Automated Reading of Tables from PDF Documents
By Bas Machielsen
May 31, 2020
Introduction
In this post, I will briefly explain how to read in a table easily from the Indonesian data sources that I have been planning to use (see here). First, I download the file, containing data about municipalities in every province and their GRDP:
if(!is.element("hello.pdf", list.files())){
download.file("https://www.bps.go.id/publication/download.html?nrbvfeve=OTgxMmExYzRlYTI1Mjk4MDA0ODM5NTk2&xzmn=aHR0cHM6Ly93d3cuYnBzLmdvLmlkL3B1YmxpY2F0aW9uLzIwMTkvMTAvMDQvOTgxMmExYzRlYTI1Mjk4MDA0ODM5NTk2L3Byb2R1ay1kb21lc3Rpay1yZWdpb25hbC1icnV0by1rYWJ1cGF0ZW4ta290YS1kaS1pbmRvbmVzaWEtMjAxNC0yMDE4Lmh0bWw%3D&twoadfnoarfeauf=MjAyMi0wOC0wMyAwMDo1NDoyMg%3D%3D", destfile = "hello.pdf")}
Reading in the table
There are different ways in which I can read the table. First, I use the tabulizer package, from which I can use the extract_tables function, with two arguments:
- The file path (in this case: “hello.pdf”)
- The page number (in this case, we start at page 24, so let us take that page as an example)
library(tabulizer)
#remotes::install_github(c("ropensci/tabulizerjars", "ropensci/tabulizer"), INSTALL_opts = "--no-multiarch")
file <- "hello.pdf"
tabulizer::extract_tables(file, pages = 24)
## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] "KABUPATEN/KOTA" "" "" "" "" ""
## [2,] "" "2014" "2015" "2016" "2017*" "2018**"
## [3,] "Regency/Municipality" "" "" "" "" ""
## [4,] "(1)" "(2)" "(3)" "(4)" "(5)" "(6)"
## [5,] "01. Kab. Simeulue" "1.517" "1.640" "1.773" "1.897" "2.048"
## [6,] "02. Kab. Aceh Singkil" "1.686" "1.816" "1.981" "2.127" "2.259"
## [7,] "03. Kab. Aceh Selatan" "3.930" "4.227" "4.554" "4.855" "5.210"
## [8,] "04. Kab. Aceh Tenggara" "3.314" "3.567" "3.883" "4.236" "4.558"
## [9,] "05. Kab. Aceh Timur" "8.695" "8.196" "8.510" "9.105" "9.700"
## [10,] "06. Kab. Aceh Tengah" "5.463" "5.875" "6.307" "6.722" "7.184"
## [11,] "07. Kab. Aceh Barat" "5.469" "5.828" "6.184" "6.943" "7.651"
## [12,] "08. Kab. Aceh Besar" "9.650" "10.327" "10.969" "11.637" "12.501"
## [13,] "09. Kab. Pidie" "7.255" "7.862" "8.490" "9.187" "9.904"
## [14,] "10. Kab. Bireun" "9.392" "10.069" "10.726" "11.411" "12.139"
## [15,] "11. Kab. Aceh Utara" "19.941" "16.340" "16.696" "17.542" "18.953"
## [16,] "12. Kab. Aceh Barat Daya" "2.780" "2.968" "3.175" "3.395" "3.635"
## [17,] "13. Kab. Gayo Lues" "1.934" "2.076" "2.234" "2.428" "2.597"
## [18,] "14. Kab. Aceh Tamiang" "5.648" "5.763" "6.063" "6.518" "7.008"
## [19,] "15. Kab. Nagan Raya" "5.457" "5.775" "6.159" "6.571" "6.931"
## [20,] "16. Kab. Aceh Jaya" "1.839" "1.981id" "2.116" "2.275" "2.434"
## [21,] "17. Kab. Bener Meriah" "3.312" "3.55.0" "3.802" "3.998" "4.203"
## [22,] "18. Kab. Pidie