6  Advanced ggplot

library(tidyverse)
source("data_AJPS2021/0_ajps_recode.R")

Grab a subset of variables (i.e. psychological measurements), and show the distribution of conspiracy thinking when respondents report a particular trait:

x <- c("argue1", "argue2", "argue3", "manip1", "manip2", "manip3", "manip4", 
       "attent1", "attent2", "attent3", "attent4",
       "insens1","insens2","insens3","insens4")

#d1$nondark <- ifelse(d1$insens3 <=2 & d1$manip4 <=2 & d1$insens1<=2, 1,0)
d1$nondark <- ifelse(d1$manipulate2 <=.2 & d1$sociopathy2 <= .2 & d1$attend2 <=.2, 1,0)

The most important step is the following:

d1 %>% select(consp_Index2,
              nondark,
              all_of(x)) %>% 
  pivot_longer(cols = -consp_Index2)

# A tibble: 32,000 × 3
   consp_Index2 name    value             
          <dbl> <chr>   <dbl+lbl>         
 1        0.938 nondark 0                 
 2        0.938 argue1  3 [Neither]       
 3        0.938 argue2  4 [Agree]         
 4        0.938 argue3  4 [Agree]         
 5        0.938 manip1  5 [Strongly agree]
 6        0.938 manip2  5 [Strongly agree]
 7        0.938 manip3  5 [Strongly agree]
 8        0.938 manip4  5 [Strongly agree]
 9        0.938 attent1 4 [Agree]         
10        0.938 attent2 3 [Neither]       
# ℹ 31,990 more rows

And the full plot can be generated with the following code:

d1 %>% select(consp_Index2,
              nondark,
              all_of(x)) %>% 
  pivot_longer(cols = -consp_Index2) %>%
  mutate(name2 = case_when(
    name == "argue1" ~  "I like to argue online with other people",
    name == "argue2" ~  "I enjoy a good argument over a controversial issue",
    name == "argue3" ~  "I am willing to express my opinion online\neven if others strongly disagree with me",
    name == "manip1" ~  "I tend to manipulate others to get my way",
    name == "manip2" ~  "I have used deceit or lied to get my way",
    name == "manip3" ~  "I have used flattery to get my way",
    name == "manip4" ~  "I tend to exploit others towards my own end",
    name == "attent1" ~  "I tend to want others to admire me",
    name == "attent2" ~  "I tend to want others to pay attention to me",
    name == "attent3" ~  "I tend to seek prestige or status",
    name == "attent4" ~  "I tend to expect special favors from others",
    name == "insens1" ~ "I tend to lack remorse",
    name == "insens2" ~ "I tend to be unconcerned with the morality\nof my actions",
    name == "insens3" ~ "I tend to be callous or insensitive",
    name == "insens4" ~ "I tend to be cynical",
    name == "nondark" ~ "Low on narcissism, mach., and sociopathy"
  ),
  concept = case_when(
    name %in% c("argue1","argue2","argue3") ~ "Argumentativeness",
    name %in% c("manip1", "manip2", "manip3", "manip4") ~ "Machiavellianism",
    name %in% c("attent1", "attent2", "attent3", "attent4") ~ "Narcissism",
    name %in% c("insens1","insens2","insens3","insens4") ~ "Sociopathy",
    name %in% c("nondark") ~ "Non-dark"
  )) %>%
  filter(!is.na(value) & !is.na(consp_Index2)) %>%
  mutate(agree = ifelse(value>=5,1,0)) %>% 
  group_by(name) %>% mutate(avg_agree = mean(agree)) %>%
  group_by(name,agree) %>% mutate(avg_cons = mean(consp_Index2)) %>%
  ungroup() %>%
  filter(agree==1 | (name=="nondark" & value==1)) %>%
  ggplot(aes(x=consp_Index2,y=fct_reorder(name2,avg_cons),
             fill=concept)) +
  ggridges::geom_density_ridges() +
  theme_bw()  +
  theme(legend.position = "right") +
  labs(x="Conspiracy thinking",y="",color="",fill="Concept",
       title = "Distribution of conspiracy thinking\nbroken down by responses to psychological questions\nand color-coded by trait (dark triad + argumentativeness)") +
  #scale_fill_manual(values=Prism5) +
  see::scale_fill_social() +
  scale_x_continuous(limits = c(0,1))

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[See the lecture notes on the course website for additional code]