Objective: Calculate stochastics with three different values for nFastK
for all variables using TTR::stoch and tidyquant packages.

Topic 1: Error message

The snippet below works, but throws an error
with option: bounded = TRUE. What is the reason for the error?

rm(list = ls())

library(tidyquant)

library(lubridate)



my_data <- tibble(  Symbol = as_factor(c( rep("a", 100), rep("b", 100)))

                    , Date   = rep( ymd("2017-11-14") + 7 * (0:99), 2) # weekly 

                    , major  = c (10000 + sample(-800:300, 100), (8000 + sample(-100:900, 100)))

                    , v1     = sample(-1000:1000, 200 ) / 100

                    , v2     = sample(-100:1200, 200) / 100

)





my_final <- my_data %>% 

  gather( -Date, -Symbol, key = "kkeys", value = "wwords") %>%

  mutate(kkeys = as_factor(kkeys)) %>%

  group_by(Symbol, kkeys) %>%

  tq_mutate(

    # tq_mutate args

    select     = wwords,

    mutate_fun = stoch, 

    # args to mutate_fun

    nFastK = 10

    # , bounded = FALSE  # <- uncomment this line for error!

  ) %>%

  select( -wwords, -fastD, -stoch ) %>% 

  mutate( fastK = round(fastK, digits = 2)) %>%

  spread( kkeys, fastK) 

Topic 2: Functional programming on this issue.

A for loop produces three values of nFastK
calling the above and then renaming and
right-joining to the final table like so.

This is just a brief illustration of my original code:

my_periods <- c(5, 10, 20)

my_vars    <- my_data %>% select (-Date, -Symbol) %>% colnames()

my_final   <- my_data



for (i in seq_along(my_periods)) {



  # Create unique Colnames

  my_vars_to <- str_c( my_vars, "_pk", my_periods[i])



  my_final <- 



    my_data %>% 

    # Do all of the above from topic 1 plus this 

    rename_at( vars(my_vars), ~ my_vars_to) %>%

    right_join(my_final,  by = c("Symbol", "Date"))



}

This loop works and gets me what I want. Still being in the steep learning curve, there are two questions:

Question 1: Acc. to Wickham with solutions provided by Arnold, preallocation operates faster. How would this code need to be written to pre-allocate the memory compared to right_join()? Or is this an OK solution? I looked at https://jrnold.github.io/r4ds-exercise-solutions/iteration.html

Question 2: After reading a few tutorials, purrr::map()
appears to be appropriate instead of the for loop.
Even after reading tutorials and questions here I can't get my head around how to write it properly. Would you please provide an example or point in the direction of more reading?

Finally:

Thank you all the help via examples, vignettes and other posts. This is probably one of the most active, helpful and knowledgable communities I have ever come across. As a new user to R I appreciate the many examples on stackoverflow and any other websites. This is my first post. Thanks, A.