My first post!

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 assignment 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. This was my peer assessment for the course Reproducible Reasearch, part of the Data Science Specialization by Johns Hopkins University on Coursera.

Loading and preprocessing the data

# 1. Load the data
unzip('activity.zip')
dataset <- read.csv('activity.csv', header=TRUE, sep = ',', na.strings = NA)
str(dataset)
## '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 ...
# 2. Process/transform the data (if necessary) into a format suitable for your analysis
dataset$date <- as.Date(dataset$date, format = "%Y-%m-%d")
str(dataset)
## 'data.frame':    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Date, format: "2012-10-01" "2012-10-01" ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...

What is mean total number of steps taken per day?

# 3. Calculate the total number of steps taken per day
stepsday <- aggregate(x = dataset$steps , by = list(dataset$date), FUN = sum , na.rm=TRUE)
names(stepsday) <- c("date","steps")

# 4. Make a histogram of the total number of steps taken each day

hist(stepsday$steps, col = "red", xlab = "Total steps", main = "Histogram of daily steps", breaks = 10)

_config.yml

# 5. Calculate and report the mean and median of the total number of steps taken per day

mean(stepsday$steps, na.rm = TRUE)
## [1] 9354.23
median(stepsday$steps, na.rm = TRUE)
## [1] 10395

What is the average daily activity pattern?

# 6. Make a times series plot of the 5-minute interval (x-axis) and the average number of steps taken,
# averaged across all days (y-axis)
avsteps <- aggregate(x = dataset$steps , by = list(dataset$interval), FUN = mean ,na.rm=TRUE)
names(avsteps) <- c("interval","steps")

# Plot
with(avsteps, plot(interval, steps, type = "l", main = "Times Series Plot of Average Steps by Interval"))

_config.yml

# 7. Which 5-minute interval, on average across all the days in the dataset, contains the maximum
# number of steps

avsteps[which.max(avsteps$steps),1]
## [1] 835

Imputing missing values

# 8. Calculate and report the total number of missing values in the dataset

nrow(dataset[is.na(dataset),])
## [1] 2304
# 9. Create a new dataset that is equal to the original dataset but with the missing data filled in.
## Strategy: imputing missing values with average step values at the time interval

imputed <- merge(x = dataset, y = avsteps, by = "interval", all.x = TRUE)
imputed[is.na(imputed$steps.x),c("steps.x")] <- imputed[is.na(imputed$steps.x),c("steps.y")]

# Cleaning and reordering data
imputed <- imputed[,-4]
imputed <- imputed[,c(2,3,1)]
names(imputed)[1] <- "steps"

# 10. 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?

newstepsday <- aggregate(x = imputed$steps , by = list(imputed$date), FUN = sum ,na.rm=TRUE)
names(newstepsday) <- c("date","steps")

# Histogram
hist(newstepsday$steps, col = "red", xlab = "Total steps", main = "Histogram of daily steps (corrected)", breaks = 10)

_config.yml

# Mean and Median
mean(newstepsday$steps, na.rm = TRUE)
## [1] 10766.19
## (Previous: [1] 9354.23)

median(newstepsday$steps, na.rm = TRUE)
## [1] 10766.19
## (Previous: [1] 10395)

Are there differences in activity patterns between weekdays and weekends?

# 11. 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.

imputed$weekday <- as.factor(ifelse(weekdays(imputed$date) %in% c("Saturday","Sunday"), "Weekend", "Weekday")) 

# 12. 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). 

avsteps.by.interval.and.weekday  <- aggregate(x = imputed$steps , 
                                                    by = list(imputed$interval,imputed$weekday), FUN = mean ,na.rm=TRUE)
names(avsteps.by.interval.and.weekday) <- c("interval","weekday","steps")

# Plot

weekdays <- subset(avsteps.by.interval.and.weekday,weekday == "Weekday", c(interval,weekday, steps))
weekend <- subset(avsteps.by.interval.and.weekday,weekday == "Weekend", c(interval,weekday, steps))

par(mfrow = c(2, 1), mar = c(4,4,3,2), oma = c(0,0,2,0))
with(weekdays, plot(interval, steps, xlab = "Weekday", type = "l"))
with(weekend, plot(interval, steps, xlab = "Weekend", type = "l"))
mtext("Average steps by type of day", outer = TRUE)

_config.yml

Written on January 12, 2016