Import your data

data(flights)

flights %>% skimr::skim()
Data summary
Name Piped data
Number of rows 336776
Number of columns 19
_______________________
Column type frequency:
character 4
numeric 14
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
carrier 0 1.00 2 2 0 16 0
tailnum 2512 0.99 5 6 0 4043 0
origin 0 1.00 3 3 0 3 0
dest 0 1.00 3 3 0 105 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1.00 2013.00 0.00 2013 2013 2013 2013 2013 ▁▁▇▁▁
month 0 1.00 6.55 3.41 1 4 7 10 12 ▇▆▆▆▇
day 0 1.00 15.71 8.77 1 8 16 23 31 ▇▇▇▇▆
dep_time 8255 0.98 1349.11 488.28 1 907 1401 1744 2400 ▁▇▆▇▃
sched_dep_time 0 1.00 1344.25 467.34 106 906 1359 1729 2359 ▁▇▇▇▃
dep_delay 8255 0.98 12.64 40.21 -43 -5 -2 11 1301 ▇▁▁▁▁
arr_time 8713 0.97 1502.05 533.26 1 1104 1535 1940 2400 ▁▃▇▇▇
sched_arr_time 0 1.00 1536.38 497.46 1 1124 1556 1945 2359 ▁▃▇▇▇
arr_delay 9430 0.97 6.90 44.63 -86 -17 -5 14 1272 ▇▁▁▁▁
flight 0 1.00 1971.92 1632.47 1 553 1496 3465 8500 ▇▃▃▁▁
air_time 9430 0.97 150.69 93.69 20 82 129 192 695 ▇▂▂▁▁
distance 0 1.00 1039.91 733.23 17 502 872 1389 4983 ▇▃▂▁▁
hour 0 1.00 13.18 4.66 1 9 13 17 23 ▁▇▇▇▅
minute 0 1.00 26.23 19.30 0 8 29 44 59 ▇▃▆▃▅

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
time_hour 0 1 2013-01-01 05:00:00 2013-12-31 23:00:00 2013-07-03 10:00:00 6936

Create Data frame functions

Example 1: count columns

code snippets

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

ncol_num
## [1] 14

Turn them into a function

count_ncol_numeric <- function(.data) {
    
    # body
   ncol_num <- .data %>%
    
    # Select a type of variables
    select(where(is.numeric)) %>%
    
    # Count columns
    ncol()
   
   # Return the new variable
   return(ncol_num)

    
}

flights %>% count_ncol_numeric()
## [1] 14
flights %>% .[1:10 , -1:-13] %>% count_ncol_numeric()
## [1] 4

Adding arguments for details of operation

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

    
}

flights %>% count_ncol_type()
## [1] 14
flights %>% count_ncol_type(type_data = "character")
## [1] 4
flights %>% .[1:10, 1:5] %>% count_ncol_type(type_data = "character")
## [1] 0

Example 2: count rows

code snippets

nrow_num <- flights %>%
    
    # filter rows that meet a condition
    filter(carrier == "DL") %>%
    
    # Count rows
    nrow()

nrow_num
## [1] 48110

Turn them into a function

count_num_flights_by_carrier <- function(.data, carrier_name) {
    
    # body 
    nrow_num <- .data %>%
    
        # filter rows that meet a condition
        filter(carrier == carrier_name) %>%
    
        # Count rows
        nrow()
    
    
    # return the new variable
    return(nrow_num)
}

flights %>% .[1:10, "carrier"] %>% count_num_flights_by_carrier(carrier_name = "AA")
## [1] 2

Example 3: count rows

Create your own.

symbols <- c("WMT", "TGT", "COST")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")

symbols
## [1] "WMT"  "TGT"  "COST"
prices
## # A tibble: 3,780 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 WMT    2012-12-31  22.5  22.8  22.5  22.7 21037500     17.8
##  2 WMT    2013-01-02  23.0  23.1  22.8  23.1 31172400     18.1
##  3 WMT    2013-01-03  23.1  23.1  22.8  22.9 26730300     18.0
##  4 WMT    2013-01-04  22.9  23.1  22.8  23.0 19314000     18.0
##  5 WMT    2013-01-07  22.9  23.0  22.7  22.8 18604200     17.9
##  6 WMT    2013-01-08  22.8  23.0  22.7  22.9 17600700     17.9
##  7 WMT    2013-01-09  22.9  22.9  22.7  22.9 15165600     17.9
##  8 WMT    2013-01-10  22.9  23.0  22.6  22.8 34361400     17.9
##  9 WMT    2013-01-11  22.9  22.9  22.7  22.9 18673500     17.9
## 10 WMT    2013-01-14  22.8  22.9  22.7  22.8 16471200     17.8
## # ℹ 3,770 more rows

code snippets

Use the filter() function to select rows that meet a condition. Refer to Chapter 5.2 Filter rows with filter()

nrow_num <- prices %>%
    
    # filter rows that meet a condition
    filter(symbol == "WMT") %>%
    
    # Count rows
    nrow()

nrow_num
## [1] 1260

Turn them into a function

count_num_prices_by_symbol <- function(.data, symbol_name) {
    
    # body 
    nrow_num <- .data %>%
    
        # filter rows that meet a condition
        filter(symbol == symbol_name) %>%
    
        # Count rows
        nrow()
    
    
    # return the new variable
    return(nrow_num)
}

prices %>% .[1:10, "symbol"] %>% count_num_prices_by_symbol(symbol_name = "TGT")
## [1] 0