3-Gráficos com ggplot2

Aula 3

Dr. Gabriel Bertolini

2º semestre 2023 - PPGGEO UFRGS

Oficina de programação.

Aula introdutória sobre gráficos com ggplot.

Instalar pacotes

#install.packages("ggtern")
#install.packages("patchwork")
#install.packages("tidypaleo")

Importar biblioteca

library(dplyr)  # biblioteca do tidyverse de manipulação de banco de dados
library(tidyr)  # biblioteca do tidyverse para transformar banco de dados
library(ggtern) # biblioteca que adiciona o gráfico ternário ao ggplot (evitar importar ggplot2 ao importar ggtern)
library(forcats) # biblioteca do tidyverse para lidar com fatores
library(patchwork) # biblioteca para organizar gráficos em um layout
library(tidypaleo) # biblioteca para plots estratigráficos
library(RColorBrewer) # biblioteca de paleta de cores
library(gt)
# Bibliotecas espaciais
library(sf) # biblioteca para criação de objetos espaciais
library(mapview) # biblioteca para visualização interativa de dados espaciais
library(geobr)
library(ggspatial)
library(ggdal)

Importar os bancos

Banco obtido em @bonin2020 .

BD<-read.csv("./data/geoquimica_granito.csv") %>%
    as_tibble() 

BD %>% 
  gt_preview()

	X.1	X	Tectonic_setting	Location_notes	Pluton	Colour	Symbol	Size_rel	Size	SubGroup	Group	DebonPQ	TASMiddlemostPlut	Villaseca	QANOR	LaRoche	Geol_unit_notes	Rock_type_notes	Ref	Age	Latitude	Longitude	SiO2	TiO2	Al2O3	FeOt	MnO	MgO	CaO	K2O	Na2O	P2O5	H2O	H2Ot	H2O.MINUS	Li	Be	B	Sc	Cr	Ni	Cu	Zn	Rb	Sr	Y	Zr	Nb	Cs	Ba	La	Ce	Pr	Nd	Sm	Eu	Gd	Tb	Dy	Ho	Er	Tm	Yb	Lu	Hf	Ta	Pb	Th	U	Co	W	Ga	Ge	In	Q	C	Or	Ab	An	Ne	Ns	Ks	Di	Wo	Hy	Ol	Il	Tn	Pf	Ru	Ap	Sum_CIPW	Orthoclase	Albite	Anorthite	Quartz	Apatite	Ilmenite	Biotite	Amphibole	Corundum	Rest	sum_meso	P	K	Ti	Cr2O3	A.NK	A.CNK	K2O.Na2O
1	1	CR03	OROGENIC BELT	////////Peninsula//////	Cape Peninsula	brown4	16	1.6	1.28	Mafic S-types	1- CPG & MPG	gr	quartz monzonite	h-P	3b granite	granodiorite	//////////	//////	Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009)		NA	NA	65.40690	0.8270369	17.08490	4.914410	0.07714897	1.7960280	1.813515	5.131949	2.660096	0.2880228	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	269.0154	97.21508	52.24591	305.2263	25.35902	NA	390.4759	56.40085	112.16620	13.223848	52.34722	11.393739	1.2494124	10.838077	1.6265380	9.835326	2.024970	6.206267	0.8976863	6.077240	NA	8.747229	NA	NA	26.782157	NA	NA	NA	NA	NA	NA	24.54893	4.546013	30.32835	22.50899	7.115483	0	0	0	0	0	4.474137	0	0.16503878	0	0	0.7405025	0.6822185	95.10966	25.62016	22.53101	7.109292	27.20246	0.6797338	0.7856851	6.932014	0	4.544585	-0.3193515	95.08560	1257.1043	42601.85	4958.086	NA	1.720334	1.291518	1.929235
2	2	BB202	OROGENIC BELT	////////Peninsula//////	Cape Peninsula	brown4	16	1.6	1.28	Mafic S-types	1- CPG & MPG	gr	granodiorite	h-P	3b granite	granodiorite	//////////	//////	Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009)		NA	NA	69.64232	0.7087655	14.88713	4.186309	0.05397209	1.6293460	1.436880	5.018386	2.215911	0.22098	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	214.2795	98.96108	38.28057	260.1457	19.91177	NA	415.6909	44.29357	90.02860	10.382656	39.39793	7.655565	1.0286014	8.160105	1.1358404	8.071263	1.516150	3.946216	0.5438978	3.740191	NA	7.342101	NA	NA	19.492634	NA	NA	NA	NA	NA	NA	32.66955	3.726173	29.65722	18.75042	5.684928	0	0	0	0	0	4.058911	0	0.11545828	0	0	0.6482846	0.5234192	95.83437	25.43081	18.76877	5.680122	35.07460	0.5215128	0.6733272	6.222967	0	3.726782	-0.2852007	95.81369	964.4893	41659.13	4249.049	NA	1.640003	1.273484	2.264706
3	3	CB02	OROGENIC BELT	////////Peninsula//////	Cape Peninsula	brown4	16	1.6	1.28	Mafic S-types	1- CPG & MPG	gr	granite	h-P	3b granite	granite	//////////	//////	Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009)		NA	NA	70.61077	0.5705854	15.28393	2.822104	0.05318077	1.3306273	1.204323	5.597277	2.329983	0.197212	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	238.5302	90.38265	29.96433	186.0415	15.13822	NA	547.5218	32.36167	63.93163	7.248746	28.7628	6.040634	1.076092	5.3337	0.8584825	5.574644	1.088590	3.174243	0.4446103	2.755472	NA	5.434977	NA	NA	14.798949	NA	NA	NA	NA	NA	NA	31.63569	3.674628	33.07830	19.71567	4.686449	0	0	0	0	0	3.314764	0	0.11376549	0	0	0.5109352	0.4671216	97.19733	29.62021	19.73496	4.682256	33.59253	0.4654203	0.5420561	5.098139	0	3.676313	-0.2339899	97.17789	860.7515	46464.68	3420.659	NA	1.545114	1.265067	2.402282
4	4	HB02	OROGENIC BELT	////////Peninsula//////	Cape Peninsula	brown4	16	1.6	1.28	Mafic S-types	1- CPG & MPG	gr	granite	h-P	3b granite	granite	//////////	//////	Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009)		NA	NA	70.61724	0.4775028	15.70859	2.508109	0.04066072	0.9779424	1.311569	5.280680	2.877528	0.2001758	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	254.2501	116.29881	36.86868	181.9237	15.15780	NA	499.1298	34.12151	70.41246	8.06835	30.55275	6.613892	1.2179917	6.696204	1.0960613	7.270367	1.316706	3.774652	0.5311750	3.354008	NA	5.152241	NA	NA	15.483984	NA	NA	NA	NA	NA	NA	29.97338	3.353324	31.20731	24.34884	5.199155	0	0	0	0	0	2.436180	0	0.08698231	0	0	0.4319138	0.4741418	97.51123	28.63039	24.37266	5.194790	31.40372	0.4724149	0.4536277	3.789478	0	3.351382	-0.1765739	97.49189	873.6873	43836.51	2862.629	NA	1.503237	1.223927	1.835145
5	5	CR01	OROGENIC BELT	////////Peninsula//////	Cape Peninsula	brown4	16	1.6	1.28	Mafic S-types	1- CPG & MPG	gr	granite	h-P	3a granite	granite	//////////	//////	Unpub. Data, A.Villaros, G. Stevens, R. Scheepers (Villaros et al. 2009)		NA	NA	70.70878	0.5322106	15.28031	2.535746	0.06085569	1.2071555	1.235924	5.569955	2.642243	0.2268257	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	233.7445	91.32626	38.96812	172.6349	14.63283	NA	408.2444	32.54389	62.92283	7.49205	29.71011	6.455299	1.0194147	6.264685	1.0442407	6.880438	1.272111	3.816830	0.4902676	3.014657	NA	4.650457	NA	NA	14.926382	NA	NA	NA	NA	NA	NA	30.22193	3.200335	32.91684	22.35792	4.649750	0	0	0	0	0	3.007180	0	0.13018383	0	0	0.4638989	0.5372654	97.48530	29.75506	22.37980	4.645108	31.99386	0.5353087	0.5056001	4.664697	0	3.200603	-0.2157710	97.46426	990.0035	46237.87	3190.603	NA	1.472673	1.210504	2.108040
6..3049
3050	3050	SGMV	OROGENIC BELT	Carajas Province, Amazonia	Velho Guilherme Suite	lightgreen	18	2.0	1.60	PostOro (A2)	5- KCG								Teixeira et al. 2019	1860-1880	NA	NA	75.95000	0.0600000	12.62000	2.745275	NA	0.0100000	0.580000	4.530000	2.590000		NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	786.0000	18.00000	170.00000	164.0000	76.00000	NA	28.0000		NA						NA	NA	NA	NA	NA	NA	NA	NA	NA	NA		NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Banco obtido em @bertolini2020

