There are many occasions when a column of data needs to be created from an already existing column for ease of data manipulation. For example, perhaps you have a body of text as a pathology report and you want to extract all the reports where the diagnosis is dysplasia. You could just subset the data using grepl so that you only get the reports that mention this word…but what if the data needs to be cleaned prior to subsetting like excluding reports where the diagnosis is normal but the phrase ‘No evidence of dysplasia’ is present. Or perhaps there are other manipulations needed prior to subsetting.

This is where data accordionisation is useful. This simply means the creation of data from (usually) a column into another column in the same dataframe.

The neatest way to do this is with the mutate function from the ‘dplyr’ package which is devoted to this. There are also other ways which I will demonstrate at the end.



The input data here will be an endoscopy data set:

Age<-sample(1:100, 130, replace=TRUE)
Dx<-sample(c("NDBE","LGD","HGD","IMC"), 130, replace = TRUE)
TimeOfEndoscopy<-sample(1:60, 130, replace=TRUE)


EMRdf<-data.frame(Age,Dx,TimeOfEndoscopy,stringsAsFactors=F)

Perhaps you need to calculate the number of hours spent doing each endoscopy rather than the number of minutes

EMRdftbb<-EMRdf%>%mutate(TimeOfEndoscopy/60)
#Just show the top 20 results
kable(head(EMRdftbb,20))
Age Dx TimeOfEndoscopy TimeOfEndoscopy/60
64 LGD 40 0.6666667
17 HGD 60 1.0000000
96 IMC 43 0.7166667
41 LGD 13 0.2166667
15 NDBE 5 0.0833333
61 NDBE 13 0.2166667
10 HGD 41 0.6833333
28 LGD 42 0.7000000
79 NDBE 60 1.0000000
27 LGD 27 0.4500000
2 IMC 5 0.0833333
99 IMC 8 0.1333333
22 LGD 42 0.7000000
38 NDBE 3 0.0500000
37 HGD 2 0.0333333
64 LGD 15 0.2500000
51 LGD 14 0.2333333
71 NDBE 33 0.5500000
55 HGD 17 0.2833333
76 IMC 10 0.1666667

That is useful but what if you want to classify the amount of time spent doing each endoscopy as follows: <0.4 hours is too little time and >0.4 hours is too long.



Using ifelse() with mutate for conditional accordionisation

For this we would use ifelse(). However this can be combined with mutate() so that the result gets put in another column as follows

EMRdf2<-EMRdf%>%mutate(TimeInHours=TimeOfEndoscopy/60)%>%mutate(TimeClassification = ifelse(TimeInHours>0.4, "Too Long", "Too Short"))
#Just show the top 20 results
kable(head(EMRdf2,20))
Age Dx TimeOfEndoscopy TimeInHours TimeClassification
64 LGD 40 0.6666667 Too Long
17 HGD 60 1.0000000 Too Long
96 IMC 43 0.7166667 Too Long
41 LGD 13 0.2166667 Too Short
15 NDBE 5 0.0833333 Too Short
61 NDBE 13 0.2166667 Too Short
10 HGD 41 0.6833333 Too Long
28 LGD 42 0.7000000 Too Long
79 NDBE 60 1.0000000 Too Long
27 LGD 27 0.4500000 Too Long
2 IMC 5 0.0833333 Too Short
99 IMC 8 0.1333333 Too Short
22 LGD 42 0.7000000 Too Long
38 NDBE 3 0.0500000 Too Short
37 HGD 2 0.0333333 Too Short
64 LGD 15 0.2500000 Too Short
51 LGD 14 0.2333333 Too Short
71 NDBE 33 0.5500000 Too Long
55 HGD 17 0.2833333 Too Short
76 IMC 10 0.1666667 Too Short

Note how we can chain the mutate() function together.



Using multiple ifelse()

What if we want to get more complex and put several classifiers in? We just use more ifelse’s:

EMRdf2<-EMRdf%>%mutate(TimeInHours=TimeOfEndoscopy/60)%>%mutate(TimeClassification = ifelse(TimeInHours>0.8, "Too Long", ifelse(TimeInHours<0.5,"Too Short",ifelse(TimeInHours>=0.5&TimeInHours<=0.8,"Just Right","N"))))
#Just show the top 20 results
kable(head(EMRdf2,20))
Age Dx TimeOfEndoscopy TimeInHours TimeClassification
64 LGD 40 0.6666667 Just Right
17 HGD 60 1.0000000 Too Long
96 IMC 43 0.7166667 Just Right
41 LGD 13 0.2166667 Too Short
15 NDBE 5 0.0833333 Too Short
61 NDBE 13 0.2166667 Too Short
10 HGD 41 0.6833333 Just Right
28 LGD 42 0.7000000 Just Right
79 NDBE 60 1.0000000 Too Long
27 LGD 27 0.4500000 Too Short
2 IMC 5 0.0833333 Too Short
99 IMC 8 0.1333333 Too Short
22 LGD 42 0.7000000 Just Right
38 NDBE 3 0.0500000 Too Short
37 HGD 2 0.0333333 Too Short
64 LGD 15 0.2500000 Too Short
51 LGD 14 0.2333333 Too Short
71 NDBE 33 0.5500000 Just Right
55 HGD 17 0.2833333 Too Short
76 IMC 10 0.1666667 Too Short



Using multiple ifelse() with grepl() or string_extract

Of course we need to extract information from text as well as numeric data. We can do this using grepl or string_extract from the library(stringr). We have used this before here so you may want to refresh yourself.

Let’s say we want to extract all the samples that had IMC. We don’t want to subset the data, just extract IMC into a column that says IMC and the rest say ’Non-IMC

Using the dataset above:

library(stringr)
EMRdf$MyIMC_Column<-str_extract(EMRdf$Dx,"IMC")

#to fill the NA's we would do:
EMRdf$MyIMC_Column<-ifelse(grepl("IMC",EMRdf$Dx),"IMC","NoIMC")
#Another way to do this (really should be for more complex examples when you want to extract the entire contents of the cell that has the match)

EMRdf$MyIMC_Column<-ifelse(grepl("IMC",EMRdf$Dx),str_extract(EMRdf$Dx,"IMC"),"NoIMC")

So data can be usefully created from data for further analysis