함수를 사용해야 하는 이유

R 함수 작성 입문

Richie Cotton

Data Evangelist at DataCamp

mean()의 인수

mean의 인수 3개

  • x: 숫자 또는 날짜-시간 벡터
  • trim: 계산 전 양끝에서 제거할 비율
  • na.rm: 결측값 제거 여부
R 함수 작성 입문

mean() 호출하기

위치로 인수 전달

mean(numbers, 0.1, TRUE)

이름으로 인수 전달

mean(na.rm = TRUE, trim = 0.1, x = numbers)

자주 쓰는 인수는 위치로, 드문 인수는 이름으로

mean(numbers, trim = 0.1, na.rm = TRUE)

R 함수 작성 입문

시험 점수 분석하기

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)
R 함수 작성 입문
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)
R 함수 작성 입문
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)
R 함수 작성 입문
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))
R 함수 작성 입문
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))
R 함수 작성 입문

함수를 작성하는 이점

함수는 코드의 반복을 없애므로

  • 작업량이 줄고
  • 오류를 피할 수 있습니다.

또한 재사용과 공유가 쉬워집니다.

R 함수 작성 입문

연습해 봅시다!

R 함수 작성 입문

Preparing Video For Download...