"PA1_template.md" completed and "PA1_template.html" generated.,

PA1_template.Rmd

@@ -5,21 +5,107 @@ output:
     keep_md: true
 ---
 
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+# Activity Monitoring Data Analysis
 
-## Loading and preprocessing the data
 
+## 0. Load the necessary library
+```{r}
+library(utils)
+library(ggplot2)
+```
+## 1. Loading and preprocessing the data
+```{r}
+# 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)
+```
 
-## What is mean total number of steps taken per day?
+## 2. What is mean total number of steps taken per day?
+```{r}
+# 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")
+cat("Median total number of steps taken per day: ", medianSteps, "\n")
+```
 
-## What is the average daily activity pattern?
+## 3. What is the average daily activity pattern?
+```{r}
+# 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
 
-## Imputing missing values
+# Print the result
+cat("The 5-minute interval with the maximum number of steps on average is:", maxInterval)
+```
 
+## 4. Imputing missing values
+```{r}
+# Calculate and report the total number of missing values
+totalNAs <- sum(is.na(data$steps))
+cat("Total number of missing values: ", totalNAs, "\n")
 
+# Calculate the mean for each interval
+averageStepsByInterval <- aggregate(steps ~ interval, data, mean, na.rm = TRUE)
 
-## Are there differences in activity patterns between weekdays and weekends?
+# 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")
+cat("Median total number of steps taken per day (Filled Data): ", medianStepsFilled, "\n")
+```
+
+## 5. Are there differences in activity patterns between weekdays and weekends?
+```{r}
+# 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")
+```