# 1. 调用自定义函数
robjects.r('''
        f <- function(r){pi * r}
        ''')
t3=robjects.r['f'](3) # 3 为传递的参数
print('%.3f'%t3[0])

# 复杂的例子
r_script = ''' 
library(randomForest) # 导入随机森林包
## use data set iris
data = iris # 使用鸢尾花数据集
table(data$Species)
## create a randomForest model to classfy the iris species
# 创建随机森林模型给鸢尾花分类
iris.rf <- randomForest(Species~., data = data, importance=T, proximity=T)
print('--------here is the random model-------')
print(iris.rf)
print('--------here is the names of model-----')
print(names(iris.rf))
confusion = iris.rf$confusion
print(confusion)
'''
robjects.r(r_script)

# 2. 调用R自带的函数
# internal function in R
t4=robjects.r['ls']()#可使用 ls()函数列出工作区中的所有变量；ls() 函数可以使用模式来匹配变量名称，eg：ls(pattern="var")，列出以“var”模式开头的变量
print(t4[0])

# another internal function
l = robjects.r['letters']
print(l)
print(len(l))
print(robjects.r['paste'](l, collapse = '-'))
output:
  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
  [20] "t" "u" "v" "w" "x" "y" "z"
  26
  [1] "a-b-c-d-e-f-g-h-i-j-k-l-m-n-o-p-q-r-s-t-u-v-w-x-y-z"

# an alternative way of getting 'paste' function in R
# eval the R code
coder = 'paste(%s, collapse = "-")' % (l.r_repr())
print(robjects.r(coder))
[1] "a-b-c-d-e-f-g-h-i-j-k-l-m-n-o-p-q-r-s-t-u-v-w-x-y-z"

# 3. 执行R的脚本文件
robjects.r.source('/home/rongzhengqin2/learn/rpy2/test01.r')
x = robjects.r('x')#获取脚本里的变量
y = robjects.r('y')
print(x) #[1] 1 2 3 4
print(y) #[1]  1  4  9 16

# 4. 载入和使用R包
from rpy2.robjects.packages import importr
stats = importr('stats')
print('stats.rnorm(10):',stats.rnorm(10))
output:
  stats.rnorm(10):  [1] -0.02499746  1.92827632  0.93832232  0.62033276  2.15107656 -0.26479198
   [7]  0.59109714 -0.34845466  0.32339794 -1.78048630