Tag: information

# Play Your Charts Right: Tips for Effective Data Visualization – by Geckoboard

In a world where data really matters, we all want to create effective charts. But data visualization is rarely taught in schools, or covered in on-the-job training. Most of us learn as we go along, and therefore we often make choices or mistakes that confuse and disorient our audience.
From overcomplicating or overdressing our charts, to conveying an entirely inaccurate message, there are common design pitfalls that can easily be avoided. We’ve put together these pointers to help you create simpler charts that effectively get across the meaning of your data.

Geckoboard

Based on work by experts such as Stephen Few, Dona Wong, Albert Cairo, Cole Nussbaumer Knaflic, and Andy Kirk, the authors at Geckoboard wrote down a list of recommendations which I summarize below:

## Present the facts

• Start your axis at zero whenever possible, to prevent misinterpretation. Particularly bar charts.
• The width and height of line and scatter plots influence its messages.
• Area and size are hard to interpret. Hence, there’s often a better alternative to the pie chart. Read also this.

## Less is more

• Use colors for communication, not decoration.
• Diminish non-data ink, to draw attention to that which matters.
• Do not use the third dimension, unless you are plotting it.
• Avoid overselling numerical accuracy with precise decimal values.

## Keep it simple

• Annotate your plots; include titles, labels or scales.
• Avoid squeezing too much information in a small space. For example, avoid a second x- or y-axis whenever possible.
• Align your numbers right, literally.
• Don’t go for fancy; go for clear. If you have few values, just display the values.

# Harry Plotter: Part 2 – Hogwarts Houses and their Stereotypes

Two weeks ago, I started the Harry Plotter project to celebrate the 20th anniversary of the first Harry Potter book. I could not have imagined that the first blog would be so well received. It reached over 4000 views in a matter of days thanks to the lovely people in the data science and #rstats community that were kind enough to share it (special thanks to MaraAverick and DataCamp). The response from the Harry Potter community, for instance on reddit, was also just overwhelming

# Part 2: Hogwarts Houses

All in all, I could not resist a sequel and in this second post we will explore the four houses of Hogwarts: GryffindorHufflepuffRavenclaw, and Slytherin. At the end of today’s post we will end up with visualizations like this:

Various stereotypes exist regarding these houses and a textual analysis seemed a perfect way to uncover their origins. More specifically, we will try to identify which words are most unique, informative, important or otherwise characteristic for each house by means of ratio and tf-idf statistics. Additionally, we will try to estime a personality profile for each house using these characteristic words and the emotions they relate to. Again, we rely strongly on `ggplot2` for our visualizations, but we will also be using the treemaps of `treemapify`. Moreover, I have a special surprise this second post, as I found the orginal Harry Potter font, which will definately make the visualizations feel more authentic. Of course, we will conduct all analyses in a tidy manner using `tidytext` and the `tidyverse`.

I hope you will enjoy this blog and that you’ll be back for more. To be the first to receive new content, please subscribe to my website www.paulvanderlaken.com, follow me on Twitter, or add me on LinkedIn. Additionally, if you would like to contribute to, collaborate on, or need assistance with a data science project or venture, please feel free to reach out.

## R Setup

All analysis were performed in RStudio, and knit using `rmarkdown` so that you can follow my steps.

