Load the data:

setwd("C:/Users/JP/Desktop/Data Science Certification/5.- Reproducible Research/Project 1")

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

What is mean total number of steps taken per day?

#Steps per day
SPD <- tapply(act$steps,act$date,sum,na.rm=TRUE)
#Histogram
hist(SPD,xlab = "Steps per day",col = "red")

#Mean and Median
mean(SPD)
## [1] 9354.23
median(SPD)
## [1] 10395

What is the average daily activity pattern?

#Average steps per interval
SPI <- tapply(act$steps,act$interval,mean,na.rm=TRUE)
plot(SPI,type = "l",col = "red",xaxt="n",main="Average number of steps per interval across all days", 
     xlab="Interval", ylab="Average # of steps across all days")
axis(1,at=1:288,labels = rownames(SPI))

The interval number 835 is the one with the max steps in average

#Five minute interval with the max average
head(sort(SPI,decreasing = TRUE),1)
##      835 
## 206.1698
#Five minute interval with the max average
head(sort(SPI,decreasing = TRUE),1)
##      835 
## 206.1698

Imputing missing values

Total number of missing values in the dataset

#Total number of missing values in the dataset
nrow(act[complete.cases(act) == FALSE,])
## [1] 2304

To fill all the missing values I use the average of the interval that was in the SPI variable. The new dataset with the missing values filled in is act1.

#Filling all the missing values
SPI1 <- cbind(as.numeric(rownames(SPI)),as.numeric(SPI))
colnames(SPI1) <- c("interval","value")

act1 <- merge(act, as.data.frame(SPI1), by = "interval")

act1$NewSteps <- ifelse(is.na(act1$steps) == TRUE, act1$value, act1$steps)

#New dataset
act1 <- act1[,c(1,3,5)]
head(act1)
##   interval       date NewSteps
## 1        0 2012-10-01 1.716981
## 2        0 2012-11-23 0.000000
## 3        0 2012-10-28 0.000000
## 4        0 2012-11-06 0.000000
## 5        0 2012-11-24 0.000000
## 6        0 2012-11-15 0.000000

Steps per day with the new dataset

#Steps per day
SPD1 <- tapply(act1$NewSteps,act1$date,sum,na.rm=TRUE)
#Histogram
hist(SPD1,xlab = "Steps per day",col = "blue")

#Mean and Median
mean(SPD1)
## [1] 10766.19
median(SPD1)
## [1] 10766.19

Compare both datasets

#Both histograms
par(mfrow = c(1,2))
hist(SPD,xlab = "Steps per day (with missing values)",col = "red")
hist(SPD1,xlab = "Steps per day (with NO missing values)",col = "blue")

We can see that the data has a more simetric shape after imputong the missing values

Are there differences in activity patterns between weekdays and weekends?

Create a new factor variable in the dataset with two levels - “weekday” and “weekend”

#Create a new factor variable in the dataset with two levels - "weekday" and "weekend" 
act1$Day <- ifelse(as.POSIXlt(act1$date)$wday > 0 & as.POSIXlt(act1$date)$wday < 6, "weekday", "weekend")

Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).

#intervals in weekdays ve weekends
WD <- subset(act1,act1$Day == "weekday")
WE <- subset(act1,act1$Day == "weekend")

#Make a panel plot
WD1 <- tapply(WD$NewSteps,WD$interval,mean,na.rm=TRUE)
WE1 <- tapply(WE$NewSteps,WE$interval,mean,na.rm=TRUE)

par(mfrow = c(2,1),oma = c(2,2,2,2),mar = c(2,2,2,2))
plot(WD1,type = "l",col = "red",xaxt="n", xlab = "Interval", ylab = "Average Steps",ylim = c(0,220),main = "Weekdays")
axis(1,at=1:288,labels = rownames(WD1))

plot(WE1,type = "l",col = "blue",xaxt="n", xlab = "Interval", ylab = "Average Steps",ylim = c(0,220),main = "Weekends")
axis(1,at=1:288,labels = rownames(WE1))

title("Compare weekdays with weekend", outer = TRUE)