```{r setup, include=FALSE} knitr::opts_chunk\$set(echo = FALSE) ``` ## What you guys thought... Some of you got very close. Someone was even just a few days away from being exactly right. Below is some summary statistics and some graphs of the guesses.
```{r, echo = FALSE, message = FALSE, warning = FALSE} library(tidyverse) guesses <- tibble::tribble( ~age, ~own_age, 32, 37, 35.49, 31, 31, 25, 29.2, NA, 32, 25, 28.5, NA, 30.5, 23.25, 31, NA, 31, 33, 34, 31, 29.1, NA, 34, 29, 32, 25, 30, 23, 34, 36, 32.6, 29, 26.5, 38, 27.8, NA, 32.4, 26, 33, 30 ) guesses %>% summarize(mean = mean(age), sd = sd(age), median = median(age), min = min(age), max = max(age)) %>% pander::pander(caption = "Table 1. Summary Statistics of the Guesses") ```
```{r} p1 <- ggplot(guesses, aes(x = age)) + geom_histogram(bins = 8, alpha = .5, fill = "chartreuse3", color = "chartreuse3") + geom_dotplot(binwidth = 1.05, fill = "dodgerblue3", color = "dodgerblue4", alpha = .8) + geom_rug(color = "dodgerblue4") + theme_minimal() + labs(y = "Count", x = "Guess of Age (in Years)") + geom_vline(xintercept = 30.2, color = "coral2") + annotate("text", x = 30.1, y = 4.5, hjust = 1, label = "True Age", color = "coral2") p1 ``` Let's change the x-axis. Let's say we wanted to see if you guessed close based on working ages (20 - 65 years old). ```{r} p1 + coord_cartesian(xlim = c(20,65)) ``` ## Relationship between Your Guess and Your Age ```{r} guesses %>% lm(age ~ own_age, data = .) %>% summary() ``` ```{r} guesses %>% ggplot(aes(own_age, age)) + geom_point() + geom_smooth(method = "lm") + labs(x = "Your Own Age", y = "Your Guess") ``` ## Relationship between How Wrong Your Guess Was and Your Age ```{r} guesses %>% mutate(wrongness = abs(age - 30)) %>% lm(wrongness ~ own_age, data = .) %>% summary() ``` ```{r} guesses %>% mutate(wrongness = abs(age - 30)) %>% ggplot(aes(own_age, wrongness)) + geom_point() + geom_smooth(method = "lm") + labs(x = "Your Own Age", y = "Your Guess") ```