This assignment reinforces ideas in Iteration.

Due: November 16 at 11:59pm.

Please submit (via courseworks) the web address of the GitHub repo containing your work for this assignment; git commits after the due date will cause the assignment to be considered late.

R Markdown documents included as part of your solutions must not install packages, and should only load the packages necessary for your submission to knit.

Problem | Points |
---|---|

Problem 0 | 20 |

Problem 1 | – |

Problem 2 | 40 |

Problem 3 | 40 |

Optional survey | No points |

This “problem” focuses on structure of your submission, especially the use git and GitHub for reproducibility, R Projects to organize your work, R Markdown to write reproducible reports, relative paths to load data from local files, and reasonable naming structures for your files. To that end:

- create a public GitHub repo + local R Project; we suggest naming
this repo / directory
`p8105_hw5_YOURUNI`

(e.g.`p8105_hw5_ajg2202`

for Jeff), but that’s not required - create a single .Rmd file named
`p8105_hw5_YOURUNI.Rmd`

that renders to`github_document`

- create a subdirectory to store the local data files used in the assignment, and use relative paths to access these data files
- submit a link to your repo via Courseworks

Your solutions to Problems 1, 2, and 3 should be implemented in your .Rmd file, and your git commit history should reflect the process you used to solve these Problems.

For this Problem, we will assess adherence to the instructions above regarding repo structure, git commit history, and whether we are able to knit your .Rmd to ensure that your work is reproducible. Adherence to appropriate styling and clarity of code will be assessed in Problems 1+ using the style rubric.

This homework includes figures; the readability of your embedded plots (e.g. font sizes, axis labels, titles) will be assessed in Problems 1+.

This zip file contains data from a longitudinal study that included a control arm and an experimental arm. Data for each participant is included in a separate file, and file names include the subject ID and arm.

Create a tidy dataframe containing data from all participants, including the subject ID, arm, and observations over time:

- Start with a dataframe containing all file names; the
`list.files`

function will help - Iterate over file names and read in data for each subject using
`purrr::map`

and saving the result as a new variable in the dataframe - Tidy the result; manipulate file names to include control arm and subject ID, make sure weekly observations are “tidy”, and do any other tidying that’s necessary

Make a spaghetti plot showing observations on each subject over time, and comment on differences between groups.

The *Washington Post* has gathered data on homicides in 50
large U.S. cities and made the data available through a GitHub
repository here. You
can read their accompanying article here.

Describe the raw data. Create a `city_state`

variable
(e.g. “Baltimore, MD”) and then summarize within cities to obtain the
total number of homicides and the number of unsolved homicides (those
for which the disposition is “Closed without arrest” or “Open/No
arrest”).

For the city of Baltimore, MD, use the `prop.test`

function to estimate the proportion of homicides that are unsolved; save
the output of `prop.test`

as an R object, apply the
`broom::tidy`

to this object and pull the estimated
proportion and confidence intervals from the resulting tidy
dataframe.

Now run `prop.test`

for each of the cities in your
dataset, and extract both the proportion of unsolved homicides and the
confidence interval for each. Do this within a “tidy” pipeline, making
use of `purrr::map`

, `purrr::map2`

, list columns
and `unnest`

as necessary to create a tidy dataframe with
estimated proportions and CIs for each city.

Create a plot that shows the estimates and CIs for each city – check
out `geom_errorbar`

for a way to add error bars based on the
upper and lower limits. Organize cities according to the proportion of
unsolved homicides.

When designing an experiment or analysis, a common question is whether it is likely that a true effect will be detected – put differently, whether a false null hypothesis will be rejected. The probability that a false null hypothesis is rejected is referred to as power, and it depends on several factors, including: the sample size; the effect size; and the error variance. In this problem, you will conduct a simulation to explore power in a one-sample t-test.

First set the following design elements:

- Fix \(n=30\)
- Fix \(\sigma = 5\)

Set \(\mu=0\). Generate 5000 datasets from the model

\[ x \sim Normal[\mu, \sigma] \]

For each dataset, save \(\hat{\mu}\)
and the p-value arising from a test of \(H :
\mu = 0\) using \(\alpha =
0.05\). *Hint: to obtain the estimate and p-value, use
broom::tidy to clean the output of
t.test.*

Repeat the above for \(\mu = \{1, 2, 3, 4, 5, 6\}\), and complete the following:

- Make a plot showing the proportion of times the null was rejected (the power of the test) on the y axis and the true value of \(\mu\) on the x axis. Describe the association between effect size and power.
- Make a plot showing the average estimate of \(\hat{\mu}\) on the y axis and the true
value of \(\mu\) on the x axis. Make a
second plot (or overlay on the first) the average estimate of \(\hat{\mu}\)
**only in samples for which the null was rejected**on the y axis and the true value of \(\mu\) on the x axis. Is the sample average of \(\hat{\mu}\) across tests for which the null is rejected approximately equal to the true value of \(\mu\)? Why or why not?

Please complete this survey regarding extra topics to cover at the end of the semester.

If you’d like, a you can complete this short survey after you’ve finished the assignment.