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 |
|---|---|---|---|---|---|---|---|---|---|
### =========================================================================
### TENxMatrixSeed objects
### -------------------------------------------------------------------------
setClass("TENxMatrixSeed",
contains="Array",
representation(
filepath="character", # Absolute path to the HDF5 file so the
# object doesn't break when the user
# changes the working directory (e.g. with
# setwd()).
group="character", # Name of the group in the HDF5 file
# containing the 10x Genomics data.
dim="integer",
dimnames="list",
col_ranges="data.frame" # Can't use an IRanges object for this at the
# moment because they don't support Linteger
# start/end values yet.
)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### path() getter/setter
###
### Does NOT access the file.
setMethod("path", "TENxMatrixSeed", function(object) object@filepath)
### Just a placeholder for now. Doesn't actually allow changing the path of
### the object yet.
setReplaceMethod("path", "TENxMatrixSeed",
function(object, value)
{
new_filepath <- normarg_path(value, "the supplied path",
"10x Genomics dataset")
old_filepath <- path(object)
if (new_filepath != old_filepath)
stop(wmsg("changing the path of a TENxMatrixSeed object ",
"is not supported yet"))
object
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### dim() getter
###
### Does NOT access the file.
setMethod("dim", "TENxMatrixSeed", function(x) x@dim)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### dimnames() getter
###
### Does NOT access the file.
setMethod("dimnames", "TENxMatrixSeed",
function(x) DelayedArray:::simplify_NULL_dimnames(x@dimnames)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Low-level internal disk data extractors
###
### All the 10xGenomics components are monodimensional.
.read_tenx_component <- function(filepath, group, name, start=NULL, count=NULL,
as.integer=FALSE)
{
name <- paste0(group, "/", name)
if (!is.null(start))
start <- list(start)
if (!is.null(count))
count <- list(count)
as.vector(h5mread(filepath, name, starts=start, counts=count,
as.integer=as.integer))
}
### Return the dimensions of the matrix.
.get_tenx_shape <- function(filepath, group)
.read_tenx_component(filepath, group, "shape")
.get_tenx_indptr <- function(filepath, group)
.read_tenx_component(filepath, group, "indptr")
.get_tenx_data <- function(filepath, group, start=NULL, count=NULL)
.read_tenx_component(filepath, group, "data", start=start, count=count)
### The row indices in the HDF5 file are 0-based but we return them 1-based.
.get_tenx_row_indices <- function(filepath, group, start=NULL, count=NULL)
.read_tenx_component(filepath, group, "indices", start=start, count=count,
as.integer=TRUE) + 1L
### Return the rownames of the matrix.
.get_tenx_genes <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/genes")))
return(NULL)
.read_tenx_component(filepath, group, "genes")
}
### Currently unused.
.get_tenx_gene_names <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/gene_names")))
return(NULL)
.read_tenx_component(filepath, group, "gene_names")
}
### Return the colnames of the matrix.
.get_tenx_barcodes <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/barcodes")))
return(NULL)
.read_tenx_component(filepath, group, "barcodes")
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .get_data_indices_by_col()
###
### S4Vectors:::fancy_mseq() does not accept 'offset' of type double yet so
### we implement a version that does.
### Will this work if sum(lengths) is > .Machine$integer.max?
.fancy_mseq <- function(lengths, offset=0)
{
lengths_len <- length(lengths)
if (lengths_len == 0L)
return(numeric(0))
offsets <- offset - cumsum(c(0L, lengths[-lengths_len]))
seq_len(sum(lengths)) + rep.int(offsets, lengths)
}
### 'j' must be an integer vector containing valid col indices.
### Return data indices in a NumericList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
.get_data_indices_by_col <- function(x, j)
{
col_ranges <- S4Vectors:::extract_data_frame_rows(x@col_ranges, j)
start2 <- col_ranges[ , "start"]
width2 <- col_ranges[ , "width"]
idx2 <- .fancy_mseq(width2, offset=start2 - 1L)
### Will this work if 'idx2' is a long vector?
relist(idx2, PartitioningByWidth(width2))
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .extract_data_from_adjacent_cols()
