https://xgboost.readthedocs.io/en/latest/R-package/discoverYourData.html
importanceRaw <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sparse_matrix, label = output_vector)

Cleaning for better display

importanceClean <- importanceRaw[,:=(Cover=NULL, Frequency=NULL)]

head(importanceClean)

Feature Split Gain RealCover RealCover %

1: TreatmentPlacebo -1.00136e-05 0.28575061 7 0.2500000

2: Age 61.5 0.16374034 12 0.4285714

3: Age 39 0.08705750 8 0.2857143

4: Age 57.5 0.06947553 11 0.3928571

5: SexMale -1.00136e-05 0.04874405 4 0.1428571

6: Age 53.5 0.04620627 10 0.3571429

our results is : Feature Gain
1: Age 0.60965369
2: TreatmentPlacebo 0.34017103
3: SexMale 0.02340126
4: AgeDiscret6 0.01514658
5: AgeDiscret4 0.01162745