Figure 5. Words related to appreciation
sel.apprec <-which(rownames(as.data.frame(res.mfact.23$freq.sup))
%in%c("agréable","agressif","asséchant","beau","bon","chaleureux","complexe","curieux","cos",
"défaut","dens","desequilibri","équilibré","généreux","onctueux","particulier",
"puissant","rond","souple","structuré","complex","velouté","gouleyant",
"malaqualitat","pinassa","potencia","rodó","secant","limite","plat","mou"))
sel.apprec
|
1 2 |
[1] 5 13 17 20 28 30 37 39 49 69 70 81 88 97 98 102 109 115 119 [20] 120 133 159 163 165 166 190 204 214 216 |
plot.MFA(res.mfact.23,choix=c("freq"),invisible=c("row","col"),axes=c(1,2),select=sel.apprec ,unselect=1,
legend=list(plot=FALSE),habillage="none",autoLab = c("yes"),cex=0.8,
title="",graph.type="classic")
French translation of 22 words
df.apprec.Fr <- data.frame(orig= "agréable", transl="pleasant")
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "agressif", transl="aggressive") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "asséchant", transl="drying") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "beau", transl="beautiful") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "bon", transl="good") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "chaleureux", transl="warm") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "complexe", transl="complex") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "curieux", transl="curious") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "défaut", transl="defect") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "équilibré", transl="balanced") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "généreux", transl="generous") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "gouleyant", transl="tasteful") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "limite", transl="limit") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "mou", transl="soft") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "onctueux", transl="unctuous") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "particulier", transl="particular") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "plat", transl="flat") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "puissant", transl="powerful") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "rond", transl="round") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "souple", transl="supple") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "structuré", transl="structured") )
df.apprec.Fr <- rbind(df.apprec.Fr, data.frame(orig= "velouté", transl="velvet") )
df.apprec.Fr$lang <- "Fr"
Catalan translation of 8 words
df.apprec.Cat <- data.frame(orig= "complex", transl="high")
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "cos", transl="full_bodied") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "dens", transl="dense") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "desequilibri", transl="imbalance") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "malaqualitat", transl="poorquality") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "pinassa", transl="pinassa") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "rodó", transl="round") )
df.apprec.Cat <- rbind(df.apprec.Cat, data.frame(orig= "secant", transl="drying") )
df.apprec.Cat$lang <- "Cat"
To join French coordinates and their translation:
df.apprec.Fr
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |
orig transl lang 1 agréable pleasant Fr 2 agressif aggressive Fr 3 asséchant drying Fr 4 beau beautiful Fr 5 bon good Fr 6 chaleureux warm Fr 7 complexe complex Fr 8 curieux curious Fr 9 défaut defect Fr 10 équilibré balanced Fr 11 généreux generous Fr 12 gouleyant tasteful Fr 13 limite limit Fr 14 mou soft Fr 15 onctueux unctuous Fr 16 particulier particular Fr 17 plat flat Fr 18 puissant powerful Fr 19 rond round Fr 20 souple supple Fr 21 structuré structured Fr 22 velouté velvet Fr |
Coord.Fr.Fig5 <- merge(res.mfact.23$freq.sup$coord[1:135,], df.apprec.Fr, by.x=0, by.y="orig")
Coord.Fr.Fig5[,c(1:5,9,10)]
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
Row.names Dim.1 Dim.2 Dim.3 Dim.4 transl lang 1 agréable -0.3622113 -0.754415703 -0.03913020 -0.18872073 pleasant Fr 2 asséchant -0.6733810 -0.803561875 -0.10146317 0.27194795 drying Fr 3 beau 0.1846424 0.693693657 -0.09449363 -0.34464145 beautiful Fr 4 bon -0.3661349 0.786808717 0.47009306 0.30805796 good Fr 5 chaleureux 0.6657340 0.554044958 0.10360208 -0.21357552 warm Fr 6 complexe 0.5960483 0.507511318 0.86628447 0.57018982 complex Fr 7 curieux -0.3052359 -1.593741775 0.60169221 -0.23240807 curious Fr 8 défaut -0.5966568 -0.571192552 -0.15997103 -0.21422006 defect Fr 9 équilibré -0.2666819 0.078640165 0.40186324 0.52297614 balanced Fr 10 généreux 1.5848955 -0.002238351 -0.54439416 0.38322473 generous Fr 11 gouleyant -0.1072808 1.406847717 1.91993029 0.58127433 tasteful Fr 12 limite -0.3052359 -1.593741775 0.60169221 -0.23240807 limit Fr 13 mou -0.7423672 -0.059917941 -0.54080265 -0.20512605 soft Fr 14 onctueux 0.2819501 0.455975637 -0.76596827 -0.65328005 unctuous Fr 15 particulier -0.3052359 -1.593741775 0.60169221 -0.23240807 particular Fr 16 plat -0.7423672 -0.059917941 -0.54080265 -0.20512605 flat Fr 17 puissant 0.2324995 0.244534689 0.06489716 0.28386548 powerful Fr 18 rond -0.5120008 0.979784095 0.58862337 0.08993399 round Fr 19 souple -0.0343926 0.902010962 0.32855234 -0.57750861 supple Fr 20 structuré -0.1072808 1.406847717 1.91993029 0.58127433 structured Fr 21 velouté -0.0738231 -0.198647063 0.25657926 -0.24860346 velvet Fr |
To join Catalan coordinates and their translation:
df.apprec.Cat
|
1 2 3 4 5 6 7 8 9 |
orig transl lang 1 complex high Cat 2 cos full_bodied Cat 3 dens dense Cat 4 desequilibri imbalance Cat 5 malaqualitat poorquality Cat 6 pinassa pinassa Cat 7 rodó round Cat 8 secant drying Cat |
Coord.Cat.Fig5 <- merge(res.mfact.23$freq.sup$coord[136:nrow(res.mfact.23$freq.sup$coord), ], df.apprec.Cat, by.x=0, by.y="orig")
Coord.Cat.Fig5[,c(1:5,9,10)]
|
1 2 3 4 5 6 7 8 9 |
Row.names Dim.1 Dim.2 Dim.3 Dim.4 transl lang 1 complex 0.25621245 0.22501723 1.2599178 0.30005544 high Cat 2 cos 0.86235771 -0.09456111 0.4304749 0.27162192 full_bodied Cat 3 dens 0.37408790 -0.18174291 0.9994396 0.21541158 dense Cat 4 desequilibri -1.42400036 0.33008205 -0.9472114 1.37192587 imbalance Cat 5 malaqualitat -0.29536903 -1.55529185 0.7231142 -0.25969536 poorquality Cat 6 pinassa -0.84676461 -0.31271027 -0.4607370 0.43321992 pinassa Cat 7 rodó 0.02363637 -0.46725895 0.6915393 -0.35041152 round Cat 8 secant -0.53676643 -0.94764429 0.1811539 0.05678312 drying Cat |
To join French and Catalan coordinates:
Coord.Fig5 <- rbind(Coord.Fr.Fig5, Coord.Cat.Fig5)
Coord.Fig5[,c(1:5,9,10)]
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
