R Create new columns with ifelse-function for multiple dataframes - r

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))