###
### 'j1' and 'j2' must be 2 single integers representing a valid range of
### col indices.
### If 'as.sparse=FALSE', return a NumericList or IntegerList object parallel
### to 'j1:j2' i.e. with one list element per col index in 'j1:j2'.
### If 'as.sparse=TRUE', return a SparseArraySeed object.
.extract_data_from_adjacent_cols <- function(x, j1, j2, as.sparse=FALSE)
{
j12 <- j1:j2
start <- x@col_ranges[j1, "start"]
count_per_col <- x@col_ranges[j12, "width"]
count <- sum(count_per_col)
ans_nzdata <- .get_tenx_data(x@filepath, x@group, start=start, count=count)
if (!as.sparse)
return(relist(ans_nzdata, PartitioningByWidth(count_per_col)))
row_indices <- .get_tenx_row_indices(x@filepath, x@group,
start=start, count=count)
col_indices <- rep.int(j12, count_per_col)
ans_nzindex <- cbind(row_indices, col_indices, deparse.level=0L)
SparseArraySeed(dim(x), ans_nzindex, ans_nzdata, check=FALSE)
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .extract_nonzero_data_by_col()
###
### Extract nonzero data using the "random" method.
### This method is based on h5mread( , starts=list(start)) which retrieves
### an arbitrary/random subset of the data.
### 'j' must be an integer vector containing valid col indices. It cannot
### be NULL.
.random_extract_nonzero_data_by_col <- function(x, j)
{
data_indices <- .get_data_indices_by_col(x, j)
idx2 <- unlist(data_indices, use.names=FALSE)
data <- .get_tenx_data(x@filepath, x@group, start=idx2)
relist(data, data_indices)
}
### Extract nonzero data using the "linear" method.
### This method is based on h5mread( , starts=list(start), counts=list(count))
### which retrieves a linear subset of the data and should be more efficient
### than doing h5mread( , starts=list(seq(start, length.out=count))).
### 'j' must be NULL or an integer vector containing valid col indices. It
### should not be empty.
.linear_extract_nonzero_data_by_col <- function(x, j)
{
if (is.null(j)) {
j1 <- 1L
j2 <- ncol(x)
} else {
stopifnot(is.numeric(j), length(j) != 0L)
j1 <- min(j)
j2 <- max(j)
}
nonzero_data <- .extract_data_from_adjacent_cols(x, j1, j2)
if (is.null(j))
return(nonzero_data)
nonzero_data[match(j, j1:j2)]
}
.normarg_method <- function(method, j)
{
if (method != "auto")
return(method)
if (is.null(j))
return("linear")
if (length(j) == 0L)
return("random")
j1 <- min(j)
j2 <- max(j)
## 'ratio' is > 0 and <= 1. A value close to 1 indicates that the columns
## to extract are close from each other (a value of 1 indicating that
## they are adjacent e.g. j <- 18:25). A value close to 0 indicates that
## they are far apart from each other i.e. that they are separated by many
## columns that are not requested. The "linear" method is very efficient
## when 'ratio' is close to 1. It is so much more efficient than the
## "random" method (typically 10x or 20x faster) that we choose it when
## 'ratio' is >= 0.2
ratio <- length(j) / (j2 - j1 + 1L)
if (ratio >= 0.2) "linear" else "random"
}
### 'j' must be NULL or an integer vector containing valid col indices.
### Return a NumericList or IntegerList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
.extract_nonzero_data_by_col <-
function(x, j, method=c("auto", "random", "linear"))
{
method <- match.arg(method)
method <- .normarg_method(method, j)
if (method == "random") {
.random_extract_nonzero_data_by_col(x, j)
} else {
.linear_extract_nonzero_data_by_col(x, j)
}
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .load_SparseArraySeed_from_TENxMatrixSeed()
###
### Load sparse data using the "random" method.
### This method is based on h5mread( , starts=list(start)) which retrieves
### an arbitrary/random subset of the data.
### 'i' must be NULL or an integer vector containing valid row indices.
### 'j' must be an integer vector containing valid col indices. It cannot
### be NULL.
### Both 'i' and 'j' can contain duplicates. Duplicates in 'i' have no effect
### on the output but duplicates in 'j' will produce duplicates in the output.
### Return a SparseArraySeed object.
.random_load_SparseArraySeed_from_TENxMatrixSeed <- function(x, i, j)
{
stopifnot(is.null(i) || is.numeric(i), is.numeric(j))
data_indices <- .get_data_indices_by_col(x, j)
idx2 <- unlist(data_indices, use.names=FALSE)
row_indices <- .get_tenx_row_indices(x@filepath, x@group, start=idx2)
col_indices <- rep.int(j, lengths(data_indices))
if (!is.null(i)) {
keep_idx <- which(row_indices %in% i)
idx2 <- idx2[keep_idx]
row_indices <- row_indices[keep_idx]
col_indices <- col_indices[keep_idx]
}
ans_nzindex <- cbind(row_indices, col_indices, deparse.level=0L)
ans_nzdata <- .get_tenx_data(x@filepath, x@group, start=idx2)
SparseArraySeed(dim(x), ans_nzindex, ans_nzdata, check=FALSE)
}
### Load sparse data using the "linear" method.
### This method is based on h5mread( , starts=list(start), counts=list(count))
### which retrieves a linear subset of the data and should be more efficient
### than doing h5mread( , starts=list(seq(start, length.out=count))).
### 'j' must be NULL or a non-empty integer vector containing valid
### col indices. The output is not affected by duplicates in 'j'.
### Return a SparseArraySeed object.
.linear_load_SparseArraySeed_from_TENxMatrixSeed <- function(x, j)
{
if (is.null(j)) {
j1 <- 1L
j2 <- ncol(x)
} else {
stopifnot(is.numeric(j), length(j) != 0L)
j1 <- min(j)
j2 <- max(j)
}
.extract_data_from_adjacent_cols(x, j1, j2, as.sparse=TRUE)
}
### Duplicates in 'index[[1]]' are ok and won't affect the output.
### Duplicates in 'index[[2]]' are ok but might introduce duplicates
### in the output so should be avoided.
### Return a SparseArraySeed object.
.load_SparseArraySeed_from_TENxMatrixSeed <-
function(x, index, method=c("auto", "random", "linear"))
{
i <- index[[1L]]
j <- index[[2L]]
method <- match.arg(method)
method <- .normarg_method(method, j)
if (method == "random") {
.random_load_SparseArraySeed_from_TENxMatrixSeed(x, i, j)
} else {
.linear_load_SparseArraySeed_from_TENxMatrixSeed(x, j)
}
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### extract_array()
###
.extract_array_from_TENxMatrixSeed <- function(x, index)
{
ans_dim <- DelayedArray:::get_Nindex_lengths(index, dim(x))
if (any(ans_dim == 0L)) {
## Return an empty matrix.
data0 <- .get_tenx_data(x@filepath, x@group, start=integer(0))
return(array(data0, dim=ans_dim))
}
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
extract_array(sas, index) # in-memory
}
setMethod("extract_array", "TENxMatrixSeed",
.extract_array_from_TENxMatrixSeed
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### chunkdim() getter
###
### Does NOT access the file.
setMethod("chunkdim", "TENxMatrixSeed", function(x) c(nrow(x), 1L))
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Constructor
###
TENxMatrixSeed <- function(filepath, group="mm10")
{
filepath <- normarg_path(filepath, "'filepath'", "10x Genomics dataset")
if (!isSingleString(group))
stop(wmsg("'group' must be a single string specifying the name ",
"of the group in the HDF5 file containing the ",
"10x Genomics data"))
if (group == "")
stop(wmsg("'group' cannot be the empty string"))
## dim
dim <- .get_tenx_shape(filepath, group)
stopifnot(length(dim) == 2L)
## dimnames
rownames <- .get_tenx_genes(filepath, group)
stopifnot(is.null(rownames) || length(rownames) == dim[[1L]])
colnames <- .get_tenx_barcodes(filepath, group)
stopifnot(is.null(colnames) || length(colnames) == dim[[2L]])
dimnames <- list(rownames, colnames)
## col_ranges
data_len <- h5length(filepath, paste0(group, "/data"))
indices_len <- h5length(filepath, paste0(group, "/indices"))
stopifnot(data_len == indices_len)
indptr <- .get_tenx_indptr(filepath, group)
stopifnot(length(indptr) == dim[[2L]] + 1L,
indptr[[1L]] == 0L,
indptr[[length(indptr)]] == data_len)
col_ranges <- data.frame(start=indptr[-length(indptr)] + 1,
width=as.integer(diff(indptr)))
new2("TENxMatrixSeed", filepath=filepath,
group=group,
dim=dim,
dimnames=dimnames,
col_ranges=col_ranges)
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Show
###
setMethod("show", "TENxMatrixSeed",
function(object)
{
cat(DelayedArray:::array_as_one_line_summary(object), ":\n", sep="")
cat("# dirname: ", dirname(object), "\n", sep="")
cat("# basename: ", basename(object), "\n", sep="")
cat("# group: ", object@group, "\n", sep="")
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Taking advantage of sparsity
###
setMethod("sparsity", "TENxMatrixSeed",
function(x)
{
data_len <- h5length(x@filepath, paste0(x@group, "/data"))
1 - data_len / length(x)
}
)
