StarTrek voice commands

Contribution to #TidyTuesday week 34

Last updated on Aug 20, 2021 4 min read

Two weeks in a row! Kudos for me! I’ve decided that I’ll try to participate every week in #TidyTuesday, even if it’s a single graph. What matters to me is to keep up practizing and to find a new topic to explore each week.

(For those who haven’t met them: #TidyTuesday is a collaborative project which consists in working each week with a new real data set, to practice visualization skills and such. You can find more about it in their GitHub repo and under the Twitter hashtag.)

This week dataset has the voice commands used by StarTrek characters. I know this must have been a great opportunity to practice text analysis; I, ironically, still haven’t learned how to do it. Worse, I haven’t even seen StarTrek, so I didn’t have background to understand how to really make the most of it.

So, to be faithful to my goal, I’ve decided to create a modest graph: counting how many utterances were madre, organizing them by type of utterance and dividing them by character type (person or computer). Then, I thought what kind of modifications I could add. Understanding the functioning of theme() has always been a great challenge for me; I tend to chose a theme and then stick with the default options, because I don’t have a lot of visual skills.

So, what have I learned this week?

I did a lollipop chart; even though I had already used them before, this time I took the time to read more about them. I learned that some people think that the increase of visual attractiveness that the chart provides, actually decreases their precision (because the circle in the point of the line is more imprecise than the strict line of a bar).
I did modify a lot of the theme’s component: I learned that the chart’s background color and the image background color are different objects. I changed the font and it’s size; I reinforced the functioning of the faceted graph labels.
I learned about the tidytext::reorder_within() function, that was first mentioned in this Tyler Rinker blogpost, and then added to the tidytext package (in this Julia Silge post you can find a more detailed explanation of the process). But, as with great power comes great responsability, this function duplicated all the factors labels (who knows why), so I had to edit them… by hand. Whatever.

The code

library(tidyverse)
library(tidytext)
library(ggthemes)

tuesdata <- tidytuesdayR::tt_load(2021, week = 34)

## 
## 	Downloading file 1 of 1: `computer.csv`

computer <- tuesdata$computer

computer <- computer %>% 
  mutate_all(.funs = tolower) %>%  # to unify rows
  mutate_if(is.character, as.factor)

computer$char_type <- factor(computer$char_type, levels = c("computer", "person"), 
                             labels = c("Computer", "Person")) # arrange

computer %>% 
  group_by(char_type, type) %>% 
  summarise(count = n()) %>%  # command quantity
  mutate(type = reorder_within(type, count, char_type)) %>% # arranged by size
  ggplot(aes(x=reorder(type, count), y = count))+
  geom_point(color = "red")+
  geom_segment(aes(x=reorder(type, count), xend =type, y=0, yend=count, color = "red"))+
  facet_wrap(~char_type, 
             scales = "free", # to prevent blank spots
             labeller = labeller( # facet title
               Computer = computer,
               Person = person))+
  coord_flip()+
  scale_y_reordered()+ # rearrange elements within the facet
  scale_x_discrete(labels = c("response___Computer" = "Response",
                              "alert___Computer" = "Alert",
                              "info___Computer" = "Information",
                              "countdown___Computer" = "Countdown",
                              "clarification___Computer" = "Clarification",
                              "progress___Computer" = "Progress",
                              "conversation___Computer" = "Conversation",
                              "command___Person" = "Command",
                              "wake word___Person" = "Wake word",
                              "statement___Person" = "Statement",
                              "question___Person" = "Question",
                              "conversation___Person" = "Conversation",
                              "password___Person" = "Password",
                              "comment___Person" = "Comment"))+
  theme_few()+
  theme(plot.background = element_rect(fill = "black"), # image background
        panel.background = element_rect(fill = "black"), # chart background
        axis.text = element_text(color = "white", size = 12), # axis text
        plot.title = element_text(color = "white", 
                                  size = 20), # chart title
        plot.caption = element_text(color = "white"), # font
        legend.position = "none", # remove reference 
        text = element_text(family = "Kefa"), # chart font
        panel.grid.major.x = element_line(size = 4), # shrink space between ticks
        strip.text = element_text(size = 12) # facet title font size
  )+
  labs(x = "", y = "",
       title = "What kind of utterances produce each one?",
       caption = "Source: Speech Interactions | http://www.speechinteraction.org/TNG/ | @_msquiroga"
    )

I hope you find this useful. Let me know!

As always, remember you can suscribe to my blog to stay updated, and if you have any questions, don’t hesitate to contact me. And if you like what I do, you can buy me a cafecito from Argentina or a kofi.

StarTrek voice commands

So, what have I learned this week?

The code

Macarena Quiroga

Linguist/PhD student

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