#### Set-up and Library ####
# *********************************************************************** #
# Set-up and Library #
# *********************************************************************** #Raw data cleaning and processing to generate Health Canada diet simulation data
# ********************************************** #
# Library #
# ********************************************** #
## data
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidylog)Warning: package 'tidylog' was built under R version 4.3.2
Attaching package: 'tidylog'The following objects are masked from 'package:dplyr':
add_count, add_tally, anti_join, count, distinct, distinct_all,
distinct_at, distinct_if, filter, filter_all, filter_at, filter_if,
full_join, group_by, group_by_all, group_by_at, group_by_if,
inner_join, left_join, mutate, mutate_all, mutate_at, mutate_if,
relocate, rename, rename_all, rename_at, rename_if, rename_with,
right_join, sample_frac, sample_n, select, select_all, select_at,
select_if, semi_join, slice, slice_head, slice_max, slice_min,
slice_sample, slice_tail, summarise, summarise_all, summarise_at,
summarise_if, summarize, summarize_all, summarize_at, summarize_if,
tally, top_frac, top_n, transmute, transmute_all, transmute_at,
transmute_if, ungroupThe following object is masked from 'package:stats':
filterlibrary(readxl)
library(janitor)
Attaching package: 'janitor'The following objects are masked from 'package:stats':
chisq.test, fisher.testlibrary(hefi2019) # Install with <devtools::install_github("didierbrassard/hefi2019")>
## presentation
library(gt)Warning: package 'gt' was built under R version 4.3.3## project
library(here)here() starts at /Users/DidierBrassard/RStudio/NuAge_protocol## suppress scientific notation
options(scipen = 9999)# ********************************************** #
# Directories #
# ********************************************** #
## Local directory
dir_scripts <- here::here("scripts")
dir_metadata <- here::here("data", "metadata")
dir_processed <- here::here("data", "processed")
dir_raw <- here::here("data","raw")
dir_results <- here::here("data", "results")
dir_temp <- here::here("data","temp")
if(dir.exists(dir_temp)==FALSE){
dir.create(dir_temp)
}# ********************************************** #
# Load functions and data #
# ********************************************** #
source(file.path(dir_scripts, "save_and_summarize_data.R"))#### Preparing Health Canada diet simulation ####
# *********************************************************************** #
# Preparing Health Canada diet simulation #
# *********************************************************************** #
# Objective:
# 1) read and format simulation data;
# 2) output table of dietary constituents and nutrients (1 diet = wide format)##### 1) Download/read data #####
# ********************************************** #
# Download/read data #
# ********************************************** #
# Omnivore
if(file.exists(file.path(dir_raw,"HC_simulated_omni.xlsx"))==FALSE){
download.file("https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be/resource/94064331-b1b9-41f5-9ba8-1bf77b880053/download/simulated-composite-diets-omnivore.xlsx",
destfile =file.path(dir_raw,"HC_simulated_omni.xlsx"))
} else {
message("dir_raw/HC_simulated_omni.xlsx found. Date modified:", file.info(file.path(dir_raw,"HC_simulated_omni.xlsx"))$mtime)
}dir_raw/HC_simulated_omni.xlsx found. Date modified:2024-05-29 14:50:11.318581# Omnivore, no beverages
if(file.exists(file.path(dir_raw,"HC_simulated_omni_nobev.xlsx"))==FALSE){
download.file("https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be/resource/66919cac-5d2d-4617-be01-9faafd539f2f/download/simulated-composite-diets-omnivore-no-healthy-beverages.xlsx",
destfile =file.path(dir_raw,"HC_simulated_omni_nobev.xlsx"))
} else {
message("dir_raw/HC_simulated_omni_nobev.xlsx found. Date modified:", file.info(file.path(dir_raw,"HC_simulated_omni_nobev.xlsx"))$mtime)
}dir_raw/HC_simulated_omni_nobev.xlsx found. Date modified:2024-05-29 14:50:11.322398# Lacto-ovo-vege
if(file.exists(file.path(dir_raw,"HC_simulated_vege.xlsx"))==FALSE){
download.file("https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be/resource/3813380e-9978-4d19-995b-c4617ad58019/download/simulated-composite-diets-lacto-ovo-vegetarian.xlsx",
destfile =file.path(dir_raw,"HC_simulated_vege.xlsx"))
} else {
message("dir_raw/HC_simulated_vege.xlsx found. Date modified:", file.info(file.path(dir_raw,"HC_simulated_vege.xlsx"))$mtime)
}dir_raw/HC_simulated_vege.xlsx found. Date modified:2024-05-29 14:50:11.325357##### 2) Function to read and output data #####
# ********************************************** #
# Function to read and output data #
# ********************************************** #Read Nutrient Data and Output
This function reads diet simulation data from an Excel file, performs some cleaning operations, and outputs a list containing serving data, total nutrients, and composite food-specific nutrients.
