In R, can I integrate different classifying algorithms in one bagging model? I use R to do data analysis. I have a dataset. When I use different classifying algorithms, such as random forest, SVM, etc, I have the different accuracy. So, I want to integrate all the algorithms into one framework, let's say adaboost.  

We know that adaboost framework use multiple "weak" classifying algorithms to combine a strong classifier. So, can I customize the "weak" classifying algorithms as I want? Here is just my current idea: In this framework, I use SVM first. Then give the data that are classified incorrectly more weights. Then, I use random forest. ... In the end, all the classifiers in this framework will work together. 

This is just what I think about this issue. If there is other method working such as voting, please let me know too.

Any help is appreciated.
What you're looking for is called an ensemble model which means it is a compilation of several models to improve the results. This is a very common technique for winners in Kaggle competitions. Since you're using R and caret is a popular way to do ML in R, here's a package just for that purpose on caret:

https://cran.r-project.org/web/packages/caretEnsemble/vignettes/caretEnsemble-intro.html