Join our workshop on Introduction to Causal Machine Learning estimators in R, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Introduction to Causal Machine Learning estimators in R
Date: Thursday, April 11th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Michael Knaus is Assistant Professor of “Data Science in Economics” at the University of Tübingen. He is working at the intersection of causal inference and machine learning for policy evaluation and recommendation.
Description: You want to learn about Double Machine Learning and/or Causal Forests for causal effect estimation but are hesitant to start because of the heavy formulas involved? Or you are already using them and curious to (better) understand what happens under the hood? In this course, we take a code first, formulas second approach. You will see how to manually replicate the output of the powerful DoubleML and grf packages using at most five lines of code and nothing more than OLS. After seeing that everything boils down to simple recipes, the involved formulas will look more friendly. The course establishes therefore how things work and gives references to further understand why things work.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
WhatsApp is one of the most heavily used mobile instant messaging applications around the world. It is especially popular for everyday communication with friends and family and most users communicate on a daily or a weekly basis through the app. Interestingly, it is possible for WhatsApp users to extract a log file from each of their chats. This log file contains all textual communication in the chat that was not manually deleted or is not too far in the past.
This logging of digital communication is on the one hand interesting for researchers seeking to investigate interpersonal communication, social relationships, and linguistics, and can on the other hand also be interesting for individuals seeking to learn more about their own chatting behavior (or their social relationships).
The WhatsR R-package enables users to transform exported WhatsApp chat logs into a usable data frame object with one row per sent message and multiple variables of interest. In this blog post, I will demonstrate how the package can be used to process and visualize chat log files.
Installing the Package The package can either be installed via CRAN or via GitHub for the most up-to-date version. I recommend to install the GitHub version for the most recent features and bugfixes.
# from CRAN
# install.packages("WhatsR")
# from GitHub
devtools::install_github("gesiscss/WhatsR")
The package also needs to be attached before it can be used. For creating nicer plots, I recommend to also install and attach the patchwork package.
Obtaining a Chat Log You can export one of your own chat logs from your phone to your email address as explained in this tutorial. If you do this, I recommend to use the “without media” export option as this allows you to export more messages.
If you don’t want to use one of your own chat logs, you can create an artificial chat log with the same structure as a real one but with made up text using the WhatsR package!
## creating chat log for demonstration purposes
# setting seed for reproducibility
set.seed(1234)
# simulating chat log
# (and saving it automatically as a .txt file in the working directory)
create_chatlog(n_messages = 20000,
n_chatters = 5,
n_emoji = 5000,
n_diff_emoji = 50,
n_links = 999,
n_locations = 500,
n_smilies = 2500,
n_diff_smilies = 10,
n_media = 999,
n_sdp = 300,
startdate = "01.01.2019",
enddate = "31.12.2023",
language = "english",
time_format = "24h",
os = "android",
path = getwd(),
chatname = "Simulated_WhatsR_chatlog")
Parsing Chat Log File Once you have a chat log on your device, you can use the WhatsR package to import the chat log and parse it into a usable data frame structure.
data <- parse_chat("Simulated_WhatsR_chatlog.txt", verbose = TRUE)
Checking the parsed Chat Log You should now have a data frame object with one row per sent message and 19 variables with information extracted from the individual messages. For a detailed overview what each column contains and how it is computed, you can check the related open source publication for the package. We also add a tabular overview here.
## Checking the chat log
dim(data)
colnames(data)
Column Name
Description
DateTime
Timestamp for date and time the message was sent. Formatted as yyyy-mm-dd hh:mm:ss
Sender
Name of the sender of the message as saved in the contact list of the exporting phone or telephone number. Messages inserted by WhatsApp into the chat are coded with “WhatsApp System Message”
Message
Text of user-generated messages with all information contained in the exported chat log
Flat
Simplified version of the message with emojis, numbers, punctuation, and URLs removed. Better suited for some text mining or machine learning tasks
TokVec
Tokenized version of the Flat column. Instead of one text string, each cell contains a list of individual words. Better suited for some text mining or machine learning tasks
URL
A list of all URLs or domains contained in the message body
Media
A list of all media attachment filenames contained in the message body
Location
A list of all shared location URLs or indicators in the message body, or indicators for shared live locations
Emoji
A list of all emoji glyphs contained in the message body
EmojiDescriptions
A list of all emojis as textual representations contained in the message body
Smilies
A list of all smileys contained in the message body
SystemMessage
Messages that are inserted by WhatsApp into the conversation and not generated by users
TokCount
Amount of user-generated tokens per message
TimeOrder
Order of messages as per the timestamps on the exporting phone
DisplayOrder
Order of messages as they appear in the exported chat log
Checking Descriptives of Chat Logs Now, you can have a first look at the overall statistics of the chat log. You can check the number of messages, sent tokens, number of chat participants, date of first message, date of last message, the timespan of the chat, and the number of emoji, smilies, links, media files, as well as locations in the chat log.
