Reference: Verbs
Description: Miller documentation
Keywords: John Kerl, Kerl, Miller, miller, mlr, OLAP, data analysis software, regression, correlation, variance, data tools,

Miller Overview: • About Miller • Miller in 10 minutes • File formats • Miller features in the context of the Unix toolkit • Record-heterogeneity • Internationalization Using Miller: • FAQ • Sharing data with other languages • Cookbook part 1 • Cookbook part 2 • Cookbook part 3 • Data-diving examples • Manpage • Reference • Reference: Verbs • Reference: DSL • Documents by release • Installation, portability, dependencies, and testing Background: • Why? • Why C? • Why call it Miller? • How original is Miller? • Performance Repository: • Things to do • Contact information • GitHub repo | Reference: Verbs Contents: • Overview • bar • bootstrap • cat • check • count-distinct • count-similar • 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
# count-similar
    $ mlr count-similar --help
    Usage: mlr count-similar [options]
    Ingests all records, then emits each record augmented by a count of
    the number of other records having the same group-by field values.
    Options:
    -g {d,e,f} Group-by-field names for counts.
    -o {name}  Field name for output count. Default "count".
    $ mlr --opprint head -n 20 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
    wye pan 5  0.5732889198020006  0.8636244699032729
    zee pan 6  0.5271261600918548  0.49322128674835697
    eks zee 7  0.6117840605678454  0.1878849191181694
    zee wye 8  0.5985540091064224  0.976181385699006
    hat wye 9  0.03144187646093577 0.7495507603507059
    pan wye 10 0.5026260055412137  0.9526183602969864
    pan pan 11 0.7930488423451967  0.6505816637259333
    zee pan 12 0.3676141320555616  0.23614420670296965
    eks pan 13 0.4915175580479536  0.7709126592971468
    eks zee 14 0.5207382318405251  0.34141681118811673
    eks pan 15 0.07155556372719507 0.3596137145616235
    pan pan 16 0.5736853980681922  0.7554169353781729
    zee eks 17 0.29081949506712723 0.054478717073354166
    hat zee 18 0.05727869223575699 0.13343527626645157
    zee pan 19 0.43144132839222604 0.8442204830496998
    eks wye 20 0.38245149780530685 0.4730652428100751
    $ mlr --opprint head -n 20 then count-similar -g a data/medium
    a   b   i  x                   y                    count
    pan pan 1  0.3467901443380824  0.7268028627434533   4
    pan wye 10 0.5026260055412137  0.9526183602969864   4
    pan pan 11 0.7930488423451967  0.6505816637259333   4
    pan pan 16 0.5736853980681922  0.7554169353781729   4
    eks pan 2  0.7586799647899636  0.5221511083334797   7
    eks wye 4  0.38139939387114097 0.13418874328430463  7
    eks zee 7  0.6117840605678454  0.1878849191181694   7
    eks pan 13 0.4915175580479536  0.7709126592971468   7
    eks zee 14 0.5207382318405251  0.34141681118811673  7
    eks pan 15 0.07155556372719507 0.3596137145616235   7
    eks wye 20 0.38245149780530685 0.4730652428100751   7
    wye wye 3  0.20460330576630303 0.33831852551664776  2
    wye pan 5  0.5732889198020006  0.8636244699032729   2
    zee pan 6  0.5271261600918548  0.49322128674835697  5
    zee wye 8  0.5985540091064224  0.976181385699006    5
    zee pan 12 0.3676141320555616  0.23614420670296965  5
    zee eks 17 0.29081949506712723 0.054478717073354166 5
    zee pan 19 0.43144132839222604 0.8442204830496998   5
    hat wye 9  0.03144187646093577 0.7495507603507059   2
    hat zee 18 0.05727869223575699 0.13343527626645157  2
    $ mlr --opprint head -n 20 then count-similar -g a then sort -f a data/medium
    a   b   i  x                   y                    count
    eks pan 2  0.7586799647899636  0.5221511083334797   7
    eks wye 4  0.38139939387114097 0.13418874328430463  7
    eks zee 7  0.6117840605678454  0.1878849191181694   7
    eks pan 13 0.4915175580479536  0.7709126592971468   7
    eks zee 14 0.5207382318405251  0.34141681118811673  7
    eks pan 15 0.07155556372719507 0.3596137145616235   7
    eks wye 20 0.38245149780530685 0.4730652428100751   7
    hat wye 9  0.03144187646093577 0.7495507603507059   2
    hat zee 18 0.05727869223575699 0.13343527626645157  2
    pan pan 1  0.3467901443380824  0.7268028627434533   4
