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Intro to Visualizations & String Data

Daniel Anderson

Week 2

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Agenda

  • Visualizing distributions
    • histograms
    • density plots
    • Empirical cumulative density plots
    • QQ plots
  • Visualizing amounts
    • bar plots
    • dot plots
    • heatmaps
  • Working textual data
    • Splitting into words, n-grams
    • Visualizing word frequencies
  • Cleaning string data
    • Substrings, basic transformations
    • Substitutions and quick pattern matching
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Agenda

  • Visualizing distributions
    • histograms
    • density plots
    • Empirical cumulative density plots
    • QQ plots
  • Visualizing amounts
    • bar plots
    • dot plots
    • heatmaps
  • Working textual data
    • Splitting into words, n-grams
    • Visualizing word frequencies
  • Cleaning string data
    • Substrings, basic transformations
    • Substitutions and quick pattern matching

A promise: You will have at least 30 minutes for lab, even if we have to pause this lecture early

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Learning Objectives

  • Understand various ways the same underlying data can be displayed

  • Think through pros/cons of each

  • Understand the basic structure of the code to produce the various plots

  • Create structured data from text and be able to visualize word frequencies

  • Be able to replace patterns in strings and understand which character need to be "escaped"

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One continuous variable

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Histogram

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Density plot

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(Empirical) Cumulative Density

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QQ Plot

Compare to theoretical quantiles (for normality)

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Empirical examples

I'll move fast, but if you want to (try to) follow along, feel free.

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Penguin data

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library(palmerpenguins)
penguins
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm
##    <fct>   <fct>              <dbl>         <dbl>
##  1 Adelie  Torgersen           39.1          18.7
##  2 Adelie  Torgersen           39.5          17.4
##  3 Adelie  Torgersen           40.3          18  
##  4 Adelie  Torgersen           NA            NA  
##  5 Adelie  Torgersen           36.7          19.3
##  6 Adelie  Torgersen           39.3          20.6
##  7 Adelie  Torgersen           38.9          17.8
##  8 Adelie  Torgersen           39.2          19.6
##  9 Adelie  Torgersen           34.1          18.1
## 10 Adelie  Torgersen           42            20.2
## # … with 334 more rows, and 4 more variables:
## #   flipper_length_mm <int>, body_mass_g <int>,
## #   sex <fct>, year <int>
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Basic histogram

ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better
## value with `binwidth`.

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Make it a little prettier

ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9)
## `stat_bin()` using `bins = 30`. Pick better
## value with `binwidth`.

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Change the number of bins

ggplot(penguins, aes(x = bill_length_mm)) +
  geom_histogram(fill = "#56B4E9",
                 color = "white",
                 alpha = 0.9,
                 bins = 50)

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Vary the number of bins

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Denisty plot

ugly 😫

ggplot(penguins, aes(bill_length_mm)) +
  geom_density()

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Denisty plot

Change the fill 😌

ggplot(penguins, aes(bill_length_mm)) +
  geom_density(fill = "#56B4E9")

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Density plot estimation

  • Kernal density estimation

    • Different kernal shapes can be selected
    • Bandwidth matters most
    • Smaller bands = bend more to the data

  • Approximation of the underlying continuous probability function

    • Integrates to 1.0 (y-axis is somewhat difficult to interpret)
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Denisty plot

change the bandwidth

ggplot(penguins, aes(bill_length_mm)) +
  geom_density(fill = "#56B4E9", 
               bw = 5)

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Quickly

How well does it approximate a normal distribution?

ggplot(penguins, aes(sample = bill_length_mm)) +
  stat_qq_line(color = "#56B4E9") +
  geom_qq(color = "gray40")

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Grouped data

Distributions

How do we display more than one distribution at a time?

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Boxplots

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Violin plots

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Jittered points

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Sina plots

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Stacked histograms

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Overlapping densities

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Ridgeline densities

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Quick empirical examples

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Boxplots

ggplot(penguins, aes(island, bill_length_mm)) +
  geom_boxplot(fill = "#A9E5C5")

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Violin plots

ggplot(penguins, aes(island, bill_length_mm)) +
  geom_violin(fill = "#A9E5C5")

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Jittered point plots

ggplot(penguins, aes(island, bill_length_mm)) +
  geom_jitter(width = 0.3, height = 0)

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Sina plot

ggplot(penguins, aes(island, bill_length_mm)) +
  ggforce::geom_sina()

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Stacked histogram

ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island))

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Stacked histogram

ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island))

🤨

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Dodged

ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island), 
                 position = "dodge")

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Dodged

ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(aes(fill = island), 
                 position = "dodge")

Note position = "dodge" does not go into aes (not accessing a variable in your dataset)

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Better

ggplot(penguins, aes(bill_length_mm)) +
  geom_histogram(fill = "#A9E5C5",
                 color = "white",
                 alpha = 0.9,) +
  facet_wrap(~island)

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Overlapping densities

ggplot(penguins, aes(bill_length_mm)) +
  geom_density(aes(fill = island),
               color = "white",
               alpha = 0.4)

