set.seed(11)
rbinom(2500, 1, 0.5) -> ipp
df <- data.frame(
sex=factor(rep(c("A", "B"), each=2500)), weight=round(c(rnorm(2500, mean=55, sd=5),
ipp* rnorm(2500, mean=65, sd=5) + (1-ipp)*rnorm(2500, mean=45, sd=3) ) ))
head(df) ; tail(df)| sex | weight | |
|---|---|---|
| <fct> | <dbl> | |
| 1 | A | 56 |
| 2 | A | 64 |
| 3 | A | 57 |
| 4 | A | 48 |
| 5 | A | 55 |
| 6 | A | 57 |
| sex | weight | |
|---|---|---|
| <fct> | <dbl> | |
| 4995 | B | 65 |
| 4996 | B | 45 |
| 4997 | B | 68 |
| 4998 | B | 66 |
| 4999 | B | 47 |
| 5000 | B | 63 |
library(ggplot2)
ggplot(df, aes(x=weight)) + geom_histogram()
library(ggplot2)
ggplot(df, aes(x=weight)) + geom_histogram() + geom_histogram(color='blue', fill='orange')`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
library(ggplot2)
ggplot(df, aes(x=weight)) + geom_histogram(binwidth=10, color="blue", fill="orange")
library(ggplot2)
ggplot(df, aes(x=weight)) + geom_histogram(binwidth=0.1, color="blue", fill="orange")
ggplot(df, aes(x=weight)) + geom_histogram(aes(y=..density..),
colour="black", fill="white")+ geom_density(alpha=.2, fill="#FF6666") +
# geom_histogram(position="dodge")+
theme(legend.position="top") + theme(axis.title = element_text(size=20),
legend.title = element_text(size=20),
legend.text = element_text(size=20), axis.text = element_text(size=20))Warning message:
“The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.”
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df, aes(x=weight, color=sex, fill=sex)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom_density(alpha= 0.2, fill="#FF6666") +
theme(legend.position="top") +
theme(axis.title = element_text(size=20), legend.title = element_text(size=20),
legend.text = element_text(size=20), axis.text = element_text(size=20))`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
head(OrchardSprays)
ggplot(OrchardSprays, aes(x=factor(treatment), y=decrease, fill=factor(treatment)))+
geom_violin(adjust=0.5, scale='width', color = rainbow(1))+
geom_boxplot(size=1, width=0.5)+ theme_minimal()| decrease | rowpos | colpos | treatment | |
|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <fct> | |
| 1 | 57 | 1 | 1 | D |
| 2 | 95 | 2 | 1 | E |
| 3 | 8 | 3 | 1 | B |
| 4 | 69 | 4 | 1 | H |
| 5 | 92 | 5 | 1 | G |
| 6 | 90 | 6 | 1 | F |
data(VADeaths)
# par(mfrow=c(1,1))
colnames(VADeaths) = c('rM','rF','uM','uF')
barplot(round(VADeaths[1,]), col=2:5, border='white', ylim=c(0,25), main= 'VADeaths (50-54)')
require(ggplot2) ; data(UCBAdmissions) ;
str(UCBAdmissions)
addmargins(apply(UCBAdmissions, c(2,1), sum))
require(graphics)
mosaicplot(apply(UCBAdmissions, c(2,1), sum),
color=c('gray','red'), main ='UC Berkeley Admissions' ) 'table' num [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
- attr(*, "dimnames")=List of 3
..$ Admit : chr [1:2] "Admitted" "Rejected"
..$ Gender: chr [1:2] "Male" "Female"
..$ Dept : chr [1:6] "A" "B" "C" "D" ...| Admitted | Rejected | Sum | |
|---|---|---|---|
| Male | 1198 | 1493 | 2691 |
| Female | 557 | 1278 | 1835 |
| Sum | 1755 | 2771 | 4526 |
require(graphics)
mosaicplot(apply(UCBAdmissions, c(2,1), sum),
color=c('gray','red'), main ='UC Berkeley Admissions' )
mosaicplot(~Dept+Gender+Admit, data=UCBAdmissions,
color=c('gray','red'), dir = c('v','v','h'),
main ='UC Berkeley Admissions' )
library(treemap)
data(GNI2014) ; str(GNI2014)
treemap(GNI2014, index = c('continent','iso3'),
vSize='population', vColor='GNI', type='value')'data.frame': 188 obs. of 5 variables:
$ iso3 : chr "BMU" "NOR" "QAT" "CHE" ...
$ country : chr "Bermuda" "Norway" "Qatar" "Switzerland" ...
$ continent : Factor w/ 8 levels "Africa","Antarctica",..: 5 4 3 4 3 4 6 4 4 5 ...
$ population: num 67837 4676305 833285 7604467 559846 ...
$ GNI : int 106140 103630 92200 88120 76270 75990 64540 61610 61310 55200 ...
GNI2014$GNI.total = GNI2014$GNI * GNI2014$population
GNI2014.a = aggregate(GNI2014[,4:6], by = list(GNI2014$continent), sum)
GNI2014.a$GNI = GNI2014.a$GNI.total/GNI2014.a$population
treemap(GNI2014.a, index = c('Group.1'), vSize='population',
vColor='GNI', type='value' )