/
0_prep.R
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0_prep.R
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# Shinyfit data preparation
## Read your data to the object `alldata`
## Work down script to prepare data object
## This is mostly about choosing variable names for menus
## Ensure numeric and factor variables are specified appropriately
# Dataset 1 ------------------------------
# Provide data to app
library(finalfit)
library(dplyr)
library(forcats)
# colon_s example
# Read data to "alldata"
alldata = finalfit::colon_s
# Display variable names
names(alldata)
# Select subset of variables to keep
alldata = alldata %>%
dplyr::select(8:9, 14:23, 25, 27, 29, 32)
# View dataset
ff_glimpse(alldata)
# Add variable labels if wish
## e.g.
alldata$nodes %<>% ff_label("Lymph node number")
# Recode factor levels if wish
# alldata %<>%
# mutate(
# var1 =
# forcats::fct_recode(var1,
# "New level 1" = "Old level 1",
# "New level 2" = "Old level 2"
# )
# )
# Extract variable names and labels.
alldata_names = names(alldata)
names(alldata_names) = extract_variable_label(alldata)
# Arrange variable names for purposes of dropdown display
matrix(alldata_names)
# Choose how to arrange the above list (order respected):
alldata_names_list = list(Outcomes = alldata_names[c(15, 2, 3)],
Explanatory = alldata_names[c(13, 4:12, 1, 14)],
Groups = alldata_names[16]
)
# Remove outcomes from explanatory list
alldata_names_list_explanatory = alldata_names_list[-1]
# Create lookup table of names
alldata_names_lookup = extract_labels(alldata)
# Create list for subsetting data, this is limited to factors
alldata %>%
dplyr::select_if(is.factor) -> alldata_subset
alldata_subset_names = names(alldata_subset)
names(alldata_subset_names) = extract_variable_label(alldata_subset)
rm(alldata_subset)
# SUBSETING: Arrange variable names for dropdown list
matrix(alldata_subset_names)
# Choose how to arrange the above list:
alldata_subset_names_list = list(Outcomes = alldata_subset_names[c(12)],
Explanatory = alldata_subset_names[c(10,1,2:9)],
Groups = alldata_subset_names[13]
)
rm(alldata_subset_names)
# Name project
shinyfit_name = "Colon dataset"
dataset_label = "colon_s"
# Make final list for app
alldata_list = list(alldata=alldata,
alldata_names = alldata_names,
alldata_names_list=alldata_names_list,
alldata_names_list_explanatory=alldata_names_list_explanatory,
alldata_names_lookup=alldata_names_lookup,
alldata_subset_names_list=alldata_subset_names_list,
shinyfit_name=shinyfit_name,
dataset_label=dataset_label)
class(alldata_list) = "shinyfit"
save(alldata_list, file="data/alldata.rda")
# Clear workspace
rm(list=ls())
# Dataset 2-------------------------------
# Provide data to app
library(finalfit)
library(dplyr)
library(forcats)
# Melanoma example
# Read data to "alldata"
alldata = boot::melanoma
# Display variable names
names(alldata)
# Select subset of variables to keep
alldata = alldata %>%
dplyr::select(1:7)
# Recode factor levels if necessary
alldata %<>%
mutate(
sex = factor(sex) %>%
fct_recode(Male = "1",
Female = "0"),
ulcer = factor(ulcer) %>%
fct_recode(Yes = "1",
No = "0")
)
# Add variable labels if wish
alldata$time %<>% ff_label("Time since operation (days)")
alldata$status %<>% ff_label("Status")
alldata$sex %<>% ff_label("Sex")
alldata$age %<>% ff_label("Age (years)")
alldata$year %<>% ff_label("Year of operation")
alldata$thickness %<>% ff_label("Thickness (mm)")
alldata$ulcer %<>% ff_label("Ulcer")
# View dataset
ff_glimpse(alldata)
# Extract variable names and labels.
alldata_names = names(alldata)
names(alldata_names) = extract_variable_label(alldata)
# Arrange variable names for purposes of dropdown display
matrix(alldata_names)
# Choose how to arrange the above list:
alldata_names_list = list(Outcomes = alldata_names[c(1, 2)],
Explanatory = alldata_names[c(3:7)],
Groups = ""
)
# Remove outcomes from explanatory list
alldata_names_list_explanatory = alldata_names_list[-1]
# Create lookup table of names (required)
alldata_names_lookup = extract_labels(alldata)
# Create list for subsetting data, this is limited to factors
alldata %>%
dplyr::select_if(is.factor) -> alldata_subset
alldata_subset_names = names(alldata_subset)
names(alldata_subset_names) = extract_variable_label(alldata_subset)
rm(alldata_subset)
# Arrange variable names for SUBSET dropdown list
matrix(alldata_subset_names)
# Choose how to arrange the above list:
alldata_subset_names_list = list(#Outcomes = "",
Explanatory = alldata_subset_names[c(1,2)]#,
#Groups = ""
)
rm(alldata_subset_names)
# Name project
shinyfit_name = "Melanoma survival dataset"
dataset_label = "melanoma"
# Make final list for app
alldata_list = list(alldata=alldata,
alldata_names = alldata_names,
alldata_names_list=alldata_names_list,
alldata_names_list_explanatory=alldata_names_list_explanatory,
alldata_names_lookup=alldata_names_lookup,
alldata_subset_names_list=alldata_subset_names_list,
shinyfit_name=shinyfit_name,
dataset_label=dataset_label)
class(alldata_list) = "shinyfit"
save(alldata_list, file="data/alldata.rda")
# Clear workspace prior to assembling second dataset
rm(list=ls())