Відео

Say it would be great if you created a update to this video because some of the functions used changed.

The excel sheet looks dreadful.

I find the part with cbind confusing.

You guys are code ninjas!

Why not use lubridate for dates? By the way I like the discussion around adressing the different issues that arise in attempting to solve a given problem.

I know wikipedia allows scraping but that is not true for other sites. I typically check the permissions with the bow(url) function from the polite package.

Does broom work with lists or only data frames?

I got the p-values with the following code: mutate(model_p = map(model_summary, ~.x$coef[1 , 4]) ) %>% unnest(cols = model_p)

You can simply add as.character(x) to the function (map_chr) without using the pipe.

The . in split does not work on my end if I use the base pipe.

Thank you guys! this was really helpful

Flame tree next, please.

gganimate? it will be great one comparison between packages

very cool and so useful simply because event-level dataframes are ubiquitous (especially in a work/company setting) especially with fetching all this data from a nested/tree like structured jsons - you have a big fan in me here gentlemen - thanks !! (i'm willing to bet Cohen is a data analyst/scientist for a company involved in sports/NBA bookkeeping or analytics lmao)

great stuff as always ! clear and easy to understand what easily can be confusing so, thanks ! (very) unrelated to this but would you be interested in diving (either deep) or as an introduction into the logger package, i think it's created with the intent of mimicking python's version and while i find python's pretty straightforward, for some reason R's version is a little more obscure/harder to grasp for me

Great content 🎉

Why was the panel data package (plm) or LSDV Reg method not used?

Nice video! Do you know how the modify() function works in purrr? I really struggle to see the difference with the map() function.

You can try use the \(df) notation for anon functions. purrr::map(my_list, \(df) lm(y~x1+X2, data = df)) This function expects a list of data frames called my_list. It then regress in each of these data frames y against x1+x2, and specifies each of the data frames in the list as the data for the regression. It is the same as what you guys did, but i think this notation was introduced and is now considered better than ~ .x notation

Great job!

This was great! Pretty concise and learnt a lot. Cleaning data is a lot less time consuming and intuitive for me now. Didn't know base R was so good at dealing with strings

Take the code I commented on episode 144 and replace purrr::map() functions with furrr::future_map() functions.

Awesome. One package I like a lot is the furrr package. You use the function "future_map" as you do with the "map" function from the purrr package, but in parallel. Pretty easy.

Great approach for doing multiple comparisons. Could you not just replace filter(r1 != r2) with filter(as.numeric(r1) > as.numeric(r2))? I think broom::tidy() on the output of the t test might have made it a bit easier to combine and extract the data you wanted. although your approach works fine.

Good stuff gents. Appreciate the extra package shared tho.

Another great episodw. Looking forward to the episode 200 party edition! In your plots the y axis was count data. It irritates me that ggplot will often put decimal points on scales even if you bother to define the variable as an integer. For one plot it's not too hard to manually input the breaks etc. Is there an easy way of getting round this problem when you are generating lots of plots, as in your example? It should be as simple as saying "this is integer data, 10.5 is meanigless!"

Great epidode! I learnt the invisible function which seems particularly appropriate for an episode on the Lord of the Rings!

How do you introduce and make predictions on other new dataset (not the test data)

Any plans to do any more NBA stuff? I'd love to see something like trying to predict some of the awards as the season is winding down, maybe Most Improved player as predicted using ML - similar to the HOF pitching

This was a fun one

Great video, guys! And it's great that the baseball season around the corner, so lots of people should be itching for new data points (I mean, games lol)

Excellent showcasing but we also need to know more about map function of purr

🐈🐈

now do barry bonds

I'm not sure if the "A Aron" was a Key and Peele joke or not, but an interesting video either way

You guys rockin', keep it up!

Good work. You guys can get the innings pitched if you divide the IPOuts by 3 (IPOuts = Outs pitched)

Great!

Nice work guys! It would be great if you could do this with tidymodels framework.

First

You should really focus on advanced ggplot, tidy models and shiny stuff....

Fantastic presentation! Thanks! I am very interested in learning more on Bayesian statistics.

If you must use a for loop... # Using a for loop library(rlang) wgt_stat_list <- list() for (i in 1:5) { wgt_stat_list[[i]] <- get_wgt_score( fake_dat, sym(str_c("stat", i)), sym(str_c("stat", i, "_n")) ) }

Also, thank you both for this video!

I'd like to offer to solutions, both from the Tidyverse; the second one uses {rlang} for Tidyevaluation (avoid for loops!!!): # Doint it with a pivot_longer and pivot_wider fake_dat |> pivot_longer(starts_with("stat")) |> mutate( stat_number = str_extract(name, "\\d"), name = str_remove(name, "\\d") ) |> pivot_wider( names_from = name, values_from = value ) |> group_by(athlete, stat_number) |> summarise( total_obs = sum(stat_n), wgt_stat = weighted.mean(stat, stat_n) ) # Using tidy_eval library(rlang) get_wgt_score <- function(dat, variable, N) { dat |> group_by(athlete) |> summarise( total_obs = sum(!!variable), wgt_stat = weighted.mean(!!variable, !!N) ) |> mutate(stat = as_string(variable)) } map2( str_c("stat", 1:5), str_c("stat", 1:5, "_n"), \(x, y) fake_dat |> get_wgt_score(sym(x), sym(y))) ) |> list_rbind()

Hey Ellis - great walk-through. I see you predicted both Beltre and Mauer, both of who made it. A-Rod won't make it due to external shenanigans, as not you note. The other player who made it was Todd Helton. Going to do pitchers next?

Really appreciate the for loop solution as often running into this situation myself and I always wondered if you can do this without running the same code multiple times. This has been super helpful!

You can accomplish it with a single pivot longer if the column names have a consistent pattern/separator. I gave the stat variables a new suffix ("_score") to make that happen. fake_dat |> rename_with(\(x) str_replace(x, "(.+\\d$)", "\\1_score")) |> pivot_longer( c(ends_with("_score"), ends_with("_n")), names_to = c("stat", ".value"), names_sep = "_", ) |> summarize( total_obs = sum(n), wgt_stat = weighted.mean(score, n), .by = c(athlete, stat) )

Keeping Patrick on his toes :)

I am right now building a shiny application prototype that I would like to demo to my colleagues. We use Dataiku in my organization and it has the capability to host shiny apps. However, since our tech guys are all pythonistas, most R packages that I am using don't run well on our Dataiku deployment (our tech guys don't care). I explored other alternatives for shiny app deployment but I couldn't find one that is practical enough. I am now going to try this *shinylive* option. Wish me luck.