Read in the data.
unzip(zipfile="repdata_data_activity.zip")
data <- read.csv("activity.csv")
First, calculate the mean number of steps for each day:
data.sum = aggregate(x=list(steps=data$steps),by=list(Date=data$date),FUN=sum,na.rm=T)Let's look at the mean and median for the total steps per day:
mean(data.sum$steps, na.rm=TRUE)[1] 9354
median(data.sum$steps, na.rm=TRUE)[1] 10395
And let's take a look at the distribution of total number of steps per day with a histogram:
library(ggplot2)
qplot(total.steps, xlab = "Total steps", ylab = "Frequency")## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
Calculate the mean steps for each five minute interval.
averages <- aggregate(x=list(steps=data$steps), by=list(interval=data$interval),
FUN=mean, na.rm=TRUE)Let's take a look at a time series plot for the mean steps.
ggplot(data=averages, aes(x=interval, y=steps,ymax=max(averages$steps))) +
geom_line(colour="coral1", size=1.0) +
xlab("5-minute interval") +
ylab("average number of steps taken")
Which five minute interval has the highest mean number of steps?
averages[which.max(averages$steps),][1] interval day steps 104 835 weekday 230.3782
Identify the number of intervals with missing step counts ("NA's"):
all.na <- sum(is.na(data$steps))
Replacing each missing value with the mean value of its 5-minute interval
fill.value <- function(steps, interval) {
filled <- NA
if (!is.na(steps))
filled <- c(steps)
else
filled <- (averages[averages$interval==interval, "steps"])
return(filled)
}
Comparing the mean and median steps for each day between the original data set with NAvalues and the imputed data set.
total.steps <- tapply(filled.data$steps, filled.data$date, FUN=sum)
#mean and median total number of steps taken per day
mean_noNA <- mean(total.steps)
median_noNA <- median(total.steps)
mean_withNA <- mean(data.sum$steps, na.rm=TRUE)
median_withNA <- median(data.sum$steps, na.rm=TRUE)
And a histogram of the imputed dataset.
qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
Imputing the missing data has increased the average number of steps.
Day <- 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=Day)
Now let's compare the patterns between weekdays and weekends.
ggplot(averages, aes(interval, steps)) + facet_grid(day ~ .) +
geom_line(colour="coral1", size=1.0) +
xlab("5-minute interval") + ylab("Number of steps")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent