NormalizeB圜haracter = TRUE, # normalization by figure normalizeByField = TRUE # normalization by fieldĭstat # corpus drama character Liebe Familie Ratio 0, # the text fieldnames = # fields we want to measure (from base_dictionary) c( "Liebe", "Familie", "Ratio", "Krieg", "Religion"), The following examples use the base_dictionary, i.e., a specific version of the fields we have been using in QuaDramA. Some of the options can also be specified through dictionaryStatistics(), as exemplified below. Entries for each keyword should then be stored in a file named like the keyword, and ending with txt (by default, can be overwritten). The latter can be achieved by specifying the directory as baseurl.
![analyze quick word analyze quick word](https://exceltmp.com/wp-content/uploads/2017/06/example-of-data-analysis-word-template-free-download.jpg)
The function loadFields() offers parameters to load from different URLs via http or to load from plain text files that are stored locally. It is also the default dictionary used by dictionaryStatistics(). Since version 2.3.0, this dictionary is included in the package as base_dictionary and can be used right away (without internet connection). By default, the function loads this dictionary from GitHub (that we used in publications), for the keywords Liebe and Familie (family).
![analyze quick word analyze quick word](https://www.wintips.org/wp-content/uploads/2018/02/image-55.png)
#ANALYZE QUICK WORD DOWNLOAD#
In addition, the function loadFields() can be used to download dictionary content from a URL or a directory. Dictionaries can be created in code, like shown above. This dictionary contains the two entries Liebe (love) and Hass (hate), with 3 respective 2 entries. New dictionaries can be easily created like this: wf <- list( Liebe= c( "liebe", "herz", "schmerz"), Hass= c( "hass", "hassen")) The (named) outer list contains the keywords, the vectors are just words associated with the keyword. To this end, dictionaries are represented as lists of character vectors. The function dictionaryStatistics() can be used to analyse multiple dictionaries at once. 0) # reformat character names # remove figures not using these words at all This will divide the number of words in this field by the total number of words a character speaks. This can be enabled by setting the argument normalizeB圜haracter=TRUE. When comparing characters, it often (but not always) makes sense to normalize the counts according to the total number of spoken words by a character. # create a bar plot barplot(dstat $x, # what to plot names.arg = dstat $character, # x axis labels las= 2 # turn axis labelsĪpparently, the prince and Marinelli are mentioning these words a lot more than the other characters. # remove characters not using these words at all We can visualise these counts in a simple bar chart: # retrieve counts and replace character ids by names What this table shows us the number of times the characters in the play use words that appear in this list. 0, # the text we want to process wordfield=wf_love # the word field
![analyze quick word analyze quick word](https://res.cloudinary.com/hy4kyit2a/f_auto,fl_lossy,q_70/learn/modules/lightning-experience-productivity/analyze-your-data-with-reports-and-dashboards/images/18aae968847878a1b15015f57286234e_lex_getstarted_rd_reportread_1.png)
Therefore, we use the function called dictionaryStatisticsSingle() (single, because we only want to analyse a single word field): dictionaryStatisticsSingle( The core of the word field analysis is collecting statistics about a dictionary.
![analyze quick word analyze quick word](https://cdn.shopify.com/s/files/1/0855/1446/files/smartmockups_jjkjaadb_large.png)
11.6 Enriching the word fields distributionally.11.4 Development over the course of the play.6.1 Character names instead of identifiers.