@param hc_file Character string, the name of the Excel file. @param dri_group Character string, the sheet name in the Excel file. @param total_rownb Numeric, the total number of rows to read from the table in the dri_group-specific sheet
@return A list containing serving data, total nutrients, and composite food-specific nutrients.
@examples
@import readxl @import janitor @import stringr @import dplyr
@export
read_n_output <- function(hc_file, dri_group, total_rownb = 20) {
message(paste0("Reading: ",hc_file, "; sheet: ", dri_group))
# Helper function for reading Excel data
read_excel_data <- function(range) {
readxl::read_excel(
file.path(dir_raw, hc_file),
sheet = dri_group,
range = range
)
}
cat("Composite food serving ...\n")
# Composite servings
serving <- read_excel_data(paste0("A3:C", total_rownb-1)) |>
janitor::clean_names() |>
select(-x3) |>
setNames(c("NB_RA", "COMPOSITE")) |>
mutate(COMPOSITE = stringr::str_squish(COMPOSITE)) |>
select(COMPOSITE, NB_RA)
# Total nutrients
cat("Total nutrient ...\n")
varnames_nut <- read_excel_data("E2:AJ3") |> names()
total_nut <- read_excel_data(paste0("E3:AJ", total_rownb)) |>
janitor::clean_names() |>
setNames(varnames_nut)
# Composite food-specific nutrients
cat("Composite food-specific nutrients ...\n")
composite_nut <- read_excel_data(paste0("A3:AJ", total_rownb-1)) |>
janitor::clean_names() |>
janitor::remove_empty(which = "cols") |>
setNames(c("NB_RA", "COMPOSITE", varnames_nut)) |>
mutate(COMPOSITE = stringr::str_squish(COMPOSITE))
return(list(serving, total_nut, composite_nut))
}##### 3) Apply function to each DRI group #####
# ********************************************** #
# Apply function to each DRI group #
# ********************************************** #
# note: using omnivore diets with beverage as most likely reflect majority of Canadians
hc_file <- "HC_simulated_omni.xlsx"
drig12 <-
read_n_output(
hc_file ,
dri_group = "M 51-70",
total_rownb = 19
)Reading: HC_simulated_omni.xlsx; sheet: M 51-70Composite food serving ...New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`select: dropped one variable (x3)
mutate: no changes
select: columns reordered (COMPOSITE, NB_RA)Total nutrient ...New names:
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• `` -> `...32`Composite food-specific nutrients ...New names:
mutate: no changes
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• `` -> `...36`drig14 <-
read_n_output(
hc_file ,
dri_group = "M 70 +",
total_rownb = 22
)Reading: HC_simulated_omni.xlsx; sheet: M 70 +Composite food serving ...New names:
select: dropped one variable (x3)
mutate: no changes
select: columns reordered (COMPOSITE, NB_RA)
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`Total nutrient ...New names:
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• `` -> `...31`
• `` -> `...32`Composite food-specific nutrients ...New names:
mutate: no changes
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• `` -> `...36`drig13 <-
read_n_output(
hc_file ,
dri_group = "F 51-70",
total_rownb = 20
)Reading: HC_simulated_omni.xlsx; sheet: F 51-70Composite food serving ...New names:
select: dropped one variable (x3)
mutate: no changes
select: columns reordered (COMPOSITE, NB_RA)
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`Total nutrient ...New names:
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• `` -> `...32`Composite food-specific nutrients ...New names:
mutate: no changes
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• `` -> `...36`drig15 <-
read_n_output(
hc_file ,
dri_group = "F 70 +",
total_rownb = 20
)Reading: HC_simulated_omni.xlsx; sheet: F 70 +Composite food serving ...New names:
select: dropped one variable (x3)
mutate: no changes
select: columns reordered (COMPOSITE, NB_RA)
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`Total nutrient ...New names:
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• `` -> `...32`Composite food-specific nutrients ...New names:
mutate: no changes
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• `` -> `...36`##### 4) Calculate total intakes for each DRI group #####
# ********************************************** #
# Calculate total intakes for each DRI group #
# ********************************************** #Calculate DRI group Nutrients
This function calculates nutrient summary for a specific DRI group.
