Handling non-English data

General
,
1

Hi, I am trying to analyse food prices from several countries, Israel is among them. The problem is that data is given in Hebrew and translating it in Excel makes the work a bit clumsy.
Is there a way to translate everything in R using Tokens, or kind of a dictionary, which I already prepared?

Thank you!

Here is part of my data. price is the last original variable and from there, it is my translation.

data <- data.frame(
  date = as.Date(c("2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01")),
  id = c(81, 83, 86, 90, 95, 13, 14, 16, 18, 19, 22, 139, 24, 25, 26, 27, 29, 30),
  sector_heb = c("ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช"),
  title_heb = c("ืืฉื›ื•ืœื™ืช", "ืœื™ืžื•ืŸ", "ืคื•ืžืœื™ืช", "ืงืœื™ืคื™ื (ืงืœืžื ื˜ื™ื ื”)", "ืชืคื•ื–", "ื‘ื˜ื˜ื”", "ื‘ื˜ื˜ื”", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื’ื•ื™ื‘ื•ืช", "ื’ื–ืจ", "ื’ื–ืจ", "ื“ืœื•ืจื™ืช", "ื“ืœืขืช", "ื—ืกื”", "ื—ืฆื™ืœ"),
  subtitle_heb = c("ืื“ื•ืžื•ืช", NA, NA, "ืžื™ื›ืœ", "ื•ืœื ืกื™ื”", NA, NA, "ืื“ื•ื", "ื‘ื™ืช-ืืœืคื", "ื™ื‘ืฉ", NA, NA, NA, NA, NA, NA, "ืขืจื‘ื™ืช", "ื—ืžืžื”"),
  spec_heb = c(NA, "ืงื•ื˜ืจ 7", NA, NA, NA, NA, NA, NA, "ืงื•ื˜ืจ 45-77", "ื™ื‘ื•ื", NA, NA, "ืืจื•ื–", "ืชืคื–ื•ืจืช", NA, "10-25 ืง\"ื’", "ื™ื—' 1", NA),
  quality_heb = c("ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืžื•ื‘ื—ืจ", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื"),
  price = c(3.4, 5.1, 3.0, 3.5, 4.9, 2.7, 5.1, 3.0, 2.9, 2.3, 2.5, 7.0, 4.5, 4.0, 2.5, 2.5, 4.0, 3.0),
  sector = c("citrus", "citrus", "citrus", "citrus", "citrus", "vegetables", "vegetables", "vegetables", "vegetables", "vegetables", "vegetables", "fruit", "vegetables", "vegetables", "vegetables", "vegetables", "vegetables", "vegetables"),
  group = c("grapefruit", "lemon", "grapefruit", "clementine", "orange", "potato", "potato", "onion", "onion", "onion", "onion", "fruit_other", "carrot", "carrot", "cucurbita", "cucurbita", "lettuce", "aubergine"),
  title = c("grapefruit", "lemon", "oroblanco", "clementine", "orange", "sweetpotato", "sweetpotato", "onion", "onion", "onion", "onion", "psidium", "carrot", "carrot", "butternut", "pumpkin", "lettuce", "aubergine"),
  quality = c("normal", "normal", "normal", "normal", "normal", "normal", "premium", "normal", "normal", "normal", "normal", "normal", "normal", "normal", "normal", "normal", "normal", "normal")
)
2

I'm sure there are more powerful text processing functions that exist somewhere, here I will only discuss two "easy" solutions.

dataset

First, making sure I understand what you have, one one hand, the data in Hebrew:

data <- data.frame(
  date = as.Date(c("2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01", "2015-11-01")),
  id = c(81, 83, 86, 90, 95, 13, 14, 16, 18, 19, 22, 139, 24, 25, 26, 27, 29, 30),
  sector_heb = c("ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื”ื“ืจื™ื", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช", "ื™ืจืงื•ืช"),
  title_heb = c("ืืฉื›ื•ืœื™ืช", "ืœื™ืžื•ืŸ", "ืคื•ืžืœื™ืช", "ืงืœื™ืคื™ื (ืงืœืžื ื˜ื™ื ื”)", "ืชืคื•ื–", "ื‘ื˜ื˜ื”", "ื‘ื˜ื˜ื”", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื‘ืฆืœ", "ื’ื•ื™ื‘ื•ืช", "ื’ื–ืจ", "ื’ื–ืจ", "ื“ืœื•ืจื™ืช", "ื“ืœืขืช", "ื—ืกื”", "ื—ืฆื™ืœ"),
  subtitle_heb = c("ืื“ื•ืžื•ืช", NA, NA, "ืžื™ื›ืœ", "ื•ืœื ืกื™ื”", NA, NA, "ืื“ื•ื", "ื‘ื™ืช-ืืœืคื", "ื™ื‘ืฉ", NA, NA, NA, NA, NA, NA, "ืขืจื‘ื™ืช", "ื—ืžืžื”"),
  spec_heb = c(NA, "ืงื•ื˜ืจ 7", NA, NA, NA, NA, NA, NA, "ืงื•ื˜ืจ 45-77", "ื™ื‘ื•ื", NA, NA, "ืืจื•ื–", "ืชืคื–ื•ืจืช", NA, "10-25 ืง\"ื’", "ื™ื—' 1", NA),
  quality_heb = c("ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืžื•ื‘ื—ืจ", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื", "ืกื•ื’ ื"),
  price = c(3.4, 5.1, 3.0, 3.5, 4.9, 2.7, 5.1, 3.0, 2.9, 2.3, 2.5, 7.0, 4.5, 4.0, 2.5, 2.5, 4.0, 3.0))

and on the other a dictionary, here I'm assuming a simple dataframe:

dict <- tribble(
  ~hebrew,	~english,
  "ื”ื“ืจื™ื",	"citrus",
  "ืืฉื›ื•ืœื™ืช"	, "grapefruit",
  "ืกื•ื’ ื"	, "normal",
  "ืœื™ืžื•ืŸ"	, "lemon",
  "ืคื•ืžืœื™ืช"	, "oroblanco",
  "ืงืœื™ืคื™ื (ืงืœืžื ื˜ื™ื ื”)"	, "clementine",
  "ืชืคื•ื–"	, "orange",
  "ื™ืจืงื•ืช"	, "vegetables",
  "ื‘ื˜ื˜ื”"	, "sweetpotato",
  "ืžื•ื‘ื—ืจ"	, "premium",
  "ื‘ืฆืœ"	, "onion",
  "ื’ื•ื™ื‘ื•ืช"	, "psidium",
  "ื’ื–ืจ"	, "carrot",
  "ื“ืœื•ืจื™ืช"	, "butternut",
  "ื“ืœืขืช"	, "pumpkin",
  "ื—ืกื”"	, "lettuce",
  "ื—ืฆื™ืœ"	, "aubergine"
)

Translate whole words

It's pretty easy if you just replace whole "cells", i.e. if the Hebrew says "ืงืœื™ืคื™ื (ืงืœืžื ื˜ื™ื ื”)" you replace with "clementine". This is what is done with e.g. a join:

data |>
  left_join(dict |> rename(sector = english),
            by = c(sector_heb = "hebrew"))

or to make things more convenient, you can put the code in a function:

translate <- function(x, dict){
  dict$english[match(x, dict$hebrew)]
}

data |>
  mutate(sector = translate(sector_heb, dict),
         title = translate(title_heb, dict))

That way, you can create columns in English, as long as each Hebrew entry has an exact match in the dictionary.

Partial translations

Things get messier for spec_heb: I'm assuming that for "ืงื•ื˜ืจ 7" you'd want to extract 7 and ืงื•ื˜ืจ and translate them separately. One solution would be to just replace individual words:

minidict <- c("ืงื•ื˜ืจ" = "diameter")

str_replace_all(data$spec_heb, minidict)

Perfect translations

For your particular case, that looks like it could be enough, you just have to make sure you have the right function to translate the right column (and look at the results to make sure you're not forgetting special cases).

If things get more complicated, I suspect you'd end up having to take into account a lot of special cases (because human languages are complicated). In this situation, it might be a good case for Deep Learning Models, maybe you could use a Google Cloud or Microsoft Azure translation API. I don't have direct experience with those.

3

Thanks so much ! Such a simple, yet effective idea