Biostatistics × R × Data Visualization
@rforbiostats.bsky.social
R-powered data visualization for biostatistics and health sciences.
From raw data to publication-ready figures.
#r #rstats #dataviz #healthdata #posit #positron
From raw data to publication-ready figures.
#r #rstats #dataviz #healthdata #posit #positron
Pinned
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
My goal is to make medical statistics clear, reproducible, and interpretable.
#rstats #biostatistics #healthdata
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)
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)
January 3, 2026 at 9:04 AM
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)
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
It handles censoring, Kaplan–Meier curves, and Cox models with rigor and transparency.
#rstats #biostatistics #survivalanalysis
January 3, 2026 at 9:01 AM
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
It handles censoring, Kaplan–Meier curves, and Cox models with rigor and transparency.
#rstats #biostatistics #survivalanalysis
January 2, 2026 at 5:47 PM
January 2, 2026 at 5:46 PM
January 2, 2026 at 5:46 PM
Biostatistics is not about finding significance.
It’s about estimating effects and uncertainty.
#rstats #biostatistics
It’s about estimating effects and uncertainty.
#rstats #biostatistics
January 2, 2026 at 5:46 PM
Biostatistics is not about finding significance.
It’s about estimating effects and uncertainty.
#rstats #biostatistics
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
My goal is to make medical statistics clear, reproducible, and interpretable.
#rstats #biostatistics #healthdata
January 2, 2026 at 5:44 PM
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
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
R makes effect sizes and uncertainty easier to report.
#rstats #biostats #medicalstats
January 2, 2026 at 5:42 PM
"p-value" don’t tell the whole story.
R makes effect sizes and uncertainty easier to report.
#rstats #biostats #medicalstats
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
R encourages transparent workflows from raw data to results.
#rstats #healthdata #clinicalresearch
January 2, 2026 at 5:42 PM
Good biostatistics starts with data structure.
R encourages transparent workflows from raw data to results.
#rstats #healthdata #clinicalresearch
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
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata
January 2, 2026 at 5:41 PM
Clinical research moves faster when tables are reproducible.
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata
gtsummary saves time without sacrificing rigor.
#rstats #gtsummary #clinicaldata
Standardized tables reduce analytical noise.
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience
January 2, 2026 at 5:41 PM
Standardized tables reduce analytical noise.
gtsummary supports transparent and defensible medical statistics.
#rstats #gtsummary #openscience
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
gtsummary turns complex medical data into interpretable summaries.
#rstats #healthdata #medicalstats
January 2, 2026 at 5:41 PM
Readable tables lead to better decisions.
gtsummary turns complex medical data into interpretable summaries.
#rstats #healthdata #medicalstats
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
With gtsummary, tables stay aligned across analyses and revisions.
#rstats #clinicalresearch #reproducibility
January 2, 2026 at 5:40 PM
Consistency matters in clinical reporting.
With gtsummary, tables stay aligned across analyses and revisions.
#rstats #clinicalresearch #reproducibility
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
gtsummary helps reviewers focus on results, not table formatting.
#rstats #gtsummary #peerreview #biostatistics
January 2, 2026 at 5:40 PM
Clear summaries improve peer review.
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
Reproducibility starts with structure.
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
January 2, 2026 at 5:40 PM
Reproducibility starts with structure.
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
Good methods deserve clear reporting.
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
January 2, 2026 at 5:39 PM
Good methods deserve clear reporting.
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
From raw clinical data to decision-ready tables.
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats
January 2, 2026 at 5:39 PM
From raw clinical data to decision-ready tables.
gtsummary bridges statistical analysis and scientific communication.
#rstats #biostats #scicomm #medicalstats
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
With gtsummary, clinical summary and regression tables stay transparent and consistent.
#rstats #healthdata #reproducibleresearch
January 2, 2026 at 5:38 PM
Manual table formatting is a reproducibility risk.
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
library(gtsummary); data(trial)
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
January 2, 2026 at 5:37 PM
library(gtsummary); data(trial)
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
library(gtsummary); data(trial)
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
January 2, 2026 at 5:33 PM
library(gtsummary); data(trial)
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
library(gtsummary); data(trial)
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
January 2, 2026 at 5:31 PM
library(gtsummary); data(trial)
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
Clear tables matter in medical research.
gtsummary helps turn raw clinical data into reproducible, publication-ready results—without manual formatting.
#rstats #gtsummary #biostatistics #clinicalresearch
gtsummary helps turn raw clinical data into reproducible, publication-ready results—without manual formatting.
#rstats #gtsummary #biostatistics #clinicalresearch
January 2, 2026 at 5:30 PM
Clear tables matter in medical research.
gtsummary helps turn raw clinical data into reproducible, publication-ready results—without manual formatting.
#rstats #gtsummary #biostatistics #clinicalresearch
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
fit <- survfit(Surv(time, status) ~ group, data = surv_data)
km_plot <- ggsurvplot()
#RStats #DataScience #Biostatistics #DataVisualization #RStudio
#Statistics #ggplot2
January 2, 2026 at 1:34 PM
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
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
#rstats #gtsummary #biostatistics #clinicalresearch
January 2, 2026 at 1:28 PM
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
#rstats #gtsummary #biostatistics #clinicalresearch
library(gtsummary)
data(trial)
trial |>
tbl_summary(
by = trt,
statistic = all_continuous() ~ "{mean} ({sd})"
) |>
add_p() |>
add_overall()
data(trial)
trial |>
tbl_summary(
by = trt,
statistic = all_continuous() ~ "{mean} ({sd})"
) |>
add_p() |>
add_overall()
January 2, 2026 at 1:27 PM
library(gtsummary)
data(trial)
trial |>
tbl_summary(
by = trt,
statistic = all_continuous() ~ "{mean} ({sd})"
) |>
add_p() |>
add_overall()
data(trial)
trial |>
tbl_summary(
by = trt,
statistic = all_continuous() ~ "{mean} ({sd})"
) |>
add_p() |>
add_overall()