::: {.cell hash='03_Data_preprocessing_cache/revealjs/unnamed-chunk-73_bb0b00ff5396723b2a2de0bdfeeedf35'}

```{.r .cell-code}
# This is the same dataset we used in the assignment
url_education <- "https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?file=data/educ_uoe_grad05.tsv.gz"
# We load it from the file, dealing with the NA encodings we already know of
education_raw <- read_tsv(url_education, na=c("", ":"))
education_raw %>%
  # Then we pivot the columns for more convenient processing
  pivot_longer(`2020`:`2013`, names_to="year", values_to="value") %>%
  # Once you find the problematic parts of the strings, you can uncomment the 
  # following line to remove the problematic characters
  #   mutate(value = str_remove(value, "d")) %>%
  # Here we keep only the values whose parsing results in a NA
  filter(is.na(as.double(value))) %>%
  # With distinct you remove the duplicates, getting an overview of the problematic values
  distinct(value)
```

::: {.cell-output .cell-output-stdout}
```
# A tibble: 126 × 1
   value 
   <chr> 
 1 <NA>  
 2 3.8 d 
 3 : d   
 4 : z   
 5 8.6 d 
 6 7.7 d 
 7 9.3 d 
 8 8.0 d 
 9 22.8 d
10 23.3 d
# ℹ 116 more rows
```
:::
:::