Increasing this parameter gives more weight to early observations than to later ones.ĭES, c2]: double exponential smoothing (Holt's method) with optional parameters c1, c2 (default c1=0.2, c2=0.3). This method uses its own weight system, using the single parameter c. SES: single exponential smoothing with parameter c (default c=0.2). You might want to increase the weight if the corresponding observation is important, or decrease the weight if the corresponding observation is an outlier or is unreliable. In general, the weights are equal to 1 by default. LR: standard linear regression with possible weights assigned to each data point and an optional seasonal adjustment period, where is the length of the period.

Method parameters may be any of the following: Method parameters must be numeric values, not member names. methodĪ choice among LR (linear regression), SES (single exponential smoothing), DES (double exponential smoothing), and TES (triple exponential smoothing). If you want to move the forecasting period forward, specify the new period with XforecastList.Ī member list into which the forecast values are placed. If you forecast consecutively from where Ylist stops, you do not need to specify XforecastList. If you do not specify XforecastList, the values are assumed to be as follows: up to (last value in Xlist + the number of values in YforecastList) Expression list that contains the underlying variable values for which the forecasting is sought. Member list that represents the differences between the data points in Ylist and the data points on the line or curve (as specified for method). Negative weights are replaced with their absolute values. Weights for methods other than linear regression are ignored. If values in weightList are #MISSING, the default is 1. An expression list that contains weights for the data points in Ylist, for the linear regression method only. If you do not specify Xlist, the default variable values are 1,2,3, and so on, up to the number of values in Ylist. For example, for each sales figure in Ylist, Xlist may contain a value for associated time periods. An expression list that contains underlying variable values. An expression list that contains known observations for example, sales figures over a period of time. Mutate.tbl_log could look like: mutate.tbl_log <- function(.data. That way, when it passes off to mutate(), the correct mutate.tbl_log method is called.

Where as_tbl_log() and init_logger() would just add a tbl_log class to the existing object. The API would probably look more like: mtcars %>% It might work to instead use an S3 method for filter instead. #> mpg cyl disp hp drat wt qsec vs am gear carb x #> The following objects are masked from 'package:base': #> The following objects are masked from 'package:stats': #> select_if, semi_join, transmute, transmute_all, transmute_at, #> mutate_if, right_join, select, select_all, select_at, #> inner_join, left_join, mutate, mutate_all, mutate_at, #> full_join, group_by, group_by_all, group_by_at, group_by_if, #> anti_join, distinct, filter, filter_all, filter_at, filter_if, #> The following objects are masked from 'package:tidylog': #> The following object is masked from 'package:stats': The most immediate issue with this is that if you flip the order of the library calls, it doesn't work! # devtools::install_github('elbersb/tidylog') The most obvious to me is that the S3 methods get clobbered by defining a new function called filter() rather than adding a filter() method. Since you asked for feedback, I do have a few thoughts!

I think it could be pretty helpful for beginners, or for just general logging and understanding of what dplyr is doing. This is a pretty neat package! Congrats on the love it has received so far.

Apply lag operation to xlist