Week 4: Visualization I

Author

Quinton Neville

Published

February 11, 2022

Data Visualization I

Today we are going to –

Learn how to wrangle and visualize data to answer real world questions
Learn what separates a good plot/table from a bad plot/table
Learn how to make the good kind

But before we discuss how, we ought to ask ourselves what is the fundamental purpose of a data visualization?

A data visualization is nothing more or less than a communication tool, and a good visualization tells a compelling story. One which –

Explains a phenomenon
Supports a hypothesis
Persuades us of an argument
Answers or investigates a real world question of interest by
- Drawing inference from the past
- Predicting the future

How do we tell a compelling story with data?

If we wrangle, clean, and reshape our data appropriately, pretty easily!

The four keys to good data visualizations are
1. Clean, wrangled, tidy data
2. Appropriate choice of communication tool or figure type
3. Clear, organized, self-explanatory structure
4. Attention to detail

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Fundamentals

Visualization with `ggplot()`

ggplot’s, much like onions, have layers –

Unlike an onion, however, the layers in ggplot are more analogous to the layers of a hearty sandwich –

The `ggplot` sandwich

Meat – mapping & geom(s)
1. DataExplorer \(\longrightarrow\) Data Wrangling I \(\longrightarrow\) tidy data %>%
2. ggplot(aes(x = , y =, colour = , fill = , group = , size = , ...)) +
3. geom_point, geom_line, geom_histogram, geom_density, geom_smooth, ...
Fixings – aesthetics & facets
1. alpha, size, shape, linetype, span, jitter etc.
2. facet_wrap(~ v1 + v2, ncol = , nrow = , scales = "free?")
Sauce – theme & colour palette
1. ggthemes
2. scale_colour_viridis colourblind friendly
3. RColorBrewer
4. paleteer
5. theme(axis.text.x = element_text(angle =, vjust =, hjust =, size = ...))
Bread – scale/axes, titles, names
1. scale_x_continuous, scale_x_date_time, ..,_discrete, scale_y_...
2. labs(x = , y = , title = , subtitle = , caption = )

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Tables with `gt()`

gt is like the ggplot of tables, but functions differently with different syntax (due to being developed much later than ggplot). gt’s also have layers, except unlike ggplot, the order of aesthetics doesn’t matter – which implies that gt is exactly like an onion! Note- ggplot is piped with + and gt with %>% (like everything else).

While the full functionality of gt is expansive, and greater detail / function description can be found here and here, today we’re going to cover the 4-5 main features you’ll need to generate a beautifully layered gt onion.

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The `gt` onion

Layer I – gt(data, rowname_col = "rowname", groupname_col = dplyr::group_vars(data))
Layer II – Titles & headers
1. tab_header(title, subtitle) (main title, subtitle)
2. tab_spanner(data, label, columns) (spans columns)
Layer III – Colours, footnotes, source notes
1. data_color(columns = , colors = scales::col_numeric(palette = , domain =))
2. tab_footnote(footnote = "", locations = cells_column_labels(columns = )))
3. tab_source_note(source_note = "")
Layer IV – Style & Options
1. tab_style()
2. tab_options()

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Warning: Note about `gt` & `github_documents`

Not everything that normally works in .html will work in github_documents; and one of those things is gt()’s displaying properly. To get around that, we’ll have to gtsave our gt's as a .png and call it directly from Rmarkdown like so –

#Save gt
#Create a figures directory and save the gt as a .png in there
dir.create("./figures/")  #create directory
gtsave(table.gt, "./figures/gt_table.png") #save it

#Display the .png inline with ![](./figures/gt_table.png)

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Telling a Story

Today, the story we are going to tell is one of fantasy, quite literally, as it pertains to customer reviews from the most magical place(s) on earth: Disney Land

These data were acquired from Kaggle and describe ~42,000 customer reviews from Disneyland(s) in California, Hong Kong, and Paris, extracted from posts on Trip Advisor. The data may be found on Canvas here.

