content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2 values | repo_name large_stringlengths 5 125 | language large_stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.46M | extension large_stringclasses 75 values | text stringlengths 0 6.46M |
|---|---|---|---|---|---|---|---|---|---|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/enumerationSetApi.r
\name{enumerationSet$update}
\alias{enumerationSet$update}
\title{Update an enumeration set by replacing items in its definition.}
\arguments{
\item{webId}{The ID of the enumeration set to update.}
\item{PIEnumerationSet}{A partial enumeration set containing the desired changes.}
}
\value{
The enumeration set was updated.
}
\description{
Update an enumeration set by replacing items in its definition.
}
| /man/enumerationSet-cash-update.Rd | permissive | frbl/PI-Web-API-Client-R | R | false | true | 505 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/enumerationSetApi.r
\name{enumerationSet$update}
\alias{enumerationSet$update}
\title{Update an enumeration set by replacing items in its definition.}
\arguments{
\item{webId}{The ID of the enumeration set to update.}
\item{PIEnumerationSet}{A partial enumeration set containing the desired changes.}
}
\value{
The enumeration set was updated.
}
\description{
Update an enumeration set by replacing items in its definition.
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trav_out_node.R
\name{trav_out_node}
\alias{trav_out_node}
\title{Traverse from one or more selected edges onto
adjacent, outward nodes}
\usage{
trav_out_node(graph, conditions = NULL, copy_attrs_from = NULL,
agg = "sum")
}
\arguments{
\item{graph}{a graph object of class
\code{dgr_graph}.}
\item{conditions}{an option to use filtering
conditions for the traversal.}
\item{copy_attrs_from}{providing an edge attribute
name will copy those edge attribute values to the
traversed nodes. If the edge attribute already exists,
the values will be merged to the traversed nodes;
otherwise, a new node attribute will be created.}
\item{agg}{if an edge attribute is provided
to \code{copy_attrs_from}, then an aggregation
function is required since there may be cases where
multiple edge attribute values will be passed onto
the traversed node(s). To pass only a single value,
the following aggregation functions can be used:
\code{sum}, \code{min}, \code{max}, \code{mean}, or
\code{median}.}
}
\value{
a graph object of class \code{dgr_graph}.
}
\description{
From a graph object of class
\code{dgr_graph} with an active selection of
edges move opposite to the edge direction to
connected nodes, replacing the current edge selection
with the selection with those nodes traversed to. An
optional filter by node attribute can limit the set
of nodes traversed to.
}
\examples{
# Set a seed
set.seed(23)
# Create a simple graph
graph <-
create_graph() \%>\%
add_n_nodes(
2, type = "a",
label = c("asd", "iekd")) \%>\%
add_n_nodes(
3, type = "b",
label = c("idj", "edl", "ohd")) \%>\%
add_edges_w_string(
"1->2 1->3 2->4 2->5 3->5",
rel = c(NA, "A", "B", "C", "D"))
# Create a data frame with node ID values
# representing the graph edges (with `from`
# and `to` columns), and, a set of numeric values
df_edges <-
data.frame(
from = c(1, 1, 2, 2, 3),
to = c(2, 3, 4, 5, 5),
values = round(rnorm(5, 5), 2))
# Create a data frame with node ID values
# representing the graph nodes (with the `id`
# columns), and, a set of numeric values
df_nodes <-
data.frame(
id = 1:5,
values = round(rnorm(5, 7), 2))
# Join the data frame to the graph's internal
# edge data frame (edf)
graph <-
graph \%>\%
join_edge_attrs(df_edges) \%>\%
join_node_attrs(df_nodes)
get_node_df(graph)
#> id type label values
#> 1 1 a asd 8.58
#> 2 2 a iekd 7.22
#> 3 3 b idj 5.95
#> 4 4 b edl 6.71
#> 5 5 b ohd 7.48
get_edge_df(graph)
#> id from to rel values
#> 1 1 1 2 <NA> 6.00
#> 2 2 1 3 A 6.11
#> 3 3 2 4 B 4.72
#> 4 4 2 5 C 6.02
#> 5 5 3 5 D 5.05
# Perform a simple traversal from the
# edge `1`->`3` to the attached node
# in the direction of the edge; here, no
# conditions are placed on the nodes
# traversed to
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node() \%>\%
get_selection()
#> [1] 1
# Traverse from edges `2`->`5` and
# `3`->`5` to the attached node along
# the direction of the edge; here, the
# traversals lead to different nodes
graph \%>\%
select_edges(from = 2, to = 5) \%>\%
select_edges(from = 3, to = 5) \%>\%
trav_out_node() \%>\%
get_selection()
#> [1] 2 3
# Traverse from the edge `1`->`3`
# to the attached node where the edge
# is outgoing, this time filtering
# numeric values greater than `7.0` for
# the `values` node attribute
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node(
conditions = "values > 7.0") \%>\%
get_selection()
#> [1] 1
# Traverse from the edge `1`->`3`
# to the attached node where the edge
# is outgoing, this time filtering
# numeric values less than `7.0` for
# the `values` node attribute (the
# condition is not met so the original
# selection of edge `1`->`3` remains)
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node(
conditions = "values < 7.0") \%>\%
get_selection()
#> [1] 2
# Traverse from the edge `1`->`2` to
# the node `2` using multiple conditions
# with a single-length vector (here, using
# a `|` to create a set of `OR` conditions)
graph \%>\%
select_edges(from = 1, to = 2) \%>\%
trav_out_node(
conditions = "grepl('.*d$', label) | values < 6.0") \%>\%
get_selection()
#> [1] 1
# Create another simple graph to demonstrate
# copying of edge attribute values to traversed
# nodes
graph <-
create_graph() \%>\%
add_node() \%>\%
select_nodes() \%>\%
add_n_nodes_ws(2, "from") \%>\%
clear_selection() \%>\%
select_nodes_by_id(2) \%>\%
set_node_attrs_ws("value", 8) \%>\%
clear_selection() \%>\%
select_edges_by_edge_id(1) \%>\%
set_edge_attrs_ws("value", 5) \%>\%
clear_selection() \%>\%
select_edges_by_edge_id(2) \%>\%
set_edge_attrs_ws("value", 5) \%>\%
clear_selection() \%>\%
select_edges()
# Show the graph's internal edge data frame
graph \%>\% get_edge_df()
#> id from to rel value
#> 1 1 1 2 <NA> 5
#> 2 2 1 3 <NA> 5
# Show the graph's internal node data frame
graph \%>\% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> NA
#> 2 2 <NA> <NA> 8
#> 3 3 <NA> <NA> NA
# Perform a traversal from the edges to
# the central node (`1`) while also applying
# the edge attribute `value` to the node (in
# this case summing the `value` of 5 from
# both edges before adding as a node attribute)
graph <-
graph \%>\%
trav_out_node(
copy_attrs_from = "value",
agg = "sum")
# Show the graph's internal node data frame
# after this change
graph \%>\% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> 10
#> 2 2 <NA> <NA> 8
#> 3 3 <NA> <NA> NA
}
| /man/trav_out_node.Rd | no_license | KID4978/DiagrammeR | R | false | true | 5,713 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trav_out_node.R
\name{trav_out_node}
\alias{trav_out_node}
\title{Traverse from one or more selected edges onto
adjacent, outward nodes}
\usage{
trav_out_node(graph, conditions = NULL, copy_attrs_from = NULL,
agg = "sum")
}
\arguments{
\item{graph}{a graph object of class
\code{dgr_graph}.}
\item{conditions}{an option to use filtering
conditions for the traversal.}
\item{copy_attrs_from}{providing an edge attribute
name will copy those edge attribute values to the
traversed nodes. If the edge attribute already exists,
the values will be merged to the traversed nodes;
otherwise, a new node attribute will be created.}
\item{agg}{if an edge attribute is provided
to \code{copy_attrs_from}, then an aggregation
function is required since there may be cases where
multiple edge attribute values will be passed onto
the traversed node(s). To pass only a single value,
the following aggregation functions can be used:
\code{sum}, \code{min}, \code{max}, \code{mean}, or
\code{median}.}
}
\value{
a graph object of class \code{dgr_graph}.
}
\description{
From a graph object of class
\code{dgr_graph} with an active selection of
edges move opposite to the edge direction to
connected nodes, replacing the current edge selection
with the selection with those nodes traversed to. An
optional filter by node attribute can limit the set
of nodes traversed to.
}
\examples{
# Set a seed
set.seed(23)
# Create a simple graph
graph <-
create_graph() \%>\%
add_n_nodes(
2, type = "a",
label = c("asd", "iekd")) \%>\%
add_n_nodes(
3, type = "b",
label = c("idj", "edl", "ohd")) \%>\%
add_edges_w_string(
"1->2 1->3 2->4 2->5 3->5",
rel = c(NA, "A", "B", "C", "D"))
# Create a data frame with node ID values
# representing the graph edges (with `from`
# and `to` columns), and, a set of numeric values
df_edges <-
data.frame(
from = c(1, 1, 2, 2, 3),
to = c(2, 3, 4, 5, 5),
values = round(rnorm(5, 5), 2))
# Create a data frame with node ID values
# representing the graph nodes (with the `id`
# columns), and, a set of numeric values
df_nodes <-
data.frame(
id = 1:5,
values = round(rnorm(5, 7), 2))
# Join the data frame to the graph's internal
# edge data frame (edf)
graph <-
graph \%>\%
join_edge_attrs(df_edges) \%>\%
join_node_attrs(df_nodes)
get_node_df(graph)
#> id type label values
#> 1 1 a asd 8.58
#> 2 2 a iekd 7.22
#> 3 3 b idj 5.95
#> 4 4 b edl 6.71
#> 5 5 b ohd 7.48
get_edge_df(graph)
#> id from to rel values
#> 1 1 1 2 <NA> 6.00
#> 2 2 1 3 A 6.11
#> 3 3 2 4 B 4.72
#> 4 4 2 5 C 6.02
#> 5 5 3 5 D 5.05
# Perform a simple traversal from the
# edge `1`->`3` to the attached node
# in the direction of the edge; here, no
# conditions are placed on the nodes
# traversed to
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node() \%>\%
get_selection()
#> [1] 1
# Traverse from edges `2`->`5` and
# `3`->`5` to the attached node along
# the direction of the edge; here, the
# traversals lead to different nodes
graph \%>\%
select_edges(from = 2, to = 5) \%>\%
select_edges(from = 3, to = 5) \%>\%
trav_out_node() \%>\%
get_selection()
#> [1] 2 3
# Traverse from the edge `1`->`3`
# to the attached node where the edge
# is outgoing, this time filtering
# numeric values greater than `7.0` for
# the `values` node attribute
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node(
conditions = "values > 7.0") \%>\%
get_selection()
#> [1] 1
# Traverse from the edge `1`->`3`
# to the attached node where the edge
# is outgoing, this time filtering
# numeric values less than `7.0` for
# the `values` node attribute (the
# condition is not met so the original
# selection of edge `1`->`3` remains)
graph \%>\%
select_edges(from = 1, to = 3) \%>\%
trav_out_node(
conditions = "values < 7.0") \%>\%
get_selection()
#> [1] 2
# Traverse from the edge `1`->`2` to
# the node `2` using multiple conditions
# with a single-length vector (here, using
# a `|` to create a set of `OR` conditions)
graph \%>\%
select_edges(from = 1, to = 2) \%>\%
trav_out_node(
conditions = "grepl('.*d$', label) | values < 6.0") \%>\%
get_selection()
#> [1] 1
# Create another simple graph to demonstrate
# copying of edge attribute values to traversed
# nodes
graph <-
create_graph() \%>\%
add_node() \%>\%
select_nodes() \%>\%
add_n_nodes_ws(2, "from") \%>\%
clear_selection() \%>\%
select_nodes_by_id(2) \%>\%
set_node_attrs_ws("value", 8) \%>\%
clear_selection() \%>\%
select_edges_by_edge_id(1) \%>\%
set_edge_attrs_ws("value", 5) \%>\%
clear_selection() \%>\%
select_edges_by_edge_id(2) \%>\%
set_edge_attrs_ws("value", 5) \%>\%
clear_selection() \%>\%
select_edges()
# Show the graph's internal edge data frame
graph \%>\% get_edge_df()
#> id from to rel value
#> 1 1 1 2 <NA> 5
#> 2 2 1 3 <NA> 5
# Show the graph's internal node data frame
graph \%>\% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> NA
#> 2 2 <NA> <NA> 8
#> 3 3 <NA> <NA> NA
# Perform a traversal from the edges to
# the central node (`1`) while also applying
# the edge attribute `value` to the node (in
# this case summing the `value` of 5 from
# both edges before adding as a node attribute)
graph <-
graph \%>\%
trav_out_node(
copy_attrs_from = "value",
agg = "sum")
# Show the graph's internal node data frame
# after this change
graph \%>\% get_node_df()
#> id type label value
#> 1 1 <NA> <NA> 10
#> 2 2 <NA> <NA> 8
#> 3 3 <NA> <NA> NA
}
|
testlist <- list(a = 3538943L, b = -1L, x = c(370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 385822975L, -1L))
result <- do.call(grattan:::anyOutside,testlist)
str(result) | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610056513-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 331 | r | testlist <- list(a = 3538943L, b = -1L, x = c(370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 370546198L, 385822975L, -1L))
result <- do.call(grattan:::anyOutside,testlist)
str(result) |
#PCA_hist function generates the PCA plot for all datapoints with density distribution along PC1
#data = dataframe of gene expression, genes in columns
#clus = no. of clusters to be generated
#seed = initialization seed to be set
#pointsize = size of point on PCA plot
#V = Loading scores of genes for principal components
PCA_hist <- function(data,pointsize = 0.5, V = NULL, title){
require(factoextra)
require(ggpubr)
data[!is.finite(as.matrix(data))] <- 0
if(is.null(V)){
pca <- FactoMineR::PCA(data, scale = FALSE, graph = F)
df <- as.data.frame(pca$ind$coord[,1:2])
}else{
#Determining coordinates for fixed PC1 loading scores (For control network)
data <- as.matrix(data)
df <- data %*% V
df <- as.data.frame(df)[,1:2]
}
names(df) <- c("PC1", "PC2")
df <- as.data.frame(df[complete.cases(df),])
sp <- ggscatter(df, x = "PC1", y = "PC2",
color = "#0073C299",
size = pointsize)+
border()
sp <- sp + rremove("legend")
xplot <- ggdensity(df[,1],xlab="PC1", fill = "black")+ ylim(0,0.5)
library(cowplot)
p <- print(plot_grid(xplot, sp, ncol = 1, align = "hv",
rel_widths = c(2, 1), rel_heights = c(1, 2)))
title1 <- ggdraw() + draw_label(paste0(title), fontface='bold')
print(plot_grid(title1, p, ncol=1, rel_heights=c(0.1, 1)) )
}
#To determine V (PC1 loading scores)
df <- read.delim("Datasets/KD/Control.txt")
pca <- FactoMineR::PCA(df, graph = F, scale.unit = F)
V <- pca$svd$V
jpeg("Figures/Fig. 7/S8_Control.jpeg", width = 350, height =350)
PCA_hist(df, V = V, title = "Control")
dev.off()
list <- c("NFIC", "AHR", "JUN", "SMAD3", "KLF4", "TBX3", "NR3C1","MITF","FOS", "SMAD4")
for (i in list) {
df <- read.delim(paste0("Datasets/KD/",i," KD.txt"))
jpeg(paste0("Figures/Fig. 7/S8_",i,"_KD.jpeg"), width = 350, height = 350)
PCA_hist(df, V = V, title = paste0(i, " KD"))
dev.off()
}
| /Figures/Fig. 7/Code/FigS8_PCA_hist.R | no_license | csbBSSE/Melanoma | R | false | false | 2,020 | r | #PCA_hist function generates the PCA plot for all datapoints with density distribution along PC1
#data = dataframe of gene expression, genes in columns
#clus = no. of clusters to be generated
#seed = initialization seed to be set
#pointsize = size of point on PCA plot
#V = Loading scores of genes for principal components
PCA_hist <- function(data,pointsize = 0.5, V = NULL, title){
require(factoextra)
require(ggpubr)
data[!is.finite(as.matrix(data))] <- 0
if(is.null(V)){
pca <- FactoMineR::PCA(data, scale = FALSE, graph = F)
df <- as.data.frame(pca$ind$coord[,1:2])
}else{
#Determining coordinates for fixed PC1 loading scores (For control network)
data <- as.matrix(data)
df <- data %*% V
df <- as.data.frame(df)[,1:2]
}
names(df) <- c("PC1", "PC2")
df <- as.data.frame(df[complete.cases(df),])
sp <- ggscatter(df, x = "PC1", y = "PC2",
color = "#0073C299",
size = pointsize)+
border()
sp <- sp + rremove("legend")
xplot <- ggdensity(df[,1],xlab="PC1", fill = "black")+ ylim(0,0.5)
library(cowplot)
p <- print(plot_grid(xplot, sp, ncol = 1, align = "hv",
rel_widths = c(2, 1), rel_heights = c(1, 2)))
title1 <- ggdraw() + draw_label(paste0(title), fontface='bold')
print(plot_grid(title1, p, ncol=1, rel_heights=c(0.1, 1)) )
}
#To determine V (PC1 loading scores)
df <- read.delim("Datasets/KD/Control.txt")
pca <- FactoMineR::PCA(df, graph = F, scale.unit = F)
V <- pca$svd$V
jpeg("Figures/Fig. 7/S8_Control.jpeg", width = 350, height =350)
PCA_hist(df, V = V, title = "Control")
dev.off()
list <- c("NFIC", "AHR", "JUN", "SMAD3", "KLF4", "TBX3", "NR3C1","MITF","FOS", "SMAD4")
for (i in list) {
df <- read.delim(paste0("Datasets/KD/",i," KD.txt"))
jpeg(paste0("Figures/Fig. 7/S8_",i,"_KD.jpeg"), width = 350, height = 350)
PCA_hist(df, V = V, title = paste0(i, " KD"))
dev.off()
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility_functions.R
\name{dense2sparse}
\alias{dense2sparse}
\title{Converts matrix given in dense format to sparse format data frame.}
\usage{
dense2sparse(mtx, add.diagonal = TRUE, name = NULL)
}
\arguments{
\item{mtx}{matrix in dense format}
\item{add.diagonal}{logical, if true an additional column indicating diagonal of each cell will be appended to resulting data frame}
\item{name}{character, additional argument, if specified column with name will be appended to resulting data frame}
}
\value{
data.frame with columns \code{c("i","j","val")} and optionally \code{c("diagonal","name")} columns; every row of resulting dataframe corresponds to cell in given dense matrix with i-th row, j-th column and value val
}
\description{
This function only keeps non-zero cells. In case given dense matix is symmetric \code{dense2sparse} will return upper triangular part of the matrix (i.e. where rows <= columns)
}
\examples{
dense2sparse(matrix(1:24, ncol = 3))
dense2sparse(matrix(1:24, ncol = 3), name = "some.matrix")
dense2sparse(matrix(1:24, ncol = 3), add.diagonal = FALSE)
# symmetric matrix
mtx.sym <- matrix(1:25, ncol = 5)
mtx.sym <- mtx.sym + t(mtx.sym)
dense2sparse(matrix(mtx.sym))
}
| /man/dense2sparse.Rd | permissive | rz6/DIADEM | R | false | true | 1,279 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility_functions.R
\name{dense2sparse}
\alias{dense2sparse}
\title{Converts matrix given in dense format to sparse format data frame.}
\usage{
dense2sparse(mtx, add.diagonal = TRUE, name = NULL)
}
\arguments{
\item{mtx}{matrix in dense format}
\item{add.diagonal}{logical, if true an additional column indicating diagonal of each cell will be appended to resulting data frame}
\item{name}{character, additional argument, if specified column with name will be appended to resulting data frame}
}
\value{
data.frame with columns \code{c("i","j","val")} and optionally \code{c("diagonal","name")} columns; every row of resulting dataframe corresponds to cell in given dense matrix with i-th row, j-th column and value val
}
\description{
This function only keeps non-zero cells. In case given dense matix is symmetric \code{dense2sparse} will return upper triangular part of the matrix (i.e. where rows <= columns)
}
\examples{
dense2sparse(matrix(1:24, ncol = 3))
dense2sparse(matrix(1:24, ncol = 3), name = "some.matrix")
dense2sparse(matrix(1:24, ncol = 3), add.diagonal = FALSE)
# symmetric matrix
mtx.sym <- matrix(1:25, ncol = 5)
mtx.sym <- mtx.sym + t(mtx.sym)
dense2sparse(matrix(mtx.sym))
}
|
### run_ssm_standard_filters.R ####################################################################
# Run the standard simple somatic filters on MuTect2 output. Output will be separate for SNVs and
# indels.
### HISTORY #######################################################################################
# Version Date Developer Comments
# 0.01 2017-04-10 rdeborja initial development
# 0.02 2017-04-13 rdeborja removed MT from dataframe due to
# clip filtering issues
### NOTES #########################################################################################
#
### PREAMBLE ######################################################################################
library('getopt')
usage <- function() {
usage.text <- '\nUsage: run_ssm_standard_filters.R --path </path/to/directory/containing/files> --sample <sample name> --source <WGS|WXS|CPANEL>\n\n'
return(usage.text)
}
params = matrix(
c(
'path', 'p', 1, 'character',
'sample', 's', 1, 'character',
'source', 'c', 1, 'character'
),
ncol = 4,
byrow = TRUE
)
opt = getopt(params)
# verify arguments
if (is.null(opt$path)) { stop(usage()) }
output <- paste(sep='.', paste(sep='_', opt$sample, 'annotated'), 'rda')
snv_filtered <- paste(sep='.', paste(sep='_', opt$sample, 'annotated_filtered_snv'), 'rda')
indel_filtered <- paste(sep='.', paste(sep='_', opt$sample, 'annotated_filtered_indel'), 'rda')
### LIBRARIES #####################################################################################
library(ShlienLab.Core.SSM)
### FUNCTIONS #####################################################################################
### GET DATA ######################################################################################
data <- get.mutect2.data(path=opt$path)
### PROCESS DATA ##################################################################################
# add additional annotations to the dataframe
data <- ShlienLab.Core.SSM::annotate.mutect2.data(data=data)
# currently there is a bug in downstream filtering causing a pre-filter step to remove the
# mitochondrial DNA from the output
data <- data %>% filter(annovar_chr != 'MT')
data <- data %>% filter(annovar_chr != 'M')
# separately filter the snv and indel data
data.snv.filtered <- ShlienLab.Core.SSM::filter_snv(data=data, source='WGS')
data.indel.filtered <- ShlienLab.Core.SSM::filter_indel(data=data, source='WGS')
# save the dataframes
save(data.snv.filtered, file=snv_filtered)
save(data.indel.filtered, file=indel_filtered)
### ANALYSIS ######################################################################################
### PLOTTING ######################################################################################
### SESSION INFORMATION ###########################################################################
sessionInfo()
| /exec/run_ssm_standard_filters.R | no_license | rdeborja/ShlienLab.Core.SSM | R | false | false | 3,023 | r | ### run_ssm_standard_filters.R ####################################################################
# Run the standard simple somatic filters on MuTect2 output. Output will be separate for SNVs and
# indels.
### HISTORY #######################################################################################
# Version Date Developer Comments
# 0.01 2017-04-10 rdeborja initial development
# 0.02 2017-04-13 rdeborja removed MT from dataframe due to
# clip filtering issues
### NOTES #########################################################################################
#
### PREAMBLE ######################################################################################
library('getopt')
usage <- function() {
usage.text <- '\nUsage: run_ssm_standard_filters.R --path </path/to/directory/containing/files> --sample <sample name> --source <WGS|WXS|CPANEL>\n\n'
return(usage.text)
}
params = matrix(
c(
'path', 'p', 1, 'character',
'sample', 's', 1, 'character',
'source', 'c', 1, 'character'
),
ncol = 4,
byrow = TRUE
)
opt = getopt(params)
# verify arguments
if (is.null(opt$path)) { stop(usage()) }
output <- paste(sep='.', paste(sep='_', opt$sample, 'annotated'), 'rda')
snv_filtered <- paste(sep='.', paste(sep='_', opt$sample, 'annotated_filtered_snv'), 'rda')
indel_filtered <- paste(sep='.', paste(sep='_', opt$sample, 'annotated_filtered_indel'), 'rda')
### LIBRARIES #####################################################################################
library(ShlienLab.Core.SSM)
### FUNCTIONS #####################################################################################
### GET DATA ######################################################################################
data <- get.mutect2.data(path=opt$path)
### PROCESS DATA ##################################################################################
# add additional annotations to the dataframe
data <- ShlienLab.Core.SSM::annotate.mutect2.data(data=data)
# currently there is a bug in downstream filtering causing a pre-filter step to remove the
# mitochondrial DNA from the output
data <- data %>% filter(annovar_chr != 'MT')
data <- data %>% filter(annovar_chr != 'M')
# separately filter the snv and indel data
data.snv.filtered <- ShlienLab.Core.SSM::filter_snv(data=data, source='WGS')
data.indel.filtered <- ShlienLab.Core.SSM::filter_indel(data=data, source='WGS')
# save the dataframes
save(data.snv.filtered, file=snv_filtered)
save(data.indel.filtered, file=indel_filtered)
### ANALYSIS ######################################################################################
### PLOTTING ######################################################################################
### SESSION INFORMATION ###########################################################################
sessionInfo()
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{print.netrankr_interval}
\alias{print.netrankr_interval}
\title{Print netrankr_interval object to terminal}
\usage{
\method{print}{netrankr_interval}(x, ...)
}
\arguments{
\item{x}{A netrankr_interval object}
\item{...}{additional arguments to print}
}
\description{
Prints the result of an object obtained from \link{rank_intervals} to terminal
}
\author{
David Schoch
}
| /man/print.netrankr_interval.Rd | permissive | schochastics/netrankr | R | false | true | 465 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{print.netrankr_interval}
\alias{print.netrankr_interval}
\title{Print netrankr_interval object to terminal}
\usage{
\method{print}{netrankr_interval}(x, ...)
}
\arguments{
\item{x}{A netrankr_interval object}
\item{...}{additional arguments to print}
}
\description{
Prints the result of an object obtained from \link{rank_intervals} to terminal
}
\author{
David Schoch
}
|
EBS <- function(object, model=1, block.length=NULL, alpha.boot=0.05, field.sig=0.05, bootR=1000, ntrials=1000, verbose=FALSE) {
a <- attributes(object)
out <- list()
attributes(out) <- a
X <- object[[1]]
Xhat <- object[[2]]
if(!is.array(Xhat)) {
if(!is.numeric(model)) {
nf <- a$nforecast
dn <- a$data.name
if(length(dn) == nf + 2) mod.names <- dn[-(1:2)]
else mod.names <- dn[-1]
model <- (1:nf)[dn == model]
if(is.na(model)) stop("datagrabber: invalid model argument.")
}
Xhat <- Xhat[[model]]
} # end of if 'Xhat' is not an array (i.e., if more than one model in object) stmts.
X <- t(apply(X, 3, c))
Xhat <- t(apply(Xhat, 3, c))
loc <- a$loc
if(!is.null(a$subset)) {
id <- a$subset
X <- X[,id]
Xhat <- Xhat[,id]
loc <- loc[id,]
}
res <- spatbiasFS(X=X, Y=Xhat, loc=a$loc, block.length=block.length,
alpha.boot=alpha.boot, field.sig=field.sig, bootR=bootR,
ntrials=ntrials, verbose=verbose)
out$block.boot.results <- res$block.boot.results
out$sig.results <- res$sig.results
sig.values <- c(res$field.significance, res$alpha.boot, bootR, ntrials)
names(sig.values) <- c("field sig.", "alpha boot", "bootstrap replicates", "number of trials")
attr(out, "arguments") <- sig.values
attr(out, "which.model") <- model
class(out) <- "EBS"
return(out)
} # end of 'EBS' function.
LocSig <- function(Z, numrep=1000, block.length=NULL, bootfun="mean", alpha=0.05, bca=FALSE, ...) {
if(bootfun=="mean") bootfun <- function(data) return(colMeans(data, na.rm=TRUE))
else if(is.character(bootfun)) bootfun <- get(bootfun)
zdim <- dim(Z)
n <- zdim[1]
m <- zdim[2]
out <- data.frame(Lower = numeric(m), Estimate = numeric(m), Upper = numeric(m))
if(is.null(block.length)) block.length <- floor(sqrt(n))
if(block.length==1) booted <- boot(Z, bootfun, R=numrep, ...)
else booted <- tsboot(Z, bootfun, l=block.length, R=numrep, sim="fixed", ...)
out$Estimate <- booted$t0
if((block.length==1) & bca) {
for(i in 1:m) {
tmp <- boot.ci( booted, conf=1-alpha, type="bca", index=i)
out$Lower[i] <- tmp$bca[,4]
out$Upper[i] <- tmp$bca[,5]
} # end of for 'i' loop.
} else {
if(bca) warning("LocSig: You chose to use the BCa method, but block.length != 1. Using percentile method with circular block bootstrap instead.")
for(i in 1:m) {
tmp <- boot.ci( booted, conf=1-alpha, type="perc", index=i)
out$Lower[i] <- tmp$perc[,4]
out$Upper[i] <- tmp$perc[,5]
} # end of for 'i' loop.
} # end of if else do "BCa" (IID bootstrap only) or percentile confidence limits.
class(out) <- "LocSig"
return(out)
} # end of 'LocSig' function.
MCdof <- function(x, ntrials=5000, field.sig=0.05, zfun="rnorm", zfun.args=NULL, which.test=c("t", "Z", "cor.test"), verbose=FALSE, ...) {
if(verbose) begin.time <- Sys.time()
if(length(which.test)>1) which.test <- "t"
xdim <- dim(x)
tlen <- xdim[1]
B.dof.test <- numeric(ntrials)
if(which.test=="cor.test") cortester <- function(x,y,...) return(cor.test(x=x,y=y,...)$p.value)
if(verbose) cat("\n", "Looping through ", ntrials, " times to simulate data and take correlations. Enjoy!\n")
for(i in 1:ntrials) {
if(verbose & (i < 100 | i%%100==0)) cat(i, " ")
z <- do.call(zfun, args=c(list(n=tlen), zfun.args))
if(which.test=="cor.test") tmp <- apply(x, 2, cortester, y=z, ...)
else {
cor.value <- abs(cor(x, z, use = "pairwise.complete.obs"))
if(which.test=="t") tmp <- sig.cor.t(cor.value, len=tlen, ...)
else if(which.test=="Z") tmp <- sig.cor.Z(cor.value, len=tlen, ...)
}
B.dof.test[i] <- mean(field.sig > tmp, na.rm = TRUE)
} # end of for 'i' loop.
if(verbose) print(Sys.time() - begin.time)
return(list(MCprops=B.dof.test, minsigcov=quantile(B.dof.test, probs=1-field.sig, na.rm=TRUE)))
} # end of 'MCdof' function.
sig.cor.t <- function(r, len = 40, ...)
{
#
# A function to determine if a correlation is significantly different from x
#
# Input -
# r - the (unsigned) correlation coefficient
# len - the length of the vectors used to generate the correlation (default = 40)
# Alpha - the significance level (default = 0.05)
#
# Output -
# palpha.cor - the p-level of the correlation
#
# v1.0
# KLE 2/3/2009
# Ported to R -- 03/30/2011
#
t <- abs(r) * sqrt((len - 2)/(1 - r^2))
palpha.cor <- 1 - pt(t, len - 2)
return(palpha.cor)
} # end of 'sig.cor.t' function.
sig.cor.Z <- function(r, len = 40, H0 = 0)
{
#
# A function to find the significance of a correlation from H0 using Fisher's Z transform.
#
# Input -
# r - the correlation
# len - the length of the crrelated vectors
# Ho0- the null hypothesis correlation default = 0
#
# Output -
# palpha.cor - the p-value of the correlation.
#
# KLE 02/20/2009
# Ported to R -- 03/30/2011
#
W <- fisherz(abs(r))
stderr <- 1/sqrt(len - 3)
zscore <- (W - H0)/stderr
palpha.cor <- 1 - pnorm(zscore)
return(palpha.cor)
} # end of 'sig.cor.Z' function.
fisherz <- function(r)
{
#
# The sampling distribution of Pearson's r is not normally distributed. Fisher
# developed a transformation now called "Fisher's z' transformation" that converts
# Pearson's r's to the normally distributed variable z'.
#
# A function to perfoem Fisher's Z transformation on a correlation value, allowing
# a t-test for significant correlation.
#
# Input -
# r - the correlation
#
# Output -
# W - the transformed corelation
#
# v1.0
# KLE 2 Feb 2009
# Ported to R -- 03/30/2011
#
W <- 0.5 * (log((1 + r)/(1 - r)))
return(W)
}
plot.LocSig <- function(x, loc=NULL, nx=NULL, ny=NULL, ...){
n <- dim(x)[1]
if(is.null(loc)) {
if(is.null(nx) | is.null(ny)) stop("plot.LocSig: must specify either loc or both nx and ny")
loc <- cbind(rep(1:nx, ny), rep(1:ny, each=nx))
}
mean.i <- as.image(x$Estimate, x=loc)
thk.i <- as.image((x$Upper - x$Lower), x=loc)
output <- list(mean.i = mean.i, thk.i = thk.i)
par(mfrow=c(1,2))
image.plot(mean.i, main="Mean of Estimate", ...)
image.plot(thk.i, main="CI range of Estimate", ...)
invisible(output)
}
plot.EBS <- function(x, ..., set.pw=FALSE, col, horizontal) {
if(missing(col)) col <- c("gray", tim.colors(64))
if(missing(horizontal)) horizontal <- TRUE
if(set.pw) par(mfrow=c(1,2), oma=c(0,0,2,0))
else par(oma=c(0,0,2,0))
Zest <- x$block.boot.results$Estimate
ZciR <- x$block.boot.results$Upper - x$block.boot.results$Lower
a <- attributes(x)
loc.byrow <- a$loc.byrow
xd <- a$xdim
if(is.null(a$subset)) {
if(!is.matrix(Zest)) Zest <- matrix(Zest, xd[1], xd[2])
if(!is.matrix(ZciR)) ZciR <- matrix(ZciR, xd[1], xd[2])
}
if(a$projection && is.null(a$subset)) {
xloc <- matrix(a$loc[,1], xd[1], xd[2], byrow=loc.byrow)
yloc <- matrix(a$loc[,2], xd[1], xd[2], byrow=loc.byrow)
}
if(!is.null(a$subset)) {
if(is.logical(a$subset)) Ns <- sum(a$subset, na.rm=TRUE)
else Ns <- length(a$subset)
Zest <- as.image(Zest, nx=ceiling(Ns/2), ny=ceiling(Ns/2), x=a$loc[a$subset,], na.rm=TRUE)
ZciR <- as.image(ZciR, nx=ceiling(Ns/2), ny=ceiling(Ns/2), x=a$loc[a$subset,], na.rm=TRUE)
} else if(!a$reg.grid) {
Zest <- as.image(Zest, nx=xd[1], ny=xd[2], x=a$loc, na.rm=TRUE)
ZciR <- as.image(ZciR, nx=xd[1], ny=xd[2], x=a$loc, na.rm=TRUE)
}
if(a$map) {
ax <- list(x=pretty(round(a$loc[,1], digits=2)), y=pretty(round(a$loc[,2], digits=2)))
if(is.null(a$subset)) r <- apply(a$loc, 2, range, finite=TRUE)
else r <- apply(a$loc[a$subset,], 2, range, finite=TRUE)
map(xlim=r[,1], ylim=r[,2], type="n")
axis(1, at=ax$x, labels=ax$x)
axis(2, at=ax$y, labels=ax$y)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
map(add=TRUE, lwd=1.5)
map(database="state", add=TRUE)
map(xlim=r[,1], ylim=r[,2], type="n")
axis(1, at=ax$x, labels=ax$x)
axis(2, at=ax$y, labels=ax$y)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, ZciR, add=TRUE, col=col, main="CI Range", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(ZciR, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(ZciR, add=TRUE, col=col, main="CI Range", horizontal=horizontal, ...)
map(add=TRUE, lwd=1.5)
map(database="state", add=TRUE)
} else {
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, ZciR, col=col, main="CI Range", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(ZciR, col=col, main="Mean of Estimate", ...)
else image.plot(ZciR, col=col, main="CI Range", horizontal=horizontal, ...)
} # end of if else 'map' stmts.
if(length(a$data.name) == a$nforecast + 2) {
msg <- paste(a$data.name[1], ": ", a$data.name[2], " vs ", a$data.name[a$which.model+2], sep="")
} else msg <- paste(a$data.name[2], " vs ", a$data.name[a$which.model+1], sep="")
if(a$field.type != "" && a$units != "") msg <- paste(msg, "\n", a$field.type, " (", a$units, ")", sep="")
else if(a$field.type != "") msg <- paste(msg, a$field.type, sep="\n")
else if(a$units != "") msg <- paste(msg, "\n(", a$units, ")", sep="")
mtext(msg, line=0.05, outer=TRUE)
invisible()
} # end of 'plot.EBS' function.
inside <- function(DF)
{
#
# A function to determine if the mean error at a data point is
# inside the confidence limits.
#
# Input
# DF - data frame containing mean error values and upper and lower
# confidence limits
#
# Output
# logical T if outside, F if inside.
#
result <- !is.na(CI.fun(DF$Upper - DF$Lower, DF$Estimate))
return(result)
} # end of 'inside' function.
CI.fun <- function(CI.field, est.field, replacement = NA)
{
test <- ifelse((0.5 * CI.field < abs(est.field)), est.field, replacement)
return(test)
} # end of 'CI.fun' function.
sig.coverage <- function(DF)
{
tmp <- inside(DF)
out <- sum(tmp,na.rm=TRUE)/(length(tmp[!is.na(tmp)])) - sum(is.na(DF$Estimate))
return(out)
} # end of 'sig.coverage' function.
is.sig <- function(X, blockboot.results.df, n = 3000, fld.sig = 0.05, verbose=FALSE)
{
#
# Input -
# X - matrix of errors at gridpoints. n gridpoints by m days
# blockboot.results.df - dataframe of errors and CI at each
# gridpoint.
# n - number of Monte Carlo trials.
# field.sig - significnace alpha for field significance.
#
# Output -
# List
# name - Name of the data beig tested
# results - The minimum amount of areal coverage needed for
# significance at
# the given fld.sig
# actual - The actual coverage of sigificant gridpoint
# results.
# issig - Logical variable: T for significant results, F for
# non-significant results.
#
# KLE 01/2005
#
# Remove missing rows
#
sig.results <- MCdof(X, ntrials = n, field.sig = fld.sig, verbose=verbose)$minsigcov
actual.coverage <- sig.coverage(blockboot.results.df)
sig <- (actual.coverage > sig.results)
output <- list(name = as.character(deparse(substitute(X))),
required = as.numeric(sig.results), actual = as.numeric(
actual.coverage), issig = as.logical(sig))
return(output)
}
spatbiasFS <- function(X, Y, loc=NULL, block.length=NULL, alpha.boot=0.05, field.sig=0.05, bootR=1000, ntrials=1000, verbose=FALSE) {
out <- list()
if(!is.null(loc)) {
data.name <- c(as.character(substitute(X)),as.character(substitute(Y)),as.character(substitute(loc)))
names(data.name) <- c("verification","forecast","locations")
} else {
data.name <- c(as.character(substitute(X)),as.character(substitute(Y)))
names(data.name) <- c("verification","forecast")
}
out$data.name <- data.name
errfield <- Y - X
hold <- LocSig(Z=errfield, numrep=bootR, block.length=block.length, alpha=alpha.boot)
res <- is.sig(errfield, hold, n=ntrials, fld.sig=field.sig, verbose=verbose)
out$block.boot.results <- hold
out$sig.results <- res
out$field.significance <- field.sig
out$alpha.boot <- alpha.boot
out$bootR <- bootR
out$ntrials <- ntrials
class(out) <- "spatbiasFS"
return(out)
} # end of 'spatbiasFS' function.
summary.spatbiasFS <- function(object, ...) {
cat("\n")
msg <- paste("Results for ", object$data.name[2], " compared against ", object$data.name[1], sep="")
print(msg)
cat("\n", "\n")
cat("Field significance level: ", object$field.significance, "\n")
cat("Observed coverage of significant difference: ", object$sig.results$actual, "\n")
cat("Required coverage for field significance: ", object$sig.results$required, "\n")
invisible()
} # end of 'summary.spatbiasFS' function.
plot.spatbiasFS <- function(x, ...) {
# TO DO: Try to make a better plot (without using as.image).
msg <- paste("Mean Error: ", x$data.name[2], " vs ", x$data.name[1], sep="")
# X <- get(x$data.name[1])
# Y <- get(x$data.name[2])
if(length(x$data.name)==3) loc <- get(x$data.name[3])
else stop("plot.spatbiasFS: No entry loc. Must supply location information.")
est.i <- as.image(x$block.boot.results$Estimate, x=loc)
CIrange <- as.image(x$block.boot.results$Upper - x$block.boot.results$Lower, x=loc)
par(mfrow=c(1,2))
image.plot(est.i, col=tim.colors(64), axes=FALSE, main=msg)
# map(add=TRUE)
# map(add=TRUE,database="state")
image.plot(CIrange, col=tim.colors(64), axes=FALSE, xlab=paste("Req. Coverage: ", round(x$sig.results$required,digits=2), " vs Obs. coverage: ",
round(x$sig.results$actual,digits=2), sep=""),
main=paste((1-x$alpha.boot)*100, "% CI range", sep=""))
invisible()
} # end of 'plot.spatbiasFS' function.
| /SpatialVx/R/SigFuns.R | no_license | ingted/R-Examples | R | false | false | 14,453 | r | EBS <- function(object, model=1, block.length=NULL, alpha.boot=0.05, field.sig=0.05, bootR=1000, ntrials=1000, verbose=FALSE) {
a <- attributes(object)
out <- list()
attributes(out) <- a
X <- object[[1]]
Xhat <- object[[2]]
if(!is.array(Xhat)) {
if(!is.numeric(model)) {
nf <- a$nforecast
dn <- a$data.name
if(length(dn) == nf + 2) mod.names <- dn[-(1:2)]
else mod.names <- dn[-1]
model <- (1:nf)[dn == model]
if(is.na(model)) stop("datagrabber: invalid model argument.")
}
Xhat <- Xhat[[model]]
} # end of if 'Xhat' is not an array (i.e., if more than one model in object) stmts.
X <- t(apply(X, 3, c))
Xhat <- t(apply(Xhat, 3, c))
loc <- a$loc
if(!is.null(a$subset)) {
id <- a$subset
X <- X[,id]
Xhat <- Xhat[,id]
loc <- loc[id,]
}
res <- spatbiasFS(X=X, Y=Xhat, loc=a$loc, block.length=block.length,
alpha.boot=alpha.boot, field.sig=field.sig, bootR=bootR,
ntrials=ntrials, verbose=verbose)
out$block.boot.results <- res$block.boot.results
out$sig.results <- res$sig.results
sig.values <- c(res$field.significance, res$alpha.boot, bootR, ntrials)
names(sig.values) <- c("field sig.", "alpha boot", "bootstrap replicates", "number of trials")
attr(out, "arguments") <- sig.values
attr(out, "which.model") <- model
class(out) <- "EBS"
return(out)
} # end of 'EBS' function.
LocSig <- function(Z, numrep=1000, block.length=NULL, bootfun="mean", alpha=0.05, bca=FALSE, ...) {
if(bootfun=="mean") bootfun <- function(data) return(colMeans(data, na.rm=TRUE))
else if(is.character(bootfun)) bootfun <- get(bootfun)
zdim <- dim(Z)
n <- zdim[1]
m <- zdim[2]
out <- data.frame(Lower = numeric(m), Estimate = numeric(m), Upper = numeric(m))
if(is.null(block.length)) block.length <- floor(sqrt(n))
if(block.length==1) booted <- boot(Z, bootfun, R=numrep, ...)
else booted <- tsboot(Z, bootfun, l=block.length, R=numrep, sim="fixed", ...)
out$Estimate <- booted$t0
if((block.length==1) & bca) {
for(i in 1:m) {
tmp <- boot.ci( booted, conf=1-alpha, type="bca", index=i)
out$Lower[i] <- tmp$bca[,4]
out$Upper[i] <- tmp$bca[,5]
} # end of for 'i' loop.
} else {
if(bca) warning("LocSig: You chose to use the BCa method, but block.length != 1. Using percentile method with circular block bootstrap instead.")
for(i in 1:m) {
tmp <- boot.ci( booted, conf=1-alpha, type="perc", index=i)
out$Lower[i] <- tmp$perc[,4]
out$Upper[i] <- tmp$perc[,5]
} # end of for 'i' loop.
} # end of if else do "BCa" (IID bootstrap only) or percentile confidence limits.
class(out) <- "LocSig"
return(out)
} # end of 'LocSig' function.
MCdof <- function(x, ntrials=5000, field.sig=0.05, zfun="rnorm", zfun.args=NULL, which.test=c("t", "Z", "cor.test"), verbose=FALSE, ...) {
if(verbose) begin.time <- Sys.time()
if(length(which.test)>1) which.test <- "t"
xdim <- dim(x)
tlen <- xdim[1]
B.dof.test <- numeric(ntrials)
if(which.test=="cor.test") cortester <- function(x,y,...) return(cor.test(x=x,y=y,...)$p.value)
if(verbose) cat("\n", "Looping through ", ntrials, " times to simulate data and take correlations. Enjoy!\n")
for(i in 1:ntrials) {
if(verbose & (i < 100 | i%%100==0)) cat(i, " ")
z <- do.call(zfun, args=c(list(n=tlen), zfun.args))
if(which.test=="cor.test") tmp <- apply(x, 2, cortester, y=z, ...)
else {
cor.value <- abs(cor(x, z, use = "pairwise.complete.obs"))
if(which.test=="t") tmp <- sig.cor.t(cor.value, len=tlen, ...)
else if(which.test=="Z") tmp <- sig.cor.Z(cor.value, len=tlen, ...)
}
B.dof.test[i] <- mean(field.sig > tmp, na.rm = TRUE)
} # end of for 'i' loop.
if(verbose) print(Sys.time() - begin.time)
return(list(MCprops=B.dof.test, minsigcov=quantile(B.dof.test, probs=1-field.sig, na.rm=TRUE)))
} # end of 'MCdof' function.
sig.cor.t <- function(r, len = 40, ...)
{
#
# A function to determine if a correlation is significantly different from x
#
# Input -
# r - the (unsigned) correlation coefficient
# len - the length of the vectors used to generate the correlation (default = 40)
# Alpha - the significance level (default = 0.05)
#
# Output -
# palpha.cor - the p-level of the correlation
#
# v1.0
# KLE 2/3/2009
# Ported to R -- 03/30/2011
#
t <- abs(r) * sqrt((len - 2)/(1 - r^2))
palpha.cor <- 1 - pt(t, len - 2)
return(palpha.cor)
} # end of 'sig.cor.t' function.
sig.cor.Z <- function(r, len = 40, H0 = 0)
{
#
# A function to find the significance of a correlation from H0 using Fisher's Z transform.
#
# Input -
# r - the correlation
# len - the length of the crrelated vectors
# Ho0- the null hypothesis correlation default = 0
#
# Output -
# palpha.cor - the p-value of the correlation.
#
# KLE 02/20/2009
# Ported to R -- 03/30/2011
#
W <- fisherz(abs(r))
stderr <- 1/sqrt(len - 3)
zscore <- (W - H0)/stderr
palpha.cor <- 1 - pnorm(zscore)
return(palpha.cor)
} # end of 'sig.cor.Z' function.
fisherz <- function(r)
{
#
# The sampling distribution of Pearson's r is not normally distributed. Fisher
# developed a transformation now called "Fisher's z' transformation" that converts
# Pearson's r's to the normally distributed variable z'.
#
# A function to perfoem Fisher's Z transformation on a correlation value, allowing
# a t-test for significant correlation.
#
# Input -
# r - the correlation
#
# Output -
# W - the transformed corelation
#
# v1.0
# KLE 2 Feb 2009
# Ported to R -- 03/30/2011
#
W <- 0.5 * (log((1 + r)/(1 - r)))
return(W)
}
plot.LocSig <- function(x, loc=NULL, nx=NULL, ny=NULL, ...){
n <- dim(x)[1]
if(is.null(loc)) {
if(is.null(nx) | is.null(ny)) stop("plot.LocSig: must specify either loc or both nx and ny")
loc <- cbind(rep(1:nx, ny), rep(1:ny, each=nx))
}
mean.i <- as.image(x$Estimate, x=loc)
thk.i <- as.image((x$Upper - x$Lower), x=loc)
output <- list(mean.i = mean.i, thk.i = thk.i)
par(mfrow=c(1,2))
image.plot(mean.i, main="Mean of Estimate", ...)
image.plot(thk.i, main="CI range of Estimate", ...)
invisible(output)
}
plot.EBS <- function(x, ..., set.pw=FALSE, col, horizontal) {
if(missing(col)) col <- c("gray", tim.colors(64))
if(missing(horizontal)) horizontal <- TRUE
if(set.pw) par(mfrow=c(1,2), oma=c(0,0,2,0))
else par(oma=c(0,0,2,0))
Zest <- x$block.boot.results$Estimate
ZciR <- x$block.boot.results$Upper - x$block.boot.results$Lower
a <- attributes(x)
loc.byrow <- a$loc.byrow
xd <- a$xdim
if(is.null(a$subset)) {
if(!is.matrix(Zest)) Zest <- matrix(Zest, xd[1], xd[2])
if(!is.matrix(ZciR)) ZciR <- matrix(ZciR, xd[1], xd[2])
}
if(a$projection && is.null(a$subset)) {
xloc <- matrix(a$loc[,1], xd[1], xd[2], byrow=loc.byrow)
yloc <- matrix(a$loc[,2], xd[1], xd[2], byrow=loc.byrow)
}
if(!is.null(a$subset)) {
if(is.logical(a$subset)) Ns <- sum(a$subset, na.rm=TRUE)
else Ns <- length(a$subset)
Zest <- as.image(Zest, nx=ceiling(Ns/2), ny=ceiling(Ns/2), x=a$loc[a$subset,], na.rm=TRUE)
ZciR <- as.image(ZciR, nx=ceiling(Ns/2), ny=ceiling(Ns/2), x=a$loc[a$subset,], na.rm=TRUE)
} else if(!a$reg.grid) {
Zest <- as.image(Zest, nx=xd[1], ny=xd[2], x=a$loc, na.rm=TRUE)
ZciR <- as.image(ZciR, nx=xd[1], ny=xd[2], x=a$loc, na.rm=TRUE)
}
if(a$map) {
ax <- list(x=pretty(round(a$loc[,1], digits=2)), y=pretty(round(a$loc[,2], digits=2)))
if(is.null(a$subset)) r <- apply(a$loc, 2, range, finite=TRUE)
else r <- apply(a$loc[a$subset,], 2, range, finite=TRUE)
map(xlim=r[,1], ylim=r[,2], type="n")
axis(1, at=ax$x, labels=ax$x)
axis(2, at=ax$y, labels=ax$y)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(Zest, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
map(add=TRUE, lwd=1.5)
map(database="state", add=TRUE)
map(xlim=r[,1], ylim=r[,2], type="n")
axis(1, at=ax$x, labels=ax$x)
axis(2, at=ax$y, labels=ax$y)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, ZciR, add=TRUE, col=col, main="CI Range", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(ZciR, add=TRUE, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(ZciR, add=TRUE, col=col, main="CI Range", horizontal=horizontal, ...)
map(add=TRUE, lwd=1.5)
map(database="state", add=TRUE)
} else {
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
else image.plot(Zest, col=col, main="Mean of Estimate", horizontal=horizontal, ...)
if(a$projection && a$reg.grid && is.null(a$subset)) image.plot(xloc, yloc, ZciR, col=col, main="CI Range", horizontal=horizontal, ...)
else if(a$reg.grid && is.null(a$subset)) image.plot(ZciR, col=col, main="Mean of Estimate", ...)
else image.plot(ZciR, col=col, main="CI Range", horizontal=horizontal, ...)
} # end of if else 'map' stmts.
if(length(a$data.name) == a$nforecast + 2) {
msg <- paste(a$data.name[1], ": ", a$data.name[2], " vs ", a$data.name[a$which.model+2], sep="")
} else msg <- paste(a$data.name[2], " vs ", a$data.name[a$which.model+1], sep="")
if(a$field.type != "" && a$units != "") msg <- paste(msg, "\n", a$field.type, " (", a$units, ")", sep="")
else if(a$field.type != "") msg <- paste(msg, a$field.type, sep="\n")
else if(a$units != "") msg <- paste(msg, "\n(", a$units, ")", sep="")
mtext(msg, line=0.05, outer=TRUE)
invisible()
} # end of 'plot.EBS' function.
inside <- function(DF)
{
#
# A function to determine if the mean error at a data point is
# inside the confidence limits.
#
# Input
# DF - data frame containing mean error values and upper and lower
# confidence limits
#
# Output
# logical T if outside, F if inside.
#
result <- !is.na(CI.fun(DF$Upper - DF$Lower, DF$Estimate))
return(result)
} # end of 'inside' function.
CI.fun <- function(CI.field, est.field, replacement = NA)
{
test <- ifelse((0.5 * CI.field < abs(est.field)), est.field, replacement)
return(test)
} # end of 'CI.fun' function.
sig.coverage <- function(DF)
{
tmp <- inside(DF)
out <- sum(tmp,na.rm=TRUE)/(length(tmp[!is.na(tmp)])) - sum(is.na(DF$Estimate))
return(out)
} # end of 'sig.coverage' function.
is.sig <- function(X, blockboot.results.df, n = 3000, fld.sig = 0.05, verbose=FALSE)
{
#
# Input -
# X - matrix of errors at gridpoints. n gridpoints by m days
# blockboot.results.df - dataframe of errors and CI at each
# gridpoint.
# n - number of Monte Carlo trials.
# field.sig - significnace alpha for field significance.
#
# Output -
# List
# name - Name of the data beig tested
# results - The minimum amount of areal coverage needed for
# significance at
# the given fld.sig
# actual - The actual coverage of sigificant gridpoint
# results.
# issig - Logical variable: T for significant results, F for
# non-significant results.
#
# KLE 01/2005
#
# Remove missing rows
#
sig.results <- MCdof(X, ntrials = n, field.sig = fld.sig, verbose=verbose)$minsigcov
actual.coverage <- sig.coverage(blockboot.results.df)
sig <- (actual.coverage > sig.results)
output <- list(name = as.character(deparse(substitute(X))),
required = as.numeric(sig.results), actual = as.numeric(
actual.coverage), issig = as.logical(sig))
return(output)
}
spatbiasFS <- function(X, Y, loc=NULL, block.length=NULL, alpha.boot=0.05, field.sig=0.05, bootR=1000, ntrials=1000, verbose=FALSE) {
out <- list()
if(!is.null(loc)) {
data.name <- c(as.character(substitute(X)),as.character(substitute(Y)),as.character(substitute(loc)))
names(data.name) <- c("verification","forecast","locations")
} else {
data.name <- c(as.character(substitute(X)),as.character(substitute(Y)))
names(data.name) <- c("verification","forecast")
}
out$data.name <- data.name
errfield <- Y - X
hold <- LocSig(Z=errfield, numrep=bootR, block.length=block.length, alpha=alpha.boot)
res <- is.sig(errfield, hold, n=ntrials, fld.sig=field.sig, verbose=verbose)
out$block.boot.results <- hold
out$sig.results <- res
out$field.significance <- field.sig
out$alpha.boot <- alpha.boot
out$bootR <- bootR
out$ntrials <- ntrials
class(out) <- "spatbiasFS"
return(out)
} # end of 'spatbiasFS' function.
summary.spatbiasFS <- function(object, ...) {
cat("\n")
msg <- paste("Results for ", object$data.name[2], " compared against ", object$data.name[1], sep="")
print(msg)
cat("\n", "\n")
cat("Field significance level: ", object$field.significance, "\n")
cat("Observed coverage of significant difference: ", object$sig.results$actual, "\n")
cat("Required coverage for field significance: ", object$sig.results$required, "\n")
invisible()
} # end of 'summary.spatbiasFS' function.
plot.spatbiasFS <- function(x, ...) {
# TO DO: Try to make a better plot (without using as.image).
msg <- paste("Mean Error: ", x$data.name[2], " vs ", x$data.name[1], sep="")
# X <- get(x$data.name[1])
# Y <- get(x$data.name[2])
if(length(x$data.name)==3) loc <- get(x$data.name[3])
else stop("plot.spatbiasFS: No entry loc. Must supply location information.")
est.i <- as.image(x$block.boot.results$Estimate, x=loc)
CIrange <- as.image(x$block.boot.results$Upper - x$block.boot.results$Lower, x=loc)
par(mfrow=c(1,2))
image.plot(est.i, col=tim.colors(64), axes=FALSE, main=msg)
# map(add=TRUE)
# map(add=TRUE,database="state")
image.plot(CIrange, col=tim.colors(64), axes=FALSE, xlab=paste("Req. Coverage: ", round(x$sig.results$required,digits=2), " vs Obs. coverage: ",
round(x$sig.results$actual,digits=2), sep=""),
main=paste((1-x$alpha.boot)*100, "% CI range", sep=""))
invisible()
} # end of 'plot.spatbiasFS' function.
|
/02-Profit/00-3-RviseMain.R | no_license | Ravin515/STN-DE | R | false | false | 803 | r | ||
% Generated by roxygen2 (4.0.1): do not edit by hand
\name{wwz}
\alias{wwz}
\title{Runs the Wang-Wei-Zhu decomposition}
\usage{
wwz(x)
}
\arguments{
\item{x}{an object of the class decompr}
}
\value{
the decomposed table
}
\description{
This function runs the Wang-Wei-Zhu decomposition.
}
\details{
Adapted from code by Fei Wang.
}
\author{
Bastiaan Quast
}
\references{
Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu.
Quantifying international production sharing at the bilateral and sector levels.
No. w19677. National Bureau of Economic Research, 2013.
}
| /man/wwz.Rd | no_license | vkummritz/decompr | R | false | false | 553 | rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{wwz}
\alias{wwz}
\title{Runs the Wang-Wei-Zhu decomposition}
\usage{
wwz(x)
}
\arguments{
\item{x}{an object of the class decompr}
}
\value{
the decomposed table
}
\description{
This function runs the Wang-Wei-Zhu decomposition.
}
\details{
Adapted from code by Fei Wang.
}
\author{
Bastiaan Quast
}
\references{
Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu.
Quantifying international production sharing at the bilateral and sector levels.
No. w19677. National Bureau of Economic Research, 2013.
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check.blocksDS.R
\name{check.blocksDS}
\alias{check.blocksDS}
\title{Name imputation blocks
This helper function names any unnamed elements in the \code{blocks}
specification. This is a convenience function.}
\usage{
check.blocksDS(blocks, data, calltype = "type")
}
\arguments{
\item{prefix}{A character vector of length 1 with the prefix to
be using for naming any unnamed blocks with two or more variables.}
}
\value{
A named list of character vectors with variables names.
}
\description{
Name imputation blocks
This helper function names any unnamed elements in the \code{blocks}
specification. This is a convenience function.
}
\details{
This function will name any unnamed list elements specified in
the optional argument \code{blocks}. Unnamed blocks
consisting of just one variable will be named after this variable.
Unnamed blocks containing more than one variables will be named
by the \code{prefix} argument, padded by an integer sequence
stating at 1.
}
\examples{
blocks <- list(c("hyp", "chl"), AGE = "age", c("bmi", "hyp"), "edu")
name.blocks(blocks)
}
| /man/check.blocksDS.Rd | no_license | paularaissa/dsMice | R | false | true | 1,149 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check.blocksDS.R
\name{check.blocksDS}
\alias{check.blocksDS}
\title{Name imputation blocks
This helper function names any unnamed elements in the \code{blocks}
specification. This is a convenience function.}
\usage{
check.blocksDS(blocks, data, calltype = "type")
}
\arguments{
\item{prefix}{A character vector of length 1 with the prefix to
be using for naming any unnamed blocks with two or more variables.}
}
\value{
A named list of character vectors with variables names.
}
\description{
Name imputation blocks
This helper function names any unnamed elements in the \code{blocks}
specification. This is a convenience function.
}
\details{
This function will name any unnamed list elements specified in
the optional argument \code{blocks}. Unnamed blocks
consisting of just one variable will be named after this variable.
Unnamed blocks containing more than one variables will be named
by the \code{prefix} argument, padded by an integer sequence
stating at 1.
}
\examples{
blocks <- list(c("hyp", "chl"), AGE = "age", c("bmi", "hyp"), "edu")
name.blocks(blocks)
}
|
#' Function for creating simple D3 JavaScript force directed network graphs.
#'
#' \code{simpleNetwork} creates simple D3 JavaScript force directed network
#' graphs.
#'
#' @param Data a data frame object with three columns. The first two are the
#' names of the linked units. The third records an edge value. (Currently the
#' third column doesn't affect the graph.)
#' @param Source character string naming the network source variable in the data
#' frame. If \code{Source = NULL} then the first column of the data frame is
#' treated as the source.
#' @param Target character string naming the network target variable in the data
#' frame. If \code{Target = NULL} then the second column of the data frame is
#' treated as the target.
#' @param height height for the network graph's frame area in pixels (if
#' \code{NULL} then height is automatically determined based on context)
#' @param width numeric width for the network graph's frame area in pixels (if
#' \code{NULL} then width is automatically determined based on context)
#' @param linkDistance numeric distance between the links in pixels (actually
#' arbitrary relative to the diagram's size).
#' @param charge numeric value indicating either the strength of the node
#' repulsion (negative value) or attraction (positive value).
#' @param fontSize numeric font size in pixels for the node text labels.
#' @param fontFamily font family for the node text labels.
#' @param linkColour character string specifying the colour you want the link
#' lines to be. Multiple formats supported (e.g. hexadecimal).
#' @param nodeColour character string specifying the colour you want the node
#' circles to be. Multiple formats supported (e.g. hexadecimal).
#' @param nodeClickColour character string specifying the colour you want the
#' node circles to be when they are clicked. Also changes the colour of the
#' text. Multiple formats supported (e.g. hexadecimal).
#' @param textColour character string specifying the colour you want the text to
#' be before they are clicked. Multiple formats supported (e.g. hexadecimal).
#' @param opacity numeric value of the proportion opaque you would like the
#' graph elements to be.
#' @param zoom logical value to enable (\code{TRUE}) or disable (\code{FALSE})
#' zooming
#'
#' @examples
#' # Fake data
#' Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D")
#' Target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I")
#' NetworkData <- data.frame(Source, Target)
#'
#' # Create graph
#' simpleNetwork(NetworkData)
#' simpleNetwork(NetworkData, fontFamily = "sans-serif")
#'
#' @source D3.js was created by Michael Bostock. See \url{http://d3js.org/} and,
#' more specifically for directed networks
#' \url{https://github.com/mbostock/d3/wiki/Force-Layout}
#'
#' @export
simpleNetwork <- function(Data,
Source = NULL,
Target = NULL,
height = NULL,
width = NULL,
linkDistance = 50,
charge = -200,
fontSize = 7,
fontFamily = "serif",
linkColour = "#666",
nodeColour = "#3182bd",
nodeClickColour = "#E34A33",
textColour = "#3182bd",
opacity = 0.6,
zoom = F)
{
# validate input
if (!is.data.frame(Data))
stop("data must be a data frame class object.")
# If tbl_df convert to plain data.frame
Data <- tbl_df_strip(Data)
# create links data
if (is.null(Source) && is.null(Target))
links <- Data[, 1:2]
else if (!is.null(Source) && !is.null(Target))
links <- data.frame(Data[, Source], Data[, Target])
names(links) <- c("source", "target")
# Check if data is zero indexed
check_zero(links[, 'source'], links[, 'target'])
# create options
options = list(
linkDistance = linkDistance,
charge = charge,
fontSize = fontSize,
fontFamily = fontFamily,
linkColour = linkColour,
nodeColour = nodeColour,
nodeClickColour = nodeClickColour,
textColour = textColour,
opacity = opacity,
zoom = zoom
)
# create widget
htmlwidgets::createWidget(
name = "simpleNetwork",
x = list(links = links, options = options),
width = width,
height = height,
htmlwidgets::sizingPolicy(padding = 10, browser.fill = TRUE),
package = "networkD3"
)
}
#' @rdname networkD3-shiny
#' @export
simpleNetworkOutput <- function(outputId, width = "100%", height = "500px") {
shinyWidgetOutput(outputId, "simpleNetwork", width, height,
package = "networkD3")
}
#' @rdname networkD3-shiny
#' @export
renderSimpleNetwork <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
shinyRenderWidget(expr, simpleNetworkOutput, env, quoted = TRUE)
}
| /R/simpleNetwork.R | no_license | fbreitwieser/networkD3 | R | false | false | 5,115 | r | #' Function for creating simple D3 JavaScript force directed network graphs.
#'
#' \code{simpleNetwork} creates simple D3 JavaScript force directed network
#' graphs.
#'
#' @param Data a data frame object with three columns. The first two are the
#' names of the linked units. The third records an edge value. (Currently the
#' third column doesn't affect the graph.)
#' @param Source character string naming the network source variable in the data
#' frame. If \code{Source = NULL} then the first column of the data frame is
#' treated as the source.
#' @param Target character string naming the network target variable in the data
#' frame. If \code{Target = NULL} then the second column of the data frame is
#' treated as the target.
#' @param height height for the network graph's frame area in pixels (if
#' \code{NULL} then height is automatically determined based on context)
#' @param width numeric width for the network graph's frame area in pixels (if
#' \code{NULL} then width is automatically determined based on context)
#' @param linkDistance numeric distance between the links in pixels (actually
#' arbitrary relative to the diagram's size).
#' @param charge numeric value indicating either the strength of the node
#' repulsion (negative value) or attraction (positive value).
#' @param fontSize numeric font size in pixels for the node text labels.
#' @param fontFamily font family for the node text labels.
#' @param linkColour character string specifying the colour you want the link
#' lines to be. Multiple formats supported (e.g. hexadecimal).
#' @param nodeColour character string specifying the colour you want the node
#' circles to be. Multiple formats supported (e.g. hexadecimal).
#' @param nodeClickColour character string specifying the colour you want the
#' node circles to be when they are clicked. Also changes the colour of the
#' text. Multiple formats supported (e.g. hexadecimal).
#' @param textColour character string specifying the colour you want the text to
#' be before they are clicked. Multiple formats supported (e.g. hexadecimal).
#' @param opacity numeric value of the proportion opaque you would like the
#' graph elements to be.
#' @param zoom logical value to enable (\code{TRUE}) or disable (\code{FALSE})
#' zooming
#'
#' @examples
#' # Fake data
#' Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D")
#' Target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I")
#' NetworkData <- data.frame(Source, Target)
#'
#' # Create graph
#' simpleNetwork(NetworkData)
#' simpleNetwork(NetworkData, fontFamily = "sans-serif")
#'
#' @source D3.js was created by Michael Bostock. See \url{http://d3js.org/} and,
#' more specifically for directed networks
#' \url{https://github.com/mbostock/d3/wiki/Force-Layout}
#'
#' @export
simpleNetwork <- function(Data,
Source = NULL,
Target = NULL,
height = NULL,
width = NULL,
linkDistance = 50,
charge = -200,
fontSize = 7,
fontFamily = "serif",
linkColour = "#666",
nodeColour = "#3182bd",
nodeClickColour = "#E34A33",
textColour = "#3182bd",
opacity = 0.6,
zoom = F)
{
# validate input
if (!is.data.frame(Data))
stop("data must be a data frame class object.")
