In z2thet/MoffittFunctions: Moffitt Functions
\listoftables
\clearpage
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(xtable.comment = FALSE, datatable.verbose = FALSE, scipen = 10, knitr.kable.NA = '', knitr.table.format = 'latex', kableExtra.latex.load_packages = FALSE)
Overview
MoffittFunctions is a collection of useful functions designed to assist in analysis and creation of professional reports. The current MoffittFunctions functions can be broken down to the following sections:
- Testing Functions
- two_samp_bin_test
- two_samp_cont_test
-
cor_test
-
Fancy Output Functions
- pretty_pvalues
- stat_paste
- paste_tbl_grp
- pretty_model_output
- run_pretty_model_output
- pretty_km_output
- run_pretty_km_output
- pretty_bayesian_output
-
pretty_bayesian_regression_tests
-
Utility Functions
- round_away_0
- get_session_info
-
get_full_name
-
Example Dataset
- Bladder_Cancer
Getting Started
# Loading MoffittFunctions and Example Dataset
library(MoffittFunctions)
data("Bladder_Cancer")
# Loading dplyr for this vignette code
library(dplyr)
MoffittTemplates Package
The MoffittTemplates package makes extensive use of the MoffittFunctions package, and is a great way get started making professional statistical reports.
Code to initially download MoffittTemplates package:
cred = git2r::cred_ssh_key(
publickey = "MYPATH/.ssh/id_rsa.pub",
privatekey = "MYPATH/.ssh/id_rsa")
devtools::install_git(
"git@gitlab.moffitt.usf.edu:ReproducibleResearch/R_Markdown_Templates.git",
credentials = cred)
RStudio you can Global Options -> Git/SVN to see SSH path, and to make SSH key if needed
Once installed, in RStudio go to File -> New File -> R Markdown -> From Template -> Moffitt PDF Report to start a new Markdown report using the template. Within the template there is code to load and make use of most of the MoffittFunctions functionality.
Example Dataset
The Bladder_Cancer dataset is a real world example dataset used throughout this vignette and most example in the documentation. The dataset is cleaned, using factor variables for categorical variables, and also using labels for all variables (created by the Hmisc::label() function).
Testing Functions
There are currently three testing functions, performing the appropriate statistical test depending on the data and options, returning a p value.
Comparing Two Groups (Binary Variable) for a Binary Variable
two_samp_bin_test() is used for comparing a binary variable to a binary (two group) variable, with options for Barnard, Fisher's Exact, Chi-Sq, and McNemar tests.
table(Bladder_Cancer$Vital_Status, Bladder_Cancer$PT0N0)
two_samp_bin_test(x = Bladder_Cancer$Vital_Status, y = Bladder_Cancer$PT0N0,
method = 'fisher')
Comparing Two Groups (Binary Variable) for a Continuous Variable
two_samp_cont_test() is used for comparing a continuous variable to a binary (two group) variable, with parametric (t.test) and non-parametric (Wilcox Rank-Sum) options. Also pair data is allowed, where there are parametric (paired t.test) and non-parametric (Wilcox Signed-Rank) options.
by(Bladder_Cancer$Survival_Days, Bladder_Cancer$PT0N0, summary)
two_samp_cont_test(x = Bladder_Cancer$Survival_Days, y = Bladder_Cancer$PT0N0,
method = 'wilcox')
Comparing Two Continuous Variables (Correlation)
cor_test() is used for comparing two continuous variables, with Pearson, Kendall, and Spearman methods. If Spearman method is chosen and either variable has a tie the approximate distribution is use in the coin::spreaman_test() function. This is usually the preferred method over the asymptotic approximation, which is the method stats:cor.test() uses in cases of ties.
cor(Bladder_Cancer$Age_At_Diagnosis, Bladder_Cancer$Survival_Days,
method = 'spearman')
cor_test(x = Bladder_Cancer$Age_At_Diagnosis, y = Bladder_Cancer$Survival_Days,
method = 'spearman')
Fancy Output Functions
There are currently seven functions designed to produce professional output that can easily printed in reports.