In term of setup, we will be needing some of the same packages as last time. Bradley Boehmke gathered the text of the Harry Potter books in his `harrypotter` package. We need `devtools` to install that package the first time, but from then on can load it in as usual. We need plyr for `ldply()`. We load in most other `tidyverse` packages in a single bundle and add tidytext. Finally, I load the Harry Potter font and set some default plotting options.

``````# SETUP ####
# library(devtools)
library(harrypotter)
library(plyr)
library(tidyverse)
library(tidytext)

# VIZUALIZATION SETTINGS
# custom Harry Potter font
# http://www.fontspace.com/category/harry%20potter
library(extrafont)
font_import(paste0(getwd(),"/fontomen_harry-potter"), prompt = F) # load in custom Harry Potter font
windowsFonts(HP = windowsFont("Harry Potter"))
theme_set(theme_light(base_family = "HP")) # set default ggplot theme to light
default_title = "Harry Plotter: References to the Hogwarts houses" # set default title
default_caption = "www.paulvanderlaken.com" # set default caption
dpi = 600 # set default dpi``````

## Importing and Transforming Data

Before we import and transform the data in one large piping chunk, I need to specify some variables.

First, I tell R the house names, which we are likely to need often, so standardization will help prevent errors. Next, my girlfriend was kind enough to help me (colorblind) select the primary and secondary colors for the four houses. Here, the `ggplot2` color guide in my R resources list helped a lot! Finally, I specify the regular expression (tutorials) which we will use a couple of times in order to identify whether text includes either of the four house names.

``````# DATA PREPARATION ####
houses <- c('gryffindor', 'ravenclaw', 'hufflepuff', 'slytherin') # define house names
houses_colors1 <- c("red3", "yellow2", "blue4", "#006400") # specify primary colors
houses_colors2 <- c("#FFD700", "black", "#B87333", "#BCC6CC") # specify secondary colors
regex_houses <- paste(houses, collapse = "|") # regular expression``````

### Import Data and Tidy

Ok, let’s import the data now. You may recognize pieces of the code below from last time, but this version runs slightly smoother after some optimalization. Have a look at the current data format.

``````# LOAD IN BOOK TEXT
houses_sentences <- list(
`Philosophers Stone` = philosophers_stone,
`Chamber of Secrets` = chamber_of_secrets,
`Prisoner of Azkaban` = prisoner_of_azkaban,
`Goblet of Fire` = goblet_of_fire,
`Order of the Phoenix` = order_of_the_phoenix,
`Half Blood Prince` = half_blood_prince,
`Deathly Hallows` = deathly_hallows
) %>%
# TRANSFORM TO TOKENIZED DATASET
ldply(cbind) %>% # bind all chapters to dataframe
mutate(.id = factor(.id, levels = unique(.id), ordered = T)) %>% # identify associated book
unnest_tokens(sentence, `1`, token = 'sentences') %>% # seperate sentences
filter(grepl(regex_houses, sentence)) %>% # exclude sentences without house reference
cbind(sapply(houses, function(x) grepl(x, .\$sentence)))# identify references
# examine
max.char = 30 # define max sentence length
houses_sentences %>%
mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
paste0(substring(sentence, 1, max.char), "..."),
sentence)) %>%
``````##                  .id                          sentence gryffindor
## 1 Philosophers Stone "well, no one really knows unt...      FALSE
## 2 Philosophers Stone "and what are slytherin and hu...      FALSE
## 3 Philosophers Stone everyone says hufflepuff are a...      FALSE
## 4 Philosophers Stone "better hufflepuff than slythe...      FALSE
## 5 Philosophers Stone "there's not a single witch or...      FALSE
##   ravenclaw hufflepuff slytherin
## 1     FALSE       TRUE      TRUE
## 2     FALSE       TRUE      TRUE
## 3     FALSE       TRUE     FALSE
## 4     FALSE       TRUE      TRUE
## 5     FALSE      FALSE      TRUE``````

