Reference: Verbs

Contents:
• Overview
• bar
• bootstrap
• cat
• check
• count-distinct
• cut
• decimate
• filter
    • Features which filter shares with put
• fraction
• grep
• group-by
• group-like
• having-fields
• head
• histogram
• join
• label
• least-frequent
• merge-fields
• most-frequent
• nest
• nothing
• put
    • Features which put shares with filter
• regularize
• rename
• reorder
• repeat
• reshape
• sample
• sec2gmt
• sec2gmtdate
• seqgen
• shuffle
• sort
• stats1
• stats2
• step
• tac
• tail
• tee
• top
• uniq
• unsparsify

Overview

When you type mlr {something} myfile.dat, the {something} part is called a verb. It specifies how you want to transform your data. (See also here for a breakdown.) The following is an alphabetical list of verbs with their descriptions.

The verbs put and filter are special in that they have a rich expression language (domain-specific language, or “DSL”). More information about them can be found here.

Here’s a comparison of verbs and put/filter DSL expressions:

Example:

$ mlr stats1 -a sum -f x -g a data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892

  • Verbs are coded in C
  • They run a bit faster
  • They take fewer keystrokes
  • There is less to learn
  • Their customization is limited to each verb’s options
Example:

$ mlr  put -q '@x_sum[$a] += $x; end{emit @x_sum, "a"}' data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892

  • You get to write your own DSL expressions
  • They run a bit slower
  • They take more keystrokes
  • There is more to learn
  • They are highly customizable

bar

Cheesy bar-charting.

$ mlr bar -h
Usage: mlr bar [options]
Replaces a numeric field with a number of asterisks, allowing for cheesy
bar plots. These align best with --opprint or --oxtab output format.
Options:
-f   {a,b,c}      Field names to convert to bars.
-c   {character}  Fill character: default '*'.
-x   {character}  Out-of-bounds character: default '#'.
-b   {character}  Blank character: default '.'.
--lo {lo}         Lower-limit value for min-width bar: default '0.000000'.
--hi {hi}         Upper-limit value for max-width bar: default '100.000000'.
-w   {n}          Bar-field width: default '40'.
--auto            Automatically computes limits, ignoring --lo and --hi.
                  Holds all records in memory before producing any output.

$ mlr --opprint cat data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint bar --lo 0 --hi 1 -f x,y data/small
a   b   i x                                        y
pan pan 1 *************........................... *****************************...........
eks pan 2 ******************************.......... ********************....................
wye wye 3 ********................................ *************...........................
eks wye 4 ***************......................... *****...................................
wye pan 5 **********************.................. **********************************......

$ mlr --opprint bar --lo 0.4 --hi 0.6 -f x,y data/small
a   b   i x                                        y
pan pan 1 #....................................... ***************************************#
eks pan 2 ***************************************# ************************................
wye wye 3 #....................................... #.......................................
eks wye 4 #....................................... #.......................................
wye pan 5 **********************************...... ***************************************#

$ mlr --opprint bar --auto -f x,y data/small
a   b   i x                                                           y
pan pan 1 [0.204603]**********..............................[0.75868] [0.134189]********************************........[0.863624]
eks pan 2 [0.204603]***************************************#[0.75868] [0.134189]*********************...................[0.863624]
wye wye 3 [0.204603]#.......................................[0.75868] [0.134189]***********.............................[0.863624]
eks wye 4 [0.204603]************............................[0.75868] [0.134189]#.......................................[0.863624]
wye pan 5 [0.204603]**************************..............[0.75868] [0.134189]***************************************#[0.863624]

bootstrap

$ mlr bootstrap --help
Usage: mlr bootstrap [options]
Emits an n-sample, with replacement, of the input records.
Options:
-n {number} Number of samples to output. Defaults to number of input records.
            Must be non-negative.
See also mlr sample and mlr shuffle.

The canonical use for bootstrap sampling is to put error bars on statistical quantities, such as mean. For example:

$ mlr --opprint stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color  u_mean   u_count
yellow 0.497129 1413
red    0.492560 4641
purple 0.494005 1142
green  0.504861 1109
blue   0.517717 1470
orange 0.490532 303

$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color  u_mean   u_count
yellow 0.500651 1380
purple 0.501556 1111
green  0.503272 1068
red    0.493895 4702
blue   0.512529 1496
orange 0.521030 321

$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color  u_mean   u_count
yellow 0.498046 1485
blue   0.513576 1417
red    0.492870 4595
orange 0.507697 307
green  0.496803 1075
purple 0.486337 1199

$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color  u_mean   u_count
blue   0.522921 1447
red    0.490717 4617
yellow 0.496450 1419
purple 0.496523 1192
green  0.507569 1111
orange 0.468014 292

cat

Most useful for format conversions (see File formats), and concatenating multiple same-schema CSV files to have the same header:

$ mlr cat -h
Usage: mlr cat [options]
Passes input records directly to output. Most useful for format conversion.
Options:
-n        Prepend field "n" to each record with record-counter starting at 1
-g {comma-separated field name(s)} When used with -n/-N, writes record-counters
          keyed by specified field name(s).
-N {name} Prepend field {name} to each record with record-counter starting at 1

$ cat data/a.csv
a,b,c
1,2,3
4,5,6

$ cat data/b.csv
a,b,c
7,8,9

$ mlr --csv cat data/a.csv data/b.csv
a,b,c
1,2,3
4,5,6
7,8,9

$ mlr --icsv --oxtab cat data/a.csv data/b.csv
a 1
b 2
c 3

a 4
b 5
c 6

a 7
b 8
c 9

$ mlr --csv cat -n data/a.csv data/b.csv
n,a,b,c
1,1,2,3
2,4,5,6
3,7,8,9

$ mlr --opprint cat data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint cat -n -g a data/small
n a   b   i x                   y
1 pan pan 1 0.3467901443380824  0.7268028627434533
1 eks pan 2 0.7586799647899636  0.5221511083334797
1 wye wye 3 0.20460330576630303 0.33831852551664776
2 eks wye 4 0.38139939387114097 0.13418874328430463
2 wye pan 5 0.5732889198020006  0.8636244699032729

check

$ mlr check --help
Usage: mlr check
Consumes records without printing any output.
Useful for doing a well-formatted check on input data.

count-distinct

$ mlr count-distinct --help
Usage: mlr count-distinct [options]
-f {a,b,c}    Field names for distinct count.
-n            Show only the number of distinct values. Not compatible with -u.
-o {name}     Field name for output count. Default "count".
              Ignored with -u.
-u            Do unlashed counts for multiple field names. With -f a,b and
              without -u, computes counts for distinct combinations of a
              and b field values. With -f a,b and with -u, computes counts
              for distinct a field values and counts for distinct b field
              values separately.
Prints number of records having distinct values for specified field names.
Same as uniq -c.

$ mlr count-distinct -f a,b then sort -nr count data/medium
a=zee,b=wye,count=455
a=pan,b=eks,count=429
a=pan,b=pan,count=427
a=wye,b=hat,count=426
a=hat,b=wye,count=423
a=pan,b=hat,count=417
a=eks,b=hat,count=417
a=eks,b=eks,count=413
a=pan,b=zee,count=413
a=zee,b=hat,count=409
a=eks,b=wye,count=407
a=zee,b=zee,count=403
a=pan,b=wye,count=395
a=wye,b=pan,count=392
a=zee,b=eks,count=391
a=zee,b=pan,count=389
a=hat,b=eks,count=389
a=wye,b=eks,count=386
a=hat,b=zee,count=385
a=wye,b=zee,count=385
a=hat,b=hat,count=381
a=wye,b=wye,count=377
a=eks,b=pan,count=371
a=hat,b=pan,count=363
a=eks,b=zee,count=357

$ mlr count-distinct -u -f a,b data/medium
field=a,value=pan,count=2081
field=a,value=eks,count=1965
field=a,value=wye,count=1966
field=a,value=zee,count=2047
field=a,value=hat,count=1941
field=b,value=pan,count=1942
field=b,value=wye,count=2057
field=b,value=zee,count=1943
field=b,value=eks,count=2008
field=b,value=hat,count=2050

$ mlr count-distinct -f a,b -o someothername then sort -nr someothername data/medium
a=zee,b=wye,someothername=455
a=pan,b=eks,someothername=429
a=pan,b=pan,someothername=427
a=wye,b=hat,someothername=426
a=hat,b=wye,someothername=423
a=pan,b=hat,someothername=417
a=eks,b=hat,someothername=417
a=eks,b=eks,someothername=413
a=pan,b=zee,someothername=413
a=zee,b=hat,someothername=409
a=eks,b=wye,someothername=407
a=zee,b=zee,someothername=403
a=pan,b=wye,someothername=395
a=wye,b=pan,someothername=392
a=zee,b=eks,someothername=391
a=zee,b=pan,someothername=389
a=hat,b=eks,someothername=389
a=wye,b=eks,someothername=386
a=hat,b=zee,someothername=385
a=wye,b=zee,someothername=385
a=hat,b=hat,someothername=381
a=wye,b=wye,someothername=377
a=eks,b=pan,someothername=371
a=hat,b=pan,someothername=363
a=eks,b=zee,someothername=357

$ mlr count-distinct -n -f a,b data/medium
count=25

cut

$ mlr cut --help
Usage: mlr cut [options]
Passes through input records with specified fields included/excluded.
-f {a,b,c}       Field names to include for cut.
-o               Retain fields in the order specified here in the argument list.
                 Default is to retain them in the order found in the input data.
-x|--complement  Exclude, rather than include, field names specified by -f.
-r               Treat field names as regular expressions. "ab", "a.*b" will
                 match any field name containing the substring "ab" or matching
                 "a.*b", respectively; anchors of the form "^ab$", "^a.*b$" may
                 be used. The -o flag is ignored when -r is present.
Examples:
  mlr cut -f hostname,status
  mlr cut -x -f hostname,status
  mlr cut -r -f '^status$,sda[0-9]'
  mlr cut -r -f '^status$,"sda[0-9]"'
  mlr cut -r -f '^status$,"sda[0-9]"i' (this is case-insensitive)

$ mlr --opprint cat data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint cut -f y,x,i data/small
i x                   y
1 0.3467901443380824  0.7268028627434533
2 0.7586799647899636  0.5221511083334797
3 0.20460330576630303 0.33831852551664776
4 0.38139939387114097 0.13418874328430463
5 0.5732889198020006  0.8636244699032729

$ echo 'a=1,b=2,c=3' | mlr cut -f b,c,a
a=1,b=2,c=3

$ echo 'a=1,b=2,c=3' | mlr cut -o -f b,c,a
b=2,c=3,a=1

decimate

$ mlr decimate --help
Usage: mlr decimate [options]
-n {count}    Decimation factor; default 10
-b            Decimate by printing first of every n.
-e            Decimate by printing last of every n (default).
-g {a,b,c}    Optional group-by-field names for decimate counts
Passes through one of every n records, optionally by category.

filter

$ mlr filter --help
Usage: mlr filter [options] {expression}
Prints records for which {expression} evaluates to true.
If there are multiple semicolon-delimited expressions, all of them are
evaluated and the last one is used as the filter criterion.

