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")
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