Extract a table from a pdf

Last updated on Nov 1, 2021 3 min read R

In a couple of days, I am going to conduct a pre-conference workshop for Malaysian R conference 2021. So, some of the data that I am going to use for this workshop is available in a table in pdf form. Hence, this post is about how I get that particular table from the pdf into R for further analysis.

So, this is a table we going to extract.

Extracting a table from pdf

We going to use tabulizer package for this. However, not every pdf works with this package. In our case, it works but need further preprocessing.

Load the required packages.

library(tabulizer)
library(dplyr)
library(stringr)

Read a table from a pdf.

raw_table <- extract_tables("https://static-content.springer.com/esm/art%3A10.1038%2Fs41440-021-00720-3/MediaObjects/41440_2021_720_MOESM1_ESM.pdf", 
                          pages = 17, 
                          output = "data.frame")

So, this is the extracted table.

raw_table[[1]] %>% head(10)
##                X     X.1     X.2     X.3  X.4     X.5 X.6     X.7  X.8
## 1                                                                     
## 2                                                                     
## 3    Ahmed, 2019 Unclear Unclear Unclear High Unclear Low Unclear High
## 4                                                                     
## 5   Badrov, 2013 Unclear    High    High High Unclear Low Unclear High
## 6   Baross, 2012 Unclear Unclear    High High Unclear Low Unclear High
## 7   Baross, 2013 Unclear Unclear    High High Unclear Low Unclear High
## 8  Carlson, 2016     Low    High    High  Low Unclear Low     Low High
## 9  Correia, 2020     Low     Low     Low High Unclear Low     Low High
## 10                                                                    
##                              X.9
## 1      1- selection bias: random
## 2            sequence generation
## 3  2- selection bias: allocation
## 4                    concealment
## 5                               
## 6   3- reporting bias: selective
## 7                      reporting
## 8                               
## 9  4- Performance bias: blinding
## 10  (participants and personnel)

So, a few preprocessing steps needed:

  1. Remove column X.9 - this column supposed to be a header
  2. Rename a header based on column X.9
  3. Remove a space between the author name - “Ahmed,2019” instead of “Ahmed, 2019”
  4. Remove empty rows
irt_rob <- 
  raw_table[[1]] %>% 
  select(-X.9) %>%  
  rename(Study = X, 
         Random.sequence.generation. = X.1, 
         Allocation.concealment. = X.2,
         Selective.reporting. = X.3,
         Blinding.of.participants.and.personnel. = X.4, 
         Blinding.of.outcome.assessment = X.5, 
         Incomplete.outcome.data = X.6, 
         Other.sources.of.bias. = X.7, 
         Overall = X.8) %>% 
  as_tibble() %>% 
  mutate(Study = str_replace_all(Study, " ", "")) %>% 
  mutate(id_del = str_match(Study, ".")) %>% 
  filter(!is.na(id_del)) %>% 
  select(-id_del)

Finally, our data is ready.

irt_rob
##          Study Random.sequence.generation. Allocation.concealment.
## 1   Ahmed,2019                     Unclear                 Unclear
## 2  Badrov,2013                     Unclear                    High
## 3  Baross,2012                     Unclear                 Unclear
## 4  Baross,2013                     Unclear                 Unclear
## 5 Carlson,2016                         Low                    High
##   Selective.reporting. Blinding.of.participants.and.personnel.
## 1              Unclear                                    High
## 2                 High                                    High
## 3                 High                                    High
## 4                 High                                    High
## 5                 High                                     Low
##   Blinding.of.outcome.assessment Incomplete.outcome.data Other.sources.of.bias.
## 1                        Unclear                     Low                Unclear
## 2                        Unclear                     Low                Unclear
## 3                        Unclear                     Low                Unclear
## 4                        Unclear                     Low                Unclear
## 5                        Unclear                     Low                    Low
##   Overall
## 1    High
## 2    High
## 3    High
## 4    High
## 5    High
Data exploration
Tengku Muhammad Hanis
Tengku Muhammad Hanis
Lead academic trainer

My research interests include medical statistics and machine learning application.

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