Pros:
Convenient, mirrors how we think of building plots and analyzing data
Cons:
Can’t go back once plot has started (i.e. to adjust margins); need to plan in advance Difficult to “translate” to others once a new plot has been created (no graphical “language”). Plot is just a series of R commands
plot(x = pressure$temperature, y= pressure$pressure,
type = "b", col = "blue", pch = 20,
xlab = "Temperature", ylab = "Pressure", main = "Plot Example")
Plots are created with a single function call (xyplot, bwplot, etc.)
Pros:
Most useful for conditioning types of plots: Looking at how y changes with x across levels of z Thinks like margins/spacing set automatically because entire plot is specified at once Good for putting many many plots on a screen Cons:
Sometimes awkward to specify an entire plot in a single function call Annotation in plot is not intuitive Use of panel functions and subscripts difficult to wield and requires intense preparation Cannot ‘add’ to the plot once it’s created
library(lattice)
Depth <- equal.count(quakes$depth, number=8, overlap=.1)
xyplot(lat ~ long | Depth, data = quakes)
states <- data.frame(state.x77,
state.name = dimnames(state.x77)[[1]],
state.region = state.region)
xyplot(Murder ~ Population | state.region, data = states,
groups = state.name,
panel = function(x, y, subscripts, groups) {
ltext(x = x, y = y, labels = groups[subscripts], cex=1,
fontfamily = "HersheySans")
})
library(ggplot2)
str(mpg)## Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: chr "audi" "audi" "audi" "audi" ...
## $ model : chr "a4" "a4" "a4" "a4" ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr "f" "f" "f" "f" ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr "p" "p" "p" "p" ...
## $ class : chr "compact" "compact" "compact" "compact" ...qplot(displ, hwy, data = mpg)
qplot(displ, hwy, data = mpg, color = drv)
qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))
qplot(hwy, data = mpg)
qplot(displ, hwy, data = mpg, facets = drv ~.)
qplot(hwy, data = mpg, facets = drv ~.)
g <- ggplot(mpg, aes(class))
g <- g + geom_bar()
print(g)
g <- g + geom_bar(aes(fill = drv), color = "black")
print(g)
g <- g + scale_fill_manual(values=c("DarkMagenta", "DarkOrange", "DodgerBlue"),
name = "",
labels = c("4-wheel drive","front-wheel drive","rear wheel drive"))
print(g)
g <- g + labs(x = "Vehicle Class", y = "Number of Cars", title = "Example of ggplot2", subtitle = "Meetup, Phoenix, 2018")
print(g)
g <- g + coord_flip()
print(g)
library(scales)
t <- table(mpg$class)
t##
## 2seater compact midsize minivan pickup subcompact
## 5 47 41 11 33 35
## suv
## 62text.df <- data.frame(class = names(t), count = as.numeric(t), rate = percent(as.numeric(t)/ sum(as.numeric(t))))
text.df ## class count rate
## 1 2seater 5 2.1%
## 2 compact 47 20.1%
## 3 midsize 41 17.5%
## 4 minivan 11 4.7%
## 5 pickup 33 14.1%
## 6 subcompact 35 15.0%
## 7 suv 62 26.5%g <- g + geom_text(data = text.df, aes(x = class,y = count, label = rate), hjust=-0.5, fontface = "bold")
g <- g + ylim(0, 90)
print(g)
library(dplyr)
g <- ggplot(mpg %>%
inner_join(text.df %>% mutate(lbl = as.factor(count)),"class"), aes(x= lbl))
g <- g + geom_bar()
g <- g + geom_bar(aes(fill = drv), color = "black")
g <- g + scale_fill_manual(values=c("DarkMagenta", "DarkOrange", "DodgerBlue"),
name = "",
labels = c("4-wheel drive","front-wheel drive","rear wheel drive"))
g <- g + labs(x = "Vehicle Class", y = "Number of Cars", title = "Example of ggplot2", subtitle = "Meetup, Phoenix, 2018")
g <- g + coord_flip()
g <- g + geom_text(data = text.df, aes(x = as.factor(count),y = count, label = rate), hjust=-0.5, fontface = "bold")
g <- g + ylim(0, 80)
g <- g + scale_x_discrete(labels = levels(reorder(text.df$class, text.df$count)))
print(g)
p <- g + theme_dark()
p <- p + geom_text(data = text.df, aes(x = as.factor(count),y = count, label = rate), hjust=-0.5, fontface = "bold", color = "yellow")
print(p)
p <- g + theme_bw()
print(p)
p <- g + theme(legend.position = "right",
strip.text.x = element_text(family = "sans", face = "bold"),
plot.background = element_rect(fill = "white"),
panel.background = element_rect(fill="White", color = "black"),
panel.border = element_rect(fill=NA, color = "black"),
panel.grid.major = element_line(colour = "LightGrey"),
axis.text.y = element_text(family = "sans", colour = "black", size = 10, face = "bold"),
axis.text.x = element_text(family = "sans", colour = "black", size = 8),
panel.grid.major.y = element_line(colour = "LightGrey", linetype = 3),
axis.ticks = element_blank())
p <- p + xlab("")
plot(p)
