如何从Sample列提取数据创建tissue、plant、stage新列？

Absolutely—your sample IDs follow a clear, consistent pattern: [tissue code][plant number].[stage number], which makes it straightforward to extract those three components into new columns. Below are step-by-step solutions using two common data analysis tools: Python's Pandas and R.

Solution 1: Using Python Pandas #

Assuming your data is stored in a Pandas DataFrame, here's how to split the sample column:

1. Set up your data:

import pandas as pd

# Example data (replace with your actual dataset)
data = {
    'sample': ['P10.1', 'P11.2', 'S1.1', 'S3.3'],
    # Include your existing measurement columns here (P10.1, P11.2, etc.)
}
df = pd.DataFrame(data)

2. Extract components with regex:
   Use str.extract() to pull out each part using a regular expression tailored to your ID structure:

# Regex breakdown:
# ^([A-Za-z]) → Captures the first letter (tissue code)
# (\d+) → Captures digits before the dot (plant number)
# \. → Matches the dot separator
# (\d+)$ → Captures digits after the dot (stage number)
df[['tissue', 'plant', 'stage']] = df['sample'].str.extract(r'^([A-Za-z])(\d+)\.(\d+)$')

# Optional: Convert plant/stage to numeric types for easier analysis
df['plant'] = df['plant'].astype(int)
df['stage'] = df['stage'].astype(int)

3. Final Result:
   Your DataFrame will now include the three new columns, exactly matching your expected output.

Solution 2: Using R #

If you prefer working in R, here are two clean methods to split the sample column:

Option A: Using tidyr::separate() #

This method uses regex to split the column at logical points:

library(tidyr)

# Example data
df <- data.frame(
    sample = c("P10.1", "P11.2", "S1.1", "S3.3"),
    # Add your existing measurement columns here
)

# Split the sample column into three parts
df <- df %>%
    separate(
        sample,
        into = c("tissue", "plant", "stage"),
        sep = "(?<=[A-Za-z])(?=\\d)|\\."  # Splits after the letter and at the dot
    )

# Convert plant/stage to numeric
df$plant <- as.integer(df$plant)
df$stage <- as.integer(df$stage)

Option B: Using stringr for direct regex extraction #

If you want more control over each component:

library(stringr)

df$tissue <- str_extract(df$sample, "^[A-Za-z]")
df$plant <- as.integer(str_extract(df$sample, "(?<=^[A-Za-z])\\d+(?=\\.)"))
df$stage <- as.integer(str_extract(df$sample, "(?<=\\.)\\d+$"))

Both approaches will populate your tissue, plant, and stage columns perfectly from the sample IDs. Just adjust the code to match your actual dataset's structure.

内容的提问来源于stack exchange，提问作者Lauren Maynard