Computes an checksum from columns
Arguments
- .data
(
data.frame(1),tibble(1),data.table(1), ortbl_dbi(1))
Data object.- col
(
character(1))
Name of the column to put the checksums in. Will be generated if missing.- exclude
(
character())
Columns to exclude from the checksum generation.
Details
In most cases, the md5 algorithm is used to compute the checksums. For Microsoft SQL Server, the SHA-256 algorithm is used.
Examples
digest_to_checksum(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> checksum
#> Mazda RX4 cc15ed979f975a3146e36b47b2ab0ebf
#> Mazda RX4 Wag 9738c8ae1037fa9f686c4de092f2cb4b
#> Datsun 710 01524a6ad903dccc72597f9dec5b925f
#> Hornet 4 Drive 096336924b8ef0abb69ce48fa8e967b7
#> Hornet Sportabout 156d66b5a25c6432856113300fafb9e1
#> Valiant 07b710a4eb565552a9368910b582a9ca
#> Duster 360 1855f1a3577b214fc997ee8f8d6fa4b0
#> Merc 240D 476a317a1bfc11bc14cb733b0c7ec648
#> Merc 230 f6801ecb87a9ac46e52167436073ff7a
#> Merc 280 d0d16567f6feb2749fbd851cf5933611
#> Merc 280C 96e1ac29420f4b2bdf0d44d213523f3a
#> Merc 450SE 0d6f19a3b4e6d345856e25721541d50e
#> Merc 450SL 8a22bd4e61cede746156a197c9167b35
#> Merc 450SLC 7cde159b5d54d8456510287d592e177c
#> Cadillac Fleetwood c7c23741b3b507260200a7f0dd460c9c
#> Lincoln Continental 8ca072e4dead5168faf744a64158a7fc
#> Chrysler Imperial f51de5b8dcb7c9cb42d241a5a88aa627
#> Fiat 128 438ba093f9a6b70c6e299f441e6b1a78
#> Honda Civic f290c1c19224018b951fae74e54aeae9
#> Toyota Corolla 2c7a947184089c1e417840ddd01f4d76
#> Toyota Corona 37fc848c004b4e1108097213cf3254d9
#> Dodge Challenger 766486bd531e78bff5cda183321b38b1
#> AMC Javelin 2b3d01c07a9797c8eda0e41d730e9cf1
#> Camaro Z28 20e47bc65cfbfac2f39e76b8317228c0
#> Pontiac Firebird 6a823af356328cddf9113d76d0f29354
#> Fiat X1-9 3f0d3eb97097b28139b69c744ffd74b7
#> Porsche 914-2 403acdf33454004fad2aba60d863e273
#> Lotus Europa 0f5e497657ffd117f2354d39c4164ecd
#> Ford Pantera L 7232cf3cf178c45559cf020a7c8bdfb7
#> Ferrari Dino 96739c21cfe5d9939591c7c257a6fe77
#> Maserati Bora 6f492f2bc98fddc742d8b1f277861980
#> Volvo 142E f390bc2b5608f4d23ad34b39d0b1d636