Conversion options:
-S: Keeps field values as strings with no type inference to int or float.
-F: Keeps field values as strings or floats with no inference to int.
All field values are type-inferred to int/float/string unless this behavior is
suppressed with -S or -F.

Output/formatting options:
--oflatsep {string}: Separator to use when flattening multi-level @-variables
    to output records for emit. Default ":".
--jknquoteint: For dump output (JSON-formatted), do not quote map keys if non-string.
--jvquoteall: For dump output (JSON-formatted), quote map values even if non-string.
Any of the output-format command-line flags (see mlr -h). Example: using
  mlr --icsv --opprint ... then put --ojson 'tee > "mytap-".$a.".dat", $*' then ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.
--no-fflush: for emit, tee, print, and dump, don't call fflush() after every
    record.

Expression-specification options:
-f {filename}: the DSL expression is taken from the specified file rather
    than from the command line. Outer single quotes wrapping the expression
    should not be placed in the file. If -f is specified more than once,
    all input files specified using -f are concatenated to produce the expression.
    (For example, you can define functions in one file and call them from another.)
-e {expression}: You can use this after -f to add an expression. Example use
    case: define functions/subroutines in a file you specify with -f, then call
    them with an expression you specify with -e.
(If you mix -e and -f then the expressions are evaluated in the order encountered.
Since the expression pieces are simply concatenated, please be sure to use intervening
semicolons to separate expressions.)

Tracing options:
-v: Prints the expressions's AST (abstract syntax tree), which gives
    full transparency on the precedence and associativity rules of
    Miller's grammar, to stdout.
-a: Prints a low-level stack-allocation trace to stdout.
-t: Prints a low-level parser trace to stderr.
-T: Prints a every statement to stderr as it is executed.

Other options:
-x: Prints records for which {expression} evaluates to false.

Please use a dollar sign for field names and double-quotes for string
literals. If field names have special characters such as "." then you might
use braces, e.g. '${field.name}'. Miller built-in variables are
NF NR FNR FILENUM FILENAME PI E, and ENV["namegoeshere"] to access environment
variables. The environment-variable name may be an expression, e.g. a field
value.

Use # to comment to end of line.

Examples:
  mlr filter 'log10($count) > 4.0'
  mlr filter 'FNR == 2          (second record in each file)'
  mlr filter 'urand() < 0.001'  (subsampling)
  mlr filter '$color != "blue" && $value > 4.2'
  mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)'
  mlr filter '($name =~ "^sys.*east$") || ($name =~ "^dev.[0-9]+"i)'
  mlr filter '$ab = $a+$b; $cd = $c+$d; $ab != $cd'
  mlr filter '
    NR == 1 ||
   #NR == 2 ||
    NR == 3
  '

Please see http://johnkerl.org/miller/doc/reference.html for more information
including function list. Or "mlr -f". Please also also "mlr grep" which is
useful when you don't yet know which field name(s) you're looking for.

Features which filter shares with put

Please see Expression language for filter and put for more information about the expression language for mlr filter.

fraction

$ mlr fraction --help
Usage: mlr fraction [options]
For each record's value in specified fields, computes the ratio of that
value to the sum of values in that field over all input records.
E.g. with input records  x=1  x=2  x=3  and  x=4, emits output records
x=1,x_fraction=0.1  x=2,x_fraction=0.2  x=3,x_fraction=0.3  and  x=4,x_fraction=0.4

Note: this is internally a two-pass algorithm: on the first pass it retains
input records and accumulates sums; on the second pass it computes quotients
and emits output records. This means it produces no output until all input is read.

Options:
-f {a,b,c}    Field name(s) for fraction calculation
-g {d,e,f}    Optional group-by-field name(s) for fraction counts
-p            Produce percents [0..100], not fractions [0..1]. Output field names
              end with "_percent" rather than "_fraction"
-c            Produce cumulative distributions, i.e. running sums: each output
              value folds in the sum of the previous for the specified group
              E.g. with input records  x=1  x=2  x=3  and  x=4, emits output records
              x=1,x_cumulative_fraction=0.1  x=2,x_cumulative_fraction=0.3
              x=3,x_cumulative_fraction=0.6  and  x=4,x_cumulative_fraction=1.0

For example, suppose you have the following CSV file:

u=female,v=red,n=2458
u=female,v=green,n=192
u=female,v=blue,n=337
u=female,v=purple,n=468
u=female,v=yellow,n=3
u=female,v=orange,n=17
u=male,v=red,n=143
u=male,v=green,n=227
u=male,v=blue,n=2034
u=male,v=purple,n=12
u=male,v=yellow,n=1192
u=male,v=orange,n=448

Then we can see what each record’s n contributes to the total n:

$ mlr --opprint fraction -f n data/fraction-example.csv
u      v      n    n_fraction
female red    2458 0.326384
female green  192  0.025495
female blue   337  0.044748
female purple 468  0.062143
female yellow 3    0.000398
female orange 17   0.002257
male   red    143  0.018988
male   green  227  0.030142
male   blue   2034 0.270084
male   purple 12   0.001593
male   yellow 1192 0.158279
male   orange 448  0.059487

Using -g we can split those out by gender, or by color:

$ mlr --opprint fraction -f n -g u data/fraction-example.csv
u      v      n    n_fraction
female red    2458 0.707338
female green  192  0.055252
female blue   337  0.096978
female purple 468  0.134676
female yellow 3    0.000863
female orange 17   0.004892
male   red    143  0.035256
male   green  227  0.055966
male   blue   2034 0.501479
male   purple 12   0.002959
male   yellow 1192 0.293886
male   orange 448  0.110454

$ mlr --opprint fraction -f n -g v data/fraction-example.csv
u      v      n    n_fraction
female red    2458 0.945021
female green  192  0.458234
female blue   337  0.142134
female purple 468  0.975000
female yellow 3    0.002510
female orange 17   0.036559
male   red    143  0.054979
male   green  227  0.541766
male   blue   2034 0.857866
male   purple 12   0.025000
male   yellow 1192 0.997490
male   orange 448  0.963441

We can see, for example, that 70.9% of females have red (on the left) while 94.5% of reds are for females.

To convert fractions to percents, you may use -p:

$ mlr --opprint fraction -f n -p data/fraction-example.csv
u      v      n    n_percent
female red    2458 32.638428
female green  192  2.549462
female blue   337  4.474837
female purple 468  6.214314
female yellow 3    0.039835
female orange 17   0.225734
male   red    143  1.898818
male   green  227  3.014208
male   blue   2034 27.008365
male   purple 12   0.159341
male   yellow 1192 15.827911
male   orange 448  5.948745

Another often-used idiom is to convert from a point distribution to a cumulative distribution, also known as “running sums”. Here, you can use -c:

$ mlr --opprint fraction -f n -p -c data/fraction-example.csv
u      v      n    n_cumulative_percent
female red    2458 32.638428
female green  192  35.187890
female blue   337  39.662727
female purple 468  45.877042
female yellow 3    45.916877
female orange 17   46.142611
male   red    143  48.041429
male   green  227  51.055637
male   blue   2034 78.064002
male   purple 12   78.223344
male   yellow 1192 94.051255
male   orange 448  100

$ mlr --opprint fraction -f n -g u -p -c data/fraction-example.csv
u      v      n    n_cumulative_percent
female red    2458 70.733813
female green  192  76.258993
female blue   337  85.956835
female purple 468  99.424460
female yellow 3    99.510791
female orange 17   100
male   red    143  3.525641
male   green  227  9.122288
male   blue   2034 59.270217
male   purple 12   59.566075
male   yellow 1192 88.954635
male   orange 448  100

grep

$ mlr grep -h
Usage: mlr grep [options] {regular expression}
Passes through records which match {regex}.
Options:
-i    Use case-insensitive search.
-v    Invert: pass through records which do not match the regex.
Note that "mlr filter" is more powerful, but requires you to know field names.
By contrast, "mlr grep" allows you to regex-match the entire record. It does
this by formatting each record in memory as DKVP, using command-line-specified
ORS/OFS/OPS, and matching the resulting line against the regex specified
here. In particular, the regex is not applied to the input stream: if you
have CSV with header line "x,y,z" and data line "1,2,3" then the regex will
be matched, not against either of these lines, but against the DKVP line
"x=1,y=2,z=3".  Furthermore, not all the options to system grep are supported,
and this command is intended to be merely a keystroke-saver. To get all the
features of system grep, you can do
  "mlr --odkvp ... | grep ... | mlr --idkvp ..."

group-by

$ mlr group-by --help
Usage: mlr group-by {comma-separated field names}
Outputs records in batches having identical values at specified field names.

This is similar to sort but with less work. Namely, Miller’s sort has three steps: read through the data and append linked lists of records, one for each unique combination of the key-field values; after all records are read, sort the key-field values; then print each record-list. The group-by operation simply omits the middle sort. An example should make this more clear.

$ mlr --opprint group-by a data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
wye wye 3 0.20460330576630303 0.33831852551664776
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint sort -f a data/small
a   b   i x                   y
eks pan 2 0.7586799647899636  0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
pan pan 1 0.3467901443380824  0.7268028627434533
wye wye 3 0.20460330576630303 0.33831852551664776
wye pan 5 0.5732889198020006  0.8636244699032729

In this example, since the sort is on field a, the first step is to group together all records having the same value for field a; the second step is to sort the distinct a-field values pan, eks, and wye into eks, pan, and wye; the third step is to print out the record-list for a=eks, then the record-list for a=pan, then the record-list for a=wye. The group-by operation omits the middle sort and just puts like records together, for those times when a sort isn’t desired. In particular, the ordering of group-by fields for group-by is the order in which they were encountered in the data stream, which in some cases may be more interesting to you.

group-like

$ mlr group-like --help
Usage: mlr group-like
Outputs records in batches having identical field names.

This groups together records having the same schema (i.e. same ordered list of field names) which is useful for making sense of time-ordered output as described in Record-heterogeneity — in particular, in preparation for CSV or pretty-print output.

