Plotting a single Shapley value observation (similar to a Python output from the original author)

I would like to create something similar to the below plot (from Python) here.

The Python code is the following:

import xgboost
import shap

# load JS visualization code to notebook
shap.initjs()

# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)

# explain the model's predictions using SHAP
# (same syntax works for LightGBM, CatBoost, scikit-learn and spark models)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)

It plots a single observations prediction based on Shapley values.

I think I have managed to re-create up to this point in R using:

library(MASS)
library(xgboost)
library(SHAPforxgboost)
data(Boston)
Boston


X_train <- Boston %>% 
  dplyr::select(-crim) %>% 
  as.matrix()

Y_train <- Boston %>% 
  dplyr::select(crim) %>% 
  as.matrix()

dtrain <- xgb.DMatrix(data = X_train, label = Y_train)


params <- list(
  "eta" = 0.3,
  "max_depth" = 6,
  "colsample_bytree" = 1,
  "min_child_weight" = 3,
  "subsample"= 0.8,
  "objective"="reg:linear",
  "gamma" = 1,
  "lambda" = 1,
  "alpha" = 0,
  "max_delta_step" = 0,
  "colsample_bylevel" = 1,
  "eval_metric"= "rmse",
  "set.seed" = 176
)

watchlist <- list("train" = dtrain)
nround = 50 
xgb.model <- xgb.train(params, dtrain, nround, watchlist)

shapValues <- shap.values(xgb_model = xgb.model, X_train = X_train)

shapForceData <- X_train %>% 
  data.frame(shapValues$shap_score)

singleShapObs <- data.frame(shapForceData[1, ]) # want to plot this data

Now I have a singleShapObs which contains the "real" values and the "Shapley" values (the variables which contain the .1 are the Shaply values. How can I plot something similar to the above Python example?

Note: This leads on from a previous question I had answered here. In that solution each geom_col is an observation. I basically want to extract one of those geom_cols and represent it in a similar fashion to the Python Shap package. (does not have to be 100% exactly the same as the Python plot)