library(lubridate)
if(!file.exists('activity.csv'))
unzip('activity.zip')
myData <- read.csv("activity.csv")
#format the date
myData$date <- as.Date(myData$date, format="%Y-%m-%d")
#Create date/time column*-+
myData$date.time <- as.POSIXct(
paste(myData$date, formatC(myData$interval/100, 2, format = "f")),
format = "%Y-%m-%d %H.%M",
tz = "GMT")
#Format the time
myData$time <- format(myData$date.time, format = "%H:%M:%S")
myData$time <- as.POSIXct(myData$time, format = "%H:%M:%S")
myData$interval <- as.factor(myData$interval)
days <- as.factor(myData$date)
intervals <- as.numeric(levels(myData$interval))
Calculate the mean and median number of steps taken per day
numSteps <- tapply(myData$steps, myData$date, sum, na.rm=TRUE)
mean(numSteps)
## [1] 9354.23
median(numSteps)
## [1] 10395
Let's show a histogram of the total number of steps taken each day
library(ggplot2)
qplot(
numSteps,
xlab='Total Steps Each Day',
ylab='Frequency')
Let's make a time series plot of the 5 minute intervals (x axis) and the average number of steps taken, averaged across all days (y axis)
#calculate the mean steps for each interval
avgSteps <- tapply(myData$steps, myData$time, mean, na.rm=TRUE)
#create date frame with mean steps
activityPattern <- data.frame(time=as.POSIXct(names(avgSteps)),
avgSteps=avgSteps)
myPlot <- ggplot(
activityPattern,
aes(intervals, avgSteps),
)
myPlot +
geom_line(colour="red") +
ggtitle("Time Series Plot of the 5m Interval and the avg No. Steps Taken") +
xlab("5 Min Intervals") +
ylab("Avg Num Steps Taken")
Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
maxSteps <- which.max(activityPattern$avgSteps)
format(
activityPattern[maxSteps, 'time'],
format = "%H:%M"
)
## [1] "08:35"
Find missing values:
#find rows of missing values
isNAs <- is.na(myData)
There are 2304 missing values in the dataset (total number of rows with “NAs”)
Fill in missing values; we'll use the 5 minute interval mean to keep the data simliar
#create a copy to preserve original
cleanData <- myData
cleanData$steps[is.na(cleanData$steps)] <-
tapply(cleanData$steps, cleanData$interval, mean, na.rm = TRUE)
#compare the two
summary(myData)
## steps date interval
## Min. : 0.00 Min. :2012-10-01 0 : 61
## 1st Qu.: 0.00 1st Qu.:2012-10-16 5 : 61
## Median : 0.00 Median :2012-10-31 10 : 61
## Mean : 37.38 Mean :2012-10-31 15 : 61
## 3rd Qu.: 12.00 3rd Qu.:2012-11-15 20 : 61
## Max. :806.00 Max. :2012-11-30 25 : 61
## NA's :2304 (Other):17202
## date.time time
## Min. :2012-10-01 00:00:00 Min. :2014-12-24 00:00:00
## 1st Qu.:2012-10-16 05:58:45 1st Qu.:2014-12-24 05:58:45
## Median :2012-10-31 11:57:30 Median :2014-12-24 11:57:30
## Mean :2012-10-31 11:57:30 Mean :2014-12-24 11:57:30
## 3rd Qu.:2012-11-15 17:56:15 3rd Qu.:2014-12-24 17:56:15
## Max. :2012-11-30 23:55:00 Max. :2014-12-24 23:55:00
##
summary(cleanData)
## steps date interval
## Min. : 0.00 Min. :2012-10-01 0 : 61
## 1st Qu.: 0.00 1st Qu.:2012-10-16 5 : 61
## Median : 0.00 Median :2012-10-31 10 : 61
## Mean : 37.38 Mean :2012-10-31 15 : 61
## 3rd Qu.: 27.00 3rd Qu.:2012-11-15 20 : 61
## Max. :806.00 Max. :2012-11-30 25 : 61
## (Other):17202
## date.time time
## Min. :2012-10-01 00:00:00 Min. :2014-12-24 00:00:00
## 1st Qu.:2012-10-16 05:58:45 1st Qu.:2014-12-24 05:58:45
## Median :2012-10-31 11:57:30 Median :2014-12-24 11:57:30
## Mean :2012-10-31 11:57:30 Mean :2014-12-24 11:57:30
## 3rd Qu.:2012-11-15 17:56:15 3rd Qu.:2014-12-24 17:56:15
## Max. :2012-11-30 23:55:00 Max. :2014-12-24 23:55:00
##
Histogram of the new Data Set:
cleanNumSteps <- tapply(cleanData$steps, cleanData$date, sum)
qplot(
cleanNumSteps,
xlab='Total Steps Each Day',
ylab='Frequency')
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.
#add Day to our cleanData
cleanData$Day <- weekdays(as.Date(as.character(cleanData$date)))
#add weekend value to our cleanData
cleanData$DayType <- as.factor(c("Weekend","Weekday"))
weekendDays <- cleanData$Day == "Saturday" | cleanData$Day == "Sunday"
cleanData$DayType[weekendDays] = "Weekend"
cleanData$DayType[!weekendDays] = "Weekday"
cleanAvgSteps <- tapply(cleanData$steps,
interaction(cleanData$time, cleanData$DayType),
mean)
cleanPattern <- data.frame(time=as.POSIXct(names(cleanAvgSteps)),
cleanAvgSteps = cleanAvgSteps,
DayType = as.factor(c("Weekend","Weekday")))
Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).
library(scales)
ggplot(cleanPattern, aes(time, cleanAvgSteps)) +
geom_line(color="blue") +
xlab("Daily 5 minute Intervals") +
ylab("Average Number of Steps") +
scale_x_datetime(labels = date_format(format =' %H:%M')) +
facet_grid(. ~ DayType)
