I have a longitudinal data-set that looks like this:
id date group
1 jan-13 1
2 jan-13 1
3 jan-13 2
1 fev-13 3
2 fev-13 4
2 fev-13 3
3 fev-13 4
1 mar-13 5
2 mar-13 6
3 mar-13 5
It represents a network, each individual is connected to other individuals in period t if they were in the same group in any period before t (including t). Therefore in feb-13 indidual 1 is only conected to individual 2.
I want to calculate the degrees for every individual at every period. In this case the final dataset that I want to create would look like this:
id date degree
1 jan-13 1
2 jan-13 1
3 jan-13 0
1 fev-13 1
2 fev-13 2
3 fev-13 1
1 mar-13 2
2 mar-13 2
3 mar-13 2
I have tried some things using for and aggregate but it is not very efficient (it is taking more than a day and hasn't finished). The data-set is very large, so usual packages that work with networks are not working here.
Edit:
Ok, sorry, it seems I misunterstood your question. Did you check if any of the network data packages for R does what you want? If you create a relational data set it should be easy to get what you want, maybe this tutorial helps:
https://statnet.org/trac/raw-attachment/wiki/Resources/introToSNAinR_sunbelt_2012_tutorial.pdf
Related
Is it possible to split episode by a given variable in survival analysis in R, similar to in STATA using stsplit in the following way: stsplit var, at(0) after(time=time)?
I am aware that the survival package allows one to split episode by given cut points such as c(0,5,10,15) in survSplit, but if a variable, say time of divorce, differs by each individual, then providing cutpoints for each individual would be impossible, and the split would have to be based on the value of a variable (say graduation, or divorce, or job termination).
Is anyone aware of a package or know a resource I might be able to tap into?
Perhaps Epi package is what you are looking for. It offers multiple ways to cut/split the follow-up time using the Lesix objects. Here is the documentation of cutLesix().
After some poking around, I think tmerge() in the survival package can achieve what stsplit var can do, which is to split episodes not just by a given cut points (same for all observations), but by when an event occurs for an individual.
This is the only way I knew how to split data
id<-c(1,2,3)
age<-c(19,20,29)
job<-c(1,1,0)
time<-age-16 ## create time since age 16 ##
data<-data.frame(id,age,job,time)
id age job time
1 1 19 1 3
2 2 20 1 4
3 3 29 0 13
## simple split by time ##
## 0 to up 2 years, 2-5 years, 5+ years ##
data2<-survSplit(data,cut=c(0,2,5),end="time",start="start",
event="job")
id age start time job
1 1 19 0 2 0
2 1 19 2 3 1
3 2 20 0 2 0
4 2 20 2 4 1
5 3 29 0 2 0
6 3 29 2 5 0
7 3 29 5 13 0
However, if I want to split by a certain variable, such as when each individuals finished school, each person might have a different cut point (finished school at different ages).
## split by time dependent variable (age finished school) ##
d1<-data.frame(id,age,time,job)
scend<-c(17,21,24)-16
d2<-data.frame(id,scend)
## create start/stop time ##
base<-tmerge(d1,d1,id=id,tstop=time)
## create time-dependent covariate ##
s1<-tmerge(base,d2,id=id,
finish=tdc(scend))
id age time job tstart tstop finish
1 1 19 3 1 0 1 0
2 1 19 3 1 1 3 1
3 2 20 4 1 0 4 0
4 3 29 13 0 0 8 0
5 3 29 13 0 8 13 1
I think tmerge() is more or less comparable with stsplit function in STATA.
I realize this is a topic that's covered somewhat well but I couldn't find anything that approaches this specific concern:
I have a df with 800 columns, 10 iterations of 80 columns (each column represents an item) - Each column is named something like: 1_BL_PRE.1 1_FU_PRE.1 1_BL_PRE.1 1_BL_POST.1
Where the first '1' indicates the item number and the second '1' indicates the iteration number.
What I'm trying to figure out is how to get the sums of specific groups of items from all 10 iterations.
As a short example let's say I want to take the 1st and 3rd item of BL_PRE and get the sum of all 10 iterations for those 2 items - how would I do this?
subject 1_BL_PRE.1 2_BL_PRE.1 3_BL_PRE.1 1_BL_PRE.2 2_BL_PRE.2
1 40002 3 4 3 1 2
2 40004 1 2 3 4 4
3 40006 4 3 3 3 1
4 40008 2 3 1 2 3
5 40009 3 4 1 2 3
Expected output (where A represents the sum of 1_BL_PRE.1, 3_BL_PRE.1, 1_BL_PRE.2 and so on):
subject BL_PRE_A
1 40002 12
2 40004 14
3 40006 15
4 40008 20
5 40009 12
My hunch is the solution is related to a for-loop or lappy (and I'm not familiar at all with either). I'm trying to work with apply(finaldata,1,function(x) {sum(x ...)}) but I haven't been able to figure out the conditional statement for the function of sum.
If there's an implementation with plyr I'd be really curious to see what that looks like. (and if there's a thread that answers this, apologies and just re-direct!)
**Edited to include small example + code I'm trying to get to work
Thanks!
Say I have data that look like this:
level start end
1 1 133.631 825.141
2 2 133.631 155.953
3 3 146.844 155.953
4 2 293.754 302.196
5 3 293.754 302.196
6 4 293.754 301.428
7 2 326.253 343.436
8 3 326.253 343.436
9 4 333.827 343.436
10 2 578.066 611.766
11 3 578.066 611.766
12 4 578.066 587.876
13 4 598.052 611.766
14 2 811.228 825.141
15 3 811.228 825.141
or this:
level start end
1 1 3.60353 1112.62000
2 2 3.60353 20.35330
3 3 3.60353 8.77526
4 2 72.03720 143.60700
5 3 73.50530 101.13200
6 4 73.50530 81.64660
7 4 92.19030 101.13200
8 3 121.28500 143.60700
9 4 121.28500 128.25900
10 2 167.19700 185.04800
11 3 167.19700 183.44600
12 4 167.19700 182.84600
13 2 398.12300 418.64300
14 3 398.12300 418.64300
15 2 445.83600 454.54500
16 2 776.59400 798.34800
17 3 776.59400 796.64700
18 4 776.59400 795.91300
19 2 906.68800 915.89700
20 3 906.68800 915.89700
21 2 1099.44000 1112.62000
22 3 1099.44000 1112.62000
23 4 1100.14000 1112.62000
They produce the following graphs:
As you can see there are several time intervals at different levels. The level-1 interval always spans the entire duration of the time of interest. Levels 2+ have time intervals that are shorter.
What I would like to do is select the maximum number of non-overlapping time intervals covering each period that contain the maximum number of total time within them. I have marked in pink which ones those would be.
For small dataframes it is possible to brute force this, but obviously there should be some more logical way of doing this. I'm interested in hearing some ideas about what I should try.
EDIT:
I think one thing that could help here is the column 'level'. The results come from Kleinberg's burst detection algorithm (package 'bursts'). You will note that the levels are hierarchically organized. Levels of the same number cannot overlap. However levels successively increasing e.g. 2,3,4 in successive rows can overlap.
In essence, I think the problem could be shortened to this. Take the levels produced, but remove level 1. This would be the vector for the 2nd example:
2 3 2 3 4 4 3 4 2 3 4 2 3 2 2 3 4 2 3 2 3 4
Then, look at the 2s... if there are fewer than or only one '3' then that 2 is the longest interval. But if there are two or more 3's between successive 2's, then those 3s should be counted. Do this iteratively for each level. I think that should work...?
e.g.
vec<-df$level %>% as.vector() %>% .[-1]
vec
#[1] 2 3 2 3 4 4 3 4 2 3 4 2 3 2 2 3 4 2 3 2 3 4
max(vec) #4
vec3<-vec #need to find two or more 4's between 3s
vec3[vec3==3]<-NA
names(vec3)<-cumsum(is.na(vec3))
0 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 8 8
2 NA 2 NA 4 4 NA 4 2 NA 4 2 NA 2 2 NA 4 2 NA 2 NA 4
vec3.res<-which(table(vec3,names(vec3))["4",]>1)
which(names(vec3)==names(vec3.res) & vec3==4) #5 6
The above identifies rows 5 and 6 (which equate to rows 6 and 7 in original df) as having two fours that lie between 3's. Perhaps something using this sort of approach might work?
