R: Plot several lines in the same plot: ggplot + data tables or frames vs matrices

I would suggest to familiarize yourself with the concept of what is known as Tidy Data, which are principles for data handling and storage that are adopted by ggplot2 and a number of other packages.

You are free to use a matrix or list of matrices to store your data; however, you can certainly store the data as you describe it (and as I understand it) in a data frame or single table following the following convention of columns:

context | sample | x | freq

I'll show you how I would convert the tablefreqs dataset you shared with us into that format, then how I would go about creating a plot as you are describing it in your question. I'm assuming in this case you only have the two values for context, although you allude to there being more. I'm going to try to interpret correctly what you stated in your question.

Create the Tidy Data frame

Your data frame as shown contains columns x1 through x5 that have values for x spread across more than one column, when you really need these to be converted in the format shown above. This is called "gathering" your data, and you can do that with tidyr::gather().

First, I also need to replicate the naming of your samples according to the matrix dataset, so I'll do that and gather your data:

library(dplyr)
library(tidyr)
library(ggplot2)

# create the sample names
tablefreqs$sample <- rep(paste0('sample',1:7), 2)

# gather the columns together
df <- tablefreqs %>%
  gather(key='x', value='freq', -c(context, sample))

Note that in the gather() function, we have to specify to leave alone the two columns df$context and df$sample, as they are not part of the gathering effort. But now we are left with df$x containing character vectors. We can't plot that, because we want the to be in the form of a number (at least... I'm assuming you do). For that, we'll convert using:

df$x <- as.numeric(gsub("[^[:digit:].]", "", df$x))

That extracts the number from each value in df$x and represents it as a number, not a character. We have the opposite issue with df$context, which is actually a discrete factor, and we should represent it as such in order to make plotting a bit easier:

df$context <- factor(df$context)

Create the Plot

Now we're ready to create the plot. From your description, I may not have this perfectly right, but it seems that you want a plot containing both context = 1 and context = 0, and when context = 1 the data should be "upside down". By that, I'm assuming you are talking about plotting df$freq when df$context == 0 and -df$freq when df$context == 1. We could do that using some fancy logic in the ggplot() call, but I find it's easier just to create a new column in your dataset to represent what we will be plotting on the y axis. We'll call this column df$freq_adj and use that for plotting:

df$freq_adj <- ifelse(df$context==1, -df$freq, df$freq)

Then we create the plot. I'll explain a bit below the result:

ggplot(df, aes(x=x, y=freq_adj)) +
  geom_line(
    aes(color=context, linetype=sample)
  ) +
  geom_hline(yintercept=0, color='gray50') +
  scale_x_continuous(expand=expansion(mult=0)) +
  theme_bw()

Without some clearer description or picture of what you were looking to do, I took some liberties here. I used color to discriminate between the two values for context, and I'm using linetype to discriminate the different samples. I also added a line at 0, since it seemed appropriate to do so here, and the scale_x_continuous() command is removing the extra white space that is put in place at the extreme ends of the data.

An alternative that is maybe closer to your description would be to physically have a separation between the two plots, and represent context = 1 as a physically separate plot compared to context = 0, with one over top of the other.

Here's the code and plot:

ggplot(df, aes(x=x, y=freq_adj)) +
  geom_line(aes(group=sample), alpha=0.3) +
  facet_grid(context ~ ., scales='free_y') +
  scale_x_continuous(expand=expansion(mult=0)) +
  theme_bw()

There the use of aes(group=sample) is quite important, since I want all the lines for each sample to be the same (alpha setting and color), yet ggplot2 needs to know that the connections between the points should be based on "sample". This is done using the group= aesthetic. The scales='free_y' argument on facet_grid() allows the y axis scale to shrink and fit the data according to each facet.