You can use the following syntax to find the column with the max value for each row in a data frame in R:

df$max_col <- colnames(df)[max.col(df, ties.method='first')]

Note that the argument ties.method=’first’ specifies that the first max column should be returned if there are multiple columns with a max value in a given row.

Other values you can provide to this argument include random and last, if you’d like to return a random max column or the last max column instead.

The following example shows how to use this syntax in practice.

Example: Find Column with Max Value for Each Row in R

Suppose we have the following data frame in R that contains information about the number of points scored by six different basketball players during three games:

#create data frame
df <- data.frame(game1=c(23, 20, 14, 12, 19, 15),
                 game2=c(9, 10, 11, 13, 13, 15),
                 game3=c(29, 11, 22, 19, 14, 15))

#view data frame
df

  game1 game2 game3
1    23     9    29
2    20    10    11
3    14    11    22
4    12    13    19
5    19    13    14
6    15    15    15

Suppose we would like to create a new column that contains the name of the column with the max value in each row of the data frame.

We can use the following syntax to do so:

#create new column that contains column with max value for each row
df$max_col <- colnames(df)[max.col(df, ties.method='first')]

#view updated data frame
df

  game1 game2 game3 max_col
1    23     9    29   game3
2    20    10    11   game1
3    14    11    22   game3
4    12    13    19   game3
5    19    13    14   game1
6    15    15    15   game1

The new column called max_col contains the name of the column with the max value in each row.

For example:

• In the first row, game3 contained the max value.
• In the second row, game1 contained the max value.
• In the third row, game3 contained the max value.

And so on.

Note that each column in the last row has the same value.

Since we specified ties.method=’first’ in the max.col() function, the code returned game1 as the column with the max value since this is the first max column.