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mixed_assoc.R
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mixed_assoc.R
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require(tidyverse)
pacman::p_load(rcompanion)
# Calculate a pairwise association between all variables in a data-frame. In particular nominal vs nominal with Chi-square, numeric vs numeric with Pearson correlation, and nominal vs numeric with ANOVA.
# Adopted from https://stackoverflow.com/a/52557631/590437
mixed_assoc = function(df, cor_method="spearman", adjust_cramersv_bias=TRUE){
df_comb = expand.grid(names(df), names(df), stringsAsFactors = F) %>% purrr::set_names("X1", "X2")
is_nominal = function(x) class(x) %in% c("factor", "character")
# https://community.rstudio.com/t/why-is-purr-is-numeric-deprecated/3559
# https://github.com/r-lib/rlang/issues/781
is_numeric <- function(x) { is.integer(x) || is_double(x)}
f = function(xName,yName) {
x = pull(df, xName)
y = pull(df, yName)
result = if(is_nominal(x) && is_nominal(y)){
# use bias corrected cramersV as described in https://rdrr.io/cran/rcompanion/man/cramerV.html
cv = cramerV(as.character(x), as.character(y), bias.correct = adjust_cramersv_bias)
data.frame(xName, yName, assoc=cv, type="cramersV")
}else if(is_numeric(x) && is_numeric(y)){
correlation = cor(x, y, method=cor_method, use="complete.obs")
data.frame(xName, yName, assoc=correlation, type="correlation")
}else if(is_numeric(x) && is_nominal(y)){
# from https://stats.stackexchange.com/questions/119835/correlation-between-a-nominal-iv-and-a-continuous-dv-variable/124618#124618
r_squared = summary(lm(x ~ y))$r.squared
data.frame(xName, yName, assoc=sqrt(r_squared), type="anova")
}else if(is_nominal(x) && is_numeric(y)){
r_squared = summary(lm(y ~x))$r.squared
data.frame(xName, yName, assoc=sqrt(r_squared), type="anova")
}else {
warning(paste("unmatched column type combination: ", class(x), class(y)))
}
# finally add complete obs number and ratio to table
result %>% mutate(complete_obs_pairs=sum(!is.na(x) & !is.na(y)), complete_obs_ratio=complete_obs_pairs/length(x)) %>% rename(x=xName, y=yName)
}
# apply function to each variable combination
map2_df(df_comb$X1, df_comb$X2, f)
}
if(F){
mixed_assoc(iris)
require(corrr)
msleep %>%
select(- name) %>%
mixed_assoc() %>%
select(x, y, assoc) %>%
spread(y, assoc) %>%
column_to_rownames("x") %>%
as.matrix %>%
as_cordf %>%
network_plot()
}