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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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| ``` | ||
| def calculate_average(numbers): | ||
| total = 0 | ||
| for number in numbers: | ||
| total += number | ||
| average = total / len(numbers) | ||
| return average | ||
| numbers = [1, 2, 3, 4, "5"] | ||
| average = calculate_average(numbers) | ||
| print("The average of the numbers is:", average) | ||
| ``` | ||
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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 | ||
| devtools::session_info() | ||
| ``` | ||
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| ``` | ||
| ## ─ Session info ─────────────────────────────────────────────────────────────── | ||
| ## setting value | ||
| ## version R version 4.0.2 (2020-06-22) | ||
| ## os Ubuntu 20.04.5 LTS | ||
| ## system x86_64, linux-gnu | ||
| ## ui X11 | ||
| ## language (EN) | ||
| ## collate en_US.UTF-8 | ||
| ## ctype en_US.UTF-8 | ||
| ## tz Etc/UTC | ||
| ## date 2023-11-01 | ||
| ## | ||
| ## ─ Packages ─────────────────────────────────────────────────────────────────── | ||
| ## package * version date lib source | ||
| ## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.5) | ||
| ## bookdown 0.24 2023-03-28 [1] Github (rstudio/bookdown@88bc4ea) | ||
| ## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.0.2) | ||
| ## cachem 1.0.7 2023-02-24 [1] CRAN (R 4.0.2) | ||
| ## callr 3.5.0 2020-10-08 [1] RSPM (R 4.0.2) | ||
| ## cli 3.6.1 2023-03-23 [1] CRAN (R 4.0.2) | ||
| ## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0) | ||
| ## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3) | ||
| ## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3) | ||
| ## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0) | ||
| ## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3) | ||
| ## evaluate 0.20 2023-01-17 [1] CRAN (R 4.0.2) | ||
| ## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0) | ||
| ## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.0.2) | ||
| ## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3) | ||
| ## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.5) | ||
| ## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0) | ||
| ## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.0.2) | ||
| ## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2) | ||
| ## jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2) | ||
| ## knitr 1.33 2023-03-28 [1] Github (yihui/knitr@a1052d1) | ||
| ## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.0.2) | ||
| ## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.0.2) | ||
| ## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2) | ||
| ## ottrpal 1.0.1 2023-03-28 [1] Github (jhudsl/ottrpal@151e412) | ||
| ## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.0.2) | ||
| ## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2) | ||
| ## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3) | ||
| ## pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.3) | ||
| ## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3) | ||
| ## processx 3.4.4 2020-09-03 [1] RSPM (R 4.0.2) | ||
| ## ps 1.4.0 2020-10-07 [1] RSPM (R 4.0.2) | ||
| ## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0) | ||
| ## readr 1.4.0 2020-10-05 [1] RSPM (R 4.0.2) | ||
| ## remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.3) | ||
| ## rlang 1.1.0 2023-03-14 [1] CRAN (R 4.0.2) | ||
| ## rmarkdown 2.10 2023-03-28 [1] Github (rstudio/rmarkdown@02d3c25) | ||
| ## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.0.2) | ||
| ## sass 0.4.5 2023-01-24 [1] CRAN (R 4.0.2) | ||
| ## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3) | ||
| ## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3) | ||
| ## stringr 1.4.0 2019-02-10 [1] RSPM (R 4.0.3) | ||
| ## testthat 3.0.1 2023-03-28 [1] Github (R-lib/testthat@e99155a) | ||
| ## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.0.2) | ||
| ## usethis 1.6.3 2020-09-17 [1] RSPM (R 4.0.2) | ||
| ## utf8 1.1.4 2018-05-24 [1] RSPM (R 4.0.3) | ||
| ## vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.0.2) | ||
| ## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2) | ||
| ## xfun 0.26 2023-03-28 [1] Github (yihui/xfun@74c2a66) | ||
| ## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3) | ||
| ## | ||
| ## [1] /usr/local/lib/R/site-library | ||
| ## [2] /usr/local/lib/R/library | ||
| ``` | ||
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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 | ||
| import tweepy | ||
| import pandas | ||
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| # Enter your API keys and access tokens here | ||
| consumer_key = 'your_consumer_key' | ||
| consumer_secret = "your_consumer_secret" | ||
| access_token = 'your_access_token' | ||
| access_token_secret = 'your_access_token_secret' | ||
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| # Authenticate with Twitter API | ||
| auth = tweepy.OAuthHandler(consumer_key, consumer_secret) | ||
| auth.set_access_token(access_token, access_token_secret) | ||
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| # Search for tweets containing the search term | ||
| 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 | ||
| date_data = [] | ||
| location_data = [] | ||
| text_data = [] | ||
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| def get_tweet_length(tweet): | ||
| # Return the length of the tweet text | ||
| return len(tweet.full_text) | ||
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| # Loop through each tweet and extract desired data | ||
| for tweet in tweets: | ||
| date_info = { | ||
| 'date': tweet.created_at | ||
| } | ||
| date_data.append(date_info) | ||
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| # Search for tweets containing the search term | ||
| 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 | ||
| for tweet in tweets: | ||
| location_info = { | ||
| 'location': tweet.user.location | ||
| } | ||
| location_data.append(location_info) | ||
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| # Search for tweets containing the search term | ||
| 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 | ||
| for tweet in tweets: | ||
| text_info = { | ||
| 'text': tweet.full_text | ||
| } | ||
| text_data.append(text_info) | ||
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| # Combine lists into a dictionary | ||
| data = {'date': date_data, | ||
| 'location': location_data, 'text': text_data} | ||
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| # Store results in pandas dataframe | ||
| df = pandas.DataFrame(data) | ||
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| # Print dataframe | ||
| print(df) | ||
| ``` | ||
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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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