在R语言中为声学遥测数据创建昼间时间区间列的求助

Hey there! Let's tackle that time period column for your acoustic telemetry data—this will get you all set for your ANOVA analysis in no time. Since you've already handled datetime conversion and extracted the hours column, here are a couple of straightforward ways to create your labeled time periods:

Method 1: Using dplyr::case_when() (Intuitive for Custom Intervals) #

If you prefer explicit control over each time window, case_when() is perfect. First, make sure you have the dplyr package installed and loaded:

# Install if you haven't already
install.packages("dplyr")
library(dplyr)

# Assume your data frame is named `df` with an integer `hours` column
df <- df %>%
  mutate(
    # Define your time period labels
    time_period = case_when(
      hours >= 0 & hours < 6 ~ "early",       # 00:00:01 to 06:00:00
      hours >= 6 & hours < 12 ~ "morning",    # 06:00:01 to 12:00:00
      hours >= 12 & hours < 18 ~ "afternoon", # 12:00:01 to 18:00:00
      hours >= 18 & hours <= 23 ~ "evening"   # 18:00:01 to 23:59:59
    ),
    # Convert to factor (critical for ANOVA, ensures proper group ordering)
    time_period = factor(time_period, levels = c("early", "morning", "afternoon", "evening"))
  )

This lets you easily adjust the intervals or labels if you need to tweak your time windows later.

Method 2: Using Base R cut() (Concise for Interval Binning) #

For a more compact approach, use cut() to bin your hours column directly into labeled groups:

# Define interval breaks and corresponding labels
breaks <- c(-1, 5, 11, 17, 23)  # -1 ensures 0 is included in the first interval
labels <- c("early", "morning", "afternoon", "evening")

# Create the time_period column as a factor
df$time_period <- cut(
  df$hours,
  breaks = breaks,
  labels = labels,
  include.lowest = TRUE  # Includes the lowest value (0) in the first interval
)

Verify Your Groups #

Before running ANOVA, double-check that your time periods are correctly assigned with:

table(df$time_period)

This will show you the count of observations in each group, so you can confirm no data is missing or misclassified.

Quick ANOVA Example #

Once your time_period column is ready, you can run a one-way ANOVA (assuming your response variable is something like detection_frequency):

# Fit the ANOVA model
anova_model <- aov(detection_frequency ~ time_period, data = df)

# View results
summary(anova_model)

内容的提问来源于stack exchange，提问作者Hillary Ann Dean