Visualizing Chat Logs The chat characteristics can now be visualized using the custom functions from the WhatsR package. These functions are basically wrappers to ggplot2 with some options for customizing the plots. Most plots have multiple ways of visualizing the data. For the visualizations, we can exclude the WhatsApp System Messages using ‘exclude_sm= TRUE’. Lets try it out:
Four different ways of visualizing the amount of sent emoji in a WhatsApp chat log. Click image to zoom in. Distribution of reaction times
# Plotting distribution of reaction times
p26 <- plot_replytimes(data,
type = "replytime",
exclude_sm = TRUE)
p27 <- plot_replytimes(data,
type = "reactiontime",
exclude_sm = TRUE)
# Printing plots with patchwork package
free(p26) | free(p27)
Average response times and times it takes to answer to messages for each individual chat participant in a WhatsApp chat log. Click image to zoom in. Lexical Dispersion A lexical dispersion plot is a visualization of where specific words occur within a text corpus. Because the simulated chat log in this example is using lorem ipsum text where all words occur similarly often, we add the string “testword” to a random subsample of messages. For visualizing real chat logs, this would of course not be necessary.
# Adding "testword" to random subset of messages for demonstration # purposes
set.seed(12345)
word_additions <- sample(dim(data)[1],50)
data$TokVec[word_additions]
sapply(data$TokVec[word_additions],function(x){c(x,"testword")})
data$Flat[word_additions] <- sapply(data$Flat[word_additions],
function(x){x <- paste(x,"testword");return(x)})
Network graph showing how often each chat participant directly responded to the previous messages (a subsequent message is counted as a “response” here). Click image to zoom in.
Issues and long-term availability.
Unfortunately, WhatsApp chat logs are a moving target when it comes to plotting and visualization. The structure of exported WhatsApp chat logs keeps changing from time to time. On top of that, the structure of chat logs is different for chats exported from different operating systems (Android & iOS) and for different time (am/pm vs. 24h format) and language (e.g. English & German) settings on the exporting phone. When the structure changes, the WhatsR package can be limited in its functionality or become completely dysfunctional until it is updated and tested. Should you encounter any issues, all reports on the GitHub issues page are welcome. Should you want to contribute to improving on or maintaining the package, pull requests and collaborations are also welcome!
Join our workshop on Creating R packages for data analysis and reproducible research, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Creating R packages for data analysis and reproducible research
Date: Thursday, February 29th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Fred Boehm is a biostatistics and translational medicine researcher living in Michigan, USA. His research focuses on statistical questions that arise in human genetics studies and their applications to clinical medicine and public health. He has extensive teaching experience as a statistics lecturer at the University of Wisconsin-Madison (https://www.wisc.edu) and as a workshop instructor for The Carpentries (https://carpentries.org/index.html). He enjoys spending time with his nieces and nephews and his two dogs. He also blogs (occasionally) at https://fboehm.us/blog/.
Description: Participants will learn to use functions from several packages, including `devtools` and `rrtools`, in the R ecosystem, while learning and adhering to practices to promote reproducible research. Participants will learn to create their own R packages for software development or data analysis. We will also motivate the need to follow reproducible research practices and will discuss strategies and open source tools.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Join our workshop onModeling Non-Linear Relationships: An Introduction to Gaussian Process Regression in R and Stan, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Modeling Non-Linear Relationships: An Introduction to Gaussian Process Regression in R and Stan
Date: Thursday, February 13th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Jan Failenschmid is a Ph.D. candidate in Psychological Research Methods and Statistics at Tilburg University, Netherlands. His research focusses on studying and developing methods for modeling and analyzing non-linear trends and dynamics in psychological time-series and intensive longitudinal data. Given the scarcity of established theories about non-linear associations in Psychology, his work focusses mainly on non-parametric statistical techniques like Gaussian Processes, which make it possible to learn complex non-linear relationships from data while providing interpretable insights.
