Reference: DSL
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: DSL Contents: • Overview • Syntax • Expression formatting • Expressions from files • Semicolons, commas, newlines, and curly braces • Variables • Built-in variables • Field names • Out-of-stream variables • Indexed out-of-stream variables • Local variables • Map literals • Type-checking • Type-test and type-assertion expressions • Type-declarations for local variables, function parameter, and function return values • Null data: empty and absent • Aggregate variable assignments • Keywords for filter and put • Operator precedence • Operator and function semantics • Control structures • Pattern-action blocks • If-statements • While and do-while loops • For-loops • Key-only for-loops • Key-value for-loops • C-style triple-for loops • Begin/end blocks • Output statements • Print statements • Dump statements • Tee statements • Redirected-output statements • Emit statements • Multi-emit statements • Emit-all statements • Unset statements • Filter statements • Built-in functions for filter and put • + • - • * • / • // • .+ • .- • .* • ./ • .// • % • ** • | • ^ • & • ~ • << • >> • == • != • =~ • !=~ • > • >= • < • <= • && • || • ^^ • ! • ? : • . • abs • acos • acosh • asin • asinh • asserting_absent • asserting_bool • asserting_boolean • asserting_empty • asserting_empty_map • asserting_float • asserting_int • asserting_map • asserting_nonempty_map • asserting_not_empty • asserting_not_map • asserting_not_null • asserting_null • asserting_numeric • asserting_present • asserting_string • atan • atan2 • atanh • bitcount • boolean • cbrt • ceil • cos • cosh • depth • dhms2fsec • dhms2sec • erf • erfc • exp • expm1 • float • floor • fmtnum • fsec2dhms • fsec2hms • gmt2sec • gsub • haskey • hexfmt • hms2fsec • hms2sec • int • invqnorm • is_absent • is_bool • is_boolean • is_empty • is_empty_map • is_float • is_int • is_map • is_nonempty_map • is_not_empty • is_not_map • is_not_null • is_null • is_numeric • is_present • is_string • joink • joinkv • joinv • leafcount • length • log • log10 • log1p • logifit • madd • mapdiff • mapexcept • mapselect • mapsum • max • mexp • min • mmul • msub • pow • qnorm • round • roundm • sec2dhms • sec2gmt • sec2gmtdate • sec2hms • sgn • sin • sinh • splitkv • splitkvx • splitnv • splitnvx • sqrt • strftime • string • strlen • strptime • sub • substr • systime • tan • tanh • tolower • toupper • typeof • urand • urand32 • urandint • User-defined functions and subroutines • User-defined functions • User-defined subroutines • Errors and transparency • A note on the complexity of Miller’s expression language # Overview Here’s 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
Please see here for information on verbs other than `put` and `filter`. The essential usages of `mlr filter` and `mlr put` are for record-selection and record-updating expressions, respectively. For example, given the following input data:
    $ cat data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
you might retain only the records whose `a` field has value `eks`:
    $ mlr filter '$a == "eks"' data/small
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
or you might add a new field which is a function of existing fields:
    $ mlr put '$ab = $a . "_" . $b ' data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,ab=pan_pan
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,ab=eks_pan
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,ab=wye_wye
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,ab=eks_wye
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,ab=wye_pan
The two verbs `mlr filter` and `mlr put` are essentially the same. The only differences are: Expressions sent to `mlr filter` must end with a boolean expression, which is the filtering criterion; `mlr filter` expressions may not reference the `filter` keyword within them; and `mlr filter` expressions may not use `tee`, `emit`, `emitp`, or `emitf`. All the rest is the same: in particular, you can define and invoke functions and subroutines to help produce the final boolean statement, and record fields may be assigned to in the statements preceding the final boolean statement. There are more details and more choices, of course, as detailed in the following sections. # Syntax ## Expression formatting Multiple expressions may be given, separated by semicolons, and each may refer to the ones before:
    $ ruby -e '10.times{|i|puts "i=#{i}"}' | mlr --opprint put '$j = $i + 1; $k = $i +$j'
    i j  k
    0 1  1
    1 2  3
    2 3  5
    3 4  7
    4 5  9
    5 6  11
    6 7  13
    7 8  15
    8 9  17
    9 10 19
Newlines within the expression are ignored, which can help increase legibility of complex expressions:
    $ mlr --opprint put '
      $nf       = NF;
      $nr       = NR;
      $fnr      = FNR;
      $filenum  = FILENUM;
      $filename = FILENAME
    ' data/small data/small2
    a   b   i     x                    y                    nf nr fnr filenum filename
    pan pan 1     0.3467901443380824   0.7268028627434533   5  1  1   1       data/small
    eks pan 2     0.7586799647899636   0.5221511083334797   5  2  2   1       data/small
    wye wye 3     0.20460330576630303  0.33831852551664776  5  3  3   1       data/small
    eks wye 4     0.38139939387114097  0.13418874328430463  5  4  4   1       data/small
    wye pan 5     0.5732889198020006   0.8636244699032729   5  5  5   1       data/small
    pan eks 9999  0.267481232652199086 0.557077185510228001 5  6  1   2       data/small2
    wye eks 10000 0.734806020620654365 0.884788571337605134 5  7  2   2       data/small2
    pan wye 10001 0.870530722602517626 0.009854780514656930 5  8  3   2       data/small2
    hat wye 10002 0.321507044286237609 0.568893318795083758 5  9  4   2       data/small2
    pan zee 10003 0.272054845593895200 0.425789896597056627 5  10 5   2       data/small2
    $ mlr --opprint filter '($x > 0.5 && $y < 0.5) || ($x < 0.5 && $y > 0.5)' then stats2 -a corr -f x,y data/medium
    x_y_corr
    -0.747994
## Expressions from files The simplest way to enter expressions for `put` and `filter` is between single quotes on the command line, e.g.
    $ mlr --from data/small put '$xy = sqrt($x**2 + $y**2)'
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584
    $ mlr --from data/small put 'func f(a, b) { return sqrt(a**2 + b**2) } $xy = f($x, $y)'
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584
You may, though, find it convenient to put expressions into files for reuse, and read them using the -f option. For example:
    $ cat data/fe-example-3.mlr
    func f(a, b) {
      return sqrt(a**2 + b**2)
    }
    $xy = f($x, $y)
    $ mlr --from data/small put -f data/fe-example-3.mlr
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584
If you have some of the logic in a file and you want to write the rest on the command line, you can use the -f and -e options together:
    $ cat data/fe-example-4.mlr
    func f(a, b) {
      return sqrt(a**2 + b**2)
    }
    $ mlr --from data/small put -f data/fe-example-4.mlr -e '$xy = f($x, $y)'
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584
A suggested use-case here is defining functions in files, and calling them from command-line expressions. Another suggested use-case is putting default parameter values in files, e.g. using `begin{@count=is_present(@count)?@count:10}` in the file, where you can precede that using `begin{@count=40}` using `-e`. Moreover, you can have one or more `-f` expressions (maybe one function per file, for example) and one or more `-e` expressions on the command line. If you mix `-f` and `-e` then the expressions are evaluated in the order encountered. (Since the expressions are all simply concatenated together in order, don’t forget intervening semicolons: e.g. not `mlr put -e '$x=1' -e '$y=2 ...'` but rather `mlr put -e '$x=1;' -e '$y=2' ...`.) ## Semicolons, commas, newlines, and curly braces Miller uses semicolons as statement separators, not statement terminators. This means you can write:
    mlr put 'x=1'
    mlr put 'x=1;$y=2'
    mlr put 'x=1;$y=2;'
    mlr put 'x=1;;;;$y=2;'
Semicolons are optional after closing curly braces (which close conditionals and loops as discussed below).
    $ echo x=1,y=2 | mlr put 'while (NF < 10) { $[NF+1] = ""}  $foo = "bar"'
    x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar
    $ echo x=1,y=2 | mlr put 'while (NF < 10) { $[NF+1] = ""}; $foo = "bar"'
    x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar
Semicolons are required between statements even if those statements are on separate lines. Newlines are for your convenience but have no syntactic meaning: line endings do not terminate statements. For example, adjacent assignment statements must be separated by semicolons even if those statements are on separate lines:
    mlr put '
      $x = 1
      $y = 2 # Syntax error
    '

    mlr put '
      $x = 1;
      $y = 2 # This is OK
    '
Trailing commas are allowed in function/subroutine definitions, function/subroutine callsites, and map literals. This is intended for (although not restricted to) the multi-line case:
    $ mlr --csvlite --from data/a.csv put '
      func f(
        num a,
        num b,
      ): num {
        return a**2 + b**2;
      }
      $* = {
        "s": $a + $b,
        "t": $a - $b,
        "u": f(
          $a,
          $b,
        ),
        "v": NR,
      }
    '
    s,t,u,v
    3,-1,5.000000,1
    9,-1,41.000000,2
Bodies for all compound statements must be enclosed in curly braces, even if the body is a single statement:
    mlr put 'if ($x == 1) $y = 2' # Syntax error
    mlr put 'if ($x == 1) { $y = 2 }' # This is OK
Bodies for compound statements may be empty:
    mlr put 'if ($x == 1) { }' # This no-op is syntactically acceptable
# Variables Miller has the following kinds of variables: Built-in variables such as `NF`, `NF`, `FILENAME`, `M_PI`, and `M_E`. These are all capital letters and are read-only (although some of them change value from one record to another). Fields of stream records, accessed using the `$` prefix. These refer to fields of the current data-stream record. For example, in `echo x=1,y=2 | mlr put '$z = $x + $y'`, `$x` and `$y` refer to input fields, and `$z` refers to a new, computed output field. In a few contexts, presented below, you can refer to the entire record as `$*`. Out-of-stream variables accessed using the `@` prefix. These refer to data which persist from one record to the next, including in `begin` and `end` blocks (which execute before/after the record stream is consumed, respectively). You use them to remember values across records, such as sums, differences, counters, and so on. In a few contexts, presented below, you can refer to the entire out-of-stream-variables collection as `@*`. Local variables are limited in scope and extent to the current statements being executed: these include function arguments, bound variables in for loops, and explicitly declared local variables. Keywords are not variables, but since their names are reserved, you cannot use these names for local variables. ## Built-in variables These are written all in capital letters, such as `NR`, `NF`, `FILENAME`, and only a small, specific set of them is defined by Miller. Namely, Miller supports the following five built-in variables for `filter` and `put`, all `awk`-inspired: `NF`, `NR`, `FNR`, `FILENUM`, and `FILENAME`, as well as the mathematical constants `M_PI` and `M_E`. Lastly, the `ENV` hashmap allows read access to environment variables, e.g. `ENV["HOME"]` or `ENV["foo_".$hostname]`.
    $ mlr filter 'FNR == 2' data/small*
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    1=pan,2=pan,3=1,4=0.3467901443380824,5=0.7268028627434533
    a=wye,b=eks,i=10000,x=0.734806020620654365,y=0.884788571337605134
    $ mlr put '$fnr = FNR' data/small*
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,fnr=1
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,fnr=2
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,fnr=3
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,fnr=4
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,fnr=5
    1=a,2=b,3=i,4=x,5=y,fnr=1
    1=pan,2=pan,3=1,4=0.3467901443380824,5=0.7268028627434533,fnr=2
    1=eks,2=pan,3=2,4=0.7586799647899636,5=0.5221511083334797,fnr=3
    1=wye,2=wye,3=3,4=0.20460330576630303,5=0.33831852551664776,fnr=4
    1=eks,2=wye,3=4,4=0.38139939387114097,5=0.13418874328430463,fnr=5
    1=wye,2=pan,3=5,4=0.5732889198020006,5=0.8636244699032729,fnr=6
    a=pan,b=eks,i=9999,x=0.267481232652199086,y=0.557077185510228001,fnr=1
    a=wye,b=eks,i=10000,x=0.734806020620654365,y=0.884788571337605134,fnr=2
    a=pan,b=wye,i=10001,x=0.870530722602517626,y=0.009854780514656930,fnr=3
    a=hat,b=wye,i=10002,x=0.321507044286237609,y=0.568893318795083758,fnr=4
    a=pan,b=zee,i=10003,x=0.272054845593895200,y=0.425789896597056627,fnr=5
Their values of `NF`, `NR`, `FNR`, `FILENUM`, and `FILENAME` change from one record to the next as Miller scans through your input data stream. The mathematical constants, of course, do not change; `ENV` is populated from the system environment variables at the time Miller starts and is read-only for the remainder of program execution. Their scope is global: you can refer to them in any `filter` or `put` statement. Their values are assigned by the input-record reader:
    $ mlr --csv put '$nr = NR' data/a.csv
    a,b,c,nr
    1,2,3,1
    4,5,6,2
    $ mlr --csv repeat -n 3 then put '$nr = NR' data/a.csv
    a,b,c,nr
    1,2,3,1
    1,2,3,1
    1,2,3,1
    4,5,6,2
    4,5,6,2
    4,5,6,2
The extent is for the duration of the put/filter: in a `begin` statement (which executes before the first input record is consumed) you will find `NR=1` and in an `end` statement (which is executed after the last input record is consumed) you will find `NR` to be the total number of records ingested. These are all read-only for the `mlr put` and `mlr filter` DSLs: they may be assigned from, e.g. `$nr=NR`, but they may not be assigned to: `NR=100` is a syntax error. ## Field names Names of fields within stream records must be specified using a `$` in `filter` and `put` expressions, even though the dollar signs don’t appear in the data stream itself. For integer-indexed data, this looks like `awk`’s `$1,$2,$3`, except that Miller allows non-numeric names such as `$quantity` or `$hostname`. Likewise, enclose string literals in double quotes in `filter` expressions even though they don’t appear in file data. In particular, `mlr filter '$x=="abc"'` passes through the record `x=abc`. If field names have special characters such as `.` then you can use braces, e.g. `'${field.name}'`. You may also use a computed field name in square brackets, e.g.
    $ echo a=3,b=4 | mlr filter '$["x"] < 0.5'
    $ echo s=green,t=blue,a=3,b=4 | mlr put '$[$s."_".$t] = $a * $b'
    s=green,t=blue,a=3,b=4,green_blue=12
The names of record fields depend on the contents of your input data stream, and their values change from one record to the next as Miller scans through your input data stream. Their extent is limited to the current record; their scope is the `filter` or `put` command in which they appear. These are read-write: you can do `$y=2*$x`, `$x=$x+1`, etc. Records are Miller’s output: field names present in the input stream are passed through to output (written to standard output) unless fields are removed with `cut`, or records are excluded with `filter` or `put -q`, etc. Simply assign a value to a field and it will be output. ## Out-of-stream variables These are prefixed with an at-sign, e.g. `@sum`. Furthermore, unlike built-in variables and stream-record fields, they are maintained in an arbitrarily nested hashmap: you can do `@sum += $quanity`, or `@sum[$color] += $quanity`, or `@sum[$color][$shape] += $quanity`. The keys for the multi-level hashmap can be any expression which evaluates to string or integer: e.g. `@sum[NR] = $a + $b`, `@sum[$a."-".$b] = $x`, etc. Their names and their values are entirely under your control; they change only when you assign to them. Just as for field names in stream records, if you want to define out-of-stream variables with special characters such as `.` then you can use braces, e.g. `'@{variable.name}["index"]'`. You may use a computed key in square brackets, e.g.
    $ echo s=green,t=blue,a=3,b=4 | mlr put -q '@[$s."_".$t] = $a * $b; emit all'
    green_blue=12
Out-of-stream variables are scoped to the `put` command in which they appear. In particular, if you have two or more `put` commands separated by `then`, each put will have its own set of out-of-stream variables:
    $ cat data/a.dkvp
    a=1,b=2,c=3
    a=4,b=5,c=6
    $ mlr put '@sum += $a; end {emit @sum}' then put 'is_present($a) {$a=10*$a; @sum += $a}; end {emit @sum}' data/a.dkvp
    a=10,b=2,c=3
    a=40,b=5,c=6
    sum=5
    sum=50
Out-of-stream variables’ extent is from the start to the end of the record stream, i.e. every time the `put` or `filter` statement referring to them is executed. Out-of-stream variables are read-write: you can do `$sum=@sum`, `@sum=$sum`, etc. ## Indexed out-of-stream variables Using an index on the `@count` and `@sum` variables, we get the benefit of the `-g` (group-by) option which `mlr stats1` and various other Miller commands have:
    $ mlr put -q '
      @x_count[$a] += 1;
      @x_sum[$a] += $x;
      end {
        emit @x_count, "a";
        emit @x_sum, "a";
      }
    ' ../data/small
    a=pan,x_count=2
    a=eks,x_count=3
    a=wye,x_count=2
    a=zee,x_count=2
    a=hat,x_count=1
    a=pan,x_sum=0.849416
    a=eks,x_sum=1.751863
    a=wye,x_sum=0.777892
    a=zee,x_sum=1.125680
    a=hat,x_sum=0.031442
    $ mlr stats1 -a count,sum -f x -g a ../data/small
    a=pan,x_count=2,x_sum=0.849416
    a=eks,x_count=3,x_sum=1.751863
    a=wye,x_count=2,x_sum=0.777892
    a=zee,x_count=2,x_sum=1.125680
    a=hat,x_count=1,x_sum=0.031442
Indices can be arbitrarily deep — here there are two or more of them:
    $ mlr --from data/medium put -q '
      @x_count[$a][$b] += 1;
      @x_sum[$a][$b] += $x;
      end {
        emit (@x_count, @x_sum), "a", "b";
      }
    '
    a=pan,b=pan,x_count=427,x_sum=219.185129
    a=pan,b=wye,x_count=395,x_sum=198.432931
    a=pan,b=eks,x_count=429,x_sum=216.075228
    a=pan,b=hat,x_count=417,x_sum=205.222776
    a=pan,b=zee,x_count=413,x_sum=205.097518
    a=eks,b=pan,x_count=371,x_sum=179.963030
    a=eks,b=wye,x_count=407,x_sum=196.945286
    a=eks,b=zee,x_count=357,x_sum=176.880365
    a=eks,b=eks,x_count=413,x_sum=215.916097
    a=eks,b=hat,x_count=417,x_sum=208.783171
    a=wye,b=wye,x_count=377,x_sum=185.295850
    a=wye,b=pan,x_count=392,x_sum=195.847900
    a=wye,b=hat,x_count=426,x_sum=212.033183
    a=wye,b=zee,x_count=385,x_sum=194.774048
    a=wye,b=eks,x_count=386,x_sum=204.812961
    a=zee,b=pan,x_count=389,x_sum=202.213804
    a=zee,b=wye,x_count=455,x_sum=233.991394
    a=zee,b=eks,x_count=391,x_sum=190.961778
    a=zee,b=zee,x_count=403,x_sum=206.640635
    a=zee,b=hat,x_count=409,x_sum=191.300006
    a=hat,b=wye,x_count=423,x_sum=208.883010
    a=hat,b=zee,x_count=385,x_sum=196.349450
    a=hat,b=eks,x_count=389,x_sum=189.006793
    a=hat,b=hat,x_count=381,x_sum=182.853532
    a=hat,b=pan,x_count=363,x_sum=168.553807
The idea is that `stats1`, and other Miller verbs, encapsulate frequently-used patterns with a minimum of keystroking (and run a little faster), whereas using out-of-stream variables you have more flexibility and control in what you do. Begin/end blocks can be mixed with pattern/action blocks. For example:
    $ mlr put '
      begin {
        @num_total = 0;
        @num_positive = 0;
      };
      @num_total += 1;
      $x > 0.0 {
        @num_positive += 1;
        $y = log10($x); $z = sqrt($y)
      };
      end {
        emitf @num_total, @num_positive
      }
    ' data/put-gating-example-1.dkvp
    x=-1
    x=0
    x=1,y=0.000000,z=0.000000
    x=2,y=0.301030,z=0.548662
    x=3,y=0.477121,z=0.690740
    num_total=5,num_positive=3
## Local variables Local variables are similar to out-of-stream variables, except that their extent is limited to the expressions in which they appear (and their basenames can’t be computed using square brackets). There are three kinds of local variables: arguments to functions/subroutines, variables bound within for-loops, and locals defined within control blocks. They may be untyped using `var`, or typed using `num`, `int`, `float`, `str`, `bool`, and `map`. For example:
    $ # Here I'm using a specified random-number seed so this example always
    # produces the same output for this web document: in everyday practice we
    # would leave off the --seed 12345 part.
    mlr --seed 12345 seqgen --start 1 --stop 10 then put '
      func f(a, b) {                          # function arguments a and b
          r = 0.0;                            # local r scoped to the function
          for (int i = 0; i < 6; i += 1) {    # local i scoped to the for-loop
              num u = urand();                # local u scoped to the for-loop
              r += u;                         # updates r from the enclosing scope
          }
          r /= 6;
          return a + (b - a) * r;
      }
      num o = f(10, 20);                      # local to the top-level scope
      $o = o;
    '
    i=1,o=14.662901
    i=2,o=17.881983
    i=3,o=14.586560
    i=4,o=16.402409
    i=5,o=16.336598
    i=6,o=14.622701
    i=7,o=15.983753
    i=8,o=13.852177
    i=9,o=15.472899
    i=10,o=15.643912
Things which are completely unsurprising, resembling many other languages: Parameter names are bound to their arguments but can be reassigned, e.g. if there is a parameter named `a` then you can reassign the value of `a` to be something else within the function if you like. However, you cannot redeclare the type of an argument or a local: `var a=1; var a=2` is an error but `var a=1; a=2` is OK. All argument-passing is positional rather than by name; arguments are passed by value, not by reference. (This is also true for map-valued variables: they are not, and cannot be, passed by reference) You can define locals (using `var`, `num`, etc.) at any scope (if-statements, else-statements, while-loops, for-loops, or the top-level scope), and nested scopes will have access (more details on scope in the next section). If you define a local variable with the same name inside an inner scope, then a new variable is created with the narrower scope. If you assign to a local variable for the first time in a scope without declaring it as `var`, `num`, etc. then: if it exists in an outer scope, that outer-scope variable will be updated; if not, it will be defined in the current scope as if `var` had been used. (See also here for an example.) I recommend always declaring variables explicitly to make the intended scoping clear. Functions and subroutines never have access to locals from their callee (unless passed by value as arguments). Things which are perhaps surprising compared to other languages: Type declarations using `var`, or typed using `num`, `int`, `float`, `str`, and `bool` are necessary to declare local variables. Function arguments and variables bound in for-loops over stream records and out-of-stream variables are implicitly declared using `var`. (Some examples are shown below.) Type-checking is done at assignment time. For example, `float f = 0` is an error (since `0` is an integer), as is `float f = 0.0; f = 1`. For this reason I prefer to use `num` over `float` in most contexts since `num` encompasses integer and floating-point values. More information about type-checking is here. Bound variables in for-loops over stream records and out-of-stream variables are implicitly local to that block. E.g. in `for (k, v in $*) { ... }` `for ((k1, k2), v in @*) { ... }` if there are `k`, `v`, etc. in the enclosing scope then those will be masked by the loop-local bound variables in the loop, and moreover the values of the loop-local bound variables are not available after the end of the loop. For C-style triple-for loops, if a for-loop variable is defined using `var`, `int`, etc. then it is scoped to that for-loop. E.g. `for (i = 0; i < 10; i += 1) { ... }` and `for (int i = 0; i < 10; i += 1) { ... }`. (This is unsurprising.). If there is no typedecl and an outer-scope variable of that name exists, then it is used. (This is also unsurprising.) But of there is no outer-scope variable of that name then the variable is scoped to the for-loop only. The following example demonstrates the scope rules:
    $ cat data/scope-example.mlr
    func f(a) {      # argument is local to the function
      var b = 100;   # local to the function
      c = 100;       # local to the function; does not overwrite outer c
      return a + 1;
    }
    var a = 10;      # local at top level
    var b = 20;      # local at top level
    c = 30;          # local at top level; there is no more-outer-scope c
    if (NR == 3) {
      var a = 40;    # scoped to the if-statement; doesn't overwrite outer a
      b = 50;        # not scoped to the if-statement; overwrites outer b
      c = 60;        # not scoped to the if-statement; overwrites outer c
      d = 70;        # there is no outer d so a local d is created here

      $inner_a = a;
      $inner_b = b;
      $inner_c = c;
      $inner_d = d;
    }
    $outer_a = a;
    $outer_b = b;
    $outer_c = c;
    $outer_d = d;    # there is no outer d defined so no assignment happens
    $ cat data/scope-example.dat
    n=1,x=123
    n=2,x=456
    n=3,x=789
    $ mlr --oxtab --from data/scope-example.dat put -f data/scope-example.mlr
    n       1
    x       123
    outer_a 10
    outer_b 20
    outer_c 30

    n       2
    x       456
    outer_a 10
    outer_b 20
    outer_c 30

    n       3
    x       789
    inner_a 40
    inner_b 50
    inner_c 60
    inner_d 70
    outer_a 10
    outer_b 50
    outer_c 60
And this example demonstrates the type-declaration rules:
    $ cat data/type-decl-example.mlr
    subr s(a, str b, int c) {                         # a is implicitly var (untyped).
                                                      # b is explicitly str.
                                                      # c is explicitly int.
                                                      # The type-checking is done at the callsite
                                                      # when arguments are bound to parameters.
                                                      #
        var b = 100;     # error                      # Re-declaration in the same scope is disallowed.
        int n = 10;                                   # Declaration of variable local to the subroutine.
        n = 20;                                       # Assignment is OK.
        int n = 30;      # error                      # Re-declaration in the same scope is disallowed.
        str n = "abc";   # error                      # Re-declaration in the same scope is disallowed.
                                                      #
        float f1 = 1;    # error                      # 1 is an int, not a float.
        float f2 = 2.0;                               # 2.0 is a float.
        num f3 = 3;                                   # 3 is a num.
        num f4 = 4.0;                                 # 4.0 is a num.
    }                                                 #
                                                      #
    call s(1, 2, 3);                                  # Type-assertion '3 is int' is done here at the callsite.
                                                      #
    k = "def";                                        # Top-level variable k.
                                                      #
    for (str k, v in $*) {                            # k and v are bound here, masking outer k.
      print k . ":" . v;                              # k is explicitly str; v is implicitly var.
    }                                                 #
                                                      #
    print "k is".k;                                   # k at this scope level is still "def".
    print "v is".v;                                   # v is undefined in this scope.
                                                      #
    i = -1;                                           #
    for (i = 1, int j = 2; i <= 10; i += 1, j *= 2) { # C-style triple-for variables use enclosing scope, unless
                                                      # declared local: i is outer, j is local to the loop.
      print "inner i =" . i;                          #
      print "inner j =" . j;                          #
    }                                                 #
    print "outer i =" . i;                            # i has been modified by the loop.
    print "outer j =" . j;                            # j is undefined in this scope.
## Map literals Miller’s `put`/`filter` DSL has four kinds of hashmaps. Stream records are (single-level) maps from name to value. Out-of-stream variables and local variables can also be maps, although they can be multi-level hashmaps (e.g. `@sum[$x][$y]`). The fourth kind is map literals. These cannot be on the left-hand side of assignment expressions. Syntactically they look like JSON, although Miller allows string and integer keys in its map literals while JSON allows only string keys (e.g. `"3"` rather than `3`). For example, the following swaps the input stream’s `a` and `i` fields, modifies `y`, and drops the rest:
    $ mlr --opprint put '
      $* = {
        "a": $i,
        "i": $a,
        "y": $y * 10,
      }
    ' data/small
    a i   y
    1 pan 7.268029
    2 eks 5.221511
    3 wye 3.383185
    4 eks 1.341887
    5 wye 8.636245
Likewise, you can assign map literals to out-of-stream variables or local variables; pass them as arguments to user-defined functions, return them from functions, and so on:
    $ mlr --from ../c/s put '
      func f(map m): map {
        m["x"] *= 200;
        return m;
      }
      $* = f({"a": $a, "x": $x});
    '
    a=pan,x=69.358029
    a=eks,x=151.735993
    a=wye,x=40.920661
    a=eks,x=76.279879
    a=wye,x=114.657784
    a=zee,x=105.425232
    a=eks,x=122.356812
    a=zee,x=119.710802
    a=hat,x=6.288375
    a=pan,x=100.525201
Like out-of-stream and local variables, map literals can be multi-level:
    $ mlr --from data/small put -q '
      begin {
        @o = {
          "nrec": 0,
          "nkey": {"numeric":0, "non-numeric":0},
        };
      }
      @o["nrec"] += 1;
      for (k, v in $*) {
        if (is_numeric(v)) {
          @o["nkey"]["numeric"] += 1;
        } else {
          @o["nkey"]["non-numeric"] += 1;
        }
      }
      end {
        dump @o;
      }
    '
    {
      "nrec": 5,
      "nkey": {
        "numeric": 15,
        "non-numeric": 10
      }
    }
By default, map-valued expressions are dumped using JSON formatting. If you use `dump` to print a hashmap with integer keys and you don’t want them double-quoted (JSON-style) then you can use `mlr put --jknquoteint`. See also `mlr put --help`. ## Type-checking Miller’s `put`/`filter` DSLs support two optional kinds of type-checking. One is inline type-tests and type-assertions within expressions. The other is type declarations for assignments to local variables, binding of arguments to user-defined functions, and return values from user-defined functions, These are discussed in the following subsections. Use of type-checking is entirely up to you: omit it if you want flexibility with heterogeneous data; use it if you want to help catch misspellings in your DSL code or unexpected irregularities in your input data. ### Type-test and type-assertion expressions The following `is...` functions take a value and return a boolean indicating whether the argument is of the indicated type. The `assert_...` functions return their argument if it is of the specified type, and cause a fatal error otherwise:
    $ mlr -F | grep ^is
    is_absent
    is_bool
    is_boolean
    is_empty
    is_empty_map
    is_float
    is_int
    is_map
    is_nonempty_map
    is_not_empty
    is_not_map
    is_not_null
    is_null
    is_numeric
    is_present
    is_string
    $ mlr -F | grep ^assert
    asserting_absent
    asserting_bool
    asserting_boolean
    asserting_empty
    asserting_empty_map
    asserting_float
    asserting_int
    asserting_map
    asserting_nonempty_map
    asserting_not_empty
    asserting_not_map
    asserting_not_null
    asserting_null
    asserting_numeric
    asserting_present
    asserting_string
Please see the Cookbook part 1 for examples of how to use these. ### Type-declarations for local variables, function parameter, and function return values Local variables can be defined either untyped as in `x = 1`, or typed as in `int x = 1`. Types include var (explicitly untyped), int, float, num (int or float), str, bool, and map. These optional type declarations are enforced at the time values are assigned to variables: whether at the initial value assignment as in `int x = 1` or in any subsequent assignments to the same variable farther down in the scope. The reason for `num` is that `int` and `float` typedecls are very precise:
      float a = 0;   # Runtime error since 0 is int not float
      int   b = 1.0; # Runtime error since 1.0 is float not int
      num   c = 0;   # OK
      num   d = 1.0; # OK
A suggestion is to use `num` for general use when you want numeric content, and use `int` when you genuinely want integer-only values, e.g. in loop indices or map keys (since Miller map keys can only be strings or ints). The `var` type declaration indicates no type restrictions, e.g. `var x = 1` has the same type restrictions on `x` as `x = 1`. The difference is in intentional shadowing: if you have `x = 1` in outer scope and `x = 2` in inner scope (e.g. within a for-loop or an if-statement) then outer-scope `x` has value 2 after the second assignment. But if you have `var x = 2` in the inner scope, then you are declaring a variable scoped to the inner block.) For example:
      x = 1;
      if (NR == 4) {
        x = 2; # Refers to outer-scope x: value changes from 1 to 2.
      }
      print x; # Value of x is now two
      x = 1;
      if (NR == 4) {
        var x = 2; # Defines a new inner-scope x with value 2
      }
      print x;     # Value of this x is still 1
Likewise function arguments can optionally be typed, with type enforced when the function is called:
      func f(map m, int i) {
        ...
      }
      $a = f({1:2, 3:4}, 5);     # OK
      $b = f({1:2, 3:4}, "abc"); # Runtime error
      $c = f({1:2, 3:4}, $x);    # Runtime error for records with non-integer field named x
      if (NR == 4) {
        var x = 2; # Defines a new inner-scope x with value 2
      }
      print x;     # Value of this x is still 1
Thirdly, function return values can be type-checked at the point of `return` using `:` and a typedecl after the parameter list:
      func f(map m, int i): bool {
        ...
        ...
        if (...) {
          return "false"; # Runtime error if this branch is taken
        }
        ...
        ...
        if (...) {
          return retval; # Runtime error if this function doesn't have an in-scope
                         # boolean-valued variable named retval
        }
        ...
        ...
        # In Miller if your functions don't explicitly return a value, they return absent-null.
        # So it would also be a runtime error on reaching the end of this function without
        # an explicit return statement.
      }
## Null data: empty and absent Please see here. ## Aggregate variable assignments There are three remaining kinds of variable assignment using out-of-stream variables, the last two of which use the `$*` syntax: Recursive copy of out-of-stream variables Out-of-stream variable assigned to full stream record Full stream record assigned to an out-of-stream variable Example recursive copy of out-of-stream variables:
    $ mlr --opprint put -q '@v["sum"] += $x; @v["count"] += 1; end{dump; @w = @v; dump}' data/small
    {
      "v": {
        "sum": 2.264762,
        "count": 5
      }
    }
    {
      "v": {
        "sum": 2.264762,
        "count": 5
      },
      "w": {
        "sum": 2.264762,
        "count": 5
      }
    }
Example of out-of-stream variable assigned to full stream record, where the 2nd record is stashed, and the 4th record is overwritten with that:
    $ mlr put 'NR == 2 {@keep = $*}; NR == 4 {$* = @keep}' data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
Example of full stream record assigned to an out-of-stream variable, finding the record for which the `x` field has the largest value in the input stream:
    $ cat data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    $ mlr --opprint put -q 'is_null(@xmax) || $x > @xmax {@xmax=$x; @recmax=$*}; end {emit @recmax}' data/small
    a   b   i x                  y
    eks pan 2 0.7586799647899636 0.5221511083334797
## Keywords for filter and put
    $ mlr --help-all-keywords
    all: used in "emit", "emitp", and "unset" as a synonym for @*

    begin: defines a block of statements to be executed before input records
    are ingested. The body statements must be wrapped in curly braces.
    Example: 'begin { @count = 0 }'

    bool: declares a boolean local variable in the current curly-braced scope.
    Type-checking happens at assignment: 'bool b = 1' is an error.

    break: causes execution to continue after the body of the current
    for/while/do-while loop.

    call: used for invoking a user-defined subroutine.
    Example: 'subr s(k,v) { print k . " is " . v} call s("a", $a)'

    continue: causes execution to skip the remaining statements in the body of
    the current for/while/do-while loop. For-loop increments are still applied.

    do: with "while", introduces a do-while loop. The body statements must be wrapped
    in curly braces.

    dump: prints all currently defined out-of-stream variables immediately
      to stdout as JSON.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump }'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >  "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >> "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump | "jq .[]"}'

    edump: prints all currently defined out-of-stream variables immediately
      to stderr as JSON.

      Example: mlr --from f.dat put -q '@v[NR]=$*; end { edump }'

    elif: the way Miller spells "else if". The body statements must be wrapped
    in curly braces.

    else: terminates an if/elif/elif chain. The body statements must be wrapped
    in curly braces.

    emit: inserts an out-of-stream variable into the output record stream. Hashmap
      indices present in the data but not slotted by emit arguments are not output.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put 'emit >  "/tmp/data-".$a, $*'
      Example: mlr --from f.dat put 'emit >  "/tmp/data-".$a, mapexcept($*, "a")'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums'
      Example: mlr --from f.dat put --ojson '@sums[$a][$b]+=$x; emit > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "grep somepattern", @*, "index1", "index2"'

      Please see http://johnkerl.org/miller/doc for more information.

    emitf: inserts non-indexed out-of-stream variable(s) side-by-side into the
      output record stream.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a'
      Example: mlr --from f.dat put --oxtab '@a=$i;@b+=$x;@c+=$y; emitf > "tap-".$i.".dat", @a'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf >> "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > stderr, @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern > mytap.dat", @a, @b, @c'

      Please see http://johnkerl.org/miller/doc for more information.

    emitp: inserts an out-of-stream variable into the output record stream.
      Hashmap indices present in the data but not slotted by emitp arguments are
      output concatenated with ":".

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums'
      Example: mlr --from f.dat put --opprint '@sums[$a][$b]+=$x; emitp > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "grep somepattern", @*, "index1", "index2"'

      Please see http://johnkerl.org/miller/doc for more information.

    end: defines a block of statements to be executed after input records
    are ingested. The body statements must be wrapped in curly braces.
    Example: 'end { emit @count }'
    Example: 'end { eprint "Final count is " . @count }'

    eprint: prints expression immediately to stderr.
      Example: mlr --from f.dat put -q 'eprint "The sum of x and y is ".($x+$y)'
      Example: mlr --from f.dat put -q 'for (k, v in $*) { eprint k . " => " . v }'
      Example: mlr --from f.dat put  '(NR % 1000 == 0) { eprint "Checkpoint ".NR}'

    eprintn: prints expression immediately to stderr, without trailing newline.
      Example: mlr --from f.dat put -q 'eprintn "The sum of x and y is ".($x+$y); eprint ""'

    false: the boolean literal value.

    filter: includes/excludes the record in the output record stream.

      Example: mlr --from f.dat put 'filter (NR == 2 || $x > 5.4)'

      Instead of put with 'filter false' you can simply use put -q.  The following
      uses the input record to accumulate data but only prints the running sum
      without printing the input record:

      Example: mlr --from f.dat put -q '@running_sum += $x * $y; emit @running_sum'

    float: declares a floating-point local variable in the current curly-braced scope.
    Type-checking happens at assignment: 'float x = 0' is an error.

    for: defines a for-loop using one of three styles. The body statements must
    be wrapped in curly braces.
    For-loop over stream record:
      Example:  'for (k, v in $*) { ... }'
    For-loop over out-of-stream variables:
      Example: 'for (k, v in @counts) { ... }'
      Example: 'for ((k1, k2), v in @counts) { ... }'
      Example: 'for ((k1, k2, k3), v in @*) { ... }'
    C-style for-loop:
      Example:  'for (var i = 0, var b = 1; i < 10; i += 1, b *= 2) { ... }'

    func: used for defining a user-defined function.
    Example: 'func f(a,b) { return sqrt(a**2+b**2)} $d = f($x, $y)'

    if: starts an if/elif/elif chain. The body statements must be wrapped
    in curly braces.

    in: used in for-loops over stream records or out-of-stream variables.

    int: declares an integer local variable in the current curly-braced scope.
    Type-checking happens at assignment: 'int x = 0.0' is an error.

    map: declares an map-valued local variable in the current curly-braced scope.
    Type-checking happens at assignment: 'map b = 0' is an error. map b = {} is
    always OK. map b = a is OK or not depending on whether a is a map.

    num: declares an int/float local variable in the current curly-braced scope.
    Type-checking happens at assignment: 'num b = true' is an error.

    print: prints expression immediately to stdout.
      Example: mlr --from f.dat put -q 'print "The sum of x and y is ".($x+$y)'
      Example: mlr --from f.dat put -q 'for (k, v in $*) { print k . " => " . v }'
      Example: mlr --from f.dat put  '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'

    printn: prints expression immediately to stdout, without trailing newline.
      Example: mlr --from f.dat put -q 'printn "."; end { print "" }'

    return: specifies the return value from a user-defined function.
    Omitted return statements (including via if-branches) result in an absent-null
    return value, which in turns results in a skipped assignment to an LHS.

    stderr: Used for tee, emit, emitf, emitp, print, and dump in place of filename
      to print to standard error.

    stdout: Used for tee, emit, emitf, emitp, print, and dump in place of filename
      to print to standard output.

    str: declares a string local variable in the current curly-braced scope.
    Type-checking happens at assignment.

    subr: used for defining a subroutine.
    Example: 'subr s(k,v) { print k . " is " . v} call s("a", $a)'

    tee: prints the current record to specified file.
      This is an immediate print to the specified file (except for pprint format
      which of course waits until the end of the input stream to format all output).

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output. See also mlr -h.

      emit with redirect and tee with redirect are identical, except tee can only
      output $*.

      Example: mlr --from f.dat put 'tee >  "/tmp/data-".$a, $*'
      Example: mlr --from f.dat put 'tee >> "/tmp/data-".$a.$b, $*'
      Example: mlr --from f.dat put 'tee >  stderr, $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\]", $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\] > /tmp/data-".$a, $*'
      Example: mlr --from f.dat put -q 'tee | "gzip > /tmp/data-".$a.".gz", $*'
      Example: mlr --from f.dat put -q --ojson 'tee | "gzip > /tmp/data-".$a.".gz", $*'

    true: the boolean literal value.

    unset: clears field(s) from the current record, or an out-of-stream or local variable.

      Example: mlr --from f.dat put 'unset $x'
      Example: mlr --from f.dat put 'unset $*'
      Example: mlr --from f.dat put 'for (k, v in $*) { if (k =~ "a.*") { unset $[k] } }'
      Example: mlr --from f.dat put '...; unset @sums'
      Example: mlr --from f.dat put '...; unset @sums["green"]'
      Example: mlr --from f.dat put '...; unset @*'

    var: declares an untyped local variable in the current curly-braced scope.
    Examples: 'var a=1', 'var xyz=""'

    while: introduces a while loop, or with "do", introduces a do-while loop.
    The body statements must be wrapped in curly braces.

    ENV: access to environment variables by name, e.g. '$home = ENV["HOME"]'

    FILENAME: evaluates to the name of the current file being processed.

    FILENUM: evaluates to the number of the current file being processed,
    starting with 1.

    FNR: evaluates to the number of the current record within the current file
    being processed, starting with 1. Resets at the start of each file.

    IFS: evaluates to the input field separator from the command line.

    IPS: evaluates to the input pair separator from the command line.

    IRS: evaluates to the input record separator from the command line,
    or to LF or CRLF from the input data if in autodetect mode (which is
    the default).

    M_E: the mathematical constant e.

    M_PI: the mathematical constant pi.

    NF: evaluates to the number of fields in the current record.

    NR: evaluates to the number of the current record over all files
    being processed, starting with 1. Does not reset at the start of each file.

    OFS: evaluates to the output field separator from the command line.

    OPS: evaluates to the output pair separator from the command line.

    ORS: evaluates to the output record separator from the command line,
    or to LF or CRLF from the input data if in autodetect mode (which is
    the default).
# Operator precedence Operators are listed in order of decreasing precedence, highest first.
    Operators              Associativity
    ---------              -------------
    ()                     left to right
    **                     right to left
    ! ~ unary+ unary- &    right to left
    binary* / // %         left to right
    binary+ binary- .      left to right
    << >>                  left to right
    &                      left to right
    ^                      left to right
    |                      left to right
    < <= > >=              left to right
    == != =~ !=~           left to right
    &&                     left to right
    ^^                     left to right
    ||                     left to right
    ? :                    right to left
    =                      N/A for Miller (there is no $a=$b=$c)
# Operator and function semantics Functions are in general pass-throughs straight to the system-standard C library. The `min` and `max` functions are different from other multi-argument functions which return null if any of their inputs are null: for `min` and `max`, by contrast, if one argument is absent-null, the other is returned. Empty-null loses min or max against numeric or boolean; empty-null is less than any other string. Symmetrically with respect to the bitwise OR, XOR, and AND operators `|`, `^`, `&`, Miller has logical operators `||`, `^^`, `&&`: the logical XOR not existing in C. The exponentiation operator `**` is familiar from many languages. The regex-match and regex-not-match operators `=~` and `!=~` are similar to those in Ruby and Perl. # Control structures ## Pattern-action blocks These are reminiscent of `awk` syntax. They can be used to allow assignments to be done only when appropriate — e.g. for math-function domain restrictions, regex-matching, and so on:
    $ mlr cat data/put-gating-example-1.dkvp
    x=-1
    x=0
    x=1
    x=2
    x=3
    $ mlr put '$x > 0.0 { $y = log10($x); $z = sqrt($y) }' data/put-gating-example-1.dkvp
    x=-1
    x=0
    x=1,y=0.000000,z=0.000000
    x=2,y=0.301030,z=0.548662
    x=3,y=0.477121,z=0.690740
    $ mlr cat data/put-gating-example-2.dkvp
    a=abc_123
    a=some other name
    a=xyz_789
    $ mlr put '$a =~ "([a-z]+)_([0-9]+)" { $b = "left_\1"; $c = "right_\2" }' data/put-gating-example-2.dkvp
    a=abc_123,b=left_abc,c=right_123
    a=some other name
    a=xyz_789,b=left_xyz,c=right_789
This produces heteregenous output which Miller, of course, has no problems with (see Record-heterogeneity). But if you want homogeneous output, the curly braces can be replaced with a semicolon between the expression and the body statements. This causes `put` to evaluate the boolean expression (along with any side effects, namely, regex-captures `\1`, `\2`, etc.) but doesn’t use it as a criterion for whether subsequent assignments should be executed. Instead, subsequent assignments are done unconditionally:
    $ mlr put '$x > 0.0; $y = log10($x); $z = sqrt($y)' data/put-gating-example-1.dkvp
    x=-1,y=nan,z=nan
    x=0,y=-inf,z=nan
    x=1,y=0.000000,z=0.000000
    x=2,y=0.301030,z=0.548662
    x=3,y=0.477121,z=0.690740
    $ mlr put '$a =~ "([a-z]+)_([0-9]+)"; $b = "left_\1"; $c = "right_\2"' data/put-gating-example-2.dkvp
    a=abc_123,b=left_abc,c=right_123
    a=some other name,b=left_,c=right_
    a=xyz_789,b=left_xyz,c=right_789
## If-statements These are again reminiscent of `awk`. Pattern-action blocks are a special case of `if` with no `elif` or `else` blocks, no `if` keyword, and parentheses optional around the boolean expression:
    mlr put 'NR == 4 {$foo = "bar"}'
    mlr put 'if (NR == 4) {$foo = "bar"}'
Compound statements use `elif` (rather than `elsif` or `else if`):
    mlr put '
      if (NR == 2) {
        ...
      } elif (NR ==4) {
        ...
      } elif (NR ==6) {
        ...
      } else {
        ...
      }
    '
## While and do-while loops Miller’s `while` and `do-while` are unsurprising in comparison to various languages, as are `break` and `continue`:
    $ echo x=1,y=2 | mlr put '
      while (NF < 10) {
        $[NF+1] = ""
      }
      $foo = "bar"
    '
    x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar
    $ echo x=1,y=2 | mlr put '
      do {
        $[NF+1] = "";
        if (NF == 5) {
          break
        }
      } while (NF < 10);
      $foo = "bar"
    '
    x=1,y=2,3=,4=,5=,foo=bar
A `break` or `continue` within nested conditional blocks or if-statements will, of course, propagate to the innermost loop enclosing them, if any. A `break` or `continue` outside a loop is a syntax error that will be flagged as soon as the expression is parsed, before any input records are ingested. The existence of `while`, `do-while`, and `for` loops in Miller’s DSL means that you can create infinite-loop scenarios inadvertently. In particular, please recall that DSL statements are executed once if in `begin` or `end` blocks, and once per record otherwise. For example, `while (NR < 10)` will never terminate as `NR` is only incremented between records. ## For-loops While Miller’s `while` and `do-while` statements are much as in many other languages, `for` loops are more idiosyncratic to Miller. They are loops over key-value pairs, whether in stream records, out-of-stream variables, local variables, or map-literals: more reminiscent of `foreach`, as in (for example) PHP. There are for-loops over map keys and for-loops over key-value tuples. Additionally, Miller has a C-style triple-for loop with initialize, test, and update statements. As with `while` and `do-while`, a `break` or `continue` within nested control structures will propagate to the innermost loop enclosing them, if any, and a `break` or `continue` outside a loop is a syntax error that will be flagged as soon as the expression is parsed, before any input records are ingested. ### Key-only for-loops The `key` variable is always bound to the key of key-value pairs:
    $ mlr --from data/small put '
      print "NR = ".NR;
      for (key in $*) {
        value = $[key];
        print "  key:" . key . "  value:".value;
      }

    '
    NR = 1
      key:a  value:pan
      key:b  value:pan
      key:i  value:1
      key:x  value:0.346790
      key:y  value:0.726803
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    NR = 2
      key:a  value:eks
      key:b  value:pan
      key:i  value:2
      key:x  value:0.758680
      key:y  value:0.522151
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    NR = 3
      key:a  value:wye
      key:b  value:wye
      key:i  value:3
      key:x  value:0.204603
      key:y  value:0.338319
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    NR = 4
      key:a  value:eks
      key:b  value:wye
      key:i  value:4
      key:x  value:0.381399
      key:y  value:0.134189
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    NR = 5
      key:a  value:wye
      key:b  value:pan
      key:i  value:5
      key:x  value:0.573289
      key:y  value:0.863624
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    $ mlr -n put '
      end {
        o = {1:2, 3:{4:5}};
        for (key in o) {
          print "  key:" . key . "  valuetype:" . typeof(o[key]);
        }
      }
    '
      key:1  valuetype:int
      key:3  valuetype:map
Note that the value corresponding to a given key may be gotten as through a computed field name using square brackets as in `$[key]` for stream records, or by indexing the looped-over variable using square brackets. ### Key-value for-loops Single-level keys may be gotten at using either `for(k,v)` or `for((k),v)`; multi-level keys may be gotten at using `for((k1,k2,k3),v)` and so on. The `v` variable will be bound to to a scalar value (a string or a number) if the map stops at that level, or to a map-valued variable if the map goes deeper. If the map isn’t deep enough then the loop body won’t be executed.
    $ cat data/for-srec-example.tbl
    label1 label2 f1  f2  f3
    blue   green  100 240 350
    red    green  120 11  195
    yellow blue   140 0   240
    $ mlr --pprint --from data/for-srec-example.tbl put '
      $sum1 = $f1 + $f2 + $f3;
      $sum2 = 0;
      $sum3 = 0;
      for (key, value in $*) {
        if (key =~ "^f[0-9]+") {
          $sum2 += value;
          $sum3 += $[key];
        }
      }
    '
    label1 label2 f1  f2  f3  sum1 sum2 sum3
    blue   green  100 240 350 690  690  690
    red    green  120 11  195 326  326  326
    yellow blue   140 0   240 380  380  380
    $ mlr --from data/small --opprint put 'for (k,v in $*) { $[k."_type"] = typeof(v) }'
    a   b   i x                   y                   a_type b_type i_type x_type y_type
    pan pan 1 0.3467901443380824  0.7268028627434533  string string int    float  float
    eks pan 2 0.7586799647899636  0.5221511083334797  string string int    float  float
    wye wye 3 0.20460330576630303 0.33831852551664776 string string int    float  float
    eks wye 4 0.38139939387114097 0.13418874328430463 string string int    float  float
    wye pan 5 0.5732889198020006  0.8636244699032729  string string int    float  float
Note that the value of the current field in the for-loop can be gotten either using the bound variable `value`, or through a computed field name using square brackets as in `$[key]`. Important note: to avoid inconsistent looping behavior in case you’re setting new fields (and/or unsetting existing ones) while looping over the record, Miller makes a copy of the record before the loop: loop variables are bound from the copy and all other reads/writes involve the record itself:
    $ mlr --from data/small --opprint put '
      $sum1 = 0;
      $sum2 = 0;
      for (k,v in $*) {
        if (is_numeric(v)) {
          $sum1 +=v;
          $sum2 += $[k];
        }
      }
    '
    a   b   i x                   y                   sum1     sum2
    pan pan 1 0.3467901443380824  0.7268028627434533  2.073593 8.294372
    eks pan 2 0.7586799647899636  0.5221511083334797  3.280831 13.123324
    wye wye 3 0.20460330576630303 0.33831852551664776 3.542922 14.171687
    eks wye 4 0.38139939387114097 0.13418874328430463 4.515588 18.062353
    wye pan 5 0.5732889198020006  0.8636244699032729  6.436913 25.747654
It can be confusing to modify the stream record while iterating over a copy of it, so instead you might find it simpler to use a local variable in the loop and only update the stream record after the loop:
    $ mlr --from data/small --opprint put '
      sum = 0;
      for (k,v in $*) {
        if (is_numeric(v)) {
          sum += $[k];
        }
      }
      $sum = sum
    '
    a   b   i x                   y                   sum
    pan pan 1 0.3467901443380824  0.7268028627434533  2.073593
    eks pan 2 0.7586799647899636  0.5221511083334797  3.280831
    wye wye 3 0.20460330576630303 0.33831852551664776 3.542922
    eks wye 4 0.38139939387114097 0.13418874328430463 4.515588
    wye pan 5 0.5732889198020006  0.8636244699032729  6.436913
You can also start iterating on sub-hashmaps of an out-of-stream or local variable; you can loop over nested keys; you can loop over all out-of-stream variables. The bound variables are bound to a copy of the sub-hashmap as it was before the loop started. The sub-hashmap is specified by square-bracketed indices after `in`, and additional deeper indices are bound to loop key-variables. The terminal values are bound to the loop value-variable whenever the keys are not too shallow. The value-variable may refer to a terminal (string, number) or it may be map-valued if the map goes deeper. Example indexing is as follows:
    # Parentheses are optional for single key:
    for (k1,           v in @a["b"]["c"]) { ... }
    for ((k1),         v in @a["b"]["c"]) { ... }
    # Parentheses are required for multiple keys:
    for ((k1, k2),     v in @a["b"]["c"]) { ... } # Loop over subhashmap of a variable
    for ((k1, k2, k3), v in @a["b"]["c"]) { ... } # Ditto
    for ((k1, k2, k3), v in @a { ... }            # Loop over variable starting from basename
    for ((k1, k2, k3), v in @* { ... }            # Loop over all variables (k1 is bound to basename)
That’s confusing in the abstract, so a concrete example is in order. Suppose the out-of-stream variable `@myvar` is populated as follows:
    $ mlr -n put --jknquoteint -q '
      begin {
        @myvar = {
          1: 2,
          3: { 4 : 5 },
          6: { 7: { 8: 9 } }
        }
      }
      end { dump }
    '
    {
      "myvar": {
        1: 2,
        3: {
          4: 5
        },
        6: {
          7: {
            8: 9
          }
        }
      }
    }
Then we can get at various values as follows:
    $ mlr -n put --jknquoteint -q '
      begin {
        @myvar = {
          1: 2,
          3: { 4 : 5 },
          6: { 7: { 8: 9 } }
        }
      }
      end {
        for (k, v in @myvar) {
          print
            "key=" . k .
            ",valuetype=" . typeof(v);
        }
      }
    '
    key=1,valuetype=int
    key=3,valuetype=map
    key=6,valuetype=map
    $ mlr -n put --jknquoteint -q '
      begin {
        @myvar = {
          1: 2,
          3: { 4 : 5 },
          6: { 7: { 8: 9 } }
        }
      }
      end {
        for ((k1, k2), v in @myvar) {
          print
            "key1=" . k1 .
            ",key2=" . k2 .
            ",valuetype=" . typeof(v);
        }
      }
    '
    key1=3,key2=4,valuetype=int
    key1=6,key2=7,valuetype=map
    $ mlr -n put --jknquoteint -q '
      begin {
        @myvar = {
          1: 2,
          3: { 4 : 5 },
          6: { 7: { 8: 9 } }
        }
      }
      end {
        for ((k1, k2), v in @myvar[6]) {
          print
            "key1=" . k1 .
            ",key2=" . k2 .
            ",valuetype=" . typeof(v);
        }
      }
    '
    key1=7,key2=8,valuetype=int
### C-style triple-for loops These are supported as follows:
    $ mlr --from data/small --opprint put '
      num suma = 0;
      for (a = 1; a <= NR; a += 1) {
        suma += a;
      }
      $suma = suma;
    '
    a   b   i x                   y                   suma
    pan pan 1 0.3467901443380824  0.7268028627434533  1
    eks pan 2 0.7586799647899636  0.5221511083334797  3
    wye wye 3 0.20460330576630303 0.33831852551664776 6
    eks wye 4 0.38139939387114097 0.13418874328430463 10
    wye pan 5 0.5732889198020006  0.8636244699032729  15
    $ mlr --from data/small --opprint put '
      num suma = 0;
      num sumb = 0;
      for (num a = 1, num b = 1; a <= NR; a += 1, b *= 2) {
        suma += a;
        sumb += b;
      }
      $suma = suma;
      $sumb = sumb;
    '
    a   b   i x                   y                   suma sumb
    pan pan 1 0.3467901443380824  0.7268028627434533  1    1
    eks pan 2 0.7586799647899636  0.5221511083334797  3    3
    wye wye 3 0.20460330576630303 0.33831852551664776 6    7
    eks wye 4 0.38139939387114097 0.13418874328430463 10   15
    wye pan 5 0.5732889198020006  0.8636244699032729  15   31
Notes: In `for (start; continuation; update) { body }`, the start, continuation, and update statements may be empty, single statements, or multiple comma-separated statements. If the continuation is empty (e.g. `for(i=1;;i+=1)`) it defaults to true. In particular, you may use `$`-variables and/or `@`-variables in the start, continuation, and/or update steps (as well as the body, of course). The typedecls such as `int` or `num` are optional. If a typedecl is provided (for a local variable), it binds a variable scoped to the for-loop regardless of whether a same-name variable is present in outer scope. If a typedecl is not provided, then the variable is scoped to the for-loop if no same-name variable is present in outer scope, or if a same-name variable is present in outer scope then it is modified. Miller has no `++` or `--` operators. As with all for/if/while statements in Miller, the curly braces are required even if the body is a single statement, or empty. ## Begin/end blocks Miller supports an `awk`-like `begin/end` syntax. The statements in the `begin` block are executed before any input records are read; the statements in the `end` block are executed after the last input record is read. (If you want to execute some statement at the start of each file, not at the start of the first file as with `begin`, you might use a pattern/action block of the form `FNR == 1 { ... }`.) All statements outside of `begin` or `end` are, of course, executed on every input record. Semicolons separate statements inside or outside of begin/end blocks; semicolons are required between begin/end block bodies and any subsequent statement. For example:
    $ mlr put '
      begin { @sum = 0 };
      @x_sum += $x;
      end { emit @x_sum }
    ' ../data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    a=zee,b=pan,i=6,x=0.5271261600918548,y=0.49322128674835697
    a=eks,b=zee,i=7,x=0.6117840605678454,y=0.1878849191181694
    a=zee,b=wye,i=8,x=0.5985540091064224,y=0.976181385699006
    a=hat,b=wye,i=9,x=0.03144187646093577,y=0.7495507603507059
    a=pan,b=wye,i=10,x=0.5026260055412137,y=0.9526183602969864
    x_sum=4.536294
Since uninitialized out-of-stream variables default to 0 for addition/substraction and 1 for multiplication when they appear on expression right-hand sides (as in `awk`), the above can be written more succinctly as
    $ mlr put '
      @x_sum += $x;
      end { emit @x_sum }
    ' ../data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    a=zee,b=pan,i=6,x=0.5271261600918548,y=0.49322128674835697
    a=eks,b=zee,i=7,x=0.6117840605678454,y=0.1878849191181694
    a=zee,b=wye,i=8,x=0.5985540091064224,y=0.976181385699006
    a=hat,b=wye,i=9,x=0.03144187646093577,y=0.7495507603507059
    a=pan,b=wye,i=10,x=0.5026260055412137,y=0.9526183602969864
    x_sum=4.536294
The put -q option is a shorthand which suppresses printing of each output record, with only `emit` statements being output. So to get only summary outputs, one could write
    $ mlr put -q '
      @x_sum += $x;
      end { emit @x_sum }
    ' ../data/small
    x_sum=4.536294
We can do similarly with multiple out-of-stream variables:
    $ mlr put -q '
      @x_count += 1;
      @x_sum += $x;
      end {
        emit @x_count;
        emit @x_sum;
      }
    ' ../data/small
    x_count=10
    x_sum=4.536294
This is of course not much different than
    $ mlr stats1 -a count,sum -f x ../data/small
    x_count=10,x_sum=4.536294
Note that it’s a syntax error for begin/end blocks to refer to field names (beginning with `$`), since these execute outside the context of input records. # Output statements You can output variable-values or expressions in five ways: Assign them to stream-record fields. For example, `$cumulative_sum = @sum`. For another example, `$nr = NR` adds a field named `nr` to each output record, containing the value of the built-in variable `NR` as of when that record was ingested. Use the print or eprint keywords which immediately print an expression directly to standard output or standard error, respectively. Note that `dump`, `edump`, `print`, and `eprint` don’t output records which participate in `then`-chaining; rather, they’re just immediate prints to stdout/stderr. The `printn` and `eprintn` keywords are the same except that they don’t print final newlines. Additionally, you can print to a specified file instead of stdout/stderr. Use the dump or edump keywords, which immediately print all out-of-stream variables as a JSON data structure to the standard output or standard error (respectively). Use tee which formats the current stream record (not just an arbitrary string as with print) to a specific file. Use emit/emitp/emitf to send out-of-stream variables’ current values to the output record stream, e.g. `@sum += $x; emit @sum` which produces an extra output record such as `sum=3.1648382`. For the first two options you are populating the output-records stream which feeds into the next verb in a `then`-chain (if any), or which otherwise is formatted for output using `--o...` flags. For the last three options you are sending output directly to standard output, standard error, or a file. ## Print statements The `print` statement is perhaps self-explanatory, but with a few light caveats: There are four variants: `print` goes to stdout with final newline, `printn` goes to stdout without final newline (you can include one using "\n" in your output string), `eprint` goes to stderr with final newline, and `eprintn` goes to stderr without final newline. Output goes directly to stdout/stderr, respectively: data produced this way do not go downstream to the next verb in a `then`-chain. (Use `emit` for that.) Print statements are for strings (`print "hello"`), or things which can be made into strings: numbers (`print 3`, `print $a + $b`, or concatenations thereof (`print "a + b = " . ($a + $b)`). Maps (in `$*`, map-valued out-of-stream or local variables, and map literals) aren’t convertible into strings. If you print a map, you get `{is-a-map}` as output. Please use `dump` to print maps. You can redirect print output to a file: `mlr --from myfile.dat put 'print > "tap.txt", $x'` `mlr --from myfile.dat put 'o=$*; print > $a.".txt", $x'`. See also the section on redirected output for examples. ## Dump statements The `dump` statement is for printing expressions, including maps, directly to stdout/stderr, respectively: There are two variants: `dump` prints to stdout; `edump` prints to stderr. Output goes directly to stdout/stderr, respectively: data produced this way do not go downstream to the next verb in a `then`-chain. (Use `emit` for that.) You can use `dump` to output single strings, numbers, or expressions including map-valued data. Map-valued data are printed as JSON. Miller allows string and integer keys in its map literals while JSON allows only string keys, so use `mlr put --jknquoteint` if you want integer-valued map keys not double-quoted. If you use `dump` (or `edump`) with no arguments, you get a JSON structure representing the current values of all out-of-stream variables. As with `print`, you can redirect output to files. See also the section on redirected output for examples. ## Tee statements Records produced by a `mlr put` go downstream to the next verb in your `then`-chain, if any, or otherwise to standard output. If you want to additionally copy out records to files, you can do that using `tee`. The syntax is, by example, `mlr --from myfile.dat put 'tee > "tap.dat", $*' then sort -n index`. First is `tee >`, then the filename expression (which can be an expression such as `"tap.".$a.".dat"`), then a comma, then `$*`. (Nothing else but `$*` is teeable.) See also the section on redirected output for examples. ## Redirected-output statements The print, dump tee, emitf, emit, and emitp keywords all allow you to redirect output to one or more files or pipe-to commands. The filenames/commands are strings which can be constructed using record-dependent values, so you can do things like splitting a table into multiple files, one for each account ID, and so on. Details: The `print` and `dump` keywords produce output immediately to standard output, or to specified file(s) or pipe-to command if present.
    $ mlr --help-keyword print
    print: prints expression immediately to stdout.
      Example: mlr --from f.dat put -q 'print "The sum of x and y is ".($x+$y)'
      Example: mlr --from f.dat put -q 'for (k, v in $*) { print k . " => " . v }'
      Example: mlr --from f.dat put  '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'
    $ mlr --help-keyword dump
    dump: prints all currently defined out-of-stream variables immediately
      to stdout as JSON.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump }'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >  "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >> "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump | "jq .[]"}'
`mlr put` sends the current record (possibly modified by the `put` expression) to the output record stream. Records are then input to the following verb in a `then`-chain (if any), else printed to standard output (unless `put -q`). The tee keyword additionally writes the output record to specified file(s) or pipe-to command, or immediately to `stdout`/`stderr`.
    $ mlr --help-keyword tee
    tee: prints the current record to specified file.
      This is an immediate print to the specified file (except for pprint format
      which of course waits until the end of the input stream to format all output).

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output. See also mlr -h.

      emit with redirect and tee with redirect are identical, except tee can only
      output $*.

      Example: mlr --from f.dat put 'tee >  "/tmp/data-".$a, $*'
      Example: mlr --from f.dat put 'tee >> "/tmp/data-".$a.$b, $*'
      Example: mlr --from f.dat put 'tee >  stderr, $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\]", $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\] > /tmp/data-".$a, $*'
      Example: mlr --from f.dat put -q 'tee | "gzip > /tmp/data-".$a.".gz", $*'
      Example: mlr --from f.dat put -q --ojson 'tee | "gzip > /tmp/data-".$a.".gz", $*'
`mlr put`’s `emitf`, `emitp`, and `emit` send out-of-stream variables to the output record stream. These are then input to the following verb in a `then`-chain (if any), else printed to standard output. When redirected with `>`, `>>`, or `|`, they instead write the out-of-stream variable(s) to specified file(s) or pipe-to command, or immediately to `stdout`/`stderr`.
    $ mlr --help-keyword emitf
    emitf: inserts non-indexed out-of-stream variable(s) side-by-side into the
      output record stream.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a'
      Example: mlr --from f.dat put --oxtab '@a=$i;@b+=$x;@c+=$y; emitf > "tap-".$i.".dat", @a'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf >> "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > stderr, @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern > mytap.dat", @a, @b, @c'

      Please see http://johnkerl.org/miller/doc for more information.
    $ mlr --help-keyword emitp
    emitp: inserts an out-of-stream variable into the output record stream.
      Hashmap indices present in the data but not slotted by emitp arguments are
      output concatenated with ":".

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums'
      Example: mlr --from f.dat put --opprint '@sums[$a][$b]+=$x; emitp > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "grep somepattern", @*, "index1", "index2"'

      Please see http://johnkerl.org/miller/doc for more information.
    $ mlr --help-keyword emit
    emit: inserts an out-of-stream variable into the output record stream. Hashmap
      indices present in the data but not slotted by emit arguments are not output.

      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.

      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.

      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.

      Example: mlr --from f.dat put 'emit >  "/tmp/data-".$a, $*'
      Example: mlr --from f.dat put 'emit >  "/tmp/data-".$a, mapexcept($*, "a")'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums'
      Example: mlr --from f.dat put --ojson '@sums[$a][$b]+=$x; emit > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "grep somepattern", @*, "index1", "index2"'

      Please see http://johnkerl.org/miller/doc for more information.
## Emit statements There are three variants: `emitf`, `emit`, and `emitp`. Keep in mind that out-of-stream variables are a nested, multi-level hashmap (directly viewable as JSON using `dump`), whereas Miller output records are lists of single-level key-value pairs. The three emit variants allow you to control how the multilevel hashmaps are flatten down to output records. You can emit any map-valued expression, including `$*`, map-valued out-of-stream variables, the entire out-of-stream-variable collection `@*`, map-valued local variables, map literals, or map-valued function return values. Use emitf to output several out-of-stream variables side-by-side in the same output record. For `emitf` these mustn’t have indexing using `@name[...]`. Example:
    $ mlr put -q '@count += 1; @x_sum += $x; @y_sum += $y; end { emitf @count, @x_sum, @y_sum}' data/small
    count=5,x_sum=2.264762,y_sum=2.585086
Use emit to output an out-of-stream variable. If it’s non-indexed you’ll get a simple key-value pair:
    $ cat data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    $ mlr put -q '@sum += $x; end { dump }' data/small
    {
      "sum": 2.264762
    }
    $ mlr put -q '@sum += $x; end { emit @sum }' data/small
    sum=2.264762
If it’s indexed then use as many names after `emit` as there are indices:
    $ mlr put -q '@sum[$a] += $x; end { dump }' data/small
    {
      "sum": {
        "pan": 0.346790,
        "eks": 1.140079,
        "wye": 0.777892
      }
    }
    $ mlr put -q '@sum[$a] += $x; end { emit @sum, "a" }' data/small
    a=pan,sum=0.346790
    a=eks,sum=1.140079
    a=wye,sum=0.777892
    $ mlr put -q '@sum[$a][$b] += $x; end { dump }' data/small
    {
      "sum": {
        "pan": {
          "pan": 0.346790
        },
        "eks": {
          "pan": 0.758680,
          "wye": 0.381399
        },
        "wye": {
          "wye": 0.204603,
          "pan": 0.573289
        }
      }
    }
    $ mlr put -q '@sum[$a][$b] += $x; end { emit @sum, "a", "b" }' data/small
    a=pan,b=pan,sum=0.346790
    a=eks,b=pan,sum=0.758680
    a=eks,b=wye,sum=0.381399
    a=wye,b=wye,sum=0.204603
    a=wye,b=pan,sum=0.573289
    $ mlr put -q '@sum[$a][$b][$i] += $x; end { dump }' data/small
    {
      "sum": {
        "pan": {
          "pan": {
            "1": 0.346790
          }
        },
        "eks": {
          "pan": {
            "2": 0.758680
          },
          "wye": {
            "4": 0.381399
          }
        },
        "wye": {
          "wye": {
            "3": 0.204603
          },
          "pan": {
            "5": 0.573289
          }
        }
      }
    }
    $ mlr put -q '@sum[$a][$b][$i] += $x; end { emit @sum, "a", "b", "i" }' data/small
    a=pan,b=pan,i=1,sum=0.346790
    a=eks,b=pan,i=2,sum=0.758680
    a=eks,b=wye,i=4,sum=0.381399
    a=wye,b=wye,i=3,sum=0.204603
    a=wye,b=pan,i=5,sum=0.573289
Now for emitp: if you have as many names following `emit` as there are levels in the out-of-stream variable’s hashmap, then `emit` and `emitp` do the same thing. Where they differ is when you don’t specify as many names as there are hashmap levels. In this case, Miller needs to flatten multiple map indices down to output-record keys: `emitp` includes full prefixing (hence the `p` in `emitp`) while `emit` takes the deepest hashmap key as the output-record key:
    $ mlr put -q '@sum[$a][$b] += $x; end { dump }' data/small
    {
      "sum": {
        "pan": {
          "pan": 0.346790
        },
        "eks": {
          "pan": 0.758680,
          "wye": 0.381399
        },
        "wye": {
          "wye": 0.204603,
          "pan": 0.573289
        }
      }
    }
    $ mlr put -q '@sum[$a][$b] += $x; end { emit @sum, "a" }' data/small
    a=pan,pan=0.346790
    a=eks,pan=0.758680,wye=0.381399
    a=wye,wye=0.204603,pan=0.573289
    $ mlr put -q '@sum[$a][$b] += $x; end { emit @sum }' data/small
    pan=0.346790
    pan=0.758680,wye=0.381399
    wye=0.204603,pan=0.573289
    $ mlr put -q '@sum[$a][$b] += $x; end { emitp @sum, "a" }' data/small
    a=pan,sum:pan=0.346790
    a=eks,sum:pan=0.758680,sum:wye=0.381399
    a=wye,sum:wye=0.204603,sum:pan=0.573289
    $ mlr put -q '@sum[$a][$b] += $x; end { emitp @sum }' data/small
    sum:pan:pan=0.346790,sum:eks:pan=0.758680,sum:eks:wye=0.381399,sum:wye:wye=0.204603,sum:wye:pan=0.573289
    $ mlr --oxtab put -q '@sum[$a][$b] += $x; end { emitp @sum }' data/small
    sum:pan:pan 0.346790
    sum:eks:pan 0.758680
    sum:eks:wye 0.381399
    sum:wye:wye 0.204603
    sum:wye:pan 0.573289
Use --oflatsep to specify the character which joins multilevel keys for `emitp` (it defaults to a colon):
    $ mlr put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum, "a" }' data/small
    a=pan,sum/pan=0.346790
    a=eks,sum/pan=0.758680,sum/wye=0.381399
    a=wye,sum/wye=0.204603,sum/pan=0.573289
    $ mlr put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum }' data/small
    sum/pan/pan=0.346790,sum/eks/pan=0.758680,sum/eks/wye=0.381399,sum/wye/wye=0.204603,sum/wye/pan=0.573289
    $ mlr --oxtab put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum }' data/small
    sum/pan/pan 0.346790
    sum/eks/pan 0.758680
    sum/eks/wye 0.381399
    sum/wye/wye 0.204603
    sum/wye/pan 0.573289
## Multi-emit statements You can emit multiple map-valued expressions side-by-side by including their names in parentheses:
    $ mlr --from data/medium --opprint put -q '
      @x_count[$a][$b] += 1;
      @x_sum[$a][$b] += $x;
      end {
          for ((a, b), _ in @x_count) {
              @x_mean[a][b] = @x_sum[a][b] / @x_count[a][b]
          }
          emit (@x_sum, @x_count, @x_mean), "a", "b"
      }
    '
    a   b   x_sum      x_count x_mean
    pan pan 219.185129 427     0.513314
    pan wye 198.432931 395     0.502362
    pan eks 216.075228 429     0.503672
    pan hat 205.222776 417     0.492141
    pan zee 205.097518 413     0.496604
    eks pan 179.963030 371     0.485076
    eks wye 196.945286 407     0.483895
    eks zee 176.880365 357     0.495463
    eks eks 215.916097 413     0.522799
    eks hat 208.783171 417     0.500679
    wye wye 185.295850 377     0.491501
    wye pan 195.847900 392     0.499612
    wye hat 212.033183 426     0.497730
    wye zee 194.774048 385     0.505907
    wye eks 204.812961 386     0.530604
    zee pan 202.213804 389     0.519830
    zee wye 233.991394 455     0.514267
    zee eks 190.961778 391     0.488393
    zee zee 206.640635 403     0.512756
    zee hat 191.300006 409     0.467726
    hat wye 208.883010 423     0.493813
    hat zee 196.349450 385     0.509999
    hat eks 189.006793 389     0.485879
    hat hat 182.853532 381     0.479931
    hat pan 168.553807 363     0.464336
What this does is walk through the first out-of-stream variable (`@x_sum` in this example) as usual, then for each keylist found (e.g. `pan,wye`), include the values for the remaining out-of-stream variables (here, `@x_count` and `@x_mean`). You should use this when all out-of-stream variables in the emit statement have the same shape and the same keylists. ## Emit-all statements Use emit all (or `emit @*` which is synonymous) to output all out-of-stream variables. You can use the following idiom to get various accumulators output side-by-side (reminiscent of `mlr stats1`):
    $ mlr --from data/small --opprint put -q '@v[$a][$b]["sum"] += $x; @v[$a][$b]["count"] += 1; end{emit @*,"a","b"}'
    a   b   sum      count
    pan pan 0.346790 1
    eks pan 0.758680 1
    eks wye 0.381399 1
    wye wye 0.204603 1
    wye pan 0.573289 1
    $ mlr --from data/small --opprint put -q '@sum[$a][$b] += $x; @count[$a][$b] += 1; end{emit @*,"a","b"}'
    a   b   sum
    pan pan 0.346790
    eks pan 0.758680
    eks wye 0.381399
    wye wye 0.204603
    wye pan 0.573289

    a   b   count
    pan pan 1
    eks pan 1
    eks wye 1
    wye wye 1
    wye pan 1
    $ mlr --from data/small --opprint put -q '@sum[$a][$b] += $x; @count[$a][$b] += 1; end{emit (@sum, @count),"a","b"}'
    a   b   sum      count
    pan pan 0.346790 1
    eks pan 0.758680 1
    eks wye 0.381399 1
    wye wye 0.204603 1
    wye pan 0.573289 1
# Unset statements You can clear a map key by assigning the empty string as its value: `$x=""` or `@x=""`. Using `unset` you can remove the key entirely. Examples:
    $ cat data/small
    a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    $ mlr put 'unset $x, $a' data/small
    b=pan,i=1,y=0.7268028627434533
    b=pan,i=2,y=0.5221511083334797
    b=wye,i=3,y=0.33831852551664776
    b=wye,i=4,y=0.13418874328430463
    b=pan,i=5,y=0.8636244699032729
This can also be done, of course, using `mlr cut -x`. You can also clear out-of-stream or local variables, at the base name level, or at an indexed sublevel:
    $ mlr put -q '@sum[$a][$b] += $x; end { dump; unset @sum; dump }' data/small
    {
      "sum": {
        "pan": {
          "pan": 0.346790
        },
        "eks": {
          "pan": 0.758680,
          "wye": 0.381399
        },
        "wye": {
          "wye": 0.204603,
          "pan": 0.573289
        }
      }
    }
    {
    }
    $ mlr put -q '@sum[$a][$b] += $x; end { dump; unset @sum["eks"]; dump }' data/small
    {
      "sum": {
        "pan": {
          "pan": 0.346790
        },
        "eks": {
          "pan": 0.758680,
          "wye": 0.381399
        },
        "wye": {
          "wye": 0.204603,
          "pan": 0.573289
        }
      }
    }
    {
      "sum": {
        "pan": {
          "pan": 0.346790
        },
        "wye": {
          "wye": 0.204603,
          "pan": 0.573289
        }
      }
    }
If you use `unset all` (or `unset @*` which is synonymous), that will unset all out-of-stream variables which have been defined up to that point. # Filter statements You can use `filter` within `put`. In fact, the following two are synonymous:
    $ mlr filter 'NR==2 || NR==3' data/small
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    $ mlr put 'filter NR==2 || NR==3' data/small
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
The former, of course, is much easier to type. But the latter allows you to define more complex expressions for the filter, and/or do other things in addition to the filter:
    $ mlr put '@running_sum += $x; filter @running_sum > 1.3' data/small
    a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
    a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
    $ mlr put '$z = $x * $y; filter $z > 0.3' data/small
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,z=0.396146
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,z=0.495106
# Built-in functions for filter and put Each function takes a specific number of arguments, as shown below, except for functions marked as variadic such as `min` and `max`. (The latter compute min and max of any number of numerical arguments.) There is no notion of optional or default-on-absent arguments. All argument-passing is positional rather than by name; arguments are passed by value, not by reference. You can get a list of all functions using mlr -F. ## +
    + (class=arithmetic #args=2): Addition.

    + (class=arithmetic #args=1): Unary plus.
## -
    - (class=arithmetic #args=2): Subtraction.

    - (class=arithmetic #args=1): Unary minus.
## *
    * (class=arithmetic #args=2): Multiplication.
## /
    / (class=arithmetic #args=2): Division.
## //
    // (class=arithmetic #args=2): Integer division: rounds to negative (pythonic).
## .+
    .+ (class=arithmetic #args=2): Addition, with integer-to-integer overflow

    .+ (class=arithmetic #args=1): Unary plus, with integer-to-integer overflow.
## .-
    .- (class=arithmetic #args=2): Subtraction, with integer-to-integer overflow.

    .- (class=arithmetic #args=1): Unary minus, with integer-to-integer overflow.
## .*
    .* (class=arithmetic #args=2): Multiplication, with integer-to-integer overflow.
## ./
    ./ (class=arithmetic #args=2): Division, with integer-to-integer overflow.
## .//
    .// (class=arithmetic #args=2): Integer division: rounds to negative (pythonic), with integer-to-integer overflow.
## %
    % (class=arithmetic #args=2): Remainder; never negative-valued (pythonic).
## **
    ** (class=arithmetic #args=2): Exponentiation; same as pow, but as an infix
    operator.
## |
    | (class=arithmetic #args=2): Bitwise OR.
## ^
    ^ (class=arithmetic #args=2): Bitwise XOR.
## &
    & (class=arithmetic #args=2): Bitwise AND.
## ~
    ~ (class=arithmetic #args=1): Bitwise NOT. Beware '$y=~$x' since =~ is the
    regex-match operator: try '$y = ~$x'.
## <<
    << (class=arithmetic #args=2): Bitwise left-shift.
## >>
    >> (class=arithmetic #args=2): Bitwise right-shift.
## ==
    == (class=boolean #args=2): String/numeric equality. Mixing number and string
    results in string compare.
## !=
    != (class=boolean #args=2): String/numeric inequality. Mixing number and string
    results in string compare.
## =~
    =~ (class=boolean #args=2): String (left-hand side) matches regex (right-hand
    side), e.g. '$name =~ "^a.*b$"'.
## !=~
    !=~ (class=boolean #args=2): String (left-hand side) does not match regex
    (right-hand side), e.g. '$name !=~ "^a.*b$"'.
## >
    > (class=boolean #args=2): String/numeric greater-than. Mixing number and string
    results in string compare.
## >=
    >= (class=boolean #args=2): String/numeric greater-than-or-equals. Mixing number
    and string results in string compare.
## <
    < (class=boolean #args=2): String/numeric less-than. Mixing number and string
    results in string compare.
## <=
    <= (class=boolean #args=2): String/numeric less-than-or-equals. Mixing number
    and string results in string compare.
## &&
    && (class=boolean #args=2): Logical AND.
## ||
    || (class=boolean #args=2): Logical OR.
## ^^
    ^^ (class=boolean #args=2): Logical XOR.
## !
    ! (class=boolean #args=1): Logical negation.
## ? :
    ? : (class=boolean #args=3): Ternary operator.
## .
    . (class=string #args=2): String concatenation.
## abs
    abs (class=math #args=1): Absolute value.
## acos
    acos (class=math #args=1): Inverse trigonometric cosine.
## acosh
    acosh (class=math #args=1): Inverse hyperbolic cosine.
## asin
    asin (class=math #args=1): Inverse trigonometric sine.
## asinh
    asinh (class=math #args=1): Inverse hyperbolic sine.
## asserting_absent
    asserting_absent (class=typing #args=1): Returns argument if it is absent in the input data, else
    throws an error.
## asserting_bool
    asserting_bool (class=typing #args=1): Returns argument if it is present with boolean value, else
    throws an error.
## asserting_boolean
    asserting_boolean (class=typing #args=1): Returns argument if it is present with boolean value, else
    throws an error.
## asserting_empty
    asserting_empty (class=typing #args=1): Returns argument if it is present in input with empty value,
    else throws an error.
## asserting_empty_map
    asserting_empty_map (class=typing #args=1): Returns argument if it is a map with empty value, else
    throws an error.
## asserting_float
    asserting_float (class=typing #args=1): Returns argument if it is present with float value, else
    throws an error.
## asserting_int
    asserting_int (class=typing #args=1): Returns argument if it is present with int value, else
    throws an error.
## asserting_map
    asserting_map (class=typing #args=1): Returns argument if it is a map, else throws an error.
## asserting_nonempty_map
    asserting_nonempty_map (class=typing #args=1): Returns argument if it is a non-empty map, else throws
    an error.
## asserting_not_empty
    asserting_not_empty (class=typing #args=1): Returns argument if it is present in input with non-empty
    value, else throws an error.
## asserting_not_map
    asserting_not_map (class=typing #args=1): Returns argument if it is not a map, else throws an error.
## asserting_not_null
    asserting_not_null (class=typing #args=1): Returns argument if it is non-null (non-empty and non-absent),
    else throws an error.
## asserting_null
    asserting_null (class=typing #args=1): Returns argument if it is null (empty or absent), else throws
    an error.
## asserting_numeric
    asserting_numeric (class=typing #args=1): Returns argument if it is present with int or float value,
    else throws an error.
## asserting_present
    asserting_present (class=typing #args=1): Returns argument if it is present in input, else throws
    an error.
## asserting_string
    asserting_string (class=typing #args=1): Returns argument if it is present with string (including
    empty-string) value, else throws an error.
## atan
    atan (class=math #args=1): One-argument arctangent.
## atan2
    atan2 (class=math #args=2): Two-argument arctangent.
## atanh
    atanh (class=math #args=1): Inverse hyperbolic tangent.
## bitcount
    bitcount (class=arithmetic #args=1): Count of 1-bits
## boolean
    boolean (class=conversion #args=1): Convert int/float/bool/string to boolean.
## cbrt
    cbrt (class=math #args=1): Cube root.
## ceil
    ceil (class=math #args=1): Ceiling: nearest integer at or above.
## cos
    cos (class=math #args=1): Trigonometric cosine.
## cosh
    cosh (class=math #args=1): Hyperbolic cosine.
## depth
    depth (class=maps #args=1): Prints maximum depth of hashmap: ''. Scalars have depth 0.
## dhms2fsec
    dhms2fsec (class=time #args=1): Recovers floating-point seconds as in
    dhms2fsec("5d18h53m20.250000s") = 500000.250000
## dhms2sec
    dhms2sec (class=time #args=1): Recovers integer seconds as in
    dhms2sec("5d18h53m20s") = 500000
## erf
    erf (class=math #args=1): Error function.
## erfc
    erfc (class=math #args=1): Complementary error function.
## exp
    exp (class=math #args=1): Exponential function e**x.
## expm1
    expm1 (class=math #args=1): e**x - 1.
## float
    float (class=conversion #args=1): Convert int/float/bool/string to float.
## floor
    floor (class=math #args=1): Floor: nearest integer at or below.
## fmtnum
    fmtnum (class=conversion #args=2): Convert int/float/bool to string using
    printf-style format string, e.g. '$s = fmtnum($n, "%06lld")'. WARNING: Miller numbers
    are all long long or double. If you use formats like %d or %f, behavior is undefined.
## fsec2dhms
    fsec2dhms (class=time #args=1): Formats floating-point seconds as in
    fsec2dhms(500000.25) = "5d18h53m20.250000s"
## fsec2hms
    fsec2hms (class=time #args=1): Formats floating-point seconds as in
    fsec2hms(5000.25) = "01:23:20.250000"
## gmt2sec
    gmt2sec (class=time #args=1): Parses GMT timestamp as integer seconds since
    the epoch.
## gsub
    gsub (class=string #args=3): Example: '$name=gsub($name, "old", "new")'
    (replace all).
## haskey
    haskey (class=maps #args=2): True/false if map has/hasn't key, e.g. 'haskey($*, "a")' or
    'haskey(mymap, mykey)'. Error if 1st argument is not a map.
## hexfmt
    hexfmt (class=conversion #args=1): Convert int to string, e.g. 255 to "0xff".
## hms2fsec
    hms2fsec (class=time #args=1): Recovers floating-point seconds as in
    hms2fsec("01:23:20.250000") = 5000.250000
## hms2sec
    hms2sec (class=time #args=1): Recovers integer seconds as in
    hms2sec("01:23:20") = 5000
## int
    int (class=conversion #args=1): Convert int/float/bool/string to int.
## invqnorm
    invqnorm (class=math #args=1): Inverse of normal cumulative distribution
    function. Note that invqorm(urand()) is normally distributed.
## is_absent
    is_absent (class=typing #args=1): False if field is present in input, false otherwise
## is_bool
    is_bool (class=typing #args=1): True if field is present with boolean value. Synonymous with is_boolean.
## is_boolean
    is_boolean (class=typing #args=1): True if field is present with boolean value. Synonymous with is_bool.
## is_empty
    is_empty (class=typing #args=1): True if field is present in input with empty string value, false otherwise.
## is_empty_map
    is_empty_map (class=typing #args=1): True if argument is a map which is empty.
## is_float
    is_float (class=typing #args=1): True if field is present with value inferred to be float
## is_int
    is_int (class=typing #args=1): True if field is present with value inferred to be int
## is_map
    is_map (class=typing #args=1): True if argument is a map.
## is_nonempty_map
    is_nonempty_map (class=typing #args=1): True if argument is a map which is non-empty.
## is_not_empty
    is_not_empty (class=typing #args=1): False if field is present in input with empty value, false otherwise
## is_not_map
    is_not_map (class=typing #args=1): True if argument is not a map.
## is_not_null
    is_not_null (class=typing #args=1): False if argument is null (empty or absent), true otherwise.
## is_null
    is_null (class=typing #args=1): True if argument is null (empty or absent), false otherwise.
## is_numeric
    is_numeric (class=typing #args=1): True if field is present with value inferred to be int or float
## is_present
    is_present (class=typing #args=1): True if field is present in input, false otherwise.
## is_string
    is_string (class=typing #args=1): True if field is present with string (including empty-string) value
## joink
    joink (class=maps #args=2): Makes string from map keys. E.g. 'joink($*, ",")'.
## joinkv
    joinkv (class=maps #args=3): Makes string from map key-value pairs. E.g. 'joinkv(@v[2], "=", ",")'
## joinv
    joinv (class=maps #args=2): Makes string from map keys. E.g. 'joinv(mymap, ",")'.
## leafcount
    leafcount (class=maps #args=1): Counts total number of terminal values in hashmap. For single-level maps,
    same as length.
## length
    length (class=maps #args=1): Counts number of top-level entries in hashmap. Scalars have length 1.
## log
    log (class=math #args=1): Natural (base-e) logarithm.
## log10
    log10 (class=math #args=1): Base-10 logarithm.
## log1p
    log1p (class=math #args=1): log(1-x).
## logifit
    logifit (class=math #args=3): Given m and b from logistic regression, compute
    fit: $yhat=logifit($x,$m,$b).
## madd
    madd (class=math #args=3): a + b mod m (integers)
## mapdiff
    mapdiff (class=maps variadic): With 0 args, returns empty map. With 1 arg, returns copy of arg.
    With 2 or more, returns copy of arg 1 with all keys from any of remaining argument maps removed.
## mapexcept
    mapexcept (class=maps variadic): Returns a map with keys from remaining arguments, if any, unset.
    E.g. 'mapexcept({1:2,3:4,5:6}, 1, 5, 7)' is '{3:4}'.
## mapselect
    mapselect (class=maps variadic): Returns a map with only keys from remaining arguments set.
    E.g. 'mapselect({1:2,3:4,5:6}, 1, 5, 7)' is '{1:2,5:6}'.
## mapsum
    mapsum (class=maps variadic): With 0 args, returns empty map. With >= 1 arg, returns a map with
    key-value pairs from all arguments. Rightmost collisions win, e.g. 'mapsum({1:2,3:4},{1:5})' is '{1:5,3:4}'.
## max
    max (class=math variadic): max of n numbers; null loses
## mexp
    mexp (class=math #args=3): a ** b mod m (integers)
## min
    min (class=math variadic): Min of n numbers; null loses
## mmul
    mmul (class=math #args=3): a * b mod m (integers)
## msub
    msub (class=math #args=3): a - b mod m (integers)
## pow
    pow (class=math #args=2): Exponentiation; same as **.
## qnorm
    qnorm (class=math #args=1): Normal cumulative distribution function.
## round
    round (class=math #args=1): Round to nearest integer.
## roundm
    roundm (class=math #args=2): Round to nearest multiple of m: roundm($x,$m) is
    the same as round($x/$m)*$m
## sec2dhms
    sec2dhms (class=time #args=1): Formats integer seconds as in sec2dhms(500000)
    = "5d18h53m20s"
## sec2gmt
    sec2gmt (class=time #args=1): Formats seconds since epoch (integer part)
    as GMT timestamp, e.g. sec2gmt(1440768801.7) = "2015-08-28T13:33:21Z".
    Leaves non-numbers as-is.

