Figure 4 with the best represented global words in the first plane (cos2 higher than 0.5 in either of the two factors). Names and positions.
sel.12.BRGW <-which((res.mfact.23$freq.sup$cos2[,1]>0.5)|(res.mfact.23$freq.sup$cos2[,2] > 0.5))
sel.12.BRGW
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
aspect asséchant beau bois boisé carton charpenté cuit 12 13 17 18 19 24 29 36 curieux dominé étable évent évolué évolution farineux finale 37 41 50 51 52 53 55 58 fraîcheur fruit fumée généreux humide limite matière mou 63 65 67 69 75 81 85 88 neuf noir nonboisé particulier plat réduction rond sec 91 94 95 98 102 112 115 117 souple sucrée sucrosité toasté vanillé vin alt bota 119 121 122 126 131 135 142 148 cafè cartró cedre cítric claudolor cos especiat floral 151 153 155 157 158 163 168 172 formatge fruitsec fum fusta gerani glicerol greix iode 174 176 177 179 180 181 182 184 liniment malaqualitat mantegós marcat neopre regalèssia secant sutja 187 190 191 192 198 213 216 221 taní torrat vainilla xocolata 222 225 227 230 |
plot.MFA(res.mfact.23,choix=c("freq"),invisible=c("row","col"),axes=c(1,2),select=sel.12.BRGW,unselect=1,
legend=list(plot=FALSE),habillage="none",autoLab = c("yes"),cex=0.8,
title="",graph.type="classic")
Figure with the best represented global words in the plane (3,4) (cos2 higher than 0.5 in either of the two factors). Names and positions.
sel.34.BRGW<-which((res.mfact.23$freq.sup$cos2[,3]>0.5)|(res.mfact.23$freq.sup$cos2[,4] > 0.5) )
plot.MFA(res.mfact.23,choix=c("freq"),invisible=c("row","col"),axes=c(3,4),select=sel.34.BRGW,unselect=1,
legend=list(plot=FALSE),habillage="none",autoLab = c("yes"),cex=0.8,
title="The best represented global words",graph.type="classic")
38 characteristic French words for the plane (1,2)
sel.12.BRGW.Fr <-which((res.mfact.23$freq.sup$cos2[c(1:135),1]>0.5)|(res.mfact.23$freq.sup$cos2[c(1:135),2] > 0.5))
sel.12.BRGW.Fr
1 2 3 4 5 6 7 8 9 10 |
aspect asséchant beau bois boisé carton charpenté cuit curieux 12 13 17 18 19 24 29 36 37 dominé étable évent évolué évolution farineux finale fraîcheur fruit 41 50 51 52 53 55 58 63 65 fumée généreux humide limite matière mou neuf noir nonboisé 67 69 75 81 85 88 91 94 95 particulier plat réduction rond sec souple sucrée sucrosité toasté 98 102 112 115 117 119 121 122 126 vanillé vin 131 135 |
cat(length(sel.12.BRGW.Fr))
1 |
38 |
Words12.BRGW.Fr <- data.frame(posit=as.vector(sel.12.BRGW.Fr), wordF4=names(sel.12.BRGW.Fr), lang="Fr")
Words12.BRGW.Fr
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 31 32 33 34 35 36 37 38 39 |
posit wordF4 lang 1 12 aspect Fr 2 13 asséchant Fr 3 17 beau Fr 4 18 bois Fr 5 19 boisé Fr 6 24 carton Fr 7 29 charpenté Fr 8 36 cuit Fr 9 37 curieux Fr 10 41 dominé Fr 11 50 étable Fr 12 51 évent Fr 13 52 évolué Fr 14 53 évolution Fr 15 55 farineux Fr 16 58 finale Fr 17 63 fraîcheur Fr 18 65 fruit Fr 19 67 fumée Fr 20 69 généreux Fr 21 75 humide Fr 22 81 limite Fr 23 85 matière Fr 24 88 mou Fr 25 91 neuf Fr 26 94 noir Fr 27 95 nonboisé Fr 28 98 particulier Fr 29 102 plat Fr 30 112 réduction Fr 31 115 rond Fr 32 117 sec Fr 33 119 souple Fr 34 121 sucrée Fr 35 122 sucrosité Fr 36 126 toasté Fr 37 131 vanillé Fr 38 135 vin Fr |
French translation
df.WTR.Fr <- data.frame(orig= "aspect", transl="aspect")
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "asséchant", transl="drying") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "beau", transl="beautiful") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "bois", transl="wood") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "boisé", transl="woody") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "carton", transl="cardboard") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "charpenté", transl="structured") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "cuit", transl="cooked") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "curieux", transl="curious") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "dominé", transl="dominated") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "étable", transl="stable") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "évent", transl="staleness") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "évolué", transl="evolved") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "évolution", transl="development") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "farineux", transl="floury") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "finale", transl="final") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "fraîcheur", transl="freshness") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "fruit", transl="fruit") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "fumée", transl="smoke") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "généreux", transl="generous") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "humide", transl="wet") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "limite", transl="limit") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "matière", transl="material") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "mou", transl="soft") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "neuf", transl="new") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "noir", transl="black") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "nonboisé", transl="unwooded") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "particulier", transl="particular") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "plat", transl="flat") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "réduction", transl="reduction") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "rond", transl="round") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "sec", transl="dry") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "souple", transl="supple") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "sucrée", transl="sweet") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "sucrosité", transl="sweetness") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "toasté", transl="toasted") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "vanillé", transl="vanillin") )
df.WTR.Fr <- rbind(df.WTR.Fr, data.frame(orig= "vin", transl="wine") )
dim(df.WTR.Fr)
1 |
38 2 |
df.WTR.Fr
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 31 32 33 34 35 36 37 38 39 |
orig transl 1 aspect aspect 2 asséchant drying 3 beau beautiful 4 bois wood 5 boisé woody 6 carton cardboard 7 charpenté structured 8 cuit cooked 9 curieux curious 10 dominé dominated 11 étable stable 12 évent staleness 13 évolué evolved 14 évolution development 15 farineux floury 16 finale final 17 fraîcheur freshness 18 fruit fruit 19 fumée smoke 20 généreux generous 21 humide wet 22 limite limit 23 matière material 24 mou soft 25 neuf new 26 noir black 27 nonboisé unwooded 28 particulier particular 29 plat flat 30 réduction reduction 31 rond round 32 sec dry 33 souple supple 34 sucrée sweet 35 sucrosité sweetness 36 toasté toasted 37 vanillé vanillin 38 vin wine |
To join French coordinates and their translation:
Coord.Fr.Fig4 <- data.frame(res.mfact.23$freq.sup$coord[sel.12.BRGW.Fr,], lang="Fr")
Coord.Fr.Fig4 <- merge(Coord.Fr.Fig4, df.WTR.Fr, by.x=0, by.y="orig")
Coord.Fr.Fig4[,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 31 32 33 34 35 36 37 38 39 |
Row.names Dim.1 Dim.2 Dim.3 Dim.4 lang transl 1 aspect 0.01597913 -0.853134728 0.009069728 0.03228269 Fr aspect 2 asséchant -0.67338104 -0.803561875 -0.101463172 0.27194795 Fr drying 3 beau 0.18464237 0.693693657 -0.094493630 -0.34464145 Fr beautiful 4 bois 1.30114701 0.051069203 -0.483807494 0.08205348 Fr wood 5 boisé 0.66641435 -0.480026128 -0.044177786 0.10843598 Fr woody 6 carton -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr cardboard 7 charpenté 1.24802969 -0.336910906 -0.255218575 0.22412317 Fr structured 8 cuit 0.01597913 -0.853134728 0.009069728 0.03228269 Fr cooked 9 curieux -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr curious 10 dominé 1.48972280 -0.132100595 -0.425383218 0.44185159 Fr dominated 11 étable -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr stable 12 évent -0.92317035 0.072908887 -0.754317378 0.28753767 Fr staleness 13 évolué 0.22963518 -1.193102877 0.338674359 0.03142973 Fr evolved 14 évolution -0.01757719 0.724909260 -0.116715947 -0.00354856 Fr development 15 farineux -0.74236722 -0.059917941 -0.540802652 -0.20512605 Fr floury 16 finale 1.23519276 -0.323182362 -0.272182884 0.14037764 Fr final 17 fraîcheur -0.60089901 0.385991015 0.172514233 0.19735824 Fr freshness 18 fruit -0.47749854 0.355953911 -0.027557246 0.06103203 Fr fruit 19 fumée -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr smoke 20 généreux 1.58489551 -0.002238351 -0.544394158 0.38322473 Fr generous 21 humide -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr wet 22 limite -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr limit 23 matière 1.58489551 -0.002238351 -0.544394158 0.38322473 Fr material 24 mou -0.74236722 -0.059917941 -0.540802652 -0.20512605 Fr soft 25 neuf 1.58489551 -0.002238351 -0.544394158 0.38322473 Fr new 26 