How to simplify handling with nested ifelse() structures in base R?

Question

I'm facing nested ifelse() structures:

df1$var <- ifelse(x < a, u, ifelse(x < b, v, ifelse(x < c, w, ...)))

whereby the u, v, w, ...s are actually functions.

A dumbed down working example would be

df1 <- data.frame(x = rbinom(100, 5, .5))
df1$y <- ifelse(x == 1, "s", ifelse(x == 2, "t", 
                                    ifelse(x == 3, "u", ifelse(x == 4, "v", "w"))))

I presume there could be ideally a base R method (for sake of speed) to simplify such code; eventually a function as

rave.ifelse(x, 1=s, 2=t, ...)

I took a glance at cut(x, 5) but it confused me from this point of view.

Note: Values of x could be either numbers or factors, == could also be any logical operator and the s, t, ... are actually functions.

edit:

Note: The number of ifelse()s is known and large. The solution really should fit to the df1$var <- ifelse(x < a, u, ifelse(x < b, v, ifelse(x < c, w, ...))) situation, when the u, v, w, ...s are functions, e.g. u=sample(0:9, 1), v=runif(1),.... It should not be significantly slower than ifelse().

Answer 1

You could use case_when from the dplyr library:

df1$y <- case_when(
    x == 1 ~ "s",
    x == 2 ~ "t",
    x == 3 ~ "u",
    x == 4 ~ "v",
    TRUE ~ "w"
)

Note that the final case above (TRUE) is the blanket else condition which will catch all cases not matching any earlier conditions.

Answer 2

Since you insist on base R, here are two possibilities:

Define a mapping data.frame:

# Define mapping
map <- cbind.data.frame(
    x = c(1, 2, 3, 4, NA),
    y = c("s", "t", "u", "v", "w"));

Method 1: match entries from map to df1.

# match entries
df1$y <- map[match(df1$x, map$x), 2];
df1$y[is.na(df1$y2)] <- "w";

Method 2: Loop through all mappings, and replace using direct indexing:

# for loop
df1$y <- factor("w", levels = map$y);
for (i in 1:nrow(map)) df1$y[df1$x == map$x[i]] <- map$y[i];

Output:

tail(df1);
#    x y
#95  4 v
#96  1 s
#97  4 v
#98  2 t
#99  4 v
#100 1 s

Note, the second method will also work for inequalities.

---

Sample data

set.seed(2017);
df1 <- data.frame(x = rbinom(100, 5, .5))