msagastya::reproducible_research-peer-ass-1

.gitignore

@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

PA1_template.Rmd

@@ -7,19 +7,103 @@ output:
 
 
 ## Loading and preprocessing the data
-
-
+```{r loaddata}
+unzip(zipfile="activity.zip")
+data <- read.csv("activity.csv")
+head(data)
+```
 
 ## What is mean total number of steps taken per day?
-
+```{r}
+library(ggplot2)
+total.steps <- tapply(data$steps, data$date, FUN=sum, na.rm=TRUE)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+mean(total.steps, na.rm=TRUE)
+median(total.steps, na.rm=TRUE)
+```
 
 
 ## What is the average daily activity pattern?
+```{r}
+library(ggplot2)
+averages <- aggregate(x=list(steps=data$steps), by=list(interval=data$interval),
+                      FUN=mean, na.rm=TRUE)
+ggplot(data=averages, aes(x=interval, y=steps)) +
+    geom_line() +
+    xlab("5-minute interval") +
+    ylab("average number of steps taken")
+```
 
+On average across all the days in the dataset, the 5-minute interval contains
+the maximum number of steps?
+```{r}
+averages[which.max(averages$steps),]
+```
 
 
 ## Imputing missing values
+There are many days/intervals where there are missing values (coded as `NA`). The presence of missing days may introduce bias into some calculations or summaries of the data.
+
+```{r how_many_missing}
+missing <- is.na(data$steps)
+# How many missing
+table(missing)
+```
 
+All of the missing values are filled in with mean value for that 5-minute
+interval.
 
+```{r}
+# Replace each missing value with the mean value of its 5-minute interval
+fill.value <- function(steps, interval) {
+    filled <- NA
+    if (!is.na(steps))
+        filled <- c(steps)
+    else
+        filled <- (averages[averages$interval==interval, "steps"])
+    return(filled)
+}
+filled.data <- data
+filled.data$steps <- mapply(fill.value, filled.data$steps, filled.data$interval)
+```
+Now, using the filled data set, let's make a histogram of the total number of steps taken each day and calculate the mean and median total number of steps.
+
+```{r}
+total.steps <- tapply(filled.data$steps, filled.data$date, FUN=sum)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+mean(total.steps)
+median(total.steps)
+```
+
+Mean and median values are higher after imputing missing data. The reason is
+that in the original data, there are some days with `steps` values `NA` for 
+any `interval`. The total number of steps taken in such days are set to 0s by
+default. However, after replacing missing `steps` values with the mean `steps`
+of associated `interval` value, these 0 values are removed from the histogram
+of total number of steps taken each day.
 
 ## Are there differences in activity patterns between weekdays and weekends?
+First, let's find the day of the week for each measurement in the dataset. In
+this part, we use the dataset with the filled-in values.
+
+```{r}
+weekday.or.weekend <- function(date) {
+    day <- weekdays(date)
+    if (day %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
+        return("weekday")
+    else if (day %in% c("Saturday", "Sunday"))
+        return("weekend")
+    else
+        stop("invalid date")
+}
+filled.data$date <- as.Date(filled.data$date)
+filled.data$day <- sapply(filled.data$date, FUN=weekday.or.weekend)
+```
+
+Now, let's make a panel plot containing plots of average number of steps taken
+on weekdays and weekends.
+```{r}
+averages <- aggregate(steps ~ interval + day, data=filled.data, mean)
+ggplot(averages, aes(interval, steps)) + geom_line() + facet_grid(day ~ .) +
+    xlab("5-minute interval") + ylab("Number of steps")
+```

