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Reproducible Research: Peer Assessment 1
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Loading and preprocessing the data

In this data section I read the data from the shared folder.

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.1     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

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.

data<- activity %>% 
    group_by(date) %>% 
    summarize(Steps_per_day=sum(steps))
## `summarise()` ungrouping output (override with `.groups` argument)

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.

mean<-mean(data$Steps_per_day,na.rm=TRUE)
median<-median(data$Steps_per_day,na.rm=TRUE)

The mean number of steps is 10766 and the median is 10765.

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.

data2<- activity %>% 
    group_by(interval) %>% 
    summarize(Steps_per_interval=mean(steps,na.rm=TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)

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

max<-max(data2$Steps_per_interval)

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

Interval 835 is the one with the maximum or higher number of steps. The total number of steps is 206.

#Imputing missing values

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 2304.

#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.

data_imp<- activity2 %>% 
    group_by(date) %>% 
    summarize(Steps_per_day_imp=sum(steps_imp))
## `summarise()` ungrouping output (override with `.groups` argument)

hist(data_imp$Steps_per_day_imp)

Are there differences in activity patterns between weekdays and weekends?

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"))
library(lattice)
weekday_series<- activity3 %>% 
    group_by(weekend,interval) %>% 
    summarize(Steps_per_int_imp=mean(steps_imp))
## `summarise()` regrouping output by 'weekend' (override with `.groups` argument)

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