second commit

Author: bilydr

PA1_template.Rmd

@@ -67,6 +67,9 @@ Find how many NA values that the dataset contains with summary function.
 ```{r}
 summary(dfOrig)
 ```
+2304 rows contain missing values.
+
+As one strategy for filling missing values, I plug in average steps for the 5-min interval as calculated above.  
 
 
 ## Are there differences in activity patterns between weekdays and weekends?

PA1_template.html

@@ -0,0 +1,108 @@
+# Reproducible Research: Peer Assessment 1
+
+
+## Loading and preprocessing the data
+
+Read the orignial csv file into a data frame and load dplyr package for further analysis
+
+```r
+require(dplyr)
+dfOrig <- read.csv("activity.csv", colClasses = c("integer", "Date", "integer"))
+dfOrig <- tbl_df(dfOrig)
+```
+
+## What is mean total number of steps taken per day?
+
+Calculate the total number of steps taken each day into a new data frame
+
+```r
+dfDay<- dfOrig %>% 
+    group_by(date) %>% 
+    summarize(totalSteps = sum(steps, na.rm = T))
+```
+
+Make a histogram
+
+```r
+hist(dfDay$totalSteps, 
+     main = "Historgram of total number of steps taken each day",
+     xlab = "total steps taken per day")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-3-1.png) 
+
+Calculate the mean and the median
+
+```r
+mean(dfDay$totalSteps)
+```
+
+```
+## [1] 9354.23
+```
+
+```r
+median(dfDay$totalSteps)
+```
+
+```
+## [1] 10395
+```
+
+## What is the average daily activity pattern?
+
+Calculate the average number of steps taken by 5-min interval into a new data frame
+
+```r
+dfInterval <- dfOrig %>% 
+    group_by(interval) %>% 
+    summarize(avgSteps = mean(steps, na.rm =T))                      
+```
+
+Make a time series plot
+
+```r
+with(dfInterval, plot(interval, avgSteps, 
+                      type = "l",
+                      main = "Average Number of Steps by 5-min Interval",
+                      xlab = "5-min interval",
+                      ylab = "avg. number of steps each day"))
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-6-1.png) 
+
+Identify the top interval with highest average steps
+
+```r
+arrange(dfInterval, desc(avgSteps)) [1,]
+```
+
+```
+## Source: local data frame [1 x 2]
+## 
+##   interval avgSteps
+## 1      835 206.1698
+```
+It turns out to be the interval from 8h35 to 8h40.
+
+## Imputing missing values
+
+Find how many NA values that the dataset contains with summary function.
+
+```r
+summary(dfOrig)
+```
+
+```
+##      steps             date               interval     
+##  Min.   :  0.00   Min.   :2012-10-01   Min.   :   0.0  
+##  1st Qu.:  0.00   1st Qu.:2012-10-16   1st Qu.: 588.8  
+##  Median :  0.00   Median :2012-10-31   Median :1177.5  
+##  Mean   : 37.38   Mean   :2012-10-31   Mean   :1177.5  
+##  3rd Qu.: 12.00   3rd Qu.:2012-11-15   3rd Qu.:1766.2  
+##  Max.   :806.00   Max.   :2012-11-30   Max.   :2355.0  
+##  NA's   :2304
+```
+
+
+## Are there differences in activity patterns between weekdays and weekends?