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Quarto Demo

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(knitr)

Graphs

ggplot(mtcars, aes(x=mpg, y=hp, color = cyl)) +
  geom_point() + 
  theme_classic() +
  labs(x = 'Miles Per Gallon', y = 'Horse Power', color = 'Cylinders')

Table

kable(head(mtcars), digits=1)

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.9	2.6	16.5	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.9	2.9	17.0	0	1	4	4
Datsun 710	22.8	4	108	93	3.9	2.3	18.6	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.1	3.2	19.4	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.1	3.4	17.0	0	0	3	2
Valiant	18.1	6	225	105	2.8	3.5	20.2	1	0	3	1

Statistics

mod <- lm(mpg ~ am + cyl * hp, data= mtcars)

summary(mod)

Call:
lm(formula = mpg ~ am + cyl * hp, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2202 -1.5052 -0.2882  1.0368  5.9707 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 44.113013   6.059124   7.280 7.86e-08 ***
am           3.760837   1.199249   3.136  0.00411 ** 
cyl         -2.911001   0.943814  -3.084  0.00467 ** 
hp          -0.178592   0.060300  -2.962  0.00631 ** 
cyl:hp       0.018541   0.007691   2.411  0.02301 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.593 on 27 degrees of freedom
Multiple R-squared:  0.8388,    Adjusted R-squared:  0.8149 
F-statistic: 35.13 on 4 and 27 DF,  p-value: 2.451e-10

mod |> 
  broom::tidy() |> 
  kable(digits = 3)

term	estimate	std.error	statistic	p.value
(Intercept)	44.113	6.059	7.280	0.000
am	3.761	1.199	3.136	0.004
cyl	-2.911	0.944	-3.084	0.005
hp	-0.179	0.060	-2.962	0.006
cyl:hp	0.019	0.008	2.411	0.023

Writing some stats.

t_test <- t.test(mtcars$mpg[mtcars$am == 0],
       mtcars$mpg[mtcars$am == 1])

t_test

    Welch Two Sample t-test

data:  mtcars$mpg[mtcars$am == 0] and mtcars$mpg[mtcars$am == 1]
t = -3.7671, df = 18.332, p-value = 0.001374
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -11.280194  -3.209684
sample estimates:
mean of x mean of y 
 17.14737  24.39231 

There is a statistically significant difference in mpg between automatic and manual cars t(18.33) = -3.77, p = 0.001.