used in the experiment reported in the article #Knowledge: Improving food-related knowledge via seeding implemented as a social media intervention

Published

October 9, 2024

Load packages 
# Packages
library(tidyverse) 
library(patchwork)
library(latex2exp)
library(kableExtra)
library(papaja)
library(extrafont)
Define ggplot theme 
# Plot colors
clrs <- c("#54AA8F","#00335B",
          "#22A884FF","#414487FF",
          "#496aa2","#e46c0a","#90b6d4")


# ggplot theme
theme_nice <- function(){
  theme_minimal(base_family = "Jost") +  
    theme(plot.title       = element_text(hjust = 0.5,size = 20),
          panel.grid.minor = element_blank(),
          text             = element_text(size  = 20),
          panel.border     = element_rect(colour = "black", linewidth = 0.5, fill = NA),
          axis.title.x     = element_text(margin = unit(c(3, 0, 0, 0), "mm")),
          axis.title.y     = element_text(margin = unit(c(3, 3, 0, 0), "mm"), angle = 90),
          legend.title     = element_text(face = "bold",size=16),
          strip.text       = element_text(face = "bold"),
          legend.position  = "bottom"
    )}

p_size <- 3
lwd    <- 1.5
Read in item data 
# Read in items
items <- read_csv2("../Materials/Seeding Fact Items/Items.csv")

Descriptive Summary

by Food Category

Code 
items %>% 
  summarise(M_CO2    = mean(`g CO2 pro kg`),
            SD_CO2   = sd(`g CO2 pro kg`),
            Min_CO2  = min(`g CO2 pro kg`),
            Max_CO2  = max(`g CO2 pro kg`),
            M_Kcal   = mean(`Kcal pro 100g`),
            SD_Kcal  = sd(`Kcal pro 100g`),
            Min_Kcal = min(`Kcal pro 100g`),
            Max_Kcal = max(`Kcal pro 100g`),
            .by="category") %>% 
  mutate_if(is.numeric,printnum) %>% 
  kbl(col.names = c("Category",rep(c("M","SD","Min","Max"),2))) %>% 
  kable_paper() %>% 
  add_header_above(c(" " = 1, "g CO2 pro kg" = 4, "Kcal pro 100g" = 4))
g CO2 pro kg
Kcal pro 100g
Category M SD Min Max M SD Min Max
fruit & vegetables 740.83 297.58 310.00 1,190.00 48.42 22.20 16.00 93.00
meat & fish 11,070.83 8,074.22 4,640.00 26,920.00 165.58 117.63 60.00 502.00
dairy 5,264.17 3,705.50 1,560.00 13,090.00 235.17 194.97 46.00 741.00
grain products 1,257.50 835.20 560.00 3,390.00 303.33 71.48 150.00 379.00
sweets 2,899.17 1,702.25 1,030.00 6,250.00 413.08 130.07 160.00 567.00

by Seeding Sets

Code 
items %>%
  filter(seeding_CO2 == 1) %>% 
  summarise(M_CO2    = mean(`g CO2 pro kg`),
            SD_CO2   = sd(`g CO2 pro kg`),
            Min_CO2  = min(`g CO2 pro kg`),
            Max_CO2  = max(`g CO2 pro kg`),
            .by=c("category")) %>% 
  mutate_if(is.numeric,printnum) %>% 
  kbl(col.names = c("Category","M","SD","Min","Max"))   %>% 
  kable_paper()
Category M SD Min Max
fruit & vegetables 736.67 414.05 360.00 1,180.00
meat & fish 11,710.00 10,571.43 5,060.00 23,900.00
dairy 5,846.67 4,197.35 1,800.00 10,180.00
grain products 1,363.33 781.37 670.00 2,210.00
sweets 3,150.00 2,289.08 1,100.00 5,620.00
Code 
items %>%
  filter(seeding_Kcal == 1) %>% 
  summarise(M_Kcal   = mean(`Kcal pro 100g`),
            SD_Kcal  = sd(`Kcal pro 100g`),
            Min_Kcal = min(`Kcal pro 100g`),
            Max_Kcal = max(`Kcal pro 100g`),
            .by=c("category")) %>%
  mutate_if(is.numeric,printnum) %>% 
  kbl(col.names = c("Category","M","SD","Min","Max"))   %>% 
  kable_paper()
Category M SD Min Max
fruit & vegetables 42.67 25.70 19.00 70.00
meat & fish 156.67 74.78 73.00 217.00
dairy 248.00 152.83 78.00 374.00
grain products 305.33 83.27 212.00 372.00
sweets 416.00 117.04 306.00 539.00

