sampcompR (version 0.2.6)

plot_biv_compare: Plot Comparison of Multiple Data Frames on a Bivariate Level

Description

Plot a object generated by biv_compare function.

Usage

plot_biv_compare(
  biv_data_object,
  plot_title = NULL,
  plots_label = NULL,
  p_value = NULL,
  varlabels = NULL,
  mar = c(0, 0, 0, 0),
  note = FALSE,
  grid = "white",
  diff_perc = TRUE,
  diff_perc_size = 4.5,
  perc_diff_transparance = 0,
  gradient = FALSE,
  sum_weights = NULL,
  missings_x = TRUE,
  order = NULL,
  breaks = NULL,
  colors = NULL,
  ncol_facet = 3
)

Value

A object generated with the help of ggplot2::ggplot2(), used to visualize the differences between the data frames and benchmarks.

Arguments

biv_data_object

A object generated by the biv_compare function.

plot_title

A character string containing the title of the plot.

plots_label

A character string or vector of character strings containing the new labels of the data frames that are used in the plot.

p_value

A number between 0 and one to determine the maximum significance niveau.

varlabels

A character string or vector of character strings containing the new labels of variables that are used in the plot.

mar

A vector that determines the margins of the plot.

note

If note = TRUE, a note will be displayed to describe the plot.

grid

A character string, that determines the color of the lines between the tiles of the heatmap.

diff_perc

If TRUE a percental measure of difference between dfs and benchmarks is displayed in the plot.

diff_perc_size

A number to determine the size of the displayed percental difference between surveys in the plot.

perc_diff_transparance

A number to determine the transparency of the displayed percental-difference between surveys in the plot.

gradient

If gradient = TRUE, colors in the heatmap will be more or less transparent, depending on the difference in Pearson's r of the data frames of comparison.

sum_weights

A vector containing information for every variable to weigh them in the displayed percental difference calculation. It can be used if some variables are over- or underrepresented in the analysis.

missings_x

If TRUE, missing pairs in the plot will be marked with an X.

order

A character vector to determine in which order the variables should be displayed in the plot.

breaks

A vector to label the color scheme in the legend.

colors

A vector to determine the colors in the plot.

ncol_facet

Number of columns used in faced_wrap() for the plots.

Details

The plot shows a heatmap of a correlation matrix, where the colors are determined by the similarity of the Pearson's r value in both sets of respondents. Leaving default breaks and colors,

• Same (green) indicates, that the Pearson's r correlation is not significant > 0 in the related data frame or benchmark or the Pearson's r correlations are not significant different, between data frame and benchmark.

• Small Diff (yellow) indicates that the Pearson's r correlation is significant > 0 in the related data frame or benchmark and the Pearson's r correlations are significant different, between data frame and benchmark.

• Large Diff (red) indicates, that the same coditions of yellow are fulfilled, and the correlations are either in opposite directions,or one is double the size of the other.

Examples

## Get Data for comparison

data("card")

north <- card[card$south==0,]
white <- card[card$black==0,]

## use the function to plot the data 
bivar_data<-sampcompR::biv_compare(dfs = c("north","white"),
                                   benchmarks = c("card","card"),
                                   variables= c("age","educ","fatheduc","motheduc","wage","IQ"),
                                   data=TRUE)
                        
sampcompR::plot_biv_compare(bivar_data)