title: “Reproducible_project1” author: “Lakshmi Kovvuri” date: “6/6/2020” output: html_document ———————————————-

Download the data file

temp<-tempfile()

fileUrl<-"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"

download.file(fileUrl, temp, mode="wb")

# Unzip the file and read the csv file

unzip(temp, "activity.csv")

activity<-read.csv("activity.csv")

Loading libraries

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

Task1: Calculate the total number of steps taken per day.

totalSteps<-aggregate(steps~date, data=activity, FUN=sum, na.rm=TRUE)

Task2: Make a Histogram of the total number of steps taken per day.

hist(totalSteps$steps, main="Total steps taken per day", xlab="Steps", col="deeppink", breaks=20)

# Task3: Mean and Median of the total number of steps

 MeanWithNA<-mean(totalSteps$steps, na.rm=TRUE)

 MedianWithNA<-median(totalSteps$steps, na.rm=TRUE)

Task4: Time series plot of the average steps taken

 averageSteps<-aggregate(steps~interval, data=activity, FUN=mean, na.rm=TRUE)

# creating plot for the average steps

   g1<-ggplot(averageSteps, aes(interval, steps))


  # Setting shape and color for the plot

   g1<-g1+geom_line(col="blue", lwd=1.2)

  # Setting titles for the plot

 g1<-g1+ggtitle("Average daily activity pattern")+ xlab ("Time Interval") + ylab("Steps")
 print(g1)

Task 5: Which 5-min interval contains the maximum number of steps

  maxSteps<-averageSteps$interval [which.max (averageSteps$steps) ]

Task 6: Calculate the total number of missing values in the dataset

 missingSteps<-sum(is.na(activity$steps))

# Devise a strategey for filling all missing values with mean

  matchSteps<-round(averageSteps$steps[match(activity$interval, averageSteps$interval)],0)

imputeSteps<- ifelse(is.na(activity$steps), matchSteps, activity$steps)

# create a new data frame with the missing data filled in

revisedActivity<-data.frame(steps=imputeSteps, interval=activity$interval, date=activity$date)

Task 7: Histogram of the steps taken after missing values are imputed

 # Grouping imputed steps with date

ImputedSteps<-aggregate(revisedActivity$steps, list(revisedActivity$date), FUN=sum)

 # setting column names for the table

colnames(ImputedSteps)<-c("Date", "Steps")

# Creating histogram for the imputed steps

g2<-ggplot(ImputedSteps, aes(Steps))

# setting shape and colors for the plot

g2<-g2+geom_histogram(boundary=0, binwidth=2000, col="chartreuse4", fill="chartreuse3")

# setting titles for the plot

g2<-g2+ggtitle("Histogram of the steps with missing data filled")+xlab("Steps")+ylab("Interval")
print(g2)

# Mean and median of the steps taken after missing data filled

MeanWithoutNA<-mean(ImputedSteps$Steps)

MedianWithoutNA<-median(ImputedSteps$Steps)

# Difference of 'Steps taken per day' and
#   'Steps taken per day after imputing missing data'

 MeanWithNA-MeanWithoutNA

## [1] 0.549335

MedianWithNA-MedianWithoutNA

## [1] 3

# Impact of imputing missing data on the estimates of the steps

summary(totalSteps)

##          date        steps      
##  2012-10-02: 1   Min.   :   41  
##  2012-10-03: 1   1st Qu.: 8841  
##  2012-10-04: 1   Median :10765  
##  2012-10-05: 1   Mean   :10766  
##  2012-10-06: 1   3rd Qu.:13294  
##  2012-10-07: 1   Max.   :21194  
##  (Other)   :47

summary(ImputedSteps)

##          Date        Steps      
##  2012-10-01: 1   Min.   :   41  
##  2012-10-02: 1   1st Qu.: 9819  
##  2012-10-03: 1   Median :10762  
##  2012-10-04: 1   Mean   :10766  
##  2012-10-05: 1   3rd Qu.:12811  
##  2012-10-06: 1   Max.   :21194  
##  (Other)   :55

#Task 8: Panel plot comparing the average number of steps

# Task 8: Panel plot comparing the average number of steps 
#         across weekdays and weekends

#Converting date to date type

revisedActivity$date<-as.Date(revisedActivity$date, format="%Y-%m-%d")

  # function to check whether the given date is weekend of weekday

TypeOfweek<-sapply(revisedActivity$date, 
               function(x)
               {
                 if(weekdays(x)=="Saturday"|weekdays(x)=="Sunday")
                  {
                   y<-"Weekend"
                  }
                 else
                  {
                    y<-"Weekday"
                   }
                 y
                })

  # Adding the field 'TypeOfweek' to the dataset

Activity2<-mutate(revisedActivity, TypeOfweek )

# Calculating average for the steps taken across weekends & weekdays

weeksActivity<-aggregate(steps~interval, data=Activity2, FUN=mean, na.rm=TRUE)

# Creating plot for the average daily steps across weekends & weekdays

g3<-ggplot(Activity2, aes(x=interval, y=steps, color=TypeOfweek))

# Setting shape for the plot

g3<-g3+geom_line()+ facet_wrap(~TypeOfweek, ncol=1,nrow=2)

# Setting names for the plot

g3<-g3+labs(title="Average steps for Weekdays & Weekends", x="Interval", y="Steps")
print(g3)

#END

message("Cheers'echo=TRUE'...")

## Cheers'echo=TRUE'...