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There are two data-sets embedded in meatrics: expert_graders and devices. Both datasets emulate the expected format of the input data-frame(s) for all functions.

# load human grader data
data("expert_graders")
expert_graders
## # A tibble: 17,148 × 9
## # Groups:   kill_date, body_no [5,716]
##    kill_date  body_no cold_grader fat_co…¹ meat_…² ribfa…³ aus_m…⁴ msa_m…⁵   ema
##    <date>     <chr>   <fct>       <fct>    <chr>     <dbl> <fct>     <dbl> <dbl>
##  1 2021-01-29 181     G1          2        2             3 3            NA    99
##  2 2021-01-29 182     G1          1        3            NA 2            NA    86
##  3 2021-01-29 184     G1          0        2            NA 3            NA    99
##  4 2021-01-29 185     G3          1        3            NA 3            NA    86
##  5 2021-01-29 231     G2          0        3            NA 3            NA    95
##  6 2021-01-29 233     G3          1        2            NA 2            NA    95
##  7 2021-01-29 236     G3          0        2            NA 3            NA    99
##  8 2021-01-29 238     G3          0        3            NA 2            NA    86
##  9 2021-01-29 239     G1          1        4            NA 2            NA    95
## 10 2021-01-29 240     G3          0        3            NA 3            NA    95
## # … with 17,138 more rows, and abbreviated variable names ¹​fat_colour,
## #   ²​meat_colour, ³​ribfat_cold, ⁴​aus_marbling, ⁵​msa_marbling
## # ℹ Use `print(n = ...)` to see more rows
# load device data
data("devices")
devices
## # A tibble: 5,544 × 10
## # Groups:   kill_date, body_no [608]
##    kill_date  body_no cold_grader fat_co…¹ meat_…² ribfa…³ aus_m…⁴ msa_m…⁵   ema
##    <date>     <chr>   <fct>       <fct>    <fct>     <dbl> <fct>     <dbl> <dbl>
##  1 2021-12-06 192     C1          0        4            NA 1           360    74
##  2 2021-12-06 192     C1          1        4            NA 1           370    74
##  3 2021-12-06 192     C1          0        4            NA 1           340    75
##  4 2021-12-06 194     C1          0        3            NA 1           370    65
##  5 2021-12-06 192     C3          0        4            NA 1           310    72
##  6 2021-12-06 194     C1          0        3            NA 2           410    66
##  7 2021-12-06 192     C3          1        4            NA 1           300    74
##  8 2021-12-06 194     C1          0        3            NA 1           400    67
##  9 2021-12-06 192     C3          0        4            NA 1           350    73
## 10 2021-12-06 195     C3          0        4            NA 1           300    82
## # … with 5,534 more rows, 1 more variable: image <int>, and abbreviated
## #   variable names ¹​fat_colour, ²​meat_colour, ³​ribfat_cold, ⁴​aus_marbling,
## #   ⁵​msa_marbling
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Traits should follow the naming conventions of these sample datasets as some functions may rely on these to recognize the appropriate trait. Most functions rely on pivoting the data-frame to a wide format, using to_wide() . Other data-processing functions include merge_device_grader() which outputs a merged data-frame in wide format and merge_device_grader_long() which outputs a merged data-frame in long format.