testFile.Rmd

---
title: "Untitled"
author: "Luca Mannino"
date: "19/09/2022"
output:
  pdf_document: default
  html_document: default
---




my_log <- function(x) log(x - 1)

my_fun <- function(a, b) {
  # browser()
  la <- my_log(a) 
  lb <- my_log(b)
  la + lb
}
my_fun(1, 0)

#> Warning in log(x - 1): NaNs produced

#> [1] NaN

Try to uncomment browser() or use debugonce(my_fun):

debugonce(my_fun)
my_fun(1, 0)


```{r}
mat <- matrix(sample(c(TRUE, FALSE), 12, replace = TRUE), 3)
```

```{r}
matrix(as.integer(mat),3)
```

```{r}


mat + 0

```


```{r}
(vec <- 1:12)

#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12

dim(vec) <- c(3, 4)
vec
```



```{r}
head(iris)
```

```{r}
iris[,3,drop=FALSE]
```

```{r}
iris[,3]
```

```{r}
iris[3]

```

```{r}
advr38pkg::sum_every(1:10, 2)
```


```{r}
head(iris)
```

```{r}
colMeans(iris[-5])
```

```{r}
mat <- matrix(0, 10, 2); mat[c(5, 8, 9, 12, 15, 16, 17, 19)] <- 1; mat
```



```{r}
advr38pkg::sum_every(1:10, 2)
```

```{r}
function(sum_all) {
  temp_C <- (temp_F - 32) * 5 / 9
  return(temp_C)
}
```
```{r}
x <- 1:10
```
x

```{r}
n <- 2
```


```{r}
matrix(x, nrow = n)
```
```{r}
colSums(matrix(x, nrow = n))
```

```{r}
sum_all <- function(x, n) {
  sum <- colSums(matrix(x, nrow = n))
  return(sum)
}
```
```{r}
sum_all(x,n)
```


```{r}
colMeans(iris[-5])
```


```{r}
iris[sapply(iris, is.numeric)]
```
```{r}
colMeans(iris[sapply(iris, is.numeric)])
```


```{r}
mat <- matrix(0, 10, 2); mat[c(5, 8, 9, 12, 15, 16, 17, 19)] <- 1; mat
```


```{r}
decode <- matrix(c(0, NA, 1, 2), 2)
```

(0, 0) -> 0; (0, 1) -> 1; (1, 1) -> 2; (1, 0) -> NA
```{r}
#decode <- mat +1
```


```{r}
apply(mat,1, function(row) decode[row[1] +1, row[2] +1])
```

```{r}
apply(mat,1, function(row) decode[row+1])
```



```{r}
str(iris)
```

```{r}
skimr::skim(iris)
```


```{r}
x <- -5
abs(x)
```






```{r}
advr38pkg::split_ind(1:40, 3)
```

```{r}
sample(1:10)
```
```{r}
rep_len(1:3, 8)
```

Use sample(), rep_len() and split() to make a function that randomly splits some indices in a list of K groups of indices (like for cross-validation). [Which are the special cases that you should consider?]


```{r}
split(1:12, rep(letters[1:3], 4))
```

```{r}
x <- sample(1:10)
k <- 5
split(x,rep(letters[1:5],k))
```


```{r}
x <- 1:40
n <- 3

sample(seq_len(n), size = length(x), replace = TRUE)

rep_len(seq_len(n), length(x))
split(x, sample(n))
```
sample x with replacement 
compute the mean
do this 1000 times
get quantiles 2.5% and 97.5%

replicate()
```{r}
set.seed(1)
(x <- rnorm(10))
```



```{r}
set.seed(1)
x <- replicate(1000, mean(sample(rnorm(10))))
```



```{r}
set.seed(1)
x <- rnorm(10)

all_mean <- replicate(1000, {
          x2 <- sample(x, replace =TRUE)
          mean(x2)})
```

```{r}
hist(all_mean)
c1 <- quantile(all_mean, probs = c(0.025, 0.975))
c1
```

