Standard Deviation Nones

std_dev.R

rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) }

whtevan <- rnorm2(100000, 5.13, 1.99)
oevan <- rnorm2(100000, 3.16, 2.13)
whtml <- rnorm2(100000, 4.21, 2.28)
bprot <- rnorm2(100000, 2.00, 1.56)
whtcath <- rnorm2(100000, 4.20, 2.24)
ocath <- rnorm2(100000, 3.01, 2.01)
ath <- rnorm2(100000, 2.42, 1.63)
agn <- rnorm2(100000, 2.730, 1.74)
nip <- rnorm2(100000, 3.37, 1.96)
mormon <- rnorm2(100000, 4.99, 1.87)
muslim <- rnorm2(100000, 2.62, 1.69)
jew <- rnorm2(100000, 2.95, 2.17)
buddhist <- rnorm2(100000, 2.76, 1.72)
hindu <- rnorm2(100000, 3.06, 1.91)
all <- rnorm2(100000, 3.73, 2.19)


full <- tibble(whtevan, oevan, whtml, bprot, whtcath, ocath, ath, agn, nip, mormon, muslim, jew, buddhist, hindu, all)

full <- melt(full) %>% as_tibble() 

full <- full %>% 
  mutate(variable1 = frcode(variable == "whtml" ~ "White Mainline", 
                            variable == "whtcath" ~ "White Catholic",
                            variable == "all" ~ "Entire Sample",
                            variable == "jew" ~ "Jewish", 
                            variable == "oevan" ~ "Non-White Evangelical",
                            variable == "ocath" ~ "Non-White Catholic",
                            variable == "whtevan" ~ "White Evangelical", 
                            variable == "nip" ~ "Nothing in Particular", 
                            variable == "hindu" ~ "Hindu",
                            variable == "mormon" ~ "Mormon", 
                            variable == "agn" ~ "Agnostic", 
                            variable == "buddhist" ~ "Buddhist", 
                            variable == "muslim" ~ "Muslim", 
                            variable == "ath" ~ "Atheist",
                            variable == "bprot" ~ "Black Protestant"
                          ))

full <- full %>% 
  mutate(sd = frcode(variable == "whtml" ~ "SD: 2.28", 
                     variable == "whtcath" ~ "SD: 2.24",
                     variable == "all" ~ "SD: 2.19",
                     variable == "jew" ~ "SD: 2.17", 
                     variable == "oevan" ~ "SD: 2.13",
                     variable == "ocath" ~ "SD: 2.01",
                     variable == "whtevan" ~ "SD: 1.99", 
                     variable == "nip" ~ "SD: 1.96", 
                     variable == "hindu" ~ "SD: 1.91",
                     variable == "mormon" ~ "SD: 1.87",
                     variable == "agn" ~ "SD: 1.74",
                     variable == "buddhist" ~ "SD: 1.72", 
                     variable == "muslim" ~ "SD: 1.69", 
                     variable == "ath" ~ "SD: 1.63", 
                     variable == "bprot" ~ "SD: 1.56"
                     ))


full <- full %>% 
  mutate(mean = frcode( variable == "whtml" ~ 4.21,
                        variable == "whtcath" ~ 4.2,
                        variable == "all" ~ 3.73,
                        variable == "jew" ~ 2.95, 
                        variable == "oevan" ~ 3.16,
                        variable == "ocath" ~ 3.01,
                        variable == "whtevan" ~ 5.13, 
                        variable == "nip" ~ 3.37, 
                        variable == "hindu" ~ 3.06,
                        variable == "mormon" ~ 4.99, 
                        variable == "agn" ~ 2.73, 
                        variable == "buddhist" ~ 2.76,
                        variable == "muslim" ~ 2.62,
                        variable == "ath" ~ 2.42,
                        variable == "bprot" ~ 2.00 
                       
                      ))

means <- full %>% 
  distinct(variable1, mean) %>% 
  mutate(mean = as.numeric(as.character(mean)))


labels <- full %>% 
  distinct(variable1, sd)

full <- full %>% 
  mutate(sd1= as.character(sd))

full$sd1 <- parse_number(full$sd1)


full %>% 
  ggplot(aes(value)) + 
  geom_density(aes(fill = variable1), alpha = .7) +
  facet_wrap(~ variable1) +
  theme_gg("Abel") +
  scale_x_continuous(limits = c(1, 7), breaks = c(1,2,3,4,5,6,7), labels = c("Dem", "", "", "Ind", "", "", "Rep")) + 
  scale_fill_d3(palette = c("category20")) +
  scale_y_continuous(labels = percent) +
  theme(panel.spacing = unit(1, "lines")) +
  theme(strip.text.x = element_text(size = 9)) +
  theme(axis.text.y = element_text(size = 8)) +
  geom_vline(data = means,aes(xintercept = mean),color = "black", linetype = "dashed") +
  labs(x = "Party Identification", y = "", title = "Visualizing the Heterogeneity of Party Identification", caption = "@ryanburge\nData: CCES 2018", subtitle = "Mean Denoted by Dashed Vertical Line") +
  geom_text(data = labels, aes(label=sd), x = Inf, y = -Inf, hjust=1.1, vjust=-12, inherit.aes = FALSE, family = "font", size = 3) +
  ggsave("E://distribution_sd_pid7.png", type = "cairo-png", height = 8)



big <- rnorm2(100000, 45, 20)
med <- rnorm2(100000, 45, 10)
small <- rnorm2(100000, 45, 2)


full <- tibble(big, med, small)

full <- melt(full) %>% as_tibble() 


full <- full %>% 
  mutate(variable1 = frcode(variable == "big" ~ "Large Std. Dev.", 
                            variable == "med" ~ "Medium Std. Dev.", 
                            variable == "small" ~ "Small Std. Dev."))

full <- full %>% 
  mutate(sd = frcode(variable == "big" ~ "SD: 20", 
                     variable == "med" ~ "SD: 10", 
                     variable == "small" ~ "SD: 2"))

labels <- full %>% 
  distinct(variable1, sd)


full %>% 
  ggplot(aes(value)) + 
  geom_density(aes(fill = variable1), alpha = .4) +
  facet_wrap(~ variable1) +
  theme_gg("Abel") +
  scale_x_continuous(limits = c(0, 100)) + 
  scale_fill_d3(palette = c("category20")) +
  scale_y_continuous(labels = percent) +
  theme(panel.spacing = unit(1, "lines")) +
  theme(strip.text.x = element_text(size = 10)) +
  theme(axis.text.y = element_text(size = 8)) +
  labs(x = "Age", y = "", title = "Visualizing Three Levels of Standard Deviation", caption = "@ryanburge\nData: Hypothetical Data") +
  geom_text(data = labels, aes(label=sd), x = Inf, y = -Inf, hjust=2, vjust=-9, inherit.aes = FALSE, family = "font", size = 3) +
  ggsave("E://distribution_sd_example.png", type = "cairo-png")