docxtractr is an R package for extracting tables & comments out of Word documents (docx). Development versions are available here and production versions are on CRAN.
Microsoft Word docx files provide an XML structure that is fairly straightforward to navigate, especially when it applies to Word tables. The docxtractr package provides tools to determine table count, table structure and extract tables from Microsoft Word docx documents.
Many tables in Word documents are in twisted formats where there may be labels or other oddities mixed in that make it difficult to work with the underlying data. docxtractr provides a function—assign_colnames—that makes it easy to identify a particular row in a scraped (or any, really) data.frame as the one containing column names and have it become the column names, removing it and (optionally) all of the rows before it (since that's usually what needs to be done).
The following functions are implemented:
read_docx: Read in a Word document for table extractiondocx_describe_tbls: Returns a description of all the tables in the Word documentdocx_describe_cmntss: Returns a description of all the comments in the Word documentdocx_extract_tbl: Extract a table from a Word documentdocx_extract_cmnts: Extract comments from a Word documentdocx_extract_all: Extract all tables from a Word document (deprecated)docx_tbl_count: Get number of tables in a Word documentdocx_cmnt_count: Get number of comments in a Word documentassign_colnames: Make a specific row the column names for the specified data.frame
The following data file are included:
system.file("examples/data.docx", package="docxtractr"): Word docx with 1 tablesystem.file("examples/data3.docx", package="docxtractr"): Word docx with 3 tablessystem.file("examples/none.docx", package="docxtractr"): Word docx with 0 tablessystem.file("examples/complex.docx", package="docxtractr"): Word docx with non-uniform tablessystem.file("examples/comments.docx", package="docxtractr"): Word docx with commentssystem.file("examples/realworld.docx", package="docxtractr"): A "real world" Word docx file with tables of all shapes and sizes
# devtools::install_github("hrbrmstr/docxtractr")
# OR
install.packages("docxtractr")library(docxtractr)
library(tibble)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# current verison
packageVersion("docxtractr")
#> [1] '0.2.0'
# one table
doc <- read_docx(system.file("examples/data.docx", package="docxtractr"))
docx_tbl_count(doc)
#> [1] 1
docx_describe_tbls(doc)
#> Word document [/Library/Frameworks/R.framework/Versions/3.3/Resources/library/docxtractr/examples/data.docx]
#>
#> Table 1
#> total cells: 16
#> row count : 4
#> uniform : likely!
#> has header : likely! => possibly [This, Is, A, Column]
docx_extract_tbl(doc, 1)
#> This Is A Column
#> 1 1 Cat 3.4 Dog
#> 2 3 Fish 100.3 Bird
#> 3 5 Pelican -99 Kangaroo
docx_extract_tbl(doc)
#> This Is A Column
#> 1 1 Cat 3.4 Dog
#> 2 3 Fish 100.3 Bird
#> 3 5 Pelican -99 Kangaroo
docx_extract_tbl(doc, header=FALSE)
#> NOTE: header=FALSE but table has a marked header row in the Word document
#> V1 V2 V3 V4
#> 1 This Is A Column
#> 2 1 Cat 3.4 Dog
#> 3 3 Fish 100.3 Bird
#> 4 5 Pelican -99 Kangaroo
# url
budget <- read_docx("http://rud.is/dl/1.DOCX")
docx_tbl_count(budget)
#> [1] 2
docx_describe_tbls(budget)
#> Word document [http://rud.is/dl/1.DOCX]
#>
#> Table 1
#> total cells: 24
#> row count : 6
#> uniform : likely!
#> has header : unlikely
#>
#> Table 2
#> total cells: 28
#> row count : 4
#> uniform : likely!
#> has header : unlikely
docx_extract_tbl(budget, 1)
#> Short-term Portfolio Long-term Portfolio Total Portfolio Values
#> 1 Portfolio Balance (Market Value) * $ 123,651,911 $ 294,704,136 $ 418,356,047
#> 2 Effective Yield 0.16 % 1.42 % 1.05 %
#> 3 Avg. Weighted Maturity 11 Days 2.4 Years 1.7 Years
#> 4 Net Earnings $ 18,470 $ 350,554 $ 369,024
#> 5 Benchmark** 0.02 % 0.41 % 0.27 %
docx_extract_tbl(budget, 2)
#> Amount of Funds (Market Value) Maturity Effective Yield Interpolated Yield
#> 1 Short-Term Portfolio $ 123,651,911 11 days 0.16 % 0.01 %
#> 2 Long-Term Portfolio $ 294,704,136 2.4 years 1.42 % 0.41 %
#> 3 Total Portfolio $ 418,356,047 1.7 years 1.05 % 0.27 %
#> Total Return Monthly Total Return Annual
#> 1 0.013 0.160
#> 2 0.437 0.250
#> 3 0.298 0.222
# three tables
doc3 <- read_docx(system.file("examples/data3.docx", package="docxtractr"))
docx_tbl_count(doc3)
#> [1] 3
docx_describe_tbls(doc3)
#> Word document [/Library/Frameworks/R.framework/Versions/3.3/Resources/library/docxtractr/examples/data3.docx]
#>
#> Table 1
#> total cells: 16
#> row count : 4
#> uniform : likely!
