Analysis of Personal Movement Using Activity Monitoring Devices
Introduction
It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.
This project makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day. The goal of this project is to write a report that answers the questions detailed below.
Loading and preprocessing the data
1 - Reading the data from the “activity.csv”
data <- read.csv("activity.csv", header = TRUE, sep = ",", na.strings = "NA")2 - Looking at a summary for the dataset using “summary” and “str” methods:
summary(data)## steps date interval
## Min. : 0.0 2012-10-01: 288 Min. : 0
## 1st Qu.: 0.0 2012-10-02: 288 1st Qu.: 589
## Median : 0.0 2012-10-03: 288 Median :1178
## Mean : 37.4 2012-10-04: 288 Mean :1178
## 3rd Qu.: 12.0 2012-10-05: 288 3rd Qu.:1766
## Max. :806.0 2012-10-06: 288 Max. :2355
## NA's :2304 (Other) :15840str(data)## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...3 - Looking at the first 6 rows of the dataset:
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 2012-10-01 254 - Converting the “date” variable to a Date classe and the “interval” variable to a factor:
data$date <- as.Date(data$date, format = "%Y-%m-%d")
data$interval <- factor(data$interval)What is mean total number of steps taken per day?
1 - Subsitting the dataset to ignore missing values
NA_index <- is.na(as.character(data$steps))
data_no_NA <- data[!NA_index,]
head(data_no_NA)## steps date interval
## 289 0 2012-10-02 0
## 290 0 2012-10-02 5
## 291 0 2012-10-02 10
## 292 0 2012-10-02 15
## 293 0 2012-10-02 20
## 294 0 2012-10-02 252 - Aggregating the number of steps taken each day:
#Creating a data frame with the steps taken for each day
steps_each_day <- aggregate(steps ~ date, data = data_no_NA, sum)
#Adding column names to the created data frame
colnames(steps_each_day) <- c("date", "steps")3 - Making a histogram of the total number of steps taken each day:
hist(as.numeric(steps_each_day$steps), breaks = 20, col = "red", xlab = "Number of Steps", main= "Histogram of the total number of steps taken each day")
4 - Calculating the mean and median total number of steps taken per day:
#Mean
mean(steps_each_day$steps)## [1] 10766#Median
median(steps_each_day$steps)## [1] 10765What is the average daily activity pattern?
1 - Calculating the average number of steps taken, averaged across all days:
#Calculating the average
steps_per_interval <- aggregate(data_no_NA$steps, by=list(interval=data_no_NA$interval), FUN=mean)
#Adding columns names
colnames(steps_per_interval) <- c("interval", "average_steps")
#ploting the average daily activity pattern
plot(as.integer(levels(steps_per_interval$interval)), steps_per_interval$average_steps, type="l",
xlab = "Interval", ylab = "Average Number of Steps", main = "Average Daily Activity Pattern", col ="blue")
2 - The 5-minute interval that contains the maximum number of steps:
#The maximum number of average steps
max_steps <- max(steps_per_interval$average_steps)
max_steps## [1] 206.2#The 5-minute interval that contains the maximum number of steps
intervale_max_steps<-steps_per_interval[which.max(steps_per_interval$average_steps),]$interval
intervale_max_steps## [1] 835
## 288 Levels: 0 5 10 15 20 25 30 35 40 45 50 55 100 105 110 115 120 ... 2355So, the 5-minute interval that contains the maximum number of steps ( 206.2 steps ) is the interval 835.
Imputing missing values
1 - The total number of missing values in the dataset (for each variable):
For the “steps” variable:
sum(is.na(as.character(data$steps)))## [1] 2304For the “date” variable:
sum(is.na(as.character(data$date)))## [1] 0For the “interval” variable:
sum(is.na(as.character(data$interval)))## [1] 0So, the total number of missing values in the dataset is 2304.
