make spelling corrections - sync with dev versions

Dockerfile_R

@@ -7,7 +7,7 @@
 FROM r-base:latest
 
 ################################################################################
-## HACKATHON USERS - EDITS THE FOLLOWING LINES TO INSTLL NEEDED DEPENDENCIES  ##
+## HACKATHON USERS - EDIT THE FOLLOWING LINES TO INSTALL NEEDED DEPENDENCIES  ##
 
 # system
 # RUN apt-get install -qy libxml2-dev

Dockerfile_py

@@ -1,6 +1,6 @@
 ################################################################################
 # HACKATHON USERS - Only edit the rows needed to install system dependencies and
-# additional R packages.
+# additional Python packages.
 #
 ################################################################################
 ##                HACKATHON USERS --- DO NOT EDIT THIS SECTION                ##
@@ -9,7 +9,7 @@ FROM continuumio/miniconda3:latest
 RUN conda install -c conda-forge rpy2
 
 ################################################################################
-## HACKATHON USERS - EDITS THE FOLLOWING LINES TO INSTLL NEEDED DEPENDENCIES  ##
+## HACKATHON USERS - EDIT THE FOLLOWING LINES TO INSTALL NEEDED DEPENDENCIES  ##
 
 # system
 # RUN apt-get install -qy libxml2-dev

mortality_model.R

@@ -39,13 +39,13 @@ mortality_model <- function(data) {
 #           part of the hackathon.
 #
 # Return:
-#   A charcter vector of length equal to the nrow(newdata) with values
+#   A character vector of length equal to the nrow(newdata) with values
 #   "Mortality" and "Alive"
 #
 predict.hackathon_mortality_model <- function(object, newdata, ...) {
 
   ##############################################################################
-  # User Defined data preperation code starts here
+  # User Defined data preparation code starts here
 
   p <- stats::predict.glm(object, newdata, type = "response", ...)
   ifelse(p > 0.25, "Mortality", "Alive")

mortality_model.py

@@ -36,7 +36,7 @@ def mortality_model(data):
 def predict_mortality(model, newdata):
 
     ############################################################################
-    # User Defined data preperation code starts here
+    # User Defined data preparation code starts here
     xmat = newdata[["age", "female", "gcs_use", "icpyn1"]]
     p = model.predict_proba(xmat)
     return np.where(p[:, 1] > 0.25, "Mortality", "Alive")

prepare_fss_data.py

@@ -1,15 +1,15 @@
 ################################################################################
 # Prepare FSS Data
 #
-# Define data procesing steps to apply to the data set used to train and test
-# models for predicting fss.
+# Define data processing steps to apply to the data set used to train and test
+# models for predicting FSS.
 #
 # Args:
-#   training  (logicial) if the data set to read in is the training or testing
+#   training  (logical) if the data set to read in is the training or testing
 #             data set.
 #
 # Return:
-#   A pandas data.frame with the defnined primary outcome and any user specific
+#   A pandas data.frame with the defined primary outcome and any user-specific
 #   elements needed for training and testing their model.
 #
 

prepare_mortality_data.R

@@ -1,15 +1,15 @@
 ################################################################################
 # Prepare Mortality Data
 #
-# Define data procesing steps to apply to the data set used to train and test
+# Define data processing steps to apply to the data set used to train and test
 # models for predicting mortality.
 #
 # Args:
-#   training  (logicial) if the data set to read in is the training or testing
+#   training  (logical) if the data set to read in is the training or testing
 #             data set.
 #
 # Return:
-#   A data.frame with the defnined primary outcome and any user specific
+#   A data.frame with the defined primary outcome and any user specific
 #   elements needed for training and testing their model.
 #
 prepare_mortality_data <- function(training = TRUE) {
@@ -30,7 +30,7 @@ prepare_mortality_data <- function(training = TRUE) {
   # Omit some elements - FSS is omitted from this data set.  FSS could not be
   # assessed for patients who died.  To reduce confusion FSS related elements
   # are omitted as missing values for FSS are be highly correlated with
-  # mortalily.
+  # mortality.
   hackathon_mortality_data[-grep("fss", names(hackathon_mortality_data))]
 
   ##############################################################################

prepare_mortality_data.py

@@ -36,7 +36,7 @@ def prepare_mortality_data(training = True):
     # Omit some elements - FSS is omitted from this data set.  FSS could not be
     # assessed for patients who died.  To reduce confusion FSS related elements
     # are omitted as missing values for FSS are be highly correlated with
-    # mortalily.
+    # mortality.
     #for c in hackathon_mortality_data.filter(regex = "fss").columns:
     #    hackathon_mortality_data = hackathon_mortality_data.drop(columns = c)
     hackathon_mortality_data = hackathon_mortality_data.filter(regex = "^(?!.*fss.*)")