#tinyplot — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #tinyplot, aggregated by home.social.
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CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
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CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
-
CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
-
CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
-
CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
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@lwpembleton I just came across your post. I took it as inspiration to achieve sth similar with base #Rstats and #tinyplot
There is one small issue that I could not solve instantly with the Cylinder as a factor. Therefore, this `Cylinders <- as.factor(mtcars$cyl)` hack. Maybe @gmcd or @zeileis has a quick suggestion?
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Gráficos feitos com #RStats, usando #RDataTable para ler e tabular os dados, e #TinyPlot (@gmcd) para plotar
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#rdatatable is a pleasure to work with. Sometimes mind bending (why does it not work? 🤔 … ahh, lists 🤦♂️)
but most of the time great.I use it in combination with #tinyplot. I had situations where my code got faster and shorter by doing this.
Enjoy
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Christmal special for the R base plot folks:
take a look at the latest #tinyplot developments.
@gmcd et al. have a nice tool for you.
install.packages("tinyplot", repos = "https://grantmcdermott.r-universe.dev")
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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Hi #rstats
Can anybody point me to good base R plot resources (templates, tutorials …) please?
Something like #tinyplot and Zeileis & Murrell, "Coloring in R's Blind Spot", The R Journal, 2023 https://doi.org/10.32614/RJ-2023-071
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
