Carbohydrates (calculated by difference) Calculator
Source:R/CHOAVLDFg_calculator.R
CHOAVLDFg_calculator.RdCalculates CHOAVLDFg_calculated = (100 - (WATERg + PROTg + FATg_combined + FBGTg + ASHg + ALCg)). Column names are case sensitive and an error is returned if not found. Non-numeric columns will be converted if possible in function to be numeric.
Usage
CHOAVLDFg_calculator(
df,
WATERg_column = "WATERg",
PROCNTg_column = "PROCNTg",
FAT_g_combined_column = "FAT_g_combined",
FIBTGg_combined_column = "FIBTGg_combined",
ALCg_column = "ALCg",
ASHg_column = "ASHg",
comment = TRUE,
comment_col = "comments",
NegativeValueReplacement = 0,
NegativeValueDF = FALSE
)Arguments
- df
Required - the data.frame the data is currently stored in.
- WATERg_column
Required - default:
'WATERg'- The name of the column containing Water/moisture content in grams per 100g of Edible Portion (EP).- PROCNTg_column
Required - default:
'PROCNTg'- Protein in grams per 100g of Edible Portion (EP), as reported in the original FCT and assumed to be calculated from nitrogen (NTg) content.- FAT_g_combined_column
Required - default:
'FAT_g_combined'- Fat content, unknown method of calculation, in grams per 100g of Edible Portion (EP).- FIBTGg_combined_column
Required - default:
'FIBTGg_combined'- Fibre content from combined Tagnames, with preference of Total dietary fibre by AOAC Prosky method, expressed in grams per 100g of Edible Portion (EP).- ALCg_column
Required - default:
'ALCg'- Alcohol in grams per 100g of Edible Portion (EP).- ASHg_column
Required - default:
'ASHg'- Ashes in grams per 100g of Edible Portion (EP).- comment
Required - default:
TRUE-TRUEorFALSE. Ifcommentis set toTRUE(as it is by default), when the function is run a comment describing the source of theCHOAVLDFg_calculatedcolumn is added to thecomment_colIf nocomment_colis selected, andcomment = TRUE, one is created.- comment_col
Optional - default:
'comments'- A potential input variable; the column which contains the metadata comments for the food item in question. Not required ifcommentis set toFALSE. Ifcommentis set toTRUE, and thecomment_colinput is not the name of a column found in thedf, the function will create a column with the name of thecomment_colinput to store comments in.- NegativeValueReplacement
Required - default:
0- Options are0,NA,'remove', or'nothing'. Choose what happens to negative values. If set to0, then negative values are set to 0. If set toNA, they are replaced with NA. if set to'remove', then those entries in thedfare removed. if set to'nothing', then they are left as negative values.- NegativeValueDF
Required - default:
FALSE-TRUEorFALSE. If set toTRUE, Then the output switches from being a copy of the inputdfwith the theCHOAVLDFg_calculatedcolumn to a subset of that dataframe only showingCHOAVLDFg_calculatedvalues that are less than 0, for manual inspection.
Value
Original data.frame with a new CHOAVLDFg_calculated column, and
(depending on the options selected) an additional comment/comments column
and comment.
Examples
# Two example data.frames have been prepared to illustrate the
# CHOAVLDFg_calculator The first is a dataset of fictional food values to
# illustrate the various options in the function, and the second is a dataset
# with non-standard column names, to show how to specify columns.
# This is the first data.frame - before the CHOAVLDFg_calculator has been used on it.
breakfast_df <- breakfast_df[,c("food_code", "food_name", "WATERg",
"PROCNTg", "FAT_g_combined", "FIBTGg_combined", "ALCg", "ASHg",
"comments")]
breakfast_df
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg
#> 1 F0001 Bacon 10 15 21 12 12
#> 2 F0002 Beans 15 10 12 3 43
#> 3 F0003 Toast 20 20 NA 8 8
#> 4 F0004 Mushroom 25 15 16 15 15
#> 5 F0005 Eggs 30 21 11 6 6
#> 6 F0006 Tomato 35 28 33 2 2
#> 7 F0007 Sausage 40 10 13 9 9
#> 8 F0008 Butter NA 27 16 13 13
#> 9 F0009 Brown Sauce NA NA NA NA NA
#> 10 F0010 Tomato Ketchup NA NA NA NA NA
#> ASHg comments
#> 1 12
#> 2 3 These are imaginary food items
#> 3 28 <NA>
#> 4 15 With imaginary nutrient values
#> 5 6
#> 6 2 And blanks
#> 7 9 <NA>
#> 8 13 To test different outputs
#> 9 NA
#> 10 NA And scenarios
#
#
# First, an example of the standard usecase - calculate the CHOAVLDFg_calculated
# value, while resetting negative values to 0.
NegativeToZeroResults <- CHOAVLDFg_calculator(breakfast_df, NegativeValueReplacement = 0)
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative values set to 0, as per user input.
#>
#> ---------------------------
#
NegativeToZeroResults
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg
#> 1 F0001 Bacon 10 15 21 12 12
#> 2 F0002 Beans 15 10 12 3 43
#> 3 F0003 Toast 20 20 NA 8 8
#> 4 F0004 Mushroom 25 15 16 15 15
#> 5 F0005 Eggs 30 21 11 6 6
#> 6 F0006 Tomato 35 28 33 2 2
#> 7 F0007 Sausage 40 10 13 9 9
#> 8 F0008 Butter NA 27 16 13 13
#> 9 F0009 Brown Sauce NA NA NA NA NA
#> 10 F0010 Tomato Ketchup NA NA NA NA NA
#> ASHg
#> 1 12
#> 2 3
#> 3 28
#> 4 15
#> 5 6
#> 6 2
#> 7 9
#> 8 13
#> 9 NA
#> 10 NA
#> comments
#> 1 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 2 These are imaginary food items; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 3 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 4 With imaginary nutrient values; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -1 reset to 0
#> 5 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 6 And blanks; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -2 reset to 0
#> 7 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 8 To test different outputs; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 9
#> 10 And scenarios
#> CHOAVLDFg_calculated
#> 1 18
#> 2 14
#> 3 16
#> 4 0
#> 5 20
#> 6 0
#> 7 10
#> 8 18
#> 9 NA
#> 10 NA
# See the changes - the addition of the CHOAVLDFg_calculated column, and
# the additions to the comments column. Notice how some comments are
# specially formulated to show if the corresponding CHOAVLDFg_calculated
# value has been reset to 0.
#
#
# The second example shows the results when the Replacement option is set to NA
NegativeToNA_results <- CHOAVLDFg_calculator(breakfast_df, NegativeValueReplacement = NA)
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative values set to NA, as per user input.
#>
#> ---------------------------
#
NegativeToNA_results
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg
#> 1 F0001 Bacon 10 15 21 12 12
#> 2 F0002 Beans 15 10 12 3 43
#> 3 F0003 Toast 20 20 NA 8 8
#> 4 F0004 Mushroom 25 15 16 15 15
#> 5 F0005 Eggs 30 21 11 6 6
#> 6 F0006 Tomato 35 28 33 2 2
#> 7 F0007 Sausage 40 10 13 9 9
#> 8 F0008 Butter NA 27 16 13 13
#> 9 F0009 Brown Sauce NA NA NA NA NA
#> 10 F0010 Tomato Ketchup NA NA NA NA NA
#> ASHg
#> 1 12
#> 2 3
#> 3 28
#> 4 15
#> 5 6
#> 6 2
#> 7 9
#> 8 13
#> 9 NA
#> 10 NA
#> comments
#> 1 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 2 These are imaginary food items; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 3 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 4 With imaginary nutrient values; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -1 reset to NA
#> 5 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 6 And blanks; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -2 reset to NA
#> 7 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 8 To test different outputs; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 9
#> 10 And scenarios
#> CHOAVLDFg_calculated
#> 1 18
#> 2 14
#> 3 16
#> 4 NA
#> 5 20
#> 6 NA
#> 7 10
#> 8 18
#> 9 NA
#> 10 NA
# Check the CHOAVLDFg_calculated column and comments column again - see how
# values below zero have been replaced with NA, and a note of this change
# logged in the comments column.
#
#
# The third example shows the results when the Replacement option is set to 'remove'
remove_results <- CHOAVLDFg_calculator(breakfast_df, NegativeValueReplacement = "remove")
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative value rows removed, as per user input.