P Values
pretty_pvalues() can be used on p values, rounding them to a specified digit amount and using < for low p values, as opposed to scientific notation (i.e. "p < 0.0001" if rounding to 4 digits), allows options for emphasizing p-values and specific characters for missing.
pvalue_example = c(1, 0.06753, 0.004435, NA, 1e-16, 0.563533)
# For simple p value display
pretty_pvalues(pvalue_example, digits = 3)
# For display in report
table_p_Values <- pretty_pvalues(pvalue_example, digits = 3, background = NULL)
kableExtra::kable(table_p_Values, format = 'latex', escape = FALSE,
col.names = c("P-values"), caption = 'Fancy P Values')
You can also specify if you want p= pasted on the front of the p values.
Basic Combining of Variables
stat_paste() is used to combine two or three statistics together, allowing for different rounding and bound character specifications. Common uses for this function are for:
- Mean (sd)
- Median [min, max]
- Estimate (SE of Estimate)
- Estimate (95% CI Lower Bound, Upper Bound)
- Estimate/Statistic (p value)
# Simple Examples
stat_paste(stat1 = 2.45, stat2 = 0.214, stat3 = 55.3,
digits = 2, bound_char = '[')
stat_paste(stat1 = 6.4864, stat2 = pretty_pvalues(0.0004, digits = 3),
digits = 3, bound_char = '(')
Bladder_Cancer %>%
group_by(Gender) %>%
summarise(`Survival Info (Median [Range])` =
stat_paste(stat1 = median(Survival_Months),
stat2 = min(Survival_Months),
stat3 = max(Survival_Months),
digits = 2, bound_char = '['))
Bladder_Cancer %>%
summarise(
`Age vs. Survival Months Cor(p value)` =
stat_paste(stat1 = cor(Age_At_Diagnosis, Survival_Months, method = 'spearman'),
stat2 =
pretty_pvalues(
cor_test(Age_At_Diagnosis, Survival_Months, method = 'spearman'),
digits = 3, include_p = TRUE),
digits = 2, bound_char = '('))
\clearpage
Advanced Combining of Variables
paste_tbl_grp() paste together information, often statistics, from two groups. There are two predefined combinations: mean(sd) and median[min,max], but user may also paste any single measure together.
# summary_info <- Bladder_Cancer %>%
# group_by(Gender, Any_Downstaging) %>%
# summarise_at("Survival_Months", funs(n = length, mean, sd, median, min, max)) %>%
# tidyr::gather(variable, value, -Any_Downstaging, -Gender) %>%
# tidyr::unite(var, Any_Downstaging, variable) %>%
# tidyr::spread(var, value) %>%
# mutate(`No Downstaging` = "No Downstaging", Downstaging = "Downstaging") %>%
# paste_tbl_grp(vars_to_paste = c('n', 'mean_sd', 'median_min_max'),
# first_name = 'No Downstaging', second_name = 'Downstaging')
summary_info <- Bladder_Cancer %>%
group_by(Gender, Any_Downstaging) %>%
summarise_at("Survival_Months", funs(n = length, mean, sd, min, max)) %>%
tidyr::gather(variable, value, -Any_Downstaging, -Gender) %>%
tidyr::unite(var, Any_Downstaging, variable) %>%
tidyr::spread(var, value) %>%
mutate(`No Downstaging` = "No Downstaging")%>%
mutate( `Downstaging` = "Downstaging") %>%
paste_tbl_grp(vars_to_paste = c('n', 'mean','sd', 'min','max'),
first_name = 'No Downstaging', second_name = 'Downstaging')
kableExtra::kable(summary_info, format = 'latex', escape = TRUE, booktabs = TRUE,
caption = 'Summary Information Comparison') %>%
kableExtra::kable_styling(font_size = 6.5) %>%
kableExtra::footnote(
'Summary Information for Downstaging vs. No-Downstaging, by Gender')
Model Output Functions
pretty_model_output() and run_pretty_model_output() are used to produce professional tables for single or multiple Linear, Logistic, or Cox Proportional-Hazards Regression Models, calculating estimates, odds ratios, or hazard ratios, respectively, with confidence intervals. P values are also produced. For categorical variables with 3+ levels overall Type 3 p values are calculated (matches SAS's default overall p values), in addition to p values comparing to the first level (reference).
pretty_model_output() uses the model fits, while run_pretty_model_output() uses the variables and dataset, running the desired model. The run_pretty_model_output() will use variable labels if they exist (created by the Hmisc::label() function). Many details can be adjusted, such as overall test method ("Wald" or "LR"), title (will be added as column), confidence level, estimate and p value rounded digits, significant alpha level for highlighting along with color, italic, and bolding p value options, and latex or non-latex desired output.
