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b748819aa6494b1593f6fdb654177cf7f1f55297
|
a58b45b89139bf17831f51890021131f84cf1f04
|
/script/rep_info.R
|
39f01b6a2ed2d8c0e22e3d0dfc65f735ba397d39
|
[] |
no_license
|
BETAPANDERETA/rocket-engine-data-analysis
|
c0ee07cb19cb8c84e96ebf6c55cdff7e3205c92d
|
a187c561f1d326a492ae26f75407f16d3f4e104c
|
refs/heads/master
| 2023-01-20T21:55:48.929723
| 2020-11-25T03:01:49
| 2020-11-25T03:01:49
| 305,259,434
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,357
|
r
|
rep_info.R
|
# ---------------------------------------------
# AUTOR: Leonardo Betancur A.K.A BETAPANDERETA|
# CONTACTO: <lbetancurd@unal.edu.co> |
# ---------------------------------------------
library(extrafont) # Cargando fonts --> Revisar disponibles con windosFonts()
library(ggplot2) # Gráficos
library(mgcv) #---> Usar GAM
library(lme4) # Usar BIC para estimar la regresión
#_______________________________________________
# DATOS PROCESADOS_M1 |
#_______________________________________________|
#data_csv = "data/clean_data.csv"
#data_util = read.table(data_csv,header = TRUE,sep=",")
#data_y = c(data_util$Impulso)
#data_x = c(data_util$t)
#_______________________________________________
# DATOS SIN PROCESAR_M1 |
#_______________________________________________
#data_csv = "data/dirt_data.csv"
#data_util = read.table(data_csv,header = TRUE,sep=",")
#data_y = c(data_util$Impulso)
#data_x = c(data_util$t)
#_______________________________________________
# DATOS PROCESADOS_M2 |
#_______________________________________________|
#data_csv = "data/clean_data_m2.csv"
#data_util = read.table(data_csv,header = TRUE,sep=",")
#data_y = c(data_util$Impulso)
#data_x = c(data_util$t)
#_______________________________________________
# DATOS SIN PROCESADOS_M2 |
#_______________________________________________|
data_csv = "data/dirt_data_m2.csv"
data_util = read.table(data_csv,header = TRUE,sep=",")
data_y = c(data_util$Impulso)
data_x = c(data_util$t)
g_p = 2 # Grado de polinomio - regresión
gam_curve = gam(data_y ~ s(data_x)) # Regresión GAM
loes_curve = loess(data_y ~ data_x) # Regresión LOESS
sm_curve = lm(data_y ~ poly(data_x,degree = g_p,raw = TRUE)) # Regresión polinomial
est_mr = BIC(sm_curve) # Índice BIC, tomar la regresión que dé menor BIC [4]
bic = paste("BIC:",est_mr)
print(bic)
print(summary(sm_curve)) # Stats de la regresión polinomial
#_______________________________________________
# GRÁFICOS CON GGPLOT2 |
#_______________________________________________|
plot_gg = function(data,x,y,lb_x,lb_y,title){ # Gráficos usando ggplot2
ggplot(data,aes(x=x,y=y))+
ggtitle(title)+
theme_bw()+
geom_point()+
geom_line(aes(colour="Registros"))+
geom_hline(yintercept=0)+
#geom_smooth( # Regresión GAM
# method = "gam",
# se = FALSE,
# linetype = "dashed",
# aes(colour="Regresión GAM")
#)+
#geom_smooth( # Regresión LOESS
# method = "loess",
# se = FALSE,
# linetype = "twodash",
# aes(colour="Regresión LOESS")
#)+
#geom_smooth( # Regresión polinomial
# method = "lm",
# formula = y ~ poly(x,degree = g_p,raw = TRUE),
# se = FALSE,
# aes(colour="Regresión polinómica")
#)+
#stat_smooth( # Área bajo la curva basado en [1]
# geom = 'area',
# method = 'lm',
# formula = y ~ poly(x,degree = g_p,raw = TRUE),
# alpha = .4, aes(fill = "Impulso Total")
#) +
scale_fill_manual(
name="Conv. área sombreada",
values="pink"
)+
theme(
text=element_text(family="RomanS_IV25"),
plot.title = element_text(color="red", size=14,family = "Technic"),
legend.title = element_text(size=10)
)+
xlab(lb_x)+ylab(lb_y)+ # leyendas en el plot tomado de [2]
scale_colour_manual(
name="Convenciones lineas",
values=c("black","blue","grey","red")
)
}
#_______________________________________________
# GRÁFICOS CON R |
#_______________________________________________|
plot_r = function(x,y,r_pol,r_gam,r_loess){
plot(
x,y,
bty = "l",
pch = 16,
cex = .9
)
legend("topright",
legend=c("Regresión P(x)", "Regresión GAM","Regresión LOESS"),
col=c("blue", "green","red"),
lty=1,lwd = 2, cex=0.8
)
lines(predict(r_pol), # Regresión polinomial
col = "blue",
lwd = 2)
lines(predict(r_gam), # Regresión GAM
col = "green",
lwd = 2)
lines(predict(r_loess), # Regresión LOESS
col = "red",
lwd = 2)
}
#_______________________________________________
# GRAFICANDO |
#_______________________________________________|
lab_g =c("Tiempo (s)","Gramos - fuerza (g)","Curva de impulso")
graf_gg = plot_gg(data_util,data_x,data_y,lab_g[1],lab_g[2],lab_g[3])
print(graf_gg)
#_______________________________________________
# REFERENCIAS |
#_______________________________________________|
# [1] https://stackoverflow.com/questions/40133833/smooth-data-for-a-geom-area-graph
# [2] https://stackoverflow.com/questions/36276240/how-to-add-legend-to-geom-smooth-in-ggplot-in-r
# Diferencia entre poly(raw = TRUE) y I(x):
# [3] https://stackoverflow.com/questions/19484053/what-does-the-r-function-poly-really-do
# [4] https://youtu.be/QptI-vDle8Y
|
d688aef5e830e7fab6d5f93194e416b2dfdb12fa
|
87f20607fc468c291f31f46c2718ddbbc69d3a75
|
/man/preprocess_secuences.Rd
|
472e83e6d47e3a7c6b05fed67d4be38d6bc91481
|
[
"MIT"
] |
permissive
|
santiago1234/iCodon
|
ffce61abc8b69b38b138861e5c098454311c4c83
|
c6739dde6b03e3516e8a464aea3e64449c0f980d
|
refs/heads/master
| 2023-04-07T20:43:43.191022
| 2022-07-30T19:10:45
| 2022-07-30T19:10:45
| 238,309,734
| 13
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 741
|
rd
|
preprocess_secuences.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ml-preprocessing.R
\name{preprocess_secuences}
\alias{preprocess_secuences}
\title{Preprocess sequence for prediction mRNA stability}
\usage{
preprocess_secuences(secuencias, specie_ = "human")
}
\arguments{
\item{secuencias}{character, a vector of dna sequences to predict. If
more than one sequence is supplied, then the sequences should be unique. (No
repeated sequences)}
\item{specie_}{character: one of human, mouse, fish, or xenopus}
}
\value{
A tibble: \code{length(secuencia)} x 423, preprocessed data for estimating
mrna stability
}
\description{
Preprocess sequence for prediction mRNA stability
}
\examples{
preprocess_secuences(test_seq, "mouse")
}
|
361b5a14d31fdf0fa02d207ae8a48c19d85b0486
|
d8ff92d904d21c1ab52d056fe6e624abfad939b4
|
/R/prVis.R
|
2624c6bb833db20a8920a34f24e8ded244e5a4cf
|
[] |
no_license
|
guhjy/prVis
|
4c1ea6cc85b7843808725bdce331a5116026fba2
|
587582d463f66a87e91a84ec0b4ff50b554ff045
|
refs/heads/master
| 2020-04-11T12:00:30.488013
| 2018-12-13T21:16:27
| 2018-12-13T21:16:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,952
|
r
|
prVis.R
|
# two-dimensional visualization of the X data in classification problems,
# similar in spirit to ordinary PCA and t-sne, color coded by Y values
# (class IDs in classification case, subinternvals of Y in regression
# case)
# t-sne, e.g. in the Rtsne package, applied in dimension k, attempts to
# find a k-dimensional manifold for which most of the data are "near";
# for visualization purposes, typically k = 2, which is assumed here
# the idea here is to expand the data with polynomial terms, using
# getPoly(), then apply PCA to the result
# typically these methods are applied only to a subsample of the data,
# due both to the lengthy computation time and the "black screen
# problem" (having a lot of points fills the screen, rendering the plot
# useless)
# arguments:
# xy: data frame
# labels: if TRUE, last column is Y for a classification problem;
# must be an R factor, unless nIntervals is non-NULL, in
# which case Y will be discretized to make labels
# deg: degree of polynomial expansion
# scale: if TRUE, first call scale() on the X data
# nSubSam: number of rows to randomly select; 0 means get all
# nIntervals: in regression case, number of intervals to use for
# partioning Y range to create labels
# outliersRemoved: specify how many outliers to remove from
# the plot, calculated using mahalanobis distance. if
# outliersRemoved is between 0 and 1, a corresponding
# percentage of the data will be removed
# pcaMethod: specify how eigenvectors will be calculated, using
# prcomp or RSpectra
# saveOutputs: specify the name of the file where the results will be saved.
# default file is 'lastPrVisOut'. set to the empty string to
# not save results.
# cex: argument to R plot(), controlling point size
# alpha: a number between 0 and 1 that can be used to specify transparency
# for alpha blending. If alpha is specified ggplot2 will be used to
# create the plot
prVis <- function(xy,labels=FALSE,yColumn = ncol (xy), deg=2,
scale=FALSE,nSubSam=0,nIntervals=NULL,
outliersRemoved=0,pcaMethod="prcomp",
saveOutputs="lastPrVisOut",cex=0.5, alpha=0)
{
# safety check
if (!pcaMethod %in% c('prcomp','RSpectra'))
stop("pcaMethod should be either NULL, prcomp, or RSpectra")
nrxy <- nrow(xy)
ncxy <- ncol(xy)
if (labels) {
if (yColumn > ncol(xy) || yColumn <= 0)
stop("The column specified is out of range")
tmp <- xy[, ncxy]
xy[, ncxy] <- xy[, yColumn]
# swapping the last column with the user-specified column
xy[, yColumn] <- tmp
}
rns <- row.names(xy)
if (scale) {
if (labels) {
xy[,-ncxy] <- scale(xy[,-ncxy])
} else xy <- scale(xy)
row.names(xy) <- rns
}
if (nSubSam < nrxy && nSubSam > 0)
xy <- xy[sample(1:nrxy,nSubSam),]
if (labels) {
ydata <- xy[,ncxy]
if (is.null(nIntervals) && !is.factor(ydata))
stop('Y must be a factor for classif.; set nIntervals for regress.')
if (!is.null(nIntervals)) {
rng <- range(ydata)
increm <- (rng[2] - rng[1]) / nIntervals
ydata <- round((ydata - rng[1]) / increm)
ydata <- as.factor(ydata)
}
xdata <- xy[,-ncxy, drop=FALSE]
} else xdata <- xy
xdata <- as.matrix(xdata)
polyMat <- getPoly(xdata, deg)$xdata
if (pcaMethod == "prcomp") {
x.pca <- prcomp(polyMat,center=TRUE)
xdata <- x.pca$x[,1:2]
} else {
require(RSpectra)
x.cov <- cov(polyMat)
x.eig <- eigs(x.cov,2)
x.pca <- x.eig
xdata <- as.matrix(polyMat) %*% x.eig$vectors[,1:2]
colnames(xdata) <- c("PC1","PC2")
}
if (outliersRemoved > 0 && outliersRemoved <= nrow(xdata)){
# percentage based outlier removal
if (outliersRemoved < 1){
outliersRemoved = floor(outliersRemoved * nrow(xdata))
}
# calculate mahalanobis distances for each data point
xdataCov <- var(xdata)
distances <- mahalanobis(xdata,colMeans(xdata),xdataCov)
# find which row the max distances correspond to
rownames(xdata) <- 1:nrow(xdata)
if (labels) names(ydata) <- 1:nrow(xdata)
names(distances) <- rownames(xdata)
sortedDistances <- sort(distances, decreasing=TRUE)
outliers <- names(sortedDistances)[1:outliersRemoved]
# remove outliers
xdata <- xdata[!rownames(xdata) %in% outliers,]
if (labels) ydata <- ydata[!names(ydata) %in% outliers]
}
if (alpha) {
require(ggplot2)
if (labels) {
plotObject <- qplot(x=xdata[,1],y=xdata[,2],xlab="PC1",ylab="PC2",
alpha=alpha,col=ydata,size=I(cex))
} else {
plotObject <- qplot(x=xdata[,1],y=xdata[,2],xlab="PC1",ylab="PC2",
alpha=alpha,size=I(cex))
}
print(plotObject)
} else {
if (labels) {
plot(xdata, col=ydata, pch=15, cex=cex)
} else {
plot(xdata, pch=15, cex=cex)
}
}
if (saveOutputs != ""){
if (labels && is.factor(ydata)) #xy has factor column, colName stores the name for all continuou
#yname stores the name for the factor col
outputList <- list(gpOut=polyMat,prout=x.pca,
colName=colnames(xy[, -ncxy]), yCol = ydata, yname=colnames(xy)[ncxy])
else # xy has no factor column
outputList <- list(gpOut=polyMat,prout=x.pca, colName=colnames(xy))
save(outputList,file=saveOutputs)
}
}
# intended to be used when a plot produced by prVis() is on the screen;
# chooses np points at random from the PCA output, writing their row
# numbers on the plot; these are the numbers from the full dataset, even
# if nSubSam > 0; the argument savedPrVisOut is the return value of
# prVis()
#
# arguments:
# np: the number of points to add row numbers to. if no value of np is
# provided, rownumbers will be added to all datapoints
# savedPrVisOut: the name of the file where a previous call to prVis was
# stored.
# area: vector with in the form of [x_start, x_finish, y_start, y_finish].
# x_start, x_finish, y_start, and y_finish should all be between 0
# and 1. These values correspond to percentages of the graph from
# left to right and bottom to top. [0,1,0,1] specifies the entirety
# of the graph. [0,0.5,0.5,1] specifies upper-left quadrant. x_start
# must be less than x_finish and y_start must be less than y_finish
addRowNums <- function(np=0,area=c(0,1,0,1),savedPrVisOut="lastPrVisOut")
{
load(savedPrVisOut)
pcax <- outputList$prout$x[,1:2]
if(is.null(row.names(pcax)))
row.names(outputList$prout$x) <-
as.character(1:nrow(outputList$prout$x))
if(identical(area, c(0,1,0,1))){
# get boundaries of graph
xMin <- min(outputList$prout$x[,1])
xMax <- max(outputList$prout$x[,1])
yMin <- min(outputList$prout$x[,2])
yMax <- max(outputList$prout$x[,2])
# error checking on inputs
xI <- area[1]
xF <- area[2]
yI <- area[3]
yF <- area[4]
if (xI < 0 | xI > 1 | xF < 0 | xF > 1 | xI > xF)
if (yI < 0 | yI > 1 | yF < 0 | yF > 1 | yI > yF){
stop('invalid area boundaries, 0 < x_start < x_finish < 1, 0 < y_start <
y_finish < 1. area is in the form of c(x_start,x_finish,y_start,
y_finish)')
}
# scale x interval
xI <- (xMax - xMin)*xI + xMin
xF <- (xMax - xMin)*xF + xMin
# scale y interval
yI <- (yMax - yMin)*yI + yMin
yF <- (yMax - yMin)*yF + yMin
# filter to datapoints within specified range
pcax <- pcax[which(pcax[,1] <= xF & pcax[,1] >= xI & pcax[,2] <=
yF & pcax[,2] > yI),]
}
npcax <- nrow(pcax)
tmp <- sample(1:npcax,np,replace=FALSE)
rowNames <- row.names(pcax[tmp,])
print('highlighted rows:')
sorted <- sort(as.numeric(rowNames))
for (i in 1:length(rowNames)) {
rn <- rowNames[i]
print(sorted[i])
coords <- pcax[rn,]
text(coords[1],coords[2],rn)
}
}
# colorCode: display color coding for user-specified vairables or expressions.
# Normally called after prVis, produce a new coloring of the same plot produced
# by prVis.
# arguments:
# colName: user can specify the column that he or she wants to produce
# the color on. The column specified must be a continuous one.
# If you want to produce the coloring based on a factor column,
# prVis has already had the functionality.
# n: rainbow parameter. We produce the coloring of a continuous variable
# using function rainbow, and n is passed in to the function as an
# option.
# exps: expressions that create a label column that produces coloring.
# If user specifies colName, he or she cannot provide arguments
# for exps, since they are two ways to produce coloring (should
# be mutually exclusive). User can supply several expressions,
# each one corresponding to a group (a label, a color, a level),
# and concatenating by c(). The expression should be mutually
# exclusive, since a data point cannot be in two colors.
# expression format:
# <exp> ::= <subexpression> [(+|*) <subexpression>]
# <subexpression> ::= columnname relationalOperator value
# Note: * represents logic and, + represents logic or
# savedPrVisOut: the file that stores a prVis object
colorCode <- function(colName="",n=256,exps="", savedPrVisOut="lastPrVisOut",
cex = 0.5)
{
load(savedPrVisOut)
xdata <- outputList$gpOut[,1:length(outputList$colName)]
plotData <- outputList$prout$x[,1:2]
if (colName == "" && exps == "")
stop("colName and expressions(exps) not specified")
if (colName != "" && exps == "")
{
if (!colName %in% outputList$colName)
stop("The column specified is not a continuous one or not found")
else { # do continue color (rainbow)
colNum = which(colName == outputList$colName)
d <- xdata[,colNum]
minVal <- min(d)
maxVal <- max(d)
diffVal <- maxVal - minVal
colorPalette <- rev(rainbow(n,start=0,end=0.7))
colorIndexes <- sapply(d, function(x) ((x - minVal) * n) %/% diffVal)
plot(plotData, col=colorPalette[colorIndexes])
}
}
else if(colName != "" && exps != "") # illegal specify both
stop("colName for rainbow, exps for createFactor column")
else { # create a label column with potentially more than one labels
numberOfRows <- length(outputList$prout$x[,1])
userCol <- rep(NA, numberOfRows) # initialize label column
hasY <- !(is.null(outputList$yname))
if (hasY) #original dataset has a factor column
factorCol <- outputList$yCol
hasLabel <- c() # track rows that has already had a label, check for relable
for (i in 1:length(exps)) {
# delete all white spaces (compress the string)
exp <- gsub(" ", "", exps[i], fixed = TRUE)
labelName <- exp #use the expression as the label name
subExp <- unlist(strsplit(exp, "\\+|\\*"))
for (m in 1:length(subExp))
exp <- sub(subExp[m], "", exp, fixed = T)
exp <- unlist(strsplit(exp, split="")) #string to vector of characters
#number of +/* operators should be one less than the number of constraints
if (length(exp) != length(subExp) - 1)
stop (length(exp)," +/* not match ",length(subExp)," constraints")
for (j in 1:length(subExp)) { # solve one expression by solving all constraints
# Ex has one constraint but the relational operator is extracted
# EX : "Male", "1"
Ex <- unlist(strsplit(subExp[j],"(==|>=|<=|>|<|!=)")) #relational ops
if (length(Ex) != 2) # Ex should have two components: column name, value
stop ("The constraint must follow the format: 'yourCol'
'relationalOperator' 'value'")
else {
tmp <- paste("\\b", Ex[1], sep="")
tmp <- paste(tmp, "\\b", sep="")
columnNum <- grep(tmp, outputList$colName)
if (!length(columnNum) && (!hasY || tmp != outputList$yname))
stop("The specified column ",Ex[1]," is not found in the data frame xy")
# restore the relational operator
relationalOp <- sub(Ex[1], "", subExp[j], fixed= TRUE)
relationalOp <- sub(Ex[2], "", relationalOp, fixed= TRUE)
if (hasY && tmp == outputList$yname) # Ex[1] is the factorcol
{
if (!Ex[2] %in% levels(factorCol))
stop ("The label ", Ex[2], " is not in the factor column")
# when ecounter operations between labels, only == and != make sense
if (!relationalOp %in% c("==", "!="))
stop ("Use of the inappropriate operator ", relationalOp)
# get the row numbers of data that satisfy the constraint userExp[i]
rowBelong <- switch(relationalOp, "==" = which(factorCol == Ex[2]),
"!=" = which (factorCol != Ex[2]))
}
else { # EX[1] is a continuous column, so Ex[2] should be a number
val <- as.double(Ex[2])
if (is.null(val)||val< min(xdata[[columnNum]])||val > max(xdata[[columnNum]]))
stop("The value ", Ex[2], " is out of the range")
# get the row numbers of data that satisfy the constraint userExp[i]
rowBelong <- switch(relationalOp, "==" = which(xdata[[columnNum]] == val),
"!=" = which (xy[[columnNum]] != val),">="= which(xdata[[columnNum]]>=val),
"<="=which(xdata[[columnNum]] <= val), ">" = which (xdata[[columnNum]] > val),
"<" = which(xdata[[columnNum]] < val))
}
}
if (j == 1) # initialize labelData
labelData <- rowBelong
else {
if (exp[j-1] == "*") # And, get the intersection of the row numbers
labelData <- intersect(labelData, rowBelong)
else # Or, get the union
labelData <- union(labelData, rowBelong)
}
} # end for loop
# check for overlaps! will cause relabel of certain data that satisfy two or
# more expressions. Enforcing mutually exclusiveness between expressions
if (length(intersect(labelData, hasLabel)) != 0)
stop ("The expression ", i, " tries to relabel some data,
the groups must be mutually exclusive")
# gives the label to data that satisfy the expression
userCol[labelData] <- labelName
# update hasLabel to keep track of all data has been labeled
hasLabel <- union(labelData, hasLabel)
} # end big for loop
if (length(hasLabel) == 0) # no matching data of the expressions
stop ("Expression(s) match no data points")
userCol[-hasLabel] <- "others"
userCol <- as.factor(userCol)
plot (plotData, col=userCol, pch=15, cex=cex)
} # end createCol
}
|
2a87270d132e197c8a76039782220e6ce2f3df94
|
c7d84bf201e560096c7925b9586343fac438d74a
|
/lectuer2-2.R
|
2f202e5fb2e3a0c9e9317b48bdc75557fe5dad02
|
[] |
no_license
|
narendrameena/diffrentRScripts
|
029301f875d97c1ec5cea5c706e27fef9995f55a
|
e83942da0021a36284bea4124a3b6930032d9f63
|
refs/heads/master
| 2021-01-01T16:47:24.060418
| 2017-07-21T07:56:24
| 2017-07-21T07:56:24
| 97,921,634
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,528
|
r
|
lectuer2-2.R
|
library(devtools)
install_github("jennybc/gapminder")
library(gapminder)
data(gapminder)
head(gapminder)
s =gapminder$year[[1952]]
table(gapminder$year ==1952)
hist(gapminder$lifeExp[which(gapminder$year==1952)])
hist(log10(gapminder$lifeExp[which(gapminder$year==1952)]))
mean(gapminder$lifeExp[which(gapminder$year==1952)] <=40)
mean(gapminder$lifeExp[which(gapminder$year==1952)]<=60 )-mean( gapminder$lifeExp[which(gapminder$year==1952)] <=40)
prop =function(q){
mean(50<=q)
}
qs =seq(from=min(1),to=max(100),length=20)
props =sapply(qs,prop)
plot(qs,props)
props =sapply(qs,function(q) mean(30<=q))
plot(ecdf)
pop = gapminder$pop[which(gapminder$year==1952)]
s = split(gapminder$pop[which(gapminder$year==1952)],gapminder$country[which(gapminder$year==1952)])
s
s<- plot(gapminder$country[which(gapminder$year==1952)],log10(gapminder$pop[which(gapminder$year==1952)]), type="h")
s
hist(log10(gapminder$pop[which(gapminder$year==1952)]))
sd(log10(gapminder$pop[which(gapminder$year==1952)]))
mean(log10(gapminder$pop[which(gapminder$year==1952)]))
fun <- function(q){
return(pnorm(q,mean=mean(log10(gapminder$pop[which(gapminder$year==1952)])),sd=sd(log10(gapminder$pop[which(gapminder$year==1952)]))))
}
nd<-fun(7)-fun(6)
nd
n = length(log10(gapminder$pop[which(gapminder$year==1952)]))
n
nc=n*nd
nc
qqnorm(log10(gapminder$pop[which(gapminder$year==1952)]))
ps=((1:n)-0.5)/n
ps
qqnorm(ps)
sort(log10(gapminder$pop[which(gapminder$year==1952)]))
plot(qnorm(ps),sort(log10(gapminder$pop[which(gapminder$year==1952)])))
|
61dbe80fa10793473067f427c00f7c4a87269cf7
|
4daca2e1978ed5fb927db5af6e6e36a372608c16
|
/R/functions/descriptorImportancePlots.R
|
916a972555bc04db265ad988ba791c6f84027ee3
|
[
"CC-BY-4.0"
] |
permissive
|
jasenfinch/Index_measures_for_oak_decline_severity_using_phenotypic_descriptors
|
518ac57f78d23bad929e26284e4dbbaa30fa8ca8
|
c4928890a4993dcca5797d4f854027f1f6d70404
|
refs/heads/master
| 2023-03-17T11:57:03.033059
| 2021-03-09T20:31:14
| 2021-03-09T20:31:14
| 165,732,718
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,419
|
r
|
descriptorImportancePlots.R
|
descriptorImportancePlots <- function(PDI_descriptor_importance,DAI_descriptor_importance){
importance_plots <- list(
a = {
dat <- PDI_descriptor_importance %>%
arrange(`%IncMSE`) %>%
mutate(Feature = factor(Feature,levels = Feature),
rev_Rank = Feature %>%
seq_along())
descriptorLabels <- dat$Feature %>%
as.character() %>%
{
.[str_detect(.,coll('Agrilus exit hole density (m^-2)'))] <- expression(Agrillus~exit~hole~density ( m^-2 ) )
.[str_detect(.,coll('Crown volume (m^3)'))] <- expression(Crown~volume ( m^3 ) )
return(.)
}
ggplot(dat,aes(y = Feature,x = `%IncMSE`)) +
geom_segment(aes(y = rev_Rank,yend = rev_Rank,x = -5,xend = `%IncMSE`)) +
geom_point(fill = ptol_pal()(1),shape = 21,size = 2) +
theme_bw() +
theme(plot.title = element_text(face = 'bold',hjust = 0.5),
axis.title = element_text(face = 'bold',size = 10),
panel.grid = element_blank(),
panel.border = element_blank(),
plot.margin = unit(c(5.5, 0, 5.5, 5.5), "pt"),
axis.line.x = element_line(),
axis.text.y = element_text(colour = 'black'),
axis.ticks.y = element_blank()) +
labs(title = 'a) PDI',
x = '% increase in\nMSE',
y = NULL) +
scale_y_discrete(labels = descriptorLabels) +
scale_x_reverse(limits = c(112,-5),
expand = c(0,0))
},
b = {
dat <- DAI_descriptor_importance %>%
arrange(`%IncMSE`) %>%
mutate(Feature = factor(Feature,levels = Feature),
rev_Rank = Feature %>%
seq_along())
descriptorLabels <- dat$Feature %>%
as.character() %>%
{
.[str_detect(.,coll('Agrilus exit hole density (m^-2)'))] <- expression(Agrillus~exit~hole~density ( m^-2 ) )
.[str_detect(.,coll('Crown volume (m^3)'))] <- expression(Crown~volume ( m^3 ) )
return(.)
}
ggplot(dat,aes(y = Feature,x = `%IncMSE`)) +
geom_segment(aes(y = rev_Rank,yend = rev_Rank,x = -5,xend = `%IncMSE`)) +
geom_point(fill = ptol_pal()(1),shape = 21,size = 2) +
theme_bw() +
theme(plot.title = element_text(face = 'bold',hjust = 0.5),
axis.title = element_text(face = 'bold',size = 10),
panel.grid = element_blank(),
panel.border = element_blank(),
plot.margin = unit(c(5.5, 5.5, 5.5, 0), "pt"),
axis.line.x = element_line(),
axis.text.y = element_text(colour = 'black'),
axis.ticks.y = element_blank()) +
labs(title = 'b) DAI',
x = '% increase in\nMSE',
y = NULL) +
scale_y_discrete(labels = descriptorLabels,
position = 'right') +
scale_x_continuous(limits = c(-5,112),
expand = c(0,0))
}
)
rank_links <- PDI_descriptor_importance %>%
arrange(`%IncMSE`) %>%
mutate(Feature = factor(Feature,levels = Feature),
PDI_Rank = Feature %>%
seq_along()) %>%
select(-`%IncMSE`,-IncNodePurity) %>%
left_join(DAI_descriptor_importance %>%
arrange(`%IncMSE`) %>%
mutate(Feature = factor(Feature,levels = Feature),
DAI_Rank = Feature %>%
seq_along()) %>%
select(-`%IncMSE`,-IncNodePurity),
by = "Feature") %>%
ggplot() +
geom_segment(x = 0,
xend = 0,
y = 1,yend = 36) +
geom_segment(x = 1,
xend = 1,
y = 1,yend = 36) +
geom_segment(aes(y = PDI_Rank,
yend = DAI_Rank,
x = 0,
xend = 1),
linetype = 5) +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
plot.margin = unit(c(5.5, 0, 5.5, 0), "pt")) +
scale_x_continuous(expand = c(0,0)) +
scale_y_discrete(labels = descriptorLabels)
wrap_plots(importance_plots$a,rank_links,importance_plots$b,widths = c(2,1,2))
}
|
5525d708ae110e18d54f817be141e08c67eda71c
|
a4bf8ea2ca052a6ebaa8d32ea427eb4f747e3b67
|
/inst/doc/edl.R
|
1da1a0657ee594f760b1f6a5de31c290b7f21d7f
|
[] |
no_license
|
cran/edl
|
941498a8f2b8d8df00ffa7c317d707ff25c189f9
|
c40160df056e31bfafda4197405bddf89158d4b4
|
refs/heads/master
| 2023-08-11T09:11:13.818275
| 2021-09-20T06:40:05
| 2021-09-20T06:40:05
| 357,249,841
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,970
|
r
|
edl.R
|
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, fig.width = 4, fig.height = 4)
## ----load-packages,message=FALSE----------------------------------------------
library(edl)
## -----------------------------------------------------------------------------
data(dat)
head(dat)
## -----------------------------------------------------------------------------
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- paste(dat$Category)
dat$Frequency <- dat$Frequency2
# remove remaining columns to simplify this example:
dat <- dat[, c("Cues", "Outcomes", "Frequency")]
# add ID for learning events:
dat$ID <- 1:nrow(dat)
head(dat)
## -----------------------------------------------------------------------------
table(dat$Outcomes)
## -----------------------------------------------------------------------------
# by default 1 run, with tokens randomized:
train <- createTrainingData(dat)
head(train)
# Frequency is always 1:
unique(train$Frequency)
# total counts per outcome match original frequencies:
table(train$Outcomes)
table(train$ID)
## ---- eval=FALSE--------------------------------------------------------------
# wm <- RWlearning(train)
## ---- include=FALSE-----------------------------------------------------------
wm <- RWlearning(train, progress = FALSE)
## -----------------------------------------------------------------------------
length(wm)
# ... which is the same as the number of rows in the training data:
nrow(train)
## -----------------------------------------------------------------------------
# after the first learning event:
wm[[1]]
# the final state of the network:
wm[[length(wm)]]
## -----------------------------------------------------------------------------
# after the first learning event:
getWM(wm,1)
## -----------------------------------------------------------------------------
wm2 <- sapply(1:length(wm), function(x){getWM(wm,x)}, simplify = FALSE)
# inspect the list of states:
length(wm2)
wm2[[1]]
## -----------------------------------------------------------------------------
# weights for outcome "plant"
weights <- getWeightsByOutcome(wm, outcome="plant")
head(weights)
tail(weights)
## -----------------------------------------------------------------------------
# weights for cue "red"
weights <- getWeightsByCue(wm, cue="red")
head(weights)
tail(weights)
## -----------------------------------------------------------------------------
act <- getActivations(wm, data=train)
head(act)
## -----------------------------------------------------------------------------
act <- getActivations(wm, data=train, select.outcomes = TRUE)
head(act)
## -----------------------------------------------------------------------------
act$Activation <- apply(act, 1, function(x){
out <- x['Outcomes']
return(as.numeric(x[out]))
})
head(act)
## ----plots-1, fig.width=8-----------------------------------------------------
oldpar <- par(mfrow=c(1,2), cex=1.1)
# plot left:
plotCueWeights(wm, cue="brown")
# plot right:
plotOutcomeWeights(wm, outcome="animal")
par(oldpar)
## ----plots-2, fig.width=8-----------------------------------------------------
oldpar <- par(mfrow=c(1,2), cex=1.1)
# plot left:
# 1. get outcome values:
out <- getValues(train$Outcomes, unique=TRUE)
out <- out[out != "animal"]
# 2. plot all outcomes, except 'plural':
lab <- plotCueWeights(wm, cue="brown", select.outcomes = out,
col=1, add.labels=FALSE, xlab='', ylim=range(getWM(wm)))
# 3. add plural:
lab2 <- plotCueWeights(wm, cue="brown", select.outcomes = "animal", col=2, lwd=2, adj=0, add=TRUE, font=2)
# 4. add legend:
legend_margin('bottom', ncol=4,
legend=c(lab2$labels, lab$labels),
col=c(lab2$col, lab$col), lty=c(lab2$lty, lab$lty),
lwd=c(lab2$lwd, lab$lwd), bty='n', cex=.85)
# plot right, different layout variant:
out <- getValues(dat$Cues, unique=TRUE)
out <- out[out != "animal"]
lab <- plotOutcomeWeights(wm, outcome="animal", select.cues = out,
col=alpha(1, f=.25), lty=1, pos=4, ylim=c(-.02,.2), font=2, ylim=range(getWM(wm)))
lab2 <- plotOutcomeWeights(wm, outcome="animal", select.cues = "brown", col='red', lwd=2, pos=4, add=TRUE, font=2)
par(oldpar)
## ----getWeights-1-------------------------------------------------------------
weights <- getWeightsByCue(wm, cue="brown")
head(weights)
## ---- fig.width=8, results='hold'---------------------------------------------
oldpar <- par(mfrow=c(1,2), cex=1.1)
# an observed cueset:
plotActivations(wm, cueset="BG_cat_brown")
# an un-observed cueset:
plotActivations(wm, cueset="BG_cat_yellow")
par(oldpar)
## ----continue-1---------------------------------------------------------------
# create a second data set with different frequencies:
data(dat)
head(dat)
## -----------------------------------------------------------------------------
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- paste(dat$Category)
dat$Frequency <- dat$Frequency1
# remove remaining columns to simplify this example:
dat <- dat[, c("Cues", "Outcomes", "Frequency")]
# add ID for learning events:
dat$ID <- 1:nrow(dat)
head(dat)
# create training data:
train2 <- createTrainingData(dat)
## -----------------------------------------------------------------------------
# continue learning from last weight matrix:
wm2 <- RWlearning(train2, wm=getWM(wm), progress = FALSE)
# number of learned event matches rows in dat2:
nrow(train2)
length(wm2)
# Alternatively, add the learning events to the existing output list wm1:
wm3 <- RWlearning(train2, wm=wm, progress = FALSE)
# number of learned event are now added to wm1:
length(wm3)
## -----------------------------------------------------------------------------
out <- getValues(dat$Cues, unique=TRUE)
out <- out[out != "animal"]
lab <- plotOutcomeWeights(wm3, outcome="animal",
select.cues = out,
col=alpha(1, f=.25), lty=1, pos=4,
ylim=c(-.02,.2), font=2, ylim=range(getWM(wm3)),
xmark=TRUE, ymark=TRUE, las=1)
lab2 <- plotOutcomeWeights(wm3, outcome="animal",
select.cues = "brown", col='red',
lwd=2, pos=4, add=TRUE, font=2)
abline(v=length(wm), lty=3)
## -----------------------------------------------------------------------------
# select weight matrix:
mat <- getWM(wm)
# for a cueset:
activationsMatrix(mat,cues="BG_cat_brown")
# for a specific outcome:
activationsMatrix(mat,cues="BG_cat_brown", select.outcomes = "animal")
# for a group of cuesets (all connection weights will be added):
activationsMatrix(mat,cues=c("BG_cat_brown", "BG_cat_blue"))
## -----------------------------------------------------------------------------
# new dummy data:
dat <- data.frame(Cues = c("noise", "noise", "light"),
Outcomes = c("food", "other", "food_other"),
Frequency = c(5, 10, 15) )
dat$Cues <- paste("BG", dat$Cues, sep="_")
train <- createTrainingData(dat)
wm <- RWlearning(train, progress = FALSE)
# list with activations for observed outcomes:
act <- activationsEvents(wm, data=train)
head(act)
# calculate max activation:
maxact <- lapply(act, function(x){ return(max(x, na.rm=TRUE)) })
unlist(maxact)
# Using argument 'fun':
act <- activationsEvents(wm, data=train, fun="max")
head(act)
## -----------------------------------------------------------------------------
# list with activations for observed outcomes:
act <- activationsCueSet(wm, cueset=c("BG_noise", "BG_light", "BG_somethingelse"))
names(act)
head(act[[1]])
# also activations for non-trained connections:
head(act[[3]])
## -----------------------------------------------------------------------------
# list with activations for observed outcomes:
act <- activationsOutcomes(wm, data=train)
head(act)
|
f9e45ef6b8af83ad12427af9b37ece404d0e5608
|
2fb32969061efc165421d571e688848240e537a5
|
/man/plostitle.Rd
|
60d20941deb0ddf912ad83625b881df69757ceec
|
[] |
no_license
|
phillord/rplos
|
cb740bb57eb9f249e94aa360ca11c6d36144f439
|
a0e75c4399d85fd98709fcccdc28bf7e5ff32471
|
refs/heads/master
| 2021-01-15T18:41:41.776122
| 2012-10-23T15:18:37
| 2012-10-23T15:18:37
| 8,477,914
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 985
|
rd
|
plostitle.Rd
|
\name{plostitle}
\alias{plostitle}
\title{Search PLoS Journals titles.}
\usage{
plostitle(terms, fields = NULL, limit = NULL,
url = "http://api.plos.org/search",
key = getOption("PlosApiKey", stop("need an API key for PLoS Journals")))
}
\arguments{
\item{terms}{search terms for article titles (character)}
\item{fields}{fields to return from search (character)
[e.g., 'id,title'], any combination of search fields [see
plosfields$field]}
\item{limit}{number of results to return (integer)}
\item{url}{the PLoS API url for the function (should be
left to default)}
\item{key}{your PLoS API key, either enter, or loads from
.Rprofile}
}
\value{
Titles, in addition to any other fields requested in a
data.frame.
}
\description{
Search PLoS Journals titles.
}
\examples{
\dontrun{
plostitle(terms='drosophila', fields='title', limit=99)
plostitle(terms='drosophila', fields='title,journal', limit=10)
plostitle(terms='drosophila', limit = 5)
}
}
|
a2bcbe3fce797b854786d6827d60bca3ba8d7bfc
|
64257a0e57cf928b0ae7676a108a3688001181bd
|
/tests/testthat/test-count_events.R
|
5b81b9c4419bcb0c048109af32c7c2d9dea6ff8e
|
[
"BSD-3-Clause"
] |
permissive
|
marcpaterno/artsupport
|
9842a678c8070468dd93a258810b84067fe22f32
|
803310561741c4aa54bdd44e393da9ae8551bfa0
|
refs/heads/master
| 2020-06-30T06:49:04.780687
| 2020-04-20T23:14:48
| 2020-04-20T23:14:48
| 74,387,093
| 0
| 1
|
NOASSERTION
| 2019-02-07T07:11:33
| 2016-11-21T17:15:33
|
R
|
UTF-8
|
R
| false
| false
| 468
|
r
|
test-count_events.R
|
context("count_events")
test_that("counting (distinct) events works for unlabeled data", {
a <- load_module_timing("timing-a.db")
expect_equal(count_events(a), 120)
b <- load_module_timing("timing-b.db")
expect_equal(count_events(b), 240)
})
test_that("counting (distinct) events works for labeled data", {
a <- load_module_timing("timing-a.db", "a")
b <- load_module_timing("timing-b.db", "b")
x <- rbind(a, b)
expect_equal(count_events(x), 360)
})
|
3fa9eeef2f9df5e8b2e60f6fc9ffc80b2ce0ca47
|
9713c2a057cb71e8641e6f40fea276ac4e722a34
|
/Paper/Descriptive_Analysis.R
|
90eadbb3b234cd39d7934c70a16555d4a75ac330
|
[] |
no_license
|
Insper-Data/Data_Macro
|
4da269c71d2af6d72a1965659083a547e2403688
|
c805895f45ddc162d614829e8ca1b70677a3b4f7
|
refs/heads/master
| 2023-07-12T02:30:39.424353
| 2021-08-25T22:11:04
| 2021-08-25T22:11:04
| 296,466,536
| 4
| 4
| null | 2020-11-02T21:52:55
| 2020-09-17T23:44:59
|
HTML
|
UTF-8
|
R
| false
| false
| 20,078
|
r
|
Descriptive_Analysis.R
|
# JANUARY 2021
# Script to generate figures used in our paper
# Authors: Augusto Netto, Gabriela Garcia, Maria Clara Drzeviechi and Victor H. Alexandrino
#--------------------------------------------------------------------------------------------
# Libraries
library(scales)
library(dplyr)
library(ggthemes)
library(spData)
library(readr)
library(readxl)
library(spData)
library(plotly)
library(sf)
library(viridis)
library(ggcharts)
library(ggrepel)
library(cowplot)
library(tidyr)
library(dplyr)
library(grid)
library(forecast)
# Calling our dataset
dataset_total_jan_2021 <- read.csv("https://raw.githubusercontent.com/Insper-Data/Data_Macro/master/Paper/Datasets/dataset_total_jan_2021.csv")
dataset_total <- dataset_total_jan_2021
#--------------------------------------------------------------------------------------------
# STYLIZED FACTS ABOUT THE DATABASE
#--------------------------------------------------------------------------------------------
# 1. The importance of debt
debt_to_gdp_graph <- dataset_total %>%
rename(Development = develop) %>%
group_by(Development, year) %>%
summarise(debt_to_GDP = mean(debt_to_GDP)) %>%
ggplot(aes(x = year, y = debt_to_GDP, color=Development )) +
scale_color_manual(values = c("EM" = "red4", "AM" = "navyblue"))+
geom_point() +
geom_line()+
labs(x = "Year", y = "Debt-to-GDP Ratio (%)", title = "", subtitle = "") +
scale_x_continuous(limits = c(2004, 2019), seq(2004,2019,by=2), name = "Year") +
ylim(30,90)+
theme_bw()
debt_to_gdp_graph
# 2.
dataset_total %>%
filter(year %in% c(2004, 2010, 2014, 2019), !is.na(develop)) %>%
rename(Development = develop) %>%
bar_chart(x = country, y = debt_to_GDP, facet = year, top_n = 10, fill = Development) +
labs( x = "", y = "Debt-to-GDP Ratio (%)",
title = "", fill = "Development") +
theme_classic() +
scale_fill_manual("Development", values = c("EM" = "red4", "AM" = "navyblue"))+
theme(legend.title = element_text(face = "bold", size = 10))
# 3.
graph_debt_foreign_pp <- dataset_total %>%
filter(!is.na(develop)) %>%
rename(Development = develop) %>%
group_by(year, Development) %>%
summarise(foreign_participation_percent_GDP = mean(foreign_participation_percent_GDP)) %>%
ggplot(aes(x = year, y = foreign_participation_percent_GDP, color=Development )) +
scale_color_manual(values = c("EM" = "red4", "AM" = "navyblue"))+
geom_point() +
geom_line()+
labs(x = "Year", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)", title = "", subtitle = "") +
scale_x_continuous(limits = c(2004, 2019), seq(2004,2019,by=2), name = "Year") +
#ylim(50,160)+
theme_light()
graph_debt_foreign_pp
# 4.
dataset_total %>%
rename(Development = develop) %>%
filter(year %in% c(2004, 2010, 2014, 2019), !is.na(Development)) %>%
bar_chart(x = country, y = (foreign_participation_percent_GDP), facet = year, top_n = 10, fill = Development) +
labs( x = "", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)",
title = "", fill = "Development") +
theme_classic() +
theme(legend.title = element_text(face = "bold", size = 10)) +
scale_fill_manual("Development", values = c("EM" = "red4", "AM" = "navyblue"))
################################################ FUNDAMENTALS ################################################################################################################
# 5.1
dataset_total %>%
filter(develop == "AM") %>%
ggplot(aes(x = fx_volatility, y = foreign_participation_percent_GDP)) +
geom_point(color="navyblue")+
labs(x = "Exchange Rate Volatility", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)") +
xlim(0,1)+
theme_bw()
# 5.2
dataset_total %>%
filter(develop == "EM" ) %>%
ggplot(aes(x = fx_volatility, y = foreign_participation_percent_GDP)) +
geom_point(color="red4")+
labs(x = "Exchange Rate Volatility", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)") +
xlim(0,1)+
theme_bw()
# 5.3 Mostra a relação da inflação com a participação na dívida e o tamanho da bolinha é o PIB percapita (note que ele vai diminuindo)
dataset_total %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T),
upper = max(foreign_participation_percent_GDP),
lower = min(foreign_participation_percent_GDP),
GDP_percapita_cur_USD = GDP_percapita_cur_USD/1000) %>%
ggplot() +
geom_point(aes(x = mean_inflation, y = foreign_participation_percent_GDP, colour = develop,
size = GDP_percapita_cur_USD), alpha = .2) +
#geom_errorbar(aes(ymin = lower, ymax = upper), width = .2) +
labs(x = "Mean Inflation Between 2004-2019 (%)", y = "Foreign Participation in Sovereign\n Debt in Terms of GDP (%)") +
scale_color_manual(values = c("navyblue", "red4")) +
guides(col=guide_legend(""),
size=guide_legend("GDP per capita \n(thousand USD)")) +
theme_light() +
theme(axis.text.y = element_text(margin = margin(l = 8)),
axis.text.x = element_text(margin = margin(b = 8)))
# 5.4 Mesmo que no de cima, mas colorindo por país
dataset_total %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T)) %>%
ggplot() +
geom_point(aes(x = mean_inflation, y = foreign_participation_percent_GDP, colour = country,
size = political_stability_rank), alpha = .5)+
#labs(x = "ln(GDP per capita USD)", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)") +
#scale_color_viridis_d("magma") +
theme_light() +
theme(legend.position = "none")
# 5.5 Relação entre volatilidade do crescimento real e a participação
dataset_total %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T),
GDP_growth = (GDP_cte_billions - lag(GDP_cte_billions, k = 1))/lag(GDP_cte_billions, k = 1),
sd_GDP_growth = sd(GDP_growth, na.rm = T),
mean_share = mean(foreign_participation_percent_GDP)) %>%
ggplot() +
geom_label(aes(x = sd_GDP_growth, y = mean_share, colour = develop,
size = mean_indebt, label = country), alpha = .3) +
#labs(x = "ln(GDP per capita USD)", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)") +
scale_color_manual(values = c("navyblue", "red4")) +
theme_light() +
facet_wrap(~develop) +
theme(legend.position = "none")
# 5.6 Relação entre média do crescimento real e a participação - sensibilizando por Rule of Law
dataset_total %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T),
GDP_growth = (GDP_cte_billions - lag(GDP_cte_billions, k = 1))/lag(GDP_cte_billions, k = 1),
mean_GDP_growth = mean(GDP_growth, na.rm = T),
sd_GDP_growth = sd(GDP_growth, na.rm = T),
mean_rule = mean(rule_of_law_rank, na.rm = T),
mean_share_ex_off = mean(foreign_ex_officials_participation_percent_GDP)) %>%
ggplot() +
geom_label(aes(x = mean_GDP_growth, y = mean_share_ex_off, colour = develop,
size = mean_rule, label = country), alpha = .3) +
guides(size = guide_legend("Rule of\nLaw"),
colour = FALSE) +
labs(x = "Mean GDP Growth Between 2004-2019 (%)", y = "Mean Foreign Participation in\nSovereign Debt in Terms of GDP (%)") +
scale_color_manual(values = c("navyblue", "red4")) +
theme_light() +
theme(axis.text.y = element_text(margin = margin(l = 8)),
axis.text.x = element_text(margin = margin(b = 8))) +
facet_wrap(~develop)
# 5.7 Relação entre volatilidade do cambio e a participação - sensibilizando por debt-to-gdp
dataset_5.7_int <- dataset_total %>%
filter(country == "United States") %>%
group_by() %>%
select(year, inflation_end) %>%
rename(US_inflation_rate = inflation_end)
dataset_5.7 <- dataset_total %>%
left_join(dataset_5.7_int, by = "year")
dataset_5.7 %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T),
GDP_growth = (GDP_cte_billions - lag(GDP_cte_billions, k = 1))/lag(GDP_cte_billions, k = 1),
mean_GDP_growth = mean(GDP_growth, na.rm = T),
sd_GDP_growth = sd(GDP_growth, na.rm = T),
x = US_inflation_rate/inflation_end,
fx_vol_real = mean(x, na.rm = T),
mean_share_ex_off = mean(foreign_ex_officials_participation_percent_GDP)) %>%
ggplot() +
geom_point(aes(x = log(fx_volatility*10000), y = foreign_participation_percent_GDP, colour = develop,
size = debt_to_GDP), alpha = .3) +
#labs(x = "ln(GDP per capita USD)", y = "Foreign Participation in Sovereign Debt in Terms of GDP (%)") +
scale_color_manual(values = c("navyblue", "red4")) +
theme_light() #+
facet_wrap(~develop) +
theme(legend.position = "none")
# 5.8 Níveis de inflação dividindo por desenvolvimento
x_order <- c("From -1 to 2.5", "From 2.5 to 5", "From 5 to 7.5", "From 7.5 to 10", "From 10 to 12.5",
"From 12.5 to 15", "15 +")
dataset_total %>%
mutate(inflation_level = ifelse(inflation_end > - 1 & inflation_end <= 2.5, "From -1 to 2.5",
ifelse(inflation_end < 5, "From 2.5 to 5",
ifelse(inflation_end < 7.5, "From 5 to 7.5",
ifelse(inflation_end < 10, "From 7.5 to 10",
ifelse(inflation_end < 12.5, "From 10 to 12.5",
ifelse(inflation_end < 15, "From 12.5 to 15", "15 +"))))))) %>%
filter(!is.na(inflation_level)) %>%
ggplot() +
geom_violin(aes(factor(inflation_level, levels = x_order), foreign_participation_percent_GDP, fill = develop,
colour = develop), trim = T) +
geom_vline(xintercept = 1.5,
color = "black", size = .6) +
geom_vline(xintercept = 2.5,
color = "black", size = .6) +
geom_vline(xintercept = 3.5,
color = "black", size = .6) +
geom_vline(xintercept = 4.5,
color = "black", size = .6) +
geom_vline(xintercept = 5.5,
color = "black", size = .6) +
geom_vline(xintercept = 6.5,
color = "black", size = .6) +
scale_color_manual(values = c("navyblue", "red4")) +
scale_fill_manual(values = c("navyblue", "red4")) +
guides(fill = guide_legend(""),
color = guide_legend("")) +
theme_light() +
labs(x = "Inflation Level (%)", y = "Foreign Participation in Sovereign\n Debt in Terms of GDP (%)") +
theme(axis.line.x = element_line(colour = "black", size = .6),
axis.line.y = element_line(colour = "black", size = .6),
axis.text.y = element_text(margin = margin(l = 8)),
axis.text.x = element_text(margin = margin(b = 8)))
# 5.8 Níveis de inflação
x_order <- c("From -1 to 2.5", "From 2.5 to 5", "From 5 to 7.5", "From 7.5 to 10", "From 10 to 12.5",
"From 12.5 to 15", "15 +")
dataset_total %>%
mutate(inflation_level = ifelse(inflation_end > - 1 & inflation_end <= 2.5, "From -1 to 2.5",
ifelse(inflation_end < 5, "From 2.5 to 5",
ifelse(inflation_end < 7.5, "From 5 to 7.5",
ifelse(inflation_end < 10, "From 7.5 to 10",
ifelse(inflation_end < 12.5, "From 10 to 12.5",
ifelse(inflation_end < 15, "From 12.5 to 15", "15 +"))))))) %>%
filter(!is.na(inflation_level)) %>%
ggplot() +
geom_violin(aes(factor(inflation_level, levels = x_order), foreign_participation_percent_GDP), fill = "black") +
scale_color_manual(values = c("navyblue", "red4")) +
scale_fill_manual(values = c("navyblue", "red4")) +
theme_light() +
xlab("Inflation Levels (%)")
# 5.9 Relação entre volatilidade do cambio e a participação - sensibilizando por rule of law
dataset_5.9_int <- dataset_total %>%
filter(country == "United States") %>%
group_by() %>%
select(year, inflation_end) %>%
rename(US_inflation_rate = inflation_end)
dataset_5.9 <- dataset_total %>%
left_join(dataset_5.9_int, by = "year")
dataset_5.9 %>%
group_by(country) %>%
mutate(mean_indebt = mean(debt_to_GDP, na.rm = T),
mean_inflation = mean(inflation_end, na.rm = T),
mean_fiscal = mean(lending_borrowing_percent_GDP, na.rm = T),
mean_percapita = mean(GDP_percapita_cur_USD, na.rm = T),
GDP_growth = (GDP_cte_billions - lag(GDP_cte_billions, k = 1))/lag(GDP_cte_billions, k = 1),
mean_GDP_growth = mean(GDP_growth, na.rm = T),
sd_GDP_growth = sd(GDP_growth, na.rm = T),
x = US_inflation_rate/inflation_end,
fx_vol_real = mean(x, na.rm = T),
mean_share_ex_off = mean(foreign_ex_officials_participation_percent_GDP)) %>%
ggplot() +
geom_point(aes(x = log(fx_volatility*10000), y = foreign_participation_percent_GDP, colour = develop,
size = rule_of_law_rank), alpha = .4) +
labs(x = "FX Volatility*", y = "Foreign Participation in Sovereign\n Debt in Terms of GDP (%)") +
scale_color_manual(values = c("navyblue", "red4")) +
guides(col=guide_legend(""),
size=guide_legend("Rule of\nLaw Rank")) +
theme_light() +
theme(axis.text.y = element_text(margin = margin(l = 8)),
axis.text.x = element_text(margin = margin(b = 8)))
################################## EXTERNAL FACTORS ##############################################################
dataset_total2 <- dataset_total %>%
select(vix_EUA, foreign_participation_percent_GDP, year, develop, country)
dataset_total2 <- dataset_total2 %>%
group_by(year) %>%
mutate(VIX = mean(vix_EUA))
dataset_total2 <- dataset_total2 %>%
group_by(year, develop) %>%
mutate(foreign_GDP = mean(foreign_participation_percent_GDP))
dataset_total2 <- dataset_total2 %>%
select(3,4,6,7)
dataset_total2 <- dataset_total2 %>%
distinct()
dataset3 <- dataset_total2
dataset3 <- dataset3 %>%
mutate(foreign_AM = ifelse(develop == "AM", foreign_GDP, 0))
dataset3 <- dataset3 %>%
filter(foreign_AM != 0)
dataset4 <- dataset_total2
dataset4 <- dataset4 %>%
mutate(foreign_EM = ifelse(develop =="EM", foreign_GDP, 0))
dataset4 <- dataset4 %>%
filter(foreign_EM != 0)
dataset3 <- dataset3 %>%
select(1,3,5)
dataset4 <- dataset4 %>%
select(1,3,5)
dataset4 <- dataset4 %>%
left_join(dataset3, by="year")
dataset4 <- dataset4 %>%
select(2,3,4,7)
dataset4 <- dataset4 %>%
rename(VIX = 2,
AM = 4,
EM = 3 ) %>%
mutate(VIX = (VIX/100))
dataset4 <- dataset4 %>%
select(year, VIX, AM, EM) %>%
gather(key = "variable", value = "value", -year)
ggplot(dataset4, aes(x = year, y = value)) +
geom_line(aes(color = variable), size = 1) +
scale_color_manual(values = c("navyblue", "red4" , "black")) +
labs(x = "Year", y = "", title = "", subtitle = "") +
scale_x_continuous(limits = c(2004, 2019), seq(2004, 2019, by=2), name = "Year") +
scale_y_continuous(breaks=NULL) +
theme_bw()
# Another way to display the same graph:
dataset5 <- dataset_total %>%
select(year, vix_EUA) %>%
filter(row_number() <= 16) %>%
rename(value = vix_EUA) %>%
mutate(variable = "US VIX")
dataset6 <- dataset_total %>%
filter(!is.na(foreign_participation_percent_GDP)) %>%
group_by(year, develop) %>%
summarise(foreign_GDP = mean(foreign_participation_percent_GDP), year = year) %>%
select(c(year, foreign_GDP)) %>%
distinct() %>%
rename(variable = develop, value = foreign_GDP)
p1 <- dataset5 %>%
ggplot() +
geom_line(aes(x = year, y = value, colour = variable), size = 1) +
scale_color_manual(values = c("black")) +
labs(x = "", y = "US VIX", title = "", subtitle = "") +
scale_x_continuous(limits = c(2004, 2019), seq(2004, 2019, by=2), name = "Year") +
#scale_y_continuous(breaks=NULL) +
theme_light() +
theme(legend.title = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_line(color = "black"),
panel.border = element_blank(),
plot.caption = element_blank(),
axis.text.y = element_text(margin = margin(l = 8)))
(p2 <- dataset6 %>%
ggplot(aes(x = year, y = value, fill = variable)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("navyblue", "red4")) +
labs(x = "Year", y = "Foreign Participation in \nSovereign Debt (% of GDP)", title = "", subtitle = "") +
scale_x_continuous(limits = c(2003, 2020), seq(2004, 2019, by = 2), name = "Year") +
#scale_y_continuous(breaks=NULL) +
theme_light() +
theme(legend.title = element_blank(),
plot.title = element_blank(),
axis.line.x = element_line(color = "black"),
panel.border = element_blank(),
plot.subtitle = element_blank(),
axis.text.y = element_text(margin = margin(l = 8))))
grid.newpage()
grid.draw(rbind(ggplotGrob(p1),
ggplotGrob(p2),
size = "last"))
#DXY
# 3.
dataset7 <- dataset_total %>%
select(year, dxy) %>%
filter(row_number() <= 16) %>%
rename(value = dxy) %>%
mutate(variable = "DXY")
dataset8 <- dataset_total %>%
filter(!is.na(foreign_participation_percent_GDP)) %>%
group_by(year, develop) %>%
summarise(foreign_GDP = mean(foreign_participation_percent_GDP), year = year) %>%
select(c(year, foreign_GDP)) %>%
distinct() %>%
rename(variable = develop, value = foreign_GDP)
p3 <- dataset7 %>%
ggplot() +
geom_line(aes(x = year, y = value, colour = variable), size = 1) +
scale_color_manual(values = c("black")) +
labs(x = "", y = "DXY", title = "", subtitle = "") +
scale_x_continuous(limits = c(2004, 2019), seq(2004, 2019, by=2), name = "Year") +
#scale_y_continuous(breaks=NULL) +
theme_light() +
theme(legend.title = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_line(color = "black"),
panel.border = element_blank(),
plot.caption = element_blank(),
axis.text.y = element_text(margin = margin(l = 8)))
(p4 <- dataset8 %>%
ggplot(aes(x = year, y = value, fill = variable)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("navyblue", "red4")) +
labs(x = "Year", y = "Foreign Participation in \nSovereign Debt (% of GDP)", title = "", subtitle = "") +
scale_x_continuous(limits = c(2003, 2020), seq(2004, 2019, by = 2), name = "Year") +
#scale_y_continuous(breaks=NULL) +
theme_light() +
theme(legend.title = element_blank(),
plot.title = element_blank(),
axis.line.x = element_line(color = "black"),
panel.border = element_blank(),
plot.subtitle = element_blank(),
axis.text.y = element_text(margin = margin(l = 8))))
grid.newpage()
grid.draw(rbind(ggplotGrob(p3),
ggplotGrob(p4),
size = "last"))
|
c99243c954f65096e4ddcc8921b21bf586d24444
|
b7e84b97452dde338b7bc8f9d8e9df33168a61d1
|
/project/man/wrangle.TablesList.Rd
|
569b91539d302080b4d12078709192366639ed17
|
[
"MIT"
] |
permissive
|
melissachamary/RDataWrangler
|
338bf153e55bc25cb954312cdab61223413a73e8
|
5337ae7793738c6d2bd698a5559757cb65101a10
|
refs/heads/master
| 2020-04-27T06:42:39.176883
| 2019-05-07T07:08:58
| 2019-05-07T07:08:58
| 173,745,360
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 782
|
rd
|
wrangle.TablesList.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DataWrangle.R
\name{wrangle.TablesList}
\alias{wrangle.TablesList}
\title{wrangle.TablesList
Import data table list into R (according source_file description) and wrangle into new data table list following wrangle_parameter_file description.}
\usage{
wrangle.TablesList(wrangler_parameter, table_list, call_customFunction)
}
\arguments{
\item{wrangle_parameter}{file of parameter data frame (see format)}
\item{sources_file}{name of data frame on which foreign key constraint is checked}
}
\value{
list of data table
}
\description{
wrangle.TablesList
Import data table list into R (according source_file description) and wrangle into new data table list following wrangle_parameter_file description.
}
|
25a665f012d90ee8bf88d2282d58d94de73fe12a
|
45a5b0036c075cf4ecb71d0018443b1b125e1fac
|
/man/print.predict.ocm.Rd
|
084ab1f4f4bb34e540b5dceed8a627fa77fb0218
|
[] |
no_license
|
SvetiStefan/ordinalCont
|
54a45adfc0b6638a904529e569e773a2277d4d27
|
fc94c4c90a71f920103f877a7d3abb0a8c77c0f8
|
refs/heads/master
| 2021-05-28T20:07:44.093122
| 2015-05-26T05:46:23
| 2015-05-26T05:46:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 867
|
rd
|
print.predict.ocm.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/ocm.methods.R
\name{print.predict.ocm}
\alias{print.predict.ocm}
\title{Print the output of predict method}
\usage{
\method{print}{predict.ocm}(x, ...)
}
\arguments{
\item{x}{an object of class \code{predict.ocm}}
\item{...}{further arguments passed to or from other methods}
}
\description{
Print method for class \code{predict.ocm}
}
\details{
The table of predictions from \code{predict.ocm} is printed.
}
\examples{
ANZ0001.ocm <- ANZ0001[ANZ0001$cycleno==0 | ANZ0001$cycleno==5,]
ANZ0001.ocm$cycleno[ANZ0001.ocm$cycleno==5] <- 1
fit.overall <- ocm(overall ~ cycleno + age + bsa + treatment, data=ANZ0001.ocm)
pred <- predict(fit.overall)
print(pred)
}
\author{
Maurizio Manuguerra, Gillian Heller
}
\seealso{
\code{\link{predict.ocm}}, \code{\link{ocm}}
}
\keyword{predict}
|
e97a560a3485fa2044fbac55b58fb294c3b6f61e
|
f7da4c0a8eabae8bcc23f15095cb534204d5794d
|
/scripts/lake_meta.r
|
3cc9bbbad67c24513278a243dc1d4d0fe7dbcc95
|
[] |
no_license
|
calandryll/coursey_pond
|
9e94e8640cfedba2d68326029329408c36fc4728
|
a35baa50c17397e31d6a760017c8b2b123c874a4
|
refs/heads/master
| 2021-04-28T13:27:54.662225
| 2018-02-19T19:06:43
| 2018-02-19T19:06:43
| 122,106,407
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,039
|
r
|
lake_meta.r
|
source('scripts/fun.prod.new.r')
coursey = read.csv("csv/CP_cleaned.csv")
coursey = coursey %>% mutate(Date = ymd_hms(Date, tz = 'EST'))
coursey = coursey %>% mutate(Wind_cor = WSpd * (10 / 4.5)^0.15, k600 = k.crusius.base(Wind_cor, method = 'bilinear'), K_wind = k600.2.kGAS.base(k600, Temp, gas = 'O2'))
prod.ols = production(coursey, wind = 'Wind_cor', method = 'ols')
prod.mle = production(coursey, wind = 'Wind_cor', method = 'mle', error.type = 'OE')
prod.classic = production(coursey, wind = 'Wind_cor', method = 'classic')
prod.kal = production(coursey, wind = 'Wind_cor', method = 'kalman')
prod.bayes = production(coursey, wind = 'Wind_cor', method = 'bayesian')
coursey.stats = coursey %>%
mutate(Wind_cor = (WSpd * (10/4.5)^0.15)) %>%
group_by(Date_2) %>%
summarise(Avg_Temp = mean(Temp, na.rm = T),
Avg_ODO = mean(ODO_mgL, na.rm = T),
Avg_Sat = mean(ODO_sat, na.rm = T),
Max_ODO = max(ODO_mgL),
Min_ODO = min(ODO_mgL),
Max_Sat = max(ODO_sat),
Min_Sat = min(ODO_sat),
Avg_chl = mean(Chl, na.rm = T),
Avg_BGA = mean(BGA, na.rm = T),
Max_PAR = max(TotPAR, na.rm = T),
Avg_Per = mean(TotPrcp, na.rm = T),
Avg_Wind = mean(Wind_cor, na.rm = T)) %>%
mutate(Date = ymd(Date_2, tz = 'EST')) %>%
select(-Date_2)
prod.ols = inner_join(prod.ols, coursey.stats)
prod.mle = inner_join(prod.mle, coursey.stats)
prod.classic = inner_join(prod.classic, coursey.stats)
prod.kal = inner_join(prod.kal, coursey.stats)
prod.bayes = inner_join(prod.bayes, coursey.stats)
write.csv(prod.ols, 'csv/prod_ols.csv', row.names = FALSE)
write.csv(prod.mle, 'csv/prod_mle.csv', row.names = FALSE)
write.csv(prod.classic, 'csv/prod_classic.csv', row.names = FALSE)
write.csv(prod.kal, 'csv/prod_kalman.csv', row.names = FALSE)
write.csv(prod.bayes, 'csv/prod_bayesian.csv', row.names = FALSE)
write.csv(coursey, 'csv/coursey_lakemeta.csv', row.names = FALSE)
|
a29967e0d692bbfd5135abb7b83729a2750c2c18
|
6b39480379545b6a50d863f8263bb0c0a5e56cfb
|
/scripts/1_getting_started.R
|
d0aa1dbbf8bbdfe3b0038f7b89082bc9b73600b4
|
[] |
no_license
|
loremarchi/hands-on-machine-learning-R-module-3
|
6b9dbe6aba6d871a41eb83361bb2aa9e100674b8
|
4077d3fd4cc84ad3dc507d66203a2ceae0e90454
|
refs/heads/main
| 2023-07-13T00:33:08.777046
| 2021-08-29T09:24:41
| 2021-08-29T09:24:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 715
|
r
|
1_getting_started.R
|
##############################################
# Getting started
##############################################
## --------------------------------------------------------------------------------------------------------------------------------------------------
library(keras)
x <- k_constant(c(1, 2, 3, 4, 5, 6),
shape = c(3, 2))
x
## --------------------------------------------------------------------------------------------------------------------------------------------------
k_mean(x, axis = 1)
k_mean(x, axis = 2)
## Your Turn!
## --------------------------------------------------------------------------------------------------------------------------------------------------
|
4e885f06bc322551fa1319b45232de972c58055e
|
38a5a35e74e487f400fccb327749a1a97e0309a8
|
/code/create_biccn_signac.R
|
365498d9d962a05482908d66b0d55aa5d72137c4
|
[] |
no_license
|
timoast/signac-paper
|
1d0f303f20ab018aa69e8929f6a66cc110e1c81f
|
1cdbb6dd6a5ad817bd23bb7d65319d5bf802455f
|
refs/heads/master
| 2023-08-25T10:59:33.951761
| 2021-11-01T21:05:01
| 2021-11-01T21:05:01
| 309,495,686
| 7
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,453
|
r
|
create_biccn_signac.R
|
library(Signac)
library(Seurat)
library(GenomicRanges)
library(future)
plan("multicore", workers = 8)
options(future.globals.maxSize = +Inf)
annot <- readRDS("data/biccn/annotations.rds")
# load metadata
metadata <- read.table("data/biccn/Supplementary Table 2 - Metatable of nuclei.tsv", sep="\t", skip=1)
rownames(metadata) <- metadata$V1
colnames(metadata) <- c("cell", "sample", "barcode", "logUM", "TSSe", "class", "MajorType", "SubType", "na")
cells <- metadata$cell
frags <- "data/biccn/fragments.bed.gz"
peaks <- read.table(file = "data/biccn/unified_peaks.bed", sep = "\t", header = TRUE)
peaks <- makeGRangesFromDataFrame(peaks)
start.time <- Sys.time()
fragments <- CreateFragmentObject(
path = frags,
cells = cells
)
# quantify
counts <- FeatureMatrix(
fragments = fragments,
features = peaks,
cells = cells
)
# create object
assay <- CreateChromatinAssay(counts = counts, fragments = fragments, annotation = annot)
obj <- CreateSeuratObject(counts = assay, assay = "ATAC")
gc()
# QC
obj <- NucleosomeSignal(obj)
obj <- TSSEnrichment(obj)
# LSI
obj <- FindTopFeatures(obj)
obj <- RunTFIDF(obj)
obj <- RunSVD(obj)
# clustering
obj <- FindNeighbors(obj, reduction = "lsi", dims = 2:100)
obj <- FindClusters(obj)
# UMAP
obj <- RunUMAP(obj, reduction = "lsi", dims = 2:100)
elapsed <- as.numeric(Sys.time() - start.time, units = "secs")
writeLines(text = as.character(elapsed), con = "data/biccn/signac_total_runtime.txt")
|
cdf869e8520eb555d7a0c6126c5d8f298072709f
|
7870272b64347ed7e88f26be3d4082a9ba4c5ba6
|
/IJCAJ/dividetest.R
|
51e44eff2bb2e5afb1865a0736ddf0c901bc0d07
|
[] |
no_license
|
jiahui-qin/MachineLearning
|
6ded04dca27f2024f0bd386fc78cd3ef1d844e3c
|
e80f48ca7946cd6baee3a9939099fd118b1831b2
|
refs/heads/master
| 2021-09-16T03:25:37.483320
| 2018-06-15T16:02:43
| 2018-06-15T16:02:43
| 104,068,207
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 252
|
r
|
dividetest.R
|
#划分测试集
#先用item_id 举例子
train_list <- fct_count(subset(train,train$context_timestamp != 6)$item_id)$f
ifelse(train[which(train$context_timestamp == 6),]$item_id %in% train_list, train[which(train$context_timestamp == 6),]$item_id ,-1)
|
355e985731c26e8b954fb0926e646dc8ae3ebf77
|
bb660ae1c6194d054248ca508475493ee264a6ae
|
/man/near_channel_stats.Rd
|
e10bb2ffbf2108ac2813235ef5d48d3e7f9887c4
|
[
"MIT"
] |
permissive
|
hrvg/RiverML
|
bdfdd6d47f2bb7a7a26c255fa4bf268b11f59c43
|
22b3223f31f310e00313096b7f1fb3a9ab267990
|
refs/heads/master
| 2023-04-10T05:02:17.788230
| 2020-10-08T16:13:21
| 2020-10-08T16:13:21
| 288,584,365
| 0
| 0
|
NOASSERTION
| 2020-12-04T01:19:50
| 2020-08-18T23:18:50
|
R
|
UTF-8
|
R
| false
| true
| 798
|
rd
|
near_channel_stats.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tam-dm.R
\name{near_channel_stats}
\alias{near_channel_stats}
\title{Derive near-channel statistics: "median","mean", "min", "max", "sd", "skew"}
\usage{
near_channel_stats(i, .ls, .lines, .stat, bf = 100)
}
\arguments{
\item{i}{numeric, indice}
\item{.ls}{a RasterStack of terrain analysis rasters, passed from \code{get_stats_df()}}
\item{.lines}{a SpatialLinesDataFrame, passed from \code{get_stats_df()}}
\item{.stat}{character, list of statistics to derive, passed from \code{get_stats_df()}}
\item{bf}{numeric, size of the riparian buffer, default to 100 m}
}
\value{
a numeric vector of statistics
}
\description{
Derive near-channel statistics: "median","mean", "min", "max", "sd", "skew"
}
\keyword{tam-dm}
|
34157f936498a529391bf1d5a936f500837e0638
|
eb469ba06f74d3fc458b5daa94fec2a78b339bc9
|
/man/nysr.Rd
|
4091cf01298e891ec5378d392b44e3ed341b49d1
|
[] |
no_license
|
cran/thickmatch
|
a4eb817c8c7af842826b14b12bd855dce108e41d
|
061dad4bca821e1a2bef6fdade4a6f94c2a0a17a
|
refs/heads/master
| 2020-07-21T23:44:36.260401
| 2020-04-11T12:30:02
| 2020-04-11T12:30:02
| 207,004,052
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,271
|
rd
|
nysr.Rd
|
\name{nysr}
\alias{nysr}
\docType{data}
\title{
Adolescent Work Intensity and Substance Use
}
\description{
NYSR data on adolescent work intensity and substance Use.
}
\usage{data("nysr")}
\format{
A data frame with 2816 observations on the following 18 variables.
\describe{
\item{\code{IDS}}{NYSR identification number}
\item{\code{intense}}{Based on question ``During the school year, about how many hours per week did you normally work at a paid job, or did you not have a job".
``Never": student did not have a job;
``Moderate": 1-19 hours;
``Intense": >=20 hours.
}
\item{\code{family.income}}{Household income with 5000 = (between 0-10,000), 15000= (between 10,000 and 20,000),..., 95000 = (between 90,000 and 100,000) and 105,000 (above 100,000).}
\item{\code{family.income.impute}}{Household income with 5000 = (between 0-10,000), 15000= (between 10,000 and 20,000),..., 95000 = (between 90,000 and 100,000) and 105,000 (above 100,000). For subjects with missing family income, the mean is imputed.}
\item{\code{family.income.mis}}{dummy variable for whether household income is missing and the mean is imputed.}
\item{\code{family.structure}}{``Two Parent Biological": both biological father and mother living with child; ``Two Parent Nonbiological": someone assuming a mother role (biological, adoptive, stepparent) living with a husband who assumes a father role (biological, adoptive, step parent) where both parents are biological; ``Single Parent/Other": any other living situation for child.
}
\item{\code{highest.education.parent.in.household}}{Maximum education level of household resident who assumes a mother role (biological, adoptive, stepparent) and household resident who assumes a father role (biological, adoptive, stepparent). If the child is living with a single parent, then this is just the education level of that single parent.}
\item{\code{highest.education.parent.in.household.impute}}{Maximum education level of household resident who assumes a mother role (biological, adoptive, stepparent) and household resident who assumes a father role (biological, adoptive, stepparent). If the child is living with a single parent, then this is just the education level of that single parent. For subjects with missing highest education of parent in household, the mean is imputed.}
\item{\code{highest.education.parent.in.household.mis}}{Dummy variable for whether household income is missing and the mean is imputed.}
\item{\code{female}}{1 = female, 0 = male}
\item{\code{race.black}}{1=black race, 0=other}
\item{\code{race.hispanic}}{1=hispanic race, 0=other}
\item{\code{age.teenager}}{age of teenager. Age is imputed with the mean if it is missing}
\item{\code{school.dropout}}{Dummy variable of whether student has dropped out of school}
\item{\code{alcohol.use}}{Based on question ``How often, if at all, do you drink alcohol, such as beer, wine or mixed drinks, not including at religious services".
``Never": answered ``Never";
``Moderate": answered ``A few times year" or ``About once a month";
``Heavy": answered ``A few times a month", ``About once a week", ``A few times a week" or ``Almost every day".
}
\item{\code{marijuana.use}}{Based on question ``How often, if ever, have you used marijuana?".
``Never": answered ``Never";
``Experimenter" answered ``You tried it once or twice";
``Continual User": answered ``You use it occasionally" or ``You use it regularly".
}
\item{\code{p}}{Propensity score.
}
\item{\code{plogit}}{Logit of propensity score.
}
}
}
\details{
The following code constructed the data as used here.
wave1data$family.income=rep(NA,nrow(wave1data))
wave1data$family.income[wave1data$PINCOME1==1 & wave1data$PINCOME2==4]=5000
wave1data$family.income[wave1data$PINCOME1==1 & wave1data$PINCOME2==3]=15000
wave1data$family.income[wave1data$PINCOME1==1 & wave1data$PINCOME2==2]=25000
wave1data$family.income[wave1data$PINCOME1==1 & wave1data$PINCOME2==1]=35000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==1]=45000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==2]=55000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==3]=65000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==4]=75000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==5]=85000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==6]=95000
wave1data$family.income[wave1data$PINCOME1==2 & wave1data$PINCOME3==7]=105000
# For subjects with missing family income data, fill in mean and create a missing data indicator
wave1data$family.income.mis=is.na(wave1data$family.income)
#wave1data$family.income[wave1data$family.income.mis==1]=mean(wave1data$family.income,na.rm=TRUE)
# Find family structure variable
wave1data$family.structure=rep(NA,nrow(wave1data))
# wave1data$family.structure[wave1data$PMOTHER==1 & wave1data$PLIVE==1 & wave1data$PSPRELAT==1]="Two Parent Biological"
# wave1data$family.structure[wave1data$PMOTHER==1 & wave1data$PLIVE==2 & wave1data$PPARTPAR==1]="Two Parent Biological"
# wave1data$family.structure[wave1data$PFATHER==1 & wave1data$PLIVE==1 & wave1data$PSPRELAT==1]="Two Parent Biological"
# wave1data$family.structure[wave1data$PFATHER==1 & wave1data$PLIVE==2 & wave1data$PPARTPAR==1]="Two Parent Biological"
# wave1data$family.structure[wave1data$PMOTHER==1 & (wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]="Two Parent Nonbiological"
# wave1data$family.structure[wave1data$PFATHER==1 & (wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]="Two Parent Nonbiological"
# wave1data$family.structure[(wave1data$PMOTHER==2 | wave1data$PMOTHER==3) & (wave1data$PSPRELAT==1 | wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]="Two Parent Nonbiological"
# wave1data$family.structure[(wave1data$PFATHER==2 | wave1data$PFATHER==3) & (wave1data$PSPRELAT==1 | wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]="Two Parent Nonbiological"
# wave1data$family.structure[is.na(wave1data$family.structure)]="Single Parent/Other"
wave1data$family.structure[wave1data$PMOTHER==1 & wave1data$PLIVE==1 & wave1data$PSPRELAT==1]=1
wave1data$family.structure[wave1data$PMOTHER==1 & wave1data$PLIVE==2 & wave1data$PPARTPAR==1]=1
wave1data$family.structure[wave1data$PFATHER==1 & wave1data$PLIVE==1 & wave1data$PSPRELAT==1]=1
wave1data$family.structure[wave1data$PFATHER==1 & wave1data$PLIVE==2 & wave1data$PPARTPAR==1]=1
wave1data$family.structure[wave1data$PMOTHER==1 & (wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]=1
wave1data$family.structure[wave1data$PFATHER==1 & (wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]=1
wave1data$family.structure[(wave1data$PMOTHER==2 | wave1data$PMOTHER==3) & (wave1data$PSPRELAT==1 | wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]=1
wave1data$family.structure[(wave1data$PFATHER==2 | wave1data$PFATHER==3) & (wave1data$PSPRELAT==1 | wave1data$PSPRELAT==2 | wave1data$PSPRELAT==3)]=1
wave1data$family.structure[is.na(wave1data$family.structure)]=0
# Highest parent education in household
dadeductemp=rep(NA,nrow(wave1data))
dadeductemp[wave1data$PDADEDUC==0 | wave1data$PDADEDUC==1 | wave1data$PDADEDUC==2]=0
dadeductemp[wave1data$PDADEDUC==3 | wave1data$PDADEDUC==4 | wave1data$PDADEDUC==5 | wave1data$PDADEDUC==7]=1
dadeductemp[wave1data$PDADEDUC==6 | wave1data$PDADEDUC==8]=2
dadeductemp[wave1data$PDADEDUC==9 | wave1data$PDADEDUC==10]=3
dadeductemp[wave1data$PDADEDUC>=11 & wave1data$PDADEDUC<=14]=4
momeductemp=rep(NA,nrow(wave1data))
momeductemp[wave1data$PMOMEDUC==0 | wave1data$PMOMEDUC==1 | wave1data$PMOMEDUC==2]=0
momeductemp[wave1data$PMOMEDUC==3 | wave1data$PMOMEDUC==4 | wave1data$PMOMEDUC==5 | wave1data$PMOMEDUC==7]=1
momeductemp[wave1data$PMOMEDUC==6 | wave1data$PMOMEDUC==8]=2
momeductemp[wave1data$PMOMEDUC==9 | wave1data$PMOMEDUC==10]=3
momeductemp[wave1data$PMOMEDUC>=11 & wave1data$PMOMEDUC<=14]=4
parents.highest.educ=pmax(dadeductemp,momeductemp,na.rm=TRUE)
# wave1data$highest.education.parent.in.household=rep(NA,nrow(wave1data))
# wave1data$highest.education.parent.in.household[parents.highest.educ==0]="Less than high school"
# wave1data$highest.education.parent.in.household[parents.highest.educ==1]="High school degree"
# wave1data$highest.education.parent.in.household[parents.highest.educ==2]="AA/vocational degree"
# wave1data$highest.education.parent.in.household[parents.highest.educ==3]="BA/BS degree"
# wave1data$highest.education.parent.in.household[parents.highest.educ==4]="Higher degree"
# wave1data$highest.education.parent.in.household[is.na(parents.highest.educ)]="Missing"
wave1data$highest.education.parent.in.household=rep(NA,nrow(wave1data))
wave1data$highest.education.parent.in.household[parents.highest.educ==0]=0
wave1data$highest.education.parent.in.household[parents.highest.educ==1]=1
wave1data$highest.education.parent.in.household[parents.highest.educ==2]=1
wave1data$highest.education.parent.in.household[parents.highest.educ==3]=2
wave1data$highest.education.parent.in.household[parents.highest.educ==4]=3
#wave1data$highest.education.parent.in.household[is.na(parents.highest.educ)]=mean(parents.highest.educ,na=T)
wave1data$highest.education.parent.in.household.mis=is.na(parents.highest.educ)
# Gender of teenager
wave1data$gender=rep(NA,nrow(wave1data))
#wave1data$gender[wave1data$TEENSEX==0]="MALE"
#wave1data$gender[wave1data$TEENSEX==1]="FEMALE"
wave1data$female=wave1data$TEENSEX
# Race/ethnicity of teenager
wave1data$race.ethnicity=rep(NA,nrow(wave1data))
# wave1data$race.ethnicity[wave1data$TEENRACE==1]="White/Other"
# wave1data$race.ethnicity[wave1data$TEENRACE==2]="African American"
# wave1data$race.ethnicity[wave1data$TEENRACE==3]="Hispanic"
# wave1data$race.ethnicity[wave1data$TEENRACE>=4]="White/Other"
wave1data$race.black=wave1data$TEENRACE==2
wave1data$race.hispanic=wave1data$TEENRACE==3
# Age of teenager
wave1data$age.teenager=wave1data$AGE
wave1data$age.missing=(wave1data$AGE==888)
# Fill in mean value for teenager with missing age
wave1data$age.teenager[wave1data$AGE==888]=NA
#wave1data$age.teenager[is.na(wave1data$age.teenager)]=mean(wave1data$age.teenager,na.rm=TRUE)
# Has student dropped out of school
wave1data$school.dropout=(wave1data$PSCHTYP==4)
# Work intensity (intensity of adolescent employment)
wave1data$work.intensity=rep(NA,nrow(wave1data))
wave1data$work.intensity[wave1data$WORKHRS==0]="Nonworker"
# Intense: >=20 hours
wave1data$work.intensity[wave1data$WORKHRS>=1 & wave1data$WORKHRS<20]="Moderate"
wave1data$work.intensity[wave1data$WORKHRS>=20 & wave1data$WORKHRS<200]="Intense"
# Alcohol use
wave1data$alcohol.use=rep(NA,nrow(wave1data))
wave1data$alcohol.use[wave1data$DRINK==7]="Never"
wave1data$alcohol.use[wave1data$DRINK==5 | wave1data$DRINK==6]="Moderate"
wave1data$alcohol.use[wave1data$DRINK<=4]="Heavy"
# Marijuana use
wave1data$marijuana.use=rep(NA,nrow(wave1data))
wave1data$marijuana.use[wave1data$POT==1]="Never"
wave1data$marijuana.use[wave1data$POT==2]="Experimenter"
wave1data$marijuana.use[wave1data$POT==3 | wave1data$POT==4]="Continual User"
## Drop from consideration for matching fifth and sixth graders; students missing work intnsity, alcohol use and marijuana use; students with moderate working intensity
wave1data$not.included.in.sample=(wave1data$PSCHGRA2==5 | wave1data$PSCHGRA2==6 | wave1data$age.missing==TRUE | is.na(wave1data$work.intensity) | is.na(wave1data$alcohol.use) | is.na(wave1data$marijuana.use) | wave1data$work.intensity=="Moderate")
# Create variable which identifies whether wave 1 interview exists for subject
interviewerdata=read.csv("C:/Users/ruoqi/Desktop/Penn/research/Dylan-ThickDescription/ivlink.csv")
wave1interviews=interviewerdata$ids[!(interviewerdata$iver=="W3" | interviewerdata$iver=="W4")]
wave1data$wave1.interview=wave1data$IDS%in%wave1interviews
wave1data$wave1.interview=wave1data$wave1.interview& (!wave1data$family.income.mis) & (!wave1data$highest.education.parent.in.household.mis)
data=wave1data
dsub=data[which(data$not.included.in.sample==FALSE),]
dim(dsub) #2816 932
dsub=dsub[which(dsub$work.intensity!='Moderate'),]
dim(dsub) # 2816 932
dsub$intense=rep(0,dim(dsub)[1])
dsub$intense[which(dsub$work.intensity=='Intense')]=1
#propensity score
dsub$family.income.impute=dsub$family.income
dsub$family.income.impute[dsub$family.income.mis==1]=mean(dsub$family.income,na.rm=TRUE)
dsub$highest.education.parent.in.household.impute=dsub$highest.education.parent.in.household
dsub$highest.education.parent.in.household.impute[dsub$highest.education.parent.in.household.mis==1]=mean(dsub$highest.education.parent.in.household,na.rm=T)
model<-glm(intense~family.income.impute+family.income.mis+
highest.education.parent.in.household.impute+highest.education.parent.in.household.mis+
female+race.black+race.hispanic+age.teenager+school.dropout,
family=binomial(link='logit'),data=dsub,x=TRUE)
x=subset(dsub[c('family.income.impute','family.income.mis','family.structure',
'highest.education.parent.in.household.impute','highest.education.parent.in.household.mis',
'female','race.black','race.hispanic','age.teenager','school.dropout')])
pred <- predict(model, newdata = x, type = 'response')
dsub$p=pred
dsub$plogit=car::logit(pred)
#boxplot(prop~intense,data=dsub)
dsub=subset(dsub[c('IDS','intense','family.income','family.income.impute','family.income.mis','family.structure',
'highest.education.parent.in.household','highest.education.parent.in.household.impute','highest.education.parent.in.household.mis',
'female','race.black','race.hispanic','age.teenager','school.dropout','alcohol.use','marijuana.use','p','plogit')])
nysr=dsub
save(nysr, file = "nysr.rda")
}
\source{
The National Survey of Youth and Religion.
}
\references{
Longest, K. C. and Shanahan M. J., Adolescent Work Intensity and Substance Use: The Mediational and Moderational Roles of Parenting, Journal of Marriage and Family, Vol. 69, No. 3, pp. 703-720.
}
\examples{
data("nysr")
summary(nysr)
}
\keyword{datasets}
|
3fcacdd933e4f58403a5e1a6c0efa60f05e8aea3
|
eb4883c3d4c45d719c16109f9113869059488c22
|
/plot2.R
|
97b970dfb952ee84d7991db8f19509cbf836911c
|
[] |
no_license
|
maridelf/ExData_Plotting1
|
f2f949417385bbb56a253ba2c606e85478e46d99
|
3d141b798d29ea186628731710feb98ba867aa27
|
refs/heads/master
| 2021-01-23T10:34:53.847674
| 2017-06-01T21:08:20
| 2017-06-01T21:08:20
| 93,072,555
| 0
| 0
| null | 2017-06-01T15:33:18
| 2017-06-01T15:33:17
| null |
UTF-8
|
R
| false
| false
| 555
|
r
|
plot2.R
|
plot2 <- function(feb1_2 = data.frame(), readdata = TRUE) {
library(dplyr)
library(lubridate)
source("getDataFeb1_2.R")
if(readdata) {feb1_2 <- getDataFeb1_2()}
with(feb1_2, plot(dt, Global_active_power,
main = "",
type="l",
xlab = "",
ylab = "Global Active Powers (kilowatts)")
)
## by default png is 480x480 px
dev.copy(png, file="plot2.png")
dev.off()
}
|
678c2616a061e8fd8d62d8c28f7ea5793841c8c8
|
485ad4123816e24842bf7c388d084b24e772b926
|
/R/tif.R
|
9854b5e2609addefd49925d37f936fe2d662a6ea
|
[] |
no_license
|
cran/cacIRT
|
57c446a880fb52552b231a19e666765f96260df8
|
8fde3135204cb26982e22ad7ca5842a5b744d3cd
|
refs/heads/master
| 2021-01-01T18:34:41.707166
| 2015-08-28T01:08:33
| 2015-08-28T01:08:33
| 17,694,912
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 230
|
r
|
tif.R
|
tif <-
function (ip, x, D = 1.7)
{
i = iif(ip, x, D)
if (is.null(dim(i$f)))
dim(i$f) = c(length(i$x), length(i$f))
f = apply(i$f, 1, sum)
r = list(x = i$x, f = f, ni = ncol(i$f))
return(r)
}
|
d03ab76db827a6db4c789028eaa6de5a13eee5bf
|
83cd2dd744e10494b4461b70b8804b21b2b78341
|
/file2tokens.R
|
6eebf6cd61842b6da6168c17ceb10306cfb911f8
|
[
"MIT"
] |
permissive
|
dmitrytoda/SwiftPredict
|
ece6ead11282ebff935656019ccf145d9c2afcfb
|
9693bac0dd52ce07ca23461e35d866014ee6a7d8
|
refs/heads/main
| 2023-02-12T04:26:16.378593
| 2021-01-08T18:03:35
| 2021-01-08T18:03:35
| 315,111,716
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 746
|
r
|
file2tokens.R
|
library(tm)
file2tokens <- function (infile) {
# Reads ALL lines from infile and converts to a list of
# char vectors, each element of a vector representing a token
con <- file(infile, 'r')
text <- readLines(con)
close(con)
# remove empty strings (artifact of sampling)
text <- text[text!='']
# A token = starts with a letter or a digit
# then may have some more letters, digits, or &-'’ symbols
# alternatively, acronyms like U.S. are also tokens
my_tokenizer <- as.Token_Tokenizer(Regexp_Tokenizer(
"[a-zA-Z0-9]+[a-zA-Z0-9&-'’]*|([a-zA-Z]\\.){2,}"
))
sapply(text, my_tokenizer)
}
|
4606d642408a9d76bcd5165f9848c015965f02b3
|
d5cd873bd84c6294df226984346fdf68e0f395aa
|
/CellChat/AnalyseCellCellCommunicationInIntestinalData.R
|
15585ea1661ca2fde0c51177e665a947af19581b
|
[] |
no_license
|
Wenxue-PKU/scRNASeqAnalysisAndModelling
|
9056cb0820ea4fba15b05154083d453a8b35c865
|
69a249dce6862a7ab359b9570596997da61d84c3
|
refs/heads/master
| 2023-01-06T14:00:08.560215
| 2020-10-10T03:43:42
| 2020-10-10T03:43:42
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 716
|
r
|
AnalyseCellCellCommunicationInIntestinalData.R
|
### Application of Suoqin Jin's CellChat package to various datas on intestinal tissue (healthy and UC)
# Load the relevant packages
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(CellChat)
library(ggalluvial)
### Set the ligand-receptor database. Here we will use the "Secreted signalling" database for cell-cell communication (let's look at ECM-receptor in the future )
CellChatDB <- CellChatDB.mouse # The othe roption is CellChatDB.human
showDatabaseCategory(CellChatDB)
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # Other options include ECM-Receptor and Cell-Cell Contact
# Unlike the wound healing data, we first need to pre-process the data in Seurat.
|
c9d546a3e4eb8d527c2cc57ebc954a8926754d16
|
c1034eb8f34b18105acf3244bf9a0b0339d6ca8d
|
/man/jm.prob.Rd
|
0d19d20ce1e950e6b2ad20f41126294cf5f37806
|
[
"MIT"
] |
permissive
|
svkucheryavski/mdatools
|
f8d4eafbb34d57283ee753eceea1584aed6da3b9
|
2e3d262e8ac272c254325a0a56e067ebf02beb59
|
refs/heads/master
| 2023-08-17T16:11:14.122769
| 2023-08-12T16:58:49
| 2023-08-12T16:58:49
| 11,718,739
| 31
| 11
|
NOASSERTION
| 2020-07-23T18:50:22
| 2013-07-28T11:10:36
|
R
|
UTF-8
|
R
| false
| true
| 520
|
rd
|
jm.prob.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ldecomp.R
\name{jm.prob}
\alias{jm.prob}
\title{Calculate probabilities for distance values and given parameters using Hotelling T2 distribution}
\usage{
jm.prob(u, eigenvals, ncomp)
}
\arguments{
\item{u}{vector with distances}
\item{eigenvals}{vector with eigenvalues for PCA components}
\item{ncomp}{number of components}
}
\description{
Calculate probabilities for distance values and given parameters using Hotelling T2 distribution
}
|
0b629146921d411a1ff2c0c676ecbf9769bd38f4
|
be2e286124b952221135f0ac736423b3032f0906
|
/dist/main.js
|
de702e8022a7f2270884a693fce2f52256cf2aed
|
[
"MIT"
] |
permissive
|
moelders/react-windowed-select
|
9529ce177c3b97d489f0ad64ac9115de7890c408
|
ca52057ff5315f23c6b2f7ceb3b0f05fe427cf63
|
refs/heads/master
| 2023-05-14T16:07:33.515525
| 2021-06-02T15:04:03
| 2021-06-02T15:04:03
| 373,188,020
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 15,650
|
js
|
main.js
|
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|
6d52c1d40090e7e2bb8c9efdad66750417587f39
|
2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0
|
/fuzzedpackages/gasper/R/GVN.R
|
416e22e407722ebd6ead7588d845b502a9f3c7cf
|
[] |
no_license
|
akhikolla/testpackages
|
62ccaeed866e2194652b65e7360987b3b20df7e7
|
01259c3543febc89955ea5b79f3a08d3afe57e95
|
refs/heads/master
| 2023-02-18T03:50:28.288006
| 2021-01-18T13:23:32
| 2021-01-18T13:23:32
| 329,981,898
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 864
|
r
|
GVN.R
|
#' Graph Von Neumann Estimator.
#'
#' Graph equivalent of the Von Neummann variance estimator.
#'
#' @export GVN
#' @param y Noisy data.
#' @param A Adjacency matrix.
#' @param L Laplacian matrix.
#' @examples
#' data(minnesota)
#' A <- minnesota$A
#' L <- laplacian_mat(A)
#' x <- minnesota$xy[ ,1]
#' n <- length(x)
#' f <- sin(x)
#' sigma <- 0.1
#' noise <- rnorm(n, sd = sigma)
#' y <- f + noise
#' sigma^2
#' GVN(y, A, L)
#' @references
#' von Neumann, J. (1941). Distribution of the ratio of the mean square successive difference to the variance. \emph{Ann. Math. Statistics}, 35(3), 433--451.
#'
#' de Loynes, B., Navarro, F., Olivier, B. (2019). Data-driven Thresholding in Denoising with Spectral Graph Wavelet Transform. arXiv preprint arXiv:1906.01882.
GVN <- function(y, A, L) {
sig <- 0.5 * sum(A * outer(y, y, "-")^2)/sum(diag(L))
return(sig)
}
|
dd8df6c4d5742a2146b7416cfdaca749da281e55
|
4697a51bdf1a57dae9559774b2b20624257881bf
|
/lib/helpers.R
|
df36bb2ae31486a36bb32410f0b1e310c143eeeb
|
[] |
no_license
|
Castdeath97/future-learn-analysis
|
f24eaf5f10e7c8ab25f6191a3886843a9aee4f44
|
1f3508d0ec97faa6df0a5f1b4b84c521ea05fa2a
|
refs/heads/master
| 2021-10-26T17:32:06.990862
| 2021-10-07T13:17:40
| 2021-10-07T13:17:40
| 156,971,824
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,966
|
r
|
helpers.R
|
## @knitr archetypes-clean
idIndex = 1
responseIndex = 3
archetypesClean = function(df){
# Remove fields that won't be used (id, responded_at)
df = df[-responseIndex]
df = df[-idIndex]
# fix id format
df$learner_id = as.character(df$learner_id)
return(df)
}
## @knitr questions-clean
clozeIndex = 8
qTypeIndex = 3
questionsClean = function(df){
# Remove empty cloze response field
df = df[-clozeIndex]
# Remove question type field (doesn't change)
df = df[-qTypeIndex]
# Remove redudant quiz_number field
df = df[-stepFieldIndex]
# Remove rows with id empty fields
df = drop_na(df, learner_id)
# fix date format
df$submitted_at = as.Date(df$submitted_at)
# fix 'correct' field format
df$correct = as.logical(df$correct)
# fix id format
df$learner_id = as.character(df$learner_id)
return(df)
}
## @knitr steps-clean
stepFieldIndex = 2
stepsClean = function(df){
# Remove redudant step field
df = df[-2]
# Replace empty strings with NA to stop date formatting issues
df$last_completed_at[df$last_completed_at == ''] = NA
# fix date format
df$first_visited_at = as.Date(df$first_visited_at)
df$last_completed_at = as.Date(df$last_completed_at)
# fix id format
df$learner_id = as.character(df$learner_id)
return(df)
}
## @knitr enrol-clean
otherFieldsIndexes = 2:12
enrolClean = function(df){
# Remove unimportant fields
df = df[-otherFieldsIndexes]
# Replace '--' with NA with not detected
df$detected_country =
factor(df$detected_country, levels=c(levels(df$detected_country), 'Not Detected'))
df$detected_country[as.character(df$detected_country) == '--'] = 'Not Detected'
# Drop unused levels (-- dropped)
df = droplevels(df)
# fix id format
df$learner_id = as.character(df$learner_id)
return(df)
}
## @knitr generic-agg
# aggregate function used to aggregate both tables
# takes function to carry aggregation by week on a field
agg = function(df, func){
# add fields for completed steps/marks in all weeks
df = left_join(func(df, 1), func(df, 2), by = 'learner_id') %>%
left_join(., func(df, 3), by = 'learner_id')
# replace any NAs (dropped off) with 0s
df[is.na(df)] = 0
return(df)
}
## @knitr steps-agg
# steps aggregate just calls the general aggregates and passes
# the function it needs to aggregate steps comppleted by week
stepsAgg = function(df){
agg(df, stepsWeekComp)
}
# function used to aggregate amount of steps completed each week (not na)
stepsWeekComp = function(df, week){
# subset and aggregate (count completed (not NA))
weekDf = subset(df, week_number == week)
comps = aggregate(weekDf$last_completed_at,
list(learner_id= weekDf$learner_id),
function(x) sum(!is.na(x)))
# rename default x for completed tasks
colnames(comps) =
c(colnames(comps)[1],
paste('week', week, '_completed_steps', sep = ''))
return(comps)
}
## @knitr questions-agg
# question aggregate just calls the general aggregates and passes
# the function it needs to aggregate marks (# of correct questions) by week
# and number of tries
questionsAgg = function(df){
agg(df, questionWeekMarks)
}
# function used to aggregate amount of marks (# of correct questions)
# earned each week (not na) and attempts
questionWeekMarks = function(df, week){
# subset and aggregate (count marks by summing (1 is true) and length for attempts)
weekDf = subset(df, week_number == week)
marks = aggregate(weekDf$correct,
list(learner_id= weekDf$learner_id),
function(x) c(sum = sum(x), n = length(x)))
# unpack the vector x and rename
marks[paste('week', week, '_total_marks', sep = '')] =
marks[,2][,1]
marks[paste('week', week, '_total_attempts', sep = '')] =
marks[,2][,2]
# remove vector and top row
marks = marks[-2]
marks = marks[-1,]
return(marks)
}
## @knitr merge1
mergeDfs = function(archetypesDf,questionsAggDf, stepsAggDf){
# join by learner id
progressByArchetypeDf =
left_join(stepsAggDf, questionsAggDf, by = 'learner_id')
# replace nas with 0 for incompleted items
progressByArchetypeDf[is.na(progressByArchetypeDf)] = 0
# join by learner id for last df
progressByArchetypeDf =
left_join(progressByArchetypeDf, archetypesDf, by = 'learner_id')
# remove NAs for archetype by replacing with unspecified
progressByArchetypeDf$archetype =
fct_explicit_na(progressByArchetypeDf$archetype, 'Unspecified')
return(progressByArchetypeDf)
}
## @knitr merge2
countryMergeDfs = function(progressByArchetypeDf, countryDf){
# join by learner id
progressByArchetypeDf =
left_join(progressByArchetypeDf, countryDf, by = 'learner_id')
# Remove rows with empty fields (single user)
progressByArchetypeDf = drop_na(progressByArchetypeDf)
return(progressByArchetypeDf)
}
|
f7a6e2412e0f1c4844100ce20bebd53c0c611a58
|
2f18e19c490e350fc4a0f93579dfb8fffa2813f9
|
/SuddenlyExpandingPopulation/Simulated_densities_of_T_Total.R
|
67e46b2f841faf09b28a1490208ebbbfd649e469
|
[] |
no_license
|
JetteS/Inhomogeneous_phase-type_distributions_in_population_genetics-
|
276f574506a1f78558b3a7a095b7a9ee5a7168fe
|
ce38663bc84b9e5f618beb9c4a26a30f27cdd979
|
refs/heads/main
| 2023-02-16T18:20:51.427009
| 2021-01-09T16:50:21
| 2021-01-09T16:50:21
| 313,293,604
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,533
|
r
|
Simulated_densities_of_T_Total.R
|
## Creating plots of the simulated density of T_Total
## under a suddenly expanding population growth model
##################################################
## Simulating the time to the most recent
## common ancestor and the total branch length
## under a model with variable population size
##################################################
## Name : simT
##
## Author: Jette Steinbach
##
## Purpose : Simulating the time to the most
## recent common ancestor and the total
## branch length.
##
## Input:
## - n : a natural number representing the sample
## size. Must be greater than one.
## Defaults to 10.
## - out : One of the following
## - 'all'
## - 'htimes'
## - 'wtimes'
## For further information see Output below.
## - LambdaInv: A function that
## represents the inverse of the
## integrated intensity
## g^{-1}(t) = int_0^t lambda(u) du.
## - ... Arguments for the function LambdaInv
##
## Output:
## - A vector holding the following numbers:
## - If out = 'all', the vector includes the
## holding times T_2,...,T_n as well as the
## time to the most recent common ancestor
## and the total branch length. That is:
## res = (T_2,...,T_n, T_MRCA, T_Total).
## - If out = 'htimes' the vector includes the
## holding times T_2,...,T_n. That is:
## res = (T_2,...,T_n).
## - If out = 'wtimes' the vector holds the
## time to the most recent common ancestor
## and the total branch length. That is:
## res = (T_MRCA, T_Total).
simT <- function(n=10, out, LambdaInv,...){
## Checking whether n is a positive natural number:
if(n <=1 | abs(n - round(n)) > .Machine$double.eps^0.5) stop("The sample size must be a natural number greater than 1.")
## Checking whether out is one of 'all','htimes' and 'wtimes'.
if(sum(out==c('all','htimes','wtimes'))==0) stop("out must be either 'all', 'htimes' or 'wtimes.")
## For a sample size of n=2, all vectors are simply numbers
if(n==2){
## Generating one independent and identically distributed random variable
## having the uniform distribution on (0,1):
U <- runif(n=1)
## Defining the vector holding the single waiting time T_2
Tvec <- -log(U)
## In this case, s_2 = t_2 and hence,
## t_2^v = Lambda^(-1)(t_2)
TVvec <- LambdaInv(Tvec,...)
}else{ ## For a sample of size n>2, all parts are vectors
## Generating independent and identically distributed random variables
## having the uniform distribution on (0,1):
U <- runif(n=(n-1))
## Defining the vector holding the waiting times
## generated by t_j = -2*log(U_j)/(j*(j-1)) for j=2,3,...,n
Tvec <- -2*log(U)/((2:n)*(1:(n-1)))
names(Tvec) <- paste0("T_",2:n)
## Forming s_n = t_n and s_j = t_n +...+ t_j for j=2,3,...,n-1
Svec <- cumsum(rev(Tvec))
names(Svec) <- paste0("S_",n:2)
## Computing t_n^v = Lambda^(-1)(s_n)
## and t_j^v = Lambda^(-1)(s_j)- Lambda^(-1)(s_(j+1))
## for j=2,3,...,n-1
TVvec <- LambdaInv(Svec,...) - c(0,LambdaInv(Svec,...)[-(n-1)])
}
if(out=="all"){
res <- c(rev(TVvec), sum(TVvec), sum((n:2)*TVvec))
names(res) <- c(paste0("T_",2:n),"T_MRCA","T_Total")
}else if(out == "htimes"){
res <- rev(TVvec)
names(res) <- paste0("T_",2:n)
}else{
res <- c(sum(TVvec), sum((n:2)*TVvec))
names(res) <- c("T_MRCA","T_Total")
}
return(res)
}
##################################################
## Suddenly expanding population
##################################################
##################################################
## The inverse of the integrated intensity under
## a model with a suddenly expanding population
##################################################
## Name : LambdaInvSEP
##
## Author: Jette Steinbach
##
## Purpose : Compute the value of the inverse of
## the integrated intensity
## Lambda(t) = int_0^t lambda(u) du.
##
## Input:
## - t : a vector holding the time points measured
## in units of N generations at which
## the function should be evaluated.
## - a : a number between 0 and 1 that represents
## the factor by which the population size
## decreases.
## - b : The generation scaled in units of N
## generations where the expansion occurred.
## Output:
## - The value(s) of the the inverse of the integrated
## intensity evaluated at the point(s) (in) t.
LambdaInvSEP <- function(t, a, b){
## Checking whether t is a positive real vector
if(sum(t <= 0)>0) stop("All time points in t must be positive real numbers.")
## Checking whether a is a real number between 0 and 1
if(a <= 0) stop("The factor a must be strictly positive.")
if(a > 1) stop("The factor a must be less than or equal to one.")
## Checking whether b is a positive real number
if(b <= 0) stop("b must be a positive real number.")
## Defining the vector holding the results
res <- replicate(n=length(t), NA)
## Computing the inverse of the integrated intensity
## for all values of t
for (i in 1:length(t)) {
res[i] <- ifelse(t[i] <= b, t[i], a*t[i]-a*b+b)
}
return(res)
}
##################################################
## Plotting the simulated density of the total
## branch length under a model with a suddenly
## expanding population size
##################################################
## Name : plot_simfTotal
##
## Author: Jette Steinbach
##
## Purpose : Plotting the simulated probability
## density function of the total branch
## length under a model with a suddenly
## expanding population size.
##
## Input:
## - n : a natural number representing the
## sample size. Must be greater
## than one.
## - a : a number between 0 and 1 that represents
## the factor by which the population size
## decreases. Defaults to 1.
## - b : A real number that represents the
## generation scaled in units of N
## generations where the expansions occurred.
## Output:
## - A ggplot of the simulated probability density
## function of the total branch length.
##
## Remark: Requires the package gglplot2
plot_simfTotal <- function(n, a, b){
## Simulating the waiting time T_Total once
Dx <- data.frame(TTotal = simT(n=n, out = 'wtimes', LambdaInv = LambdaInvSEP, a=a, b=b)[2])
## and 10000 times more
for(i in 1:10000){
Dx <- rbind(Dx, simT(n=n, out = 'wtimes', LambdaInv = LambdaInvSEP, a=a, b=b)[2])
}
## Creating a simple ggplot
p <- ggplot(Dx, aes(x=TTotal)) +
ggtitle(expression(paste("Probability density function for ", tau["Total"])),
subtitle=paste("n =",n,", b =",b, "and a =", a)) +
xlab("x") +
ylab(expression(paste(f[tau["Total"]], "(x)"))) +
theme_minimal() +
geom_density(color = "red") +
geom_histogram(aes(y=..density..), bins = 60, alpha=0.5) +
ylim(0,1) +
xlim(0,15)
print(p)
}
## Plotting the simulated density for n=2,5,10,15 and a=b=1
plot_simfTotal(n=2,a=1,b=1)
plot_simfTotal(n=5,a=1,b=1)
plot_simfTotal(n=10,a=1,b=1)
plot_simfTotal(n=15,a=1,b=1)
## plotting the simulated density for n=2,10, a=0.2,0.8 and b=1,3.
plot_simfTotal(n=2,a=0.2,b=1)
plot_simfTotal(n=10,a=0.2,b=1)
plot_simfTotal(n=2,a=0.2,b=3)
plot_simfTotal(n=10,a=0.2,b=3)
plot_simfTotal(n=2,a=0.8,b=1)
plot_simfTotal(n=10,a=0.8,b=1)
plot_simfTotal(n=2,a=0.8,b=3)
plot_simfTotal(n=10,a=0.8,b=3)
|
0668cffc350c5d1d3527bcb45ff6f0ff61e546f5
|
570e3adb41c325d7b3357b27fb39c7ea2b346f60
|
/Plot1.R
|
b721a06f5342b4b44afc3987991ae0b4ab9430ac
|
[] |
no_license
|
kathy0305/ExData_Project1
|
89e79a75d0b9b82616108f3917d0d50d0fd5a7ac
|
f9bf09e94c3be4f55dd310f92a86b4b445d75e02
|
refs/heads/master
| 2020-12-31T04:17:40.199973
| 2016-05-13T19:13:26
| 2016-05-13T19:13:26
| 58,765,762
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,182
|
r
|
Plot1.R
|
## Assignment: Course Project 1
## Plot1.R
## Checking if the file has laready been downloaded, if it hasn't then create a directory
if(!file.exists('Week1Project.zip')){
dir.create('Week1Project')
## extracting data set from website
fileUrl<-"http://archive.ics.uci.edu/ml/machine-learning-databases/00235/household_power_consumption.zip"
download.file(fileUrl,destfile = "./Week1Project/Data.zip")
}
setwd("./DataScientist/expData/Week1Project")##set working directory
unzippedData <-unzip("Data.zip")##unzip file
list.files()## double check to see if the file did indeed download and unzipped
## keep track of when the data got downloaded
dateDownLoaded <- date()
dateDownLoaded
## read only data needed (DN=DataNeeded)
## using function 'grep' which searches for matches to argument pattern
## also replace missing values from symbol ? to NA
DN <- read.table(text = grep("^[1,2]/2/2007", readLines(unzippedData), value = TRUE),na.strings = "?", sep = ";", header = FALSE)
head(DN) ## get an idea of what the data looks like
##Renaming Columns (it looks like grep is not reading the headers correctly)
names(DN) <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
## check to see if only the 1/2/2007 and 2/2/2007 got subsetted
table(DN$Date)
## Assignment asking to use 'strptime()' function to format Data and Time
##You may find it useful to convert the Date and Time variables to Date/Time classes
## in R using the strptime() and as.Date() functions.
## Combine the Date and Time in one column and format
## ?strptime
DN$DateTime <- strptime(paste(DN$Date, DN$Time, sep=" "), format="%d/%m/%Y %H:%M:%S")
# Open png graphic device
png("plot1.png", width=480, height=480)
## Using Base hist function
## fill color red
## x-axis label "Global Active Power (kilowatts)"
## default y-axes label 'frequency'
## Main title "Global Active Power"
hist(DN$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
# Turn off png graphic device
dev.off()
|
c872bd9829746f81a7a9e8a52b918b2b1130a80a
|
5027d65a10985981f7e275b0c9da8e68329b87b5
|
/scripts/plot_PS.R
|
7084b3459180980d575cb1d30f2cfd2084ffe348
|
[] |
no_license
|
DPCscience/GenomicPrediction_2018
|
3f52a7247dd4153591d327d2ff52ea94841434b1
|
507bf5e8ddad6dccec56b6e73910a52017772e79
|
refs/heads/master
| 2021-09-10T21:40:55.990635
| 2018-04-02T18:37:57
| 2018-04-02T18:37:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,203
|
r
|
plot_PS.R
|
### Comparison of model performace across the Grid Search
ids <- c('rice_DP_Spindel','sorgh_DP_Fernan','soy_NAM_xavier','spruce_Dial_beaulieu','swgrs_DP_Lipka')
setwd('/Volumes/azodichr/03_GenomicSelection/')
cls <- c(ID="character", MSE="numeric",Model="character",Trait="character",params="character")
data <- read.csv('parameter_search_results.csv', colClasses=cls, stringsAsFactors=FALSE)
data$MSE <- abs(data$MSE)
# Aggregate by parameter/model
data_ag <- aggregate(MSE ~ ID + Model + Trait + params, data, mean)
# Remove weird strings in parameter lines
data_ag <- separate(data_ag, params, c("Feat1", "Feat2", 'Feat3', 'Feat4'), ",", fill='right')
data_ag <- as.data.frame(lapply(data_ag, gsub, pattern = "\\'[A-z]+\\'\\:", replacement = ""))
data_ag <- as.data.frame(lapply(data_ag, gsub, pattern = "\\{", replacement = ""))
data_ag <- as.data.frame(lapply(data_ag, gsub, pattern = "}", replacement = ""))
data_ag <- as.data.frame(lapply(data_ag, gsub, pattern = " ", replacement = ""))
library(ggplot2)
ggplot(data_ag, aes(Feat1, Feat2)) + geom_tile(aes(fill=as.numeric(MSE)), colour='white') +
#scale_fill_gradient(limits=c(0,0.5), low='white',high='firebrick')+
theme_minimal(10)
|
7c36ac38c98d39e6381278fadaada1c51947feeb
|
563c6cb8816345083ec54fffef8e10148d3642e9
|
/run_analysis.R
|
feb507ce04b6e69a91c9239dfd77baf7b4694eaa
|
[] |
no_license
|
rtomasc/GettingCleaningCourseProject
|
fddda9541ab3da11a801979888fce24f5de3fa78
|
d70da385cdcd3a75b4568ccc30fbf4c1fa815e32
|
refs/heads/master
| 2021-01-10T04:29:10.104014
| 2015-12-27T23:02:50
| 2015-12-27T23:02:50
| 46,667,885
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,779
|
r
|
run_analysis.R
|
#PART 0. Downloading the zip file, loading libraries and getting data
FileUrl<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(FileUrl, destfile="Dataset.zip")
downloaded<-date()
# Look at the file names in Dataset.zip. Get the file names to extract them.
unzip("Dataset.zip", files=NULL,list=TRUE )
unzip("Dataset.zip", files="UCI HAR Dataset/test/X_test.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/train/X_train.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/test/Y_test.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/train/Y_train.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/activity_labels.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/test/subject_test.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/features.txt")
unzip("Dataset.zip", files="UCI HAR Dataset/train/subject_train.txt")
#Loading libraries
library(data.table)
library(dplyr)
#Reading variable names for features(measurements) and activity labels
featureNames <- read.table("UCI HAR Dataset/features.txt")
activityLabels <- read.table("UCI HAR Dataset/activity_labels.txt", header = FALSE)
#Reading 2 datasets: train (21 subjects) and test (9 subjects).
#Each dataset is splitted into subject, activity and features (measurements), same length each
#Reading train data
subjectTrain <- read.table("UCI HAR Dataset/train/subject_train.txt", header = FALSE)
activityTrain <- read.table("UCI HAR Dataset/train/y_train.txt", header = FALSE)
featuresTrain <- read.table("UCI HAR Dataset/train/X_train.txt", header = FALSE)
#Read test data
subjectTest <- read.table("UCI HAR Dataset/test/subject_test.txt", header = FALSE)
activityTest <- read.table("UCI HAR Dataset/test/y_test.txt", header = FALSE)
featuresTest <- read.table("UCI HAR Dataset/test/X_test.txt", header = FALSE)
#PART 1 - Merge the training and the test sets to create one data set for each: subject, activity and features
#Get featureNames as column names for features, and adding the 3 datasets in one dataset (completeData)
subject <- rbind(subjectTrain, subjectTest)
activity <- rbind(activityTrain, activityTest)
features <- rbind(featuresTrain, featuresTest)
#Name the column names from the features file in variable featureNames
colnames(features) <- t(featureNames[2])
#Add activity and subject as a column to features
colnames(activity) <- "Activity"
colnames(subject) <- "Subject"
completeData <- cbind(features,activity,subject)
#PART 2 - Extracts only the column numbers of the mean or std of each measurement("mean" and "std" in variable names).
#Adds Activity and Subject columns
columnsWithMeanSTD <- grep(".*Mean.*|.*Std.*", names(completeData), ignore.case=TRUE)
#Look at the number of variables in completeData
dim(completeData)
#building a vector with the required columns numbers, adding Activity and Subject columns (the last 2 columns)
requiredColumns <- c(columnsWithMeanSTD, 562, 563)
#extracting the required columns
extractedData <- completeData[,requiredColumns]
#Look at the number of variables in extractedData
dim(extractedData)
#PART 3 - Renaming variable Activity with activity names from activityLabels dataset (6 activities)
extractedData$Activity <- as.character(extractedData$Activity)
for (i in 1:6){
extractedData$Activity[extractedData$Activity == i] <- as.character(activityLabels[i,2])
}
#Set the activity variable in the data as a factor
extractedData$Activity <- as.factor(extractedData$Activity)
#PART 4 - Appropriately labelling the data set with more descriptive variable names.
#Look at variable names
names(extractedData)
#Acc to be replaced with Accelerometer
#Gyro to be replaced with Gyroscope
#BodyBody to be replaced with Body
#Mag to be replaced with Magnitude
#Character 'f' to be replaced with Frequency
#Character 't' to be replaced with Time
#-mean() to be replaced with Mean
#-std() to be replaced with STD
#-freq() to be replaced with Frequency
#-angle to be replaced with Angle
#gravity to be replaced with Gravity
names(extractedData)<-gsub("Acc", "Accelerometer", names(extractedData))
names(extractedData)<-gsub("Gyro", "Gyroscope", names(extractedData))
names(extractedData)<-gsub("BodyBody", "Body", names(extractedData))
names(extractedData)<-gsub("Mag", "Magnitude", names(extractedData))
names(extractedData)<-gsub("^t", "Time", names(extractedData))
names(extractedData)<-gsub("^f", "Frequency", names(extractedData))
names(extractedData)<-gsub("tBody", "TimeBody", names(extractedData))
names(extractedData)<-gsub("-mean()", "Mean", names(extractedData), ignore.case = TRUE)
names(extractedData)<-gsub("-std()", "STD", names(extractedData), ignore.case = TRUE)
names(extractedData)<-gsub("-freq()", "Frequency", names(extractedData), ignore.case = TRUE)
names(extractedData)<-gsub("angle", "Angle", names(extractedData))
names(extractedData)<-gsub("gravity", "Gravity", names(extractedData))
#Look at new resulting variable names
names(extractedData)
#PART 5 - From the data set in step 4, creates a second, independent tidy data set with the average of
#each variable for each activity and each subject.
#Set the subject variable in the data as a factor. Activity is already a factor (PART 3)
extractedData$Subject <- as.factor(extractedData$Subject)
extractedData <- data.table(extractedData)
#Create tidyData as a set with average for each activity and subject
tidyData <- aggregate(. ~Subject + Activity, extractedData, mean)
#Order tidayData according to subject and activity
tidyData <- tidyData[order(tidyData$Subject,tidyData$Activity),]
#Writing tidyData into a text file
write.table(tidyData, file = "Tidy.txt", row.names = FALSE)
#Making a txt with variable names. to be used in the CodeBook
write.table(tidyData_variables, file = "CodeBook.txt", row.names = FALSE)
|
5de1eb315522ba5a1fa36a1e1f3328b45bf9e61b
|
29585dff702209dd446c0ab52ceea046c58e384e
|
/ParamHelpers/R/OptPath_getter.R
|
1e6eec00950b480f8758232d7fd0363d09e4b392
|
[] |
no_license
|
ingted/R-Examples
|
825440ce468ce608c4d73e2af4c0a0213b81c0fe
|
d0917dbaf698cb8bc0789db0c3ab07453016eab9
|
refs/heads/master
| 2020-04-14T12:29:22.336088
| 2016-07-21T14:01:14
| 2016-07-21T14:01:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,250
|
r
|
OptPath_getter.R
|
#' Get the length of the optimization path.
#'
#' Dependent parameters whose requirements are not satisfied are represented by a scalar
#' NA in the output.
#'
#' @template arg_op
#' @return [\code{integer(1)}]
#' @export
#' @family optpath
getOptPathLength = function(op) {
UseMethod("getOptPathLength")
}
#' Get an element from the optimization path.
#'
#' Dependent parameters whose requirements are not satisfied are represented by a scalar NA
#' in the elements of \code{x} of the return value.
#'
#' @template arg_op
#' @param index [\code{integer(1)}]\cr
#' Index of element.
#' @return List with elements \code{x} [named \code{list}], \code{y} [named \code{numeric}],
#' \code{dob} [\code{integer(1)}], \code{eol} [\code{integer(1)}].
#' The elements \code{error.message} [\code{character(1)}],
#' \code{exec.time} [\code{numeric(1)}] and \code{extra} [named \code{list}] are
#' there if the respective options in \code{\link{OptPath}} are enabled.
#' @rdname getOptPathEl
#' @export
#' @family optpath
getOptPathEl = function(op, index) {
UseMethod("getOptPathEl")
}
#' Get data.frame of input points (X-space) referring to the param set from the optimization path.
#'
#' @template arg_op
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return [\code{data.frame}].
#' @export
#' @family optpath
getOptPathX = function(op, dob, eol) {
UseMethod("getOptPathX")
}
#' Get y-vector or y-matrix from the optimization path.
#'
#' @template arg_op
#' @param names [\code{character}]\cr
#' Names of performance measure.
#' Default is all performance measures in path.
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @param drop [\code{logical(1)}]\cr
#' Return vector instead of matrix when only one y-column was selected?
#' Default is \code{TRUE}.
#' @return [\code{numeric} | \code{matrix}]. The columns of the matrix are always named.
#' @export
#' @family optpath
getOptPathY = function(op, names, dob, eol, drop = TRUE) {
UseMethod("getOptPathY")
}
#' Get date-of-birth vector from the optimization path.
#'
#' @template arg_op
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return [\code{integer}].
#' @export
#' @family optpath
getOptPathDOB = function(op, dob, eol) {
UseMethod("getOptPathDOB")
}
#' Get end-of-life vector from the optimization path.
#'
#' @template arg_op
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return [\code{integer}].
#' @export
#' @family optpath
getOptPathEOL = function(op, dob, eol) {
UseMethod("getOptPathEOL")
}
#' Get error-message vector from the optimization path.
#'
#' @template arg_op
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return [\code{character}].
#' @export
#' @family optpath
getOptPathErrorMessages = function(op, dob, eol) {
UseMethod("getOptPathErrorMessages")
}
#' Get exec-time vector from the optimization path.
#'
#' @template arg_op
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return [\code{numeric}].
#' @export
#' @family optpath
getOptPathExecTimes = function(op, dob, eol) {
UseMethod("getOptPathExecTimes")
}
#' Get column from the optimization path.
#'
#' @template arg_op
#' @param name [\code{character(1)}]\cr
#' Name of the column.
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @return Single column as a vector.
#' @export
#' @family optpath
getOptPathCol = function(op, name, dob, eol) {
UseMethod("getOptPathCol")
}
#' Get columns from the optimization path.
#'
#' @template arg_op
#' @param names [\code{character}]\cr
#' Names of the columns.
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @inheritParams as.data.frame.OptPathDF
#' @return [\code{data.frame}].
#' @export
#' @family optpath
getOptPathCols = function(op, names, dob, eol, row.names = NULL) {
UseMethod("getOptPathCols")
}
#' Get index of the best element from optimization path.
#'
#' @template arg_op
#' @param y.name [\code{character(1)}]\cr
#' Name of target value to decide which element is best.
#' Default is \code{y.names[1]}.
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @param ties [\code{character(1)}]\cr
#' How should ties be broken when more than one optimal element is found?
#' \dQuote{all}: return all indices,
#' \dQuote{first}: return first optimal element in path,
#' \dQuote{last}: return last optimal element in path,
#' \dQuote{random}: return random optimal element in path.
#' Default is \dQuote{last}.
#' @return [\code{integer}]
#' Index or indices into path. See \code{ties}.
#' @export
#' @family optpath
#' @examples
#' ps = makeParamSet(makeNumericParam("x"))
#' op = makeOptPathDF(par.set = ps, y.names = "y", minimize = TRUE)
#' addOptPathEl(op, x = list(x = 1), y = 5)
#' addOptPathEl(op, x = list(x = 2), y = 3)
#' addOptPathEl(op, x = list(x = 3), y = 9)
#' addOptPathEl(op, x = list(x = 4), y = 3)
#' as.data.frame(op)
#' getOptPathBestIndex(op)
#' getOptPathBestIndex(op, ties = "first")
getOptPathBestIndex = function(op, y.name = op$y.names[1], dob = op$env$dob, eol = op$env$eol, ties = "last") {
assertClass(op, "OptPath")
assertChoice(y.name, choices = op$y.names)
dob = asInteger(dob, any.missing = TRUE)
eol = asInteger(eol, any.missing = TRUE)
assertChoice(ties, c("all", "first", "last", "random"))
life.inds = which(op$env$dob %in% dob & op$env$eol %in% eol)
if (length(life.inds) == 0)
stop("No element found which matches dob and eol restrictions!")
y = getOptPathY(op, y.name)[life.inds]
if (all(is.na(y))) {
best.inds = life.inds
} else {
if (op$minimize[y.name])
best.inds = which(min(y, na.rm = TRUE) == y)
else
best.inds = which(max(y, na.rm = TRUE) == y)
best.inds = life.inds[best.inds]
}
if (length(best.inds) > 1) {
if (ties == "all")
return(best.inds)
else if (ties == "first")
return(best.inds[1])
else if (ties == "last")
return(best.inds[length(best.inds)])
else if (ties == "random")
return(best.inds[sample(length(best.inds), 1)])
} else {
return(best.inds)
}
}
#' Get indices of pareto front of optimization path.
#'
#' @template arg_op
#' @param y.names [\code{character}]\cr
#' Names of performance measures to construct pareto front for.
#' Default is all performance measures.
#' @template arg_opgetter_dob
#' @template arg_opgetter_eol
#' @param index [\code{logical(1)}]\cr
#' Return indices into path of front or y-matrix of nondominated points?
#' Default is \code{FALSE}.
#' @return [\code{matrix} | \code{integer}]. Either matrix (with named columns) of points of front
#' in objective space or indices into path for front.
#' @export
#' @family optpath
#' @examples
#' ps = makeParamSet(makeNumericParam("x"))
#' op = makeOptPathDF(par.set = ps, y.names = c("y1", "y2"), minimize = c(TRUE, TRUE))
#' addOptPathEl(op, x = list(x = 1), y = c(5, 3))
#' addOptPathEl(op, x = list(x = 2), y = c(2, 4))
#' addOptPathEl(op, x = list(x = 3), y = c(9, 4))
#' addOptPathEl(op, x = list(x = 4), y = c(4, 9))
#' as.data.frame(op)
#' getOptPathParetoFront(op)
#' getOptPathParetoFront(op, index = TRUE)
getOptPathParetoFront = function(op, y.names = op$y.names, dob = op$env$dob, eol = op$env$eol, index = FALSE) {
assertClass(op, "OptPath")
assertCharacter(y.names, min.len = 2)
assertSubset(y.names, op$y.names, empty.ok = FALSE)
dob = asInteger(dob, any.missing = TRUE)
eol = asInteger(eol, any.missing = TRUE)
assertFlag(index, na.ok = TRUE)
requirePackages("emoa", default.method = "load")
life.inds = which(op$env$dob %in% dob & op$env$eol %in% eol)
if (length(life.inds) == 0)
stop("No element found which matches dob and eol restrictions!")
y = getOptPathY(op, y.names, drop = FALSE)[life.inds, , drop = FALSE]
# multiply columns with -1 if maximize
k = ifelse(op$minimize, 1, -1)
y2 = t(y) * k
# is_dominated has kind of buggy behavoiur if y2 is a row
# (it hinks, we have a 1-dimensional optimization prob und returns the min index)
# so we have to treat this case manually
if (nrow(y2) == 1)
nondom = 1
else
nondom = which(!emoa::is_dominated(y2))
if (index)
return(life.inds[nondom])
else
return(y[nondom, , drop = FALSE])
}
|
a965be02387d68721ca7be86329bd3f425f15834
|
c944ff7ec610960d106391311ceb27869c979741
|
/station_density_walking.R
|
e3f5e09cba87b1a9c64c7aa6d579cc79a51f27e2
|
[] |
no_license
|
sjrodahl/BDA17-Bike-Share-Analysis
|
924686722d4528ef2ced7ba6c646f60225791b3a
|
4f53c7c9be3023ce69dafbbc056f5727379c0d51
|
refs/heads/master
| 2021-01-20T12:44:53.686341
| 2017-06-13T05:33:58
| 2017-06-13T05:33:58
| 90,402,869
| 0
| 0
| null | 2017-06-13T05:33:59
| 2017-05-05T17:56:15
|
R
|
UTF-8
|
R
| false
| false
| 1,789
|
r
|
station_density_walking.R
|
#Station density - stations closer than a five minute walk to the next one
setwd("D:/Sondre/Dokumenter/UCSD/IRGN452 BigDataAnalytics/Project/BDA17-Bike-Share-Analysis/")
stations <- read.csv("data/station.csv" , stringsAsFactors=FALSE)
distance.matrix<-read.csv('gmapsWalkTimeMatrix.csv')
distance.matrix[distance.matrix==0] <- 10000
limit = 300
columnMins <- apply(distance.matrix, 2, min)
min.df <- as.data.frame(columnMins)
min.df <- min.df[-c(1,2),,drop=FALSE]
row.names(min.df) <- substr(rownames(min.df), 6, nchar(rownames(min.df))) #Remove "Time." from rownames
min.df$columnMins <- as.numeric(as.character(min.df$columnMins))
min.df$close<- min.df$columnMins< limit
st_coords_split <- strsplit(rownames(min.df), "[.][.]") # Split coordinates
#Only need latitude to uniqely match with station
min.df$lat <- lapply(st_coords_split, `[[`, 1)
min.df.mergeable <- min.df[c("lat", "close", "columnMins")]
stations <- merge(stations, min.df.mergeable, by="lat")
#-------Plot
lat<-c(47.58, 47.68)
lon<-c(-122.25, -122.38)
seattle_impr<-get_map(location = c(lon = mean(lon), lat = mean(lat)), zoom = 13, maptype = "satellite", source = "google")
map_fromPoints<-ggmap(seattle_impr) +
geom_point(data = stations,
aes(x = long,y = lat, color = ifelse(columnMins<=300, "green", ifelse(columnMins<=600, "yellow", "red"))), size=5) +
labs(title = "Stations and their walking distance to nearest neighbor\n", color = "Nearest neighboring station is:\n")+
scale_color_manual(labels = c("<= 5 minutes away", "> 10 minutes away"," > 5 minutes away"), values=c("green" ,"red", "yellow"))+
theme(axis.title = element_text(size=18), axis.text = element_text(size=14, face="bold"), title = element_text(size=22), legend.text = element_text(size=14))
map_fromPoints
|
03f8fd923e23343a9174ec1d9964780dbbad1a98
|
b2f61fde194bfcb362b2266da124138efd27d867
|
/code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Cashmore-Fox-Giunchiglia/Planning-CTE/pipesnotankage04_6/pipesnotankage04_6.R
|
b4fd2e50e7deea7b984e4c994c586a9ee893fe7d
|
[] |
no_license
|
arey0pushpa/dcnf-autarky
|
e95fddba85c035e8b229f5fe9ac540b692a4d5c0
|
a6c9a52236af11d7f7e165a4b25b32c538da1c98
|
refs/heads/master
| 2021-06-09T00:56:32.937250
| 2021-02-19T15:15:23
| 2021-02-19T15:15:23
| 136,440,042
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 69
|
r
|
pipesnotankage04_6.R
|
c17a968585a5d9c0c35855d2b0671a55 pipesnotankage04_6.qdimacs 839 21727
|
e63e4ae47da8de1a00c6a4713d4b59adf3c93175
|
79f9f86aa6cc9fdee3aa152c326a08b08c4b9533
|
/man/prefs_reset_to_defaults.Rd
|
377d1a33c0585dcc9ecf151e375ef7484f4f694f
|
[] |
no_license
|
charliejhadley/rstudioprefswitcher
|
bb888a96e6fb7ec51834dc2e8a3fde26f5264cca
|
ee10a3f3cbc2665e1a15be2270cff0a9c3af1239
|
refs/heads/master
| 2023-04-13T16:06:35.066160
| 2021-04-16T09:38:06
| 2021-04-16T09:38:06
| 331,698,487
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 472
|
rd
|
prefs_reset_to_defaults.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set_prefs.R
\name{prefs_reset_to_defaults}
\alias{prefs_reset_to_defaults}
\title{Restore default preferences}
\usage{
prefs_reset_to_defaults(improve_code_reproducibility = TRUE)
}
\description{
\code{prefs_reset_to_defaults()} resets RStudio preferences to defaults. You are
\strong{strongly} recommend to run \code{prefs_improve_reproducibility()} after running
prefs_reset_to_defaults().
}
|
957dbc7fa0fbf2c48601a29c8e57b0f8e69360d3
|
9279ca3701e7a6ae465c3e482f8deae70fb2daa5
|
/variableDistributions_WK3.R
|
25bf0363ee05171d32c6fccc711778c11515f83d
|
[] |
no_license
|
zliu008/dataManageVisualization
|
4fb16ba88b54c251338d70e6b505f0c008ad8a56
|
89716bc0801fdedb9649b677d6d011bcf253a92e
|
refs/heads/master
| 2021-01-10T01:51:05.014230
| 2015-10-23T00:09:26
| 2015-10-23T00:09:26
| 43,625,093
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,099
|
r
|
variableDistributions_WK3.R
|
rm(list=ls(all=TRUE))
#function to calculate distribution
caculateDist <- function(data_in, varName) {
freq <- data.frame(table((data_in), exclude = NULL));
colnames(freq)[1] = varName;
freq$Perc <- freq$Freq/sum(freq$Freq);
print(sprintf("the distribution of %s:", varName));
print(freq);
freq;
}
nesar_pds <- read.csv('nesarc_pds.csv');
age_group <-rep('middle', dim(nesar_pds)[1]);
age_group[nesar_pds[,'AGE'] < 30] <- 'young';
age_group[nesar_pds[,'AGE'] >= 60] <- 'old';
freq_agegroup <- caculateDist(age_group, 'AgeGroup');
#recode NA, this NA is not a missing data
nesar_pds$S4AQ55[is.na(nesar_pds$S4AQ55)] <- 0;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ55[nesar_pds$S4AQ55 == 9] <- NA;
#recode NA, this NA is not a missing data
nesar_pds$S4AQ56[is.na(nesar_pds$S4AQ56)] <- 0;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ56[nesar_pds$S4AQ56 == 9] <- NA;
#recode NA, this NA is not a missing data
nesar_pds$S4AQ57[is.na(nesar_pds$S4AQ57)] <- 0;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ57[nesar_pds$S4AQ57 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A8[nesar_pds$S4AQ4A8 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A6[nesar_pds$S4AQ4A6 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A5[nesar_pds$S4AQ4A5 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A9[nesar_pds$S4AQ4A9 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A14[nesar_pds$S4AQ4A14 == 9] <- NA;
# NA is missing data
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ4A15[nesar_pds$S4AQ4A15 == 9] <- NA;
#recode NA, this NA is not a missing data
nesar_pds$S4AQ51[is.na(nesar_pds$S4AQ51)] <- 0;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ51[nesar_pds$S4AQ51 == 9] <- NA;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4BQ1[nesar_pds$S4BQ1 == 9] <- NA;
#recode number 9, it is unknow, which is missing data
nesar_pds$S4BQ2[nesar_pds$S4BQ2 == 9] <- NA;
#NA is missing data, NA in S4AQ6A meaning no major depression
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ6A[nesar_pds$S4AQ6A == 99] <- NA;
#NA is missing data, NA in S4AQ7 meaning no major depression
#recode number 9, it is unknow, which is missing data
nesar_pds$S4AQ7[nesar_pds$S4AQ7 == 99] <- NA;
##caculate distribution percentage
freq_S4AQ1 <- caculateDist(nesar_pds[,'S4AQ1'], 'S4AQ1');
freq_S4AQ2 <- caculateDist(nesar_pds[,'S4AQ2'], 'S4AQ2');
freq_S4AQ56 <- caculateDist(nesar_pds[,'S4AQ56'], 'S4AQ56');
freq_S4AQ57 <- caculateDist(nesar_pds[,'S4AQ57'], 'S4AQ57');
freq_S4AQ55 <- caculateDist(nesar_pds[,'S4AQ55'], 'S4AQ55');
freq_S4AQ54 <- caculateDist(nesar_pds[,'S4AQ54'], 'S4AQ54');
freq_S4AQ4A8 <- caculateDist(nesar_pds[,'S4AQ4A8'], 'S4AQ4A8');
freq_S4AQ4A6 <- caculateDist(nesar_pds[,'S4AQ4A6'], 'S4AQ4A6');
freq_S4AQ4A5 <- caculateDist(nesar_pds[,'S4AQ4A5'], 'S4AQ4A5');
freq_S4AQ4A9 <- caculateDist(nesar_pds[,'S4AQ4A9'], 'S4AQ4A9');
freq_S4AQ4A14 <- caculateDist(nesar_pds[,'S4AQ4A14'], 'S4AQ4A14');
freq_S4AQ4A15 <- caculateDist(nesar_pds[,'S4AQ4A15'], 'S4AQ4A15');
freq_S4AQ51 <- caculateDist(nesar_pds[,'S4AQ51'], 'S4AQ51');
freq_S4BQ1 <- caculateDist(nesar_pds[,'S4BQ1'], 'S4BQ1');
freq_S4BQ2 <- caculateDist(nesar_pds[,'S4BQ2'], 'S4BQ2');
nesar_pds$S4AQ6A_GRP <-rep('middle', dim(nesar_pds)[1]);
nesar_pds$S4AQ6A_GRP[nesar_pds[,'S4AQ6A'] < 30] <- 'young';
nesar_pds$S4AQ6A_GRP[nesar_pds[,'S4AQ6A'] >= 60] <- 'old';
freq_S4AQ6A <- caculateDist(nesar_pds[,'S4AQ6A_GRP'], 'S4AQ6A_GRP');
#group number of epsode
nesar_pds$S4AQ7_GRP[nesar_pds$S4AQ7 < 5] <- 'Low';
nesar_pds$S4AQ7_GRP[nesar_pds$S4AQ7 >= 5 & nesar_pds$S4AQ7 < 20] <- 'Moderate';
nesar_pds$S4AQ7_GRP[nesar_pds$S4AQ7 >= 20] <- 'High';
freq_S4AQ7 <- caculateDist(nesar_pds[,'S4AQ7_GRP'], 'S4AQ7_GRP');
|
be321676a1c3a7ae5b3650615f65f75b7d05e960
|
2f66f60715bf67758d1f93b650886500b04ed431
|
/man/cyan2yellow.Rd
|
76cf2e3b11a40e4ac9f363867cc7fc4d6127384a
|
[] |
no_license
|
SymbolixAU/colourvalues
|
6e2e32b29c3ce2a0a0699e36e86cb6dc77ccf5db
|
dfc75685389ebcfcbac15dccbbd0c024c2854117
|
refs/heads/master
| 2023-04-11T13:42:57.481895
| 2023-04-08T22:43:28
| 2023-04-08T22:43:28
| 147,190,723
| 34
| 9
| null | 2022-12-30T02:33:38
| 2018-09-03T10:44:56
|
C++
|
UTF-8
|
R
| false
| true
| 226
|
rd
|
cyan2yellow.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/palettes.R
\name{cyan2yellow}
\alias{cyan2yellow}
\title{Cyan2yellow}
\usage{
cyan2yellow()
}
\description{
Data Frame of the cyan2yellow palette
}
|
1be1ecaf861f1c44e7c27e6dd9d002d56641ca92
|
e462ad72e1db20ece2432b27e266a8dee927bf7f
|
/讨论班/分位数回归/ADMMRQ.r
|
880e4303ce3212e27decf82504f53677e9018453
|
[] |
no_license
|
dujiangbjut/dujiangbjut.github.io
|
1c4ca37dd8f9b7c0d63cff600a679f2ba2a13ee8
|
6a9ce1bebd054023bd6e4c431f2bdf291093569c
|
refs/heads/master
| 2020-04-19T10:18:32.461941
| 2019-09-07T14:12:03
| 2019-09-07T14:12:03
| 168,131,265
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,066
|
r
|
ADMMRQ.r
|
ADMMRQ <- function(X,y,tau,iter=200,esp=1e-03){
n=length(y)
e.new=e.old=rep(0,n)
X=as.matrix(X)
p=ncol(X)
beta.new=beta.old=rep(0,p)
u.new=u.old=rep(0,n)
step=0
error=1
while(step<iter&error>esp){
beta.old=beta.new
e.old=e.new
u.old=u.new
step=step+1
c=y-X%*%beta.old+u.old/1.2
c1=c-(2*tau-1)/1.2
e.new=pmax(c1-1/1.2,0)-pmax(-c1-1/1.2,0)
beta.new=solve(t(X)%*%X)%*%t(X)%*%(y-e.new+u.old/1.2)
temp=y-e.new-X%*%beta.new
u.new=u.old+temp*1.2
temp1=y-e.new-X%*%beta.new
temp2=1.2*t(X)%*%(e.new-e.old)
error1=max(sqrt(sum((X%*%beta.new)^2)),sqrt(sum(e.new^2)),sqrt(sum(y^2)))
error2=sqrt(p)*0.01+esp*sqrt(sum((X%*%beta.new)^2))
if(sqrt(sum(temp1^2))<sqrt(n)*0.01+esp*error1&sqrt(sum(temp2^2))<error2) step=iter+1
error=sqrt(sum((beta.new-beta.old)^2))
}
e.hat=y-X%*%beta.new
loss=sum(e.hat*(tau-1*(e.hat<0)))
tmp.out = list(beta.hat=beta.new, loss=loss,ind=step)
return(tmp.out)
}
|
8111ebac8eb6a8fae9f0c41aa1b19681fc356059
|
62cfdb440c9f81b63514c9e545add414dc4d5f63
|
/man/qat_plot_roc_rule_dynamic_2d.Rd
|
de0569163f6d029d860bf68c1ac4bd436f44cdc8
|
[] |
no_license
|
cran/qat
|
7155052a40947f6e45ba216e8fd64a9da2926be4
|
92975a7e642997eac7b514210423eba2e099680c
|
refs/heads/master
| 2020-04-15T16:53:45.041112
| 2016-07-24T01:26:59
| 2016-07-24T01:26:59
| 17,698,828
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,700
|
rd
|
qat_plot_roc_rule_dynamic_2d.Rd
|
\name{qat_plot_roc_rule_dynamic_2d}
\alias{qat_plot_roc_rule_dynamic_2d}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Plot a dynamic ROC rule result}
\description{
A plot of the result of a dynamic ROC rule check will be produced.
}
\usage{
qat_plot_roc_rule_dynamic_2d(flagvector, filename, measurement_vector = NULL,
max_upward_vector = NULL, max_downward_vector = NULL, upward_vector_name = NULL,
downward_vector_name = NULL, measurement_name = "", directoryname = "",
plotstyle = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{flagvector}{The resulting flagvector of qat\_analyse\_roc\_rule\_dynamic\_2d}
\item{filename}{Name of the file without extension.}
\item{measurement_vector}{The measurement vector, which should be plotted}
\item{max_upward_vector}{The vector (2d array) with the upward values.}
\item{max_downward_vector}{The vector (2d array) with the downward values.}
\item{upward_vector_name}{Name of the vector of the upward values. }
\item{downward_vector_name}{Name of the vector of the downward values. }
\item{measurement_name}{Name of the measurement.}
\item{directoryname}{Directory, where the resulted file should be stored.}
\item{plotstyle}{A list with a qat color scheme.}
}
\details{
A plot will be produced, which base on the resulting flagvector of qat\_analyse\_roc\_rule\_dynamic\_2d. Additional information on the parameters, which were used while performing the test, will be added to the plot. When no plotstyle is defined the standard-colorscheme will be used. The resulting plot will be stored in the folder, which is defined by directory under the given filename, with the extension png.
}
\value{
No return value.
