Thanks for the great explanation!

Great job! Thank you very much!

I can't believe you only have 30k subs. You're the best R teacher I've encountered so far.

Very interesting. I do still think that in this dataset little is gained by imputation. With so few missings it has practically no effect. However, the exercise itself is of great value because it demonstrates how (and perhaps when) to impute different types of missing values. I would like to see more on imputation and perhaps a bit of dive into the different types of missings (MAR, MCAR and MNAR) which boggle the mind. Thanks for another great video.

Thank you for this Prof.

for working_day I would sort the data (already done) then group_by day and then tidyr::fill and then ungroup

Excellent explanation and well presented, appreciated! Would be nice to make a video about using formula in smoothing function in ggplot2, e.g. sometimes square term is used as a predictor in poisson regression and then just glm method in smoothing function will not be sufficient.

Nice!😊

for folks using R studio, if you want info about a function, put the cursor on the function and hit the F1 key. It does the same thing as ?my_func in the console but is faster if you have already included the function in your code.

You can find materials supporting this vid (and others) at github.com/equitable-equations/youtube.

❤

How do you do code chunks for imported datasets? I keep getting “object not found.”

interpretation of the results missing

Thank you so much. These error messages should be given in English and not only in jargon.

hi it is not saving in smits data after you split! right?

I come from Python (Pandas) and have been wanting to increase my skillset to include R. This was an excellent introduction, thank you!

any advices for a 20 yearold freshman in data science and has no skills at all trying to build himeself a good income

halo dr

good

Cool links: Heather Mattie on Google Scholar: scholar.google.com/citations?user=f_02DZ0AAAAJ On LInkedIn: www.linkedin.com/in/heather-mattie-71b98039/ On X: @HeatherMathy Obermeyer et. al, Dissecting racial bias in an algorithm used to manage the health of populations: www.science.org/doi/10.1126/science.aax2342

Great Videos. been watching the whole playlist and really helped me reviewing all the necessary ggplot arguments. keep it up and add more advanced lessons please.

this was evry helpful thank you so much

Nice presentation. In a quarto book with chapters how do you reference to one chapter from another. It your chapters have: title: Cross Refs with ---- above and below, then how do you add a cross-ref to that chapter. The quarto website suggest using number one heading (#) e.g. # Cross Ref #sec-crossref . but I don't see how that works with the ---- setup. Any ideas (other just changing all the chapter titles from the ---- format???

I didn't understand the concept of k what it would be, can you solve by letting k equals to some value

why not in 5 minutes?

I do appreciate it. R to me is a new programming language and I was browsing TY to see what should I and where to start. Yours in my opinion is the best one. Thank you

Does the fill command works only on NA cells or also in empty cells?

Will have to watch this video many more times at my pace. Otherwise, very nice and simplified video. Thanks Richard.

thanks a lot

Hi apologies I'm not quite sure how to search within the channel but does Andrew have an intro into Shiny apps?

Thank you so much for the tutorial video! Very helpful for me on learning a lot about data!!!

Great tutorial! Thank you! I've been frustrated with replicating R commands that I don't really understand, but your video have made that clearer

Thanks for making me dealwith R in Just one Video !!!

thanks this was a great help!

Just a note - the conv_multiunit function from the measurements package does a nice job on unit conversions (miles per gallon to km per liter)

Very nice. I am torn whether one should start with base R or jump straight into the tidyverse when teaching R. I get the feeling that one does not appreciate the beauty and simplicity of the tidy (and pipe) approach until one has "suffered" through a little bit of base R s nested approach. After all some things may not run or be needlessly complex in the tidyverse but simple in base R. Any thoughts? - Thanks.

Andrew, Your point about the missings is interesting but it only amounts to a percent (113 hrs) and may thus not be worth trying to impute. Besides that there could be simple explaination for the missing hours. Perhaps the bike company was closed for a holiday and/or buisness-related activities. Imputation might therefore not be warranted.

There is only ONE value with heavy rain/ snow so why not just skip it??? (run Bikeshare |> count(weathersit) for a table).

Good discussion - especially of why you chose to use Poisson.

Believe me, tons of hours and days are wasted to finally run over with content of value like this. Thank you so much

hi, do you offer live online lessons to teach R?

Good stuff!

There is nothing better than good old statistics in the era of ml/nn

You can find materials supporting this vid (and others) at github.com/equitable-equations/youtube. Here's the link to my vid on the {broom} package: kzfaq.info/get/bejne/hd9hkqSQv86niWQ.html

awesome video!

for those who couldn't get arrange function done: library(dplyr) df=diamonds_sm df2<-df %>%arrange(color) view(df2)

I'm getting error in library(tidymodels)=not found and also in "initial_split" not found

How do you download air-quality files?

You're awesome. I heard to come back here, having watched one of your videos; learning R in 39 minutes. I have a question please I'm doing the data analysis for my research titled; modeling of selected classification methods and comparison of their performance in the classification of anaemia patients among Nigerian children. Now, I've not been using much of R before this work. So I ran into an issue with my dataset. I have one on of my predictor variables to be continuous. When I declared its class in R as numeric, all values for that variable were overwritten as NAs. I can't start checking each cell because there are almost 11,000 observations. How do I fish out where the problem is?

You're awesome. I heard to come back here, having watched one of your videos; learning R in 39 minutes. I have a question please I'm doing the data analysis for my research titled; modeling of selected classification methods and comparison of their performance in the classification of anaemia patients among Nigerian children. Now, I've not been using much of R before this work. So I ran into an issue with my dataset. I have one on of my predictor variables to be continuous. When I declared its class in R as numeric, all values for that variable were overwritten as NAs. I can't start checking each cell because there are almost 11,000 observations. How do I fish out where the problem is?