Step 1. Read, clean and get comfortable

First, we utilize DataExplorer to

Conduct an initial exploration to inform our cleaning and tidying
Generate potential Data Science questions
Re-write our pipe to read, clean, and tidy appropriately

#Read data and do some initial cleaning
disney.df <- read_csv("./data/disneyland_reviews.csv",
                      show_col_types = FALSE,
                      na = "missing") %>%
             janitor::clean_names()

#Explore a little with DataExplorer
#Table of general info
DataExplorer::introduce(disney.df)

# A tibble: 1 × 9
   rows columns discrete_columns continuous_columns all_missing_columns
  <int>   <int>            <int>              <int>               <int>
1 42656       6                4                  2                   0
# ℹ 4 more variables: total_missing_values <int>, complete_rows <int>,
#   total_observations <int>, memory_usage <dbl>

#A random few observations
set.seed(36)
sample <- sample(1:nrow(disney.df), 10)

#Display
disney.df %>%
  slice(sample)

# A tibble: 10 × 6
   review_id rating year_month reviewer_location review_text              branch
       <dbl>  <dbl> <chr>      <chr>             <chr>                    <chr> 
 1 326116517      4 2015-10    United States     My family had a good ti… Disne…
 2 561114590      4 2018-2     Indonesia         I'll start by reminding… Disne…
 3  65297363      5 <NA>       Canada            I just got back from a … Disne…
 4 174308145      3 2013-8     United Kingdom    The two parks are withi… Disne…
 5 155788938      5 <NA>       Belgium           The only ride that REAL… Disne…
 6 421639027      5 2016-3     Philippines       I enjoyed our whole day… Disne…
 7 126485364      5 2012-3     United States     After visiting Disney W… Disne…
 8 202672130      4 2014-4     Australia         Having not been to a Di… Disne…
 9 574949379      5 2018-4     United States     Great fun for first or … Disne…
10 250866714      5 2015-1     United States     I actually went Tuesday… Disne…

#Plot str() (names and types)
DataExplorer::plot_str(disney.df)

We note here that there are NAs in year_month, they just aren’t labeled appropriately. The year_month and branch variables need to be mutated with stringr (or lubridate). Some variable names still require more tidying and variable types need to be fixed appropriately.

#Plot missing
DataExplorer::plot_missing(disney.df)

#Plot categorical
DataExplorer::plot_bar(disney.df)

#Plot conintuous distributions
DataExplorer::plot_histogram(disney.df)

We also note that the number of California reviews is nearly double that of Hong Kong, and ~35% larger than that of Paris. In addition, the distribution of reviews is heavily left-skewed, with mostly 4 and 5 star reviews and very few 1’s, 2’s, and 3’s.

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Step 2. Wrangle the data

Next, we re-write our data wrangling pipeline to include the necessary steps to clean, transform, rename, and tidy thse data to answer any potential questions of interest. This includes

Declaring NA = r“missing”`
Renaming redundant review_ from features
Extracting/creating time variables (numeric & factor)
Creating text analysis variables like word count, avg. length, etc.
Retaining only the features relevant to our analysis
Checking for unique observations / multiple reviews per ID
Retaining only unique/valid observations

#Read data and do some initial cleaning
disney.df <- read_csv("./data/disneyland_reviews.csv",
                      na = c("missing"),
                      show_col_types = FALSE) %>%
             janitor::clean_names() %>%
             rename(
               id       = review_id,
               text     = review_text, #trimming unnecessary review_
               location = reviewer_location
             ) %>% 
             mutate( #Time
               id    = as.factor(id),
               date  = lubridate::ym(year_month), #Cleaning up time as date, y/m factors
               year  = lubridate::year(date) %>% #See the lubridate package
                       as.factor(),
               month = month.name[lubridate::month(date)] %>%
                       as.factor()
             ) %>%
             mutate( #Text analysis
               n_words    = str_split(text, " ") %>% #proxy for number of words
                            map_dbl(length),
               avg_length = str_split(text, " ") %>% #proxy for average length
                            map(str_length) %>% 
                            map_dbl(mean),
               n_unique   = str_split(text, " ") %>% #proxy for no. unique words
                            map(unique) %>%
                            map_dbl(length),
               branch     = str_split(branch, "_") %>% #extracting branch w stringr
                            map_chr(2) %>%
                            as.factor() %>%
                            fct_recode("Hong Kong" = "HongKong") %>%
                            fct_relevel("California", "Paris")
             ) %>%
             dplyr::select(id, rating, location, branch, everything(), -c(year_month, text))

#Let's check it out now
disney.df %>%
  slice(sample)