lvsd<-read.csv("./data/lvsd.csv") %>%
    as_tibble()

lvsd %>% 
   gt_preview()

	X	sample	well	depth	h1	h2	h3	h4	h5	h6	h7	h8	h9	h10	h_mean	pw_t1	pw_t2	pw_t3	sw_t1	sw_t2	p_wave	s_wave	dry_mass	subm_mass	sat_mass	bulk_vol	pore_volum	porosity	density	lava_1	lava_2	lava_dist_base	exp_1	exp_2	total_points	comp_quartz	comp_feldspar	comp_lithic	qtz_mono	qtz_poly	qtz_deformed	f_plagio	f_microcline	f_ortoclase	lt_metamorphic	lt_plutonic	lt_vulcanic	lt_sedimentary	por_primary	por_moldic	por_intragranular	por_total	por_IGV	con_float	con_point	con_linear	con_cc	con_sutured	con_tontal	cem_zeolite	cem_qtz	cem_calcite	cem_semctite	cem_ilite	cem_albite	cem_chloriet	cem_ironoxide	cem_gypsum	cem_dolomite	cem_total	gs_silt	gs_vfs	gs_fs	gs_ms	gs_cs	gs_vcs	gs_sorting	gm_high_sphericity	gm_low_sphericity	gm_angular	gm_subangular	gm_subrounded	gm_rounded	lava_1_base	lava_1_top	lava_2_base	lava_2_top	dis_1_top	dis_1_base	dis_2_top	dis_2_base	dis_l1	dis_l2	distance
1	1	RB-01	TG-27	1.25	25.98	26.86	25.70	26.56	25.46	25.93	26.62	25.28	26.45	26.48	26.132	4.78	4.68	NA	7.38	7.33	5.52	3.55	50.45	29.35	50.50	21.15	0.05	0.24	2.39	97.0	NA	89.7	7.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	97.0	89.6	NA	NA	88.35	-95.75	NA	NA	88.35	NA	88.35
2	2	RB-02	TG-27	1.90	44.92	44.93	45.04	44.94	44.78	44.80	44.88	44.92	45.03	44.89	44.913	13.63	13.93	NA	18.63	18.83	3.26	2.40	52.10	31.81	55.33	23.52	3.23	13.73	2.22	97.0	NA	89.7	7.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	97.0	89.6	NA	NA	87.70	-95.10	NA	NA	87.70	NA	87.70
3	3	RB-03	TG-27	2.90	44.04	43.49	43.50	42.96	42.76	43.45	43.58	43.44	43.56	43.71	43.449	13.53	12.73	NA	15.93	15.63	3.31	2.75	35.26	26.50	37.76	11.26	2.50	22.20	3.13	97.0	NA	89.7	7.4	NA	356	64.47368	21.71053	13.815789	93.36735	0.5102041	6.122449	43.54839	53.22581	3.225806	5.128205	76.92308	15.384615	2.564103	16.01124	0.2808989	4.775281	21.06742	23.59551	0.2139037	28.44920	42.8877	21.28342	7.165775	100	0	0	2	5	0	0	0	2	0	0	9	4.109589	8.904110	21.91781	28.08219	33.56164	3.424658	1.204	53.62319	46.37681	24.087591	18.24818	39.41606	18.24818	97.0	89.6	NA	NA	86.70	-94.10	NA	NA	86.70	NA	86.70
4	4	RB-04	TG-27	3.90	47.45	46.67	46.45	46.14	46.52	47.06	47.43	48.79	47.82	47.68	47.201	13.68	13.73	NA	16.23	16.93	3.44	2.85	54.56	34.03	58.86	24.83	4.30	17.32	2.20	97.0	NA	89.7	7.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	97.0	89.6	NA	NA	85.70	-93.10	NA	NA	85.70	NA	85.70
5	5	RB-05	TG-27	4.90	45.59	45.43	45.28	45.34	45.30	45.24	45.20	45.25	45.28	45.36	45.327	13.48	12.58	NA	14.23	14.23	3.48	3.19	56.21	35.50	59.39	23.89	3.18	13.31	2.35	97.0	NA	89.7	7.4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	97.0	89.6	NA	NA	84.70	-92.10	NA	NA	84.70	NA	84.70
6..71
72	72	RB-71	CBM-100UF	95.00	34.55	34.44	34.79	35.01	35.80	36.32	36.57	35.89	35.20	34.77	35.334	8.48	8.83	NA	11.03	12.63	4.08	2.99	131.68	77.95	138.79	60.84	7.11	11.69	2.16	4.7	89.7	5.9	4.8	85	312	67.61566	23.84342	8.540925	95.78947	0.0000000	4.210526	33.92857	51.78571	14.285714	16.666667	75.00000	8.333333	0.000000	10.89744	0.3205128	1.602564	12.82051	17.62821	0.0000000	4.61361	52.7105	34.25606	8.419839	100	3	0	4	0	0	7	0	0	1	0	15	1.204819	2.409639	16.86747	32.53012	45.78313	1.204819	0.915	70.19868	29.80132	0.621118	22.36025	56.52174	20.49689	4.7	-0.1	89.7	4.7	-95.10	90.30	-90.3	5.3	90.30	5.3	5.30

Banco dummy de paleocorrents

PALEOCURRENTS<-read.csv("paleocurrents_dummy.csv")

PALEOCURRENTS %>% 
  gt_preview()

	X	direction	facies
1	1	53.06491	WINDRIPPLES
2	2	40.29703	WINDRIPPLES
3	3	73.98197	WINDRIPPLES
4	4	67.22886	WINDRIPPLES
5	5	66.95277	WINDRIPPLES
6..899
900	900	149.29671	DUNES

Gráficos ggplot2

Scatterplot

PLOT1<-BD %>%
  select(SiO2,TiO2) %>%
  ggplot(aes(x=TiO2,y=SiO2))+
  #parametro aes()=aestetic para parametro 
  #relacionado ao banco de dado (coluna)
  geom_point(size=2)+
  #tipo de elemento gráfico a ser usado,
  #no caso pontos
  scale_x_log10()
  #parametro adicionado para usar
  #escala log10 no eixo x

PLOT1

Gráfico interativo com o plotly

plotly::ggplotly(PLOT1)

Aqui, vamos fazer um gráfico de dispersão utilizando diferentes transparencias, adicionadas com função mutate e criação da coluna transp. Notar uso do aes() para mapear o banco de dados.

BD %>%
  select(SiO2,TiO2,MgO,Tectonic_setting) %>%
  mutate(transp=ifelse(Tectonic_setting=="CONTINENTAL FLOOD BASALT",0.8,0.3)) %>%
  ggplot(aes(x=SiO2,y=TiO2,size=MgO,color=Tectonic_setting))+
  #geom_point(alpha=0.5) # Parametro alpha para transparência
  geom_point(aes(alpha=transp))

A função ggsave() pode salvar o plot.

#ggsave("./plots/plot1.jpg")

Plot múltiplos com facet_wrap()

BD %>%
  select(SiO2,TiO2,MgO,Tectonic_setting) %>%
  ggplot(aes(x=SiO2,y=TiO2,size=MgO,col=Tectonic_setting))+
  geom_point()+
  labs(x="Óxido de Silicio",
       y="Óxido de Titânio",
       size="Proporção de Magnésio",
       col="Ambiente tectonico",
       title = " Relação entre Silicio e Titânio em diferentes ambientes geotectônicos")+
  facet_wrap(~Tectonic_setting)

ou facet_grid()

BD %>% 
  select(SiO2,TiO2,MgO,Tectonic_setting,TASMiddlemostPlut,SubGroup) %>%
  filter(Tectonic_setting=="ARCHAEAN CRATONS") %>% 
  ggplot(aes(x=SiO2,y=TiO2,col=SubGroup))+
  geom_point()+ 
  facet_grid(TASMiddlemostPlut~SubGroup)

Exercício 1

Qual a distribuição das séries de granitos utilizando o diagrama P-Q (Debon & Fort, 1983, 1988)

\[ P = K - (Na + Ca) \]

\[ Q = \frac{Si}{3} - (K + Na + 2 \times \frac{Ca}{3}) \]

P vs Q

Fazer gráfico de dispersão P vs. Q no banco dos granitos

BD %>% select(K=K2O,Na=Na2O,Si=SiO2,Ca=CaO,SubGroup) %>%
       mutate(P = K - (Na + Ca),
              Q = Si/3 - (K + Na + 2*(Ca/3))) %>%
      ggplot(aes(P,Q,col=SubGroup))+
      geom_point()+
      geom_smooth(method="lm")

Criar vários plots pela coluna SubGroup.

BD %>% select(K=K2O,Na=Na2O,Si=SiO2,Ca=CaO,SubGroup) %>%
       mutate(P = K - (Na + Ca),
              Q = Si/3 - (K + Na + 2*(Ca/3))) %>%
       ggplot(aes(P,Q,col=SubGroup))+
       geom_point()+
       geom_smooth(method="lm",col="black")+
       facet_wrap(~SubGroup)

Boxplot

BD %>%
    select(SiO2, TiO2, MgO, Tectonic_setting) %>%
    ggplot(aes(x = Tectonic_setting, y = TiO2, fill = Tectonic_setting)) +
    geom_boxplot()+
    theme(axis.title.x=element_blank(), # retirar nome escala
          axis.text.x=element_blank(), # retirar texto escala
          axis.ticks.x=element_blank()) # retirar ticks escala

Ou, versão violino

BD %>%
    select(SiO2, TiO2, MgO, Tectonic_setting) %>%
    ggplot(aes(x = Tectonic_setting, y = TiO2, fill = Tectonic_setting)) +
    geom_violin()+
    theme(axis.title.x=element_blank(), # retirar nome escala
          axis.text.x=element_blank(), # retirar texto escala
          axis.ticks.x=element_blank()) # retirar ticks escala

Histograma

BD %>% select(SiO2,TiO2,MgO,Tectonic_setting) %>%
       ggplot(aes(x=SiO2))+
       geom_histogram(bins=10)+
       geom_vline(aes(xintercept=mean(SiO2)))+ # Adiciona média
       geom_vline(col="red",aes(xintercept=median(SiO2))) # adiciona mediana

histo_ploter <- function(df,bins = 10) {
  
  df %>%
    ggplot(aes(x = SiO2) ) +
    geom_histogram(bins = bins) +
    geom_vline(aes(xintercept = mean(SiO2))) +
    geom_vline(col = "red", aes(xintercept = median(SiO2)))
}

histo_ploter(BD,5)

plotly::ggplotly(histo_ploter(BD))

Histograma pivotado

BD %>%
  select(SiO2:P2O5) %>%
  mutate(across(.cols = SiO2:P2O5, as.numeric)) %>%
  pivot_longer(cols = SiO2:P2O5,
               names_to = "element",
               values_to = "values") %>%
  group_by(element) %>%
  mutate(media = median(values, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(values)) +
  geom_histogram() +
  geom_vline(col = "red", aes(xintercept = media)) +
  facet_wrap( ~ element, scales = "free")

Densidade

BD %>%
  select(SiO2, Tectonic_setting) %>%
  ggplot(aes(x = SiO2, fill = Tectonic_setting)) +
  geom_density(bw=1, #tamanho da banda
               alpha = 0.6) +
  facet_wrap( ~ Tectonic_setting)+
  labs(fill="Ambiente\nTectônico")

Ternário

simples

BD %>% 
  filter(Tectonic_setting=="OROGENIC BELT") %>% 
  select(Q = Quartz,
              Ab = Albite,
              An = Anorthite) %>%
  ggtern(aes(x=Ab, y=Q, z=An)) +
  geom_point() +
  ggtern::geom_confidence_tern()

ou pivotado

BD %>% 
  filter(Tectonic_setting=="ARCHAEAN CRATONS") %>% 
  select(Q = Quartz,
              Ab = Albite,
              An = Anorthite,
              SubGroup) %>%
  ggtern(aes(x= Ab, y=Q, z=An, col = SubGroup)) +
  geom_point() +
  facet_wrap( ~ SubGroup)

Barras

Horizontal

BD %>% 
  reframe(n=n(),
          .by = Tectonic_setting) %>%
  ggplot(aes(x=n,
             y=fct_reorder(Tectonic_setting,n),
             fill=Tectonic_setting))+
  geom_bar(stat="identity")+
  labs(y="Ambientes tectônicos") +
  theme(axis.title.x = element_blank(),
        legend.position = "none")

Vertical

BD %>% 
  reframe(n=n(),
          .by = Tectonic_setting) %>%
  ggplot(aes(x=fct_reorder(Tectonic_setting,desc(n)),
             y=n,
             fill=Tectonic_setting))+
  geom_bar(stat="identity")+
  scale_x_discrete(guide = guide_axis(n.dodge = 4))+
  theme(axis.title.x = element_blank(),
        legend.position = "none")

Pie-Chart

Simples, calculando a média com reframe() + mean()

BD %>%
  select(
    Q = Quartz,
    Ab = Albite,
    An = Anorthite,
    Tectonic = Tectonic_setting
  ) %>%
  reframe(
    Q = mean(Q, na.rm = TRUE),
    Ab = mean(Ab, na.rm = TRUE),
    An = mean(An, na.rm = TRUE)
  ) %>%
  pivot_longer(cols = Q:An,
               names_to = "mineral",
               values_to = "value") %>%
  ggplot(aes(x = "", y = value, fill = mineral)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y")

Ou então, pivotado

BD %>%
  select(
    Q = Quartz,
    Ab = Albite,
    Or = Orthoclase,
    Tectonic = Tectonic_setting
  ) %>%
  drop_na(Q,Ab,Or) %>% 
  reframe(
    Q = mean(Q, na.rm = TRUE),
    Ab = mean(Ab, na.rm = TRUE),
    Or = mean(Or, na.rm = TRUE),
              .by=Tectonic)  %>%
  group_by(Tectonic) %>% 
  mutate(total=sum(Q,Ab,Or),
         across(.cols=Q:Or,~.x*100/total)) %>% 
  ungroup() %>%       
  pivot_longer(cols = Q:Or,
               names_to = "mineral",
               values_to = "value") %>%
  ggplot(aes(x = "", y = value, fill = mineral)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y")+
  facet_wrap(~Tectonic)

Direcional

Roseta simples

PALEOCURRENTS %>% 
  ggplot(aes(x=direction,fill=facies))+
  geom_histogram(color="white",bins=30,breaks=seq(0, 360,by=10))+
  coord_polar()+
  scale_x_continuous("", limits = c(0, 360), 
                     breaks = seq(0, 360,by=30), 
                     labels =  seq(0, 360,by=30))

Ou facetado

PALEOCURRENTS %>% 
  ggplot(aes(x=direction,fill=facies))+
  geom_histogram(color="white",bins=30,breaks=seq(0, 360,by=10))+
  coord_polar()+
  scale_x_continuous("", limits = c(0, 360), 
                     breaks = seq(0, 360,by=30), 
                     labels =  seq(0, 360,by=30)) +
  facet_wrap(~facies)

Estratigráfico

lvsd %>%
  filter(well == "TG-227") %>%
  pivot_longer(
    cols = c(p_wave:s_wave, porosity, density),
    names_to = "var",
    values_to = "value"
  ) %>%
  dplyr::mutate(var = factor(var, levels = c("porosity", "density", "p_wave", "s_wave"))) %>%
  ggplot(aes(x = value, y = depth)) +
  geom_rect(
    mapping = aes(
      ymin = 283.45,
      ymax = 298.10,
      xmin = -Inf,
      xmax = Inf
    ),
    alpha = 0.5,
    fill = "gray",
    inherit.aes = FALSE
  ) +
  geom_rect(
    mapping = aes(
      ymin = 298.10,
      ymax = 340,
      xmin = -Inf,
      xmax = Inf
    ),
    alpha = 0.3,
    fill = "lightyellow",
    inherit.aes = FALSE
  ) +
  geom_hline(
    yintercept = c(283.45, 298.10, 340),
    col = "red",
    lty = 2,
    alpha = 0.7
  ) +
  geom_lineh() +
  geom_point() +
  scale_y_reverse() +
  facet_geochem_gridh(vars(var), scales = "free") +
  labs(x = NULL,
       y = "Depth (m)") +
  theme_paleo()

Mapas

Para usar o R para mapas, algumas bibliotecas são fundamentais, como:

sf: Cria objetos espaciais com sistemas de referência de coordenadas e diversas funções de geoprocessamento.
terra e raster: Pacotes indicados para trabalhar com dados do tipo raster, cada um com suas particularidades.

