library(ggpubr)
# Define color palette
my_cols <- c("#0D0887FF", "#6A00A8FF", "#B12A90FF",
"#E16462FF", "#FCA636FF", "#F0F921FF")
# Standard contingency table
#:::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Read a contingency table: housetasks
# Repartition of 13 housetasks in the couple
data <- read.delim(
system.file("demo-data/housetasks.txt", package = "ggpubr"),
row.names = 1
)
data Wife Alternating Husband Jointly
Laundry 156 14 2 4
Main_meal 124 20 5 4
Dinner 77 11 7 13
Breakfeast 82 36 15 7
Tidying 53 11 1 57
Dishes 32 24 4 53
Shopping 33 23 9 55
Official 12 46 23 15
Driving 10 51 75 3
Finances 13 13 21 66
Insurance 8 1 53 77
Repairs 0 3 160 2
Holidays 0 1 6 153
# Basic ballon plot
ggballoonplot(data) 
# Change color and fill
ggballoonplot(data, color = "#0073C2FF", fill = "#0073C2FF")
改变颜色
# Change color according to the value of table cells
ggballoonplot(data, fill = "value")+
scale_fill_gradientn(colors = my_cols)
改变形状
# Change the plotting symbol shape
ggballoonplot(data, fill = "value", shape = 23)+
gradient_fill(c("blue", "white", "red"))
# Set points size to 8, but change fill color by values
# Sow labels
ggballoonplot(data, fill = "value", color = "lightgray",
size = 10, show.label = TRUE)+
gradient_fill(c("blue", "white", "red"))
# Streched contingency table
#:::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Create an Example Data Frame Containing Car x Color data
carnames <- c("bmw","renault","mercedes","seat")
carcolors <- c("red","white","silver","green")
datavals <- round(rnorm(16, mean=100, sd=60),1)
car_data <- data.frame(Car = rep(carnames,4),
Color = rep(carcolors, c(4,4,4,4) ),
Value=datavals )
car_data Car Color Value
1 bmw red 79.7
2 renault red 51.2
3 mercedes red 134.3
4 seat red 58.5
5 bmw white 181.9
6 renault white 12.8
7 mercedes white 197.5
8 seat white 82.2
9 bmw silver 49.3
10 renault silver 10.9
11 mercedes silver 91.4
12 seat silver 150.4
13 bmw green 195.6
14 renault green 180.4
15 mercedes green 166.7
16 seat green 126.9ggballoonplot(car_data, x = "Car", y = "Color",
size = "Value", fill = "Value") +
scale_fill_gradientn(colors = my_cols) +
guides(size = FALSE)
# Grouped frequency table
#:::::::::::::::::::::::::::::::::::::::::::::::::::::::::
data("Titanic")
dframe <- as.data.frame(Titanic)
head(dframe) Class Sex Age Survived Freq
1 1st Male Child No 0
2 2nd Male Child No 0
3 3rd Male Child No 35
4 Crew Male Child No 0
5 1st Female Child No 0
6 2nd Female Child No 0ggballoonplot(
dframe, x = "Class", y = "Sex",
size = "Freq", fill = "Freq",
facet.by = c("Survived", "Age"),
ggtheme = theme_bw()
)+
scale_fill_gradientn(colors = my_cols)
# Hair and Eye Color of Statistics Students32 ggbarplot
data(HairEyeColor)
ggballoonplot( as.data.frame(HairEyeColor),
x = "Hair", y = "Eye", size = "Freq",
ggtheme = theme_gray()) %>%
facet("Sex") 
以我自己数据为例
head(data_plot) Outcome Pesticides Time Method time logp group2
1024 commun Chlorpyrifos_ethyl_log 1year ASQ 1year 1.03 P>=0.05
1025 commun Dimethipin_log 1year ASQ 1year 0.79 P>=0.05
1026 commun Formothion_log 1year ASQ 1year 0.16 P>=0.05
1027 commun Dimethoate_log 1year ASQ 1year 0.20 P>=0.05
1028 commun Diphenamid_log 1year ASQ 1year 0.21 P>=0.05
1029 commun Mirex_log 1year ASQ 1year 0.26 P>=0.05 data_plot$Time<-ordered(data_plot$Time,levels=c("1year","18month","2year"))
ggballoonplot(data_plot, x = "Time",y = "Pesticides",fill = "logp",
shape=23,
#show.label = T,
size = "logp")+#,
geom_point(aes(shape=group2,size=1))+
scale_shape_manual(values=c("*", ""))+
# facet.by = c("Outcome"))+
# scale_fill_viridis_c(option = "C")+
# scale_color_gradient(low="#00AFBB",high = "#FC4E07")+
gradient_fill(c("#00AFBB", "white", "#FC4E07"))+
#scale_color_discrete()+
scale_y_discrete(limits=rev(pes_name),labels=rev(pes_name2))+
scale_x_discrete(limits=c("1year","18month","2year"))+
facet_wrap(~ Outcome, ncol=5)+
labs(fill="-log10(P)")
Reference: