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🆕 ALẸ́ DÁTÀ: a week session on R Programming in Yorùbá!

Exciting news! Our R programming class in Yorùbá is back, and this time, weekly (every Tuesday, 9pm Nigeria time)!

Whether you're new to coding or looking to expand your skills, this weekly session is designed to empower you with the tools to excel in data science.

In the pilot stage, I'll dive into:
- Introduction to R Programming: Get started with the basics.

- Data Manipulation & Visualization: Turn raw data into meaningful insights.

- Statistics in R: Master the key concepts.

And that's not all—future sessions will also introduce Python, making sure you have a comprehensive toolkit at your disposal.

Get ready for engaging lab sessions where we'll practice hands-on coding and tackle challenging exercises together.

Don't miss out on this unique opportunity to learn R programming in a way that resonates with you - in Yoruba.

Let's embark on this data journey together

For updates on the session:


https:///LuSWM7hT3C81QKLdxo7h9l

And this one that Yoruba's don't have R in their mother tongue...

This is unique.
Baarakallahu fiihi!

By God's grace, we're starting this course tonight.

As earlier announced, the course is designed for beginners, covering the basics of R programming and data handling. As we are all aware, R is a powerful language for statistical analysis and data visualization.

By the end of this course, you should be able to:

- Program in R
- Import and work with your own data files
- Manipulate and summarize data entries with package such as dplyr
- Create engaging visualizations with ggplot2

No prior programming experience is required.

I'll provide example data files, and both R and RStudio software are free to download (I'll send instructions to download them soon).

I hope we'll all enjoy the opportunity to learn R together in our mother's tongue - Yorùbá!

Episode 1 - Introduction | R studio

This week, I began the weekly class with an introduction to the purpose of the session: why R programming? and why the course in our mother's tongue - Yoruba? The first part of the session focused on these topics. In the second part, I provided a preview of what to expect in the course and concluded the lesson by demonstrating how to use RStudio (an R IDE), explaining the R studio GUI & its functions, as well as performing arithmetic operations in R and introducing variables.


https://www.youtube.com/watch?v=_LpwgrRd8O8

We'll continue our weekly class, R 101 - Introduction to R programming in Yorùbá tonight at 9 PM Nigeria time. Please don't forget to join us. You can catch up on the last class below:


https://www.youtube.com/watch?v=_LpwgrRd8O8

Thanks.

Episode 2 - data types and structures

This week (Episode 2), I continued from where we left off in the previous session, where we explored how to use RStudio, the RStudio GUI and its functions, performed arithmetic operations in R, and introduced variables.

In this session, I began explaining data types and structures, focusing on atomic or primitive data types—the simplest building blocks of the programming language, which include double, integer, character, and logical types. These were covered in the first part of the session. Since these building blocks can be combined within data structures to create objects containing data, I introduced the concept of vectors, which are one-dimensional, single-class data containers.



https://www.youtube.com/watch?v=lvSfaVtHHQI

📢 ANNOUNCEMENT

Due to some technical issues, I'm afraid our weekly R101 - R Programming in Yorùbá may not hold tonight as scheduled unless I'm able to resolve the problem before class time. If not, I will make it up to you tomorrow by God's grace. I sincerely apologize for any inconvenience this may cause.

Thanks for your understanding.

✍🏾
Lukgaf

https://www.youtube.com/watch?v=ToFa5_sbjKg

In Episode 3, I continued our discussion on data structures, focusing on additional structures such as factors, matrices, and data frames. While all of these, including the vectors we covered in the previous session, are homogeneous (as they can only contain one data type), data frames are heterogeneous, allowing for multiple data types. Before diving into the session, I demonstrated how to generate reports by knitting them into PDF/Word formats using R Markdown, which we have now adopted for our Lab submissions.