@param drig A list containing DRI group data. @param drig_suffix Numeric, the DRI group suffix (e.g., 12). @param nut_list Character, vector of nutrients (i.e., variable names) for which totals should be calculated
@return A data frame containing the calculated nutrient summary for the specified DRI group.
@examples
@import dplyr
@export
calculate_drig_nutrients <- function(drig, drig_suffix, nut_list = c("EKC","FSUG","FAS","FAM","FAP","SOD","PRO")) {
# Output detailed summary to calculate fat intake (missing)
all_but_total <- drig[[2]][1:nrow(drig[[2]])-1,]
drig_nut <-
drig[[2]] |>
select(all_of(nut_list)) |>
slice_tail() |>
mutate(
# add missing nutrients
FAM = sum(all_but_total$FAM, na.rm = TRUE),
FAP = sum(all_but_total$FAP, na.rm = TRUE),
# add drig id
drig = drig_suffix
)
return(drig_nut)
}
# ************************** #
# Apply function on list #
# ************************** #
drig_all <- list(drig12, drig14, drig13, drig15)
drig_suffix_all <- c(12, 14, 13, 15)
drig_nut_all <-
Map(calculate_drig_nutrients, drig_all, drig_suffix_all) |>
purrr::reduce(rbind)select: dropped 25 variables (CAR, FI, SUG, FAT, FAL, …)
slice_tail: removed 15 rows (94%), one row remaining
mutate: changed one value (100%) of 'FAM' (one fewer NA)
changed one value (100%) of 'FAP' (one fewer NA)
new variable 'drig' (double) with one unique value and 0% NA
select: dropped 25 variables (CAR, FI, SUG, FAT, FAL, …)
slice_tail: removed 18 rows (95%), one row remaining
mutate: changed one value (100%) of 'FAM' (one fewer NA)
changed one value (100%) of 'FAP' (one fewer NA)
new variable 'drig' (double) with one unique value and 0% NA
select: dropped 25 variables (CAR, FI, SUG, FAT, FAL, …)
slice_tail: removed 16 rows (94%), one row remaining
mutate: new variable 'drig' (double) with one unique value and 0% NA
select: dropped 25 variables (CAR, FI, SUG, FAT, FAL, …)
slice_tail: removed 16 rows (94%), one row remaining
mutate: changed one value (100%) of 'FAM' (one fewer NA)
changed one value (100%) of 'FAP' (one fewer NA)
new variable 'drig' (double) with one unique value and 0% NA# ************************** #
# Males, 51-70: drig=12 #
# ************************** #
drig12_ra <-
drig12[[1]] |>
mutate(drig=12)mutate: new variable 'drig' (double) with one unique value and 0% NA# ************************** #
# Males, 71 +: drig=14 #
# ************************** #
drig14_ra <-
drig14[[1]] |>
mutate(drig=14)mutate: new variable 'drig' (double) with one unique value and 0% NA# ************************** #
# Females, 51-70: drig=13 #
# ************************** #
drig13_ra <-
drig13[[1]] |>
mutate(drig=13)mutate: new variable 'drig' (double) with one unique value and 0% NA# ************************** #
# Males, 71 +: drig=15 #
# ************************** #
drig15_ra <-
drig15[[1]] |>
mutate(drig=15)mutate: new variable 'drig' (double) with one unique value and 0% NA##### 5) Append data together #####
# ********************************************** #
# Append data together #
# ********************************************** #
dietsim_bydrig <-
rbind(
drig12_ra , drig14_ra , drig13_ra , drig15_ra) |>
mutate(
# Add HEFI-2019 classification
hefi2019subgrp =
case_when(
grepl("TOTAL VEGETABLES",COMPOSITE)>0 ~ "vf",
grepl("TOTAL WHOLE",COMPOSITE)>0 ~ "wg",
grepl("ANIMAL-BASED",COMPOSITE)>0 ~ "pfab",
grepl("PLANT-BASED",COMPOSITE)>0 ~ "pfpb",
grepl("HEALTHY BEVERAGES",COMPOSITE)>0 ~ "milk_plantbev",
grepl("UNSATURATED OILS",COMPOSITE)>0 ~ "ufa"
) # end of case_when
