├── API ├── Presentation_WHYR2019_Warsaw_Bourgeois.pdf ├── Presentation_WHYR2019_Warsaw_PageSpeed.pdf ├── README.md └── WhyR_2019_Automating_GoogleSlides.pdf ├── BIO ├── 20190928_R_at_the_Ministry.pdf ├── 20190928_whyr_2019_talk_tidysq_red_size.pdf ├── AmyloGram.html ├── BipolarDisorder_whyR_28_09.pdf ├── README.md └── hadex.pdf ├── Business ├── README.md ├── Reproducibility and collaboration in business analytics_RL.pdf ├── quantup.pdf └── quantup.pptx ├── EDA ├── MasteR-of-Tables.pdf ├── README.md ├── staniak_autoEDA.pdf └── why_r_kolakowska.pptx ├── GEO ├── README.md ├── Spatial matrix approach whyR Mikos.pptx └── WhyR2019_pres.pdf ├── Keynotes ├── 20190929_WhyR_ABD_Wit_Jakuczun.pdf ├── Are we experimenting on people.pptx ├── Marvin_Wright_RF.pdf ├── README.md └── WhyR2019_PBrito.pdf ├── Lightnings ├── 2019WhyR_RGPL_commercial.pptx ├── Crazy_Sequential_Representations__Anne_Bras.pdf ├── MateuszKobylka_RME.pdf ├── PepBay_WhyR2019.pdf ├── README.md ├── R_in_marketing.pptx ├── Using_R6_classes.pdf ├── amylogram_2.pdf ├── bdl.pptx ├── d3 dalex.pdf ├── dont walk run.pdf ├── hbaniecki_modelStudio_whyr2019.pdf ├── hbaniecki_modelStudio_whyr2019.pptx ├── vivo.pdf ├── what_we_dont_have.pdf ├── whyR_RUcausal_IoanGabrielBucur_fixed.pdf └── whyr2019.pdf ├── Modelling ├── Custom loss functions for binary classification problems with highly imbalanced dataset using Extreme Gradient Boosted Trees.pdf ├── GAMs_for_demand_forecasting.pdf ├── Investment Portfolio Optimization.pdf ├── Jancewicz_Multidimensional Scaling.pdf ├── README.md ├── Tamas_Burghard_why-r-2019-categorical-embeddings.pdf ├── WhyR 2019 presentation - Quanteda.pptx ├── WhyR_prezentacja_Bie_.pdf └── nlp_models_for_masses.md.pptx ├── Opening_and_Closing ├── closing.pdf └── opening.pdf ├── Philosophy ├── README.md └── Traits of a world class data scientist WhyR 2019.pdf ├── README.md ├── Scoring ├── README.md └── klimas.pdf ├── Shiny ├── AlgoTrad_WhyR2019.pdf └── README.md ├── Vision ├── README.md ├── Semantic segmentation WhyR2019.pdf ├── Semantic segmentation WhyR2019.pptx ├── WhyR - DeepSport.pdf ├── _stepanek_talk_presentation_29_09_2019_.pdf ├── met_kolektory_panele_ang.pdf └── use_case_transfer_learning.pptx ├── XAI ├── Compare predictive models created in different languages with.pdf ├── Compare predictive models created in different languages with.pptx ├── Interpretable survival models.pdf ├── Interpretable survival models.pptx ├── README.md ├── Szymon_Maksymiuk_WhyR2019.html └── WHYR_pres_BKochanski.pdf └── presentations.Rproj /API/Presentation_WHYR2019_Warsaw_Bourgeois.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/API/Presentation_WHYR2019_Warsaw_Bourgeois.pdf -------------------------------------------------------------------------------- /API/Presentation_WHYR2019_Warsaw_PageSpeed.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/API/Presentation_WHYR2019_Warsaw_PageSpeed.pdf -------------------------------------------------------------------------------- /API/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 API Session Presentations 2 | -------------------------------------------------------------------------------- /API/WhyR_2019_Automating_GoogleSlides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/API/WhyR_2019_Automating_GoogleSlides.pdf -------------------------------------------------------------------------------- /BIO/20190928_R_at_the_Ministry.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/BIO/20190928_R_at_the_Ministry.pdf -------------------------------------------------------------------------------- /BIO/20190928_whyr_2019_talk_tidysq_red_size.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/BIO/20190928_whyr_2019_talk_tidysq_red_size.pdf -------------------------------------------------------------------------------- /BIO/BipolarDisorder_whyR_28_09.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/BIO/BipolarDisorder_whyR_28_09.pdf -------------------------------------------------------------------------------- /BIO/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 BIO Session Presentations 2 | -------------------------------------------------------------------------------- /BIO/hadex.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/BIO/hadex.pdf -------------------------------------------------------------------------------- /Business/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 Business Session Presentations 2 | -------------------------------------------------------------------------------- /Business/Reproducibility and collaboration in business analytics_RL.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Business/Reproducibility and collaboration in business analytics_RL.pdf -------------------------------------------------------------------------------- /Business/quantup.