$ mlr cat data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false

$ mlr --opprint group-like data/het.dkvp
resource             loadsec ok
/path/to/file        0.45    true
/path/to/second/file 0.32    true
/some/other/path     0.97    false

record_count resource
100          /path/to/file
150          /path/to/second/file

having-fields

$ mlr having-fields --help
Usage: mlr having-fields [options]
Conditionally passes through records depending on each record's field names.
Options:
  --at-least      {comma-separated names}
  --which-are     {comma-separated names}
  --at-most       {comma-separated names}
  --all-matching  {regular expression}
  --any-matching  {regular expression}
  --none-matching {regular expression}
Examples:
  mlr having-fields --which-are amount,status,owner
  mlr having-fields --any-matching 'sda[0-9]'
  mlr having-fields --any-matching '"sda[0-9]"'
  mlr having-fields --any-matching '"sda[0-9]"i' (this is case-insensitive)

Similar to group-like, this retains records with specified schema.

$ mlr cat data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false

$ mlr having-fields --at-least resource data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false

$ mlr having-fields --which-are resource,ok,loadsec data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
resource=/path/to/second/file,loadsec=0.32,ok=true
resource=/some/other/path,loadsec=0.97,ok=false

head

$ mlr head --help
Usage: mlr head [options]
-n {count}    Head count to print; default 10
-g {a,b,c}    Optional group-by-field names for head counts
Passes through the first n records, optionally by category.
Without -g, ceases consuming more input (i.e. is fast) when n
records have been read.

Note that head is distinct from tophead shows fields which appear first in the data stream; top shows fields which are numerically largest (or smallest).

$ mlr --opprint head -n 4 data/medium
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463

$ mlr --opprint head -n 1 -g b data/medium
a   b   i  x                   y
pan pan 1  0.3467901443380824  0.7268028627434533
wye wye 3  0.20460330576630303 0.33831852551664776
eks zee 7  0.6117840605678454  0.1878849191181694
zee eks 17 0.29081949506712723 0.054478717073354166
wye hat 24 0.7286126830627567  0.19441962592638418

histogram

$ mlr histogram --help
Usage: mlr histogram [options]
-f {a,b,c}    Value-field names for histogram counts
--lo {lo}     Histogram low value
--hi {hi}     Histogram high value
--nbins {n}   Number of histogram bins
--auto        Automatically computes limits, ignoring --lo and --hi.
              Holds all values in memory before producing any output.
-o {prefix}   Prefix for output field name. Default: no prefix.
Just a histogram. Input values < lo or > hi are not counted.

This is just a histogram; there’s not too much to say here. A note about binning, by example: Suppose you use --lo 0.0 --hi 1.0 --nbins 10 -f x. The input numbers less than 0 or greater than 1 aren’t counted in any bin. Input numbers equal to 1 are counted in the last bin. That is, bin 0 has 0.0 ≤ x < 0.1, bin 1 has 0.1 ≤ x < 0.2, etc., but bin 9 has 0.9 ≤ x ≤ 1.0.

$ mlr --opprint put '$x2=$x**2;$x3=$x2*$x' then histogram -f x,x2,x3 --lo 0 --hi 1 --nbins 10 data/medium
bin_lo   bin_hi   x_count x2_count x3_count
0.000000 0.100000 1072    3231     4661
0.100000 0.200000 938     1254     1184
0.200000 0.300000 1037    988      845
0.300000 0.400000 988     832      676
0.400000 0.500000 950     774      576
0.500000 0.600000 1002    692      476
0.600000 0.700000 1007    591      438
0.700000 0.800000 1007    560      420
0.800000 0.900000 986     571      383
0.900000 1.000000 1013    507      341

$ mlr --opprint put '$x2=$x**2;$x3=$x2*$x' then histogram -f x,x2,x3 --lo 0 --hi 1 --nbins 10 -o my_ data/medium
my_bin_lo my_bin_hi my_x_count my_x2_count my_x3_count
0.000000  0.100000  1072       3231        4661
0.100000  0.200000  938        1254        1184
0.200000  0.300000  1037       988         845
0.300000  0.400000  988        832         676
0.400000  0.500000  950        774         576
0.500000  0.600000  1002       692         476
0.600000  0.700000  1007       591         438
0.700000  0.800000  1007       560         420
0.800000  0.900000  986        571         383
0.900000  1.000000  1013       507         341

join

$ mlr join --help
Usage: mlr join [options]
Joins records from specified left file name with records from all file names
at the end of the Miller argument list.
Functionality is essentially the same as the system "join" command, but for
record streams.
Options:
  -f {left file name}
  -j {a,b,c}   Comma-separated join-field names for output
  -l {a,b,c}   Comma-separated join-field names for left input file;
               defaults to -j values if omitted.
  -r {a,b,c}   Comma-separated join-field names for right input file(s);
               defaults to -j values if omitted.
  --lp {text}  Additional prefix for non-join output field names from
               the left file
  --rp {text}  Additional prefix for non-join output field names from
               the right file(s)
  --np         Do not emit paired records
  --ul         Emit unpaired records from the left file
  --ur         Emit unpaired records from the right file(s)
  -s|--sorted-input  Require sorted input: records must be sorted
               lexically by their join-field names, else not all records will
               be paired. The only likely use case for this is with a left
               file which is too big to fit into system memory otherwise.
  -u           Enable unsorted input. (This is the default even without -u.)
               In this case, the entire left file will be loaded into memory.
  --prepipe {command} As in main input options; see mlr --help for details.
               If you wish to use a prepipe command for the main input as well
               as here, it must be specified there as well as here.
File-format options default to those for the right file names on the Miller
argument list, but may be overridden for the left file as follows. Please see
the main "mlr --help" for more information on syntax for these arguments.
  -i {one of csv,dkvp,nidx,pprint,xtab}
  --irs {record-separator character}
  --ifs {field-separator character}
  --ips {pair-separator character}
  --repifs
  --repips
  --use-mmap
  --no-mmap
Please use "mlr --usage-separator-options" for information on specifying separators.
Please see http://johnkerl.org/miller/doc/reference.html for more information
including examples.

Examples:

Join larger table with IDs with smaller ID-to-name lookup table, showing only paired records:

$ mlr --icsvlite --opprint cat data/join-left-example.csv
id  name
100 alice
200 bob
300 carol
400 david
500 edgar

$ mlr --icsvlite --opprint cat data/join-right-example.csv
status  idcode
present 400
present 100
missing 200
present 100
present 200
missing 100
missing 200
present 300
missing 600
present 400
present 400
present 300
present 100
missing 400
present 200
present 200
present 200
present 200
present 400
present 300

$ mlr --icsvlite --opprint join -u -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
id  name  status
400 david present
100 alice present
200 bob   missing
100 alice present
200 bob   present
100 alice missing
200 bob   missing
300 carol present
400 david present
400 david present
300 carol present
100 alice present
400 david missing
200 bob   present
200 bob   present
200 bob   present
200 bob   present
400 david present
300 carol present

Same, but with sorting the input first:

$ mlr --icsvlite --opprint sort -f idcode then join -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
id  name  status
100 alice present
100 alice present
100 alice missing
100 alice present
200 bob   missing
200 bob   present
200 bob   missing
200 bob   present
200 bob   present
200 bob   present
200 bob   present
300 carol present
300 carol present
300 carol present
400 david present
400 david present
400 david present
400 david missing
400 david present

Same, but showing only unpaired records:

$ mlr --icsvlite --opprint join --np --ul --ur -u -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
status  idcode
missing 600

id  name
500 edgar

Use prefixing options to disambiguate between otherwise identical non-join field names:

$ mlr --csvlite --opprint cat data/self-join.csv data/self-join.csv
a b c
1 2 3
1 4 5
1 2 3
1 4 5

$ mlr --csvlite --opprint join -j a --lp left_ --rp right_ -f data/self-join.csv data/self-join.csv
a left_b left_c right_b right_c
1 2      3      2       3
1 4      5      2       3
1 2      3      4       5
1 4      5      4       5

Use zero join columns:

$ mlr --csvlite --opprint join -j "" --lp left_ --rp right_ -f data/self-join.csv data/self-join.csv
left_a left_b left_c right_a right_b right_c
1      2      3      1       2       3
1      4      5      1       2       3
1      2      3      1       4       5
1      4      5      1       4       5

label

$ mlr label --help
Usage: mlr label {new1,new2,new3,...}
Given n comma-separated names, renames the first n fields of each record to
have the respective name. (Fields past the nth are left with their original
names.) Particularly useful with --inidx or --implicit-csv-header, to give
useful names to otherwise integer-indexed fields.
Examples:
  "echo 'a b c d' | mlr --inidx --odkvp cat"       gives "1=a,2=b,3=c,4=d"
  "echo 'a b c d' | mlr --inidx --odkvp label s,t" gives "s=a,t=b,3=c,4=d"

See also rename.

Example: Files such as /etc/passwd, /etc/group, and so on have implicit field names which are found in section-5 manpages. These field names may be made explicit as follows:

% grep -v '^#' /etc/passwd | mlr --nidx --fs : --opprint label name,password,uid,gid,gecos,home_dir,shell | head
name                  password uid gid gecos                         home_dir           shell
nobody                *        -2  -2  Unprivileged User             /var/empty         /usr/bin/false
root                  *        0   0   System Administrator          /var/root          /bin/sh
daemon                *        1   1   System Services               /var/root          /usr/bin/false
_uucp                 *        4   4   Unix to Unix Copy Protocol    /var/spool/uucp    /usr/sbin/uucico
_taskgated            *        13  13  Task Gate Daemon              /var/empty         /usr/bin/false
_networkd             *        24  24  Network Services              /var/networkd      /usr/bin/false
_installassistant     *        25  25  Install Assistant             /var/empty         /usr/bin/false
_lp                   *        26  26  Printing Services             /var/spool/cups    /usr/bin/false
_postfix              *        27  27  Postfix Mail Server           /var/spool/postfix /usr/bin/false

Likewise, if you have CSV/CSV-lite input data which has somehow been bereft of its header line, you can re-add a header line using --implicit-csv-header and label:

$ cat data/headerless.csv
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present

$ mlr  --csv --implicit-csv-header cat data/headerless.csv
1,2,3
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present

$ mlr  --csv --implicit-csv-header label name,age,status data/headerless.csv
name,age,status
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present

$ mlr --icsv --implicit-csv-header --opprint label name,age,status data/headerless.csv
name  age status
John  23  present
Fred  34  present
Alice 56  missing
Carol 45  present

least-frequent

merge-fields

$ mlr merge-fields --help
Usage: mlr merge-fields [options]
Computes univariate statistics for each input record, accumulated across
specified fields.
Options:
-a {sum,count,...}  Names of accumulators. One or more of:
  count     Count instances of fields
  mode      Find most-frequently-occurring values for fields; first-found wins tie
  antimode  Find least-frequently-occurring values for fields; first-found wins tie
  sum       Compute sums of specified fields
  mean      Compute averages (sample means) of specified fields
  stddev    Compute sample standard deviation of specified fields
  var       Compute sample variance of specified fields
  meaneb    Estimate error bars for averages (assuming no sample autocorrelation)
  skewness  Compute sample skewness of specified fields
  kurtosis  Compute sample kurtosis of specified fields
  min       Compute minimum values of specified fields
  max       Compute maximum values of specified fields
-f {a,b,c}  Value-field names on which to compute statistics. Requires -o.
-r {a,b,c}  Regular expressions for value-field names on which to compute
            statistics. Requires -o.
-c {a,b,c}  Substrings for collapse mode. All fields which have the same names
            after removing substrings will be accumulated together. Please see
            examples below.
-i          Use interpolated percentiles, like R's type=7; default like type=1.
            Not sensical for string-valued fields.
-o {name}   Output field basename for -f/-r.
-k          Keep the input fields which contributed to the output statistics;
            the default is to omit them.
-F          Computes integerable things (e.g. count) in floating point.

