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##This section downloads, unzips, and reads the data
install.packages("downloader")
library(downloader)
download("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
dest="dataset.zip", mode="wb")
unzip("dataset.zip", exdir = "./")
dfHPC <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?",
stringsAsFactors = FALSE)
##This section formats date column and subsets by date
dfHPC$Date <- as.Date(dfHPC$Date, "%d/%m/%Y")
Date1 <- as.Date("2007-02-01")
Date2 <- as.Date("2007-02-02")
dfSubset <- dfHPC[dfHPC$Date %in% Date1:Date2, ]
##This section creates a new column that is properly formatted date/time for later use
z <- paste(dfSubset$Date, dfSubset$Time)
dfSubset$DT <- strptime(z, "%Y-%m-%d %H:%M:%S")
##Begin Plot 1
par(mfrow = c(1,1))
with(dfSubset, hist(Global_active_power,
col = "red",
xlab = "Global Active Power (kilowatts)",
main = "Global Active Power"))
dev.copy(png, file = "plot1.png", width = 480, height = 480, units = "px")
dev.off()
|
/plot1.R
|
no_license
|
emlaughlin/ExData_Plotting1
|
R
| false
| false
| 1,130
|
r
|
##This section downloads, unzips, and reads the data
install.packages("downloader")
library(downloader)
download("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
dest="dataset.zip", mode="wb")
unzip("dataset.zip", exdir = "./")
dfHPC <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?",
stringsAsFactors = FALSE)
##This section formats date column and subsets by date
dfHPC$Date <- as.Date(dfHPC$Date, "%d/%m/%Y")
Date1 <- as.Date("2007-02-01")
Date2 <- as.Date("2007-02-02")
dfSubset <- dfHPC[dfHPC$Date %in% Date1:Date2, ]
##This section creates a new column that is properly formatted date/time for later use
z <- paste(dfSubset$Date, dfSubset$Time)
dfSubset$DT <- strptime(z, "%Y-%m-%d %H:%M:%S")
##Begin Plot 1
par(mfrow = c(1,1))
with(dfSubset, hist(Global_active_power,
col = "red",
xlab = "Global Active Power (kilowatts)",
main = "Global Active Power"))
dev.copy(png, file = "plot1.png", width = 480, height = 480, units = "px")
dev.off()
|
# calculate the root mean square error of a model's predictions
rmse_calc = function(true_value, predictions) {
rmse = sqrt(mean((predictions - true_value)^2))
return(rmse)
}
|
/general_functions/general_utils.R
|
no_license
|
marccgrau/dml_estimator_plm
|
R
| false
| false
| 178
|
r
|
# calculate the root mean square error of a model's predictions
rmse_calc = function(true_value, predictions) {
rmse = sqrt(mean((predictions - true_value)^2))
return(rmse)
}
|
library(qmethod)
### Name: make.distribution
### Title: Q methodology: create Q normal distribution
### Aliases: make.distribution
### ** Examples
## Make Q distribution
make.distribution(nstat=76, max.bin=7)
|
/data/genthat_extracted_code/qmethod/examples/make.distribution.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 220
|
r
|
library(qmethod)
### Name: make.distribution
### Title: Q methodology: create Q normal distribution
### Aliases: make.distribution
### ** Examples
## Make Q distribution
make.distribution(nstat=76, max.bin=7)
|
testlist <- list(data = structure(0, .Dim = c(1L, 1L)), x = structure(c(0, 1.33091576009481e-309, 5.00368698948671e-304, 1.3545163781073e+248, 2.02822087722601e-110, 7.80639368600506e+115, 2.84878894080431e-306, 5.66569438147973e-217, 4.94433309099852e-312, 2.48588604007856e-265, 1.79809443502751e-309, 0, 2.50632319422251e-304, 0, 1.25205814058912e-312, 1.60553053506776e-306, 0, 0, 3.95252516672997e-323, 8.12983395216297e-261, 0, 6.84076707059454e-304, 0, 8.70026139148914e-316, 2.08882847642057e-314, 4.10547562541273e+62, 1.62916584656708e-260, 2.6425885427944e-260, 1.23055912552855e-269, 5.26246667113329e-312, 0, 0, 4.88059031922013e-312, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.4886982972039e-314, 2.81776900841821e-202, 2.81772601270452e-202, 7.80639044993566e+115, 1.46942191467086e-105, 5.50828115013509e+125, 7.3055433066634e-304), .Dim = c(10L, 5L )))
result <- do.call(distr6:::C_EmpiricalMVPdf,testlist)
str(result)
|
/distr6/inst/testfiles/C_EmpiricalMVPdf/libFuzzer_C_EmpiricalMVPdf/C_EmpiricalMVPdf_valgrind_files/1610035707-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false
| false
| 935
|
r
|
testlist <- list(data = structure(0, .Dim = c(1L, 1L)), x = structure(c(0, 1.33091576009481e-309, 5.00368698948671e-304, 1.3545163781073e+248, 2.02822087722601e-110, 7.80639368600506e+115, 2.84878894080431e-306, 5.66569438147973e-217, 4.94433309099852e-312, 2.48588604007856e-265, 1.79809443502751e-309, 0, 2.50632319422251e-304, 0, 1.25205814058912e-312, 1.60553053506776e-306, 0, 0, 3.95252516672997e-323, 8.12983395216297e-261, 0, 6.84076707059454e-304, 0, 8.70026139148914e-316, 2.08882847642057e-314, 4.10547562541273e+62, 1.62916584656708e-260, 2.6425885427944e-260, 1.23055912552855e-269, 5.26246667113329e-312, 0, 0, 4.88059031922013e-312, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.4886982972039e-314, 2.81776900841821e-202, 2.81772601270452e-202, 7.80639044993566e+115, 1.46942191467086e-105, 5.50828115013509e+125, 7.3055433066634e-304), .Dim = c(10L, 5L )))
result <- do.call(distr6:::C_EmpiricalMVPdf,testlist)
str(result)
|
######
# Reproducible Research
# This is the raw working file where script was developed to complete Project 1
#
#######
# Loading and preprocessing the data
#
# Show any code that is needed to
#
# 1. Load the data (i.e. read.csv())
if(!exists("activity")){
activity<-read.csv("activity.csv")
}
#
# 2. Process/transform the data (if necessary) into a format suitable for your analysis
#seems fine as is
#
# What is mean total number of steps taken per day?
#
# For this part of the assignment, you can ignore the missing values in the dataset.
#
# 1. Calculate the total number of steps taken per day
total_steps_daily <- aggregate(steps ~ date, activity, sum)
# 2. If you do not understand the difference between a histogram and a barplot, research the difference between them. Make a histogram of the total number of steps taken each day
#http://stackoverflow.com/questions/10770698/understanding-dates-and-plotting-a-histogram-with-ggplot2-in-r
#total_steps_daily$date <- as.Date(total_steps_daily$date) # format the date field as a Date
# hist(total_steps_daily$steps,
# col=1,
# breaks = 53,
# main="Distribution of steps per day",
# # axes = FALSE,
# xlab="number of steps",
# ylab="number of days",
# angle = 45)
#
# 3. Calculate and report the mean and median of the total number of steps taken per day
#mean(total_steps_daily$steps, na.rm = TRUE) # 10766.19
#median(total_steps_daily$steps, na.rm = TRUE) # 10765
#sum(total_steps_daily$steps, na.rm = TRUE) # 570608
#
# What is the average daily activity pattern?
#
# 1. Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
# calculate the mean number of steps per interval as a numeric vector
mean_steps <- tapply(activity$steps, activity$interval, mean, na.rm = TRUE)
# # calculate the x values
intervals <- strptime(sprintf("%04d", as.numeric(names(mean_steps))), format="%H%M")
# # plot the mean number of steps per day
# plot(intervals, mean_steps,
# type="l",
# main="Mean steps per interval across days",
# xlab="5 minute intervals",
# ylab="Mean steps per interval"
# )
#abline(v=round(max(mean_steps)), lty=3, col="blue")
#
# # 2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
# max_steps<-which.max(time_series)
# max_steps[1]
#
# Imputing missing values
#
# Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
#
# 1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
# originalValue <- complete.cases(activity)
# missingValues <- length(originalValue[originalValue==FALSE]) # number of records with NA
# missingValues
# nComplete <- length(originalValue[originalValue==TRUE]) # number of complete records
# title="Missing vs. Complete Cases"
# barplot(table(originalValue),main=title,xaxt='n') # render Complete Cases barplot
# axis(side=1,at=c(.7,1.9),labels=c("Missing","Complete"),tick=FALSE) # render axis
# text(.7,0,labels=nMissing, pos=3) # label the NA's bar
# text(1.9,0,labels=nComplete, pos=3)
#
# 2. Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
activity2 <- activity # copy original df into a new one
for (i in 1:nrow(activity2)){ # loop through the new df
if (is.na(activity2$steps[i])){ # if a value is missing (NA)
activity2$steps[i] <- mean_steps[i] # replace it with the value previously
# calculated in mean_steps for the given
} # interval
}
#
# 3. Create a new dataset that is equal to the original dataset but with the missing data filled in.
#already did above
#
# 4. Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
total_steps_daily2 <- aggregate(strtoi(steps) ~ date, activity2, sum)
# mean(strtoi(total_steps_daily2$steps), na.rm = TRUE) # 10766.19
# median(total_steps_daily2$steps, na.rm = TRUE) # 10765
# sum(total_steps_daily2$steps, na.rm = TRUE) # 570608
#
# Are there differences in activity patterns between weekdays and weekends?
#
# For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.
#
# 1. Create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.
weekdays(as.Date(total_steps_daily2$date), abbreviate=TRUE)
daytype <- function(date) {
if (weekdays(as.Date(date)) %in% c("Saturday", "Sunday")) {
"weekend"
} else {
"weekday"
}
}
activity2$daytype <- as.factor(sapply(activity2$date, daytype))
#
# 2. Make a panel plot containing a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.
par(mfrow = c(2, 1))
for (type in c("weekend", "weekday")) {
steps.type <- aggregate(steps ~ interval, data = activity2, subset = activity2$daytype ==
type, FUN = mean)
plot(steps.type, type = "l", main = type)
}
|
/project1.R
|
no_license
|
gregrgay/RepData_PeerAssessment1
|
R
| false
| false
| 6,021
|
r
|
######
# Reproducible Research
# This is the raw working file where script was developed to complete Project 1
#
#######
# Loading and preprocessing the data
#
# Show any code that is needed to
#
# 1. Load the data (i.e. read.csv())
if(!exists("activity")){
activity<-read.csv("activity.csv")
}
#
# 2. Process/transform the data (if necessary) into a format suitable for your analysis
#seems fine as is
#
# What is mean total number of steps taken per day?
#
# For this part of the assignment, you can ignore the missing values in the dataset.
#
# 1. Calculate the total number of steps taken per day
total_steps_daily <- aggregate(steps ~ date, activity, sum)
# 2. If you do not understand the difference between a histogram and a barplot, research the difference between them. Make a histogram of the total number of steps taken each day
#http://stackoverflow.com/questions/10770698/understanding-dates-and-plotting-a-histogram-with-ggplot2-in-r
#total_steps_daily$date <- as.Date(total_steps_daily$date) # format the date field as a Date
# hist(total_steps_daily$steps,
# col=1,
# breaks = 53,
# main="Distribution of steps per day",
# # axes = FALSE,
# xlab="number of steps",
# ylab="number of days",
# angle = 45)
#
# 3. Calculate and report the mean and median of the total number of steps taken per day
#mean(total_steps_daily$steps, na.rm = TRUE) # 10766.19
#median(total_steps_daily$steps, na.rm = TRUE) # 10765
#sum(total_steps_daily$steps, na.rm = TRUE) # 570608
#
# What is the average daily activity pattern?
#
# 1. Make a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
# calculate the mean number of steps per interval as a numeric vector
mean_steps <- tapply(activity$steps, activity$interval, mean, na.rm = TRUE)
# # calculate the x values
intervals <- strptime(sprintf("%04d", as.numeric(names(mean_steps))), format="%H%M")
# # plot the mean number of steps per day
# plot(intervals, mean_steps,
# type="l",
# main="Mean steps per interval across days",
# xlab="5 minute intervals",
# ylab="Mean steps per interval"
# )
#abline(v=round(max(mean_steps)), lty=3, col="blue")
#
# # 2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
# max_steps<-which.max(time_series)
# max_steps[1]
#
# Imputing missing values
#
# Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
#
# 1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
# originalValue <- complete.cases(activity)
# missingValues <- length(originalValue[originalValue==FALSE]) # number of records with NA
# missingValues
# nComplete <- length(originalValue[originalValue==TRUE]) # number of complete records
# title="Missing vs. Complete Cases"
# barplot(table(originalValue),main=title,xaxt='n') # render Complete Cases barplot
# axis(side=1,at=c(.7,1.9),labels=c("Missing","Complete"),tick=FALSE) # render axis
# text(.7,0,labels=nMissing, pos=3) # label the NA's bar
# text(1.9,0,labels=nComplete, pos=3)
#
# 2. Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
activity2 <- activity # copy original df into a new one
for (i in 1:nrow(activity2)){ # loop through the new df
if (is.na(activity2$steps[i])){ # if a value is missing (NA)
activity2$steps[i] <- mean_steps[i] # replace it with the value previously
# calculated in mean_steps for the given
} # interval
}
#
# 3. Create a new dataset that is equal to the original dataset but with the missing data filled in.
#already did above
#
# 4. Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
total_steps_daily2 <- aggregate(strtoi(steps) ~ date, activity2, sum)
# mean(strtoi(total_steps_daily2$steps), na.rm = TRUE) # 10766.19
# median(total_steps_daily2$steps, na.rm = TRUE) # 10765
# sum(total_steps_daily2$steps, na.rm = TRUE) # 570608
#
# Are there differences in activity patterns between weekdays and weekends?
#
# For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.
#
# 1. Create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.
weekdays(as.Date(total_steps_daily2$date), abbreviate=TRUE)
daytype <- function(date) {
if (weekdays(as.Date(date)) %in% c("Saturday", "Sunday")) {
"weekend"
} else {
"weekday"
}
}
activity2$daytype <- as.factor(sapply(activity2$date, daytype))
#
# 2. Make a panel plot containing a time series plot (i.e. type = "l") of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.
par(mfrow = c(2, 1))
for (type in c("weekend", "weekday")) {
steps.type <- aggregate(steps ~ interval, data = activity2, subset = activity2$daytype ==
type, FUN = mean)
plot(steps.type, type = "l", main = type)
}
|
Get2dBIRObj <- function(X, Y, theta, W.est, lambda){
W.est <- as.matrix(W.est)
LS <- (1/(2*nrow(X)))*sum((Y%*%Rot(theta) - as.matrix(X)%*%W.est)^2)
L0 <- (sum(sign(abs(W.est[,1])))+sum(sign(abs(W.est[,2]))))
LS + lambda*L0
}
|
/Functions/get_2d_BIR_obj.R
|
permissive
|
AdrienBibal/BIR
|
R
| false
| false
| 235
|
r
|
Get2dBIRObj <- function(X, Y, theta, W.est, lambda){
W.est <- as.matrix(W.est)
LS <- (1/(2*nrow(X)))*sum((Y%*%Rot(theta) - as.matrix(X)%*%W.est)^2)
L0 <- (sum(sign(abs(W.est[,1])))+sum(sign(abs(W.est[,2]))))
LS + lambda*L0
}
|
# Set working directory
setwd("C:/Users/jess_chen/Dropbox/Jess-LISA/Simulations/Data/")
###################### Read Files ######################
data5 = read.csv("DATA_Local cluster_05_heter.csv")
data10 = read.csv("DATA_Local cluster_10_heter.csv")
data15 = read.csv("DATA_Local cluster_15_heter.csv")
data20 = read.csv("DATA_Local cluster_20_heter.csv")
data30 = read.csv("DATA_Local cluster_30_heter.csv")
###################### Test Plots ######################
# Get the mean count generated for each county
# County is red if it exceeds the nth percentile
# Where n is based on cluster size
sum_5=rowMeans(data5[,6:1005])
sum_10=rowMeans(data10[,6:1005])
sum_15=rowMeans(data15[,6:1005])
sum_20=rowMeans(data20[,6:1005])
sum_30=rowMeans(data30[,6:1005])
par(mfrow=c(3,2))
plot(data5$longitude, data5$latitude, main = "5% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_5> quantile(sum_5, 0.95),'red','green'))
plot(data5$longitude, data5$latitude, main = "10% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_10> quantile(sum_10, 0.9),'red','green'))
plot(data5$longitude, data5$latitude, main = "15% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_15> quantile(sum_15, 0.85),'red','green'))
plot(data5$longitude, data5$latitude, main = "20% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_20> quantile(sum_20, 0.8),'red','green'))
plot(data5$longitude, data5$latitude, main = "30% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_30> quantile(sum_30, 0.7),'red','green'))
###################### Output for ArcGIS ######################
sum_5[sum_5>quantile(sum_5, 0.95)]<-1
sum_5[sum_5<=quantile(sum_5, 0.95)]<-0
sum_10[sum_10>quantile(sum_10, 0.95)]<-1
sum_10[sum_10<=quantile(sum_10, 0.95)]<-0
sum_15[sum_15>quantile(sum_15, 0.95)]<-1
sum_15[sum_15<=quantile(sum_15, 0.95)]<-0
sum_20[sum_20>quantile(sum_20, 0.95)]<-1
sum_20[sum_20<=quantile(sum_20, 0.95)]<-0
sum_30[sum_30>quantile(sum_30, 0.95)]<-1
sum_30[sum_30<=quantile(sum_30, 0.95)]<-0
cluster5 = cbind(data5[,1:5], sum_5)
cluster10 = cbind(data10[,1:5], sum_10)
cluster15 = cbind(data15[,1:5], sum_15)
cluster20 = cbind(data20[,1:5], sum_20)
cluster30 = cbind(data30[,1:5], sum_30)
write.table(countypop[closest,], file = filename,
sep = ",", row.names = FALSE)
|
/CODE_Plot_Clusters.R
|
no_license
|
jesschen32/LISA
|
R
| false
| false
| 2,348
|
r
|
# Set working directory
setwd("C:/Users/jess_chen/Dropbox/Jess-LISA/Simulations/Data/")
###################### Read Files ######################
data5 = read.csv("DATA_Local cluster_05_heter.csv")
data10 = read.csv("DATA_Local cluster_10_heter.csv")
data15 = read.csv("DATA_Local cluster_15_heter.csv")
data20 = read.csv("DATA_Local cluster_20_heter.csv")
data30 = read.csv("DATA_Local cluster_30_heter.csv")
###################### Test Plots ######################
# Get the mean count generated for each county
# County is red if it exceeds the nth percentile
# Where n is based on cluster size
sum_5=rowMeans(data5[,6:1005])
sum_10=rowMeans(data10[,6:1005])
sum_15=rowMeans(data15[,6:1005])
sum_20=rowMeans(data20[,6:1005])
sum_30=rowMeans(data30[,6:1005])
par(mfrow=c(3,2))
plot(data5$longitude, data5$latitude, main = "5% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_5> quantile(sum_5, 0.95),'red','green'))
plot(data5$longitude, data5$latitude, main = "10% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_10> quantile(sum_10, 0.9),'red','green'))
plot(data5$longitude, data5$latitude, main = "15% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_15> quantile(sum_15, 0.85),'red','green'))
plot(data5$longitude, data5$latitude, main = "20% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_20> quantile(sum_20, 0.8),'red','green'))
plot(data5$longitude, data5$latitude, main = "30% Cluster",
xlab = "Longitude", ylab = "Latitude",
col = ifelse(sum_30> quantile(sum_30, 0.7),'red','green'))
###################### Output for ArcGIS ######################
sum_5[sum_5>quantile(sum_5, 0.95)]<-1
sum_5[sum_5<=quantile(sum_5, 0.95)]<-0
sum_10[sum_10>quantile(sum_10, 0.95)]<-1
sum_10[sum_10<=quantile(sum_10, 0.95)]<-0
sum_15[sum_15>quantile(sum_15, 0.95)]<-1
sum_15[sum_15<=quantile(sum_15, 0.95)]<-0
sum_20[sum_20>quantile(sum_20, 0.95)]<-1
sum_20[sum_20<=quantile(sum_20, 0.95)]<-0
sum_30[sum_30>quantile(sum_30, 0.95)]<-1
sum_30[sum_30<=quantile(sum_30, 0.95)]<-0
cluster5 = cbind(data5[,1:5], sum_5)
cluster10 = cbind(data10[,1:5], sum_10)
cluster15 = cbind(data15[,1:5], sum_15)
cluster20 = cbind(data20[,1:5], sum_20)
cluster30 = cbind(data30[,1:5], sum_30)
write.table(countypop[closest,], file = filename,
sep = ",", row.names = FALSE)
|
#### UBER ASSIGNMENT ####
#Loading the required packages
library(lubridate)
library(stringr)
library(ggplot2)
library(dplyr)
#Getting uber request data into R
Uber.Request<-read.csv("Uber Request Data.csv",stringsAsFactors = FALSE)
#Observing the structure of dataframe Uber.Requests
str(Uber.Request)
#or you can also have a glimpse of data frame using Uber.Requests
glimpse(Uber.Request)
####Handling data quality issues/Data Cleaning####
#checking for any duplicate rows
cat ('there are',(nrow(Uber.Request)-nrow(Uber.Request %>% unique)),'duplicate rows')
#there are 0 duplicate rows
# checking for any NA values in all columns, one after other
# checking for any NA values in column Request.id
anyNA(Uber.Request$Request.id) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Driver.id) # NA's present(2650)
# checking for any NA values in column
anyNA(Uber.Request$Pickup.point) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Status) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Request.timestamp) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Drop.timestamp) # NA's present(3914)
# It looks appropriate to leave the NA's untouched as they
# look valid in the columns Driver.id and Drop.timestamp the NA's
# in the respective columns are present when there is a value
# "no cars available" in column status
#checking for any spelling mistakes in categorical columns
#In column Pickup.point
unique(Uber.Request$Pickup.point)
# there are only two unique values "Airport" "City" in column Pickup.point.There are no spelling mistakes
#In column Status
unique(Uber.Request$Status)
#there are only three unique values Trip Completed,Cancelled,No Cars Available in column Status.There are no spelling mistakes
###########DATA PREPERATION####################
####Handling date and time columns Request.timestamp and Drop.timestamp which are read as type character####
##Request.timestamp##
#Parsing Request.timestamp and storing it in the column Request_Date
Uber.Request$Request_Date<- lubridate::parse_date_time(Uber.Request$Request.timestamp,orders = c("d/m/Y H:M","d-m-Y H-M-S"))
# checking if there are any NA's coerced because of invalid data values
(Uber.Request$Request.timestamp %>% is.na %>% sum) == (Uber.Request$Request_Date %>% is.na %>% sum)
#It gives TRUE means NA's are not coerced.This also means there are no invalid data values in Request.timestamp
#spliting date from Request_Date and storing it in column Request.Date
Uber.Request$Request.Date<- as.Date(Uber.Request$Request_Date)
#Extracting Day of the week from Request.Date and storing it in Request.Day column
Uber.Request$Request.Day<- weekdays(Uber.Request$Request_Date)
#spliting date form Request_Date and storing it in column Request.Time
Uber.Request$Request.Time<-format(Uber.Request$Request_Date,"%H:%M:%S")
#Extracting hours mins and sec from column Request.timestamp
Uber.Request$Request.hour<-lubridate::hour(Uber.Request$Request_Date)
Uber.Request$Request.minute<-lubridate::minute(Uber.Request$Request_Date)
Uber.Request$Request.second<-lubridate::second(Uber.Request$Request_Date)
#grouping the data into different time slots morning,noon,evening and night
#based on values in request.hour
#defineing a timeslot function
timeslot<-function(request.hour){
if(request.hour>=4 & request.hour<10){
return("Morning Slot")
}else if(request.hour>=10 & request.hour<16){
return("Noon Slot")
}else if(request.hour>=16 & request.hour<22){
return("Evening Slot")
}else if(request.hour>=22 | request.hour<4){
return("Night Slot")
}else{
return(NA)
}
}
#creating timeslot column using timeslot function
Uber.Request$Request.TimeSlot<-sapply(Uber.Request$Request.hour,timeslot) %>% unlist
#checking for any coerced NA values in column Request.TimeSlot
anyNA(Uber.Request$Request.TimeSlot)#FALSE no NA's
#Dropping request_date column
Uber.Request$Request.timestamp<-NULL
#####Drop.timestamp#####
#Parsing Drop.timestamp and storing it in the column Drop_Date
Uber.Request$Drop_Date<- parse_date_time(Uber.Request$Drop.timestamp,orders = c("d/m/Y H:M","d-m-Y H-M-S"))
# checking if there are any NA's coerced because of invalid data values
(Uber.Request$Drop.timestamp %>% is.na %>% sum) == (Uber.Request$Drop_Date%>% is.na %>% sum)
#It gives TRUE means NA's are not coerced. This also means there are no invalid data values in Drop.timestamp
#spliting date form Drop_Date and storing it in column Drop.Date
Uber.Request$Drop.Date<- as.Date(Uber.Request$Drop_Date)
#spliting date form Drop_Date and storing it in column Drop.Time
Uber.Request$Drop.Time<-format(Uber.Request$Drop_Date,"%T")
#Extracting hours mins and sec from column Drop.timestamp
Uber.Request$Drop.hour<-lubridate::hour(Uber.Request$Drop_Date)
Uber.Request$Drop.minute<-lubridate::minute(Uber.Request$Drop_Date)
Uber.Request$Drop.second<-lubridate::second(Uber.Request$Drop_Date)
#Dropping request_date column
Uber.Request$Drop.timestamp<-NULL
#checking for the time duration of data present
data.interval<-as.interval(min(Uber.Request$Request_Date),max(Uber.Request$Request_Date))
#checking the duration of the interval
as.duration(data.interval)
#Total 5 Days of data is present
######DATA ANALYSIS#####
# Since Request.id and Driver.id are id or unique values there isn't much sense analysing them
#Defining the Pickup.point variable as factor and ordering it's levels in a particuler order
Uber.Request$Pickup.point<-factor(Uber.Request$Pickup.point,levels=c("City","Airport"))
#Analysing variable Pickup.point by plotting a bar chart on it (and looking for some insights)
ggplot(Uber.Request,aes(Pickup.point,fill=Pickup.point))+geom_bar(col="black")+annotate("text",x=c("Airport","City"),y=c(3450,3700),label=c("48%","51%"))+theme_bw()
#The above plot shows that ,there isn't much difference between Airport and city pickup requests
#Analysing variable Status by plotting a bar chart on it (and looking for some insights)
ggplot(Uber.Request,aes(Status,fill=Status))+geom_bar(col="black")+annotate("text",x=c("Cancelled","No Cars Available","Trip Completed"),y=c(1400,2800,2950),label=c("19%","39%","41%"))+theme_bw()
#The above plot clearly depicts that only 41% of the requests from city and
#airport gets completed and the remaining 59% trips either get cancelled or
#there is no car availability
##########################################################################
#The proportions of above plot can be obtained from the following code
prop.table(table(Uber.Request$Pickup.point))
prop.table(table(Uber.Request$Status))
##########################################################################
#segemnting pickup.point over status
ggplot(Uber.Request,aes(x=Pickup.point,fill=Status))+geom_bar(position = "dodge",col="black")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#The above plot shows that for most of the Airport pickup requests there are
#no cars available and most requests that get cancelled are city pickup requests
#Analysing variable Request.hour by plotting a bar chart on it (and looking for
#some insights)
ggplot(Uber.Request,aes(Request.hour))+geom_bar(fill="royalblue1",col="black")+annotate("text",x=c(7.2,19),y=c(500,550),label=("High Request Rate"))+theme_bw()
#The above plot clearly depicts that there are high request rates from 5AM TO 10AM
#and 5pm to 10 pm
#To get a better understanding of requests raised at different hours of the day
#lets plot a chart on variable timeslot(which contains hours grouped into
#different timeslots)
#Defining the timeslot variable as factor and ordering it's levels in a particuler order
Uber.Request$Request.TimeSlot<-factor(Uber.Request$Request.TimeSlot,levels=c("Morning Slot","Noon Slot","Evening Slot","Night Slot"))
#plotting a bargraph on time slots
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Request.TimeSlot))+xlab("TimeSlot")+geom_bar(col="black")+annotate("text",x=c("Evening Slot","Morning Slot","Night Slot","Noon Slot"),y=c(2590,2400,975,1150),label=c("37%","34%","13%","16%"))+theme_bw()
#the proportions can be obtained from the following code
prop.table(table(Uber.Request$Request.TimeSlot))
#From the above plot it is clear that most of the requests are raised in
#morning(34%) and evening(37%) slots
#segmenting the timeslot variable by pickup point may give some more information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Pickup.point))+xlab("Timeslot")+geom_bar(position = "dodge",col="black")+ geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#During morning slot city pickup requests are high
#and during evening slot airport pickup requests are high
#segmenting the timeslot variable by Status may give some information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Status))+xlab("Timeslot")+geom_bar(position = "dodge",col="black")+ geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#segmenting the timeslot variable by both Status and pickup point may give some more information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Status))+xlab("Timeslot")+geom_bar(col="black")+theme_bw()+facet_grid(Pickup.point~Status)+stat_count(aes(label = ..count..), geom = "text",vjust=-0.5,size=2)+theme(axis.text.x = element_text(face="bold", color = "#993333",size = 10,angle=90))+theme_bw()
#From the above plot it is clear that most city requests get cancelled in the
#morningslot(during which there is high city request rate) and for most of the
#airport requests during the evening slot(during which there is high airport request rate)
#there are no cars available
#From the above plots it can be assumed that although there is not much difference city requests and airport requests ,
#city requests are problametic requests because most of city requests gets cancelled by drivers.
#Most Uber drivers are not ready to drive to airport because they may have to wait long before they get a trip back to city
#This is the same reason for most airport requests cars are not available
###############SUPPLY AND DEMAND GAP CALCULATION#######################
demandd<-function(status){
if (status=="Cancelled"|status=="No Cars Available"|status=="Trip Completed"){
return("demand")
}else{
return(NA)
}
}
supply<-function(status){
if (status=="Cancelled"|status=="No Cars Available"){
return( "gap")
}else{
return("supply")
}
}
#creating supply column using supply function
Uber.Request$supply<-sapply(Uber.Request$Status,supply) %>% unlist
#checking for NA values in Uber.Request$supply
anyNA(Uber.Request$supply) #No NA's
#creating demand column using demand function
Uber.Request$demand<-sapply(Uber.Request$Status,demandd) %>% unlist
#checking for NA values in Uber.Request$demand
anyNA(Uber.Request$demand) #No NA's
#Finding supply and demand gap
demand<-sum
addmargins(table(Uber.Request$supply),FUN = demand)
#so overall gap is 2831,it would be better if we look at this values in propotions
addmargins(prop.table(table(Uber.Request$supply)),FUN = demand)
# gap is 58% of demand
#Demand And Supply plot
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+geom_bar(aes(x=demand),col="black",fill="royalblue2")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()+annotate("text",x="demand",y=6970,label="6745")
#Finding supply and demand gap for each time slot seperately
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN = demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#For morning slot the gap is about 59%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#For noon slot the gap is about 40%
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportion
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#For evening slot the gap is about 65%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportion
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Night Slot"])),FUN = demand)
#For night slot the gap is about 54%
#From the above calculations the gap for evening slot(4pm to 10pm) is high(65%)
#Demand And Supply plot for each time slot
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+ theme_bw()+facet_wrap(~Request.TimeSlot)+geom_text(stat='count',aes(label=..count..),vjust=1,position = position_dodge(width = 1))
#or
ggplot(Uber.Request,aes(x=supply,fill=supply))+theme_bw()+geom_bar(col="black")+geom_bar(aes(x=demand),col="black",fill="royalblue2")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+facet_grid(~Uber.Request$Request.TimeSlot)
#From the above graph the gap for evening slot(4pm to 10pm) is high
#Finding Gap for each pickup point seperately
#City
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"])),FUN=demand)
#gap for city pickup requests is 57%
#city pickup requests demand and supply gap on diffrent time slots
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#gap for city pickup requests on morning slots is 71%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#gap for city pickup requests on noon slots is 47 %
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#gap for city pickup requests on evening slots is 27%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Night Slot"])),FUN=demand)
#gap for city pickup requests on night slots is 55%
#From the above calculations the gap for city pickup requests is high on morning slots i.e 71%
#Airport
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"])),FUN=demand)
#gap for airport pickup requests is 59%
#Airport pickup requests demand and supply gap on diffrent time slots
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#gap for airport pickup requests on morning slots is 16%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#gap for airport pickup requests on noon slots is 30%
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#gap for airport pickup requests on evening slots is 77%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Night Slot"])),FUN=demand)
#gap for airport pickup requests on night slots is 54%
#From the above calculations the gap for Airport pickup requests is high on evening slots i.e 77%
#If we consider all time slots the gap for airport pickup request is high which is 59% than city pickup requests 54%
#Plot showing demand and supply gap for different pickup requests on various timeslots
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+theme_bw()+geom_bar(aes(x=demand),col="black",fill="royalblue3")+facet_grid(Pickup.point~Request.TimeSlot)+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))
#In my opinion the reason for the supply demand gap is mainly due to drivers not being ready to take airport trips from city pickups
#because of this there is a huge gap of demand and supply in the morning slot for city pickup requests.As drivers are not ready
#to take airport trips from city there will be shortage of vehicles at airport.Hence customers who want to book uber will get no cars available as indication
#so there is a high gap for airport pickup requests in the evening slot.
#Possible suggestions to fill the supply demand gap
#1) Increasing trip rates for airport pickups and drops.Which may make drivers interested to take up the trips with out cancelling .
#2)Making drivers work on shift basis,where one shift starts in the morning at city and the other start at the airport during evening and so on
#3)A fixed number of cars should be specially assigned for airport trips and they should accept only airport pickups and drops
#4)Keeping drivers updated with the flight schedule details on a reguler basis,will also help them plan their work productively
|
/Uber Assignment new.R
|
no_license
|
warthineha/DataScienceProjects
|
R
| false
| false
| 19,083
|
r
|
#### UBER ASSIGNMENT ####
#Loading the required packages
library(lubridate)
library(stringr)
library(ggplot2)
library(dplyr)
#Getting uber request data into R
Uber.Request<-read.csv("Uber Request Data.csv",stringsAsFactors = FALSE)
#Observing the structure of dataframe Uber.Requests
str(Uber.Request)
#or you can also have a glimpse of data frame using Uber.Requests
glimpse(Uber.Request)
####Handling data quality issues/Data Cleaning####
#checking for any duplicate rows
cat ('there are',(nrow(Uber.Request)-nrow(Uber.Request %>% unique)),'duplicate rows')
#there are 0 duplicate rows
# checking for any NA values in all columns, one after other
# checking for any NA values in column Request.id
anyNA(Uber.Request$Request.id) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Driver.id) # NA's present(2650)
# checking for any NA values in column
anyNA(Uber.Request$Pickup.point) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Status) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Request.timestamp) # no NA's
# checking for any NA values in column
anyNA(Uber.Request$Drop.timestamp) # NA's present(3914)
# It looks appropriate to leave the NA's untouched as they
# look valid in the columns Driver.id and Drop.timestamp the NA's
# in the respective columns are present when there is a value
# "no cars available" in column status
#checking for any spelling mistakes in categorical columns
#In column Pickup.point
unique(Uber.Request$Pickup.point)
# there are only two unique values "Airport" "City" in column Pickup.point.There are no spelling mistakes
#In column Status
unique(Uber.Request$Status)
#there are only three unique values Trip Completed,Cancelled,No Cars Available in column Status.There are no spelling mistakes
###########DATA PREPERATION####################
####Handling date and time columns Request.timestamp and Drop.timestamp which are read as type character####
##Request.timestamp##
#Parsing Request.timestamp and storing it in the column Request_Date
Uber.Request$Request_Date<- lubridate::parse_date_time(Uber.Request$Request.timestamp,orders = c("d/m/Y H:M","d-m-Y H-M-S"))
# checking if there are any NA's coerced because of invalid data values
(Uber.Request$Request.timestamp %>% is.na %>% sum) == (Uber.Request$Request_Date %>% is.na %>% sum)
#It gives TRUE means NA's are not coerced.This also means there are no invalid data values in Request.timestamp
#spliting date from Request_Date and storing it in column Request.Date
Uber.Request$Request.Date<- as.Date(Uber.Request$Request_Date)
#Extracting Day of the week from Request.Date and storing it in Request.Day column
Uber.Request$Request.Day<- weekdays(Uber.Request$Request_Date)
#spliting date form Request_Date and storing it in column Request.Time
Uber.Request$Request.Time<-format(Uber.Request$Request_Date,"%H:%M:%S")
#Extracting hours mins and sec from column Request.timestamp
Uber.Request$Request.hour<-lubridate::hour(Uber.Request$Request_Date)
Uber.Request$Request.minute<-lubridate::minute(Uber.Request$Request_Date)
Uber.Request$Request.second<-lubridate::second(Uber.Request$Request_Date)
#grouping the data into different time slots morning,noon,evening and night
#based on values in request.hour
#defineing a timeslot function
timeslot<-function(request.hour){
if(request.hour>=4 & request.hour<10){
return("Morning Slot")
}else if(request.hour>=10 & request.hour<16){
return("Noon Slot")
}else if(request.hour>=16 & request.hour<22){
return("Evening Slot")
}else if(request.hour>=22 | request.hour<4){
return("Night Slot")
}else{
return(NA)
}
}
#creating timeslot column using timeslot function
Uber.Request$Request.TimeSlot<-sapply(Uber.Request$Request.hour,timeslot) %>% unlist
#checking for any coerced NA values in column Request.TimeSlot
anyNA(Uber.Request$Request.TimeSlot)#FALSE no NA's
#Dropping request_date column
Uber.Request$Request.timestamp<-NULL
#####Drop.timestamp#####
#Parsing Drop.timestamp and storing it in the column Drop_Date
Uber.Request$Drop_Date<- parse_date_time(Uber.Request$Drop.timestamp,orders = c("d/m/Y H:M","d-m-Y H-M-S"))
# checking if there are any NA's coerced because of invalid data values
(Uber.Request$Drop.timestamp %>% is.na %>% sum) == (Uber.Request$Drop_Date%>% is.na %>% sum)
#It gives TRUE means NA's are not coerced. This also means there are no invalid data values in Drop.timestamp
#spliting date form Drop_Date and storing it in column Drop.Date
Uber.Request$Drop.Date<- as.Date(Uber.Request$Drop_Date)
#spliting date form Drop_Date and storing it in column Drop.Time
Uber.Request$Drop.Time<-format(Uber.Request$Drop_Date,"%T")
#Extracting hours mins and sec from column Drop.timestamp
Uber.Request$Drop.hour<-lubridate::hour(Uber.Request$Drop_Date)
Uber.Request$Drop.minute<-lubridate::minute(Uber.Request$Drop_Date)
Uber.Request$Drop.second<-lubridate::second(Uber.Request$Drop_Date)
#Dropping request_date column
Uber.Request$Drop.timestamp<-NULL
#checking for the time duration of data present
data.interval<-as.interval(min(Uber.Request$Request_Date),max(Uber.Request$Request_Date))
#checking the duration of the interval
as.duration(data.interval)
#Total 5 Days of data is present
######DATA ANALYSIS#####
# Since Request.id and Driver.id are id or unique values there isn't much sense analysing them
#Defining the Pickup.point variable as factor and ordering it's levels in a particuler order
Uber.Request$Pickup.point<-factor(Uber.Request$Pickup.point,levels=c("City","Airport"))
#Analysing variable Pickup.point by plotting a bar chart on it (and looking for some insights)
ggplot(Uber.Request,aes(Pickup.point,fill=Pickup.point))+geom_bar(col="black")+annotate("text",x=c("Airport","City"),y=c(3450,3700),label=c("48%","51%"))+theme_bw()
#The above plot shows that ,there isn't much difference between Airport and city pickup requests
#Analysing variable Status by plotting a bar chart on it (and looking for some insights)
ggplot(Uber.Request,aes(Status,fill=Status))+geom_bar(col="black")+annotate("text",x=c("Cancelled","No Cars Available","Trip Completed"),y=c(1400,2800,2950),label=c("19%","39%","41%"))+theme_bw()
#The above plot clearly depicts that only 41% of the requests from city and
#airport gets completed and the remaining 59% trips either get cancelled or
#there is no car availability
##########################################################################
#The proportions of above plot can be obtained from the following code
prop.table(table(Uber.Request$Pickup.point))
prop.table(table(Uber.Request$Status))
##########################################################################
#segemnting pickup.point over status
ggplot(Uber.Request,aes(x=Pickup.point,fill=Status))+geom_bar(position = "dodge",col="black")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#The above plot shows that for most of the Airport pickup requests there are
#no cars available and most requests that get cancelled are city pickup requests
#Analysing variable Request.hour by plotting a bar chart on it (and looking for
#some insights)
ggplot(Uber.Request,aes(Request.hour))+geom_bar(fill="royalblue1",col="black")+annotate("text",x=c(7.2,19),y=c(500,550),label=("High Request Rate"))+theme_bw()
#The above plot clearly depicts that there are high request rates from 5AM TO 10AM
#and 5pm to 10 pm
#To get a better understanding of requests raised at different hours of the day
#lets plot a chart on variable timeslot(which contains hours grouped into
#different timeslots)
#Defining the timeslot variable as factor and ordering it's levels in a particuler order
Uber.Request$Request.TimeSlot<-factor(Uber.Request$Request.TimeSlot,levels=c("Morning Slot","Noon Slot","Evening Slot","Night Slot"))
#plotting a bargraph on time slots
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Request.TimeSlot))+xlab("TimeSlot")+geom_bar(col="black")+annotate("text",x=c("Evening Slot","Morning Slot","Night Slot","Noon Slot"),y=c(2590,2400,975,1150),label=c("37%","34%","13%","16%"))+theme_bw()
#the proportions can be obtained from the following code
prop.table(table(Uber.Request$Request.TimeSlot))
#From the above plot it is clear that most of the requests are raised in
#morning(34%) and evening(37%) slots
#segmenting the timeslot variable by pickup point may give some more information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Pickup.point))+xlab("Timeslot")+geom_bar(position = "dodge",col="black")+ geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#During morning slot city pickup requests are high
#and during evening slot airport pickup requests are high
#segmenting the timeslot variable by Status may give some information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Status))+xlab("Timeslot")+geom_bar(position = "dodge",col="black")+ geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()
#segmenting the timeslot variable by both Status and pickup point may give some more information
ggplot(Uber.Request,aes(x=Request.TimeSlot,fill=Status))+xlab("Timeslot")+geom_bar(col="black")+theme_bw()+facet_grid(Pickup.point~Status)+stat_count(aes(label = ..count..), geom = "text",vjust=-0.5,size=2)+theme(axis.text.x = element_text(face="bold", color = "#993333",size = 10,angle=90))+theme_bw()
#From the above plot it is clear that most city requests get cancelled in the
#morningslot(during which there is high city request rate) and for most of the
#airport requests during the evening slot(during which there is high airport request rate)
#there are no cars available
#From the above plots it can be assumed that although there is not much difference city requests and airport requests ,
#city requests are problametic requests because most of city requests gets cancelled by drivers.
#Most Uber drivers are not ready to drive to airport because they may have to wait long before they get a trip back to city
#This is the same reason for most airport requests cars are not available
###############SUPPLY AND DEMAND GAP CALCULATION#######################
demandd<-function(status){
if (status=="Cancelled"|status=="No Cars Available"|status=="Trip Completed"){
return("demand")
}else{
return(NA)
}
}
supply<-function(status){
if (status=="Cancelled"|status=="No Cars Available"){
return( "gap")
}else{
return("supply")
}
}
#creating supply column using supply function
Uber.Request$supply<-sapply(Uber.Request$Status,supply) %>% unlist
#checking for NA values in Uber.Request$supply
anyNA(Uber.Request$supply) #No NA's
#creating demand column using demand function
Uber.Request$demand<-sapply(Uber.Request$Status,demandd) %>% unlist
#checking for NA values in Uber.Request$demand
anyNA(Uber.Request$demand) #No NA's
#Finding supply and demand gap
demand<-sum
addmargins(table(Uber.Request$supply),FUN = demand)
#so overall gap is 2831,it would be better if we look at this values in propotions
addmargins(prop.table(table(Uber.Request$supply)),FUN = demand)
# gap is 58% of demand
#Demand And Supply plot
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+geom_bar(aes(x=demand),col="black",fill="royalblue2")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+theme_bw()+annotate("text",x="demand",y=6970,label="6745")
#Finding supply and demand gap for each time slot seperately
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN = demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#For morning slot the gap is about 59%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#For noon slot the gap is about 40%
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportion
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#For evening slot the gap is about 65%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportion
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Request.TimeSlot=="Night Slot"])),FUN = demand)
#For night slot the gap is about 54%
#From the above calculations the gap for evening slot(4pm to 10pm) is high(65%)
#Demand And Supply plot for each time slot
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+ theme_bw()+facet_wrap(~Request.TimeSlot)+geom_text(stat='count',aes(label=..count..),vjust=1,position = position_dodge(width = 1))
#or
ggplot(Uber.Request,aes(x=supply,fill=supply))+theme_bw()+geom_bar(col="black")+geom_bar(aes(x=demand),col="black",fill="royalblue2")+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))+facet_grid(~Uber.Request$Request.TimeSlot)
#From the above graph the gap for evening slot(4pm to 10pm) is high
#Finding Gap for each pickup point seperately
#City
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"])),FUN=demand)
#gap for city pickup requests is 57%
#city pickup requests demand and supply gap on diffrent time slots
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#gap for city pickup requests on morning slots is 71%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#gap for city pickup requests on noon slots is 47 %
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#gap for city pickup requests on evening slots is 27%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="City"& Uber.Request$Request.TimeSlot=="Night Slot"])),FUN=demand)
#gap for city pickup requests on night slots is 55%
#From the above calculations the gap for city pickup requests is high on morning slots i.e 71%
#Airport
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"])),FUN=demand)
#gap for airport pickup requests is 59%
#Airport pickup requests demand and supply gap on diffrent time slots
#morning slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Morning Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Morning Slot"])),FUN=demand)
#gap for airport pickup requests on morning slots is 16%
#noon slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Noon Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Noon Slot"])),FUN=demand)
#gap for airport pickup requests on noon slots is 30%
#evening slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Evening Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Evening Slot"])),FUN=demand)
#gap for airport pickup requests on evening slots is 77%
#night slot
addmargins(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Night Slot"]),FUN=demand)
#proportions
addmargins(prop.table(table(Uber.Request$supply[Uber.Request$Pickup.point=="Airport"& Uber.Request$Request.TimeSlot=="Night Slot"])),FUN=demand)
#gap for airport pickup requests on night slots is 54%
#From the above calculations the gap for Airport pickup requests is high on evening slots i.e 77%
#If we consider all time slots the gap for airport pickup request is high which is 59% than city pickup requests 54%
#Plot showing demand and supply gap for different pickup requests on various timeslots
ggplot(Uber.Request,aes(x=supply,fill=supply))+geom_bar(col="black")+theme_bw()+geom_bar(aes(x=demand),col="black",fill="royalblue3")+facet_grid(Pickup.point~Request.TimeSlot)+geom_text(stat='count',aes(label=..count..),vjust=-1,position = position_dodge(width = 1))
#In my opinion the reason for the supply demand gap is mainly due to drivers not being ready to take airport trips from city pickups
#because of this there is a huge gap of demand and supply in the morning slot for city pickup requests.As drivers are not ready
#to take airport trips from city there will be shortage of vehicles at airport.Hence customers who want to book uber will get no cars available as indication
#so there is a high gap for airport pickup requests in the evening slot.
#Possible suggestions to fill the supply demand gap
#1) Increasing trip rates for airport pickups and drops.Which may make drivers interested to take up the trips with out cancelling .
#2)Making drivers work on shift basis,where one shift starts in the morning at city and the other start at the airport during evening and so on
#3)A fixed number of cars should be specially assigned for airport trips and they should accept only airport pickups and drops
#4)Keeping drivers updated with the flight schedule details on a reguler basis,will also help them plan their work productively
|
#The file gene_expression.tsv downloaded from the github repository.
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part1_files/gene_expression.tsv",
destfile = "try.tsv")
#Read the file with gene accession numbers as row numbers
a <- read.table("try.tsv", header = TRUE, row.names = 1)
#Displaying the values of the first six genes
head(a)
#Making a new column which contains the mean of other columns
a$Mean <- rowMeans(a)
#Displaying the values of the first six genes
head(a)
#Listing the 10 genes with highest mean expression
list <- a[order(-a$Mean),]
head(list,10)
#Number of genes with a mean>10
nrow( subset(a, a$Mean<10))
#histogram plot of the means
a$Mean <- as.matrix(a)
range(a$Mean)
hist(a$Mean)
hist(as.matrix(a$Mean),10, xlab = "Mean", breaks = 50, col = "blue", xlim = c(0,75000))
#download file
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part1_files/growth_data.csv",
destfile = "try2.csv")
y <- read.table("try2.csv", header = TRUE, sep = ",",stringsAsFactors = FALSE)
colnames(y)
#subset
y[1:50,]
northeast <- y[1:50, ]
#calculate mean and sd
mean(northeast$Circumf_2004_cm)
sd(northeast$Circumf_2004_cm)
mean(northeast$Circumf_2019_cm)
sd(northeast$Circumf_2019_cm)
#subset
y[51:100,]
southwest <- y[51:100, ]
#calculate mean and sd
mean(southwest$Circumf_2004_cm)
sd(southwest$Circumf_2004_cm)
mean(southwest$Circumf_2019_cm)
sd(southwest$Circumf_2019_cm)
#boxplot
boxplot(southwest$Circumf_2004_cm,southwest$Circumf_2019_cm,northeast$Circumf_2004_cm,northeast$Circumf_2019_cm,
names = c("SW2004","SW2019","NE2004","NE2019"),ylab="Cirumference (cm)",
main="Growth at Two Plantation Sites")
#mean growth for 10 years
GrowthSW <- (southwest$Circumf_2019_cm-southwest$Circumf_2009_cm)
GrowthNE <- (northeast$Circumf_2019_cm-northeast$Circumf_2009_cm)
mean(GrowthSW)
mean(GrowthNE)
head(y)
#t.test
res <- t.test(GrowthSW,GrowthNE, var.equal = FALSE)
res
#part 2
#libraries that are required
library("seqinr")
library("rBLAST")
library("R.utils")
#Download the E.coli CDS sequence from the Ensembl FTP page
download.file("ftp://ftp.ensemblgenomes.org/pub/bacteria/release-42/fasta/bacteria_0_collection/escherichia_coli_str_k_12_substr_mg1655/cds/Escherichia_coli_str_k_12_substr_mg1655.ASM584v2.cds.all.fa.gz",
destfile = "ecoli.fa.gz")
# uncompress the file
gunzip("ecoli.fa.gz")
# create the blast DB
makeblastdb("ecoli.fa",dbtype="nucl", "-parse_seqids")
#Download the sample file
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part2_files/sample.fa",
destfile = "sample.fa")
#Read the sample file into R
d <- read.fasta("sample.fa")
mygene <- d[[3]]
mygene
#Length of sequence in bp
str(mygene)
length(mygene)
#Proportion of GC content
seqinr::GC(mygene)
#function to create blast
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part2_files/mutblast_functions.R",
destfile = "mutblast.R")
source("mutblast.R")
#Blast search for E. coli genes that matches best
res <- myblastn_tab(myseq = mygene, db = "ecoli.fa")
#Blast results
res
View(res)
str(res)
head(res)
#making mutations to mygene
mygene_mutation <- mutator(mygene,20)
res <- myblastn_tab(myseq = mygene_mutation, db = "ecoli.fa")
res
#first need to write a blast index
write.fasta(mygene,names="mygene",file.out = "mygene.fa")
makeblastdb(file = "mygene.fa",dbtype = "nucl")
# test with 100 mismatches
mygene_mutation <- mutator(myseq=mygene,100)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 150 mismatches
mygene_mutation <- mutator(myseq=mygene,150)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 200 mismatches
mygene_mutation <- mutator(myseq=mygene,200)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 250 mismatches
mygene_mutation <- mutator(myseq=mygene,250)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 300 mismatches
mygene_mutation <- mutator(myseq=mygene,450)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
plot(res$bitscore)
|
/Assignment.R
|
no_license
|
nvdesilva/Assignment-Three
|
R
| false
| false
| 4,914
|
r
|
#The file gene_expression.tsv downloaded from the github repository.
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part1_files/gene_expression.tsv",
destfile = "try.tsv")
#Read the file with gene accession numbers as row numbers
a <- read.table("try.tsv", header = TRUE, row.names = 1)
#Displaying the values of the first six genes
head(a)
#Making a new column which contains the mean of other columns
a$Mean <- rowMeans(a)
#Displaying the values of the first six genes
head(a)
#Listing the 10 genes with highest mean expression
list <- a[order(-a$Mean),]
head(list,10)
#Number of genes with a mean>10
nrow( subset(a, a$Mean<10))
#histogram plot of the means
a$Mean <- as.matrix(a)
range(a$Mean)
hist(a$Mean)
hist(as.matrix(a$Mean),10, xlab = "Mean", breaks = 50, col = "blue", xlim = c(0,75000))
#download file
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part1_files/growth_data.csv",
destfile = "try2.csv")
y <- read.table("try2.csv", header = TRUE, sep = ",",stringsAsFactors = FALSE)
colnames(y)
#subset
y[1:50,]
northeast <- y[1:50, ]
#calculate mean and sd
mean(northeast$Circumf_2004_cm)
sd(northeast$Circumf_2004_cm)
mean(northeast$Circumf_2019_cm)
sd(northeast$Circumf_2019_cm)
#subset
y[51:100,]
southwest <- y[51:100, ]
#calculate mean and sd
mean(southwest$Circumf_2004_cm)
sd(southwest$Circumf_2004_cm)
mean(southwest$Circumf_2019_cm)
sd(southwest$Circumf_2019_cm)
#boxplot
boxplot(southwest$Circumf_2004_cm,southwest$Circumf_2019_cm,northeast$Circumf_2004_cm,northeast$Circumf_2019_cm,
names = c("SW2004","SW2019","NE2004","NE2019"),ylab="Cirumference (cm)",
main="Growth at Two Plantation Sites")
#mean growth for 10 years
GrowthSW <- (southwest$Circumf_2019_cm-southwest$Circumf_2009_cm)
GrowthNE <- (northeast$Circumf_2019_cm-northeast$Circumf_2009_cm)
mean(GrowthSW)
mean(GrowthNE)
head(y)
#t.test
res <- t.test(GrowthSW,GrowthNE, var.equal = FALSE)
res
#part 2
#libraries that are required
library("seqinr")
library("rBLAST")
library("R.utils")
#Download the E.coli CDS sequence from the Ensembl FTP page
download.file("ftp://ftp.ensemblgenomes.org/pub/bacteria/release-42/fasta/bacteria_0_collection/escherichia_coli_str_k_12_substr_mg1655/cds/Escherichia_coli_str_k_12_substr_mg1655.ASM584v2.cds.all.fa.gz",
destfile = "ecoli.fa.gz")
# uncompress the file
gunzip("ecoli.fa.gz")
# create the blast DB
makeblastdb("ecoli.fa",dbtype="nucl", "-parse_seqids")
#Download the sample file
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part2_files/sample.fa",
destfile = "sample.fa")
#Read the sample file into R
d <- read.fasta("sample.fa")
mygene <- d[[3]]
mygene
#Length of sequence in bp
str(mygene)
length(mygene)
#Proportion of GC content
seqinr::GC(mygene)
#function to create blast
download.file("https://raw.githubusercontent.com/markziemann/SLE712_files/master/bioinfo_asst3_part2_files/mutblast_functions.R",
destfile = "mutblast.R")
source("mutblast.R")
#Blast search for E. coli genes that matches best
res <- myblastn_tab(myseq = mygene, db = "ecoli.fa")
#Blast results
res
View(res)
str(res)
head(res)
#making mutations to mygene
mygene_mutation <- mutator(mygene,20)
res <- myblastn_tab(myseq = mygene_mutation, db = "ecoli.fa")
res
#first need to write a blast index
write.fasta(mygene,names="mygene",file.out = "mygene.fa")
makeblastdb(file = "mygene.fa",dbtype = "nucl")
# test with 100 mismatches
mygene_mutation <- mutator(myseq=mygene,100)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 150 mismatches
mygene_mutation <- mutator(myseq=mygene,150)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 200 mismatches
mygene_mutation <- mutator(myseq=mygene,200)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 250 mismatches
mygene_mutation <- mutator(myseq=mygene,250)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
#test with 300 mismatches
mygene_mutation <- mutator(myseq=mygene,450)
res <- myblastn_tab(myseq = mygene_mutation, db = "mygene.fa")
res
cointoss <- function(mygene_mutation) {
sample(c(0,1),1,replace = TRUE)
}
mean(replicate(100,cointoss(mygene_mutation)))
plot(res$bitscore)
|
par(mfrow=c(2,2), mar=c(4,4,2,1))
with(epc, {
plot(Global_active_power~dateTime, type="l",
ylab="Global Active Power (kilowatts)")
plot(Voltage~dateTime, type="l",
ylab="Voltage (volt)", xlab="")
plot(Sub_metering_1~dateTime, type="l",
ylab="Global Active Power (kilowatts)")
lines(Sub_metering_2~dateTime,col='Red')
lines(Sub_metering_3~dateTime,col='Blue')
legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2,
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
plot(Global_reactive_power~dateTime, type="l",
ylab="Global Rective Power (kilowatts)")
})
dev.copy(png, file="plot4.png", height=480, width=480)
dev.off()
|
/plot4.R
|
no_license
|
anupkumarsahu/ExData_Plotting1
|
R
| false
| false
| 700
|
r
|
par(mfrow=c(2,2), mar=c(4,4,2,1))
with(epc, {
plot(Global_active_power~dateTime, type="l",
ylab="Global Active Power (kilowatts)")
plot(Voltage~dateTime, type="l",
ylab="Voltage (volt)", xlab="")
plot(Sub_metering_1~dateTime, type="l",
ylab="Global Active Power (kilowatts)")
lines(Sub_metering_2~dateTime,col='Red')
lines(Sub_metering_3~dateTime,col='Blue')
legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2,
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
plot(Global_reactive_power~dateTime, type="l",
ylab="Global Rective Power (kilowatts)")
})
dev.copy(png, file="plot4.png", height=480, width=480)
dev.off()
|
# Unit tests
test_that("calckl calculates KL correctly", {
temp <- 25
sal <- 35
atemp <- 25
wspd <- 5
bp <- 1013
height <- 10
expected_KL <- 1.177
result <- round(calckl(temp, sal, atemp, wspd, bp, height), 3)
# Check if the result is equal to the expected value
expect_equal(result, expected_KL)
})
|
/tests/testthat/test-calckl.R
|
no_license
|
cran/SWMPr
|
R
| false
| false
| 341
|
r
|
# Unit tests
test_that("calckl calculates KL correctly", {
temp <- 25
sal <- 35
atemp <- 25
wspd <- 5
bp <- 1013
height <- 10
expected_KL <- 1.177
result <- round(calckl(temp, sal, atemp, wspd, bp, height), 3)
# Check if the result is equal to the expected value
expect_equal(result, expected_KL)
})
|
function(input, output, session) {
# Define a reactive expression for the document term matrix
terms <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing corpus...")
getTermMatrix(input$city)
})
})
})
terms2 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing Emotion")
getbanana(input$city)
})
})
})
terms3 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Calculating a score!")
getGrape(input$city)
})
})
})
terms4 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Show me a histogram")
getPickle(input$city)
})
})
})
output$plot1 <- renderPlot({
v <- terms()
wordcloud(names(v), v, scale=c(5,0.5),
min.freq = input$freq, max.words=input$max,
colors=brewer.pal(8, "Dark2"))
})
output$plot2 <- renderPlot({
b <- terms2()
comparison.cloud(b, random.order=FALSE,
colors = c("#00B2FF", "red", "#FF0099", "#6600CC", "green", "orange", "blue", "brown"),
title.size=2, max.words=input$max, scale=c(5, 0.5),rot.per=0.4)
})
output$plot3 <- renderText({
value = terms3()
value = round(value,digits = 2)
if (value > 1) {
print(paste0('Everyone is loving this weather! Score: ', value))
} else if (value < -1){
print(paste0('Weather potentially unsafe! Score: ', value))
} else if (value > 0 & value < .5){
print(paste0('People are not real happy about the weather. Score: ', value))
} else if (value < 0 & value > -1){
print(paste0('The weather is bad, be cautious. Score: ', value))
} else if (value > 0.5 & value < 1){
print(paste0('Nice weather today! Score: ', value))
}
else {
print("Something has happened to earth :(")
}
})
output$plot4 <- renderPlot({
df = terms4()
ggplot(df, aes(x=syuzhet_emotion.df)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", bins = 15)+
geom_density(alpha=.2, fill="#64c651")
})
}
|
/server.R
|
no_license
|
JeremyWhittier/MIS510
|
R
| false
| false
| 2,721
|
r
|
function(input, output, session) {
# Define a reactive expression for the document term matrix
terms <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing corpus...")
getTermMatrix(input$city)
})
})
})
terms2 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Processing Emotion")
getbanana(input$city)
})
})
})
terms3 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Calculating a score!")
getGrape(input$city)
})
})
})
terms4 <- reactive({
# Change when the "update" button is pressed...
input$update
# ...but not for anything else
isolate({
withProgress({
setProgress(message = "Show me a histogram")
getPickle(input$city)
})
})
})
output$plot1 <- renderPlot({
v <- terms()
wordcloud(names(v), v, scale=c(5,0.5),
min.freq = input$freq, max.words=input$max,
colors=brewer.pal(8, "Dark2"))
})
output$plot2 <- renderPlot({
b <- terms2()
comparison.cloud(b, random.order=FALSE,
colors = c("#00B2FF", "red", "#FF0099", "#6600CC", "green", "orange", "blue", "brown"),
title.size=2, max.words=input$max, scale=c(5, 0.5),rot.per=0.4)
})
output$plot3 <- renderText({
value = terms3()
value = round(value,digits = 2)
if (value > 1) {
print(paste0('Everyone is loving this weather! Score: ', value))
} else if (value < -1){
print(paste0('Weather potentially unsafe! Score: ', value))
} else if (value > 0 & value < .5){
print(paste0('People are not real happy about the weather. Score: ', value))
} else if (value < 0 & value > -1){
print(paste0('The weather is bad, be cautious. Score: ', value))
} else if (value > 0.5 & value < 1){
print(paste0('Nice weather today! Score: ', value))
}
else {
print("Something has happened to earth :(")
}
})
output$plot4 <- renderPlot({
df = terms4()
ggplot(df, aes(x=syuzhet_emotion.df)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", bins = 15)+
geom_density(alpha=.2, fill="#64c651")
})
}
|
source("../../../src/config")
library(cowplot)
load("tft-glasso.RData")
## Read files
theme_set(theme_cowplot(font_size=12)) # reduce default font size
plot.thyroid <- ggplot(plot.thyroid, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Thyroid")
plot.muscle <- ggplot(plot.muscle, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7)) +
xlab("Recall") + ylab("Precision")+ggtitle("Muscle - Skeletal")
plot.lung <- ggplot(plot.lung, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
theme(legend.title=element_blank())+
xlab("Recall") + ylab("Precision")+ggtitle("Lung")
plot.blood <- ggplot(plot.blood, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Whole Blood")
plot.sub <- ggplot(plot.sub, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Adipose - Subcutaneous")
legend <- get_legend(plot.lung +
theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5),
legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) +
guides(colour = guide_legend(override.aes = list(size= 1))))
fig2 <- plot_grid(plot.sub + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.thyroid + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.lung + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.muscle + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.blood + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
legend,
# align = 'vh',
labels = c("a", "b", "c", "d", "e", ""),
hjust = -1,
nrow = 3
)
pdf("supp_fig10.pdf", height = 7.2, width = 7.2)
print(fig2)
dev.off()
|
/publication_figures/supp_figs/suppfig9_10/fig_tft_glasso.R
|
no_license
|
MMesbahU/networks_correction
|
R
| false
| false
| 2,134
|
r
|
source("../../../src/config")
library(cowplot)
load("tft-glasso.RData")
## Read files
theme_set(theme_cowplot(font_size=12)) # reduce default font size
plot.thyroid <- ggplot(plot.thyroid, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Thyroid")
plot.muscle <- ggplot(plot.muscle, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7)) +
xlab("Recall") + ylab("Precision")+ggtitle("Muscle - Skeletal")
plot.lung <- ggplot(plot.lung, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
theme(legend.title=element_blank())+
xlab("Recall") + ylab("Precision")+ggtitle("Lung")
plot.blood <- ggplot(plot.blood, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Whole Blood")
plot.sub <- ggplot(plot.sub, aes(x = recall, y = precision, colour = type)) + geom_point(size = 0.3) +
# theme(text = element_text(size=7))+
xlab("Recall") + ylab("Precision")+ggtitle("Adipose - Subcutaneous")
legend <- get_legend(plot.lung +
theme(legend.key = element_rect(color = "black", linetype = "solid", size = 0.5),
legend.key.size = unit(0.3, "cm"), legend.key.height=unit(1.5,"line")) +
guides(colour = guide_legend(override.aes = list(size= 1))))
fig2 <- plot_grid(plot.sub + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.thyroid + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.lung + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.muscle + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
plot.blood + xlim(0,0.015) + ylim(0, 0.015) + theme(legend.position="none"),
legend,
# align = 'vh',
labels = c("a", "b", "c", "d", "e", ""),
hjust = -1,
nrow = 3
)
pdf("supp_fig10.pdf", height = 7.2, width = 7.2)
print(fig2)
dev.off()
|
\name{ESummary}
\alias{ESummary}
\title{New ESummary Object}
\usage{ESummary(NCBIObject)}
\description{This function creates an object of the ESummary class and returns it.}
\value{An object of the ESummary class.}
\seealso{\code{\link[=ESummaryClass-class]{ESummaryClass}}}
\author{Martin Schumann}
\arguments{\item{NCBIObject}{The current instance of the NCBIClass.}}
|
/RNCBI-0.9/R-Source/man/ESummary.Rd
|
no_license
|
MartinUndDerWolf/rncbi
|
R
| false
| false
| 370
|
rd
|
\name{ESummary}
\alias{ESummary}
\title{New ESummary Object}
\usage{ESummary(NCBIObject)}
\description{This function creates an object of the ESummary class and returns it.}
\value{An object of the ESummary class.}
\seealso{\code{\link[=ESummaryClass-class]{ESummaryClass}}}
\author{Martin Schumann}
\arguments{\item{NCBIObject}{The current instance of the NCBIClass.}}
|
#' Building a null hypothesis data
#'
#' Create a null sampling data (N times) and write them to a file
#'
#'
#' @param X The spectral dataset in the matrix format in which each row contains a single sample
#' @param groupLabel Group label of samples in the dataset
#' @param N The number of iteration for creating null sample distribution
#' @param verbose A boolean value to allow print out process information
#'
#' @return A matrix with N rows containing the null distribution.
#'
#'
#' @author Trung Nghia Vu
#'
#' @examples
#' res=makeSimulatedData();
#' X=res$data;
#' groupLabel=res$label;
#' peakList <- detectSpecPeaks(X,
#' nDivRange = c(128),
#' scales = seq(1, 16, 2),
#' baselineThresh = 50000,
#' SNR.Th = -1,
#' verbose=FALSE
#' );
#' resFindRef<- findRef(peakList);
#' refInd <- resFindRef$refInd;
#' maxShift = 50;
#' Y <- dohCluster(X,
#' peakList = peakList,
#' refInd = refInd,
#' maxShift = maxShift,
#' acceptLostPeak = TRUE, verbose=FALSE);
#' # find the BW-statistic
#' BW = BWR(Y, groupLabel);
#' H0 = createNullSampling(Y, groupLabel, N = 100,verbose=FALSE)
#'
#' @export
#'
#'
createNullSampling <-function(X, groupLabel, N=100,
verbose=TRUE){
groupNum=length(levels(groupLabel));
samplePool=X;
groupMean=list();
for (i in 1:groupNum){
groupLabeli=which(groupLabel==levels(groupLabel)[i]);
Xi=X[groupLabeli,]
mi=colMeans(Xi);
groupMean[[i]]=mi;
}
for (i in 1:nrow(samplePool)){
samplePool[i,]=
X[i,]-groupMean[[which(levels(groupLabel)==groupLabel[i])]];
}
L=nrow(X);
H0=matrix(data=0,nrow=N,ncol=ncol(X));
for(i in 1 : N){
if (verbose) cat("\n Permutation th",i);
index=sample(L);
H0[i,]=BWR(samplePool[index,],groupLabel);
}
return(H0);
}
|
/R/createNullSampling.R
|
permissive
|
fenglb/speaq
|
R
| false
| false
| 2,073
|
r
|
#' Building a null hypothesis data
#'
#' Create a null sampling data (N times) and write them to a file
#'
#'
#' @param X The spectral dataset in the matrix format in which each row contains a single sample
#' @param groupLabel Group label of samples in the dataset
#' @param N The number of iteration for creating null sample distribution
#' @param verbose A boolean value to allow print out process information
#'
#' @return A matrix with N rows containing the null distribution.
