pivot_wider causing "! Can't subset columns that don't exist." Error in R

I have data that I'm trying to pivot in R. But when I run my code, I get a Can't subset columns that don't exist error. My data currently looks like this, however, there are hundreds of different Titles, not just three as my simplified data below looks like.

ID    Title         Training_Time    Percent_Complete

1     New             1                    100
1     Hazmat          5                    100
1     Management      12                   100

And I would like for it to look like this, and if possible also include Columns for Percent Complete, though I can make do with just having the days it took to complete training, like I have below.

ID    Training_New     Training_Hazmat          Training_Managerment

1         1                   5                        12                                                      

I've tried several different versions of the code below, based on stackoverflow responses to similar questions.

LMS_df <- LMS_df %>%
tidyr::pivot_wider(-ID,
  names_from = LMS_df$Title,
  values_from = LMS_df$Training_Time
)

Any advice on getting rid of this error? Do I need to create new columns for every training title that I have in my dataset?

Here a small reproducible example from the data provided by the original poster. The name of the data frame in dollar syntax (i.e. LMS_df$) is not needed. Without, it works so far. A name prefix can be added with names_prefix = "Training_":

library(dplyr)
library(tidyr)

df <- tribble(
~ID,    ~Title,       ~Training_Time,  ~Percent_Complete,
1,     "New",         1,                100,
1,     "Hazmat",      5,                100,
1,     "Management",  12,               100
)

df %>%
  pivot_wider(-ID,
    names_from = Title,
    values_from = Training_Time,
    names_prefix = "Training_"
  )
#> # A tibble: 1 × 4
#>   Percent_Complete Training_New Training_Hazmat Training_Management
#>              <dbl>        <dbl>           <dbl>               <dbl>
#> 1              100            1               5                  12

^{Created on 2023-03-09 with reprex v2.0.2}

A final note: This is a special case as Percent_Complete is equally 100 for all Titles. Otherwise, you would get an individual row for each percentage and an NA for all non-matching cases.

One possible way is:

library(dplyr)
library(tidyr)
df %>% 
  pivot_wider(names_from = Title,
              values_from = Training_Time, 
              names_glue = "Training_{Title}") %>% 
  select(-ID)

 Percent_Complete Training_New Training_Hazmat Training_Management
             <int>        <int>           <int>               <int>
1              100            1               5                  12

An option with data.table

library(data.table)
 dcast(setDT(df), Percent_Complete ~ paste0("Training_", Title), value.var = "Training_Time")

-output

   Percent_Complete Training_Hazmat Training_Management Training_New
1:              100               5                  12            1