AndreaCirilloAC

      
      
    library(rio)
    library(dplyr)
    
    
    read_file = function(file_path) {
      file_connection <- file(file_path, "r")
      read_data       <-  c()
      while ( TRUE ) {

AndreaCirilloAC

library(dplyr)

data <- data.frame(cod = c(rep("A",9),rep("B",10)),id =c(c(1,3,4,5,6,7,8,9,10),seq(1,10)))

data_complete <- filter(data,cod == "A") 
min_step      <-  min (data_complete$id)
max_step      <-  max(data_complete$id)  
expected_sum <- (1/2)*max(data$id)*(min_step+max_step)
actual_sum   <- sum(data$id)
check        <- expected_sum - actual_sum

AndreaCirilloAC

library(rio)
library(dplyr)
library(ggplot2)
# data from: https://www.kaggle.com/c/bike-sharing-demand/data
rides_stat <- import ("train.csv")
rides_stat %>% 
  group_by(season) %>% 
  summarise(total_rides = sum(count),mean_temp = mean(temp)) %>% 
  ggplot(aes(x=season,y=total_rides, fill  = mean_temp)) +
  geom_bar(stat='identity')+

AndreaCirilloAC

require(ggplot2)
#bernoulli
bernoulli_trial <- function(p_succ,n_trials){ # probability of success (1) and number of trials
  p_insucc     <-  1-p_succ # rpobability of failure
  trials       <<- c() 
  p_cumulative <<- c()
  for(i in 1:n_trials){
    trials       <<- rbind(trials,i)
    p_cumulative <<- rbind(p_cumulative,(((p_insucc)^(i -1))*p_succ)) # (p of success within n trials) = ((1-p)^n-1)*(p)
  }

AndreaCirilloAC

updateR(admin_password = "os_admin_user_password")

AndreaCirilloAC

library(dplyr)
library(ggplot2)

create_file <- function(name){
  path   <- paste(getwd(),"/",name,".png",sep = '') %>% 
            file.path() %>% png(,width=960,height=480)
}

#this is the template: change the theme (and the name argument) to produce the other plots