# If tbl_df convert to plain data.frame
Data <- tbl_df_strip(Data)
# create links data
if (is.null(Source) && is.null(Target))
links <- Data[, 1:2]
else if (!is.null(Source) && !is.null(Target))
links <- data.frame(Data[, Source], Data[, Target])
names(links) <- c("source", "target")
# Check if data is zero indexed
check_zero(links[, 'source'], links[, 'target'])
# create options
options = list(
linkDistance = linkDistance,
charge = charge,
fontSize = fontSize,
fontFamily = fontFamily,
linkColour = linkColour,
nodeColour = nodeColour,
nodeClickColour = nodeClickColour,
textColour = textColour,
opacity = opacity,
zoom = zoom
)
# create widget
htmlwidgets::createWidget(
name = "simpleNetwork",
x = list(links = links, options = options),
width = width,
height = height,
htmlwidgets::sizingPolicy(padding = 10, browser.fill = TRUE),
package = "networkD3"
)
}
#' @rdname networkD3-shiny
#' @export
simpleNetworkOutput <- function(outputId, width = "100%", height = "500px") {
shinyWidgetOutput(outputId, "simpleNetwork", width, height,
package = "networkD3")
}
#' @rdname networkD3-shiny
#' @export
renderSimpleNetwork <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
shinyRenderWidget(expr, simpleNetworkOutput, env, quoted = TRUE)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Pr_S_cnd.R
\name{Pr_S_cnd}
\alias{Pr_S_cnd}
\title{Probability of selection event, conditonal on theta}
\usage{
Pr_S_cnd(theta, sigma, t)
}
\arguments{
\item{theta}{the given value of mean of sampling distribution for y}
\item{sigma}{standard deviation for y}
\item{t}{truncation point}
}
\description{
Probability of selection event, conditonal on theta
}
| /man/Pr_S_cnd.Rd | no_license | spencerwoody/saFAB | R | false | true | 437 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Pr_S_cnd.R
\name{Pr_S_cnd}
\alias{Pr_S_cnd}
\title{Probability of selection event, conditonal on theta}
\usage{
Pr_S_cnd(theta, sigma, t)
}
\arguments{
\item{theta}{the given value of mean of sampling distribution for y}
\item{sigma}{standard deviation for y}
\item{t}{truncation point}
}
\description{
Probability of selection event, conditonal on theta
}
|
if (!file.exists("household_power_consumption.txt")) {
URL <- "http://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(URL, destfile="./household_power_consumption.zip")
unzip("household_power_consumption.zip")
}
## Read in data from only two dates
hpc <- subset(read.table("household_power_consumption.txt", header=TRUE, sep=";", colClasses=c(rep('character', 2), rep('numeric', 7)), na.strings="?"), Date=="1/2/2007" | Date=="2/2/2007")
## Load lubridate, format dates and time and create datetime column
library(lubridate)
hpc$datetime <- dmy_hms(paste(hpc$Date, hpc$Time))
##Open png device
png(file="plot4.png")
par(mfrow = c(2,2))
with(hpc, {
##First plot
plot(hpc$datetime, hpc$Global_active_power, type="n", xlab="", ylab="Global Active Power")
lines(hpc$datetime, hpc$Global_active_power, type="l")
##Second plot
plot(hpc$datetime, hpc$Voltage, xlab="datetime", ylab="Voltage", type="n")
lines(hpc$datetime, hpc$Voltage, type="l")
##Third plot
plot(hpc$datetime, hpc$Sub_metering_1, type="n", xlab="", ylab="Energy sub metering")
lines(hpc$datetime, hpc$Sub_metering_1, type="l")
lines(hpc$datetime, hpc$Sub_metering_2, type="l", col="red")
lines(hpc$datetime, hpc$Sub_metering_3, type="l", col="blue")
legend("topright", lty=c(1,1,1), col=c("black", "red", "blue"), legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), bty="n")
##Forth plot
plot(hpc$datetime, hpc$Global_reactive_power, xlab="datetime", ylab="Global_reactive_power", type="n")
lines(hpc$datetime, hpc$Global_reactive_power, type="l")
})
##Close png file device
dev.off() | /plot4.R | no_license | wavygravy18/ExData_Plotting1 | R | false | false | 1,643 | r | if (!file.exists("household_power_consumption.txt")) {
URL <- "http://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(URL, destfile="./household_power_consumption.zip")
unzip("household_power_consumption.zip")
}
## Read in data from only two dates
hpc <- subset(read.table("household_power_consumption.txt", header=TRUE, sep=";", colClasses=c(rep('character', 2), rep('numeric', 7)), na.strings="?"), Date=="1/2/2007" | Date=="2/2/2007")
## Load lubridate, format dates and time and create datetime column
library(lubridate)
hpc$datetime <- dmy_hms(paste(hpc$Date, hpc$Time))
##Open png device
png(file="plot4.png")
par(mfrow = c(2,2))
with(hpc, {
##First plot
plot(hpc$datetime, hpc$Global_active_power, type="n", xlab="", ylab="Global Active Power")
lines(hpc$datetime, hpc$Global_active_power, type="l")
##Second plot
plot(hpc$datetime, hpc$Voltage, xlab="datetime", ylab="Voltage", type="n")
lines(hpc$datetime, hpc$Voltage, type="l")
##Third plot
plot(hpc$datetime, hpc$Sub_metering_1, type="n", xlab="", ylab="Energy sub metering")
lines(hpc$datetime, hpc$Sub_metering_1, type="l")
lines(hpc$datetime, hpc$Sub_metering_2, type="l", col="red")
lines(hpc$datetime, hpc$Sub_metering_3, type="l", col="blue")
legend("topright", lty=c(1,1,1), col=c("black", "red", "blue"), legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), bty="n")
##Forth plot
plot(hpc$datetime, hpc$Global_reactive_power, xlab="datetime", ylab="Global_reactive_power", type="n")
lines(hpc$datetime, hpc$Global_reactive_power, type="l")
})
##Close png file device
dev.off() |
#!/usr/bin/env Rscript
library(ggplot2)
args <- commandArgs(trailingOnly = TRUE)
parse_1d_str_args <- function(str) {
return(unlist(strsplit(str,",")))
}
parse_2d_numeric_args <- function(str) {
return(lapply(unlist(strsplit(str,";")),function(s) as.numeric(parse_1d_str_args(s))))
}
data <- unlist(parse_2d_numeric_args(args[1]))
data_labels <- parse_1d_str_args(args[2])
stats <- parse_1d_str_args(args[3])
stat_colors <- parse_1d_str_args(args[4])
ylabel <- args[5]
xlabel <- args[6]
output_files <- parse_1d_str_args(args[7])
df <- data.frame(
opts = factor(rep(stats,length(data_labels)),levels=stats),
numParallel = factor(
unlist(lapply(data_labels, function(x) rep(x,length(stats)))),
levels=data_labels),
data=data)
ggplot(data=df, aes(x=numParallel, y=data, fill=opts)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
theme_bw() +
theme(legend.title=element_blank(),legend.position="bottom") +
xlab(xlabel) +
ylab(ylabel) +
scale_fill_manual(values=stat_colors)
lapply(output_files, function(f) ggsave(f,width=7,height=6))
| /nic_of_time/r-scripts/grouped_bars.r | no_license | nic-of-time/nic-of-time | R | false | false | 1,094 | r | #!/usr/bin/env Rscript
library(ggplot2)
args <- commandArgs(trailingOnly = TRUE)
parse_1d_str_args <- function(str) {
return(unlist(strsplit(str,",")))
}
parse_2d_numeric_args <- function(str) {
return(lapply(unlist(strsplit(str,";")),function(s) as.numeric(parse_1d_str_args(s))))
}
data <- unlist(parse_2d_numeric_args(args[1]))
data_labels <- parse_1d_str_args(args[2])
stats <- parse_1d_str_args(args[3])
stat_colors <- parse_1d_str_args(args[4])
ylabel <- args[5]
xlabel <- args[6]
output_files <- parse_1d_str_args(args[7])
df <- data.frame(
opts = factor(rep(stats,length(data_labels)),levels=stats),
numParallel = factor(
unlist(lapply(data_labels, function(x) rep(x,length(stats)))),
levels=data_labels),
data=data)
ggplot(data=df, aes(x=numParallel, y=data, fill=opts)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
theme_bw() +
theme(legend.title=element_blank(),legend.position="bottom") +
xlab(xlabel) +
ylab(ylabel) +
scale_fill_manual(values=stat_colors)
lapply(output_files, function(f) ggsave(f,width=7,height=6))
|
.onAttach <- function(libname, pkgname){
options(stringsAsFactors = F)
library(glmnet)
library(plyr)
library(dplyr)
library(RColorBrewer)
library(e1071)
library(ggplot2)
library(Hmisc)
library(randomForest)
}
| /R/zzz.R | no_license | menghaomiao/ITR.Forest | R | false | false | 235 | r | .onAttach <- function(libname, pkgname){
options(stringsAsFactors = F)
library(glmnet)
library(plyr)
library(dplyr)
library(RColorBrewer)
library(e1071)
library(ggplot2)
library(Hmisc)
library(randomForest)
}
|
#' @param pg.where Character length one. If left at the default 'def', the value
#' from the settings.r file is read in (parameter \code{gen_plot_pgWhereDefault}).
#' For plotting to PDFs provide "pdf", for plotting to graphics device provide
#' anything but "pdf".
#' @param pg.main Character length one. The additional text on the title of each
#' single plot.
#' @param pg.sub Character length one. The additional text on the subtitle of
#' each single plot.
#' @param pg.fns Character length one. The additional text in the filename of
#' the pdf.
| /man-roxygen/mr_pg_genParams.r | no_license | bpollner/aquap2 | R | false | false | 558 | r | #' @param pg.where Character length one. If left at the default 'def', the value
#' from the settings.r file is read in (parameter \code{gen_plot_pgWhereDefault}).
#' For plotting to PDFs provide "pdf", for plotting to graphics device provide
#' anything but "pdf".
#' @param pg.main Character length one. The additional text on the title of each
#' single plot.
#' @param pg.sub Character length one. The additional text on the subtitle of
#' each single plot.
#' @param pg.fns Character length one. The additional text in the filename of
#' the pdf.
|
source("load_data.R")
plot3 <- function(dt=NULL) {
if(is.null(dt))
dt <- load_data()
png("plot3.png", width=400, height=400)
plot(dt$Time, dt$Sub_metering_1, type="l", col="black",
xlab="", ylab="Energy sub metering")
lines(dt$Time, dt$Sub_metering_2, col="red")
lines(dt$Time, dt$Sub_metering_3, col="blue")
legend("topright",
col=c("black", "red", "blue"),
c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty=1)
dev.off()
} | /plot3.R | no_license | Drj2015/ExData_Plotting1 | R | false | false | 496 | r | source("load_data.R")
plot3 <- function(dt=NULL) {
if(is.null(dt))
dt <- load_data()
png("plot3.png", width=400, height=400)
plot(dt$Time, dt$Sub_metering_1, type="l", col="black",
xlab="", ylab="Energy sub metering")
lines(dt$Time, dt$Sub_metering_2, col="red")
lines(dt$Time, dt$Sub_metering_3, col="blue")
legend("topright",
col=c("black", "red", "blue"),
c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty=1)
dev.off()
} |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/crossReferenceList.R
\name{crossReferenceList}
\alias{crossReferenceList}
\title{crossReferenceList}
\usage{
crossReferenceList(crossReferenceFile, mySubjectClass)
}
\arguments{
\item{crossReferenceFile}{The filepath of sequence database whose
sequences have pdb in text.}
\item{mySubjectClass}{A S4 class storing all information from the
given sequence database in fasta.}
}
\value{
A dataframe with column "entry", "crossReference" which stores
entry names and their corresponding PDB name
}
\description{
Create a dataframe storing entries and corresponding cross reference.
There might be more than one pdb names for one sequence, the package
only pick the last one in given file.
}
\examples{
# Go to UniPort, download IPR005814 family entry database in text that is reviewed and has
# 3D structure (databaseExample.fasta, databaseExample.txt).
mySubject <- Biostrings::readAAStringSet("databaseExample.fasta")
crossReferenceList("databaseExample.txt", mySubject)
}
| /man/crossReferenceList.Rd | permissive | MichelleMengzhi/hmtp | R | false | true | 1,052 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/crossReferenceList.R
\name{crossReferenceList}
\alias{crossReferenceList}
\title{crossReferenceList}
\usage{
crossReferenceList(crossReferenceFile, mySubjectClass)
}
\arguments{
\item{crossReferenceFile}{The filepath of sequence database whose
sequences have pdb in text.}
\item{mySubjectClass}{A S4 class storing all information from the
given sequence database in fasta.}
}
\value{
A dataframe with column "entry", "crossReference" which stores
entry names and their corresponding PDB name
}
\description{
Create a dataframe storing entries and corresponding cross reference.
There might be more than one pdb names for one sequence, the package
only pick the last one in given file.
}
\examples{
# Go to UniPort, download IPR005814 family entry database in text that is reviewed and has
# 3D structure (databaseExample.fasta, databaseExample.txt).
mySubject <- Biostrings::readAAStringSet("databaseExample.fasta")
crossReferenceList("databaseExample.txt", mySubject)
}
|
#' The application User-Interface
#'
#' @param request Internal parameter for `{shiny}`.
#' DO NOT REMOVE.
#' @import shiny
#' @import shinydashboard
#' @noRd
app_ui <- function(request) {
tagList(
# Leave this function for adding external resources
golem_add_external_resources(),
# List the first level UI elements here
dashboardPage(
title = "Etherpad Dashboard",
skin = "black",
dashboardHeader(title = "OULAD dashboard"),
dashboardSidebar(
sidebarMenu(
menuItem("Overview", tabName = "overview", icon = icon("table"))
)
),
dashboardBody(
tabItems(
tabItem(tabName = "overview",
fluidRow(
mod_module_presentation_switch_ui("module_presentation_switch_ui_1")
),
fluidRow(
mod_activity_graph_ui("activity_graph_ui_1")
),
fluidRow(
mod_general_stats_ui("general_stats_ui_1")
),
fluidRow(
mod_student_overview_ui("student_overview_ui_1")
)
),
tabItem(tabName = "groups")
)
)
)
)
}
#' Add external Resources to the Application
#'
#' This function is internally used to add external
#' resources inside the Shiny application.
#'
#' @import shiny
#' @importFrom golem add_resource_path activate_js favicon bundle_resources
#' @noRd
golem_add_external_resources <- function(){
add_resource_path(
'www', app_sys('app/www')
)
tags$head(
favicon(),
bundle_resources(
path = app_sys('app/www'),
app_title = 'oulad.dashboard'
)
# Add here other external resources
# for example, you can add shinyalert::useShinyalert()
)
}
| /image/oulad.dashboard/R/app_ui.R | permissive | jakubkuzilek/LA-Dashboards-course | R | false | false | 1,839 | r | #' The application User-Interface
#'
#' @param request Internal parameter for `{shiny}`.
#' DO NOT REMOVE.
#' @import shiny
#' @import shinydashboard
#' @noRd
app_ui <- function(request) {
tagList(
# Leave this function for adding external resources
golem_add_external_resources(),
# List the first level UI elements here
dashboardPage(
title = "Etherpad Dashboard",
skin = "black",
dashboardHeader(title = "OULAD dashboard"),
dashboardSidebar(
sidebarMenu(
menuItem("Overview", tabName = "overview", icon = icon("table"))
)
),
dashboardBody(
tabItems(
tabItem(tabName = "overview",
fluidRow(
mod_module_presentation_switch_ui("module_presentation_switch_ui_1")
),
fluidRow(
mod_activity_graph_ui("activity_graph_ui_1")
),
fluidRow(
mod_general_stats_ui("general_stats_ui_1")
),
fluidRow(
mod_student_overview_ui("student_overview_ui_1")
)
),
tabItem(tabName = "groups")
)
)
)
)
}
#' Add external Resources to the Application
#'
#' This function is internally used to add external
#' resources inside the Shiny application.
#'
#' @import shiny
#' @importFrom golem add_resource_path activate_js favicon bundle_resources
#' @noRd
golem_add_external_resources <- function(){
add_resource_path(
'www', app_sys('app/www')
)
tags$head(
favicon(),
bundle_resources(
path = app_sys('app/www'),
app_title = 'oulad.dashboard'
)
# Add here other external resources
# for example, you can add shinyalert::useShinyalert()
)
}
|
###############################################################################
# Create simulation scenarios
###############################################################################
path.output_data <- Sys.getenv("path.output_data")
this.folder <- "exchangeable"
source(file.path(path.output_data, this.folder, "create-scenarios-exch.R"))
###############################################################################
# Check which values of rho will result in a positive definite
# correlation matrix (for Z_{it}'s)
###############################################################################
# Fix other.corr.params and increase rho from 0 to 1 --------------------------
path.code <- Sys.getenv("path.code")
source(file.path(path.code,"datagen-utils.R"))
input.rand.time <- 2
input.tot.time <- 6
list.check.pd.results <- list()
for(curr.rho in seq(from = 0, to = 1, by = 0.05)){
pd <- CheckPositiveDefinite(tot.time = input.tot.time,
rand.time = input.rand.time,
rho = curr.rho,
corr.str = "exch",
other.corr.params = curr.rho/2)
# If pd==0, then positive definite, else, if pd!=0, then not positive definite
list.check.pd.results <- append(list.check.pd.results,
list(data.frame(rho = curr.rho, is.pd = 1*(pd==0))))
}
check.pd.results <- do.call(rbind, list.check.pd.results)
print(check.pd.results)
# Clean up environment
remove(list = ls())
###############################################################################
# Calculate power for fixed value of means and proportion of zeros within
# this.folder while varying total sample size and rho
###############################################################################
path.output_data <- Sys.getenv("path.output_data")
this.folder <- "exchangeable"
for(i in 1:10){
this.scenario <- paste("sim_vary_effect/sim_results_", i, sep="")
use.grid <- expand.grid(N = seq(100,550,50), rho = c(0.2, 0.4, 0.6))
dat.all.results <- data.frame(N = rep(NA_real_, nrow(use.grid)),
rho = rep(NA_real_, nrow(use.grid)),
power.diff.eos.means = rep(NA_real_, nrow(use.grid)),
power.diff.AUC = rep(NA_real_, nrow(use.grid)),
elapsed.secs = rep(NA_real_, nrow(use.grid)))
for(idx in 1:nrow(use.grid)){
path.code <- Sys.getenv("path.code")
path.output_data <- Sys.getenv("path.output_data")
input.means <- read.csv(file.path(path.output_data, this.folder, this.scenario, "input_means.csv"))
input.prop.zeros <- read.csv(file.path(path.output_data, this.folder, this.scenario, "input_prop_zeros.csv"))
input.M <- 5000
input.N <- use.grid[idx, "N"]
input.rho <- use.grid[idx, "rho"]
input.n4 <- NA_real_
input.rand.time <- 2
input.tot.time <- 6
input.cutoff <- 0
input.corr.str <- "exch"
input.other.corr.params <- input.rho/2
use.working.corr <- "ar1"
start.time <- Sys.time()
source(file.path(path.code,"calc-covmat.R"))
this.pair <- 2
source(file.path(path.code,"calc-estimated-contrasts.R"))
input.alpha <- 0.05
source(file.path(path.code,"calc-estimated-power.R"))
end.time <- Sys.time()
elapsed.secs <- difftime(time1 = end.time, time2 = start.time, units = "secs")
elapsed.secs <- as.numeric(elapsed.secs)
dat.all.results[idx,"N"] <- input.N
dat.all.results[idx,"rho"] <- input.rho
dat.all.results[idx,"power.diff.eos.means"] <- power.diff.eos.means
dat.all.results[idx,"power.diff.AUC"] <- power.diff.AUC
dat.all.results[idx,"elapsed.secs"] <- elapsed.secs
}
write.csv(dat.all.results, file = file.path(path.output_data, this.folder, this.scenario, "power.csv"), row.names = FALSE)
}
# Clean up environment
remove(list = ls())
###############################################################################
# Calculate relationship between rho and tau
###############################################################################
path.code <- Sys.getenv("path.code")
path.output_data <- Sys.getenv("path.output_data")
for(i in 1:10){
# Input parameters
input.M <- 5000
input.N <- 1000
input.rand.time <- 2
input.tot.time <- 6
input.cutoff <- 0
input.corr.str <- "exch"
min.rho <- 0
max.rho <- 0.6
this.folder <- "exchangeable"
this.scenario <- paste("sim_vary_effect/sim_results_", i, sep="")
# Calculate correspondence between rho and tau
source(file.path(path.code,"calc-corr-params-curve.R"))
# Save output
save(collect.seq.cormat, file = file.path(path.output_data, this.folder, this.scenario, "collect_seq_cormat.RData"))
write.csv(collect.correlation.tau, file.path(path.output_data, this.folder, this.scenario, "collect_tau.csv"), row.names = FALSE)
}
# Clean up environment
remove(list = ls())
| /output/exchangeable/simulation-study-pipeline-exch.R | permissive | jamieyap/CountSMART | R | false | false | 4,969 | r | ###############################################################################
# Create simulation scenarios
###############################################################################
path.output_data <- Sys.getenv("path.output_data")
this.folder <- "exchangeable"
source(file.path(path.output_data, this.folder, "create-scenarios-exch.R"))
###############################################################################
# Check which values of rho will result in a positive definite
# correlation matrix (for Z_{it}'s)
###############################################################################
# Fix other.corr.params and increase rho from 0 to 1 --------------------------
path.code <- Sys.getenv("path.code")
source(file.path(path.code,"datagen-utils.R"))
input.rand.time <- 2
input.tot.time <- 6
list.check.pd.results <- list()
for(curr.rho in seq(from = 0, to = 1, by = 0.05)){
pd <- CheckPositiveDefinite(tot.time = input.tot.time,
rand.time = input.rand.time,
rho = curr.rho,
corr.str = "exch",
other.corr.params = curr.rho/2)
# If pd==0, then positive definite, else, if pd!=0, then not positive definite
list.check.pd.results <- append(list.check.pd.results,
list(data.frame(rho = curr.rho, is.pd = 1*(pd==0))))
}
check.pd.results <- do.call(rbind, list.check.pd.results)
print(check.pd.results)
# Clean up environment
remove(list = ls())
###############################################################################
# Calculate power for fixed value of means and proportion of zeros within
# this.folder while varying total sample size and rho
###############################################################################
path.output_data <- Sys.getenv("path.output_data")
this.folder <- "exchangeable"
for(i in 1:10){
this.scenario <- paste("sim_vary_effect/sim_results_", i, sep="")
use.grid <- expand.grid(N = seq(100,550,50), rho = c(0.2, 0.4, 0.6))
dat.all.results <- data.frame(N = rep(NA_real_, nrow(use.grid)),
rho = rep(NA_real_, nrow(use.grid)),
power.diff.eos.means = rep(NA_real_, nrow(use.grid)),
power.diff.AUC = rep(NA_real_, nrow(use.grid)),
elapsed.secs = rep(NA_real_, nrow(use.grid)))
for(idx in 1:nrow(use.grid)){
path.code <- Sys.getenv("path.code")
path.output_data <- Sys.getenv("path.output_data")
input.means <- read.csv(file.path(path.output_data, this.folder, this.scenario, "input_means.csv"))
input.prop.zeros <- read.csv(file.path(path.output_data, this.folder, this.scenario, "input_prop_zeros.csv"))
input.M <- 5000
input.N <- use.grid[idx, "N"]
input.rho <- use.grid[idx, "rho"]
input.n4 <- NA_real_
input.rand.time <- 2
input.tot.time <- 6
input.cutoff <- 0
input.corr.str <- "exch"
input.other.corr.params <- input.rho/2
use.working.corr <- "ar1"
start.time <- Sys.time()
source(file.path(path.code,"calc-covmat.R"))
this.pair <- 2
source(file.path(path.code,"calc-estimated-contrasts.R"))
input.alpha <- 0.05
source(file.path(path.code,"calc-estimated-power.R"))
end.time <- Sys.time()
elapsed.secs <- difftime(time1 = end.time, time2 = start.time, units = "secs")
elapsed.secs <- as.numeric(elapsed.secs)
dat.all.results[idx,"N"] <- input.N
dat.all.results[idx,"rho"] <- input.rho
dat.all.results[idx,"power.diff.eos.means"] <- power.diff.eos.means
dat.all.results[idx,"power.diff.AUC"] <- power.diff.AUC
dat.all.results[idx,"elapsed.secs"] <- elapsed.secs
}
write.csv(dat.all.results, file = file.path(path.output_data, this.folder, this.scenario, "power.csv"), row.names = FALSE)
}
# Clean up environment
remove(list = ls())
###############################################################################
# Calculate relationship between rho and tau
###############################################################################
path.code <- Sys.getenv("path.code")
path.output_data <- Sys.getenv("path.output_data")
for(i in 1:10){
# Input parameters
input.M <- 5000
input.N <- 1000
input.rand.time <- 2
input.tot.time <- 6
input.cutoff <- 0
input.corr.str <- "exch"
min.rho <- 0
max.rho <- 0.6
this.folder <- "exchangeable"
this.scenario <- paste("sim_vary_effect/sim_results_", i, sep="")
# Calculate correspondence between rho and tau
source(file.path(path.code,"calc-corr-params-curve.R"))
# Save output
save(collect.seq.cormat, file = file.path(path.output_data, this.folder, this.scenario, "collect_seq_cormat.RData"))
write.csv(collect.correlation.tau, file.path(path.output_data, this.folder, this.scenario, "collect_tau.csv"), row.names = FALSE)
}
# Clean up environment
remove(list = ls())
|
library(tidyverse)
library(glmmML)
library(rstan)
## Variance by individual difference
d <- read_csv('https://kuboweb.github.io/-kubo/stat/iwanamibook/fig/hbm/data7a.csv')
d %>% ggplot(aes(x=y)) + geom_histogram()
##GLM
fit.glm <- glm(cbind(y, 8-y)~1, data=d, family=binomial())
summary(fit.glm)
1/(1+exp(-fit.glm$coefficients[1]))
##GLMM
fit.glmm <- glmmML(cbind(y, 8-y)~1, data=d, family=binomial(), cluster=id)
summary(fit.glmm)
1/(1+exp(-fit.glmm$coefficients[1]))
##Stan
d.list <- list(
N = nrow(d),
y = d$y
)
fit.stan <- stan(
file = './Chapter10/model2.stan',
data = d.list)
print(fit.stan)
plot(fit.stan)
beta <- summary(fit.stan)$summary['beta','mean']
r <- summary(fit.stan)$summary[2:101,'mean']
sigma <- summary(fit.stan)$summary['sigma','mean']
rm(list=ls(all.names=TRUE))
## Variance by individual difference and by pot difference
d <- read_csv('https://kuboweb.github.io/-kubo/stat/iwanamibook/fig/hbm/nested/d1.csv')
d$f <- as.numeric(d$f=='T')
d$pot <- as.numeric(factor(d$pot))
##Stan
d.list <- list(
N = nrow(d),
N_pot = length(unique(d$pot)),
y = d$y,
f = d$f,
pot = d$pot
)
fit.stan <- stan(
file = './Chapter10/model3.stan',
data = d.list)
print(fit.stan)
plot(fit.stan)
## Reference
## https://fisproject.jp/2015/06/glm-stan/
## https://ito-hi.blog.ss-blog.jp/2012-09-03
## https://ito-hi.blog.ss-blog.jp/2012-09-04 | /Chapter10/Chapter10.R | no_license | kenyam1979/Statistical-modeling-for-data-analysis | R | false | false | 1,391 | r | library(tidyverse)
library(glmmML)
library(rstan)
## Variance by individual difference
d <- read_csv('https://kuboweb.github.io/-kubo/stat/iwanamibook/fig/hbm/data7a.csv')
d %>% ggplot(aes(x=y)) + geom_histogram()
##GLM
fit.glm <- glm(cbind(y, 8-y)~1, data=d, family=binomial())
summary(fit.glm)
1/(1+exp(-fit.glm$coefficients[1]))
##GLMM
fit.glmm <- glmmML(cbind(y, 8-y)~1, data=d, family=binomial(), cluster=id)
summary(fit.glmm)
1/(1+exp(-fit.glmm$coefficients[1]))
##Stan
d.list <- list(
N = nrow(d),
y = d$y
)
fit.stan <- stan(
file = './Chapter10/model2.stan',
data = d.list)
print(fit.stan)
plot(fit.stan)
beta <- summary(fit.stan)$summary['beta','mean']
r <- summary(fit.stan)$summary[2:101,'mean']
sigma <- summary(fit.stan)$summary['sigma','mean']
rm(list=ls(all.names=TRUE))
## Variance by individual difference and by pot difference
d <- read_csv('https://kuboweb.github.io/-kubo/stat/iwanamibook/fig/hbm/nested/d1.csv')
d$f <- as.numeric(d$f=='T')
d$pot <- as.numeric(factor(d$pot))
##Stan
d.list <- list(
N = nrow(d),
N_pot = length(unique(d$pot)),
y = d$y,
f = d$f,
pot = d$pot
)
fit.stan <- stan(
file = './Chapter10/model3.stan',
data = d.list)
print(fit.stan)
plot(fit.stan)
## Reference
## https://fisproject.jp/2015/06/glm-stan/
## https://ito-hi.blog.ss-blog.jp/2012-09-03
## https://ito-hi.blog.ss-blog.jp/2012-09-04 |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getARTData.R
\name{calcARTCov}
\alias{calcARTCov}
\title{calcARTCov}
\usage{
calcARTCov(dat = getEverART(), Args = setArgs())
}
\arguments{
\item{dat}{A dataset from \code{\link{getEverART}}.}
\item{Args}{requires Args, see \code{\link{setArgs}}}
}
\value{
data.frame
}
\description{
Calculate ART coverage for AHRI data. (This is a very crude measure of
ART coverage. More work needed on an appropriate measure.)
}
| /man/calcARTCov.Rd | no_license | hkim207/ahri-1 | R | false | true | 495 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getARTData.R
\name{calcARTCov}
\alias{calcARTCov}
\title{calcARTCov}
\usage{
calcARTCov(dat = getEverART(), Args = setArgs())
}
\arguments{
\item{dat}{A dataset from \code{\link{getEverART}}.}
\item{Args}{requires Args, see \code{\link{setArgs}}}
}
\value{
data.frame
}
\description{
Calculate ART coverage for AHRI data. (This is a very crude measure of
ART coverage. More work needed on an appropriate measure.)
}
|
import_path = "Import/180216_no_comments_and_pull_requests_per_owner.csv"
df1 = read.csv(import_path, header=TRUE, sep=",", stringsAsFactors = TRUE)
import_path = "Import/180216_count_commits_per_owner.csv"
df2 = read.csv(import_path, header=TRUE, sep=",", stringsAsFactors = TRUE)
df1$comment_count_log = log(df1$comment_count)
df1$pull_request_count_log = log(df1$pull_request_count)
df2$commit_count_log = log(df2$commit_count)
r_comment_count <- hist(df1$comment_count_log)
r_pull_request_count <- hist(df1$pull_request_count_log)
r_commit_count <- hist(df2$commit_count_log)
logplot <- function(r, title, xlab){
plot(r$breaks[-1],
r$counts,
type='l',
log = 'y',
main = title,
xlab = xlab,
ylab = "log(frequency)")
}
logplot(r=r_comment_count,
title="log-log plot of no comments to frequency per owner
(considers all comments between 2014-01-01 and 2017-07-31)",
xlab = "log(count comments per owner)")
logplot(r_pull_request_count,
title = "log-log plot of no pull_requests to frequency per owner
(considers all pull_requtests between 2014-01-01 and 2017-07-31)",
xlab = "log(count pull requests per owner)")
logplot(r_commit_count,
title = "log-log plot of no commits to frequency per owner
(considers all commits between 2014-01-01 and 2017-07-31)",
xlab = "log(count commits per owner)")
quantile(df1$comment_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
quantile(df1$pull_request_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
quantile(df2$commit_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
# hist(df$count)
summary(df1)
summary(df2)
boxplot_sample <- function(data, c_name, sample_factor){
df_sample <- data.frame(na.omit(data[, c_name]))
sample_size = round(nrow(df_sample)*sample_factor)
df_sample <- df_sample[sample(nrow(df_sample), sample_size), ]
boxplot(df_sample,
ylab = c_name,
sub = paste(
"population: ", toString(nrow(data)),
"\nsample size: ", toString(sample_size),
" (", toString(sample_factor*100),"%)" ),
log = "y")
}
boxplot_sample(df1, "comment_count", 0.002)
boxplot_sample(df1, "pull_request_count", 0.002)
boxplot_sample(df2, "commit_count", 0.001)
df3 = merge(x = df1,
y = df2,
by = "owner_id",
all = TRUE)
############# filters
lim_commit_count = 500
lim_pull_request_count = 100
lim_comment_count = 2000
df4 <- select(filter(df3, comment_count >= lim_comment_count), c(owner_id, comment_count, pull_request_count, commit_count))
df5 <- select(filter(df4, (pull_request_count >= lim_pull_request_count | commit_count >= lim_commit_count)),
c(owner_id, comment_count, pull_request_count, commit_count))
boxplot(df5[,-1], log='y',
ylab = 'count (log-transformed)',
main = 'Distribution of the remaining owner data after applying selection criteria',
sub = paste(
'remaining number of owners: ', toString(nrow(df5)),
'(', toString(round(nrow(df5)/nrow(df3)*100, digits = 2)), '% of original data)',
'\ncriteria: ',
'no commits >= ', toString(lim_commit_count), ' OR',
' no pull requests >= ', toString(lim_pull_request_count), 'AND',
' no comments >= ', toString(lim_comment_count))
)
df3_log = data.frame(commit_count = log(df3$commit_count),
pull_request_count = log(df3$pull_request_count),
comment_count = log(df3$comment_count))
plot(df3_log)
r <- hist(na.omit(df3_log$commit_count))
plot(r$breaks[-1],
r$counts,
type='l',
main = "Number of commits to owners on a log scale to frequency",
sub = "considers data between 2014-01-01 and 2017-07-31",
xlab = "log(count commits per owner)",
ylab = "frequency")
abline(v=log(lim_commit_count), lty = 2, col = 'blue')
| /project_selection/180216_boxplots_comment_and_pullrequest_count.R | no_license | maxthemillion/gitNetAnalyzer | R | false | false | 3,997 | r | import_path = "Import/180216_no_comments_and_pull_requests_per_owner.csv"
df1 = read.csv(import_path, header=TRUE, sep=",", stringsAsFactors = TRUE)
import_path = "Import/180216_count_commits_per_owner.csv"
df2 = read.csv(import_path, header=TRUE, sep=",", stringsAsFactors = TRUE)
df1$comment_count_log = log(df1$comment_count)
df1$pull_request_count_log = log(df1$pull_request_count)
df2$commit_count_log = log(df2$commit_count)
r_comment_count <- hist(df1$comment_count_log)
r_pull_request_count <- hist(df1$pull_request_count_log)
r_commit_count <- hist(df2$commit_count_log)
logplot <- function(r, title, xlab){
plot(r$breaks[-1],
r$counts,
type='l',
log = 'y',
main = title,
xlab = xlab,
ylab = "log(frequency)")
}
logplot(r=r_comment_count,
title="log-log plot of no comments to frequency per owner
(considers all comments between 2014-01-01 and 2017-07-31)",
xlab = "log(count comments per owner)")
logplot(r_pull_request_count,
title = "log-log plot of no pull_requests to frequency per owner
(considers all pull_requtests between 2014-01-01 and 2017-07-31)",
xlab = "log(count pull requests per owner)")
logplot(r_commit_count,
title = "log-log plot of no commits to frequency per owner
(considers all commits between 2014-01-01 and 2017-07-31)",
xlab = "log(count commits per owner)")
quantile(df1$comment_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
quantile(df1$pull_request_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
quantile(df2$commit_count, prob = c(0.8, 0.9, 0.98, 0.985, 0.99, 0.999))
# hist(df$count)
summary(df1)
summary(df2)
boxplot_sample <- function(data, c_name, sample_factor){
df_sample <- data.frame(na.omit(data[, c_name]))
sample_size = round(nrow(df_sample)*sample_factor)
df_sample <- df_sample[sample(nrow(df_sample), sample_size), ]
boxplot(df_sample,
ylab = c_name,
sub = paste(
"population: ", toString(nrow(data)),
"\nsample size: ", toString(sample_size),
" (", toString(sample_factor*100),"%)" ),
log = "y")
}
boxplot_sample(df1, "comment_count", 0.002)
boxplot_sample(df1, "pull_request_count", 0.002)
boxplot_sample(df2, "commit_count", 0.001)
df3 = merge(x = df1,
y = df2,
by = "owner_id",
all = TRUE)
############# filters
lim_commit_count = 500
lim_pull_request_count = 100
lim_comment_count = 2000
df4 <- select(filter(df3, comment_count >= lim_comment_count), c(owner_id, comment_count, pull_request_count, commit_count))
df5 <- select(filter(df4, (pull_request_count >= lim_pull_request_count | commit_count >= lim_commit_count)),
c(owner_id, comment_count, pull_request_count, commit_count))
boxplot(df5[,-1], log='y',
ylab = 'count (log-transformed)',
main = 'Distribution of the remaining owner data after applying selection criteria',
sub = paste(
'remaining number of owners: ', toString(nrow(df5)),
'(', toString(round(nrow(df5)/nrow(df3)*100, digits = 2)), '% of original data)',
'\ncriteria: ',
'no commits >= ', toString(lim_commit_count), ' OR',
' no pull requests >= ', toString(lim_pull_request_count), 'AND',
' no comments >= ', toString(lim_comment_count))
)
df3_log = data.frame(commit_count = log(df3$commit_count),
pull_request_count = log(df3$pull_request_count),
comment_count = log(df3$comment_count))
plot(df3_log)
r <- hist(na.omit(df3_log$commit_count))
plot(r$breaks[-1],
r$counts,
type='l',
main = "Number of commits to owners on a log scale to frequency",
sub = "considers data between 2014-01-01 and 2017-07-31",
xlab = "log(count commits per owner)",
ylab = "frequency")
abline(v=log(lim_commit_count), lty = 2, col = 'blue')
|
library(foreign)
library(reshape2)
library(rgdal)
library(maptools)
rm(list = ls())
# Set working directory
setwd("D:\\Dropbox\\Fishing_effects_model\\to_maedhbh")
gear = "all" ## Pelagic trawls: "PTR", Non-pelagic trawls: "NPT", Hook and line: "HAL", Jig: "JIG", Pots/traps: "POT"
habFeature = "both" ## Keep "b":biological, "g": geological, or "both"
if(habFeature == "both"){
habFeatToKeep = c("G", "B")
}else{
habFeatToKeep = toupper(habFeature)
}
if(gear == "all"){
gearToKeep = c("PTR", "NPT", "HAL", "JIG", "POT")
}else{
gearToKeep = toupper(gear)
}
### Output file name
year.now = substr(Sys.time(), 1,4)
month.now = substr(Sys.time(), 6,7)
day.now = substr(Sys.time(), 9,10)
outFile = paste("disturbProps_deepSteep_", gear, "Gear_",habFeature, "Struct_", year.now, month.now, day.now, sep = "")
# Import data
grid5k = readOGR(dsn = "Fishing_effects_model.gdb", layer = "Grid5k")
grid5k.dat = grid5k@data
fe = read.csv("R_input_tables\\aggregated_fishing_effort_fake_data.csv")
#fe = subset(fe, YEAR <2016) ## CIA only goes through June of 2015
gt = read.csv("R_input_tables\\GearWidthTable_022316.csv")
recovery_table = read.csv("R_input_tables\\Recovery_table_DeepCorals.csv")
### Convert total fishing to contact adjusted
fe = merge(fe, gt[,c("GearID", "Gear", "SASI_gear", "adjLow","adjMed", "adjHigh")], by.x = "GEARID", by.y = "GearID", all.x = T)
## Gear mods
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjLow = 1 # Pre 2011 gear change
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjMed = 1
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjHigh = 1
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjLow = 1 # Pre Feb 2014 gear change
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjMed = 1
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjHigh = 1
## None of these gears in Jan 2014
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjLow = 1 # Pre Feb 2014 gear change
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjMed = 1
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjHigh = 1
fe[!(fe$Gear %in% gearToKeep), ]$SUM_Shape_ = 0 # Change area of gears not interested in keeping to zero
fe$adjArea = fe$SUM_Shape_ * runif(nrow(fe), min = fe$adjLow, max = fe$adjHigh) ## Random uniform contact adj from min and max
## aggregate fishing adjusted fishing effort
fe.agg = aggregate(adjArea ~ Grid5k_ID + YEAR + MONTH + SASI_gear, data = fe, sum)
grid_order = sort(unique(fe$Grid5k_ID))
# Sediment
sed = read.dbf("R_input_tables\\sediment_v4_deepCorals.dbf")
# Create sediment matrix for model. Make sure grid order is same as I_a
# and keep only sediment areas
sedProps = as.matrix(sed[match(grid_order, sed$Grid5k_ID),
c("mudProp","sandProp","grpeProp","cobProp","bouldProp", "deepProp") ])
subst_types = c("Mud", "Sand", "Gran.Peb", "Cobble", "Boulder", "DeepSteep")
SASI_gears = c("trawl", "longline", "trap")
# Set parameters
nYears = length(unique(fe$YEAR))
nSubAnnual = 12
nGrid = length(unique(fe$Grid5k_ID))
nGear = 3 # Number of SASI gears: Trawl, Trap, Longline
nSubst = ncol(sedProps) #Number of substrates
gear_types = levels(fe$SASI_gear)
eg = expand.grid(Grid5k_ID=unique(fe$Grid5k_ID), SASI_gear=unique(fe$SASI_gear)) # create all combos of gear and grid cells
m = merge(grid5k.dat, fe.agg, by = "Grid5k_ID")
m$prop = m$adjAre/m$Shape_Area
m$MONTH = as.numeric(as.character(m$MONTH))
# Populate Fishing effort array
F_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nGear)) #create empty array
year.i = 1 # year index counter
for(year in min(m$YEAR):max(m$YEAR)){
my = subset(m, YEAR == year)
for(month.i in 1:12){
mym = subset(my, MONTH==month.i)
mym = merge(x = eg, y = mym,
by.x = c("Grid5k_ID","SASI_gear"),
by.y = c("Grid5k_ID", "SASI_gear"),
all.x=T)
mym[is.na(mym$prop),]$prop = 0
mym.x = dcast(Grid5k_ID ~ SASI_gear, data=mym, value.var = "prop", fun.aggregate = function(x) sum(x))
mym.x = mym.x[order(mym.x$Grid5k_ID),]
mym.x = mym.x[,c(gear_types)]
F_a[year.i, month.i, ,] = as.matrix(mym.x)
}
year.i = year.i + 1
}
# Suceptibility
suscept.f = function(){
gear.q = matrix(NA, nrow = nGear, ncol = nSubst)
i = 1
for(gear in SASI_gears){
gear.m = read.csv(paste("R_input_tables\\Susceptibilty_table_deepCoral_",
gear, ".csv", sep=""))
gear.m = subset(gear.m, FeatureClass %in% habFeatToKeep) ## Choose geological or biological features
gear.m = gear.m[,subst_types]
for(column in 1:ncol(gear.m)){
gear.m[gear.m[,column] %in% 0, column] =
runif(sum(gear.m[,column] %in% 0), min = 0, max = 0.1)
gear.m[gear.m[,column] %in% 1, column] =
runif(sum(gear.m[,column] %in% 1), min = 0.1, max = 0.25)
gear.m[gear.m[,column] %in% 2, column] =
runif(sum(gear.m[,column] %in% 2), min = 0.25, max = 0.5)
gear.m[gear.m[,column] %in% 3, column] =
runif(sum(gear.m[,column] %in% 3), min = 0.5, max = 1)
}
gear.q[i,] = colMeans(gear.m, na.rm=T)
i = i + 1
}
gear.q.df = data.frame(SASI_gear = SASI_gears, gear.q)
names(gear.q.df)[-1] = subst_types
q_m = as.matrix(gear.q.df[,subst_types])
return(q_m)
}
#Fishing impacts (I')
I.prime_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nSubst))
for(y in 1:nYears){
for(m in 1:nSubAnnual){
q_m = suscept.f() # Get new susceptibility table for each month
I_m = F_a[y,m,,] %*% q_m
I.prime_a[y,m,,] = 1-exp(-I_m)
}
}
# Recovery (rho')
recovery_table = recovery_table[,subst_types]
tau_m = recovery_table[,subst_types] # Make sure sediments are in correct order
recovery.f = function(){
for(column in 1:ncol(tau_m)){
tau_m[recovery_table[,column] %in% 0, column] =
runif(sum(tau_m[,column] %in% 0), min = 0, max = 1)
tau_m[recovery_table[,column] %in% 1, column] =
runif(sum(tau_m[,column] %in% 1), min = 1, max = 2)
tau_m[recovery_table[,column] %in% 2, column] =
runif(sum(tau_m[,column] %in% 2), min = 2, max = 5)
tau_m[recovery_table[,column] %in% 3, column] =
runif(sum(tau_m[,column] %in% 3), min = 5, max = 10)
tau_m[recovery_table[,column] %in% 4, column] =
runif(sum(tau_m[,column] %in% 4), min = 10, max = 50)
}
tau_v = colMeans(tau_m, na.rm=T) # Average recovery over all habitat features
rho_v = 1 / (tau_v * nSubAnnual) # Convert recovery time in years to rates per month
return(rho_v)
}
rho.prime_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nSubst))
for(y in 1:nYears){
for(m in 1:nSubAnnual){
rho_v = recovery.f() # Get new recovery values for each month
for(i in 1:nGrid){
rho.prime_a[y,m,i,] = 1-exp(-rho_v)
}
}
}
# Fishing Effects Model function
FishingEffectsModel = function(I.prime_a, rho.prime_a, H_prop_0){
model_nYears = dim(I.prime_a)[1]
model_nSubAnnual = dim(I.prime_a)[2]
model_nGrid = dim(I.prime_a)[3]
model_nSubst = dim(I.prime_a)[4]
#Make array to hold H
H_prop = array(dim = c(model_nYears, model_nSubAnnual, model_nGrid, model_nSubst))
for(y in 1:model_nYears){
for(m in 1:model_nSubAnnual){
if(y == 1 & m == 1){ # First time step use H_prop_0 for t-1
prior_state = H_prop_0
} else if (m == 1){
prior_state = H_prop[y-1,model_nSubAnnual,,]
} else{
prior_state = H_prop[y,m-1,,]
}
H_from_H = (1-I.prime_a[y,m,,])*prior_state # undisturbed remaining undisturbed
H_from_h = (1-prior_state) * (rho.prime_a[y,m,,]) # disturbed recovered to undisturbed
H_prop[y,m,,] = H_from_H + H_from_h # Total proportion disturbed
}
}
return(H_prop)
} # end function
##### Run model with H =burnin initial conditions
# Define five year burnin in
H_prop_0 = matrix(runif(nGrid*nSubst, 0,1), nrow = nGrid, ncol = nSubst) # First start with random conditions
# Cycle through 2003-2005 ten times to create burnin starting conditions
for(i in 1:10){
H_burn = FishingEffectsModel(I.prime_a[1:3,,,], rho.prime_a[1:3,,,], H_prop_0)
H_prop_0 = H_burn[3,12,,] # extract only the last time step as the new intial condition
}
##### Run model
H_tot = FishingEffectsModel(I.prime_a, rho.prime_a, H_prop_0)
# Calculate undisturbed Areas
fished_cells = grid5k.dat[match(grid_order, grid5k.dat$Grid5k_ID), ]
undistProps = matrix(NA, ncol = nSubAnnual*nYears, nrow = length(grid_order))
i = 1
for(y in 1:nYears){
for(m in 1:12){
undistProps[,i] = rowSums(H_tot[y,m,,]*sedProps)
i = i + 1
}
}
undistProps = data.frame(Grid5k_ID = grid_order, undistProps)
disturbProps = data.frame(Grid5k_ID = undistProps[,1],
apply(undistProps[,-1],2,
function(x) 1 - x))
## Write results to a shapefile
grid5k@data$orderID = 1:nrow(grid5k@data)
grid5k_results = grid5k@data
grid5k_results = merge(grid5k_results, disturbProps, by = "Grid5k_ID", all.x = T)
months = c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec")
counter = 6 # Start counter at first column that has disturbAreas
for(y in 2003:2016){
for(m in 1:12){
names(grid5k_results)[counter] = paste(months[m], y, sep = "")
counter = counter + 1
}
}
dist.out = grid5k
dist.out@data = grid5k_results[order(grid5k_results$orderID),]
## Create file name and write it to output
writeSpatialShape(dist.out, paste("FE_model_output\\", outFile, ".shp", sep = ""))
| /Fishing_effects_model_102716.R | no_license | pipelino93/Fishing_Effects_Model | R | false | false | 10,079 | r | library(foreign)
library(reshape2)
library(rgdal)
library(maptools)
rm(list = ls())
# Set working directory
setwd("D:\\Dropbox\\Fishing_effects_model\\to_maedhbh")
gear = "all" ## Pelagic trawls: "PTR", Non-pelagic trawls: "NPT", Hook and line: "HAL", Jig: "JIG", Pots/traps: "POT"
habFeature = "both" ## Keep "b":biological, "g": geological, or "both"
if(habFeature == "both"){
habFeatToKeep = c("G", "B")
}else{
habFeatToKeep = toupper(habFeature)
}
if(gear == "all"){
gearToKeep = c("PTR", "NPT", "HAL", "JIG", "POT")
}else{
gearToKeep = toupper(gear)
}
### Output file name
year.now = substr(Sys.time(), 1,4)
month.now = substr(Sys.time(), 6,7)
day.now = substr(Sys.time(), 9,10)
outFile = paste("disturbProps_deepSteep_", gear, "Gear_",habFeature, "Struct_", year.now, month.now, day.now, sep = "")
# Import data
grid5k = readOGR(dsn = "Fishing_effects_model.gdb", layer = "Grid5k")
grid5k.dat = grid5k@data
fe = read.csv("R_input_tables\\aggregated_fishing_effort_fake_data.csv")
#fe = subset(fe, YEAR <2016) ## CIA only goes through June of 2015
gt = read.csv("R_input_tables\\GearWidthTable_022316.csv")
recovery_table = read.csv("R_input_tables\\Recovery_table_DeepCorals.csv")
### Convert total fishing to contact adjusted
fe = merge(fe, gt[,c("GearID", "Gear", "SASI_gear", "adjLow","adjMed", "adjHigh")], by.x = "GEARID", by.y = "GearID", all.x = T)
## Gear mods
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjLow = 1 # Pre 2011 gear change
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjMed = 1
fe[fe$GEARID %in% 45:53 & fe$YEAR < 2011, ]$adjHigh = 1
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjLow = 1 # Pre Feb 2014 gear change
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjMed = 1
fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR < 2014, ]$adjHigh = 1
## None of these gears in Jan 2014
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjLow = 1 # Pre Feb 2014 gear change
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjMed = 1
#fe[fe$GEARID %in% c(6,7,9,10) & fe$YEAR == 2014 & fe$MONTH == 1, ]$adjHigh = 1
fe[!(fe$Gear %in% gearToKeep), ]$SUM_Shape_ = 0 # Change area of gears not interested in keeping to zero
fe$adjArea = fe$SUM_Shape_ * runif(nrow(fe), min = fe$adjLow, max = fe$adjHigh) ## Random uniform contact adj from min and max
## aggregate fishing adjusted fishing effort
fe.agg = aggregate(adjArea ~ Grid5k_ID + YEAR + MONTH + SASI_gear, data = fe, sum)
grid_order = sort(unique(fe$Grid5k_ID))
# Sediment
sed = read.dbf("R_input_tables\\sediment_v4_deepCorals.dbf")
# Create sediment matrix for model. Make sure grid order is same as I_a
# and keep only sediment areas
sedProps = as.matrix(sed[match(grid_order, sed$Grid5k_ID),
c("mudProp","sandProp","grpeProp","cobProp","bouldProp", "deepProp") ])
subst_types = c("Mud", "Sand", "Gran.Peb", "Cobble", "Boulder", "DeepSteep")
SASI_gears = c("trawl", "longline", "trap")
# Set parameters
nYears = length(unique(fe$YEAR))
nSubAnnual = 12
nGrid = length(unique(fe$Grid5k_ID))
nGear = 3 # Number of SASI gears: Trawl, Trap, Longline
nSubst = ncol(sedProps) #Number of substrates
gear_types = levels(fe$SASI_gear)
eg = expand.grid(Grid5k_ID=unique(fe$Grid5k_ID), SASI_gear=unique(fe$SASI_gear)) # create all combos of gear and grid cells
m = merge(grid5k.dat, fe.agg, by = "Grid5k_ID")
m$prop = m$adjAre/m$Shape_Area
m$MONTH = as.numeric(as.character(m$MONTH))
# Populate Fishing effort array
F_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nGear)) #create empty array
year.i = 1 # year index counter
for(year in min(m$YEAR):max(m$YEAR)){
my = subset(m, YEAR == year)
for(month.i in 1:12){
mym = subset(my, MONTH==month.i)
mym = merge(x = eg, y = mym,
by.x = c("Grid5k_ID","SASI_gear"),
by.y = c("Grid5k_ID", "SASI_gear"),
all.x=T)
mym[is.na(mym$prop),]$prop = 0
mym.x = dcast(Grid5k_ID ~ SASI_gear, data=mym, value.var = "prop", fun.aggregate = function(x) sum(x))
mym.x = mym.x[order(mym.x$Grid5k_ID),]
mym.x = mym.x[,c(gear_types)]
F_a[year.i, month.i, ,] = as.matrix(mym.x)
}
year.i = year.i + 1
}
# Suceptibility
suscept.f = function(){
gear.q = matrix(NA, nrow = nGear, ncol = nSubst)
i = 1
for(gear in SASI_gears){
gear.m = read.csv(paste("R_input_tables\\Susceptibilty_table_deepCoral_",
gear, ".csv", sep=""))
gear.m = subset(gear.m, FeatureClass %in% habFeatToKeep) ## Choose geological or biological features
gear.m = gear.m[,subst_types]
for(column in 1:ncol(gear.m)){
gear.m[gear.m[,column] %in% 0, column] =
runif(sum(gear.m[,column] %in% 0), min = 0, max = 0.1)
gear.m[gear.m[,column] %in% 1, column] =
runif(sum(gear.m[,column] %in% 1), min = 0.1, max = 0.25)
gear.m[gear.m[,column] %in% 2, column] =
runif(sum(gear.m[,column] %in% 2), min = 0.25, max = 0.5)
gear.m[gear.m[,column] %in% 3, column] =
runif(sum(gear.m[,column] %in% 3), min = 0.5, max = 1)
}
gear.q[i,] = colMeans(gear.m, na.rm=T)
i = i + 1
}
gear.q.df = data.frame(SASI_gear = SASI_gears, gear.q)
names(gear.q.df)[-1] = subst_types
q_m = as.matrix(gear.q.df[,subst_types])
return(q_m)
}
#Fishing impacts (I')
I.prime_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nSubst))
for(y in 1:nYears){
for(m in 1:nSubAnnual){
q_m = suscept.f() # Get new susceptibility table for each month
I_m = F_a[y,m,,] %*% q_m
I.prime_a[y,m,,] = 1-exp(-I_m)
}
}
# Recovery (rho')
recovery_table = recovery_table[,subst_types]
tau_m = recovery_table[,subst_types] # Make sure sediments are in correct order
recovery.f = function(){
for(column in 1:ncol(tau_m)){
tau_m[recovery_table[,column] %in% 0, column] =
runif(sum(tau_m[,column] %in% 0), min = 0, max = 1)
tau_m[recovery_table[,column] %in% 1, column] =
runif(sum(tau_m[,column] %in% 1), min = 1, max = 2)
tau_m[recovery_table[,column] %in% 2, column] =
runif(sum(tau_m[,column] %in% 2), min = 2, max = 5)
tau_m[recovery_table[,column] %in% 3, column] =
runif(sum(tau_m[,column] %in% 3), min = 5, max = 10)
tau_m[recovery_table[,column] %in% 4, column] =
runif(sum(tau_m[,column] %in% 4), min = 10, max = 50)
}
tau_v = colMeans(tau_m, na.rm=T) # Average recovery over all habitat features
rho_v = 1 / (tau_v * nSubAnnual) # Convert recovery time in years to rates per month
return(rho_v)
}
rho.prime_a = array(NA, dim = c(nYears, nSubAnnual, nGrid, nSubst))
for(y in 1:nYears){
for(m in 1:nSubAnnual){
rho_v = recovery.f() # Get new recovery values for each month
for(i in 1:nGrid){
rho.prime_a[y,m,i,] = 1-exp(-rho_v)
}
}
}
# Fishing Effects Model function
FishingEffectsModel = function(I.prime_a, rho.prime_a, H_prop_0){
model_nYears = dim(I.prime_a)[1]
model_nSubAnnual = dim(I.prime_a)[2]
model_nGrid = dim(I.prime_a)[3]
model_nSubst = dim(I.prime_a)[4]
#Make array to hold H
H_prop = array(dim = c(model_nYears, model_nSubAnnual, model_nGrid, model_nSubst))
for(y in 1:model_nYears){
for(m in 1:model_nSubAnnual){
if(y == 1 & m == 1){ # First time step use H_prop_0 for t-1
prior_state = H_prop_0
} else if (m == 1){
prior_state = H_prop[y-1,model_nSubAnnual,,]
} else{
prior_state = H_prop[y,m-1,,]
}
H_from_H = (1-I.prime_a[y,m,,])*prior_state # undisturbed remaining undisturbed
H_from_h = (1-prior_state) * (rho.prime_a[y,m,,]) # disturbed recovered to undisturbed
H_prop[y,m,,] = H_from_H + H_from_h # Total proportion disturbed
}
}
return(H_prop)
} # end function
##### Run model with H =burnin initial conditions
# Define five year burnin in
H_prop_0 = matrix(runif(nGrid*nSubst, 0,1), nrow = nGrid, ncol = nSubst) # First start with random conditions
# Cycle through 2003-2005 ten times to create burnin starting conditions
for(i in 1:10){
H_burn = FishingEffectsModel(I.prime_a[1:3,,,], rho.prime_a[1:3,,,], H_prop_0)
H_prop_0 = H_burn[3,12,,] # extract only the last time step as the new intial condition
}
##### Run model
H_tot = FishingEffectsModel(I.prime_a, rho.prime_a, H_prop_0)
# Calculate undisturbed Areas
fished_cells = grid5k.dat[match(grid_order, grid5k.dat$Grid5k_ID), ]
undistProps = matrix(NA, ncol = nSubAnnual*nYears, nrow = length(grid_order))
i = 1
for(y in 1:nYears){
for(m in 1:12){
undistProps[,i] = rowSums(H_tot[y,m,,]*sedProps)
i = i + 1
}
}
undistProps = data.frame(Grid5k_ID = grid_order, undistProps)
disturbProps = data.frame(Grid5k_ID = undistProps[,1],
apply(undistProps[,-1],2,
function(x) 1 - x))
## Write results to a shapefile
grid5k@data$orderID = 1:nrow(grid5k@data)
grid5k_results = grid5k@data
grid5k_results = merge(grid5k_results, disturbProps, by = "Grid5k_ID", all.x = T)
months = c("jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec")
counter = 6 # Start counter at first column that has disturbAreas
for(y in 2003:2016){
for(m in 1:12){
names(grid5k_results)[counter] = paste(months[m], y, sep = "")
counter = counter + 1
}
}
dist.out = grid5k
dist.out@data = grid5k_results[order(grid5k_results$orderID),]
## Create file name and write it to output
writeSpatialShape(dist.out, paste("FE_model_output\\", outFile, ".shp", sep = ""))
|
# Normalize a table of ascents into ascent, page and route tables.
# Copyright 2019, 2020 Dean Scarff
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Make sure to source("00-data_prep_functions.R") before sourcing this script.
period_length <- 604800 # seconds in 1 week
# Read in the ascents table.
df_raw <- read.csv(
file.path(data_dir, "raw_ascents.csv"),
comment.char = "#",
colClasses = c(
ascentId = "character",
route = "factor",
climber = "factor",
tick = "factor",
grade = "integer",
timestamp = "integer"
)
)
df_clean <- CleanAscents(df_raw)
message(SummarizeAscents(df_clean))
dfs <- NormalizeTables(df_clean, period_length)
dfs$routes <- mutate(dfs$routes, gamma = TransformGrade(grade))
WriteNormalizedTables(dfs, data_dir)
| /01-data_prep.R | permissive | scottwedge/climbing_ratings | R | false | false | 1,275 | r | # Normalize a table of ascents into ascent, page and route tables.
# Copyright 2019, 2020 Dean Scarff
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Make sure to source("00-data_prep_functions.R") before sourcing this script.
period_length <- 604800 # seconds in 1 week
# Read in the ascents table.
df_raw <- read.csv(
file.path(data_dir, "raw_ascents.csv"),
comment.char = "#",
colClasses = c(
ascentId = "character",
route = "factor",
climber = "factor",
tick = "factor",
grade = "integer",
timestamp = "integer"
)
)
df_clean <- CleanAscents(df_raw)
message(SummarizeAscents(df_clean))
dfs <- NormalizeTables(df_clean, period_length)
dfs$routes <- mutate(dfs$routes, gamma = TransformGrade(grade))
WriteNormalizedTables(dfs, data_dir)
|
if(!require(gdalUtils)){install.packages("gdalUtils"); library(gdalUtils)} # sostituire
library(rgdal)
library(raster)
library(rgeos)
library(spsann)
library(sp)
library(ICSNP)
library(velox)
library(spcosa)
library(spatstat)
library(dplyr)
library(tibble)
library(tidyr)
library(geosphere)
library(Rfast)
sampleboost <- function(x, ignorance, boundary, nplot, radius, perm, quant){
ndvi.vx <-velox(x)
igno.vx <- velox(ignorance)
result<-list()
distanze<-matrix(ncol=1, nrow = perm)
pb <- txtProgressBar(min = 0, max = perm, style = 3)
for (i in 1:perm){
punti_random <- spsample(boundary, n=nplot, type='random')
sampling_points <- as(punti_random, "data.frame")
xy <- sampling_points[,c(1,2)]
spdf <- SpatialPointsDataFrame(coords = xy, data = sampling_points,
proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
spols <- gBuffer(spgeom=spdf, width=radius, byid=TRUE)
estratti <- data.frame(coordinates(spdf), ndvi.vx$extract(sp=spols, fun=median))
names(estratti) <- c("x", "y", "ndvi")
estratti$ignorance <- igno.vx$extract(sp=spols, fun=median)
result[[i]]<-data.frame(estratti)
dataset_points <- cbind(xy, ID = 1:NROW(xy))
pairwise_distances <- distm(dataset_points[,1:2])
distanze[[i]] <- total.dist(pairwise_distances, method = "euclidean", square = FALSE, p = 0)
setTxtProgressBar(pb, i)
}
new_mat<-plyr::ldply(result, data.frame)
new_mat$try<-as.factor(rep(1:perm, each= nplot))
agg1<-aggregate(new_mat$ndvi,by=list(new_mat$try),FUN=var)
agg_igno<-aggregate(new_mat$ignorance,by=list(new_mat$try),FUN=mean)
agg2<-data.frame(agg1,distanze,agg_igno[[2]])
colnames(agg2)<-c('Try','Variance','Mean Dist', 'Mean Ignorance')
agg2 <- na.omit(agg2)
agg3 <- agg2[agg2$Variance > quantile(agg2$Variance, quant),]
ordered_solutions <- agg3[order(agg3[,'Mean Dist'], decreasing = TRUE),]
best <- ordered_solutions[order(ordered_solutions[,3], decreasing = TRUE),]
Index <- as.numeric(best[1,1])
sol <- subset(new_mat[new_mat$try %in% Index,])
sol2 <- subset(agg2[agg2$Try %in% Index,])
return(list("Full matrix"=new_mat, "Aggregated matrix"=agg2, "Best"= sol, "Variance of sampling points"=sol2[,'Variance'],
"Spatial Median of Distance"= sol2[,'Mean Dist']))
## Plot best solution
xy_out1 <- out1$Best[,c(1,2)]
out1_points <- SpatialPointsDataFrame(coords = xy_out1, data = out1$Best,
proj4string = crs(boundary))
p <- rasterVis::levelplot(x, layers=1, margin = list(FUN = median))+
latticeExtra::layer(sp.points(out1_points, lwd= 0.8, col='darkgray'))
p2 <- rasterVis::levelplot(mfi2, layers=1, margin = list(FUN = median))+
latticeExtra::layer(sp.points(out1_points, lwd= 0.8, col='darkgray'))
p3 <- ggplot(out1$`Full matrix`, aes(x = ndvi, group = try)) +
geom_density(colour = "lightgrey")+
theme(legend.position = "none")+
geom_density(data = out1$Best, aes(x = ndvi, colour = "red"))
print(p)
print(p2)
print(p3)
}
out1 <- sampleboost(x=ndvi_clip, ignorance = ignorance_map, nplot= 9, radius=0.2, quant = 0.99, perm = 1000, boundary=area_studio)
out1
##### PLOTTO LA SOLUZIONE OUT1, BEST SOLUTION
xy_out1 <- out1$Best[,c(1,2)]
out1_points <- SpatialPointsDataFrame(coords = xy_out1, data = out1$Best,
proj4string = CRS("+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))
plot(ndvi_clip)
plot(site, add=TRUE)
plot(out1_points, add=TRUE)
sp::plot(site)
sp::plot(mfi2, add=TRUE)
sp::plot(out1_points, add=TRUE)
plot(mfi2)
plot(mystratification@centroids, add=TRUE, col="red")
#### Ordino per valori decrescenti di varianza con il quantile 0.99 #########
out1 <- tent6
out_filter <- na.omit(out1$`Aggregated matrix`)
out_new <- out_filter[out_filter$Variance > quantile(out_filter$Variance, quantile_threshold),]
ordered_solutions <- out_new[order(out_new[,2], decreasing = TRUE),]
ordered_solutions_2 <- ordered_solutions[order(ordered_solutions[,3], decreasing = TRUE),]
head(ordered_solutions_2, 10)
########################################################################
####### Plotto la soluzione scelta #####################################
prova <- out1$`Full matrix`[is.element(out1$`Full matrix`$try, 3313),]
xy_prova <- prova[,c(1,2)]
prova_points <- SpatialPointsDataFrame(coords = xy_prova, data = prova,
proj4string = CRS("+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))
plot(ndvi_clip)
#plotRGB(rgb_crop, r= 1, g= 2, b = 3, stretch = "lin")
plot(area_studio, add=TRUE)
plot(prova_points, add=TRUE, col="black")
##############################
REFERENCE_SYSTEM <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
file_export <- spTransform(prova_points, crs(REFERENCE_SYSTEM))
#### salvo il csv ---- rinominare il file
write.csv(file_export, "Sampling points_a.csv", row.names = TRUE)
saveRDS(out1, file = "Sampling points_a.rds")
a <- df %>%
column_to_rownames("ID") %>% #make the ID the rownames. dist will use these> NB will not work on a tibble
dist() %>%
as.matrix() %>%
as.data.frame() %>%
rownames_to_column(var = "ID.x") %>% #capture the row IDs
gather(key = ID.y, value = dist, -ID.x) %>%
filter(ID.x < ID.y) %>%
as_tibble()
a <-as.data.frame(a)
distance <- sum(a$dist)
| /Sampling code_con buffers.R | no_license | interacquas/Optmised-sampling | R | false | false | 5,519 | r | if(!require(gdalUtils)){install.packages("gdalUtils"); library(gdalUtils)} # sostituire
library(rgdal)
library(raster)
library(rgeos)
library(spsann)
library(sp)
library(ICSNP)
library(velox)
library(spcosa)
library(spatstat)
library(dplyr)
library(tibble)
library(tidyr)
library(geosphere)
library(Rfast)
sampleboost <- function(x, ignorance, boundary, nplot, radius, perm, quant){
ndvi.vx <-velox(x)
igno.vx <- velox(ignorance)
result<-list()
distanze<-matrix(ncol=1, nrow = perm)
pb <- txtProgressBar(min = 0, max = perm, style = 3)
for (i in 1:perm){
punti_random <- spsample(boundary, n=nplot, type='random')
sampling_points <- as(punti_random, "data.frame")
xy <- sampling_points[,c(1,2)]
spdf <- SpatialPointsDataFrame(coords = xy, data = sampling_points,
proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
spols <- gBuffer(spgeom=spdf, width=radius, byid=TRUE)
estratti <- data.frame(coordinates(spdf), ndvi.vx$extract(sp=spols, fun=median))
names(estratti) <- c("x", "y", "ndvi")
estratti$ignorance <- igno.vx$extract(sp=spols, fun=median)
result[[i]]<-data.frame(estratti)
dataset_points <- cbind(xy, ID = 1:NROW(xy))
pairwise_distances <- distm(dataset_points[,1:2])
distanze[[i]] <- total.dist(pairwise_distances, method = "euclidean", square = FALSE, p = 0)
setTxtProgressBar(pb, i)
}
new_mat<-plyr::ldply(result, data.frame)
new_mat$try<-as.factor(rep(1:perm, each= nplot))
agg1<-aggregate(new_mat$ndvi,by=list(new_mat$try),FUN=var)
agg_igno<-aggregate(new_mat$ignorance,by=list(new_mat$try),FUN=mean)
agg2<-data.frame(agg1,distanze,agg_igno[[2]])
colnames(agg2)<-c('Try','Variance','Mean Dist', 'Mean Ignorance')
agg2 <- na.omit(agg2)
agg3 <- agg2[agg2$Variance > quantile(agg2$Variance, quant),]
ordered_solutions <- agg3[order(agg3[,'Mean Dist'], decreasing = TRUE),]
best <- ordered_solutions[order(ordered_solutions[,3], decreasing = TRUE),]
Index <- as.numeric(best[1,1])
sol <- subset(new_mat[new_mat$try %in% Index,])
sol2 <- subset(agg2[agg2$Try %in% Index,])
return(list("Full matrix"=new_mat, "Aggregated matrix"=agg2, "Best"= sol, "Variance of sampling points"=sol2[,'Variance'],
"Spatial Median of Distance"= sol2[,'Mean Dist']))
## Plot best solution
xy_out1 <- out1$Best[,c(1,2)]
out1_points <- SpatialPointsDataFrame(coords = xy_out1, data = out1$Best,
proj4string = crs(boundary))
p <- rasterVis::levelplot(x, layers=1, margin = list(FUN = median))+
latticeExtra::layer(sp.points(out1_points, lwd= 0.8, col='darkgray'))
p2 <- rasterVis::levelplot(mfi2, layers=1, margin = list(FUN = median))+
latticeExtra::layer(sp.points(out1_points, lwd= 0.8, col='darkgray'))
p3 <- ggplot(out1$`Full matrix`, aes(x = ndvi, group = try)) +
geom_density(colour = "lightgrey")+
theme(legend.position = "none")+
geom_density(data = out1$Best, aes(x = ndvi, colour = "red"))
print(p)
print(p2)
print(p3)
}
out1 <- sampleboost(x=ndvi_clip, ignorance = ignorance_map, nplot= 9, radius=0.2, quant = 0.99, perm = 1000, boundary=area_studio)
out1
##### PLOTTO LA SOLUZIONE OUT1, BEST SOLUTION
xy_out1 <- out1$Best[,c(1,2)]
out1_points <- SpatialPointsDataFrame(coords = xy_out1, data = out1$Best,
proj4string = CRS("+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))
plot(ndvi_clip)
plot(site, add=TRUE)
plot(out1_points, add=TRUE)
sp::plot(site)
sp::plot(mfi2, add=TRUE)
sp::plot(out1_points, add=TRUE)
plot(mfi2)
plot(mystratification@centroids, add=TRUE, col="red")
#### Ordino per valori decrescenti di varianza con il quantile 0.99 #########
out1 <- tent6
out_filter <- na.omit(out1$`Aggregated matrix`)
out_new <- out_filter[out_filter$Variance > quantile(out_filter$Variance, quantile_threshold),]
ordered_solutions <- out_new[order(out_new[,2], decreasing = TRUE),]
ordered_solutions_2 <- ordered_solutions[order(ordered_solutions[,3], decreasing = TRUE),]
head(ordered_solutions_2, 10)
########################################################################
####### Plotto la soluzione scelta #####################################
prova <- out1$`Full matrix`[is.element(out1$`Full matrix`$try, 3313),]
xy_prova <- prova[,c(1,2)]
prova_points <- SpatialPointsDataFrame(coords = xy_prova, data = prova,
proj4string = CRS("+proj=utm +zone=32 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))
plot(ndvi_clip)
#plotRGB(rgb_crop, r= 1, g= 2, b = 3, stretch = "lin")
plot(area_studio, add=TRUE)
plot(prova_points, add=TRUE, col="black")
##############################
REFERENCE_SYSTEM <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
file_export <- spTransform(prova_points, crs(REFERENCE_SYSTEM))
#### salvo il csv ---- rinominare il file
write.csv(file_export, "Sampling points_a.csv", row.names = TRUE)
saveRDS(out1, file = "Sampling points_a.rds")
a <- df %>%
column_to_rownames("ID") %>% #make the ID the rownames. dist will use these> NB will not work on a tibble
dist() %>%
as.matrix() %>%
as.data.frame() %>%
rownames_to_column(var = "ID.x") %>% #capture the row IDs
gather(key = ID.y, value = dist, -ID.x) %>%
filter(ID.x < ID.y) %>%
as_tibble()
a <-as.data.frame(a)
distance <- sum(a$dist)
|
# Clear variables and close windows
rm(list = ls(all = TRUE))
graphics.off()
# Please change working directory setwd('C:/...')
data <- read.delim2("SP1997-2005s.txt")
time <- (1:length(data[, 1]))
dat0 <- data[, 1] - c(mean(data[, 1]))
dat0 <- dat0/sd(dat0)
timet <- (time - 1078)/250 + 2001
plot(timet[time >= 1075], dat0[time >= 1075], xaxp = c(2001, 2005, 4), xlab = "Time", ylab = "Log-returns", type = "l")
| /STF2tvch01/STF2tvch01.R | no_license | QuantLet/STF | R | false | false | 417 | r | # Clear variables and close windows
rm(list = ls(all = TRUE))
graphics.off()
# Please change working directory setwd('C:/...')
data <- read.delim2("SP1997-2005s.txt")
time <- (1:length(data[, 1]))
dat0 <- data[, 1] - c(mean(data[, 1]))
dat0 <- dat0/sd(dat0)
timet <- (time - 1078)/250 + 2001
plot(timet[time >= 1075], dat0[time >= 1075], xaxp = c(2001, 2005, 4), xlab = "Time", ylab = "Log-returns", type = "l")
|
# sapply() takes the same arguments as lapply but tries to simplify the result
# into a vector or matrix.
# If this is not possible, it returns the same result as lapply()
# This is the structure of temp:
> str(temp)
List of 7
$ : num [1:5] 3 7 9 6 -1
$ : num [1:5] 6 9 12 13 5
$ : num [1:5] 4 8 3 -1 -3
$ : num [1:5] 1 4 7 2 -2
$ : num [1:5] 5 7 9 4 2
$ : num [1:5] -3 5 8 9 4
$ : num [1:5] 3 6 9 4 1
# Define a function
extremes_avg <- function(x) {
( min(x) + max(x) ) / 2
}
# Apply extremes_avg() over temp using sapply()
> sapply(temp, extremes_avg)
[1] 4.0 9.0 2.5 2.5 5.5 3.0 5.0
# Notice that this is the same as:
> unlist(lapply(temp, extremes_avg))
[1] 4.0 9.0 2.5 2.5 5.5 3.0 5.0
# In the previous example, we have seen that sapply simplifies the result into a vector.
# If the function passed to sapply() returns a vector of length > 1, then a matrix is returned:
extremes <- function(x) {
c(min = min(x), max = max(x))
}
> sapply(temp, extremes)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
min -1 5 -3 -2 2 -3 1
max 9 13 8 7 9 9 9
# If sapply cannot simplify the result into a meaningful vector or matrix, then it returs the
# same output as lapply:
below_zero <- function(x) {
return(x[x < 0])
}
freezing_s <- sapply(temp, below_zero)
# Apply below_zero over temp using lapply(): freezing_l
freezing_l <- lapply(temp, below_zero)
# Are freezing_s and freezing_l identical?
> identical(freezing_s, freezing_l)
[1] TRUE
# Finally, here is an example demonstrating sapply()'s power:
> sapply(list(runif (10), runif (10)),
+ function(x) c(min = min(x), mean = mean(x), max = max(x)))
[,1] [,2]
min 0.01998662 0.07075416
mean 0.50691288 0.58029329
max 0.98786178 0.93855891
| /R/R_Apply_Family/1.sapply.R | no_license | MrfksIv/R_Python_Tutorials | R | false | false | 1,768 | r | # sapply() takes the same arguments as lapply but tries to simplify the result
# into a vector or matrix.
# If this is not possible, it returns the same result as lapply()
# This is the structure of temp:
> str(temp)
List of 7
$ : num [1:5] 3 7 9 6 -1
$ : num [1:5] 6 9 12 13 5
$ : num [1:5] 4 8 3 -1 -3
$ : num [1:5] 1 4 7 2 -2
$ : num [1:5] 5 7 9 4 2
$ : num [1:5] -3 5 8 9 4
$ : num [1:5] 3 6 9 4 1
# Define a function
extremes_avg <- function(x) {
( min(x) + max(x) ) / 2
}
# Apply extremes_avg() over temp using sapply()
> sapply(temp, extremes_avg)
[1] 4.0 9.0 2.5 2.5 5.5 3.0 5.0
# Notice that this is the same as:
> unlist(lapply(temp, extremes_avg))
[1] 4.0 9.0 2.5 2.5 5.5 3.0 5.0
# In the previous example, we have seen that sapply simplifies the result into a vector.
# If the function passed to sapply() returns a vector of length > 1, then a matrix is returned:
extremes <- function(x) {
c(min = min(x), max = max(x))
}
> sapply(temp, extremes)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
min -1 5 -3 -2 2 -3 1
max 9 13 8 7 9 9 9
# If sapply cannot simplify the result into a meaningful vector or matrix, then it returs the
# same output as lapply:
below_zero <- function(x) {
return(x[x < 0])
}
freezing_s <- sapply(temp, below_zero)
# Apply below_zero over temp using lapply(): freezing_l
freezing_l <- lapply(temp, below_zero)
# Are freezing_s and freezing_l identical?
> identical(freezing_s, freezing_l)
[1] TRUE
# Finally, here is an example demonstrating sapply()'s power:
> sapply(list(runif (10), runif (10)),
+ function(x) c(min = min(x), mean = mean(x), max = max(x)))
[,1] [,2]
min 0.01998662 0.07075416
mean 0.50691288 0.58029329
max 0.98786178 0.93855891
|
library(ape)
testtree <- read.tree("10136_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10136_0_unrooted.txt") | /codeml_files/newick_trees_processed/10136_0/rinput.R | no_license | DaniBoo/cyanobacteria_project | R | false | false | 137 | r | library(ape)
testtree <- read.tree("10136_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10136_0_unrooted.txt") |
#***Problem 1***
#$$P1: Question 1$$
AK = .67
LA = .61
NC = .52
#assuming all these events are independent
#4 diff ways to win
pr_AK_LA_NC = AK*LA*NC
pr_AK_LA = AK*LA*(1-NC)
pr_AK_NC = AK*(1-LA)*NC
pr_LA_NC = (1-AK)*LA*NC
answerP1Q1 = pr_AK_LA_NC + pr_AK_LA + pr_AK_NC + pr_LA_NC
##0.649252
margin = .8-answerP1Q1
#$$P1: Question 2$$
##The betting markets probability is .150748 higher than our answer.
