knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
get_var_corr
As can probably(hopefully) be guessed from the name, this provides a convenient way to get variable correlations. It enables one to get correlation between one variable and all other variables in the data set.
Previously, one would set get_all
to TRUE
if they wanted to get correlations between all variables. This argument has been dropped in favor of simply supplying an optional other_vars
vector if one does not want to get all correlations.
library(manymodelr) # getall correlations # default pearson head( corrs <- get_var_corr(mtcars,comparison_var="mpg") )
Previously, one would also set drop_columns
to TRUE
if they wanted to drop factor columns. Now, a user simply provides a character vector specifying which column types(classes) should be dropped. It defaults to c("character","factor")
.
data("yields", package="manymodelr") # purely demonstrative get_var_corr(yields,"height",other_vars="weight", drop_columns=c("factor","character"),method="spearman", exact=FALSE)
Similarly, get_var_corr_
(note the underscore at the end) provides a convenient way to get combination-wise correlations.
head(get_var_corr_(yields),6)
To use only a subset of the data, we can use provide a list of columns to subset_cols
. By default, the first value(vector) in the list is mapped to comparison_var
and the other to other_Var
. The list is therefore of length 2.
head(get_var_corr_(mtcars,subset_cols=list(c("mpg","vs"),c("disp","wt")), method="spearman",exact=FALSE))
plot_corr
Obtaining correlations would mostly likely benefit from some form of visualization. plot_corr
aims to achieve just that. There are currently two plot styles, squares
and circles
. circles
has a shape
argument that can allow for more flexibility. It should be noted that the correlation matrix supplied to this function is an object produced by get_var_corr_
.
To modify the plot a bit, we can choose to switch the x and y values as shown below.
plot_corr(mtcars,show_which = "corr", round_which = "correlation",decimals = 2,x="other_var", y="comparison_var",plot_style = "squares" ,width = 1.1,custom_cols = c("green","blue","red"),colour_by = "correlation")
To show significance of the results instead of the correlations themselves, we can set show_which
to "signif" as shown below. By default, significance is set to 0.05. You can override this by supplying a different signif_cutoff
.
# color by p value # change custom colors by supplying custom_cols # significance is default set.seed(233) plot_corr(mtcars, x="other_var", y="comparison_var",plot_style = "circles",show_which = "signif", colour_by = "p.value", sample(colours(),3))
To explore more options, please take a look at the documentation.
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