datamash1

Primary operations:

groupby, crosstab, transpose, reverse, check

Line-Filtering operations:

rmdup

Per-Line operations:

base64, debase64, md5, sha1, sha224, sha256, sha384, sha512, bin, strbin, round, floor, ceil, trunc, frac, dirname, basename, barename, extname, getnum, cut

Numeric Grouping operations:

sum, min, max, absmin, absmax, range

Textual/Numeric Grouping operations:

count, first, last, rand, unique, collapse, countunique

Statistical Grouping operations:

mean, geomean, harmmean, trimmean, median, q1, q3, iqr, perc, mode, antimode, pstdev, sstdev, pvar, svar, ms, rms, mad, madraw, pskew, sskew, pkurt, skurt, dpo, jarque, scov, pcov, spearson, ppearson, dotprod

Grouping Options:

-C, --skip-comments

skip comment lines (starting with '#' or ';' and optional whitespace)

-f, --full

print entire input line before op results (default: print only the grouped keys)

This option is only sensible for linewise operations. Other uses are deprecated and will be removed in a future version of GNU Datamash.

-g, --group=X[,Y,Z]

group via fields X,[Y,Z]; equivalent to primary operation 'groupby'

--header-in

first input line is column headers

--header-out

print column headers as first line

-H, --headers

same as '--header-in --header-out'

--vnlog

Reads and writes data in the vnlog format. Implies -C -H -W

-i, --ignore-case

ignore upper/lower case when comparing text; this affects grouping, and string operations

-s, --sort

sort the input before grouping; this removes the need to manually pipe the input through 'sort'

-S, --seed

set a seed for operations that use randomization

-c, --collapse-delimiter=X

use X to separate elements in collapse and unique lists (default: comma)

File Operation Options:

--no-strict

allow lines with varying number of fields

--filler=X

fill missing values with X (default N/A)

General Options:

-t, --field-separator=X

use X instead of TAB as field delimiter

--format=FORMAT

print numeric values with printf style floating-point FORMAT.

--output-delimiter=X

use X instead as output field delimiter (default: use same delimiter as -t/-W)

--narm

skip NA/NaN values

-R, --round=N

round numeric output to N decimal places

-W, --whitespace

use whitespace (one or more spaces and/or tabs) for field delimiters

-z, --zero-terminated

end lines with 0 byte, not newline

--sort-cmd=/path/to/sort

Alternative sort(1) to use.

-h, --help

display this help and exit

-V, --version

output version information and exit

Primary Operations

Primary operations affect the way the file is processed. If used, the primary operation must be listed first. If primary operation is not listed the entire file is processed - either line-by-line (for 'per-line' operations) or all lines as one group (for grouping operations). See Examples section below.

groupby X,Y,... op fld ...

group the file by given fields. Equivalent to option '-g'. For each group perform operation op on field fld.

crosstab X,Y [op fld ...]

cross-tabulate a file by two fields (cross-tabulation is also known as pivot tables). If no operation is specified, counts how many incidents exist of X,Y.

transpose

transpose rows, columns of the input file

reverse

reverse field order in each line

check [N lines] [N fields]

verify the input file has same number of fields in all lines, or the expected number of lines/fields. number of lines and fields are printed to STDOUT. Exits with non-zero code and prints the offending line if there's a mismatch in the number of lines/ fields.

Line-Filtering operations

rmdup

remove lines with duplicated key value

Per-Line operations

base64

Encode the field as base64

debase64

Decode the field as base64, exit with error if invalid base64 string

md5/sha1/sha224/sha256/sha384/sha512

Calculate md5/sha1/sha224/sha256/sha384/sha512 hash of the field value

bin[:BUCKET-SIZE]

bin numeric values into buckets of size BUCKET-SIZE (defaults to 100).

strbin[:BUCKET-SIZE]

hashes the input and returns a numeric integer value between zero and BUCKET-SIZE (defaults to 10).

round/floor/ceil/trunc/frac

numeric rounding operations. round (round half away from zero), floor (round down), ceil (ceiling, round up), trunc (truncate, round towards zero), frac (fraction, return fraction part of a decimal-point value).

dirname/basename

extract the directory name and the base file name from a given string (same as to dirname(1) and basename(1)).

extname

extract the extension of the file name (without the '.').

barename

extract the base file name without the extension.

getnum[:TYPE]

extract a number from the field. TYPE is optional single letter option n/i/d/p/h/o (see examples below).

cut/echo

copy input field to output field (similar to cut(1)). The echo command is simply an alias to cut.

