Session 1 — Introduction: R workflow and first steps

Language Analytics in R

Peter Gilles

Learning objectives

By the end of this session you will:

• Use RStudio and run R code in the console and in a Quarto document.
• Create and inspect objects with the assignment operator (<-).
• Write clear names and comments, and apply basic code style.
• Call functions with correct syntax and use the pipe (|>).
• Run and render a minimal Quarto (.qmd) script.
• Load and inspect text/language data (e.g. CSV) and understand basics of data import for corpora.
• Use tidytext and quanteda to turn short example text into tokens and a corpus, and run minimal counts.

This course focuses on linguistic and language data in R. Examples and exercises use words, frequencies, and simple text data so you can quickly apply the same ideas to your own corpora and experiments.

The material draws on R for Data Science (2e) (chapters 2–4), R for Linguists, and other linguistics-oriented R resources (see Further reading).

RStudio and running R code

The RStudio interface

• Console: type R code and see results.
• Editor: write and save scripts (e.g. .qmd or .R).
• Environment: view objects you have created (datasets, variables).
• Files / Plots / Help: navigate files, view plots, read help.

Run code from the editor with Cmd+Enter (Mac) or Ctrl+Enter (Windows/Linux).

Getting help

Prefix a command with ?, e.g. ?mean to call the help page for this command. Or use the Help pane in RStudio.

# Access help for a function by name:
?mean

# Alternatively, use the help() function:
help("mean")

# Search help topics with apropos() or help.search():
apropos("lin")  # Shows all objects/functions with "lin" in the name

 [1] ".linearGradientPattern"           ".QuartoInlineRender"             
 [3] ".tilingPattern"                   "[.DLLInfoList"                   
 [5] "$.DLLInfo"                        "abline"                          
 [7] "ceiling"                          "contourLines"                    
 [9] "file.link"                        "file.symlink"                    
[11] "findLineNum"                      "getCallingDLL"                   
[13] "getCallingDLLe"                   "getDLLRegisteredRoutines.DLLInfo"
[15] "getNativeSymbolInfo"              "getSrcLines"                     
[17] "globalCallingHandlers"            "line"                            
[19] "lines"                            "lines.default"                   
[21] "loglin"                           "make.link"                       
[23] "matlines"                         "print.DLLInfo"                   
[25] "print.DLLInfoList"                "qqline"                          
[27] "readline"                         "readLines"                       
[29] "smooth.spline"                    "spline"                          
[31] "splinefun"                        "splinefunH"                      
[33] "Sys.readlink"                     "unlink"                          
[35] "withCallingHandlers"              "writeLines"                      
[37] "xspline"                         

help.search("linear model")  # Looks up help topics related to linear models

# View example usage for functions:
example(mean)

mean> x <- c(0:10, 50)

mean> xm <- mean(x)

mean> c(xm, mean(x, trim = 0.10))
[1] 8.75 5.50

# See the vignette (long-form documentation) for a package:
vignette("dplyr")

starting httpd help server ... done

There are numerous R cheatsheets that summarise command usages in a structured way.

Packages

Install a package once with install.packages("package_name") or use the packages tab in RStudio. Load it every time you start R or open a document with library(package_name):

# Core packages for data import and manipulation
library(tidyverse)

Warning: package 'ggplot2' was built under R version 4.5.2

Warning: package 'tibble' was built under R version 4.5.2

Warning: package 'tidyr' was built under R version 4.5.2

Warning: package 'readr' was built under R version 4.5.2

Warning: package 'purrr' was built under R version 4.5.2

Warning: package 'dplyr' was built under R version 4.5.2

Coding basics

You can use R as a calculator (e.g. for reaction times, accuracy, or counts):

1 / 200 * 30

[1] 0.15

(59 + 73 + 2) / 3

[1] 44.66667

sin(pi / 2)

[1] 1

Create objects with assignment:

calc <- (59 + 73 + 2) / 3

# e.g. store a value like VOT (ms) or word length
vot_ms <- 25
word_length <- nchar("linguistics")

Every assignment has the form:

object_name <- value

Read it as: “object name gets value”.

Tip: In RStudio use Alt + - (minus) to insert <- with spaces.

The value is stored but not printed. Type the object name to inspect it:

vot_ms

[1] 25

word_length

[1] 11

Combine values into a vector with c() — like a column of observations (e.g. reaction times, word lengths, or a list of words):

# numeric vector: e.g. reaction times in ms
rt_ms <- c(320, 415, 380, 290, 410)

# character vector: e.g. word forms or conditions
words <- c("cat", "dog", "mouse")

# a rose is a rose — last expression is printed in the report
stein <- c("a", "rose", "is", "a", "rose", "is", "a", "rose")
print(stein)

[1] "a"    "rose" "is"   "a"    "rose" "is"   "a"    "rose"

Instead of referencing every token in a vector individually, we can convert them to factors.

stein.fac <- as.factor(c("a", "rose", "is", "a", "rose", "is", "a", "rose"))
print(stein.fac)

[1] a    rose is   a    rose is   a    rose
Levels: a is rose

Tibbles and data frames

Vectors and factors hold a single column of values. For tabular data (rows and columns), R uses data frames. A tibble is a modern, tidyverse-style data frame: it prints nicely, doesn’t convert strings to factors by default, and works well with the pipe and dplyr.