    sec2gmt (class=time #args=2): Formats seconds since epoch as GMT timestamp with n
    decimal places for seconds, e.g. sec2gmt(1440768801.7,1) = "2015-08-28T13:33:21.7Z".
    Leaves non-numbers as-is.
## sec2gmtdate
    sec2gmtdate (class=time #args=1): Formats seconds since epoch (integer part)
    as GMT timestamp with year-month-date, e.g. sec2gmtdate(1440768801.7) = "2015-08-28".
    Leaves non-numbers as-is.
## sec2hms
    sec2hms (class=time #args=1): Formats integer seconds as in
    sec2hms(5000) = "01:23:20"
## sgn
    sgn (class=math #args=1): +1 for positive input, 0 for zero input, -1 for
    negative input.
## sin
    sin (class=math #args=1): Trigonometric sine.
## sinh
    sinh (class=math #args=1): Hyperbolic sine.
## splitkv
    splitkv (class=maps #args=3): Splits string by separators into map with type inference.
    E.g. 'splitkv("a=1,b=2,c=3", "=", ",")' gives '{"a" : 1, "b" : 2, "c" : 3}'.
## splitkvx
    splitkvx (class=maps #args=3): Splits string by separators into map without type inference (keys and
    values are strings). E.g. 'splitkv("a=1,b=2,c=3", "=", ",")' gives
    '{"a" : "1", "b" : "2", "c" : "3"}'.
## splitnv
    splitnv (class=maps #args=2): Splits string by separator into integer-indexed map with type inference.
    E.g. 'splitnv("a,b,c" , ",")' gives '{1 : "a", 2 : "b", 3 : "c"}'.
## splitnvx
    splitnvx (class=maps #args=2): Splits string by separator into integer-indexed map without type
    inference (values are strings). E.g. 'splitnv("4,5,6" , ",")' gives '{1 : "4", 2 : "5", 3 : "6"}'.
## sqrt
    sqrt (class=math #args=1): Square root.
## strftime
    strftime (class=time #args=2): Formats seconds since the epoch as timestamp, e.g.
    strftime(1440768801.7,"%Y-%m-%dT%H:%M:%SZ") = "2015-08-28T13:33:21Z", and
    strftime(1440768801.7,"%Y-%m-%dT%H:%M:%3SZ") = "2015-08-28T13:33:21.700Z".
    Format strings are as in the C library (please see "man strftime" on your system),
    with the Miller-specific addition of "%1S" through "%9S" which format the seocnds
    with 1 through 9 decimal places, respectively. ("%S" uses no decimal places.)
## string
    string (class=conversion #args=1): Convert int/float/bool/string to string.
## strlen
    strlen (class=string #args=1): String length.
## strptime
    strptime (class=time #args=2): Parses timestamp as floating-point seconds since the epoch,
    e.g. strptime("2015-08-28T13:33:21Z","%Y-%m-%dT%H:%M:%SZ") = 1440768801.000000,
    and  strptime("2015-08-28T13:33:21.345Z","%Y-%m-%dT%H:%M:%SZ") = 1440768801.345000.
## sub
    sub (class=string #args=3): Example: '$name=sub($name, "old", "new")'
    (replace once).
## substr
    substr (class=string #args=3): substr(s,m,n) gives substring of s from 0-up position m to n
    inclusive. Negative indices -len .. -1 alias to 0 .. len-1.
## systime
    systime (class=time #args=0): Floating-point seconds since the epoch,
    e.g. 1440768801.748936.
## tan
    tan (class=math #args=1): Trigonometric tangent.
## tanh
    tanh (class=math #args=1): Hyperbolic tangent.
## tolower
    tolower (class=string #args=1): Convert string to lowercase.
## toupper
    toupper (class=string #args=1): Convert string to uppercase.
## typeof
    typeof (class=conversion #args=1): Convert argument to type of argument (e.g.
    MT_STRING). For debug.
## urand
    urand (class=math #args=0): Floating-point numbers on the unit interval.
    Int-valued example: '$n=floor(20+urand()*11)'.
## urand32
    urand32 (class=math #args=0): Integer uniformly distributed 0 and 2**32-1
    inclusive.
## urandint
    urandint (class=math #args=2): Integer uniformly distributed between inclusive
    integer endpoints.
# User-defined functions and subroutines As of Miller 5.0.0 you can define your own functions, as well as subroutines. ## User-defined functions Here’s the obligatory example of a recursive function to compute the factorial function:
    $ mlr --opprint --from data/small put '
        func f(n) {
            if (is_numeric(n)) {
                if (n > 0) {
                    return n * f(n-1);
                } else {
                    return 1;
                }
            }
            # implicitly return absent-null if non-numeric
        }
        $ox = f($x + NR);
        $oi = f($i);
    '
    a   b   i x                   y                   ox         oi
    pan pan 1 0.3467901443380824  0.7268028627434533  0.467054   1
    eks pan 2 0.7586799647899636  0.5221511083334797  3.680838   2
    wye wye 3 0.20460330576630303 0.33831852551664776 1.741251   6
    eks wye 4 0.38139939387114097 0.13418874328430463 18.588349  24
    wye pan 5 0.5732889198020006  0.8636244699032729  211.387310 120
Properties of user-defined functions: Function bodies start with `func` and a parameter list, defined outside of `begin`, `end`, or other `func` or `subr` blocks. (I.e. the Miller DSL has no nested functions.) A function (uniqified by its name) may not be redefined: either by redefining a user-defined function, or by redefining a built-in function. However, functions and subroutines have separate namespaces: you can define a subroutine `log` which does not clash with the mathematical `log` function. Functions may be defined either before or after use (there is an object-binding/linkage step at startup). More specifically, functions may be either recursive or mutually recursive. Functions may not call subroutines. Functions may be defined and called either within `mlr put` or `mlr put`. Functions have read access to `$`-variables and `@`-variables but may not modify them. See also this cookbook item for an example. Argument values may be reassigned: they are not read-only. When a return value is not implicitly returned, this results in a return value of absent-null. (In the example above, if there were records for which the argument to `f` is non-numeric, the assignments would be skipped.) See also the section on empty_and_absent null data. See the section on local variables for information on scope and extent of arguments, as well as for information on the use of local variables within functions. See the section on expressions from files for information on the use of `-f` and `-e` flags. ## User-defined subroutines Example:
    $ mlr --opprint --from data/small put -q '
      begin {
        @call_count = 0;
      }
      subr s(n) {
        @call_count += 1;
        if (is_numeric(n)) {
          if (n > 1) {
            call s(n-1);
          } else {
            print "numcalls=" . @call_count;
          }
        }
      }
      print "NR=" . NR;
      call s(NR);
    '
    NR=1
    numcalls=1
    NR=2
    numcalls=3
    NR=3
    numcalls=6
    NR=4
    numcalls=10
    NR=5
    numcalls=15
Properties of user-defined subroutines: Subroutine bodies start with `subr` and a parameter list, defined outside of `begin`, `end`, or other `func` or `subr` blocks. (I.e. the Miller DSL has no nested subroutines.) A subroutine (uniqified by its name) may not be redefined. However, functions and subroutines have separate namespaces: you can define a subroutine `log` which does not clash with the mathematical `log` function. Subroutines may be defined either before or after use (there is an object-binding/linkage step at startup). More specifically, subroutines may be either recursive or mutually recursive. Subroutines may call functions. Subroutines may be defined and called either within `mlr put` or `mlr put`. Subroutines have read/write access to `$`-variables and `@`-variables. Argument values may be reassigned: they are not read-only. See the section on local variables for information on scope and extent of arguments, as well as for information on the use of local variables within functions. See the section on expressions from files for information on the use of `-f` and `-e` flags. # Errors and transparency As soon as you have a programming language, you start having the problem What is my code doing, and why? This includes getting syntax errors — which are always annoying — as well as the even more annoying problem of a program which parses without syntax error but doesn’t do what you expect. The `syntax error` message is cryptic: it says `syntax error at` followed by the next symbol it couldn’t parse. This is good, but (as of 5.0.0) it doesn’t say things like `syntax error at line 17, character 22`. Here are some common causes of syntax errors: Don’t forget `;` at end of line, before another statement on the next line. Miller’s DSL lacks the `++` and `--` operators. Curly braces are required for the bodies of `if`/`while`/`for` blocks, even when the body is a single statement. Now for transparency: As in any language, you can do `print` (or `eprint` to print to stderr). See also `dump` and `emit`. The `-v` option to `mlr put` and `mlr filter` prints abstract syntax trees for your code. While not all details here will be of interest to everyone, certainly this makes questions such as operator precedence completely unambiguous. The `-T` option prints a trace of each statement executed. The `-t` and `-a` options show low-level details for the parsing process and for stack-variable-index allocation, respectively. These will likely be of interest to people who enjoy compilers, and probably less useful for a more general audience. Please see the type-checking section for type declarations and type-assertions you can use to make sure expressions and the data flowing them are evaluating as you expect. I made them optional because one of Miller’s important use-cases is being able to say simple things like `mlr put '$y = $x + 1' myfile.dat` with a minimum of punctuational bric-a-brac — but for programs over a few lines I generally find that the more type-specification, the better. # A note on the complexity of Miller’s expression language One of Miller’s strengths is its brevity: it’s much quicker — and less error-prone — to type `mlr stats1 -a sum -f x,y -g a,b` than having to track summation variables as in `awk`, or using Miller’s out-of-stream variables. And the more language features Miller’s put-DSL has (for-loops, if-statements, nested control structures, user-defined functions, etc.) then the less powerful it begins to seem: because of the other programming-language features it doesn’t have (classes, execptions, and so on). When I was originally prototyping Miller in 2015, the decision I had was whether to hand-code in a low-level language like C or Rust, with my own hand-rolled DSL, or whether to use a higher-level language (like Python or Lua or Nim) and let the `put` statements be handled by the implementation language’s own `eval`: the implementation language would take the place of a DSL. Multiple performance experiments showed me I could get better throughput using the former, and using C in particular — by a wide margin. So Miller is C under the hood with a hand-rolled DSL. I do want to keep focusing on what Miller is good at — concise notation, low latency, and high throughput — and not add too much in terms of high-level-language features to the DSL. That said, some sort of customizability is a basic thing to want. As of 4.1.0 we have recursive for/while/if structures on about the same complexity level as `awk`; as of 5.0.0 we have user-defined functions and map-valued variables, again on about the same complexity level as `awk` along with optional type-declaration syntax. While I’m excited by these powerful language features, I hope to keep new features beyond 5.0.0 focused on Miller’s sweet spot which is speed plus simplicity.