Perché usare le funzioni
Introduzione alla scrittura di funzioni in R
Richie Cotton
Data Evangelist at DataCamp
Gli argomenti di mean()
mean ha 3 argomenti
x: un vettore numerico o di data-ora.trim: la quota di valori anomali da ciascuna estremità da rimuovere prima del calcolona.rm: rimuovi prima del calcolo
Chiamare mean()
Passa argomenti per posizione
mean(numbers, 0.1, TRUE)
Passa argomenti per nome
mean(na.rm = TRUE, trim = 0.1, x = numbers)
Argomenti comuni per posizione, rari per nome
mean(numbers, trim = 0.1, na.rm = TRUE)
Analizzare i punteggi dei test
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_english_raw <- read_csv("test_scores_english.csv")
library(dplyr)
test_scores_english_clean <- test_scores_english_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_art_raw <- read_csv("test_scores_art.csv")
library(dplyr)
test_scores_art_clean <- test_scores_art_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_spanish_raw <- read_csv("test_scores_spanish.csv")
library(dplyr)
test_scores_spanish_clean <- test_scores_spanish_raw %>%
select(person_id, first_name, last_name, test_date, score)
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
library(lubridate)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date))
library(readr)
test_scores_english_raw <- read_csv("test_scores_english.csv")
library(dplyr)
library(lubridate)
test_scores_english_clean <- test_scores_english_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date))
library(readr)
test_scores_art_raw <- read_csv("test_scores_art.csv")
library(dplyr)
library(lubridate)
test_scores_art_clean <- test_scores_art_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date))
library(readr)
test_scores_spanish_raw <- read_csv("test_scores_spanish.csv")
library(dplyr)
library(lubridate)
test_scores_spanish_clean <- test_scores_spanish_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date))
library(readr)
test_scores_geography_raw <- read_csv("test_scores_geography.csv")
library(dplyr)
library(lubridate)
test_scores_geography_clean <- test_scores_geography_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date)) %>%
filter(!is.na(score))
library(readr)
test_scores_english_raw <- read_csv("test_scores_english.csv")
library(dplyr)
library(lubridate)
test_scores_english_clean <- test_scores_english_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date)) %>%
filter(!is.na(score))
library(readr)
test_scores_art_raw <- read_csv("test_scores_art.csv")
library(dplyr)
library(lubridate)
test_scores_art_clean <- test_scores_art_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date)) %>%
filter(is.na(score))
library(readr)
test_scores_spanish_raw <- read_csv("test_scores_spanish.csv")
library(dplyr)
library(lubridate)
test_scores_spanish_clean <- test_scores_spanish_raw %>%
select(person_id, first_name, last_name, test_date, score) %>%
mutate(test_date = mdy(test_date)) %>%
filter(!is.na(score))
Vantaggi di scrivere funzioni
Le funzioni eliminano la ripetizione dal codice, il che
- può ridurre il lavoro, e
- aiuta a evitare errori.
Le funzioni permettono anche riuso e condivisione del codice.
Esercitiamoci!
Introduzione alla scrittura di funzioni in R
