---
title: "Foundations for statistical inference - Sampling distributions"
runtime: shiny
output:
  html_document:
    css: www/lab.css
    highlight: pygments
    theme: cerulean
    toc: true
    toc_float: true
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(eval = FALSE)
library(tidyverse)
library(openintro)
library(infer)
```

In this lab, you will investigate the ways in which the statistics from a random 
sample of data can serve as point estimates for population parameters. We're 
interested in formulating a *sampling distribution* of our estimate in order 
to learn about the properties of the estimate, such as its distribution.

<div id="boxedtext">
**Setting a seed:** We will take some random samples and build sampling distributions
in this lab, which means you should set a seed at the start of your lab. If this
concept is new to you, review the lab concerning probability.
</div>

## Getting Started

### Load packages

In this lab, we will explore and visualize the data using the **tidyverse** suite of packages. 
The data can be found in the companion package for OpenIntro resources, **openintro**.
Lastly, we will use the **infer** package for resampling.

Let's load the packages.

```{r load-packages, message=FALSE}
library(tidyverse)
library(openintro)
library(infer)
```

### Creating a reproducible lab report

To create your new lab report, start by opening a new R Markdown document... From 
Template... then select Lab Report from the **openintro** package. 

### The data

You will be analyzing real estate data from the city of Ames, Iowa. The details of 
every real estate transaction in Ames is recorded by the City Assessor's 
office. Your particular focus for this lab will be all residential home sales 
in Ames between 2006 and 2010.  This collection represents your population of 
interest. In this lab, you will learn about these home sales by taking 
smaller samples from the full population. Let's load the data.

```{r load-data}
data(ames)
```

You can see that there are quite a few variables in the data set, enough to do a 
very in-depth analysis. For this lab, you will focus on just two of the variables:
the above ground living area of the house in square feet (`area`) and the sale 
price (`price`).

You can explore the distribution of areas of homes in the population of home
sales visually and with summary statistics. Let's first create a visualization,
a histogram:

```{r area-hist}
ggplot(data = ames, aes(x = area)) +
  geom_histogram(binwidth = 250)
```

Let's also obtain some summary statistics. Note that you can do this using the
`summarise` function. You can calculate as many statistics as you want using this
function, and just combine the results. Some of the functions below should
be self explanatory (like `mean`, `median`, `sd`, `IQR`, `min`, and `max`). A
new function here is the `quantile` function, which you can use to calculate 
values corresponding to specific percentile cutoffs in the distribution. For
example `quantile(x, 0.25)` will yield the cutoff value for the 25th percentile (Q1)
in the distribution of `x`. Finding these values is useful for describing the 
distribution, as you can use them for descriptions like *"the middle 50% of the 
homes have areas between such and such square feet"*.

```{r area-stats}
ames %>%
  summarise(mu = mean(area), pop_med = median(area), 
            sigma = sd(area), pop_iqr = IQR(area),
            pop_min = min(area), pop_max = max(area),
            pop_q1 = quantile(area, 0.25),  # first quartile, 25th percentile
            pop_q3 = quantile(area, 0.75))  # third quartile, 75th percentile
```

1.  Describe this population distribution using a visualization and the summary
    statistics listed above. You don't have to use all of the summary statistics
    in your description, and you will need to decide which ones are relevant based
    on the shape of the distribution. Make sure to include the plot and the summary
    statistics output in your report along with your narrative.

## The unknown sampling distribution

In this lab, you have access to the entire population, but this is rarely the 
case in real life. Gathering information on an entire population is often 
extremely costly or impossible. Because of this, we often take a sample of 
the population and use that to understand the properties of the population.

If you were interested in estimating the mean living area in Ames based on a 
sample, you can use the `sample_n` command to survey the population.

```{r samp1}
samp1 <- ames %>%
  sample_n(50)
```

This command collects a simple random sample of size 50 from the `ames` dataset, 
and assigns the result to `samp1`. This is similar to going into the City 
Assessor's database and pulling up the files on 50 random home sales. Working 
with these 50 files is considerably simpler than working with all 2930 home sales.

1.  Describe the distribution of area in this sample. How does it compare to the 
    distribution of the population? **Hint:** Although the `sample_n` function 
    takes a random sample of observations (i.e. rows) from the dataset, you can 
    still refer to the variables in the dataset with the same names. Code you used
    in the previous exercise will also be helpful for visualizing and summarizing 
    the sample, however be careful to not label values `mu` and `sigma` anymore 
    since these are sample statistics, not population parameters. You can customize
    the labels of any of the statistics to indicate that these come from the sample.

If you're interested in estimating the average living area in homes in Ames 
using the sample, your best single guess is the sample mean.

