# I create a dummy list to iterate through all the pages and push a ame in This is a lengthy process and very specific to each table parsed so i will only provide an example script: library(dplyr) What i would propose to do instead is to parse each page individually and perform data cleaning according to your desired output on each of them and then bind them together. I can see that your first table has 13 columns, second 17, 3rd 12, 4th 10 and the last three 11 columns. Every page is parsed differently but all the data seem to be retained. From what I can see when I try to extract your tables its not only the table of page No. This is a data prep and wrangling problem, and not a parsing issue in my experience, as the parsing algorithms of tabulizer don't offer much leeway apart from changing between methods, in this case. How Can I fix it such that I haveĪ combined table with columns Ciclo ,Graus Dias/dias, Epcaja de Plantion and Regiao de adaptacao in one csv file. In summary, the extract_tables function is not doing consistent column position and merging columns in some tables.
# Column 1: 4 are merged into a single column. However, when I extract the 7th table: temp <- ame(out]) Out % dplyr::select(X3, X4, X5, X12) # these are the columns corresponding to `Ciclo`, `Graus Dias/dias`, Epcaja de Plantion` and `Regiao de adaptacao`
In each table, I am only interested in columns 6 - 8 and 17 column: Ciclo, Graus Dias/dias, Epcaja de Plantion andRegiao de adaptacao` All of these tables have the same structure (18 columns) and headings. I want to extract tables from page 15 - page 21.