Tableau Saves Me Time

View my Tableau Public page for all the charts i've created.

It's easier and faster to conduct exploratory analysis on Tableau versus R and the charts look prettier too.

For example, Instead of writing lines of code, copy-pasting, and editing this using R:

	library("pacman")
	pacman::p_load(ggplot2, grid, gridExtra, robustbase)
	data = read.csv(file.choose())
	str(data)
	attach(data)
	#Transform to factors
	PsychRegions <- as.factor(PsychRegions)
	region <- as.factor(region)
	division <- as.factor(division)
	# Using boxplots for each variable separately
	# data.science
	u01 <- qplot(data = data, y = data.science, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab=NULL, ylab = NULL,
	main="data.science") +
	geom_text(aes(label = ifelse(data.science %in%
	boxplot.stats(data.science)$out,
	as.character(state_code), "")), hjust = 1.5)
	u01
	# cluster.analysis
	u02 <- qplot(data = data,y = cluster.analysis, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main = "cluster.analysis") +
	geom_text(aes(label = ifelse(cluster.analysis %in%
	boxplot.stats(cluster.analysis)$out,
	as.character(state_code), "")), hjust = 1.5)
	u02
	# college
	u03 <- qplot(data = data, y = college, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="college") +
	geom_text(aes(label = ifelse(college %in%
	boxplot.stats(college)$out,
	as.character(state_code), "")), hjust = 1.5)
	u03
	# startup
	u04 <- qplot(data = data, y = startup, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="startup") +
	geom_text(aes(label = ifelse(startup %in%
	boxplot.stats(startup)$out,
	as.character(state_code), "")), hjust = 1.5)
	u04
	# entrepreneur
	u05 <- qplot(data = data, y = entrepreneur, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="entrepreneur") +
	geom_text(aes(label = ifelse(entrepreneur %in%
	boxplot.stats(entrepreneur)$out,
	as.character(state_code), "")), hjust = 1.5)
	u05
	# ceo
	u06 <- qplot(data = data, y = ceo, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="ceo") +
	geom_text(aes(label = ifelse(ceo %in%
	boxplot.stats(ceo)$out,
	as.character(state_code), "")), hjust = 1.5)
	u06
	# mortgage
	u07 <- qplot(data = data, y = mortgage, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="mortgage") +
	geom_text(aes(label = ifelse(mortgage %in%
	boxplot.stats(mortgage)$out,
	as.character(state_code), "")), hjust = 1.5)
	u07
	# nba
	u08 <- qplot(data = data, y = nba, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="nba") +
	geom_text(aes(label = ifelse(nba %in%
	boxplot.stats(nba)$out,
	as.character(state_code), "")), hjust = 1.5)
	u08
	# royal.family
	u09 <- qplot(data = data, y = royal.family, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="royal.family") +
	geom_text(aes(label = ifelse(royal.family %in%
	boxplot.stats(royal.family)$out,
	as.character(state_code), "")), hjust = 1.5)
	u09
	# Neuroticism
	u10 <- qplot(data = data, y = Neuroticism, x = 1,
	geom = "boxplot", outlier.colour = "#E38942",
	xlim = c(0, 2), xlab = NULL, ylab = NULL,
	main="Neuroticism") +
	geom_text(aes(label = ifelse(Neuroticism %in%
	boxplot.stats(Neuroticism)$out,
	as.character(state_code), "")), hjust = 1.5)
	u10
	# Plot all 10 together
	grid.arrange(u01, u02, u03, u04, u05,
	u06, u07, u08, u09, u10,
	nrow = 2,
	top = "Boxplots: Univariate outliers")

view raw Anomaly Detection (Univariate) hosted with ❤ by GitHub

In order to ultimately get this:

I could just drag and drop pills, write 2 lines of Calculated Fields, and do some optional layouting on Tableau to do this: