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
75	LGD	37	0.6166667
44	IMC	30	0.5000000
21	IMC	51	0.8500000
46	LGD	4	0.0666667
85	NDBE	39	0.6500000
57	LGD	53	0.8833333
13	HGD	34	0.5666667
55	LGD	7	0.1166667
99	NDBE	33	0.5500000
86	HGD	17	0.2833333
88	LGD	21	0.3500000
89	LGD	51	0.8500000
47	HGD	8	0.1333333
65	LGD	8	0.1333333
78	HGD	29	0.4833333
26	LGD	19	0.3166667
77	HGD	22	0.3666667
96	LGD	44	0.7333333
29	LGD	14	0.2333333
2	NDBE	2	0.0333333

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
75	LGD	37	0.6166667	Too Long
44	IMC	30	0.5000000	Too Long
21	IMC	51	0.8500000	Too Long
46	LGD	4	0.0666667	Too Short
85	NDBE	39	0.6500000	Too Long
57	LGD	53	0.8833333	Too Long
13	HGD	34	0.5666667	Too Long
55	LGD	7	0.1166667	Too Short
99	NDBE	33	0.5500000	Too Long
86	HGD	17	0.2833333	Too Short
88	LGD	21	0.3500000	Too Short
89	LGD	51	0.8500000	Too Long
47	HGD	8	0.1333333	Too Short
65	LGD	8	0.1333333	Too Short
78	HGD	29	0.4833333	Too Long
26	LGD	19	0.3166667	Too Short
77	HGD	22	0.3666667	Too Short
96	LGD	44	0.7333333	Too Long
29	LGD	14	0.2333333	Too Short
2	NDBE	2	0.0333333	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
75	LGD	37	0.6166667	Just Right
44	IMC	30	0.5000000	Just Right
21	IMC	51	0.8500000	Too Long
46	LGD	4	0.0666667	Too Short
85	NDBE	39	0.6500000	Just Right
57	LGD	53	0.8833333	Too Long
13	HGD	34	0.5666667	Just Right
55	LGD	7	0.1166667	Too Short
99	NDBE	33	0.5500000	Just Right
86	HGD	17	0.2833333	Too Short
88	LGD	21	0.3500000	Too Short
89	LGD	51	0.8500000	Too Long
47	HGD	8	0.1333333	Too Short
65	LGD	8	0.1333333	Too Short
78	HGD	29	0.4833333	Too Short
26	LGD	19	0.3166667	Too Short
77	HGD	22	0.3666667	Too Short
96	LGD	44	0.7333333	Just Right
29	LGD	14	0.2333333	Too Short
2	NDBE	2	0.0333333	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.