In run_pretty_model_output(), y_in, event_in, and event_level are used defined differently, depending on the type of model. For Linear Regression y_in is the dependent variable, and event_in and event_level are left NULL. For Logistic Regression y_in is the dependent variable, event_level is the event level of the variable (i.e. "1" or "Response"), and event_in is left NULL. For Cox Regression y_in is the time component, event_in is the event status variable, and event_level is the event level of the event_in variable (i.e. "1" or "Dead").
\clearpage
Linear Regression Example
# Using pretty_model_output() for a single multivariable model
my_fit <- lm(
Surgery_Year ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped +
Histology_Grouped, data = Bladder_Cancer)
pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped')
# Using run_pretty_model_output() for multiple univariate linear regression models
# Use purrr::map_dfr function to run the run_pretty_model_output() multiple times
linear_univariate_output <- purrr::map_dfr(
vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer,
y_in = 'Surgery_Year', event_in = NULL, event_level = NULL,
output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
linear_univariate_output, 'latex', escape = F, booktabs = TRUE,
linesep = '', caption = 'Linear Regression Univariate Models') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable linear regression model
linear_multivariable_output <- run_pretty_model_output(
x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Surgery_Year',
event_in = NULL, event_level = NULL, output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
linear_multivariable_output %>% dplyr::select(-n), 'latex',
escape = F, booktabs = TRUE, linesep = '',
caption = 'Linear Regression Multivariable Model') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10) %>%
kableExtra::footnote(
paste0('Model sample size is ',
linear_multivariable_output %>% select(n) %>% slice(1)))
\clearpage
Logistic Regression Example
# Using pretty_model_output() for a single multivariable model
my_fit <- glm(
Any_Downstaging == 'Downstaging' ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped +
Histology_Grouped, data = Bladder_Cancer, family = binomial(link = "logit"))
pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped')
# Using run_pretty_model_output() for multiple univariate logistic regression models
# Use purrr::map_dfr function to run the run_pretty_model_output() multiple times
logistic_univariate_output <- purrr::map_dfr(
vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer,
y_in = 'Any_Downstaging', event_in = NULL, event_level = 'Downstaging',
output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
logistic_univariate_output, 'latex', escape = F, booktabs = TRUE,
linesep = '', caption = 'Logistic Regression Univariate Models') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable logistic regression model
logistic_multivariable_output <- run_pretty_model_output(
x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Any_Downstaging',
event_in = NULL, event_level = 'Downstaging', output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
logistic_multivariable_output %>% dplyr::select(-n), 'latex',
escape = F, booktabs = TRUE, linesep = '',
caption = 'Logistic Regression Multivariable Model') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10) %>%
kableExtra::footnote(
paste0('Model sample size is ',
logistic_multivariable_output %>% select(n) %>% slice(1)))
\clearpage
Bayesian Output functions
pretty_bayesian_output() and pretty_bayesian_regression_tests() are used to produce professional tables for single or multivariable Bayesian Linear or Logistic regression.
pretty_bayesian_output takes a stanreg fit object ( family = binomial or guassian), and calculates estimates of regression coefficients or odds ratios.