##This is so much higher because the events of the states are
##most likely not independent, but dependent
#***Problem 2***
##This is not enough info for me to make the decision.
##I would need to know the length of the delays because
#If Delta's delays were significantly longer than those of United's
#the 2% difference would not make up for the greater amount of lost time
#***Problem 3***
#$$P3: Section B$$
#$$P3: Section C$$
#$$P3: Section D$$
| /HW1.R | no_license | wonathanjong/Stat371-H-W | R | false | false | 861 | r | #***Problem 1***
#$$P1: Question 1$$
AK = .67
LA = .61
NC = .52
#assuming all these events are independent
#4 diff ways to win
pr_AK_LA_NC = AK*LA*NC
pr_AK_LA = AK*LA*(1-NC)
pr_AK_NC = AK*(1-LA)*NC
pr_LA_NC = (1-AK)*LA*NC
answerP1Q1 = pr_AK_LA_NC + pr_AK_LA + pr_AK_NC + pr_LA_NC
##0.649252
margin = .8-answerP1Q1
#$$P1: Question 2$$
##The betting markets probability is .150748 higher than our answer.
##This is so much higher because the events of the states are
##most likely not independent, but dependent
#***Problem 2***
##This is not enough info for me to make the decision.
##I would need to know the length of the delays because
#If Delta's delays were significantly longer than those of United's
#the 2% difference would not make up for the greater amount of lost time
#***Problem 3***
#$$P3: Section B$$
#$$P3: Section C$$
#$$P3: Section D$$
|
# Please do three things to ensure this template is correctly modified:
# 1. Rename this file based on the content you are testing using
# `test-functionName.R` format so that your can directly call `moduleCoverage`
# to calculate module coverage information.
# `functionName` is a function's name in your module (e.g., `fireSense_spreadDataPrepEvent1`).
# 2. Copy this file to the tests folder (i.e., `C:/Users/Tati/Documents/GitHub/fireSense_spreadDataPrep/tests/testthat`).
# 3. Modify the test description based on the content you are testing:
test_that("test Event1 and Event2.", {
module <- list("fireSense_spreadDataPrep")
path <- list(modulePath = "C:/Users/Tati/Documents/GitHub",
outputPath = file.path(tempdir(), "outputs"))
parameters <- list(
#.progress = list(type = "graphical", interval = 1),
.globals = list(verbose = FALSE),
fireSense_spreadDataPrep = list(.saveInitialTime = NA)
)
times <- list(start = 0, end = 1)
# If your test function contains `time(sim)`, you can test the function at a
# particular simulation time by defining the start time above.
object1 <- "object1" # please specify
object2 <- "object2" # please specify
objects <- list("object1" = object1, "object2" = object2)
mySim <- simInit(times = times,
params = parameters,
modules = module,
objects = objects,
paths = path)
# You may need to set the random seed if your module or its functions use the
# random number generator.
set.seed(1234)
# You have two strategies to test your module:
# 1. Test the overall simulation results for the given objects, using the
# sample code below:
output <- spades(mySim, debug = FALSE)
# is output a simList?
expect_is(output, "simList")
# does output have your module in it
expect_true(any(unlist(modules(output)) %in% c(unlist(module))))
# did it simulate to the end?
expect_true(time(output) == 1)
# 2. Test the functions inside of the module using the sample code below:
# To allow the `moduleCoverage` function to calculate unit test coverage
# level, it needs access to all functions directly.
# Use this approach when using any function within the simList object
# (i.e., one version as a direct call, and one with `simList` object prepended).
if (exists("fireSense_spreadDataPrepEvent1", envir = .GlobalEnv)) {
simOutput <- fireSense_spreadDataPrepEvent1(mySim)
} else {
simOutput <- myEvent1(mySim)
}
expectedOutputEvent1Test1 <- " this is test for event 1. " # please define your expection of your output
expect_is(class(simOutput$event1Test1), "character")
expect_equal(simOutput$event1Test1, expectedOutputEvent1Test1) # or other expect function in testthat package.
expect_equal(simOutput$event1Test2, as.numeric(999)) # or other expect function in testthat package.
if (exists("fireSense_spreadDataPrepEvent2", envir = .GlobalEnv)) {
simOutput <- fireSense_spreadDataPrepEvent2(mySim)
} else {
simOutput <- myEvent2(mySim)
}
expectedOutputEvent2Test1 <- " this is test for event 2. " # please define your expection of your output
expect_is(class(simOutput$event2Test1), "character")
expect_equal(simOutput$event2Test1, expectedOutputEvent2Test1) # or other expect function in testthat package.
expect_equal(simOutput$event2Test2, as.numeric(777)) # or other expect function in testthat package.
}) | /tests/testthat/test-template.R | no_license | tati-micheletti/fireSense_dataPrep | R | false | false | 3,482 | r |
# Please do three things to ensure this template is correctly modified:
# 1. Rename this file based on the content you are testing using
# `test-functionName.R` format so that your can directly call `moduleCoverage`
# to calculate module coverage information.
# `functionName` is a function's name in your module (e.g., `fireSense_spreadDataPrepEvent1`).
# 2. Copy this file to the tests folder (i.e., `C:/Users/Tati/Documents/GitHub/fireSense_spreadDataPrep/tests/testthat`).
# 3. Modify the test description based on the content you are testing:
test_that("test Event1 and Event2.", {
module <- list("fireSense_spreadDataPrep")
path <- list(modulePath = "C:/Users/Tati/Documents/GitHub",
outputPath = file.path(tempdir(), "outputs"))
parameters <- list(
#.progress = list(type = "graphical", interval = 1),
.globals = list(verbose = FALSE),
fireSense_spreadDataPrep = list(.saveInitialTime = NA)
)
times <- list(start = 0, end = 1)
# If your test function contains `time(sim)`, you can test the function at a
# particular simulation time by defining the start time above.
object1 <- "object1" # please specify
object2 <- "object2" # please specify
objects <- list("object1" = object1, "object2" = object2)
mySim <- simInit(times = times,
params = parameters,
modules = module,
objects = objects,
paths = path)
# You may need to set the random seed if your module or its functions use the
# random number generator.
set.seed(1234)
# You have two strategies to test your module:
# 1. Test the overall simulation results for the given objects, using the
# sample code below:
output <- spades(mySim, debug = FALSE)
# is output a simList?
expect_is(output, "simList")
# does output have your module in it
expect_true(any(unlist(modules(output)) %in% c(unlist(module))))
# did it simulate to the end?
expect_true(time(output) == 1)
# 2. Test the functions inside of the module using the sample code below:
# To allow the `moduleCoverage` function to calculate unit test coverage
# level, it needs access to all functions directly.
# Use this approach when using any function within the simList object
# (i.e., one version as a direct call, and one with `simList` object prepended).
if (exists("fireSense_spreadDataPrepEvent1", envir = .GlobalEnv)) {
simOutput <- fireSense_spreadDataPrepEvent1(mySim)
} else {
simOutput <- myEvent1(mySim)
}
expectedOutputEvent1Test1 <- " this is test for event 1. " # please define your expection of your output
expect_is(class(simOutput$event1Test1), "character")
expect_equal(simOutput$event1Test1, expectedOutputEvent1Test1) # or other expect function in testthat package.
expect_equal(simOutput$event1Test2, as.numeric(999)) # or other expect function in testthat package.
if (exists("fireSense_spreadDataPrepEvent2", envir = .GlobalEnv)) {
simOutput <- fireSense_spreadDataPrepEvent2(mySim)
} else {
simOutput <- myEvent2(mySim)
}
expectedOutputEvent2Test1 <- " this is test for event 2. " # please define your expection of your output
expect_is(class(simOutput$event2Test1), "character")
expect_equal(simOutput$event2Test1, expectedOutputEvent2Test1) # or other expect function in testthat package.
expect_equal(simOutput$event2Test2, as.numeric(777)) # or other expect function in testthat package.
}) |
#' @export
makeRLearner.classif.lqa = function() {
makeRLearnerClassif(
cl = "classif.lqa",
package = "lqa",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "penalty",
values = c("adaptive.lasso", "ao", "bridge", "enet", "fused.lasso", "genet", "icb", "lasso",
"licb", "oscar", "penalreg", "ridge", "scad", "weighted.fusion")),
makeNumericLearnerParam(id = "lambda", lower = 0,
requires = quote(penalty %in% c("adaptive.lasso", "ao", "bridge", "genet", "lasso",
"oscar", "penalreg", "ridge", "scad"))),
makeNumericLearnerParam(id = "gamma", lower = 1 + .Machine$double.eps,
requires = quote(penalty %in% c("ao", "bridge", "genet", "weighted.fusion"))),
makeNumericLearnerParam(id = "alpha", lower = 0, requires = quote(penalty == "genet")),
makeNumericLearnerParam(id = "c", lower = 0, requires = quote(penalty == "oscar")),
makeNumericLearnerParam(id = "a", lower = 2 + .Machine$double.eps,
requires = quote(penalty == "scad")),
makeNumericLearnerParam(id = "lambda1", lower = 0,
requires = quote(penalty %in% c("enet", "fused.lasso", "icb", "licb", "weighted.fusion"))),
makeNumericLearnerParam(id = "lambda2", lower = 0,
requires = quote(penalty %in% c("enet", "fused.lasso", "icb", "licb", "weighted.fusion"))),
makeDiscreteLearnerParam(id = "method", default = "lqa.update2",
values = c("lqa.update2", "ForwardBoost", "GBlockBoost")),
makeNumericLearnerParam(id = "var.eps", default = .Machine$double.eps, lower = 0),
makeIntegerLearnerParam(id = "max.steps", lower = 1L, default = 5000L),
makeNumericLearnerParam(id = "conv.eps", default = 0.001, lower = 0),
makeLogicalLearnerParam(id = "conv.stop", default = TRUE),
makeNumericLearnerParam(id = "c1", default = 1e-08, lower = 0),
makeIntegerLearnerParam(id = "digits", default = 5L, lower = 1L)
),
properties = c("numerics", "prob", "twoclass", "weights"),
par.vals = list(penalty = 'lasso', lambda = 0.1),
name = "Fitting penalized Generalized Linear Models with the LQA algorithm",
short.name = "lqa",
note = "`penalty` has been set to *lasso* and `lambda` to 0.1 by default."
)
}
#' @export
trainLearner.classif.lqa = function(.learner, .task, .subset, .weights = NULL,
var.eps, max.steps, conv.eps, conv.stop, c1, digits, ...) {
ctrl = learnerArgsToControl(lqa::lqa.control, var.eps, max.steps, conv.eps, conv.stop, c1, digits)
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "01")
args = c(list(x = d$data, y = d$target, family = binomial(), control = ctrl), list(...))
rm(d)
if (!args$penalty %in% c("adaptive.lasso", "ao", "bridge", "genet", "lasso",
"oscar", "penalreg", "ridge", "scad")) {
args$lambda = NULL
}
is.tune.param = names(args) %in% c("lambda", "gamma", "alpha", "c", "a", "lambda1", "lambda2")
penfun = getFromNamespace(args$penalty, "lqa")
args$penalty = do.call(penfun, list(lambda = unlist(args[is.tune.param])))
args = args[!is.tune.param]
if (!is.null(.weights))
args$weights = .weights
do.call(lqa::lqa.default, args)
}
#' @export
predictLearner.classif.lqa = function(.learner, .model, .newdata, ...) {
p = lqa::predict.lqa(.model$learner.model, new.x = cbind(1, .newdata), ...)$mu.new
levs = c(.model$task.desc$negative, .model$task.desc$positive)
if (.learner$predict.type == "prob") {
p = propVectorToMatrix(p, levs)
} else {
p = as.factor(ifelse(p > 0.5, levs[2L], levs[1L]))
names(p) = NULL
}
return(p)
}
| /R/RLearner_classif_lqa.R | no_license | abhik1368/mlr | R | false | false | 3,618 | r | #' @export
makeRLearner.classif.lqa = function() {
makeRLearnerClassif(
cl = "classif.lqa",
package = "lqa",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "penalty",
values = c("adaptive.lasso", "ao", "bridge", "enet", "fused.lasso", "genet", "icb", "lasso",
"licb", "oscar", "penalreg", "ridge", "scad", "weighted.fusion")),
makeNumericLearnerParam(id = "lambda", lower = 0,
requires = quote(penalty %in% c("adaptive.lasso", "ao", "bridge", "genet", "lasso",
"oscar", "penalreg", "ridge", "scad"))),
makeNumericLearnerParam(id = "gamma", lower = 1 + .Machine$double.eps,
requires = quote(penalty %in% c("ao", "bridge", "genet", "weighted.fusion"))),
makeNumericLearnerParam(id = "alpha", lower = 0, requires = quote(penalty == "genet")),
makeNumericLearnerParam(id = "c", lower = 0, requires = quote(penalty == "oscar")),
makeNumericLearnerParam(id = "a", lower = 2 + .Machine$double.eps,
requires = quote(penalty == "scad")),
makeNumericLearnerParam(id = "lambda1", lower = 0,
requires = quote(penalty %in% c("enet", "fused.lasso", "icb", "licb", "weighted.fusion"))),
makeNumericLearnerParam(id = "lambda2", lower = 0,
requires = quote(penalty %in% c("enet", "fused.lasso", "icb", "licb", "weighted.fusion"))),
makeDiscreteLearnerParam(id = "method", default = "lqa.update2",
values = c("lqa.update2", "ForwardBoost", "GBlockBoost")),
makeNumericLearnerParam(id = "var.eps", default = .Machine$double.eps, lower = 0),
makeIntegerLearnerParam(id = "max.steps", lower = 1L, default = 5000L),
makeNumericLearnerParam(id = "conv.eps", default = 0.001, lower = 0),
makeLogicalLearnerParam(id = "conv.stop", default = TRUE),
makeNumericLearnerParam(id = "c1", default = 1e-08, lower = 0),
makeIntegerLearnerParam(id = "digits", default = 5L, lower = 1L)
),
properties = c("numerics", "prob", "twoclass", "weights"),
par.vals = list(penalty = 'lasso', lambda = 0.1),
name = "Fitting penalized Generalized Linear Models with the LQA algorithm",
short.name = "lqa",
note = "`penalty` has been set to *lasso* and `lambda` to 0.1 by default."
)
}
#' @export
trainLearner.classif.lqa = function(.learner, .task, .subset, .weights = NULL,
var.eps, max.steps, conv.eps, conv.stop, c1, digits, ...) {
ctrl = learnerArgsToControl(lqa::lqa.control, var.eps, max.steps, conv.eps, conv.stop, c1, digits)
d = getTaskData(.task, .subset, target.extra = TRUE, recode.target = "01")
args = c(list(x = d$data, y = d$target, family = binomial(), control = ctrl), list(...))
rm(d)
if (!args$penalty %in% c("adaptive.lasso", "ao", "bridge", "genet", "lasso",
"oscar", "penalreg", "ridge", "scad")) {
args$lambda = NULL
}
is.tune.param = names(args) %in% c("lambda", "gamma", "alpha", "c", "a", "lambda1", "lambda2")
penfun = getFromNamespace(args$penalty, "lqa")
args$penalty = do.call(penfun, list(lambda = unlist(args[is.tune.param])))
args = args[!is.tune.param]
if (!is.null(.weights))
args$weights = .weights
do.call(lqa::lqa.default, args)
}
#' @export
predictLearner.classif.lqa = function(.learner, .model, .newdata, ...) {
p = lqa::predict.lqa(.model$learner.model, new.x = cbind(1, .newdata), ...)$mu.new
levs = c(.model$task.desc$negative, .model$task.desc$positive)
if (.learner$predict.type == "prob") {
p = propVectorToMatrix(p, levs)
} else {
p = as.factor(ifelse(p > 0.5, levs[2L], levs[1L]))
names(p) = NULL
}
return(p)
}
|
# Pie2
expenditure=c(600,300,150,100,200)
names(expenditure)=c('Housing','Food','Cloths','Entertainment',
'Other')
expenditure
pie(expenditure)
pie(expenditure,
labels=as.character(expenditure),
main="Monthly Expenditure Breakdown",
col=c("red","orange","yellow","blue","green"),
border="brown",
clockwise=TRUE
)
| /32-basicGraphs/27c-pie2.R | no_license | DUanalytics/rAnalytics | R | false | false | 347 | r | # Pie2
expenditure=c(600,300,150,100,200)
names(expenditure)=c('Housing','Food','Cloths','Entertainment',
'Other')
expenditure
pie(expenditure)
pie(expenditure,
labels=as.character(expenditure),
main="Monthly Expenditure Breakdown",
col=c("red","orange","yellow","blue","green"),
border="brown",
clockwise=TRUE
)
|
library(kcde)
library(pdtmvn)
library(plyr)
library(dplyr)
library(lubridate)
library(doMC)
options(error = recover)
all_data_sets <- c("ili_national", "dengue_sj")
all_prediction_horizons <- seq_len(52) # make predictions at every horizon from 1 to 52 weeks ahead
all_max_lags <- 1L # use incidence at times t^* and t^* - 1 to predict incidence after t^*
all_max_seasonal_lags <- 0L # not used
all_filtering_values <- FALSE # not used
all_differencing_values <- FALSE # not used
all_seasonality_values <- c(FALSE, TRUE) # specifications without and with periodic kernel
all_bw_parameterizations <- c("diagonal", "full") # specifications with diagonal and full bandwidth
## Test values for debugging
#all_data_sets <- c("ili_national")
#all_prediction_horizons <- 4L
#all_max_lags <- 1L
#all_filtering_values <- c(FALSE)
#all_differencing_values <- c(FALSE)
#all_seasonality_values <- c(TRUE)
#all_bw_parameterizations <- c("diagonal")
num_cores <- 3L
registerDoMC(cores = num_cores)
for(data_set in all_data_sets) {
## Set path where fit object is stored
results_path <- file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"estimation-results")
if(identical(data_set, "ili_national")) {
## Load data for nationally reported influenza like illness
usflu <- read.csv("/media/evan/data/Reich/infectious-disease-prediction-with-kcde/data-raw/usflu.csv")
# ## This is how I originally got the data -- have saved it to
# ## csv for the purposes of stable access going forward.
# library(cdcfluview)
# usflu <- get_flu_data("national", "ilinet", years=1997:2014)
## A little cleanup
data <- transmute(usflu,
region.type = REGION.TYPE,
region = REGION,
year = YEAR,
week = WEEK,
weighted_ili = as.numeric(X..WEIGHTED.ILI))
## Subset data to do prediction using only data up through 2014
data <- data[data$year <= 2014, , drop = FALSE]
## Row indices in data corresponding to times at which we want to make a prediction
prediction_time_inds <- which(data$year %in% 2011:2014)
## Add time column. This is used for calculating times to drop in cross-validation
data$time <- ymd(paste(data$year, "01", "01", sep = "-"))
week(data$time) <- data$week
## Add time_index column. This is used for calculating the periodic kernel.
## Here, this is calculated as the number of days since some origin date (1970-1-1 in this case).
## The origin is arbitrary.
data$time_index <- as.integer(data$time - ymd(paste("1970", "01", "01", sep = "-")))
## Season column: for example, weeks of 2010 up through and including week 30 get season 2009/2010;
## weeks after week 30 get season 2010/2011
data$season <- ifelse(
data$week <= 30,
paste0(data$year - 1, "/", data$year),
paste0(data$year, "/", data$year + 1)
)
## Season week column: week number within season
data$season_week <- sapply(seq_len(nrow(data)), function(row_ind) {
sum(data$season == data$season[row_ind] & data$time_index <= data$time_index[row_ind])
})
} else if(identical(data_set, "dengue_sj")) {
## Load data for Dengue fever in San Juan
data <- read.csv("/media/evan/data/Reich/infectious-disease-prediction-with-kcde/data-raw/San_Juan_Testing_Data.csv")
## Form variable with total cases + 0.5 which can be logged, and its seasonally lagged ratio
data$total_cases_plus_0.5 <- data$total_cases + 0.5
## Row indices in data corresponding to times at which we want to make a prediction
prediction_time_inds <- which(data$season %in% paste0(2009:2012, "/", 2010:2013))
## convert dates
data$time <- ymd(data$week_start_date)
## Add time_index column. This is used for calculating the periodic kernel.
## Here, this is calculated as the number of days since some origin date (1970-1-1 in this case).
## The origin is arbitrary.
data$time_index <- as.integer(data$time - ymd(paste("1970", "01", "01", sep = "-")))
}
data_set_results <- foreach(prediction_horizon=all_prediction_horizons,
.packages=c("kcde", "pdtmvn", "plyr", "dplyr", "lubridate"),
.combine="rbind") %dopar% {
results_row_ind <- 1L
## Allocate data frame to store results for this prediction horizon
num_rows <- length(all_max_lags) *
length(all_max_seasonal_lags) *
length(all_filtering_values) *
length(all_differencing_values) *
length(all_seasonality_values) *
length(all_bw_parameterizations) *
length(prediction_time_inds)
ph_results <- data.frame(data_set = data_set,
prediction_horizon = rep(NA_integer_, num_rows),
max_lag = rep(NA_integer_, num_rows),
max_seasonal_lag = rep(NA_integer_, num_rows),
filtering = rep(NA, num_rows),
differencing = rep(NA, num_rows),
seasonality = rep(NA, num_rows),
bw_parameterization = rep(NA_character_, num_rows),
model = "kcde",
prediction_time = rep(NA, num_rows),
log_score = rep(NA_real_, num_rows),
pt_pred = rep(NA_real_, num_rows),
AE = rep(NA_real_, num_rows),
interval_pred_lb_95 = rep(NA_real_, num_rows),
interval_pred_ub_95 = rep(NA_real_, num_rows),
interval_pred_lb_50 = rep(NA_real_, num_rows),
interval_pred_ub_50 = rep(NA_real_, num_rows),
stringsAsFactors = FALSE
)
class(ph_results$prediction_time) <- class(data$time)
for(max_lag in all_max_lags) {
for(max_seasonal_lag in all_max_seasonal_lags) {
for(filtering in all_filtering_values) {
for(differencing in all_differencing_values) {
## Set prediction target var
if(identical(data_set, "ili_national")) {
if(differencing) {
prediction_target_var <- "weighted_ili_ratio"
orig_prediction_target_var <- "weighted_ili"
} else {
prediction_target_var <- "weighted_ili"
}
} else if(identical(data_set, "dengue_sj")) {
if(differencing) {
prediction_target_var <- "total_cases_plus_0.5_ratio"
orig_prediction_target_var <- "total_cases_plus_0.5"
} else {
prediction_target_var <- "total_cases_plus_0.5"
}
}
for(seasonality in all_seasonality_values) {
for(bw_parameterization in all_bw_parameterizations) {
for(prediction_time_ind in prediction_time_inds) {
## Set values describing case in ph_results
ph_results$prediction_horizon[results_row_ind] <-
prediction_horizon
ph_results$max_lag[results_row_ind] <-
max_lag
ph_results$max_seasonal_lag[results_row_ind] <-
max_seasonal_lag
ph_results$filtering[results_row_ind] <-
filtering
ph_results$differencing[results_row_ind] <-
differencing
ph_results$seasonality[results_row_ind] <-
seasonality
ph_results$bw_parameterization[results_row_ind] <-
bw_parameterization
ph_results$prediction_time[results_row_ind] <-
data$time[prediction_time_ind]
## Load kcde_fit object. Estimation was performed previously.
case_descriptor <- paste0(
data_set,
"-prediction_horizon_", prediction_horizon,
"-max_lag_", max_lag,
"-max_seasonal_lag_", max_seasonal_lag,
"-filtering_", filtering,
"-differencing_", differencing,
"-seasonality_", seasonality,
"-bw_parameterization_", bw_parameterization
)
kcde_fit_file_path <- file.path(results_path,
paste0("kcde_fit-", case_descriptor, ".rds"))
kcde_fit <- readRDS(kcde_fit_file_path)
## If Dengue fit, fix a bug in the in_range function for discrete variables
## that was supplied at time of estimation. This function was not used in
## estimation, but is required here. Original definition referenced a function
## that may not be available now.
if(identical(data_set, "dengue_sj")) {
for(kernel_component_ind in seq_along(kcde_fit$kcde_control$kernel_components)) {
kernel_component <- kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]
if(!is.null(kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns)) {
for(var_ind in seq_along(kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns)) {
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns[[var_ind]]$in_range <-
function(x, tolerance = .Machine$double.eps^0.5) {
return(sapply(x,
function(x_i) {
return(isTRUE(all.equal(x_i,
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns[[var_ind]]$discretizer(x_i))))
}))
}
}
kcde_fit$theta_hat[[kernel_component_ind]]$discrete_var_range_fns <-
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns
}
}
}
## fix rkernel_fn for pdtmvn-based kernel functions
## I supplied a buggy version of this in the call to the estimation routine
## that did not ensure that variables were supplied in a consistent order.
## This did not affect estimation as rkernel_fn is not called there
## But it does need to be fixed here.
for(kernel_component_ind in seq(from = (as.logical(seasonality) + 1), to = length(kcde_fit$kcde_control$kernel_components))) {
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$rkernel_fn <-
function(n,
conditioning_obs,
center,
bw,
bw_continuous,
conditional_bw_discrete,
conditional_center_discrete_offset_multiplier,
continuous_vars,
discrete_vars,
continuous_var_col_inds,
discrete_var_col_inds,
discrete_var_range_fns,
lower,
upper,
x_names,
...) {
if(missing(conditioning_obs) || is.null(conditioning_obs)) {
log_conditioning_obs <- NULL
} else {
log_conditioning_obs <- log(conditioning_obs)
}
if(missing(bw_continuous)) {
bw_continuous <- NULL
}
if(missing(conditional_bw_discrete)) {
conditional_bw_discrete <- NULL
}
if(missing(conditional_center_discrete_offset_multiplier)) {
conditional_center_discrete_offset_multiplier <- NULL
}
## center parameter of pdtmvn_kernel is mean of log
## mode of resulting log-normal distribution is
## mode = exp(mu - bw %*% 1) (where 1 is a column vector of 1s)
## therefore mu = log(mode) + bw %*% 1
reduced_x_names <- names(center)
inds_x_vars_in_orig_vars <- which(x_names %in% reduced_x_names)
x_names_for_call <- x_names[inds_x_vars_in_orig_vars]
mean_offset <- apply(bw, 1, sum)[x_names %in% colnames(center)]
return(exp(rpdtmvn_kernel(n = n,
conditioning_obs = log_conditioning_obs,
center = sweep(log(center)[, x_names_for_call, drop = FALSE], 2, mean_offset, `+`),
bw = bw,
bw_continuous = bw_continuous,
conditional_bw_discrete = conditional_bw_discrete,
conditional_center_discrete_offset_multiplier = conditional_center_discrete_offset_multiplier,
continuous_vars = continuous_vars,
discrete_vars = discrete_vars,
continuous_var_col_inds = continuous_var_col_inds,
discrete_var_col_inds = discrete_var_col_inds,
discrete_var_range_fns = discrete_var_range_fns,
lower = lower,
upper = upper,
x_names = x_names)[, reduced_x_names, drop = FALSE]))
}
}
## Get index of analysis time in data set
## (time from which we predict forward)
analysis_time_ind <- prediction_time_ind - prediction_horizon
## Compute log score
observed_prediction_target <-
data[prediction_time_ind, prediction_target_var, drop = FALSE]
colnames(observed_prediction_target) <-
paste0(prediction_target_var, "_horizon", prediction_horizon)
ph_results$log_score[results_row_ind] <-
kcde_predict(
kcde_fit = kcde_fit,
prediction_data =
data[seq_len(analysis_time_ind), , drop = FALSE],
leading_rows_to_drop = 0L,
trailing_rows_to_drop = 0L,
additional_training_rows_to_drop = NULL,
prediction_type = "distribution",
prediction_test_lead_obs = observed_prediction_target,
log = TRUE
)
if(differencing) {
ph_results$log_score[results_row_ind] <-
ph_results$log_score[results_row_ind] -
(abs(data[analysis_time_ind - 52, orig_prediction_target_var]))
}
## Compute point prediction and interval predictions -- quantiles
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
kcde_predict(
p = c(0.5, 0.025, 0.975, 0.25, 0.75),
n = 100000,
kcde_fit = kcde_fit,
prediction_data =
data[seq_len(analysis_time_ind), , drop = FALSE],
leading_rows_to_drop = 0L,
trailing_rows_to_drop = 0L,
additional_training_rows_to_drop = NULL,
prediction_type = "quantile",
log = TRUE
)
if(differencing) {
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] *
data[prediction_time_ind - 52, prediction_target_var]
}
## Correction by subtracting 0.5 for Dengue data set
if(identical(data_set, "dengue_sj")) {
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] - 0.5
}
## Compute absolute error of point prediction
ph_results$AE[results_row_ind] <-
abs(ph_results$pt_pred[results_row_ind] -
observed_prediction_target)
## Increment results row
results_row_ind <- results_row_ind + 1L
} # prediction_time_ind
} # bw_parameterization
} # seasonality
} # differencing
} # filtering
} # max_seasonal_lag
} # max_lag
saveRDS(ph_results, file = file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"prediction-results",
paste0("kcde-predictions-ph_", prediction_horizon, ".rds")))
return(ph_results)
} # prediction_horizon -- in foreach/dopar statement
## Save results for the given data set
saveRDS(data_set_results, file = file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"prediction-results/kcde-predictions.rds"))
} # data_set
| /inst/code/prediction/kcde-prediction.R | no_license | MartinPDLE/article-disease-pred-with-kcde | R | false | false | 23,557 | r | library(kcde)
library(pdtmvn)
library(plyr)
library(dplyr)
library(lubridate)
library(doMC)
options(error = recover)
all_data_sets <- c("ili_national", "dengue_sj")
all_prediction_horizons <- seq_len(52) # make predictions at every horizon from 1 to 52 weeks ahead
all_max_lags <- 1L # use incidence at times t^* and t^* - 1 to predict incidence after t^*
all_max_seasonal_lags <- 0L # not used
all_filtering_values <- FALSE # not used
all_differencing_values <- FALSE # not used
all_seasonality_values <- c(FALSE, TRUE) # specifications without and with periodic kernel
all_bw_parameterizations <- c("diagonal", "full") # specifications with diagonal and full bandwidth
## Test values for debugging
#all_data_sets <- c("ili_national")
#all_prediction_horizons <- 4L
#all_max_lags <- 1L
#all_filtering_values <- c(FALSE)
#all_differencing_values <- c(FALSE)
#all_seasonality_values <- c(TRUE)
#all_bw_parameterizations <- c("diagonal")
num_cores <- 3L
registerDoMC(cores = num_cores)
for(data_set in all_data_sets) {
## Set path where fit object is stored
results_path <- file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"estimation-results")
if(identical(data_set, "ili_national")) {
## Load data for nationally reported influenza like illness
usflu <- read.csv("/media/evan/data/Reich/infectious-disease-prediction-with-kcde/data-raw/usflu.csv")
# ## This is how I originally got the data -- have saved it to
# ## csv for the purposes of stable access going forward.
# library(cdcfluview)
# usflu <- get_flu_data("national", "ilinet", years=1997:2014)
## A little cleanup
data <- transmute(usflu,
region.type = REGION.TYPE,
region = REGION,
year = YEAR,
week = WEEK,
weighted_ili = as.numeric(X..WEIGHTED.ILI))
## Subset data to do prediction using only data up through 2014
data <- data[data$year <= 2014, , drop = FALSE]
## Row indices in data corresponding to times at which we want to make a prediction
prediction_time_inds <- which(data$year %in% 2011:2014)
## Add time column. This is used for calculating times to drop in cross-validation
data$time <- ymd(paste(data$year, "01", "01", sep = "-"))
week(data$time) <- data$week
## Add time_index column. This is used for calculating the periodic kernel.
## Here, this is calculated as the number of days since some origin date (1970-1-1 in this case).
## The origin is arbitrary.
data$time_index <- as.integer(data$time - ymd(paste("1970", "01", "01", sep = "-")))
## Season column: for example, weeks of 2010 up through and including week 30 get season 2009/2010;
## weeks after week 30 get season 2010/2011
data$season <- ifelse(
data$week <= 30,
paste0(data$year - 1, "/", data$year),
paste0(data$year, "/", data$year + 1)
)
## Season week column: week number within season
data$season_week <- sapply(seq_len(nrow(data)), function(row_ind) {
sum(data$season == data$season[row_ind] & data$time_index <= data$time_index[row_ind])
})
} else if(identical(data_set, "dengue_sj")) {
## Load data for Dengue fever in San Juan
data <- read.csv("/media/evan/data/Reich/infectious-disease-prediction-with-kcde/data-raw/San_Juan_Testing_Data.csv")
## Form variable with total cases + 0.5 which can be logged, and its seasonally lagged ratio
data$total_cases_plus_0.5 <- data$total_cases + 0.5
## Row indices in data corresponding to times at which we want to make a prediction
prediction_time_inds <- which(data$season %in% paste0(2009:2012, "/", 2010:2013))
## convert dates
data$time <- ymd(data$week_start_date)
## Add time_index column. This is used for calculating the periodic kernel.
## Here, this is calculated as the number of days since some origin date (1970-1-1 in this case).
## The origin is arbitrary.
data$time_index <- as.integer(data$time - ymd(paste("1970", "01", "01", sep = "-")))
}
data_set_results <- foreach(prediction_horizon=all_prediction_horizons,
.packages=c("kcde", "pdtmvn", "plyr", "dplyr", "lubridate"),
.combine="rbind") %dopar% {
results_row_ind <- 1L
## Allocate data frame to store results for this prediction horizon
num_rows <- length(all_max_lags) *
length(all_max_seasonal_lags) *
length(all_filtering_values) *
length(all_differencing_values) *
length(all_seasonality_values) *
length(all_bw_parameterizations) *
length(prediction_time_inds)
ph_results <- data.frame(data_set = data_set,
prediction_horizon = rep(NA_integer_, num_rows),
max_lag = rep(NA_integer_, num_rows),
max_seasonal_lag = rep(NA_integer_, num_rows),
filtering = rep(NA, num_rows),
differencing = rep(NA, num_rows),
seasonality = rep(NA, num_rows),
bw_parameterization = rep(NA_character_, num_rows),
model = "kcde",
prediction_time = rep(NA, num_rows),
log_score = rep(NA_real_, num_rows),
pt_pred = rep(NA_real_, num_rows),
AE = rep(NA_real_, num_rows),
interval_pred_lb_95 = rep(NA_real_, num_rows),
interval_pred_ub_95 = rep(NA_real_, num_rows),
interval_pred_lb_50 = rep(NA_real_, num_rows),
interval_pred_ub_50 = rep(NA_real_, num_rows),
stringsAsFactors = FALSE
)
class(ph_results$prediction_time) <- class(data$time)
for(max_lag in all_max_lags) {
for(max_seasonal_lag in all_max_seasonal_lags) {
for(filtering in all_filtering_values) {
for(differencing in all_differencing_values) {
## Set prediction target var
if(identical(data_set, "ili_national")) {
if(differencing) {
prediction_target_var <- "weighted_ili_ratio"
orig_prediction_target_var <- "weighted_ili"
} else {
prediction_target_var <- "weighted_ili"
}
} else if(identical(data_set, "dengue_sj")) {
if(differencing) {
prediction_target_var <- "total_cases_plus_0.5_ratio"
orig_prediction_target_var <- "total_cases_plus_0.5"
} else {
prediction_target_var <- "total_cases_plus_0.5"
}
}
for(seasonality in all_seasonality_values) {
for(bw_parameterization in all_bw_parameterizations) {
for(prediction_time_ind in prediction_time_inds) {
## Set values describing case in ph_results
ph_results$prediction_horizon[results_row_ind] <-
prediction_horizon
ph_results$max_lag[results_row_ind] <-
max_lag
ph_results$max_seasonal_lag[results_row_ind] <-
max_seasonal_lag
ph_results$filtering[results_row_ind] <-
filtering
ph_results$differencing[results_row_ind] <-
differencing
ph_results$seasonality[results_row_ind] <-
seasonality
ph_results$bw_parameterization[results_row_ind] <-
bw_parameterization
ph_results$prediction_time[results_row_ind] <-
data$time[prediction_time_ind]
## Load kcde_fit object. Estimation was performed previously.
case_descriptor <- paste0(
data_set,
"-prediction_horizon_", prediction_horizon,
"-max_lag_", max_lag,
"-max_seasonal_lag_", max_seasonal_lag,
"-filtering_", filtering,
"-differencing_", differencing,
"-seasonality_", seasonality,
"-bw_parameterization_", bw_parameterization
)
kcde_fit_file_path <- file.path(results_path,
paste0("kcde_fit-", case_descriptor, ".rds"))
kcde_fit <- readRDS(kcde_fit_file_path)
## If Dengue fit, fix a bug in the in_range function for discrete variables
## that was supplied at time of estimation. This function was not used in
## estimation, but is required here. Original definition referenced a function
## that may not be available now.
if(identical(data_set, "dengue_sj")) {
for(kernel_component_ind in seq_along(kcde_fit$kcde_control$kernel_components)) {
kernel_component <- kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]
if(!is.null(kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns)) {
for(var_ind in seq_along(kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns)) {
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns[[var_ind]]$in_range <-
function(x, tolerance = .Machine$double.eps^0.5) {
return(sapply(x,
function(x_i) {
return(isTRUE(all.equal(x_i,
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns[[var_ind]]$discretizer(x_i))))
}))
}
}
kcde_fit$theta_hat[[kernel_component_ind]]$discrete_var_range_fns <-
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$theta_fixed$discrete_var_range_fns
}
}
}
## fix rkernel_fn for pdtmvn-based kernel functions
## I supplied a buggy version of this in the call to the estimation routine
## that did not ensure that variables were supplied in a consistent order.
## This did not affect estimation as rkernel_fn is not called there
## But it does need to be fixed here.
for(kernel_component_ind in seq(from = (as.logical(seasonality) + 1), to = length(kcde_fit$kcde_control$kernel_components))) {
kcde_fit$kcde_control$kernel_components[[kernel_component_ind]]$rkernel_fn <-
function(n,
conditioning_obs,
center,
bw,
bw_continuous,
conditional_bw_discrete,
conditional_center_discrete_offset_multiplier,
continuous_vars,
discrete_vars,
continuous_var_col_inds,
discrete_var_col_inds,
discrete_var_range_fns,
lower,
upper,
x_names,
...) {
if(missing(conditioning_obs) || is.null(conditioning_obs)) {
log_conditioning_obs <- NULL
} else {
log_conditioning_obs <- log(conditioning_obs)
}
if(missing(bw_continuous)) {
bw_continuous <- NULL
}
if(missing(conditional_bw_discrete)) {
conditional_bw_discrete <- NULL
}
if(missing(conditional_center_discrete_offset_multiplier)) {
conditional_center_discrete_offset_multiplier <- NULL
}
## center parameter of pdtmvn_kernel is mean of log
## mode of resulting log-normal distribution is
## mode = exp(mu - bw %*% 1) (where 1 is a column vector of 1s)
## therefore mu = log(mode) + bw %*% 1
reduced_x_names <- names(center)
inds_x_vars_in_orig_vars <- which(x_names %in% reduced_x_names)
x_names_for_call <- x_names[inds_x_vars_in_orig_vars]
mean_offset <- apply(bw, 1, sum)[x_names %in% colnames(center)]
return(exp(rpdtmvn_kernel(n = n,
conditioning_obs = log_conditioning_obs,
center = sweep(log(center)[, x_names_for_call, drop = FALSE], 2, mean_offset, `+`),
bw = bw,
bw_continuous = bw_continuous,
conditional_bw_discrete = conditional_bw_discrete,
conditional_center_discrete_offset_multiplier = conditional_center_discrete_offset_multiplier,
continuous_vars = continuous_vars,
discrete_vars = discrete_vars,
continuous_var_col_inds = continuous_var_col_inds,
discrete_var_col_inds = discrete_var_col_inds,
discrete_var_range_fns = discrete_var_range_fns,
lower = lower,
upper = upper,
x_names = x_names)[, reduced_x_names, drop = FALSE]))
}
}
## Get index of analysis time in data set
## (time from which we predict forward)
analysis_time_ind <- prediction_time_ind - prediction_horizon
## Compute log score
observed_prediction_target <-
data[prediction_time_ind, prediction_target_var, drop = FALSE]
colnames(observed_prediction_target) <-
paste0(prediction_target_var, "_horizon", prediction_horizon)
ph_results$log_score[results_row_ind] <-
kcde_predict(
kcde_fit = kcde_fit,
prediction_data =
data[seq_len(analysis_time_ind), , drop = FALSE],
leading_rows_to_drop = 0L,
trailing_rows_to_drop = 0L,
additional_training_rows_to_drop = NULL,
prediction_type = "distribution",
prediction_test_lead_obs = observed_prediction_target,
log = TRUE
)
if(differencing) {
ph_results$log_score[results_row_ind] <-
ph_results$log_score[results_row_ind] -
(abs(data[analysis_time_ind - 52, orig_prediction_target_var]))
}
## Compute point prediction and interval predictions -- quantiles
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
kcde_predict(
p = c(0.5, 0.025, 0.975, 0.25, 0.75),
n = 100000,
kcde_fit = kcde_fit,
prediction_data =
data[seq_len(analysis_time_ind), , drop = FALSE],
leading_rows_to_drop = 0L,
trailing_rows_to_drop = 0L,
additional_training_rows_to_drop = NULL,
prediction_type = "quantile",
log = TRUE
)
if(differencing) {
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] *
data[prediction_time_ind - 52, prediction_target_var]
}
## Correction by subtracting 0.5 for Dengue data set
if(identical(data_set, "dengue_sj")) {
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] <-
ph_results[results_row_ind,
c("pt_pred", "interval_pred_lb_95", "interval_pred_ub_95", "interval_pred_lb_50", "interval_pred_ub_50")] - 0.5
}
## Compute absolute error of point prediction
ph_results$AE[results_row_ind] <-
abs(ph_results$pt_pred[results_row_ind] -
observed_prediction_target)
## Increment results row
results_row_ind <- results_row_ind + 1L
} # prediction_time_ind
} # bw_parameterization
} # seasonality
} # differencing
} # filtering
} # max_seasonal_lag
} # max_lag
saveRDS(ph_results, file = file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"prediction-results",
paste0("kcde-predictions-ph_", prediction_horizon, ".rds")))
return(ph_results)
} # prediction_horizon -- in foreach/dopar statement
## Save results for the given data set
saveRDS(data_set_results, file = file.path(
"/media/evan/data/Reich/infectious-disease-prediction-with-kcde/inst/results",
data_set,
"prediction-results/kcde-predictions.rds"))
} # data_set
|
data = matrix(c(1,0,0,0,1,0,0,0,0,0,
0,1,0,1,0,1,0,0,1,0,
1,1,0,0,0,0,1,0,0,0,
1,1,1,0,0,0,1,0,0,0,
0,1,1,1,0,0,0,1,0,0,
1,0,1,1,1,0,1,1,1,1,
1,1,0,1,0,1,0,1,1,1,
0,0,0,1,1,0,1,0,1,0,
0,1,1,0,0,0,1,1,0,0,
1,0,0,0,1,1,1,0,0,0),
nrow=10,
ncol=10);
# Imagine the matrix represents presence/absence across a
# geographic space. The row and columm index corresponds
# to the pixels longitudinal and latitudinal offset.
# The goal is to calculate a "clumping" score for the matrix
# that is the product over each cell, x, of:
# \epsilon + \sum_{i\in N_x} I(i == x)
# score(x) = ----------------------------------------
# | N_x | + 2 \epsilon
# where:
# N_x is the set of cells that neighbor x
# | N_x | is the size of this set (3 for corner cells, 5 for
# cells on the side, and 8 of other cells).
# \epsilon is a positive # (we'll use .1)
# I(i == x) is an "indicator" function that is 1 if the cell
# x and the cell i have the same value. It is 0
# if the cells differ.
#
# If \epsilon is 1. The score function can be thought of as
# a simple model that states that "probability of a cell being 1
# is the proportion of neighbors who display 1".
# As epsilon increases, we nudge the probability of 0 or 1 closer
# to 0.5
#
# The value for the matrix is the product of this score for each cell
epsilon <- 0.1;
total.score = 1.0;
count_matches <- function(cell, row, ind) {
num.matching <- 0;
if (row[ind] == cell) {
num.matching <- num.matching + 1;
}
if (ind > 1 && row[ind - 1] == cell) {
num.matching <- num.matching + 1;
}
if (ind < length(row) && row[ind + 1] == cell) {
num.matching <- num.matching + 1;
}
return(num.matching);
}
for (r in 1 : nrow(data)) {
row <- data[r,]
top.or.bottom <- r == 1 || r == nrow(data);
for (c in 1 : length(row)) {
cell <- row[c];
left.or.right <- c == 1 || c == length(row);
if (left.or.right) {
if (top.or.bottom) {
num.neighbors = 3; # corner
} else {
num.neighbors = 5; # left or right side
}
} else if (top.or.bottom) {
num.neighbors = 5; # top or bottom side
} else {
num.neighbors = 8;
}
num.matching <- count_matches(cell, row, c) - 1;
if (r > 1) {
prev.row <- data[(r - 1),];
m <- count_matches(cell, prev.row, c);
num.matching <- num.matching + m;
}
if (r < nrow(data)) {
nr = r + 1;
next.row <- data[nr,];
m <- count_matches(cell, next.row, c);
num.matching <- num.matching + m;
}
cell.score <- (epsilon + num.matching)/(2*epsilon + num.neighbors);
total.score <- total.score * cell.score;
#cat(r, " ", c, " ", cell, " ", num.neighbors, " ", num.matching, "\n");
}
}
cat('score = ', total.score, "\n"); | /flow-control/spatial.R | permissive | mtholder/some-simple-r-examples | R | false | false | 3,172 | r | data = matrix(c(1,0,0,0,1,0,0,0,0,0,
0,1,0,1,0,1,0,0,1,0,
1,1,0,0,0,0,1,0,0,0,
1,1,1,0,0,0,1,0,0,0,
0,1,1,1,0,0,0,1,0,0,
1,0,1,1,1,0,1,1,1,1,
1,1,0,1,0,1,0,1,1,1,
0,0,0,1,1,0,1,0,1,0,
0,1,1,0,0,0,1,1,0,0,
1,0,0,0,1,1,1,0,0,0),
nrow=10,
ncol=10);
# Imagine the matrix represents presence/absence across a
# geographic space. The row and columm index corresponds
# to the pixels longitudinal and latitudinal offset.
# The goal is to calculate a "clumping" score for the matrix
# that is the product over each cell, x, of:
# \epsilon + \sum_{i\in N_x} I(i == x)
# score(x) = ----------------------------------------
# | N_x | + 2 \epsilon
# where:
# N_x is the set of cells that neighbor x
# | N_x | is the size of this set (3 for corner cells, 5 for
# cells on the side, and 8 of other cells).
# \epsilon is a positive # (we'll use .1)
# I(i == x) is an "indicator" function that is 1 if the cell
# x and the cell i have the same value. It is 0
# if the cells differ.
#
# If \epsilon is 1. The score function can be thought of as
# a simple model that states that "probability of a cell being 1
# is the proportion of neighbors who display 1".
# As epsilon increases, we nudge the probability of 0 or 1 closer
# to 0.5
#
# The value for the matrix is the product of this score for each cell
epsilon <- 0.1;
total.score = 1.0;
count_matches <- function(cell, row, ind) {
num.matching <- 0;
if (row[ind] == cell) {
num.matching <- num.matching + 1;
}
if (ind > 1 && row[ind - 1] == cell) {
num.matching <- num.matching + 1;
}
if (ind < length(row) && row[ind + 1] == cell) {
num.matching <- num.matching + 1;
}
return(num.matching);
}
for (r in 1 : nrow(data)) {
row <- data[r,]
top.or.bottom <- r == 1 || r == nrow(data);
for (c in 1 : length(row)) {
cell <- row[c];
left.or.right <- c == 1 || c == length(row);
if (left.or.right) {
if (top.or.bottom) {
num.neighbors = 3; # corner
} else {
num.neighbors = 5; # left or right side
}
} else if (top.or.bottom) {
num.neighbors = 5; # top or bottom side
} else {
num.neighbors = 8;
}
num.matching <- count_matches(cell, row, c) - 1;
if (r > 1) {
prev.row <- data[(r - 1),];
m <- count_matches(cell, prev.row, c);
num.matching <- num.matching + m;
}
if (r < nrow(data)) {
nr = r + 1;
next.row <- data[nr,];
m <- count_matches(cell, next.row, c);
num.matching <- num.matching + m;
}
cell.score <- (epsilon + num.matching)/(2*epsilon + num.neighbors);
total.score <- total.score * cell.score;
#cat(r, " ", c, " ", cell, " ", num.neighbors, " ", num.matching, "\n");
}
}
cat('score = ', total.score, "\n"); |
#' @name kro.ref
#' @title LMS Parameters for German reference data (Kromeyer Hauschild, 2001) for height, weight, bmi, and waist circumference, including preterm correction (Voigt)
#' @docType data
#' @usage kro.ref
#' @source Perzentile fuer den Body-mass-Index fuer das Kindes- und Jugendalter unter Heranziehung verschiedener deutscher Stichproben, Monatsschrift Kinderheilkunde August 2001, Volume 149, Issue 8, pp 807-818; Fruehgeborenenkorrektur nach Voigt
NULL
| /childsds/R/kroref-data.r | no_license | ingted/R-Examples | R | false | false | 469 | r | #' @name kro.ref
#' @title LMS Parameters for German reference data (Kromeyer Hauschild, 2001) for height, weight, bmi, and waist circumference, including preterm correction (Voigt)
#' @docType data
#' @usage kro.ref
#' @source Perzentile fuer den Body-mass-Index fuer das Kindes- und Jugendalter unter Heranziehung verschiedener deutscher Stichproben, Monatsschrift Kinderheilkunde August 2001, Volume 149, Issue 8, pp 807-818; Fruehgeborenenkorrektur nach Voigt
NULL
|
## KAGGLE CONTEST: BNPP DATASET
## MATH 289 FINAL REPORT
## AUTHORS: MITESH GADGIL & sAURABH KULKARNI
## PREDICTIVE MODELLING USING XGBOOST
library(xgboost)
# Inputting data
test <- read.csv("test.csv")
train <- read.csv("train.csv")
# Replacing missing data with NA
train[train ==""] <- NA
test[test ==""] <- NA
# Storing "target" as a different vector and equalizing test and train data columns
y <- train[, 'target']
train <- train[, -2]
# the number of NA's in a data point are stored to be used as a feature
feature_NA.train <- apply(train,1,function(x)sum(is.na(x)))
feature_NA.test <- apply(test,1,function(x)sum(is.na(x)))
train[is.na(train)] <- -1
test[is.na(test)] <- -1
# Find factor variables and translate to numeric
# store column numbers of factor attributes in f and f.t
f <- c()
for(i in 1:ncol(train)) {
if (is.factor(train[, i])) f <- c(f, i)
}
f.t <- c()
for(i in 1:ncol(test)) {
if (is.factor(test[, i])) f.t <- c(f.t, i)
}
# COnverting the test and training sets into numeric variables
ttrain <- rbind(train, test)
for (i in f) {
ttrain[, i] <- as.numeric(ttrain[, i])
}
train <- ttrain[1:nrow(train), ]
train <- cbind.data.frame(train,"feature"=feature_NA.train)
test <- ttrain[(nrow(train)+1):nrow(ttrain), ]
test <- cbind.data.frame(test,"feature"=feature_NA.test)
# the function that returns probability vector for test set after training according to
#parameters in param0 for number of iterations given in 'iter'
doPrediction <- function(y, train, test, param0, iter) {
n<- nrow(train)
xgtrain <- xgb.DMatrix(as.matrix(train), label = y,missing = NaN)
xgval = xgb.DMatrix(as.matrix(test),missing = NaN)
watchlist <- list('train' = xgtrain)
model = xgb.train(
nrounds = iter
, params = param0
, data = xgtrain
, watchlist = watchlist
, print.every.n = 100
, nthread = 8
)
p <- predict(model, xgval)
rm(model)
gc()
p
}
# Set parameters of the algorithm: eta and max_depth can control overfitting
# logloss is the evaluation metric used for evaluation
param0 <- list(
# general , non specific params - just guessing
"objective" = "binary:logistic"
, "eval_metric" = "logloss"
, "eta" = 0.01
, "subsample" = 0.8
, "colsample_bytree" = 0.8
, "min_child_weight" = 1
, "max_depth" = 10
)
# Preparing submission file
submission <- read.table("sample_submission.csv", header=TRUE, sep=',')
ensemble <- rep(0, nrow(test))
# change to 1:n to get an ensemble of 'n' trained models
for (i in 1:2) {
p <- doPrediction(y, train, test, param0, 1200)
ensemble <- ensemble + p
}
submission$PredictedProb <- ensemble/i
# writing the submission file
write.csv(submission, "submission.csv", row.names=F, quote=F)
| /BNP-Paribas-Claims-Management/XgBoost_SSK_MAG.R | no_license | saurabhkulkarni2312/R-Projects | R | false | false | 2,713 | r | ## KAGGLE CONTEST: BNPP DATASET
## MATH 289 FINAL REPORT
## AUTHORS: MITESH GADGIL & sAURABH KULKARNI
## PREDICTIVE MODELLING USING XGBOOST
library(xgboost)
# Inputting data
test <- read.csv("test.csv")
train <- read.csv("train.csv")
# Replacing missing data with NA
train[train ==""] <- NA
test[test ==""] <- NA
# Storing "target" as a different vector and equalizing test and train data columns
y <- train[, 'target']
train <- train[, -2]
# the number of NA's in a data point are stored to be used as a feature
feature_NA.train <- apply(train,1,function(x)sum(is.na(x)))
feature_NA.test <- apply(test,1,function(x)sum(is.na(x)))
train[is.na(train)] <- -1
test[is.na(test)] <- -1
# Find factor variables and translate to numeric
# store column numbers of factor attributes in f and f.t
f <- c()
for(i in 1:ncol(train)) {
if (is.factor(train[, i])) f <- c(f, i)
}
f.t <- c()
for(i in 1:ncol(test)) {
if (is.factor(test[, i])) f.t <- c(f.t, i)
}
# COnverting the test and training sets into numeric variables
ttrain <- rbind(train, test)
for (i in f) {
ttrain[, i] <- as.numeric(ttrain[, i])
}
train <- ttrain[1:nrow(train), ]
train <- cbind.data.frame(train,"feature"=feature_NA.train)
test <- ttrain[(nrow(train)+1):nrow(ttrain), ]
test <- cbind.data.frame(test,"feature"=feature_NA.test)
# the function that returns probability vector for test set after training according to
#parameters in param0 for number of iterations given in 'iter'
doPrediction <- function(y, train, test, param0, iter) {
n<- nrow(train)
xgtrain <- xgb.DMatrix(as.matrix(train), label = y,missing = NaN)
xgval = xgb.DMatrix(as.matrix(test),missing = NaN)
watchlist <- list('train' = xgtrain)
model = xgb.train(
nrounds = iter
, params = param0
, data = xgtrain
, watchlist = watchlist
, print.every.n = 100
, nthread = 8
)
p <- predict(model, xgval)
rm(model)
gc()
p
}
# Set parameters of the algorithm: eta and max_depth can control overfitting
# logloss is the evaluation metric used for evaluation
param0 <- list(
# general , non specific params - just guessing
"objective" = "binary:logistic"
, "eval_metric" = "logloss"
, "eta" = 0.01
, "subsample" = 0.8
, "colsample_bytree" = 0.8
, "min_child_weight" = 1
, "max_depth" = 10
)
# Preparing submission file
submission <- read.table("sample_submission.csv", header=TRUE, sep=',')
ensemble <- rep(0, nrow(test))
# change to 1:n to get an ensemble of 'n' trained models
for (i in 1:2) {
p <- doPrediction(y, train, test, param0, 1200)
ensemble <- ensemble + p
}
submission$PredictedProb <- ensemble/i
# writing the submission file
write.csv(submission, "submission.csv", row.names=F, quote=F)
|
\name{add_names.ggplot2}
\alias{add_names.ggplot2}
\title{Add names to ggplot2 scatter plot points}
\usage{
add_names.ggplot2(g, idx, label, ...)
}
\arguments{
\item{g}{a ggplot2 object}
\item{idx}{index of the names to add. If \code{idx} is
not specified, \code{\link{identify.ggplot2}} is called.}
\item{label}{the name of the variable to be used for the
names. If \code{label} is not specified, the indexes
\code{idx} are used instead.}
\item{...}{additional arguments to
\code{\link{geom_text}}}
}
\value{
The ggplot2 object \code{g} with names added.
}
\description{
Add names to ggplot2 scatter plot points
}
\examples{
\donttest{
g=ggplot(data=mtcars,aes(x=wt,y=disp))+
geom_point(aes(color=as.factor(cyl)))
add_names.ggplot2(g)
}
}
| /man/add_names.ggplot2.Rd | no_license | kuremon/iggplot2 | R | false | false | 800 | rd | \name{add_names.ggplot2}
\alias{add_names.ggplot2}
\title{Add names to ggplot2 scatter plot points}
\usage{
add_names.ggplot2(g, idx, label, ...)
}
\arguments{
\item{g}{a ggplot2 object}
\item{idx}{index of the names to add. If \code{idx} is
not specified, \code{\link{identify.ggplot2}} is called.}
\item{label}{the name of the variable to be used for the
names. If \code{label} is not specified, the indexes
\code{idx} are used instead.}
\item{...}{additional arguments to
\code{\link{geom_text}}}
}
\value{
The ggplot2 object \code{g} with names added.
}
\description{
Add names to ggplot2 scatter plot points
}
\examples{
\donttest{
g=ggplot(data=mtcars,aes(x=wt,y=disp))+
geom_point(aes(color=as.factor(cyl)))
add_names.ggplot2(g)
}
}
|
#Challenge_06.1 'Everlasting Love'
# Your name is Julie, and you're a brand new freshman at BYU-Idaho
# In the last three weeks you've given your number to 32 different boys, but only kissed 8 of them.
# But no more of that because you have a boyfriend. His name is Cody and he's the most wonderful man in the world.
# You've already had the marriage talk, and you're both positive that you're the most perfect match ever.
# Lately though, you've been noticing how cute Kody's roommate, Dylan is. You've written your (soon to be) ex boyfriend pages and pages
# Of love letters already and it would be a downright shame to let all that effort go to waste on someone that
# Isn't as cute as Dylan.
# Challenge: Replace all mentions of Cody in the love letter with Dylan. Don't mess up or else you could be in
# A terribly awkward situation!
#Bonus: That's not my name
# Input Data (data/input_data_06.1.txt):
#sample:
#Cody, after being your best friend and telling you all my hopes and desires,
#I need to tell you what will make me happy.
#Your code here:
#Answer: | /user_challenges/love_letter_challenge.R | no_license | dylanjm/dss_coding_challenge | R | false | false | 1,075 | r | #Challenge_06.1 'Everlasting Love'
# Your name is Julie, and you're a brand new freshman at BYU-Idaho
# In the last three weeks you've given your number to 32 different boys, but only kissed 8 of them.
# But no more of that because you have a boyfriend. His name is Cody and he's the most wonderful man in the world.
# You've already had the marriage talk, and you're both positive that you're the most perfect match ever.
# Lately though, you've been noticing how cute Kody's roommate, Dylan is. You've written your (soon to be) ex boyfriend pages and pages
# Of love letters already and it would be a downright shame to let all that effort go to waste on someone that
# Isn't as cute as Dylan.
# Challenge: Replace all mentions of Cody in the love letter with Dylan. Don't mess up or else you could be in
# A terribly awkward situation!
#Bonus: That's not my name
# Input Data (data/input_data_06.1.txt):
#sample:
#Cody, after being your best friend and telling you all my hopes and desires,
#I need to tell you what will make me happy.
#Your code here:
#Answer: |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/systat.reader.R
\name{systat.reader}
\alias{systat.reader}
\title{Read a Systat file with a .sys or .syd file extension.}
\usage{
systat.reader(data.file, filename, variable.name)
}
\arguments{
\item{data.file}{The name of the data file to be read.}
\item{filename}{The path to the data set to be loaded.}
\item{variable.name}{The name to be assigned to in the global environment.}
}
\value{
No value is returned; this function is called for its side effects.
}
\description{
This function will load the specified Systat file into memory.
}
\examples{
library('ProjectTemplate2')
\dontrun{systat.reader('example.sys', 'data/example.sys', 'example')}
}
| /man/systat.reader.Rd | no_license | connectedblue/ProjectTemplate2 | R | false | true | 733 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/systat.reader.R
\name{systat.reader}
\alias{systat.reader}
\title{Read a Systat file with a .sys or .syd file extension.}
\usage{
systat.reader(data.file, filename, variable.name)
}
\arguments{
\item{data.file}{The name of the data file to be read.}
\item{filename}{The path to the data set to be loaded.}
\item{variable.name}{The name to be assigned to in the global environment.}
}
\value{
No value is returned; this function is called for its side effects.
}
\description{
This function will load the specified Systat file into memory.
}
\examples{
library('ProjectTemplate2')
\dontrun{systat.reader('example.sys', 'data/example.sys', 'example')}
}
|
args = commandArgs(trailingOnly=TRUE)
gibbsFile = args[1]
sampleNum = args[2]
g <- read.table(gibbsFile)
g$samples <- rep(1:sampleNum, length(unique(g$V2)))
gS <- spread(g, samples, V3)
write.csv(gS, "Gibbs1.csv", row.names=FALSE, quote=FALSE)
| /PICExample/RearrangeGibbs1.R | permissive | janaobsteter/Genotype_CODES | R | false | false | 248 | r | args = commandArgs(trailingOnly=TRUE)
gibbsFile = args[1]
sampleNum = args[2]
g <- read.table(gibbsFile)
g$samples <- rep(1:sampleNum, length(unique(g$V2)))
gS <- spread(g, samples, V3)
write.csv(gS, "Gibbs1.csv", row.names=FALSE, quote=FALSE)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sjPlotGroupPropTable.R
\name{sjp.gpt}
\alias{sjp.gpt}
\title{Plot grouped proportional tables}
\usage{
sjp.gpt(x, y, groups, geom.colors = "Set1", geom.size = 2.5,
shape.fill.color = "#f0f0f0", shapes = c(15, 16, 17, 18, 21, 22, 23, 24,
25, 7, 8, 9, 10, 12), title = NULL, axis.labels = NULL,
axis.titles = NULL, legend.title = NULL, legend.labels = NULL,
wrap.title = 50, wrap.labels = 15, wrap.legend.title = 20,
wrap.legend.labels = 20, axis.lim = NULL, grid.breaks = NULL,
show.total = TRUE, annotate.total = TRUE, show.p = TRUE,
show.n = TRUE, prnt.plot = TRUE)
}
\arguments{
\item{x}{categorical variable, where the proportion of each category in
\code{x} for the highest category of \code{y} will be printed
along the x-axis.}
\item{y}{categorical or numeric variable. If not a binary variable, \code{y}
will be recoded into a binary variable, dichtomized at the highest
category and all remaining categories.}
\item{groups}{grouping variable, which will define the y-axis}
\item{geom.colors}{user defined color for geoms. See 'Details' in \code{\link{sjp.grpfrq}}.}
\item{geom.size}{size resp. width of the geoms (bar width, line thickness or point size,
depending on plot type and function). Note that bar and bin widths mostly
need smaller values than dot sizes.}
\item{shape.fill.color}{optional color vector, fill-color for non-filled shapes}
\item{shapes}{numeric vector with shape styles, used to map the different
categories of \code{x}.}
\item{title}{character vector, used as plot title. Depending on plot type and function,
will be set automatically. If \code{title = ""}, no title is printed.}
\item{axis.labels}{character vector with labels used as axis labels. Optional
argument, since in most cases, axis labels are set automatically.}
\item{axis.titles}{character vector of length one or two, defining the title(s)
for the x-axis and y-axis.}
\item{legend.title}{character vector, used as title for the plot legend.}
\item{legend.labels}{character vector with labels for the guide/legend.}
\item{wrap.title}{numeric, determines how many chars of the plot title are displayed in
one line and when a line break is inserted.}
\item{wrap.labels}{numeric, determines how many chars of the value, variable or axis
labels are displayed in one line and when a line break is inserted.}
\item{wrap.legend.title}{numeric, determines how many chars of the legend's title
are displayed in one line and when a line break is inserted.}
\item{wrap.legend.labels}{numeric, determines how many chars of the legend labels are
displayed in one line and when a line break is inserted.}
\item{axis.lim}{numeric vector of length 2, defining the range of the plot axis.
Depending on plot type, may effect either x- or y-axis, or both.
For multiple plot outputs (e.g., from \code{type = "eff"} or
\code{type = "slope"} in \code{\link{sjp.glm}}), \code{axis.lim} may
also be a list of vectors of length 2, defining axis limits for each
plot (only if non-faceted).}
\item{grid.breaks}{numeric; sets the distance between breaks for the axis,
i.e. at every \code{grid.breaks}'th position a major grid is being printed.}
\item{show.total}{logical, if \code{TRUE}, a total summary line for all aggregated
\code{groups} is added.}
\item{annotate.total}{logical, if \code{TRUE} and \code{show.total = TRUE},
the total-row in the figure will be highlighted with a slightly
shaded background.}
\item{show.p}{logical, adds significance levels to values, or value and
variable labels.}
\item{show.n}{logical, if \code{TRUE}, adds total number of cases for each
group or category to the labels.}
\item{prnt.plot}{logical, if \code{TRUE} (default), plots the results as graph. Use \code{FALSE} if you don't
want to plot any graphs. In either case, the ggplot-object will be returned as value.}
}
\value{
(Insisibily) returns the ggplot-object with the complete plot
(\code{plot}) as well as the data frame that
was used for setting up the ggplot-object (\code{df}).
}
\description{
Plot grouped proportional crosstables, where the proportion of
each level of \code{x} for the highest category in \code{y}
is plotted, for each subgroup of \code{groups}.
}
\details{
The p-values are based on \code{\link[stats]{chisq.test}} of \code{x}
and \code{y} for each \code{groups}.
