You don't need to call `data.frame()` around the extract - the `@data` slot already is a data.frame. Just do

      df <- asf52@data

to pull out a copy. That said, you may be better served by using the newer `sf` library for this task:

    library(sf)
    ob_kml <- file.path(getwd(), 'Outbreaks 56 (OIE).kml')    

There is more than one layer in your KML - list them with e.g.
    
    st_layers(ob_kml)
    
Use `read_sf()` with the `layers` argument to choose your point data specifically and read it in. `read_sf()` defaults to stringsAsFactors = FALSE which may be preferable.
 
    asf_c <- read_sf(ob_kml, layer = 'ASF in China.xlsx')
    
To get a plain dataframe, just drop the geometry as follows:

    asf_c_df <- st_set_geometry(asf_c, NULL)

**EDIT:** I see your secondary issue now; it looks like neither `sf` nor `sp` look at the `<ExtendedData>` tags that hold the attribute data you want (open the KML in Notepad++ if you want to see what I mean). QGIS does detect and import them as separate attribute columns, so @Jella's advice is sound. I'm not sure if the issue here lies with `sf`/`sp` or GDAL, but it may be worth raising an issue of the `sf` github page.

In the meantime, your instinct to go with `tidyr` functions is sound, its just a little tricky to get a clean separation. The following looks pretty good: 

    asf_c_df <- st_set_geometry(asf_c, NULL) %>%
      # remove duplicate <br> tags
      dplyr::mutate(Description = gsub('<br><br>', '<br>', Description)) %>%
      # split on <br>
      tidyr::separate(., col = Description, 
                      into = c('Date', 'Province', 'City', 'County', 'Location', 
                               'Total_herd_size', 'Affected_animals', 'Deaths',
                               'Culled', 'Latitude', 'Longitude', 'Source'), 
                      sep = '<br>') %>%
      # ditch the key: part of key: value
      dplyr::mutate_all(., funs(gsub('^.*: ', '', .))) %>%
      # data type fixes 
      dplyr::mutate_at(vars(7:10), as.integer) %>%
      dplyr::mutate_at(vars(11,12), as.numeric) %>%
      # bonus points: proper dates. First, fix September, then cast to Date datatype
      dplyr::mutate(Date = gsub('Sept', 'Sep', Date),
                    Date = as.POSIXct(Date, format = '%b %d, %Y')) %>%
      # double bonus! proper NA for missing data
      dplyr::mutate_if(is.character, funs(ifelse(. == '', NA, .)))