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dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
There is also an active R community around the tidyverse. For example, there is the TidyTuesday social data project organised by the Data Science Learning Community (DSLC), [16] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier. [17]
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
RExcel is an add-on for Microsoft Excel that allows access to the statistics package R from within Excel. It uses the statconnDCOM server and, for certain configurations, the room package. It uses the statconnDCOM server and, for certain configurations, the room package.
R Commander (Rcmdr) is a GUI for the R programming language, licensed under the GNU General Public License, and developed and maintained by John Fox in the sociology department at McMaster University. [2] Rcmdr looks and works similarly to SPSS GUI by providing a menu of analytic and graphical methods. It also displays the underlying R code ...
Pivot tables are not created automatically. For example, in Microsoft Excel one must first select the entire data in the original table and then go to the Insert tab and select "Pivot Table" (or "Pivot Chart"). The user then has the option of either inserting the pivot table into an existing sheet or creating a new sheet to house the pivot table.
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.