ybin <- sample(0:1, 100, replace = TRUE)
y <- rexp(100,.1)
x1 <- rnorm(100)
x2 <- y + rnorm(100)
x3 <- factor(sample(letters[1:4],100,replace = TRUE))
my_data <- data.frame(y, ybin, x1, x2, x3)
library(rstanarm)
lm_fit <- stan_glm(y ~ x1+ x2 + x3, data = my_data, refresh = 0 )
pretty_bayesian_output(fit = lm_fit, model_data = my_data)
library(dplyr)
# Logistic Regression
my_fit <- stan_glm(ybin ~ x1 + x2 + x3, data = my_data, family = binomial, refresh = 0)
my_pretty_model_output <- pretty_bayesian_output(fit = my_fit, model_data = my_data)
\clearpage
# Printing of Fancy table in HTML
kableExtra::kable(my_pretty_model_output, 'latex', caption = 'Bayesian Logistic regression model') #%>%
# kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack')
\clearpage
Cox Proportional-Hazards Regression Example
# Using pretty_model_output() for a single multivariable model
surv_obj <- survival::Surv(Bladder_Cancer$Survival_Months, Bladder_Cancer$Vital_Status == 'Dead')
my_fit <- survival::coxph(
surv_obj ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped +
Histology_Grouped, data = Bladder_Cancer)
pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped')
# Using run_pretty_model_output() for multiple univariate Cox regression models
# Use purrr::map_dfr function to run the run_pretty_model_output() multiple times
cox_univariate_output <- purrr::map_dfr(
vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer,
y_in = 'Survival_Months', event_in = 'Vital_Status', event_level = 'Dead',
output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
cox_univariate_output, 'latex', escape = F, booktabs = TRUE,
linesep = '', caption = 'Cox Proportional-Hazards Regression Univariate Models') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable Cox regression model
cox_multivariable_output <- run_pretty_model_output(
x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Survival_Months',
event_in = 'Vital_Status', event_level = 'Dead', output_type = 'latex')
#Use kableExtra to make fancy output
kableExtra::kable(
cox_multivariable_output %>% dplyr::select(-`n (events)`), 'latex',
escape = F, booktabs = TRUE, linesep = '',
caption = 'Cox Proportional-Hazards Regression Multivariable Model') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 10) %>%
kableExtra::footnote(
paste0('Model sample size (events) is ',
cox_multivariable_output %>% select(`n (events)`) %>% slice(1)))
Kaplan–Meier Output Functions
pretty_km_output() and run_pretty_km_output() are used to produce professional tables with Kaplan–Meier median survival estimates, and the estimates at given time points, if listed. pretty_km_output() uses a survfit object, while run_pretty_km_output() uses the variables, and strata if applicable, and runs creates the survfit objects, also calculating the log-rank p value, if applicable.
Many details can be adjusted, such as title (will be added as column), strata name, confidence level, survival estimate prefix (default is "Time"), survival estimate, median estimate, and p value rounded digits, significant alpha level for highlighting along with color, italic, and bolding p value options, and latex or non-latex desired output.
# Using pretty_km_output() for a single comparisons
surv_obj <- survival::Surv(Bladder_Cancer$Survival_Months,
Bladder_Cancer$Vital_Status == 'Dead')
downstage_fit <- survival::survfit(surv_obj ~ PT0N0, data = Bladder_Cancer)
pretty_km_output(fit = downstage_fit, time_est = 60,
surv_est_prefix = 'Month', surv_est_digits = 3)
# Using run_pretty_km_output() for multiple comparisons
# First create vector of strata to compare (NA for no strate).
vars_to_run = c(NA, 'Gender', 'Clinical_Stage_Grouped', 'PT0N0', 'Any_Downstaging')
# Next use purrr::map_dfr function to run the run_pretty_km_output() multiple times
km_output <- purrr::map_dfr(
vars_to_run, run_pretty_km_output, model_data = Bladder_Cancer,
time_in = 'Survival_Months', event_in = 'Vital_Status', event_level = 'Dead',
time_est = c(24,60), surv_est_prefix = 'Month', p_digits = 5,
output_type = 'latex') %>%
select(Group, Level, everything())
#Finally use kableExtra to make fancy output
kableExtra::kable(km_output, 'latex', escape = F, booktabs = TRUE, linesep = '',
caption = 'Kaplan–Meier Output (Multiple Comparisons)') %>%
kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack',
headers_to_remove = 1:2, latex_hline = 'major') %>%
kableExtra::kable_styling(font_size = 7.5) %>%
kableExtra::footnote('Survival Percentage Estimates at 24 and 60 Months')
\clearpage
Utility Functions
round_away_0() is a function to properly perform mathematical rounding (i.e. rounding away from 0 when tied), as opposed to the round() function, which rounds to the nearest even number when tied. Also round_away_0() allows for trailing zeros (i.e. 0.100 if rounding to 3 digits).
vals_to_round = c(NA,-3.5:3.5)
vals_to_round
round(vals_to_round)
round_away_0(vals_to_round)
round_away_0(vals_to_round, digits = 2, trailing_zeros = TRUE)
get_session_info() produces reproducible tables, which are great to add to the end of reports. The first table gives Software Session Information and the second table gives Software Package Version Information get_full_name() is a function used by get_session_info() to get the user's name, based on user's ID.