}
\author{Andre Duesterhus}
\seealso{\code{\link{qat_plot_roc_rule_dynamic_1d}}, \code{\link{qat_analyse_roc_rule_dynamic_2d}}, \code{\link{qat_plot_roc_rule_static_2d}}}
\examples{
vec <- array(rnorm(500), c(25,20))
min_vector <- array(rnorm(500)+2, c(25,20))
max_vector <- array(rnorm(500)+2, c(25,20))
result <- qat_analyse_roc_rule_dynamic_2d(vec, min_vector, max_vector,
upward_vector_name="upward vector", downward_vector_name="downward vector")
# this example produce a file exampleplot_roc_dyn.png in the current directory
qat_plot_roc_rule_dynamic_2d(result$flagvector, "exampleplot_roc_dyn",
measurement_vector=vec, max_upward_vector=result$max_upward_vector,
max_downward_vector=result$max_downward_vector, upward_vector_name=result$upward_vector_name,
downward_vector_name=result$downward_vector_name, measurement_name="Result of Check")
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ts}
|
c698dd575392abe5da295a989a4c965a26faa256
|
382df78024f588acea08039a0b0a9e24f297b6a3
|
/r/math/Pi.R
|
9c351ab133daaa8d23ff33c150cd852013cde2f9
|
[] |
no_license
|
id774/sandbox
|
c365e013654790bfa3cda137b0a64d009866d19b
|
aef67399893988628e0a18d53e71e2038992b158
|
refs/heads/master
| 2023-08-03T05:04:20.111543
| 2023-07-31T14:01:55
| 2023-07-31T14:01:55
| 863,038
| 4
| 1
| null | 2020-03-05T06:18:03
| 2010-08-26T01:05:11
|
TeX
|
UTF-8
|
R
| false
| false
| 64
|
r
|
Pi.R
|
s <- 10000000
x <- runif(s)
y <- runif(s)
sum(x^2+y^2 <= 1)*4/s
|
c2caf6918238f15fda90158a45d7682db05142af
|
51fb651cdd636bf2b0f08c77ab6aa9d1341920a3
|
/R/ahn_gen.R
|
c64149fd73acb4ca50195bb77ee6aa32c1a25fad
|
[] |
no_license
|
ecopeng/AnimalHabitatNetwork
|
4ee45cce56c298d5b47d926c932a3ca41e464a12
|
ff4fc3a80477a222742222b1c5615b5db4b2699a
|
refs/heads/master
| 2023-02-03T03:49:42.038460
| 2020-12-22T10:20:51
| 2020-12-22T10:20:51
| 272,998,264
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,296
|
r
|
ahn_gen.R
|
#' @title Generate networks characterising habitat physical configurations
#' @description Generate undirected networks (weighted or unweighted, connected or disconnected) characterising the physical attributes and spatial organizations (or distributions) of habitat components (i.e. habitat configurations).
#'
#' @param N The number of nodes
#' @param L A side length of the rectangle landscape within which nodes are anchored
#' @param mu the critical \code{Dij} (i.e. Euclidean distance between node \code{i} and \code{j}) at which the link removing probability curve \code{P(Dij, mu, lamda)} transits from concave to convex (see \code{\link{ahn_prob}})
#' @param lamda the steepness of the link removing probability curve \code{P(Dij, mu, lamda)}, see \code{\link{ahn_prob}}
#' @param Connected \code{TRUE} for connected while \code{FALSE} ignores whether the networks are connected or not
#' @param Weighted \code{TRUE} for weighted while \code{FALSE} for unweighted networks
#' @param eta mediates the weight, i.e. \code{(Dij)^-eta}, of the link rewiring node \code{i} from one network component and node \code{j} from another network component (\code{i} and \code{j} are with an Euclidean distance of \code{Dij}) when the network becomes disconnected after removing links from the initial complete network with the probability \code{P(Dij, mu, lamda) = [1 + exp(-lamda(Dij - mu))]^-1} when both \code{Connected = TRUE} and \code{Weighted = TRUE}
#' @param A The area of the rectangle landscape within which the network is defined
#' @param X A vector of \code{X} coordinates for the \code{N} nodes (sampled from \code{[0, L]} uniformly at random if \code{NULL})
#' @param Y A vector of \code{Y} coordinates for the \code{N} nodes (sampled from \code{[0, A/L]} uniformly at random if \code{NULL})
#' @param U A vector with \code{N} elements specifying node attributes (qualitative or quantitive), by default \code{NULL}
#' @param V A vector with \code{N} elements specifying node attributes (qualitative or quantitive), by default \code{NULL}
#'
#' @importFrom stats runif dist rnorm
#' @import igraph
#' @export
#' @return Return an animal habitat network (an \code{igraph} object)
#' @examples
#' # generate a connected and weighted network
#' ahn_gen(N = 10, L = 5, mu = 1, lamda = 5)
#'
#'\donttest{
#'
#' N <- 10
#' x <- runif(N, 0, 5)
#' ql <- sample(LETTERS, N, replace = TRUE)
#' qn <- sample(1:20, N, replace = TRUE)
#'
#' # specify the X coordinates, node attributes U and V for a connected and unweighted network
#' ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = FALSE, X = x, U = ql, V = qn)
#'
#' # specify the Y coordinates, node attributes U and V for a weighted network, no matter if the
#' # network will be connected or not
#' ahn_gen(N, L = 5, mu = 1, lamda = 5, Weighted = TRUE, Connected = FALSE, Y = x, U = ql, V = qn)
#'
#'}
#'
ahn_gen <- function(N, L, mu, lamda, Connected = TRUE, Weighted = TRUE, eta = 1, A = 25, X = NULL, Y = NULL, U = NULL, V = NULL){
ifelse(is.null(X), x <- runif(N, 0, L), ifelse(max(X) > L || min(X) < 0 || length(X) != N, stop('Wrong X coordinate(s)!'), x <- X))
ifelse(is.null(Y), y <- runif(N, 0, A/L), ifelse(max(Y) > A/L || min(Y) < 0 || length(Y) != N, stop('Wrong Y coordinate(s)!'), y <- Y))
xy_coords <- data.frame(x = x, y = y)
dm <- as.matrix(dist(xy_coords), method = 'euclidean', diag = FALSE)
dm_0 <- 1/dm
dm_0[is.infinite(dm_0)] <- 0
ahn_wei_matrix <- dm_0
ahn_wei_matrix[lower.tri(ahn_wei_matrix, diag = TRUE)] <- NA
tr <- which(!is.na(ahn_wei_matrix))
prob <- 1/(1 + exp(-lamda*(dm[tr] - mu)))
for(u in 1:length(tr)){
if(sample(c(1, 0), size = 1, prob = c(prob[u], 1 - prob[u]))){
ahn_wei_matrix[tr][u] <- 0
}
}
ahn_wei_matrix[lower.tri(ahn_wei_matrix)] <- t(ahn_wei_matrix)[lower.tri(ahn_wei_matrix)]
if(Weighted){
ahn <- graph_from_adjacency_matrix(ahn_wei_matrix, mode = 'undirected', diag = FALSE, weighted = TRUE)
} else{
ahn_wei_matrix[ahn_wei_matrix > 0] <- 1
ahn <- graph_from_adjacency_matrix(ahn_wei_matrix, mode = 'undirected', diag = FALSE, weighted = NULL)
}
if(!is.connected(ahn) && Connected){
memb <- unname(components(ahn)$membership)
ncomp <- max(memb)
while(ncomp > 1){
r_memb <- sample(memb, 1)
temp <- dm_0[which(memb == r_memb), which(memb != r_memb), drop = FALSE]
rn <- as.numeric(rownames(temp)[which(temp == max(temp), arr.ind = T)[1]])
cn <- as.numeric(colnames(temp)[which(temp == max(temp), arr.ind = T)[2]])
if(Weighted){
ahn_wei_matrix[rn, cn] <- (dm_0[rn, cn])^eta
ahn_wei_matrix[cn, rn] <- (dm_0[rn, cn])^eta
ahn <- graph_from_adjacency_matrix(ahn_wei_matrix, mode = 'undirected', diag = FALSE, weighted = TRUE)
} else{
ahn_wei_matrix[rn, cn] <- 1
ahn_wei_matrix[cn, rn] <- 1
ahn_wei_matrix[ahn_wei_matrix > 0] <- 1
ahn <- graph_from_adjacency_matrix(ahn_wei_matrix, mode = 'undirected', diag = FALSE, weighted = NULL)
}
memb <- unname(components(ahn)$membership)
ncomp <- max(memb)
}
}
if(!is.null(U)){vertex_attr(ahn, name = 'U') <- U}
if(!is.null(V)){vertex_attr(ahn, name = 'V') <- V}
vertex_attr(ahn, name = 'X') <- xy_coords$x
vertex_attr(ahn, name = 'Y') <- xy_coords$y
return(ahn)
}
|
473919ffbcd2258f11093304ff4b9cb9883814b3
|
95bb91fd4c03527735c4eb4ac273f17ace035042
|
/R/formatting.R
|
bb6c9392dfb8f351aeeb253df155ebfa2ce4b041
|
[
"MIT"
] |
permissive
|
garborg/clean.dw
|
f69344d82e5486bb5f2d1defc8b3d5aae2d6a86a
|
bba685ad7f94b93d1847b91f620a9a5fd777aa6e
|
refs/heads/master
| 2021-01-06T20:38:30.506900
| 2014-05-02T21:56:33
| 2014-05-02T21:56:33
| 12,885,336
| 1
| 0
| null | 2014-05-02T21:56:33
| 2013-09-17T03:25:20
|
R
|
UTF-8
|
R
| false
| false
| 567
|
r
|
formatting.R
|
options(scipen=20)
enquote = function(names) {
if (length(names)) {
ifelse(nzchar(names), paste0('"', names, '"'), "")
} else
names
}
enquoteNames = function(x) {
names(x) = enquote(names(x))
x
}
chrEscape = function(chr) {
if (length(chr)) {
paste0("'", gsub("'", "''", chr), "'")
} else
chr
}
tf = function(table, field) {
paste0(table, '.', field)
}
indent = function() {
' '
}
indentWith = function(v, sep) {
if (lv <- length(v))
paste0(indent(), v, c(rep_len(sep, lv-1), ''))
}
|
2e85ceadc25a610fbca401c6c609e603744a4e7f
|
3bf3c24531fdf87ca1d5e03fb0a591d650f062b5
|
/man/ad_setup.Rd
|
4acd27b9aa7596a0dee86c0dc752b249a19a18c6
|
[] |
no_license
|
hiendn/autodiffr
|
c8eba7519a8cee22d6c53fabcef53bc23b3038a9
|
d59407ce1289358afc4fadb1501ad743f5280e28
|
refs/heads/master
| 2020-06-19T17:19:43.480408
| 2018-12-18T23:41:57
| 2018-12-18T23:41:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 931
|
rd
|
ad_setup.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autodiff.R
\name{ad_setup}
\alias{ad_setup}
\title{Do initial setup for package autodiffr.}
\usage{
ad_setup(JULIA_HOME = NULL, reverse = TRUE, forward = TRUE,
verbose = TRUE, ...)
}
\arguments{
\item{JULIA_HOME}{the file folder which contains julia binary,
if not set, it will try to look at global option JULIA_HOME,
if the global option is not set,
it will then look at the environmental variable JULIA_HOME,
if still not found, julia in path will be used.}
\item{reverse}{whether to use load reverse mode automatic differentiation.}
\item{forward}{whether to use forward mode automatic differentiation.}
\item{verbose}{whether to print package startup messages.}
\item{...}{arguments passed to \code{JuliaCall::julia_setup}.}
}
\description{
\code{ad_setup} does the initial setup for package autodiffr.
}
\examples{
\dontrun{
ad_setup()
}
}
|
9e9bb32e15010f9469836a2f755101b6c349efd5
|
8124be31adfe738983227380f28b6694b62efdea
|
/man/data2HTML.Rd
|
6984c87421482f90fb74eac509394c5bdf6033f2
|
[] |
no_license
|
guhjy/rrtable
|
7a1b849bca2a9157972add2507ae5ee4ed849698
|
587eeee021768a2dc34639dd8c422b3c2c622c21
|
refs/heads/master
| 2020-04-08T19:49:43.445657
| 2018-10-16T04:26:30
| 2018-10-16T04:26:30
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,051
|
rd
|
data2HTML.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data2HTML.R
\name{data2HTML}
\alias{data2HTML}
\title{Make a HTML5 file with a data.frame}
\usage{
data2HTML(data, preprocessing = "", path = NULL,
filename = "report.HTML", rawDataName = NULL,
rawDataFile = "rawData.RDS", vanilla = FALSE, echo = TRUE,
showself = FALSE)
}
\arguments{
\item{data}{A data.frame}
\item{preprocessing}{A character string of R code}
\item{path}{A name of destination file path}
\item{filename}{A name of destination file}
\item{rawDataName}{The name of the rawData}
\item{rawDataFile}{The name of the rawData file which the data are to be read from.}
\item{vanilla}{logical. Whether or not make vanilla table}
\item{echo}{Logical. Whether or not show R code of plot and table}
\item{showself}{Logical. Whether or not show R code for the paragraph}
}
\description{
Make a HTML5 file with a data.frame
}
\examples{
\donttest{
library(moonBook)
library(ztable)
library(rrtable)
library(ggplot2)
data2HTML(sampleData2,path="tmp")
}
}
|
2749d6f5d4a2a3a7170bf1c3a0447a8289d583a2
|
66332bb30c8d14f824af71a9d418c5d6345f58d1
|
/source/get.R
|
508fcd28addd774bc40462aa3f72d1a4a204c93f
|
[] |
no_license
|
moggces/ActivityProfilingGUI
|
f2c2f073733e6c3feba6679e8620e81cf4d78c1e
|
f2bb6533cceba979e31aa91a3a42cfe538c9052e
|
refs/heads/master
| 2020-04-15T23:48:14.361354
| 2017-10-06T15:50:04
| 2017-10-06T15:50:04
| 17,751,883
| 1
| 0
| null | 2017-03-27T15:21:38
| 2014-03-14T16:08:17
|
R
|
UTF-8
|
R
| false
| false
| 14,497
|
r
|
get.R
|
# specific to activity file generated from KNIME
get_property_name <- function (master)
{
col_list <- strsplit(colnames(master), '.', fixed=TRUE)
#unique(unlist(lapply(col_list, function (x) x[[length(x)]]))) # get the unique assay name
names <- lapply(col_list, function (x)
{
if (length(x) == 3)
{
return(paste(x[[1]], '.', x[[2]], sep=""))
} else {return(x[[1]])}
}
)
names <- unique(unlist(names))
return(names)
}
# a wrapper for join, it can detect, CAS, GSID automatically
get_lookup_list <- function (input, master)
{
#result <- subset(master, select=c(CAS, Chemical.Name, StructureID))
#result <- merge(input,result, by='CAS', all.x=TRUE)
result <- join(input, master)
return(result)
}
# filter the matrix by chemical input
get_input_chemical_mat <- function (input, full)
{
partial <- list()
for (name in names(full))
{
if ((name == 'struct') )
{
# for the ones that are removed due to purity issue
partial[[name]] <- full[[name]][as.character(rownames(full[[name]])) %in% as.character(input[['GSID']]),] # CAS here
if (! is.null(partial[['npod']]))
{
partial[[name]] <- partial[[name]][as.character(rownames(partial[[name]])) %in% as.character(rownames(partial[['npod']])),]
} else if (! is.null(partial[['nwauc.logit']]))
{
partial[[name]] <- partial[[name]][as.character(rownames(partial[[name]])) %in% as.character(rownames(partial[['nwauc.logit']])),]
} else if (! is.null(partial[['nec50']]))
{
partial[[name]] <- partial[[name]][as.character(rownames(partial[[name]])) %in% as.character(rownames(partial[['nec50']])),]
}
} else
{
partial[[name]] <- full[[name]][as.character(rownames(full[[name]])) %in% as.character(input[['GSID']]),] # CAS here
}
}
#print(rownames(partial[['npod']]))
return(partial)
}
# filter the matrix by assays regular expression
get_assay_mat <- function (partial, regSel, invSel=FALSE)
{
for (name in names(partial))
{
if (name != 'struct' )
{
partial[[name]] <- partial[[name]][,grep(regSel, colnames(partial[[name]]), value = TRUE, invert = invSel)]
}
}
return(partial)
}
# its linked with nwauc.logit matrix results. if active and high CV -> mark
get_cv_mark_mat <- function(cv, nwauc)
{
cv_mark <- cv
cv_mark[is.na(cv_mark) ] <- -0.1
cv_mark[cv_mark > 1.4 & nwauc > 0.0001 ] <- "#"
cv_mark[cv_mark != "#"] <- ''
return(cv_mark)
}
# dependent on conversion
# d: is the distance matrix
# input: chemical identification (GSID + Cluster)
# master: all mapping info
# dmat
get_heatmap_annotation <- function (d, input, master, input_chemical_name=NULL, cutoff=0.7, method="average", dmat, actType='')
{
chemical_name_ref <- input_chemical_name
# chemical structure clustering
hc <- hclust(d, method=method)
group <- cutree(hc, h=cutoff)
group_cp <- group
group_t <- sort(table(group), decreasing=TRUE)
for (i in 1:length(group_t))
{
if (group_t[i] == 1)
{
group_cp[group == names(group_t)[i]] <- 0
} else
{
group_cp[group == names(group_t)[i]] <- i
}
}
#print(str_c("get:line100", names(group_cp)))
# create annotations: chemClust
annotation <- data.frame(chemClust = as.factor(group_cp))
rownames(annotation) <- names(group_cp)
#print("get:line104")
#print(annotation)
# create annoations: userClust
annotation2 <- data.frame(userClust = as.factor(input[['Cluster']]))
if (nrow(annotation2) > 0)
{
# can get the chemical name outside this function
# do not need this because there is input name chemical name
#rownames(annotation2) <- as.character(input[['GSID']])
#if (is.null(chemical_name_ref)) chemical_name_ref <- conversion(master, inp='GSID', out='Chemical.Name')
if (is.null(chemical_name_ref)) {
chemical_name_ref <- make.unique(input[['Chemical.Name']])
rownames(annotation2) <- chemical_name_ref
#print(str_c("get:line115", chemical_name_ref))
} else
{
rownames(annotation2) <- input[['Chemical.Name']]
rownames(annotation2) <- chemical_name_ref[as.character(rownames(annotation2))]
}
annotation <- merge(annotation, annotation2, by="row.names")
#print("get:line122")
#print(annotation)
rownames(annotation) <- annotation$Row.names
annotation <- annotation[,-which(colnames(annotation) == 'Row.names')]
}
# create annotations: toxScore
annotation3 <- data.frame()
if (actType == 'nwauc.logit' )
{
annotation3 <- data.frame(toxScore = rowSums(abs(dmat[[actType]]) ))
} else if (actType == 'npod' | actType == 'nec50' )
{
if ( ! is.null(dmat[['nwauc.logit']]))
{
annotation3 <- data.frame(toxScore = unlist(lapply(1:nrow(dmat[[actType]]), function (x) sum(abs(dmat[[actType]][x,])*dmat[['nwauc.logit']][x,]) )))
} else
{
annotation3 <- data.frame(toxScore = rowSums(abs(dmat[[actType]]) ))
}
}
if (nrow(annotation3) > 0)
{
rownames(annotation3) <- rownames(dmat[[1]])
annotation <- merge(annotation, annotation3, by="row.names")
rownames(annotation) <- annotation$Row.names
annotation <- annotation[,-which(colnames(annotation) == 'Row.names')]
}
return(annotation)
}
# rainbow color to generate unique colors
# toxScore is a continuous color
get_heatmap_annotation_color <- function(annotation, actType='')
{
user <- rainbow(length(unique(annotation[['userClust']])))
names(user) <- sort(unique(annotation[['userClust']])) # for the CAS not avaiable, more levels than values
chem <- rainbow(length(unique(annotation[['chemClust']])))
names(chem) <- sort(unique(annotation[['chemClust']]))
if (actType != '')
#if (! is.null(actType))
{
tox <- c("#F7F4F9", "#E7E1EF", "#D4B9DA", "#C994C7", "#DF65B0", "#E7298A", "#CE1256", "#980043", "#67001F") #PuRd
return(list(userClust=user, chemClust=chem, toxScore=tox))
} else
{
return(list(userClust=user, chemClust=chem))
}
}
get_output_df <- function (paras, id_data, isUpload=FALSE, actwithflag=FALSE)
{
act <- paras[['act']]
annotation <- paras[['annotation']]
label <- paras[['label']]
cv <- paras[['cv']]
# the reverse act flag won't show up (but will show up if not removing inconclusive)
# the high_source_variation will only show up if you include the acts
# not removing inconclusive could be confusing in the output
result <-
inner_join(act %>% rownames_to_column("Chemical.Name"),
annotation %>% rownames_to_column("Chemical.Name"))
if (isUpload)
{
if(!is.null(id_data$input_Chemical.Name))
{
id_data[, "Chemical.Name"] <- id_data$input_Chemical.Name
} #else { id_data <- master}
} else if (actwithflag )
{
result <-
act %>% rownames_to_column("Chemical.Name") %>% gather(call_name, act, -Chemical.Name) %>%
inner_join( label %>% rownames_to_column("Chemical.Name") %>% gather(call_name, label, -Chemical.Name)) %>% #label df
inner_join( cv %>% rownames_to_column("Chemical.Name") %>% gather(call_name, cv, -Chemical.Name)) %>%
mutate(label = ifelse(label == 'b_autofluor', 'autofluorescent',
ifelse(label == 'c_contradict', 'not_supported_by_ch2',
ifelse(label == 'd_cytotoxic', 'cytotoxicity',
ifelse(label == 'e_weaknoisy', 'weaknoisy_in_rep',
label))))) %>%
mutate(comb_data =
ifelse(
label != 'a_normal', str_c(round(act,4), " (", label, ")"),
ifelse( label == "", str_c(round(act,4), " (not_tested)"),
ifelse( cv != '', str_c(round(act,4), " (high_source_variation)"),
round(act,4))))) %>% #merge act & label
select( -label, -act, -cv) %>%
spread(call_name, comb_data) %>%
inner_join(annotation %>% rownames_to_column("Chemical.Name")) # add the annotation
}
result[,"Chemical.Name_original"] <- result$Chemical.Name
result[,"Chemical.Name_original"] <- sub("\\.[0-9]+$", "", result$Chemical.Name_original)
result <- left_join(result, subset(id_data, select=c(CAS, Chemical.Name)), by=c("Chemical.Name_original" = "Chemical.Name")) # join by Chemical.Name
result <- result[, c("CAS", grep("CAS", colnames(result), invert=TRUE, value=TRUE))]
return(result)
}
get_pod_boxplot <- function (pod, fontsize, sortby, dcols, global_para)
{
# order the chemical.name
h <- hclust(dcols, method='average')
pod[, 'Chemical.Name'] <- ordered(pod$Chemical.Name, levels=h$label[h$order])
if (sortby == 'toxscore') pod[, 'Chemical.Name'] <- ordered(pod$Chemical.Name, levels=pod$Chemical.Name[order(pod$toxScore)])
# melt the data and exclude the all inactives
pod_m <- melt(pod, id.vars = c( 'CAS', 'Chemical.Name', 'chemClust', 'userClust', 'toxScore'), value.name = "pod_value", variable.name = 'pathway')
pod_m <- subset(pod_m, pod_value > 1) # Chemical.Name is a factor. So if completely inactve. it won't be removed
mat <- pod_m
#create conversion
#let <- conversion(global_para, inp='common_name', out='letters')
let <- conversion(global_para, inp='protocol_call_db.name', out='_letters4boxplot')
let2 <- paste(let, names(let), sep="=") # color legend
names(let2) <- names(let)
#add a new column
mat[, 'path_abb'] <- let[as.character(mat$pathway)]
p <- ggplot(data=mat, aes(x=Chemical.Name, y=pod_value*-1+6)) +
geom_boxplot(outlier.shape = NA) +
geom_text(aes(label=path_abb, color=pathway), size=7, alpha=0.7, position="jitter") +
scale_color_discrete("",labels=let2) +
scale_x_discrete("", drop=FALSE) + # keep the no activity ones
theme(text=element_text(size=fontsize),
axis.text.x = element_text( angle=90, color="black")) +
scale_y_continuous(expression(paste("concentration ", "(", mu, "M", ")", sep="")), breaks=seq(-10+6, -3+6, by=1), limits=c(-10+6, -3+6), labels = math_format(10^.x)) +
#theme_bw(base_size = fontsize) +
annotation_logticks(sides = "l")
return(p)
}
get_published_data_only_commonname <- function (dd, assay_dd)
{
id_cols <- c('CAS','Chemical.Name','chemClust','userClust','toxScore')
ok_assays <- unlist(subset(assay_dd, ! is.na(`PubChem AID`), select="common_name"))
result <- dd[, colnames(dd) %in% c(id_cols, ok_assays)]
return(result)
}
get_clust_assay_enrichment <- function (partial_act, full_act, annotation, calZscore=FALSE)
{
pp <- partial_act %>% add_rownames() %>% left_join(select(add_rownames(annotation), -toxScore)) %>%
mutate(allClust = "all") %>% gather(assay, act, -matches('rowname|Clust'), na.rm = TRUE) %>%
gather(clust_group, clust, matches('Clust')) %>%
group_by(assay, clust_group, clust) %>% filter(clust != 'unassigned') %>%
#summarize(n=sum(act != 0.0001), n_p=sum(act > 0.0001), n_mean=mean(act), n_std=sd(act))
summarize(n=sum(act != 0.0001), n_p=sum(act > 0.0001))
ff_long <- full_act %>% select(one_of(colnames(partial_act))) %>% gather(assay, act, na.rm = TRUE) %>%
group_by(assay)
ff <- ff_long %>% summarise(N=sum(act != 0.0001), N_P=sum(act > 0.0001))
if (calZscore)
{
zz <- bind_rows(lapply(1:2000, function (x) pp %>% filter(n_p > 1) %>%
left_join(ff_long, by="assay") %>%
group_by(assay, clust_group, clust) %>%
sample_frac(1) %>% slice(1:unique(n)) %>% group_by(assay, clust_group, clust) %>%
summarize(ns_p=sum(act > 0.0001)))) %>% group_by(assay, clust_group, clust) %>%
summarize(ns_mean=mean(ns_p), ns_std=sd(ns_p))
result <- pp %>% filter(n_p > 1) %>% left_join(zz) %>% left_join(ff)
result <- result %>% rowwise() %>%
mutate(pvalue = get_fisher_pvalue(n, n_p, N_P, N)$p.value, zscore = (n_p-ns_mean)/ns_std)
} else
{
result <- pp %>% filter(n_p > 1) %>% left_join(ff)
if (nrow(result) > 0)
{
result <- result %>% rowwise() %>%
mutate(pvalue = get_fisher_pvalue(n, n_p, N_P, N)$p.value)
}
}
return(result)
}
get_fisher_pvalue <- function (n, n_p, N_P, N)
{
conti <- matrix ( c( n_p-1, n-n_p, N_P-n_p, N-n-(N_P-n_p)), nrow=2, dimnames = list(active = c('In', 'notIn'), clust = c('In', 'notIn')))
fish <- fisher.test( conti , alternative="greater")
return(fish)
}
get_source_data_long <- function(source_acts, chem_id_master, filtered_act)
{
chem_id_filtered <- chem_id_master %>% select(CAS, Chemical.Name, Tox21.ID,
Purity_Rating_T0, Purity_Rating_T4, Purity_Rating_Comb) %>%
unnest(Tox21.ID = str_split(Tox21.ID, "\\|"),
Purity_Rating_T0 = str_split(Purity_Rating_T0, "\\|"),
Purity_Rating_T4 = str_split(Purity_Rating_T4, "\\|"),
Purity_Rating_Comb = str_split(Purity_Rating_Comb, "\\|")) %>%
filter(Chemical.Name %in% rownames(filtered_act)) # filter by the filtered act Chemical.Name
# the activity type to retrieve
value_type <- c('hitcall', 'label', 'nwauc', 'npod', 'nec50', 'ncmax', 'nwauc.logit', 'wauc_fold_change' )
source_acts <- source_acts[value_type]
acts_collect <- lapply(names(source_acts), function (x){
result <- source_acts[[x]] %>% rownames_to_column("Tox21AgencyID") %>%
separate(Tox21AgencyID, c("Tox21.ID", "Library"), sep="@") %>%
filter(Tox21.ID %in% chem_id_filtered$Tox21.ID) %>%
gather_("call_name", x, grep("Tox21.ID|Library", colnames(.), value=TRUE, invert=TRUE)) %>%
filter(call_name %in% colnames(filtered_act))
return(result)
})
acts_collect <- left_join(chem_id_filtered, Reduce("full_join", acts_collect)) %>%
mutate(label = ifelse(label == 'b_autofluor', 'autofluorescent',
ifelse(label == 'c_contradict', 'not_supported_by_ch2',
ifelse(label == 'd_cytotoxic', 'cytotoxicity',
ifelse(label == 'e_weaknoisy', 'weaknoisy_in_rep',
ifelse(label == 'a_normal', '',
ifelse(label == '', 'not_tested', label))))))) %>%
rename(flag = label, efficacy = ncmax, POD=npod, EC50=nec50, wAUC=nwauc,
wAUC.logit=nwauc.logit, wAUC.fold.change = wauc_fold_change)
return(acts_collect)
}
|
53fd0350f89cb1c6a03fb4ce4baf7f8c407272a3
|
40962c524801fb9738e3b450dbb8129bb54924e1
|
/Day - 1/Assignment/Q2 - UserInput.R
|
36edb2808efbb9da098ea49c577a1751118cee21
|
[] |
no_license
|
klmsathish/R_Programming
|
628febe334d5d388c3dc51560d53f223585a0843
|
93450028134d4a9834740922ff55737276f62961
|
refs/heads/master
| 2023-01-14T12:08:59.068741
| 2020-11-15T13:23:31
| 2020-11-15T13:23:31
| 309,288,498
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 270
|
r
|
Q2 - UserInput.R
|
#UserInput
Id = readline("Enter your UserID:") #Prompts user for input(id)
Branch = readline(prompt="Enter you Branch/Group:") #Prompts user for input(branch)
cat("Your UserID is",Id,"and you belong to",Branch,"group") #Displaying Values
|
112a58fb89bdf1037197144d51edca7fb80b92ff
|
b32a5bca6aeac9ec9c0761079164540b21103dfa
|
/visualization-project-shiny.R
|
a53004252a0fc8b8c4ef79b921db911878765340
|
[] |
no_license
|
chelseypaulsen/Fall2Orange2
|
5533f479bf91c1262bc0b6b8d8b696d5c61c7b30
|
f807c5ca9d548b72c75a3d791dd67c69bfb8e8f3
|
refs/heads/master
| 2020-03-30T20:38:44.468347
| 2018-10-25T04:53:41
| 2018-10-25T04:53:41
| 151,597,178
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 24,257
|
r
|
visualization-project-shiny.R
|
rm(list=ls())
library(shiny)
library(ggplot2)
#install.packages(c('maps','mapproj','caschrono'))
library(maps)
library(mapproj)
library(tidyverse)
library(readxl)
library(zoo)
library(forecast)
library(haven)
library(fma)
library(expsmooth)
library(lmtest)
library(seasonal)
library(lubridate)
library(tseries)
library(foreign)
library(caschrono)
library(TSA)
library(quantmod)
library(imputeTS)
library(dplyr)
#install.packages('rlang')
#install.packages(c('stringi'))
library(rlang)
#install.packages('shinydashboard')
library(shinydashboard)
#install.packages(c('shiny','ggplot2','dplyr','tidyverse','readxl','forecast','haven','fma','expsmooth','lubridate','caschrono','imputeTS'))
# Set up the working directory and initialize the well list
data.dir <- 'C:/Users/johnb/OneDrive/Documents/MSA/Fall 2/Well Data/'
#data.dir <- 'C:/Users/Steven/Documents/MSA/Analytics Foundations/Forecasting/data/Well Data/'
wells <- c('G-852','F-45','F-179','F-319','G-561_T','G-580A','G-860','G-1220_T',
'G-1260_T','G-2147_T','G-2866_T','G-3549','PB-1680_T')
rainlist <- c('G852_RAIN','F45_RAIN','F179_RAIN','F319_RAIN','G561_T_RAIN','G580A_RAIN','G860_RAIN','G1220_T_RAIN',
'G1260_T_RAIN','G2147_T_RAIN','G2866_T_RAIN','G3549_RAIN','PB1680_T_RAIN')
welllist <- c('G852','F45','F179','F319','G561_T','G580A','G860','G1220_T',
'G1260_T','G2147_T','G2866_T','G3549','PB1680_T')
# Create a sequence of dates and convert to dataframe to find missing dates in well data
start <- ymd_h("2007/10/01 00", tz='UTC')
end <- ymd_h("2018/06/12 23", tz='UTC')
hourseq <- seq(start,end+(168*3600), by='hours')
full_df <- data.frame(datetime=hourseq)
hourseqdf <- as.data.frame(hourseq)
names(hourseqdf) <- c('datetime')
end_df = data.frame(names = wells,
ends = c('','3/26/2018 10','6/4/2018 10','4/9/2018 12','6/12/2018 23','4/9/2018 11','6/4/2018 12','','6/8/2018 11','6/8/2018 09',
'6/8/2018 09','6/12/2018 23','2/8/2018 09'),
starts = c('10/1/2007 00','10/1/2007 01','10/1/2007 01','10/1/2007 01','10/5/2007 00','10/1/2007 00','10/1/2007 01',
'10/1/2007 00','10/1/2007 01','10/10/2007 00','10/1/2007 01','10/1/2007 01','10/1/2007 01'),
stringsAsFactors = F)
# pred_model5 <- function(final_well, well.2){
# # TODO, incoporate and check that this function works in the for loop
#
# # this function takes in a dataframe of well datetimes,well data, and rain data
# # it returns the original dataframe w/ new cols for forecast and confidence intervals
# # it also depends on startwell and endwell vectors
# # returned df also has new names
#
# #splitting newly assembled well data for clean model generation
# if (well.2 == 'G-852' | well.2 == 'G-1220_T'){end_dt <- max(well_df_clean$datetime)
# }
#
# else{end_dt <- mdy_h(endwell)}
# train <- final_well %>%
# filter(datetime >= mdy_h(startwell) & datetime <= end_dt)
# # Generating model on training data
# yearly <- 24*365.25
# x.reg <- train$RAIN_FT
# seasons <- msts(train$filled, start=1, seasonal.periods = c(yearly))
# model5 <- Arima(seasons,order=c(2,0,2), xreg=cbind(fourier(seasons,K=1),x.reg))
#
# # forecasting across last 168 days
# rain.ts <- read.zoo(train$RAIN_FT)
# rain.impute <- na.approx(rain.ts)
# rain.model <- auto.arima(rain.impute)
# rain.preds <- forecast(rain.model, h=168)
# newx <- rain.preds$mean # using actual rainfall data, because our rain model is really bad
# final.pred=forecast(model5,xreg=cbind(fourier(seasons,K=1),newx),h=168)
#
# # building df from results
# df_results <- as.data.frame(cbind(
# final.pred$mean,
# final.pred$upper[,1],
# final.pred$upper[,2],
# final.pred$lower[,1],
# final.pred$lower[,2]))
# colnames(df_results) <- c('Forecast', 'Upper80', 'Upper95', 'Low80', 'Low95') #probably an unnecessary line
# df_results$datetime <- test$datetime
# final_well <- final_well %>%
# left_join(df_results, by="datetime")
#
# # Rename the column to the appropriate names
# well2 <- gsub('-','',well)
# names <- c('','_RAIN', '_Forecast', '_Up80', '_Up95', '_Lo80', '_Lo95')
# uniq_names <- paste(well2,names,sep="")
# colnames(final_well) <- c("datetime", uniq_names)
#
#
# return(final_well)
# }
# Read the excel files in and clean them
for (well in wells){
if (well == 'G-852'){
df <- read_excel(paste(data.dir,well,'.xlsx',sep=''),sheet='Well',
col_names=c('date','time','tz_code','well_ft',
'Code','Corrected'),skip=1)
}
else{
df <- read_excel(paste(data.dir,well,'.xlsx',sep=''),sheet='Well')
}
well_df <- data.frame(df)
print(well)
startwell <- (end_df %>% filter(names==well) %>% select(starts))
endwell <- (end_df %>% filter(names==well) %>% select(ends))
well_df_clean <- mutate(well_df, time=hour(time)) # adds date to datetime
well_df_clean$datetime <- as.POSIXct(paste(well_df_clean$date,well_df_clean$time), format='%Y-%m-%d %H',tz='UTC')
if ((endwell) != ''){
well_df_clean <- well_df_clean %>% # summarizes to hourly data and
group_by(datetime) %>% # averages Corrected values when multiple rows have same datetime
summarise(well_ft=mean(Corrected)) %>%
filter(datetime >= mdy_h(startwell) & # filters to dates defined in Simmons instructions
datetime <= mdy_h(endwell))}
else{
well_df_clean <- well_df_clean %>%
group_by(datetime) %>%
summarise(well_ft=mean(Corrected)) %>%
filter(datetime >= mdy_h(startwell) & datetime <= mdy_h('6/12/2018 23'))
}
well_df_clean <- well_df_clean %>% select(datetime,well_ft)
if(endwell != ''){
hourseq2 <- seq(mdy_h(startwell),mdy_h(endwell), by='hours')
}
else{
hourseq2 <- seq(mdy_h(startwell),max(well_df_clean$datetime),by='hours')
}
hourseq2df <- as.data.frame(hourseq2)
names(hourseq2df) <- c('datetime')
# Join the data onto the date sequence to find missing values
full_well_df <- left_join(hourseq2df,well_df_clean,by='datetime')
# Create the timeseries object and then impute missing values using imputeTS package
startday <- as.numeric(strftime(mdy_h(startwell), format='%j'))
timeseries <- ts(full_well_df$well_ft, start=c(2007,startday*24), frequency=(365.25*24))
imputed <- na.seadec(timeseries, algorithm='locf')
full_well_df$filled <- imputed
final_well <- left_join(hourseqdf,full_well_df, by='datetime')
final_well <- final_well %>% select(datetime, filled)
#####################
##### Rain Data #####
#####################
rain <- read_xlsx(paste(data.dir,well,'.xlsx',sep=""),sheet='Rain')
rain$date<- as.Date(rain$Date)
rain$time<- format(as.POSIXct(rain$Date, "%H:%M:%S"))
rain_df <-data.frame(rain)
rain_df_clean <- mutate(rain_df, datetime=date(date)) # adds date to datetime
hour(rain_df_clean$datetime) <- hour(rain_df_clean$time) # Adds hour to datetime. Removes minutes from all hours
rain_df_clean$datetime <- as.POSIXct(rain_df_clean$datetime) # change time type of newly created Datetime
if(endwell != ''){
rain_df_clean <- rain_df_clean %>% # summarizes to hourly data and
group_by(datetime) %>% # averages Corrected values when multiple rows have same datetime
summarise(RAIN_FT=mean(RAIN_FT)) %>%
filter(datetime >= mdy_h(startwell) & # filters to dates defined in Simmons instructions
datetime <= mdy_h(endwell))}
else{
rain_df_clean <- rain_df_clean %>%
group_by(datetime) %>%
summarise(RAIN_FT=mean(RAIN_FT)) %>%
filter(datetime >= mdy_h(startwell))
}
final_well <- left_join(final_well, rain_df_clean, by='datetime')
#####################
##### Forecasting ####
#####################
# Run function to get forecast
# final_well <- pred_model5(rain_n_well) # DISFUNCTIONAL FUNCTION
# splitting newly assembled well data for clean model generation
if (well == 'G-852' | well == 'G-1220_T'){end_dt <- max(well_df_clean$datetime)
}
else{end_dt <- mdy_h(endwell)}
train <- final_well %>% select(datetime,filled,RAIN_FT) %>%
filter(datetime >= mdy_h(startwell) & datetime <= end_dt)
# Generating model on training data
yearly <- 24*365.25
x.reg <- train$RAIN_FT
seasons <- msts(train$filled, start=1, seasonal.periods = c(yearly))
model5 <- Arima(seasons,order=c(2,0,2), xreg=cbind(fourier(seasons,K=1),x.reg))
# forecasting across last 168 days
rain.ts <- read.zoo(train %>% select(datetime,RAIN_FT))
rain.impute <- na.approx(rain.ts)
rain.model <- auto.arima(rain.impute)
autoplot(rain.impute)
rain.preds <- forecast(rain.model, h=168)
r.f <- rain.preds$mean
r.f.df <- as.data.frame(r.f)
num <- length(seasons)
index <- num-(365.25*24)
sine <- fourier(seasons,K=1)[,1]
cose <- fourier(seasons,K=1)[,2]
final.pred <- forecast(model5,xreg=cbind(sine[index:(index+167)],cose[index:(index+167)],r.f.df$x),h=168)
# building df from results
df_results <- as.data.frame(cbind(
final.pred$mean,
final.pred$upper[,1],
final.pred$upper[,2],
final.pred$lower[,1],
final.pred$lower[,2]))
colnames(df_results) <- c('Forecast', 'Upper80', 'Upper95', 'Low80', 'Low95') #probably an unnecessary line
df_results$datetime <- seq(end_dt+hours(1), end_dt+hours(168), by='1 hour')
final_well <- final_well %>%
left_join(df_results, by="datetime")
# Rename the column to the appropriate names
well2 <- gsub('-','',well)
names <- c('','_RAIN', '_Forecast', '_Up80', '_Up95', '_Lo80', '_Lo95')
uniq_names <- paste(well2,names,sep="")
colnames(final_well) <- c("datetime", uniq_names)
# Join all the well columns together into one master dataframe
full_df <- full_df %>% left_join(final_well, by='datetime')
}
head(full_df)
full_df %>% filter(datetime <= mdy_h('6/13/2018 03') & datetime >= mdy_h('6/12/2018 22'))
# to save time on the above steps. Be careful to not save it to the Git repository. That'll eventually take up a lot of space.
save(full_df, welllist, file="Well_Viz_Full.RData") # need to add model to this save effort
load("C:/Users/johnb/OneDrive/Documents/MSA/Fall 2/Well Data/Well_Viz_Full.RData")
load("C:/Users/johnb/OneDrive/Documents/MSA/Fall 2/Well Data/Well_Viz_10_24.RData")
full_df %>% select(datetime,G852,G852_Forecast) %>% filter(is.na(!!as.symbol('G852')))
###############################
# Below is the shiny app code #
###############################
ui <- dashboardPage(
# The UI code
dashboardHeader(
title='Options'),
# Set up the conditional panels that are dependent on the user's first selection
dashboardSidebar(
sidebarMenu(id='menu1',
menuItem('Explore', tabName='explore', icon=icon('compass')),
menuItem('Predict', tabName='predict', icon=icon('bullseye')
),
# 'Explor' sidebar panels
conditionalPanel(
#condition = 'input.choice == "Explore"',
condition = 'input.menu1 == "explore"',
checkboxGroupInput('well_check','Well',
choices=welllist,selected='G852'),
dateRangeInput('dateRange_Input', 'Date Range',
start='2016-01-01',
end='2018-01-01',
min='2007-10-01',
max='2018-06-12'),
selectInput('year_Input','Year',unique(year(full_df$datetime)),selected='2009'),
selectInput('month_Input','Month',''),
selectInput('day_Input','Day','')),
# 'Predict' sidebar panels
conditionalPanel(
#condition = 'input.choice == "Predict"',
condition = 'input.menu1 == "predict"',
selectInput('well_Input','Well',welllist,selected='G852'),
numericInput('range_Input','Hours Predicted (max. 168)',68,0,168,1),
radioButtons('decomp_Input','Effects',choices=c('Rain','Seasonal'),selected='Rain'),
dateInput('start_date','Initial Plot Date',value='2018-06-01',min='2007-10-01',max='2018-07-01')
)
)),
dashboardBody(
mainPanel(
tabItems(
tabItem(tabName='explore',
fluidRow(
box(#title='Timeseries Plot of Selected Well(s)',
plotOutput('timeOutput'), width=12),
box(#title='Well Elevation on Selected Date',
plotOutput('dateOutput'), width=12)
)
),
tabItem(tabName='predict',
fluidRow(
box(#title='Forecast for Selected Well',
plotOutput('predictOutput'), width=12),
conditionalPanel(
condition = 'input.decomp_Input == "Rain"',
box(#title='Rain Influence on Predictions',
plotOutput('rainefctOutput'),width=12)
),
conditionalPanel(
condition = 'input.decomp_Input == "Seasonal"',
box(#title='Seasonal Influence on Predictions',
plotOutput('seasefctOutput'),width=12)
)
))
))
# 'Explore' panels
# conditionalPanel(
# condition = 'input.choice == "Explore"',
# h4('Timeseries Plot of Selected Well'),
# plotOutput('timeOutput'),
# br(),
# h4('Well Heights on Selected Date'),
# plotOutput('dateOutput')),
# br(),
# # 'Predict' panels
# conditionalPanel(
# condition = 'input.choice == "Predict"',
# h4('Well Prediction for Selected Well and Hours'),
# plotOutput('predictOutput'),
# br(),
# h4('Rain Measurements'),
# plotOutput('rainOutput')),
# br()))
)
)
# Below is the server code for shiny
server <- function(input,output,session){
reactive_data_well <- reactive({
full_df %>% select(datetime,input$well_check) %>% gather(well, depth, -datetime)
})
observe({
print(input$well_check)
print(names(reactive_data_well()))
})
observe({
print(input$well_Input)
print(input$range_Input)
})
reactive_data_year <- reactive({
full_df %>% filter(year(datetime) == input$year_Input)
})
reactive_TS_date <- reactive({
as.POSIXct(input$dateRange_Input)
})
# Need observe function to make the dropdown menu option reactive
# observe({
# updateDateRangeInput(session,'dateRange_Input',
# start=min(reactive_data_well()$datetime),
# end=max(reactive_data_well()$datetime))
# })
observe({
updateSelectInput(session,'month_Input',
choices=unique(month((reactive_data_year())$datetime)))
})
# First allow for month input to not have a value to prevent error
# If it has a value, use it
observe({
if(input$month_Input == ''){
return()
}
else{
reactive_data_month <- reactive({(full_df %>%
filter(year(datetime) == input$year_Input) %>%
filter(month(datetime) == input$month_Input))})
updateSelectInput(session,'day_Input',
choices=unique(day((reactive_data_month())$datetime)))
}
})
# Again use observe to allow the ggplot to have a variable number of lines in it
observe({
if(is.null(input$well_check)){
output$timeOutput <- renderPlot({
ggplot(reactive_data_well(), aes(x=datetime))
})
}
else{
# Below the plot iterates over however many wells are selected and adds them to the graph
alphas <- c(1,0.7,0.5,0.3)
if(length(input$well_check) == 1){
a2 <- alphas[1]
}
else if(length(input$well_check) < 5){
a2 <- alphas[2]
}
else if(length(input$well_check) < 9){
a2 <- alphas[3]
}
else{
a2 <- alphas[4]
}
cbbPalette <- c('G852'='#000000','F45'='#a6cee3','F179'='#1f78b4','F319'='#b2df8a','G561_T'='#33a02c',
'G580A'='#fb9a99','G860'='#e31a1c','G1220_T'='#fdbf6f','G1260_T'='#ff7f00',
'G2147_T'='#cab2d6','G2866_T'='#6a3d9a','G3549'='#ffff99','PB1680_T'='#b15928')
output$timeOutput <- renderPlot({
p <- ggplot(reactive_data_well(), aes(x=datetime, y=depth, color=well)) + geom_line(alpha=a2) +
xlim(reactive_TS_date())
#TODO attempts at this failed: +geom_vline(xintercept = )
# Need better colors
#cbbPalette <- c('#000000','#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c',
# '#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928')
p <- p + theme(legend.position='right') +
labs(y='Well Elevation (ft)', x='Year') + scale_color_manual(values=cbbPalette) +
guides(color=guide_legend(title='Well'))+theme_minimal()+ ggtitle("Timeseries Plot of Selected Well(s)")+
theme(axis.title=element_text(size=20),plot.title=element_text(size=28, hjust=0.5),
axis.text = element_text(size=12),
legend.text=element_text(size=12),
legend.title=element_text(size=12))
p
})}
})
# The bar chart is below, need observe because the inputs are reactive to other inputs
observe({
if(input$month_Input == '' | input$day_Input == ''){
return()
}
else{
reactive_prelim <- reactive({(full_df %>% select(datetime, one_of(welllist)) %>% filter(year(datetime) == input$year_Input,month(datetime) == input$month_Input,
day(datetime) == input$day_Input) %>% summarise_all(funs(mean)) %>% select(-datetime) %>%
gather(well, depth))})
reactive_data_date <- reactive({new <- reactive_prelim()
new$sign <- as.factor(reactive_prelim()$depth > 0)
new})
cols = c('TRUE'='#00BFC4','FALSE'='#F8766D')
output$dateOutput <- renderPlot({
ggplot(reactive_data_date(), aes(x=well,y=depth,fill=sign)) +
geom_col() +
labs(x='Well',y='Well Elevation (ft)') +
guides(fill=F) + geom_text(aes(label=round(depth, digits=2), vjust = ifelse(depth >= 0, -0.25, 1.25)), size=5) +
scale_fill_manual(values=cols)+theme_minimal()+ggtitle("Well Elevation on Selected Date")+
theme(axis.title=element_text(size=20),plot.title=element_text(size=28, hjust=0.5),
axis.text = element_text(size=12))
#cale_fill_manual(values=c('red','blue'))
})
}
})
vars <- c('_Forecast','_Up80','_Up95','_Lo80','_Lo95')
reactive_predict <- reactive({full_df %>% select(datetime,input$well_Input,paste(input$well_Input,vars,sep='')) %>%
filter(!is.na(!!as.symbol(input$well_Input)) | !is.na(!!as.symbol(paste(input$well_Input,'_Forecast',sep=''))))})
reactive_rain_pred <- reactive({full_df %>% select(datetime,paste(input$well_Input,'_RAIN',sep=''),
paste(input$well_Input,'.rain.efct',sep=''),
paste(input$well_Input,'.seas.efct',sep=''))})
observe({
# need to use the as.symbol function to make the string into a symbol so the filter function works
# reactive_predict <- reactive({full_df %>% select(datetime,input$well_Input,paste(input$well_Input,vars,sep='')) %>%
# filter(!is.na(!!as.symbol(input$well_Input)) | !is.na(!!as.symbol(paste(input$well_Input,'_Forecast',sep=''))))})
#
# reactive_rain_pred <- reactive({full_df %>% select(datetime,paste(input$well_Input,'_RAIN',sep=''),
# paste(input$well_Input,'.rain.efct',sep=''),
# paste(input$well_Input,'.seas.efct',sep=''))})
updateDateInput(session,'start_date',
min=(max(reactive_predict()$datetime)-years(1)),
max=(max(reactive_predict()$datetime)-days(14)))
# if(input$start_date == ''){
# return()
# }
# else{
# https://stackoverflow.com/questions/17148679/construct-a-manual-legend-for-a-complicated-plot
output$predictOutput <- renderPlot({ggplot(reactive_predict(), aes_string(x='datetime',y=paste(input$well_Input,'_Forecast',sep=''))) +
geom_line(color='#F8766D') +
geom_vline(xintercept=max((reactive_predict() %>% filter(!is.na(!!as.symbol(input$well_Input))))$datetime), linetype=2, alpha=0.7) +
geom_line(aes_string(y=input$well_Input)) +
geom_line(aes_string(y=paste(input$well_Input,'_Up95',sep='')),color='#00BFC4',alpha=0.7) +
geom_line(aes_string(y=paste(input$well_Input,'_Lo95',sep='')),color='#00BFC4',alpha=0.7) +
scale_x_datetime(limits=c(as.POSIXct(ymd(input$start_date)),(max(reactive_predict()$datetime) - hours(168-input$range_Input)))) +
#scale_y_continuous(limits=c(min(reactive_predict() %>% select(input$well_Input)) - 1, max(reactive_predict() %>% select(input$well_Input)) + 1)) +
geom_line(aes_string(y=paste(input$well_Input,'_Up80',sep='')),color='#00BFC4',linetype=2) +
geom_line(aes_string(y=paste(input$well_Input,'_Lo80',sep='')),color='#00BFC4',linetype=2) +
labs(x='Time',y='Well Elevation (ft)')+ggtitle("Forecast for Selected Well")+theme_minimal()+
guides(color = guide_legend(order = 1), lines = guide_legend(order = 2)) +
theme(axis.title=element_text(size=20),
plot.title=element_text(size=28, hjust=0.5),
axis.text = element_text(size=12),
plot.margin = unit(c(5,40,5,5),'points'))
})
# if(input$start_date == ''){
# return()
# }
# else{
output$rainefctOutput <- renderPlot({
ggplot(reactive_rain_pred(), aes(x=datetime)) +
geom_line(aes_string(y=paste(input$well_Input,'.rain.efct',sep='')), linetype=5) +
geom_vline(xintercept=max((reactive_predict() %>% filter(!is.na(!!as.symbol(input$well_Input))))$datetime), linetype=2, alpha=0.7) +
geom_histogram(stat='identity',aes_string(y=paste(input$well_Input,'_RAIN*12',sep='')),fill='#00BFC4') +
scale_x_datetime(limits=c(as.POSIXct(ymd(input$start_date)),(max(reactive_predict()$datetime) - hours(168-input$range_Input)))) +
scale_y_continuous(sec.axis = sec_axis(~.*12, name = "Rainfall (in)")) +
labs(x='Time',y='Rain Effect (ft)')+ theme_minimal()+ggtitle("Rain Influence")+
theme(axis.title=element_text(size=20),
plot.title=element_text(size=28, hjust=0.5),
axis.text = element_text(size=12),
axis.title.y.right = element_text(color = '#00BFC4'),
axis.text.y.right = element_text(color ='#00BFC4', margin = margin(t = 0, r = 0, b = 0, l = 10)),
plot.margin = unit(c(5,5,5,5),'points')
)
})
output$seasefctOutput <- renderPlot({
ggplot(reactive_rain_pred(), aes(x=datetime)) +
geom_line(aes_string(y=paste(input$well_Input,'.seas.efct',sep=''))) +
geom_vline(xintercept=max((reactive_predict() %>% filter(!is.na(!!as.symbol(input$well_Input))))$datetime), linetype=2, alpha=0.7) +
scale_x_datetime(limits=c(as.POSIXct(ymd(input$start_date)),(max(reactive_predict()$datetime) - hours(168-input$range_Input)))) +
labs(x='Time',y='Seasonal Effect (ft)')+theme_minimal()+ggtitle('Seasonal Influence on Predictions')+
theme(axis.title=element_text(size=20),
plot.title=element_text(size=28, hjust=0.5),
axis.text = element_text(size=12),
plot.margin = unit(c(5,40,5,5),'points'))
})
})
}
# Call the app
shinyApp(ui=ui, server=server, options=list(height=1080))
|
4ed7443a61480e153b81d20832617c75ce2987b3
|
e54a704f3197440a47f987a4e080716e8db55cc7
|
/tests/testthat/test-RE-stan_data.R
|
560a898fba345acfc22d115580d66e2e4330327e
|
[] |
no_license
|
dimbage/epidemia
|
ccb2b13c25b0dcb8a4857590cf6dc6d2494af3b2
|
c68720cabbc3856fa903038f603e9af6f6afa799
|
refs/heads/master
| 2023-07-13T08:51:39.922234
| 2021-06-23T10:59:35
| 2021-06-23T10:59:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,090
|
r
|
test-RE-stan_data.R
|
context("Random effects stan data")
load(file = "../data/NYWA.RData")
args <- list()
args$data <- NYWA$data
args$inf <- epiinf(gen=NYWA$si)
expect_warning(args$obs <- epiobs(deaths ~ 1, i2o = NYWA$inf2death * 0.02))
args$chains <- 0
test_that("Correct dimensions and values for quantities t, p, q and l in the stan data", {
# check terms for random effects are null if there are none
args$rt <- epirt(
formula = R(code, date) ~ 1 + av_mobility
)
sdat <- do.call("epim", args=args)
expect_equal(sdat$t, 0)
expect_equal(sdat$q, 0)
expect_equal(sdat$len_theta_L,0)
expect_equal(length(sdat$p),0)
expect_equal(length(sdat$l),0)
# check quantities for a simple random effects term
args$rt <- epirt(
formula = R(code, date) ~ 1 + (av_mobility | code)
)
sdat <- do.call("epim", args=args)
expect_equal(sdat$t, 1)
expect_equal(sdat$q, sum(sdat$p * sdat$l))
expect_equal(as.numeric(sdat$p), 2)
expect_equal(as.numeric(sdat$l), 3)
expect_equal(sdat$len_theta_L, sum(factorial(sdat$p + 1)/2))
# Setting with no intercept
args$rt <- epirt(
formula = R(code, date) ~ (0 + av_mobility | code)
)
sdat <- do.call("epim", args=args)
expect_equal(sdat$t, 1)
expect_equal(sdat$q, sum(sdat$p * sdat$l))
expect_equal(as.numeric(sdat$p), 1)
expect_equal(as.numeric(sdat$l), 3)
expect_equal(sdat$len_theta_L, sum(factorial(sdat$p + 1)/2))
# Multiple terms
args$rt <- epirt(
formula = R(code, date) ~ (av_mobility | code) + (0 + residential | code)
)
sdat <- do.call("epim", args=args)
expect_equal(sdat$t, 2)
expect_equal(sdat$q, sum(sdat$p * sdat$l))
expect_equal(as.numeric(sdat$p), c(2,1))
expect_equal(as.numeric(sdat$l), c(3,3))
expect_equal(sdat$len_theta_L,sum(factorial(sdat$p + 1)/2))
})
test_that("CSR matrix vectors for random slope model", {
# test CSR storage vectors on intercept example
args$rt <- epirt(
formula = R(code, date) ~ (1 | code)
)
sdat <- do.call("epim", args = args)
len <- nrow(args$data)
colidx <- 1 - (args$data$code == "NY")
expect_equal(sdat$w, rep(1,len))
expect_equal(sdat$v, colidx)
# each row has a single entry
expect_equal(sdat$u, 0:len)
expect_equal(sdat$num_non_zero, len)
# check empty if there are no random effect terms
args$rt <- epirt(
formula = R(code, date) ~ 1
)
sdat <- do.call("epim", args = args)
expect_equal(length(sdat$w), 0)
expect_equal(length(sdat$v), 0)
expect_equal(length(sdat$u), 0)
expect_equal(sdat$num_non_zero, 0)
})
test_that("Correct usage of special_case flag in stan data", {
# Only FE
args$rt <- epirt(
formula = R(code, date) ~ 1
)
sdat <- do.call("epim", args=args)
expect_equal(sdat$special_case, 0)
# Only random intercept (special case == TRUE)
args$rt <- epirt(
formula = R(code, date) ~ (1 | code)
)
sdat <- do.call("epim", args=args)
expect_true(sdat$special_case)
# Random slopes (special_case == FALSE)
args$rt <- epirt(
formula = R(code, date) ~ (av_mobility | code)
)
sdat <- do.call("epim", args=args)
expect_false(sdat$special_case)
})
|
6254930780e33489118c6548709de47705a7aaca
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/pmxTools/examples/calc_sd_2cmt_linear_oral_1_lag.Rd.R
|
7a65f618132392e356e05ed6782b799fb1f06bce
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 370
|
r
|
calc_sd_2cmt_linear_oral_1_lag.Rd.R
|
library(pmxTools)
### Name: calc_sd_2cmt_linear_oral_1_lag
### Title: Calculate C(t) for a 2-compartment linear model after a single
### first-order oral dose with a lag time
### Aliases: calc_sd_2cmt_linear_oral_1_lag
### ** Examples
Ctrough <- calc_sd_2cmt_linear_oral_1_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30, Q = 0.5,
dose = 1000, ka = 1, tlag = 2)
|
440ac7a6cd985447816c5de8fec889be81ee6d08
|
f30cc1c33978ca5a708a7e0a493403ea88550160
|
/R/neuronlist_interactive_3d.R
|
be1488ecb8e9b486c865769c0d07514d2a5cfdae
|
[] |
no_license
|
natverse/nat
|
044384a04a17fd0c9d895e14979ce43e43a283ba
|
1d161fa463086a2d03e7db3d2a55cf4d653dcc1b
|
refs/heads/master
| 2023-08-30T21:34:36.623787
| 2023-08-25T07:23:44
| 2023-08-26T19:02:50
| 15,578,625
| 35
| 10
| null | 2023-01-28T19:03:03
| 2014-01-02T07:54:01
|
R
|
UTF-8
|
R
| false
| false
| 8,757
|
r
|
neuronlist_interactive_3d.R
|
#' Find neurons within a 3D selection box (usually drawn in rgl window)
#'
#' @details Uses \code{\link{subset.neuronlist}}, so can work on dotprops or
#' neuron lists.
#' @param sel3dfun A \code{\link{select3d}} style function to indicate if points
#' are within region
#' @param indices Names of neurons to search (defaults to all neurons in list)
#' @param db \code{neuronlist} to search. Can also be a character vector naming
#' the neuronlist. Defaults to \code{options('nat.default.neuronlist')}.
#' @param threshold More than this many points must be present in region
#' @param invert Whether to return neurons outside the selection box (default
#' \code{FALSE})
#' @param rval What to return (character vector, default='names')
#'
#' @return Character vector of names of selected neurons, neuronlist, or
#' data.frame of attached metadata according to the value of \code{rval}.
#' @export
#' @seealso \code{\link{select3d}, \link{find.soma}, \link{subset.neuronlist}}
#' @examples
#' \dontrun{
#' plot3d(kcs20)
#' # draw a 3D selection e.g. around tip of vertical lobe when ready
#' find.neuron(db=kcs20)
#' # would return 9 neurons
#' # make a standalone selection function
#' vertical_lobe=select3d()
#' find.neuron(vertical_lobe, db=kcs20)
#' # use base::Negate function to invert the selection function
#' # i.e. choose neurons that do not overlap the selection region
#' find.neuron(Negate(vertical_lobe), db=kcs20)
#' }
find.neuron<-function(sel3dfun=select3d(), indices=names(db),
db=getOption("nat.default.neuronlist"), threshold=0,
invert=FALSE, rval=c("names",'data.frame',"neuronlist")){
if(is.null(db))
stop("Please pass a neuronlist in argument db or set options",
"(nat.default.neuronlist='myfavneuronlist'). See ?nat for details.")
if(is.character(db)) db=get(db)
if(!missing(sel3dfun) && !is.function(sel3dfun))
stop("Please provide a selection function!")
selfun=function(x){
pointsinside=sel3dfun(na.omit(xyzmatrix(x)))
sum(pointsinside, na.rm=T)>threshold
}
if(invert) selfun=Negate(selfun)
rval=match.arg(rval)
subset(db, subset=indices, filterfun=selfun, rval=rval)
}
#' Find neurons with soma inside 3D selection box (usually drawn in rgl window)
#'
#' @details Can work on \code{neuronlist}s containing \code{neuron} objects
#' \emph{or} \code{neuronlist}s whose attached data.frame contains soma
#' positions specified in columns called X,Y,Z .
#' @inheritParams find.neuron
#' @return Character vector of names of selected neurons
#' @export
#' @seealso \code{\link{select3d}, \link{subset.neuronlist}, \link{find.neuron}}
find.soma <- function (sel3dfun = select3d(), indices = names(db),
db = getOption("nat.default.neuronlist"),
invert=FALSE,
rval=c("names", "neuronlist", "data.frame"))
{
if (is.null(db))
stop("Please pass a neuronlist in argument db or set options",
"(nat.default.neuronlist='myfavneuronlist'). See ?nat for details.")
if (is.character(db))
db = get(db)
df=attr(db, 'df')
rval=match.arg(rval)
if(all(c("X","Y","Z") %in% colnames(df))){
# assume these represent soma position
somapos=df[indices,c("X","Y","Z")]
sel_neurons=indices[sel3dfun(somapos)]
if(invert) sel_neurons=setdiff(indices, sel_neurons)
if(rval=='names') sel_neurons else subset(db, indices=sel_neurons, rval=rval)
} else {
selfun = function(x) {
somapos=x$d[x$StartPoint, c("X","Y","Z")]
isTRUE(sel3dfun(somapos))
}
if(invert) selfun=Negate(selfun)
subset(db, subset = indices, filterfun = selfun, rval = rval)
}
}
#' Scan through a set of neurons, individually plotting each one in 3D
#'
#' Can also choose to select specific neurons along the way and navigate
#' forwards and backwards.
#'
#' @param neurons a \code{neuronlist} object or a character vector of names of
#' neurons to plot from the neuronlist specified by \code{db}.
#' @inheritParams plot3d.character
#' @param col the color with which to plot the neurons (default \code{'red'}).
#' @param Verbose logical indicating that info about each selected neuron should
#' be printed (default \code{TRUE}).
#' @param Wait logical indicating that there should be a pause between each
#' displayed neuron.
#' @param sleep time to pause between each displayed neuron when
#' \code{Wait=TRUE}.
#' @param extrafun an optional function called when each neuron is plotted, with
#' two arguments: the current neuron name and the current \code{selected}
#' neurons.
#' @param selected_file an optional path to a \code{yaml} file that already
#' contains a selection.
#' @param selected_col the color in which selected neurons (such as those
#' specified in \code{selected_file}) should be plotted.
#' @param yaml a logical indicating that selections should be saved to disk in
#' (human-readable) \code{yaml} rather than (machine-readable) \code{rda}
#' format.
#' @param ... extra arguments to pass to \code{\link{plot3d}}.
#'
#' @return A character vector of names of any selected neurons, of length 0 if
#' none selected.
#' @importFrom yaml yaml.load_file as.yaml
#' @seealso \code{\link{plot3d.character}}, \code{\link{plot3d.neuronlist}}
#' @export
#' @examples
#' \dontrun{
#' # scan a neuronlist
#' nlscan(kcs20)
#'
#' # using neuron names
#' nlscan(names(kcs20), db=kcs20)
#' # equivalently using a default neuron list
#' options(nat.default.neuronlist='kcs20')
#' nlscan(names(kcs20))
#' }
#' # scan without waiting
#' nlscan(kcs20[1:4], Wait=FALSE, sleep=0)
#' \dontrun{
#' # could select e.g. the gamma neurons with unbranched axons
#' gammas=nlscan(kcs20)
#' nclear3d()
#' plot3d(kcs20[gammas])
#'
#' # plot surface model of brain first
#' # nb depends on package only available on github
#' devtools::install_github(username = "natverse/nat.flybrains")
#' library(nat.flybrains)
#' plot3d(FCWB)
#' # could select e.g. the gamma neurons with unbranched axons
#' gammas=nlscan(kcs20)
#'
#' nclear3d()
#' plot3d(kcs20[gammas])
#' }
nlscan <- function(neurons, db=NULL,
col='red', Verbose=T, Wait=T, sleep=0.1,
extrafun=NULL, selected_file=NULL, selected_col='green',
yaml=TRUE, ..., plotengine = "rgl") {
if(!isTRUE(plotengine=="rgl"))
stop("nlscan only supports the rgl plotengine at present!")
if(is.neuronlist(neurons)) {
db=neurons
neurons=names(db)
}
frames <- length(neurons)
if(length(col)==1) col <- rep(col,frames)
selected <- character()
i <- 1
if(!is.null(selected_file) && file.exists(selected_file)) {
selected <- yaml.load_file(selected_file)
if(!all(names(selected) %in% neurons)) stop("Mismatch between selection file and neurons.")
}
savetodisk <- function(selected, selected_file) {
if(is.null(selected_file)) selected_file <- file.choose(new=TRUE)
if(yaml){
if(!grepl("\\.yaml$",selected_file)) selected_file <- paste(selected_file,sep="",".yaml")
message("Saving selection to disk as ", selected_file, ".")
writeLines(as.yaml(selected), con=selected_file)
} else {
if(!grepl("\\.rda$", selected_file)) selected_file <- paste(selected_file, sep="", ".rda")
save(selected, file=selected_file)
message("Saving selection to disk as ", selected_file)
}
selected_file
}
chc <- NULL
while(TRUE){
if(i > length(neurons) || i < 1) break
n <- neurons[i]
cat("Current neuron:", n, "(", i, "/", length(neurons), ")\n")
pl <- plot3d(n, db=db, plotengine = plotengine,
col=substitute(ifelse(n %in% selected, selected_col, col[i])), ..., SUBSTITUTE=FALSE)
# call user supplied function
more_rgl_ids <- list()
if(!is.null(extrafun))
more_rgl_ids <- extrafun(n, selected=selected)
if(Wait){
chc <- readline("Return to continue, b to go back, s to select, d [save to disk], t to stop, c to cancel (without returning a selection): ")
if(chc=="c" || chc=='t'){
sapply(pl, pop3d, type='shape')
sapply(more_rgl_ids, pop3d, type='shape')
break
}
if(chc=="s") {
if(n %in% selected) {
message("Deselected: ", n)
selected <- setdiff(selected, n)
} else selected <- union(selected, n)
}
if(chc=="b") i <- i-1
else if (chc=='d') savetodisk(selected, selected_file)
else i <- i+1
} else {
Sys.sleep(sleep)
i <- i+1
}
sapply(pl, pop3d, type='shape')
sapply(more_rgl_ids, pop3d, type='shape')
}
if(is.null(chc) || chc=='c') return(NULL)
if(!is.null(selected_file)) savetodisk(selected, selected_file)
selected
}
|
486ae5a58b059238b5689733158f2627f6da5f7d
|
01f3d2abd6a99a98d3a26441c6508e8994f310d8
|
/Project.r
|
848aaa79f1f61680f1d37249409d8f3420cc8fe6
|
[] |
no_license
|
gauthamsaimr/TimeSeriesAnalysisPanelData
|
1b453d06b3d0d75b98f1d305df7b3ea0767cf153
|
83a094fb270cade8b3664cfcc2a78d9f11df77ba
|
refs/heads/master
| 2020-06-04T03:18:32.071659
| 2019-06-14T01:22:22
| 2019-06-14T01:22:22
| 191,852,443
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,419
|
r
|
Project.r
|
library(haven)
library(data.table)
library(ggplot2)
library(broom)
library(forecast)
library(plm)
library(margins)
library(sandwich)
library(lmtest)
library(tseries)
library(DBI)
library(RSQLite)
library(tidyverse)
library(car)
library(TSA)
library(deeplr)
library(dplyr)
library(partykit)
mydata<- read_dta("Guns.dta")
summary(mydata)
qplot(mydata$incarc_rate, geom = 'histogram', binwidth = 2) + xlab('Incarc rate')
qplot(mydata$density, geom = 'histogram', binwidth = 1) + xlab('density')
## this tells us that incarc_rate and density are highly skewed and we need to make Log transformations on it
corr<-cor(mydata)
#we can see that there is high correaltion between pb1064 and pw1064
library(ggcorrplot)
ggcorrplot(corr)
summary(lm(log(vio)~shall,data = mydata))
coeftest(lm(log(vio)~shall,data=mydata),vcovHC)
# rsquare is just 0.08 which is very low
#Regression without control variables|^
# we can see that there is 44% reduce in violent crime rates after issuing shall laws
# now lets see Regression with Control Variables|>
summary(lm(log(vio)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata))
coeftest(lm(log(vio)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata),vcovHC)
# rsquare is 0.6713
# We can see that still there is a large effect with a small drop in the coefficient, which is 28.26% reduce in violent crime rate after issuing shall laws.
#correlation
cor(mydata$pb1064,mydata$pw1064)
#These are highly correlated (-0.98)
#Pooled regression data frame
gunsvio<-pdata.frame(mydata, index = c("stateid","year"))
#violence as Dependent
#pooled without Cluster Robust SE
vio_polled<-plm(log(vio)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density)+pw1064,data=gunsvio, model="pooling")
summary(vio_polled)
# we see that pb1064 is highly correlated with pw1064 as we saw before so we are removing pw1064
# pooled with cluster Robust and no pb1064
# reason being the OLS estimates of the model are still unbiased and linear but no longer the best and the standard errors are incorrect which makes the confidence intervals and hypothesis tests misleading.
vio_polled1<-plm(log(vio)~shall+log(incarc_rate)+pm1029+pop+avginc+log(density)+pb1064,data=gunsvio, model="pooling")
summary(vio_polled1)
coeftest(vio_polled1,vcovHC)
##We notice that pw1064 is highly insignificant.
##we corrected the OLS standard errors, but these estimates are still not the best as the model is inefficient.
##This could be because of the omitted variable bias. So, we next wanted to implement a fixed effects model which is immune to omitted variable bias from variables that are constant over time and vary between states and not within states. For example, the attitude of the people committing crime or quality of police cannot be quantified using a pooled OLS model where as it won't introduce any bias in a fixed effects model.
#Fixed effects without robust SE
vio_fixed1<-plm(log(vio)~shall+log(incarc_rate)+pop+avginc+log(density)+pw1064+pb1064+pm1029 ,data=gunsvio, model="within")
summary(vio_fixed1)
#we see that avginc is insignificant and we check on Ftest
Hnull <- c("avginc=0")
linearHypothesis(vio_fixed1,Hnull)
#this shows that avginc is insignificant and we can drop
vio_fixed2<-plm(log(vio)~shall+log(incarc_rate)+pop+log(density)+pw1064++pb1064+pm1029 ,data=gunsvio, model="within")
summary(vio_fixed2)
# do the interpretations
#We are not planning to use Cluster Robust Standard errors for Entity Fixed effects because fixed effects controls for omitted variable bias because of variables that are constant over time and change with states.
#Still there can be omitted variables which can possibly vary over time but are constant across states. We then implemented used entity fixed and time fixed effects model to address the bias from such omitted variables.
#fixed effects with time
vio_fixed3 <- plm(log(vio)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pm1029+pw1064+factor(year)-1, data=gunsvio, model="within")
summary(vio_fixed3)
Hnull <- c("pb1064=0","pop=0","avginc=0","pw1064=0")
linearHypothesis(vio_fixed3,Hnull)
vio_fixed4 <- plm(log(vio)~log(incarc_rate)+pm1029+log(density)+shall+factor(year)-1, data=gunsvio, model="within")
summary(vio_fixed4)
#{r murder}
mydata<- read_dta("Guns.dta")
summary(mydata)
summary(lm(log(mur)~shall,data = mydata))
coeftest(lm(log(mur)~shall,data=mydata),vcovHC)
# rsquare is just 0.08 which is very low with 47% decrease in violent crime and is highly significant
summary(lm(log(mur)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata))
coeftest(lm(log(mur)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata),vcovHC)
# r square is 0.64 and the coefficient has reduced to 21% and still significant
## pooled ols without cluster Robust SE
gunsmur<-pdata.frame(mydata, index = c("stateid","year"))
mur_pooled<-plm(log(mur)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density)+pw1064,data=gunsmur, model="pooling")
summary(mur_pooled)
##We have observed that "pw1064" is insignificant and it might be because of high correlation with "pb1064", which has been observed earlier. Therefore, removing "pw1064" and executing pooled model.
##pooled ols without pw1064
mur_pooled<-plm(log(mur)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density),data=gunsmur, model="pooling")
summary(mur_pooled)
# there is a decrease n murder rate
##lets check for CLuster Robust
coeftest(mur_pooled,vcovHC)
## now we see that pb1064 is insignificant
##Using the robust standard errors, we corrected the OLS standard errors but these estimates are still not the best as the model is inefficient. This could be because of the omitted variable bias. So, we next wanted to implement a fixed effects model which is immune to omitted variable bias from variables that are constant over time and vary between states and not within states
##state fixed entity effect
mur_fixed<-plm(log(mur)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density),data=gunsmur, model="within")
summary(mur_fixed)
##Pm1029 and pop are insignificant.
Hnull <- c("pm1029=0","pop=0")
linearHypothesis(mur_fixed,Hnull)
#drop these 2
mur_fixed1<-plm(log(mur)~shall+log(incarc_rate)+pb1064+avginc+log(density),data=gunsmur, model="within")
summary(mur_fixed1)
#interpret
## Fixed Time effect
mur_fixed2<-plm(log(mur)~shall+log(incarc_rate)+pb1064+pm1029+avginc+log(density)+factor(year)-1,data=gunsmur, model="within")
summary(mur_fixed2)
#shall is insignificant
##We further wanted to address any bias from unobserved omitted variables. So, we decided to try and implement Random effects model. But we saw that the data is not collected using random sampling. So, we should not implement Random effects model. \
#---------------robbery---------
mydata<- read_dta("Guns.dta")
summary(mydata)
summary(lm(log(rob)~shall,data = mydata))
coeftest(lm(log(rob)~shall,data=mydata),vcovHC)
# rsquare is just 0.12 which is very low with 77% decrease in robbery crime and is highly significant
summary(lm(log(rob)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata))
coeftest(lm(log(rob)~shall+log(incarc_rate)+pop+avginc+log(density)+pb1064+pw1064+pm1029, data=mydata),vcovHC)
# rsquare is 0.6899 with a decrease of 41% in robbery crime and is highly significant
##pooled ols without Robust SE
gunsrob<-pdata.frame(mydata, index = c("stateid","year"))
rob_pooled<-plm(log(rob)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density)+pw1064,data=gunsrob, model="pooling")
summary(rob_pooled)
#We have observed that "pw1064" is insignificant and it might be because of high correlation with "pb1064", which has been observed earlier. Therefore, removing "pw1064" and executing pooled model.
rob_pooled1<-plm(log(rob)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density),data=gunsrob, model="pooling")
summary(rob_pooled1)
##heterosckedasticity
coeftest(rob_pooled1,vcovHC)
##Only "pb1064" becomes insignificant at p value of 0.1
#Using the robust standard errors, we corrected the OLS standard errors but these estimates are still not the best as the model is inefficient. This could be because of the omitted variable bias. So, we next wanted to implement a fixed effects model which is immune to omitted variable bias from variables that are constant over time and vary between states and not within states.
#state Fixed entity effect
rob_fixed<-plm(log(rob)~shall+log(incarc_rate)+pb1064+pm1029+pop+avginc+log(density),data=gunsrob, model="within")
summary(rob_fixed)
#after testing for all insignificant variables on Ftest we remove them
rob_fixed1<-plm(log(rob)~shall+log(incarc_rate)+pb1064,data=gunsrob, model="within")
summary(rob_fixed1)
##After controlling for entity fixed effects, the direction of impact of log (incarceration_rate) has changed in comparison to the pooled model. Controlling for omitted variables bias has led to this change.
##fixed time and entity
rob_fixed2<-plm(log(rob)~shall+log(incarc_rate)+pb1064+pm1029+avginc+log(density)+factor(year)-1,data=gunsrob, model="within")
summary(rob_fixed2)
#shall variable is insignificant
#analysis on murder rate
|
2ae390c37aa9798c221c6971d85c73aea786e4db
|
bf8802140cc5e08b9e0a9abcb060f2e15eb83b55
|
/man/cvglasso.Rd
|
2edbbd6f2095553001f72326739c702ee35d6a82
|
[] |
no_license
|
cran/robustcov
|
b2415fa2d141a74ef54d49471dd26983f6276b46
|
35d25b8a3df67f09172ee1dbb74ffa1a3a0af669
|
refs/heads/master
| 2023-07-08T16:52:39.466320
| 2021-08-04T09:00:05
| 2021-08-04T09:00:05
| 392,744,128
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,035
|
rd
|
cvglasso.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/glasso_CV.R
\name{cvglasso}
\alias{cvglasso}
\title{Cross validation to chose tuning parameter of glasso}
\usage{
cvglasso(
data,
k = 10,
covest = cov,
rhos = seq(0.1, 1, 0.1),
evaluation = negLLrobOmega,
...
)
}
\arguments{
\item{data}{The full dataset, should be a matrix or a data.frame, row as sample}
\item{k}{number of folds}
\item{covest}{a *function* or name of a function (string) that takes a matrix to estimate covariance}
\item{rhos}{a vector of tuning parameter to be tested}
\item{evaluation}{a *function* or name of a function (string) that takes only two arguments, the estimated covariance and the test covariance, when NULL, we use negative log likelihood on test sets}
\item{...}{extra arguments send to glasso}
}
\value{
a matrix with k rows, each row is the evaluation loss of that fold
}
\description{
This routine use k fold cross validation to chose tuning parameter
}
\examples{
cvglasso(matrix(rnorm(100),20,5))
}
|
254f0ae649cd1c98d1dc54b97372a965bd7a7334
|
9b62eff47bbe5b29e362778d855bd7eac1ee7595
|
/man/launch_shinytmb.Rd
|
e442c979c2e755dfd88ab066fe31aa45f133a2a7
|
[] |
no_license
|
jmannseth/adnuts
|
2876ff9d3993b68235de3d2b01e136160e1b4610
|
055fa3e5899c0d521607c0aa05a3d673e2af8cd1
|
refs/heads/master
| 2021-06-21T03:33:59.678604
| 2017-08-18T21:26:48
| 2017-08-18T21:26:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 465
|
rd
|
launch_shinytmb.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helper.R
\name{launch_shinytmb}
\alias{launch_shinytmb}
\title{A high level wrapper to launch shinystan for a TMB fit.}
\usage{
launch_shinytmb(fit)
}
\arguments{
\item{fit}{A named list returned by \code{sample_tmb}.}
}
\description{
A high level wrapper to launch shinystan for a TMB fit.
}
\details{
This function simply calls
\code{launch_shinystan(as.shinystan.tmb(tmb.fit))}.
}
|
a07c60aa60b6a23a55f6b7ee9b9fca472ed20891
|
538e7a274b5e7442b61d03df954e1355d2ebe683
|
/R/CI_script_transform.R
|
2af996a40697fffb6227e7156b1ef2957a35e763
|
[] |
no_license
|
WilliamsPaleoLab/Farley_Optimizing_Computing_SDMs
|
7fdbe26c620dcadc136f01e4826234ffabd27e38
|
0613a8d2274078d9c7de422c0cdb0441bf11db9e
|
refs/heads/master
| 2021-06-18T20:47:28.320997
| 2017-05-12T17:50:32
| 2017-05-12T17:50:32
| 58,968,939
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,325
|
r
|
CI_script_transform.R
|
library(plyr)
con <- dbConnect(dbDriver("MySQL"), host='104.154.235.236', password = 'Thesis-Scripting123!', dbname='timeSDM', username='Scripting')
## get results from database
r <- dbGetQuery(con, "Select * From Experiments Inner Join Results on Results.experimentID = Experiments.experimentID
WHERE experimentStatus = 'DONE' AND cores < 8 AND model = 'GBM-BRT' OR model='GAM' OR model = 'MARS';")
## separate models
r.brt <- r[which(r$model == 'GBM-BRT'), ]
r.gam <- r[which(r$model == 'GAM'), ]
r.mars <- r[which(r$model == 'MARS'), ]
f <- as.formula(log(totalTime) ~ cores + GBMemory + (cores * GBMemory) + trainingExamples + spatialResolution)
## build testing and training set
r.brt.testingInd <- sample(nrow(r.brt), 100)
r.brt.testing <- r.brt[r.brt.testingInd, ]
r.brt.training <- r.brt[-r.brt.testingInd, ]
r.gam.testingInd <- sample(nrow(r.gam), 100)
r.gam.testing <- r.gam[r.gam.testingInd, ]
r.gam.training <- r.gam[-r.gam.testingInd, ]
r.mars.testingInd <- sample(nrow(r.mars), 100)
r.mars.testing <- r.mars[r.mars.testingInd, ]
r.mars.training <- r.mars[-r.mars.testingInd, ]
## build the GBM models
library(gbm)
r.brt.brt <- gbm(f, data=r.brt.training, n.trees = 15000, bag.fraction = 0.75)
r.gam.brt <- gbm(f, data=r.gam.training, n.trees = 15000, bag.fraction = 0.75)
r.mars.brt <- gbm(f, data=r.mars.training, n.trees = 15000, bag.fraction = 0.75)
## predict the results
# find best iteration
r.brt.brt.bestIter <- gbm.perf(r.brt.brt)
r.gam.brt.bestIter <- gbm.perf(r.gam.brt)
r.mars.brt.bestIter <- gbm.perf(r.mars.brt)
r.brt.brt.predict = predict(r.brt.brt, r.brt.testing, n.trees = r.brt.brt.bestIter)
r.gam.brt.predict = predict(r.gam.brt, r.gam.testing, n.trees = r.gam.brt.bestIter)
r.mars.brt.predict = predict(r.mars.brt, r.mars.testing, n.trees = r.mars.brt.bestIter)
## evaluate the prediction
cor(r.brt.brt.predict, log(r.brt.testing$totalTime))
cor(r.gam.brt.predict, log(r.gam.testing$totalTime))
cor(r.mars.brt.predict, log(r.mars.testing$totalTime))
r.brt.brt.delta <- r.brt.brt.predict - log(r.brt.testing$totalTime)
r.gam.brt.delta <- r.gam.brt.predict - log(r.gam.testing$totalTime)
r.mars.brt.delta <- r.mars.brt.predict - log(r.mars.testing$totalTime)
mean(r.brt.brt.delta)
mean(r.gam.brt.delta)
mean(r.mars.brt.delta)
sd(r.brt.brt.delta)
sd(r.gam.brt.delta)
sd(r.mars.brt.delta)
plot(r.brt.brt.predict ~ log(r.brt.testing$totalTime), main='Performance Model (GBM)', xlab='log(Observed Execution Time) [Seconds]', ylab='Predicted Execution Time [Seconds]', pch=3, col='darkgreen')
points(r.gam.brt.predict ~ log(r.gam.testing$totalTime), pch=3, col='darkred')
points(r.mars.brt.predict ~ log(r.mars.testing$totalTime), pch=3, col='dodgerblue')
legend('bottomright', c('GBM-BRT', 'GAM', 'MARS'), col=c('darkgreen', 'darkred', 'dodgerblue'), pch=3)
abline(0, 1)
## Build the linear models
r.brt.lm <- lm(f, data=r.brt.training)
r.gam.lm <- lm(f, data=r.gam.training)
r.mars.lm <- lm(f, data=r.mars.training)
## predict
r.brt.lm.predict = predict(r.brt.lm, r.brt.testing)
r.gam.lm.predict = predict(r.gam.lm, r.gam.testing)
r.mars.lm.predict = predict(r.mars.lm, r.mars.testing)
## evaluate the prediction
cor(r.brt.lm.predict, log(r.brt.testing$totalTime))
cor(r.gam.lm.predict, log(r.gam.testing$totalTime))
cor(r.mars.lm.predict, log(r.mars.testing$totalTime))
r.brt.lm.delta <- r.brt.lm.predict - log(r.brt.testing$totalTime)
r.gam.lm.delta <- r.gam.lm.predict - log(r.gam.testing$totalTime)
r.mars.lm.delta <- r.mars.brt.predict - log(r.mars.testing$totalTime)
mean(r.brt.lm.delta)
mean(r.gam.lm.delta)
mean(r.mars.lm.delta)
sd(r.brt.lm.delta)
sd(r.gam.lm.delta)
sd(r.mars.lm.delta)
plot(r.brt.lm.predict ~ log(r.brt.testing$totalTime), main='Performance Model (Linear Model)', xlab='log(Observed Execution Time) [Seconds]', ylab='Predicted Execution Time [Seconds]', pch=3, col='darkgreen')
points(r.gam.lm.predict ~ log(r.gam.testing$totalTime), pch=3, col='darkred')
points(r.mars.lm.predict ~ log(r.mars.testing$totalTime), pch=3, col='dodgerblue')
legend('bottomright', c('GBM-BRT', 'GAM', 'MARS'), col=c('darkgreen', 'darkred', 'dodgerblue'), pch=3)
abline(0, 1)
## build accuracy prediction
a <- dbGetQuery(con, "Select * From Experiments Inner Join Results on Results.experimentID = Experiments.experimentID WHERE experimentStatus = 'DONE' and experimentCategory = 'nSensitivity';")
a.testingInd = sample(nrow(a), 75)
a.testing <- a[a.testingInd, ]
a.training <- a[-a.testingInd, ]
a.gbm <- gbm(testingAUC ~ trainingExamples, data=a.training, n.trees = 15000)
a.gbm.bestIter <- gbm.perf(a.gbm)
a.gbm.predict <- predict(a.gbm, a.testing, n.trees = a.gbm.bestIter)
cor(a.gbm.predict, a.testing$testingAUC)
a.gbm.delta <- a.gbm.predict - a.testing$testingAUC
mean(a.gbm.delta)
a.s <- ddply(a, .(cores, GBMemory, trainingExamples, taxon, cellID, spatialResolution),summarise, var = var(testingAUC), sd=sd(testingAUC), mean=mean(testingAUC), median=median(testingAUC))
plot(a.gbm, n.trees=a.gbm.bestIter, main='AUC Accuracy of GBM-BRT SDM', xlim=c(0, 10000))
points(a.training$testingAUC ~ a.training$trainingExamples, col=rgb(0.5, 0.5, 0, 0.5))
#points(a.s$median ~ a.s$trainingExamples, col=rgb(0.5, 0.5, 0, 1))
legend('bottomright', c('Observed Values', 'Predictive Model'), col=c(rgb(0.5, 0.5, 0), 'black'), lty=c(NA, 1), pch=c(1, NA))
|
8fee2ebed7a3fde56c2128d9e702db09d45fd52e
|
bb3fd8e814b3210022371974c95684066034ec39
|
/man/slide2.Rd
|
953808050518272a24646247ada7e2070fd0a955
|
[] |
no_license
|
xtmgah/tsibble
|
34f9990f5db6c39552e446ebee7b399d05fa12b2
|
1c5ec296036d86cae1a4c24bc01fa9e676cd63ed
|
refs/heads/master
| 2020-03-23T00:40:20.332753
| 2018-07-13T13:06:38
| 2018-07-13T13:06:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,917
|
rd
|
slide2.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slide.R
\name{slide2}
\alias{slide2}
\alias{slide2}
\alias{slide2_dfr}
\alias{slide2_dfc}
\alias{pslide}
\alias{pslide}
\alias{pslide_dfr}
\alias{pslide_dfc}
\title{Sliding window calculation over multiple inputs simultaneously}
\usage{
slide2(.x, .y, .f, ..., .size = 1, .fill = NA, .partial = FALSE)
slide2_dfr(.x, .y, .f, ..., .size = 1, .fill = NA, .partial = FALSE,
.id = NULL)
slide2_dfc(.x, .y, .f, ..., .size = 1, .fill = NA, .partial = FALSE)
pslide(.l, .f, ..., .size = 1, .fill = NA, .partial = FALSE)
pslide_dfr(.l, .f, ..., .size = 1, .fill = NA, .partial = FALSE,
.id = NULL)
pslide_dfc(.l, .f, ..., .size = 1, .fill = NA, .partial = FALSE)
}
\arguments{
\item{.x, .y}{Objects to slide over simultaneously.}
\item{.f}{A function, formula, or atomic vector.
If a \strong{function}, it is used as is.
If a \strong{formula}, e.g. \code{~ .x + 2}, it is converted to a function. There
are three ways to refer to the arguments:
\itemize{
\item For a single argument function, use \code{.}
\item For a two argument function, use \code{.x} and \code{.y}
\item For more arguments, use \code{..1}, \code{..2}, \code{..3} etc
}
This syntax allows you to create very compact anonymous functions.
If \strong{character vector}, \strong{numeric vector}, or \strong{list}, it
is converted to an extractor function. Character vectors index by name
and numeric vectors index by position; use a list to index by position
and name at different levels. Within a list, wrap strings in \code{\link[=get-attr]{get-attr()}}
to extract named attributes. If a component is not present, the value of
\code{.default} will be returned.}
\item{...}{Additional arguments passed on to \code{.f}.}
\item{.size}{An integer for window size. If positive, moving forward from left
to right; if negative, moving backward (from right to left).}
\item{.fill}{A single value or data frame to replace \code{NA}.}
\item{.partial}{if \code{TRUE}, partial sliding.}
\item{.id}{If not \code{NULL} a variable with this name will be created
giving either the name or the index of the data frame.}
\item{.l}{A list of lists. The length of \code{.l} determines the
number of arguments that \code{.f} will be called with. List
names will be used if present.}
}
\description{
Rolling window with overlapping observations:
\itemize{
\item \code{slide2()} and \code{pslide()} always returns a list.
\item \code{slide2_lgl()}, \code{slide2_int()}, \code{slide2_dbl()}, \code{slide2_chr()} use the same
arguments as \code{slide2()}, but return vectors of the corresponding type.
\item \code{slide2_dfr()} \code{slide2_dfc()} return data frames using row-binding & column-binding.
}
}
\examples{
.x <- 1:5
.y <- 6:10
.z <- 11:15
.lst <- list(x = .x, y = .y, z = .z)
.df <- as.data.frame(.lst)
slide2(.x, .y, sum, .size = 2)
slide2(.lst, .lst, ~ ., .size = 2)
slide2(.df, .df, ~ ., .size = 2)
pslide(.lst, ~ ., size = 1)
pslide(list(.lst, .lst), ~ ., .size = 2)
## window over 2 months
pedestrian \%>\%
filter(Sensor == "Southern Cross Station") \%>\%
index_by(yrmth = yearmonth(Date_Time)) \%>\%
nest(-yrmth) \%>\%
mutate(ma = slide_dbl(data, ~ mean(do.call(rbind, .)$Count), .size = 2))
# row-oriented workflow
\dontrun{
my_diag <- function(...) {
data <- list(...)
fit <- lm(data$Count ~ data$Time)
tibble::tibble(fitted = fitted(fit), resid = residuals(fit))
}
pedestrian \%>\%
filter(Date <= as.Date("2015-01-31")) \%>\%
nest(-Sensor) \%>\%
mutate(diag = purrr::map(data, ~ pslide_dfr(., my_diag, .size = 48)))
}
}
\seealso{
\itemize{
\item \link{slide}
\item \link{tile2} for tiling window without overlapping observations
\item \link{stretch2} for expanding more observations
}
}
\alias{slide2_lgl}
\alias{slide2_chr}
\alias{slide2_int}
\alias{slide2_dbl}
\alias{pslide_lgl}
\alias{pslide_chr}
\alias{pslide_int}
\alias{pslide_dbl}
|
71ffa13e50e7599dbd37d0e8ea2f5b0474792412
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/flux/examples/plot.reco.Rd.R
|
b6b22eac0898e96bd7b000a297a8220e26abe86a
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 173
|
r
|
plot.reco.Rd.R
|
library(flux)
### Name: plot.reco
### Title: Plot diagnostic plots for Reco models.
### Aliases: plot.reco
### Keywords: hplot
### ** Examples
## see examples at gpp
|
525010b2e0ac02ed11cca4340dd52417a339d99e
|
510e69ed40a0db235877d7f5e531ddbd2e118af6
|
/Copy_MySQL_2_CBIPMySQL.R
|
61db7a2bde4c9310945d97661417f95504d2d36b
|
[] |
no_license
|
predsci/Climate_Data-BSVE
|
fd2afcf2c6f79cdc9d0527f7a49db742d2d7b0e0
|
656050ad249849c0a34bdfc42c7102c71364a259
|
refs/heads/master
| 2020-04-17T09:20:34.404417
| 2019-06-24T18:33:46
| 2019-06-24T18:33:46
| 166,454,178
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 17,164
|
r
|
Copy_MySQL_2_CBIPMySQL.R
|
rm(list=ls())
# require(DICE)
require(dplyr)
require(lubridate)
require(RMySQL)
# require(RPostgreSQL)
# load functions for opening connections
# source("~/Dropbox/MyLEPR/SQL/BSVE/containers/docker-NOAA_proc/source/NOAA_Funs.R")
source("/home/source/NOAA_Funs.R")
MySQLdb = OpenCon("predsci")
# CBIP_db = OpenCon("dcs_aws")
CBIP_db = OpenCon("cbip")
updt_dep_table <- function(table_name=NULL, ref_cols=NULL, data_cols=NULL, table_data=NULL, table_data2=NULL, P_SQLdb=NULL) {
# determine rows of table_data that are not in table_data2
append_rows = anti_join(x=table_data, y=table_data2, by=ref_cols)
if (nrow(append_rows)>0) {
# append to database
dbWriteTable(CBIP_db, name=table_name, value=append_rows, row.names=FALSE, overwrite=F, append=T)
}
# determine rows that overlap (and keep data cols from table_data)
update_rows = semi_join(x=table_data, y=table_data2, by=ref_cols)
# map overlap rows
outer_mat = matrix(TRUE, nrow=nrow(update_rows), ncol=nrow(table_data2))
for (var in ref_cols) {
outer_mat = outer_mat & outer(update_rows[[var]], table_data2[[var]], "==")
}
match_ind = which(outer_mat, arr.ind=T)
# test rows for equality
for (ii in 1:nrow(match_ind)) {
is_equal = update_rows[match_ind[ii, 1], data_cols] == table_data2[match_ind[ii, 2], data_cols]
if (any(!is_equal)) {
write_vars = update_rows[match_ind[ii, 1], data_cols[!is_equal]]
char_vars = unlist(lapply(write_vars, FUN=is.character))
write_vars[char_vars] = paste0("'", write_vars[char_vars], "'")
set_char = paste0(data_cols[!is_equal], " = ", write_vars)
set_char = paste(set_char, collapse=", ")
where_vars = update_rows[match_ind[ii, 1], ref_cols]
char_vars = unlist(lapply(where_vars, FUN=is.character))
nchar_vars = sum(char_vars)
if (nchar_vars>0) {
jj = which(char_vars)[1]
where_char = paste0(ref_cols[jj], " = '", where_vars[jj], "'")
if (nchar_vars>1) {
for (jj in which(char_vars)[2:nchar_vars]) {
where_char = paste0(where_char, " AND ", ref_cols[jj], " = '", where_vars[jj], "'")
}
}
}
nnum_vars = sum(!char_vars)
if (nnum_vars>0) {
jj = which(!char_vars)[1]
if (nchar_vars>0) {
where_char = paste0(where_char, " AND ", ref_cols[jj], " = ", where_vars[jj])
} else {
where_char = paste0(ref_cols[jj], " = ", where_vars[jj])
}
if (nnum_vars>1) {
for (jj in which(!char_vars)[2:nnum_vars]) {
where_char = paste0(where_char, " AND ", ref_cols[jj], " = ", where_vars[jj])
}
}
}
dbExecute(conn=CBIP_db, statement=paste0("UPDATE ", table_name ," SET ", set_char, " WHERE ", where_char, ";"))
}
}
}
table_name = "data_sources"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("source_key", "cadence", "disease")
data_cols = c("source", "source_abbv", "source_desc", "data_cols", "col_names", "col_units", "bsve_use", "countries", "max_lev")
table_data = dbReadTable(MySQLdb, table_name)
# table_data$bsve_use = as.logical(table_data$bsve_use)
# remove Quidel entry
table_data = table_data[table_data$source_abbv!="quidel", ]
if (!dbExistsTable(CBIP_db, table_name)) {
# Create and populate table
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
source_key SMALLINT PRIMARY KEY,
cadence SMALLINT NOT NULL,
disease VARCHAR(50),
source TEXT,
source_abbv VARCHAR(50),
source_desc VARCHAR(255),
data_cols SMALLINT,
col_names VARCHAR(255),
col_units VARCHAR(255),
bsve_use TINYINT,
countries VARCHAR(765),
max_lev SMALLINT,
min_lev SMALLINT
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "clim_by_disease"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("disease")
data_cols = c("n_clim", "clim_names")
table_data = dbReadTable(MySQLdb, table_name)
clim_by_disease = table_data
if (!dbExistsTable(CBIP_db, table_name)) {
# dbExecute(conn=CBIP_db, statement=paste0("DROP TABLE IF EXISTS ", table_name))
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
disease VARCHAR(15) PRIMARY KEY,
n_clim SMALLINT NOT NULL,
clim_names VARCHAR(50)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "transmission_names"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("trans_abbv")
data_cols = c("trans_desc")
table_data = dbReadTable(MySQLdb, table_name)
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
trans_abbv VARCHAR(5) PRIMARY KEY,
trans_desc VARCHAR(50) NOT NULL
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "disease_transmission"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("dtype_id", "disease_abbv")
data_cols = c("disease_desc", "trans_abbv")
table_data = dbReadTable(MySQLdb, table_name)
diseases = table_data$disease_abbv
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
dtype_id SERIAL,
disease_abbv VARCHAR(15) UNIQUE NOT NULL,
disease_desc VARCHAR(50) NOT NULL,
trans_abbv VARCHAR(5) REFERENCES transmission_names (trans_abbv),
ui_name VARCHAR(30),
PRIMARY KEY (dtype_id, disease_abbv)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "unit_types"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("unit_key", "unit_type")
data_cols = c("factor", "aggregate_method")
table_data = dbReadTable(MySQLdb, table_name)
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
unit_key serial PRIMARY KEY,
unit_type VARCHAR(50) NOT NULL,
factor VARCHAR(200),
aggregate_method VARCHAR(25),
UNIQUE INDEX (unit_type)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "col_units"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("col_key", "col_unit")
data_cols = c("unit_type")
table_data = dbReadTable(MySQLdb, table_name)
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
col_key SERIAL PRIMARY KEY,
col_unit VARCHAR(50) NOT NULL,
unit_type VARCHAR(50) NOT NULL,
FOREIGN KEY (unit_type) REFERENCES unit_types(unit_type)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
table_name = "season_se_dates"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("abbv_1", "abbv_2", "season", "disease", "cadence")
data_cols = c("start_date", "end_date")
table_data = dbReadTable(MySQLdb, table_name)
# names(table_data) = tolower(names(table_data))
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
ABBV_1 CHAR(2) NOT NULL,
ABBV_2 VARCHAR(10) NOT NULL,
season SMALLINT NOT NULL,
disease VARCHAR(15) REFERENCES disease_transmission (disease_abbv),
cadence SMALLINT NOT NULL,
start_date DATE NOT NULL,
end_date DATE NOT NULL,
PRIMARY KEY (ABBV_1, ABBV_2, season, disease, cadence)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# read existing table
table_data2 = dbReadTable(CBIP_db, table_name)
# perform table update
updt_dep_table(table_name=table_name, ref_cols=ref_cols, data_cols=data_cols, table_data=table_data, table_data2=table_data2, P_SQLdb=CBIP_db)
}
for (disease in diseases) {
# update incidence data
# The data itself can be updated in weird ways, with cases being added or deleted at various intervals after the initial report. For this reason, we simply truncate and re-write the entire table.
table_name = paste0(disease, "_data")
cat("Updating table ", table_name, ". \n", sep="")
table_data = dbReadTable(MySQLdb, table_name)
if (dbExistsTable(conn=CBIP_db, name=table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("TRUNCATE TABLE ", table_name))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
# clim_cols = strsplit(clim_by_disease$clim_names[clim_by_disease$disease==disease], ";")[[1]]
data_cols = sum(substr(names(table_data), start=1,stop=4)=="data")
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
master_key CHAR(8) NOT NULL,
source_key SMALLINT NOT NULL,
date DATE NOT NULL, ",
paste0("data", 1:data_cols, " REAL", collapse=", "),
", PRIMARY KEY (master_key, source_key, date)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
}
table_name = paste0(disease, "_lut")
table_data = dbReadTable(MySQLdb, table_name)
# names(table_data) = tolower(names(table_data))
if (dbExistsTable(conn=CBIP_db, name=table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("TRUNCATE TABLE ", table_name))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
spatial_levels = sum(substr(names(table_data), start=1, stop=4)=="name")
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
identifier VARCHAR(50) PRIMARY KEY,
level SMALLINT NOT NULL,
", paste0("NAME_", 1:spatial_levels, " VARCHAR(50) \n", collapse=", "),",
", paste0("ID_", 1:spatial_levels, " REAL \n", collapse=", "),",
", paste0("ABBV_", 1:spatial_levels, " VARCHAR(10) \n", collapse=", "),",
inc_key INTEGER NOT NULL,
master_key CHAR(8) UNIQUE NOT NULL,
gadm_name VARCHAR(50),
gadm_lvl SMALLINT,
clim_ident VARCHAR(50) NOT NULL,
gadm_noaa_sedac_ident VARCHAR(30),
gadm_lat REAL,
gadm_lon REAL,
gadm_area REAL,
sedac_lat REAL,
sedac_lon REAL,
sedac_pop INT
);"))
# Also create index on master_key. Additional indices.....?
dbExecute(conn=CBIP_db, statement=paste0("CREATE UNIQUE INDEX ", disease,"_key_idx ON ",table_name ," (master_key)"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
}
}
# school schedule tables
for (cadence in c("daily", "weekly", "monthly")) {
table_name = paste0("school_", cadence)
cat("Updating table ", table_name, ". \n", sep="")
table_data = dbReadTable(MySQLdb, table_name)
if (dbExistsTable(conn=CBIP_db, name=table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("TRUNCATE TABLE ", table_name))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE school_weekly (
master_key CHAR(8) NOT NULL,
date DATE NOT NULL,
school REAL,
PRIMARY KEY (master_key, date)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
}
}
# population table
table_name = "pop_yearly"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("master_key", "date")
data_cols = c("pop")
table_data = dbReadTable(MySQLdb, table_name)
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
master_key CHAR(8) NOT NULL,
date DATE NOT NULL,
pop INT NULL,
PRIMARY KEY (master_key, date)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
dbExecute(conn=CBIP_db, statement=paste0("TRUNCATE TABLE ", table_name))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
}
# season onset
table_name = "season_onset"
cat("Updating table ", table_name, ". \n", sep="")
ref_cols = c("master_key", "year")
data_cols = c("onset")
table_data = dbReadTable(MySQLdb, table_name)
if (!dbExistsTable(CBIP_db, table_name)) {
dbExecute(conn=CBIP_db, statement=paste0("CREATE TABLE ",table_name ," (
master_key CHAR(8) NOT NULL,
year INT NOT NULL,
onset REAL NULL,
PRIMARY KEY (master_key, year)
);"))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
} else {
dbExecute(conn=CBIP_db, statement=paste0("TRUNCATE TABLE ", table_name))
dbWriteTable(CBIP_db, name=table_name, value=table_data, row.names=FALSE, overwrite=F, append=T)
}
dbDisconnect(conn=CBIP_db)
dbDisconnect(MySQLdb)
|
09362ea0de0e86f189b4f02723b5b50897347b61
|
124e3a9cd70c301798fa4201a6ccd27e3c15e7dc
|
/Data Cleaning/clean_subnational_ihme.R
|
9f6c7f79a24c1f1c29b4774ee19c15f0be04f587
|
[] |
no_license
|
shinnakayama/aquatic_food_justice_model
|
540b195b6f54dacac51917eb876d7351ebeddaf5
|
86d3bbc886e6fab3fbaf90880dcb10f0a2027453
|
refs/heads/master
| 2023-01-25T03:22:22.722033
| 2020-12-07T03:00:49
| 2020-12-07T03:00:49
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,591
|
r
|
clean_subnational_ihme.R
|
#######################
# Extracting and cleaning harmonized nighttime lights data
# Zach Koehn
# zkoehn@stanford.edu
#######################
library(tidyverse)
library(pbapply)
library(countrycode)
library(raster)
library(sf)
library(spData)
library(exactextractr)
library(viridis)
library(readxl)
library(rnaturalearth)
library(rnaturalearthhires)
# read in data
# data from Table S2 in https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228912#sec010
directory <- "/Volumes/GoogleDrive/My Drive/BFA_Papers/BFA_Justice/section_model/aquatic_food_justice_model"
rasterOptions(progress = 'text',timer=TRUE) #allows for progress timer... helps multitask :)
ihme_second_women_15to49 <- read.csv(
file.path(
directory,
"data",
"data_raw",
"ihme",
"educational_attainment",
"Data [CSV]",
"IHME_LMIC_EDU_2000_2017_SECONDARYPROP_15_49_FEMALE_AD1_Y2019M12D24.CSV"
)
)
sub_gis <- ne_states() #subnational polygons
ihme_second_men_15to49 <- read.csv(
file.path(
directory,
"data",
"data_raw",
"ihme",
"educational_attainment",
"Data [CSV]",
"IHME_LMIC_EDU_2000_2017_SECONDARYPROP_15_49_MALE_AD1_Y2019M12D24.CSV"
)
)
rast_ihme_second_men_15to49 <- raster(
file.path(
directory,
"data",
"data_raw",
"ihme",
"educational_attainment",
"IHME_LMIC_EDU_2000_2017_SECONDARYPROP_1549_MALE_MEAN_Y2019M12D24.TIF"
)
)
rast_ihme_second_women_15to49 <- raster(
file.path(
directory,
"data",
"data_raw",
"ihme",
"educational_attainment",
"IHME_LMIC_EDU_2000_2017_SECONDARYPROP_1549_FEMALE_MEAN_Y2019M12D24.TIF"
)
)
sub_nat_boundaries <- ne_states()
sub_nat_boundaries <- st_as_sf(sub_nat_boundaries)
extract_subnational_stats <- function(rast_layer,i) {
# i=7
# rast_layer=rast
subnational_a2_code <- sub_nat_boundaries$iso_3166_2[i]
subnational_poly <- st_geometry(sub_nat_boundaries[i,])
subnational_extract <- exact_extract(
rast_layer,subnational_poly,
c("mean")
)
subnational_stats <- cbind(subnational_a2_code,subnational_extract)
return(subnational_stats)
}
beginCluster()
education_ratio <- rast_ihme_second_women_15to49/rast_ihme_second_men_15to49
endCluster()
beginCluster()
education_ratio_subnational_stats <- t(
pbsapply(1:dim(sub_nat_boundaries)[1],function(c) extract_subnational_stats(rast_layer=education_ratio,i=c))
)
endCluster()
education_ratio_subnational_stats_df <- data.frame(matrix(unlist(education_ratio_subnational_stats), nrow=dim(sub_nat_boundaries)[1], byrow=F),stringsAsFactors=FALSE)
names(education_ratio_subnational_stats_df) <- c("iso_3166_2","gender_secondary_education_ratio_mean")
beginCluster()
women_education_subnational_stats <- t(
pbsapply(1:dim(sub_nat_boundaries)[1],function(c) extract_subnational_stats(rast_layer=rast_ihme_second_women_15to49,i=c))
)
endCluster()
women_education_subnational_stats_df <- data.frame(matrix(unlist(women_education_subnational_stats), nrow=dim(sub_nat_boundaries)[1], byrow=F),stringsAsFactors=FALSE)
names(women_education_subnational_stats_df) <- c("iso_3166_2","women_secondary_education_mean")
write.csv(
education_ratio_subnational_stats_df,
file.path(
directory,
"data",
"data_clean",
"clean_subnational",
"education_ratio_subnational_mean_2000_2017.csv"
),
row.names=FALSE
)
write.csv(
women_education_subnational_stats_df,
file.path(
directory,
"data",
"data_clean",
"clean_subnational",
"women_education_subnational_mean_2000_2017.csv"
),
row.names=FALSE
)
|
39bdc4d6331bc5d46c2ced51b0fbd44f590cf035
|
1fb22fa3ac1f5a78e0cce797d6d7bdcf78d8236f
|
/man/plot_adjmatrix.Rd
|
4b4e18fdc1df3753dbd2252036ed199a0f2163c2
|
[] |
no_license
|
scramblingbalam/graphclass
|
2fb0a77d6fa984a9dd6c56b39487ea43a8cbda34
|
930d2ff47319e5fd5c70c6d73afdea2763eee3ab
|
refs/heads/master
| 2021-04-12T04:21:09.096338
| 2018-02-23T23:01:18
| 2018-02-23T23:01:18
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 577
|
rd
|
plot_adjmatrix.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Plots.R
\name{plot_adjmatrix}
\alias{plot_adjmatrix}
\title{Plot a vectorized adjacency matrix.}
\usage{
plot_adjmatrix(beta, type = "intersection")
}
\arguments{
\item{beta}{Vectorized adjacency matrix. For undirected networks use only upper triangle in column-major order, for directed use both}
\item{type}{Either intersection for undirected networks, union for directed.}
}
\description{
Plot a vectorized adjacency matrix.
}
\examples{
B = runif(34453)
plot_adjmatrix(B)
}
|
aea40445e4ec4dd508273349b5c80bcd8610811e
|
431719d48e8567140216bdfdcd27c76cc335a490
|
/man/TagResource.Rd
|
887428e23b31150bf861c719b6dac20455af2198
|
[
"BSD-3-Clause"
] |
permissive
|
agaveplatform/r-sdk
|
4f32526da4889b4c6d72905e188ccdbb3452b840
|
b09f33d150103e7ef25945e742b8d0e8e9bb640d
|
refs/heads/master
| 2018-10-15T08:34:11.607171
| 2018-09-21T23:40:19
| 2018-09-21T23:40:19
| 118,783,778
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 416
|
rd
|
TagResource.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TagResource.r
\docType{data}
\name{TagResource}
\alias{TagResource}
\title{TagResource Class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
TagResource
}
\description{
Resource to which a tag has been associated
}
\section{Fields}{
\describe{
\item{\code{id}}{uuid of the API resource}
}}
\keyword{datasets}
|
561abbf77b0c3d3a863283db47b1796ab65da59a
|
9a3ed38b823f090cd9826def42fb77f50c2f8492
|
/USAspending/cfda_extraction_datatable.r
|
09f6e7813a0a1b296aaf685e8a59fd1daf6aa17a
|
[
"MIT"
] |
permissive
|
cenuno/Spatial_Visualizations
|
fded362213663882103d0dea10ed73ab0fe4231c
|
7a4776dd5b87449497b7615887c42f05a3d2e694
|
refs/heads/master
| 2021-01-02T08:44:56.313875
| 2017-12-23T03:47:02
| 2017-12-23T03:47:02
| 99,060,719
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,106
|
r
|
cfda_extraction_datatable.r
|
#
# Author: Cristian Nuno
# Date: August 20, 2017
# Purpose: To write an API function with USA Spending data
# regarding CFDA inqueries
#
#
#
# Example URL: https://api.usaspending.gov/api/v1/references/cfda/
# Summary: Returns information about CFDA programs
#
require(compiler)
# compilePKGS()
setCompilerOptions(suppressAll = TRUE )
# enableJIT enables or disables just-in-time (JIT) compilation.
# JIT is disabled if the argument is 0.
# If level is 1 then larger closures are compiled before their first use.
# If level is 2, then some small closures are also compiled before their second use.
# If level is 3 then in addition all top level loops are compiled before they are executed.
enableJIT(3) # 0
# load necessary packages
library( httr )
library( jsonlite )
library( dplyr )
library( DT )
# Create function
endpoint_df_extraction <- function( path ) {
# Create empty list
pages <- list()
# Create initial API url
url <- modify_url("https://api.usaspending.gov"
, path = path
, query = list(page = 1
, limit = 400
)
)
# Get API url
raw.resp <- GET(url)
if (http_type(raw.resp) != "application/json") {
stop("API did not return json. Check 'status code = 200'"
, call. = FALSE)
}
this.char.resp <- rawToChar( raw.resp$content) # convert from raw to char
# convert JSON object into R object
this.clean.resp <- fromJSON(this.char.resp
, flatten = TRUE
)
# identify a boolean operator
# data_api$page_metadata$has_next_page = TRUE
this.clean.resp$page_metadata$has_next_page = TRUE
# while loop to grab and store data as long as the variable
# this.clean.resp$page_metadata$has_next_page == TRUE
# Set initial page number
page_number <- 1
# while loop with boolean condition
while( this.clean.resp$page_metadata$has_next_page == TRUE ) {
# identify current page url
current.page.url <- this.clean.resp$page_metadata$current
# subsitute "&page=XX" with "&page='page_number'"
next.page.url <- gsub( pattern = "&page=[[:digit:]]+"
, replacement = paste0( "&page=", page_number)
, x = current.page.url
)
# Get new API url
raw.resp <- GET( url = next.page.url )
# Convert raw vector to character vector
this.char.resp <- rawToChar( raw.resp$content )
# Convert JSON object into R object
this.clean.resp <- fromJSON( this.char.resp
, flatten = TRUE
)
# For every page number (1, 2, 3...), insert that page's "results" inside the list
pages[[ page_number ]] <- this.clean.resp$results
# Add to the page number and restart the loop
page_number <- page_number + 1
}
# once all the pages have been collected,
data_api_data <- rbind_pages(pages)
# return what we've collected
return( data_api_data )
# Turn API errors into R errors
if (http_error( raw.resp )) {
stop(
sprintf(
"USASpending.gov API request failed [%s]\n%s\n<%s>",
status_code( raw.resp),
this.clean.resp$message,
this.clean.resp$documentation_url
),
call. = FALSE
)
}
# add some structure stuff
structure(
list(
content = this.clean.resp
, path = "/api/v1/references/cfda/"
, response = raw.resp
)
, class = "usa_spending_api"
)
} # end of function
# Call function
cfda_program_info <- endpoint_df_extraction("/api/v1/references/cfda/")
# Waiting...3 seconds!
fancy_table <- datatable( data = select( cfda_program_info
, program_number
, program_title
, popular_name
, objectives
, website_address
)
, rownames = FALSE
, colnames = c("Program Number"
, "Program Title"
, "Popular Name"
, "Objectives"
, "Website Address"
)
, caption = "Catalog of Federal Domestic Assistance (CFDA) Programs"
, extensions = 'Buttons'
, options = list(
dom = "Blfrtip"
, buttons =
list("copy", list(
extend = "collection"
, buttons = c("csv", "excel", "pdf")
, text = "Download"
) ) # end of buttons customization
# customize the length menu
, lengthMenu = list( c(100, 500, -1) # declare values
, c(100, 500, "All") # declare titles
) # end of lengthMenu customization
, pageLength = 100
) # end of options
)
# Display fancy table
fancy_table
|
a5ac1487ea95831fbe0ec9498ea97a9f17d6be7e
|
478e2bacd8621f8846fca224134b8b121ea6f193
|
/graphics/drawGraph.r
|
3212fef06d93104c6d11688e914b119d0b58b59f
|
[] |
no_license
|
krprls/Variant-graph-analysis
|
aae8c419bfca91a9bb69f2fa21c4764c1ed11c8a
|
981de650670baf25bea19659b2e5e9b1668bd044
|
refs/heads/master
| 2020-06-03T05:53:02.225539
| 2017-06-01T07:51:12
| 2017-06-01T07:51:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,216
|
r
|
drawGraph.r
|
##############################################################################
# drawGraph.r
#
# Analyses for master thesis
# ======================================
#
# Feeds the edgeList from BuildGraph.java into igraph for visualization
###############################################################################
require(igraph)
data = read.table("g1edgeList.txt", header = TRUE, sep = " ")
edgeList = as.matrix(data)
labels = read.table("g1labelList.txt", header = TRUE, sep = " ")
g1 = graph_from_edgelist(edgeList, directed = TRUE)
V(g1)$label = paste(labels[,1])
V(g1)$name = paste(labels[,1])
pdf("g1.pdf", width = 7, height = 6)
plot(g1, vertex.size = 25, edge.arrow.size = .7, vertex.label.cex = 1.8,
layout = layout.fruchterman.reingold(g1))
dev.off()
data = read.table("g2edgeList.txt", header = TRUE, sep = " ")
edgeList = as.matrix(data)
labels = read.table("g2labelList.txt", header = TRUE, sep = " ")
g2 = graph_from_edgelist(edgeList, directed = TRUE)
V(g2)$label = paste(labels[,1])
V(g2)$name = paste(labels[,1])
pdf("g2.pdf", width = 7, height = 6)
plot(g2, vertex.size = 25, edge.arrow.size = .7, vertex.label.cex = 1.8,
layout = layout.fruchterman.reingold(g2))
dev.off()
|
510c7fc4e951aff2c10c7800e4276a2b9d12fe47
|
31fbbb9f41d6540933a9dd69f43a3b7f97577423
|
/user_jc/overlap_between_disease_sets.R
|
5f5ddedaac099133c6dd57cbcfc351317ae69a9f
|
[] |
no_license
|
potentece/ses
|
ecd75f5705344443d30d2560003ebf57f5416871
|
3fb7cd5d6c1a8e413f4ebd929b68ceef31b9bc38
|
refs/heads/master
| 2022-12-24T02:27:57.515238
| 2020-10-01T10:05:20
| 2020-10-01T10:05:20
| 280,169,302
| 0
| 0
| null | 2020-09-01T11:44:02
| 2020-07-16T14:02:43
| null |
UTF-8
|
R
| false
| false
| 2,671
|
r
|
overlap_between_disease_sets.R
|
#' ---
#' title: Examples
#' output:
#' html_document:
#' toc: true
#' highlight: zenburn
#' ---
#' <!-- rmarkdown::render("supervised_play/nice_code.R") -->
#' <!-- [See here.](http://brooksandrew.github.io/simpleblog/articles/render-reports-directly-from-R-scripts/) -->
#' Set global options
#+ setup, warning=FALSE, message=FALSE
# knitr::opts_chunk$set(echo = FALSE)
set.seed(123)
library(here)
library(tidyverse)
library(Biobase)
walk(dir(path = here("R"), full.names = TRUE), source)
############################################################
# LOAD DATA, DEFINE VARIABLES, RECODE VARIABLES
############################################################
load_data(reconciled = FALSE)
define_treatments_and_controls()
sigs = signatures$outcome_set[c(table1, "ctra_mRNA")]
get_intersection =
function(intersection_order, op = intersect) {
# GET ALL INTERSECTIONS OF A GIVEN ORDER
# y = combinations of sets
# nm = names of such sets
# z = intersection of such sets
tibble(y = combn(sigs, intersection_order, simplify = FALSE),
nm = map(y, names),
z = map(y, reduce, op)) %>%
arrange(-lengths(z)) %>%
filter(lengths(z) > 0)
}
prettify = . %>%
select(-y) %>%
knitr::kable()
# overlap between the disease sets and the 1k.
a = sigs[-3] # less 1k
b = sigs[3] # 1k
crossing(a,b) %>%
pmap_dbl(function(a,b) length(intersect(a,b))/length(a)) %>%
enframe(name = "signature", value = "proportion which overlaps with 1k")
# how many n-way intersections?
map(2:5, compose(prettify, get_intersection))
# Inflammation1k intersects with all other disease sets, by at least one gene (rarely the same gene)
map(2:5, get_intersection) %>% map(pluck("nm")) %>% map(unlist) %>% map(table) %>% map(sort) %>% map(enframe)
# Only 1 gene belongs to as many as 4 signatures (TCF7).
# non-monogomous genes (appearing in at 2 or more signatures)
map(2:5, get_intersection) %>% map(pluck, "z") %>% map(unlist) %>% map(unique) %>% lengths()
# how many n-way intersections does each gene belong to
map(2:5, get_intersection) %>%
map(pluck, "z") %>%
map(compose(fct_count, factor, unlist)) %>%
map(arrange, -n) %>%
walk(print, n = Inf)
# GOC "genes of co-morbidity".
sigs %>% lengths()
ns = lengths(sigs)
map(2:5, get_intersection) %>% map(transmute, nm, n = lengths(z)) %>% map(unnest_wider, "nm")
# size of intersection and the size of the disease sets
map(2, get_intersection) %>%
map(transmute, nm, n = lengths(z)) %>%
map(unnest_wider, "nm") %>%
map(mutate, n1 = ns[`...1`], n2 = ns[`...2`]) %>%
map(mutate, p1 = n/n1, p2 = n/n2) %>%
map(print, n = Inf)
|
3083de1ff392fb9a7049d66edf09ee1613544a16
|
df02a3254469994aee94ba9783ae5823a138d6a8
|
/scRNA_operation.R
|
ff432147fada5b794c583034ee52aecc0416845f
|
[] |
no_license
|
liuzhe93/FengLab-PCNSL
|
57022228e177b018135696d8b44f03027d28240d
|
77c3eac98be00ed2c5ec804888e2c61d996a8eae
|
refs/heads/main
| 2023-07-03T21:24:13.972032
| 2021-08-07T13:49:00
| 2021-08-07T13:49:00
| 386,863,041
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 63,014
|
r
|
scRNA_operation.R
|
# set workpath and load R packages
# conda activate Renv
setwd("/ifs1/Grp8/liuzhe/scRNA/")
rm(list = ls())
#remotes::install_github("satijalab/seurat", ref = "release/4.0.0")
#remotes::install_github("jlmelville/uwot")
#install.packages('Seurat')
library("Seurat")
#install.packages('tidyverse')
library("tidyverse")
library("Matrix")
library("scales")
library("cowplot")
library("RCurl")
options(stringsAsFactors = F)
library("scRNAseq")
library("scater")
# devtools::install_github(repo = "satijalab/seurat", ref = "loom")
library("loomR")
library("patchwork")
library("scuttle")
library("dplyr")
library("tibble")
library("HCAData")
library("SingleR")
library("org.Hs.eg.db")
library("clusterProfiler")
library("vroom")
library("celldex")
library("dittoSeq")
set.seed(2020)
library("monocle3")
suppressPackageStartupMessages(library("SingleCellExperiment"))
library("ggplot2"); theme_set(theme_bw())
library("DuoClustering2018")
require(scry)
library("scran")
library("DoubletFinder")
## Pre-process Seurat object (standard) --------------------------------------------------------------------------------------
# load the scRNA-seq data
# create each individual Seurat object for every sample
# create Seurat object cancer and normal
seurat_data<-Read10X(data.dir=paste0("0_counts/","cancer"))
#seurat_obj<-CreateSeuratObject(counts=seurat_data,project="cancer")
seurat_obj<-CreateSeuratObject(counts=seurat_data,project="cancer")
assign("cancer",seurat_obj)
cancer<-NormalizeData(cancer)
cancer <- FindVariableFeatures(cancer, selection.method = "vst", nfeatures = 3000)
cancer <- ScaleData(cancer)
cancer <- RunPCA(cancer)
cancer <- RunUMAP(cancer, reduction = "pca", dims = 1:21)
cancer <- RunTSNE(cancer, reduction = "pca", dims = 1:21)
## Pre-process Seurat object (sctransform) -----------------------------------------------------------------------------------
cancer <- SCTransform(cancer)
cancer <- RunPCA(cancer)
cancer <- RunUMAP(cancer, reduction = "pca", dims = 1:21)
cancer <- RunTSNE(cancer, reduction = "pca", dims = 1:21)
## pK Identification (no ground-truth) ---------------------------------------------------------------------------------------
sweep.res.list_brain <- paramSweep_v3(cancer, PCs = 1:21, sct = FALSE)
sweep.stats_brain <- summarizeSweep(sweep.res.list_brain, GT = FALSE)
head(sweep.stats_brain)
bcmvn_brain <- find.pK(sweep.stats_brain)
mpK<-as.numeric(as.vector(bcmvn_brain$pK[which.max(bcmvn_brain$BCmetric)]))
DoubletRate = ncol(cancer)*8*1e-6 #按每增加1000个细胞，双细胞比率增加千分之8来计算
#Calculate by increasing the ratio of doulets by 0.008 for every additional 1000 cells
#DoubletRate = 0.075
## Homotypic Doublet Proportion Estimate -------------------------------------------------------------------------------------
cancer <- FindNeighbors(cancer, reduction = "pca", dims = 1:21)
cancer <- FindClusters(cancer, resolution = 0.6)
levels(cancer)
cancer <- RunUMAP(cancer, reduction = "pca", dims = 1:21)
cancer <- RunTSNE(cancer, reduction = "pca", dims = 1:21)
annotations <- cancer@meta.data$seurat_clusters
homotypic.prop <- modelHomotypic(annotations)
nExp_poi <- round(DoubletRate*nrow(cancer@meta.data))
nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))
## Run DoubletFinder with varying classification stringencies ----------------------------------------------------------------
cancer <- doubletFinder_v3(cancer, PCs = 1:21, pN = 0.05, pK = 5e-04, nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)
cancer <- doubletFinder_v3(cancer, PCs = 1:21, pN = 0.05, pK = 5e-04, nExp = nExp_poi.adj, reuse.pANN = "pANN_0.05_5e-04_9717", sct = FALSE)
pdf("test_Double.pdf", width = 10, height = 10)
DimPlot(cancer, reduction = "tsne", group.by = "DF.classifications_0.05_5e-04_8535")
dev.off()
cancer@meta.data$singledouble<-cancer@meta.data$'DF.classifications_0.05_5e-04_8535'
cancer.singlet <- subset(cancer, subset = singledouble == "Singlet")
cancer$'DF.classifications_0.05_5e-04_8535'<-Idents(cancer)
pdf("test_Double.pdf", width = 10, height = 10)
DimPlot(cancer, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8, group.by = 'DF.classifications_0.05_5e-04_8535')
dev.off()
cancer_singlet<-cancer.singlet
pdf("1_preoperation/figures/pre-operation/cancer.counts.vs.features.pdf")
plot(x=cancer_singlet@meta.data$nCount_RNA,y=cancer_singlet@meta.data$nFeature_RNA)
dev.off()
# check the metadata in the new Seurat objects
head(cancer_singlet@meta.data)
tail(cancer_singlet@meta.data)
# Create .RData object to load at any time
save(cancer_singlet, file="1_preoperation/data/cancer.combined.RData")
cancer_singlet$log10GenesPerUMI <- log10(cancer_singlet$nFeature_RNA) / log10(cancer_singlet$nCount_RNA)
cancer_singlet$mitoRatio <- PercentageFeatureSet(object = cancer_singlet, pattern = "^MT-")
cancer_singlet$mitoRatio <- cancer_singlet@meta.data$mitoRatio / 100
cancermetadata <- cancer_singlet@meta.data
cancermetadata$cells <- rownames(cancermetadata)
cancermetadata <- cancermetadata %>%
dplyr::rename(seq_folder = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA)
cancer_singlet
cancer_singlet@meta.data <- cancermetadata
counts <- GetAssayData(object = cancer_singlet, slot = "counts")
cancer_singlet <- CreateSeuratObject(counts, meta.data = cancer@meta.data)
cancer_singlet$label <- "cancer"
cancer_norm <- NormalizeData(cancer_singlet, normalization.method = "LogNormalize", scale.factor = 10000)
cancer_norm <- FindVariableFeatures(cancer_norm, selection.method = "vst", nfeatures = 3000)
pdf("1_preoperation/figures/pre-operation/cancer_Visualize_QC.pdf", width = 12, height = 6)
VlnPlot(cancer_singlet, features = c("nFeature_SCT", "nCount_SCT", "mitoRatio"), ncol = 3)
dev.off()
plot1 <- FeatureScatter(cancer_singlet, feature1 = "nCount_SCT", feature2 = "mitoRatio")
plot2 <- FeatureScatter(cancer_singlet, feature1 = "nCount_SCT", feature2 = "nFeature_SCT")
pdf("1_preoperation/figures/pre-operation/cancer_FeatureScatter.pdf", width = 12, height = 6)
CombinePlots(plots = list(plot1, plot2))
dev.off()
top30 <- head(VariableFeatures(cancer_norm), 30)
pdf("1_preoperation/figures/pre-operation/cancer_VariableFeatures.pdf", width = 12, height = 6)
plot1 <- VariableFeaturePlot(cancer_norm)
plot2 <- LabelPoints(plot = plot1, points = top30, repel = TRUE)
CombinePlots(plots = list(plot1, plot2))
dev.off()
# filter cancer
filtered_cancer <- subset(x = cancer_singlet,
subset= (nUMI < 20000) &
(nGene > 200) &
(nGene < 2000) &
(log10GenesPerUMI > 0.80) &
(mitoRatio < 0.1))
filtered_cancer
cancer_singlet[["percent_HBA1"]] <- PercentageFeatureSet(filtered_cancer, features = "HBA1")
cancer_singlet[["percent_HBB"]] <- PercentageFeatureSet(filtered_cancer, features = "HBB")
cancer_singlet <- subset(x = cancer_singlet, subset= percent_HBA1 < 0.001 | percent_HBB < 0.001)
counts <- GetAssayData(object = filtered_cancer, slot = "counts")
nonzero <- counts > 0
keep_genes <- Matrix::rowSums(nonzero) >= 20
filtered_counts <- counts[keep_genes, ]
filtered_cancer <- CreateSeuratObject(filtered_counts, meta.data = cancer_singlet@meta.data)
filtered_cancer$label <- "cancer"
save(filtered_cancer, file="1_preoperation/data/filtered_cancer.RData")
filtered_cancer_norm<-NormalizeData(filtered_cancer)
setwd("/ifs1/Grp8/liuzhe/scRNA/")
seurat_data<-Read10X(data.dir=paste0("0_counts/normal/","HFA567_total.filtered_gene_matrices"))
seurat_obj<-CreateSeuratObject(counts=seurat_data,project="normal")
assign("normal1",seurat_obj)
normal1<-NormalizeData(normal1)
seurat_data<-Read10X(data.dir=paste0("0_counts/normal/","HFA570_total.filtered_gene_matrices"))
seurat_obj<-CreateSeuratObject(counts=seurat_data,project="normal")
assign("normal2",seurat_obj)
normal2<-NormalizeData(normal2)
seurat_data<-Read10X(data.dir=paste0("0_counts/normal/","HFA571_total.filtered_gene_matrices"))
seurat_obj<-CreateSeuratObject(counts=seurat_data,project="normal")
assign("normal3",seurat_obj)
normal3<-NormalizeData(normal3)
normal.normalized.combined <- merge(normal1, y = c(normal2, normal3), add.cell.ids = c("N1", "N2", "N3"), project = "normal", merge.data = TRUE)
normal<-normal.normalized.combined
pdf("1_preoperation/figures/pre-operation/normal.counts.vs.features.pdf")
plot(x=normal@meta.data$nCount_RNA,y=normal@meta.data$nFeature_RNA)
dev.off()
# check the metadata in the new Seurat objects
head(normal@meta.data)
tail(normal@meta.data)
# Create .RData object to load at any time
save(normal, file="1_preoperation/data/normal.combined.RData")
normal$log10GenesPerUMI <- log10(normal$nFeature_RNA) / log10(normal$nCount_RNA)
normal$mitoRatio <- PercentageFeatureSet(object = normal, pattern = "^MT-")
normal$mitoRatio <- normal@meta.data$mitoRatio / 100
normalmetadata <- normal@meta.data
normalmetadata$cells <- rownames(normalmetadata)
normalmetadata <- normalmetadata %>%
dplyr::rename(seq_folder = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA)
normal
normal@meta.data <- normalmetadata
counts <- GetAssayData(object = normal, slot = "counts")
normal <- CreateSeuratObject(counts, meta.data = normal@meta.data)
normal$label <- "normal"
normal_norm <- NormalizeData(normal, normalization.method = "LogNormalize", scale.factor = 10000)
normal_norm <- FindVariableFeatures(normal_norm, selection.method = "vst", nfeatures = 3000)
pdf("1_preoperation/figures/pre-operation/normal_Visualize_QC.pdf", width = 12, height = 6)
VlnPlot(normal, features = c("nFeature_RNA", "nCount_RNA", "mitoRatio"), ncol = 3)
dev.off()
plot1 <- FeatureScatter(normal, feature1 = "nCount_RNA", feature2 = "mitoRatio")
plot2 <- FeatureScatter(normal, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
pdf("1_preoperation/figures/pre-operation/normal_FeatureScatter.pdf", width = 12, height = 6)
CombinePlots(plots = list(plot1, plot2))
dev.off()
top30 <- head(VariableFeatures(normal_norm), 30)
pdf("1_preoperation/figures/pre-operation/normal_VariableFeatures.pdf", width = 12, height = 6)
plot1 <- VariableFeaturePlot(normal_norm)
plot2 <- LabelPoints(plot = plot1, points = top30, repel = TRUE)
CombinePlots(plots = list(plot1, plot2))
dev.off()
# filter normal
filtered_normal <- subset(x = normal,
subset= (nUMI < 20000) &
(nGene > 100) &
(nGene < 3000) &
(log10GenesPerUMI > 0.80) &
(mitoRatio < 0.1))
counts <- GetAssayData(object = filtered_normal, slot = "counts")
nonzero <- counts > 0
keep_genes <- Matrix::rowSums(nonzero) >= 20
filtered_counts <- counts[keep_genes, ]
filtered_normal <- CreateSeuratObject(filtered_counts, meta.data = normal@meta.data)
filtered_normal$label <- "normal"
save(filtered_normal, file="1_preoperation/data/filtered_normal.RData")
filtered_normal_norm<-NormalizeData(filtered_normal)
WBY.anchors <- FindIntegrationAnchors(object.list = list(filtered_cancer_norm, filtered_normal_norm), dims = 1:30)
save(WBY.anchors, file="1_preoperation/data/integrated.anchors_seurat20210604.RData")
WBY.combined <- IntegrateData(anchorset = WBY.anchors, dims = 1:30)
WBY.combined <- FindVariableFeatures(WBY.combined, selection.method = "vst", nfeatures = 3000)
save(WBY.combined, file="1_preoperation/data/integrated.combined_seurat20210510.RData")
DefaultAssay(WBY.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
WBY.combined <- ScaleData(WBY.combined, verbose = FALSE, vars.to.regress = c("nUMI", "mitoRatio"))
#Scaling is an essential step in the Seurat workflow, but only on genes that will be used as input to PCA. Therefore, the default in ScaleData() is only to perform scaling on the previously identified variable features (2,000 by default).
WBY.combined <- ScaleData(WBY.combined)
save(WBY.combined, file="1_preoperation/data/WBY.combined_scaled.RData")
WBY.combined <- RunPCA(WBY.combined, npcs = 30, verbose = FALSE)
print(WBY.combined[["pca"]], dims = 1:5, nfeatures = 5)
pdf("1_preoperation/data/VizDimLoadings.pdf")
VizDimLoadings(WBY.combined, dims = 1:2, reduction = "pca")
dev.off()
pdf("1_preoperation/data/DimPlot.pdf")
DimPlot(WBY.combined, reduction = "pca")
dev.off()
pdf("1_preoperation/data/DimHeatmap.pc1.pdf")
DimHeatmap(WBY.combined, dims = 1, cells = 500, balanced = TRUE)
dev.off()
pdf("1_preoperation/data/DimHeatmap.all.pdf")
DimHeatmap(WBY.combined, dims = 1:30, cells = 500, balanced = TRUE)
dev.off()
WBY.combined <- JackStraw(WBY.combined, num.replicate = 100, dims = 30)
WBY.combined <- ScoreJackStraw(WBY.combined, dims = 1:30)
pdf("1_preoperation/data/Determine_dimensionality.pdf", width = 24, height = 18)
p1 <- JackStrawPlot(WBY.combined, dims = 1:30)
p2 <- ElbowPlot(WBY.combined,ndims = 30)
plot_grid(p1, p2)
dev.off()
save(WBY.combined, file="1_preoperation/data/integrated.combined_beforepcs.RData")
# determine the resolution
library(Seurat)
library(clustree)
WBY.combined <- FindNeighbors(WBY.combined, reduction = "pca", dims = 1:21)
WBY.combined <- FindClusters(WBY.combined, resolution = 0.5)
WBY.combined <- RunUMAP(WBY.combined, reduction = "pca", dims = 1:21)
WBY.combined <- RunTSNE(WBY.combined, reduction = "pca", dims = 1:21)
save(WBY.combined, file="1_preoperation/data/integrated.combined_beforepcs.RData")
#sce <- as.SingleCellExperiment(WBY.combined)
#sce <- FindNeighbors(sce, dims = 1:21)
#save(sce, file="1_preoperation/data/integrated.combined.sce_beforepcs.RData")
#sce <- FindClusters(
# object = sce,
# resolution = c(seq(.1,1.6,.2))
#)
#pdf("1_preoperation/data/clusters.pdf",width=30,height=15)
#clustree(sce@meta.data, prefix = "integrated_snn_res.")
