# Issue

*This Content is from Stack Overflow. Question asked by fsneden *

I have a data frame of correlation coefficients and p-values that I have created externally and want to read into R so I can use corrplot() and create a correlation matrix map. Corrplot requires the data to be in a correlation matrix, and even though I have read other questions, I am still confused how to create it. A preview of my data is as follows (with some random values):

```
var1, var2, cor, p
lcca lica 0.9 0.01
lcca rcca 0.4 0.10
lcca rica 0.4 0.24
```

where var1 and var2 are the two variables listed by name, cor is the coefficient and p is the significance value.

Currently, my incorrect code is as follows:

```
correlations <- read_csv("correlations.csv")
correlations <- rcorr(as.matrix(correlations))
```

If I understand correctly, I need to pivot_wider() before I can do the second line, but after I have it in a correlation matrix, it SHOULD run fine? I’ve managed it before on data that I created within R itself, with this code:

```
rawdata <- read_csv("rawdata.csv")
dfcor <- cór(rawdata, method = "spearman", use = "complete.obs")
round(dfcor, 2)
dfcor <- rcorr(as.matrix(dfcor))
plot <- corrplot(dfcor$r,
method = "circle",
type = "upper",
order = "original",
p.mat = dfcor$P,
sig.level = 0.05,
insig = "blank",
tl.col = "black",
tl.srt = 90,
diag = FALSE)
```

I feel I know what SHOULD be done, but I don’t necessarily know HOW to do it. Can anyone help?

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