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Published in International Journal of Climatology, 2024
Recommended citation: Türkeş, M., Özdemir, O., & Yozgatlıgil, C. (2024). Forecasting drought phenomena using a statistical and Machine Learning‐Based analysis for the Central Anatolia Region, Turkey. International Journal of Climatology. https://doi.org/10.1002/joc.8742 https://doi.org/10.1002/joc.8742
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I think everyone agrees on the fact that the Economist magazine produces very-well designed graphics, sometimes the best in the world. The success behind their graph lies on the ability of explaining complex matters in a simpler way by employing traditional data visualization techniques such as line graph or bar plot. They put emphasis on the message they want to convey rather than the aesthetics of the graph itself. They also have a clear hiearchy in their plots and use colors, fonts and lines which represents the brand identity of the magazine.
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Forecasting is more than just fitting a model. Here are 10 essential rules to ensure your time series models are robust, reliable, and realistic.
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This tutorial breaks down the development of an R Shiny application titled S&P 500 Monitoring Dashboard for the 2025 Posit Table Dashboard. This app effectively combines interactive financial data visualization (plotly), beautiful data tables (gt, gtExtras), web scraping (rvest), and external API integration (riingo, ellmer/Gemini AI) within a custom, sleek dark theme. You can access the app through this link
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Hello folks!
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Candlestick charts are a type of financial chart used to depict the price movements of an asset over a specific period. Each “candlestick” represents a time frame—such as a day, hour, or minute—and displays four key pieces of data: the opening price, closing price, highest price, and lowest price within that period. The body of the candlestick shows the range between the opening and closing prices, while the wicks (also known as shadows) extend to the highest and lowest prices. If the closing price is higher than the opening price, the candlestick is typically colored green or left hollow to indicate a price increase. Conversely, if the closing price is lower than the opening price, it is colored red or filled to signify a price decrease.
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2018 yılında hazırlamış olduğum R’da ggplot2 ve maps Kutuphanelerini Kullanarak Harita Cizdirmek adlı yazımda, R’da ggplot2 ve maps paketlerini kullanarak harita çizdirmeyi anlatmıştım. Yıllar içinde oldukça fazla bu yazıyla ilgili mailler aldım, ancak aldığım son mailler bu yazıda kullandığım kodların, kullandığım veri kaynağı olan GADM platformunun paylaştığı veri içeriğini değiştirmesi nedeniyle istenilen sonucu vermediğine dairdi, o nedenle yıllar sonra bu içeriği güncellemek istedim. Bu içerikte de yine GADM de bu sefer JSON olarak paylaşılan verileri kullanarak bir Türkiye haritası oluşturacağız ve bir örnek üzerinden gradyan (gradient) renklendirmeler yapacağız.
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LLM provides many advantages to the users, especially for coding. Once user had to switch the windows from the coding environment to the browser to search for the solution. But now, thanks to the newly advancements, users can chat with the LLM and get the solution for their queries on the same coding environment in R.
ggsegmentedtotalbar Published:
Kevin Flerlage, who is a data visualization specialist, suggested a great alternative to stacked bar plot on his blog. He called this new alternative “segmented total bar plot”. This R package ggsegmentedtotalbar implements this idea. The package is built on top of the ggplot2 package, which is a popular data visualisation package in R. The ggsegmentedtotalbar function creates a segmented total bar plot with custom annotations (boxes) added for each group. The height of each box is determined by the Total value associated with each group.
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Pie Chart… The unloved boy of visualization family. However, it is getting popularity especially when it is in conjuction with maps. For example, the following chart was publised by to illustrate the vote distribution across the country.
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Published in 4th National Insurance and Actuary Congress, 2019
A machine learning study on predicting earthquake magnitude and direction in Kütahya and Muğla.
Recommended citation: Özdemir, O., & Yozgatlıgil, C. (2019). Makine Öğrenmesi Teknikleri ile Muğla ve Kütahya İllerindeki Deprem Büyüklüğünün ve Yönünün Tahminlenmesi. 4th National Insurance and Actuary Congress. https://www.researchgate.net/publication/360112382_Makine_Ogrenmesi_Teknikleri_ile_Mugla_ve_Kutahya_Illerindeki_Deprem_Buyuklugunun_ve_Yonunun_Tahminlenmesi
Published in Environmental Research, 2021
A study on how lockdown policies were associated with air pollution indicators and human mobility patterns across Turkey.
Recommended citation: Orak, N. H., & Ozdemir, O. (2021). The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey. Environmental Research, 197, 111018. https://doi.org/10.1016/j.envres.2021.111018. https://www.sciencedirect.com/science/article/pii/S0013935121003121?via%3Dihub
Published in The R Journal, 2021
A conference report on Why R? Turkey 2021, documenting the event, community, talks, and the growing R ecosystem in Turkey.