) |> # end of mutate
# remove non-hefi food groups (i.e., subclassification)
filter(is.na(hefi2019subgrp)==FALSE) |>
# Transpose long data to wide
select(-COMPOSITE) |>
pivot_wider(
names_from = "hefi2019subgrp",
values_from = "NB_RA"
) |>
# add hefi-2019 nutrients
full_join(
drig_nut_all,
by = "drig"
) |>
# ensure variable names are not capital letters
janitor::clean_names()mutate: new variable 'hefi2019subgrp' (character) with 7 unique values and 63% NA
filter: removed 41 rows (63%), 24 rows remaining
select: dropped one variable (COMPOSITE)
pivot_wider: reorganized (NB_RA, hefi2019subgrp) into (vf, wg, pfpb, pfab, milk_plantbev, …) [was 24x3, now 4x7]
full_join: added 7 columns (EKC, FSUG, FAS, FAM, FAP, …)
> rows only in pivot_wider(select(filt.. 0
> rows only in drig_nut_all 0
> matched rows 4
> ===
> rows total 4# Generate an 'overall' row
dietsim_drig0 <-
dietsim_bydrig |>
# calculate mean
summarise(
across(.cols=all_of(names(dietsim_bydrig[-1])),
function(x) mean(x),
.names ="{col}" )
) |>
mutate(
drig=0
)summarise: now one row and 13 columns, ungrouped
mutate: new variable 'drig' (double) with one unique value and 0% NAdim(dietsim_drig0); head(dietsim_drig0)[1] 1 14# A tibble: 1 × 14
vf wg pfpb pfab milk_plantbev ufa ekc fsug fas fam fap
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 9.03 3.92 2.04 1.88 1 1 1865. 6.37 10.2 21.5 20.1
# ℹ 3 more variables: sod <dbl>, pro <dbl>, drig <dbl># Add 'overall' row to drig-specific rows
dietsim <-
rbind(dietsim_bydrig, dietsim_drig0) |>
arrange(drig) |>
mutate(
drig_f =
factor(drig,
levels = c(0, 12, 13, 14, 15),
labels = c("Male and female, 51 y or older",
"Male, 51-70 y",
"Female, 51-70 y",
"Male, 71y+",
"Female, 71y+"))
)mutate: new variable 'drig_f' (factor) with 5 unique values and 0% NAdim(dietsim); names(dietsim); head(dietsim)[1] 5 15 [1] "drig" "vf" "wg" "pfpb"
[5] "pfab" "milk_plantbev" "ufa" "ekc"
[9] "fsug" "fas" "fam" "fap"
[13] "sod" "pro" "drig_f" # A tibble: 5 × 15
drig vf wg pfpb pfab milk_plantbev ufa ekc fsug fas fam
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 9.03 3.92 2.04 1.88 1 1 1865. 6.37 10.2 21.5
2 12 9.93 5 2.42 2.29 1 1 2259. 7.10 11.7 24.4
3 13 8.56 3.5 2 1.85 1 1 1737. 5.52 9.74 20.8
4 14 9.93 4.2 2 1.84 1 1 1959. 6.98 10.6 22.3
5 15 7.71 3 1.72 1.56 1 1 1507. 5.87 8.71 18.5
# ℹ 4 more variables: fap <dbl>, sod <dbl>, pro <dbl>, drig_f <fct># ************************** #
# Add labels #
# ************************** #
labelled::var_label(dietsim) <-
list(
drig = "DRI age and sex group",
drig_f = "DRI age and sex group",
vf = "Vegetables and fruits, RA/d",
wg = "Whole-grain foods, RA/d",
pfpb = "Protein foods, plant-based, RA/d",
pfab = "Protein foods, animal-based, RA/d",
milk_plantbev = "Unsweetened milk and plant beverage with protein, RA/d",
ufa = "Unsaturated fats, RA/d",
ekc = "Total energy intake, kcal/d",
fsug = "Free sugars, grams/d",
fas = "Saturated fats, grams/d",
fam = "Monounsaturated fats, grams/d",
fap = "Polyunsaturated fats, grams/d",
sod = "Sodium, mg/d",
pro = "Protein, grams/d")#### Calculate HEFI-2019 scores ####
# ********************************************** #
# Calculate HEFI-2019 scores #
# ********************************************** #
# Apply the hefi-2019 scoring algorithm
dietsim_hefi <-
dietsim |>
mutate(
# Change milk_plantbev RA to grams
milk_plantbev = milk_plantbev*258,
# Without further information for milk_plantbev = split half/half
milk = milk_plantbev/2,
plantbev = milk_plantbev/2
) |>