String-valued data make sense unless arithmetic on them is required,
e.g. for sum, mean, interpolated percentiles, etc. In case of mixed data,
numbers are less than strings.

Example input data: "a_in_x=1,a_out_x=2,b_in_y=4,b_out_x=8".
Example: mlr merge-fields -a sum,count -f a_in_x,a_out_x -o foo
  produces "b_in_y=4,b_out_x=8,foo_sum=3,foo_count=2" since "a_in_x,a_out_x" are
  summed over.
Example: mlr merge-fields -a sum,count -r in_,out_ -o bar
  produces "bar_sum=15,bar_count=4" since all four fields are summed over.
Example: mlr merge-fields -a sum,count -c in_,out_
  produces "a_x_sum=3,a_x_count=2,b_y_sum=4,b_y_count=1,b_x_sum=8,b_x_count=1"
  since "a_in_x" and "a_out_x" both collapse to "a_x", "b_in_y" collapses to
  "b_y", and "b_out_x" collapses to "b_x".

This is like mlr stats1 but all accumulation is done across fields within each given record: horizontal rather than vertical statistics, if you will.

Examples:

$ mlr --csvlite --opprint cat data/inout.csv
a_in a_out b_in b_out
436  490   446  195
526  320   963  780
220  888   705  831

$ mlr --csvlite --opprint merge-fields -a min,max,sum -c _in,_out data/inout.csv
a_min a_max a_sum b_min b_max b_sum
436   490   926   195   446   641
320   526   846   780   963   1743
220   888   1108  705   831   1536

$ mlr --csvlite --opprint merge-fields -k -a sum -c _in,_out data/inout.csv
a_in a_out b_in b_out a_sum b_sum
436  490   446  195   926   641
526  320   963  780   846   1743
220  888   705  831   1108  1536

most-frequent

nest

$ mlr nest -h
Usage: mlr nest [options]
Explodes specified field values into separate fields/records, or reverses this.
Options:
  --explode,--implode   One is required.
  --values,--pairs      One is required.
  --across-records,--across-fields One is required.
  -f {field name}       Required.
  --nested-fs {string}  Defaults to ";". Field separator for nested values.
  --nested-ps {string}  Defaults to ":". Pair separator for nested key-value pairs.
  --evar {string}       Shorthand for --explode --values ---across-records --nested-fs {string}
Please use "mlr --usage-separator-options" for information on specifying separators.

Examples:

  mlr nest --explode --values --across-records -f x
  with input record "x=a;b;c,y=d" produces output records
    "x=a,y=d"
    "x=b,y=d"
    "x=c,y=d"
  Use --implode to do the reverse.

  mlr nest --explode --values --across-fields -f x
  with input record "x=a;b;c,y=d" produces output records
    "x_1=a,x_2=b,x_3=c,y=d"
  Use --implode to do the reverse.

  mlr nest --explode --pairs --across-records -f x
  with input record "x=a:1;b:2;c:3,y=d" produces output records
    "a=1,y=d"
    "b=2,y=d"
    "c=3,y=d"

  mlr nest --explode --pairs --across-fields -f x
  with input record "x=a:1;b:2;c:3,y=d" produces output records
    "a=1,b=2,c=3,y=d"

Notes:
* With --pairs, --implode doesn't make sense since the original field name has
  been lost.
* The combination "--implode --values --across-records" is non-streaming:
  no output records are produced until all input records have been read. In
  particular, this means it won't work in tail -f contexts. But all other flag
  combinations result in streaming (tail -f friendly) data processing.
* It's up to you to ensure that the nested-fs is distinct from your data's IFS:
  e.g. by default the former is semicolon and the latter is comma.
See also mlr reshape.

nothing

$ mlr nothing -h
Usage: mlr nothing [options]
Drops all input records. Useful for testing, or after tee/print/etc. have
produced other output.

put

$ mlr put --help
Usage: mlr put [options] {expression}
Adds/updates specified field(s). Expressions are semicolon-separated and must
either be assignments, or evaluate to boolean.  Booleans with following
statements in curly braces control whether those statements are executed;
booleans without following curly braces do nothing except side effects (e.g.
regex-captures into \1, \2, etc.).

Conversion options:
-S: Keeps field values as strings with no type inference to int or float.
-F: Keeps field values as strings or floats with no inference to int.
All field values are type-inferred to int/float/string unless this behavior is
suppressed with -S or -F.

Output/formatting options:
--oflatsep {string}: Separator to use when flattening multi-level @-variables
    to output records for emit. Default ":".
--jknquoteint: For dump output (JSON-formatted), do not quote map keys if non-string.
--jvquoteall: For dump output (JSON-formatted), quote map values even if non-string.
Any of the output-format command-line flags (see mlr -h). Example: using
  mlr --icsv --opprint ... then put --ojson 'tee > "mytap-".$a.".dat", $*' then ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.
--no-fflush: for emit, tee, print, and dump, don't call fflush() after every
    record.

Expression-specification options:
-f {filename}: the DSL expression is taken from the specified file rather
    than from the command line. Outer single quotes wrapping the expression
    should not be placed in the file. If -f is specified more than once,
    all input files specified using -f are concatenated to produce the expression.
    (For example, you can define functions in one file and call them from another.)
-e {expression}: You can use this after -f to add an expression. Example use
    case: define functions/subroutines in a file you specify with -f, then call
    them with an expression you specify with -e.
(If you mix -e and -f then the expressions are evaluated in the order encountered.
Since the expression pieces are simply concatenated, please be sure to use intervening
semicolons to separate expressions.)

Tracing options:
-v: Prints the expressions's AST (abstract syntax tree), which gives
    full transparency on the precedence and associativity rules of
    Miller's grammar, to stdout.
-a: Prints a low-level stack-allocation trace to stdout.
-t: Prints a low-level parser trace to stderr.
-T: Prints a every statement to stderr as it is executed.

Other options:
-q: Does not include the modified record in the output stream. Useful for when
    all desired output is in begin and/or end blocks.

Please use a dollar sign for field names and double-quotes for string
literals. If field names have special characters such as "." then you might
use braces, e.g. '${field.name}'. Miller built-in variables are
NF NR FNR FILENUM FILENAME PI E, and ENV["namegoeshere"] to access environment
variables. The environment-variable name may be an expression, e.g. a field
value.

Use # to comment to end of line.

Examples:
  mlr put '$y = log10($x); $z = sqrt($y)'
  mlr put '$x>0.0 { $y=log10($x); $z=sqrt($y) }' # does {...} only if $x > 0.0
  mlr put '$x>0.0;  $y=log10($x); $z=sqrt($y)'   # does all three statements
  mlr put '$a =~ "([a-z]+)_([0-9]+);  $b = "left_\1"; $c = "right_\2"'
  mlr put '$a =~ "([a-z]+)_([0-9]+) { $b = "left_\1"; $c = "right_\2" }'
  mlr put '$filename = FILENAME'
  mlr put '$colored_shape = $color . "_" . $shape'
  mlr put '$y = cos($theta); $z = atan2($y, $x)'
  mlr put '$name = sub($name, "http.*com"i, "")'
  mlr put -q '@sum += $x; end {emit @sum}'
  mlr put -q '@sum[$a] += $x; end {emit @sum, "a"}'
  mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}'
  mlr put -q '@min=min(@min,$x);@max=max(@max,$x); end{emitf @min, @max}'
  mlr put -q 'is_null(@xmax) || $x > @xmax {@xmax=$x; @recmax=$*}; end {emit @recmax}'
  mlr put '
    $x = 1;
   #$y = 2;
    $z = 3
  '

Please see also 'mlr -k' for examples using redirected output.

Please see http://johnkerl.org/miller/doc/reference.html for more information
including function list. Or "mlr -f".
Please see in particular:
  http://www.johnkerl.org/miller/doc/reference.html#put

Features which put shares with filter

Please see Expression language for filter and put for more information about the expression language for mlr put.

regularize

rename

$ mlr rename --help
Usage: mlr rename [options] {old1,new1,old2,new2,...}
Renames specified fields.
Options:
-r         Treat old field  names as regular expressions. "ab", "a.*b"
           will match any field name containing the substring "ab" or
           matching "a.*b", respectively; anchors of the form "^ab$",
           "^a.*b$" may be used. New field names may be plain strings,
           or may contain capture groups of the form "\1" through
           "\9". Wrapping the regex in double quotes is optional, but
           is required if you wish to follow it with 'i' to indicate
           case-insensitivity.
-g         Do global replacement within each field name rather than
           first-match replacement.
Examples:
mlr rename old_name,new_name'
mlr rename old_name_1,new_name_1,old_name_2,new_name_2'
mlr rename -r 'Date_[0-9]+,Date,'  Rename all such fields to be "Date"
mlr rename -r '"Date_[0-9]+",Date' Same
mlr rename -r 'Date_([0-9]+).*,\1' Rename all such fields to be of the form 20151015
mlr rename -r '"name"i,Name'       Rename "name", "Name", "NAME", etc. to "Name"

$ mlr --opprint cat data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint rename i,INDEX,b,COLUMN2 data/small
a   COLUMN2 INDEX x                   y
pan pan     1     0.3467901443380824  0.7268028627434533
eks pan     2     0.7586799647899636  0.5221511083334797
wye wye     3     0.20460330576630303 0.33831852551664776
eks wye     4     0.38139939387114097 0.13418874328430463
wye pan     5     0.5732889198020006  0.8636244699032729