#colnames(sce@meta.data)
#dev.off()
#DefaultAssay(WBY.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
#WBY.combined<-FindVariableFeatures(WBY.combined, selection.method = "vst", nfeatures = 3000)
#WBY.combined <- ScaleData(WBY.combined)
#save(WBY.combined, file="1_preoperation/data/WBY.combined_scaled.RData")
#WBY.combined <- RunPCA(WBY.combined, npcs = 30, verbose = FALSE)
#print(WBY.combined[["pca"]], dims = 1:5, nfeatures = 5)
#pdf("1_preoperation/figures/pre-operation/VizDimLoadings.pdf")
#VizDimLoadings(WBY.combined, dims = 1:2, reduction = "pca")
#dev.off()
#pdf("1_preoperation/figures/pre-operation/DimPlot.pdf")
#DimPlot(WBY.combined, reduction = "pca")
#dev.off()
#pdf("1_preoperation/figures/pre-operation/DimHeatmap.pc1.pdf")
#DimHeatmap(WBY.combined, dims = 1, cells = 500, balanced = TRUE)
#dev.off()
#pdf("1_preoperation/figures/pre-operation/DimHeatmap.all.pdf")
#DimHeatmap(WBY.combined, dims = 1:30, cells = 500, balanced = TRUE)
#dev.off()
#WBY.combined <- JackStraw(WBY.combined, num.replicate = 100, dims = 30)
#WBY.combined <- ScoreJackStraw(WBY.combined, dims = 1:30)
#pdf("1_preoperation/figures/pre-operation/Determine_dimensionality.pdf", width = 24, height = 18)
#p1 <- JackStrawPlot(WBY.combined, dims = 1:30)
#p2 <- ElbowPlot(WBY.combined,ndims = 30)
#plot_grid(p1, p2)
#dev.off()
#save(WBY.combined, file="1_preoperation/data/integrated.combined_beforepcs.RData")
# determine the resolution
r=0.7
WBY.combined <- FindClusters(WBY.combined, resolution = r)
levels(WBY.combined)
save(WBY.combined,file="2_annotation/seurat/WBY.combined.res0.7.RData")
WBY.pca21.markers <- FindAllMarkers(object = WBY.combined, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
save(WBY.pca21.markers,file="2_annotation/seurat/WBY.pca21.markers.res0.7.RData")
top30<-WBY.pca21.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
write.table(top30,"2_annotation/seurat/top30.pca21.markers.csv",sep=",",quote=F)
head(Idents(WBY.combined), 5)
write.table(WBY.pca21.markers,"2_annotation/seurat/WBY.pca21.markers.csv",sep=",",quote=F)
#library(tidyverse)
#umap_tx = WBY.combined.pca25@reductions$umap@cell.embeddings %>% as.data.frame() %>% cbind(label = WBY.combined.pca25@meta.data$label) %>% cbind(subpop = WBY.combined.pca25@meta.data$integrated_snn_res.0.5) %>% cbind(indiv = WBY.combined.pca25@meta.data$seq_folder)
#write.csv(umap_tx,file="2_scRNA-seq/annotation/Seurat/umap_tx.csv",quote=F)
#tsne_tx = WBY.combined.pca25@reductions$tsne@cell.embeddings %>% as.data.frame() %>% cbind(label = WBY.combined.pca25@meta.data$label) %>% cbind(subpop = WBY.combined.pca25@meta.data$integrated_snn_res.0.5) %>% cbind(indiv = WBY.combined.pca25@meta.data$seq_folder)
#write.csv(tsne_tx,file="2_scRNA-seq/annotation/Seurat/tsne_tx.csv",quote=F)
#umap_tx_ind = WBY.combined.pca25@reductions$umap@cell.embeddings %>% as.data.frame() %>% cbind(tx = WBY.combined.pca25@meta.data$seq_folder)
#write.csv(umap_tx_ind,file="annotation/Seurat/umap_tx_ind.csv",quote=F)
#ggplot(umap_tx, aes(x=UMAP_1, y=UMAP_2, color=tx)) + geom_point() +
#scale_color_manual(values=c("group1_untreated" = "darkblue",
# "group1_treated" = "darkred"))
#tsne_tx = WBY.combined.pca25@reductions$tsne@cell.embeddings %>% as.data.frame() %>% cbind(tx = WBY.combined.pca25@meta.data$label)
#tsne_tx_ind = WBY.combined.pca25@reductions$tsne@cell.embeddings %>% as.data.frame() %>% cbind(tx = WBY.combined.pca25@meta.data$seq_folder)
# Visualization
pdf(paste0("2_annotation/seurat/umap.pca21.res",r,".splitbyLabel.pdf"),width=20,height=10)
DimPlot(WBY.combined, reduction = "umap", label = TRUE, pt.size=1,label.size = 8, split.by = 'label', group.by = 'integrated_snn_res.0.7')
dev.off()
pdf(paste0("2_annotation/seurat/umap.pca21.res",r,".pdf"),width=10,height=10)
DimPlot(WBY.combined, reduction = "umap", label = TRUE, pt.size=1,label.size = 8, group.by = 'integrated_snn_res.0.7')
dev.off()
pdf(paste0("2_annotation/seurat/tsne.pca21.res",r,".splitbyLabel.pdf"),width=20,height=10)
DimPlot(WBY.combined, reduction = "tsne", label = TRUE, pt.size=0.1,label.size = 8, split.by = 'label', group.by = 'integrated_snn_res.0.7')
dev.off()
pdf(paste0("2_annotation/seurat/tsne.pca21.res",r,".pdf"),width=10,height=10)
DimPlot(WBY.combined, reduction = "tsne", label = TRUE, pt.size=0.1,label.size = 8, group.by = 'integrated_snn_res.0.7')
dev.off()
prop.table(table(Idents(WBY.combined), WBY.combined$label))
allsampleprop.each <-as.data.frame(prop.table(table(Idents(WBY.combined), WBY.combined$label)))
prop.table(table(Idents(WBY.combined)))
allsampleprop.total <-as.data.frame(prop.table(table(Idents(WBY.combined))))
write.csv(x = allsampleprop.each,file = '2_annotation/seurat/anno.allsample.each.prop.csv',quote = T,row.names = T)
write.csv(x = allsampleprop.total,file = '2_annotation/seurat/anno.allsample.total.prop.csv',quote = T,row.names = T)
table(Idents(WBY.combined))
pro.total <- table(Idents(WBY.combined),WBY.combined$label)
table(Idents(WBY.combined),WBY.combined$label)
pro.each <- table(Idents(WBY.combined),WBY.combined$label)
write.csv(x =pro.total,file = '2_annotation/seurat/anno.pro.total.csv',quote = T,row.names = T)
write.csv(x =pro.each,file = '2_annotation/seurat/anno.pro.each.csv',quote = T,row.names = T)
save(WBY.combined,file="2_annotation/seurat/WBY.combined.pca21.res0.7.17clusters.aftercluster.autoSeurat.nolabel.RData")
save(WBY.combined,file="2_annotation/seurat/WBY.combined.pca21.aftercluster.autoSeurat.nolabel.RData")
# annotation
new.cluster.ids<-c("B_cell", "T_cell", "EN", "IN", "IN", "RG", "Macrophage", "EN", "INP", "Dendritic_cell", "ENP", "Astrocyte", "Oligodendrocyte", "Miningeal_cell", "OPC", "EN")
names(new.cluster.ids) <- levels(WBY.combined)
WBY.combined <- RenameIdents(WBY.combined, new.cluster.ids)
WBY.combined$celltype<-Idents(WBY.combined)
save(WBY.combined,file="/ifs1/Grp8/liuzhe/scRNA/2_annotation/seurat/WBY.combined.pca21.res0.7.afteranno.RData")
WBY.combined.markers <- FindAllMarkers(object = WBY.combined, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(WBY.combined.markers,"2_annotation/seurat/WBY.pca21.markers.csv",sep=",",quote=F)
top30<-WBY.combined.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
write.table(top30,"2_annotation/seurat/top30.pca21.markers.csv",sep=",",quote=F)
save(WBY.combined.markers,file="/ifs1/Grp8/liuzhe/scRNA/2_annotation/seurat/WBY.combined.pca21.res0.7.marker.afteranno.RData")
# calculate the percentage and count the numbers
prop.table(table(Idents(WBY.combined), WBY.combined$label))
allsampleprop.each <-as.data.frame(prop.table(table(Idents(WBY.combined), WBY.combined$label)))
prop.table(table(Idents(WBY.combined)))
allsampleprop.total <-as.data.frame(prop.table(table(Idents(WBY.combined))))
write.csv(x = allsampleprop.each,file = '2_annotation/seurat/anno.allsample.each.prop.csv',quote = T,row.names = T)
write.csv(x = allsampleprop.total,file = '2_annotation/seurat/anno.allsample.total.prop.csv',quote = T,row.names = T)
table(Idents(WBY.combined))
pro.total <- table(Idents(WBY.combined),WBY.combined$label)
table(Idents(WBY.combined),WBY.combined$label)
pro.each <- table(Idents(WBY.combined),WBY.combined$label)
write.csv(x =pro.total,file = '2_annotation/seurat/anno.pro.total.csv',quote = T,row.names = T)
write.csv(x =pro.each,file = '2_annotation/seurat/anno.pro.each.csv',quote = T,row.names = T)
# visualization
WBY.combined$celltype <- Idents(WBY.combined)
pdf(paste0("2_annotation/seurat/umap.pca21.res",r,".splitbyLabel.pdf"),width=20,height=10)
DimPlot(WBY.combined, reduction = "umap", label = TRUE, pt.size=1.5,label.size = 8, split.by = 'label', group.by = "celltype")
dev.off()
pdf(paste0("2_annotation/seurat/umap.pca21.res",r,".pdf"),width=10,height=10)
DimPlot(WBY.combined, reduction = "umap", label = TRUE, pt.size=1.5,label.size = 8, group.by = 'celltype')
dev.off()
pdf(paste0("2_annotation/seurat/tsne.pca21.res",r,".splitbyLabel.pdf"),width=20,height=10)
DimPlot(WBY.combined, reduction = "tsne", label = TRUE, pt.size=1,label.size = 8, split.by = 'label', group.by = 'celltype')
dev.off()
pdf(paste0("2_annotation/seurat/tsne.pca21.res",r,".pdf"),width=10,height=10)
DimPlot(WBY.combined, reduction = "tsne", label = TRUE, pt.size=1,label.size = 8, group.by = 'label')
dev.off()
delta.genes <- c("CD79A","CD37","CCL5","CRIP1","ENC1","EGR1","HBA2","CXCR4","MSMO1","RPS26","LYZ","C1QA","HIST1H4C","TOP2A","CST3","CPVL","EOMES","CORO1C","TTYH1","FGFBP3","PLP1","MBP","TIMP1","IGFBP7","APOD","S100B")
pdf("2_annotation/anno/dittoDotPlot.pdf",width=15,height=8)
dittoDotPlot(WBY.combined, vars = delta.genes, group.by = "celltype",scale = FALSE)
dev.off()
pdf("2_annotation/anno/heatmap.top30.pdf",width=24,height=18)
DoHeatmap(WBY.combined,features=top30$gene,cells = 1:500, size = 4, angle = 90, disp.min=-2, disp.max=2) + scale_fill_gradientn(colours=c("blue","white","red"))
dev.off()
library(psych)
library(pheatmap)
AverageExp<-AverageExpression(WBY.combined,features=unique(top30$gene))
typeof(AverageExp)
head(AverageExp$RNA)
DefaultAssay(WBY.combined) <- "integrated"
pdf("2_annotation/anno/averageExptop30.clusters.pdf")
coorda<-corr.test(AverageExp$RNA,AverageExp$RNA,method="spearman")
pheatmap(coorda$r,cluster_row = FALSE,cluster_col = FALSE)
dev.off()
pdf("2_annotation/anno/heatmap.top30.pdf",width=24,height=18)
DoHeatmap(WBY.combined,features=top30$gene,cells = 1:500, size = 4, angle = 90, disp.min=-2, disp.max=2) + scale_fill_gradientn(colours=c("blue","white","red"))
dev.off()
DefaultAssay(WBY.combined) <- "RNA"
features.plot <- c("CD37","CD79A","EEF1B2","CCL5","CRIP1","TRAC","ENC1","SLA","NRP1","PLS3","PDE4DIP","MEG3","MSMO1","FDFT1","TSPAN13","LYZ","FTL","FTH1","TOP2A","UBE2C","ZWINT","CST3","SNX3","VIM","EOMES","CORO1C","ADGRG1","PON2","CLU","GFAP","PLP1","CRYAB","CLDN11","MGP","C1S","OLIG1","PLLP","CMTM5")
pdf("2_annotation/anno/markergenes.dotplot.pdf",width = 10, height = 8)
DotPlot(object = WBY.combined, features = features.plot, cols = c("lightgrey", "red"))
dev.off()
pdf("2_annotation/anno/markergenes.dotplot.pdf",width = 14, height = 8)
DotPlot(object = WBY.combined, features=features.plot,dot.scale = 10,cols = c("gray90", "red")) + RotatedAxis()
dev.off()
pdf("2_annotation/anno/markergenes.Bcell.pdf", width = 16, height = 8)
p1 <- FeaturePlot(WBY.combined, features = c("CD37", "CD79A"), pt.size = 1.5, combine = FALSE, reduction="tsne" )
fix.sc <- scale_color_gradientn( colours = c('lightgrey', 'red'), limits = c(2, 5))
p2 <- lapply(p1, function (x) x + fix.sc)
CombinePlots(p2)
dev.off()
pdf("2_annotation/anno/markergenes.MPC.pdf", width = 16, height = 8)
p1 <- FeaturePlot(WBY.combined, features = c("TUBA1B", "HMGB2"), pt.size = 1.5, combine = FALSE, reduction="tsne" )
fix.sc <- scale_color_gradientn( colours = c('lightgrey', 'red'), limits = c(3, 5))
p2 <- lapply(p1, function (x) x + fix.sc)
CombinePlots(p2)
dev.off()
WBY.combined_cancer<-subset(x=WBY.combined,subset = label == "cancer")
cellcom <- subset(WBY.combined_cancer, subset = (celltype == "B_cell" | celltype == "T_cell" | celltype == "Macrophage" | celltype == "Dendritic_cell"))
write.table(as.matrix(cellcom@assays$RNA@data), 'cellphonedb_count.txt', sep='\t', quote=F)
meta_data <- cbind(rownames(cellcom@meta.data), cellcom@meta.data[,'celltype', drop=F])
meta_data <- as.matrix(meta_data)
meta_data[is.na(meta_data)] = "Unkown" # ????????в?????NA
write.table(meta_data, 'cellphonedb_meta.txt', sep='\t', quote=F, row.names=F)
write.table(as.matrix(WBY.combined@assays$RNA@data), '3_cellphone/data/cellphonedb_count.txt', sep='\t', quote=F)
meta_data <- cbind(rownames(WBY.combined@meta.data), WBY.combined@meta.data[,'celltype', drop=F])
meta_data <- as.matrix(meta_data)
meta_data[is.na(meta_data)] = "Unkown"
write.table(meta_data, '3_cellphone/data/cellphonedb_meta.txt', sep='\t', quote=F, row.names=F)
require(org.Hs.eg.db)
library(topGO)
library(DOSE)
#devtools::install_github("eliocamp/ggnewscale")
library("ggnewscale")
x=as.list(org.Hs.egALIAS2EG)
geneList<-rep(0,nrow(WBY.combined))
names(geneList)<-row.names(WBY.combined)
geneList<-geneList[intersect(names(geneList),names(x))]
newwallgenes=names(geneList)
for (ii in 1:length(geneList)){
names(geneList)[ii]<-x[[names(geneList)[ii]]][1]
}
gene_erichment_results=list()
for (c1 in as.character(unique(levels(WBY.combined.markers$cluster)))){
print(paste0("RUN ", c1))
testgenes<-subset(WBY.combined.markers,cluster==c1)$gene
gene_erichment_results[[c1]]=list()
testgeneList=geneList
testgeneList[which(newwallgenes %in% testgenes)]= 1
#gene_erichment_results=list()
tab1=c()
for(ont in c("BP","MF")){
sampleGOdata<-suppressMessages(new("topGOdata",description="Simple session",ontology=ont,allGenes=as.factor(testgeneList),
nodeSize=10,annot=annFUN.org,mapping="org.Hs.eg.db",ID="entrez"))
resultTopGO.elim<-suppressMessages(runTest(sampleGOdata,algorithm="elim",statistic="Fisher"))
resultTopGO.classic<-suppressMessages(runTest(sampleGOdata,algorithm="classic",statistic="Fisher"))
tab1<-rbind(tab1,GenTable(sampleGOdata,Fisher.elim=resultTopGO.elim,Fisher.classic=resultTopGO.classic,orderBy="Fisher.elim",
topNodes=200))
}
gene_erichment_results[[c1]][["topGO"]]=tab1
x<-suppressMessages(enrichDO(gene=names(testgeneList)[testgeneList==1],ont="DO",pvalueCutoff=1,pAdjustMethod="BH",universe=names(testgeneList),
minGSSize=5,maxGSSize=500,readable=T))
gene_erichment_results[[c1]][["DO"]]=x
dgn<-suppressMessages(enrichDGN(names(testgeneList)[testgeneList==1]))
gene_erichment_results[[c1]][["DGN"]]=dgn
}
save(gene_erichment_results,file="2_annotation/anno/gene_erichment_results.RData")
write.csv(gene_erichment_results[["B_cell"]][["topGO"]],"2_annotation/anno/GO_B_cell.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["T_cell"]][["topGO"]],"2_annotation/anno/GO_T_cell.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["EN"]][["topGO"]],"2_annotation/anno/GO_EN.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["IN"]][["topGO"]],"2_annotation/anno/GO_IN.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["RG"]][["topGO"]],"2_annotation/anno/GO_RG.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Macrophage"]][["topGO"]],"2_annotation/anno/GO_Macrophage.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["INP"]][["topGO"]],"2_annotation/anno/GO_INP.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Dendritic_cell"]][["topGO"]],"2_annotation/anno/GO_Dendritic_cell.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["ENP"]][["topGO"]],"2_annotation/anno/GO_ENP.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Astrocyte"]][["topGO"]],"2_annotation/anno/GO_Astrocyte.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Oligodendrocyte"]][["topGO"]],"2_annotation/anno/GO_Oligodendrocyte.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Miningeal_cell"]][["topGO"]],"2_annotation/anno/GO_Minigeal_cell.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["OPC"]][["topGO"]],"2_annotation/anno/GO_OPC.GO.csv",quote=F,row.names=F)
#Bcell
gene_erichment_results[["B_cell"]][["topGO"]][1:5,]
pdf("2_annotation/anno/clusterAnalysis_B_cell.1.pdf",width=8,height=10)
library(enrichplot)
dotplot(gene_erichment_results[["B_cell"]][["DGN"]], showCategory=30)
dev.off()
WBY.combined$celltype<-Idents(WBY.combined)
WBY_cancer<-subset(x=WBY.combined,subset = label == "cancer")
B_cell.subset <- subset(WBY_cancer@meta.data, celltype=="B_cell")
scRNAsub.Bcell <- subset(WBY_cancer, cells=row.names(B_cell.subset))
scRNAsub.Bcell <- FindVariableFeatures(scRNAsub.Bcell, selection.method = "vst", nfeatures = 2000)
scale.genes.Bcell <- rownames(scRNAsub.Bcell)
scRNAsub.Bcell <- ScaleData(scRNAsub.Bcell, features = scale.genes.Bcell)
scRNAsub.Bcell <- RunPCA(scRNAsub.Bcell, features = VariableFeatures(tme))
pdf("2_annotation/subcluster/Determine.Bcell.pcnumber.pdf")
ElbowPlot(scRNAsub.Bcell, ndims=20, reduction="pca")
dev.off()
pc.num=1:8
scRNAsub.Bcell <- FindNeighbors(scRNAsub.Bcell, dims = pc.num)
scRNAsub.Bcell <- FindClusters(scRNAsub.Bcell, resolution = 0.6)
table(scRNAsub.Bcell@meta.data$seurat_clusters)
metadata <- scRNAsub.Bcell@meta.data
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'2_annotation/subcluster/Bcell.cell_cluster.csv',row.names = F)
#tSNE
scRNAsub.Bcell = RunTSNE(scRNAsub.Bcell, dims = pc.num)
embed_tsne <- Embeddings(scRNAsub.Bcell, 'tsne')
write.csv(embed_tsne,'2_annotation/subcluster/tme.embed_tsne.csv')
pdf("2_annotation/subcluster/tsne_Bcell.pdf")
DimPlot(scRNAsub.Bcell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8)
dev.off()
diff.wilcox = FindAllMarkers(scRNAsub.Bcell)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top30 = all.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
write.csv(all.markers, "2_annotation/subcluster/tme.diff_genes_wilcox.csv", row.names = F)
write.csv(top30, "2_annotation/subcluster/tme.top30_diff_genes_wilcox.csv", row.names = F)
save(scRNAsub.Bcell,file="2_annotation/subcluster/scRNAsub.tme.RData")
new.cluster.ids<-c("B_cell-1", "B_cell-2", "B_cell-3", "B_cell-3", "B_cell-1", "B_cell-2", "Plasma_cell")
names(new.cluster.ids) <- levels(scRNAsub.tme)
scRNAsub.Bcell <- RenameIdents(scRNAsub.Bcell, new.cluster.ids)
scRNAsub.Bcell <- RunUMAP(scRNAsub.Bcell, dims = 1:8)
scRNAsub.Bcell$celltype<-Idents(scRNAsub.Bcell)
save(scRNAsub.Bcell,file="2_annotation/subcluster/scRNAsub.Bcell.afteranno.RData")
scRNAsub.Bcell.markers <- FindAllMarkers(object = scRNAsub.Bcell, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(scRNAsub.Bcell.markers,"2_annotation/subcluster/scRNAsub.tme.markers.anno.csv",sep=",",quote=F)
save(scRNAsub.Bcell.markers,file="2_annotation/subcluster/scRNAsub.tme.markers.afteranno.RData")
pdf("2_annotation/subcluster/tsne.bcell.integrate.pdf", width = 10, height = 10)
DimPlot(scRNAsub.Bcell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8, split.by = 'label', group.by = 'celltype')
dev.off()
top30<-scRNAsub.tme.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
write.table(top30,"2_annotation/subcluster/scRNAsub.tme.top30.anno.csv",sep=",",quote=F)
require(org.Hs.eg.db)
library(topGO)
library(DOSE)
#devtools::install_github("eliocamp/ggnewscale")
library("ggnewscale")
x=as.list(org.Hs.egALIAS2EG)
geneList<-rep(0,nrow(scRNAsub.Bcell))
names(geneList)<-row.names(scRNAsub.Bcell)
geneList<-geneList[intersect(names(geneList),names(x))]
newwallgenes=names(geneList)
for (ii in 1:length(geneList)){
names(geneList)[ii]<-x[[names(geneList)[ii]]][1]
}
gene_erichment_results=list()
for (c1 in as.character(unique(levels(scRNAsub.Bcell.markers$cluster)))){
print(paste0("RUN ", c1))
testgenes<-subset(scRNAsub.Bcell.markers,cluster==c1)$gene
gene_erichment_results[[c1]]=list()
testgeneList=geneList
testgeneList[which(newwallgenes %in% testgenes)]= 1
#gene_erichment_results=list()
tab1=c()
for(ont in c("BP","MF")){
sampleGOdata<-suppressMessages(new("topGOdata",description="Simple session",ontology=ont,allGenes=as.factor(testgeneList),
nodeSize=10,annot=annFUN.org,mapping="org.Hs.eg.db",ID="entrez"))
resultTopGO.elim<-suppressMessages(runTest(sampleGOdata,algorithm="elim",statistic="Fisher"))
resultTopGO.classic<-suppressMessages(runTest(sampleGOdata,algorithm="classic",statistic="Fisher"))
tab1<-rbind(tab1,GenTable(sampleGOdata,Fisher.elim=resultTopGO.elim,Fisher.classic=resultTopGO.classic,orderBy="Fisher.elim",
topNodes=200))
}
gene_erichment_results[[c1]][["topGO"]]=tab1
x<-suppressMessages(enrichDO(gene=names(testgeneList)[testgeneList==1],ont="DO",pvalueCutoff=1,pAdjustMethod="BH",universe=names(testgeneList),
minGSSize=5,maxGSSize=500,readable=T))
gene_erichment_results[[c1]][["DO"]]=x
dgn<-suppressMessages(enrichDGN(names(testgeneList)[testgeneList==1]))
gene_erichment_results[[c1]][["DGN"]]=dgn
}
save(gene_erichment_results,file="2_annotation/anno/Bcellsub.gene_erichment_results.RData")
gene_erichment_results[["B_cell-1"]][["topGO"]][1:5,]
write.csv(gene_erichment_results[["B_cell-1"]][["topGO"]],"2_annotation/subcluster/Bcellsub.B_cell-1.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Plasma_cell"]][["topGO"]],"2_annotation/subcluster/Bcellsub.Plasma_cell.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["B_cell-2"]][["topGO"]],"2_annotation/subcluster/Bcellsub.B_cell-2.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["B_cell-3"]][["topGO"]],"2_annotation/subcluster/Bcellsub.B_cell-3.GO.csv",quote=F,row.names=F)
names(new.cluster.ids) <- levels(scRNAsub.Bcell)
scRNAsub.Bcell <- RenameIdents(scRNAsub.Bcell, new.cluster.ids)
scRNAsub.Bcell <- RunUMAP(scRNAsub.Bcell, dims = 1:8)
save(scRNAsub.Bcell,file="data/scRNAsub.Bcell.afteranno.RData")
scRNAsub.Bcell.markers <- FindAllMarkers(object = scRNAsub.Bcell, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(scRNAsub.Bcell.markers,"subcluster/scRNAsub.Bcell.markers.anno.csv",sep=",",quote=F)
save(scRNAsub.Bcell.markers,file="subcluster/scRNAsub.Bcell.markers.afteranno.RData")
pdf("subcluster/scRNAsub.Bcell.integrate.pdf", width = 36, height = 18)
p1 <- DimPlot(scRNAsub.Bcell, reduction = "umap", group.by = "label")
p2<-DimPlot(scRNAsub.Bcell, reduction = "umap", label = TRUE)
plot_grid(p1, p2)
dev.off()
prop.table(table(Idents(scRNAsub.Bcell), scRNAsub.Bcell$label))
allsampleprop.each <-as.data.frame(prop.table(table(Idents(scRNAsub.Bcell), scRNAsub.Bcell$label)))
prop.table(table(Idents(scRNAsub.Bcell)))
allsampleprop.total <-as.data.frame(prop.table(table(Idents(scRNAsub.Bcell))))
write.csv(x = allsampleprop.each,file = '2_annotation/subcluster/Bcellprop.each.prop.csv',quote = T,row.names = T)
write.csv(x = allsampleprop.total,file = '2_annotation/subcluster/Bcellprop.prop.csv',quote = T,row.names = T)
table(Idents(scRNAsub.Bcell))
pro.total <- table(Idents(scRNAsub.Bcell),scRNAsub.tme$label)
table(Idents(scRNAsub.tme),scRNAsub.Bcell$label)
pro.each <- table(Idents(scRNAsub.Bcell),scRNAsub.Bcell$label)
write.csv(x =pro.total,file = '2_annotation/subcluster/Bcellanno.pro.total.csv',quote = T,row.names = T)
write.csv(x =pro.each,file = '2_annotation/subcluster/Bcellanno.pro.each.csv',quote = T,row.names = T)
pdf("results/pca16res0.3/umap.pca16.res0.3.integrate.pdf", width = 36, height = 18)
p1 <- DimPlot(glioma.combined.pca16, reduction = "umap", group.by = "label")
p2<-DimPlot(glioma.combined.pca16, reduction = "umap", label = TRUE)
plot_grid(p1, p2)
Bcell.markers <- FindAllMarkers(object = scRNAsub.Bcell, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
save(WBY.pca21.markers,file="2_annotation/seurat/WBY.pca21.markers.res0.7.RData")
top30<-WBY.pca21.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
plot1 = DimPlot(scRNAsub.Bcell, reduction = "tsne")
ggsave("2_annotation/subcluster/Bcell.tSNE.pdf", plot = plot1, width = 8, height = 7)
ggsave("2_annotation/subcluster/Bcell.tSNE.png", plot = plot1, width = 8, height = 7)
###DEGs analysis
macrophage.cells <- subset(WBY.combined, idents = "Macrophage")
Idents(macrophage.cells) <- "label"
avg.macrophage.cells <- as.data.frame(log1p(AverageExpression(macrophage.cells, verbose = FALSE)$RNA))
avg.macrophage.cells$gene <- rownames(avg.macrophage.cells)
write.table(avg.macrophage.cells,"2_annotation/DEGs/avg.macrophage.cells.csv",sep=",",quote=F)
WBY.combined$celltype.label <- paste(Idents(WBY.combined), WBY.combined$label, sep = "_")
WBY.combined$celltype <- Idents(WBY.combined)
Idents(WBY.combined) <- "celltype.label"
scRNAsub.macrophage.data <- subset(WBY.combined, subset = (celltype.label == "Macrophage_cancer" | celltype.label == "Macrophage_normal"))
write.table(GetAssayData(scRNAsub.macrophage.data),"macrophage.data.csv",sep=",",quote=F)
immune.response <- FindMarkers(WBY.combined, ident.1 = "Macrophage_cancer", ident.2 = "Macrophage_normal", verbose = FALSE)
head(immune.response, n = 15)
write.table(immune.response,"2_annotation/DEGs/macrophage.degs.csv",sep=",")
degs_macrophage<-immune.response
degs_filtered<-degs_macrophage[(degs_macrophage$avg_log2FC>log2(1.5)|degs_macrophage$avg_log2FC<=-log2(1.5) ) & (degs_macrophage$p_val_adj<0.05),]
features_degs<-degs_filtered[order(degs_filtered$avg_log2FC),]
pdf("2_annotation/DEGs/macrophage.heatmap.pdf",width = 20, height = 18)
DoHeatmap(macrophage.cells, features = row.names(features_degs)) + NoLegend()
dev.off()
pdf("2_annotation/DEGs/macrophage.heatmap.pdf",width = 20, height = 18)
DoHeatmap(macrophage.cells, features = row.names(features_degs))
dev.off()
### T cell
WBY.combined$celltype<-Idents(WBY.combined)
WBY_cancer<-subset(x=WBY.combined,subset = label == "cancer")
T_cell.subset <- subset(WBY_cancer@meta.data, celltype=="T_cell")
scRNAsub.T_cell <- subset(WBY_cancer, cells=row.names(T_cell.subset))
scRNAsub.T_cell <- FindVariableFeatures(scRNAsub.T_cell, selection.method = "vst", nfeatures = 2000)
scale.genes.T_cell <- rownames(scRNAsub.T_cell)
scRNAsub.T_cell <- ScaleData(scRNAsub.T_cell, features = scale.genes.T_cell)
scRNAsub.T_cell <- RunPCA(scRNAsub.T_cell, features = VariableFeatures(scRNAsub.T_cell))
pdf("2_annotation/subcluster/Determine.T_cell.pcnumber.pdf")
ElbowPlot(scRNAsub.T_cell, ndims=20, reduction="pca")
dev.off()
pc.num=1:9
##???????
scRNAsub.T_cell <- FindNeighbors(scRNAsub.T_cell, dims = pc.num)
scRNAsub.T_cell <- FindClusters(scRNAsub.T_cell, resolution = 0.8)
table(scRNAsub.T_cell@meta.data$seurat_clusters)
metadata <- scRNAsub.T_cell@meta.data
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'2_annotation/subcluster/T_cell.cell_cluster.csv',row.names = F)
##????????
#tSNE
scRNAsub.T_cell = RunTSNE(scRNAsub.T_cell, dims = pc.num)
embed_tsne <- Embeddings(scRNAsub.T_cell, 'tsne')
write.csv(embed_tsne,'2_annotation/subcluster/T_cell.embed_tsne.csv')
pdf("2_annotation/subcluster/tsne_T_cell.pdf")
DimPlot(scRNAsub.T_cell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8) + NoLegend()
dev.off()
diff.wilcox = FindAllMarkers(scRNAsub.T_cell)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top30 = all.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
write.csv(all.markers, "2_annotation/subcluster/T_cell.diff_genes_wilcox.csv", row.names = F)
write.csv(top30, "2_annotation/subcluster/T_cell.top30_diff_genes_wilcox.csv", row.names = F)
save(scRNAsub.T_cell,file="2_annotation/subcluster/scRNAsub.T_cell.RData")
library(SingleR)
#refdata <- MonacoImmuneData()
hpca.se <- HumanPrimaryCellAtlasData()
bpe.se <- BlueprintEncodeData()
#immu.se <- DatabaseImmuneCellExpressionData()
testdata <- GetAssayData(scRNAsub.T_cell, slot="data")
clusters <- scRNAsub.T_cell@meta.data$seurat_clusters
cellpred <- SingleR(test = testdata,
ref = list(HP = hpca.se , BP = bpe.se),
labels = list(hpca.se$label.main , bpe.se$label.main),
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts",de.method="wilcox")
table(cellpred$labels)
pdf("2_annotation/subcluster/test.pdf", width=18 ,height=9)
plotScoreHeatmap(cellpred)
dev.off()
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,"2_annotation/subcluster/T_cell.celltype_singleR.csv",row.names = F)
scRNAsub.T_cell@meta.data$celltype = "NA"
for(i in 1:nrow(celltype)){
scRNAsub.T_cell@meta.data[which(scRNAsub.T_cell@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
p1 = DimPlot(scRNAsub.T_cell, group.by="celltype", label=T, label.size=5, reduction='tsne')
p2 = DimPlot(scRNAsub.T_cell, group.by="celltype", label=T, label.size=5, reduction='umap')
p3 = plotc <- p1+p2+ plot_layout(guides = 'collect')
ggsave("2_annotation/subcluster/T_cell.tSNE_celltype.pdf", p1, width=7 ,height=6)
ggsave("2_annotation/subcluster/T_cell.UMAP_celltype.pdf", p2, width=10 ,height=6)
ggsave("2_annotation/subcluster/T_cell.celltype.pdf", p3, width=10 ,height=5)
ggsave("2_annotation/subcluster/T_cell.celltype.png", p3, width=10 ,height=5)
diff.wilcox = FindAllMarkers(scRNAsub.T_cell)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top30 = all.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
require(org.Hs.eg.db)
library(topGO)
library(DOSE)
#devtools::install_github("eliocamp/ggnewscale")
library("ggnewscale")
x=as.list(org.Hs.egALIAS2EG)
geneList<-rep(0,nrow(scRNAsub.T_cell))
names(geneList)<-row.names(scRNAsub.T_cell)
geneList<-geneList[intersect(names(geneList),names(x))]
newwallgenes=names(geneList)
for (ii in 1:length(geneList)){
names(geneList)[ii]<-x[[names(geneList)[ii]]][1]
}
gene_erichment_results=list()
for (c1 in as.character(unique(levels(all.markers$cluster)))){
print(paste0("RUN ", c1))
testgenes<-subset(all.markers,cluster==c1)$gene
gene_erichment_results[[c1]]=list()
testgeneList=geneList
testgeneList[which(newwallgenes %in% testgenes)]= 1
#gene_erichment_results=list()
tab1=c()
for(ont in c("BP","MF")){
sampleGOdata<-suppressMessages(new("topGOdata",description="Simple session",ontology=ont,allGenes=as.factor(testgeneList),
nodeSize=10,annot=annFUN.org,mapping="org.Hs.eg.db",ID="entrez"))
resultTopGO.elim<-suppressMessages(runTest(sampleGOdata,algorithm="elim",statistic="Fisher"))
resultTopGO.classic<-suppressMessages(runTest(sampleGOdata,algorithm="classic",statistic="Fisher"))
tab1<-rbind(tab1,GenTable(sampleGOdata,Fisher.elim=resultTopGO.elim,Fisher.classic=resultTopGO.classic,orderBy="Fisher.elim",
topNodes=200))
}
gene_erichment_results[[c1]][["topGO"]]=tab1
x<-suppressMessages(enrichDO(gene=names(testgeneList)[testgeneList==1],ont="DO",pvalueCutoff=1,pAdjustMethod="BH",universe=names(testgeneList),
minGSSize=5,maxGSSize=500,readable=T))
gene_erichment_results[[c1]][["DO"]]=x
dgn<-suppressMessages(enrichDGN(names(testgeneList)[testgeneList==1]))
gene_erichment_results[[c1]][["DGN"]]=dgn
}
save(gene_erichment_results,file="2_annotation/subcluster/T_cell_gene_erichment_results.RData")
write.csv(gene_erichment_results[["T_helper"]][["topGO"]],"2_annotation/subcluster/Tcellsub.T_helper.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["NKT"]][["topGO"]],"2_annotation/subcluster/Tcellsub.NKT.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["Multilymphoid_progenitor_cell"]][["topGO"]],"2_annotation/subcluster/Tcellsub.MPC.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["T_cell"]][["topGO"]],"2_annotation/subcluster/Tcellsub.T_cell.csv",quote=F,row.names=F)
load("2_annotation/subcluster/scRNAsub.T_cell.RData")
new.cluster.ids<-c("T_helper", "NKT", "Multilymphoid_progenitor_cell", "NKT", "Multilymphoid_progenitor_cell","NA", "T_cell", "NA")
names(new.cluster.ids) <- levels(scRNAsub.T_cell)
scRNAsub.T_cell <- RenameIdents(scRNAsub.T_cell, new.cluster.ids)
scRNAsub.T_cell$celltype<-Idents(scRNAsub.T_cell )
scRNAsub.T_cell_rmNA <- subset(scRNAsub.T_cell, subset = (celltype == "T_helper" | celltype == "NKT" | celltype == "Multilymphoid_progenitor_cell" | celltype == "T_cell"))
scRNAsub.T_cell<-scRNAsub.T_cell_rmNA
scRNAsub.T_cell <- RunUMAP(scRNAsub.T_cell, dims = 1:8)
scRNAsub.T_cell$celltype<-Idents(scRNAsub.T_cell)
save(scRNAsub.T_cell,file="2_annotation/subcluster/scRNAsub.T_cell.afteranno.RData")
scRNAsub.T_cell.markers <- FindAllMarkers(object = scRNAsub.T_cell, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(scRNAsub.T_cell.markers,"2_annotation/subcluster/scRNAsub.T_cell.markers.anno.csv",sep=",",quote=F)
save(scRNAsub.T_cell.markers,file="2_annotation/subcluster/scRNAsub.T_cell.markers.afteranno.RData")
pdf("2_annotation/subcluster/tsne.Tcell.integrate.pdf", width = 10, height = 10)
DimPlot(scRNAsub.T_cell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8, split.by = 'label', group.by = 'celltype')
dev.off()
top30<-scRNAsub.T_cell.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
write.table(top30,"2_annotation/subcluster/scRNAsub.T_cell.top30.anno.csv",sep=",",quote=F)
features.plot <- c("LTB","NEAT1","IL7R","DUSP1","CCL5","JUNB","UBE2C","STMN1","BIRC5","NCL","HNRNPA3","DUT")
pdf("2_annotation/subcluster/Tcellsub.dittoDotPlot.pdf",width = 9, height = 4)
DotPlot(object = scRNAsub.T_cell, features=features.plot,dot.scale = 12,cols = c("lightgrey", "red")) + RotatedAxis()
dev.off()
prop.table(table(Idents(scRNAsub.T_cell), scRNAsub.T_cell$label))
allsampleprop.each <-as.data.frame(prop.table(table(Idents(scRNAsub.T_cell), scRNAsub.T_cell$label)))
prop.table(table(Idents(scRNAsub.T_cell)))
allsampleprop.total <-as.data.frame(prop.table(table(Idents(scRNAsub.T_cell))))
write.csv(x = allsampleprop.each,file = '2_annotation/subcluster/Tcellprop.each.prop.csv',quote = T,row.names = T)
write.csv(x = allsampleprop.total,file = '2_annotation/subcluster/Tcellprop.prop.csv',quote = T,row.names = T)
table(Idents(scRNAsub.T_cell))
pro.total <- table(Idents(scRNAsub.T_cell),scRNAsub.T_cell$label)
table(Idents(scRNAsub.T_cell),scRNAsub.T_cell$label)
pro.each <- table(Idents(scRNAsub.T_cell),scRNAsub.T_cell$label)
write.csv(x =pro.total,file = '2_annotation/subcluster/Tcellanno.pro.total.csv',quote = T,row.names = T)
write.csv(x =pro.each,file = '2_annotation/subcluster/Tcellanno.pro.each.csv',quote = T,row.names = T)
## Dendritic_cell
WBY.combined$celltype<-Idents(WBY.combined)
WBY_cancer<-subset(x=WBY.combined,subset = label == "cancer")
CD8PlusDendritic_cell.subset <- subset(WBY_cancer@meta.data, celltype=="Dendritic_cell")
scRNAsub.CD8PlusDendritic_cell <- subset(WBY_cancer, cells=row.names(CD8PlusDendritic_cell.subset))
scRNAsub.CD8PlusDendritic_cell <- FindVariableFeatures(scRNAsub.CD8PlusDendritic_cell, selection.method = "vst", nfeatures = 2000)
scale.genes.CD8PlusDendritic_cell <- rownames(scRNAsub.CD8PlusDendritic_cell)
scRNAsub.CD8PlusDendritic_cell <- ScaleData(scRNAsub.CD8PlusDendritic_cell, features = scale.genes.CD8PlusDendritic_cell)
scRNAsub.CD8PlusDendritic_cell <- RunPCA(scRNAsub.CD8PlusDendritic_cell, features = VariableFeatures(scRNAsub.CD8PlusDendritic_cell))
pdf("2_annotation/subcluster/Determine.CD8PlusDendritic_cell.pcnumber.pdf")
ElbowPlot(scRNAsub.CD8PlusDendritic_cell, ndims=20, reduction="pca")
dev.off()
pc.num=1:18
scRNAsub.CD8PlusDendritic_cell <- FindNeighbors(scRNAsub.CD8PlusDendritic_cell, dims = pc.num)
scRNAsub.CD8PlusDendritic_cell <- FindClusters(scRNAsub.CD8PlusDendritic_cell, resolution = 1)
table(scRNAsub.CD8PlusDendritic_cell@meta.data$seurat_clusters)
metadata <- scRNAsub.CD8PlusDendritic_cell@meta.data
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'2_annotation/subcluster/CD8PlusDendritic_cell.cell_cluster.csv',row.names = F)
#tSNE
scRNAsub.CD8PlusDendritic_cell = RunTSNE(scRNAsub.CD8PlusDendritic_cell, dims = pc.num)
embed_tsne <- Embeddings(scRNAsub.CD8PlusDendritic_cell, 'tsne')
write.csv(embed_tsne,'2_annotation/subcluster/CD8PlusDendritic_cell.embed_tsne.csv')
pdf("2_annotation/subcluster/tsne_CD8PlusDendritic_cell.pdf")
DimPlot(scRNAsub.CD8PlusDendritic_cell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8)
dev.off()
diff.wilcox = FindAllMarkers(scRNAsub.CD8PlusDendritic_cell)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top30 = all.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
write.csv(all.markers, "2_annotation/subcluster/CD8PlusDendritic_cell.diff_genes_wilcox.csv", row.names = F)
write.csv(top30, "2_annotation/subcluster/CD8PlusDendritic_cell.top30_diff_genes_wilcox.csv", row.names = F)
save(scRNAsub.CD8PlusDendritic_cell,file="2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.RData")
library(SingleR)
#refdata <- MonacoImmuneData()
hpca.se <- HumanPrimaryCellAtlasData()
bpe.se <- BlueprintEncodeData()
#immu.se <- DatabaseImmuneCellExpressionData()
testdata <- GetAssayData(scRNAsub.CD8PlusDendritic_cell, slot="data")
clusters <- scRNAsub.CD8PlusDendritic_cell@meta.data$seurat_clusters
cellpred <- SingleR(test = testdata,
ref = list(HP = hpca.se , BP = bpe.se),
labels = list(hpca.se$label.main , bpe.se$label.main),
method = "cluster", clusters = clusters,
assay.type.test = "logcounts", assay.type.ref = "logcounts",de.method="wilcox")
table(cellpred$labels)
pdf("2_annotation/subcluster/test.pdf", width=18 ,height=9)
plotScoreHeatmap(cellpred)
dev.off()
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,"2_annotation/subcluster/CD8PlusDendritic_cell.celltype_singleR.csv",row.names = F)
scRNAsub.CD8PlusDendritic_cell@meta.data$celltype = "NA"
for(i in 1:nrow(celltype)){
scRNAsub.CD8PlusDendritic_cell@meta.data[which(scRNAsub.CD8PlusDendritic_cell@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
p1 = DimPlot(scRNAsub.CD8PlusDendritic_cell, group.by="celltype", label=T, label.size=5, reduction='tsne')
p2 = DimPlot(scRNAsub.CD8PlusDendritic_cell, group.by="celltype", label=T, label.size=5, reduction='umap')
p3 = plotc <- p1+p2+ plot_layout(guides = 'collect')
ggsave("2_annotation/subcluster/CD8PlusDendritic_cell.tSNE_celltype.pdf", p1, width=7 ,height=6)
ggsave("2_annotation/subcluster/CD8PlusDendritic_cell.UMAP_celltype.pdf", p2, width=10 ,height=6)
ggsave("2_annotation/subcluster/CD8PlusDendritic_cell.celltype.pdf", p3, width=10 ,height=5)
ggsave("2_annotation/subcluster/CD8PlusDendritic_cell.celltype.png", p3, width=10 ,height=5)
diff.wilcox = FindAllMarkers(scRNAsub.CD8PlusDendritic_cell)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top30 = all.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
write.csv(top30, "2_annotation/subcluster/CD8PlusDendritic_cell.top30_diff_genes_wilcox.csv", row.names = F)
prop.table(table(Idents(scRNAsub.CD8PlusDendritic_cell), scRNAsub.CD8PlusDendritic_cell$label))
allsampleprop.each <-as.data.frame(prop.table(table(Idents(scRNAsub.CD8PlusDendritic_cell), scRNAsub.CD8PlusDendritic_cell$label)))
prop.table(table(Idents(scRNAsub.CD8PlusDendritic_cell)))
allsampleprop.total <-as.data.frame(prop.table(table(Idents(scRNAsub.CD8PlusDendritic_cell))))
write.csv(x = allsampleprop.each,file = '2_annotation/subcluster/Denprop.each.prop.csv',quote = T,row.names = T)
write.csv(x = allsampleprop.total,file = '2_annotation/subcluster/Denprop.prop.csv',quote = T,row.names = T)
table(Idents(scRNAsub.CD8PlusDendritic_cell))
pro.total <- table(Idents(scRNAsub.CD8PlusDendritic_cell),scRNAsub.CD8PlusDendritic_cell$label)
table(Idents(scRNAsub.CD8PlusDendritic_cell),scRNAsub.CD8PlusDendritic_cell$label)
pro.each <- table(Idents(scRNAsub.CD8PlusDendritic_cell),scRNAsub.CD8PlusDendritic_cell$label)
write.csv(x =pro.total,file = '2_annotation/subcluster/Denanno.pro.total.csv',quote = T,row.names = T)
write.csv(x =pro.each,file = '2_annotation/subcluster/Denanno.pro.each.csv',quote = T,row.names = T)
require(org.Hs.eg.db)
library(topGO)
library(DOSE)
#devtools::install_github("eliocamp/ggnewscale")
library("ggnewscale")
x=as.list(org.Hs.egALIAS2EG)
geneList<-rep(0,nrow(scRNAsub.CD8PlusDendritic_cell))
names(geneList)<-row.names(scRNAsub.CD8PlusDendritic_cell)
geneList<-geneList[intersect(names(geneList),names(x))]
newwallgenes=names(geneList)
for (ii in 1:length(geneList)){
names(geneList)[ii]<-x[[names(geneList)[ii]]][1]
}
gene_erichment_results=list()
for (c1 in as.character(unique(levels(all.markers$cluster)))){
print(paste0("RUN ", c1))
testgenes<-subset(all.markers,cluster==c1)$gene
gene_erichment_results[[c1]]=list()
testgeneList=geneList
testgeneList[which(newwallgenes %in% testgenes)]= 1
#gene_erichment_results=list()
tab1=c()
for(ont in c("BP","MF")){
sampleGOdata<-suppressMessages(new("topGOdata",description="Simple session",ontology=ont,allGenes=as.factor(testgeneList),
nodeSize=10,annot=annFUN.org,mapping="org.Hs.eg.db",ID="entrez"))
resultTopGO.elim<-suppressMessages(runTest(sampleGOdata,algorithm="elim",statistic="Fisher"))
resultTopGO.classic<-suppressMessages(runTest(sampleGOdata,algorithm="classic",statistic="Fisher"))
tab1<-rbind(tab1,GenTable(sampleGOdata,Fisher.elim=resultTopGO.elim,Fisher.classic=resultTopGO.classic,orderBy="Fisher.elim",
topNodes=200))
}
gene_erichment_results[[c1]][["topGO"]]=tab1
x<-suppressMessages(enrichDO(gene=names(testgeneList)[testgeneList==1],ont="DO",pvalueCutoff=1,pAdjustMethod="BH",universe=names(testgeneList),
minGSSize=5,maxGSSize=500,readable=T))
gene_erichment_results[[c1]][["DO"]]=x
dgn<-suppressMessages(enrichDGN(names(testgeneList)[testgeneList==1]))
gene_erichment_results[[c1]][["DGN"]]=dgn
}
save(gene_erichment_results,file="2_annotation/subcluster/CD8PlusDendritic_cell_gene_erichment_results.RData")
write.csv(gene_erichment_results[["cDC"]][["topGO"]],"2_annotation/subcluster/CD8PlusDendritic_cellsub.cDC.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["pDC"]][["topGO"]],"2_annotation/subcluster/CD8PlusDendritic_cellsub.pDC.GO.csv",quote=F,row.names=F)
write.csv(gene_erichment_results[["mDC"]][["topGO"]],"2_annotation/subcluster/CD8PlusDendritic_cellsub.mDC.GO.csv",quote=F,row.names=F)
library(enrichplot)
#CD8PlusDendritic_cell_0
gene_erichment_results[["Dendritic_cell"]][["topGO"]][1:5,]
pdf("2_annotation/subcluster/CD8PlusDendritic_cell_Dendritic_cell.pdf",width=8,height=10)
dotplot(gene_erichment_results[["Dendritic_cell"]][["DGN"]], showCategory=30)
dev.off()
#CD8PlusDendritic_cell_1
gene_erichment_results[["Monocyte_derived_dendritic_cell"]][["topGO"]][1:5,]
pdf("2_annotation/subcluster/CD8PlusDendritic_cell_Monocyte_derived_dendritic_cell.pdf",width=8,height=10)
dotplot(gene_erichment_results[["Monocyte_derived_dendritic_cell"]][["DGN"]], showCategory=30)
dev.off()
#CD8PlusDendritic_cell_2
gene_erichment_results[["NPC"]][["topGO"]][1:5,]
pdf("2_annotation/subcluster/CD8PlusDendritic_cell_NPC.pdf",width=8,height=10)
dotplot(gene_erichment_results[["NPC"]][["DGN"]], showCategory=30)
dev.off()
#CD8PlusDendritic_cell_3
gene_erichment_results[["Lymphoid_dendritic_cells"]][["topGO"]][1:5,]
pdf("2_annotation/subcluster/CD8PlusDendritic_cell_Lymphoid_dendritic_cells.pdf",width=8,height=10)
dotplot(gene_erichment_results[["Lymphoid_dendritic_cells"]][["DGN"]], showCategory=30)
dev.off()
load("2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.RData")
new.cluster.ids<-c("cDC", "pDC", "mDC", "NA","NA")
names(new.cluster.ids) <- levels(scRNAsub.CD8PlusDendritic_cell)
scRNAsub.CD8PlusDendritic_cell <- RenameIdents(scRNAsub.CD8PlusDendritic_cell, new.cluster.ids)
scRNAsub.CD8PlusDendritic_cell$celltype<-Idents(scRNAsub.CD8PlusDendritic_cell )
scRNAsub.CD8PlusDendritic_cell_rmNA <- subset(scRNAsub.CD8PlusDendritic_cell, subset = (celltype == "cDC" | celltype == "pDC" | celltype == "mDC"))
scRNAsub.CD8PlusDendritic_cell<-scRNAsub.CD8PlusDendritic_cell_rmNA
scRNAsub.CD8PlusDendritic_cell <- RunUMAP(scRNAsub.CD8PlusDendritic_cell, dims = 1:18)
scRNAsub.CD8PlusDendritic_cell$celltype<-Idents(scRNAsub.CD8PlusDendritic_cell)
save(scRNAsub.CD8PlusDendritic_cell,file="2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.afteranno.RData")
scRNAsub.CD8PlusDendritic_cell.markers <- FindAllMarkers(object = scRNAsub.CD8PlusDendritic_cell, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(scRNAsub.CD8PlusDendritic_cell.markers,"2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.markers.anno.csv",sep=",",quote=F)
save(scRNAsub.CD8PlusDendritic_cell.markers,file="2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.markers.afteranno.RData")
pdf("2_annotation/subcluster/tsne.CD8PlusDendritic_cell.integrate.pdf", width = 10, height = 10)
DimPlot(scRNAsub.CD8PlusDendritic_cell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8, split.by = 'label', group.by = 'celltype')
dev.off()
pdf("2_annotation/subcluster/tsne.CD8PlusDendritic_cell.integrate.nolegend.pdf", width = 10, height = 10)
DimPlot(scRNAsub.CD8PlusDendritic_cell, reduction = "tsne", label = TRUE, pt.size=1.5,label.size = 8, split.by = 'label', group.by = 'celltype')+NoLegend()
dev.off()
top30<-scRNAsub.CD8PlusDendritic_cell.markers %>% group_by(cluster) %>% top_n(n=30,wt=avg_log2FC)
write.table(top30,"2_annotation/subcluster/scRNAsub.CD8PlusDendritic_cell.top30.anno.csv",sep=",",quote=F)
features.plot <- c("HIST1H4C","STMN1","HMGB2","IGLC2","GAS5","IL32","CST3","HLA-DPA1","HLA-DQB1")
pdf("2_annotation/subcluster/CD8PlusDendritic_cell.dittoDotPlot.pdf",width = 10, height = 5)
DotPlot(object = scRNAsub.CD8PlusDendritic_cell, features = features.plot,dot.scale = 12,cols = c("lightgrey", "red")) + RotatedAxis()
dev.off()
Meningeal_cell.cells <- subset(WBY.combined, idents = "Meningeal_cell")
Idents(Meningeal_cell.cells) <- "label"
avg.Meningeal_cell.cells <- as.data.frame(log1p(AverageExpression(Meningeal_cell.cells, verbose = FALSE)$RNA))
avg.Meningeal_cell.cells$gene <- rownames(avg.Meningeal_cell.cells)
write.table(avg.Meningeal_cell.cells,"2_annotation/DEGs/avg.Meningeal_cell.cells.csv",sep=",",quote=F)
WBY.combined$celltype.label <- paste(Idents(WBY.combined), WBY.combined$label, sep = "_")
WBY.combined$celltype <- Idents(WBY.combined)
Idents(WBY.combined) <- "celltype.label"
Meningeal_cell.response <- FindMarkers(WBY.combined, ident.1 = "Meningeal_cell_cancer", ident.2 = "Meningeal_cell_normal", verbose = FALSE)
head(Meningeal_cell.response, n = 15)
write.table(Meningeal_cell.response,"2_annotation/DEGs/Meningeal_cell.degs.csv",sep=",")
degs_meningeal<-Meningeal_cell.response
degs_filtered<-degs_meningeal[(degs_meningeal$avg_log2FC>log2(1.5)|degs_meningeal$avg_log2FC<=-log2(1.5) ) & (degs_meningeal$p_val_adj<0.05),]
features_degs<-degs_filtered[order(degs_filtered$avg_log2FC),]
pdf("2_annotation/DEGs/meningeal.heatmap.pdf",width = 20, height = 18)
DoHeatmap(Meningeal_cell.cells, features = row.names(features_degs)) + NoLegend()
dev.off()
degs_meningeal<-Meningeal_cell.response
degs_filtered<-degs_meningeal[(degs_meningeal$avg_log2FC>log2(1.5)|degs_meningeal$avg_log2FC<=-log2(1.5) ) & (degs_meningeal$p_val_adj<0.05),]
features_degs<-degs_filtered[order(degs_filtered$avg_log2FC),]
pdf("2_annotation/DEGs/meningeal.heatmap.label.pdf",width = 20, height = 18)
DoHeatmap(Meningeal_cell.cells, features = row.names(features_degs))
dev.off()
pdf("2_annotation/DEGs/Meningeal_cell.figure1.pdf",width = 20, height = 18)
FeaturePlot(WBY.combined, features = c("IL6ST","IRF1","PRDM1","IGFBP1","IGFBP2"), reduction = "tsne", split.by = "label", max.cutoff = 3,
cols = c("grey", "red"))
dev.off()
pdf("2_annotation/DEGs/Meningeal_cell.figure2.pdf",width = 10, height = 18)
plots <- VlnPlot(WBY.combined, features = c("CARD16","IRF1","PRDM1","IGFBP2","IGFBP7"), split.by = "label", group.by = "celltype",
pt.size = 0, combine = FALSE)
wrap_plots(plots = plots, ncol = 1)
dev.off()
pdf("2_annotation/DEGs/Meningeal_cell.figure3.pdf")
genes.to.label = c("CARD16","IRF1","PRDM1","IGFBP2","IGFBP7")
p1 <- ggplot(avg.Meningeal_cell.cells, aes(cancer, normal)) + geom_point() + ggtitle("Meningeal_cell")
p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE)
p1
dev.off()
##############################################################################################################################
|
4fef26fd9dbc9ca88ab2da0f41a37e8076bcd20a
|
5d6ac20f8e20d5c7739c68e7d44542f3bc5f8ba7
|
/R/grid2.R
|
a55f90ad65b8ebd026c426a240307f5d844da0ee
|
[] |
no_license
|
kl-lab/fformpp
|
d4585c8a8359e1d0a15bbcea379ffa54488e0992
|
1ec46c507e0c4781a8d274fb69354f7b778df92e
|
refs/heads/master
| 2022-11-16T14:36:59.297031
| 2020-07-14T14:51:23
| 2020-07-14T14:51:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 200
|
r
|
grid2.R
|
##' Add grids on plot
##'
##' @export
grid2 <- function(x.at = NA, y.at = NA, col = "black", lty="dotted", lwd = 0.5, ...)
{
abline(h = y.at, v = x.at, col = col, lty = lty, lwd = lwd, ...)
}
|
ec40df02cfe540c751e8221ad834516f07f72228
|
ccf77c83c0c4a328b7d6e353ad85ce4d2bf199d6
|
/R/summarize_model_results.R
|
c11e8fe0f879fcab213c8cdaeb52c35a79938c0a
|
[] |
no_license
|
FerreiraAM/CyTOFpower
|
6c314ad5e842dcc3ac0c5481168f38b5f6c0fe01
|
0ba3553ad1c288fc35c89bbd25e46c4f1cf76f66
|
refs/heads/master
| 2023-08-29T10:09:27.221017
| 2021-10-18T20:40:35
| 2021-10-18T20:40:35
| 355,366,603
| 0
| 0
| null | 2021-10-13T01:51:48
| 2021-04-07T00:32:49
|
R
|
UTF-8
|
R
| false
| false
| 1,323
|
r
|
summarize_model_results.R
|
# Functions to summarize results from the different models
#' Summarize data and results.
#'
#' @details Function to do a summary of the tested data and model's results
#' for the CytoGLMM and diffcyt packages.
#'
#' @param summary_from_model list, output from the functions running the models.
#' @param package character, package used to run the test: "CytoGLMM" or "diffcyt".
#'
#' @return data.frame of results for each simulation.
#'
#' @keywords internal
function_summary_results_models <- function(summary_from_model, package){
# Get package argument
package <- match.arg(package, choices = c("CytoGLMM", "diffcyt"))
# Rename colnames
# if the cytoGLMM package has been used, the column name in the results table
# is "protein_name"
if(package == "CytoGLMM"){
res_sum <- as.data.frame(summary_from_model$result_summary)
colnames(res_sum) <- c("marker_id", "p_val", "p_adj")
} else if(package =="diffcyt"){
# if the diffcyt package has been used, the column name in the results table
# is "marker_id"
res_sum <- as.data.frame(summary_from_model$result_summary[,c("marker_id",
"p_val",
"p_adj")])
}
# Return
return(as.data.frame(res_sum))
}
|
1cbd46b39aa98746196511a1c9563d6ad7ef39be
|
265fd47d1e5471091acf34c02769f0cf87a931c3
|
/ml02/ej_e_plot.R
|
c29aaffc73e38a3f3273d2310d8f0e381e02e9d7
|
[] |
no_license
|
hgurmendi/machine-learning
|
1646e4e2d96841afa02f025a101130ae997a844b
|
72181f5a27dba1f25f498ecfc4f0b66899d16c59
|
refs/heads/master
| 2018-10-19T19:04:57.322637
| 2018-07-23T00:53:56
| 2018-07-23T00:53:56
| 86,375,207
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,438
|
r
|
ej_e_plot.R
|
#!/usr/bin/Rscript
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 3) {
message("USAGE: ./ej07_plot.R parallel_errors diagonal_errors anns_errors")
quit()
}
message("parallel")
message(args[1])
message("diagonal")
message(args[2])
parallel <- read.csv(args[1], header = TRUE)
diagonal <- read.csv(args[2], header = TRUE)
anns <- read.csv(args[3], header = FALSE)
anns_diagonal <- subset(anns, V1=="diagonal")
anns_parallel <- subset(anns, V1=="parallel")
minX <- min(parallel$d)
maxX <- max(parallel$d)
minY <- min(diagonal$TestEAP, parallel$TestEAP, anns_diagonal$V3, anns_parallel$V3)
maxY <- max(diagonal$TestEAP, parallel$TestEAP, anns_diagonal$V3, anns_parallel$V3)
# red -> diagonal
# green -> parallel
# solid -> decision tree
# dashed -> neural network
png("ej_e.png")
par(mar=c(4,4,1,1)) # par = parametros de plot, mar = margenes, c(bottom, left, top, right)
plot(diagonal$d
, diagonal$TestEAP
, col = "red"
, type = "o"
, xlim = c(minX, maxX)
, ylim = c(minY, maxY)
, xlab = "Dimensions"
, ylab = "Error percentage"
, lwd = 2
, lty = 1)
lines(parallel$d
, parallel$TestEAP
, col = "green"
, type = "o"
, lwd = 2
, lty = 1)
lines(anns_parallel$V2
, anns_parallel$V3
, col = "green"
, type = "o"
, lwd = 2
, lty = 2)
lines(anns_diagonal$V2
, anns_diagonal$V3
, col = "red"
, type = "o"
, lwd = 2
, lty = 2)
|
71d8c9f8d30402dbee3c95359cd7b00143d50376
|
18fb84b6b827113aea196209b55b34fc846f718c
|
/man/print.callfest.Rd
|
84fb24f08fdd3fc427191df2d84c622217d49c92
|
[] |
no_license
|
vst/callfest
|
b291ce49598884ff4eecbeb3f43aa28bfca4949b
|
177c348cd2401b252c574ae07c43bf4a72766d39
|
refs/heads/master
| 2021-01-01T05:49:10.127641
| 2014-12-13T10:03:06
| 2014-12-13T10:03:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 376
|
rd
|
print.callfest.Rd
|
% Generated by roxygen2 (4.0.2): do not edit by hand
\name{print.callfest}
\alias{print.callfest}
\title{Pretty prints the \code{callfest} instance}
\usage{
\method{print}{callfest}(x, ...)
}
\arguments{
\item{x}{A \code{callfest} instance to be pretty printed.}
\item{...}{Additional arguments to print method.}
}
\description{
Pretty prints the \code{callfest} instance
}
|
537b03771687fdd357f02cf471e6393c35b695ea
|
10fe70854c82345f8bd8669330306c0e5fdcffb4
|
/function/test_what.R
|
2608019cbc74e8cde1b3cfbf2def124466c67ada
|
[] |
no_license
|
yahcong/travel
|
724f7c40ff91c89cf1580df223af52b241cf55c7
|
1863d1d89476b81b4dc42e9ded65c4479535982b
|
refs/heads/master
| 2021-05-13T15:27:12.628805
| 2018-01-14T13:16:57
| 2018-01-14T13:16:57
| 116,768,809
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 361
|
r
|
test_what.R
|
rm(list=ls())
library(data.table)
setwd("F:/DataMining/R/travel")
load("model/randomForest2.rda")
library(randomForest)
test_action=new_aciton_test_hour
str(test_action)
test_action$predict_type=predict(fit2,test_action)
table(test_action$predict_type)
#-------------------predict----------------------#
load("data/output/new_userProfile_comment_test.rda")
|
46f8960aed6fbe78dc4dcf1b7d25dcc5bb6f2270
|
efde884c9fe091891cc24f765a5d3dfe08eed653
|
/tests/testthat/test-normalisation.R
|
6beb66efa0681dce82e029ff2b35b8d637de3529
|
[] |
no_license
|
koenvandenberge/dynnormaliser
|
7cd07ff17e4f4903491034dfa4f73fc9940d47a7
|
41e08d28a0ab83781ed550f3eb3f0f204b4d0752
|
refs/heads/master
| 2020-03-09T23:50:26.359309
| 2018-04-11T09:20:36
| 2018-04-11T09:20:36
| 129,066,110
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 502
|
r
|
test-normalisation.R
|
context("Normalisation")
test_that("Testing normalise function", {
counts <- matrix(round(2^rnorm(30*50, mean = 6, sd = 2)), ncol=50)
counts[sample(c(T, F), length(counts), prob = c("T"=.1, "F"=.9), replace = TRUE)] <- 0
rownames(counts) <- paste0("Cell", seq_len(nrow(counts)))
colnames(counts) <- paste0("Gene", seq_len(ncol(counts)))
# todo: add mitochondrial and ercc spikeins
normd <- normalise_filter_counts(counts)
expect_error(normd <- normalise_filter_counts(counts), NA)
})
|
3e5a61973c8b1dde5140b8114ec5f4e3694cd209
|
297dd41b2457e3d2b2fd0ea9f4fd6dc91ccc0217
|
/cimis/get_cimis_et.R
|
b102526ab2c94f1dfc2ccc436f0a84ae0bfd2774
|
[] |
no_license
|
richpauloo/junkyard
|
51ee1bdbca9fa343872f03e753dddba08af5cc11
|
9a38f823805cc86d43a2a2b2523573062ef7ce56
|
refs/heads/master
| 2022-08-29T04:31:47.949705
| 2022-08-15T21:34:33
| 2022-08-15T21:34:33
| 164,265,351
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,927
|
r
|
get_cimis_et.R
|
##########################################################################
# This script scrapes the last month's ET data from active stations in
# California's Central Valley from CIMIS:
# * web: https://cimis.water.ca.gov/
# * API: https://et.water.ca.gov/Rest/Index
##########################################################################
# packages
library(xml2)
library(httr)
library(dplyr)
library(stringr)
library(tidyr)
library(sp)
library(lubridate)
##########################################################################
# check for active stations in the central valley
##########################################################################
s <- GET("http://et.water.ca.gov/api/station")
s <- content(s)
cn <- s$Stations[[1]] %>% names() %>% .[-14]
l <- lapply(s$Stations, function(x) as.data.frame(x[-14], col.names = cn)) %>%
do.call(rbind.data.frame, .)
# drop unnecerssary data and clean lat/lon
l <- select(l, StationNbr, IsActive, IsEtoStation, HmsLatitude, HmsLongitude) %>%
separate(col = HmsLatitude, into = letters[1:2], "/ ") %>%
separate(col = HmsLongitude, into = letters[3:4], "/ ") %>%
select(- c("a", "c")) %>%
rename(lat = b, lng = d) %>%
mutate(lat = as.numeric(lat),
lng = as.numeric(lng),
StationNbr = as.numeric(as.character(StationNbr)))
# load central valley shapefile
ll <- "+init=epsg:4269 +proj=longlat +ellps=GRS80 +datum=NAD83 +no_defs +towgs84=0,0,0"
cv <- raster::shapefile("C:/Users/rpauloo/Documents/Github/junkyard/cimis/data/Alluvial_Bnd.shp")
cv <- spTransform(cv, ll)
# make stations into spatial object
spdf <- SpatialPointsDataFrame(coords = as.matrix(l[,c("lng","lat")]),
data = l,
proj4string = CRS(ll))
# sanity check
plot(cv); points(spdf)
# subset of points within cv
spdf_in <- spdf[cv, ]
# sanity check
plot(cv); points(spdf_in, col = "red", pch = 16)
# subset for active stations that record Eto
# sanity check
plot(cv); points(spdf_in[(spdf_in$IsActive == 'True' &
spdf_in$IsEtoStation == 'True'), ],
col = "red", pch = 16)
# active station numbers
active_stations <- spdf_in[(spdf_in$IsActive == 'True' &
spdf_in$IsEtoStation == 'True'), ]@data %>%
pull(StationNbr) %>%
as.numeric()
# all stations including currently inactive
active_stations <- spdf_in[spdf_in$IsEtoStation == 'True', ]@data %>%
pull(StationNbr) %>%
as.numeric()
##########################################################################
# query Eto at active stations for the last month
##########################################################################
# length of ET window (today's date minus this many days)
len <- 30
# query parameters
key <- "43e46605-8d96-491e-aa09-be9a5ca4bcf3"
targets <- paste(active_stations, collapse = ",")
startdate <- "2001-01-01" #as.character(ymd(substr(Sys.time(), 1, 10)) - len)
enddate <- substr(Sys.time(), 1, 10)
dataitems <- "day-asce-eto"
# url
g <- paste0("http://et.water.ca.gov/api/data?appKey=",
key,
"&targets=",
targets,
"&startDate=",
startdate,
"&endDate=",
enddate,
"&dataItems=",
dataitems)
r <- GET(g) # GET HTTP response
r <- content(r) # response content as list
# get data from list
# deal with variable number of unequal column lengths using
# data.table::rbindlist(. , fill = TRUE) and do light cleaning
d <- r$Data$Providers[[1]]$Records %>%
lapply(., unlist) %>%
lapply(., as.data.frame) %>%
lapply(., t) %>%
lapply(., as.data.frame) %>%
data.table::rbindlist(., fill = TRUE) %>%
rename(date = Date,
julian = Julian,
station = Station,
et = DayAsceEto.Value) %>%
mutate(date = ymd(date),
julian = as.numeric(as.character(julian)),
station = as.numeric(as.character(station)),
et = as.numeric(as.character(et))) %>%
select(date, julian, station, et)
# join to station info
d <- left_join(d, l, by = c("station" = "StationNbr"))
##########################################################################
# split into list by date/julian, interpolate, and save raster output
##########################################################################
# make into spatial object
dsp <- SpatialPointsDataFrame(coords = as.matrix(d[,c("lng","lat")]),
data = d,
proj4string = CRS(ll))
dsp <- split(dsp, dsp$date)
# interpolate and turn raster into data frame
tps <- vector("list", length(dsp))
library(fields)
library(raster)
dtt <- names(dsp)
for(i in 1:length(tps)){
mod <- Tps(dsp[[i]]@data[, c("lng", "lat")], dsp[[i]]@data[, "et"])
r <- raster(cv, nrow = 100, ncol = 100) # res: 0.002008855, 0.002061255 (x, y)
r <- interpolate(r, mod)
r <- mask(r, cv)
tps[[i]] <- as.data.frame(r, xy = TRUE) %>%
rename(et = layer) %>%
mutate(date = ymd(dtt[i]))
}
tpsdf <- bind_rows(tps) %>% filter(!is.na(et))
# spplot style binning
brks <- seq(min(tpsdf$et), max(tpsdf$et), (max(tpsdf$et) - min(tpsdf$et))/16) %>% round(2)
labs <- paste(brks[1:length(brks)-1], brks[2:length(brks)], sep = " - ")
tpsdf$etb <- cut(tpsdf$et, breaks = brks, labels = labs)
library(gganimate)
gifa <- ggplot(tpsdf, aes(x, y)) +
geom_raster(aes(fill = et)) +
geom_path(data = rename(fortify(cv), x = long, y = lat),
aes(x,y, group = group)) +
scale_fill_viridis_c(option = "A") +
coord_fixed(1.3) +
theme_void() +
transition_time(date) +
labs(title = "Date: {frame_time}",
fill = "ET (in)", x = "Lng", y = "Lat")
#anim_save("etb.gif", gifa, nframes = length(dtt), fps = 3) # save to root
# mercator projection in m and surface area of each cell
merc <- "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
sa <- prod(res(raster(spTransform(cv, merc), nrow = 100, ncol = 100)))
etdf <- tpsdf %>%
group_by(date) %>%
mutate(etv = et * 0.0254 * sa * 1e-9) %>% # convert inches to m and m3 to km3
summarise(tot_et = sum(etv)) # km3
gifb <- ggplot(etdf, aes(date, tot_et)) +
geom_line() +
geom_point() +
theme_minimal() +
labs(x = "Date", y = expression(paste("Total ET ( ", km ^ 3, " )")),
title = "Total Daily ET in the Central Valley") +
transition_reveal(date)
a_gif <- animate(gifa, width = 240, height = 240)
b_gif <- animate(gifb, width = 360, height = 240)
library(magick)
a_mgif <- image_read(a_gif)
b_mgif <- image_read(b_gif)
new_gif <- image_append(c(a_mgif[1], b_mgif[1]))
for(i in 2:100){
combined <- image_append(c(a_mgif[i], b_mgif[i]))
new_gif <- c(new_gif, combined)
}
new_gif
anim_save("etcomb.gif", new_gif) # save to root
|
01ab62b2f986b9af96721ee0548763e576bacfe0
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/StreamMetabolism/examples/simp.Rd.R
|
a27fdc87b2958605ceba56358e97f39857997dd2
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 324
|
r
|
simp.Rd.R
|
library(StreamMetabolism)
### Name: simp
### Title: Numeric Integration Using Simpson's method
### Aliases: simp
### Keywords: math
### ** Examples
# 4-x^2-y^2
fun <- function(x, y){
a <- 4
b <- x^2
d <- y^2
z <- a-b-d
return(z)
}
a <- fun(seq(-1000,1000,1), seq(-1000,1000,1))
simp(a, x=-1000:1000, n=1000)
|
b9df6252ef6cba18186a808cde95fe4a07d7df8f
|
5ac57449f8a0cfbc0e9c8f716ab0a578d8606806
|
/man/temax.Rd
|
0053701f48ac16c9094b1c71dff69acf00b2b25c
|
[] |
no_license
|
hugaped/MBNMAtime
|
bfb6913e25cacd148ed82de5456eb9c5d4f93eab
|
04de8baa16bf1be4ad7010787a1feb9c7f1b84fd
|
refs/heads/master
| 2023-06-09T01:23:14.240105
| 2023-06-01T12:51:48
| 2023-06-01T12:51:48
| 213,945,629
| 5
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 5,233
|
rd
|
temax.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/time.functions.R
\name{temax}
\alias{temax}
\title{Emax time-course function}
\usage{
temax(
pool.emax = "rel",
method.emax = "common",
pool.et50 = "rel",
method.et50 = "common",
pool.hill = NULL,
method.hill = NULL,
p.expon = FALSE
)
}
\arguments{
\item{pool.emax}{Pooling for Emax parameter. Can take \code{"rel"} or \code{"abs"} (see details).}
\item{method.emax}{Method for synthesis of Emax parameter. Can take \verb{"common}, \code{"random"}, or be assigned a numeric value (see details).}
\item{pool.et50}{Pooling for ET50 parameter. Can take \code{"rel"} or \code{"abs"} (see details).}
\item{method.et50}{Method for synthesis of ET50 parameter. Can take \verb{"common}, \code{"random"}, or be assigned a numeric value (see details).}
\item{pool.hill}{Pooling for Hill parameter. Can take \code{"rel"} or \code{"abs"} (see details).}
\item{method.hill}{Method for synthesis of Hill parameter. Can take \verb{"common}, \code{"random"}, or be assigned a numeric value (see details).}
\item{p.expon}{Should parameters that can only take positive values be modeled on the exponential scale (\code{TRUE})
or should they be assigned a prior that restricts the posterior to positive values (\code{FALSE})}
}
\value{
An object of \code{class("timefun")}
}
\description{
** For version 0.2.3: to ensure positive posterior values, et50 and hill parameters are now
modeled on the natural scale using a half-normal prior rather than a symmetrical prior
on the exponential scale to improve model stability **
}
\details{
\itemize{
\item Emax represents the maximum response.
\item ET50 represents the time at which 50\% of the maximum response is achieved. This can only take
positive values and so is modeled on the exponential scale and assigned a symmetrical normal prior
Alternatively it can be assigned a normal prior truncated at zero (half-normal) (this
will be the default in MBNMAtime version >=0.2.3).
\item Hill is the Hill parameter, which allows for a sigmoidal function. This can only take
positive values and so is modeled on the exponential scale and assigned a symmetrical normal prior
Alternatively it can be assigned a normal prior truncated at zero (half-normal) (this
will be the default in MBNMAtime version >=0.2.3).
}
Without Hill parameter:
\deqn{\frac{E_{max}\times{x}}{ET_{50}+x}}
With Hill parameter:
\deqn{\frac{E_{max}\times{x^{hill}}}{ET_{50}\times{hill}+x^{hill}}}
}
\section{Time-course parameters}{
Time-course parameters in the model must be specified using a \code{pool} and a \code{method} prefix.
\code{pool} is used to define the approach used for pooling of a given time-course parameter and
can take any of:\tabular{ll}{
\strong{Argument} \tab \strong{Model specification} \cr
\code{"rel"} \tab Indicates that \emph{relative} effects should be pooled for this time-course parameter. Relative effects preserve randomisation within included studies, are likely to vary less between studies (only due to effect modification), and allow for testing of consistency between direct and indirect evidence. Pooling follows the general approach for Network Meta-Analysis proposed by \insertCite{lu2004;textual}{MBNMAtime}. \cr
\code{"abs"} \tab Indicates that study arms should be pooled across the whole network for this time-course parameter \emph{independently of assigned treatment} to estimate an \emph{absolute} effect. This implies estimating a single value across the network for this time-course parameter, and may therefore be making strong assumptions of similarity. \cr
}
\code{method} is used to define the model used for meta-analysis for a given time-course parameter
and can take any of the following values:\tabular{ll}{
\strong{Argument} \tab \strong{Model specification} \cr
\code{"common"} \tab Implies that all studies estimate the same true effect (often called a "fixed effect" meta-analysis) \cr
\code{"random"} \tab Implies that all studies estimate a separate true effect, but that each of these true effects vary randomly around a true mean effect. This approach allows for modelling of between-study heterogeneity. \cr
\code{numeric()} \tab Assigned a numeric value, indicating that this time-course parameter should not be estimated from the data but should be assigned the numeric value determined by the user. This can be useful for fixing specific time-course parameters (e.g. Hill parameters in Emax functions, power parameters in fractional polynomials) to a single value. \cr
}
When relative effects are modelled on more than one time-course parameter,
correlation between them is automatically estimated using a vague inverse-Wishart prior.
This prior can be made slightly more informative by specifying the scale matrix \code{omega}
and by changing the degrees of freedom of the inverse-Wishart prior
using the \code{priors} argument in \code{mb.run()}.
}
\examples{
# Model without a Hill parameter
temax(pool.emax="rel", method.emax="random", pool.et50="abs", method.et50="common")
# Model including a Hill parameter and defaults for Emax and ET50 parameters
temax(pool.hill="abs", method.hill="common")
}
\references{
\insertAllCited
}
|
defeb0db1217403fc2a13f825d911f416947f194
|
9dd936c7b849d368e9ef2a450ebd32f09ed924da
|
/man/BrainIQ.Rd
|
97990df82d6b07288239dd7c64a5fbcb0bf0978e
|
[] |
no_license
|
cran/MPsychoR
|
0d12d19c6044ebc9207ae27a41061301f217ae0c
|
717f2223eecdd97ad55ed9d073410512b1e94077
|
refs/heads/master
| 2021-04-03T09:19:37.646636
| 2020-06-18T05:17:05
| 2020-06-18T05:17:05
| 124,736,769
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,591
|
rd
|
BrainIQ.Rd
|
\name{BrainIQ}
\alias{BrainIQ}
\docType{data}
\title{
Brain Size and Intelligence
}
\description{
Willerman et al. (1991) conducted their study at a large southwestern university. They selected a sample of 40 right-handed Anglo introductory psychology students who had indicated no history of alcoholism, unconsciousness, brain damage, epilepsy, or heart disease. These subjects were drawn from a larger pool of introductory psychology students with total Scholastic Aptitude Test Scores higher than 1350 or lower than 940 who had agreed to satisfy a course requirement by allowing the administration of four subtests (Vocabulary, Similarities, Block
Design, and Picture Completion) of the Wechsler (1981) Adult Intelligence Scale-Revised. With prior approval of the University's research review board, students selected for MRI were required to obtain prorated full-scale IQs of greater than 130 or less than 103, and were equally divided by sex and IQ classification.
