Difference between revisions of "Food intake in Finland"
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[[Category:Beneris]] | [[Category:Beneris]] | ||
[[Category:Intake]] | [[Category:Intake]] | ||
+ | [[Category:Contains R code]] | ||
{{variable}} | {{variable}} | ||
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==Answer== | ==Answer== | ||
+ | |||
+ | * [http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=crAJyAlr9taPQlIG Fish intake per age group] | ||
+ | ** [http://194.187.214.42/rtools_server/runs/crAJyAlr9taPQlIG_plot001.png Larger graph] | ||
<rcode graphics="1"> | <rcode graphics="1"> | ||
+ | library(OpasnetUtils) | ||
+ | library(ggplot2) | ||
+ | |||
+ | objects.get("84Sg6BQDs7potVJr") | ||
+ | |||
+ | #oprint(head(foodFI)) | ||
+ | |||
+ | fishlong <- c( | ||
+ | "Atlantic salmon (Salmo salar)", | ||
+ | "Baltic herring (Clupea harengus membras)", | ||
+ | "Fish and other seafood(by species, these are examples - variation by country):", | ||
+ | "Herring (Clupea harengus)", | ||
+ | "Pike (Esox lucius)", | ||
+ | "Rainbow trout (Onchorchys mykiss)" | ||
+ | ) | ||
+ | |||
+ | fishshort <- c( | ||
+ | "Atlantic salmon", | ||
+ | "Baltic herring", | ||
+ | "Fish and seafood", | ||
+ | "Herring", | ||
+ | "Pike", | ||
+ | "Rainbow trout" | ||
+ | ) | ||
+ | |||
+ | # Why do we need to reorder the factor? It should be ordered already. | ||
+ | |||
+ | age = c("6 mo", "7 mo", "8 mo", "9 mo", "7-11 mo", "10 mo", "11 mo", "1", "2", "3", "4-5", "6-9", "10-13", "14-17", | ||
+ | "18-24", "25-34", "35-44", "45-54", "55-64", "65-74") | ||
+ | foodFI$Age <- factor(foodFI$Age, age, ordered = TRUE) | ||
+ | |||
+ | #levels(foodFI$Food)[levels(foodFI$Food) %in% fishlong] | ||
+ | levels(foodFI$Food)[levels(foodFI$Food) %in% fishlong] <- fishshort | ||
+ | |||
+ | cat("Fish intake data based on results from Beneris project (child data mainly from DIPP study.)\n") | ||
+ | |||
+ | #head(foodFI[foodFI$Food %in% fishshort , ]) | ||
+ | |||
+ | ggplot(foodFI[foodFI$Parameter == "Fractile0.5" & foodFI$Food == fishshort , ], aes(x = Age, y = Result)) + # | ||
+ | geom_point(aes(colour = Food)) + | ||
+ | theme_grey(base_size = 24) + | ||
+ | theme(axis.text.x = element_text(angle = 90, hjust = 1)) | ||
</rcode> | </rcode> | ||
− | + | ||
== Rationale == | == Rationale == | ||
Line 58: | Line 104: | ||
=== Calculations === | === Calculations === | ||
− | <rcode> | + | <rcode graphics="1"> |
+ | library(OpasnetUtils) | ||
+ | library(reshape2) | ||
+ | library(ggplot2) | ||
+ | |||
+ | importFoodTables <- function( | ||
+ | dat = table, # Data sheet from Excel or elsewhere | ||
+ | cols = c("mean", "SD", "Fractile0.05", "Fractile0.25", "Fractile0.5", "Fractile0.75", "Fractile0.95", "Userfraction"), | ||
+ | sheet = "General population", | ||
+ | obs, | ||
+ | sex = c("Male", "Female"), | ||
+ | block, | ||
+ | age | ||
+ | ) { | ||
+ | out <- data.frame() | ||
+ | for(i in 1:length(obs)) { | ||
+ | for(j in 1:length(block)) { | ||
+ | temp <- dat[obs[[i]], block[[j]]] | ||
+ | colnames(temp) <- cols | ||
+ | temp <- data.frame(Population = sheet, Sex = sex[[i]], Age = age[[j]], Food = dat[obs[[i]], 1], temp) | ||
+ | temp[["Userfraction"]] <- as.numeric(gsub("%", "", temp[["Userfraction"]])) / 100 | ||
+ | temp <- melt(temp, measure.vars = cols, variable.name = "Parameter", value.name = "Result") | ||
+ | out <- rbind(out, temp) | ||
+ | } | ||
+ | } | ||
+ | out$Result <- as.numeric(out$Result) | ||
+ | out$Age <- factor(out$Age, age, ordered = TRUE) | ||
+ | return(out) | ||
+ | } | ||
+ | |||
+ | # Food consumption FINLAND.xls / Main food categories. http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=7YsAYvWNMy7wRqIR | ||
+ | |||
+ | objects.get("7YsAYvWNMy7wRqIR") | ||
+ | |||
+ | fooddata1 <- importFoodTables( | ||
+ | obs = list(c(5:22, 25:33, 35:37), c(40:57, 60:68, 70:72)), | ||
+ | block = list(10:17, 26:33, 42:49, 74:81, 82:89, 90:97, 98:105), | ||
+ | age = c("1", "3", "6", "25-34", "35-44", "45-54", "55-64") | ||
+ | ) | ||
+ | |||
+ | # Food consumption FINLAND.xls / Fish categories. http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=E03tfupLAj1MNRur | ||
+ | |||
+ | objects.get("E03tfupLAj1MNRur") | ||
+ | |||
+ | fooddata2 <- importFoodTables( | ||
+ | obs = list(4:16, 19:31), | ||
+ | block = list(3:10, 12:19, 21:28, 30:37, 39:46, 48:55, 57:64, c(65, 65:71), 73:80, 82:89, 91:98, 100:107, 109:116, | ||
