##Assignment Instructions 1.Code for reading in the dataset and/or processing the data 2.Histogram of the total number of steps taken each day 3.Mean and median number of steps taken each day 4.Time series plot of the average number of steps taken 5.The 5-minute interval that, on average, contains the maximum number of steps 6.Code to describe and show a strategy for imputing missing data 7.Histogram of the total number of steps taken each day after missing values are imputed 8.Panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends 9.All of the R code needed to reproduce the results (numbers, plots, etc.) in the report

1. Code for reading in the dataset and/or processing the data

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

Exploring the basics of this data

dim(activity)
## [1] 17568     3
names(activity)
## [1] "steps"    "date"     "interval"
head(activity)
##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10
## 4    NA 2012-10-01       15
## 5    NA 2012-10-01       20
## 6    NA 2012-10-01       25
str(activity)
## 'data.frame':    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : chr  "2012-10-01" "2012-10-01" "2012-10-01" "2012-10-01" ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...
#total number of missing data
sum(is.na(activity$steps))/dim(activity)[[1]]
## [1] 0.1311475
#transforming the date column into date format using lubridate
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
activity$date<-ymd(activity$date)
length(unique(activity$date))
## [1] 61

2. Histogram of the total number of steps taken each day

library(ggplot2)
Q2<-data.frame(tapply(activity$steps,activity$date,sum,na.rm=TRUE))
Q2$date<-rownames(Q2)
rownames(Q2)<-NULL
names(Q2)[[1]]<-"Total Steps"
png("plot1.png")
#Total Steps by date bar chart
ggplot(Q2,aes(y=Q2$`Total Steps`,x=Q2$date))+geom_bar(stat="identity") + ylab("Total Steps")+xlab("Date")+ggtitle("Total Steps by date")
## Warning: Use of `Q2$date` is discouraged.
## ℹ Use `date` instead.
## Warning: Use of `` Q2$`Total Steps` `` is discouraged.
## ℹ Use `Total Steps` instead.
dev.off()
## png 
##   2
ggplot(Q2,aes(y=Q2$`Total Steps`,x=Q2$date))+geom_bar(stat="identity") + ylab("Total Steps")+xlab("Date")+ggtitle("Total Steps by date")
## Warning: Use of `Q2$date` is discouraged.
## ℹ Use `date` instead.
## Use of `` Q2$`Total Steps` `` is discouraged.
## ℹ Use `Total Steps` instead.

#Histogram of total steps
qplot(Q2$`Total Steps`,geom="histogram",xlab="Total Steps",ylab="Counts",main="Total Steps Historgram")
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

png("plot1.1.png")
qplot(Q2$`Total Steps`,geom="histogram",xlab="Total Steps",ylab="Counts",main="Total Steps Historgram")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dev.off()
## png 
##   2

3. Mean and median number of steps taken each day

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
Q3<-data.frame(round(tapply(activity$steps,activity$date,mean,na.rm=TRUE),2))
Q3$date<-rownames(Q3)
rownames(Q3)<-NULL
names(Q3)[[1]]<-"Mean Steps"
temp<-activity%>%select(date,steps) %>% group_by(date) %>% summarise(median(steps))
names(temp)[[2]]<-"Median Steps"
Q3$median<-temp$`Median Steps`
Q3<-Q3 %>% select(date,`Mean Steps`,median)

4. Time series plot of the average number of steps taken

Q4<-Q3
Q4$date<-as.Date(Q4$date,format="%Y-%m-%d")
ggplot(Q4,aes(x=Q4$date,y=Q4$`Mean Steps`))+geom_bar(stat="identity")+scale_x_date()+ylab("Mean Steps Every day")+xlab("Date")+ggtitle("Mean Steps by Date")
## Warning: Use of `Q4$date` is discouraged.
## ℹ Use `date` instead.
## Warning: Use of `` Q4$`Mean Steps` `` is discouraged.
## ℹ Use `Mean Steps` instead.
## Warning: Removed 8 rows containing missing values (`position_stack()`).