my_session_info <- get_session_info()
kableExtra::kable(my_session_info$platform_table, 'latex', booktabs = TRUE,
linesep = '', caption = "Reproducibility Software Session Information") %>%
kableExtra::kable_styling(font_size = 8)
my_session_info <- get_session_info()
kableExtra::kable(my_session_info$packages_table, 'latex', booktabs = TRUE,
linesep = '', caption = "Reproducibility Software Package Version Information") %>%
kableExtra::kable_styling(font_size = 8)
z2thet/MoffittFunctions documentation built on July 17, 2021, 9:51 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
\listoftables
\clearpage
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(xtable.comment = FALSE, datatable.verbose = FALSE, scipen = 10, knitr.kable.NA = '', knitr.table.format = 'latex', kableExtra.latex.load_packages = FALSE)
Overview
MoffittFunctions is a collection of useful functions designed to assist in analysis and creation of professional reports. The current MoffittFunctions functions can be broken down to the following sections:
- Testing Functions
- two_samp_bin_test
- two_samp_cont_test
-
cor_test
-
Fancy Output Functions
- pretty_pvalues
- stat_paste
- paste_tbl_grp
- pretty_model_output
- run_pretty_model_output
- pretty_km_output
- run_pretty_km_output
- pretty_bayesian_output
-
pretty_bayesian_regression_tests
-
Utility Functions
- round_away_0
- get_session_info
-
get_full_name
-
Example Dataset
- Bladder_Cancer
Getting Started
# Loading MoffittFunctions and Example Dataset library(MoffittFunctions) data("Bladder_Cancer") # Loading dplyr for this vignette code library(dplyr)
MoffittTemplates Package
The MoffittTemplates package makes extensive use of the MoffittFunctions package, and is a great way get started making professional statistical reports.
Code to initially download MoffittTemplates package:
cred = git2r::cred_ssh_key( publickey = "MYPATH/.ssh/id_rsa.pub", privatekey = "MYPATH/.ssh/id_rsa") devtools::install_git( "git@gitlab.moffitt.usf.edu:ReproducibleResearch/R_Markdown_Templates.git", credentials = cred)
RStudio you can Global Options -> Git/SVN to see SSH path, and to make SSH key if needed
Once installed, in RStudio go to File -> New File -> R Markdown -> From Template -> Moffitt PDF Report to start a new Markdown report using the template. Within the template there is code to load and make use of most of the MoffittFunctions functionality.
Example Dataset
The Bladder_Cancer dataset is a real world example dataset used throughout this vignette and most example in the documentation. The dataset is cleaned, using factor variables for categorical variables, and also using labels for all variables (created by the Hmisc::label() function).
Testing Functions
There are currently three testing functions, performing the appropriate statistical test depending on the data and options, returning a p value.
Comparing Two Groups (Binary Variable) for a Binary Variable
two_samp_bin_test() is used for comparing a binary variable to a binary (two group) variable, with options for Barnard, Fisher's Exact, Chi-Sq, and McNemar tests.
table(Bladder_Cancer$Vital_Status, Bladder_Cancer$PT0N0) two_samp_bin_test(x = Bladder_Cancer$Vital_Status, y = Bladder_Cancer$PT0N0, method = 'fisher')
Comparing Two Groups (Binary Variable) for a Continuous Variable
two_samp_cont_test() is used for comparing a continuous variable to a binary (two group) variable, with parametric (t.test) and non-parametric (Wilcox Rank-Sum) options. Also pair data is allowed, where there are parametric (paired t.test) and non-parametric (Wilcox Signed-Rank) options.
by(Bladder_Cancer$Survival_Days, Bladder_Cancer$PT0N0, summary) two_samp_cont_test(x = Bladder_Cancer$Survival_Days, y = Bladder_Cancer$PT0N0, method = 'wilcox')
Comparing Two Continuous Variables (Correlation)
cor_test() is used for comparing two continuous variables, with Pearson, Kendall, and Spearman methods. If Spearman method is chosen and either variable has a tie the approximate distribution is use in the coin::spreaman_test() function. This is usually the preferred method over the asymptotic approximation, which is the method stats:cor.test() uses in cases of ties.
cor(Bladder_Cancer$Age_At_Diagnosis, Bladder_Cancer$Survival_Days, method = 'spearman') cor_test(x = Bladder_Cancer$Age_At_Diagnosis, y = Bladder_Cancer$Survival_Days, method = 'spearman')
Fancy Output Functions
There are currently seven functions designed to produce professional output that can easily printed in reports.
