我有一个编码的调查文件和数据字典，它描述了每列的每个编码值。Example of inputs and desired output here。
数据字典中的“字段”列列出了调查数据中的每一列。根据列名的不同，这些值具有不同的说明。例如，如果“1”在“满意度”列中，则表示“非常满意”，但如果“Metro”在“地区”列中，则表示“Metro”。
我已经让这段代码在Python中工作，但我需要它在R中工作。
我对其他方法持开放态度，这段代码正是我提出的。它在大约5秒内运行150列和40，000行。

import pandas as pd

# Read the data dictionary and raw survey data
dictionary_df = pd.read_excel(filepath)
dictionary_df["Value"] = dictionary_df["Value"].astype(int)

survey_df = pd.read_excel(filepath)

# Replaces the original unalbelled survey_df with a labelled survey_df.

lookup_dict = {}
cols = list(dictionary_df.Field.unique())
for i in cols:
    subset = dictionary_df.loc[dictionary_df.Field==i]
    lookup_dict_vals = dict(zip(subset['Value'], subset['Description']))
    lookup_dict[i] = lookup_dict_vals

# This try/except is necessary where columns that are in the raw file are not in the data dictionary. These columns will remain untouched, but columns to the right will get labelled.

for i in cols:
    try:
        survey_df[i] = survey_df[i].map(lookup_dict[i])
    except:
        continue

我试图在R中复制这一点，但得到了一个充满NA的 Dataframe 。有没有办法修复下面的代码？

library(dplyr)

# Read the data dictionary and raw survey data
dictionary_df <- readxl::read_excel(filepath)
dictionary_df$Value <- as.integer(dictionary_df$Value)

survey_df <- readxl::read_excel(filepath)

# Replaces the original unlabelled survey_df with a labelled survey_df.

lookup_dict <- list()
cols <- unique(dictionary_df$Field)

for (i in cols) {
  subset <- filter(dictionary_df, Field == i)
  lookup_dict_vals <- setNames(subset$Description, subset$Value)
  lookup_dict[[i]] <- lookup_dict_vals
}

# This try/except is necessary where columns that are in the raw file are not in the data dictionary. These columns will remain untouched, but columns to the right will get labelled.

for (i in colnames(survey_df)) {
  tryCatch({
    survey_df <- survey_df %>% mutate({{i}} := lookup_dict[[i]][{{i}}])
  }, error = function(e) {
    next
  })
}