title: Knit2html is not generating MD file, but referring me to rmarkdown::render
body: <p>I wasn't able to find much pertinent to this on Stack-Overflow, or the web.</p>
  
  <p>I'm getting this error:</p>
  
  <pre><code>&gt; library(knitr)
  &gt; knit2html("pa1_template.rmd")
  Error in knit2html("pa1_template.rmd") : 
    It seems you should call rmarkdown::render() instead of knitr::knit2html() because pa1_template.rmd appears to be an R Markdown v2 document.
  </code></pre>
  
  <p>I just ran it with rmarkdown::render(), and it created the HTML file.  However, my assignment wants me to run it through knit2html() and create an md file.</p>
  
  <p>When I run the Rmd file through the RStudio "Knit HTML" menu option, it creates the HTML file fine.  </p>
  
  <p>Any pointers appreciated.  </p>
  
  <p>Here is the content of the rmd file:</p>
  
  <pre><code>## Loading and preprocessing the data
  
  Read the data file in.
  ```{r readfile}
  steps&lt;-read.csv("activity.csv",header=TRUE, sep=",")
  steps_good&lt;-subset(steps, !is.na(steps))
  
  ```
  
  Sum the number of steps per day
  ```{r summarize/day}
  steps_day&lt;-aggregate(steps~date, data=steps_good, sum)
  ```
  
  Create a histogram of the results
  ```{r histogram}
  hist(steps_day$steps, main="Frequency of Steps/day", xlab="Steps/Day", border="blue", col="orange")
  ```
  
  # What is the mean total number of steps taken per day?
  Calculate the mean of the steps per day
  ```{r means_steps/day}
  mean_steps&lt;-mean(steps_day$steps)
  mean_steps
  ```
  Calculate the median of the steps per day
  ```{r median_steps/day}
  med_steps&lt;-median(steps_day$steps)
  med_steps
  ```
  
  #What is the average daily activity pattern?
  
  Get the average steps per 5 minute interval
  ```{r avg_5_min}
  step_5min&lt;-aggregate(steps~interval, data=steps_good, mean)
  ```
  Plot steps against time interval, averaged across all days
  ```{r plot_interval}
  plot(step_5min$interval,step_5min$steps, type="l", main="steps per time interval",ylab="Steps",xlab="Interval")
  ```
  
  On average, which interval during the day has the most steps.
  ```{r max_interval}
  step_5min$interval[which.max(step_5min$steps)]
  ```
  
  #Imputing missing values
  
  How many NAs are there in the original table?
  ```{r NAs}
    steps_na&lt;-which(is.na(steps))
    length(steps_na)
  ```
  Merge 5 minute interval with original steps table
  ```{r merge}
  
    steps_filled&lt;-merge(steps, step_5min,by="interval")
  ```
  
  Replace NA values with mean of steps values for that time interval
  ```{r replace_na}
    steps_na&lt;-which(is.na(steps_filled$steps.x))
    steps_filled$steps.x[steps_na]&lt;-steps_filled$steps.y[steps_na]
  ```
  
  Create a histogram of the results
  ```{r new_hist}
  steps_day_new&lt;-aggregate(steps.x~date, data=steps_filled, sum)
  hist(steps_day_new$steps.x, main="Frequency of Steps/day", xlab="Steps/Day", border="blue", col="orange")
  ```
  
  It looks like the imputing of NA values increases the middle bar (mean/median) height, but other bars seem unchanged.
  
  
  Calculate the new mean of the steps per day
  ```{r new_means_steps/day}
  mean_steps&lt;-mean(steps_day_new$steps.x)
  mean_steps
  ```
  Calculate the new median of the steps per day
  ```{r new_median_steps/day}
  med_steps&lt;-median(steps_day_new$steps.x)
  med_steps
  ```
  
  It looks like the mean did not change, but the median took on the value of the mean, now that some non-integer values were plugged in. 
  
  
  #Are there differences in activity patterns between weekdays and weekends?
  Regenerate steps_filled, and flag whether a date is a weekend or a weekday.
  Convert resulting column to factor.
  ```{r fill_weekdays}
    steps_filled&lt;-merge(steps, step_5min,by="interval")
    steps_filled$steps.x[steps_na]&lt;-steps_filled$steps.y[steps_na]
    steps_filled&lt;-cbind(steps_filled, wkday=weekdays(as.Date(steps_filled$date)))
    steps_filled&lt;-cbind(steps_filled,    day_type="", stringsAsFactors=FALSE)
  
    for(i in 1:nrow(steps_filled)){
      if(steps_filled$wkday[i] %in% c("Saturday","Sunday"))
        steps_filled$day_type[i]="Weekend"
      else
        steps_filled$day_type[i]="Weekday"
    }
    steps_filled$day_type&lt;-as.factor(steps_filled$day_type)
  ```
  
  Get average steps per interval and day_type
  ```{r plot_interva_day_type}
  steps_interval_day&lt;-aggregate(steps_filled$steps.x,by=list(steps_filled$interval,steps_filled$day_type),mean)
  ```
  
  Plot the weekend and weekday results in a panel plot.
  ```{r day_type_plot}
  weekday_intervals&lt;-subset(steps_interval_day, steps_interval_day$Group.2=="Weekday",select=c("Group.1","x"))
  weekend_intervals&lt;-subset(steps_interval_day, steps_interval_day$Group.2=="Weekend",select=c("Group.1","x"))
  par(mfrow=c(1,2))
  plot(weekday_intervals$Group.1,weekday_intervals$x,type="l",xlim=c(0,2400), ylim=c(0,225),main="Weekdays",xlab="Intervals",ylab="Mean Steps/day")
  plot(weekend_intervals$Group.1,weekend_intervals$x,type="l",xlim=c(0,2400), ylim=c(0,225),main="Weekends",xlab="Intervals",ylab="")
  </code></pre>