Mutate a new columns based positive and negative consistency of two columns

Question

I am trying to add a new column result in my dataframe df1, as specific columns (value1 and value2 columns) met the following conditions:

1. Both of them row-wisely are positive, negative or 0, or one of them is 0 and another is either negative or positive, then result=="True";

2. if row-wisely one of them are positive and another is negative or verse versa, then result=="False";

3. if row-wisely both of them are NAs or one of them is NA but another is either negative or positive, then result=="-"

Input:

df1 <- data.frame(ID= c("ID1","ID2","ID3","ID4","ID5"), value1 = c(1.2, -1, NA, -1.5, 0), value2 = c(0.8, -1.1, -1, 1.3, 0.9))

Expected output:

df2 <- data.frame(ID= c("ID1","ID2","ID3","ID4","ID5"), value1 = c(1.2, -1, NA, -1.5, 0), value2 = c(0.8, -1.1, -1, 1.3, 0.9), result = c("True","True","-", "False", 'True'))

Out:

Any helps would be appreciated.

Reference link:

Add a new column if multiple columns have negative value

Answer 1

You may try

library(dplyr)

df1 %>%
  mutate(result = case_when(
    is.na(value1 * value2) ~ "-",
    value1 * value2 >=0 ~ "True",
    T ~ "False"
  ))

   ID value1 value2 result
1 ID1    1.2    0.8   True
2 ID2   -1.0   -1.1   True
3 ID3     NA   -1.0      -
4 ID4   -1.5    1.3  False
5 ID5    0.0    0.9   True