Reproducible research: Peer graded assignment 1

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1. Loading and preprocessing data

dat <- read.csv("activity.csv")
dat$date <- as.Date(as.character(dat$date), format = "%Y-%m-%d")

2. Total Number of steps per 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
library(knitr)
sub1 <- subset(dat, steps >= 0, select = c(steps, date))
grouped <- group_by(sub1, date)
Totals <- summarise(grouped, Total.Steps = sum(steps, na.rm = TRUE))
kable(Totals, caption = "Total number of steps per day", align = "l", format = "markdown")
date Total.Steps
2012-10-02 126
2012-10-03 11352
2012-10-04 12116
2012-10-05 13294
2012-10-06 15420
2012-10-07 11015
2012-10-09 12811
2012-10-10 9900
2012-10-11 10304
2012-10-12 17382
2012-10-13 12426
2012-10-14 15098
2012-10-15 10139
2012-10-16 15084
2012-10-17 13452
2012-10-18 10056
2012-10-19 11829
2012-10-20 10395
2012-10-21 8821
2012-10-22 13460
2012-10-23 8918
2012-10-24 8355
2012-10-25 2492
2012-10-26 6778
2012-10-27 10119
2012-10-28 11458
2012-10-29 5018
2012-10-30 9819
2012-10-31 15414
2012-11-02 10600
2012-11-03 10571
2012-11-05 10439
2012-11-06 8334
2012-11-07 12883
2012-11-08 3219
2012-11-11 12608
2012-11-12 10765
2012-11-13 7336
2012-11-15 41
2012-11-16 5441
2012-11-17 14339
2012-11-18 15110
2012-11-19 8841
2012-11-20 4472
2012-11-21 12787
2012-11-22 20427
2012-11-23 21194
2012-11-24 14478
2012-11-25 11834
2012-11-26 11162
2012-11-27 13646
2012-11-28 10183
2012-11-29 7047 
hist(Totals$Total.Steps, xlab = "Total steps", main = "Total number of steps", col = "blue", breaks = 8)

mean.med <- summarise(grouped, Mean = round(mean(steps, na.rm = TRUE)), Median = median(steps, na.rm = TRUE))
kable(mean.med, caption = "Mean and Median steps per day",align = "c", format = "markdown")
date Mean Median
2012-10-02 0 0
2012-10-03 39 0
2012-10-04 42 0
2012-10-05 46 0
2012-10-06 54 0
2012-10-07 38 0
2012-10-09 44 0
2012-10-10 34 0
2012-10-11 36 0
2012-10-12 60 0
2012-10-13 43 0
2012-10-14 52 0
2012-10-15 35 0
2012-10-16 52 0
2012-10-17 47 0
2012-10-18 35 0
2012-10-19 41 0
2012-10-20 36 0
2012-10-21 31 0
2012-10-22 47 0
2012-10-23 31 0
2012-10-24 29 0
2012-10-25 9 0
2012-10-26 24 0
2012-10-27 35 0
2012-10-28 40 0
2012-10-29 17 0
2012-10-30 34 0
2012-10-31 54 0
2012-11-02 37 0
2012-11-03 37 0
2012-11-05 36 0
2012-11-06 29 0
2012-11-07 45 0
2012-11-08 11 0
2012-11-11 44 0
2012-11-12 37 0
2012-11-13 25 0
2012-11-15 0 0
2012-11-16 19 0
2012-11-17 50 0
2012-11-18 52 0
2012-11-19 31 0
2012-11-20 16 0
2012-11-21 44 0
2012-11-22 71 0
2012-11-23 74 0
2012-11-24 50 0
2012-11-25 41 0
2012-11-26 39 0
2012-11-27 47 0
2012-11-28 35 0
2012-11-29 24 0

3. Average daily activity

library(ggplot2)
sub2 <- subset(dat, steps >= 0, select = c(steps, interval))
grouped2 <- group_by(sub2, interval)
daily_interval_avg <- summarise(grouped2, Avg.Steps = mean(steps))
g <- ggplot(daily_interval_avg, aes(interval, Avg.Steps))
g+geom_line(colour = "purple")+ggtitle("Daily average steps taken per inteval")+ ylab("Average Steps")

i <- which.max(daily_interval_avg$Avg.Steps)
daily_interval_avg$interval[i]
## [1] 835

The interval with maximun number of steps averaged by all days is 835

4. Missing values

icc <- !complete.cases(dat)
Nas <- sum(icc)

There are 2304 rows with missing values

dat1 <- dat
m <- round(mean(dat1[,1], na.rm = TRUE))
for(i in 1: nrow(dat1)){
        if (is.na(dat1[i,1]) == TRUE){
            dat1[i,1] <- m
        }else{
                dat1[i,1] <- dat1[i,1]
        }
}
Totals1 <- summarise(group_by(subset(dat1, select = c(steps,date)),date), Total.Steps = sum(steps, na.rm = TRUE))
hist(Totals1$Total.Steps, xlab = "Total steps", main = "Total number of steps", col = "brown", breaks = 8)

mean.med1 <- summarise(group_by(subset(dat1, select = c(steps,date)),date), Mean = round(mean(steps, na.rm = TRUE)), Median = median(steps, na.rm = TRUE))
kable(mean.med1, caption = "Mean and Median steps per day", align = "c", format = "markdown")
date Mean Median
2012-10-01 37 37
2012-10-02 0 0
2012-10-03 39 0
2012-10-04 42 0
2012-10-05 46 0
2012-10-06 54 0
2012-10-07 38 0
2012-10-08 37 37
2012-10-09 44 0
2012-10-10 34 0
2012-10-11 36 0
2012-10-12 60 0
2012-10-13 43 0
2012-10-14 52 0
2012-10-15 35 0
2012-10-16 52 0
2012-10-17 47 0
2012-10-18 35 0
2012-10-19 41 0
2012-10-20 36 0
2012-10-21 31 0
2012-10-22 47 0
2012-10-23 31 0
2012-10-24 29 0
2012-10-25 9 0
2012-10-26 24 0
2012-10-27 35 0
2012-10-28 40 0
2012-10-29 17 0
2012-10-30 34 0
2012-10-31 54 0
2012-11-01 37 37
2012-11-02 37 0
2012-11-03 37 0
2012-11-04 37 37
2012-11-05 36 0
2012-11-06 29 0
2012-11-07 45 0
2012-11-08 11 0
2012-11-09 37 37
2012-11-10 37 37
2012-11-11 44 0
2012-11-12 37 0
2012-11-13 25 0
2012-11-14 37 37
2012-11-15 0 0
2012-11-16 19 0
2012-11-17 50 0
2012-11-18 52 0
2012-11-19 31 0
2012-11-20 16 0
2012-11-21 44 0
2012-11-22 71 0
2012-11-23 74 0
2012-11-24 50 0
2012-11-25 41 0
2012-11-26 39 0
2012-11-27 47 0
2012-11-28 35 0
2012-11-29 24 0
2012-11-30 37 37

5. Weekdays Vs Weekends

dat1 <- mutate(dat1, Weekday = weekdays(dat1$date))
for(i in 1:nrow(dat1)){
        if(dat1[i,4] ==  "Saturday" | dat1[i,4] == "Sunday"){
                dat1[i,4] <- "weekend"
        }else if(dat1[i,4] == "Monday" | dat1[i,4] == "Tuesday" | dat1[i,4] == "Wednesday" | dat1[i,4] == "Thursday" | dat1[i,4] == "Friday"){
                dat1[i,4] <- "weekday"
        }
}
dat1$Weekday <- as.factor(dat1$Weekday)
sub3 <- subset(dat1, select = c(steps, interval, Weekday))
grouped3 <- group_by(sub3, Weekday, interval)
interval_Avg.Steps <- summarise(grouped3, Avg.Steps = mean(steps))
g <- ggplot(interval_Avg.Steps, aes(interval, Avg.Steps))
g <- g + geom_line(colour = "brown") +facet_grid(Weekday ~.)
g <- g+ xlab("Interval") + ylab("Number of Steps") + ggtitle("5-minute interval average number of steps taken")
g