README.md

---
license: unlicense
dataset_info:
  features:
  - name: title
    dtype: string
  - name: selftext
    dtype: string
  - name: top_comment
    dtype: string
  - name: subreddit
    dtype: string
  splits:
  - name: train
    num_bytes: 12747912959
    num_examples: 15689260
  download_size: 7773494765
  dataset_size: 12747912959
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

## Top comments from subbreddits

These are comments from a select group of subbreddits (see below) and all the posts were filtered to select only the top comment from that post.  

The filter criteria was that it must have had at least one up vote.

It covers the dates from 2005-2022.

I picked the subreddits that were the most popular.  I did not pick NSFW but there is probably some NSFW language in here so be aware.

The subreddits in the dataset are:

* AskReddit
* worldnews
* todayilearned
* Music
* movies
* science
* Showerthoughts
* Jokes
* space
* books
* WritingPrompts
* tifu
* wallstreetbets
* explainlikeimfive
* askscience
* history
* technology
* relationship_advice
* relationships
* Damnthatsinteresting
* CryptoCurrency
* television
* politics
* Parenting
* Bitcoin
* creepy
* nosleep

## Loading the dataset

Each entry in the dataset includes the following columns:
- **title**: The title of the Reddit post.
- **selftext**: The body text of the Reddit post.
- **top_comment**: The top comment on the Reddit post.
- **subreddit**: The subreddit where the post was made.

### 1. Loading the Entire Dataset

To load the entire dataset, use the following code:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("cowWhySo/reddit_top_comments")
```

### 2. Loading Specific Splits
To load specific splits of the dataset:

```python
from datasets import load_dataset

# Load the train split
train_dataset = load_dataset("cowWhySo/reddit_top_comments", split="train")

# Load the validation split
validation_dataset = load_dataset("cowWhySo/reddit_top_comments", split="validation")

# Load the test split
test_dataset = load_dataset("cowWhySo/reddit_top_comments", split="test")
```

### 3. Streaming the Dataset
You can stream the data:

```python
from datasets import load_dataset

# Stream the train split
train_streaming = load_dataset("cowWhySo/reddit_top_comments", split="train", streaming=True)

# Iterate through the dataset
for example in train_streaming:
    print(example)
    break  # Just print the first example for demonstration
```

### 4. Loading a Specific Slice
To load a specific portion of the dataset:

```python
from datasets import load_dataset

# Load the first 10% of the train split
train_slice = load_dataset("your-username/your-dataset-name", split="train[:10%]")

# Print the first few examples
print(train_slice[:5])
```

## Code to download subredditt's

dl_subbreddits.sh:
```
#!/bin/bash
#
# Directions:
#./dl_reddit_comments.sh submissions
# or
#./dl_reddit_comments.sh comments

# Check if the argument is provided and valid
if [ "$#" -ne 1 ] || { [ "$1" != "submissions" ] && [ "$1" != "comments" ]; }; then
  echo "Usage: $0 <submissions|comments>"
  exit 1
fi

# Create the reddit_data folder if it doesn't exist
mkdir -p reddit_data

# Base URL
base_url="https://the-eye.eu/redarcs/files/"

# Array of subreddit names
subreddits=(
  "AskReddit"
  "worldnews"
  "todayilearned"
  "Music"
  "movies"
  "science"
  "Showerthoughts"
  "Jokes"
  "space"
  "books"
  "WritingPrompts"
  "tifu"
  "wallstreetbets"
  "explainlikeimfive"
  "askscience"
  "history"
  "technology"
  "relationship_advice"
  "relationships"
  "Damnthatsinteresting"
  "CryptoCurrency"
  "television"
  "politics"
  "Parenting"
  "Bitcoin"
  "creepy"
  "nosleep"
)

# Export base_url so it can be used by xargs
export base_url

# Argument to determine whether to download comments or submissions
type=$1

# Generate file names based on the argument
file_names=()
for subreddit in "${subreddits[@]}"; do
  file_names+=("${subreddit}_${type}.zst")
done

# Download each file using wget in parallel
printf "%s\n" "${file_names[@]}" | xargs -n 1 -P 8 -I {} wget -P reddit_data "${base_url}{}"
```

## Code to process for top comments

This may need some work. There is some chunking that needed to be done because some of the comment files are very large. 

AskReddit subbreddit was 50gb of comments so processing that to a csv was a bit painful.

```
import zstandard
import os
import json
import sys
import csv
from datetime import datetime
import logging
from concurrent.futures import ProcessPoolExecutor

log = logging.getLogger("bot")
log.setLevel(logging.DEBUG)
log.addHandler(logging.StreamHandler())

def read_and_decode(reader, chunk_size, max_window_size, previous_chunk=None, bytes_read=0):
    chunk = reader.read(chunk_size)
    bytes_read += chunk_size
    if previous_chunk is not None:
        chunk = previous_chunk + chunk
    try:
        return chunk.decode()
    except UnicodeDecodeError:
        if bytes_read > max_window_size:
            raise UnicodeError(f"Unable to decode frame after reading {bytes_read:,} bytes")
        log.info(f"Decoding error with {bytes_read:,} bytes, reading another chunk")
        return read_and_decode(reader, chunk_size, max_window_size, chunk, bytes_read)

