anndataR/man/generate_dataset.Rd at 0d6341ee6ee6c92585c138114560f2d669d8033b · scverse/anndataR · GitHub

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generate_dataset.R
\name{generate_dataset}
\alias{generate_dataset}
\title{Generate a dataset}
\usage{
generate_dataset(
  n_obs = 10L,
  n_vars = 20L,
  x_type = "numeric_matrix",
  layer_types = c("numeric_matrix", "numeric_dense", "numeric_csparse",
    "numeric_rsparse", "numeric_matrix_with_nas", "numeric_dense_with_nas",
    "numeric_csparse_with_nas", "numeric_rsparse_with_nas", "integer_matrix",
    "integer_dense", "integer_csparse", "integer_rsparse", "integer_matrix_with_nas",
    "integer_dense_with_nas", "integer_csparse_with_nas", "integer_rsparse_with_nas"),
  obs_types = c("character", "integer", "factor", "factor_ordered", "logical", "numeric",
    "character_with_nas", "integer_with_nas", "factor_with_nas",
    "factor_ordered_with_nas", "logical_with_nas", "numeric_with_nas"),
  var_types = c("character", "integer", "factor", "factor_ordered", "logical", "numeric",
    "character_with_nas", "integer_with_nas", "factor_with_nas",
    "factor_ordered_with_nas", "logical_with_nas", "numeric_with_nas"),
  obsm_types = c("numeric_matrix", "numeric_dense", "numeric_csparse", "numeric_rsparse",
    "numeric_matrix_with_nas", "numeric_dense_with_nas", "numeric_csparse_with_nas",
    "numeric_rsparse_with_nas", "integer_matrix", "integer_dense", "integer_csparse",
    "integer_rsparse", "integer_matrix_with_nas", "integer_dense_with_nas",
    "integer_csparse_with_nas", "integer_rsparse_with_nas", "character", "integer",
    "factor", "factor_ordered", "logical", "numeric", "character_with_nas",
    "integer_with_nas", "factor_with_nas",
     "factor_ordered_with_nas",
    "logical_with_nas", "numeric_with_nas"),
  varm_types = c("numeric_matrix", "numeric_dense", "numeric_csparse", "numeric_rsparse",
    "numeric_matrix_with_nas", "numeric_dense_with_nas", "numeric_csparse_with_nas",
    "numeric_rsparse_with_nas", "integer_matrix", "integer_dense", "integer_csparse",
    "integer_rsparse", "integer_matrix_with_nas", "integer_dense_with_nas",
    "integer_csparse_with_nas", "integer_rsparse_with_nas", "character", "integer",
    "factor", "factor_ordered", "logical", "numeric", "character_with_nas",
    "integer_with_nas", "factor_with_nas",
     "factor_ordered_with_nas",
    "logical_with_nas", "numeric_with_nas"),
  obsp_types = c("numeric_matrix", "numeric_dense", "numeric_csparse", "numeric_rsparse",
    "numeric_matrix_with_nas", "numeric_dense_with_nas", "numeric_csparse_with_nas",
    "numeric_rsparse_with_nas", "integer_matrix", "integer_dense", "integer_csparse",
    "integer_rsparse", "integer_matrix_with_nas", "integer_dense_with_nas",
    "integer_csparse_with_nas", "integer_rsparse_with_nas"),
  varp_types = c("numeric_matrix", "numeric_dense", "numeric_csparse", "numeric_rsparse",
    "numeric_matrix_with_nas", "numeric_dense_with_nas", "numeric_csparse_with_nas",
    "numeric_rsparse_with_nas", "integer_matrix", "integer_dense", "integer_csparse",
    "integer_rsparse", "integer_matrix_with_nas", "integer_dense_with_nas",
    "integer_csparse_with_nas", "integer_rsparse_with_nas"),
  uns_types = c("scalar_character", "scalar_integer", "scalar_factor",
    "scalar_factor_ordered", "scalar_logical", "scalar_numeric",
    "scalar_character_with_nas", "scalar_integer_with_nas", "scalar_factor_with_nas",
    "scalar_factor_ordered_with_nas", "scalar_logical_with_nas",
    "scalar_numeric_with_nas", "vec_character", "vec_integer", "vec_factor",
    "vec_factor_ordered", "vec_logical", "vec_numeric", "vec_character_with_nas",
    "vec_integer_with_nas", "vec_factor_with_nas", "vec_factor_ordered_with_nas",
    "vec_logical_with_nas",
     "vec_numeric_with_nas", "df_character", "df_integer",
    "df_factor", "df_factor_ordered", "df_logical", "df_numeric",
    "df_character_with_nas", "df_integer_with_nas", "df_factor_with_nas",
    "df_factor_ordered_with_nas", "df_logical_with_nas", "df_numeric_with_nas",
    "mat_numeric_matrix", "mat_numeric_dense", "mat_numeric_csparse",
    "mat_numeric_rsparse", "mat_numeric_matrix_with_nas", "mat_numeric_dense_with_nas",
    "mat_numeric_csparse_with_nas", "mat_numeric_rsparse_with_nas", "mat_integer_matrix",

     "mat_integer_dense", "mat_integer_csparse", "mat_integer_rsparse",
    "mat_integer_matrix_with_nas", "mat_integer_dense_with_nas",
    "mat_integer_csparse_with_nas", "mat_integer_rsparse_with_nas", "list"),
  example = FALSE,
  format = c("list", "AnnData", "SingleCellExperiment", "Seurat")
)
}
\arguments{
\item{n_obs}{Number of observations to generate}

\item{n_vars}{Number of variables to generate}

\item{x_type}{Type of matrix to generate for X}

\item{layer_types}{Types of matrices to generate for layers}

\item{obs_types}{Types of vectors to generate for obs}

\item{var_types}{Types of vectors to generate for var}

\item{obsm_types}{Types of matrices to generate for obsm}

\item{varm_types}{Types of matrices to generate for varm}

\item{obsp_types}{Types of matrices to generate for obsp}

\item{varp_types}{Types of matrices to generate for varp}

\item{uns_types}{Types of objects to generate for uns}

\item{example}{If \code{TRUE}, the types will be overridden to a small set of
types. This is useful for documentations.}

\item{format}{Object type to output, one of "list", "AnnData",
"SingleCellExperiment", or "Seurat".}
}
\value{
Object containing the generated dataset as defined by \code{output}
}
\description{
Generate a dataset with different types of columns and layers
}
\examples{
dummy <- generate_dataset()
}