為什麼要用函式
R 函式撰寫入門
Richie Cotton
Data Evangelist at DataCamp
mean() 的引數
mean 有 3 個引數
x：數值或日期時間向量。trim：計算前要從兩端移除的離群值比例。na.rm：計算前是否移除遺漏值。
呼叫 mean()
依位置傳入引數
mean(numbers, 0.1, TRUE)
依名稱傳入引數
mean(na.rm = TRUE, trim = 0.1, x = numbers)
常用引數用位置，少見引數用名稱
mean(numbers, trim = 0.1, na.rm = TRUE)
分析測驗分數
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))
撰寫函式的好處
函式可消除重複程式碼，
- 能減少你的工作量，
- 並幫助避免錯誤。
函式也能促進程式碼重用與分享。
一起來練習吧！
R 函式撰寫入門
