Peer-graded Assignment: Course Project 1
Hoang Lam (Nancy Lam)
March 4, 2019
I - Loading and preprocessing the data
1. Load packages
library(dplyr)
library(ggplot2)
library(readr)2. Load data
activity <- read_csv("C:/Users/tuuye/Desktop/Data Science course/Reproducible Research/activity.csv")# Dimension of the data
dim(activity)## [1] 17568 3The dataset has 17568 observation and 3 variables
head(activity)## # A tibble: 6 x 3
## steps date interval
## <dbl> <date> <dbl>
## 1 NA 2012-10-01 0
## 2 NA 2012-10-01 5
## 3 NA 2012-10-01 10
## 4 NA 2012-10-01 15
## 5 NA 2012-10-01 20
## 6 NA 2012-10-01 25Process the data
activity$day <- weekdays(as.Date(activity$date))
activity$DateTime <- as.POSIXct(activity$date)
head(activity)## # A tibble: 6 x 5
## steps date interval day DateTime
## <dbl> <date> <dbl> <chr> <dttm>
## 1 NA 2012-10-01 0 Monday 2012-09-30 17:00:00
## 2 NA 2012-10-01 5 Monday 2012-09-30 17:00:00
## 3 NA 2012-10-01 10 Monday 2012-09-30 17:00:00
## 4 NA 2012-10-01 15 Monday 2012-09-30 17:00:00
## 5 NA 2012-10-01 20 Monday 2012-09-30 17:00:00
## 6 NA 2012-10-01 25 Monday 2012-09-30 17:00:00II - What is mean total number of steps taken per day?
1. Calculate the total number of steps taken per day
sum_steps <- aggregate(activity$steps ~ activity$date, FUN = sum)
head(sum_steps)## activity$date activity$steps
## 1 2012-10-02 126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015We will rename the columns
colnames(sum_steps) <- c('Date', 'Steps')
head(sum_steps)## Date Steps
## 1 2012-10-02 126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 110152. Make a histogram of the total number of steps taken each day
hist(sum_steps$Steps, breaks = 5, main = 'Histogram of the total number of steps taken each day', xlab = 'Steps', col = 'brown')
3. Calculate and report the mean and median of the total number of steps taken per day
MEAN <- round(mean(sum_steps$Steps))
MEDIAN <- round(median(sum_steps$Steps))
print(paste('MEAN of the total number of steps taken per day =', MEAN))## [1] "MEAN of the total number of steps taken per day = 10766"print(paste('MEDIAN of the total number of steps taken per day=', MEDIAN))## [1] "MEDIAN of the total number of steps taken per day= 10765"III-What is the average daily activity pattern?
1. Make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
library(plyr)
library(ggplot2)
clean <- na.omit(activity)
interval_steps <- ddply(clean, .(interval), summarize, Avg = mean(steps))
ggplot(interval_steps, aes(x = interval, y = Avg)) +
geom_line(col = 'blue') +
labs(title = 'Time series plot of the 5-minute interval and the average number of steps taken, averaged across all days', xlab = 'Interval', ylab = 'Average number of steps')
2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
interval_steps[which.max(interval_steps$Avg),]## interval Avg
## 104 835 206.1698IV-Imputing missing values
1. Calculate and report the total number of missing values in the dataset
na_count <- colSums(is.na(activity))
na_count## steps date interval day DateTime
## 2304 0 0 0 02. Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
We will use average number of step from cleaning data to replace all NA in the original data
data_fill_NA <- activity
data_fill_NA$steps[is.na(data_fill_NA$steps)] <- round(mean(clean$steps))3. Create a new dataset that is equal to the original dataset but with the missing data filled in.
head(data_fill_NA)## # A tibble: 6 x 5
## steps date interval day DateTime
## <dbl> <date> <dbl> <chr> <dttm>
## 1 37 2012-10-01 0 Monday 2012-09-30 17:00:00
## 2 37 2012-10-01 5 Monday 2012-09-30 17:00:00
## 3 37 2012-10-01 10 Monday 2012-09-30 17:00:00
## 4 37 2012-10-01 15 Monday 2012-09-30 17:00:00
## 5 37 2012-10-01 20 Monday 2012-09-30 17:00:00
## 6 37 2012-10-01 25 Monday 2012-09-30 17:00:004. Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
sum_steps_fill <- aggregate(data_fill_NA$steps ~ data_fill_NA$date, FUN=sum)
colnames(sum_steps_fill) <- c('Date', "Steps")
MEAN_FILL <- round(mean(sum_steps_fill$Steps))
MEDIAN_FILL <- round(median(sum_steps_fill$Steps))
MEAN_FILL## [1] 10752MEDIAN_FILL## [1] 10656Before filling NA : Mean = 10766, Median = 10765 After filling NA: Mean = 10752, Median = 10656
5. Make a histogram
hist(sum_steps_fill$Steps, breaks = 5, xlab = "Steps", main = "Total Steps per Day with NAs Fixed", col='Purple')
**The overall shape of the distribution has not change even though the new mean and median decreased*
V- Are there differences in activity patterns between weekdays and weekends?
1. 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.
data_fill_NA$DayCategory <- ifelse(data_fill_NA$day %in% c("Saturday", "Sunday"), "Weekend", "Weekday")2. Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).
library(lattice)
interval_steps2 <- ddply(data_fill_NA, .(interval, DayCategory), summarize, Avg = mean(steps))
xyplot(Avg~interval|DayCategory, data=interval_steps2, type="l", layout = c(1,2),
main="Average Steps per Interval Based on Type of Day",
ylab="Average Number of Steps", xlab="Interval")