diff --git a/r4babs4/week-4/workshop.qmd b/r4babs4/week-4/workshop.qmd
index beb21e42..f5ccf7f6 100644
--- a/r4babs4/week-4/workshop.qmd
+++ b/r4babs4/week-4/workshop.qmd
@@ -25,7 +25,7 @@ In this workshop you will use the tools you used in the previous workshop (and b
 
 🎬 If you have not already done so, save your data files to the project. Are the file names going to be easy for you to work with? If not, rename them. [Remember](../week-2/workshop.html#make-data-easier-to-work-with) that we used the file names to label to rows with their treatment (Media, LPS or ECOLIGreen) and antibody (ISOTYPE or TNFAPC) so if you do not match the names you will need to change the code in "Add columns for treatment and antibody by extracting that information from the sample name." and in "We can use the `fct_relevel()` function to put groups in order so that our graphs are better to interpret."
 
-🎬 The sample data had [22 columns](../week-2/workshop.html#explore-the-data-structure). We used [meta.csv](../week-2/data-meta/meta.csv) to rename the columns. Some of those columns were not used and we [dropped them](../week-2/workshop.html#drop-the-unused-channels-columns). You data has been created without those columns. You will therefore use a different [meta.csv](../week-2/data-meta/meta.csv) file and you will **not** need to drop any columns.
+🎬 The sample data had [22 columns](../week-2/workshop.html#explore-the-data-structure). We used [meta.csv](../week-2/data-meta/meta.csv) to rename the columns. Some of those columns were not used and we [dropped them](../week-2/workshop.html#drop-the-unused-channels-columns). You data has been created without those columns. You will therefore use a different [meta.csv](../week-4/data-meta/meta.csv) file and you will **not** need to drop any columns.
 
 
 🎬 Open your R script and begin to analyse your data.