Trying to convert column to numeric

I'm trying to take the following dataframe and take the averages of some of the columns (like reference_1 and reference_2) but I can't seem to get the columns to register as numeric.

Data:

> dput(head(all.data))
structure(list(`Identified Proteins (3404)` = c("Casein kinase II subunit beta", 
"No_match_UP_pBlast", "DNA helicase", "Methanethiol oxidase_protein transport and brown fat cell differentation_pBlast", 
"No_match_UP_pBlast", "Thylakoid lumenal protein, chlorplastic_pBlast"
), `Accession Number` = c("A0A383V1G7", "A0A383V1H7", "A0A383V1J2", 
"A0A383V1M7", "A0A383V1R6", "A0A383V1S1"), reference_1 = c("8.0699999999999994E-2", 
"1.52E-2", "-0.1225", "4.4299999999999999E-2", "-2.41E-2", "-5.5999999999999999E-3"
), reference_2 = c("-0.12620000000000001", "-3.3999999999999998E-3", 
"0.113", "-5.1999999999999998E-2", "4.9799999999999997E-2", "1.5100000000000001E-2"
), An_24h_1 = c("0.4803", "-0.247", "0.2999", "0.2437", "-0.97709999999999997", 
"-1.0880000000000001"), An_24h_2 = c("0.2009", "-0.40379999999999999", 
"0.30609999999999998", "0.24909999999999999", "-1.0182", "-1.0642"
), An_28d_1 = c("-0.39629999999999999", "-1.0501", "0.94269999999999998", 
"-0.17030000000000001", "0.46289999999999998", "-1.3814"), An_28d_2 = c("9.9299999999999999E-2", 
"-0.53979999999999995", "0.83540000000000003", "-0.1983", "0.35149999999999998", 
"-1.4052"), C_24h_1 = c("0.17380000000000001", "0.22459999999999999", 
"0.13550000000000001", "-0.15820000000000001", "-0.96909999999999996", 
"0.29039999999999999"), C_24h_2 = c("0.32090000000000002", "0.20250000000000001", 
"-0.1242", "-3.0700000000000002E-2", "-0.74780000000000002", 
"9.8599999999999993E-2"), C_28d_1 = c("0.61170000000000002", 
"-0.35260000000000002", "0.96399999999999997", "9.4200000000000006E-2", 
"-0.40229999999999999", "-0.99809999999999999"), C_28d_2 = c("0.49249999999999999", 
"-0.30109999999999998", "0.55520000000000003", "3.44E-2", "-0.3841", 
"-0.79159999999999997")), row.names = c(NA, -6L), class = c("tbl_df", 
"tbl", "data.frame"))

My current code:

all.data<-readxl::read_xlsx("Output of all algae proteins for blast sequences.xlsx") %>% 
  select("Identified Proteins (3404)","Accession Number","Reference...3",
         "Reference...4","Anaerobic_24h...5","Anaerobic_24h...6",
         "Anaerobic_28d...7","Anaerobic_28d...8","Citric_24h...9",
         "Citric_24h...10","Citric_28d...11","Citric_28d...12")

colnames(all.data)<-gsub("Reference...3","reference_1",colnames(all.data))
colnames(all.data)<-gsub("Reference...4","reference_2",colnames(all.data))
colnames(all.data)<-gsub("Anaerobic_24h...5","An_24h_1",colnames(all.data))
colnames(all.data)<-gsub("Anaerobic_24h...6","An_24h_2",colnames(all.data))
colnames(all.data)<-gsub("Anaerobic_28d...7","An_28d_1",colnames(all.data))
colnames(all.data)<-gsub("Anaerobic_28d...8","An_28d_2",colnames(all.data))
colnames(all.data)<-gsub("Citric_24h...9","C_24h_1",colnames(all.data))
colnames(all.data)<-gsub("Citric_24h...10","C_24h_2",colnames(all.data))
colnames(all.data)<-gsub("Citric_28d...11","C_28d_1",colnames(all.data))
colnames(all.data)<-gsub("Citric_28d...12","C_28d_2",colnames(all.data))
colnames(all.data)<-gsub("Identified Proteins (3404)","Protein",colnames(all.data))

I've tried using the as.numeric() command, but get the following:

> all.data %>% 
+   as.numeric(all.data$reference_1)
Error in all.data %>% as.numeric(all.data$reference_1) : 
  'list' object cannot be coerced to type 'double'

And I've tried doing the following, which worked in my prior work but now it just puts NA into the new column:

> all.data<-all.data %>% 
+   rowwise() %>% 
+   mutate(reference_avg=mean(c_across(c(`reference_1`,`reference_2`))))
There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: Problem while computing `reference_avg = mean(c_across(c(reference_1, reference_2)))`.
ℹ argument is not numeric or logical: returning NA
ℹ The warning occurred in row 1.
2: Problem while computing `reference_avg = mean(c_across(c(reference_1, reference_2)))`.
ℹ argument is not numeric or logical: returning NA
ℹ The warning occurred in row 2.
3: Problem while computing `reference_avg = mean(c_across(c(reference_1, reference_2)))`.
ℹ argument is not numeric or logical: returning NA
ℹ The warning occurred in row 3.

I'm still very much a beginner in any sort of coding, so any advice is very helpful.

It should be:

all.data <- all.data %>%
    mutate( # create a new variable
    reference_1 = as.numeric(reference_1) # named reference_1 where reference_1 is numeric
  )

That works, thanks. Do you know of a shortcut to due this to all the columns except the first without having to do it as multiple lines of code, just to make the code a little neater?

And actually, it did convert the column to numeric, but I still can't get it to find the average. Instead I get the following:

> all.data<-all.data %>% 
+   mutate(ref_avg=mean(reference_1,reference_2,na.rm=TRUE))
Error in `mutate()`:
! Problem while computing `ref_avg = mean(reference_1, reference_2, na.rm = TRUE)`.
Caused by error in `mean.default()`:
! 'trim' must be numeric of length one
Run `rlang::last_error()` to see where the error occurred.
# mean for each variable
all.data %>%
  mutate(
    across(`Accession Number`:C_28d_2, as.numeric) # mutate selected variables to numeric
  ) %>%
  summarise( # mean of each variable
    ref_1_avg = mean(reference_1), # mean of ref 1
    ref_2_avg = mean(reference_2) # mean of ref 2
  )
  
# mean for reference variables 
all.data %>%
  mutate(
    across(`Accession Number`:C_28d_2, as.numeric) # mutate selected variables to numeric
  ) %>%
  select( # select columns to pivot 
    `Identified Proteins (3404)`, 
    reference_1,
    reference_2
  ) %>%
  pivot_longer(
    !`Identified Proteins (3404)` # make the data table, long
  ) %>%
  summarise( # mean of refence variables
    ref_avg = mean(value)
  )

Sorry, there may be some confusion, so each row is a separate observation. I want a column that has the average of reference_1 and reference_2 across each row. So I can't just do an average of the whole column. So like I want a reference_avg for each row, then a An_24h_avg for each row, so on and so forth, does that make sense?

all.data %>%
  mutate(
    across(`Accession Number`:C_28d_2, as.numeric) # mutate selected variables to numeric
  ) %>%
  mutate(
    ref_avg = (reference_1 + reference_2) /2)

Or, like the solution found in this thread (rowwise):