16 Personalities with Circlize

16 Personalities displayed in circle with circlize package

16 Personalities

While back, my husband introduced me to this fun online personalities test called 16 personalities. I’ve taken the test, and my personalities type came back as Mediator (Code: INFP - Role: Diplomat)

There were difinitely some traits that sounded like me, and a lot of statement I can relate for sure. It’s definitely some fun test to do.

There was a one part that stated “INFP - compromising just 4% of the population”. This read me to think which traits are more popular than the others, but so far I haven’t found details on population breakdown. Later if I do find population breakdown of personalities trait, then I thought it would be interesting to make the plot proportional to the ratio.

Anyways, I thought I’d get all 16 personalities and plot them using circlize package as practice. The documentation on circlize was extremely helpful to get me started!

Below is the code I wrote for now, so I can remember what I have done in case I want to utilize circlize packages.

Here’s snippets of code I’ve used to create above.

First I’ve used package rvest to get some data about all 16 personalities.

## Get 16 Different Personalities from 16 Personalities Website

per.df <- tibble(
type = personalities %>% html_nodes(".type-item h4") %>%
html_text() %>% gsub(pattern="(“|”)",replacement=""),
type.code = personalities %>% html_nodes("h5") %>% html_text(),
short.descr = personalities %>% html_nodes(".type-snippet") %>% html_text(),
img = personalities %>% html_nodes(".type-item img") %>% html_attr("src")
)

## Add few more details to table.
per.df <- per.df %>% mutate(type.code=substr(type.code,1,4), mind=substr(type.code,1,1),
energy = substr(type.code,2,2), nature = substr(type.code,3,3),
tactic = substr(type.code,4,4))

## There are 4 "Roles" based on followig rules ## I only really recently discovered case_when statment!
### case_when statement is amazing! I've been writing crazy nested ifelse statement till very recently.
per.df <- per.df %>% mutate(roles = case_when(
str_detect(.$type.code,".NT.") ~ "Analyst", str_detect(.$type.code,".NF.") ~ "Diplomat",
str_detect(.$type.code,".S.J") ~ "Sentinels", str_detect(.$type.code,".S.P") ~ "Explorers"
))

Here comes exciting drawing with circlize package!

circos.clear()
circos.par(start.degree=90)

## initialize
circos.initialize(factor=per.df$type, xlim=c(0,1)) ## Layout basic tracks circos.track(ylim=c(0,1), panel.fun = function(x,y){ circos.text(x=CELL_META$xcenter, y=CELL_META$ycenter, labels=CELL_META$sector.index,
facing = "inside",
niceFacing= TRUE, font=2)
})

## I definitely think there's better way to write below....
## But at the moment, I'm building it section by section...
highlight.sector(per.df$type[1:4], track.index = 1, text = per.df$roles[1],
facing = "bending.inside", niceFacing = TRUE,
text.vjust = "15mm", cex = 1.5, col="#734C5F60")

highlight.sector(per.df$type[5:8], track.index = 1, text = per.df$roles[5],
facing = "bending.inside", niceFacing = TRUE,
text.vjust = "15mm", cex = 1.5, col="#728D5660")

highlight.sector(per.df$type[9:12], track.index = 1, text = per.df$roles[9],
facing = "bending.inside", niceFacing = TRUE,
text.vjust = "15mm", cex = 1.5, col="#51A9AB60")

highlight.sector(per.df$type[13:16], track.index = 1, text = per.df$roles[13],
facing = "bending.inside", niceFacing = TRUE,
text.vjust = "15mm", cex = 1.5, col="#E5C82E60")

###  Now the inner track.  I want to place images.
circos.track(ylim=c(0,1),
panel.fun = function(x,y){
circos.raster(image=image_read(per.df$img[CELL_META$sector.numeric.index]),
x=CELL_META$xcenter, y=CELL_META$ycenter,
width="2.5cm", facing = "inside", niceFacing=TRUE)
},bg.border = NA, track.height=0.3)

## Place another track with type code.
circos.track(ylim=c(0,1),
panel.fun = function(x,y){
circos.text(x=CELL_META$xcenter, y=CELL_META$ycenter,
labels=per.df$type.code[CELL_META$sector.numeric.index],
facing = "inside",
niceFacing= TRUE)
},bg.border = NA , track.height=0.15)