Subsetting matrices and data frames
WebSubsetting on lists works slightly different than on vectors and matrices. The basic concepts stay the same, however, due to the more complex structure of the object, additional operators for subsetting become available. Note that there is a distinct difference between single brackets ( [...]) and double brackets ( [ [...]] ). WebDataset arrays are used to collect heterogeneous data and metadata including variable and observation names into a single container variable. Dataset arrays are suitable for storing column-oriented or tabular data that are often stored as columns in a text file or in a spreadsheet, and can accommodate variables of different types, sizes, units, etc.
Subsetting matrices and data frames
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Web8 Nov 2024 · subset() function in R programming is used to create a subset of vectors, matrices, or data frames based on the conditions provided in the parameters. Syntax: …
WebThe most common way of subsetting matrices (2d) and arrays (>2d) is a simple generalisation of 1d subsetting: you supply a 1d index for each dimension, separated by a … Web4 Mar 2024 · Techniques for assignments and subsetting matrices and data frames. # same principle applied to both dimensions of a matrix m <- matrix(data=1:12,nrow=3) …
WebCN_subset_int[indexes, colnames(CN_mat_subset)] <- as.matrix(CN_mat_subset[driver_genes[indexes], ]) return(CN_subset_int)} #' Get a list of essential genes #' #' The function takes in RSA data and returns a vector of essential genes #' #' @param data data frame of RSA normalised values #' @param x primary site. Web21 Jul 2016 · 1: Basic Building Blocks 2: Workspace and Files 3: Sequences of Numbers 4: Vectors 5: Missing Values 6: Subsetting Vectors 7: Matrices and Data Frames 8: Logic 9: Functions 10: lapply and sapply 11: vapply and tapply 12: Looking at Data 13: Simulation 14: Dates and Times 15: Base Graphics Selection: 7 0%
Web23 Sep 2024 · Method 2: Subset dataframe Rows Based On Multiple Factor Levels. In this approach to subset the dataframe rows based on the multiple factor levels, the user simply needs to use the in-built operators of the R programming language, which will be including $,% and a vector which will be containing the required multiple factor levels, as per the …
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