Here's my code:

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library(rvest)

# create data frames for each section of data
data_A <- data.frame(
  protocol_url_segment = character(),
  A.1 = character(), A.2 = character(), A.3 = character(), A.3.1 = character(), A.3.2 = character(), A.4.1 = character(), A.5.1 = character(), A.5.2 = character(),  A.5.3 = character(), A.5.4 = character(), A.7 = character(), A.8 = character(),
  stringsAsFactors = FALSE
)

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data_P <- data.frame(
  protocol_url_segment = character(),
  P.A = character(),  P.B = character(),
  stringsAsFactors = FALSE
)

# specify the base URL of the protocol page
protocol_url_segment <- "2015-003589-10/GR"
base_url <- "https://www.clinicaltrialsregister.eu/ctr-search/trial/"
url <- paste0(base_url, protocol_url_segment)

# read in the HTML content of the page
page <- read_html(url)

# extract the tricell rows as a list of nodes
tricell_rows <- page %>% html_nodes("tr.tricell")

# function to extract the text from row td elements and create a row vector 
extract_td_text <- function(row) {
  td_elements <- html_children(row)
  td_texts <- sapply(td_elements, html_text)
  td_texts <- trimws(td_texts)
  return(as.vector(td_texts))
}

# extract the text from the td elements in each row and store them in rows_list
rows_list <- lapply(tricell_rows, extract_td_text)

# create a data frame from the rows_list
tricell_df <- do.call(rbind.data.frame, c(rows_list, stringsAsFactors = FALSE))
colnames(tricell_df) <- c("Column1", "Column2", "Column3")

# Create a data frame with the codes and their corresponding replacements
lookup_df <- data.frame(
  code = c("E.8.9.1", "E.8.9.1", "E.8.9.1", "E.8.9.2", "E.8.9.2", "E.8.9.2", 
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)
)

# Loop through each row in the target data frame and check if the code and value match
for (i in 1:nrow(tricell_df)) {
  code <- tricell_df[i, 1]
  value <- tricell_df[i, 2]
  replacement <- lookup_df[lookup_df$code == code & lookup_df$value == value, "replacement"]
  # If a match is found, replace the code with the corresponding replacement
  if (length(replacement) > 0) {
    tricell_df[i, 1] <- replacement
  }
}

# Function that filters dataframe by the first letter in ‘Column1’ and returns the filtered dataframe
filter_by_first_letter <- function(df, letter) {
  index <- grep(paste0("^", letter), df$Column1)
  filtered_df <- df[index,]
  return(filtered_df)
}

# Filter the dataframe for each letter, then create separate dataframes
df_A <- filter_by_first_letter(tricell_df, "A")

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df_P <- filter_by_first_letter(tricell_df, "P")



data_list <- list(data_A, data_B, data_D, data_E, data_F, data_G, data_H, data_N, data_P)
df_list <- list(df_A, df_B, df_D, df_E, df_F, df_G, df_H, df_N, df_P)

# Loop through each pair of data_ and df_ dataframes
for (k in 1:length(data_list)) {
  data_df <- data_list[[k]]
  merge_df <- df_list[[k]]
  
  if (nrow(merge_df) == 0) {
    next
  }
  
  new_row <- data_df[1,] # Create a new row based on the structure of the data_ dataframe
  
  # Set all values in the new row to NA except the protocol_url_segment column
  new_row[,-1] <- NA 
  new_row$protocol_url_segment <- protocol_url_segment 
  
  
  # Loop through each row in the df_ dataframe
  for (i in 1:nrow(merge_df)) {
    code <- merge_df[i, 1]
    value <- merge_df[i, 3]
    
    # Check if the code exists in the data_ dataframe
    if (code %in% colnames(data_df)) {
      # Insert the value at the corresponding column in the new row
      new_row[, code] <- value
    }
  }
  
  # Add the new row to the data_ dataframe
  data_list[[k]] <- rbind(data_list[[k]], new_row)
}

# Update the data_ dataframes with the merged values
data_A <- data_list[[1]]

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data_N <- data_list[[8]]


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We need to change a thing... Handling of multiple entries is not working.

The problem is with the data from B, D and G. 
While for the others, there's only one entry per df_, for B, D and G, each protocol_url may have multiple entries. If there are multiple entries I want them saved with a row each in the data_ dataframes. 
Multiple entries in those dataframes will appear sequentially, and not in multiple columns. You can detect it's a new entry if the codes start repeating. 

It may help to know that each entry will always start with the same code: "B.1.1", "D.1.2andD.1.3" or "G.4.1", respectively.

If there are multiple, they will be sequentially in the df dataframe, per example:

 "D.1.2andD.1.3" "D.2.1"         "D.2.5"         "D.2.5.1"       "D.3.1"         "D.3.2"         "D.3.4"         "D.3.4.1"      
 "D.3.7"         "D.3.8"         "D.3.9.1"       "D.3.9.2"       "D.3.10.1"      "D.3.10.2"      "D.3.10.3"      "D.3.11.1"     
 "D.3.11.2"      "D.3.11.3"      "D.3.11.3.1"    "D.3.11.3.2"    "D.3.11.3.3"    "D.3.11.3.4"    "D.3.11.3.5"    "D.3.11.4"     
 "D.3.11.5"      "D.3.11.6"      "D.3.11.7"      "D.3.11.8"      "D.3.11.9"      "D.3.11.10"     "D.3.11.11"     "D.3.11.12"    
 "D.3.11.13"     "D.1.2andD.1.3" "D.2.1"         "D.2.5"