na

问题 I am using the excel tools OFFSET and INDEX to import an array from a different sheet, into a workbook with room for 7 entries. The sheet that is being referenced has an ID number and the number could be listed several times for different entries. If you are not familiar with the offset function, the second to last input is how many rows you want returned in the array. The number I have here is a COUNTIF for how many times an ID appears. My code works, however because it is in an array format

问题 I am wondering why indexing Julia's DataArrays with NA values is not possible. Excuting the snipped below results in an error(NAException("cannot index an array with a DataArray containing NA values")): dm = data([1 4 7; 2 5 8; 3 1 9]) dm[dm .== 5] = NA dm[dm .< 3] = 1 #Error dm[(!isna(dm)) & (dm .< 3)] = 1 #Working There is a solutions to ignore NA's in a DataFrame with isna() , like answered here. At a first glance it works like it should and ignoring NA's in DataFrames is the same approach

问题 I have a matrix, where rows can have NA's for all columns. I want to replace these NA rows with previous row's non-NA value and K-th column. For example, this matrix: [,1] [,2] [1,] NA NA [2,] NA NA [3,] 1 2 [4,] 2 3 [5,] NA NA [6,] NA NA [7,] NA NA [8,] 6 7 [9,] 7 8 [10,] 8 9 Must be transformed to this non-NA matrix, where we use 2-th column for replacement: [,1] [,2] [1,] NA NA [2,] NA NA [3,] 1 2 [4,] 2 3 [5,] 3 3 [6,] 3 3 [7,] 3 3 [8,] 6 7 [9,] 7 8 [10,] 8 9 I wrote a function for this,

问题 Is any elegant solution to clean a dataframe from NA values without remove the row or column where the NA is? Example: Input dataframe C1 C2 C3 R1 A <NA> <NA> R2 <NA> A <NA> R3 <NA> <NA> A R4 B <NA> <NA> R5 <NA> B <NA> R6 <NA> <NA> <NA> R7 C <NA> B R8 C <NA> R9 <NA> R10 <NA> R11 C Output dataframe C1 C2 C3 R1 A A A R2 B B B R3 C C C For example, here is a messy dataframe (df1) full of NA values A B C D E F G H I J K 1 Healthy <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 2 <NA> Healthy

问题 I read in some data via: it.data <- read.csv("inputData/rstar.data.it.csv", header = T, sep = ",") then the second and fourth columns are inflation resp. interest: inflation.it <- it.data[2] and interest.it <- it.data[4] . However, the trouble starts when I am trying to reform the data into a time-series object, because there are leading and trailing NA values in the columns. I have tried na.omit() , it.data[complete.cases(it.data),] , na.contiguous , without luck. What happens now is that

问题 I am spreading multiple categorical variables to Boolean columns using tidyr::spread() . As the data contains NAs, spread creates a new column without a name. What I'm looking for is a way to get rid off the NAs using a) a piping solution (I've tried select_() and '['() , but don't know how to refer to the NA column's name or index) or b) a custom function, which would be even better c) a way to simply not generate the NA columns, Hadleyverse compatible, if possible. Below is my current (and

问题 I want to extract the coefficients (estimates, tvalues, etc) of LM ouputs to a dataframe. I need to store all coefficients in a dataframe for all regression outputs, since I have 949 separate outputs. The PROBLEM is that some of the outputs include NA's for a number of variables. When I export these summaries, it excludes the NA's and only outputs the variables that have true values. Since I need to bind all the values in rows, I want to maintain the same structure of all estimates (and so NA

问题 I have a dataframe as follows: hospital <- c("PROVIDENCE ALASKA MEDICAL CENTER", "ALASKA REGIONAL HOSPITAL", "FAIRBANKS MEMORIAL HOSPITAL", "CRESTWOOD MEDICAL CENTER", "BAPTIST MEDICAL CENTER EAST", "ARKANSAS HEART HOSPITAL", "MEDICAL CENTER NORTH LITTLE ROCK", "CRITTENDEN MEMORIAL HOSPITAL") state <- c("AK", "AK", "AK", "AL", "AL", "AR", "AR", "AR") rank <- c(1,2,3,1,2,1,2,3) df <- data.frame(hospital, state, rank) df hospital state rank 1 PROVIDENCE ALASKA MEDICAL CENTER AK 1 2 ALASKA

问题 I wrote a leading-NA count function, it works on vectors. However: a) Can you simplify my version? b) Can you also generalize it to work directly on matrix, dataframe (must still work on individual vector), so I don't need apply() ? Try to avoid all *apply functions, fully vectorize, it must still work on a vector, and no special-casing if at all possible. leading_NA_count <- function(x) { max(cumsum((1:length(x)) == cumsum(is.na(x)))) } # v0.1: works but seems clunky, slow and unlikely to be

问题 This question already has an answer here : remove rows where all columns are NA except 2 columns [duplicate] (1 answer) Closed 6 months ago . I have a data.table which was formed by taking the differences between two panel observations using: tab <- tab[, lapply(.SD, function(x) x - shift(x)), by = A, .SDcols = (sapply(tab, is.numeric)) ] tab = data.table(A = c(1, 1, 2, 2), B = c(NA, 2, NA, 1), C = c(NA, NA, NA, 2), D=c(NA, 3, NA, 2) tab A B C D 1: 1 NA NA NA 2: 1 2 NA 3 3: 2 NA NA NA 4: 2 1