Creating a tibble

Use tibble() to build a table from named vectors (each vector becomes a column). Columns can be referred to by name in later code.

library(tibble)

# Example: a small table (e.g. word, frequency, part of speech)
lex <- tibble(
  word = c("the", "cat", "sat", "on", "mat"),
  freq = c(100, 5, 3, 50, 2),
  pos  = c("det", "noun", "verb", "prep", "noun")
)
lex

# A tibble: 5 × 3
  word   freq pos  
  <chr> <dbl> <chr>
1 the     100 det  
2 cat       5 noun 
3 sat       3 verb 
4 on       50 prep 
5 mat       2 noun 

With data.frame() you get a classic R data frame; tibbles are usually preferred in the tidyverse because of their print format and consistent behaviour.

Inspecting structure

• Print: typing the name shows the first rows and column types (tibbles don’t flood the console).
• glimpse() (dplyr): one row per column, with type and a few values.
• nrow(), ncol(), names(): number of rows, number of columns, column names.

glimpse(lex)

Rows: 5
Columns: 3
$ word <chr> "the", "cat", "sat", "on", "mat"
$ freq <dbl> 100, 5, 3, 50, 2
$ pos  <chr> "det", "noun", "verb", "prep", "noun"

nrow(lex)

[1] 5

names(lex)

[1] "word" "freq" "pos" 

Accessing columns

Use $ to get one column as a vector: lex$word, lex$freq. You can use that in expressions (e.g. mean(lex$freq)) or pass it to functions. Later you’ll use dplyr verbs (filter, select, mutate) to work with whole tables without pulling columns out by hand.

lex$word

[1] "the" "cat" "sat" "on"  "mat"

mean(lex$freq)

[1] 32

Comments and names

Comments

R ignores everything after # on a line. Use comments to explain why you did something (e.g. why you chose a threshold or excluded a condition), not just what the code does.

# word list for lexical decision practice
words <- c("cat", "dog", "mouse")

# mean reaction time (ms)
mean(rt_ms)

[1] 363

Naming conventions

• Names must start with a letter and contain only letters, numbers, _, and ..
• Use snake_case: lowercase words separated by _ (e.g. word_list, response_time).

response_time_ms <- 320
word_list <- c("the", "cat", "sat")
# Avoid: responseTimeMs, Word.List

R is case-sensitive and does not fix typos: word_list and Word_list are different objects.

Tip: Type a prefix and press Tab for autocomplete; use ↑ or Cmd+↑ to recall previous commands.

Calling functions

Functions are called like this:

function_name(argument1 = value1, argument2 = value2, ...)

Example: seq() creates sequences (e.g. for item IDs or conditions):

seq(from = 1, to = 10)

 [1]  1  2  3  4  5  6  7  8  9 10

seq(1, 5, by = 0.5)

[1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

You can omit argument names for the first arguments. Use Tab after typing a function name to see arguments and help.

Strings (text) need matched quotation marks — essential when working with words and corpora:

# quotation marks around strings
word <- "hello"

# Compare strings with == (e.g. for accuracy coding)
"linguistics" == "linguistics"

[1] TRUE

"linguistics" == "lingiustics"

[1] FALSE

If you see a + in the console, R is waiting for more input (often a missing " or )). Press Escape to cancel.

Code style

Good style makes code easier to read for you and others. We follow the tidyverse style guide.

Names (reminder)

• Use lowercase, numbers, and _.
• Prefer long, descriptive names (e.g. participant_response_time over prt).

Spaces

• Put spaces around +, -, ==, <, etc. (except ^).
• Put spaces around <-.
• Put a space after every comma.

# Strive for: spaces around operators and after comma
n_char <- nchar("word")
mean(rt_ms, na.rm = TRUE)

[1] 363

# Avoid: n_char<-nchar("word") or mean(rt_ms,na.rm=TRUE)

Pipes

The pipe |> (alternatively %>% when using package magrittr) passes the result on the left into the function on the right. Read it as “then”.

• Put a space before |> and put |> at the end of the line.
• Indent the next line by two spaces.

c("Méindeg", "Dënschdeg", "Mëttwoch") |>
  nchar()

[1] 7 9 8

We’ll use the pipe with language data in the next section.

Sectioning comments

Break long scripts into sections:

# Load data --------------------------------------

# Plot data --------------------------------------

In RStudio: Cmd+Shift+R (Mac) or Ctrl+Shift+R (Windows/Linux) inserts a section header.

Tip: Install the styler package and use the command palette (Cmd+Shift+P) to format code automatically.

Scripting in Quarto

Why Quarto?

• Combine text, code, and output in one document — ideal for reporting on corpus or experiment results.
• Reproducible: re-run and re-render to update tables and figures.
• Output to HTML, PDF, or other formats.

Running code in a .qmd file

• Create an empty Quarto file in RStudio: File > New File > Quarto Document …
• Code chunks are enclosed in ```{r} and ```.
• Run one chunk: Cmd+Enter (Mac) or Ctrl+Enter (Windows/Linux).
• Render the whole document: Cmd+Shift+K (or Ctrl+Shift+K) or click Render.

Example chunk options:

#| label: my-chunk
#| echo: true
#| eval: true

• label: unique name for the chunk.
• echo: whether to show the code in the output.
• eval: whether to run the code.

Working with text data and corpora (data import)

For language analytics we usually work with tables (e.g. CSV or Excel) or text files. Here we cover reading tabular data with readr, then tidytext and quanteda for turning text into a format you can analyze. Later sessions will go deeper (frequency lists, sentiment, document classification, etc.).

Reading a CSV file

Use read_csv() from the readr package (included in tidyverse) to read comma-separated files. Example: population by commune (Luxembourg), file rnrpp-population-commune.csv (same folder as this document, or adjust the path if needed).

# A tibble: 102 × 6
   COMMUNE_CODE COMMUNE_NOM FEMMES_MINEURES HOMMES_MINEURS FEMMES_MAJEURES
   <chr>        <chr>                 <dbl>          <dbl>           <dbl>
 1 1101         Beaufort                312            306            1250
 2 1102         Bech                    104            134             557
 3 0802         Beckerich               271            287            1178
 4 1103         Berdorf                 216            244             906
 5 0401         Bertrange               839            953            3899
 6 0301         Bettembourg            1091           1167            4821
 7 0702         Bettendorf              269            293            1203
 8 1201         Betzdorf                394            487            1663
 9 0502         Bissen                  337            314            1462
10 1202         Biwer                   194            194             727
# ℹ 92 more rows
# ℹ 1 more variable: HOMMES_MAJEURS <dbl>

Inspecting the dataset

• Print the data frame: type its name.
• View(pop): opens a spreadsheet-like viewer in RStudio.
• glimpse(): compact view of columns and types.

glimpse(pop)

Rows: 102
Columns: 6
$ COMMUNE_CODE    <chr> "1101", "1102", "0802", "1103", "0401", "0301", "0702"…
$ COMMUNE_NOM     <chr> "Beaufort", "Bech", "Beckerich", "Berdorf", "Bertrange…
$ FEMMES_MINEURES <dbl> 312, 104, 271, 216, 839, 1091, 269, 394, 337, 194, 160…
$ HOMMES_MINEURS  <dbl> 306, 134, 287, 244, 953, 1167, 293, 487, 314, 194, 174…
$ FEMMES_MAJEURES <dbl> 1250, 557, 1178, 906, 3899, 4821, 1203, 1663, 1462, 72…
$ HOMMES_MAJEURS  <dbl> 1234, 575, 1177, 927, 3798, 4573, 1286, 1587, 1450, 79…

nrow(pop)

[1] 102

ncol(pop)

[1] 6

names(pop)

[1] "COMMUNE_CODE"    "COMMUNE_NOM"     "FEMMES_MINEURES" "HOMMES_MINEURS" 
[5] "FEMMES_MAJEURES" "HOMMES_MAJEURS" 

head(pop, 5)

# A tibble: 5 × 6
  COMMUNE_CODE COMMUNE_NOM FEMMES_MINEURES HOMMES_MINEURS FEMMES_MAJEURES
  <chr>        <chr>                 <dbl>          <dbl>           <dbl>
1 1101         Beaufort                312            306            1250
2 1102         Bech                    104            134             557
3 0802         Beckerich               271            287            1178
4 1103         Berdorf                 216            244             906
5 0401         Bertrange               839            953            3899
# ℹ 1 more variable: HOMMES_MAJEURS <dbl>

Accessing columns

Use $ to get one column as a vector (e.g. to compute totals or means):