noir -0.51200080 0.979784095 0.588623369 0.08993399 Fr black 27 nonboisé -0.68346651 0.164631867 0.048985206 0.30313197 Fr unwooded 28 particulier -0.30523593 -1.593741775 0.601692209 -0.23240807 Fr particular 29 plat -0.74236722 -0.059917941 -0.540802652 -0.20512605 Fr flat 30 réduction -0.74236722 -0.059917941 -0.540802652 -0.20512605 Fr reduction 31 rond -0.51200080 0.979784095 0.588623369 0.08993399 Fr round 32 sec -0.88066723 0.010392073 -0.646368809 0.11574179 Fr dry 33 souple -0.03439260 0.902010962 0.328552340 -0.57750861 Fr supple 34 sucrée 1.58489551 -0.002238351 -0.544394158 0.38322473 Fr sweet 35 sucrosité 0.34183745 -0.534850990 -0.070969914 -0.21484109 Fr sweetness 36 toasté 0.29012704 -1.118934073 0.300860299 -0.16743876 Fr toasted 37 vanillé 0.70680948 -0.354176022 -0.232543146 -0.06772137 Fr vanillin 38 vin 0.32738937 -0.090612082 0.085744578 0.12380635 Fr wine |
30 characteristic Catalan words for the plane (1,2)
sel.12.BRGW.Cat <- which((res.mfact.23$freq.sup$cos2[c(136:230),1]>0.5)|(res.mfact.23$freq.sup$cos2[c(136:230),2] > 0.5))
sel.12.BRGW.Cat
1 2 3 4 5 6 7 8 |
alt bota cafè cartró cedre cítric claudolor cos 7 13 16 18 20 22 23 28 especiat floral formatge fruitsec fum fusta gerani glicerol 33 37 39 41 42 44 45 46 greix iode liniment malaqualitat mantegós marcat neopre regalèssia 47 49 52 55 56 57 63 78 secant sutja taní torrat vainilla xocolata 81 86 87 90 92 95 |
cat(length(sel.12.BRGW.Cat))
1 |
30 |
Words12.BRGW.Cat <- data.frame(posit=as.vector(sel.12.BRGW.Cat), wordF4=names(sel.12.BRGW.Cat), lang="Cat")
Words12.BRGW.Cat
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 31 |
posit wordF4 lang 1 7 alt Cat 2 13 bota Cat 3 16 cafè Cat 4 18 cartró Cat 5 20 cedre Cat 6 22 cítric Cat 7 23 claudolor Cat 8 28 cos Cat 9 33 especiat Cat 10 37 floral Cat 11 39 formatge Cat 12 41 fruitsec Cat 13 42 fum Cat 14 44 fusta Cat 15 45 gerani Cat 16 46 glicerol Cat 17 47 greix Cat 18 49 iode Cat 19 52 liniment Cat 20 55 malaqualitat Cat 21 56 mantegós Cat 22 57 marcat Cat 23 63 neopre Cat 24 78 regalèssia Cat 25 81 secant Cat 26 86 sutja Cat 27 87 taní Cat 28 90 torrat Cat 29 92 vainilla Cat 30 95 xocolata Cat |
French translation
df.WTR.Cat <- data.frame(orig= "alt", transl="high")
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "bota", transl="barrel") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "cafè", transl="coffee") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "cartró", transl="cardboard") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "cedre", transl="cedar") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "cítric", transl="citrus") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "claudolor", transl="clove") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "cos", transl="full_bodied") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "especiat", transl="spicy") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "floral.1", transl="floral") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "formatge", transl="cheese") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "fruitsec", transl="nuts") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "fum", transl="smoke") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "fusta", transl="wood") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "gerani", transl="geranium") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "glicerol", transl="glycerol") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "greix", transl="fat") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "iode", transl="iodine") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "liniment", transl="liniment") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "malaqualitat", transl="poorquality") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "mantegós", transl="buttered") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "marcat", transl="marked") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "neopre", transl="neoprene") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "regalèssia", transl="licorice") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "secant", transl="drying") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "sutja", transl="soot") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "taní", transl="tannin") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "torrat", transl="toasted") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "vainilla", transl="vanilla") )
df.WTR.Cat <- rbind(df.WTR.Cat , data.frame(orig= "xocolata", transl="chocolate") )
