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Data Analytics | Our Portfolio
1. Basic Statistics (5%) (a) Exam Grades Analysis for Module A The exam grades for 20 students in Module A are: 5, 31, 19, 21, 33, 8, 15, 43, 67, 50, 93, 28, 41, 41, 35, 10, 23, 77, 97, 63 (i) Mean: module_a <- c(5…
Violin Plots in R with ggplot2 | Comprehensive Guide
Ever wondered how to visualize complex data distributions in a way that’s both insightful and aesthetically pleasing? Use the violin plot. It is a powerful tool that combines the best of boxplots and density plots, offering a comprehensive view of your data’s distributi…
FAQs on RStudio | Installation, Features, and Tips
Welcome to the ultimate resource for Frequently Asked Questions related to RStudio! If you've ever felt overwhelmed by the vast world of data analysis or uncertain about how to get started with downloading RStudio, installing R, or integrating Python with RStudio, you'r…
How to use read.table Function in R | Read table in r
Key Point The read.table function in R is essential for importing tabular data into data frames. It supports various file formats with customizable parameters, allowing you to handle complex datasets accurately.Adjusting the sep parameter allows it to manage diffe…
How to do F test in R | Compare variances in Rstudio
The f-test in R is a powerful tool for comparing variances and drawing significant conclusions from your data. Understanding how to perform an F-test can transform your data analysis capabilities, allowing you to determine whether the variances in two samples are statis…
Remove rows from dataframe based on condition in R
In data science, the ability to manipulate data frames is essential. Whether you're a seasoned data scientist or a budding analyst, removing specific rows from a data frame based on certain conditions is a fundamental skill. It's the digital equivalent of spring cleani…
Count Function in R I dplyr::count()
Data analysis is all about turning raw data into actionable insights. I was working on a research project analyzing survey data from thousands of respondents. The clock was ticking, and I needed to summarize responses to hundreds of questions quickly. Manually counting …
Guide to Remove Legends in ggplot2 in R Programming
The data visualization with R, ggplot2 reigns supreme. However, a well-designed legend from a plot can undermine even the most elegant plots. Legends have the power to clarify or confuse – they're the key that unlocks the insights hidden within your graphs. Learning how…
How to Install ggplot2 in R: A Comprehensive Guide
As a researcher, I remember the frustration of wrestling with clunky graphing tools. My results deserved better than the generic charts I could produce. Then, I discovered ggplot2. The ability to layer elements, customize visuals, and follow intuitive logic changed how…
FAQ: RStudio SOS- Your Lifeline for Common Questions
Hi, how are you? If you've scratched your head over peculiar RStudio errors, puzzling over which panes do what, or wondering if GitHub Copilot can streamline your coding life, you're not alone! RStudio is a powerful tool, but it has its own quirks and com…
Unload All Packages in R: Best Practices
Key pointsR packages expand functionality but require management. Packages offer essential tools for analysis but can clutter your workspace and cause conflicts.detach() is the basic unloading tool. Start with detach("package:package_name", unload=TRUE) for individual p…
Maximize Your Research Time: Essential RStudio FAQs
As researchers, we know that time is our most valuable resource. Every hour spent solving RStudio errors is an hour lost for the in-depth analysis that drives our discoveries. That's why I'm creating this "Frequently Asked Questions" section, which is published weekly –…
Rotate ggplot2 Axis Labels in R: Improve Plot Readability
Key PointsLabels Matter! Axis labels are like signposts on your graph – they tell the reader what they're looking at. Without them, it's just pretty colours and confusion.The Trouble with Defaults: ggplot2 is awesome, but sometimes those labels automatically overlap. Th…
How to Convert Data Frame to Raster Object in R
Are you a data analyst who works with spatial data in R? Do you want to learn how to convert data frames to rasters in R and why it is useful and important for spatial data analysis and visualization? If yes, then this article is for you.# Convert the dataframe to a ras…
Comparing LDA with Other classification
Have you ever considered how we train machine learning models that could mirror our biases? As a seasoned data analyst, I frequently confront this problem. We desire objective systems yet inadvertently impose our preconceived notions of what patterns or 'ideal' data sho…
Linear Discriminant Analysis (LDA) in R
Are you looking for a simple, robust, and efficient method to help you with classification and dimensionality reduction? Do you want to learn how to implement and evaluate LDA in R programming, one of the most popular and powerful techniques for these tasks? If yes, the…
Understanding Linear Discriminant Analysis (LDA)
Key Points Linear discriminant analysis (LDA) is a supervised learning technique that can be used for classification, dimensionality reduction, feature extraction, clustering, or visualization.It assumes that the data follow a multivariate normal distribution with class…
Create New Variables in R with dplyr
Key TakeawaysThe mutate function from the dplyr package allows you to create new variables or modify existing variables in a data frame or a tibble in R.The variants of mutate, such as mutate_all, mutate_at, mutate_if, and mutate_across, allow you to apply functions to …
Creating & Transforming Variables in RStudio: Your Essential Guide
