From e55aa64dd76fe3f87a9fffa8a6550dbda8334e21 Mon Sep 17 00:00:00 2001
From: Dom <dom@itsallbroken.com>
Date: Mon, 5 Jul 2021 23:25:29 +0100
Subject: [PATCH] fix: freq undefined when scale set for some tables

This commit removes references to 'freq' which is not assigned when
the argument 'scale' != NA for the following tables:

	* table.Distributions
	* table.DownsideRiskRatio
	* table.Variability

Fixes #128.
---
 R/table.Distributions.R     | 7 +++++--
 R/table.DownsideRiskRatio.R | 5 ++++-
 R/table.Variability.R       | 5 ++++-
 3 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/R/table.Distributions.R b/R/table.Distributions.R
index 3e942ab1..5e169919 100644
--- a/R/table.Distributions.R
+++ b/R/table.Distributions.R
@@ -42,7 +42,8 @@ function (R, scale = NA, digits = 4)
 
     #set up frequency
     if(is.na(scale)) {
-        freq = periodicity(y)
+        freq = periodicity(R)
+        name <- paste0(freq$scale," Std Dev")
         switch(freq$scale,
             minute = {stop("Data periodicity too high")},
             hourly = {stop("Data periodicity too high")},
@@ -52,13 +53,15 @@ function (R, scale = NA, digits = 4)
             quarterly = {scale = 4},
             yearly = {scale = 1}
         )
+    } else {
+        name <- paste0("Std Dev")
     }
 
     # for each column, do the following:
     for(column in 1:columns) {
         z = c(StdDev.annualized(y[,column,drop=FALSE])/sqrt(scale), skewness(y[,column,drop=FALSE], method = "moment"), kurtosis(y[,column,drop=FALSE], method = "moment"), kurtosis(y[,column,drop=FALSE], method = "excess"), skewness(y[,column,drop=FALSE], method = "sample"), kurtosis(y[,column,drop=FALSE], method = "sample_excess"))
 
-        znames = c(paste(freq$scale," Std Dev"), "Skewness",  "Kurtosis", "Excess kurtosis", "Sample skewness", "Sample excess kurtosis")
+        znames = c(name, "Skewness",  "Kurtosis", "Excess kurtosis", "Sample skewness", "Sample excess kurtosis")
 
 
         if(column == 1) {
diff --git a/R/table.DownsideRiskRatio.R b/R/table.DownsideRiskRatio.R
index aaa30718..647a3487 100644
--- a/R/table.DownsideRiskRatio.R
+++ b/R/table.DownsideRiskRatio.R
@@ -46,6 +46,7 @@ function (R, MAR = 0, scale = NA, digits = 4)
     #set up frequency
     if(is.na(scale)) {
         freq = periodicity(R)
+        name <- paste0(freq$scale," downside risk")
         switch(freq$scale,
             minute = {stop("Data periodicity too high")},
             hourly = {stop("Data periodicity too high")},
@@ -55,13 +56,15 @@ function (R, MAR = 0, scale = NA, digits = 4)
             quarterly = {scale = 4},
             yearly = {scale = 1}
         )
+    } else {
+        name <- paste0("downside risk")
     }
 
     # for each column, do the following:
     for(column in 1:columns) {
         z = c(DownsideDeviation(y[,column,drop=FALSE], MAR = MAR), DownsideDeviation(y[,column,drop=FALSE], MAR = MAR)*sqrt(scale), DownsidePotential(y[,column,drop=FALSE], MAR = MAR), UpsideRisk(y[,column,drop=FALSE], MAR = MAR, stat = "potential")/DownsidePotential(y[,column,drop=FALSE], MAR = MAR), SortinoRatio(y[,column,drop=FALSE], MAR = MAR), UpsideRisk(y[,column,drop=FALSE], MAR = MAR, stat = "potential"), UpsidePotentialRatio(y[,column,drop=FALSE], MAR = MAR), OmegaSharpeRatio(y[,column,drop=FALSE], MAR = MAR))
 
-        znames = c(paste0(freq$scale," downside risk"), "Annualised downside risk", "Downside potential", "Omega", "Sortino ratio", "Upside potential", "Upside potential ratio", "Omega-sharpe ratio")
+        znames = c(name, "Annualised downside risk", "Downside potential", "Omega", "Sortino ratio", "Upside potential", "Upside potential ratio", "Omega-sharpe ratio")
         if(column == 1) {
             resultingtable = data.frame(Value = z, row.names = znames)
         }
diff --git a/R/table.Variability.R b/R/table.Variability.R
index b86d2e44..eae8e876 100644
--- a/R/table.Variability.R
+++ b/R/table.Variability.R
@@ -46,6 +46,7 @@ function (R, scale = NA, geometric = TRUE, digits = 4)
     #set up frequency
     if(is.na(scale)) {
         freq = periodicity(R)
+        name <- paste0(freq$scale," Std Dev")
         switch(freq$scale,
             minute = {stop("Data periodicity too high")},
             hourly = {stop("Data periodicity too high")},
@@ -55,13 +56,15 @@ function (R, scale = NA, geometric = TRUE, digits = 4)
             quarterly = {scale = 4},
             yearly = {scale = 1}
         )
+    } else {
+        name <- paste0("Std Dev")
     }
 
     # for each column, do the following:
     for(column in 1:columns) {
         z = c(MeanAbsoluteDeviation(y[,column,drop=FALSE]), StdDev.annualized(y[,column,drop=FALSE], scale = scale)/sqrt(scale), StdDev.annualized(y[,column,drop=FALSE], scale = scale))
 
-        znames = c("Mean Absolute deviation", paste0(freq$scale," Std Dev"), "Annualized Std Dev")
+        znames = c("Mean Absolute deviation", name, "Annualized Std Dev")
 
 
         if(column == 1) {