#>
#> ---------------------------
#
remove_results
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg ASHg
#> 1 F0001 Bacon 10 15 21 12 12 12
#> 2 F0002 Beans 15 10 12 3 43 3
#> 3 F0003 Toast 20 20 NA 8 8 28
#> 5 F0005 Eggs 30 21 11 6 6 6
#> 7 F0007 Sausage 40 10 13 9 9 9
#> 8 F0008 Butter NA 27 16 13 13 13
#> comments
#> 1 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 2 These are imaginary food items; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 3 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 5 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 7 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 8 To test different outputs; CHOAVLDFg_calculated calculated from 100-[constituents]
#> CHOAVLDFg_calculated
#> 1 18
#> 2 14
#> 3 16
#> 5 20
#> 7 10
#> 8 18
# See how the out of bounds values have been removed.
#
#
# The fourth example is of the nothing results - i.e. nothing happens to the
# negative values.
NegativeNoChangeResults <- CHOAVLDFg_calculator(breakfast_df, NegativeValueReplacement = "nothing")
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative values left untouched, as per user input.
#>
#> ---------------------------
#
NegativeNoChangeResults
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg
#> 1 F0001 Bacon 10 15 21 12 12
#> 2 F0002 Beans 15 10 12 3 43
#> 3 F0003 Toast 20 20 NA 8 8
#> 4 F0004 Mushroom 25 15 16 15 15
#> 5 F0005 Eggs 30 21 11 6 6
#> 6 F0006 Tomato 35 28 33 2 2
#> 7 F0007 Sausage 40 10 13 9 9
#> 8 F0008 Butter NA 27 16 13 13
#> 9 F0009 Brown Sauce NA NA NA NA NA
#> 10 F0010 Tomato Ketchup NA NA NA NA NA
#> ASHg
#> 1 12
#> 2 3
#> 3 28
#> 4 15
#> 5 6
#> 6 2
#> 7 9
#> 8 13
#> 9 NA
#> 10 NA
#> comments
#> 1 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 2 These are imaginary food items; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 3 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 4 With imaginary nutrient values; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 5 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 6 And blanks; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 7 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 8 To test different outputs; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 9 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 10 And scenarios
#> CHOAVLDFg_calculated
#> 1 18
#> 2 14
#> 3 16
#> 4 -1
#> 5 20
#> 6 -2
#> 7 10
#> 8 18
#> 9 NA
#> 10 NA
# Look at the CHOAVLDFg_calculator values - and see how they've been left,
# even if they're negative.
#
#
# The fifth example is of the negative dataframe - an option useful for identifying
# and examining negative results.
NegativeCarb_results <- CHOAVLDFg_calculator(breakfast_df, NegativeValueDF = TRUE)
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative values set to 0, as per user input.
