Finding If Boolean Is Ever True by Groups in R

finding if boolean is ever true by groups in R

A base R option would be

 df1$c <- with(df1, ave(b, a, FUN=any))

Or

 library(sqldf)
 sqldf('select * from df1
      left join(select a, b,
         (sum(b))>0 as c
         from df1 
         group by a)
         using(a)')

R: return one TRUE / FALSE from a group of boolean values

Use all:

all(abs(mu.iter - mu) > 1.5)

check if a value exists group by id in R

Here is a base R option with transform

transform(df1, new_var = as.integer(id %in% id[reference>0]))
#   id reference new_var
#1  A         0       1
#2  A         1       1
#3  B         0       0
#4  B         0       0

R check if a list of TRUE/FALSE values evaluate to TRUE

Maybe try the all function:

> x <- c(rep(TRUE, 5), FALSE)
> y <- c(rep(TRUE, 6))
> all(x)
[1] FALSE
> all(y)
[1] TRUE

As its name says, it checks if all the values are TRUE.

Create Aggregate of Boolean Values in R

Try using summarise as the terminal step in the pipeline:

library(dplyr)
new %>%
    group_by(ctfips) %>% 
    summarise(vote = sum(MinorityTract))

Actually, there is nothing wrong with summing a boolean column, and that should already be giving the correct sums.

Group by and summarise number of boolean values of a column corresponding to each unique value of another column on a specific date

We can subset the 'id' based on the logical vector and get the n_distinct

library(dplyr)
data_frame %>% 
   group_by(date) %>% 
   summarise(total_ids =n_distinct(id), 
              total_true_values = n_distinct(id[boolean_value]))

-output

# A tibble: 3 x 3
#  date       total_ids total_true_values
#  <date>         <int>             <int>
#1 2020-01-01         1                 1
#2 2020-01-02         2                 2
#3 2020-01-03         1                 0

If ID contains all elements in vector return TRUE for that ID in dplyr

Here, we need filter with all. Create a logical vector with %in% and wrap with all to get a single TRUE/FALSE output after grouping by 'id'

library(dplyr)
df1 %>%
    group_by(id) %>% 
    filter(all(4:6 %in% time))

NOTE: The OP created a matrix with cbind. Inorder to create a data.frame, use the data.frame constructor. Also, logical vector TRUE/FALSE ('solution') should be without any quotes i.e. TRUE or FALSE instead of "TRUE" or "FALSE"

data

df1 <- data.frame(id, time)

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