posit conf 2024

talk

Author

Tony Duan

Published

September 18, 2024

Posit conf 2024 Link:

https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog?tab.day=20240813

video:https://www.youtube.com/@PositPBC/videos

1 1 Beyond Dashboards: Dynamic Data Storytelling with Python, R, and Quarto Emails

Talk by Sean Nguyen

using quarto parameters to make multiple quarto output

2 2 A Future of Data Science(not watch yet)

Talk by Allen Downey

2.1 Reference:

video:https://www.youtube.com/watch?v=YKMZIzYBgTk

3 3 Quarto, AI, and the Art of Getting Your Life Back

Talk by Tyler Morgan-Wall

3.1 Reference:

video:https://www.youtube.com/watch?v=U8_Dc_ru8fg

4 4 Quarto: A Multifaceted Publishing Powerhouse for Medical Researchers

Talk by Joshua J. Cook

4.1 include

this is _data.qmd

Code

import os
os.system('pip3 install great_tables')

Code

os.system('pip3 install pandas')

Code

import great_tables as gt
from great_tables.data import sp500
print(sp500.head())

         date       open       high  ...      close        volume  adj_close
0  2015-12-31  2060.5901  2062.5400  ...  2043.9399  2.655330e+09  2043.9399
1  2015-12-30  2077.3401  2077.3401  ...  2063.3601  2.367430e+09  2063.3601
2  2015-12-29  2060.5400  2081.5601  ...  2078.3601  2.542000e+09  2078.3601
3  2015-12-28  2057.7700  2057.7700  ...  2056.5000  2.492510e+09  2056.5000
4  2015-12-24  2063.5200  2067.3601  ...  2060.9900  1.411860e+09  2060.9900

[5 rows x 7 columns]

4.2 embed

embed other code result into this quarto doc(only result).

Source: _penguins.qmd

4.3 Reference:

video:https://www.youtube.com/watch?v=PITfti4JRiU

https://quarto.org/docs/authoring/notebook-embed.html

https://quarto.org/docs/authoring/includes.html

5 5 Creating reproducible static reports.

Talk by Orla Doyle

5.1 Reference:

video:https://www.youtube.com/watch?v=HoTl0oYUX3c

6 6 Empowering Reproducible Finance through Tidy Finance with R and Python

Talk by Christoph Scheuch

in R
in Python

Code

#install.packages("tidyfinance")

Code

library(tidyverse)
library(tidyfinance)
library(scales)

Code

prices <- download_data(
  type = "stock_prices",
  symbols = "AAPL",
  start_date = "2000-01-01",
  end_date = "2025-12-31"
) |> arrange(desc(date))

Code

head(prices)

# A tibble: 6 × 8
  symbol date         volume  open   low  high close adjusted_close
  <chr>  <date>        <dbl> <dbl> <dbl> <dbl> <dbl>          <dbl>
1 AAPL   2024-11-04 44903300  221.  220.  223.  222.           222.
2 AAPL   2024-11-01 65276700  221.  220.  225.  223.           223.
3 AAPL   2024-10-31 64370100  229.  225.  230.  226.           226.
4 AAPL   2024-10-30 47070900  233.  230.  233.  230.           230.
5 AAPL   2024-10-29 35417200  233.  232.  234.  234.           234.
6 AAPL   2024-10-28 36087100  233.  233.  235.  233.           233.

Code

prices |>
  ggplot(aes(x = date, y = adjusted_close)) +
  geom_line() +
  labs(
    x = NULL,
    y = NULL,
    title = "Apple stock prices between beginning of 2000 and end of 2023"
  )

Code

import os
#os.system('pip3 install yfinance')
#os.system('pip3 install plotnine')
#os.system('pip3 install seaborn')
#os.system('pip3 install siuba')

Code

import pandas as pd
import numpy as np
import yfinance as yf
#from siuba import *

Code

# prices = (yf.download(
#     tickers="AAPL", 
#     start="2000-01-01", 
#     end="2025-12-31",
# )

prices= yf.Ticker("AAPL").history(start="2000-01-01", end="2025-12-31")

prices = prices.reset_index()

Code

prices.head()

