Reproducible Research Assignment 1
Selcuk Fidan
March 24, 2016
Loading and preprocessing the data
require(ggplot2)## Loading required package: ggplot2require(lubridate)## Loading required package: lubridaterequire(mice)## Loading required package: mice## Loading required package: Rcpp## mice 2.25 2015-11-09require(VIM)## Loading required package: VIM## Loading required package: colorspace## Loading required package: grid## Loading required package: data.table##
## Attaching package: 'data.table'## The following objects are masked from 'package:lubridate':
##
## hour, mday, month, quarter, wday, week, yday, year## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleep# call packages
library(ggplot2)
library(lubridate)
library(mice)
library(VIM)
# 1_code for reading dataset and/or processing the data
# activity monitoring data
act.mon.data <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
# make sure the site is live, if it is not live stop function terminate the program
check.url <- file(act.mon.data,"r")
if (!isOpen(check.url)) {
stop(paste("There's a problem with the data:",geterrmessage()))
}
# zipfile.data is the variable to keep the *.zip file
zipfile.data = "repdata-data-activity.zip"
# make sure the data in the working directory if not download the zip file into the to zipfile.data and unzip the zipfile.data
if(!file.exists(zipfile.data)) {
download.file(act.mon.data,zipfile.data)
data.file <- unzip(zipfile.data)
} else {
data.file <-"activity.csv"
}
activity.mon.data <- read.csv(data.file)
str(activity.mon.data)## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...What is mean total number of steps taken per day?
- Missing values in the dataset are ignored.
new.activity.mon.data<-na.omit(activity.mon.data)
str(new.activity.mon.data)## 'data.frame': 15264 obs. of 3 variables:
## $ steps : int 0 0 0 0 0 0 0 0 0 0 ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
## - attr(*, "na.action")=Class 'omit' Named int [1:2304] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..- attr(*, "names")= chr [1:2304] "1" "2" "3" "4" ...- Total number of steps taken per day:
new.activity.mon.data$date<-as.Date(new.activity.mon.data$date)
str(new.activity.mon.data) ## 'data.frame': 15264 obs. of 3 variables:
## $ steps : int 0 0 0 0 0 0 0 0 0 0 ...
## $ date : Date, format: "2012-10-02" "2012-10-02" ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
## - attr(*, "na.action")=Class 'omit' Named int [1:2304] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..- attr(*, "names")= chr [1:2304] "1" "2" "3" "4" ...per.day<-tapply(new.activity.mon.data$steps, new.activity.mon.data$date, FUN=sum)# 2_Histogram of the total number steps taken each day
# picture size arrangements
number.add.width<-800
number.add.height<-720
png("plot1.png", width=number.add.width, height=number.add.height)
# lines(density(per.day)) #Get a density curve to go along with your AirPassengers histogram
# dev.off()hist(per.day,53, main = "Total number of steps taken per day", xlab = "steps per day",col="black")
abline(v = mean(per.day), col = "red", lwd = 2)
- Mean of the total number of steps taken per day
# 3_Mean and median number of steps taken each day
mean.per.day<-mean(per.day)
mean.per.day## [1] 10766.19- Median of the total number of steps taken per day
median.per.day<-median(per.day)
median.per.day## [1] 10765What is the average daily activity pattern?
- Make a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
# 4_Time series plot of the average number of steps taken
daily.act.pattern<-tapply(new.activity.mon.data$steps, new.activity.mon.data$interval, FUN=mean)
# alternative solution for five.min.interval
five.min.interval.alt<-daily.act.pattern[match(max(daily.act.pattern),daily.act.pattern)]
#png("plot2.png", width=number.add.width, height=number.add.height)
#dev.off()plot( x = names(daily.act.pattern),y = daily.act.pattern, type = "l", xlab = "5-Minute-Interval",
main = "Daily Activity Pattern", ylab = "Average number of steps",col="red",lwd=3)
grid()
- On average across all the days in the dataset, the 5-minute interval contains the maximum number of steps?
