Quarto template

This is a Quarto template for multi-language website. We present below example with R and Python.

Setup

Load R packages
Load Python packages
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import session_info

The pinguin dataset

The goal of palmerpenguins is to provide a great dataset for data exploration & visualization.

Code
dt <- read.csv("./docs/data/palmer-penguins.csv") %>%
  mutate(
    species = factor(species),
    island = factor(island),
    sex = factor(sex)
  )

dt %>%
  summary() %>%
  knitr::kable()
rowid species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Min. : 1.00 Adelie :152 Biscoe :168 Min. :32.10 Min. :13.10 Min. :172.0 Min. :2700 female:165 Min. :2007
1st Qu.: 86.75 Chinstrap: 68 Dream :124 1st Qu.:39.23 1st Qu.:15.60 1st Qu.:190.0 1st Qu.:3550 male :168 1st Qu.:2007
Median :172.50 Gentoo :124 Torgersen: 52 Median :44.45 Median :17.30 Median :197.0 Median :4050 NA’s : 11 Median :2008
Mean :172.50 Mean :43.92 Mean :17.15 Mean :200.9 Mean :4202 Mean :2008
3rd Qu.:258.25 3rd Qu.:48.50 3rd Qu.:18.70 3rd Qu.:213.0 3rd Qu.:4750 3rd Qu.:2009
Max. :344.00 Max. :59.60 Max. :21.50 Max. :231.0 Max. :6300 Max. :2009
NA’s :2 NA’s :2 NA’s :2 NA’s :2
Code
str(dt)
'data.frame':   344 obs. of  9 variables:
 $ rowid            : int  1 2 3 4 5 6 7 8 9 10 ...
 $ species          : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ island           : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ bill_length_mm   : num  39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
 $ bill_depth_mm    : num  18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
 $ flipper_length_mm: int  181 186 195 NA 193 190 181 195 193 190 ...
 $ body_mass_g      : int  3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
 $ sex              : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
 $ year             : int  2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
Code
dt
Code
df = pd.read_csv('./docs/data/palmer-penguins.csv')

df.describe(include='all')
             rowid species  island  ...  body_mass_g   sex         year
count   344.000000     344     344  ...   342.000000   333   344.000000
unique         NaN       3       3  ...          NaN     2          NaN
top            NaN  Adelie  Biscoe  ...          NaN  male          NaN
freq           NaN     152     168  ...          NaN   168          NaN
mean    172.500000     NaN     NaN  ...  4201.754386   NaN  2008.029070
std      99.448479     NaN     NaN  ...   801.954536   NaN     0.818356
min       1.000000     NaN     NaN  ...  2700.000000   NaN  2007.000000
25%      86.750000     NaN     NaN  ...  3550.000000   NaN  2007.000000
50%     172.500000     NaN     NaN  ...  4050.000000   NaN  2008.000000
75%     258.250000     NaN     NaN  ...  4750.000000   NaN  2009.000000
max     344.000000     NaN     NaN  ...  6300.000000   NaN  2009.000000

[11 rows x 9 columns]
Code
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 9 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   rowid              344 non-null    int64  
 1   species            344 non-null    object 
 2   island             344 non-null    object 
 3   bill_length_mm     342 non-null    float64
 4   bill_depth_mm      342 non-null    float64
 5   flipper_length_mm  342 non-null    float64
 6   body_mass_g        342 non-null    float64
 7   sex                333 non-null    object 
 8   year               344 non-null    int64  
dtypes: float64(4), int64(2), object(3)
memory usage: 24.3+ KB
Code
df
     rowid    species     island  ...  body_mass_g     sex  year
0        1     Adelie  Torgersen  ...       3750.0    male  2007
1        2     Adelie  Torgersen  ...       3800.0  female  2007
2        3     Adelie  Torgersen  ...       3250.0  female  2007
3        4     Adelie  Torgersen  ...          NaN     NaN  2007
4        5     Adelie  Torgersen  ...       3450.0  female  2007
..     ...        ...        ...  ...          ...     ...   ...
339    340  Chinstrap      Dream  ...       4000.0    male  2009
340    341  Chinstrap      Dream  ...       3400.0  female  2009
341    342  Chinstrap      Dream  ...       3775.0    male  2009
342    343  Chinstrap      Dream  ...       4100.0    male  2009
343    344  Chinstrap      Dream  ...       3775.0  female  2009

[344 rows x 9 columns]

Visualization

See Figure 1 for an exploration of bill sizes by species.

Code
ggplot(dt, aes(x = bill_length_mm, y = bill_depth_mm, color = species, fill = species)) +
  geom_point(size = 3, alpha = 0.8) +
  geom_smooth(method = "lm", formula = y ~ x, se = T, show.legend = F) +
  scale_discrete_manual(aesthetics = c("colour", "fill"), values = c("darkorange","purple","cyan4")) +
  labs(x = "Bill length (mm)",
       y = "Bill depth (mm)",
       color = "Penguin species",
       fill = "Penguin species") +
  theme_bw()
Figure 1: Bill length and depth for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER

See Figure 2 for an exploration of bill sizes by species.

Code
sns.set_style('whitegrid')

(
  sns.lmplot(x = "bill_length_mm",
               y = "bill_depth_mm",
               hue = "species",
               height = 7,
               data = df,
               palette = ['#FF8C00','#159090','#A034F0'])
  .set_xlabels('Bill length (mm)')
  .set_ylabels('Bill depth (mm)')
);

plt.show()
Figure 2: Bill length and depth for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER

Code 
R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Paris
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.5.1 dplyr_1.1.4  

loaded via a namespace (and not attached):
 [1] Matrix_1.6-5      gtable_0.3.5      jsonlite_1.8.8    compiler_4.3.3   
 [5] tidyselect_1.2.1  Rcpp_1.0.12       splines_4.3.3     scales_1.3.0     
 [9] png_0.1-8         yaml_2.3.8        fastmap_1.2.0     reticulate_1.37.0
[13] lattice_0.22-6    here_1.0.1        R6_2.5.1          labeling_0.4.3   
[17] generics_0.1.3    knitr_1.45        htmlwidgets_1.6.4 tibble_3.2.1     
[21] munsell_0.5.1     rprojroot_2.0.4   pillar_1.9.0      rlang_1.1.3      
[25] utf8_1.2.4        xfun_0.44         cli_3.6.2         withr_3.0.0      
[29] magrittr_2.0.3    mgcv_1.9-1        digest_0.6.35     grid_4.3.3       
[33] rstudioapi_0.16.0 nlme_3.1-164      lifecycle_1.0.4   vctrs_0.6.5      
[37] evaluate_0.23     glue_1.7.0        farver_2.1.2      fansi_1.0.6      
[41] colorspace_2.1-0  rmarkdown_2.27    tools_4.3.3       pkgconfig_2.0.3  
[45] htmltools_0.5.8.1
Code
session_info.show() # html=False
-----
matplotlib          3.7.1
pandas              1.5.3
seaborn             0.13.0
session_info        1.0.0
-----
Python 3.8.16 | packaged by conda-forge | (default, Feb  1 2023, 16:13:45) [Clang 14.0.6 ]
macOS-12.6.1-x86_64-i386-64bit
-----
Session information updated at 2024-09-07 17:40