在R语言中对多列计算累积和并生成Pre前缀变量的技术问询

Hey there! Let's work through creating those PreYYYY cumulative sum variables for your dataset. Here's a couple of straightforward approaches that fit your needs:

First, Let's Recap Your Data Setup #

First, let's confirm your initial data and renaming step (since R automatically prepends X to column names that start with numbers):

# Define your original dataset
test <- data.frame("1991" = c(1,5,3), "1992" = c(4,3,3), "1993" = c(10,5,3), "1994" = c(1,1,1), "1995" = c(2,2,6))

# Rename columns to remove the auto-added X prefix
test <- plyr::rename(test, c("X1991"="1991", "X1992"="1992", "X1993"="1993", "X1994"="1994", "X1995"="1995"))

Approach 1: Base R (No Extra Packages Needed) #

If you prefer sticking to base R, you can use apply to calculate row-wise cumulative sums, then rename the columns and merge back to your original data:

# Calculate row-wise cumulative sums
cumulative_values <- t(apply(test, 1, cumsum))

# Add the "Pre" prefix to column names
colnames(cumulative_values) <- paste0("Pre", colnames(test))

# Merge the cumulative columns into your original data frame
test <- cbind(test, cumulative_values)

Approach 2: Using dplyr (More Intuitive Syntax) #

If you're open to using the dplyr package (part of the tidyverse), this method is cleaner and more readable:

library(dplyr)

# Add cumulative sum columns with the "Pre" prefix directly
test <- test %>%
  mutate(across(everything(), cumsum, .names = "Pre{col}"))

What This Does #

Both methods will generate exactly what you need:

• Pre1991 = test$1991 (since it's the first year, cumulative sum is just the value itself)
• Pre1992 = test$1991 + test$1992
• Pre1993 = test$1991 + test$1992 + test$1993
• And so on for the remaining years

Quick Check of the Result #

If you print test after running either method, you'll see the new cumulative columns:

print(test)
#   1991 1992 1993 1994 1995 Pre1991 Pre1992 Pre1993 Pre1994 Pre1995
# 1    1    4   10    1    2       1       5      15      16      18
# 2    5    3    5    1    2       5       8      13      14      16
# 3    3    3    3    1    6       3       6       9      10      16

内容的提问来源于stack exchange，提问作者Data Science Beginner