Outras bibliotecas são muito úteis para coisas em especifico

chronosphere: Focado em dados de períodos geológicos distintos, com API para dados paleogeográficos e paleoclimáticos.
mapview: Biblioteca para visualização rapida de dados espaciais de forma interativa
ggspatial, ggdal, tidyterra: Extensões do ggplot2 para manipulação e visualização de dados espaciais.
geobr: Fornece bases cartográficas brasileiras.
vapour: Leitura eficiente de arquivos raster, como imagens de satélite e DEMs.

A estrutura para mapas com ggplot é igual as anteriores. Vamos começar criando um banco de dados dummy com coordenadas dentro do brasil

coords_br <- data.frame(
  id=1:10,
  latitude = runif(10, min = -33, max = 0), # Valores aleatórios para latitude
  longitude = runif(10, min = -54, max = -40) # Valores aleatórios para longitude
)

coords_br %>% 
  gt_preview()

	id	latitude	longitude
1	1	-23.639832	-52.76330
2	2	-32.875365	-47.86329
3	3	-15.526395	-53.90008
4	4	-19.012403	-43.36837
5	5	-19.344231	-42.91496
6..9
10	10	-3.356715	-45.23681

Vamos visualizar rapidamente com mapview, mas para isso, primeiro precisamos converter o dataframe coords_br para um dado espacial. Para isso, vamos usar a função st_as_sf() da biblioteca sf:

# Coordinate Ref System WGS84 latlong = 4326
# Funciona melhor no viewer
library(sf)

coords_br %>% 
  st_as_sf(coords = c("longitude","latitude"), # informar nome colunas com X,Y
           crs=4326) ->BDS_SPATIAL

BDS_SPATIAL %>% # informar crs WGS84 / EPSG = european petroleum society...
  mapview(.)

Vamos tilizar agora o pacote geobr para baixar dados cartograficos do brasil.

# baixar shapefile estados brasileiros
estados<-geobr::read_state()
POA<-geobr::read_municipality() %>% 
  filter(name_muni=="POA")
# filtrar para RS
rs <- estados %>% 
  filter(abbrev_state=="RS")
# criar coordenadas dummies dentro do RS
coords<-st_sample(rs, 100)

Criação do mapa com ggplot: - Primeiro vamos fazer um mapa regional

inset<-  ggplot()+
  geom_sf(data = estados,fill="black")+
  geom_sf(data = rs,fill="red")+
  theme_void() +  # Remove elementos do gráfico
  theme(plot.background = element_rect(fill = "transparent", colour = NA))  # Define o fundo como transparente

inset

Segundo seguimos com o mapa de interesse, no caso o estado do RS

RS<-ggplot()+
    geom_sf(data = rs,linewidth=1)+
    geom_sf(data = coords,color="grey50",size=2)+
    geom_sf(data=POA,fill="red")+
    coord_sf(xlim=c(-48,-59),
             ylim=c(-26,-34))+
    ggspatial::annotation_north_arrow(location="tr")+
    ggspatial::annotation_scale(location="bl")

RS

3 unir os plots com a biblioteca patchwork

#Patchwork

RS + inset_element(inset, left = 0, bottom = 0.7, right = 0.3, top = 1)

Outra opção, é utilziar o pacote ggdal para utilizar layers de imagem de satélites.

library(ggdal)

ggplot()+
    ggdal::annotation_gdal(dsn = "virtualearth")+
    geom_sf(data = rs,
            linewidth=1.5,
            fill=NA,col="white")+
    geom_sf(data = coords,color="red",size=2)

Cores

Cores podem inseridas através de:

Seus nomes (“white”, “aliceblue”, “antiquewhite”). Cheque todas usando a função colors()

colors()