Row.names Dim.1 Dim.2 Dim.3 Dim.4 transl lang 1 agréable -0.36221131 -0.754415703 -0.03913020 -0.18872073 pleasant Fr 2 asséchant -0.67338104 -0.803561875 -0.10146317 0.27194795 drying Fr 3 beau 0.18464237 0.693693657 -0.09449363 -0.34464145 beautiful Fr 4 bon -0.36613490 0.786808717 0.47009306 0.30805796 good Fr 5 chaleureux 0.66573397 0.554044958 0.10360208 -0.21357552 warm Fr 6 complexe 0.59604830 0.507511318 0.86628447 0.57018982 complex Fr 7 curieux -0.30523593 -1.593741775 0.60169221 -0.23240807 curious Fr 8 défaut -0.59665679 -0.571192552 -0.15997103 -0.21422006 defect Fr 9 équilibré -0.26668190 0.078640165 0.40186324 0.52297614 balanced Fr 10 généreux 1.58489551 -0.002238351 -0.54439416 0.38322473 generous Fr 11 gouleyant -0.10728079 1.406847717 1.91993029 0.58127433 tasteful Fr 12 limite -0.30523593 -1.593741775 0.60169221 -0.23240807 limit Fr 13 mou -0.74236722 -0.059917941 -0.54080265 -0.20512605 soft Fr 14 onctueux 0.28195009 0.455975637 -0.76596827 -0.65328005 unctuous Fr 15 particulier -0.30523593 -1.593741775 0.60169221 -0.23240807 particular Fr 16 plat -0.74236722 -0.059917941 -0.54080265 -0.20512605 flat Fr 17 puissant 0.23249948 0.244534689 0.06489716 0.28386548 powerful Fr 18 rond -0.51200080 0.979784095 0.58862337 0.08993399 round Fr 19 souple -0.03439260 0.902010962 0.32855234 -0.57750861 supple Fr 20 structuré -0.10728079 1.406847717 1.91993029 0.58127433 structured Fr 21 velouté -0.07382310 -0.198647063 0.25657926 -0.24860346 velvet Fr 22 complex 0.25621245 0.225017231 1.25991777 0.30005544 high Cat 23 cos 0.86235771 -0.094561115 0.43047490 0.27162192 full_bodied Cat 24 dens 0.37408790 -0.181742905 0.99943959 0.21541158 dense Cat 25 desequilibri -1.42400036 0.330082052 -0.94721141 1.37192587 imbalance Cat 26 malaqualitat -0.29536903 -1.555291851 0.72311423 -0.25969536 poorquality Cat 27 pinassa -0.84676461 -0.312710270 -0.46073696 0.43321992 pinassa Cat 28 rodó 0.02363637 -0.467258947 0.69153926 -0.35041152 round Cat 29 secant -0.53676643 -0.947644286 0.18115389 0.05678312 drying Cat |
Coord.Fig5$lang <- as.factor(Coord.Fig5$lang )
set.seed(1234)
Figure5 <- ggplot(Coord.Fig5)+
theme_light() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
xlab(labx)+ ylab(laby) + coord_fixed()+
geom_hline(yintercept=0, linetype="dashed", color = "grey")+
geom_vline(xintercept=0, linetype="dashed", color = "grey")+
geom_text_repel(size=5, fontface = font.type.FRCat[Coord.Fig5$lang], max.overlaps=100,
box.padding = unit(0.35, "lines"),
aes(x=Dim.1, y=Dim.2, label = transl, color=lang))+
theme( axis.text.x = element_text(size=rel(1.6)))+
theme( axis.text.y = element_text(size=rel(1.6)))+
labs(x=labx)+labs(y=laby)+
theme(axis.title.x= element_text(size=17, face="bold"))+
theme(axis.title.y= element_text(size=17, face="bold"))+
theme(plot.margin = grid::unit(c(t=5,r= 2,b=5, l=2), "mm"))+
scale_color_manual(name="Language",
labels=c("Catalan","French"),
values = setNames(col.margin, levels(Coord.Fig4$lang))) +
theme(axis.title.x = element_text(margin=margin(t=10))) +
theme(panel.border = element_rect(colour = "black", fill=NA, linewidth=1)) +
theme(legend.position = "none")+
labs(title = "Words originally in <b style='color:#FF0000'>**_French_**</b> and **Catalan**")+
theme(plot.title = element_markdown(lineheight = 1.1, hjust=1, size=20))+
ylim(-1.9, 1.1)+
ggtitle("Figure 5. Words related to appreciation")
To plot Figure 5:
set.seed(1234)
Figure5