# A tibble: 10 × 10
   id        rating location    branch date       year  month n_words avg_length
   <fct>      <dbl> <chr>       <fct>  <date>     <fct> <fct>   <dbl>      <dbl>
 1 326116517      4 United Sta… Calif… 2015-10-01 2015  Octo…     120       4.25
 2 561114590      4 Indonesia   Hong … 2018-02-01 2018  Febr…     126       4.71
 3 65297363       5 Canada      Calif… NA         <NA>  <NA>      160       4.25
 4 174308145      3 United Kin… Calif… 2013-08-01 2013  Augu…     120       4.18
 5 155788938      5 Belgium     Paris  NA         <NA>  <NA>       34       4.76
 6 421639027      5 Philippines Hong … 2016-03-01 2016  March      38       4.82
 7 126485364      5 United Sta… Calif… 2012-03-01 2012  March     117       4.32
 8 202672130      4 Australia   Hong … 2014-04-01 2014  April     140       4.34
 9 574949379      5 United Sta… Calif… 2018-04-01 2018  April      85       4.35
10 250866714      5 United Sta… Calif… 2015-01-01 2015  Janu…      84       4.26
# ℹ 1 more variable: n_unique <dbl>

Note that not every observation is independent, as 20 ID’s submitted 2 reviews –

#Repeated id data frame
disney.df %>%
  pull(id) %>%
  fct_count() %>%
  arrange(desc(n)) %>%
  filter(n > 1)

# A tibble: 20 × 2
   f             n
   <fct>     <int>
 1 121568004     2
 2 121570980     2
 3 121578357     2
 4 121580686     2
 5 121586148     2
 6 121615136     2
 7 121615246     2
 8 129207323     2
 9 129214104     2
10 129231609     2
11 164830205     2
12 164862064     2
13 166730734     2
14 166753649     2
15 166754595     2
16 166784597     2
17 166787525     2
18 166787635     2
19 168489234     2
20 226905150     2

#Extract only the first occurance of these id's
keep_index <- disney.df %>% pull(id) %>% match(unique(.), .)

#Retain only the first occurance (final df 20 less obs)
disney.df <- disney.df %>% slice(keep_index)

I went back and cross-referenced these repeated ID’s with their reviews and they are indeed ‘true’ duplicates even though the computer hasn’t recognized that due to small changes in punctuation/spacing/text parsing. Above is a small bit of code to keep only the first occurence of each review ID by index.

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Step 3. Ask some real world questions of interest

Some natural questions we may ask of these data, given our cleaning and transformation, are –

3.1 What are the top 5 reviewer location(s) of origin by branch?

3.2 What is the average rating by branch?
- Has the average rating changed over time?
- Does the avg. rating vary within each branch by top 5 location? vs. other?

3.3 Is there any association between rating and text analysis variables?

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Step 4. Do data science to answer these questions

Activity 3.1 What are the top 5 reviewer location(s) of origin by branch?

#gt, need the paletteer package for colours
if (!require("paleteer")) {
  install.packages("paleteer")
}

#Make a table of top 5 w/ dplyr
top_5.df <- disney.df %>%
  group_by(branch, location) %>%
  summarise(
    N = n()
  ) %>%
  arrange(branch, desc(N)) %>%
  group_by(branch) %>%
  slice(1:5)

#Display table with gt
top_5.df %>%
  rename(
    `# Reviews` = N,
    `Location of Origin` = location) %>%
  gt() %>%
  tab_header("Top 5 Reviewer Locations by Branch") %>%
  data_color(
    columns = `# Reviews`,
    colors = scales::col_numeric(
      palette = c("white", my_purple),
      domain  = c(0, 13000)
    )
  ) %>%
  tab_footnote(
    footnote = "Reviews extracted from Trip Advisor 2010-2019",
    locations = cells_column_labels(
      columns = `# Reviews`
    )
  )

Top 5 Reviewer Locations by Branch
Location of Origin	# Reviews¹
California
United States	12335
Australia	2447
Canada	1842
United Kingdom	1019
New Zealand	527
Paris
United Kingdom	7992
United States	1330
Australia	595
Ireland	430
India	293
Hong Kong
Australia	1634
India	1082
Philippines	991
United States	877
Singapore	849
¹ Reviews extracted from Trip Advisor 2010-2019

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Activity 3.2 What is the average rating by branch?