### Transform to Long Format

Ok, looking great, but not tidy yet. We need gather the columns and put them in a long dataframe. Thinking ahead, it would be nice to already capitalize the house names for which I wrote a custom `Capitalize()` function.

``````# custom capitalization function
Capitalize = function(text){
paste0(substring(text,1,1) %>% toupper(),
substring(text,2))
}

# TO LONG FORMAT
houses_long <- houses_sentences %>%
gather(key = house, value = test, -sentence, -.id) %>%
mutate(house = Capitalize(house)) %>% # capitalize names
filter(test) %>% select(-test) # delete rows where house not referenced
# examine
houses_long %>%
mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
paste0(substring(sentence, 1, max.char), "..."),
sentence)) %>%
``````##                   .id                          sentence      house
## 1  Philosophers Stone i've been asking around, and i... Gryffindor
## 2  Philosophers Stone           "gryffindor," said ron. Gryffindor
## 3  Philosophers Stone "the four houses are called gr... Gryffindor
## 4  Philosophers Stone you might belong in gryffindor... Gryffindor
## 5  Philosophers Stone " brocklehurst, mandy" went to... Gryffindor
## 6  Philosophers Stone "finnigan, seamus," the sandy-... Gryffindor
## 7  Philosophers Stone                     "gryffindor!" Gryffindor
## 8  Philosophers Stone when it finally shouted, "gryf... Gryffindor
## 9  Philosophers Stone well, if you're sure -- better... Gryffindor
## 10 Philosophers Stone he took off the hat and walked... Gryffindor
## 11 Philosophers Stone "thomas, dean," a black boy ev... Gryffindor
## 12 Philosophers Stone harry crossed his fingers unde... Gryffindor
## 13 Philosophers Stone resident ghost of gryffindor t... Gryffindor
## 14 Philosophers Stone looking pleased at the stunned... Gryffindor
## 15 Philosophers Stone gryffindors have never gone so... Gryffindor
## 16 Philosophers Stone the gryffindor first years fol... Gryffindor
## 17 Philosophers Stone they all scrambled through it ... Gryffindor
## 18 Philosophers Stone nearly headless nick was alway... Gryffindor
## 19 Philosophers Stone professor mcgonagall was head ... Gryffindor
## 20 Philosophers Stone over the noise, snape said, "a... Gryffindor``````

## Visualize House References

Woohoo, so tidy! Now comes the fun part: visualization. The following plots how often houses are mentioned overall, and in each book seperately.

``````# set plot width & height
w = 10; h = 6

# PLOT REFERENCE FREQUENCY
houses_long %>%
group_by(house) %>%
summarize(n = n()) %>% # count sentences per house
ggplot(aes(x = desc(house), y = n)) +
geom_bar(aes(fill = house), stat = 'identity') +
geom_text(aes(y = n / 2, label = house, col = house),  # center text
size = 8, family = 'HP') +
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = 'none') +
labs(title = default_title,
subtitle = "Combined references in all Harry Potter books",
caption = default_caption,
x = '', y = 'Name occurence') +
coord_flip()``````

``````# PLOT REFERENCE FREQUENCY OVER TIME
houses_long %>%
group_by(.id, house) %>%
summarize(n = n()) %>% # count sentences per house per book
ggplot(aes(x = .id, y = n, group = house)) +
geom_line(aes(col = house), size = 2) +
scale_color_manual(values = houses_colors1) +
theme(legend.position = 'bottom',
axis.text.x = element_text(angle = 15, hjust = 0.5, vjust = 0.5)) + # rotate x axis text
labs(title = default_title,
subtitle = "References throughout the Harry Potter books",
caption = default_caption,
x = NULL, y = 'Name occurence', color = 'House') ``````

The Harry Potter font looks wonderful, right?

In terms of the data, Gryffindor and Slytherin definitely play a larger role in the Harry Potter stories. However, as the storyline progresses, Slytherin as a house seems to lose its importance. Their downward trend since the Chamber of Secrets results in Ravenclaw being mentioned more often in the final book (Edit – this is likely due to the diadem horcrux, as you will see later on).

I can’t but feel sorry for house Hufflepuff, which never really gets to involved throughout the saga.

## Retrieve Reference Words & Data

Let’s dive into the specific words used in combination with each house. The following code retrieves and counts the single words used in the sentences where houses are mentioned.

``````# IDENTIFY WORDS USED IN COMBINATION WITH HOUSES
words_by_houses <- houses_long %>%
unnest_tokens(word, sentence, token = 'words') %>% # retrieve words
mutate(word = gsub("'s", "", word)) %>% # remove possesive determiners
group_by(house, word) %>%
summarize(word_n = n()) # count words per house
# examine
``````## # A tibble: 6 x 3
## # Groups:   house [1]
##        house        word word_n
##        <chr>       <chr>  <int>
## 1 Gryffindor         104      1
## 2 Gryffindor        22nd      1
## 3 Gryffindor           a    251
## 4 Gryffindor   abandoned      1
## 5 Gryffindor  abandoning      1
## 6 Gryffindor abercrombie      1``````

## Visualize Word-House Combinations

Now we can visualize which words relate to each of the houses. Because `facet_wrap()` has trouble reordering the axes (because words may related to multiple houses in different frequencies), I needed some custom functionality, which I happily recycled from dgrtwo’s github. With these `reorder_within()` and `scale_x_reordered()` we can now make an ordered barplot of the top-20 most frequent words per house.

``````# custom functions for reordering facet plots
# https://github.com/dgrtwo/drlib/blob/master/R/reorder_within.R
reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
new_x <- paste(x, within, sep = sep)
reorder(new_x, by, FUN = fun)
}

scale_x_reordered <- function(..., sep = "___") {
reg <- paste0(sep, ".+\$")
ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}

# set plot width & height
w = 10; h = 7;

# PLOT MOST FREQUENT WORDS PER HOUSE
words_per_house = 20 # set number of top words
words_by_houses %>%
group_by(house) %>%
arrange(house, desc(word_n)) %>%
mutate(top = row_number()) %>% # count word top position
filter(top <= words_per_house) %>% # retain specified top number
ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
word_n, fill = house)) +
geom_col(show.legend = F) +
scale_x_reordered() + # rectify x axis labels
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
facet_wrap(~ house, scales = "free_y") + # facet wrap and free y axis
coord_flip() +
labs(title = default_title,
subtitle = "Words most commonly used together with houses",
caption = default_caption,
x = NULL, y = 'Word Frequency')``````

Unsurprisingly, several stop words occur most frequently in the data. Intuitively, we would rerun the code but use `dplyr::anti_join()` on `tidytext::stop_words` to remove stop words.

``````# PLOT MOST FREQUENT WORDS PER HOUSE
# EXCLUDING STOPWORDS
words_by_houses %>%
anti_join(stop_words, 'word') %>% # remove stop words
group_by(house) %>%
arrange(house, desc(word_n)) %>%
mutate(top = row_number()) %>% # count word top position
filter(top <= words_per_house) %>% # retain specified top number
ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
word_n, fill = house)) +
geom_col(show.legend = F) +
scale_x_reordered() + # rectify x axis labels
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
facet_wrap(~ house, scales = "free") + # facet wrap and free scales
coord_flip() +
labs(title = default_title,
subtitle = "Words most commonly used together with houses, excluding stop words",
caption = default_caption,
x = NULL, y = 'Word Frequency')``````

However, some stop words have a different meaning in the Harry Potter universe. points are for instance quite informative to the Hogwarts houses but included in `stop_words`.

Moreover, many of the most frequent words above occur in relation to multiple or all houses. Take, for instance, Harry and Ron, which are in the top-10 of each house, or words like tablehouse, and professor.

