Problem: Understanding the Difference in Column Count between meth_df and class_df

Overview

The problem presents two dataframes, class_df and meth_df, where class_df has 941 rows but only three columns. The task is to understand why there are fewer columns in meth_df compared to the number of rows in class_df.

Steps Taken

1. Subsetting of class_df: The code provided first subsets class_df by removing any row where the “survival” column equals an empty string.
2. Subsetting of meth_df: Then, it further subsets meth_df to only include columns that have names matching the row names in class_df.

Issue Analysis

The issue at hand seems to stem from how the original data was structured. Since there are no duplicates found using table(table(colnames(meth_df)) > 1) or table(table(rownames(class_df)) > 1), we can infer that every column in class_df is also present in meth_df. However, not all columns in meth_df appear to have matching names in class_df.

Solution Approach

To address this discrepancy and achieve the desired output where there are as many columns in meth_df as there are rows in class_df, consider the following steps:

1. Ensure Matching Names: Verify that each column name in meth_df matches a row name in class_df. If there are discrepancies, you will need to either rename columns in meth_df or remove those that don’t match.

2. Verify Data Integrity: Review how data was originally structured and populated into both dataframes. Ensure no data manipulation steps inadvertently led to the discrepancy observed.

3. Rethink Subsetting Strategy: If every column in class_df should be present in meth_df, reconsider the subsetting strategy, especially for class_df. This might involve ensuring that no rows are removed during the initial subset operation.

4. Data Reconciliation: Use methods like data merging or joining to reconcile the discrepancy if the above steps fail.

5. Output Format Adjustment: If the goal is strictly to have as many columns in meth_df as there are rows in class_df, ensure that all necessary columns are included, possibly by adding dummy variables or similar to fill gaps if direct matching isn’t possible.

Example Code

# Load necessary libraries
library(dplyr)

# Assuming class_df and meth_df have been loaded

# Subset class_df without empty strings in survival column
class_df_sub <- class_df[!class_df["survival"] %in% c("lts", "non-lts"), ]

# Get matching row names for meth_df subset
rownames_to_match <- rownames(class_df_sub)

# Verify the presence of these row names as column names in meth_df
column_names_match <- colnames(meth_df) == rownames_to_match

# If there are discrepancies, consider renaming or removing columns to match
if (!all(column_names_match)) {
  # Code to address discrepancy goes here
}

# Finally, subset meth_df based on the matching row names and column names
meth_df_sub <- meth_df[rownames_to_match, ]

Conclusion

The problem revolves around understanding why there are fewer columns in meth_df compared to rows in class_df. By examining how data was structured initially and implementing steps to ensure all necessary columns from class_df are included in the final subset of meth_df, you can achieve your desired output.

Last modified on 2025-01-29