```{r mean-samp1}
samp1 %>%
  summarise(x_bar = mean(area))
```

Depending on which 50 homes you selected, your estimate could be a bit above 
or a bit below the true population mean of `r round(mean(ames$area),2)` square 
feet. In general, though, the sample mean turns out to be a pretty good estimate 
of the average living area, and you were able to get it by sampling less than 3\%
of the population.

1.  Would you expect the mean of your sample to match the mean of another team's 
    sample? Why, or why not? If the answer is no, would you expect the means to
    just be somewhat different or very different? Ask a neighboring team to confirm 
    your answer.

1.  Take a second sample, also of size 50, and call it `samp2`. How does the 
    mean of `samp2` compare with the mean of `samp1`? Suppose we took two 
    more samples, one of size 100 and one of size 1000. Which would you think 
    would provide a more accurate estimate of the population mean?

Not surprisingly, every time you take another random sample, you get a different 
sample mean. It's useful to get a sense of just how much variability you 
should expect when estimating the population mean this way. The distribution 
of sample means, called the *sampling distribution (of the mean)*, can help you 
understand this variability. In this lab, because you have access to the population,
you can build up the sampling distribution for the sample mean by repeating the 
above steps many times. Here, you will generate 15,000 samples and compute the 
sample mean of each. Note that we specify that `replace = TRUE` since sampling 
distributions are constructed by sampling with replacement.

```{r loop}
sample_means50 <- ames %>%
                    rep_sample_n(size = 50, reps = 15000, replace = TRUE) %>%
                    summarise(x_bar = mean(area))

ggplot(data = sample_means50, aes(x = x_bar)) +
  geom_histogram(binwidth = 20)
```

Here, we use R to take 15,000 different samples of size 50 from the population, 
calculate the mean of each sample, and store each result in a vector called 
`sample_means50`. Next, you will review how this set of code works.

1.  How many elements are there in `sample_means50`?  Describe the sampling 
    distribution, and be sure to specifically note its center. Make sure to include 
    a plot of the distribution in your answer.

## Interlude: Sampling distributions

The idea behind the `rep_sample_n` function is *repetition*. Earlier, you took
a single sample of size `n` (50) from the population of all houses in Ames. With
this new function, you can repeat this sampling procedure `rep` times in order
to build a distribution of a series of sample statistics, which is called the 
**sampling distribution**. 

Note that in practice one rarely gets to build true sampling distributions, 
because one rarely has access to data from the entire population. 

Without the `rep_sample_n` function, this would be painful. We would have to 
manually run the following code 15,000 times 
```{r sample-code, eval=FALSE}
ames %>%
  sample_n(size = 50) %>%
  summarise(x_bar = mean(area))
```
as well as store the resulting sample means each time in a separate vector.

Note that for each of the 15,000 times we computed a mean, we did so from a 
**different** sample!

1.  To make sure you understand how sampling distributions are built, and exactly
    what the `rep_sample_n` function does, try modifying the code to create a
    sampling distribution of **25 sample means** from **samples of size 10**, 
    and put them in a data frame named `sample_means_small`. Print the output. 
    How many observations are there in this object called `sample_means_small`? 
    What does each observation represent?

## Sample size and the sampling distribution

Mechanics aside, let's return to the reason we used the `rep_sample_n` function: to 
compute a sampling distribution, specifically, the sampling distribution of the 
mean home area for samples of 50 houses. 