png("plot4.png")
ggplot(Q4,aes(x=Q4$date,y=Q4$`Mean Steps`))+geom_bar(stat="identity")+scale_x_date()+ylab("Mean Steps Every day")+xlab("Date")+ggtitle("Mean Steps by Date")
## Warning: Use of `Q4$date` is discouraged.
## ℹ Use `date` instead.
## Warning: Use of `` Q4$`Mean Steps` `` is discouraged.
## ℹ Use `Mean Steps` instead.
## Warning: Removed 8 rows containing missing values (`position_stack()`).
dev.off()
## png 
##   2

5. The 5-minute interval that, on average, contains the maximum number of steps

# Assuming that the words on average means averaging steps by date and interval
activity$interval<-factor(activity$interval)
Q5<-aggregate(data=activity,steps~date+interval,FUN="mean")
Q5<-aggregate(data=Q5,steps~interval,FUN="max")

6. Code to describe and show a strategy for imputing missing data

There are multiple strategies to deal with multiple value imputations. The common strategies include: 1. Constant value imputations 2. Regression model value imputations 3. Mean/mode value substitutions For the purpose of simplicity, in this question, I will use the mean/mode value substitution strategy to impute missing values. That is, using the mean values to substitute out the missing values in the original data set Before doing any sort of imputation, it is helpful to understand what are the distributions of missing values by date and interval

Q6<-activity
Q6$Missing<-is.na(Q6$steps)
Q6<-aggregate(data=Q6,Missing~date+interval,FUN="sum")
Q6.1<-data.frame(tapply(Q6$Missing,Q6$date,sum))
Q6.1$date<-rownames(Q6.1)
rownames(Q6.1)<-NULL
names(Q6.1)<-c("Missing","date")
Q6.1$date<-as.Date(Q6.1$date,format="%Y-%m-%d")

Q6.2<-data.frame(tapply(Q6$Missing,Q6$interval,sum))
Q6.2$date<-rownames(Q6.2)
rownames(Q6.2)<-NULL
names(Q6.2)<-c("Missing","Interval")

par(mfrow=c(1,2))
plot(y=Q6.1$Missing,x=Q6.1$date,main="Missing Value Distribution by Date")
plot(y=Q6.2$Missing,x=Q6.2$Interval,main="Missing Value Distribution by Interval")

table(activity$date)
## 
## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06 2012-10-07 
##        288        288        288        288        288        288        288 
## 2012-10-08 2012-10-09 2012-10-10 2012-10-11 2012-10-12 2012-10-13 2012-10-14 
##        288        288        288        288        288        288        288 
## 2012-10-15 2012-10-16 2012-10-17 2012-10-18 2012-10-19 2012-10-20 2012-10-21 
##        288        288        288        288        288        288        288 
## 2012-10-22 2012-10-23 2012-10-24 2012-10-25 2012-10-26 2012-10-27 2012-10-28 
##        288        288        288        288        288        288        288 
## 2012-10-29 2012-10-30 2012-10-31 2012-11-01 2012-11-02 2012-11-03 2012-11-04 
##        288        288        288        288        288        288        288 
## 2012-11-05 2012-11-06 2012-11-07 2012-11-08 2012-11-09 2012-11-10 2012-11-11 
##        288        288        288        288        288        288        288 
## 2012-11-12 2012-11-13 2012-11-14 2012-11-15 2012-11-16 2012-11-17 2012-11-18 
##        288        288        288        288        288        288        288 
## 2012-11-19 2012-11-20 2012-11-21 2012-11-22 2012-11-23 2012-11-24 2012-11-25 
##        288        288        288        288        288        288        288 
## 2012-11-26 2012-11-27 2012-11-28 2012-11-29 2012-11-30 
##        288        288        288        288        288

By observing the plot, a clear pattern emerges in the distribution of missing values. It becomes evident that there is a consistent pattern where each interval exhibits exactly 8 missing values. Similarly, the dates consistently show 288 missing values. In total, there are 8 dates with missing values. While the precise cause of these missing values remains unknown, the pattern is noticeable. In light of this pattern, opting for mean value imputation seems to be a suitable choice.