# 5_The 5-minute interval that, on average, contains the maximum number of steps
five.min.interval<-daily.act.pattern[daily.act.pattern==max(daily.act.pattern)]
five.min.interval## 835
## 206.1698Imputing missing values
Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with ’NA’s)
md.pattern(activity.mon.data)## date interval steps
## 15264 1 1 1 0
## 2304 1 1 0 1
## 0 0 2304 2304#plot pattern
#png("plot3.png", width=number.add.width, height=number.add.height)
#dev.off()
#png("plot4.png", width=number.add.width, height=number.add.height)
#dev.off()aggr_plot <- aggr(activity.mon.data, col=c('navyblue','red'), numbers=TRUE, sortVars=TRUE, labels=names(data), cex.axis=.7, gap=3, ylab=c("Histogram of missing data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## steps 0.1311475
## date 0.0000000
## interval 0.0000000marginmatrix(activity.mon.data)
Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
Create a new dataset that is equal to the original dataset but with the missing data filled in.
new.activity.mon.data<-activity.mon.data
new.activity.mon.data[which(is.na(new.activity.mon.data$steps)),1]<-daily.act.pattern[as.character(new.activity.mon.data[which(is.na(new.activity.mon.data$steps)),3])]
# No missing values are now in the new dataset:
sum(is.na(new.activity.mon.data))## [1] 0# 6_Code to describe and show a strategy for imputing missing data
# Now let's make the same histogram, that we made in the first part of the analysis, in order to visually see if there is a big effect.
new.per.day <- tapply(new.activity.mon.data$steps, new.activity.mon.data$date, FUN=sum)# 7_Histogram of the total number of steps taken each day after missing values are imputed
#png("plot5.png", width=number.add.width, height=number.add.height)
hist(new.per.day,53, main = "Total number of steps taken per day", xlab = "steps per day",col="black")
abline(v = mean(new.per.day), col = "red", lwd = 2)
#dev.off()- Mean of the total number of steps taken per day after imputed missing data
mean(new.per.day)## [1] 10766.19- Median of the total number of steps taken per day after imputed missing data
median(new.per.day)## [1] 10766.19Compare the new mean, median values before imputed missing data:
mean(new.per.day)-mean(per.day)## [1] 0median(new.per.day)-median(per.day)## [1] 1.188679So, after imputing the missing data, the new mean of total steps taken per day is the same as that of the old mean; the new median of total steps taken per day is slightly greater than that of the old median.
- Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day.
# Comparison before imputed and after imputed
#png("plot6.png", width=number.add.width, height=number.add.height)
#dev.off()par(mfrow=c(1,2))
hist(per.day,53, main = "Total number of steps taken per day", xlab = "steps per day",col="black")
abline(v = median(per.day), col = "red", lwd = 2)
hist(new.per.day,53, main = "Total number of steps taken per day
(missing values replaced with mean of interval)", xlab = "steps per day",col="black")
abline(v = median(new.per.day), col = "red", lwd = 2)
Are there differences in activity patterns between weekdays and weekends?
# 8_Panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends
weekday.or.weekend <- function(date) {
day <- weekdays(date)
if (day %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
return("weekday") else if (day %in% c("Saturday", "Sunday"))
return("weekend") else stop("invalid date")
}
new.activity.mon.data$date <- as.Date(new.activity.mon.data$date)
new.activity.mon.data$day <- sapply(new.activity.mon.data$date, FUN = weekday.or.weekend)
#png("plot7.png", width=number.add.width, height=number.add.height)
aggregated.avgs <- aggregate(steps ~ interval + day, data = new.activity.mon.data, mean)
weekday.aggregated.avgs <- subset(aggregated.avgs, day=="weekday")
weekend.aggregated.avgs <- subset(aggregated.avgs, day!="weekday")
c.y1<-max(weekend.aggregated.avgs$steps)
c.y2<-max(weekday.aggregated.avgs$steps)
c.x1<-weekend.aggregated.avgs$interval[match(c.y1,weekend.aggregated.avgs$steps)]
c.x2<-weekday.aggregated.avgs$interval[match(c.y2,weekday.aggregated.avgs$steps)]
#dev.off()ggplot(aggregated.avgs, aes(interval, steps,color = day))+scale_colour_manual(values=c("red","black")) + geom_line(size=2) + facet_grid(. ~ day,scales = "free", space = "free") +
xlab("5-minute interval") + ylab("Number of steps")+theme(axis.text=element_text(size=20,colour="blue"),legend.position='none',
axis.title=element_text(size=20,face="bold"),legend.background = element_rect(),panel.background = element_rect(fill = "grey"),strip.text.x = element_text(size=24, face="bold",colour="white"),
strip.background = element_rect(colour="red", fill="blue"))