#>
#> ---------------------------
#
NegativeCarb_results
#> food_code food_name WATERg PROCNTg FAT_g_combined FIBTGg_combined ALCg
#> 4 F0004 Mushroom 25 15 16 15 15
#> 6 F0006 Tomato 35 28 33 2 2
#> NA <NA> <NA> NA NA NA NA NA
#> NA.1 <NA> <NA> NA NA NA NA NA
#> ASHg comments CHOAVLDFg_calculated
#> 4 15 With imaginary nutrient values -1
#> 6 2 And blanks -2
#> NA NA <NA> NA
#> NA.1 NA <NA> NA
# Only the out of bounds results are present, in their original form, for inspection.
#
#
# The sixth example is of the CHOAVLDFg_calculator working on a dataframe with
# non-standard column names. It uses a modified example data frame, shown below.
#
breakfast_df_nonstandard <- breakfast_df_nonstandard[,c("food_code",
"food_name", "Water_values_g", "PROCNT_values_g", "FAT_values_g_combined",
"FIBTG_values_g_combined", "ALC_values_g", "ASH_values_g", "comments_column")]
breakfast_df_nonstandard
#> food_code food_name Water_values_g PROCNT_values_g
#> 1 F0001 Bacon 10 15
#> 2 F0002 Beans 15 10
#> 3 F0003 Toast 20 20
#> 4 F0004 Mushroom 25 15
#> 5 F0005 Eggs 30 21
#> 6 F0006 Tomato 35 28
#> 7 F0007 Sausage 40 10
#> 8 F0008 Butter NA 27
#> 9 F0009 Brown Sauce NA NA
#> 10 F0010 Tomato Ketchup NA NA
#> FAT_values_g_combined FIBTG_values_g_combined ALC_values_g ASH_values_g
#> 1 21 12 12 12
#> 2 12 3 43 3
#> 3 NA 8 8 28
#> 4 16 15 15 15
#> 5 11 6 6 6
#> 6 33 2 2 2
#> 7 13 9 9 9
#> 8 16 13 13 13
#> 9 NA NA NA NA
#> 10 NA NA NA NA
#> comments_column
#> 1
#> 2 These are imaginary food items
#> 3 <NA>
#> 4 With imaginary nutrient values
#> 5
#> 6 And blanks
#> 7 <NA>
#> 8 To test different outputs
#> 9
#> 10 And scenarios
# Notice how the column names are different, and differ from the assumed names.
#
#
# Because of the different names, the column names for each input must be specified.
nothing_results_NonStandardInput <- CHOAVLDFg_calculator(
breakfast_df_nonstandard,
WATERg_column = "Water_values_g",
PROCNTg_column = "PROCNT_values_g",
FAT_g_combined_column = "FAT_values_g_combined",
FIBTGg_combined_column = "FIBTG_values_g_combined",
ALCg_column = "ALC_values_g",
ASHg_column = "ASH_values_g",
comment_col = "comments_column"
)
#> ---------------------------
#>
#> 4 CHOAVLDFg_calculated values calculated to be less than 0. Minimum result: -2. Number of values below -5: 0. Number of NA results: 2. Please rerun the function with NegativeValueDF = TRUE if you wish to inspect these values.
#>
#> Negative values set to 0, as per user input.
#>
#> ---------------------------
nothing_results_NonStandardInput
#> food_code food_name Water_values_g PROCNT_values_g
#> 1 F0001 Bacon 10 15
#> 2 F0002 Beans 15 10
#> 3 F0003 Toast 20 20
#> 4 F0004 Mushroom 25 15
#> 5 F0005 Eggs 30 21
#> 6 F0006 Tomato 35 28
#> 7 F0007 Sausage 40 10
#> 8 F0008 Butter NA 27
#> 9 F0009 Brown Sauce NA NA
#> 10 F0010 Tomato Ketchup NA NA
#> FAT_values_g_combined FIBTG_values_g_combined ALC_values_g ASH_values_g
#> 1 21 12 12 12
#> 2 12 3 43 3
#> 3 NA 8 8 28
#> 4 16 15 15 15
#> 5 11 6 6 6
#> 6 33 2 2 2
#> 7 13 9 9 9
#> 8 16 13 13 13
#> 9 NA NA NA NA
#> 10 NA NA NA NA
#> comments_column
#> 1 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 2 These are imaginary food items; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 3 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 4 With imaginary nutrient values; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -1 reset to 0
#> 5 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 6 And blanks; CHOAVLDFg_calculated calculated from 100-[constituents] - Original value of -2 reset to 0
#> 7 CHOAVLDFg_calculated calculated from 100-[constituents]
#> 8 To test different outputs; CHOAVLDFg_calculated calculated from 100-[constituents]
#> 9
#> 10 And scenarios
#> CHOAVLDFg_calculated
#> 1 18
#> 2 14
#> 3 16
#> 4 0
#> 5 20
#> 6 0
#> 7 10
#> 8 18
#> 9 NA
#> 10 NA
#
# The resulting CHOAVLDFg_calculated column is the same as in the first example,
# despite the different names.