QUARTO TIANA

Author

Tiana Isaacs

Published

November 1, 2025

Quarto

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

WE CAN DO ITALICS, BULLETED LISTS

FIRST
SECOND

And other things.

Artisanal Gillnet Fishing in Trinidad & Tobago — Gillnet Fishing

Running Code

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

library(tidyverse)

Include a plot

Code

ggplot(diamonds,aes(x=cut))+
  geom_bar()

More Description — Figure 1: A bar graph of diamond cuts

This is a reference: Figure 1.

Using LaTex

We can inset formulas in line like : \(\alpha\)

We indent like this

\[\pi\in\mathbb{R}\] Google for more information and specific symbols

Using Python

x, y = 4, 7
mtcars_py = r.mtcars
print(mtcars_py)

{'mpg': [21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26.0, 30.4, 15.8, 19.7, 15.0, 21.4], 'cyl': [6.0, 6.0, 4.0, 6.0, 8.0, 6.0, 8.0, 4.0, 4.0, 6.0, 6.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 4.0, 4.0, 4.0, 4.0, 8.0, 8.0, 8.0, 8.0, 4.0, 4.0, 4.0, 8.0, 6.0, 8.0, 4.0], 'disp': [160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0, 304.0, 350.0, 400.0, 79.0, 120.3, 95.1, 351.0, 145.0, 301.0, 121.0], 'hp': [110.0, 110.0, 93.0, 110.0, 175.0, 105.0, 245.0, 62.0, 95.0, 123.0, 123.0, 180.0, 180.0, 180.0, 205.0, 215.0, 230.0, 66.0, 52.0, 65.0, 97.0, 150.0, 150.0, 245.0, 175.0, 66.0, 91.0, 113.0, 264.0, 175.0, 335.0, 109.0], 'drat': [3.9, 3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.0, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76, 3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11], 'wt': [2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19, 3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2, 1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14, 1.513, 3.17, 2.77, 3.57, 2.78], 'qsec': [16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.0, 22.9, 18.3, 18.9, 17.4, 17.6, 18.0, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87, 17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6], 'vs': [0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0], 'am': [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'gear': [4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0], 'carb': [4.0, 4.0, 1.0, 1.0, 2.0, 1.0, 4.0, 2.0, 2.0, 4.0, 4.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 1.0, 2.0, 1.0, 1.0, 2.0, 2.0, 4.0, 2.0, 1.0, 2.0, 2.0, 4.0, 6.0, 8.0, 2.0]}

library(reticulate)
library(gt)
mtcars_new <- as.data.frame(py$mtcars_py)
head(mtcars_new)

   mpg cyl disp  hp drat    wt  qsec vs am gear carb
1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1