R and RStudio Data Analysis
Learn biostatistics using R with real-world public health and epidemiology data. This channel covers data analysis in R, data visualization (ggplot2), statistical modelling, and machine learning in R for beginners and researchers.
We work with real datasets such as Demographic and Health Survey (DHS) data to teach practical skills in health data analysis, regression models, survey analysis, and data cleaning in R.
Whether you are a student, researcher, or public health professional, this channel will help you apply R programming for biostatistics and epidemiology step by step.
R and RStudio Data Analysis
Learn_Data_Science_&_Biostatistics_with_R_and_RStudio
5 months ago | [YT] | 0
View 1 reply
R and RStudio Data Analysis
World Map Visualization in R | Hypothetical Facebook User Prevalence by Country
Please note: This dataset is only for learning and practice purposes. These are not true values and should not be used for reporting, research conclusions, or real-world interpretation. Please download the data from our FB group, link in the commentBox
#----
library(readxl)
library(tidyverse)
library(sf)
library(viridis)
mydata <- read_excel('fb_dat.xlsx')
g_map <- st_read('ne_10m_admin_0_countries.shp')
map_data <- g_map %>%
left_join(mydata, by=c('ADM0_A3' = 'ADM0_A3'))
map_data %>%
filter(is.na(fb)) %>%
select(ADM0_A3, GEOUNIT)
ggplot(map_data) +
geom_sf(aes(fill = fb),
color = "white", linewidth = 0.15) +
scale_fill_viridis_c(
option = "plasma",
name = "Facebook users (%)",
na.value = "grey90"
) +
labs(
title = "Hypothetical Prevalence of Facebook Users by Country",
subtitle = "Simulated data for learning purpose only"
) +
theme_void() +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(size = 11),
legend.position = "right"
)+
coord_sf(
expand = FALSE
)
5 months ago | [YT] | 0
View 1 reply
R and RStudio Data Analysis
R Programmingf
7 months ago | [YT] | 0
View 0 replies
R and RStudio Data Analysis
Hi everyone,
Here is a simple example of a boxplot in R using ggplot2.
This plot compares an example variable across place of residence and wealth groups, and highlights distribution and outliers.
#----------------
library(ggplot2)
dat_a %>%
ggplot(aes(y=bmi, x=place, fill = ses))+
geom_boxplot(
outliers = TRUE,
outlier.color = 'red',
outlier.size = 2,
notch = TRUE
)+
labs(
y = "Body mass index",
x = "Place of residence",
fill= "Wealth index"
)+
theme_classic()+
theme(
axis.title = element_text(size = 12, face = "italic"),
legend.position = 'top'
)+
scale_fill_brewer(palette = 'Set3')
7 months ago (edited) | [YT] | 0
View 0 replies
R and RStudio Data Analysis
Data Visualization with ggplot & Machine Learning in R
7 months ago | [YT] | 0
View 0 replies