  [1] "white"                "aliceblue"            "antiquewhite"        
  [4] "antiquewhite1"        "antiquewhite2"        "antiquewhite3"       
  [7] "antiquewhite4"        "aquamarine"           "aquamarine1"         
 [10] "aquamarine2"          "aquamarine3"          "aquamarine4"         
 [13] "azure"                "azure1"               "azure2"              
 [16] "azure3"               "azure4"               "beige"               
 [19] "bisque"               "bisque1"              "bisque2"             
 [22] "bisque3"              "bisque4"              "black"               
 [25] "blanchedalmond"       "blue"                 "blue1"               
 [28] "blue2"                "blue3"                "blue4"               
 [31] "blueviolet"           "brown"                "brown1"              
 [34] "brown2"               "brown3"               "brown4"              
 [37] "burlywood"            "burlywood1"           "burlywood2"          
 [40] "burlywood3"           "burlywood4"           "cadetblue"           
 [43] "cadetblue1"           "cadetblue2"           "cadetblue3"          
 [46] "cadetblue4"           "chartreuse"           "chartreuse1"         
 [49] "chartreuse2"          "chartreuse3"          "chartreuse4"         
 [52] "chocolate"            "chocolate1"           "chocolate2"          
 [55] "chocolate3"           "chocolate4"           "coral"               
 [58] "coral1"               "coral2"               "coral3"              
 [61] "coral4"               "cornflowerblue"       "cornsilk"            
 [64] "cornsilk1"            "cornsilk2"            "cornsilk3"           
 [67] "cornsilk4"            "cyan"                 "cyan1"               
 [70] "cyan2"                "cyan3"                "cyan4"               
 [73] "darkblue"             "darkcyan"             "darkgoldenrod"       
 [76] "darkgoldenrod1"       "darkgoldenrod2"       "darkgoldenrod3"      
 [79] "darkgoldenrod4"       "darkgray"             "darkgreen"           
 [82] "darkgrey"             "darkkhaki"            "darkmagenta"         
 [85] "darkolivegreen"       "darkolivegreen1"      "darkolivegreen2"     
 [88] "darkolivegreen3"      "darkolivegreen4"      "darkorange"          
 [91] "darkorange1"          "darkorange2"          "darkorange3"         
 [94] "darkorange4"          "darkorchid"           "darkorchid1"         
 [97] "darkorchid2"          "darkorchid3"          "darkorchid4"         
[100] "darkred"              "darksalmon"           "darkseagreen"        
[103] "darkseagreen1"        "darkseagreen2"        "darkseagreen3"       
[106] "darkseagreen4"        "darkslateblue"        "darkslategray"       
[109] "darkslategray1"       "darkslategray2"       "darkslategray3"      
[112] "darkslategray4"       "darkslategrey"        "darkturquoise"       
[115] "darkviolet"           "deeppink"             "deeppink1"           
[118] "deeppink2"            "deeppink3"            "deeppink4"           
[121] "deepskyblue"          "deepskyblue1"         "deepskyblue2"        
[124] "deepskyblue3"         "deepskyblue4"         "dimgray"             
[127] "dimgrey"              "dodgerblue"           "dodgerblue1"         
[130] "dodgerblue2"          "dodgerblue3"          "dodgerblue4"         
[133] "firebrick"            "firebrick1"           "firebrick2"          
[136] "firebrick3"           "firebrick4"           "floralwhite"         
[139] "forestgreen"          "gainsboro"            "ghostwhite"          
[142] "gold"                 "gold1"                "gold2"               
[145] "gold3"                "gold4"                "goldenrod"           
[148] "goldenrod1"           "goldenrod2"           "goldenrod3"          
[151] "goldenrod4"           "gray"                 "gray0"               
[154] "gray1"                "gray2"                "gray3"               
[157] "gray4"                "gray5"                "gray6"               
[160] "gray7"                "gray8"                "gray9"               
[163] "gray10"               "gray11"               "gray12"              
[166] "gray13"               "gray14"               "gray15"              
[169] "gray16"               "gray17"               "gray18"              
[172] "gray19"               "gray20"               "gray21"              
[175] "gray22"               "gray23"               "gray24"              
[178] "gray25"               "gray26"               "gray27"              
[181] "gray28"               "gray29"               "gray30"              
[184] "gray31"               "gray32"               "gray33"              
[187] "gray34"               "gray35"               "gray36"              
[190] "gray37"               "gray38"               "gray39"              
[193] "gray40"               "gray41"               "gray42"              
[196] "gray43"               "gray44"               "gray45"              
[199] "gray46"               "gray47"               "gray48"              
[202] "gray49"               "gray50"               "gray51"              
[205] "gray52"               "gray53"               "gray54"              
[208] "gray55"               "gray56"               "gray57"              
[211] "gray58"               "gray59"               "gray60"              
[214] "gray61"               "gray62"               "gray63"              
[217] "gray64"               "gray65"               "gray66"              
[220] "gray67"               "gray68"               "gray69"              
[223] "gray70"               "gray71"               "gray72"              
[226] "gray73"               "gray74"               "gray75"              
[229] "gray76"               "gray77"               "gray78"              
[232] "gray79"               "gray80"               "gray81"              
[235] "gray82"               "gray83"               "gray84"              
[238] "gray85"               "gray86"               "gray87"              
[241] "gray88"               "gray89"               "gray90"              
[244] "gray91"               "gray92"               "gray93"              
[247] "gray94"               "gray95"               "gray96"              
[250] "gray97"               "gray98"               "gray99"              
[253] "gray100"              "green"                "green1"              
[256] "green2"               "green3"               "green4"              
[259] "greenyellow"          "grey"                 "grey0"               
[262] "grey1"                "grey2"                "grey3"               
[265] "grey4"                "grey5"                "grey6"               
[268] "grey7"                "grey8"                "grey9"               
[271] "grey10"               "grey11"               "grey12"              
[274] "grey13"               "grey14"               "grey15"              
[277] "grey16"               "grey17"               "grey18"              
[280] "grey19"               "grey20"               "grey21"              
[283] "grey22"               "grey23"               "grey24"              
[286] "grey25"               "grey26"               "grey27"              
[289] "grey28"               "grey29"               "grey30"              
[292] "grey31"               "grey32"               "grey33"              
[295] "grey34"               "grey35"               "grey36"              
[298] "grey37"               "grey38"               "grey39"              
[301] "grey40"               "grey41"               "grey42"              
[304] "grey43"               "grey44"               "grey45"              
[307] "grey46"               "grey47"               "grey48"              
[310] "grey49"               "grey50"               "grey51"              
[313] "grey52"               "grey53"               "grey54"              
[316] "grey55"               "grey56"               "grey57"              
[319] "grey58"               "grey59"               "grey60"              
[322] "grey61"               "grey62"               "grey63"              
[325] "grey64"               "grey65"               "grey66"              
