apply

问题 I'd like to collect terms under multiple columns of the annot data.frame . Below is the first row of information for a toy datset for annot. colnames(annot) # [1] "HUGO.Name" "Common.Name" "Gene.Class" "Cell.Type" "Annotation" annot[1,] # HUGO.Name Common.Name Gene.Class Cell.Type # 1 CCL1 CCL1 Immune Response - Cell Type specific aDC # Annotation # 1 Cell Type specific, Chemokines and receptors, Inflammatory response So far, I've been writing the colnames iteratively, but I'd like to learn

问题 Given a dataset Dat where I have species (SP), Area (AR), and Time (TM) (in POSIXct). I want to subset the data for individuals that were present with Species A, within a half hour prior and after it was recorded, and within the same area, including two adjacent areas (+ and - 1). For example, if species A was present at 1:00 on area 4, I wish to subset all species present from 12:30 to 1:30 in the same day in areas 3,4 and 5. As an example: SP TM AR B 1-jan-03 07:22 1 F 1-jan-03 09:22 4 A 1

问题 I have a dataframe with 5 dimensions of data that looks like this: > dim(alldata) [1] 162 6 > head(alldata) value layer Kmultiplier Resolution Season Variable 1: 0.01308008 b .01K 1km Baseflow Evapotranspiration 2: 0.03974779 b .01K 1km Peak Flow Evapotranspiration 3: 0.02396524 b .01K 1km Summer Flow Evapotranspiration 4: -0.15670996 b .01K 1km Baseflow Discharge 5: 0.06774948 b .01K 1km Peak Flow Discharge 6: -0.04138313 b .01K 1km Summer Flow Discharge What I'd like to do is get the mean

问题 I feel this should be easy in base R but I just can't figure it out. I have a simple dataframe, let's say it looks like this tbl <- read.table(text = "Field1 Field2 100 200 150 180 200 160 280 250 300 300 300 250", header = TRUE) Now, what I want to do is create a function that will apply a rolling % addition, something like: fn <- function(tbl, pct) {} which accepts the dataframe above as tbl . It adds a percentage fraction of the current row to the NEXT row down based on pct , and rolls

问题 I have a huge dataset ( > 2.5 Million). A small subset looks like this (code reproducible) temp <- data.frame(list(col1 = c("424", "560", "557"), col2 = c("276", "427", "V46"), col3 = c("780", "V45", "584"), col4 = c("276", "V45", "995"), col5 = c("428", "799", "427"))) > temp col1 col2 col3 col4 col5 1 424 276 780 276 428 2 560 427 V45 V45 799 3 557 V46 584 995 427 I am trying to remove duplicates per row, and shifting values left, using this code library(plyr) temp <- apply(temp,1,function

问题 As part of some Eulerian travails, I'm trying to code a Sieve of Eratosthenes with a factorization wheel. My code so far is: (defun ring (&rest content) "Returns a circular list containing the elements in content. The returned list starts with the first element of content." (setf (cdr (last content)) content)) (defun factorization-wheel (lst) "Returns a circular list containing a factorization wheel using the list of prime numbers in lst" (let ((circumference (apply #'* lst))) (loop for i

问题 It seems apply will not re-assemble 3D arrays when operating on just one margin. Consider: arr <- array( runif(2*4*3), dim=c(2, 4, 3), dimnames=list(a=paste0("a", 1:2), b=paste0("b", 1:4), c=paste0("c", 1:3)) ) # , , c = c1 # # b # a b1 b2 b3 b4 # a1 0.7321399 0.8851802 0.2469866 0.9307044 # a2 0.5896138 0.6183046 0.7732842 0.6652637 # # , , c = c2 # b # a b1 b2 b3 b4 # a1 0.5894680 0.7839048 0.3854357 0.56555024 # a2 0.6158995 0.6530224 0.8401427 0.04044974 # # , , c = c3 # b # a b1 b2 b3 b4

问题 New to pandas, I already want to parallelize a row-wise apply operation. So far I found Parallelize apply after pandas groupby However, that only seems to work for grouped data frames. My use case is different: I have a list of holidays and for my current row/date want to find the no-of-days before and after this day to the next holiday. This is the function I call via apply: def get_nearest_holiday(x, pivot): nearestHoliday = min(x, key=lambda x: abs(x- pivot)) difference = abs(nearesHoliday

问题 I want to use the apply function that: - Takes 2 columns as inputs - Outputs two new columns based on a function. An example is with this add_multiply function. #function with 2 column inputs and 2 outputs def add_multiply (a,b): return (a+b, a*b ) #example dataframe df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) #this doesn't work df[['add', 'multiply']] = df.apply(lambda x: add_multiply(x['col1'], x['col2']), axis=1) ideal result: col1 col2 add multiply 1 3 4 3 2 4 6 8 回答1: You can add

问题 In this case (more details could be found in this question: Count how many observations in the rest of the dat fits multiple conditions? (R)) This is a dataset called event, containing thousands of events (observations) and I selected several rows to show you the data structure. It contains the "STATEid", "date" of occurrence, and geographical coordinates in two variables "LON" "LAT". I am writing to calculate a new variable (column) for each row. This new variable should be: "Given any