check this package
one more cool thing is 3d plotting
library(tidyr)
library(dplyr)
data("EuStockMarkets")
df<-as.data.frame(EuStockMarkets) %>%
gather(index, price) %>%
mutate(time = rep(time(EuStockMarkets),4))
plot_ly(df, x = ~time, y = ~price, color = ~index, mode = "lines")
plot_ly(iris, y = ~Sepal.Length, color = ~Species, type = "box")
which cars are close in terms of (normalized) Euclidean distance
map<-as.matrix(dist(scale(mtcars)))
plot_ly(z = ~map, type = "heatmap")
for this the base heatmap is actualy better
map<-as.matrix(dist(scale(mtcars)))
heatmap(map)
mat<-matrix(NA,100,100)
for (i in 1:100){
vec<-rnorm(100, mean = i, sd = i/10)
mat[ ,i]<-vec
}
for (i in 1:100){
mat[i,]<-mat[i,]+rnorm(100, mean = (100-i)^0.9, sd = 100/i)
}
plot_ly(z = ~mat, type = "surface")
library(ggplot2)
g<-ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width))+
geom_point()+
facet_grid(Species~.)+
geom_smooth(method = "lm")
ggplotly(g)
bonus:
during my studies I have worked with data from thr Israeli Central Bureau of Statistics (CBS), and I played with Israelies wage data.
thats it for now