                       Date      Open  ...  Dividends  Stock Splits
0 2000-01-03 00:00:00-05:00  0.790753  ...        0.0           0.0
1 2000-01-04 00:00:00-05:00  0.816200  ...        0.0           0.0
2 2000-01-05 00:00:00-05:00  0.782270  ...        0.0           0.0
3 2000-01-06 00:00:00-05:00  0.800178  ...        0.0           0.0
4 2000-01-07 00:00:00-05:00  0.727606  ...        0.0           0.0

[5 rows x 8 columns]

Code

prices.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6250 entries, 0 to 6249
Data columns (total 8 columns):
 #   Column        Non-Null Count  Dtype                           
---  ------        --------------  -----                           
 0   Date          6250 non-null   datetime64[ns, America/New_York]
 1   Open          6250 non-null   float64                         
 2   High          6250 non-null   float64                         
 3   Low           6250 non-null   float64                         
 4   Close         6250 non-null   float64                         
 5   Volume        6250 non-null   int64                           
 6   Dividends     6250 non-null   float64                         
 7   Stock Splits  6250 non-null   float64                         
dtypes: datetime64[ns, America/New_York](1), float64(6), int64(1)
memory usage: 390.8 KB

Code

prices.tail().round(3)

                          Date    Open  ...  Dividends  Stock Splits
6245 2024-10-29 00:00:00-04:00  233.10  ...        0.0           0.0
6246 2024-10-30 00:00:00-04:00  232.61  ...        0.0           0.0
6247 2024-10-31 00:00:00-04:00  229.34  ...        0.0           0.0
6248 2024-11-01 00:00:00-04:00  220.97  ...        0.0           0.0
6249 2024-11-04 00:00:00-05:00  220.99  ...        0.0           0.0

[5 rows x 8 columns]

Code

from plotnine import *
prices_figure = (
  ggplot(prices, 
         aes(y="Close", x="Date")) +
  geom_line() +
  labs(x="", y="",
       title="Apple stock prices from 2000 to 2023")
)

Code

print(prices_figure)

<ggplot: (640 x 480)>

Code

import seaborn as sns
sns.lineplot(data=prices,x='Date',y='Close')

6.1 Reference:

video:https://www.youtube.com/watch?v=vV7JECvFXNE

tidy finance book:https://www.tidy-finance.org/

7 Datapages for interactive data sharing using Quarto - posit conf 2024

https://www.youtube.com/watch?v=1gUl-vWqScc

7.1 download data from redivis

Code

# https://github.com/redivis/redivis-r
#install.packages("devtools")
#install.packages('sf')
#install.packages('arrow')
#devtools::install_github("redivis/redivis-r", ref="main")

library(redivis)

user <- redivis::user("datapages")
dataset <- user$dataset("palmerpenguins:9g3v:v1_0")
table <- dataset$table("penguins:79km")

# Load table as tidyverse tibble
data <- table$to_tibble(max_results=100)
head(data)

7.2 create datapage

first remove renv in the project renv::deactivate()

page:https://tonyfly3000.github.io/datapage_example/ code:https://github.com/TonyFly3000/datapage_example

7.3 change data location in quarto doc `data.qmd`

Code

{{< redivis-table >}}

<!-- {{< redivis-table verykoala.penguinsdata.penguins >}} -->

7.4 change data location in `_quarto.yml`

Code

project:
  title: "Penguins datapage example"
  type: datapage
  output-dir: docs

redivis:
  user: verykoala
  dataset: penguinsdata
  table: penguins

7.5 add redivis api in `.Renviron`

REDIVIS_API_TOKEN=xxxxxxx

7.6 Reference:

video :https://www.youtube.com/watch?v=1gUl-vWqScc
datapage github page: https://github.com/datapages/datapage
data page excample :https://datapages.github.io/datapage/

8 Shiny x AI - posit conf 2024

8.1 Reference:

video:https://www.youtube.com/watch?v=AP8BWGhCRZc

9 Keynote Session: Updates from Posit

link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712004520449001rbm2

Posit’s mission and products by Hadley Wickham
Databricks and Snowflake by James Blair
Quarto by Charlotte Wickham
webR by George Stagg