2- The strategy for filling in all of the missing values in the dataset. Missing values are replaced by the mean of that 5-minute interval.
#finding the indices of missing values (NAs)
NA_index <- which(is.na(as.character(data$steps)))
complete_data <- data
#Imputing missing values using the mean for that 5-minute interval
complete_data[NA_index, ]$steps<-unlist(lapply(NA_index, FUN=function(NA_index){
steps_per_interval[data[NA_index,]$interval==steps_per_interval$interval,]$average_steps
}))3 - Creating a new dataset that is equal to the original dataset but with the missing data filled in.
#Checking the complete data with the summary and str methods
summary(complete_data)## steps date interval
## Min. : 0.0 Min. :2012-10-01 0 : 61
## 1st Qu.: 0.0 1st Qu.:2012-10-16 5 : 61
## Median : 0.0 Median :2012-10-31 10 : 61
## Mean : 37.4 Mean :2012-10-31 15 : 61
## 3rd Qu.: 27.0 3rd Qu.:2012-11-15 20 : 61
## Max. :806.0 Max. :2012-11-30 25 : 61
## (Other):17202str(complete_data)## 'data.frame': 17568 obs. of 3 variables:
## $ steps : num 1.717 0.3396 0.1321 0.1509 0.0755 ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: Factor w/ 288 levels "0","5","10","15",..: 1 2 3 4 5 6 7 8 9 10 ...4 - Making a histogram of the total number of steps taken each day for the complete dataset:
#Creating a data frame with the steps taken for each day
steps_each_day_complete <- aggregate(steps ~ date, data = complete_data, sum)
#Adding column names to the created data frame
colnames(steps_each_day_complete) <- c("date", "steps")
#Making the histogram
hist(as.numeric(steps_each_day_complete$steps), breaks = 20, col = "red", xlab = "Number of Steps", main= "Histogram of the total number of steps taken each day")
5 - Calculating the mean and median total number of steps taken per day for the complete dataset:
#Mean
mean(steps_each_day_complete$steps)## [1] 10766#Median
median(steps_each_day_complete$steps)## [1] 10766We notice that the mean of the complete dataset (10766) is equal to the mean of the dataset without missing values. The median of the complete dataset has shifted from 10765 to 10766. Therefore, the mean and median for the complete dataset are almost identical.
Are there differences in activity patterns between weekdays and weekends?
#Creating a factor variable "day "to store the day of the week:
complete_data$day <- as.factor(weekdays(complete_data$date))
#Creating a logical variable "is_weekday" (weekday=TRUE, weekend = FALE) :
complete_data$is_weekday <- ifelse(!(complete_data$day %in% c("Saturday","Sunday")), TRUE, FALSE)
#Calculating the average number of steps for weekdays
weekdays_data <- complete_data[complete_data$is_weekday,]
steps_per_interval_weekdays <- aggregate(weekdays_data$steps, by=list(interval=weekdays_data$interval), FUN=mean)
#Calculating the average number of steps for weekends
weekends_data <- complete_data[!complete_data$is_weekday,]
steps_per_interval_weekends <- aggregate(weekends_data$steps, by=list(interval=weekends_data$interval), FUN=mean)
#Adding columns names
colnames(steps_per_interval_weekdays) <- c("interval", "average_steps")
colnames(steps_per_interval_weekends) <- c("interval", "average_steps")
#Adding a column to indecate the day
steps_per_interval_weekdays$day <- "Weekday"
steps_per_interval_weekends$day <- "Weekend"
#Merging the two togather
week_data <- rbind(steps_per_interval_weekends, steps_per_interval_weekdays)
#Converting the day variabke to a factor
week_data$day <- as.factor(week_data$day)
#Making the plot
library(lattice)
xyplot(average_steps ~ interval | day, data = week_data, layout = c(1,2), type ="l", ylab="Number of Steps")
The plot shows that that activity on the weekends tends to be more spread out over the day compared to the weekdays.