disney.df %>%
  group_by(branch) %>%
  summarise(
    avg_rating = mean(rating)
  ) %>%
  arrange(desc(avg_rating)) %>%
  rename(
    Branch = branch,
    `Average Rating` = avg_rating
  ) %>%
  gt() %>%
  tab_header("Average Rating by Branch") %>%
  tab_footnote(
    footnote = "Reviews extracted from Trip Advisor 2010-2019",
    locations = cells_column_labels(
      columns = `Average Rating`
    )
  )

Average Rating by Branch
Branch	Average Rating¹
California	4.405
Hong Kong	4.204
Paris	3.960
¹ Reviews extracted from Trip Advisor 2010-2019

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Activity 3.2.1 Has this average rating changed over time?

disney.df %>%
  group_by(branch, date) %>%
  summarise(
    avg_rating = mean(rating)
  ) %>%
  drop_na() %>%
  ungroup() %>%
  mutate(branch = fct_reorder(branch, avg_rating, .desc = TRUE)) %>%
  ggplot(aes(x = ymd(date), y = avg_rating, colour = branch, fill = branch)) +
  stat_smooth(alpha   = 0.2, 
              size    = 1.2, 
              method  = "loess",
              formula = "y ~ x",
              span    = 0.2,
              se      = FALSE) +
  geom_point(alpha    = 0.24, 
             position = "jitter",
             size     = 2, 
             shape    = 16) +
  labs(
    x = "Year",
    y = "Average Rating",
    title = "Average Rating by Branch from 2010-2019"
  ) + 
  annotate(geom = "text",
           x = ymd("2013-07-1"), 
           y = 4.66,
           label  = "Mean Trend",
           #family = "AvantGarde",
           colour = my_purple) +
  scale_colour_viridis_d("Disneyland Branch") +
  scale_fill_viridis_d("Disneyland Branch") +
  scale_x_date(
    date_breaks = "1 year",
    date_minor_breaks = "1 year",
    date_labels = "%Y"
  ) +
  theme(axis.text.x = element_text(angle = 45,
                                   vjust = 1.24,
                                   hjust = 1.2,
                                   size  = 11),
        axis.text.y = element_text(size  = 11)) +
  ylim(c(3, 5))

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Activity 3.2.2 Does the avg. rating vary within each branch by top 5 location? vs. other?

#Display table with gt
disney.df %>%
  group_by(branch, location) %>%
  summarise(
    avg_rating = mean(rating),
    N          = n()
  ) %>%
  arrange(branch, desc(N)) %>%
  group_by(branch) %>%
  slice(1:5) %>%
  arrange(branch, desc(avg_rating), desc(N)) %>%
  rename(
    `# Reviews`          = N,
    `Average Rating`     = avg_rating,
    `Location of Origin` = location) %>%
  gt() %>%
  tab_header("Top 5 Reviewer Locations by Branch") %>%
  data_color(
    columns = `# Reviews`,
    colors = scales::col_numeric(
      palette = c("white", "red"),
      domain  = c(0, 13000)
    )
  ) %>%
  data_color(
    columns = `Average Rating`,
    colors = scales::col_numeric(
      palette = c("blue", "white", "red"),
      domain  = c(3.2, 4.6)
    )
  ) %>%
  tab_footnote(
    footnote = "Reviews extracted from Trip Advisor 2010-2019",
    locations = cells_column_labels(
      columns = `Average Rating`
    )
  )

Top 5 Reviewer Locations by Branch
Location of Origin	Average Rating¹	# Reviews
California
Australia	4.539	2447
New Zealand	4.488	527
Canada	4.412	1842
United States	4.394	12335
United Kingdom	4.290	1019
Paris
Ireland	4.260	430
India	4.215	293
United Kingdom	4.016	7992
United States	3.735	1330
Australia	3.649	595
Hong Kong
India	4.512	1082
Philippines	4.403	991
Australia	4.213	1634
United States	4.146	877
Singapore	4.046	849
¹ Reviews extracted from Trip Advisor 2010-2019

Activity 3.3 Is there any association between rating and text analysis variables?

disney.df %>%
  pivot_longer(
    cols      = contains(c("n_", "avg")),
    names_to  = "text_var",
    values_to = "measurement"
  ) %>%
  mutate(
    text_var = case_when(
                text_var %in% "n_words"  ~ "Word Count",
                text_var %in% "n_unique" ~ "Unique Word Count",
                TRUE                     ~ "Avg. Word Length"
                ) %>%
                as_factor()
  ) %>%
  ggplot(aes(x = measurement, y = rating, colour = text_var)) +
#  stat_smooth(alpha   = 0.2, 
#              size    = 1.2, 
#              method  = "loess",
#              span    = 1,
#              se      = FALSE) +
  geom_point(alpha    = 0.16, 
             position = position_jitter(w = 2, h = 0.36),
             size     = 2, 
             shape     = 16) +
  labs(
    x = "Observed Measurement",
    y = "Rating",
    title = "Text-derived Associations with Rating"
  ) +
  scale_colour_viridis_d("Text-derived Variable") +
  facet_wrap(~ text_var, scales = "free_x")

disney.df %>%
  filter(
    n_words <= 1000,
    n_unique <= 750,
    avg_length <= 6,
    avg_length >= 3
  ) %>%              #to handle outliers rigorously
  pivot_longer(
    cols      = contains(c("n_", "avg")), #Turning text analysis vars from wide to long
    names_to  = "text_var",
    values_to = "measurement"
  ) %>%
  mutate(
    text_var = case_when(
                text_var %in% "n_words"  ~ "Word Count",
                text_var %in% "n_unique" ~ "Unique Word Count", #Recoding variable w meaningful names
                TRUE                     ~ "Avg. Word Length"
                ) %>%
                as_factor()
  ) %>%
  ggplot(aes(x = measurement, colour = as.factor(rating), fill = as.factor(rating))) +
  geom_density(adjust = 2, alpha = 0.44, position = "stack") +
  labs(
    x = "Observed Measurement",
    y = "Rating 1-5",
    title = "Text-derived Associations with Rating"
  ) +
  scale_colour_viridis_d("Rating") +
  scale_fill_viridis_d("Rating") +
  facet_wrap(~ text_var, scales = "free")

`ggridges`

#Load the ggridges package for density plots
if (!require("ggridges")) {
  install.packages("ggridges")
}
#Load data
library(ggridges)

disney.df %>%
  filter(
    n_words <= 1000,
    n_unique <= 750,
    avg_length <= 6,
    avg_length >= 3
  ) %>%
  pivot_longer(
    cols      = contains(c("n_", "avg")),
    names_to  = "text_var",
    values_to = "measurement"
  ) %>%
  mutate(
    text_var = case_when(
                text_var %in% "n_words"  ~ "Word Count",
                text_var %in% "n_unique" ~ "Unique Word Count",
                TRUE                     ~ "Avg. Word Length"
                ) %>%
                as_factor(),
    rating = as.factor(rating) %>% 
             fct_rev()
  ) %>%
  ggplot(aes(x = measurement, y = rating, colour = rating, fill = rating)) +
#  geom_histogram(aes(y = stat(density)),
#                 binwidth = 10,  colour = "black",
#                 alpha    = 0.24) +
  geom_density_ridges2(adjust = 2,
                       alpha  = 0.44,
                       scale  = 2) +
  stat_summary(fun = mean, geom = "point", size = 6, shape = "|") +
  labs(
    x = "Observed Measurement",
    y = "Rating 1-5",
    title = "Text-derived Associations with Rating"
  ) +
  scale_colour_viridis_d("Rating (Mean |)") +
  scale_fill_viridis_d("Rating (Mean |)") +
  facet_wrap(~ text_var, scales = "free")

Data Visualization I

But before we discuss how, we ought to ask ourselves what is the fundamental purpose of a data visualization?

How do we tell a compelling story with data?

Fundamentals

Visualization with ggplot()

The ggplot sandwich

Tables with gt()

The gt onion

Warning: Note about gt & github_documents

Telling a Story

Step 1. Read, clean and get comfortable

Step 2. Wrangle the data

Step 3. Ask some real world questions of interest

Step 4. Do data science to answer these questions

Activity 3.1 What are the top 5 reviewer location(s) of origin by branch?

Activity 3.2 What is the average rating by branch?

Activity 3.2.1 Has this average rating changed over time?

Activity 3.2.2 Does the avg. rating vary within each branch by top 5 location? vs. other?

Activity 3.3 Is there any association between rating and text analysis variables?

ggridges

Visualization with `ggplot()`

The `ggplot` sandwich

Tables with `gt()`

The `gt` onion

Warning: Note about `gt` & `github_documents`

`ggridges`