Module 12: Apply it to your data 11

Chapter 19 Functions

Import your data

games <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-02-04/games.csv')

## Rows: 5324 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (11): week, home_team, away_team, winner, tie, day, date, home_team_nam...
## dbl   (7): year, pts_win, pts_loss, yds_win, turnovers_win, yds_loss, turnov...
## time  (1): time
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Create Data frame functions

Example 1: count columns

code snippets

ncol_num <- games %>%
    
    # Select a type of variables
    select(where(is.numeric)) %>%
    
    # Count columns
    ncol()

ncol_num

## [1] 7

Turn them into a function

count_ncol_num <- function(.data){
    
    ncol_num <- .data %>%
    
    # Select a type of variables
    select(where(is.numeric)) %>%
    
    # Count columns
    ncol()
 
    return(ncol_num)   
}

games %>% count_ncol_num

## [1] 7

games %>% .[1:10, 1:5] %>% count_ncol_num()

## [1] 1

Adding arguments for details of operation

count_ncol_type <- function(.data, type_data = "numeric"){
    
    #if statement
    if(type_data == "numeric") {
    
        ncol_type <- .data %>%
    
        # Select a type of variables
        select(where(is.numeric)) %>%
    
        # Count columns
        ncol()
    
    }else if(type_data == "character") {
     
          ncol_type <- .data %>%
    
        # Select a type of variables
        select(where(is.character)) %>%
        
        # Count columns
        ncol() }
        
    return(ncol_type)  }

games %>% count_ncol_type(type_data = "character")

## [1] 11

games %>% .[1:10, 1:5] %>% count_ncol_type(type_data = "character")

## [1] 4

Example 2: count rows

code snippets

nrow_num <- games %>%
    
    # filter rows that meet a condition
    filter(day == "Thu") %>%
    
    # Count rows
    nrow()

nrow_num

## [1] 214

count_num_games_by_day <- function(.data, day_name) {
    
    nrow_num <- .data %>%
        
    # filter rows that meet a condition
    filter(day == day_name) %>%
    
    # Count rows
    nrow()

nrow_num
    
}

games %>% count_num_games_by_day(day_name = "Mon")

## [1] 339

games %>% count_num_games_by_day(day_name = "Thu")

## [1] 214

games %>% count_num_games_by_day("Sun")

## [1] 4588

Turn them into a function

Example 3: count rows

Create your own.

code snippets

nrow_num <- games %>%
    
    # filter rows that meet a condition
    filter(week == "1") %>%
    
    # Count rows
    nrow()

nrow_num

## [1] 317

Turn them into a function

count_nrow_num_by_week <- function(.data, week_num){
    
    nrow_num <- .data %>%
    
    # filter rows that meet a condition
    filter(week == week_num) %>%
    
    # Count rows
    nrow()
    
    nrow_num
}
    
games %>% count_nrow_num_by_week(1)

## [1] 317

games %>% count_nrow_num_by_week(5)

## [1] 282

games %>% count_nrow_num_by_week(10)

## [1] 285