write a loop that takes each row of strings in multiple columns in a dataframe and removes the rows that contains those variables of another dataframe across multiple columns in r

You can achieve this by using the %in% operator in R along with the apply function to check each row of the dataframe against the columns of another dataframe. Here is an example:

main.r
# Sample dataframes
df1 <- data.frame(
  col1 = c("var1", "var2", "var3"),
  col2 = c("var4", "var5", "var6")
)

df2 <- data.frame(
  colA = c("var1", "var7"),
  colB = c("var4", "var8")
)

# Find rows in df1 that contain variables in df2
rows_to_remove <- apply(df1, 1, function(x) any(unlist(x) %in% unlist(df2)))

# Remove rows from df1
df1_filtered <- df1[!rows_to_remove, ]

print(df1_filtered)
400 chars
19 lines

In this example, apply is used to apply a function to each row of df1. The function checks if any of the values in the row are present in df2 using the %in% operator. The unlist function is used to convert the dataframes into vectors, which can be compared using %in%. The result is a boolean vector indicating which rows to remove. The ! operator is used to negate this vector and select the rows that do not contain the variables.

Alternatively, you can use the dplyr package to achieve the same result:

main.r
library(dplyr)

# Sample dataframes
df1 <- data.frame(
  col1 = c("var1", "var2", "var3"),
  col2 = c("var4", "var5", "var6")
)

df2 <- data.frame(
  colA = c("var1", "var7"),
  colB = c("var4", "var8")
)

# Find rows in df1 that contain variables in df2
df1_filtered <- df1 %>%
  rowwise() %>%
  filter(!(any(c_across() %in% unlist(df2))))

print(df1_filtered)
362 chars
20 lines

In this example, rowwise is used to perform the comparison row by row, c_across is used to select all columns, and filter is used to remove the rows that contain the variables.