myData <- read_csv("../00_data/myData.csv")
## Rows: 1222 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): months, state
## dbl (8): year, colony_n, colony_max, colony_lost, colony_lost_pct, colony_ad...
##
## 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.
ncol_num <- myData %>%
#select a type of variable
select(where(is.numeric)) %>%
#count columns
ncol()
ncol_num
## [1] 8
count_ncol_numeric <- function(.data) {
#body
ncol_num <- .data %>%
#select a type of variable
select(where(is.numeric)) %>%
#count columns
ncol()
#return the new variable
return(ncol_num)
}
myData %>% count_ncol_numeric()
## [1] 8
myData %>% .[1:10,-1:-5] %>% count_ncol_numeric()
## [1] 5
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 variable
select(where(is.numeric)) %>%
#count columns
ncol()
} else if(type_data == "character") {
#body
ncol_type <- .data %>%
#select a type of variable
select(where(is.numeric)) %>%
#count columns
ncol()
}
#return the new variable
return(ncol_num)
## [1] 8
nrow_num <- myData %>%
#Filter rows that meet a condition
filter(state == "Arizona") %>%
#count_rows
nrow()
nrow_num
## [1] 26
##Turn them into a function
count_number_of_colonys_by_state <- function(.data, state) {
#body
nrow_num <- .data %>%
#filter rows by condition
filter(state == "Arizona") %>%
#count columns
nrow()
#return the new variable
return(nrow_num)
}