Suppose I have the following dataframe:
x <- c(1, 1, 2, 3, 4, 5)
y <- c(1, 1, 1, 3, 4, 5)
z <- c(NA, 1, 1, 3, 4, NA)
to get:
x y z
1 1 NA
1 1 1
2 1 1
3 3 3
4 4 4
5 4 NA
and I wanted to get a conditional statement such that if all of the non-NA x, y, and z values are equal to 1, then it would be flagged as 1, how would I go about writing this script?
For instance, what I want is the following:
x y z flag1
1 1 NA 1
1 1 1 1
2 1 1 0
3 3 3 0
4 4 4 0
5 4 NA 0
Additionally, I would also want to flag if any of the variables contained a 4, ignoring NA, so that I can get:
x y z flag1 flag2
1 1 NA 1 0
1 1 1 1 0
2 1 1 0 0
3 3 3 0 0
4 4 4 0 1
5 4 NA 0 1
CodePudding user response:
Easiest is with rowSums
df$flag <- (!rowSums(df != 1, na.rm = TRUE) & !!rowSums(!is.na(df)))
df$flag2 <- (rowSums(df == 4, na.rm = TRUE) > 0 & !!rowSums(!is.na(df)))
-output
> df
x y z flag flag2
1 1 1 NA 1 0
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 4 NA 0 1
In tidyverse
, we may use if_all
with if_any
for creating those columns
library(dplyr)
df %>%
mutate(flag1 = (if_all(everything(), ~is.na(.)| . %in% 1)),
flag2 = (if_any(x:z, ~ . %in% 4)))
x y z flag1 flag2
1 1 1 NA 1 0
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 4 NA 0 1
data
df <-structure(list(x = c(1, 1, 2, 3, 4, 5), y = c(1, 1, 1, 3, 4,
4), z = c(NA, 1, 1, 3, 4, NA)), class = "data.frame", row.names = c(NA,
-6L))
CodePudding user response:
Here's a version that more verbose than @Akrun's answer (and slower on larger datasets), but more customizable:
flag1 <- ifelse( (x == 1 | is.na(x) ) &
(y == 1 | is.na(y) ) &
(z == 1 | is.na(z) ), 1, 0)
flag2 <- ifelse( x == 4 | y == 4 | z == 4, 1, 0)
If you had a bunch of these vectors, you could store them in a matrix or data.frame so you don't need to list each column in order to do the calculation:
mat <- cbind(x,y,z)
flag1 <- apply(mat, 1, function(r) sum(r==1 | is.na(r)) == length(r))
flag2 <- apply(mat, 1, function(r) any(r==4, na.rm=T))
CodePudding user response:
Using apply function:
apply(df, 1, function(x) all(x == 1,na.rm = 1))
[1] 1 1 0 0 0 0
apply(df, 1, function(x) any(x == 4,na.rm = 1))
[1] 0 0 0 0 1 0
Data used:
df
x y z
1 1 1 NA
2 1 1 1
3 2 1 1
4 3 3 3
5 4 4 4
6 5 5 NA
CodePudding user response:
Here is an additional alternative way with pivoting using all
and any
:
library(tidyr)
library(dplyr)
df %>%
pivot_longer(
cols=everything()
) %>%
mutate(id = as.integer(gl(n(), 3, n()))) %>%
group_by(id) %>%
mutate(flag1 = ifelse(all(value == 1, na.rm=TRUE), 1,0),
flag2 = ifelse(any(value == 4, na.rm=TRUE), 1,0)) %>%
pivot_wider(
names_from = name,
values_from = value
) %>%
ungroup() %>%
select(x,y,z,flag1, flag2)
output:
x y z flag1 flag2
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA 1 0
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 4 NA 0 1
CodePudding user response:
library(tidyverse)
df = tibble(
x = c(1, 1, 2, 3, 4, 5),
y = c(1, 1, 1, 3, 4, 5),
z = c(NA, 1, 1, 3, 4, NA)
)
df %>% mutate(
flag1 = ifelse((x==1 | is.na(x)) & (y==1 | is.na(y)) & (z==1 | is.na(z)), 1, 0),
flaf2 = ifelse((x==4 | is.na(x)) | (y==4 | is.na(y)) | (z==4 | is.na(z)), 1, 0)
)
output
# A tibble: 6 x 5
x y z flag1 flaf2
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA 1 1
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 5 NA 0 1
Update 1
Note, you can't forget to decide what to do when all variables are NA
. Here is a corrected version of one of the possible solutions.
library(tidyverse)
df = tibble(
x = c(1, 1, 2, 3, 4, 5, NA),
y = c(1, 1, 1, 3, 4, 5, NA),
z = c(NA, 1, 1, 3, 4, NA, NA)
)
df %>% mutate(
flag1 = ifelse(is.na(x) & is.na(y) & is.na(z), NA,
ifelse((x==1 | is.na(x)) & (y==1 | is.na(y)) & (z==1 | is.na(z)), 1, 0)),
flag2 = ifelse(is.na(x) & is.na(y) & is.na(z), NA,
ifelse((x==4 | is.na(x)) | (y==4 | is.na(y)) | (z==4 | is.na(z)), 1, 0))
)
output
# A tibble: 7 x 5
x y z flag1 flag2
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA 1 1
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 5 NA 0 1
7 NA NA NA NA NA
CodePudding user response:
Here's an option using rowwise
and c_across
:
library(dplyr)
df %>%
rowwise() %>%
mutate(flag1 = as.numeric(all(c_across() == 1, na.rm = T)),
flag2 = as.numeric(any(c_across() == 4, na.rm = T))) %>%
ungroup()
c_across
will combine each row into an atomic vector for comparison to your condition.
Note: by default c_across
works across all columns. You can change this with any tidyselect syntax. For example, x:z
.
Output
x y z flag1 flag2
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA 1 0
2 1 1 1 1 0
3 2 1 1 0 0
4 3 3 3 0 0
5 4 4 4 0 1
6 5 4 NA 0 1