corp_by {quest}

Bivariate Correlations with Significant Symbols by Group

Description

corp_by computes a correlation data.frame for each group within numeric data. The correlation coefficients are appended by their significant symbols based on their associated p-values. If only the correlation coefficients are desired, use cor_by which returns a list of numeric matrices. corp_by is simply corp + by2.

Usage

corp_by(
  data,
  vrb.nm,
  grp.nm,
  use = "pairwise.complete.obs",
  method = "pearson",
  sep = ".",
  digits = 3L,
  p.10 = "",
  p.05 = "*",
  p.01 = "**",
  p.001 = "***",
  lead.zero = FALSE,
  trail.zero = TRUE,
  plus = FALSE,
  diags = FALSE,
  lower = TRUE,
  upper = FALSE
)

Arguments

data

data.frame of data.

vrb.nm

character vector of colnames from data specifying the variables.

grp.nm

character vector of colnames from data specifying the groups.

use

character vector of length 1 specifying how to handle missing data when computing the correlations. The options are 1) "pairwise.complete.obs", 2) "complete.obs", 3) "na.or.complete", 4) "all.obs", or 5) "everything". See details of cor.

method

character vector of length 1 specifying the type of correlations to be computed. The options are 1) "pearson", 2) "kendall", or 3) "spearman". See details of cor.

sep

character vector of length 1 specifying the string to combine the group values together with. sep is only used if there are multiple grouping variables (i.e., length(grp.nm) > 1).

digits

integer vector of length 1 specifying the number of decimals to round to.

p.10

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .10 level.

p.05

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .05 level.

p.01

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .01 level.

p.001

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .001 level.

lead.zero

logical vector of length 1 specifying whether to retain a zero in front of the decimal place.

trail.zero

logical vector of length 1 specifying whether to retain zeros after the decimal place (due to rounding).

plus

logical vector of length 1 specifying whether to include a plus sign in front of positive correlations (minus signs are always in front of negative correlations).

diags

logical vector of length 1 specifying whether to retain the values in the diagonal of the correlation matrix. If TRUE, then the diagonal will be 1s with digits number of zeros after the decimal place (and no significant symbols). If FALSE, then the diagonal will be NA.

lower

logical vector of length 1 specifying whether to retain the lower triangle of the correlation matrix. If TRUE, then the lower triangle correlations and their significance symbols are retained. If FAlSE, then the lower triangle will all be NA.

upper

logical vector of length 1 specifying whether to retain the upper triangle of the correlation matrix. If TRUE, then the upper triangle correlations and their significance symbols are retained. If FAlSE, then the upper triangle will all be NA.

Value

list of data.frames containing the correlation coefficients and their appended significance symbols based upon their associated p-values. The listnames are the unique combinations of the grouping variables, separated by "sep" if multiple grouping variables (i.e., length(grp.nm) > 1) are input: unique(interaction(data[grp.nm], sep = sep)). For each data.frame, the rownames and colnames = vrb.nm. The significance symbols are specified by the arguments p.10, p.05, p.01, and p.001, after the correlation value. The specific elements of the return object are determined by the other arguments.

See Also

corp cor_by cor

Examples

# one grouping variable
corp_by(airquality, vrb.nm = c("Ozone","Solar.R","Wind"), grp.nm = "Month")
corp_by(airquality, vrb.nm = c("Ozone","Solar.R","Wind"), grp.nm = "Month",
   use = "complete.obs", method = "spearman")

# two grouping variables
corp_by(mtcars, vrb.nm = c("mpg","disp","drat","wt"), grp.nm = c("vs","am"))
corp_by(mtcars, vrb.nm = c("mpg","disp","drat","wt"), grp.nm = c("vs","am"),
   use = "complete.obs", method = "spearman", sep = "_")