R Function A Day @rfunctionaday
R function a day keeps the madness away 🧘♀️ — If you’re a new follower, check out older posts as well 😊 — From 24.01.2021 to 24.01.2022 — by @patilindrajeets Joined January 2021-
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This function no longer resides in {parameters} 📦. It can now be found in {datawizard} 📦: easystats.github.io/datawizard/ref…
If you want to get a quick overview of descriptive statistics for a numeric variable, {describe_distribution} from {parameters} 📦 is your friend 🙌 easystats.github.io/parameters/ref… Also works with a {grouped_} dataframe from {dplyr} 👏 #rstats #DataScience
This function no longer resides in {parameters} 📦. It can now be found in {performance} 📦: easystats.github.io/performance/re…
Factor analysis (FA) can help reduce many features to a few latent features. But one first needs to check if data is suitable for FA. The {check_factorstructure} function from {parameters} provides an informative and verbose way 🔍 easystats.github.io/parameters/ref… #rstats #DataScience
@IsmaelLCalandri @patilindrajeets
Today concludes this 1-year hobby project. There won’t be new posts, so feel free to unfollow, but do keep revisiting! 🙃 With > 22K followers in 365 posts, I guess you enjoyed reading them as I much as I’d preparing them. 😅 Best luck with your jouRneys! 🤞 @patilindrajeets
@malte_grosser @patilindrajeets Ask and ye shall receive 😅 bookdown.org/IndrajeetPatil… Thanks a lot for the suggestion! 🙌
@malte_grosser @patilindrajeets Good idea! Might work on that soon, but until then this timeline widget should come in handy 🧵
@Bishal_Bhardwaj @patilindrajeets The tweets from the past will always be accessible. They will neither be removed nor changed in any way.
If we need to bind multiple dataframes by rows, we may first wish to check if this can be done successfully. The {compare_df_cols} function from {janitor} 📦 provides a summary of data types to check this! 🍎🍏 sfirke.github.io/janitor/refere… #rstats #DataScience
@JonGretar @kiwiSteveTaylor @rstats4ds In real use cases, this routine will actually be used to access a variable - specified by the user - from a data frame. Note that now `data$x` won’t work because we don’t know a priori what `x` is going to be.
While working in a piped workflow, if you need to extract a single column, the syntax to do so can be a bit cumbersome. The {pull} function from {dplyr} 📦 provides a more readable syntax to this end! 💄 dplyr.tidyverse.org/reference/pull… #rstats #DataScience
@ThomVolker Yes, I have shared it in the past 👇 But, AFAIK, that operator is used for side effects. So you will be able to print that column but won’t be able to assign it to an object. I think.
Although a number of functions tend to have a {data} argument, some don’t. For such functions, the pipe operator (%>%) from {magrittr} 📦 won’t work. In such contexts, one can use the exposition pipe operator (%$%) 👏 magrittr.tidyverse.org/reference/expo… #rstats #DataScience
Visualizing data along an Archimedean spiral can efficiently reveal periodic patterns in time series data. The {spiral_*} function family from {spiralize} 📦 draws such spiral plots flexibly! 🌀 jokergoo.github.io/spiralize_vign… #rstats #DataScience
Serialization changes objects to a byte stream that can be saved to a binary file, while deserializing does the reverse. The {q*} function family from {qs} 📦 provides a performant way to serialize or deserialize any R object! ⚡️ rdrr.io/cran/qs/man/ #rstats #DataScience
Sometimes you may wish to include some patterns or images in plots for enhanced storytelling. The {image*} or {pattern*} function family from {patternplot} 📦 provides a way to do so! ⚜️ rdrr.io/cran/patternpl… #rstats #DataScience
@Fischuu None at all. In fact, AFAIK, the implementation of this data type in {reticulate} is based on names lists. Dictionary just provides a familiar name for bilingual users to a data structure that is not native to R but can be easily implemented.
Python dictionary is an unordered data type with key-value pairs that allows accessing values, not by indexing, but via unique keys. The {py_dict} function from {reticulate} 📦 gives you access to this data type in R! 📖 rstudio.github.io/reticulate/ref… #rstats #DataScience
Sometimes, especially in the context of statistical modeling, there might be infinite or NaN's present in outputs that we may wish to replace with NAs. The {zap_inf} helper function from {sjmisc} 📦 does exactly this! ♾ strengejacke.github.io/sjmisc/referen… #rstats #DataScience
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