I have the following:
ID Value1 Value2 Code 0001 3.3 432 A 0001 0 654 A 0001 0 63 A 0002 0 78 B 0002 1 98 B 0003 0 22 C 0003 0 65 C 0003 0 91 C
I need the following:
ID Value1 Value2 Code 0001 3.3 432 A 0001 0 0 A 0001 0 0 A 0002 0 0 B 0002 1 98 B 0003 0 22 C 0003 0 65 C 0003 0 91 C
i.e., for the same "Code" if there is at least one row with Value1 !=0 then all the other rows referred to the same Code will be set to 0 (meaning that 654 and 63 for 0001 relative to Value2 will be set to 0). If this is not the case (like for 0003 nothing will be done).
Can anyone help me please?
Thank you in advance
CodePudding user response：
dplyr
library(dplyr)
quux %>%
group_by(Code) %>%
mutate(Value2 = if_else(abs(Value1) > 0 | !any(abs(Value1) > 0),
Value2, 0L)) %>%
ungroup()
# # A tibble: 8 x 4
# ID Value1 Value2 Code
# <int> <dbl> <int> <chr>
# 1 1 3.3 432 A
# 2 1 0 0 A
# 3 1 0 0 A
# 4 2 0 0 B
# 5 2 1 98 B
# 6 3 0 22 C
# 7 3 0 65 C
# 8 3 0 91 C
base R
quux |>
transform(Value2 = ifelse(ave(abs(Value1), Code, FUN = function(v) abs(v) > 0 | !any(abs(v) > 0)),
Value2, 0L))
# ID Value1 Value2 Code
# 1 1 3.3 432 A
# 2 1 0.0 0 A
# 3 1 0.0 0 A
# 4 2 0.0 0 B
# 5 2 1.0 98 B
# 6 3 0.0 22 C
# 7 3 0.0 65 C
# 8 3 0.0 91 C
data.table
library(data.table)
as.data.table(quux)[, Value2 := fifelse(abs(Value1) > 0 | !any(abs(Value1) > 0), Value2, 0L), by = Code][]
# ID Value1 Value2 Code
# <int> <num> <int> <char>
# 1: 1 3.3 432 A
# 2: 1 0.0 0 A
# 3: 1 0.0 0 A
# 4: 2 0.0 0 B
# 5: 2 1.0 98 B
# 6: 3 0.0 22 C
# 7: 3 0.0 65 C
# 8: 3 0.0 91 C
Data
quux <- structure(list(ID = c(1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L), Value1 = c(3.3, 0, 0, 0, 1, 0, 0, 0), Value2 = c(432L, 654L, 63L, 78L, 98L, 22L, 65L, 91L), Code = c("A", "A", "A", "B", "B", "C", "C", "C")), class = "data.frame", row.names = c(NA, -8L))
CodePudding user response：
We can use an if_else here. For example
library(dplyr)
dd %>%
group_by(ID) %>%
mutate(Value2=if_else(any(Value1!=0) & Value1==0, 0L, Value2))
Basically we use any() to check for non-zero values and then replace with 0s if one is found.
CodePudding user response：
This should do it:
df %>% group_by(Code) %>%
mutate(Value2 = if_else(row_number() == 1 & any(Value1 != 0), Value2, 0))
# A tibble: 8 × 4
# Groups: Code [3]
# ID Value1 Value2 Code
# <int> <dbl> <dbl> <fct>
# 1 1 3.3 432 A
# 2 1 0 0 A
# 3 1 0 0 A
# 4 2 0 78 B
# 5 2 1 0 B
# 6 3 0 0 C
# 7 3 0 0 C
# 8 3 0 0 C