#'
#'
#' @author Trung Nghia Vu
#'
#' @examples
#' res=makeSimulatedData();
#' X=res$data;
#' groupLabel=res$label;
#' peakList <- detectSpecPeaks(X,
#' nDivRange = c(128),
#' scales = seq(1, 16, 2),
#' baselineThresh = 50000,
#' SNR.Th = -1,
#' verbose=FALSE
#' );
#' resFindRef<- findRef(peakList);
#' refInd <- resFindRef$refInd;
#' maxShift = 50;
#' Y <- dohCluster(X,
#' peakList = peakList,
#' refInd = refInd,
#' maxShift = maxShift,
#' acceptLostPeak = TRUE, verbose=FALSE);
#' # find the BW-statistic
#' BW = BWR(Y, groupLabel);
#' H0 = createNullSampling(Y, groupLabel, N = 100,verbose=FALSE)
#'
#' @export
#'
#'
createNullSampling <-function(X, groupLabel, N=100,
verbose=TRUE){
groupNum=length(levels(groupLabel));
samplePool=X;
groupMean=list();
for (i in 1:groupNum){
groupLabeli=which(groupLabel==levels(groupLabel)[i]);
Xi=X[groupLabeli,]
mi=colMeans(Xi);
groupMean[[i]]=mi;
}
for (i in 1:nrow(samplePool)){
samplePool[i,]=
X[i,]-groupMean[[which(levels(groupLabel)==groupLabel[i])]];
}
L=nrow(X);
H0=matrix(data=0,nrow=N,ncol=ncol(X));
for(i in 1 : N){
if (verbose) cat("\n Permutation th",i);
index=sample(L);
H0[i,]=BWR(samplePool[index,],groupLabel);
}
return(H0);
}
|
Read.wRef <- function(ibase, iref, MissingValue) {
# read-in data from base_data_file and ref_data_file, put MissingValue as NA set several global variables as output: Nt -- total numbers of seasonal factors,
# say, monthly=12, daily=365 Icy -- catelog of seasonal factors, say, monthly 1:12 Y0 -- data vector of base - ref, take common period for both non-missing IY0
# -- cor-responding seasonal vector of Y0 IY0flg -- flg(integer, 0 or 1) for IY0, 1 -- continouse, 0 -- not continouse for autocorrelation calculation bdata --
# matrix of non-missing base data, 4 columns, yyyy,mm,dd,data ori.bdata -- original base data matrix, also 4 columns, same as bdata
# ErrorMSG<-NA assign('ErrorMSG',ErrorMSG,envir=.GlobalEnv)
# if(!file.exists(ibase)) { ErrorMSG<<-paste('Input basefile',ibase,'does not exist!', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) } if(!file.exists(iref))
# { ErrorMSG<<-paste('Input ref file',iref,'does not exist!', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) } if(is.csv(ibase)){
# itmp<-try(read.table(ibase,sep=',',header=F,na.strings=MissingValue, colClasses='real'),silent=T) if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage()
# return(-1) } else itable<-itmp } else{ itmp<-try(read.table(ibase,sep='',header=F,na.strings=MissingValue, colClasses='real'),silent=T)
# if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage() return(-1) } else itable<-itmp } if(is.csv(iref)){
# itmp<-try(read.table(iref,sep=',',header=F,na.strings=MissingValue, colClasses='real'),silent=T) if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage()
# return(-1) } else rtable<-itmp } else{ itmp<-try(read.table(iref,sep='',header=F,na.strings=MissingValue, colClasses='real'),silent=T)
# if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage() return(-1) } else rtable<-itmp } check input data (both base and ref), if column!=4, return error
# if(dim(itable)[2]!=4){ ErrorMSG<-paste('Input base data column number error', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) }
# colnames(itable)<-c('id1','id2','id3','data')
itable <- as.data.frame(ibase)
itable[which(itable[, 4] == MissingValue), 4] <- NA
names(itable) <- c("id1", "id2", "id3", "data")
# if(dim(rtable)[2]!=4){ ErrorMSG<-paste('Input reference data column number error', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) }
# colnames(rtable)<-c('id1','id2','id3','data')
rtable <- as.data.frame(iref)
# rtable[rtable==MissingValue,4]<-NA
names(rtable) <- c("id1", "id2", "id3", "data")
# keep input base data as ori.itable
ori.itable <- itable
owflg <- is.na(ori.itable[, 4]) == F & ((itable[, 2] * 100 + itable[, 3]) != 229)
# get rid of Feb 29th data
itable <- itable[!(itable[, 2] == 2 & itable[, 3] == 29), ]
rtable <- rtable[!(rtable[, 2] == 2 & rtable[, 3] == 29), ]
# check input data (both base and ref), no jump with begin and end
Icy <- sort(unique(itable[, 2] * 100 + itable[, 3]))
Nt <- length(Icy)
# construct YYYYMMDD for base series
imdbegin <- itable[1, 2] * 100 + itable[1, 3] # begin MMDD for base series
iyrbegin <- itable[1, 1] # begin year for base series
Nx1 <- dim(itable)[1]
imdend <- itable[Nx1, 2] * 100 + itable[Nx1, 3] # end MMDD for base series
iyrend <- itable[Nx1, 1] # end year for base series
Ind1 <- iyrbegin * 10000 + Icy[Icy >= imdbegin] # first year
if (iyrend > (iyrbegin + 1))
for (i in (iyrbegin + 1):(iyrend - 1)) Ind1 <- c(Ind1, i * 10000 + Icy)
Ind1 <- c(Ind1, iyrend * 10000 + Icy[Icy <= imdend])
YMD.base <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
for (i in 1:length(Ind1)) {
if (Ind1[i] != YMD.base[i] | is.na(YMD.base[i])) {
InsertMessagesTxt(main.txt.out, paste("input base series not continuous at:", Ind1[i], YMD.base[i]), format = TRUE)
return(-1)
# stop(paste('input base series not continuous at:',Ind1[i],YMD.base[i]))
}
}
# construct YYYYMMDD for ref series
imdbegin <- rtable[1, 2] * 100 + rtable[1, 3] # begin MMDD for ref series
iyrbegin <- rtable[1, 1] # begin year for base series
Nx2 <- dim(rtable)[1]
imdend <- rtable[Nx2, 2] * 100 + rtable[Nx2, 3] # end MMDD for ref series
iyrend <- rtable[Nx2, 1] # end year for ref series
Ind2 <- iyrbegin * 10000 + Icy[Icy >= imdbegin] # first year
if (iyrend > (iyrbegin + 1))
for (i in (iyrbegin + 1):(iyrend - 1)) Ind2 <- c(Ind2, i * 10000 + Icy)
Ind2 <- c(Ind2, iyrend * 10000 + Icy[Icy <= imdend])
YMD.ref <- rtable[, 1] * 10000 + rtable[, 2] * 100 + rtable[, 3]
for (i in 1:length(Ind2)) if (Ind2[i] != YMD.ref[i] | is.na(YMD.ref[i])) {
InsertMessagesTxt(main.txt.out, paste("input ref series not continuous at:", Ind2[i], YMD.base[i]), format = TRUE)
# cat(paste('input ref series not continuous at:',Ind2[i],YMD.base[i]),'\n') ErrorMSG<-paste('input ref series not continuous at:',Ind2[i],YMD.base[i],
# '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
# take non-missing data only
icol.nmbase <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
itable <- itable[is.na(itable[, 4]) == F, ]
# itable.nm<-itable
itable.nmb <- merge(itable, rtable, by.x = c(1:3), by.y = c(1:3), all.x = T, all.y = F, sort = F)
colnames(itable.nmb) <- c(colnames(itable), "data.ref")
rtable <- rtable[is.na(rtable[, 4]) == F, ]
Nx1 <- dim(itable)[1]
Nx2 <- dim(rtable)[1]
icol.base <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
icol.ref <- rtable[, 1] * 10000 + rtable[, 2] * 100 + rtable[, 3]
ind.base <- cbind(icol.base, seq(1, Nx1))
ind.ref <- cbind(icol.ref, seq(1, Nx2))
ind.base <- ind.base[is.na(itable[, 4]) == F, ]
ind.ref <- ind.ref[is.na(rtable[, 4]) == F, ]
colnames(ind.base) <- c("IY0", "ind")
colnames(ind.ref) <- c("IY0", "ind")
cind <- merge(ind.base, ind.ref, by.x = "IY0", by.y = "IY0", suffixes = c(".base", ".ref"))
IY0 <- cind[, "IY0"]
IY0flg <- rep(0, length(IY0))
# construct flag vector for autocor calculation
Iyr <- floor(IY0/10000)
Imd <- IY0 - Iyr * 10000
Ti <- IY0
for (i in 1:length(IY0)) {
ith <- match(Imd[i], Icy)
Ti[i] <- (Iyr[i] - iyrbegin) * Nt + ith
}
IyrB <- floor(icol.base/10000)
ImdB <- icol.base - IyrB * 10000
TiB <- rep(0, Nx1)
for (i in 1:Nx1) {
ith <- match(ImdB[i], Icy)
TiB[i] <- (IyrB[i] - iyrbegin) * Nt + ith
}
for (i in 1:(length(IY0) - 1)) {
if (Ti[i + 1] - Ti[i] == 1)
IY0flg[i] <- 1
}
IYBflg <- rep(0, length(TiB))
for (i in 1:(length(TiB) - 1)) if (TiB[i + 1] - TiB[i] == 1)
IYBflg[i] <- 1
ind.base <- cind[, "ind.base"]
ind.ref <- cind[, "ind.ref"]
# check data qualification
itmp <- cbind(itable[, 2] * 100 + itable[, 3], rep(NA, dim(itable)[1]))
itmp[ind.base, 2] <- itable[ind.base, 4]
idenind <- unique(itmp[, 1])
for (i in 1:Nt) {
if (sum(is.na(itmp[itmp[, 1] == Icy[i]]) == F) < 10) {
InsertMessagesTxt(main.txt.out, paste("input data too few at:", Icy[i]), format = TRUE)
# cat(paste('input data too few at:',Icy[i]),'\n')
# ErrorMSG<<-paste('input data too few at:',Icy[i], '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
}
itmp1 <- 0
for (i in 1:(dim(itmp)[1] - 1)) if (is.na(itmp[i, 2]) == F & is.na(itmp[(i + 1), 2]) == F)
itmp1 <- itmp1 + 1
if (itmp1 < 10) {
InsertMessagesTxt(main.txt.out, "input data too few for autocorrelation calculating", format = TRUE)
# cat(paste('input data too few for autocorrelation calculating','\n'))
# ErrorMSG<<-paste('input data too few for autocorrelation calculating', '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
# finish checking
Y0 <- itable[ind.base, 4] - rtable[ind.ref, 4]
rtmp <- itable[ind.base, ]
otmp <- rmCycle(rtmp)
EBb <- otmp$EB
rtmp <- rtable[ind.ref, ]
otmp <- rmCycle(rtmp)
EBr <- otmp$EB
itmp <- itable[ind.base, 2] * 100 + itable[ind.base, 3]
for (i in 1:length(Y0)) {
indd <- itmp[i] # mmdd for Y0[i]
indf <- NULL
for (j in 1:Nt) if (Icy[j] == indd)
indf <- j
Y0[i] <- Y0[i] + EBr[indf] - EBb[indf]
}
assign("Ti", Ti, envir = .GlobalEnv) # Time index for LS fitting
assign("TiB", TiB, envir = .GlobalEnv)
assign("Y0", Y0, envir = .GlobalEnv) # Data series for Base-Ref
assign("IY0", IY0, envir = .GlobalEnv) # Cycle index for Base-Ref
assign("IY0flg", IY0flg, envir = .GlobalEnv) # continuous flag for Base-Ref
assign("IYBflg", IYBflg, envir = .GlobalEnv) # continuous flag for Base-Ref
assign("bdata", itable.nmb, envir = .GlobalEnv) # non-missing table for base data
assign("ori.bdata", ori.itable, envir = .GlobalEnv) # original base data
assign("owflg", owflg, envir = .GlobalEnv)
assign("Icy", Icy, envir = .GlobalEnv) # Cycle index
assign("Nt", Nt, envir = .GlobalEnv) # Cycle length
}
|
/RHtestsV4/Read.wRef.R
|
no_license
|
rijaf/CDT
|
R
| false
| false
| 9,296
|
r
|
Read.wRef <- function(ibase, iref, MissingValue) {
# read-in data from base_data_file and ref_data_file, put MissingValue as NA set several global variables as output: Nt -- total numbers of seasonal factors,
# say, monthly=12, daily=365 Icy -- catelog of seasonal factors, say, monthly 1:12 Y0 -- data vector of base - ref, take common period for both non-missing IY0
# -- cor-responding seasonal vector of Y0 IY0flg -- flg(integer, 0 or 1) for IY0, 1 -- continouse, 0 -- not continouse for autocorrelation calculation bdata --
# matrix of non-missing base data, 4 columns, yyyy,mm,dd,data ori.bdata -- original base data matrix, also 4 columns, same as bdata
# ErrorMSG<-NA assign('ErrorMSG',ErrorMSG,envir=.GlobalEnv)
# if(!file.exists(ibase)) { ErrorMSG<<-paste('Input basefile',ibase,'does not exist!', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) } if(!file.exists(iref))
# { ErrorMSG<<-paste('Input ref file',iref,'does not exist!', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) } if(is.csv(ibase)){
# itmp<-try(read.table(ibase,sep=',',header=F,na.strings=MissingValue, colClasses='real'),silent=T) if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage()
# return(-1) } else itable<-itmp } else{ itmp<-try(read.table(ibase,sep='',header=F,na.strings=MissingValue, colClasses='real'),silent=T)
# if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage() return(-1) } else itable<-itmp } if(is.csv(iref)){
# itmp<-try(read.table(iref,sep=',',header=F,na.strings=MissingValue, colClasses='real'),silent=T) if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage()
# return(-1) } else rtable<-itmp } else{ itmp<-try(read.table(iref,sep='',header=F,na.strings=MissingValue, colClasses='real'),silent=T)
# if(inherits(itmp,'try-error')){ ErrorMSG<<-geterrmessage() return(-1) } else rtable<-itmp } check input data (both base and ref), if column!=4, return error
# if(dim(itable)[2]!=4){ ErrorMSG<-paste('Input base data column number error', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) }
# colnames(itable)<-c('id1','id2','id3','data')
itable <- as.data.frame(ibase)
itable[which(itable[, 4] == MissingValue), 4] <- NA
names(itable) <- c("id1", "id2", "id3", "data")
# if(dim(rtable)[2]!=4){ ErrorMSG<-paste('Input reference data column number error', get('ErrorMSG',env=.GlobalEnv),'\n') return(-1) }
# colnames(rtable)<-c('id1','id2','id3','data')
rtable <- as.data.frame(iref)
# rtable[rtable==MissingValue,4]<-NA
names(rtable) <- c("id1", "id2", "id3", "data")
# keep input base data as ori.itable
ori.itable <- itable
owflg <- is.na(ori.itable[, 4]) == F & ((itable[, 2] * 100 + itable[, 3]) != 229)
# get rid of Feb 29th data
itable <- itable[!(itable[, 2] == 2 & itable[, 3] == 29), ]
rtable <- rtable[!(rtable[, 2] == 2 & rtable[, 3] == 29), ]
# check input data (both base and ref), no jump with begin and end
Icy <- sort(unique(itable[, 2] * 100 + itable[, 3]))
Nt <- length(Icy)
# construct YYYYMMDD for base series
imdbegin <- itable[1, 2] * 100 + itable[1, 3] # begin MMDD for base series
iyrbegin <- itable[1, 1] # begin year for base series
Nx1 <- dim(itable)[1]
imdend <- itable[Nx1, 2] * 100 + itable[Nx1, 3] # end MMDD for base series
iyrend <- itable[Nx1, 1] # end year for base series
Ind1 <- iyrbegin * 10000 + Icy[Icy >= imdbegin] # first year
if (iyrend > (iyrbegin + 1))
for (i in (iyrbegin + 1):(iyrend - 1)) Ind1 <- c(Ind1, i * 10000 + Icy)
Ind1 <- c(Ind1, iyrend * 10000 + Icy[Icy <= imdend])
YMD.base <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
for (i in 1:length(Ind1)) {
if (Ind1[i] != YMD.base[i] | is.na(YMD.base[i])) {
InsertMessagesTxt(main.txt.out, paste("input base series not continuous at:", Ind1[i], YMD.base[i]), format = TRUE)
return(-1)
# stop(paste('input base series not continuous at:',Ind1[i],YMD.base[i]))
}
}
# construct YYYYMMDD for ref series
imdbegin <- rtable[1, 2] * 100 + rtable[1, 3] # begin MMDD for ref series
iyrbegin <- rtable[1, 1] # begin year for base series
Nx2 <- dim(rtable)[1]
imdend <- rtable[Nx2, 2] * 100 + rtable[Nx2, 3] # end MMDD for ref series
iyrend <- rtable[Nx2, 1] # end year for ref series
Ind2 <- iyrbegin * 10000 + Icy[Icy >= imdbegin] # first year
if (iyrend > (iyrbegin + 1))
for (i in (iyrbegin + 1):(iyrend - 1)) Ind2 <- c(Ind2, i * 10000 + Icy)
Ind2 <- c(Ind2, iyrend * 10000 + Icy[Icy <= imdend])
YMD.ref <- rtable[, 1] * 10000 + rtable[, 2] * 100 + rtable[, 3]
for (i in 1:length(Ind2)) if (Ind2[i] != YMD.ref[i] | is.na(YMD.ref[i])) {
InsertMessagesTxt(main.txt.out, paste("input ref series not continuous at:", Ind2[i], YMD.base[i]), format = TRUE)
# cat(paste('input ref series not continuous at:',Ind2[i],YMD.base[i]),'\n') ErrorMSG<-paste('input ref series not continuous at:',Ind2[i],YMD.base[i],
# '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
# take non-missing data only
icol.nmbase <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
itable <- itable[is.na(itable[, 4]) == F, ]
# itable.nm<-itable
itable.nmb <- merge(itable, rtable, by.x = c(1:3), by.y = c(1:3), all.x = T, all.y = F, sort = F)
colnames(itable.nmb) <- c(colnames(itable), "data.ref")
rtable <- rtable[is.na(rtable[, 4]) == F, ]
Nx1 <- dim(itable)[1]
Nx2 <- dim(rtable)[1]
icol.base <- itable[, 1] * 10000 + itable[, 2] * 100 + itable[, 3]
icol.ref <- rtable[, 1] * 10000 + rtable[, 2] * 100 + rtable[, 3]
ind.base <- cbind(icol.base, seq(1, Nx1))
ind.ref <- cbind(icol.ref, seq(1, Nx2))
ind.base <- ind.base[is.na(itable[, 4]) == F, ]
ind.ref <- ind.ref[is.na(rtable[, 4]) == F, ]
colnames(ind.base) <- c("IY0", "ind")
colnames(ind.ref) <- c("IY0", "ind")
cind <- merge(ind.base, ind.ref, by.x = "IY0", by.y = "IY0", suffixes = c(".base", ".ref"))
IY0 <- cind[, "IY0"]
IY0flg <- rep(0, length(IY0))
# construct flag vector for autocor calculation
Iyr <- floor(IY0/10000)
Imd <- IY0 - Iyr * 10000
Ti <- IY0
for (i in 1:length(IY0)) {
ith <- match(Imd[i], Icy)
Ti[i] <- (Iyr[i] - iyrbegin) * Nt + ith
}
IyrB <- floor(icol.base/10000)
ImdB <- icol.base - IyrB * 10000
TiB <- rep(0, Nx1)
for (i in 1:Nx1) {
ith <- match(ImdB[i], Icy)
TiB[i] <- (IyrB[i] - iyrbegin) * Nt + ith
}
for (i in 1:(length(IY0) - 1)) {
if (Ti[i + 1] - Ti[i] == 1)
IY0flg[i] <- 1
}
IYBflg <- rep(0, length(TiB))
for (i in 1:(length(TiB) - 1)) if (TiB[i + 1] - TiB[i] == 1)
IYBflg[i] <- 1
ind.base <- cind[, "ind.base"]
ind.ref <- cind[, "ind.ref"]
# check data qualification
itmp <- cbind(itable[, 2] * 100 + itable[, 3], rep(NA, dim(itable)[1]))
itmp[ind.base, 2] <- itable[ind.base, 4]
idenind <- unique(itmp[, 1])
for (i in 1:Nt) {
if (sum(is.na(itmp[itmp[, 1] == Icy[i]]) == F) < 10) {
InsertMessagesTxt(main.txt.out, paste("input data too few at:", Icy[i]), format = TRUE)
# cat(paste('input data too few at:',Icy[i]),'\n')
# ErrorMSG<<-paste('input data too few at:',Icy[i], '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
}
itmp1 <- 0
for (i in 1:(dim(itmp)[1] - 1)) if (is.na(itmp[i, 2]) == F & is.na(itmp[(i + 1), 2]) == F)
itmp1 <- itmp1 + 1
if (itmp1 < 10) {
InsertMessagesTxt(main.txt.out, "input data too few for autocorrelation calculating", format = TRUE)
# cat(paste('input data too few for autocorrelation calculating','\n'))
# ErrorMSG<<-paste('input data too few for autocorrelation calculating', '\n',get('ErrorMSG',env=.GlobalEnv))
return(-1)
}
# finish checking
Y0 <- itable[ind.base, 4] - rtable[ind.ref, 4]
rtmp <- itable[ind.base, ]
otmp <- rmCycle(rtmp)
EBb <- otmp$EB
rtmp <- rtable[ind.ref, ]
otmp <- rmCycle(rtmp)
EBr <- otmp$EB
itmp <- itable[ind.base, 2] * 100 + itable[ind.base, 3]
for (i in 1:length(Y0)) {
indd <- itmp[i] # mmdd for Y0[i]
indf <- NULL
for (j in 1:Nt) if (Icy[j] == indd)
indf <- j
Y0[i] <- Y0[i] + EBr[indf] - EBb[indf]
}
assign("Ti", Ti, envir = .GlobalEnv) # Time index for LS fitting
assign("TiB", TiB, envir = .GlobalEnv)
assign("Y0", Y0, envir = .GlobalEnv) # Data series for Base-Ref
assign("IY0", IY0, envir = .GlobalEnv) # Cycle index for Base-Ref
assign("IY0flg", IY0flg, envir = .GlobalEnv) # continuous flag for Base-Ref
assign("IYBflg", IYBflg, envir = .GlobalEnv) # continuous flag for Base-Ref
assign("bdata", itable.nmb, envir = .GlobalEnv) # non-missing table for base data
assign("ori.bdata", ori.itable, envir = .GlobalEnv) # original base data
assign("owflg", owflg, envir = .GlobalEnv)
assign("Icy", Icy, envir = .GlobalEnv) # Cycle index
assign("Nt", Nt, envir = .GlobalEnv) # Cycle length
}
|
samp.default <- function(x, summaries=FALSE, ...) {
if (inherits(x, "Mefa")) return(x@xtab)
if (inherits(x, "mefa")) {
if (is.null(x$samp))
return(NULL) else if (summaries)
return(as.data.frame(x, fun=mss, ...)) else return(x$samp)
}
stop("not mefa class")
}
|
/mefa/R/samp.default.R
|
no_license
|
ingted/R-Examples
|
R
| false
| false
| 322
|
r
|
samp.default <- function(x, summaries=FALSE, ...) {
if (inherits(x, "Mefa")) return(x@xtab)
if (inherits(x, "mefa")) {
if (is.null(x$samp))
return(NULL) else if (summaries)
return(as.data.frame(x, fun=mss, ...)) else return(x$samp)
}
stop("not mefa class")
}
|
#' Drop a node attribute column
#' @description Within a graph's internal NDF, remove
#' an existing node attribute.
#' @param graph a graph object of class
#' @param node_attr the name of the node attribute
#' column to drop.
#' @return a graph object of class
#' \code{dgr_graph}.
#' @examples
#' # Create a random graph
#' graph <-
#' create_random_graph(
#' 5, 10, set_seed = 3)
#'
#' # Get the graph's internal ndf to show which
#' # node attributes are available
#' get_node_df(graph)
#' #> nodes type label value
#' #> 1 1 1 2
#' #> 2 2 2 8.5
#' #> 3 3 3 4
#' #> 4 4 4 3.5
#' #> 5 5 5 6.5
#'
#' # Drop the `value` node attribute
#' graph <-
#' graph %>%
#' drop_node_attrs("value")
#'
#' # Get the graph's internal ndf to show that the
#' # node attribute had been removed
#' get_node_df(graph)
#' #> nodes type label
#' #> 1 1 1
#' #> 2 2 2
#' #> 3 3 3
#' #> 4 4 4
#' #> 5 5 5
#' @export drop_node_attrs
drop_node_attrs <- function(graph,
node_attr) {
# Stop function if length of `node_attr` is
# greater than one
if (length(node_attr) > 1) {
stop("You can only provide a single column.")
}
# Stop function if `node_attr` is any of
# `nodes`, `node`, `type`, or `label`
if (any(c("nodes", "node", "type", "label") %in%
node_attr)) {
stop("You cannot drop this column.")
}
# Get the number of nodes ever created for
# this graph
nodes_created <- graph$last_node
# Extract the graph's ndf
nodes <- get_node_df(graph)
# Get column names from the graph's ndf
column_names_graph <- colnames(nodes)
# Stop function if `node_attr` is not one
# of the graph's column
if (!any(column_names_graph %in% node_attr)) {
stop("The node attribute to drop is not in the ndf.")
}
# Get the column number for the node attr to drop
col_num_drop <-
which(colnames(nodes) %in% node_attr)
# Remove the column
nodes <- nodes[, -col_num_drop]
# Create a new graph object
dgr_graph <-
create_graph(
nodes_df = nodes,
edges_df = graph$edges_df,
graph_attrs = graph$graph_attrs,
node_attrs = graph$node_attrs,
edge_attrs = graph$edge_attrs,
directed = graph$directed,
graph_name = graph$graph_name,
graph_time = graph$graph_time,
graph_tz = graph$graph_tz)
# Update the `last_node` counter
dgr_graph$last_node <- nodes_created
return(dgr_graph)
}
|
/R/drop_node_attrs.R
|
no_license
|
timelyportfolio/DiagrammeR
|
R
| false
| false
| 2,566
|
r
|
#' Drop a node attribute column
#' @description Within a graph's internal NDF, remove
#' an existing node attribute.
#' @param graph a graph object of class
#' @param node_attr the name of the node attribute
#' column to drop.
#' @return a graph object of class
#' \code{dgr_graph}.
#' @examples
#' # Create a random graph
#' graph <-
#' create_random_graph(
#' 5, 10, set_seed = 3)
#'
#' # Get the graph's internal ndf to show which
#' # node attributes are available
#' get_node_df(graph)
#' #> nodes type label value
#' #> 1 1 1 2
#' #> 2 2 2 8.5
#' #> 3 3 3 4
#' #> 4 4 4 3.5
#' #> 5 5 5 6.5
#'
#' # Drop the `value` node attribute
#' graph <-
#' graph %>%
#' drop_node_attrs("value")
#'
#' # Get the graph's internal ndf to show that the
#' # node attribute had been removed
#' get_node_df(graph)
#' #> nodes type label
#' #> 1 1 1
#' #> 2 2 2
#' #> 3 3 3
#' #> 4 4 4
#' #> 5 5 5
#' @export drop_node_attrs
drop_node_attrs <- function(graph,
node_attr) {
# Stop function if length of `node_attr` is
# greater than one
if (length(node_attr) > 1) {
stop("You can only provide a single column.")
}
# Stop function if `node_attr` is any of
# `nodes`, `node`, `type`, or `label`
if (any(c("nodes", "node", "type", "label") %in%
node_attr)) {
stop("You cannot drop this column.")
}
# Get the number of nodes ever created for
# this graph
nodes_created <- graph$last_node
# Extract the graph's ndf
nodes <- get_node_df(graph)
# Get column names from the graph's ndf
column_names_graph <- colnames(nodes)
# Stop function if `node_attr` is not one
# of the graph's column
if (!any(column_names_graph %in% node_attr)) {
stop("The node attribute to drop is not in the ndf.")
}
# Get the column number for the node attr to drop
col_num_drop <-
which(colnames(nodes) %in% node_attr)
# Remove the column
nodes <- nodes[, -col_num_drop]
# Create a new graph object
dgr_graph <-
create_graph(
nodes_df = nodes,
edges_df = graph$edges_df,
graph_attrs = graph$graph_attrs,
node_attrs = graph$node_attrs,
edge_attrs = graph$edge_attrs,
directed = graph$directed,
graph_name = graph$graph_name,
graph_time = graph$graph_time,
graph_tz = graph$graph_tz)
# Update the `last_node` counter
dgr_graph$last_node <- nodes_created
return(dgr_graph)
}
|
#' Raw mass spectrum proteomics log abundance for 4 pairs of technical replicates.
#' @format A data frame of 85 rows and 8 columns with missing peaks' abundance as NA.
#'
'replicates'
|
/R/replicates.R
|
no_license
|
cran/GMSimpute
|
R
| false
| false
| 192
|
r
|
#' Raw mass spectrum proteomics log abundance for 4 pairs of technical replicates.
#' @format A data frame of 85 rows and 8 columns with missing peaks' abundance as NA.
#'
'replicates'
|
#
# RUnit tests TTR moving averages
#
# test reclass works and throws error
# test xtsAttributes, both CLASS and USER
# test all.equal(CLASS) and !all.equal(CLASS) cases
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
input <- list( all=ttrc[1:250,], top=ttrc[1:250,], mid=ttrc[1:250,] )
input$top[1:10,] <- NA
input$mid[9:20,] <- NA
# Load output data
load('unitTests/output.overlays.rda')
#################################################
# Bollinger Bands
test.BBands <- function() {
ia <- input$all[,c('High','Low','Close')]
it <- input$top[,c('High','Low','Close')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- NULL
oa <- BBands(ia)
ot <- BBands(it)
rownames(oa) <- rownames(ot) <- rownames(input$all)
checkEqualsNumeric( oa, output$allBBands )
checkEquals( attributes(oa), attributes(output$allBBands) )
checkEqualsNumeric( ot, output$topBBands )
checkEquals( attributes(ot), attributes(output$topBBands) )
checkException( BBands(im) )
}
# SAR
test.SAR <- function() {
ia <- input$all[,c('High','Low')]
it <- input$top[,c('High','Low')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
checkEqualsNumeric( SAR(ia), output$allSAR )
checkEquals( attributes(SAR(ia)), attributes(output$allSAR) )
checkEqualsNumeric( SAR(it), output$topSAR )
checkEquals( attributes(SAR(it)), attributes(output$topSAR) )
checkException( SAR(im) )
}
# Zig Zag
test.ZigZag <- function() {
ia <- input$all[,c('High','Low')]
it <- input$top[,c('High','Low')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
checkEqualsNumeric( ZigZag(ia), output$allZZ )
checkEquals( attributes(ZigZag(ia)), attributes(output$allZZ) )
checkEqualsNumeric( ZigZag(it), output$topZZ )
checkEquals( attributes(ZigZag(it)), attributes(output$topZZ) )
checkException( ZigZag(im) )
}
|
/TTR/tests/unitTests/runit.TTR.Overlays.R
|
no_license
|
codeview2/codeview
|
R
| false
| false
| 1,937
|
r
|
#
# RUnit tests TTR moving averages
#
# test reclass works and throws error
# test xtsAttributes, both CLASS and USER
# test all.equal(CLASS) and !all.equal(CLASS) cases
# Create input data
data(ttrc)
rownames(ttrc) <- ttrc$Date
ttrc$Date <- NULL
input <- list( all=ttrc[1:250,], top=ttrc[1:250,], mid=ttrc[1:250,] )
input$top[1:10,] <- NA
input$mid[9:20,] <- NA
# Load output data
load('unitTests/output.overlays.rda')
#################################################
# Bollinger Bands
test.BBands <- function() {
ia <- input$all[,c('High','Low','Close')]
it <- input$top[,c('High','Low','Close')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- NULL
oa <- BBands(ia)
ot <- BBands(it)
rownames(oa) <- rownames(ot) <- rownames(input$all)
checkEqualsNumeric( oa, output$allBBands )
checkEquals( attributes(oa), attributes(output$allBBands) )
checkEqualsNumeric( ot, output$topBBands )
checkEquals( attributes(ot), attributes(output$topBBands) )
checkException( BBands(im) )
}
# SAR
test.SAR <- function() {
ia <- input$all[,c('High','Low')]
it <- input$top[,c('High','Low')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
checkEqualsNumeric( SAR(ia), output$allSAR )
checkEquals( attributes(SAR(ia)), attributes(output$allSAR) )
checkEqualsNumeric( SAR(it), output$topSAR )
checkEquals( attributes(SAR(it)), attributes(output$topSAR) )
checkException( SAR(im) )
}
# Zig Zag
test.ZigZag <- function() {
ia <- input$all[,c('High','Low')]
it <- input$top[,c('High','Low')]
im <- input$mid[,c('High','Low')]
rownames(ia) <- rownames(it) <- rownames(im) <- NULL
checkEqualsNumeric( ZigZag(ia), output$allZZ )
checkEquals( attributes(ZigZag(ia)), attributes(output$allZZ) )
checkEqualsNumeric( ZigZag(it), output$topZZ )
checkEquals( attributes(ZigZag(it)), attributes(output$topZZ) )
checkException( ZigZag(im) )
}
|
# plot number of strokes per character vs year of learning the kanji
# from list on wikipedia
# https://en.wikipedia.org/wiki/Ky%C5%8Diku_kanji
library(ggplot2)
kanjidata_file = "~/git/misc-analyses/language_difficulty/data/kanji_by_school_year_1-6.txt"
kanjidata = read.table(kanjidata_file, header=TRUE, sep="\t", encoding="UTF-8", stringsAsFactors=FALSE)
strokejitter=jitter(kanjidata$Strokes, factor=0.5)
gradejitter=jitter(kanjidata$grade)
replace_numbers = c(116, 120, 237,
436, 241,
582, 608, 613, 629, 635, 636, 640,
769, 774, 792, 793, 824,
946, 963, 964, 989, 1003 )
replace_xjitters = c(1.9, 2.1, 2.0,
3.0, 3.0,
3.9, 3.8, 4.1, 3.9, 4.1, 4.0, 4.2,
4.8, 5.0, 5.0, 5.0, 5.2,
5.8, 5.9, 6.0, 6.1, 6.2 )
gradejitter_fixed = replace(gradejitter, replace_numbers, replace_xjitters)
has_many_strokes = kanjidata$Strokes >= 18
kanjidata[has_many_strokes,2]
p = ggplot(data=kanjidata , aes(x = grade, y = Strokes, group=grade) ) +
theme(text = element_text(family="Japan1"),
axis.text.y=element_text(size=16),
axis.text.x=element_text(size=13),
axis.title=element_text(size=18)) +
labs(x="Grade", y="Strokes",
title="Number of strokes per character for Kyouiku Kanji",
subtitle="for primary school") +
scale_x_continuous(breaks=1:6) +
geom_boxplot( outlier.size = 5, outlier.shape = NA) +
#geom_jitter( width=0.25, height=0.2, color="#086a33", size=5, alpha=0.5)
geom_point(data=kanjidata, aes(x = gradejitter_fixed, y=strokejitter), size=5, alpha=0.5, color="#fe9929") +
geom_text(data=kanjidata, aes( x = gradejitter_fixed, y=strokejitter, label=ifelse(Strokes >= 18 , as.character(Kanji), '' ) ) , vjust=0.5, size=5 )
p
pdf("~/git/misc-analyses/language_difficulty/images/kanji_by_school_year_w_outliers.pdf", height=7, width=8, family="Japan1" )
print(p)
dev.off()
#ggsave("~/git/misc-analyses/language_difficulty/images/kanji_by_school_year_w_outliers.pdf", p, device="pdf", encoding="default", height=7, width=8, fonts="Japan1")
#
|
/language_difficulty/kanji_difficulty.R
|
no_license
|
wrf/misc-analyses
|
R
| false
| false
| 2,171
|
r
|
# plot number of strokes per character vs year of learning the kanji
# from list on wikipedia
# https://en.wikipedia.org/wiki/Ky%C5%8Diku_kanji
library(ggplot2)
kanjidata_file = "~/git/misc-analyses/language_difficulty/data/kanji_by_school_year_1-6.txt"
kanjidata = read.table(kanjidata_file, header=TRUE, sep="\t", encoding="UTF-8", stringsAsFactors=FALSE)
strokejitter=jitter(kanjidata$Strokes, factor=0.5)
gradejitter=jitter(kanjidata$grade)
replace_numbers = c(116, 120, 237,
436, 241,
582, 608, 613, 629, 635, 636, 640,
769, 774, 792, 793, 824,
946, 963, 964, 989, 1003 )
replace_xjitters = c(1.9, 2.1, 2.0,
3.0, 3.0,
3.9, 3.8, 4.1, 3.9, 4.1, 4.0, 4.2,
4.8, 5.0, 5.0, 5.0, 5.2,
5.8, 5.9, 6.0, 6.1, 6.2 )
gradejitter_fixed = replace(gradejitter, replace_numbers, replace_xjitters)
has_many_strokes = kanjidata$Strokes >= 18
kanjidata[has_many_strokes,2]
p = ggplot(data=kanjidata , aes(x = grade, y = Strokes, group=grade) ) +
theme(text = element_text(family="Japan1"),
axis.text.y=element_text(size=16),
axis.text.x=element_text(size=13),
axis.title=element_text(size=18)) +
labs(x="Grade", y="Strokes",
title="Number of strokes per character for Kyouiku Kanji",
subtitle="for primary school") +
scale_x_continuous(breaks=1:6) +
geom_boxplot( outlier.size = 5, outlier.shape = NA) +
#geom_jitter( width=0.25, height=0.2, color="#086a33", size=5, alpha=0.5)
geom_point(data=kanjidata, aes(x = gradejitter_fixed, y=strokejitter), size=5, alpha=0.5, color="#fe9929") +
geom_text(data=kanjidata, aes( x = gradejitter_fixed, y=strokejitter, label=ifelse(Strokes >= 18 , as.character(Kanji), '' ) ) , vjust=0.5, size=5 )
p
pdf("~/git/misc-analyses/language_difficulty/images/kanji_by_school_year_w_outliers.pdf", height=7, width=8, family="Japan1" )
print(p)
dev.off()
#ggsave("~/git/misc-analyses/language_difficulty/images/kanji_by_school_year_w_outliers.pdf", p, device="pdf", encoding="default", height=7, width=8, fonts="Japan1")
#
|
library(glmnet)
mydata = read.table("./TrainingSet/ReliefF/stomach.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.25,family="gaussian",standardize=FALSE)
sink('./Model/EN/ReliefF/stomach/stomach_040.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
/Model/EN/ReliefF/stomach/stomach_040.R
|
no_license
|
leon1003/QSMART
|
R
| false
| false
| 356
|
r
|
library(glmnet)
mydata = read.table("./TrainingSet/ReliefF/stomach.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.25,family="gaussian",standardize=FALSE)
sink('./Model/EN/ReliefF/stomach/stomach_040.txt',append=TRUE)
print(glm$glmnet.fit)
sink()
|
## The functions herein cache the inverse of a matrix.
## Matrix inversion is usually a costly computation.
## Caching matrix inversions stores the data produced by the
## matrix inversion so that we can refer to that data repeatedly without
## having to compute the inversion over and over again.
makeCacheMatrix <- function(x = matrix(), ...) {
## This function creates a special "matrix" object that can
## cache its inverse:
m <- NULL ## This initializes "m", the variable within which the function caches
set <- function(y) { ## This enables user to reset m as NULL, as well as x.
x <<- y
m <<- NULL
}
get <- function() x ## This allows $get() to take on the value of argument x.
setinverse <- function(solve) m <<- solve ## This allows the inverse to be reset.
getinverse <- function() m ## This receives the inverse matrix from cacheSolve.
list(set = set, get = get, ## This coerces the functions above to list form.
setinverse = setinverse,
getinverse = getinverse)
}
cacheSolve <- function(x, ...) {
## This function then computes the inverse of the special matrix
## which was created by the makeCacheMatrix function above. If the inverse
## has already been calculated (and the matrix has not changed), then
## the function below will just retrieve the data from the cache.
## Ultimately, it returns a matrix that is the inverse of 'x'.
m <- x$getinverse() ## This makes the local "m" become getinverse()
if(!is.null(m)) { ## If the local m is not NULL, then global m is returned.
message("getting cached data...")
return(m)
}
data <- x$get() ## Else, the original matrix from makeCacheMatrix is stored in "data".
m <- solve(data, ...) ## And "data" is used to store the inverse value into local m
x$setinverse(m) ## This sets global m as the local m here
m ## This returns local m
}
|
/cachematrix.R
|
no_license
|
ericchoi53/ProgrammingAssignment2
|
R
| false
| false
| 2,057
|
r
|
## The functions herein cache the inverse of a matrix.
## Matrix inversion is usually a costly computation.
## Caching matrix inversions stores the data produced by the
## matrix inversion so that we can refer to that data repeatedly without
## having to compute the inversion over and over again.
makeCacheMatrix <- function(x = matrix(), ...) {
## This function creates a special "matrix" object that can
## cache its inverse:
m <- NULL ## This initializes "m", the variable within which the function caches
set <- function(y) { ## This enables user to reset m as NULL, as well as x.
x <<- y
m <<- NULL
}
get <- function() x ## This allows $get() to take on the value of argument x.
setinverse <- function(solve) m <<- solve ## This allows the inverse to be reset.
getinverse <- function() m ## This receives the inverse matrix from cacheSolve.
list(set = set, get = get, ## This coerces the functions above to list form.
setinverse = setinverse,
getinverse = getinverse)
}
cacheSolve <- function(x, ...) {
## This function then computes the inverse of the special matrix
## which was created by the makeCacheMatrix function above. If the inverse
## has already been calculated (and the matrix has not changed), then
## the function below will just retrieve the data from the cache.
## Ultimately, it returns a matrix that is the inverse of 'x'.
m <- x$getinverse() ## This makes the local "m" become getinverse()
if(!is.null(m)) { ## If the local m is not NULL, then global m is returned.
message("getting cached data...")
return(m)
}
data <- x$get() ## Else, the original matrix from makeCacheMatrix is stored in "data".
m <- solve(data, ...) ## And "data" is used to store the inverse value into local m
x$setinverse(m) ## This sets global m as the local m here
m ## This returns local m
}
|
library(fitdistrplus)
# (1) fit of two distributions by maximum likelihood estimation
# to the serving size data
# and comparison of goodness-of-fit statistics
#
data(groundbeef)
serving <- groundbeef$serving
(fitg <- fitdist(serving, "gamma"))
gg <- gofstat(fitg)
(fitln <- fitdist(serving, "lnorm"))
gn <- gofstat(fitln)
gofstat(list(fitg, fitln))
# (2) fit of two discrete distributions to toxocara data
# and comparison of goodness-of-fit statistics
#
data(toxocara)
number <- toxocara$number
fitp <- fitdist(number, "pois")
summary(fitp)
plot(fitp)
gp <- gofstat(fitp)
gp
fitnb <- fitdist(number, "nbinom")
summary(fitnb)
plot(fitnb)
gnb <- gofstat(fitnb)
gnb
gofstat(list(fitp, fitnb))
attributes(gofstat(list(fitp, fitnb)))
# (3) Use of Chi-squared results in addition to
# recommended statistics for continuous distributions
#
set.seed(1234)
x4 <- rweibull(n=10,shape=2,scale=1)
# fit of the good distribution
f4 <- fitdist(x4, "weibull")
g4 <- gofstat(f4, meancount=10)
print(g4)
# fit of a bad distribution
f4b <- fitdist(x4, "cauchy")
g4b <- gofstat(f4b, meancount=10)
print(g4b)
# (4) estimation of the standard deviation of a normal distribution
# by maximum likelihood with the mean fixed at 10 using the argument fix.arg
#
f1b <- fitdist(serving, "norm", start=list(sd=5), fix.arg=list(mean=10), lower=0)
gofstat(f1b)
|
/tests/t-gofstat.R
|
no_license
|
frontierkodiak/fitdistrplusExperimental
|
R
| false
| false
| 1,357
|
r
|
library(fitdistrplus)
# (1) fit of two distributions by maximum likelihood estimation
# to the serving size data
# and comparison of goodness-of-fit statistics
#
data(groundbeef)
serving <- groundbeef$serving
(fitg <- fitdist(serving, "gamma"))
gg <- gofstat(fitg)
(fitln <- fitdist(serving, "lnorm"))
gn <- gofstat(fitln)
gofstat(list(fitg, fitln))
# (2) fit of two discrete distributions to toxocara data
# and comparison of goodness-of-fit statistics
#
data(toxocara)
number <- toxocara$number
fitp <- fitdist(number, "pois")
summary(fitp)
plot(fitp)
gp <- gofstat(fitp)
gp
fitnb <- fitdist(number, "nbinom")
summary(fitnb)
plot(fitnb)
gnb <- gofstat(fitnb)
gnb
gofstat(list(fitp, fitnb))
attributes(gofstat(list(fitp, fitnb)))
# (3) Use of Chi-squared results in addition to
# recommended statistics for continuous distributions
#
set.seed(1234)
x4 <- rweibull(n=10,shape=2,scale=1)
# fit of the good distribution
f4 <- fitdist(x4, "weibull")
g4 <- gofstat(f4, meancount=10)
print(g4)
# fit of a bad distribution
f4b <- fitdist(x4, "cauchy")
g4b <- gofstat(f4b, meancount=10)
print(g4b)
# (4) estimation of the standard deviation of a normal distribution
# by maximum likelihood with the mean fixed at 10 using the argument fix.arg
#
f1b <- fitdist(serving, "norm", start=list(sd=5), fix.arg=list(mean=10), lower=0)
gofstat(f1b)
|
## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
#this is defineed to set a value to the matrix and c
#inverse from the cache
i <- NULL
set <- function(y) {
x <<- y # setting the value
i <<- NULL # clear the cache
}
get <- function() x
# Define function to set the inverse. This is only used by getinverse() when
# there is no cached inverse
setInverse <- function(inverse) i <<- inverse
getInverse <- function() i
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## Write a short comment describing this function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i <- x$getInverse()
if (!is.null(i)) {
message("getting cached data")
return(i)
}
mat <- x$get()
i <- solve(mat, ...)
x$setInverse(i)
i
}
|
/cachematrix.R
|
no_license
|
kgpavan/ProgrammingAssignment2
|
R
| false
| false
| 984
|
r
|
## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
#this is defineed to set a value to the matrix and c
#inverse from the cache
i <- NULL
set <- function(y) {
x <<- y # setting the value
i <<- NULL # clear the cache
}
get <- function() x
# Define function to set the inverse. This is only used by getinverse() when
# there is no cached inverse
setInverse <- function(inverse) i <<- inverse
getInverse <- function() i
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## Write a short comment describing this function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i <- x$getInverse()
if (!is.null(i)) {
message("getting cached data")
return(i)
}
mat <- x$get()
i <- solve(mat, ...)
x$setInverse(i)
i
}
|
#' @title durumWC
#' @description 200 sites from durum wheat collection and their world clim data.
#' @docType data
#' @usage data(durumWC)
#' @format The data includes the site unique identifier, longitude, latitude and 55 worldclim data \href{https://www.worldclim.org}{worldclim}
#'
#' @examples
#' if(interactive()){
#' # Load durum wheat data with world climatic variables obtained from WorldClim database
#' data(durumWC)
#' }
"durumWC"
#' @title durumDaily
#' @description 200 sites from durum wheat collection and their daily climatic data.
#' @docType data
#' @usage data(durumDaily)
#' @format The data includes the site unique identifier and daily data for 4 climatic variables (tmin, tmax, precipitation and relative humidity)
#'
#' @examples
#' if(interactive()){
#' # Load durum wheat data with their daily climatic variables obtained from ICARDA database
#' data(durumDaily)
#' }
"durumDaily"
#' @title septoriaDurumWC
#' @description A sample data including daily data for 4 climatic variables (tmin, tmax, precipitation and relative humidity) and evaluation for Septoria Tritici
#' @docType data
#' @usage data(septoriaDurumWC)
#' @format 200 sites from durum wheat collection and their daily climatic data and evaluation for Septoria Tritici.
#'
#' @examples
#' if(interactive()){
#' #Load durum wheat data with septoria scores and climatic variables obtained from WorldClim database
#' data(septoriaDurumWC)
#' }
"septoriaDurumWC"
#' @title FIGS subset for wheat sodicity resistance
#'
#' @description FIGS subset for wheat sodicity resistance
#' constructed using the harmonized world soil database HWSD
#'
#' @docType data
#'
#' @usage data(FIGS)
#'
#' @format A data frame with 201 rows and 15 variables
#'
#'
#' @references
#' \href{http://www.fao.org/3/aq361e/aq361e.pdf}{HWSD}
#'
#'
#' @examples
#' if(interactive()){
#' data(FIGS)
#' }
"FIGS"
|
/R/data.R
|
no_license
|
khadijaaziz/icardaFIGSr
|
R
| false
| false
| 1,887
|
r
|
#' @title durumWC
#' @description 200 sites from durum wheat collection and their world clim data.
#' @docType data
#' @usage data(durumWC)
#' @format The data includes the site unique identifier, longitude, latitude and 55 worldclim data \href{https://www.worldclim.org}{worldclim}
#'
#' @examples
#' if(interactive()){
#' # Load durum wheat data with world climatic variables obtained from WorldClim database
#' data(durumWC)
#' }
"durumWC"
#' @title durumDaily
#' @description 200 sites from durum wheat collection and their daily climatic data.
#' @docType data
#' @usage data(durumDaily)
#' @format The data includes the site unique identifier and daily data for 4 climatic variables (tmin, tmax, precipitation and relative humidity)
#'
#' @examples
#' if(interactive()){
#' # Load durum wheat data with their daily climatic variables obtained from ICARDA database
#' data(durumDaily)
#' }
"durumDaily"
#' @title septoriaDurumWC
#' @description A sample data including daily data for 4 climatic variables (tmin, tmax, precipitation and relative humidity) and evaluation for Septoria Tritici
#' @docType data
#' @usage data(septoriaDurumWC)
#' @format 200 sites from durum wheat collection and their daily climatic data and evaluation for Septoria Tritici.
#'
#' @examples
#' if(interactive()){
#' #Load durum wheat data with septoria scores and climatic variables obtained from WorldClim database
#' data(septoriaDurumWC)
#' }
"septoriaDurumWC"
#' @title FIGS subset for wheat sodicity resistance
#'
#' @description FIGS subset for wheat sodicity resistance
#' constructed using the harmonized world soil database HWSD
#'
#' @docType data
#'
#' @usage data(FIGS)
#'
#' @format A data frame with 201 rows and 15 variables
#'
#'
#' @references
#' \href{http://www.fao.org/3/aq361e/aq361e.pdf}{HWSD}
#'
#'
#' @examples
#' if(interactive()){
#' data(FIGS)
#' }
"FIGS"
|
###########################################################################/**
# @RdocClass Arguments
#
# @title "Static class to validate and process arguments"
#
# \description{
# @classhierarchy
# }
#
# \section{Fields and Methods}{
# @allmethods
# }
#
# @author
#
# @keyword programming
#*/###########################################################################
setConstructorS3("Arguments", function(...) {
extend(Object(), "Arguments");
})
#########################################################################/**
# @RdocMethod getFilename
#
# @title "Gets and validates a filename"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{filename}{A @character string.}
# \item{nchar}{An @integer @vector of length two specifying the range
# of valid filename lengths.}
# \item{class}{A @character string specifying the class of valid
# filenames.}
# \item{.name}{The name of the argument validated.}
# \item{.type}{Not used.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string if filename is valid,
# otherwise an exception is thrown.
# }
#
# \section{Missing values}{
# If \code{filename} is a missing value, then an exception is thrown.
# }
#
# \details{
# When argument \code{class="safe"}, the following 86 ASCII characters
# are allowed in filenames:
# \preformatted{
# #$%&'()+,-.0123456789;= (24 including initial space)
# @ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_ (31)
# `abcdefghijklmnopqrstuvwxyz{|}~ (31)
# }
# This class of filenames has been extensively tested on for
# cross-platform support on Microsoft Windows, OSX and various
# Unix flavors.
# }
#
# \references{
# [1] Microsoft, \emph{Naming Files, Paths, and Namespaces} (Section 'Windows Naming Conventions'), 2012. \url{http://msdn.microsoft.com/en-us/library/aa365247.aspx#naming_conventions}.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#*/#########################################################################
setMethodS3("getFilename", "Arguments", function(static, filename, nchar=c(1,128), class=c("safe"), .name=NULL, .type="filename", ...) {
##
## OLD NOTES:
## Valid filename characters:
## * The FTP RFCs require (7-bit) ASCII characters (and presumably not control
## characters either). The 95 printable ASCII characters are (note initial
## space):
##
## !"#$%&'()*+,-./0123456789:;<=>? (32)
## @ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_ (32)
## `abcdefghijklmnopqrstuvwxyz{|}~ (31)
##
## * On Windows the following 9 characters aren't allowed: \ / : * ? " < > !.
## This leaves us with:
##
## #$%&'()+,-.0123456789;= (24)
## @ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_ (31)
## `abcdefghijklmnopqrstuvwxyz{|}~ (31)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument '.name':
if (is.null(.name)) {
.name <- as.character(deparse(substitute(filename)));
}
# Argument 'filename':
if (is.na(filename)) {
throw("Argument 'filename' cannot be a missing value: ", filename)
}
filename <- getCharacter(static, filename, nchar=nchar, .name=.name);
# Argument 'class':
class <- match.arg(class);
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Filter out valid characters
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
chars <- filename;
# Always valid characters
chars <- gsub("[abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0-9_.,]", "", chars);
chars <- gsub("[-]", "", chars);
chars <- gsub("[+]", "", chars);
# Filter out according to classes.
if ("safe" %in% class) {
chars <- gsub("[ ]", "", chars);
chars <- gsub("[\\[\\]]", "", chars);
chars <- gsub("[#$%&'()`{|}~]", "", chars);
chars <- gsub("[=]", "", chars);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Check for remaining (=invalid) characters
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (nchar(chars, type="chars") > 0L) {
chars <- unlist(strsplit(chars, split=""));
chars <- sort(unique(chars));
chars <- sprintf("'%s'", chars);
chars <- paste(chars, collapse=", ");
throw(sprintf("Not a valid %s. Argument '%s' contains non-valid %s characters (%s): %s", .type, .name, .type, chars, filename));
}
filename;
}, static=TRUE, private=TRUE)
#########################################################################/**
# @RdocMethod getReadablePathname
#
# @title "Gets a readable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{file}{A @character string specifying the file.}
# \item{path}{A @character string specifying the path.}
# \item{mustExist}{If @TRUE, the pathname must exists and be readable,
# otherwise an exception is thrown. If @FALSE, no such test is
# performed.}
# \item{absolutePath}{If @TRUE, the absolute pathname is returned.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string of the absolute pathname of the file.
# }
#
# \section{Missing values}{
# If \code{file} or \code{path} is @NA and \code{mustExist} is @FALSE,
# then (character) @NA is returned, otherwise an exception is thrown.
# }
#
# \section{Windows}{
# If a too long pathname is detected on Windows, an informative warning
# is given.
# The maximum number of symbols in a Windows pathname is 256, including
# file separators '/' or '\', but excluding the drive letter, and initial
# file separator (e.g. 'C:/'), and the string terminator ('\\0'), cf.
# 'MSDN - Naming a File or Directory', Microsoft. In R, the limit is
# one symbol less, i.e. 255.
# }
#
# @author
#
# \seealso{
# @seemethod "getWritablePathname"
# @see "R.utils::filePath".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getReadablePathname", "Arguments", function(static, file=NULL, path=NULL, mustExist=TRUE, absolutePath=FALSE, adjust=c("none", "url"), ...) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'file':
if (!is.null(file)) {
if (inherits(file, "connection")) {
throw("In this context, argument 'file' cannot be a connection.");
}
file <- getCharacter(static, file, length=c(1,1));
}
# Ignore 'path'?
if (isAbsolutePath(file)) path <- NULL
# Argument 'path':
if (!is.null(path)) {
path <- getCharacter(static, path, length=c(1,1));
}
if (is.null(file) && is.null(path)) {
throw("Both argument 'file' and 'path' are NULL.");
}
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'absolutePath':
absolutePath <- getLogical(static, absolutePath);
# Argument 'adjust':
adjust <- match.arg(adjust);
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Process arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (mustExist) {
if (!is.null(file) && is.na(file)) {
throw("No such file/directory because argument 'file' is NA.");
}
if (!is.null(path) && is.na(path)) {
throw("No such file/directory because argument 'path' is NA.");
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Make sure <path>/<file> is properly split up
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (is.null(path)) {
pathname <- file;
} else if (is.null(file)) {
pathname <- path;
} else {
pathname <- file.path(path, file);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Windows: The maximum number of symbols in a Windows pathname is 256,
# in R it's 255. For more details, see:
# https://msdn.microsoft.com/en-us/library/aa365247(VS.85).aspx
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (.Platform$OS.type == "windows") {
if (!is.na(pathname) && nchar(pathname, type="chars") > 255L) {
msg <- sprintf("A too long pathname (%d characters) was detected on Windows, where maximum number of symbols is 256 and in R it is one less: %s", nchar(pathname, type="chars"), pathname);
warning(msg);
}
}
path <- dirname(pathname);
file <- basename(pathname);
pathname <- NULL;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Adjust filename?
# FIXME: Adjust also directory names. /HB 2014-05-04
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (adjust == "url") {
# Decode non-problematic filename characters, e.g. '%20' -> ' '
file <- URLdecode(file);
# But encode problematic ones, e.g. ':', '*'
file <- gsub(":", "%3A", file, fixed=TRUE)
file <- gsub("*", "%2A", file, fixed=TRUE)
file <- gsub("\\", "%5C", file, fixed=TRUE)
# Encode tilde (~) unless first character
# FIX ME: Needed or not? /HB 2014-05-04
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Expand links
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# NB: Here 'mustExist=TRUE' means that filePath() will always return
# a pathname, not that it will give an error if file does not exist.
pathname <- filePath(path, file, expandLinks="any", mustExist=TRUE);
if (absolutePath) {
pathname <- getAbsolutePath(pathname);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Windows: The maximum number of symbols in a Windows pathname is 256,
# in R it's 255. For more details, see:
# https://msdn.microsoft.com/en-us/library/aa365247(VS.85).aspx
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (.Platform$OS.type == "windows") {
if (!is.na(pathname) && nchar(pathname, type="chars") > 255L) {
msg <- sprintf("A too long pathname (%d characters) was detected on Windows, where maximum number of symbols is 256 and in R it is one less: %s", nchar(pathname, type="chars"), pathname);
warning(msg);
}
}
if (mustExist) {
# Check if file exists
if (!file.exists(pathname)) {
# Locate the first parent directory that does not exist
depth <- 1;
while(TRUE) {
parent <- getParent(pathname, depth=depth);
if (is.na(parent) || is.null(parent) || isDirectory(parent))
break;
depth <- depth + 1;
} # while()
reason <- NULL;
if (is.na(parent) || is.null(parent)) {
parent <- getParent(pathname);
if (is.na(parent) || is.null(parent)) {
reason <- "no such file in the current working directory";
} else {
reason <- sprintf("none of the parent directories [%s/] exist", parent);
}
} else {
reason <- sprintf("%s/ exists, but nothing beyond", parent);
}
if (!is.null(reason) && !isAbsolutePath(pathname)) {
reason <- sprintf("%s; current directory is '%s'", reason, getwd());
}
reason <- sprintf(" (%s)", reason);
throw("Pathname not found: ", pathname, reason);
}
# Check if file permissions allow reading
if (fileAccess(pathname, mode=4) == -1) {
throw("Pathname exists, but there is no permission to read file: ", pathname);
}
} # if (mustExist)
pathname;
}, static=TRUE)
setMethodS3("getReadablePath", "Arguments", function(static, path=NULL, mustExist=TRUE, ...) {
if (is.null(path))
return(NULL);
path <- getReadablePathname(static, path=path, mustExist=mustExist, ...);
if (mustExist && !is.na(path) && !isDirectory(path)) {
throw("Argument 'path' is not a directory: ", path);
}
path;
}, static=TRUE, protected=TRUE)
#########################################################################/**
# @RdocMethod getReadablePathnames
#
# @title "Gets a readable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{files}{A @character @vector of filenames.}
# \item{paths}{A @character @vector of paths.}
# \item{...}{Arguments passed to @seemethod "getReadablePathname".}
# }
#
# \value{
# Returns a @character @vector of the pathnames for the files.
# }
#
# @author
#
# \seealso{
# @seemethod "getReadablePathname"
# @see "R.utils::filePath".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getReadablePathnames", "Arguments", function(static, files=NULL, paths=NULL, ...) {
nbrOfFiles <- length(files);
# Argument 'paths':
if (length(paths) > nbrOfFiles) {
throw("Argument 'paths' is longer than argument 'files': ",
length(paths), " > ", nbrOfFiles);
}
# Expand argument 'paths' to be of same length as 'files'
if (!is.null(paths)) {
paths <- rep(paths, length.out=nbrOfFiles);
}
pathnames <- list();
for (kk in seq(length=nbrOfFiles)) {
pathnames[[kk]] <- getReadablePathname(static, files[kk],
path=paths[kk], ...);
}
unlist(pathnames);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getWritablePathname
#
# @title "Gets a writable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @seemethod "getReadablePathname".}
# \item{mustExist}{If @TRUE and the pathname does not exists,
# an Exception is thrown, otherwise not.}
# \item{mustNotExist}{If the file exists, and \code{mustNotExist} is
# @TRUE, an Exception is thrown. If the file exists, and
# \code{mustNotExist} is @FALSE, or the file does not exists, the
# pathname is accepted.}
# \item{mkdirs}{If @TRUE, \code{mustNotExist} is @FALSE, and the path to
# the file does not exist, it is (recursively) created.}
# \item{maxTries}{A positive @integer specifying how many times the
# method should try to create a missing directory before giving up.
# For more details, see @see "R.utils::mkdirs".}
# }
#
# \value{
# Returns a @character string of the pathname of the file.
# If the argument was invalid an @see "R.oo::Exception" is thrown.
# }
#
# \section{Missing values}{
# If any argument in \code{...} is @NA, an exception is thrown.
# }
#
# @author
#
# \seealso{
# @seemethod "getReadablePathname".
# @see "R.utils::filePath".
# @see "R.utils::mkdirs".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getWritablePathname", "Arguments", function(static, ..., mustExist=FALSE, mustNotExist=FALSE, mkdirs=TRUE, maxTries=5L) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'mustNotExist':
mustNotExist <- getLogical(static, mustNotExist);
# Argument 'mkdirs':
mkdirs <- getLogical(static, mkdirs);
# Create pathname
pathname <- getReadablePathname(static, ..., mustExist=mustExist);
if (is.na(pathname)) {
throw("Cannot retrieve writable file/directory because it is NA.");
}
if (isFile(pathname)) {
# Check if it is ok that the file already exists
if (mustNotExist) {
throw("File already exists: ", pathname);
}
# Check if file permissions allow to modify existing
if (fileAccess(pathname, mode=2) == -1) {
throw("No permission to modify existing file: ", pathname);
}
} else {
# Check if directory exists
path <- getParent(pathname);
if (!isDirectory(path)) {
# Does the directory have to exists (mkdirs=FALSE)?
if (!mkdirs) {
path <- getReadablePath(static, path, mustExist=TRUE);
}
# If not, first try to create the parent directory, iff missing.
# This should give a more informative error message, if it fails.
pathP <- getParent(path);
createParent <- !isDirectory(pathP);
if (createParent) {
pathnameP <- getWritablePathname(static, file="dummy-not-tested", path=pathP, mustExist=FALSE, mustNotExist=FALSE, mkdirs=TRUE, maxTries=maxTries);
}
# Try to create the directory
mkdirs(path, mustWork=TRUE, maxTries=maxTries)
}
filename <- basename(pathname);
if (filename != "dummy-not-tested") {
# Check if file permissions allow to create a file in the directory
pathT <- ifelse(is.null(path), ".", path);
if (fileAccess(pathT, mode=2) == -1) {
throw("No write permission for directory: ", path);
}
# Try to create a file
filenameT <- basename(tempfile());
pathnameT <- filePath(path, filenameT);
on.exit({
if (isFile(pathnameT)) {
# Try to remove the temporary file
res <- FALSE;
suppressWarnings({
for (tt in 1:maxTries) {
res <- file.remove(pathnameT);
if (res) break;
# If not, wait a bit and try again...
Sys.sleep(0.5);
}
})
if (!res) {
warning("Failed to remove temporary file: ", sQuote(pathnameT));
}
}
}, add=TRUE);
tryCatch({
cat(file=pathnameT, Sys.time());
}, error = function(ex) {
throw("No permission to create a new file in directory: ", path);
});
} # if (filename != "dummy-not-tested")
} # if (isFile(pathname))
pathname;
}, static=TRUE)
setMethodS3("getWritablePath", "Arguments", function(static, path=NULL, ...) {
# Special case: If path == NULL, the skip
if (is.null(path))
return(NULL);
pathname <- getWritablePathname(static, file="dummy-not-created", path=path, ...);
getParent(pathname);
}, static=TRUE, protected=TRUE)
setMethodS3("getDirectory", "Arguments", function(static, path=NULL, ..., mustExist=FALSE, mkdirs=TRUE) {
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'mkdirs':
mkdirs <- getLogical(static, mkdirs);
# Create pathname
pathname <- getReadablePathname(static, path=path, ..., mustExist=mustExist);
if (is.na(pathname)) {
throw("Cannot retrieve directory because it is NA.");
}
# Nothing to do?
if (isDirectory(pathname)) {
return(pathname);
}
if (!mkdirs) {
throw("Directory does not exist: ", pathname);
}
mkdirs(pathname, mustWork=TRUE)
pathname;
}, static=TRUE, protected=TRUE)
#########################################################################/**
# @RdocMethod getVector
#
# @title "Validates a vector"
#
# \description{
# @get "title" by checking its length (number of elements).
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A single @vector.}
# \item{length}{A @numeric @vector of length two or more. If two, it
# is the minimum and maximum length of \code{x}. Elsewise it is the
# set of possible lengths of \code{x}.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns the same @vector, if it is valid. Otherwise an exception is
# thrown.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getVector", "Arguments", function(static, x, length=NULL, .name=NULL, ...) {
if (length(length) == 0)
return(x);
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
# See ?is.vector for how it is defined. /HB 2009-05-19
attrs <- attributes(x);
attributes(x) <- attrs[intersect(names(attrs), c("names", "dim"))];
if (length[1] > 0 && !is.vector(x)) {
throw(sprintf("Argument '%s' is not a vector: %s", .name, storage.mode(x)));
}
xlen <- length(x);
if (length(length) == 1)
length <- c(1,length);
if (length(length) == 2) {
if (xlen < length[1] || xlen > length[2]) {
if (length[1] == length[2] && length[1] == 1) {
throw(sprintf("Argument '%s' should be a single value not %d values.", .name, xlen));
} else if (length[1] == length[2]) {
throw(sprintf("Number of elements in argument '%s' should be exactly %d not %d value(s).", .name, length[1], xlen));
} else {
throw(sprintf("Number of elements in argument '%s' is out of range [%d,%d]: %d", .name, length[1], length[2], xlen));
}
}
} else {
if (!is.element(xlen, length)) {
throw(sprintf("Number of elements in argument '%s' is not in {%s}: %d",
.name, seqToHumanReadable(length), xlen, ));
}
}
attributes(x) <- attrs;
x;
}, static=TRUE, private=TRUE)
#########################################################################/**
# @RdocMethod getCharacters
# @aliasmethod getCharacter
#
# @title "Coerces to a character vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{s}{A @vector.}
# \item{nchar}{A @numeric @vector of length one or two. If one,
# the maximum number of characters ("length") in \code{s}. If two,
# the minimum and maximum length of \code{s}.}
# \item{useNames}{If @TRUE, the 'names' attribute is preserved, otherwise
# it is dropped.}
# \item{asGString}{If @TRUE, each string is treated as a @see "GString".}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character @vector, if it is valid. Otherwise an exception is
# thrown.
# }
#
# \section{Missing values}{
# If \code{s} contains missing values, and \code{nchar} is not @NULL,
# then an exception is thrown.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getCharacters", "Arguments", function(static, s, length=NULL, trim=FALSE, nchar=NULL, useNames=TRUE, asGString=getOption("Arguments$getCharacters/args/asGString", TRUE), .name=NULL, ...) {
if (is.null(.name))
.name <- as.character(deparse(substitute(s)));
s <- getVector(static, s, length=length, .name=.name);
# Nothing to check?
if (length(s) == 0L)
return(s);
# Coerce GString:s to character strings?
if (asGString) {
# Treat only strings with GString markup. This avoids lots of
# GString overhead if there are no GStrings.
hasMarkup <- (regexpr("${", s, fixed=TRUE) != -1);
idxs <- which(hasMarkup & !is.na(s));
s[idxs] <- unlist(lapply(s[idxs], FUN=function(x) {
x <- GString(x);
as.character(x);
}), use.names=FALSE);
}
if (trim) {
# Trim the strings
# (using s[] to preserve attributes)
s[] <- unlist(lapply(s, FUN=trim), use.names=FALSE);
}
# Coerce to character strings
# (using s[] to preserve attributes)
s[] <- unlist(lapply(s, FUN=as.character), use.names=FALSE);
if (!useNames) {
names(s) <- NULL;
}
# Nothing to check?
if (is.null(nchar))
return(s);
# At this point, missing values are not allowed
if (any(is.na(s))) {
throw("Argument 'nchar' cannot be specified if character vector contains missing values: ", hpaste(sQuote(s)))
}
if (length(nchar) == 1L)
nchar <- c(1L, nchar);
# Check the string length of each character string
for (kk in seq(length=length(s))) {
slen <- nchar(s[kk], type="chars");
if (slen < nchar[1L] || slen > nchar[2L]) {
throw(sprintf("String length of elements #%d in '%s' is out of range [%d,%d]: %d '%s'", kk, .name, nchar[1L], nchar[2L], slen, s[kk]));
}
}
s;
}, static=TRUE)
setMethodS3("getCharacter", "Arguments", function(static, ..., length=c(0,1)) {
getCharacters(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getNumerics
# @aliasmethod getNumeric
#
# @title "Coerces to a numeric vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A @vector.}
# \item{range}{Two @numerics for the allowed ranged. If @NULL, range is
# not checked.}
# \item{asMode}{A @character specifying the mode to coerce to.}
# \item{disallow}{A @character @vector specifying diallowed value sets,
# i.e. \code{"NA"}, \code{"NaN"}, and/or \code{"Inf"}.}
# \item{...}{Arguments passed to @method "getVector".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @numeric @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getNumerics", "Arguments", function(static, x, range=NULL, asMode=NULL, disallow=NULL, ..., .name=NULL) {
# Argument '.name':
if (is.null(.name)) {
.name <- as.character(deparse(substitute(x)));
}
x <- getVector(static, x, ..., .name=.name);
xMode <- storage.mode(x);
# Coerce the mode of 'x'
if (is.null(asMode)) {
if (is.element(xMode, c("integer", "double"))) {
asMode <- xMode;
} else {
asMode <- "double";
}
}
# Update/coerce mode?
if (xMode != asMode) {
storage.mode(x) <- asMode;
}
# Nothing to do?
if (length(x) == 0)
return(x);
if (!is.null(disallow)) {
if (is.element("NaN", disallow) && any(is.nan(x))) {
throw(sprintf("Argument '%s' contains %d NaN value(s).",
.name, sum(is.nan(x))));
}
if (is.element("NA", disallow) && any(is.na(x) & !is.nan(x))) {
throw(sprintf("Argument '%s' contains %d NA value(s).",
.name, sum(is.na(x))));
}
# For conveniency, disallow 'Inf' here too; other range takes care of it.
if (is.element("Inf", disallow) && any(is.infinite(x))) {
throw(sprintf("Argument '%s' contains %d (-/+)Inf value(s).",
.name, sum(is.infinite(x))));
}
}
# Nothing to check?
if (is.null(range))
return(x);
# Argument 'range':
if (length(range) != 2) {
throw("Argument 'range' should be of length 2: ", length(range));
}
if (range[2] < range[1]) {
throw(sprintf("Argument 'range' is not ordered: c(%s,%s)", range[1], range[2]));
}
# Suppress warnings when there are no finite values in x.
suppressWarnings({
xrange <- range(x, na.rm=TRUE);
})
if (xrange[1] < range[1] || xrange[2] > range[2]) {
xrange <- as.character(xrange);
range <- as.character(range);
if (length(x) == 1) {
throw(sprintf("Argument '%s' is out of range [%s,%s]: %s",
.name, range[1], range[2], x));
} else {
throw(sprintf("Range of argument '%s' is out of range [%s,%s]: [%s,%s]",
.name, range[1], range[2], xrange[1], xrange[2]));
}
}
x;
}, static=TRUE)
setMethodS3("getNumeric", "Arguments", function(static, ..., length=1) {
getNumerics(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getDoubles
# @aliasmethod getDouble
#
# @title "Coerces to a double vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @method "getNumeric".}
# \item{disallow}{Disallowed values. See @method "getNumerics" for details.}
# }
#
# \value{
# Returns a @double @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getDoubles", "Arguments", function(static, ..., disallow=c("NA","NaN")) {
getNumerics(static, ..., asMode="double", disallow=disallow);
}, static=TRUE)
setMethodS3("getDouble", "Arguments", function(static, ..., length=1) {
getDoubles(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getIntegers
# @aliasmethod getInteger
#
# @title "Coerces to a integer vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @method "getNumeric".}
# \item{disallow}{Disallowed values. See @method "getNumerics" for details.}
# }
#
# \value{
# Returns a @integer @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getIntegers", "Arguments", function(static, ..., disallow=c("NA","NaN")) {
getNumerics(static, ..., asMode="integer", disallow=disallow);
}, static=TRUE)
setMethodS3("getInteger", "Arguments", function(static, ..., length=1) {
getIntegers(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getIndices
# @aliasmethod getIndex
#
# @title "Coerces to a integer vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A single @vector. If @logical, @see "base::which" is used.}
# \item{...}{Arguments passed to @method "getIntegers".}
# \item{range}{Allowed range. See @method "getNumerics" for details.}
# \item{max}{The maximum of the default range.}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns an @integer @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getIndices", "Arguments", function(static, x, ..., max=Inf, range=c(1*(max > 0L),max), .name=NULL) {
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
# Argument 'x':
if (is.logical(x)) {
x <- which(x);
}
# Argument 'max':
if (length(max) != 1) {
throw("Argument 'max' must be a single value: ", length(max));
}
max <- as.numeric(max);
if (is.na(max)) {
throw("Argument 'max' is NA/NaN: ", max);
} else if (max < 0) {
throw("Argument 'max' must be positive: ", max);
}
# Argument 'range':
if (!is.null(range)) {
if (length(range) != 2) {
throw("Argument 'range' should be of length 2: ", length(range));
}
if (range[2] < range[1]) {
throw(sprintf("Argument 'range' is not ordered: c(%s,%s)", range[1], range[2]));
}
}
# Identify indices
x <- getIntegers(static, x, ..., range=range, .name=.name);
# Special dealing with range = c(0,0)
if (!is.null(range)) {
if (range[2] < 1L) {
xt <- x[is.finite(x)];
if (length(xt) > 0) {
throw(sprintf("Argument 'x' contains %d non-missing indices although the range ([%s,%s]) implies that there should be none.", length(xt), range[1L], range[2L]));
}
}
}
x;
}, static=TRUE)
setMethodS3("getIndex", "Arguments", function(static, ..., length=1) {
getIndices(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getLogicals
# @aliasmethod getLogical
#
# @title "Coerces to a logical vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A @vector.}
# \item{disallow}{A @character @vector specifying diallowed value sets
# after coercing, i.e. \code{"NA"}.}
# \item{...}{Arguments passed to @method "getVector".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @numeric @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getLogicals", "Arguments", function(static, x, ..., disallow=c("NA", "NaN"), coerce=FALSE, .name=NULL) {
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
x <- getVector(static, x, ..., .name=.name);
# Coerce to logicals?
if (coerce)
x <- as.logical(x);
if (!is.null(disallow)) {
if (is.element("NA", disallow) && any(is.na(x))) {
throw(sprintf("Argument '%s' contains %d NA value(s).",
.name, sum(is.na(x))));
}
}
# Assert that 'x' is logical before returning
if (any(!is.logical(x)))
throw(sprintf("Argument '%s' is non-logical: %s", .name, class(x)));
x;
}, static=TRUE)
setMethodS3("getLogical", "Arguments", function(static, ..., length=1) {
getLogicals(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getVerbose
#
# @title "Coerces to Verbose object"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{verbose}{A single object. If a @see "Verbose", it is immediately
# returned. If a @numeric value, it is used as the threshold.
# Otherwise the object is coerced to a @logical value and if @TRUE,
# the threshold is \code{defaultThreshold}.}
# \item{defaultThreshold}{A @numeric value for the default threshold, if
# \code{verbose} was interpreted as a @logical value.}
# \item{useNullVerbose}{If \code{verbose} can be interpreted as @FALSE,
# return a @see NullVerbose object if @TRUE.}
# \item{...}{Passed to the constructor of @see "Verbose".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @see Verbose (or a @see "NullVerbose") object.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getVerbose", "Arguments", function(static, verbose, defaultThreshold=-1, useNullVerbose=TRUE, ..., .name=NULL) {
if (inherits(verbose, "Verbose"))
return(verbose);
if (is.null(.name))
.name <- as.character(deparse(substitute(verbose)));
if (is.numeric(verbose)) {
verbose <- getDouble(static, verbose, .name=.name);
verbose <- Verbose(threshold=verbose, ...);
} else {
verbose <- getLogical(static, verbose, .name=.name);
if (!verbose && useNullVerbose) {
verbose <- NullVerbose();
} else {
defaultThreshold <- getNumeric(static, defaultThreshold);
verbose <- Verbose(threshold=defaultThreshold, ...);
}
}
verbose;
}, static=TRUE)
#########################################################################/**
# @RdocMethod getRegularExpression
#
# @title "Gets a valid regular expression pattern"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{pattern}{A @character string to be validated.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string.
# }
#
# @author
#
# \seealso{
# @see "base::grep".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getRegularExpression", "Arguments", function(static, pattern=NULL, ..., .name=NULL) {
if (is.null(.name)) {
.name <- as.character(deparse(substitute(pattern)));
}
if (is.null(pattern)) {
throw(sprintf("Argument '%s' is not a valid regular expression: NULL",
.name));
}
pattern <- getCharacter(static, pattern, .name=.name, length=c(1,1));
# Validate it
tryCatch({
regexpr(pattern, "dummy string", ...);
}, error = function(ex) {
throw(sprintf("Argument '%s' is not a valid regular expression: %s. Error message from regexpr() was: %s", .name, pattern, ex$message));
})
pattern;
}, static=TRUE)
#########################################################################/**
# @RdocMethod getEnvironment
#
# @title "Gets an existing environment"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{envir}{An @environment, the name of a loaded package, or @NULL.
# If @NULL, the global environment is returned.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns an @environment.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getEnvironment", "Arguments", function(static, envir=NULL, .name=NULL, ...) {
if (is.null(.name))
.name <- as.character(deparse(substitute(envir)));
if (is.null(envir)) {
return(.GlobalEnv);
}
if (is.character(envir)) {
name <- getCharacter(static, envir, length=c(1,1));
envirs <- gsub("^package:", "", search());
pos <- which(name == envirs);
if (length(pos) == 0)
throw("Argument 'envir' is not the name of a loaded package: ", envir);
envir <- pos.to.env(pos);
}
if (!is.environment(envir)) {
throw(sprintf("Argument '%s' is not an environment: %s",
.name, class(envir)[1]));
}
}, static=TRUE)
#########################################################################/**
# @RdocMethod getInstanceOf
#
# @title "Gets an instance of the object that is of a particular class"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{object}{The object that should be returned as an instance of
# class \code{class}.}
# \item{class}{A @character string specifying the name of the class that
# the returned object should inherit from.}
# \item{coerce}{If @TRUE and the object is not of the wanted class, then
# method will be coerced to that class, if possible. Otherwise,
# an error is thrown.}
# \item{...}{Not used.}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns an object inheriting from class \code{class}.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword programming
#*/#########################################################################
setMethodS3("getInstanceOf", "Arguments", function(static, object, class, coerce=FALSE, ..., .name=NULL) {
if (is.null(.name)) {
.name <- as.character(deparse(substitute(object)));
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'class':
class <- getCharacter(static, class);
# Argument 'coerce':
coerce <- getLogical(static, coerce);
# Argument 'object':
if (!inherits(object, class)) {
if (coerce) {
object <- as(object, class, ...);
} else {
throw(sprintf("Argument '%s' is neither of nor inherits class %s: %s",
.name, class[1], paste(class(object), collapse=", ")));
}
}
# Return the object
object;
}, static=TRUE, protected=TRUE)
withoutGString <- function(..., envir=parent.frame()) {
# Temporarily disable 'asGString' for Arguments$getCharacters()
oopts <- options("Arguments$getCharacters/args/asGString"=FALSE);
on.exit(options(oopts));
eval(..., envir=envir);
} # withoutGString()
############################################################################
# HISTORY:
# 2015-02-05
# o Now getReadablePathname() warns about too long pathnames on Windows.
# 2014-10-03
# o Now Arguments$getReadablePathname(file, path) ignores 'path' if
# 'file' specifies an absolute pathname.
# 2014-05-04
# o Added argument 'adjust' to Arguments$getReadablePathname().
# 2014-01-12
# o Made argument 'useNames' to getCharacters() default to TRUE.
# o Now Arguments$getCharacters() preserves attributes.
# 2013-12-15
# o Added withoutGString().
# 2013-12-13
# o Now argument 'asGString' for Arguments$getCharacters() defaults to
# getOption("Arguments$getCharacters/args/asGString", TRUE). This makes
# it possible to disable this feature, even when it is not possible to
# directly pass that argument. This will also make it possible to
# set the default to FALSE in the future (instead of TRUE as today).
# 2013-11-15
# o CLEANUP: Arguments$getNumerics(NA, range=c(0,1)) no longer gives
# warnings on "no non-missing arguments to min()" etc.
# 2013-08-26
# o CLEANUP: Arguments$getReadablePathnames(files, paths=NULL) no longer
# warns about "rep(paths, length.out = nbrOfFiles) : 'x' is NULL so
# the result will be NULL" if length(files) > 0.
# 2012-12-01
# o BUG FIX: Arguments$getIndices(NA_integer_, max=0, disallow="NaN")
# would give "Exception: Argument 'x' is of length 1 although the range
# ([0,0]) implies that is should be empty." although it should return
# NA_integer.
# 2012-10-21
# o ROBUSTNESS: Added argument 'maxTries' to Arguments$getWritablePathname()
# to have the method try to create missing directories multiple times
# before giving up.
# 2012-10-16
# o Moved Arguments$getFilename() from R.filesets to R.utils.
# Added Rd help.
# 2012-09-24
# o BUG FIX: Arguments$getReadablePath(..., mustExist=FALSE) did not work.
# 2011-11-15
# o SPEEDUP: Now Arguments$getCharacters(s, asGString=TRUE) is much
# faster for elements of 's' that are non-GStrings. For long character
# vectors the speedup is 100-200x times.
# 2011-10-16
# o CORRECTION: Arguments$getNumerics(c(Inf), disallow="Inf") would report
# that it contains "NA" instead of "Inf" values".
# 2011-03-08
# o Now Arguments$getWritablePath(NULL) returns NULL without asserting
# write permission, which is analogue to how it is done with
# Arguments$getReadablePath(NULL).
# 2010-11-19
# o TYPO: Static methods getVector() and getRegularExpression() of
# Arguments would report the incorrect argument name.
# 2010-01-25
# o ROBUSTNESS: Added validation of argument 'range' in Arguments methods.
# 2010-01-01
# o Now Arguments$getNumerics(x) displays the value of 'x' in the error
# message if it is a *single* value and out of range.
# o Added argument 'max' to Arguments$getIndices().
# 2009-12-30
# o Now Arguments$getWritablePath() and Arguments$getWritablePathname()
# throws an error is an NA file/directory is specified.
# o Now Arguments$getReadablePath() and Arguments$getReadablePathname()
# throws an error is an NA file/directory is specified, unless
# 'mustExist' is FALSE.
# o Added Arguments$getInstanceOf(...).
# o BUG FIX: Arguments$getCharacters(s) would return a *logical* instead
# of a *character* vector if 's' contained all NAs.
# 2009-11-20
# o If 'x' is a logical vector, Arguments$getIndices(x) will now return
# the same as if x <- which(x).
# 2009-10-30
# o Now Arguments$getWritablePathname(path) validates that there is enough
# file permissions so that a file can be created in the 'path' directory.
# 2009-06-29
# o Added argument 'useNames=FALSE' to getCharacters() of Arguments.
# Don't remember why I didn't want names in the first place (see below).
# 2009-05-18
# o UPDATE: Now getWritablePathname() gives a more precise error message
# if the file exists but the rights to modifies it does not.
# o UPDATE: Now getEnvironment(), getRegularExpression(), and
# getReadablePathname() give clearer error messages if more the input
# contains more than one element.
# 2009-05-15
# o Changed argument 'asMode' for Arguments$getNumerics() to default to
# NULL instead of "numeric". This will case the method to return integer
# if the input is integer, and double if the input is double. The
# previous default was alway returning doubles, cf. notes on common
# misconception of how as.numeric() works. In the case when the input
# is neither integer or double, the default is to coerce to doubles.
# Also, the method is now using storage.mode() instead of mode().
# 2009-04-04
# o Now getReadablePathname(..., mustExist=TRUE) of Arguments reports also
# the working directory if the a relative pathname is missing.
# o BUG FIX: getReadablePathname(..., mustExist=TRUE) of Arguments gave an
# internal error if the pathname was in the current directory and did
# not exist.
# 2008-12-27
# o Now getReadablePathname(..., mustExist=TRUE) and
# getWritablePathname(..., mkdirs=FALSE) of Arguments report which
# of the parent directories exists when the requested pathname is not
# found. This will help troubleshooting missing pathnames.
# 2008-12-01
# o Now getReadablePathname() and getWritablePathname() use the more
# trusted fileAccess() of R.utils.
# 2008-02-26
# o Now the '...' arguments to Arguments$getVerbose() are passed to the
# constructor of Verbose. This allows the construct of
# Arguments$getVerbose(-10, timestamp=TRUE).
# 2005-12-05
# o getNumerics(Inf, range=c(0,Inf)) would give a warning "no finite
# arguments to min; returning Inf". Fixed with a withCallingHandlers().
# 2005-11-22
# o Added Rdoc comments for getReadablePathnames().
# 2005-11-13
# o Added getReadablePathnames().
# o Now getCharacter() only accept vectors of length zero or one.
# 2005-10-25
# o BUG FIX: New 'mustNotExist' argument got logically the inverse.
# 2005-10-21
# o Renamed argument 'overwrite' in getWritablePathname() in Arguments to
# 'mustNotExist'. Renamed all 'mustExists' to 'mustExist' in all methods
# of class Arguments.
# 2005-09-06
# o Replace argument 'gString' of getCharacters() to 'asGString', cf.
# Verbose class.
# o Now Arguments$getReadablePathname() follows Windows shortcut files.
# 2005-08-01
# o getReadablePathname() no longer returns the absolute pathname by
# default. This is because on some systems the relative pathname can
# be queried wheras the absolute one may not be access due to missing
# file permissions.
# o Added getEnvironment(), getRegularExpression(),
# getReadablePath(), getWritablePath().
# 2005-07-19
# o BUG FIX: getCharacters() would not coerce Object:s correctly.
# 2005-07-07
# o getCharacters() returned attribute 'names' too. Removed.
# 2005-06-20
# o Added argument 'absolutePath' to getReadablePathname().
# 2005-06-18
# o Added static methods getVector(), getNumeric/s(), getDouble/s(),
# getInteger/s(), getIndices/getIndex(), and getLogical/s(). These should
# be very handy. Also added getVector().
# Not sure if getVector() should be renamed to checkLength(), and even
# be moved to the Assert class. Not sure where the assert class is
# heading.
# 2005-05-31
# o Created from former File$validateFileAndPath().
############################################################################
|
/R/Arguments.R
|
no_license
|
monoguerin/sears-routes
|
R
| false
| false
| 46,658
|
r
|
###########################################################################/**
# @RdocClass Arguments
#
# @title "Static class to validate and process arguments"
#
# \description{
# @classhierarchy
# }
#
# \section{Fields and Methods}{
# @allmethods
# }
#
# @author
#
# @keyword programming
#*/###########################################################################
setConstructorS3("Arguments", function(...) {
extend(Object(), "Arguments");
})
#########################################################################/**
# @RdocMethod getFilename
#
# @title "Gets and validates a filename"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{filename}{A @character string.}
# \item{nchar}{An @integer @vector of length two specifying the range
# of valid filename lengths.}
# \item{class}{A @character string specifying the class of valid
# filenames.}
# \item{.name}{The name of the argument validated.}
# \item{.type}{Not used.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string if filename is valid,
# otherwise an exception is thrown.
# }
#
# \section{Missing values}{
# If \code{filename} is a missing value, then an exception is thrown.
# }
#
# \details{
# When argument \code{class="safe"}, the following 86 ASCII characters
# are allowed in filenames:
# \preformatted{
# #$%&'()+,-.0123456789;= (24 including initial space)
# @ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_ (31)
# `abcdefghijklmnopqrstuvwxyz{|}~ (31)
# }
# This class of filenames has been extensively tested on for
# cross-platform support on Microsoft Windows, OSX and various
# Unix flavors.
# }
#
# \references{
# [1] Microsoft, \emph{Naming Files, Paths, and Namespaces} (Section 'Windows Naming Conventions'), 2012. \url{http://msdn.microsoft.com/en-us/library/aa365247.aspx#naming_conventions}.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#*/#########################################################################
setMethodS3("getFilename", "Arguments", function(static, filename, nchar=c(1,128), class=c("safe"), .name=NULL, .type="filename", ...) {
##
## OLD NOTES:
## Valid filename characters:
## * The FTP RFCs require (7-bit) ASCII characters (and presumably not control
## characters either). The 95 printable ASCII characters are (note initial
## space):
##
## !"#$%&'()*+,-./0123456789:;<=>? (32)
## @ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_ (32)
## `abcdefghijklmnopqrstuvwxyz{|}~ (31)
##
## * On Windows the following 9 characters aren't allowed: \ / : * ? " < > !.
## This leaves us with:
##
## #$%&'()+,-.0123456789;= (24)
## @ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_ (31)
## `abcdefghijklmnopqrstuvwxyz{|}~ (31)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument '.name':
if (is.null(.name)) {
.name <- as.character(deparse(substitute(filename)));
}
# Argument 'filename':
if (is.na(filename)) {
throw("Argument 'filename' cannot be a missing value: ", filename)
}
filename <- getCharacter(static, filename, nchar=nchar, .name=.name);
# Argument 'class':
class <- match.arg(class);
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Filter out valid characters
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
chars <- filename;
# Always valid characters
chars <- gsub("[abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0-9_.,]", "", chars);
chars <- gsub("[-]", "", chars);
chars <- gsub("[+]", "", chars);
# Filter out according to classes.
if ("safe" %in% class) {
chars <- gsub("[ ]", "", chars);
chars <- gsub("[\\[\\]]", "", chars);
chars <- gsub("[#$%&'()`{|}~]", "", chars);
chars <- gsub("[=]", "", chars);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Check for remaining (=invalid) characters
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (nchar(chars, type="chars") > 0L) {
chars <- unlist(strsplit(chars, split=""));
chars <- sort(unique(chars));
chars <- sprintf("'%s'", chars);
chars <- paste(chars, collapse=", ");
throw(sprintf("Not a valid %s. Argument '%s' contains non-valid %s characters (%s): %s", .type, .name, .type, chars, filename));
}
filename;
}, static=TRUE, private=TRUE)
#########################################################################/**
# @RdocMethod getReadablePathname
#
# @title "Gets a readable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{file}{A @character string specifying the file.}
# \item{path}{A @character string specifying the path.}
# \item{mustExist}{If @TRUE, the pathname must exists and be readable,
# otherwise an exception is thrown. If @FALSE, no such test is
# performed.}
# \item{absolutePath}{If @TRUE, the absolute pathname is returned.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string of the absolute pathname of the file.
