first commit

draft, still working on problem rdpeng#3

Author: acrost

PA1_template.Rmd

@@ -7,19 +7,85 @@ output:
 
 
 ## Loading and preprocessing the data
+```{r, echo=TRUE}
+#library(dplyr)
+library(lattice)
+activity<-read.csv("activity.csv")
+activity$date<-as.Date(activity$date)
+clean_activity<-activity[!is.na(activity$steps),]
 
+```
 
 
 ## What is mean total number of steps taken per day?
+```{r, echo=TRUE}
 
-
+part1<-aggregate(steps ~date, clean_activity, sum)
+hist(part1$steps, 
+     main= "Total number of steps per day",
+     xlab="Steps", 
+     breaks=15,
+     col="blue")
+mean(part1$steps)
+median(part1$steps)
+```
 
 ## What is the average daily activity pattern?
 
+```{r, echo=TRUE}
+interval_steps<-aggregate(steps ~ interval, clean_activity, mean)
+
+plot(interval_steps$interval,
+     interval_steps$steps, 
+     type="l", 
+     xlab="Time interval", 
+     ylab="Number of steps")
+
+# Use which.max() to find largest mean number of steps per day,
+# then return the associated interval
 
+interval_steps[which.max(interval_steps$steps), 1]
+
+
+```
+So the interval with the hightest mean number of steps is 835
 
 ## Imputing missing values
+```{r}
+# count the number of NA values 
+nrow(activity[is.na(activity$steps),])
+```
+1. The total number of missing values in the dataset is 2304
+
+2. The strategy is to replace "NA" values with the average steps taken for
+    that time interval across all days
+    
+```{r, echo=TRUE}
 
+fixed_activity<-activity
 
+# strategy: fill NA with average for that time interval
+
+
+```
 
 ## Are there differences in activity patterns between weekdays and weekends?
+```{r, echo=TRUE}
+
+# use weekdays() function to label each day
+clean_activity$weekend <- ifelse(weekdays(clean_activity$date) %in% 
+                c("Saturday", "Sunday"),
+                "weekend", "weekday")
+clean_activity$weekend<-as.factor(clean_activity$weekend)
+
+# same strategy as before but add the weekend factor variable
+avg_steps_w<-aggregate(steps ~ interval + weekend, clean_activity, mean)
+
+#lattice plot separated by weekend and weekday
+xyplot(avg_steps_w$steps ~ avg_steps_w$interval | avg_steps_w$weekend, 
+       type="l", 
+       layout=c(1,2),
+       main="Comparison between activity on weekdays and weekends",
+       xlab="Time intervals",
+       ylab="Average steps")
+```

PA1_template.html

@@ -0,0 +1,114 @@
+# Reproducible Research: Peer Assessment 1
+
+
+## Loading and preprocessing the data
+
+```r
+#library(dplyr)
+library(lattice)
+activity<-read.csv("activity.csv")
+activity$date<-as.Date(activity$date)
+clean_activity<-activity[!is.na(activity$steps),]
+```
+
+
+## What is mean total number of steps taken per day?
+
+```r
+part1<-aggregate(steps ~date, clean_activity, sum)
+hist(part1$steps, 
+     main= "Total number of steps per day",
+     xlab="Steps", 
+     breaks=15,
+     col="blue")
+```
+
+![](./PA1_template_files/figure-html/unnamed-chunk-2-1.png) 
+
+```r
+mean(part1$steps)
+```
+
+```
+## [1] 10766.19
+```
+
+```r
+median(part1$steps)
+```
+
+```
+## [1] 10765
+```
+
+## What is the average daily activity pattern?
+
+
+```r
+interval_steps<-aggregate(steps ~ interval, clean_activity, mean)
+
+plot(interval_steps$interval,
+     interval_steps$steps, 
+     type="l", 
+     xlab="Time interval", 
+     ylab="Number of steps")
+```
+
+![](./PA1_template_files/figure-html/unnamed-chunk-3-1.png) 
+
+```r
+# Use which.max() to find largest mean number of steps per day,
+# then return the associated interval
+
+interval_steps[which.max(interval_steps$steps), 1]
+```
+
+```
+## [1] 835
+```
+So the interval with the hightest mean number of steps is 835
+
+## Imputing missing values
+
+```r
+# count the number of NA values 
+nrow(activity[is.na(activity$steps),])
+```
+
+```
+## [1] 2304
+```
+1. The total number of missing values in the dataset is 2304
+
+2. The strategy is to replace "NA" values with the average steps taken for
+    that time interval across all days
+    
+
+```r
+fixed_activity<-activity
+
+# strategy: fill NA with average for that time interval
+```
+
+## Are there differences in activity patterns between weekdays and weekends?
+
+```r
+# use weekdays() function to label each day
+clean_activity$weekend <- ifelse(weekdays(clean_activity$date) %in% 
+                c("Saturday", "Sunday"),
+                "weekend", "weekday")
+clean_activity$weekend<-as.factor(clean_activity$weekend)
+
+# same strategy as before but add the weekend factor variable
+avg_steps_w<-aggregate(steps ~ interval + weekend, clean_activity, mean)
+
+#lattice plot separated by weekend and weekday
+xyplot(avg_steps_w$steps ~ avg_steps_w$interval | avg_steps_w$weekend, 
+       type="l", 
+       layout=c(1,2),
+       main="Comparison between activity on weekdays and weekends",
+       xlab="Time intervals",
+       ylab="Average steps")
+```
+
+![](./PA1_template_files/figure-html/unnamed-chunk-6-1.png)