STRAEQ-2: items rating and selection

# STRAEQ-2 120 items scale
gs4_deauth() # indicate there is no need for a token.

items_120 <- read_sheet("https://docs.google.com/spreadsheets/d/1nEfuMVtYh5kclsDS1qxKsbMRiIw-libDEp7epfjZDq0/edit#gid=0") 

DT::datatable(items_120)

Ratings distribution of the full list of items

General ratings distribution

pallete <- c("#85D4E3", "#F4B5BD", "#9C964A", "#C94A7D", "#CDC08C", "#FAD77B", "#7294D4", "#DC863B", "#972D15")

all_exp_plot <- df_tidy %>%
  ggplot(aes(rating, fill = expert)) + 
  geom_bar() +
  scale_fill_manual(values = pallete)

all_exp_plot

Expert’s ratings distribution

e1 <- df %>%
  ggplot(aes(adeyemi)) +
  geom_bar(fill = "#85D4E3") 

e2 <- df %>%
  ggplot(aes(daniela)) +
  geom_bar(fill = "#F4B5BD") 

e3 <- df %>%
  ggplot(aes(hans)) +
  geom_bar(fill = "#9C964A")

e4 <- df %>%
  ggplot(aes(mattie)) +
  geom_bar(fill = "#C94A7D")

e5 <- df %>%
  ggplot(aes(olivier)) +
  geom_bar(fill = "#CDC08C")

e6 <- df %>%
  ggplot(aes(qinglan)) +
  geom_bar(fill = "#FAD77B")

e7 <- df %>%
  ggplot(aes(rodrigo)) +
  geom_bar(fill = "#7294D4") 

e8 <- df %>%
  ggplot(aes(siegwart)) +
  geom_bar(fill = "#DC863B")

e9 <- df %>%
  ggplot(aes(soufian)) +
  geom_bar(fill = "#972D15")

plot_grid(e1, e2, e3, e4, e5, e6, e7, e8, e9)

Expert’s ratings distribution per subscales

#  for face grid labs
subscale.labs <- c("T1", "T2", "T3", "T4", "R1", "R2", "R3", "R4", "F1", "F2", "F3", "F4")
names(subscale.labs) <- c("temp_sens","temp_soli","temp_desi", "temp_conf", "risk_sens", "risk_soli", "risk_desi", "risk_conf", "food_sens", "food_soli", "food_desi", "food_conf")

expert_plot <- df_tidy %>%
  ggplot(aes(rating, fill = expert, show.legend = FALSE)) + 
  geom_bar(show.legend = FALSE) +
  facet_grid(expert ~ subscale, labeller = labeller(subscale = subscale.labs)) +
  scale_fill_manual(values = pallete) 

expert_plot

Full list of items per dimension and subscale

Temperature regulation dimension

Temperature: sensitivity subscale

temp_sens <- df %>% 
  filter(grepl('temp_sens', subscale)) %>% #select rows "temp_sens" in the subscale column
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(temp_sens)

Temperature: solitary subscale

temp_soli <- df %>% 
  filter(grepl('temp_soli', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(temp_soli)

Temperature: desire to outsource with others subscale

temp_desi <- df %>% 
  filter(grepl('temp_desi', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(temp_desi)

Temperature: confidence in others subscale

temp_conf <- df %>% 
  filter(grepl('temp_conf', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(temp_conf)

Risk avoidance dimension

Risk: sensitivity subscale

risk_sens <-  df %>% 
  filter(grepl('risk_sens', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(risk_sens)

Risk: solitary subscale

risk_soli <-  df %>% 
  filter(grepl('risk_soli', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(risk_soli)

Risk: desire to outsource with others subscale

risk_desi <- df %>% 
  filter(grepl('risk_desi', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(risk_desi)

Risk: confidence in others subscale

risk_conf <-  df %>% 
  filter(grepl('risk_conf', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(risk_conf)

Food intake dimension

Food: sensitivity subscale

food_sens <-  df %>% 
  filter(grepl('food_sens', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(food_sens)

Food: solitary subscale

food_soli <-  df %>% 
  filter(grepl('food_soli', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(food_soli)

Food: desire to outsource with others subscale

food_desi <-  df %>% 
  filter(grepl('food_desi', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(food_desi)

Food: confidence in others subscale

food_conf <- df %>% 
  filter(grepl('food_conf', subscale)) %>%
  arrange(sd) %>%
  arrange(desc(mean)) %>% 
  select(item, mean, sd, country)

DT::datatable(food_conf)

World map of the 120 selected items

# import the world.cities data frame from 'maps' in order to have GPS positions of the countries
data(world.cities)

# remane some country to match the world.cities data base
items_120$country[items_120$country == "United Kingdom"] <- "UK"
items_120$country[items_120$country == "United States"] <- "USA"
items_120$country[items_120$country == "Serbia"] <- "Serbia and Montenegro"
  
# merge the desired cols from that the world.cities data frame with mine by country = add lat and lng 
items_120 <- world.cities %>%
    filter(capital == 1) %>%
    dplyr::select(country = country.etc, lat, lng = long) %>%
    left_join(items_120, ., by = "country")

# identify the number of items per country, this is needed to vary the size of the dots in the map
df_country_count <- items_120 %>%
  group_by(country) %>%
  summarise(n()) %>%
  rename(n = "n()")

# create the data frame to generate the map
items_120 <- left_join(items_120, df_country_count)

# create the map
m <- leaflet(items_120) %>% addTiles() %>%
                         addCircles(lng = ~lng, lat = ~lat, weight = 1,
                                    radius = ~ log(n + 5) * 100000, popup = ~paste(country, ":", item, "(", subscale, ")"),
                                     stroke = T, opacity = 1, fill = T, color = "#a500a5", fillOpacity = 0.09)

# print the map
m