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datamash - Online in the Cloud

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PROGRAM:

NAME


datamash - command-line calculations

SYNOPSIS


datamash [OPTION] op [col] [op col ...]

DESCRIPTION


Performs numeric/string operations on input from stdin.

'op' is the operation to perform; For grouping,per-line operations 'col' is the input
field to use; 'col' can be a number (1=first field), or a column name when using -H or
--header-in options.

File operations:
transpose, reverse

Line-Filtering operations:
rmdup

Per-Line operations:
base64, debase64, md5, sha1, sha256, sha512

Numeric Grouping operations:
sum, min, max, absmin, absmax

Textual/Numeric Grouping operations:
count, first, last, rand, unique, collapse, countunique

Statistical Grouping operations:
mean, median, q1, q3, iqr, mode, antimode, pstdev, sstdev, pvar svar, mad, madraw,
pskew, sskew, pkurt, skurt, dpo, jarque

OPTIONS


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]

--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

AVAILABLE OPERATIONS


File operations:
transpose transpose rows, columns of the input file

reverse reverse field order in each line

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

reverse reverse field order in each line

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

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
mean mean of the values

median median value

q1 1st quartile value

q3 3rd quartile value

iqr inter-quartile range

mode mode value (most common value)

antimode anti-mode value (least common value)

pstdev population standard deviation

sstdev sample standard deviation

pvar population variance

svar sample variance

mad median absolute deviation, scaled by constant 1.4826 for normal distributions

madraw median absolute deviation, unscaled

sskew skewness of the (sample) group

pskew skewness of the (population) 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

skurt excess Kurtosis of the (sample) group

pkurt excess Kurtosis of the (population) group

jarque p-value of the Jarque-Beta test for normality

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

EXAMPLES


Print the sum and the mean of values from column 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

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

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 columns)
$ 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

Reverse field order in each line:

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

Transpose rows, columns:

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

Remove lines with duplicate key value from column 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 (column 1):
$ datamash rmdup 1 < INPUT
(or used named column)
$ datamash -H rmdup SampleID < INPUT
SampleID File
2 cc.txt
3 dd.txt
1 ab.txt

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

ADDITIONAL INFORMATION


See GNU Datamash Website (http://www.gnu.org/software/datamash)

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