hist(all_mean)


```{r}
set.seed(1); (x <- rnorm(10))
all_mean <- replicate(1000, {
  (x2 <- sample(x, replace = TRUE))
  mean(x2)
})
hist(all_mean)
(ci <- quantile(all_mean, probs = c(0.025, 0.975)))
abline(v = ci, col = "chartreuse3")
abline(v = 0, col = "red")
```





```{r}
my_mtcars <- mtcars[c("mpg", "hp")]
my_mtcars$my_col <- sample(c("mpg", "hp"), size = nrow(my_mtcars), replace = TRUE)
head(my_mtcars)
```



```{r}
#my_mtcars$my_val <- my_mtcars$my_col

#my_mtcars$my_val[] == "hp" <-  my_mtcars$my_val[] = my_mtcars$hp[]
```


```{r}
col <- match(my_mtcars$my_col, colnames(my_mtcars))
row <- seq_len(nrow(my_mtcars))

sapply(seq_along(row), function(k) {
  my_mtcars[row[k], col[k]]
  
  
  
  
})


my_mtcars[1:2][cbind(row,col)]
```


```{r}
count1 <- 0
count2 <- 0
f <- function(i) {
  count1 <-  count1 + 1  ## will assign a new (temporary) count1
  count2 <<- count2 + 1  ## will assign count2 on top
  i + 1
}
sapply(1:10, f)
count1
count2
```


```{r}
f1 <- function(...) {
  list(...)
}
f1(a = 2, b = 3)
```
```{r}
summary(iris)
```




```{r}
agent007 <- list(first = "James", last = "Bond")
agent007

class(agent007) <- "Person"  ## "agent007" is now an object of class "Person"
# Just make a function called <method_name>.<class_name>()
print.Person <- function(x) {
  print(glue::glue("My name is {x$last}, {x$first} {x$last}."))
  invisible(x)
}


Person <- function(first, last) {
  structure(list(first = first, last = last), class = "Person")
}
(me <- Person("Florian", "Privé"))

Worker <- function(first, last, job) {
  obj <- Person(first, last)
  obj$job <- job
  class(obj) <- c("Worker", class(obj))
  obj
}
print.Worker <- function(x) {
  print.Person(x) 
  print(glue::glue("I am a {x$job}."))
  invisible(x)
}

(worker_007 <- Worker("James", "Bond", "secret agent"))
```





```{r}
methods(summary)
```


```{r}
list_of_int <- as.list(1:5)
do.call("c", list_of_int)
```


df <- data.frame(
  id1 = c("a", "f", "a"),
  id2 = c("b", "e", "e"), 
  id3 = c("c", "d", "f"),
  inter = c(7.343, 2.454, 3.234),
  stringsAsFactors = FALSE
)
df

```{r}
df <- data.frame(
  id1 = c("a", "f", "a"),
  id2 = c("b", "e", "e"), 
  id3 = c("c", "d", "f"),
  inter = c(7.343, 2.454, 3.234),
  stringsAsFactors = FALSE
)
df
```


```{r}
(code <- setNames(1:6, letters[1:6]))
```


```{r}

```

```{r}
library(dplyr)
as_tibble(iris)
```

```{r}
ggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3)
```

```{r}
ggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_point(aes(color = Species, shape = Species), size = 3) + 
  geom_smooth(color = "black")
```

```{r}
ggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_smooth(color = "black") + 
  geom_point(aes(color = Species, shape = Species), size = 3)
```

```{r}
ggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_smooth(aes(group = Species), color = "black") + 
  geom_point(aes(color = Species, shape = Species), size = 3)
```


```{r}
ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + 
  geom_smooth() + 
  geom_point(aes(shape = Species), size = 3)
```



```{r}
ggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3) + 
  facet_grid(~ Species)
```

```{r}
(var <- names(iris)[1:4])
p_list <- list()
for (i in seq_along(var)) {
  p_list[[i]] <- ggplot(iris, coeff = 0.6) + 
    geom_density(aes_string(var[i], fill = "Species"), alpha = 0.6)
}
str(p_list, max.level = 1)

```

```{r}
p_list
```


```{r}
cowplot::plot_grid(plotlist = p_list, ncol = 2, align = "hv",
                   labels = LETTERS[1:4], label_size = 15)
```

```{r}
lapply(p_list, function(p) p + theme(legend.position = "none")) %>%
  cowplot::plot_grid(plotlist = ., ncol = 2, align = "hv",
                   labels = LETTERS[1:4], label_size = 15) %>%
  cowplot::plot_grid(cowplot::get_legend(p_list[[1]]),
                     rel_widths = c(1, 0.3))
```

```{r}
(iris_tidy <- tidyr::pivot_longer(iris, -Species))
```

```{r}
p <- ggplot(iris_tidy, aes(value, col=Species, fill=Species)
       ) + geom_density(alpha=0.8) +
  facet_wrap(~name,scales = "free") 
  
