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
2 IMC 11 0.1833333
30 LGD 29 0.4833333
98 NDBE 4 0.0666667
41 NDBE 35 0.5833333
36 LGD 20 0.3333333
5 HGD 1 0.0166667
32 HGD 30 0.5000000
25 NDBE 13 0.2166667
95 NDBE 6 0.1000000
40 IMC 52 0.8666667
90 NDBE 29 0.4833333
85 HGD 54 0.9000000
78 IMC 45 0.7500000
72 HGD 9 0.1500000
17 IMC 51 0.8500000
24 NDBE 23 0.3833333
74 HGD 4 0.0666667
67 IMC 42 0.7000000
45 NDBE 12 0.2000000
37 NDBE 51 0.8500000

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
2 IMC 11 0.1833333 Too Short
30 LGD 29 0.4833333 Too Long
98 NDBE 4 0.0666667 Too Short
41 NDBE 35 0.5833333 Too Long
36 LGD 20 0.3333333 Too Short
5 HGD 1 0.0166667 Too Short
32 HGD 30 0.5000000 Too Long
25 NDBE 13 0.2166667 Too Short
95 NDBE 6 0.1000000 Too Short
40 IMC 52 0.8666667 Too Long
90 NDBE 29 0.4833333 Too Long
85 HGD 54 0.9000000 Too Long
78 IMC 45 0.7500000 Too Long
72 HGD 9 0.1500000 Too Short
17 IMC 51 0.8500000 Too Long
24 NDBE 23 0.3833333 Too Short
74 HGD 4 0.0666667 Too Short
67 IMC 42 0.7000000 Too Long
45 NDBE 12 0.2000000 Too Short
37 NDBE 51 0.8500000 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
2 IMC 11 0.1833333 Too Short
30 LGD 29 0.4833333 Too Short
98 NDBE 4 0.0666667 Too Short
41 NDBE 35 0.5833333 Just Right
36 LGD 20 0.3333333 Too Short
5 HGD 1 0.0166667 Too Short
32 HGD 30 0.5000000 Just Right
25 NDBE 13 0.2166667 Too Short
95 NDBE 6 0.1000000 Too Short
40 IMC 52 0.8666667 Too Long
90 NDBE 29 0.4833333 Too Short
85 HGD 54 0.9000000 Too Long
78 IMC 45 0.7500000 Just Right
72 HGD 9 0.1500000 Too Short
17 IMC 51 0.8500000 Too Long
24 NDBE 23 0.3833333 Too Short
74 HGD 4 0.0666667 Too Short
67 IMC 42 0.7000000 Just Right
45 NDBE 12 0.2000000 Too Short
37 NDBE 51 0.8500000 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 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.

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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For attribution, please cite this work as:
Zeki, Sebastian. 2018. “How to Extrapolate Data from Data.” July 23, 2018. https://nhs-r-community.github.io/nhs-r-community/blog/how-to-extrapolate-data-from-data.html.