Author

Sebastian Zeki

Published

July 23, 2018

Modified

March 23, 2024

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 data cleaning. 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)

library(dplyr)

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)

# install.packages("knitr")
library(knitr)
library(kableExtra)

# Just show the top 20 results

kable(head(EMRdftbb, 20))
Age Dx TimeOfEndoscopy TimeOfEndoscopy/60
63 LGD 30 0.5000000
56 IMC 48 0.8000000
65 IMC 46 0.7666667
39 NDBE 21 0.3500000
92 LGD 57 0.9500000
92 IMC 19 0.3166667
58 HGD 34 0.5666667
54 NDBE 27 0.4500000
95 IMC 48 0.8000000
73 HGD 6 0.1000000
40 IMC 18 0.3000000
4 HGD 60 1.0000000
89 IMC 7 0.1166667
84 HGD 52 0.8666667
96 LGD 60 1.0000000
15 HGD 24 0.4000000
76 HGD 22 0.3666667
62 NDBE 50 0.8333333
77 LGD 49 0.8166667
28 NDBE 38 0.6333333

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
63 LGD 30 0.5000000 Too Long
56 IMC 48 0.8000000 Too Long
65 IMC 46 0.7666667 Too Long
39 NDBE 21 0.3500000 Too Short
92 LGD 57 0.9500000 Too Long
92 IMC 19 0.3166667 Too Short
58 HGD 34 0.5666667 Too Long
54 NDBE 27 0.4500000 Too Long
95 IMC 48 0.8000000 Too Long
73 HGD 6 0.1000000 Too Short
40 IMC 18 0.3000000 Too Short
4 HGD 60 1.0000000 Too Long
89 IMC 7 0.1166667 Too Short
84 HGD 52 0.8666667 Too Long
96 LGD 60 1.0000000 Too Long
15 HGD 24 0.4000000 Too Short
76 HGD 22 0.3666667 Too Short
62 NDBE 50 0.8333333 Too Long
77 LGD 49 0.8166667 Too Long
28 NDBE 38 0.6333333 Too Long

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
63 LGD 30 0.5000000 Just Right
56 IMC 48 0.8000000 Just Right
65 IMC 46 0.7666667 Just Right
39 NDBE 21 0.3500000 Too Short
92 LGD 57 0.9500000 Too Long
92 IMC 19 0.3166667 Too Short
58 HGD 34 0.5666667 Just Right
54 NDBE 27 0.4500000 Too Short
95 IMC 48 0.8000000 Just Right
73 HGD 6 0.1000000 Too Short
40 IMC 18 0.3000000 Too Short
4 HGD 60 1.0000000 Too Long
89 IMC 7 0.1166667 Too Short
84 HGD 52 0.8666667 Too Long
96 LGD 60 1.0000000 Too Long
15 HGD 24 0.4000000 Too Short
76 HGD 22 0.3666667 Too Short
62 NDBE 50 0.8333333 Too Long
77 LGD 49 0.8166667 Too Long
28 NDBE 38 0.6333333 Just Right

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).

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 NAs 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.

Hopefully this way of extrapolating data and especially using conditional expressions to categorise data according to some rules is a helpful way to get more out of your data.

Please follow @gastroDS on twitter

This article originally appeared on https://sebastiz.github.io/gastrodatascience/ and has been edited to render in Quarto and had NHS-R styles applied.

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