Recommended citation: Cavus, M., Ozdemir, O., et al. (2021). Conference Report of Why R? Turkey 2021. The R Journal. https://journal.r-project.org/archive/2021-1/whyr2021.pdf
Published in Education Reform Initiative (ERG), 2022
An analysis of reading skills in the context of gender using PISA 2018 data.
Recommended citation: Cin, M., Ilhan, A., Özdemir, O., Düşkün, Y., & Korlu, Ö. (2022). Okuma Becerilerinin Toplumsal Cinsiyet Bağlamında Değerlendirilmesi: PISA 2018 verileriyle bir analiz. Education Reform Initiative (ERG). https://doi.org/10.13140/RG.2.2.26165.35044 https://doi.org/10.13140/RG.2.2.26165.35044
Published in The R Journal, 2022
A conference report on Why R? Turkey 2022, the first R conference with a call for papers in Turkey.
Recommended citation: Cavus, M., Ozdemir, O., et al. (2022). The Conference Report of Why R? Turkey 2022: The First R Conference with Call for Papers in Turkey. The R Journal. https://journal.r-project.org/news/RJ-2022-2-whyrturkey/
Published in Statistica Neerlandica, 2023
A comparative study of machine learning, classical time series, and hybrid methods for low- and high-frequency forecasting tasks.
Recommended citation: Özdemir, O., & Yozgatlıgil, C. (2023). Forecasting performance of machine learning, time series, and hybrid methods for low- and high-frequency time series. Statistica Neerlandica. https://doi.org/10.1111/stan.12326 https://onlinelibrary.wiley.com/doi/10.1111/stan.12326
Published in Journal of Statistics and Applied Sciences, 2024
An R package paper introducing turkeyelections, a comprehensive package for working with election results in Turkey.
Recommended citation: Özdemir, O. (2024). turkeyelections: The Most Comprehensive Initial R Package Developed on Election Results in Turkey. Journal of Statistics and Applied Sciences, 9, 67–76. https://doi.org/10.52693/jsas.1456233 https://dergipark.org.tr/tr/download/article-file/3810011
Published in Journal of Data Applications, 2024
A case study on teaching data visualisation to undergraduate statistics students in Turkey.
Recommended citation: Özdemir, O., & Yozgatlıgil, C. (2024). How to teach Data Visualisation to Fresh Statisticians: A Case Study in Turkey. Journal of Data Applications, 3, 1–16. https://doi.org/10.26650/JODA.1472118 https://doi.org/10.26650/JODA.1472118
Published in International Journal of Climatology, 2024
A statistical and machine learning-based study on forecasting drought phenomena in the Central Anatolia Region of Turkey.
Recommended citation: Türkeş, M., Özdemir, O., & Yozgatlıgil, C. (2024). Forecasting drought phenomena using a statistical and machine learning-based analysis for the Central Anatolia Region, Turkey. International Journal of Climatology. https://doi.org/10.1002/joc.8742 https://doi.org/10.1002/joc.8742
Published in SN Computer Science, 2025
A forecasting study comparing ensemble and hybrid tools to improve prediction accuracy in a Turkey COVID-19 case study.
Recommended citation: Evkaya, O. O., Kurnaz, F. S., Ozdemir, O., & Yigit, P. (2025). Leveraging ensemble and hybrid forecasting tools to increase accuracy: Turkey COVID-19 case study. SN Computer Science, 6(2). https://doi.org/10.1007/s42979-025-03658-2 https://doi.org/10.1007/s42979-025-03658-2
Published in arXiv, 2026
A preprint on detecting AI-generated content in Turkish news media using a fine-tuned Turkish BERT classifier.
Recommended citation: Ozdemir, O. (2026). From Perceptions to Evidence: Detecting AI-Generated Content in Turkish News Media with a Fine-Tuned BERT Classifier. arXiv:2602.13504. https://doi.org/10.48550/arXiv.2602.13504 https://arxiv.org/abs/2602.13504
Published in Sosyal Veri Bilimi: Programlama, Modelleme ve Sosyal Bilimlerde Dijital/Hesaplamalı Yöntemler, 2026
A book chapter on open data sources in Turkey and their use in social sciences, with an application of tree-based machine learning models to election-related economic indicators.
Recommended citation: Özdemir, O. (2026). Türkiye’deki Açık Veri Kaynaklarına Genel Bakış ve Sosyal Bilimlerde Kullanımı: Ağaç Tabanlı Yapay Öğrenme Modelleriyle Seçim Sonuçlarını Etkileyen Ekonomik Göstergelerin Tahmini. In H. Akın Ünver (Ed.), Sosyal Veri Bilimi: Programlama, Modelleme ve Sosyal Bilimlerde Dijital/Hesaplamalı Yöntemler. İstanbul Bilgi Üniversitesi Yayınları. https://bilgiyay.com/kitap/sosyal-veri-bilimi/
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This is a podcast record on Spotify. You can listen to it from here.
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The record of the talk can be watched from here
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The record of the talk can be watched from here
, METU & RUG , 2025