Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.

  retx = TRUE,
  center = TRUE,
  scale. = TRUE,
  tol = NULL,
  rank. = NULL



a data.frame containing numeric columns


columns of x to be selected for PCA, can be unquoted since it supports quasiquotation.


a logical value indicating whether the rotated variables should be returned.


a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale.


a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale.


a value indicating the magnitude below which components should be omitted. (Components are omitted if their standard deviations are less than or equal to tol times the standard deviation of the first component.) With the default null setting, no components are omitted (unless rank. is specified less than min(dim(x)).). Other settings for tol could be tol = 0 or tol = sqrt(.Machine$double.eps), which would omit essentially constant components.


optionally, a number specifying the maximal rank, i.e., maximal number of principal components to be used. Can be set as alternative or in addition to tol, useful notably when the desired rank is considerably smaller than the dimensions of the matrix.


An object of classes pca and prcomp


The pca() function takes a data.frame as input and performs the actual PCA with the R function prcomp().

The result of the pca() function is a prcomp object, with an additional attribute non_numeric_cols which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by ggplot_pca().

Stable Lifecycle

The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

Read more on Our Website!

On our website you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.


# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.

# \donttest{
if (require("dplyr")) {
  # calculate the resistance per group first 
  resistance_data <- example_isolates %>% 
    group_by(order = mo_order(mo),       # group on anything, like order
             genus = mo_genus(mo)) %>%   #   and genus as we do here;
    summarise_if(is.rsi, resistance)     # then get resistance of all drugs
  # now conduct PCA for certain antimicrobial agents
  pca_result <- resistance_data %>%         
    pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT) 
  ggplot_pca(pca_result) # a new and convenient plot function
# }