As discussed in Performance, sed is significantly faster than Miller at doing this. However, Miller is format-aware, so it knows to do renames only within specified field keys and not any others, nor in field values which may happen to contain the same pattern. Example:

$ sed 's/y/COLUMN5/g' data/small
a=pan,b=pan,i=1,x=0.3467901443380824,COLUMN5=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,COLUMN5=0.5221511083334797
a=wCOLUMN5e,b=wCOLUMN5e,i=3,x=0.20460330576630303,COLUMN5=0.33831852551664776
a=eks,b=wCOLUMN5e,i=4,x=0.38139939387114097,COLUMN5=0.13418874328430463
a=wCOLUMN5e,b=pan,i=5,x=0.5732889198020006,COLUMN5=0.8636244699032729

$ mlr rename y,COLUMN5 data/small
a=pan,b=pan,i=1,x=0.3467901443380824,COLUMN5=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,COLUMN5=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,COLUMN5=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,COLUMN5=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,COLUMN5=0.8636244699032729

See also label.

reorder

$ mlr reorder --help
Usage: mlr reorder [options]
-f {a,b,c}   Field names to reorder.
-e           Put specified field names at record end: default is to put
             them at record start.
Examples:
mlr reorder    -f a,b sends input record "d=4,b=2,a=1,c=3" to "a=1,b=2,d=4,c=3".
mlr reorder -e -f a,b sends input record "d=4,b=2,a=1,c=3" to "d=4,c=3,a=1,b=2".

This pivots specified field names to the start or end of the record — for example when you have highly multi-column data and you want to bring a field or two to the front of line where you can give a quick visual scan.

$ mlr --opprint cat data/small
a   b   i x                   y
pan pan 1 0.3467901443380824  0.7268028627434533
eks pan 2 0.7586799647899636  0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006  0.8636244699032729

$ mlr --opprint reorder -f i,b data/small
i b   a   x                   y
1 pan pan 0.3467901443380824  0.7268028627434533
2 pan eks 0.7586799647899636  0.5221511083334797
3 wye wye 0.20460330576630303 0.33831852551664776
4 wye eks 0.38139939387114097 0.13418874328430463
5 pan wye 0.5732889198020006  0.8636244699032729

$ mlr --opprint reorder -e -f i,b data/small
a   x                   y                   i b
pan 0.3467901443380824  0.7268028627434533  1 pan
eks 0.7586799647899636  0.5221511083334797  2 pan
wye 0.20460330576630303 0.33831852551664776 3 wye
eks 0.38139939387114097 0.13418874328430463 4 wye
wye 0.5732889198020006  0.8636244699032729  5 pan

repeat

$ mlr repeat --help
Usage: mlr repeat [options]
Copies input records to output records multiple times.
Options must be exactly one of the following:
  -n {repeat count}  Repeat each input record this many times.
  -f {field name}    Same, but take the repeat count from the specified
                     field name of each input record.
Example:
  echo x=0 | mlr repeat -n 4 then put '$x=urand()'
produces:
 x=0.488189
 x=0.484973
 x=0.704983
 x=0.147311
Example:
  echo a=1,b=2,c=3 | mlr repeat -f b
produces:
  a=1,b=2,c=3
  a=1,b=2,c=3
Example:
  echo a=1,b=2,c=3 | mlr repeat -f c
produces:
  a=1,b=2,c=3
  a=1,b=2,c=3
  a=1,b=2,c=3

This is useful in at least two ways: one, as a data-generator as in the above example using urand(); two, for reconstructing individual samples from data which has been count-aggregated:

$ cat data/repeat-example.dat
color=blue,count=5
color=red,count=4
color=green,count=3

$ mlr repeat -f count then cut -x -f count data/repeat-example.dat
color=blue
color=blue
color=blue
color=blue
color=blue
color=red
color=red
color=red
color=red
color=green
color=green
color=green

After expansion with repeat, such data can then be sent on to stats1 -a mode, or (if the data are numeric) to stats1 -a p10,p50,p90, etc.

reshape

$ mlr reshape --help
Usage: mlr reshape [options]
Wide-to-long options:
  -i {input field names}   -o {key-field name,value-field name}
  -r {input field regexes} -o {key-field name,value-field name}
  These pivot/reshape the input data such that the input fields are removed
  and separate records are emitted for each key/value pair.
  Note: this works with tail -f and produces output records for each input
  record seen.
Long-to-wide options:
  -s {key-field name,value-field name}
  These pivot/reshape the input data to undo the wide-to-long operation.
  Note: this does not work with tail -f; it produces output records only after
  all input records have been read.

Examples:

  Input file "wide.txt":
    time       X           Y
    2009-01-01 0.65473572  2.4520609
    2009-01-02 -0.89248112 0.2154713
    2009-01-03 0.98012375  1.3179287

  mlr --pprint reshape -i X,Y -o item,value wide.txt
    time       item value
    2009-01-01 X    0.65473572
    2009-01-01 Y    2.4520609
    2009-01-02 X    -0.89248112
    2009-01-02 Y    0.2154713
    2009-01-03 X    0.98012375
    2009-01-03 Y    1.3179287

  mlr --pprint reshape -r '[A-Z]' -o item,value wide.txt
    time       item value
    2009-01-01 X    0.65473572
    2009-01-01 Y    2.4520609
    2009-01-02 X    -0.89248112
    2009-01-02 Y    0.2154713
    2009-01-03 X    0.98012375
    2009-01-03 Y    1.3179287

  Input file "long.txt":
    time       item value
    2009-01-01 X    0.65473572
    2009-01-01 Y    2.4520609
    2009-01-02 X    -0.89248112
    2009-01-02 Y    0.2154713
    2009-01-03 X    0.98012375
    2009-01-03 Y    1.3179287

  mlr --pprint reshape -s item,value long.txt
    time       X           Y
    2009-01-01 0.65473572  2.4520609
    2009-01-02 -0.89248112 0.2154713
    2009-01-03 0.98012375  1.3179287
See also mlr nest.

sample

$ mlr sample --help
Usage: mlr sample [options]
Reservoir sampling (subsampling without replacement), optionally by category.
-k {count}    Required: number of records to output, total, or by group if using -g.
-g {a,b,c}    Optional: group-by-field names for samples.
See also mlr bootstrap and mlr shuffle.

This is reservoir-sampling: select k items from n with uniform probability and no repeats in the sample. (If n is less than k, then of course only n samples are produced.) With -g {field names}, produce a k-sample for each distinct value of the specified field names.

$ mlr --opprint sample -k 4 data/colored-shapes.dkvp
color  shape    flag i     u                   v                    w                   x
purple triangle 0    90122 0.9986871176198068  0.3037738877233719   0.5154934457238382  5.365962021016529
red    circle   0    3139  0.04835898233323954 -0.03964684310055758 0.5263660881848111  5.3758779366493625
orange triangle 0    67847 0.36746306902109926 0.5161574810505635   0.5176199566173642  3.1748088656576567
yellow square   1    33576 0.3098376725521097  0.8525628505287842   0.49774122460981685 4.494754378604669

$ mlr --opprint sample -k 4 data/colored-shapes.dkvp
color  shape  flag i     u                     v                   w                   x
blue   square 1    16783 0.09974385090654347   0.7243899920872646  0.5353718443278438  4.431057737383438
orange square 1    93291 0.5944176543007182    0.17744449786454086 0.49262281749172077 3.1548117990710653
yellow square 1    54436 0.5268161165014636    0.8785588662666121  0.5058773791931063  7.019185838783636
yellow square 1    55491 0.0025440267883102274 0.05474106287787284 0.5102729153751984  3.526301273728043

$ mlr --opprint sample -k 2 -g color data/colored-shapes.dkvp
color  shape    flag i     u                    v                   w                    x
yellow triangle 1    11    0.6321695890307647   0.9887207810889004  0.4364983936735774   5.7981881667050565
yellow square   1    917   0.8547010348386344   0.7356782810796262  0.4531511689924275   5.774541777078352
red    circle   1    4000  0.05490416175132373  0.07392337815122155 0.49416101516594396  5.355725080701707
red    square   0    87506 0.6357719216821314   0.6970867759393995  0.4940826462055272   6.351579417310387
purple triangle 0    14898 0.7800986870203719   0.23998073813992293 0.5014775988383656   3.141006771777843
purple triangle 0    151   0.032614487569017414 0.7346633365041219  0.7812143304483805   2.6831992610568047
green  triangle 1    126   0.1513010528347546   0.40346767294704544 0.051213231883952326 5.955109300797182
green  circle   0    17635 0.029856606049114442 0.4724542934246524  0.49529606749929744  5.239153910272168
blue   circle   1    1020  0.414263129226617    0.8304946402876182  0.13151094520189244  4.397873687920433
blue   triangle 0    220   0.441773289968473    0.44597731903759075 0.6329360666849821   4.3064608776550894
orange square   0    1885  0.8079311983747106   0.8685956833908394  0.3116410800256374   4.390864584500387
orange triangle 0    1533  0.32904497195507487  0.23168161807490417 0.8722623057355134   5.164071635714438

$ mlr --opprint sample -k 2 -g color then sort -f color data/colored-shapes.dkvp
color  shape    flag i     u                   v                    w                   x
blue   circle   0    215   0.7803586969333292  0.33146680638888126  0.04289047852629113 5.725365736377487
blue   circle   1    3616  0.8548431579124808  0.4989623130006362   0.3339426415875795  3.696785877560498
green  square   0    356   0.7674272008085286  0.341578843118008    0.4570224877870851  4.830320062215299
green  square   0    152   0.6684429446914862  0.016056003736548696 0.4656148241291592  5.434588759225423
orange triangle 0    587   0.5175826237797857  0.08989091493635304  0.9011709461770973  4.265854207755811
orange triangle 0    1533  0.32904497195507487 0.23168161807490417  0.8722623057355134  5.164071635714438
purple triangle 0    14192 0.5196327866973567  0.7860928603468063   0.4964368415453642  4.899167143824484
purple triangle 0    65    0.6842806710360729  0.5823723856331258   0.8014053396013747  5.805148213865135
red    square   1    2431  0.38378504852300466 0.11445015005595527  0.49355539228753786 5.146756570128739
red    triangle 0    57097 0.43763430414406546 0.3355450325004481   0.5322349637512487  4.144267240289442
yellow triangle 1    11    0.6321695890307647  0.9887207810889004   0.4364983936735774  5.7981881667050565
yellow square   1    158   0.41527900739142165 0.7118027080775757   0.4200799665161291  5.33279067554884

Note that no output is produced until all inputs are in. Another way to do sampling, which works in the streaming case, is mlr filter 'urand() & 0.001' where you tune the 0.001 to meet your needs.

sec2gmt

$ mlr sec2gmt -h
Usage: mlr sec2gmt [options] {comma-separated list of field names}
Replaces a numeric field representing seconds since the epoch with the
corresponding GMT timestamp; leaves non-numbers as-is. This is nothing
more than a keystroke-saver for the sec2gmt function:
  mlr sec2gmt time1,time2
is the same as
  mlr put '$time1=sec2gmt($time1);$time2=sec2gmt($time2)'
Options:
-1 through -9: format the seconds using 1..9 decimal places, respectively.

sec2gmtdate

$ mlr sec2gmtdate -h
Usage: mlr sec2gmtdate {comma-separated list of field names}
Replaces a numeric field representing seconds since the epoch with the
corresponding GMT year-month-day timestamp; leaves non-numbers as-is.
This is nothing more than a keystroke-saver for the sec2gmtdate function:
  mlr sec2gmtdate time1,time2
is the same as
  mlr put '$time1=sec2gmtdate($time1);$time2=sec2gmtdate($time2)'

seqgen

$ mlr seqgen -h
Usage: mlr seqgen [options]
Produces a sequence of counters.  Discards the input record stream. Produces
output as specified by the following options:
-f {name} Field name for counters; default "i".
--start {number} Inclusive start value; default "1".
--stop  {number} Inclusive stop value; default "100".
--step  {number} Step value; default "1".
Start, stop, and/or step may be floating-point. Output is integer if start,
stop, and step are all integers. Step may be negative. It may not be zero
unless start == stop.

$ mlr seqgen --stop 10
i=1
i=2
i=3
i=4
i=5
i=6
i=7
i=8
i=9
i=10

$ mlr seqgen --start 20 --stop 40 --step 4
i=20
i=24
i=28
i=32
i=36
i=40

$ mlr seqgen --start 40 --stop 20 --step -4
i=40
i=36
i=32
i=28
i=24
i=20

shuffle

$ mlr shuffle -h
Usage: mlr shuffle {no options}
Outputs records randomly permuted. No output records are produced until
all input records are read.
See also mlr bootstrap and mlr sample.

sort

$ mlr sort --help
Usage: mlr sort {flags}
Flags:
  -f  {comma-separated field names}  Lexical ascending
  -n  {comma-separated field names}  Numerical ascending; nulls sort last
  -nf {comma-separated field names}  Numerical ascending; nulls sort last
  -r  {comma-separated field names}  Lexical descending
  -nr {comma-separated field names}  Numerical descending; nulls sort first
Sorts records primarily by the first specified field, secondarily by the second
field, and so on.  (Any records not having all specified sort keys will appear
at the end of the output, in the order they were encountered, regardless of the
specified sort order.) The sort is stable: records that compare equal will sort
in the order they were encountered in the input record stream.

Example:
  mlr sort -f a,b -nr x,y,z
which is the same as:
  mlr sort -f a -f b -nr x -nr y -nr z

Example:

$ mlr --opprint sort -f a -nr x data/small
a   b   i x                   y
eks pan 2 0.7586799647899636  0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
pan pan 1 0.3467901443380824  0.7268028627434533
wye pan 5 0.5732889198020006  0.8636244699032729
wye wye 3 0.20460330576630303 0.33831852551664776

Here’s an example filtering log data: suppose multiple threads (labeled here by color) are all logging progress counts to a single log file. The log file is (by nature) chronological, so the progress of various threads is interleaved:

$ head -n 10 data/multicountdown.dat
upsec=0.002,color=green,count=1203
upsec=0.083,color=red,count=3817
upsec=0.188,color=red,count=3801
upsec=0.395,color=blue,count=2697
upsec=0.526,color=purple,count=953
upsec=0.671,color=blue,count=2684
upsec=0.899,color=purple,count=926
upsec=0.912,color=red,count=3798
upsec=1.093,color=blue,count=2662
upsec=1.327,color=purple,count=917

We can group these by thread by sorting on the thread ID (here, color). Since Miller’s sort is stable, this means that timestamps within each thread’s log data are still chronological:

$ head -n 20 data/multicountdown.dat | mlr --opprint sort -f color
upsec              color  count
0.395              blue   2697
0.671              blue   2684
1.093              blue   2662
2.064              blue   2659
2.2880000000000003 blue   2647
0.002              green  1203
1.407              green  1187
1.448              green  1177
2.313              green  1161
0.526              purple 953
0.899              purple 926
1.327              purple 917
1.703              purple 908
0.083              red    3817
0.188              red    3801
0.912              red    3798
1.416              red    3788
1.587              red    3782
1.601              red    3755
1.832              red    3717

Any records not having all specified sort keys will appear at the end of the output, in the order they were encountered, regardless of the specified sort order:

$ mlr sort -n  x data/sort-missing.dkvp
x=1
x=2
x=4
a=3

$ mlr sort -nr x data/sort-missing.dkvp
x=4
x=2
x=1
a=3

stats1

$ mlr stats1 --help
Usage: mlr stats1 [options]
Computes univariate statistics for one or more given fields, accumulated across
the input record stream.
Options:
-a {sum,count,...}  Names of accumulators: p10 p25.2 p50 p98 p100 etc. and/or
                    one or more of:
   count     Count instances of fields
   mode      Find most-frequently-occurring values for fields; first-found wins tie
   antimode  Find least-frequently-occurring values for fields; first-found wins tie
   sum       Compute sums of specified fields
   mean      Compute averages (sample means) of specified fields
   stddev    Compute sample standard deviation of specified fields
   var       Compute sample variance of specified fields
   meaneb    Estimate error bars for averages (assuming no sample autocorrelation)
   skewness  Compute sample skewness of specified fields
   kurtosis  Compute sample kurtosis of specified fields
   min       Compute minimum values of specified fields
   max       Compute maximum values of specified fields
-f {a,b,c}   Value-field names on which to compute statistics
--fr {regex} Regex for value-field names on which to compute statistics
             (compute statsitics on values in all field names matching regex)
--fx {regex} Inverted regex for value-field names on which to compute statistics
             (compute statsitics on values in all field names not matching regex)
-g {d,e,f}   Optional group-by-field names
--gr {regex} Regex for optional group-by-field names
             (group by values in field names matching regex)
--gx {regex} Inverted regex for optional group-by-field names
             (group by values in field names not matching regex)
--grfx {regex} Shorthand for --gr {regex} --fx {that same regex}
-i           Use interpolated percentiles, like R's type=7; default like type=1.
             Not sensical for string-valued fields.
-s           Print iterative stats. Useful in tail -f contexts (in which
             case please avoid pprint-format output since end of input
             stream will never be seen).
-F           Computes integerable things (e.g. count) in floating point.
Example: mlr stats1 -a min,p10,p50,p90,max -f value -g size,shape
Example: mlr stats1 -a count,mode -f size
Example: mlr stats1 -a count,mode -f size -g shape
Example: mlr stats1 -a count,mode --fr '^[a-h].*$' -gr '^k.*$'
         This computes count and mode statistics on all field names beginning
         with a through h, grouped by all field names starting with k.
Notes:
* p50 and median are synonymous.
* min and max output the same results as p0 and p100, respectively, but use
  less memory.
* String-valued data make sense unless arithmetic on them is required,
  e.g. for sum, mean, interpolated percentiles, etc. In case of mixed data,
  numbers are less than strings.
* count and mode allow text input; the rest require numeric input.
  In particular, 1 and 1.0 are distinct text for count and mode.
* When there are mode ties, the first-encountered datum wins.

These are simple univariate statistics on one or more number-valued fields (count and mode apply to non-numeric fields as well), optionally categorized by one or more other fields.

$ mlr --oxtab stats1 -a count,sum,min,p10,p50,mean,p90,max -f x,y data/medium
x_count 10000
x_sum   4986.019682
x_min   0.000045
x_p10   0.093322
x_p50   0.501159
x_mean  0.498602
x_p90   0.900794
x_max   0.999953
y_count 10000
y_sum   5062.057445
y_min   0.000088
y_p10   0.102132
y_p50   0.506021
y_mean  0.506206
y_p90   0.905366
y_max   0.999965

$ mlr --opprint stats1 -a mean -f x,y -g b then sort -f b data/medium
b   x_mean   y_mean
eks 0.506361 0.510293
hat 0.487899 0.513118
pan 0.497304 0.499599
wye 0.497593 0.504596
zee 0.504242 0.502997

$ mlr --opprint stats1 -a p50,p99 -f u,v -g color then put '$ur=$u_p99/$u_p50;$vr=$v_p99/$v_p50' data/colored-shapes.dkvp
color  u_p50    u_p99    v_p50    v_p99    ur       vr
yellow 0.501019 0.989046 0.520630 0.987034 1.974069 1.895845
red    0.485038 0.990054 0.492586 0.994444 2.041189 2.018823
purple 0.501319 0.988893 0.504571 0.988287 1.972582 1.958668
green  0.502015 0.990764 0.505359 0.990175 1.973574 1.959350
blue   0.525226 0.992655 0.485170 0.993873 1.889958 2.048505
orange 0.483548 0.993635 0.480913 0.989102 2.054884 2.056717

$ mlr --opprint count-distinct -f shape then sort -nr count data/colored-shapes.dkvp
shape    count
square   4115
triangle 3372
circle   2591

$ mlr --opprint stats1 -a mode -f color -g shape data/colored-shapes.dkvp
shape    color_mode
triangle red
square   red
circle   red

stats2

$ mlr stats2 --help
Usage: mlr stats2 [options]
Computes bivariate statistics for one or more given field-name pairs,
accumulated across the input record stream.
-a {linreg-ols,corr,...}  Names of accumulators: one or more of:
  linreg-pca   Linear regression using principal component analysis
  linreg-ols   Linear regression using ordinary least squares
  r2           Quality metric for linreg-ols (linreg-pca emits its own)
  logireg      Logistic regression
  corr         Sample correlation
  cov          Sample covariance
  covx         Sample-covariance matrix
-f {a,b,c,d}   Value-field name-pairs on which to compute statistics.
               There must be an even number of names.
-g {e,f,g}     Optional group-by-field names.
-v             Print additional output for linreg-pca.
-s             Print iterative stats. Useful in tail -f contexts (in which
               case please avoid pprint-format output since end of input
               stream will never be seen).
--fit          Rather than printing regression parameters, applies them to
               the input data to compute new fit fields. All input records are
               held in memory until end of input stream. Has effect only for
               linreg-ols, linreg-pca, and logireg.
Only one of -s or --fit may be used.
Example: mlr stats2 -a linreg-pca -f x,y
Example: mlr stats2 -a linreg-ols,r2 -f x,y -g size,shape
Example: mlr stats2 -a corr -f x,y

These are simple bivariate statistics on one or more pairs of number-valued fields, optionally categorized by one or more fields.

$ mlr --oxtab put '$x2=$x*$x; $xy=$x*$y; $y2=$y**2' then stats2 -a cov,corr -f x,y,y,y,x2,xy,x2,y2 data/medium
x_y_cov    0.000043
x_y_corr   0.000504
y_y_cov    0.084611
y_y_corr   1.000000
x2_xy_cov  0.041884
x2_xy_corr 0.630174
x2_y2_cov  -0.000310
x2_y2_corr -0.003425

$ mlr --opprint put '$x2=$x*$x; $xy=$x*$y; $y2=$y**2' then stats2 -a linreg-ols,r2 -f x,y,y,y,xy,y2 -g a data/medium
a   x_y_ols_m x_y_ols_b x_y_ols_n x_y_r2   y_y_ols_m y_y_ols_b y_y_ols_n y_y_r2   xy_y2_ols_m xy_y2_ols_b xy_y2_ols_n xy_y2_r2
pan 0.017026  0.500403  2081      0.000287 1.000000  0.000000  2081      1.000000 0.878132    0.119082    2081        0.417498
eks 0.040780  0.481402  1965      0.001646 1.000000  0.000000  1965      1.000000 0.897873    0.107341    1965        0.455632
wye -0.039153 0.525510  1966      0.001505 1.000000  0.000000  1966      1.000000 0.853832    0.126745    1966        0.389917
zee 0.002781  0.504307  2047      0.000008 1.000000  0.000000  2047      1.000000 0.852444    0.124017    2047        0.393566
hat -0.018621 0.517901  1941      0.000352 1.000000  0.000000  1941      1.000000 0.841230    0.135573    1941        0.368794

Here’s an example simple line-fit. The x and y fields of the data/medium dataset are just independent uniformly distributed on the unit interval. Here we remove half the data and fit a line to it.


# Prepare input data:
mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)' data/medium > data/medium-squares

# Do a linear regression and examine coefficients:
mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
x_y_pca_m=1.014419
x_y_pca_b=0.000308
x_y_pca_quality=0.861354

# Option 1 to apply the regression coefficients and produce a linear fit:
#   Set x_y_pca_m and x_y_pca_b as shell variables:
eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
#   In addition to x and y, make a new yfit which is the line fit, then plot
#   using your favorite tool:
mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
  | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1

# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
  | pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1

I use pgr for plotting; here’s a screenshot.

(Thanks Drew Kunas for a good conversation about PCA!)

Here’s an example estimating time-to-completion for a set of jobs. Input data comes from a log file, with number of work units left to do in the count field and accumulated seconds in the upsec field, labeled by the color field:

$ head -n 10 data/multicountdown.dat
upsec=0.002,color=green,count=1203
upsec=0.083,color=red,count=3817
upsec=0.188,color=red,count=3801
upsec=0.395,color=blue,count=2697
upsec=0.526,color=purple,count=953
upsec=0.671,color=blue,count=2684
upsec=0.899,color=purple,count=926
upsec=0.912,color=red,count=3798
upsec=1.093,color=blue,count=2662
upsec=1.327,color=purple,count=917

We can do a linear regression on count remaining as a function of time: with c = m*u+b we want to find the time when the count goes to zero, i.e. u=-b/m.

$ mlr --oxtab stats2 -a linreg-pca -f upsec,count -g color then put '$donesec = -$upsec_count_pca_b/$upsec_count_pca_m' data/multicountdown.dat
color                   green
upsec_count_pca_m       -32.756917
upsec_count_pca_b       1213.722730
upsec_count_pca_n       24
upsec_count_pca_quality 0.999984
donesec                 37.052410

color                   red
upsec_count_pca_m       -37.367646
upsec_count_pca_b       3810.133400
upsec_count_pca_n       30
upsec_count_pca_quality 0.999989
donesec                 101.963431

color                   blue
upsec_count_pca_m       -29.231212
upsec_count_pca_b       2698.932820
upsec_count_pca_n       25
upsec_count_pca_quality 0.999959
donesec                 92.330514

color                   purple
upsec_count_pca_m       -39.030097
upsec_count_pca_b       979.988341
upsec_count_pca_n       21
upsec_count_pca_quality 0.999991
donesec                 25.108529

step

$ mlr step --help
Usage: mlr step [options]
Computes values dependent on the previous record, optionally grouped
by category.

Options:
-a {delta,rsum,...}   Names of steppers: comma-separated, one or more of:
  delta    Compute differences in field(s) between successive records
  shift    Include value(s) in field(s) from previous record, if any
  from-first Compute differences in field(s) from first record
  ratio    Compute ratios in field(s) between successive records
  rsum     Compute running sums of field(s) between successive records
  counter  Count instances of field(s) between successive records
  ewma     Exponentially weighted moving average over successive records
-f {a,b,c} Value-field names on which to compute statistics
-g {d,e,f} Optional group-by-field names
-F         Computes integerable things (e.g. counter) in floating point.
-d {x,y,z} Weights for ewma. 1 means current sample gets all weight (no
           smoothing), near under under 1 is light smoothing, near over 0 is
           heavy smoothing. Multiple weights may be specified, e.g.
           "mlr step -a ewma -f sys_load -d 0.01,0.1,0.9". Default if omitted
           is "-d 0.5".
-o {a,b,c} Custom suffixes for EWMA output fields. If omitted, these default to
           the -d values. If supplied, the number of -o values must be the same
           as the number of -d values.

Examples:
  mlr step -a rsum -f request_size
  mlr step -a delta -f request_size -g hostname
  mlr step -a ewma -d 0.1,0.9 -f x,y
  mlr step -a ewma -d 0.1,0.9 -o smooth,rough -f x,y
  mlr step -a ewma -d 0.1,0.9 -o smooth,rough -f x,y -g group_name

Please see http://johnkerl.org/miller/doc/reference.html#filter or
https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
for more information on EWMA.

Most Miller commands are record-at-a-time, with the exception of stats1, stats2, and histogram which compute aggregate output. The step command is intermediate: it allows the option of adding fields which are functions of fields from previous records. Rsum is short for running sum.

$ mlr --opprint step -a shift,delta,rsum,counter -f x data/medium | head -15
a   b   i     x                      y                      x_shift                x_delta   x_rsum      x_counter
pan pan 1     0.3467901443380824     0.7268028627434533     -                      0         0.346790    1
eks pan 2     0.7586799647899636     0.5221511083334797     0.3467901443380824     0.411890  1.105470    2
wye wye 3     0.20460330576630303    0.33831852551664776    0.7586799647899636     -0.554077 1.310073    3
eks wye 4     0.38139939387114097    0.13418874328430463    0.20460330576630303    0.176796  1.691473    4
wye pan 5     0.5732889198020006     0.8636244699032729     0.38139939387114097    0.191890  2.264762    5
zee pan 6     0.5271261600918548     0.49322128674835697    0.5732889198020006     -0.046163 2.791888    6
eks zee 7     0.6117840605678454     0.1878849191181694     0.5271261600918548     0.084658  3.403672    7
zee wye 8     0.5985540091064224     0.976181385699006      0.6117840605678454     -0.013230 4.002226    8
hat wye 9     0.03144187646093577    0.7495507603507059     0.5985540091064224     -0.567112 4.033668    9
pan wye 10    0.5026260055412137     0.9526183602969864     0.03144187646093577    0.471184  4.536294    10
pan pan 11    0.7930488423451967     0.6505816637259333     0.5026260055412137     0.290423  5.329343    11
zee pan 12    0.3676141320555616     0.23614420670296965    0.7930488423451967     -0.425435 5.696957    12
eks pan 13    0.4915175580479536     0.7709126592971468     0.3676141320555616     0.123903  6.188474    13
eks zee 14    0.5207382318405251     0.34141681118811673    0.4915175580479536     0.029221  6.709213    14

$ mlr --opprint step -a shift,delta,rsum,counter -f x -g a data/medium | head -15
a   b   i     x                      y                      x_shift                x_delta   x_rsum      x_counter
pan pan 1     0.3467901443380824     0.7268028627434533     -                      0         0.346790    1
eks pan 2     0.7586799647899636     0.5221511083334797     -                      0         0.758680    1
wye wye 3     0.20460330576630303    0.33831852551664776    -                      0         0.204603    1
eks wye 4     0.38139939387114097    0.13418874328430463    0.7586799647899636     -0.377281 1.140079    2
wye pan 5     0.5732889198020006     0.8636244699032729     0.20460330576630303    0.368686  0.777892    2
zee pan 6     0.5271261600918548     0.49322128674835697    -                      0         0.527126    1
eks zee 7     0.6117840605678454     0.1878849191181694     0.38139939387114097    0.230385  1.751863    3
zee wye 8     0.5985540091064224     0.976181385699006      0.5271261600918548     0.071428  1.125680    2
hat wye 9     0.03144187646093577    0.7495507603507059     -                      0         0.031442    1
pan wye 10    0.5026260055412137     0.9526183602969864     0.3467901443380824     0.155836  0.849416    2
pan pan 11    0.7930488423451967     0.6505816637259333     0.5026260055412137     0.290423  1.642465    3
zee pan 12    0.3676141320555616     0.23614420670296965    0.5985540091064224     -0.230940 1.493294    3
eks pan 13    0.4915175580479536     0.7709126592971468     0.6117840605678454     -0.120267 2.243381    4
eks zee 14    0.5207382318405251     0.34141681118811673    0.4915175580479536     0.029221  2.764119    5