Lists

Target Fact Items

Code 
items %>% 
  select(ID_item,name,category,`g CO2 pro kg`,`Kcal pro 100g`,`Seeditems CO2` = seeding_CO2,`Seeditems Kcal` = seeding_Kcal) %>% 
  kbl(align = "c") %>% 
  column_spec(4,color = "white",background = spec_color(items$`g CO2 pro kg`)) %>% 
  column_spec(5,color = "white",background = spec_color(items$`Kcal pro 100g`,option = "D")) %>%
  kable_styling(fixed_thead = T)
ID_item name category g CO2 pro kg Kcal pro 100g Seeditems CO2 Seeditems Kcal
1 Zucchinis fruit & vegetables 860 19 0 1
2 Potatoes fruit & vegetables 360 71 1 0
3 Bell pepper fruit & vegetables 1190 38 0 0
4 Carrots fruit & vegetables 310 39 0 1
5 Broccoli fruit & vegetables 1180 34 1 0
6 Lettuce (Iceberg) fruit & vegetables 590 16 0 0
7 Pineapples fruit & vegetables 680 57 0 0
8 Apple fruit & vegetables 500 54 0 0
9 Banana fruit & vegetables 990 93 0 0
10 Orange fruit & vegetables 670 47 1 0
11 Grapes fruit & vegetables 580 70 0 1
12 Raspberries fruit & vegetables 980 43 0 0
13 Goat meat & fish 20960 198 0 0
14 Fish fingers (frozen) meat & fish 6400 188 0 0
15 Meatballs meat & fish 5670 217 0 1
16 Salami meat & fish 5060 502 1 0
17 Beef meat & fish 26920 98 0 0
18 Pork meat & fish 6620 147 0 0
19 Chicken meat & fish 4640 102 0 0
20 Lamb meat & fish 23900 112 1 0
21 Octopus meat & fish 6170 73 1 1
22 Salmon meat & fish 6820 180 0 1
23 Shrimp (frozen) meat & fish 12100 60 0 0
24 Tuna (canned) meat & fish 7590 110 0 0
25 Quark (low-fat) dairy 2450 70 0 0
26 Butter dairy 10180 741 1 0
27 Milk dairy 1800 46 1 0
28 Cream (30%) dairy 5010 292 0 1
29 Eggs dairy 2540 137 0 0
30 Sour Cream dairy 4260 147 0 0
31 Gouda dairy 13090 364 0 0
32 Mozzarella dairy 7660 238 0 0
33 Parmesan dairy 7410 374 0 1
34 Feta dairy 5560 234 1 0
35 Yogurt dairy 1650 78 0 1
36 Fruit Yogurt dairy 1560 101 0 0
37 Rye bread grain products 1210 212 1 1
38 Wheat bread grain products 720 285 0 0
39 Croissant grain products 3390 332 0 1
40 Rice (raw) grain products 2210 361 1 0
41 Pizza dough grain products 720 235 0 0
42 Gnocchi grain products 600 150 0 0
43 Tortilla bread, wheat grain products 740 304 0 0
44 Bulgur (raw) grain products 560 314 0 0
45 Corn flakes grain products 1510 379 0 0
46 Pasta (raw) grain products 1460 330 0 0
47 Quinoa (raw) grain products 1300 366 0 0
48 Oat flakes grain products 670 372 1 1
49 Chocolate chip cookie sweets 2730 434 1 0
50 Chocolate cake sweets 2360 360 0 0
51 Chips sweets 3290 503 0 0
52 Nutella sweets 3970 539 0 0
53 Popcorn sweets 1100 403 1 1
54 Ice cream sweets 3310 160 0 0
55 Sugar sweets 1030 405 0 0
56 Milk Chocolate sweets 5620 539 1 1
57 Marzipan sweets 6250 512 0 0
58 Strawberry jam sweets 1800 229 0 0
59 Peanuts sweets 2150 567 0 0
60 Honey sweets 1180 306 0 1

Trivia Items

15 trivia facts will be shown during the seeding phase (type = Post) and 6 will be used in a 2AFC-recognition test in the final questionnaire (type = Test).

Code 
trivia <- c("An average ear of corn has an even number of rows, usually 16",
  "A watermelon consists of about 95 percent water.",
  "Every German needs about 2,700 square meters of farmland for food supply.",
  "Children see junk food advertisements an average of 27 times a day.",
  "Approximately 500 million snails are eaten in France every year.",
  "The largest cashew tree in the world covers an area of 8,500 square meters.",
  "The price of food increased by 9 percent between August 2022 and August 2023.",
  "Germans eat about 20 kilos of tomatoes per year.",
  "Caffeine has a half-life of four to six hours.",
  "Chia seeds contain five times more calcium than milk.",
  "In Germany, peas were grown on 82,600 hectares of land in 2020.",
  "In Germany, approximately 36,000 tons of pears were harvested in 2023.",
  "Rosemary can be harvested after a germination period of about four weeks.",
  "There are about 30mg of caffeine in a cup of green tea.",
  "Farmers plant radishes with approximately 200 seeds per square meter.",
  "The cultivation area of corn in Germany is around 472,200 hectares.",
  "The German Nutrition Society recommends 400 grams of vegetables per day.",
  "McDonald's sells 75 hamburgers per second.",
  "Wheat requires about 500 liters of water per kilogram of dry matter.",
  "China produced approximately 63 million tons of salt in 2019.",
  "There are over 40 different types of kiwi.")


