "posts/2024-10-31/index.html#problem-2-customer-analysis", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Problem 2: Customer Analysis", + "text": "Problem 2: Customer Analysis\n\n# Create sample customer data\ncustomers <- data.frame(\n age = c(25, 35, 42, 19, 55),\n purchase = c(150, 450, 200, 100, 300),\n loyal = c(TRUE, TRUE, FALSE, FALSE, TRUE)\n)\n\n# Find high-value or loyal customers\n# Your code here\n\nSolution:\n\nvaluable_customers <- customers[customers$purchase > 250 | customers$loyal == TRUE, ]\nprint(valuable_customers)\n\n age purchase loyal\n1 25 150 TRUE\n2 35 450 TRUE\n5 55 300 TRUE" + }, + { + "objectID": "posts/2024-10-31/index.html#using-or-with-dplyr-and-tidyverse", + "href": "posts/2024-10-31/index.html#using-or-with-dplyr-and-tidyverse", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Using OR with dplyr and tidyverse", + "text": "Using OR with dplyr and tidyverse\nFrom R-bloggers’ advanced examples:\n\nlibrary(tidyverse)\n\nmtcars %>%\n filter(mpg > 20 | hp > 200) %>%\n arrange(desc(mpg)) %>%\n select(mpg, hp) %>%\n head(5)\n\n mpg hp\nToyota Corolla 33.9 65\nFiat 128 32.4 66\nHonda Civic 30.4 52\nLotus Europa 30.4 113\nFiat X1-9 27.3 66" + }, + { + "objectID": "posts/2024-10-31/index.html#or-operations-in-data.table", + "href": "posts/2024-10-31/index.html#or-operations-in-data.table", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "OR Operations in data.table", + "text": "OR Operations in data.table\n\nlibrary(data.table)\n\ndt <- as.data.table(mtcars)\nresult <- dt[mpg > 20 | hp > 200]\nprint(result)\n\n mpg cyl disp hp drat wt qsec vs am gear carb\n <num> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>\n 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4\n 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4\n 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\n 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1\n 5: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4\n 6: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2\n 7: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2\n 8: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4\n 9: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4\n10: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4\n11: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1\n12: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2\n13: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1\n14: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1\n15: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4\n16: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1\n17: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2\n18: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2\n19: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4\n20: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8\n21: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2\n mpg cyl disp hp drat wt qsec vs am gear carb" + }, + { + "objectID": "posts/2024-10-31/index.html#common-issues-and-solutions", + "href": "posts/2024-10-31/index.html#common-issues-and-solutions", + "title": "How to Use ‘OR’ Operator in R: A Comprehensive Guide for Beginners", + "section": "Common Issues and Solutions", + "text": "Common Issues and Solutions\nFrom GeeksforGeeks and DataMentor:\n\nVector Length Mismatch\n\n\n# Problem\nx <- c(TRUE, FALSE)\ny <- c(TRUE, FALSE, TRUE) # Different length\n\n# Solution\n# Ensure equal lengths\nlength(y) <- length(x)\n\n\nNA Handling\n\n\n# Problem\ndata <- c(1, NA, 3, 4)\nresult <- data > 2 | data < 2 # Contains NA\nprint(result)\n\n[1] TRUE NA TRUE TRUE\n\n# Solution\nresult <- data > 2 | data < 2 | is.na(data)\nprint(result)\n\n[1] TRUE TRUE TRUE TRUE" } ] \ No newline at end of file diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 328388f5..703bf6c9 