Working with Chat GPT

Tyler George

Cornell College
DSC 223 - Spring 2024 Block 7

Warm up

The 🐘 in the room: Chat GPT

Using Chat GPT

How are you using Chat GPT for this class?

Example

Each CSI franchise includes a number of series, each with the character string "CSI" (in capital letters) in their title. Using this information, create a new data frame called csi that only has titles from the CSI-verse and all of the columns from tv for these titles. Then, display the first 10 rows of csi. Note: The titles and seasons included in the dataset may not be all CSI titles and titles that have ever aired.

Chat GPT - Take 1 prompt

Each CSI franchise includes a number of series, each with the character string "CSI" (in capital letters) in their title. Using this information, create a new data frame called csi that only has titles from the CSI-verse and all of the columns from tv for these titles. Then, display the first 10 rows of csi. Note: The titles and seasons included in the dataset may not be all CSI titles and titles that have ever aired.

Chat GPT - Take 1 response

Certainly! To filter the tv data frame to include only titles from the CSI-verse, you can use the filter function from the dplyr package. Here’s the code:
library(dplyr)

# Assuming your dataset is named 'tv'
csi <- tv %>%
  filter(str_detect(title, "CSI", case_insensitive = TRUE))

# Display the first 10 rows of the 'csi' data frame
head(csi, 10)
This code uses the filter function to select rows where the title column contains the string “CSI” (case-insensitive). The resulting data frame, csi, will include only titles from the CSI-verse along with all the columns from the original tv data frame. Make sure to replace ‘tv’ with the actual name of your dataset.

Chat GPT - Take 1 code

What is wrong with the following code?

tv <- read_csv("data/tv.csv")

library(dplyr)

# Assuming your dataset is named 'tv'
csi <- tv %>%
  filter(str_detect(title, "CSI", case_insensitive = TRUE))

Error in `filter()`:
ℹ In argument: `str_detect(title, "CSI", case_insensitive = TRUE)`.
Caused by error in `str_detect()`:
! unused argument (case_insensitive = TRUE)

# Display the first 10 rows of the 'csi' data frame
head(csi, 10)

Error in eval(expr, envir, enclos): object 'csi' not found

Chat GPT - Take 2 prompt

str_detect doesn’t have a case_insensitive argument, can you try again?

Chat GPT - Take 2 response

You are correct, and I appreciate your understanding. The str_detect function itself doesn’t have a case_insensitive argument. Instead, you can use the str_which function along with the case_insensitive argument in the filter function. Here’s the corrected code:
library(dplyr)

# Assuming your dataset is named 'tv'
csi <- tv %>%
  filter(str_detect(title, "CSI", ignore_case = TRUE))

# Display the first 10 rows of the 'csi' data frame
head(csi, 10)
This code uses str_detect within the filter function and includes the ignore_case = TRUE argument to perform a case-insensitive search. The resulting csi data frame will contain only titles from the CSI-verse along with all the columns from the original tv data frame. Make sure to replace ‘tv’ with the actual name of your dataset.

Chat GPT - Take 2 code

library(dplyr)

# Assuming your dataset is named 'tv'
csi <- tv %>%
  filter(str_detect(title, "CSI", ignore_case = TRUE))

Error in `filter()`:
ℹ In argument: `str_detect(title, "CSI", ignore_case = TRUE)`.
Caused by error in `str_detect()`:
! unused argument (ignore_case = TRUE)

# Display the first 10 rows of the 'csi' data frame
head(csi, 10)

Error in eval(expr, envir, enclos): object 'csi' not found

Chat GPT - Take 3 prompt

str_detect doesn’t have an ignore_case argument either, can you try again?