[328] "grey67"               "grey68"               "grey69"              
[331] "grey70"               "grey71"               "grey72"              
[334] "grey73"               "grey74"               "grey75"              
[337] "grey76"               "grey77"               "grey78"              
[340] "grey79"               "grey80"               "grey81"              
[343] "grey82"               "grey83"               "grey84"              
[346] "grey85"               "grey86"               "grey87"              
[349] "grey88"               "grey89"               "grey90"              
[352] "grey91"               "grey92"               "grey93"              
[355] "grey94"               "grey95"               "grey96"              
[358] "grey97"               "grey98"               "grey99"              
[361] "grey100"              "honeydew"             "honeydew1"           
[364] "honeydew2"            "honeydew3"            "honeydew4"           
[367] "hotpink"              "hotpink1"             "hotpink2"            
[370] "hotpink3"             "hotpink4"             "indianred"           
[373] "indianred1"           "indianred2"           "indianred3"          
[376] "indianred4"           "ivory"                "ivory1"              
[379] "ivory2"               "ivory3"               "ivory4"              
[382] "khaki"                "khaki1"               "khaki2"              
[385] "khaki3"               "khaki4"               "lavender"            
[388] "lavenderblush"        "lavenderblush1"       "lavenderblush2"      
[391] "lavenderblush3"       "lavenderblush4"       "lawngreen"           
[394] "lemonchiffon"         "lemonchiffon1"        "lemonchiffon2"       
[397] "lemonchiffon3"        "lemonchiffon4"        "lightblue"           
[400] "lightblue1"           "lightblue2"           "lightblue3"          
[403] "lightblue4"           "lightcoral"           "lightcyan"           
[406] "lightcyan1"           "lightcyan2"           "lightcyan3"          
[409] "lightcyan4"           "lightgoldenrod"       "lightgoldenrod1"     
[412] "lightgoldenrod2"      "lightgoldenrod3"      "lightgoldenrod4"     
[415] "lightgoldenrodyellow" "lightgray"            "lightgreen"          
[418] "lightgrey"            "lightpink"            "lightpink1"          
[421] "lightpink2"           "lightpink3"           "lightpink4"          
[424] "lightsalmon"          "lightsalmon1"         "lightsalmon2"        
[427] "lightsalmon3"         "lightsalmon4"         "lightseagreen"       
[430] "lightskyblue"         "lightskyblue1"        "lightskyblue2"       
[433] "lightskyblue3"        "lightskyblue4"        "lightslateblue"      
[436] "lightslategray"       "lightslategrey"       "lightsteelblue"      
[439] "lightsteelblue1"      "lightsteelblue2"      "lightsteelblue3"     
[442] "lightsteelblue4"      "lightyellow"          "lightyellow1"        
[445] "lightyellow2"         "lightyellow3"         "lightyellow4"        
[448] "limegreen"            "linen"                "magenta"             
[451] "magenta1"             "magenta2"             "magenta3"            
[454] "magenta4"             "maroon"               "maroon1"             
[457] "maroon2"              "maroon3"              "maroon4"             
[460] "mediumaquamarine"     "mediumblue"           "mediumorchid"        
[463] "mediumorchid1"        "mediumorchid2"        "mediumorchid3"       
[466] "mediumorchid4"        "mediumpurple"         "mediumpurple1"       
[469] "mediumpurple2"        "mediumpurple3"        "mediumpurple4"       
[472] "mediumseagreen"       "mediumslateblue"      "mediumspringgreen"   
[475] "mediumturquoise"      "mediumvioletred"      "midnightblue"        
[478] "mintcream"            "mistyrose"            "mistyrose1"          
[481] "mistyrose2"           "mistyrose3"           "mistyrose4"          
[484] "moccasin"             "navajowhite"          "navajowhite1"        
[487] "navajowhite2"         "navajowhite3"         "navajowhite4"        
[490] "navy"                 "navyblue"             "oldlace"             
[493] "olivedrab"            "olivedrab1"           "olivedrab2"          
[496] "olivedrab3"           "olivedrab4"           "orange"              
[499] "orange1"              "orange2"              "orange3"             
[502] "orange4"              "orangered"            "orangered1"          
[505] "orangered2"           "orangered3"           "orangered4"          
[508] "orchid"               "orchid1"              "orchid2"             
[511] "orchid3"              "orchid4"              "palegoldenrod"       
[514] "palegreen"            "palegreen1"           "palegreen2"          
[517] "palegreen3"           "palegreen4"           "paleturquoise"       
[520] "paleturquoise1"       "paleturquoise2"       "paleturquoise3"      
[523] "paleturquoise4"       "palevioletred"        "palevioletred1"      
[526] "palevioletred2"       "palevioletred3"       "palevioletred4"      
[529] "papayawhip"           "peachpuff"            "peachpuff1"          
[532] "peachpuff2"           "peachpuff3"           "peachpuff4"          
[535] "peru"                 "pink"                 "pink1"               
[538] "pink2"                "pink3"                "pink4"               
[541] "plum"                 "plum1"                "plum2"               
[544] "plum3"                "plum4"                "powderblue"          
[547] "purple"               "purple1"              "purple2"             
[550] "purple3"              "purple4"              "red"                 
[553] "red1"                 "red2"                 "red3"                
[556] "red4"                 "rosybrown"            "rosybrown1"          
[559] "rosybrown2"           "rosybrown3"           "rosybrown4"          
[562] "royalblue"            "royalblue1"           "royalblue2"          
[565] "royalblue3"           "royalblue4"           "saddlebrown"         
[568] "salmon"               "salmon1"              "salmon2"             
[571] "salmon3"              "salmon4"              "sandybrown"          
[574] "seagreen"             "seagreen1"            "seagreen2"           
[577] "seagreen3"            "seagreen4"            "seashell"            
[580] "seashell1"            "seashell2"            "seashell3"           
[583] "seashell4"            "sienna"               "sienna1"             
[586] "sienna2"              "sienna3"              "sienna4"             
[589] "skyblue"              "skyblue1"             "skyblue2"            
[592] "skyblue3"             "skyblue4"             "slateblue"           
[595] "slateblue1"           "slateblue2"           "slateblue3"          
[598] "slateblue4"           "slategray"            "slategray1"          
[601] "slategray2"           "slategray3"           "slategray4"          
[604] "slategrey"            "snow"                 "snow1"               
[607] "snow2"                "snow3"                "snow4"               
[610] "springgreen"          "springgreen1"         "springgreen2"        
[613] "springgreen3"         "springgreen4"         "steelblue"           
[616] "steelblue1"           "steelblue2"           "steelblue3"          
[619] "steelblue4"           "tan"                  "tan1"                
[622] "tan2"                 "tan3"                 "tan4"                
[625] "thistle"              "thistle1"             "thistle2"            
[628] "thistle3"             "thistle4"             "tomato"              
[631] "tomato1"              "tomato2"              "tomato3"             
[634] "tomato4"              "turquoise"            "turquoise1"          
[637] "turquoise2"           "turquoise3"           "turquoise4"          
[640] "violet"               "violetred"            "violetred1"          
[643] "violetred2"           "violetred3"           "violetred4"          
[646] "wheat"                "wheat1"               "wheat2"              
[649] "wheat3"               "wheat4"               "whitesmoke"          
[652] "yellow"               "yellow1"              "yellow2"             
[655] "yellow3"              "yellow4"              "yellowgreen"

