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‎docs/01-intro.md‎

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- [Phind](https://www.phind.com/) is a AI-powered search engine designed specifically for developers asking technical questions. In addition to answering your coding question, it will provide links to the internet sources used to find the answer.
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- [Poe](https://poe.com/) is Quora's platform that gives you access to multiple AI chatbots, including Sage, based on OpenAI's GPT-3.5 architecture; Claude, an AI natural language bot created by [Anthropic](https://www.anthropic.com); and ChatGPT.
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- [Poe](https://poe.com/) is Quora's platform that gives you access to multiple AI chatbots, including Sage, based on OpenAI's GPT-3.5 architecture; Claude, an AI natural language bot created by [Anthropic](https://www.anthropic.com); Llama 2, created by [Meta](https://ai.meta.com/llama/), and ChatGPT.
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**IDE-based chatbots**
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## About This Book
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We used AI bots (including ChatGPT, Bard, Sage, and Claude) to develop and write much of this course. We firmly believe this new technology can be used in ethical ways and we put into practice all the rules and suggestions offered over the following modules while we created this book.
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We used AI bots (including ChatGPT, Bard, Sage, and Claude) as part of the process of creating this book. The words are all original content, but we used AI bots to edit for grammar and tone. We firmly believe this new technology can be used in ethical ways and we put into practice all the rules and suggestions offered over the following modules while we created this book.

‎docs/03-writing_code.md‎

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<img src="resources/images/03-writing_code_files/figure-html//1MCNeSO4aOm1iESWDLOGTcx3aLEbnu8UttV0QGVAeafE_g230c4a00725_31_0.png" title="Claude-instant directly compares Python and Java as languages for the blackjack game, suggesting Python is best for a simple game and Java is better if you want to build a more complex game." alt="Claude-instant directly compares Python and Java as languages for the blackjack game, suggesting Python is best for a simple game and Java is better if you want to build a more complex game." width="100%" style="display: block; margin: auto;" />
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## Hands On Exercise
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Now it’s your turn to try!
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### Code
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Here's the code you need for question #2.
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```
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def calculate_average(numbers):
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total = 0
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for number in numbers:
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total += number
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average = total / len(numbers)
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return average
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numbers = [1, 2, 3, 4, "5"]
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average = calculate_average(numbers)
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print("The average of the numbers is:", average)
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```
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### Questions
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1. Write a prompt to create a function that replaces all instances of the string "three" with the number 3, then create a prompt that allows you to modify the function so that "three", "Three", and "THREE" are all replaced with the number 3.
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1. Write a query that helps you debug the code snippet from above.
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1. Create a query that explains how to retrieve data from an online database. Don't forget to find out what the source of the code is.
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1. Write a prompt to plan out a program that creates and plays a game of Rock, Paper, Scissors. You should create a multi-step plan as well as figure out what your first coding task should be.
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1. Create a prompt that identifies the best coding language for the above game.
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## Summary
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* Coding has become an essential skill in today's world. AI can increase the efficiency of planning, writing, and debugging code, as well as make coding more accessible for beginners.
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## collate en_US.UTF-8
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## ctype en_US.UTF-8
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## tz Etc/UTC
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## date 2023-08-22
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## date 2023-11-01
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##
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## ─ Packages ───────────────────────────────────────────────────────────────────
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## package * version date lib source