}
\usage{
data("BrainIQ")
}
\format{
A data frame with 40 individuals and the following 7 variables.
\describe{
\item{\code{Gender}}{Participant's gender.}
\item{\code{FSIQ}}{Full Scale IQ.}
\item{\code{VIQ}}{Verbal IQ.}
\item{\code{PIQ}}{Performance IQ.}
\item{\code{Weight}}{Body weight.}
\item{\code{Height}}{Body height.}
\item{\code{MRI_Count}}{MRI pixel count (brain size).}
}
}
\source{
Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. (1991). In vivo brain size and intelligence. Intelligence, 15, 223-228.
}
\examples{
data(BrainIQ)
str(BrainIQ)
}
\keyword{datasets}
|
eb16e09400d638de2b4fd5ba1692d738f7ef6aab
|
8cbfb091e9444261b9a957bbb745ac04a5ef2c98
|
/PCAWG_timing.R
|
c7cbf2eb6e0b30f1841f6562f2c76abc8c8ad225
|
[] |
no_license
|
smm19900210/PCAWG11-Timing_and_Signatures
|
2ee400c7828e463a7486aa42126be144cc723c89
|
015bc714d8e9d45b61fb829b2e66bb634a2e8b43
|
refs/heads/master
| 2022-02-11T15:27:11.941919
| 2019-08-25T16:46:27
| 2019-08-25T16:46:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,157
|
r
|
PCAWG_timing.R
|
# Mutational timing of gains in single samples
# Libraries and additional code
library(VariantAnnotation)
library(cancerTiming)
source("cancerTiming.WGD.R")
# each VCF file is input as argument to script
args = commandArgs(TRUE)
vcf_filename = args[1]
# clustering results
clustering_output_path = args[2]
# copy number
cn_path = args[3]
# samplename from VCF
id = gsub(".consensus.20160830.somatic.snv_mnv.vcf.gz","",basename(vcf_filename))
print(id)
# function to assign mutations as clonal or subclonal
assignMuts <- function(subclones_file){
clonal.rows = NULL
# define clonal cluster and subclones
if (nrow(subclones[subclones$fraction_cancer_cells > 0.9 & subclones$fraction_cancer_cells < 1.1, ]) == 1){
# if there is only one clone between 0.9 and 1.1
# take this as the clone, and add the superclonal mutations
# subclones are everything below
clonal.row = subclones[subclones$fraction_cancer_cells > 0.9 & subclones$fraction_cancer_cells < 1.1, ]
clonal.rows = subset(subclones, fraction_cancer_cells >= clonal.row$fraction_cancer_cells)
subclonal.rows = subset(subclones, fraction_cancer_cells < clonal.row$fraction_cancer_cells)
no.subclones = nrow(subclonal.rows)
# if there is nothing in the range of 0.9 to 1.1
# remove superclone
# and count only subclonal clusters and mutations
} else if (nrow(subclones[subclones$fraction_cancer_cells > 0.9 & subclones$fraction_cancer_cells < 1.1, ]) == 0){
subclones = subset(subclones, fraction_cancer_cells < 1)
clonal.rows = as.data.frame(matrix(ncol=3, nrow=0))
subclonal.rows = subset(subclones, fraction_cancer_cells < 1)
no.subclones = nrow(subclonal.rows)
# if there are multiple clusters in the range of 0.9 and 1.1
# look between 0.95 and 1.05
} else if (nrow(subclones[subclones$fraction_cancer_cells > 0.9 & subclones$fraction_cancer_cells < 1.1, ]) > 1){
clonal.row.strict = subclones[subclones$fraction_cancer_cells > 0.95 & subclones$fraction_cancer_cells < 1.05,]
# if there is only 1 cluster in the strict range
if (nrow(clonal.row.strict) == 1){
clonal.row = clonal.row.strict
clonal.rows = subset(subclones, fraction_cancer_cells >= clonal.row$fraction_cancer_cells)
subclonal.rows = subset(subclones, fraction_cancer_cells < clonal.row$fraction_cancer_cells)
no.subclones = nrow(subclonal.rows)
# if there are still two clusters in the strict range
# then take the larger ccf
} else if (nrow(clonal.row.strict) > 1){
clonal.row = clonal.row.strict[which.max(clonal.row.strict$fraction_cancer_cells),]
clonal.rows = subset(subclones, fraction_cancer_cells >= clonal.row$fraction_cancer_cells)
subclonal.rows = subset(subclones, fraction_cancer_cells < clonal.row$fraction_cancer_cells)
no.subclones = nrow(subclonal.rows)
# if there is nothing between 0.95 and 1.05
# but two within 0.9 and 1.1
} else if (nrow(clonal.row.strict) == 0){
clonal.row.relaxed = subclones[subclones$fraction_cancer_cells > 0.9 & subclones$fraction_cancer_cells < 1.1, ]
clonal.row = clonal.row.relaxed[which.max(clonal.row.relaxed$n_snvs),]
clonal.rows = subset(subclones, fraction_cancer_cells >= clonal.row$fraction_cancer_cells)
subclonal.rows = subset(subclones, fraction_cancer_cells < clonal.row$fraction_cancer_cells)
no.subclones = nrow(subclonal.rows)
}
}
return(clonal.rows)
}
# these are all files available for the clustering
clustering_output_files = list.files(clustering_output_path, pattern = "cluster_assignments.txt.gz", recursive=FALSE, full.names = TRUE)
subclonal_structure_files = list.files(clustering_output_path, pattern = "subclonal_structure.txt.gz", recursive=FALSE, full.names = TRUE)
# the file containing tumour purity and ploidy
purity_ploidy = read.table("consensus_subclonal_reconstruction_v1.1_20181121_summary_table.txt", header = TRUE)
sample_purity = subset(purity_ploidy, samplename == id)$purity
norm_cont = 1 - sample_purity
# the copy number files
cn_files = list.files(cn_path, recursive=FALSE, full.names = TRUE)
# get mutations and read counts from vcf
sample_vcf = vcf_filename
# first read in vcf file and format
vcf = readVcfAsVRanges(sample_vcf, "hg19", param = ScanVcfParam(fixed=c("ALT","FILTER"),geno=NA))
print(sample_vcf)
vcf_df = as.data.frame(vcf)
vcf_df = subset(vcf_df, !is.na(vcf_df$t_alt_count) & !is.na(vcf_df$t_ref_count))
vcf_df = vcf_df[,c(1,2,3,20,21)]
# for each sample, get the clustering output
sample_clustering_output = clustering_output_files[grep(id, clustering_output_files)]
print(sample_clustering_output)
# get DP output files
if (file.exists(sample_clustering_output)){
cl_output = read.table(gzfile(sample_clustering_output), header=TRUE)
print("1. Sample has clustering output")
# get subclonal structure file
sample_subclones = subclonal_structure_files[grep(id, subclonal_structure_files)]
print(sample_subclones)
if (file.exists(sample_subclones)){
subclones = read.table(gzfile(sample_subclones), header=TRUE)
print("2. Sample has subclonal structure file")
# remove clusters with less than 1%
subclones$n_snvs_prop = subclones$n_snvs/sum(subclones$n_snvs)
subclones = subset(subclones, n_snvs_prop > 0.01)
subclones$cluster = paste0("cluster_", subclones$cluster)
clonal.rows = assignMuts(subclones)
clonal = clonal.rows
# remove anything not SNV and assign muts to subclones
cl_output = subset(cl_output, mut_type == "SNV")
clusters = unique(subclones$cluster)
cluster.cols = subset(cl_output, select=c(clusters))
cl_output[, "max_cluster"] = colnames(cluster.cols)[max.col(cluster.cols,ties.method="first")]
clonal_mutations = subset(cl_output, max_cluster %in% clonal$cluster)
if (is.data.frame(clonal_mutations) == TRUE & nrow(clonal_mutations) > 0){
print("3. Sample has clonal mutations")
# convert vcf and clonal dp_output to granges, subset vcf to get only clonal mutations
vcf_gr = makeGRangesFromDataFrame(vcf_df, keep.extra.columns = T)
clonal_mutations$start = clonal_mutations$position
clonal_mutations$end = clonal_mutations$position
clonal_mutations$position = NULL
clonal_mutations_gr = makeGRangesFromDataFrame(clonal_mutations)
# get overlap between vcf and clonal mutations
hits = findOverlaps(vcf_gr, clonal_mutations_gr, type = "start")
idx = hits@from
vcf_clonal_gr = unique(vcf_gr[idx])
# read in consensus copy number file and get clonal regions
sample_cn = cn_files[grep(id, cn_files)]
print(sample_cn)
if (file.exists(sample_cn)) {
print("4. Sample has copy number")
cn = read.table(gzfile(sample_cn), header=TRUE)
# get clonal gain segments and reduce file
# take only a-d segments
cn_gains = subset(cn, level %in% c("a","b","c","d") & major_cn > 1)[,1:6]
if (is.data.frame(cn_gains) == TRUE & nrow(cn_gains) > 0){
print("5. Sample has clonal gains")
# make GRanges of clonal gain position
cn_gains_gr = makeGRangesFromDataFrame(cn_gains, keep.extra.columns=TRUE)
# get mutations in regions of clonal copy number
vcf_gains_gr = mergeByOverlaps(vcf_clonal_gr, cn_gains_gr, type = "within")
muts_df = as.data.frame(vcf_gains_gr$vcf_clonal_gr)
cn_df = as.data.frame(vcf_gains_gr$cn_gains_gr, row.names=NULL)
muts_clonal_gains = cbind(muts_df, cn_df)
names(muts_clonal_gains)[9] = "segment.start"
names(muts_clonal_gains)[10] = "segment.end"
if (is.data.frame(muts_clonal_gains) == TRUE & nrow(muts_clonal_gains) > 0){
print("6. Sample has clonal mutations in clonal gains")
muts_clonal_gains$type = "none"
muts_clonal_gains$type[muts_clonal_gains$major_cn == 2 & muts_clonal_gains$minor_cn == 1] = "SingleGain"
muts_clonal_gains$type[muts_clonal_gains$major_cn == 2 & muts_clonal_gains$minor_cn == 0] = "CNLOH"
muts_clonal_gains$type[muts_clonal_gains$major_cn == 3 & muts_clonal_gains$minor_cn == 1] = "DoubleGain"
muts_clonal_gains$type[muts_clonal_gains$major_cn == 2 & muts_clonal_gains$minor_cn == 2] = "WGD"
names(muts_clonal_gains)[1] = "chr"
muts_clonal_gains$segId = paste0(muts_clonal_gains$chr,"_",muts_clonal_gains$segment.start, "_", muts_clonal_gains$segment.end, "_", muts_clonal_gains$type)
muts_clonal_gains = subset(muts_clonal_gains, type != "none")
if (is.data.frame(muts_clonal_gains) == TRUE & nrow(muts_clonal_gains) > 0){
print("7. Sample has clonal mutations within clonal timeable gains")
muts_clonal_gains$sample = id
# do formatting for eventTiming
names(muts_clonal_gains)[6] = "nMutAllele"
muts_clonal_gains$nReads = muts_clonal_gains$t_ref_count + muts_clonal_gains$nMutAllele
muts_clonal_gains$mutationId = paste0(muts_clonal_gains$chr, "_", muts_clonal_gains$start)
clonal_events_list = split(muts_clonal_gains, muts_clonal_gains$sample)
arguments = list(bootstrapCI="nonparametric", minMutations=2)
x = eventTimingOverList.WGD(dfList = clonal_events_list, normCont = norm_cont, eventArgs = arguments)
# format results
y = getPi0Summary(x, CI=TRUE)
piSum = na.omit(y)
rownames(piSum) = NULL
print("8. Writing output")
# save as gz files
write.table(piSum, file=gzfile(paste0(id, "_timed_segments.txt.gz")), sep="\t", quote = FALSE, row.names=FALSE)
}
}
}
}
}
}
}
|
7b952bc6613fcf35c106048469c0de4e36a5c52c
|
7f83f684b76b225e21f00ef721f846d371025521
|
/join.R
|
61f56e1c0a0f43156fedb22a595ec83132484d2a
|
[] |
no_license
|
vladmonteiro/eletiva_analise_de_dados
|
2225771ba7c145742f478d1835fc3852605f6c7d
|
650d283a68148ece6fda730f2fda237ea6c67d04
|
refs/heads/master
| 2023-06-10T13:56:19.802780
| 2021-07-05T02:33:37
| 2021-07-05T02:33:37
| 356,731,299
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 413
|
r
|
join.R
|
library(tidyverse)
library(dplyr)
library(foreign)
conflicts <- read.csv2('base_original/MIDB4.3.CSV', sep = ',')
politics <- read.csv2('base_original/institutions.csv', sep = ',')
banco <- inner_join(conflicts, politics,
by = c("styear" = "year", "stabb" ="ifs"))
banco2 <- inner_join(banco, mundo,
by = c("countryname" = "country") )
|
6d5221b595a9e71b9791d0c46e2642665ad47d20
|
83840bba98c2ed74d2256f9bddc436a4c59a8126
|
/tests/testthat/test_common_artifact_part.R
|
03ace8f4bdad5b6f93409aba1fac7346498c5607
|
[] |
no_license
|
botchkoAI/VertaRegistryService
|
4290e1d5fbb67c979ead090f4c042f2d3dedf5f7
|
0bc53050d6ddc4ab9ff540e8116d571cd9eb5699
|
refs/heads/main
| 2023-06-03T08:37:22.340430
| 2021-06-16T23:01:05
| 2021-06-16T23:01:05
| 377,648,143
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 563
|
r
|
test_common_artifact_part.R
|
# Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test CommonArtifactPart")
model.instance <- CommonArtifactPart$new()
test_that("part_number", {
# tests for the property `part_number` (character)
# uncomment below to test the property
#expect_equal(model.instance$`part_number`, "EXPECTED_RESULT")
})
test_that("etag", {
# tests for the property `etag` (character)
# uncomment below to test the property
#expect_equal(model.instance$`etag`, "EXPECTED_RESULT")
})
|
5cfac08d714fb53f0f9dd3f073442e503afdf481
|
5a952d0bc4237afd8c732186f263f6016f99d508
|
/run_analysis.R
|
d00627e6408c968d290006c0e2c91c0a4cac40f4
|
[] |
no_license
|
pjohnston/gettingandcleaningdata
|
cd2f4fb24024b2b1046c2f141aa56cbe870572d1
|
c276bbf732f60d9551af09aa145d0dcd89f10aad
|
refs/heads/master
| 2020-05-07T05:46:37.075361
| 2014-12-19T16:51:04
| 2014-12-19T16:51:04
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,962
|
r
|
run_analysis.R
|
# Getting and Cleaning Data - Coursera Course. Course Project
#
# Paul Johnston
# 12/8/2014
# One of the most exciting areas in all of data science right now is wearable computing
# Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract
# new users. The data linked to from the course website represent data collected from the accelerometers
# from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:
# http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
# Here are the data for the project:
# https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
# This script assumes the above data has been downloaded and
# unzipped into the current working directory and then does the following:
# 1. Merges the training and the test sets to create one data set.
# 2. Extracts only the measurements on the mean and standard deviation for each measurement.
# 3. Uses descriptive activity names to name the activities in the data set
# 4. Appropriately labels the data set with descriptive variable names. Data is stored in the "activityData" variable.
# 5, Writes a tidy data set with the average of each variable for each activity and each subject to "tidy.txt"
# file in current working directory
# Merges the training and the test sets to create one data set.
# Note this script makes extensive use of the "dplyr" and "tidyr" R packages. Make sure those are loaded
library(dplyr)
library(tidyr)
# Load the "activities_labels.txt" file
# Label the column actiivyt_id and activity_name
activities <- read.table("activity_labels.txt", col.names=c("activity_id","activity_name"))
# Load the "features.txt" file
# Label the column actiivity_id and activity_name
features <- read.table("features.txt", col.names=c("feature_id","feature_name"))
#
# Load the test data - subject_test.txt, X_test.txt, y_test.txt
#
# - Subject file 1 column
testSubject <- read.table("test/subject_test.txt", col.names=c("subject_id"))
# Activities file - 1 column
testActivities <- read.table("test/y_test.txt", col.names=c("activity_id"))
# Features file - 561 columns, one per feature
testFeatures <- read.table("test/X_test.txt")
# Create a combined data.frame from the 3 test tables.
testData <- data.frame(testSubject,testActivities,testFeatures)
#
# Load the training data - subject_train.txt, X_train.txt, y_train.txt
#
# - Subject file 1 column
trainSubject <- read.table("train/subject_train.txt", col.names=c("subject_id"))
# Activities file - 1 column
trainActivities <- read.table("train/y_train.txt", col.names=c("activity_id"))
# Features file - 561 columns, one per feature
trainFeatures <- read.table("train/X_train.txt")
# Create a combined data.frame from the 3 test tables.
trainData <- data.frame(trainSubject,trainActivities,trainFeatures)
# Now combine the test and training data into a single data frame
mergedData <- rbind(testData,trainData)
# Set the column headers to the names of id plus "-" plus name of the measure (measure names are not unique and
# the dplyr "select" function requires them to be unique)
colnames(mergedData) <- c("subject_id","activity_id", as.character(paste(features$feature_id,"-",features$feature_name)))
meansAndStds <- mergedData %>%
# Select columns using select that end in _id, or contain -std() or -mean()
select(matches("_id$|-mean()|-std()"))
# We have unique column names now (guaranteed by select) so get rid of the "id - " from the front of them
colnames(meansAndStds) <- gsub("^(.* - )(.*$)","\\2",colnames(meansAndStds))
# and replace () and - with underscores and lowercase the column names
colnames(meansAndStds) <- tolower(gsub("\\(\\)$","",gsub("-","_",gsub("\\(\\)-+","_",colnames(meansAndStds)))))
# join in the names of the activities from the activity table using dplyr inner_join function
activityTmp <- tbl_df(inner_join(activities,meansAndStds,by="activity_id"))
# and get rid of the activity_id column.
activityData <-
activityTmp %>%
select(-contains("activity_id")
)
# So "activityData" variable now contains the (untidy) data we want.
# Now tidy it using tidyr into a variable called "tidyActivityData"
tidyActivityData <-
activityData %>%
# 1. Gather all the measures, to convert to a 4 column layout with activty_name, subject_id, measure and value
gather(measure, value, tbodyacc_mean_x:fbodybodygyrojerkmag_meanfreq) %>%
# 2. Group the data by activity and subject and measure
group_by(activity_name,subject_id,measure) %>%
# 3. Calculate the average measure by activity and subject
summarize(mean_value = mean(value)) %>%
# 4. Sort output data by activity, subject and measure
arrange(activity_name, subject_id, measure)
# write it out to a txt file in the current working directory named "tidy.txt" without rownames
write.table(tidyActivityData,file="tidy.txt",row.names=FALSE)
|
5fa60a8d85d6a4f56250c88bacd14a554e764b0a
|
161940a51deb66a640489adcd953caa85317e107
|
/R/import.R
|
0ecf9f5cc6111d35386f2ec3ca392983ff8f64c0
|
[] |
no_license
|
baifengbai/modules
|
f8d00c9ac573efb01b78343189c437981c2c3849
|
9873a6ada76f7d1f24d7dc6293459498f3e59f3c
|
refs/heads/master
| 2020-04-02T12:03:38.991021
| 2018-06-24T20:50:46
| 2018-06-24T20:50:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,124
|
r
|
import.R
|
#' @rdname module
#' @export
import <- function(from, ..., attach = TRUE, where = parent.frame()) {
where <- importCheckAttach(where, attach)
pkg <- importGetPkgName(match.call())
objectsToImport <- importGetSelection(match.call(), pkg)
addDependency(pkg, objectsToImport, where, makeDelayedAssignment, pkg)
invisible(parent.env(where))
}
importCheckAttach <- function(where, attach) {
if (!attach) new.env(parent = baseenv()) else where
}
importGetPkgName <- function(mc) {
pkg <- Map(deparse, mc)$from
pkg <- deleteQuotes(pkg)
importCheckInstall(pkg)
}
importCheckInstall <- function(pkg) {
ind <- !is.element(pkg, installed.packages()[, "Package"])
if (ind) stop(
"'package:", pkg, "' is not installed!"
) else pkg
}
importGetSelection <- function(mc, pkg) {
objectsToImport <- importDeparseEllipses(mc)
if (length(objectsToImport) == 0) getNamespaceExports(pkg)
else objectsToImport
}
importDeparseEllipses <- function(mc) {
args <- Map(deparse, mc)
args[[1]] <- NULL
args$from <- NULL
args$where <- NULL
args$attach <- NULL
args <- unlist(args)
deleteQuotes(args)
}
|
8caae52bfd7dc0f9566c438460b74d4b554c954c
|
414754f98514fec8c8c8230e064f742f2a4ae6b3
|
/machine_learning/matrix_factorization.R
|
71f694e59b892a49dbb31e70cc25cf05f3f87784
|
[] |
no_license
|
brambloemen/HarvardX_GenomicsClass
|
38da74d6eb56c5704ee4af7bfc052c614af7baf9
|
2a0a2357b21866832d7ccef5b7a2dd37b8561562
|
refs/heads/main
| 2023-05-08T15:00:38.847068
| 2021-05-29T12:01:58
| 2021-05-29T12:01:58
| 371,959,067
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,119
|
r
|
matrix_factorization.R
|
.libPaths('C:/Users/BrBl1834/R/win-library')
library(tidyverse)
# set.seed(1987)
#if using R 3.6 or later, use `set.seed(1987, sample.kind="Rounding")` instead
set.seed(1987, sample.kind="Rounding")
n <- 100
k <- 8
Sigma <- 64 * matrix(c(1, .75, .5, .75, 1, .5, .5, .5, 1), 3, 3)
m <- MASS::mvrnorm(n, rep(0, 3), Sigma)
m <- m[order(rowMeans(m), decreasing = TRUE),]
y <- m %x% matrix(rep(1, k), nrow = 1) + matrix(rnorm(matrix(n*k*3)), n, k*3)
colnames(y) <- c(paste(rep("Math",k), 1:k, sep="_"),
paste(rep("Science",k), 1:k, sep="_"),
paste(rep("Arts",k), 1:k, sep="_"))
# Q1
my_image <- function(x, zlim = range(x), ...){
colors = rev(RColorBrewer::brewer.pal(9, "RdBu"))
cols <- 1:ncol(x)
rows <- 1:nrow(x)
image(cols, rows, t(x[rev(rows),,drop=FALSE]), xaxt = "n", yaxt = "n",
xlab="", ylab="", col = colors, zlim = zlim, ...)
abline(h=rows + 0.5, v = cols + 0.5)
axis(side = 1, cols, colnames(x), las = 2)
}
my_image(y)
# Q2
my_image(cor(y), zlim = c(-1,1))
range(cor(y))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)
# Q3
s <- svd(y)
names(s)
y_svd <- s$u %*% diag(s$d) %*% t(s$v)
max(abs(y - y_svd))
ss_y <- colSums(y^2)
ss_yv <- colSums((y%*%s$v)^2)
sum(ss_y)
sum(ss_yv)
# Q4
plot(1:ncol(y),ss_y)
plot(1:ncol(y),ss_yv)
# Q5
plot(s$d,sqrt(ss_yv))
# Q6
# first 3 columns of YV
ss_yv_first3 <- colSums((y%*%s$v[,1:3])^2)
# total variation explained by first 3 of YV
sum(ss_yv_first3)/sum(ss_y)
# Q7
identical(s$u %*% diag(s$d), sweep(s$u, 2, s$d, FUN = "*"))
# Q8
s$u %*% diag(s$d)
# first component of UD
U1d1.1 <- s$u[,1]*s$d[1]
# average per student
student_avgs <- rowMeans(y)
plot(U1d1.1,student_avgs)
# Q9
cols <- 1:ncol(s$v)
rows <- 1:nrow(s$v)
my_image(s$v)
# Q10
plot(s$u[,1])
range(s$u[,1])
plot(s$v[,1])
plot(t(s$v[,1]))
U1d1.1.tsv1 <- U1d1.1 %*% t(s$v[,1])
my_image(U1d1.1.tsv1)
my_image(y)
# Q11
resid <- y - with(s,(u[, 1, drop=FALSE]*d[1]) %*% t(v[, 1, drop=FALSE]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)
plot(s$u[,2])
range(s$u[,2])
plot(s$v[,2])
plot(t(s$v[,2]))
U2d2.2 <- s$u[,2]*s$d[2]
U2d2.2.tsv2 <- U2d2.2 %*% t(s$v[,2])
my_image(U2d2.2.tsv2)
my_image(y)
# Q12
# variance explained by first two columns
sum(s$d[1:2]^2)/sum(s$d^2) * 100
resid <- y - with(s,sweep(u[, 1:2], 2, d[1:2], FUN="*") %*% t(v[, 1:2]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)
plot(s$u[,3])
range(s$u[,3])
plot(s$v[,3])
plot(t(s$v[,3]))
U3d3.3 <- s$u[,3]*s$d[3]
U3d3.3.tsv3 <- U3d3.3 %*% t(s$v[,3])
my_image(U3d3.3.tsv3)
my_image(y)
# Q13
sum(s$d[1:3]^2)/sum(s$d^2) * 100
resid <- y - with(s,sweep(u[, 1:3], 2, d[1:3], FUN="*") %*% t(v[, 1:3]))
my_image(cor(resid), zlim = c(-1,1))
axis(side = 2, 1:ncol(y), rev(colnames(y)), las = 2)
my_image(U1d1.1.tsv1 + U2d2.2.tsv2 +U3d3.3.tsv3)
my_image(y)
my_image(y-(U1d1.1.tsv1 + U2d2.2.tsv2 +U3d3.3.tsv3))
y_hat <- with(s,sweep(u[, 1:3], 2, d[1:3], FUN="*") %*% t(v[, 1:3]))
my_image(y, zlim = range(y))
my_image(y_hat, zlim = range(y))
my_image(y - y_hat, zlim = range(y))
|
acb19f34fa4d702a781b02ed18ed50be8a7b58f0
|
9c0aa0ced21d27e9dd0618fef4785943d59c420d
|
/ui.R
|
457d1085d47fd7381d8c5c1206c06d83592ae9dc
|
[] |
no_license
|
travisfell/10---Capstone
|
d7107c1cf341ca0dc2ee30f06241226d208d6031
|
cf74dd63b9c308e66d27085f0ccda9e90fdcad68
|
refs/heads/master
| 2021-01-21T13:07:26.466000
| 2016-04-21T04:13:25
| 2016-04-21T04:13:25
| 53,469,004
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 833
|
r
|
ui.R
|
#ui.R
# This is the UI definition script for the
#Capstone final project
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Text Prediction Cloud"),
sidebarPanel(
h3('Enter your text.'),
p("This app predicts the next word based on the words you enter."),
p("Type your text in the text box below. Then click Submit."),
p("Please allow 5-10 seconds for the app to return results."),
textInput('enteredtext', 'Enter your text here: '),
#submitButton('Submit')
actionButton('Submit', "Submit")
),
mainPanel(
h3('The top 3 words associated with your text are: '),
verbatimTextOutput("prediction"),
p("The word cloud below shows upto the top 25 words predicted from the entered text."),
plotOutput("textcloudplot")
)
))
|
aa7880fa2d6a93677a5efe2ab1b6b72cdd0a221a
|
e3495708f48030176093019ba38a08b0afc6fe5a
|
/waterfall.R
|
bc4621e4bcf45ae6ab6f943d429972f86fddd4ca
|
[] |
no_license
|
plus4u/Data-Analytics
|
32ae849aeb0c40484e733d02a4edc6e5b64b1c5c
|
811ea7ac651019d6cb03f71d47d008f7074beb25
|
refs/heads/master
| 2022-07-07T09:47:05.876879
| 2022-07-03T23:01:36
| 2022-07-03T23:01:36
| 146,960,490
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,834
|
r
|
waterfall.R
|
### waterfall
### 2 methods : simple ,
###################################
### @ 1
install.packages("waterfalls")
library(waterfalls)
group <- LETTERS[1:6]
value <- c(2000, 4000, 2000,
-1500, -1000, -2500)
df <- data.frame(x = group, y = value)
waterfall(values = value, labels = group)
### @ 2
install.packages("tidyverse")
library(tidyverse)
df <- tribble(
~Category, ~Value,
# --------- header record ----------
"Prev Budget", 5,
"Salaries", 0.1,
"CapEx", 0.175,
"Travel", -0.2,
"Contracting", -0.1,
"Operations", -0.2,
"RealEstate", -0.1,
"Gap to Target", -0.175,
"Current Budget", -4.5
)
df
##
levels <- df$Category
data1 <- df %>%
mutate(Category = factor(Category, levels = levels),
ymin = round(cumsum(Value), 3),
ymax = lag(cumsum(Value), default = 0),
xmin = c(head(Category, -1), NA),
xmax = c(tail(Category, -1), NA),
Impact = ifelse(Category %in% c(as.character(df$Category[1]), as.character(df$Category[nrow(df)])),"Budget",
ifelse(Value > 0, "Increase", "Decrease")
))
data1
##
g <- ggplot(data1) +
theme_bw()+
theme(legend.position = "right", panel.grid = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5)) +
labs(y = "USD Millions", x = "Expense Category", title = "Explanation of Budget Gap Closure")
w <- 0.4 #use to set width of bars
g <- g +
geom_rect(aes(xmin = as.integer(Category) - w/2,
xmax = as.integer(Category) + w/2, ymin = ymin, ymax = ymax,
fill = Impact), colour = "black") +
scale_x_discrete(limits = levels) +
scale_fill_manual(values = (c("Decrease" = "blue", "Increase" = "orange", "Budget" = "black")))
g
|
fcecc45d7a099f97e517c1aea7be4e7e059828e6
|
6984fd7eca0e35087b50c245a2c4f55b3b1c0964
|
/WIP/Evaluating Variational Inference/Figure_2_linear_reg.R
|
939bf09637210d7b610a9dae11d56003a592cc15
|
[
"MIT"
] |
permissive
|
junpenglao/Planet_Sakaar_Data_Science
|
20620b5b0de0705fd7f7dcf0a477745325fa43c1
|
4366de036cc608c942fdebb930e96f2cc8b83d71
|
refs/heads/main
| 2023-04-13T15:06:08.128262
| 2023-04-05T08:56:32
| 2023-04-05T08:56:32
| 127,780,588
| 55
| 14
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,526
|
r
|
Figure_2_linear_reg.R
|
###### Figure_2, a bayesian linear regression, ##############################
#############the result is sensitive to the tolorance, which scales with the problem complexity. K hat gives a convergence diagnoistic
library(rstan)
library(loo) ### loo is used for PSIS
setwd("")
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
stan_code='
data {
int <lower=0> N;
int <lower=0> D;
matrix [N,D] x ;
vector [N] y;
}
parameters {
vector [D] b;
real <lower=0> sigma;
}
model {
y ~ normal(x * b, sigma);
b~normal(0,1);
sigma~gamma(0.5,0.5);
}
generated quantities{
real log_density;
log_density=normal_lpdf(y |x * b, sigma)+normal_lpdf(b|0,1)+gamma_lpdf(sigma|0.5,0.5)+log(sigma);
}
'
# linear regression model
##log(sigma) in the last line: the jacobian term in the joint density using log(sigma) as the transformed parameters.
m=stan_model(model_code = stan_code) # the function for PSIS re-weighting.
ip_weighted_average=function(lw, x){
ip_weights=exp(lw-min(lw))
return( t(ip_weights)%*%x /sum(ip_weights) )
}
set.seed(1000)
N=10000 # a linear regression with 10^5 data and 100 variables
D=100
beta=rnorm(D,0,1)
x=matrix(rnorm(N*D,0,1), N, D)
y=as.vector(x%*%beta+rnorm(N,0,2))
time_temp=proc.time()
fit_stan=stan(model_code=stan_code, data=list(x=x,y=y, D=D,N=N), iter=3000)
time_temp2=proc.time()
time_diff=c(time_temp2-time_temp)
running_time_stan=sum(get_elapsed_time(fit_stan))
stan_sample=extract(fit_stan)
trans_para=cbind(stan_sample$b, log(stan_sample$sigma))
stan_mean= apply(trans_para, 2, mean)
stan_square= apply(trans_para^2, 2, mean)
tol_vec=c(0.03,0.01,0.003,0.001,0.0003,0.0001,0.00003,0.00001) # choose relative tolerance. The default in the published ADVI paper is 0.01.
sim_N=50 # repeat simulations. 50 is enough as we can check the sd. The only uncertainty is stochastic optimization in ADVI
I=length(tol_vec)
K_hat=running_time=matrix(NA,sim_N,I )
bias_mean=bias_square=array(NA,c(3,sim_N,I,length(stan_mean)) )
set.seed(1000)
for(i in c(I) )
for(sim_n in 1:sim_N)
{
tol=tol_vec[i]
time_temp=proc.time()
fit_vb=vb(m, data=list(x=x,y=y, D=D,N=N), iter=1e5,output_samples=5e4,tol_rel_obj=tol,eta=0.05,adapt_engaged=F) ## it is also sensitive to eta.
time_temp2=proc.time()
time_diff=c(time_temp2-time_temp)
running_time[sim_n,i]= time_diff[3]
vb_samples=extract(fit_vb)
trans_parameter=cbind(vb_samples$b,log(vb_samples$sigma))
vi_parameter_mean=apply(trans_parameter, 2, mean)
vi_parameter_sd=apply(trans_parameter, 2, sd)
normal_likelihood=function(trans_parameter){
one_data_normal_likelihood=function(vec){
return( sum( dnorm(vec,mean=vi_parameter_mean,sd=vi_parameter_sd, log=T)))
}
return( apply(trans_parameter, 1, one_data_normal_likelihood))
}
lp_vi=normal_likelihood(trans_parameter)
lp_target=vb_samples$log_density
ip_ratio=lp_target-lp_vi
ok=complete.cases(ip_ratio)
joint_diagnoistics=psislw(lw=ip_ratio[ok])
bias_mean[1,sim_n,i,]=vi_parameter_mean-stan_mean
bias_square[1,sim_n,i,]=apply(trans_parameter^2, 2, mean)-stan_square
K_hat[sim_n,i]=joint_diagnoistics$pareto_k
trans_parameter=trans_parameter[ok,]
psis_lw=joint_diagnoistics$lw_smooth
bias_mean[2,sim_n,i,]=ip_weighted_average(lw=ip_ratio, x=trans_parameter)-stan_mean
bias_square[2,sim_n,i,]=ip_weighted_average(lw=ip_ratio, x=trans_parameter^2)-stan_square
bias_mean[3,sim_n,i,]=ip_weighted_average(lw=psis_lw, x=trans_parameter)-stan_mean
bias_square[3,sim_n,i,]=ip_weighted_average(lw=psis_lw, x=trans_parameter^2)-stan_square
print(paste("======================= i=",i," ========================"))
print(paste("=======================iter",sim_n,"========================"))
}
running_time=matrix(NA,5,I) ## calculate the running time again. most of the elapsed time calculated above is on sampling. now we skip the sampling procudure.
set.seed(1000)
for(i in 1:I )
for(sim_n in 1:5)
{ tol=tol_vec[i]
time_temp=proc.time()
fit_vb=vb(m, data=list(x=x,y=y, D=D,N=N), iter=1e5,output_samples=2,tol_rel_obj=tol,eta=0.09,adapt_engaged=F)
time_temp2=proc.time()
time_diff=c(time_temp2-time_temp)
running_time[sim_n,i]= time_diff[3]
}
time_vec=apply(running_time, 2, mean, na.rm=T)
#save(K_hat,running_time_vec,bias_mat, bias_mean_new,bias_square_new,running_time,running_time_stan,file="linear_1e52.RData")
#save(K_hat, bias_mean,bias_square,running_time,running_time_stan,file="linear_1e52_copy.RData")
#load("linear_1e52.RData")
k_vec=apply(K_hat, 2, mean, na.rm=T) ## average among all repeated simulations
time_vec=apply(running_time, 2, mean, na.rm=T) ## average among all repeated simulations
bias_mat=matrix(NA,I,3) ## average among all repeated simulations
for(i in 1:I)
for(j in 1:3)
bias_mat[i,j]= sqrt((D+1)*mean(bias_mean_new[j,,i,]^2,na.rm=T)) # L_2 norm of first order error (RMSE)
bias_sq_mat=matrix(NA,I,3)
for(i in 1:I)
for(j in 1:3)
bias_sq_mat[i,j]= sqrt((D+1)*mean(bias_square_new[j,,i,]^2,na.rm=T)) # L_2 norm of second order error (RMSE of x^2)
## Figure 2 PSIS in linear regression ########
library(plotrix)
pdf("~/Desktop/linear_large_n.pdf",width=4,height=1.5) ## Figure 2 linear regression
par(mfcol=c(1,3),oma=c(0.5,0.8,0.1,0.2), pty='m',mar=c(1,0.6,0.5,0.7) ,mgp=c(1.5,0.25,0), lwd=0.5,tck=-0.01, cex.axis=0.6, cex.lab=0.9, cex.main=0.9)
plot(log10(tol_vec),k_vec,xlim=c(-4.3,-1.5),ylim=c(0,2.8), type='l' , xlab="",ylab="" ,axes=F,lwd=1,yaxs='i',xpd=T)
abline(h=c(0.7),lty=2,lwd=0.6, col='grey')
points(log10(tol_vec),k_vec,pch=19,cex=0.3)
axis(1,padj=-0.5, at=c(-5,-4,-3,-2),labels = c(NA,expression(10^-4),expression(10^-3), expression(10^-2) ) ,lwd=0.5, cex.axis=0.7)
at.x <- outer(c(2,4,6,8) , 10^(-3:-5))
lab.x <- ifelse(log10(at.x) %% 1 == 0, at.x, NA)
axis(1,padj=-1, at=log10(at.x), labels=NA,lwd=0.2,lwd.ticks=0.4,tck=-0.007)
axis(2, at=c(0,0.5,0.7,1, 2) ,labels=c(0,".5",".7",1,2) ,lwd=0.5,las=2)
box(bty='l',lwd=0.5)
mtext(2, text="k hat", cex=0.7, line = 0.5)
mtext(1, text="relative tolerance", cex=0.7, line = 0.5,las=1)
plot( time_vec,k_vec,xlim=c(0,72),ylim=c(0,2.8), type='l' , xlab="",ylab="" ,axes=F,lwd=1,yaxs='i',xpd=T,xaxs='i')
abline(h=c(0.7),lty=2,lwd=0.6, col='grey')
points(time_vec,k_vec,pch=19,cex=0.3)
axis(1,padj=-1, at=c(0,20,40,60),lwd=0.5)
axis(2, at=c(0,0.5,0.7,1, 2) ,labels=c(0,".5",".7",1,2) ,lwd=0.5,las=2)
box(bty='l',lwd=0.5)
mtext(2, text="k hat", cex=0.7, line = 0.5)
mtext(1, text="running time (s)", cex=0.7, line = 0.5,las=1)
axis.break(1,65,style="zigzag")
axis(1,padj=-0.5, at=c(70),lwd=0.5,col=2,labels = NA, tick=0.5, cex.axis=0.4)
round(running_time_stan,-2)
text(67,0.44,labels = "NUTS\n sampling\n time=2300",xpd=T, cex=0.65,col=2)
lines(x=c(70,70),y=c(0,0.15),col=2,lwd=0.5,lty=2)
plot(bias_mat,xlim=c(0.4,1.57),ylim=c(0,0.05),type='n', xlab="",ylab="" ,axes=F,lwd=1,yaxs='i')
for(i in 1:3)
lines(k_vec,bias_mat[,i],col=c("blue","red","forest green")[i],lwd=1,xpd=T)
axis(1,padj=-1, at=c(0.5,1,1.5),labels = c(.5,1,1.5),lwd=0.5)
axis(2, at=c(0,0.02,0.04) ,labels=c(0,".02",".04") ,lwd=0.5,las=2)
mtext(2, text="RMSE", cex=0.6, line = 1)
mtext(1, text="k hat", cex=0.7, line = 0.5,las=1)
text(1.46,0.04,labels = "Raw ADVI",col="blue",xpd=T,cex=0.8)
text(1.48,0.047,labels = "IS",col="forest green",xpd=T,cex=0.8)
text(1.4,0.015,labels = "PSIS",col="red",xpd=T,cex=0.8)
box(bty='l',lwd=0.5)
dev.off()
|
b3c4cbdd4e4d8e035e9fec1277cfbb92f2adbe1a
|
644cf30f84ed01751811c124c9c3594f62a25de0
|
/Part 2/Prediction/Random Forest/RF_FULL.R
|
34c5708d9247077cb5b7261e81f547ee7b03d302
|
[] |
no_license
|
ahmeduncc/Team_1_Energy_Consumption_modeling
|
f35c369cb1e9d4668c2cc71190b75f9c9e6ddb6f
|
a9593549c582042719fabb1b545a2bc295ad883d
|
refs/heads/master
| 2021-06-08T23:19:12.311840
| 2016-11-19T05:24:59
| 2016-11-19T05:24:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,929
|
r
|
RF_FULL.R
|
install.packages("hydroGOF")
install.packages("randomForest")
library("hydroGOF")
library("randomForest")
install.packages("rjson")
library("rjson")
config_json<- fromJSON(file="config_RF.json")
n_tree_value <- as.integer(config_json$ntree)
ab <- read.csv("final_sample_format_Part1.csv")
#View(ab)
final_file <- data.frame()
outlier_tag_file <- data.frame()
#View(ab)
ab$Base_hour_Flag <- as.character(ab$Base_hour_Flag)
ab$Base_hour_Flag[ab$Base_hour_Flag=='True'] <- 0
ab$Base_hour_Flag[ab$Base_hour_Flag=='False'] <- 1
ab$Base_hour_Flag <- as.numeric(ab$Base_hour_Flag)
ab$X..address.y<-NULL
ab$area_floor._m.sqr.y<-NULL
ab$BuildingID <-NULL
ab$building<-NULL
ab$meternumb<-NULL
ab$airport_code<-NULL
#ab$type<- NULL
ab$date<- NULL
ab$year<-NULL
ab$Base_hour_usage <- NULL
ab$Consumption <- NULL
ab$Base_Hour_Class <- NULL
ab$VisibilityMPH <- NULL ## more than 50% data is negative
ab$Gust_SpeedMPH<-NULL #601105 values = '-'
ab$PrecipitationIn<-NULL #614116 values N/A
ab$Wind_Direction <- NULL # wind dir deg is numreical for wind_direction
ab$Events <- NULL # 350626 values empty
ab$Weekday <- as.numeric(ab$Weekday)
ab$Holiday <- as.numeric(ab$Holiday)
ab$Base_hour_Flag <- as.numeric(ab$Base_hour_Flag)
#ab$month <- factor(ab$month)
smp_size <- floor(0.60 * nrow(ab))
#Set the seed to make your partition reproductible
set.seed(123)
train_ind <- sample(seq_len(nrow(ab)), size = smp_size)
#Split the data into training and testing
train <- ab[train_ind, ]
test <- ab[-train_ind, ]
#View(ab)
y=train$Norm_Consumption
train$month <- factor(train$month)
train$Weekday <- as.numeric(train$Weekday)
train$Holiday <- as.numeric(train$Holiday)
train$Base_hour_Flag <- as.numeric(train$Base_hour_Flag)
rf_fit <- randomForest(y~ train$Base_hour_Flag +train$Weekday +train$Holiday+ train$month
+train$TemperatureF +train$Dew_PointF ,data=train,ntree = n_tree_value)
#summary(rf_fit)
selected_model <- formula(rf_fit$terms)
test_pred_rf <- predict(rf_fit,data=train)
library("forecast")
accuracy(test_pred_rf,train$Norm_Consumption)
# summary(rf_fit)
#Prediction parameters
selected_model <- formula(rf_fit$terms)
pred_rf <- predict(rf_fit,train)
acc <- accuracy(pred_rf,train$Norm_Consumption)
acc_df <- as.data.frame(acc)
acc_df$model <- as.character(selected_model[3])
train$predicted_value <- pred_rf
residual_values <- train$Norm_Consumption - pred_rf
train$residual_values <- residual_values
std_dev_residuals<- sd(residual_values)
#train$outlier <- "False"
train$outlier <- ifelse(train$residual_values>=2*std_dev_residuals,"True","False")
train$residual_values <- NULL
final_file<- rbind(final_file,acc_df)
outlier_tag_file <- rbind(outlier_tag_file,train)
final_file$ME<-NULL
final_file$MPE<-NULL
final_file$algorithm <- "Random Forest"
write.csv(final_file,file="RF_Pred_model_FULL.csv",row.names=FALSE)
write.csv(outlier_tag_file,file = "Outlier_RF_Pred_FULL.csv")
|
c14778450a5d4b477f0bc797481fb3eb8ad2bf18
|
8ced7c6508be6fbc0b3c3ba6a84440a6353f33b9
|
/bsem_analyses/process_models.R
|
b0cb55dd74676b74853b751ef016ee2d9d4f214a
|
[] |
no_license
|
UNCDEPENdLab/attachment_physio_coreg
|
ae7bff7c288874204a6900addff101cd005d5d37
|
5a73c065e5b82f168a93dceebdad46adf02c7643
|
refs/heads/master
| 2022-12-16T08:44:25.337138
| 2020-09-25T17:46:43
| 2020-09-25T17:46:43
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 46,652
|
r
|
process_models.R
|
# Set up paths ------------------------------------------------------------
output_dir <- "../output"
bsem_process_res_dir <- "../output/bsem_process"
bsem_personalitymod_res_dir <- "../output/bsem_personalitymod"
if(!dir.exists(output_dir)) {dir.create(output_dir)}
if(!dir.exists(bsem_process_res_dir)) {dir.create(bsem_process_res_dir)}
if(!dir.exists(bsem_personalitymod_res_dir)) {dir.create(bsem_personalitymod_res_dir)}
# Source functions --------------------------------------------------------
source(paste0(project_dir, "/code/support_fx.R"))
# Load Packages -----------------------------------------------------------
if (!require(pacman)) { install.packages("pacman"); library(pacman) }
p_load(tidyverse, R.matlab,lavaan,lattice, MplusAutomation)
# Load Data ---------------------------------------------------------------
posnegint_personality <- read.csv("../data/posnegint_personality.csv")
# Process Model for Negint Avo --------------------------------------------
avo_allfree <- "
pavo1 ~ scpt + ccpt + scpr + ccpr
pavo0 ~ scpt + ccpt + scpr + ccpr
scpt ~ pravo1 + pravo0
ccpt ~ pravo1 + pravo0
scpr ~ pravo1 + pravo0
ccpr ~ pravo1 + pravo0
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1~pravo1
pavo0 ~ pravo0
"
avo_allfree_m <- sem(model = avo_allfree, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
avo_afixed <- "
pavo1 ~ a*scpt
pavo1 ~ b*ccpt
pavo1 ~ scpr
pavo1 ~ ccpr
pavo0 ~ scpt
pavo0 ~ ccpt
pavo0 ~ a*scpr
pavo0 ~ b*ccpr
scpt ~ c*pravo1
scpt ~ pravo0
ccpt ~ d*pravo1
ccpt ~ pravo0
scpr ~ pravo1
scpr ~c*pravo0
ccpr ~ pravo1
ccpr ~ d*pravo0
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1~pravo1
pavo0 ~ pravo0
"
avo_afixed_m <- sem(model = avo_afixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
avo_pfixed <- "
pavo1 ~ scpt
pavo1 ~ ccpt
pavo1 ~ a*scpr
pavo1 ~ b*ccpr
pavo0 ~ a*scpt
pavo0 ~ b*ccpt
pavo0 ~ scpr
pavo0 ~ ccpr
scpt ~ pravo1
scpt ~ c*pravo0
ccpt ~ pravo1
ccpt ~ d*pravo0
scpr ~ c*pravo1
scpr ~pravo0
ccpr ~ d*pravo1
ccpr ~ pravo0
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1~pravo1
pavo0 ~ pravo0
"
avo_pfixed_m <- sem(model = avo_pfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
avo_apfixed <- "
pavo1 ~ e*scpt
pavo1 ~ f*ccpt
pavo1 ~ a*scpr
pavo1 ~ b*ccpr
pavo0 ~ a*scpt
pavo0 ~ b*ccpt
pavo0 ~ e*scpr
pavo0 ~ f*ccpr
scpt ~ g*pravo1
scpt ~ c*pravo0
ccpt ~ h*pravo1
ccpt ~ d*pravo0
scpr ~ c*pravo1
scpr ~g*pravo0
ccpr ~ d*pravo1
ccpr ~ h*pravo0
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1~pravo1
pavo0 ~ pravo0
"
avo_apfixed_m <- sem(model = avo_apfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
avo_aonly <- "
pavo1 ~ scpt
pavo1 ~ a*ccpt
pavo0 ~ scpr
pavo0 ~ ccpr
scpt ~ pravo1
ccpt ~ c*pravo1
scpr ~pravo0
ccpr ~ pravo0
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
ac:=a*c
pavo1~pravo1
pavo0 ~ pravo0
"
avo_aonly_m <- sem(model = avo_aonly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
avo_ponly <- "
pavo1 ~ scpr
pavo1 ~ ccpr
pavo0 ~ scpt
pavo0 ~ ccpt
scpt ~ pravo0
ccpt ~ pravo0
scpr ~ pravo1
ccpr ~ pravo1
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1~pravo1
pavo0 ~ pravo0
"
avo_ponly_m <- sem(model = avo_ponly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
# adds in paths that were significant from competitive model and fixes paths that are approximately zero (i.e., P > .4)
avo_mod <- "
#paths that are not particularly strong
pavo1 ~ 0*scpt
pavo0 ~ scpr
pavo0 ~ 0*ccpr
scpt ~ 0*pravo1
scpt ~ 0*pravo0
scpr ~ 0*pravo0
ccpr ~ 0*pravo0
#strong paths
pavo1 ~ a*ccpt
ccpt ~ c*pravo1
scpr ~ pravo1
ccpr ~ c*pravo1
pravo1 ~~ pravo0
pavo1~~pavo0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
ac:=a*c
"
avo_mod_m <- sem(model = avo_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
#Re do so only cc
avo_mod <- "
#paths that are not particularly strong
pavo0 ~ 0*ccpr
ccpr ~ 0*pravo0
#strong paths
pavo1 ~ a*ccpt
ccpt ~ c*pravo1
ccpr ~ c*pravo1
pravo1 ~~ pravo0
pavo1~~pavo0
ccpt ~~ ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
ac:=a*c
"
avo_mod_m <- sem(model = avo_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(avo_allfree_m, avo_apfixed_m, avo_afixed_m, avo_pfixed_m, avo_aonly_m, avo_ponly_m, avo_mod_m) # avo mod m preferred but ponly is close second
model_list <- c(avo_allfree_m, avo_apfixed_m, avo_afixed_m, avo_pfixed_m, avo_aonly_m, avo_ponly_m, avo_mod_m) # avo mod m preferred but ponly is close second
syn_model_list <- c(avo_allfree, avo_apfixed, avo_afixed, avo_pfixed, avo_aonly, avo_ponly, avo_mod)
syn_model_list_full <- syn_model_list
df <- mapply(pull_fitmeasures, model_list, syn_model_list, list(posnegint_personality))
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "negint_avo"
fulldf <- df
# Process Model for Negint Anx --------------------------------------------
anx_allfree <- "
panx1 ~ scpt + ccpt + scpr + ccpr
panx0 ~ scpt + ccpt + scpr + ccpr
scpt ~ pranx1 + pranx0
ccpt ~ pranx1 + pranx0
scpr ~ pranx1 + pranx0
ccpr ~ pranx1 + pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_allfree_m <- sem(model = anx_allfree, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_afixed <- "
panx1 ~ a*scpt
panx1 ~ b*ccpt
panx1 ~ scpr
panx1 ~ ccpr
panx0 ~ scpt
panx0 ~ ccpt
panx0 ~ a*scpr
panx0 ~ b*ccpr
scpt ~ c*pranx1
scpt ~ pranx0
ccpt ~ d*pranx1
ccpt ~ pranx0
scpr ~ pranx1
scpr ~c*pranx0
ccpr ~ pranx1
ccpr ~ d*pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_afixed_m <- sem(model = anx_afixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_pfixed <- "
panx1 ~ scpt
panx1 ~ ccpt
panx1 ~ a*scpr
panx1 ~ b*ccpr
panx0 ~ a*scpt
panx0 ~ b*ccpt
panx0 ~ scpr
panx0 ~ ccpr
scpt ~ pranx1
scpt ~ c*pranx0
ccpt ~ pranx1
ccpt ~ d*pranx0
scpr ~ c*pranx1
scpr ~pranx0
ccpr ~ d*pranx1
ccpr ~ pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_pfixed_m <- sem(model = anx_pfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_apfixed <- "
panx1 ~ e*scpt
panx1 ~ f*ccpt
panx1 ~ a*scpr
panx1 ~ b*ccpr
panx0 ~ a*scpt
panx0 ~ b*ccpt
panx0 ~ e*scpr
panx0 ~ f*ccpr
scpt ~ g*pranx1
scpt ~ c*pranx0
ccpt ~ h*pranx1
ccpt ~ d*pranx0
scpr ~ c*pranx1
scpr ~g*pranx0
ccpr ~ d*pranx1
ccpr ~ h*pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_apfixed_m <- sem(model = anx_apfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_aonly <- "
panx1 ~ scpt
panx1 ~ a*ccpt
panx0 ~ scpr
panx0 ~ ccpr
scpt ~ pranx1
ccpt ~ c*pranx1
scpr ~pranx0
ccpr ~ pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
ac:=a*c
panx1~pranx1
panx0 ~ pranx0
"
anx_aonly_m <- sem(model = anx_aonly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_ponly <- "
panx1 ~ scpr
panx1 ~ ccpr
panx0 ~ scpt
panx0 ~ ccpt
scpt ~ pranx0
ccpt ~ pranx0
scpr ~ pranx1
ccpr ~ pranx1
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_ponly_m <- sem(model = anx_ponly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anx_mod <- "
panx1 ~ f*ccpt
panx1 ~ scpr
panx0 ~ ccpt
panx0 ~ scpr
panx0 ~ f*ccpr
scpt ~ a*pranx0
scpt ~ b*pranx1
scpr ~ a*pranx0
scpr ~ b*pranx1
ccpt ~ h*pranx1
ccpt ~ d*pranx0
ccpr ~ d*pranx1
ccpr ~ h*pranx0
pranx1 ~~ pranx0
panx1~~panx0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~~0*scpt
panx0~~0*scpt
panx1~pranx1
panx0 ~ pranx0
"
anx_mod_m <- sem(model = anx_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
# Re do so only cc
anx_mod <- "
panx1 ~ f*ccpt
panx0 ~ ccpt
panx0 ~ f*ccpr
ccpt ~ h*pranx1
ccpt ~ d*pranx0
ccpr ~ d*pranx1
ccpr ~ h*pranx0
pranx1 ~~ pranx0
panx1~~panx0
ccpt ~~ ccpr
panx1~pranx1
panx0 ~ pranx0
"
anx_mod_m <- sem(model = anx_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(anx_allfree_m, anx_apfixed_m, anx_afixed_m, anx_pfixed_m, anx_aonly_m, anx_ponly_m, anx_mod_m)
model_list <- c(anx_allfree_m, anx_apfixed_m, anx_afixed_m, anx_pfixed_m, anx_aonly_m, anx_ponly_m, anx_mod_m)
syn_model_list <- c(anx_allfree, anx_apfixed, anx_afixed, anx_pfixed, anx_aonly, anx_ponly, anx_mod)
syn_model_list_full <- c(syn_model_list_full, syn_model_list)
df <- mapply(pull_fitmeasures, model_list, syn_model_list)
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "negint_anx"
fulldf <- bind_rows(fulldf, df)
# Process model for Negint Sec --------------------------------------------
sec_allfree <- "
psec1 ~ scpt + ccpt + scpr + ccpr
psec0 ~ scpt + ccpt + scpr + ccpr
scpt ~ prsec1 + prsec0
ccpt ~ prsec1 + prsec0
scpr ~ prsec1 + prsec0
ccpr ~ prsec1 + prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_allfree_m <- sem(model = sec_allfree, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_afixed <- "
psec1 ~ a*scpt
psec1 ~ b*ccpt
psec1 ~ scpr
psec1 ~ ccpr
psec0 ~ scpt
psec0 ~ ccpt
psec0 ~ a*scpr
psec0 ~ b*ccpr
scpt ~ c*prsec1
scpt ~ prsec0
ccpt ~ d*prsec1
ccpt ~ prsec0
scpr ~ pravo1
scpr ~c*prsec0
ccpr ~ prsec1
ccpr ~ d*prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_afixed_m <- sem(model = sec_afixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_pfixed <- "
psec1 ~ scpt
psec1 ~ ccpt
psec1 ~ a*scpr
psec1 ~ b*ccpr
psec0 ~ a*scpt
psec0 ~ b*ccpt
psec0 ~ scpr
psec0 ~ ccpr
scpt ~ prsec1
scpt ~ c*prsec0
ccpt ~ prsec1
ccpt ~ d*prsec0
scpr ~ c*prsec1
scpr ~prsec0
ccpr ~ d*prsec1
ccpr ~ prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_pfixed_m <- sem(model = sec_pfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_apfixed <- "
psec1 ~ e*scpt
psec1 ~ f*ccpt
psec1 ~ a*scpr
psec1 ~ b*ccpr
psec0 ~ a*scpt
psec0 ~ b*ccpt
psec0 ~ e*scpr
psec0 ~ f*ccpr
scpt ~ g*prsec1
scpt ~ c*prsec0
ccpt ~ h*prsec1
ccpt ~ d*prsec0
scpr ~ c*prsec1
scpr ~g*prsec0
ccpr ~ d*prsec1
ccpr ~ h*prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_apfixed_m <- sem(model = sec_apfixed, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_aonly <- "
psec1 ~ scpt
psec1 ~ a*ccpt
psec0 ~ scpr
psec0 ~ ccpr
scpt ~ prsec1
ccpt ~ c*prsec1
scpr ~prsec0
ccpr ~ prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
ac:=a*c
psec1~prsec1
psec0 ~ prsec0
"
sec_aonly_m <- sem(model = sec_aonly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_ponly <- "
psec1 ~ scpr
psec1 ~ ccpr
psec0 ~ scpt
psec0 ~ ccpt
scpt ~ prsec0
ccpt ~ prsec0
scpr ~ prsec1
ccpr ~ prsec1
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_ponly_m <- sem(model = sec_ponly, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_mod <- "
psec1 ~ e*scpt
psec1 ~ f*ccpt
psec1 ~ 0*scpr
psec1 ~ 0*ccpr
psec0 ~ a*scpt
psec0 ~ b*ccpt
psec0 ~ 0*scpr
psec0 ~ 0*ccpr
scpt ~ g*prsec1
scpt ~ h*prsec0
ccpt ~ 0*prsec1
ccpt ~ 0*prsec0
scpr ~ 0*prsec1
scpr ~0*prsec0
ccpr ~ h*prsec1
ccpr ~ 0*prsec0
prsec1 ~~ prsec0
psec1~~psec0
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_mod_m <- sem(model = sec_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
sec_mod <- "
psec1 ~ f*ccpt
psec1 ~ 0*ccpr
psec0 ~ b*ccpt
psec0 ~ 0*ccpr
ccpt ~ 0*prsec1
ccpt ~ 0*prsec0
ccpr ~ h*prsec1
ccpr ~ 0*prsec0
prsec1 ~~ prsec0
psec1~~psec0
ccpt ~~ ccpr
psec1~prsec1
psec0 ~ prsec0
"
sec_mod_m <- sem(model = sec_mod, data = posnegint_personality, missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(sec_allfree_m, sec_apfixed_m, sec_afixed_m, sec_pfixed_m, sec_aonly_m, sec_ponly_m, sec_mod_m)
model_list <- c(sec_allfree_m, sec_apfixed_m, sec_afixed_m, sec_pfixed_m, sec_aonly_m, sec_ponly_m, sec_mod_m)
syn_model_list <- c(sec_allfree, sec_apfixed, sec_afixed, sec_pfixed, sec_aonly, sec_ponly, sec_mod)
syn_model_list_full <- c(syn_model_list_full, syn_model_list)
df <- mapply(pull_fitmeasures, model_list, syn_model_list, list(posnegint_personality))
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "negint_sec"
fulldf <- bind_rows(fulldf, df)
# Process Model for Posint Avo --------------------------------------------
posint_avo_allfree <- "
p_scpt ~ pavo1 # actor
p_scpt ~ pravo1 # actor
p_scpt ~pavo0 #partner
p_scpt ~pravo0 #p
p_scpr ~ pavo1 #p
p_scpr ~ pravo1 #p
p_scpr ~ pavo0 #a
p_scpr ~ pravo0 #a
p_ccpt ~ pavo1 #a
p_ccpt ~ pravo1 #a
p_ccpt ~ pavo0 #p
p_ccpt ~ pravo0 #p
p_ccpr ~ pavo1 #p
p_ccpr ~ pravo1 #p
p_ccpr ~ pavo0 # a
p_ccpr ~ pravo0 #a
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_allfree_m <- sem(model = posint_avo_allfree, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_afixed <- "
p_scpt ~ a*pavo1 # actor
p_scpt ~ b*pravo1 # actor
p_scpt ~pavo0 #partner
p_scpt ~pravo0 #p
p_scpr ~ pavo1 #p
p_scpr ~ pravo1 #p
p_scpr ~ a*pavo0 #a
p_scpr ~ b*pravo0 #a
p_ccpt ~ c*pavo1 #a
p_ccpt ~ d*pravo1 #a
p_ccpt ~ pavo0 #p
p_ccpt ~ pravo0 #p
p_ccpr ~ pavo1 #p
p_ccpr ~ pravo1 #p
p_ccpr ~ c*pavo0 # a
p_ccpr ~ d*pravo0 #a
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_afixed_m <- sem(model = posint_avo_afixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_pfixed <- "
p_scpt ~ pavo1 # actor
p_scpt ~ pravo1 # actor
p_scpt ~a*pavo0 #partner
p_scpt ~b*pravo0 #p
p_scpr ~ a*pavo1 #p
p_scpr ~ b*pravo1 #p
p_scpr ~ pavo0 #a
p_scpr ~ pravo0 #a
p_ccpt ~ pavo1 #a
p_ccpt ~ pravo1 #a
p_ccpt ~ c*pavo0 #p
p_ccpt ~ d*pravo0 #p
p_ccpr ~ c*pavo1 #p
p_ccpr ~ d*pravo1 #p
p_ccpr ~ pavo0 # a
p_ccpr ~ pravo0 #a
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_pfixed_m <- sem(model = posint_avo_pfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_apfixed <- "
p_scpt ~ a*pavo1 # actor
p_scpt ~ b*pravo1 # actor
p_scpt ~c*pavo0 #partner
p_scpt ~d*pravo0 #p
p_scpr ~ c*pavo1 #p
p_scpr ~ d*pravo1 #p
p_scpr ~ a*pavo0 #a
p_scpr ~ b*pravo0 #a
p_ccpt ~ e*pavo1 #a
p_ccpt ~ f*pravo1 #a
p_ccpt ~ g*pavo0 #p
p_ccpt ~ h*pravo0 #p
p_ccpr ~ g*pavo1 #p
p_ccpr ~ h*pravo1 #p
p_ccpr ~ e*pavo0 # a
p_ccpr ~ f*pravo0 #a
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_apfixed_m <- sem(model = posint_avo_apfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_aonly <- "
p_scpt ~ pavo1 # actor
p_scpt ~ pravo1 # actor
p_scpr ~ pavo0 #a
p_scpr ~ pravo0 #a
p_ccpt ~ pavo1 #a
p_ccpt ~ pravo1 #a
p_ccpr ~ pavo0 # a
p_ccpr ~ pravo0 #a
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_aonly_m <- sem(model = posint_avo_aonly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_ponly <- "
p_scpt ~pavo0 #partner
p_scpt ~pravo0 #p
p_scpr ~ pavo1 #p
p_scpr ~ pravo1 #p
p_ccpt ~ pavo0 #p
p_ccpt ~ pravo0 #p
p_ccpr ~ pavo1 #p
p_ccpr ~ pravo1 #p
pravo1 ~~ pravo0
pavo1~~pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~pravo0
"
posint_avo_ponly_m <- sem(model = posint_avo_ponly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_mod <- "
p_scpt ~ a*pavo0 #partner
p_scpt ~ b*pravo0 #p
p_scpr ~ a*pavo1 #p
p_scpr ~ b*pravo1 #p
p_ccpr ~ pravo1 #p
pravo1 ~~ pravo0
pavo1 ~~ pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
pavo1 ~ pravo1
pavo0 ~ pravo0
p_ccpt ~ 0*pavo0
p_ccpt ~ 0*pavo1
p_ccpt ~ 0*pravo0
p_ccpt ~ 0*pravo1
"
posint_avo_mod_m <- sem(model = posint_avo_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_mod2 <- "
p_scpt ~pravo0 #partner
p_scpr ~ pravo1 #p
p_ccpr ~ pravo1 #p
pavo1 ~ pravo1
pavo0 ~pravo0
pravo1 ~~ pravo0
pavo1 ~~ pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
p_scpt ~~ 0*pavo1
p_scpt ~~ 0*pavo0
p_scpr ~~ 0*pavo1
p_scpr ~~ 0*pavo0
p_ccpr ~~ 0*pavo1
p_ccpr ~~ 0*pavo0
p_ccpt ~ 0*pavo0
p_ccpt ~ 0*pavo1
p_ccpt ~ 0*pravo0
p_ccpt ~ 0*pravo1
"
posint_avo_mod2_m <- sem(model = posint_avo_mod2, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_mod3 <- "
p_scpt ~pavo0 #partner
p_scpr ~ pavo1 #p
p_ccpr ~ pravo1 #p
pavo1 ~ pravo1
pavo0 ~pravo0
pravo1 ~~ pravo0
pavo1 ~~ pavo0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
p_scpt ~~ 0*pavo1
p_scpt ~~ 0*pavo0
p_scpr ~~ 0*pavo1
p_scpr ~~ 0*pavo0
p_ccpr ~~ 0*pavo1
p_ccpr ~~ 0*pavo0
"
posint_avo_mod3_m <- sem(model = posint_avo_mod3, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_avo_mod <- posint_avo_mod2
posint_avo_mod_m <- posint_avo_mod2_m
posint_avo_mod <- "
p_ccpr ~ pravo1 #p
pavo1 ~ pravo1
pavo0 ~pravo0
pravo1 ~~ pravo0
pavo1 ~~ pavo0
p_ccpt ~~ p_ccpr
p_ccpr ~~ 0*pavo1
p_ccpr ~~ 0*pavo0
p_ccpt ~ 0*pavo0
p_ccpt ~ 0*pavo1
p_ccpt ~ 0*pravo0
p_ccpt ~ 0*pravo1
"
posint_avo_mod_m <- sem(model = posint_avo_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(posint_avo_allfree_m, posint_avo_apfixed_m, posint_avo_afixed_m, posint_avo_pfixed_m, posint_avo_aonly_m, posint_avo_ponly_m, posint_avo_mod2_m, posint_avo_mod_m) # favors ponly (which by frequentist measures fits well), greater
model_list <- c(posint_avo_allfree_m, posint_avo_apfixed_m, posint_avo_afixed_m, posint_avo_pfixed_m, posint_avo_aonly_m, posint_avo_ponly_m, posint_avo_mod_m) # favors ponly (which by frequentist measures fits well), greater
syn_model_list <- c(posint_avo_allfree, posint_avo_apfixed, posint_avo_afixed, posint_avo_pfixed, posint_avo_aonly, posint_avo_ponly, posint_avo_mod) # favors ponly (which by frequentist measures fits well), greater
syn_model_list_full <- c(syn_model_list_full, syn_model_list)
df <- mapply(pull_fitmeasures, model_list, syn_model_list, list(dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.))) )
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "posint_avo"
fulldf <- bind_rows(fulldf, df)
# Process Model for Posint Anx --------------------------------------------
posint_anx_allfree <- "
p_scpt ~ panx1 # actor
p_scpt ~ pranx1 # actor
p_scpt ~panx0 #partner
p_scpt ~pranx0 #p
p_scpr ~ panx1 #p
p_scpr ~ pranx1 #p
p_scpr ~ panx0 #a
p_scpr ~ pranx0 #a
p_ccpt ~ panx1 #a
p_ccpt ~ pranx1 #a
p_ccpt ~ panx0 #p
p_ccpt ~ pranx0 #p
p_ccpr ~ panx1 #p
p_ccpr ~ pranx1 #p
p_ccpr ~ panx0 # a
p_ccpr ~ pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_allfree_m <- sem(model = posint_anx_allfree, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_afixed <- "
p_scpt ~ a*panx1 # actor
p_scpt ~ b*pranx1 # actor
p_scpt ~panx0 #partner
p_scpt ~pranx0 #p
p_scpr ~ panx1 #p
p_scpr ~ pranx1 #p
p_scpr ~ a*panx0 #a
p_scpr ~ b*pranx0 #a
p_ccpt ~ c*panx1 #a
p_ccpt ~ d*pranx1 #a
p_ccpt ~ panx0 #p
p_ccpt ~ pranx0 #p
p_ccpr ~ panx1 #p
p_ccpr ~ pranx1 #p
p_ccpr ~ c*panx0 # a
p_ccpr ~ d*pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_afixed_m <- sem(model = posint_anx_afixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_pfixed <- "
p_scpt ~ panx1 # actor
p_scpt ~ pranx1 # actor
p_scpt ~a*panx0 #partner
p_scpt ~b*pranx0 #p
p_scpr ~ a*panx1 #p
p_scpr ~ b*pranx1 #p
p_scpr ~ panx0 #a
p_scpr ~ pranx0 #a
p_ccpt ~ panx1 #a
p_ccpt ~ pranx1 #a
p_ccpt ~ c*panx0 #p
p_ccpt ~ d*pranx0 #p
p_ccpr ~ c*panx1 #p
p_ccpr ~ d*pranx1 #p
p_ccpr ~ panx0 # a
p_ccpr ~ pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_pfixed_m <- sem(model = posint_anx_pfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_apfixed <- "
p_scpt ~ a*panx1 # actor
p_scpt ~ b*pranx1 # actor
p_scpt ~c*panx0 #partner
p_scpt ~d*pranx0 #p
p_scpr ~ c*panx1 #p
p_scpr ~ d*pranx1 #p
p_scpr ~ a*panx0 #a
p_scpr ~ b*pranx0 #a
p_ccpt ~ e*panx1 #a
p_ccpt ~ f*pranx1 #a
p_ccpt ~ g*panx0 #p
p_ccpt ~ h*pranx0 #p
p_ccpr ~ g*panx1 #p
p_ccpr ~ h*pranx1 #p
p_ccpr ~ e*panx0 # a
p_ccpr ~ f*pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_apfixed_m <- sem(model = posint_anx_apfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_aonly <- "
p_scpt ~ panx1 # actor
p_scpt ~ pranx1 # actor
p_scpr ~ panx0 #a
p_scpr ~ pranx0 #a
p_ccpt ~ panx1 #a
p_ccpt ~ pranx1 #a
p_ccpr ~ panx0 # a
p_ccpr ~ pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_aonly_m <- sem(model = posint_anx_aonly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_ponly <- "
p_scpt ~panx0 #partner
p_scpt ~pranx0 #p
p_scpr ~ panx1 #p
p_scpr ~ pranx1 #p
p_ccpt ~ panx0 #p
p_ccpt ~ pranx0 #p
p_ccpr ~ panx1 #p
p_ccpr ~ pranx1 #p
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_ponly_m <- sem(model = posint_anx_ponly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_mod <- "
p_scpt ~ c*panx1 # actor
p_scpr ~ c*pranx1 #p
p_ccpt ~ b*panx1 #a
p_ccpt ~ a*pranx1 #a
p_ccpr ~ b*panx0 # a
p_ccpr ~ a*pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_mod_m <- sem(model = posint_anx_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_mod2 <- "
p_scpt ~ a*pranx1 #+ pranx0 # actor
p_scpr ~ a*pranx1 #+ pranx0 #p
p_ccpt ~ pranx1 #+ pranx0 #a
#p_ccpt ~ pranx1 #a
p_ccpr ~ pranx0 #+ pranx1 # a
#p_ccpr ~ pranx0 #a
pranx1 ~~ pranx0
#panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
#panx1 ~ pranx1
#panx0 ~pranx0
#p_scpt ~~ 0*panx1
p_scpt ~~ 0*pranx0
#p_scpr ~~ 0*pranx1
p_scpr ~~ 0*pranx0
#p_ccpr ~~ 0*pranx1
p_ccpr ~~ 0*pranx0
p_ccpt ~~ 0*pranx0
"
posint_anx_mod2_m <- sem(model = posint_anx_mod2, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_mod3 <- "
p_scpt ~ a*panx1 + panx0 # actor
p_scpr ~ a*panx1 + panx0 #p
p_ccpt ~ panx1 #+ panx0 #a
p_ccpr ~ panx0# + panx1 # a
panx1~~panx0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
"
posint_anx_mod3_m <- sem(model = posint_anx_mod3, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_anx_mod <- "
p_ccpt ~ b*panx1 #a
p_ccpt ~ a*pranx1 #a
p_ccpr ~ b*panx0 # a
p_ccpr ~ a*pranx0 #a
pranx1 ~~ pranx0
panx1~~panx0
p_ccpt ~~ p_ccpr
panx1 ~ pranx1
panx0 ~pranx0
"
posint_anx_mod_m <- sem(model = posint_anx_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(posint_anx_allfree_m, posint_anx_apfixed_m, posint_anx_afixed_m, posint_anx_pfixed_m, posint_anx_aonly_m, posint_anx_ponly_m, posint_anx_mod_m) # favors modified version of model
model_list <- c(posint_anx_allfree_m, posint_anx_apfixed_m, posint_anx_afixed_m, posint_anx_pfixed_m, posint_anx_aonly_m, posint_anx_ponly_m, posint_anx_mod_m) # favors modified version of model
syn_model_list <- c(posint_anx_allfree, posint_anx_apfixed, posint_anx_afixed, posint_anx_pfixed, posint_anx_aonly, posint_anx_ponly, posint_anx_mod) # favors modified version of model
syn_model_list_full <- c(syn_model_list_full, syn_model_list)
df <- mapply(pull_fitmeasures, model_list, syn_model_list, list(dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.))) )
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "posint_anx"
fulldf <- bind_rows(fulldf, df)
# Process Model for Posint Sec --------------------------------------------
posint_sec_allfree <- "
p_scpt ~ psec1 # actor
p_scpt ~ prsec1 # actor
p_scpt ~psec0 #partner
p_scpt ~prsec0 #p
p_scpr ~ psec1 #p
p_scpr ~ prsec1 #p
p_scpr ~ psec0 #a
p_scpr ~ prsec0 #a
p_ccpt ~ psec1 #a
p_ccpt ~ prsec1 #a
p_ccpt ~ psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ psec0 # a
p_ccpr ~ prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_allfree_m <- sem(model = posint_sec_allfree, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_afixed <- "
p_scpt ~ a*psec1 # actor
p_scpt ~ b*prsec1 # actor
p_scpt ~psec0 #partner
p_scpt ~prsec0 #p
p_scpr ~ psec1 #p
p_scpr ~ prsec1 #p
p_scpr ~ a*psec0 #a
p_scpr ~ b*prsec0 #a
p_ccpt ~ c*psec1 #a
p_ccpt ~ d*prsec1 #a
p_ccpt ~ psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ c*psec0 # a
p_ccpr ~ d*prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_afixed_m <- sem(model = posint_sec_afixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_pfixed <- "
p_scpt ~ psec1 # actor
p_scpt ~ prsec1 # actor
p_scpt ~a*psec0 #partner
p_scpt ~b*prsec0 #p
p_scpr ~ a*psec1 #p
p_scpr ~ b*prsec1 #p
p_scpr ~ psec0 #a
p_scpr ~ prsec0 #a
p_ccpt ~ psec1 #a
p_ccpt ~ prsec1 #a
p_ccpt ~ c*psec0 #p
p_ccpt ~ d*prsec0 #p
p_ccpr ~ c*psec1 #p
p_ccpr ~ d*prsec1 #p
p_ccpr ~ psec0 # a
p_ccpr ~ prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_pfixed_m <- sem(model = posint_sec_pfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_apfixed <- "
p_scpt ~ a*psec1 # actor
p_scpt ~ b*prsec1 # actor
p_scpt ~c*psec0 #partner
p_scpt ~d*prsec0 #p
p_scpr ~ c*psec1 #p
p_scpr ~ d*prsec1 #p
p_scpr ~ a*psec0 #a
p_scpr ~ b*prsec0 #a
p_ccpt ~ e*psec1 #a
p_ccpt ~ f*prsec1 #a
p_ccpt ~ g*psec0 #p
p_ccpt ~ h*prsec0 #p
p_ccpr ~ g*psec1 #p
p_ccpr ~ h*prsec1 #p
p_ccpr ~ e*psec0 # a
p_ccpr ~ f*prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_apfixed_m <- sem(model = posint_sec_apfixed, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_aonly <- "
p_scpt ~ psec1 # actor
p_scpt ~ prsec1 # actor
p_scpr ~ psec0 #a
p_scpr ~ prsec0 #a
p_ccpt ~ psec1 #a
p_ccpt ~ prsec1 #a
p_ccpr ~ psec0 # a
p_ccpr ~ prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_aonly_m <- sem(model = posint_sec_aonly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_ponly <- "
p_scpt ~psec0 #partner
p_scpt ~prsec0 #p
p_scpr ~ psec1 #p
p_scpr ~ prsec1 #p
p_ccpt ~ psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ psec1 #p
p_ccpr ~ prsec1 #p
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_ponly_m <- sem(model = posint_sec_ponly, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_mod <- "
p_scpt ~ 0*psec1 # actor
p_scpt ~ prsec1 # actor
p_scpt ~0*psec0 #partner
p_scpt ~0*prsec0 #p
p_scpr ~ psec1 #p
p_scpr ~ 0*prsec1 #p
p_scpr ~ 0*psec0 #a
p_scpr ~ 0*prsec0 #a
p_ccpt ~ 0*psec1 #a
p_ccpt ~ 0*prsec1 #a
p_ccpt ~ 0*psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ 0*psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ 0*psec0 # a
p_ccpr ~ prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_mod_m <- sem(model = posint_sec_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
posint_sec_mod <- "
p_ccpt ~ 0*psec1 #a
p_ccpt ~ 0*prsec1 #a
p_ccpt ~ 0*psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ 0*psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ 0*psec0 # a
p_ccpr ~ prsec0 #a
prsec1 ~~ prsec0
psec1~~psec0
p_ccpt ~~ p_ccpr
psec1 ~ prsec1
psec0 ~prsec0
"
posint_sec_mod_m <- sem(model = posint_sec_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
anova(posint_sec_allfree_m, posint_sec_apfixed_m, posint_sec_afixed_m, posint_sec_pfixed_m, posint_sec_aonly_m, posint_sec_ponly_m, posint_sec_mod_m)
model_list <- c(posint_sec_allfree_m, posint_sec_apfixed_m, posint_sec_afixed_m, posint_sec_pfixed_m, posint_sec_aonly_m, posint_sec_ponly_m, posint_sec_mod_m) # fsecrs ponly (which by frequentist measures fits well), greater
syn_model_list <- c(posint_sec_allfree, posint_sec_apfixed, posint_sec_afixed, posint_sec_pfixed, posint_sec_aonly, posint_sec_ponly, posint_sec_mod) # favors ponly (which by frequentist measures fits well), greater
syn_model_list_full <- c(syn_model_list_full, syn_model_list)
df <- mapply(pull_fitmeasures, model_list, syn_model_list, list(dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.))) )
if(class(df) == "list") {df <- as.data.frame(do.call(rbind, df)) } else {df <- as.data.frame(t(df))}
df <- df%>% mutate_all(funs(as.numeric(.)))
