R and RStudio: Data Science & Biostatistics

Udemy is a highly popular and user-friendly learning platform. Here are three effective paid courses on R programming offered on Udemy.

Learn Data Science & Biostatistics with R and RStudio

Demographic and Health Survey Data Analysis in R and RStudio

R Programming & RStudio for Epidemiologic Data Analysis

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I am excited to share my course:

**Crash Course: R Programming for Biostatistical Data Analysis**

This course is designed for students, researchers, public health professionals, and beginners who want to learn practical data analysis in RStudio for research and manuscript writing.

In this course, you will learn how to:

✅ Set up RStudio and required packages
✅ Import Excel data into R
✅ Clean and prepare variables
✅ Create factor and numeric variables
✅ Generate descriptive summary tables using `gtsummary`
✅ Perform univariate and bivariate analysis
✅ Report n (%), mean ± SD, median and IQR
✅ Conduct chi-square and t-tests
✅ Create publication-ready tables
✅ Make bar diagrams and boxplots using `ggplot2`
✅ Run simple and multiple logistic regression
✅ Interpret crude and adjusted odds ratios
✅ Estimate risk ratios using log-binomial and modified Poisson regression

This is a practical, beginner-friendly course focused on real research data analysis. It will help you build confidence in RStudio and prepare clean, manuscript-ready statistical outputs.

: R Programming for Biostatistical Data Statistical Data analysis Guru
📌 Platform: Udemy
Link in the Comment Box

library(haven)
library(tidyverse)

d1 %
mutate(
v025 = as_factor(v025),
v190 = as_factor(v190),
bmi = ifelse(v445 > 9000, NA, v445/100),
haz = ifelse(hw70 > 9000, NA, hw70/100),
stunting = ifelse(haz < -2, 1, 0)
) %>%
drop_na()

dat_a %>%
ggplot(aes(x = v025, y = bmi, fill = v190)) +
geom_violin(
trim = FALSE,
alpha = 0.75,
linewidth = 0.3,
color = "black"
) +
labs(
x = "Place of residence",
y = "Maternal BMI (kg/m²)",
fill = "Wealth index",
title = "Distribution of Maternal BMI among Nigerian Women"
) +
scale_fill_brewer(palette = "Set3") +
theme_classic(base_size = 12) +
theme(
plot.title = element_text(
size = 14,
face = "bold",
hjust = 0.5
),
axis.title = element_text(
size = 12,
face = "bold"
),
axis.text = element_text(
size = 11,
color = "black"
),
legend.position = "top",
legend.title = element_text(
size = 11,
face = "bold"
),
legend.text = element_text(size = 10)
)

Created in R
A violin plot showing the distribution of maternal BMI among Nigerian women across place of residence and wealth index categories. This visualization helps compare BMI patterns, variability, and differences between urban and rural women.

+ Maternal BMI Distribution among Nigerian Women by Residence and Wealth Index
#---

library(haven)
library(tidyverse)

d1 %
mutate(
v025 = as_factor(v025),
v190 = as_factor(v190),
bmi = ifelse(v445 > 9000, NA, v445/100),
haz = ifelse(hw70 > 9000, NA, hw70/100),
stunting = ifelse(haz < -2, 1, 0)
) %>%
drop_na()

dat_a %>%
ggplot(aes(x = v025, y = bmi, fill = v190)) +
geom_violin(
trim = FALSE,
alpha = 0.75,
linewidth = 0.3,
color = "black"
) +
geom_boxplot(
width = 0.15,
linewidth = 0.3,
outlier.colour = "red",
outlier.size = 1.2,
outlier.alpha = 0.6,
position = position_dodge(width = 0.9)
) +
labs(
x = "Place of residence",
y = "Maternal BMI (kg/m²)",
fill = "Wealth index",
title = "Distribution of Maternal BMI among Nigerian Women"
) +
scale_fill_brewer(palette = "Set3") +
theme_classic(base_size = 12) +
theme(
plot.title = element_text(
size = 14,
face = "bold",
hjust = 0.5
),
axis.title = element_text(
size = 12,
face = "bold"
),
axis.text = element_text(
size = 11,
color = "black"
),
legend.position = "top",
legend.title = element_text(
size = 11,
face = "bold"
),
legend.text = element_text(size = 10)
)

This my Course

Learn Data Science & Biostatistics with R and RStudio