14:46 rather than typing out the breaks and labels manually, how about using: breaks = 10^(0:6), labels = format(10^(0:6), scientific = FALSE, big.mark = ",") It's a lot shorter, but also less susceptible to typos.
I started dabbling in R about 10 years ago in the starting phases of tidyverse and my code was a mixture of base-R and early tidyverse and lots of loooong codelines. When COVID-19 hit in 2020, I picked up the pace of data analysis with R more and started really learning both base and tidyverse a lot better. Comparing what I do now vs even 2020 is a big(!!!) difference in terms of clean coding and efficiency. Tip on capitalizing: stringr (loaded with tidyverse) has the functions str_to_*() which can help. dog [1] "The Quick Brown Dog" str_to_sentence("the quick brown dog") #> [1] "The quick brown dog
Can you tell us the difference between working with RStudio o Visual Studio? I know this question kind sound a bit silly but I have been using Visual Studio for making SSIS Packages, and altought I knew it can be set up for work with code (python , R, you name it) I never used it this way (mainly because I use Rstudio for R and Jupyter notebooks or Spyder for Python). Maybe a video comparing the two idles? Also I know that I can use Python with RStudio, maybe in the future I can transition to only one idle, and just use Visual Studio.
Hey Matias - thanks for watching! If you're coding in R on a local computer, I think R studio is the way to go. It's just got all the bells and whistles that you need. For this (and the previous) video, my data were living up on a computer cluster and I couldn't easily use RStudio. So I use VSCode when I'm working on the cluster. I'm still getting my legs under me when it comes to using VSCode, but when I have a better feeling about it, I'll circle back and do a comparison video. Thanks for the suggestion!
@@mabenba I don't have any resources to recommend to you, I only follow through the VS Code documentation in their IDE to setup my own and that is where I write my R code now
Hello 👋 there, now I've been following you a great deal you can tell by email as I am also subscribed for the daily updates. I have made it my goal to become even one proficient in R and also pick up machine Learning. However, I am now finding myself somewhat limited by my machine (laptop). [2015 surface book ] I know I am pushing it over here, this is not the sort of thing you do. But if you had to recommend a machine, I noticed you use a MacBook. Would you reckon the new MacBook air m2 should do it? It's affordable and would be a step up for me to much more competent machine that's up to the task. My current machine turns on the fans when putting together a simple ggplot graph. It's embarrassing honestly.
I hate spending other people’s money! I love Macs and they’re a good on ramp to Linux which I think you’ll need to learn to do real heavy lifting for big datasets.
Thanks Pat. Normally I let R choose my factor variable colors when geom_pointing or geom_jittering. Will now use scale_color_manual more often.
Great! I’m not a fan of the default colors 😂
14:46 rather than typing out the breaks and labels manually, how about using:
breaks = 10^(0:6), labels = format(10^(0:6), scientific = FALSE, big.mark = ",")
It's a lot shorter, but also less susceptible to typos.
Can you also do a video on Sankey plot, if possible ?
I’ll keep it in mind but it requires a special dataset and it’s not my usual practice to use toy datasets
I started dabbling in R about 10 years ago in the starting phases of tidyverse and my code was a mixture of base-R and early tidyverse and lots of loooong codelines. When COVID-19 hit in 2020, I picked up the pace of data analysis with R more and started really learning both base and tidyverse a lot better. Comparing what I do now vs even 2020 is a big(!!!) difference in terms of clean coding and efficiency.
Tip on capitalizing: stringr (loaded with tidyverse) has the functions str_to_*() which can help.
dog [1] "The Quick Brown Dog"
str_to_sentence("the quick brown dog")
#> [1] "The quick brown dog
Thanks!
Can you tell us the difference between working with RStudio o Visual Studio? I know this question kind sound a bit silly but I have been using Visual Studio for making SSIS Packages, and altought I knew it can be set up for work with code (python , R, you name it) I never used it this way (mainly because I use Rstudio for R and Jupyter notebooks or Spyder for Python). Maybe a video comparing the two idles?
Also I know that I can use Python with RStudio, maybe in the future I can transition to only one idle, and just use Visual Studio.
Hey Matias - thanks for watching! If you're coding in R on a local computer, I think R studio is the way to go. It's just got all the bells and whistles that you need. For this (and the previous) video, my data were living up on a computer cluster and I couldn't easily use RStudio. So I use VSCode when I'm working on the cluster. I'm still getting my legs under me when it comes to using VSCode, but when I have a better feeling about it, I'll circle back and do a comparison video. Thanks for the suggestion!
@Matias VS Code is a very good IDE and I prefare using it to RStudio because it has a lot of features and extensions
@@oluwafemioyedele thanks for your answer! Can you recommend me a resource to learn more or do you think that I just have to jump right in?
@@mabenba I don't have any resources to recommend to you, I only follow through the VS Code documentation in their IDE to setup my own and that is where I write my R code now
Hello 👋 there, now I've been following you a great deal you can tell by email as I am also subscribed for the daily updates.
I have made it my goal to become even one proficient in R and also pick up machine Learning. However, I am now finding myself somewhat limited by my machine (laptop). [2015 surface book ]
I know I am pushing it over here, this is not the sort of thing you do. But if you had to recommend a machine, I noticed you use a MacBook. Would you reckon the new MacBook air m2 should do it? It's affordable and would be a step up for me to much more competent machine that's up to the task.
My current machine turns on the fans when putting together a simple ggplot graph. It's embarrassing honestly.
I hate spending other people’s money! I love Macs and they’re a good on ramp to Linux which I think you’ll need to learn to do real heavy lifting for big datasets.