df$mname <- "posint_sec"
fulldf <- bind_rows(fulldf, df)
ordering <- c("allfree", "afixed", "pfixed", "apfixed", "aonly", "ponly", "mod")
fulldf <- fulldf %>% group_by(mname) %>% mutate(model_v = ordering) %>% ungroup() %>% mutate(analysis_name = paste0(mname, "_", model_v))
names(syn_model_list_full) <- as.vector(fulldf$analysis_name)
save(syn_model_list_full, file ="../output/bsem_process/model_strings.RData")
df_order <- data.frame(mname = c("negint_avo", "negint_anx", "negint_sec", "posint_avo", "posint_anx", "posint_sec"), model_type = 1:length(unique(fulldf$mname)))
fulldf <- left_join(fulldf, df_order)
fulldf <- group_by(fulldf, model_type) %>% mutate(mnumber = 1:length(V1)) %>% ungroup()
fulldf <- arrange(fulldf, fulldf$model_type)
write.csv(fulldf, "../output/bsem_process/fit_indices_allprocess_models.csv", row.names= FALSE)
# Combined Models -- not nested within others -----------------------------
posnegint_avo_mod <- "
#positive interaction regressions
p_scpt ~pravo0 #partner
p_scpr ~ pravo1 #p
p_ccpr ~ pravo1 #p
p_scpt ~~ 0*pavo1
p_scpt ~~ 0*pavo0
p_scpr ~~ 0*pavo1
p_scpr ~~ 0*pavo0
p_ccpr ~~ 0*pavo1
p_ccpr ~~ 0*pavo0
p_ccpt ~ 0*pavo0
p_ccpt ~ 0*pavo1
p_ccpt ~ 0*pravo0
p_ccpt ~ 0*pravo1
#negint regressions
pavo0 ~ scpr
pavo1 ~ a*ccpt
ccpt ~ c*pravo1
scpr ~ pravo1
ccpr ~ c*pravo1
pavo1 ~ 0*scpt
pavo0 ~ 0*ccpr
scpt ~ 0*pravo1
scpt ~ 0*pravo0
scpr ~ 0*pravo0
ccpr ~ 0*pravo0
pavo1 ~ pravo1
pavo0 ~pravo0
#state covariation
pravo1 ~~ pravo0
pavo1~~pavo0
#posint covariation
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
#negint covariation
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
#posnegint covariation
p_scpt ~ scpt
p_scpr ~ scpr
#indirect paths
ac:=a*c
"
posnegint_avo_mod_m <- sem(model = posnegint_avo_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_avo_mod))
posnegint_anx_mod <- "
#posint regressions
p_scpt ~ cc*panx1 # actor
p_scpr ~ cc*pranx1 #p
p_ccpt ~ bb*panx1 #a
p_ccpt ~ aa*pranx1 #a
p_ccpr ~ bb*panx0 # a
p_ccpr ~ aa*pranx0 #a
#negint regressions
panx1 ~ f*ccpt
panx1 ~ scpr
panx0 ~ ccpt
panx0 ~ scpr
panx0 ~ f*ccpr
scpt ~ a*pranx0
scpt ~ b*pranx1
scpr ~ a*pranx0
scpr ~ b*pranx1
ccpt ~ h*pranx1
ccpt ~ d*pranx0
ccpr ~ d*pranx1
ccpr ~ h*pranx0
panx1~pranx1
panx0 ~ pranx0
#state covariation
pranx1 ~~ pranx0
panx1~~panx0
#negint covariation
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
panx1~~0*scpt
panx0~~0*scpt
#posint covariation
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
#posnegint covariation
p_scpr ~ scpr
p_scpt ~ 0*scpt
p_scpt ~ 0*ccpt
p_scpt ~ 0*scpr
p_scpt ~ 0*ccpr
p_ccpt ~ 0*scpt
p_ccpt ~ 0*ccpt
p_ccpt ~ 0*scpr
p_ccpt ~ 0*ccpr
p_ccpr ~ 0*scpt
p_ccpr ~ 0*ccpt
p_ccpr ~ 0*scpr
p_ccpr ~ 0*ccpr
p_scpr ~ 0*ccpt
p_scpr ~ 0*scpt
p_scpr ~ 0*ccpr
"
posnegint_anx_mod_m <- sem(model = posnegint_anx_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_anx_mod))
# trying to reduce the number of parameters - omitting sc from model; go with mod2 because preserves paths present and/of interest in simpler model and increases K:n ratio
posnegint_anx_mod2 <- "
#posint regressions
p_ccpt ~ bb*panx1 #a
p_ccpt ~ aa*pranx1 #a
p_ccpr ~ bb*panx0 # a
p_ccpr ~ aa*pranx0 #a
#negint regressions
panx1 ~ f*ccpt
panx0 ~ ccpt
panx0 ~ f*ccpr
ccpt ~ h*pranx1
ccpt ~ d*pranx0
ccpr ~ d*pranx1
ccpr ~ h*pranx0
panx1~pranx1
panx0 ~ pranx0
#state covariation
pranx1 ~~ pranx0
panx1~~panx0
#negint covariation
ccpt ~~ ccpr
#posint covariation
p_ccpt ~~ p_ccpr
#posnegint covariation
p_ccpt ~ 0*ccpt
p_ccpt ~ 0*ccpr
p_ccpr ~ 0*ccpt
p_ccpr ~ 0*ccpr
"
posnegint_anx_mod2_m <- sem(model = posnegint_anx_mod2, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_anx_mod2))
posnegint_avo_mod2 <- "
#positive interaction regressions
p_ccpr ~ pravo1 #p
p_ccpr ~~ 0*pavo1
p_ccpr ~~ 0*pavo0
p_ccpt ~ 0*pavo0
p_ccpt ~ 0*pavo1
p_ccpt ~ 0*pravo0
p_ccpt ~ 0*pravo1
#negint regressions
pavo1 ~ a*ccpt
ccpt ~ c*pravo1
ccpr ~ c*pravo1
pavo0 ~ 0*ccpr
ccpr ~ 0*pravo0
pavo1 ~ pravo1
pavo0 ~pravo0
#state covariation
pravo1 ~~ pravo0
pavo1~~pavo0
#posint covariation
p_ccpt ~~ p_ccpr
#negint covariation
ccpt ~~ ccpr
#posnegint covariation
p_ccpr ~ 0*ccpr
p_ccpt ~ 0*ccpt
p_ccpr ~ 0*ccpt
p_ccpt ~ 0*ccpr
#indirect paths
ac:=a*c
"
posnegint_avo_mod2_m <- sem(model = posnegint_avo_mod2, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_avo_mod2))
posnegint_sec_mod <- "
#posint regressions
p_scpt ~ 0*psec1 # actor
p_scpt ~ prsec1 # actor
p_scpt ~0*psec0 #partner
p_scpt ~0*prsec0 #p
p_scpr ~ psec1 #p
p_scpr ~ 0*prsec1 #p
p_scpr ~ 0*psec0 #a
p_scpr ~ 0*prsec0 #a
p_ccpt ~ 0*psec1 #a
p_ccpt ~ 0*prsec1 #a
p_ccpt ~ 0*psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ 0*psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ 0*psec0 # a
p_ccpr ~ prsec0 #a
#negint regressions
psec1 ~ e*scpt
psec1 ~ f*ccpt
psec1 ~ 0*scpr
psec1 ~ 0*ccpr
psec0 ~ a*scpt
psec0 ~ b*ccpt
psec0 ~ 0*scpr
psec0 ~ 0*ccpr
scpt ~ g*prsec1
scpt ~ h*prsec0
ccpt ~ 0*prsec1
ccpt ~ 0*prsec0
scpr ~ 0*prsec1
scpr ~0*prsec0
ccpr ~ h*prsec1
ccpr ~ 0*prsec0
psec1~prsec1
psec0 ~ prsec0
#state covariation
prsec1 ~~ prsec0
psec1~~psec0
#posint covariation
p_scpt ~~ p_ccpt
p_scpt ~~ p_scpr
p_scpt ~~ p_ccpr
p_ccpt ~~ p_scpr
p_ccpt ~~ p_ccpr
p_scpr ~~ p_ccpr
#negint covariation
scpt ~~ ccpt
scpt ~~ scpr
scpt ~~ ccpr
ccpt ~~ scpr
ccpt ~~ ccpr
scpr ~~ ccpr
#posnegint covariation
p_scpr ~ scpr
p_scpt ~ scpt
p_scpt ~ 0*ccpt
p_scpt ~ 0*scpr
p_scpt ~ 0*ccpr
p_ccpt ~ 0*scpt
p_ccpt ~ 0*ccpt
p_ccpt ~ 0*scpr
p_ccpt ~ 0*ccpr
p_ccpr ~ 0*scpt
p_ccpr ~ 0*ccpt
p_ccpr ~ 0*scpr
p_ccpr ~ 0*ccpr
p_scpr ~ 0*ccpt
p_scpr ~ 0*scpt
p_scpr ~ 0*ccpr
"
posnegint_sec_mod_m <- sem(model = posnegint_sec_mod, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_sec_mod))
posnegint_sec_mod2 <- "
#posint regressions
p_ccpt ~ 0*psec1 #a
p_ccpt ~ 0*prsec1 #a
p_ccpt ~ 0*psec0 #p
p_ccpt ~ prsec0 #p
p_ccpr ~ 0*psec1 #p
p_ccpr ~ prsec1 #p
p_ccpr ~ 0*psec0 # a
p_ccpr ~ prsec0 #a
#negint regressions
psec1 ~ f*ccpt
psec1 ~ 0*ccpr
psec0 ~ b*ccpt
psec0 ~ 0*ccpr
ccpt ~ 0*prsec1
ccpt ~ 0*prsec0
ccpr ~ h*prsec1
ccpr ~ 0*prsec0
psec1~prsec1
psec0 ~ prsec0
#state covariation
prsec1 ~~ prsec0
psec1~~psec0
#posint covariation
p_ccpt ~~ p_ccpr
#negint covariation
ccpt ~~ ccpr
#posnegint covariation
p_ccpt ~ 0*ccpt
p_ccpt ~ 0*ccpr
p_ccpr ~ 0*ccpt
p_ccpr ~ 0*ccpr
"
posnegint_sec_mod2_m <- sem(model = posnegint_sec_mod2, data = dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), missing = "ML",estimator = "MLR", meanstructure = TRUE, mimic = "Mplus", conditional.x = FALSE)
runBSEM(modsyntax(dat =dplyr::mutate_at(posnegint_personality, vars(starts_with("p_")), list(~100*.)), posnegint_sec_mod2))
# so psec0 ~ ccpt goes from sig to marginal (.025 to .028) and ccpr ~ prsec1 goes from .024 to .036. But other effects remain. End up with 25 additional free parameters too... Ask Michael but feels like should still go with the simpler model SINCE adding additional parameters
syn_comb_models <- c(posnegint_avo_mod, posnegint_avo_mod2,posnegint_anx_mod,posnegint_anx_mod2, posnegint_sec_mod, posnegint_sec_mod2)
names(syn_comb_models) <- c("posnegint_avo_mod", "posnegint_avo_mod2","posnegint_anx_mod","posnegint_anx_mod2", "posnegint_sec_mod", "posnegint_sec_mod2")
save(syn_comb_models, file = "../output/bsem_process/model_comb_strings.RData")
## Summary of findings from process results
# Baseline avoidance in patient predicts contrarian in both patient and partner during negative interaction and contrarianism during the positive interaction for the partner. Contrarianism is sticky with patient where it subsequently is associated with heightened feels of attachment avoidance in patient.
#Baseline momentary anxiety predicts within person tendency to be contrarian during the positive interaction and marginally so in the negative interaction. This is true in both patients and partners. Within person effect such that exhibiting dependency during the interaction amplifies anxiety (being contrarian CAN lead to decreases in anxiety too, not just weaker increases).
|
566202ed1c8769f5ad66f66c9118f800f26a6830
|
ef572bd2b0515892d1f59a073b8bf99f81d6a734
|
/data-raw/update_cached_map_datapack_cogs.R
|
3bdd2f4eda1c99d04a387d4149aa3829bb28f290
|
[
"CC0-1.0"
] |
permissive
|
pepfar-datim/datapackr
|
5bc604caa1ae001b6c04e1d934c0c613c59df1e6
|
9275632673e45948db6846513a53c1436cfc0e47
|
refs/heads/master
| 2023-08-30T23:26:48.454382
| 2023-08-11T13:01:57
| 2023-08-11T13:01:57
| 170,350,211
| 9
| 7
|
CC0-1.0
| 2023-09-11T21:53:24
| 2019-02-12T16:19:47
|
R
|
UTF-8
|
R
| false
| false
| 1,229
|
r
|
update_cached_map_datapack_cogs.R
|
# Load PSNUs into package from DATIM ####
library(magrittr)
library(datapackr)
# Point to DATIM login secrets ####
secrets <- Sys.getenv("SECRETS_FOLDER") %>% paste0(., "cop-test.json")
datimutils::loginToDATIM(secrets)
# Processing
datapack_cogs <- datapackr::datapack_cogs
datapack_cogs$COP22 <-
datimutils::getMetadata(categoryOptionGroups,
fields = "id,name,categoryOptions[id,name]", # nolint
"groupSets.id:eq:hdEmBvPF3iq",
d2_session = d2_default_session)
datapack_cogs$COP23 <-
datimutils::getMetadata(categoryOptionGroups,
fields = "id,name,categoryOptions[id,name]", # nolint
"groupSets.id:eq:CIqgMytqbMA",
d2_session = d2_default_session)
datapack_cogs$COP24 <-
# Patch before MER 3.0 deployment:
datapack_cogs$COP23
# datimutils::getMetadata(categoryOptionGroups,
# fields = "id,name,categoryOptions[id,name]", # nolint
# "groupSets.id:eq:CIqgMytqbMA",
# d2_session = d2_default_session)
save(datapack_cogs, file = "./data/datapack_cogs.rda", compress = "xz")
|
91d889ea878a9b28d6c06ecc0e1c0abec2e7add0
|
6be70ffdb95ed626d05b5ef598b842c5864bac4d
|
/old/senate_party_calls_replicate_figures_emIRT_only.R
|
2c458e2313bd3927b81feb88c1111310d53d268b
|
[] |
no_license
|
Hershberger/partycalls
|
c4f7a539cacd3120bf6b0bfade327f269898105a
|
8d9dc31dd3136eae384a8503ba71832c78139870
|
refs/heads/master
| 2021-09-22T17:54:29.106667
| 2018-09-12T21:16:56
| 2018-09-12T21:16:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,906
|
r
|
senate_party_calls_replicate_figures_emIRT_only.R
|
library(partycalls)
library(data.table)
options(stringsAsFactors = FALSE)
# load data for analysis
load("data/senator_year_data_emIRT_only.RData")
senator_year_data <- senator_year_data[senator_year_data$drop == 0]
# whoheeds13 <- readstata13::read.dta13(
# "inst/extdata/who-heeds-replication-archive.dta")
# setDT(new_whoheeds13)
# setDT(whoheeds13)
# new_whoheeds13 <- merge(new_whoheeds13, whoheeds13, by = c("congress", "icpsr"))
# setDT(new_whoheeds13)
# # regression formulas and functions
# f.meddist <- new_pirate100 ~ new_distance_from_floor_median +
# responsiveness_noncalls + vote_share * pres_vote_share + south13 + female +
# afam + latino + up_for_reelection + seniority + freshman + retiree +
# best_committee + leader + power_committee + chair
f.extremism <- responsiveness_party_calls ~ ideological_extremism +
responsiveness_noncalls + vote_share * pres_vote_share + south13 + female +
afam + latino + up_for_reelection + seniority + freshman + retiree +
best_committee + leader + power_committee + chair
# To see a regression from a particular congress, use these functions:
# fm.meddist <- function(i, j) {
# summary(lm(f.meddist,
# data=subset(senator_year_data, congress==i & majority==j)),
# vcov=vcovHC(type="HC1"))
# }
fm.extremism <- function(i, j) {
summary(lm(f.extremism,
data=subset(senator_year_data, congress==i & majority==j)),
vcov=vcovHC(type="HC1"))
}
# Uncomment to use
# fm.meddist(93, 1) # <- produces regression results for Distance from the
# Median Model for majority party in 93rd Congress;
# change 1 to 0 for minority party
# Produce results for Figure 2
B <- SE <- data.frame(row.names=93:112)
# B$meddist.maj <- do.call(c, lapply(93:112, function(x)
# fm.meddist(x, 1)$coef[2, 1]))
# B$meddist.min <- do.call(c, lapply(93:112, function(x)
# fm.meddist(x, 0)$coef[2, 1]))
B$extremism.maj <- do.call(c, lapply(93:112, function(x)
fm.extremism(x, 1)$coef[2, 1]))
B$extremism.min <- do.call(c, lapply(93:112, function(x)
fm.extremism(x, 0)$coef[2, 1]))
# SE$meddist.maj <- do.call(c, lapply(93:112, function(x)
# fm.meddist(x, 1)$coef[2, 2]))
# SE$meddist.min <- do.call(c, lapply(93:112, function(x)
# fm.meddist(x, 0)$coef[2, 2]))
SE$extremism.maj <- do.call(c, lapply(93:112, function(x)
fm.extremism(x, 1)$coef[2, 2]))
SE$extremism.min <- do.call(c, lapply(93:112, function(x)
fm.extremism(x, 0)$coef[2, 2]))
pdf(file="plots/who-heeds-figure2-senate_emIRT_only.pdf", ## RENAME
width=4, height = 8, family="Times")
layout(matrix(1:2, 2, 1, byrow=TRUE))
par(mar=c(2.5, 4, 2, 0.3) + 0.1, font.lab=2)
x <- (93:112)[-12]
x.ticks <- c(94, 99, 104, 109)
y.ticks <- c(-12, 0, 12, 24, 36)
# b <- B$meddist.maj[-12]
# se <- SE$meddist.maj[-12]
# plot(0, 0, type='n', ylim=c(-6, 36), xlim=c(93, 109),
# cex.lab=1.15, xaxt="n", yaxt="n", xlab="", ylab="Majority Party")
# axis(1, x.ticks, cex.axis=1.1, labels=FALSE, xpd=TRUE)
# axis(2, y.ticks, cex.axis=1.1, labels=TRUE)
# abline(h=0, col="gray", xpd=FALSE)
# title(main="Distance from Floor Median", cex.main=1.15, line=0.75, font.main=2)
# points(x, b, pch=19, col="black", cex=.8)
# segments(x, b-qnorm(.750)*se, x, b+qnorm(.750)*se, lwd=2)
# segments(x, b-qnorm(.975)*se, x, b+qnorm(.975)*se, lwd=.9)
b <- B$extremism.maj[-12]
se <- SE$extremism.maj[-12]
plot(0, 0, type='n', ylim=c(-18, 42), xlim=c(93, 112),
cex.lab=1.15, xaxt="n", yaxt="n", xlab="", ylab="")
axis(1, x.ticks, cex.axis=1.1, labels=TRUE)
axis(2, y.ticks, cex.axis=1.1, labels=TRUE)
abline(h=0, col="gray", xpd=FALSE)
title(main="Ideological Extremism", cex.main=1.15, line=0.75, font.main=2)
points(x, b, pch=19, col="black", cex=.8)
segments(x, b-qnorm(.750)*se, x, b+qnorm(.750)*se, lwd=2)
segments(x, b-qnorm(.975)*se, x, b+qnorm(.975)*se, lwd=.9)
# b <- B$meddist.min[-12]
# se <- SE$meddist.min[-12]
# plot(0, 0, type='n', ylim=c(-6, 36), xlim=c(93, 109),
# cex.lab=1.15, xaxt="n", yaxt="n", xlab="", ylab="Minority Party")
# axis(1, x.ticks, cex.axis=1.1, labels=TRUE, xpd=TRUE)
# axis(2, y.ticks, cex.axis=1.1, labels=TRUE)
# abline(h=0, col="gray", xpd=FALSE)
# title(main="", cex.main=1.15, line=0.75, font.main=2)
# points(x, b, pch=19, col="black", cex=.8)
# segments(x, b-qnorm(.750)*se, x, b+qnorm(.750)*se, lwd=2)
# segments(x, b-qnorm(.975)*se, x, b+qnorm(.975)*se, lwd=.9)
b <- B$extremism.min[-12]
se <- SE$extremism.min[-12]
plot(0, 0, type='n', ylim=c(-18, 42), xlim=c(93, 112),
cex.lab=1.15, xaxt="n", yaxt="n", xlab="", ylab="")
axis(1, x.ticks, cex.axis=1.1, labels=TRUE, xpd=TRUE)
axis(2, y.ticks, cex.axis=1.1, labels=TRUE)
abline(h=0, col="gray", xpd=FALSE)
title(main="", cex.main=1.15, line=0.75, font.main=2)
points(x, b, pch=19, col="black", cex=.8)
segments(x, b-qnorm(.750)*se, x, b+qnorm(.750)*se, lwd=2)
segments(x, b-qnorm(.975)*se, x, b+qnorm(.975)*se, lwd=.9)
# NAME FIGURE WITH CLASSIFICATION METHOD IN TEX CAPTION
dev.off()
|
7722403a336a399bd19c2208149d259764073cb8
|
727e491d1d19394960d52f732e367f37be038956
|
/dieroller/dieroller.Rcheck/00_pkg_src/dieroller/R/hello.R
|
35e2345e0f7a968fc2f454eb8a51cb0acd9431d7
|
[] |
no_license
|
JustinRiverNg/Computing-Data
|
a0a3ddd9a0ae11699bdf3efe34cda7dc45b369fd
|
4ff7415cb09b5148fadb51310b27dbdd1746603b
|
refs/heads/master
| 2020-03-30T10:34:10.423755
| 2018-04-27T18:12:36
| 2018-04-27T18:12:36
| 151,125,755
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,881
|
r
|
hello.R
|
# Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Build and Reload Package: 'Cmd + Shift + B'
# Check Package: 'Cmd + Shift + E'
# Test Package: 'Cmd + Shift + T'
#1) Object "die"
#' @title Die Creation
#' @description Creates a die of object class "die"
#' @param sides a vector of length 6 representing the sides of the die
#' @param prob a vector of length 6 with sum of 1 representing the probability of rolling each respective side
#' @return A data table expressing each side of the die and its respective probabilities
die <- function(sides = c("1", "2", "3", "4", "5", "6"), prob = c(1/6, 1/6, 1/6, 1/6, 1/6, 1/6)) {
check_sides(sides)
check_prob(prob)
res <- list(sides = sides, prob = prob)
class(res) <- "die"
return(res)
}
#' @title Print Die Method
#' @description Print method for objects of class "die"
#' @param x a "die" object
#' @return A data table expressing each side of the die and its respective probabilities
print.die <- function(x) {
cat('object "die"\n\n')
cd <- data.frame(
side = x$sides, prob = x$prob
)
print(cd)
invisible(x)
}
#' @title Check Probability Function
#' @description Checks that the vector of probabilities is valid
#' @param x a vector of probabilties
#' @return True if the vectors of probabilities is of length 6, sums to 1, and that none of the probabilities is less than 0 or greater than 1. Else, error
check_prob <- function(prob) {
if(length(prob) !=6 | !is.numeric(prob)) {
stop("\n'prob' must be a numeric vector of length 6")
}
if(any(prob < 0) | any(prob > 1)) {
stop("\n'prob' values must be between 0 and 1")
}
if(sum(prob) !=1) {
stop("\n'prob' values must be between 0 and 1")
}
TRUE
}
#' @title Check Sides Function
#' @description Checks that the vector of sides is of length 6
#' @param x A vector of sides
#' @return True if the vector contains 6 values. Else, error
check_sides <- function(sides) {
if (length(sides) != 6) {
stop("\n'sides' must be on length 6")
}
}
fair_die <- die()
fair_die
weird_die <- die(sides = c('i', 'ii', 'iii', 'iv', 'v', 'vi'))
weird_die
loaded_die <- die(prob = c(0.075, 0.1, 0.125, 0.15, 0.20, 0.35))
loaded_die
bad_die <- die(sides = c('a', 'b', 'c', 'd', 'e'))
bad_die2 <- die(sides = c('a', 'b', 'c', 'd', 'e', 'f'),
prob = c(0.2, 0.1, 0.1, 0.1, 0.5, 0.1))
practice_die <- die(sides = c('1', '2', '3', '4', '5', '6'),
prob = c(0, 0, 0, .34, .33, .33))
#2) Object "roll"
#' @title Check Times Function
#' @description Checks that the number of times of rolling is valid
#' @param times an integer of number of times to roll
#' @return TRUE if times is an integer >= 0. Else, error
check_times <- function(times) {
if (times <= 0 | !is.numeric(times)) {
stop("\n'times' must be a positive integer")
} else {
TRUE
}
}
#' @title Roll
#' @description Computes the results of rolling a specified die a specificied number of times
#' @param z a die of class "die"
#' @param times number of times the die is being rolled
#' @return The results from rolling the die the specified number of times
roll <- function(z, times = 1) {
check_times(times)
if(class(z) !="die") {
stop("\nroll() requires an object 'die'")
}
rolls <- sample(z$sides, size = times, replace = TRUE, prob = z$prob)
res <- list(
sides = z$sides,
rolls = rolls,
prob = z$prob,
total = length(rolls))
class(res) <- "roll"
res
}
#' @title Print Die Method
#' @description A print method for the roll function
#' @param q a roll function
#' @return A transformed version of the results from the roll function
print.roll <- function(q) {
cat('object "roll"\n')
print(q$rolls)
}
set.seed(123)
fair50 <- roll(fair_die, times = 50)
fair50
names(fair50)
fair50$rolls
fair50$sides
fair50$prob
fair50$total
str_die <- die(
sides = c('a', 'b', 'c', 'd', 'e', 'f'),
prob = c(0.075, 0.1, 0.125, 0.15, 0.20, 0.35))
set.seed(123)
str_rolls <- roll(str_die, times = 20)
names(str_rolls)
str_rolls
#3) Summary Method for "Roll" Objects
#' @title Roll Summary
#' @description Gives the summary of a sample of rolls.
#' @param w a "roll" object that contains a sample of rolls with a specified die
#' @return A data table listing a summary of the sample
summary.roll <- function(w) {
if(class(w) !="roll") {
stop("\nsummary() requires an object 'roll'")
}
side <- w$sides
count <- as.data.frame(table(w$rolls))[ ,2]
prop <- (count/w$total)
freqs <- data.frame(side, count, prop)
res2 <- list(freqs = freqs)
class(res2) <- "summary.roll"
res2
}
#' @title Print Summary Roll
#' @description A print method for summary.roll
#' @param e a summary.roll function
#' @return A modified data table listing a summary of the sample
print.summary.roll <- function(e) {
cat('summary "roll" \n\n')
print(as.data.frame(e$freqs))
}
set.seed(123)
fair_50rolls <- roll(fair_die, times = 50)
fair50_sum <- summary(fair_50rolls)
fair50_sum
class(fair50_sum)
names(fair50_sum)
fair50_sum$freqs
#4) Plot method for "roll" objects
one_freqs <- function(x) {
(cumsum(x$rolls == x$sides[1]) / 1:x$total)[x$total]
}
two_freqs <- function(x) {
(cumsum(x$rolls == x$sides[2]) / 1:x$total)[x$total]
}
three_freqs <- function(x) {
(cumsum(x$rolls == x$sides[3]) / 1:x$total)[x$total]
}
four_freqs <- function(x) {
(cumsum(x$rolls == x$sides[4]) / 1:x$total)[x$total]
}
five_freqs <- function(x) {
(cumsum(x$rolls == x$sides[5]) / 1:x$total)[x$total]
}
six_freqs <- function(x) {
(cumsum(x$rolls == x$sides[6]) / 1:x$total)[x$total]
}
#' @title Frequencies
#' @description Determines what relative frequency to call upon
#' @param x the roll function
#' @param side the number of side to call upon
#' @return The relative frequency of the called upon side
frequencies <- function(x, side = 1) {
if(side == 1) {
return(one_freqs(x))
}
if(side == 2) {
return(two_freqs(x))
}
if(side == 3) {
return(three_freqs(x))
}
if(side == 4) {
return(four_freqs(x))
}
if(side == 5) {
return(five_freqs(x))
}
if(side == 6) {
return(six_freqs(x))
}
}
#' @title Plot Roll
#' @description Creates a bar plot of a roll function
#' @param x a sample of rolls
#' @return A barplot for the sample of rolls
plot.roll <- function(x) {
barplot(c(frequencies(x), frequencies(x, 2), frequencies(x, 3),
frequencies(x, 4), frequencies(x, 5), frequencies(x, 6)),
as.numeric(x$side), type = 'n'
, ylim = c(0,.2), las = 1, xlab = "sides of die"
, bty = 'n', ylab = sprintf("relative frequencies")
, names.arg = c(x$side[1], x$side[2], x$side[3],
x$side[4], x$side[5], x$side[6]))
}
plot(fair_50rolls)
#5) Additional Methods
"[.roll" <- function(x, i) {
x$rolls[i]
}
set.seed(123)
fair500 <- roll(fair_die, times = 500)
fair500[500]
"[<-.roll" <- function(x, i, value) {
x$rolls[i] <- value
return(x$rolls)
}
fair500[500] <- 1
fair500
fair500[500]
summary(fair500)
#5) Additional Methods
"[.roll" <- function(x, i) {
x$rolls[i]
}
set.seed(123)
fair500 <- roll(fair_die, times = 500)
fair500[500]
"[<-.roll" <- function(x, i, value) {
x$rolls[i] <- value
return(x$rolls)
}
fair500[500] <- 1
fair500
fair500[500]
"+.roll" <- function(obj, incr) {
if (length(incr) != 1 | incr <= 0) {
stop("\ninvalid increament (must be positive)")
}
more_rolls <- roll(obj$x, times = incr)
rolls <- sample(x$sides, size = times, replace = TRUE, prob = x$prob)
res <- list(
sides = x$sides,
rolls = rolls,
prob = x$prob,
total = length(rolls))
class(res) <- "roll"
res
}
fair600 <- fair500 + 100
|
334141a545dd8c654fe3bee70856ff9fb2a18ba0
|
0b5a137ce9998b30efd1a2b3061712951602f4c0
|
/analysis/fit_model.R
|
10329eb427475f05efe3e618fad22b753f043fe9
|
[] |
no_license
|
jsphillips2/midge_pi
|
0c272ba3acdef5faddb999240251382ea917d41b
|
33e28ff1a250620f201299feadb67a6a33ea8b9e
|
refs/heads/master
| 2022-12-01T13:36:36.001157
| 2020-07-28T13:54:10
| 2020-07-28T13:54:10
| 277,593,334
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,578
|
r
|
fit_model.R
|
#==========
#========== Preliminaries
#==========
# load packages
library(tidyverse)
library(rstan)
source("analysis/model_fn.R")
# stan settings
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores()-2)
# import data
data <- read_csv("data/metabolism_2015.csv")
# process data
dd <- data %>%
filter(trial=="a") %>%
mutate(
rateo2 = 15*(do_f - do_i)/(100*duration),
day = as.numeric(date) - min(as.numeric(date)) + 3,
time = as.numeric(as.factor(day))-1,
temp = (temp_i + temp_f)/2
) %>%
rename(par = light) %>%
select(core, rack, date, day, time, par, midge, rateo2, temp) %>%
na.omit() %>%
filter(rateo2 > -3)
# data frame for summary
dd_sum <- dd %>%
mutate(id = interaction(time, midge)) %>%
split(.$id) %>%
lapply(function(x) {
x %>%
tidyr::expand(par = seq(min(par), max(par), length.out = 100)) %>%
mutate(time = unique(x$time),
midge = unique(x$midge))
}) %>%
bind_rows()
#==========
#========== Fit model
#==========
# model form
forms <- c("both","midge_b","midge_a","none")
form <- forms[1]
if(form == "both") {b = formula(~ 1 + time * midge)
a = formula(~ 1 + time * midge)
r = formula(~ 1 + time * midge)}
if(form == "midge_b") {b = formula(~ 1 + time * midge)
a = formula(~ 1 + time)
r = formula(~ 1 + time * midge)}
if(form == "midge_a") {b = formula(~ 1 + time)
a = formula(~ 1 + time * midge)
r = formula(~ 1 + time * midge)}
if(form == "none") {b = formula(~ 1 + time)
a = formula(~ 1 + time)
r = formula(~ 1 + time * midge)}
# package data
data_list <- model_fn(b_ = b,
a_ = a,
r_ = r,
dd_ = dd,
dd_sum_ = dd_sum)
# MCMC specifications
chains <- 6
iter <- 4000
adapt_delta <- 0.9
max_treedepth <- 10
# fit model
fit <- stan(file = "analysis/midge_pi.stan", data = data_list, seed=2e3,
chains = chains, iter = iter,
control = list(adapt_delta = adapt_delta, max_treedepth = max_treedepth))
# summary of fit
fit_summary <- summary(fit, probs=c(0.16, 0.5, 0.84))$summary %>%
{as_tibble(.) %>%
mutate(var = rownames(summary(fit)$summary))}
# export
write_rds(list(b = b,
a = a,
r = r,
data_list = data_list,
fit = fit,
fit_summary = fit_summary),
paste0("analysis/model_fit_ii/",form,".rds"))
|
7cc5954d2799faec76769c7ae25ca5dd11bfc3db
|
1dc421a198c86a888f2029ce96d09924aac6cab2
|
/R/testIndSpeedglm.R
|
f79b939f052f93648e27825b5a645c5a13fa5487
|
[] |
no_license
|
JokerWhy233/MXM
|
519b8216a1a2a965a8e43531fd52fb0a7c460f86
|
035673338ed6647239a4859981918ddf3b8ce38e
|
refs/heads/master
| 2021-08-22T14:54:05.597447
| 2017-11-30T10:41:38
| 2017-11-30T10:41:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,790
|
r
|
testIndSpeedglm.R
|
testIndSpeedglm = function(target, dataset, xIndex, csIndex, wei = NULL, dataInfo = NULL, univariateModels = NULL, hash = FALSE,
stat_hash = NULL, pvalue_hash = NULL, target_type = 0, robust = FALSE) {
# TESTINDSPEEDGLM Conditional independence test for large sample sized data (tens and hundreds of thousands) for normal, binary discrete or ordinal class variables
# provides a p-value PVALUE for the null hypothesis: X independent by target
# given CS. The pvalue is calculated by comparing a logistic model based
# on the conditioning set CS against a model containing both X and CS.
# The comparison is performed through a chi-square test with one degree
# of freedom on the difference between the deviances of the two models.
# TESTINDSPEEDGLM requires the following inputs:
# target: a vector containing the values of the target variable.
# target must be a vector with percentages, binay data, numerical values or integers
# dataset: a numeric data matrix containing the variables for performing
# the conditional independence test. They can be mixed variables, either continous or categorical
# xIndex: the index of the variable whose association with the target
# must be tested. Can be any type of variable, either continous or categorical.
# csIndex: the indices of the variables to condition on. They can be mixed variables, either continous or categorical
# target_Type: the type of the target
# target_type == 1 (normal target)
# target_type == 2 (binary target)
# target_type == 3 (discrete target)
# default target_type=0
# this method returns: the pvalue PVALUE, the statistic STAT and a control variable FLAG.
# if FLAG == 1 then the test was performed succesfully
#cast factor into numeric vector
target = as.numeric( as.vector(target) );
csIndex[which(is.na(csIndex))] = 0
if ( hash ) {
csIndex2 = csIndex[which(csIndex!=0)]
csIndex2 = sort(csIndex2)
xcs = c(xIndex,csIndex2)
key = paste(as.character(xcs) , collapse=" ");
if (is.null(stat_hash[[key]]) == FALSE) {
stat = stat_hash[[key]];
pvalue = pvalue_hash[[key]];
flag = 1;
results <- list(pvalue = pvalue, stat = stat, flag = flag, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
}
#if the test cannot performed succesfully these are the returned values
pvalue = log(1);
stat = 0;
flag = 0;
#if the xIndex is contained in csIndex, x does not bring any new
#information with respect to cs
if (!is.na( match(xIndex, csIndex) ) ) {
if ( hash ) { #update hash objects
stat_hash$key <- 0;#.set(stat_hash , key , 0)
pvalue_hash$key <- log(1);#.set(pvalue_hash , key , 1)
}
results <- list(pvalue = log(1), stat = 0, flag = 1, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
#check input validity
if (xIndex < 0 || csIndex < 0) {
message(paste("error in testIndSpeedglm : wrong input of xIndex or csIndex"))
results <- list(pvalue = pvalue, stat = stat, flag = flag, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
#xIndex = unique(xIndex);
#csIndex = unique(csIndex);
#extract the data
x = dataset[ , xIndex];
cs = dataset[ , csIndex];
#if x or target is constant then there is no point to perform the test
# if ( Rfast::Var( as.numeric(x) ) == 0 ) {
# if ( hash ) { #update hash objects
# stat_hash$key <- 0; #.set(stat_hash , key , 0)
# pvalue_hash$key <- log(1); #.set(pvalue_hash , key , 1)
# }
# results <- list(pvalue = log(1), stat = 0, flag = 1, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
# return(results);
# }
#remove NAs-zeros from cs
#csIndex = csIndex[csIndex!=0]
if(length(cs) == 0 || is.na(cs) ) cs = NULL;
#if x = any of the cs then pvalue = log(1) and flag = 1.
#That means that the x variable does not add more information to our model due to an exact copy of this in the cs, so it is independent from the target
if ( length(cs) != 0 ) {
if ( is.null(dim(cs)[2]) ) { #cs is a vector
if ( identical(x, cs) ) { #if(!any(x == cs) == FALSE)
if ( hash ) { #update hash objects
stat_hash$key <- 0; #.set(stat_hash , key , 0)
pvalue_hash$key <- log(1); #.set(pvalue_hash , key , 1)
}
results <- list(pvalue = log(1), stat = 0, flag = 1 , stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
} else { #more than one var
for (col in 1:dim(cs)[2]) {
if ( identical(x, cs[, col]) ) { #if(!any(x == cs) == FALSE)
if ( hash ) { #update hash objects
stat_hash$key <- 0; #.set(stat_hash , key , 0)
pvalue_hash$key <- log(1); #.set(pvalue_hash , key , 1)
}
results <- list(pvalue = log(1), stat = 0, flag = 1 , stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
}
}
}
#binomial or multinomial target?
yCounts = length( unique(target) );
if (yCounts == 2) {
target_type = 1;
} else if ( identical(floor(target), target) ) {
target_type = 2
} else target_type = 3
#if the conditioning set (cs) is empty, we use the t-test on the coefficient of x.
if (length(cs) == 0) {
if ( target_type == 3 ) {
fit2 = speedglm::speedlm( target ~ x, weights = wei, data = as.data.frame(x) )
if ( any( is.na(coef(fit2)) ) ) {
stat = 0
pvalue = log(1)
flag = 1;
} else {
suma = summary(fit2)[[ 13 ]]
stat = suma[1]
dof = suma[3]
pvalue = pf(stat, 1, dof, lower.tail = FALSE, log.p = TRUE)
flag = 1;
}
} else if (target_type == 1){
fit2 = speedglm::speedglm(target ~ x, weights = wei, data = as.data.frame(x), family = binomial(logit) )
stat = fit2$nulldev - fit2$deviance
dof = length( coef(fit2) ) - 1
pvalue = pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE)
flag = 1;
} else {
fit2 = speedglm::speedglm(target ~ x, weights = wei, data = as.data.frame(x), family = poisson(log) )
stat = fit2$nulldev - fit2$deviance
dof = length( coef(fit2) ) - 1
pvalue = pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE)
flag = 1;
}
} else {
if ( target_type == 3 ) {
fit1 = speedglm::speedlm( target ~ dataset[, csIndex], data = as.data.frame( dataset[, c(csIndex, xIndex)] ), weights = wei )
fit2 = speedglm::speedlm( target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), weights = wei )
d1 = length( coef(fit1) )
d2 = length( coef(fit2) )
df1 = d2 - d1
df2 = length(target) - d2
stat = ( (fit1$RSS - fit2$RSS)/df1 ) / ( fit2$RSS /df2 )
pvalue = pf(stat, df1, df2, lower.tail = FALSE, log.p = TRUE)
flag = 1;
} else if (target_type == 1) {
fit1 = speedglm::speedglm( target ~ dataset[, csIndex], data = as.data.frame( dataset[, c(xIndex, csIndex)] ), family = binomial(logit), weights = wei )
fit2 = speedglm::speedglm( target ~ ., data = as.data.frame( dataset[, c(xIndex, csIndex)] ), family = binomial(logit), weights = wei )
stat = fit1$deviance - fit2$deviance
dof = length( coef(fit2) ) - length( coef(fit1) )
pvalue = pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE)
flag = 1;
} else {
fit1 = speedglm::speedglm( target ~ dataset[, csIndex], data = as.data.frame( dataset[, c(xIndex, csIndex)] ), family = poisson(log), weights = wei )
fit2 = speedglm::speedglm( target ~ ., data = as.data.frame( dataset[, c(xIndex, csIndex)] ), family = poisson(log), weights = wei )
stat = fit1$deviance - fit2$deviance
dof = length( coef(fit2) ) - length( coef(fit1) )
pvalue = pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE)
flag = 1;
}
}
#update hash objects
if( hash ) {
stat_hash$key <- stat;#.set(stat_hash , key , stat)
pvalue_hash$key <- pvalue;#.set(pvalue_hash , key , pvalue)
}
#last error check
if ( is.na(pvalue) || is.na(stat) ) {
pvalue = log(1);
stat = 0;
flag = 0;
} else {
#update hash objects
if( hash ) {
stat_hash[[key]] <- stat;#.set(stat_hash , key , stat)
pvalue_hash[[key]] <- pvalue;#.set(pvalue_hash , key , pvalue)
}
}
results <- list(pvalue = pvalue, stat = stat, flag = flag, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
|
94e5857e3b152851644083bb8737371274f5fe63
|
aff8c05c8d8c6f7ac6b95d535aa3a00576c14798
|
/fuzzycmeans/fuzzycmeans.R
|
8d928cf9bcbd094afbb4f2a258832bcef0a0dffb
|
[] |
no_license
|
coppolalab/Chandran_pipeline
|
6905263d043bd34c82e4d5a0deda9744d6d2f16a
|
84fbf25398deb9e2e47ad2df7bdbdcc6f87d988e
|
refs/heads/master
| 2021-01-22T22:13:22.137333
| 2017-03-20T00:52:41
| 2017-03-20T00:52:41
| 85,520,556
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,717
|
r
|
fuzzycmeans.R
|
#String operations
library(stringr)
#Reading and writing tables
library(readr)
library(openxlsx)
#For plotting
library(ggplot2)
#For microarray stuff
library(Biobase)
library(matrixStats)
library(abind)
library(lumi)
library(lumiHumanAll.db)
library(annotate)
library(sva)
library(peer)
library(limma)
#Longitudinal analysis
library(Mfuzz)
#Plotting
library(Cairo)
library(WGCNA)
library(heatmap.plus)
library(flashClust)
enableWGCNAThreads()
#Data arrangement
library(reshape2)
library(plyr)
library(dplyr)
library(tidyr)
library(doBy)
#Functional programming
library(magrittr)
library(purrr)
library(functional)
library(vadr)
saveRDS.gz <- function(object,file,threads=parallel::detectCores()) {
con <- pipe(paste0("pigz -p",threads," > ",file),"wb")
saveRDS(object, file = con)
close(con)
}
readRDS.gz <- function(file,threads=parallel::detectCores()) {
con <- pipe(paste0("pigz -d -c -p",threads," ",file))
object <- readRDS(file = con)
close(con)
return(object)
}
csel.repeat <- function(eset, m, crange, max.runs, csel.mat = matrix(), count.runs = 0)
{
csel.out <- cselection(eset, m = m, crange = crange, repeats = 5, visu = FALSE)
if (count.runs == 0)
{
csel.return <- csel.out
}
else
{
csel.return <- rbind(csel.mat, csel.out)
}
count.runs <- count.runs + 1
print(count.runs)
if (count.runs < max.runs)
{
csel.repeat(eset, m, crange, max.runs, csel.return, count.runs)
}
else
{
return(csel.return)
}
}
dmin.repeat <- function(eset, m, crange, max.runs, dmin.mat = vector(), count.runs = 0)
{
dmin.out <- Dmin(eset, m = m, crange = crange, repeats = 5, visu = FALSE)
if (count.runs == 0)
{
dmin.return <- dmin.out
}
else
{
dmin.return <- rbind(dmin.mat, dmin.out)
}
count.runs <- count.runs + 1
if (count.runs < max.runs)
{
dmin.repeat(eset, m, crange, max.runs, dmin.return, count.runs)
}
else
{
return(dmin.return)
}
}
match.exact <- mkchain(map_chr(paste %<<<% "^" %<<% c("$", sep = "")), paste(collapse = "|"))
intensities.mean <- readRDS.gz("../dtw/save/intensities.means.rda")
intensities.tgdox <- intensities.mean$Tg.DOX
tgdox.betr.genes <- read.xlsx("../betr/tgdox.out.xlsx") %>% select(Symbol)
tgdox.timecourse.genes <- read.xlsx("../timecourse/tgdox.out.xlsx") %>% select(Symbol)
longnet.tgdox <- readRDS.gz("../longnet/save/ica.tgdox.rda")
ica.tgdox <- readRDS.gz("../longnet/save/ica.tgdox.rda")
tgdox.genes <- c(unlist(tgdox.betr.genes), unlist(tgdox.timecourse.genes)) %>% unique %>% match.exact
#tgdox.genes <- match.exact(ica.tgdox)
tgdox.cluster <- intensities.tgdox[grepl(tgdox.genes, rownames(intensities.tgdox)),]
tgdox.cluster <- intensities.tgdox[match(longnet.tgdox, rownames(intensities.tgdox)),]
tgdox.eset <- ExpressionSet(assayData = as.matrix(tgdox.cluster)) %>% standardise
m.estimate <- mestimate(tgdox.eset)
csel.runs <- csel.repeat(tgdox.eset, m = m.estimate, crange = 4:20, max.runs = 5)
csel.ratio <- (4:20) - colMeans(csel.runs)
dmin.runs <- dmin.repeat(tgdox.eset, m = m.estimate, crange = seq(4, 40, 4), max.runs = 5)
seed.mfuzz <- function(eset, c.num, m, mfuzz.list = list(), iter.count = 0)
{
if (iter.count < 250)
{
mfuzz.new <- mfuzz(eset, c = c.num, m = m)
iter.count <- iter.count + 1
print(iter.count)
mfuzz.add <- mfuzz.new$membership
colnames(mfuzz.add) <- paste("X", 1:c.num, sep = "")
mfuzz.list[[iter.count]] <- mfuzz.add
seed.mfuzz(eset = eset, c.num = c.num, m = m, mfuzz.list = mfuzz.list, iter.count = iter.count)
}
else
{
return(mfuzz.list)
}
}
cluster.tgdox7 <- seed.mfuzz(eset = tgdox.eset, c.num = 4, m = m.estimate)
median.tgdox7 <- melt(cluster.tgdox7) %>% dcast(Var1 ~ Var2, median)
cluster.tgdox13 <- seed.mfuzz(eset = tgdox.eset, c.num = 13, m = m.estimate)
median.tgdox13 <- melt(cluster.tgdox13) %>% dcast(Var1 ~ Var2, median)
cluster.tgdox14 <- seed.mfuzz(eset = tgdox.eset, c.num = 14, m = m.estimate)
median.tgdox14 <- melt(cluster.tgdox14) %>% dcast(Var1 ~ Var2, median)
cluster.tgdox <- mfuzz(tgdox.eset, c = 7, m = m.estimate)
mfuzz.plot(tgdox.eset, cl = cluster.tgdox, mfrow = c(4,4), time.labels = 1:4)
#cluster.filter <- (cluster.patient$membership > 0.4) %>% apply(1, any)
#cluster.filtered <- cluster.patient$membership[cluster.filter,]
#patient.members <- apply(cluster.patient$membership, 1, which.max)
patient.df <- data.frame(Symbol = names(cluster.patient$cluster), Cluster = cluster.patient$cluster)
write.xlsx(patient.df, "./patient.cmeans.xlsx")
partcoef.tgdox <- partcoef(tgdox.eset)
permut.timeseries <- function()
|
23e9d58f3ada650c4cc1d8e9f3b3b1b73e70850d
|
b6e7d568d3c01b5fb2ee63a8c4594efc995e37c3
|
/R/msrep.R
|
e9da95c6fef6ad34a9feb034ebb1472ec6f2917c
|
[] |
no_license
|
cran/longit
|
4b87d6851d600a900f59b5e4ebd09f1d1e725a7e
|
2aa442df666057b016cc191b5da3e182dda3eacc
|
refs/heads/master
| 2023-04-08T05:09:29.130163
| 2021-04-15T07:00:05
| 2021-04-15T07:00:05
| 358,312,090
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 580
|
r
|
msrep.R
|
#' @title longitudinal data
#'
#' @description Longitudinal observation on single variable at different timepoints. Observations arranged in a column as the patient with corresponding column of ID.
#' @usage data(msrep)
#' @format A \code{tibble} with 7 columns which are :
#' \describe{
#' \item{Subject}{Patient ID}
#' \item{Gender}{Categorical numeric variable, 1 if Males and 0 if female}
#' \item{Age}{Time or age at which observations were taken from every subjects}
#' \item{x1,...,x4}{Columns stating number of observations at age 18,10,12 and 14}}
#'
"msrep"
|
9c281d9e44c2e9f52f9bd5dc3b10f26c7be5d005
|
f2bcc38a5ece9b14a3d8b68c5d4a09edff06314d
|
/man/Seq.Rd
|
c42e2c811247151c7ec36bbc3170080006a3d33d
|
[
"Apache-2.0"
] |
permissive
|
charlieccarey/monarchr.biolink
|
8eb618c9c61091ac3371e7d67636520a07d3db6b
|
5b488a06cedcafc9df44368e2d665555bc5cd53d
|
refs/heads/master
| 2020-03-09T08:40:56.748770
| 2018-04-11T04:33:54
| 2018-04-11T04:33:54
| 128,694,973
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 681
|
rd
|
Seq.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Seq.r
\docType{data}
\name{Seq}
\alias{Seq}
\title{Seq Class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
Seq
}
\description{
Seq Class
}
\section{Fields}{
\describe{
\item{\code{id}}{}
\item{\code{label}}{}
\item{\code{categories}}{}
\item{\code{types}}{}
\item{\code{description}}{}
\item{\code{consider}}{}
\item{\code{deprecated}}{}
\item{\code{replaced_by}}{}
\item{\code{synonyms}}{}
\item{\code{xrefs}}{}
\item{\code{taxon}}{}
\item{\code{md5checksum}}{}
\item{\code{residues}}{}
\item{\code{alphabet}}{}
\item{\code{seqlen}}{}
}}
\keyword{datasets}
|
b5de92a0fa9796eadd12ad1f26e2e2eb30b67176
|
72341bedc0e8951ced5f1afa53239d7ee02b03b1
|
/split.R
|
e0a3517e2d629fc9744ecdcb3fd830846ef0c7c7
|
[] |
no_license
|
vippro169/Load-profile-simulator
|
e9cba74dc2d9bb4172836fdd697f7bdfbf2e9174
|
c80aeb38fb67059206955307d9db78b125db874b
|
refs/heads/master
| 2021-09-13T11:35:26.326489
| 2017-11-26T04:51:35
| 2017-11-26T04:51:35
| 111,104,055
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,122
|
r
|
split.R
|
setwd('~/Documents/Project 1/household 1/01_plugs_csv/01/06')
filename <- list.files(pattern = "*.csv")
for(i in 1:length(filename)){
power <- read.csv(filename[i])
time <- seq(ISOdate(2017,11,7), by='sec', length.out = 86399)
smr <- data.frame(unclass(summary(power)))
preference <- power[,1]
preference[preference <= 20] = 0
a <- data.frame(unclass(rle(preference)))
#preference <- round(preference$X49.2516/10)*10
listOfSignature <- split(a,cumsum(a$lengths>100), cumsum(a$values==0))
for(l in 1:length(listOfSignature)){
listOfSignature[[l]]$lengths[listOfSignature[[l]]$values == 0 && listOfSignature[[l]]$lengths > 100] = 1
}
outpath=paste('~/Documents/Project 1/household 1/01_plugs_csv/Output/app_6',sep='')
setwd(outpath)
for(j in 1:length(listOfSignature)){
tmp <- inverse.rle(listOfSignature[[j]])
savename <- paste('6__',filename[i],'__filenum_',j,'.csv', sep='')
write.csv(tmp, file = savename)
}
setwd('~/Documents/Project 1/household 1/01_plugs_csv/01/06')
# tmp <- inverse.rle(listOfSignature[[1]])
# plot(tmp, type='l', ylim=c(0,600))
}
|
65b345974ac63d9d915815a535be763133073b8a
|
abedec3e252d97868aea5b3385a77313ae26a92f
|
/baby_names02.R
|
6128e229d267fd910e6d688ec033a35353f83f13
|
[] |
no_license
|
dk81/R_projects
|
b4129a88ab48060727cde75810d69baa2e50e5f3
|
f55ba52f4864593bc3970bc9de22f9b96f02ec43
|
refs/heads/master
| 2022-03-13T23:45:35.582464
| 2022-03-06T16:33:59
| 2022-03-06T16:33:59
| 140,731,173
| 5
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,831
|
r
|
baby_names02.R
|
# Sideways Bar Graph
# Analyzing Baby Names Part 2:
# A Bar Graph Approach
library(babynames) # Baby Names dataset:
library(ggplot2) # Data visualization
library(data.table) # For data wrangling and manipulation
# Save the babynames data into baby_data:
baby_data <- data.table(babynames)
# Preview the data:
head(baby_data); tail(baby_data)
# Structure of data:
str(baby_data)
# Change column names:
colnames(baby_data) <- c("Year", "Sex", "Name", "Count", "Proportion")
### --------------------------------------
## Finding The Top 20 Baby Names:
# Sort names from most popular to least popular (adds duplicates too):
sorted_names <- baby_data[ , .(Name.Count = sum(Count)), by = Name]
sorted_names <- sorted_names[order(-Name.Count)]
# Preview:
head(sorted_names, n = 20)
top_twenty_babynames <- sorted_names[1:20, ]
# Preview:
top_twenty_babynames
# Ggplot Bar Graph:
ggplot(top_twenty_babynames, aes(x = Name, y = Name.Count)) +
geom_bar(stat = "identity") +
labs(x = "Name \n", y = "\n Count \n", title = "Top 20 Baby Names") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(angle = 90, vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
# Ggplot Sideways Bar Graph:
ggplot(top_twenty_babynames, aes(x = Name, y = Name.Count)) +
geom_bar(stat = "identity") + coord_flip() +
labs(x = "Name \n", y = "\n Count \n", title = "Top 20 Baby Names") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(angle = 90, vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
# Ggplot Sideways Bar Graph (Scaled):
ggplot(top_twenty_babynames, aes(x = Name, y = Name.Count/1000000)) +
geom_bar(stat = "identity") + coord_flip() +
scale_y_continuous(breaks=seq(0, 6, 1)) +
labs(x = "Name \n", y = "\n Count (Millions) \n", title = "Top 20 Baby Names") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
# Getting The Sorted Bar Graph In ggplot2:
# We have the sorted top 20 baby names but ggplot would not recognize this
# ordering.
# To have ggplot recognize this ordering, we have these 20 names
# as factors in the order of the most popular female
# name to the 20th most popular female name
# http://rstudio-pubs-static.s3.amazonaws.com/7433_4537ea5073dc4162950abb715f513469.html
top_twenty_babynames$Name <- factor(top_twenty_babynames$Name,
levels = top_twenty_babynames$Name[order(top_twenty_babynames$Name.Count)])
# Ggplot Sideways Bar Graph (Fixed & Sorted):
ggplot(top_twenty_babynames, aes(x = Name, y = Name.Count/1000000)) +
geom_bar(stat = "identity") + coord_flip() +
scale_y_continuous(breaks=seq(0, 6, 1)) +
geom_text(aes(label = round(Name.Count/1000000, 3)), hjust = 1.2, colour = "white", fontface = "bold") +
labs(x = "Name \n", y = "\n Count (Millions) \n", title = "The Twenty Most Popular Baby Names") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
#-------------------------
### Male Baby Names:
male_babynames <- baby_data[Sex == "M" , .(Name.Count = sum(Count)), by = Name][order(-Name.Count)]
head(male_babynames , n = 20)
# Order male baby names in descending order by Count:
# Reference: http://www.statmethods.net/management/sorting.html
male_babynames <- male_babynames[order(-Name.Count), ]
head(male_babynames, n = 20)
# Eliminate weird row numbers:
#rownames(male_babynames) <- NULL
# Top 20 Male Baby Names:
toptwenty_m <- male_babynames[1:20, ]
toptwenty_m
toptwenty_m$Name <- factor(toptwenty_m$Name,
levels = toptwenty_m$Name[order(toptwenty_m$Name.Count)])
# Ggplot Sideways Bar Graph (Fixed & Sorted):
ggplot(toptwenty_m, aes(x = Name, y = Name.Count/1000000)) +
geom_bar(stat = "identity") + coord_flip() +
scale_y_continuous(breaks=seq(0, 6, 1)) +
geom_text(aes(label = round(Name.Count/1000000, 3)), hjust = 1.2, colour = "white", fontface = "bold") +
labs(x = "Name \n", y = "\n Count (Millions) \n",
title = "The Twenty Most Popular \n Male Baby Names \n") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
#-----------------------------------------
# Getting the Top 20 Female Baby Names:
female_babynames <- baby_data[Sex == "F" , .(Name.Count = sum(Count)), by = Name][order(-Name.Count)]
head(female_babynames , n = 20)
# Order female baby names in descending order by Count:
# Reference: http://www.statmethods.net/management/sorting.html
female_babynames <- female_babynames[order(-Name.Count), ]
head(female_babynames, n = 20)
# Eliminate weird row numbers:
#rownames(male_babynames) <- NULL
# Top 20 Female Baby Names:
toptwenty_f <- female_babynames[1:20, ]
toptwenty_f
toptwenty_f$Name <- factor(toptwenty_f$Name,
levels = toptwenty_f$Name[order(toptwenty_f$Name.Count)])
# Ggplot Sideways Bar Graph (Fixed & Sorted):
ggplot(toptwenty_f, aes(x = Name, y = Name.Count/1000000)) +
geom_bar(stat = "identity") + coord_flip() +
scale_y_continuous(breaks=seq(0, 6, 1)) +
geom_text(aes(label = round(Name.Count/1000000, 3)), hjust = 1.2, colour = "white", fontface = "bold") +
labs(x = "Name \n", y = "\n Count (Millions) \n",
title = "The Twenty Most Popular \n Female Baby Names") +
theme(plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(face="bold", colour="#FF7A33", size = 12, vjust = 1),
axis.title.y = element_text(face="bold", colour="#FF7A33", size = 12),
axis.text.x = element_text(vjust = 0.1, hjust = 0.1),
legend.title = element_text(face="bold", size = 10))
|
da428483de4c57b4a9763d492de1b4cd5154eb9c
|
074d650201fbf5c15482e7a733e5f2a0e6330dcd
|
/NetLogo R Analyses/Spacial Statistics.R
|
160fd885d986aa5e0bf9c4be03c3bcd780d18539
|
[] |
no_license
|
smwoodman/Bee-Lab-Thesis
|
b2944b0bbda8fa05c1ba1abb616577695213a3c3
|
87c4219bcc68f2c4ac1859715142aaed5654dc4f
|
refs/heads/master
| 2023-05-01T21:21:08.467938
| 2021-05-21T00:09:20
| 2021-05-21T00:09:20
| 50,375,952
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,117
|
r
|
Spacial Statistics.R
|
# Sam Woodman
# Analysis of spacial statistic R
# General, Desne, and Sparse Specific Testing
library(dplyr)
setwd("~/Google Drive/Semester 8/Thesis/NetLogo+R GitHub/BehaviorSpace Data/R Value/")
### General Testing
g.data <- read.csv("Bees R extra_sparse testing1-table.csv", header = TRUE, skip = 6)
## Filter data by dense and sparse
data.e.sparse <- filter(g.data, g.data$resource_density == "\"extra_sparse\"")
data.e.sparse.r <- data.e.sparse$R
data.e.sparse$grp <- (floor((data.e.sparse$X.run.number.-1) / 7)) + 1
data.e.sparse <- data.e.sparse %>% filter(c1_mult >= c2_mult)
## Divide data into groups .03 away from: R = 0.4, 0.6, 0.8, 1.0
# Extra-sparse
data.e.sparse.4 <- data.e.sparse %>% filter(abs(R - 0.4) <= 0.07)
data.e.sparse.6 <- data.e.sparse %>% filter(abs(R - 0.6) <= 0.03)
table(data.e.sparse.6$grp)
temp.6 <- which.max(table(data.e.sparse.6$grp))
es.idx.6 <- (7 * as.numeric(names(temp.6)))
data.e.sparse.8 <- data.e.sparse %>% filter(abs(R - 0.8) <= 0.03)
table(data.e.sparse.8$grp)
temp.8 <- which.max(table(data.e.sparse.8$grp))
es.idx.8 <- (7 * as.numeric(names(temp.8)))
### Dense Testing
d.data.all <- read.csv("Bees R dense testing-table.csv", header = TRUE, skip = 6)[-c(6,7,9)]
d.data.all$grp <- (floor((d.data.all$X.run.number.-1) / 20)) + 1
d.data <- d.data.all %>% filter(c1_mult > c2_mult)
## Divide data into groups d.num away from: R = 0.4, 0.6, 0.8
d.num <- 0.03
d.data.4 <- d.data %>% filter(abs(R - 0.4) <= d.num)
temp.4 <- which.max(table(d.data.4$grp))
table(d.data.4$grp)
d.idx.4 <- (20 * as.numeric(names(temp.4)))
d.data.all[d.idx.4,] # Best sequence is d.data.all[3941:3960,]
d.data.6 <- d.data %>% filter(abs(R - 0.6) <= d.num)
temp.6 <- which.max(table(d.data.6$grp))
table(d.data.6$grp)
d.idx.6 <- 20 * as.numeric(names(temp.6))
d.data.all[d.idx.6,] # Best sequence is d.data.all[2221:2240,]
d.data.8 <- d.data %>% filter(abs(R - 0.8) <= d.num)
temp.8 <- which.max(table(d.data.8$grp))
table(d.data.8$grp)
d.idx.8 <- 20 * as.numeric(names(temp.8))
d.data.all[d.idx.8,] # Best sequence is d.data.all[821:840,]
### Sparse Testing
#s.data.all <- read.csv("Bees R sparse testing_10rep-table.csv", header = TRUE, skip = 6)[-c(6,7,9)]
#s.data.all <- read.csv("Bees R sparse testing_10rep_detailed-table.csv", header = TRUE, skip = 6)
s.data.all <- read.csv("Bees R sparse testing_1500_2-table.csv", header = TRUE, skip = 6)
s.data.all$grp <- (floor((s.data.all$X.run.number.-1) / 4)) + 1
s.data <- s.data.all %>% filter(c1_mult > c2_mult)
s.num <- 0.03
## Divide data into groups s.num away from: R = 0.4, 0.6, 0.8
s.data.4 <- s.data %>% filter(abs(R - 0.4) <= s.num)
temp.4 <- which.max(table(s.data.4$grp))
table(s.data.4$grp)
s.idx.4 <- (4 * as.numeric(names(temp.4)))
s.data.all[s.idx.4,]
s.data.6 <- s.data %>% filter(abs(R - 0.6) <= s.num)
temp.6 <- which.max(table(s.data.6$grp))
table(s.data.6$grp)
s.idx.6 <- 4 * as.numeric(names(temp.6))
s.data.all[s.idx.6,]
s.data.8 <- s.data %>% filter(abs(R - 0.8) <= s.num)
temp.8 <- which.max(table(s.data.8$grp))
table(s.data.8$grp)
s.idx.8 <- 4 * as.numeric(names(temp.8))
s.data.all[s.idx.8,]
|
47426d3f488d8a71d89632133776d65fab14ecc2
|
63ce3e48f2217972353de20d58bf2a56d58a31b0
|
/R/some.R
|
54c8ecf46ed1846ff16b6441974e253ae0bcaed1
|
[] |
no_license
|
cran/car
|
6c8c284a58be9798daa0407f65cd5dd1fc808d34
|
d67a2f5ed4013c8766e8d3e827dcaf6aaaf7f0fa
|
refs/heads/master
| 2023-04-06T03:29:23.872258
| 2023-03-30T09:40:02
| 2023-03-30T09:40:02
| 17,694,945
| 8
| 20
| null | 2022-06-06T07:18:20
| 2014-03-13T04:11:37
|
R
|
UTF-8
|
R
| false
| false
| 776
|
r
|
some.R
|
# adapted from head() and tail()
# 3/10/2017: S. Weisberg modified to add an argument 'cols'
# cols = num will display only the first num cols
some <- function(x, ...) UseMethod("some")
some.default <- function(x, n=10, ...){
len <- length(x)
ans <- x[sort(sample(len, min(n, len)))]
if (length(dim(x)) == 1)
array(ans, n, list(names(ans)))
else ans
}
some.matrix <- function(x, n=10, cols=NULL, ...){
nr <- nrow(x)
nc <- ncol(x)
cols <- if(is.null(cols)) 1:nc else cols
x[sort(sample(nr, min(n, nr))), cols, drop = FALSE]
}
some.data.frame <- function(x, n=10, cols=NULL, ...){
nr <- nrow(x)
nc <- ncol(x)
cols <- if(is.null(cols)) 1:nc else cols
x[sort(sample(nr, min(n, nr))), cols, drop=FALSE]
}
|
dc56a85d7d36a36a3e0f32f4da2f80b6b2d23301
|
55a2bdd215a66d17bf5e170019cb8de960de760d
|
/mymain.R
|
488bff2c1ad9a98eea927780d9dbec93588202ba
|
[] |
no_license
|
yvonnechanlove97/Movie-Review
|
8569f102f690a5f5ed03efa4354b3d8ee350ef91
|
89c3568044b903dea7679139adff0b54b71716aa
|
refs/heads/master
| 2020-12-13T12:44:03.257227
| 2020-02-18T19:52:46
| 2020-02-18T19:52:46
| 234,420,103
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,870
|
r
|
mymain.R
|
rm(list=ls())
library(text2vec)
library(magrittr)
library(glmnet)
#get data
all = read.table("data.tsv",stringsAsFactors = F,header = T)
all$review = gsub('<.*?>', ' ', all$review)
splits = read.table("splits.csv", header = T)
s = 3 # Here we get the 3rd training/test split.
myvocab=as.matrix(read.table("myVocab.txt"))
train = all[-which(all$new_id%in%splits[,s]),]
test = all[which(all$new_id%in%splits[,s]),]
#build the vocabulary
prep_fun = tolower
tok_fun = word_tokenizer
train_tokens = train$review %>%
prep_fun %>%
tok_fun
it_train = itoken(train_tokens,
ids = train$new_id,
progressbar = FALSE)
it_test = test$review %>%
prep_fun %>% tok_fun %>%
itoken(ids = test$new_id, progressbar = FALSE)
stop_words2 = c('a','about',
'above','after','again','against','ain','all','am','an','and','any',
'are','aren',"aren't",'as','at','be','because','been','before',
'being','below','between','both','but','by','can','couldn',"couldn't",
'd','did','didn',"didn't",'do','does','doesn',"doesn't",
'doing','don',"don't",'down','during','each','few','for','from',
'further','had','hadn',"hadn't",'has','hasn',"hasn't",
'have','haven',"haven't",'having','he','her','here','hers',
'herself','him','himself','his','how','i','if','in','into','is',
'isn',"isn't",'it',"it's",'its','itself','just','ll','m','ma',
'me','mightn',"mightn't",'more','most','mustn',"mustn't",
'my','myself','needn',"needn't",'no','nor','not','now',
'o','of','off','on','once','only','or','other','our',
'ours','ourselves','out','over','own','re','s',
'same','shan',"shan't",'she',"she's",
'should',"should've",'shouldn',"shouldn't",'so','some',
'such','t','than','that',"that'll",'the','their',
'theirs','them','themselves','then','there','these',
'they','this','those','through','to','too','under',
'until','up','ve','very','was','wasn',"wasn't",'we','were',
'weren',"weren't",'what','when','where','which',
'while','who','whom','why','will','with','won',
"won't",'wouldn',"wouldn't",'y','you',"you'd","you'll",
"you're","you've",'your','yours','yourself','yourselves'
)
vocab=create_vocabulary(it_train,ngram = c(1L,4L),stopwords = stop_words2)
#clean train vocab
pruned_vocab = prune_vocabulary(vocab,
term_count_min = 5,
doc_proportion_max = 0.5,
doc_proportion_min = 0.001)
vectorizer = vocab_vectorizer(pruned_vocab)
dtm_train = create_dtm(it_train, vectorizer)
dtm_test = create_dtm(it_test, vectorizer)
train_x=dtm_train[,which(colnames(dtm_train)%in%myvocab)]
test_x=dtm_test[,which(colnames(dtm_test)%in%myvocab)]
#ridge
NFOLDS = 10
mycv = cv.glmnet(x=train_x, y=train$sentiment,
family='binomial',type.measure = "auc",
nfolds = NFOLDS, alpha=0)
myfit = glmnet(x=train_x, y=train$sentiment,
lambda = mycv$lambda.min, family='binomial', alpha=0)
logit_pred = predict(myfit, test_x, type = "response")
glmnet:::auc(test$sentiment, logit_pred)
write.table(result,"mysubmission.txt",row.names=FALSE, col.names = c('new_id','prob'), sep=", ")
#result=cbind(test$new_id,logit_pred)
#write.table(result,"Result_3.txt",row.names=FALSE, col.names = c('new_id','prob'), sep=", ")
#final_vocab2=words[id]
#write.table(final_vocab2,"myVocab1.txt",row.names=FALSE, col.names=FALSE,sep=", ")
#three
#3 0.9609737
#2 0.9635941
#1 0.9643016
|
87a7b183c2f42c7f07258dd02b328c7415a690e6
|
2e7fcffe2b532125bdc6662db16363ee36696df8
|
/RepRes_Peer_Assessment_1.r
|
7802225a7cdb113fd4a49c628e7ddecfb0bfde18
|
[] |
no_license
|
megerex/RepData_PeerAssessment1
|
802ef20c7c64d8a32821cbcb7dea9f1c94cbf5e7
|
5ecb62b522354e694a8c24ac6702ebbbf6edba5a
|
refs/heads/master
| 2020-05-29T11:55:16.073065
| 2015-05-14T13:59:49
| 2015-05-14T13:59:49
| 35,497,221
| 0
| 0
| null | 2015-05-12T15:43:04
| 2015-05-12T15:43:04
| null |
UTF-8
|
R
| false
| false
| 1,521
|
r
|
RepRes_Peer_Assessment_1.r
|
my.data <- read.csv(file="activity.csv", header=TRUE, sep=",", na.string="NA")
my.data$date <- as.Date(my.data$date)
my.data.aveSteps <- aggregate(formula = steps ~ date, data = my.data, FUN = mean)
require(ggplot2)
ggplot(data=my.data.aveSteps, aes(x=date, y=steps)) +
geom_histogram(stat="identity", fill="lightblue", colour="black")
my.data.aveIntervalSteps <- aggregate(formula = steps ~ interval, data = my.data, FUN = mean)
my.data.maxIntervalSteps <- my.data.aveIntervalSteps[which.max(my.data.aveIntervalSteps$steps),]
DailySteps.mean <- mean(my.data.aveSteps$steps)
DailySteps.median <- median(my.data.aveSteps$steps)
my.data.sansNA <- my.data
for (index in 1:nrow(my.data.sansNA)){
if(is.na(my.data.sansNA$steps[index])){
my.data.sansNA$steps[index]<- my.data.aveIntervalSteps$steps[my.data.aveIntervalSteps$interval == my.data.sansNA$interval[index]]
}
}
is.weekday <- function(date) {
day.test <- weekdays(date)
if (day.test %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"))
return("weekday")
else if (day.test %in% c("Saturday", "Sunday"))
return("weekend")
else
stop("invalid date")
}
my.data.sansNA$wday <- sapply(my.data.sansNA$date, FUN=is.weekday)
my.data.wday.aveIntervalSteps <- aggregate(formula = steps ~ interval + wday, data = my.data.sansNA, FUN = mean)
ggplot(data=my.data.wday.aveIntervalSteps, aes(x=interval, y=steps)) +
facet_grid(wday ~ . ) +
geom_line(colour="red", size=0.5) +
xlab("5-min interval") +
ylab("average steps taken")
|
e24aeebc182541bed949e7761d51b1a101b05a44
|
ca26b58313dc16a137f31f45e39aefe0a1a8f1ba
|
/rprog-data-ProgAssignment3-data/rankhospital.R
|
1f121f24ff966b30b20401dd6dad4ed95fa50850
|
[] |
no_license
|
ChuckChekuri/datasciencecoursera
|
06c4a95ef37c4fde39ab55dc98de615be98f56cf
|
8e81a42c3a1a43372390aa44fdb35bafa21c08fd
|
refs/heads/master
| 2020-05-23T10:06:24.413977
| 2017-04-08T14:18:33
| 2017-04-08T14:19:12
| 80,387,047
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,040
|
r
|
rankhospital.R
|
rankhospital = function(state, outcome, rank) {
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
states <- unique(data$State)
if (!state %in% states) stop("invalid state")
outcomes <- c("heart attack", "heart failure", "pneumonia")
if (!outcome %in% outcomes ) stop("invalid outcome")
# Hopital.Name - col 2
# get the column number based on passed in outcome
# columns for the mortality rates in the same order as outcomes
m_cols <- c(11,17,23)
outcol <-m_cols[which(outcomes == outcome)]
# subset the data by state and select hospital and metric
df <- data[data$State == state, c(2,outcol)]
# convert the character to number
df[,2] <- suppressWarnings(as.numeric(df[,2]))
# remove the NAs
df <- na.omit(df)
# order the hospitals by mortality rate
df <- df[order(df[,2],df[,1]),]
# return Hospital Name
if (rank == "best") rank <- 1
if (rank == "worst") rank <- nrow(df)
if (suppressWarnings(is.na(as.numeric(rank))))
stop("invalid rank")
if (rank > nrow(df) ) NA else df[rank,1]
}
|
006fe86376259fba85bd2d7d9e6a2dc9db7fa9a3
|
cd0e51bf6d007e8f540f22833d8e6ce2d4bba92f
|
/man/poisson.process.Rd
|
1652fd76a39d1d55edb379c39732072d06c292a2
|
[
"MIT"
] |
permissive
|
David-Statistics/Clancy-Functions
|
07daef7b20f2064e25f42bf987bba6243855cd27
|
2921e42a14f1cd39c5208d5772ebdbb915bb6d03
|
refs/heads/master
| 2021-05-04T06:46:31.702577
| 2016-11-30T19:21:19
| 2016-11-30T19:21:19
| 70,514,224
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 819
|
rd
|
poisson.process.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/poisson.process.r
\name{poisson.process}
\alias{poisson.process}
\title{Interarrival times and arrival times of a poisson process}
\usage{
poisson.process(rate = 1, events = NULL, time.running = NULL)
}
\arguments{
\item{rate}{The rate/intensity of the arrivals in the poisson process}
\item{events}{The number of events in the process (or \code{NULL} if defining
the cut off point by the time)}
\item{time.running}{The amount of time for the process to run (or \code{NULL}
if defining the cut off by the number of events)}
}
\value{
An numeric matrix with 2 columns. The first column contains the interarrival
times and the second contains the arrival times.
