Retrieve rows from one or all IFCT 2017 nutrient tables, filtered by food name or food code.
Usage
get_nutrition(
food = NULL,
table = NULL,
nutrients = NULL,
se = FALSE,
match = c("fixed", "regex")
)Arguments
- food
Character string matched against food name (case-insensitive substring) or food code (exact).
NULLreturns all foods.- table
One of
"proximate","vitamins_water","vitamins_fat","carotenoids","minerals","sugars","fatty_acids","amino_acids","organic_acids","polyphenols","oligosaccharides","edible_oils".NULLjoins all tables on food code and returns a wide result.- nutrients
Optional character vector of column name substrings to select (applied after table filter).
NULLreturns all columns.- se
If
TRUE, return standard errors instead of point estimates.- match
One of
"fixed"(default) or"regex".
Examples
# All proximate data for rice
get_nutrition("rice", table = "proximate")
#> ✔ Retrieved 6 rows for 6 foods.
#> # A tibble: 6 × 12
#> `Food code` `Food name` `No. of region` `Water(g)` `Protein_PROTCNT(g)`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 A011 "(Oryza sativa )\… 6 10.4 7.44
#> 2 A012 "Rice puffed (Ory… 6 9.4 7.47
#> 3 A013 "Rice, raw, brown… 6 9.33 9.16
#> 4 A014 "Rice, parboiled,… 6 10.1 7.81
#> 5 A015 "Rice, raw, mille… 6 9.93 7.94
#> 6 B023 "Ricebean (Vigna … 1 11.1 20.0
#> # ℹ 7 more variables: `Ash(g)` <dbl>, `TotalFat_FATCE(g)` <dbl>,
#> # `TotalDietaryFibre_FIBTG(g)` <dbl>, `InsolubleFibre_FIBINS(g)` <dbl>,
#> # `SolubleFibre_FINSOL(g)` <dbl>, `AvailableCarbohydrate_CHOAVLDF(g)` <dbl>,
#> # `Energy_ENERC(KJ)` <dbl>
# Amino acid profile for dal (any lentil/legume)
get_nutrition("dal", table = "amino_acids")
#> ✔ Retrieved 8 rows for 8 foods.
#> # A tibble: 8 × 21
#> `Food code` `Food name` `No. of region` `Histidine_HIS(g)` `Isoleucine_ILE(g)`
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 B001 "Bengal gr… 6 2.39 4.25
#> 2 B004 "Black gra… 6 2.85 3.42
#> 3 B010 "Vigna rad… 6 2.55 4.07
#> 4 B013 "Lentil da… 6 1.93 3.74
#> 5 B021 "Red gram,… 6 3.16 3.42
#> 6 H001 "Almond (P… 6 2.22 2.38
#> 7 P020 "Kadal bra… 1 NA 5
#> 8 P021 "Kadali (N… 1 NA 6.23
#> # ℹ 16 more variables: `Leucine_LEU(g)` <dbl>, `Lysine_LYS(g)` <dbl>,
#> # `Methionine_MET(g)` <dbl>, `Cysteine_CYS(g)` <dbl>,
#> # `Phenylalanine_PHE(g)` <dbl>, `Threonine_THR(g)` <dbl>,
#> # `Tryptophan_TRP(g)` <dbl>, `Valine_VAL(g)` <dbl>, `Alanine_ALA(g)` <dbl>,
#> # `Arginine_ARG(g)` <dbl>, `AsparticAcid_ASP(g)` <dbl>,
#> # `GlutamicAcid_GLU(g)` <dbl>, `Glycine_GLY(g)` <dbl>,
#> # `Proline_PRO(g)` <dbl>, `Serine_SER(g)` <dbl>, `Tyrosine_TYR(g)` <dbl>
# Iron and calcium for all foods
get_nutrition(table = "minerals", nutrients = c("Iron", "Calcium"))
#> ✔ Retrieved 528 rows for 528 foods.
#> # A tibble: 528 × 5
#> `Food code` `Food name` `No. of region` `Calcium_Ca(mg)` `Iron_Fe(mg)`
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 A001 Amaranth seed, bl… 1 181 9.33
#> 2 A002 Amaranth seed, pa… 6 162 8.02
#> 3 A003 Bajra (Pennisetum… 6 27.4 6.42
#> 4 A004 Barley (Hordeum v… 6 28.6 1.56
#> 5 A005 Jowar (Sorghum vu… 6 27.6 3.95
#> 6 A006 Maize, dry (Zea m… 6 8.91 2.49
#> 7 A007 Maize, tender, lo… 6 6.35 0.71
#> 8 A008 Maize, tender, sw… 4 6.37 0.54
#> 9 A009 Quinoa(Chenopodiu… 1 198 7.51
#> 10 A010 Ragi (Eleusine co… 5 364 4.62
#> # ℹ 518 more rows
# Standard errors for protein content
get_nutrition("wheat", table = "proximate", se = TRUE)
#> ✔ Retrieved 7 rows for 7 foods.
#> # A tibble: 7 × 12
#> `Food code` `Food name` `No. of region` `Water(g)` `Protein_PROTCNT(g)`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 A018 Wheat flour, refi… 6 0.93 0.29
#> 2 A019 Wheat flour, atta… 6 0.35 0.37
#> 3 A020 Wheat, whole (Tri… 6 1.11 0.6
#> 4 A021 Wheat, bulgur (Tr… 6 0.32 0.75
#> 5 A022 Wheat, semolina (… 6 0.68 0.37
#> 6 A023 Wheat, vermicelli… 6 0.37 0.52
#> 7 A024 Wheat, vermicelli… 6 0.47 0.7
#> # ℹ 7 more variables: `Ash(g)` <dbl>, `TotalFat_FATCE(g)` <dbl>,
#> # `TotalDietaryFibre_FIBTG(g)` <dbl>, `InsolubleFibre_FIBINS(g)` <dbl>,
#> # `SolubleFibre_FINSOL(g)` <dbl>, `AvailableCarbohydrate_CHOAVLDF(g)` <dbl>,
#> # `Energy_ENERC(KJ)` <dbl>