+
+
+
+
+
+
+
+
+
Loading and preprocessing the data
+
unzip(zipfile="activity.zip")
+data <- read.csv("activity.csv")
+head(data)
+
## steps date interval
+## 1 NA 2012-10-01 0
+## 2 NA 2012-10-01 5
+## 3 NA 2012-10-01 10
+## 4 NA 2012-10-01 15
+## 5 NA 2012-10-01 20
+## 6 NA 2012-10-01 25
+
+
+
What is mean total number of steps taken per day?
+
library(ggplot2)
+total.steps <- tapply(data$steps, data$date, FUN=sum, na.rm=TRUE)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
+## This warning is displayed once every 8 hours.
+## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
+## generated.
+
+
mean(total.steps, na.rm=TRUE)
+
## [1] 9354.23
+
median(total.steps, na.rm=TRUE)
+
## [1] 10395
+
+
+
What is the average daily activity pattern?
+
library(ggplot2)
+averages <- aggregate(x=list(steps=data$steps), by=list(interval=data$interval),
+ FUN=mean, na.rm=TRUE)
+ggplot(data=averages, aes(x=interval, y=steps)) +
+ geom_line() +
+ xlab("5-minute interval") +
+ ylab("average number of steps taken")
+
+
On average across all the days in the dataset, the 5-minute interval
+contains the maximum number of steps?
+
averages[which.max(averages$steps),]
+
## interval steps
+## 104 835 206.1698
+
+
+
Imputing missing values
+
There are many days/intervals where there are missing values (coded
+as NA). The presence of missing days may introduce bias
+into some calculations or summaries of the data.
+
missing <- is.na(data$steps)
+# How many missing
+table(missing)
+
## missing
+## FALSE TRUE
+## 15264 2304
+
All of the missing values are filled in with mean value for that
+5-minute interval.
+
# Replace each missing value with the mean value of its 5-minute interval
+fill.value <- function(steps, interval) {
+ filled <- NA
+ if (!is.na(steps))
+ filled <- c(steps)
+ else
+ filled <- (averages[averages$interval==interval, "steps"])
+ return(filled)
+}
+filled.data <- data
+filled.data$steps <- mapply(fill.value, filled.data$steps, filled.data$interval)
+
Now, using the filled data set, let’s make a histogram of the total
+number of steps taken each day and calculate the mean and median total
+number of steps.
+
total.steps <- tapply(filled.data$steps, filled.data$date, FUN=sum)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+
+
mean(total.steps)
+
## [1] 10766.19
+
median(total.steps)
+
## [1] 10766.19
+
Mean and median values are higher after imputing missing data. The
+reason is that in the original data, there are some days with
+steps values NA for any interval.
+The total number of steps taken in such days are set to 0s by default.
+However, after replacing missing steps values with the mean
+steps of associated interval value, these 0
+values are removed from the histogram of total number of steps taken
+each day.
+
+
+
Are there differences in activity patterns between weekdays and
+weekends?
+
First, let’s find the day of the week for each measurement in the
+dataset. In this part, we use the dataset with the filled-in values.
+
weekday.or.weekend <- function(date) {
+ day <- weekdays(date)
+ if (day %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
+ return("weekday")
+ else if (day %in% c("Saturday", "Sunday"))
+ return("weekend")
+ else
+ stop("invalid date")
+}
+filled.data$date <- as.Date(filled.data$date)
+filled.data$day <- sapply(filled.data$date, FUN=weekday.or.weekend)
+
Now, let’s make a panel plot containing plots of average number of
+steps taken on weekdays and weekends.
+
averages <- aggregate(steps ~ interval + day, data=filled.data, mean)
+ggplot(averages, aes(interval, steps)) + geom_line() + facet_grid(day ~ .) +
+ xlab("5-minute interval") + ylab("Number of steps")
+
+
+
+
+
+
+