Por seus códigos

par(mfrow=c(1,2))

# Hexadecimal
plot(1:10, col = "#FF5733", main="Hexadecimal")

# RGB
plot(1:10, col = rgb(255, 87, 51, maxColorValue = 255),main="RGB")

par(mfrow=c(1,1))

Paletas prontas de cores- as mais utilizadas são Viridis e rColorBrewer

As paletas de cores são em geral convergentes, divergentes ou categóricas. Outra subdivisão são valores continuos ou discretos.

Viridis

Scale_*_viridis_b

Usar scale_*_viridis_b para dados continuos separados em grupos (bins). * representa ou color ou fill.

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_b(option = "magma",n.breaks=3) +
  labs(title = "Viridis - Magma (A)")

Scale_*_viridis_c

Usar scale_*_viridis_c para dados continuos. * representa ou color ou fill : color para as linhas (no caso o ponto) e fill para colunas.

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "magma") +
  labs(title = "Viridis - Magma (A)")->A

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "inferno") +
  labs(title = "Viridis - Inferno (B)")->B


BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "plasma") +
  labs(title = "Viridis - Plasma (C)")->C

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "viridis") +
  labs(title = "Viridis - Viridis (D)")->D

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "cividis") +
  labs(title = "Viridis - Cividis (E)")->E

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "rocket") +
  labs(title = "Viridis - rocket (F)")->F1


BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "mako") +
  labs(title = "Viridis - Mako (G)")->G

BD %>% 
  ggplot(aes(x=SiO2,
             y=TiO2,
             color=Al2O3))+
  geom_point()+
  scale_color_viridis_c(option = "turbo") +
  labs(title = "Viridis - Turbo(H)")->H


A+B+C+D+E+F1+G+H+
plot_layout(ncol = 2)

Scale_*_viridis_d

Usar scale_*_viridis_dpara dados discretos. * representa ou color ou fill : color para as linhas (no caso o ponto) e fill para colunas.

BD %>% 
  ggplot(aes(x=fct_reorder(Tectonic_setting,TiO2),
             y=TiO2,
             fill=fct_reorder(Tectonic_setting,TiO2)))+
  geom_boxplot()+
  scale_fill_viridis_d(option = "magma") +
  labs(title = "Viridis - Magma (A)",
       fill="Ambiente Tectônico")+
  theme(axis.title.x=element_blank(), # retirar nome escala
          axis.text.x=element_blank(), # retirar texto escala
          axis.ticks.x=element_blank()) # retirar ticks escala

ColorBrew

Paleta de cores “color-blind friendly” facilitando visualização universal de gráficos. O conjunto completo de cores pode ser obtido com a função display.brewer.all().

display.brewer.all()

A biblioteca colorbrewer divide suas pallete (conjunto de cores) em sequencial, divergente e categórico, usando a função scale_*_brewer() e o parâmetro pallete para determinar pallete.

BD %>% 
   drop_na(CaO,Na2O,MgO) %>% 
   ggplot(aes(x=CaO,
              y=Na2O,
              colour=MgO))+
  geom_point()+
  scale_color_distiller(palette = "Greens",direction = 1) +
  labs(title = "Sequencial Greens") -> A

BD %>% 
   drop_na(CaO,Na2O,MgO) %>% 
   ggplot(aes(x=CaO,
              y=Na2O,
              colour=MgO))+
  geom_point()+
  scale_color_distiller(palette = "YlOrRd",direction = 1) +
  labs(title = "Sequencial YlOrRd") -> B


BD %>% 
  drop_na(Cu) %>% 
  ggplot(aes(x=fct_reorder(Tectonic_setting,Cu),
             y=Cu,
             fill=fct_reorder(Tectonic_setting,Cu)))+
  geom_boxplot()+
  scale_fill_brewer(palette = "Set1",direction = 1) +
  scale_y_log10()+
  labs(title = "Categórico Set1",
       fill="Ambiente\nTectônico")+
  theme(axis.title.x=element_blank(), # retirar nome escala
          axis.text.x=element_blank(), # retirar texto escala
          axis.ticks.x=element_blank())->C # retirar ticks escala

BD %>% 
  drop_na(Cu) %>% 
  ggplot(aes(x=fct_reorder(Tectonic_setting,Cu),
             y=Cu,
             fill=fct_reorder(Tectonic_setting,Cu)))+
  geom_violin()+
  scale_fill_brewer(palette = "Pastel1",direction = 1) +
  scale_y_log10()+
  labs(title = "Categórico Pastel1",
       fill="Ambiente\nTectônico")+
  theme(axis.title.x=element_blank(), # retirar nome escala
        axis.text.x=element_blank(), # retirar texto escala
        axis.ticks.x=element_blank())->D # retirar ticks escala

BD %>% 
  drop_na(Cu) %>% 
  ggplot(aes(x=SiO2,y=Al2O3,colour=log10(Cu)))+
  geom_point()+
  scale_colour_distiller(palette = "Spectral") +
  labs(title = "Divergente Spectral",
       fill="log10\nCobre")->E

BD %>% 
  drop_na(FeOt) %>% 
  filter(FeOt>0) %>% 
  ggplot(aes(x=SiO2,y=Al2O3,colour=FeOt))+
  geom_point()+
  scale_colour_distiller(palette = "RdBu") +
  labs(title = "Divergente RdBu",
       fill="Ferro\ntotal")->F1

A+B+C+D+E+F1+plot_layout(ncol=1)

Cores manuais

No banco de dados

BD %>% 
  select(Tectonic_setting,TiO2,Na2O) %>% 
 # 
  mutate(colors=case_when(
    Tectonic_setting=="CONVERGENT MARGIN"~"red",
    Tectonic_setting=="ARCHAEAN CRATONS"~"blue",
    Tectonic_setting=="OROGENIC BELT"~"yellow",
    TRUE~"black"
  )) %>% 
  ggplot(aes(x=TiO2,y=Na2O,color=colors))+
  geom_point()+
  scale_color_identity()

Vetor

cols<-c("red","blue","yellow")

BD %>% 
  filter(Tectonic_setting %in% c("CONVERGENT MARGIN", "ARCHAEAN CRATONS", "OROGENIC BELT") ) %>% 
ggplot(aes(x=TiO2,y=Na2O,color=Tectonic_setting))+
  geom_point()+
  scale_color_manual(values=cols)

Manuais

BD %>% 
  filter(Tectonic_setting %in% c("CONVERGENT MARGIN", "ARCHAEAN CRATONS", "OROGENIC BELT","RIFT VOLCANICS") ) %>% 
ggplot(aes(x=TiO2,y=Na2O,color=Tectonic_setting))+
  geom_point()+
  scale_color_manual(values=c("red","blue","yellow","black"))