P Values
pretty_pvalues() can be used on p values, rounding them to a specified digit amount and using < for low p values, as opposed to scientific notation (i.e. "p < 0.0001" if rounding to 4 digits), allows options for emphasizing p-values and specific characters for missing.
pvalue_example = c(1, 0.06753, 0.004435, NA, 1e-16, 0.563533) # For simple p value display pretty_pvalues(pvalue_example, digits = 3) # For display in report table_p_Values <- pretty_pvalues(pvalue_example, digits = 3, background = NULL) kableExtra::kable(table_p_Values, format = 'latex', escape = FALSE, col.names = c("P-values"), caption = 'Fancy P Values')
You can also specify if you want p= pasted on the front of the p values.
Basic Combining of Variables
stat_paste() is used to combine two or three statistics together, allowing for different rounding and bound character specifications. Common uses for this function are for:
- Mean (sd)
- Median [min, max]
- Estimate (SE of Estimate)
- Estimate (95% CI Lower Bound, Upper Bound)
- Estimate/Statistic (p value)
# Simple Examples stat_paste(stat1 = 2.45, stat2 = 0.214, stat3 = 55.3, digits = 2, bound_char = '[') stat_paste(stat1 = 6.4864, stat2 = pretty_pvalues(0.0004, digits = 3), digits = 3, bound_char = '(') Bladder_Cancer %>% group_by(Gender) %>% summarise(`Survival Info (Median [Range])` = stat_paste(stat1 = median(Survival_Months), stat2 = min(Survival_Months), stat3 = max(Survival_Months), digits = 2, bound_char = '[')) Bladder_Cancer %>% summarise( `Age vs. Survival Months Cor(p value)` = stat_paste(stat1 = cor(Age_At_Diagnosis, Survival_Months, method = 'spearman'), stat2 = pretty_pvalues( cor_test(Age_At_Diagnosis, Survival_Months, method = 'spearman'), digits = 3, include_p = TRUE), digits = 2, bound_char = '('))
\clearpage
Advanced Combining of Variables
paste_tbl_grp() paste together information, often statistics, from two groups. There are two predefined combinations: mean(sd) and median[min,max], but user may also paste any single measure together.
# summary_info <- Bladder_Cancer %>% # group_by(Gender, Any_Downstaging) %>% # summarise_at("Survival_Months", funs(n = length, mean, sd, median, min, max)) %>% # tidyr::gather(variable, value, -Any_Downstaging, -Gender) %>% # tidyr::unite(var, Any_Downstaging, variable) %>% # tidyr::spread(var, value) %>% # mutate(`No Downstaging` = "No Downstaging", Downstaging = "Downstaging") %>% # paste_tbl_grp(vars_to_paste = c('n', 'mean_sd', 'median_min_max'), # first_name = 'No Downstaging', second_name = 'Downstaging') summary_info <- Bladder_Cancer %>% group_by(Gender, Any_Downstaging) %>% summarise_at("Survival_Months", funs(n = length, mean, sd, min, max)) %>% tidyr::gather(variable, value, -Any_Downstaging, -Gender) %>% tidyr::unite(var, Any_Downstaging, variable) %>% tidyr::spread(var, value) %>% mutate(`No Downstaging` = "No Downstaging")%>% mutate( `Downstaging` = "Downstaging") %>% paste_tbl_grp(vars_to_paste = c('n', 'mean','sd', 'min','max'), first_name = 'No Downstaging', second_name = 'Downstaging') kableExtra::kable(summary_info, format = 'latex', escape = TRUE, booktabs = TRUE, caption = 'Summary Information Comparison') %>% kableExtra::kable_styling(font_size = 6.5) %>% kableExtra::footnote( 'Summary Information for Downstaging vs. No-Downstaging, by Gender')
Model Output Functions
pretty_model_output() and run_pretty_model_output() are used to produce professional tables for single or multiple Linear, Logistic, or Cox Proportional-Hazards Regression Models, calculating estimates, odds ratios, or hazard ratios, respectively, with confidence intervals. P values are also produced. For categorical variables with 3+ levels overall Type 3 p values are calculated (matches SAS's default overall p values), in addition to p values comparing to the first level (reference).
pretty_model_output() uses the model fits, while run_pretty_model_output() uses the variables and dataset, running the desired model. The run_pretty_model_output() will use variable labels if they exist (created by the Hmisc::label() function). Many details can be adjusted, such as overall test method ("Wald" or "LR"), title (will be added as column), confidence level, estimate and p value rounded digits, significant alpha level for highlighting along with color, italic, and bolding p value options, and latex or non-latex desired output.
