PA1_template.md

title	output
Reproducible Research: Peer Assessment 1	html_document keep_md true

Loading and preprocessing the data

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

unzip("activity.zip")
data <- read.csv("activity.csv")
data$date <- ymd(data$date)

What is mean total number of steps taken per day?

data$date <- factor(data$date)
sumsteps <- aggregate(data$steps ~ data$date, FUN = sum)
names(sumsteps) <- c("Date", "Steps")
hist(sumsteps$Steps, main = "Total Steps Taken Each Day", xlab = "Steps")

totalmean <- mean(sumsteps$Steps)
totalmean

## [1] 10766.19

totalmedian <- median(sumsteps$Steps)
totalmedian

## [1] 10765

What is the average daily activity pattern?

library(ggplot2)
intervalsteps <- aggregate(data$steps ~ data$interval, FUN = mean, na.rm = TRUE)
names(intervalsteps) <- c("Interval", "Steps")
ggplot(data = intervalsteps, aes(x=Interval, y=Steps)) + geom_line()

intervalsteps[which.max(intervalsteps$Steps),]

##     Interval    Steps
## 104      835 206.1698

Imputing missing values

nas <- data[is.na(data$steps),]
nrow(nas)

## [1] 2304

for (i in 1:nrow(nas)){
        tmp_interval <- nas[i,"interval"]
        tmp_mean <- intervalsteps[intervalsteps$Interval==tmp_interval,"Steps"]
        nas[i,"steps"] <- tmp_mean
}

filled.data <- data
nasvec <- setNames(nas$steps, rownames(nas))
filled.data[rownames(nas), "steps"] <- nasvec

sumsteps_final <- aggregate(filled.data$steps ~ filled.data$date, FUN = sum)
names(sumsteps_final) <- c("Date", "Steps")
hist(sumsteps_final$Steps, main = "Total Steps Taken Each Day", xlab = "Steps")

finalmean <- mean(sumsteps_final$Steps)
finalmean

## [1] 10766.19

finalmedian <- median(sumsteps_final$Steps)
finalmedian

## [1] 10766.19

Imputing the missing values causes no changes, because the imputed values are the small parts of the mean. (sorry for bad expression)

Are there differences in activity patterns between weekdays and weekends?

filled.data$day <- weekdays(as.Date(filled.data$date))

for (i in 1:nrow(filled.data)){
        ifelse(filled.data[i,"day"] == "Saturday"|
                filled.data[i,"day"] == "Sunday", 
                filled.data[i,"day"] <- "Weekend", 
                filled.data[i,"day"] <- "Weekday")
}

filled.data$day <- factor(filled.data$day)
filled.data$interval <- factor(filled.data$interval)

weeksteps <- aggregate(steps~interval+day, filled.data, mean)

ggplot(data=weeksteps, aes(interval,steps, group = 1)) + geom_line() + facet_grid(rows = ~day)