- Copy and paste the example broken script into R
Basic Manual Debugging Strategy
- How do we figure out what’s wrong with a program?
- Be a scientist.
- Hypothesize about what is wrong.
- Make one change that is expected to fix error.
- Check if change worked/fixed error.
- Do not change something without a reason.
- Developing hypotheses (observe)
- See where the code failed.
- Read the error message.
- Observe what the code is doing.
- Look at the current state of the environment (snapshot of what’s going on)
- Talk through the code.
- Rubber duck programming
- Run the code line by line checking each step.
- Have a LLM look for problems
- Be a scientist.
Example
- Create a file with the following code in it
- Run it
- Work through debugging the code
library(dplyr)
library(readr)
surveys <- read_csv('surveys.csv')
species <- read_csv('species.csv')
do_counts_by_year <- survey |>
filter(species = "DO") |>
group_by(year)
summarize(count = n())
ggplot(do_count_by_year, aes(x = year, y = count)) +
geom_point() +
geom_line()
labs(x = "Year", y = "Count")
Debugged version of example
library(dplyr)
library(ggplot2)
library(readr)
surveys <- read_csv('surveys.csv')
do_counts_by_year <- surveys |>
filter(species_id == "DO") |>
group_by(year) |>
summarize(count = n())
ggplot(do_counts_by_year, aes(x = year, y = count)) +
geom_point() +
geom_line() +
labs(x = "Year", y = "Count")
- Now copy the code into a LLM and see what it finds and what it gets wrong