}
\examples{
library(sjmisc)
data(efc)
# the proportion of dependency levels in female
# elderly, for each family carer's relationship
# to elderly
sjp.gpt(efc$e42dep, efc$e16sex, efc$e15relat)
# proportion of educational levels in highest
# dependency category of elderly, for different
# care levels
sjp.gpt(efc$c172code, efc$e42dep, efc$n4pstu)
}
| /man/sjp.gpt.Rd | no_license | RogerBorras/devel | R | false | true | 4,796 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sjPlotGroupPropTable.R
\name{sjp.gpt}
\alias{sjp.gpt}
\title{Plot grouped proportional tables}
\usage{
sjp.gpt(x, y, groups, geom.colors = "Set1", geom.size = 2.5,
shape.fill.color = "#f0f0f0", shapes = c(15, 16, 17, 18, 21, 22, 23, 24,
25, 7, 8, 9, 10, 12), title = NULL, axis.labels = NULL,
axis.titles = NULL, legend.title = NULL, legend.labels = NULL,
wrap.title = 50, wrap.labels = 15, wrap.legend.title = 20,
wrap.legend.labels = 20, axis.lim = NULL, grid.breaks = NULL,
show.total = TRUE, annotate.total = TRUE, show.p = TRUE,
show.n = TRUE, prnt.plot = TRUE)
}
\arguments{
\item{x}{categorical variable, where the proportion of each category in
\code{x} for the highest category of \code{y} will be printed
along the x-axis.}
\item{y}{categorical or numeric variable. If not a binary variable, \code{y}
will be recoded into a binary variable, dichtomized at the highest
category and all remaining categories.}
\item{groups}{grouping variable, which will define the y-axis}
\item{geom.colors}{user defined color for geoms. See 'Details' in \code{\link{sjp.grpfrq}}.}
\item{geom.size}{size resp. width of the geoms (bar width, line thickness or point size,
depending on plot type and function). Note that bar and bin widths mostly
need smaller values than dot sizes.}
\item{shape.fill.color}{optional color vector, fill-color for non-filled shapes}
\item{shapes}{numeric vector with shape styles, used to map the different
categories of \code{x}.}
\item{title}{character vector, used as plot title. Depending on plot type and function,
will be set automatically. If \code{title = ""}, no title is printed.}
\item{axis.labels}{character vector with labels used as axis labels. Optional
argument, since in most cases, axis labels are set automatically.}
\item{axis.titles}{character vector of length one or two, defining the title(s)
for the x-axis and y-axis.}
\item{legend.title}{character vector, used as title for the plot legend.}
\item{legend.labels}{character vector with labels for the guide/legend.}
\item{wrap.title}{numeric, determines how many chars of the plot title are displayed in
one line and when a line break is inserted.}
\item{wrap.labels}{numeric, determines how many chars of the value, variable or axis
labels are displayed in one line and when a line break is inserted.}
\item{wrap.legend.title}{numeric, determines how many chars of the legend's title
are displayed in one line and when a line break is inserted.}
\item{wrap.legend.labels}{numeric, determines how many chars of the legend labels are
displayed in one line and when a line break is inserted.}
\item{axis.lim}{numeric vector of length 2, defining the range of the plot axis.
Depending on plot type, may effect either x- or y-axis, or both.
For multiple plot outputs (e.g., from \code{type = "eff"} or
\code{type = "slope"} in \code{\link{sjp.glm}}), \code{axis.lim} may
also be a list of vectors of length 2, defining axis limits for each
plot (only if non-faceted).}
\item{grid.breaks}{numeric; sets the distance between breaks for the axis,
i.e. at every \code{grid.breaks}'th position a major grid is being printed.}
\item{show.total}{logical, if \code{TRUE}, a total summary line for all aggregated
\code{groups} is added.}
\item{annotate.total}{logical, if \code{TRUE} and \code{show.total = TRUE},
the total-row in the figure will be highlighted with a slightly
shaded background.}
\item{show.p}{logical, adds significance levels to values, or value and
variable labels.}
\item{show.n}{logical, if \code{TRUE}, adds total number of cases for each
group or category to the labels.}
\item{prnt.plot}{logical, if \code{TRUE} (default), plots the results as graph. Use \code{FALSE} if you don't
want to plot any graphs. In either case, the ggplot-object will be returned as value.}
}
\value{
(Insisibily) returns the ggplot-object with the complete plot
(\code{plot}) as well as the data frame that
was used for setting up the ggplot-object (\code{df}).
}
\description{
Plot grouped proportional crosstables, where the proportion of
each level of \code{x} for the highest category in \code{y}
is plotted, for each subgroup of \code{groups}.
}
\details{
The p-values are based on \code{\link[stats]{chisq.test}} of \code{x}
and \code{y} for each \code{groups}.
}
\examples{
library(sjmisc)
data(efc)
# the proportion of dependency levels in female
# elderly, for each family carer's relationship
# to elderly
sjp.gpt(efc$e42dep, efc$e16sex, efc$e15relat)
# proportion of educational levels in highest
# dependency category of elderly, for different
# care levels
sjp.gpt(efc$c172code, efc$e42dep, efc$n4pstu)
}
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/DendSer.R
\name{DendSer.dendrogram}
\alias{DendSer.dendrogram}
\title{Tries to run DendSer on a dendrogram}
\usage{
DendSer.dendrogram(dend, ser_weight, ...)
}
\arguments{
\item{dend}{An object of class dendrogram}
\item{ser_weight}{Used by cost function to evaluate
ordering. For cost=costLS, this is a vector of
object weights. Otherwise is a dist or symmetric matrix.
passed to DendSer.
If it is missing, the cophenetic distance is used instead.}
\item{...}{parameters passed to \link[DendSer]{DendSer}}
}
\value{
Numeric vector giving an optimal dendrogram order
}
\description{
Implements dendrogram seriation.
The function tries to turn the dend into hclust, on
which it runs \link[DendSer]{DendSer}.
Also, if a distance matrix is missing, it will try
to use the \link{cophenetic} distance.
}
\examples{
\dontrun{
library(DendSer) # already used from within the function
hc <- hclust(dist(USArrests[1:4,]), "ave")
dend <- as.dendrogram(hc)
DendSer.dendrogram(dend)
}
}
\seealso{
\code{\link[DendSer]{DendSer}}, \link{DendSer.dendrogram} ,
\link{untangle_DendSer}, \link{rotate_DendSer}
}
| /man/DendSer.dendrogram.Rd | no_license | timelyportfolio/dendextend | R | false | false | 1,187 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/DendSer.R
\name{DendSer.dendrogram}
\alias{DendSer.dendrogram}
\title{Tries to run DendSer on a dendrogram}
\usage{
DendSer.dendrogram(dend, ser_weight, ...)
}
\arguments{
\item{dend}{An object of class dendrogram}
\item{ser_weight}{Used by cost function to evaluate
ordering. For cost=costLS, this is a vector of
object weights. Otherwise is a dist or symmetric matrix.
passed to DendSer.
If it is missing, the cophenetic distance is used instead.}
\item{...}{parameters passed to \link[DendSer]{DendSer}}
}
\value{
Numeric vector giving an optimal dendrogram order
}
\description{
Implements dendrogram seriation.
The function tries to turn the dend into hclust, on
which it runs \link[DendSer]{DendSer}.
Also, if a distance matrix is missing, it will try
to use the \link{cophenetic} distance.
}
\examples{
\dontrun{
library(DendSer) # already used from within the function
hc <- hclust(dist(USArrests[1:4,]), "ave")
dend <- as.dendrogram(hc)
DendSer.dendrogram(dend)
}
}
\seealso{
\code{\link[DendSer]{DendSer}}, \link{DendSer.dendrogram} ,
\link{untangle_DendSer}, \link{rotate_DendSer}
}
|
##########################################
# Coliphage analysis - 6 beaches
# v1 by Jade 11/3/15
# Illness figures for main text
##########################################
rm(list=ls())
# --------------------------------------
# load the and pre-preprocess the
# analysis dataset
# (refer to the base functions script
# for details on the pre-processing)
# --------------------------------------
beaches13=read.csv("~/Documents/CRG/coliphage/13beaches-data/final/13beaches-analysis.csv")
# load base functions
source("~/Documents/CRG/coliphage/13beaches-coliphage/src/Analysis/0-base-functions.R")
data=preprocess.6beaches(beaches13)
# restrict to 6 beaches with coliphage data
beach.list=c("Avalon","Doheny","Malibu","Mission Bay",
"Fairhope","Goddard")
all=data[data$beach %in% beach.list,]
# drop individuals with no water quality information
all=subset(all,nowq==0)
# --------------------------------------
# % swimmers in waters with coliphage
# --------------------------------------
sum(table(all$fmc.pres[all$bodycontact=="Yes"]))
sum(table(all$fpc.pres[all$bodycontact=="Yes"]))
prop.table(table(all$fmc.pres[all$bodycontact=="Yes"]))
prop.table(table(all$fpc.pres[all$bodycontact=="Yes"]))
# --------------------------------------
# illness rates
# --------------------------------------
# illness rates among all enrollees
table(all$gici10)
prop.table(table(all$gici10))
prop.table(table(all$gici10,all$beach),2)
# illness rates among swimmers
table(all$gici10[all$bodycontact=="Yes"])
prop.table(table(all$gici10[all$bodycontact=="Yes"]))
prop.table(table(all$gici10[all$bodycontact=="Yes"],all$beach[all$bodycontact=="Yes"]),2)
# illness rates among non-swimmers
table(all$gici10[all$anycontact=="No"])
prop.table(table(all$gici10[all$anycontact=="No"]))
prop.table(table(all$gici10[all$anycontact=="No"],all$beach[all$anycontact=="No"]),2)
# --------------------------------------
# regression for swimming only
# --------------------------------------
swim.reg=glm(gici10~bodycontact+agecat+female+racewhite+gichron+anim_any+gicontactbase+
rawfood+beach,family=poisson(link="log"),data=all)
# CIR for swimming
exp(swim.reg$coef[["bodycontactYes"]])
exp(swim.reg$coef[["bodycontactYes"]]-
qnorm(.975)*summary(swim.reg)$coefficients[2,2])
exp(swim.reg$coef[["bodycontactYes"]]+
qnorm(.975)*summary(swim.reg)$coefficients[2,2])
swimrisk.reg=glm(gici10~bodycontact*risk+agecat+female+racewhite+gichron+anim_any+gicontactbase+
rawfood+beach,family=poisson(link="log"),data=all)
# CIR for swimming under high risk
exp(swimrisk.reg$coef[["bodycontactYes"]]+
swimrisk.reg$coef[["bodycontactYes:riskHigh"]])
# CIR for swimming under low risk
exp(swimrisk.reg$coef[["bodycontactYes"]])
lrtest(swim.reg,swimrisk.reg)
save(swim.reg,file="~/Documents/CRG/coliphage/results/rawoutput/regress-10day-swim.Rdata")
| /src/Analysis/7-illness-analysis.R | no_license | jadebc/13beaches-coliphage | R | false | false | 2,864 | r | ##########################################
# Coliphage analysis - 6 beaches
# v1 by Jade 11/3/15
# Illness figures for main text
##########################################
rm(list=ls())
# --------------------------------------
# load the and pre-preprocess the
# analysis dataset
# (refer to the base functions script
# for details on the pre-processing)
# --------------------------------------
beaches13=read.csv("~/Documents/CRG/coliphage/13beaches-data/final/13beaches-analysis.csv")
# load base functions
source("~/Documents/CRG/coliphage/13beaches-coliphage/src/Analysis/0-base-functions.R")
data=preprocess.6beaches(beaches13)
# restrict to 6 beaches with coliphage data
beach.list=c("Avalon","Doheny","Malibu","Mission Bay",
"Fairhope","Goddard")
all=data[data$beach %in% beach.list,]
# drop individuals with no water quality information
all=subset(all,nowq==0)
# --------------------------------------
# % swimmers in waters with coliphage
# --------------------------------------
sum(table(all$fmc.pres[all$bodycontact=="Yes"]))
sum(table(all$fpc.pres[all$bodycontact=="Yes"]))
prop.table(table(all$fmc.pres[all$bodycontact=="Yes"]))
prop.table(table(all$fpc.pres[all$bodycontact=="Yes"]))
# --------------------------------------
# illness rates
# --------------------------------------
# illness rates among all enrollees
table(all$gici10)
prop.table(table(all$gici10))
prop.table(table(all$gici10,all$beach),2)
# illness rates among swimmers
table(all$gici10[all$bodycontact=="Yes"])
prop.table(table(all$gici10[all$bodycontact=="Yes"]))
prop.table(table(all$gici10[all$bodycontact=="Yes"],all$beach[all$bodycontact=="Yes"]),2)
# illness rates among non-swimmers
table(all$gici10[all$anycontact=="No"])
prop.table(table(all$gici10[all$anycontact=="No"]))
prop.table(table(all$gici10[all$anycontact=="No"],all$beach[all$anycontact=="No"]),2)
# --------------------------------------
# regression for swimming only
# --------------------------------------
swim.reg=glm(gici10~bodycontact+agecat+female+racewhite+gichron+anim_any+gicontactbase+
rawfood+beach,family=poisson(link="log"),data=all)
# CIR for swimming
exp(swim.reg$coef[["bodycontactYes"]])
exp(swim.reg$coef[["bodycontactYes"]]-
qnorm(.975)*summary(swim.reg)$coefficients[2,2])
exp(swim.reg$coef[["bodycontactYes"]]+
qnorm(.975)*summary(swim.reg)$coefficients[2,2])
swimrisk.reg=glm(gici10~bodycontact*risk+agecat+female+racewhite+gichron+anim_any+gicontactbase+
rawfood+beach,family=poisson(link="log"),data=all)
# CIR for swimming under high risk
exp(swimrisk.reg$coef[["bodycontactYes"]]+
swimrisk.reg$coef[["bodycontactYes:riskHigh"]])
# CIR for swimming under low risk
exp(swimrisk.reg$coef[["bodycontactYes"]])
lrtest(swim.reg,swimrisk.reg)
save(swim.reg,file="~/Documents/CRG/coliphage/results/rawoutput/regress-10day-swim.Rdata")
|
IterEigv <- function(CovM, StartV, m){
v <- matrix(0, nrow = nrow(CovM), ncol = m)
v[,1] <- StartV
w <- 0
for(i in 2:m){
w <- CovM%*%v[,i-1]
v[,i] <- w/(sqrt(sum(w^2)))
}
return(v)
} | /Blatt2/IterEigv.R | no_license | alexanderlange53/DataMining_in_Bioinformatics | R | false | false | 201 | r | IterEigv <- function(CovM, StartV, m){
v <- matrix(0, nrow = nrow(CovM), ncol = m)
v[,1] <- StartV
w <- 0
for(i in 2:m){
w <- CovM%*%v[,i-1]
v[,i] <- w/(sqrt(sum(w^2)))
}
return(v)
} |
depvar.icc <- function(depvar, include.r3=TRUE, stratify="branch.office", treatments=c("credit", "cash", "info")) {
rhs <- treatments
reg.variables <- c(depvar, "village", "incentive", stratify, rhs)
round2.data[, reg.variables] %>%
mutate(round=2) %>% {
if (include.r3) {
bind_rows(., round3.data[, reg.variables] %>%
mutate(round=3))
} else {
return(.)
}
} %>%
filter(incentive %in% treatments) %>%
mutate_each(funs(factor), round) %>%
icc(depvar, cluster="village", rhs=rhs, stratify=stratify)
}
foreach(depvar=c(depvars_T1_08), .combine=bind_rows) %do% {
depvar.icc(depvar) %>% t %>% as.data.frame %>% mutate(depvar=depvar)
} %>%
bind_rows(depvar.icc("migrant", include.r3=FALSE) %>% t %>% as.data.frame %>% mutate(depvar="migrant")) %>%
bind_rows(depvar.icc("ngo.help.migrate", stratify=c("branch.office"), include.r3=FALSE) %>% t %>% as.data.frame %>% mutate(depvar="ngo.help.migrate"))
| /calc_icc_0809.R | no_license | evidenceaction/seasonal-migration | R | false | false | 995 | r |
depvar.icc <- function(depvar, include.r3=TRUE, stratify="branch.office", treatments=c("credit", "cash", "info")) {
rhs <- treatments
reg.variables <- c(depvar, "village", "incentive", stratify, rhs)
round2.data[, reg.variables] %>%
mutate(round=2) %>% {
if (include.r3) {
bind_rows(., round3.data[, reg.variables] %>%
mutate(round=3))
} else {
return(.)
}
} %>%
filter(incentive %in% treatments) %>%
mutate_each(funs(factor), round) %>%
icc(depvar, cluster="village", rhs=rhs, stratify=stratify)
}
foreach(depvar=c(depvars_T1_08), .combine=bind_rows) %do% {
depvar.icc(depvar) %>% t %>% as.data.frame %>% mutate(depvar=depvar)
} %>%
bind_rows(depvar.icc("migrant", include.r3=FALSE) %>% t %>% as.data.frame %>% mutate(depvar="migrant")) %>%
bind_rows(depvar.icc("ngo.help.migrate", stratify=c("branch.office"), include.r3=FALSE) %>% t %>% as.data.frame %>% mutate(depvar="ngo.help.migrate"))
|
suppressPackageStartupMessages(c(
library(shinythemes),
library(shiny),
library(tm),
library(stringr),
library(markdown),
library(stylo),library(stringi),
library(ggplot2),
library(magrittr),
library(markdown),
library(RWeka),
library(openNLP),
library(wordcloud),
library(tm),
library(NLP),
library(qdap),
library(RColorBrewer),
library(dplyr)))
y <- readRDS(file="./fourgramTable.RData")
z <- readRDS(file="./threegramTable.RData")
k <- readRDS(file="./twogramTable.RData")
prediction_model <- function(x,y,z,k){
t<- tolower(x)
t <- removePunctuation(t)
t <- removeNumbers(t)
t <- str_replace_all(t, "[^[:alnum:]]", " ")
m<- paste(tail(unlist(strsplit(t,' ')),3), collapse=" ")
u<- paste(tail(unlist(strsplit(t,' ')),2), collapse=" ")
v<- paste(tail(unlist(strsplit(t,' ')),1), collapse=" ")
if (stri_count_words(x)>2){
if (m %in% y$nminusgram){
i <- y %>% filter(nminusgram==m) %>% .$lastword
print(i[1])
} else if (u %in% z$nminusgram){
i <- z %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else {print('the')}
} else if(stri_count_words(x)==2){
if (u %in% z$nminusgram){
i <- z %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else {print('the')}
} else if(stri_count_words(x)==1){
if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
}else {print('the')}
} else {print('wrong input')}
}
| /PredictionFunction.R | no_license | abharga6/word-prediction-capstone | R | false | false | 1,727 | r | suppressPackageStartupMessages(c(
library(shinythemes),
library(shiny),
library(tm),
library(stringr),
library(markdown),
library(stylo),library(stringi),
library(ggplot2),
library(magrittr),
library(markdown),
library(RWeka),
library(openNLP),
library(wordcloud),
library(tm),
library(NLP),
library(qdap),
library(RColorBrewer),
library(dplyr)))
y <- readRDS(file="./fourgramTable.RData")
z <- readRDS(file="./threegramTable.RData")
k <- readRDS(file="./twogramTable.RData")
prediction_model <- function(x,y,z,k){
t<- tolower(x)
t <- removePunctuation(t)
t <- removeNumbers(t)
t <- str_replace_all(t, "[^[:alnum:]]", " ")
m<- paste(tail(unlist(strsplit(t,' ')),3), collapse=" ")
u<- paste(tail(unlist(strsplit(t,' ')),2), collapse=" ")
v<- paste(tail(unlist(strsplit(t,' ')),1), collapse=" ")
if (stri_count_words(x)>2){
if (m %in% y$nminusgram){
i <- y %>% filter(nminusgram==m) %>% .$lastword
print(i[1])
} else if (u %in% z$nminusgram){
i <- z %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else {print('the')}
} else if(stri_count_words(x)==2){
if (u %in% z$nminusgram){
i <- z %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
} else {print('the')}
} else if(stri_count_words(x)==1){
if (v %in% k$nminusgram){
i <- k %>% filter(nminusgram==u) %>% .$lastword
print(i[1])
}else {print('the')}
} else {print('wrong input')}
}
|
################################################################################
################################################################################
# Objective: Geometric approach.
# Author: Tiago Tambonis and Marcelo Boareto.
# Additional informations:
# Date: 08/2017.
################################################################################
################################################################################
GA<- function(counts.dat, group){
colnames(counts.dat) <- factor(group)
condA <- counts.dat[,colnames(counts.dat)==1]
condB <- counts.dat[,colnames(counts.dat)==2]
f_diff <- data.frame()
f_diff_temp = 0
n_diff = 0
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1, dim(condA)[2])){
for (j in seq(1, dim(condB)[2])){
f_diff_temp = f_diff_temp + abs(condA[k,i] - condB[k,j])
n_diff = n_diff + 1
}
}
f_diff = rbind(f_diff, f_diff_temp)
f_diff_temp = 0
}
n_same = 0
f_same_A = data.frame()
f_same_temp = 0
combinations <- combn(1:(dim(condA)[2]), 2)
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1:dim(combinations)[2])){
f_same_temp = f_same_temp + abs(condA[k,combinations[1,i]] - condA[k,combinations[2,i]])
n_same = n_same + 1
}
f_same_A = rbind(f_same_A, f_same_temp)
f_same_temp = 0
}
f_same_B = data.frame()
f_same_temp = 0
combinations <- combn(1:(dim(condB)[2]), 2)
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1:dim(combinations)[2])){
f_same_temp = f_same_temp + abs(condB[k,combinations[1,i]] - condB[k,combinations[2,i]])
n_same = n_same + 1
}
f_same_B = rbind(f_same_B, f_same_temp)
f_same_temp = 0
}
f_same = f_same_A + f_same_B
w = (f_diff/n_diff)**2 - (f_same/n_same)**2
w[w<0] = 0
w = w/sqrt(sum((w**2)))
rownames(w) <- rownames(counts.dat)
colnames(w) <- "Results"
return(w)
}
| /Codes/Fold-change/Geometric_Approach.R | no_license | tambonis/GA_RNA_Seq | R | false | false | 1,957 | r | ################################################################################
################################################################################
# Objective: Geometric approach.
# Author: Tiago Tambonis and Marcelo Boareto.
# Additional informations:
# Date: 08/2017.
################################################################################
################################################################################
GA<- function(counts.dat, group){
colnames(counts.dat) <- factor(group)
condA <- counts.dat[,colnames(counts.dat)==1]
condB <- counts.dat[,colnames(counts.dat)==2]
f_diff <- data.frame()
f_diff_temp = 0
n_diff = 0
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1, dim(condA)[2])){
for (j in seq(1, dim(condB)[2])){
f_diff_temp = f_diff_temp + abs(condA[k,i] - condB[k,j])
n_diff = n_diff + 1
}
}
f_diff = rbind(f_diff, f_diff_temp)
f_diff_temp = 0
}
n_same = 0
f_same_A = data.frame()
f_same_temp = 0
combinations <- combn(1:(dim(condA)[2]), 2)
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1:dim(combinations)[2])){
f_same_temp = f_same_temp + abs(condA[k,combinations[1,i]] - condA[k,combinations[2,i]])
n_same = n_same + 1
}
f_same_A = rbind(f_same_A, f_same_temp)
f_same_temp = 0
}
f_same_B = data.frame()
f_same_temp = 0
combinations <- combn(1:(dim(condB)[2]), 2)
for (k in seq(1, dim(counts.dat)[1])){
for (i in seq(1:dim(combinations)[2])){
f_same_temp = f_same_temp + abs(condB[k,combinations[1,i]] - condB[k,combinations[2,i]])
n_same = n_same + 1
}
f_same_B = rbind(f_same_B, f_same_temp)
f_same_temp = 0
}
f_same = f_same_A + f_same_B
w = (f_diff/n_diff)**2 - (f_same/n_same)**2
w[w<0] = 0
w = w/sqrt(sum((w**2)))
rownames(w) <- rownames(counts.dat)
colnames(w) <- "Results"
return(w)
}
|
# Distance to points - parallelized
# H. Achicanoy
# Jul - 2013
source(paste(src.dir,"/000.zipWrite.R",sep=""))
populationDistance <- function(bdir,spID) {
idir <- paste(bdir, "/maxent_modeling", sep="")
odir <- paste(bdir, "/samples_calculations", sep="")
spOutFolder <- paste(odir,"/",spID, sep="")
cat("Loading occurrences \n")
occ <- read.csv(paste(bdir, "/occurrence_files/",spID, ".csv", sep=""))
xy <- occ[,2:3]
cat("Loading mask \n")
msk <- raster(paste(bdir, "/masks/mask.asc", sep=""))
cat("Distance from points \n")
dgrid <- distanceFromPoints(msk, xy)
dgrid[which(is.na(msk[]))] <- NA
cat("Writing output \n")
dumm <- zipWrite(dgrid, spOutFolder, "pop-dist.asc.gz")
return(dgrid)
}
summarizeDistances <- function(bdir) {
spList <- list.files(paste(bdir, "/occurrence_files", sep=""))
sppC <- 1
for (spp in spList) {
spp <- unlist(strsplit(spp, ".", fixed=T))[1]
pdir <- paste(bdir,"/samples_calculations/",spp,sep="")
#pop=sum(pdir=="pop-dist.asc.gz")
if(file.exists(paste(pdir,"/pop-dist.asc.gz",sep=""))){
cat("The file already exists",spp,"\n")
}else{
cat("Processing taxon", spp, "\n")
if(!file.exists(pdir)){dir.create(pdir)}
dg <- populationDistance(bdir, spp)}
}
return(spList)
}
ParProcess <- function(bdir, ncpu) {
spList <- list.files(paste(bdir, "/occurrence_files", sep=""),pattern=".csv")
pDist_wrapper <- function(i) {
library(raster)
library(rgdal)
sp <- spList[i]
sp <- unlist(strsplit(sp, ".", fixed=T))[1]
cat("\n")
cat("...Species", sp, "\n")
out <- summarizeDistances(bdir)
}
library(snowfall)
sfInit(parallel=T,cpus=ncpu)
sfExport("populationDistance")
sfExport("summarizeDistances")
sfExport("zipWrite")
sfExport("bdir")
sfExport("src.dir")
sfExport("spList")
#run the control function
system.time(sfSapply(as.vector(1:length(spList)), pDist_wrapper))
#stop the cluster
sfStop()
return("Done!")
}
| /011.distanceToPopulations2.R | no_license | vbern/gap-analysis-maxent | R | false | false | 2,008 | r | # Distance to points - parallelized
# H. Achicanoy
# Jul - 2013
source(paste(src.dir,"/000.zipWrite.R",sep=""))
populationDistance <- function(bdir,spID) {
idir <- paste(bdir, "/maxent_modeling", sep="")
odir <- paste(bdir, "/samples_calculations", sep="")
spOutFolder <- paste(odir,"/",spID, sep="")
cat("Loading occurrences \n")
occ <- read.csv(paste(bdir, "/occurrence_files/",spID, ".csv", sep=""))
xy <- occ[,2:3]
cat("Loading mask \n")
msk <- raster(paste(bdir, "/masks/mask.asc", sep=""))
cat("Distance from points \n")
dgrid <- distanceFromPoints(msk, xy)
dgrid[which(is.na(msk[]))] <- NA
cat("Writing output \n")
dumm <- zipWrite(dgrid, spOutFolder, "pop-dist.asc.gz")
return(dgrid)
}
summarizeDistances <- function(bdir) {
spList <- list.files(paste(bdir, "/occurrence_files", sep=""))
sppC <- 1
for (spp in spList) {
spp <- unlist(strsplit(spp, ".", fixed=T))[1]
pdir <- paste(bdir,"/samples_calculations/",spp,sep="")
#pop=sum(pdir=="pop-dist.asc.gz")
if(file.exists(paste(pdir,"/pop-dist.asc.gz",sep=""))){
cat("The file already exists",spp,"\n")
}else{
cat("Processing taxon", spp, "\n")
if(!file.exists(pdir)){dir.create(pdir)}
dg <- populationDistance(bdir, spp)}
}
return(spList)
}
ParProcess <- function(bdir, ncpu) {
spList <- list.files(paste(bdir, "/occurrence_files", sep=""),pattern=".csv")
pDist_wrapper <- function(i) {
library(raster)
library(rgdal)
sp <- spList[i]
sp <- unlist(strsplit(sp, ".", fixed=T))[1]
cat("\n")
cat("...Species", sp, "\n")
out <- summarizeDistances(bdir)
}
library(snowfall)
sfInit(parallel=T,cpus=ncpu)
sfExport("populationDistance")
sfExport("summarizeDistances")
sfExport("zipWrite")
sfExport("bdir")
sfExport("src.dir")
sfExport("spList")
#run the control function
system.time(sfSapply(as.vector(1:length(spList)), pDist_wrapper))
#stop the cluster
sfStop()
return("Done!")
}
|
setwd("C:/Users/James/Documents/GitHub/ExData_Plotting1")
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip","rawdata.zip")
unzip("rawdata.zip")
rawdb <- read.table("household_power_consumption.txt",
header = T,sep = ";",na.strings = c("NA","?"),
colClasses = c("character","character","numeric",
"numeric","numeric","numeric",
"numeric","numeric","numeric"))
rawdb <- rawdb[rawdb$Date %in% c("1/2/2007","2/2/2007"),]
rawdb$DateTime <- paste(rawdb$Date,rawdb$Time)
rawdb$DateTime <- strptime(rawdb$DateTime,"%d/%m/%Y %H:%M:%S")
db <- rawdb
png("plot3.png",
width = 480, height = 480)
plot(db$DateTime,db$Sub_metering_1,
type = "l",
xlab = "",
ylab = "Energy sub metering")
lines(db$DateTime,db$Sub_metering_2,col = "red")
lines(db$DateTime,db$Sub_metering_3,col = "blue")
legend("topright", lty = c(1,1,1), col = c("black","red","blue"), legend = c("Sub_metering_1", "Sub_metering_2","Sub_metering_3"))
dev.off() | /plot3.R | no_license | jferre/ExData_Plotting1 | R | false | false | 1,099 | r | setwd("C:/Users/James/Documents/GitHub/ExData_Plotting1")
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip","rawdata.zip")
unzip("rawdata.zip")
rawdb <- read.table("household_power_consumption.txt",
header = T,sep = ";",na.strings = c("NA","?"),
colClasses = c("character","character","numeric",
"numeric","numeric","numeric",
"numeric","numeric","numeric"))
rawdb <- rawdb[rawdb$Date %in% c("1/2/2007","2/2/2007"),]
rawdb$DateTime <- paste(rawdb$Date,rawdb$Time)
rawdb$DateTime <- strptime(rawdb$DateTime,"%d/%m/%Y %H:%M:%S")
db <- rawdb
png("plot3.png",
width = 480, height = 480)
plot(db$DateTime,db$Sub_metering_1,
type = "l",
xlab = "",
ylab = "Energy sub metering")
lines(db$DateTime,db$Sub_metering_2,col = "red")
lines(db$DateTime,db$Sub_metering_3,col = "blue")
legend("topright", lty = c(1,1,1), col = c("black","red","blue"), legend = c("Sub_metering_1", "Sub_metering_2","Sub_metering_3"))
dev.off() |
#==================================================================
# Install packages not already installed in a list
#==================================================================
rm(list=ls())
list=c("tidyverse","stringr","forcats","ggmap","rvest","tm","SnowballC","dplyr","calibrate","doParallel",
"stringi","ggplot2","maps","httr","rsdmx","devtools","plyr","dplyr","ggplot2","caret","elasticnet",
"magrittr","broom","glmnet","Hmisc",'knitr',"RSQLite","RANN","lubridate","ggvis","plotly","lars",
"ggcorrplot","GGally","ROCR","lattice","car","corrgram","ggcorrplot","parallel","readxl","ggmosaic",
"vcd","Amelia","d3heatmap","ResourceSelection","ROCR","plotROC","DT","aod","mice","Hmisc","data.table",
"corrplot","gvlma")
list_packages <- list
new.packages <- list_packages[!(list_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
R<-suppressWarnings(suppressMessages(sapply(list, require, character.only = TRUE)))
setwd("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines")
data=data.table::fread("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/Take_home_Stats_test_Data.xlsx")
#install.packages("xlsx")
require(xlsx)
FirstTable <- read.xlsx("Take_home_Stats_test_Data.xlsx", 1 , stringsAsFactors=F)
read.xlsx("Take_home_Stats_test_Data.xlsx", sheetName = "Sheet1")
read.xlsx2("Take_home_Stats_test_Data.xlsx", sheetName = "Sheet1")
#devtools::install_github("hadley/readxl") # development version
library(readxl)
# read_excel reads both xls and xlsx files
uniteddata=read_excel("Take_home_Stats_test_Data.xlsx")
# Specify sheet with a number or name
#read_excel("my-spreadsheet.xls", sheet = "data")
#read_excel("my-spreadsheet.xls", sheet = 2)
# If NAs are represented by something other than blank cells,
# set the na argument
#read_excel("my-spreadsheet.xls", na = "NA")
model1=glm(Y~X1+X2,data=uniteddata)
broom::tidy(model1)
tidyr::gather(uniteddata)
head(broom::augment(model1))
broom::glance(model1)
#install.packages("plsdepot")
library(pls)
model2=pls::plsr(Y~X1+X2,data=uniteddata,ncomp=1)
summary(model2)
biplot(model2)
plot(model2, plottype = "biplot")
library(plsdepot)
model3=plsdepot::plsreg1(uniteddata[,3:4],uniteddata["Y"],comps=1, crosval=TRUE)
model3=mvr(Y~X1+X2,data=uniteddata,comps=1, crosval=TRUE)
model3$cor.xyt
model3$reg.coefs
model3$R2
plot(model3)
selectNcomp(model3)
pls::crossval(model3)
pls::coefplot(model3)
#load semPLS and datasets
#install.packages("BiplotGUI")
library(semPLS)
data(mobi)
data(ECSImobi)
#runs PLS SEM
ecsi <- sempls(Y=X1+X2, data=uniteddata, E="C")
ecsi
#calculate percent variation
(prcomp(scale(mobi))$sdev^2)/24
#load and open BiPlotGUI
library(BiplotGUI)
Biplots(uniteddata, PointLabels=NULL)
#right click on biplot
#select "Predict points closest to cursor positions"
#Select Prediction Tab in top-right frame
pcr_model <- pcr(Y~., data = uniteddata, scale = TRUE, validation = "CV")
summary(pcr_model)
# Plot the root mean squared error
validationplot(pcr_model)
# Plot the cross validation MSE
validationplot(pcr_model, val.type="MSEP")
# Plot the R2
validationplot(pcr_model, val.type = "R2")
predplot(pcr_model)
coefplot(pcr_model)
# Train-test split
# Pricipal Components Analysis
# entering raw data and extracting PCs
# from the correlation matrix
fit <- princomp(uniteddata, cor=TRUE)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
# Maximum Likelihood Factor Analysis
# entering raw data and extracting 3 factors,
# with varimax rotation
fit <- factanal(uniteddata[,-1], 1, rotation="varimax")
print(fit, digits=2, cutoff=.3, sort=TRUE)
# plot factor 1 by factor 2
load <- fit$loadings[,1:2]
plot(load,type="n") # set up plot
text(load,labels=names(uniteddata[,-1]),cex=.7) # add variable names
# Varimax Rotated Principal Components
# retaining 5 components
library(psych)
fit <- principal(uniteddata[,-1], nfactors=1, rotate="varimax")
fit # print results
factanal(uniteddata[,-1],1)
y_test <-uniteddata["Y"]
index <- createDataPartition(uniteddata$Y,p=0.70, list=FALSE)
trainSet <- uniteddata[index,]
testSet <- uniteddata[-index,]
pcr_model <- pcr(Y~., data = trainSet,scale =TRUE, validation = "CV",ncomp=1)
pcr_pred <- predict(pcr_model, testSet, ncomp = 1)
mean((pcr_pred - y_test)^2)
data.table::melt.data.table(as.data.table(uniteddata))
tidyr::gather(uniteddata)
data.table::dcast.data.table()
reshape2::melt(uniteddata)
uniteddata2=data_frame(No.Trial=c(seq(1:13)),No=c(29,16,17,4,3,9,4,5,1,1,1,3,7)
,Yes=c(198,107,55,38,18,22,7,9,5,3,6,6,12))
m1=reshape2::melt(uniteddata2,id.vars ="No.Trial",variable.name ="Driving.school",value.name ="Frequency")
m2= reshape2::dcast(reshape2::melt(uniteddata2,id.vars ="No.Trial"),No.Trial~variable )
hist(m1$Frequency,breaks = "fd")
hist(log(m1$Trials),breaks = "fd")
l=list(xtabs(~Trials+Driving.school+No.Trial,data=m1))
do.call(chisq.test,xtabs(~No.Trial+Driving.school+Trials,data=m1))
tmp <- expand.grid(letters[1:2], 1:3, c("+", "-"))
do.call("paste", c(tmp, sep = ""))
by(m1, m1["No.Trial"], function(x) chisq.test)
fisher.test(xtabs(No.Trial~Driving.school+Trials, data=m1),workspace = 200000000)
chisq.test(xtabs(No.Trial~Driving.school+Frequency, data=m1),simulate.p.value = T)%>%tidy()
ggplot(m1, aes(No.Trial,Frequency, group=Driving.school, linetype=Driving.school, shape=Driving.school)) +
geom_line() +geom_point()
p=ggplot(m1, aes(No.Trial,Frequency, color=Driving.school)) +
geom_line() +
geom_point()+theme_bw()+xlab("Number of Trials")
ggplotly(p)
ggsave("p.pdf")
p1=ggplot(m1, aes( No.Trial,Frequency, color=Driving.school)) + theme_bw()+
geom_smooth(se = FALSE, method = "loess")
ggplotly(p1)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/p1.pdf")
p2=ggplot(m1, aes( No.Trial,Frequency, color=Driving.school)) + theme_bw()+
geom_histogram()
ggplotly(p2)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/p2.pdf")
p2=ggplot(m1, aes(No.Trial,Frequency, color=Driving.school)) +
geom_line() +
theme_bw()
ggplotly(p2)
model4=glm(log(Frequency)~(No.Trial)+Driving.school+No.Trial*Driving.school,data=m1)
summary(model4)
m1$Driving.school=if_else(m1$Driving.school=="Yes",1,0)
model5=glm(Driving.school~(No.Trial)+log(Frequency)+No.Trial*log(Frequency),data=m1,family="binomial")
summary(model5)
model6=glm(Driving.school~(No.Trial)+log(Frequency)+No.Trial*log(Frequency),data=m1,family="binomial")
summary(model6)
str(m1)
pq=qplot(log( m1$Frequency), geom="histogram",binwidth = 0.5)+theme_minimal()+xlab("log(Number of Trials)")
ggplotly(pq)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/plott.pdf")
qplot((m1$Frequency), geom="histogram",binwidth = 2)+theme_minimal()+xlab("Number of Trials")
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/plot3.pdf")
?tapply
| /United.R | no_license | NanaAkwasiAbayieBoateng/United | R | false | false | 7,265 | r |
#==================================================================
# Install packages not already installed in a list
#==================================================================
rm(list=ls())
list=c("tidyverse","stringr","forcats","ggmap","rvest","tm","SnowballC","dplyr","calibrate","doParallel",
"stringi","ggplot2","maps","httr","rsdmx","devtools","plyr","dplyr","ggplot2","caret","elasticnet",
"magrittr","broom","glmnet","Hmisc",'knitr',"RSQLite","RANN","lubridate","ggvis","plotly","lars",
"ggcorrplot","GGally","ROCR","lattice","car","corrgram","ggcorrplot","parallel","readxl","ggmosaic",
"vcd","Amelia","d3heatmap","ResourceSelection","ROCR","plotROC","DT","aod","mice","Hmisc","data.table",
"corrplot","gvlma")
list_packages <- list
new.packages <- list_packages[!(list_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
R<-suppressWarnings(suppressMessages(sapply(list, require, character.only = TRUE)))
setwd("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines")
data=data.table::fread("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/Take_home_Stats_test_Data.xlsx")
#install.packages("xlsx")
require(xlsx)
FirstTable <- read.xlsx("Take_home_Stats_test_Data.xlsx", 1 , stringsAsFactors=F)
read.xlsx("Take_home_Stats_test_Data.xlsx", sheetName = "Sheet1")
read.xlsx2("Take_home_Stats_test_Data.xlsx", sheetName = "Sheet1")
#devtools::install_github("hadley/readxl") # development version
library(readxl)
# read_excel reads both xls and xlsx files
uniteddata=read_excel("Take_home_Stats_test_Data.xlsx")
# Specify sheet with a number or name
#read_excel("my-spreadsheet.xls", sheet = "data")
#read_excel("my-spreadsheet.xls", sheet = 2)
# If NAs are represented by something other than blank cells,
# set the na argument
#read_excel("my-spreadsheet.xls", na = "NA")
model1=glm(Y~X1+X2,data=uniteddata)
broom::tidy(model1)
tidyr::gather(uniteddata)
head(broom::augment(model1))
broom::glance(model1)
#install.packages("plsdepot")
library(pls)
model2=pls::plsr(Y~X1+X2,data=uniteddata,ncomp=1)
summary(model2)
biplot(model2)
plot(model2, plottype = "biplot")
library(plsdepot)
model3=plsdepot::plsreg1(uniteddata[,3:4],uniteddata["Y"],comps=1, crosval=TRUE)
model3=mvr(Y~X1+X2,data=uniteddata,comps=1, crosval=TRUE)
model3$cor.xyt
model3$reg.coefs
model3$R2
plot(model3)
selectNcomp(model3)
pls::crossval(model3)
pls::coefplot(model3)
#load semPLS and datasets
#install.packages("BiplotGUI")
library(semPLS)
data(mobi)
data(ECSImobi)
#runs PLS SEM
ecsi <- sempls(Y=X1+X2, data=uniteddata, E="C")
ecsi
#calculate percent variation
(prcomp(scale(mobi))$sdev^2)/24
#load and open BiPlotGUI
library(BiplotGUI)
Biplots(uniteddata, PointLabels=NULL)
#right click on biplot
#select "Predict points closest to cursor positions"
#Select Prediction Tab in top-right frame
pcr_model <- pcr(Y~., data = uniteddata, scale = TRUE, validation = "CV")
summary(pcr_model)
# Plot the root mean squared error
validationplot(pcr_model)
# Plot the cross validation MSE
validationplot(pcr_model, val.type="MSEP")
# Plot the R2
validationplot(pcr_model, val.type = "R2")
predplot(pcr_model)
coefplot(pcr_model)
# Train-test split
# Pricipal Components Analysis
# entering raw data and extracting PCs
# from the correlation matrix
fit <- princomp(uniteddata, cor=TRUE)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
# Maximum Likelihood Factor Analysis
# entering raw data and extracting 3 factors,
# with varimax rotation
fit <- factanal(uniteddata[,-1], 1, rotation="varimax")
print(fit, digits=2, cutoff=.3, sort=TRUE)
# plot factor 1 by factor 2
load <- fit$loadings[,1:2]
plot(load,type="n") # set up plot
text(load,labels=names(uniteddata[,-1]),cex=.7) # add variable names
# Varimax Rotated Principal Components
# retaining 5 components
library(psych)
fit <- principal(uniteddata[,-1], nfactors=1, rotate="varimax")
fit # print results
factanal(uniteddata[,-1],1)
y_test <-uniteddata["Y"]
index <- createDataPartition(uniteddata$Y,p=0.70, list=FALSE)
trainSet <- uniteddata[index,]
testSet <- uniteddata[-index,]
pcr_model <- pcr(Y~., data = trainSet,scale =TRUE, validation = "CV",ncomp=1)
pcr_pred <- predict(pcr_model, testSet, ncomp = 1)
mean((pcr_pred - y_test)^2)
data.table::melt.data.table(as.data.table(uniteddata))
tidyr::gather(uniteddata)
data.table::dcast.data.table()
reshape2::melt(uniteddata)
uniteddata2=data_frame(No.Trial=c(seq(1:13)),No=c(29,16,17,4,3,9,4,5,1,1,1,3,7)
,Yes=c(198,107,55,38,18,22,7,9,5,3,6,6,12))
m1=reshape2::melt(uniteddata2,id.vars ="No.Trial",variable.name ="Driving.school",value.name ="Frequency")
m2= reshape2::dcast(reshape2::melt(uniteddata2,id.vars ="No.Trial"),No.Trial~variable )
hist(m1$Frequency,breaks = "fd")
hist(log(m1$Trials),breaks = "fd")
l=list(xtabs(~Trials+Driving.school+No.Trial,data=m1))
do.call(chisq.test,xtabs(~No.Trial+Driving.school+Trials,data=m1))
tmp <- expand.grid(letters[1:2], 1:3, c("+", "-"))
do.call("paste", c(tmp, sep = ""))
by(m1, m1["No.Trial"], function(x) chisq.test)
fisher.test(xtabs(No.Trial~Driving.school+Trials, data=m1),workspace = 200000000)
chisq.test(xtabs(No.Trial~Driving.school+Frequency, data=m1),simulate.p.value = T)%>%tidy()
ggplot(m1, aes(No.Trial,Frequency, group=Driving.school, linetype=Driving.school, shape=Driving.school)) +
geom_line() +geom_point()
p=ggplot(m1, aes(No.Trial,Frequency, color=Driving.school)) +
geom_line() +
geom_point()+theme_bw()+xlab("Number of Trials")
ggplotly(p)
ggsave("p.pdf")
p1=ggplot(m1, aes( No.Trial,Frequency, color=Driving.school)) + theme_bw()+
geom_smooth(se = FALSE, method = "loess")
ggplotly(p1)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/p1.pdf")
p2=ggplot(m1, aes( No.Trial,Frequency, color=Driving.school)) + theme_bw()+
geom_histogram()
ggplotly(p2)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/p2.pdf")
p2=ggplot(m1, aes(No.Trial,Frequency, color=Driving.school)) +
geom_line() +
theme_bw()
ggplotly(p2)
model4=glm(log(Frequency)~(No.Trial)+Driving.school+No.Trial*Driving.school,data=m1)
summary(model4)
m1$Driving.school=if_else(m1$Driving.school=="Yes",1,0)
model5=glm(Driving.school~(No.Trial)+log(Frequency)+No.Trial*log(Frequency),data=m1,family="binomial")
summary(model5)
model6=glm(Driving.school~(No.Trial)+log(Frequency)+No.Trial*log(Frequency),data=m1,family="binomial")
summary(model6)
str(m1)
pq=qplot(log( m1$Frequency), geom="histogram",binwidth = 0.5)+theme_minimal()+xlab("log(Number of Trials)")
ggplotly(pq)
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/plott.pdf")
qplot((m1$Frequency), geom="histogram",binwidth = 2)+theme_minimal()+xlab("Number of Trials")
ggsave("/Users/nanaakwasiabayieboateng/Documents/memphisclassesbooks/DataMiningscience/UnitedAirlines/plot3.pdf")
?tapply
|
\name{dccforecast-methods}
\docType{methods}
\alias{dccforecast}
\alias{dccforecast,ANY-method}
\alias{dccforecast,DCCfit-method}
\title{function: DCC-GARCH Forecast}
\description{
Method for creating a DCC-GARCH forecast object.
}
\usage{
dccforecast(fit, n.ahead = 1, n.roll = 0,
external.forecasts = list(mregfor = NULL, vregfor = NULL), cluster = NULL, ...)
}
\arguments{
\item{fit}{ A \code{\linkS4class{DCCfit}} object created by calling
\code{\link{dccfit}}.}
\item{n.ahead}{ The forecast horizon.}
\item{n.roll}{ The no. of rolling forecasts to create beyond the first one
(see details).}
\item{external.forecasts}{ A list with forecasts for the external regressors
in the mean and/or variance equations if specified (see details).}
\item{cluster}{ A cluster object created by calling \code{makeCluster} from
the parallel package. If it is not NULL, then this will be used for parallel
estimation (remember to stop the cluster on completion).}
\item{...}{.}
}
\value{
A \code{\linkS4class{DCCforecast}} object containing details of the DCC-GARCH
forecast.
}
\details{
When using \code{n.roll}, it is assumed that \code{\link{dccfit}} was called
with argument \sQuote{out.sample} being large enough to cover n-rolling
forecasts.\cr
When n.roll = 0, all forecasts are based on an unconditional n-ahead forecast
routine based on the approximation method described in ENGLE and SHEPPARD (2001)
paper (see reference below). If any external regressors are present, then the
user must pass in their unconditional forecasts in the \sQuote{external.forecasts}
list, as matrices with dimensions equal to n.ahead x n.assets. This assumes
that the univariate GARCH specifications share common external regressors
(this may change in the future).\cr
When n.roll>0 and n.ahead = 1, then this is a pure rolling forecast based on the
available out.sample data provided for in the call to the fit routine. It is
also assumed that if any external regressors were passed to the fit routine that
they contained enough values to cover the out.sample period so that they could
be used in this forecast scenario.\cr
The case of n.roll > 0 AND n.ahead > 1 is not implemented.\cr
}
\references{
Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of
dynamic conditional correlation multivariate GARCH, \emph{NBER Working Paper}.\cr
}
\author{Alexios Ghalanos}
\keyword{methods} | /fuzzedpackages/rmgarch/man/dccforecast-methods.Rd | no_license | akhikolla/testpackages | R | false | false | 2,488 | rd | \name{dccforecast-methods}
\docType{methods}
\alias{dccforecast}
\alias{dccforecast,ANY-method}
\alias{dccforecast,DCCfit-method}
\title{function: DCC-GARCH Forecast}
\description{
Method for creating a DCC-GARCH forecast object.
}
\usage{
dccforecast(fit, n.ahead = 1, n.roll = 0,
external.forecasts = list(mregfor = NULL, vregfor = NULL), cluster = NULL, ...)
}
\arguments{
\item{fit}{ A \code{\linkS4class{DCCfit}} object created by calling
\code{\link{dccfit}}.}
\item{n.ahead}{ The forecast horizon.}
\item{n.roll}{ The no. of rolling forecasts to create beyond the first one
(see details).}
\item{external.forecasts}{ A list with forecasts for the external regressors
in the mean and/or variance equations if specified (see details).}
\item{cluster}{ A cluster object created by calling \code{makeCluster} from
the parallel package. If it is not NULL, then this will be used for parallel
estimation (remember to stop the cluster on completion).}
\item{...}{.}
}
\value{
A \code{\linkS4class{DCCforecast}} object containing details of the DCC-GARCH
forecast.
}
\details{
When using \code{n.roll}, it is assumed that \code{\link{dccfit}} was called
with argument \sQuote{out.sample} being large enough to cover n-rolling
forecasts.\cr
When n.roll = 0, all forecasts are based on an unconditional n-ahead forecast
routine based on the approximation method described in ENGLE and SHEPPARD (2001)
paper (see reference below). If any external regressors are present, then the
user must pass in their unconditional forecasts in the \sQuote{external.forecasts}
list, as matrices with dimensions equal to n.ahead x n.assets. This assumes
that the univariate GARCH specifications share common external regressors
(this may change in the future).\cr
When n.roll>0 and n.ahead = 1, then this is a pure rolling forecast based on the
available out.sample data provided for in the call to the fit routine. It is
also assumed that if any external regressors were passed to the fit routine that
they contained enough values to cover the out.sample period so that they could
be used in this forecast scenario.\cr
The case of n.roll > 0 AND n.ahead > 1 is not implemented.\cr
}
\references{
Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of
dynamic conditional correlation multivariate GARCH, \emph{NBER Working Paper}.\cr
}
\author{Alexios Ghalanos}
\keyword{methods} |
## Coursera - Exploratory Data Analysis - Course Project 1 - Plot 4
##
## Create and Combine 4 different plots in one plot and export and save it as PNG Files
## Download the neccessary data file and save it into a file in the local working directory
filename = "exdata_consumption.zip"
if (!file.exists(filename)) {
retval = download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
destfile = filename)
}
## Unzip the file and read the data from the file
Epc = read.csv(unz(filename, "household_power_consumption.txt"), header=T,
sep=";", stringsAsFactors=F, na.strings="?",
colClasses=c("character", "character", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
## Format the date and time and then subset the data to time period 2007-02-01 to 2007-02-02
Epc$timestamp = strptime(paste(Epc$Date, Epc$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC")
FromDate = strptime("01/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
ToDate = strptime("02/02/2007 23:59:59", format="%d/%m/%Y %H:%M:%S", tz="UTC")
Epc = Epc[Epc$timestamp >= FromDate & Epc$timestamp <= ToDate, ]
## Create the plot
png(filename="plot4.png", width=480, height=480)
# Use par() to combine the 4 plots
par(mfcol=c(2,2))
# Create the first plot
plot(Epc$timestamp, Epc$Global_active_power, type="l", xlab="",
ylab="Global Active Power")
# Create the second plot
plot(Epc$timestamp, Epc$Sub_metering_1, type="l", xlab="",
ylab="Energy sub metering")
lines(Epc$timestamp, Epc$Sub_metering_2, col="red")
lines(Epc$timestamp, Epc$Sub_metering_3, col="blue")
legend("topright", legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
col=c("black", "red", "blue"), lwd=par("lwd"), bty="n")
# Create the third Plot
plot(Epc$timestamp, Epc$Voltage, type="l",
xlab="datetime", ylab="Voltage")
# Create the fourth plot
plot(Epc$timestamp, Epc$Global_reactive_power, type="l",
xlab="datetime", ylab="Global_reactive_power")
dev.off()
| /Plot4.R | no_license | Divyaratna/ExData_Plotting1 | R | false | false | 2,128 | r | ## Coursera - Exploratory Data Analysis - Course Project 1 - Plot 4
##
## Create and Combine 4 different plots in one plot and export and save it as PNG Files
## Download the neccessary data file and save it into a file in the local working directory
filename = "exdata_consumption.zip"
if (!file.exists(filename)) {
retval = download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
destfile = filename)
}
## Unzip the file and read the data from the file
Epc = read.csv(unz(filename, "household_power_consumption.txt"), header=T,
sep=";", stringsAsFactors=F, na.strings="?",
colClasses=c("character", "character", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
## Format the date and time and then subset the data to time period 2007-02-01 to 2007-02-02
Epc$timestamp = strptime(paste(Epc$Date, Epc$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC")
FromDate = strptime("01/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
ToDate = strptime("02/02/2007 23:59:59", format="%d/%m/%Y %H:%M:%S", tz="UTC")
Epc = Epc[Epc$timestamp >= FromDate & Epc$timestamp <= ToDate, ]
## Create the plot
png(filename="plot4.png", width=480, height=480)
# Use par() to combine the 4 plots
par(mfcol=c(2,2))
# Create the first plot
plot(Epc$timestamp, Epc$Global_active_power, type="l", xlab="",
ylab="Global Active Power")
# Create the second plot
plot(Epc$timestamp, Epc$Sub_metering_1, type="l", xlab="",
ylab="Energy sub metering")
lines(Epc$timestamp, Epc$Sub_metering_2, col="red")
lines(Epc$timestamp, Epc$Sub_metering_3, col="blue")
legend("topright", legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
col=c("black", "red", "blue"), lwd=par("lwd"), bty="n")
# Create the third Plot
plot(Epc$timestamp, Epc$Voltage, type="l",
xlab="datetime", ylab="Voltage")
# Create the fourth plot
plot(Epc$timestamp, Epc$Global_reactive_power, type="l",
xlab="datetime", ylab="Global_reactive_power")
dev.off()
|
#' @rdname dgmcm.loglik
dgmm.loglik <- function (theta, z, marginal.loglik = FALSE) {
if (!is.theta(theta))
stop("theta is not formatted correctly")
if (!is.matrix(z))
stop("z is not a matrix")
if (ncol(z) != theta$d)
stop("Number of colums of z does not equal theta$d")
dgmm_loglik(mus = theta$mu, sigmas = theta$sigma, pie = theta$pie, z = z,
marginal_loglik = marginal.loglik)
}
# dgmm.loglik2 <- function (theta, z, marginal.loglik = FALSE) {
# TempFuncJoint <- function (k) {
# theta$pie[k]*
# dmvnormal(z, mu = theta$mu[[k]], sigma = theta$sigma[[k]])
# }
# loglik <- log(rowSums(rbind(sapply(1:theta$m, FUN = TempFuncJoint))))
# if (!marginal.loglik)
# loglik <- sum(loglik)
# return(loglik)
# } | /fuzzedpackages/GMCM/R/dgmm.loglik.R | no_license | akhikolla/testpackages | R | false | false | 791 | r | #' @rdname dgmcm.loglik
dgmm.loglik <- function (theta, z, marginal.loglik = FALSE) {
if (!is.theta(theta))
stop("theta is not formatted correctly")
if (!is.matrix(z))
stop("z is not a matrix")
if (ncol(z) != theta$d)
stop("Number of colums of z does not equal theta$d")
dgmm_loglik(mus = theta$mu, sigmas = theta$sigma, pie = theta$pie, z = z,
marginal_loglik = marginal.loglik)
}
# dgmm.loglik2 <- function (theta, z, marginal.loglik = FALSE) {
# TempFuncJoint <- function (k) {
# theta$pie[k]*
# dmvnormal(z, mu = theta$mu[[k]], sigma = theta$sigma[[k]])
# }
# loglik <- log(rowSums(rbind(sapply(1:theta$m, FUN = TempFuncJoint))))
# if (!marginal.loglik)
# loglik <- sum(loglik)
# return(loglik)
# } |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bin_helpers.R
\name{extractLeadAuthor}
\alias{extractLeadAuthor}
\title{Title}
\usage{
extractLeadAuthor(x)
}
\arguments{
\item{x}{}
}
\value{
}
\description{
Title
}
| /man/extractLeadAuthor.Rd | permissive | benjamincrary/CrossRefEDNA | R | false | true | 246 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bin_helpers.R
\name{extractLeadAuthor}
\alias{extractLeadAuthor}
\title{Title}
\usage{
extractLeadAuthor(x)
}
\arguments{
\item{x}{}
}
\value{
}
\description{
Title
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filechoose.R, R/dirchoose.R, R/filesave.R
\name{shinyFiles-buttons}
\alias{shinyFiles-buttons}
\alias{shinyFilesButton}
\alias{shinyFilesLink}
\alias{shinyDirButton}
\alias{shinyDirLink}
\alias{shinySaveButton}
\alias{shinySaveLink}
\title{Create a button to summon a shinyFiles dialog}
\usage{
shinyFilesButton(
id,
label,
title,
multiple,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinyFilesLink(
id,
label,
title,
multiple,
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinyDirButton(
id,
label,
title,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
...
)
shinyDirLink(id, label, title, class = NULL, icon = NULL, style = NULL, ...)
shinySaveButton(
id,
label,
title,
filename = "",
filetype,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinySaveLink(
id,
label,
title,
filename = "",
filetype,
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
}
\arguments{
\item{id}{The id matching the \code{\link[=shinyFileChoose]{shinyFileChoose()}}}
\item{label}{The text that should appear on the button}
\item{title}{The heading of the dialog box that appears when the button is
pressed}
\item{multiple}{A logical indicating whether or not it should be possible to
select multiple files}
\item{buttonType}{The Bootstrap button markup used to colour the button.
Defaults to 'default' for a neutral appearance but can be changed for another
look. The value will be pasted with 'btn-' and added as class.}
\item{class}{Additional classes added to the button}
\item{icon}{An optional \href{http://shiny.rstudio.com/reference/shiny/latest/icon.html}{icon} to appear on the button.}
\item{style}{Additional styling added to the button (e.g., "margin-top: 25px;")}
\item{viewtype}{View type to use in the file browser. One of "detail" (default), "list", or "icon"}
\item{...}{Named attributes to be applied to the button or link (e.g., 'onclick')}
\item{filename}{A predefined filename to be filed in. Can be modified by the
user during saving.}
\item{filetype}{A named list of file extensions. The name of each element
gives the name of the filetype and the content of the element the possible
extensions e.g. \code{list(picture=c('jpg', 'jpeg'))}. The first extension
will be used as default if it is not supplied by the user.}
}
\value{
This function is called for its side effects
}
\description{
This function adds the required html markup for the client to access the file
system. The end result will be the appearance of a button on the webpage that
summons one of the shinyFiles dialog boxes. The last position in the file
system is automatically remembered between instances, but not shared between
several shinyFiles buttons. For a button to have any functionality it must
have a matching observer on the server side. shinyFilesButton() is matched
with shinyFileChoose() and shinyDirButton with shinyDirChoose(). The id
argument of two matching calls must be the same. See
\code{\link[=shinyFiles-observers]{shinyFiles-observers()}} on how to handle client input on the
server side.
}
\details{
\strong{Selecting files}
When a user selects one or several files the corresponding input variable is
set to a list containing a character vector for each file. The character
vectors gives the traversal route from the root to the selected file(s). The
reason it does not give a path as a string is that the client has no
knowledge of the file system on the server and can therefore not ensure
proper formatting. The \code{\link[=parseFilePaths]{parseFilePaths()}} function can be used on
the server to format the input variable into a format similar to that
returned by \code{\link[shiny:fileInput]{shiny::fileInput()}}.
\strong{Selecting folders}
When a folder is selected it will also be available in its respective input
variable as a list giving the traversal route to the selected folder. To
properly format it, feed it into \code{\link[=parseDirPath]{parseDirPath()}} and a string with
the full folder path will be returned.
\strong{Creating files (saving)}
When a new filename is created it will become available in the respective
input variable and can be formatted with \code{\link[=parseSavePath]{parseSavePath()}} into a
data.frame reminiscent that returned by fileInput. There is no size column
and the type is only present if the filetype argument is used in
\code{shinySaveButton}. In that case it will be the name of the chosen type
(not the extension).
\strong{Manual markup}
For users wanting to design their html markup manually it is very easy to add
a shinyFiles button. The only markup required is:
\emph{shinyFilesButton}
\verb{<button id="inputId" type="button" class="shinyFiles btn btn-default" data-title="title" data-selecttype="single"|"multiple">label</button>}
\emph{shinyDirButton}
\verb{<button id="inputId" type="button" class="shinyDirectories btn-default" data-title="title">label</button>}
\emph{shinySaveButton}
\code{<button id="inputId" type="button" class="shinySave btn-default" data-title="title" data-filetype="[{name: 'type1', ext: ['txt']}, {name: 'type2', ext: ['exe', 'bat']}]">label</button>}
where the id tag matches the inputId parameter, the data-title tag matches
the title parameter, the data-selecttype is either "single" or "multiple"
(the non-logical form of the multiple parameter) and the internal textnode
matches the label parameter. The data-filetype tag is a bit more involved as
it is a json formatted array of objects with the properties 'name' and 'ext'.
'name' gives the name of the filetype as a string and 'ext' the allowed
extensions as an array of strings. The non-exported
\code{\link[=formatFiletype]{formatFiletype()}} function can help convert from a named R list
into the string representation. In the example above "btn-default" is used as
button styling, but this can be changed to any other Bootstrap style.
Apart from this the html document should link to a script with the
following path 'sF/shinyFiles.js' and a stylesheet with the following path
'sF/styles.css'.
The markup is bootstrap compliant so if the bootstrap css is used in the page
the look will fit right in. There is nothing that hinders the developer from
ignoring bootstrap altogether and designing the visuals themselves. The only
caveat being that the glyphs used in the menu buttons are bundled with
bootstrap. Use the css ::after pseudoclasses to add alternative content to
these buttons. Additional filetype specific icons can be added with css using
the following style:
\preformatted{
.sF-file .sF-file-icon .yourFileExtension{
content: url(path/to/16x16/pixel/png);
}
.sF-fileList.sF-icons .sF-file .sF-file-icon .yourFileExtension{
content: url(path/to/32x32/pixel/png);
}
}
If no large version is specified the small version gets upscaled.
\strong{Client side events}
If the shiny app uses custom Javascript it is possible to react to selections
directly from the javascript. Once a selection has been made, the button will
fire of the event 'selection' and pass the selection data along with the
event. To listen for this event you simple add:
\preformatted{
$(button).on('selection', function(event, path) {
// Do something with the paths here
})
}
in the same way a 'cancel' event is fired when a user dismisses a selection
box. In that case, no path is passed on.
Outside events the current selection is available as an object bound to the
button and can be accessed at any time:
\preformatted{
// For a shinyFilesButton
$(button).data('files')
// For a shinyDirButton
$(button).data('directory')
// For a shinySaveButton
$(button).data('file')
}
}
\references{
The file icons used in the file system navigator is taken from
FatCows Farm-Fresh Web Icons (\url{http://www.fatcow.com/free-icons})
}
\seealso{
Other shinyFiles:
\code{\link{shinyFiles-observers}},
\code{\link{shinyFiles-parsers}},
\code{\link{shinyFilesExample}()}
}
\concept{shinyFiles}
| /man/shinyFiles-buttons.Rd | no_license | kacoster/shinyfileslite | R | false | true | 8,186 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filechoose.R, R/dirchoose.R, R/filesave.R
\name{shinyFiles-buttons}
\alias{shinyFiles-buttons}
\alias{shinyFilesButton}
\alias{shinyFilesLink}
\alias{shinyDirButton}
\alias{shinyDirLink}
\alias{shinySaveButton}
\alias{shinySaveLink}
\title{Create a button to summon a shinyFiles dialog}
\usage{
shinyFilesButton(
id,
label,
title,
multiple,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinyFilesLink(
id,
label,
title,
multiple,
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinyDirButton(
id,
label,
title,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
...
)
shinyDirLink(id, label, title, class = NULL, icon = NULL, style = NULL, ...)
shinySaveButton(
id,
label,
title,
filename = "",
filetype,
buttonType = "default",
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
shinySaveLink(
id,
label,
title,
filename = "",
filetype,
class = NULL,
icon = NULL,
style = NULL,
viewtype = "detail",
...
)
}
\arguments{
\item{id}{The id matching the \code{\link[=shinyFileChoose]{shinyFileChoose()}}}
\item{label}{The text that should appear on the button}
\item{title}{The heading of the dialog box that appears when the button is
pressed}
\item{multiple}{A logical indicating whether or not it should be possible to
select multiple files}
\item{buttonType}{The Bootstrap button markup used to colour the button.
Defaults to 'default' for a neutral appearance but can be changed for another
look. The value will be pasted with 'btn-' and added as class.}
\item{class}{Additional classes added to the button}
\item{icon}{An optional \href{http://shiny.rstudio.com/reference/shiny/latest/icon.html}{icon} to appear on the button.}
\item{style}{Additional styling added to the button (e.g., "margin-top: 25px;")}
\item{viewtype}{View type to use in the file browser. One of "detail" (default), "list", or "icon"}
\item{...}{Named attributes to be applied to the button or link (e.g., 'onclick')}
\item{filename}{A predefined filename to be filed in. Can be modified by the
user during saving.}
\item{filetype}{A named list of file extensions. The name of each element
gives the name of the filetype and the content of the element the possible
extensions e.g. \code{list(picture=c('jpg', 'jpeg'))}. The first extension
will be used as default if it is not supplied by the user.}
}
\value{
This function is called for its side effects
}
\description{
This function adds the required html markup for the client to access the file
system. The end result will be the appearance of a button on the webpage that
summons one of the shinyFiles dialog boxes. The last position in the file
system is automatically remembered between instances, but not shared between
several shinyFiles buttons. For a button to have any functionality it must
have a matching observer on the server side. shinyFilesButton() is matched
with shinyFileChoose() and shinyDirButton with shinyDirChoose(). The id
argument of two matching calls must be the same. See
\code{\link[=shinyFiles-observers]{shinyFiles-observers()}} on how to handle client input on the
server side.
}
\details{
\strong{Selecting files}
When a user selects one or several files the corresponding input variable is
set to a list containing a character vector for each file. The character
vectors gives the traversal route from the root to the selected file(s). The
reason it does not give a path as a string is that the client has no
knowledge of the file system on the server and can therefore not ensure
proper formatting. The \code{\link[=parseFilePaths]{parseFilePaths()}} function can be used on
the server to format the input variable into a format similar to that
returned by \code{\link[shiny:fileInput]{shiny::fileInput()}}.
\strong{Selecting folders}
When a folder is selected it will also be available in its respective input
variable as a list giving the traversal route to the selected folder. To
properly format it, feed it into \code{\link[=parseDirPath]{parseDirPath()}} and a string with
the full folder path will be returned.
\strong{Creating files (saving)}
When a new filename is created it will become available in the respective
input variable and can be formatted with \code{\link[=parseSavePath]{parseSavePath()}} into a
data.frame reminiscent that returned by fileInput. There is no size column
and the type is only present if the filetype argument is used in
\code{shinySaveButton}. In that case it will be the name of the chosen type
(not the extension).
\strong{Manual markup}
For users wanting to design their html markup manually it is very easy to add
a shinyFiles button. The only markup required is:
\emph{shinyFilesButton}
\verb{<button id="inputId" type="button" class="shinyFiles btn btn-default" data-title="title" data-selecttype="single"|"multiple">label</button>}
\emph{shinyDirButton}
\verb{<button id="inputId" type="button" class="shinyDirectories btn-default" data-title="title">label</button>}
\emph{shinySaveButton}
\code{<button id="inputId" type="button" class="shinySave btn-default" data-title="title" data-filetype="[{name: 'type1', ext: ['txt']}, {name: 'type2', ext: ['exe', 'bat']}]">label</button>}
where the id tag matches the inputId parameter, the data-title tag matches
the title parameter, the data-selecttype is either "single" or "multiple"
(the non-logical form of the multiple parameter) and the internal textnode
matches the label parameter. The data-filetype tag is a bit more involved as
it is a json formatted array of objects with the properties 'name' and 'ext'.
'name' gives the name of the filetype as a string and 'ext' the allowed
extensions as an array of strings. The non-exported
\code{\link[=formatFiletype]{formatFiletype()}} function can help convert from a named R list
into the string representation. In the example above "btn-default" is used as
button styling, but this can be changed to any other Bootstrap style.
Apart from this the html document should link to a script with the
following path 'sF/shinyFiles.js' and a stylesheet with the following path
'sF/styles.css'.
The markup is bootstrap compliant so if the bootstrap css is used in the page
the look will fit right in. There is nothing that hinders the developer from
ignoring bootstrap altogether and designing the visuals themselves. The only
caveat being that the glyphs used in the menu buttons are bundled with
bootstrap. Use the css ::after pseudoclasses to add alternative content to
these buttons. Additional filetype specific icons can be added with css using
the following style:
\preformatted{
.sF-file .sF-file-icon .yourFileExtension{
content: url(path/to/16x16/pixel/png);
}
.sF-fileList.sF-icons .sF-file .sF-file-icon .yourFileExtension{
content: url(path/to/32x32/pixel/png);
}
}
If no large version is specified the small version gets upscaled.
\strong{Client side events}
If the shiny app uses custom Javascript it is possible to react to selections
directly from the javascript. Once a selection has been made, the button will
fire of the event 'selection' and pass the selection data along with the
event. To listen for this event you simple add:
\preformatted{
$(button).on('selection', function(event, path) {
// Do something with the paths here
})
}
in the same way a 'cancel' event is fired when a user dismisses a selection
box. In that case, no path is passed on.