Description: This workshop will provide an introduction to Gaussian Process regression, a flexible and powerful Bayesian non-parametric technique, for modeling non-linear relationships in R and Stan. The Workshop will start by providing a brief introduction to Bayesian statistics, including the principles underlying Bayesian inference and its implementation in Stan. Building on this foundation, we will delve into Gaussian Processes, exploring their conceptual framework and practical applications. The final part of the workshop will feature hands-on examples illustrating different applications to the kinds of data found in the social and behavioral sciences.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Please note that the registration confirmation email will be sent 1 day before the workshop.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Excitement is building as we approach ShinyConf 2024, organized by Appsilon. We are thrilled to announce the Call for Speakers. This is a unique opportunity for experts, industry leaders, and enthusiasts to disseminate their knowledge, insights, and expertise to a diverse and engaged audience.
Why Speak at ShinyConf?
Becoming a speaker at ShinyConf is not just about sharing your expertise; it’s about enriching the community, networking with peers, and contributing to the growth and innovation in your field. It’s an experience that extends beyond the conference, fostering a sense of camaraderie and collaboration among professionals.
Conference Tracks
ShinyConf 2024 features several tracks, each tailored to different aspects of our industry. Our track chairs, experts in their respective fields, will guide these sessions.
Shiny Innovation Hub – Led by Jakub Nowicki, Lab Lead at Appsilon, this track focuses on the latest developments and creative applications within the R Shiny framework. We’re looking for talks on advanced Shiny programming techniques, case studies, and how Shiny drives data communication advancements.
Shiny in Enterprise – Chaired by Maria Grycuk, Senior Delivery Manager at Appsilon. This track delves into R Shiny’s role in shaping business outcomes, including case studies, benefits and challenges in enterprise environments, and integration strategies.
Shiny in Life Sciences – Guided by Eric Nantz, a Statistician/Developer/Podcaster. This track focuses on R Shiny’s application in data science and life sciences, including interactive visualization, drug discovery, and clinical research.
Shiny for Good – Overseen by Jon Harmon, Data Science Leader and Expert R Programmer. This track highlights R Shiny’s impact on social good, community initiatives, and strategies for engaging diverse communities.
Submission Guidelines
Topics of Interest: Tailored to each track, ranging from advanced programming techniques to real-world applications in life sciences, social good and enterprise.
Submission Types:
Talks (20 min)
Shiny app showcases (5 min)
Tutorials (40 min)
Who Can Apply: Open to both seasoned and new speakers. Unsure about your idea? Submit it anyway!
Join us at the Shiny Conf as a speaker and shine! We look forward to receiving your submissions and creating an inspiring and educational event together.
Follow us on social media (LinkedIn and Twitter) for updates. Registration opens this month! Contact us at [email protected] for any queries.
Useful Links
Join our community, Shiny 4 All, to keep up with the latest updates
Navigating the volatile world of cryptocurrencies requires a keen understanding of market sentiment. This blog post explores some of the essential tools and techniques for analyzing the mood of the crypto market, using the cryptoQuotes-package.
The Cryptocurrency Fear and Greed Index in R
The Fear and Greed Index is a market sentiment tool that measures investor emotions, ranging from 0 (extreme fear) to 100 (extreme greed). It analyzes data like volatility, market momentum, and social media trends to indicate potential overvaluation or undervaluation of cryptocurrencies. This index helps investors identify potential buying or selling opportunities by gauging the market’s emotional extremes.