# }
#
# \section{Missing values}{
# If \code{file} or \code{path} is @NA and \code{mustExist} is @FALSE,
# then (character) @NA is returned, otherwise an exception is thrown.
# }
#
# \section{Windows}{
# If a too long pathname is detected on Windows, an informative warning
# is given.
# The maximum number of symbols in a Windows pathname is 256, including
# file separators '/' or '\', but excluding the drive letter, and initial
# file separator (e.g. 'C:/'), and the string terminator ('\\0'), cf.
# 'MSDN - Naming a File or Directory', Microsoft. In R, the limit is
# one symbol less, i.e. 255.
# }
#
# @author
#
# \seealso{
# @seemethod "getWritablePathname"
# @see "R.utils::filePath".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getReadablePathname", "Arguments", function(static, file=NULL, path=NULL, mustExist=TRUE, absolutePath=FALSE, adjust=c("none", "url"), ...) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'file':
if (!is.null(file)) {
if (inherits(file, "connection")) {
throw("In this context, argument 'file' cannot be a connection.");
}
file <- getCharacter(static, file, length=c(1,1));
}
# Ignore 'path'?
if (isAbsolutePath(file)) path <- NULL
# Argument 'path':
if (!is.null(path)) {
path <- getCharacter(static, path, length=c(1,1));
}
if (is.null(file) && is.null(path)) {
throw("Both argument 'file' and 'path' are NULL.");
}
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'absolutePath':
absolutePath <- getLogical(static, absolutePath);
# Argument 'adjust':
adjust <- match.arg(adjust);
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Process arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (mustExist) {
if (!is.null(file) && is.na(file)) {
throw("No such file/directory because argument 'file' is NA.");
}
if (!is.null(path) && is.na(path)) {
throw("No such file/directory because argument 'path' is NA.");
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Make sure <path>/<file> is properly split up
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (is.null(path)) {
pathname <- file;
} else if (is.null(file)) {
pathname <- path;
} else {
pathname <- file.path(path, file);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Windows: The maximum number of symbols in a Windows pathname is 256,
# in R it's 255. For more details, see:
# https://msdn.microsoft.com/en-us/library/aa365247(VS.85).aspx
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (.Platform$OS.type == "windows") {
if (!is.na(pathname) && nchar(pathname, type="chars") > 255L) {
msg <- sprintf("A too long pathname (%d characters) was detected on Windows, where maximum number of symbols is 256 and in R it is one less: %s", nchar(pathname, type="chars"), pathname);
warning(msg);
}
}
path <- dirname(pathname);
file <- basename(pathname);
pathname <- NULL;
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Adjust filename?
# FIXME: Adjust also directory names. /HB 2014-05-04
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (adjust == "url") {
# Decode non-problematic filename characters, e.g. '%20' -> ' '
file <- URLdecode(file);
# But encode problematic ones, e.g. ':', '*'
file <- gsub(":", "%3A", file, fixed=TRUE)
file <- gsub("*", "%2A", file, fixed=TRUE)
file <- gsub("\\", "%5C", file, fixed=TRUE)
# Encode tilde (~) unless first character
# FIX ME: Needed or not? /HB 2014-05-04
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Expand links
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# NB: Here 'mustExist=TRUE' means that filePath() will always return
# a pathname, not that it will give an error if file does not exist.
pathname <- filePath(path, file, expandLinks="any", mustExist=TRUE);
if (absolutePath) {
pathname <- getAbsolutePath(pathname);
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Windows: The maximum number of symbols in a Windows pathname is 256,
# in R it's 255. For more details, see:
# https://msdn.microsoft.com/en-us/library/aa365247(VS.85).aspx
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (.Platform$OS.type == "windows") {
if (!is.na(pathname) && nchar(pathname, type="chars") > 255L) {
msg <- sprintf("A too long pathname (%d characters) was detected on Windows, where maximum number of symbols is 256 and in R it is one less: %s", nchar(pathname, type="chars"), pathname);
warning(msg);
}
}
if (mustExist) {
# Check if file exists
if (!file.exists(pathname)) {
# Locate the first parent directory that does not exist
depth <- 1;
while(TRUE) {
parent <- getParent(pathname, depth=depth);
if (is.na(parent) || is.null(parent) || isDirectory(parent))
break;
depth <- depth + 1;
} # while()
reason <- NULL;
if (is.na(parent) || is.null(parent)) {
parent <- getParent(pathname);
if (is.na(parent) || is.null(parent)) {
reason <- "no such file in the current working directory";
} else {
reason <- sprintf("none of the parent directories [%s/] exist", parent);
}
} else {
reason <- sprintf("%s/ exists, but nothing beyond", parent);
}
if (!is.null(reason) && !isAbsolutePath(pathname)) {
reason <- sprintf("%s; current directory is '%s'", reason, getwd());
}
reason <- sprintf(" (%s)", reason);
throw("Pathname not found: ", pathname, reason);
}
# Check if file permissions allow reading
if (fileAccess(pathname, mode=4) == -1) {
throw("Pathname exists, but there is no permission to read file: ", pathname);
}
} # if (mustExist)
pathname;
}, static=TRUE)
setMethodS3("getReadablePath", "Arguments", function(static, path=NULL, mustExist=TRUE, ...) {
if (is.null(path))
return(NULL);
path <- getReadablePathname(static, path=path, mustExist=mustExist, ...);
if (mustExist && !is.na(path) && !isDirectory(path)) {
throw("Argument 'path' is not a directory: ", path);
}
path;
}, static=TRUE, protected=TRUE)
#########################################################################/**
# @RdocMethod getReadablePathnames
#
# @title "Gets a readable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{files}{A @character @vector of filenames.}
# \item{paths}{A @character @vector of paths.}
# \item{...}{Arguments passed to @seemethod "getReadablePathname".}
# }
#
# \value{
# Returns a @character @vector of the pathnames for the files.
# }
#
# @author
#
# \seealso{
# @seemethod "getReadablePathname"
# @see "R.utils::filePath".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getReadablePathnames", "Arguments", function(static, files=NULL, paths=NULL, ...) {
nbrOfFiles <- length(files);
# Argument 'paths':
if (length(paths) > nbrOfFiles) {
throw("Argument 'paths' is longer than argument 'files': ",
length(paths), " > ", nbrOfFiles);
}
# Expand argument 'paths' to be of same length as 'files'
if (!is.null(paths)) {
paths <- rep(paths, length.out=nbrOfFiles);
}
pathnames <- list();
for (kk in seq(length=nbrOfFiles)) {
pathnames[[kk]] <- getReadablePathname(static, files[kk],
path=paths[kk], ...);
}
unlist(pathnames);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getWritablePathname
#
# @title "Gets a writable pathname"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @seemethod "getReadablePathname".}
# \item{mustExist}{If @TRUE and the pathname does not exists,
# an Exception is thrown, otherwise not.}
# \item{mustNotExist}{If the file exists, and \code{mustNotExist} is
# @TRUE, an Exception is thrown. If the file exists, and
# \code{mustNotExist} is @FALSE, or the file does not exists, the
# pathname is accepted.}
# \item{mkdirs}{If @TRUE, \code{mustNotExist} is @FALSE, and the path to
# the file does not exist, it is (recursively) created.}
# \item{maxTries}{A positive @integer specifying how many times the
# method should try to create a missing directory before giving up.
# For more details, see @see "R.utils::mkdirs".}
# }
#
# \value{
# Returns a @character string of the pathname of the file.
# If the argument was invalid an @see "R.oo::Exception" is thrown.
# }
#
# \section{Missing values}{
# If any argument in \code{...} is @NA, an exception is thrown.
# }
#
# @author
#
# \seealso{
# @seemethod "getReadablePathname".
# @see "R.utils::filePath".
# @see "R.utils::mkdirs".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getWritablePathname", "Arguments", function(static, ..., mustExist=FALSE, mustNotExist=FALSE, mkdirs=TRUE, maxTries=5L) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'mustNotExist':
mustNotExist <- getLogical(static, mustNotExist);
# Argument 'mkdirs':
mkdirs <- getLogical(static, mkdirs);
# Create pathname
pathname <- getReadablePathname(static, ..., mustExist=mustExist);
if (is.na(pathname)) {
throw("Cannot retrieve writable file/directory because it is NA.");
}
if (isFile(pathname)) {
# Check if it is ok that the file already exists
if (mustNotExist) {
throw("File already exists: ", pathname);
}
# Check if file permissions allow to modify existing
if (fileAccess(pathname, mode=2) == -1) {
throw("No permission to modify existing file: ", pathname);
}
} else {
# Check if directory exists
path <- getParent(pathname);
if (!isDirectory(path)) {
# Does the directory have to exists (mkdirs=FALSE)?
if (!mkdirs) {
path <- getReadablePath(static, path, mustExist=TRUE);
}
# If not, first try to create the parent directory, iff missing.
# This should give a more informative error message, if it fails.
pathP <- getParent(path);
createParent <- !isDirectory(pathP);
if (createParent) {
pathnameP <- getWritablePathname(static, file="dummy-not-tested", path=pathP, mustExist=FALSE, mustNotExist=FALSE, mkdirs=TRUE, maxTries=maxTries);
}
# Try to create the directory
mkdirs(path, mustWork=TRUE, maxTries=maxTries)
}
filename <- basename(pathname);
if (filename != "dummy-not-tested") {
# Check if file permissions allow to create a file in the directory
pathT <- ifelse(is.null(path), ".", path);
if (fileAccess(pathT, mode=2) == -1) {
throw("No write permission for directory: ", path);
}
# Try to create a file
filenameT <- basename(tempfile());
pathnameT <- filePath(path, filenameT);
on.exit({
if (isFile(pathnameT)) {
# Try to remove the temporary file
res <- FALSE;
suppressWarnings({
for (tt in 1:maxTries) {
res <- file.remove(pathnameT);
if (res) break;
# If not, wait a bit and try again...
Sys.sleep(0.5);
}
})
if (!res) {
warning("Failed to remove temporary file: ", sQuote(pathnameT));
}
}
}, add=TRUE);
tryCatch({
cat(file=pathnameT, Sys.time());
}, error = function(ex) {
throw("No permission to create a new file in directory: ", path);
});
} # if (filename != "dummy-not-tested")
} # if (isFile(pathname))
pathname;
}, static=TRUE)
setMethodS3("getWritablePath", "Arguments", function(static, path=NULL, ...) {
# Special case: If path == NULL, the skip
if (is.null(path))
return(NULL);
pathname <- getWritablePathname(static, file="dummy-not-created", path=path, ...);
getParent(pathname);
}, static=TRUE, protected=TRUE)
setMethodS3("getDirectory", "Arguments", function(static, path=NULL, ..., mustExist=FALSE, mkdirs=TRUE) {
# Argument 'mustExist':
mustExist <- getLogical(static, mustExist);
# Argument 'mkdirs':
mkdirs <- getLogical(static, mkdirs);
# Create pathname
pathname <- getReadablePathname(static, path=path, ..., mustExist=mustExist);
if (is.na(pathname)) {
throw("Cannot retrieve directory because it is NA.");
}
# Nothing to do?
if (isDirectory(pathname)) {
return(pathname);
}
if (!mkdirs) {
throw("Directory does not exist: ", pathname);
}
mkdirs(pathname, mustWork=TRUE)
pathname;
}, static=TRUE, protected=TRUE)
#########################################################################/**
# @RdocMethod getVector
#
# @title "Validates a vector"
#
# \description{
# @get "title" by checking its length (number of elements).
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A single @vector.}
# \item{length}{A @numeric @vector of length two or more. If two, it
# is the minimum and maximum length of \code{x}. Elsewise it is the
# set of possible lengths of \code{x}.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns the same @vector, if it is valid. Otherwise an exception is
# thrown.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getVector", "Arguments", function(static, x, length=NULL, .name=NULL, ...) {
if (length(length) == 0)
return(x);
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
# See ?is.vector for how it is defined. /HB 2009-05-19
attrs <- attributes(x);
attributes(x) <- attrs[intersect(names(attrs), c("names", "dim"))];
if (length[1] > 0 && !is.vector(x)) {
throw(sprintf("Argument '%s' is not a vector: %s", .name, storage.mode(x)));
}
xlen <- length(x);
if (length(length) == 1)
length <- c(1,length);
if (length(length) == 2) {
if (xlen < length[1] || xlen > length[2]) {
if (length[1] == length[2] && length[1] == 1) {
throw(sprintf("Argument '%s' should be a single value not %d values.", .name, xlen));
} else if (length[1] == length[2]) {
throw(sprintf("Number of elements in argument '%s' should be exactly %d not %d value(s).", .name, length[1], xlen));
} else {
throw(sprintf("Number of elements in argument '%s' is out of range [%d,%d]: %d", .name, length[1], length[2], xlen));
}
}
} else {
if (!is.element(xlen, length)) {
throw(sprintf("Number of elements in argument '%s' is not in {%s}: %d",
.name, seqToHumanReadable(length), xlen, ));
}
}
attributes(x) <- attrs;
x;
}, static=TRUE, private=TRUE)
#########################################################################/**
# @RdocMethod getCharacters
# @aliasmethod getCharacter
#
# @title "Coerces to a character vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{s}{A @vector.}
# \item{nchar}{A @numeric @vector of length one or two. If one,
# the maximum number of characters ("length") in \code{s}. If two,
# the minimum and maximum length of \code{s}.}
# \item{useNames}{If @TRUE, the 'names' attribute is preserved, otherwise
# it is dropped.}
# \item{asGString}{If @TRUE, each string is treated as a @see "GString".}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character @vector, if it is valid. Otherwise an exception is
# thrown.
# }
#
# \section{Missing values}{
# If \code{s} contains missing values, and \code{nchar} is not @NULL,
# then an exception is thrown.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getCharacters", "Arguments", function(static, s, length=NULL, trim=FALSE, nchar=NULL, useNames=TRUE, asGString=getOption("Arguments$getCharacters/args/asGString", TRUE), .name=NULL, ...) {
if (is.null(.name))
.name <- as.character(deparse(substitute(s)));
s <- getVector(static, s, length=length, .name=.name);
# Nothing to check?
if (length(s) == 0L)
return(s);
# Coerce GString:s to character strings?
if (asGString) {
# Treat only strings with GString markup. This avoids lots of
# GString overhead if there are no GStrings.
hasMarkup <- (regexpr("${", s, fixed=TRUE) != -1);
idxs <- which(hasMarkup & !is.na(s));
s[idxs] <- unlist(lapply(s[idxs], FUN=function(x) {
x <- GString(x);
as.character(x);
}), use.names=FALSE);
}
if (trim) {
# Trim the strings
# (using s[] to preserve attributes)
s[] <- unlist(lapply(s, FUN=trim), use.names=FALSE);
}
# Coerce to character strings
# (using s[] to preserve attributes)
s[] <- unlist(lapply(s, FUN=as.character), use.names=FALSE);
if (!useNames) {
names(s) <- NULL;
}
# Nothing to check?
if (is.null(nchar))
return(s);
# At this point, missing values are not allowed
if (any(is.na(s))) {
throw("Argument 'nchar' cannot be specified if character vector contains missing values: ", hpaste(sQuote(s)))
}
if (length(nchar) == 1L)
nchar <- c(1L, nchar);
# Check the string length of each character string
for (kk in seq(length=length(s))) {
slen <- nchar(s[kk], type="chars");
if (slen < nchar[1L] || slen > nchar[2L]) {
throw(sprintf("String length of elements #%d in '%s' is out of range [%d,%d]: %d '%s'", kk, .name, nchar[1L], nchar[2L], slen, s[kk]));
}
}
s;
}, static=TRUE)
setMethodS3("getCharacter", "Arguments", function(static, ..., length=c(0,1)) {
getCharacters(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getNumerics
# @aliasmethod getNumeric
#
# @title "Coerces to a numeric vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A @vector.}
# \item{range}{Two @numerics for the allowed ranged. If @NULL, range is
# not checked.}
# \item{asMode}{A @character specifying the mode to coerce to.}
# \item{disallow}{A @character @vector specifying diallowed value sets,
# i.e. \code{"NA"}, \code{"NaN"}, and/or \code{"Inf"}.}
# \item{...}{Arguments passed to @method "getVector".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @numeric @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getNumerics", "Arguments", function(static, x, range=NULL, asMode=NULL, disallow=NULL, ..., .name=NULL) {
# Argument '.name':
if (is.null(.name)) {
.name <- as.character(deparse(substitute(x)));
}
x <- getVector(static, x, ..., .name=.name);
xMode <- storage.mode(x);
# Coerce the mode of 'x'
if (is.null(asMode)) {
if (is.element(xMode, c("integer", "double"))) {
asMode <- xMode;
} else {
asMode <- "double";
}
}
# Update/coerce mode?
if (xMode != asMode) {
storage.mode(x) <- asMode;
}
# Nothing to do?
if (length(x) == 0)
return(x);
if (!is.null(disallow)) {
if (is.element("NaN", disallow) && any(is.nan(x))) {
throw(sprintf("Argument '%s' contains %d NaN value(s).",
.name, sum(is.nan(x))));
}
if (is.element("NA", disallow) && any(is.na(x) & !is.nan(x))) {
throw(sprintf("Argument '%s' contains %d NA value(s).",
.name, sum(is.na(x))));
}
# For conveniency, disallow 'Inf' here too; other range takes care of it.
if (is.element("Inf", disallow) && any(is.infinite(x))) {
throw(sprintf("Argument '%s' contains %d (-/+)Inf value(s).",
.name, sum(is.infinite(x))));
}
}
# Nothing to check?
if (is.null(range))
return(x);
# Argument 'range':
if (length(range) != 2) {
throw("Argument 'range' should be of length 2: ", length(range));
}
if (range[2] < range[1]) {
throw(sprintf("Argument 'range' is not ordered: c(%s,%s)", range[1], range[2]));
}
# Suppress warnings when there are no finite values in x.
suppressWarnings({
xrange <- range(x, na.rm=TRUE);
})
if (xrange[1] < range[1] || xrange[2] > range[2]) {
xrange <- as.character(xrange);
range <- as.character(range);
if (length(x) == 1) {
throw(sprintf("Argument '%s' is out of range [%s,%s]: %s",
.name, range[1], range[2], x));
} else {
throw(sprintf("Range of argument '%s' is out of range [%s,%s]: [%s,%s]",
.name, range[1], range[2], xrange[1], xrange[2]));
}
}
x;
}, static=TRUE)
setMethodS3("getNumeric", "Arguments", function(static, ..., length=1) {
getNumerics(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getDoubles
# @aliasmethod getDouble
#
# @title "Coerces to a double vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @method "getNumeric".}
# \item{disallow}{Disallowed values. See @method "getNumerics" for details.}
# }
#
# \value{
# Returns a @double @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getDoubles", "Arguments", function(static, ..., disallow=c("NA","NaN")) {
getNumerics(static, ..., asMode="double", disallow=disallow);
}, static=TRUE)
setMethodS3("getDouble", "Arguments", function(static, ..., length=1) {
getDoubles(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getIntegers
# @aliasmethod getInteger
#
# @title "Coerces to a integer vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{...}{Arguments passed to @method "getNumeric".}
# \item{disallow}{Disallowed values. See @method "getNumerics" for details.}
# }
#
# \value{
# Returns a @integer @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getIntegers", "Arguments", function(static, ..., disallow=c("NA","NaN")) {
getNumerics(static, ..., asMode="integer", disallow=disallow);
}, static=TRUE)
setMethodS3("getInteger", "Arguments", function(static, ..., length=1) {
getIntegers(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getIndices
# @aliasmethod getIndex
#
# @title "Coerces to a integer vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A single @vector. If @logical, @see "base::which" is used.}
# \item{...}{Arguments passed to @method "getIntegers".}
# \item{range}{Allowed range. See @method "getNumerics" for details.}
# \item{max}{The maximum of the default range.}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns an @integer @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getIndices", "Arguments", function(static, x, ..., max=Inf, range=c(1*(max > 0L),max), .name=NULL) {
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
# Argument 'x':
if (is.logical(x)) {
x <- which(x);
}
# Argument 'max':
if (length(max) != 1) {
throw("Argument 'max' must be a single value: ", length(max));
}
max <- as.numeric(max);
if (is.na(max)) {
throw("Argument 'max' is NA/NaN: ", max);
} else if (max < 0) {
throw("Argument 'max' must be positive: ", max);
}
# Argument 'range':
if (!is.null(range)) {
if (length(range) != 2) {
throw("Argument 'range' should be of length 2: ", length(range));
}
if (range[2] < range[1]) {
throw(sprintf("Argument 'range' is not ordered: c(%s,%s)", range[1], range[2]));
}
}
# Identify indices
x <- getIntegers(static, x, ..., range=range, .name=.name);
# Special dealing with range = c(0,0)
if (!is.null(range)) {
if (range[2] < 1L) {
xt <- x[is.finite(x)];
if (length(xt) > 0) {
throw(sprintf("Argument 'x' contains %d non-missing indices although the range ([%s,%s]) implies that there should be none.", length(xt), range[1L], range[2L]));
}
}
}
x;
}, static=TRUE)
setMethodS3("getIndex", "Arguments", function(static, ..., length=1) {
getIndices(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getLogicals
# @aliasmethod getLogical
#
# @title "Coerces to a logical vector and validates"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{x}{A @vector.}
# \item{disallow}{A @character @vector specifying diallowed value sets
# after coercing, i.e. \code{"NA"}.}
# \item{...}{Arguments passed to @method "getVector".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @numeric @vector.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getLogicals", "Arguments", function(static, x, ..., disallow=c("NA", "NaN"), coerce=FALSE, .name=NULL) {
if (is.null(.name))
.name <- as.character(deparse(substitute(x)));
x <- getVector(static, x, ..., .name=.name);
# Coerce to logicals?
if (coerce)
x <- as.logical(x);
if (!is.null(disallow)) {
if (is.element("NA", disallow) && any(is.na(x))) {
throw(sprintf("Argument '%s' contains %d NA value(s).",
.name, sum(is.na(x))));
}
}
# Assert that 'x' is logical before returning
if (any(!is.logical(x)))
throw(sprintf("Argument '%s' is non-logical: %s", .name, class(x)));
x;
}, static=TRUE)
setMethodS3("getLogical", "Arguments", function(static, ..., length=1) {
getLogicals(static, ..., length=length);
}, static=TRUE)
#########################################################################/**
# @RdocMethod getVerbose
#
# @title "Coerces to Verbose object"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{verbose}{A single object. If a @see "Verbose", it is immediately
# returned. If a @numeric value, it is used as the threshold.
# Otherwise the object is coerced to a @logical value and if @TRUE,
# the threshold is \code{defaultThreshold}.}
# \item{defaultThreshold}{A @numeric value for the default threshold, if
# \code{verbose} was interpreted as a @logical value.}
# \item{useNullVerbose}{If \code{verbose} can be interpreted as @FALSE,
# return a @see NullVerbose object if @TRUE.}
# \item{...}{Passed to the constructor of @see "Verbose".}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns a @see Verbose (or a @see "NullVerbose") object.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getVerbose", "Arguments", function(static, verbose, defaultThreshold=-1, useNullVerbose=TRUE, ..., .name=NULL) {
if (inherits(verbose, "Verbose"))
return(verbose);
if (is.null(.name))
.name <- as.character(deparse(substitute(verbose)));
if (is.numeric(verbose)) {
verbose <- getDouble(static, verbose, .name=.name);
verbose <- Verbose(threshold=verbose, ...);
} else {
verbose <- getLogical(static, verbose, .name=.name);
if (!verbose && useNullVerbose) {
verbose <- NullVerbose();
} else {
defaultThreshold <- getNumeric(static, defaultThreshold);
verbose <- Verbose(threshold=defaultThreshold, ...);
}
}
verbose;
}, static=TRUE)
#########################################################################/**
# @RdocMethod getRegularExpression
#
# @title "Gets a valid regular expression pattern"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{pattern}{A @character string to be validated.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns a @character string.
# }
#
# @author
#
# \seealso{
# @see "base::grep".
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getRegularExpression", "Arguments", function(static, pattern=NULL, ..., .name=NULL) {
if (is.null(.name)) {
.name <- as.character(deparse(substitute(pattern)));
}
if (is.null(pattern)) {
throw(sprintf("Argument '%s' is not a valid regular expression: NULL",
.name));
}
pattern <- getCharacter(static, pattern, .name=.name, length=c(1,1));
# Validate it
tryCatch({
regexpr(pattern, "dummy string", ...);
}, error = function(ex) {
throw(sprintf("Argument '%s' is not a valid regular expression: %s. Error message from regexpr() was: %s", .name, pattern, ex$message));
})
pattern;
}, static=TRUE)
#########################################################################/**
# @RdocMethod getEnvironment
#
# @title "Gets an existing environment"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{envir}{An @environment, the name of a loaded package, or @NULL.
# If @NULL, the global environment is returned.}
# \item{.name}{A @character string for name used in error messages.}
# \item{...}{Not used.}
# }
#
# \value{
# Returns an @environment.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword IO
#*/#########################################################################
setMethodS3("getEnvironment", "Arguments", function(static, envir=NULL, .name=NULL, ...) {
if (is.null(.name))
.name <- as.character(deparse(substitute(envir)));
if (is.null(envir)) {
return(.GlobalEnv);
}
if (is.character(envir)) {
name <- getCharacter(static, envir, length=c(1,1));
envirs <- gsub("^package:", "", search());
pos <- which(name == envirs);
if (length(pos) == 0)
throw("Argument 'envir' is not the name of a loaded package: ", envir);
envir <- pos.to.env(pos);
}
if (!is.environment(envir)) {
throw(sprintf("Argument '%s' is not an environment: %s",
.name, class(envir)[1]));
}
}, static=TRUE)
#########################################################################/**
# @RdocMethod getInstanceOf
#
# @title "Gets an instance of the object that is of a particular class"
#
# \description{
# @get "title".
# }
#
# @synopsis
#
# \arguments{
# \item{object}{The object that should be returned as an instance of
# class \code{class}.}
# \item{class}{A @character string specifying the name of the class that
# the returned object should inherit from.}
# \item{coerce}{If @TRUE and the object is not of the wanted class, then
# method will be coerced to that class, if possible. Otherwise,
# an error is thrown.}
# \item{...}{Not used.}
# \item{.name}{A @character string for name used in error messages.}
# }
#
# \value{
# Returns an object inheriting from class \code{class}.
# }
#
# @author
#
# \seealso{
# @seeclass
# }
#
# @keyword programming
#*/#########################################################################
setMethodS3("getInstanceOf", "Arguments", function(static, object, class, coerce=FALSE, ..., .name=NULL) {
if (is.null(.name)) {
.name <- as.character(deparse(substitute(object)));
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Argument 'class':
class <- getCharacter(static, class);
# Argument 'coerce':
coerce <- getLogical(static, coerce);
# Argument 'object':
if (!inherits(object, class)) {
if (coerce) {
object <- as(object, class, ...);
} else {
throw(sprintf("Argument '%s' is neither of nor inherits class %s: %s",
.name, class[1], paste(class(object), collapse=", ")));
}
}
# Return the object
object;
}, static=TRUE, protected=TRUE)
withoutGString <- function(..., envir=parent.frame()) {
# Temporarily disable 'asGString' for Arguments$getCharacters()
oopts <- options("Arguments$getCharacters/args/asGString"=FALSE);
on.exit(options(oopts));
eval(..., envir=envir);
} # withoutGString()
############################################################################
# HISTORY:
# 2015-02-05
# o Now getReadablePathname() warns about too long pathnames on Windows.
# 2014-10-03
# o Now Arguments$getReadablePathname(file, path) ignores 'path' if
# 'file' specifies an absolute pathname.
# 2014-05-04
# o Added argument 'adjust' to Arguments$getReadablePathname().
# 2014-01-12
# o Made argument 'useNames' to getCharacters() default to TRUE.
# o Now Arguments$getCharacters() preserves attributes.
# 2013-12-15
# o Added withoutGString().
# 2013-12-13
# o Now argument 'asGString' for Arguments$getCharacters() defaults to
# getOption("Arguments$getCharacters/args/asGString", TRUE). This makes
# it possible to disable this feature, even when it is not possible to
# directly pass that argument. This will also make it possible to
# set the default to FALSE in the future (instead of TRUE as today).
# 2013-11-15
# o CLEANUP: Arguments$getNumerics(NA, range=c(0,1)) no longer gives
# warnings on "no non-missing arguments to min()" etc.
# 2013-08-26
# o CLEANUP: Arguments$getReadablePathnames(files, paths=NULL) no longer
# warns about "rep(paths, length.out = nbrOfFiles) : 'x' is NULL so
# the result will be NULL" if length(files) > 0.
# 2012-12-01
# o BUG FIX: Arguments$getIndices(NA_integer_, max=0, disallow="NaN")
# would give "Exception: Argument 'x' is of length 1 although the range
# ([0,0]) implies that is should be empty." although it should return
# NA_integer.
# 2012-10-21
# o ROBUSTNESS: Added argument 'maxTries' to Arguments$getWritablePathname()
# to have the method try to create missing directories multiple times
# before giving up.
# 2012-10-16
# o Moved Arguments$getFilename() from R.filesets to R.utils.
# Added Rd help.
# 2012-09-24
# o BUG FIX: Arguments$getReadablePath(..., mustExist=FALSE) did not work.
# 2011-11-15
# o SPEEDUP: Now Arguments$getCharacters(s, asGString=TRUE) is much
# faster for elements of 's' that are non-GStrings. For long character
# vectors the speedup is 100-200x times.
# 2011-10-16
# o CORRECTION: Arguments$getNumerics(c(Inf), disallow="Inf") would report
# that it contains "NA" instead of "Inf" values".
# 2011-03-08
# o Now Arguments$getWritablePath(NULL) returns NULL without asserting
# write permission, which is analogue to how it is done with
# Arguments$getReadablePath(NULL).
# 2010-11-19
# o TYPO: Static methods getVector() and getRegularExpression() of
# Arguments would report the incorrect argument name.
# 2010-01-25
# o ROBUSTNESS: Added validation of argument 'range' in Arguments methods.
# 2010-01-01
# o Now Arguments$getNumerics(x) displays the value of 'x' in the error
# message if it is a *single* value and out of range.
# o Added argument 'max' to Arguments$getIndices().
# 2009-12-30
# o Now Arguments$getWritablePath() and Arguments$getWritablePathname()
# throws an error is an NA file/directory is specified.
# o Now Arguments$getReadablePath() and Arguments$getReadablePathname()
# throws an error is an NA file/directory is specified, unless
# 'mustExist' is FALSE.
# o Added Arguments$getInstanceOf(...).
# o BUG FIX: Arguments$getCharacters(s) would return a *logical* instead
# of a *character* vector if 's' contained all NAs.
# 2009-11-20
# o If 'x' is a logical vector, Arguments$getIndices(x) will now return
# the same as if x <- which(x).
# 2009-10-30
# o Now Arguments$getWritablePathname(path) validates that there is enough
# file permissions so that a file can be created in the 'path' directory.
# 2009-06-29
# o Added argument 'useNames=FALSE' to getCharacters() of Arguments.
# Don't remember why I didn't want names in the first place (see below).
# 2009-05-18
# o UPDATE: Now getWritablePathname() gives a more precise error message
# if the file exists but the rights to modifies it does not.
# o UPDATE: Now getEnvironment(), getRegularExpression(), and
# getReadablePathname() give clearer error messages if more the input
# contains more than one element.
# 2009-05-15
# o Changed argument 'asMode' for Arguments$getNumerics() to default to
# NULL instead of "numeric". This will case the method to return integer
# if the input is integer, and double if the input is double. The
# previous default was alway returning doubles, cf. notes on common
# misconception of how as.numeric() works. In the case when the input
# is neither integer or double, the default is to coerce to doubles.
# Also, the method is now using storage.mode() instead of mode().
# 2009-04-04
# o Now getReadablePathname(..., mustExist=TRUE) of Arguments reports also
# the working directory if the a relative pathname is missing.
# o BUG FIX: getReadablePathname(..., mustExist=TRUE) of Arguments gave an
# internal error if the pathname was in the current directory and did
# not exist.
# 2008-12-27
# o Now getReadablePathname(..., mustExist=TRUE) and
# getWritablePathname(..., mkdirs=FALSE) of Arguments report which
# of the parent directories exists when the requested pathname is not
# found. This will help troubleshooting missing pathnames.
# 2008-12-01
# o Now getReadablePathname() and getWritablePathname() use the more
# trusted fileAccess() of R.utils.
# 2008-02-26
# o Now the '...' arguments to Arguments$getVerbose() are passed to the
# constructor of Verbose. This allows the construct of
# Arguments$getVerbose(-10, timestamp=TRUE).
# 2005-12-05
# o getNumerics(Inf, range=c(0,Inf)) would give a warning "no finite
# arguments to min; returning Inf". Fixed with a withCallingHandlers().
# 2005-11-22
# o Added Rdoc comments for getReadablePathnames().
# 2005-11-13
# o Added getReadablePathnames().
# o Now getCharacter() only accept vectors of length zero or one.
# 2005-10-25
# o BUG FIX: New 'mustNotExist' argument got logically the inverse.
# 2005-10-21
# o Renamed argument 'overwrite' in getWritablePathname() in Arguments to
# 'mustNotExist'. Renamed all 'mustExists' to 'mustExist' in all methods
# of class Arguments.
# 2005-09-06
# o Replace argument 'gString' of getCharacters() to 'asGString', cf.
# Verbose class.
# o Now Arguments$getReadablePathname() follows Windows shortcut files.
# 2005-08-01
# o getReadablePathname() no longer returns the absolute pathname by
# default. This is because on some systems the relative pathname can
# be queried wheras the absolute one may not be access due to missing
# file permissions.
# o Added getEnvironment(), getRegularExpression(),
# getReadablePath(), getWritablePath().
# 2005-07-19
# o BUG FIX: getCharacters() would not coerce Object:s correctly.
# 2005-07-07
# o getCharacters() returned attribute 'names' too. Removed.
# 2005-06-20
# o Added argument 'absolutePath' to getReadablePathname().
# 2005-06-18
# o Added static methods getVector(), getNumeric/s(), getDouble/s(),
# getInteger/s(), getIndices/getIndex(), and getLogical/s(). These should
# be very handy. Also added getVector().
# Not sure if getVector() should be renamed to checkLength(), and even
# be moved to the Assert class. Not sure where the assert class is
# heading.
# 2005-05-31
# o Created from former File$validateFileAndPath().
############################################################################
|
#' Rboretum Unique Clade Fetcher
#'
#' This function takes a named multiPhylo object and a focal tree, and returns clades that occur in the focal clade but not all other trees in the multiPhylo
#' @param trees Named multiPhylo object
#' @param focal_tree Name of focal tree
#' @return Character vector of clades that occur in the focal tree, and are missing from at least one other tree in the multiPhylo
#' @export
#'
get.uniqueClades <- function(trees,focal_tree){
if(!Rboretum::is.multiPhylo(trees)){
stop("'trees' does not appear to be a valid multiPhlyo object with 2+ trees")
} else if(!Rboretum::check.shared(trees)){
stop("'trees' don't appear to share at least 3 taxa in common.")
} else if(Rboretum::same.topology(trees)){
stop("'trees' have identical topology. No comparison possible.")
} else if(is.null(names(trees))){
stop("'trees' must be named for get.uniqueClades. Name multiPhylo by assigning through names(trees) <- c('name1','name2',etc)")
}
tree_count <- length(trees)
if(missing(focal_tree)){
stop("'focal_tree' name not assigned.")
} else{
focal_tree <- as.character(focal_tree)
}
if(has_error(tree <- trees[[focal_tree]])){
stop("'focal_tree' must be the name of a tree in the multiPhylo. Check names(trees).")
}
clade_compare <- Rboretum::compare.clades(trees) %>%
filter(Tree_Percent < 100)
clades <- c()
for(i in 1:nrow(clade_compare)){
if(str_detect(clade_compare$Trees_with_Clade[i],";")){
check <- semiVector(clade_compare$Trees_with_Clade[i])
} else{ check <- clade_compare$Trees_with_Clade[i] }
if(focal_tree %in% check){
clades <- c(clades,clade_compare$Clade[i])
}
}
return(clades)
}
|
/R/get.uniqueClades.R
|
no_license
|
erenada/Rboretum
|
R
| false
| false
| 1,734
|
r
|
#' Rboretum Unique Clade Fetcher
#'
#' This function takes a named multiPhylo object and a focal tree, and returns clades that occur in the focal clade but not all other trees in the multiPhylo
#' @param trees Named multiPhylo object
#' @param focal_tree Name of focal tree
#' @return Character vector of clades that occur in the focal tree, and are missing from at least one other tree in the multiPhylo
#' @export
#'
get.uniqueClades <- function(trees,focal_tree){
if(!Rboretum::is.multiPhylo(trees)){
stop("'trees' does not appear to be a valid multiPhlyo object with 2+ trees")
} else if(!Rboretum::check.shared(trees)){
stop("'trees' don't appear to share at least 3 taxa in common.")
} else if(Rboretum::same.topology(trees)){
stop("'trees' have identical topology. No comparison possible.")
} else if(is.null(names(trees))){
stop("'trees' must be named for get.uniqueClades. Name multiPhylo by assigning through names(trees) <- c('name1','name2',etc)")
}
tree_count <- length(trees)
if(missing(focal_tree)){
stop("'focal_tree' name not assigned.")
} else{
focal_tree <- as.character(focal_tree)
}
if(has_error(tree <- trees[[focal_tree]])){
stop("'focal_tree' must be the name of a tree in the multiPhylo. Check names(trees).")
}
clade_compare <- Rboretum::compare.clades(trees) %>%
filter(Tree_Percent < 100)
clades <- c()
for(i in 1:nrow(clade_compare)){
if(str_detect(clade_compare$Trees_with_Clade[i],";")){
check <- semiVector(clade_compare$Trees_with_Clade[i])
} else{ check <- clade_compare$Trees_with_Clade[i] }
if(focal_tree %in% check){
clades <- c(clades,clade_compare$Clade[i])
}
}
return(clades)
}
|
\name{show_news}
\alias{show_news}
\title{Show package news...}
\usage{
show_news(pkg = NULL, latest = TRUE, ...)
}
\arguments{
\item{pkg}{package description, can be path or package
name. See \code{\link{as.package}} for more information}
\item{latest}{if \code{TRUE}, only show the news for the
most recent version.}
\item{...}{other arguments passed on to \code{news}}
}
\description{
Show package news
}
|
/man/show_news.Rd
|
no_license
|
BrianDiggs/devtools
|
R
| false
| false
| 426
|
rd
|
\name{show_news}
\alias{show_news}
\title{Show package news...}
\usage{
show_news(pkg = NULL, latest = TRUE, ...)
}
\arguments{
\item{pkg}{package description, can be path or package
name. See \code{\link{as.package}} for more information}
\item{latest}{if \code{TRUE}, only show the news for the
most recent version.}
\item{...}{other arguments passed on to \code{news}}
}
\description{
Show package news
}
|
#Define colors
phyla.col <- c("Acidimicrobiia"="#AA4488",
"Actinobacteria" = "#DDAA77",
"Alphaproteobacteria"= "#771155",
"Bacilli"="#117744",
"Bacteroidia"= "#77AADD",
"Campylobacteria" = "#FF2222",
#"Chlamydiae"= "#DD1232" ,
#"Clostridia"= "#77CCCC",
"Cyanobacteriia"= "#AAAA44",
"Clostridia" ="#CC1234",
#"Desulfobulbia"= "#117744",
"Gammaproteobacteria"="#117777",
"Gracilibacteria"= "#44AA77",
"Kiritimatiellae" = "#DD7788",
"Negativicutes"= "#34ABAA",
"Paracubacteria"= "#11BBCC",
#"NB1-j_uncl" = "#774411",
"Rhodothermia" = "#E69F00",
"Parcubacteria"= "#88CCAA",
"Planctomycetes"= "#777711",
#"OM190"= "#009E73",
#"SAR324_clade(Marine_group_B)_uncl"="#CC99BB",
"Saccharimonadia" = "#AACC45",
#"Thermoplasmata" = "#0072B2",
"Verrucomicrobiae" = "#AA7744",
#"Vicinamibacteria" ="#DDDD77",
"Other taxa"= "#114477")
indicators.col <- c("Aeromonadaceae"="#AA4488",
"Arcobacteraceae" = "#771155",
"Bacteroidaceae"= "#DDAA77",
"Bdellovibrionaceae"="#AAAA44",
"Carnobacteriaceae"= "#77AADD",
"Campylobacteraceae" = "#FF2222",
"Chlamydiaceae"= "#E5C494" ,
"Enterococcaceae"= "#117777",
"Clostridiaceae" ="#CC1234",
"Desulfovibrionaceae"= "#117744",
"Enterobacteriaceae"="#77CCCC",
"Flavobacteriaceae"= "#44AA77",
"Helicobacteraceae" = "#88CCAA",
"Lachnospiraceae"= "#34ABAA",
"Legionellaceae"= "#11BBCC",
"Leptospiraceae" = "#774411",
"Listeriaceae" = "#E69F00",
"Moraxellaceae"= "#DDDD77",
"Mycobacteriaceae"= "#777711",
"Porphyromonadaceae"= "#009E73",
"Pseudomonadaceae"="#CC99BB",
"Ruminococcaceae" = "#AACC45",
"Staphylococcaceae" = "#0072B2",
"Streptococcaceae" = "#AA7744",
"Vibrionaceae" = "#DD7788",
"Yersiniaceae"= "#117744",
"Other taxa"= "#114477")
location.col <- c("R-Estuary-1" = "#FC8D62",
"R-Estuary-2" = "#FFD92F",
"NS-Marine" = "#E5C494",
"OS-Marine" = "#8DA0CB",
"SM-Outfall" = "#66C2A5",
"R-Mouth" = "#E41A1C")
season.col <- c("winter" = "#8960b3",
"spring" = "#56ae6c",
"summer" = "#ba495b",
"autumn" = "#b0923b")
tol21rainbow<- c("#771155",
"#AA4488",
"#CC99BB",
"#114477",
"#4477AA",
"#117744",
"#117777",
"#88CCAA",
"#77CCCC",
"#00ffff",
"#44AA77",
"#44AAAA",
"#777711",
"#AAAA44",
"#DDDD77",
"#774411",
"#AA7744",
"#DDAA77",
"#771122",
"#AA4455",
"#DD7788")
|
/scripts/Color_palettes.R
|
no_license
|
Orel-N/microbiome
|
R
| false
| false
| 3,601
|
r
|
#Define colors
phyla.col <- c("Acidimicrobiia"="#AA4488",
"Actinobacteria" = "#DDAA77",
"Alphaproteobacteria"= "#771155",
"Bacilli"="#117744",
"Bacteroidia"= "#77AADD",
"Campylobacteria" = "#FF2222",
#"Chlamydiae"= "#DD1232" ,
#"Clostridia"= "#77CCCC",
"Cyanobacteriia"= "#AAAA44",
"Clostridia" ="#CC1234",
#"Desulfobulbia"= "#117744",
"Gammaproteobacteria"="#117777",
"Gracilibacteria"= "#44AA77",
"Kiritimatiellae" = "#DD7788",
"Negativicutes"= "#34ABAA",
"Paracubacteria"= "#11BBCC",
#"NB1-j_uncl" = "#774411",
"Rhodothermia" = "#E69F00",
"Parcubacteria"= "#88CCAA",
"Planctomycetes"= "#777711",
#"OM190"= "#009E73",
#"SAR324_clade(Marine_group_B)_uncl"="#CC99BB",
"Saccharimonadia" = "#AACC45",
#"Thermoplasmata" = "#0072B2",
"Verrucomicrobiae" = "#AA7744",
#"Vicinamibacteria" ="#DDDD77",
"Other taxa"= "#114477")
indicators.col <- c("Aeromonadaceae"="#AA4488",
"Arcobacteraceae" = "#771155",
"Bacteroidaceae"= "#DDAA77",
"Bdellovibrionaceae"="#AAAA44",
"Carnobacteriaceae"= "#77AADD",
"Campylobacteraceae" = "#FF2222",
"Chlamydiaceae"= "#E5C494" ,
"Enterococcaceae"= "#117777",
"Clostridiaceae" ="#CC1234",
"Desulfovibrionaceae"= "#117744",
"Enterobacteriaceae"="#77CCCC",
"Flavobacteriaceae"= "#44AA77",
"Helicobacteraceae" = "#88CCAA",
"Lachnospiraceae"= "#34ABAA",
"Legionellaceae"= "#11BBCC",
"Leptospiraceae" = "#774411",
"Listeriaceae" = "#E69F00",
"Moraxellaceae"= "#DDDD77",
"Mycobacteriaceae"= "#777711",
"Porphyromonadaceae"= "#009E73",
"Pseudomonadaceae"="#CC99BB",
"Ruminococcaceae" = "#AACC45",
"Staphylococcaceae" = "#0072B2",
"Streptococcaceae" = "#AA7744",
"Vibrionaceae" = "#DD7788",
"Yersiniaceae"= "#117744",
"Other taxa"= "#114477")
location.col <- c("R-Estuary-1" = "#FC8D62",
"R-Estuary-2" = "#FFD92F",
"NS-Marine" = "#E5C494",
"OS-Marine" = "#8DA0CB",
"SM-Outfall" = "#66C2A5",
"R-Mouth" = "#E41A1C")
season.col <- c("winter" = "#8960b3",
"spring" = "#56ae6c",
"summer" = "#ba495b",
"autumn" = "#b0923b")
tol21rainbow<- c("#771155",
"#AA4488",
"#CC99BB",
"#114477",
"#4477AA",
"#117744",
"#117777",
"#88CCAA",
"#77CCCC",
"#00ffff",
"#44AA77",
"#44AAAA",
"#777711",
"#AAAA44",
"#DDDD77",
"#774411",
"#AA7744",
"#DDAA77",
"#771122",
"#AA4455",
"#DD7788")
|
# Downloading the data and loading it into R
if(!file.exists('data')){
dir.create('data')
}
fileUrl <- 'https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip'
download.file(fileUrl, destfile = './data/EmissionData.zip')
unzip('./data/EmissionData.zip')
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Question 2
# Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") from 1999 to
# 2008? Use the base plotting system to make a plot answering this question.
Baltimore <- NEI[NEI$fips == '24510', ]
baltimore <- aggregate(Baltimore$Emission, by = list(Baltimore$year), FUN = sum)
names(baltimore) <- c('Year', 'Emission')
png(filename = 'plot2.png')
plot(x = baltimore$Year, y = baltimore$Emission, type = 'b', main = 'M2.5 Emission in Baltimore (1999 - 2008)', xlab = 'Year', ylab = 'Total Emission [tons]')
dev.off()
|
/plot2.R
|
no_license
|
ajladz/ExploratoryDataAnalysis---CourseProject2
|
R
| false
| false
| 927
|
r
|
# Downloading the data and loading it into R
if(!file.exists('data')){
dir.create('data')
}
fileUrl <- 'https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip'
download.file(fileUrl, destfile = './data/EmissionData.zip')
unzip('./data/EmissionData.zip')
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Question 2
# Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") from 1999 to
# 2008? Use the base plotting system to make a plot answering this question.
Baltimore <- NEI[NEI$fips == '24510', ]
baltimore <- aggregate(Baltimore$Emission, by = list(Baltimore$year), FUN = sum)
names(baltimore) <- c('Year', 'Emission')
png(filename = 'plot2.png')
plot(x = baltimore$Year, y = baltimore$Emission, type = 'b', main = 'M2.5 Emission in Baltimore (1999 - 2008)', xlab = 'Year', ylab = 'Total Emission [tons]')
dev.off()
|
library(sf)
library(sp)
library(mapview)
library(tidyverse)
library(plotrix)
library(spatstat)
library(maptools)
library(raster)
setwd("C:/Users/Rocket/Google Drive/1.Materias/Analisis_espacial/2_Analisis_puntos")
data<-read.csv("input/crime-lat-long.csv")
str(data)
summary(data$lat)
summary(data$long)
data<-data[!is.na(data$long)&!is.na(data$lat),]
data<-data[data$year==2013,]
summary(data$lat)
summary(data$long)
#https://hoyodecrimen.com/
#zero <- zerodist(data)
border <- shapefile("input/DF_Delegaciones.shp")
#crime_sf = st_as_sf(data, coords = c("long", "lat"), crs = 4326)
#rs<-st_crs(border)
#st_crs(c)<-rs
coordinates(data)=~long+lat
plot(data,pch="+",cex=0.5,main="",col=data$crime)
plot(border,add=T)
legend(x=-0.53,y=51.41,pch="+",legend=unique(data$crime),cex=0.4)
projection(data)=projection(border)
#crime_utm<-st_transform(crime_sf,crs=6369)
#border_utm<-st_transform(border,crs=6369)
data_utm<-spTransform(data, CRS("+init=epsg:6369"))
border_utm<-spTransform(border, CRS("+init=epsg:6369"))
x_coord<-data_utm@coords[,1]
y_coord<-data_utm@coords[,2]
mean_centerX<-mean(data_utm@coords[,1])
mean_centerY<-mean(data_utm@coords[,2])
standard_deviationX <- sd(x_coord)
standard_deviationY <- sd(y_coord)
standard_distance <- sqrt(sum(((x_coord-mean_centerX)^2+(y_coord-mean_centerY)^2))/(nrow(data_utm)))
plot(data_utm,pch="+",cex=0.05,main="")
plot(border,add=T,cex=0.001)
points(mean_centerX,mean_centerY,col="red",pch=16)
draw.circle(mean_centerX,mean_centerY,radius=standard_distance,border="red",lwd=2)
plot(data_utm,pch="+",cex=0.05,main="")
plot(border,add=T,cex=0.001)
points(mean_centerX,mean_centerY,col="red",pch=16)
draw.ellipse(mean_centerX,mean_centerY,a=standard_deviationX,b=standard_deviationY,border="red",lwd=2)
#Homicidios
homicides<-data_utm[data_utm$crime=="HOMICIDIO DOLOSO",]
homicides <- remove.duplicates(homicides)
w<-as(border_utm, "owin")
homicides.ppp <- ppp(x=homicides@coords[,1],y=homicides@coords[,2],window=w)
homicides.ppp$n/sum(sapply(slot(border_utm, "polygons"), slot, "area"))
q<-quadratcount(homicides.ppp, nx = 8, ny = 8)
plot(homicides.ppp,pch=20, cols="grey70",cex=0.05,main="Homicidios")
plot(q,add=TRUE,col="red")
Local.Intensity <- data.frame(Mun=factor(),Number=numeric())
for(i in unique(border_utm$Name)){
sub.pol <- border_utm[border_utm$Name==i,]
sub.ppp <- ppp(x=homicides.ppp$x,y=homicides.ppp$y,window=as.owin(sub.pol))
Local.Intensity <- rbind(Local.Intensity,data.frame(Mun=factor(i,levels=border_utm$Name),Number=sub.ppp$n))
}
colorScale <- color.scale(Local.Intensity[order(Local.Intensity[,2]),2],color.spec="rgb",extremes=c("green","red"),alpha=0.8)
barplot(Local.Intensity[order(Local.Intensity[,2]),2],names.arg=Local.Intensity[order(Local.Intensity[,2]),1],horiz=T,las=2,space=1,col=colorScale)
sigma1<- bw.diggle(homicides.ppp)
sigma2<- bw.ppl(homicides.ppp)
sigma3<- bw.scott(homicides.ppp)[1]
sigma4<- bw.scott(homicides.ppp)[2]
d1<-density.ppp(homicides.ppp, sigma =sigma1,edge=T)
d2<-density.ppp(homicides.ppp, sigma =sigma2,edge=T)
d3<-density.ppp(homicides.ppp, sigma =sigma3,edge=T)
d4<-density.ppp(homicides.ppp, sigma =sigma4,edge=T)
plot(d1,main=paste("h =",round(sigma1,2)))
plot(d2,main=paste("h =",round(sigma2,2)))
plot(d3,main=paste("h =",round(sigma2,2)))
plot(d3,main=paste("h =",round(sigma2,2)))
plot(Gest(homicides.ppp),main="Homicidios")
|
/2_Analisis_puntos/Script02.R
|
no_license
|
alequech/Curso-analisis-espacial2018
|
R
| false
| false
| 3,397
|
r
|
library(sf)
library(sp)
library(mapview)
library(tidyverse)
library(plotrix)
library(spatstat)
library(maptools)
library(raster)
setwd("C:/Users/Rocket/Google Drive/1.Materias/Analisis_espacial/2_Analisis_puntos")
data<-read.csv("input/crime-lat-long.csv")
str(data)
summary(data$lat)
summary(data$long)
data<-data[!is.na(data$long)&!is.na(data$lat),]
data<-data[data$year==2013,]
summary(data$lat)
summary(data$long)
#https://hoyodecrimen.com/
#zero <- zerodist(data)
border <- shapefile("input/DF_Delegaciones.shp")
#crime_sf = st_as_sf(data, coords = c("long", "lat"), crs = 4326)
#rs<-st_crs(border)
#st_crs(c)<-rs
coordinates(data)=~long+lat
plot(data,pch="+",cex=0.5,main="",col=data$crime)
plot(border,add=T)
legend(x=-0.53,y=51.41,pch="+",legend=unique(data$crime),cex=0.4)
projection(data)=projection(border)
#crime_utm<-st_transform(crime_sf,crs=6369)
#border_utm<-st_transform(border,crs=6369)
data_utm<-spTransform(data, CRS("+init=epsg:6369"))
border_utm<-spTransform(border, CRS("+init=epsg:6369"))
x_coord<-data_utm@coords[,1]
y_coord<-data_utm@coords[,2]
mean_centerX<-mean(data_utm@coords[,1])
mean_centerY<-mean(data_utm@coords[,2])
standard_deviationX <- sd(x_coord)
standard_deviationY <- sd(y_coord)
standard_distance <- sqrt(sum(((x_coord-mean_centerX)^2+(y_coord-mean_centerY)^2))/(nrow(data_utm)))
plot(data_utm,pch="+",cex=0.05,main="")
plot(border,add=T,cex=0.001)
points(mean_centerX,mean_centerY,col="red",pch=16)
draw.circle(mean_centerX,mean_centerY,radius=standard_distance,border="red",lwd=2)
plot(data_utm,pch="+",cex=0.05,main="")
plot(border,add=T,cex=0.001)
points(mean_centerX,mean_centerY,col="red",pch=16)
draw.ellipse(mean_centerX,mean_centerY,a=standard_deviationX,b=standard_deviationY,border="red",lwd=2)
#Homicidios
homicides<-data_utm[data_utm$crime=="HOMICIDIO DOLOSO",]
homicides <- remove.duplicates(homicides)
w<-as(border_utm, "owin")
homicides.ppp <- ppp(x=homicides@coords[,1],y=homicides@coords[,2],window=w)
homicides.ppp$n/sum(sapply(slot(border_utm, "polygons"), slot, "area"))
q<-quadratcount(homicides.ppp, nx = 8, ny = 8)
plot(homicides.ppp,pch=20, cols="grey70",cex=0.05,main="Homicidios")
plot(q,add=TRUE,col="red")
Local.Intensity <- data.frame(Mun=factor(),Number=numeric())
for(i in unique(border_utm$Name)){
sub.pol <- border_utm[border_utm$Name==i,]
sub.ppp <- ppp(x=homicides.ppp$x,y=homicides.ppp$y,window=as.owin(sub.pol))
Local.Intensity <- rbind(Local.Intensity,data.frame(Mun=factor(i,levels=border_utm$Name),Number=sub.ppp$n))
}
colorScale <- color.scale(Local.Intensity[order(Local.Intensity[,2]),2],color.spec="rgb",extremes=c("green","red"),alpha=0.8)
barplot(Local.Intensity[order(Local.Intensity[,2]),2],names.arg=Local.Intensity[order(Local.Intensity[,2]),1],horiz=T,las=2,space=1,col=colorScale)
sigma1<- bw.diggle(homicides.ppp)
sigma2<- bw.ppl(homicides.ppp)
sigma3<- bw.scott(homicides.ppp)[1]
sigma4<- bw.scott(homicides.ppp)[2]
d1<-density.ppp(homicides.ppp, sigma =sigma1,edge=T)
d2<-density.ppp(homicides.ppp, sigma =sigma2,edge=T)
d3<-density.ppp(homicides.ppp, sigma =sigma3,edge=T)
d4<-density.ppp(homicides.ppp, sigma =sigma4,edge=T)
plot(d1,main=paste("h =",round(sigma1,2)))
plot(d2,main=paste("h =",round(sigma2,2)))
plot(d3,main=paste("h =",round(sigma2,2)))
plot(d3,main=paste("h =",round(sigma2,2)))
plot(Gest(homicides.ppp),main="Homicidios")
|
\name{redisMove}
\alias{redisMove}
\title{
Move the specified key/value pair to another database.
}
\description{
Move the specified key/value pair in the currently selected database to
another database.
}
\usage{
redisMove(key, dbindex)
}
\arguments{
\item{key}{The key to move.}
\item{dbindex}{The destination database index number.}
}
\details{
This command returns TRUE only if the key was successfully moved,
and FALSE if the target key was already there or if the source key was
not found at all. It is possible to use \code{redisMove} as
a locking primitive.
}
\value{
Returns TRUE if the key/value pair was moved, or FALSE otherwise.
}
\references{
http://redis.io/commands
}
\author{
B. W. Lewis
}
\seealso{
\code{\link{redisSelect}}
}
\examples{
\dontrun{
redisConnect()
redisSelect(1)
redisSet('x',5)
redisMove('x',2)
redisSelect(2)
redisGet('x')
}
}
|
/man/redisMove.Rd
|
no_license
|
bwlewis/rredis
|
R
| false
| false
| 869
|
rd
|
\name{redisMove}
\alias{redisMove}
\title{
Move the specified key/value pair to another database.
}
\description{
Move the specified key/value pair in the currently selected database to
another database.
}
\usage{
redisMove(key, dbindex)
}
\arguments{
\item{key}{The key to move.}
\item{dbindex}{The destination database index number.}
}
\details{
This command returns TRUE only if the key was successfully moved,
and FALSE if the target key was already there or if the source key was
not found at all. It is possible to use \code{redisMove} as
a locking primitive.
}
\value{
Returns TRUE if the key/value pair was moved, or FALSE otherwise.
}
\references{
http://redis.io/commands
}
\author{
B. W. Lewis
}
\seealso{
\code{\link{redisSelect}}
}
\examples{
\dontrun{
redisConnect()
redisSelect(1)
redisSet('x',5)
redisMove('x',2)
redisSelect(2)
redisGet('x')
}
}
|
library(dplyr)
library(ggplot2)
library(gridExtra)
library(rmarkdown)
library(car)
library(yhat)
library(lme4)
library(grplasso)
library(polycor)
#Columns we care about
cols = c('resident_status', 'detail_age_type', 'detail_age', 'age_recode_27',
'education_reporting_flag', 'education_2003_revision', 'education_1989_revision',
'sex', 'marital_status',
'race_recode_5', 'hispanic_originrace_recode', 'X39_cause_recode', 'current_data_year' )
#Read the data from 2011 - 2015 and subsample each of them to 40K rows
set.seed(100) #to reproduce the results
Data1 = read.csv("2015_data.csv",header = TRUE)
Data1 = Data1[,cols]
Data1s <- Data1[sample(1:nrow(Data1), 40*10^3, replace=FALSE),]
Data2 = read.csv("2014_data.csv",header = TRUE)
Data2 = Data2[,cols]
Data2s <- Data2[sample(1:nrow(Data2), 40*10^3, replace=FALSE),]
Data3 = read.csv("2013_data.csv",header = TRUE)
Data3 = Data3[,cols]
Data3s <- Data3[sample(1:nrow(Data3), 40*10^3, replace=FALSE),]
Data4 = read.csv("2012_data.csv",header = TRUE)
Data4 = Data4[,cols]
Data4s <- Data4[sample(1:nrow(Data4), 40*10^3, replace=FALSE),]
Data5 = read.csv("2011_data.csv",header = TRUE)
Data5 = Data5[,cols]
Data5s <- Data5[sample(1:nrow(Data5), 40*10^3, replace=FALSE),]
#Data = Data[,cols]
#Data2 = read.csv("2014_data.csv",header = TRUE)
Data = rbind(Data1s, Data2s, Data3s, Data4s, Data5s)
#Save Memory by removing the data we've stitched together
rm(Data1, Data1s, Data2, Data2s, Data3, Data3s, Data4, Data4s, Data5, Data5s)
#########################################################################
## Data Cleaning
#Age
#get the cases where age is in years
Data = Data[Data$detail_age_type == 1, ]
Data = Data[Data$age_recode_27 != 27, ] #remove where age is not present
Data = Data[Data$age_recode_27 >= 10, ] #filter for only adults >=19
Data$age = Data$detail_age
#Data$age_recode_27 = as.factor(Data$age_recode_27)
#Education
Data = Data[Data$education_reporting_flag != 2, ] #remove where education is not present
#recoding education into 3 levels : high school or less, less than 4 year college, greater than 4 years college
Data[, 'education'] <- ifelse(is.na(Data$education_2003_revision), cut(Data$education_1989_revision, breaks=c(-1, 12, 15, 17, 100)), cut(Data$education_2003_revision, breaks=c(0,3,5,8, 100)))
Data$education = as.factor(Data$education)
Data = Data[Data$education != 4,] #remove where education is not reported
levels(Data$education) <- c('Up to High School','College < 4 years', 'College > 4 years', 'unknown' )
Data$education <- recode(Data$education, 1 = '')
Data$education <- factor(Data$education)
#Resident Status
Data$resident_status = as.factor(Data$resident_status)
levels(Data$resident_status) <- c('Resident', 'Intrastate NR', 'Interstate NR', 'Foreign Residents')
#gender
#nothing to be done, already a categorical variable
#Race
Data$race = as.factor(Data$race_recode_5)
levels(Data$race) <- c('White', 'Black', 'American Indian', 'Asian/Pacific')
#recode hispanic into 3 levels: hispanic, nonhispanic, unreported
Data$hispanic_originrace_recode = as.numeric(Data$hispanic_originrace_recode)
Data[, 'hispanic'] <- cut(Data$hispanic_originrace_recode, breaks=c(0,5,8,10), labels=c('hispanic','non-hispanic','unknown'))
Data = Data[Data$hispanic !='unknown',] #remove where hispanic origin not reported
Data$hispanic <- factor(Data$hispanic)
#marital_status
levels(Data$marital_status) <- c('Divorced', 'Married', 'Never Married', 'Unknown', 'Widowed')
#X39_recode_cause
Data$cause_of_death = as.factor(Data$X39_cause_recode)
levels(Data$cause_of_death) = c("Tuberculosis",
"Syphilis",
"HIV",
"Malignant neoplasms",
"Malignant neoplasm Stomach",
"Malignant neoplasms of Colon",
"Malignant neoplasm of Pancreas (C25)",
"Malignant neoplasms of Lung",
"Malignant neoplasm of breast",
"Malignant neoplasms of Ovary",
"Malignant neoplasm of prostate",
"Malignant neoplasms of urinary tract",
"Non-Hodgkin's lymphoma",
"Leukemia",
"Other malignant neoplasms",
"Diabetes",
"Alzheimer's",
"Major cardiovascular diseases",
"Diseases of heart",
"Hypertensive heart disease",
"Ischemic heart diseases",
"Other diseases of heart",
"hypertension and hypertensive renal disease",
"Cerebrovascular diseases",
"Atherosclerosis",
"Other diseases of circulatory system",
"Influenza and pneumonia",
"Chronic lower respiratory diseases",
"Peptic ulcer",
"Chronic liver disease and cirrhosis",
"Nephritis",
"Pregnancy",
"Perinatal period",
"Congenital malformations",
"Sudden infant death syndrome",
"Abnormal clinical and laboratory findings",
"All other diseases (Residual)",
"Motor vehicle accidents",
"All other and unspecified accidents",
"Suicide",
"Assault",
"external causes")
#current year
Data$current_data_year = as.factor(Data$current_data_year)
Data$year = Data$current_data_year
#####################################################################
cols = c('resident_status', 'age',
'education',
'sex', 'marital_status',
'race', 'hispanic', 'cause_of_death', 'year' )
Data = Data[,cols]
########################################################################
# Exploratory Data Analysis
#histogram of the response variable
hist(Data$age, breaks=100, xlab='Age at Time of Death', main='') #histogram of age at TOD, makes since it's left tailed
# boxplots of response vs qualitative predictors
boxplot(Data$age ~ Data$resident_status, ylab='Age', xlab='Resident Status', main='Variation of Age by Residency', col= rainbow(10))
boxplot(Data$age ~ Data$sex, ylab='Age', xlab='Sex', main='Variation of Age by Sex', col= rainbow(10))
boxplot(Data$age ~ Data$race, ylab='Age', xlab='Race', main='Variation of Age by Race', col= rainbow(10))
boxplot(Data$age ~ Data$hispanic, ylab='Age', xlab='Hispanic', main='Variation of Age by Hispanic Origin', col= rainbow(10))
boxplot(Data$age ~ Data$education, ylab='Age', xlab='Education Level', main='Variation of Age by Education', col= rainbow(10))
boxplot(Data$age ~ Data$marital_status, ylab='Age', xlab='Marital Status', main='Variation of Age by Marital Status', col= rainbow(10))
boxplot(Data$age ~ Data$year, ylab='Age', xlab='Year', main='Variation of Age by Year', col= rainbow(10))
boxplot(Data$age ~ Data$cause_of_death, ylab='Age', xlab='Cause of Death', main='Variation of Age by Cause of Death', col= rainbow(30))
#Correlation matrix
cor = hetcor(Data)
cor$correlations
cor$correlations > 0.2
#Build the model
model <- lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death + year, data=Data)
summary(model)$r.squared
summary(model)$adjusted.r.squared
ggplot(Data[,c('age', 'sex')], aes(x = sex, y = age)) +
geom_point(position = position_dodge(width = 0.4))
#Check for multicollinearity
vif = vif(model)
vif
threshold = 1/(1-summary(model)$r.squared)
threshold
threshold = max(10, threshold)
vif > threshold
#this shows that there is no multicollinearity
########################################################################
# model selection
attach(Data)
lambda = seq(0, 10, by=0.25)
# VARIABLE SELECTION
# Group Lasso
predictors = cbind(resident_status, sex, marital_status, race, hispanic, education, cause_of_death)
index <- c(4,2,5,4,2,3,42)
grouplasso_model <- grplasso(y=Data$age,predictors, lambda=lambda, model=LinReg(), index=index,center=T, standardize=T)
summary(grouplasso_model)
grouplasso_model$coefficients
# None excluded
# MODEL BUIDING
# MLR Full Model
max_model <- lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=Data)
summary(max_model)
# SubSampling for Full model - looking for significant variables
count=1
n = nrow(Data)
p = matrix(0,nrow = length(summary(max_model)$coefficients[,4]),ncol = 100)
while (count<101) {
set.seed(100)
subsample = sample(n, ceiling(n*0.02), replace = FALSE)
subdata = Data[subsample,]
submod = lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=subdata)
p[,count] = summary(submod)$coefficients[,4]
count= count + 1
}
p[p>0.01]
# All significant at 0.05 level
summary(submod)
# CHECKING ASSUMPTIONS - FULL MODEL
full.resid = rstandard(max_model)
fits = max_model$fitted
cook = cooks.distance(max_model)
par(mfrow =c(1,1))
plot(fits, full.resid, xlab="Fitted Values", ylab="Residuals", main="Scatter Plot")
abline(0,0,col='blue')
plot(cook, type="h", lwd=3, col="red", ylab = "Cook's Distance", main="Cook's Distance")
qqPlot(full.resid, ylab="Residuals", main = "QQ Plot")
hist(full.resid, xlab="Residuals", main = "Histogram")
# LOG TRANSFORMATION OF AGE (RESPONSE) TO SEE IF FIT IMPROVES
log_model <- lm(log(age) ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=Data)
summary(log_model)
# CHECKING MODEL ASSUMPTIONS - LOG TRANFORMED MODEL
log.resid = rstandard(log_model)
fits = log_model$fitted
plot(fits, log.resid, xlab="Fitted Values", ylab="Residuals", main="Scatter Plot")
abline(0,0,col='red')
qqPlot(log.resid, ylab="Residuals", main = "QQ Plot")
hist(log.resid, xlab="Residuals", main = "Histogram")
# MIXED EFFECTS MODEL
Data = within(Data,race<-relevel(race,ref='White'))
mixed_model <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=Data)
summary(mixed_model)
# vcov(mixed_model)
# CHECKING MODEL ASSUMPTIONS - MIXED EFFECTS MODEL
# Fitted Values VS Residuals
mixed.resid = resid(mixed_model)
plot(mixed_model,xlab="Fitted Values", ylab="Residuals")
qqPlot(mixed.resid, ylab="Residuals", main = "QQ Plot")
# qqnorm(mixed.resid, ylab="Residuals", main = "QQ Plot")
qqline(mixed.resid)
hist(mixed.resid, xlab="Residuals", main = "Histogram")
# MIXED EFFECTS MODEL WITH SUBSET OF DATA with AGE > 40
mixed_age40 <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=subdata[subdata$age>=40, ])
summary(mixed_age40)
mixed_age40.resid = resid(mixed_age40)
plot(mixed_age40,xlab="Fitted Values", ylab="Residuals")
abline(0,0,col="purple")
qqPlot(mixed_age40.resid, ylab="Residuals", main = "QQ Plot")
hist(mixed_age40.resid, xlab="Residuals", main = "Histogram")
# ANOVA
reduced.resident <- lm(age~resident_status,data=subdata[subdata$age>=40, ])
anova(mixed_age40, reduced.resident)
reduced.sex <- lm(age~resident_status + sex,data=subdata[subdata$age>=40, ])
anova(reduced.resident, reduced.sex)
reduced.marital_status <- lm(age~resident_status + sex + marital_status,data=subdata[subdata$age>=40, ])
anova(reduced.sex, reduced.marital_status)
reduced.race <- lm(age~resident_status + sex + marital_status + race,data=subdata[subdata$age>=40, ])
anova(reduced.marital_status, reduced.race)
reduced.education <- lm(age~resident_status + sex + marital_status + race + hispanic,data=subdata[subdata$age>=40, ])
anova(reduced.race, reduced.education)
reduced.hispanic <- lm(age~resident_status + sex + marital_status + race + hispanic,data=subdata[subdata$age>=40, ])
anova(reduced.race, reduced.hispanic)
# MIXED EFFECTS MODEL ON POPULATIONS WITH AGE <80
mixed_age80 <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=subdata[subdata$age<=80, ])
summary(mixed_age80)
mixed_age80.resid = resid(mixed_age80)
plot(mixed_age80,xlab="Fitted Values", ylab="Residuals")
abline(0,0,col="purple")
qqPlot(mixed_age80.resid, ylab="Residuals", main = "QQ Plot")
hist(mixed_age80.resid, xlab="Residuals", main = "Histogram")
|
/moratality.R
|
no_license
|
kunaalahuja/Mortality-Model
|
R
| false
| false
| 13,158
|
r
|
library(dplyr)
library(ggplot2)
library(gridExtra)
library(rmarkdown)
library(car)
library(yhat)
library(lme4)
library(grplasso)
library(polycor)
#Columns we care about
cols = c('resident_status', 'detail_age_type', 'detail_age', 'age_recode_27',
'education_reporting_flag', 'education_2003_revision', 'education_1989_revision',
'sex', 'marital_status',
'race_recode_5', 'hispanic_originrace_recode', 'X39_cause_recode', 'current_data_year' )
#Read the data from 2011 - 2015 and subsample each of them to 40K rows
set.seed(100) #to reproduce the results
Data1 = read.csv("2015_data.csv",header = TRUE)
Data1 = Data1[,cols]
Data1s <- Data1[sample(1:nrow(Data1), 40*10^3, replace=FALSE),]
Data2 = read.csv("2014_data.csv",header = TRUE)
Data2 = Data2[,cols]
Data2s <- Data2[sample(1:nrow(Data2), 40*10^3, replace=FALSE),]
Data3 = read.csv("2013_data.csv",header = TRUE)
Data3 = Data3[,cols]
Data3s <- Data3[sample(1:nrow(Data3), 40*10^3, replace=FALSE),]
Data4 = read.csv("2012_data.csv",header = TRUE)
Data4 = Data4[,cols]
Data4s <- Data4[sample(1:nrow(Data4), 40*10^3, replace=FALSE),]
Data5 = read.csv("2011_data.csv",header = TRUE)
Data5 = Data5[,cols]
Data5s <- Data5[sample(1:nrow(Data5), 40*10^3, replace=FALSE),]
#Data = Data[,cols]
#Data2 = read.csv("2014_data.csv",header = TRUE)
Data = rbind(Data1s, Data2s, Data3s, Data4s, Data5s)
#Save Memory by removing the data we've stitched together
rm(Data1, Data1s, Data2, Data2s, Data3, Data3s, Data4, Data4s, Data5, Data5s)
#########################################################################
## Data Cleaning
#Age
#get the cases where age is in years
Data = Data[Data$detail_age_type == 1, ]
Data = Data[Data$age_recode_27 != 27, ] #remove where age is not present
Data = Data[Data$age_recode_27 >= 10, ] #filter for only adults >=19
Data$age = Data$detail_age
#Data$age_recode_27 = as.factor(Data$age_recode_27)
#Education
Data = Data[Data$education_reporting_flag != 2, ] #remove where education is not present
#recoding education into 3 levels : high school or less, less than 4 year college, greater than 4 years college
Data[, 'education'] <- ifelse(is.na(Data$education_2003_revision), cut(Data$education_1989_revision, breaks=c(-1, 12, 15, 17, 100)), cut(Data$education_2003_revision, breaks=c(0,3,5,8, 100)))
Data$education = as.factor(Data$education)
Data = Data[Data$education != 4,] #remove where education is not reported
levels(Data$education) <- c('Up to High School','College < 4 years', 'College > 4 years', 'unknown' )
Data$education <- recode(Data$education, 1 = '')
Data$education <- factor(Data$education)
#Resident Status
Data$resident_status = as.factor(Data$resident_status)
levels(Data$resident_status) <- c('Resident', 'Intrastate NR', 'Interstate NR', 'Foreign Residents')
#gender
#nothing to be done, already a categorical variable
#Race
Data$race = as.factor(Data$race_recode_5)
levels(Data$race) <- c('White', 'Black', 'American Indian', 'Asian/Pacific')
#recode hispanic into 3 levels: hispanic, nonhispanic, unreported
Data$hispanic_originrace_recode = as.numeric(Data$hispanic_originrace_recode)
Data[, 'hispanic'] <- cut(Data$hispanic_originrace_recode, breaks=c(0,5,8,10), labels=c('hispanic','non-hispanic','unknown'))
Data = Data[Data$hispanic !='unknown',] #remove where hispanic origin not reported
Data$hispanic <- factor(Data$hispanic)
#marital_status
levels(Data$marital_status) <- c('Divorced', 'Married', 'Never Married', 'Unknown', 'Widowed')
#X39_recode_cause
Data$cause_of_death = as.factor(Data$X39_cause_recode)
levels(Data$cause_of_death) = c("Tuberculosis",
"Syphilis",
"HIV",
"Malignant neoplasms",
"Malignant neoplasm Stomach",
"Malignant neoplasms of Colon",
"Malignant neoplasm of Pancreas (C25)",
"Malignant neoplasms of Lung",
"Malignant neoplasm of breast",
"Malignant neoplasms of Ovary",
"Malignant neoplasm of prostate",
"Malignant neoplasms of urinary tract",
"Non-Hodgkin's lymphoma",
"Leukemia",
"Other malignant neoplasms",
"Diabetes",
"Alzheimer's",
"Major cardiovascular diseases",
"Diseases of heart",
"Hypertensive heart disease",
"Ischemic heart diseases",
"Other diseases of heart",
"hypertension and hypertensive renal disease",
"Cerebrovascular diseases",
"Atherosclerosis",
"Other diseases of circulatory system",
"Influenza and pneumonia",
"Chronic lower respiratory diseases",
"Peptic ulcer",
"Chronic liver disease and cirrhosis",
"Nephritis",
"Pregnancy",
"Perinatal period",
"Congenital malformations",
"Sudden infant death syndrome",
"Abnormal clinical and laboratory findings",
"All other diseases (Residual)",
"Motor vehicle accidents",
"All other and unspecified accidents",
"Suicide",
"Assault",
"external causes")
#current year
Data$current_data_year = as.factor(Data$current_data_year)
Data$year = Data$current_data_year
#####################################################################
cols = c('resident_status', 'age',
'education',
'sex', 'marital_status',
'race', 'hispanic', 'cause_of_death', 'year' )
Data = Data[,cols]
########################################################################
# Exploratory Data Analysis
#histogram of the response variable
hist(Data$age, breaks=100, xlab='Age at Time of Death', main='') #histogram of age at TOD, makes since it's left tailed
# boxplots of response vs qualitative predictors
boxplot(Data$age ~ Data$resident_status, ylab='Age', xlab='Resident Status', main='Variation of Age by Residency', col= rainbow(10))
boxplot(Data$age ~ Data$sex, ylab='Age', xlab='Sex', main='Variation of Age by Sex', col= rainbow(10))
boxplot(Data$age ~ Data$race, ylab='Age', xlab='Race', main='Variation of Age by Race', col= rainbow(10))
boxplot(Data$age ~ Data$hispanic, ylab='Age', xlab='Hispanic', main='Variation of Age by Hispanic Origin', col= rainbow(10))
boxplot(Data$age ~ Data$education, ylab='Age', xlab='Education Level', main='Variation of Age by Education', col= rainbow(10))
boxplot(Data$age ~ Data$marital_status, ylab='Age', xlab='Marital Status', main='Variation of Age by Marital Status', col= rainbow(10))
boxplot(Data$age ~ Data$year, ylab='Age', xlab='Year', main='Variation of Age by Year', col= rainbow(10))
boxplot(Data$age ~ Data$cause_of_death, ylab='Age', xlab='Cause of Death', main='Variation of Age by Cause of Death', col= rainbow(30))
#Correlation matrix
cor = hetcor(Data)
cor$correlations
cor$correlations > 0.2
#Build the model
model <- lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death + year, data=Data)
summary(model)$r.squared
summary(model)$adjusted.r.squared
ggplot(Data[,c('age', 'sex')], aes(x = sex, y = age)) +
geom_point(position = position_dodge(width = 0.4))
#Check for multicollinearity
vif = vif(model)
vif
threshold = 1/(1-summary(model)$r.squared)
threshold
threshold = max(10, threshold)
vif > threshold
#this shows that there is no multicollinearity
########################################################################
# model selection
attach(Data)
lambda = seq(0, 10, by=0.25)
# VARIABLE SELECTION
# Group Lasso
predictors = cbind(resident_status, sex, marital_status, race, hispanic, education, cause_of_death)
index <- c(4,2,5,4,2,3,42)
grouplasso_model <- grplasso(y=Data$age,predictors, lambda=lambda, model=LinReg(), index=index,center=T, standardize=T)
summary(grouplasso_model)
grouplasso_model$coefficients
# None excluded
# MODEL BUIDING
# MLR Full Model
max_model <- lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=Data)
summary(max_model)
# SubSampling for Full model - looking for significant variables
count=1
n = nrow(Data)
p = matrix(0,nrow = length(summary(max_model)$coefficients[,4]),ncol = 100)
while (count<101) {
set.seed(100)
subsample = sample(n, ceiling(n*0.02), replace = FALSE)
subdata = Data[subsample,]
submod = lm(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=subdata)
p[,count] = summary(submod)$coefficients[,4]
count= count + 1
}
p[p>0.01]
# All significant at 0.05 level
summary(submod)
# CHECKING ASSUMPTIONS - FULL MODEL
full.resid = rstandard(max_model)
fits = max_model$fitted
cook = cooks.distance(max_model)
par(mfrow =c(1,1))
plot(fits, full.resid, xlab="Fitted Values", ylab="Residuals", main="Scatter Plot")
abline(0,0,col='blue')
plot(cook, type="h", lwd=3, col="red", ylab = "Cook's Distance", main="Cook's Distance")
qqPlot(full.resid, ylab="Residuals", main = "QQ Plot")
hist(full.resid, xlab="Residuals", main = "Histogram")
# LOG TRANSFORMATION OF AGE (RESPONSE) TO SEE IF FIT IMPROVES
log_model <- lm(log(age) ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death, data=Data)
summary(log_model)
# CHECKING MODEL ASSUMPTIONS - LOG TRANFORMED MODEL
log.resid = rstandard(log_model)
fits = log_model$fitted
plot(fits, log.resid, xlab="Fitted Values", ylab="Residuals", main="Scatter Plot")
abline(0,0,col='red')
qqPlot(log.resid, ylab="Residuals", main = "QQ Plot")
hist(log.resid, xlab="Residuals", main = "Histogram")
# MIXED EFFECTS MODEL
Data = within(Data,race<-relevel(race,ref='White'))
mixed_model <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=Data)
summary(mixed_model)
# vcov(mixed_model)
# CHECKING MODEL ASSUMPTIONS - MIXED EFFECTS MODEL
# Fitted Values VS Residuals
mixed.resid = resid(mixed_model)
plot(mixed_model,xlab="Fitted Values", ylab="Residuals")
qqPlot(mixed.resid, ylab="Residuals", main = "QQ Plot")
# qqnorm(mixed.resid, ylab="Residuals", main = "QQ Plot")
qqline(mixed.resid)
hist(mixed.resid, xlab="Residuals", main = "Histogram")
# MIXED EFFECTS MODEL WITH SUBSET OF DATA with AGE > 40
mixed_age40 <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=subdata[subdata$age>=40, ])
summary(mixed_age40)
mixed_age40.resid = resid(mixed_age40)
plot(mixed_age40,xlab="Fitted Values", ylab="Residuals")
abline(0,0,col="purple")
qqPlot(mixed_age40.resid, ylab="Residuals", main = "QQ Plot")
hist(mixed_age40.resid, xlab="Residuals", main = "Histogram")
# ANOVA
reduced.resident <- lm(age~resident_status,data=subdata[subdata$age>=40, ])
anova(mixed_age40, reduced.resident)
reduced.sex <- lm(age~resident_status + sex,data=subdata[subdata$age>=40, ])
anova(reduced.resident, reduced.sex)
reduced.marital_status <- lm(age~resident_status + sex + marital_status,data=subdata[subdata$age>=40, ])
anova(reduced.sex, reduced.marital_status)
reduced.race <- lm(age~resident_status + sex + marital_status + race,data=subdata[subdata$age>=40, ])
anova(reduced.marital_status, reduced.race)
reduced.education <- lm(age~resident_status + sex + marital_status + race + hispanic,data=subdata[subdata$age>=40, ])
anova(reduced.race, reduced.education)
reduced.hispanic <- lm(age~resident_status + sex + marital_status + race + hispanic,data=subdata[subdata$age>=40, ])
anova(reduced.race, reduced.hispanic)
# MIXED EFFECTS MODEL ON POPULATIONS WITH AGE <80
mixed_age80 <- lmer(age ~ resident_status + sex + marital_status + race + hispanic + education + cause_of_death+ (1|year), data=subdata[subdata$age<=80, ])
summary(mixed_age80)
mixed_age80.resid = resid(mixed_age80)
plot(mixed_age80,xlab="Fitted Values", ylab="Residuals")
abline(0,0,col="purple")
qqPlot(mixed_age80.resid, ylab="Residuals", main = "QQ Plot")
hist(mixed_age80.resid, xlab="Residuals", main = "Histogram")
