brunaw

# In case you're installing, building, or removing the package:
# remove.packages("hebartBase")
# devtools::document()
# devtools::check()
# devtools::install()

# Exemplifying:
# Package loading  ----------------------------------
library(magrittr)
library(ggplot2)

brunaw

library(tidyverse)
library(patchwork)
iris_species <- unique(iris$Species)
colours <- c("pink", "red", "blue")

plots <- purrr::map2(
  iris_species, colours,
  ~{                  
    p <- iris |> 
      dplyr::filter(Species == .x) |> 

brunaw

# Loading packages
library(tidyverse)
library(tidymodels)
library(hebart)
library(lme4)
library(dbarts)
library(patchwork)

# Loading Andrew's friedman data
df <- read.table("https://raw.githubusercontent.com/andrewcparnell/rBART/master/friedman.txt")

brunaw

# Loading packages
library(tidyverse)
library(hebart)

df <- read.table("https://raw.githubusercontent.com/andrewcparnell/rBART/master/friedman.txt")
df$y <- rnorm(nrow(df))
df$group <- sample(1:5, nrow(df), replace = TRUE)

group_variable = "group"
formula <- y ~  V2 + V3 + V4 + V5 + V6

brunaw

create_dat <- function(artist, track){
notes = c("A", "B", "C", "D", "E", "F", "G")
flats = "b"
minor = "m"
sharps = "#"
all_notes = c(notes, paste0(notes, flats), paste0(notes, 
                                                  sharps), paste0(notes, minor), paste0(notes, flats, sharps), 
              paste0(notes, minor, sharps), paste0(notes, flats, minor), 
              paste0(notes, flats, sharps, minor))
artist <- chorrrds::get_songs(artist)

brunaw

library(tidyverse)
library(MASS)

det2 <- function(k_1_d, k_2_d, M_d) {
  n <- nrow(M_d)
  n_j <- colSums(M_d)
  tMM <- crossprod(x = M_d)
  Psi_tilde_inv <- diag(n) - M_d%*%diag(k_1_d/(1 + k_1_d*n_j))%*%t(M_d)
  return(log(k_2_d) + log(1/k_2_d + sum(Psi_tilde_inv)) + sum(log(1 + k_1_d*n_j)))
}

brunaw

library(tidyverse)

data <- data.frame(
  cidade = 
    c("cidade_1", "urbana", "rural",
      "cidade_2", "urbana", "rural",
      "cidade_3", "urbana", "rural"), 
  valor_1 = rpois(9, 100),
  valor_2 = rpois(9, 1000))

brunaw

library(tidyverse)
library(factoextra)

agg_transp <- read_csv("data/aggregated_transport.csv")

transp_border <- agg_transp %>% 
  group_by(location) %>% 
  mutate(mean_value = mean_value/max(mean_value))

brunaw

library(tidyverse)

# Read the data 
df <- read_csv("RIP_towns_6May_w_county_edited.csv")

# Change the format from wide to long
df_long <- df %>% 
  gather(key = date, value = deaths, -Town, -County) %>% 
  mutate(year = paste0("20", str_extract(date, "[0-9]{2}")), 
         year = as.numeric(year), 

brunaw

eliminate_words <- function(first_sentence, second_sentence){
  if(is.na(second_sentence)) return(first_sentence)
  
  first_word <- word(second_sentence, 1)
  characters_first_word <- strsplit(first_word, split = "")[[1]]
  
  # Get all possible words that could have remained in the previous 
  # sentence
  words_list <- vector()