Jaya" "2.389" "2o.598" "2.770" "3.013" "3.242"
## [23,] "71. Kota Banda Aceh" "13.502" "g14.494" "15.801" "16.685" "17.571"
##
## [[2]]
## [,1] [,2]
## [1,] "71. Kota Banda Aceh" ""
## [2,] "72. Kota Sabang" ""
## [3,] "73. Kota Langsa 74. Kota Lhokseumawe" ""
## [4,] "75. Kota Subulussalam" ""
## [5,] "Jml Kab./Kota Total of Reg./Mun. Provinsi/Province" "ww 128.246"
## [,3] [,4] [,5] [,6] [,7]
## [1,] "13.502" "" "14.494" "15.801" "16.685"
## [2,] "992 ." "" "1.070" "1.158" "1.273"
## [3,] "3.562ps9.321" "" "3.875 7.636" "4.217 7.729" "4.538 8.070"
## [4,] ". 1.b201" "" "1.295" "1.400" "1.536"
## [5,] "127.897" "" "128.831 129.093" "136.698 136.844" "145.962 145.807"
## [,8]
## [1,] "17.571"
## [2,] "1.399"
## [3,] "4.8908.454"
## [4,] "1.642"
## [5,] "156.114155.912"
Not bad at all! But as you can see, it read two tables instead of one, and in the second table, it also missed some spaced. Finally ,the row summarizing the total is very confusing for the algorithm. A super useful feature of this package is the ability to select a part of the picture to extract:
extract_areas(file, 24)
You can also do this in two or three (or more) parts, and then combine the results to get a data.frame you want. Sometimes, or actually, most of the time, this will still not help you in getting exactly the table you want. One option is to resort to reading the table as a text, and then creating the table you want.
Reading the table as text
That is what we will do next. The same tabulizer package also allows us to read in text, in the following way:
extract_text(file, 24)
## [1] "KABUPATEN/KOTA\nRegency/Municipality\n(1) (2) (3) (4) (5) (6)\n01. Kab. Simeulue 1.517 1.640 1.773 1.897 2.048 \n02. Kab. Aceh Singkil 1.686 1.816 1.981 2.127 2.259 \n03. Kab. Aceh Selatan 3.930 4.227 4.554 4.855 5.210 \n04. Kab. Aceh Tenggara 3.314 3.567 3.883 4.236 4.558 \n05. Kab. Aceh Timur 8.695 8.196 8.510 9.105 9.700 \n06. Kab. Aceh Tengah 5.463 5.875 6.307 6.722 7.184 \n07. Kab. Aceh Barat 5.469 5.828 6.184 6.943 7.651 \n08. Kab. Aceh Besar 9.650 10.327 10.969 11.637 12.501 \n09. Kab. Pidie 7.255 7.862 8.490 9.187 9.904 \n10. Kab. Bireun 9.392 10.069 10.726 11.411 12.139 \n11. Kab. Aceh Utara 19.941 16.340 16.696 17.542 18.953 \n12. Kab. Aceh Barat Daya 2.780 2.968 3.175 3.395 3.635 \n13. Kab. Gayo Lues 1.934 2.076 2.234 2.428 2.597 \n14. Kab. Aceh Tamiang 5.648 5.763 6.063 6.518 7.008 \n15. Kab. Nagan Raya 5.457 5.775 6.159 6.571 6.931 \n16. Kab. Aceh Jaya 1.839 1.981 2.116 2.275 2.434 \n17. Kab. Bener Meriah 3.312 3.550 3.802 3.998 4.203 \n18. Kab. Pidie Jaya 2.389 2.598 2.770 3.013 3.242 \n71. Kota Banda Aceh 13.502 14.494 15.801 16.685 17.571 \n72. Kota Sabang 992 1.070 1.158 1.273 1.399 \n73. Kota Langsa 3.562 3.875 4.217 4.538 4.890 \n74. Kota