Outside events the current selection is available as an object bound to the
button and can be accessed at any time:
\preformatted{
// For a shinyFilesButton
$(button).data('files')
// For a shinyDirButton
$(button).data('directory')
// For a shinySaveButton
$(button).data('file')
}
}
\references{
The file icons used in the file system navigator is taken from
FatCows Farm-Fresh Web Icons (\url{http://www.fatcow.com/free-icons})
}
\seealso{
Other shinyFiles:
\code{\link{shinyFiles-observers}},
\code{\link{shinyFiles-parsers}},
\code{\link{shinyFilesExample}()}
}
\concept{shinyFiles}
|
#load libraries
library(dplyr)
library(haven)
library(patchwork)
library(caTools)
options(scipen = 999)
"
Data Download Link:
https://hrsdata.isr.umich.edu/data-products/2018-rand-hrs-fat-file
Data Codebook:
https://hrs.isr.umich.edu/sites/default/files/meta/2018/core/codebook/h18_00.html
The raw SAS file was read into R and saved as an RDS file.
Read in the file below
"
dat <- readRDS(file="../h18e1a_SAS/h18e1a.rds")
#Data processing
mydat <- dat %>%
select(QX060_R, QX065_R, QA019, QA099,
QA100, QA101, QA113, QB063,
QB000,QC001,QC005,QC010, QC018, QC257, QC053,
QC271,QC272, QC080,QC117,QC129, QC229,
QC150, QD101,QD114,QD115, QJ005M1, QJ612,
QJK014, QJ067,QJ547, QJ549, QG059,
QH004, QJ179, QJ3568, QJ3570, QJ3478,
QN001,QP097,QT011, QT012,
QJ3578, QQ317, QQ331,QQ345,
QQ357, QQ371,QQ376, QJ2W009_1,
QJ2W009_2, QJ2W009_3, QJ2W009_4,QJ2W009_5,
QJ2W009_6,QJ2W009_7,QJ2W009_8,
QJ2W009_9,QJ2W009_10,
#friends
QG198, QG097,
#housing - mortage
QH020,QH016,QH166, QH032,
#debts
QQ478, QQ519)%>%
filter(QJ3578 %in% c(1,3,5)) %>%
mutate(retirement = ifelse(QJ3578 %in% c(1,3), "Retired", "Not Retired"),
sex = ifelse(QX060_R ==1, "Male","Female"),
partner_status = ifelse(QX065_R ==1, "Married",
ifelse(QX065_R ==3, "Partnered",
ifelse(QX065_R ==6, "Other",NA))),
age = QA019,
resident_children = QA099,
nonresident_children = QA100,
children_nonspouse = QA101,
children = resident_children + nonresident_children + children_nonspouse,
#grandchildren = QA113, #don't think we need this - "COUNT OF CHILD CHILDLAW AND GRANDCHILD"
marital_status =ifelse(QB063 ==1, "Married",
ifelse(QB063 ==3, "Separated",
ifelse(QB063 ==4, "Divorced",
ifelse(QB063 ==5, "Widowed",
ifelse(QB063 ==6, "Never Married",
ifelse(QB063 %in% c(2, 7, 8, 9), "Other",NA)))))),
life_satisfaction = ifelse(QB000 ==1, "Completely Satisfied",
ifelse(QB000 ==2, "Very Satisfied",
ifelse(QB000 ==3, "Somewhat Satisfied",
ifelse(QB000 ==4, "Not Very Satisfied",
ifelse(QB000 ==5, "Not At All Satisfied",
ifelse(QB000 == 8 | QB000 == 9, NA,QB000)))))),
health_rate = ifelse(QC001 ==1, "Excellent",
ifelse(QC001 ==2, "Very Good",
ifelse(QC001 ==3, "Good",
ifelse(QC001 ==4, "Fair",
ifelse(QC001 ==5, "Poor",
ifelse(QC001 == 8 | QC001 == 9, NA,QC001)))))),
high_BP = ifelse(QC005 ==1, "Yes",
ifelse(QC005 == 4, "No",
ifelse(QC005 == 5, "No",
ifelse(QC005 == 6, "No",
ifelse(QC005 == 8 | QC001 == 9, NA,QC005))))),
diabetes = ifelse(QC010 ==1, "Yes",
ifelse(QC010 ==4, "No",
ifelse(QC010 ==5, "No",
ifelse(QC010 ==6, "No",
ifelse(QC010 == 8 | QC010 == 9, NA,QC010))))),
cancer = ifelse(QC018 == 1, "Yes",
ifelse(QC018 ==4, "No",
ifelse(QC018 ==5, "No",
ifelse(QC018 == 8 | QC018 == 9, NA, QC018)))),
heart_attack = ifelse(QC257 == 1, "Yes",
ifelse(QC257 ==4, "No",
ifelse(QC257 ==5, "No",
ifelse(QC257 == 8 | QC257 == 9, NA, QC257)))),
depression = ifelse(QC271 == 1, "Yes",
ifelse(QC271 ==6, "No",
ifelse(QC271 ==4, "No",
ifelse(QC271 ==5, "No",
ifelse(QC271 == 8 | QC271 == 9, NA, QC271))))),
times_fallen = ifelse(QC080 == 99 | QC080 ==98, NA, QC080),
alc_days = ifelse(QC129 == 9 | QC129 ==8, NA, QC129),
days_in_bed = ifelse(QC229 == 98 | QC229 ==99, NA, QC229),
memory = ifelse(QD101 ==1, "Excellent",
ifelse(QD101 ==2, "Very Good",
ifelse(QD101 ==3, "Good",
ifelse(QD101 ==4, "Fair",
ifelse(QD101 ==5, "Poor",
ifelse(QD101 == 8 | QD101 == 9, NA,QD101)))))),
lonely_pw = ifelse(QD114 == 1, "Yes",
ifelse(QD114 == 5, "No",
ifelse(QD114 == 8 | QD114 == 9, NA, QD114))),
enjoy_life_pw = ifelse(QD115 == 1, "Yes",
ifelse(QD115 == 5, "No",
ifelse(QD115 == 8 | QD115 == 9, NA, QD115))),
help_friends_py = ifelse(QG198 == 1, "Yes",
ifelse(QG198 ==5, "No",
ifelse(QG198 == 8 | QG198 == 9, NA, QG198))),
have_friends = ifelse(QG097 == 1, "Yes",
ifelse(QG097 ==5, "No",
ifelse(QG097 == 8 | QG097 == 9, NA, QG097))),
job_status =ifelse(QJ005M1 ==1, "Working Now",
ifelse(QJ005M1 ==2, "Unemployed",
ifelse(QJ005M1 ==3, "Laid Off",
ifelse(QJ005M1 ==4, "Disabled",
ifelse(QJ005M1 ==5, "Retired",
ifelse(QJ005M1 ==6, "Homemaker",
ifelse(QJ005M1 ==7, "Other",
ifelse(QJ005M1 == 8,"On Leave",
ifelse(QJ005M1 == 98 | QJ005M1 == 99, NA, NA))))))))),
weeks_worked_py = ifelse(QJ612 == 98 | QJ612 ==99, NA, QJ612),
amount_earn_when_left = ifelse(QJ067 == 9999998 | QJ067 == 9999999, NA,
QJ067),
difficulty_managing_mny = ifelse(QG059 ==1, "Yes",
ifelse(QG059 == 5, "No",
ifelse(QG059 == 8 |
QG059 == 9 |
QG059 == 6 | QG059 == 7, NA,QG059))),
own_rent_home = ifelse(QH004 ==1, "Own",
ifelse(QH004 ==2, "Rent",
ifelse(QH004 ==3, "Lives Rent Free",
ifelse(QH004 ==7, "Other",
ifelse(QH004 == 8 | QH004 == 9, NA,QH004))))),
age_plan_stop_wrk = ifelse(QJ3568 == 96, NA,
ifelse(QJ3568 == 98 |QJ3568 == 99, NA,QJ3568)),
#age to plan to stop working: if never (95), then we will go to the maximum (95)
social_security = ifelse(QJ3478 == 1, "Yes",
ifelse(QJ3478 == 5, "No",
ifelse(QJ3478 == 8 | QJ3478 == 9, NA, QJ3478))),
medicare = ifelse(QN001 == 1, "Yes",
ifelse(QN001 == 5, "No",
ifelse(QN001 == 8 | QN001 == 9, NA, QN001))),
follow_stockmarket = ifelse(QP097 == 1, "Very Closely",
ifelse(QP097 == 2, "Somewhat Closely",
ifelse(QP097 == 3, "Not At All",
ifelse(QP097 == 8 | QP097 == 9, NA, QP097)))),
life_insurance = ifelse(QT011 == 1, "Yes",
ifelse(QT011 == 5, "No",
ifelse(QT011 == 8 | QT011 == 9, NA, QT011))),
num_lifeinsur_policies = ifelse(QT012 ==1, "1",
ifelse(QT012 ==2, "2",
ifelse(QT012 ==3, "3",
ifelse(QT012 ==4, "4",
ifelse(QT012 ==5, "5 or more",
ifelse(QT012 == 8 | QT012 == 9, NA,QT012)))))),
stocks_mf = ifelse(QQ317 == 9999998 | QQ317 == 9999999, NA, QQ317),
bonds = ifelse(QQ331 == 99999998 | QQ331 == 99999999, NA, QQ331),
savings = ifelse(QQ345 == 99999998 | QQ345 == 99999999, NA, QQ345),
cds = ifelse(QQ357 == 99999998 | QQ357 == 99999999, NA, QQ357),
vehicles = ifelse(QQ371 == 99999998 | QQ371 == 99999999, NA, QQ371),
other_savings = ifelse(QQ376 == 99999998 | QQ376 == 99999999, NA, QQ376),
pension_1= ifelse(QJ2W009_1 == 99999998 | QJ2W009_1 == 99999999, NA, QJ2W009_1),
pension_2= ifelse(QJ2W009_2 == 99999998 | QJ2W009_2 == 99999999, NA, QJ2W009_2),
pension_3= ifelse(QJ2W009_3 == 99999998 | QJ2W009_3 == 99999999, NA, QJ2W009_3),
pension_4= ifelse(QJ2W009_4 == 99999998 | QJ2W009_4 == 99999999, NA, QJ2W009_4),
pension_5= ifelse(QJ2W009_5 == 99999998 | QJ2W009_5 == 99999999, NA, QJ2W009_5),
pension_6= ifelse(QJ2W009_6 == 99999998 | QJ2W009_6 == 99999999, NA, QJ2W009_6),
pension_7= ifelse(QJ2W009_7 == 99999998 | QJ2W009_7 == 99999999, NA, QJ2W009_7),
pension_8= ifelse(QJ2W009_8 == 99999998 | QJ2W009_8 == 99999999, NA, QJ2W009_8),
pension_9= ifelse(QJ2W009_9 == 99999998 | QJ2W009_9 == 99999999, NA, QJ2W009_9),
pension_10= ifelse(QJ2W009_10 == 99999998 | QJ2W009_10 == 99999999, NA, QJ2W009_10),
home_value = ifelse(QH020 == 9999999998 | QH020 == 9999999999, NA, QH020),
mobile_home_value = ifelse(QH016 == 99999998 | QH016 == 99999999, NA, QH016),
second_home_value = ifelse(QH166 == 9999999998 | QH166 == 9999999999, NA, QH166),
mortgage = ifelse(QH032 == 99999998 | QH032 == 99999999, NA, QH032),
debts = ifelse(QQ478 == 9999998 | QQ478 == 9999999, NA, QQ478),
credit_card_debt = ifelse(QQ519 == 9999998 | QQ519 == 9999999, NA, QQ519)) %>%
select(retirement, sex, age, children, marital_status,
life_satisfaction, health_rate, high_BP, diabetes,
cancer, heart_attack, depression, times_fallen, alc_days, days_in_bed,
memory, lonely_pw, enjoy_life_pw, job_status, weeks_worked_py,
help_friends_py,have_friends,
amount_earn_when_left, difficulty_managing_mny,
own_rent_home, age_plan_stop_wrk, social_security, medicare, follow_stockmarket, life_insurance,
num_lifeinsur_policies,
stocks_mf, bonds, savings, cds, vehicles, other_savings, home_value, mobile_home_value,
second_home_value, mortgage, debts, credit_card_debt,
pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10) %>%
rowwise() %>%
mutate(
pension = sum(pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10, na.rm = TRUE),
assets = sum(stocks_mf, bonds, savings, cds, vehicles,
other_savings, pension,
na.rm = TRUE),
housing_assets = sum(home_value, second_home_value,mobile_home_value, -mortgage,
na.rm = TRUE),
net_assets = assets + housing_assets + pension) %>%
ungroup() %>%
#we now remove the values that have lots of missingness and are shown to be seen in other variables
select(-heart_attack, -amount_earn_when_left, -age_plan_stop_wrk,
-times_fallen, -weeks_worked_py, -num_lifeinsur_policies, -alc_days,
-job_status) %>%
#remove variables that are used to compute the outcome variable
select(-c(stocks_mf, bonds, savings, cds, vehicles,
other_savings, pension,home_value, second_home_value,mobile_home_value, mortgage,
assets, housing_assets,debts, credit_card_debt,
pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10)) %>%
data.frame() %>%
mutate(net_assets = ifelse(net_assets <0, 0, net_assets)) %>%
data.frame()
#attribute removal:
mydat[] <- lapply(mydat, function(x) { attributes(x) <- NULL; x })
#turn all character variables into factors:
mydat[sapply(mydat, is.character)] <- lapply(mydat[sapply(mydat, is.character)],
as.factor)
############## Training & Test Split ##############
#Note that the data has 16,309 rows
#Create a test data set with 20% of these rows (3262 rows)
#The test data set must include complete cases.
#The remaining data will be imputed
set.seed(11+05+21)
all_rownum <- nrow(mydat)
test_rownums <- ceiling(all_rownum*0.2)
#data with complete cases:
data_complete <- mydat[complete.cases(mydat), ]
#calculate ratio percentage with new rows
#math to solve percentage
new_p <- 1 - (test_rownums / nrow(data_complete))
new_p <- round(new_p, 1)
#find test set
sample <- sample.split(data_complete$net_assets, SplitRatio = new_p)
#define test data
test <- subset(data_complete, sample == FALSE)
#define training data by
#rbind'ing the rest of the complete cases with "sample == TRUE"
pre_train <- rbind(mydat[!complete.cases(mydat), ],
subset(data_complete, sample == TRUE))
| /scripts/R/processing/proposal_preprocessing.R | no_license | delashu/health_retirement | R | false | false | 14,647 | r | #load libraries
library(dplyr)
library(haven)
library(patchwork)
library(caTools)
options(scipen = 999)
"
Data Download Link:
https://hrsdata.isr.umich.edu/data-products/2018-rand-hrs-fat-file
Data Codebook:
https://hrs.isr.umich.edu/sites/default/files/meta/2018/core/codebook/h18_00.html
The raw SAS file was read into R and saved as an RDS file.
Read in the file below
"
dat <- readRDS(file="../h18e1a_SAS/h18e1a.rds")
#Data processing
mydat <- dat %>%
select(QX060_R, QX065_R, QA019, QA099,
QA100, QA101, QA113, QB063,
QB000,QC001,QC005,QC010, QC018, QC257, QC053,
QC271,QC272, QC080,QC117,QC129, QC229,
QC150, QD101,QD114,QD115, QJ005M1, QJ612,
QJK014, QJ067,QJ547, QJ549, QG059,
QH004, QJ179, QJ3568, QJ3570, QJ3478,
QN001,QP097,QT011, QT012,
QJ3578, QQ317, QQ331,QQ345,
QQ357, QQ371,QQ376, QJ2W009_1,
QJ2W009_2, QJ2W009_3, QJ2W009_4,QJ2W009_5,
QJ2W009_6,QJ2W009_7,QJ2W009_8,
QJ2W009_9,QJ2W009_10,
#friends
QG198, QG097,
#housing - mortage
QH020,QH016,QH166, QH032,
#debts
QQ478, QQ519)%>%
filter(QJ3578 %in% c(1,3,5)) %>%
mutate(retirement = ifelse(QJ3578 %in% c(1,3), "Retired", "Not Retired"),
sex = ifelse(QX060_R ==1, "Male","Female"),
partner_status = ifelse(QX065_R ==1, "Married",
ifelse(QX065_R ==3, "Partnered",
ifelse(QX065_R ==6, "Other",NA))),
age = QA019,
resident_children = QA099,
nonresident_children = QA100,
children_nonspouse = QA101,
children = resident_children + nonresident_children + children_nonspouse,
#grandchildren = QA113, #don't think we need this - "COUNT OF CHILD CHILDLAW AND GRANDCHILD"
marital_status =ifelse(QB063 ==1, "Married",
ifelse(QB063 ==3, "Separated",
ifelse(QB063 ==4, "Divorced",
ifelse(QB063 ==5, "Widowed",
ifelse(QB063 ==6, "Never Married",
ifelse(QB063 %in% c(2, 7, 8, 9), "Other",NA)))))),
life_satisfaction = ifelse(QB000 ==1, "Completely Satisfied",
ifelse(QB000 ==2, "Very Satisfied",
ifelse(QB000 ==3, "Somewhat Satisfied",
ifelse(QB000 ==4, "Not Very Satisfied",
ifelse(QB000 ==5, "Not At All Satisfied",
ifelse(QB000 == 8 | QB000 == 9, NA,QB000)))))),
health_rate = ifelse(QC001 ==1, "Excellent",
ifelse(QC001 ==2, "Very Good",
ifelse(QC001 ==3, "Good",
ifelse(QC001 ==4, "Fair",
ifelse(QC001 ==5, "Poor",
ifelse(QC001 == 8 | QC001 == 9, NA,QC001)))))),
high_BP = ifelse(QC005 ==1, "Yes",
ifelse(QC005 == 4, "No",
ifelse(QC005 == 5, "No",
ifelse(QC005 == 6, "No",
ifelse(QC005 == 8 | QC001 == 9, NA,QC005))))),
diabetes = ifelse(QC010 ==1, "Yes",
ifelse(QC010 ==4, "No",
ifelse(QC010 ==5, "No",
ifelse(QC010 ==6, "No",
ifelse(QC010 == 8 | QC010 == 9, NA,QC010))))),
cancer = ifelse(QC018 == 1, "Yes",
ifelse(QC018 ==4, "No",
ifelse(QC018 ==5, "No",
ifelse(QC018 == 8 | QC018 == 9, NA, QC018)))),
heart_attack = ifelse(QC257 == 1, "Yes",
ifelse(QC257 ==4, "No",
ifelse(QC257 ==5, "No",
ifelse(QC257 == 8 | QC257 == 9, NA, QC257)))),
depression = ifelse(QC271 == 1, "Yes",
ifelse(QC271 ==6, "No",
ifelse(QC271 ==4, "No",
ifelse(QC271 ==5, "No",
ifelse(QC271 == 8 | QC271 == 9, NA, QC271))))),
times_fallen = ifelse(QC080 == 99 | QC080 ==98, NA, QC080),
alc_days = ifelse(QC129 == 9 | QC129 ==8, NA, QC129),
days_in_bed = ifelse(QC229 == 98 | QC229 ==99, NA, QC229),
memory = ifelse(QD101 ==1, "Excellent",
ifelse(QD101 ==2, "Very Good",
ifelse(QD101 ==3, "Good",
ifelse(QD101 ==4, "Fair",
ifelse(QD101 ==5, "Poor",
ifelse(QD101 == 8 | QD101 == 9, NA,QD101)))))),
lonely_pw = ifelse(QD114 == 1, "Yes",
ifelse(QD114 == 5, "No",
ifelse(QD114 == 8 | QD114 == 9, NA, QD114))),
enjoy_life_pw = ifelse(QD115 == 1, "Yes",
ifelse(QD115 == 5, "No",
ifelse(QD115 == 8 | QD115 == 9, NA, QD115))),
help_friends_py = ifelse(QG198 == 1, "Yes",
ifelse(QG198 ==5, "No",
ifelse(QG198 == 8 | QG198 == 9, NA, QG198))),
have_friends = ifelse(QG097 == 1, "Yes",
ifelse(QG097 ==5, "No",
ifelse(QG097 == 8 | QG097 == 9, NA, QG097))),
job_status =ifelse(QJ005M1 ==1, "Working Now",
ifelse(QJ005M1 ==2, "Unemployed",
ifelse(QJ005M1 ==3, "Laid Off",
ifelse(QJ005M1 ==4, "Disabled",
ifelse(QJ005M1 ==5, "Retired",
ifelse(QJ005M1 ==6, "Homemaker",
ifelse(QJ005M1 ==7, "Other",
ifelse(QJ005M1 == 8,"On Leave",
ifelse(QJ005M1 == 98 | QJ005M1 == 99, NA, NA))))))))),
weeks_worked_py = ifelse(QJ612 == 98 | QJ612 ==99, NA, QJ612),
amount_earn_when_left = ifelse(QJ067 == 9999998 | QJ067 == 9999999, NA,
QJ067),
difficulty_managing_mny = ifelse(QG059 ==1, "Yes",
ifelse(QG059 == 5, "No",
ifelse(QG059 == 8 |
QG059 == 9 |
QG059 == 6 | QG059 == 7, NA,QG059))),
own_rent_home = ifelse(QH004 ==1, "Own",
ifelse(QH004 ==2, "Rent",
ifelse(QH004 ==3, "Lives Rent Free",
ifelse(QH004 ==7, "Other",
ifelse(QH004 == 8 | QH004 == 9, NA,QH004))))),
age_plan_stop_wrk = ifelse(QJ3568 == 96, NA,
ifelse(QJ3568 == 98 |QJ3568 == 99, NA,QJ3568)),
#age to plan to stop working: if never (95), then we will go to the maximum (95)
social_security = ifelse(QJ3478 == 1, "Yes",
ifelse(QJ3478 == 5, "No",
ifelse(QJ3478 == 8 | QJ3478 == 9, NA, QJ3478))),
medicare = ifelse(QN001 == 1, "Yes",
ifelse(QN001 == 5, "No",
ifelse(QN001 == 8 | QN001 == 9, NA, QN001))),
follow_stockmarket = ifelse(QP097 == 1, "Very Closely",
ifelse(QP097 == 2, "Somewhat Closely",
ifelse(QP097 == 3, "Not At All",
ifelse(QP097 == 8 | QP097 == 9, NA, QP097)))),
life_insurance = ifelse(QT011 == 1, "Yes",
ifelse(QT011 == 5, "No",
ifelse(QT011 == 8 | QT011 == 9, NA, QT011))),
num_lifeinsur_policies = ifelse(QT012 ==1, "1",
ifelse(QT012 ==2, "2",
ifelse(QT012 ==3, "3",
ifelse(QT012 ==4, "4",
ifelse(QT012 ==5, "5 or more",
ifelse(QT012 == 8 | QT012 == 9, NA,QT012)))))),
stocks_mf = ifelse(QQ317 == 9999998 | QQ317 == 9999999, NA, QQ317),
bonds = ifelse(QQ331 == 99999998 | QQ331 == 99999999, NA, QQ331),
savings = ifelse(QQ345 == 99999998 | QQ345 == 99999999, NA, QQ345),
cds = ifelse(QQ357 == 99999998 | QQ357 == 99999999, NA, QQ357),
vehicles = ifelse(QQ371 == 99999998 | QQ371 == 99999999, NA, QQ371),
other_savings = ifelse(QQ376 == 99999998 | QQ376 == 99999999, NA, QQ376),
pension_1= ifelse(QJ2W009_1 == 99999998 | QJ2W009_1 == 99999999, NA, QJ2W009_1),
pension_2= ifelse(QJ2W009_2 == 99999998 | QJ2W009_2 == 99999999, NA, QJ2W009_2),
pension_3= ifelse(QJ2W009_3 == 99999998 | QJ2W009_3 == 99999999, NA, QJ2W009_3),
pension_4= ifelse(QJ2W009_4 == 99999998 | QJ2W009_4 == 99999999, NA, QJ2W009_4),
pension_5= ifelse(QJ2W009_5 == 99999998 | QJ2W009_5 == 99999999, NA, QJ2W009_5),
pension_6= ifelse(QJ2W009_6 == 99999998 | QJ2W009_6 == 99999999, NA, QJ2W009_6),
pension_7= ifelse(QJ2W009_7 == 99999998 | QJ2W009_7 == 99999999, NA, QJ2W009_7),
pension_8= ifelse(QJ2W009_8 == 99999998 | QJ2W009_8 == 99999999, NA, QJ2W009_8),
pension_9= ifelse(QJ2W009_9 == 99999998 | QJ2W009_9 == 99999999, NA, QJ2W009_9),
pension_10= ifelse(QJ2W009_10 == 99999998 | QJ2W009_10 == 99999999, NA, QJ2W009_10),
home_value = ifelse(QH020 == 9999999998 | QH020 == 9999999999, NA, QH020),
mobile_home_value = ifelse(QH016 == 99999998 | QH016 == 99999999, NA, QH016),
second_home_value = ifelse(QH166 == 9999999998 | QH166 == 9999999999, NA, QH166),
mortgage = ifelse(QH032 == 99999998 | QH032 == 99999999, NA, QH032),
debts = ifelse(QQ478 == 9999998 | QQ478 == 9999999, NA, QQ478),
credit_card_debt = ifelse(QQ519 == 9999998 | QQ519 == 9999999, NA, QQ519)) %>%
select(retirement, sex, age, children, marital_status,
life_satisfaction, health_rate, high_BP, diabetes,
cancer, heart_attack, depression, times_fallen, alc_days, days_in_bed,
memory, lonely_pw, enjoy_life_pw, job_status, weeks_worked_py,
help_friends_py,have_friends,
amount_earn_when_left, difficulty_managing_mny,
own_rent_home, age_plan_stop_wrk, social_security, medicare, follow_stockmarket, life_insurance,
num_lifeinsur_policies,
stocks_mf, bonds, savings, cds, vehicles, other_savings, home_value, mobile_home_value,
second_home_value, mortgage, debts, credit_card_debt,
pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10) %>%
rowwise() %>%
mutate(
pension = sum(pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10, na.rm = TRUE),
assets = sum(stocks_mf, bonds, savings, cds, vehicles,
other_savings, pension,
na.rm = TRUE),
housing_assets = sum(home_value, second_home_value,mobile_home_value, -mortgage,
na.rm = TRUE),
net_assets = assets + housing_assets + pension) %>%
ungroup() %>%
#we now remove the values that have lots of missingness and are shown to be seen in other variables
select(-heart_attack, -amount_earn_when_left, -age_plan_stop_wrk,
-times_fallen, -weeks_worked_py, -num_lifeinsur_policies, -alc_days,
-job_status) %>%
#remove variables that are used to compute the outcome variable
select(-c(stocks_mf, bonds, savings, cds, vehicles,
other_savings, pension,home_value, second_home_value,mobile_home_value, mortgage,
assets, housing_assets,debts, credit_card_debt,
pension_1, pension_2,pension_3,pension_4,pension_5,
pension_6,pension_7,pension_8,pension_9,pension_10)) %>%
data.frame() %>%
mutate(net_assets = ifelse(net_assets <0, 0, net_assets)) %>%
data.frame()
#attribute removal:
mydat[] <- lapply(mydat, function(x) { attributes(x) <- NULL; x })
#turn all character variables into factors:
mydat[sapply(mydat, is.character)] <- lapply(mydat[sapply(mydat, is.character)],
as.factor)
############## Training & Test Split ##############
#Note that the data has 16,309 rows
#Create a test data set with 20% of these rows (3262 rows)
#The test data set must include complete cases.
#The remaining data will be imputed
set.seed(11+05+21)
all_rownum <- nrow(mydat)
test_rownums <- ceiling(all_rownum*0.2)
#data with complete cases:
data_complete <- mydat[complete.cases(mydat), ]
#calculate ratio percentage with new rows
#math to solve percentage
new_p <- 1 - (test_rownums / nrow(data_complete))
new_p <- round(new_p, 1)
#find test set
sample <- sample.split(data_complete$net_assets, SplitRatio = new_p)
#define test data
test <- subset(data_complete, sample == FALSE)
#define training data by
#rbind'ing the rest of the complete cases with "sample == TRUE"
pre_train <- rbind(mydat[!complete.cases(mydat), ],
subset(data_complete, sample == TRUE))
|
/SCRIPT_S3R/GUI_tab2.r | no_license | palou26/testgit | R | false | false | 5,646 | r | ||
#preprocessing data
library(lubridate)
library(dplyr)
d <- read.table("household_power_consumption.txt",header=TRUE,sep=";")
cDate <- as.character(d$Date)
dDate <- dmy(cDate)
d <- d %>% mutate(Date=dDate)
ad <- d %>% filter(Date == "2007-02-01" | Date == "2007-02-02")
#plot 2
Date <- ad$Date
Time <- ad$Time
NewTime <- paste(Date,Time)
NewTime <- ymd_hms(NewTime)
GAP <- as.numeric(ad$Global_active_power)
plot(NewTime,GAP,type="l",xlab = "",ylab = "Global Active Power (kilowatts)",yaxt="n")
axis(2,at = c(0,1000,2000,3000),labels = c(0,2,4,6))
dev.copy(png,"plot2.png")
dev.off()
| /plot2.R | no_license | MGitting/plotting-homework | R | false | false | 603 | r | #preprocessing data
library(lubridate)
library(dplyr)
d <- read.table("household_power_consumption.txt",header=TRUE,sep=";")
cDate <- as.character(d$Date)
dDate <- dmy(cDate)
d <- d %>% mutate(Date=dDate)
ad <- d %>% filter(Date == "2007-02-01" | Date == "2007-02-02")
#plot 2
Date <- ad$Date
Time <- ad$Time
NewTime <- paste(Date,Time)
NewTime <- ymd_hms(NewTime)
GAP <- as.numeric(ad$Global_active_power)
plot(NewTime,GAP,type="l",xlab = "",ylab = "Global Active Power (kilowatts)",yaxt="n")
axis(2,at = c(0,1000,2000,3000),labels = c(0,2,4,6))
dev.copy(png,"plot2.png")
dev.off()
|
bb_individuals<-function(bb_probabilitydensity=bb, #Output from IndividualBB
tracksums=tracksums.out,
cellsize=3000){
## NOTE: ud estimates have been scaled by multiplying each value by cellsize^2 to make prob vol that sums to 1.00 accross pixel space
#individuals also may be split across groups (year, season) if the tracks were segmented useing these
require(adehabitatHR)
require(SDMTools)
#grp.meta<-data.matrix(meta[grping.var])
bb<-bb_probabilitydensity
burst<-names(bb)
tracksums$bbref<-1:nrow(tracksums)
# get unique groups (use tracksums b/c these points are contained in polygon - not a tracks will necessarily be represented in a given polygon)
(grp.ids<-unique(tracksums$timegrp))
#### initialize lists to house data by desired grouping variable (group.uniq)
# list to house uds normalized by track duration
bbindis <- vector ("list", length(grp.ids))
#### loop through groups
for (h in 1:length(grp.ids)) {
grp.id<-grp.ids[h]
#tracksums.want<-tracksums[which(tracksums$grp==grp.ids[k]),]
tracksums.want<-tracksums%>%dplyr::filter(timegrp==grp.id)
# create summary table of # of segments from each track
(track.freq<-tracksums.want%>%group_by(uniID)%>%
dplyr::summarize(n=n_distinct(seg_id),minbb=min(bbref),maxbb=max(bbref)))
# initialize lists to house data for segment based on deploy_id
ud.track <- vector("list", nrow(track.freq))
track.days <- vector ("list", nrow(track.freq))
# sum up segments for each track
# run through track.freq table summing segments >1
for (j in 1:nrow(track.freq)) {
if (track.freq$n[j]==1) {
# operation for only one segment in polygon
bbIndx<-track.freq$minbb[j]
ud.seg<-bb[[bbIndx]]
a<- slot(ud.seg,"data")
slot(ud.seg,"data")<-a*(cellsize^2)
ud.track[[j]]<-ud.seg
# get number of track days (in decimal days)
track.days[[j]]<-tracksums.want$days[tracksums.want$uniID==track.freq$uniID[j]]
#paste(paste("bird:",track.freq$uniID[j],
# "segnum:",track.freq$n[j],
# "area:",sum(slot(ud.track[[j]],"data")[,1])))
} else {
# get multiple segments
days.segs<-tracksums.want$days[tracksums.want$uniID==track.freq$uniID[j]]
bbIndx.segs<-seq(from=track.freq$minbb[track.freq$uniID==track.freq$uniID[j]],
to=track.freq$maxbb[track.freq$uniID==track.freq$uniID[j]])
# list to house each segment
ud.segs.new <- vector ("list", length(bbIndx.segs))
for (k in 1:length(bbIndx.segs)) {
bbIndx<-bbIndx.segs[k]
ud.seg <- bb[[bbIndx]]
# weigh each segment it's proportion of total hours tracked within the clipperName (Freiberg 20XX paper)
a<- slot(ud.seg,"data")*(days.segs[k]/sum(days.segs))
#slot(ud.seg,"data")<-a
ud.segs.new[[k]] <- a
}
# adds the segments from one bird together into one UD
spdf<-Reduce("+",ud.segs.new)*(cellsize^2)
sum(spdf)
estUDsum<-ud.seg#steal UD formatting from
slot(estUDsum,"data")<-spdf
ud.track[[j]]<-estUDsum
# get number of track days
track.days[[j]]<-sum(days.segs)}
#print(paste(j,k,sum(slot(ud.track[[j]],"data"))))
}
names(ud.track)<-track.freq$uniID
class(ud.track) <- "estUDm"
bbindis[[h]]<-ud.track
}
names(bbindis)<-grp.ids
#class(bbindis)<-"estUD"
return(bbindis)
}
| /R/bb_individuals.R | no_license | raorben/seabird_tracking_atlas | R | false | false | 3,615 | r | bb_individuals<-function(bb_probabilitydensity=bb, #Output from IndividualBB
tracksums=tracksums.out,
cellsize=3000){
## NOTE: ud estimates have been scaled by multiplying each value by cellsize^2 to make prob vol that sums to 1.00 accross pixel space
#individuals also may be split across groups (year, season) if the tracks were segmented useing these
require(adehabitatHR)
require(SDMTools)
#grp.meta<-data.matrix(meta[grping.var])
bb<-bb_probabilitydensity
burst<-names(bb)
tracksums$bbref<-1:nrow(tracksums)
# get unique groups (use tracksums b/c these points are contained in polygon - not a tracks will necessarily be represented in a given polygon)
(grp.ids<-unique(tracksums$timegrp))
#### initialize lists to house data by desired grouping variable (group.uniq)
# list to house uds normalized by track duration
bbindis <- vector ("list", length(grp.ids))
#### loop through groups
for (h in 1:length(grp.ids)) {
grp.id<-grp.ids[h]
#tracksums.want<-tracksums[which(tracksums$grp==grp.ids[k]),]
tracksums.want<-tracksums%>%dplyr::filter(timegrp==grp.id)
# create summary table of # of segments from each track
(track.freq<-tracksums.want%>%group_by(uniID)%>%
dplyr::summarize(n=n_distinct(seg_id),minbb=min(bbref),maxbb=max(bbref)))
# initialize lists to house data for segment based on deploy_id
ud.track <- vector("list", nrow(track.freq))
track.days <- vector ("list", nrow(track.freq))
# sum up segments for each track
# run through track.freq table summing segments >1
for (j in 1:nrow(track.freq)) {
if (track.freq$n[j]==1) {
# operation for only one segment in polygon
bbIndx<-track.freq$minbb[j]
ud.seg<-bb[[bbIndx]]
a<- slot(ud.seg,"data")
slot(ud.seg,"data")<-a*(cellsize^2)
ud.track[[j]]<-ud.seg
# get number of track days (in decimal days)
track.days[[j]]<-tracksums.want$days[tracksums.want$uniID==track.freq$uniID[j]]
#paste(paste("bird:",track.freq$uniID[j],
# "segnum:",track.freq$n[j],
# "area:",sum(slot(ud.track[[j]],"data")[,1])))
} else {
# get multiple segments
days.segs<-tracksums.want$days[tracksums.want$uniID==track.freq$uniID[j]]
bbIndx.segs<-seq(from=track.freq$minbb[track.freq$uniID==track.freq$uniID[j]],
to=track.freq$maxbb[track.freq$uniID==track.freq$uniID[j]])
# list to house each segment
ud.segs.new <- vector ("list", length(bbIndx.segs))
for (k in 1:length(bbIndx.segs)) {
bbIndx<-bbIndx.segs[k]
ud.seg <- bb[[bbIndx]]
# weigh each segment it's proportion of total hours tracked within the clipperName (Freiberg 20XX paper)
a<- slot(ud.seg,"data")*(days.segs[k]/sum(days.segs))
#slot(ud.seg,"data")<-a
ud.segs.new[[k]] <- a
}
# adds the segments from one bird together into one UD
spdf<-Reduce("+",ud.segs.new)*(cellsize^2)
sum(spdf)
estUDsum<-ud.seg#steal UD formatting from
slot(estUDsum,"data")<-spdf
ud.track[[j]]<-estUDsum
# get number of track days
track.days[[j]]<-sum(days.segs)}
#print(paste(j,k,sum(slot(ud.track[[j]],"data"))))
}
names(ud.track)<-track.freq$uniID
class(ud.track) <- "estUDm"
bbindis[[h]]<-ud.track
}
names(bbindis)<-grp.ids
#class(bbindis)<-"estUD"
return(bbindis)
}
|
library(dtwclust)
library(dtw)
words = list("nula", "jedan", "dva", "tri", "cetiri", "pet", "sest", "sedam", "osam", "devet", "plus", "minus", "puta", "dijeljeno", "jednako")
for(word in words){
path = 'C:/Users/Mira/Documents/DIPLOMSKI RAD - ALGORITAM PORAVNANJA VREMENSKIH NIZOVA/speach-recognition/Dataset/Train/'
directory = paste(path, word, sep="")
files = Sys.glob(file.path(directory, "*.txt"))
mfccs = list()
i = 1
for(file in files){
data = as.matrix(read.table(file ,header=FALSE,sep=" "))
n = ncol(data)
data=t(data)
data = na.omit(data)
mfccs[[i]] = data
i = i + 1
}
average = DBA(mfccs)
filename = paste(directory, "/", word, "_average1.txt", sep="")
print(filename)
write.table(t(average), file=filename, row.names=FALSE, col.names=FALSE, sep=" ")
} | /speach-recognition/baricenter-averaging.R | no_license | mjukicbraculj/Diplomski-rad | R | false | false | 833 | r | library(dtwclust)
library(dtw)
words = list("nula", "jedan", "dva", "tri", "cetiri", "pet", "sest", "sedam", "osam", "devet", "plus", "minus", "puta", "dijeljeno", "jednako")
for(word in words){
path = 'C:/Users/Mira/Documents/DIPLOMSKI RAD - ALGORITAM PORAVNANJA VREMENSKIH NIZOVA/speach-recognition/Dataset/Train/'
directory = paste(path, word, sep="")
files = Sys.glob(file.path(directory, "*.txt"))
mfccs = list()
i = 1
for(file in files){
data = as.matrix(read.table(file ,header=FALSE,sep=" "))
n = ncol(data)
data=t(data)
data = na.omit(data)
mfccs[[i]] = data
i = i + 1
}
average = DBA(mfccs)
filename = paste(directory, "/", word, "_average1.txt", sep="")
print(filename)
write.table(t(average), file=filename, row.names=FALSE, col.names=FALSE, sep=" ")
} |
## First function takes a square matrix and returns a list of functions to both set and get the matrix and inverse
## This list is then used as the input to the next function so the use of <-- to write to write outside the environment is present
makeCacheMatrix <- function(x = matrix()) {
i<- NULL
set<-function(y){
x<<-y #<-- has been used to write to the outside environment
i<<-NULL #<-- has been used to write to the outside environment
}
get <-function() x
seti<-function(inverse) i<<-inverse #<-- has been used to write to the outside environment
geti<-function() i
list(set=set, get=get,
seti=seti,
geti=geti)
}
## Second funtion takes the output of the first as mentioned above and returns the invers of the matrix first entered to the first function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i<-x$geti()
if(!is.null(i)){
message("getting cached data")
return(i)
}
data<-x$get()
i<-solve(data)
x$seti(i)
return(i)
} | /cachematrix.R | no_license | CodeManLearner/ProgrammingAssignment2 | R | false | false | 1,031 | r | ## First function takes a square matrix and returns a list of functions to both set and get the matrix and inverse
## This list is then used as the input to the next function so the use of <-- to write to write outside the environment is present
makeCacheMatrix <- function(x = matrix()) {
i<- NULL
set<-function(y){
x<<-y #<-- has been used to write to the outside environment
i<<-NULL #<-- has been used to write to the outside environment
}
get <-function() x
seti<-function(inverse) i<<-inverse #<-- has been used to write to the outside environment
geti<-function() i
list(set=set, get=get,
seti=seti,
geti=geti)
}
## Second funtion takes the output of the first as mentioned above and returns the invers of the matrix first entered to the first function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i<-x$geti()
if(!is.null(i)){
message("getting cached data")
return(i)
}
data<-x$get()
i<-solve(data)
x$seti(i)
return(i)
} |
##Assignment3##
##Q0
firstName<- "JINGNI"
lastName <- "LI"
print(
paste(firstName,
lastName
)
)
studentID<- "1505021"
print(studentID)
print("jli239@ucsc.edu")
## Q1
##loding data
library(foreign)
df.ex<-read.dta("https://github.com/EconomiCurtis/econ294_2015/raw/master/data/org_example.dta")
View(df.ex)
##Q2
##filter
install.packages("dplyr")
library(dplyr)
df.ex.lastyear2013<- dplyr::filter(df.ex,year == 2013 & month == 12)
print(nrow(df.ex.lastyear2013))
july <- sum(with(df.ex,year==2013&month==7))
august <- sum(with(df.ex,year==2013&month==8))
sept <-sum(with(df.ex,year==2013&month==9))
summer2013<-sum(july+august+sept)
print(summer2013)
##Q3
install.packages("dplyr")
library(foreign)
df.ex.3a <-arrange(df.ex, year, month)
View(df.ex.3a)
##Q4
df.ex.4a<-select(df.ex,year,age)
View(df.ex.4a)
df.ex.4b <- select(df.ex, year,month,starts_with("i"))
state<-distinct(select(df.ex, state))
View(state)
##Q5
stndz <- function(x){(x - mean(x, na.rm = T)) / sd(x, na.rm = T)}
nrmlz <-function(x) {(x-min(x,na.rm = T))/(max(x,na.rm = T)-min(x,na.rm=T))}
df.ex.5a <- dplyr::mutate(df.ex, rw.stndz = stndz(rw), rw_nrmlz = nrmlz(rw)) %>%
select(rw.stndz, rw_nrmlz)
df.ex.5b <- df.ex %>% group_by(year,month) %>%
mutate(rw.stndz = stndz(rw), rw_nrmlz = nrmlz(rw),count = n()) %>%
select(rw.stndz, rw_nrmlz,count)
##Q6
df.ex.6 <-
dplyr::group_by(df.ex,year, month, state) %>%
dplyr::summarise(
rw.min = min(rw, na.rm = T),
rw.1stq = quantile(rw, 0.25, na.rm = T),
rw.mean = mean(rw, na.rm = T),
rw.median = median(rw, na.rm = T),
rw.3rdq = quantile(rw, 0.75, na.rm = T),
rw.max = max(rw, na.rm = T),
count = n()
)
print(nrow(df.ex.6))
View(df.ex.6) | /jingniliAssignment3Creator.R | no_license | JINGNILItina/TINA | R | false | false | 1,717 | r | ##Assignment3##
##Q0
firstName<- "JINGNI"
lastName <- "LI"
print(
paste(firstName,
lastName
)
)
studentID<- "1505021"
print(studentID)
print("jli239@ucsc.edu")
## Q1
##loding data
library(foreign)
df.ex<-read.dta("https://github.com/EconomiCurtis/econ294_2015/raw/master/data/org_example.dta")
View(df.ex)
##Q2
##filter
install.packages("dplyr")
library(dplyr)
df.ex.lastyear2013<- dplyr::filter(df.ex,year == 2013 & month == 12)
print(nrow(df.ex.lastyear2013))
july <- sum(with(df.ex,year==2013&month==7))
august <- sum(with(df.ex,year==2013&month==8))
sept <-sum(with(df.ex,year==2013&month==9))
summer2013<-sum(july+august+sept)
print(summer2013)
##Q3
install.packages("dplyr")
library(foreign)
df.ex.3a <-arrange(df.ex, year, month)
View(df.ex.3a)
##Q4
df.ex.4a<-select(df.ex,year,age)
View(df.ex.4a)
df.ex.4b <- select(df.ex, year,month,starts_with("i"))
state<-distinct(select(df.ex, state))
View(state)
##Q5
stndz <- function(x){(x - mean(x, na.rm = T)) / sd(x, na.rm = T)}
nrmlz <-function(x) {(x-min(x,na.rm = T))/(max(x,na.rm = T)-min(x,na.rm=T))}
df.ex.5a <- dplyr::mutate(df.ex, rw.stndz = stndz(rw), rw_nrmlz = nrmlz(rw)) %>%
select(rw.stndz, rw_nrmlz)
df.ex.5b <- df.ex %>% group_by(year,month) %>%
mutate(rw.stndz = stndz(rw), rw_nrmlz = nrmlz(rw),count = n()) %>%
select(rw.stndz, rw_nrmlz,count)
##Q6
df.ex.6 <-
dplyr::group_by(df.ex,year, month, state) %>%
dplyr::summarise(
rw.min = min(rw, na.rm = T),
rw.1stq = quantile(rw, 0.25, na.rm = T),
rw.mean = mean(rw, na.rm = T),
rw.median = median(rw, na.rm = T),
rw.3rdq = quantile(rw, 0.75, na.rm = T),
rw.max = max(rw, na.rm = T),
count = n()
)
print(nrow(df.ex.6))
View(df.ex.6) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extract-prm.R
\name{extract_prm}
\alias{extract_prm}
\alias{extract_prm_cohort}
\title{Extract values for Atlantis parameters from the biological parameter file.}
\usage{
extract_prm(prm_biol, variables, ignore_duplicates = FALSE)
extract_prm_cohort(prm_biol, variables)
}
\arguments{
\item{prm_biol}{Character string giving the connection to the biological parameterfile.
The filename usually contains \code{biol_fishing} and does end in \code{.prm}.}
\item{variables}{Character string giving the flag to search for. This should be
a combination of the parameter name and the group-Code.}
\item{ignore_duplicates}{TRUE to just return first value in case of duplicates, FALSE for error}
}
\value{
numeric vector.
}
\description{
Extract values for Atlantis parameters from the biological parameter file.
}
\examples{
d <- system.file("extdata", "setas-model-new-trunk", package = "atlantistools")
prm_biol <- file.path(d, "VMPA_setas_biol_fishing_Trunk.prm")
# You can pass a single variable
extract_prm(prm_biol, variables = "KWRR_FVS")
# Or multiple variables
extract_prm(prm_biol, variables = paste("KWRR", c("FVS", "FPS"), sep = "_"))
# Use extract_prm_cohort do extract data for age specific parameters.
# They are usually stored in the next line following the parameter tag.
extract_prm_cohort(prm_biol, variables = "C_FVS")
extract_prm_cohort(prm_biol, variables = paste("C", c("FVS", "FPS"), sep = "_"))
}
| /man/extract_prm.Rd | no_license | cbracis/atlantistools | R | false | true | 1,498 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extract-prm.R
\name{extract_prm}
\alias{extract_prm}
\alias{extract_prm_cohort}
\title{Extract values for Atlantis parameters from the biological parameter file.}
\usage{
extract_prm(prm_biol, variables, ignore_duplicates = FALSE)
extract_prm_cohort(prm_biol, variables)
}
\arguments{
\item{prm_biol}{Character string giving the connection to the biological parameterfile.
The filename usually contains \code{biol_fishing} and does end in \code{.prm}.}
\item{variables}{Character string giving the flag to search for. This should be
a combination of the parameter name and the group-Code.}
\item{ignore_duplicates}{TRUE to just return first value in case of duplicates, FALSE for error}
}
\value{
numeric vector.
}
\description{
Extract values for Atlantis parameters from the biological parameter file.
}
\examples{
d <- system.file("extdata", "setas-model-new-trunk", package = "atlantistools")
prm_biol <- file.path(d, "VMPA_setas_biol_fishing_Trunk.prm")
# You can pass a single variable
extract_prm(prm_biol, variables = "KWRR_FVS")
# Or multiple variables
extract_prm(prm_biol, variables = paste("KWRR", c("FVS", "FPS"), sep = "_"))
# Use extract_prm_cohort do extract data for age specific parameters.
# They are usually stored in the next line following the parameter tag.
extract_prm_cohort(prm_biol, variables = "C_FVS")
extract_prm_cohort(prm_biol, variables = paste("C", c("FVS", "FPS"), sep = "_"))
}
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/metab.kalman.R
\name{metab.kalman}
\alias{metab.kalman}
\title{Metabolism calculated from parameters estimated using a Kalman filter}
\usage{
metab.kalman(do.obs, do.sat, k.gas, z.mix, irr, wtr, ...)
}
\arguments{
\item{do.obs}{Vector of dissovled oxygen concentration observations, \eqn{mg O[2] L^{-1}}{mg O2 / L}}
\item{do.sat}{Vector of dissolved oxygen saturation values based on water temperature. Calculate using \link{o2.at.sat}}
\item{k.gas}{Vector of kGAS values calculated from any of the gas flux models
(e.g., \link{k.cole}) and converted to kGAS using \link{k600.2.kGAS}}
\item{z.mix}{Vector of mixed-layer depths in meters. To calculate, see \link{ts.meta.depths}}
\item{irr}{Vector of photosynthetically active radiation in \eqn{\mu mol\ m^{-2} s^{-1}}{micro mols / m^2 / s}}
\item{wtr}{Vector of water temperatures in \eqn{^{\circ}C}{degrees C}. Used in scaling respiration with temperature}
\item{...}{additional arguments; currently "datetime" is the only recognized argument passed through \code{...}}
}
\value{
A data.frame with columns corresponding to components of metabolism
\describe{
\item{GPP}{numeric estimate of Gross Primary Production, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
\item{R}{numeric estimate of Respiration, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
\item{NEP}{numeric estimate of Net Ecosystem production, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
}
Use \link{attributes} to access more model output:
\item{smoothDO}{smoothed time series of oxygen concentration (\eqn{mg O[2] L^{-1}}{mg O2 / L}), from Kalman smoother}
\item{params}{parameters estimated by the Kalman filter (\eqn{c_1, c_2, Q, H}{c1, c2, Q, H})}
}
\description{
A state space model accounting for process and observation error, with the maximum likelihood of parameteres estimated using a Kalman filter.
Also provides a smoothed time series of oxygen concentration.
}
\details{
The model has four parameters, \eqn{c_1, c_2, Q, H}{c1, c2, Q, H}, and consists of equations involving the prediction of upcoming state conditional on information of the previous state (\eqn{a_{t|t-1}}{a[t|t-1]}, \eqn{P_{t|t-1}}{P[t|t-1]}), as well as updates of those predictions that are conditional upon information of the current state (\eqn{a_{t|t}}{a[t|t]}, \eqn{P_{t|t}}{P[t|t]}). \eqn{a} is the
\deqn{v=k.gas/z.mix}{v=k.gas/z.mix}
\deqn{a_t = c_1*irr_{t-1} + c_2*log_e(wtr_{t-1}) + v_{t-1}*do.sat_{t-1}}{a[t] = c1*irr[t-1] + c2*log(wtr[t-1]) + v[t-1]*do.sat[t-1]}
\deqn{\beta = e^{-v}}{beta = exp(-v)}
\deqn{do.obs_t = a_t/v_{t-1} + -e^{-v_{t-1}}*a_t/v_{t-1} + \beta_{t-1}*\do.obs_{t-1} + \epsilon_t}{do.obs[t] = a[t]/v[t-1] + -exp(-v[t-1])*a[t]/v[t-1] + beta[t-1]*do.obs[t-1] + epsilon[t]}
The above model is used during model fitting, but if gas flux is not integrated between time steps, those equations simplify to the following:
\deqn{F_{t-1} = k.gas_{t-1}*(do.sat_{t-1} - do.obs_{t-1})/z.mix_{t-1}}{F[t-1] = k.gas[t-1]*(do.sat[t-1] - do.obs[t-1])/z.mix[t-1]}
\deqn{do.obs_t=do.obs_{t-1}+c_1*irr_{t-1}+c_2*log_e(wtr_{t-1}) + F_{t-1} + \epsilon_t}{do.obs[t] = do.obs[t-1] + c1*irr[t-1] + c2*log(wtr[t-1]) + F[t-1] + epsilon[t]}
The parameters are fit using maximum likelihood, and the optimization (minimization of the negative log likelihood function) is performed by \code{optim} using default settings.
GPP is then calculated as \code{mean(c1*irr, na.rm=TRUE)*freq}, where \code{freq} is the number of observations per day, as estimated from the typical size between time steps. Thus, generally \code{freq==length(do.obs)}.
Similarly, R is calculated as \code{mean(c2*log(wtr), na.rm=TRUE)*freq}.
NEP is the sum of GPP and R.
}
\note{
If observation error is substantial, consider applying a Kalman filter to the water temperature time series by supplying
\code{wtr} as the output from \link{temp.kalman}
}
\examples{
library(rLakeAnalyzer)
doobs <- load.ts(system.file('extdata',
'sparkling.doobs', package="LakeMetabolizer"))
wtr <- load.ts(system.file('extdata',
'sparkling.wtr', package="LakeMetabolizer"))
wnd <- load.ts(system.file('extdata',
'sparkling.wnd', package="LakeMetabolizer"))
irr <- load.ts(system.file('extdata',
'sparkling.par', package="LakeMetabolizer"))
#Subset a day
Sys.setenv(TZ='GMT')
mod.date <- as.POSIXct('2009-07-08', 'GMT')
doobs <- doobs[trunc(doobs$datetime, 'day') == mod.date, ]
wtr <- wtr[trunc(wtr$datetime, 'day') == mod.date, ]
wnd <- wnd[trunc(wnd$datetime, 'day') == mod.date, ]
irr <- irr[trunc(irr$datetime, 'day') == mod.date, ]
k600 <- k.cole.base(wnd[,2])
k.gas <- k600.2.kGAS.base(k600, wtr[,3], 'O2')
do.sat <- o2.at.sat.base(wtr[,3], altitude=300)
metab.kalman(irr=irr[,2], z.mix=rep(1, length(k.gas)),
do.sat=do.sat, wtr=wtr[,2],
k.gas=k.gas, do.obs=doobs[,2])
}
\author{
Ryan Batt, Luke A. Winslow
}
\references{
Batt, Ryan D. and Stephen R. Carpenter. 2012. \emph{Free-water lake metabolism:
addressing noisy time series with a Kalman filter}. Limnology and Oceanography: Methods 10: 20-30. doi: 10.4319/lom.2012.10.20
}
\seealso{
\link{temp.kalman}, \link{watts.in}, \link{metab}, \link{metab.bookkeep}, \link{metab.ols}, \link{metab.mle}, \link{metab.bayesian}
}
| /man/metab.kalman.Rd | no_license | aappling-usgs/LakeMetabolizer | R | false | false | 5,366 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/metab.kalman.R
\name{metab.kalman}
\alias{metab.kalman}
\title{Metabolism calculated from parameters estimated using a Kalman filter}
\usage{
metab.kalman(do.obs, do.sat, k.gas, z.mix, irr, wtr, ...)
}
\arguments{
\item{do.obs}{Vector of dissovled oxygen concentration observations, \eqn{mg O[2] L^{-1}}{mg O2 / L}}
\item{do.sat}{Vector of dissolved oxygen saturation values based on water temperature. Calculate using \link{o2.at.sat}}
\item{k.gas}{Vector of kGAS values calculated from any of the gas flux models
(e.g., \link{k.cole}) and converted to kGAS using \link{k600.2.kGAS}}
\item{z.mix}{Vector of mixed-layer depths in meters. To calculate, see \link{ts.meta.depths}}
\item{irr}{Vector of photosynthetically active radiation in \eqn{\mu mol\ m^{-2} s^{-1}}{micro mols / m^2 / s}}
\item{wtr}{Vector of water temperatures in \eqn{^{\circ}C}{degrees C}. Used in scaling respiration with temperature}
\item{...}{additional arguments; currently "datetime" is the only recognized argument passed through \code{...}}
}
\value{
A data.frame with columns corresponding to components of metabolism
\describe{
\item{GPP}{numeric estimate of Gross Primary Production, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
\item{R}{numeric estimate of Respiration, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
\item{NEP}{numeric estimate of Net Ecosystem production, \eqn{mg O_2 L^{-1} d^{-1}}{mg O2 / L / d}}
}
Use \link{attributes} to access more model output:
\item{smoothDO}{smoothed time series of oxygen concentration (\eqn{mg O[2] L^{-1}}{mg O2 / L}), from Kalman smoother}
\item{params}{parameters estimated by the Kalman filter (\eqn{c_1, c_2, Q, H}{c1, c2, Q, H})}
}
\description{
A state space model accounting for process and observation error, with the maximum likelihood of parameteres estimated using a Kalman filter.
Also provides a smoothed time series of oxygen concentration.
}
\details{
The model has four parameters, \eqn{c_1, c_2, Q, H}{c1, c2, Q, H}, and consists of equations involving the prediction of upcoming state conditional on information of the previous state (\eqn{a_{t|t-1}}{a[t|t-1]}, \eqn{P_{t|t-1}}{P[t|t-1]}), as well as updates of those predictions that are conditional upon information of the current state (\eqn{a_{t|t}}{a[t|t]}, \eqn{P_{t|t}}{P[t|t]}). \eqn{a} is the
\deqn{v=k.gas/z.mix}{v=k.gas/z.mix}
\deqn{a_t = c_1*irr_{t-1} + c_2*log_e(wtr_{t-1}) + v_{t-1}*do.sat_{t-1}}{a[t] = c1*irr[t-1] + c2*log(wtr[t-1]) + v[t-1]*do.sat[t-1]}
\deqn{\beta = e^{-v}}{beta = exp(-v)}
\deqn{do.obs_t = a_t/v_{t-1} + -e^{-v_{t-1}}*a_t/v_{t-1} + \beta_{t-1}*\do.obs_{t-1} + \epsilon_t}{do.obs[t] = a[t]/v[t-1] + -exp(-v[t-1])*a[t]/v[t-1] + beta[t-1]*do.obs[t-1] + epsilon[t]}
The above model is used during model fitting, but if gas flux is not integrated between time steps, those equations simplify to the following:
\deqn{F_{t-1} = k.gas_{t-1}*(do.sat_{t-1} - do.obs_{t-1})/z.mix_{t-1}}{F[t-1] = k.gas[t-1]*(do.sat[t-1] - do.obs[t-1])/z.mix[t-1]}
\deqn{do.obs_t=do.obs_{t-1}+c_1*irr_{t-1}+c_2*log_e(wtr_{t-1}) + F_{t-1} + \epsilon_t}{do.obs[t] = do.obs[t-1] + c1*irr[t-1] + c2*log(wtr[t-1]) + F[t-1] + epsilon[t]}
The parameters are fit using maximum likelihood, and the optimization (minimization of the negative log likelihood function) is performed by \code{optim} using default settings.
GPP is then calculated as \code{mean(c1*irr, na.rm=TRUE)*freq}, where \code{freq} is the number of observations per day, as estimated from the typical size between time steps. Thus, generally \code{freq==length(do.obs)}.
Similarly, R is calculated as \code{mean(c2*log(wtr), na.rm=TRUE)*freq}.
NEP is the sum of GPP and R.
}
\note{
If observation error is substantial, consider applying a Kalman filter to the water temperature time series by supplying
\code{wtr} as the output from \link{temp.kalman}
}
\examples{
library(rLakeAnalyzer)
doobs <- load.ts(system.file('extdata',
'sparkling.doobs', package="LakeMetabolizer"))
wtr <- load.ts(system.file('extdata',
'sparkling.wtr', package="LakeMetabolizer"))
wnd <- load.ts(system.file('extdata',
'sparkling.wnd', package="LakeMetabolizer"))
irr <- load.ts(system.file('extdata',
'sparkling.par', package="LakeMetabolizer"))
#Subset a day
Sys.setenv(TZ='GMT')
mod.date <- as.POSIXct('2009-07-08', 'GMT')
doobs <- doobs[trunc(doobs$datetime, 'day') == mod.date, ]
wtr <- wtr[trunc(wtr$datetime, 'day') == mod.date, ]
wnd <- wnd[trunc(wnd$datetime, 'day') == mod.date, ]
irr <- irr[trunc(irr$datetime, 'day') == mod.date, ]
k600 <- k.cole.base(wnd[,2])
k.gas <- k600.2.kGAS.base(k600, wtr[,3], 'O2')
do.sat <- o2.at.sat.base(wtr[,3], altitude=300)
metab.kalman(irr=irr[,2], z.mix=rep(1, length(k.gas)),
do.sat=do.sat, wtr=wtr[,2],
k.gas=k.gas, do.obs=doobs[,2])
}
\author{
Ryan Batt, Luke A. Winslow
}
\references{
Batt, Ryan D. and Stephen R. Carpenter. 2012. \emph{Free-water lake metabolism:
addressing noisy time series with a Kalman filter}. Limnology and Oceanography: Methods 10: 20-30. doi: 10.4319/lom.2012.10.20
}
\seealso{
\link{temp.kalman}, \link{watts.in}, \link{metab}, \link{metab.bookkeep}, \link{metab.ols}, \link{metab.mle}, \link{metab.bayesian}
}
|
hybrid.pc.backend = function(x, cluster = NULL, whitelist, blacklist,
test, alpha, B, max.sx = ncol(x), complete, debug = FALSE) {
nodes = names(x)
mb = smartSapply(cluster, as.list(nodes), hybrid.pc.heuristic, data = x,
nodes = nodes, alpha = alpha, B = B, whitelist = whitelist,
blacklist = blacklist, test = test, max.sx = max.sx,
complete = complete, debug = debug)
names(mb) = nodes
# make up a set of believable Markov blankets, using all the nodes within
# distance 2 from the target node (which is a superset).
for (node in nodes)
mb[[node]]$mb = fake.markov.blanket(mb, node)
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, debug = debug)
return(mb)
}#HYBRID.PC.BACKEND
hybrid.pc.heuristic = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, max.sx = ncol(data), complete, debug = FALSE) {
# identify the parents-and-children superset.
pvalues = hybrid.pc.de.pcs(x = x, data = data, nodes = nodes, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist,
test = test, complete = complete, debug = debug)
pc.superset = names(pvalues)
# if the superset contains zero or just one nodes, there is nothing else to do
# (the superset is not super, it is the right set).
if (length(pc.superset) < 2)
return(list(nbr = pc.superset, mb = NULL))
# identify the spouses superset (some spouses may already be in the
# parents-and-children superset).
sp.superset = hybrid.pc.de.sps(x = x, data = data, nodes = nodes,
pc.superset = pc.superset, dsep.set = attr(pvalues, "dsep.set"),
alpha = alpha, B = B, test = test, max.sx = max.sx,
complete = complete, debug = debug)
# if there are just two nodes in the parents-and-children set and no spouse,
# the superset is necessarily the same as the set..
if ((length(pc.superset) == 2) && (length(sp.superset) == 0))
return(list(nbr = pc.superset, mb = pc.superset))
# the two nodes with the smallest p-values would be examined again using the
# same low-order tests as before, so just include them.
start = names(sort(pvalues))[1:min(length(pvalues), 2)]
# identify the real parents and children from the supersets.
pc = hybrid.pc.nbr.search(x, data = data,
nodes = c(x, pc.superset, sp.superset), alpha = alpha, B = B,
whitelist = whitelist, blacklist = blacklist, test = test,
max.sx = max.sx, complete = complete, start = start, debug = debug)
# one more scan to identify possible false negatives.
for (node in setdiff(pc.superset, pc)) {
fn = hybrid.pc.nbr.search(node, data = data,
nodes = c(x, pc.superset, sp.superset), alpha = alpha, B = B,
whitelist = whitelist, blacklist = blacklist, test = test,
max.sx = max.sx, complete = complete, start = start,
debug = debug, looking.for = x)
# add the nodes which appear to be neighbours.
if (x %in% fn) {
pc = c(pc, node)
mb = c(mb, node)
if (debug)
cat(" @", node, "added to the parents and children. (HPC's OR)\n")
}#THEN
}#FOR
return(list(nbr = pc, mb = c(pc.superset, sp.superset)))
}#HYBRID.PC.HEURISTIC
hybrid.pc.nbr.search = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, max.sx = ncol(data), complete, debug = FALSE, start = start,
looking.for = NULL) {
mb = ia.fdr.markov.blanket(x, data = data, nodes = nodes, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist, start = start,
test = test, max.sx = max.sx, complete = complete, debug = debug)
# if the node is not in the markov blanket it cannot be a neighbour either.
if (!is.null(looking.for) && (looking.for %!in% mb))
return(NULL)
pc = hybrid.pc.filter(x, pc.superset = mb, sp.superset = NULL, data = data,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, max.sx = max.sx, complete = complete, debug = debug)
return(pc)
}#HYBRID.PC.NBR.SEARCH
hybrid.pc.de.pcs = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, complete, debug = FALSE) {
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
blacklisted = nodes[sapply(nodes,
function(y) { is.blacklisted(blacklist, c(x, y), both = TRUE) })]
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the parents and children superset of", x, ".\n")
}#THEN
if (debug)
cat(" * nodes to be tested for inclusion: '",
nodes[nodes %!in% x], "'.\n")
# all nodes are candidates initially, except for those whose status is already
# determined (from whitelist and blacklist).
to.check = setdiff(nodes, c(x, whitelisted, blacklisted))
# exclude nodes that are marginally independent from the target.
association = indep.test(to.check, x, sx = character(0), data = data,
test = test, B = B, alpha = alpha, complete = complete)
to.keep = names(association[association <= alpha])
to.drop = names(association[association > alpha])
pvalues = association[to.keep]
if (debug) {
cat(" * checking nodes for association.\n")
if (length(to.keep) > 0) {
cat(" * nodes that are still candidates for inclusion.\n")
sapply(to.keep,
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
if (length(to.drop) > 0) {
cat(" * nodes that will be disregarded from now on.\n")
sapply(to.drop,
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
}#THEN
# sort the candidates in order of increasing association, so that nodes with
# weak associations are checked for exclusion first.
pvalues = sort(pvalues, decreasing = TRUE)
to.check = names(pvalues)
fixed = whitelisted
if (debug)
cat(" * nodes to be tested for exclusion: '", to.check, "'.\n")
pvalues = structure(c(rep(0, length(fixed)), pvalues),
names = c(fixed, names(pvalues)))
dsep.set = list()
# if there is only a single node left to check, it would be conditional on
# the empty set which would just repeat an earlier test; nothing left to do.
if (length(to.check) == 1)
return(structure(pvalues, dsep.set = dsep.set))
# exlcude nodes that are independent given a single conditioning node.
for (node in to.check) {
# sort the candidates in order of decreasing association, so that nodes with
# strong associations are checked first.
to.check.against = setdiff(names(sort(pvalues, decreasing = FALSE)), node)
if (length(to.check.against) == 0)
next
a = allsubs.test(x = x, y = node, sx = to.check.against, min = 1,
max = 1, data = data, test = test, alpha = alpha, B = B,
complete = complete, debug = debug)
if (a["p.value"] > alpha) {
pvalues = pvalues[names(pvalues) != node]
dsep.set[[node]] = attr(a, "dsep.set")
}#THEN
else {
pvalues[node] = max(pvalues[node], a["max.p.value"])
}#ELSE
}#FOR
return(structure(pvalues, dsep.set = dsep.set))
}#HYBRID.PC.DE.PCS
hybrid.pc.de.sps = function(x, data, nodes, pc.superset, dsep.set, alpha, B,
test, max.sx, complete, debug = FALSE) {
spouses.superset = character(0)
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the spouses superset of", x, ".\n")
cat(" * nodes still to be tested for inclusion:",
nodes[nodes %!in% c(x, pc.superset)], "\n")
}#THEN
for (cpc in pc.superset) {
pvalues = numeric(0)
# forward selection.
for (y in setdiff(nodes, c(x, pc.superset))) {
# if the candidate node d-separates the current node from a node that is
# not in the superset, it is potentially in the markov blanket and thus a
# potential spouse.
if (cpc %in% dsep.set[[y]])
next
# skip tests whose conditioning sets are too large (assuming independence
# means not adding the node to the superset).
if (length(c(dsep.set[[y]], cpc)) > max.sx)
next
if (debug)
cat(" > checking node", y, "for inclusion.\n")
a = indep.test(x = x, y = y, sx = c(dsep.set[[y]], cpc), data = data,
test = test, B = B, alpha = alpha, complete = complete)
if (debug)
cat(" > node", x, "is",
ifelse(a > alpha, "independent from", "dependent on"), y, "given",
c(dsep.set[[y]], cpc), " ( p-value:", a, ").\n")
if (a <= alpha) {
pvalues[y] = a
if (debug)
cat(" @ node", y, "added to the spouses superset.\n")
}#THEN
}#FOR
# sort the candidates in order of increasing association, so that nodes with
# weak associations are checked for exclusion first.
pvalues = sort(pvalues, decreasing = TRUE)
# backward selection, to remove false positives.
for (y in names(pvalues)) {
sx = setdiff(names(pvalues), y)
if (length(sx) == 0)
next
if (debug)
cat(" > checking node", y, "for removal.\n")
a = allsubs.test(x = x, y = y, sx = sx, fixed = cpc, data = data,
test = test, B = B, alpha = alpha, complete = complete, min = 1,
max = 1, debug = debug)
if (a["p.value"] > alpha) {
pvalues = pvalues[names(pvalues) != y]
if (debug)
cat(" @ node", y, "removed from the spouses superset.\n")
}#THEN
}#FOR
spouses.superset = union(spouses.superset, names(pvalues))
}#FOR
return(spouses.superset)
}#HYBRID.PC.DE.SPS
hybrid.pc.filter = function(x, pc.superset, sp.superset, data, alpha, B = B,
whitelist, blacklist, test, max.sx, complete, debug = FALSE) {
nodes = names(data)
mb.superset = c(pc.superset, sp.superset)
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
blacklisted = nodes[sapply(nodes,
function(y) { is.blacklisted(blacklist, c(x, y), both = TRUE) })]
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* filtering parents and children of", x, ".\n")
cat(" * blacklisted nodes: '", blacklisted, "'\n")
cat(" * whitelisted nodes: '", whitelisted, "'\n")
cat(" * starting with neighbourhood superset: '", pc.superset, "'\n")
cat(" * with spouses superset: '", sp.superset, "'\n")
}#THEN
# make sure blacklisted nodes are not included, and add whitelisted nodes.
pc.superset = union(setdiff(pc.superset, blacklisted), whitelisted)
mb.superset = union(mb.superset, whitelisted)
# if the markov blanket is empty, the neighbourhood is empty as well.
if (length(mb.superset) == 0)
return(character(0))
nbr = function(node) {
a = allsubs.test(x = x, y = node, sx = setdiff(mb.superset, node),
data = data, test = test, B = B, alpha = alpha, max = max.sx,
complete = complete, debug = debug)
return(as.logical(a["p.value"] < alpha))
