I have multiple datasets that I would like to visualize in R. Unfortunately, the nomenclatur across datasets is not consistent or uses synonyms (e.g. "apple" is spelled "apple", "Apple" and "APPLE").

I have a dataframe that references the nomenclatur across datasets:

I would like to make things consistent, e.g. to iterate through datasets B and C and replace their nomenclatur with that of dataset A (if available). Would anyone have any recommendations?

Thanks in advance!

CodePudding user response：

If you only want to modify the capitalization of some characters, perhaps you can convert the data to a list and then apply a function recursively. You can try something like this:

df = data.frame(col1 =c("Apple", "Pear", "Melon"))

df1 = data.frame(col1 =c("APPLE", "PEAR", NA))

df2 = data.frame(col1 =c("apple", NA, "melon"))

dflist = mget(ls(pattern = "df")) # Put all the data frames in a list

Then you can apply the functions to each element, e.g., transform all the words to lower case using rapply

thelist = rapply(dflist, tolower, how = "list")

Output

$df
$df$col1
[1] "apple" "pear"  "melon"


$df1
$df1$col1
[1] "apple" "pear"  NA     


$df2
$df2$col1
[1] "apple" NA      "melon"

Additional string manipulation can be applied to the list, e.g., searching for a pattern and replace using gsub() and lapply():

thelist2 = lapply(thelist, "[[", "col1") |> # Extracting "col1"
    lapply(\(x) gsub('apple', 'pink lady', x)) # Replace 'apple' with 'pink lady'

Output

$df
[1] "pink lady" "pear"      "melon"    

$df1
[1] "pink lady" "pear"      NA         

$df2
[1] "pink lady" NA          "melon"  

You can also have a similar approach using rapply:

thelist3 = rapply(thelist, \(x) gsub('apple', 'pink lady', x), how = 'list')

Depending on the structure of your data, you can also join the data frames and then apply the functions as needed.

CodePudding user response：

If you have names and data like this:

df_names = data.frame(names_for_a = c("apple", "orange"),
                      names_for_b = c("pink lady", "ORANGE"))
df_b = data.frame(index = 1:9, name = rep(c("pink lady", "ORANGE", "ORANGE"), 3))

I'd do something like:

df_b$name = df_names$names_for_a[match(df_b$name, df_names$names_for_b)]

This thread may be helpful for what you are doing too: Replace values in a dataframe based on lookup table