Aula 16 outubro 2020 - ggplot & patchwork
Professor Marcus H Jones
Prática em Software de Pesquisa Médica - Turma 2020/2
mt <- mtcars
mt## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Explorando ggplot
# carregando tidyverse
library(tidyverse)## ── Attaching packages ──────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# criando grafico disp x hp com pipe e ggplot
mt %>% ggplot(aes(disp, hp))+geom_point()
# obtendo o mesmo resultado sem usar o pipe
ggplot(mt, aes(disp, hp))+geom_point()
# mudando o tema
ggplot(mt, aes(disp, hp))+geom_point()+theme_bw()
# atribuindo cor aos pontos
ggplot(mt, aes(disp, hp))+geom_point(col= "Red")+theme_bw()
# mudando o tamanho dos pontos
ggplot(mt, aes(disp, hp))+geom_point(size=4, col= "Red")+theme_bw()
# criando grafico de disp x mpg com triangulos azuis
ggplot(mt, aes(disp, mpg))+geom_point(size=4, col= "Blue", shape=17)+theme_bw()
# criando grafico de hp x mpg com definicao de cor e formatos
ggplot(mt, aes(hp, mpg))+geom_point(size=2, col= "#49A556FF", shape=15)+theme_bw()
# mudando tamanho, formato e cor
ggplot(mt, aes(hp, mpg))+geom_point(size=5, col= "Tomato2", shape=18)+theme_bw()
# colocando nomes nos eixos (Potencia do Motor e Milhas por Galao)
ggplot(mt, aes(hp, mpg))+geom_point(size=5, col= "Tomato2", shape=18)+theme_bw()+labs(y="Milhas por Galao", x="Potencia do motor (hp)" )
Gráfico boxplot
# convertendo gear em fator
mt$gear <- as.factor(mt$gear)
# criando grafico boxplot de gear x mpg
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Orange3")+theme_bw()
# mudando cor
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Tomato")+theme_bw()
# definindo cores por nomes ---> colors()
ggplot(mt, aes(gear, mpg, gear))+geom_boxplot(fill=c("royalblue", "steelblue", "lightblue"))+theme_bw()
# Definindo cores por hexadecimal
ggplot(mt, aes(gear, mpg, gear))+geom_boxplot(fill=c("#03256C", "#1768AC", "#06BEE1"))+theme_bw()
# Acrecentando nova camada de dados com geom_point
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Tomato")+geom_point(size=3)+theme_bw()
# Acrecentando nova camada de dados, agora com geom_jitter
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Tomato")+geom_jitter()+theme_bw()
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Tomato")+geom_jitter(width=0.1, size=3)+theme_bw()
# adicionando tranparencia com alpha em tons de azul
ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="steelblue1", alpha=0.6)+geom_jitter(width=0.1, size=3, col="royalblue", alpha=0.9)+theme_bw()
# mudando para violin
ggplot(mt, aes(gear, mpg))+geom_violin(fill="steelblue1", alpha=0.6)+geom_jitter(width=0.1, size=3, col="royalblue", alpha=0.9)+theme_bw()
Trabalhando com patchwork
# instalar package patchwork
# install.packages("patchwork")
# carregando library patchwork
library(patchwork)
# criando objetos a1 a2 a3 a4 com os graficos
a1 <- ggplot(mt, aes(disp, hp))+geom_point(size=2, col= "Red")+theme_bw()
a2 <- ggplot(mt, aes(disp, mpg))+geom_point(size=2, col= "Blue", shape=17)+theme_bw()
a3 <- ggplot(mt, aes(hp, mpg))+geom_point(size=2, col= "#49A556FF", shape=15)+theme_bw()
a4 <- ggplot(mt, aes(gear, mpg))+geom_boxplot(fill="Orange3")+theme_bw()
# criando figuras combinadas
a1+a2
a3+a4
a1/(a2+a3+a4)
a1+a2 / (a3+a4)
(a1+a2)/(a3+a4)
# acrescentando tags
a1+a2+plot_annotation(tag_levels = "A")
a3+a4+plot_annotation(tag_levels = "a")
a1/(a2+a3+a4)+plot_annotation(tag_levels = 1)
a1+a2 / (a3+a4)+plot_annotation(tag_levels = "I")
(a1+a2)/(a3+a4)+plot_annotation(tag_levels = "A")
# Acrescentando tag e prefixo
a1/(a2+a3+a4)+plot_annotation(tag_levels = 1, tag_prefix = "Fig. ")
Linha de tendencia
ggplot(mt, aes(disp, hp))+geom_point()+geom_smooth()+theme_bw()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(mt, aes(disp, hp))+geom_point()+geom_smooth(method = lm, size= 2, col="gray35")+theme_bw()## `geom_smooth()` using formula 'y ~ x'
colocando rotulos nos eixos x & y
# Adicionando titulos aos eixos
ggplot(mt, aes(disp, hp))+geom_point()+geom_smooth(method=lm, size= 2, col="gray35")+theme_bw()+labs(x="Displacement (cu.in.)", y="Gross horsepower (hp)")## `geom_smooth()` using formula 'y ~ x'
# alterando tamanho da fonte no grafico
ggplot(mt, aes(disp, hp))+geom_point()+geom_smooth(method=lm, size= 2, col="gray35")+theme_bw(base_size = 20)+labs(x="Displacement (cu.in.)", y="Gross horsepower (hp)")## `geom_smooth()` using formula 'y ~ x'
Salvando arquivo mt como arquivo .Rdata
# salvando arquivo como .Rdata no diretorio de trabalho
save(mt, file="mt.Rdata")
# carregando arquivo mt.Rdata do diretorio de trabalho
load("mt.Rdata")