Annotations:
filter() only includes rows where the condition is TRUE; it excludes both FALSE and NA values
Missing values are always sorted at the end:
cumsum(), cumprod(), cummin(), cummax()
Offsets: lead() and lag() allow you to refer to leading or lagging values.
Instead of relying on ==, use near():
(flights, time_hour, air_time, everything())
5.4 Select columns with select()
rename(flights, tail_num = tailnum)
matches x1, x2 and x3
Annotations:
Search for ECS Optimized to locate the Amazon ECS-Optimized AMI. Amazon created this image for use with its EC2 Container Service. We won't be using ECS and will opt instead to use Docker and later, Docker Swarm. This choice will allow you to use the skills you acquire here on other cloud providers such as Google Cloud and Microsoft's Azure. The reason we're using an ECS Optimized AMI is because it has Docker pre-installed!
Annotations:
You can avoid this type of repetition by passing a set of mappings to ggplot(). ggplot2 will treat these mappings as global mappings that apply to each geom in the graph. In other words, this code will produce the same plot as the previous code:
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_point(mapping = aes(color = class)) + geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
facet_grid(drv ~ cyl)
coord_quickmap() sets the aspect ratio correctly for maps. This is very important if you’re plotting spatial data with ggplot2 (which unfortunately we don’t have the space to cover in this book).
3.5 Facets
group = drv
If you place mappings in a geom function, ggplot2 will treat them as local mappings for the layer. It will use these mappings to extend or overwrite the global mappings for that layer only. This makes it possible to display different aesthetics in different layers.
ggplot2 provides over 20 stats for you to use. Each stat is a function, so you can get help in usual way, e.g. ?stat_bin. To see a complete list of stats, try the ggplot2 cheatsheet.
.prop.., group = 1
Annotations:
Cell segmentation, cell tracking and phenotype quantification. Continuous long-term imaging of single cells at high spatiotemporal resolution provides researchers with voluminous multidimensional, information-rich data sets. These can be mined to analyze a large number of cell phenotypical attributes, including molecular dynamics, morphology and behavior, and relationships and evolving dynamics across generations (Fig. 3). The wealth of these data can be fully explored only with the use of efficient automatic cell segmentation, tracking and phenotype quantification algorithms. For segmentation, a large number of approaches have been developed to date70, 71, but most methods are not broadly generalizable and are currently limited to the specific types of experiments, cells or images for which they were designed. Unfortunately, as others have said, “rather than converging to a robust, unified solution, it thus seems that the field is diverging, and by now almost as many cell segmentation methods have been developed as there exist cell analysis problems”72.