--- title: "Exercise 7" author: "Put your name here" date: "Put the date here" output: html_document --- # Task 1: Reflection Put your reflection here # Task 2: Combining plots ```{r load-libraries-data} library(tidyverse) library(patchwork) library(broom) results_2016 <- read_csv("data/results_2016.csv") ``` Make 2–3 plots of anything you want from the `results_2016` data (histogram, density, boxplot, scatterplot, whatever) and combine them with **patchwork**. Look at [the documentation](https://patchwork.data-imaginist.com/articles/guides/assembly.html) to see fancy ways of combining them, like having two rows inside a column. ```{r combine-plots} # Make some plots and combine them here ``` # Task 3: Visualizing regression ## Coefficient plot Use the `results_2016` data to create a model that predicts the percent of Democratic votes in a precinct based on age, race, income, rent, and state (hint: the formula will look like this: `percent_dem ~ median_age + percent_white + per_capita_income + median_rent + state`) Use `tidy()` in the **broom** package and `geom_pointrange()` to create a coefficient plot for the model estimates. You'll have 50 rows for all the states, and that's excessive for a plot like this, so you'll want to filter out the state rows. You can do that by adding this: ```{r example-filtering, eval=FALSE} tidy(...) %>% filter(!str_detect(term, "state")) ``` The `str_detect()` function looks for the characters "state" in the term column. The `!` negates it. This is thus saying "only keep rows where the word 'state' is not in the term name". You should also get rid of the intercept (`filter(term != "(Intercept)")`). ## Predicted values Show what happens to `percent_dem` as one (or more) of your model's variables changes. To make life easy, refer to the ["Predicted values and marginal effects in 2022"](/example/07-example/#predicted-values-and-marginal-effects-in-2022) section in this session's example and use `predictions()` rather than creating your own `newdata` data set by hand. You'll do something like this (assuming you're manipulating `per_capita_income`; try using a different variable when you do the assignment, though): ```{r example-predictions, eval=FALSE} my_predictions <- predictions( model_name, newdata = datagrid(per_capita_income = seq(9000, 60000, by = 100), state = "Georgia")) ``` Plot your varied variable on the x-axis, the fitted values (`predicted`) on the y-axis, show the relationship with a line, and add a ribbon to show the 95% confidence interval. # Bonus task 1! Correlograms **This is entirely optional but might be fun.** For extra fun times, if you feel like it, create a correlogram heatmap, either with `geom_tile()` or with points sized by the correlation. Use any variables you want from `results_2016`. # Bonus task 2! Marginal effects **This is also entirely optional but will be super useful if you use regression for anything in your own work.** For extra super bonus fun times, create a more complex model that predicts `percent_dem` that uses polynomial terms (e.g. age squared) and/or interaction terms (e.g. age × state). Plot predictions from the model, use `marginaleffects()` to find the slopes of those predictions at different values, and plot the slopes in a marginal effects plot. (The ["Predicted values and marginal effects in 2022"](/example/07-example/#predicted-values-and-marginal-effects-in-2022) section from the example will be indispensable here.)