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Amazingly expressed

permission to learn sir. thank you

Muchas gracias

I would avoid editing and cleaning raw data prior to importing into R. You should be able to do so safely in R and with less risk of error. Copy and pasting in excel files can lead to unintended errors. There are packages and functions in R for doing so including janitor, tidyxl, openxlsx, readxl and others. If you are sharing a file with someone else it is best to edit and clean it in R (keep the original raw somewhere) and then export it as a .csv file. That is a pretty uniform format. If you are sharing with another R user you can use .rds or .rdata formats.

Excel spreadsheets are simply bad.

There are a couple built-in functions available in R for the Birthday Problem. Those are pbirthday and qbirthday. They come with the stats package.

I wonder how the frequency of birthdays is affected by c-sections which are quite common in the US.

I don't know why so many complain about for loops. They seem quite simple.

Good presentation 👏 👌

This was excellent! However, the link to the rmarkdown file doesn't work. Any chance of updating it?

This VDO is very useful but sound is unclear.

That is so descriptive and makes me understand so much

Thank you for this series. It has been very helpful. I liked your systematic approach and the constant repetition. that is how we learn.

Thank you 😊

Hola Alex, sabes algo sobre como corregir este error: Error in check.data(X) : Data are not matrix or data.frame. Saludos

How vould any apparatus logically acceptable psykotrsphy experiments with scientific proves.

Well done. Great video. I am curious, what software did you use to generate the animated presentation? Perhaps you should make a video about how you did it!! Thanks again.

This is great and insightful. The nodes, bridgers and influencers bit I like very much and it has helped me to understand network analysis. My question is: These nodes, clusters etc. are dynamic over time and across subject matter. How is this factored in?

thank you very much, what a useful lesson <3

Excelente explicación, muchas gracias por la información, es de mucho valor.

Hi Sam, I am curious to hear your professional opinion about how to fit a model that I find to be challenging. I have measurements of oxygen consumption taken at 4 different temperatures for 520 eggs belonging to 80 species of insects. The goal of my model is to look at how a series of predictors like location, mortality ecc. influence the slopes of the lines that describe the change in oxygen consumption across the 4 measurement temperatures. Oxygen consumption intuitively increases with egg mass, measurement temperature and age of the egg. What I do not understand is how to make these slopes the dependent variables. If I first make a model like oxygen~egg mass+age+measurement temperature and then I use the slopes for each species as dependent variable in a model like slopes~site+mortality+ambient temperature is that correct? Or is that discourage because I am doing statistics on statistics? Conversely, if I build only one model like oxygen~egg mass+age+measurement temperature+site+mortality+ambient temperature is this model actually looking at slopes? Sorry for the long question but I find it hard to get a reasonable answer by myself. Cheers

Awesome stuff!

Thanks for the vid. At the minute six, I can see the intercept value (beta 0) at the summary is a high number completely different (-3.258) from where the line cross over the graph (y axy) . Is there a reason about that? Thank you

Great

Absolutely awesome tutorial on the foundations of ggplot2. Beyond any reasonable dought, ggplot is powerful and stands high among competitor packages and it is worthy investing your time to get grip on it, but plotly shines when it comes to interactive side, specially when the task is exploratory analysis. Of course you can zoom in and out with ggplot by changing the axis limit with the ylim() or xlim(). Thank you professor for the amazing tutorial!

This is awesome life savertutorial. Neaty and informtive. I always found hard to use the colour scales and had to go for the automatic option. I am aware of the critical importance of getting the colours right in vizualisations. From this tutorial it is now clear just like water under the bridge. I will be from now enjoying my favourite veredis scale. This colour scale accounts for discrete and continous variables type mapped to axis. Thank you a lot Professor for such an amazing tutorial

Thank you so much, it's really wonderful.

Wonderful series. I have found this content very useful! Nicely presented, easy to understand, free tools and resources,.. I am so impressed! THANK YOU

i did a random word generator and looked it up on youtube and found this

Thank you so much indeed! You unlocked ODK locks of mine.

This has been my one stop course to ODK. Very simple, well crafted and high quality not to mention making all resources available including pptx. Thanks so much.

Hello, Could we have a session with you to ask about ODK? Thanks

Excellent. Very nice presentation. Clearly this can be extended to other phenomena. In similar vein have you considered doing a presentation on Benford's Law? I would find that quite interesting.

Ahh - the Provisional was an artifact - it did not get generated with the code I used.

Under the yyyy variable I am not seeing Provisional. The data has probably been fixed since this video was recorded. That climate data appears to be updated at the end of each month.

This worked for me: read_table(oxford_url, skip = 5) using readr. And then I removed the first row (with the mismatched units).

This is classic messy data!

Well explained. Would a map function have worked here?

Great Video. Thanks

Excellent! Thank you. Regards from Brazil.

Thanks Sam! You are a good teacher on statistical modelling in R. This is an amazing video...

Amazing

Good job. Thanks

this is amazing

It is a good presentation and really helpful,can you please share the link to the ppt

Superb video my friend

quite hilarious and informative

Clarity of the writing