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<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<pre><code class="r">
setwd("C:\\Users\\Kingsbury\\Documents\\GitHub\\RepData_PeerAssessment1")
# DataLoad
activity <- read.csv("activity.csv", header = TRUE, sep = ",", na.strings = "NA")
## Convert the Date column to date format
activity$date <- as.Date(activity$date, format = "%Y-%m-%d")
# show the table of NAs
table(is.na(activity$steps))
</code></pre>
<pre><code>##
## FALSE TRUE
## 15264 2304
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<pre><code class="r">
# calc the total nbr steps taken per day
total_nbr_steps_day <- aggregate(activity$steps, by = list(activity$date), FUN = sum)
# rename columns
colnames(total_nbr_steps_day) <- c("date", "nbr_steps")
# calculate mean and median, remove NAs
mean_raw <- mean(total_nbr_steps_day$nbr_steps, na.rm = TRUE)
median_raw <- median(total_nbr_steps_day$nbr_steps, na.rm = TRUE)
</code></pre>
<pre><code class="r"># print the values to screen
mean_raw
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">median_raw
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<h3>Histogram of total number of steps taken per day</h3>
<pre><code class="r"># draw the histogram
hist(total_nbr_steps_day$nbr_steps[is.na(total_nbr_steps_day$nbr_steps) == FALSE],
main = "Histogram of tot nbr steps per day", xlab = "Nbr steps per day")
rug(total_nbr_steps_day$nbr_steps[is.na(total_nbr_steps_day$nbr_steps) == FALSE],
ticksize = 0.02)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk historgram_tot_nbr_steps_day"/> </p>
<h2>What is the average daily activity pattern?</h2>
<h3>Time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)</h3>
<pre><code class="r"># removing NAs
clean_activity <- subset(activity, is.na(activity$steps) == FALSE)
# calculating
clean_activity$avg_by_date <- ave(clean_activity$steps, clean_activity$date)
# draw the time series plot
</code></pre>
<pre><code class="r">plot(clean_activity$date, clean_activity$avg_by_date, type = "l", ylab = "Avg nbr steps per 5m interval",
xlab = "Date")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Avg_nbr_steps_per_5m_interval"/> </p>
<h3>Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</h3>
<pre><code class="r"># calculate average nbr steps per interval
clean_activity$avg_by_interval <- ave(clean_activity$steps, clean_activity$interval)
# plot average nbr steps per interval show maximum
max(clean_activity[, "avg_by_interval"])
</code></pre>
<pre><code>## [1] 206.2
</code></pre>
<pre><code class="r"># which interval contains the maximum
with(clean_activity, interval[avg_by_interval == max(avg_by_interval)])[1]
</code></pre>
<pre><code>## [1] 835
</code></pre>
<pre><code class="r">plot(clean_activity$interval, clean_activity$avg_by_interval, ylab = "Avg nbr steps",
xlab = "Interval", main = "Average nbr of steps per five min interval")
text(1250, 205, "Interval 835 with the maximum steps", cex = 0.7)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Average_nbr_of_steps_per_five_min_interval"/> </p>
<h2>Imputing missing values</h2>
<h3>Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)</h3>
<pre><code class="r"># report NAs
colSums(is.na(activity))
</code></pre>
<pre><code>## steps date interval
## 2304 0 0
</code></pre>
<h3>New data set with imputed values</h3>
<pre><code class="r"># load a time series library zoo
library(zoo)
</code></pre>
<pre><code>##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
</code></pre>
<pre><code class="r"># convert the data frame into a time series object for usage in zoo
activity_imputed <- read.zoo(activity, index.column = 2, format = "%Y-%m-%d")
</code></pre>
<pre><code>## Warning: some methods for "zoo" objects do not work if the index entries
## in 'order.by' are not unique
</code></pre>
<pre><code class="r">
# fill NAs be using median of the five minutes intervals in the zoo ts
# object
activity_filled <- na.aggregate(activity_imputed, by = 3, FUN = median)
## helper function to convert a time series object to a dataframe
zoo.to.data.frame <- function(x, index.name = "date") {
stopifnot(is.zoo(x))
xn <- if (is.null(dim(x)))
deparse(substitute(x)) else colnames(x)
setNames(data.frame(index(x), x, row.names = NULL), c(index.name, xn))
}
# convert ts object back to a dataframe
activity_filled_df <- zoo.to.data.frame(activity_filled)
# check NAs
table(is.na(activity_filled_df$steps))
</code></pre>
<pre><code>##
## FALSE
## 17568
</code></pre>
<h3>Calculations of median and mean with new NAs filled dataset</h3>
<pre><code class="r">
# calc the total nbr steps taken per day
total_nbr_steps_day_fill <- aggregate(activity_filled_df$steps, by = list(activity_filled_df$date),
FUN = sum)
# rename columns
colnames(total_nbr_steps_day_fill) <- c("date", "nbr_steps")
# calculate mean and median, remove NAs
mean_na_filled <- mean(total_nbr_steps_day_fill$nbr_steps, na.rm = TRUE)
median_na_filled <- median(total_nbr_steps_day_fill$nbr_steps, na.rm = TRUE)
</code></pre>
<h3>Do these values differ from the estimates from the first part of the assignment?</h3>
<pre><code class="r">mean_raw - mean_na_filled
</code></pre>
<pre><code>## [1] 1412
</code></pre>
<pre><code class="r">median_raw - median_na_filled
</code></pre>
<pre><code>## [1] 370
</code></pre>
<pre><code class="r">
## Yes, there is a difference between mean/median before and after filling of
## NAs
</code></pre>
<h3>Histogram of total number of steps taken per day</h3>
<pre><code class="r"># draw histogram
hist(total_nbr_steps_day_fill$nbr_steps[is.na(total_nbr_steps_day_fill$nbr_steps) ==
FALSE], main = "NAs filled: histogram of tot nbr steps per day", xlab = "Nbr steps per day")
rug(total_nbr_steps_day_fill$nbr_steps[is.na(total_nbr_steps_day_fill$nbr_steps) ==
FALSE], ticksize = 0.02)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Histogram_NA_filled"/> </p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<h3>Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day</h3>
<pre><code class="r"># create a new column with weekdays
activity_filled_df$weekdays <- weekdays(activity_filled_df$date)
# create a new column with two values weekday and weekend
activity_filled_df[((activity_filled_df[, 4] == "Saturday") | (activity_filled_df[,
4] == "Sunday")), "wd"] <- "weekend"
activity_filled_df[(!((activity_filled_df[, 4] == "Saturday") | (activity_filled_df[,
4] == "Sunday"))), "wd"] <- "weekday"
# new column with averages per 5 min interval
activity_filled_df$ave <- ave(activity_filled_df$step, activity_filled_df$interval,
activity_filled_df$wd)
</code></pre>
<h3>Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken</h3>
<pre><code class="r">
library(lattice)
xyplot(activity_filled_df$ave ~ activity_filled_df$interval | activity_filled_df$wd,
layout = c(1, 2), xlab = "inteval", ylab = "Number of steps", type = "l")
</code></pre>
<p><img 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" alt="plot of chunk Lattice_timeseries_panel_plot"/> </p>
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