How to extrapolate data from data – NHS-R Community Quarto website

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
25	HGD	24	0.4000000
51	IMC	27	0.4500000
42	NDBE	21	0.3500000
41	NDBE	16	0.2666667
76	NDBE	14	0.2333333
89	IMC	48	0.8000000
23	HGD	37	0.6166667
8	HGD	26	0.4333333
81	IMC	12	0.2000000
12	NDBE	45	0.7500000
57	HGD	10	0.1666667
81	HGD	44	0.7333333
55	LGD	55	0.9166667
95	NDBE	26	0.4333333
43	IMC	21	0.3500000
31	NDBE	53	0.8833333
35	IMC	32	0.5333333
99	LGD	29	0.4833333
75	IMC	21	0.3500000
20	LGD	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
25	HGD	24	0.4000000	Too Short
51	IMC	27	0.4500000	Too Long
42	NDBE	21	0.3500000	Too Short
41	NDBE	16	0.2666667	Too Short
76	NDBE	14	0.2333333	Too Short
89	IMC	48	0.8000000	Too Long
23	HGD	37	0.6166667	Too Long
8	HGD	26	0.4333333	Too Long
81	IMC	12	0.2000000	Too Short
12	NDBE	45	0.7500000	Too Long
57	HGD	10	0.1666667	Too Short
81	HGD	44	0.7333333	Too Long
55	LGD	55	0.9166667	Too Long
95	NDBE	26	0.4333333	Too Long
43	IMC	21	0.3500000	Too Short
31	NDBE	53	0.8833333	Too Long
35	IMC	32	0.5333333	Too Long
99	LGD	29	0.4833333	Too Long
75	IMC	21	0.3500000	Too Short
20	LGD	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
25	HGD	24	0.4000000	Too Short
51	IMC	27	0.4500000	Too Short
42	NDBE	21	0.3500000	Too Short
41	NDBE	16	0.2666667	Too Short
76	NDBE	14	0.2333333	Too Short
89	IMC	48	0.8000000	Just Right
23	HGD	37	0.6166667	Just Right
8	HGD	26	0.4333333	Too Short
81	IMC	12	0.2000000	Too Short
12	NDBE	45	0.7500000	Just Right
57	HGD	10	0.1666667	Too Short
81	HGD	44	0.7333333	Just Right
55	LGD	55	0.9166667	Too Long
95	NDBE	26	0.4333333	Too Short
43	IMC	21	0.3500000	Too Short
31	NDBE	53	0.8833333	Too Long
35	IMC	32	0.5333333	Just Right
99	LGD	29	0.4833333	Too Short
75	IMC	21	0.3500000	Too Short
20	LGD	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.

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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.

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