Numeric Grouping operations

Textual/Numeric Grouping operations

count

count number of elements in the group

first

the first value of the group

last

the last value of the group

rand

one random value from the group

unique/uniq

comma-separated sorted list of unique values The uniq command is simply an alias to unique.

collapse

comma-separated list of all input values

countunique

number of unique/distinct values

Statistical Grouping operations

A p/s prefix indicates the variant: population or sample. Typically, the sample variant is equivalent with GNU R's internal functions (e.g datamash's sstdev operation is equivalent to R's sd() function).

mean

mean of the values

geomean

geometric mean of the values

harmmean

harmonic mean of the values

trimmean[:PERCENT]

trimmed mean of the values. PERCENT should be between 0 and 0.5. (trimmean:0 is equivalent to mean. trimmean:0.5 is equivalent to median).

ms

mean square of the values

rms

root mean square of the values

median

median value

q1

1st quartile value

q3

3rd quartile value

iqr

inter-quartile range

perc[:PERCENTILE]

percentile value PERCENTILE (defaults to 95).

mode

mode value (most common value)

antimode

anti-mode value (least common value)

pstdev/sstdev

population/sample standard deviation

pvar/svar

population/sample variance

mad

median absolute deviation, scaled by constant 1.4826 for normal distributions

madraw

median absolute deviation, unscaled

pskew/sskew

skewness of the group
values x reported by 'sskew' and 'pskew' operations:

          x > 0       -  positively skewed / skewed right
      0 > x           -  negatively skewed / skewed left
          x > 1       -  highly skewed right
      1 > x >  0.5    -  moderately skewed right
    0.5 > x > -0.5    -  approximately symmetric
   -0.5 > x > -1      -  moderately skewed left
     -1 > x           -  highly skewed left

pkurt/skurt

excess Kurtosis of the group

jarque/dpo

p-value of the Jarque-Beta (jarque) and D'Agostino-Pearson Omnibus (dpo) tests for normality:
null hypothesis is normality;
low p-Values indicate non-normal data;
high p-Values indicate null-hypothesis cannot be rejected.

pcov/scov [X:Y]

covariance of fields X and Y

ppearson/spearson [X:Y]

Pearson product-moment correlation coefficient [Pearson's R] of fields X and Y

dotprod [X:Y]

Scalar product (aka dot product or Euclidean inner product) of fields X and Y

Basic usage

Print the sum and the mean of values from field 1:

$ seq 10 | datamash sum 1 mean 1
55  5.5

Group input based on field 1, and sum values (per group) on field 2:

$ cat example.txt
A  10
A  5
B  9
B  11
$ datamash -g 1 sum 2 < example.txt
A  15
B  20
$ datamash groupby 1 sum 2 < example.txt
A  15
B  20

Unsorted input must be sorted (with '-s'):

$ cat example.txt
A  10
C  4
B  9
C  1
A  5
B  11
$ datamash -s -g1 sum 2 < example.txt
A  15
B  20
C  5

Which is equivalent to:

$ cat example.txt | sort -k1,1 | datamash -g 1 sum 2

Header lines

Use -H (--headers) if the input file has a header line:

# Given a file with student name, field, test score...
$ head -n5 scores_h.txt
Name           Major            Score
Shawn          Engineering      47
Caleb          Business         87
Christian      Business         88
Derek          Arts             60
# Calculate the mean and standard deviation for each major
$ datamash --sort --headers --group 2 mean 3 pstdev 3 < scores_h.txt
 (or use short form)
$ datamash -sH -g2 mean 3 pstdev 3 < scores_h.txt
 (or use named fields)
$ datamash -sH -g Major mean Score pstdev Score < scores_h.txt
GroupBy(Major)    mean(Score)   pstdev(Score)
Arts              68.9          10.1
Business          87.3           4.9
Engineering       66.5          19.1
Health-Medicine   90.6           8.8
Life-Sciences     55.3          19.7
Social-Sciences   60.2          16.6

Field names must be escaped with a backslash if they start with a digit or contain special characters (dash/minus, colons, commas). Note the interplay between escaping with backslash and shell quoting. The following equivalent command sum the values of a field named "FOO-BAR":

$ datamash -H sum FOO\\-BAR < input.txt
$ datamash -H sum 'FOO\-BAR' < input.txt
$ datamash -H sum "FOO\\-BAR" < input.txt

Skipping comment lines

Use -C (--skip-comments) to skip lines starting with '#' or ';' characters (and optional whitespace before them):

$ cat in.txt
 #foo   3
bar     5
;baz    7
$ datamash sum 2 < in.txt
15
$ datamash -C sum 2 < in.txt
5

Multiple fields

Use comma or dash to specify multiple fields. The following are equivalent:

$ seq 9 | paste - - -
1   2   3
4   5   6
7   8   9
$ seq 9 | paste - - - | datamash sum 1 sum 2 sum 3
12  15  18
$ seq 9 | paste - - - | datamash sum 1,2,3
12  15  18
$ seq 9 | paste - - - | datamash sum 1-3
12  15  18

Rounding

The following demonstrate the different rounding operations:

$ ( echo X ; seq -1.25 0.25 1.25 ) \
      | datamash --full -H round 1 ceil 1 floor 1 trunc 1 frac 1
  X     round(X)  ceil(X)  floor(X)  trunc(X)   frac(X)
-1.25   -1        -1       -2        -1         -0.25
-1.00   -1        -1       -1        -1          0
-0.75   -1         0       -1         0         -0.75
-0.50   -1         0       -1         0         -0.5
-0.25    0         0       -1         0         -0.25
 0.00    0         0        0         0          0
 0.25    0         1        0         0          0.25
 0.50    1         1        0         0          0.5
 0.75    1         1        0         0          0.75
 1.00    1         1        1         1          0
 1.25    1         2        1         1          0.25

Reversing fields

$ seq 6 | paste - - | datamash reverse
2    1
4    3
6    5

Transposing a file

$ seq 6 | paste - - | datamash transpose
1    3    5
2    4    6

Removing Duplicated lines

Remove lines with duplicate key value from field 1 (Unlike first,last operations, rmdup is much faster and does not require sorting the file with -s):

# Given a list of files and sample IDs:
$ cat INPUT
SampleID  File
2         cc.txt
3         dd.txt
1         ab.txt
2         ee.txt
3         ff.txt
# Remove lines with duplicated Sample-ID (field 1):
$ datamash rmdup 1 < INPUT
# or use named field:
$ datamash -H rmdup SampleID < INPUT
SampleID  File
2         cc.txt
3         dd.txt
1         ab.txt

Checksums

Calculate the sha1 hash value of each TXT file, after calculating the sha1 value of each file's content:

$ sha1sum *.txt | datamash -Wf sha1 2

Check file structure

Check the structure of the input file: ensure all lines have the same number of fields, or expected number of lines/fields:

$ seq 10 | paste - - | datamash check && echo ok || echo fail
5 lines, 2 fields
ok
$ seq 13 | paste - - - | datamash check && echo ok || echo fail
line 4 (3 fields):
  10  11  12
line 5 (2 fields):
  13
datamash: check failed: line 5 has 2 fields (previous line had 3)
fail
$ seq 10 | paste - - | datamash check 2 fields 5 lines
5 lines, 2 fields
$ seq 10 | paste - - | datamash check 4 fields
line 1 (2 fields):
  1     2
datamash: check failed: line 1 has 2 fields (expecting 4)
$ seq 10 | paste - - | datamash check 7 lines
datamash: check failed: input had 5 lines (expecting 7)

Cross-Tabulation

Cross-tabulation compares the relationship between two fields. Given the following input file:

$ cat input.txt
a    x    3
a    y    7
b    x    21
a    x    40

Show cross-tabulation between the first field (a/b) and the second field (x/y) - counting how many times each pair appears (note: sorting is required):

$ datamash -s crosstab 1,2 < input.txt
     x    y
a    2    1
b    1    N/A

An optional grouping operation can be used instead of counting:

Binning numeric values

Bin input values into buckets of size 5:

$  ( echo X ; seq -10 2.5 10 ) \
      | datamash -H --full bin:5 1
    X  bin(X)
-10.0    -10
 -7.5    -10
 -5.0     -5
 -2.5     -5
  0.0      0
  2.5      0
  5.0      5
  7.5      5
 10.0     10

Binning string values

Hash any input value into a numeric integer. A typical usage would be to split an input file into N chunks, ensuring that all values of a certain key will be stored in the same chunk:

$ cat input.txt
PatientA   10
PatientB   11
PatientC   12
PatientA   14
PatientC   15
Each patient ID is hashed into a bin between 0 and 9
and printed in the last field:
$ datamash --full strbin 1 < input.txt
PatientA   10    5
PatientB   11    6
PatientC   12    7
PatientA   14    5
PatientC   15    7
Splitting the input into chunks can be done with awk:
$ cat input.txt \
    | datamash --full strbin 1 \
    | awk '{print > $NF ".txt"}'

Extracting numbers with getnum

The 'getnum' operation extracts a numeric value from the field:

$ echo zoom-123.45xyz | datamash getnum 1
123.45
getnum accepts an optional single-letter TYPE option:
    getnum:n - natural numbers (positive integers, including zero)
    getnum:i - integers
    getnum:d - decimal point numbers
    getnum:p - positive decimal point numbers (this is the default)
    getnum:h - hex numbers
    getnum:o - octal numbers

Examples:

$ echo zoom-123.45xyz | datamash getnum 1
123.45
$ echo zoom-123.45xyz | datamash getnum:n 1
123
$ echo zoom-123.45xyz | datamash getnum:i 1
-123
$ echo zoom-123.45xyz | datamash getnum:d 1
123.45
$ echo zoom-123.45xyz | datamash getnum:p 1
-123.45
# Hex 0x123 = 291 Decimal
$ echo zoom-123.45xyz | datamash getnum:h 1
291
# Octal 0123 = 83 Decimal
$ echo zoom-123.45xyz | datamash getnum:o 1
83