Key takeawaysMultiple methods exist for creating new variables in R, each with advantages and limitations. Understanding these options empowers you to choose the best tool for your needs and data context.Best practices prioritize clarity and efficiency. Opt for descript…
Repeated Measures ANCOVA in R | A Complete Guide
The repeated measures ANCOVA in R tests whether the average values of one or more variables measured repeatedly on the same subjects differ significantly after adjusting for a covariate.The code for performing a one-way repeated measures ANOVA in R is:# Fit the repeated…
How to Perform ANCOVA with R: A Simple Guide
ANCOVA is short for analysis of covariance, a statistical method that lets you compare the means of an outcome variable among two or more groups while accounting for (or correcting for) the variability of other variables, known as covariates. Covariates are variabl…
How to Use scale_color_manual in R | A Complete Guide
Key Pointsscale_color_manual is a function in R that allows you to specify the colors of your plots manually.You can use color names, hex codes, or RGB values to choose the colors for scale_color_manual.You can create different plots using different color palettes and s…
Customize R Plots with scale_fill_gradient: Your Guide
Key pointsScale_fill_gradient function allows us to create a continuous color gradient based on a numeric variable for our graphs.We can use the scale_fill_gradient function with the ggplot2 package, a powerful and popular R data visualization tool.We can customize the …
Colour Gradient Scale with scale_fill_gradientn in R
Key pointsThe scale_fill_gradientn is a powerful function of ggplot2 for data visualization customization.It is a function in the ggplot2 package that allows you to create an n-color gradient scale for fill aesthetics by specifying a vector of colors and adjusting other…
Learn Color Gradients with scale_fill_gradient2 in R
KeyPointsscale_fill_gradient2 is a function in the ggplot2 package that creates a diverging color gradient for fill aesthetics.fill aesthetic is an aesthetic that controls the color of the interior of a geometric object, such as a bar, a point, or a polygon.diverging co…
Enhance Scatterplots with ggplot Shapes plot in R
Key Pointsggplot shapes represent the points in a scatter plot, and they can be used to distinguish different groups of data, highlight outliers, or add more information to the plot.We can use 25 different ggplot shapes, ranging from simple circles and squares to more c…
Create Stunning Data Visualization in R
Key PointsData visualization is the art and science of presenting data visually, making it easy to understand and explore.R is a powerful and popular programming language for data analysis and visualization, and ggplot2 is one of the most popular and powerful packages f…
Exploratory Data Analysis (EDA) for Journal Submissions
Key pointsExploratory data analysis (EDA) is crucial in any data analysis project. It involves exploring, summarizing, and visualizing your data to gain insights, identify patterns, and detect outliers.EDA can also help you formulate hypotheses, choose appropriate stati…
Contingency Tables in R: Insights from a PhD
Key pointsA contingency table is a way to show how often different categories of two or more variables occur together. You can make a two-way contingency table in R with the table() function. You can also add the totals and percentages of each category with the add…
Exploring R Date Format Complexities
Key Points R date format is a skill that can help you handle one of the most common and important data types in any data analysis project.R has a special class for representing dates called the date class. It stores dates as the number of days since January 1st, 19…
Calculate Confidence Intervals in R: Your Practical Guide
Key points Confidence intervals are a way of expressing the uncertainty associated with a point estimate. They provide a range of values likely to contain the true population parameter with a certain confidence level. R has several built-in functions that can calculate…
How to R Count Number of Words in a String?
Key points A word count is the number of words in a text or a collection of texts. It can be useful for many purposes, such as measuring the length and complexity of a text by comparing texts from different sources.In R, there are many ways to count words in a dataset,…
How to Fix Plots Not Showing in RStudio?
Key points Plots not showing in RStudio can be caused by various reasons, such as outdated versions, closed graphic devices, changed default graphic devices, errors, warnings, or bugs.To diagnose the problem, check your R and RStudio versions, current and default gr…
Remove Outliers and Perform Data Cleaning in R
Key Points Outliers are data points that are significantly different from the rest of the data and can affect the results of statistical tests and machine learning models. There are different ways to detect outliers, such as graphical methods (boxplots and histograms) …
ggplot2 multiple plots in R
Key Points To create multiple panels within one plot based on one or more factors, you can use one of the faceting functions: facet_wrap() or facet_grid(). To arrange multiple plots in a simple grid layout using the grid system, use the gridExtra&nb…
Did You Know How to Calculate Z Score in R?
Z-scores, also known as standard scores, z-values, normal scores, z score or standardized values, measure how many standard deviations away a value is from the mean of a distribution. They are useful for comparing data with different units, scales, or ranges. They ca…
How to Set Working Directory setwd in R
Key pointsIn R setwd () function is used to set the working directory to a new location. R looks for and saves files, such as data, scripts, or plots in the working directory.The getwd () function in R is used to get the current working directory. It returns a charac…

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