}#NBR
# do not even try to remove whitelisted nodes.
pc = names(which(sapply(setdiff(pc.superset, whitelisted), nbr)))
# make sure whitelisted nodes are always included.
pc = unique(c(pc, whitelisted))
return(pc)
}#HYBRID.PC.FILTER
| /R/hybrid-pc.R | no_license | cran/bnlearn | R | false | false | 11,462 | r |
hybrid.pc.backend = function(x, cluster = NULL, whitelist, blacklist,
test, alpha, B, max.sx = ncol(x), complete, debug = FALSE) {
nodes = names(x)
mb = smartSapply(cluster, as.list(nodes), hybrid.pc.heuristic, data = x,
nodes = nodes, alpha = alpha, B = B, whitelist = whitelist,
blacklist = blacklist, test = test, max.sx = max.sx,
complete = complete, debug = debug)
names(mb) = nodes
# make up a set of believable Markov blankets, using all the nodes within
# distance 2 from the target node (which is a superset).
for (node in nodes)
mb[[node]]$mb = fake.markov.blanket(mb, node)
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, debug = debug)
return(mb)
}#HYBRID.PC.BACKEND
hybrid.pc.heuristic = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, max.sx = ncol(data), complete, debug = FALSE) {
# identify the parents-and-children superset.
pvalues = hybrid.pc.de.pcs(x = x, data = data, nodes = nodes, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist,
test = test, complete = complete, debug = debug)
pc.superset = names(pvalues)
# if the superset contains zero or just one nodes, there is nothing else to do
# (the superset is not super, it is the right set).
if (length(pc.superset) < 2)
return(list(nbr = pc.superset, mb = NULL))
# identify the spouses superset (some spouses may already be in the
# parents-and-children superset).
sp.superset = hybrid.pc.de.sps(x = x, data = data, nodes = nodes,
pc.superset = pc.superset, dsep.set = attr(pvalues, "dsep.set"),
alpha = alpha, B = B, test = test, max.sx = max.sx,
complete = complete, debug = debug)
# if there are just two nodes in the parents-and-children set and no spouse,
# the superset is necessarily the same as the set..
if ((length(pc.superset) == 2) && (length(sp.superset) == 0))
return(list(nbr = pc.superset, mb = pc.superset))
# the two nodes with the smallest p-values would be examined again using the
# same low-order tests as before, so just include them.
start = names(sort(pvalues))[1:min(length(pvalues), 2)]
# identify the real parents and children from the supersets.
pc = hybrid.pc.nbr.search(x, data = data,
nodes = c(x, pc.superset, sp.superset), alpha = alpha, B = B,
whitelist = whitelist, blacklist = blacklist, test = test,
max.sx = max.sx, complete = complete, start = start, debug = debug)
# one more scan to identify possible false negatives.
for (node in setdiff(pc.superset, pc)) {
fn = hybrid.pc.nbr.search(node, data = data,
nodes = c(x, pc.superset, sp.superset), alpha = alpha, B = B,
whitelist = whitelist, blacklist = blacklist, test = test,
max.sx = max.sx, complete = complete, start = start,
debug = debug, looking.for = x)
# add the nodes which appear to be neighbours.
if (x %in% fn) {
pc = c(pc, node)
mb = c(mb, node)
if (debug)
cat(" @", node, "added to the parents and children. (HPC's OR)\n")
}#THEN
}#FOR
return(list(nbr = pc, mb = c(pc.superset, sp.superset)))
}#HYBRID.PC.HEURISTIC
hybrid.pc.nbr.search = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, max.sx = ncol(data), complete, debug = FALSE, start = start,
looking.for = NULL) {
mb = ia.fdr.markov.blanket(x, data = data, nodes = nodes, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist, start = start,
test = test, max.sx = max.sx, complete = complete, debug = debug)
# if the node is not in the markov blanket it cannot be a neighbour either.
if (!is.null(looking.for) && (looking.for %!in% mb))
return(NULL)
pc = hybrid.pc.filter(x, pc.superset = mb, sp.superset = NULL, data = data,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, max.sx = max.sx, complete = complete, debug = debug)
return(pc)
}#HYBRID.PC.NBR.SEARCH
hybrid.pc.de.pcs = function(x, data, nodes, alpha, B, whitelist, blacklist,
test, complete, debug = FALSE) {
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
blacklisted = nodes[sapply(nodes,
function(y) { is.blacklisted(blacklist, c(x, y), both = TRUE) })]
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the parents and children superset of", x, ".\n")
}#THEN
if (debug)
cat(" * nodes to be tested for inclusion: '",
nodes[nodes %!in% x], "'.\n")
# all nodes are candidates initially, except for those whose status is already
# determined (from whitelist and blacklist).
to.check = setdiff(nodes, c(x, whitelisted, blacklisted))
# exclude nodes that are marginally independent from the target.
association = indep.test(to.check, x, sx = character(0), data = data,
test = test, B = B, alpha = alpha, complete = complete)
to.keep = names(association[association <= alpha])
to.drop = names(association[association > alpha])
pvalues = association[to.keep]
if (debug) {
cat(" * checking nodes for association.\n")
if (length(to.keep) > 0) {
cat(" * nodes that are still candidates for inclusion.\n")
sapply(to.keep,
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
if (length(to.drop) > 0) {
cat(" * nodes that will be disregarded from now on.\n")
sapply(to.drop,
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
}#THEN
# sort the candidates in order of increasing association, so that nodes with
# weak associations are checked for exclusion first.
pvalues = sort(pvalues, decreasing = TRUE)
to.check = names(pvalues)
fixed = whitelisted
if (debug)
cat(" * nodes to be tested for exclusion: '", to.check, "'.\n")
pvalues = structure(c(rep(0, length(fixed)), pvalues),
names = c(fixed, names(pvalues)))
dsep.set = list()
# if there is only a single node left to check, it would be conditional on
# the empty set which would just repeat an earlier test; nothing left to do.
if (length(to.check) == 1)
return(structure(pvalues, dsep.set = dsep.set))
# exlcude nodes that are independent given a single conditioning node.
for (node in to.check) {
# sort the candidates in order of decreasing association, so that nodes with
# strong associations are checked first.
to.check.against = setdiff(names(sort(pvalues, decreasing = FALSE)), node)
if (length(to.check.against) == 0)
next
a = allsubs.test(x = x, y = node, sx = to.check.against, min = 1,
max = 1, data = data, test = test, alpha = alpha, B = B,
complete = complete, debug = debug)
if (a["p.value"] > alpha) {
pvalues = pvalues[names(pvalues) != node]
dsep.set[[node]] = attr(a, "dsep.set")
}#THEN
else {
pvalues[node] = max(pvalues[node], a["max.p.value"])
}#ELSE
}#FOR
return(structure(pvalues, dsep.set = dsep.set))
}#HYBRID.PC.DE.PCS
hybrid.pc.de.sps = function(x, data, nodes, pc.superset, dsep.set, alpha, B,
test, max.sx, complete, debug = FALSE) {
spouses.superset = character(0)
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the spouses superset of", x, ".\n")
cat(" * nodes still to be tested for inclusion:",
nodes[nodes %!in% c(x, pc.superset)], "\n")
}#THEN
for (cpc in pc.superset) {
pvalues = numeric(0)
# forward selection.
for (y in setdiff(nodes, c(x, pc.superset))) {
# if the candidate node d-separates the current node from a node that is
# not in the superset, it is potentially in the markov blanket and thus a
# potential spouse.
if (cpc %in% dsep.set[[y]])
next
# skip tests whose conditioning sets are too large (assuming independence
# means not adding the node to the superset).
if (length(c(dsep.set[[y]], cpc)) > max.sx)
next
if (debug)
cat(" > checking node", y, "for inclusion.\n")
a = indep.test(x = x, y = y, sx = c(dsep.set[[y]], cpc), data = data,
test = test, B = B, alpha = alpha, complete = complete)
if (debug)
cat(" > node", x, "is",
ifelse(a > alpha, "independent from", "dependent on"), y, "given",
c(dsep.set[[y]], cpc), " ( p-value:", a, ").\n")
if (a <= alpha) {
pvalues[y] = a
if (debug)
cat(" @ node", y, "added to the spouses superset.\n")
}#THEN
}#FOR
# sort the candidates in order of increasing association, so that nodes with
# weak associations are checked for exclusion first.
pvalues = sort(pvalues, decreasing = TRUE)
# backward selection, to remove false positives.
for (y in names(pvalues)) {
sx = setdiff(names(pvalues), y)
if (length(sx) == 0)
next
if (debug)
cat(" > checking node", y, "for removal.\n")
a = allsubs.test(x = x, y = y, sx = sx, fixed = cpc, data = data,
test = test, B = B, alpha = alpha, complete = complete, min = 1,
max = 1, debug = debug)
if (a["p.value"] > alpha) {
pvalues = pvalues[names(pvalues) != y]
if (debug)
cat(" @ node", y, "removed from the spouses superset.\n")
}#THEN
}#FOR
spouses.superset = union(spouses.superset, names(pvalues))
}#FOR
return(spouses.superset)
}#HYBRID.PC.DE.SPS
hybrid.pc.filter = function(x, pc.superset, sp.superset, data, alpha, B = B,
whitelist, blacklist, test, max.sx, complete, debug = FALSE) {
nodes = names(data)
mb.superset = c(pc.superset, sp.superset)
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
blacklisted = nodes[sapply(nodes,
function(y) { is.blacklisted(blacklist, c(x, y), both = TRUE) })]
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* filtering parents and children of", x, ".\n")
cat(" * blacklisted nodes: '", blacklisted, "'\n")
cat(" * whitelisted nodes: '", whitelisted, "'\n")
cat(" * starting with neighbourhood superset: '", pc.superset, "'\n")
cat(" * with spouses superset: '", sp.superset, "'\n")
}#THEN
# make sure blacklisted nodes are not included, and add whitelisted nodes.
pc.superset = union(setdiff(pc.superset, blacklisted), whitelisted)
mb.superset = union(mb.superset, whitelisted)
# if the markov blanket is empty, the neighbourhood is empty as well.
if (length(mb.superset) == 0)
return(character(0))
nbr = function(node) {
a = allsubs.test(x = x, y = node, sx = setdiff(mb.superset, node),
data = data, test = test, B = B, alpha = alpha, max = max.sx,
complete = complete, debug = debug)
return(as.logical(a["p.value"] < alpha))
}#NBR
# do not even try to remove whitelisted nodes.
pc = names(which(sapply(setdiff(pc.superset, whitelisted), nbr)))
# make sure whitelisted nodes are always included.
pc = unique(c(pc, whitelisted))
return(pc)
}#HYBRID.PC.FILTER
|
# Various objects have attributes that are worth preserving. This does that.
modify_object <- function(object, new_object) {
x_attr <- attributes(object)
x_class <- class(object)
attrs <- names(x_attr)
attrs <- attrs[!(attrs %in% c("class", "names", "dim", "dimnames", "row.names"))]
for(obj in attrs) {
attr(new_object, obj) <- x_attr[[obj]]
}
class(new_object) <- x_class
new_object
}
| /R/modify_object.R | no_license | byandell/intermediate | R | false | false | 410 | r | # Various objects have attributes that are worth preserving. This does that.
modify_object <- function(object, new_object) {
x_attr <- attributes(object)
x_class <- class(object)
attrs <- names(x_attr)
attrs <- attrs[!(attrs %in% c("class", "names", "dim", "dimnames", "row.names"))]
for(obj in attrs) {
attr(new_object, obj) <- x_attr[[obj]]
}
class(new_object) <- x_class
new_object
}
|
library(limma)
library(stats)
library(VennDiagram)
library(gtools)
library(ggplot2)
setwd('/group/stranger-lab/immvar/meta')
load('/group/stranger-lab/immvar_rep/mappings/annot.Robj')
cd4.meta <- read.table(file='cd4_meta1.txt', header=T)
cd14.meta <- read.table(file='cd14_meta1.txt', header=T)
cd4_gene_counts <- table(annots[as.character(cd4.meta$MarkerName),"chr"])
cd14_gene_counts <- table(annots[as.character(cd14.meta$MarkerName),"chr"])
cd4.meta$Q.value <- p.adjust(cd4.meta$P.value, method="BH")
cd4.meta <- cd4.meta[order(cd4.meta$Q.value),]
cd14.meta$Q.value <- p.adjust(cd14.meta$P.value, method="BH")
cd14.meta <- cd14.meta[order(cd14.meta$Q.value),]
meta_sig_cd4_bh <- cd4.meta[cd4.meta$Q.value<=0.05, ]
meta_sig_cd4_bh <- meta_sig_cd4_bh[order(meta_sig_cd4_bh$P.value),]
rownames(meta_sig_cd4_bh) <- as.character(meta_sig_cd4_bh$MarkerName)
cd4_meta_percent <- table(annots[rownames(meta_sig_cd4_bh), "chr"]) / cd4_gene_counts
cd4_gene_pers <- data.frame(chr_name=names(cd4_meta_percent), per = as.numeric(cd4_meta_percent))
g <- ggplot(cd4_gene_pers, aes(x=chr_name, y=per))
pdf(file='/group/stranger-lab/czysz/cd4.meta.locs.pdf')
g + geom_bar(stat="identity")
dev.off()
meta_sig_cd14_bh <- cd14.meta[cd14.meta$Q.value<=0.05, ]
meta_sig_cd14_bh <- meta_sig_cd14_bh[order(meta_sig_cd14_bh$P.value),]
rownames(meta_sig_cd14_bh) <- as.character(meta_sig_cd14_bh$MarkerName)
cd14_meta_percent <- table(annots[rownames(meta_sig_cd14_bh), "chr"]) / cd14_gene_counts
cd14_gene_pers <- data.frame(chr=mixedorder(names(cd14_meta_percent)), chr_name = names(cd14_meta_percent), per = as.numeric(cd14_meta_percent))
setwd('/group/stranger-lab/immvar_data')
cd14.fits<-list()
cd4.fits<-list()
for ( pop in c('Caucasian', 'African-American', 'Asian')) {
for ( cell in c('CD14', 'CD4') ) {
if (cell=='CD14') { load(paste('fit',pop, cell, 'Robj', sep='.'))
eb.fit$Q.value <- p.adjust(eb.fit$p.value, method='fdr')
cd14.fits[[pop]]<-data.frame(eb.fit)
} else { load(paste('fit',pop, cell, 'Robj', sep='.'))
eb.fit$Q.value <- p.adjust(eb.fit$p.value, method='fdr')
cd4.fits[[pop]]<-data.frame(eb.fit) }
}
}
# CD14 Venn Diagram
cd14.venn <- list(cau=rownames(subset(cd14.fits[[1]], Q.value<0.05)),
afr=rownames(subset(cd14.fits[[2]], Q.value<0.05)),
asn=rownames(subset(cd14.fits[[3]], Q.value<0.05)))
cd14.shared <- intersect(cd14.venn[[1]], intersect(cd14.venn[[2]], cd14.venn[[3]]))
print(table(annots[cd14.shared, "chr"]))
venn.diagram(cd14.venn,
filename='/group/stranger-lab/czysz/cd14_separate_venn.tiff',
fontfamily="Helvetica",
main.fontfamily="Helvetica",
cat.fontfamily="Helvetica",
sub.fontfamily="Helvetica",
fill=topo.colors(3),
main="CD14 - Separate VennDiagram", sub.cex=1.5,
width=10, height=10, units="in")
# CD4
cd4.venn <- list(cau=rownames(subset(cd4.fits[[1]], Q.value<0.05)),
afr=rownames(subset(cd4.fits[[2]], Q.value<0.05)),
asn=rownames(subset(cd4.fits[[3]], Q.value<0.05)))
cd4.shared <- intersect(cd4.venn[[1]], intersect(cd4.venn[[2]], cd4.venn[[3]]))
print(table(annots[cd4.shared, "chr"]))
venn.diagram(cd4.venn,
filename='/group/stranger-lab/czysz/cd4_separate_venn.tiff',
fontfamily="Helvetica",
main.fontfamily="Helvetica",
cat.fontfamily="Helvetica",
sub.fontfamily="Helvetica",
fill=topo.colors(3),
main="CD4 - Separate VennDiagram", sub.cex=1.5,
width=10, height=10, units="in")
| /meta/analyze_sep_meta.R | permissive | cczysz/immvar | R | false | false | 3,376 | r | library(limma)
library(stats)
library(VennDiagram)
library(gtools)
library(ggplot2)
setwd('/group/stranger-lab/immvar/meta')
load('/group/stranger-lab/immvar_rep/mappings/annot.Robj')
cd4.meta <- read.table(file='cd4_meta1.txt', header=T)
cd14.meta <- read.table(file='cd14_meta1.txt', header=T)
cd4_gene_counts <- table(annots[as.character(cd4.meta$MarkerName),"chr"])
cd14_gene_counts <- table(annots[as.character(cd14.meta$MarkerName),"chr"])
cd4.meta$Q.value <- p.adjust(cd4.meta$P.value, method="BH")
cd4.meta <- cd4.meta[order(cd4.meta$Q.value),]
cd14.meta$Q.value <- p.adjust(cd14.meta$P.value, method="BH")
cd14.meta <- cd14.meta[order(cd14.meta$Q.value),]
meta_sig_cd4_bh <- cd4.meta[cd4.meta$Q.value<=0.05, ]
meta_sig_cd4_bh <- meta_sig_cd4_bh[order(meta_sig_cd4_bh$P.value),]
rownames(meta_sig_cd4_bh) <- as.character(meta_sig_cd4_bh$MarkerName)
cd4_meta_percent <- table(annots[rownames(meta_sig_cd4_bh), "chr"]) / cd4_gene_counts
cd4_gene_pers <- data.frame(chr_name=names(cd4_meta_percent), per = as.numeric(cd4_meta_percent))
g <- ggplot(cd4_gene_pers, aes(x=chr_name, y=per))
pdf(file='/group/stranger-lab/czysz/cd4.meta.locs.pdf')
g + geom_bar(stat="identity")
dev.off()
meta_sig_cd14_bh <- cd14.meta[cd14.meta$Q.value<=0.05, ]
meta_sig_cd14_bh <- meta_sig_cd14_bh[order(meta_sig_cd14_bh$P.value),]
rownames(meta_sig_cd14_bh) <- as.character(meta_sig_cd14_bh$MarkerName)
cd14_meta_percent <- table(annots[rownames(meta_sig_cd14_bh), "chr"]) / cd14_gene_counts
cd14_gene_pers <- data.frame(chr=mixedorder(names(cd14_meta_percent)), chr_name = names(cd14_meta_percent), per = as.numeric(cd14_meta_percent))
setwd('/group/stranger-lab/immvar_data')
cd14.fits<-list()
cd4.fits<-list()
for ( pop in c('Caucasian', 'African-American', 'Asian')) {
for ( cell in c('CD14', 'CD4') ) {
if (cell=='CD14') { load(paste('fit',pop, cell, 'Robj', sep='.'))
eb.fit$Q.value <- p.adjust(eb.fit$p.value, method='fdr')
cd14.fits[[pop]]<-data.frame(eb.fit)
} else { load(paste('fit',pop, cell, 'Robj', sep='.'))
eb.fit$Q.value <- p.adjust(eb.fit$p.value, method='fdr')
cd4.fits[[pop]]<-data.frame(eb.fit) }
}
}
# CD14 Venn Diagram
cd14.venn <- list(cau=rownames(subset(cd14.fits[[1]], Q.value<0.05)),
afr=rownames(subset(cd14.fits[[2]], Q.value<0.05)),
asn=rownames(subset(cd14.fits[[3]], Q.value<0.05)))
cd14.shared <- intersect(cd14.venn[[1]], intersect(cd14.venn[[2]], cd14.venn[[3]]))
print(table(annots[cd14.shared, "chr"]))
venn.diagram(cd14.venn,
filename='/group/stranger-lab/czysz/cd14_separate_venn.tiff',
fontfamily="Helvetica",
main.fontfamily="Helvetica",
cat.fontfamily="Helvetica",
sub.fontfamily="Helvetica",
fill=topo.colors(3),
main="CD14 - Separate VennDiagram", sub.cex=1.5,
width=10, height=10, units="in")
# CD4
cd4.venn <- list(cau=rownames(subset(cd4.fits[[1]], Q.value<0.05)),
afr=rownames(subset(cd4.fits[[2]], Q.value<0.05)),
asn=rownames(subset(cd4.fits[[3]], Q.value<0.05)))
cd4.shared <- intersect(cd4.venn[[1]], intersect(cd4.venn[[2]], cd4.venn[[3]]))
print(table(annots[cd4.shared, "chr"]))
venn.diagram(cd4.venn,
filename='/group/stranger-lab/czysz/cd4_separate_venn.tiff',
fontfamily="Helvetica",
main.fontfamily="Helvetica",
cat.fontfamily="Helvetica",
sub.fontfamily="Helvetica",
fill=topo.colors(3),
main="CD4 - Separate VennDiagram", sub.cex=1.5,
width=10, height=10, units="in")
|
# Read the BPRS data
BPRS <- read.table("https://raw.githubusercontent.com/KimmoVehkalahti/MABS/master/Examples/data/BPRS.txt", sep =" ", header = T)
# Look at the (column) names of BPRS
names(BPRS)
# Look at the structure of BPRS
str(BPRS)
# Print out summaries of the variables
summary(BPRS)
# The data BPRS is available
# Access the packages dplyr and tidyr
library(dplyr)
library(tidyr)
# Factor treatment & subject
BPRS$treatment <- factor(BPRS$treatment)
BPRS$subject <- factor(BPRS$subject)
# Convert to long form
BPRSL <- BPRS %>% gather(key = weeks, value = bprs, -treatment, -subject)
# Extract the week number
BPRSL <- BPRSL %>% mutate(week = as.integer(substr(weeks,5,5)))
# Take a glimpse at the BPRSL data
glimpse(BPRSL)
#Access the package ggplot2
library(ggplot2)
# Draw the plot
ggplot(BPRSL, aes(x = week, y = bprs, linetype = subject)) +
geom_line() +
scale_linetype_manual(values = rep(1:10, times=4)) +
facet_grid(. ~ treatment, labeller = label_both) +
theme(legend.position = "none") +
scale_y_continuous(limits = c(min(BPRSL$bprs), max(BPRSL$bprs)))
# dplyr, tidyr and ggplot2 packages and BPRSL are available
# Standardise the variable bprs
BPRSL <- BPRSL %>%
group_by(week) %>%
mutate(stdbprs = (bprs - mean(bprs))/sd(bprs) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSL)
# Plot again with the standardised bprs
ggplot(BPRSL, aes(x = week, y = stdbprs, linetype = subject)) +
geom_line() +
scale_linetype_manual(values = rep(1:10, times=4)) +
facet_grid(. ~ treatment, labeller = label_both) +
scale_y_continuous(name = "standardized bprs")
# dplyr, tidyr & ggplot2 packages and BPRSL are available
# Number of weeks, baseline (week 0) included
n <- BPRSL$week %>% unique() %>% length()
# Summary data with mean and standard error of bprs by treatment and week
BPRSS <- BPRSL %>%
group_by(treatment, week) %>%
summarise( mean = mean(bprs), se = sd(bprs)/sqrt(n) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSS)
# A very nice picture! i think going to be useful.
# Plot the mean profiles
ggplot(BPRSS, aes(x = week, y = mean, linetype = treatment, shape = treatment)) +
geom_line() +
scale_linetype_manual(values = c(1,2)) +
geom_point(size=3) +
scale_shape_manual(values = c(1,2)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se, linetype="1"), width=0.3) +
theme(legend.position = c(0.8,0.8)) +
scale_y_continuous(name = "mean(bprs) +/- se(bprs)")
# dplyr, tidyr & ggplot2 packages and BPRSL are available
# Create a summary data by treatment and subject with mean as the summary variable (ignoring baseline week 0).
BPRSL8S <- BPRSL %>%
filter(week > 0) %>%
group_by(treatment, subject) %>%
summarise( mean=mean(bprs) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSL8S)
# Draw a boxplot of the mean versus treatment
ggplot(BPRSL8S, aes(x = treatment, y = mean)) +
geom_boxplot() +
stat_summary(fun.y = "mean", geom = "point", shape=23, size=4, fill = "white") +
scale_y_continuous(name = "mean(bprs), weeks 1-8")
# Create a new data by filtering the outlier and adjust the ggplot code the draw the plot again with the new data
BPRSL8S1 <- BPRSL8S %>%
filter(mean < 65)
# dplyr, tidyr & ggplot2 packages and BPRSL8S & BPRSL8S1 data are available
# Perform a two-sample t-test
t.test(mean ~ treatment, data = BPRSL8S1, var.equal = TRUE)
# Add the baseline from the original data as a new variable to the summary data
BPRSL8S2 <- BPRSL8S %>%
mutate(baseline = BPRS$week0)
# Fit the linear model with the mean as the response
fit <- lm(mean ~ baseline + treatment, data = BPRSL8S2)
summary(fit)
# Compute the analysis of variance table for the fitted model with anova()
# this shows about the same as summary(fit)
anova(fit)
########################################################
# read the RATS data
RATS <- read.table("https://raw.githubusercontent.com/KimmoVehkalahti/MABS/master/Examples/data/rats.txt", header = TRUE, sep = '\t')
# Factor variables ID and Group
RATS$ID <- factor(RATS$ID)
RATS$Group <- factor(RATS$Group)
# Glimpse the data
glimpse(RATS)
# dplyr, tidyr and RATS are available
# Convert data to long form
str(RATS)
RATSL <- RATS %>%
gather(key = WD, value = Weight, -ID, -Group) %>%
mutate(Time = as.integer(substr(WD,3,4)))
# Glimpse the data
glimpse(RATSL)
# dplyr, tidyr and RATSL are available
# Check the dimensions of the data
dim(RATSL)
# Plot the RATSL data
ggplot(RATSL, aes(x = Time, y = Weight, group = ID)) +
geom_line()
# dplyr, tidyr, RATS and RATSL are available
# create a regression model RATS_reg
RATS_reg <- lm(Weight ~ Time + Group, data = RATSL)
# print out a summary of the model
summary(RATS_reg)
# dplyr, tidyr, RATS and RATSL are available
# access library lme4
library(lme4)
# Create a random intercept model
RATS_ref <- lmer(Weight ~ Time + Group + (1 | ID), data = RATSL, REML = FALSE)
# Print the summary of the model
# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available
# create a random intercept and random slope model
RATS_ref1 <- lmer(Weight ~ Time + Group + (Time | ID), data = RATSL, REML = FALSE)
# print a summary of the model
summary(RATS_ref1)
# perform an ANOVA test on the two models
anova(RATS_ref1, RATS_ref)
# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available
# create a random intercept and random slope model with the interaction
RATS_ref2 <- lmer(Weight ~ Group + Time + (Time|ID) +Time*Group, REML= FALSE, data= RATSL)
# print a summary of the model
summary(RATS_ref2)
# perform an ANOVA test on the two models
anova(RATS_ref2, RATS_ref1)
# draw the plot of RATSL with the observed Weight values
ggplot(RATSL, aes(x = Time, y = Weight, group = ID)) +
geom_line(aes(linetype = Group)) +
scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
scale_y_continuous(name = "Observed weight (grams)") +
theme(legend.position = "top")
# Create a vector of the fitted values
Fitted <- fitted(RATS_ref2)
help(fitted)
# Create a new column fitted to RATSL
RATSL <- RATSL %>%
mutate(Fitted)
# draw the plot of RATSL with the Fitted values of weight
ggplot(RATSL, aes(x = Time, y = Fitted, group = ID)) +
geom_line(aes(linetype = Group)) +
scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
scale_y_continuous(name = "Fitted weight (grams)") +
theme(legend.position = "top")
| /week6_Datacamp_lmm.R | no_license | atiitu/IODS-project | R | false | false | 6,363 | r | # Read the BPRS data
BPRS <- read.table("https://raw.githubusercontent.com/KimmoVehkalahti/MABS/master/Examples/data/BPRS.txt", sep =" ", header = T)
# Look at the (column) names of BPRS
names(BPRS)
# Look at the structure of BPRS
str(BPRS)
# Print out summaries of the variables
summary(BPRS)
# The data BPRS is available
# Access the packages dplyr and tidyr
library(dplyr)
library(tidyr)
# Factor treatment & subject
BPRS$treatment <- factor(BPRS$treatment)
BPRS$subject <- factor(BPRS$subject)
# Convert to long form
BPRSL <- BPRS %>% gather(key = weeks, value = bprs, -treatment, -subject)
# Extract the week number
BPRSL <- BPRSL %>% mutate(week = as.integer(substr(weeks,5,5)))
# Take a glimpse at the BPRSL data
glimpse(BPRSL)
#Access the package ggplot2
library(ggplot2)
# Draw the plot
ggplot(BPRSL, aes(x = week, y = bprs, linetype = subject)) +
geom_line() +
scale_linetype_manual(values = rep(1:10, times=4)) +
facet_grid(. ~ treatment, labeller = label_both) +
theme(legend.position = "none") +
scale_y_continuous(limits = c(min(BPRSL$bprs), max(BPRSL$bprs)))
# dplyr, tidyr and ggplot2 packages and BPRSL are available
# Standardise the variable bprs
BPRSL <- BPRSL %>%
group_by(week) %>%
mutate(stdbprs = (bprs - mean(bprs))/sd(bprs) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSL)
# Plot again with the standardised bprs
ggplot(BPRSL, aes(x = week, y = stdbprs, linetype = subject)) +
geom_line() +
scale_linetype_manual(values = rep(1:10, times=4)) +
facet_grid(. ~ treatment, labeller = label_both) +
scale_y_continuous(name = "standardized bprs")
# dplyr, tidyr & ggplot2 packages and BPRSL are available
# Number of weeks, baseline (week 0) included
n <- BPRSL$week %>% unique() %>% length()
# Summary data with mean and standard error of bprs by treatment and week
BPRSS <- BPRSL %>%
group_by(treatment, week) %>%
summarise( mean = mean(bprs), se = sd(bprs)/sqrt(n) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSS)
# A very nice picture! i think going to be useful.
# Plot the mean profiles
ggplot(BPRSS, aes(x = week, y = mean, linetype = treatment, shape = treatment)) +
geom_line() +
scale_linetype_manual(values = c(1,2)) +
geom_point(size=3) +
scale_shape_manual(values = c(1,2)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se, linetype="1"), width=0.3) +
theme(legend.position = c(0.8,0.8)) +
scale_y_continuous(name = "mean(bprs) +/- se(bprs)")
# dplyr, tidyr & ggplot2 packages and BPRSL are available
# Create a summary data by treatment and subject with mean as the summary variable (ignoring baseline week 0).
BPRSL8S <- BPRSL %>%
filter(week > 0) %>%
group_by(treatment, subject) %>%
summarise( mean=mean(bprs) ) %>%
ungroup()
# Glimpse the data
glimpse(BPRSL8S)
# Draw a boxplot of the mean versus treatment
ggplot(BPRSL8S, aes(x = treatment, y = mean)) +
geom_boxplot() +
stat_summary(fun.y = "mean", geom = "point", shape=23, size=4, fill = "white") +
scale_y_continuous(name = "mean(bprs), weeks 1-8")
# Create a new data by filtering the outlier and adjust the ggplot code the draw the plot again with the new data
BPRSL8S1 <- BPRSL8S %>%
filter(mean < 65)
# dplyr, tidyr & ggplot2 packages and BPRSL8S & BPRSL8S1 data are available
# Perform a two-sample t-test
t.test(mean ~ treatment, data = BPRSL8S1, var.equal = TRUE)
# Add the baseline from the original data as a new variable to the summary data
BPRSL8S2 <- BPRSL8S %>%
mutate(baseline = BPRS$week0)
# Fit the linear model with the mean as the response
fit <- lm(mean ~ baseline + treatment, data = BPRSL8S2)
summary(fit)
# Compute the analysis of variance table for the fitted model with anova()
# this shows about the same as summary(fit)
anova(fit)
########################################################
# read the RATS data
RATS <- read.table("https://raw.githubusercontent.com/KimmoVehkalahti/MABS/master/Examples/data/rats.txt", header = TRUE, sep = '\t')
# Factor variables ID and Group
RATS$ID <- factor(RATS$ID)
RATS$Group <- factor(RATS$Group)
# Glimpse the data
glimpse(RATS)
# dplyr, tidyr and RATS are available
# Convert data to long form
str(RATS)
RATSL <- RATS %>%
gather(key = WD, value = Weight, -ID, -Group) %>%
mutate(Time = as.integer(substr(WD,3,4)))
# Glimpse the data
glimpse(RATSL)
# dplyr, tidyr and RATSL are available
# Check the dimensions of the data
dim(RATSL)
# Plot the RATSL data
ggplot(RATSL, aes(x = Time, y = Weight, group = ID)) +
geom_line()
# dplyr, tidyr, RATS and RATSL are available
# create a regression model RATS_reg
RATS_reg <- lm(Weight ~ Time + Group, data = RATSL)
# print out a summary of the model
summary(RATS_reg)
# dplyr, tidyr, RATS and RATSL are available
# access library lme4
library(lme4)
# Create a random intercept model
RATS_ref <- lmer(Weight ~ Time + Group + (1 | ID), data = RATSL, REML = FALSE)
# Print the summary of the model
# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available
# create a random intercept and random slope model
RATS_ref1 <- lmer(Weight ~ Time + Group + (Time | ID), data = RATSL, REML = FALSE)
# print a summary of the model
summary(RATS_ref1)
# perform an ANOVA test on the two models
anova(RATS_ref1, RATS_ref)
# dplyr, tidyr, lme4, ggplot2, RATS and RATSL are available
# create a random intercept and random slope model with the interaction
RATS_ref2 <- lmer(Weight ~ Group + Time + (Time|ID) +Time*Group, REML= FALSE, data= RATSL)
# print a summary of the model
summary(RATS_ref2)
# perform an ANOVA test on the two models
anova(RATS_ref2, RATS_ref1)
# draw the plot of RATSL with the observed Weight values
ggplot(RATSL, aes(x = Time, y = Weight, group = ID)) +
geom_line(aes(linetype = Group)) +
scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
scale_y_continuous(name = "Observed weight (grams)") +
theme(legend.position = "top")
# Create a vector of the fitted values
Fitted <- fitted(RATS_ref2)
help(fitted)
# Create a new column fitted to RATSL
RATSL <- RATSL %>%
mutate(Fitted)
# draw the plot of RATSL with the Fitted values of weight
ggplot(RATSL, aes(x = Time, y = Fitted, group = ID)) +
geom_line(aes(linetype = Group)) +
scale_x_continuous(name = "Time (days)", breaks = seq(0, 60, 20)) +
scale_y_continuous(name = "Fitted weight (grams)") +
theme(legend.position = "top")
|
#' ---
#' title: test Fox forces data
#' ---
library("tidyverse")
library("stringr")
library("assertthat")
fox_forces <- read_csv("data/fox_forces.csv") %>%
mutate(start_date = as.Date(start_date),
end_date = as.Date(end_date))
# Check validity of IDs
assert_that(length(unique(fox_forces$battle_id)) == nrow(fox_forces))
assert_that(all(str_detect(fox_forces$battle_id, "[UC]\\d+[A-Z]?")))
# belligerents
BELLIGERENTS <- c("US", "Confederate")
assert_that(all(!is.na(fox_forces$belligerent)))
assert_that(all(fox_forces$belligerent %in% BELLIGERENTS))
# states
STATES <- c("AR", "DC", "FL", "GA", "KY", "LA", "MD", "MO",
"MS", "NC", "PA", "SC", "TN", "VA", "WV")
assert_that(all(!is.na(fox_forces$state)))
assert_that(all(fox_forces$state %in% STATES))
# battle names
assert_that(all(!is.na(fox_forces$battle_name)))
assert_that(is.character(fox_forces$battle_name))
# dates
MIN_DATE <- as.Date("1861-04-10")
MAX_DATE <- as.Date("1865-04-17")
MAX_DURATION <- 119
assert_that(all(!is.na(fox_forces$start_date)))
assert_that(all(!is.na(fox_forces$end_date)))
assert_that(!nrow(filter(fox_forces, start_date < MIN_DATE)))
# assert_that(!nrow(filter(fox_forces, end_date > MAX_DATE)))
assert_that(!nrow(filter(fox_forces, end_date < start_date)))
# longest entry is 119 days (Atlanta Campaign)
assert_that(!nrow(filter(fox_forces,
as.integer(end_date - start_date)
<= UQ(MAX_DURATION))))
# killed
MAX_KILLED <- 4423
assert_that(min(fox_forces$killed, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$killed, na.rm = TRUE) <= MAX_KILLED)
# wounded
# Number wounded is so large because of the Atlanta Campaign
MAX_WOUNDED <- 22822
assert_that(min(fox_forces$wounded, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$wounded, na.rm = TRUE) <= MAX_WOUNDED)
# killed
MAX_MISSING <- 13829
assert_that(min(fox_forces$missing, na.rm = TRUE) >= 0)
assert_that(max(fox_forces$missing, na.rm = TRUE) <= MAX_MISSING)
# total casualties
MAX_CASUALTIES <- 31687
assert_that(min(fox_forces$casualties, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$casualties, na.rm = TRUE)
<= MAX_CASUALTIES)
| /tests/test_fox_forces.R | permissive | jrnold/acw_battle_data | R | false | false | 2,167 | r | #' ---
#' title: test Fox forces data
#' ---
library("tidyverse")
library("stringr")
library("assertthat")
fox_forces <- read_csv("data/fox_forces.csv") %>%
mutate(start_date = as.Date(start_date),
end_date = as.Date(end_date))
# Check validity of IDs
assert_that(length(unique(fox_forces$battle_id)) == nrow(fox_forces))
assert_that(all(str_detect(fox_forces$battle_id, "[UC]\\d+[A-Z]?")))
# belligerents
BELLIGERENTS <- c("US", "Confederate")
assert_that(all(!is.na(fox_forces$belligerent)))
assert_that(all(fox_forces$belligerent %in% BELLIGERENTS))
# states
STATES <- c("AR", "DC", "FL", "GA", "KY", "LA", "MD", "MO",
"MS", "NC", "PA", "SC", "TN", "VA", "WV")
assert_that(all(!is.na(fox_forces$state)))
assert_that(all(fox_forces$state %in% STATES))
# battle names
assert_that(all(!is.na(fox_forces$battle_name)))
assert_that(is.character(fox_forces$battle_name))
# dates
MIN_DATE <- as.Date("1861-04-10")
MAX_DATE <- as.Date("1865-04-17")
MAX_DURATION <- 119
assert_that(all(!is.na(fox_forces$start_date)))
assert_that(all(!is.na(fox_forces$end_date)))
assert_that(!nrow(filter(fox_forces, start_date < MIN_DATE)))
# assert_that(!nrow(filter(fox_forces, end_date > MAX_DATE)))
assert_that(!nrow(filter(fox_forces, end_date < start_date)))
# longest entry is 119 days (Atlanta Campaign)
assert_that(!nrow(filter(fox_forces,
as.integer(end_date - start_date)
<= UQ(MAX_DURATION))))
# killed
MAX_KILLED <- 4423
assert_that(min(fox_forces$killed, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$killed, na.rm = TRUE) <= MAX_KILLED)
# wounded
# Number wounded is so large because of the Atlanta Campaign
MAX_WOUNDED <- 22822
assert_that(min(fox_forces$wounded, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$wounded, na.rm = TRUE) <= MAX_WOUNDED)
# killed
MAX_MISSING <- 13829
assert_that(min(fox_forces$missing, na.rm = TRUE) >= 0)
assert_that(max(fox_forces$missing, na.rm = TRUE) <= MAX_MISSING)
# total casualties
MAX_CASUALTIES <- 31687
assert_that(min(fox_forces$casualties, nr.rm = TRUE) >= 0)
assert_that(max(fox_forces$casualties, na.rm = TRUE)
<= MAX_CASUALTIES)
|
testlist <- list(bytes1 = c(892679477L, 3487029L, 838860799L, -1L, -8585216L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), pmutation = 0)
result <- do.call(mcga:::ByteCodeMutation,testlist)
str(result) | /mcga/inst/testfiles/ByteCodeMutation/libFuzzer_ByteCodeMutation/ByteCodeMutation_valgrind_files/1612802295-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 328 | r | testlist <- list(bytes1 = c(892679477L, 3487029L, 838860799L, -1L, -8585216L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), pmutation = 0)
result <- do.call(mcga:::ByteCodeMutation,testlist)
str(result) |
## This R script takes the household power consumption data that you have,
## plots a histogram of the global active power in kilowatts for Feb 01 and 02 2007,
## and saves this histogram to a PNG file.
## reads household power consumption to a csv file
power <- read.csv("household_power_consumption.txt", sep=";")
## converts Date column to the right format
power$Date <- as.Date(power$Date, format="%d/%m/%Y")
## subsets the rows for which the date is 2007-02-01 or 2007-02-02
powersub <- power[(power$Date == "2007-02-01" | power$Date == "2007-02-02"), ]
## Turns the column Global_active_power into numeric
powersub$Global_active_power <- as.numeric(powersub$Global_active_power)
## initiates dplyr library
library(dplyr)
## creates a new column called Global_active_power_kilowatts that is the global active power in kilowatts (divided by 1000)
powersub <- mutate(powersub, Global_active_power_kilowatts = Global_active_power/1000)
## draws the histogram for you and saves it to a PNG file called plot1.png
png("plot1.png")
hist(powersub$Global_active_power_kilowatts, xlab="Global Active Power (kilowatts)", main="Global Active Power", col = "red")
dev.off()
| /plot1.R | no_license | gracechua/ExData_Plotting1 | R | false | false | 1,182 | r | ## This R script takes the household power consumption data that you have,
## plots a histogram of the global active power in kilowatts for Feb 01 and 02 2007,
## and saves this histogram to a PNG file.
## reads household power consumption to a csv file
power <- read.csv("household_power_consumption.txt", sep=";")
## converts Date column to the right format
power$Date <- as.Date(power$Date, format="%d/%m/%Y")
## subsets the rows for which the date is 2007-02-01 or 2007-02-02
powersub <- power[(power$Date == "2007-02-01" | power$Date == "2007-02-02"), ]
## Turns the column Global_active_power into numeric
powersub$Global_active_power <- as.numeric(powersub$Global_active_power)
## initiates dplyr library
library(dplyr)
## creates a new column called Global_active_power_kilowatts that is the global active power in kilowatts (divided by 1000)
powersub <- mutate(powersub, Global_active_power_kilowatts = Global_active_power/1000)
## draws the histogram for you and saves it to a PNG file called plot1.png
png("plot1.png")
hist(powersub$Global_active_power_kilowatts, xlab="Global Active Power (kilowatts)", main="Global Active Power", col = "red")
dev.off()
|
#' Standardise the column headers in the Summary Statistics files
#'
#' Use a reference data table of common column header names (stored in sumstatsColHeaders.rda) convert them to a standard set, i.e. chromosome --> CHR
#'
#' This function does not check that all the required column headers are present
#'
#' The amended header is written directly back into the file
#'
#' @param path Filepath for the summary statistics file
#'
#' @return The amended column headers (also the column headers will be written directly into the summary statistics file)
#'
#' @examples
#' col_headers = standardise.sumstats.column.headers("~/Downloads/202040.assoc.tsv")
#'
#' @export
standardise.sumstats.column.headers.linux <- function(path){
# Check the sumstats file exists
if(!file.exists(path)){stop("Path to GWAS sumstats is not valid")}
# Read in the first line of the file only
con <- file(path,"r") ; first_line <- readLines(con,n=1) ; close(con)
column_headers = strsplit(first_line,"\t")[[1]]
# Show to the user what the header is
print("First line of summary statistics file: ")
print(first_line)
print(column_headers)
# Amend the column headers based on a data table of commonly used names
data(sumstatsColHeaders)
column_headers = toupper(column_headers)
for(headerI in 1:dim(sumstatsColHeaders)[1]){
un = sumstatsColHeaders[headerI,1]
cr = sumstatsColHeaders[headerI,2]
#print(un)
if(un %in% column_headers & (!cr %in% column_headers)){column_headers=gsub(sprintf("^%s$",un),cr,column_headers)}
#if(tolower(un) %in% column_headers){column_headers=gsub(sprintf("^%s$",tolower(un)),cr,column_headers)}
}
new_first_line = paste(column_headers,collapse = "\t")
# Write the new column headers to file
sed_command = sprintf("sed -i.bak '1s/%s/%s/' %s",first_line,new_first_line,path)
system2("/bin/bash", args = c("-c", shQuote(sed_command)))
system(sprintf("rm %s.bak", path))
#column_headers = strsplit(column_headers," ")[[1]]
return(column_headers)
}
| /R/standardise.sumstats.column.headers.linux.r | no_license | rbutleriii/MAGMA_Celltyping | R | false | false | 2,125 | r | #' Standardise the column headers in the Summary Statistics files
#'
#' Use a reference data table of common column header names (stored in sumstatsColHeaders.rda) convert them to a standard set, i.e. chromosome --> CHR
#'
#' This function does not check that all the required column headers are present
#'
#' The amended header is written directly back into the file
#'
#' @param path Filepath for the summary statistics file
#'
#' @return The amended column headers (also the column headers will be written directly into the summary statistics file)
#'
#' @examples
#' col_headers = standardise.sumstats.column.headers("~/Downloads/202040.assoc.tsv")
#'
#' @export
standardise.sumstats.column.headers.linux <- function(path){
# Check the sumstats file exists
if(!file.exists(path)){stop("Path to GWAS sumstats is not valid")}
# Read in the first line of the file only
con <- file(path,"r") ; first_line <- readLines(con,n=1) ; close(con)
column_headers = strsplit(first_line,"\t")[[1]]
# Show to the user what the header is
print("First line of summary statistics file: ")
print(first_line)
print(column_headers)
# Amend the column headers based on a data table of commonly used names
data(sumstatsColHeaders)
column_headers = toupper(column_headers)
for(headerI in 1:dim(sumstatsColHeaders)[1]){
un = sumstatsColHeaders[headerI,1]
cr = sumstatsColHeaders[headerI,2]
#print(un)
if(un %in% column_headers & (!cr %in% column_headers)){column_headers=gsub(sprintf("^%s$",un),cr,column_headers)}
#if(tolower(un) %in% column_headers){column_headers=gsub(sprintf("^%s$",tolower(un)),cr,column_headers)}
}
new_first_line = paste(column_headers,collapse = "\t")
# Write the new column headers to file
sed_command = sprintf("sed -i.bak '1s/%s/%s/' %s",first_line,new_first_line,path)
system2("/bin/bash", args = c("-c", shQuote(sed_command)))
system(sprintf("rm %s.bak", path))
#column_headers = strsplit(column_headers," ")[[1]]
return(column_headers)
}
|
#Question 6: Compare emissions from motor vehicle sources in Baltimore City
#with emissions from motor vehicle sources in Los Angeles County, California
#(\color{red}{\verb|fips == "06037"|}fips=="06037").
#Which city has seen greater changes over time in motor vehicle emissions?
#Unzip the dataset
unzip(zipfile = "./data/exdata_data_NEI_data.zip", exdir = "./data")
library(ggplot2)
#Reading data
NEI <- readRDS("./data/summarySCC_PM25.rds")
SCC <- readRDS("./data/Source_Classification_Code.rds")
#subsetting Los Angeles and Baltimore data
LA <- subset(NEI, fips == "06037")
Baltimore <- subset(NEI, fips=="24510")
# subsetting SCC with vehicle values
vehicle <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case=TRUE)
subsetSCC <- SCC[vehicle, ]
# merging Baltimore and LA data with SCC vehicles
data_Balt <- merge(Baltimore, subsetSCC, by="SCC")
data_LA <- merge(LA, subsetSCC, by="SCC")
#adding city variable
data_Balt$city <- make.names("Baltimore City")
data_LA$city <- make.names("Los Angeles County")
#merging Baltimore and Los Angeles data
data_merged <- rbind(data_Balt, data_LA)
# summing emission data per year by city
data_LA_Balt<-aggregate(Emissions ~ year + city, data_merged, sum)
# plotting
png("plot6.png", width=560, height=480)
g <- ggplot(data_LA_Balt, aes(year, Emissions, color = city))
g + geom_line() +
xlab("Year") +
ylab(expression("Total PM"[2.5]*" Emissions")) +
ggtitle("Total Emissions from motor sources in Baltimore,MD and Los Angeles, CA")
dev.off()
| /Course 4- Exploratory Data Analysis/Week 4/Assignment/plot6.R | no_license | shovitraj/DataScienceSpecialization-JHU | R | false | false | 1,526 | r | #Question 6: Compare emissions from motor vehicle sources in Baltimore City
#with emissions from motor vehicle sources in Los Angeles County, California
#(\color{red}{\verb|fips == "06037"|}fips=="06037").
#Which city has seen greater changes over time in motor vehicle emissions?
#Unzip the dataset
unzip(zipfile = "./data/exdata_data_NEI_data.zip", exdir = "./data")
library(ggplot2)
#Reading data
NEI <- readRDS("./data/summarySCC_PM25.rds")
SCC <- readRDS("./data/Source_Classification_Code.rds")
#subsetting Los Angeles and Baltimore data
LA <- subset(NEI, fips == "06037")
Baltimore <- subset(NEI, fips=="24510")
# subsetting SCC with vehicle values
vehicle <- grepl("vehicle", SCC$SCC.Level.Two, ignore.case=TRUE)
subsetSCC <- SCC[vehicle, ]
# merging Baltimore and LA data with SCC vehicles
data_Balt <- merge(Baltimore, subsetSCC, by="SCC")
data_LA <- merge(LA, subsetSCC, by="SCC")
#adding city variable
data_Balt$city <- make.names("Baltimore City")
data_LA$city <- make.names("Los Angeles County")
#merging Baltimore and Los Angeles data
data_merged <- rbind(data_Balt, data_LA)
# summing emission data per year by city
data_LA_Balt<-aggregate(Emissions ~ year + city, data_merged, sum)
# plotting
png("plot6.png", width=560, height=480)
g <- ggplot(data_LA_Balt, aes(year, Emissions, color = city))
g + geom_line() +
xlab("Year") +
ylab(expression("Total PM"[2.5]*" Emissions")) +
ggtitle("Total Emissions from motor sources in Baltimore,MD and Los Angeles, CA")
dev.off()
|
# My
library(shiny)
shinyUI(fluidPage(
titlePanel("StockComp"),
sidebarLayout(
sidebarPanel(
helpText("Select two stocks and a time frame to compare.
Information will be collected from yahoo finance."),
textInput("symb1", "1st Stock Symbol", "GOOG"),
textInput("symb2", "2nd Stock Symbol", "AAPL"),
dateRangeInput("dates",
"Date range",
start = "2012-01-01",
end = as.character(Sys.Date())),
actionButton("get", "Compare Stocks"),
br(),
br(),
checkboxInput("log", "Plot y axis on log scale",
value = FALSE)
),
mainPanel(
plotOutput("plot"),
plotOutput("plot2")
)
)
)) | /ui.R | no_license | EricHPei/MyApp | R | false | false | 730 | r | # My
library(shiny)
shinyUI(fluidPage(
titlePanel("StockComp"),
sidebarLayout(
sidebarPanel(
helpText("Select two stocks and a time frame to compare.
Information will be collected from yahoo finance."),
textInput("symb1", "1st Stock Symbol", "GOOG"),
textInput("symb2", "2nd Stock Symbol", "AAPL"),
dateRangeInput("dates",
"Date range",
start = "2012-01-01",
end = as.character(Sys.Date())),
actionButton("get", "Compare Stocks"),
br(),
br(),
checkboxInput("log", "Plot y axis on log scale",
value = FALSE)
),
mainPanel(
plotOutput("plot"),
plotOutput("plot2")
)
)
)) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/JieZ_2017.R
\name{JieZ_2017}
\alias{JieZ_2017}
\alias{JieZ_2017.genefamilies_relab.stool}
\alias{JieZ_2017.marker_abundance.stool}
\alias{JieZ_2017.marker_presence.stool}
\alias{JieZ_2017.metaphlan_bugs_list.stool}
\alias{JieZ_2017.pathabundance_relab.stool}
\alias{JieZ_2017.pathcoverage.stool}
\title{Data from the JieZ_2017 study}
\description{
Data from the JieZ_2017 study
}
\section{Datasets}{
\subsection{JieZ_2017.genefamilies_relab.stool}{
An ExpressionSet with 385 samples and 1,976,093 features specific to the stool body site
}
\subsection{JieZ_2017.marker_abundance.stool}{
An ExpressionSet with 385 samples and 158,072 features specific to the stool body site
}
\subsection{JieZ_2017.marker_presence.stool}{
An ExpressionSet with 385 samples and 145,642 features specific to the stool body site
}
\subsection{JieZ_2017.metaphlan_bugs_list.stool}{
An ExpressionSet with 385 samples and 1,666 features specific to the stool body site
}
\subsection{JieZ_2017.pathabundance_relab.stool}{
An ExpressionSet with 385 samples and 13,222 features specific to the stool body site
}
\subsection{JieZ_2017.pathcoverage.stool}{
An ExpressionSet with 385 samples and 13,222 features specific to the stool body site
}
}
\section{Source}{
\subsection{Title}{
The gut microbiome in atherosclerotic cardiovascular disease.
}
\subsection{Author}{
Jie Z, Xia H, Zhong SL, Feng Q, Li S, Liang S, Zhong H, Liu Z, Gao Y, Zhao H, Zhang D, Su Z, Fang Z, Lan Z, Li J, Xiao L, Li J, Li R, Li X, Li F, Ren H, Huang Y, Peng Y, Li G, Wen B, Dong B, Chen JY, Geng QS, Zhang ZW, Yang H, Wang J, Wang J, Zhang X, Madsen L, Brix S, Ning G, Xu X, Liu X, Hou Y, Jia H, He K, Kristiansen K
}
\subsection{Lab}{
[1] BGI-Shenzhen, Shenzhen, 518083, China., [2] China National Genebank, Shenzhen, 518120, China., [3] Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, 518083, China., [4] Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, 510080, China., [5] Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China., [6] Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, 518083, China., [7] Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, Denmark., [8] Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, 250012, China., [9] BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China., [10] School of Bioscience and Biotechnology, South China University of Technology, Guangzhou, 510006, China., [11] Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, 100853, China., [12] Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA., [13] James D. Watson Institute of Genome Sciences, Hangzhou, 310000, China., [14] Macau University of Science and Technology, Macau, 999078, China., [15] iCarbonX, Shenzhen, 518053, China., [16] Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China., [17] National Institute of Nutrition and Seafood Research, (NIFES), Postboks 2029, Nordnes, N-5817, Bergen, Norway., [18] Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), 2800, Kongens Lyngby, Denmark., [19] Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China., [20] BGI-Shenzhen, Shenzhen, 518083, China. jiahuijue@genomics.cn., [21] China National Genebank, Shenzhen, 518120, China. jiahuijue@genomics.cn., [22] Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, 518083, China. jiahuijue@genomics.cn., [23] Macau University of Science and Technology, Macau, 999078, China. jiahuijue@genomics.cn., [24] Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, 100853, China. hekl301@aliyun.com., [25] BGI-Shenzhen, Shenzhen, 518083, China. kk@bio.ku.dk., [26] China National Genebank, Shenzhen, 518120, China. kk@bio.ku.dk., [27] Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, Denmark. kk@bio.ku.dk.
}
\subsection{PMID}{
29018189
}
}
\examples{
JieZ_2017.metaphlan_bugs_list.stool()
}
| /man/JieZ_2017.Rd | permissive | vjcitn/curatedMetagenomicData | R | false | true | 5,109 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/JieZ_2017.R
\name{JieZ_2017}
\alias{JieZ_2017}
\alias{JieZ_2017.genefamilies_relab.stool}
\alias{JieZ_2017.marker_abundance.stool}
\alias{JieZ_2017.marker_presence.stool}
\alias{JieZ_2017.metaphlan_bugs_list.stool}
\alias{JieZ_2017.pathabundance_relab.stool}
\alias{JieZ_2017.pathcoverage.stool}
\title{Data from the JieZ_2017 study}
\description{
Data from the JieZ_2017 study
}
\section{Datasets}{
\subsection{JieZ_2017.genefamilies_relab.stool}{
An ExpressionSet with 385 samples and 1,976,093 features specific to the stool body site
}
\subsection{JieZ_2017.marker_abundance.stool}{
An ExpressionSet with 385 samples and 158,072 features specific to the stool body site
}
\subsection{JieZ_2017.marker_presence.stool}{
An ExpressionSet with 385 samples and 145,642 features specific to the stool body site
}
\subsection{JieZ_2017.metaphlan_bugs_list.stool}{
An ExpressionSet with 385 samples and 1,666 features specific to the stool body site
}
\subsection{JieZ_2017.pathabundance_relab.stool}{
An ExpressionSet with 385 samples and 13,222 features specific to the stool body site
}
\subsection{JieZ_2017.pathcoverage.stool}{
An ExpressionSet with 385 samples and 13,222 features specific to the stool body site
}
}
\section{Source}{
\subsection{Title}{
The gut microbiome in atherosclerotic cardiovascular disease.
}
\subsection{Author}{
Jie Z, Xia H, Zhong SL, Feng Q, Li S, Liang S, Zhong H, Liu Z, Gao Y, Zhao H, Zhang D, Su Z, Fang Z, Lan Z, Li J, Xiao L, Li J, Li R, Li X, Li F, Ren H, Huang Y, Peng Y, Li G, Wen B, Dong B, Chen JY, Geng QS, Zhang ZW, Yang H, Wang J, Wang J, Zhang X, Madsen L, Brix S, Ning G, Xu X, Liu X, Hou Y, Jia H, He K, Kristiansen K
}
\subsection{Lab}{
[1] BGI-Shenzhen, Shenzhen, 518083, China., [2] China National Genebank, Shenzhen, 518120, China., [3] Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, 518083, China., [4] Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, 510080, China., [5] Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China., [6] Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, 518083, China., [7] Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, Denmark., [8] Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, 250012, China., [9] BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China., [10] School of Bioscience and Biotechnology, South China University of Technology, Guangzhou, 510006, China., [11] Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, 100853, China., [12] Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA., [13] James D. Watson Institute of Genome Sciences, Hangzhou, 310000, China., [14] Macau University of Science and Technology, Macau, 999078, China., [15] iCarbonX, Shenzhen, 518053, China., [16] Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China., [17] National Institute of Nutrition and Seafood Research, (NIFES), Postboks 2029, Nordnes, N-5817, Bergen, Norway., [18] Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), 2800, Kongens Lyngby, Denmark., [19] Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China., [20] BGI-Shenzhen, Shenzhen, 518083, China. jiahuijue@genomics.cn., [21] China National Genebank, Shenzhen, 518120, China. jiahuijue@genomics.cn., [22] Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, BGI-Shenzhen, Shenzhen, 518083, China. jiahuijue@genomics.cn., [23] Macau University of Science and Technology, Macau, 999078, China. jiahuijue@genomics.cn., [24] Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, 100853, China. hekl301@aliyun.com., [25] BGI-Shenzhen, Shenzhen, 518083, China. kk@bio.ku.dk., [26] China National Genebank, Shenzhen, 518120, China. kk@bio.ku.dk., [27] Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, Denmark. kk@bio.ku.dk.
}
\subsection{PMID}{
29018189
}
}
\examples{
JieZ_2017.metaphlan_bugs_list.stool()
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wasserstein.R
\name{wasserstein}
\alias{wasserstein}
\title{wasserstein}
\usage{
wasserstein(p, q, cost_matrix, epsilon, niterations)
}
\value{
a list with "distances", "transportmatrix", "u" and "v"
}
\description{
Compute regularized Wasserstein distance between two empirical distributions,
p and q, specified as vector of probabilities summing to one.
The third argument is the cost matrix, i.e. a matrix of pair-wise distances,
the fourth argument is the regularization parameter, e.g. 0.05*median(cost_matrix),
and the last argument is the number of Sinkhorn iterations to perform, e.g. 100.
Important references are
- Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (NIPS), pages 2292-2300.
- Cuturi, M. and Doucet, A. (2014). Fast computation of Wasserstein barycenters. In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 685-693.
}
| /man/wasserstein.Rd | no_license | ramcqueary/winference | R | false | true | 1,045 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wasserstein.R
\name{wasserstein}
\alias{wasserstein}
\title{wasserstein}
\usage{
wasserstein(p, q, cost_matrix, epsilon, niterations)
}
\value{
a list with "distances", "transportmatrix", "u" and "v"
}
\description{
Compute regularized Wasserstein distance between two empirical distributions,
p and q, specified as vector of probabilities summing to one.
The third argument is the cost matrix, i.e. a matrix of pair-wise distances,
the fourth argument is the regularization parameter, e.g. 0.05*median(cost_matrix),
and the last argument is the number of Sinkhorn iterations to perform, e.g. 100.
Important references are
- Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (NIPS), pages 2292-2300.
- Cuturi, M. and Doucet, A. (2014). Fast computation of Wasserstein barycenters. In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 685-693.
}
|
#Autor: Suárez Pérez Juan Pablo
#Funciónn Principal del programa
main <- function() {
cat("Bienvenido\n")
cat("Calculadora de ahorro por viajes compartidos...\n\n")
#Registro de viajes.
viajes <- registrar_viajes()
#Impresión del los viajes realizados.
print(viajes)
View(viajes)
#Resultado por viajes: costo del viaje, costo por persona y ahorrado.
resultados_viajes <- obtener_resultados(viajes)
print(resultados_viajes)
View(resultados_viajes)
#Obtención de resultados diarios.
calcular_resultados(resultados_viajes)
#Resultados del día: costo diario, costo diario por persona y ahorrado diario.
generar_graficos(viajes, resultados_viajes)
}
registrar_viajes <- function() {
respuesta <- "Si"
bandera = TRUE
cat("Es necesario que ingreses los siguientes datos:\n")
cat("1. Kilometros conducidos\n")
cat("2. Costo por litro de gasolina\n")
cat("3. Kilometros por litro\n")
cat("4. Peaje\n")
cat("5. Cuotas por estacionamiento\n")
cat("6. Número de integrantes por viaje\n\n")
#Obtención del primer carácter de la respuesta.
respuesta <- substr(respuesta, start = 1, stop = 1)
while (respuesta == 'S' || respuesta == 's') {
#Ingreso de los resultados del usuario.
#Se realizan algunas validaciones de los datos de entrada.
cat("1. ¿Cuántos kilometros recorriste?")
km_recorridos <- as.numeric(readLines(n = 1))
if (is.na(km_recorridos)) km_recorridos = 0
cat("2. Ingresa el precio por litro de gasolina")
costo_lt_gas <- as.numeric(readLines(n = 1))
#Promedio del costo de gasolina.
if (is.na(costo_lt_gas)) costo_lt_gas = 20
cat("3. ¿Cuántos kilometros por litro de gasolina?")
km_lt <- as.numeric(readLines(n = 1))
#Promedio de km por litro de gasolina.
if (is.na(km_lt)) km_lt = 12
cat("4. ¿Cuánto gastaste en peajes?")
peaje_recorrido <- as.numeric(readLines(n = 1))
if (is.na(peaje_recorrido)) peaje_recorrido = 0
cat("5. ¿Cuánto gastaste en estacionamiento?")
cuota_estacionamiento <- as.numeric(readLines(n = 1))
if (is.na(cuota_estacionamiento)) cuota_estacionamiento = 0
cat("6. ¿Cuántas personas viajaron contigo? (incluyendote)")
integrantes <- as.numeric(readLines(n = 1))
if (is.na(integrantes)) integrantes = 1
cat("\n")
if (bandera == TRUE) {
#Creación de la matriz llamada viajes.
viajes <- matrix(c(km_recorridos, costo_lt_gas, km_lt,
peaje_recorrido, cuota_estacionamiento,
integrantes), ncol = 6, byrow = T)
colnames(viajes) <- c("km_recorridos", "costo_lt_gas",
"km_lt", "peaje_recorrido",
"cuota_estacionamiento", "integrantes")
bandera = FALSE
} else {
#Actualización de la matriz llamda viajes.
viajes <- rbind(viajes, c(km_recorridos, costo_lt_gas,
km_lt, peaje_recorrido,
cuota_estacionamiento, integrantes))
}
cat("¿Quieres realizar más viajes?")
respuesta <- readLines(n = 1)
cat("\n")
respuesta <- substr(respuesta, start = 1, stop = 1)
}
#Retorno de la matriz viajes.
return(viajes)
}
obtener_resultados <- function(viajes) {
bandera = TRUE
#Obtención de las dimensiones del viaje.
dim_viajes <- dim(viajes)
cat("\n")
for (i in 1:dim_viajes[1]) {
#Obtención de los resultados obtenidos por viajes.
litros_necesarios <- viajes[i, "km_recorridos"] / viajes[i, "km_lt"]
costo_total_gasolina <- litros_necesarios * viajes[i, "costo_lt_gas"]
costo_total <- costo_total_gasolina +
viajes[i, "peaje_recorrido"] + viajes[i, "cuota_estacionamiento"]
costo_persona <- costo_total / viajes[i, "integrantes"]
ahorrado <- costo_total - costo_persona
#Impresión de los resultados obtenidos por viajes.
cat("Resultados obtenidos del viaje ", i, "...\n")
cat("Litros necesarios para tu viaje: ", litros_necesarios, "\n")
cat("Costo total de la gasolina: $", costo_total_gasolina, "\n")
cat("El costo total del viaje es de: $", costo_total, "\n")
cat("El costo por persona: $", costo_persona, "\n")
cat("Te ahorraste: $", ahorrado, "\n\n")
if (bandera == TRUE) {
#Creación de la matriz con los resultados.
resultados_viajes <- matrix(c(costo_total, costo_persona, ahorrado),
ncol = 3, byrow = TRUE)
colnames(resultados_viajes) <- c("costo_total", "costo_persona",
"ahorrado")
bandera = FALSE
} else {
#Actualización de la matriz con los resultados.
resultados_viajes <- rbind(resultados_viajes,
c(costo_total, costo_persona, ahorrado))
}
}
#Retorno del la matriz de resultados.
return(resultados_viajes)
}
calcular_resultados <- function(resultados_viajes) {
#Resultados finales.
res_diarios <- colSums(resultados_viajes)
cat("\n")
cat("Costo total diario: $", res_diarios[1], "\n")
cat("Costo por persona diario: $", res_diarios[2], "\n")
cat("Ahorrado diario: $", res_diarios[3], "\n")
}
generar_graficos <- function(viajes, resultados_viajes) {
#Generación de gráfico.
plot(x = viajes[, "integrantes"], y = resultados_viajes[, "ahorrado"],
main = "Ahorrado por integrantes", xlab = "Integrantes",
ylab = "Ahorrado")
}
main() | /calculadora_viajes.R | no_license | breko151/Calculadora_Viajes | R | false | false | 5,489 | r | #Autor: Suárez Pérez Juan Pablo
#Funciónn Principal del programa
main <- function() {
cat("Bienvenido\n")
cat("Calculadora de ahorro por viajes compartidos...\n\n")
#Registro de viajes.
viajes <- registrar_viajes()
#Impresión del los viajes realizados.
print(viajes)
View(viajes)
#Resultado por viajes: costo del viaje, costo por persona y ahorrado.
resultados_viajes <- obtener_resultados(viajes)
print(resultados_viajes)
View(resultados_viajes)
#Obtención de resultados diarios.
calcular_resultados(resultados_viajes)
#Resultados del día: costo diario, costo diario por persona y ahorrado diario.
generar_graficos(viajes, resultados_viajes)
}
registrar_viajes <- function() {
respuesta <- "Si"
bandera = TRUE
cat("Es necesario que ingreses los siguientes datos:\n")
cat("1. Kilometros conducidos\n")
cat("2. Costo por litro de gasolina\n")
cat("3. Kilometros por litro\n")
cat("4. Peaje\n")
cat("5. Cuotas por estacionamiento\n")
cat("6. Número de integrantes por viaje\n\n")
#Obtención del primer carácter de la respuesta.
respuesta <- substr(respuesta, start = 1, stop = 1)
while (respuesta == 'S' || respuesta == 's') {
#Ingreso de los resultados del usuario.
