Catalan panel
25 most frequent Catalan words ordered by frequency
res.TD.Cat.Before <-TextData(baseCat,var.text=c(1:ncol(baseCat)), stop.word.user=str.Cat.stopworduser,Fmin=1)
summary(res.TD.Cat.Before, ndoc=0, nword=25, info=FALSE)
TextData summary
Before After
Documents 8.00 8.00
Occurrences 323.00 319.00
Words 97.00 95.00
Mean-length 40.38 39.88
Index of the 25 most frequent words
Word Frequency N.Documents
1 confitura 13 6
2 fruita 12 6
3 tànnic 10 7
4 fusta 9 4
5 vainilla 9 7
6 madur 8 5
7 nas 8 8
8 aroma 7 7
9 astringent 7 5
10 boca 7 5
11 greix 7 6
12 regalèssia 7 5
13 balsàmic 6 5
14 rodó 6 5
15 secant 6 4
16 acetatdetil 5 3
17 cos 5 4
18 especiat 5 3
19 fum 5 4
20 xocolata 5 5
21 alt 4 2
22 cirera 4 4
23 claudolor 4 3
24 complex 4 3
25 floral 4 3
To translate the name of 15 most frequent Catalan words.
Building a copy of res.TD.Cat.Before object and creating a vector (original.Cat) with the 15 most frequent Catalan words.
res.Cat.Trans <- res.TD.Cat.Before
original.Cat <- rownames(res.TD.Cat.Before$indexW[1:15,])
cat(original.Cat)
confitura fruita tànnic fusta vainilla madur nas aroma astringent boca greix regalèssia balsàmic rodó secant
Creating a vector translation.Cat with the words in English with the same order than original.Cat:
translation.Cat <- c("confitura (jelly)", "fruita (fruit)", "tànnic (tannic)", "fusta (wood)", "vainilla (vanilla)", "madur (mature)", "nas (nose)", "aroma (bouquet)", "astringent (astringent)", "boca (mouth)", "greix (unctuous/fat)", "regalèssia (liquorice)", "balsàmic (balsamic)", "rodó (round)", "secant (drying)" )
Creating a data frame with the original words and translation:
df.CatChange <- data.frame(original.Cat, translation.Cat)
To change Catalan DocTerm object (only for the 15 most frequent words):
res.Cat.Trans$DocTerm$dimnames$Terms[match(df.CatChange$original.Cat , res.Cat.Trans$DocTerm$dimnames$Terms)] <- df.CatChange$translation.Cat cat(res.Cat.Trans$DocTerm$dimnames$Terms)
acetatdetil acètic acidesabaix acidulat afrutat alcohòliques alt arbust aroma (bouquet) astringent (astringent) balsàmic (balsamic) boca (mouth) bota brisa cacau cafè caramel cartró
cassis cedre cirera cítric claudolor complex compostos confitada confitura (jelly) cos cosmitjabaix dens desequilibri dolç especiat espigola eucaliptus farigola floral florsseques
formatge fruita (fruit) fruitsec fum fumat fusta (wood) gerani glicerol greix (unctuous/fat) herbesseques iode jove làctic liniment lleuger madur (mature) malaqualitat mantegós marcat
mel melós mentolat mora nas (nose) neopre oxidat pebrotverd pegadolça picant picat pinassa pla pocaaroma pocestructurat pocpersistent pollen potència prunasec químic
regalèssia (liquorice) rodó (round) roure secant (drying) sensacions sofre sotabosc sucrositat sutja taní tànnic (tannic) toffe torrat torrefacte vainilla (vanilla) vegetal vi xocolataTo change indexW with the frequencies (only for the 15 most frequent words)
rownames(res.Cat.Trans$indexW)[match(df.CatChange$original.Cat , rownames(res.Cat.Trans$indexW))] <- df.CatChange$translation.Cat res.Cat.Trans$indexW[1:20,]
Frequency N.Documents
confitura (jelly) 13 6
fruita (fruit) 12 6
tànnic (tannic) 10 7
fusta (wood) 9 4
vainilla (vanilla) 9 7
madur (mature) 8 5
nas (nose) 8 8
aroma (bouquet) 7 7
astringent (astringent) 7 5
boca (mouth) 7 5
greix (unctuous/fat) 7 6
regalèssia (liquorice) 7 5
balsàmic (balsamic) 6 5
rodó (round) 6 5
secant (drying) 6 4
acetatdetil 5 3
cos 5 4
especiat 5 3
fum 5 4
xocolata 5 5Other way to check changes:
summary(res.Cat.Trans, ndoc=0, nword=15, info=FALSE)
TextData summary
Before After
Documents 8.00 8.00
Occurrences 323.00 319.00
Words 97.00 95.00
Mean-length 40.38 39.88
Index of the 15 most frequent words
Word Frequency N.Documents
1 confitura (jelly) 13 6
2 fruita (fruit) 12 6
3 tànnic (tannic) 10 7
4 fusta (wood) 9 4
5 vainilla (vanilla) 9 7
6 madur (mature) 8 5
7 nas (nose) 8 8
8 aroma (bouquet) 7 7
9 astringent (astringent) 7 5
10 boca (mouth) 7 5
11 greix (unctuous/fat) 7 6
12 regalèssia (liquorice) 7 5
13 balsàmic (balsamic) 6 5
14 rodó (round) 6 5
15 secant (drying) 6 4
Building a datafrane with the frequency of Catalan words. Two ways:
df.CatW <- data.frame(res.Cat.Trans$indexW[1:15,])
df.CatW <- data.frame(rownames(df.CatW), df.CatW)
df.CatW
rownames.df.CatW. Frequency N.Documents
confitura (jelly) confitura (jelly) 13 6
fruita (fruit) fruita (fruit) 12 6
tànnic (tannic) tànnic (tannic) 10 7
fusta (wood) fusta (wood) 9 4
vainilla (vanilla) vainilla (vanilla) 9 7
madur (mature) madur (mature) 8 5
nas (nose) nas (nose) 8 8
aroma (bouquet) aroma (bouquet) 7 7
astringent (astringent) astringent (astringent) 7 5
boca (mouth) boca (mouth) 7 5
greix (unctuous/fat) greix (unctuous/fat) 7 6
regalèssia (liquorice) regalèssia (liquorice) 7 5
balsàmic (balsamic) balsàmic (balsamic) 6 5
rodó (round) rodó (round) 6 5
secant (drying) secant (drying) 6 4Building the table
row.names(df.CatW) <- NULL
colnames(df.CatW) <- c("Words", "Count", "No.docs")
df.CatW
Words Count No.docs
1 confitura (jelly) 13 6
2 fruita (fruit) 12 6
3 tànnic (tannic) 10 7
4 fusta (wood) 9 4
5 vainilla (vanilla) 9 7
6 madur (mature) 8 5
7 nas (nose) 8 8
8 aroma (bouquet) 7 7
9 astringent (astringent) 7 5
10 boca (mouth) 7 5
11 greix (unctuous/fat) 7 6
12 regalèssia (liquorice) 7 5
13 balsàmic (balsamic) 6 5
14 rodó (round) 6 5
15 secant (drying) 6 4Table 2.b. Most frequent Catalan words
kableExtra::kable(df.CatW,
caption = "<left><strong>Table 2.b. Most frequent Catalan words</strong></left>") %>%
column_spec(1, bold = T) %>% kable_classic(full_width = F, html_font = "Cambria") %>%
row_spec(seq(2,nrow(df.FrW),2), background="#CCFFFF")
Table 2. Joining Catalan and French words