In run_pretty_model_output(), y_in, event_in, and event_level are used defined differently, depending on the type of model. For Linear Regression y_in is the dependent variable, and event_in and event_level are left NULL. For Logistic Regression y_in is the dependent variable, event_level is the event level of the variable (i.e. "1" or "Response"), and event_in is left NULL. For Cox Regression y_in is the time component, event_in is the event status variable, and event_level is the event level of the event_in variable (i.e. "1" or "Dead").
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Linear Regression Example
# Using pretty_model_output() for a single multivariable model my_fit <- lm( Surgery_Year ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped + Histology_Grouped, data = Bladder_Cancer) pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped') # Using run_pretty_model_output() for multiple univariate linear regression models # Use purrr::map_dfr function to run the run_pretty_model_output() multiple times linear_univariate_output <- purrr::map_dfr( vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer, y_in = 'Surgery_Year', event_in = NULL, event_level = NULL, output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( linear_univariate_output, 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Linear Regression Univariate Models') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable linear regression model linear_multivariable_output <- run_pretty_model_output( x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Surgery_Year', event_in = NULL, event_level = NULL, output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( linear_multivariable_output %>% dplyr::select(-n), 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Linear Regression Multivariable Model') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10) %>% kableExtra::footnote( paste0('Model sample size is ', linear_multivariable_output %>% select(n) %>% slice(1)))
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Logistic Regression Example
# Using pretty_model_output() for a single multivariable model my_fit <- glm( Any_Downstaging == 'Downstaging' ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped + Histology_Grouped, data = Bladder_Cancer, family = binomial(link = "logit")) pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped') # Using run_pretty_model_output() for multiple univariate logistic regression models # Use purrr::map_dfr function to run the run_pretty_model_output() multiple times logistic_univariate_output <- purrr::map_dfr( vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer, y_in = 'Any_Downstaging', event_in = NULL, event_level = 'Downstaging', output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( logistic_univariate_output, 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Logistic Regression Univariate Models') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable logistic regression model logistic_multivariable_output <- run_pretty_model_output( x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Any_Downstaging', event_in = NULL, event_level = 'Downstaging', output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( logistic_multivariable_output %>% dplyr::select(-n), 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Logistic Regression Multivariable Model') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10) %>% kableExtra::footnote( paste0('Model sample size is ', logistic_multivariable_output %>% select(n) %>% slice(1)))
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Bayesian Output functions
pretty_bayesian_output() and pretty_bayesian_regression_tests() are used to produce professional tables for single or multivariable Bayesian Linear or Logistic regression.
pretty_bayesian_output takes a stanreg fit object ( family = binomial or guassian), and calculates estimates of regression coefficients or odds ratios.
ybin <- sample(0:1, 100, replace = TRUE) y <- rexp(100,.1) x1 <- rnorm(100) x2 <- y + rnorm(100) x3 <- factor(sample(letters[1:4],100,replace = TRUE)) my_data <- data.frame(y, ybin, x1, x2, x3) library(rstanarm) lm_fit <- stan_glm(y ~ x1+ x2 + x3, data = my_data, refresh = 0 ) pretty_bayesian_output(fit = lm_fit, model_data = my_data) library(dplyr) # Logistic Regression my_fit <- stan_glm(ybin ~ x1 + x2 + x3, data = my_data, family = binomial, refresh = 0) my_pretty_model_output <- pretty_bayesian_output(fit = my_fit, model_data = my_data)
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# Printing of Fancy table in HTML kableExtra::kable(my_pretty_model_output, 'latex', caption = 'Bayesian Logistic regression model') #%>% # kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack')
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Cox Proportional-Hazards Regression Example