#Se realizan algunas validaciones de los datos de entrada.
cat("1. ¿Cuántos kilometros recorriste?")
km_recorridos <- as.numeric(readLines(n = 1))
if (is.na(km_recorridos)) km_recorridos = 0
cat("2. Ingresa el precio por litro de gasolina")
costo_lt_gas <- as.numeric(readLines(n = 1))
#Promedio del costo de gasolina.
if (is.na(costo_lt_gas)) costo_lt_gas = 20
cat("3. ¿Cuántos kilometros por litro de gasolina?")
km_lt <- as.numeric(readLines(n = 1))
#Promedio de km por litro de gasolina.
if (is.na(km_lt)) km_lt = 12
cat("4. ¿Cuánto gastaste en peajes?")
peaje_recorrido <- as.numeric(readLines(n = 1))
if (is.na(peaje_recorrido)) peaje_recorrido = 0
cat("5. ¿Cuánto gastaste en estacionamiento?")
cuota_estacionamiento <- as.numeric(readLines(n = 1))
if (is.na(cuota_estacionamiento)) cuota_estacionamiento = 0
cat("6. ¿Cuántas personas viajaron contigo? (incluyendote)")
integrantes <- as.numeric(readLines(n = 1))
if (is.na(integrantes)) integrantes = 1
cat("\n")
if (bandera == TRUE) {
#Creación de la matriz llamada viajes.
viajes <- matrix(c(km_recorridos, costo_lt_gas, km_lt,
peaje_recorrido, cuota_estacionamiento,
integrantes), ncol = 6, byrow = T)
colnames(viajes) <- c("km_recorridos", "costo_lt_gas",
"km_lt", "peaje_recorrido",
"cuota_estacionamiento", "integrantes")
bandera = FALSE
} else {
#Actualización de la matriz llamda viajes.
viajes <- rbind(viajes, c(km_recorridos, costo_lt_gas,
km_lt, peaje_recorrido,
cuota_estacionamiento, integrantes))
}
cat("¿Quieres realizar más viajes?")
respuesta <- readLines(n = 1)
cat("\n")
respuesta <- substr(respuesta, start = 1, stop = 1)
}
#Retorno de la matriz viajes.
return(viajes)
}
obtener_resultados <- function(viajes) {
bandera = TRUE
#Obtención de las dimensiones del viaje.
dim_viajes <- dim(viajes)
cat("\n")
for (i in 1:dim_viajes[1]) {
#Obtención de los resultados obtenidos por viajes.
litros_necesarios <- viajes[i, "km_recorridos"] / viajes[i, "km_lt"]
costo_total_gasolina <- litros_necesarios * viajes[i, "costo_lt_gas"]
costo_total <- costo_total_gasolina +
viajes[i, "peaje_recorrido"] + viajes[i, "cuota_estacionamiento"]
costo_persona <- costo_total / viajes[i, "integrantes"]
ahorrado <- costo_total - costo_persona
#Impresión de los resultados obtenidos por viajes.
cat("Resultados obtenidos del viaje ", i, "...\n")
cat("Litros necesarios para tu viaje: ", litros_necesarios, "\n")
cat("Costo total de la gasolina: $", costo_total_gasolina, "\n")
cat("El costo total del viaje es de: $", costo_total, "\n")
cat("El costo por persona: $", costo_persona, "\n")
cat("Te ahorraste: $", ahorrado, "\n\n")
if (bandera == TRUE) {
#Creación de la matriz con los resultados.
resultados_viajes <- matrix(c(costo_total, costo_persona, ahorrado),
ncol = 3, byrow = TRUE)
colnames(resultados_viajes) <- c("costo_total", "costo_persona",
"ahorrado")
bandera = FALSE
} else {
#Actualización de la matriz con los resultados.
resultados_viajes <- rbind(resultados_viajes,
c(costo_total, costo_persona, ahorrado))
}
}
#Retorno del la matriz de resultados.
return(resultados_viajes)
}
calcular_resultados <- function(resultados_viajes) {
#Resultados finales.
res_diarios <- colSums(resultados_viajes)
cat("\n")
cat("Costo total diario: $", res_diarios[1], "\n")
cat("Costo por persona diario: $", res_diarios[2], "\n")
cat("Ahorrado diario: $", res_diarios[3], "\n")
}
generar_graficos <- function(viajes, resultados_viajes) {
#Generación de gráfico.
plot(x = viajes[, "integrantes"], y = resultados_viajes[, "ahorrado"],
main = "Ahorrado por integrantes", xlab = "Integrantes",
ylab = "Ahorrado")
}
main() |
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# ProjectName: Bluebook 2021-Universe
# Purpose: Price
# programmer: Zhe Liu
# Date: 2021-03-12
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
##---- Price ----
## origin price
price.origin <- raw.total %>%
group_by(packid, quarter, province, city) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price = sales / units) %>%
select(-sales, -units)
## mean price by city year
price.city <- raw.total %>%
group_by(packid, year, province, city) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_city = sales / units) %>%
select(-sales, -units)
## mean price by province quarter
price.province <- raw.total %>%
group_by(packid, quarter, province) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_prov = sales / units) %>%
select(-sales, -units)
## mean price by province year
price.year <- raw.total %>%
group_by(packid, year, province) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_year = sales / units) %>%
select(-sales, -units)
## mean price by pack quarter
price.pack <- raw.total %>%
group_by(packid, quarter) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_pack = sales / units) %>%
select(-sales, -units)
## mean price by pack year
price.pack.year <- raw.total %>%
group_by(packid, year) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_pack_year = sales / units) %>%
select(-sales, -units)
##---- Result ----
proj.price <- proj.nation %>%
left_join(price.origin, by = c('province', 'city', 'quarter', 'packid')) %>%
left_join(price.city, by = c('province', 'city', 'year', 'packid')) %>%
left_join(price.province, by = c('province', 'quarter', 'packid')) %>%
left_join(price.year, by = c('province', 'year', 'packid')) %>%
left_join(price.pack, by = c('quarter', 'packid')) %>%
left_join(price.pack.year, by = c('year', 'packid')) %>%
mutate(price = if_else(is.na(price), price_city, price),
price = if_else(is.na(price), price_prov, price),
price = if_else(is.na(price), price_year, price),
price = if_else(is.na(price), price_pack, price),
price = if_else(is.na(price), price_pack_year, price)) %>%
mutate(units = sales / price) %>%
filter(units > 0, sales > 0) %>%
select(year, quarter, date, province, city, market, atc4, molecule, packid,
units, sales)
write_feather(proj.price, '03_Outputs/Universe/05_Bluebook_2020_Universe_Projection_Price.feather')
| /04_Codes/Universe/05_Price.R | no_license | Zaphiroth/Bluebook_2021 | R | false | false | 3,112 | r | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# ProjectName: Bluebook 2021-Universe
# Purpose: Price
# programmer: Zhe Liu
# Date: 2021-03-12
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
##---- Price ----
## origin price
price.origin <- raw.total %>%
group_by(packid, quarter, province, city) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price = sales / units) %>%
select(-sales, -units)
## mean price by city year
price.city <- raw.total %>%
group_by(packid, year, province, city) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_city = sales / units) %>%
select(-sales, -units)
## mean price by province quarter
price.province <- raw.total %>%
group_by(packid, quarter, province) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_prov = sales / units) %>%
select(-sales, -units)
## mean price by province year
price.year <- raw.total %>%
group_by(packid, year, province) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_year = sales / units) %>%
select(-sales, -units)
## mean price by pack quarter
price.pack <- raw.total %>%
group_by(packid, quarter) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_pack = sales / units) %>%
select(-sales, -units)
## mean price by pack year
price.pack.year <- raw.total %>%
group_by(packid, year) %>%
summarise(sales = sum(sales, na.rm = TRUE),
units = sum(units, na.rm = TRUE)) %>%
ungroup() %>%
mutate(price_pack_year = sales / units) %>%
select(-sales, -units)
##---- Result ----
proj.price <- proj.nation %>%
left_join(price.origin, by = c('province', 'city', 'quarter', 'packid')) %>%
left_join(price.city, by = c('province', 'city', 'year', 'packid')) %>%
left_join(price.province, by = c('province', 'quarter', 'packid')) %>%
left_join(price.year, by = c('province', 'year', 'packid')) %>%
left_join(price.pack, by = c('quarter', 'packid')) %>%
left_join(price.pack.year, by = c('year', 'packid')) %>%
mutate(price = if_else(is.na(price), price_city, price),
price = if_else(is.na(price), price_prov, price),
price = if_else(is.na(price), price_year, price),
price = if_else(is.na(price), price_pack, price),
price = if_else(is.na(price), price_pack_year, price)) %>%
mutate(units = sales / price) %>%
filter(units > 0, sales > 0) %>%
select(year, quarter, date, province, city, market, atc4, molecule, packid,
units, sales)
write_feather(proj.price, '03_Outputs/Universe/05_Bluebook_2020_Universe_Projection_Price.feather')
|
##### we evaluate health outcome from various potential intervetions in this script
# setwd("~/Desktop/Spring 2020/coronavirus/codes")
setwd("~/Dropbox/codes")
rm(list = ls())
library(lubridate)
require("sfsmisc")
require("deSolve")
library(matrixStats)
###### source wuhan model and other city model
source('wuhan_simulation_policy_by_age.R')
source('other_city_simulation_policy_by_age.R')
# load('m_calib_res.rda')
load('m_calib_res_revision.rda')
load('model_inputs.rda')
################ Intervention 1: change quarantine start time
##### we assume massive social distancing start on the same date of city-quarantine
id = 1
milestone = as.Date("03/31/20", "%m/%d/%y")
# delta_t_range = -30:30
start = -30
incre = 2
end = 30
delta_t_range = seq(start, end, by = incre)
### debug
# delta_t_range = 25:30
nage = length(f_cq)
CI_and_mean = 3
date = structure(rep(NA_real_, length(delta_t_range)), class="Date")
cum_exportion = array(0, c(length(delta_t_range), CI_and_mean))
cum_infected = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
cum_death = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
disease_burden = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
economy_loss = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
# THS_effectiveness = array(0, c(length(delta_t_range), 3, CI_and_mean)) # only for CQ, BJ, SH
screening_cost = array(0, c(length(delta_t_range), 3, CI_and_mean))
SD_end_dates = structure(rep(NA_real_, length(nage)), class="Date")
n_sample = 1000
temp_disease_burden = matrix(0, 4, n_sample)
temp_cum_infected = matrix(0, 4, n_sample)
temp_cum_death = matrix(0, 4, n_sample)
temp_economy_loss = matrix(0, 4, n_sample)
temp_screening_cost = matrix(0, 3, n_sample)
# sample_param_indexes = which.min(abs(runif(n_sample) - m_calib_res[,"c_posterior_prob"]))
t_init = Sys.time()
for (delta_t in delta_t_range){
# delta_t_ind = delta_t - delta_t_range[1] + 1
delta_t_ind = as.integer(as.character((delta_t - start)/incre)) + 1
cat( "delta_t is ", delta_t, "\n")
base_line_quarantine_startdate = as.Date("01/23/20", "%m/%d/%y")
for (ind in 1:n_sample){
# if (ind %% 50 == 0){
# cat('ind is ', ind, "\n")
# }
# param_ind = which.min(abs(runif(1) - m_calib_res[,"c_posterior_prob"]))
param_ind = ind
symptomatic_ratio = as.numeric(m_calib_res[param_ind, 1:3])
mu_1 = as.numeric(m_calib_res[param_ind, 4:6])
beta = as.numeric(m_calib_res[param_ind, 7])
# ratio = ratio_range[beta_ind]
quarantine_start = base_line_quarantine_startdate + delta_t
quarantine_end = simulation_end
### we assume social_distancing start as the same time as
social_distancing_start = quarantine_start
SD_end_dates[1] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[2] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[3] = as.Date("03/31/20", "%m/%d/%y")
# social_distancing_end = delta_t + base_line_social_distancing_end
time_knot_vec = list('simulation_start' = simulation_start,'simulation_end' = simulation_end,
'chunyun_start' = chunyun_start, 'chunyun_end' = chunyun_end,
'quarantine_start' = quarantine_start, 'quarantine_end' = quarantine_end,
'social_distancing_start' = social_distancing_start, 'SD_end_dates' = SD_end_dates)
mu = mu_1 * 2
WH_output <- wuhan_simulation(N_wh, time, beta, z_0, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, symptomatic_ratio)
# ratio = ratio_range[2]
#### for other cities
exported = WH_output$exported
ths_efficiency = 1
ths_window = 14
ths_start = quarantine_start ###ths: travel history screening
ths_end = quarantine_end ### ths: travel history screening
# social_distancing_start = as.Date("01/23/20", "%m/%d/%y")
social_distancing_start = ths_start
# social_distancing_end = quarantine_end
SD_end_dates[1] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[2] = as.Date("02/29/20", "%m/%d/%y")
SD_end_dates[3] = as.Date("03/31/20", "%m/%d/%y")
time_knot_vec = list('simulation_start' = simulation_start,'simulation_end' = simulation_end,
'chunyun_start' = chunyun_start, 'chunyun_end' = chunyun_end,
'ths_start' = ths_start, 'ths_end' = ths_end,
'social_distancing_start' = social_distancing_start, 'SD_end_dates' = SD_end_dates)
mu = mu_1/7 * 2
CQ_output <- other_city_simulation(N_cq, f_cq, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_cq, ths_efficiency, ths_window, symptomatic_ratio)
BJ_output <- other_city_simulation(N_bj, f_bj, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_bj, ths_efficiency, ths_window, symptomatic_ratio)
SH_output <- other_city_simulation(N_sh, f_sh, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_sh, ths_efficiency, ths_window, symptomatic_ratio)
diff_t = as.integer(milestone - simulation_start)
temp_disease_burden[1, ind] = sum((WH_output$IS[diff_t + 1, ] + WH_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(WH_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[2, ind] = sum((CQ_output$IS[diff_t + 1, ] + CQ_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(CQ_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[3, ind] = sum((BJ_output$IS[diff_t + 1, ] + BJ_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(BJ_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[4, ind] = sum((SH_output$IS[diff_t + 1, ] + SH_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(SH_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_cum_infected[1, ind] = sum(WH_output$incidence[1:diff_t + 1])/WH_output$N_t[diff_t + 1] * 10000
temp_cum_infected[2, ind] = sum(CQ_output$incidence[1:diff_t + 1])/CQ_output$N_t[diff_t + 1] * 10000
temp_cum_infected[3, ind] = sum(BJ_output$incidence[1:diff_t + 1])/BJ_output$N_t[diff_t + 1] * 10000
temp_cum_infected[4, ind] = sum(SH_output$incidence[1:diff_t + 1])/SH_output$N_t[diff_t + 1] * 10000
temp_cum_death[1, ind] = WH_output$D[diff_t + 1 - time_to_death[1], 1] + WH_output$D[diff_t + 1 - time_to_death[2], 2] + WH_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[2, ind] = CQ_output$D[diff_t + 1 - time_to_death[1], 1] + CQ_output$D[diff_t + 1 - time_to_death[2], 2] + CQ_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[3, ind] = BJ_output$D[diff_t + 1 - time_to_death[1], 1] + BJ_output$D[diff_t + 1 - time_to_death[2], 2] + BJ_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[4, ind] = SH_output$D[diff_t + 1 - time_to_death[1], 1] + SH_output$D[diff_t + 1 - time_to_death[2], 2] + SH_output$D[diff_t + 1 - time_to_death[3], 3]
temp_screening_cost[1, ind] = CQ_output$screening_counts * 16.438/1e+06
temp_screening_cost[2, ind] = BJ_output$screening_counts * 16.438/1e+06
temp_screening_cost[3, ind] = SH_output$screening_counts * 16.438/1e+06
temp_economy_loss[, ind] = temp_economy_loss[ ,ind] +
GDP_loss_matrix[1,] * (milestone - quarantine_start + 1) +
GDP_loss_matrix[2, ] * (SD_end_dates[2] - social_distancing_start + 1) +
temp_disease_burden[ ,ind] + c(0, temp_screening_cost[ ,ind])
temp_economy_loss[ , ind] = round(temp_economy_loss[ ,ind]/1000,2)
}
# cum_exportion[delta_t_ind, beta_ind] = sum(WH_output$exported[1:diff_t + 1,,])
disease_burden[delta_t_ind, ,] = rowQuantiles(temp_disease_burden, probs = c(0.025, 0.5, 0.975))
cum_infected[delta_t_ind, ,] = rowQuantiles(temp_cum_infected, probs = c(0.025, 0.5, 0.975))
cum_death[delta_t_ind, ,] = rowQuantiles(temp_cum_death, probs = c(0.025, 0.5, 0.975))
screening_cost[delta_t_ind, ,] = rowQuantiles(temp_screening_cost, probs = c(0.025, 0.5, 0.975))
economy_loss[delta_t_ind, ,] = rowQuantiles(temp_economy_loss, probs = c(0.025, 0.5, 0.975))
### change meadian to mean
disease_burden[delta_t_ind, ,2] = rowMeans(temp_disease_burden)
cum_infected[delta_t_ind, ,2] = rowMeans(temp_cum_infected)
cum_death[delta_t_ind, ,2] = rowMeans(temp_cum_death)
screening_cost[delta_t_ind, ,2] = rowMeans(temp_screening_cost)
economy_loss[delta_t_ind, ,2] = rowMeans(temp_economy_loss)
date[delta_t_ind] = base_line_quarantine_startdate + delta_t
# date[delta_t_ind, 1] = as.Date(base_line_quarantine_startdate + delta_t)
}
comp_time = Sys.time() - t_init
comp_time
save(economy_loss, disease_burden, cum_infected, cum_death, date, delta_t_range, screening_cost, file = "vary_start_date_all_doubled_mortality.rda")
| /simulation_by_policy/vary_start_date_all_doubled_moratlity.R | no_license | Anthony-zh-Zhang/COVID_19_model | R | false | false | 9,199 | r | ##### we evaluate health outcome from various potential intervetions in this script
# setwd("~/Desktop/Spring 2020/coronavirus/codes")
setwd("~/Dropbox/codes")
rm(list = ls())
library(lubridate)
require("sfsmisc")
require("deSolve")
library(matrixStats)
###### source wuhan model and other city model
source('wuhan_simulation_policy_by_age.R')
source('other_city_simulation_policy_by_age.R')
# load('m_calib_res.rda')
load('m_calib_res_revision.rda')
load('model_inputs.rda')
################ Intervention 1: change quarantine start time
##### we assume massive social distancing start on the same date of city-quarantine
id = 1
milestone = as.Date("03/31/20", "%m/%d/%y")
# delta_t_range = -30:30
start = -30
incre = 2
end = 30
delta_t_range = seq(start, end, by = incre)
### debug
# delta_t_range = 25:30
nage = length(f_cq)
CI_and_mean = 3
date = structure(rep(NA_real_, length(delta_t_range)), class="Date")
cum_exportion = array(0, c(length(delta_t_range), CI_and_mean))
cum_infected = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
cum_death = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
disease_burden = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
economy_loss = array(0, c(length(delta_t_range), 4, CI_and_mean)) # 4 cities: Wuhan, CQ, BJ, SH
# THS_effectiveness = array(0, c(length(delta_t_range), 3, CI_and_mean)) # only for CQ, BJ, SH
screening_cost = array(0, c(length(delta_t_range), 3, CI_and_mean))
SD_end_dates = structure(rep(NA_real_, length(nage)), class="Date")
n_sample = 1000
temp_disease_burden = matrix(0, 4, n_sample)
temp_cum_infected = matrix(0, 4, n_sample)
temp_cum_death = matrix(0, 4, n_sample)
temp_economy_loss = matrix(0, 4, n_sample)
temp_screening_cost = matrix(0, 3, n_sample)
# sample_param_indexes = which.min(abs(runif(n_sample) - m_calib_res[,"c_posterior_prob"]))
t_init = Sys.time()
for (delta_t in delta_t_range){
# delta_t_ind = delta_t - delta_t_range[1] + 1
delta_t_ind = as.integer(as.character((delta_t - start)/incre)) + 1
cat( "delta_t is ", delta_t, "\n")
base_line_quarantine_startdate = as.Date("01/23/20", "%m/%d/%y")
for (ind in 1:n_sample){
# if (ind %% 50 == 0){
# cat('ind is ', ind, "\n")
# }
# param_ind = which.min(abs(runif(1) - m_calib_res[,"c_posterior_prob"]))
param_ind = ind
symptomatic_ratio = as.numeric(m_calib_res[param_ind, 1:3])
mu_1 = as.numeric(m_calib_res[param_ind, 4:6])
beta = as.numeric(m_calib_res[param_ind, 7])
# ratio = ratio_range[beta_ind]
quarantine_start = base_line_quarantine_startdate + delta_t
quarantine_end = simulation_end
### we assume social_distancing start as the same time as
social_distancing_start = quarantine_start
SD_end_dates[1] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[2] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[3] = as.Date("03/31/20", "%m/%d/%y")
# social_distancing_end = delta_t + base_line_social_distancing_end
time_knot_vec = list('simulation_start' = simulation_start,'simulation_end' = simulation_end,
'chunyun_start' = chunyun_start, 'chunyun_end' = chunyun_end,
'quarantine_start' = quarantine_start, 'quarantine_end' = quarantine_end,
'social_distancing_start' = social_distancing_start, 'SD_end_dates' = SD_end_dates)
mu = mu_1 * 2
WH_output <- wuhan_simulation(N_wh, time, beta, z_0, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, symptomatic_ratio)
# ratio = ratio_range[2]
#### for other cities
exported = WH_output$exported
ths_efficiency = 1
ths_window = 14
ths_start = quarantine_start ###ths: travel history screening
ths_end = quarantine_end ### ths: travel history screening
# social_distancing_start = as.Date("01/23/20", "%m/%d/%y")
social_distancing_start = ths_start
# social_distancing_end = quarantine_end
SD_end_dates[1] = as.Date("03/31/20", "%m/%d/%y")
SD_end_dates[2] = as.Date("02/29/20", "%m/%d/%y")
SD_end_dates[3] = as.Date("03/31/20", "%m/%d/%y")
time_knot_vec = list('simulation_start' = simulation_start,'simulation_end' = simulation_end,
'chunyun_start' = chunyun_start, 'chunyun_end' = chunyun_end,
'ths_start' = ths_start, 'ths_end' = ths_end,
'social_distancing_start' = social_distancing_start, 'SD_end_dates' = SD_end_dates)
mu = mu_1/7 * 2
CQ_output <- other_city_simulation(N_cq, f_cq, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_cq, ths_efficiency, ths_window, symptomatic_ratio)
BJ_output <- other_city_simulation(N_bj, f_bj, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_bj, ths_efficiency, ths_window, symptomatic_ratio)
SH_output <- other_city_simulation(N_sh, f_sh, time, beta, D_E, D_I, time_knot_vec, E_I_beta_ratio, contact_mat, contact_ratio, mu, exported, exportion_perc_sh, ths_efficiency, ths_window, symptomatic_ratio)
diff_t = as.integer(milestone - simulation_start)
temp_disease_burden[1, ind] = sum((WH_output$IS[diff_t + 1, ] + WH_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(WH_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[2, ind] = sum((CQ_output$IS[diff_t + 1, ] + CQ_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(CQ_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[3, ind] = sum((BJ_output$IS[diff_t + 1, ] + BJ_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(BJ_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_disease_burden[4, ind] = sum((SH_output$IS[diff_t + 1, ] + SH_output$RS[diff_t + 1, ]) * ratio * (cost_treatment + cost_survived) +
(SH_output$D[diff_t + 1, ]) * cost_death) /1e+06
temp_cum_infected[1, ind] = sum(WH_output$incidence[1:diff_t + 1])/WH_output$N_t[diff_t + 1] * 10000
temp_cum_infected[2, ind] = sum(CQ_output$incidence[1:diff_t + 1])/CQ_output$N_t[diff_t + 1] * 10000
temp_cum_infected[3, ind] = sum(BJ_output$incidence[1:diff_t + 1])/BJ_output$N_t[diff_t + 1] * 10000
temp_cum_infected[4, ind] = sum(SH_output$incidence[1:diff_t + 1])/SH_output$N_t[diff_t + 1] * 10000
temp_cum_death[1, ind] = WH_output$D[diff_t + 1 - time_to_death[1], 1] + WH_output$D[diff_t + 1 - time_to_death[2], 2] + WH_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[2, ind] = CQ_output$D[diff_t + 1 - time_to_death[1], 1] + CQ_output$D[diff_t + 1 - time_to_death[2], 2] + CQ_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[3, ind] = BJ_output$D[diff_t + 1 - time_to_death[1], 1] + BJ_output$D[diff_t + 1 - time_to_death[2], 2] + BJ_output$D[diff_t + 1 - time_to_death[3], 3]
temp_cum_death[4, ind] = SH_output$D[diff_t + 1 - time_to_death[1], 1] + SH_output$D[diff_t + 1 - time_to_death[2], 2] + SH_output$D[diff_t + 1 - time_to_death[3], 3]
temp_screening_cost[1, ind] = CQ_output$screening_counts * 16.438/1e+06
temp_screening_cost[2, ind] = BJ_output$screening_counts * 16.438/1e+06
temp_screening_cost[3, ind] = SH_output$screening_counts * 16.438/1e+06
temp_economy_loss[, ind] = temp_economy_loss[ ,ind] +
GDP_loss_matrix[1,] * (milestone - quarantine_start + 1) +
GDP_loss_matrix[2, ] * (SD_end_dates[2] - social_distancing_start + 1) +
temp_disease_burden[ ,ind] + c(0, temp_screening_cost[ ,ind])
temp_economy_loss[ , ind] = round(temp_economy_loss[ ,ind]/1000,2)
}
# cum_exportion[delta_t_ind, beta_ind] = sum(WH_output$exported[1:diff_t + 1,,])
disease_burden[delta_t_ind, ,] = rowQuantiles(temp_disease_burden, probs = c(0.025, 0.5, 0.975))
cum_infected[delta_t_ind, ,] = rowQuantiles(temp_cum_infected, probs = c(0.025, 0.5, 0.975))
cum_death[delta_t_ind, ,] = rowQuantiles(temp_cum_death, probs = c(0.025, 0.5, 0.975))
screening_cost[delta_t_ind, ,] = rowQuantiles(temp_screening_cost, probs = c(0.025, 0.5, 0.975))
economy_loss[delta_t_ind, ,] = rowQuantiles(temp_economy_loss, probs = c(0.025, 0.5, 0.975))
### change meadian to mean
disease_burden[delta_t_ind, ,2] = rowMeans(temp_disease_burden)
cum_infected[delta_t_ind, ,2] = rowMeans(temp_cum_infected)
cum_death[delta_t_ind, ,2] = rowMeans(temp_cum_death)
screening_cost[delta_t_ind, ,2] = rowMeans(temp_screening_cost)
economy_loss[delta_t_ind, ,2] = rowMeans(temp_economy_loss)
date[delta_t_ind] = base_line_quarantine_startdate + delta_t
# date[delta_t_ind, 1] = as.Date(base_line_quarantine_startdate + delta_t)
}
comp_time = Sys.time() - t_init
comp_time
save(economy_loss, disease_burden, cum_infected, cum_death, date, delta_t_range, screening_cost, file = "vary_start_date_all_doubled_mortality.rda")
|
% Generated by roxygen2 (4.0.1): do not edit by hand
\name{geom_widerect}
\alias{geom_widerect}
\title{ggplot2 geom with ymin and ymax aesthetics that covers the entire x range, useful for clickSelects background elements.}
\usage{
geom_widerect(mapping = NULL, data = NULL, stat = "identity",
position = "identity", ...)
}
\arguments{
\item{mapping}{aesthetic mapping}
\item{data}{data set}
\item{stat}{statistic mapping, defaults to identity}
\item{position}{position mapping, defaults to identity}
\item{...}{other arguments}
}
\value{
ggplot2 layer
}
\description{
ggplot2 geom with ymin and ymax aesthetics that covers the
entire x range, useful for clickSelects background
elements.
}
\examples{
\dontrun{
source(system.file("examples/WorldBank.R", package = "animint"))
}
}
| /man/geom_widerect.Rd | no_license | cesine/animint | R | false | false | 802 | rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{geom_widerect}
\alias{geom_widerect}
\title{ggplot2 geom with ymin and ymax aesthetics that covers the entire x range, useful for clickSelects background elements.}
\usage{
geom_widerect(mapping = NULL, data = NULL, stat = "identity",
position = "identity", ...)
}
\arguments{
\item{mapping}{aesthetic mapping}
\item{data}{data set}
\item{stat}{statistic mapping, defaults to identity}
\item{position}{position mapping, defaults to identity}
\item{...}{other arguments}
}
\value{
ggplot2 layer
}
\description{
ggplot2 geom with ymin and ymax aesthetics that covers the
entire x range, useful for clickSelects background
elements.
}
\examples{
\dontrun{
source(system.file("examples/WorldBank.R", package = "animint"))
}
}
|
unemp = scan('unemp_nz_quarterly_1986Q1-2019Q2.txt')
unemp = ts(unemp, frequency = 4, start = 1986)
diff1 = diff(unemp)
Box.test(diff1, lag = 20) | /092019TA/p43a.R | no_license | ding05/time_series | R | false | false | 148 | r | unemp = scan('unemp_nz_quarterly_1986Q1-2019Q2.txt')
unemp = ts(unemp, frequency = 4, start = 1986)
diff1 = diff(unemp)
Box.test(diff1, lag = 20) |
## Exploratory Data Analyis Project 1
## source: graph2.r
## result: graph2.png
## file is being kept in the .\data directory
setwd("C:\Users\x\devel\DataScience\ExploratoryDataAnalysis\ExData_Plotting1")
## Read in the data from the csv file, header values are true
powdf <- read.csv("data\\household_power_consumption.txt", header=T, sep=";", stringsAsFactors=F, na.strings="?", colClasses=c("character","character","numeric","numeric","numeric","numeric","numeric","numeric","numeric"))
## convert the start/end date first then compare
## in the next statement to get subset of data we are interested in.
sdttm <- strptime("01/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
edttm <- strptime("03/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
powdfsubset <- powdf[strptime(paste(powdf$Date, powdf$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC") >= sdttm & strptime(paste(powdf$Date, powdf$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC") <= edttm,]
powdfsubset$dttm <- strptime(paste(powdfsubset$Date, powdfsubset$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC")
## Initialize PNG device
png(filename="graph2.png", width=500, height=500)
## create histogram
plot(powdfsubset$dttm,powdfsubset$Global_active_power, xlab="", ylab="Global Active Power (kilowatts)")
## turn off the PNG device ...
dev.off
| /graph2.r | no_license | aeamaea/ExData_Plotting1 | R | false | false | 1,319 | r | ## Exploratory Data Analyis Project 1
## source: graph2.r
## result: graph2.png
## file is being kept in the .\data directory
setwd("C:\Users\x\devel\DataScience\ExploratoryDataAnalysis\ExData_Plotting1")
## Read in the data from the csv file, header values are true
powdf <- read.csv("data\\household_power_consumption.txt", header=T, sep=";", stringsAsFactors=F, na.strings="?", colClasses=c("character","character","numeric","numeric","numeric","numeric","numeric","numeric","numeric"))
## convert the start/end date first then compare
## in the next statement to get subset of data we are interested in.
sdttm <- strptime("01/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
edttm <- strptime("03/02/2007 00:00:00", format="%d/%m/%Y %H:%M:%S", tz="UTC")
powdfsubset <- powdf[strptime(paste(powdf$Date, powdf$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC") >= sdttm & strptime(paste(powdf$Date, powdf$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC") <= edttm,]
powdfsubset$dttm <- strptime(paste(powdfsubset$Date, powdfsubset$Time),format="%d/%m/%Y %H:%M:%S", tz="UTC")
## Initialize PNG device
png(filename="graph2.png", width=500, height=500)
## create histogram
plot(powdfsubset$dttm,powdfsubset$Global_active_power, xlab="", ylab="Global Active Power (kilowatts)")
## turn off the PNG device ...
dev.off
|
temp <- tempfile()
Url <- "https://d396qusza40orc.cloudfront.net/exdata/data/household_power_consumption.zip"
download.file(Url, temp)
data <- read.table(unz(temp, "household_power_consumption.txt"), sep = ";", na.strings = "?", nrows = 2880, skip = 66637)house
unlink(temp)
names(data) <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
data[,3:9] <- sapply(data[,3:9], as.numeric)
library(lubridate)
data[,1] <- dmy(data[,1])
datetime <- as.POSIXct(paste(data$Date, as.character(data$Time)))
data <- cbind(data, datetime)
png(file="plot2.png", width = 480, height = 480, unit = "px", bg = "white")
plot(data$datetime, data$Global_active_power, type = "l", xlab ="", ylab = "Global Active Power (kilowatts)")
mtext("Plot 2", side = 10, adj = 0, line = 16, at = 2, outer = TRUE)
dev.off() | /plot2.R | no_license | panyvino/ExData_Plotting1 | R | false | false | 915 | r | temp <- tempfile()
Url <- "https://d396qusza40orc.cloudfront.net/exdata/data/household_power_consumption.zip"
download.file(Url, temp)
data <- read.table(unz(temp, "household_power_consumption.txt"), sep = ";", na.strings = "?", nrows = 2880, skip = 66637)house
unlink(temp)
names(data) <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
data[,3:9] <- sapply(data[,3:9], as.numeric)
library(lubridate)
data[,1] <- dmy(data[,1])
datetime <- as.POSIXct(paste(data$Date, as.character(data$Time)))
data <- cbind(data, datetime)
png(file="plot2.png", width = 480, height = 480, unit = "px", bg = "white")
plot(data$datetime, data$Global_active_power, type = "l", xlab ="", ylab = "Global Active Power (kilowatts)")
mtext("Plot 2", side = 10, adj = 0, line = 16, at = 2, outer = TRUE)
dev.off() |
# Cria um modelo preditivo usando randomForest
# Este código foi criado para executar tanto no Azure, quanto no RStudio.
# Para executar no Azure, altere o valor da variavel Azure para TRUE.
# Se o valor for FALSE, o codigo será executado no RStudio
# Obs: Caso tenha problemas com a acentuação, consulte este link:
# https://support.rstudio.com/hc/en-us/articles/200532197-Character-Encoding
# Configurando o diretório de trabalho
# Coloque entre aspas o diretório de trabalho que você está usando no seu computador
# Não use diretórios com espaço no nome
# setwd("C:/FCD/BigDataRAzure/Cap14/Projeto")
# getwd()
# Função para tratar as datas
set.asPOSIXct <- function(inFrame) {
dteday <- as.POSIXct(
as.integer(inFrame$dteday),
origin = "1970-01-01")
as.POSIXct(strptime(
paste(as.character(dteday),
" ",
as.character(inFrame$hr),
":00:00",
sep = ""),
"%Y-%m-%d %H:%M:%S"))
}
char.toPOSIXct <- function(inFrame) {
as.POSIXct(strptime(
paste(inFrame$dteday, " ",
as.character(inFrame$hr),
":00:00",
sep = ""),
"%Y-%m-%d %H:%M:%S")) }
# Variável que controla a execução do script
Azure <- FALSE
if(Azure){
dataset$dteday <- set.asPOSIXct(dataset)
}else{
bikes <- bikes
}
require(randomForest)
model <- randomForest(cnt ~ xformWorkHr + dteday + temp + hum,
data = bikes, # altere o nome do objeto data para "dataset" de estiver trabalhando no Azure ML
ntree = 40,
nodesize = 5)
print(model)
| /pt_02_regressao/Projeto/06-CreateModel.R | no_license | ralsouza/azure_machine_learning | R | false | false | 1,656 | r | # Cria um modelo preditivo usando randomForest
# Este código foi criado para executar tanto no Azure, quanto no RStudio.
# Para executar no Azure, altere o valor da variavel Azure para TRUE.
# Se o valor for FALSE, o codigo será executado no RStudio
# Obs: Caso tenha problemas com a acentuação, consulte este link:
# https://support.rstudio.com/hc/en-us/articles/200532197-Character-Encoding
# Configurando o diretório de trabalho
# Coloque entre aspas o diretório de trabalho que você está usando no seu computador
# Não use diretórios com espaço no nome
# setwd("C:/FCD/BigDataRAzure/Cap14/Projeto")
# getwd()
# Função para tratar as datas
set.asPOSIXct <- function(inFrame) {
dteday <- as.POSIXct(
as.integer(inFrame$dteday),
origin = "1970-01-01")
as.POSIXct(strptime(
paste(as.character(dteday),
" ",
as.character(inFrame$hr),
":00:00",
sep = ""),
"%Y-%m-%d %H:%M:%S"))
}
char.toPOSIXct <- function(inFrame) {
as.POSIXct(strptime(
paste(inFrame$dteday, " ",
as.character(inFrame$hr),
":00:00",
sep = ""),
"%Y-%m-%d %H:%M:%S")) }
# Variável que controla a execução do script
Azure <- FALSE
if(Azure){
dataset$dteday <- set.asPOSIXct(dataset)
}else{
bikes <- bikes
}
require(randomForest)
model <- randomForest(cnt ~ xformWorkHr + dteday + temp + hum,
data = bikes, # altere o nome do objeto data para "dataset" de estiver trabalhando no Azure ML
ntree = 40,
nodesize = 5)
print(model)
|
\name{poolSeq-package}
\alias{poolSeq-package}
\alias{poolSeq}
\docType{package}
\title{
Analyze Pool-seq Data
}
\description{
The \code{poolSeq} package provides a variety of functions to simulate and analyze Pool-seq data, including estimation of the effective population size, selection coefficients and dominance parameters.
}
\details{
}
\author{
\packageAuthor{poolSeq}
Maintainer: \packageMaintainer{poolSeq}
}
\references{
Waples R. S.: A generalized approach for estimating effective population size from temporal changes in allele frequency, \emph{Genetics} \bold{1989}, 121, 379–391.
Agresti A.: Categorical data analysis (second edition). \emph{New York: Wiley} \bold{2002}
Jorde P. E. and Ryman N.: Unbiased estimator for genetic drift and effective population size, \emph{Genetics} \bold{2007}, 177 927–935.
Frick K., Munk, A. and Sieling, H.: Multiscale Change-Point Inference, \emph{Journal of the Royal Statistical Society: Series B} \bold{2014}, 76, 495-580.
Futschik A., Hotz T., Munk A. and Sieling H.: Multiresolution DNA partitioning: statistical evidence for segments, \emph{Bioinformatics} \bold{2014}, 30, 2255-2262.
Jónás A., Taus T., Kosiol C., Schlötterer C. & Futschik A.: Estimating effective population size from temporal allele frequency changes in experimental evolution, manuscript in preparation.
}
\keyword{ package }
\seealso{
\code{\link{wf.traj}}, \code{\link{sample.alleles}}, \code{\link{estimateNe}}, \code{\link{cmh.test}}, \code{\link{chi.sq.test}} and \code{\link{read.sync}}.
}
\examples{
}
| /man/poolSeq-package.Rd | permissive | ThomasTaus/poolSeq | R | false | false | 1,552 | rd | \name{poolSeq-package}
\alias{poolSeq-package}
\alias{poolSeq}
\docType{package}
\title{
Analyze Pool-seq Data
}
\description{
The \code{poolSeq} package provides a variety of functions to simulate and analyze Pool-seq data, including estimation of the effective population size, selection coefficients and dominance parameters.
}
\details{
}
\author{
\packageAuthor{poolSeq}
Maintainer: \packageMaintainer{poolSeq}
}
\references{
Waples R. S.: A generalized approach for estimating effective population size from temporal changes in allele frequency, \emph{Genetics} \bold{1989}, 121, 379–391.
Agresti A.: Categorical data analysis (second edition). \emph{New York: Wiley} \bold{2002}
Jorde P. E. and Ryman N.: Unbiased estimator for genetic drift and effective population size, \emph{Genetics} \bold{2007}, 177 927–935.
Frick K., Munk, A. and Sieling, H.: Multiscale Change-Point Inference, \emph{Journal of the Royal Statistical Society: Series B} \bold{2014}, 76, 495-580.
Futschik A., Hotz T., Munk A. and Sieling H.: Multiresolution DNA partitioning: statistical evidence for segments, \emph{Bioinformatics} \bold{2014}, 30, 2255-2262.
Jónás A., Taus T., Kosiol C., Schlötterer C. & Futschik A.: Estimating effective population size from temporal allele frequency changes in experimental evolution, manuscript in preparation.
}
\keyword{ package }
\seealso{
\code{\link{wf.traj}}, \code{\link{sample.alleles}}, \code{\link{estimateNe}}, \code{\link{cmh.test}}, \code{\link{chi.sq.test}} and \code{\link{read.sync}}.
}
\examples{
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xtabs.R
\name{collect_and_normalize_xtab}
\alias{collect_and_normalize_xtab}
\title{Collect data and normalize result}
\usage{
collect_and_normalize_xtab(connection, disconnect = FALSE)
}
\arguments{
\item{connection}{SQlite connection}
\item{disconnect}{Optional parameter to determine if the SQlite connection should be closed, `FALSE` by default.}
}
\value{
A tibble with the data in long form
}
\description{
Collect data and normalize result
}
| /man/collect_and_normalize_xtab.Rd | permissive | mountainMath/statcanXtabs | R | false | true | 528 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xtabs.R
\name{collect_and_normalize_xtab}
\alias{collect_and_normalize_xtab}
\title{Collect data and normalize result}
\usage{
collect_and_normalize_xtab(connection, disconnect = FALSE)
}
\arguments{
\item{connection}{SQlite connection}
\item{disconnect}{Optional parameter to determine if the SQlite connection should be closed, `FALSE` by default.}
}
\value{
A tibble with the data in long form
}
\description{
Collect data and normalize result
}
|
remove(list= ls())
options(stringsAsFactors = FALSE)
options(scipen = 999)
setwd("/Users/francisco06121988/Desktop/coursera_files/applied_time_series/")
library(astsa);library(tidyverse)
# Johnson & Johnson quarterly earnings ------------------------------------
plot(JohnsonJohnson, main = "Johnson & Johnson earnings per share",
col ="red", lwd = 2, ylab = "Earnings per share")
# Log return of Johnson & Johnson -----------------------------------------
jj_log_return <- diff(log(JohnsonJohnson))
jj_log_return_mean_0 <- jj_log_return - mean(jj_log_return)
# Obtain time plot, ACF, and PACF -----------------------------------------
par(mfrow = c(3,1))
plot(jj_log_return_mean_0, main = "Mean 0 log returns of Johnson & Johnson shares",
col = "blue", lwd = 2, ylab = "Log returns")
acf(jj_log_return_mean_0, main = "ACF")
pacf(jj_log_return_mean_0, main = "PACF")
# Obtain the r vector -----------------------------------------------------
r <- acf(jj_log_return_mean_0, main = "ACF", plot = FALSE)$acf[2:5]
# Obtain matrix R ---------------------------------------------------------
R <- matrix(1,4,4)
for(i in 1:4){
for(j in 1:4){
if(i!=j)
R[i,j]=r[abs(i-j)]
}
}
# Solve for sigma hat -----------------------------------------------------
phi_hat <- solve(R) %*% r
# Obtain the estimate of the variance of residuals ------------------------
c_0 <- acf(jj_log_return_mean_0, type = "covariance",plot = FALSE)$acf[1]
sigma_sq_hat <- c_0*(1 - sum(phi_hat*r))
sigma_sq_hat
# Obtain the constant in the model ----------------------------------------
phi_0_hat <- mean(jj_log_return)*(1 - sum(phi_hat)) ##Normal mean coefficient
phi_0_hat
# Predictions from the model ----------------------------------------------
##predicting diff log
par(mfrow = c(1,1))
predictions <- vector(length = length(jj_log_return_mean_0), mode = "numeric")
predictions <- vector(length = 83, mode = "numeric")
for(i in 5:length(jj_log_return_mean_0)){
predictions[i] <- phi_0_hat +
phi_hat[1]*jj_log_return[i-1] +
phi_hat[2]*jj_log_return[i-2] +
phi_hat[3]*jj_log_return[i-3] +
phi_hat[4]*jj_log_return[i-4]
}
remove(i)
tibble(
time = 1:80,
earnings = jj_log_return[4:83],
predictions = predictions[4:83]
)%>%
gather(type, value, - time) %>%
ggplot(aes(x = time, y = value, color = str_to_title(type))) +
geom_line() +
hrbrthemes::theme_ipsum_rc() +
theme(
legend.position = "bottom"
) +
guides(color = guide_legend(title = "Series",title.position = "top",
title.hjust = 0.5, label.position = "bottom")) +
labs(x = "Time", y = "Log difference of earnings")
# Back to original scale --------------------------------------------------
same_scale <- vector(length = 83, mode = "numeric")
for(i in 5:length(predictions)){
same_scale[i] <- exp(predictions[i]) + JohnsonJohnson[i-1]
}
tibble(
time = 1:83,
earnings = JohnsonJohnson[2:84],
predictions = same_scale[2:84]
) %>%
gather(
measure, value, -time
) %>%
ggplot(aes(x = time, y = value,
color = str_to_title(measure))) +
geom_line() +
hrbrthemes::theme_ipsum_rc() +
theme(
legend.position = "bottom"
) + guides(color = guide_legend(title = "Series",title.position = "top",
title.hjust = 0.5, label.position = "bottom")) +
labs(x = "Time", y = "Earnings") | /applied_time_series/Week 4 - Johnson & Johnson Earnings.R | no_license | ssh352/coursera_files | R | false | false | 3,487 | r | remove(list= ls())
options(stringsAsFactors = FALSE)
options(scipen = 999)
setwd("/Users/francisco06121988/Desktop/coursera_files/applied_time_series/")
library(astsa);library(tidyverse)
# Johnson & Johnson quarterly earnings ------------------------------------
plot(JohnsonJohnson, main = "Johnson & Johnson earnings per share",
col ="red", lwd = 2, ylab = "Earnings per share")
# Log return of Johnson & Johnson -----------------------------------------
jj_log_return <- diff(log(JohnsonJohnson))
jj_log_return_mean_0 <- jj_log_return - mean(jj_log_return)
# Obtain time plot, ACF, and PACF -----------------------------------------
par(mfrow = c(3,1))
plot(jj_log_return_mean_0, main = "Mean 0 log returns of Johnson & Johnson shares",
col = "blue", lwd = 2, ylab = "Log returns")
acf(jj_log_return_mean_0, main = "ACF")
pacf(jj_log_return_mean_0, main = "PACF")
# Obtain the r vector -----------------------------------------------------
r <- acf(jj_log_return_mean_0, main = "ACF", plot = FALSE)$acf[2:5]
# Obtain matrix R ---------------------------------------------------------
R <- matrix(1,4,4)
for(i in 1:4){
for(j in 1:4){
if(i!=j)
R[i,j]=r[abs(i-j)]
}
}
# Solve for sigma hat -----------------------------------------------------
phi_hat <- solve(R) %*% r
# Obtain the estimate of the variance of residuals ------------------------
c_0 <- acf(jj_log_return_mean_0, type = "covariance",plot = FALSE)$acf[1]
sigma_sq_hat <- c_0*(1 - sum(phi_hat*r))
sigma_sq_hat
# Obtain the constant in the model ----------------------------------------
phi_0_hat <- mean(jj_log_return)*(1 - sum(phi_hat)) ##Normal mean coefficient
phi_0_hat
# Predictions from the model ----------------------------------------------
##predicting diff log
par(mfrow = c(1,1))
predictions <- vector(length = length(jj_log_return_mean_0), mode = "numeric")
predictions <- vector(length = 83, mode = "numeric")
for(i in 5:length(jj_log_return_mean_0)){
predictions[i] <- phi_0_hat +
phi_hat[1]*jj_log_return[i-1] +
phi_hat[2]*jj_log_return[i-2] +
phi_hat[3]*jj_log_return[i-3] +
phi_hat[4]*jj_log_return[i-4]
}
remove(i)
tibble(
time = 1:80,
earnings = jj_log_return[4:83],
predictions = predictions[4:83]
)%>%
gather(type, value, - time) %>%
ggplot(aes(x = time, y = value, color = str_to_title(type))) +
geom_line() +
hrbrthemes::theme_ipsum_rc() +
theme(
legend.position = "bottom"
) +
guides(color = guide_legend(title = "Series",title.position = "top",
title.hjust = 0.5, label.position = "bottom")) +
labs(x = "Time", y = "Log difference of earnings")
# Back to original scale --------------------------------------------------
same_scale <- vector(length = 83, mode = "numeric")
for(i in 5:length(predictions)){
same_scale[i] <- exp(predictions[i]) + JohnsonJohnson[i-1]
}
tibble(
time = 1:83,
earnings = JohnsonJohnson[2:84],
predictions = same_scale[2:84]
) %>%
gather(
measure, value, -time
) %>%
ggplot(aes(x = time, y = value,
color = str_to_title(measure))) +
geom_line() +
hrbrthemes::theme_ipsum_rc() +
theme(
legend.position = "bottom"
) + guides(color = guide_legend(title = "Series",title.position = "top",
title.hjust = 0.5, label.position = "bottom")) +
labs(x = "Time", y = "Earnings") |
#The 3-factor model (can follow the 3-factor notebook for more details)
library(stats)
library(psych)
library(polycor)
library(lavaan)
####### NOTES 1 THE CORRELATION MATRIX ############################
#read in the coded undergraduate response data and take the items (questions) of interest
all_student_data <- read.csv('path_to_your_data_here.csv')
#student_data <- all_student_data[,c(6,7,9,15,16,17,19,21,23,24)]
student_data <- all_student_data[,c(6,7,15,16,17,19,21,23,24)] #Q8 dropped for KMO test
#make sure the incoming data is interpreted as ordinal, not as interval - you can check that without this line, hetcor produces Pearson correlations
student_data_cat <- sapply(student_data, as.factor)
#apply the hetcor function which produces the polychoric correlation matrix and associated information
#check for 0 cell values in contingency tables
corr_info = hetcor(student_data_cat)
PCM <- corr_info$cor #the correlation matrix
PCM_type <- corr_info$type #the type of correlation computed for each pair - all should be polychoric
PCM_assumption_test <- corr_info$tests #the p-value for each test of bivariate normality (uses pearson, want < .1)
#Assessing correlation matrix as input to FA
# eyeball correlations, want some but not too large
# check Bartlett's test of sphericity - tests the null hypothesis that PCM is an identity matrix (reject with small p)
BToS_pvalue <- cortest.bartlett(PCM, n = 82)$p.value #n is the number of samples
# check for multicolinearity and singularity by checking the determinant
detMtest = det(PCM)
# check eigenvalues - should all be positive and not too close to zero (smoothing)
eigsPCM = eigen(PCM, only.values = TRUE)$values
# Kaiser-Meyer-Olkin (KMO) test of sampling adequacy
KMO_summary <- KMO(PCM)
########## NOTES 2 EFA ##################
######### PART 2.1 DETERMINING NUMBER OF FACTORS TO RETAIN #######
#take a look at the eigenvalues of the correlation matrix and plot them
#run the parallel analysis - which also will show you the scree plot and eigs greater than 1
fa.parallel(PCM, n.obs = 82, fm="pa", fa ="both") #note: this analysis runs 1-factor PA for many randomly simulated corr matices, it's not unusual that some warnings will be produced
######## PART 2.2 RUNNING FACTOR ANALYSIS #######
fa_3 <- fa(r = PCM, nfactors = 3, fm = "uls", rotate = "promax")
######## PART 3 CFA ########
### 3-factor model ###
#define the model
model_3 <- 'F3_A =~ Q3_6 + Q3_14 + Q3_15 + Q3_18 + Q3_23
F3_B =~ Q3_5 + Q3_22 + Q3_23
F3_C =~ Q3_5 + Q3_16 + Q3_20'
#we fit our model providing the entire dataset
#ordinal data must be labelled as ordered; doing so will use the WLSMV estimator for (robust) test statistics
fit_3 <- cfa(model_3, data = student_data, ordered = c("Q3_5","Q3_6", "Q3_14", "Q3_15", "Q3_16", "Q3_18", "Q3_20","Q3_22", "Q3_23"))
#look at some output
#summary(fit_1, fit.measures=TRUE)
sanity_3 <- standardizedSolution(fit_3) #compare factor loadings and uniquenesses to EFA soln (double check output is sensible)
#check the modifaction indices
mi <- modindices(fit_3)
#print(mi)
####### PART 4: RELIABILITY ##########
# check ordinal alpha for the item measure, and each factor
#entire survey (since we use a polychoric corr matrix, we don't have to do anything except call alpha on the correct matrix)
overall_alpha <- alpha(PCM, check.keys=TRUE)
#find the sub-correlation matrices associated with each factor
corr_FA <- hetcor(sapply(all_student_data[,c(7,15,16,19,24)],as.factor))$cor
corr_FB <- hetcor(sapply(all_student_data[,c(6,23,24)],as.factor))$cor
corr_FC <- hetcor(sapply(all_student_data[,c(6,17,21)],as.factor))$cor
#check their alphas
alpha_A <- alpha(corr_FA)
alpha_B <- alpha(corr_FB)
alpha_C <- alpha(corr_FC)
| /3FactorScript.R | no_license | hillary-dawkins/ValidationNotes | R | false | false | 3,845 | r | #The 3-factor model (can follow the 3-factor notebook for more details)
library(stats)
library(psych)
library(polycor)
library(lavaan)
####### NOTES 1 THE CORRELATION MATRIX ############################
#read in the coded undergraduate response data and take the items (questions) of interest
all_student_data <- read.csv('path_to_your_data_here.csv')
#student_data <- all_student_data[,c(6,7,9,15,16,17,19,21,23,24)]
student_data <- all_student_data[,c(6,7,15,16,17,19,21,23,24)] #Q8 dropped for KMO test
#make sure the incoming data is interpreted as ordinal, not as interval - you can check that without this line, hetcor produces Pearson correlations
student_data_cat <- sapply(student_data, as.factor)
#apply the hetcor function which produces the polychoric correlation matrix and associated information
#check for 0 cell values in contingency tables
corr_info = hetcor(student_data_cat)
PCM <- corr_info$cor #the correlation matrix
PCM_type <- corr_info$type #the type of correlation computed for each pair - all should be polychoric
PCM_assumption_test <- corr_info$tests #the p-value for each test of bivariate normality (uses pearson, want < .1)
#Assessing correlation matrix as input to FA
# eyeball correlations, want some but not too large
# check Bartlett's test of sphericity - tests the null hypothesis that PCM is an identity matrix (reject with small p)
BToS_pvalue <- cortest.bartlett(PCM, n = 82)$p.value #n is the number of samples
# check for multicolinearity and singularity by checking the determinant
detMtest = det(PCM)
# check eigenvalues - should all be positive and not too close to zero (smoothing)
eigsPCM = eigen(PCM, only.values = TRUE)$values
# Kaiser-Meyer-Olkin (KMO) test of sampling adequacy
KMO_summary <- KMO(PCM)
########## NOTES 2 EFA ##################
######### PART 2.1 DETERMINING NUMBER OF FACTORS TO RETAIN #######
#take a look at the eigenvalues of the correlation matrix and plot them
#run the parallel analysis - which also will show you the scree plot and eigs greater than 1
fa.parallel(PCM, n.obs = 82, fm="pa", fa ="both") #note: this analysis runs 1-factor PA for many randomly simulated corr matices, it's not unusual that some warnings will be produced
######## PART 2.2 RUNNING FACTOR ANALYSIS #######
fa_3 <- fa(r = PCM, nfactors = 3, fm = "uls", rotate = "promax")
######## PART 3 CFA ########
### 3-factor model ###
#define the model
model_3 <- 'F3_A =~ Q3_6 + Q3_14 + Q3_15 + Q3_18 + Q3_23
F3_B =~ Q3_5 + Q3_22 + Q3_23
F3_C =~ Q3_5 + Q3_16 + Q3_20'
#we fit our model providing the entire dataset
#ordinal data must be labelled as ordered; doing so will use the WLSMV estimator for (robust) test statistics
fit_3 <- cfa(model_3, data = student_data, ordered = c("Q3_5","Q3_6", "Q3_14", "Q3_15", "Q3_16", "Q3_18", "Q3_20","Q3_22", "Q3_23"))
#look at some output
#summary(fit_1, fit.measures=TRUE)
sanity_3 <- standardizedSolution(fit_3) #compare factor loadings and uniquenesses to EFA soln (double check output is sensible)
#check the modifaction indices
mi <- modindices(fit_3)
#print(mi)
####### PART 4: RELIABILITY ##########
# check ordinal alpha for the item measure, and each factor
#entire survey (since we use a polychoric corr matrix, we don't have to do anything except call alpha on the correct matrix)
overall_alpha <- alpha(PCM, check.keys=TRUE)
#find the sub-correlation matrices associated with each factor
corr_FA <- hetcor(sapply(all_student_data[,c(7,15,16,19,24)],as.factor))$cor
corr_FB <- hetcor(sapply(all_student_data[,c(6,23,24)],as.factor))$cor
corr_FC <- hetcor(sapply(all_student_data[,c(6,17,21)],as.factor))$cor
#check their alphas
alpha_A <- alpha(corr_FA)
alpha_B <- alpha(corr_FB)
alpha_C <- alpha(corr_FC)
|
# https://stanford.edu/~wpmarble/webscraping_tutorial/webscraping_tutorial.pdf
## Webscraping tutorial
# Will Marble - August 2016
# This code goes along with the tutorial found at
# http://stanford.edu/~wpmarble/webscraping_tutorial/webscraping_tutorial.pdf
## Before going through this tutorial, you should download google chrome
## and the SelectorGadget chrome extension (http://selectorgadget.com/)
## Then run the following code to make sure you have all the required packages:
rm(list=ls())
pkgs = c("rvest", "magrittr", "httr", "stringr", "ggplot2", "rjson")
for (pkg in pkgs){
if (!require(pkg, character.only = T)){
install.packages(pkg)
library(pkg)
}
}
# simple example ----------------------------------------------------------
## Read my example html with read_html()
silly_webpage = read_html("http://stanford.edu/~wpmarble/webscraping_tutorial/html/silly_webpage.html")
# get paragraphs (css selector "p")
my_paragraphs = html_nodes(silly_webpage, "p")
my_paragraphs
# get elements with class "thisOne" -- use a period to denote class
thisOne_elements = html_nodes(silly_webpage, ".thisOne")
thisOne_elements
# get elements with id "myDivID" -- use a hashtag to denote id
myDivID_elements = html_nodes(silly_webpage, "#myDivID")
myDivID_elements
# extract text from myDivID_elements
myDivID_text = html_text(myDivID_elements)
myDivID_text
# extract links from myDivID_elements. first i extract all the "a" nodes (as in a href="website.com")
# and then extract the "href" attribute from those nodes
myDivID_link = html_nodes(myDivID_elements, "a") %>% html_attr("href")
myDivID_link
# harder example ----------------------------------------------------------
# STEP 1, OUTSIDE OF R
# Open that webpage on Chrome and search for the relevant set of ballot measures
# (in this case, everything from 2016). Then download the page source.
# I did this and saved it to my website.
# STEP 2
# Use rvest to read the html file
measures = read_html("http://stanford.edu/~wpmarble/webscraping_tutorial/html/ballot_measures_2016.html")
# STEP 3
# Select the nodes I want -- I can use the | character to return both types of
# Xpath selectors I want
selector = '//*[contains(concat( " ", @class, " " ), concat( " ", "divRepeaterResults", " " ))]|//*[contains(concat( " ", @class, " " ), concat( " ", "h2Headers", " " ))]'
my_nodes = measures %>% html_nodes(xpath=selector)
# let's look at what we got
my_nodes[1:9]
# the first 6 nodes don't have information I want, so get rid of them
my_nodes = my_nodes[-c(1:6)]
## work thru one entry first ##
# randomly chose 128 as an example to work thru
thetext = html_text(my_nodes[[128]])
# get rid of all those extra spaces
thetext = gsub(pattern = "[ ]+", replacement = " ", thetext)
# let's split up the string using the "\r\n \r\n" identifier plus the one field that's
# not separated by two line breaks -- topic areas
thetext = strsplit(thetext, split= "\r\n \r\n|\r\n Topic")[[1]]
thetext
# get rid of the \r\n, extra whitespace, and empty entries
thetext = gsub(pattern="\\r|\\n", replacement="", thetext) %>% str_trim
thetext = thetext[thetext != ""]
thetext
# finally extract results
title = thetext[1]
election = thetext[grepl(pattern = "^Election", thetext)] %>% gsub("Election:", "", x = .) %>% str_trim
type = thetext[grepl(pattern = "^Type", thetext)] %>% gsub("Type:", "", x = .) %>% str_trim
status = thetext[grepl(pattern = "^Status", thetext)] %>% gsub("Status:", "", x = .) %>% str_trim
topic_areas = thetext[grepl(pattern = "^Area:|Areas:", thetext)] %>% gsub("Area:|Areas:", "", x = .) %>% str_trim
# summary is a little trickier to get because the actual summary comes
# the entry after the one that says "Summary: Click for Summary"
summary_index = grep(pattern="^Summary", thetext) + 1
summary = thetext[summary_index]
# we're done! print the results:
for (x in c("title", "election", "type", "status", "summary", "topic_areas")){
cat(x,": ", get(x), "\n")
}
## Now loop thru all our nodes ##
# create state / info indicator vector
state_or_info = my_nodes %>% html_attr("class")
state_or_info = ifelse(state_or_info == "h2Headers", "state", "info")
# set up data frame to store results
results_df = data.frame(state = rep(NA_character_, length(my_nodes)),
title = NA_character_,
election = NA_character_,
type = NA_character_,
status = NA_character_,
topic_areas = NA,
summary = NA_character_,
stringsAsFactors = F)
state = NA_character_ # this variable will keep track of what state we're in
# loop through all the nodes
for (i in 1:length(my_nodes)){
# first see if the node tells us what state we're in; if so, update
# the state variable
if (state_or_info[i] == "state") {
state = html_text(my_nodes[[i]])
}
# if it doesn't say what state we're in, apply the parsing code from above
else {
results_df$state[i] = state # fill in state
# parse text like above
thetext = html_text(my_nodes[[i]])
thetext = gsub(pattern = "[ ]+", replacement = " ", thetext)
thetext = strsplit(thetext, split= "\r\n \r\n|\r\n Topic")[[1]]
thetext = gsub(pattern="\\r|\\n", replacement="", thetext) %>% str_trim
thetext = thetext[thetext != ""]
results_df$title[i] = thetext[1]
results_df$election[i] = thetext[grepl(pattern = "^Election", thetext)] %>%
gsub("Election:", "", x = .) %>% str_trim
results_df$type[i] = thetext[grepl(pattern = "^Type", thetext)] %>%
gsub("Type:", "", x = .) %>% str_trim
results_df$status[i] = thetext[grepl(pattern = "^Status", thetext)] %>%
gsub("Status:", "", x = .) %>% str_trim
results_df$topic_areas[i] = thetext[grepl(pattern = "^Area:|Areas:", thetext)] %>%
gsub("Area:|Areas:", "", x = .) %>% str_trim
summary_index = grep(pattern="^Summary", thetext) + 1
results_df$summary[i] = thetext[summary_index]
}
}
results_df = results_df[!is.na(results_df$state),]
# let's have a look at a bit of the final product (some variables omitted for space)
head(results_df)
View(results_df)
# Briefly on API's --------------------------------------------------------
list_of_shows = c("breaking bad", "mad men", "game of thrones",
"homeland", "house of cards", "true detective",
"orange is the new black", "the americans", "mr robot",
"boardwalk empire", "the good wife", "dexter",
"lost", "true blood", "house", "big love", "downton abbey",
"damages", "boston legal", "grey's anatomy", "the sopranos",
"heroes", "better call saul")
show_db = data.frame(title = list_of_shows,
year = NA, genre = NA, plot = NA, country = NA,
awards = NA, metascore = NA, imdbrating = NA,
imdbvotes = NA, imdbid = NA, totalseasons = NA)
# construct the url for each show by pasting the name of the show after
# the API base, and encoding using URLencode().
for (show in list_of_shows){
show_url = paste0("http://omdbapi.com/?&t=", URLencode(show, reserved = T))
show_info = read_html(show_url) %>% html_text %>% fromJSON
show_db$year[show_db$title==show] = show_info$Year
show_db$genre[show_db$title==show] = show_info$Genre
show_db$plot[show_db$title==show] = show_info$Plot
show_db$country[show_db$title==show] = show_info$Country
show_db$awards[show_db$title==show] = show_info$Awards
show_db$metascore[show_db$title==show] = show_info$Metascore
show_db$imdbrating[show_db$title==show] = show_info$imdbRating
show_db$imdbvotes[show_db$title==show] = show_info$imdbVotes
show_db$imdbid[show_db$title==show] = show_info$imdbID
show_db$totalseasons[show_db$title==show] = show_info$totalSeasons
}
show_db[1:5, c(1:3, 8)]
# make a plot
show_db = show_db[order(show_db$imdbrating),]
show_db$title = factor(show_db$title, levels = show_db$title)
ggplot(show_db, aes(x = title, y = as.numeric(imdbrating))) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle=65, hjust=1)) +
scale_y_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(ylim = c(0, 10)) +
labs(x = NULL, y = "IMDb rating")
| /webscaping1/webscraping_example.R | no_license | 106035007/Text-Mining | R | false | false | 8,361 | r | # https://stanford.edu/~wpmarble/webscraping_tutorial/webscraping_tutorial.pdf
## Webscraping tutorial
# Will Marble - August 2016
# This code goes along with the tutorial found at
# http://stanford.edu/~wpmarble/webscraping_tutorial/webscraping_tutorial.pdf
## Before going through this tutorial, you should download google chrome
## and the SelectorGadget chrome extension (http://selectorgadget.com/)
## Then run the following code to make sure you have all the required packages:
rm(list=ls())
pkgs = c("rvest", "magrittr", "httr", "stringr", "ggplot2", "rjson")
for (pkg in pkgs){
if (!require(pkg, character.only = T)){
install.packages(pkg)
library(pkg)
}
}
# simple example ----------------------------------------------------------
## Read my example html with read_html()
silly_webpage = read_html("http://stanford.edu/~wpmarble/webscraping_tutorial/html/silly_webpage.html")
# get paragraphs (css selector "p")
my_paragraphs = html_nodes(silly_webpage, "p")
my_paragraphs
# get elements with class "thisOne" -- use a period to denote class
thisOne_elements = html_nodes(silly_webpage, ".thisOne")
thisOne_elements
# get elements with id "myDivID" -- use a hashtag to denote id
myDivID_elements = html_nodes(silly_webpage, "#myDivID")
myDivID_elements
# extract text from myDivID_elements
myDivID_text = html_text(myDivID_elements)
myDivID_text
# extract links from myDivID_elements. first i extract all the "a" nodes (as in a href="website.com")
# and then extract the "href" attribute from those nodes
myDivID_link = html_nodes(myDivID_elements, "a") %>% html_attr("href")
myDivID_link
# harder example ----------------------------------------------------------
# STEP 1, OUTSIDE OF R
# Open that webpage on Chrome and search for the relevant set of ballot measures
# (in this case, everything from 2016). Then download the page source.
# I did this and saved it to my website.
# STEP 2
# Use rvest to read the html file
measures = read_html("http://stanford.edu/~wpmarble/webscraping_tutorial/html/ballot_measures_2016.html")
# STEP 3
# Select the nodes I want -- I can use the | character to return both types of
# Xpath selectors I want
selector = '//*[contains(concat( " ", @class, " " ), concat( " ", "divRepeaterResults", " " ))]|//*[contains(concat( " ", @class, " " ), concat( " ", "h2Headers", " " ))]'
my_nodes = measures %>% html_nodes(xpath=selector)
# let's look at what we got
my_nodes[1:9]
# the first 6 nodes don't have information I want, so get rid of them
my_nodes = my_nodes[-c(1:6)]
## work thru one entry first ##
# randomly chose 128 as an example to work thru
thetext = html_text(my_nodes[[128]])
# get rid of all those extra spaces
thetext = gsub(pattern = "[ ]+", replacement = " ", thetext)
# let's split up the string using the "\r\n \r\n" identifier plus the one field that's
# not separated by two line breaks -- topic areas
thetext = strsplit(thetext, split= "\r\n \r\n|\r\n Topic")[[1]]
thetext
# get rid of the \r\n, extra whitespace, and empty entries
thetext = gsub(pattern="\\r|\\n", replacement="", thetext) %>% str_trim
thetext = thetext[thetext != ""]
thetext
# finally extract results
title = thetext[1]
election = thetext[grepl(pattern = "^Election", thetext)] %>% gsub("Election:", "", x = .) %>% str_trim
type = thetext[grepl(pattern = "^Type", thetext)] %>% gsub("Type:", "", x = .) %>% str_trim
status = thetext[grepl(pattern = "^Status", thetext)] %>% gsub("Status:", "", x = .) %>% str_trim
topic_areas = thetext[grepl(pattern = "^Area:|Areas:", thetext)] %>% gsub("Area:|Areas:", "", x = .) %>% str_trim
# summary is a little trickier to get because the actual summary comes
# the entry after the one that says "Summary: Click for Summary"
summary_index = grep(pattern="^Summary", thetext) + 1
summary = thetext[summary_index]
# we're done! print the results:
for (x in c("title", "election", "type", "status", "summary", "topic_areas")){
cat(x,": ", get(x), "\n")
}
## Now loop thru all our nodes ##
# create state / info indicator vector
state_or_info = my_nodes %>% html_attr("class")
state_or_info = ifelse(state_or_info == "h2Headers", "state", "info")
# set up data frame to store results
results_df = data.frame(state = rep(NA_character_, length(my_nodes)),
title = NA_character_,
election = NA_character_,
type = NA_character_,
status = NA_character_,
topic_areas = NA,
summary = NA_character_,
stringsAsFactors = F)
state = NA_character_ # this variable will keep track of what state we're in
# loop through all the nodes
for (i in 1:length(my_nodes)){
# first see if the node tells us what state we're in; if so, update
# the state variable
if (state_or_info[i] == "state") {
state = html_text(my_nodes[[i]])
}
# if it doesn't say what state we're in, apply the parsing code from above
else {
results_df$state[i] = state # fill in state
# parse text like above
thetext = html_text(my_nodes[[i]])
thetext = gsub(pattern = "[ ]+", replacement = " ", thetext)
thetext = strsplit(thetext, split= "\r\n \r\n|\r\n Topic")[[1]]
thetext = gsub(pattern="\\r|\\n", replacement="", thetext) %>% str_trim
thetext = thetext[thetext != ""]
results_df$title[i] = thetext[1]
results_df$election[i] = thetext[grepl(pattern = "^Election", thetext)] %>%
gsub("Election:", "", x = .) %>% str_trim
results_df$type[i] = thetext[grepl(pattern = "^Type", thetext)] %>%
gsub("Type:", "", x = .) %>% str_trim
results_df$status[i] = thetext[grepl(pattern = "^Status", thetext)] %>%
gsub("Status:", "", x = .) %>% str_trim
results_df$topic_areas[i] = thetext[grepl(pattern = "^Area:|Areas:", thetext)] %>%
gsub("Area:|Areas:", "", x = .) %>% str_trim
summary_index = grep(pattern="^Summary", thetext) + 1
results_df$summary[i] = thetext[summary_index]
}
}
results_df = results_df[!is.na(results_df$state),]
# let's have a look at a bit of the final product (some variables omitted for space)
head(results_df)
View(results_df)
# Briefly on API's --------------------------------------------------------
list_of_shows = c("breaking bad", "mad men", "game of thrones",
"homeland", "house of cards", "true detective",
"orange is the new black", "the americans", "mr robot",
"boardwalk empire", "the good wife", "dexter",
"lost", "true blood", "house", "big love", "downton abbey",
"damages", "boston legal", "grey's anatomy", "the sopranos",
"heroes", "better call saul")
show_db = data.frame(title = list_of_shows,
year = NA, genre = NA, plot = NA, country = NA,
awards = NA, metascore = NA, imdbrating = NA,
imdbvotes = NA, imdbid = NA, totalseasons = NA)
# construct the url for each show by pasting the name of the show after
# the API base, and encoding using URLencode().
for (show in list_of_shows){
show_url = paste0("http://omdbapi.com/?&t=", URLencode(show, reserved = T))
show_info = read_html(show_url) %>% html_text %>% fromJSON
show_db$year[show_db$title==show] = show_info$Year
show_db$genre[show_db$title==show] = show_info$Genre
show_db$plot[show_db$title==show] = show_info$Plot
show_db$country[show_db$title==show] = show_info$Country
show_db$awards[show_db$title==show] = show_info$Awards
show_db$metascore[show_db$title==show] = show_info$Metascore
show_db$imdbrating[show_db$title==show] = show_info$imdbRating
show_db$imdbvotes[show_db$title==show] = show_info$imdbVotes
show_db$imdbid[show_db$title==show] = show_info$imdbID
show_db$totalseasons[show_db$title==show] = show_info$totalSeasons
}
show_db[1:5, c(1:3, 8)]
# make a plot
show_db = show_db[order(show_db$imdbrating),]
show_db$title = factor(show_db$title, levels = show_db$title)
ggplot(show_db, aes(x = title, y = as.numeric(imdbrating))) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle=65, hjust=1)) +
scale_y_continuous(breaks = seq(0, 10, 1)) +
coord_cartesian(ylim = c(0, 10)) +
labs(x = NULL, y = "IMDb rating")
|
library(dplyr)
library(reshape2)