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1848,4 +1848,8 @@ https://www.spsanderson.com/steveondata/posts/2024-10-30/index.html 2024-10-30T01:55:52.088Z + + https://www.spsanderson.com/steveondata/posts/2024-10-31/index.html + 2024-10-31T02:00:38.594Z + diff --git a/posts/2024-10-31/index.qmd b/posts/2024-10-31/index.qmd new file mode 100644 index 00000000..7405bbfd --- /dev/null +++ b/posts/2024-10-31/index.qmd @@ -0,0 +1,307 @@ +--- +title: "How to Use 'OR' Operator in R: A Comprehensive Guide for Beginners" +author: "Steven P. Sanderson II, MPH" +date: "2024-10-31" +categories: [code, rtip, operations] +toc: TRUE +description: "Learn how to effectively use the OR operator in R programming with practical examples. Master boolean logic and conditional filtering for better data manipulation." +keywords: [Programming, R logical operators, Boolean operations in R, R conditional statements, R vector filtering, R data frame filtering, OR operator syntax, R programming operators, R boolean logic, R conditional filtering, R data manipulation, OR operator in R, R logical operators, R programming operators, R boolean operations, R conditional statements, R vector filtering, R data frame filtering, Single pipe vs double pipe R, R logical operations examples, Boolean logic R programming, how to use OR operator in R with data frames, difference between | and || operators in R, filtering data in R using OR operator, R programming OR operator with NA values, combining AND and OR operators in R programming] +draft: TRUE +--- + +# Introduction + +The OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis. + +# Understanding OR Operators in R + +## Types of OR Operators + +R provides two distinct OR operators (source: DataMentor): + +- `|`: Element-wise OR operator +- `||`: Logical OR operator + +```{r} +# Basic syntax comparison +x <- c(TRUE, FALSE) +y <- c(FALSE, TRUE) + +# Element-wise OR +x | y # Returns: TRUE TRUE + +# Logical OR (only first elements) +x[1] || y[1] # Returns: TRUE +x[2] || y[2] +``` + +## Comparison Table: | vs || + +```r +|--------------------|------------------|-------------------| +| Feature | Single | (|) | Double || (||) | +|--------------------|------------------|-------------------| +| Vector Operation | Yes | No | +| Short-circuit | No | Yes | +| Performance | Slower | Faster | +| Use Case | Vectors/Arrays | Single values | +|--------------------|------------------|-------------------| +``` + +# Working with Numeric Values + +## Basic Numeric Examples + +```{r} +# Example from Statistics Globe +numbers <- c(2, 5, 8, 12, 15) +result <- numbers < 5 | numbers > 10 +print(result) # Returns: TRUE FALSE FALSE TRUE TRUE +``` + +## Real-World Application with mtcars Dataset + +```{r} +# Example from R-bloggers +data(mtcars) +# Find cars with high MPG or low weight +efficient_cars <- mtcars[mtcars$mpg > 25 | mtcars$wt < 2.5, ] +print(head(efficient_cars)) +``` + +# Advanced Applications + +## Using OR with dplyr (source: DataCamp) + +```{r message=FALSE, warning=FALSE} +library(dplyr) + +mtcars %>% + filter(mpg > 25 | wt < 2.5) %>% + select(mpg, wt) +``` + +## Performance Optimization Tips + +According to Statistics Globe, consider these performance best practices: + +1. Use `||` for single conditions in if statements +2. Place more likely conditions first when using `||` +3. Use vectorized operations with `|` for large datasets + +```r +# Efficient code example +if(nrow(df) > 1000 || any(is.na(df))) { + # Process large or incomplete datasets +} +``` + +# Common Pitfalls and Solutions + +## Handling NA Values + +```{r} +# Example from GeeksforGeeks +x <- c(TRUE, FALSE, NA) +y <- c(FALSE, FALSE, TRUE) + +# Standard OR operation +x | y # Returns: TRUE FALSE NA + +# Handling NAs explicitly +x | y | is.na(x) # Returns: TRUE FALSE TRUE +``` + +## Vector