hefi2019::hefi2019(#indata = dietsim,
vegfruits = vf,
wholegrfoods = wg,
nonwholegrfoods = 0,
profoodsanimal = pfab,
profoodsplant = pfpb,
otherfoods = 0,
waterhealthybev = 0,
unsweetmilk = milk,
unsweetplantbevpro = plantbev,
otherbeverages = 0 ,
mufat = fam ,
pufat = fap ,
satfat = fas ,
freesugars = fsug,
sodium = sod,
energy = ekc
)Healthy Eating Food Index-2019 Scoring Algorithm R version 1.4mutate: changed 5 values (100%) of 'milk_plantbev' (0 new NAs)
new variable 'milk' (double) with one unique value and 0% NA
new variable 'plantbev' (double) with one unique value and 0% NA# ************************** #
# Add labels #
# ************************** #
labelled::var_label(dietsim_hefi) <-
list(
drig = "DRI age and sex group",
drig_f = "DRI age and sex group",
vf = "Vegetables and fruits, RA/d",
wg = "Whole-grain foods, RA/d",
pfpb = "Protein foods, plant-based, RA/d",
pfab = "Protein foods, animal-based, RA/d",
milk_plantbev = "Unsweetened milk and plant beverage with protein, RA/d",
ufa = "Unsaturated fats, RA/d",
ekc = "Total energy intake, kcal/d",
fsug = "Free sugars, grams/d",
fas = "Saturated fats, grams/d",
fam = "Monounsaturated fats, grams/d",
fap = "Polyunsaturated fats, grams/d",
sod = "Sodium, mg/d",
pro = "Protein, grams/d")#### Summary and save ####
# ********************************************** #
# Summary and save #
# ********************************************** #
# summary
dietsim |>
select(-c("drig", "ekc":"pro")) |>
gt::gt() |>
gt::cols_move_to_start("drig_f") |>
gt::fmt_number(columns = everything(), decimals = 1) |>
gt::tab_header("Intakes of major food categories in Health Canada simulated diets, by DRI group") |>
gt::tab_source_note(source_note = "Data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be")select: dropped 8 variables (drig, ekc, fsug, fas, fam, …)| Intakes of major food categories in Health Canada simulated diets, by DRI group | ||||||
|---|---|---|---|---|---|---|
| DRI age and sex group | Vegetables and fruits, RA/d | Whole-grain foods, RA/d | Protein foods, plant-based, RA/d | Protein foods, animal-based, RA/d | Unsweetened milk and plant beverage with protein, RA/d | Unsaturated fats, RA/d |
| Male and female, 51 y or older | 9.0 | 3.9 | 2.0 | 1.9 | 1.0 | 1.0 |
| Male, 51-70 y | 9.9 | 5.0 | 2.4 | 2.3 | 1.0 | 1.0 |
| Female, 51-70 y | 8.6 | 3.5 | 2.0 | 1.9 | 1.0 | 1.0 |
| Male, 71y+ | 9.9 | 4.2 | 2.0 | 1.8 | 1.0 | 1.0 |
| Female, 71y+ | 7.7 | 3.0 | 1.7 | 1.6 | 1.0 | 1.0 |
| Data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be | ||||||
dietsim |>
select("drig_f", "ekc":"pro") |>
gt::gt() |>
gt::cols_move_to_start("drig_f") |>
gt::fmt_number(columns = everything(), decimals = 1) |>
gt::tab_header("Intakes of major nutrients in Health Canada simulated diets, by DRI group") |>
gt::tab_source_note(source_note = "Data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be")select: dropped 7 variables (drig, vf, wg, pfpb, pfab, …)| Intakes of major nutrients in Health Canada simulated diets, by DRI group | |||||||
|---|---|---|---|---|---|---|---|
| DRI age and sex group | Total energy intake, kcal/d | Free sugars, grams/d | Saturated fats, grams/d | Monounsaturated fats, grams/d | Polyunsaturated fats, grams/d | Sodium, mg/d | Protein, grams/d |
| Male and female, 51 y or older | 1,865.3 | 6.4 | 10.2 | 21.5 | 20.1 | 1,065.8 | 97.6 |
| Male, 51-70 y | 2,258.8 | 7.1 | 11.7 | 24.4 | 20.8 | 1,174.3 | 118.7 |
| Female, 51-70 y | 1,736.7 | 5.5 | 9.7 | 20.8 | 20.5 | 992.5 | 91.7 |
