In today’s world, we have the capability to collect vast amounts of data related to personal activities using devices like Fitbit, Nike Fuelband, or Jawbone Up. These devices are integral to the “quantified self” movement, where individuals regularly gather data about themselves to improve their health, identify behavioral patterns, or simply indulge their curiosity as tech enthusiasts. Despite the abundance of data, there are challenges in acquiring raw data and a scarcity of statistical methods and software for effectively processing and interpreting this data.
This assignment focuses on utilizing data from a personal activity monitoring device that records data at 5-minute intervals throughout the day. The dataset spans two months, covering October and November 2012, and it contains information about the number of steps taken in 5-minute intervals on each day.
The dataset for this assignment can be downloaded from the course website:
- Dataset: Activity Monitoring Data
The dataset consists of the following variables:
- steps: The number of steps taken in a 5-minute interval (missing values are denoted as 𝙽𝙰).
- date: The date on which the measurement was taken in YYYY-MM-DD format.
- interval: An identifier for the 5-minute interval during which the measurement was recorded.
The dataset is stored in a comma-separated-value (CSV) file and comprises a total of 17,568 observations.
library(data.table)
library(ggplot2)
dataURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
download.file(url = dataURL,destfile = paste0(getwd(), '/repdata%2Fdata%2Factivity.zip'), method = "curl")
unzip("repdata%2Fdata%2Factivity.zip",exdir = "data")activity <- data.table::fread(input = "data/activity.csv")Total_Steps <- activity[, c(lapply(.SD, sum, na.rm = FALSE)), .SDcols = c("steps"), by = .(date)]
head(Total_Steps, 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
ggplot(Total_Steps, aes(x = steps))+
geom_histogram(fill = "magenta", binwidth = 800)+
labs(title = "Daily Number of Steps", x = "steps", y = "frequency")## Warning: Removed 8 rows containing non-finite values (stat_bin).
### The Report including the mean and the median number of step taken
each day
Total_Steps[, .(Mean_Steps = mean(steps, na.rm = TRUE), Median_Steps = median(steps, na.rm = TRUE))]## Mean_Steps Median_Steps
## 1: 10766.19 10765
IntervalDT <- activity[, c(lapply(.SD, mean, na.rm = TRUE)), .SDcols = c("steps"), by = .(interval)]
ggplot(IntervalDT, aes(x = interval , y = steps)) + geom_line(color="magenta", size=1) + labs(title = "Avg. Daily Steps", x = "Interval", y = "Avg. Steps per day")
### Which 5-minute interval, on average across all the days in the
dataset, contains the maximum number of steps?
IntervalDT[steps == max(steps), .(max_interval = interval)]## max_interval
## 1: 835
activity[is.na(steps), .N ]## [1] 2304
# Filling in missing values with median of dataset.
activity[is.na(steps), "steps"] <- activity[, c(lapply(.SD, median, na.rm = TRUE)), .SDcols = c("steps")]data.table::fwrite(x = activity, file = "data/tidyData.csv", quote = FALSE)A histogram of the total number of steps taken each day and 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? What is the impact of imputing missing data on the estimates of the total daily number of steps?
# total number of steps taken per day
Total_Steps <- activity[, c(lapply(.SD, sum)), .SDcols = c("steps"), by = .(date)]
# mean and median total number of steps taken per day
Total_Steps[, .(Mean_Steps = mean(steps), Median_Steps = median(steps))]## Mean_Steps Median_Steps
## 1: 9354.23 10395
ggplot(Total_Steps, aes(x = steps)) + geom_histogram(fill = "magenta", binwidth = 1000) + labs(title = "Daily Steps", x = "Steps", y = "Frequency")
## 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.
# Just recreating activity from scratch then making the new factor variable. (No need to, just want to be clear on what the entire process is.)
activity <- data.table::fread(input = "data/activity.csv")
activity[, date := as.POSIXct(date, format = "%Y-%m-%d")]
activity[, `Day of Week`:= weekdays(x = date)]
activity[grepl(pattern = "Monday|Tuesday|Wednesday|Thursday|Friday", x = `Day of Week`), "weekday or weekend"] <- "weekday"
activity[grepl(pattern = "Saturday|Sunday", x = `Day of Week`), "weekday or weekend"] <- "weekend"
activity[, `weekday or weekend` := as.factor(`weekday or weekend`)]
head(activity, 10)## steps date interval Day of Week weekday or weekend
## 1: NA 2012-10-01 0 Monday weekday
## 2: NA 2012-10-01 5 Monday weekday
## 3: NA 2012-10-01 10 Monday weekday
## 4: NA 2012-10-01 15 Monday weekday
## 5: NA 2012-10-01 20 Monday weekday
## 6: NA 2012-10-01 25 Monday weekday
## 7: NA 2012-10-01 30 Monday weekday
## 8: NA 2012-10-01 35 Monday weekday
## 9: NA 2012-10-01 40 Monday weekday
## 10: NA 2012-10-01 45 Monday weekday
A panel plot containing a time series plot of the 5-minute interval and the average number of steps taken, averaged across all weekday days or weekend days .
activity[is.na(steps), "steps"] <- activity[, c(lapply(.SD, median, na.rm = TRUE)), .SDcols = c("steps")]
Interval <- activity[, c(lapply(.SD, mean, na.rm = TRUE)), .SDcols = c("steps"), by = .(interval, `weekday or weekend`)]
ggplot(Interval , aes(x = interval , y = steps, color=`weekday or weekend`)) + geom_line() + labs(title = "Avg. Daily Steps by Weektype", x = "Interval", y = "No. of Steps") + facet_wrap(~`weekday or weekend` , ncol = 1, nrow=2)