Thanks again for yet another video which rises the bar high! I gained a lot of valuable lessons. I do have one suggestion to you, would you please start a lesson on the programming aspect of R programming from basics to advanced level in a structured way by doing you your channel will stand out from the rest of the crowd sir. By the way I am following you from Ethiopia, Addis Ababa.
Thanks for watching and your suggestion! My struggle with covering basic programming concepts is that it's hard to create a story out of the lesson that I think is interesting enough. I'll keep it in mind though for the future.
Great video as always 💯...thank you. These past days I have been diving into diversity in ecology (alpha, beta, gamma, Shannon, Simpson...), since I don't have a lot of experience in statistics with ecological data I am learning as I go, and when it comes to R I always turn to your videos 😅. I found your previous code club videos about dissimilarity matrices, NMDS, Mantel...very helpful of course, but in my research I came across Hill numbers and "hill.div" package...it would be so great if you make some videos about the package and the Hill numbers. Thank you again...always keep making these videos🤗🤗
Great work, but I'm not a huge fan of this heavy use of annotations. They won't change when your data changes. Instead you could use something like: ... scale_y_continuous(labels = scales::label_currency())+ coord_cartesian(ylim = c(0, NA), xlim = c(-0.6, 6.6), clip = "off", expand = FALSE) + ... theme( ... axis.text.y = element_text(hjust = 0, vjust = -0.3, size = 13, face = "bold", margin = margin(r = -58)), ... ) The definition of margin is not ideal (it's hardcoded and might need adaption) but I think it's overall a more "robust" solution
Thanks for your code! It's great to see people riffing off of what I'm sharing 🤓. To get yours to match mine, I had to adjust the margin (mainly the left) since the gridlines were going all the way to the left edge of the figure... axis.text.y = element_text(hjust = 0, vjust = -0.3, size = 13, face = "bold", margin = margin(l = 10, r = -40)), I think I still prefer the annotate() approach since the meaning of the x and y positions are a bit more clear than the units on the margin and the vjust. For what it's worth, I tried adding fake 2024 data and I don't think it would take much/any fiddling to get it to look right. Thanks again for sharing - there's certainly multiple ways to do this
Thanks again for yet another video which rises the bar high! I gained a lot of valuable lessons. I do have one suggestion to you, would you please start a lesson on the programming aspect of R programming from basics to advanced level in a structured way by doing you your channel will stand out from the rest of the crowd sir. By the way I am following you from Ethiopia, Addis Ababa.
Thanks for watching and your suggestion! My struggle with covering basic programming concepts is that it's hard to create a story out of the lesson that I think is interesting enough. I'll keep it in mind though for the future.
I loved your presentation!
Your fresh perspective on data visualization is really inspiring.
Thanks for sharing your knowledge!
My pleasure! 🤓
Great video as always 💯...thank you.
These past days I have been diving into diversity in ecology (alpha, beta, gamma, Shannon, Simpson...), since I don't have a lot of experience in statistics with ecological data I am learning as I go, and when it comes to R I always turn to your videos 😅.
I found your previous code club videos about dissimilarity matrices, NMDS, Mantel...very helpful of course, but in my research I came across Hill numbers and "hill.div" package...it would be so great if you make some videos about the package and the Hill numbers.
Thank you again...always keep making these videos🤗🤗
My pleasure - thanks for watching! I'll add hill numbers to the list of things to cover in the future
So good, love to see you work. For me a lot of job is now being done by Calude. I think it is ruining my skills in terms of thinking how to do it.
There's only one AI that I encourage people to use: *ACTUAL* intelligence 🤓
Great work, but I'm not a huge fan of this heavy use of annotations. They won't change when your data changes.
Instead you could use something like:
...
scale_y_continuous(labels = scales::label_currency())+
coord_cartesian(ylim = c(0, NA),
xlim = c(-0.6, 6.6), clip = "off", expand = FALSE) +
...
theme(
...
axis.text.y = element_text(hjust = 0, vjust = -0.3, size = 13, face = "bold", margin = margin(r = -58)),
...
)
The definition of margin is not ideal (it's hardcoded and might need adaption) but I think it's overall a more "robust" solution
Thanks for your code! It's great to see people riffing off of what I'm sharing 🤓. To get yours to match mine, I had to adjust the margin (mainly the left) since the gridlines were going all the way to the left edge of the figure...
axis.text.y = element_text(hjust = 0, vjust = -0.3,
size = 13, face = "bold", margin = margin(l = 10, r = -40)),
I think I still prefer the annotate() approach since the meaning of the x and y positions are a bit more clear than the units on the margin and the vjust. For what it's worth, I tried adding fake 2024 data and I don't think it would take much/any fiddling to get it to look right. Thanks again for sharing - there's certainly multiple ways to do this