Lhokseumawe 9.321 7.636 7.729 8.070 8.454 \n75. Kota Subulussalam 1.201 1.295 1.400 1.536 1.642 \nJml Kab./Kota Total of Reg./Mun. 128.246 128.831 136.698 145.962 156.114 \nProvinsi/Province 127.897 129.093 136.844 145.807 155.912 \nCatatan/Note: \n#) Merupakan pecahan dari kabupaten yang berada diatasnya/ As a part of Regency/Municipality above\n* Angka sementara/Preliminary figures\n** Angka sangat sementara/Very preliminary figures\nTabel/Table 1.\nPDRB Provinsi Aceh Atas Dasar Harga Berlaku \nMenurut Kabupaten/Kota (miliar rupiah), 2014-2018\nGRDP of Aceh Province at Current Market Prices \nby Regency/Municipality (billion rupiahs), 2014-2018\n2018**201620152014 2017*\n3\nht\ntp\ns:\n//w\nww\n.b\nps\n.g\no.\nid\n"
This seems kinda messy, but you will shortly see that this is in fact just what we need. To make this clear, let’s do some cleaning:
extract_text(file, 24) %>%
stringr::str_split("\n") %>%
lapply(stringr::str_squish) %>%
magrittr::extract2(1) %>%
magrittr::extract(4:26)
## [1] "01. Kab. Simeulue 1.517 1.640 1.773 1.897 2.048"
## [2] "02. Kab. Aceh Singkil 1.686 1.816 1.981 2.127 2.259"
## [3] "03. Kab. Aceh Selatan 3.930 4.227 4.554 4.855 5.210"
## [4] "04. Kab. Aceh Tenggara 3.314 3.567 3.883 4.236 4.558"
## [5] "05. Kab. Aceh Timur 8.695 8.196 8.510 9.105 9.700"
## [6] "06. Kab. Aceh Tengah 5.463 5.875 6.307 6.722 7.184"
## [7] "07. Kab. Aceh Barat 5.469 5.828 6.184 6.943 7.651"
## [8] "08. Kab. Aceh Besar 9.650 10.327 10.969 11.637 12.501"
## [9] "09. Kab. Pidie 7.255 7.862 8.490 9.187 9.904"
## [10] "10. Kab. Bireun 9.392 10.069 10.726 11.411 12.139"
## [11] "11. Kab. Aceh Utara 19.941 16.340 16.696 17.542 18.953"
## [12] "12. Kab. Aceh Barat Daya 2.780 2.968 3.175 3.395 3.635"
## [13] "13. Kab. Gayo Lues 1.934 2.076 2.234 2.428 2.597"
## [14] "14. Kab. Aceh Tamiang 5.648 5.763 6.063 6.518 7.008"
## [15] "15. Kab. Nagan Raya 5.457 5.775 6.159 6.571 6.931"
## [16] "16. Kab. Aceh Jaya 1.839 1.981 2.116 2.275 2.434"
## [17] "17. Kab. Bener Meriah 3.312 3.550 3.802 3.998 4.203"
## [18] "18. Kab. Pidie Jaya 2.389 2.598 2.770 3.013 3.242"
## [19] "71. Kota Banda Aceh 13.502 14.494 15.801 16.685 17.571"
## [20] "72. Kota Sabang 992 1.070 1.158 1.273 1.399"
## [21] "73. Kota Langsa 3.562 3.875 4.217 4.538 4.890"
## [22] "74. Kota Lhokseumawe 9.321 7.636 7.729 8.070 8.454"
## [23] "75. Kota Subulussalam 1.201 1.295 1.400 1.536 1.642"
Only the last command, magrittr:extract contains a parameter, because that’s where the actual content of the table is separated from things like the header and footer. In this case, I wanted to extract only the municipalities’ info, and not the total or headers or anything, so that’s why I went with row 4 to row 26, but I could’ve included more if I wanted to.
