Learn efficient ways to collapse text by group in R! Explore base R's aggregate(), dplyr's group_by() and summarise(), and data.table's grouping. Mastering these techniques enhances data preprocessing skills. Try these examples with your datasets to optimize workflows. Happy coding! 📊💻
👍 In R, you can easily extract specific columns from a data frame by their numerical positions. For instance, to grab the second column from a data frame df, you can use df[, 2].
🙅♂️ You can also exclude columns by using negative indexing, such as df[, -2] to exclude the second column.
#KDE will mentor ten projects in Google Summer of Code (#GSoC) this year, including two projects for #LabPlot, a FREE, open source and cross-platform #DataVisualization and #DataAnalysis software.
The plotting, statistical, and data selection tools in the mapdata.py data explorer (https://pypi.org/project/mapdata/) can be used even if you don't have any map data. Just add dummy latitude and longitude values to the data table. Zeroes will do. The map and the dummy columns can both be hidden, and you can then explore the data table with the other available tools.
@hyp3r00t Here is the response I got from the publisher:
"our books are released internationally, including in India, and are distributed through over 45 channels. Examples include Amazon, O'Reilly, Barnes and Noble, Safari, Packt and more.
We have Print-on-Demand vendor who prints copies."
We dive deep into simplifying outlier detection in R using #easystats to follow good practices and make your data analysis more robust and replicable. Check it out! #Rstats#DataAnalysis@rstats