## Creating a column whose values are dependent on multiple other columns

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I'm trying to create a new column ("newcol") in a dataframe ("data"), whose values will be determined by the contents of up to two other columns in the dataframe ("B_stance" and "C_stance"). The values within B_stance are either "L", "R", "U" or "N". Within C_stance they are either "L" or "R".

Please excuse the semi-logical language, but I need R code which will achieve this for the contents of newcol:

```if (data\$B_stance = "L" AND data\$C_stance = "L") then (data\$newcol = "N")
if (data\$B_stance = "L" AND data\$C_stance = "R") then (data\$newcol = "Y")
if (data\$B_stance = "R" AND data\$C_stance = "R") then (data\$newcol = "N")
if (data\$B_stance = "R" AND data\$C_stance = "L") then (data\$newcol = "Y")
if (data\$B_stance = "U") then (data\$newcol = "N")
if (data\$B_stance = "N") then (data\$newcol = "N")
```

I've tried to see if/how "ifelse" could achieve this, but cannot find an example of how to draw from multiple column values in determining the new value.

In base R the `ifelse` function is most useful for these conditions. The `dplyr` library includesa more robust `if_else` function and a `case_when` function. The `ifelse` returns the second argument if the first is true and returns the third argument if the first argument is false.

```data <- read.table(text="
B_stance C_stance
L R
L L
U X
R L
R R
N X
X X

data\$newcol = ifelse(data\$B_stance == "L" & data\$C_stance == "L", "N",
ifelse(data\$B_stance == "L" & data\$C_stance == "R", "Y",
ifelse(data\$B_stance == "R" & data\$C_stance == "R", "N",
ifelse(data\$B_stance == "R" & data\$C_stance == "L", "Y",
ifelse(data\$B_stance == "U", "N",
ifelse(data\$B_stance == "N", "N",
NA))))))

data

# B_stance C_stance newcol
# 1        L        R      Y
# 2        L        L      N
# 3        U        X      N
# 4        R        L      Y
# 5        R        R      N
# 6        N        X      N
# 7        X        X   <NA>
```

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It may be easier to create a `key/val` dataset and then do a join

```keydat <- data.frame(B_stance = c('L', 'L', 'R', 'R'),
C_stance = c('L', 'R', 'R', 'L'),
newcol = c('N', 'Y', 'N', 'Y'),
stringsAsFactors = FALSE)
library(dplyr)
left_join(data, keydat) %>%
mutate(newcol = replace(newcol, is.na(newcol), 'N'))
```

Database Modeling Step by Step, A determinant is a column whose value other columns may depend on for their breakdown into multiple tables created from the original table, for any values  I have a pandas dataframe with one column showing currencies and another showing prices. I want to create a new column that standardizes the prices to USD based on the values from the other two columns. eg. currency price SGD 100 USD 80 EUR 75 the new column would have conditions similar to

With `dplyr` you can use `case_when`. It's a little cleaner than nested `if_else`s if you have numerous conditions.

```df <- data.frame(
B_stance = c('L', 'L', 'R', 'R'),
C_stance = c('L', 'R', 'R', 'L'),
stringsAsFactors = FALSE
)

df %>% mutate(
newcol = case_when(
B_stance == 'U'                   ~ 'N',
B_stance == 'N'                   ~ 'N',
B_stance == 'L' & C_stance == 'L' ~ 'N',
B_stance == 'L' & C_stance == 'R' ~ 'Y',
B_stance == 'R' & C_stance == 'L' ~ 'Y',
B_stance == 'R' & C_stance == 'R' ~ 'N',
TRUE                              ~ B_stance
)
)

#   B_stance C_stance newcol
# 1        L        L      N
# 2        L        R      Y
# 3        R        R      N
# 4        R        L      Y
```

Note that the conditioning within `case_when` is lazy; the first true statement is executed. The final `TRUE` ensures there's a fallback in case no statement is true.

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