This index can be retrieved by using the cryptoQuotes::getFGIndex()-function, which returns the daily index within a specified time-frame,
## Fear and Greed Index
## from the last 14 days
tail(
FGI <- cryptoQuotes::getFGIndex(
from = Sys.Date() - 14
)
)
#> FGI
#> 2024-01-03 70
#> 2024-01-04 68
#> 2024-01-05 72
#> 2024-01-06 70
#> 2024-01-07 71
#> 2024-01-08 71
The Long-Short Ratio of a Cryptocurrency Pair in R
The Long-Short Ratio is a financial metric indicating market sentiment by comparing the number of long positions (bets on price increases) against short positions (bets on price decreases) for an asset. A higher ratio signals bullish sentiment, while a lower ratio suggests bearish sentiment, guiding traders in making informed decisions.
The Long-Short Ratio can be retrieved by using the cryptoQuotes::getLSRatio()-function, which returns the ratio within a specified time-frame and granularity. Below is an example using the Daily Long-Short Ratio on Bitcoin (BTC),
## Long-Short Ratio
## from the last 14 days
tail(
LSR <- cryptoQuotes::getLSRatio(
ticker = "BTCUSDT",
interval = '1d',
from = Sys.Date() - 14
)
)
#> Long Short LSRatio
#> 2024-01-03 0.5069 0.4931 1.0280
#> 2024-01-04 0.6219 0.3781 1.6448
#> 2024-01-05 0.5401 0.4599 1.1744
#> 2024-01-06 0.5499 0.4501 1.2217
#> 2024-01-07 0.5533 0.4467 1.2386
#> 2024-01-08 0.5364 0.4636 1.1570
Putting it all together
Even though cryptoQuotes::getLSRatio() is an asset-specific sentiment indicator, and cryptoQuotes::getFGIndex() is a general sentiment indicator, there is much information to be gathered by combining this information.
This information can be visualized by using the the various charting-functions in the cryptoQuotes-package,
## get the BTCUSDT
## pair from the last 14 days
BTCUSDT <- cryptoQuotes::getQuote(
ticker = "BTCUSDT",
interval = "1d",
from = Sys.Date() - 14
)
Note: The latest price may vary depending on time of publication relative to the rendering time of the document. This document were rendered at 2024-01-08 23:30 CET
Join our workshop on Factor Analysis in R, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Factor Analysis in R
Date: Thursday, February 1st, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Gagan Atreya is a quantitative social scientist and data science consultant based in Los Angeles, California. He has graduate degrees in Experimental Psychology and Quantitative Political Science from The College of William & Mary in Virginia and The University of Minnesota respectively. He has multiple years of experience in data analysis and visualization in the social sciences – both as a researcher and a consultant with faculty and researchers around the world. You can find him in Bluesky at @gaganatreya.bsky.social.
Description:This workshop will go through the basics of Exploratory and Confirmatory Factor Analysis in the R programming language. Factor Analysis is a valuable statistical technique widely used in Psychology, Economics, Political Science, and related disciplines that allows us to uncover the underlying structure of our data by reducing it to coherent factors. The workshop will heavily (but not exclusively) utilize the “psych” and “lavaan” packages in R. Although open to everyone, a beginner level familiarity with R and some background/interest in survey data analysis will be ideal to make the most out of this workshop.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Getting cryptocurrency OHLCV data in R without having to depend on low-level coding using, for example, curl or httr2, have not been easy for the R community.
There is now a high-level API Client available on CRAN which fetches all the market data without having to rely on web-scrapers, API keys or low-level coding.
Bitcoin Prices in R (Example)
This high-level API-client have one main function, getQuotes(), which returns cryptocurrency market data with a xts– and zoo-class. The returned objects contains Open, High, Low, Close and Volume data with different granularity, from the currently supported exchanges.