Chat GPT - Take 3 response

I apologize for the oversight. It seems there was a mistake in my responses. To perform a case-insensitive search, you can use the stringi package along with stri_detect_regex. Here’s the corrected code:
library(dplyr)
library(stringi)

# Assuming your dataset is named 'tv'
csi <- tv %>%
  filter(stri_detect_regex(title, "CSI", case_insensitive = TRUE))

# Display the first 10 rows of the 'csi' data frame
head(csi, 10)
This code uses stri_detect_regex from the stringi package within the filter function, and it includes the case_insensitive = TRUE argument for a case-insensitive search. The resulting csi data frame will contain only titles from the CSI-verse along with all the columns from the original tv data frame. Make sure to replace ‘tv’ with the actual name of your dataset.

Let’s go back to Take 1

and edit it a bit:

tv |>
  filter(str_detect(title, "CSI"))

# A tibble: 36 × 9
   season title                         year month   day av_rating genre_1 genre_2 genre_3
    <dbl> <chr>                        <dbl> <dbl> <dbl>     <dbl> <chr>   <chr>   <chr>  
 1      1 CSI: Crime Scene Investigat…  2001     1    20      8.32 Crime   Drama   Mystery
 2      2 CSI: Crime Scene Investigat…  2002     1    10      8.26 Crime   Drama   Mystery
 3      3 CSI: Crime Scene Investigat…  2003     1    15      8.30 Crime   Drama   Mystery
 4      4 CSI: Crime Scene Investigat…  2004     1    18      8.33 Crime   Drama   Mystery
 5      5 CSI: Crime Scene Investigat…  2005     1    24      8.38 Crime   Drama   Mystery
 6      6 CSI: Crime Scene Investigat…  2006     1    16      8.21 Crime   Drama   Mystery
 7      7 CSI: Crime Scene Investigat…  2007     1    14      8.43 Crime   Drama   Mystery
 8      8 CSI: Crime Scene Investigat…  2008     1     7      8.07 Crime   Drama   Mystery
 9      9 CSI: Crime Scene Investigat…  2009     1    27      7.80 Crime   Drama   Mystery
10     10 CSI: Crime Scene Investigat…  2010     1    23      7.69 Crime   Drama   Mystery
# ℹ 26 more rows

Let’s go back to Take 1

csi <- tv |>
  filter(str_detect(title, "CSI"))

csi

# A tibble: 36 × 9
   season title                         year month   day av_rating genre_1 genre_2 genre_3
    <dbl> <chr>                        <dbl> <dbl> <dbl>     <dbl> <chr>   <chr>   <chr>  
 1      1 CSI: Crime Scene Investigat…  2001     1    20      8.32 Crime   Drama   Mystery
 2      2 CSI: Crime Scene Investigat…  2002     1    10      8.26 Crime   Drama   Mystery
 3      3 CSI: Crime Scene Investigat…  2003     1    15      8.30 Crime   Drama   Mystery
 4      4 CSI: Crime Scene Investigat…  2004     1    18      8.33 Crime   Drama   Mystery
 5      5 CSI: Crime Scene Investigat…  2005     1    24      8.38 Crime   Drama   Mystery
 6      6 CSI: Crime Scene Investigat…  2006     1    16      8.21 Crime   Drama   Mystery
 7      7 CSI: Crime Scene Investigat…  2007     1    14      8.43 Crime   Drama   Mystery
 8      8 CSI: Crime Scene Investigat…  2008     1     7      8.07 Crime   Drama   Mystery
 9      9 CSI: Crime Scene Investigat…  2009     1    27      7.80 Crime   Drama   Mystery
10     10 CSI: Crime Scene Investigat…  2010     1    23      7.69 Crime   Drama   Mystery
# ℹ 26 more rows

Guidelines and best practices for using Chat GPT

Do not just copy-paste the prompt – for appropriate academic conduct, for your own learning, and for getting to better results faster
Engineer the prompt until the response starts to look like code you’re learning in the course
If the response is not correct, ask for a correction
If the response doesn’t follow the guidelines, ask for a correction
Do not just copy-paste code from Chat GPT responses, run it line-by-line and edit as needed
Watch out for clear mistakes in the response: do not keep loading packages that are already loaded, use the base pipe |>, use tidyverse-based code, etc.