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Overlapping densities

ggplot(penguins, aes(bill_length_mm)) +
  geom_density(aes(fill = island),
               color = "white",
               alpha = 0.4)

Note the default colors generally don't work great in most of these

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ggplot(penguins, aes(bill_length_mm)) +
  geom_density(aes(color = island, fill = island),
               alpha = 0.6) +
  scale_fill_manual(values = c("#003326", "#009973", "#d6fff5")) +
  scale_color_manual(
    values = darken(c("#003326", "#009973", "#d6fff5"), .1)
  )

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Ridgeline densities

ggplot(penguins, aes(bill_length_mm, island)) +
  ggridges::geom_density_ridges(color = "white",
                                fill = "#A9E5C5")

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Visualizing amounts

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Bar plots

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Flipped bars

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Dotplot

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Heatmap

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Empirical examples

How much does college cost?

library(here)
library(rio)
tuition <- import(here("data", "us_avg_tuition.xlsx"),
                  setclass = "tbl_df")
head(tuition)
## # A tibble: 6 × 13
##   State      `2004-05` `2005-06` `2006-07`
##   <chr>          <dbl>     <dbl>     <dbl>
## 1 Alabama     5682.838  5840.550  5753.496
## 2 Alaska      4328.281  4632.623  4918.501
## 3 Arizona     5138.495  5415.516  5481.419
## 4 Arkansas    5772.302  6082.379  6231.977
## 5 California  5285.921  5527.881  5334.826
## 6 Colorado    4703.777  5406.967  5596.348
## # … with 9 more variables: `2007-08` <dbl>,
## #   `2008-09` <dbl>, `2009-10` <dbl>,
## #   `2010-11` <dbl>, `2011-12` <dbl>,
## #   `2012-13` <dbl>, `2013-14` <dbl>,
## #   `2014-15` <dbl>, `2015-16` <dbl>
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By state: 2015-16

ggplot(tuition, aes(State, `2015-16`)) +
  geom_col()

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By state: 2015-16

ggplot(tuition, aes(State, `2015-16`)) +
  geom_col()

🤮🤮🤮

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Two puke emoji version

🤮🤮

ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))

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One puke emoji version

🤮

ggplot(tuition, aes(State, `2015-16`)) +
  geom_col() +
  coord_flip()
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Kinda smiley version

😏

ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  coord_flip()
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Highlight Oregon

🙂

ggplot(tuition, aes(fct_reorder(State, `2015-16`), `2015-16`)) +
  geom_col() +
  geom_col(fill = "cornflowerblue", 
           data = filter(tuition, State == "Oregon")) +
  coord_flip()
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Not always good to sort

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Much better

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Average tuition by year

How?

head(tuition)
## # A tibble: 6 × 13
##   State      `2004-05` `2005-06` `2006-07`
##   <chr>          <dbl>     <dbl>     <dbl>
## 1 Alabama     5682.838  5840.550  5753.496
## 2 Alaska      4328.281  4632.623  4918.501
## 3 Arizona     5138.495  5415.516  5481.419
## 4 Arkansas    5772.302  6082.379  6231.977
## 5 California  5285.921  5527.881  5334.826
## 6 Colorado    4703.777  5406.967  5596.348
## # … with 9 more variables: `2007-08` <dbl>,
## #   `2008-09` <dbl>, `2009-10` <dbl>,
## #   `2010-11` <dbl>, `2011-12` <dbl>,
## #   `2012-13` <dbl>, `2013-14` <dbl>,
## #   `2014-15` <dbl>, `2015-16` <dbl>
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Rearrange

tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition")
## # A tibble: 600 × 3
##    State   year    avg_tuition
##    <chr>   <chr>         <dbl>
##  1 Alabama 2004-05    5682.838
##  2 Alabama 2005-06    5840.550
##  3 Alabama 2006-07    5753.496
##  4 Alabama 2007-08    6008.169
##  5 Alabama 2008-09    6475.092
##  6 Alabama 2009-10    7188.954
##  7 Alabama 2010-11    8071.134
##  8 Alabama 2011-12    8451.902
##  9 Alabama 2012-13    9098.069
## 10 Alabama 2013-14    9358.929
## # … with 590 more rows
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Compute summaries

annual_means <- tuition %>%
  pivot_longer(`2004-05`:`2015-16`,
               names_to = "year", 
               values_to = "avg_tuition")  %>%
  group_by(year) %>%
  summarize(mean_tuition = mean(avg_tuition))
annual_means
## # A tibble: 12 × 2
##    year    mean_tuition
##    <chr>          <dbl>
##  1 2004-05     6409.564
##  2 2005-06     6654.177
##  3 2006-07     6809.914
##  4 2007-08     7085.881
##  5 2008-09     7156.560
##  6 2009-10     7761.810
##  7 2010-11     8228.834
##  8 2011-12     8539.115
##  9 2012-13     8842.357
## 10 2013-14     8947.938
## 11 2014-15     9037.357
## 12 2015-16     9317.633
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Good

ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col()

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Better?

ggplot(annual_means, aes(year, mean_tuition)) +
  geom_col() +
  coord_flip()

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Better still?

ggplot(annual_means, aes(year, mean_tuition)) +
  geom_point() +
  coord_flip()

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Even better

annual_means %>%
  mutate(year = readr::parse_number(year)) %>%
  ggplot(aes(year, mean_tuition)) +
    geom_line(color = "cornflowerblue") +
    geom_point()

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Even better

annual_means %>%
  mutate(year = readr::parse_number(year)) %>%
  ggplot(aes(year, mean_tuition)) +
    geom_line(color = "cornflowerblue") +
    geom_point()

Treat time (year) as a continuous variable

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Grouped points

Show change in tuition from 05-06 to 2015-16

tuition %>% 
  select(State, `2005-06`, `2015-16`)
## # A tibble: 50 × 3
##    State       `2005-06` `2015-16`
##    <chr>           <dbl>     <dbl>
##  1 Alabama      5840.550  9751.101
##  2 Alaska       4632.623  6571.340
##  3 Arizona      5415.516 10646.28 
##  4 Arkansas     6082.379  7867.297
##  5 California   5527.881  9269.844
##  6 Colorado     5406.967  9748.188
##  7 Connecticut  8249.074 11397.34 
##  8 Delaware     8610.597 11676.22 
##  9 Florida      3924.234  6360.159
## 10 Georgia      4492.167  8446.961
## # … with 40 more rows
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lt <- tuition %>% 
  select(State, `2005-06`, `2015-16`) %>% 
  pivot_longer(`2005-06`:`2015-16`,
               names_to = "Year", 
               values_to = "Tuition")
lt
## # A tibble: 100 × 3
##    State      Year      Tuition
##    <chr>      <chr>       <dbl>
##  1 Alabama    2005-06  5840.550
##  2 Alabama    2015-16  9751.101
##  3 Alaska     2005-06  4632.623
##  4 Alaska     2015-16  6571.340
##  5 Arizona    2005-06  5415.516
##  6 Arizona    2015-16 10646.28 
##  7 Arkansas   2005-06  6082.379
##  8 Arkansas   2015-16  7867.297
##  9 California 2005-06  5527.881
## 10 California 2015-16  9269.844
## # … with 90 more rows
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ggplot(lt, aes(State, Tuition)) +
  geom_line(aes(group = State), color = "gray40") +
  geom_point(aes(color = Year)) +
  coord_flip()
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Extensions

We need to move on to other things, but we definitely would want to keep going here:

  • Order states according to something more meaningful (starting tuition, ending tuition, or difference in tuition)

  • Meaningful title, e.g., "Change in average tuition over a decade"

  • Consider better color scheme for points

  • Potentially color the difference line by magnitude

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Let's back up a bit

  • Lets go back to our full data, but in a format that we can have a year variable.
tuition_l <- tuition %>%
    pivot_longer(-State,
                 names_to = "year", 
                 values_to = "avg_tuition") 
tuition_l
## # A tibble: 600 × 3
##    State   year    avg_tuition
##    <chr>   <chr>         <dbl>
##  1 Alabama 2004-05    5682.838
##  2 Alabama 2005-06    5840.550
##  3 Alabama 2006-07    5753.496
##  4 Alabama 2007-08    6008.169
##  5 Alabama 2008-09    6475.092
##  6 Alabama 2009-10    7188.954
##  7 Alabama 2010-11    8071.134
##  8 Alabama 2011-12    8451.902
##  9 Alabama 2012-13    9098.069
## 10 Alabama 2013-14    9358.929
## # … with 590 more rows
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Heatmap

ggplot(tuition_l, aes(year, State)) +
  geom_tile(aes(fill = avg_tuition))

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Better heatmap

ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition))

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Even better heatmap

ggplot(tuition_l, aes(year, fct_reorder(State, avg_tuition))) +
  geom_tile(aes(fill = avg_tuition)) +
  scale_fill_viridis_c(option = "magma")

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Quick aside

  • Think about the data you have
  • Given that these are state-level data, they have a geographic component
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Quick aside

  • Think about the data you have
  • Given that these are state-level data, they have a geographic component
#install.packages("maps")
state_data <- map_data("state") %>% # ggplot2::map_data
  rename(State = region)
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Join it

Obviously we'll talk more about joins later

tuition <- tuition %>% 
  mutate(State = tolower(State))
states <- left_join(state_data, tuition)
head(states)
##        long      lat group order   State
## 1 -87.46201 30.38968     1     1 alabama
## 2 -87.48493 30.37249     1     2 alabama
## 3 -87.52503 30.37249     1     3 alabama
## 4 -87.53076 30.33239     1     4 alabama
## 5 -87.57087 30.32665     1     5 alabama
## 6 -87.58806 30.32665     1     6 alabama
##   subregion  2004-05 2005-06  2006-07  2007-08
## 1      <NA> 5682.838 5840.55 5753.496 6008.169
## 2      <NA> 5682.838 5840.55 5753.496 6008.169
## 3      <NA> 5682.838 5840.55 5753.496 6008.169
## 4      <NA> 5682.838 5840.55 5753.496 6008.169
## 5      <NA> 5682.838 5840.55 5753.496 6008.169
## 6      <NA> 5682.838 5840.55 5753.496 6008.169
##    2008-09  2009-10  2010-11  2011-12  2012-13
## 1 6475.092 7188.954 8071.134 8451.902 9098.069
## 2 6475.092 7188.954 8071.134 8451.902 9098.069
## 3 6475.092 7188.954 8071.134 8451.902 9098.069
## 4 6475.092 7188.954 8071.134 8451.902 9098.069
## 5 6475.092 7188.954 8071.134 8451.902 9098.069
## 6 6475.092 7188.954 8071.134 8451.902 9098.069
##    2013-14  2014-15  2015-16
## 1 9358.929 9496.084 9751.101
## 2 9358.929 9496.084 9751.101
## 3 9358.929 9496.084 9751.101
## 4 9358.929 9496.084 9751.101
## 5 9358.929 9496.084 9751.101
## 6 9358.929 9496.084 9751.101
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Rearrange

states <- states %>% 
  gather(year, tuition, `2004-05`:`2015-16`)
head(states)
##        long      lat group order   State
## 1 -87.46201 30.38968     1     1 alabama
## 2 -87.48493 30.37249     1     2 alabama
## 3 -87.52503 30.37249     1     3 alabama
## 4 -87.53076 30.33239     1     4 alabama
## 5 -87.57087 30.32665     1     5 alabama
## 6 -87.58806 30.32665     1     6 alabama
##   subregion    year  tuition
## 1      <NA> 2004-05 5682.838
## 2      <NA> 2004-05 5682.838
## 3      <NA> 2004-05 5682.838
## 4      <NA> 2004-05 5682.838
## 5      <NA> 2004-05 5682.838
## 6      <NA> 2004-05 5682.838
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Plot

ggplot(states) +
  geom_polygon(aes(long, lat, group = group, fill = tuition)) +
  coord_fixed(1.3) +
  scale_fill_viridis_c(option = "magma") +
  facet_wrap(~year)

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Intro to textual data

Disclaimer:

This is going to be relatively fast, with not much depth. It's an intro, and I can work with you more individually if you end up working with text data.

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Structured vs unstructured

  • Most every dataset you've ever worked with is what is referred to as a structured dataset - it has rows and columns.

  • But there is an incredible amount of data out there that is unstructured - it just sort of exists

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Structured vs unstructured

  • Most every dataset you've ever worked with is what is referred to as a structured dataset - it has rows and columns.

  • But there is an incredible amount of data out there that is unstructured - it just sort of exists

  • Most text data is unstructured. How would you analyze the contents of a book? No rows or columns there

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Getting text data

There are many ways to get text data. Any digital text could potentially be used as textual data.

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Getting text data

There are many ways to get text data. Any digital text could potentially be used as textual data.

How about Wikipedia?

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Getting text data

There are many ways to get text data. Any digital text could potentially be used as textual data.

How about Wikipedia?

Anything that lives on the web is a common use case. Social media data being perhaps primary among them.

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"Screen" scraping

Short foray into web scraping. It's not expected you fully follow this. More about "exposure" and less about building competencies.

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"Screen" scraping

Short foray into web scraping. It's not expected you fully follow this. More about "exposure" and less about building competencies.

Use the rvest package to scrape the data you see "on the screen".

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"Screen" scraping

Short foray into web scraping. It's not expected you fully follow this. More about "exposure" and less about building competencies.

Use the rvest package to scrape the data you see "on the screen".

Let's read in the Wikipedia page on Eugene

library(rvest)
eugene <- read_html("https://en.wikipedia.org/wiki/Eugene%2C_Oregon")
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Grab paragraphs

The "#mw-content-text > div.mw-parser-output > p" is the CSS selector that I pulled from the website

paragraphs <- eugene %>% 
  html_elements("#mw-content-text > div.mw-parser-output > p") %>%
  html_text2()

The first paragraph is just an empty line, so they are numbered p + 1

Print the first paragraph

cat(stringr::str_wrap(paragraphs[2], 50))
## Eugene (/juːˈdʒiːn/ yoo-JEEN) is a city in the
## U.S. state of Oregon, in the Pacific Northwest. It
## is at the southern end of the Willamette Valley,
## near the confluence of the McKenzie and Willamette
## rivers, about 50 miles (80 km) east of the Oregon
## Coast.[7]
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Print the fourth paragraph

cat(stringr::str_wrap(paragraphs[5], 50))
## The first people to settle in the Eugene area were
## known as the Kalapuyans, also written Calapooia
## or Calapooya. They made "seasonal rounds," moving
## around the countryside to collect and preserve
## local foods, including acorns, the bulbs of the
## wapato and camas plants, and berries. They stored
## these foods in their permanent winter village.
## When crop activities waned, they returned to their
## winter villages and took up hunting, fishing, and
## trading.[19][20] They were known as the Chifin
## Kalapuyans and called the Eugene area where they
## lived "Chifin", sometimes recorded as "Chafin" or
## "Chiffin".[21][22]
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Analysis

How do we analyze the text? What we we even analyze?

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Analysis

How do we analyze the text? What we we even analyze?

First, let's structure it! Turn the text into a simple data frame.

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Analysis

How do we analyze the text? What we we even analyze?

First, let's structure it! Turn the text into a simple data frame.

library(tidyverse)
eugene_df <- tibble(
  paragraph = seq_along(paragraphs),
  description = paragraphs
)
eugene_df
## # A tibble: 130 × 2
##    paragraph
##        <int>
##  1         1
##  2         2
##  3         3
##  4         4
##  5         5
##  6         6
##  7         7
##  8         8
##  9         9
## 10        10
## # … with 120 more rows, and 1 more variable:
## #   description <chr>
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Can we analyze it now?

Not really... what would we analyze?

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Can we analyze it now?

Not really... what would we analyze?

Words!

Let's break it into words. This is where the tidytext package comes into play.

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The unnest_tokens() function

Just like most functions in the tidyverse, we pipe our data to unnest_tokens()

  • First argument is the name of the new column we want in our data

  • Second argument is the text data to process

  • Third argument is how the text should processed. The default is "words", meaning the text will be broken into words.

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Example

library(tidytext)
eugene_tidy_words <- eugene_df %>% 
  unnest_tokens(word, description)
eugene_tidy_words
## # A tibble: 7,814 × 2
##    paragraph word     
##        <int> <chr>    
##  1         2 eugene   
##  2         2 juːˈdʒiːn
##  3         2 yoo      
##  4         2 jeen     
##  5         2 is       
##  6         2 a        
##  7         2 city     
##  8         2 in       
##  9         2 the      
## 10         2 u.s      
## # … with 7,804 more rows

Not perfect, but pretty good

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What to do now?

Let's count some words!

eugene_tidy_words %>% 
  count(word, sort = TRUE)
## # A tibble: 2,478 × 2
##    word       n
##    <chr>  <int>
##  1 the      597
##  2 and      249
##  3 of       231
##  4 in       230
##  5 eugene   178
##  6 a        136
##  7 to       116
##  8 is        95
##  9 for       76
## 10 was       76
## # … with 2,468 more rows
90 / 132

Plot the top 15 words

eugene_tidy_words %>% 
  count(word, sort = TRUE) %>% 
  mutate(word = fct_reorder(word, n)) %>% 
  slice(1:15) %>% # select only the first 15 rows
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue")

91 / 132

Not very informative

Why?

92 / 132

Not very informative

Why?

Most of the words are common words like "the", "and", "of" (top three words)

92 / 132

Not very informative

Why?

Most of the words are common words like "the", "and", "of" (top three words)

These are referred to as "stop words".

92 / 132

Not very informative

Why?

Most of the words are common words like "the", "and", "of" (top three words)

These are referred to as "stop words".

Luckily, tidytext provides us with a dictionary of stop words. We can use an anti_join() with this dictionary to remove these words.

92 / 132

Quick refresher

A semi_join() works just like an inner_join(), but without adding any columns. A semi_join() works by keeping only rows that are in common with the two datasets.

93 / 132

Quick refresher

A semi_join() works just like an inner_join(), but without adding any columns. A semi_join() works by keeping only rows that are in common with the two datasets.

An anti_join() does basically the opposite, by removing any rows that are in common between the two datasets.

93 / 132

Look at the stop words

This dataset is available to you as soon as you load tidytext.

There are three lexicons - I usually use all three.

stop_words
## # A tibble: 1,149 × 2
##    word        lexicon
##    <chr>       <chr>  
##  1 a           SMART  
##  2 a's         SMART  
##  3 able        SMART  
##  4 about       SMART  
##  5 above       SMART  
##  6 according   SMART  
##  7 accordingly SMART  
##  8 across      SMART  
##  9 actually    SMART  
## 10 after       SMART  
## # … with 1,139 more rows
94 / 132

Count

Let's try counting again without the stop words included.

eugene_tidy_words %>% 
  anti_join(stop_words) %>%
  count(word, sort = TRUE)
## # A tibble: 2,199 × 2
##    word           n
##    <chr>      <int>
##  1 eugene       178
##  2 city          54
##  3 oregon        50
##  4 university    40
##  5 community     27
##  6 lane          24
##  7 eugene's      23
##  8 college       20
##  9 home          20
## 10 center        19
## # … with 2,189 more rows

So much more informative!