#> has header : likely! => possibly [This, Is, A, Column]
#>
#> Table 2
#> total cells: 12
#> row count : 4
#> uniform : likely!
#> has header : likely! => possibly [Foo, Bar, Baz]
#>
#> Table 3
#> total cells: 14
#> row count : 7
#> uniform : likely!
#> has header : likely! => possibly [Foo, Bar]
docx_extract_tbl(doc3, 3)
#> Foo Bar
#> 1 Aa Bb
#> 2 Dd Ee
#> 3 Gg Hh
#> 4 1 2
#> 5 Zz Jj
#> 6 Tt ii
# no tables
none <- read_docx(system.file("examples/none.docx", package="docxtractr"))
docx_tbl_count(none)
#> [1] 0
# wrapping in try since it will return an error
# use docx_tbl_count before trying to extract in scripts/production
try(docx_describe_tbls(none))
#> No tables in document
try(docx_extract_tbl(none, 2))
# 5 tables, with two in sketchy formats
complx <- read_docx(system.file("examples/complex.docx", package="docxtractr"))
docx_tbl_count(complx)
#> [1] 5
docx_describe_tbls(complx)
#> Word document [/Library/Frameworks/R.framework/Versions/3.3/Resources/library/docxtractr/examples/complex.docx]
#>
#> Table 1
#> total cells: 16
#> row count : 4
#> uniform : likely!
#> has header : likely! => possibly [This, Is, A, Column]
#>
#> Table 2
#> total cells: 12
#> row count : 4
#> uniform : likely!
#> has header : likely! => possibly [Foo, Bar, Baz]
#>
#> Table 3
#> total cells: 14
#> row count : 7
#> uniform : likely!
#> has header : likely! => possibly [Foo, Bar]
#>
#> Table 4
#> total cells: 11
#> row count : 4
#> uniform : unlikely => found differing cell counts (3, 2) across some rows
#> has header : likely! => possibly [Foo, Bar, Baz]
#>
#> Table 5
#> total cells: 21
#> row count : 7
#> uniform : likely!
#> has header : unlikely
docx_extract_tbl(complx, 3, header=TRUE)
#> Foo Bar
#> 1 Aa Bb
#> 2 Dd Ee
#> 3 Gg Hh
#> 4 1 2
#> 5 Zz Jj
#> 6 Tt ii
docx_extract_tbl(complx, 4, header=TRUE)
#> Foo Bar Baz
#> 1 Aa BbCc <NA>
#> 2 Dd Ee Ff
#> 3 Gg Hh ii
docx_extract_tbl(complx, 5, header=TRUE)
#> Foo Bar Baz
#> 1 Aa Bb Cc
#> 2 Dd Ee Ff
#> 3 Gg Hh Ii
#> 4 Jj88 Kk Ll
#> 5 Uu Ii
#> 6 Hh Ii h
# a "real" Word doc
real_world <- read_docx(system.file("examples/realworld.docx", package="docxtractr"))
docx_tbl_count(real_world)
#> [1] 8
# get all the tables
tbls <- docx_extract_all(real_world)
#> docx_extract_all() is deprecated; use docx_extract_all_tbls()
# see table 1
tbls[[1]]
#> V1 V2 V3 V4 V5
#> 1 Lesson 1: Step 1 <NA> <NA> <NA> <NA>
#> 2 Country Birthrate Death Rate Population Growth 2005 Population Growth 2050
#> 3 USA 2.06 0.51% 0.92% -0.06%
#> 4 China 1.62 0.3% 0.6% -0.58%
#> 5 Egypt 2.83 0.41% 2.0% 1.32%
#> 6 India 2.35 0.34% 1.56% 0.76%
#> 7 Italy 1.28 0.72% 0.35% -1.33%
#> 8 Mexico 2.43 0.25% 1.41% 0.96%
#> 9 Nigeria 4.78 0.26% 2.46% 3.58%
#> V6 V7 V8 V9
#> 1 <NA> <NA> <NA> <NA>
#> 2 Relative place in Transition Social Factors 1 Social Factors 2 Social Factors 3
#> 3 Post- Industrial Female Independence Stable Birth Rate Good technology
#> 4 Post- Industrial Government intervention Technology Urbanization
#> 5 Mature Industrial Not yet industrialized More children needed Slightly higher life expectancy
#> 6 Post Industrial Economic growth Poverty Becoming more industrialized
