shv38339

# place list levels in object
list_levels <- 
  mtcars %>% select(vs, gear) %>% 
  transmute(vs_fctr = factor(vs, labels = c("0. Zero", "1. One")), 
            gear_fctr = factor(gear, labels = c("3. Three", "4. Four", "5. Five"))) %>% 
  sapply(levels) # sapply extracts unique levels from all variables

# i still don't understand this code but it works...
lapply(seq_along(list_levels), 
       function(i) paste(names(list_levels)[[i]], list_levels[[i]], sep = "")) %>% 

shv38339

# Quite a few requirements so read closely
# - variable must filter out NAs
# - variable must be a factor
# - variable must have factor labels (i think)
# - must be slightly comfortable with dplyr

# FUNCTION
tbl_steele <- function(data, names){
  require(htmlTable)
  a <- lapply(data, function(x) freq(x, plot = F))

shv38339

model_output <- function(mod_obj){
  # libraries
  require(geepack); require(tibble); require(dplyr)
  xvar <- names(lm_object$model)[-1]
  yvar <- names(lm_object$model)[1] # idky i need this but just in case
  data <- lm_object$data
  
  # xvar levels
  xvar_levels <- sapply(data[, xvar], levels)
  xvar_levels_unlist <- unlist(lapply(seq_along(xvar_levels), 

shv38339

# try to do table function in base R

tbl_steele <- function(data, var, ...){
  require(descr)
  require(htmlTable)
  
  levels_logic <- lapply(data[, var], function(x) levels(x))
  levels_logic1 <- lapply(levels_logic, function(x) is.null(x)) # combine these statements in the future
  if(sum(unlist(levels_logic1)) < length(var)){
    cat("Are all of your variables properly labelled?\n")

shv38339

ggplot(${1:data = }, aes(${2:x = )}) + 
		geom_histogram(aes(y = ..density..), ${3:binwidth = }, colour = "black", fill = "white") + 
		geom_density(alpha = .2, fill = "#FF6666") + 
		geom_vline(aes(xintercept = mean(${4:}), color = "mean"), linetype = "dashed", size = 2) + 
		geom_vline(aes(xintercept = median(${5:}), color = "median"), linetype = "dashed", size = 2) + 
		scale_color_manual(name = "Dispersion", values = c(median = "blue", mean = "red"))

shv38339

#libraries
library(shiny)
library(ggplot2)
library(dplyr)
library(DT)
library(tableone)

# data
# mtcars[, c("cyl", "vs", "am", "gear", "carb")] <- lapply(mtcars[, c("cyl", "vs", "am", "gear", "carb")], factor)
# data1

shv38339

# ANOVA testing by group
data %>% 
  group_by(your_group) %>%
  do(tidy(aov(y ~ factor(x), data = .))) %>% 
  select(your_group, p.value)
  
# CHISQUARE testing by group
data %>% 
  group_by(your_group) %>%
  summarise(pvalue = chisq.test(x, y)$p.value) %>%