plot_biv_compare {sampcompR} | R Documentation |
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
)
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 |
grid |
A character string, that determines the color of the lines between the tiles of the heatmap. |
diff_perc |
If |
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.
Value
A object generated with the help of ggplot2::ggplot2()
, used to visualize
the differences between the data frames and benchmarks.
Examples
## Get Data for comparison
require(wooldridge)
card<-wooldridge::card
south <- card[card$south==1,]
north <- card[card$south==0,]
black <- card[card$black==1,]
white <- card[card$black==0,]
## use the function to plot the data
bivar_data<-sampcompR::biv_compare(dfs = c("north","white"),
benchmarks = c("south","black"),
variables= c("age","educ","fatheduc","motheduc","wage","IQ"),
data=TRUE)
sampcompR::plot_biv_compare(bivar_data)