In jverzani/questionr: A package to write quizzes, ... using R markdown

``` {r echo=FALSE, results="asis"}

Nothing to see here ... move along

require("questionr", quietly=TRUE) page <- questionr:::Page$new() nav <- questionr:::NavBar$new() cat(page$write_header())

`r I(nav$write_header("Reading the common graphics of univariate statistics"))`

``` {r echo=FALSE}
## put startup R commands here
require(ggplot2)

This web page quizzes you on your ability to read the basic graphics of exploratory data analysis. Just answer the questions below by eyeballing the graphs. To see how you did, click the "grade" button at the bottom.

r I(nav$add("Stem and Leaf"))

For moderate-sized data sets the stem-and-leaf plot lets us quickly identify the center, spread and shape of a distribution, as well as identify quantiles.

Suppose our data set is x.

``` {r echo=FALSE} sc <- sample(2:4, 1) x <- sample((10^sc):(10^sc + 99), 13) m <- min(x); M <- max(x); iqr <- IQR(x); med <- median(x); mean <- mean(x)

The stem and leaf plot of x is found by:
``` {r}
stem(x, scale=2)

r I(page$new_problem()) What is the maximum value of x recorded in the data?

r I(page$numeric_choice(M, M))

r I(page$new_problem()) What is the median value of x recorded in the data?

r I(page$numeric_choice(med, med, hint="find the middle..."))

r I(page$new_problem()) Is the shape of x "long-tailed"

r I(page$radio_choice(c("Yes", "No"), "No", hint="some hint"))

r I(nav$add("Boxplots"))

Boxplots allow us to quickly see the center, spread and rough shape of a distribution in a graphic that invites comparison of many distributions together (side-by-side boxplots).

p <- ggplot(morley, aes(x=factor(Expt), y=Speed))
p + geom_boxplot()

The graphic shows a summary of the measured speed of light (in some scale) for each of 5 experiments recorded in the morley data set.

r I(page$new_problem()) Which of the 5 experiments have "outliers" as determined by the 1.5 IQR rule?

r I(page$checkgroup_choice(as.character(1:5), as.character(c(1,3)), hint="Outlier values are marked individually"))

r I(page$new_problem()) Which of the 5 experiments had the largest median value? r I(page$radio_choice(as.character(1:5), "1"))

r I(page$new_problem()) Which of the 5 experiments had the smallest recorded value? r I(page$radio_choice(as.character(1:5), "3"))

r I(page$new_problem()) For experiment 1, the value of the Q3 is more than the maximum value of which experiments? r I(page$checkgroup_choice(as.character(2:5), as.character(c(2:5)), hint="Outlier values are marked individually"))

r I(nav$add("Histograms"))

Histograms allow us to quickly identify the center, spread and shape of a distribution for arbitrarily large data sets. This is unlike the stem and leaf plot, an excellent graphic that unfortunately doesn't scale well to larger sets of numbers.

``` {r echo=FALSE} mu <- sample(5:25,1) s <- sample(3:10, 1) x <- rnorm(100, mean=mu, sd=s)

```r
qplot(x, binwidth=diff(range(x))/30)

Answer the following questions based on the histogram of x:

r I(page$new_problem()) What is the median value of x?

r I(page$numeric_choice(median(x) - s/2, median(x) + s/2, hint="split the area in half"))

r I(page$new_problem()) What is the mean value of x? r I(page$numeric_choice(mean(x) - s/2, mean(x) + s/2, hint="Look to balance the (weighted) area"))

r I(page$new_problem()) Which boxplot best represents x:

x1 <- mu + s*rt(100, df=3)
x2 <- mu -s + s*rexp(100)
x3 <- x
m <- data.frame(x1=x1, x2=x2, x3=x3)
d <- stack(m)

p <- ggplot(d, aes(y=values, x=ind))
p + geom_boxplot()

r I(page$radio_choice(c("x1", "x2", "x3"), "x3", comment=list(x1="too long tailed", x2="this is skewed right")))

r I(nav$add("Density plots"))

A density plot is often seen overlaid a histogram, as in the figure below. This is a bit redundant, both give a visual estimate of the parent population of a random sample. The histogram has more chart junk, as Tufte might say, but the density plot is less familiar.

p <- ggplot(diamonds, aes(x=carat))
p + geom_histogram(aes(y=..density..)) + geom_density(alpha=.2, fill="#FF6666") 

r I(page$new_problem()) Based on the plot of carat, estimate the mean value for this data:

x <- diamonds$carat; xbar <- mean(x); s <- sd(x)
cat(page$numeric_choice(xbar - s/2, xbar + s/2))

r I(page$new_problem()) Based on the shape of the graph, would you say the distribution is r I(page$radio_choice(c("symmetric", "skewed", "neither"), "skewed"))

r I(page$new_problem()) Based on the shape of the graph, would you say the distribution is r I(page$radio_choice(c("short-tailed", "long-tailed", "neither"), "long-tailed"))

r I(nav$add("qqplots"))

The quantile-quantile plot allows one to compare one distribution against the other. The two are similar up to changes of scale and spread if the qqplot is essentially straight.

df <- data.frame(rivers) 
ggplot(df, aes(sample = rivers)) + stat_qq() 

r I(page$new_problem()) Based on the graphic above, is the rivers data approximately normal? r I(page$radio_choice(c("Yes", "No"), "No", comment=c("Yes"="Are the points basically falling on a straight line?")))

cat(nav$add("Grade", header=FALSE))
cat(nav$write_footer())
cat(page$grade_button())
cat(page$write_footer())

jverzani/questionr documentation built on May 20, 2019, 5:20 a.m.