Calculate Row-Wise Maximum

Calculate row-wise maximum

I won't vouch for its speed, but this at least avoids coercing to a matrix:

data[,mymax:=do.call(pmax,.SD)]

rowwise maximum for R

Here's one possibility:

df$colMax <- apply(df, 1, function(x) max(x[x != 9]))

Finding row-wise maximum value column with distinct column indices in a numpy matrix

Assuming the minimum value in the matrix is 0

for i in range(len(arr)): # for the ith row
    maxcol = np.argmax(arr[i])
    arr[:,maxcol] = np.zeros( len(arr[:,maxcol]) )
    out.append((i,maxcol))

This works by finding the index of the max column for a row (using argmax), and then setting all of that column's elements to an arbitrary minimum value (0 here, given efficiently by np.zeros)

This should work, I'm trying it out rn. And is definitely less than O(n^2) but don't know exactly how much.

Edit: I tried it and out is [(0, 0), (1, 1), (2, 3), (3, 2), (4, 0)]. So it does have the desired output, but an extra term (4,0). [which is there because iterates over all rows].
If you want to iterate till you 'run out' of columns, you can use min(arr.shape) instead of len(arr)

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Time Complexity

We're running a for loop once, that takes O(n)

Inside the for loop, we have np.argmax, which is also O(n)

Then comes the replacements which are constant time [O(1)]

So it's not that optimised. You can write your own code for that, by directly coding the edge cases and other improvements like checking only the required parts, etc...

In R: row wise return max value and corresponding column name

Try this tidyverse approach. It can be more practical reshaping data to long previously creating an id per row and then extract the desired values using filter. Using pivot_wider() you can have the desired values and then the filter for max values is applied. Finally you can merge to your original data using left_join() and the id you created based on rows. Here the code:

library(dplyr)
library(tidyr)
#Code
newdf <- df %>% mutate(id=1:n()) %>%
  left_join(df %>% mutate(id=1:n()) %>%
  pivot_longer(-id) %>%
  separate(name,c('Var','Day'),sep='_') %>%
  pivot_wider(names_from=Var,values_from=value) %>%
  group_by(id) %>%
  filter(measure==max(measure)) %>%
  mutate(Day=paste0('measure_',Day)) %>% select(-measure) %>%
  rename(measure_max=Day,temp_day_measure_max=temp)) %>% select(-id)

Output:

  measure_day1 measure_day2 measure_day3 temp_day1 temp_day2 temp_day3  measure_max temp_day_measure_max
1            1            5            2        25        31        14 measure_day2                   31
2            2            7            3        27        33        16 measure_day2                   33
3            5            1            9        29        35        19 measure_day3                   19

Calculate the maximum value across all rows without manually typing the names of every column

You get some data:

m <- tibble(matrix(runif(1000 * 500), ncol = 500))

Make sure every column is a double, then this should ideally work:

m_with_max_col <- m %>% 
  rowwise() %>% 
  mutate(max = max(c_across(where(is.numeric))))

This also works, but might be less desirable:

m_with_max_col <- m %>% 
  rowwise() %>% 
  mutate(max = max(across()))

Solution is taken from : Row-wise operations

Calculate the rowwise mean when a maximum number of NA values is given for a set of columns using dplyr

We can use across to select column of interest.

library(dplyr)

dat %>% 
  mutate(mean = ifelse(rowSums(is.na(across(-colA))) > 2, 
                       NA, 
                       rowMeans(across(-colA), na.rm = T)))

# A tibble: 4 × 6
   colA  colB  colC  colD  colE  mean
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     1     2     3     4     5   3.5
2     2     3    NA     4     5   4  
3     3    NA    NA    NA     4  NA  
4     4    NA    NA     5     6   5.5

How to calculate max and min of multiple columns (row wise) using awk

You may use this awk:

awk 'BEGIN{FS=OFS=","} NR==1 {print $0, "New_col"; next} {print $0, ($2 > $4 ? $2 : $4) - ($3 < $5 ? $3 : $5)}' df.csv

col1,col2,col3,col4,col5,New_col
A,2,5,7,9,2
B,6,10,2,3,3
C,3,4,6,8,2

A more readable version:

awk '
BEGIN { FS = OFS = "," }
NR == 1 {
   print $0, "New_col"
   next
}
{
   print $0, ($2 > $4 ? $2 : $4) - ($3 < $5 ? $3 : $5)
}' df.csv

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