R and RStudio: Data Science & Biostatistics

R and RStudio: Data Science & Biostatistics Learn data science and biostatistics using R and RStudio.

Explore data management, visualization, statistical modeling, and reproducible analysis through hands-on tutorials and practical examples.

library(ggplot2)library(tidyr)library(dplyr) # Datastunting
29/08/2026

library(ggplot2)
library(tidyr)
library(dplyr)

# Data
stunting

27/08/2026

📊 Free Course: Logistic Regression Analysis in R for Manuscript Writing

Are you a researcher, student, or health professional who wants to improve your data analysis skills and write publication-ready research results?

This practical course by Mr. Haque, Statistician and Researcher, will guide you step by step through:

✅ Descriptive analysis in R
✅ Data visualization using ggplot2
✅ Logistic regression analysis
✅ Odds ratio, confidence interval, and p-value interpretation
✅ Manuscript-style presentation of statistical results

Learn how to use R programming for research data analysis and gain confidence in performing statistical analysis for scientific publications.

🎓 Suitable for:
✔ Beginners learning R
✔ Researchers working with health and epidemiological data
✔ Students interested in statistical analysis and manuscript writing

🚀 Start learning today and strengthen your research analysis skills.

🔗 Free course link available in the comment box.

R packages----library(readxl)library(tidyverse)library(gtsummary)library(gt)library(cards)library(labelled) # Read da...
27/08/2026

R packages----
library(readxl)
library(tidyverse)
library(gtsummary)
library(gt)
library(cards)
library(labelled)

# Read data ----
mydata %
as_gt() %>%
gtsave(
filename = "Table2.docx",
path = "P:/abc"
)

Sample size calculation: Case-Control StudyThe objective of a study is to identify that smoking status is associated wit...
26/08/2026

Sample size calculation: Case-Control Study
The objective of a study is to identify that smoking status is associated with heart disease in a City, 2025

---Risk Ratio Estmation--library(readxl)library(tidyverse)library(labelled)library(gtsummary)mydata % tbl_regression(...
25/08/2026

---Risk Ratio Estmation--

library(readxl)
library(tidyverse)
library(labelled)
library(gtsummary)

mydata %
tbl_regression(
exponentiate = TRUE
)

--Bar diagram ----Dataset link in the comment boxlibrary(readxl)library(tidyverse)library(labelled)library(gtsummary)m...
25/08/2026

--Bar diagram ----Dataset link in the comment box
library(readxl)
library(tidyverse)
library(labelled)
library(gtsummary)

mydata %
group_by(sugar_cons, income_level) %>%
summarise(
p = mean(heart_disease)*100,
.groups = 'drop'
)

bar1 %
ggplot(aes(y =p, x=sugar_cons, fill = income_level))+
geom_col(
position = position_dodge(width = 0.7),
width = 0.5
)+
labs(
y = "Percentage of heart disease",
x = "Sugar consumption",
fill = "Income level"
)+
geom_text(
aes(label = sprintf("%.1f",p)),
position = position_dodge(width = 0.7),
vjust = -0.5,
size = 4
)+
theme_classic(base_size = 12)+
theme(
legend.position = 'top'
)

print(bar1)

ggsave(
filename = "P:/abc/Fig2.tiff",
plot = bar1,
width = 7,
height = 5,
dpi = 300,
compression = 'lzw'
)

24/08/2026

Learn Logistic Regression in R Programming!

Are you interested in learning how to build and interpret a Logistic Regression model using R? 📊

In this tutorial, you will learn:

✅ How to run Logistic Regression in R
✅ How to interpret Odds Ratios (OR) and 95% Confidence Intervals
✅ How to create publication-ready regression tables using R
✅ How to understand and explain model outputs step by step

🎁 FREE Course Link is available in the Comment Box!

Start your journey with R Programming and statistical analysis today. 💻📈

23/08/2026

-----Descriptive statistics and p-value----

Tab1 %
tbl_summary(
missing = 'no',
by = gender,
statistic = list(
all_categorical() ~ "{n} ({p})",
all_continuous() ~"{mean} ± {sd}"
),
digits = list(
all_categorical() ~ c(0, 2),
all_continuous() ~ c(2,2)
)
) %>%
add_overall(last=TRUE) %>%
bold_labels() %>%
add_p(
test = list(
all_categorical() ~ "chisq.test",
all_continuous() ~ "t.test"
),
pvalue_fun =~ style_pvalue(.x, digits = 3)
)

print(Tab1)

#--Save the Table--

Tab1 %>%
as_gt() %>%
gtsave(
filename = "Table1.docx",
path = "P:/abc"
)

Box plotlibrary(readxl) library(tidyverse)mydata % ggplot( aes(y=bmi, x= sugar_cons, fill=income_level) ) + geo...
18/08/2026

Box plot
library(readxl)
library(tidyverse)

mydata %
ggplot(
aes(y=bmi, x= sugar_cons, fill=income_level)
) +
geom_boxplot(
linewidth=0.2,
outlier.colour = 'red',
notch = TRUE
)+
labs(
y = "Body mass index",
x = "Sugar consumption",
fill = "Income level"
)+
theme_classic()+
theme(
legend.position = 'top'
)

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Bathurst, NSW
2795

Telephone

+61406481476

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