trivia_df <- data.frame(ID   = 1:length(trivia),
                        fact = trivia,
                        type = c(rep("Post",15),rep("Test",6)))


trivia_df %>%
  kbl(align = "c") %>%
  kable_styling(fixed_thead = T)
ID fact type
1 An average ear of corn has an even number of rows, usually 16 Post
2 A watermelon consists of about 95 percent water. Post
3 Every German needs about 2,700 square meters of farmland for food supply. Post
4 Children see junk food advertisements an average of 27 times a day. Post
5 Approximately 500 million snails are eaten in France every year. Post
6 The largest cashew tree in the world covers an area of 8,500 square meters. Post
7 The price of food increased by 9 percent between August 2022 and August 2023. Post
8 Germans eat about 20 kilos of tomatoes per year. Post
9 Caffeine has a half-life of four to six hours. Post
10 Chia seeds contain five times more calcium than milk. Post
11 In Germany, peas were grown on 82,600 hectares of land in 2020. Post
12 In Germany, approximately 36,000 tons of pears were harvested in 2023. Post
13 Rosemary can be harvested after a germination period of about four weeks. Post
14 There are about 30mg of caffeine in a cup of green tea. Post
15 Farmers plant radishes with approximately 200 seeds per square meter. Post
16 The cultivation area of corn in Germany is around 472,200 hectares. Test
17 The German Nutrition Society recommends 400 grams of vegetables per day. Test
18 McDonald's sells 75 hamburgers per second. Test
19 Wheat requires about 500 liters of water per kilogram of dry matter. Test
20 China produced approximately 63 million tons of salt in 2019. Test
21 There are over 40 different types of kiwi. Test

Plot

CO2e per kg

Code 
ggplot(items,aes(x = fct_reorder(name,`g CO2 pro kg`),
                 y = `g CO2 pro kg`,
                 color = factor(seeding_CO2))) +
  geom_point(size=p_size) +
  geom_linerange(aes(ymax = `g CO2 pro kg`, ymin = 0),linewidth=lwd) +
  facet_wrap(.~category,scales="free",nrow=2)+
  labs(title = "",
       x     = "",
       y     = "",
       color = "Seed Item") +
  theme_nice() +
  scale_color_manual(values = c("black","#22A884FF")) +
  theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.2)))

Code 
ggplot(items,aes(x = fct_reorder(name,`g CO2 pro kg`),
                 y = `g CO2 pro kg`,
                 color = factor(seeding_CO2))) +
  geom_point(size=p_size) +
  geom_linerange(aes(ymax = `g CO2 pro kg`, ymin = 0),linewidth=lwd) +
  facet_wrap(.~category,scales="free",nrow=2)+
  labs(title = "",
       x     = "",
       y     = "",
       color = "Seed Item") +
  scale_color_manual(values = c("black","#22A884FF")) +
  theme_nice() +
  theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
  scale_y_continuous(expand = c(0, 0), limits = c(0,28500))

Kcal per 100g

Code 
ggplot(items,aes(x = fct_reorder(name,`Kcal pro 100g`),
                 y = `Kcal pro 100g`,
                 color = factor(seeding_Kcal))) +
  geom_point(size=p_size) +
  geom_linerange(aes(ymax = `Kcal pro 100g`, ymin = 0),linewidth=lwd) +
  facet_wrap(.~category,scales="free",nrow=2)+
  labs(title = "",
       x     = "",
       y     = "",
       color = "Seed Item") +
  scale_color_manual(values = c("black","#22A884FF")) +
  theme_nice() +
  theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
  scale_y_continuous(expansion(mult = c(0, 0.2)))

Code 
ggplot(items,aes(x = fct_reorder(name,`Kcal pro 100g`),
                 y = `Kcal pro 100g`,
                 color = factor(seeding_Kcal))) +
  geom_point(size=p_size) +
  geom_linerange(aes(ymax = `Kcal pro 100g`, ymin = 0),linewidth=lwd) +
  facet_wrap(.~category,scales="free",nrow=2)+
  labs(title = "",
       x     = "",
       y     = "",
       color = "Seed Item") +
  scale_color_manual(values = c("black","#22A884FF")) +
  theme_nice() +
  theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
  scale_y_continuous(expand = c(0, 0), limits = c(0,800))

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