+ | 118:125, 127:134, 136:143, 151:158, 160:167, 169:176, 178:185), | ||
+ | age = c("6 mo", "7 mo", "8 mo", "9 mo", "10 mo", "11 mo", "1", "7-11 mo", "2", "3", "4-5", "6-9", "10-13", "14-17", | ||
+ | "18-24", "25-34", "35-44", "45-54", "55-64", "65-74") | ||
+ | ) | ||
+ | |||
+ | foodFI <- rbind(fooddata1, fooddata2) | ||
+ | |||
+ | oprint(head(foodFI)) | ||
+ | |||
+ | temp <- fooddata2 | ||
+ | temp <- temp[temp$Parameter == "Fractile0.5" & | ||
+ | temp$Food %in% c( | ||
+ | "Fish and other seafood(by species, these are examples - variation by country):", | ||
+ | "Baltic herring (Clupea harengus membras)", | ||
+ | "Herring (Clupea harengus)", | ||
+ | "Pike (Esox lucius)", | ||
+ | "Rainbow trout (Onchorchys mykiss)", | ||
+ | "Atlantic salmon (Salmo salar)" | ||
+ | ) , ] | ||
+ | |||
+ | oprint(head(temp)) | ||
+ | ggplot(temp, aes(x = Age, y = Result)) + geom_point(aes(colour = Food)) + theme_grey(base_size = 24) | ||
+ | |||
+ | objects.put(foodFI) | ||
+ | |||
+ | cat("Object foodFI saved.\n") | ||
</rcode> | </rcode> | ||
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* [[Food intake in Spain]] | * [[Food intake in Spain]] | ||
* [[Food intake in Denmark]] | * [[Food intake in Denmark]] | ||
+ | * {{#l:Food consumption_FINLAND.xls}} | ||
+ | * {{#l:Beneris_Food consumption pregnant women_FINLAND_300507_th.xls}} | ||
+ | * {{#l:Beneris_Food consumption_SPAIN_130407_lr.xls}} | ||
+ | * {{#l:Fish consumption and POPs_IRELAND.xls}} | ||
+ | * {{#l:Food consumption_DK.xls}} | ||
==References== | ==References== | ||
<references/> | <references/> |
Latest revision as of 15:45, 15 April 2016
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Question
What is intake of different food items (especially fish) in Finland in age groups 0-64 years?
Answer
Rationale
Data
The data contains:
a) Main food groups (ingredient level, classification used in EPIC)
b) Nutrients
c) Nutrient supplements
d) Fish and other seafood by species
Age groups are: 0, 1, 2, 3, 4-5, 6-9, 10-13, 14-17, 18-24, 25-34, 35-44, 45-54, 55-64,
The data here is generated from the DIPP study, a Type 1 Diabetes Prediction and Prevention (DIPP) project in Finland, where the cohort of 5993 children (77 % of those invited) participated in the main study, and 117 randomly selected infants in the validation study. [1] [2] This was carried out in the university hospitals of Turku, Tampere and Oulu.[3]
- Food intake in Spain: [1] [2] [3] [4]
- Food intake in Denmark: [5] [6]
- Domestic fish consumption of the general population in Finland: Fish oil intake in Beneris [7]
- Food consumption FINLAND.xls [4]. The Excel sheets are uploaded as tables (R data.frames): Main food categories, Fish categories
- Food intake in subpopulations in Finland:
- Domestic fish consumption of the pregnant women in Finland
- Beneris_D18_subpopulation_intakes_Finland_th.xls [5]. The Excel sheets are uploaded as tables (R data.frames): Main food categories_children, Fish categories_children, Main food categories_adults, Fish categories_adults, Main food categories_pregnant, Fish categories_pregnant
- D14 Dietary patterns
- Beneris Food consumption pregnant women FINLAND 300507 th.xls The Excel sheets are uploaded as tables (R data.frames): Main food categories, Fish categories
- Metals in Finnish human placentae
- Food and contaminant intake in Ireland: [8] [9] [10]
- Dioxin intakes in Finnish children
Unit
g/day AMONG THOSE WHO USE THIS PRODUCT
Calculations
See also
- Food intake in Finland
- Food intake in Spain
- Food intake in Denmark
- Food consumption_FINLAND
- Beneris_Food consumption pregnant women_FINLAND_300507_th
- Beneris_Food consumption_SPAIN_130407_lr
- Fish consumption and POPs_IRELAND
- Food consumption_DK
References
- ↑ Virtanen SM, Aro A. Dietary factors in the aetiology of diabetes. Ann Med 1994;26:469-478. Virtanen et al 1994
- ↑ Virtanen SM, Knip M. Nutritional risk predictors of beta-cell autoimmunity and type 1 diabetes at a young age. Am J Clin Nutr 2003;78:1053-67. Virtanen et al 2003
- ↑ DIPP study
- ↑ File is also in N:\YTOS\Projects\BENERIS\WP2\Datat pyydetyssa muodossa\Food consumption_FINLAND.xls (original data accessible only for the THL))
- ↑ D18 Subpopulation intakes. The Excel data file is in N:\YMTO\PROJECTS\BENERIS\Admin\Deliverables\D18 Subpopulation intakes\Beneris_D18_subpopulation_intakes_Finland_th.xls.