95 / 132

Plot the top 15 words

eugene_tidy_words %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  mutate(word = reorder(word, n)) %>% # make y-axis ordered by n
  slice(1:15) %>% # select only the first 15 rows
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue")

96 / 132

Add the headers in

I hid the code here because it's weird and inefficient but the HTML structure made it difficult. You can look at the source if you want. The new dataframe is called eugene_tidy_words2.

## # A tibble: 130 × 3
##    paragraph header 
##        <int> <chr>  
##  1         1 Intro  
##  2         2 Intro  
##  3         3 Intro  
##  4         4 Intro  
##  5         5 History
##  6         6 History
##  7         7 History
##  8         8 History
##  9         9 History
## 10        10 History
## # … with 120 more rows, and 1 more variable:
## #   description <chr>
97 / 132

Count words by header

Not surprisingly, "eugene" appears to be the most common among multiple categories.

We might want to remove "eugene" as well.

eugene_tidy_words2 %>% 
  unnest_tokens(word, description) %>% 
  count(header, word, sort = TRUE) %>% 
  anti_join(stop_words)
## # A tibble: 3,029 × 3
##    header           word       n
##    <chr>            <chr>  <int>
##  1 Arts and culture eugene    54
##  2 History          eugene    21
##  3 Infrastructure   eugene    20
##  4 Arts and culture church    19
##  5 Education        eugene    15
##  6 Geography        eugene    15
##  7 Economy          eugene    14
##  8 Education        school    14
##  9 Government       eugene    14
## 10 Arts and culture center    12
## # … with 3,019 more rows
98 / 132

Plot

Top 10 words by header

p <- eugene_tidy_words2 %>% 
  unnest_tokens(word, description) %>% 
  count(header, word, sort = TRUE) %>% 
  anti_join(stop_words) %>% 
  group_by(header) %>% 
  slice(1:10) %>% 
  mutate(word = reorder_within(word, n, header)) %>%
  ggplot(aes(n, word)) +
    geom_col(fill = "cornflowerblue") +
    scale_y_reordered() +
    facet_wrap(~header, scales = "free_y", ncol = 3)
99 / 132

100 / 132

String manipulations

101 / 132

Base vs tidyverse

The tidyverse package is {stringr}

It is more consistent than base functions and occassionally faster

However, I tend to prefer the base functions, and they are still more commonly seen "in the wild" than stringr.

We'll therefore briefly cover both.

102 / 132

Inconsistencies

Super common example

set.seed(123)
ex <- tibble(
  gender = sample(
    c("f", "F", "Fem", "Female", "FEMALE", 
      "m", "M", "Male", "MALE",
      "nb", "NB", "non-binary", 
      "agender", "AGENDER", "Agender",
      "gender-fluid", "fluid",
      "No response"),
    500, 
    replace = TRUE),
  score = rnorm(500)
)
103 / 132

Have vs want

Have

## # A tibble: 18 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         25
##  2 Agender         26
##  3 AGENDER         29
##  4 f               24
##  5 F               24
##  6 Fem             27
##  7 Female          27
##  8 FEMALE          25
##  9 fluid           28
## 10 gender-fluid    28
## 11 m               29
## 12 M               34
## 13 Male            37
## 14 MALE            27
## 15 nb              36
## 16 NB              27
## 17 No response     21
## 18 non-binary      26

Want

## # A tibble: 6 × 2
##   gender          n
##   <chr>       <int>
## 1 agender        80
## 2 female        127
## 3 fluid          56
## 4 male          127
## 5 no response    21
## 6 non-binary     89
104 / 132

Walkthrough

Getting to what we want takes a few steps. Let's do it together!

105 / 132

Consistent case

  • The first thing we might want to do is change everything to uppercase or lowercase. This will fix many of our inconsistencies.

  • Options are stringr::str_to_upper(), stringr::str_to_lower(), base::toupper() or base::tolower()

106 / 132

Consistent case

Original

ex %>% 
  count(gender)
## # A tibble: 18 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         25
##  2 Agender         26
##  3 AGENDER         29
##  4 f               24
##  5 F               24
##  6 Fem             27
##  7 Female          27
##  8 FEMALE          25
##  9 fluid           28
## 10 gender-fluid    28
## 11 m               29
## 12 M               34
## 13 Male            37
## 14 MALE            27
## 15 nb              36
## 16 NB              27
## 17 No response     21
## 18 non-binary      26

Modified

ex %>%
  mutate(gender = tolower(gender)) %>% 
  count(gender)
## # A tibble: 11 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         80
##  2 f               48
##  3 fem             27
##  4 female          52
##  5 fluid           28
##  6 gender-fluid    28
##  7 m               63
##  8 male            64
##  9 nb              63
## 10 no response     21
## 11 non-binary      26
107 / 132

What next?

108 / 132

What next?

Collapse the genders that have "fluid"?

108 / 132

What next?

Collapse the genders that have "fluid"?

Use grepl() (global regular expression parser logical) with ifelse() to replace if a pattern is found

108 / 132

What next?

Collapse the genders that have "fluid"?

