####Show any code that is needed to
## Get necessary packages loaded
if(!require(tidyr)){install.packages("tidyr")}## Loading required package: tidyr
library(tidyr)
if(!require(dplyr)){install.packages("dplyr")}## Loading required package: 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
library(dplyr)
if(!require(lubridate)){install.packages("lubridate")}## Loading required package: lubridate
library(lubridate)
if(!require(lattice)){install.packages("lattice")}## Loading required package: lattice
library(lattice)
## little functions annoying but necessary b/c I haven't been able to find a better way
as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
## pull down a fresh dataset if it doesn't already exist in your working directory
## Load the data & process
dataset_url <-"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
if(!file.exists("Dataset.zip")){
download.file(dataset_url, destfile="Dataset.zip")}
if(!file.exists("activity.csv")){
unzip("Dataset.zip", overwrite= TRUE)}
if(!exists("activity")){
activity <- read.csv("activity.csv", sep = ",")}
dateDownloaded <- date()
cat("Data file downloaded & evaluated ", dateDownloaded," from ", dataset_url)## Data file downloaded & evaluated Mon Feb 09 09:54:53 2015 from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip
####For this part of the assignment, you can ignore the missing values in the dataset.
TotalbyDay<-aggregate(activity$steps, list(Day=activity$date), FUN = sum, na.rm=TRUE)
names(TotalbyDay)<-c("Day", "TotalSteps")
head(TotalbyDay)## Day TotalSteps
## 1 2012-10-01 0
## 2 2012-10-02 126
## 3 2012-10-03 11352
## 4 2012-10-04 12116
## 5 2012-10-05 13294
## 6 2012-10-06 15420
2) If you do not understand the difference between a histogram and a barplot, research the difference between them. Make a histogram of the total number of steps taken each day
hist(TotalbyDay$TotalSteps, col = "green", breaks = 100
,xlab = c("Total Number of Steps per Day")
,xlim = c(0,25000)
, ylab = c("Frequency of Number of Daily Steps")
,main = "Histogram of Number of Daily Steps"
)
sum1<- as.data.frame(rbind(
c("Total Number of Steps", sum(TotalbyDay$TotalSteps)),
c("Mean Number of Steps", trunc(mean(TotalbyDay$TotalSteps))),
c("Median Number of Steps", median(TotalbyDay$TotalSteps))
))
colnames(sum1)<-c("Description", "Number")
print(sum1)## Description Number
## 1 Total Number of Steps 570608
## 2 Mean Number of Steps 9354
## 3 Median Number of Steps 10395
1)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)
AvgbyInterval<-aggregate(activity$steps, list(interval=activity$interval)
, FUN = mean, na.rm=TRUE)
names(AvgbyInterval)<-c("interval", "AvgSteps")
x<- which.max(AvgbyInterval$AvgSteps)
maxAvg<-AvgbyInterval[x,]
label <- paste("Peak Avg Steps:",trunc(maxAvg$AvgSteps),"@ Time:"
,trunc(maxAvg$interval))
plot(AvgbyInterval$interval,AvgbyInterval$AvgSteps, type = "l"
, col = "blue", lwd = 2
,xlab = "Time Stamp (24HRMM)"
,ylab = "Average Number of Steps"
)
title("Average Number of Steps Across All Days per 5 Minute Interval")
text (maxAvg$interval+500, maxAvg$AvgSteps, label, cex = 0.8)
# dev.copy(png, file = "Plot1.png", width = 480, height = 480)2)Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
print(paste("Time:",maxAvg$interval))## [1] "Time: 835"
####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.
1) Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
nrow(activity)## [1] 17568
colSums(is.na(activity))## steps date interval
## 2304 0 0
2)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.