‎docs/03-writing_code_activity.md‎

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# Writing Code: Hands-On Exercise
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Now it’s your turn to try!
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## Code
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Here's the code you need for question #2.
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```
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def calculate_average(numbers):
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total = 0
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for number in numbers:
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total += number
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average = total / len(numbers)
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return average
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numbers = [1, 2, 3, 4, "5"]
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average = calculate_average(numbers)
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print("The average of the numbers is:", average)
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```
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## Questions
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1. Write a prompt to create a function that replaces all instances of the string "three" with the number 3, then create a prompt that allows you to modify the function so that "three", "Three", and "THREE" are all replaced with the number 3.
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1. Write a query that helps you debug the code snippet from above.
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1. Create a query that explains how to retrieve data from an online database. Don't forget to find out what the source of the code is.
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1. Write a prompt to plan out a program that creates and plays a game of Rock, Paper, Scissors. You should create a multi-step plan as well as figure out what your first coding task should be.
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1. Create a prompt that identifies the best coding language for the above game.
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```r
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devtools::session_info()
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```
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```
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## ─ Session info ───────────────────────────────────────────────────────────────
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## setting value
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## version R version 4.0.2 (2020-06-22)
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## os Ubuntu 20.04.5 LTS
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## system x86_64, linux-gnu
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## ui X11
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## language (EN)
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## collate en_US.UTF-8
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## ctype en_US.UTF-8
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## tz Etc/UTC
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## date 2023-11-01
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##
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## ─ Packages ───────────────────────────────────────────────────────────────────
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## package * version date lib source
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## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.5)
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## bookdown 0.24 2023-03-28 [1] Github (rstudio/bookdown@88bc4ea)
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## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.0.2)
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## cachem 1.0.7 2023-02-24 [1] CRAN (R 4.0.2)
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## callr 3.5.0 2020-10-08 [1] RSPM (R 4.0.2)
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## cli 3.6.1 2023-03-23 [1] CRAN (R 4.0.2)
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## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0)
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## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3)
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## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3)
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## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0)
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## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3)
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## evaluate 0.20 2023-01-17 [1] CRAN (R 4.0.2)
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## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0)
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## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.0.2)
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## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3)
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## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.5)
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## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0)
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## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.0.2)
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## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2)
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## jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2)
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## knitr 1.33 2023-03-28 [1] Github (yihui/knitr@a1052d1)
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## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.0.2)
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## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.0.2)
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## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2)
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## ottrpal 1.0.1 2023-03-28 [1] Github (jhudsl/ottrpal@151e412)
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## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.0.2)
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## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2)
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## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3)
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## pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.3)
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## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3)
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## processx 3.4.4 2020-09-03 [1] RSPM (R 4.0.2)
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## ps 1.4.0 2020-10-07 [1] RSPM (R 4.0.2)
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## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0)
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## readr 1.4.0 2020-10-05 [1] RSPM (R 4.0.2)
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## remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.3)
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## rlang 1.1.0 2023-03-14 [1] CRAN (R 4.0.2)
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## rmarkdown 2.10 2023-03-28 [1] Github (rstudio/rmarkdown@02d3c25)
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## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.0.2)
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## sass 0.4.5 2023-01-24 [1] CRAN (R 4.0.2)
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## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3)
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## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3)
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## stringr 1.4.0 2019-02-10 [1] RSPM (R 4.0.3)
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## testthat 3.0.1 2023-03-28 [1] Github (R-lib/testthat@e99155a)
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## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.0.2)
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## usethis 1.6.3 2020-09-17 [1] RSPM (R 4.0.2)
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## utf8 1.1.4 2018-05-24 [1] RSPM (R 4.0.3)
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## vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.0.2)
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## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2)
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## xfun 0.26 2023-03-28 [1] Github (yihui/xfun@74c2a66)
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## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3)
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##
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## [1] /usr/local/lib/R/site-library
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## [2] /usr/local/lib/R/library
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```
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‎docs/04-refactoring.md‎

