Social media check-in services have enabled people to share their activity-related choices, providing a new source of human activity and social networks data. Geolocation data from these services offer us information, in new ways, to understand social influence on individual choices. In this paper, we investigate the extent of social influence on individual activity and lifestyle choices from social media check-in data. We first collect user check-ins and their social network information by linking two social media systems (Twitter and Foursquare) and analyze the structure of the underlying social network. We next infer user check-in and geo lifestyle patterns using topic models. We analyze the correlation between the social relationships and individual-level patterns. We investigate whether or not two individuals have similar activity choice and geo lifestyle patterns, if they are socially connected. We find that the similarity between two users, in their check-in behavior and lifestyle patterns, increases with the increase of the friendship probability.