Hello,
I have a large dataframe (1 000 000 rows, 1000 columns) where the
columns contain a character. I would like to determine the most common
character for each row.
In the example below, I can parse one row at the time and find the
most common character (apart for ties...). But I think this will be
very slow and memory consuming.
Is there a way to run it more efficiently?
Thank you
```
V = c("A", "B", "C", "D")
df = data.frame(n = 1:10,
col_01 = sample(V, 10, replace = TRUE, prob = NULL),
col_02 = sample(V, 10, replace = TRUE, prob = NULL),
col_03 = sample(V, 10, replace = TRUE, prob = NULL),
col_04 = sample(V, 10, replace = TRUE, prob = NULL),
col_05 = sample(V, 10, replace = TRUE, prob = NULL),
stringsAsFactors = FALSE)
q = vector()
for(i in 1:nrow(df)) {
x = as.vector(t(df[i,2:ncol(df)]))
q[i] = names(which.max(table(x)))
}
df$most = q
```
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