Since I've already calculated the mean for the 5-minute intervals, I'll
recycle that data for the strategy by merging the AvgbyInterval.AvgSteps
with the data from activity.
activity2<-merge(activity, AvgbyInterval, by.x = "interval")
remove<-subset(activity2, is.na(steps))
remove<-remove[,c(1,4,3)]
colnames(remove)<-c("interval", "steps","date")
keep<-subset(activity2, !is.na(steps))
keep<-keep[,1:3]
activity.imputed<-rbind(keep, remove)
nrow(activity.imputed)## [1] 17568
colSums(is.na(activity.imputed))## interval steps date
## 0 0 0
4a)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.
TotalbyDay.Imputed<-aggregate(activity.imputed$steps
,list(Day=activity.imputed$date)
, FUN = sum, na.rm=TRUE)
names(TotalbyDay.Imputed)<-c("Day", "TotalSteps")
hist(TotalbyDay.Imputed$TotalSteps, col = "blue", breaks = 100
,xlab = c("Total Number of Steps per Day")
,xlim = c(0,25000)
, ylab = c("Frequency of Number of Daily Steps")
,main = "Histogram of Number of Daily Steps"
)
sum2<- as.data.frame(rbind(
c("Total Number of Steps", trunc(sum(TotalbyDay.Imputed$TotalSteps))),
c("Mean Number of Steps", trunc(mean(TotalbyDay.Imputed$TotalSteps))),
c("Median Number of Steps", trunc(median(TotalbyDay.Imputed$TotalSteps)))
))
colnames(sum2)<-c("Description", "Number")
print(sum2)## Description Number
## 1 Total Number of Steps 656737
## 2 Mean Number of Steps 10766
## 3 Median Number of Steps 10766
yes
summary<- merge(sum1, sum2, by = "Description")
summary$Number.x<-as.numeric.factor(summary$Number.x)
summary$Number.y<-as.numeric.factor(summary$Number.y)
colnames(summary)<-c("Description", "UnadjustedSteps", "AdjustedSteps")
summary<-mutate(summary,
PctChange.From.UnAdjusted =
trunc(100*((UnadjustedSteps - AdjustedSteps)/UnadjustedSteps)))
print(summary[,c(1,4)] )## Description PctChange.From.UnAdjusted
## 1 Mean Number of Steps -15
## 2 Median Number of Steps -3
## 3 Total Number of Steps -15
For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.
1) Create a new factor variable in the dataset with two levels - "weekday" and "weekend" indicating whether a given date is a weekday or weekend day.
activity3<-mutate(activity.imputed, DOW = wday(activity$date, label = TRUE))
day.id<-as.data.frame(cbind(
DOW = c("Sun","Mon","Wed","Fri","Sat","Thurs","Tues"),
End = c("weekend","weekday","weekday","weekday"
,"weekend","weekday","weekday")
))
activity3<-merge(activity3, day.id, by.x = "DOW")
print(str(activity3))## 'data.frame': 17568 obs. of 5 variables:
## $ DOW : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tues"<..: 6 6 6 6 6 6 6 6 6 6 ...
## $ interval: int 210 2050 210 2055 215 2055 210 210 210 520 ...
## $ steps : num 0 0 56 0 0 0 0 0 0 6 ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 57 2 9 33 5 17 27 44 21 27 ...
## $ End : Factor w/ 2 levels "weekday","weekend": 1 1 1 1 1 1 1 1 1 1 ...
## NULL
2) Make a panel plot containing 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 weekday days or weekend days (y-axis). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.
AvgByEnd.Imputed<-aggregate(activity3$steps,
list(TimeStamp=activity3$interval
,WeekEnd.WeekDay = activity3$End)
,FUN = mean, na.rm=TRUE)
names(AvgByEnd.Imputed)<-c("TimeStamp", "WeekEnd.WeekDay", "AvgSteps")
xyplot(AvgByEnd.Imputed$AvgSteps ~ AvgByEnd.Imputed$TimeStamp
| AvgByEnd.Imputed$WeekEnd.WeekDay
,layout = c(1, 2)
,type = "l"
,xlab = "5 minute Time Stamp"
,ylab = "Average Number of Steps")