10 Introducing Positron, a new data science IDE

link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712707493462001KPj8

10.1 Introducing Positron - Julia Silge

10.2 Exploratory Data Analysis with Python in Positron - Isabel Zimmerman

10.3 Debugging Data with the Positron Data Explorer - Tom Mock

10.4 Architecture and Design of Positron - Jonathan McPherson

10.5 Introducing Ark, Modern Tooling for R - Lionel Henry and Davis Vaughan

10.6 Positron for R and RStudio users - Jenny Bryan

https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712707493462001KPj8

https://github.com/posit-dev/positron

11 Machine Learning And Statistical Modeling

Link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712003957019001aS0p

11.1 Making sense of marginal effects - Demetri Pananos

11.2 Understanding, Generating, and Evaluating Prediction Intervals - Bryan Shalloway

11.3 Keras 3: Deep Learning made easy - Tomasz Kalinowski

11.4 Quality Control to avoid GIGO in Deep Learning Models - Vasant Marur

12 Navigating A Career In Data Science

Link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1711986791933001rL5i

12.1 Beyond the Classroom: Unspoken Realities of a Data Science Career - Brandon Sucher

12.2 GitHub: How To Tell Your Professional Story - Abigail Haddad

12.3 Getting Data Done with a Pragmatic Data Team - Alan Schussman

12.4 Oops I’m A Manager - Finding your Minimal Viable Process - Andrew Holz

13 Reference

https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog?search=&tab.day=20240813