Recycling Issues + +```{r} +# Potential issue +vec1 <- c(TRUE, FALSE, TRUE) +vec2 <- c(FALSE) +result <- vec1 | vec2 # Recycling occurs + +# Better approach +vec2 <- rep(FALSE, length(vec1)) +result <- vec1 | vec2 +print(result) +``` + +# Your Turn! Real-World Practice Problems + +## Problem 1: Data Analysis Challenge + +Using the built-in `iris` dataset, find all flowers that meet either of these conditions: +- Sepal length greater than 6.5 +- Petal width greater than 1.8 + +```r +# Your code here +``` + +Solution: + +```{r} +# From DataCamp's practical examples +data(iris) +selected_flowers <- iris[iris$Sepal.Length > 6.5 | iris$Petal.Width > 1.8, ] +print(head(selected_flowers)) +``` + +## Problem 2: Customer Analysis + +```{r} +# Create sample customer data +customers <- data.frame( + age = c(25, 35, 42, 19, 55), + purchase = c(150, 450, 200, 100, 300), + loyal = c(TRUE, TRUE, FALSE, FALSE, TRUE) +) + +# Find high-value or loyal customers +# Your code here +``` + +Solution: + +```{r} +valuable_customers <- customers[customers$purchase > 250 | customers$loyal == TRUE, ] +print(valuable_customers) +``` + +# Integration with Popular R Packages + +## Using OR with dplyr and tidyverse + +From R-bloggers' advanced examples: + +```{r warning=FALSE, message=FALSE} +library(tidyverse) + +mtcars %>% + filter(mpg > 20 | hp > 200) %>% + arrange(desc(mpg)) %>% + select(mpg, hp) %>% + head(5) +``` + +## OR Operations in data.table + +```{r warning=FALSE, message=FALSE} +library(data.table) + +dt <- as.data.table(mtcars) +result <- dt[mpg > 20 | hp > 200] +print(result) +``` + +# Quick Takeaways + +Based on Statistics Globe's expert analysis: + +1. Use `|` for vectorized operations across entire datasets +2. Implement `||` for single logical comparisons in control structures +3. Consider NA handling in logical operations +4. Leverage package-specific implementations for better performance +5. Always test with small datasets first + +# Enhanced Troubleshooting Guide + +## Common Issues and Solutions + +From GeeksforGeeks and DataMentor: + +1. Vector Length Mismatch + +```{r} +# Problem +x <- c(TRUE, FALSE) +y <- c(TRUE, FALSE, TRUE) # Different length + +# Solution +# Ensure equal lengths +length(y) <- length(x) +``` + +2. NA Handling + +```{r} +# Problem +data <- c(1, NA, 3, 4) +result <- data > 2 | data < 2 # Contains NA +print(result) + +# Solution +result <- data > 2 | data < 2 | is.na(data) +print(result) +``` + +# FAQs + +**Q: How does OR operator performance compare in large datasets?** + +According to DataCamp, vectorized operations with `|` are more efficient for large datasets, while `||` is faster for single conditions. + +**Q: Can I use OR operators with factor variables?** + +Yes, but convert factors to character or numeric first for reliable results (Statistics Globe). + +**Q: How do OR operators work with different data types?** + +R coerces values to logical before applying OR operations. See type conversion rules in R documentation. + +**Q: What's the best practice for complex conditions?** + +R-bloggers recommends using parentheses and breaking complex conditions into smaller, readable chunks. + +**Q: How do I optimize OR operations in data.table?** + +data.table provides optimized methods for logical operations within its syntax. + +# References + +1. [DataMentor: "R Operators Guide"](https://www.datamentor.io/r-programming/operator/) + +2. [GeeksforGeeks: "R Programming Logical Operators"](https://www.geeksforgeeks.org/r-operators/?ref=header_outind#logical-operators) + +# Engage! + +Share your OR operator experiences or questions in the comments below! Follow us for more R programming tutorials and tips. + +For hands-on practice, try our example code in RStudio and experiment with different conditions. Join our R programming community to discuss more advanced techniques and best practices. + +------------------------------------------------------------------------ + +Happy Coding! 