```
```{r}
p + theme_bw()
```

```{r}
ggplot(iris_tidy) +
  geom_density(aes(value, fill = Species), alpha = 0.6,linetype = 4) +
  facet_wrap(~ name, scales = "free")+
  scale_fill_viridis_d()
```


```{r}
ggplot(iris, aes(Petal.Length, Petal.Width, 
                   color = Species, shape = Species)) + 
  geom_point(size = 3)
```

```{r}
library(plotly)
```

```{r}


a <- ggplot(iris, aes(Petal.Length, Petal.Width, 
                   color = Species, shape = Species)) + 
  geom_point(size = 3)
plotly::ggplotly(a)

```

```{r}
ggplot(iris, aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species)) + 
  geom_point(size = 3)
ggsave("myggplot.pdf", scale = 1/100, width = 888, height = 725)
```


```{r}
(df <- dplyr::filter(gapminder::gapminder, year == 1992))
```

(iris_tidy <- tidyr::pivot_longer(iris, -Species))
```{r}
(df_tidy <- tidyr::pivot_longer(df, -country))
```

```{r}

df$GDP <- df$gdpPercap * df$pop
df
ggplot(df) +
  geom_point(aes(gdpPercap, lifeExp, color = continent,size = pop)) +
  scale_x_log10() +
  ggtitle("Gapminder for 1992" ) +
  theme_bw()
  

#ggsave("myggplot.pdf", scale = 1/100, width = 888, height = 725)
```


```{r}
ggplot(df) + 
  geom_point(aes(gdpPercap, lifeExp, size = pop, 
                 color = continent)) +
  scale_x_log10(breaks = c(300, 1e3, 3e3, 10e3, 30e3)) + 
  labs(title = "Gapminder for 1992",
       x = "Gross Domestic Product (log scale)",
       y = "Life Expectancy at birth (years)",
       color = "Continent", size = "Population")
```




```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
```

```{r}
?mpg
```

```{r}
mpg
```
color size and shape
```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = displ, size = displ))
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = year>2000, size = displ))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = ifelse(year>2000,"yes","no"), size = displ))+
  #labs() check what labs does

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = displ, size = displ, shape = displ))

```

gganimate
```{r}
?geom_point
```



```{r}
vignette("ggplot2-specs")
```

```{r}
tb <- tibble(
  `:)` = "smile", 
  ` ` = "space",
  `2000` = "number"
)
tb
```



```{r}
tribble(
  ~x, ~y, ~z,
  #--|--|----
  "a", 2, 3.6,
  "b", 1, 8.5
)
```

```{r}
df_iris <- iris

tbl_iris <- tibble::as_tibble(iris)
```

```{r}
df_iris[,1]
tbl_iris[,1]
```
partial matching with $ only available on dataframes not on tibble


```{r}
df_iris$Spec
df_iris[["Spe"]]
tbl_iris[["Spe"]]
```

```{r}
str(df_iris)
str(tbl_iris)
```


```{r}
df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]
df
```

If you have the name of a variable stored in an object, e.g. var <- "mpg" , how can you extract the reference variable from a tibble? You can use the double bracket, like df[[var]] . You cannot use the dollar sign, because df$var would look for a column named var .

```{r}
var <- "Species"
tbl_iris[["Species"]]
tbl_iris$Species
tbl_iris[[var]]
tbl_iris$var
```

```{r}
annoying <- tibble(
  `1` = 1:10,
  `2` = `1` * 2 + rnorm(length(`1`))
)
```

```{r}
annoying
a<- annoying[["1"]]
a

ggplot(annoying)+
  geom_point(aes(x= annoying[["1"]], y= annoying[["2"]]))
```

```{r}
tibble::enframe(a)
```


```{r}
?tibble::enframe()
```


```{r}
library(nycflights13)
library(tidyverse)
```
```{r}
filter(flights, month == 1, day == 1)
```

```{r}
filter(flights, month == 11 | month == 12)
```
```{r}
nov_dec <- filter(flights, month %in% c(11, 12))
```
```{r}
nov_dec
```


