## 1.Loading and preprocessing the data
activity_data <- read.csv('activity.csv', header = TRUE, sep = ",",colClasses=c("numeric", "character", "numeric"))
activity_data$date <- as.Date(activity_data$date, format = "%m/%d/%Y")
activity_data$interval <- as.factor(activity_data$interval)
## What is mean total number of steps taken per day?
total_steps_1day <- aggregate(steps ~ date, activity_data, sum)
colnames(total_steps_1day) <- c("date","steps")
####### 2. Make a histogram of the total number of steps taken each day
ggplot(total_steps_1day, aes(x=steps)) + geom_histogram(fill="yellow",binwidth = 1000) + xlab("Steps per Day") + ylab("Count in a day") +
ggtitle("Histogram-Steps Taken per Day")+ theme_bw()
##3.Mean and median number of steps taken each day
mean_steps_per_day <- mean(total_steps_1day$steps, na.rm=TRUE)
median_steps_per_day <- median(total_steps_1day$steps, na.rm=TRUE)
mean_steps_per_day
median_steps_per_day
#######What is the average daily activity pattern?
daily_activity_pattern <- aggregate(activity_data$steps,
by = list(interval = activity_data$interval),
FUN=mean, na.rm=TRUE)
#convert to integers this helps in plotting
daily_activity_pattern$interval <- as.integer(levels(daily_activity_pattern$interval)[daily_activity_pattern$interval])
colnames(daily_activity_pattern) <- c("interval", "steps")
####### 4.Make a time series plotof the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days
ggplot(daily_activity_pattern, aes(x=interval, y=steps)) +
geom_line(color="blue", size=1) + xlab("Interval") + ylab("steps_count") +
ggtitle("Average Daily Steps")+ theme_bw()
####### 5. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
max_5minute_interval <- daily_activity_pattern[which.max(daily_activity_pattern$steps),]
max_5minute_interval
####### 6. Imputing missing values
#######Calculate and report the total number of missing values in the dataset
sum(is.na(activity_data))
#######Create a new dataset that is equal to the original dataset but with the missing data filled in.Means for the 5-minute intervals are used as fillers for missing values.
activity_data <- merge(activity_data, daily_activity_pattern, by = "interval", suffixes = c("", ".y"))
step_count <- is.na(activity_data$steps)
activity_data$steps[step_count] <- activity_data$steps.y[step_count]
activity_data <- activity_data[, c(1:3)]
str(activity_data)
####### 7.Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
steps_taken_in_day <- aggregate(steps ~ date, data = activity_data, FUN = sum)
ggplot(steps_taken_in_day, aes(x=steps)) + geom_histogram(fill="magenta",binwidth = 1000) + xlab("Steps per Day") + ylab("Count in a day") +
ggtitle("Histogram-Total no. of Steps Taken per Day")+ theme_bw()
barplot(steps_taken_in_day$steps, names.arg = steps_taken_in_day$date, xlab = "date", ylab = "steps")
mean2 <- mean(steps_taken_in_day$steps)
median2 <- median(steps_taken_in_day$steps)
mean2
median2
##Mean and median are same now after imputing data earlier it was different
###Mean and Median After Imputing Data - 10766.19 and 10766.19
##Mean and Median Before Imputing Data - 10766.19 and 10765
#######Are there differences in activity patterns between weekdays and weekends?
#######Create a new factor variable in the dataset with two levels - "weekday" and "weekend" indicating whether a given date is a weekday or weekend day.
####### 8 .Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).
activity_data$weekdays <- factor(format(activity_data$date, "%A"))
levels(activity_data$weekdays)
levels(activity_data$weekdays) <- list(weekday = c("Monday", "Tuesday",
"Wednesday",
"Thursday", "Friday"),
weekend = c("Saturday", "Sunday"))
levels(activity_data$weekdays)
table(activity_data$weekdays)
days_average <- aggregate(activity_data$steps,
list(interval = as.numeric(as.character(activity_data$interval)),
weekdays = activity_data$weekdays),
FUN = "mean")
names(days_average)[3] <- "mean_Steps"
####### 8 .Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).
xyplot(days_average$mean_Steps ~ days_average$interval | days_average$weekdays,
layout = c(1, 2), type = "l",
xlab = "Interval", ylab = "Number of steps")
## [1] 10766.19
## [1] 10765
## interval steps
## 104 835 206.1698
## [1] 2304
## 'data.frame': 17568 obs. of 3 variables:
## $ interval: Factor w/ 288 levels "0","5","10","15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ steps : num 1.72 0 0 0 0 ...
## $ date : Date, format: "2012-10-01" "2012-11-23" ...
## [1] 10766.19
## [1] 10766.19
## [1] "Friday" "Monday" "Saturday" "Sunday" "Thursday" "Tuesday"
## [7] "Wednesday"
## [1] "weekday" "weekend"
##
## weekday weekend
## 12960 4608