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
46	HGD	8	0.1333333
1	IMC	54	0.9000000
50	NDBE	13	0.2166667
45	IMC	46	0.7666667
2	NDBE	52	0.8666667
11	LGD	14	0.2333333
49	HGD	32	0.5333333
85	NDBE	24	0.4000000
62	HGD	56	0.9333333
99	LGD	9	0.1500000
65	LGD	39	0.6500000
98	IMC	28	0.4666667
35	IMC	50	0.8333333
45	HGD	59	0.9833333
48	HGD	51	0.8500000
5	IMC	29	0.4833333
11	IMC	21	0.3500000
49	HGD	46	0.7666667
28	LGD	10	0.1666667
22	LGD	53	0.8833333

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	HGD	8	0.1333333	Too Short
1	IMC	54	0.9000000	Too Long
50	NDBE	13	0.2166667	Too Short
45	IMC	46	0.7666667	Too Long
2	NDBE	52	0.8666667	Too Long
11	LGD	14	0.2333333	Too Short
49	HGD	32	0.5333333	Too Long
85	NDBE	24	0.4000000	Too Short
62	HGD	56	0.9333333	Too Long
99	LGD	9	0.1500000	Too Short
65	LGD	39	0.6500000	Too Long
98	IMC	28	0.4666667	Too Long
35	IMC	50	0.8333333	Too Long
45	HGD	59	0.9833333	Too Long
48	HGD	51	0.8500000	Too Long
5	IMC	29	0.4833333	Too Long
11	IMC	21	0.3500000	Too Short
49	HGD	46	0.7666667	Too Long
28	LGD	10	0.1666667	Too Short
22	LGD	53	0.8833333	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
46	HGD	8	0.1333333	Too Short
1	IMC	54	0.9000000	Too Long
50	NDBE	13	0.2166667	Too Short
45	IMC	46	0.7666667	Just Right
2	NDBE	52	0.8666667	Too Long
11	LGD	14	0.2333333	Too Short
49	HGD	32	0.5333333	Just Right
85	NDBE	24	0.4000000	Too Short
62	HGD	56	0.9333333	Too Long
99	LGD	9	0.1500000	Too Short
65	LGD	39	0.6500000	Just Right
98	IMC	28	0.4666667	Too Short
35	IMC	50	0.8333333	Too Long
45	HGD	59	0.9833333	Too Long
48	HGD	51	0.8500000	Too Long
5	IMC	29	0.4833333	Too Short
11	IMC	21	0.3500000	Too Short
49	HGD	46	0.7666667	Just Right
28	LGD	10	0.1666667	Too Short
22	LGD	53	0.8833333	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 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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