Let us now write this output to a vector called data, and see if we want split up the strings and put the data in a data.frame.
data <- extract_text(file, 24) %>%
stringr::str_split("\n") %>%
lapply(stringr::str_squish) %>%
magrittr::extract2(1) %>%
magrittr::extract(4:26)
Splitting up the data in columns
Naturally, we wanted to have a data.frame in which the first and second column contain information about the specific municipality, and all the other numbers afterwards to go to separate variables. Unfortunately, the stringr::str_split function, which is the usual way to go about this, has some limitations, forcing us to use either complicated Regex’s to get the string to split in chunks we want, or to go with a second-best alternative. Fortunately, I found
this blog:
strsplit2 <- function(x,
split,
type = "remove",
perl = FALSE,
...) {
if (type == "remove") {
# use base::strsplit
out <- base::strsplit(x = x, split = split, perl = perl, ...)
} else if (type == "before") {
# split before the delimiter and keep it
out <- base::strsplit(x = x,
split = paste0("(?<=.)(?=", split, ")"),
perl = TRUE,
...)
} else if (type == "after") {
# split after the delimiter and keep it
out <- base::strsplit(x = x,
split = paste0("(?<=", split, ")"),
perl = TRUE,
...)
} else {
# wrong type input
stop("type must be remove, after or before!")
}
return(out)
}
which was exactly what I was looking for: I could use easy Regex, and clean the data in very few steps:
data %>%
strsplit2("\\s[0-9]+", type = "before") %>%
purrr::reduce(rbind) %>%
as.data.frame(row.names = F)
## V1 V2 V3 V4 V5 V6
## 1 01. Kab. Simeulue 1.517 1.640 1.773 1.897 2.048
## 2 02. Kab. Aceh Singkil 1.686 1.816 1.981 2.127 2.259
## 3 03. Kab. Aceh Selatan 3.930 4.227 4.554 4.855 5.210
## 4 04. Kab. Aceh Tenggara 3.314 3.567 3.883 4.236 4.558
## 5 05. Kab. Aceh Timur 8.695 8.196 8.510 9.105 9.700
## 6 06. Kab. Aceh Tengah 5.463 5.875 6.307 6.722 7.184
## 7 07. Kab. Aceh Barat 5.469 5.828 6.184 6.943 7.651
## 8 08. Kab. Aceh Besar 9.650 10.327 10.969 11.637 12.501
## 9 09. Kab. Pidie 7.255 7.862 8.490 9.187 9.904
## 10 10. Kab. Bireun 9.392 10.069 10.726 11.411 12.139
## 11 11. Kab. Aceh Utara 19.941 16.340 16.696 17.542 18.953
## 12 12. Kab. Aceh Barat Daya 2.780 2.968 3.175 3.395 3.635
## 13 13. Kab. Gayo Lues 1.934 2.076 2.234 2.428 2.597
## 14 14. Kab. Aceh Tamiang 5.648 5.763 6.063 6.518 7.008
## 15 15. Kab. Nagan Raya 5.457 5.775 6.159 6.571 6.931
## 16 16. Kab. Aceh Jaya 1.839 1.981 2.116 2.275 2.434
## 17 17. Kab. Bener Meriah 3.312 3.550 3.802 3.998 4.203
## 18 18. Kab. Pidie Jaya 2.389 2.598 2.770 3.013 3.242
## 19 71. Kota Banda Aceh 13.502 14.494 15.801 16.685 17.571
## 20 72. Kota Sabang 992 1.070 1.158 1.273 1.399
## 21 73. Kota Langsa 3.562 3.875 4.217 4.538 4.890
## 22 74. Kota Lhokseumawe 9.321 7.636 7.729 8.070 8.454
## 23 75. Kota Subulussalam 1.201 1.295 1.400 1.536 1.642
The example of this method is that it can be used on virtually all tables from the aforementioned .pdf file, requiring very little editing and data wrangling, which saves a lot of time. To domenstrate, let’s look at pages 25, 26 and 27, the pages following the original p 24 which I started with.
