This is assuming that the file you are searching against is unique. If the number returned is the same as the length of your search list, then all items were found. If we are only interested to see if our list of patterns exist, we can use the -c parameter. We will use the -f parameter to specify the file containing all our patterns. We will create a search list by shuffling small.txt (using the shuf tool) and taking the first 10 elements. Now to the main topic: grepping a list with a list. Two other useful parameters I often use are -B (before) and -A (after), which returns lines before and after lines that matched your pattern. If we only want to match 999 entirely, use the -w parameter this selects only those lines containing matches that form whole words. However if we searched for 999, grep will return all lines matching the 999 pattern. Say we wanted to find out if 9999 exists in small.txt. We'll generate a small file containing integers from 0 to 9999. Please cite as described in 'parallel -citation'. When using programs that use GNU Parallel to process data for publication This is free software: you are free to change and redistribute it. License GPLv3+: GNU GPL version 3 or later Ole Tange and Free Software Foundation, Inc. If you are not familiar with Docker and/or Conda, I have previously prepared some introductory material. If you are working on a machine without root (admin) privileges, you can install parallel using Conda. We will need the parallel tool, which can be installed using apt. I will use the Ubuntu:18:04 Docker image for this post for reproducibility. One use case is that you could subset a BED file with a list of IDs. In this post, I will demonstrate how you can search for a list of things in another list of things. For example, you could search a gene or SNP ID in a BED/GFF/GTF file to find out its coordinates. keep_all = TRUE ) #> # A tibble: 10 × 2 #> x y #> #> 1 6 8 #> 2 10 10 #> 3 5 2 #> 4 7 4 #> 5 6 9 #> 6 1 1 #> 7 1 6 #> 8 3 7 #> 9 1 3 #> 10 1 5 # You can also use distinct on computed variables distinct ( df, diff = abs ( x - y ) ) #> # A tibble: 9 × 1 #> diff #> #> 1 2 #> 2 0 #> 3 3 #> 4 7 #> 5 1 #> 6 5 #> 7 9 #> 8 4 #> 9 6 # Use `pick()` to select columns with tidy-select distinct ( starwars, pick ( contains ( "color" ) ) ) #> # A tibble: 67 × 3 #> hair_color skin_color eye_color #> #> 1 blond fair blue #> 2 NA gold yellow #> 3 NA white, blue red #> 4 none white yellow #> 5 brown light brown #> 6 brown, grey light blue #> 7 brown light blue #> 8 NA white, red red #> 9 black light brown #> 10 auburn, white fair blue-gray #> # ℹ 57 more rows # Grouping - df % group_by ( g ) # With grouped data frames, distinctness is computed within each group df %>% distinct ( x ) #> # A tibble: 3 × 2 #> # Groups: g #> g x #> #> 1 1 1 #> 2 2 2 #> 3 2 1 # When `.The grep command-line utility is a commonly used tool for searching plain text files for lines that match a pattern.
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