R Create new columns with ifelse-function for multiple dataframes

Question

I want to create several columns with a ifelse()-condition for multiple dataframes. In this case the dataframes are 3 time-series data for cryptocurrencies. Here is the code to download the 3 dataframes automatically:

library(tidyverse)
library(crypto)

crypto_chart <- crypto_prices()%>% select(-id, -symbol,-price_btc, -`24h_volume_usd`,-available_supply, -total_supply,-max_supply, -percent_change_1h, -percent_change_24h, -percent_change_7d, -last_updated)%>% slice(1:3)

list_cryptocurrencies <-crypto_chart$name   

map(list_cryptocurrencies,
    function(x) crypto_history(x, start_date = '20150101', end_date = '20190303')%>%
      select(-slug, -symbol, -name, -`ranknow`))%>%
set_names(list_cryptocurrencies)%>%
list2env(envir = .GlobalEnv)

##Calculating return
map(mget(list_cryptocurrencies),
function(x) x %>% mutate(`return` =   (close-open)/open * 100))%>%
list2env(mget(list_cryptocurrencies), envir = .GlobalEnv)

Now I want to detect positive overreactions (oR_pos) in the returns. I define an overreaction as a value (return) higher than the mean + 1 standard deviation. I want do this also for 1.5 and 2 standard deviations. Here ist my desired output for one cryptocurrencie (Bitcoin):

> Bitcoin
     date    open   close      return     oR_pos>1sd oR_pos>1.5sd oR_pos>2sd
1  2018-01-01 14112.2 13657.2  -3.2241607         NA           NA         NA
2  2018-01-02 13625.0 14982.1   9.9603670   9.960367     9.960367   9.960367
3  2018-01-03 14978.2 15201.0   1.4874952         NA           NA         NA
4  2018-01-04 15270.7 15599.2   2.1511784         NA           NA         NA
5  2018-01-05 15477.2 17429.5  12.6140387  12.614039    12.614039  12.614039
6  2018-01-06 17462.1 17527.0   0.3716621         NA           NA         NA
7  2018-01-07 17527.3 16477.6  -5.9889430         NA           NA         NA
8  2018-01-08 16476.2 15170.1  -7.9271919         NA           NA         NA
9  2018-01-09 15123.7 14595.4  -3.4931928         NA           NA         NA
10 2018-01-10 14588.5 14973.3   2.6376941         NA           NA         NA
11 2018-01-11 14968.2 13405.8 -10.4381288         NA           NA         NA
12 2018-01-12 13453.9 13980.6   3.9148500   3.914850           NA         NA

Now I have 3 new columns with overreactions(oR_pos) which are > 1sd; 1.5sd and 2sd.

I've already tried this code:

oR_pos_function <- function(y) {
n <- seq(1, 2, 0.5)
y[paste0("oR_pos>", n, "sd")] <-lapply(n, function(x)
ifelse(x$return > mean(x$return)+ sd(x$return),x$return, NA))
y
}

map(mget(list_cryptocurrencies), oR_pos_function)%>%
set_names(list_cryptocurrencies)%>%
list2env(envir = .GlobalEnv)

But it doesen't works. Can someone help me?

Answer 1

The following closely matches your intended function, adding the desired columns onto your crypto, while allowing the desired sd thresholds to be passed-in as parameter for flexibility. Aside note, the solution below uses > as per OP, but you may wish to consider movement +/- direction from sd. Using solution below could be done using instead:

col <- ifelse(returns > (r_mean+(r_sd*threshold)) | 
              returns < (r_mean-(r_sd*threshold)),
              returns,NA)

Solution as follows:

oR_pos_function <- function(returns,thresholds) {

  r_mean <- mean(returns,na.rm=T)
  r_sd <- sd(returns,na.rm=T)

  cols <- lapply(thresholds,function(threshold) {
    col <- ifelse(returns > (r_mean+(r_sd*threshold)),returns,NA)
    return(col)
  })
  cols <- as.data.frame(cols)
  names(cols) <- paste0("oR_pos>",thresholds,"sd")
  return(cols)  
}

new_cols <- oR_pos_function(returns=Bitcoin$return,thresholds=c(1,1.5,2))
Bitcoin <- cbind(Bitcoin,new_cols)

Results:

> head(Bitcoin[Bitcoin$date>="2018-01-01",])
           date    open    high     low   close      volume       market close_ratio spread     return oR_pos>1sd oR_pos>1.5sd oR_pos>2sd
1097 2018-01-01 14112.2 14112.2 13154.7 13657.2 10291200000 229119155396   0.5248042  957.5 -3.2241607         NA           NA         NA
1098 2018-01-02 13625.0 15444.6 13163.6 14982.1 16846600192 251377913955   0.7972381 2281.0  9.9603670   9.960367     9.960367   9.960367
1099 2018-01-03 14978.2 15572.8 14844.5 15201.0 16871900160 255080562912   0.4894961  728.3  1.4874952         NA           NA         NA
1100 2018-01-04 15270.7 15739.7 14522.2 15599.2 21783199744 261795321110   0.8845996 1217.5  2.1511784         NA           NA         NA
1101 2018-01-05 15477.2 17705.2 15202.8 17429.5 23840899072 292544135538   0.8898258 2502.4 12.6140387  12.614039    12.614039  12.614039
1102 2018-01-06 17462.1 17712.4 16764.6 17527.0 18314600448 294217423675   0.8043891  947.8  0.3716621         NA           NA         NA
> 

Alternative per comments:

oR_pos_function <- function(coin_data,thresholds) {

  returns <- coin_data$return
  r_mean <- mean(returns,na.rm=T)
  r_sd <- sd(returns,na.rm=T)

  cols <- lapply(thresholds,function(threshold) {
    col <- ifelse(returns > (r_mean+(r_sd*threshold)),returns,NA)
    return(col)
  })
  cols <- as.data.frame(cols)
  names(cols) <- paste0("oR_pos>",thresholds,"sd")
  coin_data <- cbind(coin_data,cols)
  return(coin_data)  
}

Answer 2

You can use dplyr::mutate to add any such fields

library(dplyr)
Bitcoin %>%
  mutate(oR_pos_1sd = ifelse(return > mean(return) + sd(return), return , NA),
         oR_pos_1.5sd = ifelse(return > mean(return) + 1.5*sd(return), return , NA),
         oR_pos_2sd = ifelse(return > mean(return) + 2*sd(return), return , NA))