Advanced analytics for health & electricity market data
R • Shiny • Statistical modeling • Interactive dashboards
From raw datasets to decision-ready outputs
medyanistdanismanlik.com
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
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
💬 WhatsApp: wa.me/905394893522
📸 Instagram: instagram.com/medyanistati...
🌐 medyanistdanismanlik.com
💬 WhatsApp: wa.me/905394893522
📸 Instagram: instagram.com/medyanistati...
🌐 medyanistdanismanlik.com
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)
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)
It handles censoring, Kaplan–Meier curves, and Cox models with rigor and transparency.
#rstats #biostatistics #survivalanalysis
It handles censoring, Kaplan–Meier curves, and Cox models with rigor and transparency.
#rstats #biostatistics #survivalanalysis
It’s about estimating effects and uncertainty.
#rstats #biostatistics
It’s about estimating effects and uncertainty.
#rstats #biostatistics
My goal is to make medical statistics clear, reproducible, and interpretable.
#rstats #biostatistics #healthdata
My goal is to make medical statistics clear, reproducible, and interpretable.
#rstats #biostatistics #healthdata
R makes effect sizes and uncertainty easier to report.
#rstats #biostats #medicalstats
R makes effect sizes and uncertainty easier to report.
#rstats #biostats #medicalstats
R encourages transparent workflows from raw data to results.
#rstats #healthdata #clinicalresearch
R encourages transparent workflows from raw data to results.
#rstats #healthdata #clinicalresearch
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience
gtsummary turns complex medical data into interpretable summaries.
#rstats #healthdata #medicalstats
gtsummary turns complex medical data into interpretable summaries.
#rstats #healthdata #medicalstats
With gtsummary, tables stay aligned across analyses and revisions.
#rstats #clinicalresearch #reproducibility
With gtsummary, tables stay aligned across analyses and revisions.
#rstats #clinicalresearch #reproducibility
gtsummary helps reviewers focus on results, not table formatting.
#rstats #gtsummary #peerreview #biostatistics
gtsummary helps reviewers focus on results, not table formatting.
#rstats #gtsummary #peerreview #biostatistics
gtsummary keeps medical statistics clean, consistent, and publication-ready.
#rstats #gtsummary #clinicalresearch #researchtools
gtsummary keeps medical statistics clean, consistent, and publication-ready.
#rstats #gtsummary #clinicalresearch #researchtools
gtsummary supports standardized summaries and regression outputs in medical studies.
#rstats #gtsummary #clinicaldata #openscience
gtsummary supports standardized summaries and regression outputs in medical studies.
#rstats #gtsummary #clinicaldata #openscience
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats
With gtsummary, clinical summary and regression tables stay transparent and consistent.
#rstats #healthdata #reproducibleresearch
With gtsummary, clinical summary and regression tables stay transparent and consistent.
#rstats #healthdata #reproducibleresearch
trial |> tbl_summary(by = trt,
statistic = all_continuous() ~ "{median} [{p25},{p75}]") |> add_p()
#rstats #biostats #reproducibleresearch
trial |> tbl_summary(by = trt,
statistic = all_continuous() ~ "{median} [{p25},{p75}]") |> add_p()
#rstats #biostats #reproducibleresearch
trial |> tbl_summary(by = trt, include = c(age, grade, response, marker)) |> add_n()
#rstats #healthdata #medicalstats
trial |> tbl_summary(by = trt, include = c(age, grade, response, marker)) |> add_n()
#rstats #healthdata #medicalstats
trial |> tbl_summary(by=trt) |> add_p() |> add_overall()
#rstats #gtsummary #biostatistics #clinicalresearch #posit
trial |> tbl_summary(by=trt) |> add_p() |> add_overall()
#rstats #gtsummary #biostatistics #clinicalresearch #posit