# Using pretty_model_output() for a single multivariable model surv_obj <- survival::Surv(Bladder_Cancer$Survival_Months, Bladder_Cancer$Vital_Status == 'Dead') my_fit <- survival::coxph( surv_obj ~ Age_At_Diagnosis + Gender + Clinical_Stage_Grouped + Histology_Grouped, data = Bladder_Cancer) pretty_model_output(fit = my_fit, model_data = Bladder_Cancer)
vars_to_run = c('Age_At_Diagnosis', 'Gender', 'Clinical_Stage_Grouped', 'Histology_Grouped') # Using run_pretty_model_output() for multiple univariate Cox regression models # Use purrr::map_dfr function to run the run_pretty_model_output() multiple times cox_univariate_output <- purrr::map_dfr( vars_to_run, run_pretty_model_output, model_data = Bladder_Cancer, y_in = 'Survival_Months', event_in = 'Vital_Status', event_level = 'Dead', output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( cox_univariate_output, 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Cox Proportional-Hazards Regression Univariate Models') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10)
# Using run_pretty_model_output() for a multivariable Cox regression model cox_multivariable_output <- run_pretty_model_output( x_in = vars_to_run, model_data = Bladder_Cancer, y_in = 'Survival_Months', event_in = 'Vital_Status', event_level = 'Dead', output_type = 'latex') #Use kableExtra to make fancy output kableExtra::kable( cox_multivariable_output %>% dplyr::select(-`n (events)`), 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Cox Proportional-Hazards Regression Multivariable Model') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 10) %>% kableExtra::footnote( paste0('Model sample size (events) is ', cox_multivariable_output %>% select(`n (events)`) %>% slice(1)))
Kaplan–Meier Output Functions
pretty_km_output() and run_pretty_km_output() are used to produce professional tables with Kaplan–Meier median survival estimates, and the estimates at given time points, if listed. pretty_km_output() uses a survfit object, while run_pretty_km_output() uses the variables, and strata if applicable, and runs creates the survfit objects, also calculating the log-rank p value, if applicable.
Many details can be adjusted, such as title (will be added as column), strata name, confidence level, survival estimate prefix (default is "Time"), survival estimate, median estimate, and p value rounded digits, significant alpha level for highlighting along with color, italic, and bolding p value options, and latex or non-latex desired output.
# Using pretty_km_output() for a single comparisons surv_obj <- survival::Surv(Bladder_Cancer$Survival_Months, Bladder_Cancer$Vital_Status == 'Dead') downstage_fit <- survival::survfit(surv_obj ~ PT0N0, data = Bladder_Cancer) pretty_km_output(fit = downstage_fit, time_est = 60, surv_est_prefix = 'Month', surv_est_digits = 3)
# Using run_pretty_km_output() for multiple comparisons # First create vector of strata to compare (NA for no strate). vars_to_run = c(NA, 'Gender', 'Clinical_Stage_Grouped', 'PT0N0', 'Any_Downstaging') # Next use purrr::map_dfr function to run the run_pretty_km_output() multiple times km_output <- purrr::map_dfr( vars_to_run, run_pretty_km_output, model_data = Bladder_Cancer, time_in = 'Survival_Months', event_in = 'Vital_Status', event_level = 'Dead', time_est = c(24,60), surv_est_prefix = 'Month', p_digits = 5, output_type = 'latex') %>% select(Group, Level, everything()) #Finally use kableExtra to make fancy output kableExtra::kable(km_output, 'latex', escape = F, booktabs = TRUE, linesep = '', caption = 'Kaplan–Meier Output (Multiple Comparisons)') %>% kableExtra::collapse_rows(c(1:2), row_group_label_position = 'stack', headers_to_remove = 1:2, latex_hline = 'major') %>% kableExtra::kable_styling(font_size = 7.5) %>% kableExtra::footnote('Survival Percentage Estimates at 24 and 60 Months')
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Utility Functions
round_away_0() is a function to properly perform mathematical rounding (i.e. rounding away from 0 when tied), as opposed to the round() function, which rounds to the nearest even number when tied. Also round_away_0() allows for trailing zeros (i.e. 0.100 if rounding to 3 digits).
vals_to_round = c(NA,-3.5:3.5) vals_to_round round(vals_to_round) round_away_0(vals_to_round) round_away_0(vals_to_round, digits = 2, trailing_zeros = TRUE)
get_session_info() produces reproducible tables, which are great to add to the end of reports. The first table gives Software Session Information and the second table gives Software Package Version Information get_full_name() is a function used by get_session_info() to get the user's name, based on user's ID.
my_session_info <- get_session_info() kableExtra::kable(my_session_info$platform_table, 'latex', booktabs = TRUE, linesep = '', caption = "Reproducibility Software Session Information") %>% kableExtra::kable_styling(font_size = 8)
my_session_info <- get_session_info() kableExtra::kable(my_session_info$packages_table, 'latex', booktabs = TRUE, linesep = '', caption = "Reproducibility Software Package Version Information") %>% kableExtra::kable_styling(font_size = 8)
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