--- title: "posit conf 2024" author: "Tony Duan" date: "2024-09-18" categories: [talk] execute: warning: false error: false format: html: toc: true code-fold: show code-tools: true number-sections: true code-block-bg: true code-block-border-left: "#31BAE9" image: "images/posit-conf-logo.svg" --- Posit conf 2024 Link: https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog?tab.day=20240813 video playlist:https://www.youtube.com/watch?v=YKMZIzYBgTk&list=PL9HYL-VRX0oSFkdF4fJeY63eGDvgofcbn ########## on progress ############# # Wait, that’s Shiny? Building feature-full, user-friendly interactive data explorers - Posit Conf https://www.youtube.com/watch?v=SAbV6d7Pn0U # Adam Wang - Why You Should Think Like an End-to-end Data Scientist, and How https://www.youtube.com/watch?v=Rxq4O3AhXjM # Richard Iannone - Adequate Tables? No, We Want Great Tables https://www.youtube.com/watch?v=JRW2jm8aNiI # Hannes Mühleisen - Data Wrangling [for Python or R] Like a Boss With DuckDB https://www.youtube.com/watch?v=GELhdezYmP0 #############################important finished ################################### # Greg Swinehart - We CAN have nice Shiny apps: What's new in Shiny's UI & UX https://rstudio.github.io/bslib/ https://www.youtube.com/watch?v=FPc5PJRWHsk ########################################################################################################### ######################################### finished ################################### # Kshitij Aranke - Demystifying Data Modeling https://www.youtube.com/watch?v=II8qzoFrkLg # Marcin Dubel - Shiny in Action: Transforming Film Production with TARS https://www.youtube.com/watch?v=G2GNZZ8GQPY # Becca Krouse - Stitch by Stitch: The Art of Engaging New Users https://www.youtube.com/watch?v=R3VMij_1aSE # Hubert Halun - Art of R Packages: Forging Community with Hex Stickers https://www.youtube.com/watch?v=Pw-wvDYD0Ks # Emily Riederer - Python Rgonomics https://www.youtube.com/watch?v=ILxK92HDtvU # Greg Swinehart - We CAN have nice Shiny apps: What's new in Shiny's UI & UX https://www.youtube.com/watch?v=FPc5PJRWHsk ########################################################################################################### # old # Beyond Dashboards: Dynamic Data Storytelling with Python, R, and Quarto Emails Talk by Sean Nguyen using quarto parameters to make multiple quarto output # A Future of Data Science(not watch yet) Talk by Allen Downey ## Reference: video:https://www.youtube.com/watch?v=YKMZIzYBgTk # Quarto, AI, and the Art of Getting Your Life Back Talk by Tyler Morgan-Wall ## Reference: video:https://www.youtube.com/watch?v=U8_Dc_ru8fg # gtsummary: Streamlining Summary Tables for Research and Regulatory Submissions Talk by Daniel Sjoberg video:https://www.youtube.com/watch?v=oYA_DXzN40A # Quarto: A Multifaceted Publishing Powerhouse for Medical Researchers Talk by Joshua J. Cook ## include {{{< include _data.qmd >}}} {{< include _data.qmd >}} ## embed embed other code result into this quarto doc(only result). {{{< embed _penguins.qmd#fig-size-scatter >}}} {{< embed _penguins.qmd#fig-size-scatter >}} ## Reference: video:https://www.youtube.com/watch?v=PITfti4JRiU https://quarto.org/docs/authoring/notebook-embed.html https://quarto.org/docs/authoring/includes.html # Creating reproducible static reports. Talk by Orla Doyle ![](images/paste-1.png) ## Reference: video:https://www.youtube.com/watch?v=HoTl0oYUX3c # Empowering Reproducible Finance through Tidy Finance with R and Python Talk by Christoph Scheuch ::: panel-tabset ## in R ```{r} #install.packages("tidyfinance") ``` ```{r} library(tidyverse) # Easily Install and Load the 'Tidyverse' library(tidyfinance) # Tidy Finance Helper Functions library(scales) # Scale Functions for Visualization ``` ```{r} prices <- download_data( type = "stock_prices", symbols = "AAPL", start_date = "2000-01-01", end_date = "2025-12-31" ) |> arrange(desc(date)) ``` ```{r} head(prices) ``` ```{r} prices |> ggplot(aes(x = date, y = adjusted_close)) + geom_line() + labs( x = NULL, y = NULL, title = "Apple stock prices between beginning of 2000 and end of 2023" ) ``` ## in Python ```{python} import os #os.system('pip3 install yfinance') #os.system('pip3 install plotnine') #os.system('pip3 install seaborn') #os.system('pip3 install siuba') ``` ```{python} import pandas as pd import numpy as np import yfinance as yf #from siuba import * ``` ```{python} # prices = (yf.download( # tickers="AAPL", # start="2000-01-01", # end="2025-12-31", # ) prices= yf.Ticker("AAPL").history(start="2000-01-01", end="2025-12-31") prices = prices.reset_index() ``` ```{python} prices.head() ``` ```{python} prices.info() ``` ```{python} prices.tail().round(3) ``` ```{python} from plotnine import * prices_figure = ( ggplot(prices, aes(y="Close", x="Date")) + geom_line() + labs(x="", y="", title="Apple stock prices from 2000 to 2023") ) ``` ```{python} print(prices_figure) ``` ```{python} import seaborn as sns sns.lineplot(data=prices,x='Date',y='Close') ``` ::: ## Reference: video:https://www.youtube.com/watch?v=vV7JECvFXNE tidy finance book:https://www.tidy-finance.org/ # Why’d you load that package for? Talk by Luis D. Verde Arregoitia ```{r} install.packages("annotater") ``` ```{r} library(annotater) # Annotate Package Load Calls test_string <-c("library(dplyr)\nlibrary(readr)") writeLines(annotate_pkg_calls(test_string)) ``` ```{r} writeLines(annotate_repo_source(test_string)) ``` Also can using addin to add package info for all package ![](https://raw.githubusercontent.com/luisdva/annotater/master/inst/media/repostitles.gif) ## Reference: video:https://www.youtube.com/watch?v=q4vmmlUEoQg https://annotater.liomys.mx/ # Datapages for interactive data sharing using Quarto - posit conf 2024 https://www.youtube.com/watch?v=1gUl-vWqScc ## download data from redivis ```{r} #| eval: false # https://github.com/redivis/redivis-r #install.packages("devtools") #install.packages('sf') #install.packages('arrow') #devtools::install_github("redivis/redivis-r", ref="main") library(redivis) # R interface for Redivis user <- redivis::user("datapages") dataset <- user$dataset("palmerpenguins:9g3v:v1_0") table <- dataset$table("penguins:79km") # Load table as tidyverse tibble data <- table$to_tibble(max_results=100) head(data) ``` ## create datapage first remove renv in the project renv::deactivate() page:https://tonyfly3000.github.io/datapage_example/ code:https://github.com/TonyFly3000/datapage_example ## change data location in quarto doc `data.qmd` ```{bash} #| eval: false #| {{< redivis-table >}}  ``` ## change data location in `_quarto.yml` ```{bash} #| eval: false project: title: "Penguins datapage example" type: datapage output-dir: docs redivis: user: verykoala dataset: penguinsdata table: penguins ``` ## add redivis api in `.Renviron` REDIVIS_API_TOKEN=xxxxxxx ## Reference: - video :https://www.youtube.com/watch?v=1gUl-vWqScc - datapage github page: https://github.com/datapages/datapage - data page excample :https://datapages.github.io/datapage/ # Shiny x AI - posit conf 2024 ## Reference: video:https://www.youtube.com/watch?v=AP8BWGhCRZc # Keynote Session: Updates from Posit link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712004520449001rbm2 - Posit’s mission and products by Hadley Wickham - Databricks and Snowflake by James Blair - Quarto by Charlotte Wickham - webR by George Stagg # Introducing Positron, a new data science IDE link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712707493462001KPj8 ![](images/my%20screenshots%204.png) ## Introducing Positron - Julia Silge ## Exploratory Data Analysis with Python in Positron - Isabel Zimmerman ## Debugging Data with the Positron Data Explorer - Tom Mock ## Architecture and Design of Positron - Jonathan McPherson ## Introducing Ark, Modern Tooling for R - Lionel Henry and Davis Vaughan ## Positron for R and RStudio users - Jenny Bryan https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712707493462001KPj8 https://github.com/posit-dev/positron # Machine Learning And Statistical Modeling Link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1712003957019001aS0p ## Making sense of marginal effects - Demetri Pananos ## Understanding, Generating, and Evaluating Prediction Intervals - Bryan Shalloway ## Keras 3: Deep Learning made easy - Tomasz Kalinowski ## Quality Control to avoid GIGO in Deep Learning Models - Vasant Marur # Navigating A Career In Data Science Link:https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1711986791933001rL5i ## Beyond the Classroom: Unspoken Realities of a Data Science Career - Brandon Sucher ## GitHub: How To Tell Your Professional Story - Abigail Haddad ## Getting Data Done with a Pragmatic Data Team - Alan Schussman ## Oops I'm A Manager - Finding your Minimal Viable Process - Andrew Holz # Reference https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog?search=&tab.day=20240813 https://www.youtube.com/@PositPBC/videos

