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
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.

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.

Back to top

Reuse

CC0

Citation

For attribution, please cite this work as:
Zeki, Sebastian. 2018. “How to Extrapolate Data from Data.” July 23, 2018. https://nhs-r-community.github.io/nhs-r-community/blog/how-to-extrapolate-data-from-data.html.