I have two matrix with the same number of columns, but with different number of rows:
a <- cbind(runif(5), runif(5))
b <- cbind(runif(8), runif(8))
I want to associate these in a same list, so that the first columns of a and b are associated with each other, and so on:
my_result <- list(list(a[,1], b[,1]), list(a[,2], b[,2]))
So the result would look like this:
> print(my_result)
[[1]]
[[1]][[1]]
[1] 0.9440956 0.7259602 0.7804068 0.7115368 0.2771190
[[1]][[2]]
[1] 0.4155642 0.1535414 0.6983123 0.7578231 0.2126765 0.6753884 0.8160817
[8] 0.6548915
[[2]]
[[2]][[1]]
[1] 0.7343330 0.7751599 0.4463870 0.6926663 0.9692621
[[2]][[2]]
[1] 0.5708726 0.1234482 0.2875474 0.4760349 0.2027653 0.5142006 0.4788264
[8] 0.7935544
I can't figure how to do that without a for loop, but I'm pretty sure some *pply magic could be used here.
Any directions would be much appreciated.
I'm not sure how general a solution you're looking for (arbitrary number of matrices, ability to pass a list of matrices, etc.) but this works for your specific example:
lapply(1:2,function(i){list(a[,i],b[,i])})
Related
I have a list looks like this
[[1]]
[[1]][[1]]
[[1]][[1]]$p1est.z
[1] 2.890829
[[1]][[1]]$p1se.z
[1] 0.1418367
[[1]][[2]]
[[1]][[2]]$p2est.w
[1] 4.947014
[[1]][[2]]$p2se.w
[1] 0.5986682
[[2]]
[[2]][[1]]
[[2]][[1]]$p1est.z
[1] 3.158164
[[2]][[1]]$p1se.z
[1] 0.138770
[[2]][[2]]
[[2]][[2]]$p2est.w
[1] 5.052874
[[2]][[2]]$p2se.w
[1] 0.585608
How can I extract values of "p1est.z" from both levels? since I need to compute the average of them.
Thanks!
Actually the unlist() function out of the box should probably work here:
output <- unlist(your_list)
output[names(output) == "p1est.z"]
p1est.z p1est.z
2.890829 3.158164
Data:
your_list <- list(
list(list(p1est.z=2.890829, p1se.z=0.1418367),
list(p1est.w=4.947014, p2se.w=0.5986682)),
list(list(p1est.z=3.158164, p1se.z=0.138770),
list(p1est.w=5.052874, p2se.w=0.585608)))
One way to do this, using Tim Biegeleisen's representation of your data is to make a function to extract p1est.z and apply that. Your top level list has two elements, in both, the first element has a p1est.z so you could do
fn <- function(x) { x[[1]]$p1est.z }
and then apply it
sapply(your_list, fn)
# [1] 2.890829 3.158164
I am working with a vector of strings in r. However, when I see the first item in the list I see this:
> uni_list[1]
[1] c("ENSMUSG00000000204", "ENSMUSG00000115878", "ENSMUSG00000116453", "ENSMUSG00000116134")
15940 Levels: c("ENSMUSG00000000204", "ENSMUSG00000115878", "ENSMUSG00000116453", "ENSMUSG00000116134")
How can I split this one in separate values?
Thanks in advance,
Juan
You can use split, i.e.
split(l3[[1]], seq(length(l3[[1]])))
$`1`
[1] "ENSMUSG00000000204"
$`2`
[1] "ENSMUSG00000115878"
$`3`
[1] "ENSMUSG00000116453"
$`4`
[1] "ENSMUSG00000116134"
where
l3
[[1]]
[1] "ENSMUSG00000000204" "ENSMUSG00000115878" "ENSMUSG00000116453" "ENSMUSG00000116134"
It seems a silly question, but I have searched on line, but still did not find any sufficient reply.
My question is: suppose we have a matrix M, then we use the scale() function, how can we extract the center and scale of each column by writing a line of code (I know we can see the centers and scales..), but my matrix has lots of columns, it is cumbersome to do it manually.
