Provided by: datamash_1.2.0-1_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, sha256, sha512, bin, strbin, round, floor, ceil, trunc, frac
Numeric Grouping operations:
sum, min, max, absmin, absmax, range
Textual/Numeric Grouping operations:
count, first, last, rand, unique, collapse, countunique
Statistical Grouping operations:
mean, median, q1, q3, iqr, perc, mode, antimode, pstdev, sstdev, pvar, svar, mad, madraw, pskew,
sskew, pkurt, skurt, dpo, jarque, scov, pcov, spearson, ppearson
Grouping Options:
-f, --full
print entire input line before op results (default: print only the grouped keys)
-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'
File Operation Options:
--no-strict
allow lines with varying number of fields
--filler=X
fill missing values with X (default %s)
General Options:
-t, --field-separator=X
use X instead of TAB as field delimiter
--narm skip NA/NaN values
-W, --whitespace
use whitespace (one or more spaces and/or tabs) for field delimiters
-z, --zero-terminated
end lines with 0 byte, not newline
--help display this help and exit
--version
output version information and exit
OPTIONS
AVAILABLE OPERATIONS
Primary Operations
Primary operations affect the way the file is processed. If used, the primary operation must be listed
first. Some operations require field numbers (groupby, crosstab) while others do not
(reverse,check,transpose). 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/sha256/sha512
Calculate md5/sha1/sha256/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 up), ceil
(ceiling, round down), trunc (truncate, round towards zero), frac (fraction, return fraction
part of a decimal-point value).
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 comma-separated sorted list of unique values
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
median median value
q1 1st quartile value
q3 3rd quartile value
iqr inter-quartile range
perc[:PERCENTILE]
percentile value ERCENTILE (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 devian 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
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 -15
-7.5 -10
-5.0 -10
-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"}'