🚀 + +------------------------------------------------------------------------ + +*You can connect with me at any one of the below*: + +*Telegram Channel here*: + +*LinkedIn Network here*: + +*Mastadon Social here*: [https://mstdn.social/\@stevensanderson](https://mstdn.social/@stevensanderson) + +*RStats Network here*: [https://rstats.me/\@spsanderson](https://rstats.me/@spsanderson) + +*GitHub Network here*: + +------------------------------------------------------------------------ + + diff --git a/posts/2024-10-31/todays_post.png b/posts/2024-10-31/todays_post.png new file mode 100644 index 0000000000000000000000000000000000000000..53c880ce7aa61287643d9dada69db4e6d8f3b9d3 GIT binary patch literal 1583882 zcmV)TK(W7xP)L zchoIOavugpL}pc8`}ya6`yM}VGnjz~Xh4FX00E+vNQ&g1)pDs_n&oQG9R&1|W!+kSmWQhzP*$ 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/site_libs/quarto-html/quarto-syntax-highlighting-dark.css b/site_libs/quarto-html/quarto-syntax-highlighting-dark.css new file mode 100644 index 00000000..343d2f80 --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting-dark.css @@ -0,0 +1,189 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #f07178; + --quarto-hl-an-color: #d4d0ab; + --quarto-hl-at-color: #00e0e0; + --quarto-hl-bn-color: #d4d0ab; + --quarto-hl-bu-color: #abe338; + --quarto-hl-ch-color: #abe338; + --quarto-hl-co-color: #f8f8f2; + --quarto-hl-cv-color: #ffd700; + --quarto-hl-cn-color: #ffd700; + --quarto-hl-cf-color: #ffa07a; + --quarto-hl-dt-color: #ffa07a; + --quarto-hl-dv-color: #d4d0ab; + --quarto-hl-do-color: #f8f8f2; + --quarto-hl-er-color: #f07178; + --quarto-hl-ex-color: #00e0e0; + --quarto-hl-fl-color: #d4d0ab; + --quarto-hl-fu-color: #ffa07a; + --quarto-hl-im-color: #abe338; + --quarto-hl-in-color: #d4d0ab; + --quarto-hl-kw-color: #ffa07a; + --quarto-hl-op-color: #ffa07a; + --quarto-hl-ot-color: #00e0e0; + --quarto-hl-pp-color: #dcc6e0; + --quarto-hl-re-color: #00e0e0; + --quarto-hl-sc-color: #abe338; + --quarto-hl-ss-color: #abe338; + --quarto-hl-st-color: #abe338; + --quarto-hl-va-color: #00e0e0; + --quarto-hl-vs-color: #abe338; + --quarto-hl-wa-color: #dcc6e0; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span.al { + background-color: #2a0f15; + font-weight: bold; + color: #f07178; +} + +code span.an { + color: #d4d0ab; +} + +code span.at { + color: #00e0e0; +} + +code span.bn { + color: #d4d0ab; +} + +code span.bu { + color: #abe338; +} + +code span.ch { + color: #abe338; +} + +code span.co { + font-style: italic; + color: #f8f8f2; +} + +code span.cv { + color: #ffd700; +} + +code span.cn { + color: #ffd700; +} + +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +code span.dt { + color: #ffa07a; +} + +code span.dv { + color: #d4d0ab; +} + +code span.do { + color: #f8f8f2; +} + +code span.er { + color: #f07178; + text-decoration: underline; +} + +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +code span.fl { + color: #d4d0ab; +} + +code span.fu { + color: #ffa07a; +} + +code span.im { + color: #abe338; +} + +code span.in { + color: #d4d0ab; +} + +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +pre > code.sourceCode > span { + color: #f8f8f2; +} + +code span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +code span.op { + color: #ffa07a; +} + +code span.ot { + color: #00e0e0; +} + +code span.pp { + color: #dcc6e0; +} + +code span.re { + background-color: #f8f8f2; + color: #00e0e0; +} + +code span.sc { + color: #abe338; +} + +code span.ss { + color: #abe338; +} + +code span.st { + color: #abe338; +} + +code span.va { + color: #00e0e0; +} + +code span.vs { + color: #abe338; +} + +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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