Generate correlation matrix with specific columns and only with significant values in corrplot

I would use the well established Hmisc::rcorr for the calculations. In corrplot::corrplot, subset both the corr= and the p.mat= with [1:6, 7:14].

c_df <- Hmisc::rcorr(cor(correlation_df), type='spearman')

library(corrplot)
corrplot(corr=c_df$r[1:6, 7:14], p.mat=c_df$P[1:6, 7:14], sig.level=0.05, 
         method='color', diag=FALSE, addCoef.col=1, type='upper', insig='blank',
         number.cex=.8)

This appears to correspond to the p-values.

m <- c_df$P[1:6, 7:14] < .05
m[lower.tri(m, diag=TRUE)] <- ''
as.data.frame(replace(m, lower.tri(m, diag=TRUE), ''))
#    Al    Fe    Mn   Zn    Mo Baresoil Humdepth    pH
# N     FALSE FALSE TRUE FALSE    FALSE    FALSE FALSE
# P            TRUE TRUE FALSE    FALSE    FALSE FALSE
# K                 TRUE FALSE    FALSE    FALSE  TRUE
# Ca                     FALSE     TRUE     TRUE FALSE
# Mg                               TRUE     TRUE  TRUE
# S                                        FALSE FALSE