PA1_template.Rmd

---
title: "Reproducible Research: Peer Assessment 1"
output: 
  html_document: 
    keep_md: true
---
## Loading and preprocessing the data
In this data section I read the data from the shared folder. 

```{r, echo=FALSE}
library(tidyverse) #Need for the Pipes I will use in next step.
library(knitr)
options(digits=0) #Shows full numbers
options(scipen=999) #Removes scientific notation
```

```{r, echo=TRUE}
activity <- read.csv("~/Reproducible Research/week2/activity.csv")
```

### Histogram of number of steps
Here, I do an histogram of the number of steps taken each day.

```{r, echo=TRUE}
data<- activity %>% 
    group_by(date) %>% 
    summarize(Steps_per_day=sum(steps))

hist(data$Steps_per_day)
```

# What is the mean and median number of steps taken each day? 
As instructed, I use NA.RM to ignore the missings. 
```{r ,echo=TRUE}
mean<-mean(data$Steps_per_day,na.rm=TRUE)
median<-median(data$Steps_per_day,na.rm=TRUE)
```

The mean number of steps is `r mean` and the median is `r median`. 

# What is the average daily activity pattern?
To study this, I will make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis). For this, I aggregate the data per interval.

```{r , echo=TRUE}
data2<- activity %>% 
    group_by(interval) %>% 
    summarize(Steps_per_interval=mean(steps,na.rm=TRUE))

plot(data2$interval,data2$Steps_per_interval,type="l")

max<-max(data2$Steps_per_interval)

max2<-subset(data2,(data2$Steps_per_interval)==max)
```

Interval `r max2$interval` is the one with the maximum or higher number of steps. The total number of steps is `r max2$Steps_per_interval`.

#Imputing missing values
```{r determine missing, echo=TRUE}
activity$missing<-ifelse(is.na(activity$steps)==TRUE,1,0)

data3<-activity %>% 
       summarize(Missing=sum(missing))

total_missing<-data3$Missing

rm(data3)
```

The total number of observation with a missing value is `r total_missing`.

```{r , echo=TRUE}
#Here I create a new dataset to avoid overwriting the old one
activity2<-activity

mean_steps<-mean(activity2$steps,na.rm=TRUE)

activity2$steps_imp<-ifelse(is.na(activity2$steps)==FALSE,activity2$steps,mean_steps)
```

Now, I create the new dataset per day, but using the imputed data.

```{r, echo=TRUE}
data_imp<- activity2 %>% 
    group_by(date) %>% 
    summarize(Steps_per_day_imp=sum(steps_imp))

hist(data_imp$Steps_per_day_imp)
```

## Are there differences in activity patterns between weekdays and weekends?
```{r, echo=TRUE}
activity3<-activity2
activity3$date_new <- as.Date(activity3$date)
activity3$day<-weekdays(activity3$date_new)

activity3$weekend<-ifelse(activity3$day=="Saturday","Weekend",
                  ifelse(activity3$day=="Sunday","Weekend","Weekday"))
```

```{r, echo=TRUE}
library(lattice)
weekday_series<- activity3 %>% 
    group_by(weekend,interval) %>% 
    summarize(Steps_per_int_imp=mean(steps_imp))

xyplot(Steps_per_int_imp ~ interval | weekend,
       data = weekday_series,
       type = "l")
```