dim(df.WTR.Cat)
1 |
30 2 |
df.WTR.Cat
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 31 |
orig transl 1 alt high 2 bota barrel 3 cafè coffee 4 cartró cardboard 5 cedre cedar 6 cítric citrus 7 claudolor clove 8 cos full_bodied 9 especiat spicy 10 floral.1 floral 11 formatge cheese 12 fruitsec nuts 13 fum smoke 14 fusta wood 15 gerani geranium 16 glicerol glycerol 17 greix fat 18 iode iodine 19 liniment liniment 20 malaqualitat poorquality 21 mantegós buttered 22 marcat marked 23 neopre neoprene 24 regalèssia licorice 25 secant drying 26 sutja soot 27 taní tannin 28 torrat toasted 29 vainilla vanilla 30 xocolata chocolate |
To join Catalan coordinates and their translation. 29 characteristic Catalan words for the plane (1,2):
sel.12.BRGW.Cat
1 2 3 4 5 6 7 8 |
alt bota cafè cartró cedre cítric claudolor cos 7 13 16 18 20 22 23 28 especiat floral formatge fruitsec fum fusta gerani glicerol 33 37 39 41 42 44 45 46 greix iode liniment malaqualitat mantegós marcat neopre regalèssia 47 49 52 55 56 57 63 78 secant sutja taní torrat vainilla xocolata 81 86 87 90 92 95 |
Coord.Cat.Fig4 <- data.frame(res.mfact.23$freq.sup$coord[135+sel.12.BRGW.Cat,], lang="Cat")
Coord.Cat.Fig4 <- merge(Coord.Cat.Fig4, df.WTR.Cat, by.x=0, by.y="orig")
Coord.Cat.Fig4[,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 lang transl 1 alt 1.594762412 0.03621157 -0.422972137 0.355937434 Cat high 2 bota 1.594762412 0.03621157 -0.422972137 0.355937434 Cat barrel 3 cafè 1.499589705 -0.09365067 -0.303961198 0.414564298 Cat coffee 4 cartró -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat cardboard 5 cedre -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat cedar 6 cítric 0.239502079 -1.15465295 0.460096380 0.004142437 Cat citrus 7 claudolor 1.170024478 0.33762005 -0.480315219 -0.334345362 Cat clove 8 cos 0.862357705 -0.09456111 0.430474899 0.271621921 Cat full_bodied 9 especiat 1.197868440 0.19942101 -0.397440039 -0.161112684 Cat spicy 10 formatge -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat cheese 11 fruitsec 0.239502079 -1.15465295 0.460096380 0.004142437 Cat nuts 12 fum 0.812995160 0.31113997 -0.555916604 -0.265965432 Cat smoke 13 fusta 1.217970407 0.32673601 -0.174644124 0.071150592 Cat wood 14 gerani 0.933272460 0.30822729 -0.380418640 -0.505812763 Cat geranium 15 glicerol 0.933272460 0.30822729 -0.380418640 -0.505812763 Cat glycerol 16 greix 0.008356135 -0.56804162 -0.111703813 0.213163418 Cat fat 17 iode -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat iodine 18 liniment 0.933272460 0.30822729 -0.380418640 -0.505812763 Cat liniment 19 malaqualitat -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat poorquality 20 mantegós -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat buttered 21 marcat 1.594762412 0.03621157 -0.422972137 0.355937434 Cat marked 22 neopre -0.295369026 -1.55529185 0.723114229 -0.259695358 Cat neoprene 23 regalèssia 0.623473033 -0.37652219 -0.170410215 0.325183556 Cat licorice 24 secant -0.536766429 -0.94764429 0.181153893 0.056783121 Cat drying 25 sutja 1.594762412 0.03621157 -0.422972137 0.355937434 Cat soot 26 taní 0.649696693 -0.75954014 0.150071046 0.048121038 Cat tannin 27 torrat 1.142158500 -0.24380276 -0.300656637 0.203027830 Cat toasted 28 vainilla -0.046753466 -0.52902043 -0.006036952 -0.130748840 Cat vanilla 29 xocolata -0.129192655 0.65173127 -0.167899570 -0.023157824 Cat chocolate |
Coord.Fig4 <- rbind(Coord.Fr.Fig4, Coord.Cat.Fig4)
To build Figure 4:
ax1 <-1 ; ax2 <-2
labx <- paste0("Dim 1 (", round(res.mfact.23$eig[ax1,2],1),"%)" )
laby <- paste0("Dim 2 (", round(res.mfact.23$eig[ax2,2],1),"%)" )
font.type.FRCat <- c("bold", "bold.italic")
color.type.FRCat <- c("Green", "Blue")
col.margin = c("black", "red")
Coord.Fig4$lang <- as.factor(Coord.Fig4$lang)
set.seed(1234)
Figure4 <- ggplot(Coord.Fig4)+
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.Fig4$lang], max.overlaps=100,
box.padding = unit(0.35, "lines"),
aes(x=Dim.1, y=Dim.2, label = transl, color=lang))+
theme( axis.text.x = element_text(size=rel(1.6)))+
theme( axis.text.y = element_text(size=rel(1.6)))+
labs(x=labx)+labs(y=laby)+
theme(axis.title.x= element_text(size=17, face="bold"))+
theme(axis.title.y= element_text(size=17, face="bold"))+
theme(plot.margin = grid::unit(c(t=5,r= 2,b=5, l=2), "mm"))+
scale_color_manual(name="Language",
labels=c("Catalan","French"),
values = setNames(col.margin, levels(Coord.Fig4$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 4. The best-represented global words on either of the first two axes")
To plot Figure 4: