How to extrapolate data from data

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	NDBE	29	0.4833333
21	HGD	51	0.8500000
48	IMC	23	0.3833333
52	IMC	1	0.0166667
72	LGD	42	0.7000000
9	HGD	21	0.3500000
25	HGD	42	0.7000000
59	LGD	52	0.8666667
61	NDBE	58	0.9666667
35	NDBE	47	0.7833333
47	NDBE	37	0.6166667
25	NDBE	34	0.5666667
64	NDBE	49	0.8166667
55	LGD	37	0.6166667
36	IMC	31	0.5166667
20	HGD	5	0.0833333
17	NDBE	45	0.7500000
60	NDBE	25	0.4166667
44	NDBE	31	0.5166667
47	HGD	18	0.3000000

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	NDBE	29	0.4833333	Too Long
21	HGD	51	0.8500000	Too Long
48	IMC	23	0.3833333	Too Short
52	IMC	1	0.0166667	Too Short
72	LGD	42	0.7000000	Too Long
9	HGD	21	0.3500000	Too Short
25	HGD	42	0.7000000	Too Long
59	LGD	52	0.8666667	Too Long
61	NDBE	58	0.9666667	Too Long
35	NDBE	47	0.7833333	Too Long
47	NDBE	37	0.6166667	Too Long
25	NDBE	34	0.5666667	Too Long
64	NDBE	49	0.8166667	Too Long
55	LGD	37	0.6166667	Too Long
36	IMC	31	0.5166667	Too Long
20	HGD	5	0.0833333	Too Short
17	NDBE	45	0.7500000	Too Long
60	NDBE	25	0.4166667	Too Long
44	NDBE	31	0.5166667	Too Long
47	HGD	18	0.3000000	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
46	NDBE	29	0.4833333	Too Short
21	HGD	51	0.8500000	Too Long
48	IMC	23	0.3833333	Too Short
52	IMC	1	0.0166667	Too Short
72	LGD	42	0.7000000	Just Right
9	HGD	21	0.3500000	Too Short
25	HGD	42	0.7000000	Just Right
59	LGD	52	0.8666667	Too Long
61	NDBE	58	0.9666667	Too Long
35	NDBE	47	0.7833333	Just Right
47	NDBE	37	0.6166667	Just Right
25	NDBE	34	0.5666667	Just Right
64	NDBE	49	0.8166667	Too Long
55	LGD	37	0.6166667	Just Right
36	IMC	31	0.5166667	Just Right
20	HGD	5	0.0833333	Too Short
17	NDBE	45	0.7500000	Just Right
60	NDBE	25	0.4166667	Too Short
44	NDBE	31	0.5166667	Just Right
47	HGD	18	0.3000000	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).

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.

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