This R-Hack shows how to build a habit of running one-line sanity checks after every major transformation, using a concrete example from our Exploring Diabetes Data tutorial.
One-line sanity checks you can run after every major transformation.
They are quick, cheap, and often enough to catch problems before plots or models hide them.
Why One-Line Checks Matter
Transformations are the most fragile part of a data workflow. They change structure, size, and meaning — often without throwing an error.
The goal here is not defensive programming or heavy validation. It’s about building small, automatic pauses that let you ask:
“Does the data still look the way I expect?”
Data Example: Diabetes Dataset (Before Clustering)
In the tutorial, the diabetes dataset is cleaned and prepared before moving to k-prototypes clustering.
At that point, the data typically goes through steps like:
selecting relevant variables
recoding categorical fields
filtering incomplete observations
That makes it an ideal place to pause and check assumptions. Let’s simulate a sample dataset similar to the one used in the tutorial:
library(dplyr)set.seed(123)df_before<-data.frame( id =1:300, age =round(rnorm(300, mean =55, sd =10)), bmi =round(rnorm(300, mean =28, sd =5), 1), glucose =round(rnorm(300, mean =120, sd =30)), sex =sample(c("Female", "Male"), 300, replace =TRUE), diabetes =sample(c("No", "Yes"), 300, replace =TRUE, prob =c(0.7, 0.3)))# Introduce realistic missingness (so filtering has an effect)set.seed(456)df_before$bmi[sample(seq_len(nrow(df_before)), size =20)]<-NAdf_before$glucose[sample(seq_len(nrow(df_before)), size =15)]<-NAdim(df_before)
id age bmi glucose sex diabetes
1 1 49 24.4 152 Male No
2 2 53 24.2 119 Male No
3 3 71 23.3 119 Male No
4 4 56 22.7 75 Female Yes
5 5 56 25.8 144 Male No
6 6 72 29.7 114 Female Yes
After the transformations, we get a new dataframe df_after:
id age bmi glucose sex diabetes
1 1 49 24.4 152 Male No
2 2 53 24.2 119 Male No
3 3 71 23.3 119 Male No
4 4 56 22.7 75 Female Yes
5 5 56 25.8 144 Male No
6 6 72 29.7 114 Female Yes
Check 1 – Did the Row Count Change as Expected?
After any operation that could affect the number of rows, check it explicitly.
# One-line sanity check: did we drop rows as expected?nrow(df_before)
Before summarising or modelling grouped data, look at the group counts. In particular, before using categorical variables in clustering or summaries, check their distribution.
