diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
index 3d49dc630ef5afba4c95fe813bdb0384cd79ae67..21966e1f3f03416e1a080a391894f370f9f1a5a8 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_r.md
@@ -64,7 +64,7 @@ marie@compute$ R -e 'install.packages("ggplot2")'
 
 The deep learning frameworks perform extremely fast when run on accelerators such as GPU.
 Therefore, using nodes with built-in GPUs, e.g., partitions [ml](../jobs_and_resources/power9.md)
-and [alpha](../jobs_and_resources/alpha_centauri.md, is beneficial for the examples here.
+and [alpha](../jobs_and_resources/alpha_centauri.md), is beneficial for the examples here.
 
 ### R Interface to TensorFlow