关于验证选定变量滞后值与当前值是否相等及批量处理data.frame变量差异判断的技术问询

Hey there! Let's solve this problem so you don't have to hardcode variable names ever again. You want a flexible way to generate that same column, regardless of how many variables you have or what they're named—here are two straightforward approaches:

Using dplyr (Tidyverse) #

This method is clean and readable, perfect if you're already using the tidyverse ecosystem:

library(dplyr)

# Your original dataset
df <- data.frame(id = c(1,2,3,4), var1 = c(1,1,2,2), var2 = c(1,1,2,3))

# Generate the `same` column automatically
df <- df %>%
  # Step 1: For every column except `id`, check if current row equals the previous row
  mutate(across(-id, ~ . == lag(.))) %>%
  # Step 2: For each row, check if ALL those comparisons are TRUE
  rowwise() %>%
  mutate(same = all(c_across(-id))) %>%
  ungroup() %>%
  # Step 3: Clean up temporary columns and set first row to NA
  select(-all_of(setdiff(names(.), c("id", "var1", "var2", "same")))) %>%
  mutate(same = if_else(row_number() == 1, NA, same))

How it works: #

• across(-id, ~ . == lag(.)): Creates temporary logical columns for each variable (excluding id) where each value is TRUE if it matches the row above.
• rowwise() + all(c_across(-id)): Checks every row to see if all the temporary logical values are TRUE (meaning every variable matches the previous row).
• The final steps clean up the temporary columns and set the first row's same value to NA (since there's no prior row to compare against).

Using Base R #

If you prefer not to load extra libraries, this base R approach works just as well:

# Your original dataset
df <- data.frame(id = c(1,2,3,4), var1 = c(1,1,2,2), var2 = c(1,1,2,3))

# Automatically get the columns to compare (all except `id`)
compare_cols <- setdiff(names(df), "id")

# Compare each row to the one above, then check if all matches are TRUE
row_matches <- apply(df[-1, compare_cols] == df[-nrow(df), compare_cols], 1, all)

# Add the `same` column (first row is NA, followed by our match results)
df$same <- c(NA, row_matches)

How it works: #

• setdiff(names(df), "id"): Dynamically grabs all variable names except id—no hardcoding needed, even if you add/remove variables later.
• df[-1, compare_cols] == df[-nrow(df), compare_cols]: Creates a matrix where each cell is TRUE if the current row's variable matches the previous row's.
• apply(..., 1, all): Checks each row of the matrix to see if every value is TRUE (all variables match the prior row).
• c(NA, row_matches): Adds the NA for the first row and appends our match results for the rest.

Both methods will output exactly the target dataset you wanted:

> df
  id var1 var2 same
1  1    1    1   NA
2  2    1    1 TRUE
3  3    2    2 FALSE
4  4    2    3 FALSE

内容的提问来源于stack exchange，提问作者Jirka Čep