如何基于start_time与time_end_of_experiment为每个参与者生成end_time变量（R语言）

Perfect, let's tackle this problem. You want to create an end_time column where each entry is either:

• The next row's start_time minus 10ms (0.01 seconds) for all but the last row per participant, or
• The time_end_of_experiment minus 10ms for the final row of each subject.

Here are two reliable approaches to achieve your desired output:

Method 1: Using dplyr (Tidyverse Style) #

This method is intuitive and clean, especially for grouped operations:

# Load the dplyr package (install first if needed: install.packages("dplyr"))
library(dplyr)

# Your initial data frame
df <- data.frame(
  subject_nr = c("1", "1", "1", "2", "2"), 
  start_time = c(50, 52, 55, 53, 54.5), 
  time_end_of_experiment = c(60, 60, 60, 55.5, 55.5)
)

# Create the end_time column
df <- df %>%
  group_by(subject_nr) %>%  # Group data by each participant
  mutate(
    end_time = ifelse(
      row_number() == n(),  # Check if this is the last row of the group
      time_end_of_experiment - 0.01,  # Use experiment end time minus 10ms
      lead(start_time) - 0.01  # Use next row's start time minus 10ms
    )
  ) %>%
  ungroup()  # Remove grouping to keep data frame in standard format

# View the result
print(df)

Explanation: #

• group_by(subject_nr): Ensures we process each participant's data separately.
• row_number() == n(): Identifies the final row in each participant's group.
• lead(start_time): Fetches the start_time value from the next row in the same group.
• ungroup(): Resets the data frame to an ungrouped state (good practice for subsequent operations).

Method 2: Using Base R #

If you prefer not to use external packages, this base R approach works just as well:

# Your initial data frame
df <- data.frame(
  subject_nr = c("1", "1", "1", "2", "2"), 
  start_time = c(50, 52, 55, 53, 54.5), 
  time_end_of_experiment = c(60, 60, 60, 55.5, 55.5)
)

# Calculate end_time using tapply to handle grouped logic
df$end_time <- unlist(tapply(
  seq_len(nrow(df)),  # Use row indices to group data
  df$subject_nr,
  function(idx) {
    sub_df <- df[idx, ]
    # Get next start time (NA for last row of the group)
    next_start <- c(sub_df$start_time[-1], NA)
    # Assign end_time values
    ifelse(
      is.na(next_start),
      sub_df$time_end_of_experiment - 0.01,
      next_start - 0.01
    )
  }
))

# View the result
print(df)

Explanation: #

• tapply(): Groups row indices by subject_nr and applies a custom function to each group.
• next_start <- c(sub_df$start_time[-1], NA): Creates a vector where each entry is the next row's start_time, with NA for the last row.
• ifelse(): Replaces NA values with time_end_of_experiment - 0.01 and uses next_start - 0.01 for all other rows.

Final Output #

Both methods will produce your desired data frame:

subject_nr start_time time_end_of_experiment end_time
1          1       50.0                  60.0     51.9
2          1       52.0                  60.0     54.9
3          1       55.0                  60.0     59.9
4          2       53.0                  55.5     54.4
5          2       54.5                  55.5     55.4

内容的提问来源于stack exchange，提问作者user9974638