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PA1_template.md

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+---
+title: "Week 2 Reproducible research"
+output: html_document
+---
+
+
+
+First reading the data in
+
+
+```r
+d <- read.csv("activity.csv")
+```
+
+### Total number of steps per day. Calculating the total number per day using tapply, then plotting a histogram and calculating the mean.
+
+```r
+spd <- tapply(d$steps,d$date,sum)
+hist(spd, main="Number of steps taken per day")
+```
+
+![plot of chunk unnamed-chunk-2](figure/unnamed-chunk-2-1.png)
+
+```r
+mean(spd,na.rm=TRUE)
+```
+
+```
+## [1] 10766.19
+```
+
+```r
+median(spd,na.rm=TRUE)
+```
+
+```
+## [1] 10765
+```
+
+### Average daily activity pattern.
+Calculating the means for each inerval and then plotting. Also subsetting the interval with the largest mean.
+
+
+```r
+ada <- tapply(d$steps,d$interval,mean,na.rm=TRUE)
+plot(names(ada),ada,type = "l",xlab="Interval")
+```
+
+![plot of chunk unnamed-chunk-3](figure/unnamed-chunk-3-1.png)
+
+```r
+ada[ada=max(ada)]
+```
+
+```
+##     1705 
+## 56.30189
+```
+The interval 1705 on average across all the days in the dataset, contains the maximum number of steps.
+
+### Imputing missing values.
+
+Counting the number of NAs
+
+```r
+sum(!complete.cases(d))
+```
+
+```
+## [1] 2304
+```
+
+The total number of repws with NAs = 2304.
+
+I have decided to impute missing values with the mean of that 5 minute interval.
+
+
+```r
+dc <- d
+dc$steps[is.na(dc$steps)] = ave(dc$steps, 
+                           dc$interval, 
+                             FUN=function(x)mean(x, 
+                      na.rm = T))[is.na(d$steps)] 
+```
+The code above code does that
+
+Histogram of total number of steps each day with missing data imputed. Using the same strategy as was used for the previous histogram. Getting the mean and median as well.
+
+```r
+spdc <- tapply(dc$steps,dc$date,sum)
+hist(spdc, main="Number of steps taken per day")
+```
+
+![plot of chunk unnamed-chunk-6](figure/unnamed-chunk-6-1.png)
+
+```r
+mean(spdc)
+```
+
+```
+## [1] 10766.19
+```
+
+```r
+median(spdc)
+```
+
+```
+## [1] 10766.19
+```
+
+The means of the imputed and non imputed datasets are the same, showing that imputing does not have an effect, however the median is smaller in the non imputed data. 
+
+### Are there differences in activity patterns between weekdays and weekends
+Creating a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.
+
+```r
+dc$date <- as.Date(dc$date)
+dc$day <- ifelse(weekdays(dc$date)=="Saturday","weekend","")
+dc$day <- ifelse(weekdays(dc$date)=="Sunday","weekend",dc$day)
+dc$day <- ifelse(dc$day=="","weekday",dc$day)
+dc$day <- as.factor(dc$day)
+```
+
+Creating a plot with average over weekday/weekend
+
+```r
+library(ggplot2)
+dc$interval <- as.integer(dc$interval)
+ggplot(dc, aes(x=interval, y=steps)) + stat_summary(fun.y="mean", geom="point") + geom_smooth()+ facet_grid(day~.)
+```
+
+```
+## `geom_smooth()` using method = 'gam'
+```
+
+![plot of chunk unnamed-chunk-8](figure/unnamed-chunk-8-1.png)