```{r hist}
ggplot(data = sample_means50, aes(x = x_bar)) +
  geom_histogram(binwidth = 20)
```

The sampling distribution that you computed tells you much about estimating 
the average living area in homes in Ames.  Because the sample mean is an 
unbiased estimator, the sampling distribution is centered at the true average 
living area of the population, and the spread of the distribution 
indicates how much variability is incurred by sampling only 50 home sales.

In the remainder of this section, you will work on getting a sense of the effect
that sample size has on your sampling distribution.

1.  Use the app below to create sampling distributions of means of `area`s from
    samples of size 10, 50, and 100. Use 5,000 simulations. What does each 
    observation in the sampling distribution represent? How does the mean, standard
    error, and shape of the sampling distribution change as the sample size
    increases? How (if at all) do these values change if you increase the number 
    of simulations? (You do not need to include plots in your answer.)
    
```{r shiny, echo=FALSE, eval=TRUE}
shinyApp(
  ui <- fluidPage(
    
    # Sidebar with a slider input for number of bins 
    sidebarLayout(
      sidebarPanel(
        
        selectInput("selected_var",
                    "Variable:",
                    choices = list("area", "price"),
                    selected = "area"),         
        
        numericInput("n_samp",
                     "Sample size:",
                     min = 1,
                     max = nrow(ames),
                     value = 30),
        
        numericInput("n_sim",
                     "Number of samples:",
                     min = 1,
                     max = 30000,
                     value = 15000) 
        
      ),
      
      # Show a plot of the generated distribution
      mainPanel(
        plotOutput("sampling_plot"),
        verbatimTextOutput("sampling_mean"),
        verbatimTextOutput("sampling_se")
      )
    )
  ),
  
  # Define server logic required to draw a histogram
  server <- function(input, output) {
    
    # create sampling distribution
    sampling_dist <- reactive({
      ames[[input$selected_var]] %>%
        sample(size = input$n_samp * input$n_sim, replace = TRUE) %>%
        matrix(ncol = input$n_samp) %>%
        rowMeans() %>%
        data.frame(x_bar = .)
    })
    
    # plot sampling distribution
    output$sampling_plot <- renderPlot({
      x_min <- quantile(ames[[input$selected_var]], 0.1)
      x_max <- quantile(ames[[input$selected_var]], 0.9)
      
      ggplot(sampling_dist(), aes(x = x_bar)) +
        geom_histogram() +
        xlim(x_min, x_max) +
        ylim(0, input$n_sim * 0.35) +
        ggtitle(paste0("Sampling distribution of mean ", 
                       input$selected_var, " (n = ", input$n_samp, ")")) +
        xlab(paste("mean", input$selected_var)) +
        theme(plot.title = element_text(face = "bold", size = 16))
    })
    
    # mean of sampling distribution
    output$sampling_mean <- renderText({
      paste0("mean of sampling distribution = ", round(mean(sampling_dist()$x_bar), 2))
    })
    
    # mean of sampling distribution
    output$sampling_se <- renderText({
      paste0("SE of sampling distribution = ", round(sd(sampling_dist()$x_bar), 2))
    })
  },
  
  options = list(height = 500) 
)
```


* * *

## More Practice

So far, you have only focused on estimating the mean living area in homes in 
Ames. Now, you'll try to estimate the mean home price.

Note that while you might be able to answer some of these questions using the app,
you are expected to write the required code and produce the necessary plots and
summary statistics. You are welcome to use the app for exploration.

1.  Take a sample of size 15 from the population and calculate the mean `price`
    of the homes in this sample. Using this sample, what is your best point estimate
    of the population mean of prices of homes?

1.  Since you have access to the population, simulate the sampling 
    distribution of $\overline{price}$ for samples of size 15 by taking 2000 
    samples from the population of size 15 and computing 2000 sample means. 
    Store these means in a vector called `sample_means15`. Plot the data, then 
    describe the shape of this sampling distribution. Based on this sampling 
    distribution, what would you guess the mean home price of the population to 
    be? Finally, calculate and report the population mean.

1.  Change your sample size from 15 to 150, then compute the sampling 
    distribution using the same method as above, and store these means in a 
    new vector called `sample_means150`. Describe the shape of this sampling 
    distribution and compare it to the sampling distribution for a sample 
    size of 15. Based on this sampling distribution, what would you guess to 
    be the mean sale price of homes in Ames?

1.  Of the sampling distributions from 2 and 3, which has a smaller spread?  If
    you're concerned with making estimates that are more often close to the 
    true value, would you prefer a sampling distribution with a large or small spread?


<div id="license">
This is a product of OpenIntro that is released under a [Creative Commons 
Attribution-ShareAlike 3.0 Unported](http://creativecommons.org/licenses/by-sa/3.0).
This lab was written for OpenIntro by Andrew Bray and Mine &Ccedil;etinkaya-Rundel.
</div>