Demographic plots

Lab PI?

pie(table(dr$pi), cex = .7)

Chance of contributing data.

pie(table(dr$contribution_prob), cex = .7)

Expected number of babies.

pie(table(dr$num_infants), cex = .7)

Preference plots

Aggregate data.

ms <- d %>%
  group_by(study) %>%
  summarise(MB1 = sum(interest == "MB1"), 
            MB2 = sum(interest == "MB2"),
            able = sum(ability == "Yes"), 
            maybe_able = sum(ability == "Maybe")) %>%
  gather(measure, sum, MB1, MB2, able, maybe_able) %>%
  mutate(category = ifelse(measure == "able" | measure == "maybe_able", 
                           "ability", "interest"))

Plot for ManyBabies 1 study preference.

ms$study <- factor(ms$study, levels = unique(ms$study)[sort(ms$sum[ms$measure=="MB1"], decreasing = TRUE, index.return = TRUE)$ix])

ggplot(filter(ms, category == "interest" & measure == "MB1"), 
       aes(x = study, y = sum)) + 
  geom_bar(stat="identity", position = "dodge") + 
  theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1)) + 
  scale_fill_solarized()

ManyBabies 2 study preference.

ms$study <- factor(ms$study, levels = unique(ms$study)[sort(ms$sum[ms$measure=="MB2"], decreasing = TRUE, index.return = TRUE)$ix])

ggplot(filter(ms, category == "interest" & measure == "MB2"), 
       aes(x = study, y = sum)) + 
  geom_bar(stat="identity", position = "dodge") + 
  theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1)) + 
  scale_fill_solarized()

And ability to do the studies.

ms$study <- factor(ms$study, levels = unique(ms$study)[sort(ms$sum[ms$category=="ability"], decreasing = TRUE, index.return = TRUE)$ix])

ggplot(filter(ms, category == "ability"), 
aes(x = study, y = sum, fill = measure)) + 
  geom_bar(stat="identity") + 
  theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1)) + 
  scale_fill_solarized()