Provided by: datamash_1.8-1_amd64 
NAME
datamash - command-line calculations
SYNOPSIS
datamash [OPTION] op [fld] [op fld ...]
DESCRIPTION
Performs numeric/string operations on input from stdin.
'op' is the operation to perform. If a primary operation is used, it must be listed first, optionally
followed by other operations. 'fld' is the input field to use. 'fld' can be a number (1=first field),
or a field name when using the -H or --header-in options. Multiple fields can be listed with a comma
(e.g. 1,6,8). A range of fields can be listed with a dash (e.g. 2-8). Use colons for operations which
require a pair of fields (e.g. 'pcov 2:6').
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
OPTIONS
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'
-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'
-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.
--help display this help and exit
--version
output version information and exit
AVAILABLE OPERATIONS
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
sum sum the of values
min minimum value
max maximum value
absmin minimum of the absolute values
absmax maximum of the absolute values
range the values range (max-min)
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
EXAMPLES
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:
$ datamash -s crosstab 1,2 sum 3 < input.txt
x y
a 43 7
b 21 N/A
$ datamash -s crosstab 1,2 unique 3 < input.txt
x y
a 3,40 7
b 21 N/A
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