If/else function on a data frame to create conditional matrix - r

Question

Here is the data set for reproducibility:
a=c(90.41,37.37,18.98)
b=c(103.39,39.44,51.68)
c=c(83.51,36.41,47.46)
d=c(94.60,38.57,50.22)
e=c(95.04,38.81,50.49)
xx=t(data.frame(a,b,c,d,e))
df=data.frame(xx)
And here is the if/else function I am trying run on the data frame
classify=function(df){
if (df>=110) {
class="5"}
else if (df<110 & df>=103){
class="4"}
else if (df<103 & df>=95){
class="3"}
else if (df<95 & df>=76){
class="2"}
else if (df<76){
class="1"}
else {class="none"}
}
However, what I want the if/else function to produce is a new data frame that looks like this:
df
X1 X2 X3
a 2 1 1
b 4 1 1
c 2 1 1
d 2 1 1
e 3 1 1
I am unsure as to how to do this so any help would be super appreciated. I anticipate something is wrong in the if/else function itself but I am quite inexperienced and I don't know how to detect errors in the script that easily. Thank you!

Answer 1

sapply(df, function(x) {as.numeric(as.character(cut(x, c(-Inf,76,95,103,110,Inf), seq(1:5))))})
X1 X2 X3
[1,] 2 1 1
[2,] 4 1 1
[3,] 2 1 1
[4,] 2 1 1
[5,] 3 1 1
Use cut to set the intervals (its 2nd argument) and the labels (its 3rd argument). However, it returns a factor, so convert back to numeric if you like that. Since you want to run the function over the the full dataframe, use sapply or lapply.

Answer 2

You can do this with findInterval. All you have to do is to pass it a non-decreasing vector of break points.
classify <- function(DF, breaks = c(-Inf, 76, 95, 103, 110, Inf)){
f <- function(x, breaks) findInterval(x, breaks)
DF[] <- lapply(DF, f, breaks)
DF
}
classify(df)
# X1 X2 X3
#a 2 1 1
#b 4 1 1
#c 2 1 1
#d 2 1 1
#e 3 1 1

Answer 3

Quite a similar approach to your example, using case_when from dplyr:
library(dplyr)
classify <- function(x){
case_when(
x >= 110 ~ "5",
x >= 103 & x < 110 ~ "4",
x >= 95 & x < 103 ~ "3",
x >= 76 & x < 95 ~ "2",
x < 76 ~ "1",
TRUE ~ "none"
)
}
a = c(90.41, 37.37, 18.98)
b = c(103.39, 39.44, 51.68)
c = c(83.51, 36.41, 47.46)
d = c(94.60, 38.57, 50.22)
e = c(95.04, 38.81, 50.49)
df <- data.frame(matrix(c(a, b, c, d, e), ncol = 3, byrow = T))
mutate_all(df, classify)
# X1 X2 X3
#1 2 1 1
#2 4 1 1
#3 2 1 1
#4 2 1 1
#5 3 1 1
In case if:
df
# X1 X2 X3
#1 -Inf 37.37 18.98
#2 103.39 NaN 51.68
#3 83.51 36.41 47.46
#4 94.60 Inf 50.22
#5 95.04 38.81 NA
The results look like this:
mutate_all(df, classify)
# X1 X2 X3
#1 1 1 1
#2 4 none 1
#3 2 1 1
#4 2 5 1
#5 3 1 none