OK here is a stab using your second data set to test. This might not be correct in all cases!!
library(data.table)
dat <- fread("data.csv")
dat[,use:="maybe"]
make.pass <- function(dat,low,high,the.level,use) {
check <- dat[(use!="no" & level > the.level)]
check[,contained.by.above:=(low<=start & end<=high)]
check[,consecutive.contained.by.above:=
(contained.by.above &
!is.na(shift(contained.by.above,1)) &
shift(contained.by.above,1)),by=level]
if(!any(check[,consecutive.contained.by.above])) {
#Cause a side effect where we've learned we don't care:
dat[check[(contained.by.above),rownum],use:="no"]
print(check)
return("yes")
} else {
return("no")
}
}
dat[,rownum:=.I]
dat[level==1,use:=make.pass(dat,start,end,level,use),by=rownum]
dat
dat[use=="maybe" & level==2,use:=make.pass(dat,start,end,level,use),by=rownum]
dat
dat[use=="maybe" & level==3,use:=make.pass(dat,start,end,level,use),by=rownum]
dat
#Finally correct for last level
dat[use=="maybe" & level==4,use:="yes"]
I wrote these last steps out so you can trace in your own interactive session to see what's happening (see the print to get an idea) but you can remove the print and also condense the last steps into something like lapply(1:dat[,max(level)-1], function(the.level) dat[use=="maybe" & level==the.level,use:=make.pass......]) In response to your comment if there are an arbitrary number of levels you will definitely want to use this formalism, and follow it with a final call to dat[use=="maybe" & level==max(level),use:="yes"].
Output:
> dat
level start end use rownum
1: 1 3.60353 1112.62000 no 1
2: 2 3.60353 20.35330 yes 2
3: 3 3.60353 8.77526 no 3
4: 2 72.03720 143.60700 no 4
5: 3 73.50530 101.13200 no 5
6: 4 73.50530 81.64660 yes 6
7: 4 92.19030 101.13200 yes 7
8: 3 121.28500 143.60700 yes 8
9: 4 121.28500 128.25900 no 9
10: 2 167.19700 185.04800 yes 10
11: 3 167.19700 183.44600 no 11
12: 4 167.19700 182.84600 no 12
13: 2 398.12300 418.64300 yes 13
14: 3 398.12300 418.64300 no 14
15: 2 445.83600 454.54500 yes 15
16: 2 776.59400 798.34800 yes 16
17: 3 776.59400 796.64700 no 17
18: 4 776.59400 795.91300 no 18
19: 2 906.68800 915.89700 yes 19
20: 3 906.68800 915.89700 no 20
21: 2 1099.44000 1112.62000 yes 21
22: 3 1099.44000 1112.62000 no 22
23: 4 1100.14000 1112.62000 no 23
level start end use rownum
On the off chance this is correct, the algorithm can roughly be described as follows:
Mark all the intervals as possible.
Start with a given level. Pick a particular interval (by=rownum) say called X. With X in mind, subset a copy of the data to all higher-level intervals.
Mark any of these that are contained in X as "contained in X".
If consecutive intervals at the same level are contained in X, X is no good b/c it wastes intervals. In this case label X's "use" variable as "no" so we'll never think about X again. [Note: if it's possible that non-consecutive intervals are contained in X, or that containing multiple intervals across levels could ruin X's viability, then this logic might need to be changed to count contained intervals instead of finding consecutive ones. I didn't think about this at all, but it's just occurring to me now, so use at your own risk.]
On the other hand, if X passed the test, then we've already established it's good. Mark it as a "yes." But importantly, we also have to mark any single interval contained in X as "no," or else when we iterate the step it will forget that it was contained inside a good interval and mark itself as "yes" as well. This is the side effect step.
Now, iterate, ignoring any results that we've already determined.
Finally any "maybe"s leftover at the highest level are automatically in.
Let me know what you think of this--this is a rough draft and some aspects might not be correct.
I´m obviously a novice in writing R-code.
I have tried multiple solutions to my problem from stackoverflow but I'm still stuck.
My dataset is carcinoid, patients with a small bowel cancer, with multiple variables.
i would like to know how different variables are distributed
carcinoid$met_any - with metastatic disease 1=yes, 2=no(computed variable)
carcinoid$liver_mets_y_n - liver metastases 1=yes, 2=no
carcinoid$regional_lymph_nodes_y_n - regional lymph nodes 1=yes, 2=no
peritoneal_carcinosis_y_n - peritoneal carcinosis 1=yes, 2=no
i have tried this solution which is close to my wanted result
ddply(carcinoid, .(carcinoid$met_any), summarize,
livermetastases=sum(carcinoid$liver_mets_y_n=="1"),
regionalmets=sum(carcinoid$regional_lymph_nodes_y_n=="1"),
pc=sum(carcinoid$peritoneal_carcinosis_y_n=="1"))
with the result being:
carcinoid$met_any livermetastases regionalmets pc
1 1 21 46 7
2 2 21 46 7
Now, i expected the row with 2(=no metastases), to be empty. i would also like the rows in the column carcinoid$met_any to give the number of patients.
