David - 1 year ago 221

R Question

I wasn't able to find much pertinent to this on Stack-Overflow, or the web.

I'm getting this error:

`> library(knitr)`

> 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.

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.

When I run the Rmd file through the RStudio "Knit HTML" menu option, it creates the HTML file fine.

Any pointers appreciated.

Here is the content of the rmd file:

`## Loading and preprocessing the data`

Read the data file in.

```{r readfile}

steps<-read.csv("activity.csv",header=TRUE, sep=",")

steps_good<-subset(steps, !is.na(steps))

```

Sum the number of steps per day

```{r summarize/day}

steps_day<-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<-mean(steps_day$steps)

mean_steps

```

Calculate the median of the steps per day

```{r median_steps/day}

med_steps<-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<-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<-which(is.na(steps))

length(steps_na)

```

Merge 5 minute interval with original steps table

```{r merge}

steps_filled<-merge(steps, step_5min,by="interval")

```

Replace NA values with mean of steps values for that time interval

```{r replace_na}

steps_na<-which(is.na(steps_filled$steps.x))

steps_filled$steps.x[steps_na]<-steps_filled$steps.y[steps_na]

```

Create a histogram of the results

```{r new_hist}

steps_day_new<-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<-mean(steps_day_new$steps.x)

mean_steps

```

Calculate the new median of the steps per day

```{r new_median_steps/day}

med_steps<-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<-merge(steps, step_5min,by="interval")

steps_filled$steps.x[steps_na]<-steps_filled$steps.y[steps_na]

steps_filled<-cbind(steps_filled, wkday=weekdays(as.Date(steps_filled$date)))

steps_filled<-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$day_type<-as.factor(steps_filled$day_type)

```

Get average steps per interval and day_type

```{r plot_interva_day_type}

steps_interval_day<-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<-subset(steps_interval_day, steps_interval_day$Group.2=="Weekday",select=c("Group.1","x"))

weekend_intervals<-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="")

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Answer Source

In RStudio, you can add `keep_md: true`

in your `YAML`

header:

```
---
title: "Untitled"
output:
html_document:
keep_md: true
---
```

With this option, you get both `HTML`

and `md`

files.

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