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```
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## 7.971 0.000 7.970
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```
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:::{.query}
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```
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## 0.800 0.527 0.620
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The `outer()` function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The `rowSums()` function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index `i` in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.
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```
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One optimized way to perform the same calculation is by using the `%*%` operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.
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When using AI to refactor code, the code itself is often sent to an external service or platform for analysis and transformation. This can raise concerns about the security of the code, especially if it contains sensitive information such as trade secrets, proprietary algorithms, or personal data. If your code is sensitive, it's important to carefully vet any third-party AI tools or services used in the refactoring process.
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## Hands-On Exercise
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Now it's your turn to try.
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### The Code
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Let's say you are dusting off some code from your past (no judgment here). You were investigating tweets about [Mr. Trash Wheel](https://www.mrtrashwheel.com/), a beloved Baltimore-based contraption that filters trash out of the waterways.
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**Note**: This code is just an example and was written strictly for educational purposes.
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```python
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import tweepy
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import pandas
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# Enter your API keys and access tokens here
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consumer_key = 'your_consumer_key'
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consumer_secret = "your_consumer_secret"
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access_token = 'your_access_token'
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access_token_secret = 'your_access_token_secret'
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# Authenticate with Twitter API
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auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
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auth.set_access_token(access_token, access_token_secret)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q="Mr. Trash Wheel", tweet_mode='extended').items(100)
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# Create empty list to store tweet data
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date_data = []
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location_data = []
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text_data = []
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def get_tweet_length(tweet):
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# Return the length of the tweet text
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return len(tweet.full_text)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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date_info = {
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'date': tweet.created_at
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}
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date_data.append(date_info)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q="Mr. Trash Wheel", tweet_mode='extended').items(100)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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location_info = {
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'location': tweet.user.location
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}
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location_data.append(location_info)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q='Mr. Trash Wheel', tweet_mode='extended').items(100)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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text_info = {
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'text': tweet.full_text
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}
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text_data.append(text_info)
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# Combine lists into a dictionary
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'location': location_data, 'text': text_data}
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# Store results in pandas dataframe
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df = pandas.DataFrame(data)
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# Print dataframe
718-
print(df)
719-
```
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### Questions
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1. Create an AI prompt that fixes any formatting issues with the code that would cause it not to run.
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1. Devise an AI prompt that removes any dead code from your sample above. What gets removed?
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1. Create a prompt that makes the code less repetitive, adhering to the DRY principle. What aspect of the code was repetitive?
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1. Construct a prompt that makes the code more concise. What are some trade-offs that appear in this code between readability and brevity?
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## Summary
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‎docs/04-refactoring_activity.md‎

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# Refactoring Code: Hands-On Exercise
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Now it's your turn to try.
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## The Code
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Let's say you are dusting off some code from your past (no judgment here). You were investigating tweets about [Mr. Trash Wheel](https://www.mrtrashwheel.com/), a beloved Baltimore-based contraption that filters trash out of the waterways.
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**Note**: This code is just an example and was written strictly for educational purposes.
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```python
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import tweepy
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import pandas
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# Enter your API keys and access tokens here
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consumer_key = 'your_consumer_key'
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consumer_secret = "your_consumer_secret"
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access_token = 'your_access_token'
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access_token_secret = 'your_access_token_secret'
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# Authenticate with Twitter API
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auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
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auth.set_access_token(access_token, access_token_secret)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q="Mr. Trash Wheel", tweet_mode='extended').items(100)
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# Create empty list to store tweet data
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date_data = []
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location_data = []
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text_data = []
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def get_tweet_length(tweet):
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# Return the length of the tweet text
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return len(tweet.full_text)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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date_info = {
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'date': tweet.created_at
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}
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date_data.append(date_info)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q="Mr. Trash Wheel", tweet_mode='extended').items(100)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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location_info = {
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'location': tweet.user.location
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}
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location_data.append(location_info)
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# Search for tweets containing the search term
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tweets = tweepy.Cursor(tweepy.API(auth).search_tweets, q='Mr. Trash Wheel', tweet_mode='extended').items(100)
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# Loop through each tweet and extract desired data
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for tweet in tweets:
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text_info = {
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'text': tweet.full_text
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}
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text_data.append(text_info)
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# Combine lists into a dictionary
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data = {'date': date_data,
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'location': location_data, 'text': text_data}
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# Store results in pandas dataframe
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df = pandas.DataFrame(data)
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# Print dataframe
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print(df)
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```
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## Questions
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1. Devise an AI prompt that removes any dead code from your sample above. What gets removed?
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1. Create a prompt that makes the code less repetitive, adhering to the DRY principle. What aspect of the code was repetitive?
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‎docs/05-annotation.md‎

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## date 2023-08-22
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## date 2023-11-01
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##
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## ─ Packages ───────────────────────────────────────────────────────────────────
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## package * version date lib source

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