| Male, 71y+ | 1,958.7 | 7.0 | 10.6 | 22.3 | 20.7 | 1,141.7 | 102.1 |
| Female, 71y+ | 1,507.0 | 5.9 | 8.7 | 18.5 | 18.4 | 954.5 | 77.9 |
| Data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be | |||||||
dietsim_hefi |>
select(starts_with("HEFI2019")) |>
gt::gt() |>
gt::fmt_number(columns = everything(), decimals = 1) |>
gt::tab_header("HEFI-2019 scores of Health Canada simulated diets, by DRI group") |>
gt::tab_source_note(source_note = "Based on data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be")select: dropped 28 variables (drig, vf, wg, pfpb, pfab, …)| HEFI-2019 scores of Health Canada simulated diets, by DRI group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HEFI2019 C1 Vegetables and fruits | HEFI2019 C2 Whole-grain foods | HEFI2019 C3 Grain foods ratio | HEFI2019 C4 Protein foods | HEFI2019 C5 Plant-based protein foods | HEFI2019 C6 Beverages | HEFI2019 C7 Fatty acids ratio | HEFI2019 C8 Saturated fats | HEFI2019 C9 Free sugars | HEFI2019 C10 Sodium | Total Healthy Eating Food Index (/80) |
| 20.0 | 4.4 | 5.0 | 5.0 | 4.1 | 10.0 | 5.0 | 5.0 | 10.0 | 10.0 | 78.5 |
| 19.2 | 4.8 | 5.0 | 5.0 | 4.2 | 10.0 | 5.0 | 5.0 | 10.0 | 10.0 | 78.3 |
| 20.0 | 4.1 | 5.0 | 5.0 | 4.1 | 10.0 | 5.0 | 5.0 | 10.0 | 10.0 | 78.3 |
| 20.0 | 4.4 | 5.0 | 5.0 | 4.1 | 10.0 | 5.0 | 5.0 | 10.0 | 10.0 | 78.6 |
| 20.0 | 4.0 | 5.0 | 5.0 | 4.0 | 10.0 | 5.0 | 5.0 | 10.0 | 10.0 | 78.0 |
| Based on data from Health Canada. (2022). Simulated composite diets. Available at https://open.canada.ca/data/dataset/0490749d-b0b0-410a-9577-a903c6cec2be | ||||||||||
# save intake only
save_and_summarize_data(
dietsim,
dir = dir_processed,
dir_metadata = dir_metadata
)Warning in fun(libname, pkgname): couldn't connect to display
"/private/tmp/com.apple.launchd.6tu9LhZbap/org.xquartz:0"system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)
Output file written: /Users/DidierBrassard/RStudio/NuAge_protocol/data/metadata/dietsim.html# save intake + hefi
save_and_summarize_data(
dietsim_hefi,
dir = dir_processed,
dir_metadata = dir_metadata
)Output file written: /Users/DidierBrassard/RStudio/NuAge_protocol/data/metadata/dietsim_hefi.html# *********************************************************************** #
# End of code #
# *********************************************************************** #
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.7.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Toronto
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] summarytools_1.0.1 here_1.0.1 gt_0.11.1 hefi2019_0.0.1.4
[5] janitor_2.2.0 readxl_1.4.3 tidylog_1.1.0 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 tidyr_1.3.1
[5] tcltk_4.3.1 xml2_1.3.6 stringi_1.8.4 rematch_2.0.0
[9] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 evaluate_1.0.1
[13] timechange_0.3.0 fastmap_1.2.0 plyr_1.8.9 cellranger_1.1.0
[17] rprojroot_2.0.4 jsonlite_1.8.9 rapportools_1.1 backports_1.5.0
[21] pander_0.6.5 purrr_1.0.2 fansi_1.0.6 codetools_0.2-20
[25] cli_3.6.3 labelled_2.13.0 rlang_1.1.4 base64enc_0.1-3
[29] withr_3.0.2 yaml_2.3.10 tools_4.3.1 reshape2_1.4.4
[33] pryr_0.1.6 checkmate_2.3.2 forcats_1.0.0 vctrs_0.6.5
[37] R6_2.5.1 matrixStats_1.4.1 magick_2.8.5 lifecycle_1.0.4
[41] lubridate_1.9.3 snakecase_0.11.1 stringr_1.5.1 htmlwidgets_1.6.4
[45] MASS_7.3-60.0.1 clisymbols_1.2.0 pkgconfig_2.0.3 pillar_1.9.0
[49] Rcpp_1.0.13-1 glue_1.8.0 haven_2.5.4 xfun_0.49
[53] tibble_3.2.1 tidyselect_1.2.1 rstudioapi_0.17.1 knitr_1.49
[57] htmltools_0.5.8.1 rmarkdown_2.29 compiler_4.3.1