## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = ""
)
## ----chunk and split-----------------------------------------------------
library(keysandstrings)
chunk_string('aaabbbcccddd', chunk_len=3)
split_key(c('aaabbb', 'aaaccc','bbbaaa'), chunk_len = 3)
## ----level coder---------------------------------------------------------
data = data.frame(w = rep('wkey1',8),
x = rep(c('xkey1', 'xkey2','xkey3', NA), 2),
y = c(rep(c('ykey1', 'ykey2', 'ykey3'), 2), 'ykey1', ''),
z = c(rep(c('zkey1',NA, ''), 2), 'zkey1', '')
)
lc = level_coder(data)
x = predict(lc, data, verbose = TRUE)
print(x)
predict(lc, x, rev = TRUE)
key = predict(lc,data, return_primkey = TRUE)
print(key)
predict(lc,key, is_primkey = TRUE)
z = "aaazzzzzzzzz"
attr(z, 'primkey') = TRUE
predict(lc, key[8] ,is_primkey = TRUE)
## ----tsfeatures----------------------------------------------------------
head(ts_features(AirPassengers),2)
tail(ts_features(AirPassengers),2)
params = list(time_col = 'date',
key_col = 'key',
val_col = 'val',
n_future = 2,
n_past = 2,
na_fill = NA)
data = data.frame(date = rep(1:10,2), key = c(rep('a',10), rep('b',10) ), val =c(1:10, 2:11))
ts_features(data, params)
# this demonstrates the back fill capability for missing
data = data.frame(date = c(2:10, 1:10), key = c(rep('a',9), rep('b',10) ), val =c(2:10, 1:10))
new_data = ts_features(data, params)
new_data[ order(new_data$key, new_data$date),]
## ----keyscaler-----------------------------------------------------------
data = as.matrix(data.frame(key = c(rep(1,40), rep(2,40)),
val1 = c(1:79, NA),
val2 = c(1:80),
val3 = rep(1, 80)))
head(data)
ks = keyscale(data, key_col = 'key')
x = predict(ks, data)
head(x)
head(predict(ks, x))
## ----black_list----------------------------------------------------------
data = data.frame(a = c(rep('aa', 9), 'b'), b = c(rep('aa', 10)))
bl = black_list(data, cols = c('a', 'b'), freq = 9 )
predict(bl, data, keep_rows = TRUE)
wl = black_list(data, cols = c('a', 'b'), freq = 9, white_list = TRUE )
predict(bl, data, keep_rows = TRUE)
## ----sparse encoder------------------------------------------------------
data = data.frame(x = rep(x = c(NA,letters[1:4]), 5), y = rep(c(NA,letters[5:8]), 5), z = c(1:24, NA), zz = 1:25)
lc = sparseEncoder(data)
predict(lc, data)
## ----cp functions--------------------------------------------------------
x = c(201209,201212, 201301)
cp_add(x,2)
cp_to_fp(x)
cp_to_fp(cp_to_fp(x), rev = TRUE)
cp_seq(201201,6)
x = rep(cp_seq(200011, 250),2)
system.time(cp_to_calendar(cp_seq(201201,100)))
system.time(cp_to_calendar(cp_seq(201201,100), par = FALSE))
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