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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)
#> Loading required package: caret
#> Loading required package: ggplot2
#> Loading required package: lattice
#> 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 0.3.7.
#>  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 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)
#> 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

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)
#> 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, 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")
#> 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 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))
#> 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.