All variables must be continuous.
The matrix will be returned as an element of `ggplot`

object.
This is basically a wrapper of R package
ggcorrplot.

show_cor(
data,
x_vars = colnames(data),
y_vars = x_vars,
cor_method = "spearman",
vis_method = "square",
lab = TRUE,
test = TRUE,
hc_order = FALSE,
p_adj = NULL,
...
)

## Arguments

data |
a `data.frame` . |

x_vars |
variables/column names shown in x axis. |

y_vars |
variables/column names shown in y axis. |

cor_method |
method for correlation, default is 'spearman'. |

vis_method |
visualization method, default is 'square',
can also be 'circle'. |

lab |
logical value. If TRUE, add correlation coefficient on the plot. |

test |
if `TRUE` , run test for correlation and mark significance. |

hc_order |
logical value. If `TRUE` ,
correlation matrix will be hc.ordered using `hclust` function. |

p_adj |
p adjust method, see stats::p.adjust for details. |

... |
other parameters passing to `ggcorrplot::ggcorrplot()` . |

## Value

a `ggplot`

object

## See also

## Examples

data("mtcars")
p1 <- show_cor(mtcars)
p2 <- show_cor(mtcars,
x_vars = colnames(mtcars)[1:4],
y_vars = colnames(mtcars)[5:8]
)
p3 <- show_cor(mtcars, vis_method = "circle", p_adj = "fdr")
p1
p1$cor
p2
p3
## Auto detect problem variables
mtcars$xx <- 0L
p4 <- show_cor(mtcars)
p4