R/helpers.R

In pkopps/biview: Functions to Achieve Classic GBI Report Format (Four Weeks, All Months, Run Rate, YTD, Previous Year Variance, etc.)

pretty_num <- function(x) { prettyNum(x, big.mark = ",") }

month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun",
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)

mth_num_in_yr_template <-
  tibble(
    yr_num = c(rep(year(Sys.Date()), 12), rep(year(Sys.Date()-365), 12)),
    mth_num_in_yr = rep(1:12, 2) %>% as.double(),
    mth_name = rep(factor(month.abb, month_levels), 2)
  )

formattable_spark <- function(x){
  x %>% formattable() %>%
    formattable::as.htmlwidget() %>%
    spk_add_deps()
}

neg_paren <- function(x){ # function for changing negative value format; ie: -156 -> (156)
  ind <- grepl("-", x)
  x[ind] <-  paste0("(", sub("-", "", x[ind]), ")")
  x
}

div_by_1000 <- function(x){
  y <- x/1000
  y %>% as.numeric()
}

# round_sum <- function(x){
#   round(sum(x, na.rm=TRUE), 1)
# }

# round_to_two <- function(x){
#   round(x, 2)
# }

today <- Sys.Date()
# cur_month <- lubridate::month(Sys.Date())

cur_yr <- year(Sys.Date() - 2)
prev_yr <- year(Sys.Date() - (365 + 2))

# cur_year <- year(Sys.Date())
cur_mth <- month(Sys.Date())
prev_mth <- month(Sys.Date()) - 1

cur_yr_prev_wk <- week(Sys.Date() - 7)
prev_yr_prev_wk <- week((Sys.Date() - 365) - 7)

today <- Sys.Date()
today_prev_mth <- Sys.Date() - 30 # is this the correct way to do this?

# metrics <- c("revenue",
#            "trials")

dimensions <- c("date_value",
                "yr_num",
                "mth_num_in_yr",
                "wk_num_in_yr")

# date_seq <- seq(as.Date("2017-01-01"), Sys.Date() + 100, "day")
#
# performance <- data.frame(
#   company_name = "Dunder Mifflin",
#   branch = 'Scranton',
#   state_abb = 'PA',
#   salesman_full_name = "Dwight Schrute",
#   marketplace_short_name = "US",
#   date_value = date_seq,
#   yr_num = epiyear(date_seq),
#   yr_num_2 = year(date_seq),
#   mth_num_in_yr = lubridate::month(date_seq),
#   mth_name = lubridate::month(date_seq, label = TRUE),
#   wk_num_in_yr = epiweek(date_seq),
#   wk_start_date = floor_date(date_seq, unit = 'weeks'),
#   wk_end_date = ceiling_date(date_seq, unit = 'weeks'),
#   revenue = rnorm(n = length(date_seq), mean = 500000, sd = 3582),
#   trials = round(rnorm(n = length(date_seq), mean = 500, sd = 35), 0),
#   converts = round(rnorm(n = length(date_seq), mean = 100, sd = 35), 0),
#   royalties = round(rnorm(n = length(date_seq), mean = 4666, sd = 1000), 4)
# ) %>% group_by(marketplace_short_name) %>%
#   mutate(members = cumsum(converts)) %>%
#   ungroup()
#
#
# devtools::use_data(performance, performance, overwrite = TRUE)
# readr::write_csv(performance, "data/performance.csv")

#
# sample_data %>% write_csv("sample_data.csv")
# sample_data <- read_csv("sample_data.csv")

# op2 is at monthly grain
# goals <- data.frame(yr_num = 2018,
#                   mth_num_in_yr = 1:12,
#                   revenue_op2 = 1555000,
#                   trials_op2 = 17000,
#                   converts_op2 = 12000,
#                   revenue_per_member_op2 = .05)
#
# devtools::use_data(goals, goals, overwrite = TRUE)

# sample_goals %>% write_csv("sample_budget_data.csv")

# sample_data2 <- sample_data %>% filter(date_value <= '2017-08-15')

# sample_gaap <- data.frame(
#   yr_num = 2017,
#   mth_num_in_yr = 1:12,
#   revenue_gaap = 1553000
#   )
#
# sample_gaap %>% write_csv("sample_gaap.csv")