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+Reproducible Research: Peer Assessment +1
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+Activity Monitoring Data Analysis
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+
+0. Load the necessary library
+library(utils)
+library(ggplot2)## Warning: パッケージ 'ggplot2' はバージョン 4.3.2 の R の下で造られました
+
+1. Loading and preprocessing the data
+# Read the CSV file directly from the zipped archive
+zipFilePath <- "activity.zip"
+csvFileName <- "activity.csv"
+data <- read.csv(unzip(zipFilePath, files = csvFileName))
+
+# Aggregate total steps by date
+totalStepsByDay <- aggregate(steps ~ date, data, sum, na.rm = TRUE)
+
+2. What is mean total number of steps taken per day?
+# Make a histogram of the total number of steps taken each day
+hist(totalStepsByDay$steps, main = "Total Steps Taken Each Day", xlab = "Total Steps", ylab = "Frequency", col = "blue")
# Calculate and report the mean and median total number of steps taken per day
+meanSteps <- mean(totalStepsByDay$steps)
+medianSteps <- median(totalStepsByDay$steps)
+
+# Print the results
+cat("Mean total number of steps taken per day: ", meanSteps, "\n")## Mean total number of steps taken per day: 10766.19cat("Median total number of steps taken per day: ", medianSteps, "\n")## Median total number of steps taken per day: 10765
+
+3. What is the average daily activity pattern?
+# Calculate the average number of steps taken in each 5-minute interval
+averageStepsByInterval <- aggregate(steps ~ interval, data, mean, na.rm = TRUE)
+
+# Make a time series plot
+plot(averageStepsByInterval$interval, averageStepsByInterval$steps, type = "l", xlab = "Interval", ylab = "Average Number of Steps", main = "Average Daily Activity Pattern")
# Identify the interval with the maximum average number of steps
+maxInterval <- averageStepsByInterval[which.max(averageStepsByInterval$steps), ]$interval
+
+# Print the result
+cat("The 5-minute interval with the maximum number of steps on average is:", maxInterval)## The 5-minute interval with the maximum number of steps on average is: 835
+
+4. Imputing missing values
+# Calculate and report the total number of missing values
+totalNAs <- sum(is.na(data$steps))
+cat("Total number of missing values: ", totalNAs, "\n")## Total number of missing values: 2304# Calculate the mean for each interval
+averageStepsByInterval <- aggregate(steps ~ interval, data, mean, na.rm = TRUE)
+
+# Fill in missing values and create a new dataset
+filledData <- data
+for (i in 1:nrow(filledData)) {
+ if (is.na(filledData$steps[i])) {
+ filledData$steps[i] <- averageStepsByInterval$steps[which(averageStepsByInterval$interval == filledData$interval[i])]
+ }
+}
+
+# Aggregate total steps by day for the filled dataset
+totalStepsByDayFilled <- aggregate(steps ~ date, filledData, sum)
+
+# Make a histogram
+hist(totalStepsByDayFilled$steps, main = "Total Steps Taken Each Day (Filled Data)", xlab = "Total Steps", ylab = "Frequency", col = "green")
# Calculate mean and median
+meanStepsFilled <- mean(totalStepsByDayFilled$steps)
+medianStepsFilled <- median(totalStepsByDayFilled$steps)
+
+# Print the results
+cat("Mean total number of steps taken per day (Filled Data): ", meanStepsFilled, "\n")## Mean total number of steps taken per day (Filled Data): 10766.19cat("Median total number of steps taken per day (Filled Data): ", medianStepsFilled, "\n")## Median total number of steps taken per day (Filled Data): 10766.19
+
+5. Are there differences in activity patterns between weekdays and +weekends?
+# Convert 'date' to Date type if it's not already
+filledData$date <- as.Date(filledData$date)
+
+# Create a new factor variable for weekday/weekend
+filledData$dayType <- ifelse(weekdays(filledData$date, abbreviate = TRUE) %in% c("土", "日"), "weekend", "weekday")
+filledData$dayType <- factor(filledData$dayType, levels = c("weekday", "weekend"))
+
+# Aggregate average steps by interval and dayType again to reflect the updated dayType
+averageStepsByDayType <- aggregate(steps ~ interval + dayType, filledData, mean)
+
+# Create the plot
+ggplot(averageStepsByDayType, aes(x = interval, y = steps, color = dayType)) +
+ geom_line() +
+ xlab("Interval") +
+ ylab("Average Number of Steps") +
+ ggtitle("Average Number of Steps by Interval: Weekday vs Weekend") +
+ scale_color_manual(values = c("weekday" = "blue", "weekend" = "red")) +
+ theme(legend.position = "bottom")