Reproducible Research - Project 1

Introduction

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement - a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behaviour, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

Activity Monitoring Data Set: https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip

Preliminary steps: Loading Prerequisite Packages and Downloading the Data Set

library(ggplot2)
library(data.table)

if(!file.exists('activity.csv') ){
        if (!file.exists('repdata_data_activity.zip')){
              url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
              download.file(url, destfile = paste0(getwd(), '/repdata_data_activity.zip'), method = "curl") 
        }
    unzip('repdata_data_activity.zip')
}

Code for reading in the dataset and processing the data

DT <- data.table::fread(input = "activity.csv")

What is the mean total number of steps taken per day?

#constructing a sub-set of DT data table with missing values (NA) present
columns <- "steps"

totalSteps <- DT[, c(lapply(.SD, sum, na.rm = FALSE)), .SDcols = columns, by = .(date)]

Verifying that the Data Set which was input is correct:

head(totalSteps,10)

##           date steps
##  1: 2012-10-01    NA
##  2: 2012-10-02   126
##  3: 2012-10-03 11352
##  4: 2012-10-04 12116
##  5: 2012-10-05 13294
##  6: 2012-10-06 15420
##  7: 2012-10-07 11015
##  8: 2012-10-08    NA
##  9: 2012-10-09 12811
## 10: 2012-10-10  9900

Histogram of the total number of steps taken each day

hist_plot <- ggplot(totalSteps, aes(steps)) +
        geom_histogram(fill = "darkgreen", binwidth = 500) +
                labs(title = "Total Daily Steps", x = "Steps", y = "Frequency (w/Binwidth 500)") 

plot(hist_plot)

## Warning: Removed 8 rows containing non-finite values (stat_bin).

Reporting Mean and Median number of steps taken each day:

totalSteps[, .(`Mean # of Steps` = mean(steps, na.rm = TRUE), `Median # of Steps` = median(steps, na.rm = TRUE))]

##    Mean # of Steps Median # of Steps
## 1:        10766.19             10765

What is the average daily activity pattern?

Make a time series plot (i.e. type=“l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

** Time series plot of the average number of steps taken:

interval <- DT[, c(lapply(.SD, mean, na.rm = TRUE)), .SDcols = columns, by = list(interval)]

head(interval,10)

##     interval     steps
##  1:        0 1.7169811
##  2:        5 0.3396226
##  3:       10 0.1320755
##  4:       15 0.1509434
##  5:       20 0.0754717
##  6:       25 2.0943396
##  7:       30 0.5283019
##  8:       35 0.8679245
##  9:       40 0.0000000
## 10:       45 1.4716981

ggplot(interval, aes(x = interval , y = steps)) + geom_line(color="darkgreen", size=0.5) + labs(title = "Average Daily Steps", x = "5-min Interval", y = "Average Steps per Day")

Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

interval[steps == max(steps), list(`Maximum Intreval` = interval)]

##    Maximum Intreval
## 1:              835

Imputing missing (NA) values

Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)

message("Total number of missing values (NAs) in the orig. Data Set: ", nrow(DT[is.na(steps),]))

## Total number of missing values (NAs) in the orig. Data Set: 2304

Devise a strategy for filling in all of the missing values in the dataset. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.

# Imputing the missing values (NA) in Data Set (DT) with Median values 
DT[is.na(steps), "steps"] <- DT[, c(lapply(.SD, median, na.rm = TRUE)), .SDcols = columns]

# verifying Data Set (DT) after imputing missing values
head(DT, 10)

##     steps       date interval
##  1:     0 2012-10-01        0
##  2:     0 2012-10-01        5
##  3:     0 2012-10-01       10
##  4:     0 2012-10-01       15
##  5:     0 2012-10-01       20
##  6:     0 2012-10-01       25
##  7:     0 2012-10-01       30
##  8:     0 2012-10-01       35
##  9:     0 2012-10-01       40
## 10:     0 2012-10-01       45

Create a new dataset that is equal to the original dataset but with the missing data filled in.

# saving the newly updated DT
data.table::fwrite(x = DT, file = "clean_set.csv", quote = FALSE)

1. Plotting a histogram for the total number of steps taken each day. What is the impact of imputing missing data on the estimates of the total daily number of steps?

totalSteps_imp <- DT[, c(lapply(.SD, sum)), .SDcols = columns, by = list(date)] 

head(totalSteps_imp,20)

##           date steps
##  1: 2012-10-01     0
##  2: 2012-10-02   126
##  3: 2012-10-03 11352
##  4: 2012-10-04 12116
##  5: 2012-10-05 13294
##  6: 2012-10-06 15420
##  7: 2012-10-07 11015
##  8: 2012-10-08     0
##  9: 2012-10-09 12811
## 10: 2012-10-10  9900
## 11: 2012-10-11 10304
## 12: 2012-10-12 17382
## 13: 2012-10-13 12426
## 14: 2012-10-14 15098
## 15: 2012-10-15 10139
## 16: 2012-10-16 15084
## 17: 2012-10-17 13452
## 18: 2012-10-18 10056
## 19: 2012-10-19 11829
## 20: 2012-10-20 10395

4.b) What is the impact of imputing missing data on the estimates of the total daily number of steps?

ggplot(totalSteps_imp, aes(x = steps)) + geom_histogram(fill = "darkgreen", binwidth = 500) + labs(title = "Average Daily Steps", x = "Steps", y = "Frequency (w/Binwidth 500)")

4.c) Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment?

Mean and Median # of Steps based on the imputed Data Set:

# mean and median total number of steps taken per day
totalSteps_imp[, .(`Mean # of Steps` = mean(steps), `Median # of Steps` = median(steps))]

##    Mean # of Steps Median # of Steps
## 1:         9354.23             10395

Vis-a-vis Mean and Median # of steps WITHOUT missing values (NA) getting subsituted with Medians

totalSteps[, .(`Mean # of Steps` = mean(steps, na.rm = TRUE), `Median # of Steps` = median(steps, na.rm = TRUE))]

##    Mean # of Steps Median # of Steps
## 1:        10766.19             10765

Are there differences in activity patterns between weekdays and weekends?

Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.

# re-read DT from the imputed Data Set
DT <- read.csv('clean_set.csv')

# if 'date' is Sunday or Saturday, then it is a Weekned, else: Weekday
DT$DayType <-  ifelse(as.POSIXlt(DT$date)$wday %in% c(0,6), 'Weekend', 'Weekday')

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

# create an aggregated table: group by interval, then DayType and output a 'mean' # of steps for e/grouping
aggrDT <- aggregate(steps ~ interval + DayType, data=DT, mean)

ggplot(aggrDT, aes(interval, steps)) + 
    geom_line(color="darkblue", size=0.5) + 
    facet_grid(DayType ~ .) +
    xlab("5-min Interval") + 
    ylab("Average Number of Steps")

Reproducible Research - Project 1

analytics-moses

26-May-19

Reproducible Research - Project 1

Introduction

Preliminary steps: Loading Prerequisite Packages and Downloading the Data Set

What is the mean total number of steps taken per day?

Verifying that the Data Set which was input is correct:

What is the average daily activity pattern?

Imputing missing (NA) values

Vis-a-vis Mean and Median # of steps WITHOUT missing values (NA) getting subsituted with Medians

Are there differences in activity patterns between weekdays and weekends?