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Business/quantup.pdf -------------------------------------------------------------------------------- /Business/quantup.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Business/quantup.pptx -------------------------------------------------------------------------------- /EDA/MasteR-of-Tables.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/EDA/MasteR-of-Tables.pdf -------------------------------------------------------------------------------- /EDA/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 EDA Session Presentations 2 | -------------------------------------------------------------------------------- /EDA/staniak_autoEDA.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/EDA/staniak_autoEDA.pdf -------------------------------------------------------------------------------- /EDA/why_r_kolakowska.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/EDA/why_r_kolakowska.pptx -------------------------------------------------------------------------------- /GEO/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 GEO Session Presentations 2 | -------------------------------------------------------------------------------- /GEO/Spatial matrix approach whyR Mikos.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/GEO/Spatial matrix approach whyR Mikos.pptx -------------------------------------------------------------------------------- /GEO/WhyR2019_pres.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/GEO/WhyR2019_pres.pdf -------------------------------------------------------------------------------- /Keynotes/20190929_WhyR_ABD_Wit_Jakuczun.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Keynotes/20190929_WhyR_ABD_Wit_Jakuczun.pdf -------------------------------------------------------------------------------- /Keynotes/Are we experimenting on people.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Keynotes/Are we experimenting on people.pptx -------------------------------------------------------------------------------- /Keynotes/Marvin_Wright_RF.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Keynotes/Marvin_Wright_RF.pdf -------------------------------------------------------------------------------- /Keynotes/README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 Keynotes Session Presentations 2 | 3 | Marvin Wright - [Random forests: The first-choice method for every data analysis?](https://github.com/WhyR2019/presentations/blob/master/Keynotes/Marvin_Wright_RF.pdf) 4 | 5 | Sigrid Keydana - [tfprobably correct - adding uncertainty to deep learning with TensorFlow Probability](http://rpubs.com/zkajdan/533047) 6 | 7 | Jakub Nowosad - [The landscape of spatial data analysis in R](https://nowosad.github.io/whyr_19/#1) 8 | 9 | Steph Locke - [Are we experimenting on people?](https://github.com/WhyR2019/presentations/blob/master/Keynotes/Are%20we%20experimenting%20on%20people.pptx) 10 | 11 | Wit Jakuczun - [Always Be Deploying. How to make R great for machine learning in (not only) Enterprise](https://github.com/WhyR2019/presentations/blob/master/Keynotes/20190929_WhyR_ABD_Wit_Jakuczun.pdf) 12 | 13 | Paula Brito - [Modelling and Analysing Interval Data in R](https://github.com/WhyR2019/presentations/blob/master/Keynotes/WhyR2019_PBrito.pdf) 14 | -------------------------------------------------------------------------------- /Keynotes/WhyR2019_PBrito.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Keynotes/WhyR2019_PBrito.pdf -------------------------------------------------------------------------------- /Lightnings/2019WhyR_RGPL_commercial.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Lightnings/2019WhyR_RGPL_commercial.pptx 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scientist WhyR 2019.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WhyR2019/presentations/1be8f2e7474e6f20fa9aafc018cb56813bdcbf59/Philosophy/Traits of a world class data scientist WhyR 2019.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Why R? 2019 Presentations 2 | 3 | This repository consist of presentations prepared by the authors. 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 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SessionAuthorTitle
OpeningMarcin Kosiński, Michał Burdukiewicz, Piotr WójcikWhy R? 2019 Opening Session
ClosingMarcin Kosiński, Michał Burdukiewicz, Piotr WójcikWhy R? 2019 Closing Session
KeynotesJakub NowosadThe landscape of spatial data analysis in R
KeynotesMarvin N. WrightRandom forests: The first-choice method for every data analysis?
KeynotesPaula BritoModelling and Analysing Interval Data in R
KeynotesSigrid Keydanatfprobably correct - adding uncertainty to deep learning with TensorFlow Probability
KeynotesSteph LockeIs data science experimenting on people?