pop$COMMUNE_NOM

  [1] "Beaufort"                "Bech"                   
  [3] "Beckerich"               "Berdorf"                
  [5] "Bertrange"               "Bettembourg"            
  [7] "Bettendorf"              "Betzdorf"               
  [9] "Bissen"                  "Biwer"                  
 [11] "Boulaide"                "Bourscheid"             
 [13] "Bous"                    "Bous-Waldbredimus"      
 [15] "Clervaux"                "Colmar-Berg"            
 [17] "Consdorf"                "Contern"                
 [19] "Dalheim"                 "Diekirch"               
 [21] "Differdange"             "Dippach"                
 [23] "Dudelange"               "Echternach"             
 [25] "Ell"                     "Erpeldange-sur-S\xfbre" 
 [27] "Esch-sur-Alzette"        "Esch-sur-S\xfbre"       
 [29] "Ettelbruck"              "Feulen"                 
 [31] "Fischbach"               "Flaxweiler"             
 [33] "Frisange"                "Garnich"                
 [35] "Goesdorf"                "Grevenmacher"           
 [37] "Groussbus-Wal"           "Habscht"                
 [39] "Heffingen"               "Helperknapp"            
 [41] "Hesperange"              "Junglinster"            
 [43] "K\xe4erjeng"             "Kayl"                   
 [45] "Kehlen"                  "Kiischpelt"             
 [47] "Koerich"                 "Kopstal"                
 [49] "Lac de la Haute-S\xfbre" "Larochette"             
 [51] "Lenningen"               "Leudelange"             
 [53] "Lintgen"                 "Lorentzweiler"          
 [55] "Luxembourg"              "Mamer"                  
 [57] "Manternach"              "Mersch"                 
 [59] "Mertert"                 "Mertzig"                
 [61] "Mondercange"             "Mondorf-les-Bains"      
 [63] "Niederanven"             "Nommern"                
 [65] "P\xe9tange"              "Parc Hosingen"          
 [67] "Pr\xe9izerdaul"          "Putscheid"              
 [69] "Rambrouch"               "Reckange-sur-Mess"      
 [71] "Redange/Attert"          "Reisdorf"               
 [73] "Remich"                  "Roeser"                 
 [75] "Rosport-Mompach"         "Rumelange"              
 [77] "Saeul"                   "Sandweiler"             
 [79] "Sanem"                   "Schengen"               
 [81] "Schieren"                "Schifflange"            
 [83] "Schuttrange"             "Stadtbredimus"          
 [85] "Steinfort"               "Steinsel"               
 [87] "Strassen"                "Tandel"                 
 [89] "Troisvierges"            "Useldange"              
 [91] "Vall\xe9e de l'Ernz"     "Vianden"                
 [93] "Vichten"                 "Waldbillig"             
 [95] "Waldbredimus"            "Walferdange"            
 [97] "Weiler-la-Tour"          "Weiswampach"            
 [99] "Wiltz"                   "Wincrange"              
[101] "Winseler"                "Wormeldange"            

pop$FEMMES_MAJEURES

  [1]  1250   557  1178   906  3899  4821  1203  1663  1462   727   635   687
 [13]     0  1300  2546  1013   827  2000   951  3111 12393  1973  9274  2567
 [25]   678  1117 15436  1330  4186   965   519   880  2040   960   662  2210
 [37]   938  2114   623  2052  7066  3690  4791  4116  2969   524  1093  1908
 [49]   874   911   889  1133  1399  1952 56612  4620   942  4330  2332   961
 [61]  3034  2431  2992   604  8540  1686   744   462  1969  1214  1348   557
 [73]  1765  2923  1521  2299   422  1572  8019  2135   896  4827  1763   831
 [85]  2541  2580  4504   925  1429   849  1162   923   573   782     2  3864
 [97]  1012  1042  3269  1942   623  1337

# Mean population per commune (total = sum of the four count columns)
mean(pop$FEMMES_MINEURES + pop$HOMMES_MINEURS + pop$FEMMES_MAJEURES + pop$HOMMES_MAJEURS)

[1] 6774.01

Aggregating the data

With dplyr you can aggregate by groups (e.g. totals, means). Example: add a total population per commune, then show the top 10 communes by total population; or summarize across all rows (e.g. national totals by category).

# Total population per commune (using the pipe)
pop |>
  mutate(
    total = FEMMES_MINEURES + HOMMES_MINEURS + FEMMES_MAJEURES + HOMMES_MAJEURS
  ) |>
  arrange(desc(total)) |>
  select(COMMUNE_NOM, total) |>
  head(10)

# A tibble: 10 × 2
   COMMUNE_NOM         total
   <chr>               <dbl>
 1 "Luxembourg"       137663
 2 "Esch-sur-Alzette"  38269
 3 "Differdange"       31420
 4 "Dudelange"         22377
 5 "P\xe9tange"        21290
 6 "Sanem"             19609
 7 "Hesperange"        17260
 8 "Schifflange"       11678
 9 "Bettembourg"       11652
10 "Mamer"             11582

# One-row summary: national totals by category (sum across all communes)
pop |>
  summarise(
    femmes_mineures = sum(FEMMES_MINEURES),
    hommes_mineurs  = sum(HOMMES_MINEURS),
    femmes_majeures = sum(FEMMES_MAJEURES),
    hommes_majeurs  = sum(HOMMES_MAJEURS),
    total          = sum(FEMMES_MINEURES + HOMMES_MINEURS + FEMMES_MAJEURES + HOMMES_MAJEURS)
  )

# A tibble: 1 × 5
  femmes_mineures hommes_mineurs femmes_majeures hommes_majeurs  total
            <dbl>          <dbl>           <dbl>          <dbl>  <dbl>
1           63118          66173          280678         280980 690949

So: load with read_csv(), inspect with glimpse(), names(), head(), access columns with $, and aggregate with mutate(), summarise(), arrange(), and select() (and later group_by() for group-wise summaries).

Reading text with tidytext

The tidytext package treats text as tidy data: one token per row. You start from a table with one row per document (or line) and a text column, then tokenize with unnest_tokens().

Install once: install.packages("tidytext") and install.packages("gutenbergr"). Load with library(tidytext) and library(gutenbergr) (and library(tidyverse) for tibbles and the pipe).

