add column to df according to rule - r

Question

My dataset is:
unit date total
1 2019-04-02 7
1 2020-01-01 5
2 2019-12-01 10
2 2020-01-03 2
3 2019-09-01 3
3 2020-03-03 3
I would like to add the 'category' column if any value in 'total' is higher or equal to 10 for each 'unit':
unit date total category
1 2019-04-02 7 low
1 2020-01-01 5 low
2 2019-12-01 10 high
2 2020-01-03 2 high
3 2019-09-01 3 low
3 2020-03-03 3 low
I have tried many things such as:
df$category <- "low"
for (i in df$unit){
if (rowSums(df$total >= 10) > 0){
df$category <- "high"
}
}
but none worked. Can you please advise?

Answer 1

Try working around the max values in each group and then assign the category. Here the code:
library(dplyr)
#Code
dfnew <- df %>% group_by(unit) %>% mutate(category=ifelse(max(total,na.rm=T)>=10,'High','Low'))
Output:
# A tibble: 6 x 4
# Groups: unit [3]
unit date total category
<int> <chr> <int> <chr>
1 1 2019-04-02 7 Low
2 1 2020-01-01 5 Low
3 2 2019-12-01 10 High
4 2 2020-01-03 2 High
5 3 2019-09-01 3 Low
6 3 2020-03-03 3 Low
Some data used:
#Data
df <- structure(list(unit = c(1L, 1L, 2L, 2L, 3L, 3L), date = c("2019-04-02",
"2020-01-01", "2019-12-01", "2020-01-03", "2019-09-01", "2020-03-03"
), total = c(7L, 5L, 10L, 2L, 3L, 3L)), class = "data.frame", row.names = c(NA,
-6L))

Answer 2

Does this work:
> library(dplyr)
> df %>% group_by(unit) %>% mutate(category = case_when(max(total) >= 10 ~ 'high', TRUE ~ 'low'))
# A tibble: 6 x 4
# Groups: unit [3]
unit date total category
<dbl> <dttm> <dbl> <chr>
1 1 2019-04-02 00:00:00.000 7 low
2 1 2020-01-01 00:00:00.000 5 low
3 2 2019-12-01 00:00:00.000 10 high
4 2 2020-01-03 00:00:00.000 2 high
5 3 2019-09-01 00:00:00.000 3 low
6 3 2020-03-03 00:00:00.000 3 low
>

Answer 3

One base R option using ave, e.g.,
transform(
df,
category = c("Low","High")[ave(total>=10,unit,FUN = any)+1]
)
which gives
unit date total category
1 1 2019-04-02 7 Low
2 1 2020-01-01 5 Low
3 2 2019-12-01 10 High
4 2 2020-01-03 2 High
5 3 2019-09-01 3 Low
6 3 2020-03-03 3 Low
Data
> dput(df)
structure(list(unit = c(1L, 1L, 2L, 2L, 3L, 3L), date = c("2019-04-02",
"2020-01-01", "2019-12-01", "2020-01-03", "2019-09-01", "2020-03-03"
), total = c(7L, 5L, 10L, 2L, 3L, 3L)), class = "data.frame", row.names = c(NA,
-6L))

Answer 4

For each unit you can check if any value is greater than equal to 10 and assign category value accordingly.
library(dplyr)
df %>%
group_by(unit) %>%
mutate(category = if(any(total >= 10)) 'high' else 'low')
# unit date total category
# <int> <chr> <int> <chr>
#1 1 2019-04-02 7 low
#2 1 2020-01-01 5 low
#3 2 2019-12-01 10 high
#4 2 2020-01-03 2 high
#5 3 2019-09-01 3 low
#6 3 2020-03-03 3 low
The same logic can be implemented in base R
df$category <- with(df, ave(total, unit, FUN = function(x)
if(any(x >= 10)) 'high' else 'low'))
and data.table :
library(data.table)
setDT(df)[, category := if(any(total >= 10)) 'high' else 'low', unit]