First, we load the iris dataset using seaborn in python:
import seaborn as sns
import matplotlib.pyplot as plt
iris = sns.load_dataset('iris')
iris
## sepal_length sepal_width petal_length petal_width species
## 0 5.1 3.5 1.4 0.2 setosa
## 1 4.9 3.0 1.4 0.2 setosa
## 2 4.7 3.2 1.3 0.2 setosa
## 3 4.6 3.1 1.5 0.2 setosa
## 4 5.0 3.6 1.4 0.2 setosa
## .. ... ... ... ... ...
## 145 6.7 3.0 5.2 2.3 virginica
## 146 6.3 2.5 5.0 1.9 virginica
## 147 6.5 3.0 5.2 2.0 virginica
## 148 6.2 3.4 5.4 2.3 virginica
## 149 5.9 3.0 5.1 1.8 virginica
##
## [150 rows x 5 columns]
Then, we plot this in R:
library(ggplot2)
py <- reticulate::py # Interface to python session
ggplot(py$iris, aes(x = sepal_length, y = petal_length)) +
geom_point() +
stat_smooth(method = "lm") +
theme_bw(base_size = 13) +
ggtitle("Relationship between sepal and petal length in Iris")
## `geom_smooth()` using formula 'y ~ x'
Load the iris dataset using data() in R
data(iris)
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
Then, we plot this in python
# the "r" object is the interface to the R environment.
sns.regplot(
data=r.iris,
x="Sepal.Length",
y="Petal.Length"
).set(title="Relationship between sepal and petal length in Iris")
plt.show()