Example: download a book from the Project Gutenberg corpus using gutenbergr, then turn it into a one-word-per-row tibble. The first run requires an internet connection.

library(tidytext)
library(gutenbergr)

Warning: package 'gutenbergr' was built under R version 4.5.2

# Download one book by Gutenberg ID (35 = The Time Machine, H.G. Wells)
# See https://docs.ropensci.org/gutenbergr/articles/text-mining.html
gutenberg_books <- gutenberg_download(c(35), meta_fields = "title")
head(gutenberg_books)

# A tibble: 6 × 3
  gutenberg_id text               title           
         <int> <chr>              <chr>           
1           35 "The Time Machine" The Time Machine
2           35 ""                 The Time Machine
3           35 "An Invention"     The Time Machine
4           35 ""                 The Time Machine
5           35 "by H. G. Wells"   The Time Machine
6           35 ""                 The Time Machine

# One row per line; tokenize to one row per word (punctuation removed, lowercased)
tidy_tokens <- gutenberg_books |>
  unnest_tokens(output = word, input = text)

# First rows of tokenized text
head(tidy_tokens, 12)

# A tibble: 12 × 3
   gutenberg_id title            word        
          <int> <chr>            <chr>       
 1           35 The Time Machine the         
 2           35 The Time Machine time        
 3           35 The Time Machine machine     
 4           35 The Time Machine an          
 5           35 The Time Machine invention   
 6           35 The Time Machine by          
 7           35 The Time Machine h           
 8           35 The Time Machine g           
 9           35 The Time Machine wells       
10           35 The Time Machine contents    
11           35 The Time Machine i           
12           35 The Time Machine introduction

Remove stop words (very common words like “the”, “of”) with anti_join(stop_words) from tidytext, then count words:

# Remove stop words, then count
tidy_tokens |>
  anti_join(stop_words, by = "word") |>
  count(word, sort = TRUE) |>
  head(15)

# A tibble: 15 × 2
   word          n
   <chr>     <int>
 1 time        207
 2 machine      88
 3 white        61
 4 traveller    57
 5 hand         49
 6 morlocks     48
 7 people       46
 8 weena        46
 9 found        44
10 light        43
11 night        37
12 world        37
13 round        36
14 eyes         35
15 mind         35

This follows the Text Mining with R workflow: tidy format → tokenize → filter → count. You can join with sentiment lexicons (e.g. get_sentiments()) or use gutenbergr to download more works and compare word frequencies across texts.

Reading text with quanteda

The quanteda package works with a corpus object: a collection of documents with metadata. You can build a corpus from a data frame with a text column (e.g. the same Gutenberg tibble) or from a character vector.

Install once: install.packages("quanteda"). Load with library(quanteda).

Example: use the same Gutenberg data we downloaded for tidytext. Here each line of the book is treated as one document (you could instead collapse by chapter or use one document per book):

library(quanteda)
library(quanteda.textstats)

# Use the gutenberg_books tibble from the tidytext chunk (run that chunk first)
# It has columns gutenberg_id, text, title
corp <- corpus(gutenberg_books, text_field = "text")

# Basic inspection: one row per document (line)
summary(corp, n = 5)

Corpus consisting of 3174 documents, showing 5 documents:

  Text Types Tokens Sentences gutenberg_id            title
 text1     3      3         1           35 The Time Machine
 text2     0      0         0           35 The Time Machine
 text3     2      2         1           35 The Time Machine
 text4     0      0         0           35 The Time Machine
 text5     5      6         2           35 The Time Machine

ndoc(corp)

[1] 3174

ntoken(corp)

   text1    text2    text3    text4    text5    text6    text7    text8 
       3        0        2        0        6        0        0        1 
   text9   text10   text11   text12   text13   text14   text15   text16 
       0        2        3        5        3        5        5        4 
  text17   text18   text19   text20   text21   text22   text23   text24 
       2        3        4        6        4        7        4        5 
  text25   text26   text27   text28   text29   text30   text31   text32 
       4        1        0        0        0        0        2        1 
  text33   text34   text35   text36   text37   text38   text39   text40 
       0        0       16       13       14       13       12       14 
  text41   text42   text43   text44   text45   text46   text47   text48 
      12       10       14       11       15        0       15       13 
  text49   text50   text51   text52   text53   text54   text55   text56 
      12        0       17        9        0       14       18       15 
  text57   text58   text59   text60   text61   text62   text63   text64 
      13        7        0       11        0       17        4        0 
  text65   text66   text67   text68   text69   text70   text71   text72 
      19        4        0       15        3        0        9        0 
  text73   text74   text75   text76   text77   text78   text79   text80 
      17        3        0       15       12       12       16       13 
  text81   text82   text83   text84   text85   text86   text87   text88 
      18       12       11        9       11        0       16       15 
  text89   text90   text91   text92   text93   text94   text95   text96 
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text3065 text3066 text3067 text3068 text3069 text3070 text3071 text3072 
      17       14       15       15       10        0       15       19 
text3073 text3074 text3075 text3076 text3077 text3078 text3079 text3080 
      18       17        2        0       16       14       15       11 
text3081 text3082 text3083 text3084 text3085 text3086 text3087 text3088 
       0       19       16       13       15       16       18       16 
text3089 text3090 text3091 text3092 text3093 text3094 text3095 text3096 
      12       12       12       15       14       16       16       18 
text3097 text3098 text3099 text3100 text3101 text3102 text3103 text3104 
       3        0       21        0       17       18       18       14 
text3105 text3106 text3107 text3108 text3109 text3110 text3111 text3112 
      14        5        0       13       16       16       14       12 
text3113 text3114 text3115 text3116 text3117 text3118 text3119 text3120 
      17       15        3        0       16       17       16       15 
text3121 text3122 text3123 text3124 text3125 text3126 text3127 text3128 
      14       12       12       16       15       13        0       12 
text3129 text3130 text3131 text3132 text3133 text3134 text3135 text3136 
      12       16        5        0       20        7        0       19 
text3137 text3138 text3139 text3140 text3141 text3142 text3143 text3144 
       3        0       13       14       12       17       13        7 
text3145 text3146 text3147 text3148 text3149 text3150 text3151 text3152 
       0        0        0        0        1        0        0       16 
text3153 text3154 text3155 text3156 text3157 text3158 text3159 text3160 
      13       13       12       14       10       14       17       12 
text3161 text3162 text3163 text3164 text3165 text3166 text3167 text3168 
      18       12       15       14       10       12       12       18 
text3169 text3170 text3171 text3172 text3173 text3174 
      16       15       15       11       13        9 

