https://github.com/develHector/RepData_PeerAssessment1
Update - 19 Apr, 2015 b

Loading and preprocessing the data

Download the file in case it’s not on the current directory

downloadFile <- "activity.zip";
url <- "https://d396qusza40orc.cloudfront.net/repdata/data/activity.zip";
if( !file.exists(downloadFile) ) download.file( url, downloadFile, method = "curl" );

Unzip it in memory, and process/transofrm the zip onto raw data

dataFile <- "activity.csv";
FileCon <- unz( downloadFile, dataFile );
RawData <- read.csv( FileCon, header=TRUE, nrows=17568 );

Mean total number of steps taken per day

Calculate the total number of steps taken per day (I’ll remove intervals without data)

# na.rm=TRUE fattens the left tail
StepsPerDay <- with( RawData, tapply( steps, date, sum, na.rm=FALSE ) )  ;

Make a histogram of the total number of steps taken each day

library( lattice ) ; # I chose this just because of the parameterless nice colored graphs
histogram( StepsPerDay ) ;

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

# same case as the sum, na.rm=TRUE to avoid operations with NAs
mean( StepsPerDay, na.rm=TRUE )

## [1] 10766.19

median( StepsPerDay, na.rm=TRUE )

## [1] 10765

Average daily activity pattern

Make a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken

# This time I'll use the base::aggregate method instead of tapply, just for fun
MeanStepsPerInterval <- aggregate( steps ~ interval, data=RawData, FUN=mean, na.rm=FALSE ) ;
library( lattice ) ; 
xyplot( steps ~ interval, data=MeanStepsPerInterval, type="l",
  ylab = "Avg num steps (across all days)",
  xlab  = "5 min interval" )

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

# There's no inquirying on the value, that's why only the interval I'm reporting
MeanStepsPerInterval[ which.max( MeanStepsPerInterval$steps ), "interval" ] ;

## [1] 835

Imputing missing values

Calculate and report the total number of missing values in the dataset

MissingValues <- RawData[ is.na( RawData$steps ),] ;
nrow( MissingValues ) ;

## [1] 2304

Devise a strategy for filling in all of the missing values in the dataset.

My strategy will be to fill the NA values with the Average mean of the interval from the whole complete dataset

# We aggregated the mean in previous essignment question, let's do the same for this requirement
MeanStepsPerInterval <- aggregate( steps ~ interval, data=RawData, FUN=mean, na.rm = TRUE ) ;
# Merge it on interval
CompleteData <- merge( RawData, MeanStepsPerInterval, by="interval" ) ;
# Replace NA values with the mean of that same interval, of course and being single steps, rounded 
CompleteData[ is.na( CompleteData$steps.x ), "steps.x" ] <-
    round( CompleteData[ is.na( CompleteData$steps.x ), "steps.y" ] ) ;

Create a new dataset that is equal to the original dataset but with the missing data filled in.

# Let's remove work columns and order it as the original one
CompleteData$steps <- CompleteData$steps.x;
CompleteData$steps.x <- NULL ;
CompleteData$steps.y <- NULL ;
CompleteData <- CompleteData[ order(CompleteData$date, CompleteData$interval), ] ;

Make a histogram of the total number of steps taken each day

StepsPerDayComplete <- with( CompleteData, tapply( X=steps, INDEX=date, FUN=sum ) )  ; 
histogram( StepsPerDayComplete )

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

mean(StepsPerDayComplete)

## [1] 10765.64

median(StepsPerDayComplete)

## [1] 10762

Do these values differ from the estimates from the first part of the assignment?

No, changes are minimal

What is the NA impact on the estimates of the total daily number of steps?

Really minimal or nothing, the new mean are median are kept or almost the same

Activity diff between weekdays and weekends

Create a new factor variable in the dataset indicating whether a given date is a weekday or weekend day.

# For if your language is not English as mine, adjust it with Sys.setlocale("LC_ALL", "English")
CompleteData$weekend =  factor( weekdays( as.Date( CompleteData$date ) ) %in% c("Saturday","Sunday"),
                                labels = 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(lattice)
MeanStepsPerInterval <- aggregate( steps ~ interval + weekend, data=CompleteData, FUN=mean ) ;
xyplot( steps ~ interval | weekend, data=MeanStepsPerInterval, type="l", layout = c(1,2) )  ;

Thanks guys!

Good luck with yours

Reproducible Research - Peer Assessment 1