Use grepl() (global regular expression parser logical) with ifelse() to replace if a pattern is found

You could also use stringr::str_detect() instead. The arguments are just in reversed order

grepl("fluid", gender)
str_detect(gender, "fluid")
108 / 132
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender)
  ) %>% 
  count(gender)
## # A tibble: 10 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         80
##  2 f               48
##  3 fem             27
##  4 female          52
##  5 gender-fluid    56
##  6 m               63
##  7 male            64
##  8 nb              63
##  9 no response     21
## 10 non-binary      26
109 / 132

stringr

library(stringr)
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(str_detect(gender, "fluid"), "gender-fluid", gender)
  ) %>% 
  count(gender)
## # A tibble: 10 × 2
##    gender           n
##    <chr>        <int>
##  1 agender         80
##  2 f               48
##  3 fem             27
##  4 female          52
##  5 gender-fluid    56
##  6 m               63
##  7 male            64
##  8 nb              63
##  9 no response     21
## 10 non-binary      26
110 / 132

What next?

111 / 132

What next?

How about - if it starts with "m", then "Male"?

111 / 132

What next?

How about - if it starts with "m", then "Male"?

Use "^" to denote "starts with"

111 / 132
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender),
    gender = ifelse(grepl("^m", gender), "male", gender)
  ) %>% 
  count(gender)
## # A tibble: 9 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 f               48
## 3 fem             27
## 4 female          52
## 5 gender-fluid    56
## 6 male           127
## 7 nb              63
## 8 no response     21
## 9 non-binary      26
112 / 132

Again

Replicate the same thing, but this time with "female". Note that we couldn't do this initially, but we can now because "fluid" has been collapsed with "gender-fluid".

You try first

113 / 132
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender),
    gender = ifelse(grepl("^m", gender), "male", gender),
    gender = ifelse(grepl("^f", gender), "female", gender)
  ) %>% 
  count(gender)
## # A tibble: 7 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 female         127
## 3 gender-fluid    56
## 4 male           127
## 5 nb              63
## 6 no response     21
## 7 non-binary      26
114 / 132

Again?

Can we do the same thing with "^n" for non-binary?

115 / 132

Again?

Can we do the same thing with "^n" for non-binary?

NO!

115 / 132

Again?

Can we do the same thing with "^n" for non-binary?

NO!

Little bit more complicated - use Boolean logic.

115 / 132
ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(grepl("fluid", gender), "gender-fluid", gender),
    gender = ifelse(grepl("^m", gender), "male", gender),
    gender = ifelse(grepl("^f", gender), "female", gender),
    gender = ifelse(
      grepl("^n", gender) & gender != "no response",
      "non-binary",
      gender
    )
  ) %>% 
  count(gender)
116 / 132
## # A tibble: 6 × 2
##   gender           n
##   <chr>        <int>
## 1 agender         80
## 2 female         127
## 3 gender-fluid    56
## 4 male           127
## 5 no response     21
## 6 non-binary      89
117 / 132

stringr version

ex %>%
  mutate(
    gender = tolower(gender),
    gender = ifelse(str_detect(gender, "fluid"), "gender-fluid", gender),
    gender = ifelse(str_detect(gender, "^m"), "male", gender),
    gender = ifelse(str_detect(gender, "^f"), "female", gender),
    gender = ifelse(
      str_detect(gender, "^n") & gender != "no response",
      "non-binary",
      gender
    )
  ) %>% 
  count(gender)
118 / 132

Special characters

  • ^: Anchor - matches the start of a string
  • $: Anchor - matches the end of a string
  • *: Matches the preceding character zero or more times
  • ?: Matches the preceding character zero or one times
  • +: Matches the preceding character one or more times
  • {: Used to specify number of matches, a{n}, a{n,}, and a{n, m}
  • .: Wildcard - matches any character
  • |: OR operator
  • [: Alternates, also used for character matching (e.g., [:digit:])
  • (: Used for backreferencing, look aheads, or groups
  • \: Used to escape special characters
119 / 132

More detail

Both of the below are good places to get more comprehensive information

120 / 132

One more quick example

library(edld652)
d <- get_data("EDFacts_acgr_lea_2011_2019")
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d
## # A tibble: 11,326 × 29
##    ALL_COHORT ALL_RATE CWD_COHORT CWD_RATE
##         <dbl> <chr>         <dbl> <chr>   
##  1        252 80                3 PS      
##  2        398 75               47 70-79   
##  3       1020 89               51 40-49   
##  4        750 91               35 60-69   
##  5        128 55-59            15 LT50    
##  6        166 90-94             9 GE50    
##  7        336 90               30 60-79   
##  8        273 77               11 LT50    
##  9        134 70-74             4 PS      
## 10        266 58               33 50-59   
## # … with 11,316 more rows, and 25 more variables:
## #   DATE_CUR <chr>, ECD_COHORT <dbl>,
## #   ECD_RATE <chr>, FIPST <chr>, FILEURL <chr>,
## #   LEAID <chr>, LEANM <chr>, LEP_COHORT <dbl>,
## #   LEP_RATE <chr>, MAM_COHORT <dbl>,
## #   MAM_RATE <chr>, MAS_COHORT <dbl>,
## #   MAS_RATE <chr>, MBL_COHORT <dbl>, …
121 / 132

Do three things:

  • Create a new variable that identifies if the LEA is associated with a city or a county

  • Drop "City" or "County" from the LEA name (e.g., "Albertville City" would be "Albertville")

  • Replace all . with --DOT-- in FILEURL (so they are not actual links)

122 / 132

City or county

Ideas?