To get a document-term matrix (documents × terms) for frequency analysis or modelling:

# Tokenize and build document-term matrix (remove punctuation)
doc_term_matrix <- corp |>
  tokens(remove_punct = TRUE) |>
  dfm()

# Dimensions and top terms
dim(doc_term_matrix)

[1] 3174 4832

textstat_frequency(doc_term_matrix) |>
  head(10)

   feature frequency rank docfreq group
1      the      2275    1    1604   all
2        i      1241    2    1013   all
3      and      1234    3    1053   all
4       of      1159    4     996   all
5        a       806    5     702   all
6       to       678    6     605   all
7      was       546    7     504   all
8       in       545    8     504   all
9       my       439    9     405   all
10    that       435   10     408   all

So: tidytext gives you a long tibble (one row per token); quanteda gives you a corpus and a matrix (documents × terms). For more on Gutenberg + quanteda you can combine gutenberg_download() with corpus(..., text_field = "text") or use readtext::readtext() for files on disk.

When to use which?

• tidytext: you prefer working in tibbles and dplyr (one row per token); good for word counts, sentiment, and tidy-style workflows.
• quanteda: you need a corpus object, document-term matrices, or built-in NLP (e.g. keywords, collocations, classification); good for corpus linguistics and text mining.
• readr/read_csv: for tabular data (responses, word lists, metadata); combine with tidytext or quanteda when your “text” lives in a column.

Import LOD dictionary data (XML)

Getting the file

The LOD linguistic dataset is published as open data by the Luxembourg government (Zenter fir d’Lëtzebuerger Sprooch) under CC0 (LOD – Linguistesch Daten (resource). Download the zip, extract it, and place the XML file (new_lod-art.xml, ~74 MB) in a folder in your project (e.g. data/). Below we read it from that folder and inspect its structure.

Install once: install.packages("xml2"). Then load with library(xml2).

Reading the XML file

Read the XML from the file path. The file is large, so the first parse may take a few seconds.

library(xml2)

Warning: package 'xml2' was built under R version 4.5.2

# Path to the XML file (e.g. in data/; adjust if your file is elsewhere)
lod_xml_path <- if (file.exists("data/new_lod-art.xml")) "data/new_lod-art.xml" else "new_lod-art.xml"

# Read XML from the folder
lod_xml <- read_xml(lod_xml_path)

# Root element
xml_name(lod_xml)

[1] "lod"

xml_children(lod_xml) |> length()   # number of top-level children (entries)

[1] 33737

Structure of the LOD XML

The document has a single root element <lod>, which contains many <entry> elements. Each entry represents one headword (lemma) and its dictionary information. The hierarchy is:

1. Entry level (<entry id="...">)

• <lemma> — the headword (e.g. “A”).
• <ipa> — IPA pronunciation (e.g. “aː”).
• <microStructure> — grammatical and semantic information.

2. Inside <microStructure>

• <partOfSpeech gen="..."> — part of speech (e.g. SUBST, with optional gender).
• <inflection type="..."> — inflectional forms (e.g. plural), with <form nRuleForm="..."> for each form.
• <grammaticalUnit> — groups one or more meanings.

3. Meaning level (<meaning id="..." video="...">)

• <number> — sense number.
• <targetLanguage lang="de|fr|en|pt|nl"> — translations:
  • <translation> — equivalent in the target language.
  • <semanticClarifier> — short clarification (e.g. “Sehorgan” for “Auge”).
• <examples count="..."> — example sentences:
  • <example> (optional id):
    • <text> — Luxembourgish example: <word> elements for each token, <inflectedHeadword> for the headword form used; optional <attribute> (e.g. EGS = example).
    • <gloss> (optional) — gloss/paraphrase, again with <word> elements.

So each entry → microStructure → grammaticalUnit → one or more meaning → translations in several languages and examples (with optional glosses).

How XPath works

XPath is a language for selecting nodes in an XML (or HTML) tree. In R you use it with xml2 via xml_find_first() (one node) and xml_find_all() (all matching nodes). The expression is a path that describes where to go in the tree.

XPath	Meaning	Example
nodename	Direct child with that name	lemma → children of the current node named lemma
/nodename	Child from the root	/lod → root element; /lod/entry → every entry directly under lod
//nodename	Any descendant (any depth)	//entry → every entry in the document
.//nodename	Any descendant from the current node	Inside an entry, .//lemma finds lemma anywhere under that entry
*	Any element (wildcard)	./* → all direct children of the current node
[@attr='value']	Filter by attribute	targetLanguage[@lang='de'] → targetLanguage whose lang is de
path1/path2	Then: path2 under path1	.//meaning/examples/example → example under examples under meaning

Examples on the LOD:

• //entry — every entry in the document (from root).
• //entry/lemma — every lemma that is a direct child of an entry.
• .//lemma — the lemma of the current entry (when you’re already on one entry).
• .//meaning/targetLanguage[@lang='en']/translation — English translations under the current entry.
• .//meaning/examples/example — all example nodes under the current entry.

The dot (.) is the current node; .// means “starting from here, any descendant”. Without the dot, // starts from the document root.

Inspecting one entry in R

To see the structure of a single entry and extract a few fields:

# First entry
first_entry <- xml_find_first(lod_xml, "//entry")

# Lemma and IPA
xml_find_first(first_entry, ".//lemma") |> xml_text()

[1] "A"

xml_find_first(first_entry, ".//ipa")    |> xml_text()

[1] "aː"

# Part of speech
xml_find_first(first_entry, ".//partOfSpeech") |> xml_text()

[1] "SUBST"

xml_find_first(first_entry, ".//partOfSpeech") |> xml_attr("gen")

[1] "N"

# First meaning: translations (e.g. German, English)
xml_find_all(first_entry, ".//meaning/targetLanguage[@lang='de']/translation") |> xml_text()

 [1] "Auge"             "blaues Auge"      "Veilchen"         "Glasauge"        
 [5] "vor Augen führen" "Auge"             "Fettauge"         "Auge"            
 [9] "Keim"             "Knospe"           "Auge"            

xml_find_all(first_entry, ".//meaning/targetLanguage[@lang='en']/translation") |> xml_text()

 [1] "eye"               "black eye"         "glass eye"        
 [4] "ocular prosthetic" "to make aware of"  "to show"          
 [7] "to demonstrate"    "fat globule"       "eye"              
[10] "bud"               "pip"               "spot"             

# Number of examples in the first meaning
xml_find_first(first_entry, ".//meaning/examples") |> xml_attr("count")

[1] "2+"

Exploring the examples section

Each meaning can contain an <examples> block with one or more <example> nodes. Each <example> has at least one <text> element whose children are <word> and <inflectedHeadword> (and optionally <attribute>), in order — that is the Luxembourgish example sentence. Below we collect several example sentences from random entries and put them into a tibble so you can work with them like any table (filter, count, join, etc.).

Helper: turn one <example> node into the sentence string (first <text> only):

# Sentence text from one <example>: first <text>, then concatenate child elements (word, inflectedHeadword, etc.)
get_sentence <- function(ex) {
  text_node <- xml_find_first(ex, ".//text")
  if (inherits(text_node, "xml_missing")) return(NA_character_)
  tokens <- xml_find_all(text_node, "./*")
  paste(xml_text(tokens), collapse = " ")
}

Collect examples and build a tibble: sample a number of entries, take up to two examples per entry, then combine lemma and sentence into a data frame.

library(tibble)

all_entries <- xml_find_all(lod_xml, "//entry")
set.seed(123)
n_entries  <- 30
sampled    <- sample(length(all_entries), min(n_entries, length(all_entries)))
rows       <- list()

for (i in seq_along(sampled)) {
  entry  <- all_entries[[sampled[i]]]
  lemma  <- xml_find_first(entry, ".//lemma") |> xml_text()
  exs    <- xml_find_all(entry, ".//meaning/examples/example")
  # Take at most 2 examples per entry
  exs    <- exs[1:min(2, length(exs))]
  for (ex in exs) {
    sent <- get_sentence(ex)
    if (!is.na(sent) && nchar(sent) > 0)
      rows[[length(rows) + 1]] <- list(lemma = lemma, sentence = sent)
  }
}

# One row per example sentence
lod_examples <- tibble(
  lemma    = vapply(rows, function(r) r$lemma,    character(1)),
  sentence = vapply(rows, function(r) r$sentence, character(1))
)

lod_examples

# A tibble: 34 × 2
   lemma        sentence                                                        
   <chr>        <chr>                                                           
 1 Belsch       meng bescht Frëndin ass Belsch                                  
 2 Urbanistin   d' Urbanistin schafft Mesuren aus, déi zur Berouegung vum Trafi…
 3 befalen      bei waarmem, fiichtem Wieder ginn d' Riewen nawell gär vun enge…
 4 Finanzwiesen d' EU fuerdert méi eng streng Reglementatioun vum europäesche F…
 5 Finanzwiesen als Ekonomistin hues de gutt Chancen, am Finanzwiesen eng Aarbe…
 6 Virbau       wéi mir d' Haus renovéiert hunn, hu mer eisen Hall duerch e Vir…
 7 Interferenz  fir Interferenzen tëschent den elektreschen Apparater ze vermei…
 8 Interferenz  bei Kanner, déi méisproocheg opwuessen, kënnt et dacks zu sproo…
 9 Kärebrout    eise Bäcker mécht wonnerbaart Kärebrout                         
10 Kärebrout    kanns de nach zwee Kärebrout bei de Bäcker siche goen?          
# ℹ 24 more rows

