From 7cb564c29bc0b73d3d2f9bb989dc1f616f24b21b Mon Sep 17 00:00:00 2001 From: Mary Llewellyn Date: Mon, 26 Feb 2024 11:14:09 +0000 Subject: [PATCH] address task 7, issue #112 --- _episodes_rmd/01-introduction-to-high-dimensional-data.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/_episodes_rmd/01-introduction-to-high-dimensional-data.Rmd b/_episodes_rmd/01-introduction-to-high-dimensional-data.Rmd index 89d4f7e1..82b80ff6 100644 --- a/_episodes_rmd/01-introduction-to-high-dimensional-data.Rmd +++ b/_episodes_rmd/01-introduction-to-high-dimensional-data.Rmd @@ -140,9 +140,9 @@ of the challenges we are facing when working with high-dimensional data. > Although technically not a high-dimensional dataset, the `Prostate` data > will allow us explore the problems encountered when working with many features. > -> Examine the dataset (in which each row represents a single patient) to: -> a) Determine how many observations ($n$) and features ($p$) are available (hint: see the `dim()` function) -> b) Examine what variables were measured (hint: see the `names()` and `head()` functions) +> Examine the dataset (in which each row represents a single patient) to:\ +> a) Determine how many observations ($n$) and features ($p$) are available (hint: see the `dim()` function).\ +> b) Examine what variables were measured (hint: see the `names()` and `head()` functions).\ > c) Plot the relationship between the variables (hint: see the `pairs()` function). > > > ## Solution