Biostatistics × R × Data Visualization × Energy Analytics

@medyanist.bsky.social EPİAŞ Analytics Lite is live ⚡
Real-time Türkiye electricity consumption data → interactive dashboards, KPIs, and trend charts — built with R Shiny.
From raw energy data to decision-ready visuals.

#EnergyAnalytics #RStats #ShinyApp #DataVisualization #ElectricityMarket

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library(survival)
library(survminer)

# Built-in medical dataset
data(lung)

# Recode event variable (1 = censored, 2 = dead)
lung$status <- ifelse(lung$status == 2, 1, 0)

# Kaplan–Meier model by sex
fit <- survfit(Surv(time, status) ~ sex, data = lung)

survival is a core R package for time-to-event analysis in medical research.
It handles censoring, Kaplan–Meier curves, and Cox models with rigor and transparency.
#rstats #biostatistics #survivalanalysis

From data cleaning to modeling,
R supports the full analytical workflow.
#rstats

Good R workflows reduce analytical noise.
Clarity starts with structure.
#rstats

R is not just a tool.
It’s a way of thinking about data.
#rstats

Biostatistics is not about finding significance.
It’s about estimating effects and uncertainty.
#rstats #biostatistics

I work in biostatistics using R to analyze and visualize health data.
My goal is to make medical statistics clear, reproducible, and interpretable.

#rstats #biostatistics #healthdata

"p-value" don’t tell the whole story.
R makes effect sizes and uncertainty easier to report.
#rstats #biostats #medicalstats

Good biostatistics starts with data structure.
R encourages transparent workflows from raw data to results.
#rstats #healthdata #clinicalresearch

Clinical research moves faster when tables are reproducible.
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata

Standardized tables reduce analytical noise.
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience

Readable tables lead to better decisions.
gtsummary turns complex medical data into interpretable summaries.
#rstats #healthdata #medicalstats

Consistency matters in clinical reporting.
With gtsummary, tables stay aligned across analyses and revisions.
#rstats #clinicalresearch #reproducibility

Clear summaries improve peer review.
gtsummary helps reviewers focus on results, not table formatting.
#rstats #gtsummary #peerreview #biostatistics

Reproducibility starts with structure.
gtsummary keeps medical statistics clean, consistent, and publication-ready.
#rstats #gtsummary #clinicalresearch #researchtools

Good methods deserve clear reporting.
gtsummary supports standardized summaries and regression outputs in medical studies.
#rstats #gtsummary #clinicaldata #openscience

From raw clinical data to decision-ready tables.
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats

Manual table formatting is a reproducibility risk.
With gtsummary, clinical summary and regression tables stay transparent and consistent.
#rstats #healthdata #reproducibleresearch

library(gtsummary); data(trial)
trial |> tbl_summary(by = trt,
statistic = all_continuous() ~ "{median} [{p25},{p75}]") |> add_p()

#rstats #biostats #reproducibleresearch

library(gtsummary); data(trial)
trial |> tbl_summary(by = trt, include = c(age, grade, response, marker)) |> add_n()
#rstats #healthdata #medicalstats

library(gtsummary); data(trial)
trial |> tbl_summary(by=trt) |> add_p() |> add_overall()

#rstats #gtsummary #biostatistics #clinicalresearch #posit

Clear tables matter in medical research.
gtsummary helps turn raw clinical data into reproducible, publication-ready results—without manual formatting.
#rstats #gtsummary #biostatistics #clinicalresearch

surv_data <- data.frame(time = time, status = status, group = group)
fit <- survfit(Surv(time, status) ~ group, data = surv_data)

km_plot <- ggsurvplot()

#RStats #DataScience #Biostatistics #DataVisualization #RStudio
#Statistics #ggplot2

gtsummary is an R package for creating clear, publication-ready summary and regression tables—especially for medical and biostatistics research. It integrates seamlessly with tidyverse workflows and supports reproducible clinical research.

#rstats #gtsummary #biostatistics #clinicalresearch

library(gtsummary)
data(trial)
trial |>
tbl_summary(
by = trt,
statistic = all_continuous() ~ "{mean} ({sd})"
) |>
add_p() |>
add_overall()

Presentation-Ready Data Summary and Analytic Result Tables Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function,...

Want publication-ready tables in R with minimal effort?
gtsummary turns clinical & biostat data into clean, reproducible summary and regression tables. Perfect for papers & reports.
www.danieldsjoberg.com/gtsummary/

#rstats #gtsummary #biostatistics #healthdata #dataviz