PA1_template.txt

# Reproducible Research: Peer Assessment 1


## Loading and preprocessing the data

```r
library(dplyr)
```

```
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
```

```r
library(xtable)
data <- read.csv("activity.csv")
```





## What is mean total number of steps taken per day?

```r
stepsPerDay <- data %>%
      group_by(date) %>%
      summarize(tot = sum(steps, na.rm = T))

hist(stepsPerDay$tot, main = "Histogram of Steps per Day", xlab = "Steps per Day")
```

![](PA1_template_files/figure-html/unnamed-chunk-2-1.png) 

```r
dtMean <- round(mean(stepsPerDay$tot),digits = 2)
dtMedian <- median(stepsPerDay$tot)
```
#### Mean: 9354.23     
#### Median: 10395




## What is the average daily activity pattern?

```r
stepsPerTime <- data %>%
      group_by(interval) %>%
      summarize(avg = mean(steps, na.rm=T))

plot(stepsPerTime$interval,stepsPerTime$avg,type = "l",main="Avg Steps per Interval",xlab="5 min interval",ylab="Avg Steps")
```

![](PA1_template_files/figure-html/unnamed-chunk-3-1.png) 

```r
dtMax <- filter(stepsPerTime, avg == max(stepsPerTime$avg)) %>% select(interval)
```
#### Interval with highest average steps: 835





## Imputing missing values





## Are there differences in activity patterns between weekdays and weekends?