Import your data

# Example 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 
# My data
data(troopdata)

troopdata %>% skimr::skim()
Data summary
Name Piped data
Number of rows 14435
Number of columns 10
Key NULL
_______________________
Column type frequency:
character 3
numeric 7
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
countryname 0 1.00 4 32 0 239 0
iso3c 775 0.95 3 3 0 211 0
region 14 1.00 6 26 0 8 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
ccode 0 1.00 508.68 289.76 2 317 484 705 1099 ▆▇▇▆▅
year 0 1.00 1986.35 20.65 1950 1969 1987 2004 2021 ▇▇▇▇▇
troops 0 1.00 9003.78 98785.11 0 0 7 32 2338379 ▇▁▁▁▁
army 11767 0.18 3089.68 32332.33 0 1 3 9 449563 ▇▁▁▁▁
navy 11767 0.18 2011.28 21610.86 0 0 1 6 306272 ▇▁▁▁▁
air_force 11767 0.18 1955.00 20208.21 0 0 2 11 288090 ▇▁▁▁▁
marine_corps 11767 0.18 1121.88 11832.48 0 0 6 13 164252 ▇▁▁▁▁

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_num)
    
}

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

Example 2: count rows

code snippets

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

nrow_num
## [1] 58665

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 new character
    return(nrow_num)
    
}

flights %>% .[1:10, "carrier"] %>% count_num_flights_by_carrier(carrier_name = "UA")
## [1] 3
flights %>% .[1:10, "carrier"] %>% count_num_flights_by_carrier(carrier_name = "AA")
## [1] 2

Example 3: count rows

Create your own.

code snippets

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

nrow_num <- troopdata %>%
    
    # filter rows that meet a condition
    filter(region == "Middle East & North Africa") %>%
    
    # Count rows
    nrow()

nrow_num
## [1] 1524

Turn them into a function

count_troops_deployed_by_region <- function(.data, region_name) {
    
    # body
    nrow_num <- .data %>%
    
        # filter rows that meet a condition
        filter(region == region_name) %>%
        
        # Count rows
        nrow()
    
    # return new character
    return(nrow_num)
    
}

troopdata %>% .[1:5000, "region"] %>% count_troops_deployed_by_region(region_name = "Middle East & North Africa")
## [1] 72
troopdata %>% .[1:1000, "region"] %>% count_troops_deployed_by_region(region_name = "North America")
## [1] 144
troopdata %>% .[500:2000, "region"] %>% count_troops_deployed_by_region(region_name = "Latin America & Caribbean")
## [1] 1501
troopdata %>% .[10000:14000, "region"] %>% count_troops_deployed_by_region(region_name = "East Asia & Pacific")
## [1] 2101