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
46 HGD 8 0.1333333
1 IMC 54 0.9000000
50 NDBE 13 0.2166667
45 IMC 46 0.7666667
2 NDBE 52 0.8666667
11 LGD 14 0.2333333
49 HGD 32 0.5333333
85 NDBE 24 0.4000000
62 HGD 56 0.9333333
99 LGD 9 0.1500000
65 LGD 39 0.6500000
98 IMC 28 0.4666667
35 IMC 50 0.8333333
45 HGD 59 0.9833333
48 HGD 51 0.8500000
5 IMC 29 0.4833333
11 IMC 21 0.3500000
49 HGD 46 0.7666667
28 LGD 10 0.1666667
22 LGD 53 0.8833333

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
46 HGD 8 0.1333333 Too Short
1 IMC 54 0.9000000 Too Long
50 NDBE 13 0.2166667 Too Short
45 IMC 46 0.7666667 Too Long
2 NDBE 52 0.8666667 Too Long
11 LGD 14 0.2333333 Too Short
49 HGD 32 0.5333333 Too Long
85 NDBE 24 0.4000000 Too Short
62 HGD 56 0.9333333 Too Long
99 LGD 9 0.1500000 Too Short
65 LGD 39 0.6500000 Too Long
98 IMC 28 0.4666667 Too Long
35 IMC 50 0.8333333 Too Long
45 HGD 59 0.9833333 Too Long
48 HGD 51 0.8500000 Too Long
5 IMC 29 0.4833333 Too Long
11 IMC 21 0.3500000 Too Short
49 HGD 46 0.7666667 Too Long
28 LGD 10 0.1666667 Too Short
22 LGD 53 0.8833333 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
46 HGD 8 0.1333333 Too Short
1 IMC 54 0.9000000 Too Long
50 NDBE 13 0.2166667 Too Short
45 IMC 46 0.7666667 Just Right
2 NDBE 52 0.8666667 Too Long
11 LGD 14 0.2333333 Too Short
49 HGD 32 0.5333333 Just Right
85 NDBE 24 0.4000000 Too Short
62 HGD 56 0.9333333 Too Long
99 LGD 9 0.1500000 Too Short
65 LGD 39 0.6500000 Just Right
98 IMC 28 0.4666667 Too Short
35 IMC 50 0.8333333 Too Long
45 HGD 59 0.9833333 Too Long
48 HGD 51 0.8500000 Too Long
5 IMC 29 0.4833333 Too Short
11 IMC 21 0.3500000 Too Short
49 HGD 46 0.7666667 Just Right
28 LGD 10 0.1666667 Too Short
22 LGD 53 0.8833333 Too Long

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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