def read_lines_zst(file_name):
    with open(file_name, 'rb') as file_handle:
        buffer = ''
        reader = zstandard.ZstdDecompressor(max_window_size=2**31).stream_reader(file_handle)
        while True:
            chunk = read_and_decode(reader, 2**27, (2**29) * 2)
            if not chunk:
                break
            lines = (buffer + chunk).split("\n")
            for line in lines[:-1]:
                yield line, file_handle.tell()
            buffer = lines[-1]
        reader.close()

def process_file(input_file, output_folder):
    output_file_path = os.path.join(output_folder, os.path.splitext(os.path.basename(input_file))[0] + '.csv')
    log.info(f"Processing {input_file} to {output_file_path}")

    is_submission = "submission" in input_file
    if is_submission:
        fields = ["author", "title", "score", "created", "link", "text", "url"]
    else:
        fields = ["author", "score", "created", "link", "body"]

    file_size = os.stat(input_file).st_size
    file_lines, bad_lines = 0, 0
    line, created = None, None

    # Dictionary to store the top comment for each post
    top_comments = {}

    with open(output_file_path, "w", encoding='utf-8', newline="") as output_file:
        writer = csv.DictWriter(output_file, fieldnames=fields, quoting=csv.QUOTE_MINIMAL, quotechar='"', escapechar='\\')
        writer.writeheader()

        try:
            for line, file_bytes_processed in read_lines_zst(input_file):
                try:
                    obj = json.loads(line)
                    if is_submission:
                        # Process submission data
                        submission = {
                            'author': f"u/{obj['author']}",
                            'title': obj.get('title', ''),
                            'score': obj.get('score', 0),
                            'created': datetime.fromtimestamp(int(obj['created_utc'])).strftime("%Y-%m-%d %H:%M"),
                            'link': f"https://www.reddit.com/r/{obj['subreddit']}/comments/{obj['id']}/",
                            'text': obj.get('selftext', ''),
                            'url': obj.get('url', ''),
                        }
                        writer.writerow(submission)
                    else:
                        # Process comment data and look for top comments
                        post_id = obj['link_id']
                        score = obj.get('score', 0)
                        body = obj.get('body', '')
                        
                        if "[deleted]" in body or score <= 1:
                            continue
                        
                        comment = {
                            'author': f"u/{obj['author']}",
                            'score': score,
                            'created': datetime.fromtimestamp(int(obj['created_utc'])).strftime("%Y-%m-%d %H:%M"),
                            'link': f"https://www.reddit.com/r/{obj['subreddit']}/comments/{obj['link_id'][3:]}/_/{obj['id']}/",
                            'body': body,
                        }
                        
                        if post_id not in top_comments or score > top_comments[post_id]['score']:
                            top_comments[post_id] = comment
                            writer.writerow(comment)
                    
                    created = datetime.utcfromtimestamp(int(obj['created_utc']))
                except json.JSONDecodeError as err:
                    bad_lines += 1
                file_lines += 1
                if file_lines % 100000 == 0:
                    log.info(f"{created.strftime('%Y-%m-%d %H:%M:%S')} : {file_lines:,} : {bad_lines:,} : {(file_bytes_processed / file_size) * 100:.0f}%")
        except KeyError as err:
            log.info(f"Object has no key: {err}")
            log.info(line)
        except Exception as err:
            log.info(err)
            log.info(line)

    log.info(f"Complete : {file_lines:,} : {bad_lines:,}")

def convert_to_csv(input_folder, output_folder):
    input_files = []
    for subdir, dirs, files in os.walk(input_folder):
        for filename in files:
            input_path = os.path.join(subdir, filename)
            if input_path.endswith(".zst"):
                input_files.append(input_path)

    with ProcessPoolExecutor() as executor:
        futures = [executor.submit(process_file, input_file, output_folder) for input_file in input_files]
        for future in futures:
            future.result()

if __name__ == "__main__":
    if len(sys.argv) < 3:
        print("Usage: python script.py <input_folder> <output_folder>")
        sys.exit(1)
    input_folder = sys.argv[1]
    output_folder = sys.argv[2]
    convert_to_csv(input_folder, output_folder)
```

## Combining into one dataset

Afer finishing, combined into one parquet:

```
import pandas as pd
import os

# Define the folder containing the CSV files
folder_path = 'csv'

# List of files in the folder
files = os.listdir(folder_path)

# Initialize an empty list to store dataframes
dfs = []

# Process each file
for file in files:
    if file.endswith('.csv'):
        # Extract subreddit name from the file name
        subreddit = file.split('_')[0]
        
        # Read the CSV file
        df = pd.read_csv(os.path.join(folder_path, file))
        
        # Add the subreddit name as a new column
        df['subreddit'] = subreddit
        
        # Keep only the required columns and rename them
        df = df[['title', 'selftext', 'top_comment_body', 'subreddit']]
        df.columns = ['title', 'selftext', 'top_comment', 'subreddit']
        
        # Append the dataframe to the list
        dfs.append(df)

# Concatenate all dataframes
combined_df = pd.concat(dfs, ignore_index=True)

# Save the combined dataframe to a Parquet file
combined_df.to_parquet('reddit_top_comments.parquet', index=False)
```

## Source

https://the-eye.eu/redarcs/