"Adding Missing Grouping Variables" Message in Dplyr in R

Adding missing grouping variables message in dplyr in R

For consistency sake the grouping variables should be always present when defined earlier and thus are added when select(value) is executed. ungroup should resolve it:

qu25 <- mydata %>% 
  group_by(month, day, station_number) %>%
  arrange(desc(value)) %>% 
  slice(2) %>% 
  ungroup() %>%
  select(value)

The requested result is without warnings:

> mydata %>% 
+   group_by(month, day, station_number) %>%
+   arrange(desc(value)) %>% 
+   slice(2) %>% 
+   ungroup() %>%
+   select(value)
# A tibble: 1 x 1
  value
  <dbl>
1   113

Dplyr keeps automatically adding one of my columns

Try ungroup :

df <- data.frame(trial.number=1:2,indexer=3:4)

df %>% distinct(trial.number)
#  trial.number
#1            1
#2            2

df %>% group_by(trial.number,indexer) %>% distinct(trial.number)
## A tibble: 2 x 2
## Groups:   trial.number, indexer [2]
#  trial.number indexer
#         <int>   <int>
#1            1       3
#2            2       4

df %>% group_by(trial.number,indexer) %>% ungroup %>% distinct(trial.number)
## A tibble: 2 x 1
#  trial.number
#         <int>
#1            1
#2            2

How can I keep additional variables after grouping in some other variables in dplyr in R?

From the previous solution, add .keep_all = TRUE in distinct and then fill the loc column with the previous non-NA value

library(dplyr)
library(tidyr)
library(lubridate)
data %>%   
   mutate(month = lubridate::month(date)) %>%
   group_by(var, month) %>% 
   mutate(height = sum(height)) %>%
   ungroup %>% 
   complete(var, month, fill = list(height = 0)) %>% 
   mutate(Quarter = quarter, Condition = !is.na(date)) %>% 
   distinct(var, month, Quarter, Condition, .keep_all = TRUE) %>% 
   fill(loc) %>% 
   select(-date)

-output

# A tibble: 9 × 6
  var   month loc    height Quarter Condition
  <chr> <dbl> <chr>   <dbl>   <dbl> <lgl>    
1 A         1 london     13       1 TRUE     
2 A         2 london     14       1 TRUE     
3 A         3 london     15       1 TRUE     
4 B         1 berlin     13       1 TRUE     
5 B         2 berlin      0       1 FALSE    
6 B         3 berlin     15       1 TRUE     
7 C         1 cairo      28       1 TRUE     
8 C         2 cairo      27       1 TRUE     
9 C         3 cairo      15       1 TRUE     

Grouping data by time intervals and adding missing rows

With the tidyverse:

left_join(mp, bills) %>% 
  group_by(name, surname, month = lubridate::floor_date(date, "month")) %>% 
  summarise(n = sum(!is.na(month))) %>% 
  replace_na(list(month = as.Date("2021-01-01"))) %>% 
  ungroup(month) %>% 
  complete(month = seq.Date(as.Date("2021-01-01"), as.Date("2021-12-01"), '1 month'), fill = list(n = 0))

# A tibble: 60 x 4
# Groups:   name, surname [5]
   name  surname month          n
   <chr> <chr>   <date>     <dbl>
 1 Diane Abbott  2021-01-01     1
 2 Diane Abbott  2021-02-01     0
 3 Diane Abbott  2021-03-01     0
 4 Diane Abbott  2021-04-01     0
 5 Diane Abbott  2021-05-01     1
 6 Diane Abbott  2021-06-01     0
 7 Diane Abbott  2021-07-01     0
 8 Diane Abbott  2021-08-01     0
 9 Diane Abbott  2021-09-01     0
10 Diane Abbott  2021-10-01     0

Calculate internal consistency of items by grouping variables using dplyr/tidyverse

Perhaps this helps

out1 <-  mydata %>%
    group_by(age, raterType) %>%    
     summarise(alpha = alpha(across(all_of(itemNames)))$total$raw_alpha, 
     omega = ci.reliability(across(all_of(itemNames)), 
    type = "omega", interval.type = "none")$est, .groups = 'drop')

-output

> out1
# A tibble: 15 × 4
     age raterType   alpha     omega
   <int> <fct>       <dbl>     <dbl>
 1     1 self      -0.135    2.76   
 2     1 friend     0.138    0.231  
 3     1 parent    -0.229  255.     
 4     2 self      -0.421   NA      
 5     2 friend     0.0650  58.7    
 6     2 parent     0.153   NA      
 7     3 self      -0.302    0.00836
 8     3 friend     0.147    0.334  
 9     3 parent     0.196    0.132  
10     4 self      -0.0699  NA      
11     4 friend     0.118    0.214  
12     4 parent    -0.0303  31.1    
13     5 self      -0.0166   0.246  
14     5 friend    -0.192    0.0151 
15     5 parent     0.0847  NA      

---

Or may be this

out2 <- mydata %>%
   nest_by(age, raterType) %>%
   mutate(alpha = alpha(data[, itemNames])$total$raw_alpha, 
   omega = ci.reliability(data[, itemNames], type = "omega", 
    interval.type = "none")$est)

-output

out2
# A tibble: 15 × 5
# Rowwise:  age, raterType
     age raterType               data   alpha     omega
   <int> <fct>     <list<tibble[,7]>>   <dbl>     <dbl>
 1     1 self               [100 × 7] -0.135    2.76   
 2     1 friend             [100 × 7]  0.138    0.231  
 3     1 parent             [100 × 7] -0.229  255.     
 4     2 self               [100 × 7] -0.421   NA      
 5     2 friend             [100 × 7]  0.0650  58.7    
 6     2 parent             [100 × 7]  0.153   NA      
 7     3 self               [100 × 7] -0.302    0.00836
 8     3 friend             [100 × 7]  0.147    0.334  
 9     3 parent             [100 × 7]  0.196    0.132  
10     4 self               [100 × 7] -0.0699  NA      
11     4 friend             [100 × 7]  0.118    0.214  
12     4 parent             [100 × 7] -0.0303  31.1    
13     5 self               [100 × 7] -0.0166   0.246  
14     5 friend             [100 × 7] -0.192    0.0151 
15     5 parent             [100 × 7]  0.0847  NA     

---

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