This function takes the results from `mr()`

and is particularly useful
if the MR has been applied using multiple exposures and multiple outcomes.
It creates a new data frame with the following:

Variables: exposure, outcome, category, outcome sample size, effect, upper ci, lower ci, pval, nsnp

only one estimate for each exposure-outcome

exponentiated effects if required

## Usage

```
format_mr_results(
mr_res,
exponentiate = FALSE,
single_snp_method = "Wald ratio",
multi_snp_method = "Inverse variance weighted",
ao_slc = TRUE,
priority = "Cardiometabolic"
)
```

## Arguments

- mr_res
Results from

`mr()`

.- exponentiate
Convert effects to OR? The default is

`FALSE`

.- single_snp_method
Which of the single SNP methods to use when only 1 SNP was used to estimate the causal effect? The default is

`"Wald ratio"`

.- multi_snp_method
Which of the multi-SNP methods to use when there was more than 1 SNPs used to estimate the causal effect? The default is

`"Inverse variance weighted"`

.- ao_slc
Logical; retrieve sample size and subcategory using

`available_outcomes()`

. If set to`FALSE`

`mr_res`

must contain the following additional columns:`subcategory`

and`sample_size`

.- priority
Name of category to prioritise at the top of the forest plot. The default is

`"Cardiometabolic"`

.

## Details

By default it uses the `available_outcomes()`

function to retrieve the study level characteristics for the outcome trait,
including sample size and outcome category.
This assumes the MR analysis was performed using outcome GWAS(s) contained in MR-Base.

If `ao_slc`

is set to `TRUE`

then the user must supply their own study level characteristics.
This is useful when the user has supplied their own outcome GWAS results (i.e. they are not in MR-Base).