|
##============ Sink warnings and errors to a file ==============
## use the sink() function to wrap all code within it.
##==============================================================
zz = file(paste0(Sys.getenv('REPORT_FILES_PATH'), '/.r_rendering.log.txt'))
sink(zz)
sink(zz, type = 'message')
#============== preparation ====================================
options(stringsAsFactors = FALSE)
# import libraries
#------------------------------------------------------------------
# ADD MORE LIBRARIES HERE IF YOUR TOOL DEPENDS ON OTHER R LIBRARIES
#------------------------------------------------------------------
library('getopt')
library('rmarkdown')
library('htmltools')
# load helper functions
source(paste0(Sys.getenv('TOOL_INSTALL_DIR'), '/helper.R'))
# import getopt specification matrix from a csv file
opt = getopt(getopt_specification_matrix('getopt_specification.csv',
tool_dir=Sys.getenv('TOOL_INSTALL_DIR')))
# define environment variables for all input values. this is useful when we
# want to use input values by other programming language in r markdown
do.call(Sys.setenv, opt[-1])
#===============================================================
#======================== render Rmd files =========================
# NOTICE:
# we should copy all rmarkdown files from tool install directory to REPORT_FILES_PATH directory.
# and render rmarkdown files in the REPORT_FILES_PATH directory.
file.copy(from = paste0(Sys.getenv('TOOL_INSTALL_DIR'), '/vakata-jstree-3.3.5'),
to = Sys.getenv('REPORT_FILES_PATH'), recursive = TRUE)
system(command = 'cp -r ${TOOL_INSTALL_DIR}/*.Rmd ${REPORT_FILES_PATH}')
#----------------BELOW IS WHERE YOU NEED TO CUSTOMIZE ---------------------
render(input = paste0(Sys.getenv('REPORT_FILES_PATH'), '/skewer.Rmd'))
# add more lines below if there are more Rmd files to be rendered
#===============================================================
#============== expose outputs to galaxy history ===============
system(command = 'sh ${TOOL_INSTALL_DIR}/expose-outputs.sh')
#===============================================================
##--------end of code rendering .Rmd templates----------------
sink()
##=========== End of sinking output=============================
|
/old-tools/aurora_skewer/skewer_v2.0.0/skewer_render.R
|
permissive
|
statonlab/aurora-galaxy-tools
|
R
| false
| false
| 2,299
|
r
|
##============ Sink warnings and errors to a file ==============
## use the sink() function to wrap all code within it.
##==============================================================
zz = file(paste0(Sys.getenv('REPORT_FILES_PATH'), '/.r_rendering.log.txt'))
sink(zz)
sink(zz, type = 'message')
#============== preparation ====================================
options(stringsAsFactors = FALSE)
# import libraries
#------------------------------------------------------------------
# ADD MORE LIBRARIES HERE IF YOUR TOOL DEPENDS ON OTHER R LIBRARIES
#------------------------------------------------------------------
library('getopt')
library('rmarkdown')
library('htmltools')
# load helper functions
source(paste0(Sys.getenv('TOOL_INSTALL_DIR'), '/helper.R'))
# import getopt specification matrix from a csv file
opt = getopt(getopt_specification_matrix('getopt_specification.csv',
tool_dir=Sys.getenv('TOOL_INSTALL_DIR')))
# define environment variables for all input values. this is useful when we
# want to use input values by other programming language in r markdown
do.call(Sys.setenv, opt[-1])
#===============================================================
#======================== render Rmd files =========================
# NOTICE:
# we should copy all rmarkdown files from tool install directory to REPORT_FILES_PATH directory.
# and render rmarkdown files in the REPORT_FILES_PATH directory.
file.copy(from = paste0(Sys.getenv('TOOL_INSTALL_DIR'), '/vakata-jstree-3.3.5'),
to = Sys.getenv('REPORT_FILES_PATH'), recursive = TRUE)
system(command = 'cp -r ${TOOL_INSTALL_DIR}/*.Rmd ${REPORT_FILES_PATH}')
#----------------BELOW IS WHERE YOU NEED TO CUSTOMIZE ---------------------
render(input = paste0(Sys.getenv('REPORT_FILES_PATH'), '/skewer.Rmd'))
# add more lines below if there are more Rmd files to be rendered
#===============================================================
#============== expose outputs to galaxy history ===============
system(command = 'sh ${TOOL_INSTALL_DIR}/expose-outputs.sh')
#===============================================================
##--------end of code rendering .Rmd templates----------------
sink()
##=========== End of sinking output=============================
|
#' Retrieve data from the Roswell Park Data Commons
#'
#' @rdname retrieve
#'
#' @param x An object of class `Rosy`, created and authenticated with
#' `rosy()` with subsets defined via `filter()` and `select()`.
#'
#' @param ... additional arguments; ignored.
#'
#' @return A tibble containing desired data.
#'
#' @importFrom tibble as_tibble
#'
#' @export
as_tibble.Rosy <-
function(x, ...)
{
as_tibble()
}
|
/R/retrieve.R
|
no_license
|
mtmorgan/rosy
|
R
| false
| false
| 420
|
r
|
#' Retrieve data from the Roswell Park Data Commons
#'
#' @rdname retrieve
#'
#' @param x An object of class `Rosy`, created and authenticated with
#' `rosy()` with subsets defined via `filter()` and `select()`.
#'
#' @param ... additional arguments; ignored.
#'
#' @return A tibble containing desired data.
#'
#' @importFrom tibble as_tibble
#'
#' @export
as_tibble.Rosy <-
function(x, ...)
{
as_tibble()
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot_single_uts_vector}
\alias{plot_single_uts_vector}
\title{Plot a uts_vector in a single plot}
\usage{
plot_single_uts_vector(x, ..., max_dt = ddays(Inf), xlab = "", ylab = "",
col = seq_along(x), lty = 1, lwd = 1, pch = 1, type = "l",
legend = TRUE, legend.x = "topright", legend.y = NULL)
}
\arguments{
\item{x}{a \code{"uts_vector"} object with numeric or logical observation values.}
\item{\dots}{arguments passed to \code{\link[uts]{plot.uts}}.}
\item{max_dt}{a non-negative \code{\link[lubridate]{duration}} object. Consecutive observations that are more than this amount apart in time, are not connected by a line in the graph.}
\item{xlab}{a label for the x axis.}
\item{ylab}{a label for the y axis}
\item{col, lty, lwd, pch, type}{graphical parameters. See \code{\link{plot.default}}.}
\item{legend}{boolean. Whether to add a legend to the plot.}
\item{legend.x, legend.y}{the x and y co-ordinates to be used to position the legend.}
}
\description{
A helper function that implements \code{plot.uts_vector} for argument \code{plot.type="single"}.
}
\examples{
plot_single_uts_vector(ex_uts_vector(), xlab="time")
plot_single_uts_vector(ex_uts_vector(), type="o", main="Fruit", max_dt=dhours(12))
plot_single_uts_vector(ex_uts_vector(), type="p", pch=2, ylim=c(40, 60), cex=2)
}
\seealso{
\code{\link{matplot}}
}
\keyword{internal}
|
/man/plot_single_uts_vector.Rd
|
no_license
|
yanliangs/utsMultivariate
|
R
| false
| true
| 1,446
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot_single_uts_vector}
\alias{plot_single_uts_vector}
\title{Plot a uts_vector in a single plot}
\usage{
plot_single_uts_vector(x, ..., max_dt = ddays(Inf), xlab = "", ylab = "",
col = seq_along(x), lty = 1, lwd = 1, pch = 1, type = "l",
legend = TRUE, legend.x = "topright", legend.y = NULL)
}
\arguments{
\item{x}{a \code{"uts_vector"} object with numeric or logical observation values.}
\item{\dots}{arguments passed to \code{\link[uts]{plot.uts}}.}
\item{max_dt}{a non-negative \code{\link[lubridate]{duration}} object. Consecutive observations that are more than this amount apart in time, are not connected by a line in the graph.}
\item{xlab}{a label for the x axis.}
\item{ylab}{a label for the y axis}
\item{col, lty, lwd, pch, type}{graphical parameters. See \code{\link{plot.default}}.}
\item{legend}{boolean. Whether to add a legend to the plot.}
\item{legend.x, legend.y}{the x and y co-ordinates to be used to position the legend.}
}
\description{
A helper function that implements \code{plot.uts_vector} for argument \code{plot.type="single"}.
}
\examples{
plot_single_uts_vector(ex_uts_vector(), xlab="time")
plot_single_uts_vector(ex_uts_vector(), type="o", main="Fruit", max_dt=dhours(12))
plot_single_uts_vector(ex_uts_vector(), type="p", pch=2, ylim=c(40, 60), cex=2)
}
\seealso{
\code{\link{matplot}}
}
\keyword{internal}
|
#' DataFiltering
#' @description CountResponseFractions
#' @param data data.frame with colnames \code{response}
#' @param response chartacter, that specify name of the column that represents the output response
#' @param response.min numeric, lower bound of \code{response}, default \code{response.min = 10^(0)}
#' @param response.max numeric, lower bound of \code{response}, default \code{response.max = 10^(4)}
#' @return filtered data
#' @export
DataFiltering <-
function(
data,
response,
response.min = 10^(0),
response.max = 10^(4)
){
data %>%
dplyr::filter(
!!!quos(!!sym(response) > response.min,
!!sym(response) < response.max
)
)
}
|
/R/preprocess_DataFiltering.R
|
no_license
|
stork119/SysBioSigHeterogeneity
|
R
| false
| false
| 721
|
r
|
#' DataFiltering
#' @description CountResponseFractions
#' @param data data.frame with colnames \code{response}
#' @param response chartacter, that specify name of the column that represents the output response
#' @param response.min numeric, lower bound of \code{response}, default \code{response.min = 10^(0)}
#' @param response.max numeric, lower bound of \code{response}, default \code{response.max = 10^(4)}
#' @return filtered data
#' @export
DataFiltering <-
function(
data,
response,
response.min = 10^(0),
response.max = 10^(4)
){
data %>%
dplyr::filter(
!!!quos(!!sym(response) > response.min,
!!sym(response) < response.max
)
)
}
|
# LOAD, MERGE, AND QC DATA
source("setup.R")
Mcap.ff.all$year <- ifelse(Mcap.ff.all$date < as.Date("2015-05-07"), "y1", "y2")
# # FILTER OUT COLONIES WITH <= 4 OBSERVATIONS -----
nobs <- aggregate(data.frame(obs=Mcap.ff.all$colony), by=list(colony=Mcap.ff.all$colony), FUN=length)
Mcap.ff.all <- droplevels(Mcap.ff.all[Mcap.ff.all$colony %in% nobs[nobs$obs > 4, "colony"], ])
Mcap.ff.all <- droplevels(Mcap.ff.all[Mcap.ff.all$reef!="42", ])
# WAS VISUAL BLEACHING SCORE THE SAME IN BOTH YEARS?
bscore <- aggregate(data.frame(minscore=Mcap.ff.all$score),
by=list(colony=Mcap.ff.all$colony, year=Mcap.ff.all$year), FUN=min, na.rm=T)
dcast(bscore, colony ~ year, value.var="minscore")
# IDENTIFY COLONIES THAT SHUFFLED SYMBIONTS -----
res <- ldply(levels(Mcap.ff.all$colony), plotpropD)
par(mfrow=c(4,2), mar=c(3,3,2,1))
ldply(list(11,71,119,125,40,72,78), plotpropD, method="loess")
rownames(res) <- unlist(levels(Mcap.ff.all$colony))
apply(res, 2, table) # Count number of shufflers determined by each fitting technique
Mcap.ff.all$shuff <- res[Mcap.ff.all$colony, "loess"]
# GROUP COLONIES BY SHUFFLING, BLEACHING, AND DOMINANT CLADE -----
Mcap.ff.all$bleach1 <- ifelse(Mcap.ff.all$colony %in% Mcap.ff.all[Mcap.ff.all$score==1 & Mcap.ff.all$year=="y1", "colony"], "bleach", "notbleached")
Mcap.ff.all$bleach2 <- ifelse(Mcap.ff.all$year=="y1", NA,
ifelse(is.na(Mcap.ff.all$score), NA,
ifelse(Mcap.ff.all$colony %in% Mcap.ff.all[Mcap.ff.all$score==1 & Mcap.ff.all$year=="y2", "colony"], "bleach", "notbleached")))
Mcap.ff.all$group <- as.character(droplevels(interaction(Mcap.ff.all$bleach2, Mcap.ff.all$shuff)))
Mcap.ff.all[Mcap.ff.all$group=="notbleached.noshuff", "group"] <- ifelse(Mcap.ff.all[Mcap.ff.all$group=="notbleached.noshuff", "tdom"]=="C", "notbleached.noshuff.C", "notbleached.noshuff.D")
Mcap.ff.all$group <- factor(Mcap.ff.all$group)
#Mcap.ff.all[Mcap.ff.all$colony=="207", "group"] <- "bleach.shuff" # assume this colony was C-dominated prior to bleaching
# ADDITIONAL GROUPING FACTOR BY SURVIVAL OF SECOND BLEACHING EVENT
numberofsamplesafter12042015 <- aggregate(data.frame(n=Mcap.ff.all$date>="2015-12-04"), by=list(colony=Mcap.ff.all$colony), FUN=function(x) table(x)[2])
Mcap.ff.all$survival <- ifelse(Mcap.ff.all$colony %in% numberofsamplesafter12042015[numberofsamplesafter12042015$n>=2, "colony"], TRUE, FALSE)
Mcap.ff.all$group2 <- interaction(Mcap.ff.all$survival, Mcap.ff.all$group)
# IDENTIFY AND COUNT COLONIES IN EACH GROUP
cols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group), FUN=function(x) unique(as.character(x)))
ncols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group), FUN=function(x) length(unique(as.character(x))))
cols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group2), FUN=function(x) unique(as.character(x)))
ncols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group2), FUN=function(x) length(unique(as.character(x))))
#Plot Bleaching vs non bleaching dominant symbiont clade
eight11 <- Mcap.ff.all[Mcap.ff.all$date=="2015-08-11",]
st <- table(eight11$dom, eight11$bleach2)
bars <- barplot(st, col=c("blue","red"), width=1, xlim=c(0,6), ylab="Number of Colonies", names.arg=c("Bleached", "Not Bleached"))
text(0.7, 22, labels="n=21", xpd=NA)
text(1.9, 26, labels="n=25", xpd=NA)
legend("topleft",legend=c("D","C"), bty="n", pt.cex=2, pch=22, pt.bg=c("red","blue"))
#Pie chart function for proportion D at a specific timepoint
h <- Mcap.ff.all[(Mcap.ff.all$colony=="71" & Mcap.ff.all$date=="2015-10-21"),]
htable <- c(h$propD, 1-h$propD)
pie(htable)
pieintheface <- function(x,y) {
h <- Mcap.ff.all[(Mcap.ff.all$colony==x & Mcap.ff.all$date==y),]
htable <- c(h$propD, 1-h$propD)
lbls <- c("Clade D","Clade C")
pct <- round(htable/sum(htable)*100)
lbls <- paste(lbls,pct)
lbls <- paste(lbls,"%",sep="")
pie(htable, col=c("red","blue"), labels=lbls, main=y)
}
pieintheface("71", "2016-02-11")
plotcolony <- function(colony) {
df <- Mcap.ff.all[Mcap.ff.all$colony==colony, ]
df <- df[order(df$date), ]
par(mar=c(5,3,1,1))
plot(df$date, log(df$tot.SH), type="b", pch=21, cex=2, bg=c("blue","lightblue","pink","red")[df$syms], ylim=c(-11,1), xlab="", ylab="Log SH", xaxt="n")
dates <- as.Date(c("2014-10-24","2014-11-04","2014-11-24","2014-12-16","2015-01-14","2015-05-06","2015-08-11", "2015-09-14", "2015-10-01", "2015-10-21", "2015-11-04", "2015-12-04","2015-12-17", "2016-01-20", "2016-02-11","2016-03-31"))
axis(side=1, at=dates, labels=FALSE)
text(x=dates, y=par("usr")[3]-.2, srt=45, labels=as.character(dates), xpd=NA, pos=2)
legend("topleft", legend=c("C","C>D","D>C","D"), pch=21, pt.cex=2, pt.bg=c("blue","lightblue","pink","red"))
}
plotcolony(11)
#plot mortality
dead <- condition[condition$mortality=="3",]
missing <- condition[condition$mortality=="missing",]
plot(condition$date, condition$mortality)
condition <- condition[!condition$colony %in% missing$colony,]
condition <- condition[condition$reef!="42",]
table <- table(condition$mortality, condition$date, condition$reef)
table
HIMB <- table[,,1]
HIMB <- melt(HIMB)
HIMB2or3 <- aggregate(HIMB$value, by=list(HIMB$Var1 %in% c(2,3), HIMB$Var2), FUN=sum)
HIMB2or3 <- HIMB2or3[HIMB2or3$Group.1==T, ]
HIMB2or3
plot(as.Date(HIMB2or3$Group.2), HIMB2or3$x, type="o", col="magenta", xlab="Date", ylab="Number of Colonies over 50% Dead", Main="Mortality over Time")
lines(as.Date(TF2or3$Group.2), TF2or3$x, type="o",col="purple")
lines(as.Date(FF2or3$Group.2), FF2or3$x, type="o",col="turquoise")
legend("topleft", legend=c("Reef HIMB","Reef 25", "Reef 44"), fill=c("magenta","purple","turquoise"))
TF <- table[,,2]
TF <- melt(TF)
TF2or3 <- aggregate(TF$value, by=list(TF$Var1 %in% c(2,3), TF$Var2), FUN=sum)
TF2or3 <- TF2or3[TF2or3$Group.1==T, ]
TF2or3
plot(as.Date(TF2or3$Group.2), TF2or3$x, type="o")
FF <- table[,,3]
FF <- melt(FF)
FF2or3 <- aggregate(FF$value, by=list(FF$Var1 %in% c(2,3), FF$Var2), FUN=sum)
FF2or3 <- FF2or3[FF2or3$Group.1==T, ]
FF2or3
plot(as.Date(FF2or3$Group.2), FF2or3$x, type="o")
table1 <-table(condition$mortality, condition$date)
table1
All <- melt(table1)
All
All2or3 <- aggregate(All$value, by=list(All$Var1 %in% c(2,3), All$Var2), FUN=sum)
All2or3 <- All2or3[All2or3$Group.1==T, ]
All2or3
plot(as.Date(All2or3$Group.2), All2or3$x, type="o", col="magenta", xlab="Date", ylab="Number of Colonies over 50% Dead")
abline(v=c())
nlevels(droplevels(condition$colony))
dev.off()
Byr2 <- subset(Mcap.ff.all, bleach2=="bleach")
NByr2 <- subset(Mcap.ff.all, bleach2=="notbleached")
head(Byr2)
plot(Mcap.ff.all$date, log(Mcap.ff.all$tot.SH))
table(Mcap.ff.all$date, log(Mcap.ff.all$tot.SH))
# PLOT SYMBIONT ABUNDANCE AND COMPOSITION FOR INDIVIDUAL COLONIES -----
# XYPLOT ALL COLONIES IN EACH GROUP
xyplot(log(tot.SH) ~ date | group, groups=~colony, ylim=c(-11,1), data=Mcap.ff.all, type="o", cex=0.25)
xyplot(propD ~ date | group, groups=~colony, ylim=c(-0.1,1.1), data=Mcap.ff.all, type="o", cex=0.25)
# XYPLOT INDIVIDUAL COLONIES BY GROUP, RAW DATA
for (g in levels(Mcap.ff.all$group)) {
df <- subset(Mcap.ff.all, group==g)
print(doubleYScale(
# Plot total S/H with GAM fit
xyplot(log(tot.SH) ~ date | colony, ylim=c(-11,1), data=df, type="o", cex=0.25, main=g),
# Plot propD with locfit
xyplot(propD ~ date | colony, ylim=c(-0.1, 1.1), data=df, type="o", cex=0.25)
))
}
# XYPLOT INDIVIDUAL COLONIES BY GROUP, FITTED RESPONSES
for (g in levels(Mcap.ff.all$group)) {
df <- subset(Mcap.ff.all, group==g)
print(doubleYScale(
# Plot total S/H with GAM fit
xyplot(log(tot.SH) ~ days | colony, ylim=c(-11,1), data=df, main=g, panel = function(x, y, ...) {
panel.xyplot(x, y, cex=0.5, ...)
dayrange <- seq(min(x), max(x), 1)
tryCatch({
m <- gam(y ~ s(x), family="gaussian")
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
},
error=function(e) {
m <- gam(y ~ s(x, k=3), family="gaussian")
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
},
warning=function(w) print(w))
}),
# Plot propD with locfit
xyplot(propD ~ days | colony, ylim=c(-0.1, 1.1), data=df, panel = function(x, y, ...) {
panel.xyplot(x, y, cex=0.25, ...)
dayrange <- seq(min(x), max(x), 1)
tryCatch({
m <- locfit(y ~ lp(x, nn=1), family="betar", lfproc=locfit.raw)
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
CtoD <- dayrange[which(diff(sign(p-0.5))>0)]
DtoC <- dayrange[which(diff(sign(p-0.5))<0)]
panel.xyplot(c(CtoD, DtoC), rep(0.5, length(c(CtoD, DtoC))), pch="*", cex=2, col="red")
},
error=function(e) print(e),
warning=function(w) print(w))
})
))
}
# MODEL SYMBIONT ABUNDANCE AND COMPOSITION FOR EACH GROUP -----
# Exclude groups that didn't quite make it
df <- Mcap.ff.all[as.numeric(Mcap.ff.all$group2) %in% c(2,4,6,8,10), ]
df <- droplevels(df)
# FIT PROPD GAMM BY GROUP
xyplot(propD ~ days | group2, data=df)
propDmod <- gamm4(propD ~ group2 + s(days, by=group2), random=~(1|colony), data=df)
# FIT TOTSH GAMM BY GROUP
xyplot(log(tot.SH) ~ days | group, data=df)
totSHmod <- gamm4(log(tot.SH) ~ group2 + s(days, by=group2), random=~(1|colony), data=df)
# GET FITTED VALUES FOR EACH GROUP
newdata <- expand.grid(days=seq(0,524,1), group2=levels(df$group2))
newdata$tot.SH <- predict(totSHmod$gam, newdata)
newdata$propD <- predict(propDmod$gam, newdata)
newdata$predse <- predict(totSHmod$gam, newdata, se.fit=T)$se.fit
# PLOT FITTED VALUES FOR EACH GROUP
xyplot(tot.SH ~ days, groups=~group2, newdata)
xyplot(propD ~ days, groups=~group2, newdata, ylim=c(0,1))
doubleYScale(xyplot(tot.SH ~ days | group2, newdata, type="l"),
xyplot(propD ~ days | group2, newdata, type="l", ylim=c(-0.1,1.1)))
# PLOT FITTED RESPONSES FOR EACH GROUP, MULTIPANEL SHUFFLERS vs. NONSHUFFLERS -----
rbPal <- colorRampPalette(c('dodgerblue','red'))
newdata$color <- rbPal(100)[as.numeric(cut(newdata$propD, breaks = 100))]
par(mfrow=c(2,1), mar=c(1,3,1,2), mgp=c(1.5,0.4,0), tcl=-0.25)
plot(NA, ylim=c(-7,0), xlim=range(newdata$days), xaxs="i", xaxt="n", yaxt="n", ylab="")
axis(side=2, at=seq(-7,-1,1), cex.axis=0.75)
dateticks <- seq.Date(as.Date("2014-11-01"), as.Date("2016-02-01"), by="month")
axis(side=1, at=as.numeric(dateticks-as.Date("2014-10-24")), labels=NA)
for (group2 in levels(newdata$group2)[c(1,3,4)]) {
df <- newdata[newdata$group2==group2, ]
addpoly(df$days, df$tot.SH - 1.96*df$predse, df$tot.SH + 1.96*df$predse, col=alpha("gray", 0.7))
}
points(tot.SH ~ days, newdata[as.numeric(newdata$group2) %in% c(1,3,4), ], pch=21, col=color, bg=color)
text(par("usr")[1], quantile(par("usr")[3:4], 0.9), pos=4,
expression(bold("A. Non-shuffling colonies")))
gradient.rect(quantile(par("usr")[1:2], 0.1), quantile(par("usr")[3:4], 0.1),
quantile(par("usr")[1:2], 0.35), quantile(par("usr")[3:4], 0.175),
col=rbPal(100), border=NA)
text(quantile(par("usr")[1:2], c(0.1, 0.35)), rep(quantile(par("usr")[3:4], 0.1375), 2), pos=c(2,4), labels=c("C", "D"), cex=0.75)
par(mar=c(2,3,0,2))
plot(NA, ylim=c(-7,0), xlim=range(newdata$days), xaxs="i", xlab="", ylab="", xaxt="n", yaxt="n", xpd=NA)
axis(side=2, at=seq(-7,-1,1), cex.axis=0.75)
mtext(side=2, text="Symbiont abundance (ln S/H)", line=-1.5, outer=T)
dateticks <- seq.Date(as.Date("2014-11-01"), as.Date("2016-02-01"), by="month")
axis(side=1, at=as.numeric(dateticks-as.Date("2014-10-24")), labels=format(dateticks, "%b"), cex.axis=0.75)
for (group2 in levels(df$group2)[c(2,5)]) {
df <- newdata[newdata$group2==group2, ]
addpoly(df$days, df$tot.SH - 1.96*df$predse, df$tot.SH + 1.96*df$predse, col=alpha("gray", 0.7))
}
points(tot.SH ~ days, newdata[as.numeric(newdata$group2) %in% c(2,5), ], pch=21, col=color, bg=color)
gradient.rect(quantile(par("usr")[1:2], 0.1), quantile(par("usr")[3:4], 0.1),
quantile(par("usr")[1:2], 0.35), quantile(par("usr")[3:4], 0.175),
col=rbPal(100), border=NA)
text(quantile(par("usr")[1:2], c(0.1, 0.35)), rep(quantile(par("usr")[3:4], 0.1375), 2), pos=c(2,4), labels=c("C", "D"), cex=0.75)
text(par("usr")[1], quantile(par("usr")[3:4], 0.9), pos=4,
expression(bold("B. Shuffling colonies")))
# DOES SHUFFLING DEPEND ON REEF?
MC <- unique(Mcap.ff.all[, c("colony", "reef","shuff","bleach", "group", "depth")])
MC$shuff <- factor(MC$shuff)
MCb <- droplevels(MC[MC$bleach=="bleach", ])
plot(MCb$group ~ MCb$reef)
chisq.test(MCb$reef, MCb$group)
MCnb <- droplevels(MC[MC$bleach=="notbleached", ])
plot(MCnb$group ~ MCnb$reef)
chisq.test(MCnb$reef, MCnb$group)
# DOES SHUFFLING DEPEND ON DEPTH?
plot(shuff ~ depth, MCb)
chisq.test(MCb$depth, MCb$shuff)
plot(as.numeric(shuff) ~ depth, MCnb)
# DOES SHUFFLING RELATE TO BLEACHING SEVERITY?
bsev <- aggregate(data.frame(minscore=Mcap.ff.all$tot.SH),
by=list(colony=Mcap.ff.all$colony), FUN=min, na.rm=T)
bsev <- merge(MCb, bsev)
plot(bsev$shuff ~ log(bsev$minscore))
plot(as.numeric(bsev$shuff) ~ log(bsev$minscore))
mod <- glm(shuff ~ depth * log(minscore), family="binomial", data=bsev)
plot(mod)
anova(mod, test="Chisq")
plot(effect("depth:log(minscore)", mod), x.var="minscore")
plot(effect("depth", mod))
# HEATMAP OF SHUFFLING ---------
# Create matrix for image function
clades <- melt(Mcap.f, id.vars=c("colony", "date", "vis", "reef", "tdom"), measure.vars="syms",
factorsAsStrings=FALSE)
head(clades)
clades$value <- as.numeric(factor(clades$value))
clades <- unique(clades)
clades <- dcast(clades, vis + colony + reef + tdom ~ date, drop=T)
head(clades)
clades[is.na(clades)] <- -1 # Recode missing values as -1
#clades[which(clades$colony=="129"), 8:10] <- -2 # Recode mortality as -2
clades.m0 <- clades[with(clades, order(clades[, 12], clades[, 5], clades[, 7],
clades[, 8], clades[, 9], clades[, 10])), ]
clades.m <- as.matrix(clades.m0[,5:12])
rownames(clades.m) <- as.character(clades.m0$colony)
# Plot figure
library(RColorBrewer)
par(mfrow=c(1,1), mar=c(3,5,2,2), bg="white")
image(x=seq(1, ncol(clades.m)), y=seq(1, nrow(clades.m)), z=t(clades.m),
xaxt="n", yaxt="n", xlab="", ylab="",
breaks=c(-2,-1.1,0,1,2,3,4,5),
col=c("black", "white", rev(brewer.pal(11, "RdYlBu")[c(2,1,3,9,11)])))
# Plot date axis
#axis(side=3, at=seq(1:8), labels=FALSE, cex.axis=0.75, par("tck"=-0.025), xpd=T)
text(0.5:7.5, par("usr")[4], xpd=T, cex=0.6, pos=4,
labels=levels(Mcap$fdate), srt=45, adj=-0.1)
# # Plot Bleached vs. Not Bleached rectangles
# rect(par("usr")[1] - 2.25, par("usr")[3], par("usr")[1] - 1.25, par("usr")[4], xpd=T)
# text(par("usr")[1] - 1.75, quantile(par("usr")[3:4])[c(2, 4)], labels=c("Not Bleached", "Bleached"),
# srt=90, xpd=2)
# Plot colony numbers
text(0, 1:nrow(clades.m), labels=rownames(clades.m), xpd=T, cex=0.5)
# get shufflers
Mcap[which(Mcap$colony %in% c(71,54,40,119)), ]
head(clades.m)
# Plot Row Side Colors
reefcols <- c("#bebada", "#8dd3c7", "#d9d9d9")
for (i in 1:nrow(clades.m0)) {
reef <- clades.m0$reef[i]
rect(par("usr")[1] - 1.25, par("usr")[3] + 1 * (i - 1),
par("usr")[1] - 0.25, par("usr")[3] + 1 * (i - 1) + 1, col=reefcols[as.numeric(reef)],
xpd=T, border=NA)
}
rect(par("usr")[1] - 1.25, par("usr")[3], par("usr")[1] - 0.25, par("usr")[4], xpd=T)
lines(x=c(par("usr")[1] - 2.25, par("usr")[1] - 0.25), y=rep(quantile(par("usr")[3:4], 0.5), 2), xpd=T)
# Plot tdom side boxes
breaks <- c(0, which(diff(as.numeric(clades.m0$tdom))!=0), length(clades.m0$tdom))
doms <- clades.m0$tdom[breaks]
for (i in 2:length(breaks)) {
rect(par("usr")[2] + 0.25, par("usr")[3] + breaks[i-1], par("usr")[2] + 0.75, par("usr")[3] + breaks[i], xpd=T)
}
for (i in 1:(length(breaks)-1)) {
text(par("usr")[2] + 0.5, (breaks[i] + breaks[i+1]) / 2, paste(doms[i], "dominant"), xpd=T, srt=90,
cex=0.75)
}
# Plot Row Side Color Key
for (i in 1:3) {
rect(par("usr")[1] - 1.25, quantile(par("usr")[3:4], 0) * -1.05 - ((i - 1) * 1),
par("usr")[1] - 0.25, quantile(par("usr")[3:4], 0) * -1.05 - ((i - 1) * 1) - 1, xpd=T,
border=NA, col=reefcols[i])
}
rect(par("usr")[1] - 1.25, quantile(par("usr")[3:4], 0) * -1.05,
par("usr")[1] - 0.25, quantile(par("usr")[3:4], 0) * -1.05 - 3, xpd=T)
axis(side=2, xpd=T, pos=par("usr")[1] - 1.25, lwd=0, lwd.ticks=0,
at=c(-1, -2, -3), labels=c("Rf 25", "Rf 44", "HIMB"), las=2, cex.axis=0.6, mgp=c(0,0.4,0))
# Plot Heatmap Key
x <- quantile(par("usr")[1:2], probs=seq(0, 1, length.out=7))
y <- rep(quantile(par("usr")[3:4], 0) * -1.05, 2) - c(0, 1)
rect(x[1], y[1], x[7], y[2], xpd=T)
for (i in 1:6) {
rect(x[i], y[1], x[i + 1], y[2], xpd=T,
border=NA, col=c(brewer.pal(11, "RdYlBu")[c(1,3,9,11)], "white", "black")[i])
}
text(xpd=T, y=y[1] - 0.75, pos=1, cex=0.6,
x=seq(par("usr")[1], par("usr")[2], length=7)[-7] + 0.5,
labels=c("D only", "D > C", "C > D", "C only", "no data", "dead"))
text(xpd=T, y=quantile(par("usr")[3:4], 0) * -1.05 - 2.5, pos=1, cex=0.9,
x=quantile(par("usr")[1:2], 0.5),
labels=expression(italic(Symbiodinium)~clades))
# MODEL TRAJECTORIES USING SPIDA ------
# Model trajectories of symbiont populations over time using mixed model
# Build piecewise polynomial model with knot at 82 days (January time point)
# From October to January, fit a quadratic polynomial (1st element of degree=2)
# From January to May, fit a linear model (2nd element of degree=1)
# Function is continuous at time=82 days (smooth=0)
Mcap.ff$days <- as.numeric(Mcap.ff$date - as.Date("2015-08-11"))
#offset <- 0 # optional to center "days" axis at any point
sp <- function(x) gsp(x, knots=c(51,85,128), degree=c(2,3,2,2))
#sp <- function(x) cs(x)
#sp <- function(x) bs(x, knots=c(71,128))
# Build full model with fixed effects of vis, tdom, reef, and time, random effect of colony
Mcapdf <- Mcap.ff[Mcap.ff$reef!="42",]
#mod.all.full <- lmerTest::lmer(log(Mcapdf$tot.SH) ~ sp(Mcapdf$days) * Mcapdf$vis * Mcapdf$reef + (sp(Mcapdf$days) | Mcapdf$colony))
#mod.all.full <- lmerTest::lmer(log(tot.SH) ~ poly(days, 3) * vis * reef + (1 | colony), data=Mcap.ff[Mcap.ff$reef!="42",])
#plot(Effect(c("days", "vis", "reef"), mod.all.full, xlevels=list(days=unique(Mcap.ff$days))),
# multiline=T, z.var="reef", ci.style="bars")
# Test significance of fixed effects by backwards selection
#modselect <- step(mod.all.full, lsmeans.calc=F, difflsmeans.calc=F, alpha.fixed=0.05)
#modselect
#summary(mod.all.full)
# Rebuild model omitting non-significant fixed effects
#mod.all <- mod.all.full
# Identify outliers with standardized residuals > 2.5
#out <- abs(residuals(mod.all)) > sd(residuals(mod.all)) * 2.5
#Mcap.ff[out, ] # outlying data points
# Refit model without outliers
#Mcapdf <- Mcapdf[!out, ]
mod.all <- lmerTest::lmer(log(tot.SH) ~ sp(days) * vis * reef + (1 | colony), data=Mcapdf)
#mod.all <- mod.all.full
# Print and save ANOVA table for model
anovatab <- anova(mod.all)
#write.csv(round(anovatab, digits=3), file="output/Table1.csv")
# pseudo-r2 value-- squared correlation between fitted and observed values
summary(lm(model.response(model.frame(mod.all)) ~ fitted(mod.all)))$r.squared
# Plotting function
plotreefs <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(reef=levels(dat$reef), vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$reef, dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$reef)
datlist <- lapply(datlist, function(x) rev(split(x, f=x$vis)))
pred <- expand.grid(days=seq_len(max(dat$days)), reef=levels(dat$reef), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$reef)
predlist <- lapply(predlist, function(x) rev(split(x, f=x$vis)))
par(mgp=c(1.75,0.4,0), oma=c(0,0,0,0))
par(mar=c(0,3,0.3,1))
layout(mat=matrix(seq_len(nlevels(dat$reef)+1)))
for (reef in levels(dat$reef)) {
with(datlist[[reef]], {
# Create plot frame for each reef
plot(NA, xlim=range(dat$days), ylim=c(-9,-1), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title(paste("Reef", reef), line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist[[reef]], {
lapply(predlist[[reef]], function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha(reefcols[[reef]], 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist[[reef]], function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
}
axis(side=1, at=as.numeric(as.Date(c("2015-08-01", "2015-09-01", "2015-10-01", "2015-11-01",
"2015-12-01", "2016-01-01", "2016-02-01")) - as.Date("2015-08-11")),
labels=c("Aug", "Sep", "Oct", "Nov", "Dec", "Jan", "Feb"))
return(list(predlist=predlist, datlist=datlist))
}
# Plot
reefcols <- list(`25`="#bebada", `44`="#8dd3c7", HIMB="#d9d9d9", `42`="green")
vislty <- list("bleached"=2, "not bleached"=1)
vispch <- list("bleached"=24, "not bleached"=21)
visbg <- list("bleached"="white", "not bleached"="black")
modelplot <- plotreefs(mod.all, 99)
Mcap42 <- Mcap.ff[Mcap.ff$reef==42,]
sp2 <- function(x) gsp(x, knots=c(85,128), degree=c(2,2,2))
mod.42 <- lmerTest::lmer(log(tot.SH) ~ sp2(days) * vis + (1 | colony), data=Mcap42)
plot42 <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$vis)
pred <- expand.grid(days=seq(min(dat$days), max(dat$days)), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$vis)
plot(NA, xlim=c(0, max(dat$days)), ylim=c(-9,-1), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title("Reef 42", line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist, {
lapply(predlist, function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha("green", 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist, function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
return(list(predlist=predlist, datlist=datlist))
}
plot42(mod.42, 99)
# MODEL TRAJECTORIES FOR BOTH YEARS
Mcap.ff.all$days <- as.numeric(Mcap.ff.all$date - as.Date("2014-10-24"))
Mcap.ff.all$days <- scale(Mcap.ff.all$days)
points <- unique(Mcap.ff.all$days)
knots <- points[c(5,7,9,11,13)]
sp <- function(x) gsp(x, knots=knots, degree=c(2,1,2,3,2,1), smooth=c(0,1,1,1,1))
Mcapdf <- Mcap.ff.all[Mcap.ff.all$reef!="42",]
Mcapdf$reef <- factor(Mcapdf$reef, levels=c("44","25","HIMB"))
Mcapdf$batch <- Mcapdf$days < 195
mod.all <- lmerTest::lmer(log(tot.SH) ~ sp(days) * vis * reef + (1 | colony), data=Mcapdf)
#anova(mod.all)
summary(lm(model.response(model.frame(mod.all)) ~ fitted(mod.all)))$r.squared
plotreefs <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(reef=levels(dat$reef), vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$reef, dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$reef)
datlist <- lapply(datlist, function(x) rev(split(x, f=x$vis)))
pred <- expand.grid(days=seq(min(dat$days), max(dat$days), length.out=475), reef=levels(dat$reef), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$reef)
predlist <- lapply(predlist, function(x) rev(split(x, f=x$vis)))
par(mgp=c(1.75,0.4,0), oma=c(0,0,0,0))
par(mar=c(0,3,0.3,1))
layout(mat=matrix(seq_len(nlevels(dat$reef)+1)))
for (reef in levels(dat$reef)) {
with(datlist[[reef]], {
# Create plot frame for each reef
plot(NA, xlim=range(dat$days), ylim=c(-9,0), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title(paste("Reef", reef), line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist[[reef]], {
lapply(predlist[[reef]], function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha(reefcols[[reef]], 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist[[reef]], function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
}
axdates <- seq.Date(as.Date("2014-11-01"), as.Date("2016-04-01"), by="month")
unscaled <- Mcap.ff.all$days * attr(Mcap.ff.all$days, 'scaled:scale') + attr(Mcap.ff.all$days, 'scaled:center')
axdays <- as.numeric(axdates - as.Date("2014-10-24"))
tdays <- scales::rescale(axdays, to=range(dat$days), from=range(unscaled))
axis(side=1, at=tdays, labels=format(axdates, format="%b\n%y"), padj=1)
return(list(predlist=predlist, datlist=datlist))
}
# Plot
reefcols <- list(`25`="#bebada", `44`="#8dd3c7", HIMB="#d9d9d9", `42`="green")
vislty <- list("bleached"=2, "not bleached"=1)
vispch <- list("bleached"=24, "not bleached"=21)
visbg <- list("bleached"="white", "not bleached"="black")
modelplot <- plotreefs(mod.all, 99)
|
/analysis.R
|
no_license
|
jrcunning/kbayrecov2015
|
R
| false
| false
| 28,373
|
r
|
# LOAD, MERGE, AND QC DATA
source("setup.R")
Mcap.ff.all$year <- ifelse(Mcap.ff.all$date < as.Date("2015-05-07"), "y1", "y2")
# # FILTER OUT COLONIES WITH <= 4 OBSERVATIONS -----
nobs <- aggregate(data.frame(obs=Mcap.ff.all$colony), by=list(colony=Mcap.ff.all$colony), FUN=length)
Mcap.ff.all <- droplevels(Mcap.ff.all[Mcap.ff.all$colony %in% nobs[nobs$obs > 4, "colony"], ])
Mcap.ff.all <- droplevels(Mcap.ff.all[Mcap.ff.all$reef!="42", ])
# WAS VISUAL BLEACHING SCORE THE SAME IN BOTH YEARS?
bscore <- aggregate(data.frame(minscore=Mcap.ff.all$score),
by=list(colony=Mcap.ff.all$colony, year=Mcap.ff.all$year), FUN=min, na.rm=T)
dcast(bscore, colony ~ year, value.var="minscore")
# IDENTIFY COLONIES THAT SHUFFLED SYMBIONTS -----
res <- ldply(levels(Mcap.ff.all$colony), plotpropD)
par(mfrow=c(4,2), mar=c(3,3,2,1))
ldply(list(11,71,119,125,40,72,78), plotpropD, method="loess")
rownames(res) <- unlist(levels(Mcap.ff.all$colony))
apply(res, 2, table) # Count number of shufflers determined by each fitting technique
Mcap.ff.all$shuff <- res[Mcap.ff.all$colony, "loess"]
# GROUP COLONIES BY SHUFFLING, BLEACHING, AND DOMINANT CLADE -----
Mcap.ff.all$bleach1 <- ifelse(Mcap.ff.all$colony %in% Mcap.ff.all[Mcap.ff.all$score==1 & Mcap.ff.all$year=="y1", "colony"], "bleach", "notbleached")
Mcap.ff.all$bleach2 <- ifelse(Mcap.ff.all$year=="y1", NA,
ifelse(is.na(Mcap.ff.all$score), NA,
ifelse(Mcap.ff.all$colony %in% Mcap.ff.all[Mcap.ff.all$score==1 & Mcap.ff.all$year=="y2", "colony"], "bleach", "notbleached")))
Mcap.ff.all$group <- as.character(droplevels(interaction(Mcap.ff.all$bleach2, Mcap.ff.all$shuff)))
Mcap.ff.all[Mcap.ff.all$group=="notbleached.noshuff", "group"] <- ifelse(Mcap.ff.all[Mcap.ff.all$group=="notbleached.noshuff", "tdom"]=="C", "notbleached.noshuff.C", "notbleached.noshuff.D")
Mcap.ff.all$group <- factor(Mcap.ff.all$group)
#Mcap.ff.all[Mcap.ff.all$colony=="207", "group"] <- "bleach.shuff" # assume this colony was C-dominated prior to bleaching
# ADDITIONAL GROUPING FACTOR BY SURVIVAL OF SECOND BLEACHING EVENT
numberofsamplesafter12042015 <- aggregate(data.frame(n=Mcap.ff.all$date>="2015-12-04"), by=list(colony=Mcap.ff.all$colony), FUN=function(x) table(x)[2])
Mcap.ff.all$survival <- ifelse(Mcap.ff.all$colony %in% numberofsamplesafter12042015[numberofsamplesafter12042015$n>=2, "colony"], TRUE, FALSE)
Mcap.ff.all$group2 <- interaction(Mcap.ff.all$survival, Mcap.ff.all$group)
# IDENTIFY AND COUNT COLONIES IN EACH GROUP
cols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group), FUN=function(x) unique(as.character(x)))
ncols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group), FUN=function(x) length(unique(as.character(x))))
cols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group2), FUN=function(x) unique(as.character(x)))
ncols <- aggregate(Mcap.ff.all$colony, by=list(Mcap.ff.all$group2), FUN=function(x) length(unique(as.character(x))))
#Plot Bleaching vs non bleaching dominant symbiont clade
eight11 <- Mcap.ff.all[Mcap.ff.all$date=="2015-08-11",]
st <- table(eight11$dom, eight11$bleach2)
bars <- barplot(st, col=c("blue","red"), width=1, xlim=c(0,6), ylab="Number of Colonies", names.arg=c("Bleached", "Not Bleached"))
text(0.7, 22, labels="n=21", xpd=NA)
text(1.9, 26, labels="n=25", xpd=NA)
legend("topleft",legend=c("D","C"), bty="n", pt.cex=2, pch=22, pt.bg=c("red","blue"))
#Pie chart function for proportion D at a specific timepoint
h <- Mcap.ff.all[(Mcap.ff.all$colony=="71" & Mcap.ff.all$date=="2015-10-21"),]
htable <- c(h$propD, 1-h$propD)
pie(htable)
pieintheface <- function(x,y) {
h <- Mcap.ff.all[(Mcap.ff.all$colony==x & Mcap.ff.all$date==y),]
htable <- c(h$propD, 1-h$propD)
lbls <- c("Clade D","Clade C")
pct <- round(htable/sum(htable)*100)
lbls <- paste(lbls,pct)
lbls <- paste(lbls,"%",sep="")
pie(htable, col=c("red","blue"), labels=lbls, main=y)
}
pieintheface("71", "2016-02-11")
plotcolony <- function(colony) {
df <- Mcap.ff.all[Mcap.ff.all$colony==colony, ]
df <- df[order(df$date), ]
par(mar=c(5,3,1,1))
plot(df$date, log(df$tot.SH), type="b", pch=21, cex=2, bg=c("blue","lightblue","pink","red")[df$syms], ylim=c(-11,1), xlab="", ylab="Log SH", xaxt="n")
dates <- as.Date(c("2014-10-24","2014-11-04","2014-11-24","2014-12-16","2015-01-14","2015-05-06","2015-08-11", "2015-09-14", "2015-10-01", "2015-10-21", "2015-11-04", "2015-12-04","2015-12-17", "2016-01-20", "2016-02-11","2016-03-31"))
axis(side=1, at=dates, labels=FALSE)
text(x=dates, y=par("usr")[3]-.2, srt=45, labels=as.character(dates), xpd=NA, pos=2)
legend("topleft", legend=c("C","C>D","D>C","D"), pch=21, pt.cex=2, pt.bg=c("blue","lightblue","pink","red"))
}
plotcolony(11)
#plot mortality
dead <- condition[condition$mortality=="3",]
missing <- condition[condition$mortality=="missing",]
plot(condition$date, condition$mortality)
condition <- condition[!condition$colony %in% missing$colony,]
condition <- condition[condition$reef!="42",]
table <- table(condition$mortality, condition$date, condition$reef)
table
HIMB <- table[,,1]
HIMB <- melt(HIMB)
HIMB2or3 <- aggregate(HIMB$value, by=list(HIMB$Var1 %in% c(2,3), HIMB$Var2), FUN=sum)
HIMB2or3 <- HIMB2or3[HIMB2or3$Group.1==T, ]
HIMB2or3
plot(as.Date(HIMB2or3$Group.2), HIMB2or3$x, type="o", col="magenta", xlab="Date", ylab="Number of Colonies over 50% Dead", Main="Mortality over Time")
lines(as.Date(TF2or3$Group.2), TF2or3$x, type="o",col="purple")
lines(as.Date(FF2or3$Group.2), FF2or3$x, type="o",col="turquoise")
legend("topleft", legend=c("Reef HIMB","Reef 25", "Reef 44"), fill=c("magenta","purple","turquoise"))
TF <- table[,,2]
TF <- melt(TF)
TF2or3 <- aggregate(TF$value, by=list(TF$Var1 %in% c(2,3), TF$Var2), FUN=sum)
TF2or3 <- TF2or3[TF2or3$Group.1==T, ]
TF2or3
plot(as.Date(TF2or3$Group.2), TF2or3$x, type="o")
FF <- table[,,3]
FF <- melt(FF)
FF2or3 <- aggregate(FF$value, by=list(FF$Var1 %in% c(2,3), FF$Var2), FUN=sum)
FF2or3 <- FF2or3[FF2or3$Group.1==T, ]
FF2or3
plot(as.Date(FF2or3$Group.2), FF2or3$x, type="o")
table1 <-table(condition$mortality, condition$date)
table1
All <- melt(table1)
All
All2or3 <- aggregate(All$value, by=list(All$Var1 %in% c(2,3), All$Var2), FUN=sum)
All2or3 <- All2or3[All2or3$Group.1==T, ]
All2or3
plot(as.Date(All2or3$Group.2), All2or3$x, type="o", col="magenta", xlab="Date", ylab="Number of Colonies over 50% Dead")
abline(v=c())
nlevels(droplevels(condition$colony))
dev.off()
Byr2 <- subset(Mcap.ff.all, bleach2=="bleach")
NByr2 <- subset(Mcap.ff.all, bleach2=="notbleached")
head(Byr2)
plot(Mcap.ff.all$date, log(Mcap.ff.all$tot.SH))
table(Mcap.ff.all$date, log(Mcap.ff.all$tot.SH))
# PLOT SYMBIONT ABUNDANCE AND COMPOSITION FOR INDIVIDUAL COLONIES -----
# XYPLOT ALL COLONIES IN EACH GROUP
xyplot(log(tot.SH) ~ date | group, groups=~colony, ylim=c(-11,1), data=Mcap.ff.all, type="o", cex=0.25)
xyplot(propD ~ date | group, groups=~colony, ylim=c(-0.1,1.1), data=Mcap.ff.all, type="o", cex=0.25)
# XYPLOT INDIVIDUAL COLONIES BY GROUP, RAW DATA
for (g in levels(Mcap.ff.all$group)) {
df <- subset(Mcap.ff.all, group==g)
print(doubleYScale(
# Plot total S/H with GAM fit
xyplot(log(tot.SH) ~ date | colony, ylim=c(-11,1), data=df, type="o", cex=0.25, main=g),
# Plot propD with locfit
xyplot(propD ~ date | colony, ylim=c(-0.1, 1.1), data=df, type="o", cex=0.25)
))
}
# XYPLOT INDIVIDUAL COLONIES BY GROUP, FITTED RESPONSES
for (g in levels(Mcap.ff.all$group)) {
df <- subset(Mcap.ff.all, group==g)
print(doubleYScale(
# Plot total S/H with GAM fit
xyplot(log(tot.SH) ~ days | colony, ylim=c(-11,1), data=df, main=g, panel = function(x, y, ...) {
panel.xyplot(x, y, cex=0.5, ...)
dayrange <- seq(min(x), max(x), 1)
tryCatch({
m <- gam(y ~ s(x), family="gaussian")
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
},
error=function(e) {
m <- gam(y ~ s(x, k=3), family="gaussian")
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
},
warning=function(w) print(w))
}),
# Plot propD with locfit
xyplot(propD ~ days | colony, ylim=c(-0.1, 1.1), data=df, panel = function(x, y, ...) {
panel.xyplot(x, y, cex=0.25, ...)
dayrange <- seq(min(x), max(x), 1)
tryCatch({
m <- locfit(y ~ lp(x, nn=1), family="betar", lfproc=locfit.raw)
p <- predict(m, newdata=data.frame(x=dayrange))
panel.lines(p ~ dayrange)
CtoD <- dayrange[which(diff(sign(p-0.5))>0)]
DtoC <- dayrange[which(diff(sign(p-0.5))<0)]
panel.xyplot(c(CtoD, DtoC), rep(0.5, length(c(CtoD, DtoC))), pch="*", cex=2, col="red")
},
error=function(e) print(e),
warning=function(w) print(w))
})
))
}
# MODEL SYMBIONT ABUNDANCE AND COMPOSITION FOR EACH GROUP -----
# Exclude groups that didn't quite make it
df <- Mcap.ff.all[as.numeric(Mcap.ff.all$group2) %in% c(2,4,6,8,10), ]
df <- droplevels(df)
# FIT PROPD GAMM BY GROUP
xyplot(propD ~ days | group2, data=df)
propDmod <- gamm4(propD ~ group2 + s(days, by=group2), random=~(1|colony), data=df)
# FIT TOTSH GAMM BY GROUP
xyplot(log(tot.SH) ~ days | group, data=df)
totSHmod <- gamm4(log(tot.SH) ~ group2 + s(days, by=group2), random=~(1|colony), data=df)
# GET FITTED VALUES FOR EACH GROUP
newdata <- expand.grid(days=seq(0,524,1), group2=levels(df$group2))
newdata$tot.SH <- predict(totSHmod$gam, newdata)
newdata$propD <- predict(propDmod$gam, newdata)
newdata$predse <- predict(totSHmod$gam, newdata, se.fit=T)$se.fit
# PLOT FITTED VALUES FOR EACH GROUP
xyplot(tot.SH ~ days, groups=~group2, newdata)
xyplot(propD ~ days, groups=~group2, newdata, ylim=c(0,1))
doubleYScale(xyplot(tot.SH ~ days | group2, newdata, type="l"),
xyplot(propD ~ days | group2, newdata, type="l", ylim=c(-0.1,1.1)))
# PLOT FITTED RESPONSES FOR EACH GROUP, MULTIPANEL SHUFFLERS vs. NONSHUFFLERS -----
rbPal <- colorRampPalette(c('dodgerblue','red'))
newdata$color <- rbPal(100)[as.numeric(cut(newdata$propD, breaks = 100))]
par(mfrow=c(2,1), mar=c(1,3,1,2), mgp=c(1.5,0.4,0), tcl=-0.25)
plot(NA, ylim=c(-7,0), xlim=range(newdata$days), xaxs="i", xaxt="n", yaxt="n", ylab="")
axis(side=2, at=seq(-7,-1,1), cex.axis=0.75)
dateticks <- seq.Date(as.Date("2014-11-01"), as.Date("2016-02-01"), by="month")
axis(side=1, at=as.numeric(dateticks-as.Date("2014-10-24")), labels=NA)
for (group2 in levels(newdata$group2)[c(1,3,4)]) {
df <- newdata[newdata$group2==group2, ]
addpoly(df$days, df$tot.SH - 1.96*df$predse, df$tot.SH + 1.96*df$predse, col=alpha("gray", 0.7))
}
points(tot.SH ~ days, newdata[as.numeric(newdata$group2) %in% c(1,3,4), ], pch=21, col=color, bg=color)
text(par("usr")[1], quantile(par("usr")[3:4], 0.9), pos=4,
expression(bold("A. Non-shuffling colonies")))
gradient.rect(quantile(par("usr")[1:2], 0.1), quantile(par("usr")[3:4], 0.1),
quantile(par("usr")[1:2], 0.35), quantile(par("usr")[3:4], 0.175),
col=rbPal(100), border=NA)
text(quantile(par("usr")[1:2], c(0.1, 0.35)), rep(quantile(par("usr")[3:4], 0.1375), 2), pos=c(2,4), labels=c("C", "D"), cex=0.75)
par(mar=c(2,3,0,2))
plot(NA, ylim=c(-7,0), xlim=range(newdata$days), xaxs="i", xlab="", ylab="", xaxt="n", yaxt="n", xpd=NA)
axis(side=2, at=seq(-7,-1,1), cex.axis=0.75)
mtext(side=2, text="Symbiont abundance (ln S/H)", line=-1.5, outer=T)
dateticks <- seq.Date(as.Date("2014-11-01"), as.Date("2016-02-01"), by="month")
axis(side=1, at=as.numeric(dateticks-as.Date("2014-10-24")), labels=format(dateticks, "%b"), cex.axis=0.75)
for (group2 in levels(df$group2)[c(2,5)]) {
df <- newdata[newdata$group2==group2, ]
addpoly(df$days, df$tot.SH - 1.96*df$predse, df$tot.SH + 1.96*df$predse, col=alpha("gray", 0.7))
}
points(tot.SH ~ days, newdata[as.numeric(newdata$group2) %in% c(2,5), ], pch=21, col=color, bg=color)
gradient.rect(quantile(par("usr")[1:2], 0.1), quantile(par("usr")[3:4], 0.1),
quantile(par("usr")[1:2], 0.35), quantile(par("usr")[3:4], 0.175),
col=rbPal(100), border=NA)
text(quantile(par("usr")[1:2], c(0.1, 0.35)), rep(quantile(par("usr")[3:4], 0.1375), 2), pos=c(2,4), labels=c("C", "D"), cex=0.75)
text(par("usr")[1], quantile(par("usr")[3:4], 0.9), pos=4,
expression(bold("B. Shuffling colonies")))
# DOES SHUFFLING DEPEND ON REEF?
MC <- unique(Mcap.ff.all[, c("colony", "reef","shuff","bleach", "group", "depth")])
MC$shuff <- factor(MC$shuff)
MCb <- droplevels(MC[MC$bleach=="bleach", ])
plot(MCb$group ~ MCb$reef)
chisq.test(MCb$reef, MCb$group)
MCnb <- droplevels(MC[MC$bleach=="notbleached", ])
plot(MCnb$group ~ MCnb$reef)
chisq.test(MCnb$reef, MCnb$group)
# DOES SHUFFLING DEPEND ON DEPTH?
plot(shuff ~ depth, MCb)
chisq.test(MCb$depth, MCb$shuff)
plot(as.numeric(shuff) ~ depth, MCnb)
# DOES SHUFFLING RELATE TO BLEACHING SEVERITY?
bsev <- aggregate(data.frame(minscore=Mcap.ff.all$tot.SH),
by=list(colony=Mcap.ff.all$colony), FUN=min, na.rm=T)
bsev <- merge(MCb, bsev)
plot(bsev$shuff ~ log(bsev$minscore))
plot(as.numeric(bsev$shuff) ~ log(bsev$minscore))
mod <- glm(shuff ~ depth * log(minscore), family="binomial", data=bsev)
plot(mod)
anova(mod, test="Chisq")
plot(effect("depth:log(minscore)", mod), x.var="minscore")
plot(effect("depth", mod))
# HEATMAP OF SHUFFLING ---------
# Create matrix for image function
clades <- melt(Mcap.f, id.vars=c("colony", "date", "vis", "reef", "tdom"), measure.vars="syms",
factorsAsStrings=FALSE)
head(clades)
clades$value <- as.numeric(factor(clades$value))
clades <- unique(clades)
clades <- dcast(clades, vis + colony + reef + tdom ~ date, drop=T)
head(clades)
clades[is.na(clades)] <- -1 # Recode missing values as -1
#clades[which(clades$colony=="129"), 8:10] <- -2 # Recode mortality as -2
clades.m0 <- clades[with(clades, order(clades[, 12], clades[, 5], clades[, 7],
clades[, 8], clades[, 9], clades[, 10])), ]
clades.m <- as.matrix(clades.m0[,5:12])
rownames(clades.m) <- as.character(clades.m0$colony)
# Plot figure
library(RColorBrewer)
par(mfrow=c(1,1), mar=c(3,5,2,2), bg="white")
image(x=seq(1, ncol(clades.m)), y=seq(1, nrow(clades.m)), z=t(clades.m),
xaxt="n", yaxt="n", xlab="", ylab="",
breaks=c(-2,-1.1,0,1,2,3,4,5),
col=c("black", "white", rev(brewer.pal(11, "RdYlBu")[c(2,1,3,9,11)])))
# Plot date axis
#axis(side=3, at=seq(1:8), labels=FALSE, cex.axis=0.75, par("tck"=-0.025), xpd=T)
text(0.5:7.5, par("usr")[4], xpd=T, cex=0.6, pos=4,
labels=levels(Mcap$fdate), srt=45, adj=-0.1)
# # Plot Bleached vs. Not Bleached rectangles
# rect(par("usr")[1] - 2.25, par("usr")[3], par("usr")[1] - 1.25, par("usr")[4], xpd=T)
# text(par("usr")[1] - 1.75, quantile(par("usr")[3:4])[c(2, 4)], labels=c("Not Bleached", "Bleached"),
# srt=90, xpd=2)
# Plot colony numbers
text(0, 1:nrow(clades.m), labels=rownames(clades.m), xpd=T, cex=0.5)
# get shufflers
Mcap[which(Mcap$colony %in% c(71,54,40,119)), ]
head(clades.m)
# Plot Row Side Colors
reefcols <- c("#bebada", "#8dd3c7", "#d9d9d9")
for (i in 1:nrow(clades.m0)) {
reef <- clades.m0$reef[i]
rect(par("usr")[1] - 1.25, par("usr")[3] + 1 * (i - 1),
par("usr")[1] - 0.25, par("usr")[3] + 1 * (i - 1) + 1, col=reefcols[as.numeric(reef)],
xpd=T, border=NA)
}
rect(par("usr")[1] - 1.25, par("usr")[3], par("usr")[1] - 0.25, par("usr")[4], xpd=T)
lines(x=c(par("usr")[1] - 2.25, par("usr")[1] - 0.25), y=rep(quantile(par("usr")[3:4], 0.5), 2), xpd=T)
# Plot tdom side boxes
breaks <- c(0, which(diff(as.numeric(clades.m0$tdom))!=0), length(clades.m0$tdom))
doms <- clades.m0$tdom[breaks]
for (i in 2:length(breaks)) {
rect(par("usr")[2] + 0.25, par("usr")[3] + breaks[i-1], par("usr")[2] + 0.75, par("usr")[3] + breaks[i], xpd=T)
}
for (i in 1:(length(breaks)-1)) {
text(par("usr")[2] + 0.5, (breaks[i] + breaks[i+1]) / 2, paste(doms[i], "dominant"), xpd=T, srt=90,
cex=0.75)
}
# Plot Row Side Color Key
for (i in 1:3) {
rect(par("usr")[1] - 1.25, quantile(par("usr")[3:4], 0) * -1.05 - ((i - 1) * 1),
par("usr")[1] - 0.25, quantile(par("usr")[3:4], 0) * -1.05 - ((i - 1) * 1) - 1, xpd=T,
border=NA, col=reefcols[i])
}
rect(par("usr")[1] - 1.25, quantile(par("usr")[3:4], 0) * -1.05,
par("usr")[1] - 0.25, quantile(par("usr")[3:4], 0) * -1.05 - 3, xpd=T)
axis(side=2, xpd=T, pos=par("usr")[1] - 1.25, lwd=0, lwd.ticks=0,
at=c(-1, -2, -3), labels=c("Rf 25", "Rf 44", "HIMB"), las=2, cex.axis=0.6, mgp=c(0,0.4,0))
# Plot Heatmap Key
x <- quantile(par("usr")[1:2], probs=seq(0, 1, length.out=7))
y <- rep(quantile(par("usr")[3:4], 0) * -1.05, 2) - c(0, 1)
rect(x[1], y[1], x[7], y[2], xpd=T)
for (i in 1:6) {
rect(x[i], y[1], x[i + 1], y[2], xpd=T,
border=NA, col=c(brewer.pal(11, "RdYlBu")[c(1,3,9,11)], "white", "black")[i])
}
text(xpd=T, y=y[1] - 0.75, pos=1, cex=0.6,
x=seq(par("usr")[1], par("usr")[2], length=7)[-7] + 0.5,
labels=c("D only", "D > C", "C > D", "C only", "no data", "dead"))
text(xpd=T, y=quantile(par("usr")[3:4], 0) * -1.05 - 2.5, pos=1, cex=0.9,
x=quantile(par("usr")[1:2], 0.5),
labels=expression(italic(Symbiodinium)~clades))
# MODEL TRAJECTORIES USING SPIDA ------
# Model trajectories of symbiont populations over time using mixed model
# Build piecewise polynomial model with knot at 82 days (January time point)
# From October to January, fit a quadratic polynomial (1st element of degree=2)
# From January to May, fit a linear model (2nd element of degree=1)
# Function is continuous at time=82 days (smooth=0)
Mcap.ff$days <- as.numeric(Mcap.ff$date - as.Date("2015-08-11"))
#offset <- 0 # optional to center "days" axis at any point
sp <- function(x) gsp(x, knots=c(51,85,128), degree=c(2,3,2,2))
#sp <- function(x) cs(x)
#sp <- function(x) bs(x, knots=c(71,128))
# Build full model with fixed effects of vis, tdom, reef, and time, random effect of colony
Mcapdf <- Mcap.ff[Mcap.ff$reef!="42",]
#mod.all.full <- lmerTest::lmer(log(Mcapdf$tot.SH) ~ sp(Mcapdf$days) * Mcapdf$vis * Mcapdf$reef + (sp(Mcapdf$days) | Mcapdf$colony))
#mod.all.full <- lmerTest::lmer(log(tot.SH) ~ poly(days, 3) * vis * reef + (1 | colony), data=Mcap.ff[Mcap.ff$reef!="42",])
#plot(Effect(c("days", "vis", "reef"), mod.all.full, xlevels=list(days=unique(Mcap.ff$days))),
# multiline=T, z.var="reef", ci.style="bars")
# Test significance of fixed effects by backwards selection
#modselect <- step(mod.all.full, lsmeans.calc=F, difflsmeans.calc=F, alpha.fixed=0.05)
#modselect
#summary(mod.all.full)
# Rebuild model omitting non-significant fixed effects
#mod.all <- mod.all.full
# Identify outliers with standardized residuals > 2.5
#out <- abs(residuals(mod.all)) > sd(residuals(mod.all)) * 2.5
#Mcap.ff[out, ] # outlying data points
# Refit model without outliers
#Mcapdf <- Mcapdf[!out, ]
mod.all <- lmerTest::lmer(log(tot.SH) ~ sp(days) * vis * reef + (1 | colony), data=Mcapdf)
#mod.all <- mod.all.full
# Print and save ANOVA table for model
anovatab <- anova(mod.all)
#write.csv(round(anovatab, digits=3), file="output/Table1.csv")
# pseudo-r2 value-- squared correlation between fitted and observed values
summary(lm(model.response(model.frame(mod.all)) ~ fitted(mod.all)))$r.squared
# Plotting function
plotreefs <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(reef=levels(dat$reef), vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$reef, dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$reef)
datlist <- lapply(datlist, function(x) rev(split(x, f=x$vis)))
pred <- expand.grid(days=seq_len(max(dat$days)), reef=levels(dat$reef), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$reef)
predlist <- lapply(predlist, function(x) rev(split(x, f=x$vis)))
par(mgp=c(1.75,0.4,0), oma=c(0,0,0,0))
par(mar=c(0,3,0.3,1))
layout(mat=matrix(seq_len(nlevels(dat$reef)+1)))
for (reef in levels(dat$reef)) {
with(datlist[[reef]], {
# Create plot frame for each reef
plot(NA, xlim=range(dat$days), ylim=c(-9,-1), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title(paste("Reef", reef), line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist[[reef]], {
lapply(predlist[[reef]], function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha(reefcols[[reef]], 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist[[reef]], function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
}
axis(side=1, at=as.numeric(as.Date(c("2015-08-01", "2015-09-01", "2015-10-01", "2015-11-01",
"2015-12-01", "2016-01-01", "2016-02-01")) - as.Date("2015-08-11")),
labels=c("Aug", "Sep", "Oct", "Nov", "Dec", "Jan", "Feb"))
return(list(predlist=predlist, datlist=datlist))
}
# Plot
reefcols <- list(`25`="#bebada", `44`="#8dd3c7", HIMB="#d9d9d9", `42`="green")
vislty <- list("bleached"=2, "not bleached"=1)
vispch <- list("bleached"=24, "not bleached"=21)
visbg <- list("bleached"="white", "not bleached"="black")
modelplot <- plotreefs(mod.all, 99)
Mcap42 <- Mcap.ff[Mcap.ff$reef==42,]
sp2 <- function(x) gsp(x, knots=c(85,128), degree=c(2,2,2))
mod.42 <- lmerTest::lmer(log(tot.SH) ~ sp2(days) * vis + (1 | colony), data=Mcap42)
plot42 <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$vis)
pred <- expand.grid(days=seq(min(dat$days), max(dat$days)), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$vis)
plot(NA, xlim=c(0, max(dat$days)), ylim=c(-9,-1), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title("Reef 42", line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist, {
lapply(predlist, function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha("green", 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist, function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
return(list(predlist=predlist, datlist=datlist))
}
plot42(mod.42, 99)
# MODEL TRAJECTORIES FOR BOTH YEARS
Mcap.ff.all$days <- as.numeric(Mcap.ff.all$date - as.Date("2014-10-24"))
Mcap.ff.all$days <- scale(Mcap.ff.all$days)
points <- unique(Mcap.ff.all$days)
knots <- points[c(5,7,9,11,13)]
sp <- function(x) gsp(x, knots=knots, degree=c(2,1,2,3,2,1), smooth=c(0,1,1,1,1))
Mcapdf <- Mcap.ff.all[Mcap.ff.all$reef!="42",]
Mcapdf$reef <- factor(Mcapdf$reef, levels=c("44","25","HIMB"))
Mcapdf$batch <- Mcapdf$days < 195
mod.all <- lmerTest::lmer(log(tot.SH) ~ sp(days) * vis * reef + (1 | colony), data=Mcapdf)
#anova(mod.all)
summary(lm(model.response(model.frame(mod.all)) ~ fitted(mod.all)))$r.squared
plotreefs <- function(mod, n) {
dat <- get(as.character(summary(mod)$call$data))
dat <- droplevels(dat)
levs <- expand.grid(reef=levels(dat$reef), vis=levels(dat$vis), days=as.numeric(levels(as.factor(dat$days))))
datlevs <- list(interaction(dat$reef, dat$vis, dat$days))
datsumm <- data.frame(levs,
mean=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=mean)$x),
sd=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=sd)$x),
se=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)))$x),
conf95=with(dat, aggregate(log(tot.SH), by=datlevs, FUN=function(x) sd(x)/sqrt(length(x)) * qt(0.975, length(x)-1))$x))
datlist <- split(datsumm, f=datsumm$reef)
datlist <- lapply(datlist, function(x) rev(split(x, f=x$vis)))
pred <- expand.grid(days=seq(min(dat$days), max(dat$days), length.out=475), reef=levels(dat$reef), vis=levels(dat$vis))
bootfit <- bootMer(mod, FUN=function(x) predict(x, pred, re.form=NA), nsim=n)
# Extract 90% confidence interval on predicted values
pred$fit <- predict(mod, pred, re.form=NA)
pred$lci <- apply(bootfit$t, 2, quantile, 0.05)
pred$uci <- apply(bootfit$t, 2, quantile, 0.95)
predlist <- split(pred, f=pred$reef)
predlist <- lapply(predlist, function(x) rev(split(x, f=x$vis)))
par(mgp=c(1.75,0.4,0), oma=c(0,0,0,0))
par(mar=c(0,3,0.3,1))
layout(mat=matrix(seq_len(nlevels(dat$reef)+1)))
for (reef in levels(dat$reef)) {
with(datlist[[reef]], {
# Create plot frame for each reef
plot(NA, xlim=range(dat$days), ylim=c(-9,0), xaxt="n", bty="n", tck=-0.03, ylab="ln S/H")
title(paste("Reef", reef), line=-0.9, adj=0, outer=F)
# Plot model fit line and shaded CI for bleached and/or not bleached corals
with(predlist[[reef]], {
lapply(predlist[[reef]], function(vis) {
addpoly(vis$days, vis$lci, vis$uci, col=alpha(reefcols[[reef]], 0.4), xpd=NA)
lines(vis$days, vis$fit, lty=vislty[[vis$vis[1]]])
})
})
# Plot raw data +/- standard error
lapply(datlist[[reef]], function(vis) {
arrows(vis$days, vis$mean + vis$se, vis$days, vis$mean - vis$se, code=3, angle=90, length=0.03, xpd=NA)
points(vis$days, vis$mean, pch=vispch[[vis$vis[1]]], bg=visbg[[vis$vis[1]]])
})
})
#rect(xleft = 0, ybottom = -6, xright = 82, ytop = -1, lty = 3, border="black")
}
axdates <- seq.Date(as.Date("2014-11-01"), as.Date("2016-04-01"), by="month")
unscaled <- Mcap.ff.all$days * attr(Mcap.ff.all$days, 'scaled:scale') + attr(Mcap.ff.all$days, 'scaled:center')
axdays <- as.numeric(axdates - as.Date("2014-10-24"))
tdays <- scales::rescale(axdays, to=range(dat$days), from=range(unscaled))
axis(side=1, at=tdays, labels=format(axdates, format="%b\n%y"), padj=1)
return(list(predlist=predlist, datlist=datlist))
}
# Plot
reefcols <- list(`25`="#bebada", `44`="#8dd3c7", HIMB="#d9d9d9", `42`="green")
vislty <- list("bleached"=2, "not bleached"=1)
vispch <- list("bleached"=24, "not bleached"=21)
visbg <- list("bleached"="white", "not bleached"="black")
modelplot <- plotreefs(mod.all, 99)
|
library(Matrix)
library(caret)
library(xgboost)
library(ROCR)
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
train_df <- read.csv(url, header = FALSE, na.strings = " ?")
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names"
decrp <- readLines(url)
names <- sub("(.*):.*", "\\1", decrp[97:110])
names
colnames(train_df) <- c(names, "Y")
head(train_df)
str(train_df)
## one-hot encoding
encoding <- dummyVars(~ . - Y, train_df)
train_data <- predict(encoding, train_df, na.action = na.pass)
train_data <- as(train_data, "sparseMatrix")
train_label <- as.numeric(train_df$Y) - 1
train <- xgb.DMatrix(data = train_data, label = train_label)
## test data
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
test_df <- read.csv(url, header = FALSE, na.strings = " ?", skip = 1)
colnames(test_df) <- c(names, "Y")
encoding <- dummyVars(~ . - Y, test_df)
test_data <- predict(encoding, test_df, na.action = na.pass)
test_data <- as(test_data, "sparseMatrix")
test_label <- as.numeric(test_df$Y) - 1
test <- xgb.DMatrix(data = test_data, label = test_label)
param_1 <- list(
eta = 0.3, # learning rate
gamma = 0, # minimum reduction of loss for split
max.depth = 6, # maximum depth of the tree
subsample = 1, # row subsampling
colsample_bylevel = 1, # feature subsampling
lambda = 1 # l2 penalty
)
## 5-fold cross validation
gbcv_1 <- xgb.cv(params = param_1, data = train, nfold = 5,
objective = "binary:logistic", nrounds = 200,
early_stopping_rounds = 15, eval_metric = "auc",
missing = NA, seed = 12345)
param_2 <- list(
eta = 0.15, # learning rate
gamma = 0, # minimum reduction of loss for split
max.depth = 4, # maximum depth of the tree
subsample = 0.7, # row subsampling
colsample_bylevel = 0.5, # feature subsampling
lambda = 3
)
gbcv_2 <- xgb.cv(params = param_2, data = train, nfold = 5,
objective = "binary:logistic", nrounds = 500,
early_stopping_rounds = 10, eval_metric = "auc",
missing = NA, seed = 12345)
## validation set
set.seed(12345)
index <- sample(1:nrow(train_df), 6000, replace = FALSE)
train_df_sub <- train_df[-index,]
vali_df <- train_df[index,]
encoding <- dummyVars(~ . - Y, train_df_sub)
train_sub_data <- predict(encoding, train_df_sub, na.action = na.pass)
train_sub_data <- as(train_sub_data, "sparseMatrix")
train_sub_label <- as.numeric(train_df_sub$Y) - 1
train_sub <- xgb.DMatrix(data = train_sub_data, label = train_sub_label)
encoding <- dummyVars(~ . - Y, vali_df)
vali_data <- predict(encoding, vali_df, na.action = na.pass)
vali_data <- as(vali_data, "sparseMatrix")
vali_label <- as.numeric(vali_df$Y) - 1
vali <- xgb.DMatrix(data = vali_data, label = vali_label)
watchlist <- list(train = train_sub, test = vali)
xgb_vali_1 <- xgb.train(params = param_1, data = train_sub,
watchlist = watchlist,
objective = "binary:logistic",
nrounds = 200, early_stopping_rounds = 15,
eval_metric = "auc", missing = NA, seed = 12345)
xgb_vali_2 <- xgb.train(params = param_2, data = train_sub,
watchlist = watchlist,
objective = "binary:logistic",
nrounds = 200, early_stopping_rounds = 15,
eval_metric = "auc", missing = NA, seed = 12345)
## Final model
xgbtree_1 <- xgboost(params = param_1, data = train, verbose = 0,
objective = "binary:logistic", nrounds = 25,
missing = NA, seed = 12345)
xgbtree_2 <- xgboost(params = param_2, data = train, verbose = 0,
objective = "binary:logistic", nrounds = 130,
missing = NA, seed = 12345)
performance(prediction(predict(xgbtree_1, test), test_label), "auc")
performance(prediction(predict(xgbtree_2, test), test_label), "auc")
importance <- xgb.importance(feature_names = dimnames(train)[[2]],
model = xgbtree_1)
xgb.ggplot.importance(importance, top_n = 15) + theme(legend.position="none")
|
/example_2.R
|
no_license
|
lqyliuqingyang/class-presentation
|
R
| false
| false
| 4,340
|
r
|
library(Matrix)
library(caret)
library(xgboost)
library(ROCR)
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
train_df <- read.csv(url, header = FALSE, na.strings = " ?")
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names"
decrp <- readLines(url)
names <- sub("(.*):.*", "\\1", decrp[97:110])
names
colnames(train_df) <- c(names, "Y")
head(train_df)
str(train_df)
## one-hot encoding
encoding <- dummyVars(~ . - Y, train_df)
train_data <- predict(encoding, train_df, na.action = na.pass)
train_data <- as(train_data, "sparseMatrix")
train_label <- as.numeric(train_df$Y) - 1
train <- xgb.DMatrix(data = train_data, label = train_label)
## test data
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
test_df <- read.csv(url, header = FALSE, na.strings = " ?", skip = 1)
colnames(test_df) <- c(names, "Y")
encoding <- dummyVars(~ . - Y, test_df)
test_data <- predict(encoding, test_df, na.action = na.pass)
test_data <- as(test_data, "sparseMatrix")
test_label <- as.numeric(test_df$Y) - 1
test <- xgb.DMatrix(data = test_data, label = test_label)
param_1 <- list(
eta = 0.3, # learning rate
gamma = 0, # minimum reduction of loss for split
max.depth = 6, # maximum depth of the tree
subsample = 1, # row subsampling
colsample_bylevel = 1, # feature subsampling
lambda = 1 # l2 penalty
)
## 5-fold cross validation
gbcv_1 <- xgb.cv(params = param_1, data = train, nfold = 5,
objective = "binary:logistic", nrounds = 200,
early_stopping_rounds = 15, eval_metric = "auc",
missing = NA, seed = 12345)
param_2 <- list(
eta = 0.15, # learning rate
gamma = 0, # minimum reduction of loss for split
max.depth = 4, # maximum depth of the tree
subsample = 0.7, # row subsampling
colsample_bylevel = 0.5, # feature subsampling
lambda = 3
)
gbcv_2 <- xgb.cv(params = param_2, data = train, nfold = 5,
objective = "binary:logistic", nrounds = 500,
early_stopping_rounds = 10, eval_metric = "auc",
missing = NA, seed = 12345)
## validation set
set.seed(12345)
index <- sample(1:nrow(train_df), 6000, replace = FALSE)
train_df_sub <- train_df[-index,]
vali_df <- train_df[index,]
encoding <- dummyVars(~ . - Y, train_df_sub)
train_sub_data <- predict(encoding, train_df_sub, na.action = na.pass)
train_sub_data <- as(train_sub_data, "sparseMatrix")
train_sub_label <- as.numeric(train_df_sub$Y) - 1
train_sub <- xgb.DMatrix(data = train_sub_data, label = train_sub_label)
encoding <- dummyVars(~ . - Y, vali_df)
vali_data <- predict(encoding, vali_df, na.action = na.pass)
vali_data <- as(vali_data, "sparseMatrix")
vali_label <- as.numeric(vali_df$Y) - 1
vali <- xgb.DMatrix(data = vali_data, label = vali_label)
watchlist <- list(train = train_sub, test = vali)
xgb_vali_1 <- xgb.train(params = param_1, data = train_sub,
watchlist = watchlist,
objective = "binary:logistic",
nrounds = 200, early_stopping_rounds = 15,
eval_metric = "auc", missing = NA, seed = 12345)
xgb_vali_2 <- xgb.train(params = param_2, data = train_sub,
watchlist = watchlist,
objective = "binary:logistic",
nrounds = 200, early_stopping_rounds = 15,
eval_metric = "auc", missing = NA, seed = 12345)
## Final model
xgbtree_1 <- xgboost(params = param_1, data = train, verbose = 0,
objective = "binary:logistic", nrounds = 25,
missing = NA, seed = 12345)
xgbtree_2 <- xgboost(params = param_2, data = train, verbose = 0,
objective = "binary:logistic", nrounds = 130,
missing = NA, seed = 12345)
performance(prediction(predict(xgbtree_1, test), test_label), "auc")
performance(prediction(predict(xgbtree_2, test), test_label), "auc")
importance <- xgb.importance(feature_names = dimnames(train)[[2]],
model = xgbtree_1)
xgb.ggplot.importance(importance, top_n = 15) + theme(legend.position="none")
|
setClass("DMNGroup", contains="SimpleList",
prototype=prototype(elementType="DMN"))
.DMNGroup <-
function(...)
{
new("DMNGroup", listData=list(...))
}
## dmngroup
dmngroup <-
function(count, group, k, ..., simplify=TRUE, .lapply=parallel::mclapply)
{
if (length(group) != nrow(count))
stop("'length(group)' does not equal 'nrow(count)'")
if (!is.factor(group))
group <- factor(group)
lvls <- setNames(nm=levels(group))
counts <- lapply(lvls, csubset, count, group)
tasks <- expand.grid(group=names(counts), k=k)
tid <- seq_len(nrow(tasks))
ans0 <- .lapply(tid, function(i, tasks, counts, ...) {
count <- counts[[tasks[i,"group"]]]
k <- tasks[i,"k"]
dmn(count, k, ...)
}, tasks, counts, ...)
ans <- if (simplify) {
ans1 <- split(ans0, tasks[,"group"])
opt <- lapply(ans1, function(ans) {
which.min(sapply(ans, laplace))
})
Map("[[", ans1, opt)
} else ans0
do.call(.DMNGroup, ans)
}
## predict
.predict.DMNGroup <-
function(object, newdata, ..., assign=FALSE)
{
if (2 < length(object))
stop("only 2 groups can be used for classification")
res <- lapply(object, predict, newdata, ..., logevidence=TRUE)
offset <- apply(do.call(cbind, res), 1, min)
prClass <- local({
nClass <- sapply(object, function(x) nrow(mixture(x)))
nClass / sum(nClass)
})
pr <- simplify2array(Map(function(x, alpha, prClass, offset) {
prMix <- sweep(exp(-(alpha - offset)), 2, mixturewt(x)$pi, "*")
rowSums(prMix) * prClass
}, object, res, prClass, MoreArgs=list(offset=offset)))
if (!is.matrix(pr)) {
dmnms <- list(rownames(newdata), names(prClass))
pr <- matrix(pr, nrow=1, dimnames=dmnms)
}
if (assign)
names(object)[ifelse((pr[,1] / rowSums(pr)) > .5, 1, 2)]
else
pr / rowSums(pr)
}
setMethod(predict, "DMNGroup", .predict.DMNGroup)
## cross-validation
.cv_dmngroup <-
function(dropidx, count, k, z, ..., verbose=FALSE)
## e.g., k = c(Lean=1, Obese=3) --> 1 group for lean, 3 for obese
{
tryCatch({
trainz <- z[-dropidx]
u <- unique(trainz)
train <- count[-dropidx,,drop=FALSE]
if (!is.factor(trainz))
trainz <- factor(trainz, levels=names(k))
if (any(is.na(trainz)))
stop("values of 'z' not all in 'names(k)'")
if (!all(names(k) %in% as.character(trainz)))
stop("not all names(k) in z subset")
trains <- sapply(levels(trainz), csubset, train, trainz)
fits <- Map(dmn, trains, k[levels(trainz)], ...,
verbose=verbose)
fits <- do.call(.DMNGroup, fits)
predict(fits, count[dropidx,,drop=FALSE], assign=FALSE)
}, error=function(err) {
message(".cv_dmngroup error: ", conditionMessage(err))
matrix(NA_integer_, nrow=length(dropidx), ncol=length(k),
dimnames=list(rownames(count)[dropidx], names(k)))
})
}
cvdmngroup <-
function(ncv, count, k, z, ..., verbose=FALSE, .lapply=parallel::mclapply)
{
n <- seq_len(nrow(count))
grp <- split(sample(length(n)), cut(n, ncv))
names(grp) <- seq_along(grp)
cvresult <- .lapply(names(grp), function(idx, grp, ..., verbose) {
if (verbose)
cat("cross-validation group", names(grp[idx]), "\n")
.cv_dmngroup(grp[[idx]], ..., verbose=verbose)
}, grp, count, k, z, ..., verbose=verbose)
gid <- rep(seq_along(cvresult), sapply(cvresult, nrow))
cbind(data.frame(group=gid, row.names=NULL),
do.call(rbind, cvresult))
}
## summary / print / plot
setMethod(summary, "DMNGroup",
function(object, ...)
{
k <- data.frame(k=sapply(object, function(elt) ncol(mixture(elt))))
sxt <- t(sapply(object, function(elt) {
c(samples=nrow(mixture(elt)), taxa=nrow(fitted(elt)))
}))
goodness <- t(sapply(object, goodnessOfFit))
cbind(k=k, sxt, goodness)
})
setMethod(show, "DMNGroup",
function(object)
{
cat("class:", class(object), "\n")
cat("summary:\n")
print(summary(object))
})
|
/R/dmngroup.R
|
no_license
|
mtmorgan/DirichletMultinomial
|
R
| false
| false
| 4,155
|
r
|
setClass("DMNGroup", contains="SimpleList",
prototype=prototype(elementType="DMN"))
.DMNGroup <-
function(...)
{
new("DMNGroup", listData=list(...))
}
## dmngroup
dmngroup <-
function(count, group, k, ..., simplify=TRUE, .lapply=parallel::mclapply)
{
if (length(group) != nrow(count))
stop("'length(group)' does not equal 'nrow(count)'")
if (!is.factor(group))
group <- factor(group)
lvls <- setNames(nm=levels(group))
counts <- lapply(lvls, csubset, count, group)
tasks <- expand.grid(group=names(counts), k=k)
tid <- seq_len(nrow(tasks))
ans0 <- .lapply(tid, function(i, tasks, counts, ...) {
count <- counts[[tasks[i,"group"]]]
k <- tasks[i,"k"]
dmn(count, k, ...)
}, tasks, counts, ...)
ans <- if (simplify) {
ans1 <- split(ans0, tasks[,"group"])
opt <- lapply(ans1, function(ans) {
which.min(sapply(ans, laplace))
})
Map("[[", ans1, opt)
} else ans0
do.call(.DMNGroup, ans)
}
## predict
.predict.DMNGroup <-
function(object, newdata, ..., assign=FALSE)
{
if (2 < length(object))
stop("only 2 groups can be used for classification")
res <- lapply(object, predict, newdata, ..., logevidence=TRUE)
offset <- apply(do.call(cbind, res), 1, min)
prClass <- local({
nClass <- sapply(object, function(x) nrow(mixture(x)))
nClass / sum(nClass)
})
pr <- simplify2array(Map(function(x, alpha, prClass, offset) {
prMix <- sweep(exp(-(alpha - offset)), 2, mixturewt(x)$pi, "*")
rowSums(prMix) * prClass
}, object, res, prClass, MoreArgs=list(offset=offset)))
if (!is.matrix(pr)) {
dmnms <- list(rownames(newdata), names(prClass))
pr <- matrix(pr, nrow=1, dimnames=dmnms)
}
if (assign)
names(object)[ifelse((pr[,1] / rowSums(pr)) > .5, 1, 2)]
else
pr / rowSums(pr)
}
setMethod(predict, "DMNGroup", .predict.DMNGroup)
## cross-validation
.cv_dmngroup <-
function(dropidx, count, k, z, ..., verbose=FALSE)
## e.g., k = c(Lean=1, Obese=3) --> 1 group for lean, 3 for obese
{
tryCatch({
trainz <- z[-dropidx]
u <- unique(trainz)
train <- count[-dropidx,,drop=FALSE]
if (!is.factor(trainz))
trainz <- factor(trainz, levels=names(k))
if (any(is.na(trainz)))
stop("values of 'z' not all in 'names(k)'")
if (!all(names(k) %in% as.character(trainz)))
stop("not all names(k) in z subset")
trains <- sapply(levels(trainz), csubset, train, trainz)
fits <- Map(dmn, trains, k[levels(trainz)], ...,
verbose=verbose)
fits <- do.call(.DMNGroup, fits)
predict(fits, count[dropidx,,drop=FALSE], assign=FALSE)
}, error=function(err) {
message(".cv_dmngroup error: ", conditionMessage(err))
matrix(NA_integer_, nrow=length(dropidx), ncol=length(k),
dimnames=list(rownames(count)[dropidx], names(k)))
})
}
cvdmngroup <-
function(ncv, count, k, z, ..., verbose=FALSE, .lapply=parallel::mclapply)
{
n <- seq_len(nrow(count))
grp <- split(sample(length(n)), cut(n, ncv))
names(grp) <- seq_along(grp)
cvresult <- .lapply(names(grp), function(idx, grp, ..., verbose) {
if (verbose)
cat("cross-validation group", names(grp[idx]), "\n")
.cv_dmngroup(grp[[idx]], ..., verbose=verbose)
}, grp, count, k, z, ..., verbose=verbose)
gid <- rep(seq_along(cvresult), sapply(cvresult, nrow))
cbind(data.frame(group=gid, row.names=NULL),
do.call(rbind, cvresult))
}
## summary / print / plot
setMethod(summary, "DMNGroup",
function(object, ...)
{
k <- data.frame(k=sapply(object, function(elt) ncol(mixture(elt))))
sxt <- t(sapply(object, function(elt) {
c(samples=nrow(mixture(elt)), taxa=nrow(fitted(elt)))
}))
goodness <- t(sapply(object, goodnessOfFit))
cbind(k=k, sxt, goodness)
})
setMethod(show, "DMNGroup",
function(object)
{
cat("class:", class(object), "\n")
cat("summary:\n")
print(summary(object))
})
|
# Final submission
#
# Set variable for which ensemble (QRA or QA (mean))
ensemble_dir = "qra-state-ensemble" # c("qra-state-ensemble" "qra-ensemble", "quantile-average")
# Get ensemble
submit_ensemble <- suppressMessages(readr::read_csv(here::here("ensembling", ensemble_dir, "submission-files",
paste0("latest.csv"))))
# Filter to states with minimum deaths in last week
source(here::here("utils", "states-min-last-week.R"))
keep_states <- states_min_last_week(min_last_week = 5, last_week = 1)
submit_ensemble <- dplyr::filter(submit_ensemble, location %in% c(keep_states$state_code, "US"))
# Set forecast date
forecast_date <- unique(dplyr::pull(submit_ensemble, forecast_date))
# Filter to forecasts within Rt forecast
# rt_max_date <- suppressMessages(readr::read_csv(here::here("rt-forecast/submission-files/latest.csv"))) %>%
# dplyr::pull(target_end_date) %>%
# unique() %>%
# max()
submit_ensemble <- dplyr::filter(submit_ensemble, (target_end_date - submission_date) <= 30) %>%
dplyr::select(-submission_date)
# Checks ------------------------------------------------------------------
# 1. Check population limit
pop_check <- dplyr::left_join(submit_ensemble, readr::read_csv("utils/state_pop_totals.csv"),
by = c("location" = "state_code")) %>%
dplyr::mutate(pop_check = ifelse(value > tot_pop, FALSE, TRUE)) %>%
dplyr::filter(pop_check == FALSE) %>%
dplyr::pull(location) %>%
unique()
# 2. Check for NA values
na_check <- submit_ensemble %>%
dplyr::filter(is.na(value)) %>%
dplyr::pull(location)
# 3. Incident and cumulative add up ------------------------------------------
# - check each model to find the issue
# Check incident forecast adds to cumulative
source("utils/load-submissions-function.R")
source("utils/get-us-data.R")
state_codes <- readRDS("utils/state_codes.rds")
# get cumulative data
cumulative <- get_us_deaths(data = "cumulative") %>%
dplyr::ungroup() %>%
dplyr::filter(date == forecast_date-2) %>%
dplyr::add_row(state="US", deaths = sum(.$deaths), date = forecast_date-2) %>%
dplyr::left_join(state_codes, by = "state")
# Check each model to find the issue
forecasts <- load_submission_files(dates = "all", num_last = 1, models = "single") %>%
dplyr::filter(location == "US")
us_inc <- dplyr::filter(forecasts, grepl("inc", forecasts$target))
us_cum <- dplyr::filter(forecasts, grepl("cum", forecasts$target)) %>%
dplyr::group_by(location, quantile, type) %>%
dplyr::mutate(cum_to_inc = value - dplyr::lag(value, 1)) %>%
dplyr::ungroup()
us_join <- dplyr::left_join(us_inc, us_cum, by = c("model",
"location", "target_end_date",
"type", "quantile")) %>%
dplyr::left_join(cumulative, by = c("location")) %>%
dplyr::rename(value_inc = value.x, value_cum = value.y) %>%
dplyr::mutate(cum_to_inc = ifelse(is.na(cum_to_inc), value_cum - deaths, cum_to_inc),
diff_inc_cum = value_inc - cum_to_inc) %>%
dplyr::select(model,
location, state, target_end_date, type, quantile, deaths,
value_inc, value_cum, cum_to_inc, diff_inc_cum) %>%
dplyr::group_by(model, target_end_date) %>%
dplyr::summarise(diff_inc_cum = mean(diff_inc_cum),
.groups = "drop")
if(!mean(us_join$diff_inc_cum) == 0){
warning("Incident and cumulative forecasts don't match.
Re-writing cumulative forecasts for the submission ensemble")
incident_forecast <- dplyr::filter(submit_ensemble,
grepl("wk ahead inc", target))
cumulative_data <- get_us_deaths(data = "cumulative")
cumulative_deaths <- cumulative_data %>%
dplyr::ungroup() %>%
dplyr::filter(date == min(max(date), forecast_date)) %>%
dplyr::add_row(state="US", deaths = sum(.$deaths), date = forecast_date) %>%
dplyr::left_join(state_codes, by = "state")
cumulative_forecast <- incident_forecast %>%
dplyr::left_join(dplyr::select(cumulative_deaths, location, deaths),
by = "location") %>%
dplyr::group_by(location, quantile, type) %>%
dplyr::mutate(value = cumsum(value),
value = value + deaths,
target = stringr::str_replace_all(target, "inc", "cum")) %>%
dplyr::ungroup() %>%
dplyr::select(-deaths)
# Check this also adds up
# join <- dplyr::left_join(incident_forecast, cumulative_forecast,
# by = c("forecast_date", "target_end_date",
# "location", "type", "quantile")) %>%
# dplyr::rename(value_inc = value.x, value_cum = value.y) %>%
# dplyr::group_by(location, type, quantile) %>%
# dplyr::mutate(cum_to_inc = (value_cum - dplyr::lag(value_cum))-value_inc)
submit_ensemble <- dplyr::bind_rows(incident_forecast, cumulative_forecast)
}
# Filter failing checks ---------------------------------------------------
if((length(na_check) | length(pop_check)) > 0){
message("Excluding states failing checks:")
print(dplyr::filter(state_codes, location %in% c(pop_check, na_check)) %>%
dplyr::pull(state))
}
submit_ensemble <- submit_ensemble %>%
dplyr::filter(!location %in% pop_check &
!location %in% na_check)
# Save in final-submissions
readr::write_csv(submit_ensemble,
here::here("final-submissions", "death-forecast",
paste0(unique(submit_ensemble$forecast_date),
"-epiforecasts-ensemble1.csv")))
|
/final-submissions/update-final-submission.R
|
permissive
|
signaturescience/covid-us-forecasts
|
R
| false
| false
| 5,667
|
r
|
# Final submission
#
# Set variable for which ensemble (QRA or QA (mean))
ensemble_dir = "qra-state-ensemble" # c("qra-state-ensemble" "qra-ensemble", "quantile-average")
# Get ensemble
submit_ensemble <- suppressMessages(readr::read_csv(here::here("ensembling", ensemble_dir, "submission-files",
paste0("latest.csv"))))
# Filter to states with minimum deaths in last week
source(here::here("utils", "states-min-last-week.R"))
keep_states <- states_min_last_week(min_last_week = 5, last_week = 1)
submit_ensemble <- dplyr::filter(submit_ensemble, location %in% c(keep_states$state_code, "US"))
# Set forecast date
forecast_date <- unique(dplyr::pull(submit_ensemble, forecast_date))
# Filter to forecasts within Rt forecast
# rt_max_date <- suppressMessages(readr::read_csv(here::here("rt-forecast/submission-files/latest.csv"))) %>%
# dplyr::pull(target_end_date) %>%
# unique() %>%
# max()
submit_ensemble <- dplyr::filter(submit_ensemble, (target_end_date - submission_date) <= 30) %>%
dplyr::select(-submission_date)
# Checks ------------------------------------------------------------------
# 1. Check population limit
pop_check <- dplyr::left_join(submit_ensemble, readr::read_csv("utils/state_pop_totals.csv"),
by = c("location" = "state_code")) %>%
dplyr::mutate(pop_check = ifelse(value > tot_pop, FALSE, TRUE)) %>%
dplyr::filter(pop_check == FALSE) %>%
dplyr::pull(location) %>%
unique()
# 2. Check for NA values
na_check <- submit_ensemble %>%
dplyr::filter(is.na(value)) %>%
dplyr::pull(location)
# 3. Incident and cumulative add up ------------------------------------------
# - check each model to find the issue
# Check incident forecast adds to cumulative
source("utils/load-submissions-function.R")
source("utils/get-us-data.R")
state_codes <- readRDS("utils/state_codes.rds")
# get cumulative data
cumulative <- get_us_deaths(data = "cumulative") %>%
dplyr::ungroup() %>%
dplyr::filter(date == forecast_date-2) %>%
dplyr::add_row(state="US", deaths = sum(.$deaths), date = forecast_date-2) %>%
dplyr::left_join(state_codes, by = "state")
# Check each model to find the issue
forecasts <- load_submission_files(dates = "all", num_last = 1, models = "single") %>%
dplyr::filter(location == "US")
us_inc <- dplyr::filter(forecasts, grepl("inc", forecasts$target))
us_cum <- dplyr::filter(forecasts, grepl("cum", forecasts$target)) %>%
dplyr::group_by(location, quantile, type) %>%
dplyr::mutate(cum_to_inc = value - dplyr::lag(value, 1)) %>%
dplyr::ungroup()
us_join <- dplyr::left_join(us_inc, us_cum, by = c("model",
"location", "target_end_date",
"type", "quantile")) %>%
dplyr::left_join(cumulative, by = c("location")) %>%
dplyr::rename(value_inc = value.x, value_cum = value.y) %>%
dplyr::mutate(cum_to_inc = ifelse(is.na(cum_to_inc), value_cum - deaths, cum_to_inc),
diff_inc_cum = value_inc - cum_to_inc) %>%
dplyr::select(model,
location, state, target_end_date, type, quantile, deaths,
value_inc, value_cum, cum_to_inc, diff_inc_cum) %>%
dplyr::group_by(model, target_end_date) %>%
dplyr::summarise(diff_inc_cum = mean(diff_inc_cum),
.groups = "drop")
if(!mean(us_join$diff_inc_cum) == 0){
warning("Incident and cumulative forecasts don't match.
Re-writing cumulative forecasts for the submission ensemble")
incident_forecast <- dplyr::filter(submit_ensemble,
grepl("wk ahead inc", target))
cumulative_data <- get_us_deaths(data = "cumulative")
cumulative_deaths <- cumulative_data %>%
dplyr::ungroup() %>%
dplyr::filter(date == min(max(date), forecast_date)) %>%
dplyr::add_row(state="US", deaths = sum(.$deaths), date = forecast_date) %>%
dplyr::left_join(state_codes, by = "state")
cumulative_forecast <- incident_forecast %>%
dplyr::left_join(dplyr::select(cumulative_deaths, location, deaths),
by = "location") %>%
dplyr::group_by(location, quantile, type) %>%
dplyr::mutate(value = cumsum(value),
value = value + deaths,
target = stringr::str_replace_all(target, "inc", "cum")) %>%
dplyr::ungroup() %>%
dplyr::select(-deaths)
# Check this also adds up
# join <- dplyr::left_join(incident_forecast, cumulative_forecast,
# by = c("forecast_date", "target_end_date",
# "location", "type", "quantile")) %>%
# dplyr::rename(value_inc = value.x, value_cum = value.y) %>%
# dplyr::group_by(location, type, quantile) %>%
# dplyr::mutate(cum_to_inc = (value_cum - dplyr::lag(value_cum))-value_inc)
submit_ensemble <- dplyr::bind_rows(incident_forecast, cumulative_forecast)
}
# Filter failing checks ---------------------------------------------------
if((length(na_check) | length(pop_check)) > 0){
message("Excluding states failing checks:")
print(dplyr::filter(state_codes, location %in% c(pop_check, na_check)) %>%
dplyr::pull(state))
}
submit_ensemble <- submit_ensemble %>%
dplyr::filter(!location %in% pop_check &
!location %in% na_check)
# Save in final-submissions
readr::write_csv(submit_ensemble,
here::here("final-submissions", "death-forecast",
paste0(unique(submit_ensemble$forecast_date),
"-epiforecasts-ensemble1.csv")))
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/focal.events.R
\name{read.genes.info.tsv}
\alias{read.genes.info.tsv}
\title{Read Biomart annotation file.}
\usage{
read.genes.info.tsv(build.file, filter.chr = NULL)
}
\arguments{
\item{build.file}{The TSV file exported from Biomart.}
\item{filter.chr}{A character vector of wanted chromosomes.}
}
\value{
The \code{data.table} containing the genome annotations.
}
\description{
Read Biomart annotation file.
}
\section{Warning}{
This function is expecting at least the following headers: 'Ensembl Gene ID',
'Gene Start (bp)', 'Gene End (bp)', 'Chromosome Name', 'Associated Gene Name'.
}
|
/man/read.genes.info.tsv.Rd
|
permissive
|
NKI-CCB/RUBIC
|
R
| false
| true
| 696
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/focal.events.R
\name{read.genes.info.tsv}
\alias{read.genes.info.tsv}
\title{Read Biomart annotation file.}
\usage{
read.genes.info.tsv(build.file, filter.chr = NULL)
}
\arguments{
\item{build.file}{The TSV file exported from Biomart.}
\item{filter.chr}{A character vector of wanted chromosomes.}
}
\value{
The \code{data.table} containing the genome annotations.
}
\description{
Read Biomart annotation file.
}
\section{Warning}{
This function is expecting at least the following headers: 'Ensembl Gene ID',
'Gene Start (bp)', 'Gene End (bp)', 'Chromosome Name', 'Associated Gene Name'.
}
|
context("Create a dust object")
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
test_that("Create a dust object",
{
x <- dust(fit)
expect_equal(class(x), "dust")
})
test_that("dust object has expected names",
{
x <- dust(fit)
expect_equal(names(x),
c("head", "body", "interfoot", "foot",
"border_collapse", "caption", "label", "justify",
"float", "longtable", "table_width", "tabcolsep",
"hhline", "bookdown", "print_method"))
})
test_that("dust object body component has correct dimensions",
{
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
x <- dust(fit)
Dims <- list(dim(x$head),
dim(x$body),
dim(x$interfoot),
dim(x$foot))
expect_equal(Dims,
list(c(5, 34),
c(30, 34),
NULL,
NULL))
})
test_that("dust runs when passed a data frame with tidy_df = FALSE",
{
expect_silent(dust(mtcars, tidy_df = FALSE))
})
test_that("dust runs when passed a data frame with tidy_df = TRUE",
{
expect_silent(dust(mtcars, tidy_df = TRUE))
})
test_that("dust with keep_rownames = TRUE adds rownames to object",
{
x <- dust(mtcars, keep_rownames = TRUE)
expect_equal(x$body$value[1:32], rownames(mtcars))
})
test_that("dust with additional descriptors",
{
expect_silent(dust(fit,
descriptors = c("label", "level_detail")))
})
test_that("dust with additional descriptors and term_plain numeric_label",
{
expect_silent(dust(fit,
descriptors = c("label", "level_detail"),
numeric_label = "term_plain"))
})
test_that("dust with glance_foot",
{
expect_silent(dust(fit, glance_foot = TRUE))
})
test_that("dust with glance_foot and col_pairs a divisor of total_cols",
{
fit <- lm(mpg ~ qsec + factor(am) + wt * factor(gear), data = mtcars)
expect_silent(dust(fit,
descriptors = c("label", "level_detail"),
glance_foot = TRUE,
col_pairs = 3))
})
test_that("dust a list",
{
x <- split(mtcars, list(mtcars$am, mtcars$vs))
expect_silent(dust(x))
})
|
/pixiedust/tests/testthat/test-dust.R
|
no_license
|
ingted/R-Examples
|
R
| false
| false
| 2,385
|
r
|
context("Create a dust object")
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
test_that("Create a dust object",
{
x <- dust(fit)
expect_equal(class(x), "dust")
})
test_that("dust object has expected names",
{
x <- dust(fit)
expect_equal(names(x),
c("head", "body", "interfoot", "foot",
"border_collapse", "caption", "label", "justify",
"float", "longtable", "table_width", "tabcolsep",
"hhline", "bookdown", "print_method"))
})
test_that("dust object body component has correct dimensions",
{
fit <- lm(mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars)
x <- dust(fit)
Dims <- list(dim(x$head),
dim(x$body),
dim(x$interfoot),
dim(x$foot))
expect_equal(Dims,
list(c(5, 34),
c(30, 34),
NULL,
NULL))
})
test_that("dust runs when passed a data frame with tidy_df = FALSE",
{
expect_silent(dust(mtcars, tidy_df = FALSE))
})
test_that("dust runs when passed a data frame with tidy_df = TRUE",
{
expect_silent(dust(mtcars, tidy_df = TRUE))
})
test_that("dust with keep_rownames = TRUE adds rownames to object",
{
x <- dust(mtcars, keep_rownames = TRUE)
expect_equal(x$body$value[1:32], rownames(mtcars))
})
test_that("dust with additional descriptors",
{
expect_silent(dust(fit,
descriptors = c("label", "level_detail")))
})
test_that("dust with additional descriptors and term_plain numeric_label",
{
expect_silent(dust(fit,
descriptors = c("label", "level_detail"),
numeric_label = "term_plain"))
})
test_that("dust with glance_foot",
{
expect_silent(dust(fit, glance_foot = TRUE))
})
test_that("dust with glance_foot and col_pairs a divisor of total_cols",
{
fit <- lm(mpg ~ qsec + factor(am) + wt * factor(gear), data = mtcars)
expect_silent(dust(fit,
descriptors = c("label", "level_detail"),
glance_foot = TRUE,
col_pairs = 3))
})
test_that("dust a list",
{
x <- split(mtcars, list(mtcars$am, mtcars$vs))
expect_silent(dust(x))
})
|
#script to import, analyse and produce plots for prop biomass in secondary forests
#load in necessary libraries
library(RODBC)
library(ggplot2)
library(nlme)
library(lme4)
library(MuMIn)
#connect to database
sec <- odbcConnect("Secondary/Degraded forests")
sqlTables(sec)
#import proportion query
Prop<- sqlFetch(sec, "Proportion query")
head(Prop)
#Rename columns
colnames(Prop) <- c("ID", "Site","Disturbance","Age","Type","Measurement","Prop_Ref","Prop_Sec","Tax")
head(Prop)
Prop<-data.frame(Prop)
Prop$Prop<-Prop$Prop_Sec/Prop$Prop_Ref
#subset data to remove logging, fire and missing values
Prop<-subset(Prop,Disturbance!="Fire")
Prop<-subset(Prop,Disturbance!="Logging")
Prop<-subset(Prop,Disturbance!="Agroforestry")
Tree_prop<-Prop
#new variable to rescale data
Tree_prop$proploss<-Tree_prop$Prop-1/1
Tree_prop$proploss2<-(qlogis((Tree_prop$proploss+1)))
Tree_prop$asin<-(sqrt(asin(Tree_prop$Prop)))
hist(Tree_prop$proploss2)
head(Tree_prop)
#logit transformation
Tree_prop$logprop<-log(Tree_prop$Prop)-log(1-Tree_prop$Prop)
#Mixed model of Prop prop
#null model
M0<-glmer(Prop~1+(1|ID),family=binomial(logit),data=Tree_prop,REML=F)
nulldev<--2*logLik(M0)[1]
#full model
M1<-glmer(Prop~1+Age+Type+Disturbance+(1|ID),family=binomial(logit),data=Tree_prop,REML=F)
plot(fitted(M1),resid(M1))
summary(M1)
plot(Tree_prop$Age,Tree_prop$Prop)
#model selection using AICc
MS1<- dredge(M1, trace = TRUE, rank = "AICc", REML = FALSE)
#subset models with delta<7 (to remove implausible models)
poss_mod<- get.models(MS1, subset = delta <7)
modsumm <- model.sel(poss_mod, rank = "AICc")
modsumm
plot(Tree_prop$Age,Tree_prop$Prop)
Age<-seq(0,115,0.1)
lines(Age,1/(1+1/(exp(-1.551+(0.01726*(Age))))))
lines(Age,(plogis(-2.2035532+(Age*0.0151852)*2))
#calculate deviance of model
modsumm$dev<--2*modsumm$logLik
#calculate deviance explained for each model
modsumm$dev_ex<-((nulldev-modsumm$dev)/nulldev)
modsumm$dev_ex
#output possible models
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Statistics")
write.csv(modsumm, "Model - Species pool.csv")
#calculate model averaged coefficients
#create predictions based on models >0.6 weight
averaged<-model.avg(MS1,subset=cumsum(weight)<=0.6)
averaged2<-averaged$avg.model
write.csv(averaged2, "Multimodel inferences Prop primary.csv") #save table
#set parameter values from best model
Avgest.mod$avg.model
Int<-averaged2[1]
Int
Age<-averaged2[2]
Age
#create new data for predictions
range(Tree_prop$Age)
preds<-expand.grid(Age=seq(1.5,81,0.1))
preds$predicted<-Int+(preds$Age*Age)
preds$Prop<-1/(1+1/(exp(preds$predicted)))
#produce plots of model
windowsFonts(Times=windowsFont("TT Times New Roman"))
theme_set(theme_bw(base_size=18))
a<-ggplot(Tree_prop,aes(x=Age,y=Prop))+geom_point(size=4,shape=1)+opts(panel.grid.major = theme_line(colour =NA))
a
d<-a+coord_cartesian(ylim = c(0, 1.05),xlim=c(0,120))+ylab("Proportion of primary species pool\n found in secondary forest")+geom_hline(y=1,lty=2)
e<-d+xlab("Time since last disturbance (Years)")+ scale_colour_discrete(name="Type of \nforest")
f<-e+scale_y_continuous(breaks=seq(0, 1, 0.5))+scale_x_continuous(breaks=seq(0, 120, 40))
f
f+theme(text=element_text(family="Times"))
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Figures")
ggsave(filename="Prop_trees_pres.jpeg",height=6,width=10,dpi=300,)
#produce plots of model
theme_set(theme_bw(base_size=18))
a<-ggplot(Tree_prop,aes(x=Age,y=Prop))+geom_point(size=4,shape=1)+opts(panel.grid.major = theme_line(colour =NA))
a
d<-a+coord_cartesian(ylim = c(0, 1.05),xlim=c(0,120))+ylab("Proportion of primary species pool\n found in secondary forest")+geom_hline(y=1,lty=2)
e<-d+xlab("Time since last disturbance (Years)")+ scale_colour_discrete(name="Type of \nforest")
f<-e+scale_y_continuous(breaks=seq(0, 1, 0.5))+scale_x_continuous(breaks=seq(0, 120, 40))
f+geom_hline(y=mean(Tree_prop$Prop),lty=1,size=2)
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Figures")
ggsave(filename="Prop_trees_pres_mean.jpeg",height=6,width=10,dpi=300,)
|
/Scripts/Shared species for presentations.R
|
no_license
|
fdbesanto2/SecFor
|
R
| false
| false
| 4,351
|
r
|
#script to import, analyse and produce plots for prop biomass in secondary forests
#load in necessary libraries
library(RODBC)
library(ggplot2)
library(nlme)
library(lme4)
library(MuMIn)
#connect to database
sec <- odbcConnect("Secondary/Degraded forests")
sqlTables(sec)
#import proportion query
Prop<- sqlFetch(sec, "Proportion query")
head(Prop)
#Rename columns
colnames(Prop) <- c("ID", "Site","Disturbance","Age","Type","Measurement","Prop_Ref","Prop_Sec","Tax")
head(Prop)
Prop<-data.frame(Prop)
Prop$Prop<-Prop$Prop_Sec/Prop$Prop_Ref
#subset data to remove logging, fire and missing values
Prop<-subset(Prop,Disturbance!="Fire")
Prop<-subset(Prop,Disturbance!="Logging")
Prop<-subset(Prop,Disturbance!="Agroforestry")
Tree_prop<-Prop
#new variable to rescale data
Tree_prop$proploss<-Tree_prop$Prop-1/1
Tree_prop$proploss2<-(qlogis((Tree_prop$proploss+1)))
Tree_prop$asin<-(sqrt(asin(Tree_prop$Prop)))
hist(Tree_prop$proploss2)
head(Tree_prop)
#logit transformation
Tree_prop$logprop<-log(Tree_prop$Prop)-log(1-Tree_prop$Prop)
#Mixed model of Prop prop
#null model
M0<-glmer(Prop~1+(1|ID),family=binomial(logit),data=Tree_prop,REML=F)
nulldev<--2*logLik(M0)[1]
#full model
M1<-glmer(Prop~1+Age+Type+Disturbance+(1|ID),family=binomial(logit),data=Tree_prop,REML=F)
plot(fitted(M1),resid(M1))
summary(M1)
plot(Tree_prop$Age,Tree_prop$Prop)
#model selection using AICc
MS1<- dredge(M1, trace = TRUE, rank = "AICc", REML = FALSE)
#subset models with delta<7 (to remove implausible models)
poss_mod<- get.models(MS1, subset = delta <7)
modsumm <- model.sel(poss_mod, rank = "AICc")
modsumm
plot(Tree_prop$Age,Tree_prop$Prop)
Age<-seq(0,115,0.1)
lines(Age,1/(1+1/(exp(-1.551+(0.01726*(Age))))))
lines(Age,(plogis(-2.2035532+(Age*0.0151852)*2))
#calculate deviance of model
modsumm$dev<--2*modsumm$logLik
#calculate deviance explained for each model
modsumm$dev_ex<-((nulldev-modsumm$dev)/nulldev)
modsumm$dev_ex
#output possible models
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Statistics")
write.csv(modsumm, "Model - Species pool.csv")
#calculate model averaged coefficients
#create predictions based on models >0.6 weight
averaged<-model.avg(MS1,subset=cumsum(weight)<=0.6)
averaged2<-averaged$avg.model
write.csv(averaged2, "Multimodel inferences Prop primary.csv") #save table
#set parameter values from best model
Avgest.mod$avg.model
Int<-averaged2[1]
Int
Age<-averaged2[2]
Age
#create new data for predictions
range(Tree_prop$Age)
preds<-expand.grid(Age=seq(1.5,81,0.1))
preds$predicted<-Int+(preds$Age*Age)
preds$Prop<-1/(1+1/(exp(preds$predicted)))
#produce plots of model
windowsFonts(Times=windowsFont("TT Times New Roman"))
theme_set(theme_bw(base_size=18))
a<-ggplot(Tree_prop,aes(x=Age,y=Prop))+geom_point(size=4,shape=1)+opts(panel.grid.major = theme_line(colour =NA))
a
d<-a+coord_cartesian(ylim = c(0, 1.05),xlim=c(0,120))+ylab("Proportion of primary species pool\n found in secondary forest")+geom_hline(y=1,lty=2)
e<-d+xlab("Time since last disturbance (Years)")+ scale_colour_discrete(name="Type of \nforest")
f<-e+scale_y_continuous(breaks=seq(0, 1, 0.5))+scale_x_continuous(breaks=seq(0, 120, 40))
f
f+theme(text=element_text(family="Times"))
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Figures")
ggsave(filename="Prop_trees_pres.jpeg",height=6,width=10,dpi=300,)
#produce plots of model
theme_set(theme_bw(base_size=18))
a<-ggplot(Tree_prop,aes(x=Age,y=Prop))+geom_point(size=4,shape=1)+opts(panel.grid.major = theme_line(colour =NA))
a
d<-a+coord_cartesian(ylim = c(0, 1.05),xlim=c(0,120))+ylab("Proportion of primary species pool\n found in secondary forest")+geom_hline(y=1,lty=2)
e<-d+xlab("Time since last disturbance (Years)")+ scale_colour_discrete(name="Type of \nforest")
f<-e+scale_y_continuous(breaks=seq(0, 1, 0.5))+scale_x_continuous(breaks=seq(0, 120, 40))
f+geom_hline(y=mean(Tree_prop$Prop),lty=1,size=2)
setwd("C:/Documents and Settings/Phil/My Documents/My Dropbox/Publications, Reports and Responsibilities/Chapters/4. Forest restoration trajectories/Analysis/Figures")
ggsave(filename="Prop_trees_pres_mean.jpeg",height=6,width=10,dpi=300,)
|
get_issues_by_date <- function(city,after = Sys.time()-86400, before = Sys.time(), status = "open,acknowledged,closed,archived", limit = 100) {
total <- 0
page <- 1
pagelimit <- min(100,limit)
after <- as.character(after,format="%Y-%m-%dT%H:%M:%SZ")
before <- as.character(before,format="%Y-%m-%dT%H:%M:%SZ")
url <- paste("https://seeclickfix.com/api/v2/issues?place_url=",city,"&after=",after,"&before=",before,"&status=",status, "&per_page=",pagelimit,"&page=",page,sep = "")
url <- gsub(" ","%20",x=url)
rawdata <- RCurl::getURL(url)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
# scf$issues$point$coordinates # duplicate of lat/lng
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
allout <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
total <- nrow(allout)
## check if total n issues < inputted limit:
limit <- min(limit,scf$metadata$pagination$entries)
while(limit>total){
page <- page+1
if((limit-total)<100){pagelimit <- (limit-total)}
url <- paste("https://seeclickfix.com/api/v2/issues?place_url=",city,"&after=",after,"&before=",before,"&status=",status, "&per_page=",pagelimit,"&page=",page,sep = "")
url <- gsub(" ","%20",x=url)
rawdata <- RCurl::getURL(url)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
# scf$issues$point$coordinates # duplicate of lat/lng
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
holder <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
allout <- rbind(allout,holder)
total <- nrow(allout)
}
return(allout)
}
|
/R/get_issues_by_date.R
|
no_license
|
cran/seeclickfixr
|
R
| false
| false
| 5,572
|
r
|
get_issues_by_date <- function(city,after = Sys.time()-86400, before = Sys.time(), status = "open,acknowledged,closed,archived", limit = 100) {
total <- 0
page <- 1
pagelimit <- min(100,limit)
after <- as.character(after,format="%Y-%m-%dT%H:%M:%SZ")
before <- as.character(before,format="%Y-%m-%dT%H:%M:%SZ")
url <- paste("https://seeclickfix.com/api/v2/issues?place_url=",city,"&after=",after,"&before=",before,"&status=",status, "&per_page=",pagelimit,"&page=",page,sep = "")
url <- gsub(" ","%20",x=url)
rawdata <- RCurl::getURL(url)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
# scf$issues$point$coordinates # duplicate of lat/lng
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
allout <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
total <- nrow(allout)
## check if total n issues < inputted limit:
limit <- min(limit,scf$metadata$pagination$entries)
while(limit>total){
page <- page+1
if((limit-total)<100){pagelimit <- (limit-total)}
url <- paste("https://seeclickfix.com/api/v2/issues?place_url=",city,"&after=",after,"&before=",before,"&status=",status, "&per_page=",pagelimit,"&page=",page,sep = "")
url <- gsub(" ","%20",x=url)
rawdata <- RCurl::getURL(url)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
# scf$issues$point$coordinates # duplicate of lat/lng
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
holder <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
allout <- rbind(allout,holder)
total <- nrow(allout)
}
return(allout)
}
|
# Project 1, plot 2 code:
#
# Note that this script assumes that this script and the input file are in the same directory.
# Also, the code is written in Windows Operating System through RStudio hence windows()
# graphics device is utilized to plot the figures on screen.
#
# Step: Record date and R version:
(DownloadDate <- date())
(DownloadRversion <- R.version)
# load the data, partially:
datain <- read.table("household_power_consumption.txt", nrows= 150000, sep=";", header=TRUE, na.string="?")
# Transform the date:
datain$DateTransformed <- as.Date(datain$Date, "%d/%m/%Y")
# Extract the data for the date of 2007-02-01:
DataForDate1 <- datain[datain$DateTransformed == "2007-02-01",]
# Extract the data for the date of 2007-02-02:
DataForDate2 <- datain[datain$DateTransformed == "2007-02-02",]
# Combine the extracted data sets into single data set:
DataExtracted <- rbind(DataForDate1, DataForDate2)
# Subset the Global_active_power column for the histogram:
GAP <- DataExtracted$Global_active_power
dates <- DataExtracted$Date
times <- DataExtracted$Time
x <- paste(dates,times)
xx <- strptime(x, format="%d/%m/%Y %H:%M:%S")
# Display the figure on the screen:
windows()
plot(xx,GAP, type="l", xlab=" ", ylab="Global Active Power (kilowatts)")
dev.off()
# Save the figure to PNG:
png(file = "plot2.png", units = "px", width=480, height=480, res=NA)
plot(xx,GAP, type="l", xlab=" ", ylab="Global Active Power (kilowatts)")
dev.off()
##########################################################
##########################################################
# end-of-file
|
/plot2.R
|
no_license
|
zikribayraktar/ExData_Plotting1
|
R
| false
| false
| 1,590
|
r
|
# Project 1, plot 2 code:
#
# Note that this script assumes that this script and the input file are in the same directory.
# Also, the code is written in Windows Operating System through RStudio hence windows()
# graphics device is utilized to plot the figures on screen.
#
# Step: Record date and R version:
(DownloadDate <- date())
(DownloadRversion <- R.version)
# load the data, partially:
datain <- read.table("household_power_consumption.txt", nrows= 150000, sep=";", header=TRUE, na.string="?")
# Transform the date:
datain$DateTransformed <- as.Date(datain$Date, "%d/%m/%Y")
# Extract the data for the date of 2007-02-01:
DataForDate1 <- datain[datain$DateTransformed == "2007-02-01",]
# Extract the data for the date of 2007-02-02:
DataForDate2 <- datain[datain$DateTransformed == "2007-02-02",]
# Combine the extracted data sets into single data set:
DataExtracted <- rbind(DataForDate1, DataForDate2)
# Subset the Global_active_power column for the histogram:
GAP <- DataExtracted$Global_active_power
dates <- DataExtracted$Date
times <- DataExtracted$Time
x <- paste(dates,times)
xx <- strptime(x, format="%d/%m/%Y %H:%M:%S")
# Display the figure on the screen:
windows()
plot(xx,GAP, type="l", xlab=" ", ylab="Global Active Power (kilowatts)")
dev.off()
# Save the figure to PNG:
png(file = "plot2.png", units = "px", width=480, height=480, res=NA)
plot(xx,GAP, type="l", xlab=" ", ylab="Global Active Power (kilowatts)")
dev.off()
##########################################################
##########################################################
# end-of-file
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_data_one_iso.R
\name{plot_data_one_iso}
\alias{plot_data_one_iso}
\title{Plot biotracer data (1-D)}
\usage{
plot_data_one_iso(mix, source, discr, filename, plot_save_pdf, plot_save_png)
}
\arguments{
\item{mix}{output from \code{\link{load_mix_data}}}
\item{source}{output from \code{\link{load_source_data}}}
\item{discr}{output from \code{\link{load_discr_data}}}
\item{filename}{name of the plot file(s) to save (e.g. "isospace_plot")}
\item{plot_save_pdf}{T/F, save the plot(s) as a pdf?}
\item{plot_save_png}{T/F, save the plot(s) as a png?}
}
\description{
\code{plot_data_one_iso} creates a 1-D plot of mix and source tracer data and
saves the plot to a file in the working directory
}
\details{
An important detail is that \code{plot_data_one_iso} plots the raw mix data
and \emph{adds the TDF to the source data}, since this is the polygon that the
mixing model uses to determine proportions. The plotted source means are:
\deqn{\mu_source + \mu_discr}
The source error bars are +/- 1 standard deviation, \emph{calculated as a
combination of source and TDF variances:}
\deqn{\sqrt{\sigma^2_source + \sigma^2_discr}}
\code{plot_data_one_iso} looks for 'C', 'N', 'S', and 'O' in the biotracer column
headers and assumes they are stable isotopes, labeling the axes with, e.g.,
expression(paste(delta^13, "C (u2030)",sep="")).
}
\seealso{
\code{\link{plot_data}}
}
|
/man/plot_data_one_iso.Rd
|
no_license
|
izabelabujak/MixSIAR
|
R
| false
| true
| 1,459
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_data_one_iso.R
\name{plot_data_one_iso}
\alias{plot_data_one_iso}
\title{Plot biotracer data (1-D)}
\usage{
plot_data_one_iso(mix, source, discr, filename, plot_save_pdf, plot_save_png)
}
\arguments{
\item{mix}{output from \code{\link{load_mix_data}}}
\item{source}{output from \code{\link{load_source_data}}}
\item{discr}{output from \code{\link{load_discr_data}}}
\item{filename}{name of the plot file(s) to save (e.g. "isospace_plot")}
\item{plot_save_pdf}{T/F, save the plot(s) as a pdf?}
\item{plot_save_png}{T/F, save the plot(s) as a png?}
}
\description{
\code{plot_data_one_iso} creates a 1-D plot of mix and source tracer data and
saves the plot to a file in the working directory
}
\details{
An important detail is that \code{plot_data_one_iso} plots the raw mix data
and \emph{adds the TDF to the source data}, since this is the polygon that the
mixing model uses to determine proportions. The plotted source means are:
\deqn{\mu_source + \mu_discr}
The source error bars are +/- 1 standard deviation, \emph{calculated as a
combination of source and TDF variances:}
\deqn{\sqrt{\sigma^2_source + \sigma^2_discr}}
\code{plot_data_one_iso} looks for 'C', 'N', 'S', and 'O' in the biotracer column
headers and assumes they are stable isotopes, labeling the axes with, e.g.,
expression(paste(delta^13, "C (u2030)",sep="")).
}
\seealso{
\code{\link{plot_data}}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/url.R
\name{update_seafile_url}
\alias{update_seafile_url}
\title{Update seafile URL}
\usage{
update_seafile_url()
}
\description{
update the seafile api url
}
|
/man/update_seafile_url.Rd
|
no_license
|
VincentGuyader/seafile
|
R
| false
| true
| 249
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/url.R
\name{update_seafile_url}
\alias{update_seafile_url}
\title{Update seafile URL}
\usage{
update_seafile_url()
}
\description{
update the seafile api url
}
|
getFlights <- function(status = c("ankunft", "abflug"),
hour = formatC(c(0, 3:11 * 2), flag = "0", width = 2, format = "d")) {
stopifnot(require("XML"))
status <- match.arg(status)
hour <- match.arg(hour, several.ok = TRUE)
URLs <- switch(status,
"ankunft" = paste("http://www.munich-airport.de/de/consumer/fluginfo/ankunft/h",
hour, "00_de_L.jsp", sep = ""),
"abflug" = paste("http://www.munich-airport.de/de/consumer/fluginfo/abflug/h",
hour, "00_de_S.jsp", sep = ""))
websites <- lapply(URLs, htmlTreeParse, useInternalNodes = TRUE, encoding = "UTF-8")
## raw Data List
rDL<- lapply(websites, xpathSApply,
path="//table//input[@type='hidden']", xmlAttrs)
lapply(websites, free)
### remove pages without a flight table
rDL <- rDL[sapply(rDL, length) > 0]
dataList <- lapply(rDL, function(z) {
ret <- matrix(z["value", ], ncol = 20, byrow = TRUE)
colnames(ret) <- z["name", 1:20]
as.data.frame(ret, stringsAsFactors = FALSE)
})
## ornamente
resDF <- do.call(rbind, dataList)
resDF <- resDF[, !colnames(resDF) %in% c("key", "zurueck")]
resDF$ett[resDF$ett == ""] <- resDF$stt[resDF$ett == ""]
## resDF$stt <- strftime(resDF$stt, format = "%Y-%m-%d %H:%M:%S.0")
## resDF$ett <- strftime(resDF$ett, format = "%Y-%m-%d %H:%M:%S.0")
return(resDF)
}
# muc <- rbind(getFlights("ankunft"), getFlights("abflug"))
# save(muc, file = paste("../data/", Sys.Date(), "_Flugdaten", ".RData", sep = "" ))
|
/MUCflights/R/getFlights.R
|
no_license
|
ingted/R-Examples
|
R
| false
| false
| 1,585
|
r
|
getFlights <- function(status = c("ankunft", "abflug"),
hour = formatC(c(0, 3:11 * 2), flag = "0", width = 2, format = "d")) {
stopifnot(require("XML"))
status <- match.arg(status)
hour <- match.arg(hour, several.ok = TRUE)
URLs <- switch(status,
"ankunft" = paste("http://www.munich-airport.de/de/consumer/fluginfo/ankunft/h",
hour, "00_de_L.jsp", sep = ""),
"abflug" = paste("http://www.munich-airport.de/de/consumer/fluginfo/abflug/h",
hour, "00_de_S.jsp", sep = ""))
websites <- lapply(URLs, htmlTreeParse, useInternalNodes = TRUE, encoding = "UTF-8")
## raw Data List
rDL<- lapply(websites, xpathSApply,
path="//table//input[@type='hidden']", xmlAttrs)
lapply(websites, free)
### remove pages without a flight table
rDL <- rDL[sapply(rDL, length) > 0]
dataList <- lapply(rDL, function(z) {
ret <- matrix(z["value", ], ncol = 20, byrow = TRUE)
colnames(ret) <- z["name", 1:20]
as.data.frame(ret, stringsAsFactors = FALSE)
})
## ornamente
resDF <- do.call(rbind, dataList)
resDF <- resDF[, !colnames(resDF) %in% c("key", "zurueck")]
resDF$ett[resDF$ett == ""] <- resDF$stt[resDF$ett == ""]
## resDF$stt <- strftime(resDF$stt, format = "%Y-%m-%d %H:%M:%S.0")
## resDF$ett <- strftime(resDF$ett, format = "%Y-%m-%d %H:%M:%S.0")
return(resDF)
}
# muc <- rbind(getFlights("ankunft"), getFlights("abflug"))
# save(muc, file = paste("../data/", Sys.Date(), "_Flugdaten", ".RData", sep = "" ))
|
#setwd("~/Desktop/PSM/Spring 2019/Multivariate-Stats/Project")
#Make my n=2 dimenstional data/hypershpere image
library(plotrix)
p1=data.frame(x=double(),y=double()) #Points
names(p1)=c("X", "Y")
while(dim(p1)[1]<200){ #Get 200 points for the first bubble by making circle
x=runif(1, min=-1, max=1)
y=runif(1, min=-1, max=1)
if((x**2+y**2)<=1){
p1[nrow(p1)+1,]=c(x,y)
}
}
while(dim(p1)[1]<400){ #200 more but with a shift to a new center now
x=runif(1, min=-1, max=1)
y=runif(1, min=-1, max=1)
if((x**2+y**2)<=1){
p1[nrow(p1)+1,]=c(x+3,y+3)
}
}
plot(p1)
png("circles.png")
plot(p1)
dev.off()
####### Example 12.4
library(sem)
rm(list=ls())
X <- readMoments("T12-3.DAT",diag=TRUE) #Language Linakage data
colnames(X) <- c("E","N", "Da", "Du", "G","Fr", "Sp", "I", "P", "H", "Fi")
rownames(X) <- c("E","N", "Da", "Du", "G","Fr", "Sp", "I", "P", "H", "Fi")
D <- 10-X #Convert to distance
C <- hclust(as.dist(D), method = "average") #Do an average distance cluster analysis
plot(C) #Dendro Plot
png("LanguageDendo.png")
plot(C)
dev.off()
### Example 12.5
rm(list = ls())
D <- matrix(c(0, 9, 3, 6,11,
9,0, 7 , 5, 10,
3,7,0, 9,2,
6,5,9,0,8,
11,10,2,8,0), byrow=TRUE, ncol=5) #Complete linkage example
C <- hclust(as.dist(D), method = "complete") #Desired Result
plot(C)
#This plot shows that the first cluster will be (35), then (24), then (124),
#and finally (12345)
min(D[D>0]) #Find the smallest value in distance matrix
Dnew=D
#Corresponds to (35) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(3,5),-c(3,5)]
d351 <- max(D[3,1],D[5,1])
d352 <- max(D[3,2],D[5,2])
d354 <- max(D[3,4],D[5,4])
Dnew <- cbind(c(d351,d352,d354),Dnew)
Dnew <- rbind(c(0,d351,d352,d354),Dnew)
D <- Dnew
min(D[D>0])#Find the smallest value in distance matrix
#Corresponds to (24) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(3,4),-c(3,4)]
d2435 <- max(D[1,3],D[1,4])
d241 <- max(D[3,2],D[4,2])
Dnew <- cbind(Dnew[,1],c(0,d241),Dnew[,2])
Dnew <- rbind(Dnew[1,],c(d2435,0,d241),Dnew[2,])
min(D[D>0])
#Corresponds to (124) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(2,3),-c(2,3)]
d24135 <- max(D[1,3],D[1,2])
Dnew <- cbind(c(0,d24135),c(d24135,0))
D<- Dnew
min(D[D>0])#Find the smallest value in distance matrix
#Corresponds to the (12345) cluster, we are done.
#We see here that our results matched perfectly the complete linkage method in R
### Example 12.7
rm(list=ls())
library(sem)
X <- readMoments("T12-5.DAT",diag=TRUE) #Correlations in public utility data
C <- hclust(as.dist(X), method = "complete")
plot(C)
#######################################################################
#######################################################################
############# Some Worked Problems ####################################
#######################################################################
#######################################################################
#12 .1
'''
Consider the binary values:
{1 if from South, 0 else}
{1 if elected first term, 0 else}
{1 if Democrat, 0 else}
{1 if Prior Experience, 0 else}
{1 if former Vice, 0 else}
Then, consider the following,
'''
rm(list = ls())
X <- matrix(c(0,1,0,0,0,
1,1,1,0,0,
0,0,0,1,1,
0,1,0,1,1,
1,0,1,1,1,
0,1,1,1,0), byrow=TRUE, ncol=5)
#Reagan Carter
a=1
b=3
sim1 <- matrix(c(0,0,
0,0), byrow=TRUE, ncol=2)
for(i in 1:5){
if(X[a,i]==1 && X[b,i]==1){
sim1[1,1]<- sim1[1,1]+1
}
if(X[a,i]==1 && X[b,i]==0){
sim1[1,2]<- sim1[1,2]+1
}
if(X[a,i]==0 && X[b,i]==1){
sim1[2,1]<- sim1[2,1]+1
}
if(X[a,i]==0 && X[b,i]==0){
sim1[2,2]<- sim1[2,2]+1
}
}
(simCoef1 <- (sim1[1,1]+sim1[2,2])/sum(sim1))
## 12.7 Stock Data
rm(list=ls())
X <- matrix(c(1,.63,.51,.12,.16,
.63,1,.57,.32,.21,
.51,.57,1,.18,.15,
.12,.32,.18,1,.68,
.16,.21,.15,.68,1), byrow = TRUE, ncol=5)
row.names(X) <- c("JP Morgan", "Citibank","Wells Fargo", "Royal DutchShell","ExxonMobil")
C <- hclust(as.dist(X), method = "complete")
plot(C)
#We see based on this analysis that JP Morgan and Royal DutchShell are closely related, followed by Wells fargo and ExxonMobil
C <- hclust(as.dist(X), method = "average")
plot(C)
#We see that there is a similar initial breakdown in this dendrogram, but uwing the average method actually causes the two pairs to merge before adding Citibank now
## 12.15
#This one will be k-means clustering
rm(list=ls())
X <- read.table("T11-9.dat")
X<-X[-c(1,2)]
k1 <- kmeans(X[,1:5],2)
print(k1)
k2 <- kmeans(X[,1:5],3)
print(k2)
k3 <- kmeans(X[,1:5],4)
print(k3)
hist(k1$cluster)
hist(k2$cluster)
hist(k3$cluster)
#We see that the data clusters into roughly even groups, and that each successive addtion of a group is likely just a natural partition of an existing group
# 12.26 Maloi Farm
rm(list=ls())
X <- read.table("T8-7.DAT", header=FALSE)
X <- X[-c(25,34,69,72),]#,-c(1,7:9)]
D <- matrix(0, nrow= 72, ncol=72)
for(i in 1:72){
for(j in i:72){
d <- sqrt(sum((X[i,]-X[j,])^2))
D[i,j] <- d
}
}
D<-t(D)
D <- as.dist(D)
avg <- hclust(D, method="average")
plot(avg)
ward <- hclust(D, method = "ward.D")
plot(ward)
## Based on the Ward method, we see tat there are around 7 base clusters of farms based on Euclidean Distance
library(cluster)
k1 <- kmeans(X,5)
s=silhouette(k1$cl,D)
plot(s)
clusplot(X,k1$cluster)
k2 <- kmeans(X,6)
s2=silhouette(k2$cl,D)
plot(s2)
clusplot(X,k2$cluster)
#We see that the data is well wrapped within the 5 or 6 clusters that we are using.
|
/Project/ALL FILES/Clustering.R
|
no_license
|
justinhood/Multivariate-Stats
|
R
| false
| false
| 5,637
|
r
|
#setwd("~/Desktop/PSM/Spring 2019/Multivariate-Stats/Project")
#Make my n=2 dimenstional data/hypershpere image
library(plotrix)
p1=data.frame(x=double(),y=double()) #Points
names(p1)=c("X", "Y")
while(dim(p1)[1]<200){ #Get 200 points for the first bubble by making circle
x=runif(1, min=-1, max=1)
y=runif(1, min=-1, max=1)
if((x**2+y**2)<=1){
p1[nrow(p1)+1,]=c(x,y)
}
}
while(dim(p1)[1]<400){ #200 more but with a shift to a new center now
x=runif(1, min=-1, max=1)
y=runif(1, min=-1, max=1)
if((x**2+y**2)<=1){
p1[nrow(p1)+1,]=c(x+3,y+3)
}
}
plot(p1)
png("circles.png")
plot(p1)
dev.off()
####### Example 12.4
library(sem)
rm(list=ls())
X <- readMoments("T12-3.DAT",diag=TRUE) #Language Linakage data
colnames(X) <- c("E","N", "Da", "Du", "G","Fr", "Sp", "I", "P", "H", "Fi")
rownames(X) <- c("E","N", "Da", "Du", "G","Fr", "Sp", "I", "P", "H", "Fi")
D <- 10-X #Convert to distance
C <- hclust(as.dist(D), method = "average") #Do an average distance cluster analysis
plot(C) #Dendro Plot
png("LanguageDendo.png")
plot(C)
dev.off()
### Example 12.5
rm(list = ls())
D <- matrix(c(0, 9, 3, 6,11,
9,0, 7 , 5, 10,
3,7,0, 9,2,
6,5,9,0,8,
11,10,2,8,0), byrow=TRUE, ncol=5) #Complete linkage example
C <- hclust(as.dist(D), method = "complete") #Desired Result
plot(C)
#This plot shows that the first cluster will be (35), then (24), then (124),
#and finally (12345)
min(D[D>0]) #Find the smallest value in distance matrix
Dnew=D
#Corresponds to (35) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(3,5),-c(3,5)]
d351 <- max(D[3,1],D[5,1])
d352 <- max(D[3,2],D[5,2])
d354 <- max(D[3,4],D[5,4])
Dnew <- cbind(c(d351,d352,d354),Dnew)
Dnew <- rbind(c(0,d351,d352,d354),Dnew)
D <- Dnew
min(D[D>0])#Find the smallest value in distance matrix
#Corresponds to (24) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(3,4),-c(3,4)]
d2435 <- max(D[1,3],D[1,4])
d241 <- max(D[3,2],D[4,2])
Dnew <- cbind(Dnew[,1],c(0,d241),Dnew[,2])
Dnew <- rbind(Dnew[1,],c(d2435,0,d241),Dnew[2,])
min(D[D>0])
#Corresponds to (124) cluster. Remove relevant rows, and add new row,
Dnew <- Dnew[-c(2,3),-c(2,3)]
d24135 <- max(D[1,3],D[1,2])
Dnew <- cbind(c(0,d24135),c(d24135,0))
D<- Dnew
min(D[D>0])#Find the smallest value in distance matrix
#Corresponds to the (12345) cluster, we are done.
#We see here that our results matched perfectly the complete linkage method in R
### Example 12.7
rm(list=ls())
library(sem)
X <- readMoments("T12-5.DAT",diag=TRUE) #Correlations in public utility data
C <- hclust(as.dist(X), method = "complete")
plot(C)
#######################################################################
#######################################################################
############# Some Worked Problems ####################################
#######################################################################
#######################################################################
#12 .1
'''
Consider the binary values:
{1 if from South, 0 else}
{1 if elected first term, 0 else}
{1 if Democrat, 0 else}
{1 if Prior Experience, 0 else}
{1 if former Vice, 0 else}
Then, consider the following,
'''
rm(list = ls())
X <- matrix(c(0,1,0,0,0,
1,1,1,0,0,
0,0,0,1,1,
0,1,0,1,1,
1,0,1,1,1,
0,1,1,1,0), byrow=TRUE, ncol=5)
#Reagan Carter
a=1
b=3
sim1 <- matrix(c(0,0,
0,0), byrow=TRUE, ncol=2)
for(i in 1:5){
if(X[a,i]==1 && X[b,i]==1){
sim1[1,1]<- sim1[1,1]+1
}
if(X[a,i]==1 && X[b,i]==0){
sim1[1,2]<- sim1[1,2]+1
}
if(X[a,i]==0 && X[b,i]==1){
sim1[2,1]<- sim1[2,1]+1
}
if(X[a,i]==0 && X[b,i]==0){
sim1[2,2]<- sim1[2,2]+1
}
}
(simCoef1 <- (sim1[1,1]+sim1[2,2])/sum(sim1))
## 12.7 Stock Data
rm(list=ls())
X <- matrix(c(1,.63,.51,.12,.16,
.63,1,.57,.32,.21,
.51,.57,1,.18,.15,
.12,.32,.18,1,.68,
.16,.21,.15,.68,1), byrow = TRUE, ncol=5)
row.names(X) <- c("JP Morgan", "Citibank","Wells Fargo", "Royal DutchShell","ExxonMobil")
C <- hclust(as.dist(X), method = "complete")
plot(C)
#We see based on this analysis that JP Morgan and Royal DutchShell are closely related, followed by Wells fargo and ExxonMobil
C <- hclust(as.dist(X), method = "average")
plot(C)
#We see that there is a similar initial breakdown in this dendrogram, but uwing the average method actually causes the two pairs to merge before adding Citibank now
## 12.15
#This one will be k-means clustering
rm(list=ls())
X <- read.table("T11-9.dat")
X<-X[-c(1,2)]
k1 <- kmeans(X[,1:5],2)
print(k1)
k2 <- kmeans(X[,1:5],3)
print(k2)
k3 <- kmeans(X[,1:5],4)
print(k3)
hist(k1$cluster)
hist(k2$cluster)
hist(k3$cluster)
#We see that the data clusters into roughly even groups, and that each successive addtion of a group is likely just a natural partition of an existing group
# 12.26 Maloi Farm
rm(list=ls())
X <- read.table("T8-7.DAT", header=FALSE)
X <- X[-c(25,34,69,72),]#,-c(1,7:9)]
D <- matrix(0, nrow= 72, ncol=72)
for(i in 1:72){
for(j in i:72){
d <- sqrt(sum((X[i,]-X[j,])^2))
D[i,j] <- d
}
}
D<-t(D)
D <- as.dist(D)
avg <- hclust(D, method="average")
plot(avg)
ward <- hclust(D, method = "ward.D")
plot(ward)
## Based on the Ward method, we see tat there are around 7 base clusters of farms based on Euclidean Distance
library(cluster)
k1 <- kmeans(X,5)
s=silhouette(k1$cl,D)
plot(s)
clusplot(X,k1$cluster)
k2 <- kmeans(X,6)
s2=silhouette(k2$cl,D)
plot(s2)
clusplot(X,k2$cluster)
#We see that the data is well wrapped within the 5 or 6 clusters that we are using.
|
## Take in data and create a pomp object for each individual # ---------------------------------------------------
make_pomp_panel <- function(i, infection_data = data, times_data = times, covariates = covartable, n_cov = n_covariates, test_params = guess, rprocess = rprocess_homologous_immunity, init = init_homologous_immunity ){
log_paramnames <- names(test_params)
cat("i is: ", i , "\n")
n.cov <- length(grep("cov",names(covariates)))
data_i <- infection_data[i,grep("y",names(infection_data))]
ind <- which(!is.na(data_i))
data_i_complete <- data_i[ind]
covariates_i <- subset(covariates, subjectId == infection_data[i,]$subjectId)
times_i <- times_data[i,grep("v",names(times_data))]
times_i_complete <- times_i[ind]
covs <- covariates_i[ind, grep("cov|c_i",names(covariates_i))]
n.vis <- length(times_i_complete)
print(n.vis)
t <- as.numeric(get_tbv(times_i_complete))
stopifnot(t[1] == 0)
t <- t[-1]
covartab <- data.frame(tbv = c(t,0) ,
covs
)
names(covartab) <- c("tbv", paste0("cov",c(1:n_cov)),"c_i","cov_7_2","cov_8_2")
covartab$visit <- c(1:n.vis)
pomp_data <- data.frame(y = as.numeric(data_i_complete[1:n.vis]),
visit = c(1:n.vis))
statenames = c("x","duration_remaining", "previously_cleared", "t_activate", "t_cum")
obsnames = "y"
pomp_object <- pomp(
data = pomp_data,
times ="visit",
t0=1,
params=unlist(test_params),
rprocess = discrete.time.sim(step.fun=rprocess,delta.t=1),
dmeasure = dmeasure,
rmeasure = rmeasure,
covar = covartab,
tcovar = "visit",
obsnames = obsnames,
statenames = statenames,
paramnames = log_paramnames,
initializer = init
)
return(pomp_object)
}
|
/Inference/homologous_immunity_model/data_processing/make_one_panel_pomp_unit.R
|
no_license
|
jabogaards/HPV-model
|
R
| false
| false
| 1,794
|
r
|
## Take in data and create a pomp object for each individual # ---------------------------------------------------
make_pomp_panel <- function(i, infection_data = data, times_data = times, covariates = covartable, n_cov = n_covariates, test_params = guess, rprocess = rprocess_homologous_immunity, init = init_homologous_immunity ){
log_paramnames <- names(test_params)
cat("i is: ", i , "\n")
n.cov <- length(grep("cov",names(covariates)))
data_i <- infection_data[i,grep("y",names(infection_data))]
ind <- which(!is.na(data_i))
data_i_complete <- data_i[ind]
covariates_i <- subset(covariates, subjectId == infection_data[i,]$subjectId)
times_i <- times_data[i,grep("v",names(times_data))]
times_i_complete <- times_i[ind]
covs <- covariates_i[ind, grep("cov|c_i",names(covariates_i))]
n.vis <- length(times_i_complete)
print(n.vis)
t <- as.numeric(get_tbv(times_i_complete))
stopifnot(t[1] == 0)
t <- t[-1]
covartab <- data.frame(tbv = c(t,0) ,
covs
)
names(covartab) <- c("tbv", paste0("cov",c(1:n_cov)),"c_i","cov_7_2","cov_8_2")
covartab$visit <- c(1:n.vis)
pomp_data <- data.frame(y = as.numeric(data_i_complete[1:n.vis]),
visit = c(1:n.vis))
statenames = c("x","duration_remaining", "previously_cleared", "t_activate", "t_cum")
obsnames = "y"
pomp_object <- pomp(
data = pomp_data,
times ="visit",
t0=1,
params=unlist(test_params),
rprocess = discrete.time.sim(step.fun=rprocess,delta.t=1),
dmeasure = dmeasure,
rmeasure = rmeasure,
covar = covartab,
tcovar = "visit",
obsnames = obsnames,
statenames = statenames,
paramnames = log_paramnames,
initializer = init
)
return(pomp_object)
}
|
library(MXM)
### Name: Conditional independence test for longitudinal and clustered data using GLMM
### Title: Linear mixed models conditional independence test for
### longitudinal class variables
### Aliases: testIndGLMMReg testIndGLMMLogistic testIndGLMMPois
### testIndGLMMGamma testIndGLMMNormLog testIndGLMMOrdinal testIndGLMMCR
### testIndLMM
### Keywords: Linear mixed model Conditional independence test
### ** Examples
y <- rnorm(150)
x <- matrix(rnorm(150 * 5), ncol = 5)
id <- sample(1:20, 150, replace = TRUE)
testIndGLMMReg(y, group=id, dataset=x, xIndex=1, csIndex=3)
testIndLMM(y, group=id, dataset=x, xIndex=1, csIndex=3)
|
/data/genthat_extracted_code/MXM/examples/testIndGLMMReg.Rd.R
|
no_license
|
surayaaramli/typeRrh
|
R
| false
| false
| 652
|
r
|
library(MXM)
### Name: Conditional independence test for longitudinal and clustered data using GLMM
### Title: Linear mixed models conditional independence test for
### longitudinal class variables
### Aliases: testIndGLMMReg testIndGLMMLogistic testIndGLMMPois
### testIndGLMMGamma testIndGLMMNormLog testIndGLMMOrdinal testIndGLMMCR
### testIndLMM
### Keywords: Linear mixed model Conditional independence test
### ** Examples
y <- rnorm(150)
x <- matrix(rnorm(150 * 5), ncol = 5)
id <- sample(1:20, 150, replace = TRUE)
testIndGLMMReg(y, group=id, dataset=x, xIndex=1, csIndex=3)
testIndLMM(y, group=id, dataset=x, xIndex=1, csIndex=3)
|
#### Utility for TR pattern and flanking MH identification
#### This utility requires the genome utility clousure
# sequence identity below this threshold will be denied
consensus_thresh <- 0.75
consensus_lower <- 0.5
# consensus_thresh <- 0.3
# consensus_lower <- 0.5
# 4 bases
bases <- c("A", "C", "G", "T")
# this function returns the base freq matrix given a vector of
# sequences with the same length (nchar)
makeFrequencyMatrix <- function(seqs) {
n <- nchar(seqs[1])
seqs <- matrix(unlist(strsplit(seqs, "")), n)
m <- matrix(0, 4, n)
for (j in 1:n) {
b <- seqs[j, ]
for (i in 1:4) {
m[i, j] <- sum(b == bases[i])
}
}
rownames(m) <- bases
m / ncol(seqs)
}
# this function returns a consensus sequence given a freq matrix
makeConsensus <- function(fm) {
use <- paste(apply(fm, 2, function(cl) bases[which(cl == max(cl))[1]]), collapse = "")
out <- paste(apply(fm, 2, function(cl) {
if (all(cl < 0.4)) b <- "*" else {
b <- bases[which(cl > max(cl) - 0.1)]
if (length(b) > 1) b <- paste0("[", paste(b, collapse = "/"), "]")
else b <- bases[which(cl == max(cl))[1]]
}
b
} ), collapse = "")
attr(use, "output") <- out
use
}
# this function returns the entropy of a given freq matrix
seqEntropy <- function(fm) {
baserate <- rowSums(fm) / ncol(fm)
sum(ifelse(baserate == 0, 0, -baserate * log2(baserate)))
}
# this function returns the information content of a given freq matrix
# infoContent <- function(fm) {}
# this function calculate the match between a sequence and a freq matrix
match <- function(seq, fm) {
seq <- unlist(strsplit(seq, ""))
s <- 0
for (i in 1:length(seq)) {
s <- s + fm[seq[i], (i-1)%%ncol(fm)+1]
}
s
}
# this function find if a given subsequence have inner repetitive pattern
findInnerRep <- function(seq) {
# find the inner repeating pattern of a given seq
n <- nchar(seq)
inner <- 1
for (i in (2:n)[n %% (2:n) == 0]) {
ss <- substring(seq, (1:i - 1) * n/i + 1, 1:i * n/i)
fm <- makeFrequencyMatrix(ss)
if (all(sapply(ss, match, fm = fm) > consensus_thresh * n / i))
inner <- i
}
inner
}
# this is the main function that identifies the TR given the unit info
annotateTR <- function(gu, chr, posOriginal, k) {
pos <- posOriginal
seq <- gu$extract(chr, pos, pos+k-1)
endpos <- pos + k
ir <- 1
ir <- tryCatch( { findInnerRep(seq) })
if (ir > 1) seq <- substring(seq, (1:ir - 1) * nchar(seq)/ir + 1, 1:ir * nchar(seq)/ir)
k <- nchar(seq[1])
lor <- 0
score <- numeric()
repeat {
e <- gu$extract(chr, pos - (lor+1) * k, pos - lor * k - 1)
if (e == -1) break
else {
s <- match(e, makeFrequencyMatrix(seq))
if (s < consensus_lower*k) break
else { score[length(score)+1] <- s ; seq <- c(e, seq) ; lor <- lor+1 }
}
}
lor <- which(score > consensus_thresh*k)
if (length(lor)) lor <- max(lor) else lor <- 0
seq <- seq[(length(seq)-ir-lor+1):length(seq)]
ror <- 0
score <- numeric()
repeat {
e <- gu$extract(chr, endpos + ror * k, endpos + (ror+1) * k - 1)
if (e == -1) break
else {
s <- match(e, makeFrequencyMatrix(seq))
if (s < consensus_lower*k) break
else { score[length(score)+1] <- s ; seq <- c(seq, e) ; ror <- ror+1 }
}
}
ror <- which(score > consensus_thresh*k)
if (length(ror)) ror <- max(ror) else ror <- 0
seq <- seq[1:(lor+ir+ror)]
pos <- pos - lor * k
rep <- length(seq)
if (rep > 1) {
repeat {
e <- gu$extract(chr, pos - 1)
if (e == -1) break
else {
shift <- paste0( c(e, substring(seq[1:(rep-1)], k, k)), substring(seq, 1, k-1) )
if (match(shift, makeFrequencyMatrix(shift)) >= match(seq, makeFrequencyMatrix(seq))) {
pos <- pos-1 ; seq <- shift
e <- gu$extract(chr, pos + k*rep, pos + k*(rep+1) - 1)
if (e != -1 && match(e, makeFrequencyMatrix(seq)) >= consensus_thresh*k) {
seq <- c(seq, e) ; rep <- rep+1
}
} else break
}
}
}
fl <- 0
mh <- ""
counter_to_avoid_loop = 0
repeat {
counter_to_avoid_loop = counter_to_avoid_loop + 1
if(counter_to_avoid_loop>1000){
mh = 'XXXXXXXXXXXXXXXXXXXXXX'
break
}
if (fl >= consensus_thresh*k && k-fl < 4) {
e <- gu$extract(chr, pos + rep * k, pos + (rep+1) * k-1)
if (e != -1) {
seq[rep+1] <- gu$extract(chr, pos + rep * k, pos + (rep+1) * k-1)
rep <- rep + 1 ; fl <- 0 ; mh <- ""
}
} else {
e <- gu$extract(chr, pos + rep * k, pos + rep * k + fl)
if (e == -1) break
else {
if (all(e == substring(seq, 1, fl+1))) {
fl <- fl+1 ; mh <- e
} else break
}
}
}
if (nchar(mh) < 2) mh <- ""
mat <- makeFrequencyMatrix(seq)
cons <- makeConsensus(mat)
entropy <- seqEntropy(mat)
list(chr = chr, posOriginal = posOriginal , pos = pos, rep = rep, k = k, seq = seq,
cons = attr(cons, "output"), entropy = entropy, mh = mh)
}
# annotateIndelTR <- function(gu, chr, pos, seq, is_insert = F) {
# endpos <- ifelse(is_insert, pos, pos + nchar(seq))
# ir <- findInnerRep(seq) # repeat unit count in the indel seq
# if (ir > 1) seq <- substring(seq, (1:ir - 1) * nchar(seq)/ir + 1, 1:ir * nchar(seq)/ir)
# k <- nchar(seq[1])
# lor <- 0 # repeat unit count on the left side of indel site
# while ({
# e <- gu$extract(chr, pos - (lor+1) * k + 1, pos - lor * k)
# if (e != -1) {
# seq <- c(e, seq)
# meanEntropy(makeFrequencyMatrix(seq)) < consensus_thresh
# } else {
# if (1 < pos - lor * k) {
# seq <- c(gu$extract(chr, 1, pos - lor * k), seq)
# } else seq <- c("", seq)
# FALSE
# }
# }) lor <- lor + 1
# ror <- 0 # repeat unit count on the right side of indel site
# while ({
# e <- gu$extract(chr, endpos + ror * k, endpos + (ror+1) * k - 1)
# if (e != -1) {
# seq <- c(seq, e)
# meanEntropy(makeFrequencyMatrix(seq[-1])) < consensus_thresh
# } else {
# if (endpos + ror * k < gu$getLength(chr)) {
# seq <- c(seq, gu$extract(chr, endpos + ror * k, gu$getLength(chr)))
# } else seq <- c(seq, "")
# FALSE
# }
# }) ror <- ror + 1
# mat <- makeFrequencyMatrix(seq[2:(length(seq)-1)]) # frequency matrix
# con <- makeConsensus(mat) # consensus sequence
# lf <- 0 # left flanking homology
# # while ({
# # pb <- mat[substring(seq[1], k-lf, k-lf), k-lf]
# # # pb > 0.4 && pb > max(mat[, k-lf]) - 0.1
# # pb == 1
# # }) lf <- lf + 1
# rf <- 0 # right flanking homology
# while ( (rf < nchar(seq[length(seq)])) &&
# (mat[substring(seq[length(seq)], rf+1, rf+1), rf+1] == 1) ) rf <- rf + 1
# if (lf + rf >= 3 && lf + rf <= min(15, 0.75 * k) ) { # recognize as microhomology
# if (lf > 0 && rf == 0) mh <- mat[ ,(k-lf+1):k]
# else if (lf ==0 && rf > 0) mh <- mat[, 1:rf]
# else mh <- mat[, c((k - lf + 1):k, 1:rf)]
# mh <- makeConsensus(mh)
# if (lf != 0) seq[1:(length(seq) - 1)] <- paste(
# substring(seq[1:(length(seq) - 1)], 1, k-lf),
# tolower(substring(seq[1:(length(seq) - 1)], k-lf+1, k)), sep = ""
# )
# if (rf != 0) seq[2:length(seq)] <- paste(
# tolower(substring(seq[2:length(seq)], 1, rf)),
# substring(seq[2:length(seq)], rf+1, k), sep = ""
# )
# } else mh <- ""
# rnc <- if (is_insert) { c(lor + ror, lor + ir + ror)
# } else { c(lor + ir + ror, lor + ror) } # repeat number
# # reformat sequence
# if (rnc[1] > 0) {
# seq[(lor+2)] <- paste0(ifelse(is_insert, ">", "<"), seq[(lor+2)])
# seq[(lor+1+ir)] <- paste0(seq[(lor+1+ir)], ifelse(is_insert, "<", ">"))
# seq <- paste0(chr, ":",pos - (lor+1) * k, " - ", paste(seq, collapse = " "))
# repstarts <- pos + c((-lor):(ir + ror - is_insert - 1)) * k
# repends <- repstarts + k
# mhstarts <- c(repstarts[1], repends)
# mhends <- mhstarts + nchar(mh)
# return(list(repeat_number = rnc, consensus = con, seq = seq, microhomology = mh,
# repeat_starts = repstarts, repeat_ends = repends,
# mh_starts = mhstarts, mh_ends = mhends))
# } else return(NULL)
# }
|
/reference_genome_TRs/code/tr_util.R
|
no_license
|
SarahChen0401/MicroHomologyMediatedTandemDuplications
|
R
| false
| false
| 8,943
|
r
|
#### Utility for TR pattern and flanking MH identification
#### This utility requires the genome utility clousure
# sequence identity below this threshold will be denied
consensus_thresh <- 0.75
consensus_lower <- 0.5
# consensus_thresh <- 0.3
# consensus_lower <- 0.5
# 4 bases
bases <- c("A", "C", "G", "T")
# this function returns the base freq matrix given a vector of
# sequences with the same length (nchar)
makeFrequencyMatrix <- function(seqs) {
n <- nchar(seqs[1])
seqs <- matrix(unlist(strsplit(seqs, "")), n)
m <- matrix(0, 4, n)
for (j in 1:n) {
b <- seqs[j, ]
for (i in 1:4) {
m[i, j] <- sum(b == bases[i])
}
}
rownames(m) <- bases
m / ncol(seqs)
}
# this function returns a consensus sequence given a freq matrix
makeConsensus <- function(fm) {
use <- paste(apply(fm, 2, function(cl) bases[which(cl == max(cl))[1]]), collapse = "")
out <- paste(apply(fm, 2, function(cl) {
if (all(cl < 0.4)) b <- "*" else {
b <- bases[which(cl > max(cl) - 0.1)]
if (length(b) > 1) b <- paste0("[", paste(b, collapse = "/"), "]")
else b <- bases[which(cl == max(cl))[1]]
}
b
} ), collapse = "")
attr(use, "output") <- out
use
}
# this function returns the entropy of a given freq matrix
seqEntropy <- function(fm) {
baserate <- rowSums(fm) / ncol(fm)
sum(ifelse(baserate == 0, 0, -baserate * log2(baserate)))
}
# this function returns the information content of a given freq matrix
# infoContent <- function(fm) {}
# this function calculate the match between a sequence and a freq matrix
match <- function(seq, fm) {
seq <- unlist(strsplit(seq, ""))
s <- 0
for (i in 1:length(seq)) {
s <- s + fm[seq[i], (i-1)%%ncol(fm)+1]
}
s
}
# this function find if a given subsequence have inner repetitive pattern
findInnerRep <- function(seq) {
# find the inner repeating pattern of a given seq
n <- nchar(seq)
inner <- 1
for (i in (2:n)[n %% (2:n) == 0]) {
ss <- substring(seq, (1:i - 1) * n/i + 1, 1:i * n/i)
fm <- makeFrequencyMatrix(ss)
if (all(sapply(ss, match, fm = fm) > consensus_thresh * n / i))
inner <- i
}
inner
}
# this is the main function that identifies the TR given the unit info
annotateTR <- function(gu, chr, posOriginal, k) {
pos <- posOriginal
seq <- gu$extract(chr, pos, pos+k-1)
endpos <- pos + k
ir <- 1
ir <- tryCatch( { findInnerRep(seq) })
if (ir > 1) seq <- substring(seq, (1:ir - 1) * nchar(seq)/ir + 1, 1:ir * nchar(seq)/ir)
k <- nchar(seq[1])
lor <- 0
score <- numeric()
repeat {
e <- gu$extract(chr, pos - (lor+1) * k, pos - lor * k - 1)
if (e == -1) break
else {
s <- match(e, makeFrequencyMatrix(seq))
if (s < consensus_lower*k) break
else { score[length(score)+1] <- s ; seq <- c(e, seq) ; lor <- lor+1 }
}
}
lor <- which(score > consensus_thresh*k)
if (length(lor)) lor <- max(lor) else lor <- 0
seq <- seq[(length(seq)-ir-lor+1):length(seq)]
ror <- 0
score <- numeric()
repeat {
e <- gu$extract(chr, endpos + ror * k, endpos + (ror+1) * k - 1)
if (e == -1) break
else {
s <- match(e, makeFrequencyMatrix(seq))
if (s < consensus_lower*k) break
else { score[length(score)+1] <- s ; seq <- c(seq, e) ; ror <- ror+1 }
}
}
ror <- which(score > consensus_thresh*k)
if (length(ror)) ror <- max(ror) else ror <- 0
seq <- seq[1:(lor+ir+ror)]
pos <- pos - lor * k
rep <- length(seq)
if (rep > 1) {
repeat {
e <- gu$extract(chr, pos - 1)
if (e == -1) break
else {
shift <- paste0( c(e, substring(seq[1:(rep-1)], k, k)), substring(seq, 1, k-1) )
if (match(shift, makeFrequencyMatrix(shift)) >= match(seq, makeFrequencyMatrix(seq))) {
pos <- pos-1 ; seq <- shift
e <- gu$extract(chr, pos + k*rep, pos + k*(rep+1) - 1)
if (e != -1 && match(e, makeFrequencyMatrix(seq)) >= consensus_thresh*k) {
seq <- c(seq, e) ; rep <- rep+1
}
} else break
}
}
}
fl <- 0
mh <- ""
counter_to_avoid_loop = 0
repeat {
counter_to_avoid_loop = counter_to_avoid_loop + 1
if(counter_to_avoid_loop>1000){
mh = 'XXXXXXXXXXXXXXXXXXXXXX'
break
}
if (fl >= consensus_thresh*k && k-fl < 4) {
e <- gu$extract(chr, pos + rep * k, pos + (rep+1) * k-1)
if (e != -1) {
seq[rep+1] <- gu$extract(chr, pos + rep * k, pos + (rep+1) * k-1)
rep <- rep + 1 ; fl <- 0 ; mh <- ""
}
} else {
e <- gu$extract(chr, pos + rep * k, pos + rep * k + fl)
if (e == -1) break
else {
if (all(e == substring(seq, 1, fl+1))) {
fl <- fl+1 ; mh <- e
} else break
}
}
}
if (nchar(mh) < 2) mh <- ""
mat <- makeFrequencyMatrix(seq)
cons <- makeConsensus(mat)
entropy <- seqEntropy(mat)
list(chr = chr, posOriginal = posOriginal , pos = pos, rep = rep, k = k, seq = seq,
cons = attr(cons, "output"), entropy = entropy, mh = mh)
}
# annotateIndelTR <- function(gu, chr, pos, seq, is_insert = F) {
# endpos <- ifelse(is_insert, pos, pos + nchar(seq))
# ir <- findInnerRep(seq) # repeat unit count in the indel seq
# if (ir > 1) seq <- substring(seq, (1:ir - 1) * nchar(seq)/ir + 1, 1:ir * nchar(seq)/ir)
# k <- nchar(seq[1])
# lor <- 0 # repeat unit count on the left side of indel site
# while ({
# e <- gu$extract(chr, pos - (lor+1) * k + 1, pos - lor * k)
# if (e != -1) {
# seq <- c(e, seq)
# meanEntropy(makeFrequencyMatrix(seq)) < consensus_thresh
# } else {
# if (1 < pos - lor * k) {
# seq <- c(gu$extract(chr, 1, pos - lor * k), seq)
# } else seq <- c("", seq)
# FALSE
# }
# }) lor <- lor + 1
# ror <- 0 # repeat unit count on the right side of indel site
# while ({
# e <- gu$extract(chr, endpos + ror * k, endpos + (ror+1) * k - 1)
# if (e != -1) {
# seq <- c(seq, e)
# meanEntropy(makeFrequencyMatrix(seq[-1])) < consensus_thresh
# } else {
# if (endpos + ror * k < gu$getLength(chr)) {
# seq <- c(seq, gu$extract(chr, endpos + ror * k, gu$getLength(chr)))
# } else seq <- c(seq, "")
# FALSE
# }
# }) ror <- ror + 1
# mat <- makeFrequencyMatrix(seq[2:(length(seq)-1)]) # frequency matrix
# con <- makeConsensus(mat) # consensus sequence
# lf <- 0 # left flanking homology
# # while ({
# # pb <- mat[substring(seq[1], k-lf, k-lf), k-lf]
# # # pb > 0.4 && pb > max(mat[, k-lf]) - 0.1
# # pb == 1
# # }) lf <- lf + 1
# rf <- 0 # right flanking homology
# while ( (rf < nchar(seq[length(seq)])) &&
# (mat[substring(seq[length(seq)], rf+1, rf+1), rf+1] == 1) ) rf <- rf + 1
# if (lf + rf >= 3 && lf + rf <= min(15, 0.75 * k) ) { # recognize as microhomology
# if (lf > 0 && rf == 0) mh <- mat[ ,(k-lf+1):k]
# else if (lf ==0 && rf > 0) mh <- mat[, 1:rf]
# else mh <- mat[, c((k - lf + 1):k, 1:rf)]
# mh <- makeConsensus(mh)
# if (lf != 0) seq[1:(length(seq) - 1)] <- paste(
# substring(seq[1:(length(seq) - 1)], 1, k-lf),
# tolower(substring(seq[1:(length(seq) - 1)], k-lf+1, k)), sep = ""
# )
# if (rf != 0) seq[2:length(seq)] <- paste(
# tolower(substring(seq[2:length(seq)], 1, rf)),
# substring(seq[2:length(seq)], rf+1, k), sep = ""
# )
# } else mh <- ""
# rnc <- if (is_insert) { c(lor + ror, lor + ir + ror)
# } else { c(lor + ir + ror, lor + ror) } # repeat number
# # reformat sequence
# if (rnc[1] > 0) {
# seq[(lor+2)] <- paste0(ifelse(is_insert, ">", "<"), seq[(lor+2)])
# seq[(lor+1+ir)] <- paste0(seq[(lor+1+ir)], ifelse(is_insert, "<", ">"))
# seq <- paste0(chr, ":",pos - (lor+1) * k, " - ", paste(seq, collapse = " "))
# repstarts <- pos + c((-lor):(ir + ror - is_insert - 1)) * k
# repends <- repstarts + k
# mhstarts <- c(repstarts[1], repends)
# mhends <- mhstarts + nchar(mh)
# return(list(repeat_number = rnc, consensus = con, seq = seq, microhomology = mh,
# repeat_starts = repstarts, repeat_ends = repends,
# mh_starts = mhstarts, mh_ends = mhends))
# } else return(NULL)
# }
|
Annotationbychemical_IDschild <-
function(adduct_index=NA,dataA,queryadductlist=c("M+H"),adduct_table,max.mz.diff=10,outloc, otherdbs=FALSE,otherinfo=FALSE,keggCompMZ){
#load("~/Documents/Emory/JonesLab/Projects/xMSannotator/keggCompMZ.Rda")
dataA<-as.data.frame(dataA)
adduct_names<-as.character(adduct_table[,1])
adductlist<-adduct_table[,4]
mult_charge<-adduct_table[,3]
num_mol<-adduct_table[,2]
names(adductlist)<-as.character(adduct_names)
names(mult_charge)<-as.character(adduct_names)
names(num_mol)<-as.character(adduct_names)
alladducts<-adduct_names
if(is.na(adduct_index)==FALSE){
queryadductlist=queryadductlist[adduct_index]
}
#load("~/Documents/Emory/JonesLab/Projects/xMSannotator/keggCompMZ.Rda")
alladducts<-adduct_names
#print(queryadductlist)
#print(alladducts)
if(queryadductlist[1]=="positive")
{
queryadductlist<-adduct_names[which(adduct_table[,5]=="positive")]
}else{
if(queryadductlist[1]=="negative")
{
queryadductlist<-adduct_names[which(adduct_table[,5]=="negative")]
}else{
if(queryadductlist[1]=="all"){
queryadductlist<-alladducts
}else{
if(length(which(queryadductlist%in%alladducts==FALSE))>0){
errormsg<-paste("Adduct should be one of:",sep="")
for(i in alladducts){errormsg<-paste(errormsg, i,sep=" ; ")}
stop(errormsg, "\n\n")
}
}
}
}
adduct_table<-as.data.frame(adduct_table)
adduct_table<-adduct_table[which(adduct_table$Adduct%in%queryadductlist),]
dir.create(outloc)
setwd(outloc)
#keggres<-KEGG.annotation(dataA=mz_search_list,queryadductlist = c("positive"),xMSannotator.outloc)
#cur_fname<-"/Users/karanuppal/Documents/Emory/JonesLab/Projects/MaHPIC/Exp2/c18/apLCMS_with_xMSanalyzer_merged_data/apLCMS_feature_list_at_p1_U_p2cor0.7_CV100.txt"
#dataA<-read.table(cur_fname,sep="\t",header=TRUE)
mz_search_list_1<-as.data.frame(keggCompMZ[which(keggCompMZ$Adduct%in%adduct_table$Adduct),c(1,7)])
mz_search_list_1<-apply(mz_search_list_1,2,as.numeric)
gcur<-getVenn(dataA=dataA,name_a="Experimental",name_b="DB",dataB=mz_search_list_1,mz.thresh=max.mz.diff,time.thresh=NA,
xMSanalyzer.outloc=outloc,alignment.tool=NA)
#print("here 1")
#print(length(gcur$common))
#print("here 2")
if(length(gcur$common)>0){
mcur<-merge(keggCompMZ[which(keggCompMZ$Adduct%in%adduct_table$Adduct),],gcur$common,by.x="mz",by.y="mz.data.B")
#print(mcur[1,1:4])
#print(dataA[1,])
mcur_2<-merge(mcur,dataA,by.x="mz.data.A",by.y="mz")
mcur_3<-mcur_2[order(mcur_2$Name),]
mcur_3<-mcur_3[,-c(9,10)]
cnames<-colnames(mcur_3)
cnames[1]<-"mz"
cnames[2]<-"theoretical.mz"
colnames(mcur_3)<-as.character(cnames)
mcur_4<-as.data.frame(mcur_3)
rm(keggCompMZ)
rm(dataA)
rm(mcur_2)
rm(mcur_3)
rm(mz_search_list_1)
if(dim(mcur)[1]>1){
h1<-table(mcur_4$mz) #Adduct
if(length(h1)>0){
#u1<-c(u1,which(h1<=1))
u1<-which(h1<=1)
}
match_status<-rep("Multiple",dim(h1)[1])
uniq_kegg_matches<-names(u1)
match_status[u1]<-"Unique"
match_status_mat<-cbind(rownames(h1),match_status)
colnames(match_status_mat)<-c("mz","MatchCategory")
match_status_mat<-as.data.frame(match_status_mat)
mcur_5<-merge(match_status_mat,mcur_4,by="mz")
print(dim(mcur_5))
rm(mcur_4)
#h1<-table(mcur_5$chemical_ID,mcur_5$mz)
print("here 2")
#s2<-apply(h1,2,sum)
mcur_5<-as.data.frame(mcur_5)
#mcur_5$mz<-as.numeric(mcur_5$mz)
mcur_5<-mcur_5[order(mcur_5$mz),]
}else{
MatchCategory<-"Unique"
cnames1<-colnames(mcur_4)
cnames1<-c(cnames1[1],"MatchCategory",cnames[-c(1)])
mcur_5<-cbind(mcur_4[1,1],MatchCategory,mcur_4[,-c(1)])
mcur_5<-as.data.frame(mcur_5)
colnames(mcur_5)<-as.character(cnames1)
#mcur_5$mz<-as.numeric(mcur_5$mz)
mcur_5<-mcur_5[order(mcur_5$mz),]
}
if(otherinfo==TRUE){
info_mat<-sapply(1:dim(mcur_5)[1],function(j){
b1<-keggGet(paste("cpd:",mcur_5[j,1],sep=""))
brite_inf<-paste(b1[[1]]$BRITE,collapse=";")
path_inf<-paste(b1[[1]]$PATHWAYS,collapse=";")
otherdb_inf<-paste(b1[[1]]$DBLINKS,collapse=";")
r1<-c(as.character(mcur_5[j,1]),as.character(brite_inf),as.character(path_inf),as.character(otherdb_inf))
return(r1)
})
info_mat_1<-as.data.frame(t(info_mat))
colnames(info_mat_1)<-c("chemical_ID","BriteCategory","Pathways","ExternalLinks")
mcur_6<-merge(info_mat_1,mcur_5,by="chemical_ID")
mcur_7<-unique(mcur_6)
rm(mcur_6)
if(otherdbs==TRUE){
info_mat_2<-sapply(1:dim(mcur_7)[1],function(j){
b1<-keggLink(paste("cpd:",mcur_7[j,1],"+-e",sep=""))
hmdbID<-"-"
lipidmapsID<-"-"
link_text<-b1[,2]
t2<-gregexpr(pattern="hmdb:",perl=FALSE,text=link_text)
if(length(t2)>1){
g_ind<-which(t2==1)
if(length(g_ind)>0){
if(length(g_ind)>1){
for(g in g_ind){
t3=t2[[g]]
hmdbID<-c(hmdbID,gsub(b1[g,2],pattern="hmdb:",replacement=""))
}
if(length(g_ind)>1){hmdbID<-paste(hmdbID,collapse=";")}
}else{
hmdbID<-gsub(b1[g_ind,2],pattern="hmdb:",replacement="")
}
}
}
t2<-gregexpr(pattern="lipidmaps:",perl=FALSE,text=link_text)
if(length(t2)>1){
g_ind<-which(t2==1)
if(length(g_ind)>0){
if(length(g_ind)>1){
for(g in g_ind){
t3=t2[[g]]
lipidmapsID<-c(lipidmapsID,gsub(b1[g,2],pattern="lipidmaps:",replacement=""))
}
lipidmapsID<-paste(lipidmapsID,collapse=";")
}else{lipidmapsID<-gsub(b1[g_ind,2],pattern="lipidmaps:",replacement="")}
}
}
return(list(keggid=as.character(mcur_7[j,1]),hmdb=hmdbID,lipidmaps=lipidmapsID))
c1<-c(as.character(mcur_7[j,1]),hmdbID,lipidmapsID)
c1<-as.data.frame(c1)
return(c1)
})
info_mat_3<-{}
#for(i in 1:dim(info_mat_2)[1]){
cdata<-rbind(info_mat_2[1,],info_mat_2[2,])
cdata<-rbind(cdata,info_mat_2[3,])
cdata<-as.data.frame(cdata)
info_mat_3<-rbind(info_mat_3,cdata)
#}
#info_mat_3<-as.data.frame(t(info_mat_2))
info_mat_3<-t(info_mat_3)
colnames(info_mat_3)<-c("chemical_ID","HMDBID","LIPIDMAPS")
mcur_7<-as.data.frame(mcur_7)
mcur_8<-cbind(info_mat_3,mcur_7) #,by="chemical_ID")
mcur_8<-unique(mcur_8)
rownames(mcur_8)<-NULL
return(mcur_8)
}else{
mcur_7<-as.data.frame(mcur_7)
rownames(mcur_7)<-NULL
return(mcur_7)
}
}else{
mcur_5<-unique(mcur_5)
return(mcur_5)
}
}else{return("no match found.")}
#}else{return("no match found.")}
}
|
/R/Annotationbychemical_IDschild.R
|
no_license
|
jaspershen/MSannotator
|
R
| false
| false
| 6,407
|
r
|
Annotationbychemical_IDschild <-
function(adduct_index=NA,dataA,queryadductlist=c("M+H"),adduct_table,max.mz.diff=10,outloc, otherdbs=FALSE,otherinfo=FALSE,keggCompMZ){
#load("~/Documents/Emory/JonesLab/Projects/xMSannotator/keggCompMZ.Rda")
dataA<-as.data.frame(dataA)
adduct_names<-as.character(adduct_table[,1])
adductlist<-adduct_table[,4]
mult_charge<-adduct_table[,3]
num_mol<-adduct_table[,2]
names(adductlist)<-as.character(adduct_names)
names(mult_charge)<-as.character(adduct_names)
names(num_mol)<-as.character(adduct_names)
alladducts<-adduct_names
if(is.na(adduct_index)==FALSE){
queryadductlist=queryadductlist[adduct_index]
}
#load("~/Documents/Emory/JonesLab/Projects/xMSannotator/keggCompMZ.Rda")
alladducts<-adduct_names
#print(queryadductlist)
#print(alladducts)
if(queryadductlist[1]=="positive")
{
queryadductlist<-adduct_names[which(adduct_table[,5]=="positive")]
}else{
if(queryadductlist[1]=="negative")
{
queryadductlist<-adduct_names[which(adduct_table[,5]=="negative")]
}else{
if(queryadductlist[1]=="all"){
queryadductlist<-alladducts
}else{
if(length(which(queryadductlist%in%alladducts==FALSE))>0){
errormsg<-paste("Adduct should be one of:",sep="")
for(i in alladducts){errormsg<-paste(errormsg, i,sep=" ; ")}
stop(errormsg, "\n\n")
}
}
}
}
adduct_table<-as.data.frame(adduct_table)
adduct_table<-adduct_table[which(adduct_table$Adduct%in%queryadductlist),]
dir.create(outloc)
setwd(outloc)
#keggres<-KEGG.annotation(dataA=mz_search_list,queryadductlist = c("positive"),xMSannotator.outloc)
#cur_fname<-"/Users/karanuppal/Documents/Emory/JonesLab/Projects/MaHPIC/Exp2/c18/apLCMS_with_xMSanalyzer_merged_data/apLCMS_feature_list_at_p1_U_p2cor0.7_CV100.txt"
#dataA<-read.table(cur_fname,sep="\t",header=TRUE)
mz_search_list_1<-as.data.frame(keggCompMZ[which(keggCompMZ$Adduct%in%adduct_table$Adduct),c(1,7)])
mz_search_list_1<-apply(mz_search_list_1,2,as.numeric)
gcur<-getVenn(dataA=dataA,name_a="Experimental",name_b="DB",dataB=mz_search_list_1,mz.thresh=max.mz.diff,time.thresh=NA,
xMSanalyzer.outloc=outloc,alignment.tool=NA)
#print("here 1")
#print(length(gcur$common))
#print("here 2")
if(length(gcur$common)>0){
mcur<-merge(keggCompMZ[which(keggCompMZ$Adduct%in%adduct_table$Adduct),],gcur$common,by.x="mz",by.y="mz.data.B")
#print(mcur[1,1:4])
#print(dataA[1,])
mcur_2<-merge(mcur,dataA,by.x="mz.data.A",by.y="mz")
mcur_3<-mcur_2[order(mcur_2$Name),]
mcur_3<-mcur_3[,-c(9,10)]
cnames<-colnames(mcur_3)
cnames[1]<-"mz"
cnames[2]<-"theoretical.mz"
colnames(mcur_3)<-as.character(cnames)
mcur_4<-as.data.frame(mcur_3)
rm(keggCompMZ)
rm(dataA)
rm(mcur_2)
rm(mcur_3)
rm(mz_search_list_1)
if(dim(mcur)[1]>1){
h1<-table(mcur_4$mz) #Adduct
if(length(h1)>0){
#u1<-c(u1,which(h1<=1))
u1<-which(h1<=1)
}
match_status<-rep("Multiple",dim(h1)[1])
uniq_kegg_matches<-names(u1)
match_status[u1]<-"Unique"
match_status_mat<-cbind(rownames(h1),match_status)
colnames(match_status_mat)<-c("mz","MatchCategory")
match_status_mat<-as.data.frame(match_status_mat)
mcur_5<-merge(match_status_mat,mcur_4,by="mz")
print(dim(mcur_5))
rm(mcur_4)
#h1<-table(mcur_5$chemical_ID,mcur_5$mz)
print("here 2")
#s2<-apply(h1,2,sum)
mcur_5<-as.data.frame(mcur_5)
#mcur_5$mz<-as.numeric(mcur_5$mz)
mcur_5<-mcur_5[order(mcur_5$mz),]
}else{
MatchCategory<-"Unique"
cnames1<-colnames(mcur_4)
cnames1<-c(cnames1[1],"MatchCategory",cnames[-c(1)])
mcur_5<-cbind(mcur_4[1,1],MatchCategory,mcur_4[,-c(1)])
mcur_5<-as.data.frame(mcur_5)
colnames(mcur_5)<-as.character(cnames1)
#mcur_5$mz<-as.numeric(mcur_5$mz)
mcur_5<-mcur_5[order(mcur_5$mz),]
}
if(otherinfo==TRUE){
info_mat<-sapply(1:dim(mcur_5)[1],function(j){
b1<-keggGet(paste("cpd:",mcur_5[j,1],sep=""))
brite_inf<-paste(b1[[1]]$BRITE,collapse=";")
path_inf<-paste(b1[[1]]$PATHWAYS,collapse=";")
otherdb_inf<-paste(b1[[1]]$DBLINKS,collapse=";")
r1<-c(as.character(mcur_5[j,1]),as.character(brite_inf),as.character(path_inf),as.character(otherdb_inf))
return(r1)
})
info_mat_1<-as.data.frame(t(info_mat))
colnames(info_mat_1)<-c("chemical_ID","BriteCategory","Pathways","ExternalLinks")
mcur_6<-merge(info_mat_1,mcur_5,by="chemical_ID")
mcur_7<-unique(mcur_6)
rm(mcur_6)
if(otherdbs==TRUE){
info_mat_2<-sapply(1:dim(mcur_7)[1],function(j){
b1<-keggLink(paste("cpd:",mcur_7[j,1],"+-e",sep=""))
hmdbID<-"-"
lipidmapsID<-"-"
link_text<-b1[,2]
t2<-gregexpr(pattern="hmdb:",perl=FALSE,text=link_text)
if(length(t2)>1){
g_ind<-which(t2==1)
if(length(g_ind)>0){
if(length(g_ind)>1){
for(g in g_ind){
t3=t2[[g]]
hmdbID<-c(hmdbID,gsub(b1[g,2],pattern="hmdb:",replacement=""))
}
if(length(g_ind)>1){hmdbID<-paste(hmdbID,collapse=";")}
}else{
hmdbID<-gsub(b1[g_ind,2],pattern="hmdb:",replacement="")
}
}
}
t2<-gregexpr(pattern="lipidmaps:",perl=FALSE,text=link_text)
if(length(t2)>1){
g_ind<-which(t2==1)
if(length(g_ind)>0){
if(length(g_ind)>1){
for(g in g_ind){
t3=t2[[g]]
lipidmapsID<-c(lipidmapsID,gsub(b1[g,2],pattern="lipidmaps:",replacement=""))
}
lipidmapsID<-paste(lipidmapsID,collapse=";")
}else{lipidmapsID<-gsub(b1[g_ind,2],pattern="lipidmaps:",replacement="")}
}
}
return(list(keggid=as.character(mcur_7[j,1]),hmdb=hmdbID,lipidmaps=lipidmapsID))
c1<-c(as.character(mcur_7[j,1]),hmdbID,lipidmapsID)
c1<-as.data.frame(c1)
return(c1)
})
info_mat_3<-{}
#for(i in 1:dim(info_mat_2)[1]){
cdata<-rbind(info_mat_2[1,],info_mat_2[2,])
cdata<-rbind(cdata,info_mat_2[3,])
cdata<-as.data.frame(cdata)
info_mat_3<-rbind(info_mat_3,cdata)
#}
#info_mat_3<-as.data.frame(t(info_mat_2))
info_mat_3<-t(info_mat_3)
colnames(info_mat_3)<-c("chemical_ID","HMDBID","LIPIDMAPS")
mcur_7<-as.data.frame(mcur_7)
mcur_8<-cbind(info_mat_3,mcur_7) #,by="chemical_ID")
mcur_8<-unique(mcur_8)
rownames(mcur_8)<-NULL
return(mcur_8)
}else{
mcur_7<-as.data.frame(mcur_7)
rownames(mcur_7)<-NULL
return(mcur_7)
}
}else{
mcur_5<-unique(mcur_5)
return(mcur_5)
}
}else{return("no match found.")}
#}else{return("no match found.")}
}
|
library(RInno)
library(dplyr)
library(magrittr)
depends = c("googledrive",
'magrittr',
'dplyr',
'diceSyntax',
'purrr',
'ogbox',
'glue',
'shiny',
'shinythemes',
'shinyjs',
'shinyWidgets',
'DT',
'shinyBS')
appVersion = '1.0.0'
unlink('sheet',recursive = TRUE)
unlink('sheetCI',recursive = TRUE,force = TRUE)
system('svn checkout https://github.com/oganm/import5eChar/trunk/inst/app')
unlink('app/.svn', recursive = TRUE, force = TRUE)
file.rename('app','sheet')
dir.create('sheetCI')
# git2r::clone('https://github.com/oganm/import5eChar.git',local_path = 'sheetCI')
#######################
create_app(app_name = "5eInteractiveSheet", app_dir = "sheet",include_R = TRUE)
file.copy('dice_icon.ico','sheet/default.ico',overwrite = TRUE)
file.copy('dice_icon.ico','sheet/setup.ico',overwrite = TRUE)
file.copy('infoafter.txt','sheet/infoafter.txt',overwrite = TRUE)
file.copy('infobefore.txt','sheet/infobefore.txt',overwrite = TRUE)
appR = readLines('sheet/global.R')
appR = c('options(ImThePortableClient = TRUE)',appR)
writeLines(appR,'sheet/global.R')
iss = readLines('sheet/5eInteractiveSheet.iss')
iss[2] %<>% gsub(pattern = '0.0.0',replacement = appVersion,.)
writeLines(iss,'sheet/5eInteractiveSheet.iss')
compile_iss()
#######################
create_app(
app_name = "import5eChar",
app_repo_url = "https://github.com/oganm/import5eChar",
pkgs = depends,
app_dir = 'sheetCI',include_R = TRUE
)
file.copy('dice_icon.ico','sheetCI/default.ico',overwrite = TRUE)
file.copy('dice_icon.ico','sheetCI/setup.ico',overwrite = TRUE)
file.copy('infoafter.txt','sheetCI/infoafter.txt',overwrite = TRUE)
file.copy('infobefore.txt','sheetCI/infobefore.txt',overwrite = TRUE)
appR = readLines('sheetCI/utils/app.R')
appR = c('options(ImThePortableClient = TRUE)',appR)
writeLines(appR,'sheetCI/utils/app.R')
iss = readLines('sheetCI/import5eChar.iss')
iss[2] %<>% gsub(pattern = '0.0.0',replacement = appVersion,.)
writeLines(iss,'sheetCI/import5eChar.iss')
compile_iss()
|
/appBuilder.R
|
no_license
|
chsims1/5eInteractiveSheet
|
R
| false
| false
| 2,147
|
r
|
library(RInno)
library(dplyr)
library(magrittr)
depends = c("googledrive",
'magrittr',
'dplyr',
'diceSyntax',
'purrr',
'ogbox',
'glue',
'shiny',
'shinythemes',
'shinyjs',
'shinyWidgets',
'DT',
'shinyBS')
appVersion = '1.0.0'
unlink('sheet',recursive = TRUE)
unlink('sheetCI',recursive = TRUE,force = TRUE)
system('svn checkout https://github.com/oganm/import5eChar/trunk/inst/app')
unlink('app/.svn', recursive = TRUE, force = TRUE)
file.rename('app','sheet')
dir.create('sheetCI')
# git2r::clone('https://github.com/oganm/import5eChar.git',local_path = 'sheetCI')
#######################
create_app(app_name = "5eInteractiveSheet", app_dir = "sheet",include_R = TRUE)
file.copy('dice_icon.ico','sheet/default.ico',overwrite = TRUE)
file.copy('dice_icon.ico','sheet/setup.ico',overwrite = TRUE)
file.copy('infoafter.txt','sheet/infoafter.txt',overwrite = TRUE)
file.copy('infobefore.txt','sheet/infobefore.txt',overwrite = TRUE)
appR = readLines('sheet/global.R')
appR = c('options(ImThePortableClient = TRUE)',appR)
writeLines(appR,'sheet/global.R')
iss = readLines('sheet/5eInteractiveSheet.iss')
iss[2] %<>% gsub(pattern = '0.0.0',replacement = appVersion,.)
writeLines(iss,'sheet/5eInteractiveSheet.iss')
compile_iss()
#######################
create_app(
app_name = "import5eChar",
app_repo_url = "https://github.com/oganm/import5eChar",
pkgs = depends,
app_dir = 'sheetCI',include_R = TRUE
)
file.copy('dice_icon.ico','sheetCI/default.ico',overwrite = TRUE)
file.copy('dice_icon.ico','sheetCI/setup.ico',overwrite = TRUE)
file.copy('infoafter.txt','sheetCI/infoafter.txt',overwrite = TRUE)
file.copy('infobefore.txt','sheetCI/infobefore.txt',overwrite = TRUE)
appR = readLines('sheetCI/utils/app.R')
appR = c('options(ImThePortableClient = TRUE)',appR)
writeLines(appR,'sheetCI/utils/app.R')
iss = readLines('sheetCI/import5eChar.iss')
iss[2] %<>% gsub(pattern = '0.0.0',replacement = appVersion,.)
writeLines(iss,'sheetCI/import5eChar.iss')
compile_iss()
|
#example using mapply for noise function
noise <- function(n,mean_s,std_s){
rnorm(n,mean_s,std_s)
}
different_noises_fixed_std <- function(listn,listmean,std_s){
mapply(noise,listn,listmean,std_s)
}
|
/Rprogramming.dir/Week3.dir/lesson_3_mapply.r
|
no_license
|
hhorus/dataAnalysisCoursera
|
R
| false
| false
| 204
|
r
|
#example using mapply for noise function
noise <- function(n,mean_s,std_s){
rnorm(n,mean_s,std_s)
}
different_noises_fixed_std <- function(listn,listmean,std_s){
mapply(noise,listn,listmean,std_s)
}
|
source('populateNewDirectory2.R')
source('makeNewFolder.R')
source('makeLink.R')
source('crawlSynapseObject.R')
source('makeHeadFolder.R')
#crawl folderA in projectD
synObj <- crawlSynapseObject('syn3157162')
synObj <- makeHeadFolder(synObj,'syn3157162')
populateNewDirectory2('syn3157160',synObj,topId = 'syn3157160')
|
/RFunctions/testMigration.R
|
no_license
|
alma2moon434/ampAdScripts
|
R
| false
| false
| 320
|
r
|
source('populateNewDirectory2.R')
source('makeNewFolder.R')
source('makeLink.R')
source('crawlSynapseObject.R')
source('makeHeadFolder.R')
#crawl folderA in projectD
synObj <- crawlSynapseObject('syn3157162')
synObj <- makeHeadFolder(synObj,'syn3157162')
populateNewDirectory2('syn3157160',synObj,topId = 'syn3157160')
|
rm(list = ls())
library(leaflet)
library(leaflet.extras)
library(leaflet.minicharts)
library(sf)
library(dplyr)
library(tidyr)
setwd(setwd('C:/Users/REACH/Dropbox (SSD REACH)/REACH South Sudan upscale/34_WFP/11_WFP_IACWG'))
disputed <- st_read('8. Dashboard/r_dashboard/app_plot/Disputed/SSD_Undetermined.shp')
disputed <- st_transform(disputed,"+init=epsg:4326" )
country <- st_read('8. Dashboard/r_dashboard/app_plot/Country/SSD_Country.shp')
country <- st_transform(country,"+init=epsg:4326" )
states <- st_read("8. Dashboard/r_dashboard/app_plot/States/SSD_States.shp")
states <- st_transform(states,"+init=epsg:4326" )
counties <- st_read("8. Dashboard/r_dashboard/app_plot/Counties/SSD_counties.shp")
counties <- st_transform(counties,"+init=epsg:4326" )
settlements <- st_read("Settlements/SSD_Settlements.shp")
settlements <- st_transform(settlements, "+init=epsg:4326")
rivers <- st_read("8. Dashboard/r_dashboard/app_plot/Rivers/SSD_Rivers.shp")
rivers <- st_transform(rivers, "+init=epsg:4326")
rivers_primary <- rivers %>% filter(OBJECTID == c(5, 6))
#st_write(rivers_primary, "rivers_primary.shp")
lakes <- st_read("8. Dashboard/r_dashboard/app_plot/Lakes/SSD_Lakes.shp")
lakes <- st_transform(lakes, "+init=epsg:4326")
roads <- st_read("8. Dashboard/r_dashboard/app_plot/Roads/SSD_roads.shp")
roads <- st_transform(roads, "+init=epsg:4326")
roads_primary <- roads %>% filter(CLASS == "Primary")
#st_write(roads_primary, "roads_primary.shp")
leaflet() %>%
#addTiles() %>% addProviderTiles(providers$OpenStreetMap) %>%
addPolygons(data = lakes, group = "Lakes", fill = TRUE, stroke = FALSE, fillColor = "#D5EAF1", fillOpacity = 0.75) %>%
addPolygons(data = counties, group = "Counties", fill = FALSE, stroke = TRUE, color = "#BDBDBD", weight = 0.6, opacity = 0.5) %>%
addPolygons(data = states, group = "States", fill = FALSE, stroke = TRUE, color = "#58585A", weight = 1, opacity = 0.7) %>%
#addPolygons(data = disputed, group = "Disputed Territory", fill = FALSE, stroke = TRUE, color = "#58585A", weight = 1, opacity = 0.7) %>%
addPolylines(data = rivers_primary, group = "Rivers", stroke = TRUE, color = "#94CCDC", weight = 1.3, opacity = 0.7) %>%
addPolylines(data = roads_primary, group = "Roads", stroke = TRUE, color = "#F69E61", weight = 1.5, opacity = 0.4) %>%
# addCircleMarkers(data = settlements, group = "Settlements", radius = 3, stroke = FALSE, fillOpacity = 0.5) %>%
#addLegend("bottomright", colors = c("#03F", "#03F"), labels = c("States", "Counties")) %>%
addLayersControl(
overlayGroups = c("States", "Counties", "Lakes", "Rivers", "Roads"),
options = layersControlOptions(collapsed = FALSE)) %>%
setMapWidgetStyle(style = list(background = "transparent"))
|
/app_plot/4_Practice/script.R
|
no_license
|
JonathanBuckleyREACHSSD/SSD-JMMI-Draft
|
R
| false
| false
| 2,786
|
r
|
rm(list = ls())
library(leaflet)
library(leaflet.extras)
library(leaflet.minicharts)
library(sf)
library(dplyr)
library(tidyr)
setwd(setwd('C:/Users/REACH/Dropbox (SSD REACH)/REACH South Sudan upscale/34_WFP/11_WFP_IACWG'))
disputed <- st_read('8. Dashboard/r_dashboard/app_plot/Disputed/SSD_Undetermined.shp')
disputed <- st_transform(disputed,"+init=epsg:4326" )
country <- st_read('8. Dashboard/r_dashboard/app_plot/Country/SSD_Country.shp')
country <- st_transform(country,"+init=epsg:4326" )
states <- st_read("8. Dashboard/r_dashboard/app_plot/States/SSD_States.shp")
states <- st_transform(states,"+init=epsg:4326" )
counties <- st_read("8. Dashboard/r_dashboard/app_plot/Counties/SSD_counties.shp")
counties <- st_transform(counties,"+init=epsg:4326" )
settlements <- st_read("Settlements/SSD_Settlements.shp")
settlements <- st_transform(settlements, "+init=epsg:4326")
rivers <- st_read("8. Dashboard/r_dashboard/app_plot/Rivers/SSD_Rivers.shp")
rivers <- st_transform(rivers, "+init=epsg:4326")
rivers_primary <- rivers %>% filter(OBJECTID == c(5, 6))
#st_write(rivers_primary, "rivers_primary.shp")
lakes <- st_read("8. Dashboard/r_dashboard/app_plot/Lakes/SSD_Lakes.shp")
lakes <- st_transform(lakes, "+init=epsg:4326")
roads <- st_read("8. Dashboard/r_dashboard/app_plot/Roads/SSD_roads.shp")
roads <- st_transform(roads, "+init=epsg:4326")
roads_primary <- roads %>% filter(CLASS == "Primary")
#st_write(roads_primary, "roads_primary.shp")
leaflet() %>%
#addTiles() %>% addProviderTiles(providers$OpenStreetMap) %>%
addPolygons(data = lakes, group = "Lakes", fill = TRUE, stroke = FALSE, fillColor = "#D5EAF1", fillOpacity = 0.75) %>%
addPolygons(data = counties, group = "Counties", fill = FALSE, stroke = TRUE, color = "#BDBDBD", weight = 0.6, opacity = 0.5) %>%
addPolygons(data = states, group = "States", fill = FALSE, stroke = TRUE, color = "#58585A", weight = 1, opacity = 0.7) %>%
#addPolygons(data = disputed, group = "Disputed Territory", fill = FALSE, stroke = TRUE, color = "#58585A", weight = 1, opacity = 0.7) %>%
addPolylines(data = rivers_primary, group = "Rivers", stroke = TRUE, color = "#94CCDC", weight = 1.3, opacity = 0.7) %>%
addPolylines(data = roads_primary, group = "Roads", stroke = TRUE, color = "#F69E61", weight = 1.5, opacity = 0.4) %>%
# addCircleMarkers(data = settlements, group = "Settlements", radius = 3, stroke = FALSE, fillOpacity = 0.5) %>%
#addLegend("bottomright", colors = c("#03F", "#03F"), labels = c("States", "Counties")) %>%
addLayersControl(
overlayGroups = c("States", "Counties", "Lakes", "Rivers", "Roads"),
options = layersControlOptions(collapsed = FALSE)) %>%
setMapWidgetStyle(style = list(background = "transparent"))
|
H<-matrix(readBin("histograms.bin", "double", 640000), 40000, 16)
# add small constant to empty bins
eps<-0.01
index<-which(H==0,arr.ind=TRUE)
for (i in 1:dim(index)[1]){
H[index[i,1],index[i,2]]<-eps
}
# parameters
k_1<-3
k_2<-4
k_3<-5
# EM algorithm
MultinomialEM<-function(H,k,t){
n<-dim(H)[1]
d<-dim(H)[2]
H<-H/rowSums(H) # normalize each histogram to avoid overflow/underflow
theta<-t(H[sample(1:n,k),])
count<-1
repeat {
if(count==1){
a_old<-matrix(0,n,k)
}
else{
a_old<-a
}
phi<-exp(H%*%(log(theta)))
a<-phi/colSums(phi)
b<-t(H)%*%a
theta<-b/colSums(b)
c<-norm(a-a_old,"o")
count<-count+1
if(c<t)
break
}
m<-max.col(a)
return(m)
}
# plot to check which parameter produce the best cluster result
tune_par<-function(H,k){
param<-seq(0.1,0.9,length=9)
for (i in 1:length(param)){
m<-MultinomialEM(H,k,param[i])
m<-matrix(m,nrow=200,ncol=200)
n<-matrix(NA,nrow=200,ncol=200)
for (j in 0:(200-1)){
n[,j+1]<-m[,200-j]
}
pdf(paste(k,'_cluster_',param[i],'.pdf',sep=''))
image(n,axes=FALSE,col=grey(0:k/k))
dev.off()
}
}
|
/em.R
|
no_license
|
Shemster/machine-learning
|
R
| false
| false
| 1,085
|
r
|
H<-matrix(readBin("histograms.bin", "double", 640000), 40000, 16)
# add small constant to empty bins
eps<-0.01
index<-which(H==0,arr.ind=TRUE)
for (i in 1:dim(index)[1]){
H[index[i,1],index[i,2]]<-eps
}
# parameters
k_1<-3
k_2<-4
k_3<-5
# EM algorithm
MultinomialEM<-function(H,k,t){
n<-dim(H)[1]
d<-dim(H)[2]
H<-H/rowSums(H) # normalize each histogram to avoid overflow/underflow
theta<-t(H[sample(1:n,k),])
count<-1
repeat {
if(count==1){
a_old<-matrix(0,n,k)
}
else{
a_old<-a
}
phi<-exp(H%*%(log(theta)))
a<-phi/colSums(phi)
b<-t(H)%*%a
theta<-b/colSums(b)
c<-norm(a-a_old,"o")
count<-count+1
if(c<t)
break
}
m<-max.col(a)
return(m)
}
# plot to check which parameter produce the best cluster result
tune_par<-function(H,k){
param<-seq(0.1,0.9,length=9)
for (i in 1:length(param)){
m<-MultinomialEM(H,k,param[i])
m<-matrix(m,nrow=200,ncol=200)
n<-matrix(NA,nrow=200,ncol=200)
for (j in 0:(200-1)){
n[,j+1]<-m[,200-j]
}
pdf(paste(k,'_cluster_',param[i],'.pdf',sep=''))
image(n,axes=FALSE,col=grey(0:k/k))
dev.off()
}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/configure.R
\name{configure_get}
\alias{configure_get}
\title{Get a config setting}
\usage{
configure_get(varname)
}
|
/man/configure_get.Rd
|
permissive
|
mmuurr/awscli
|
R
| false
| true
| 195
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/configure.R
\name{configure_get}
\alias{configure_get}
\title{Get a config setting}
\usage{
configure_get(varname)
}
|
# Define ko_pep
ko_pep <- ko / pep
# Make a time series plot of ko_pep
plot.zoo(ko_pep)
# Add as a reference, a horizontal line at 1
abline(h = 1)
# Define the vector values
values <- c(4000, 4000, 2000)
# Define the vector weights
weights <- values / sum(values)
# Print the resulting weights
print(weights)
# Define marketcaps
marketcaps <- c(5, 8, 9, 20, 25, 100, 100, 500, 700, 2000)
# Compute the weights
weights <- marketcaps / sum(marketcaps)
# Inspect summary statistics
summary(weights)
# Create a barplot of weights
barplot(weights)
# Vector of initial value of the assets
in_values <- c(1000, 5000, 2000)
# Vector of final values of the assets
fin_values <- c(1100, 4500, 3000)
# Weights as the proportion of total value invested in each assets
weights <- in_values / sum(in_values)
# Vector of simple returns of the assets
returns <- (fin_values - in_values) / in_values
# Compute portfolio return using the portfolio return formula
preturns <- sum(weights * returns)
# Suppose that you have an investment horizon of two periods. In the first period you make a 10% return. But in the second period you take a loss of 5%.
((1 + .10) * (1 + -.05) - 1) * 1000
|
/portfolio.analysis_01/01 - building.blocks/01 - intro.R
|
no_license
|
aliawaischeema/DataCamp
|
R
| false
| false
| 1,188
|
r
|
# Define ko_pep
ko_pep <- ko / pep
# Make a time series plot of ko_pep
plot.zoo(ko_pep)
# Add as a reference, a horizontal line at 1
abline(h = 1)
# Define the vector values
values <- c(4000, 4000, 2000)
# Define the vector weights
weights <- values / sum(values)
# Print the resulting weights
print(weights)
# Define marketcaps
marketcaps <- c(5, 8, 9, 20, 25, 100, 100, 500, 700, 2000)
# Compute the weights
weights <- marketcaps / sum(marketcaps)
# Inspect summary statistics
summary(weights)
# Create a barplot of weights
barplot(weights)
# Vector of initial value of the assets
in_values <- c(1000, 5000, 2000)
# Vector of final values of the assets
fin_values <- c(1100, 4500, 3000)
# Weights as the proportion of total value invested in each assets
weights <- in_values / sum(in_values)
# Vector of simple returns of the assets
returns <- (fin_values - in_values) / in_values
# Compute portfolio return using the portfolio return formula
preturns <- sum(weights * returns)
# Suppose that you have an investment horizon of two periods. In the first period you make a 10% return. But in the second period you take a loss of 5%.
((1 + .10) * (1 + -.05) - 1) * 1000
|
##############################################################################
# #
# PAYOFF MATRIX FOR THE QUANTUM BATTLE OF THE SEXES GAME #
# #
##############################################################################
#' @title
#' Quantum Battle of the Sexes game: Payoff Matrix
#'
#' @description
#' This function generates the payoff matrix for the Quantum Battle of Sexes game for all the four combinations of \code{p} and \code{q}. \code{moves} is a list of two possible strategies for each of the players and \code{alpha, beta, gamma} are the payoffs for the players corresponding to the choices available to them with the chain of inequalities, \code{alpha>beta>gamma}.
#'
#' @param moves a list of matrices
#' @param alpha a number
#' @param beta a number
#' @param gamma a number
#'
#' @usage
#' PayoffMatrix_QBOS(moves, alpha, beta, gamma)
#'
#' @return The payoff matrices for the two players as two elements of a list.
#'
#' @references
#' \url{https://arxiv.org/pdf/quant-ph/0506219.pdf}\cr
#' \url{https://arxiv.org/pdf/quant-ph/0208069.pdf}\cr
#' \url{https://arxiv.org/abs/quant-ph/0110096}\cr
#'
#'
#' @examples
#' init()
#' moves <- list(Q$I2, sigmaX(Q$I2))
#' PayoffMatrix_QBOS(moves, 5, 3, 1)
#'
#' @export
#'
PayoffMatrix_QBOS <- function(moves, alpha, beta, gamma){
Alice <- matrix(0, 2, 2)
Bob <- matrix(0, 2, 2)
for(i in 1:2){
for (j in 1:2){
X <- QBOS(i-1, j-1, moves, alpha, beta, gamma)
Alice[i, j] <- X[[1]]
Bob[i, j] <- X[[2]]
}
}
return(list(Alice, Bob))
}
|
/R/PayoffMatrix_QBOS.R
|
permissive
|
indrag49/QGameTheory
|
R
| false
| false
| 1,701
|
r
|
##############################################################################
# #
# PAYOFF MATRIX FOR THE QUANTUM BATTLE OF THE SEXES GAME #
# #
##############################################################################
#' @title
#' Quantum Battle of the Sexes game: Payoff Matrix
#'
#' @description
#' This function generates the payoff matrix for the Quantum Battle of Sexes game for all the four combinations of \code{p} and \code{q}. \code{moves} is a list of two possible strategies for each of the players and \code{alpha, beta, gamma} are the payoffs for the players corresponding to the choices available to them with the chain of inequalities, \code{alpha>beta>gamma}.
#'
#' @param moves a list of matrices
#' @param alpha a number
#' @param beta a number
#' @param gamma a number
#'
#' @usage
#' PayoffMatrix_QBOS(moves, alpha, beta, gamma)
#'
#' @return The payoff matrices for the two players as two elements of a list.
#'
#' @references
#' \url{https://arxiv.org/pdf/quant-ph/0506219.pdf}\cr
#' \url{https://arxiv.org/pdf/quant-ph/0208069.pdf}\cr
#' \url{https://arxiv.org/abs/quant-ph/0110096}\cr
#'
#'
#' @examples
#' init()
#' moves <- list(Q$I2, sigmaX(Q$I2))
#' PayoffMatrix_QBOS(moves, 5, 3, 1)
#'
#' @export
#'
PayoffMatrix_QBOS <- function(moves, alpha, beta, gamma){
Alice <- matrix(0, 2, 2)
Bob <- matrix(0, 2, 2)
for(i in 1:2){
for (j in 1:2){
X <- QBOS(i-1, j-1, moves, alpha, beta, gamma)
Alice[i, j] <- X[[1]]
Bob[i, j] <- X[[2]]
}
}
return(list(Alice, Bob))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualize.R
\name{TCGAvisualize_SurvivalCoxNET}
\alias{TCGAvisualize_SurvivalCoxNET}
\title{Survival analysis with univariate Cox regression package (dnet)}
\usage{
TCGAvisualize_SurvivalCoxNET(
clinical_patient,
dataGE,
Genelist,
org.Hs.string,
scoreConfidence = 700,
titlePlot = "TCGAvisualize_SurvivalCoxNET Example"
)
}
\arguments{
\item{clinical_patient}{is a data.frame using function 'clinic' with information
related to barcode / samples such as bcr_patient_barcode, days_to_death ,
days_to_last_followup , vital_status, etc}
\item{dataGE}{is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare}
\item{Genelist}{is a list of gene symbols where perform survival KM.}
\item{org.Hs.string}{an igraph object that contains a functional protein association network
in human. The network is extracted from the STRING database (version 10).}
\item{scoreConfidence}{restrict to those edges with high confidence (eg. score>=700)}
\item{titlePlot}{is the title to show in the final plot.}
}
\value{
net IGRAPH with related Cox survival genes in community (same pval and color) and with
interactions from STRING database.
}
\description{
TCGAvisualize_SurvivalCoxNET can help an user to identify a group of survival genes that are
significant from univariate Kaplan Meier Analysis and also for Cox Regression.
It shows in the end a network build with community of genes with similar range of pvalues from
Cox regression (same color) and that interaction among those genes is already validated in
literatures using the STRING database (version 9.1).
TCGAvisualize_SurvivalCoxNET perform survival analysis with univariate Cox regression
and package (dnet) using following functions wrapping from these packages:
\enumerate{
\item survival::coxph
\item igraph::subgraph.edges
\item igraph::layout.fruchterman.reingold
\item igraph::spinglass.community
\item igraph::communities
\item dnet::dRDataLoader
\item dnet::dNetInduce
\item dnet::dNetPipeline
\item dnet::visNet
\item dnet::dCommSignif
}
}
\details{
TCGAvisualize_SurvivalCoxNET allow user to perform the complete workflow using coxph
and dnet package related to survival analysis with an identification of gene-active networks from
high-throughput omics data using gene expression and clinical data.
\enumerate{
\item Cox regression survival analysis to obtain hazard ratio (HR) and p-values
\item fit a Cox proportional hazards model and ANOVA (Chisq test)
\item Network comunites
\item An igraph object that contains a functional protein association network in human.
The network is extracted from the STRING database (version 9.1).
Only those associations with medium confidence (score>=400) are retained.
\item restrict to those edges with high confidence (score>=700)
\item extract network that only contains genes in pvals
\item Identification of gene-active network
\item visualisation of the gene-active network itself
\item the layout of the network visualisation (fixed in different visuals)
\item color nodes according to communities (identified via a spin-glass model and simulated annealing)
\item node sizes according to degrees
\item highlight different communities
\item visualize the subnetwork
}
}
|
/man/TCGAvisualize_SurvivalCoxNET.Rd
|
no_license
|
BioinformaticsFMRP/TCGAbiolinks
|
R
| false
| true
| 3,290
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualize.R
\name{TCGAvisualize_SurvivalCoxNET}
\alias{TCGAvisualize_SurvivalCoxNET}
\title{Survival analysis with univariate Cox regression package (dnet)}
\usage{
TCGAvisualize_SurvivalCoxNET(
clinical_patient,
dataGE,
Genelist,
org.Hs.string,
scoreConfidence = 700,
titlePlot = "TCGAvisualize_SurvivalCoxNET Example"
)
}
\arguments{
\item{clinical_patient}{is a data.frame using function 'clinic' with information
related to barcode / samples such as bcr_patient_barcode, days_to_death ,
days_to_last_followup , vital_status, etc}
\item{dataGE}{is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare}
\item{Genelist}{is a list of gene symbols where perform survival KM.}
\item{org.Hs.string}{an igraph object that contains a functional protein association network
in human. The network is extracted from the STRING database (version 10).}
\item{scoreConfidence}{restrict to those edges with high confidence (eg. score>=700)}
\item{titlePlot}{is the title to show in the final plot.}
}
\value{
net IGRAPH with related Cox survival genes in community (same pval and color) and with
interactions from STRING database.
}
\description{
TCGAvisualize_SurvivalCoxNET can help an user to identify a group of survival genes that are
significant from univariate Kaplan Meier Analysis and also for Cox Regression.
It shows in the end a network build with community of genes with similar range of pvalues from
Cox regression (same color) and that interaction among those genes is already validated in
literatures using the STRING database (version 9.1).
TCGAvisualize_SurvivalCoxNET perform survival analysis with univariate Cox regression
and package (dnet) using following functions wrapping from these packages:
\enumerate{
\item survival::coxph
\item igraph::subgraph.edges
\item igraph::layout.fruchterman.reingold
\item igraph::spinglass.community
\item igraph::communities
\item dnet::dRDataLoader
\item dnet::dNetInduce
\item dnet::dNetPipeline
\item dnet::visNet
\item dnet::dCommSignif
}
}
\details{
TCGAvisualize_SurvivalCoxNET allow user to perform the complete workflow using coxph
and dnet package related to survival analysis with an identification of gene-active networks from
high-throughput omics data using gene expression and clinical data.
\enumerate{
\item Cox regression survival analysis to obtain hazard ratio (HR) and p-values
\item fit a Cox proportional hazards model and ANOVA (Chisq test)
\item Network comunites
\item An igraph object that contains a functional protein association network in human.
The network is extracted from the STRING database (version 9.1).
Only those associations with medium confidence (score>=400) are retained.
\item restrict to those edges with high confidence (score>=700)
\item extract network that only contains genes in pvals
\item Identification of gene-active network
\item visualisation of the gene-active network itself
\item the layout of the network visualisation (fixed in different visuals)
\item color nodes according to communities (identified via a spin-glass model and simulated annealing)
\item node sizes according to degrees
\item highlight different communities
\item visualize the subnetwork
}
}
|
#' @title Spatial Variance Inflation Factor
#'
#' @description Calculate the VIF
#'
#' @param base_model model to use as comparison.
#' @param model model to compare.
#'
#' @return res data.frame with VIF for fixed parameters
#'
#' @export
SVIF <- function(base_model, model) {
fixed_names <- rownames(base_model$summary_fixed)
vif <- (model$summary_fixed$sd^2)/(base_model$summary_fixed$sd^2)
vif <- data.frame(vif)
res <- data.frame(fixed_names, vif, stringsAsFactors = FALSE)
names(res) <- c("parameter", "VIF")
return(res)
}
#' @title Spatial Variance Retraction Factor
#'
#' @description Calculate the VRF
#'
#' @param base_model model to use as comparison
#' @param model model to compare
#'
#' @return res data.frame with VRF for fixed parameters
#'
#' @export
SVRF <- function(base_model, model) {
res <- SVIF(base_model = base_model, model = model)
res$VIF <- 1- 1/res$VIF
names(res) <- c(c("parameter", "VRF"))
return(res)
}
#' @title Projection matrix
#'
#' @description Calculate the projection matrix under several approaches
#'
#' @param X covariate matrix
#' @param groups ids for the subjects
#' @param method projection's method
#'
#' @return Px Projection matrix
#' Px_ort (I - Px)
proj_mat <- function(X, groups = NULL, method = "rhz") {
N <- nrow(X)
if(is.null(groups)) groups <- 1:N
n_groups <- length(unique(groups))
##-- Projection matrices ----
if(method == "rhz"){
if(n_groups != N){
n_group <- diag(1/tapply(groups, groups, length))
XX_inv <- solve(t(X)%*%X)
Paux <- XX_inv%*%(t(X) %r% groups)
Px <- (X %r% groups)%*%Paux
Px_ort <- diag(1, nrow = n_groups, ncol = n_groups) - n_group%*%Px
} else{
XX_inv <- solve(t(X)%*%X)
Paux <- XX_inv%*%t(X)
Px <- X%*%Paux
Px_ort <- diag(nrow(X)) - Px
}
}
return(list(Paux = Paux, Px = Px, Px_ort = Px_ort))
}
#' @title Generate data from ICAR model
#'
#' @description Generate data from ICAR model
#'
#' @param W adjcency matrix
#' @param sig standard deviation
#'
#' @importFrom stats rnorm
#'
#' @return x
ricar <- function(W, sig = 1) {
n <- ncol(W)
num <- rowSums(W)
Q <- -W
diag(Q) <- num
Q_aux <- eigen(Q)$vectors[, order(eigen(Q)$values)]
D_aux <- sort(eigen(Q)$values)
rnd <- rnorm(n-1, 0, sqrt(sig*(1/D_aux[-1])))
rnd <- Q_aux%*%c(0, rnd)
return(as.vector(rnd))
}
#' #' @title Generate data from CAR model
#' #'
#' #' @description Generate data from CAR model
#' #'
#' #' @param W adjcency matrix
#' #' @param sig standard deviation
#' #' @param rho dependence parameter
#' #'
#' #' @return x
#'
#' rcar <- function(W, sig, rho = 0.9999){
#' D <- diag(colSums(W))
#' Q <- sig*(D - rho*W)
#' sigma <- solve(Q)
#'
#' samp <- as.numeric(rmvnorm(n = 1, mean = rep(0, ncol(W)), sigma = sigma))
#' return(samp)
#' }
#' @title select_marginal
#'
#' @description Select the desired marginals on a INLA models
#'
#' @param samp a sample from ?inla.posterior.sample
#' @param ids ids to restore
select_marginal <- function(samp, ids) {
row_names <- row.names(samp$latent)
samp <- c(samp$latent[row_names %in% ids])
return(samp)
}
#' @title Append two lists
#'
#' @description Get commom parameters in two list and generate one append list
#'
#' @param x list base
#' @param y second list
#'
#' @return x
append_list <- function (x, y) {
xnames <- names(x)
for (v in names(y)) {
if(v %in% xnames && is.list(x[[v]]) && is.list(y[[v]])){
x[[v]] <- append_list(x[[v]], y[[v]])
} else{
if(!is.null(y[[v]])){
x[[v]] <- y[[v]]
}
}
}
return(x)
}
#' @title meang
#'
#' @description Mean by groups
#'
#' @param x a numeric vector
#' @param g group indexes
#' @param weighted TRUE for weighted mean
meang <- function(x, g, weighted = FALSE) {
if(weighted){
res <- tapply(X = x, INDEX = g, FUN = function(x) mean(x)*length(x))
} else{
res <- tapply(X = x, INDEX = g, FUN = mean)
}
return(res)
}
#' @title Reduction operator
#'
#' @description Reduction operator
#'
#' @param x a numeric vector or a numeric matrix
#' @param g_index group indexes
#'
#' @export
`%r%` <- function(x, g_index) {
if(!(class(x) %in% c("numeric", "matrix"))) stop("x must be a vector or a matrix")
if(is.matrix(x)){
n <- nrow(x)
p <- ncol(x)
if(!(length(g_index) %in% c(n, p))) stop("g_index should be equal to nrow(x) or ncol(x)")
dim_reduction <- ifelse(length(g_index) == p, 1, 2)
reduction <- apply(X = x, MARGIN = dim_reduction, FUN = function(y) tapply(X = y, INDEX = g_index, FUN = sum))
if(dim_reduction == 1) reduction <- t(reduction)
} else{
reduction <- tapply(X = x, INDEX = g_index, FUN = sum)
}
return(reduction)
}
#' @title Enlargement operator
#'
#' @description Enlargement operator
#'
#' @param x a numeric vector or a numeric matrix
#' @param g_index group indexes
`%e%` <- function(x, g_index) {
if(!(class(x) %in% c("numeric", "matrix"))) stop("x must be a vector or a matrix")
if(is.matrix(x)) if(is.null(colnames(x)) & is.null(row.names(x))) stop("x must be a named matrix")
if(!is.matrix(x)) if(is.null(names(x))) stop("x must be a named vector.")
if(!all(names(x) %in% unique(g_index))) stop("names(x) and g_index does not match.")
if(is.matrix(x)){
n <- nrow(x)
p <- ncol(x)
ng <- length(unique(g_index))
if(!(ng %in% c(n, p))) stop("Number of g_index groups should be equal to nrow(x) or ncol(x)")
if(ng == p){
dim_enlargement <- 1
names_x <- colnames(x)
} else{
dim_enlargement <- 2
names_x <- row.names(x)
}
n_group <- table(g_index)[names_x]
enlargement <- apply(X = x, MARGIN = dim_enlargement, FUN = function(x) (x/n_group)[g_index])
if(dim_enlargement == 1) enlargement <- t(enlargement)
} else{
names_x <- names(x)
n_group <- table(g_index)[names_x]
enlargement <- (x/table(g_index)[names(x)])[g_index]
enlargement <- as.numeric(enlargement)
names(enlargement) <- g_index
}
return(enlargement)
}
#' @title Updating INLA formula
#'
#' @description Updating INLA formula
#'
#' @param formula a formula to be updated to INLA format
#'
#' @importFrom stats terms.formula
update_inla_formula <- function(formula) {
##-- Checking formula
terms_formula <- terms.formula(formula, specials = c("f"), data = NULL)
terms_labels <- paste(attr(terms_formula, "variables"))
terms_f <- attr(terms_formula, "specials")$f + 1 ##-- + 1 for the list parameter
pos_restricted <- grep(x = terms_labels, pattern = "restricted|r_")
pos_unrestricted <- grep(x = terms_labels,
pattern = "\"(iid|besag|besag2|besagproper|besagproper2)")
##-- Updating formula
if(length(pos_restricted) > 0){
formula_char <- format(formula)
formula_char <- gsub(pattern = "restricted_besag", replacement = "besag", x = formula_char)
formula_new <- as.formula(formula_char)
terms_formula <- terms.formula(formula_new, specials = c("f"), data = NULL)
terms_labels <- paste(attr(terms_formula, "variables"))
} else{
formula_new <- formula
}
if(length(terms_f) > 0){
var_f <- list()
for(i in seq_along(terms_f)){
var_f[[i]] = eval(expr = parse(text = gsub(pattern = "^f\\(",
replacement = "INLA::f(",
x = terms_labels[terms_f[i]])),
envir = parent.frame(n = 2))
}
##-- Restricted and unrestricted components
list_models <- lapply(var_f, "[", c("label", "model", "n"))
list_restricted <- list_models[terms_f %in% pos_restricted]
var_restricted <- unlist(lapply(list_restricted, FUN = "[[", "label"))
size_restricted <- unlist(lapply(list_restricted, FUN = "[[", "n"))
list_unrestricted <- list_models[terms_f %in% pos_unrestricted]
var_unrestricted <- unlist(lapply(list_unrestricted, FUN = "[[", "label"))
size_unrestricted <- unlist(lapply(list_unrestricted, FUN = "[[", "n"))
} else{
var_restricted <- NULL
size_restricted <- NULL
var_unrestricted <- NULL
size_unrestricted <- NULL
}
return(list(formula = formula_new,
var_restricted = var_restricted,
vars_unrestricted = var_unrestricted,
size_restricted = size_restricted,
size_unrestricted = size_unrestricted))
}
#' @title Deviance Information Criterion
#'
#' @description Get the Deviance Information Criterion (DIC) from a model
#'
#' @param object a object from ?rsglmm, ?rscm or ?rsfm
#'
#' @return DIC
#'
#' @export
DIC <- function(object) {
out <- object$out
if(class(out) == "inla") {
return(out$dic$dic)
}
if(class(out) == "sparse.sglmm") {
return(out$dic)
}
stop(sprintf("Don't know how to deal with an object of class %s. Did you fit a model using rsglmm, rscm or rsfm?", class(out)))
}
#' @title Watanabe–Akaike information criterion
#'
#' @description Get the Watanabe–Akaike information criterion (WAIC) from a model
#'
#' @param object a object from ?rsglmm, ?rscm or ?rsfm
#'
#' @return WAIC
#'
#' @export
WAIC <- function(object) {
out <- object$out
if(class(out) == "inla") {
return(out$waic$waic)
}
if(class(out) == "sparse.sglmm") {
return(NA_real_)
}
stop(sprintf("Don't know how to deal with an object of class %s. Did you fit a model using rsglmm, rscm or rsfm?", class(out)))
}
|
/R/utils.R
|
no_license
|
bandyopd/RASCO
|
R
| false
| false
| 9,442
|
r
|
#' @title Spatial Variance Inflation Factor
#'
#' @description Calculate the VIF
#'
#' @param base_model model to use as comparison.
#' @param model model to compare.
#'
#' @return res data.frame with VIF for fixed parameters
#'
#' @export
SVIF <- function(base_model, model) {
fixed_names <- rownames(base_model$summary_fixed)
vif <- (model$summary_fixed$sd^2)/(base_model$summary_fixed$sd^2)
vif <- data.frame(vif)
res <- data.frame(fixed_names, vif, stringsAsFactors = FALSE)
names(res) <- c("parameter", "VIF")
return(res)
}
#' @title Spatial Variance Retraction Factor
#'
#' @description Calculate the VRF
#'
#' @param base_model model to use as comparison
#' @param model model to compare
#'
#' @return res data.frame with VRF for fixed parameters
#'
#' @export
SVRF <- function(base_model, model) {
res <- SVIF(base_model = base_model, model = model)
res$VIF <- 1- 1/res$VIF
names(res) <- c(c("parameter", "VRF"))
return(res)
}
#' @title Projection matrix
#'
#' @description Calculate the projection matrix under several approaches
#'
#' @param X covariate matrix
#' @param groups ids for the subjects
#' @param method projection's method
#'
#' @return Px Projection matrix
#' Px_ort (I - Px)
proj_mat <- function(X, groups = NULL, method = "rhz") {
N <- nrow(X)
if(is.null(groups)) groups <- 1:N
n_groups <- length(unique(groups))
##-- Projection matrices ----
if(method == "rhz"){
if(n_groups != N){
n_group <- diag(1/tapply(groups, groups, length))
XX_inv <- solve(t(X)%*%X)
Paux <- XX_inv%*%(t(X) %r% groups)
Px <- (X %r% groups)%*%Paux
Px_ort <- diag(1, nrow = n_groups, ncol = n_groups) - n_group%*%Px
} else{
XX_inv <- solve(t(X)%*%X)
Paux <- XX_inv%*%t(X)
Px <- X%*%Paux
Px_ort <- diag(nrow(X)) - Px
}
}
return(list(Paux = Paux, Px = Px, Px_ort = Px_ort))
}
#' @title Generate data from ICAR model
#'
#' @description Generate data from ICAR model
#'
#' @param W adjcency matrix
#' @param sig standard deviation
#'
#' @importFrom stats rnorm
#'
#' @return x
ricar <- function(W, sig = 1) {
n <- ncol(W)
num <- rowSums(W)
Q <- -W
diag(Q) <- num
Q_aux <- eigen(Q)$vectors[, order(eigen(Q)$values)]
D_aux <- sort(eigen(Q)$values)
rnd <- rnorm(n-1, 0, sqrt(sig*(1/D_aux[-1])))
rnd <- Q_aux%*%c(0, rnd)
return(as.vector(rnd))
}
#' #' @title Generate data from CAR model
#' #'
#' #' @description Generate data from CAR model
#' #'
#' #' @param W adjcency matrix
#' #' @param sig standard deviation
#' #' @param rho dependence parameter
#' #'
#' #' @return x
#'
#' rcar <- function(W, sig, rho = 0.9999){
#' D <- diag(colSums(W))
#' Q <- sig*(D - rho*W)
#' sigma <- solve(Q)
#'
#' samp <- as.numeric(rmvnorm(n = 1, mean = rep(0, ncol(W)), sigma = sigma))
#' return(samp)
#' }
#' @title select_marginal
#'
#' @description Select the desired marginals on a INLA models
#'
#' @param samp a sample from ?inla.posterior.sample
#' @param ids ids to restore
select_marginal <- function(samp, ids) {
row_names <- row.names(samp$latent)
samp <- c(samp$latent[row_names %in% ids])
return(samp)
}
#' @title Append two lists
#'
#' @description Get commom parameters in two list and generate one append list
#'
#' @param x list base
#' @param y second list
#'
#' @return x
append_list <- function (x, y) {
xnames <- names(x)
for (v in names(y)) {
if(v %in% xnames && is.list(x[[v]]) && is.list(y[[v]])){
x[[v]] <- append_list(x[[v]], y[[v]])
} else{
if(!is.null(y[[v]])){
x[[v]] <- y[[v]]
}
}
}
return(x)
}
#' @title meang
#'
#' @description Mean by groups
#'
#' @param x a numeric vector
#' @param g group indexes
#' @param weighted TRUE for weighted mean
meang <- function(x, g, weighted = FALSE) {
if(weighted){
res <- tapply(X = x, INDEX = g, FUN = function(x) mean(x)*length(x))
} else{
res <- tapply(X = x, INDEX = g, FUN = mean)
}
return(res)
}
#' @title Reduction operator
#'
#' @description Reduction operator
#'
#' @param x a numeric vector or a numeric matrix
#' @param g_index group indexes
#'
#' @export
`%r%` <- function(x, g_index) {
if(!(class(x) %in% c("numeric", "matrix"))) stop("x must be a vector or a matrix")
if(is.matrix(x)){
n <- nrow(x)
p <- ncol(x)
if(!(length(g_index) %in% c(n, p))) stop("g_index should be equal to nrow(x) or ncol(x)")
dim_reduction <- ifelse(length(g_index) == p, 1, 2)
reduction <- apply(X = x, MARGIN = dim_reduction, FUN = function(y) tapply(X = y, INDEX = g_index, FUN = sum))
if(dim_reduction == 1) reduction <- t(reduction)
} else{
reduction <- tapply(X = x, INDEX = g_index, FUN = sum)
}
return(reduction)
}
#' @title Enlargement operator
#'
#' @description Enlargement operator
#'
#' @param x a numeric vector or a numeric matrix
#' @param g_index group indexes
`%e%` <- function(x, g_index) {
if(!(class(x) %in% c("numeric", "matrix"))) stop("x must be a vector or a matrix")
if(is.matrix(x)) if(is.null(colnames(x)) & is.null(row.names(x))) stop("x must be a named matrix")
if(!is.matrix(x)) if(is.null(names(x))) stop("x must be a named vector.")
if(!all(names(x) %in% unique(g_index))) stop("names(x) and g_index does not match.")
if(is.matrix(x)){
n <- nrow(x)
p <- ncol(x)
ng <- length(unique(g_index))
if(!(ng %in% c(n, p))) stop("Number of g_index groups should be equal to nrow(x) or ncol(x)")
if(ng == p){
dim_enlargement <- 1
names_x <- colnames(x)
} else{
dim_enlargement <- 2
names_x <- row.names(x)
}
n_group <- table(g_index)[names_x]
enlargement <- apply(X = x, MARGIN = dim_enlargement, FUN = function(x) (x/n_group)[g_index])
if(dim_enlargement == 1) enlargement <- t(enlargement)
} else{
names_x <- names(x)
n_group <- table(g_index)[names_x]
enlargement <- (x/table(g_index)[names(x)])[g_index]
enlargement <- as.numeric(enlargement)
names(enlargement) <- g_index
}
return(enlargement)
}
#' @title Updating INLA formula
#'
#' @description Updating INLA formula
#'
#' @param formula a formula to be updated to INLA format
#'
#' @importFrom stats terms.formula
update_inla_formula <- function(formula) {
##-- Checking formula
terms_formula <- terms.formula(formula, specials = c("f"), data = NULL)
terms_labels <- paste(attr(terms_formula, "variables"))
terms_f <- attr(terms_formula, "specials")$f + 1 ##-- + 1 for the list parameter
pos_restricted <- grep(x = terms_labels, pattern = "restricted|r_")
pos_unrestricted <- grep(x = terms_labels,
pattern = "\"(iid|besag|besag2|besagproper|besagproper2)")
##-- Updating formula
if(length(pos_restricted) > 0){
formula_char <- format(formula)
formula_char <- gsub(pattern = "restricted_besag", replacement = "besag", x = formula_char)
formula_new <- as.formula(formula_char)
terms_formula <- terms.formula(formula_new, specials = c("f"), data = NULL)
terms_labels <- paste(attr(terms_formula, "variables"))
} else{
formula_new <- formula
}
if(length(terms_f) > 0){
var_f <- list()
for(i in seq_along(terms_f)){
var_f[[i]] = eval(expr = parse(text = gsub(pattern = "^f\\(",
replacement = "INLA::f(",
x = terms_labels[terms_f[i]])),
envir = parent.frame(n = 2))
}
##-- Restricted and unrestricted components
list_models <- lapply(var_f, "[", c("label", "model", "n"))
list_restricted <- list_models[terms_f %in% pos_restricted]
var_restricted <- unlist(lapply(list_restricted, FUN = "[[", "label"))
size_restricted <- unlist(lapply(list_restricted, FUN = "[[", "n"))
list_unrestricted <- list_models[terms_f %in% pos_unrestricted]
var_unrestricted <- unlist(lapply(list_unrestricted, FUN = "[[", "label"))
size_unrestricted <- unlist(lapply(list_unrestricted, FUN = "[[", "n"))
} else{
var_restricted <- NULL
size_restricted <- NULL
var_unrestricted <- NULL
size_unrestricted <- NULL
}
return(list(formula = formula_new,
var_restricted = var_restricted,
vars_unrestricted = var_unrestricted,
size_restricted = size_restricted,
size_unrestricted = size_unrestricted))
}
#' @title Deviance Information Criterion
#'
#' @description Get the Deviance Information Criterion (DIC) from a model
#'
#' @param object a object from ?rsglmm, ?rscm or ?rsfm
#'
#' @return DIC
#'
#' @export
DIC <- function(object) {
out <- object$out
if(class(out) == "inla") {
return(out$dic$dic)
}
if(class(out) == "sparse.sglmm") {
return(out$dic)
}
stop(sprintf("Don't know how to deal with an object of class %s. Did you fit a model using rsglmm, rscm or rsfm?", class(out)))
}
#' @title Watanabe–Akaike information criterion
#'
#' @description Get the Watanabe–Akaike information criterion (WAIC) from a model
#'
#' @param object a object from ?rsglmm, ?rscm or ?rsfm
#'
#' @return WAIC
#'
#' @export
WAIC <- function(object) {
out <- object$out
if(class(out) == "inla") {
return(out$waic$waic)
}
if(class(out) == "sparse.sglmm") {
return(NA_real_)
}
stop(sprintf("Don't know how to deal with an object of class %s. Did you fit a model using rsglmm, rscm or rsfm?", class(out)))
}
|
data_proc_africa <- function(outputdir){
# LOAD REQUIRED PACKAGES AND FUNCTIONS -----------------------------------------
if (!require("pacman")) install.packages("pacman")
pkgs = c("dplyr", "sf","sp") # package names
pacman::p_load(pkgs, character.only = T)
# LOAD DATA --------------------------------------------------------------------
# Cases at country level
# ECDC
# cases <- readr::read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/ecdc/new_cases.csv")
# JHU
cases <- readr::read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
# Shapefile for Africa
africa <- st_read("data/original/geodata/africa.gpkg")
# Policy index
policy <- readr::read_csv("https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv")
# DATA PREPARATION -------------------------------------------------------------
caseloc = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
africaloc = "data/original/geodata/africa.gpkg"
policyloc = "https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv"
alldata = read_all(cases=caseloc, areas=africaloc, policy=policyloc)
# Remove Countries with missing policy data
countries_to_remove <- c("Madagascar", "Western Sahara", "Guinea-Bissau", "Equatorial Guinea")
alldata$areas <- alldata$areas[!(alldata$areas$name %in% countries_to_remove), ]
# # THIS IS THE CLEANING FOR ECDC DATA
# # Change names of countries according to the shapefils
# names(cases)[grep("Congo", names(cases))] <- c("Republic of Congo", "Democratic Republic of the Congo")
# names(cases)[grep("Gambia", names(cases))] <- "The Gambia"
#
#
# # Subset the cases only to the 46 African countries we work with
# cases <- cases[, c(1, match(africa$name, names(cases)))]
# cases[is.na(cases)] <- 0
# Clean JHU data :
# - rename countries according to shapefile
# - keep only countries that we are going to analyse
# - calculate daily new cases
# - replace the 4 negatives with 0
alldata$cases <- alldata$cases %>%
select(-`Province/State`, -Lat, -Long) %>%
rename(country = `Country/Region`) %>%
mutate(country = case_when(
country == "Congo (Kinshasa)" ~ "Democratic Republic of the Congo",
country == "Gambia" ~ "The Gambia",
country == "Eswatini" ~ "Swaziland",
country == "Congo (Brazzaville)" ~ "Republic of Congo",
country == "Gambia" ~ "The Gambia",
TRUE ~ country
)) %>%
filter(country %in% alldata$areas$name)
# Check that the order of cases and countries in the shapefile are the same
alldata$cases <- alldata$cases[order(alldata$cases$country), ]
all(alldata$cases$country == alldata$areas$name)
# Reshape cases with times in the row and geographical units in the column
# so each column is the cases time series for the county
# the results should be of dimension T x N
# where T is the number of time points and N is the number of countries
# remember that this structure needs to be kept also for the
# predictors
# First change the names of the columns to proper dates
names(alldata$cases)[-1] <- paste0(names(alldata$cases)[-1], 20) %>%
as.Date(format = "%m/%d/%Y") %>%
as.character()
alldata$counts <- t(alldata$cases[, -1])
colnames(alldata$counts) <- alldata$cases$country
alldata$counts <- apply(alldata$counts, 2, diff)
alldata$counts[alldata$counts < 0] <- 0
# Clean policy data
alldata$policy_clean <- alldata$policy %>%
select(country = CountryName, date = Date,
testing = `H2_Testing policy`, sindex = StringencyIndex) %>%
mutate(date = as.Date(as.character(date), format = "%Y%m%d"),
country = case_when(
country == "Democratic Republic of Congo" ~ "Democratic Republic of the Congo",
country == "Gambia" ~ "The Gambia",
country == "Eswatini" ~ "Swaziland",
country == "Congo" ~ "Republic of Congo",
TRUE ~ country
)) %>%
filter(country %in% alldata$areas$name)
alldata$testing <- alldata$policy_clean %>%
select(-sindex) %>%
tidyr::spread(key = country, value = testing) %>%
select(-date) %>%
as.matrix()
alldata$testing[is.na(alldata$testing)] <- 0
rownames(alldata$testing) <- unique(as.character(alldata$policy_clean$date))
alldata$sindex <- alldata$policy_clean %>%
select(-testing) %>%
tidyr::spread(key = country, value = sindex) %>%
select(-date) %>%
as.matrix()
alldata$sindex[is.na(alldata$sindex)] <- 0
rownames(alldata$sindex) <- unique(as.character(alldata$policy_clean$date))
# Subset to the cases dates
alldata$testing <- alldata$testing[rownames(alldata$testing) %in% rownames(alldata$counts), ]
alldata$sindex <- alldata$sindex[rownames(alldata$sindex) %in% rownames(alldata$counts), ]
return(alldata)
}
|
/01_data-processing.R
|
no_license
|
Jacob-Snyder/chicas-stsmodel
|
R
| false
| false
| 5,088
|
r
|
data_proc_africa <- function(outputdir){
# LOAD REQUIRED PACKAGES AND FUNCTIONS -----------------------------------------
if (!require("pacman")) install.packages("pacman")
pkgs = c("dplyr", "sf","sp") # package names
pacman::p_load(pkgs, character.only = T)
# LOAD DATA --------------------------------------------------------------------
# Cases at country level
# ECDC
# cases <- readr::read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/ecdc/new_cases.csv")
# JHU
cases <- readr::read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
# Shapefile for Africa
africa <- st_read("data/original/geodata/africa.gpkg")
# Policy index
policy <- readr::read_csv("https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv")
# DATA PREPARATION -------------------------------------------------------------
caseloc = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
africaloc = "data/original/geodata/africa.gpkg"
policyloc = "https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv"
alldata = read_all(cases=caseloc, areas=africaloc, policy=policyloc)
# Remove Countries with missing policy data
countries_to_remove <- c("Madagascar", "Western Sahara", "Guinea-Bissau", "Equatorial Guinea")
alldata$areas <- alldata$areas[!(alldata$areas$name %in% countries_to_remove), ]
# # THIS IS THE CLEANING FOR ECDC DATA
# # Change names of countries according to the shapefils
# names(cases)[grep("Congo", names(cases))] <- c("Republic of Congo", "Democratic Republic of the Congo")
# names(cases)[grep("Gambia", names(cases))] <- "The Gambia"
#
#
# # Subset the cases only to the 46 African countries we work with
# cases <- cases[, c(1, match(africa$name, names(cases)))]
# cases[is.na(cases)] <- 0
# Clean JHU data :
# - rename countries according to shapefile
# - keep only countries that we are going to analyse
# - calculate daily new cases
# - replace the 4 negatives with 0
alldata$cases <- alldata$cases %>%
select(-`Province/State`, -Lat, -Long) %>%
rename(country = `Country/Region`) %>%
mutate(country = case_when(
country == "Congo (Kinshasa)" ~ "Democratic Republic of the Congo",
country == "Gambia" ~ "The Gambia",
country == "Eswatini" ~ "Swaziland",
country == "Congo (Brazzaville)" ~ "Republic of Congo",
country == "Gambia" ~ "The Gambia",
TRUE ~ country
)) %>%
filter(country %in% alldata$areas$name)
# Check that the order of cases and countries in the shapefile are the same
alldata$cases <- alldata$cases[order(alldata$cases$country), ]
all(alldata$cases$country == alldata$areas$name)
# Reshape cases with times in the row and geographical units in the column
# so each column is the cases time series for the county
# the results should be of dimension T x N
# where T is the number of time points and N is the number of countries
# remember that this structure needs to be kept also for the
# predictors
# First change the names of the columns to proper dates
names(alldata$cases)[-1] <- paste0(names(alldata$cases)[-1], 20) %>%
as.Date(format = "%m/%d/%Y") %>%
as.character()
alldata$counts <- t(alldata$cases[, -1])
colnames(alldata$counts) <- alldata$cases$country
alldata$counts <- apply(alldata$counts, 2, diff)
alldata$counts[alldata$counts < 0] <- 0
# Clean policy data
alldata$policy_clean <- alldata$policy %>%
select(country = CountryName, date = Date,
testing = `H2_Testing policy`, sindex = StringencyIndex) %>%
mutate(date = as.Date(as.character(date), format = "%Y%m%d"),
country = case_when(
country == "Democratic Republic of Congo" ~ "Democratic Republic of the Congo",
country == "Gambia" ~ "The Gambia",
country == "Eswatini" ~ "Swaziland",
country == "Congo" ~ "Republic of Congo",
TRUE ~ country
)) %>%
filter(country %in% alldata$areas$name)
alldata$testing <- alldata$policy_clean %>%
select(-sindex) %>%
tidyr::spread(key = country, value = testing) %>%
select(-date) %>%
as.matrix()
alldata$testing[is.na(alldata$testing)] <- 0
rownames(alldata$testing) <- unique(as.character(alldata$policy_clean$date))
alldata$sindex <- alldata$policy_clean %>%
select(-testing) %>%
tidyr::spread(key = country, value = sindex) %>%
select(-date) %>%
as.matrix()
alldata$sindex[is.na(alldata$sindex)] <- 0
rownames(alldata$sindex) <- unique(as.character(alldata$policy_clean$date))
# Subset to the cases dates
alldata$testing <- alldata$testing[rownames(alldata$testing) %in% rownames(alldata$counts), ]
alldata$sindex <- alldata$sindex[rownames(alldata$sindex) %in% rownames(alldata$counts), ]
return(alldata)
}
|
library(data.table); library(dplyr)
# load required dataset
nv <- readRDS("9 net value.rds")
# Net value before standardisation
mean(nv$nv_50k)
mean(nv$nv_100k)
mean(nv$nv_200k)
# classify participants by age groups and gender
df <- mutate(nv, group = cut(age, breaks=c(seq(from = 14, to = 84, by = 5), Inf), labels=c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85+"))) %>% as.data.table
df <- df[, .(nv_50k = mean(nv_50k), nv_100k = mean(nv_100k), nv_200k = mean(nv_200k), diff.spend = mean(diff.spend.mod)), keyby = .(group, female)]
# Net Value 2: Age- and sex-standardized population, using Hong Kong diabetes patient reference population for adults up to age 79 (for comparability with Japan sample)
# Net Value 3: Age- and sex-standardized population, using WHO world population referencepopulation (modified for adult population 15 - 85+ years old)
df$hkdm <- c(0.001340, 0.000997, 0.001315, 0.001293, 0.001794, 0.002307, 0.003605, 0.004583, 0.007690, 0.008642, 0.016029, 0.014628, 0.029435, 0.023546, 0.059159, 0.046097, 0.082705, 0.068599, 0.090264, 0.081155, 0.090036, 0.084927, 0.070938, 0.066088, 0.067006, 0.075821, 0, 0, 0, 0)
df$who <- rep(c(0.057318597, 0.055626862, 0.053664342, 0.051498732, 0.048386049, 0.0445964, 0.040874449, 0.03634014, 0.03079106, 0.025174285, 0.020031384, 0.014955503, 0.010286478, 0.006158347, 0.004297372), each = 2)
df$nv_50k_hkdm <- df$nv_50k * df$hkdm
df$nv_100k_hkdm <- df$nv_100k * df$hkdm
df$nv_200k_hkdm <- df$nv_200k * df$hkdm
df$nv_50k_who <- df$nv_50k * df$who
df$nv_100k_who <- df$nv_100k * df$who
df$nv_200k_who <- df$nv_200k * df$who
# Overall weighted net value, standardised to HKDM reference population
sum(df$nv_50k_hkdm) # assuming value of 1 life-year = 50k
sum(df$nv_100k_hkdm) # assuming value of 1 life-year = 100k
sum(df$nv_200k_hkdm) # assuming value of 1 life-year = 200k
# Overall weighted net value, standardised to WHO reference population
sum(df$nv_50k_who) # assuming value of 1 life-year = 50k
sum(df$nv_100k_who) # assuming value of 1 life-year = 100k
sum(df$nv_200k_who) # assuming value of 1 life-year = 200k
|
/10 Age and sex standardisation.R
|
no_license
|
janetltk/dm-net-value
|
R
| false
| false
| 2,214
|
r
|
library(data.table); library(dplyr)
# load required dataset
nv <- readRDS("9 net value.rds")
# Net value before standardisation
mean(nv$nv_50k)
mean(nv$nv_100k)
mean(nv$nv_200k)
# classify participants by age groups and gender
df <- mutate(nv, group = cut(age, breaks=c(seq(from = 14, to = 84, by = 5), Inf), labels=c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85+"))) %>% as.data.table
df <- df[, .(nv_50k = mean(nv_50k), nv_100k = mean(nv_100k), nv_200k = mean(nv_200k), diff.spend = mean(diff.spend.mod)), keyby = .(group, female)]
# Net Value 2: Age- and sex-standardized population, using Hong Kong diabetes patient reference population for adults up to age 79 (for comparability with Japan sample)
# Net Value 3: Age- and sex-standardized population, using WHO world population referencepopulation (modified for adult population 15 - 85+ years old)
df$hkdm <- c(0.001340, 0.000997, 0.001315, 0.001293, 0.001794, 0.002307, 0.003605, 0.004583, 0.007690, 0.008642, 0.016029, 0.014628, 0.029435, 0.023546, 0.059159, 0.046097, 0.082705, 0.068599, 0.090264, 0.081155, 0.090036, 0.084927, 0.070938, 0.066088, 0.067006, 0.075821, 0, 0, 0, 0)
df$who <- rep(c(0.057318597, 0.055626862, 0.053664342, 0.051498732, 0.048386049, 0.0445964, 0.040874449, 0.03634014, 0.03079106, 0.025174285, 0.020031384, 0.014955503, 0.010286478, 0.006158347, 0.004297372), each = 2)
df$nv_50k_hkdm <- df$nv_50k * df$hkdm
df$nv_100k_hkdm <- df$nv_100k * df$hkdm
df$nv_200k_hkdm <- df$nv_200k * df$hkdm
df$nv_50k_who <- df$nv_50k * df$who
df$nv_100k_who <- df$nv_100k * df$who
df$nv_200k_who <- df$nv_200k * df$who
# Overall weighted net value, standardised to HKDM reference population
sum(df$nv_50k_hkdm) # assuming value of 1 life-year = 50k
sum(df$nv_100k_hkdm) # assuming value of 1 life-year = 100k
sum(df$nv_200k_hkdm) # assuming value of 1 life-year = 200k
# Overall weighted net value, standardised to WHO reference population
sum(df$nv_50k_who) # assuming value of 1 life-year = 50k
sum(df$nv_100k_who) # assuming value of 1 life-year = 100k
sum(df$nv_200k_who) # assuming value of 1 life-year = 200k
|
#' Continuum removal by removing the convex hull
#'
#' This function applies a linearly interpolated convex hull to the spectra and returns the ratio of the deviation to the hull value
#'
#' @param spectra a matrix or data.frame with wavelengths as columns and spectra as rows
#' @export
chBLC <- function(spectra){
interval <- seq_len(ncol(spectra))
hull_spectra <- matrix(NA,ncol=ncol(spectra),nrow=nrow(spectra))
for (i in seq_len(nrow(spectra))){
tempSpect <- as.matrix(spectra[i,])
data1 <- sortedXyData(interval, tempSpect)
## calculate convex hull
c_hull <- chull(data1)
## get the appropriate region: the points of the polygon over the spectra
# Create vector which wraps around
c_hull <- c(c_hull, c_hull)
# remove all points before the first one.
c_hull <- c_hull[which.min(c_hull):length(c_hull)]
# Go until the first end
c_hull <- c_hull[1:which.max(c_hull)]
## calculate linear approximation between hull points
linear_approx <- approx(data1[c_hull,], xout = interval, method = 'linear', ties = 'mean')
## calculate the deviation from the convex hull
hull_spectra[i,] <- ( linear_approx[[2]] - tempSpect )/linear_approx[[2]]}
colnames(hull_spectra) <- colnames(spectra)
return(hull_spectra)}
|
/R/chBLC.R
|
no_license
|
mmbaye/spectroscopy
|
R
| false
| false
| 1,287
|
r
|
#' Continuum removal by removing the convex hull
#'
#' This function applies a linearly interpolated convex hull to the spectra and returns the ratio of the deviation to the hull value
#'
#' @param spectra a matrix or data.frame with wavelengths as columns and spectra as rows
#' @export
chBLC <- function(spectra){
interval <- seq_len(ncol(spectra))
hull_spectra <- matrix(NA,ncol=ncol(spectra),nrow=nrow(spectra))
for (i in seq_len(nrow(spectra))){
tempSpect <- as.matrix(spectra[i,])
data1 <- sortedXyData(interval, tempSpect)
## calculate convex hull
c_hull <- chull(data1)
## get the appropriate region: the points of the polygon over the spectra
# Create vector which wraps around
c_hull <- c(c_hull, c_hull)
# remove all points before the first one.
c_hull <- c_hull[which.min(c_hull):length(c_hull)]
# Go until the first end
c_hull <- c_hull[1:which.max(c_hull)]
## calculate linear approximation between hull points
linear_approx <- approx(data1[c_hull,], xout = interval, method = 'linear', ties = 'mean')
## calculate the deviation from the convex hull
hull_spectra[i,] <- ( linear_approx[[2]] - tempSpect )/linear_approx[[2]]}
colnames(hull_spectra) <- colnames(spectra)
return(hull_spectra)}
|
# Global ------------------------------------------------------------------
library(shiny)
library(shinymanager)
# data.frame with credentials info
credentials <- data.frame(
user = c("fanny", "victor", "benoit"),
password = c("azerty", "12345", "azerty"),
comment = c("alsace", "auvergne", "bretagne"),
applications = c("app1;shiny-sqlite", "app1", "shiny-sqlite"),
age = c(14, 20, 30),
expire = as.Date(c(NA, "2019-12-31", "2019-12-31")),
admin = c(TRUE, TRUE, FALSE),
stringsAsFactors = FALSE
)
if (!file.exists("credentials.sqlite")) {
create_db(credentials_data = credentials, sqlite_path = "credentials.sqlite", passphrase = "supersecret")
}
|
/dev/shiny-sqlite/global.R
|
no_license
|
abhik1368/shinymanager
|
R
| false
| false
| 672
|
r
|
# Global ------------------------------------------------------------------
library(shiny)
library(shinymanager)
# data.frame with credentials info
credentials <- data.frame(
user = c("fanny", "victor", "benoit"),
password = c("azerty", "12345", "azerty"),
comment = c("alsace", "auvergne", "bretagne"),
applications = c("app1;shiny-sqlite", "app1", "shiny-sqlite"),
age = c(14, 20, 30),
expire = as.Date(c(NA, "2019-12-31", "2019-12-31")),
admin = c(TRUE, TRUE, FALSE),
stringsAsFactors = FALSE
)
if (!file.exists("credentials.sqlite")) {
create_db(credentials_data = credentials, sqlite_path = "credentials.sqlite", passphrase = "supersecret")
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/elementApi.r
\name{element$createElement}
\alias{element$createElement}
\title{Create a child element.}
\arguments{
\item{webId}{The ID of the parent element on which to create the element.}
\item{piElement}{The new element definition.}
}
\value{
The element was created. The response's Location header is a link to the element.
}
\description{
Create a child element.
}
|
/man/element-cash-createElement.Rd
|
permissive
|
aj9253/PI-Web-API-Client-R
|
R
| false
| true
| 450
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/elementApi.r
\name{element$createElement}
\alias{element$createElement}
\title{Create a child element.}
\arguments{
\item{webId}{The ID of the parent element on which to create the element.}
\item{piElement}{The new element definition.}
}
\value{
The element was created. The response's Location header is a link to the element.
}
\description{
Create a child element.
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check4Gaps.R
\name{check4Gaps}
\alias{check4Gaps}
\title{Check for Discontinuities (Gaps) in a Vector & Optionally Make a Plot}
\usage{
check4Gaps(x, y = NULL, silent = FALSE, tol = NULL, ...)
}
\arguments{
\item{x}{A numeric vector to be checked for gaps.}
\item{y}{An optional vector of \code{y}-values which correspond to the \code{x}
values. Only used in \code{ChemoSpec}. If provided, a plot will be made
in the style of a \code{\link[ChemoSpec]{Spectra}} object showing the gap(s).}
\item{silent}{Logical indicating a "no gap" message
should not be reported to the console. Important because
\code{check4Gaps} is called iteratively by other functions.}
\item{tol}{A number indicating the tolerance for checking to see if the step
between successive \code{x} values are the same. Depending upon how the
\code{x} values are stored and rounded, you may need to change the value of
\code{tol} to avoid flagging trivial "gaps". If \code{NULL}, a value is
chosen which is just above the median difference between \code{x} values.}
\item{\dots}{Other parameters to be passed to the plot routines if
\code{y} is provided, e.g. \code{xlim}.}
}
\value{
A data frame giving the data chunks found, with one chunk per row.
Also a plot if {y} is provided. In the event there are no gaps found,
a data frame with one row is returned. The data frame has columns as follows:
\item{beg.freq }{The first frequency value in a given data chunk.}
\item{end.freq }{The last frequency value in a given data chunk.}
\item{size }{The length (in frequency units) of the data chunk.}
\item{beg.indx }{The index of the first frequency value in the data chunk.}
\item{end.indx }{The index of the last frequency value in the data chunk.}
}
\description{
The basic procedure is to compare x[n + 1] - x[n] for successive values of
n. When this value jumps, there is a gap which is flagged. \code{beg.indx}
and \code{end.indx} will always be contiguous as indices must be; it is the
\code{x} values that jump or have the gap. The indices are provided as they
are more convenient in some programming contexts. If not assigned, the
result appears at the console.
}
\examples{
x <- seq(0, 2 * pi, 0.1)
y <- sin(x)
remove <- c(8:11, 40:45)
x <- x[-remove]
y <- y[-remove]
gaps <- check4Gaps(x, tol = 0.11) # tol just larger than orig spacing
gaps
gaps <- check4Gaps(x, y, tol = 0.11) # show a plot if y given
}
\seealso{
\code{\link{sumSpectra}} which make extensive use of this function.
}
\author{
Bryan A. Hanson, DePauw University.
}
\keyword{utilities}
|
/man/check4Gaps.Rd
|
no_license
|
Tejasvigupta/ChemoSpecUtils
|
R
| false
| true
| 2,619
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check4Gaps.R
\name{check4Gaps}
\alias{check4Gaps}
\title{Check for Discontinuities (Gaps) in a Vector & Optionally Make a Plot}
\usage{
check4Gaps(x, y = NULL, silent = FALSE, tol = NULL, ...)
}
\arguments{
\item{x}{A numeric vector to be checked for gaps.}
\item{y}{An optional vector of \code{y}-values which correspond to the \code{x}
values. Only used in \code{ChemoSpec}. If provided, a plot will be made
in the style of a \code{\link[ChemoSpec]{Spectra}} object showing the gap(s).}
\item{silent}{Logical indicating a "no gap" message
should not be reported to the console. Important because
\code{check4Gaps} is called iteratively by other functions.}
\item{tol}{A number indicating the tolerance for checking to see if the step
between successive \code{x} values are the same. Depending upon how the
\code{x} values are stored and rounded, you may need to change the value of
\code{tol} to avoid flagging trivial "gaps". If \code{NULL}, a value is
chosen which is just above the median difference between \code{x} values.}
\item{\dots}{Other parameters to be passed to the plot routines if
\code{y} is provided, e.g. \code{xlim}.}
}
\value{
A data frame giving the data chunks found, with one chunk per row.
Also a plot if {y} is provided. In the event there are no gaps found,
a data frame with one row is returned. The data frame has columns as follows:
\item{beg.freq }{The first frequency value in a given data chunk.}
\item{end.freq }{The last frequency value in a given data chunk.}
\item{size }{The length (in frequency units) of the data chunk.}
\item{beg.indx }{The index of the first frequency value in the data chunk.}
\item{end.indx }{The index of the last frequency value in the data chunk.}
}
\description{
The basic procedure is to compare x[n + 1] - x[n] for successive values of
n. When this value jumps, there is a gap which is flagged. \code{beg.indx}
and \code{end.indx} will always be contiguous as indices must be; it is the
\code{x} values that jump or have the gap. The indices are provided as they
are more convenient in some programming contexts. If not assigned, the
result appears at the console.
}
\examples{
x <- seq(0, 2 * pi, 0.1)
y <- sin(x)
remove <- c(8:11, 40:45)
x <- x[-remove]
y <- y[-remove]
gaps <- check4Gaps(x, tol = 0.11) # tol just larger than orig spacing
gaps
gaps <- check4Gaps(x, y, tol = 0.11) # show a plot if y given
}
\seealso{
\code{\link{sumSpectra}} which make extensive use of this function.
}
\author{
Bryan A. Hanson, DePauw University.
}
\keyword{utilities}
|
# Correlation
# Prepare the Data
mydata <- mtcars[, c(1,3,4,5,6,7)]
head(mydata)
# Compute the correlation matrix - cor()
cormat <- round(cor(mydata),2)
head(cormat)
# Create the correlation heatmap with ggplot2
# The package reshape is required to melt the correlation matrix.
library(reshape2)
melted_cormat <- melt(cormat)
head(melted_cormat)
#The function geom_tile()[ggplot2 package] is used to visualize the correlation matrix :
library(ggplot2)
ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile()
#Doesnot Look Great.. Let's Enhance the viz!
#Get the lower and upper triangles of the correlation matrix
## a correlation matrix has redundant information. We'll use the functions below to set half of it to NA.
# Get lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}
# Get upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
upper_tri <- get_upper_tri(cormat)
upper_tri
# Finished correlation matrix heatmap
## Melt the correlation data and drop the rows with NA values
# Melt the correlation matrix
library(reshape2)
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Heatmap
library(ggplot2)
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
# negative correlations are in blue color and positive correlations in red.
# The function scale_fill_gradient2 is used with the argument limit = c(-1,1) as correlation coefficients range from -1 to 1.
# coord_fixed() : this function ensures that one unit on the x-axis is the same length as one unit on the y-axis.
# Reorder the correlation matrix
# This section describes how to reorder the correlation matrix according to the correlation coefficient.
# This is useful to identify the hidden pattern in the matrix.
# hclust for hierarchical clustering order is used in the example below.
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
# Reorder the correlation matrix
cormat <- reorder_cormat(cormat)
upper_tri <- get_upper_tri(cormat)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+ # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
# Print the heatmap
print(ggheatmap)
#Add correlation coefficients on the heatmap
## Use geom_text() to add the correlation coefficients on the graph
## Use a blank theme (remove axis labels, panel grids and background, and axis ticks)
## Use guides() to change the position of the legend title
ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.6, 0.7),
legend.direction = "horizontal")+
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5))
|
/5.2 DSA - R - EDA -Correlation Heatmap - 6 Dec 2020.R
|
no_license
|
sridhar-v/R-Codes
|
R
| false
| false
| 4,125
|
r
|
# Correlation
# Prepare the Data
mydata <- mtcars[, c(1,3,4,5,6,7)]
head(mydata)
# Compute the correlation matrix - cor()
cormat <- round(cor(mydata),2)
head(cormat)
# Create the correlation heatmap with ggplot2
# The package reshape is required to melt the correlation matrix.
library(reshape2)
melted_cormat <- melt(cormat)
head(melted_cormat)
#The function geom_tile()[ggplot2 package] is used to visualize the correlation matrix :
library(ggplot2)
ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile()
#Doesnot Look Great.. Let's Enhance the viz!
#Get the lower and upper triangles of the correlation matrix
## a correlation matrix has redundant information. We'll use the functions below to set half of it to NA.
# Get lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}
# Get upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
upper_tri <- get_upper_tri(cormat)
upper_tri
# Finished correlation matrix heatmap
## Melt the correlation data and drop the rows with NA values
# Melt the correlation matrix
library(reshape2)
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Heatmap
library(ggplot2)
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
# negative correlations are in blue color and positive correlations in red.
# The function scale_fill_gradient2 is used with the argument limit = c(-1,1) as correlation coefficients range from -1 to 1.
# coord_fixed() : this function ensures that one unit on the x-axis is the same length as one unit on the y-axis.
# Reorder the correlation matrix
# This section describes how to reorder the correlation matrix according to the correlation coefficient.
# This is useful to identify the hidden pattern in the matrix.
# hclust for hierarchical clustering order is used in the example below.
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
# Reorder the correlation matrix
cormat <- reorder_cormat(cormat)
upper_tri <- get_upper_tri(cormat)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+ # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
# Print the heatmap
print(ggheatmap)
#Add correlation coefficients on the heatmap
## Use geom_text() to add the correlation coefficients on the graph
## Use a blank theme (remove axis labels, panel grids and background, and axis ticks)
## Use guides() to change the position of the legend title
ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.6, 0.7),
legend.direction = "horizontal")+
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5))
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SwarnTopTags.R
\name{SwarnTopTags}
\alias{SwarnTopTags}
\title{This function selects the top genes in scRNA-seq data.}
\usage{
SwarnTopTags(results, m)
}
\arguments{
\item{results}{A output data frame from \code{SwarnSeqLRT} or \code{SwarnUnadjLRT} which contains the unclassified differential expression analysis results.}
\item{m}{A scalar representing the number of top performing genes to be selected from the scRNA-seq data.}
}
\value{
A list of the top genes along with their statistics.
}
\description{
This function selects the top genes in scRNA-seq data.
}
\examples{
#Load the test count data, spike-in counts and spike-in concentration data for SwarnSeq.
data(TestData)
counts <- TestData$CountData
Spikes <- TestData$SpikeCounts
SpikeConc <- TestData$SpikeConc
#specifying the group information, the group 1 and 2 have two hundred cells each.
group <- c(rep(1, 200), rep(2, 200))
#Specifying the cluster memberships of the cells in columns of countData.
cellcluster <- c(rep(1, 60), rep(2, 40), rep(3, 50),
rep(4, 50), rep(5, 30),
rep(6, 90),
rep(7, 80))
#results <- SwarnSeqLRT(CountData=counts, RNAspike.use=TRUE, spikes=Spikes, spike.conc=SpikeConc,
#parallel=FALSE, norm.method="TMM", group=group, CellCluster=cellcluster,
#CellAuxil=NULL, maxit=500, eps=1e-10,
#muoffset=NULL, phioffset=NULL, weights=NULL, p.adjust.method="hochberg")
#DEGtypes <- SwarnTopTags(results, m = 100)
}
\seealso{
\code{\link{SwarnSeqLRT}}, for the detection of differentially expressed genes from scRNA-seq data.
\code{\link{TestData}}, a test dataset for SwarnSeq.
}
\author{
Samarendra Das
}
|
/man/SwarnTopTags.Rd
|
no_license
|
sam-uofl/SwarnSeq
|
R
| false
| true
| 1,810
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SwarnTopTags.R
\name{SwarnTopTags}
\alias{SwarnTopTags}
\title{This function selects the top genes in scRNA-seq data.}
\usage{
SwarnTopTags(results, m)
}
\arguments{
\item{results}{A output data frame from \code{SwarnSeqLRT} or \code{SwarnUnadjLRT} which contains the unclassified differential expression analysis results.}
\item{m}{A scalar representing the number of top performing genes to be selected from the scRNA-seq data.}
}
\value{
A list of the top genes along with their statistics.
}
\description{
This function selects the top genes in scRNA-seq data.
}
\examples{
#Load the test count data, spike-in counts and spike-in concentration data for SwarnSeq.
data(TestData)
counts <- TestData$CountData
Spikes <- TestData$SpikeCounts
SpikeConc <- TestData$SpikeConc
#specifying the group information, the group 1 and 2 have two hundred cells each.
group <- c(rep(1, 200), rep(2, 200))
#Specifying the cluster memberships of the cells in columns of countData.
cellcluster <- c(rep(1, 60), rep(2, 40), rep(3, 50),
rep(4, 50), rep(5, 30),
rep(6, 90),
rep(7, 80))
#results <- SwarnSeqLRT(CountData=counts, RNAspike.use=TRUE, spikes=Spikes, spike.conc=SpikeConc,
#parallel=FALSE, norm.method="TMM", group=group, CellCluster=cellcluster,
#CellAuxil=NULL, maxit=500, eps=1e-10,
#muoffset=NULL, phioffset=NULL, weights=NULL, p.adjust.method="hochberg")
#DEGtypes <- SwarnTopTags(results, m = 100)
}
\seealso{
\code{\link{SwarnSeqLRT}}, for the detection of differentially expressed genes from scRNA-seq data.
\code{\link{TestData}}, a test dataset for SwarnSeq.
}
\author{
Samarendra Das
}
|
## Matrix inversion is usually a costly computation and there may be some
## benefit to caching the inverse of a matrix rather than compute it repeatedly.
## stores a matrix and caches its inverse.
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setInverse <- function(inverse) inv <<- inverse
getInverse <- function() inv
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## This function computes the inverse of the special "matrix" returned
## by makeCacheMatrix above. If the inverse has already been calculated
## (and the matrix has not changed), then the cachesolve should retrieve
## the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getInverse()
if (!is.null(inv)) {
message("getting cached data")
return(inv)
}
mat <- x$get()
inv <- solve(mat, ...)
x$setInverse(inv)
inv
}
|
/cachematrix.R
|
no_license
|
rtodoc/ProgrammingAssignment2
|
R
| false
| false
| 1,105
|
r
|
## Matrix inversion is usually a costly computation and there may be some
## benefit to caching the inverse of a matrix rather than compute it repeatedly.
## stores a matrix and caches its inverse.
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setInverse <- function(inverse) inv <<- inverse
getInverse <- function() inv
list(set = set,
get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## This function computes the inverse of the special "matrix" returned
## by makeCacheMatrix above. If the inverse has already been calculated
## (and the matrix has not changed), then the cachesolve should retrieve
## the inverse from the cache.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getInverse()
if (!is.null(inv)) {
message("getting cached data")
return(inv)
}
mat <- x$get()
inv <- solve(mat, ...)
x$setInverse(inv)
inv
}
|
#' @keywords internal
.pkgSnapshot <- function(instPkgs, instVers, packageVersionFile = "._packageVersionsAuto.txt") {
browser(expr = exists("aaaa"))
inst <- data.frame(instPkgs, instVers = unlist(instVers), stringsAsFactors = FALSE)
write.table(inst, file = packageVersionFile, row.names = FALSE)
inst
}
#' @importFrom utils chooseCRANmirror
#' @keywords internal
getCRANrepos <- function(repos = NULL) {
if (is.null(repos)) {
repos <- getOption("repos")["CRAN"]
}
# still might be imprecise repository, specifically ""
if (isTRUE("" == repos)) {
repos <- "@CRAN@"
}
# if @CRAN@, and non interactive session
if (isTRUE("@CRAN@" %in% repos)) {
cranRepo <- Sys.getenv("CRAN_REPO")
repos <- if (nzchar(cranRepo)) {
cranRepo
} else {
chooseCRANmirror2() ## sets repo option
getOption("repos")["CRAN"]
}
}
return(repos)
}
#' @importFrom utils chooseCRANmirror
#' @keywords internal
chooseCRANmirror2 <- function() {
if (isInteractive()) {
chooseCRANmirror()
} else {
chooseCRANmirror(ind = 1) ## https://cloud.r-project.org
}
}
#' Add a prefix or suffix to the basename part of a file path
#'
#' Prepend (or postpend) a filename with a prefix (or suffix).
#' If the directory name of the file cannot be ascertained from its path,
#' it is assumed to be in the current working directory.
#'
#' @param f A character string giving the name/path of a file.
#' @param prefix A character string to prepend to the filename.
#' @param suffix A character string to postpend to the filename.
#'
#' @author Jean Marchal and Alex Chubaty
#' @export
#' @importFrom tools file_ext file_path_sans_ext
#' @rdname prefix
#'
#' @examples
#' # file's full path is specified (i.e., dirname is known)
#' myFile <- file.path("~/data", "file.tif")
#' .prefix(myFile, "small_") ## "/home/username/data/small_file.tif"
#' .suffix(myFile, "_cropped") ## "/home/username/data/myFile_cropped.shp"
#'
#' # file's full path is not specified
#' .prefix("myFile.shp", "small") ## "./small_myFile.shp"
#' .suffix("myFile.shp", "_cropped") ## "./myFile_cropped.shp"
#'
.prefix <- function(f, prefix = "") {
file.path(dirname(f), paste0(prefix, basename(f)))
}
#' @export
#' @name suffix
#' @rdname prefix
.suffix <- function(f, suffix = "") {
file.path(dirname(f), paste0(tools::file_path_sans_ext(basename(f)), suffix,
".", tools::file_ext(f)))
}
#' Get a unique name for a given study area
#'
#' Digest a spatial object to get a unique character string (hash) of the study area.
#' Use \code{.suffix()} to append the hash to a filename, e.g., when using \code{filename2} in \code{prepInputs}.
#'
#' @param studyArea Spatial object.
#' @param ... Other arguments (not currently used)
#'
#' @export
#' @importFrom digest digest
setGeneric("studyAreaName", function(studyArea, ...) {
standardGeneric("studyAreaName")
})
#' @export
#' @rdname studyAreaName
setMethod(
"studyAreaName",
signature = "SpatialPolygonsDataFrame",
definition = function (studyArea, ...) {
digest(studyArea[, -c(1:ncol(studyArea))], algo = "xxhash64") ## TODO: use `...` to pass `algo`
})
#' Identify which formals to a function are not in the current \code{...}
#'
#' Advanced use.
#'
#' @keywords internal
#' @export
#' @param fun A function
#' @param ... The ... from inside a function. Will be ignored if \code{dots} is
#' provided explicitly.
#' @param dots Optional. If this is provided via say \code{dots = list(...)},
#' then this will cause the \code{...} to be ignored.
.formalsNotInCurrentDots <- function(fun, ..., dots) {
if (!missing(dots)) {
out <- names(dots)[!(names(dots) %in% names(formals(fun)))]
} else {
out <- names(list(...))[!(names(list(...)) %in% names(formals(fun)))]
}
out
}
#' @keywords internal
rndstr <- function(n = 1, len = 8) {
unlist(lapply(character(n), function(x) {
x <- paste0(sample(c(0:9, letters, LETTERS), size = len,
replace = TRUE), collapse = "")
}))
}
#' Alternative to \code{interactive()} for unit testing
#'
#' This is a suggestion from
#' \url{https://github.com/MangoTheCat/blog-with-mock/blob/master/Blogpost1.Rmd}
#' as a way to test interactive code in unit tests. Basically, in the unit tests,
#' we use \code{testthat::with_mock}, and inside that we redefine \code{isInteractive}
#' just for the test. In all other times, this returns the same things as
#' \code{interactive()}.
#' @keywords internal
#' @examples
#' \dontrun{
#' testthat::with_mock(
#' `isInteractive` = function() {browser(); TRUE},
#' {
#' tmpdir <- tempdir()
#' aa <- Cache(rnorm, 1, cacheRepo = tmpdir, userTags = "something2")
#' # Test clearCache -- has an internal isInteractive() call
#' clearCache(tmpdir, ask = FALSE)
#' })
#' }
isInteractive <- function() interactive()
#' A version of \code{base::basename} that is \code{NULL} resistant
#'
#' Returns \code{NULL} if x is \code{NULL}, otherwise, as \code{basename}.
#'
#' @param x A character vector of paths
#' @export
#' @return Same as \code{\link[base]{basename}}
#'
basename2 <- function(x) {
if (is.null(x)) {
NULL
} else {
basename(x)
}
}
#' A wrapper around \code{try} that retries on failure
#'
#' This is useful for functions that are "flaky", such as \code{curl}, which may fail for unknown
#' reasons that do not persist.
#'
#' @details
#' Based on \url{https://github.com/jennybc/googlesheets/issues/219#issuecomment-195218525}.
#'
#' @param expr Quoted expression to run, i.e., \code{quote(...)}
#' @param retries Numeric. The maximum number of retries.
#' @param envir The environment in which to evaluate the quoted expression, default
#' to \code{parent.frame(1)}
#' @param exponentialDecayBase Numeric > 1.0. The delay between
#' successive retries will be \code{runif(1, min = 0, max = exponentialDecayBase ^ i - 1)}
#' where \code{i} is the retry number (i.e., follows \code{seq_len(retries)})
#' @param silent Logical indicating whether to \code{try} silently.
#'
#' @export
retry <- function(expr, envir = parent.frame(), retries = 5,
exponentialDecayBase = 1.3, silent = TRUE) {
if (exponentialDecayBase <= 1)
stop("exponentialDecayBase must be greater than 1.0")
for (i in seq_len(retries)) {
if (!(is.call(expr) || is.name(expr))) warning("expr is not a quoted expression")
result <- try(expr = eval(expr, envir = envir), silent = silent)
if (inherits(result, "try-error")) {
backoff <- runif(n = 1, min = 0, max = exponentialDecayBase^i - 1)
if (backoff > 3) {
message("Waiting for ", round(backoff, 1), " seconds to retry; the attempt is failing")
}
Sys.sleep(backoff)
} else {
break
}
}
if (inherits(result, "try-error")) {
stop(result, "\nFailed after ", retries, " attempts.")
} else {
return(result)
}
}
#' Test whether system is Windows
#'
#' This is used so that unit tests can override this using \code{testthat::with_mock}.
#' @keywords internal
isWindows <- function() identical(.Platform$OS.type, "windows")
#' Provide standard messaging for missing package dependencies
#'
#' This provides a standard message format for missing packages, e.g.,
#' detected via \code{requireNamespace}.
#'
#' @export
#' @param pkg Character string indicating name of package required
#' @param minVersion Character string indicating minimum version of package
#' that is needed
#' @param messageStart A character string with a prefix of message to provide
.requireNamespace <- function(pkg = "methods", minVersion = NULL,
messageStart = paste0(pkg, if (!is.null(minVersion)) paste0("(>=", minVersion, ")"), " is required. Try: ")) {
need <- FALSE
if (suppressWarnings(!requireNamespace(pkg, quietly = TRUE, warn.conflicts = FALSE))) {
need <- TRUE
} else {
if (isTRUE(packageVersion(pkg) < minVersion))
need <- TRUE
}
if (need) {
message(messageStart,
"install.packages('",pkg,"')")
}
!need
}
|
/R/helpers.R
|
no_license
|
mdsumner/reproducible
|
R
| false
| false
| 8,067
|
r
|
#' @keywords internal
.pkgSnapshot <- function(instPkgs, instVers, packageVersionFile = "._packageVersionsAuto.txt") {
browser(expr = exists("aaaa"))
inst <- data.frame(instPkgs, instVers = unlist(instVers), stringsAsFactors = FALSE)
write.table(inst, file = packageVersionFile, row.names = FALSE)
inst
}
#' @importFrom utils chooseCRANmirror
#' @keywords internal
getCRANrepos <- function(repos = NULL) {
if (is.null(repos)) {
repos <- getOption("repos")["CRAN"]
}
# still might be imprecise repository, specifically ""
if (isTRUE("" == repos)) {
repos <- "@CRAN@"
}
# if @CRAN@, and non interactive session
if (isTRUE("@CRAN@" %in% repos)) {
cranRepo <- Sys.getenv("CRAN_REPO")
repos <- if (nzchar(cranRepo)) {
cranRepo
} else {
chooseCRANmirror2() ## sets repo option
getOption("repos")["CRAN"]
}
}
return(repos)
}
#' @importFrom utils chooseCRANmirror
#' @keywords internal
chooseCRANmirror2 <- function() {
if (isInteractive()) {
chooseCRANmirror()
} else {
chooseCRANmirror(ind = 1) ## https://cloud.r-project.org
}
}
#' Add a prefix or suffix to the basename part of a file path
#'
#' Prepend (or postpend) a filename with a prefix (or suffix).
#' If the directory name of the file cannot be ascertained from its path,
#' it is assumed to be in the current working directory.
#'
#' @param f A character string giving the name/path of a file.
#' @param prefix A character string to prepend to the filename.
#' @param suffix A character string to postpend to the filename.
#'
#' @author Jean Marchal and Alex Chubaty
#' @export
#' @importFrom tools file_ext file_path_sans_ext
#' @rdname prefix
#'
#' @examples
#' # file's full path is specified (i.e., dirname is known)
#' myFile <- file.path("~/data", "file.tif")
#' .prefix(myFile, "small_") ## "/home/username/data/small_file.tif"
#' .suffix(myFile, "_cropped") ## "/home/username/data/myFile_cropped.shp"
#'
#' # file's full path is not specified
#' .prefix("myFile.shp", "small") ## "./small_myFile.shp"
#' .suffix("myFile.shp", "_cropped") ## "./myFile_cropped.shp"
#'
.prefix <- function(f, prefix = "") {
file.path(dirname(f), paste0(prefix, basename(f)))
}
#' @export
#' @name suffix
#' @rdname prefix
.suffix <- function(f, suffix = "") {
file.path(dirname(f), paste0(tools::file_path_sans_ext(basename(f)), suffix,
".", tools::file_ext(f)))
}
#' Get a unique name for a given study area
#'
#' Digest a spatial object to get a unique character string (hash) of the study area.
#' Use \code{.suffix()} to append the hash to a filename, e.g., when using \code{filename2} in \code{prepInputs}.
#'
#' @param studyArea Spatial object.
#' @param ... Other arguments (not currently used)
#'
#' @export
#' @importFrom digest digest
setGeneric("studyAreaName", function(studyArea, ...) {
standardGeneric("studyAreaName")
})
#' @export
#' @rdname studyAreaName
setMethod(
"studyAreaName",
signature = "SpatialPolygonsDataFrame",
definition = function (studyArea, ...) {
digest(studyArea[, -c(1:ncol(studyArea))], algo = "xxhash64") ## TODO: use `...` to pass `algo`
})
#' Identify which formals to a function are not in the current \code{...}
#'
#' Advanced use.
#'
#' @keywords internal
#' @export
#' @param fun A function
#' @param ... The ... from inside a function. Will be ignored if \code{dots} is
#' provided explicitly.
#' @param dots Optional. If this is provided via say \code{dots = list(...)},
#' then this will cause the \code{...} to be ignored.
.formalsNotInCurrentDots <- function(fun, ..., dots) {
if (!missing(dots)) {
out <- names(dots)[!(names(dots) %in% names(formals(fun)))]
} else {
out <- names(list(...))[!(names(list(...)) %in% names(formals(fun)))]
}
out
}
#' @keywords internal
rndstr <- function(n = 1, len = 8) {
unlist(lapply(character(n), function(x) {
x <- paste0(sample(c(0:9, letters, LETTERS), size = len,
replace = TRUE), collapse = "")
}))
}
#' Alternative to \code{interactive()} for unit testing
#'
#' This is a suggestion from
#' \url{https://github.com/MangoTheCat/blog-with-mock/blob/master/Blogpost1.Rmd}
#' as a way to test interactive code in unit tests. Basically, in the unit tests,
#' we use \code{testthat::with_mock}, and inside that we redefine \code{isInteractive}
#' just for the test. In all other times, this returns the same things as
#' \code{interactive()}.
#' @keywords internal
#' @examples
#' \dontrun{
#' testthat::with_mock(
#' `isInteractive` = function() {browser(); TRUE},
#' {
#' tmpdir <- tempdir()
#' aa <- Cache(rnorm, 1, cacheRepo = tmpdir, userTags = "something2")
#' # Test clearCache -- has an internal isInteractive() call
#' clearCache(tmpdir, ask = FALSE)
#' })
#' }
isInteractive <- function() interactive()
#' A version of \code{base::basename} that is \code{NULL} resistant
#'
#' Returns \code{NULL} if x is \code{NULL}, otherwise, as \code{basename}.
#'
#' @param x A character vector of paths
#' @export
#' @return Same as \code{\link[base]{basename}}
#'
basename2 <- function(x) {
if (is.null(x)) {
NULL
} else {
basename(x)
}
}
#' A wrapper around \code{try} that retries on failure
#'
#' This is useful for functions that are "flaky", such as \code{curl}, which may fail for unknown
#' reasons that do not persist.
#'
#' @details
#' Based on \url{https://github.com/jennybc/googlesheets/issues/219#issuecomment-195218525}.
#'
#' @param expr Quoted expression to run, i.e., \code{quote(...)}
#' @param retries Numeric. The maximum number of retries.
#' @param envir The environment in which to evaluate the quoted expression, default
#' to \code{parent.frame(1)}
#' @param exponentialDecayBase Numeric > 1.0. The delay between
#' successive retries will be \code{runif(1, min = 0, max = exponentialDecayBase ^ i - 1)}
#' where \code{i} is the retry number (i.e., follows \code{seq_len(retries)})
#' @param silent Logical indicating whether to \code{try} silently.
#'
#' @export
retry <- function(expr, envir = parent.frame(), retries = 5,
exponentialDecayBase = 1.3, silent = TRUE) {
if (exponentialDecayBase <= 1)
stop("exponentialDecayBase must be greater than 1.0")
for (i in seq_len(retries)) {
if (!(is.call(expr) || is.name(expr))) warning("expr is not a quoted expression")
result <- try(expr = eval(expr, envir = envir), silent = silent)
if (inherits(result, "try-error")) {
backoff <- runif(n = 1, min = 0, max = exponentialDecayBase^i - 1)
if (backoff > 3) {
message("Waiting for ", round(backoff, 1), " seconds to retry; the attempt is failing")
}
Sys.sleep(backoff)
} else {
break
}
}
if (inherits(result, "try-error")) {
stop(result, "\nFailed after ", retries, " attempts.")
} else {
return(result)
}
}
#' Test whether system is Windows
#'
#' This is used so that unit tests can override this using \code{testthat::with_mock}.
#' @keywords internal
isWindows <- function() identical(.Platform$OS.type, "windows")
#' Provide standard messaging for missing package dependencies
#'
#' This provides a standard message format for missing packages, e.g.,
#' detected via \code{requireNamespace}.
#'
#' @export
#' @param pkg Character string indicating name of package required
#' @param minVersion Character string indicating minimum version of package
#' that is needed
#' @param messageStart A character string with a prefix of message to provide
.requireNamespace <- function(pkg = "methods", minVersion = NULL,
messageStart = paste0(pkg, if (!is.null(minVersion)) paste0("(>=", minVersion, ")"), " is required. Try: ")) {
need <- FALSE
if (suppressWarnings(!requireNamespace(pkg, quietly = TRUE, warn.conflicts = FALSE))) {
need <- TRUE
} else {
if (isTRUE(packageVersion(pkg) < minVersion))
need <- TRUE
}
if (need) {
message(messageStart,
"install.packages('",pkg,"')")
}
!need
}
|
testlist <- list(a = 1684825385L, b = 676545880L, x = NA_integer_)
result <- do.call(grattan:::anyOutside,testlist)
str(result)
|
/grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610388256-test.R
|
no_license
|
akhikolla/updated-only-Issues
|
R
| false
| false
| 127
|
r
|
testlist <- list(a = 1684825385L, b = 676545880L, x = NA_integer_)
result <- do.call(grattan:::anyOutside,testlist)
str(result)
|
#
# test-subset_dfdates.R
# ----------------------
#
# Test suite for testing function subset_dfdates
#
#
context("Testing function: subset_dfdates()")
library(testthat)
test_that("Testing basic functionality", {
library(testthat)
library(mlStocks)
df = Earnings[, c(1,2,13)]
timeframe = "2011-01-03/2011-01-21"
datecol = 2
keep_NAs = FALSE
#---------------------------------------------------
# Test to include both start and end boundaries
#---------------------------------------------------
bounds_rm = c(FALSE, FALSE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-03")) # include first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-21")) # include last date
#---------------------------------------------------
# Test to include start but not end boundary
#---------------------------------------------------
bounds_rm <- c(FALSE, TRUE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-03")) # include first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-20")) # EXclude last date
#---------------------------------------------------
# Test to include start but exclude end boundary
#---------------------------------------------------
bounds_rm <- c(TRUE, FALSE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-04")) # EXclude first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-21")) # include last date
#---------------------------------------------------
# Test to include start but exclude end boundary
#---------------------------------------------------
bounds_rm <- c(TRUE, TRUE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-04")) # EXclude first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-20")) # EXclude last date
})
|
/tests/testthat/test-subset_dfdates.R
|
no_license
|
jeanmarcgp/mlStocks
|
R
| false
| false
| 2,347
|
r
|
#
# test-subset_dfdates.R
# ----------------------
#
# Test suite for testing function subset_dfdates
#
#
context("Testing function: subset_dfdates()")
library(testthat)
test_that("Testing basic functionality", {
library(testthat)
library(mlStocks)
df = Earnings[, c(1,2,13)]
timeframe = "2011-01-03/2011-01-21"
datecol = 2
keep_NAs = FALSE
#---------------------------------------------------
# Test to include both start and end boundaries
#---------------------------------------------------
bounds_rm = c(FALSE, FALSE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-03")) # include first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-21")) # include last date
#---------------------------------------------------
# Test to include start but not end boundary
#---------------------------------------------------
bounds_rm <- c(FALSE, TRUE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-03")) # include first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-20")) # EXclude last date
#---------------------------------------------------
# Test to include start but exclude end boundary
#---------------------------------------------------
bounds_rm <- c(TRUE, FALSE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-04")) # EXclude first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-21")) # include last date
#---------------------------------------------------
# Test to include start but exclude end boundary
#---------------------------------------------------
bounds_rm <- c(TRUE, TRUE)
dftest <- subset_dfdates(df, timeframe = timeframe, datecol = datecol, keep_NAs = keep_NAs, bounds_rm = bounds_rm)
N <- nrow(dftest)
expect_equal(dftest[1, "dtBuy"], as.Date("2011-01-04")) # EXclude first date
expect_equal(dftest[N, "dtBuy"], as.Date("2011-01-20")) # EXclude last date
})
|
library("neuRosim")
# Valeurs constantes
dim <- c(53,63,46)
nscan <- 421
TR <- 2
total.time <- nscan * TR
onsets.F <- c((10:30),(100:120),(160:180),(190:210),(250:270),(280:300),(400:420))*TR #
onsets.H <- c((40:60),(70:90),(130:150),(220:240),(310:330),(340:360),(370:390))*TR #
lis<-(1:nscan)
event<-lis
event[lis]<-'Rest'
event[(onsets.F/TR)+1]<-'Face'
event[(onsets.H/TR)+1]<-'House'
write(event,'D:/sim/event_sim.csv')
region.1a.radius <- 3
region.1b.radius <- 3
region.4a.radius <- 3
region.2a.radius <-5
region.2b.radius <- 5
region.3a.radius <- 4
region.3b.radius <- 4
region.4b.radius <- 3
onsets <- list(onsets.H, onsets.F)
onsets.regions <- list(onsets, onsets, onsets, onsets, onsets,onsets, onsets, onsets)
dur <- list(0, 0)
dur.regions <- list(dur, dur, dur, dur, dur, dur, dur, dur)
# Valeurs qui varient selon sujet
library("oro.nifti")
mask <- readNIfTI("D:/mask.nii.gz")
Haxby <- readNIfTI("D:/restbaseline.nii.gz")
baseline <- apply(Haxby@.Data, 1:3, mean)
r1a.center<- c(12,21,9)#110
r1a.d<- c(239, 258)
r1b.center<- c(39,21,9)#697
r1b.d<- c(229,247)
r2a.center<- c(23,7,16)#93
r2a.d<- c(225,225)
r2b.center<- c(30,7,16)#46
r2b.d<- c(252,252)
r3a.center<- c(31,12,13)#200
r3a.d<- c(309,291)
r3b.center<- c(21,13,13)#716
r3b.d<- c(333,323)
r4a.center<- c(35,21,13)#860
r4a.d<- c(335,314)
r4b.center<- c(17,22,12)#680
r4b.d<- c(308,288)
snr.or <- 4.3
for(s in 2:5) {
# Assign value to each subject
region.1a.center <- round(r1a.center*runif(3,min=0.9, max=1.1),0)
region.1b.center <- round(r1b.center*runif(3,min=0.9, max=1.1),0)
region.2a.center <- round(r2a.center*runif(3,min=0.99, max=1.01),0)
region.2b.center <- round(r2b.center*runif(3,min=0.99, max=1.01),0)
region.3a.center <- round(r3a.center*runif(3,min=0.9, max=1.1),0)
region.3b.center <- round(r3b.center*runif(3,min=0.9, max=1.1),0)
region.4a.center <- round(r4a.center*runif(3,min=0.9, max=1.1),0)
region.4b.center <- round(r4b.center*runif(3,min=0.9, max=1.1),0)
region.1a.d <- list(r1a.d[1]*runif(1,min=0.95, max=1.05),r1a.d[2]*runif(1,min=0.95, max=1.05))
region.1b.d <- list(r1b.d[1]*runif(1,min=0.95, max=1.05),r1b.d[2]*runif(1,min=0.95, max=1.05))
region.2a.d <- list(r2a.d[1]*runif(1,min=0.95, max=1.05),r2a.d[2]*runif(1,min=0.95, max=1.05))
region.2b.d <- list(r2b.d[1]*runif(1,min=0.95, max=1.05),r2b.d[2]*runif(1,min=0.95, max=1.05))
region.3a.d <- list(r3a.d[1]*runif(1,min=0.95, max=1.05),r3a.d[2]*runif(1,min=0.95, max=1.05))
region.3b.d <- list(r3b.d[1]*runif(1,min=0.95, max=1.05),r3b.d[2]*runif(1,min=0.95, max=1.05))
region.4a.d<- list(r4a.d[1]*runif(1,min=0.95, max=1.05),r4a.d[2]*runif(1,min=0.95, max=1.05))
region.4b.d<- list(r4b.d[1]*runif(1,min=0.95, max=1.05),r4b.d[2]*runif(1,min=0.95, max=1.05))
effect <- list(region.1a.d, region.1b.d, region.2a.d, region.2b.d,region.3a.d,region.3b.d,region.4a.d,region.4b.d)
snr <- snr.or*runif(1,min=0.9, max=1.1)
design <- simprepTemporal(regions = 8, onsets = onsets.regions,
durations = dur.regions, hrf = "Balloon", TR = TR,
totaltime = total.time, effectsize = effect)
spatial <- simprepSpatial(regions = 8, coord = list(region.1a.center,
region.1b.center, region.2a.center, region.2b.center, region.3a.center, region.3b.center,region.4a.center, region.4b.center),
radius = c(region.1a.radius, region.1b.radius, region.2a.radius,
region.2b.radius, region.3a.radius, region.3b.radius, region.4a.radius, region.4b.radius), form = "sphere", fading = 0.01)
name_val = paste('D:/sim/sim',s,'val.txt', sep='_')
write.table(rbind(region.1a.center,region.1b.center,region.2a.center,
region.2b.center,region.3a.center,region.3b.center,region.4a.center,
region.4b.center,region.1a.d,region.1b.d,region.2a.d ,
region.2b.d,region.3a.d,region.3b.d,region.4a.d,region.4b.d,snr),file=name_val)
for(n in 1:50) {
sim.data <- simVOLfmri(design = design, image = spatial,
SNR = snr, noise = "mixture"
, type = "rician", rho.temp = c(0.142,
0.108, 0.084), base= baseline, rho.spat = 0.4, w = c(0.05, 0.1, 0.01,
0.09, 0.05, 0.7), dim = dim, nscan = nscan, vee = 0,template=mask,
spat = "gaussRF")
sim.nifti <- nifti(img = sim.data, dim=c(53,63,46,nscan),pixdim = c(-1,3,3,3,2,0,0,0),
xyzt_units=10)
name = paste('D:/sim/sim',s,n, sep='_')
writeNIfTI(sim.nifti,name)
}
}
|
/SimulationR/neuRosim_loop_haxby.R
|
no_license
|
brain-bzh/gsp-learn
|
R
| false
| false
| 4,268
|
r
|
library("neuRosim")
# Valeurs constantes
dim <- c(53,63,46)
nscan <- 421
TR <- 2
total.time <- nscan * TR
onsets.F <- c((10:30),(100:120),(160:180),(190:210),(250:270),(280:300),(400:420))*TR #
onsets.H <- c((40:60),(70:90),(130:150),(220:240),(310:330),(340:360),(370:390))*TR #
lis<-(1:nscan)
event<-lis
event[lis]<-'Rest'
event[(onsets.F/TR)+1]<-'Face'
event[(onsets.H/TR)+1]<-'House'
write(event,'D:/sim/event_sim.csv')
region.1a.radius <- 3
region.1b.radius <- 3
region.4a.radius <- 3
region.2a.radius <-5
region.2b.radius <- 5
region.3a.radius <- 4
region.3b.radius <- 4
region.4b.radius <- 3
onsets <- list(onsets.H, onsets.F)
onsets.regions <- list(onsets, onsets, onsets, onsets, onsets,onsets, onsets, onsets)
dur <- list(0, 0)
dur.regions <- list(dur, dur, dur, dur, dur, dur, dur, dur)
# Valeurs qui varient selon sujet
library("oro.nifti")
mask <- readNIfTI("D:/mask.nii.gz")
Haxby <- readNIfTI("D:/restbaseline.nii.gz")
baseline <- apply(Haxby@.Data, 1:3, mean)
r1a.center<- c(12,21,9)#110
r1a.d<- c(239, 258)
r1b.center<- c(39,21,9)#697
r1b.d<- c(229,247)
r2a.center<- c(23,7,16)#93
r2a.d<- c(225,225)
r2b.center<- c(30,7,16)#46
r2b.d<- c(252,252)
r3a.center<- c(31,12,13)#200
r3a.d<- c(309,291)
r3b.center<- c(21,13,13)#716
r3b.d<- c(333,323)
r4a.center<- c(35,21,13)#860
r4a.d<- c(335,314)
r4b.center<- c(17,22,12)#680
r4b.d<- c(308,288)
snr.or <- 4.3
for(s in 2:5) {
# Assign value to each subject
region.1a.center <- round(r1a.center*runif(3,min=0.9, max=1.1),0)
region.1b.center <- round(r1b.center*runif(3,min=0.9, max=1.1),0)
region.2a.center <- round(r2a.center*runif(3,min=0.99, max=1.01),0)
region.2b.center <- round(r2b.center*runif(3,min=0.99, max=1.01),0)
region.3a.center <- round(r3a.center*runif(3,min=0.9, max=1.1),0)
region.3b.center <- round(r3b.center*runif(3,min=0.9, max=1.1),0)
region.4a.center <- round(r4a.center*runif(3,min=0.9, max=1.1),0)
region.4b.center <- round(r4b.center*runif(3,min=0.9, max=1.1),0)
region.1a.d <- list(r1a.d[1]*runif(1,min=0.95, max=1.05),r1a.d[2]*runif(1,min=0.95, max=1.05))
region.1b.d <- list(r1b.d[1]*runif(1,min=0.95, max=1.05),r1b.d[2]*runif(1,min=0.95, max=1.05))
region.2a.d <- list(r2a.d[1]*runif(1,min=0.95, max=1.05),r2a.d[2]*runif(1,min=0.95, max=1.05))
region.2b.d <- list(r2b.d[1]*runif(1,min=0.95, max=1.05),r2b.d[2]*runif(1,min=0.95, max=1.05))
region.3a.d <- list(r3a.d[1]*runif(1,min=0.95, max=1.05),r3a.d[2]*runif(1,min=0.95, max=1.05))
region.3b.d <- list(r3b.d[1]*runif(1,min=0.95, max=1.05),r3b.d[2]*runif(1,min=0.95, max=1.05))
region.4a.d<- list(r4a.d[1]*runif(1,min=0.95, max=1.05),r4a.d[2]*runif(1,min=0.95, max=1.05))
region.4b.d<- list(r4b.d[1]*runif(1,min=0.95, max=1.05),r4b.d[2]*runif(1,min=0.95, max=1.05))
effect <- list(region.1a.d, region.1b.d, region.2a.d, region.2b.d,region.3a.d,region.3b.d,region.4a.d,region.4b.d)
snr <- snr.or*runif(1,min=0.9, max=1.1)
design <- simprepTemporal(regions = 8, onsets = onsets.regions,
durations = dur.regions, hrf = "Balloon", TR = TR,
totaltime = total.time, effectsize = effect)
spatial <- simprepSpatial(regions = 8, coord = list(region.1a.center,
region.1b.center, region.2a.center, region.2b.center, region.3a.center, region.3b.center,region.4a.center, region.4b.center),
radius = c(region.1a.radius, region.1b.radius, region.2a.radius,
region.2b.radius, region.3a.radius, region.3b.radius, region.4a.radius, region.4b.radius), form = "sphere", fading = 0.01)
name_val = paste('D:/sim/sim',s,'val.txt', sep='_')
write.table(rbind(region.1a.center,region.1b.center,region.2a.center,
region.2b.center,region.3a.center,region.3b.center,region.4a.center,
region.4b.center,region.1a.d,region.1b.d,region.2a.d ,
region.2b.d,region.3a.d,region.3b.d,region.4a.d,region.4b.d,snr),file=name_val)
for(n in 1:50) {
sim.data <- simVOLfmri(design = design, image = spatial,
SNR = snr, noise = "mixture"
, type = "rician", rho.temp = c(0.142,
0.108, 0.084), base= baseline, rho.spat = 0.4, w = c(0.05, 0.1, 0.01,
0.09, 0.05, 0.7), dim = dim, nscan = nscan, vee = 0,template=mask,
spat = "gaussRF")
sim.nifti <- nifti(img = sim.data, dim=c(53,63,46,nscan),pixdim = c(-1,3,3,3,2,0,0,0),
xyzt_units=10)
name = paste('D:/sim/sim',s,n, sep='_')
writeNIfTI(sim.nifti,name)
}
}
|
#' @title Print \code{'gainstable'} Object
#'
#' @description S3 print method to print \code{"gainstable"} object.
#'
#'
#' @param x An object of class \code{"gainstable"}, created with either
#' \code{\link{gainstable.default}} or \code{\link{gainstable.rocit}}.
#'
#' @param maxdigit How many digits after decimal to be printed.
#'
#' @param ... \code{NULL}. Used for S3 generic/method consistency.
#'
#' @examples
#' data("Loan")
#' class <- Loan$Status
#' score <- Loan$Score
#' rocit_emp <- rocit(score = score, class = class, negref = "FP")
#' # ----------------------------------------------------------------
#' gtable8 <- gainstable(rocit_emp, ngroup = 8)
#' print(gtable8)
#' print(gtable8, maxdigit = 4)
#' @method print gainstable
#' @export
print.gainstable <- function(x, maxdigit = 3, ... = NULL) {
df <- as.data.frame(
cbind(
Bucket = x$Bucket,
Obs = x$Obs,
CObs = x$CObs,
Depth = x$Depth,
Resp = x$Resp,
CResp = x$CResp,
RespRate = x$RespRate,
CRespRate = x$CRespRate,
CCapRate = x$CCapRate,
Lift = x$Lift,
CLift = x$CLift
)
)
ncol <- ncol(df)
tempindex <- NULL -> rounddigits
for (i in 1:ncol) {
tempindex <- df[, i] %% 1
rounddigits[i] <- ifelse((max(nchar(tempindex)) > maxdigit), T, F)
}
longcols <- which(rounddigits)
for (i in longcols) {
df[, i] <- round(df[, i], maxdigit)
}
print(df)
}
|
/R/printGainsTable.R
|
no_license
|
cran/ROCit
|
R
| false
| false
| 1,479
|
r
|
#' @title Print \code{'gainstable'} Object
#'
#' @description S3 print method to print \code{"gainstable"} object.
#'
#'
#' @param x An object of class \code{"gainstable"}, created with either
#' \code{\link{gainstable.default}} or \code{\link{gainstable.rocit}}.
#'
#' @param maxdigit How many digits after decimal to be printed.
#'
#' @param ... \code{NULL}. Used for S3 generic/method consistency.
#'
#' @examples
#' data("Loan")
#' class <- Loan$Status
#' score <- Loan$Score
#' rocit_emp <- rocit(score = score, class = class, negref = "FP")
#' # ----------------------------------------------------------------
#' gtable8 <- gainstable(rocit_emp, ngroup = 8)
#' print(gtable8)
#' print(gtable8, maxdigit = 4)
#' @method print gainstable
#' @export
print.gainstable <- function(x, maxdigit = 3, ... = NULL) {
df <- as.data.frame(
cbind(
Bucket = x$Bucket,
Obs = x$Obs,
CObs = x$CObs,
Depth = x$Depth,
Resp = x$Resp,
CResp = x$CResp,
RespRate = x$RespRate,
CRespRate = x$CRespRate,
CCapRate = x$CCapRate,
Lift = x$Lift,
CLift = x$CLift
)
)
ncol <- ncol(df)
tempindex <- NULL -> rounddigits
for (i in 1:ncol) {
tempindex <- df[, i] %% 1
rounddigits[i] <- ifelse((max(nchar(tempindex)) > maxdigit), T, F)
}
longcols <- which(rounddigits)
for (i in longcols) {
df[, i] <- round(df[, i], maxdigit)
}
print(df)
}
|
%%
%% WARNING! DO NOT EDIT!
%% This file is automatically generated from pc-gammacount.R
%%
\name{pc.gammacount}
\alias{inla.pc.gammacount}
\alias{pc.gammacount}
\alias{pc.rgammacount}
\alias{inla.pc.rgammacount}
\alias{pc.dgammacount}
\alias{inla.pc.dgammacount}
\alias{pc.pgammacount}
\alias{inla.pc.pgammacount}
\alias{pc.qgammacount}
\alias{inla.pc.qgammacount}
\title{Utility functions for the PC prior for the \code{gammacount} likelihood}
\description{Functions to evaluate, sample, compute quantiles and
percentiles of the PC prior for the \code{gammacount} likelihood}
\usage{
inla.pc.rgammacount(n, lambda = 1)
inla.pc.dgammacount(x, lambda = 1, log = FALSE)
inla.pc.qgammacount(p, lambda = 1)
inla.pc.pgammacount(q, lambda = 1)
}
\arguments{
\item{n}{Number of observations}
\item{lambda}{The rate parameter (see Details)}
\item{x}{Evaluation points}
\item{log}{Logical. Return the density in natural or log-scale.}
\item{p}{Vector of probabilities}
\item{q}{Vector of quantiles}
}
\details{
This gives the PC prior for the \code{gammacount} likelihood, which is the PC prior for
\code{a} in \code{Gamma(a, 1)} where \code{Gamma(1, 1)} is the base model.