PA1_template.md

@@ -0,0 +1,182 @@
+---
+title: "Reproducible Research: Peer Assessment 1"
+output: 
+  html_document:
+    keep_md: true
+---
+
+
+## Loading and preprocessing the data
+
+```r
+unzip(zipfile="activity.zip")
+data <- read.csv("activity.csv")
+head(data)
+```
+
+```
+##   steps       date interval
+## 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       25
+```
+
+## What is mean total number of steps taken per day?
+
+```r
+library(ggplot2)
+total.steps <- tapply(data$steps, data$date, FUN=sum, na.rm=TRUE)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+```
+
+```
+## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
+## This warning is displayed once every 8 hours.
+## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
+## generated.
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-1-1.png)<!-- -->
+
+```r
+mean(total.steps, na.rm=TRUE)
+```
+
+```
+## [1] 9354.23
+```
+
+```r
+median(total.steps, na.rm=TRUE)
+```
+
+```
+## [1] 10395
+```
+
+
+## What is the average daily activity pattern?
+
+```r
+library(ggplot2)
+averages <- aggregate(x=list(steps=data$steps), by=list(interval=data$interval),
+                      FUN=mean, na.rm=TRUE)
+ggplot(data=averages, aes(x=interval, y=steps)) +
+    geom_line() +
+    xlab("5-minute interval") +
+    ylab("average number of steps taken")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-2-1.png)<!-- -->
+
+On average across all the days in the dataset, the 5-minute interval contains
+the maximum number of steps?
+
+```r
+averages[which.max(averages$steps),]
+```
+
+```
+##     interval    steps
+## 104      835 206.1698
+```
+
+
+## Imputing missing values
+There are many days/intervals where there are missing values (coded as `NA`). The presence of missing days may introduce bias into some calculations or summaries of the data.
+
+
+```r
+missing <- is.na(data$steps)
+# How many missing
+table(missing)
+```
+
+```
+## missing
+## FALSE  TRUE 
+## 15264  2304
+```
+
+All of the missing values are filled in with mean value for that 5-minute
+interval.
+
+
+```r
+# Replace each missing value with the mean value of its 5-minute interval
+fill.value <- function(steps, interval) {
+    filled <- NA
+    if (!is.na(steps))
+        filled <- c(steps)
+    else
+        filled <- (averages[averages$interval==interval, "steps"])
+    return(filled)
+}
+filled.data <- data
+filled.data$steps <- mapply(fill.value, filled.data$steps, filled.data$interval)
+```
+Now, using the filled data set, let's make a histogram of the total number of steps taken each day and calculate the mean and median total number of steps.
+
+
+```r
+total.steps <- tapply(filled.data$steps, filled.data$date, FUN=sum)
+qplot(total.steps, binwidth=1000, xlab="total number of steps taken each day")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-5-1.png)<!-- -->
+
+```r
+mean(total.steps)
+```
+
+```
+## [1] 10766.19
+```
+
+```r
+median(total.steps)
+```
+
+```
+## [1] 10766.19
+```
+
+Mean and median values are higher after imputing missing data. The reason is
+that in the original data, there are some days with `steps` values `NA` for 
+any `interval`. The total number of steps taken in such days are set to 0s by
+default. However, after replacing missing `steps` values with the mean `steps`
+of associated `interval` value, these 0 values are removed from the histogram
+of total number of steps taken each day.
+
+## Are there differences in activity patterns between weekdays and weekends?
+First, let's find the day of the week for each measurement in the dataset. In
+this part, we use the dataset with the filled-in values.
+
+
+```r
+weekday.or.weekend <- function(date) {
+    day <- weekdays(date)
+    if (day %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
+        return("weekday")
+    else if (day %in% c("Saturday", "Sunday"))
+        return("weekend")
+    else
+        stop("invalid date")
+}
+filled.data$date <- as.Date(filled.data$date)
+filled.data$day <- sapply(filled.data$date, FUN=weekday.or.weekend)
+```
+
+Now, let's make a panel plot containing plots of average number of steps taken
+on weekdays and weekends.
+
+```r
+averages <- aggregate(steps ~ interval + day, data=filled.data, mean)
+ggplot(averages, aes(interval, steps)) + geom_line() + facet_grid(day ~ .) +
+    xlab("5-minute interval") + ylab("Number of steps")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-7-1.png)<!-- -->

RepData_PeerAssessment1.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX