tagsm5(USP)

Name

tagsm5 : Output the grand total

Synopsis

Usage   : tagsm5 <d1> <d2> [<s1> <s2>] <file>

          tagsm5 [dummy=<dummy>] [val=<val>] <file>

Option  : -d<string>

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

Edition : 1

Description

This tool adds a grand total line (total of all lines) to <file>.

The range of fields from tag <d1> to tag <d2> are dummy fields, and

every field in the range from tag <s1> to <f2> are summed fields. A

record is added at the end of the list containing the grand totals

of all summed fields. The dummy fields in the grand total record are

padded with "@".  When a file that has subtotal lines from the

tagsm4 command is processed, the subtotal lines that were added with

tagsm4 are ignored when calculating the grand total.

<dummy> and <val> 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.

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.

    {TAGa}/{TAGb}

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 a grand total

$ tagsm5 K1 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

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

@@            @@ @@          @@ 894 687 485 606 514

Example 2

When processing data that has subtotal lines added by "tagsm4".

$ fcols data2

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

$ tagsm5 K1 N2 V1 V5 data2 | 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

@@            @@ @@          @@ 894 687 485 606 514

Example 3

-d<string> option

$ fcols data3

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 a grand total

$ tagsm5 -d+++ K1 N2 V1 V5 data3 | 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

 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

+++           +++ +++          +++ 894 687 485 606 514

Note

Using the "tagsm" tools together:

When writing a report that summarizes data in the shell, a very

common pattern is to sub-subtotal and subtotal based on keys, and

than at the end create a grand total line.  In this case, you can

use "tagsm2", "tagsm4" and "tagsm5" in stages.

cat data                     |

tagsm2 K1       K6 V1 V5      |        <- Total based on key

tagsm4 K1 K4 K5 K6 V1 V5      |        <- Sub-subtotal

tagsm4 K1 K2 K3 K6 V1 V5      |        <- Subtotal

tagsm5 K1       K6 V1 V5      > result <- Grand total