This article walks through how you can apply custom specific gravity conversion factors or adjusted volumes to your input data. Please ensure you have read and understood the Specific gravity article and the workflow of the various underlying functions before applying custom values.
Note that the examples given below show automatic calculation of the majority of the data, and then adjustment of a single row with custom values. This adjustment could be made more automated by building a loop to check if the custom column contains a non-null value, and if so applying the custom calculation. Similarly, the automated calculation could be skipped entirely and a column filled with custom values could instead be used.
These examples follow a very similar process to the examples given in the Workflow article, except the analysis and calculation has been broken into two stages: first, the specific gravity conversion is done, then the NPM scoring and assessment is done. This allows for adjustment of specific gravity values between these two steps.
Where adjusted volume has been provided
As always, we need to load in the data set and the required libraries:
# load required libraries
library(tidyr)
library(dplyr)
library(nutrientprofiler)
# read in the data
example_data <- read.csv("data/example_data.csv")This dataset has a column called custom_sg_adjusted that
is empty except for products where a final adjusted volume based on
specific gravity has been provided.
example_data
#> name brand product_category product_type food_type
#> 1 lembas NA NA Food
#> 2 zeno's icecream NA NA Food Ice cream
#> 3 mystic rush NA NA Drink
#> 4 delta ringer drink NA NA Drink
#> 5 welter water NA NA Drink
#> 6 janus's drink NA NA Food
#> 7 beta ringer drink NA NA Drink
#> 8 zeta ringer drink NA NA Drink
#> 9 heavyweight water NA NA Drink
#> 10 bantam water NA NA Drink
#> drink_format drink_type nutrition_info
#> 1
#> 2
#> 3 Ready Carbonated/juice drink
#> 4 Powdered Preparation instructions given
#> 5 Cordial As consumed
#> 6
#> 7 Powdered As consumed
#> 8 Powdered Preparation instructions not given
#> 9 Cordial Preparation instructions given
#> 10 Cordial Preparation instructions not given
#> energy_measurement_kj energy_measurement_kcal sugar_measurement_g
#> 1 266 NA 50
#> 2 NA 24 21
#> 3 NA 194 11
#> 4 188 NA 15
#> 5 NA 205 19
#> 6 NA 24 21
#> 7 188 NA 15
#> 8 188 NA 15
#> 9 NA 205 19
#> 10 NA 205 19
#> satfat_measurement_g salt_measurement_g sodium_measurement_mg
#> 1 3 NA 0.6
#> 2 11 0.08 NA
#> 3 0 NA 100.0
#> 4 0 NA 100.0
#> 5 0 0.10 NA
#> 6 11 0.08 NA
#> 7 0 NA 100.0
#> 8 0 NA 100.0
#> 9 0 0.10 NA
#> 10 0 0.10 NA
#> fibre_measurement_nsp fibre_measurement_aoac protein_measurement_g
#> 1 3 NA 7.0
#> 2 NA 0.7 3.5
#> 3 NA 0.0 0.0
#> 4 NA 0.0 0.5
#> 5 NA 0.0 0.1
#> 6 NA 0.7 3.5
#> 7 NA 0.0 0.5
#> 8 NA 0.0 0.5
#> 9 NA 0.0 0.1
#> 10 NA 0.0 0.1
#> fvn_measurement_percent weight_g volume_ml volume_water_ml
#> 1 0 100 NA NA
#> 2 0 NA 100 NA
#> 3 0 NA 100 NA
#> 4 3 25 NA 100
#> 5 6 NA 100 NA
#> 6 0 NA 100 NA
#> 7 3 NA 50 NA
#> 8 3 25 NA NA
#> 9 6 NA 20 100
#> 10 6 NA 100 NA
#> custom_sg_adjusted
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 125
#> 10 NANow, we calculate specific gravities as usual, initially ignoring the custom values, before then modifying the output to include our custom values:
example_data_sg_calc <- example_data %>%
rowwise() %>%
mutate( sg = SGConverter(pick(everything()))) %>%
select(everything(), sg)
print("Before setting custom values:")
#> [1] "Before setting custom values:"
print(example_data_sg_calc$sg)
#> [1] 100.00 130.00 104.00 128.75 103.00 100.00 51.50 25.00 123.60 109.00
example_data_sg_calc$sg[[9]] <- example_data_sg_calc$custom_sg_adjusted[[9]]
print("After setting custom values:")
#> [1] "After setting custom values:"
print(example_data_sg_calc$sg)
#> [1] 100.00 130.00 104.00 128.75 103.00 100.00 51.50 25.00 125.00 109.00We can then continue with our analysis using the newly updated specific gravity values:
example_data_results <- example_data_sg_calc %>%
rowwise() %>%
mutate(test = NPMScore(pick(everything()), sg_adjusted_label="sg")) %>%
unnest(test) %>%
rowwise() %>%
mutate(assess = NPMAssess(pick(everything()))) %>%
unnest(assess) %>%
select(everything(), energy_score, sugar_score, salt_score, fvn_score,
protein_score, satfat_score, fibre_score, NPM_score, NPM_assessment)Our results table looks much the same as for previous examples, with
the addition of the custom_sg_adjusted column to record
where manual modification has occurred.
names(example_data_results)
#> [1] "name" "brand"
#> [3] "product_category" "product_type"
#> [5] "food_type" "drink_format"
#> [7] "drink_type" "nutrition_info"
#> [9] "energy_measurement_kj" "energy_measurement_kcal"
#> [11] "sugar_measurement_g" "satfat_measurement_g"
#> [13] "salt_measurement_g" "sodium_measurement_mg"
#> [15] "fibre_measurement_nsp" "fibre_measurement_aoac"
#> [17] "protein_measurement_g" "fvn_measurement_percent"
#> [19] "weight_g" "volume_ml"
#> [21] "volume_water_ml" "custom_sg_adjusted"
#> [23] "sg" "energy_score"
#> [25] "sugar_score" "satfat_score"
#> [27] "protein_score" "salt_score"
#> [29] "fvn_score" "fibre_score"
#> [31] "A_score" "C_score"
#> [33] "NPM_score" "NPM_assessment"
example_data_results[c("name", "sg", "custom_sg_adjusted", "NPM_score", "NPM_assessment")]
#> # A tibble: 10 × 5
#> name sg custom_sg_adjusted NPM_score NPM_assessment
#> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 lembas 100 NA 8 FAIL
#> 2 zeno's icecream 130 NA 11 FAIL
#> 3 mystic rush 104 NA 5 FAIL
#> 4 delta ringer drink 129. NA 2 FAIL
#> 5 welter water 103 NA 6 FAIL
#> 6 janus's drink 100 NA 14 FAIL
#> 7 beta ringer drink 51.5 NA 9 FAIL
#> 8 zeta ringer drink 25 NA 16 FAIL
#> 9 heavyweight water 125 125 5 FAIL
#> 10 bantam water 109 NA 5 FAILWhere a custom conversion factor has been provided
Again, we load the required libraries and data:
# load required libraries
library(tidyr)
library(dplyr)
library(nutrientprofiler)
# read in the data
example_data_2 <- read.csv("data/example_data_2.csv")This dataset has a column called custom_sg_conversion
that is empty except for products where a custom specific gravity
conversion factor has been provided.
example_data_2
#> name brand product_category product_type food_type
#> 1 lembas NA NA Food
#> 2 zeno's icecream NA NA Food Ice cream
#> 3 mystic rush NA NA Drink
#> 4 delta ringer drink NA NA Drink
#> 5 welter water NA NA Drink
#> 6 janus's drink NA NA Food
#> 7 beta ringer drink NA NA Drink
#> 8 zeta ringer drink NA NA Drink
#> 9 heavyweight water NA NA Drink
#> 10 bantam water NA NA Drink
#> drink_format drink_type nutrition_info
#> 1
#> 2
#> 3 Ready Carbonated/juice drink
#> 4 Powdered Preparation instructions given
#> 5 Cordial As consumed
#> 6
#> 7 Powdered As consumed
#> 8 Powdered Preparation instructions not given
#> 9 Cordial Preparation instructions given
#> 10 Cordial Preparation instructions not given
#> energy_measurement_kj energy_measurement_kcal sugar_measurement_g
#> 1 266 NA 50
#> 2 NA 24 21
#> 3 NA 194 11
#> 4 188 NA 15
#> 5 NA 205 19
#> 6 NA 24 21
#> 7 188 NA 15
#> 8 188 NA 15
#> 9 NA 205 19
#> 10 NA 205 19
#> satfat_measurement_g salt_measurement_g sodium_measurement_mg
#> 1 3 NA 0.6
#> 2 11 0.08 NA
#> 3 0 NA 100.0
#> 4 0 NA 100.0
#> 5 0 0.10 NA
#> 6 11 0.08 NA
#> 7 0 NA 100.0
#> 8 0 NA 100.0
#> 9 0 0.10 NA
#> 10 0 0.10 NA
#> fibre_measurement_nsp fibre_measurement_aoac protein_measurement_g
#> 1 3 NA 7.0
#> 2 NA 0.7 3.5
#> 3 NA 0.0 0.0
#> 4 NA 0.0 0.5
#> 5 NA 0.0 0.1
#> 6 NA 0.7 3.5
#> 7 NA 0.0 0.5
#> 8 NA 0.0 0.5
#> 9 NA 0.0 0.1
#> 10 NA 0.0 0.1
#> fvn_measurement_percent weight_g volume_ml volume_water_ml
#> 1 0 100 NA NA
#> 2 0 NA 100 NA
#> 3 0 NA 100 NA
#> 4 3 25 NA 100
#> 5 6 NA 100 NA
#> 6 0 NA 100 NA
#> 7 3 NA 50 NA
#> 8 3 25 NA NA
#> 9 6 NA 20 100
#> 10 6 NA 100 NA
#> custom_sg_conversion
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 0.83
#> 10 NANow, we calculate specific gravities as usual, initially ignoring the custom values.
example_data_sg_calc_2 <- example_data_2 %>%
rowwise() %>%
mutate( sg = SGConverter(pick(everything()))) %>%
select(everything(), sg)We can use the function generic_specific_gravity() to
calculate the adjusted volumes. The value we want to modify is a
cordial, and the nutrition information provided assumes the product has
been prepared as instructed
(nutrition_info == "Preparation instructions given"); This
means we need to calculate the adjusted specific gravity weight by
adding the volume (ml) and the volume of water (ml) provided and using
these as parameters along with our custom conversion factor in the
generic_specific_gravity() function.
print("Before setting custom values:")
#> [1] "Before setting custom values:"
print(example_data_sg_calc_2$sg)
#> [1] 100.00 130.00 104.00 128.75 103.00 100.00 51.50 25.00 123.60 109.00
example_data_sg_calc_2[9,]
#> # A tibble: 1 × 23
#> # Rowwise:
#> name brand product_category product_type food_type drink_format drink_type
#> <chr> <lgl> <lgl> <chr> <chr> <chr> <chr>
#> 1 heavywe… NA NA Drink "" Cordial ""
#> # ℹ 16 more variables: nutrition_info <chr>, energy_measurement_kj <int>,
#> # energy_measurement_kcal <int>, sugar_measurement_g <int>,
#> # satfat_measurement_g <int>, salt_measurement_g <dbl>,
#> # sodium_measurement_mg <dbl>, fibre_measurement_nsp <int>,
#> # fibre_measurement_aoac <dbl>, protein_measurement_g <dbl>,
#> # fvn_measurement_percent <int>, weight_g <int>, volume_ml <int>,
#> # volume_water_ml <int>, custom_sg_conversion <dbl>, sg <dbl>
# example_data_sg_calc$sg[[9]] <- example_data_sg_calc$custom_sg_adjusted[[9]]
row <- example_data_sg_calc_2[9,]
example_data_sg_calc_2$sg[[9]] <- generic_specific_gravity((as.numeric(row[["volume_water_ml"]]) + as.numeric(row[["volume_ml"]])),
row[["custom_sg_conversion"]])
print("After setting custom values:")
#> [1] "After setting custom values:"
print(example_data_sg_calc_2$sg)
#> [1] 100.00 130.00 104.00 128.75 103.00 100.00 51.50 25.00 99.60 109.00We can then continue with our analysis using the newly updated specific gravity values:
example_data_results_2 <- example_data_sg_calc_2 %>%
rowwise() %>%
mutate(test = NPMScore(pick(everything()), sg_adjusted_label="sg")) %>%
unnest(test) %>%
rowwise() %>%
mutate(assess = NPMAssess(pick(everything()))) %>%
unnest(assess) %>%
select(everything(), energy_score, sugar_score, salt_score, fvn_score,
protein_score, satfat_score, fibre_score, NPM_score, NPM_assessment)Our results table looks much the same as for previous examples, with
the addition of the custom_sg_conversion column to record
where manual modification has occurred.
names(example_data_results_2)
#> [1] "name" "brand"
#> [3] "product_category" "product_type"
#> [5] "food_type" "drink_format"
#> [7] "drink_type" "nutrition_info"
#> [9] "energy_measurement_kj" "energy_measurement_kcal"
#> [11] "sugar_measurement_g" "satfat_measurement_g"
#> [13] "salt_measurement_g" "sodium_measurement_mg"
#> [15] "fibre_measurement_nsp" "fibre_measurement_aoac"
#> [17] "protein_measurement_g" "fvn_measurement_percent"
#> [19] "weight_g" "volume_ml"
#> [21] "volume_water_ml" "custom_sg_conversion"
#> [23] "sg" "energy_score"
#> [25] "sugar_score" "satfat_score"
#> [27] "protein_score" "salt_score"
#> [29] "fvn_score" "fibre_score"
#> [31] "A_score" "C_score"
#> [33] "NPM_score" "NPM_assessment"
example_data_results_2[c("name", "sg", "custom_sg_conversion", "NPM_score", "NPM_assessment")]
#> # A tibble: 10 × 5
#> name sg custom_sg_conversion NPM_score NPM_assessment
#> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 lembas 100 NA 8 FAIL
#> 2 zeno's icecream 130 NA 11 FAIL
#> 3 mystic rush 104 NA 5 FAIL
#> 4 delta ringer drink 129. NA 2 FAIL
#> 5 welter water 103 NA 6 FAIL
#> 6 janus's drink 100 NA 14 FAIL
#> 7 beta ringer drink 51.5 NA 9 FAIL
#> 8 zeta ringer drink 25 NA 16 FAIL
#> 9 heavyweight water 99.6 0.83 6 FAIL
#> 10 bantam water 109 NA 5 FAIL