COMMAND（tagsm4）

tagsm4(USP)

Name

tagsm4 : Inserts subtotal lines.

Synopsis

Usage : tagsm4 <k1> <k2> <d1> <d2> [<s1> <s2>] <file>

tagsm4 key=<key> [dummy=<dummy>] [val=<val>] <file>

Options : -d<string>

-e

-s<c>

Version : Tue Jan 9 09:02:34 JST 2024

Edition : 1

Description

Inserts a "total line" for records in <file> that have the same key.

Tags from <k1> to <k2> and are key fields while tags from <d1> to

<d2> are dummy fields (they are not keys nor are they summed).

Tags from <s1> to <s2> are the summed fields. These fields are

summed for rows with the same key field and a "total line" is

inserted after the last line with the same key. If you do not

specify <s1> and <s2> then all fields starting with the field

following field <d2> are summed.

<key> designates the field position as fllows:

single field TAGa TAGa field

contiguous fields TAGa/TAGb from TAGa field to TAGb field

combination TAGa@TAGb TAGa field and TAGb field

There is no limit on the length of the key field or on the number

of key fields. The key field can also contain multi-byte characters

such as Japanese.

If you specify ":r" as comparison method after the field position,

the fields are compared in reverse order. If you specify ":n" as

comparison method after the field position, that field's values

will be compared as numbers. If you specify ":nr" as comparison

method after the field, the values will be compared in reverse order

as numbers. If you specify comparison method before or after the "/",

you must use the same comparison method for both fields.

TAGa:n/TAGb:n OK

TAGa:n/TAGb:nr Error

TAGa:n/TAGb:r Error

When you specify ":e" as comparison method or specify -e ootion and

no method, characters in the field are replaced as follows and

compared as string:

_ ==> 0x20 (space)

\0 ==> 0x00 (null)

\t ==> 0x09 (tab stop)

\n ==> 0x0a (new line)

\r ==> 0x0d (carrige return)

\_ ==> 0x5f (underscore)

\\ ==> 0x5c (back slash)

The tag name specfying the field can be eclosed by braces {}. In

this case, tag names can include special charcter like "/" or "@".

Moreover, tag names can include pairs of braces. When comparison

method is attached to brace enclosed tag name, ":" should be ommited.

{TAGa}n/{TAGb}n

<val> and <dummy> is same as <key> except no comparison method is

allowed.

"@" characters are inserted in the dummy fields of the total row.

If you use tagsm4 repeatedly to insert subtotals and sub-subtotals

then any record where there is at least one field equal to dummy

value "@" will be excluded.

The "-d<string>" option changes the dummy string as <string>. In

this case, the dummy string is inserted once for each dummy field

regardless the dummy field width. Records which have a dummy field

equivalent to <string> is treated as total row.

Example 1

Survey Data from Various Cities

$ fcols data

K1 N1 K2 N2 V1 V2 V3 V4 V5

01 Massachusetts 01 Boston 91 59 20 76 54

01 Massachusetts 02 Worcester 46 39 8 5 21

01 Massachusetts 03 Springfield 82 0 23 84 10

02 New_York 04 Manhattan 30 50 71 36 30

02 New_York 05 Brooklyn 78 13 44 28 51

02 New_York 06 Queens 58 71 20 10 6

02 New_York 07 Albany 82 79 16 21 80

02 New_York 08 Buffalo 50 2 33 15 62

03 New_Jersey 09 Newark 52 91 44 9 0

03 New_Jersey 10 Trenton 60 89 33 18 6

03 New_Jersey 11 Moorestown 95 60 35 93 76

04 Pennsylvania 12 Philadelphia 92 56 83 96 75

04 Pennsylvania 13 Pittsburgh 30 12 32 44 19

04 Pennsylvania 14 Lancaster 48 66 23 71 24

Output subtotals by state:

$ tagsm4 K1 N1 K2 N2 V1 V5 data | fcols

K1 N1 K2 N2 V1 V2 V3 V4 V5

01 Massachusetts 01 Boston 91 59 20 76 54

01 Massachusetts 02 Worcester 46 39 8 5 21

01 Massachusetts 03 Springfield 82 0 23 84 10

01 Massachusetts @@ @@ 219 98 51 165 85

02 New_York 04 Manhattan 30 50 71 36 30

02 New_York 05 Brooklyn 78 13 44 28 51

02 New_York 06 Queens 58 71 20 10 6

02 New_York 07 Albany 82 79 16 21 80

02 New_York 08 Buffalo 50 2 33 15 62

02 New_York @@ @@ 298 215 184 110 229

03 New_Jersey 09 Newark 52 91 44 9 0

03 New_Jersey 10 Trenton 60 89 33 18 6

03 New_Jersey 11 Moorestown 95 60 35 93 76

03 New_Jersey @@ @@ 207 240 112 120 82

04 Pennsylvania 12 Philadelphia 92 56 83 96 75

04 Pennsylvania 13 Pittsburgh 30 12 32 44 19

04 Pennsylvania 14 Lancaster 48 66 23 71 24

04 Pennsylvania @@ @@ 170 134 138 211 118

Example 2

Output subtotals and sub-subtotals.

$ fcols data2

K1 N1 K2 N2 K3 N3 V1 V2 V3 V4 V5

01 Northeast 01 Massachusetts 01 Boston 91 59 20 76 54

01 Northeast 01 Massachusetts 03 Springfield 82 0 23 84 10

01 Northeast 02 New_York 04 Manhattan 30 50 71 36 30

01 Northeast 02 New_York 05 Brooklyn 78 13 44 28 51

01 Northeast 02 New_York 07 Albany 82 79 16 21 80

02 Mid-Atlantic 01 New_Jersey 01 Trenton 91 59 20 76 54

02 Mid-Atlantic 01 New_Jersey 02 Newark 46 39 8 5 21

02 Mid-Atlantic 01 New_Jersey 03 Moorestown 82 0 23 84 10

02 Mid-Atlantic 02 Virginia 04 Arlington 30 50 71 36 30

02 Mid-Atlantic 02 Virginia 05 Reston 78 13 44 28 51

02 Mid-Atlantic 02 Virginia 06 Langley 58 71 20 10 6

02 Mid-Atlantic 02 Virginia 07 Richmond 82 79 16 21 80

02 Mid-Atlantic 02 Virginia 08 Norfolk 50 2 33 15 62

02 Mid-Atlantic 03 Delaware 09 Dover 52 91 44 9 0

02 Mid-Atlantic 03 Delaware 10 Rehobeth 60 89 33 18 6

$ tagsm4 K1 N2 K3 N3 V1 V5 data2 | tagsm4 K1 N1 K2 N3 V1 V5 | fcols

K1 N1 K2 N2 K3 N3 V1 V2 V3 V4 V5

01 Northeast 01 Massachusetts 01 Boston 91 59 20 76 54

01 Northeast 01 Massachusetts 03 Springfield 82 0 23 84 10

01 Northeast 01 Massachusetts @@ @@ 173 59 43 160 64

01 Northeast 02 New_York 04 Manhattan 30 50 71 36 30

01 Northeast 02 New_York 05 Brooklyn 78 13 44 28 51

01 Northeast 02 New_York 07 Albany 82 79 16 21 80

01 Northeast 02 New_York @@ @@ 190 142 131 85 161

01 Northeast @@ @@ @@ @@ 363 201 174 245 225

02 Mid-Atlantic 01 New_Jersey 01 Trenton 91 59 20 76 54

02 Mid-Atlantic 01 New_Jersey 02 Newark 46 39 8 5 21

02 Mid-Atlantic 01 New_Jersey 03 Moorestown 82 0 23 84 10

02 Mid-Atlantic 01 New_Jersey @@ @@ 219 98 51 165 85

02 Mid-Atlantic 02 Virginia 04 Arlington 30 50 71 36 30

02 Mid-Atlantic 02 Virginia 05 Reston 78 13 44 28 51

02 Mid-Atlantic 02 Virginia 06 Langley 58 71 20 10 6

02 Mid-Atlantic 02 Virginia 07 Richmond 82 79 16 21 80

02 Mid-Atlantic 02 Virginia 08 Norfolk 50 2 33 15 62

02 Mid-Atlantic 02 Virginia @@ @@ 298 215 184 110 229

02 Mid-Atlantic 03 Delaware 09 Dover 52 91 44 9 0

02 Mid-Atlantic 03 Delaware 10 Rehobeth 60 89 33 18 6

02 Mid-Atlantic 03 Delaware @@ @@ 112 180 77 27 6

02 Mid-Atlantic @@ @@ @@ @@ 629 493 312 302 320

Example 3

Special Case (No Dummy Field)

$ fcols data3

N1 V1 V2 V3 V4 V5

Massachusetts 91 59 20 76 54

Massachusetts 46 39 8 5 21

Massachusetts 82 0 23 84 10

New_York 30 50 71 36 30

New_York 78 13 44 28 51

New_York 58 71 20 10 6

New_York 82 79 16 21 80

New_York 50 2 33 15 62

New_Jersey 52 91 44 9 0

New_Jersey 60 89 33 18 6

New_Jersey 95 60 35 93 76

Pennsylvania 92 56 83 96 75

Pennsylvania 30 12 32 44 19

Pennsylvania 48 66 23 71 24

$ tagsm4 N1 N1 - - V1 V5 data3 | fcols

N1 V1 V2 V3 V4 V5

Massachusetts 91 59 20 76 54

Massachusetts 46 39 8 5 21

Massachusetts 82 0 23 84 10

@@ 219 98 51 165 85

New_York 30 50 71 36 30

New_York 78 13 44 28 51

New_York 58 71 20 10 6

New_York 82 79 16 21 80

New_York 50 2 33 15 62

@@ 298 215 184 110 229

New_Jersey 52 91 44 9 0

New_Jersey 60 89 33 18 6

New_Jersey 95 60 35 93 76

@@ 207 240 112 120 82

Pennsylvania 92 56 83 96 75

Pennsylvania 30 12 32 44 19

Pennsylvania 48 66 23 71 24

@@ 170 134 138 211 118

Example 4

-d<string> option

$ fcols data4

K1 N1 K2 N2 V1 V2 V3 V4 V5

01 Massachusetts 01 Boston 91 59 20 76 54

01 Massachusetts 02 Worcester 46 39 8 5 21

01 Massachusetts 03 Springfield 82 0 23 84 10

02 New_York 04 Manhattan 30 50 71 36 30

02 New_York 05 Brooklyn 78 13 44 28 51

02 New_York 06 Queens 58 71 20 10 6

02 New_York 07 Albany 82 79 16 21 80

02 New_York 08 Buffalo 50 2 33 15 62

03 New_Jersey 09 Newark 52 91 44 9 0

03 New_Jersey 10 Trenton 60 89 33 18 6

03 New_Jersey 11 Moorestown 95 60 35 93 76

04 Pennsylvania 12 Philadelphia 92 56 83 96 75

04 Pennsylvania 13 Pittsburgh 30 12 32 44 19

04 Pennsylvania 14 Lancaster 48 66 23 71 24

Output subtotals by state:

$ tagsm4 -d+++ K1 N1 K2 N2 V1 V5 data4 | fcols

K1 N1 K2 N2 V1 V2 V3 V4 V5

01 Massachusetts 01 Boston 91 59 20 76 54

01 Massachusetts 02 Worcester 46 39 8 5 21

01 Massachusetts 03 Springfield 82 0 23 84 10

01 Massachusetts +++ +++ 219 98 51 165 85

02 New_York 04 Manhattan 30 50 71 36 30

02 New_York 05 Brooklyn 78 13 44 28 51

02 New_York 06 Queens 58 71 20 10 6

02 New_York 07 Albany 82 79 16 21 80

02 New_York 08 Buffalo 50 2 33 15 62

02 New_York +++ +++ 298 215 184 110 229

03 New_Jersey 09 Newark 52 91 44 9 0

03 New_Jersey 10 Trenton 60 89 33 18 6

03 New_Jersey 11 Moorestown 95 60 35 93 76

03 New_Jersey +++ +++ 207 240 112 120 82

04 Pennsylvania 12 Philadelphia 92 56 83 96 75

04 Pennsylvania 13 Pittsburgh 30 12 32 44 19

04 Pennsylvania 14 Lancaster 48 66 23 71 24

04 Pennsylvania +++ +++ 170 134 138 211 118