class: center, middle, inverse, title-slide # Mini-Lecture 29 ## MP3 presentations ### Ben Baumer ### SDS 192Apr 15, 2019(
http://beanumber.github.io/sds192/lectures/28-mp3_workshop.html
) --- ## Extra Credit talk .pull-left[ - Stephanie Hicks Johns Hopkins Bloomberg School of Public Health Thursday, April 18th, 6 pm Seelye 106 ] .pull-right[ ![](https://www.smith.edu/sds/images/stephanie_hicks_poster_500px.jpg) ] --- ## `MULTIPOINT` ```r library(tidyverse) library(sf) library(macleish) x <- st_intersection( pluck(macleish_layers, "trails"), pluck(macleish_layers, "contours_30ft") ) western <- filter(x, name == "Western Loop") western_pts <- western %>% st_cast("MULTIPOINT") %>% st_cast("POINT") nrow(western) nrow(western_pts) ``` --- ## Takeaways from this module (I hope) you learned: -- - how to work with spatial data -- - appreciation for the inherent complexities - projections - size of the data - spatial join complexities -- - joy of visualizing data spatially -- - working with big tools (e.g. Leaflet) -- - the value of getting outside (cool stuff at MacLeish)!! <!-- > - how to write SQL queries > - new skills built atop old skills > - the joys and frustrations of remote RBDMSs > - strengths and weaknesses of SQL vs. R/Python > - appreciation for scalability/big data issues --> --- class: center, middle # Presentations --- ## Organization 1. Self-organize into groups of 3 teams - **by topic** 2. Find a spot --- ## Peer-review - at most 15 minutes per team - "Tell a story---not necessarily the whole story..." - Fill out peer-review form: - https://docs.google.com/forms/d/1EoD7mGhEPGiQyBvJvs4X4aQdyd-GzQ2wHXWxmhbEgOI/ - one review per team per presentation - reconvene by 12 pm --- class: center, middle # Go! --- ## SQL is up next... - [Homework #7 is posted](../hw/hw_sql.html) - [Mini-project #3](../mod_spatial.html) due Tuesday just before midnight