You can now use dplyr or tidyr on lod_examples (e.g. count sentences per lemma, filter by length, or join with other data). The same idea scales to all entries if you loop over every entry and every example and bind the rows.

Summary of the LOD XML structure

Level	Element	Role
Root	lod	Container for all entries.
Entry	entry (id)	One headword: lemma, ipa, microStructure.
Micro	microStructure	partOfSpeech, inflection(s), grammaticalUnit(s).
Unit	grammaticalUnit	Groups one or more meanings.
Meaning	meaning (id, video)	Sense: number, targetLanguage (translation, semanticClarifier), examples.
Example	example	text (word, inflectedHeadword, optional attribute), optional gloss.

This structure lets you extract lemmas, translations, parts of speech, inflections, and example sentences (with glosses) for lexical and corpus-oriented analyses in R.

First taste of data transformation (with language data)

We use dplyr (part of the tidyverse) to filter, select, and summarize data. Key ideas:

• First argument is always a data frame.
• You refer to column names without quotes.
• The result is a new data frame (the original is unchanged).

Use the pipe to chain steps. Example: take a small word/frequency table, filter by frequency, then sort:

# Build a tiny “corpus-like” table: word, frequency, condition
word_data <- tibble(
  word = c("the", "cat", "sat", "on", "mat", "dog", "ran"),
  freq = c(100, 5, 3, 50, 2, 8, 4),
  pos = c("det", "noun", "verb", "prep", "noun", "noun", "verb")
)

# Then: filter, arrange, show
word_data |>
  filter(freq >= 4) |>
  arrange(desc(freq)) |>
  select(word, freq, pos)

# A tibble: 5 × 3
  word   freq pos  
  <chr> <dbl> <chr>
1 the     100 det  
2 on       50 prep 
3 dog       8 noun 
4 cat       5 noun 
5 ran       4 verb 

Read this as: “Take word_data, then keep rows where freq is at least 4, then sort by freq descending, then show columns word, freq, pos.”

Main verbs you will see again in Session 2:

• filter() — keep rows that satisfy a condition (e.g. certain words or participants).
• select() — keep or drop columns.
• mutate() — add or change columns (e.g. log frequency, word length).
• arrange() — sort rows.
• summarize() — compute summaries (often with group_by()).

Hands-on exercises

1. Typo. Why does this fail? (Look very carefully at the object name.)

   my_variable <- 10
   my_varıable   # error (hint: different character than 'i')

2. Vectors and strings. In a new code chunk:

   • Create a character vector test_words with at least three words (e.g. c("word1", "word2", "word3")).
   • Use length(test_words) and nchar(test_words) and interpret the result.

3. Comparisons. Run these and say what they return (TRUE/FALSE) and why:

   "dog" == "dog"
   "dog" != "cat"
   nchar("linguistics") > 5

4. Data import and inspection. Create a short CSV string with columns word and count (e.g. three rows). Read it with read_csv(), then use glimpse() and $ to inspect the count column and compute mean(count).

5. Pipe and filter. Using the word_data table from the First taste section (re-run that chunk if needed), write a pipe that:

   • keeps only rows where pos is "noun",
   • then arranges by freq ascending.

6. Style. Restyle this into a clear, multi-line pipe (spaces, line breaks, indentation):

   word_data|>filter(freq>3)|>arrange(freq)|>select(word,freq)

7. tidytext and quanteda with Gutenberg (hands-on). Use the same book downloaded in the tidytext section (gutenberg_books) or download another (e.g. gutenberg_download(1342) for Pride and Prejudice; see gutenbergr text mining).

   (a) tidytext: Start from gutenberg_books. Tokenize with unnest_tokens(output = word, input = text), then anti_join(stop_words, by = "word"), then count(word, sort = TRUE). What are the top 5 words? How many tokens are there in total (use sum(n) on the count table)?

   (b) quanteda: Build a corpus with corpus(gutenberg_books, text_field = "text"). Call sum(ntoken(corp)) to get total tokens (this counts all tokens; in (a) you had totals after removing stop words, so the numbers differ). Build a document-term matrix with tokens(corp, remove_punct = TRUE) |> dfm(), then use textstat_freq(your_dfm) to list the most frequent terms. Compare the top 5 with tidytext’s top 5 (quanteda’s dfm doesn’t remove stop words by default, so you’ll see “the”, “and”, etc. unless you add a step to remove them).

   (c) In one sentence: what is the main difference between tidytext’s one-token-per-row (or word-count) tibble and quanteda’s document-term matrix (dfm)?