如何基于现有数据集的变量与行创建新数据集？

Hey there! Let's break down how to create your new dataset using the variables you've already calculated, plus existing columns from your esports data. I'll cover a couple of common approaches depending on your needs:

1. Basic R Approach (No Extra Packages Needed) #

If you prefer sticking to base R, these steps will work smoothly:

• Combine selected original columns with your new variables:
  First pick the columns you want to keep from the original esports dataset (e.g., Age, gender), then merge them with femininity and mac:

  # Select specific columns from the original dataset
  original_subset <- esports[, c("Age", "gender")]
  
  # Create the new dataset by combining the subset with your calculated variables
  new_dataset <- data.frame(original_subset, femininity, mac)
  

• Add new variables to the full original dataset:
  If you want to keep all existing columns plus your new variables, use cbind():

  new_dataset_full <- cbind(esports, femininity, mac)
  

• Filter rows based on conditions:
  To include only specific rows (e.g., players over 18), subset the dataset like this:

  filtered_dataset <- new_dataset[new_dataset$Age > 18, ]
  

2. Tidyverse/dplyr Approach (More Intuitive for Data Manipulation) #

If you're open to using the dplyr package (part of the tidyverse), this method is cleaner for complex data tasks:
First, make sure you have the package installed and loaded:

install.packages("dplyr")
library(dplyr)

• Option 1: Calculate variables and build the dataset in one step:
  You can even embed your rowMeans calculations directly into the workflow to avoid creating separate vectors first:

  new_dataset <- esports %>%
    # Add your calculated variables as new columns
    mutate(
      femininity = rowMeans(data.frame(.[11:15], 6 - .[16:17])),
      mac = rowMeans(data.frame(.[28:30], 6 - .[, c(31, 33)])) # Note: Fixed your original code here - use .[, c(31,33)] to get all rows for columns 31 and 33
    ) %>%
    # Select only the columns you need (remove this line if you want all columns)
    select(Age, gender, femininity, mac)
  

• Option 2: Bind pre-calculated variables to a subset of original data:
  If you already have femininity and mac ready, use bind_cols() to merge them with your chosen original columns:

  new_dataset <- esports %>%
    select(Age, gender) %>% # Pick columns from original data
    bind_cols(femininity = femininity, mac = mac)
  

• Filter rows with filter():
  Add a filter() step to keep only rows that meet your criteria:

  filtered_dataset <- new_dataset %>%
    filter(Age > 18, gender == "Female") # Example conditions - adjust as needed
  

Quick Note on Your Original mac Calculation #

I noticed a small syntax issue in your original code: 6-esports[31,33] will only select the value at row 31, column 33. To get all rows for columns 31 and 33, you need to write 6-esports[, c(31, 33)] (the comma tells R to include all rows for those columns). This fix ensures your mac variable calculates correctly across all observations.

内容的提问来源于stack exchange，提问作者Fares Hassan Almas