}
\value{%%
\code{inla.pc.dgammacount} gives the density,
\code{inla.pc.pgammacount} gives the distribution function,
\code{inla.pc.qgammacount} gives the quantile function, and
\code{inla.pc.rgammacount} generates random deviates.
}
\seealso{inla.doc("pc.gammacount")}
\author{Havard Rue \email{hrue@r-inla.org}}
\examples{
x = inla.pc.rgammacount(100, lambda = 1)
d = inla.pc.dgammacount(x, lambda = 1)
x = inla.pc.qgammacount(0.5, lambda = 1)
inla.pc.pgammacount(x, lambda = 1)
}
|
/man/pc-gammacount.Rd
|
no_license
|
jdsimkin04/shinyinla
|
R
| false
| false
| 1,727
|
rd
|
%%
%% WARNING! DO NOT EDIT!
%% This file is automatically generated from pc-gammacount.R
%%
\name{pc.gammacount}
\alias{inla.pc.gammacount}
\alias{pc.gammacount}
\alias{pc.rgammacount}
\alias{inla.pc.rgammacount}
\alias{pc.dgammacount}
\alias{inla.pc.dgammacount}
\alias{pc.pgammacount}
\alias{inla.pc.pgammacount}
\alias{pc.qgammacount}
\alias{inla.pc.qgammacount}
\title{Utility functions for the PC prior for the \code{gammacount} likelihood}
\description{Functions to evaluate, sample, compute quantiles and
percentiles of the PC prior for the \code{gammacount} likelihood}
\usage{
inla.pc.rgammacount(n, lambda = 1)
inla.pc.dgammacount(x, lambda = 1, log = FALSE)
inla.pc.qgammacount(p, lambda = 1)
inla.pc.pgammacount(q, lambda = 1)
}
\arguments{
\item{n}{Number of observations}
\item{lambda}{The rate parameter (see Details)}
\item{x}{Evaluation points}
\item{log}{Logical. Return the density in natural or log-scale.}
\item{p}{Vector of probabilities}
\item{q}{Vector of quantiles}
}
\details{
This gives the PC prior for the \code{gammacount} likelihood, which is the PC prior for
\code{a} in \code{Gamma(a, 1)} where \code{Gamma(1, 1)} is the base model.
}
\value{%%
\code{inla.pc.dgammacount} gives the density,
\code{inla.pc.pgammacount} gives the distribution function,
\code{inla.pc.qgammacount} gives the quantile function, and
\code{inla.pc.rgammacount} generates random deviates.
}
\seealso{inla.doc("pc.gammacount")}
\author{Havard Rue \email{hrue@r-inla.org}}
\examples{
x = inla.pc.rgammacount(100, lambda = 1)
d = inla.pc.dgammacount(x, lambda = 1)
x = inla.pc.qgammacount(0.5, lambda = 1)
inla.pc.pgammacount(x, lambda = 1)
}
|
conc.fun <-function(sample_data, standard_coeffs, chamber_volumes, directory, gas, print=FALSE){
#Note: edited on 8/28/15 to include a fixed standard coefficient slope and intercept
collar_surface_area <- 0.064 #meters
std_curve_nums <- unique(standard_coeffs$Std_Curve_Num)
for(j in 1:nrow(sample_data)) {
sample_data$Concentration[j] <- sapply(sample_data$Area[j], function(x) x*standard_coeffs$Slope + standard_coeffs$Intercept)
if (is.na(sample_data$Concentration[j])){
sample_data$Concentration[j]=NA
}else if(sample_data$Concentration[j]<0){
sample_data$Concentration[j]=0
}
}
sample_data$Time <- as.numeric(sample_data$Time)
sample_data$Time_min <- sapply(sample_data$Time, function(x) x/60)
conc_data <- sample_data
}
|
/conc.fun.R
|
no_license
|
SoilWaterLab/Septic-GHGs
|
R
| false
| false
| 755
|
r
|
conc.fun <-function(sample_data, standard_coeffs, chamber_volumes, directory, gas, print=FALSE){
#Note: edited on 8/28/15 to include a fixed standard coefficient slope and intercept
collar_surface_area <- 0.064 #meters
std_curve_nums <- unique(standard_coeffs$Std_Curve_Num)
for(j in 1:nrow(sample_data)) {
sample_data$Concentration[j] <- sapply(sample_data$Area[j], function(x) x*standard_coeffs$Slope + standard_coeffs$Intercept)
if (is.na(sample_data$Concentration[j])){
sample_data$Concentration[j]=NA
}else if(sample_data$Concentration[j]<0){
sample_data$Concentration[j]=0
}
}
sample_data$Time <- as.numeric(sample_data$Time)
sample_data$Time_min <- sapply(sample_data$Time, function(x) x/60)
conc_data <- sample_data
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visNetworkEditor.R
\name{visNetworkEditor-module}
\alias{visNetworkEditor-module}
\alias{visNetworkEditorServer}
\alias{visNetworkEditorUI}
\title{Module shiny for visualize and customize and get back a \code{visNetwork} object.
Using the javascript interface \link{visConfigure}.}
\usage{
visNetworkEditorServer(input, output, session, object,
filter = shiny::reactive(NULL), showButton = shiny::reactive(NULL))
visNetworkEditorUI(id, quitButton = FALSE, height = "700px")
}
\arguments{
\item{input}{\code{list} shiny input}
\item{output}{\code{list}, shiny output}
\item{session}{\code{list}, shiny session}
\item{object}{a \code{visNetwork} object. Must be a reactive.}
\item{filter}{: see \link{visConfigure}. Must be a reactive.}
\item{showButton}{: see \link{visConfigure}. Must be a reactive.}
\item{id}{\code{character} id of module, linked to \link{visNetworkEditorUI}}
\item{quitButton}{: logical. Add a button for quit shiny and get back network in R ?}
\item{height}{: height of the configuration div. Defaut to "700px"}
}
\description{
Module shiny for visualize and customize and get back a \code{visNetwork} object.
Using the javascript interface \link{visConfigure}.
}
\examples{
\dontrun{
nodes <- data.frame(id = 1:3, label = paste("Node", 1:3))
edges <- data.frame(from = c(1,2), to = c(1,3), label = paste("Edge", 1:2))
network <- visNetwork(nodes, edges)
shiny::shinyApp(ui = shiny::fluidPage(
visNetworkEditorUI(id = "id1")),
server = function(input, output, session) {
shiny::callModule(visNetworkEditorServer, "id1", object = shiny::reactive(network))
})
}
}
\references{
See online documentation \url{http://datastorm-open.github.io/visNetwork/}
}
\seealso{
\link{visConfigure}, \link{visTree}, \link{visNetworkEditor}
}
|
/man/visNetworkEditor-module.Rd
|
no_license
|
42-Discworld/visNetwork
|
R
| false
| true
| 1,901
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visNetworkEditor.R
\name{visNetworkEditor-module}
\alias{visNetworkEditor-module}
\alias{visNetworkEditorServer}
\alias{visNetworkEditorUI}
\title{Module shiny for visualize and customize and get back a \code{visNetwork} object.
Using the javascript interface \link{visConfigure}.}
\usage{
visNetworkEditorServer(input, output, session, object,
filter = shiny::reactive(NULL), showButton = shiny::reactive(NULL))
visNetworkEditorUI(id, quitButton = FALSE, height = "700px")
}
\arguments{
\item{input}{\code{list} shiny input}
\item{output}{\code{list}, shiny output}
\item{session}{\code{list}, shiny session}
\item{object}{a \code{visNetwork} object. Must be a reactive.}
\item{filter}{: see \link{visConfigure}. Must be a reactive.}
\item{showButton}{: see \link{visConfigure}. Must be a reactive.}
\item{id}{\code{character} id of module, linked to \link{visNetworkEditorUI}}
\item{quitButton}{: logical. Add a button for quit shiny and get back network in R ?}
\item{height}{: height of the configuration div. Defaut to "700px"}
}
\description{
Module shiny for visualize and customize and get back a \code{visNetwork} object.
Using the javascript interface \link{visConfigure}.
}
\examples{
\dontrun{
nodes <- data.frame(id = 1:3, label = paste("Node", 1:3))
edges <- data.frame(from = c(1,2), to = c(1,3), label = paste("Edge", 1:2))
network <- visNetwork(nodes, edges)
shiny::shinyApp(ui = shiny::fluidPage(
visNetworkEditorUI(id = "id1")),
server = function(input, output, session) {
shiny::callModule(visNetworkEditorServer, "id1", object = shiny::reactive(network))
})
}
}
\references{
See online documentation \url{http://datastorm-open.github.io/visNetwork/}
}
\seealso{
\link{visConfigure}, \link{visTree}, \link{visNetworkEditor}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/breakpoint.density.r
\docType{class}
\name{breaks-class}
\alias{breaks-class}
\alias{breaks}
\title{Class to store breakpoint annotations in association with genomic features (e.g. gene loci)}
\arguments{
\item{breaks}{(data.table): the breakpoint info containing data.table, this will be occupied by the CNV segmentation data in the case of cnv.break.annot or SV for sv.break.annot. Unique random string rownames are added to the returned breaks data.frame.}
\item{burden}{(numeric): a vector containing the total number of breakpoints in each sample}
\item{param}{(list): a list of parametres provided}
}
\value{
an instance of the class 'breaks' containing breakpoint and breakpoint burden information
}
\description{
Class to store breakpoint annotations in association with genomic features (e.g. gene loci)
}
|
/man/breaks-class.Rd
|
no_license
|
gonzolgarcia/svpluscnv
|
R
| false
| true
| 895
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/breakpoint.density.r
\docType{class}
\name{breaks-class}
\alias{breaks-class}
\alias{breaks}
\title{Class to store breakpoint annotations in association with genomic features (e.g. gene loci)}
\arguments{
\item{breaks}{(data.table): the breakpoint info containing data.table, this will be occupied by the CNV segmentation data in the case of cnv.break.annot or SV for sv.break.annot. Unique random string rownames are added to the returned breaks data.frame.}
\item{burden}{(numeric): a vector containing the total number of breakpoints in each sample}
\item{param}{(list): a list of parametres provided}
}
\value{
an instance of the class 'breaks' containing breakpoint and breakpoint burden information
}
\description{
Class to store breakpoint annotations in association with genomic features (e.g. gene loci)
}
|
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states0
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-1
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istate
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-1
5000
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NA
4
4
0
84
24
NA
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NA
NA
39
1
1
NA
NA
NA
NA
NA
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1
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6
rstate
14
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1.262517368508104e-100
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|
/Outputs/Modelrun_20110413_134157.R
|
no_license
|
Sandy4321/Tree-Death-Physiological-Models
|
R
| false
| false
| 2,896
|
r
|
RDA2
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NA
39
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|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{binMin}
\alias{binMin}
\title{Function that computes a minimum value for each bin}
\usage{
binMin(x, n)
}
\arguments{
\item{x}{NumericVector - vector of values of a bin}
\item{n}{intiger - number of bins}
}
\description{
Function that computes a minimum value for each bin
}
\keyword{internal}
|
/man/binMin.Rd
|
no_license
|
alexg9010/genomation
|
R
| false
| true
| 393
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{binMin}
\alias{binMin}
\title{Function that computes a minimum value for each bin}
\usage{
binMin(x, n)
}
\arguments{
\item{x}{NumericVector - vector of values of a bin}
\item{n}{intiger - number of bins}
}
\description{
Function that computes a minimum value for each bin
}
\keyword{internal}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/num-rpd.R
\name{rpd}
\alias{rpd}
\alias{rpd.data.frame}
\alias{rpd_vec}
\title{Ratio of performance to deviation}
\usage{
rpd(data, ...)
\method{rpd}{data.frame}(data, truth, estimate, na_rm = TRUE, ...)
rpd_vec(truth, estimate, na_rm = TRUE, ...)
}
\arguments{
\item{data}{A \code{data.frame} containing the \code{truth} and \code{estimate}
columns.}
\item{...}{Not currently used.}
\item{truth}{The column identifier for the true results
(that is \code{numeric}). This should be an unquoted column name although
this argument is passed by expression and supports
\link[rlang:quasiquotation]{quasiquotation} (you can unquote column
names). For \code{_vec()} functions, a \code{numeric} vector.}
\item{estimate}{The column identifier for the predicted
results (that is also \code{numeric}). As with \code{truth} this can be
specified different ways but the primary method is to use an
unquoted variable name. For \code{_vec()} functions, a \code{numeric} vector.}
\item{na_rm}{A \code{logical} value indicating whether \code{NA}
values should be stripped before the computation proceeds.}
}
\value{
A \code{tibble} with columns \code{.metric}, \code{.estimator},
and \code{.estimate} and 1 row of values.
For grouped data frames, the number of rows returned will be the same as
the number of groups.
For \code{rpd_vec()}, a single \code{numeric} value (or \code{NA}).
}
\description{
These functions are appropriate for cases where the model outcome is a
numeric. The ratio of performance to deviation
(\code{\link[=rpd]{rpd()}}) and the ratio of performance to inter-quartile (\code{\link[=rpiq]{rpiq()}})
are both measures of consistency/correlation between observed
and predicted values (and not of accuracy).
}
\details{
In the field of spectroscopy in particular, the ratio
of performance to deviation (RPD) has been used as the standard
way to report the quality of a model. It is the ratio between
the standard deviation of a variable and the standard error of
prediction of that variable by a given model. However, its
systematic use has been criticized by several authors, since
using the standard deviation to represent the spread of a
variable can be misleading on skewed dataset. The ratio of
performance to inter-quartile has been introduced by
Bellon-Maurel et al. (2010) to address some of these issues, and
generalise the RPD to non-normally distributed variables.
}
\examples{
# Supply truth and predictions as bare column names
rpd(solubility_test, solubility, prediction)
library(dplyr)
set.seed(1234)
size <- 100
times <- 10
# create 10 resamples
solubility_resampled <- bind_rows(
replicate(
n = times,
expr = sample_n(solubility_test, size, replace = TRUE),
simplify = FALSE
),
.id = "resample"
)
# Compute the metric by group
metric_results <- solubility_resampled \%>\%
group_by(resample) \%>\%
rpd(solubility, prediction)
metric_results
# Resampled mean estimate
metric_results \%>\%
summarise(avg_estimate = mean(.estimate))
}
\references{
Williams, P.C. (1987) Variables affecting near-infrared
reflectance spectroscopic analysis. In: Near Infrared Technology
in the Agriculture and Food Industries. 1st Ed. P.Williams and
K.Norris, Eds. Am. Cereal Assoc. Cereal Chem., St. Paul, MN.
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger,
J.M. and McBratney, A., (2010). Critical review of chemometric
indicators commonly used for assessing the quality of the
prediction of soil attributes by NIR spectroscopy. TrAC Trends
in Analytical Chemistry, 29(9), pp.1073-1081.
}
\seealso{
The closely related inter-quartile metric: \code{\link[=rpiq]{rpiq()}}
Other numeric metrics: \code{\link{ccc}},
\code{\link{huber_loss_pseudo}},
\code{\link{huber_loss}}, \code{\link{mae}},
\code{\link{mape}}, \code{\link{mase}},
\code{\link{rmse}}, \code{\link{rpiq}},
\code{\link{rsq_trad}}, \code{\link{rsq}},
\code{\link{smape}}
Other consistency metrics: \code{\link{ccc}},
\code{\link{rpiq}}, \code{\link{rsq_trad}},
\code{\link{rsq}}
}
\author{
Pierre Roudier
}
\concept{consistency metrics}
\concept{numeric metrics}
|
/man/rpd.Rd
|
no_license
|
jyuu/yardstick
|
R
| false
| true
| 4,176
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/num-rpd.R
\name{rpd}
\alias{rpd}
\alias{rpd.data.frame}
\alias{rpd_vec}
\title{Ratio of performance to deviation}
\usage{
rpd(data, ...)
\method{rpd}{data.frame}(data, truth, estimate, na_rm = TRUE, ...)
rpd_vec(truth, estimate, na_rm = TRUE, ...)
}
\arguments{
\item{data}{A \code{data.frame} containing the \code{truth} and \code{estimate}
columns.}
\item{...}{Not currently used.}
\item{truth}{The column identifier for the true results
(that is \code{numeric}). This should be an unquoted column name although
this argument is passed by expression and supports
\link[rlang:quasiquotation]{quasiquotation} (you can unquote column
names). For \code{_vec()} functions, a \code{numeric} vector.}
\item{estimate}{The column identifier for the predicted
results (that is also \code{numeric}). As with \code{truth} this can be
specified different ways but the primary method is to use an
unquoted variable name. For \code{_vec()} functions, a \code{numeric} vector.}
\item{na_rm}{A \code{logical} value indicating whether \code{NA}
values should be stripped before the computation proceeds.}
}
\value{
A \code{tibble} with columns \code{.metric}, \code{.estimator},
and \code{.estimate} and 1 row of values.
For grouped data frames, the number of rows returned will be the same as
the number of groups.
For \code{rpd_vec()}, a single \code{numeric} value (or \code{NA}).
}
\description{
These functions are appropriate for cases where the model outcome is a
numeric. The ratio of performance to deviation
(\code{\link[=rpd]{rpd()}}) and the ratio of performance to inter-quartile (\code{\link[=rpiq]{rpiq()}})
are both measures of consistency/correlation between observed
and predicted values (and not of accuracy).
}
\details{
In the field of spectroscopy in particular, the ratio
of performance to deviation (RPD) has been used as the standard
way to report the quality of a model. It is the ratio between
the standard deviation of a variable and the standard error of
prediction of that variable by a given model. However, its
systematic use has been criticized by several authors, since
using the standard deviation to represent the spread of a
variable can be misleading on skewed dataset. The ratio of
performance to inter-quartile has been introduced by
Bellon-Maurel et al. (2010) to address some of these issues, and
generalise the RPD to non-normally distributed variables.
}
\examples{
# Supply truth and predictions as bare column names
rpd(solubility_test, solubility, prediction)
library(dplyr)
set.seed(1234)
size <- 100
times <- 10
# create 10 resamples
solubility_resampled <- bind_rows(
replicate(
n = times,
expr = sample_n(solubility_test, size, replace = TRUE),
simplify = FALSE
),
.id = "resample"
)
# Compute the metric by group
metric_results <- solubility_resampled \%>\%
group_by(resample) \%>\%
rpd(solubility, prediction)
metric_results
# Resampled mean estimate
metric_results \%>\%
summarise(avg_estimate = mean(.estimate))
}
\references{
Williams, P.C. (1987) Variables affecting near-infrared
reflectance spectroscopic analysis. In: Near Infrared Technology
in the Agriculture and Food Industries. 1st Ed. P.Williams and
K.Norris, Eds. Am. Cereal Assoc. Cereal Chem., St. Paul, MN.
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger,
J.M. and McBratney, A., (2010). Critical review of chemometric
indicators commonly used for assessing the quality of the
prediction of soil attributes by NIR spectroscopy. TrAC Trends
in Analytical Chemistry, 29(9), pp.1073-1081.
}
\seealso{
The closely related inter-quartile metric: \code{\link[=rpiq]{rpiq()}}
Other numeric metrics: \code{\link{ccc}},
\code{\link{huber_loss_pseudo}},
\code{\link{huber_loss}}, \code{\link{mae}},
\code{\link{mape}}, \code{\link{mase}},
\code{\link{rmse}}, \code{\link{rpiq}},
\code{\link{rsq_trad}}, \code{\link{rsq}},
\code{\link{smape}}
Other consistency metrics: \code{\link{ccc}},
\code{\link{rpiq}}, \code{\link{rsq_trad}},
\code{\link{rsq}}
}
\author{
Pierre Roudier
}
\concept{consistency metrics}
\concept{numeric metrics}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_umap.R
\name{pal_ggplot}
\alias{pal_ggplot}
\title{UMAP Palette using ggplot2 colors}
\usage{
pal_ggplot(object, group_col, jitter = TRUE, comp = 3, alpha = 0.7)
}
\arguments{
\item{comp}{integer setting the color complementarity to be used}
}
\description{
UMAP Palette using ggplot2 colors
}
|
/man/pal_ggplot.Rd
|
no_license
|
dbrookeUAB/dbsinglecell
|
R
| false
| true
| 377
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_umap.R
\name{pal_ggplot}
\alias{pal_ggplot}
\title{UMAP Palette using ggplot2 colors}
\usage{
pal_ggplot(object, group_col, jitter = TRUE, comp = 3, alpha = 0.7)
}
\arguments{
\item{comp}{integer setting the color complementarity to be used}
}
\description{
UMAP Palette using ggplot2 colors
}
|
cols = 40
rows = 36
ncls.grid = 20
ncls.hour = 6
ncls.ia.h.g=-1
ncls.ia.h.l=-1
ncls.ia.h.w=-1
ncls.ia.g.l=-1
ncls.ia.g.w=-1
ncls.ia.l.w=-1
ncls.ia.h.g.l=-1
ncls.ia.h.g.w=-1
ncls.ia.h.l.w=-1
ncls.ia.g.l.w=-1
ncls.ia.h.g.l.w = -1
job.id = 3
job.group.id = 11
regression.formula = paste("cate_l1 ~", "ugrid.id.cid",
"+ hour.cid",
"+ last.cate_l1",
# "+ isweekend",
# "+ hour_grid.cid",
# "+ hour_last.cid",
# "+ hour_weekday.cid",
# "+ grid_last.cid",
# "+ grid_weekday.cid",
# "+ last_weekday.cid",
# "+ hour_grid_last.cid",
# "+ hour_grid_weekday.cid",
# "+ hour_last_weekday.cid",
# "+ grid_last_weekday.cid",
# "+ hour_grid_last_weekday.cid",
"- 1")
|
/Proj-Experiment/201411-predictability(CEUS)/experiments-3/JOB3/configs.R
|
no_license
|
almatrix/work-RProject
|
R
| false
| false
| 785
|
r
|
cols = 40
rows = 36
ncls.grid = 20
ncls.hour = 6
ncls.ia.h.g=-1
ncls.ia.h.l=-1
ncls.ia.h.w=-1
ncls.ia.g.l=-1
ncls.ia.g.w=-1
ncls.ia.l.w=-1
ncls.ia.h.g.l=-1
ncls.ia.h.g.w=-1
ncls.ia.h.l.w=-1
ncls.ia.g.l.w=-1
ncls.ia.h.g.l.w = -1
job.id = 3
job.group.id = 11
regression.formula = paste("cate_l1 ~", "ugrid.id.cid",
"+ hour.cid",
"+ last.cate_l1",
# "+ isweekend",
# "+ hour_grid.cid",
# "+ hour_last.cid",
# "+ hour_weekday.cid",
# "+ grid_last.cid",
# "+ grid_weekday.cid",
# "+ last_weekday.cid",
# "+ hour_grid_last.cid",
# "+ hour_grid_weekday.cid",
# "+ hour_last_weekday.cid",
# "+ grid_last_weekday.cid",
# "+ hour_grid_last_weekday.cid",
"- 1")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/move.R
\name{adjacted_move}
\alias{adjacted_move}
\title{Create adjacent movement matrix}
\usage{
adjacted_move(n_rows, n_cols, prob)
}
\arguments{
\item{n_rows}{number of rows in population matrix}
\item{n_cols}{number of columns in population matrix}
\item{prob}{probability of movement}
}
\description{
Create a transition matrix of movement to adjacent cells
}
|
/man/adjacted_move.Rd
|
no_license
|
AustralianAntarcticDivision/tagsim
|
R
| false
| true
| 445
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/move.R
\name{adjacted_move}
\alias{adjacted_move}
\title{Create adjacent movement matrix}
\usage{
adjacted_move(n_rows, n_cols, prob)
}
\arguments{
\item{n_rows}{number of rows in population matrix}
\item{n_cols}{number of columns in population matrix}
\item{prob}{probability of movement}
}
\description{
Create a transition matrix of movement to adjacent cells
}
|
# get current directory to script folder
script.directory <- dirname(sys.frame(1)$ofile)
download_dataset <- function() {
# download file
assignment_file <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
assignment_dest_file <- "assignment.zip"
# downloading file
print(paste("Downloading dataset from :", assignment_file))
download.file(assignment_file, destfile = assignment_dest_file, method = "curl")
# Unzip files
print("Extracting dataset files!")
unzip(assignment_dest_file, exdir = ".")
if (!file.exists("UCI HAR Dataset")) return (FALSE) else return (TRUE)
}
# install and load dependent packages
install_dependences <- function() {
requiredPackages = c('plyr','dplyr')
for(p in requiredPackages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}
}
set_working_folder <- function() {
if (getwd() != script.directory)
{
print(paste("Current directory NOT set correctly : ", getwd()))
setwd(script.directory)
if (getwd() != script.directory) answer <- FALSE else answer <- TRUE
print(paste("Setting current directory to correct", answer, ":", getwd()))
}
else
{
print(paste("Current directory set correctly : ", getwd()))
}
}
run_analysis <- function() {
# load dependent libraries
install_dependences()
# set working directory
set_working_folder()
# check if dataset folder exist
if (!file.exists("UCI HAR Dataset"))
{
ans <- readline(prompt="Dataset NOT exist, do you like to download it?")
if (ans != "y" && ans != "Y")
{
stop("Dataset NOT exist, exiting!!!")
}
# download and unzip dataset
if (download_dataset() == TRUE)
{
print("Dataset downloaded and extracted correctly")
}
else
{
stop("Problem occurred when downloading and extracting dataset!")
}
}
print("Dataset exist, starting to analyse it!")
# files path
activity_labels_file <- ".\\UCI HAR Dataset\\activity_labels.txt"
features_labels_file <- ".\\UCI HAR Dataset\\features.txt"
x_train_file <- ".\\UCI HAR Dataset\\train\\X_train.txt"
y_train_file <- ".\\UCI HAR Dataset\\train\\Y_train.txt"
sub_train_file <- ".\\UCI HAR Dataset\\train\\subject_train.txt"
train_files <- c(sub_train_file, x_train_file, y_train_file)
x_test_file <- ".\\UCI HAR Dataset\\test\\X_test.txt"
y_test_file <- ".\\UCI HAR Dataset\\test\\Y_test.txt"
sub_test_file <- ".\\UCI HAR Dataset\\test\\subject_test.txt"
test_files <- c(sub_test_file, x_test_file, y_test_file)
#constants
activity_label <- c("activity")
subject_label <- c("subject")
# load files
print("Loading dataset files!")
activity_labels <- read.table(activity_labels_file)
names(activity_labels) <- c("activity_index", "activity_label")
features_labels <- read.table(features_labels_file)
names(features_labels) <- c("feature_index", "feature_label")
data_train_x <- read.table(x_train_file, col.names = features_labels$feature_label, check.names = FALSE)
data_train_y <- read.table(y_train_file, col.names = activity_label)
data_train_subjects <- read.table(sub_train_file, col.names = subject_label)
data_test_x <- read.table(x_test_file, col.names = features_labels$feature_label, check.names = FALSE)
data_test_y <- read.table(y_test_file, col.names = activity_label)
data_test_subjects <- read.table(sub_test_file, col.names = subject_label)
# select only columns that have mean and std values
mean_features_idx <- grep("mean()", features_labels$feature_label)
std_features_idx <- grep("std()", features_labels$feature_label)
# extract only mean and std columns (sorted) from measurements
data_train_x <- data_train_x[,sort(c(mean_features_idx, std_features_idx))]
data_test_x <- data_test_x[,sort(c(mean_features_idx, std_features_idx))]
# merge train and test column
print("Merging data")
data_train <- cbind(data_train_subjects, data_train_y, data_train_x)
data_test <- cbind(data_test_subjects, data_test_y, data_test_x)
# join train and test data
data_total <- rbind(data_train, data_test)
# descriptive names of the activities
data_total$activity <- factor(data_total$activity, labels = activity_labels$activity_label)
data_total$subject <- as.factor(data_total$subject)
# delete unused data to clean memory
rm(list = c("data_train", "data_test", "data_train_x", "data_test_x", "data_train_y", "data_test_y", "data_train_subjects", "data_test_subjects"))
# calculate mean for each column grouped by activity and subject
data_new <- ddply(data_total, .(activity, subject), colwise(mean))
# save new dataset in files
print("Writing new dataset!")
write.table(data_new, file = "new_dataset.txt", row.names = FALSE)
if (file.exists("new_dataset.txt"))
{
print("Everything finished correctly!")
}
else
{
stop("Problem occurred when saving new dataset!")
}
}
|
/run_analysis.R
|
no_license
|
rfribeiro/GettingAndCleaningDataCourseProject
|
R
| false
| false
| 5,008
|
r
|
# get current directory to script folder
script.directory <- dirname(sys.frame(1)$ofile)
download_dataset <- function() {
# download file
assignment_file <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
assignment_dest_file <- "assignment.zip"
# downloading file
print(paste("Downloading dataset from :", assignment_file))
download.file(assignment_file, destfile = assignment_dest_file, method = "curl")
# Unzip files
print("Extracting dataset files!")
unzip(assignment_dest_file, exdir = ".")
if (!file.exists("UCI HAR Dataset")) return (FALSE) else return (TRUE)
}
# install and load dependent packages
install_dependences <- function() {
requiredPackages = c('plyr','dplyr')
for(p in requiredPackages){
if(!require(p,character.only = TRUE)) install.packages(p)
library(p,character.only = TRUE)
}
}
set_working_folder <- function() {
if (getwd() != script.directory)
{
print(paste("Current directory NOT set correctly : ", getwd()))
setwd(script.directory)
if (getwd() != script.directory) answer <- FALSE else answer <- TRUE
print(paste("Setting current directory to correct", answer, ":", getwd()))
}
else
{
print(paste("Current directory set correctly : ", getwd()))
}
}
run_analysis <- function() {
# load dependent libraries
install_dependences()
# set working directory
set_working_folder()
# check if dataset folder exist
if (!file.exists("UCI HAR Dataset"))
{
ans <- readline(prompt="Dataset NOT exist, do you like to download it?")
if (ans != "y" && ans != "Y")
{
stop("Dataset NOT exist, exiting!!!")
}
# download and unzip dataset
if (download_dataset() == TRUE)
{
print("Dataset downloaded and extracted correctly")
}
else
{
stop("Problem occurred when downloading and extracting dataset!")
}
}
print("Dataset exist, starting to analyse it!")
# files path
activity_labels_file <- ".\\UCI HAR Dataset\\activity_labels.txt"
features_labels_file <- ".\\UCI HAR Dataset\\features.txt"
x_train_file <- ".\\UCI HAR Dataset\\train\\X_train.txt"
y_train_file <- ".\\UCI HAR Dataset\\train\\Y_train.txt"
sub_train_file <- ".\\UCI HAR Dataset\\train\\subject_train.txt"
train_files <- c(sub_train_file, x_train_file, y_train_file)
x_test_file <- ".\\UCI HAR Dataset\\test\\X_test.txt"
y_test_file <- ".\\UCI HAR Dataset\\test\\Y_test.txt"
sub_test_file <- ".\\UCI HAR Dataset\\test\\subject_test.txt"
test_files <- c(sub_test_file, x_test_file, y_test_file)
#constants
activity_label <- c("activity")
subject_label <- c("subject")
# load files
print("Loading dataset files!")
activity_labels <- read.table(activity_labels_file)
names(activity_labels) <- c("activity_index", "activity_label")
features_labels <- read.table(features_labels_file)
names(features_labels) <- c("feature_index", "feature_label")
data_train_x <- read.table(x_train_file, col.names = features_labels$feature_label, check.names = FALSE)
data_train_y <- read.table(y_train_file, col.names = activity_label)
data_train_subjects <- read.table(sub_train_file, col.names = subject_label)
data_test_x <- read.table(x_test_file, col.names = features_labels$feature_label, check.names = FALSE)
data_test_y <- read.table(y_test_file, col.names = activity_label)
data_test_subjects <- read.table(sub_test_file, col.names = subject_label)
# select only columns that have mean and std values
mean_features_idx <- grep("mean()", features_labels$feature_label)
std_features_idx <- grep("std()", features_labels$feature_label)
# extract only mean and std columns (sorted) from measurements
data_train_x <- data_train_x[,sort(c(mean_features_idx, std_features_idx))]
data_test_x <- data_test_x[,sort(c(mean_features_idx, std_features_idx))]
# merge train and test column
print("Merging data")
data_train <- cbind(data_train_subjects, data_train_y, data_train_x)
data_test <- cbind(data_test_subjects, data_test_y, data_test_x)
# join train and test data
data_total <- rbind(data_train, data_test)
# descriptive names of the activities
data_total$activity <- factor(data_total$activity, labels = activity_labels$activity_label)
data_total$subject <- as.factor(data_total$subject)
# delete unused data to clean memory
rm(list = c("data_train", "data_test", "data_train_x", "data_test_x", "data_train_y", "data_test_y", "data_train_subjects", "data_test_subjects"))
# calculate mean for each column grouped by activity and subject
data_new <- ddply(data_total, .(activity, subject), colwise(mean))
# save new dataset in files
print("Writing new dataset!")
write.table(data_new, file = "new_dataset.txt", row.names = FALSE)
if (file.exists("new_dataset.txt"))
{
print("Everything finished correctly!")
}
else
{
stop("Problem occurred when saving new dataset!")
}
}
|
## this file allows for a cached retrieval of an inverted matrix.
## creates an object representing a matrix with an precomputed inverted matrix.
makeCacheMatrix <- function(m = matrix()) {
mi <- NULL
set <- function(y) {
m <<- y
mi <<- NULL
}
get <- function() m
setinverse <- function(inverse) mi <<- inverse
getinverse <- function() mi
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## computes the inverted matrix if it has not been cached yet. Returns the inverted matrix.
cacheSolve <- function(m, ...) {
inverse <- m$getinverse()
if(!is.null(inverse)) {
message("getting cached data")
return(inverse)
}
thematrix <- m$get()
inverse <- solve(thematrix, ...)
m$setinverse(inverse)
inverse
}
|
/cachematrix.R
|
no_license
|
netchkin/ProgrammingAssignment2
|
R
| false
| false
| 789
|
r
|
## this file allows for a cached retrieval of an inverted matrix.
## creates an object representing a matrix with an precomputed inverted matrix.
makeCacheMatrix <- function(m = matrix()) {
mi <- NULL
set <- function(y) {
m <<- y
mi <<- NULL
}
get <- function() m
setinverse <- function(inverse) mi <<- inverse
getinverse <- function() mi
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## computes the inverted matrix if it has not been cached yet. Returns the inverted matrix.
cacheSolve <- function(m, ...) {
inverse <- m$getinverse()
if(!is.null(inverse)) {
message("getting cached data")
return(inverse)
}
thematrix <- m$get()
inverse <- solve(thematrix, ...)
m$setinverse(inverse)
inverse
}
|
# Script para agrupar as curvas de carga com observações a cada 10 minutos, dentro de 1 dia (144 observações).
source("data_houses_10minutes.R") # Dados já normalizados
# Gráficos das 20 casas
gg1 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg2 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg3 <- ggplot() +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg4 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg5 <- ggplot() +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg6 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg7 <- ggplot() +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg8 <- ggplot() +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg9 <- ggplot() +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg10 <- ggplot() +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg11 <- ggplot() +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg12 <- ggplot() +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg13 <- ggplot() +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg14 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg15 <- ggplot() +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg16 <- ggplot() +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg17 <- ggplot() +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg18 <- ggplot() +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg19 <- ggplot() +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg20 <- ggplot() +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
grid.arrange(gg1, gg2, gg3, gg4, gg5, gg6, gg7, gg8, gg9, gg10,
gg11, gg12, gg13, gg14, gg15, gg16, gg17, gg18, gg19, gg20,
nrow = 4, ncol = 5)
# Unir os dados em um data frame
series <- dayHouse1
series <- cbind(series, dayHouse2$Aggregate, dayHouse3$Aggregate, dayHouse4$Aggregate, dayHouse5$Aggregate,
dayHouse6$Aggregate, dayHouse7$Aggregate, dayHouse8$Aggregate, dayHouse9$Aggregate,
dayHouse10$Aggregate, dayHouse11$Aggregate, dayHouse12$Aggregate, dayHouse13$Aggregate,
dayHouse15$Aggregate, dayHouse16$Aggregate, dayHouse17$Aggregate, dayHouse18$Aggregate,
dayHouse19$Aggregate, dayHouse20$Aggregate, dayHouse21$Aggregate)
series$Time <- NULL
# Fixando a quantidade de clusters
qtdCluster <- 5
# Agrupando os perfis de consumo
source("functions_cluster_profile.R")
# ---------- #
# Correlação #
# ---------- #
simCorrelacao <- as.data.frame(correlacao_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simCorrelacao <- data.frame(user1=simCorrelacao$V1, user2=simCorrelacao$V2, user3=simCorrelacao$V3, user4=simCorrelacao$V4,
user5=simCorrelacao$V5, user6=simCorrelacao$V6, user7=simCorrelacao$V7, user8=simCorrelacao$V8,
user9=simCorrelacao$V9, user10=simCorrelacao$V10, user11=simCorrelacao$V11,
user12=simCorrelacao$V12, user13=simCorrelacao$V13, user14=simCorrelacao$V14,
user15=simCorrelacao$V15, user16=simCorrelacao$V16, user17=simCorrelacao$V17,
user18=simCorrelacao$V18, user19=simCorrelacao$V19, user20=simCorrelacao$V20)
for(i in 1:nrow(simCorrelacao)){ # linha
for(j in 1:ncol(simCorrelacao)){ # coluna
simCorrelacao[i,j] <- sqrt( (1-simCorrelacao[i,j])/2 )
}
}
# Agrupamento
#qtdCluster <- wssplot(simCorrelacao, nc = nrow(simCorrelacao)-1)
hierarchical(simCorrelacao, qtdCluster) # Dendrogram
k.res <- kmeans(simCorrelacao, centers = qtdCluster, nstart = 25)
simCorrelacao$cluster <- k.res$cluster
# Validação
Dist <- as.dist(simCorrelacao[,-(ncol(simCorrelacao))])
cluster.stats(Dist, simCorrelacao$cluster)$dunn # 0.4949157
cluster.stats(Dist, simCorrelacao$cluster)$avg.silwidth # 0.266
plot(silhouette(simCorrelacao$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.1103037
# Grupos
for(i in 1:qtdCluster){
print(which(simCorrelacao$cluster == i))
}
# Sequencia: (H = house)
# H1,H2,H3,H4,H5,H6,H7,H8,H9,H10,H11,H12,H13,H15,H16,H17,H18,H19,H20,H21
# 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
# 1
# 15,16,19
# 5
# 4,6,11,21
# 2,3,7,8,9,10,12,13,17,18,20
# Gráficos
gg1 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg2 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg3 <- ggplot() +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg4 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg5 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg1,gg2,gg3,gg4,gg5, nrow = 3, ncol = 2)
# ---------- #
# Euclidiana #
# ---------- #
simEuclid <- as.data.frame(distaciaEuclid_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simEuclid <- data.frame(user1=simEuclid$V1, user2=simEuclid$V2, user3=simEuclid$V3, user4=simEuclid$V4, user5=simEuclid$V5,
user6=simEuclid$V6, user7=simEuclid$V7, user8=simEuclid$V8, user9=simEuclid$V9, user10=simEuclid$V10,
user11=simEuclid$V11, user12=simEuclid$V12, user13=simEuclid$V13, user14=simEuclid$V14,
user15=simEuclid$V15, user16=simEuclid$V16, user17=simEuclid$V17, user18=simEuclid$V18,
user19=simEuclid$V19, user20=simEuclid$V20)
# Agrupamento
#qtdCluster <- wssplot(simEuclid, nc = nrow(simEuclid)-1)
hierarchical(simEuclid, qtdCluster) # Dendrogram
k.res <- kmeans(simEuclid, centers = qtdCluster, nstart = 25)
simEuclid$cluster <- k.res$cluster
# Validação
Dist <- dist(simEuclid[,-(ncol(simEuclid))])
cluster.stats(Dist, simEuclid$cluster)$dunn # 0.3589234
cluster.stats(Dist, simEuclid$cluster)$avg.silwidth # 0.2978361
plot(silhouette(simEuclid$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.09944951
# Grupos
for(i in 1:qtdCluster){
print(which(simEuclid$cluster == i))
}
# Sequencia: (H = house)
# H1,H2,H3,H4,H5,H6,H7,H8,H9,H10,H11,H12,H13,H15,H16,H17,H18,H19,H20,H21
# 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
# 4,5,8
# 2,3,11,12,18,20,21
# 1
# 6,7,9,10,13,17
# 15,16,19
# Gráficos
gg6 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg7 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg8 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg9 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg10 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg5,gg6,gg7,gg8,gg9,gg10, nrow = 3, ncol = 2)
# --- #
# DTW #
# --- #
simDtw <- as.data.frame(distanciaDTW_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simDtw <- data.frame(user1=simDtw$V1, user2=simDtw$V2, user3=simDtw$V3, user4=simDtw$V4, user5=simDtw$V5,
user6=simDtw$V6, user7=simDtw$V7, user8=simDtw$V8, user9=simDtw$V9, user10=simDtw$V10,
user11=simDtw$V11, user12=simDtw$V12, user13=simDtw$V13, user14=simDtw$V14, user15=simDtw$V15,
user16=simDtw$V16, user17=simDtw$V17, user18=simDtw$V18, user19=simDtw$V19, user20=simDtw$V20)
# Agrupamento
#qtdCluster <- wssplot(simDtw, nc = nrow(simDtw)-1)
hierarchical(simDtw, qtdCluster) # Dendrogram
k.res <- kmeans(simDtw, centers = qtdCluster, nstart = 25)
simDtw$cluster <- k.res$cluster
# Validação
Dist <- dist(simDtw[,-(ncol(simDtw))])
cluster.stats(Dist, simDtw$cluster)$dunn # 0.3128908
cluster.stats(Dist, simDtw$cluster)$avg.silwidth # 0.2319691
plot(silhouette(simDtw$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.1008105
# Grupos
for(i in 1:qtdCluster){
print(which(simDtw$cluster == i))
}
# 1,4,5
# 16
# 6,7,9,10,13,17,18,21
# 15,19
# 2,3,8,11,12,20
# Gráficos
gg11 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg12 <- ggplot() +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg13 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg14 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg15 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg11,gg12,gg13,gg14,gg15, nrow = 3, ncol = 2)
# Todos os agrupamentos
png(filename = "../Results/cluster_20users_mean_10min_1day.png", width = 1920, height = 1080)
grid.arrange(gg1,gg6,gg11, gg2,gg7,gg12, gg3,gg8,gg13, gg4,gg9,gg14, gg5,gg10,gg15, nrow = 5, ncol = 3)
dev.off()
|
/Consumption_Profile_REFIT/Scripts/results_1_day_10_min.R
|
no_license
|
HarllanAndrye/EnergyEfficiency-clustering-
|
R
| false
| false
| 17,388
|
r
|
# Script para agrupar as curvas de carga com observações a cada 10 minutos, dentro de 1 dia (144 observações).
source("data_houses_10minutes.R") # Dados já normalizados
# Gráficos das 20 casas
gg1 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg2 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg3 <- ggplot() +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg4 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg5 <- ggplot() +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg6 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg7 <- ggplot() +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg8 <- ggplot() +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg9 <- ggplot() +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg10 <- ggplot() +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg11 <- ggplot() +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg12 <- ggplot() +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg13 <- ggplot() +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg14 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg15 <- ggplot() +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg16 <- ggplot() +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg17 <- ggplot() +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg18 <- ggplot() +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg19 <- ggplot() +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
gg20 <- ggplot() +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Date') + ylab('Power') + ggtitle('1 day of January')
grid.arrange(gg1, gg2, gg3, gg4, gg5, gg6, gg7, gg8, gg9, gg10,
gg11, gg12, gg13, gg14, gg15, gg16, gg17, gg18, gg19, gg20,
nrow = 4, ncol = 5)
# Unir os dados em um data frame
series <- dayHouse1
series <- cbind(series, dayHouse2$Aggregate, dayHouse3$Aggregate, dayHouse4$Aggregate, dayHouse5$Aggregate,
dayHouse6$Aggregate, dayHouse7$Aggregate, dayHouse8$Aggregate, dayHouse9$Aggregate,
dayHouse10$Aggregate, dayHouse11$Aggregate, dayHouse12$Aggregate, dayHouse13$Aggregate,
dayHouse15$Aggregate, dayHouse16$Aggregate, dayHouse17$Aggregate, dayHouse18$Aggregate,
dayHouse19$Aggregate, dayHouse20$Aggregate, dayHouse21$Aggregate)
series$Time <- NULL
# Fixando a quantidade de clusters
qtdCluster <- 5
# Agrupando os perfis de consumo
source("functions_cluster_profile.R")
# ---------- #
# Correlação #
# ---------- #
simCorrelacao <- as.data.frame(correlacao_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simCorrelacao <- data.frame(user1=simCorrelacao$V1, user2=simCorrelacao$V2, user3=simCorrelacao$V3, user4=simCorrelacao$V4,
user5=simCorrelacao$V5, user6=simCorrelacao$V6, user7=simCorrelacao$V7, user8=simCorrelacao$V8,
user9=simCorrelacao$V9, user10=simCorrelacao$V10, user11=simCorrelacao$V11,
user12=simCorrelacao$V12, user13=simCorrelacao$V13, user14=simCorrelacao$V14,
user15=simCorrelacao$V15, user16=simCorrelacao$V16, user17=simCorrelacao$V17,
user18=simCorrelacao$V18, user19=simCorrelacao$V19, user20=simCorrelacao$V20)
for(i in 1:nrow(simCorrelacao)){ # linha
for(j in 1:ncol(simCorrelacao)){ # coluna
simCorrelacao[i,j] <- sqrt( (1-simCorrelacao[i,j])/2 )
}
}
# Agrupamento
#qtdCluster <- wssplot(simCorrelacao, nc = nrow(simCorrelacao)-1)
hierarchical(simCorrelacao, qtdCluster) # Dendrogram
k.res <- kmeans(simCorrelacao, centers = qtdCluster, nstart = 25)
simCorrelacao$cluster <- k.res$cluster
# Validação
Dist <- as.dist(simCorrelacao[,-(ncol(simCorrelacao))])
cluster.stats(Dist, simCorrelacao$cluster)$dunn # 0.4949157
cluster.stats(Dist, simCorrelacao$cluster)$avg.silwidth # 0.266
plot(silhouette(simCorrelacao$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.1103037
# Grupos
for(i in 1:qtdCluster){
print(which(simCorrelacao$cluster == i))
}
# Sequencia: (H = house)
# H1,H2,H3,H4,H5,H6,H7,H8,H9,H10,H11,H12,H13,H15,H16,H17,H18,H19,H20,H21
# 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
# 1
# 15,16,19
# 5
# 4,6,11,21
# 2,3,7,8,9,10,12,13,17,18,20
# Gráficos
gg1 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg2 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg3 <- ggplot() +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg4 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg5 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Correlação (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg1,gg2,gg3,gg4,gg5, nrow = 3, ncol = 2)
# ---------- #
# Euclidiana #
# ---------- #
simEuclid <- as.data.frame(distaciaEuclid_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simEuclid <- data.frame(user1=simEuclid$V1, user2=simEuclid$V2, user3=simEuclid$V3, user4=simEuclid$V4, user5=simEuclid$V5,
user6=simEuclid$V6, user7=simEuclid$V7, user8=simEuclid$V8, user9=simEuclid$V9, user10=simEuclid$V10,
user11=simEuclid$V11, user12=simEuclid$V12, user13=simEuclid$V13, user14=simEuclid$V14,
user15=simEuclid$V15, user16=simEuclid$V16, user17=simEuclid$V17, user18=simEuclid$V18,
user19=simEuclid$V19, user20=simEuclid$V20)
# Agrupamento
#qtdCluster <- wssplot(simEuclid, nc = nrow(simEuclid)-1)
hierarchical(simEuclid, qtdCluster) # Dendrogram
k.res <- kmeans(simEuclid, centers = qtdCluster, nstart = 25)
simEuclid$cluster <- k.res$cluster
# Validação
Dist <- dist(simEuclid[,-(ncol(simEuclid))])
cluster.stats(Dist, simEuclid$cluster)$dunn # 0.3589234
cluster.stats(Dist, simEuclid$cluster)$avg.silwidth # 0.2978361
plot(silhouette(simEuclid$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.09944951
# Grupos
for(i in 1:qtdCluster){
print(which(simEuclid$cluster == i))
}
# Sequencia: (H = house)
# H1,H2,H3,H4,H5,H6,H7,H8,H9,H10,H11,H12,H13,H15,H16,H17,H18,H19,H20,H21
# 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
# 4,5,8
# 2,3,11,12,18,20,21
# 1
# 6,7,9,10,13,17
# 15,16,19
# Gráficos
gg6 <- ggplot() +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg7 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg8 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg9 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg10 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('Euclidiana (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg5,gg6,gg7,gg8,gg9,gg10, nrow = 3, ncol = 2)
# --- #
# DTW #
# --- #
simDtw <- as.data.frame(distanciaDTW_20U(dayHouse1,dayHouse2,dayHouse3,dayHouse4,dayHouse5,dayHouse6,dayHouse7,dayHouse8,
dayHouse9,dayHouse10,dayHouse11,dayHouse12,dayHouse13,dayHouse15,dayHouse16,
dayHouse17,dayHouse18,dayHouse19,dayHouse20,dayHouse21)
)
simDtw <- data.frame(user1=simDtw$V1, user2=simDtw$V2, user3=simDtw$V3, user4=simDtw$V4, user5=simDtw$V5,
user6=simDtw$V6, user7=simDtw$V7, user8=simDtw$V8, user9=simDtw$V9, user10=simDtw$V10,
user11=simDtw$V11, user12=simDtw$V12, user13=simDtw$V13, user14=simDtw$V14, user15=simDtw$V15,
user16=simDtw$V16, user17=simDtw$V17, user18=simDtw$V18, user19=simDtw$V19, user20=simDtw$V20)
# Agrupamento
#qtdCluster <- wssplot(simDtw, nc = nrow(simDtw)-1)
hierarchical(simDtw, qtdCluster) # Dendrogram
k.res <- kmeans(simDtw, centers = qtdCluster, nstart = 25)
simDtw$cluster <- k.res$cluster
# Validação
Dist <- dist(simDtw[,-(ncol(simDtw))])
cluster.stats(Dist, simDtw$cluster)$dunn # 0.3128908
cluster.stats(Dist, simDtw$cluster)$avg.silwidth # 0.2319691
plot(silhouette(simDtw$cluster, Dist))
# Calculando o MAE
maeSeries(series, k.res$cluster, qtdCluster) # 0.1008105
# Grupos
for(i in 1:qtdCluster){
print(which(simDtw$cluster == i))
}
# 1,4,5
# 16
# 6,7,9,10,13,17,18,21
# 15,19
# 2,3,8,11,12,20
# Gráficos
gg11 <- ggplot() +
geom_line(data = dayHouse1, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse4, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse5, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 1)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg12 <- ggplot() +
geom_line(data = dayHouse16, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 2)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg13 <- ggplot() +
geom_line(data = dayHouse6, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse7, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse9, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse10, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse13, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse17, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse18, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse21, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 3)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg14 <- ggplot() +
geom_line(data = dayHouse15, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse19, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 4)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
gg15 <- ggplot() +
geom_line(data = dayHouse2, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse3, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse8, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse11, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse12, aes(x = Time, y = Aggregate, group = 1)) +
geom_line(data = dayHouse20, aes(x = Time, y = Aggregate, group = 1)) +
xlab('Hora (1 dia)') + ylab('Consumo normalizado') +
ggtitle('DTW (grupo 5)') + theme_bw() +
theme(title = element_text(size = 18), axis.title = element_text(size = 15))
grid.arrange(gg11,gg12,gg13,gg14,gg15, nrow = 3, ncol = 2)
# Todos os agrupamentos
png(filename = "../Results/cluster_20users_mean_10min_1day.png", width = 1920, height = 1080)
grid.arrange(gg1,gg6,gg11, gg2,gg7,gg12, gg3,gg8,gg13, gg4,gg9,gg14, gg5,gg10,gg15, nrow = 5, ncol = 3)
dev.off()
|
install_learners("classif.liblinearl1l2svc")
test_that("autotest", {
learner = LearnerClassifLiblineaRL1L2SVC$new()
expect_learner(learner)
result = run_autotest(learner)
expect_true(result, info = result$error)
})
|
/tests/testthat/test_LiblineaR_classif_liblinearl1l2svc.R
|
no_license
|
0livier1O1/mlr3extralearners
|
R
| false
| false
| 224
|
r
|
install_learners("classif.liblinearl1l2svc")
test_that("autotest", {
learner = LearnerClassifLiblineaRL1L2SVC$new()
expect_learner(learner)
result = run_autotest(learner)
expect_true(result, info = result$error)
})
|
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307392677e+77, 9.53818252179844e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(8L, 3L)))
result <- do.call(CNull:::communities_individual_based_sampling_alpha,testlist)
str(result)
|
/CNull/inst/testfiles/communities_individual_based_sampling_alpha/AFL_communities_individual_based_sampling_alpha/communities_individual_based_sampling_alpha_valgrind_files/1615784318-test.R
|
no_license
|
akhikolla/updatedatatype-list2
|
R
| false
| false
| 329
|
r
|
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307392677e+77, 9.53818252179844e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(8L, 3L)))
result <- do.call(CNull:::communities_individual_based_sampling_alpha,testlist)
str(result)
|
##investment
data_csv <- read.csv("C:/Users/Huhu/Downloads/data_csv.csv", stringsAsFactors = FALSE)
head(data_csv)
# Doing stuff with dates:
# Reformatting the dates to make it readable by the system.
data_csv$ID <- seq.int(nrow(data_csv))
data_csv$Date[1513]
# S&P 500 was started in 1923; prior history is from Shiller. If you
# only want the "real" sp500 values, uncomment the line below:
#subset the data from 2007
sp500<-subset(data_csv,data_csv$ID >= 1513)
#sp500$Date<-as.Date(sp500$Date,"%Y%m%d")
#Calculate real returns (Reinvested dividends)
sp500$real.return <- 1 # Start with initial conditions. I invest one dollar at the beginning of the stock market.
for(r in 2:nrow(sp500)){
sp500$real.return[r]<-
# Start with previous value:
sp500$real.return[r-1]*
# Multiply it by the % change in stock value in the last month:
(((sp500$Real.Price[r]-sp500$Real.Price[r-1])/
(sp500$Real.Price[r-1]))+1)+
# Finally, add last month's dividends to the party; they get reinvested:
(sp500$Real.Dividend[r-1]/sp500$Real.Price[r-1])*
(sp500$real.return[r-1]/12)
}
sp500$real.return
summary(sp500$real.return)
sp500$rr.log = log(sp500$real.return)
x = sp500[ -c(1, 11, 12) ]
cor(x)
cormat<-signif(cor(x),2)
cormat
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(cormat, col=col, symm=TRUE)
# Master Loop
# If you're not regenerating the source data, uncomment this part
# Warning: May take a very long time to solve.
###############
stocks<-data.frame(NA,NA,NA,NA)
names(stocks)<-c("year","real","percent","inv.date")
for(f in 0:nrow(sp500)){
sp500$future.f<-NA #Future S&P Price
sp500$cpi.f <- NA #Future CPI
sp500$future.r <- NA #Future Real Returns
buffer<-data.frame(NA,NA,NA,NA)
names(buffer)<-c("year","real","percent","inv.date")
for(n in (f+1):nrow(sp500)){
# Get values for "f" years in the future
sp500$future.f[n-f] <- sp500$SP500[n] # Work our Future S&P Price into its own column
sp500$cpi.f[n-f] <- sp500$Consumer.Price.Index[n] # Work the Future CPI into its own column
sp500$future.r[n-f] <- sp500$real.return[n] # Work the Real Returns into its own column
buffer<-rbind(buffer,c(f/12,sp500$future.r[n-f], # Record all history
(sp500$future.r[n-f]-sp500$real.return[n-f]) /
sp500$real.return[n-f],
as.character(sp500$Date[n-f])
))
}
stocks<-rbind(stocks,buffer)
print(paste(f, " of ", nrow(sp500), " completed: ", signif(f*100/nrow(sp500),4),"%",sep=""))}
stocks<-subset(stocks,!is.na(stocks$percent))
rm(buffer)
# Use a cash multiplier instead of a percent:
stocks$multip<-as.numeric(stocks$percent)+1
stocks$year<-as.numeric(stocks$year)
stocks$real<-as.numeric(stocks$real)
stocks$percent<-as.numeric(stocks$percent)
#write.table(stocks,"returns.csv",sep=",")
head(stocks)
|
/src/investment.R
|
permissive
|
tamarakatic/financial-prediction
|
R
| false
| false
| 3,032
|
r
|
##investment
data_csv <- read.csv("C:/Users/Huhu/Downloads/data_csv.csv", stringsAsFactors = FALSE)
head(data_csv)
# Doing stuff with dates:
# Reformatting the dates to make it readable by the system.
data_csv$ID <- seq.int(nrow(data_csv))
data_csv$Date[1513]
# S&P 500 was started in 1923; prior history is from Shiller. If you
# only want the "real" sp500 values, uncomment the line below:
#subset the data from 2007
sp500<-subset(data_csv,data_csv$ID >= 1513)
#sp500$Date<-as.Date(sp500$Date,"%Y%m%d")
#Calculate real returns (Reinvested dividends)
sp500$real.return <- 1 # Start with initial conditions. I invest one dollar at the beginning of the stock market.
for(r in 2:nrow(sp500)){
sp500$real.return[r]<-
# Start with previous value:
sp500$real.return[r-1]*
# Multiply it by the % change in stock value in the last month:
(((sp500$Real.Price[r]-sp500$Real.Price[r-1])/
(sp500$Real.Price[r-1]))+1)+
# Finally, add last month's dividends to the party; they get reinvested:
(sp500$Real.Dividend[r-1]/sp500$Real.Price[r-1])*
(sp500$real.return[r-1]/12)
}
sp500$real.return
summary(sp500$real.return)
sp500$rr.log = log(sp500$real.return)
x = sp500[ -c(1, 11, 12) ]
cor(x)
cormat<-signif(cor(x),2)
cormat
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(cormat, col=col, symm=TRUE)
# Master Loop
# If you're not regenerating the source data, uncomment this part
# Warning: May take a very long time to solve.
###############
stocks<-data.frame(NA,NA,NA,NA)
names(stocks)<-c("year","real","percent","inv.date")
for(f in 0:nrow(sp500)){
sp500$future.f<-NA #Future S&P Price
sp500$cpi.f <- NA #Future CPI
sp500$future.r <- NA #Future Real Returns
buffer<-data.frame(NA,NA,NA,NA)
names(buffer)<-c("year","real","percent","inv.date")
for(n in (f+1):nrow(sp500)){
# Get values for "f" years in the future
sp500$future.f[n-f] <- sp500$SP500[n] # Work our Future S&P Price into its own column
sp500$cpi.f[n-f] <- sp500$Consumer.Price.Index[n] # Work the Future CPI into its own column
sp500$future.r[n-f] <- sp500$real.return[n] # Work the Real Returns into its own column
buffer<-rbind(buffer,c(f/12,sp500$future.r[n-f], # Record all history
(sp500$future.r[n-f]-sp500$real.return[n-f]) /
sp500$real.return[n-f],
as.character(sp500$Date[n-f])
))
}
stocks<-rbind(stocks,buffer)
print(paste(f, " of ", nrow(sp500), " completed: ", signif(f*100/nrow(sp500),4),"%",sep=""))}
stocks<-subset(stocks,!is.na(stocks$percent))
rm(buffer)
# Use a cash multiplier instead of a percent:
stocks$multip<-as.numeric(stocks$percent)+1
stocks$year<-as.numeric(stocks$year)
stocks$real<-as.numeric(stocks$real)
stocks$percent<-as.numeric(stocks$percent)
#write.table(stocks,"returns.csv",sep=",")
head(stocks)
|
meshRatio <- function(design){
#----------------------------------
# Compute the meshratio criterion
# For a regular mesh, ratio=1
# input : design of n experiments
# Example : Meshratio(matrix(runif(40),20,2))
#----------------------------------
X <- as.matrix(design)
n <- dim(X)[1]
dimension <- dim(X)[2]
if ( n < dimension ){
stop('Warning : the number of points is lower than the dimension')
}
# To check the experimental region
if ( min(X)<0 || max(X)>1 ){
warning("The design is rescaling into the unit cube [0,1]^d.")
M <- apply(X,2,max)
m <- apply(X,2,min)
for (j in 1:dim(X)[2]){
X[,j] <- (X[,j]-m[j])/(M[j]-m[j])
}
}
DistanceMax <- -1.0E30
DistanceMin <- 1.0E30
for (i in 1:(n-1)) {
DistMin <- 1.0E30
DistMax <- -1.0E30
for (k in 1 : n){
if (i != k){ # if the differs from current point
Dist <- 0
for (j in 1 : dimension){
Dist <- Dist + (X[i,j] -X[k,j])*(X[i,j] - X[k,j])
}
if (Dist > DistMax){ DistMax <- Dist; }
if (Dist < DistMin){ DistMin <- Dist; }
}
}
if (DistanceMax < DistMin){
DistanceMax <- DistMin
}
if (DistanceMin > DistMin){
DistanceMin <- DistMin
}
}
ratio <- sqrt(DistanceMax/DistanceMin)
return(ratio)
}
|
/A_github/sources/authors/2765/DiceDesign/meshRatio.R
|
no_license
|
Irbis3/crantasticScrapper
|
R
| false
| false
| 1,329
|
r
|
meshRatio <- function(design){
#----------------------------------
# Compute the meshratio criterion
# For a regular mesh, ratio=1
# input : design of n experiments
# Example : Meshratio(matrix(runif(40),20,2))
#----------------------------------
X <- as.matrix(design)
n <- dim(X)[1]
dimension <- dim(X)[2]
if ( n < dimension ){
stop('Warning : the number of points is lower than the dimension')
}
# To check the experimental region
if ( min(X)<0 || max(X)>1 ){
warning("The design is rescaling into the unit cube [0,1]^d.")
M <- apply(X,2,max)
m <- apply(X,2,min)
for (j in 1:dim(X)[2]){
X[,j] <- (X[,j]-m[j])/(M[j]-m[j])
}
}
DistanceMax <- -1.0E30
DistanceMin <- 1.0E30
for (i in 1:(n-1)) {
DistMin <- 1.0E30
DistMax <- -1.0E30
for (k in 1 : n){
if (i != k){ # if the differs from current point
Dist <- 0
for (j in 1 : dimension){
Dist <- Dist + (X[i,j] -X[k,j])*(X[i,j] - X[k,j])
}
if (Dist > DistMax){ DistMax <- Dist; }
if (Dist < DistMin){ DistMin <- Dist; }
}
}
if (DistanceMax < DistMin){
DistanceMax <- DistMin
}
if (DistanceMin > DistMin){
DistanceMin <- DistMin
}
}
ratio <- sqrt(DistanceMax/DistanceMin)
return(ratio)
}
|
#postscript("indicators_NSGAII_SPEA2.eps", horizontal=FALSE, onefile=FALSE, height=8, width=12, pointsize=10)
pdf("indicators_NSGAII_NSGAII_DKM.pdf", onefile=FALSE, width=10)
NSGAIIresultDirectory<-"."
NSGAIIqIndicator <- function(indicator)
{
fileNSGAII<-paste(NSGAIIresultDirectory, "NSGAII", sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, problem, sep="/")
fileNSGAII<-paste(fileNSGAII, indicator, sep="/")
NSGAII_results<-scan(fileNSGAII)
fileNSGAIIDKM<-paste(NSGAIIresultDirectory, "NSGAIIWithDKMutation", sep="/")
#fileDKMutation<-paste(fileDKMutation, problem, sep="/")
fileNSGAIIDKM<-paste(fileNSGAIIDKM, indicator, sep="/")
NSGAIIDKM_results<-scan(fileNSGAIIDKM)
algs<-c("NSGAII","NSGAIIDKM")
boxplot(NSGAII_results,NSGAIIDKM_results,names=algs, notch = FALSE)
titulo <-paste(indicator)
title(main=titulo)
}
#SPEA2resultDirectory<-"./MutationStudySPEA2/data/"
#SPEA2qIndicator <- function(indicator, problem)
#{
#filePolynomialMutation<-paste(SPEA2resultDirectory, "PolynomialMutation", sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, problem, sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, indicator, sep="/")
#PolynomialMutation<-scan(filePolynomialMutation)
#fileDKMutation<-paste(SPEA2resultDirectory, "DKMutation", sep="/")
#fileDKMutation<-paste(fileDKMutation, problem, sep="/")
#fileDKMutation<-paste(fileDKMutation, indicator, sep="/")
#DKMutation<-scan(fileDKMutation)
#algs<-c("Polynomial","DKMutation")
#boxplot(PolynomialMutation,DKMutation,names=algs, notch = FALSE)
#titulo <-paste(indicator, "SPEA2", sep=":")
#title(main=titulo)
#}
par(mfrow=c(2,3))
indicator<-"HV"
NSGAIIqIndicator(indicator)
indicator<-"IGD"
NSGAIIqIndicator(indicator)
indicator<-"GD"
NSGAIIqIndicator(indicator)
indicator<-"Epsilon"
NSGAIIqIndicator(indicator)
indicator<-"Spread"
NSGAIIqIndicator(indicator)
#indicator<-"HV"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"IGD"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"GD"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"Epsilon"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
|
/mutationResults/AalborgProblem/compare.indicators.NSGAII.NSGAII_DKM.R
|
no_license
|
shaikatcse/EnergyPLANDomainKnowledgeEAStep1
|
R
| false
| false
| 2,169
|
r
|
#postscript("indicators_NSGAII_SPEA2.eps", horizontal=FALSE, onefile=FALSE, height=8, width=12, pointsize=10)
pdf("indicators_NSGAII_NSGAII_DKM.pdf", onefile=FALSE, width=10)
NSGAIIresultDirectory<-"."
NSGAIIqIndicator <- function(indicator)
{
fileNSGAII<-paste(NSGAIIresultDirectory, "NSGAII", sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, problem, sep="/")
fileNSGAII<-paste(fileNSGAII, indicator, sep="/")
NSGAII_results<-scan(fileNSGAII)
fileNSGAIIDKM<-paste(NSGAIIresultDirectory, "NSGAIIWithDKMutation", sep="/")
#fileDKMutation<-paste(fileDKMutation, problem, sep="/")
fileNSGAIIDKM<-paste(fileNSGAIIDKM, indicator, sep="/")
NSGAIIDKM_results<-scan(fileNSGAIIDKM)
algs<-c("NSGAII","NSGAIIDKM")
boxplot(NSGAII_results,NSGAIIDKM_results,names=algs, notch = FALSE)
titulo <-paste(indicator)
title(main=titulo)
}
#SPEA2resultDirectory<-"./MutationStudySPEA2/data/"
#SPEA2qIndicator <- function(indicator, problem)
#{
#filePolynomialMutation<-paste(SPEA2resultDirectory, "PolynomialMutation", sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, problem, sep="/")
#filePolynomialMutation<-paste(filePolynomialMutation, indicator, sep="/")
#PolynomialMutation<-scan(filePolynomialMutation)
#fileDKMutation<-paste(SPEA2resultDirectory, "DKMutation", sep="/")
#fileDKMutation<-paste(fileDKMutation, problem, sep="/")
#fileDKMutation<-paste(fileDKMutation, indicator, sep="/")
#DKMutation<-scan(fileDKMutation)
#algs<-c("Polynomial","DKMutation")
#boxplot(PolynomialMutation,DKMutation,names=algs, notch = FALSE)
#titulo <-paste(indicator, "SPEA2", sep=":")
#title(main=titulo)
#}
par(mfrow=c(2,3))
indicator<-"HV"
NSGAIIqIndicator(indicator)
indicator<-"IGD"
NSGAIIqIndicator(indicator)
indicator<-"GD"
NSGAIIqIndicator(indicator)
indicator<-"Epsilon"
NSGAIIqIndicator(indicator)
indicator<-"Spread"
NSGAIIqIndicator(indicator)
#indicator<-"HV"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"IGD"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"GD"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
#indicator<-"Epsilon"
#SPEA2qIndicator(indicator, "OptimizeElecEnergy_SPEA2")
|
trades <- read.table("data/trades.csv",
header = T,
sep = ",",
skip = 3,
#stringsAsFactors = T,
colClasses = c("NULL",
"NULL",
"factor",
"NULL",
"factor",
"character",
"character",
"integer",
"numeric",
"factor",
"numeric",
"numeric",
"factor",
"numeric",
"factor"))
trades
str(trades)
|
/import.R
|
no_license
|
olk/examples_R
|
R
| false
| false
| 895
|
r
|
trades <- read.table("data/trades.csv",
header = T,
sep = ",",
skip = 3,
#stringsAsFactors = T,
colClasses = c("NULL",
"NULL",
"factor",
"NULL",
"factor",
"character",
"character",
"integer",
"numeric",
"factor",
"numeric",
"numeric",
"factor",
"numeric",
"factor"))
trades
str(trades)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ram_service.R
\name{ram}
\alias{ram}
\title{AWS Resource Access Manager}
\usage{
ram()
}
\description{
Use AWS Resource Access Manager to share AWS resources between AWS
accounts. To share a resource, you create a resource share, associate
the resource with the resource share, and specify the principals that
can access the resource. The following principals are supported:
\itemize{
\item The ID of an AWS account
\item The Amazon Resource Name (ARN) of an OU from AWS Organizations
\item The Amazon Resource Name (ARN) of an organization from AWS
Organizations
}
If you specify an AWS account that doesn't exist in the same
organization as the account that owns the resource share, the owner of
the specified account receives an invitation to accept the resource
share. After the owner accepts the invitation, they can access the
resources in the resource share. An administrator of the specified
account can use IAM policies to restrict access resources in the
resource share.
}
\section{Operations}{
\tabular{ll}{
\link[=ram_accept_resource_share_invitation]{accept_resource_share_invitation} \tab Accepts an invitation to a resource share from another AWS account \cr
\link[=ram_associate_resource_share]{associate_resource_share} \tab Associates the specified resource share with the specified principals and resources \cr
\link[=ram_create_resource_share]{create_resource_share} \tab Creates a resource share \cr
\link[=ram_delete_resource_share]{delete_resource_share} \tab Deletes the specified resource share \cr
\link[=ram_disassociate_resource_share]{disassociate_resource_share} \tab Disassociates the specified principals or resources from the specified resource share\cr
\link[=ram_enable_sharing_with_aws_organization]{enable_sharing_with_aws_organization} \tab Enables resource sharing within your organization \cr
\link[=ram_get_resource_policies]{get_resource_policies} \tab Gets the policies for the specifies resources \cr
\link[=ram_get_resource_share_associations]{get_resource_share_associations} \tab Gets the associations for the specified resource share \cr
\link[=ram_get_resource_share_invitations]{get_resource_share_invitations} \tab Gets the specified invitations for resource sharing \cr
\link[=ram_get_resource_shares]{get_resource_shares} \tab Gets the specified resource shares or all of your resource shares \cr
\link[=ram_list_principals]{list_principals} \tab Lists the principals with access to the specified resource \cr
\link[=ram_list_resources]{list_resources} \tab Lists the resources that the specified principal can access \cr
\link[=ram_reject_resource_share_invitation]{reject_resource_share_invitation} \tab Rejects an invitation to a resource share from another AWS account \cr
\link[=ram_tag_resource]{tag_resource} \tab Adds the specified tags to the specified resource share \cr
\link[=ram_untag_resource]{untag_resource} \tab Removes the specified tags from the specified resource share \cr
\link[=ram_update_resource_share]{update_resource_share} \tab Updates the specified resource share
}
}
\examples{
\donttest{svc <- ram()
svc$accept_resource_share_invitation(
Foo = 123
)}
}
|
/paws/man/ram.Rd
|
permissive
|
peoplecure/paws
|
R
| false
| true
| 3,221
|
rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ram_service.R
\name{ram}
\alias{ram}
\title{AWS Resource Access Manager}
\usage{
ram()
}
\description{
Use AWS Resource Access Manager to share AWS resources between AWS
accounts. To share a resource, you create a resource share, associate
the resource with the resource share, and specify the principals that
can access the resource. The following principals are supported:
\itemize{
\item The ID of an AWS account
\item The Amazon Resource Name (ARN) of an OU from AWS Organizations
\item The Amazon Resource Name (ARN) of an organization from AWS
Organizations
}
If you specify an AWS account that doesn't exist in the same
organization as the account that owns the resource share, the owner of
the specified account receives an invitation to accept the resource
share. After the owner accepts the invitation, they can access the
resources in the resource share. An administrator of the specified
account can use IAM policies to restrict access resources in the
resource share.
}
\section{Operations}{
\tabular{ll}{
\link[=ram_accept_resource_share_invitation]{accept_resource_share_invitation} \tab Accepts an invitation to a resource share from another AWS account \cr
\link[=ram_associate_resource_share]{associate_resource_share} \tab Associates the specified resource share with the specified principals and resources \cr
\link[=ram_create_resource_share]{create_resource_share} \tab Creates a resource share \cr
\link[=ram_delete_resource_share]{delete_resource_share} \tab Deletes the specified resource share \cr
\link[=ram_disassociate_resource_share]{disassociate_resource_share} \tab Disassociates the specified principals or resources from the specified resource share\cr
\link[=ram_enable_sharing_with_aws_organization]{enable_sharing_with_aws_organization} \tab Enables resource sharing within your organization \cr
\link[=ram_get_resource_policies]{get_resource_policies} \tab Gets the policies for the specifies resources \cr
\link[=ram_get_resource_share_associations]{get_resource_share_associations} \tab Gets the associations for the specified resource share \cr
\link[=ram_get_resource_share_invitations]{get_resource_share_invitations} \tab Gets the specified invitations for resource sharing \cr
\link[=ram_get_resource_shares]{get_resource_shares} \tab Gets the specified resource shares or all of your resource shares \cr
\link[=ram_list_principals]{list_principals} \tab Lists the principals with access to the specified resource \cr
\link[=ram_list_resources]{list_resources} \tab Lists the resources that the specified principal can access \cr
\link[=ram_reject_resource_share_invitation]{reject_resource_share_invitation} \tab Rejects an invitation to a resource share from another AWS account \cr
\link[=ram_tag_resource]{tag_resource} \tab Adds the specified tags to the specified resource share \cr
\link[=ram_untag_resource]{untag_resource} \tab Removes the specified tags from the specified resource share \cr
\link[=ram_update_resource_share]{update_resource_share} \tab Updates the specified resource share
}
}
\examples{
\donttest{svc <- ram()
svc$accept_resource_share_invitation(
Foo = 123
)}
}
|
#--PLOT MSE OF FILTERED VS SMOOTH--
# M monte carlo runs
# Test 2 cases sigma.meas=3 and sigma.meas=1
#load library
source("filter_mod.R")
#set model variables
x0<-0
tau<-100
sigma<-3
sigma.meas<-3 #sigma.meas=1
N<-500
M<-100
#generate states and simulated data y
set.seed(123)
x<-rand.walk.1D(tau=tau,x0=x0,sigma=sigma)
y.mat<-matrix(NA,nrow=tau,ncol=M)
for(k in 1:M){
y.mat[,k]<-rand.y.1D(x,sigma.meas=sigma.meas)
}
#compute MSE for smoothing and filtered
MSE<-matrix(NA,tau,M)
MSE.filter<-matrix(NA,tau,M)
means<-matrix(NA,tau,M)
means.filter<-matrix(NA,tau,M)
neff<-matrix(NA,tau,M)
for(k in 1:M){
obj<-particle.filter.path(N=N,x=x,y=y.mat[,k],x0=5,sigma=sigma,sigma.meas=sigma.meas,resample.type="standard",N.thr=1)
x.pf<-obj$x.pf.out
means.filter[,k]<-obj$m.out
means[,k]<-rowMeans(x.pf)
MSE[,k]<-(rowMeans(x.pf)-x)^2
MSE.filter[,k]<-(obj$m.out-x)^2
neff[,k]<-obj$N.eff.out
}
#plot MSE for smoothing and filtered
plot(rowMeans(MSE),main="MSE",ylab="MSE",xlab="Time,t",type="l")
lines(rowMeans(MSE.filter),col="red",type="l")
legend(10, 14, legend=c("Filtered", "Joint Smoothing"),
col=c( "red","black"),pch=c(1,1), cex=0.8)
#legend(75, 1.8, legend=c("Filtered", "Joint Smoothing"),
# col=c( "red","black"),pch=c(1,1), cex=0.8)
|
/R/old_scripts/smooth_vs_filter_plot.R
|
no_license
|
tintinthong/pfilter
|
R
| false
| false
| 1,270
|
r
|
#--PLOT MSE OF FILTERED VS SMOOTH--
# M monte carlo runs
# Test 2 cases sigma.meas=3 and sigma.meas=1
#load library
source("filter_mod.R")
#set model variables
x0<-0
tau<-100
sigma<-3
sigma.meas<-3 #sigma.meas=1
N<-500
M<-100
#generate states and simulated data y
set.seed(123)
x<-rand.walk.1D(tau=tau,x0=x0,sigma=sigma)
y.mat<-matrix(NA,nrow=tau,ncol=M)
for(k in 1:M){
y.mat[,k]<-rand.y.1D(x,sigma.meas=sigma.meas)
}
#compute MSE for smoothing and filtered
MSE<-matrix(NA,tau,M)
MSE.filter<-matrix(NA,tau,M)
means<-matrix(NA,tau,M)
means.filter<-matrix(NA,tau,M)
neff<-matrix(NA,tau,M)
for(k in 1:M){
obj<-particle.filter.path(N=N,x=x,y=y.mat[,k],x0=5,sigma=sigma,sigma.meas=sigma.meas,resample.type="standard",N.thr=1)
x.pf<-obj$x.pf.out
means.filter[,k]<-obj$m.out
means[,k]<-rowMeans(x.pf)
MSE[,k]<-(rowMeans(x.pf)-x)^2
MSE.filter[,k]<-(obj$m.out-x)^2
neff[,k]<-obj$N.eff.out
}
#plot MSE for smoothing and filtered
plot(rowMeans(MSE),main="MSE",ylab="MSE",xlab="Time,t",type="l")
lines(rowMeans(MSE.filter),col="red",type="l")
legend(10, 14, legend=c("Filtered", "Joint Smoothing"),
col=c( "red","black"),pch=c(1,1), cex=0.8)
#legend(75, 1.8, legend=c("Filtered", "Joint Smoothing"),
# col=c( "red","black"),pch=c(1,1), cex=0.8)
|
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