Merging rows with shared information

Question

I have a data.frame with several rows which come from a merge which are not completely merged:

b <- read.table(text = \"
      ID   Age    Steatosis               


        

Answer 1

Llopis's request to keep both rows if a given ID has different information for a column complicates matters. First let's create some example data that illustrates the situation:

b <- read.table(text = "ID   Age    Steatosis       Mallory Lille_dico Lille_3 Bili.AHHS2cat
                HA-09   16                           5             NA
                HA-09   16   <33% no/occasional             NA             1
                HA-10   20   no   2 NA
                HA-10   20   yes  0 NA NA",
                na.strings = c("NA", ""), header = T)

     ID Age Steatosis       Mallory Lille_dico Lille_3 Bili.AHHS2cat
1 HA-09  16                         NA       5            NA
2 HA-09  16      <33% no/occasional         NA      NA             1
3 HA-10  20        no                   NA       2            NA
4 HA-10  20       yes                    0      NA            NA

This can still be accomplished, but the custom function for summarization (let's call it f) gets a little more complicated:

f <- function(x) {
    x <- x[!is.na(x$value),]
    if (nrow(x) > 0) {
        y <- unique(x[colnames(x) != 'row.ID'])
        y$row.ID <- 1:nrow(y)
        return(y)
    } else {
        return(data.frame())
    }
}

Notice that this function references a column called "row.ID", which we will create before applying the function:

library(tidyverse) # gives access to dplyr and tidyr packages

b2 <- gather(b, variable, value, -ID, -Age) %>% # gather the many columns into a simplified key/value pair of columns (one called 'variable', the other, 'value') for each ID
    group_by(ID, variable) %>% # perform subsequent operations per ID and variable
    mutate(row.ID = 1:n()) %>% # add a row identifier
    do(f(.)) %>% # apply our custom function
    spread(variable, value, convert = T) %>% # un-gather the variable/value columns
    ungroup # remove grouping metadata

      ID   Age row.ID Bili.AHHS2cat Lille_3 Lille_dico       Mallory Steatosis
*                                    
1  HA-09    16      1             1       5         NA no/occasional      <33%
2  HA-10    20      1            NA       2          0                  no
3  HA-10    20      2            NA      NA         NA                 yes