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
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.

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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.