tidyr spread when you want to spread multiple summary values

tidyr-spreading.R

> head(df.tmp)
# A tibble: 6 x 9
  patientid practiceid mmt.date   mmt.val int        pre        preend     post       stage
      <dbl>      <dbl> <date>       <dbl> <date>     <date>     <date>     <date>     <chr>
1     21342       3.00 2015-12-09    83.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
2     19273       3.00 2015-12-11   120   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
3     19273       3.00 2015-12-11    50.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
4     19273       3.00 2015-12-11     0   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
5     19273       3.00 2015-12-17    72.0 2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
6     19273       3.00 2015-12-17   135   2017-03-07 2015-12-07 2016-12-07 2017-09-07 PRE  
> str(df.tmp)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':	29984 obs. of  9 variables:
 $ patientid : num  21342 19273 19273 19273 19273 ...
 $ practiceid: num  3 3 3 3 3 3 3 3 3 3 ...
 $ mmt.date  : Date, format: "2015-12-09" "2015-12-11" "2015-12-11" ...
 $ mmt.val   : num  83 120 50 0 72 135 4.5 1.7 1.7 0.4 ...
 $ int       : Date, format: "2017-03-07" "2017-03-07" "2017-03-07" ...
 $ pre       : Date, format: "2015-12-07" "2015-12-07" "2015-12-07" ...
 $ preend    : Date, format: "2016-12-07" "2016-12-07" "2016-12-07" ...
 $ post      : Date, format: "2017-09-07" "2017-09-07" "2017-09-07" ...
 $ stage     : chr  "PRE" "PRE" "PRE" "PRE" ...
 
 
 # 1. Gives you pracid, stage, variable, value
 # 2. Combines the stage and variable fields into a single field that we want to spread
 # 3. Spread, pivot-like
 
 df.tmp %>% 
    dplyr::group_by(practiceid, stage) %>%
    dplyr::summarise(n.mmt = n(),
                     n.pat = len.unique(patientid)) %>%
    tidyr::gather(variable, value, -(practiceid:stage)) %>%     ############ <--------- 1. This bit is the trick
    tidyr::unite(temp, stage, variable) %>%                     ############ <--------- 2. This bit is the trick
    tidyr::spread(temp, value)   %>%                            ############ <--------- 3. This bit is the trick 
    dplyr::select(practiceid, 
                  PRE_n.pat, PRE_n.mmt, 
                  INTV_n.pat, INTV_n.mmt, 
                  POST_n.pat, POST_n.mmt ) %>%
    sapply(., as.character) %>%
    as_data_frame(.) 
    
    
# A tibble: 31 x 7
   practiceid PRE_n.pat PRE_n.mmt INTV_n.pat INTV_n.mmt POST_n.pat POST_n.mmt
   <chr>      <chr>     <chr>     <chr>      <chr>      <chr>      <chr>     
 1 3          11        345       6          67         4          16        
 2 4          13        414       13         318        <NA>       <NA>      
 3 5          16        577       14         151        <NA>       <NA>      
 4 7          10        308       10         189        <NA>       <NA>      
 5 9          13        333       17         366        16         195       
 6 10         29        1367      22         285        <NA>       <NA>      
 7 11         4         89        4          118        <NA>       <NA>      
 8 12         4         147       4          96         <NA>       <NA>      
 9 13         30        808       31         465        <NA>       <NA>      
10 14         39        1915      34         341        <NA>       <NA>