library(tidyverse)
library(ggpubr)
library(ggplot2)
library(grid)
library(gridExtra)
theme_set(
theme_bw(base_size=26) +
theme(legend.position = "top")
)
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
library("ggpubr")
p <- ggplot(mtcars, aes(mpg, wt)) +
geom_point() +
geom_smooth(method = lm) +
stat_cor(method = "pearson", label.x = 20)
p
ggplot(mpg, aes(cty, hwy)) +
geom_jitter(size = 2, width = 1, alpha=0.3)
ggplot(mpg, aes(cty, hwy)) +
geom_count()
df <- mtcars %>%
rownames_to_column() %>%
as_data_frame() %>%
mutate(cyl = as.factor(cyl)) %>%
select(rowname, wt, mpg, cyl)
df
## # A tibble: 32 x 4
## rowname wt mpg cyl
## <chr> <dbl> <dbl> <fct>
## 1 Mazda RX4 2.62 21 6
## 2 Mazda RX4 Wag 2.88 21 6
## 3 Datsun 710 2.32 22.8 4
## 4 Hornet 4 Drive 3.22 21.4 6
## 5 Hornet Sportabout 3.44 18.7 8
## 6 Valiant 3.46 18.1 6
## 7 Duster 360 3.57 14.3 8
## 8 Merc 240D 3.19 24.4 4
## 9 Merc 230 3.15 22.8 4
## 10 Merc 280 3.44 19.2 6
## # ... with 22 more rows
ggplot(df, aes(x = reorder(rowname, mpg), y = mpg)) +
geom_col() +
rotate_x_text(angle = 65)+xlab("model")
ggplot(df, aes(x = reorder(rowname, mpg), y = mpg)) +
geom_col( aes(fill = cyl)) +
geom_text(aes(label = mpg), nudge_y = 2) +
coord_flip() +
scale_fill_viridis_d()+xlab("model")
library(ggforce)
theme_set(
theme_bw(base_size=20) +
theme(legend.position = "top")
)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
ggplot(iris, aes(Petal.Length, Petal.Width, colour = Species)) +
geom_point(cex=3) +
facet_zoom(x = Species == "versicolor")
library("ggalt")
circle.df <- iris %>% filter(Species == "setosa")
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_point(aes(colour = Species), cex=4) +
geom_encircle(data = circle.df, linetype = 4)
p= ggscatterhist(
iris, x = "Sepal.Length", y = "Sepal.Width",
color = "Species", size = 6, alpha = 0.6,
palette = c("#798E87", "#046C9A", "#C7B19C"),
margin.params = list(fill = "Species",
color = "black", size = 1)
)
ggpar(p,
font.main = c(25,"bold.italic", "red"),
font.x = c(20, "bold", "black"),
font.y = c(20, "bold", "black"),
font.xtickslab= c(14, "black"),
font.ytickslab= c(14, "black"),
font.legend= c(18, "black")
)
p= ggscatterhist(
iris, x = "Sepal.Length", y = "Sepal.Width",
color = "Species", size = 3, alpha = 0.6,
palette = c("#798E87", "#046C9A", "#B40F20"),
margin.plot = "boxplot",
ggtheme = theme_bw()
)
ggpar(p,
font.main = c(25,"bold.italic", "red"),
font.x = c(20, "bold", "black"),
font.y = c(20, "bold", "black"),
font.xtickslab= c(14, "black"),
font.ytickslab= c(14, "black"),
font.legend= c(18, "black")
)
library(ggridges)
p= ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(aes(fill = Species)) +
scale_fill_manual(values = c("#798E87", "#046C9A", "#B40F20"))
ggpar(p,
font.main = c(25,"bold.italic", "red"),
font.x = c(20, "bold", "black"),
font.y = c(20, "bold", "black"),
font.xtickslab= c(14, "black"),
font.ytickslab= c(14, "black"),
font.legend= c(18, "black")
)
# Data
df3 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
dose=rep(c("D0.5", "D1", "D2"),2),
len=c(6.8, 15, 33, 4.2, 10, 29.5))
# Line plot
ggplot(df3, aes(x = dose, y = len, group = supp)) +
geom_line(aes(linetype = supp), cex=2) +
geom_point(aes(shape = supp))
# Data preparation
df <- economics %>%
select(date, psavert, uempmed) %>%
gather(key = "variable", value = "value", -date)
head(df, 3)
## # A tibble: 3 x 3
## date variable value
## <date> <chr> <dbl>
## 1 1967-07-01 psavert 12.6
## 2 1967-08-01 psavert 12.6
## 3 1967-09-01 psavert 11.9
theme_set(
theme_bw(base_size=26) +
theme(legend.position = "top")
)
ggplot(df, aes(x = date, y = value)) +
geom_line(aes(color = variable), size = 3) +
scale_color_manual(values = c("#00AFBB", "#E7B800"))
library(ggplot2)
library(ssdtools)
data(boron_data)
fit <- ssd_fit_dists(boron_data, dists = c("llogis", "lnorm", "gamma"))
fit.plot <- autoplot(fit)
fit.plot
#### https://github.com/adamribaudo/storytelling-with-data-ggplot/tree/master/plot%20output