This is so simple and how dummy coding works but I never thought about using mean to find proportions of something. Today Dason added to a post I gave to an R user about find proportions of a value and his code was much simpler and faster.
Code:
x <- rnorm(1000, 5, 1.75)
sum(x > 0)/length(x) #gives the proportion using binary operator
mean(x > 0) #Dason way that also gives the proportion using binary operator
test replications elapsed relative user.self sys.self user.child sys.child
2 dason 100000 3.46 1.362205 3.13 0.00 NA NA
1 trinker 100000 2.54 1.000000 2.11 0.07 NA NATIL: RStudio has a neat little way to add some gWidgets type functionality to plots through their own library (only available if using RStudio).
http://www.rstudio.org/docs/advanced/manipulate
http://www.rstudio.org/docs/advanced/manipulate
Although I won't post code, I also learned today how to interpret (and use) model-averaging for Generalized Additive Models (package mgcv). Once I sat down with it, it was relatively simple. Allowed me to examine exactly which parts of my 3-D selection surfaces had a positive or negative effect (by examining confidence intervals on the link scale). Model-selection and model-averaging with GAM and GAMM is a bee-otch.
Anyway, just needed to tell someone about the small steps made today!
Anyway, just needed to tell someone about the small steps made today!
I love base and ggplot for different reasons. They're different tools for different jobs. I do construction work around the house and have a nice array of tools. I just bought a new osculating tool that saws things off flush and used it last night to saw off some trim flush. I'm glad I have this tool. It's awesome and saves time. I won't be using it to pound in nails anytime soon though. I'm not an either or kinda guy. I'm a pragmatist, best tool for the job. I'm going to eventually learn lattice as well because I believe under certain circumstances this is the best tool for the job. Why limit yourself to one visualization technique?
I stand firm and tall; after all a velociraptor's gotta stand for something.
I'm calling out the TE.
View attachment 1716
Challenge: gather the same visualization information in base in less than 1 min 55 sec
I stand firm and tall; after all a velociraptor's gotta stand for something.
I'm calling out the TE.
Code:
library(data.table)
begin.time <- Sys.time()
require(ggplot2)
p <- ggplot(mtcars, aes(hp, mpg))
p + geom_point(aes(colour=as.factor(gear))) + facet_grid(cyl~gear, margins=T)
timetaken(begin.time)
Code:
> timetaken(begin.time)
[1] "00:01:55"
Probably, partly because I made a mistake in using gear for color instead of carb
Jake I think a better way to understand the graph is to study the code:
I'm not sure if you're a user of ggplot but the faceting I used is pretty familiar to ggplot users. For me it's one of the most useful aspects of ggplot.
The plot is a facet plot that works similarly to reshape. The graph is not publishable, but for me, allows me to quickly look at plots of data by group (and includes margins) and look for trends. If you're familiar with ggplot's faceting then the graph is pretty understandable, but again the plot is one I use frequently for initial data exploration. It's not polished but to do the same thing in base would take an exorbitant amount of time. As a researcher this tool has been invaluable.
Jake I think a better way to understand the graph is to study the code:
Code:
require(ggplot2)
p <- ggplot(mtcars, aes(hp, mpg))
p + geom_point() #just a plot of hp and mpg
p + geom_point(aes(colour=as.factor(carb))) # a plot colored by carb (I meant to do this in my plot above but used gear instead
p + geom_point(aes(colour=as.factor(carb))) + facet_grid(cyl~gear) #facet plot of both cyl and gear
p + geom_point(aes(colour=as.factor(carb))) + facet_grid(cyl~gear, margins=T) #same as before but with the marginsThe plot is a facet plot that works similarly to reshape. The graph is not publishable, but for me, allows me to quickly look at plots of data by group (and includes margins) and look for trends. If you're familiar with ggplot's faceting then the graph is pretty understandable, but again the plot is one I use frequently for initial data exploration. It's not polished but to do the same thing in base would take an exorbitant amount of time. As a researcher this tool has been invaluable.
In Python there is a nice way to index that would have obvious conflicts in R. This is the negative indexing. In Python, I can say something like someList[-2] to access the second to last element of the list, or to grab the last two elements I could say someList[-2:]. This is like sequence indexing in R on a vector: someVector[2:4]. In Python, though, the empty part of the sequence implies "to the end." In R, you have to be explicit
In R, negatives are treated as "exclude" indicators. Thus, someVector[-2] would mean include all values except 2. In Python, as explained, it would grab that distance from the end of the vector.
What I learned today is that head has some nice properties that can mimic this Python behavior! I know that you can do something like
What I did not realize is that the number can be a negative amount to exclude from the end.
Be creative. There are uses for this!
Code:
someList[2:] # Python
someVector[2:length(someVector)] # RWhat I learned today is that head has some nice properties that can mimic this Python behavior! I know that you can do something like
Code:
head(x, 20) # Reveals 20 elements instead of default 5
Code:
head(x, -5) # All but last 5 elements