    pan wye 10 0.5026260055412137  0.9526183602969864   4
    pan pan 11 0.7930488423451967  0.6505816637259333   4
    pan pan 16 0.5736853980681922  0.7554169353781729   4
    wye wye 3  0.20460330576630303 0.33831852551664776  2
    wye pan 5  0.5732889198020006  0.8636244699032729   2
    zee pan 6  0.5271261600918548  0.49322128674835697  5
    zee wye 8  0.5985540091064224  0.976181385699006    5
    zee pan 12 0.3676141320555616  0.23614420670296965  5
    zee eks 17 0.29081949506712723 0.054478717073354166 5
    zee pan 19 0.43144132839222604 0.8442204830496998   5
# 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 M_PI M_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 `top` — `head` 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
      --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
    $ mlr least-frequent -h
    Usage: mlr least-frequent [options]
    Shows the least frequently occurring distinct values for specified field names.
    The first entry is the statistical anti-mode; the remaining are runners-up.
    Options:
    -f {one or more comma-separated field names}. Required flag.
    -n {count}. Optional flag defaulting to 10.
    -b          Suppress counts; show only field values.
    -o {name}   Field name for output count. Default "count".
    See also "mlr most-frequent".
    $ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape -n 5
    shape    count
    circle   2591
    triangle 3372
    square   4115
    $ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5
    shape    color  count
    circle   orange 68
    triangle orange 107
    square   orange 128
    circle   green  287
    circle   purple 289
    $ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5 -o someothername
    shape    color  someothername
    circle   orange 68
    triangle orange 107
    square   orange 128
    circle   green  287
    circle   purple 289
    $ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5 -b
    shape    color
    circle   orange
    triangle orange
    square   orange
    circle   green
    circle   purple
See also most-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
    $ mlr most-frequent -h
    Usage: mlr most-frequent [options]
    Shows the most frequently occurring distinct values for specified field names.
    The first entry is the statistical mode; the remaining are runners-up.
    Options:
    -f {one or more comma-separated field names}. Required flag.
    -n {count}. Optional flag defaulting to 10.
    -b          Suppress counts; show only field values.
    -o {name}   Field name for output count. Default "count".
    See also "mlr least-frequent".
    $ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape -n 5
    shape    count
    square   4115
    triangle 3372
    circle   2591
    $ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5
    shape    color  count
    square   red    1874
    triangle red    1560
    circle   red    1207
    square   yellow 589
    square   blue   589
    $ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5 -o someothername
    shape    color  someothername
    square   red    1874
    triangle red    1560
    circle   red    1207
    square   yellow 589
    square   blue   589
    $ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5 -b
    shape    color
    square   red
    triangle red
    circle   red
    square   yellow
    square   blue
See also least-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 M_PI M_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
    $ mlr regularize --help
    Usage: mlr regularize
    For records seen earlier in the data stream with same field names in
    a different order, outputs them with field names in the previously
    encountered order.
    Example: input records a=1,c=2,b=3, then e=4,d=5, then c=7,a=6,b=8
    output as              a=1,c=2,b=3, then e=4,d=5, then a=6,c=7,b=8
This exists since hash-map software in various languages and tools encountered in the wild does not always print similar rows with fields in the same order: `mlr regularize` helps clean that up. See also `reorder`. # 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 `head` — `head` 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