KeynotesWit JakuczunAlways Be Deploying. How to make R great for machine learning in (not only) Enterprise
APIPiotrek CiurusAutomating Google Slides creation
APIFlorent BourgeoisBringing interactivity into engineering courses with BERT-based Excel-R applications
APILeszek SieminskiGoogle PageSpeed with R
BIOJaroslaw ChilimoniukAmyloGram: the R package and a Shiny server for amyloid prediction
BIOOlga KaminskaMachine Learning usage for prediction of state change in bipolar disorder
BIOLeon Eyrich JessenTidysq for Working with Biological Sequence Data in ML Driven Epitope Prediction in Cancer Immunotherapy
BIOJagoda GlowackaMulticenter study, 33 TB of data and the goal: predicting epilepsy
BIOWeronika PuchalaR for experimentalists: HDX-MS example
BIOPiotr NowosielskiR in Ministry of Health
BusinessArtur SuchwałkoHow R helps us with delivering Machine Learning projects
BusinessRichard LoudenIntegrating R and Python for reproducible business analytics
BusinessFrancois JacquetR for Entrepreneurs : supply chain automation case
EDALidia KolakowskaHow to deal with nested lists in R? Using the purrr, furrr and future packages in practice
EDATomasz ŻółtakMasteR of Tables
EDAMateusz StaniakR Tools for Automated Exploratory Data Analysis
GEOKrystian AndruszekFeatures of districts of Warsaw visible from space
GEOÇizmeli Servet AhmetGeospatial data analysis and visualization in R
GEOMaria MikosSpatial econometrics with self-made weighting matrixes - uncovering similarity of sample with machine learning results and categorical variables
LightningsAnne BrasCrazy Sequential Representations - The 10958 Problem
LightningsHubert BanieckiD3 + DALEX = Interactive Studio with Explanations for ML Predictive Models in R
LightningsDawid KaledkowskiDon't walk, run! runner package for rolling window functions
LightningsIoan Gabriel BucurRUcausal: An R package for Representing Uncertainty in causal discovery
LightningsMateusz KobylkaRME: interpretable explainations for sequence models
LightningsKamil SijkoSelling solutions based on R (which is GPL licensed). Is this possible?
LightningsPatrik DrhlikUsing R6 classes to communicate with a REST API
LightningsDominik RafaczAmyloGram 2.0: MBO in the prediction of amyloid proteins
LightningsKrzysztof Kaniabdl: interface and tools to Local Data Bank API
LightningsKatarzyna SidorczukPepBay: Implementation of Bayesian inference in the analysis of peptide arrays
LightningsAgnieszka Otreba-SzklarczykR in marketing surveys - how to speed up the analysis of open ended questions
LightningsŁukasz WawrowskiTesting artificial intelligence algorithms in games with Shiny
LightningsAnna Kozakvivo: Is it Victoria In Variable impOrtance detection?
LightningsRafal WozniakWhat we don't have but need. Some missing R functions in teaching econometrics
ModellingBartosz Kolasa, Patryk WielopolskiCustom loss functions for binary classifications problem with highly imbalanced dataset using Extremely Gradient Boosted Trees
ModellingMichał PodsiadłoInvestment Portfolio Optimization
ModellingBarbara JancewiczMultidimensional Scaling with the smacof package
ModellingKen Benoit, Damian RodziewiczNLP models for the masses with the Quanteda package and a Shiny interface
ModellingAdam BieńDetecting topics in civil service job offers using Latent Dirichlet Allocation model
ModellingMatteo FasioloGeneralized additive models for short-term electricity demand forecasting
ModellingTamas BurghardUsing categorical embeddings (deep learning) in boosting models
PhilosophyColin GillespieHacking R as a script kiddie
PhilosophyColin FayR & MicroService
PhilosophyOlga Mierzwa-SulimaTraits of a world-class data scientist
ScoringMichal RudkoExperiment management using mlflow and R
ScoringJacek Wolak, Mateusz JałochaForecasting rental prices of flats in Krakow
ScoringKarol KlimasPredict, vote and elect with R
ShinyPawel SakowskiA Shiny Real-time Application for Backtesting Investment Strategies on Regulated and Crypto Markets
ShinyJakub Małecki, Jakub StepniakChallenges of Shiny application development at scale
ShinyTheo RoeImproving the communication of environmental data using Shiny
ShinyTomasz Koc, Piotr WójcikA Case Study for Image Classification using Transfer Learning
VisionMichel VossDetection of solar panels based on aerial images using deep learning
VisionLubomir StepanekFacial landmarking made (possible and) easy with R!
VisionPablo MaldonadoDeepSport: A Shiny app for sports video analysis
VisionMichal MajSemantic segmentation using U-Net with R
XAISzymon MaksymiukCompare predictive models created in different languages with DALEX and friends
XAIBlazej KochanskiBenefits of better credit scoring
XAIAleksandra Grudziazsurvxai: how to explain predictions for survival models?
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