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Reproducible Research: Peer Assessment 1

Load packages

library(chron)
library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lubridate)
## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:chron':
## 
##     days, hours, minutes, seconds, years

## The following object is masked from 'package:base':
## 
##     date

Loading and preprocessing the data

data<-read.csv("activity.csv", stringsAsFactors=FALSE)
data<-data %>% mutate(date=ymd(date))

What is mean total number of steps taken per day?

byDay<-data %>% group_by(date) %>% summarise(TotalSteps=sum(steps, na.rm = TRUE))
ggplot(data = byDay, aes(TotalSteps)) + geom_histogram(bins = 20)+ggtitle("Total steps per day frequency counts")

Mean of total steps by day

mean(byDay$TotalSteps)
## [1] 9354.23

Median of total steps by day

median(byDay$TotalSteps)
## [1] 10395

What is the average daily activity pattern?

byInterval<-data %>% group_by(interval) %>% summarise(MedianSteps=median(steps, na.rm=TRUE), MeanSteps=mean(steps, na.rm=TRUE))
ggplot(data = byInterval, aes(x=interval, y=MeanSteps)) + geom_line()

Interval with the maximum mean total steps

byInterval[which.max(byInterval$MeanSteps),1]
## # A tibble: 1 × 1
##   interval
##      <int>
## 1      835

Imputing missing values

Total number of missing values

sum(is.na(data$steps))
## [1] 2304

Impute some missing values

inputed<-inner_join(x=data, y=byInterval, by=c("interval","interval"))
#we can use the mean for that interval if the value is missing
inputed$extra <- ifelse(!is.na(inputed$steps), inputed$steps, inputed$MeanSteps)
ibyDay<-inputed %>% group_by(date) %>% summarise(TotalSteps=sum(extra, na.rm = TRUE))

Total number of steps per day

ggplot(data = ibyDay, aes(TotalSteps)) + geom_histogram(bin=20)+ggtitle("Total steps per day frequency counts")
## Warning: Ignoring unknown parameters: bin

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Mean and median of total steps by day

mean(ibyDay$TotalSteps)
## [1] 10766.19

median(ibyDay$TotalSteps)
## [1] 10766.19

The total number of steps has gone up, as the missing values were previously implicitly counted as zero. Also, the median is now equal to the mean, as we have used the mean to fill the missing values. This will skew the centre of the distribution to the mean.

Are there differences in activity patterns between weekdays and weekends?

inputed$isweekend<-as.factor(ifelse(is.weekend(inputed$date), "WeekEnd", "WeekDay"))

byIntervalw<-inputed %>% group_by(interval, isweekend) %>% summarise(MedianSteps=median(steps, na.rm=TRUE), MeanSteps=mean(steps, na.rm=TRUE))
qplot(interval, 
      MeanSteps, 
      data = byIntervalw, 
      type = 'l', 
      geom=c("line"),
      xlab = "Interval", 
      ylab = "Number of steps", 
      main = "") +
  facet_wrap(~ isweekend, ncol = 1)
## Warning: Ignoring unknown parameters: type