1. Digital Text and Data Processing Introduction to R

2. □ Tools themselves are often based on specific assumptions / subjective decisions □ There is subjectivity in the way in which tools are used □ Reproducible results □ Rockwell & Ramsay, in “Developing Things”: A tool is a theoryDeveloping Things Objectivity of DH Research

3. Willard McCarty, Humanities Computing (Palgrave, 2005) "The point of all modelling exercises, as of scholarly research generally, is the process seen in and by means of a developing product, not the definitive achievement" (p. 22). Models, "however finely perfected, are better understood as temporary states in a process of coming to know rather than fixed structures of knowledge" (p. 27) -> Clash between tacit and intuitive knowledge of scholar and computer’s need for consistency and explicitness

4. □ Data creation □ Data analysis Two stages in text mining

5. □ Finding distinctive vocabulary □ Finding stylistic or grammatical differences and similarities □ Examining topics or themes □ Clustering texts on the basis of quantifiable aspects Types of analyses

6. opendir (DIR, $dir) or die "Can't open directory!"; while (my $file = readdir(DIR)) { if ( $file =~ /txt$/) { push ( @files, $file ) ; } Reading a directory

7. Inverse document frequency For an application, see Stephen Ramsay, Algorithmic Criticism Algorithmic Criticism

8. □ Both a programme and a programming language □ Successor of “S” □ “a free software environment for statistical computing and graphic” □ The capabilities of R can be extended via external “packages”

10. □ Any combination of alphanumerical characters, underscore and dot □ Unlike Perl, they do not begin with a $ □ First characters cannot be a number. The second characters cannot be a number if the first character is a dot Variables in R Allowed:Not allowed: data3rdDataSet my.data.4thData.set my_2ndDataSet.myCsv

11. □ A collection of indexed values □ Can be created using the c() function, or by supplying a range □ N.B. The assignment operator in R is <- □ Examples: Vectors x <- c( 4, 5, 3, 7) ; y <- 1:30 ;

12. □ A collection of vectors, all of the same length □ Each column of the table is stored in R as a vector. Data frame V1V2V3 R13,4,5 R21,21,8 R323,5,6

13. Comma Separated Values i,you,he Emma,160416,3178,1994 Persuasion,77431,1284,918 PrideAndPrejudice,121812,2068,1356 N.B. The first row has one column less

14. □ Use the read.csv function, with parameter header = TRUE □ The CSV file will be represented as a data frame □ Values on first line and first value of each subsequent line will be used as rownames and colnames Reading data data <- read.csv( "data.csv", header = TRUE) ; colnames(data)

15. □ Can be accessed using the $ operator Data frame columns data <- read.csv( "data.csv", header = TRUE) ; data$you

16. □ max(), min(), mean(), sd() Calculations y <- data$you ; max(y) ; sd(y) ;

17. □ Run the program “typeToken.pl” □ Use the file “ratio.csv” that is created by this program. □ Print a list of all the texts that have been read □ Calculate the average number of tokens □ Calculate the total number of tokens in the full corpus □ Identify the lowest number in the column “types” □ Identify the highest number in the column “ratio” Exercise

18. d <- read.csv("data.csv") ; d <- d[ 1, 2 ] ; d <- d[ 2, ] ; od <- data[ order( data$ratio ), ] Subsetting and sorting

19. □ Qualitative data (categorical) □ Nominal scale (unordered scale), e.g. eye colour, marital status □ Ordinal scale (ordered scale), e.g. educational level □ Quantitative data □ Interval (scale with no mathematical zero) □ Ratio (multipliable scale), e.g. age Quantitative and Qualitative Source: Seminar Basic Statistics, Laura Bettens

20. □ Two quantitative variables can be clarified in a variety of ways (e.g. line chart, pie chart) □ A combination of one qualitative variable and one quantitative variable is best presented using a bar chart or a dot chart Diagrams