# ------------------------------------------------------------------------------
# solution to problem 2
# This problem focuses on Baltimore City, Maryland. We want to determine if the
# total emissions from PM2.5 has decreased in Baltimore during the period of
# this study.
Problem2 <- function(to.png = TRUE) {
# read in the data files
if (!exists('NEI')) NEI <- readRDS("summarySCC_PM25.rds")
if (!exists('SCC')) SCC <- readRDS("Source_Classification_Code.rds")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Group the data frame by the year and city
grouped.nei <- filter(NEI, fips == '24510') %>%
group_by(year)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# open the png plot device
if (to.png) png('plot2.png', width = 720, height = 720)
# plot the sum of the emissions by the year
# this is done implicitly because the data is grouped by year. Therefore, the
# summarise function returns a data frame with two columns whcih can directly
# be plotted
summary <- summarise(grouped.nei, emissions = sum(Emissions)/1e3)
plot(summary,
xlab = 'Year',
ylab = 'Total emmisions [thousands of tons]',
main = expression(paste('Total emissions of PM'[2.5],
' by year in Baltimore City, MD')),
ylim = c(0.0, 5.3),
pch = 20)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# perform linear fit to data
linear.model <- lm(emissions ~ year, summary)
abline(linear.model, lwd = 2)
# compute the confidence interval
smoother <- data.frame(year = seq(min(summary$year), max(summary$year), 0.1))
conf.interval <- predict(linear.model,
newdata=smoother,
interval="confidence")
# conf.interval <- predict(linear.model, interval="confidence")
matlines(smoother[, 'year'],
conf.interval[, c('lwr', 'upr')],
col='blue',
lty=2)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# extract the slope and R^2 value from the fit
slope <- summary(linear.model)[['coefficients']]['year', 'Estimate']
slope.uncert <- summary(linear.model)[['coefficients']]['year', 'Std. Error']
adj.r.squared <- summary(linear.model)[['adj.r.squared']]
p.value <- summary(linear.model)$coef[2,4]
# create labels and draw them to the plot
slope.label <- bquote(Slope ==
.(format(slope, digits = 2)) %+-%
.(format(slope.uncert, digits = 2)))
adj.r.sq.label <- bquote(Adj.~italic(R)^2 == .(format(adj.r.squared,
digits = 2)))
p.value.label <- bquote(italic(p) == .(format(p.value, digits = 2)))
x.pos <- 2005.5
y.pos <- 5.1
y.spacing <- 0.40
text(x.pos, y.pos, slope.label , adj = c(0,0)); y.pos <- y.pos - y.spacing
text(x.pos, y.pos, adj.r.sq.label, adj = c(0,0)); y.pos <- y.pos - y.spacing
text(x.pos, y.pos, p.value.label , adj = c(0,0)); y.pos <- y.pos - y.spacing
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (to.png) dev.off()
}
Problem2()
| /ExploratoryDataAnalysis/Project2/Problem2.R | no_license | bdjackson/CourseraDataScience | R | false | false | 3,258 | r | library(dplyr)
library(reshape2)
# ------------------------------------------------------------------------------
# solution to problem 2
# This problem focuses on Baltimore City, Maryland. We want to determine if the
# total emissions from PM2.5 has decreased in Baltimore during the period of
# this study.
Problem2 <- function(to.png = TRUE) {
# read in the data files
if (!exists('NEI')) NEI <- readRDS("summarySCC_PM25.rds")
if (!exists('SCC')) SCC <- readRDS("Source_Classification_Code.rds")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Group the data frame by the year and city
grouped.nei <- filter(NEI, fips == '24510') %>%
group_by(year)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# open the png plot device
if (to.png) png('plot2.png', width = 720, height = 720)
# plot the sum of the emissions by the year
# this is done implicitly because the data is grouped by year. Therefore, the
# summarise function returns a data frame with two columns whcih can directly
# be plotted
summary <- summarise(grouped.nei, emissions = sum(Emissions)/1e3)
plot(summary,
xlab = 'Year',
ylab = 'Total emmisions [thousands of tons]',
main = expression(paste('Total emissions of PM'[2.5],
' by year in Baltimore City, MD')),
ylim = c(0.0, 5.3),
pch = 20)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# perform linear fit to data
linear.model <- lm(emissions ~ year, summary)
abline(linear.model, lwd = 2)
# compute the confidence interval
smoother <- data.frame(year = seq(min(summary$year), max(summary$year), 0.1))
conf.interval <- predict(linear.model,
newdata=smoother,
interval="confidence")
# conf.interval <- predict(linear.model, interval="confidence")
matlines(smoother[, 'year'],
conf.interval[, c('lwr', 'upr')],
col='blue',
lty=2)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# extract the slope and R^2 value from the fit
slope <- summary(linear.model)[['coefficients']]['year', 'Estimate']
slope.uncert <- summary(linear.model)[['coefficients']]['year', 'Std. Error']
adj.r.squared <- summary(linear.model)[['adj.r.squared']]
p.value <- summary(linear.model)$coef[2,4]
# create labels and draw them to the plot
slope.label <- bquote(Slope ==
.(format(slope, digits = 2)) %+-%
.(format(slope.uncert, digits = 2)))
adj.r.sq.label <- bquote(Adj.~italic(R)^2 == .(format(adj.r.squared,
digits = 2)))
p.value.label <- bquote(italic(p) == .(format(p.value, digits = 2)))
x.pos <- 2005.5
y.pos <- 5.1
y.spacing <- 0.40
text(x.pos, y.pos, slope.label , adj = c(0,0)); y.pos <- y.pos - y.spacing
text(x.pos, y.pos, adj.r.sq.label, adj = c(0,0)); y.pos <- y.pos - y.spacing
text(x.pos, y.pos, p.value.label , adj = c(0,0)); y.pos <- y.pos - y.spacing
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (to.png) dev.off()
}
Problem2()
|
library("depmixS4")
library(parallel)
library(plyr)
getwd()
setwd("\Users\kali\Documents")
data <- read.table("Dataset3.txt",header = TRUE, sep = ",")
data$day <- as.POSIXlt(data$Date,format = "%d/%m/%Y")$wday
#Q2
sundayNights <- data[data$day == 0 & strptime(data$Time,"%H:%M:%S") >= strptime("21:00:00","%H:%M:%S")
& strptime(data$Time,"%H:%M:%S") < strptime("24:00:00","%H:%M:%S"),]
sundayNights <- sundayNights[order(as.Date(sundayNights$Date, format = "%d/%m/%Y"), strptime(sundayNights$Time,"%H:%M:%S")),]
GAP_GRP_cor <- cor(sundayNights$Global_active_power, sundayNights$Global_reactive_power, method = "pearson")
GAP_Vol_cor <- cor(sundayNights$Global_active_power, sundayNights$Voltage, method = "pearson")
GAP_GI_cor <- cor(sundayNights$Global_active_power, sundayNights$Global_intensity, method = "pearson")
Nmin <- 2
Nmax <- 20
Nstates <- Nmin:Nmax
BIC <- vector("list", Nmax - Nmin + 1)
ct <- count(sundayNights, "Date")
ct <- ct[order(as.Date(ct$Date, format = "%d/%m/%Y")),]
set.seed(2)
for (n in Nstates)
{
mod <- depmix(response = Global_active_power ~ 1, data = sundayNights, nstates = n, ntimes = ct$freq)
fm <- fit(mod)
BIC[[n-Nmin+1]] <- BIC(fm)
}
plot(Nstates, BIC, ty="b")
| /CMPT318/CMPT 318 R workspace/Assignment2Final/Group_15_Assignment_2_Q2.R | no_license | alik604/Classes | R | false | false | 1,237 | r | library("depmixS4")
library(parallel)
library(plyr)
getwd()
setwd("\Users\kali\Documents")
data <- read.table("Dataset3.txt",header = TRUE, sep = ",")
data$day <- as.POSIXlt(data$Date,format = "%d/%m/%Y")$wday
#Q2
sundayNights <- data[data$day == 0 & strptime(data$Time,"%H:%M:%S") >= strptime("21:00:00","%H:%M:%S")
& strptime(data$Time,"%H:%M:%S") < strptime("24:00:00","%H:%M:%S"),]
sundayNights <- sundayNights[order(as.Date(sundayNights$Date, format = "%d/%m/%Y"), strptime(sundayNights$Time,"%H:%M:%S")),]
GAP_GRP_cor <- cor(sundayNights$Global_active_power, sundayNights$Global_reactive_power, method = "pearson")
GAP_Vol_cor <- cor(sundayNights$Global_active_power, sundayNights$Voltage, method = "pearson")
GAP_GI_cor <- cor(sundayNights$Global_active_power, sundayNights$Global_intensity, method = "pearson")
Nmin <- 2
Nmax <- 20
Nstates <- Nmin:Nmax
BIC <- vector("list", Nmax - Nmin + 1)
ct <- count(sundayNights, "Date")
ct <- ct[order(as.Date(ct$Date, format = "%d/%m/%Y")),]
set.seed(2)
for (n in Nstates)
{
mod <- depmix(response = Global_active_power ~ 1, data = sundayNights, nstates = n, ntimes = ct$freq)
fm <- fit(mod)
BIC[[n-Nmin+1]] <- BIC(fm)
}
plot(Nstates, BIC, ty="b")
|
# # # # # #
# Analysing the data set provided by Hilary Kennedy
# Jordi F. Pagès
# 20-03-2019
# University of Barcelona
# # # # # #
# # # # # # # # # # # # # # # # # # # # #
# Loading the data clean and corrected #
# # # # # # # # # # # # # # # # # # # # #
source("01_DataImport&Corrections_CarbonReview.R")
library(gridExtra)
library(patchwork)
# library(cowplot)
# # # # # # # # # # # # # # # # # # # # #
# Data visualisation of Carbon stocks ----
# # # # # # # # # # # # # # # # # # # # #
# HISTOGRAM OF CSTOCKS ----
# Histogram for 0-20
cstocks %>%
ggplot() +
geom_histogram(aes(Stock_0_20cm), binwidth = 10, fill = "#9FDA3AFF") +
xlab("Carbon stock 0-20 cm") +
theme_bw()
# ggsave("Figs/Cstocks_histogram_0_20cm.pdf")
# Histogram for 20-50
cstocks %>%
ggplot() +
geom_histogram(aes(Stock_20_50cm), binwidth = 10, fill = "#9FDA3AFF") +
xlab("Carbon stock 20-50 cm") +
theme_bw()
# ggsave("Figs/Cstocks_histogram_20_50cm.pdf")
# Histograms all together in the same plot (for that we need to gather (tidy) the data set)
cstocks_tidy <- cstocks %>%
select(-Stock_20_50cm_estimate) %>%
gather(key = depth, value = cstocks, Stock_0_20cm:Stock_20_50cm)
cstocks_tidy %>%
filter(depth != "Stock_0_50cm") %>%
ggplot() +
geom_histogram(aes(cstocks, fill = depth, colour = depth), alpha = 0.5) +
theme_bw()
# ggsave("Figs/Cstocks_histogram_allDepths_same_plot.pdf")
# BARPLOT PER SPECIES (COUNT) ----
cstocks %>%
mutate(Species = factor(Species) %>% fct_infreq() %>% fct_rev()) %>%
filter(Meadow_type == "monospecific") %>%
group_by(Species) %>%
summarise(n = n()) %>%
ggplot() +
geom_bar(aes(x = Species, y = n), stat = "identity") +
geom_text(aes(x = Species, y = n, label = str_c("(", n, ")")), nudge_y = 6, size = 3) +
ylab("count") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face = "italic"))
# ggsave("Figs/CoreCount_by_Species.pdf")
# BARPLOT PER SPECIES PERCENT ----
cstocks %>%
mutate(Species = factor(Species) %>% fct_infreq() %>% fct_rev()) %>%
filter(Meadow_type == "monospecific") %>%
group_by(Species) %>%
summarise(n = n()) %>%
mutate(percent = 100*(n/sum(n))) %>%
ggplot() +
geom_bar(aes(x = Species, y = percent), stat = "identity") +
geom_text(aes(x = Species, y = percent, label = str_c(round(percent), "%")), nudge_y = 1, size = 3) +
ylab("%") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face = "italic"))
# ggsave("Figs/CoreCountPercent_by_Species.pdf")
# BARPLOT PER COUNTRY ----
cstocks %>%
mutate(Country = factor(Country) %>% fct_infreq() %>% fct_rev()) %>%
filter(!is.na(Country)) %>%
group_by(Country) %>%
summarise(n = n()) %>%
ggplot() +
geom_bar(aes(x = Country, y = n), stat = "identity") +
geom_text(aes(x = Country, y = n, label = str_c("(", n, ")")), nudge_y = 15, size = 3) +
ylab("count") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# ggsave("Figs/CountryCount.pdf")
# BARPLOT PER COUNTRY PERCENT ----
cstocks %>%
mutate(Country = factor(Country) %>% fct_infreq() %>% fct_rev()) %>%
filter(!is.na(Country)) %>%
group_by(Country) %>%
summarise(n = n()) %>%
mutate(percent = 100*(n/sum(n))) %>%
ggplot() +
geom_bar(aes(x = Country, y = percent), stat = "identity") +
geom_text(aes(x = Country, y = percent, label = str_c(round(percent), "%")), nudge_y = 3, size = 3) +
ylab("%") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# ggsave("Figs/CountryPercent.pdf")
# MAP OF CORES BY COUNTRY ----
cstocksCountry <- cstocks %>%
group_by(Country) %>%
filter(!is.na(Country)) %>%
summarise(n = n())
unique(cstocksCountry$Country)
map.world <- map_data('world')
unique(map.world$region)
# Checking potential join mismatches
cstocksCountry %>%
anti_join(map.world, by = c('Country' = 'region')) %>%
print(n = Inf)
# Ok, no mismatches. Useful to see what will not be joined. In this case, nothing. Everything will be joined.
# So we can proceed with the left_join
map.cstocks <- left_join(map.world, cstocksCountry, by = c('region' = 'Country'))
# Map of number of cores per country
ggplot(map.cstocks, aes(x = long, y = lat, group = group )) +
geom_polygon(aes(fill = n)) +
# scale_fill_gradientn(colours = brewer.pal(5, "YlOrRd"), trans = "log10", na.value = "#d0d0d0") +
# scale_fill_gradientn(colours = rev(c("#9FDA3AFF", "#4AC16DFF", "#1FA187FF", "#277F8EFF", "#365C8DFF",
# "#46337EFF")), trans = "log10", na.value = "#d0d0d0") +
# scale_fill_gradientn(colours = brewer.pal(5, "Blues"), trans = "log10") +
# theme_minimal()
labs(fill = '', x = NULL, y = NULL) +
theme(text = element_text(color = '#EEEEEE'), axis.ticks = element_blank(), axis.text = element_blank(),
panel.grid = element_blank(), panel.background = element_rect(fill = '#787878'),
plot.background = element_rect(fill = '#787878'), legend.position = c(.18,.36) ,
legend.background = element_blank(), legend.key = element_blank())
# ggsave(filename = "Figs/CountryMap.pdf")
# MAPPING INDIVIDUAL CORES WITH GGMAP ----
# From https://lucidmanager.org/geocoding-with-ggmap/
library(ggmap)
# Now, to use Google API you have to be registered (include a credit card) and get an API key. See below.
api <- readLines("google.api") # Text file with the API key
register_google(key = api)
getOption("ggmap")
has_google_key()
# To check we don't exceed the day_limit that Google imposes (2500)
geocodeQueryCheck()
#### Successful trial with https://blog.dominodatalab.com/geographic-visualization-with-rs-ggmaps/
# We get the lat lon coordinates in a df
Df_coord <- cstocks %>%
select(Latitude, Longitude) %>%
group_by(Latitude, Longitude) %>%
summarise(Samples = n())
# We build a world map from google
Map <- ggmap(ggmap = get_googlemap(center = c(lon = 10, lat = 0),
zoom = 1,
style = c(feature="all",element="labels",visibility="off"),
maptype = "roadmap",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Map
# We plot the frequency of samples per coordinate combination as 'bubbles'
Map +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude, size = Samples), col="#365C8DFF", alpha=0.4) +
scale_size(range = range(sqrt(Df_coord$Samples))) +
scale_y_continuous(limits = c(-70,70)) +
theme_bw()
# ggsave("Figs/CoreMapBubbles.pdf")
# Just plotting the points
Map +
geom_jitter(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col= "#0063ad", alpha=0.2, width = 1, height = 1) +
scale_y_continuous(limits = c(-55,70), breaks = c(-50, -25, 25, 50, 75)) +
theme_bw() +
xlab("Longitude (º)") +
ylab("Latitude (º)") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
text = element_text(size = 12))
ggsave("Figs/Final_Figures_March2021/GlobalCoreMapPointsNEW_sized.png", width = 180, height = 110, units = "mm", dpi = 350)
# MAPPING INDIVIDUAL CORES IN GGMAP ZOOMING IN EACH REGION ----
# Europe
MapEurope <- ggmap(ggmap = get_googlemap(center = c(lon = 10, lat = 50),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Europe <- MapEurope +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
scale_y_continuous(limits = c(35,60)) +
scale_x_continuous(limits = c(-10,30)) +
theme_bw()
# N.America
MapAmerica <- ggmap(ggmap = get_googlemap(center = c(lon = -100, lat = 40),
zoom = 3,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_America <- MapAmerica +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(15,50)) +
# scale_x_continuous(limits = c(-125,-65)) +
theme_bw()
# Brasil
MapBrasil <- ggmap(ggmap = get_googlemap(center = c(lon = -50, lat = -20),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Brasil <- MapBrasil +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Arabia
MapArabia <- ggmap(ggmap = get_googlemap(center = c(lon = 45, lat = 25),
zoom = 5,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Arabia <- MapArabia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Australia
MapAustralia <- ggmap(ggmap = get_googlemap(center = c(lon = 133, lat = -28),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Australia <- MapAustralia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Asia
MapAsia <- ggmap(ggmap = get_googlemap(center = c(lon = 120, lat = 25),
zoom = 3,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Asia <- MapAsia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# General map
# zooms <- grid.arrange(Cstocks_America,
# Cstocks_Europe,
# Cstocks_Asia,
# Cstocks_Brasil,
# Cstocks_Arabia,
# Cstocks_Australia,
# nrow = 2, top = "")
patchwork <- Cstocks_America + Cstocks_Europe + Cstocks_Asia + Cstocks_Brasil + Cstocks_Arabia + Cstocks_Australia + plot_annotation(tag_levels = 'A')
patchwork & xlab("Longitude") & ylab("Latitude")
# ggsave("Figs/Core_coordinates_MapZoom_Points.pdf")
# CSTOCKS PER SPECIES FACET PLOT ----
source('reorder_within_function.R')
cstocks_tidy <- cstocks %>%
select(-Stock_20_50cm_estimate) %>%
gather(key = depth, value = cstocks, Stock_0_20cm:Stock_20_50cm)
# ggplot(iris_gathered, aes(reorder_within(Species, value, metric), value)) +
#' geom_boxplot() +
#' scale_x_reordered() +
#' facet_wrap(~ metric, scales = "free_x")
### WORKING ON THIS!!! NOW HAVE TO REORDER
cstocks_tidy %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = Species, y = cstocks), fill = "#9FDA3AFF") +
scale_y_reordered() +
coord_flip() +
xlab("Species") +
ylab("Carbon stock") +
facet_grid(~depth, scales = "free_x") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_facet_depths.pdf", width = 12, height = 6)
# CSTOCKS PER SPECIES grid.arrange PLOT ----
p1 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
p2 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
p3 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
ml <- grid.arrange(p1, p2, p3, widths = c(2,2,2), nrow = 1, top = "")
# ggsave("Figs/Cstocks_by_Species_grid.arrange_depths.pdf", plot = ml, width = 12, height = 6)
# CSTOCKS PER SPECIES ----
# Boxplots cstocks 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_20_50cm.pdf")
# CSTOCKS PER GENUS ----
# Boxplots cstocks 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_20_50cm.pdf")
# CSTOCKS PER MEADOW TYPE ----
# Boxplots cstocks 0-20 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_0_20cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 0-20 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_0_50cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 0-50 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_20_50cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 20-50 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_20_50cm.pdf")
# DELTA13C PER SPECIES ----
# Boxplots delta13C 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_0_20cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_0_20cm, FUN = median), y = d13C_0_20cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 0-20 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_0_20cm.pdf")
# Boxplots delta13C 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_0_50cm, FUN = median), y = d13C_0_50cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 0-50 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_0_50cm.pdf")
# Boxplots delta13C 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_20_50cm, FUN = median), y = d13C_20_50cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 20-50 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_20_50cm.pdf")
# # # # # # # # # # # # # # # # # # # # #
# Data visualisation of Plant traits ----
# # # # # # # # # # # # # # # # # # # # #
# ABOVEGROUND BIOMASS PER SPECIES ----
# Dotplots mean aboveground biomass
cstocks_traits %>%
filter(!is.na(Mean_aboveground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
y = Mean_aboveground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
ymin=Mean_aboveground_biomass-SE_Mean_aboveground_biomass,
ymax=Mean_aboveground_biomass+SE_Mean_aboveground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
y = Mean_aboveground_biomass+SE_Mean_aboveground_biomass,
label = str_c("n = ", N_Mean_aboveground_biomass)),
nudge_y = 50, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Mean aboveground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MeanAbovegroundB_by_Species_.pdf")
# Dotplots max aboveground biomass
cstocks_traits %>%
filter(!is.na(Max_aboveground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
y = Max_aboveground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
ymin=Max_aboveground_biomass-SE_Max_aboveground_biomass,
ymax=Max_aboveground_biomass+SE_Max_aboveground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
y = Max_aboveground_biomass+SE_Max_aboveground_biomass,
label = str_c("n = ", N_Max_aboveground_biomass)),
nudge_y = 100, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Max aboveground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MaxAbovegroundB_by_Species_.pdf")
# BELOWGROUND BIOMASS PER SPECIES ----
# Dotplots max aboveground biomass
cstocks_traits %>%
filter(!is.na(Mean_belowground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
y = Mean_belowground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
ymin=Mean_belowground_biomass-SE_Mean_belowground_biomass,
ymax=Mean_belowground_biomass+SE_Mean_belowground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
y = Mean_belowground_biomass+SE_Mean_belowground_biomass,
label = str_c("n = ", N_Mean_belowground_biomass)),
nudge_y = 150, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Mean belowground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MeanBelowgroundB_by_Species_.pdf")
# Dotplots mean aboveground biomass
cstocks_traits %>%
filter(!is.na(Max_belowground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
y = Max_belowground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
ymin=Max_belowground_biomass-SE_Max_belowground_biomass,
ymax=Max_belowground_biomass+SE_Max_belowground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
y = Max_belowground_biomass,
label = str_c("n = ", N_Max_belowground_biomass)),
nudge_y = 550, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Max belowground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MaxBelowgroundB_by_Species_.pdf")
# ROOT:SHOOT RATIO PER SPECIES ----
cstocks_traits %>%
filter(!is.na(Root_shoot_ratio)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
y = Root_shoot_ratio), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
ymin=Root_shoot_ratio-SE_Root_shoot_ratio,
ymax=Root_shoot_ratio+SE_Root_shoot_ratio),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
y = Root_shoot_ratio+SE_Root_shoot_ratio,
label = str_c("n = ", N_Max_belowground_biomass)),
nudge_y = 1, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Root:Shoot ratio") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/RootShootRatio_by_Species_.pdf")
#########################
| /02_ExploratoryDataVis_CarbonReview.R | no_license | jordipages-repo/seagrass_Cstocks_pub | R | false | false | 24,494 | r | # # # # # #
# Analysing the data set provided by Hilary Kennedy
# Jordi F. Pagès
# 20-03-2019
# University of Barcelona
# # # # # #
# # # # # # # # # # # # # # # # # # # # #
# Loading the data clean and corrected #
# # # # # # # # # # # # # # # # # # # # #
source("01_DataImport&Corrections_CarbonReview.R")
library(gridExtra)
library(patchwork)
# library(cowplot)
# # # # # # # # # # # # # # # # # # # # #
# Data visualisation of Carbon stocks ----
# # # # # # # # # # # # # # # # # # # # #
# HISTOGRAM OF CSTOCKS ----
# Histogram for 0-20
cstocks %>%
ggplot() +
geom_histogram(aes(Stock_0_20cm), binwidth = 10, fill = "#9FDA3AFF") +
xlab("Carbon stock 0-20 cm") +
theme_bw()
# ggsave("Figs/Cstocks_histogram_0_20cm.pdf")
# Histogram for 20-50
cstocks %>%
ggplot() +
geom_histogram(aes(Stock_20_50cm), binwidth = 10, fill = "#9FDA3AFF") +
xlab("Carbon stock 20-50 cm") +
theme_bw()
# ggsave("Figs/Cstocks_histogram_20_50cm.pdf")
# Histograms all together in the same plot (for that we need to gather (tidy) the data set)
cstocks_tidy <- cstocks %>%
select(-Stock_20_50cm_estimate) %>%
gather(key = depth, value = cstocks, Stock_0_20cm:Stock_20_50cm)
cstocks_tidy %>%
filter(depth != "Stock_0_50cm") %>%
ggplot() +
geom_histogram(aes(cstocks, fill = depth, colour = depth), alpha = 0.5) +
theme_bw()
# ggsave("Figs/Cstocks_histogram_allDepths_same_plot.pdf")
# BARPLOT PER SPECIES (COUNT) ----
cstocks %>%
mutate(Species = factor(Species) %>% fct_infreq() %>% fct_rev()) %>%
filter(Meadow_type == "monospecific") %>%
group_by(Species) %>%
summarise(n = n()) %>%
ggplot() +
geom_bar(aes(x = Species, y = n), stat = "identity") +
geom_text(aes(x = Species, y = n, label = str_c("(", n, ")")), nudge_y = 6, size = 3) +
ylab("count") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face = "italic"))
# ggsave("Figs/CoreCount_by_Species.pdf")
# BARPLOT PER SPECIES PERCENT ----
cstocks %>%
mutate(Species = factor(Species) %>% fct_infreq() %>% fct_rev()) %>%
filter(Meadow_type == "monospecific") %>%
group_by(Species) %>%
summarise(n = n()) %>%
mutate(percent = 100*(n/sum(n))) %>%
ggplot() +
geom_bar(aes(x = Species, y = percent), stat = "identity") +
geom_text(aes(x = Species, y = percent, label = str_c(round(percent), "%")), nudge_y = 1, size = 3) +
ylab("%") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(face = "italic"))
# ggsave("Figs/CoreCountPercent_by_Species.pdf")
# BARPLOT PER COUNTRY ----
cstocks %>%
mutate(Country = factor(Country) %>% fct_infreq() %>% fct_rev()) %>%
filter(!is.na(Country)) %>%
group_by(Country) %>%
summarise(n = n()) %>%
ggplot() +
geom_bar(aes(x = Country, y = n), stat = "identity") +
geom_text(aes(x = Country, y = n, label = str_c("(", n, ")")), nudge_y = 15, size = 3) +
ylab("count") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# ggsave("Figs/CountryCount.pdf")
# BARPLOT PER COUNTRY PERCENT ----
cstocks %>%
mutate(Country = factor(Country) %>% fct_infreq() %>% fct_rev()) %>%
filter(!is.na(Country)) %>%
group_by(Country) %>%
summarise(n = n()) %>%
mutate(percent = 100*(n/sum(n))) %>%
ggplot() +
geom_bar(aes(x = Country, y = percent), stat = "identity") +
geom_text(aes(x = Country, y = percent, label = str_c(round(percent), "%")), nudge_y = 3, size = 3) +
ylab("%") +
coord_flip() +
theme_bw() +
theme(legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.text.align = 0,
text = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# ggsave("Figs/CountryPercent.pdf")
# MAP OF CORES BY COUNTRY ----
cstocksCountry <- cstocks %>%
group_by(Country) %>%
filter(!is.na(Country)) %>%
summarise(n = n())
unique(cstocksCountry$Country)
map.world <- map_data('world')
unique(map.world$region)
# Checking potential join mismatches
cstocksCountry %>%
anti_join(map.world, by = c('Country' = 'region')) %>%
print(n = Inf)
# Ok, no mismatches. Useful to see what will not be joined. In this case, nothing. Everything will be joined.
# So we can proceed with the left_join
map.cstocks <- left_join(map.world, cstocksCountry, by = c('region' = 'Country'))
# Map of number of cores per country
ggplot(map.cstocks, aes(x = long, y = lat, group = group )) +
geom_polygon(aes(fill = n)) +
# scale_fill_gradientn(colours = brewer.pal(5, "YlOrRd"), trans = "log10", na.value = "#d0d0d0") +
# scale_fill_gradientn(colours = rev(c("#9FDA3AFF", "#4AC16DFF", "#1FA187FF", "#277F8EFF", "#365C8DFF",
# "#46337EFF")), trans = "log10", na.value = "#d0d0d0") +
# scale_fill_gradientn(colours = brewer.pal(5, "Blues"), trans = "log10") +
# theme_minimal()
labs(fill = '', x = NULL, y = NULL) +
theme(text = element_text(color = '#EEEEEE'), axis.ticks = element_blank(), axis.text = element_blank(),
panel.grid = element_blank(), panel.background = element_rect(fill = '#787878'),
plot.background = element_rect(fill = '#787878'), legend.position = c(.18,.36) ,
legend.background = element_blank(), legend.key = element_blank())
# ggsave(filename = "Figs/CountryMap.pdf")
# MAPPING INDIVIDUAL CORES WITH GGMAP ----
# From https://lucidmanager.org/geocoding-with-ggmap/
library(ggmap)
# Now, to use Google API you have to be registered (include a credit card) and get an API key. See below.
api <- readLines("google.api") # Text file with the API key
register_google(key = api)
getOption("ggmap")
has_google_key()
# To check we don't exceed the day_limit that Google imposes (2500)
geocodeQueryCheck()
#### Successful trial with https://blog.dominodatalab.com/geographic-visualization-with-rs-ggmaps/
# We get the lat lon coordinates in a df
Df_coord <- cstocks %>%
select(Latitude, Longitude) %>%
group_by(Latitude, Longitude) %>%
summarise(Samples = n())
# We build a world map from google
Map <- ggmap(ggmap = get_googlemap(center = c(lon = 10, lat = 0),
zoom = 1,
style = c(feature="all",element="labels",visibility="off"),
maptype = "roadmap",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Map
# We plot the frequency of samples per coordinate combination as 'bubbles'
Map +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude, size = Samples), col="#365C8DFF", alpha=0.4) +
scale_size(range = range(sqrt(Df_coord$Samples))) +
scale_y_continuous(limits = c(-70,70)) +
theme_bw()
# ggsave("Figs/CoreMapBubbles.pdf")
# Just plotting the points
Map +
geom_jitter(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col= "#0063ad", alpha=0.2, width = 1, height = 1) +
scale_y_continuous(limits = c(-55,70), breaks = c(-50, -25, 25, 50, 75)) +
theme_bw() +
xlab("Longitude (º)") +
ylab("Latitude (º)") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
text = element_text(size = 12))
ggsave("Figs/Final_Figures_March2021/GlobalCoreMapPointsNEW_sized.png", width = 180, height = 110, units = "mm", dpi = 350)
# MAPPING INDIVIDUAL CORES IN GGMAP ZOOMING IN EACH REGION ----
# Europe
MapEurope <- ggmap(ggmap = get_googlemap(center = c(lon = 10, lat = 50),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Europe <- MapEurope +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
scale_y_continuous(limits = c(35,60)) +
scale_x_continuous(limits = c(-10,30)) +
theme_bw()
# N.America
MapAmerica <- ggmap(ggmap = get_googlemap(center = c(lon = -100, lat = 40),
zoom = 3,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_America <- MapAmerica +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(15,50)) +
# scale_x_continuous(limits = c(-125,-65)) +
theme_bw()
# Brasil
MapBrasil <- ggmap(ggmap = get_googlemap(center = c(lon = -50, lat = -20),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Brasil <- MapBrasil +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Arabia
MapArabia <- ggmap(ggmap = get_googlemap(center = c(lon = 45, lat = 25),
zoom = 5,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Arabia <- MapArabia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Australia
MapAustralia <- ggmap(ggmap = get_googlemap(center = c(lon = 133, lat = -28),
zoom = 4,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Australia <- MapAustralia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# Asia
MapAsia <- ggmap(ggmap = get_googlemap(center = c(lon = 120, lat = 25),
zoom = 3,
maptype = "terrain",
size = c(512, 512),
scale = 2,
color = "bw"),
extent = "panel")
Cstocks_Asia <- MapAsia +
geom_point(data=Df_coord, mapping = aes(x=Longitude, y=Latitude), col="#0063ad", size = 2, alpha = 0.4) +
# scale_y_continuous(limits = c(-30, -5)) +
# scale_x_continuous(limits = c(-70,-30)) +
theme_bw()
# General map
# zooms <- grid.arrange(Cstocks_America,
# Cstocks_Europe,
# Cstocks_Asia,
# Cstocks_Brasil,
# Cstocks_Arabia,
# Cstocks_Australia,
# nrow = 2, top = "")
patchwork <- Cstocks_America + Cstocks_Europe + Cstocks_Asia + Cstocks_Brasil + Cstocks_Arabia + Cstocks_Australia + plot_annotation(tag_levels = 'A')
patchwork & xlab("Longitude") & ylab("Latitude")
# ggsave("Figs/Core_coordinates_MapZoom_Points.pdf")
# CSTOCKS PER SPECIES FACET PLOT ----
source('reorder_within_function.R')
cstocks_tidy <- cstocks %>%
select(-Stock_20_50cm_estimate) %>%
gather(key = depth, value = cstocks, Stock_0_20cm:Stock_20_50cm)
# ggplot(iris_gathered, aes(reorder_within(Species, value, metric), value)) +
#' geom_boxplot() +
#' scale_x_reordered() +
#' facet_wrap(~ metric, scales = "free_x")
### WORKING ON THIS!!! NOW HAVE TO REORDER
cstocks_tidy %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = Species, y = cstocks), fill = "#9FDA3AFF") +
scale_y_reordered() +
coord_flip() +
xlab("Species") +
ylab("Carbon stock") +
facet_grid(~depth, scales = "free_x") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_facet_depths.pdf", width = 12, height = 6)
# CSTOCKS PER SPECIES grid.arrange PLOT ----
p1 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
p2 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
p3 <- cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
ml <- grid.arrange(p1, p2, p3, widths = c(2,2,2), nrow = 1, top = "")
# ggsave("Figs/Cstocks_by_Species_grid.arrange_depths.pdf", plot = ml, width = 12, height = 6)
# CSTOCKS PER SPECIES ----
# Boxplots cstocks 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Species") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Species_20_50cm.pdf")
# CSTOCKS PER GENUS ----
# Boxplots cstocks 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_0_20cm, FUN = median), y = Stock_0_20cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 0-20 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_0_50cm, FUN = median), y = Stock_0_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 0-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(Stock_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Genus, Stock_20_50cm, FUN = median), y = Stock_20_50cm), fill = "#9FDA3AFF") +
coord_flip() +
xlab("Genus") +
ylab("Carbon stock 20-50 cm") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/Cstocks_by_Genus_20_50cm.pdf")
# CSTOCKS PER MEADOW TYPE ----
# Boxplots cstocks 0-20 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_0_20cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 0-20 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_0_20cm.pdf")
# Boxplots cstocks 0-50 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_0_50cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 0-50 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_0_50cm.pdf")
# Boxplots cstocks 20-50 cm
cstocks %>%
ggplot() +
geom_boxplot(aes(x = Meadow_type, y = Stock_20_50cm), fill = "#9FDA3AFF") +
xlab("Meadow type") +
ylab("Carbon stock 20-50 cm") +
coord_flip() +
theme_bw()
# ggsave("Figs/Cstocks_meadowtype_20_50cm.pdf")
# DELTA13C PER SPECIES ----
# Boxplots delta13C 0-20 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_0_20cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_0_20cm, FUN = median), y = d13C_0_20cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 0-20 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_0_20cm.pdf")
# Boxplots delta13C 0-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_0_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_0_50cm, FUN = median), y = d13C_0_50cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 0-50 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_0_50cm.pdf")
# Boxplots delta13C 20-50 cm
cstocks %>%
filter(Meadow_type == "monospecific") %>%
filter(!is.na(d13C_20_50cm)) %>%
ggplot() +
geom_boxplot(aes(x = reorder(Species, d13C_20_50cm, FUN = median), y = d13C_20_50cm), fill = "#1FA187FF") +
coord_flip() +
xlab("Species") +
ylab(bquote(delta*"13C 20-50 cm")) +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/delta13C_by_Species_20_50cm.pdf")
# # # # # # # # # # # # # # # # # # # # #
# Data visualisation of Plant traits ----
# # # # # # # # # # # # # # # # # # # # #
# ABOVEGROUND BIOMASS PER SPECIES ----
# Dotplots mean aboveground biomass
cstocks_traits %>%
filter(!is.na(Mean_aboveground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
y = Mean_aboveground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
ymin=Mean_aboveground_biomass-SE_Mean_aboveground_biomass,
ymax=Mean_aboveground_biomass+SE_Mean_aboveground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Mean_aboveground_biomass, FUN = mean),
y = Mean_aboveground_biomass+SE_Mean_aboveground_biomass,
label = str_c("n = ", N_Mean_aboveground_biomass)),
nudge_y = 50, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Mean aboveground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MeanAbovegroundB_by_Species_.pdf")
# Dotplots max aboveground biomass
cstocks_traits %>%
filter(!is.na(Max_aboveground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
y = Max_aboveground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
ymin=Max_aboveground_biomass-SE_Max_aboveground_biomass,
ymax=Max_aboveground_biomass+SE_Max_aboveground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Max_aboveground_biomass, FUN = mean),
y = Max_aboveground_biomass+SE_Max_aboveground_biomass,
label = str_c("n = ", N_Max_aboveground_biomass)),
nudge_y = 100, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Max aboveground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MaxAbovegroundB_by_Species_.pdf")
# BELOWGROUND BIOMASS PER SPECIES ----
# Dotplots max aboveground biomass
cstocks_traits %>%
filter(!is.na(Mean_belowground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
y = Mean_belowground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
ymin=Mean_belowground_biomass-SE_Mean_belowground_biomass,
ymax=Mean_belowground_biomass+SE_Mean_belowground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Mean_belowground_biomass, FUN = mean),
y = Mean_belowground_biomass+SE_Mean_belowground_biomass,
label = str_c("n = ", N_Mean_belowground_biomass)),
nudge_y = 150, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Mean belowground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MeanBelowgroundB_by_Species_.pdf")
# Dotplots mean aboveground biomass
cstocks_traits %>%
filter(!is.na(Max_belowground_biomass)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
y = Max_belowground_biomass), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
ymin=Max_belowground_biomass-SE_Max_belowground_biomass,
ymax=Max_belowground_biomass+SE_Max_belowground_biomass),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Max_belowground_biomass, FUN = mean),
y = Max_belowground_biomass,
label = str_c("n = ", N_Max_belowground_biomass)),
nudge_y = 550, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Max belowground biomass") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/MaxBelowgroundB_by_Species_.pdf")
# ROOT:SHOOT RATIO PER SPECIES ----
cstocks_traits %>%
filter(!is.na(Root_shoot_ratio)) %>%
ggplot() +
geom_point(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
y = Root_shoot_ratio), fill = "#1FA187FF") +
geom_errorbar(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
ymin=Root_shoot_ratio-SE_Root_shoot_ratio,
ymax=Root_shoot_ratio+SE_Root_shoot_ratio),
width=.2, position=position_dodge(.9)) +
geom_text(aes(x = reorder(Species, Root_shoot_ratio, FUN = mean),
y = Root_shoot_ratio+SE_Root_shoot_ratio,
label = str_c("n = ", N_Max_belowground_biomass)),
nudge_y = 1, size = 3) +
coord_flip() +
xlab("Species") +
ylab("Root:Shoot ratio") +
theme_bw() +
theme(axis.text.y = element_text(face = "italic"))
# ggsave("Figs/RootShootRatio_by_Species_.pdf")
#########################
|
mtcars
km1 = kmeans(mtcars, centers = 3) #cluster into 3 groups
km1$centers
km1 = kmeans(mtcars[,c('mpg','wt')], centers = 3)
km1$centers
df=mtcars[c('mpg','wt')]
df
df2=scale(df)
df2
| /clusterexercise.R | no_license | samiksha04/analytics1 | R | false | false | 184 | r | mtcars
km1 = kmeans(mtcars, centers = 3) #cluster into 3 groups
km1$centers
km1 = kmeans(mtcars[,c('mpg','wt')], centers = 3)
km1$centers
df=mtcars[c('mpg','wt')]
df
df2=scale(df)
df2
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/amm.replace.r
\name{amm.replacemp}
\alias{amm.replacemp}
\title{Replace pattern in column by match}
\usage{
amm.replacemp(ds, ds.list, ds.string, ds.replace)
}
\arguments{
\item{ds}{data.table: data set}
\item{ds.list}{character vector of columns name needed to convert}
\item{ds.string}{character: value to replace}
\item{ds.replace}{character: replacing character}
}
\value{
Character vector of column names
}
\description{
Replace pattern in column by matching. Replaces only pattern, not the whole value
}
\seealso{
amm.match, amm.gbetween
}
| /man/amm.replacemp.Rd | no_license | ameshkoff/amfeat | R | false | true | 628 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/amm.replace.r
\name{amm.replacemp}
\alias{amm.replacemp}
\title{Replace pattern in column by match}
\usage{
amm.replacemp(ds, ds.list, ds.string, ds.replace)
}
\arguments{
\item{ds}{data.table: data set}
\item{ds.list}{character vector of columns name needed to convert}
\item{ds.string}{character: value to replace}
\item{ds.replace}{character: replacing character}
}
\value{
Character vector of column names
}
\description{
Replace pattern in column by matching. Replaces only pattern, not the whole value
}
\seealso{
amm.match, amm.gbetween
}
|
#' @template dbispec-sub-wip
#' @format NULL
#' @importFrom withr with_output_sink
#' @section Connection:
#' \subsection{Stress tests}{
spec_stress_connection <- list(
#' Open 50 simultaneous connections
simultaneous_connections = function(ctx) {
cons <- list()
on.exit(try_silent(lapply(cons, dbDisconnect)), add = TRUE)
for (i in seq_len(50L)) {
cons <- c(cons, connect(ctx))
}
inherit_from_connection <-
vapply(cons, is, class2 = "DBIConnection", logical(1))
expect_true(all(inherit_from_connection))
},
#' Open and close 50 connections
stress_connections = function(ctx) {
for (i in seq_len(50L)) {
con <- connect(ctx)
expect_s4_class(con, "DBIConnection")
expect_error(dbDisconnect(con), NA)
}
},
#' }
NULL
)
| /R/spec-stress-connection.R | no_license | wlattner/DBItest | R | false | false | 796 | r | #' @template dbispec-sub-wip
#' @format NULL
#' @importFrom withr with_output_sink
#' @section Connection:
#' \subsection{Stress tests}{
spec_stress_connection <- list(
#' Open 50 simultaneous connections
simultaneous_connections = function(ctx) {
cons <- list()
on.exit(try_silent(lapply(cons, dbDisconnect)), add = TRUE)
for (i in seq_len(50L)) {
cons <- c(cons, connect(ctx))
}
inherit_from_connection <-
vapply(cons, is, class2 = "DBIConnection", logical(1))
expect_true(all(inherit_from_connection))
},
#' Open and close 50 connections
stress_connections = function(ctx) {
for (i in seq_len(50L)) {
con <- connect(ctx)
expect_s4_class(con, "DBIConnection")
expect_error(dbDisconnect(con), NA)
}
},
#' }
NULL
)
|
# 2016-06-23
# Jake Yeung
rm(list=ls())
setwd("/home/yeung/projects/tissue-specificity")
library(dplyr)
library(ggplot2)
library(hash)
source("scripts/functions/ListFunctions.R")
source("scripts/functions/LiverKidneyFunctions.R")
source("scripts/functions/PlotGeneAcrossTissues.R")
source("scripts/functions/NcondsFunctions.R")
source("scripts/functions/SvdFunctions.R")
source("scripts/functions/GetClockGenes.R")
source("scripts/functions/BiomartFunctions.R")
# load("Robjs/liver_kidney_atger_nestle/fits.long.multimethod.filtbest.Robj", v=T)
load("Robjs/liver_kidney_atger_nestle/fits.long.multimethod.filtbest.staggeredtimepts.bugfixed.Robj", v=T)
load("Robjs/liver_kidney_atger_nestle/dat.long.liverkidneyWTKO.bugfixed.Robj", v=T)
dat.orig <- dat.long
dat.long <- CollapseTissueGeno(dat.long)
dat.long <- StaggeredTimepointsLivKid(dat.long)
# dat.long <- SameTimepointsLivKid(dat.long)
# filter NA changes
dat.long <- subset(dat.long, !is.na(gene))
# Filter to common genes --------------------------------------------------
genes.keep <- unique(as.character(fits.long.filt$gene))
dat.long <- subset(dat.long, gene %in% genes.keep)
# Project to Frequency ----------------------------------------------------
omega <- 2 * pi / 24
dat.freq <- dat.long %>%
group_by(gene, tissue) %>%
do(ProjectToFrequency2(., omega, add.tissue=TRUE))
s <- SvdOnComplex(dat.freq, value.var = "exprs.transformed")
for (i in seq(1)){
eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
}
# All periods -------------------------------------------------------------
periods <- rep(48, 6) / seq(1, 6) # 48/1, 48/2 ... 48/12
loadfile <- "Robjs/liver_kidney_atger_nestle/dat.complex.all_T.Robj"
if (file.exists(loadfile)){
load(loadfile)
} else {
library(parallel)
dat.complexes <- mclapply(periods, function(period, dat.long){
omega <- 2 * pi / period
dat.tmp <- dat.long %>%
group_by(gene, tissue) %>%
do(ProjectToFrequency2(., omega, add.tissue=TRUE))
dat.tmp$period <- period
return(dat.tmp)
}, dat.long = dat.long, mc.cores = length(periods))
dat.complex.all_T <- do.call(rbind, dat.complexes)
outfcomp <- "Robjs/liver_kidney_atger_nestle/dat.complex.all_T.bugfixed.Robj"
if (!file.exists(outfcomp)) save(dat.complex.all_T, file = outfcomp)
rm(dat.complexes)
}
outffreq <- "Robjs/liver_kidney_atger_nestle/dat.freq.bugfixed.Robj"
if (!file.exists(outffreq)) save(dat.freq, file = outffreq)
# By clusters -------------------------------------------------------------
jmeth <- "zf"
jmeth <- "g=4001"
jmeth <- "g=1001"
jmeth <- "BIC"
i <- 1
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129,Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129,Liver_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129;Liver_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129,Kidney_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129;Kidney_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 10, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_BmalKO"))$gene)
#
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method ==jmeth & model %in% c("Liver_SV129;Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = length(genes.tw) - 1, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
# Count by temporal variance ----------------------------------------------
fits.g <- subset(fits.long.filt, gene %in% genes.keep & !method %in% c("zf"))
fits.g$g <- sapply(fits.g$method, function(m){
g = tryCatch({
g <- as.numeric(strsplit(m, "=")[[1]][[2]])
}, error = function(e) {
g <- m
})
return(g)
})
by.noise <- TRUE
if (!by.noise){
# for 24hr variance
dat.freq.tvar <- subset(dat.freq) %>%
group_by(gene) %>%
summarize(tvar = sum(Mod(exprs.transformed * 2) ^ 2))
temp.var <- hash(as.character(dat.freq.tvar$gene), dat.freq.tvar$tvar)
jylab <- "24h Spectral Power"
} else {
# for 16 and 9.6 hour variance
noise.components <- periods[which(24 %% periods != 0)]
dat.freq.tvar <- subset(dat.complex.all_T, period %in% noise.components) %>%
group_by(gene) %>%
summarize(tvar = sum(Mod(exprs.transformed * 2) ^ 2))
temp.var <- hash(as.character(dat.freq.tvar$gene), dat.freq.tvar$tvar)
jylab <- paste0(paste(noise.components, collapse=","), "Spectral Power")
}
fits.g$tvar <- sapply(as.character(fits.g$gene), function(g){
tvar <- temp.var[[g]]
if (is.null(tvar)){
return(0)
} else {
return(tvar)
}
})
fits.count <- fits.g %>%
group_by(g, model) %>%
summarize(n.genes = length(model),
tvar = sum(tvar))
tvar.flat <- hash(as.character(subset(fits.count, model == "")$g), subset(fits.count, model == "")$tvar)
fits.count$tvar.flat <- sapply(as.character(fits.count$g), function(g) tvar.flat[[g]])
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#0072B2", "#D55E00", "#CC79A7", "#F0E442", "#009E73")
jmodels <- c("Liver_SV129", "Kidney_SV129", "Liver_SV129,Kidney_SV129", "Liver_SV129;Kidney_SV129", "")
jmodels <- c("Liver_SV129", "Kidney_SV129", "Liver_SV129,Kidney_SV129", "Liver_SV129;Kidney_SV129")
bic.var <- subset(fits.count, g == "BIC" & model %in% jmodels)
ggplot(subset(fits.count, model %in% jmodels & g != "BIC"), aes(x = as.numeric(g), y = tvar, colour = model, group = model)) + geom_point(size = 3) + geom_line() + xlim(0, 5001) +
theme_bw() +
theme(aspect.ratio=1,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
scale_colour_manual(values=cbPalette) +
ylab(jylab) +
xlab("g (larger g favors simpler models)") +
geom_hline(yintercept=bic.var$tvar[[1]], colour=cbPalette[[1]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[2]], colour=cbPalette[[2]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[3]], colour=cbPalette[[3]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[4]], colour=cbPalette[[4]], linetype = "dotted") +
geom_vline(xintercept = 1000)
bic.n.genes <- subset(fits.count, g == "BIC" & model %in% jmodels)
ggplot(subset(fits.count, model %in% jmodels & g != "BIC"), aes(x = as.numeric(g), y = n.genes, colour = model, group = model)) + geom_point(size = 3) + geom_line() + xlim(0, 5001) +
theme_bw() +
theme(aspect.ratio=1,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
scale_colour_manual(values=cbPalette) +
ylab("# genes") +
xlab("g (larger g favors simpler models)") +
geom_hline(yintercept=bic.var$n.genes[[1]], colour=cbPalette[[1]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[2]], colour=cbPalette[[2]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[3]], colour=cbPalette[[3]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[4]], colour=cbPalette[[4]], linetype = "dotted") +
geom_vline(xintercept = 1000)
| /scripts/liver_kidney_WTKO/nconds_downstream.analysis.R | no_license | jakeyeung/Yeung_et_al_2018_TissueSpecificity | R | false | false | 10,497 | r | # 2016-06-23
# Jake Yeung
rm(list=ls())
setwd("/home/yeung/projects/tissue-specificity")
library(dplyr)
library(ggplot2)
library(hash)
source("scripts/functions/ListFunctions.R")
source("scripts/functions/LiverKidneyFunctions.R")
source("scripts/functions/PlotGeneAcrossTissues.R")
source("scripts/functions/NcondsFunctions.R")
source("scripts/functions/SvdFunctions.R")
source("scripts/functions/GetClockGenes.R")
source("scripts/functions/BiomartFunctions.R")
# load("Robjs/liver_kidney_atger_nestle/fits.long.multimethod.filtbest.Robj", v=T)
load("Robjs/liver_kidney_atger_nestle/fits.long.multimethod.filtbest.staggeredtimepts.bugfixed.Robj", v=T)
load("Robjs/liver_kidney_atger_nestle/dat.long.liverkidneyWTKO.bugfixed.Robj", v=T)
dat.orig <- dat.long
dat.long <- CollapseTissueGeno(dat.long)
dat.long <- StaggeredTimepointsLivKid(dat.long)
# dat.long <- SameTimepointsLivKid(dat.long)
# filter NA changes
dat.long <- subset(dat.long, !is.na(gene))
# Filter to common genes --------------------------------------------------
genes.keep <- unique(as.character(fits.long.filt$gene))
dat.long <- subset(dat.long, gene %in% genes.keep)
# Project to Frequency ----------------------------------------------------
omega <- 2 * pi / 24
dat.freq <- dat.long %>%
group_by(gene, tissue) %>%
do(ProjectToFrequency2(., omega, add.tissue=TRUE))
s <- SvdOnComplex(dat.freq, value.var = "exprs.transformed")
for (i in seq(1)){
eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
}
# All periods -------------------------------------------------------------
periods <- rep(48, 6) / seq(1, 6) # 48/1, 48/2 ... 48/12
loadfile <- "Robjs/liver_kidney_atger_nestle/dat.complex.all_T.Robj"
if (file.exists(loadfile)){
load(loadfile)
} else {
library(parallel)
dat.complexes <- mclapply(periods, function(period, dat.long){
omega <- 2 * pi / period
dat.tmp <- dat.long %>%
group_by(gene, tissue) %>%
do(ProjectToFrequency2(., omega, add.tissue=TRUE))
dat.tmp$period <- period
return(dat.tmp)
}, dat.long = dat.long, mc.cores = length(periods))
dat.complex.all_T <- do.call(rbind, dat.complexes)
outfcomp <- "Robjs/liver_kidney_atger_nestle/dat.complex.all_T.bugfixed.Robj"
if (!file.exists(outfcomp)) save(dat.complex.all_T, file = outfcomp)
rm(dat.complexes)
}
outffreq <- "Robjs/liver_kidney_atger_nestle/dat.freq.bugfixed.Robj"
if (!file.exists(outffreq)) save(dat.freq, file = outffreq)
# By clusters -------------------------------------------------------------
jmeth <- "zf"
jmeth <- "g=4001"
jmeth <- "g=1001"
jmeth <- "BIC"
i <- 1
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129,Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129,Liver_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Liver_SV129;Liver_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129,Kidney_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 15, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
#
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_SV129;Kidney_BmalKO"))$gene)
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 10, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
# genes.tw <- as.character(subset(fits.long.filt, method == jmeth & model %in% c("Kidney_BmalKO"))$gene)
#
# s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
# eigens <- GetEigens(s, period = 24, comp = i, label.n = 30, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
# jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
# multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
genes.tw <- as.character(subset(fits.long.filt, method ==jmeth & model %in% c("Liver_SV129;Kidney_SV129"))$gene)
s <- SvdOnComplex(subset(dat.freq, gene %in% genes.tw), value.var = "exprs.transformed")
eigens <- GetEigens(s, period = 24, comp = i, label.n = length(genes.tw) - 1, eigenval = TRUE, adj.mag = TRUE, constant.amp = 4, peak.to.trough = TRUE)
jlayout <- matrix(c(1, 2), 1, 2, byrow = TRUE)
multiplot(eigens$u.plot, eigens$v.plot, layout = jlayout)
# Count by temporal variance ----------------------------------------------
fits.g <- subset(fits.long.filt, gene %in% genes.keep & !method %in% c("zf"))
fits.g$g <- sapply(fits.g$method, function(m){
g = tryCatch({
g <- as.numeric(strsplit(m, "=")[[1]][[2]])
}, error = function(e) {
g <- m
})
return(g)
})
by.noise <- TRUE
if (!by.noise){
# for 24hr variance
dat.freq.tvar <- subset(dat.freq) %>%
group_by(gene) %>%
summarize(tvar = sum(Mod(exprs.transformed * 2) ^ 2))
temp.var <- hash(as.character(dat.freq.tvar$gene), dat.freq.tvar$tvar)
jylab <- "24h Spectral Power"
} else {
# for 16 and 9.6 hour variance
noise.components <- periods[which(24 %% periods != 0)]
dat.freq.tvar <- subset(dat.complex.all_T, period %in% noise.components) %>%
group_by(gene) %>%
summarize(tvar = sum(Mod(exprs.transformed * 2) ^ 2))
temp.var <- hash(as.character(dat.freq.tvar$gene), dat.freq.tvar$tvar)
jylab <- paste0(paste(noise.components, collapse=","), "Spectral Power")
}
fits.g$tvar <- sapply(as.character(fits.g$gene), function(g){
tvar <- temp.var[[g]]
if (is.null(tvar)){
return(0)
} else {
return(tvar)
}
})
fits.count <- fits.g %>%
group_by(g, model) %>%
summarize(n.genes = length(model),
tvar = sum(tvar))
tvar.flat <- hash(as.character(subset(fits.count, model == "")$g), subset(fits.count, model == "")$tvar)
fits.count$tvar.flat <- sapply(as.character(fits.count$g), function(g) tvar.flat[[g]])
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#0072B2", "#D55E00", "#CC79A7", "#F0E442", "#009E73")
jmodels <- c("Liver_SV129", "Kidney_SV129", "Liver_SV129,Kidney_SV129", "Liver_SV129;Kidney_SV129", "")
jmodels <- c("Liver_SV129", "Kidney_SV129", "Liver_SV129,Kidney_SV129", "Liver_SV129;Kidney_SV129")
bic.var <- subset(fits.count, g == "BIC" & model %in% jmodels)
ggplot(subset(fits.count, model %in% jmodels & g != "BIC"), aes(x = as.numeric(g), y = tvar, colour = model, group = model)) + geom_point(size = 3) + geom_line() + xlim(0, 5001) +
theme_bw() +
theme(aspect.ratio=1,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
scale_colour_manual(values=cbPalette) +
ylab(jylab) +
xlab("g (larger g favors simpler models)") +
geom_hline(yintercept=bic.var$tvar[[1]], colour=cbPalette[[1]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[2]], colour=cbPalette[[2]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[3]], colour=cbPalette[[3]], linetype = "dotted") +
geom_hline(yintercept=bic.var$tvar[[4]], colour=cbPalette[[4]], linetype = "dotted") +
geom_vline(xintercept = 1000)
bic.n.genes <- subset(fits.count, g == "BIC" & model %in% jmodels)
ggplot(subset(fits.count, model %in% jmodels & g != "BIC"), aes(x = as.numeric(g), y = n.genes, colour = model, group = model)) + geom_point(size = 3) + geom_line() + xlim(0, 5001) +
theme_bw() +
theme(aspect.ratio=1,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
scale_colour_manual(values=cbPalette) +
ylab("# genes") +
xlab("g (larger g favors simpler models)") +
geom_hline(yintercept=bic.var$n.genes[[1]], colour=cbPalette[[1]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[2]], colour=cbPalette[[2]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[3]], colour=cbPalette[[3]], linetype = "dotted") +
geom_hline(yintercept=bic.var$n.genes[[4]], colour=cbPalette[[4]], linetype = "dotted") +
geom_vline(xintercept = 1000)
|
#=============================================================
#====================== MAIN FUNCTION ========================
#=============================================================
plotEcoCompanyGoods <- function(df_economy, output_dir, one_plot) {
name = "Company_goods_plot"
#-------------------------------------------------------------
#-------------------- DATA MANIPULATION ----------------------
#-------------------------------------------------------------
print(paste(name, " performing data manipulation", sep=""))
# CONTACTS PER GATHERING POINT PER APP USAGE SCENARIO ----------------------------
#group_by(tick) %>% summarise(total = mean(count_people_with_not_is_young_and_is_in_poverty))
# Add days converted from ticks
#df_economy$day <- dmfConvertTicksToDay(df_economy$tick)
# df_people_captial <- df_economy %>% select(tick, run_number, Scenario,
# workers = workers_average_amount_of_goods,
# retired = retirees_average_amount_of_goods,
# students = students_average_amount_of_goods)
df_essential_shop_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = essential_shop_amount_of_goods_in_stock,
)
df_essential_shop_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(essential_shop_amount_of_goods_in_stock)
,std = sd(essential_shop_amount_of_goods_in_stock)
)
df_non_essential_shop_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = non_essential_shop_amount_of_goods_in_stock,
)
df_non_essential_shop_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(non_essential_shop_amount_of_goods_in_stock)
,std = sd(non_essential_shop_amount_of_goods_in_stock)
)
df_workplace_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = workplace_amount_of_goods_in_stock,
)
df_workplace_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(workplace_amount_of_goods_in_stock)
,std = sd(workplace_amount_of_goods_in_stock)
)
#seg_people_calpital <- gather(df_mean_std, variable, measurement, mean, std)
# ----- convert to days
df_essential_shop_mean_std_day <- df_essential_shop_mean_std
df_essential_shop_mean_std_day$day <- dmfConvertTicksToDay(df_essential_shop_mean_std_day$tick)
df_essential_shop_mean_std_day <- df_essential_shop_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
df_non_essential_shop_mean_std_day <- df_non_essential_shop_mean_std
df_non_essential_shop_mean_std_day$day <- dmfConvertTicksToDay(df_non_essential_shop_mean_std_day$tick)
df_non_essential_shop_mean_std_day <- df_non_essential_shop_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
df_workplace_mean_std_day <- df_workplace_mean_std
df_workplace_mean_std_day$day <- dmfConvertTicksToDay(df_workplace_mean_std_day$tick)
df_workplace_mean_std_day <- df_workplace_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
print(paste(name, " writing CSV", sep=""))
write.csv(df_essential_shop_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_non_essential_shop_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_workplace_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_essential_shop_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_non_essential_shop_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_workplace_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
#-------------------------------------------------------------
#------------------------- Plotting --------------------------
#-------------------------------------------------------------
#seg_people_calpital <- gather(df_people_captial, variable, measurement, workers, retired)
print(paste(name, " making plots", sep=""))
dmfPdfOpen(output_dir, "eco_essential_shop_goods")
print(plot_ggplot(df_essential_shop_mean_std, "essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_essential_shop_goods_smooth")
print(plot_ggplot_smooth(df_essential_shop_mean_std, "essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods")
print(plot_ggplot(df_non_essential_shop_mean_std, "non-essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods_smooth")
print(plot_ggplot_smooth(df_non_essential_shop_mean_std, "non-essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_goods")
print(plot_ggplot(df_workplace_mean_std, "workplace", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_smooth_goodd")
print(plot_ggplot_smooth(df_workplace_mean_std, "workplace", "tick"))
dmfPdfClose()
# --- days
dmfPdfOpen(output_dir, "eco_essential_shop_goods_smooth_day")
print(plot_ggplot_smooth(df_essential_shop_mean_std_day, "essential shop", "day"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods_smooth_day")
print(plot_ggplot_smooth(df_non_essential_shop_mean_std_day, "non-essential shop", "day"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_capital_goods_day")
print(plot_ggplot_smooth(df_workplace_mean_std_day, "workplace", "day"))
dmfPdfClose()
}
#=============================================================
#=================== PLOTTING FUNCTIONS ======================
#=============================================================
plot_ggplot <- function(data_to_plot, type_of_people, timeframe) {
timeframe <- sym(timeframe)
data_to_plot %>%
ggplot(aes(x = !!timeframe,
y = mean)) +
geom_line(size=1,alpha=0.8,aes(color=Scenario, group = Scenario)) +
#geom_errorbar(aes(ymin = mean - std, ymax = mean + std,
# color=Scenario, group = Scenario)) +
#continues_colour_brewer(palette = "Spectral", name="Infected") +
xlab(paste(toupper(substring(timeframe, 1,1)), substring(timeframe, 2), "s", sep = "")) +
ylab("Goods") +
labs(title=paste("Average", type_of_people, "goods in stock", sep = " "),
subtitle=paste("Average goods in stock at", type_of_people, sep = " "),
caption="Agent-based Social Simulation of Corona Crisis (ASSOCC)") +
scale_color_manual(values = gl_plot_colours) +
gl_plot_guides + gl_plot_theme
}
plot_ggplot_smooth <- function(data_to_plot, type_of_people, timeframe) {
timeframe <- sym(timeframe)
data_to_plot %>%
ggplot(aes(x = !!timeframe,
y = mean)) +
gl_plot_smooth +
geom_ribbon(aes(ymin = mean - std, ymax = mean + std,
color= Scenario), alpha=0.025) +
#scale_colour_brewer(palette = "Spectral", name="Infected") +
xlab(paste(toupper(substring(timeframe, 1,1)), substring(timeframe, 2), "s", sep = "")) +
ylab("Goods") +
labs(title=paste("Average", type_of_people, "goods in stock", sep = " "),
subtitle=paste("Average goods in stock at", type_of_people, "(smoothed + uncertainty (std. dev.))", sep = " "),
caption="Agent-based Social Simulation of Corona Crisis (ASSOCC)") +
scale_color_manual(values = gl_plot_colours) +
gl_plot_guides + gl_plot_theme
}
| /processing/scenarios/R_ASSOCC_Economy/1.0_company_goods_plot.R | no_license | SergeStinckwich/COVID-sim | R | false | false | 8,367 | r | #=============================================================
#====================== MAIN FUNCTION ========================
#=============================================================
plotEcoCompanyGoods <- function(df_economy, output_dir, one_plot) {
name = "Company_goods_plot"
#-------------------------------------------------------------
#-------------------- DATA MANIPULATION ----------------------
#-------------------------------------------------------------
print(paste(name, " performing data manipulation", sep=""))
# CONTACTS PER GATHERING POINT PER APP USAGE SCENARIO ----------------------------
#group_by(tick) %>% summarise(total = mean(count_people_with_not_is_young_and_is_in_poverty))
# Add days converted from ticks
#df_economy$day <- dmfConvertTicksToDay(df_economy$tick)
# df_people_captial <- df_economy %>% select(tick, run_number, Scenario,
# workers = workers_average_amount_of_goods,
# retired = retirees_average_amount_of_goods,
# students = students_average_amount_of_goods)
df_essential_shop_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = essential_shop_amount_of_goods_in_stock,
)
df_essential_shop_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(essential_shop_amount_of_goods_in_stock)
,std = sd(essential_shop_amount_of_goods_in_stock)
)
df_non_essential_shop_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = non_essential_shop_amount_of_goods_in_stock,
)
df_non_essential_shop_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(non_essential_shop_amount_of_goods_in_stock)
,std = sd(non_essential_shop_amount_of_goods_in_stock)
)
df_workplace_goods <- df_economy %>% select(tick, run_number, Scenario,
goods = workplace_amount_of_goods_in_stock,
)
df_workplace_mean_std <- df_economy %>% group_by(tick, Scenario) %>% summarise(tick, Scenario,
mean = mean(workplace_amount_of_goods_in_stock)
,std = sd(workplace_amount_of_goods_in_stock)
)
#seg_people_calpital <- gather(df_mean_std, variable, measurement, mean, std)
# ----- convert to days
df_essential_shop_mean_std_day <- df_essential_shop_mean_std
df_essential_shop_mean_std_day$day <- dmfConvertTicksToDay(df_essential_shop_mean_std_day$tick)
df_essential_shop_mean_std_day <- df_essential_shop_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
df_non_essential_shop_mean_std_day <- df_non_essential_shop_mean_std
df_non_essential_shop_mean_std_day$day <- dmfConvertTicksToDay(df_non_essential_shop_mean_std_day$tick)
df_non_essential_shop_mean_std_day <- df_non_essential_shop_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
df_workplace_mean_std_day <- df_workplace_mean_std
df_workplace_mean_std_day$day <- dmfConvertTicksToDay(df_workplace_mean_std_day$tick)
df_workplace_mean_std_day <- df_workplace_mean_std_day %>% group_by(day, Scenario) %>% summarise(
day, Scenario,
mean = mean(mean),
std = mean(std))
print(paste(name, " writing CSV", sep=""))
write.csv(df_essential_shop_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_non_essential_shop_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_workplace_mean_std, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_essential_shop_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_non_essential_shop_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
write.csv(df_workplace_mean_std_day, file=paste(output_dir, "/plot_data_", name, ".csv", sep=""))
#-------------------------------------------------------------
#------------------------- Plotting --------------------------
#-------------------------------------------------------------
#seg_people_calpital <- gather(df_people_captial, variable, measurement, workers, retired)
print(paste(name, " making plots", sep=""))
dmfPdfOpen(output_dir, "eco_essential_shop_goods")
print(plot_ggplot(df_essential_shop_mean_std, "essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_essential_shop_goods_smooth")
print(plot_ggplot_smooth(df_essential_shop_mean_std, "essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods")
print(plot_ggplot(df_non_essential_shop_mean_std, "non-essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods_smooth")
print(plot_ggplot_smooth(df_non_essential_shop_mean_std, "non-essential shop", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_goods")
print(plot_ggplot(df_workplace_mean_std, "workplace", "tick"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_smooth_goodd")
print(plot_ggplot_smooth(df_workplace_mean_std, "workplace", "tick"))
dmfPdfClose()
# --- days
dmfPdfOpen(output_dir, "eco_essential_shop_goods_smooth_day")
print(plot_ggplot_smooth(df_essential_shop_mean_std_day, "essential shop", "day"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_non_essential_shop_goods_smooth_day")
print(plot_ggplot_smooth(df_non_essential_shop_mean_std_day, "non-essential shop", "day"))
dmfPdfClose()
dmfPdfOpen(output_dir, "eco_workplace_capital_goods_day")
print(plot_ggplot_smooth(df_workplace_mean_std_day, "workplace", "day"))
dmfPdfClose()
}
#=============================================================
#=================== PLOTTING FUNCTIONS ======================
#=============================================================
plot_ggplot <- function(data_to_plot, type_of_people, timeframe) {
timeframe <- sym(timeframe)
data_to_plot %>%
ggplot(aes(x = !!timeframe,
y = mean)) +
geom_line(size=1,alpha=0.8,aes(color=Scenario, group = Scenario)) +
#geom_errorbar(aes(ymin = mean - std, ymax = mean + std,
# color=Scenario, group = Scenario)) +
#continues_colour_brewer(palette = "Spectral", name="Infected") +
xlab(paste(toupper(substring(timeframe, 1,1)), substring(timeframe, 2), "s", sep = "")) +
ylab("Goods") +
labs(title=paste("Average", type_of_people, "goods in stock", sep = " "),
subtitle=paste("Average goods in stock at", type_of_people, sep = " "),
caption="Agent-based Social Simulation of Corona Crisis (ASSOCC)") +
scale_color_manual(values = gl_plot_colours) +
gl_plot_guides + gl_plot_theme
}
plot_ggplot_smooth <- function(data_to_plot, type_of_people, timeframe) {
timeframe <- sym(timeframe)
data_to_plot %>%
ggplot(aes(x = !!timeframe,
y = mean)) +
gl_plot_smooth +
geom_ribbon(aes(ymin = mean - std, ymax = mean + std,
color= Scenario), alpha=0.025) +
#scale_colour_brewer(palette = "Spectral", name="Infected") +
xlab(paste(toupper(substring(timeframe, 1,1)), substring(timeframe, 2), "s", sep = "")) +
ylab("Goods") +
labs(title=paste("Average", type_of_people, "goods in stock", sep = " "),
subtitle=paste("Average goods in stock at", type_of_people, "(smoothed + uncertainty (std. dev.))", sep = " "),
caption="Agent-based Social Simulation of Corona Crisis (ASSOCC)") +
scale_color_manual(values = gl_plot_colours) +
gl_plot_guides + gl_plot_theme
}
|
testlist <- list(Beta = 0, CVLinf = 0, FM = 3.19900290824811e-83, L50 = 0, L95 = 0, LenBins = numeric(0), LenMids = numeric(0), Linf = 0, MK = 0, Ml = numeric(0), Prob = structure(0, .Dim = c(1L, 1L)), SL50 = 1.64050245440024e-303, SL95 = 1.05376013623562e-264, nage = 847783982L, nlen = 1735852032L, rLens = numeric(0))
result <- do.call(DLMtool::LBSPRgen,testlist)
str(result) | /DLMtool/inst/testfiles/LBSPRgen/AFL_LBSPRgen/LBSPRgen_valgrind_files/1615830404-test.R | no_license | akhikolla/updatedatatype-list2 | R | false | false | 398 | r | testlist <- list(Beta = 0, CVLinf = 0, FM = 3.19900290824811e-83, L50 = 0, L95 = 0, LenBins = numeric(0), LenMids = numeric(0), Linf = 0, MK = 0, Ml = numeric(0), Prob = structure(0, .Dim = c(1L, 1L)), SL50 = 1.64050245440024e-303, SL95 = 1.05376013623562e-264, nage = 847783982L, nlen = 1735852032L, rLens = numeric(0))
result <- do.call(DLMtool::LBSPRgen,testlist)
str(result) |
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