I would like to know if there is an easier way to transform this table in the image below into a dataframe
What I can do is copy the names and numbers of the image once it's in PDF. So it goes like this:
Classifiers TOPSIS GRA VIKOR PROMETHEE II ELECTRE III
Bayes net 0.8005 0.9476 0.5309 0.8687 0.8635
Naïve Bayes 0.7336 0.6903 0.5322 0.697 0.7686
Incremental Naïve Bayes 0.7336 0.6903 0.5322 0.6162 0.7705
IB1 0.6549 0.5069 0.4339 0.3232 0.4628
AdaBoost M1 0.9633 0.9533 0.833 0.8788 0.93
HyperPipes 0 0 0 0.1717 0.0964
VFI 0.361 0.412 0.1752 0.3636 0.4367
Conjunctive rule 0.0308 0.0195 0.0152 0.2121 0.1232
Decision table 0.8307 0.8956 0.4873 0.8384 0.911
OneR 0.4106 0.3484 0.2194 0.3535 0.402
PART 1 0.9888 1 0.9394 1
ZeroR 0.0289 0.0177 0.013 0 0
Decision stump 0.1678 0.1257 0.0839 0.0808 0.136
C4.5 0.8569 0.9914 0.507 0.9293 0.9261
Grafted C4.5 0.8672 1 0.5109 1 0.9442
Random tree 0.583 0.4283 0.3034 0.404 0.4912
REP tree 0.7838 0.7665 0.4097 0.596 0.789
Hi @JojoSouza ,
There are packages in R that can help you get (scrape) the data straight from a PDF (pdftools for example). Could you upload the PDF somewhere and provide a download link? If you do, I can send you a code to do it directly so that you won't have to manually copying and pasting.
Sure! It is table 9 of the article.
Thanks @gueyenono
IJITDM-2012-Kou (1).pdf (294.1 KB)
I actually saw the link and downloaded the file. I will get to it and write the code for you soon.
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It is important to remember that scraping data from PDF is more often than not a fairly tedious task. The techniques you use to scrape data from one PDF will unfortunately not be 100% useful when trying to scrape data from another PDF. The code I provide below will scrape Table 9's data from the article.
# Load packages ----
library(here)
library(purrr)
library(pdftools)
library(stringr)
library(tesseract)
# Extract content of page 23 ----
raw_data <- pdftools::pdf_ocr_text(pdf = here("data/jojosouza.pdf"), pages = 23)
# Keep content related to Table 9 ----
split_data <- str_split(string = raw_data, pattern = "\\n")
start_i <- str_which(string = unlist(split_data), pattern = "Table 9")
end_i <- str_which(string = unlist(split_data), pattern = "Table 10")
raw_table9_data <- unlist(split_data)[(start_i + 1):(end_i - 1)]
# Write a function to clean the data ----
transform_to_row <- function(raw){
model <- str_remove_all(
string = raw,
pattern = "\\s\\d+[\\d\\.]*"
)
values <- str_extract_all(
string = raw,
pattern = "\\s\\d+[\\d\\.]*"
) %>% unlist() %>% str_squish() %>% as.numeric() %>% t() %>% as.data.frame()
data.frame(x = model, values)
}
# Apply the function and set column names
final <- map_dfr(raw_table9_data[-1], transform_to_row) %>%
setNames(c("Classifiers", "TOPSIS", "GRA", "VIKOR", "PROMETHEE II", "ELECTRE III"))
# Final data
final
Classifiers TOPSIS GRA VIKOR PROMETHEE II ELECTRE III
1 Bayes net 0.8005 0.9476 0.5309 0.8687 0.8635
2 Naive Bayes 0.7336 0.6903 0.5322 0.6970 0.7686
3 Incremental Naive Bayes 0.7336 0.6903 0.5322 0.6162 0.7705
4 IB1 0.6549 0.5069 0.4339 0.3232 0.4628
5 AdaBoost M1 0.9633 0.9533 0.8330 0.8788 0.9300
6 HyperPipes 0.0000 0.0000 0.0000 0.1717 0.0964
7 VFI 0.3610 0.4120 0.1752 0.3636 0.4367
8 Conjunctive rule 0.0308 0.0195 0.0152 0.2121 0.1232
9 Decision table 0.8307 0.8956 0.4873 0.8384 0.9110
10 OneR 0.4106 0.3484 0.2194 0.3535 0.4020
11 PART 1.0000 0.9888 1.0000 0.9394 1.0000
12 ZeroR 0.0289 0.0177 0.0130 0.0000 0.0000
13 Decision stump 0.1678 0.1257 0.0839 0.0808 0.1360
14 C4.5 0.8569 0.9914 0.5070 0.9293 0.9261
15 Grafted C4.5 0.8672 1.0000 0.5109 1.0000 0.9442
16 Random tree 0.5830 0.4283 0.3034 0.4040 0.4912
17 REP tree 0.7838 0.7665 0.4097 0.5960 0.7890
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It worked @gueyenono , thanks a lot for the help, it saved me a lot of time because depending on the table it takes a lot to do in R.
Thank you friend!
1 Like
I'm glad I was able to help.
2 Likes
system
March 17, 2022, 1:50pm
9
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