multi.gg.cor.vis: Differential correlation visualisation

In emily5/exCorr: An R package for differential correlation analyses

Description

Creates two correlation plots between two variables, separated by a grouping variable. Requires datasets with same samples.

Usage

multi.gg.cor.vis(var_1, var_2, data1, data2, sample_col, group,
  group_1_title = "Group 1 Correlation",
  group_2_title = "Group 2 Correlation")

Arguments

var_1	character string defining the first variable for differential correlation
var_2	character string defining the second variable for differential correlation
data1	dataframe containing var_1
data2	dataframe containing var_2
sample_col	character string defining the sample or identifying column in both dataframes
group	character string defining the grouping variable for comparative differential correlations
group_1_title	character string defining the first plot title
group_2_title	character string defining the second plot title

Value

two correlation plots for each group with correlation coefficents and p-values

Author(s)

Emily Mears, [e-mail], Matthew Grant, [e-mail]

Ben Day, [e-mail]

Examples

## Load example dataframes
df1 <- read.csv("example_data/excorr_df1.csv")
df2 <- read.csv("example_data/excorr_df2.csv")

## Run function
multi.gg.cor.vis(var_1 = "NTRpFI", var_2 = "X70J8B6", df1, df2, group = "sex")
multi.gg.cor.vis(var_1 = "NTRpFI", var_2 = "X70J8B6", df1, df2, group = "sex", group_1_title = "Group 1 Correlation", group_2_title = "Group 2 Correlation")

emily5/exCorr documentation built on May 22, 2020, 1:01 p.m.