How to group by consecutive rows in a R dataframe?

I have a dataframe with columns TimeStamp, Type, Value in time series data. Type refers to whether it is a peak or valley. I want to:

Group all data by consecutive types For groups of "peak" type I want to select the highest For groups if "valley" type I want to select the lowest Filter the dataframe by these highest/lowest Expectation: I would have a dataframe that alternated each row between the highest peak and lowest valley.

The only way I know how to do this is by using a for loop and then adding consecutive values into a vector and then getting the max, then shoving this in a new dataframe and so on.

For those who know python, this is what I did in that (I need to transfer my code to R though):

segmentation['min_v'] = segmentation.groupby( segmentation.pv_type.ne(segmentation.pv_type.shift()).cumsum() ).price.transform(min)
segmentation['max_p'] = segmentation.groupby( segmentation.segmentation.pv_type.ne(segmentation.pv_type.shift()).cumsum() ).price.transform(max)

EDIT

Sample data set:

types <- c('peak', 'peak', 'valley', 'peak', 'valley', 'valley', 'valley')
values <- c(1.01,   1.00,    0.4,     1.2,     0.3,      0.1,      0.2)
segmentation <- data.frame(types, values)
segmentation

expectedTypes <- c('peak', 'valley', 'peak', 'valley')
expectedValues <- c(1.00, 0.4, 1.2, 0.1 )
expectedResult <- data.frame(expectedTypes, expectedValues)
expectedResult

I dont know a better way to generate the data.