title: "monitorActivity" author: "Vishal Sharma" date: "Saturday, December 13, 2014" output: html_document
This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.
Read the data file , downloaded and copied in the current working directory The date column is converted to Date type. The data is examined by using summary and str methods on it.
library(ggplot2) # we shall use ggplot2 for plotting figures
tbl <- read.csv('activity.csv', header=T, colClasses=c("numeric", "character", "numeric"))
tbl$interval <- factor(tbl$interval)
tbl$date <- as.Date(tbl$date, format="%Y-%m-%d")
summary(tbl)## steps date interval
## Min. : 0.00 Min. :2012-10-01 0 : 61
## 1st Qu.: 0.00 1st Qu.:2012-10-16 5 : 61
## Median : 0.00 Median :2012-10-31 10 : 61
## Mean : 37.38 Mean :2012-10-31 15 : 61
## 3rd Qu.: 12.00 3rd Qu.:2012-11-15 20 : 61
## Max. :806.00 Max. :2012-11-30 25 : 61
## NA's :2304 (Other):17202
str(tbl)## 'data.frame': 17568 obs. of 3 variables:
## $ steps : num NA NA NA NA NA NA NA NA NA NA ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: Factor w/ 288 levels "0","5","10","15",..: 1 2 3 4 5 6 7 8 9 10 ...
Below is a histogram of the daily total number of steps taken, plotted with a bin interval of 1500 steps. Also marked on the plot are the mean and median of the daily total steps.
calc_steps_per_day <- function(tbl) {
steps_per_day <- aggregate(steps ~ date, tbl, sum,na.action = na.omit)
colnames(steps_per_day) <- c("date", "steps")
steps_per_day
}
plot_steps_per_day <- function(steps_per_day, mean_steps, median_steps) {
col_labels=c(paste("Mean:", mean_steps), paste("Median:", median_steps))
cols = c("green", "yellow")
ggplot(steps_per_day, aes(x=steps)) +
geom_histogram(fill="steelblue", binwidth=1500) +
geom_point(aes(x=mean_steps, y=0, color="green"), size=4, shape=15) +
geom_point(aes(x=median_steps, y=0, color="yellow"), size=4, shape=15) +
scale_color_manual(name=element_blank(), labels=col_labels, values=cols) +
labs(title="Histogram of Steps Taken per Day", x="Number of Steps", y="Count") +
theme_bw() + theme(legend.position = "bottom")
}
steps_per_day <- calc_steps_per_day(tbl)
mean_steps = round(mean(steps_per_day$steps), 2)
median_steps = round(median(steps_per_day$steps), 2)
plot_steps_per_day(steps_per_day, mean_steps, median_steps)
The mean number of steps taken across all the days is:
mean(steps_per_day$steps)## [1] 10766.19
The median total number of steps taken across all the days is:
median(steps_per_day$steps)## [1] 10765
To get the average daily activity pattern, we first calculate the average number of steps taken for each time interval, by averaging across all days.
calc_steps_per_interval <- function(tbl) {
steps_pi <- aggregate(tbl$steps, by=list(interval=tbl$interval),
FUN=mean, na.rm=T)
# convert to integers for plotting
steps_pi$interval <- as.integer(levels(steps_pi$interval)[steps_pi$interval])
colnames(steps_pi) <- c("interval", "steps")
steps_pi
}Below is a plot of the average daily pattern of the number of steps plotted against the interval number. The interval that clocks the maximum number of steps on the average is also marked.
plot_activity_pattern <- function(steps_per_interval, max_step_interval) {
col_labels=c(paste("Interval with Maximum Activity: ", max_step_interval))
cols = c("red")
ggplot(steps_per_interval, aes(x=interval, y=steps)) +
geom_line(color="steelblue", size=1) +
geom_point(aes(x=max_step_interval, y=0, color="red"), size=4, shape=15) +
scale_color_manual(name=element_blank(), labels=col_labels, values=cols) +
labs(title="Average Daily Activity Pattern", x="Interval", y="Number of steps") +
theme_bw() + theme(legend.position = "bottom")
}
steps_per_interval <- calc_steps_per_interval(tbl)
max_step_interval <- steps_per_interval[which.max(steps_per_interval$steps),]$interval
Index_With_Max_Steps <- with(steps_per_interval, which.max(steps_per_interval$steps))
plot_activity_pattern(steps_per_interval, max_step_interval)
Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
max_step_interval## [1] 835
To populate missing values, we choose to replace them with the mean value at the same interval across days. The choice is based on the assumption that activities usually follow a daily pattern
impute_means <- function(tbl, defaults) {
na_indices <- which(is.na(tbl$steps))
defaults <- steps_per_interval
na_replacements <- unlist(lapply(na_indices, FUN=function(idx){
interval = tbl[idx,]$interval
defaults[defaults$interval == interval,]$steps
}))
imp_steps <- tbl$steps
imp_steps[na_indices] <- na_replacements
imp_steps
}
complete_tbl <- data.frame(
steps = impute_means(tbl, steps_per_interval),
date = tbl$date,
interval = tbl$interval)Summarizing the new dataset with imputed values:
summary(complete_tbl)## steps date interval
## Min. : 0.00 Min. :2012-10-01 0 : 61
## 1st Qu.: 0.00 1st Qu.:2012-10-16 5 : 61
## Median : 0.00 Median :2012-10-31 10 : 61
## Mean : 37.38 Mean :2012-10-31 15 : 61
## 3rd Qu.: 27.00 3rd Qu.:2012-11-15 20 : 61
## Max. :806.00 Max. :2012-11-30 25 : 61
## (Other):17202
The total number of missing values in the dataset
na_Count_Org <- sum(is.na(tbl$steps))
na_Count_Org## [1] 2304
The total number of missing values in the Modified dataset
na_Count_Mdfy <- sum(is.na(complete_tbl$steps))
na_Count_Mdfy## [1] 0
With the imputed dataset, below is a histogram of the daily total number of steps taken, plotted with a bin interval of 1500 steps. Also marked on the plot are the mean and median of the daily total steps.
complete_steps_per_day <- calc_steps_per_day(complete_tbl)
complete_mean_steps = round(mean(complete_steps_per_day$steps), 2)
complete_median_steps = round(median(complete_steps_per_day$steps), 2)
plot_steps_per_day(complete_steps_per_day, complete_mean_steps, complete_median_steps)
The mean number of steps taken across all the days is:
mean(complete_steps_per_day$steps)## [1] 10766.19
The median total number of steps taken across all the days is:
median(complete_steps_per_day$steps)## [1] 10766.19
Comparing with the calculations done in the first section of this document, we observe that while the mean value remains unchanghed, the median value has shifted closer to the mean.Therefore, the impact of imputing missing data on the estimates of the total daily number of steps is small.
We do this comparison with the table with filled-in missing values.
1- Augment the table with a column that indicates the day of the week 2- Subset the table into two parts - weekends (Saturday and Sunday) and weekdays (Monday through Friday). 3- Tabulate the average steps per interval for each dataset. 4- Plot the two datasets side by side for comparison.
calc_day_of_week_data <- function(tbl) {
tbl$weekday <- as.factor(weekdays(tbl$date))
weekend_data <- subset(tbl, weekday %in% c("Saturday","Sunday"))
weekday_data <- subset(tbl, !weekday %in% c("Saturday","Sunday"))
weekend_spi <- calc_steps_per_interval(weekend_data)
weekday_spi <- calc_steps_per_interval(weekday_data)
weekend_spi$dayofweek <- rep("weekend", nrow(weekend_spi))
weekday_spi$dayofweek <- rep("weekday", nrow(weekday_spi))
day_of_week_data <- rbind(weekend_spi, weekday_spi)
day_of_week_data$dayofweek <- as.factor(day_of_week_data$dayofweek)
day_of_week_data
}
plot_day_of_week_comparison <- function(dow_data) {
ggplot(dow_data,
aes(x=interval, y=steps)) +
geom_line(color="steelblue", size=1) +
facet_wrap(~ dayofweek, nrow=2, ncol=1) +
labs(x="Interval", y="Number of steps") +
theme_bw()
}
day_of_week_data <- calc_day_of_week_data(complete_tbl)
plot_day_of_week_comparison(day_of_week_data)
We observe that activity on the weekends tends to be more spread out over the day compared to the weekdays. This could be due to the fact that activities on weekdays mostly follow a work related routine, whereas weekends tend to be more adhoc.