assign_colnames: Make a specific row the column names for the specified...
In docxtractr: Extract Data Tables and Comments from 'Microsoft' 'Word' Documents

Description Usage Arguments Value See Also Examples

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. This function makes it easy to identify a particular row in a scraped 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).

1	assign_colnames(dat, row, remove = TRUE, remove_previous = remove)

`dat`	can be any `data.frame` but is intended for use with ones retuned by this package
`row`	numeric value indicating the row number that is to become the column names
`remove`	remove row specified by `row` after making it the column names? (Default: `TRUE`)
`remove_previous`	remove any rows preceding `row`? (Default: `TRUE` but will be assigned whatever is given for `remove`).

data.frame

docx_extract_all, docx_extract_tbl

# a "real" Word doc
real_world <- read_docx(system.file("examples/realworld.docx", package="docxtractr"))
docx_tbl_count(real_world)

# get all the tables
tbls <- docx_extract_all_tbls(real_world)

# make table 1 better
assign_colnames(tbls[[1]], 2)

# make table 5 better
assign_colnames(tbls[[5]], 2)

[1] 8
# A tibble: 7 x 9
  Country Birthrate `Death Rate` `Population Gro… `Population Gro…
  <chr>   <chr>     <chr>        <chr>            <chr>           
1 USA     2.06      0.51%        0.92%            -0.06%          
2 China   1.62      0.3%         0.6%             -0.58%          
3 Egypt   2.83      0.41%        2.0%             1.32%           
4 India   2.35      0.34%        1.56%            0.76%           
5 Italy   1.28      0.72%        0.35%            -1.33%          
6 Mexico  2.43      0.25%        1.41%            0.96%           
7 Nigeria 4.78      0.26%        2.46%            3.58%           
# … with 4 more variables: `Relative place in Transition` <chr>, `Social
#   Factors 1` <chr>, `Social Factors 2` <chr>, `Social Factors 3` <chr>
# A tibble: 3 x 6
  Nigeria           Default Prediction    `+ 5 years` `+15 years` `-5 years`
  <chr>             <chr>   <chr>         <chr>       <chr>       <chr>     
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%