example_plot_with_rlang.r

library(rlang)
library()

set.seed(123)
new_table <- tibble(Date = seq.Date(as.Date("2016-01-01"), as.Date("2019-12-31"), 1)) %>% 
                      mutate(total_sales = rnorm(n()))




yearly_lines_fn <- function(sales_table, date_col, money_col) {

  date_col <- enquo(date_col)
  money_col <-  enquo(money_col)

  sales_table %>%
    group_by(year_month = floor_date(!! date_col, "months"),
             year = year(!! date_col)) %>%
    summarise(total_sales = sum(!! money_col)) %>%
    ungroup() %>%
    ggplot() +
    aes(year_month, total_sales, col = factor(year)) +
    geom_line(stat = "identity", size = 2) +
    geom_point(stat = "identity", size = 2, col = "black")
}


yearly_lines_fn(new_table, Date, total_sales)







new_yearly_lines_fn <- function(sales_table, date_col, money_col) {
  
  sales_table <- eval(sym(sales_table))

  sales_table %>%
    group_by(year_month = floor_date({{date_col}}, "months"),
             year = year({{date_col}})) %>%
    summarise(total_sales = sum({{money_col}})) %>%
    ungroup() %>%
    ggplot() +
    aes(year_month, total_sales, col = factor(year)) +
    geom_line(stat = "identity", size = 2) +
    geom_point(stat = "identity", size = 2, col = "black")
}




function_arguments <- c("the_dataset" = "new_table",
                         "the_date_col" = "Date",
                        "the_money_col" = "total_sales")


new_yearly_lines_fn(function_arguments[["the_dataset"]], function_arguments[["the_date_col"]], function_arguments[["the_money_col"]])