Episode 4

Today, in Episode 4, I concluded the topic of data structures by discussing lists, which are more complex data types compared to vectors (1-dimensional arrays), factors, matrices (2-dimensional arrays), and data frames. In the second part of the session, I covered conditional statements and logical operators, focusing on "if", "else", and "else if" statements. I wrapped up the session with an introduction to loops, specifically "while" and "for" loops, thus concluding the introductory part of the course. Starting next session, we will begin using packages, particularly the tidyverse.


https://www.youtube.com/watch?v=-A4UqMEO4cw

Lab 4

I will be posting your content on naitalk.com too for a wider audience. Naitalk is a discussion forum where there is a possibility to monetize your content if you use the CIRCLE feature on the forum.

I will be posting your content on naitalk too for a wider audience. Naitalk is a discussion forum where there is a possibility to monetize your content if you use the CIRCLE feature on the forum.

https://www.youtube.com/watch?v=vyw6Ze34-HE

Tonight (Episode 5), I introduced packages. So far, we have used functions available in the base distribution of R. Since R is open source, we may want to use functions written by others for our analyses. A common and useful package for data manipulation is tidyverse, which includes tools like dplyr (for data manipulation) and ggplot2 (for data visualization). Tonight, I focused on dplyr and explained some of its useful functions for data manipulation, such as filter(), select(), group_by(), summarise(), and arrange().

ggplot2 (Grammars of graphics)

This week (episode 6), I began explaining how to use the ggplot2 package, one of the components of the tidyverse – the grammar of graphics. Although you can create plots with base R, using ggplot2 is relatively easy, well-documented, and supported by a community of data enthusiasts. I covered the basic units of ggplots – data, aesthetic, and geometric – along with other components.
The class was short due to bad internet and inadequate power on my computer. We'll complete the rest in the next session.


https://www.youtube.com/watch?v=yYzNYN7F7P4

This week, I continued with ggplot2, covering its anatomy and core elements such as data, aesthetics, geometry, coordinates, and theme. I specifically explained when to use each type of plot to visualize data effectively. This marks the end of the introductory part of our course. In the next session, we'll begin using R for statistical analysis, focusing on how to conduct research, design experiments, and formulate dependent and independent variables, gather data and analysis of the data with R.


https://www.youtube.com/watch?v=t3ZSTDcPnDc

Lukgaf:
This week, I continued with ggplot2, covering its anatomy and core elements such as data, aesthetics, geometry, coordinates, and theme. I specifically explained when to use each type of plot to visualize data effectively. This marks the end of the introductory part of our course. In the next session, we'll begin using R for statistical analysis, focusing on how to conduct research, design experiments, and formulate dependent and independent variables, gather data and analysis of the data with R.


https://www.youtube.com/watch?v=t3ZSTDcPnDc

Your consistency and topics are cool. Unfortunately I don't understand your language.

BRATISLAVA:
Your consistency and topics are cool. Unfortunately I don't understand your language.

Many thanks bro

Quiz 1


https://www.youtube.com/watch?v=x3TC30TJqvM

Tonight (episode 8 ) I began the second theme of our course (statistical analysis with R programming in Yorùbá). After covering basic R data types, data manipulation with dplyr, and visualization with ggplot2, we moved into the process of getting data (like those relevant to our projects) before deciding which statistics tests are suitable and how to perform them in R.
I introduced psychology experiments as a tool in cognitive science, defining key concepts such as experiments, dependent variables (DVs), independent variables (IVs), and how to formulate them. We discussed confounds, how to control for them, and the essentials of experimental design, including important vocabulary. I also explained why Randomized Controlled Trials (RCTs) are considered the gold standard when looking for reliable evidence.


https://www.youtube.com/watch?v=KYNiBAMIvrw

Today (episode 9), as we move closer to the stage of getting data that we are going to ultimately analyze with R, I continued my explanation following our previous discussion on using Randomized Controlled Trials (RCTs) in the last session. I emphasized the importance of novelty in research, explained why it matters, and outlined steps to check for novelty effectively before carrying out the research work.

