Rounding only numeric variables with ifelse - r

Question

I have a very large dataframe (around 100 rows, 200 columns). A subset of my data looks like this:
example <- data.frame("Station" = c("012", "013", "014"), "Value1" = c(145.23453, 1.022342, 0.4432),
"Value2" = c(2.1221213, 4445.2231412, 0.3333421), "Name" = c("ABC", "SDS", "EFG"))
I would like to round all numeric variables in my table with these conditions.
if x<1, then 1 sig fig
if 1<= x < 99, then 2 sig figs
if x>= 100, then 3 sig figs
I know to do something like this for a specific column:
example$Value1 <- ifelse(example$Value1 < 1, signif(example$Value1, 1), example$Value1)
but I'm not sure what to do for a large dataframe with a mix of numeric and character values.

Answer 1

Just put the ifelse into an lapply. To identify numeric columns use negate is.character in an sapply. You also could Vectorize a small replacement FUNction with all your desired conditions to use in the lapply, which might be convenient. However, note #GKi's comment, that your conditions are not complete.
nums <- sapply(example, is.numeric)
FUN <- Vectorize(function(x) {
if (x < 1) x <- signif(x, 1)
if (1 <= x & x < 99) x <- signif(x, 2)
if (x >= 100) x <- signif(x, 3)
x
})
example[nums] <- lapply(example[nums], FUN)
# Station Value1 Value2 Name
# 1 012 145.0 2.1 ABC
# 2 013 1.0 4450.0 SDS
# 3 014 0.4 0.3 EFG

Answer 2

CODE
example %>%
pivot_longer(contains("Value")) %>%
mutate(
signf = case_when(
value < 1 ~ 1,
value >= 1 & value < 99 ~ 2,
TRUE ~ 3
),
value = map2_dbl(value, signf, ~signif(.x, .y))
) %>%
select(-signf) %>%
pivot_wider(names_from = "name", values_from = "value")
OUTPUT
# A tibble: 3 x 4
Station Name Value1 Value2
<fct> <fct> <dbl> <dbl>
1 012 ABC 145 2.1
2 013 SDS 1 4450
3 014 EFG 0.4 0.3

Answer 3

I'll give the answer using data.table instead of data.frame because it's better and I don't remember data.frame syntax that well anymore.
library(data.table)
example = data.table(
Station = c("012", "013", "014"),
Value1 = c(145.23453, 1.022342, 0.4432),
Value2 = c(2.1221213, 4445.2231412, 0.3333421),
Name = c("ABC", "SDS", "EFG"))
numeric_colnames = names(example)[sapply(example,is.numeric)]
for(x in numeric_colnames){
example[,(x):=ifelse(
get(x)<1,
signif(get(x),1),
ifelse(
get(x)<99,
signif(get(x),2),
signif(get(x),3)
))]
}
Result:
Station Value1 Value2 Name
1: 012 145.0 2.1 ABC
2: 013 1.0 4450.0 SDS
3: 014 0.4 0.3 EFG
PS: Don't worry about the 145.0 and 4450.0; that's a display issue, not a data issue:
> example[,as.character(Value1)]
[1] "145" "1" "0.4"
> example[,as.character(Value2)]
[1] "2.1" "4450" "0.3"
PPS: the 99 cutoff produces some strange results, e.g.,
> signif(98.9,2)
[1] 99
> signif(99.1,3)
[1] 99.1
Why not use a cutoff of 100 instead?
> signif(99.4,2)
[1] 99
> signif(99.5,2)
[1] 100
> signif(100.1,3)
[1] 100

Answer 4

Use applyand nested ifelse:
If you do not know in advance which columns are numeric and you want to keep the original dataframe:
example[sapply(example, is.numeric)] <- apply(example[sapply(example, is.numeric)], 2,
function(x) ifelse(x < 1, signif(x, 1),
ifelse(x >= 1 & x < 99 , signif(x, 2), signif(x, 3))))
example
Station Value1 Value2 Name
1 012 145.0 2.1 ABC
2 013 1.0 4450.0 SDS
3 014 0.4 0.3 EFG

Answer 5

You can use findInterval to set signif:
i <- sapply(example, is.numeric)
x <- unlist(example[,i])
example[,i] <- signif(x, findInterval(x, c(1, 99))+1)
example
# Station Value1 Value2 Name
#1 012 145.0 2.1 ABC
#2 013 1.0 4450.0 SDS
#3 014 0.4 0.3 EFG
findIntervall result from #webb (Thanks!) example given in the comment:
findInterval(c(145.23453, 1.022342, 0.4432, 2.1221213, 4445.2231412
, 0.3333421), c(1, 99))
#[1] 2 1 0 1 2 0