1 1 Beyond Dashboards: Dynamic Data Storytelling with Python, R, and Quarto Emails

2 2 A Future of Data Science(not watch yet)

2.1 Reference:

3 3 Quarto, AI, and the Art of Getting Your Life Back

3.1 Reference:

4 4 Quarto: A Multifaceted Publishing Powerhouse for Medical Researchers

4.1 include

4.2 embed

4.3 Reference:

5 5 Creating reproducible static reports.

5.1 Reference:

6 6 Empowering Reproducible Finance through Tidy Finance with R and Python

6.1 Reference:

7 Datapages for interactive data sharing using Quarto - posit conf 2024

7.1 download data from redivis

7.2 create datapage

7.3 change data location in quarto doc data.qmd

7.4 change data location in _quarto.yml

7.5 add redivis api in .Renviron

7.6 Reference:

8 Shiny x AI - posit conf 2024

8.1 Reference:

9 Keynote Session: Updates from Posit

10 Introducing Positron, a new data science IDE

10.1 Introducing Positron - Julia Silge

10.2 Exploratory Data Analysis with Python in Positron - Isabel Zimmerman

10.3 Debugging Data with the Positron Data Explorer - Tom Mock

10.4 Architecture and Design of Positron - Jonathan McPherson

10.5 Introducing Ark, Modern Tooling for R - Lionel Henry and Davis Vaughan

10.6 Positron for R and RStudio users - Jenny Bryan

11 Machine Learning And Statistical Modeling

11.1 Making sense of marginal effects - Demetri Pananos

11.2 Understanding, Generating, and Evaluating Prediction Intervals - Bryan Shalloway

11.3 Keras 3: Deep Learning made easy - Tomasz Kalinowski

11.4 Quality Control to avoid GIGO in Deep Learning Models - Vasant Marur

12 Navigating A Career In Data Science

12.1 Beyond the Classroom: Unspoken Realities of a Data Science Career - Brandon Sucher

12.2 GitHub: How To Tell Your Professional Story - Abigail Haddad

12.3 Getting Data Done with a Pragmatic Data Team - Alan Schussman

12.4 Oops I’m A Manager - Finding your Minimal Viable Process - Andrew Holz

13 Reference

7.3 change data location in quarto doc `data.qmd`

7.4 change data location in `_quarto.yml`

7.5 add redivis api in `.Renviron`