Had an arrival delay of two or more hours

```{r}
h2_del <- filter(flights, arr_delay > 120)
h2_del
```

Flew to Houston (IAH or HOU)
```{r}
flew <- filter(flights, dest == "IAH" | dest == "HOU")
flew
```
?flights to learn more about data
Were operated by United, American, or Delta
```{r}
operator <- filter(flights, carrier == "UA" | carrier == "AA" | carrier == "DL" )
operator
```

Departed in summer (July, August, and September)

```{r}
summer <- filter(flights, month %in% c(7,8,9) )
summer
```

Arrived more than two hours late, but didn’t leave late

```{r}
operator <- filter(flights, arr_delay > 120 & dep_delay <= 0 )
operator
```

Were delayed by at least an hour, but made up over 30 minutes in flight

```{r}
operator <- filter(flights, dep_delay > 60 & dep_delay- arr_delay >= 30 )
operator
```


Departed between midnight and 6am (inclusive)
 x > 2 & x < 5
```{r}
operator <- filter(flights, dep_time >= 0 &  dep_time <= 360)
operator
```


Another useful dplyr filtering helper is between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?
never use between not clear enough
```{r}
operator2 <- filter(flights, between(x =dep_time, 0,360))
operator2
operator == operator2
```

How many flights have a missing dep_time? What other variables are missing? What might these rows represent?
```{r}
table(is.na(flights$dep_time))
skimr::skim(flights)
```

How could you use arrange() to sort all missing values to the start? (Hint: use is.na())
slice() #check slice function
```{r}
arrange(flights, desc(is.na(dep_time)))
arrange(flights, !is.na(dep_time))
```
Sort flights to find the most delayed flights. Find the flights that left earliest.

```{r}
arrange(flights, desc(dep_delay))
arrange(flights, dep_time)
arrange(flights, dep_time != 2400)
```

Sort flights to find the fastest (highest speed) flights.

```{r}
flights$speed <- flights$distance/flights$air_time
arrange(flights, desc(distance/air_time))
arrange(flights, desc(speed))
```
```{r}
select(flights, year, month, day)
```
```{r}
# Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
```

move variables of choice to start
```{r}
select(flights, time_hour, air_time, everything())
```
```{r}
rename(flights, tail_num = tailnum)
```
rename while selecting
```{r}
select(flights,t= time_hour, a=air_time)
```

```{r}
select(flights, time_hour,time_hour, time_hour)
```

```{r}
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, any_of(vars))
select(flights, vars)
```

```{r}
select(flights, contains("TIME"))
```

```{r}
#select(flights, contains( any_of(vars)))
select(flights, contains(vars))
```

```{r}
select(flights, contains("TIME"),ignore_cases=FALSE)
```



```{r}
flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
mutate(flights_sml,
  gain = dep_delay - arr_delay,
  speed = distance / air_time * 60
)
```
```{r}
mutate(flights_sml,
  gain = dep_delay - arr_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)
```

Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they’re not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.

```{r}
flights
```
```{r}
transmute(flights,
  dep_time,
  hour = dep_time %/% 100,
  minute = dep_time %% 100
)
flights
```
```{r}
dep_time2 %>% mutate(flights,
  dep_time,
  dep_time = hour *60 + minute)
flights
```

```{r}
flights
```



```{r}
x <- flights$sched_dep_time

change_format <- function(x) ifelse(x ==2400,0,x%/%100 * 60 + x %% 100)
change_format
flights2 <- mutate(flights,
                   dep_time = change_format(dep_time),
                   sched_dep_time = change_format(sched_dep_time))
flights2

```



```{r}
change_format <- function(x) ifelse(x == 2400, 0, x %/% 100 * 60 + x %% 100)
summary(change_format(flights$dep_time))
flights2 <- mutate(flights,
                   dep_time = change_format(dep_time),
                   sched_dep_time = change_format(sched_dep_time))
flights2 <- mutate(flights, across(c(dep_time, sched_dep_time), change_format))
```




```{r}
ggplot(slice_sample(flights2, n=5000))+
  geom_point(aes(air_time, arr_time - dep_time))+
  geom_abline(color="red")

```


ask for 10 but get more because they have the same value
```{r}
min_rank(flights$arr_delay)
filter(flights, min_rank(flights$arr_delay) <=10)
```