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
63	LGD	30	0.5000000
56	IMC	48	0.8000000
65	IMC	46	0.7666667
39	NDBE	21	0.3500000
92	LGD	57	0.9500000
92	IMC	19	0.3166667
58	HGD	34	0.5666667
54	NDBE	27	0.4500000
95	IMC	48	0.8000000
73	HGD	6	0.1000000
40	IMC	18	0.3000000
4	HGD	60	1.0000000
89	IMC	7	0.1166667
84	HGD	52	0.8666667
96	LGD	60	1.0000000
15	HGD	24	0.4000000
76	HGD	22	0.3666667
62	NDBE	50	0.8333333
77	LGD	49	0.8166667
28	NDBE	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
63	LGD	30	0.5000000	Too Long
56	IMC	48	0.8000000	Too Long
65	IMC	46	0.7666667	Too Long
39	NDBE	21	0.3500000	Too Short
92	LGD	57	0.9500000	Too Long
92	IMC	19	0.3166667	Too Short
58	HGD	34	0.5666667	Too Long
54	NDBE	27	0.4500000	Too Long
95	IMC	48	0.8000000	Too Long
73	HGD	6	0.1000000	Too Short
40	IMC	18	0.3000000	Too Short
4	HGD	60	1.0000000	Too Long
89	IMC	7	0.1166667	Too Short
84	HGD	52	0.8666667	Too Long
96	LGD	60	1.0000000	Too Long
15	HGD	24	0.4000000	Too Short
76	HGD	22	0.3666667	Too Short
62	NDBE	50	0.8333333	Too Long
77	LGD	49	0.8166667	Too Long
28	NDBE	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
63	LGD	30	0.5000000	Just Right
56	IMC	48	0.8000000	Just Right
65	IMC	46	0.7666667	Just Right
39	NDBE	21	0.3500000	Too Short
92	LGD	57	0.9500000	Too Long
92	IMC	19	0.3166667	Too Short
58	HGD	34	0.5666667	Just Right
54	NDBE	27	0.4500000	Too Short
95	IMC	48	0.8000000	Just Right
73	HGD	6	0.1000000	Too Short
40	IMC	18	0.3000000	Too Short
4	HGD	60	1.0000000	Too Long
89	IMC	7	0.1166667	Too Short
84	HGD	52	0.8666667	Too Long
96	LGD	60	1.0000000	Too Long
15	HGD	24	0.4000000	Too Short
76	HGD	22	0.3666667	Too Short
62	NDBE	50	0.8333333	Too Long
77	LGD	49	0.8166667	Too Long
28	NDBE	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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