#> 7 Late Post industrial Stable birth rate People marry later Better health care
#> 8 Mature Industrial Better health care Emigration Economic growth
#> 9 End of Mechanization of Agriculture Disease People marry early People have many children
#' # make table 1 better
assign_colnames(tbls[[1]], 2)
#> Country Birthrate Death Rate Population Growth 2005 Population Growth 2050 Relative place in Transition
#> 1 USA 2.06 0.51% 0.92% -0.06% Post- Industrial
#> 2 China 1.62 0.3% 0.6% -0.58% Post- Industrial
#> 3 Egypt 2.83 0.41% 2.0% 1.32% Mature Industrial
#> 4 India 2.35 0.34% 1.56% 0.76% Post Industrial
#> 5 Italy 1.28 0.72% 0.35% -1.33% Late Post industrial
#> 6 Mexico 2.43 0.25% 1.41% 0.96% Mature Industrial
#> 7 Nigeria 4.78 0.26% 2.46% 3.58% End of Mechanization of Agriculture
#> Social Factors 1 Social Factors 2 Social Factors 3
#> 1 Female Independence Stable Birth Rate Good technology
#> 2 Government intervention Technology Urbanization
#> 3 Not yet industrialized More children needed Slightly higher life expectancy
#> 4 Economic growth Poverty Becoming more industrialized
#> 5 Stable birth rate People marry later Better health care
#> 6 Better health care Emigration Economic growth
#> 7 Disease People marry early People have many children
# see table 5
tbls[[5]]
#> V1 V2 V3 V4 V5 V6
#> 1 Lesson 2: Step 1 <NA> <NA> <NA> <NA> <NA>
#> 2 Nigeria Default Prediction + 5 years +15 years -5 years
#> 3 Birth rate 4.78 Goes Down 4.76 4.72 4.79
#> 4 Death rate 0.36% Stay the Same 0.42% 0.52% 0.3%
#> 5 Population growth 3.58% Goes Down 3.02% 2.32% 4.38%
# make table 5 better
assign_colnames(tbls[[5]], 2)
#> Nigeria Default Prediction + 5 years +15 years -5 years
#> 1 Birth rate 4.78 Goes Down 4.76 4.72 4.79
#> 2 Death rate 0.36% Stay the Same 0.42% 0.52% 0.3%
#> 3 Population growth 3.58% Goes Down 3.02% 2.32% 4.38%
# comments
cmnts <- read_docx(system.file("examples/comments.docx", package="docxtractr"))
print(cmnts)
#> No tables in document
#> Word document [/Library/Frameworks/R.framework/Versions/3.3/Resources/library/docxtractr/examples/comments.docx]
#>
#> Found 3 comments.
#> # A tibble: 1 x 2
#> author # Comments
#> <chr> <int>
#> 1 boB Rudis 3
glimpse(docx_extract_all_cmnts(cmnts))
#> Observations: 3
#> Variables: 5
#> $ id <chr> "0", "1", "2"
#> $ author <chr> "boB Rudis", "boB Rudis", "boB Rudis"
#> $ date <chr> "2016-07-01T21:09:00Z", "2016-07-01T21:09:00Z", "2016-07-01T21:09:00Z"
#> $ initials <chr> "bR", "bR", "bR"
#> $ comment_text <chr> "This is the first comment", "This is the second comment", "This is a reply to the second comm...library(docxtractr)
library(testthat)
#>
#> Attaching package: 'testthat'
#> The following object is masked from 'package:dplyr':
#>
#> matches
date()
#> [1] "Tue Jul 19 22:56:37 2016"
test_dir("tests/")
#> testthat results ========================================================================================================
#> OK: 10 SKIPPED: 0 FAILED: 0
#>
#> DONE ===================================================================================================================Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.