It’s apparent that each date comprises exactly 288 data points, suggesting that there are eight dates with an absence of any data points. To enhance our analysis, we can delve into these missing values by considering their specific Weekday and interval characteristics to align them with the overall average.

#Dates that have missing values 
library(lubridate)
Q6.3<-as.data.frame(Q6.1) %>% select(date,Missing) %>% arrange(desc(Missing))
Q6.3<-Q6.3[which(Q6.3$Missing!=0),]
Q6.3$Weekday<-wday(Q6.3$date,label=TRUE)
Q6.4<-activity
Q6.4$weekday<-wday(Q6.4$date,label=TRUE)
#Finding the mean of steps every monday, and every interval
Q6.5<-aggregate(data=Q6.4,steps~interval+weekday,FUN="mean",na.rm=TRUE)
#Merge the pre-imputation table Q6.4 table with the average table Q6.5
Q6.6<-merge(x=Q6.4,y=Q6.5,by.x=c("interval","weekday"),by.y=c("interval","weekday"),all.x=TRUE)
#Conditionally replacing the steps.x column NA value with the values from steps.y column value 
Q6.6$Steps.Updated<-0
for (i in 1:dim(Q6.6)[[1]]){
if(is.na(Q6.6[i,3])){Q6.6[i,6]=Q6.6[i,5]}
else {Q6.6[i,6]=Q6.6[i,3]}
}
#Now simplify the imputed analytical data frame
Q6.6 <-Q6.6  %>% select(date,weekday,interval,Steps.Updated)
names(Q6.6)[[4]]<-"Steps"

7. Histogram of the total number of steps taken each day after missing values are imputed

png("plot7.png")
qplot(Q6.6$Steps,geom="histogram",main="Total steps taken histogram post imputation",xlab="Steps",ylab="Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dev.off()
## png 
##   2
qplot(Q6.6$Steps,geom="histogram",main="Total steps taken histogram post imputation",xlab="Steps",ylab="Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

8. Panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends

Q8<-Q6.6
levels(Q8$weekday)<-c(1,2,3,4,5,6,7)
Q8$WDWE<-Q8$weekday %in% c(1,2,3,4,5)
Q8.1<-aggregate(data=Q8,Steps~interval+WDWE,mean,na.rm=TRUE)
Q8.1$WDWE<-as.factor(Q8.1$WDWE)
levels(Q8.1$WDWE)<-c("Weekend","Weekday")
png("plot8.png")
ggplot(data=Q8.1,aes(y=Steps,x=interval,group=1,color=WDWE))+geom_line() +scale_x_discrete(breaks = seq(0, 2500, by = 300))+ylab("Mean Steps")+xlab("Intervals")+ggtitle("Mean steps across intervals by Weekend and Weekday")
dev.off()
## png 
##   2
ggplot(data=Q8.1,aes(y=Steps,x=interval,group=1,color=WDWE))+geom_line() +scale_x_discrete(breaks = seq(0, 2500, by = 300))+ylab("Mean Steps")+xlab("Intervals")+ggtitle("Mean steps across intervals by Weekend and Weekday")

#Producing the panel plot
Q8.1$interval<-as.numeric(as.character(Q8.1$interval))
library(lattice)
xyplot(data=Q8.1,Steps~interval|WDWE, grid = TRUE, type = c("p", "smooth"), lwd = 4,panel = panel.smoothScatter)
## (loaded the KernSmooth namespace)

library(hexbin)
hexbinplot(data=Q8.1,Steps~interval|WDWE, aspect = 1, bins=50)

png("plott8.1.png")
xyplot(data=Q8.1,Steps~interval|WDWE, grid = TRUE, type = c("p", "smooth"), lwd = 4,panel = panel.smoothScatter)
dev.off()
## png 
##   2
png("plot8.2.png")
hexbinplot(data=Q8.1,Steps~interval|WDWE, aspect = 1, bins=50)
dev.off()
## png 
##   2