$ mlr --opprint step -a ewma -f x -d 0.1,0.9 ../doc/data/medium | head -15
a   b   i     x                      y                      x_ewma_0.1 x_ewma_0.9
pan pan 1     0.3467901443380824     0.7268028627434533     0.346790   0.346790
eks pan 2     0.7586799647899636     0.5221511083334797     0.387979   0.717491
wye wye 3     0.20460330576630303    0.33831852551664776    0.369642   0.255892
eks wye 4     0.38139939387114097    0.13418874328430463    0.370817   0.368849
wye pan 5     0.5732889198020006     0.8636244699032729     0.391064   0.552845
zee pan 6     0.5271261600918548     0.49322128674835697    0.404671   0.529698
eks zee 7     0.6117840605678454     0.1878849191181694     0.425382   0.603575
zee wye 8     0.5985540091064224     0.976181385699006      0.442699   0.599056
hat wye 9     0.03144187646093577    0.7495507603507059     0.401573   0.088203
pan wye 10    0.5026260055412137     0.9526183602969864     0.411679   0.461184
pan pan 11    0.7930488423451967     0.6505816637259333     0.449816   0.759862
zee pan 12    0.3676141320555616     0.23614420670296965    0.441596   0.406839
eks pan 13    0.4915175580479536     0.7709126592971468     0.446588   0.483050
eks zee 14    0.5207382318405251     0.34141681118811673    0.454003   0.516969

$ mlr --opprint step -a ewma -f x -d 0.1,0.9 -o smooth,rough ../doc/data/medium | head -15
a   b   i     x                      y                      x_ewma_smooth x_ewma_rough
pan pan 1     0.3467901443380824     0.7268028627434533     0.346790      0.346790
eks pan 2     0.7586799647899636     0.5221511083334797     0.387979      0.717491
wye wye 3     0.20460330576630303    0.33831852551664776    0.369642      0.255892
eks wye 4     0.38139939387114097    0.13418874328430463    0.370817      0.368849
wye pan 5     0.5732889198020006     0.8636244699032729     0.391064      0.552845
zee pan 6     0.5271261600918548     0.49322128674835697    0.404671      0.529698
eks zee 7     0.6117840605678454     0.1878849191181694     0.425382      0.603575
zee wye 8     0.5985540091064224     0.976181385699006      0.442699      0.599056
hat wye 9     0.03144187646093577    0.7495507603507059     0.401573      0.088203
pan wye 10    0.5026260055412137     0.9526183602969864     0.411679      0.461184
pan pan 11    0.7930488423451967     0.6505816637259333     0.449816      0.759862
zee pan 12    0.3676141320555616     0.23614420670296965    0.441596      0.406839
eks pan 13    0.4915175580479536     0.7709126592971468     0.446588      0.483050
eks zee 14    0.5207382318405251     0.34141681118811673    0.454003      0.516969

Example deriving uptime-delta from system uptime:

$ each 10 uptime | mlr -p step -a delta -f 11
...
20:08 up 36 days, 10:38, 5 users, load averages: 1.42 1.62 1.73 0.000000
20:08 up 36 days, 10:38, 5 users, load averages: 1.55 1.64 1.74 0.020000
20:08 up 36 days, 10:38, 7 users, load averages: 1.58 1.65 1.74 0.010000
20:08 up 36 days, 10:38, 9 users, load averages: 1.78 1.69 1.76 0.040000
20:08 up 36 days, 10:39, 9 users, load averages: 2.12 1.76 1.78 0.070000
20:08 up 36 days, 10:39, 9 users, load averages: 2.51 1.85 1.81 0.090000
20:08 up 36 days, 10:39, 8 users, load averages: 2.79 1.92 1.83 0.070000
20:08 up 36 days, 10:39, 4 users, load averages: 2.64 1.90 1.83 -0.020000

tac

$ mlr tac --help
Usage: mlr tac
Prints records in reverse order from the order in which they were encountered.

Prints the records in the input stream in reverse order. Note: this requires Miller to retain all input records in memory before any output records are produced.

$ mlr --icsv --opprint cat data/a.csv
a b c
1 2 3
4 5 6

$ mlr --icsv --opprint cat data/b.csv
a b c
7 8 9

$ mlr --icsv --opprint tac data/a.csv data/b.csv
a b c
7 8 9
4 5 6
1 2 3

$ mlr --icsv --opprint put '$filename=FILENAME' then tac data/a.csv data/b.csv
a b c filename
7 8 9 data/b.csv
4 5 6 data/a.csv
1 2 3 data/a.csv

tail

$ mlr tail --help
Usage: mlr tail [options]
-n {count}    Tail count to print; default 10
-g {a,b,c}    Optional group-by-field names for tail counts
Passes through the last n records, optionally by category.

Prints the last n records in the input stream, optionally by category.

$ mlr --opprint tail -n 4 data/colored-shapes.dkvp
color  shape    flag i     u                    v                   w                   x
blue   square   1    99974 0.6189062525431605   0.2637962404841453  0.5311465405784674  6.210738209085753
blue   triangle 0    99976 0.008110504040268474 0.8267274952432482  0.4732962944898885  6.146956761817328
yellow triangle 0    99990 0.3839424618160777   0.55952913620132    0.5113763011485609  4.307973891915119
yellow circle   1    99994 0.764950884927175    0.25284227383991364 0.49969878539567425 5.013809741826425

$ mlr --opprint tail -n 1 -g shape data/colored-shapes.dkvp
color  shape    flag i     u                  v                   w                   x
yellow triangle 0    99990 0.3839424618160777 0.55952913620132    0.5113763011485609  4.307973891915119
blue   square   1    99974 0.6189062525431605 0.2637962404841453  0.5311465405784674  6.210738209085753
yellow circle   1    99994 0.764950884927175  0.25284227383991364 0.49969878539567425 5.013809741826425

tee

$ mlr tee --help
Usage: mlr tee [options] {filename}
Passes through input records (like mlr cat) but also writes to specified output
file, using output-format flags from the command line (e.g. --ocsv). See also
the "tee" keyword within mlr put, which allows data-dependent filenames.
Options:
-a:          append to existing file, if any, rather than overwriting.
--no-fflush: don't call fflush() after every record.
Any of the output-format command-line flags (see mlr -h). Example: using
  mlr --icsv --opprint put '...' then tee --ojson ./mytap.dat then stats1 ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.

top

$ mlr top --help
Usage: mlr top [options]
-f {a,b,c}    Value-field names for top counts.
-g {d,e,f}    Optional group-by-field names for top counts.
-n {count}    How many records to print per category; default 1.
-a            Print all fields for top-value records; default is
              to print only value and group-by fields. Requires a single
              value-field name only.
--min         Print top smallest values; default is top largest values.
-F            Keep top values as floats even if they look like integers.
-o {name}     Field name for output indices. Default "top_idx".
Prints the n records with smallest/largest values at specified fields,
optionally by category.

Note that top is distinct from headhead shows fields which appear first in the data stream; top shows fields which are numerically largest (or smallest).

$ mlr --opprint top -n 4 -f x data/medium
top_idx x_top
1       0.999953
2       0.999823
3       0.999733
4       0.999563

$ mlr --opprint top -n 4 -f x -o someothername data/medium
someothername x_top
1             0.999953
2             0.999823
3             0.999733
4             0.999563

$ mlr --opprint top -n 2 -f x -g a then sort -f a data/medium
a   top_idx x_top
eks 1       0.998811
eks 2       0.998534
hat 1       0.999953
hat 2       0.999733
pan 1       0.999403
pan 2       0.999044
wye 1       0.999823
wye 2       0.999264
zee 1       0.999490
zee 2       0.999438

uniq

$ mlr uniq --help
Usage: mlr uniq [options]
-g {d,e,f}    Group-by-field names for uniq counts.
-c            Show repeat counts in addition to unique values.
-n            Show only the number of distinct values.
-o {name}     Field name for output count. Default "count".
Prints distinct values for specified field names. With -c, same as
count-distinct. For uniq, -f is a synonym for -g.

$ wc -l data/colored-shapes.dkvp
   10078 data/colored-shapes.dkvp

$ mlr uniq -g color,shape data/colored-shapes.dkvp
color=yellow,shape=triangle
color=red,shape=square
color=red,shape=circle
color=purple,shape=triangle
color=yellow,shape=circle
color=purple,shape=square
color=yellow,shape=square
color=red,shape=triangle
color=green,shape=triangle
color=green,shape=square
color=blue,shape=circle
color=blue,shape=triangle
color=purple,shape=circle
color=blue,shape=square
color=green,shape=circle
color=orange,shape=triangle
color=orange,shape=square
color=orange,shape=circle

$ mlr --opprint uniq -g color,shape -c then sort -f color,shape data/colored-shapes.dkvp
color  shape    count
blue   circle   384
blue   square   589
blue   triangle 497
green  circle   287
green  square   454
green  triangle 368
orange circle   68
orange square   128
orange triangle 107
purple circle   289
purple square   481
purple triangle 372
red    circle   1207
red    square   1874
red    triangle 1560
yellow circle   356
yellow square   589
yellow triangle 468

$ mlr --opprint uniq -g color,shape -c -o someothername then sort -nr someothername data/colored-shapes.dkvp
color  shape    someothername
red    square   1874
red    triangle 1560
red    circle   1207
yellow square   589
blue   square   589
blue   triangle 497
purple square   481
yellow triangle 468
green  square   454
blue   circle   384
purple triangle 372
green  triangle 368
yellow circle   356
purple circle   289
green  circle   287
orange square   128
orange triangle 107
orange circle   68

$ mlr --opprint uniq -n -g color,shape data/colored-shapes.dkvp
count
18

unsparsify

$ mlr unsparsify --help
Usage: mlr unsparsify [options]
Prints records with the union of field names over all input records.
For field names absent in a given record but present in others, fills in
a value. This verb retains all input before producing any output.

Options:
--fill-with {filler string}  What to fill absent fields with. Defaults to
                             the empty string.

Example: if the input is two records, one being 'a=1,b=2' and the other
being 'b=3,c=4', then the output is the two records 'a=1,b=2,c=' and
'a=,b=3,c=4'.

Examples:

$ cat data/sparse.json
{"a":1,"b":2,"v":3}
{"u":1,"b":2}
{"a":1,"v":2,"x":3}
{"v":1,"w":2}

$ mlr --json unsparsify data/sparse.json
{ "a": 1, "b": 2, "v": 3, "u": "", "x": "", "w": "" }
{ "a": "", "b": 2, "v": "", "u": 1, "x": "", "w": "" }
{ "a": 1, "b": "", "v": 2, "u": "", "x": 3, "w": "" }
{ "a": "", "b": "", "v": 1, "u": "", "x": "", "w": 2 }

$ mlr --ijson --opprint unsparsify data/sparse.json
a b v u x w
1 2 3 - - -
- 2 - 1 - -
1 - 2 - 3 -
- - 1 - - 2

$ mlr --ijson --opprint unsparsify --fill-with missing data/sparse.json
a       b       v       u       x       w
1       2       3       missing missing missing
missing 2       missing 1       missing missing
1       missing 2       missing 3       missing
missing missing 1       missing missing 2