}
\description{
Interarrival times and arrival times of a poisson process
}
|
fdbc7be148ef51f37de157586c2a330f54ba5f31
|
03d20ec52ea429d2bffdefa849044ab6d0ad7481
|
/03_stop_frisk/scripts/stop_and_frisk.R
|
4e000dd92f4633cb09c0abbd2cd7809b2f2d1f92
|
[] |
no_license
|
GWarrenn/dc_data
|
3f679b28aa02f1cec7b9e887d66087d44ed40d7c
|
15b358d77210644dcdd908ef05d6e95930fbf62e
|
refs/heads/master
| 2021-11-17T07:36:55.337352
| 2021-09-30T21:44:56
| 2021-09-30T21:44:56
| 98,127,130
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 58,317
|
r
|
stop_and_frisk.R
|
## Author: August Warren
## Description: Analysis of DC Stop and Frisk Data
## Date: 9/6/2018
## Status: Published
## Specs: R version 3.3.2 (2016-10-31)
###################################
##
## Load packages
##
###################################
library(ggplot2)
library(dplyr)
library(gdata)
library(rgdal)
library(sp)
library(rgeos)
library(geosphere)
library(ggmap)
library(reshape2)
library(ggthemes)
library(zoo)
library(ggridges)
library(lubridate)
library(fuzzyjoin)
library(tidyverse)
library(tweenr)
library(scales)
library(stargazer)
library(viridis)
source("03_stop_frisk/scripts/mygg_animate.r")
###################################
##
## Load Stop and Frisk Data
## Provided by MPD: https://mpdc.dc.gov/publication/stop-and-frisk-data-and-explanatory-notes
##
###################################
stop_frisk_1 <- read.xls("data/crime/SF_Field Contact_02202018.xls",sheet = 1)
stop_frisk_2017 <- read.xls("data/crime/SF_Field Contact_CY2017.xls",sheet = 1)
stop_frisk_1 <- rbind(stop_frisk_1,stop_frisk_2017)
stop_frisk_1$REASON.FOR.STOP <- "N/A"
stop_frisk_1$time_of_day <- format(round(strptime(stop_frisk_1$Report_taken_date_EST, "%m/%d/%Y %I:%M %p"), units="hours"), format="%H:%M")
stop_frisk_1$day_of_month <- format(strptime(stop_frisk_1$Report_taken_date_EST, "%m/%d/%Y %I:%M %p"), "%d")
stop_frisk_1$month <- format(as.Date(stop_frisk_1$Report_taken_date_EST,"%m/%d/%Y"), "%m")
stop_frisk_1$year_month <- as.Date(paste0(stop_frisk_1$month,"/","01/",as.numeric(stop_frisk_1$Year),sep=""),"%m/%d/%Y")
## add in 2017 data
stop_frisk_2 <- read.xls("data/crime/SF_Field Contact_02202018.xlsx",sheet = 2, quote = "")
stop_frisk_2017 <- read.xls("data/crime/SF_Field Contact_CY2017.xls",sheet = 2, quote = "")
## add 2017 data
stop_frisk_2 <- rbind(stop_frisk_2,stop_frisk_2017)
stop_frisk_2 <- as.data.frame(sapply(stop_frisk_2, function(x) gsub("\"", "", x)))
cols <- colnames(stop_frisk_2)
for (c in cols) {
fixed <- gsub(pattern = "X.|.$",
replacement = "",
x = c)
names(stop_frisk_2)[names(stop_frisk_2)==c] <- fixed
}
stop_frisk_2 <- stop_frisk_2 %>%
rename(Report_taken_date_EST = Report.taken.date,
Incident_Type = FIELD.CONTACT.TYPE,
Subject_Race = Race,
Subject_Sex = Sex,
Subject_Ethnicity = Ethnicity,
Incident.Location.PSA = PSA,
Incident.Location.District = District,
Block.Address = Block.address)
stop_frisk_2$time_of_day <- format(round(strptime(stop_frisk_2$Report_taken_date_EST, "%Y-%m-%d %H:%M"),units="hours"), "%H:%M")
stop_frisk_2$day_of_month <- format(strptime(stop_frisk_2$Report_taken_date_EST, "%Y-%m-%d %H:%M"), "%d")
stop_frisk_2$month <- format(as.Date(stop_frisk_2$Report_taken_date_EST,"%Y-%m-%d"), "%m")
stop_frisk_2$year_month <- as.Date(paste0(stop_frisk_2$month,"/","01/",as.numeric(as.character(stop_frisk_2$Year)),sep=""),"%m/%d/%Y")
stop_frisk_total <- rbind(stop_frisk_1)
stop_frisk_total$id <- seq.int(nrow(stop_frisk_total))
###################################
##
## Matching incident block address in stop & frisk file to DC block data
## in order to obtain incident lattitude and longitude
## h/t to Mahkah Wu for the idea and doing the hard: https://github.com/mahkah/dc_stop_and_frisk
##
###################################
stop_frisk_matched <- read.csv(url("https://raw.githubusercontent.com/mahkah/dc_stop_and_frisk/master/transformed_data/SF_Field%20Contact_locations.csv"))
stop_frisk_matched <- stop_frisk_matched %>%
filter(block_match == "Matched" & cause == "Unknown") %>%
rename(Age = subject_age,
Subject_Ethnicity = subject_ethnicity,
Subject_Gender = subject_gender,
Subject_Race = subject_race)
stop_frisk_matched$time_of_day <- format(round(strptime(stop_frisk_matched$incident_date, "%Y-%m-%d %H:%M"), units="hours"), format="%H:%M")
stop_frisk_matched$day_of_month <- format(strptime(stop_frisk_matched$incident_date, "%Y-%m-%d %H:%M"), "%d")
stop_frisk_matched$month <- format(as.Date(stop_frisk_matched$incident_date,"%Y-%m-%d %H:%M"), "%m")
stop_frisk_matched$year_month <- as.Date(paste0(as.numeric(stop_frisk_matched$month),"/","01/",as.numeric(stop_frisk_matched$year),sep=""),"%m/%d/%Y")
###################################
##
## Top-level descriptives
##
###################################
## add in non-forcible for analysis
stop_frisk_all_stops <- rbind(stop_frisk_1,stop_frisk_2)
## standard funciton to add demos to all forcible, matched lat/long forcible, forcible & non-forcible stops
add_demos <- function(data_frame){
data_frame$race_ethn <- ifelse(data_frame$Subject_Ethnicity=='Hispanic Or Latino','Hispanic/Latino',
data_frame$Subject_Race)
data_frame$race_ethn <- as.character(data_frame$Subject_Race)
data_frame$race_ethn[data_frame$Subject_Ethnicity == "Hispanic Or Latino"] <- "Hispanic/Latino"
data_frame$juvenile <- ifelse(data_frame$Age == "Juvenile","Juvenile","Adult")
data_frame$juvenile[data_frame$Age == "Unknown" | data_frame$Age == ""] <- "Unknown"
return(data_frame)
}
stop_frisk_total <- add_demos(stop_frisk_total) ## all forcible stop & frisks
stop_frisk_matched <- add_demos(stop_frisk_matched) ## forcible stops mapped to lat/long
stop_frisk_all_stops <- add_demos(stop_frisk_all_stops) ## both forcible & non-forcible stops
## what are the historical patterns of stop and frisk? -- monthly ts
stop_frisk_monthly <- stop_frisk_total %>%
group_by(year_month) %>%
summarise (n = n()) %>%
arrange(year_month) %>%
mutate(monthly = rollsum(n, k = 12, na.pad = TRUE, align = "right"))
monthly_sf <- ggplot(stop_frisk_monthly,aes(x=year_month,y=n,group=1)) +
geom_point(size=2) +
geom_smooth(method = lm,size=2) +
#geom_vline(aes(xintercept = as.numeric(as.Date(dmy("2/1/2015")))), col = "black") +
#stat_smooth(aes(x=year_month, y=n), method = lm, formula = y ~ poly(x, 10), se = TRUE,size=2) +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
ylab('Number of Stop and Frisks') + xlab("Month") + ggtitle("Total Number of Stop and Frisks per Month") +
scale_x_date(date_breaks = "6 months", date_labels = "%m/%y")
ggsave(plot = monthly_sf, "03_stop_frisk/images/01_monthly_sf.png", w = 10.67, h = 8,type = "cairo-png")
## hourly ts by race percentage
stop_frisk_hourly <- stop_frisk_total %>%
filter(!is.na(stop_frisk_total$time_of_day)) %>%
group_by(time_of_day) %>%
summarise (n = n()) %>%
mutate(freq=n/sum(n))
stop_frisk_total$hour <- format(round(strptime(stop_frisk_total$Report_taken_date_EST, "%m/%d/%Y %H:%M"),units="hours"), "%H")
stop_frisk_total$mins <- format(round(strptime(stop_frisk_total$Report_taken_date_EST, "%m/%d/%Y %H:%M"),units="mins"), "%M")
stop_frisk_total$mins_past_midnight <- (as.numeric(stop_frisk_total$hour) * 60) + as.numeric(stop_frisk_total$mins)
ggplot(data = filter(stop_frisk_total,race_ethn %in% c("White","Black","Hispanic/Latino")),
aes(mins_past_midnight,group=race_ethn,fill=race_ethn)) + geom_density(alpha = 0.5)
stop_frisk_hourly$race_ethn <- "Total"
stop_frisk_hourly <- stop_frisk_hourly[c("race_ethn", "time_of_day", "n","freq")]
stop_frisk_hourly_race <- stop_frisk_total %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino")) %>%
group_by(race_ethn,time_of_day) %>%
summarise (n = n()) %>%
mutate(freq=n/sum(n))
stop_frisk_hourly_comb <- rbind(as.data.frame(stop_frisk_hourly_race),as.data.frame(stop_frisk_hourly))
stop_frisk_hourly_comb$time_of_day <- factor(stop_frisk_hourly_comb$time_of_day,levels=c("07:00","08:00","09:00","10:00","11:00","12:00","13:00",
"14:00","15:00","16:00","17:00","18:00","19:00","20:00",
"21:00","22:00","23:00","00:00","01:00","02:00","03:00",
"04:00","05:00","06:00"))
stop_frisk_hourly_comb_plot <- ggplot(stop_frisk_hourly_comb, aes(x=time_of_day, y=freq, color=race_ethn,group=race_ethn)) +
geom_line(size=2) +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
ylab('Percentage of Stop and Frisk Incidents') + xlab("Time of Day") +
ggtitle("Total Stop and Frisk Incidents by Time of Day & Race/Ethnicity") +
scale_y_continuous(labels = scales::percent) +
scale_color_discrete(name="Legend")
ggsave(plot = stop_frisk_hourly_comb_plot, "03_stop_frisk/images/02_time_of_day_sf.png", w = 10.67, h = 8,type = "cairo-png")
## percent of juvenile stops by race
stop_frisk_total$juvenile <- ifelse(stop_frisk_total$Age == "Juvenile","Juveniles",
"Adults")
sf_age_race <- stop_frisk_total %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino")) %>%
group_by(race_ethn,juvenile,Subject_Sex) %>%
summarise(count = n()) %>%
mutate(freq=count/sum(count))
sf_age_race$race_ethn <- factor(sf_age_race$race_ethn,levels=c("White","Black","Hispanic/Latino"))
sf_race_youths <- ggplot(sf_age_race,aes(x=juvenile,y=freq,fill=juvenile)) +
geom_bar(stat="identity") +
facet_wrap(~race_ethn) +
geom_text(aes(x=juvenile,y=freq,label=percent(round(freq,2))),data=sf_age_race,
position=position_dodge(width=0.9), vjust=-0.5,size=5) +
theme_fivethirtyeight() +
scale_y_continuous(labels=scales::percent,limits=c(0,1)) +
theme(axis.title = element_text(),
plot.title = element_text(hjust = 0.5),
axis.text = element_text(size=12),
strip.text = element_text(size=16)) +
labs(title = "Proportion of Juvenile vs. Adult Stops by Race/Ethnicity",
x = '',
y ="",
fill="Legend")
ggsave(plot = sf_race_youths, "03_stop_frisk/images/sf_race_youths.png", w = 10.67, h = 8,type = "cairo-png")
## average age of stop and frisk by race
sf_ages <- stop_frisk_total %>%
filter(Age != "Juvenile")
sf_ages$Age <- as.double(as.character(sf_ages$Age))
sf_age_stats <- sf_ages %>%
filter(!is.na(sf_ages$Age)) %>%
group_by(race_ethn) %>%
summarise(mean = mean(Age),
median = median(Age),
min = min(Age),
max = max(Age),
iqr = IQR(Age))
white <- sf_ages %>%
filter(race_ethn=="White") ##23
black <- sf_ages %>%
filter(race_ethn=="Black") #18
hisp <- sf_ages %>%
filter(race_ethn=="Hispanic/Latino") #18
sf_age_dist$race_ethn <- factor(sf_age_dist$race_ethn,levels=c("White","Black","Hispanic/Latino"))
sf_age_dist <- ggplot(filter(sf_ages, race_ethn %in% c("White","Black","Hispanic/Latino")),
aes(x = Age, y = race_ethn)) + geom_density_ridges() +
theme_fivethirtyeight() +
theme(axis.title = element_text(),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
labs(x="Age",
y="",
title="Age of Stop and Frisk Incidents by Race/Ethnicity among Adults")
ggsave(plot = sf_age_dist, "03_stop_frisk/images/04_sf_age_dist.png", w = 10.67, h = 8,type = "cairo-png")
## what are the most cited reasons for non-forcible stops?
stop_frisk_all_stops$reason <- ifelse(grepl("Suspicious",stop_frisk_all_stops$REASON.FOR.STOP),
"Suspicious Vehicles/Persons/Activities",stop_frisk_all_stops$REASON.FOR.STOP)
reasons_for_stop <- stop_frisk_all_stops %>%
filter(!reason %in% c("N/A") & race_ethn %in% c("White","Black","Hispanic/Latino","Asian")) %>%
group_by(race_ethn,reason) %>%
summarise(n = n()) %>%
mutate(freq = n/sum(n))
reasons_for_stop <- reasons_for_stop %>%
filter(race_ethn %in% c("White", "Black", "Hispanic/Latino"))
reasons_for_stop$race_ethn <- factor(reasons_for_stop$race_ethn, levels = c("White", "Black", "Hispanic/Latino"))
reason_for_stop_plot <- ggplot(reasons_for_stop,aes(x=reason, y=freq)) +
geom_bar(stat = "identity") +
geom_text(aes(label=percent(round(freq,2))),
hjust=-.1, position=position_dodge(.5)) +
coord_flip() +
theme_fivethirtyeight() +
facet_wrap(~race_ethn) +
scale_y_continuous(labels=scales::percent,limits=c(0,.5)) +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
labs(title = "Reason for Field Contact Report by Race",
subtitle = "among all non-forcible stops",
x = 'Reason For Stop',
y ="")
ggsave(plot = reason_for_stop_plot, "03_stop_frisk/images/reason_for_stop.png", w = 10.67, h = 8,type = "cairo-png")
## race breakdown by forcible/non-forcible stops
stop_frisk_all_stops$contact_type <- ifelse(stop_frisk_all_stops$REASON.FOR.STOP=="N/A","Forcible","Non-forcible")
race_contact <- stop_frisk_all_stops %>%
group_by(contact_type,race_ethn) %>%
summarise(n=n()) %>%
mutate(freq=n/sum(n)) %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino"))
race_contact_plot <- ggplot(race_contact,aes(x=race_ethn, y=freq,fill=race_ethn)) +
geom_bar(stat="identity") +
geom_text(aes(label=percent(freq)),
vjust=-.5, position=position_dodge(.5), size=5) +
theme_fivethirtyeight() +
facet_grid(~contact_type) +
scale_y_continuous(labels=scales::percent,limits=c(0,1)) +
scale_x_discrete(limits=c("White","Black","Hispanic/Latino")) +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
xlab("") + ylab("") + ggtitle("Subject Race/Ethnicity by Contact Type")+
scale_fill_discrete(name="Legend",limits=c("White","Black","Hispanic/Latino"))
ggsave(plot = race_contact_plot, "03_stop_frisk/images/race_contact.png", w = 10.67, h = 8,type = "cairo-png")
gender_race <- stop_frisk_total %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino")) %>%
group_by(race_ethn,Subject_Sex) %>%
summarise(n=n()) %>%
mutate(freq=n/sum(n))
gender_race_plot <- ggplot(gender_race,aes(x=Subject_Sex, y=freq,fill=Subject_Sex)) +
geom_bar(stat="identity") +
geom_text(aes(label=percent(freq)),
vjust=-.5, position=position_dodge(.5), size=5) +
theme_fivethirtyeight() +
facet_wrap( ~ race_ethn) +
scale_y_continuous(labels=scales::percent,limits=c(0,1)) +
scale_x_discrete(limits=c("Male","Female")) +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5),
strip.text = element_text(size = 12)) +
xlab("") + ylab("") + ggtitle("Gender of Stop and Frisks by Race/Ethnicity")+
scale_fill_discrete(name="Legend",limits=c("Male","Female"))
ggsave(plot = gender_race_plot, "03_stop_frisk/images/gender_race.png", w = 10.67, h = 8,type = "cairo-png")
## gender/race breakdown by forcible/non-forcible
gender_race <- stop_frisk_all_stops %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino")) %>%
group_by(race_ethn,contact_type,Subject_Sex) %>%
summarise(n=n()) %>%
mutate(freq=n/sum(n))
gender_race_plot <- ggplot(gender_race,aes(x=Subject_Sex, y=freq,fill=Subject_Sex)) +
geom_bar(stat="identity") +
geom_text(aes(label=percent(freq)),
vjust=-.5, position=position_dodge(.5), size=5) +
theme_fivethirtyeight() +
facet_grid(contact_type ~ race_ethn) +
scale_y_continuous(labels=scales::percent,limits=c(0,1)) +
scale_x_discrete(limits=c("Male","Female")) +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
xlab("") + ylab("") + ggtitle("Gender by Race/Ethnicity & Contact Type")+
scale_fill_discrete(name="Legend",limits=c("Male","Female"))
ggsave(plot = gender_race_plot, "03_stop_frisk/images/gender_race_contact.png", w = 10.67, h = 8,type = "cairo-png")
###################################
##
## Matching incidents to neighborhoods using DC neighborhood shapefile
## provided by DC OpenData
## h/t: https://gis.stackexchange.com/questions/133625/checking-if-points-fall-within-polygon-shapefile
##
###################################
## neighborhoods
dc_neighborhoods <- readOGR("data/shapefiles",
layer="Neighborhood_Clusters")
coordinates(stop_frisk_matched) <- ~ X + Y
neighborhoods <- levels(dc_neighborhoods$NBH_NAMES)
nbh_sf_df <- data.frame()
for (n in neighborhoods) {
print(paste("Classifying stop and frisk incidents in",n))
test <- data.frame()
cluster <- dc_neighborhoods[dc_neighborhoods$NBH_NAMES == n , ]
proj4string(stop_frisk_matched) <- proj4string(cluster)
test <- stop_frisk_matched[complete.cases(over(stop_frisk_matched, cluster)), ]
test_df <- as.data.frame(test)
try(test_df$neighborhood <- n)
nbh_sf_df <- rbind(nbh_sf_df,test_df)
}
###################################
##
## Calculate race/age breakdowns of stop and frisk at neighborhood level
##
###################################
## neighborhoods
nbh_sf_tot <- nbh_sf_df %>%
group_by(neighborhood) %>%
summarise (n = n()) %>%
mutate(freq=n/sum(n))
nbh_sf_tot$demo_group <- "Total"
nbh_sf_tot$subgroup <- "Total"
nbh_sf_race <- nbh_sf_df %>%
group_by(neighborhood,race_ethn) %>%
summarise (n = n()) %>%
mutate(freq=n/sum(n)) %>%
rename(subgroup = race_ethn)
nbh_sf_race$demo_group <- "Race/Ethnicity"
nbh_sf_age <- nbh_sf_df %>%
group_by(neighborhood,juvenile) %>%
summarise (n = n()) %>%
mutate(freq=n/sum(n)) %>%
rename(subgroup = juvenile)
nbh_sf_age$demo_group <- "Age"
nbh_sf_demos <- dplyr::bind_rows(nbh_sf_tot,nbh_sf_age,nbh_sf_race)
additional_cluster_info <- read.csv("data/shapefiles/Neighborhood_Clusters.csv")
nbh_sf_demos <- merge(nbh_sf_demos,additional_cluster_info,by.x="neighborhood",by.y="NBH_NAMES")
###################################
##
## Process Census data
##
###################################
census_data_original <- read.csv("data/census/comp_table_cltr00_pop.csv")
census_data_tot <- census_data_original %>%
select(CLUSTER_TR2000,TotPop_2010) %>%
melt(id.vars = c("CLUSTER_TR2000"))
census_data_tot$TotPop_2010 <- census_data_tot$value
census_data <- census_data_original %>%
select(CLUSTER_TR2000,TotPop_2010,PctPopUnder18Years_2010,
PctBlackNonHispBridge_2010,PctWhiteNonHispBridge_2010,PctHisp_2010)
census_data <- melt(census_data, id.vars = c("CLUSTER_TR2000","TotPop_2010"))
census_data <- rbind(census_data,census_data_tot)
census_data$pop <- ifelse(census_data$variable == "TotPop_2010",census_data$TotPop_2010,
census_data$TotPop_2010 * (census_data$value/100))
census_data <- census_data %>%
mutate(variable = recode(census_data$variable, PctPopUnder18Years_2010 = "Juvenile",
PctBlackNonHispBridge_2010 = "Black",
PctWhiteNonHispBridge_2010 = "White",
PctHisp_2010 = "Hispanic/Latino",
TotPop_2010 ="Total")) %>%
rename(census_value = value)
nbh_racial_summary <- census_data_original %>%
select(CLUSTER_TR2000,PctBlackNonHispBridge_2010,PctHisp_2010,PctAsianPINonHispBridge_2010)
nbh_racial_summary$pct_non_white <- nbh_racial_summary$PctBlackNonHispBridge_2010 +
nbh_racial_summary$PctHisp_2010 +
nbh_racial_summary$PctAsianPINonHispBridge_2010
nbh_racial_summary$non_white_bins <- cut(nbh_racial_summary$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
nbh_racial_summary <- merge(nbh_racial_summary,additional_cluster_info,by.x="CLUSTER_TR2000",by.y="NAME")
levels(nbh_racial_summary$non_white_bins) <- c("0-25%","25-50%","50-75%","75-95%","95-100%")
nbh_black_summary <- ggplot(nbh_racial_summary,aes(x=reorder(NBH_NAMES,PctBlackNonHispBridge_2010),y=PctBlackNonHispBridge_2010/100,fill=non_white_bins)) +
geom_bar(stat='identity') +
geom_text(aes(x=reorder(NBH_NAMES,PctBlackNonHispBridge_2010),y=PctBlackNonHispBridge_2010/100,
label=percent(round(PctBlackNonHispBridge_2010/100,2))),data=nbh_racial_summary,
position=position_dodge(width=0.9), hjust=-0.1,size=3) +
theme_fivethirtyeight() +
coord_flip() +
labs(title = "DC Neighborhood Racial Composition", subtitle="Proportion of Black Residents by Neighborhood as of 2010 Census ",
x="",y="Neighborhood Percent of Black Residents",
fill = "Neighborhood % Black") +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5,size = 10)) +
scale_y_continuous(labels=scales::percent) +
guides(fill=guide_legend(nrow=2,byrow=TRUE))
ggsave(plot = nbh_black_summary, "03_stop_frisk/images/nbh_racial_profiles.png", w = 10.67, h = 8,type = "cairo-png")
###################################
##
## Merge neighborhood-level census data to stop and frisk data
##
###################################
nbh_sf_demos_census <- merge(nbh_sf_demos,census_data,by.x=c("NAME","subgroup"),by.y=c("CLUSTER_TR2000","variable"))
###################################
##
## Plot stop and frisk and census
##
###################################
overall_change <- stop_frisk_total %>%
group_by(Year) %>%
summarise(count = n())
overall_change$Year <- paste0("year_",overall_change$Year)
overall_change$ix <- "Total"
overall_change <- dcast(overall_change, ix ~ Year,value.var = "count")
overall_change$change_2017_2010 <- (overall_change$year_2017 / overall_change$year_2010) - 1
## percent change in stop & frisk by nh racial composition 2010 - 2017
yearly_nbh_counts <- nbh_sf_df %>%
group_by(year,neighborhood) %>%
summarise(n=n())
yearly_nbh_counts$year <- paste0("year_",yearly_nbh_counts$year)
# reshape to wide
yearly_nbh_counts_wide <- dcast(yearly_nbh_counts, neighborhood ~ year)
# merge in census data
yearly_nbh_counts_wide <- merge(yearly_nbh_counts_wide,additional_cluster_info,by.x="neighborhood",by.y="NBH_NAMES")
yearly_nbh_counts_wide <- merge(yearly_nbh_counts_wide,census_data_original,by.x=c("NAME"),by.y=c("CLUSTER_TR2000"))
yearly_nbh_counts_wide$pct_non_white <- yearly_nbh_counts_wide$PctAsianPINonHispBridge_2010 +
yearly_nbh_counts_wide$PctBlackNonHispBridge_2010 +
yearly_nbh_counts_wide$PctHisp_2010
yearly_nbh_counts_wide$black_bins <-cut(yearly_nbh_counts_wide$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
yearly_nbh_counts_wide$change_2017_2010 <- (yearly_nbh_counts_wide$year_2017 / yearly_nbh_counts_wide$year_2010) - 1
change_by_racial_bins <- yearly_nbh_counts_wide %>%
group_by(black_bins) %>%
summarise(avg_change = mean(change_2017_2010),
n= n())
levels(change_by_racial_bins$black_bins) <- c("0-25%","25-50%","50-75%","75-95%","95-100%")
change_in_sf <- ggplot(change_by_racial_bins,aes(x=black_bins,y=avg_change)) +
geom_bar(stat='identity') +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
ylab('Change in Total Stop and Frisk 2010 to 2017') + xlab("Neighborhood Percent of Black Residents") +
ggtitle("Average Change in Stop and Frisk 2010 to 2017\nby Neighborhood Racial Composition") +
geom_text(aes(x=black_bins,y=avg_change,label=percent(round(avg_change,2))),data=change_by_racial_bins,
position=position_dodge(width=0.9), vjust=-0.5,size=5) +
scale_y_continuous(labels=scales::percent)
ggsave(plot = change_in_sf, "03_stop_frisk/images/change_in_sf.png", w = 10.67, h = 8,type = "cairo-png")
# overall neighborhood stop and frisk
nbh_sf_totals_census_adj <- nbh_sf_demos_census %>%
filter(demo_group == "Total") %>%
mutate(adj_sf = (n/pop) * 10000,
group = 1:39) %>%
select(group,adj_sf) %>%
rename(value = adj_sf)
nbh_names <- nbh_sf_demos_census %>%
filter(demo_group == "Total") %>%
mutate(group = 1:nrow(nbh_sf_totals_census_adj)) %>%
select(neighborhood,group)
nbh_sf_totals_census <- nbh_sf_demos_census %>%
filter(demo_group == "Total") %>%
mutate(group = 1:nrow(nbh_sf_totals_census_adj)) %>%
select(group,n) %>%
rename(value = n)
#group = neighborhood)
nbh_sf_totals_census$frame <- 1
nbh_sf_totals_census_adj$frame <- 2
# Interpolate data with tweenr
ts <- list(nbh_sf_totals_census, nbh_sf_totals_census_adj,nbh_sf_totals_census_adj,nbh_sf_totals_census)
tf <- tween_states(ts, tweenlength = 0.02, statelength = 0.001, ease = c('cubic-in-out'), nframes = 30)
init_sort <- nbh_sf_totals_census %>%
arrange(-value) %>%
mutate(rank = 1:nrow(nbh_sf_totals_census)) %>%
select(rank,group)
tf <- merge(tf,nbh_names,by="group")
tf <- merge(tf,init_sort,by="group")
## h/t to: https://stackoverflow.com/questions/45569659/plot-titles-when-using-gganimate-with-tweenr
tf$type <- ifelse(tf$frame > 1.5,"per 10,000 people","")
# Make a barplot with frame
p <- ggplot(tf, aes(x=reorder(neighborhood,-rank), y=value, fill=value, frame= .frame,ttl=type)) +
geom_bar(stat='identity', position = "identity") +
coord_flip() +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
labs(x="Neighborhood",y="Total Stop & Frisk",title="Total Stop and Frisk",fill="Total Stop & Frisk")
mygg_animate(p,filename = "03_stop_frisk/images/05_nbh_sf.gif",interval = 0.1, title_frame=T,ani.height=768,ani.width=1024)
# overall racial sf & census breakdowns
# process 2018 census data from https://www.census.gov/quickfacts/fact/table/DC#viewtop
census_race <- read.csv("data/census/QuickFacts Apr-22-2018.csv",nrows = 20)
census_race <- slice(census_race, 13:19) %>%
select(Fact,District.of.Columbia) %>%
rename(value = District.of.Columbia,
variable = Fact)
census_race$variable <- gsub("([A-Za-z]+).*", "\\1", census_race$variable)
census_race$value <- gsub("%", "", census_race$value)
census_race <- census_race %>%
filter(variable %in% c("White","Black","Hispanic","Asian"))
census_race$variable[census_race$variable=="Hispanic"] <- "Hispanic/Latino"
sf_race <- stop_frisk_total %>%
group_by(race_ethn) %>%
summarise (n = n()) %>%
mutate(freq=(n/sum(n))*100) %>%
rename(variable = race_ethn,
value = freq) %>%
select(variable,value) %>%
filter(variable %in% c("White","Black","Hispanic/Latino","Asian"))
sf_race$type <- 'Stop & Frisk'
census_race$type <- 'Census'
census_sf_race <- rbind(sf_race,census_race)
census_sf_race$value <- as.numeric(as.character(census_sf_race$value))/100
census_sf_race$type <- factor(census_sf_race$type, levels=c("Census","Stop & Frisk"))
census_sf_race_plot <- ggplot(census_sf_race,aes(x=variable,y=as.numeric(value),fill=variable)) +
geom_bar(stat = "identity",position = "stack") +
theme_fivethirtyeight() +
theme(axis.title = element_text(),
plot.title = element_text(hjust = 0.5),
strip.text = element_text(size=12)) +
ylab("") + xlab("Race") + ggtitle("Stop and Frisk - Census Racial Comparison") +
scale_x_discrete(limits = c("White","Black","Hispanic/Latino","Asian")) +
scale_y_continuous(labels=scales::percent,limits=c(0,1)) +
geom_text(aes(x=variable,y=value,label=percent(round(value,2))),data=census_sf_race,
position=position_dodge(width=0.9), vjust=-0.5,size=5) +
scale_fill_discrete(name="Legend") +
facet_wrap(~type)
ggsave(plot = census_sf_race_plot, "03_stop_frisk/images/03_census_sf_race.png", w = 10.67, h = 8,type = "cairo-png")
## scatter plot of neighborhood racial composition and percent of stop and frisk by race
## label petworth?
nbh_sf_race_plot <- ggplot(data=filter(nbh_sf_demos_census,demo_group %in% c("Race/Ethnicity")),aes(x=freq,y=census_value)) +
geom_point(aes(size=n,color=subgroup),alpha=.7) + scale_x_continuous(limits = c(0, 1),labels = scales::percent) +
geom_point(data=subset(nbh_sf_demos_census,neighborhood == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
demo_group %in% c("Race/Ethnicity")),
aes(size=n),colour="black",pch=21) +
geom_text(data=subset(nbh_sf_demos_census,neighborhood == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
demo_group %in% c("Race/Ethnicity")),
aes(label=paste0("Columbia Heights\n(",subgroup,")")),
hjust="center", vjust = -.5, size=4) +
scale_y_continuous(limits = c(0, 100)) + geom_abline(intercept = 0,slope = 100) +
geom_hline(yintercept = 50) + geom_vline(xintercept = .5) +
theme_fivethirtyeight() +
labs(x = "Stop and Frisk Racial Makeup (%)", y = "Neighborhood Racial Makeup (%)") +
ggtitle("Neighborhood Population vs. Neighborhood Stop and Frisk") +
scale_color_discrete(name="Race") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text()) +
scale_size(name = "Total Stop and Frisk") + theme(legend.position="bottom", legend.box = "horizontal")
ggsave(plot = nbh_sf_race_plot, "03_stop_frisk/images/06_nbh_sf_race.png", w = 10.67, h = 10.67,type = "cairo-png")
nbh_sf_demos_census$diff <- (nbh_sf_demos_census$census_value/100) - nbh_sf_demos_census$freq
## difference of neighborhood racial composition and percent of stop and frisk by race
plot_diff <- function(racial_group,output_file) {
race_pct <- nbh_sf_demos_census %>%
filter(subgroup == racial_group) %>%
select(NAME,neighborhood,census_value) %>%
rename(pct = census_value) %>%
mutate(pct = pct/100)
nbh_sf_demos_census <- merge(nbh_sf_demos_census,race_pct)
nbh_diff_race <- ggplot(data=filter(nbh_sf_demos_census,subgroup %in% c(racial_group) & demo_group != "Total"),
aes(x = reorder(neighborhood, -diff),
y = as.numeric(diff))) +
geom_point(aes(size=n,color=pct),alpha=.7,stat='identity') + coord_flip() +
geom_hline(yintercept = 0) +
theme_fivethirtyeight() +
labs(title = "Difference in Stop and Frisk Rate & Population",
subtitle = paste("among", racial_group,"Residents (2010 - 2017)"),
y = 'Stop & Frisk - Population',
x="Neighborhood",size="Total Stop & Frisk",color=paste("Neighborhood %",racial_group)) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5,size=12),
text = element_text(size=10)) +
scale_y_continuous(labels=scales::percent) +
scale_color_viridis(labels = percent) +
geom_segment(aes(y = 0,
x = neighborhood,
yend = diff,
xend = neighborhood),
color = "black")
ggsave(plot = nbh_diff_race, paste0("03_stop_frisk/images/",output_file,".png"), w = 10.67, h = 8,type = "cairo-png")
}
plot_diff("Black","07_nbh_diff_black")
plot_diff("White","07a_nbh_diff_white")
plot_diff("Hispanic/Latino","07b_nbh_diff_hisp")
## stop & frisk & census pop among blacks by neighborhood white percentage
nbh_white <- nbh_sf_demos_census %>%
filter(subgroup %in% c("White") & demo_group != "Total") %>%
select(neighborhood,census_value) %>%
rename(nbh_white_pct = census_value)
new_df <- nbh_sf_demos_census %>%
filter(subgroup %in% c("Black") & demo_group != "Total") %>%
mutate(adj_census = census_value/100) %>%
select(neighborhood,adj_census,freq) %>%
rename(nbh_black_pct = adj_census,
black_sf = freq)
new_df_l <- melt(new_df)
num_stops <- nbh_sf_demos_census %>%
filter(subgroup %in% c("Black") & demo_group != "Total") %>%
select(neighborhood,n)
new_df_l <- merge(new_df_l,nbh_white,by="neighborhood")
new_df_l <- merge(new_df_l,num_stops,by="neighborhood")
new_df <- merge(new_df,nbh_white,by="neighborhood")
new_df$indicator <- ifelse(new_df$black_sf > new_df$nbh_black_pct,"Higher","Lower")
group.colors <- c("Higher" = "#FF0000", "Lower" = "#008000", "Black Stop & Frisk Percentage" = cols[1], "Black Census Percentage" = cols[3])
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
n = 4
cols = gg_color_hue(n)
levels(new_df_l$variable) <- c("Black Census Percentage","Black Stop & Frisk Percentage")
test <- ggplot(data=new_df_l,
aes(x = as.numeric(nbh_white_pct/100),
y = as.numeric(value)),
color = variable) +
geom_point(aes(size=n,color=variable),alpha=.5,stat='identity') +
coord_flip() +
geom_segment(data = new_df, aes(y = nbh_black_pct,
x = as.numeric(nbh_white_pct/100),
yend = black_sf,
xend = as.numeric(nbh_white_pct/100),
color=indicator),
size = 1,
alpha = .2) +
scale_color_manual(values = group.colors) +
theme_fivethirtyeight() +
labs(title = "Neighborhood Difference in Stop & Frisk Rate & Population",
subtitle = "among Black Residents (2012 - 2017)",
y = 'Neighborhood Black Stop & Frisk & Population',
x="Neighborhood White Percentage",size="Total Stop & Frisk") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.title = element_text()) +
scale_x_continuous(limits = c(0, .85),labels = scales::percent) +
scale_y_continuous(limits = c(0, 1),labels = scales::percent)
ggsave(plot = test, "03_stop_frisk/images/test.png", w = 10.67, h = 8,type = "cairo-png")
###################################
##
## Tying in crime data
##
###################################
coordinates(stop_frisk_matched) <- ~ LONGITUDE + LATITUDE
years = c(2012,2013,2014,2015,2016)
crime_all_years = data.frame()
for (y in years) {
crime <- readOGR(paste("data/shapefiles/Crime_Incidents_in_",y,".shp",sep = ""),
layer=paste("Crime_Incidents_in_",y,"",sep = ""))
neighborhoods <- levels(dc_neighborhoods$NBH_NAMES)
new_df <- data.frame()
for (n in neighborhoods) {
print(paste("Classifying crimes incidents in",n))
test <- data.frame()
cluster <- dc_neighborhoods[dc_neighborhoods$NBH_NAMES == n, ]
proj4string(crime) <- proj4string(cluster)
test <- crime[complete.cases(over(crime, cluster)), ]
test_df <- as.data.frame(test)
try(test_df$neighborhood <- n)
new_df <- rbind(new_df,test_df)
}
new_df$month <- format(as.Date(new_df$REPORT_DAT,"%Y-%m-%d"), "%m")
new_df$Year <- format(as.Date(new_df$REPORT_DAT,"%Y-%m-%d"), "%Y")
new_df$year_month <- as.Date(paste0(new_df$month,"/","01/",as.numeric(new_df$Year),sep=""),"%m/%d/%Y")
crime_neighborhood <- new_df %>%
group_by(neighborhood,Year) %>%
summarise(crime = n())
crime_neighborhood$year <- y
crime_all_years <- rbind(crime_all_years,as.data.frame(crime_neighborhood))
}
crime_total_years_all_dc <- crime_all_years %>%
group_by(year) %>%
summarise(total_crime = sum(crime))
crime_avg_years <- crime_all_years %>%
group_by(year,neighborhood) %>%
summarise(tot_crime_yr = sum(crime))
crime_avg_years <- crime_avg_years %>%
group_by(neighborhood) %>%
summarise(crime = mean(tot_crime_yr))
## By year
nbh_sf_yearly <- nbh_sf_df %>%
group_by(year,neighborhood) %>%
summarise (stop_frisks = n())
nbh_sf_yearly$prev_year <- as.numeric(as.character(nbh_sf_yearly$year)) - 1
nbh_sf_yearly <- merge(nbh_sf_yearly,crime_all_years,
by.x = c("neighborhood","prev_year"),
by.y = c("neighborhood","year"))
## relationship between previous year in crime and current year stop and frisk
crime_frisk_yearly <- ggplot(data=nbh_sf_yearly,aes(x=crime,y=stop_frisks)) +
geom_point() +
geom_point(data=subset(nbh_sf_yearly,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston" &
year == "2014"),
colour="red") +
geom_smooth(method='glm',formula=y~x) +
geom_text(data=subset(nbh_sf_yearly,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston" &
year == "2014"),
aes(label=paste0("Ivy City (",year,")")),
hjust=1.1, size=4,colour="black") +
theme_fivethirtyeight() +
labs(y = "Number Stop and Frisks in Subsequent Year", x = "Number of Crimes Reported in Year") +
ggtitle("Crime Incidents vs. Stop and Frisk Incidents in Subsequent Year\nby Neighborhood") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text())
ggsave(plot = crime_frisk_yearly, "03_stop_frisk/images/08_crime_frisks.png", w = 10.67, h = 8,type = "cairo-png")
## crime-only modelling
nbh_sf_avg <- nbh_sf_yearly %>%
group_by(neighborhood) %>%
summarise(avg_sf = mean(stop_frisks),
avg_prev_yr_crime = mean(crime))
nbh_sf_avg <- merge(nbh_sf_avg,additional_cluster_info,by.y="NBH_NAMES",by.x="neighborhood")
nbh_sf_avg <- merge(nbh_sf_avg,census_data_original,by.x="NAME",by.y="CLUSTER_TR2000")
nbh_sf_avg$pct_non_white <- nbh_sf_avg$PctAsianPINonHispBridge_2010 +
nbh_sf_avg$PctBlackNonHispBridge_2010 +
nbh_sf_avg$PctHisp_2010
nbh_sf_avg$non_white_bins <-cut(nbh_sf_avg$pct_non_white, c(0,10,20,30,40,50,60,70,80,90,100))
nbh_sf_avg$black_bins <- cut(nbh_sf_avg$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
yearly_model <- lm(formula = avg_sf ~ avg_prev_yr_crime,
data = nbh_sf_avg)
stargazer(yearly_model,align = TRUE, out="03_stop_frisk/images/models.htm")
## crime & race model
nbh_sf_yearly <- merge(nbh_sf_yearly,additional_cluster_info,by.y="NBH_NAMES",by.x="neighborhood")
nbh_sf_yearly <- merge(nbh_sf_yearly,census_data_original,by.x="NAME",by.y="CLUSTER_TR2000")
nbh_sf_yearly$pctnonwhite <- nbh_sf_yearly$PctAsianPINonHispBridge_2010 +
nbh_sf_yearly$PctBlackNonHispBridge_2010 +
nbh_sf_yearly$PctHisp_2010
nbh_sf_avg$coll_bins <-cut(nbh_sf_avg$pct_non_white, c(0,10,40,60,90,100))
nbh_sf_avg$black_bins <- cut(nbh_sf_avg$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
yearly_model_w_race <- lm(formula = avg_sf ~ avg_prev_yr_crime + black_bins,
data = nbh_sf_avg)
stargazer(yearly_model,yearly_model_w_race,align = TRUE, out="03_stop_frisk/images/models_w_race.htm")
nbh_sf_yearly$predicted_w_race <- predict(yearly_model_w_race)
nbh_sf_yearly$coll_bins <-cut(nbh_sf_yearly$pctnonwhite, c(0,10,40,60,90,100))
nbh_sf_yearly$black_bins <- cut(nbh_sf_yearly$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
levels(nbh_sf_yearly$coll_bins) <- c("0-10%","10-40%","40-60%","60-90%","90-100%")
levels(nbh_sf_yearly$black_bins) <- c("0-25%","25-50%","50-75%","75-95%","95-100%")
crime_frisk_yearly_race <- ggplot(data=nbh_sf_yearly,aes(x=crime,y=stop_frisks,color=black_bins)) +
geom_point(aes(color=black_bins)) +
geom_smooth(method = lm,size=2,se = F) +
geom_point(data=subset(nbh_sf_yearly,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston" &
year == "2014"),
colour="red",pch=21) +
geom_text(data=subset(nbh_sf_yearly,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston" &
year == "2014"),
aes(label=paste0("Ivy City (",year,")")),
hjust=1.1, size=4,colour="black") +
theme_fivethirtyeight() +
labs(x = "Number of Crimes Reported in Year",
y = "Number Stop and Frisks in Subsequent Year",
color = "Neighborhood % Black Residents") +
ggtitle("Crime Incidents vs. Stop and Frisk Incidents\nby Neighborhood Racial Composition") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text())
ggsave(plot = crime_frisk_yearly_race, "03_stop_frisk/images/crime_frisk_yearly_race.png", w = 10.67, h = 8,type = "cairo-png")
## looking at crime-only residulas by neighborhood racial composition
nbh_sf_avg$predicted <- predict(yearly_model)
nbh_sf_avg$residuals <- residuals(yearly_model)
residuals_nbh_race <- nbh_sf_avg %>%
group_by(neighborhood,PctBlackNonHispBridge_2010) %>%
summarise(avg_residuals = mean(residuals))
crime_model_residuals <- ggplot(residuals_nbh_race, aes(x = PctBlackNonHispBridge_2010, y = avg_residuals)) +
geom_smooth() +
geom_point() +
geom_point(data=subset(residuals_nbh_race,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston"),
colour="red") +
geom_text(data=subset(residuals_nbh_race,neighborhood == "Ivy City, Arboretum, Trinidad, Carver Langston"),
aes(label="Ivy City (2010-2017)"),
hjust=1.1, size=4,colour="black") +
geom_hline(yintercept = 0) +
annotate("text", label = "More stop and frisk than model predicted", x = 15, y = 95, size = 4, colour = "red") +
annotate("text", label = "Less stop and frisk than model predicted", x = 15, y = -65, size = 4, colour = "dark green") +
theme_fivethirtyeight() +
labs(x = "Neighborhood Percent of Black Residents", y = "Model Residual") +
ggtitle("Linear Model Residuals by Neighborhood Racial Composition") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text())
ggsave(plot = crime_model_residuals, "03_stop_frisk/images/09_crime_model_residuals.png", w = 10.67, h = 8,type = "cairo-png")
## total stop and frisk counts by neighborhood race from census
nbh_sf_df <- merge(nbh_sf_df,additional_cluster_info,by.x="neighborhood",by.y="NBH_NAMES")
nbh_sf_df_census <- merge(nbh_sf_df,census_data_original,by.x=c("NAME"),by.y=c("CLUSTER_TR2000"))
nbh_sf_df_census$pct_non_white <- nbh_sf_df_census$PctAsianPINonHispBridge_2010 +
nbh_sf_df_census$PctBlackNonHispBridge_2010 +
nbh_sf_df_census$PctHisp_2010
nbh_sf_df_census$non_white_bins <-cut(nbh_sf_df_census$pct_non_white, c(0,10,20,30,40,50,60,70,80,90,100))
nbh_sf_df_census$black_bins <-cut(nbh_sf_df_census$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
total_sf_nbh_race <- nbh_sf_df_census %>%
group_by(year,black_bins) %>%
summarise (sf = n())
avg_sf_nbh_race <- total_sf_nbh_race %>%
group_by(black_bins) %>%
summarise (avg_sf = mean(sf))
crime_w_census <- merge(crime_avg_years,additional_cluster_info,by.y="NBH_NAMES",by.x="neighborhood")
crime_w_census <- merge(crime_w_census,census_data_original,by.x="NAME",by.y="CLUSTER_TR2000")
crime_w_census <- crime_w_census %>%
select(NAME,neighborhood,PctBlackNonHispBridge_2010,PctHisp_2010,
PctAsianPINonHispBridge_2010,TotPop_2010,crime)
crime_w_census$pct_non_white <- crime_w_census$PctAsianPINonHispBridge_2010 +
crime_w_census$PctBlackNonHispBridge_2010 +
crime_w_census$PctHisp_2010
crime_w_census$non_white_bins <-cut(crime_w_census$pct_non_white, c(0,10,20,30,40,50,60,70,80,90,100))
crime_w_census$black_bins <-cut(crime_w_census$PctBlackNonHispBridge_2010, c(0,25,50,75,95,100))
crime_w_census <- crime_w_census %>%
group_by(black_bins) %>%
summarise (crime = mean(crime),pop=mean(TotPop_2010))
crime_sf_race_bins <- merge(crime_w_census,avg_sf_nbh_race,by="black_bins")
crime_sf_race_bins$adj_sf <- (crime_sf_race_bins$avg_sf / crime_sf_race_bins$pop) * 100
crime_sf_race_bins$adj_crime <- (crime_sf_race_bins$crime / crime_sf_race_bins$pop) * 100
crime_sf_race_l <- melt(crime_sf_race_bins, id.vars=c("black_bins")) %>%
subset(variable %in% c("adj_sf","adj_crime"))
levels(crime_sf_race_l$variable) <- c("crime","pop","","Average Stop and Frisk","Average Crime")
levels(crime_sf_race_l$black_bins) <- c("0-25%","25-50%","50-75%","75-95%","95-100%")
sf_crime_nbh_race <- ggplot(crime_sf_race_l,aes(x=black_bins,y=value,fill=variable,group=as.character(variable))) +
geom_bar(stat="identity", position="dodge") +
geom_text(aes(label = round(value,2)), position = position_dodge(0.9),vjust=-.5,size=5) +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
ylab('Crime & Stop and Frisk per 100 people') + xlab("Neighborhood Percent of Black Residents") +
scale_fill_discrete(name="Legend") +
ggtitle("Average Yearly Stop and Frisk vs. Crime per 100 Residents")
ggsave(plot = sf_crime_nbh_race, "03_stop_frisk/images/10_sf_crime_nbh_race.png", w = 10.67, h = 8,type = "cairo-png")
## crime data w/ race
crimes_race <- read.csv("data/crime/crimes_anon.csv",stringsAsFactors = F)
crimes_race <- crimes_race %>% filter(!is.na(longitude))
dc_neighborhoods <- readOGR("data/shapefiles",
layer="Neighborhood_Clusters")
coordinates(crimes_race) <- ~ longitude + latitude
neighborhoods <- levels(dc_neighborhoods$NBH_NAMES)
nbh_crimes_df <- data.frame()
for (n in neighborhoods) {
print(paste("Classifying crimes incidents in",n))
test <- data.frame()
cluster <- dc_neighborhoods[dc_neighborhoods$NBH_NAMES == n , ]
proj4string(crimes_race) <- proj4string(cluster)
test <- crimes_race[complete.cases(over(crimes_race, cluster)), ]
test_df <- as.data.frame(test)
try(test_df$neighborhood <- n)
nbh_crimes_df <- rbind(nbh_crimes_df,test_df)
}
## pulling in neighborhood-level census
census_nbh_pct_black <- census_data %>%
filter(variable %in% c("Black","Hispanic/Latino","White"))
census_nbh_pct_w <- dcast(census_nbh_pct_black,CLUSTER_TR2000 ~ variable,value.var = "census_value")
census_nbh_pct_black$bins <- cut(census_nbh_pct_w$Black, c(0,10,40,60,80,100))
nbh_crimes_df$race_ethn <- ifelse(as.character(nbh_crimes_df$ethnicity)=='Hispanic Or Latino','Hispanic/Latino',
as.character(nbh_crimes_df$race))
nbh_crimes_df$race_ethn[nbh_crimes_df$ethnicity == "Hispanic Or Latino"] <- "Hispanic/Latino"
## calculate neighborhood-level crimes by race
crimes_by_race_nbh <- nbh_crimes_df %>%
group_by(neighborhood,race_ethn) %>%
summarise(crimes=n()) %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino"))
## calculate neighborhood-level stop and frisks by race
stops_by_race_nbh <- nbh_sf_df %>%
filter(year == 2017) %>%
group_by(neighborhood,race_ethn) %>%
summarise(stop_frisks=n()) %>%
filter(race_ethn %in% c("White","Black","Hispanic/Latino"))
## merge arrests & stop and frisk then census
stops_crimes_nbh <- merge(crimes_by_race_nbh,stops_by_race_nbh,by=c("neighborhood","race_ethn"),
all = T)
additional_cluster_info <- read.csv("data/shapefiles/Neighborhood_Clusters.csv")
stops_crimes_nbh <- merge(stops_crimes_nbh,additional_cluster_info,by.x="neighborhood",by.y="NBH_NAMES")
stops_crimes_tracts_nbh <- merge(stops_crimes_nbh,census_nbh_pct_w,by.x="NAME",by.y="CLUSTER_TR2000")
stops_crimes_tracts_nbh$adj_sf <- (stops_crimes_tracts_nbh$stop_frisks / stops_crimes_tracts_nbh$pop) * 100
stops_crimes_tracts_nbh$adj_crimes <- (stops_crimes_tracts_nbh$crimes / stops_crimes_tracts_nbh$pop) * 100
## roll up neighborhoods and aggregate stop & frisk and arrests based on racial bins
stops_crimes_nbh_census_bins <- stops_crimes_tracts_nbh %>%
replace(is.na(.), 0) %>%
group_by(bins,race_ethn) %>%
summarise(total_crimes = sum(crimes),
total_sf = sum(stop_frisks))
## calculate crime to stop & frisk ratio
stops_crimes_nbh_census_bins$arrest_to_stops <- stops_crimes_nbh_census_bins$total_sf /
stops_crimes_nbh_census_bins$total_crimes
stops_crimes_ratio <- ggplot(stops_crimes_nbh_census_bins,aes(x=bins,y=arrest_to_stops,color=race_ethn,group=as.character(race_ethn))) +
geom_line(size=2) +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5)) +
ylab('Stop & Frisk to Crimes Ratio') + xlab("Neighborhood Racial Composition") +
scale_x_discrete(labels = c("< 10% black","10 - 40% black","40 - 60% black","> 60% black","80 - 100% black")) +
scale_color_discrete(name="Legend") +
#scale_y_continuous(limits = c(0,2.5)) +
ggtitle("2017 Stop & Frisk to 2016 Crimes Ratio")
ggsave(plot = stops_crimes_ratio, "03_stop_frisk/images/stops_crimes_ratio.png", w = 10.67, h = 8,type = "cairo-png")
## re-creating stop and frisk by neighborhood percent using crime instead
nbh_crimes_race <- nbh_crimes_df %>%
group_by(neighborhood,race_ethn) %>%
summarise (crime_n = n()) %>%
mutate(crime_freq=crime_n/sum(crime_n)) %>%
rename(subgroup = race_ethn)
nbh_crime_race <- merge(nbh_crimes_race,nbh_sf_race,by=c("neighborhood","subgroup"),all=T)
nbh_crime_race <- nbh_crime_race %>%
replace(., is.na(.), 0)
# adding neighborhood census data
nbh_crime_race <- merge(additional_cluster_info,nbh_crime_race,by.y="neighborhood",by.x="NBH_NAMES")
nbh_crime_race <- merge(nbh_crime_race,census_data,by.x=c("NAME","subgroup"),by.y=c("CLUSTER_TR2000","variable"),all.x=T)
nbh_crime_sf_race_scatter <- ggplot(data=filter(nbh_crime_race,subgroup %in% c("White","Black","Hispanic/Latino")),aes(x=freq,y=crime_freq)) +
geom_point(aes(size=n,color=subgroup),alpha=.7) +
#geom_point(aes(size=census_value,color=subgroup),alpha=.7) +
geom_point(data=subset(nbh_crime_race,NBH_NAMES == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
subgroup %in% c("White","Black")),
aes(size=n),colour="black",pch=21) +
geom_text(data=subset(nbh_crime_race,NBH_NAMES == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
subgroup %in% c("White","Black")),
aes(label=paste0("Columbia Heights\n(",subgroup,")")),
hjust="center", vjust = -.5, size=4) +
geom_point(data=subset(nbh_crime_race,NBH_NAMES == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
subgroup %in% c("Hispanic/Latino")),
aes(size=n),colour="black",pch=21) +
geom_text(data=subset(nbh_crime_race,NBH_NAMES == "Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View" &
subgroup %in% c("Hispanic/Latino")),
aes(label=paste0("Columbia Heights\n(",subgroup,")")),
hjust=-.1, vjust = .5, size=4) +
scale_x_continuous(limits = c(0, 1),labels = scales::percent) +
scale_y_continuous(limits = c(0, 1.0),labels = scales::percent) +
geom_abline(intercept = 0,slope = 1) +
geom_hline(yintercept = .5) + geom_vline(xintercept = .5) +
theme_fivethirtyeight() +
labs(x = "Neighborhood Stop and Frisk (2017)", y = "Neighborhood Crime (2016)") +
ggtitle("Neighborhood Crime vs. Neighborhood Stop and Frisk") +
scale_color_discrete(name="Legend") +
#scale_size_continuous(name="Total Stop & Frisk") +
scale_size_continuous(name="Neighborhood Population") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text()) +
theme(legend.position="bottom", legend.box = "horizontal")
ggsave(plot = nbh_crime_sf_race_scatter, "03_stop_frisk/images/11_nbh_crime_sf_race_scatter.png", w = 10.67, h = 10.67,type = "cairo-png")
######################################
##
## plotting crime by census
##
######################################
nbh_crimes_race <- merge(additional_cluster_info,nbh_crimes_race,by.y="neighborhood",by.x="NBH_NAMES")
nbh_crime_demos_census <- merge(nbh_crimes_race,census_data,by.x=c("NAME","subgroup"),by.y=c("CLUSTER_TR2000","variable"))
nbh_crime_demos_census <- merge(nbh_crime_demos_census,nbh_sf_demos_census[ , c("NAME","subgroup","n")],by=c("NAME","subgroup"))
crime_and_census_plot <- ggplot(data=filter(nbh_crime_demos_census,subgroup %in% c("White","Black","Hispanic/Latino")),aes(x=census_value/100,y=crime_freq)) +
geom_point(aes(size=n,color=subgroup),alpha=.7) +
#geom_point(aes(size=census_value,color=subgroup),alpha=.7) +
scale_x_continuous(limits = c(0, 1),labels = scales::percent) +
scale_y_continuous(limits = c(0, 1.0),labels = scales::percent) +
geom_abline(intercept = 0,slope = 1) +
geom_hline(yintercept = .5) + geom_vline(xintercept = .5) +
theme_fivethirtyeight() +
labs(x = "Neighborhood Population", y = "Percent of Crime") +
ggtitle("Neighborhood Crime v. Census Population") +
scale_color_discrete(name="Legend") +
scale_size_continuous(name="Total Stop & Frisk") +
theme(plot.title = element_text(hjust = 0.5),axis.title = element_text())
ggsave(plot = crime_and_census_plot, "03_stop_frisk/images/crime_and_census_plot.png", w = 10.67, h = 10.67,type = "cairo-png")
nbh_crime_race$diff <- nbh_crime_race$freq - (nbh_crime_race$crime_freq)
nbh_diff_crimes <- ggplot(data=filter(nbh_crime_race,subgroup %in% c("White","Black","Hispanic/Latino")),
aes(x = reorder(NBH_NAMES, diff),
y = as.numeric(diff))) +
geom_point(aes(size=n,color=subgroup),alpha=.7,stat='identity') +
labs(x = "Neighborhood", y = "Stop & Frisk - Population") +
coord_flip() +
ggtitle("Difference Between 2017 Stop and Frisk\n & 2016 Crime Rate by Race") +
geom_hline(yintercept = 0) +
theme_fivethirtyeight() +
theme(plot.title = element_text(hjust = 0.5),
text = element_text(size=10),
legend.title = element_text(size=12),
legend.text = element_text(size=12)) +
scale_y_continuous(labels=scales::percent) +
guides(color=guide_legend(title="Race/Ethnicity"),
size=guide_legend(title="Total Stop and Frisk"))
ggsave(plot = nbh_diff_crimes, "03_stop_frisk/images/12_nbh_diff_crimes.png", w = 10.67, h = 8,type = "cairo-png")
###################################
##
## poisson modelling
##
###################################
stops_crimes_tracts_nbh$race_ethn <- factor(stops_crimes_tracts_nbh$race_ethn,levels = c("White","Black","Hispanic/Latino"))
stops_crimes_tracts_nbh$black_bins <-cut(stops_crimes_tracts_nbh$Black, c(0,25,50,75,95,100))
stop_model <- glm(stop_frisks ~ race_ethn + Black, family=quasipoisson,
offset=log(crimes),
data = stops_crimes_tracts_nbh,
subset=crimes>0 & stop_frisks>0)
summary(stop_model)
stargazer(stop_model,align = TRUE, out="03_stop_frisk/images/poisson.htm")
coefs <- data.frame(stop_model$coefficients,check.rows = T)
coefs$ix <- "index"
coefs$values <- row.names(coefs)
coefs_w <- dcast(coefs,ix ~ values, value.var = "stop_model.coefficients")
neighborhoods_1 <- seq(0, 100, by=1)
all <- expand.grid(neighborhoods_1)
regression_output <- merge(all,coefs_w,by = NULL)
regression_output$black_value <- regression_output$`(Intercept)` +
regression_output$race_ethnBlack +
regression_output$Var1 * regression_output$Black
regression_output$hisp_value <- regression_output$`(Intercept)` +
regression_output$`race_ethnHispanic/Latino` +
regression_output$Var1 * regression_output$Black
regression_output$white_value <- regression_output$`(Intercept)` +
0 +
regression_output$Var1 * regression_output$Black
ggtern(data = regression_output,aes(x=white,y=black,z=hisp)) +
geom_point(size=1,aes(color=regression_output$black_value)) +
theme_bw() +
scale_color_gradient2(low = "blue", mid = "white", high = "red")
Black = c(exp(coefs[1,1] + coefs[2,1] + 0),
exp(coefs[1,1] + coefs[2,1] + coefs[4,1]),
exp(coefs[1,1] + coefs[2,1] + coefs[5,1]),
exp(coefs[1,1] + coefs[2,1] + coefs[6,1]),
exp(coefs[1,1] + coefs[2,1] + coefs[7,1]))
White = c(exp(coefs[1,1] + 0 + 0),
exp(coefs[1,1] + 0 + coefs[4,1]),
exp(coefs[1,1] + 0 + coefs[5,1]),
exp(coefs[1,1] + 0 + coefs[6,1]),
exp(coefs[1,1] + 0 + coefs[7,1]))
Hispanic = c(exp(coefs[1,1] + coefs[3,1] + 0),
exp(coefs[1,1] + coefs[3,1] + coefs[4,1]),
exp(coefs[1,1] + coefs[3,1] + coefs[5,1]),
exp(coefs[1,1] + coefs[3,1] + coefs[6,1]),
exp(coefs[1,1] + coefs[3,1] + coefs[7,1]))
labels <- c("0-25% black","25-50% black","50-75% black","75-95% black","95-100% black")
results <- data.frame(labels, Black, White,Hispanic)
results_l <- regression_output %>%
select(Var1,black_value,white_value,hisp_value)
results_l <- melt(results_l,id.vars = "Var1")
levels(results_l$variable) <- c("Black","White","Hispanic/Latino")
poisson_plot <- ggplot(data=results_l,aes(x=Var1/100,y=exp(as.numeric(value)),color=variable,group=as.character(variable))) +
geom_line(size=2) +
theme_fivethirtyeight() +
theme(axis.title = element_text(),plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)) +
labs(title = "Estimated Stop and Frisk by Neighborhood Racial Composition",
subtitle = "Poisson regression results using constant term + race parameters for each neighborhood composition",
y = 'Stop and Frisks per Crime',
x="Neighborhood Percent of Black Residents") +
scale_color_discrete(name="Legend") +
scale_x_continuous(labels=scales::percent)
ggsave(plot = poisson_plot, "03_stop_frisk/images/13_poisson_plot.png", w = 10.67, h = 8,type = "cairo-png")
###################################
##
## exporting nbh data to csv for table
##
###################################
nbh_sf_demos_census$census_percent <- nbh_sf_demos_census$census_value / 100
nbh_sf_demos_census$census_percent <- round(nbh_sf_demos_census$census_percent,2)
output_sf <- dcast(data = nbh_sf_demos_census, neighborhood ~ subgroup, value.var = "freq")
output_sf <- output_sf %>%
rename(Total.stop_and_frisk = Total,
Juvenile.stop_and_frisk = Juvenile,
White.stop_and_frisk = White,
Black.stop_and_frisk = Black,
"Hispanic.Latino.stop_and_frisk" = "Hispanic/Latino")
output_census <- dcast(data = nbh_sf_demos_census, neighborhood ~ subgroup, value.var = "census_percent")
output_census <- output_census %>%
rename(Total.census = Total,
Juvenile.census = Juvenile,
White.census = White,
Black.census = Black,
"Hispanic.Latino.census" = "Hispanic/Latino")
output <- merge(output_sf,output_census,by=c("neighborhood"))
output$Black.Diff <- output$Black.census - output$Black.stop_and_frisk
output$Hispanic.Latino.Diff <- output$Hispanic.Latino.census - output$Hispanic.Latino.stop_and_frisk
output$Juvenile.Diff <- output$Juvenile.census - output$Juvenile.stop_and_frisk
output$White.Diff <- output$White.census - output$White.stop_and_frisk
order_cols <- c("neighborhood","Black.stop_and_frisk","Black.census","Black.Diff",
"Hispanic.Latino.stop_and_frisk","Hispanic.Latino.census","Hispanic.Latino.Diff",
"Juvenile.stop_and_frisk","Juvenile.census","Juvenile.Diff","White.stop_and_frisk",
"White.census","White.Diff")
output <- output[, order_cols]
write.csv(output, "03_stop_frisk/scripts/shiny/sf_nbh_summary.csv")
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