Quarto demo

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 many other things.

My logo

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:

install.packages(“readxl”)

library(tidyverse)

#|out-width: 50%
#|code-fold: true

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

A bar chart of diamond cuts

Using Latex

We can put formulas inline like this: \(\alpha\) We indent like this

\[ \pi \in \mathbb{R}\]

Google for more info and specific symbols.

Using python

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

                      mpg  cyl   disp     hp  drat  ...   qsec   vs   am  gear  carb
Mazda RX4            21.0  6.0  160.0  110.0  3.90  ...  16.46  0.0  1.0   4.0   4.0
Mazda RX4 Wag        21.0  6.0  160.0  110.0  3.90  ...  17.02  0.0  1.0   4.0   4.0
Datsun 710           22.8  4.0  108.0   93.0  3.85  ...  18.61  1.0  1.0   4.0   1.0
Hornet 4 Drive       21.4  6.0  258.0  110.0  3.08  ...  19.44  1.0  0.0   3.0   1.0
Hornet Sportabout    18.7  8.0  360.0  175.0  3.15  ...  17.02  0.0  0.0   3.0   2.0
Valiant              18.1  6.0  225.0  105.0  2.76  ...  20.22  1.0  0.0   3.0   1.0
Duster 360           14.3  8.0  360.0  245.0  3.21  ...  15.84  0.0  0.0   3.0   4.0
Merc 240D            24.4  4.0  146.7   62.0  3.69  ...  20.00  1.0  0.0   4.0   2.0
Merc 230             22.8  4.0  140.8   95.0  3.92  ...  22.90  1.0  0.0   4.0   2.0
Merc 280             19.2  6.0  167.6  123.0  3.92  ...  18.30  1.0  0.0   4.0   4.0
Merc 280C            17.8  6.0  167.6  123.0  3.92  ...  18.90  1.0  0.0   4.0   4.0
Merc 450SE           16.4  8.0  275.8  180.0  3.07  ...  17.40  0.0  0.0   3.0   3.0
Merc 450SL           17.3  8.0  275.8  180.0  3.07  ...  17.60  0.0  0.0   3.0   3.0
Merc 450SLC          15.2  8.0  275.8  180.0  3.07  ...  18.00  0.0  0.0   3.0   3.0
Cadillac Fleetwood   10.4  8.0  472.0  205.0  2.93  ...  17.98  0.0  0.0   3.0   4.0
Lincoln Continental  10.4  8.0  460.0  215.0  3.00  ...  17.82  0.0  0.0   3.0   4.0
Chrysler Imperial    14.7  8.0  440.0  230.0  3.23  ...  17.42  0.0  0.0   3.0   4.0
Fiat 128             32.4  4.0   78.7   66.0  4.08  ...  19.47  1.0  1.0   4.0   1.0
Honda Civic          30.4  4.0   75.7   52.0  4.93  ...  18.52  1.0  1.0   4.0   2.0
Toyota Corolla       33.9  4.0   71.1   65.0  4.22  ...  19.90  1.0  1.0   4.0   1.0
Toyota Corona        21.5  4.0  120.1   97.0  3.70  ...  20.01  1.0  0.0   3.0   1.0
Dodge Challenger     15.5  8.0  318.0  150.0  2.76  ...  16.87  0.0  0.0   3.0   2.0
AMC Javelin          15.2  8.0  304.0  150.0  3.15  ...  17.30  0.0  0.0   3.0   2.0
Camaro Z28           13.3  8.0  350.0  245.0  3.73  ...  15.41  0.0  0.0   3.0   4.0
Pontiac Firebird     19.2  8.0  400.0  175.0  3.08  ...  17.05  0.0  0.0   3.0   2.0
Fiat X1-9            27.3  4.0   79.0   66.0  4.08  ...  18.90  1.0  1.0   4.0   1.0
Porsche 914-2        26.0  4.0  120.3   91.0  4.43  ...  16.70  0.0  1.0   5.0   2.0
Lotus Europa         30.4  4.0   95.1  113.0  3.77  ...  16.90  1.0  1.0   5.0   2.0
Ford Pantera L       15.8  8.0  351.0  264.0  4.22  ...  14.50  0.0  1.0   5.0   4.0
Ferrari Dino         19.7  6.0  145.0  175.0  3.62  ...  15.50  0.0  1.0   5.0   6.0
Maserati Bora        15.0  8.0  301.0  335.0  3.54  ...  14.60  0.0  1.0   5.0   8.0
Volvo 142E           21.4  4.0  121.0  109.0  4.11  ...  18.60  1.0  1.0   4.0   2.0

[32 rows x 11 columns]

library(reticulate)
library(gt)
mtcars_new <-py$mtcars_py
head(mtcars_new)%>%gt()

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