solmos

parseFormula <- function(event_name, vars) {
  m_formula <- paste0(
    ## Otavio/Anna change this since you are not using age as time scale!
    "Surv(age_start, age_end, ", event_name, ") ~ ",
    paste(vars, collapse = " + ")
  )

  as.formula(m_formula)
}

solmos

library(tidyverse)

getSameEpisodes <- function(h_visits_clean) {
  visits_diff <- h_visits_clean %>%
    ## TODO Remove this if we want everyone
    filter(date_visit >= "2016-01-01", date_visit <= "2020-12-31") %>%
    arrange(perm_id, date_visit) %>%
    group_by(perm_id) %>%
    summarise(
      date = date_visit,

solmos

library(tidyverse)

## Simple example dataset
time_on_study <- tibble(
  id = 1:5,
  start_followup = 0,
  time_followup = c(2, 4, 3, 1, 3),
  age_baseline = c(18, 30, 28, 20, 18),
  event = factor(c(1, 0, 1, 1, 0), labels = c("Censored", "Event"))
)

solmos

git clone --depth=1 --single-branch 
--branch emacs-27 https://github.com/emacs-mirror/emacs.git

cd emacs/

sudo apt install -y autoconf make gcc texinfo libgtk-3-dev libxpm-dev \
     libjpeg-dev libgif-dev libtiff5-dev libgnutls28-dev libncurses5-dev \
     libjansson-dev libharfbuzz-dev libharfbuzz-bin

./autogen.sh

solmos

library(splines)
library(dplyr)
library(readr)
library(tidyr)
library(ggplot2)

## Use your path
df <- read_csv("data.csv") %>%
  select(fever, mean.anom.4w, water.safe)

solmos

library(dplyr)

## Create similar but simpler data frame to yours
df <- data.frame(
  scenario = rep(1:3, 2),
  year = rep(2010:2011, each = 3),
  age_25_29 = rnorm(6),
  age_30_34 = rnorm(6),
  age_35_39 = rnorm(6)
)

solmos

library(tidyverse)
library(tidync)

readNcFiles <- function(files, lat_range = c(8, 37), lon_range = c(68, 98)) {
  nc_list <- map(files, tidync)
  names(nc_list) <- files

  filterNc <- function(nc, lat_range = c(8, 37), lon_range = c(68, 98)) {
    nc %>%
      hyper_filter(LAT = between(LAT, lat_range[1], lat_range[2]),

solmos

readFireCounts <- function(file) {
  fire_counts <- read_delim(file, delim = ";") %>%
    rename(date = "ACQ_DATE") %>%
    count(date) %>%
    as_tibble()
  dates_expanded <- seq(min(fire_counts$date), max(fire_counts$date), by = "days")
  fires_full <- tibble(date = dates_expanded) %>%
    left_join(fire_counts) %>%
    mutate(
      day = day(date),

solmos

## Scorekeeper bias in LEB Oro

library(tidyverse)

data_file <- "https://github.com/solmos/feb-data/raw/master/assists-leb-oro.csv"
assists_leb_oro <- read_csv(data_file)

# Players -----------------------------------------------------------------

ast_player <- assists_leb_oro %>%

solmos

# Elo Rating System

library(eurolig)
library(tidyverse)
library(lubridate)

load("data/results.rds")

# Helpers -----------------------------------------------------------------