R formating long data to wide data... but with linked results - r

Question

Can anyone help arranging long data to wide data, but complicated by linked results, ie listed in wide format identified by Study Number this repetitive results listed in wide format after the SN (I've shown an abbreviated table there are more results per patient listed along the bottom with repetitive in columns LabTest, LabDate, Result, Lower, Upper)...I've tried melt and recast, and binding columns but can't seem to get it to work. Over 1000 results to reformat so can't input results manually need to reformat a long data excel document in wide format in R Thank you
Original data looks like this
SN LabTest LabDate Result Lower Upper
TD62 Creat 05/12/2004 22 30 90
TD62 AST 06/12/2004 652 6 45
TD58 Creat 26/05/2007 72 30 90
TD58 Albumin 26/05/2005 22 25 35
TD14 AST 28/02/2007 234 6 45
TD14 Albumin 26/02/2007 15 25 35
Formatted data should look like this
SN LabTCode LabDate Result Lower Upper LabCode LabDate Result Lower Upper
TD62 Creat 05/12/04 22 30 90 AST 06/12/04 652 6 45
TD58 Creat 26/05/05 72 30 90 Alb 26/05/05 22 25 35
TD14 AST 28/02/07 92 30 90 Alb 26/02/07 15 25 35
Formatted data looks like this
So far I have tried:
data_wide2 <- dcast(tdl, SN + LabDate ~ LabCode, value.var="Result")
and
melt(tdl, id = c("SN", "LabDate"), measured= c("Result", "Upper", + "Lower"))

Answer 1

Your issue is that R won't like the final table because it has duplicate column names. Maybe you need the data in that format but it's a bad way to store data because it would be difficult to put the columns back into rows again without a load of manual work.
That said, if you want to do it you'll need a new column to help you transpose the data.
I've used dplyr and tidyr below, which are worth looking at rather than reshape. They're by the same author but more modern and designed to fit together as part of the 'tidyverse'.
library(dplyr)
library(tidyr)
#Recreate your data (not doing this bit in your question is what got you downvoted)
df <- data.frame(
SN = c("TD62","TD62","TD58","TD58","TD14","TD14"),
LabTest = c("Creat","AST","Creat","Albumin","AST","Albumin"),
LabDate = c("05/12/2004","06/12/2004","26/05/2007","26/05/2005","28/02/2007","26/02/2007"),
Result = c(22,652,72,22,234,15),
Lower = c(30,6,30,25,6,25),
Upper = c(90,45,90,35,45,35),
stringsAsFactors = FALSE
)
output <- df %>%
group_by(SN) %>%
mutate(id_number = row_number()) %>% #create an id number to help with tracking the data as it's transposed
gather("key", "value", -SN, -id_number) %>% #flatten the data so that we can rename all the column headers
mutate(key = paste0("t",id_number, key)) %>% #add id_number to the column names. 't' for 'test' to start name with a letter.
select(-id_number) %>% #don't need id_number anymore
spread(key, value)
SN t1LabDate t1LabTest t1Lower t1Result t1Upper t2LabDate t2LabTest t2Lower t2Result t2Upper
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 TD14 28/02/2007 AST 6 234 45 26/02/2007 Albumin 25 15 35
2 TD58 26/05/2007 Creat 30 72 90 26/05/2005 Albumin 25 22 35
3 TD62 05/12/2004 Creat 30 22 90 06/12/2004 AST 6 652 45
And you're there, possibly with some sorting issues still to crack if you need the columns in a specific order.