- Page 25:
data <- extract_text(file, 25) %>%
stringr::str_split("\n") %>%
lapply(stringr::str_squish) %>%
magrittr::extract2(1) %>%
magrittr::extract(4:36) #Remember to change this parameter
data
## [1] "01. Kab. Nias 2.443 2.677 2.966 3.234 3.509"
## [2] "02. Kab. Mandailing Natal 8.758 9.586 10.660 11.713 12.618"
## [3] "03. Kab. Tapanuli Selatan 9.310 10.058 10.965 11.983 12.902"
## [4] "04. Kab. Tapanuli Tengah 6.516 7.140 7.850 8.545 9.230"
## [5] "05. Kab. Tapanuli Utara 5.429 5.856 6.300 6.766 7.297"
## [6] "06. Kab. Toba Samosir 5.173 5.623 6.124 6.642 7.167"
## [7] "07. Kab. Labuhan Batu 22.176 24.083 26.505 29.032 31.303"
## [8] "08. Kab. Asahan 24.329 26.465 29.207 32.020 34.667"
## [9] "09. Kab. Simalungun 25.338 27.147 30.123 32.832 35.445"
## [10] "10. Kab. Dairi 6.268 6.823 7.484 8.049 8.736"
## [11] "11. Kab. Karo 13.817 15.150 16.728 18.066 19.359"
## [12] "12. Kab. Deli Serdang 69.674 76.735 85.152 93.194 101.120"
## [13] "13. Kab. Langkat 27.875 30.742 34.105 37.119 39.819"
## [14] "14. Kab. Nias Selatan 4.298 4.729 5.193 5.696 6.262"
## [15] "15. Kab. Humbang Hasundutan 4.050 4.413 4.777 5.130 5.524"
## [16] "16. Kab. Pakpak Bharat 754 826 917 994 1.083"
## [17] "17. Kab. Samosir 2.838 3.144 3.443 3.752 4.085"
## [18] "18. Kab. Serdang Bedagai 18.457 20.152 22.114 24.095 25.995"
## [19] "19. Kab. Batu Bara 23.461 25.395 27.555 29.770 31.972"
## [20] "20. Kab. Padang Lawas Utara 7.448 8.222 9.074 9.904 10.765"
## [21] "21. Kab. Padang Lawas 7.288 7.853 8.808 9.705 10.591"
## [22] "22. Kab. Labuhan Batu Selatan 17.601 19.052 21.004 23.196 25.124"
## [23] "23. Kab. Labuhan Batu Utara 16.262 17.620 19.374 21.162 22.750"
## [24] "24. Kab. Nias Utara 2.319 2.525 2.775 3.008 3.252"
## [25] "25. Kab. Nias Barat 1.184 1.289 1.414 1.548 1.672"
## [26] "71. Kota Sibolga 3.429 3.836 4.263 4.645 5.064"
## [27] "72. Kota Tanjung Balai 5.439 6.052 6.723 7.425 8.176"
## [28] "73. Kota Pematang Siantar 9.555 10.566 11.579 12.444 13.177"
## [29] "74. Kota Tebing Tinggi 3.912 4.288 4.725 5.123 5.513"
## [30] "75. Kota Medan 148.247 164.722 184.809 203.016 222.482"
## [31] "76. Kota Binjai 7.649 8.382 9.112 9.905 10.765"
## [32] "77. Kota Padang Sidempuan 4.001 4.425 4.903 5.372 5.859"
## [33] "78. Kota Gunungsitoli 3.212 3.595 4.034 4.503 5.010"
data %>%
strsplit2("\\s[0-9]+", type = "before") %>%
purrr::reduce(rbind) %>%
as.data.frame(row.names = F)
## V1 V2 V3 V4 V5 V6
## 1 01. Kab. Nias 2.443 2.677 2.966 3.234 3.509
## 2 02. Kab. Mandailing Natal 8.758 9.586 10.660 11.713 12.618
## 3 03. Kab. Tapanuli Selatan 9.310 10.058 10.965 11.983 12.902
## 4 04. Kab. Tapanuli Tengah 6.516 7.140 7.850 8.545 9.230
## 5 05. Kab. Tapanuli Utara 5.429 5.856 6.300 6.766 7.297
## 6 06. Kab. Toba Samosir 5.173 5.623 6.124 6.642 7.167
## 7 07. Kab. Labuhan Batu 22.176 24.083 26.505 29.032 31.303
## 8 08. Kab. Asahan 24.329 26.465 29.207 32.020 34.667
## 9 09. Kab. Simalungun 25.338 27.147 30.123 32.832 35.445
## 10 10. Kab. Dairi 6.268 6.823 7.484 8.049 8.736
## 11 11. Kab. Karo 13.817 15.150 16.728 18.066 19.359
## 12 12. Kab. Deli Serdang 69.674 76.735 85.152 93.194 101.120
## 13 13. Kab. Langkat 27.875 30.742 34.105 37.119 39.819
## 14 14. Kab. Nias Selatan 4.298 4.729 5.193 5.696 6.262
## 15 15. Kab. Humbang Hasundutan 4.050 4.413 4.777 5.130 5.524
## 16 16. Kab. Pakpak Bharat 754 826 917 994 1.083
## 17 17. Kab. Samosir 2.838 3.144 3.443 3.752 4.085
## 18 18. Kab. Serdang Bedagai 18.457 20.152 22.114 24.095 25.995
## 19 19. Kab. Batu Bara 23.461 25.395 27.555 29.770 31.972
## 20 20. Kab. Padang Lawas Utara 7.448 8.222 9.074 9.904 10.765
## 21 21. Kab. Padang Lawas 7.288 7.853 8.808 9.705 10.591
## 22 22. Kab. Labuhan Batu Selatan 17.601 19.052 21.004 23.196 25.124
## 23 23. Kab. Labuhan Batu Utara 16.262 17.620 19.374 21.162 22.750
## 24 24. Kab. Nias Utara 2.319 2.525 2.775 3.008 3.252
## 25 25. Kab. Nias Barat 1.184 1.289 1.414 1.548 1.672
## 26 71. Kota Sibolga 3.429 3.836 4.263 4.645 5.064
## 27 72. Kota Tanjung Balai 5.439 6.052 6.723 7.425 8.176
## 28 73. Kota Pematang Siantar 9.555 10.566 11.579 12.444 13.177
## 29 74. Kota Tebing Tinggi 3.912 4.288 4.725 5.123 5.513
## 30 75. Kota Medan 148.247 164.722 184.809 203.016 222.482
## 31 76. Kota Binjai 7.649 8.382 9.112 9.905 10.765
## 32 77. Kota Padang Sidempuan 4.001 4.425 4.903 5.372 5.859
## 33 78. Kota Gunungsitoli 3.212 3.595 4.034 4.503 5.010
- Page 26:
data <- extract_text(file, 26) %>%
stringr::str_split("\n") %>%
lapply(stringr::str_squish) %>%
magrittr::extract2(1) %>%
magrittr::extract(4:23) #Remember to change this parameter
data %>%
strsplit2("\\s[0-9]+", type = "before") %>%
purrr::reduce(rbind) %>%
as.data.frame(row.names = F)
## V1 V2 V3 V4 V5
## 1 01. Kab. Kepulauan Mentawai 3.027 3.396 3.726 4.089
## 2 02. Kab. Pesisir Selatan 9.114 10.197 11.271 12.522
## 3 03. Kab. Solok 9.408 10.165 11.053 11.980
## 4 04. Kab. Sijunjung 6.471 6.955 7.439 7.978
## 5 05. Kab. Tanah Datar 9.178 9.901 10.735 11.620
## 6 06. Kab. Padang Pariaman 14.153 15.846 17.533 19.182
## 7 07. Kab. Agam 13.918 15.248 16.693 18.220
## 8 08. Kab. Lima Puluh Kota 10.564 11.583 12.678 13.783
## 9 09. Kab. Pasaman 5.951 6.505 7.336 8.008
## 10 10. Kab. Solok Selatan 3.891 4.236 4.598 4.987
## 11 11. Kab. Dharmasraya 7.155 