We are more interested in words that describe one house, but not another. Similarly, we only want to exclude stop words which are really irrelevant. To this end, we compute a ratio-statistic below. This statistic displays how frequently a word occurs in combination with one house rather than with the others. However, we need to adjust this ratio for how often houses occur in the text as more text (and thus words) is used in reference to house Gryffindor than, for instance, Ravenclaw.

``````words_by_houses <- words_by_houses %>%
group_by(word) %>% mutate(word_sum = sum(word_n)) %>% # counts words overall
group_by(house) %>% mutate(house_n = n()) %>%
ungroup() %>%
# compute ratio of usage in combination with house as opposed to overall
# adjusted for house references frequency as opposed to overall frequency
mutate(ratio = (word_n / (word_sum - word_n + 1) / (house_n / n())))
# examine
words_by_houses %>% select(-word_sum, -house_n) %>% arrange(desc(word_n)) %>% head()``````
``````## # A tibble: 6 x 4
##        house       word word_n     ratio
##        <chr>      <chr>  <int>     <dbl>
## 1 Gryffindor        the   1057  2.373115
## 2  Slytherin        the    675  1.467926
## 3 Gryffindor gryffindor    602 13.076218
## 4 Gryffindor        and    477  2.197259
## 5 Gryffindor         to    428  2.830435
## 6 Gryffindor         of    362  2.213186``````
``````# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
group_by(house) %>%
arrange(house, desc(ratio)) %>%
mutate(top = row_number()) %>% # count word top position
filter(top <= words_per_house) %>% # retain specified top number
ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
ratio, fill = house)) +
geom_col(show.legend = F) +
scale_x_reordered() + # rectify x axis labels
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
facet_wrap(~ house, scales = "free") +  # facet wrap and free scales
coord_flip() +
labs(title = default_title,
subtitle = "Most informative words per house, by ratio",
caption = default_caption,
x = NULL, y = 'Adjusted Frequency Ratio (house vs. non-house)')``````

``````# PS. normally I would make a custom ggplot function
#    when I plot three highly similar graphs``````

This ratio statistic (x-axis) should be interpreted as follows: night is used 29 times more often in combination with Gryffindor than with the other houses.

Do you think the results make sense:

• Gryffindors spent dozens of hours during their afternoonsevenings, and nights in the, often emptytower room, apparently playing chess? Nevile Longbottom and Hermione Granger are Gryffindors, obviously, and Sirius Black is also on the list. The sword of Gryffindor is no surprise here either.
• Hannah AbbotErnie Macmillan and Cedric Diggory are Hufflepuffs. Were they mostly hot curly blondes interested in herbology? Nevertheless, wild and aggresive seem unfitting for Hogwarts most boring house.
• A lot of names on the list of Helena Ravenclaw’s house. Roger DaviesPadma Patil, Cho Chang, Miss S. FawcettStewart AckerleyTerry Boot, and Penelope Clearwater are indeed Ravenclaws, I believe. Ravenclaw’s Diadem was one of Voldemort horcruxes. AlectoCarrow, Death Eater by profession, was apparently sent on a mission by Voldemort to surprise Harry in Rawenclaw’s common room (source), which explains what she does on this list. Can anybody tell me what buststatue and spot have in relation to Ravenclaw?
• House Slytherin is best represented by Gregory Goyle, one of the members of Draco Malfoy’s gang along with Vincent CrabbePansy Parkinson also represents house SlytherinSlytherin are famous for speaking Parseltongue and their house’s gem is an emerald. House Gaunt were pure-blood descendants from Salazar Slytherin and apparently Viktor Krum would not have misrepresented the Slytherin values either. Oh, and only the heir of Slytherin could control the monster in the Chamber of Secrets.

Honestly, I was not expecting such good results! However, there is always room for improvement.

We may want to exclude words that only occur once or twice in the book (e.g., Alecto) as well as the house names. Additionally, these barplots are not the optimal visualization if we would like to include more words per house. Fortunately, Hadley Wickham helped me discover treeplots. Let’s draw one using the `ggfittext` and the `treemapify` packages.

``````# set plot width & height
w = 12; h = 8;

# PACKAGES FOR TREEMAP
# devtools::install_github("wilkox/ggfittext")
# devtools::install_github("wilkox/treemapify")
library(ggfittext)
library(treemapify)

# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
filter(word_n > 3) %>% # filter words with few occurances
filter(!grepl(regex_houses, word)) %>% # exclude house names
group_by(house) %>%
arrange(house, desc(ratio), desc(word_n)) %>%
mutate(top = seq_along(ratio)) %>%
filter(top <= words_per_house) %>% # filter top n words
ggplot(aes(area = ratio, label = word, subgroup = house, fill = house)) +
geom_treemap() + # create treemap
geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
geom_treemap_subgroup_text(aes(col = house), # add house names
family = "HP", place = 'center', alpha = 0.3, grow = T) +
geom_treemap_subgroup_border(colour = 'black') +
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
theme(legend.position = 'none') +
labs(title = default_title,
subtitle = "Most informative words per house, by ratio",
caption = default_caption)``````

A treemap can display more words for each of the houses and displays their relative proportions better. New words regarding the houses include the following, but do you see any others?

• Slytherin girls laugh out loud whereas Ravenclaw had a few little, pretty girls?
• Gryffindors, at least Harry and his friends, got in trouble often, that is a fact.
• Yellow is the color of house Hufflepuff whereas Slytherin is green indeed.
• Zacherias Smith joined Hufflepuff and Luna Lovegood Ravenclaw.
• Why is Voldemort in camp Ravenclaw?!

In the earlier code, we specified a minimum number of occurances for words to be included, which is a bit hacky but necessary to make the ratio statistic work as intended. Foruntately, there are other ways to estimate how unique or informative words are to houses that do not require such hacks.

## TF-IDF

tf-idf similarly estimates how unique / informative words are for a body of text (for more info: Wikipedia). We can calculate a tf-idf score for each word within each document (in our case house texts) by taking the product of two statistics:

• TF or term frequency, meaning the number of times the word occurs in a document.
• IDF or inverse document frequency, specifically the logarithm of the inverse number of documents the word occurs in.

A high tf-idf score means that a word occurs relatively often in a specific document and not often in other documents. Different weighting schemes can be used to td-idf’s performance in different settings but we used the simple default of `tidytext::bind_tf_idf()`.

An advantage of tf-idf over the earlier ratio statistic is that we no longer need to specify a minimum frequency: low frequency words will have low tf and thus low tf-idf. A disadvantage is that tf-idf will automatically disregard words occur together with each house, be it only once: these words have zero idf (log(4/4)) so zero tf-idf.

Let’s run the treemap gain, but not on the computed tf-idf scores.

``````words_by_houses <- words_by_houses %>%
# compute term frequency and inverse document frequency
bind_tf_idf(word, house, word_n)
# examine
``````## # A tibble: 6 x 8
##        house        word word_n word_sum    ratio           tf       idf
##        <chr>       <chr>  <int>    <int>    <dbl>        <dbl>     <dbl>
## 1 Gryffindor         104      1        1 2.671719 6.488872e-05 1.3862944
## 2 Gryffindor        22nd      1        1 2.671719 6.488872e-05 1.3862944
## 3 Gryffindor           a    251      628 1.774078 1.628707e-02 0.0000000
## 4 Gryffindor   abandoned      1        1 2.671719 6.488872e-05 1.3862944
## 5 Gryffindor  abandoning      1        2 1.335860 6.488872e-05 0.6931472
## 6 Gryffindor abercrombie      1        1 2.671719 6.488872e-05 1.3862944
## # ... with 1 more variables: tf_idf <dbl>``````
``````# PLOT MOST UNIQUE WORDS PER HOUSE BY TF_IDF
words_per_house = 30
words_by_houses %>%
filter(tf_idf > 0) %>% # filter for zero tf_idf
group_by(house) %>%
arrange(house, desc(tf_idf), desc(word_n)) %>%
mutate(top = seq_along(tf_idf)) %>%
filter(top <= words_per_house) %>%
ggplot(aes(area = tf_idf, label = word, subgroup = house, fill = house)) +
geom_treemap() + # create treemap
geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
geom_treemap_subgroup_text(aes(col = house), # add house names
family = "HP", place = 'center', alpha = 0.3, grow = T) +
geom_treemap_subgroup_border(colour = 'black') +
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
theme(legend.position = 'none') +
labs(title = default_title,
subtitle = "Most informative words per house, by tf-idf",
caption = default_caption)``````

This plot looks quite different from its predecessor. For instance, Marcus Flint and Adrian Pucey are added to house Slytherin and Hufflepuff’s main color is indeed not just yellow, but canary yellow. Severus Snape’s dual role is also nicely depicted now, with him in both house Slytherin and house Gryffindor. Do you notice any other important differences? Did we lose any important words because they occured in each of our four documents?

## House Personality Profiles (by NRC Sentiment Analysis)

We end this second Harry Plotter blog by examining to what the extent the stereotypes that exist of the Hogwarts Houses can be traced back to the books. To this end, we use the NRC sentiment dictionary, see also the the previous blog, with which we can estimate to what extent the most informative words for houses (we have over a thousand for each house) relate to emotions such as anger, fear, or trust.

The code below retains only the emotion words in our `words_by_houses` dataset and multiplies their tf-idf scores by their relative frequency, so that we retrieve one score per house per sentiment.

``````# PLOT SENTIMENT OF INFORMATIVE WORDS (TFIDF)
words_by_houses %>%
inner_join(get_sentiments("nrc"), by = 'word') %>%
group_by(house, sentiment) %>%
summarize(score = sum(word_n / house_n * tf_idf)) %>% # compute emotion score
ggplot(aes(x = house, y = score, group = house)) +
geom_col(aes(fill = house)) + # create barplots
geom_text(aes(y = score / 2, label = substring(house, 1, 1), col = house),
family = "HP", vjust = 0.5) + # add house letter in middle
facet_wrap(~ Capitalize(sentiment), scales = 'free_y') + # facet and free y axis
scale_fill_manual(values = houses_colors1) +
scale_color_manual(values = houses_colors2) +
theme(legend.position = 'none', # tidy dataviz
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.text.x = element_text(colour = 'black', size = 12)) +
labs(title = default_title,
subtitle = "Sentiment (nrc) related to houses' informative words (tf-idf)",
caption = default_caption,
y = "Sentiment score", x = NULL)``````

The results to a large extent confirm the stereotypes that exist regarding the Hogwarts houses:

• Gryffindors are full of anticipation and the most positive and trustworthy.
• Hufflepuffs are the most joyous but not extraordinary on any other front.
• Ravenclaws are distinguished by their low scores. They are super not-angry and relatively not-anticipating, not-negative, and not-sad.
• Slytherins are the angriest, the saddest, and the most feared and disgusting. However, they are also relatively joyous (optimistic?) and very surprising (shocking?).

## Conclusion and future work

With this we have come to the end of the second part of the Harry Plotter project, in which we used tf-idf and ratio statistics to examine which words were most informative / unique to each of the houses of Hogwarts. The data was retrieved using the `harrypotter` package and transformed using `tidytext` and the `tidyverse`. Visualizations were made with `ggplot2` and `treemapify`, using a Harry Potter font.

I have several ideas for subsequent posts and I’d love to hear your preferences or suggestions:

• I would like to demonstrate how regular expressions can be used to retrieve (sub)strings that follow a specific format. We could use regex to examine, for instance, when, and by whom, which magical spells are cast.
• I would like to use network analysis to examine the interactions between the characters. We could retrieve networks from the books and conduct sentiment analysis to establish the nature of relationships. Similarly, we could use unsupervised learning / clustering to explore character groups.
• I would like to use topic models, such as latent dirichlet allocation, to identify the main topics in the books. We could, for instance, try to summarize each book chapter in single sentence, or examine how topics (e.g., love or death) build or fall over time.
• Finally, I would like to build an interactive application / dashboard in Shiny (another hobby of mine) so that readers like you can explore patterns in the books yourself. Unfortunately, the free on shinyapps.io only 25 hosting hours per month : (

For now, I hope you enjoyed this blog and that you’ll be back for more. To receive new content first, please subscribe to my website www.paulvanderlaken.com, follow me on Twitter, or add me on LinkedIn.

If you would like to contribute to, collaborate on, or need assistance with a data science project or venture, please feel free to reach out