123 / 132

City or county

Ideas?

d <- d %>% 
  mutate(county = grepl("county$", tolower(LEANM)))
123 / 132

Quick Check

d %>% 
  select(LEANM, county) %>% 
  print(n = 15)
## # A tibble: 11,326 × 2
##    LEANM            county
##    <chr>            <lgl> 
##  1 Albertville City FALSE 
##  2 Marshall County  TRUE  
##  3 Hoover City      FALSE 
##  4 Madison City     FALSE 
##  5 Leeds City       FALSE 
##  6 Boaz City        FALSE 
##  7 Trussville City  FALSE 
##  8 Alexander City   FALSE 
##  9 Andalusia City   FALSE 
## 10 Anniston City    FALSE 
## 11 Arab City        FALSE 
## 12 Athens City      FALSE 
## 13 Attalla City     FALSE 
## 14 Auburn City      FALSE 
## 15 Autauga County   TRUE  
## # … with 11,311 more rows
124 / 132

Remove city/county

  • Lots of ways to do this. Use base::gsub() or stringr::str_replace_all()

  • Replace everything after the space with nothing

125 / 132

Remove city/county

  • Lots of ways to do this. Use base::gsub() or stringr::str_replace_all()

  • Replace everything after the space with nothing

d %>% 
  select(LEANM) %>% 
  mutate(new_name = gsub(" .+", "", LEANM))
## # A tibble: 11,326 × 2
##    LEANM            new_name   
##    <chr>            <chr>      
##  1 Albertville City Albertville
##  2 Marshall County  Marshall   
##  3 Hoover City      Hoover     
##  4 Madison City     Madison    
##  5 Leeds City       Leeds      
##  6 Boaz City        Boaz       
##  7 Trussville City  Trussville 
##  8 Alexander City   Alexander  
##  9 Andalusia City   Andalusia  
## 10 Anniston City    Anniston   
## # … with 11,316 more rows
125 / 132

Another way

There are other ways too, of course

d %>% 
  select(LEANM) %>% 
  mutate(new_name = gsub(" City$| County$", "", LEANM))
## # A tibble: 11,326 × 2
##    LEANM            new_name   
##    <chr>            <chr>      
##  1 Albertville City Albertville
##  2 Marshall County  Marshall   
##  3 Hoover City      Hoover     
##  4 Madison City     Madison    
##  5 Leeds City       Leeds      
##  6 Boaz City        Boaz       
##  7 Trussville City  Trussville 
##  8 Alexander City   Alexander  
##  9 Andalusia City   Andalusia  
## 10 Anniston City    Anniston   
## # … with 11,316 more rows
126 / 132

Final step

Handling the URLs. This is a bit artificial, but it illustrates escaping, which is important.

  • Remember . is a special character, so needs to be escaped

  • \ itself is a special character so it needs to be escaped. Functionally, then, you escape special characters with \\, not \.

127 / 132
d %>% 
  select(FILEURL)
## # A tibble: 11,326 × 1
## # … with 11,316 more rows, and 1 more variable:
## #   FILEURL <chr>
128 / 132
d %>% 
  select(FILEURL) %>% 
  mutate(FILEURL = gsub("\\.", "--DOT--", FILEURL)) %>% 
  as.data.frame()
##                                                                                             FILEURL
## 1    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 2    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 3    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 4    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 5    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 6    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 7    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 8    https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
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## 990  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 991  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 992  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 993  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 994  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 995  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 996  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 997  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 998  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 999  https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
## 1000 https://www2--DOT--ed--DOT--gov/about/inits/ed/edfacts/data-files/acgr-lea-sy2010-11--DOT--csv
##  [ reached 'max' / getOption("max.print") -- omitted 10326 rows ]
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Wrapping up

This was pretty quick, still a lot we didn't get to

I'll try to embed more opportunities for you to practice these skills throughout the term

130 / 132

Next time

No class - MLK Jr. Day. Please do have your lab in before then.

131 / 132

Lab 1

132 / 132

Agenda

  • Visualizing distributions
    • histograms
    • density plots
    • Empirical cumulative density plots
    • QQ plots
  • Visualizing amounts
    • bar plots
    • dot plots
    • heatmaps
  • Working textual data
    • Splitting into words, n-grams
    • Visualizing word frequencies
  • Cleaning string data
    • Substrings, basic transformations
    • Substitutions and quick pattern matching
2 / 132
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