If someone could help me it would be very much appreciated!
John
Edit
My dataset, although the column numbers are: 1, 43,28,31,33
1=yes2=no
case_nr met_any liver_mets_y_n regional_lymph_nodes_y_n pc
1 1 1 1 2
2 1 2 1 2
3 2 2 2 2
4 1 2 1 1
5 1 2 1 1
desired output - I want to count the numbers of 1:s and 2:s, if it works, all 1:s should end up in the met_any=1 row
nr liver_mets regional_lymph_nodes pc
met_any=1 4 1 4 2
met_any=2 1 4 1 3
EDIT
Although i probably was very unclear in my question, with your help i could make the table i needed!
setDT(carcinoid)[,lapply(.SD,table),.SDcols=c(43,28,31,33,17)]
gives
met_any lymph_nod liver_met paraortal extrahep
1: 50 46 21 6 15
2: 111 115 140 151 146
i am very grateful! #mtoto provided the solution
John
Based on your example data, this data.table approach works:
library(data.table)
setDT(df)[,lapply(.SD,table),.SDcols=c(2:5)]
# met_any liver_mets_y_n regional_lymph_nodes_y_n pc
# 1: 4 1 4 2
# 2: 1 4 1 3
I have binned data that looks like this:
(8.048,18.05] (-21.95,-11.95] (-31.95,-21.95] (18.05,28.05] (-41.95,-31.95]
81 76 18 18 12
(-132,-122] (-122,-112] (-112,-102] (-162,-152] (-102,-91.95]
6 6 6 5 5
(-91.95,-81.95] (-192,-182] (28.05,38.05] (38.05,48.05] (58.05,68.05]
5 4 4 4 4
(78.05,88.05] (98.05,108] (-562,-552] (-512,-502] (-482,-472]
4 4 3 3 3
(-452,-442] (-412,-402] (-282,-272] (-152,-142] (48.05,58.05]
3 3 3 3 3
(68.05,78.05] (118,128] (128,138] (-582,-572] (-552,-542]
3 3 3 2 2
(-532,-522] (-422,-412] (-392,-382] (-362,-352] (-262,-252]
2 2 2 2 2
(-252,-242] (-142,-132] (-81.95,-71.95] (148,158] (-1402,-1392]
2 2 2 2 1
(-1372,-1362] (-1342,-1332] (-942,-932] (-862,-852] (-822,-812]
1 1 1 1 1
(-712,-702] (-682,-672] (-672,-662] (-632,-622] (-542,-532]
1 1 1 1 1
(-502,-492] (-492,-482] (-472,-462] (-462,-452] (-442,-432]
1 1 1 1 1
(-432,-422] (-352,-342] (-332,-322] (-312,-302] (-302,-292]
1 1 1 1 1
(-202,-192] (-182,-172] (-172,-162] (-51.95,-41.95] (88.05,98.05]
1 1 1 1 1
(108,118] (158,168] (168,178] (178,188] (298,308]
1 1 1 1 1
(318,328] (328,338] (338,348] (368,378] (458,468]
1 1 1 1 1
How can I plot this data so that the bin is sorted from most negative on the left to most positive on the right? Currently my graph looks like this. Notice that it is not sorted at all. In particular the second bar (value = 76) is placed to the right of the first:
(8.048,18.05] (-21.95,-11.95]
81 76
This is the command I use to plot:
barplot(x,ylab="Number of Unique Tags", xlab="Expected - Observed")
I really want to help answer your question, but I gotta tell you, I can't make heads or tails of your data. I see a lot of opening parenthesis but no closing ones. The data looks sorted descending by whatever the values are on the bottom of each row. I have no idea what to make out of a value like "(8.048,18.05]"
Am I missing something obvious? Can you make a more simple example where your data structure is not a factor?
I would generally expect a data frame or a matrix with two columns, one for the X and one for the Y.
See if this example of sorting helps (I'm sort of shooting in the dark here)
tN <- table(Ni <- rpois(100, lambda=5))
r <- barplot(tN)
#stop here and examine the plot
#the next bit converts the matrix to a data frame,
# sorts it, and plots it again
df<-data.frame(tN)
df2<-df[order(df$Freq),]
barplot(df2$Freq)