I am guessing you need to do something like the following. You did not provide any data so I invented a small set. I first reshaped the data so that the type of product is listed in one column and another column contains all of the values. This is typically the preferred arrangement for plotting with ggplot. I then summed over each product and then plotted that result. I put in extra print steps to show what each step does.

```
demographic_data <- data.frame(MntWines=c(4,7),MntMeatProducts=c(4,3),
MntSweetProducts=c(6,5))
demographic_data
#> MntWines MntMeatProducts MntSweetProducts
#> 1 4 4 6
#> 2 7 3 5
library(tidyr)
library(dplyr)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.0.5
DF_Long <- demographic_data %>% pivot_longer(cols = MntWines:MntSweetProducts,
names_to="Product",values_to="Value")
DF_Long
#> # A tibble: 6 x 2
#> Product Value
#> <chr> <dbl>
#> 1 MntWines 4
#> 2 MntMeatProducts 4
#> 3 MntSweetProducts 6
#> 4 MntWines 7
#> 5 MntMeatProducts 3
#> 6 MntSweetProducts 5
DF_Sums <- DF_Long %>% group_by(Product) %>% summarize(Total=sum(Value))
#> `summarise()` ungrouping output (override with `.groups` argument)
DF_Sums
#> # A tibble: 3 x 2
#> Product Total
#> <chr> <dbl>
#> 1 MntMeatProducts 7
#> 2 MntSweetProducts 11
#> 3 MntWines 11
ggplot(DF_Sums,aes(x=Product,y=Total))+geom_col(fill="steelblue")
```

^{Created on 2021-09-11 by the reprex package (v0.3.0)}