In this blog post I will show how to get hourly Bitcoin (BTC) prices in R
using the getQuotes()-function. See the code below,
# 1) getting hourly BTC
# from the last 3 days
BTC <- cryptoQuotes::getQuote(
ticker = "BTCUSDT",
source = "binance",
futures = FALSE,
interval = "1h",
from = as.character(Sys.Date() - 3)
)
Bitcoin (BTC) OHLC-prices (Output from getQuote-function)
Index
Open
High
Low
Close
Volume
2023-12-23 19:00:00
43787.69
43821.69
43695.03
43703.81
547.96785
2023-12-23 20:00:00
43703.82
43738.74
43632.77
43711.33
486.4342
2023-12-23 21:00:00
43711.33
43779.71
43661.81
43772.55
395.6197
2023-12-23 22:00:00
43772.55
43835.94
43737.85
43745.86
577.03505
2023-12-23 23:00:00
43745.86
43806.38
43701.1
43702.16
940.55167
2023-12-24
43702.15
43722.25
43606.18
43716.72
773.85301
The returned Bitcoin prices from getQuotes() are compatible with quantmod and TTR, without further programming. Let me demonstrate this using chartSeries(), addBBands() and addMACD() from these powerful libraries,
# charting BTC
# using quantmod
quantmod::chartSeries(
x = BTC,
TA = c(
# add bollinger bands
# to the chart
quantmod::addBBands(),
# add MACD indicator
# to the chart
quantmod::addMACD()
),
theme = quantmod::chartTheme("white")
)
Charting Bitcoin prices using quantmod and TTR
Installing cryptoQuotes
Stable version
# install from CRAN
install.packages(
pkgs='cryptoQuotes',
dependencies=TRUE
)
Development version
# install from github
devtools::install_github(
repo='https://github.com/serkor1/cryptoQuotes/',
ref='main'
)
Join our workshop on Automating updates to dashboards on Shiny Server, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Automating updates to dashboards on Shiny Server
Date: Thursday, January 25th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Clinton Oyogo David is a data scientist with 7 years of experience currently working with Oxford Policy Management (OPM) under the Research and Evidence, data innovations team. Prior to joining OPM I was working at World Agroforestry Centre as a junior data scientist in the Spatial Data Science and Applied Learning Lab.
Description: In this workshop, we will talk about the configurations and set ups needed to have an automated update to R Shiny dashboards deployed on a shiny server. The talk will touch on GitHub webhooks, API (Django) and bash scripting. With the set-up in place one will not need to manually update the code on shiny server, a push event to github will be enough to have your changes to the code reflect on the dashboard in a matter of seconds.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting listhere. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Thanks to Deepanshu Bhalla (writer of above article), I’ve many inspirations and made some research to utilize Gemini API more. And I’m glad to share the results with you.
In this article, I want to highlight to How to use gemini with R and Shiny via R package for Gemini API
As today (23.12.26), Gemini API is mainly consisted with 4 things. you can see more details in official docs.
1. Gemini Pro: Is get Text and returns Text 2. Gemini Pro Vision: Is get Text and Image and returns Text 3. Gemini Pro Multi-turn: Just chat 4. Embedding: for NLP
However, offical docs doesn’t describe for how to use Gemini API in R. (How sad) But we can handle it as “REST API” ( I’ll explain it later)
Shiny application
I made very brief concept of Shiny application that uses Gemini API for get Image and Text (maybe “Explain this picture”) and returns Answer from Gemini
(Number is expected user flow)
This UI, is consisted 5 components.
1. fileInput for upload image 2. imageOutput for show uploaded Image 3. textInput for prompt 4. actionButton for send API to Gemini 5. textOutput for show return value from Gemini
And this is result of shiny and R code (Again, you can see it in github repository)
I think you may think “What is gemini_image function?”
It is function to send API to Gemini server and return result.
and it consisted with 3 main part.
1. Model query 2. API key 3. Content
I used gemini_image function in example. but I’ll gemini function first (which is function to send text and get text)
Gemini’s API example usage is looks like below. (for REST API)
Which can be transformed like below in R Also, gemini API key must set before use with “Sys.setenv” function
Anyway, I think you should note, body for API is mainly consisted with list.
Similarly, gemini_image function for Gemini Pro Vision API looks like below
is
Note that, image must encoded as base64 using base64encode function and provided as separated list.
Example
So with Shiny application and gemini.r package.
You now can run example application to ask image to Gemini.
Summary
I made very basic R package “gemini.R” to use Gemini API.
Which provides 2 function: gemini and gemini_image.
And still there’s many possiblity for develop this package.
like feature to Chat like bard or provide NLP Embedding
and finally, I want to hear feedback or contribution from you. (Really)
Thanks.
* P.S, I think just using bard / chatGPT / copilot is much better for personal usage. (unless you don’t want to provide AI service via R)
(Number is expected user flow)
Note that, image must encoded as base64 using base64encode function and provided as separated list.