How to extrapolate data from data

“Extracting data using {grepl} and {stringr}”

Strings
Author

Sebastian Zeki

Published

July 23, 2018

Modified

March 23, 2024

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
68 NDBE 53 0.8833333
62 NDBE 58 0.9666667
54 LGD 35 0.5833333
33 HGD 53 0.8833333
54 HGD 8 0.1333333
10 HGD 57 0.9500000
82 NDBE 47 0.7833333
88 IMC 30 0.5000000
12 HGD 8 0.1333333
68 LGD 2 0.0333333
29 IMC 43 0.7166667
82 LGD 7 0.1166667
72 IMC 23 0.3833333
83 LGD 58 0.9666667
43 LGD 60 1.0000000
36 LGD 13 0.2166667
78 LGD 5 0.0833333
43 HGD 46 0.7666667
53 IMC 2 0.0333333
6 LGD 35 0.5833333

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
68 NDBE 53 0.8833333 Too Long
62 NDBE 58 0.9666667 Too Long
54 LGD 35 0.5833333 Too Long
33 HGD 53 0.8833333 Too Long
54 HGD 8 0.1333333 Too Short
10 HGD 57 0.9500000 Too Long
82 NDBE 47 0.7833333 Too Long
88 IMC 30 0.5000000 Too Long
12 HGD 8 0.1333333 Too Short
68 LGD 2 0.0333333 Too Short
29 IMC 43 0.7166667 Too Long
82 LGD 7 0.1166667 Too Short
72 IMC 23 0.3833333 Too Short
83 LGD 58 0.9666667 Too Long
43 LGD 60 1.0000000 Too Long
36 LGD 13 0.2166667 Too Short
78 LGD 5 0.0833333 Too Short
43 HGD 46 0.7666667 Too Long
53 IMC 2 0.0333333 Too Short
6 LGD 35 0.5833333 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
68 NDBE 53 0.8833333 Too Long
62 NDBE 58 0.9666667 Too Long
54 LGD 35 0.5833333 Just Right
33 HGD 53 0.8833333 Too Long
54 HGD 8 0.1333333 Too Short
10 HGD 57 0.9500000 Too Long
82 NDBE 47 0.7833333 Just Right
88 IMC 30 0.5000000 Just Right
12 HGD 8 0.1333333 Too Short
68 LGD 2 0.0333333 Too Short
29 IMC 43 0.7166667 Just Right
82 LGD 7 0.1166667 Too Short
72 IMC 23 0.3833333 Too Short
83 LGD 58 0.9666667 Too Long
43 LGD 60 1.0000000 Too Long
36 LGD 13 0.2166667 Too Short
78 LGD 5 0.0833333 Too Short
43 HGD 46 0.7666667 Just Right
53 IMC 2 0.0333333 Too Short
6 LGD 35 0.5833333 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.

Please follow @gastroDS on twitter

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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