[Marcos Lancia]

Ok, my table is ready. Now, my problem is that I have non-numerical data in my columns. How can I avoid that? For example,
plot(data$log2.FC.transcriptome., data$log2.FC.translatome.)

In min(x) : no non-missing arguments to min; returning Inf

[blancha]

As always, there are many ways, and many subtleties.
If you've been using read.csv, I would recommend that you add the argument stringsAsFactors=FALSE.
You don't need to bother with fread, which won't treat strings as factors.

I'll just post two ways to do the filtering, supposing that you know the strings that you want to filter out.
I'll have to get back to work on my own script then.
There is an older, more conventional method, but dplyr has many advantages.

library(dplyr)
data.filtered <- data %>% filter(log2.FC.transcriptome. != "Inf" & log2.FC.transcriptome. != "-Inf" & log2.FC.translatome. != "Inf" & log2.FC.translatome. != "-Inf")

Older method

data.filtered <- data[data$log2.FC.transcriptome. != "Inf" & data$log2.FC.transcriptome. != "-Inf" & data$log2.FC.translatome. != "Inf" & data$log2.FC.translatome. != "-Inf", ]

Sorry, I really have to go and finish my own script now.

[Marcos Lancia]

Ok, thanks so much for your time. You helped me so much. If you can read this, there's a problem with the %>%.
Error: could not find function "%>%"

The 2nd option you gave me deleted all data. The table is empty after that command.

[blancha]

The "%>%" forward-pipe operator comes with dplyr.
Either you haven't loaded the dplyr package, or you made a mistake writing the operator.
It's just the greater sign in between two percentage sign, so the second mistake is less likely.
Just make sure you've installed dplyr and loaded the package before using "%>%".

install.packages("dplyr")
library(dplyr)

For the problems you're experiencing with the more conventional filtering step, I'm not sure what the problem could be.
I don't see any typos in my command, but there could always be a mistake on my part.
The best troubleshooting step is always to simplify the command.
Try just one filtering step at the time.

data.filtered <- data[data$log2.FC.transcriptome. != "Inf", ]

I'm going to stop procrastinating now by spending time on seqanswers, and actually finish my own R script.