Any ideas? Many thanks!
you are looking for the attributes function:
set.seed(1)
mat = matrix(rnorm(1000),,10) # Suppose you have 10 columns
s = scale(mat) # scale your data
attributes(s)#This gives you the means and the standard deviations:
$`dim`
[1] 100 10
$`scaled:center`
[1] 0.1088873669 -0.0378080766 0.0296735350 0.0516018586 -0.0391342406 -0.0445193567 -0.1995797418
[8] 0.0002549694 0.0100772648 0.0040650015
$`scaled:scale`
[1] 0.8981994 0.9578791 1.0342655 0.9916751 1.1696122 0.9661804 1.0808358 1.0973012 1.0883612 1.0548091
These values can also be obtained as:
colMeans(mat)
[1] 0.1088873669 -0.0378080766 0.0296735350 0.0516018586 -0.0391342406 -0.0445193567 -0.1995797418
[8] 0.0002549694 0.0100772648 0.0040650015
sqrt(diag(var(mat)))
[1] 0.8981994 0.9578791 1.0342655 0.9916751 1.1696122 0.9661804 1.0808358 1.0973012 1.0883612 1.0548091
you get a list that you can subset the way you want:
or you can do
attr(s,"scaled:center")
[1] 0.1088873669 -0.0378080766 0.0296735350 0.0516018586 -0.0391342406 -0.0445193567 -0.1995797418
[8] 0.0002549694 0.0100772648 0.0040650015
attr(s,"scaled:scale")
[1] 0.8981994 0.9578791 1.0342655 0.9916751 1.1696122 0.9661804 1.0808358 1.0973012 1.0883612 1.0548091
I'm working on a shiny app which plots data trees. I'm looking to incorporate the shinyTree app to permit quick comparison of plotted nodes. The issue is that the shinyTree app returns a redundant list of lists of the sub node plot.
The actual list of list is included below. I would like to keep the longest branches only. I would also like to remove the id node (integer node), I'm struggling as to why it even shows up based on the list. I have tried many different methods to work with this list but it's been a real struggle. The list concept is difficult to understand.
I create the data.tree and plot via:
dataTree.a <- FromListSimple(checkList)
plot(dataTree.a)
> checkList
[[1]]
[[1]]$Asia
[[1]]$Asia$China
[[1]]$Asia$China$Beijing
[[1]]$Asia$China$Beijing$Round
[[1]]$Asia$China$Beijing$Round$`20383994`
[1] 0
[[2]]
[[2]]$Asia
[[2]]$Asia$China
[[2]]$Asia$China$Beijing
[[2]]$Asia$China$Beijing$Round
[1] 0
[[3]]
[[3]]$Asia
[[3]]$Asia$China
[[3]]$Asia$China$Beijing
[1] 0
[[4]]
[[4]]$Asia
[[4]]$Asia$China
[[4]]$Asia$China$Shanghai
[[4]]$Asia$China$Shanghai$Round
[[4]]$Asia$China$Shanghai$Round$`23740778`
[1] 0
[[5]]
[[5]]$Asia
[[5]]$Asia$China
[[5]]$Asia$China$Shanghai
[[5]]$Asia$China$Shanghai$Round
[1] 0
[[6]]
[[6]]$Asia
[[6]]$Asia$China
[[6]]$Asia$China$Shanghai
[1] 0
[[7]]
[[7]]$Asia
[[7]]$Asia$China
[1] 0
[[8]]
[[8]]$Asia
[[8]]$Asia$India
[[8]]$Asia$India$Delhi
[[8]]$Asia$India$Delhi$Round
[[8]]$Asia$India$Delhi$Round$`25703168`
[1] 0
[[9]]
[[9]]$Asia
[[9]]$Asia$India
[[9]]$Asia$India$Delhi
[[9]]$Asia$India$Delhi$Round
[1] 0
[[10]]
[[10]]$Asia
[[10]]$Asia$India
[[10]]$Asia$India$Delhi
[1] 0
[[11]]
[[11]]$Asia
[[11]]$Asia$India
[1] 0
[[12]]
[[12]]$Asia
[[12]]$Asia$Japan
[[12]]$Asia$Japan$Tokyo
[[12]]$Asia$Japan$Tokyo$Round
[[12]]$Asia$Japan$Tokyo$Round$`38001000`
[1] 0
[[13]]
[[13]]$Asia
[[13]]$Asia$Japan
[[13]]$Asia$Japan$Tokyo
[[13]]$Asia$Japan$Tokyo$Round
[1] 0
[[14]]
[[14]]$Asia
[[14]]$Asia$Japan
[[14]]$Asia$Japan$Tokyo
[1] 0
[[15]]
[[15]]$Asia
[[15]]$Asia$Japan
[1] 0
[[16]]
[[16]]$Asia
[1] 0
Well, I did cobble together a poor hack to make this work here is what I did to the 'checkList' list
checkList <- get_selected(tree, format = "slices")
# Convert and collapse shinyTree slices to data.tree
# This is a bit of a cluge to work the graphic with
# shinyTree an alternate one liner is in works
# This transform works by finding the longest branches
# and only plotting them since the other branches are
# subsets due to the slices.
# Extract the checkList name (as characters) from the checkList
tmp <- names(unlist(checkList))
# Determine the length of the individual checkList Names
lens <- lapply(tmp, function(x) length(strsplit(x, ".", fixed=TRUE)[[1]]))
# Find the elements with the highest length returns a list of high vals
lens.max <- which(lens == max(sapply(lens, max)))
# Replace all '.' with '\' prepping for DataFrameTable Converions
tmp <- relist(str_replace_all(tmp, "\\.", "/"), skeleton=tmp)
# Add a root node to work with multiple branches
tmp <- unlist(lapply(tmp, function(x) paste0("Root/", x)))
# Create a list of only the longest branches
longBranches <- as.list(tmp[lens.max])
# Convert the list into a data.frame for convert
longBranches.df <- data.frame(pathString = do.call(rbind, longBranches))
# Publish the data.frame for use
vals$selDF <- longBranches.df
#save(checkList, file = "chkLists.RData") # Save for troubleshooting
print(vals$selDF)ode here
The new checkList looks like this:
[1] "Root/Europe/France/Paris/Round/10843285" "Root/Europe/France/Paris/Round"
[3] "Root/Europe/France/Paris" "Root/Europe/France"
[5] "Root/Europe/Germany/Berlin/Diamond/3563194" "Root/Europe/Germany/Berlin/Diamond"
[7] "Root/Europe/Germany/Berlin/Round/3563194" "Root/Europe/Germany/Berlin/Round"
[9] "Root/Europe/Germany/Berlin" "Root/Europe/Germany"
[11] "Root/Europe/Italy/Rome/Round/3717956" "Root/Europe/Italy/Rome/Round"
[13] "Root/Europe/Italy/Rome" "Root/Europe/Italy"
[15] "Root/Europe/United Kingdom/London/Round/10313307" "Root/Europe/United Kingdom/London/Round"
[17] "Root/Europe/United Kingdom/London" "Root/Europe/United Kingdom"
[19] "Root/Europe"
It works :)... but I think this could be done with a two liner.... I'll work on it again in a week or so. Any other Ideas would be appreciated.
This is the second time that I have faced this recently, so I wanted to reach out to see if there is a better way to parse dataframes returned from jsonlite when one of elements is an array stored as a column in the dataframe as a list.
I know that this part of the power with jsonlite, but I am not sure how to work with this nested structure. In the end, I suppose that I can write my own custom parsing, but given that I am almost there, I wanted to see how to work with this data.
For example:
## options
options(stringsAsFactors=F)
## packages
library(httr)
library(jsonlite)
## setup
gameid="2015020759"
SEASON = '20152016'
BASE = "http://live.nhl.com/GameData/"
URL = paste0(BASE, SEASON, "/", gameid, "/PlayByPlay.json")
## get the data
x <- GET(URL)
## parse
api_response <- content(x, as="text")
api_response <- jsonlite::fromJSON(api_response, flatten=TRUE)
## get the data of interest
pbp <- api_response$data$game$plays$play
colnames(pbp)
And exploring what comes back:
> class(pbp$aoi)
[1] "list"
> class(pbp$desc)
[1] "character"
> class(pbp$xcoord)
[1] "integer"
From above, the column pbp$aoi is a list. Here are a few entries:
> head(pbp$aoi)
[[1]]
[1] 8465009 8470638 8471695 8473419 8475792 8475902
[[2]]
[1] 8470626 8471276 8471695 8476525 8476792 8477956
[[3]]
[1] 8469619 8471695 8473492 8474625 8475727 8476525
[[4]]
[1] 8469619 8471695 8473492 8474625 8475727 8476525
[[5]]
[1] 8469619 8471695 8473492 8474625 8475727 8476525
[[6]]
[1] 8469619 8471695 8473492 8474625 8475727 8475902
I don't really care if I parse these lists in the same dataframe, but what do I have for options to parse out the data?
I would prefer to take the data out of out lists and parse them into a dataframe that can be "related" to the original record it came from.
Thanks in advance for your help.
From #hrbmstr above, I was able to get what I wanted using unnest.
select(pbp, eventid, aoi) %>% unnest() %>% head