This packages compute the v value (i.e. systemic value) from a (sub)system in a systemic functional network
gleasonCalc is a package to evaluate systemic functional valeur from a system network (see Figuredo & Figueredo, 2019). It gets agnation, delicacy and valeur magnitudes in data frames in order to visualize, correlate and sort systemic options in a gleason scale.
gleasonCalc is still under development. You can install development version of gleasonCalc from GitHub:
# Install development version from GitHub:
install.packages("devtools")
devtools::install_github("bmmedinac/gleasonCalc")
As a systemic-functional-linguistics-oriented package, gleasonCalc requires the transformation of a systemic network into a special format in a spreedsheet. Take for example the next English MOOD system network:
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In a spreedsheet, in a column named 'features', each oposition and delicacy level must be expressed. Opositions are expressed by '/' (e.g. indicative/imperative). Delicacy levels are expressed by ':' (e.g. indicative:interrogative:wh-). So, further delicacy levels must be expressed with its all lesser delicacy levels:
# features
1 imperative/indicative
2 indicative:declarative/indicative:interrogative
3 indicative:interrogative:yesno/indicative:interrogative:wh
At the moment, gleasonCalc just can evaluate valeur from system networks without co-selections. Any try to evaluate this kind of network will return error. Meanwhile, a co-selective system network in spreedsheet would look like this with '&' marking co-selection:
# features
1 imperative/indicative & positive/negative
2 indicative:declarative/indicative:interrogative & positive/negative
3 indicative:interrogative:yesno/indicative:interrogative:wh & positive/negative
gleasonCalc have two built-in systems: mood_spanish (see Quiroz, 2013) and mental_german (see Figueredo & Figueredo, 2019). First one is a system without co-selection. Second one is a system with co-selection.
Actually, gleasonCalc has three main functions: getFeatures, agnation and valeurCalc2. It's necessary to export it from library(gleasonCalc) previously to use it:
getFeaturesreturns all the features from the input system network. At the moment, can't return co-selective features. Example ofgetFeaturesis below:
library(gleasonCalc)
getFeatures(mood_spanish)
features
1 menor
2 major
4 indicative
6 imperative
9 informative
12 interrogative
15 jusive
18 hortative
21 optative
25 polar
29 elemental
33 declarative
37 exclamative
agnationreturns agnation magnitude of every option in a system network. At the moment, can't return agnation of co-selective features. Example ofagnationis below:
#library(gleasonCalc)
agnation(mood_spanish)
# A tibble: 13 x 2
features agnation
<fct> <dbl>
1 menor 2
2 major 2
3 indicative 2
4 imperative 2
5 informative 2
6 interrogative 2
7 jusive 9
8 hortative 9
9 optative 9
10 polar 2
11 elemental 2
12 declarative 2
13 exclamative 2
valeurCalc2returns a data frame with agnation, delicacy and valeur of every option in a system network. Example ofvaleurCalc2is below:
#library(gleasonCalc)
valeurCalc2(mood_spanish)
# A tibble: 13 x 4
features agnation delicacy valeur
<fct> <dbl> <dbl> <dbl>
1 menor 2 1 2
2 major 2 1 2
3 indicative 2 4 8
4 imperative 2 4 8
5 informative 2 9 18
6 interrogative 2 9 18
7 jusive 9 9 81
8 hortative 9 9 81
9 optative 9 9 81
10 polar 2 16 32
11 elemental 2 16 32
12 declarative 2 16 32
13 exclamative 2 16 32
valeurCalc2 can also sort valeur with sorted = TRUE argument:
valeurCalc2(mood_spanish, sorted = TRUE)
# A tibble: 13 x 4
features agnation delicacy valeur
<fct> <dbl> <dbl> <dbl>
1 menor 2 1 2
2 major 2 1 2
3 indicative 2 4 8
4 imperative 2 4 8
5 informative 2 9 18
6 interrogative 2 9 18
7 polar 2 16 32
8 elemental 2 16 32
9 declarative 2 16 32
10 exclamative 2 16 32
11 jusive 9 9 81
12 hortative 9 9 81
13 optative 9 9 81
GleasonCalc2package also include functions to plot interactive 3D graph. One of this functions is langspace. This function plots a language space (see Figueredo & Figuredo, 2019) from three systems. As example, GleasonCalc2 have a built-in data frame called language_space. In order to plot a language space, dataframe must have at least three systems (in this case, TRANSITIVITY trans, MOOD mood, THEME theme.
head(language_space)
# A tibble: 6 x 7
ID genre clause_ID clause trans mood theme
<chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
1 DVM 1 Procedimien… 1 Use a Balloon 232 25 9
2 DVM 1 Procedimien… 2 to Amplify Sound 232 9 0
3 DVM 1 Procedimien… 3 Small sounds can still make a big noise 232 297 65
4 DVM 1 Procedimien… 4 when you use a good sound conductor. 232 17 67
5 DVM 1 Procedimien… 5 Experiment with a balloon, compressed air and … 232 25 9
6 DVM 1 Procedimien… 6 to find out 232 9 0
From this dataframe, we can plot with langspace()function. langspace() can take seven arguments, but just four are mandatory:
langspace(df, x, y, z, grouping, instance = FALSE)
Arguments
df The dataframe with a data.
x First variable of the plot (e.g. TYPE OF PROCESS)
y Second variable of the plot (e.g. MOOD)
z Third variable of the plot (e.g. THEME)
grouping Empty by default. Create color groups from levels of a factor (e.g. genres, fields, tenors, etc)
instance FALSE by default. If provided, hover shows the textual instances of x, y and z
See the next use of langspace:
langspace(language_space, #example dataframe
x = language_space$trans, #first mandatory system
y = language_space$mood, #second mandatory system
z = language_space$theme, #third mandatory system
grouping = language_space$genre, #grouping by genre
instance = language_space$clause, #show instance saved in clause column
framing = language_space$clause_ID) #unfolding of text saved in clause_ID colum
From the example above, langspace plots the next interactive 3D graph:
%2016.27.49.png)
Actually, x-, y- and z-axis are statically defined as TYPE OF PROCESS, MOOD and THEME. Dinamyc name axis are under develope.
- Figueredo, G., & Figueredo, G. P. (2019). A systemic dynamics model of text production. Journal of Quantitative Linguistics. https://doi.org/10.1080/09296174.2019.1567301
- Quiroz, B. (2013). The interpersonal and experiential grammar of chilean spanish: Towards a principled systemic-functional description based on axial argumentation [PhD Thesis]. University of Sydney.