7.725 8.438 9.282
## 12 12. Kab. Pasaman Barat 10.703 11.713 12.794 14.068
## 13 71. Kota Padang 41.266 45.093 49.386 53.869
## 14 72. Kota Solok 2.729 2.965 3.241 3.555
## 15 73. Kota Sawah Lunto 2.514 2.715 2.938 3.214
## 16 74. Kota Padang Panjang 2.348 2.533 2.774 3.028
## 17 75. Kota Bukittinggi 5.636 6.170 6.783 7.453
## 18 76. Kota Payakumbuh 4.181 4.655 5.203 5.757
## 19 77. Kota Pariaman 3.406 3.699 4.037 4.387
## 20 Jml Kab./Kota Total of Reg./Mun. 165.612 181.302 198.656 216.982
## V6
## 1 4.397
## 2 13.643
## 3 12.801
## 4 8.516
## 5 12.393
## 6 20.639
## 7 19.506
## 8 14.739
## 9 8.530
## 10 5.303
## 11 9.917
## 12 14.997
## 13 58.272
## 14 3.835
## 15 3.461
## 16 3.269
## 17 8.069
## 18 6.342
## 19 4.765
## 20 233.394
- And finally, page 27. Let me introduce a more abstract way of recognizing the parameter, making use of
whichto find out in which character string “Jml Kab.” is located, so I know at which string to stop:
data <- extract_text(file, 27) %>%
stringr::str_split("\n") %>%
lapply(stringr::str_squish) %>%
magrittr::extract2(1)
uptothis <- which(stringr::str_detect(data, "Jml Kab."))-1
data <- data %>%
magrittr::extract(4:uptothis)
data %>%
strsplit2("\\s[0-9]+", type = "before") %>%
purrr::reduce(rbind) %>%
as.data.frame(row.names = F)
## V1 V2 V3 V4 V5 V6
## 1 01. Kab. Kuantan Singingi 24.022 25.195 27.522 29.518 30.652
## 2 02. Kab. Indragiri Hulu 33.762 34.584 37.033 38.740 40.392
## 3 03. Kab. Indragiri Hilir 47.822 51.800 57.292 60.891 60.223
## 4 04. Kab. Pelalawan 35.401 38.176 41.165 43.871 46.154
## 5 05. Kab. Siak 85.736 77.236 78.942 79.619 84.674
## 6 06. Kab. Kampar 68.817 66.285 69.676 71.587 77.197
## 7 07. Kab. Rokan Hulu 25.355 26.907 29.146 31.006 32.311
## 8 08. Kab. Bengkalis 165.899 135.505 132.201 132.994 149.407
## 9 09. Kab. Rokan Hilir 74.546 70.693 73.268 74.030 78.707
## 10 10. Kab. Kepulauan Meranti 15.127 15.152 16.044 16.731 18.186
## 11 71. Kota Pekan Baru 73.841 83.664 92.129 101.112 108.840
## 12 73. Kota Dumai 23.628 25.454 27.962 30.299 32.994
Conclusion
In this post, I attempted to explain, and show, how to easily import tables from .pdf documents in R. I also did not show a couple of other aspects, most notably, endogenous start of data extraction (a little tricker than endogenous ending), and I also did not separate the Identifiers from the names. Neither did I convert all variables to numeric, and gave variable names. If you want to know more about such basic cleaning operations, feel free to message me, or better, go to the tidyverse website.
- Posted on:
- May 31, 2020
- Length:
- 15 minute read, 3152 words
- See Also: