correlation matrices

This is the code that I'm worried about. I am not sure if it gave me reliable correlation matrices.

This is the page I used. I'm having trouble figuring out if I did a pearson correlation. Or maybe I didn't complete the correlation matrix?
http://www.sthda.com/english/wiki/correlation-matrix-a-quick-start-guide-to-analyze-format-and-visualize-a-correlation-matrix-using-r-software

WyoP/code/1-dataprocessing-ghP.R

Lines 78 to 150 in af2a5a4

    
           #correlation matrix for all cover crops (no fallow) no caco3/inorganic c 
        
           corr_data = 
        
             pall2 %>% 
        
             filter(ctrt != 'Fallow') %>%  
        
             select(cbio, wbio, pbic_percbio, amac_percbio, unavp_percbio,  
        
                    porg_percbio, amm_percbio, nit_percbio, 
        
                    pmn_percbio, pmc_percbio) %>%  
        
             na.omit() %>%  
        
             force() 
        
           matrix1 <- rcorr(as.matrix(corr_data)) 
        
           matrix1 
        
           # Extract the correlation coefficients 
        
           corrpercbio_r <- matrix1$r 
        
           # Extract p-values 
        
           corrpercbio_p <- matrix1$P 
        
           write.csv(corrpercbio_r, "corrpercbio_r.csv", row.names = TRUE) 
        
           write.csv(corrpercbio_p, "corrpercbio_p.csv", row.names = TRUE) 
        
           #correlation matrix for all cover crops with caco3/inorganic c (time1 only) 
        
           corr_data2 = 
        
             pall2 %>% 
        
             filter(time == '1') %>%  
        
             select(cbio, wbio, pbic_percbio, amac_percbio, unavp_percbio,  
        
                    porg_percbio, inorgcarbon_percbio, amm_percbio, nit_percbio, 
        
                    pmn_percbio, pmc_percbio) %>%  
        
             force() 
        
           matrix2 <- rcorr(as.matrix(corr_data2)) 
        
           matrix2 
        
           ##rcorr(matrix2, type = c("pearson")) 
        
           # Extract the correlation coefficients 
        
           corrpercbiocaco3_r <- matrix2$r 
        
           # Extract p-values 
        
           corrpercbiocaco3_p <- matrix2$P 
        
           write.csv(corrpercbiocaco3_r, "corrpercbiocaco3_r.csv", row.names = TRUE) 
        
           write.csv(corrpercbiocaco3_p, "corrpercbiocaco3_p.csv", row.names = TRUE) 
        
           #correlation matrix no standardization, no caco3 
        
           corr_data3 = 
        
             pall2 %>% 
        
             #filter(time == '1') %>%  
        
             select(cbio, wbio, pbic, amac, unavp,  
        
                    porg, amm, nit, 
        
                    pmn, pmc) %>%  
        
             force() 
        
           matrix3 <- rcorr(as.matrix(corr_data3)) 
        
           matrix3 
        
           # Extract the correlation coefficients 
        
           corr_r <- matrix3$r 
        
           # Extract p-values 
        
           corr_p <- matrix3$P 
        
           write.csv(corr_r, "corr_r.csv", row.names = TRUE) 
        
           write.csv(corr_p, "corr_p.csv", row.names = TRUE)

	#correlation matrix for all cover crops (no fallow) no caco3/inorganic c

	corr_data =
	pall2 %>%
	filter(ctrt != 'Fallow') %>%
	select(cbio, wbio, pbic_percbio, amac_percbio, unavp_percbio,
	porg_percbio, amm_percbio, nit_percbio,
	pmn_percbio, pmc_percbio) %>%
	na.omit() %>%
	force()

	matrix1 <- rcorr(as.matrix(corr_data))

	matrix1


	# Extract the correlation coefficients
	corrpercbio_r <- matrix1$r
	# Extract p-values
	corrpercbio_p <- matrix1$P

	write.csv(corrpercbio_r, "corrpercbio_r.csv", row.names = TRUE)
	write.csv(corrpercbio_p, "corrpercbio_p.csv", row.names = TRUE)


	#correlation matrix for all cover crops with caco3/inorganic c (time1 only)


	corr_data2 =
	pall2 %>%
	filter(time == '1') %>%
	select(cbio, wbio, pbic_percbio, amac_percbio, unavp_percbio,
	porg_percbio, inorgcarbon_percbio, amm_percbio, nit_percbio,
	pmn_percbio, pmc_percbio) %>%
	force()

	matrix2 <- rcorr(as.matrix(corr_data2))

	matrix2

	##rcorr(matrix2, type = c("pearson"))


	# Extract the correlation coefficients
	corrpercbiocaco3_r <- matrix2$r
	# Extract p-values
	corrpercbiocaco3_p <- matrix2$P

	write.csv(corrpercbiocaco3_r, "corrpercbiocaco3_r.csv", row.names = TRUE)
	write.csv(corrpercbiocaco3_p, "corrpercbiocaco3_p.csv", row.names = TRUE)

	#correlation matrix no standardization, no caco3

	corr_data3 =
	pall2 %>%
	#filter(time == '1') %>%
	select(cbio, wbio, pbic, amac, unavp,
	porg, amm, nit,
	pmn, pmc) %>%
	force()

	matrix3 <- rcorr(as.matrix(corr_data3))

	matrix3


	# Extract the correlation coefficients
	corr_r <- matrix3$r
	# Extract p-values
	corr_p <- matrix3$P

	write.csv(corr_r, "corr_r.csv", row.names = TRUE)
	write.csv(corr_p, "corr_p.csv", row.names = TRUE)