How to plot Stacked Bar Plots with Error Bars in R? | Data Visualization Tutorial скачать в хорошем качестве

How to plot Stacked Bar Plots with Error Bars in R? | Data Visualization Tutorial

Lets master data visualization with our latest tutorial on creating stacked bar plots with error bars using ggplot2 in R! This video demonstrates one of the trickier aspects of data visualization—accurately positioning error bars on stacked bar plots. By utilizing the cumsum() function and carefully arranging categories and subcategories, we demonstrate a robust method to ensure error bars are placed correctly atop each sub-bar. The contents of the video tutorial: Transforming the classic iris dataset into a long format with pivot_longer(). Calculating cumulative means to find the precise location for each error bar. Strategies for stacking and arranging data to facilitate error bar placement. Perfect for statisticians, data scientists, and R enthusiasts looking to enhance their plotting skills and understand complex visualization techniques! Subscribe for more insightful tutorials and hit the Like button if you find this video helpful. Share with your peers and help us grow a community passionate about data science and visualization! #RProgramming #DataScience #ggplot2 #Statistics #DataVisualization #Coding #Biostatistics #TechEducation #LearnR #Tutorial Load the necessary package, tidyverse, which includes dplyr for data manipulation and ggplot2 for data visualization library(tidyverse) Convert the iris dataset from wide to long format, specifying the columns to pivot and how to name the new columns, replace < with less than symbol and > with greater than symbol, to make the code work. also before each bold faced line in code put '#' to make it comment in your code. iris_long <- iris %>% pivot_longer(cols=1:4, names_to = "FlowerPart", values_to = "measurement") %>% Group the data by Species and FlowerPart to calculate statistics for each group group_by(Species, FlowerPart) %>% Summarise the data to calculate the mean and standard deviation of measurements for each group summarise(meanL=mean(measurement), sdL=sd(measurement)) %>% Arrange the summary statistics first by Species and then by FlowerPart in descending order arrange(Species, desc(FlowerPart)) %>% Calculate the cumulative mean across the groups and define the confidence interval bounds mutate(cmmean=cumsum(meanL), lower=cmmean - sdL, upper=cmmean + sdL) Create a bar plot using ggplot2, mapping Species to the x-axis and meanL to the y-axis, and filling bars by FlowerPart ggplot(iris_long, aes(Species, meanL, fill=FlowerPart)) + Add colored bars for each group with a grey outline geom_col(color="grey60") + Overlay error bars on the bars to represent the confidence intervals geom_errorbar(aes(ymin=lower, ymax=upper), width=0.2) 0:00 - Introduction 0:52 - Preparing iris data set using pivoe_longer() 2:45 - Summarising to get mean and sd for plotting barplot and errorbars 3:37 - Arranging the summary dataset to get the exact positioning of error bars 4:28 - Arranging Categories 5:15 - Computing lower and upper ends of errorbars 6:21 - Getting the stacked bar plot 7:02 - Plotting the error bars 8:37 - Conclusion Facebook page:   / rajendrachoureisc   Mail Id: [email protected] youtube playlist:    • R programming tutorials