Big_5_Personality_By_Country

personality.Rmd

library("tidyverse")
library("data.table")
library("ggplot2")
library("ggpubr")
library("dplyr")
library("magrittr")
library("countrycode")

# Data normalizing func
normalize <- function(x) 100*(x-min(x))/(max(x)-min(x))

data <- fread(
	# Read data pulled down from site
	"~/PROJECTS/FUN/personality/data-final.csv",
	sep = "\t",
	#na.strings=("NA"),
	stringsAsFactors = FALSE
) %>%
	# Remove some rows with data I'm not using
	select(c(1:50, 101:110)) %>%
	filter(
		IPC == 1,
		as.numeric(as.character(introelapse)) < 60,
		as.numeric(as.character(testelapse)) < 900,
		as.numeric(as.character(endelapse)) < 60,
	)   %>%
	group_by(country) %>%
	# Require at least 1000 individuals, filter any inds without a country
	filter(n() > 1000 && country != "NONE") %>%
	droplevels() %>%
	# change factors to numeric
	mutate_at(c(1:50), as.numeric, ) %>%
	# Calc scores based on https://ipip.ori.org/new_ipip-50-item-scale.htm
	mutate(
		O = normalize(OPN1 - OPN2 + OPN3 - OPN4  + OPN5 - OPN6 + OPN7 + OPN8 + OPN9 + OPN10),
		C = normalize(CSN1 - CSN2 + CSN3 - CSN4 + CSN5 - CSN6 + CSN7 - CSN8 + CSN9 + CSN10),
		E = normalize(EXT1 - EXT2 + EXT3 - EXT4 + EXT5 - EXT6 + EXT7 - EXT8 + EXT9 - EXT10),
		A = normalize(AGR1 + AGR2 - AGR3 + AGR4 - AGR5 + AGR6 - AGR7 + AGR8 + AGR9 + AGR10),
		N = normalize(EST1 - EST2 + EST3 - EST4 + EST5 + EST6 + EST7 + EST8 + EST9 + EST10)
	) 

#recode country names
data$country <- countrycode(data$country, "iso2c","country.name")
data <- data %>%
	mutate(country = recode(country,
		`Hong Kong SAR China` = "Hong Kong",
		`United States` = "U.S.",
		`United Kingdom` = "U.K.",
		`United Arab Emirates` = "U.A.E."))


O <-
	ggplot(data, aes(
		x = O,
		y = reorder(country, O),
		fill = country
	)) + geom_violin() + theme_minimal() + theme(legend.position = "none")  + labs(title = "Openess",x="", y = "") 

C <-
	ggplot(data, aes(
		x = C,
		y = reorder(country, C),
		fill = country
	)) + geom_violin() + theme_minimal() + theme(legend.position = "none")  + labs(title =
			"Conscientiousness",x="", y = "") 

E <-
	ggplot(data, aes(
		x = E,
		y = reorder(country, E),
		fill = country
	)) + geom_violin() + theme_minimal() + theme(legend.position = "none")  + labs(title =
			"Extroversion",x="", y = "") 

A <-
	ggplot(data, aes(
		x = A,
		y = reorder(country, A),
		fill = country
	)) + geom_violin() + theme_minimal() + theme(legend.position = "none")  + labs(title =
			"Agreeableness",x="", y = "") 

N <-
	ggplot(data, aes(
		x = N,
		y = reorder(country, N),
		fill = country
	)) + geom_violin() + theme_minimal() + theme(legend.position = "none")  + labs(title =
			"Neuroticism",x="", y = "")

ggsave(
	"OCEAN.png",
	plot = ggarrange(O, C, E, A, N , nrow = 1),
	height = 15,
	width = 15,
	dpi = 300
)