I concluded the lesson by discussing the importance of pilot studies in research and the potential consequences of skipping this critical step. I illustrated the significance of piloting interventions with three examples and Nigeria as a case study: one related to the 2023 election and two recent policies introduced by the current government.


https://www.youtube.com/watch?v=Ka_bWEJ5JE4

In the last episode (Episode 10), I covered the topic of power analysis and demonstrated how to carry it out using the pwr package in R. Power analysis is crucial for determining the required sample size to detect an effect if it exists, ensuring that a study is neither overpowered (wasting resources) nor underpowered (risking Type II error).

I explained key concepts and terminologies, such as power, effect, effect size, sample size, and level of significance. In addition to using R, I also demonstrated how to perform power analysis using the G*Power software.


https://www.youtube.com/watch?v=a1NRwDr2OhM

Lab 9 and 10 – Power analysis with R and the importance of novelty in research
Deadline: Tuesday, 31st December 2024 (5pm Nigeria time)

1. (a) What does power represent in power analysis? (b) what are the consequences of conducting an underpowered study? (c) What are the components of power analysis?

2. Use the pwr package in R or G*Power software to determine the sample size required for detecting a medium effect size (Cohen’s d = 0.5) with a significance level of 0.05 and a power of 0.8. Submit a screenshot of your code or software output along with an explanation of the parameters and results. Finally, compare your results from R with those obtained using G*Power. What similarities or differences do you notice?

3. Ensure that your research question is novel or includes a novel element. Start by using academic search engines such as Google Scholar, PubMed, or Scopus to verify whether your research question or topic has been addressed before. Can you identify gaps or areas where novelty can be added, such as applying the concept in a new context or population or using a different methodology? Document your findings by noting whether your research question already exists and, if it does, what novel element you will introduce. Based on these findings, refine your research question to include the identified novel element. Clearly state how your approach contributes something new or unique to existing knowledge. Document a short report that includes your original research question, the results of your novelty check (including similar studies if found), and your revised research question with the added novelty.

Episode 11 - Replication crisis and the importance of open science practices

Tonight (episode 11), before diving into statistical analysis with R in the coming week, I explained the replication crisis and the importance of open science practices like pre-registration. In the first part, I discussed why so many research findings fail to replicate and explored the underlying causes. In the second part, I focused on what we can do to improve reproducibility and how to implement these solutions. I concluded the lesson by demonstrating how to adopt open science practices using platforms like OSF.


https://www.youtube.com/watch?v=ohCDfpOkc2Q

Tonight (episode 12), I began statistical analysis by introducing hypothesis testing using the t-test in R. I explained what a t-test is, when to use it, the different types of t-tests, the hypotheses involved, how to calculate a t-test in R, and how to interpret the results.


https://www.youtube.com/watch?v=PiaH0GdI4kI

Episode 13


https://www.youtube.com/watch?v=szDAXzps8ug

Tonight's class was entirely practical. I presented our recent work on the impact of Kahoot in reducing math phobia among students, which I previously shared as a preprint. I discussed how we generated our data, including all the steps taken, how we manipulated and cleaned it, and how we analyzed it using a t-test. Additionally, I demonstrated how we visualized the data using R. This session also served as an opportunity to refresh what we have covered from the very first class.

Episode 14 - working with "loyalty" data


https://www.youtube.com/watch?v=EpwOUrWI04k

Episode 14 - working with "loyalty" data


https://www.youtube.com/watch?v=EpwOUrWI04k

Episode 15 - Lauching the Working Groups


https://www.youtube.com/watch?v=Ya4JwIP2DPA

This week, we launched the Working Groups with the aim of applying everything we’ve learned in R within the context of experimental methods, specifically using the RCT (Randomized Controlled Trial) approach in research.

I gave a summary of what we’ve covered so far, explained the position of RCTs in the hierarchy of evidence, and presented a step-by-step guideline for conducting experimental research. This is the framework we will follow as we aim to submit our findings—preliminary results to conferences, and subsequent studies to highly ranked journals for publication.

We concluded the session by evaluating the submitted research topics to determine whether they are suitable for RCTs, and participants were asked to draft their pre-registration following the provided guideline.

Pre-registration on OSF


https://www.youtube.com/watch?v=0tmJuuobK40