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
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) #> Loading required package: caret #> Loading required package: lattice #> Loading required package: ggplot2 #> Loading required package: Metrics #> #> Attaching package: 'Metrics' #> The following objects are masked from 'package:caret': #> #> precision, recall #> Loading required package: e1071 #> Welcome to manymodelr. This is manymodelr version 0.3.6. #> Please file issues and feedback at https://www.github.com/Nelson-Gon/manymodelr/issues #> Turn this message off using 'suppressPackageStartupMessages(library(manymodelr))' #> Happy Modelling! :) # getall correlations # default pearson head( corrs <- get_var_corr(mtcars,comparison_var="mpg") ) #> comparison_var other_var p.value correlation lower_ci upper_ci #> 1 mpg cyl 6.112687e-10 -0.8521620 -0.92576936 -0.7163171 #> 2 mpg disp 9.380327e-10 -0.8475514 -0.92335937 -0.7081376 #> 3 mpg hp 1.787835e-07 -0.7761684 -0.88526861 -0.5860994 #> 4 mpg drat 1.776240e-05 0.6811719 0.43604838 0.8322010 #> 5 mpg wt 1.293959e-10 -0.8676594 -0.93382641 -0.7440872 #> 6 mpg qsec 1.708199e-02 0.4186840 0.08195487 0.6696186
Previously, one would also set
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
data("yields", package="manymodelr") # purely demonstrative get_var_corr(yields,"height",other_vars="weight", drop_columns=c("factor","character"),method="spearman", exact=FALSE) #> Warning in get_var_corr.data.frame(yields, "height", other_vars = "weight", : #> Columns with classes in drop_columns have been discarded. You can disable this #> yourself by setting drop_columns to NULL. #> comparison_var other_var p.value correlation #> 1 height weight 4.204642e-07 -0.1591719
get_var_corr_ (note the underscore at the end) provides a convenient way to get combination-wise correlations.
head(get_var_corr_(yields),6) #> Warning in get_var_corr_.data.frame(yields): Columns with classes in #> drop_columns were dropped. #> comparison_var other_var p.value correlation lower_ci upper_ci #> 1 height weight 1.470866e-08 -0.17793196 -0.23730741 -0.11723201 #> 2 height yield 4.473683e-01 0.02405390 -0.03799584 0.08591886 #> 3 weight yield 2.986171e-01 0.03290108 -0.02915146 0.09470100
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)) #> comparison_var other_var p.value correlation #> 2 mpg disp 6.370336e-13 -0.9088824 #> 5 mpg wt 1.487595e-11 -0.8864220
Obtaining correlations would mostly likely benefit from some form of visualization.
plot_corr aims to achieve just that. There are currently two plot styles,
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
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") #> Warning in plot_corr(mtcars, show_which = "corr", round_which = "correlation", : #> Using colour_by for the legend title.
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
# 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)) #> Warning in plot_corr(mtcars, x = "other_var", y = "comparison_var", plot_style = #> "circles", : Using colour_by for the legend title.
To explore more options, please take a look at the documentation.