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
45	IMC	18	0.3000000
17	HGD	54	0.9000000
39	LGD	34	0.5666667
46	NDBE	55	0.9166667
31	NDBE	6	0.1000000
39	NDBE	4	0.0666667
21	IMC	17	0.2833333
29	HGD	44	0.7333333
78	IMC	18	0.3000000
56	NDBE	56	0.9333333
12	LGD	39	0.6500000
56	NDBE	2	0.0333333
56	LGD	60	1.0000000
4	HGD	28	0.4666667
68	LGD	56	0.9333333
73	NDBE	58	0.9666667
64	IMC	52	0.8666667
43	LGD	16	0.2666667
47	LGD	5	0.0833333
82	HGD	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
45	IMC	18	0.3000000	Too Short
17	HGD	54	0.9000000	Too Long
39	LGD	34	0.5666667	Too Long
46	NDBE	55	0.9166667	Too Long
31	NDBE	6	0.1000000	Too Short
39	NDBE	4	0.0666667	Too Short
21	IMC	17	0.2833333	Too Short
29	HGD	44	0.7333333	Too Long
78	IMC	18	0.3000000	Too Short
56	NDBE	56	0.9333333	Too Long
12	LGD	39	0.6500000	Too Long
56	NDBE	2	0.0333333	Too Short
56	LGD	60	1.0000000	Too Long
4	HGD	28	0.4666667	Too Long
68	LGD	56	0.9333333	Too Long
73	NDBE	58	0.9666667	Too Long
64	IMC	52	0.8666667	Too Long
43	LGD	16	0.2666667	Too Short
47	LGD	5	0.0833333	Too Short
82	HGD	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
45	IMC	18	0.3000000	Too Short
17	HGD	54	0.9000000	Too Long
39	LGD	34	0.5666667	Just Right
46	NDBE	55	0.9166667	Too Long
31	NDBE	6	0.1000000	Too Short
39	NDBE	4	0.0666667	Too Short
21	IMC	17	0.2833333	Too Short
29	HGD	44	0.7333333	Just Right
78	IMC	18	0.3000000	Too Short
56	NDBE	56	0.9333333	Too Long
12	LGD	39	0.6500000	Just Right
56	NDBE	2	0.0333333	Too Short
56	LGD	60	1.0000000	Too Long
4	HGD	28	0.4666667	Too Short
68	LGD	56	0.9333333	Too Long
73	NDBE	58	0.9666667	Too Long
64	IMC	52	0.8666667	Too Long
43	LGD	16	0.2666667	Too Short
47	LGD	5	0.0833333	Too Short
82	HGD	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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