### This is about **structural** sparsity, not about quantitative sparsity
### measured by sparsity().
setMethod("is_sparse", "TENxMatrixSeed", function(x) TRUE)
.extract_sparse_array_from_TENxMatrixSeed <- function(x, index)
{
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
extract_sparse_array(sas, index) # in-memory
}
setMethod("extract_sparse_array", "TENxMatrixSeed",
.extract_sparse_array_from_TENxMatrixSeed
)
### The default "read_sparse_block" method defined in DelayedArray would work
### just fine on a TENxMatrixSeed object (thanks to the "extract_sparse_array"
### method for TENxMatrixSeed objects defined above), but we overwrite it with
### the method below which is slightly more efficient. That's because the
### method below calls read_sparse_block() on the SparseArraySeed object
### returned by .load_SparseArraySeed_from_TENxMatrixSeed() and this is
### faster than calling extract_sparse_array() on the same object (which
### is what the "extract_sparse_array" method for TENxMatrixSeed would do
### when called by the default "read_sparse_block" method).
### Not sure the difference is significant enough for this extra method to
### be worth it though, because, time is really dominated by I/O here, that
### is, by the call to .load_SparseArraySeed_from_TENxMatrixSeed().
.read_sparse_block_from_TENxMatrixSeed <- function(x, viewport)
{
index <- makeNindexFromArrayViewport(viewport, expand.RangeNSBS=TRUE)
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
## Unlike the "extract_sparse_array" method for TENxMatrixSeed objects
## defined above, we use read_sparse_block() here, which is faster than
## using extract_sparse_array().
read_sparse_block(sas, viewport) # in-memory
}
setMethod("read_sparse_block", "TENxMatrixSeed",
.read_sparse_block_from_TENxMatrixSeed
)
### Return a NumericList or IntegerList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
### Spelling: "nonzero" preferred over "non-zero". See:
### https://gcc.gnu.org/ml/gcc/2001-10/msg00610.html
setGeneric("extractNonzeroDataByCol", signature="x",
function(x, j) standardGeneric("extractNonzeroDataByCol")
)
setMethod("extractNonzeroDataByCol", "TENxMatrixSeed",
function(x, j)
{
j <- DelayedArray:::normalizeSingleBracketSubscript2(j, ncol(x),
colnames(x))
.extract_nonzero_data_by_col(x, j)
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Coercion to dgCMatrix
###
.from_TENxMatrixSeed_to_dgCMatrix <- function(from)
{
row_indices <- .get_tenx_row_indices(from@filepath, from@group)
indptr <- .get_tenx_indptr(from@filepath, from@group)
data <- .get_tenx_data(from@filepath, from@group)
sparseMatrix(i=row_indices, p=indptr, x=data, dims=dim(from),
dimnames=dimnames(from))
}
setAs("TENxMatrixSeed", "dgCMatrix", .from_TENxMatrixSeed_to_dgCMatrix)
setAs("TENxMatrixSeed", "sparseMatrix", .from_TENxMatrixSeed_to_dgCMatrix)
| /R/TENxMatrixSeed-class.R | no_license | muschellij2/HDF5Array | R | false | false | 17,938 | r | ### =========================================================================
### TENxMatrixSeed objects
### -------------------------------------------------------------------------
setClass("TENxMatrixSeed",
contains="Array",
representation(
filepath="character", # Absolute path to the HDF5 file so the
# object doesn't break when the user
# changes the working directory (e.g. with
# setwd()).
group="character", # Name of the group in the HDF5 file
# containing the 10x Genomics data.
dim="integer",
dimnames="list",
col_ranges="data.frame" # Can't use an IRanges object for this at the
# moment because they don't support Linteger
# start/end values yet.
)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### path() getter/setter
###
### Does NOT access the file.
setMethod("path", "TENxMatrixSeed", function(object) object@filepath)
### Just a placeholder for now. Doesn't actually allow changing the path of
### the object yet.
setReplaceMethod("path", "TENxMatrixSeed",
function(object, value)
{
new_filepath <- normarg_path(value, "the supplied path",
"10x Genomics dataset")
old_filepath <- path(object)
if (new_filepath != old_filepath)
stop(wmsg("changing the path of a TENxMatrixSeed object ",
"is not supported yet"))
object
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### dim() getter
###
### Does NOT access the file.
setMethod("dim", "TENxMatrixSeed", function(x) x@dim)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### dimnames() getter
###
### Does NOT access the file.
setMethod("dimnames", "TENxMatrixSeed",
function(x) DelayedArray:::simplify_NULL_dimnames(x@dimnames)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Low-level internal disk data extractors
###
### All the 10xGenomics components are monodimensional.
.read_tenx_component <- function(filepath, group, name, start=NULL, count=NULL,
as.integer=FALSE)
{
name <- paste0(group, "/", name)
if (!is.null(start))
start <- list(start)
if (!is.null(count))
count <- list(count)
as.vector(h5mread(filepath, name, starts=start, counts=count,
as.integer=as.integer))
}
### Return the dimensions of the matrix.
.get_tenx_shape <- function(filepath, group)
.read_tenx_component(filepath, group, "shape")
.get_tenx_indptr <- function(filepath, group)
.read_tenx_component(filepath, group, "indptr")
.get_tenx_data <- function(filepath, group, start=NULL, count=NULL)
.read_tenx_component(filepath, group, "data", start=start, count=count)
### The row indices in the HDF5 file are 0-based but we return them 1-based.
.get_tenx_row_indices <- function(filepath, group, start=NULL, count=NULL)
.read_tenx_component(filepath, group, "indices", start=start, count=count,
as.integer=TRUE) + 1L
### Return the rownames of the matrix.
.get_tenx_genes <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/genes")))
return(NULL)
.read_tenx_component(filepath, group, "genes")
}
### Currently unused.
.get_tenx_gene_names <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/gene_names")))
return(NULL)
.read_tenx_component(filepath, group, "gene_names")
}
### Return the colnames of the matrix.
.get_tenx_barcodes <- function(filepath, group)
{
if (!h5exists(filepath, paste0(group, "/barcodes")))
return(NULL)
.read_tenx_component(filepath, group, "barcodes")
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .get_data_indices_by_col()
###
### S4Vectors:::fancy_mseq() does not accept 'offset' of type double yet so
### we implement a version that does.
### Will this work if sum(lengths) is > .Machine$integer.max?
.fancy_mseq <- function(lengths, offset=0)
{
lengths_len <- length(lengths)
if (lengths_len == 0L)
return(numeric(0))
offsets <- offset - cumsum(c(0L, lengths[-lengths_len]))
seq_len(sum(lengths)) + rep.int(offsets, lengths)
}
### 'j' must be an integer vector containing valid col indices.
### Return data indices in a NumericList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
.get_data_indices_by_col <- function(x, j)
{
col_ranges <- S4Vectors:::extract_data_frame_rows(x@col_ranges, j)
start2 <- col_ranges[ , "start"]
width2 <- col_ranges[ , "width"]
idx2 <- .fancy_mseq(width2, offset=start2 - 1L)
### Will this work if 'idx2' is a long vector?
relist(idx2, PartitioningByWidth(width2))
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .extract_data_from_adjacent_cols()
###
### 'j1' and 'j2' must be 2 single integers representing a valid range of
### col indices.
### If 'as.sparse=FALSE', return a NumericList or IntegerList object parallel
### to 'j1:j2' i.e. with one list element per col index in 'j1:j2'.
### If 'as.sparse=TRUE', return a SparseArraySeed object.
.extract_data_from_adjacent_cols <- function(x, j1, j2, as.sparse=FALSE)
{
j12 <- j1:j2
start <- x@col_ranges[j1, "start"]
count_per_col <- x@col_ranges[j12, "width"]
count <- sum(count_per_col)
ans_nzdata <- .get_tenx_data(x@filepath, x@group, start=start, count=count)
if (!as.sparse)
return(relist(ans_nzdata, PartitioningByWidth(count_per_col)))
row_indices <- .get_tenx_row_indices(x@filepath, x@group,
start=start, count=count)
col_indices <- rep.int(j12, count_per_col)
ans_nzindex <- cbind(row_indices, col_indices, deparse.level=0L)
SparseArraySeed(dim(x), ans_nzindex, ans_nzdata, check=FALSE)
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .extract_nonzero_data_by_col()
###
### Extract nonzero data using the "random" method.
### This method is based on h5mread( , starts=list(start)) which retrieves
### an arbitrary/random subset of the data.
### 'j' must be an integer vector containing valid col indices. It cannot
### be NULL.
.random_extract_nonzero_data_by_col <- function(x, j)
{
data_indices <- .get_data_indices_by_col(x, j)
idx2 <- unlist(data_indices, use.names=FALSE)
data <- .get_tenx_data(x@filepath, x@group, start=idx2)
relist(data, data_indices)
}
### Extract nonzero data using the "linear" method.
### This method is based on h5mread( , starts=list(start), counts=list(count))
### which retrieves a linear subset of the data and should be more efficient
### than doing h5mread( , starts=list(seq(start, length.out=count))).
### 'j' must be NULL or an integer vector containing valid col indices. It
### should not be empty.
.linear_extract_nonzero_data_by_col <- function(x, j)
{
if (is.null(j)) {
j1 <- 1L
j2 <- ncol(x)
} else {
stopifnot(is.numeric(j), length(j) != 0L)
j1 <- min(j)
j2 <- max(j)
}
nonzero_data <- .extract_data_from_adjacent_cols(x, j1, j2)
if (is.null(j))
return(nonzero_data)
nonzero_data[match(j, j1:j2)]
}
.normarg_method <- function(method, j)
{
if (method != "auto")
return(method)
if (is.null(j))
return("linear")
if (length(j) == 0L)
return("random")
j1 <- min(j)
j2 <- max(j)
## 'ratio' is > 0 and <= 1. A value close to 1 indicates that the columns
## to extract are close from each other (a value of 1 indicating that
## they are adjacent e.g. j <- 18:25). A value close to 0 indicates that
## they are far apart from each other i.e. that they are separated by many
## columns that are not requested. The "linear" method is very efficient
## when 'ratio' is close to 1. It is so much more efficient than the
## "random" method (typically 10x or 20x faster) that we choose it when
## 'ratio' is >= 0.2
ratio <- length(j) / (j2 - j1 + 1L)
if (ratio >= 0.2) "linear" else "random"
}
### 'j' must be NULL or an integer vector containing valid col indices.
### Return a NumericList or IntegerList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
.extract_nonzero_data_by_col <-
function(x, j, method=c("auto", "random", "linear"))
{
method <- match.arg(method)
method <- .normarg_method(method, j)
if (method == "random") {
.random_extract_nonzero_data_by_col(x, j)
} else {
.linear_extract_nonzero_data_by_col(x, j)
}
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### .load_SparseArraySeed_from_TENxMatrixSeed()
###
### Load sparse data using the "random" method.
### This method is based on h5mread( , starts=list(start)) which retrieves
### an arbitrary/random subset of the data.
### 'i' must be NULL or an integer vector containing valid row indices.
### 'j' must be an integer vector containing valid col indices. It cannot
### be NULL.
### Both 'i' and 'j' can contain duplicates. Duplicates in 'i' have no effect
### on the output but duplicates in 'j' will produce duplicates in the output.
### Return a SparseArraySeed object.
.random_load_SparseArraySeed_from_TENxMatrixSeed <- function(x, i, j)
{
stopifnot(is.null(i) || is.numeric(i), is.numeric(j))
data_indices <- .get_data_indices_by_col(x, j)
idx2 <- unlist(data_indices, use.names=FALSE)
row_indices <- .get_tenx_row_indices(x@filepath, x@group, start=idx2)
col_indices <- rep.int(j, lengths(data_indices))
if (!is.null(i)) {
keep_idx <- which(row_indices %in% i)
idx2 <- idx2[keep_idx]
row_indices <- row_indices[keep_idx]
col_indices <- col_indices[keep_idx]
}
ans_nzindex <- cbind(row_indices, col_indices, deparse.level=0L)
ans_nzdata <- .get_tenx_data(x@filepath, x@group, start=idx2)
SparseArraySeed(dim(x), ans_nzindex, ans_nzdata, check=FALSE)
}
### Load sparse data using the "linear" method.
### This method is based on h5mread( , starts=list(start), counts=list(count))
### which retrieves a linear subset of the data and should be more efficient
### than doing h5mread( , starts=list(seq(start, length.out=count))).
### 'j' must be NULL or a non-empty integer vector containing valid
### col indices. The output is not affected by duplicates in 'j'.
### Return a SparseArraySeed object.
.linear_load_SparseArraySeed_from_TENxMatrixSeed <- function(x, j)
{
if (is.null(j)) {
j1 <- 1L
j2 <- ncol(x)
} else {
stopifnot(is.numeric(j), length(j) != 0L)
j1 <- min(j)
j2 <- max(j)
}
.extract_data_from_adjacent_cols(x, j1, j2, as.sparse=TRUE)
}
### Duplicates in 'index[[1]]' are ok and won't affect the output.
### Duplicates in 'index[[2]]' are ok but might introduce duplicates
### in the output so should be avoided.
### Return a SparseArraySeed object.
.load_SparseArraySeed_from_TENxMatrixSeed <-
function(x, index, method=c("auto", "random", "linear"))
{
i <- index[[1L]]
j <- index[[2L]]
method <- match.arg(method)
method <- .normarg_method(method, j)
if (method == "random") {
.random_load_SparseArraySeed_from_TENxMatrixSeed(x, i, j)
} else {
.linear_load_SparseArraySeed_from_TENxMatrixSeed(x, j)
}
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### extract_array()
###
.extract_array_from_TENxMatrixSeed <- function(x, index)
{
ans_dim <- DelayedArray:::get_Nindex_lengths(index, dim(x))
if (any(ans_dim == 0L)) {
## Return an empty matrix.
data0 <- .get_tenx_data(x@filepath, x@group, start=integer(0))
return(array(data0, dim=ans_dim))
}
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
extract_array(sas, index) # in-memory
}
setMethod("extract_array", "TENxMatrixSeed",
.extract_array_from_TENxMatrixSeed
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### chunkdim() getter
###
### Does NOT access the file.
setMethod("chunkdim", "TENxMatrixSeed", function(x) c(nrow(x), 1L))
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Constructor
###
TENxMatrixSeed <- function(filepath, group="mm10")
{
filepath <- normarg_path(filepath, "'filepath'", "10x Genomics dataset")
if (!isSingleString(group))
stop(wmsg("'group' must be a single string specifying the name ",
"of the group in the HDF5 file containing the ",
"10x Genomics data"))
if (group == "")
stop(wmsg("'group' cannot be the empty string"))
## dim
dim <- .get_tenx_shape(filepath, group)
stopifnot(length(dim) == 2L)
## dimnames
rownames <- .get_tenx_genes(filepath, group)
stopifnot(is.null(rownames) || length(rownames) == dim[[1L]])
colnames <- .get_tenx_barcodes(filepath, group)
stopifnot(is.null(colnames) || length(colnames) == dim[[2L]])
dimnames <- list(rownames, colnames)
## col_ranges
data_len <- h5length(filepath, paste0(group, "/data"))
indices_len <- h5length(filepath, paste0(group, "/indices"))
stopifnot(data_len == indices_len)
indptr <- .get_tenx_indptr(filepath, group)
stopifnot(length(indptr) == dim[[2L]] + 1L,
indptr[[1L]] == 0L,
indptr[[length(indptr)]] == data_len)
col_ranges <- data.frame(start=indptr[-length(indptr)] + 1,
width=as.integer(diff(indptr)))
new2("TENxMatrixSeed", filepath=filepath,
group=group,
dim=dim,
dimnames=dimnames,
col_ranges=col_ranges)
}
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Show
###
setMethod("show", "TENxMatrixSeed",
function(object)
{
cat(DelayedArray:::array_as_one_line_summary(object), ":\n", sep="")
cat("# dirname: ", dirname(object), "\n", sep="")
cat("# basename: ", basename(object), "\n", sep="")
cat("# group: ", object@group, "\n", sep="")
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Taking advantage of sparsity
###
setMethod("sparsity", "TENxMatrixSeed",
function(x)
{
data_len <- h5length(x@filepath, paste0(x@group, "/data"))
1 - data_len / length(x)
}
)
### This is about **structural** sparsity, not about quantitative sparsity
### measured by sparsity().
setMethod("is_sparse", "TENxMatrixSeed", function(x) TRUE)
.extract_sparse_array_from_TENxMatrixSeed <- function(x, index)
{
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
extract_sparse_array(sas, index) # in-memory
}
setMethod("extract_sparse_array", "TENxMatrixSeed",
.extract_sparse_array_from_TENxMatrixSeed
)
### The default "read_sparse_block" method defined in DelayedArray would work
### just fine on a TENxMatrixSeed object (thanks to the "extract_sparse_array"
### method for TENxMatrixSeed objects defined above), but we overwrite it with
### the method below which is slightly more efficient. That's because the
### method below calls read_sparse_block() on the SparseArraySeed object
### returned by .load_SparseArraySeed_from_TENxMatrixSeed() and this is
### faster than calling extract_sparse_array() on the same object (which
### is what the "extract_sparse_array" method for TENxMatrixSeed would do
### when called by the default "read_sparse_block" method).
### Not sure the difference is significant enough for this extra method to
### be worth it though, because, time is really dominated by I/O here, that
### is, by the call to .load_SparseArraySeed_from_TENxMatrixSeed().
.read_sparse_block_from_TENxMatrixSeed <- function(x, viewport)
{
index <- makeNindexFromArrayViewport(viewport, expand.RangeNSBS=TRUE)
sas <- .load_SparseArraySeed_from_TENxMatrixSeed(x, index) # I/O
## Unlike the "extract_sparse_array" method for TENxMatrixSeed objects
## defined above, we use read_sparse_block() here, which is faster than
## using extract_sparse_array().
read_sparse_block(sas, viewport) # in-memory
}
setMethod("read_sparse_block", "TENxMatrixSeed",
.read_sparse_block_from_TENxMatrixSeed
)
### Return a NumericList or IntegerList object parallel to 'j' i.e. with
### one list element per col index in 'j'.
### Spelling: "nonzero" preferred over "non-zero". See:
### https://gcc.gnu.org/ml/gcc/2001-10/msg00610.html
setGeneric("extractNonzeroDataByCol", signature="x",
function(x, j) standardGeneric("extractNonzeroDataByCol")
)
setMethod("extractNonzeroDataByCol", "TENxMatrixSeed",
function(x, j)
{
j <- DelayedArray:::normalizeSingleBracketSubscript2(j, ncol(x),
colnames(x))
.extract_nonzero_data_by_col(x, j)
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Coercion to dgCMatrix
###
.from_TENxMatrixSeed_to_dgCMatrix <- function(from)
{
row_indices <- .get_tenx_row_indices(from@filepath, from@group)
indptr <- .get_tenx_indptr(from@filepath, from@group)
data <- .get_tenx_data(from@filepath, from@group)
sparseMatrix(i=row_indices, p=indptr, x=data, dims=dim(from),
dimnames=dimnames(from))
}
setAs("TENxMatrixSeed", "dgCMatrix", .from_TENxMatrixSeed_to_dgCMatrix)
setAs("TENxMatrixSeed", "sparseMatrix", .from_TENxMatrixSeed_to_dgCMatrix)
|
library(ape)
testtree <- read.tree("6618_5.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="6618_5_unrooted.txt") | /codeml_files/newick_trees_processed/6618_5/rinput.R | no_license | DaniBoo/cyanobacteria_project | R | false | false | 135 | r | library(ape)
testtree <- read.tree("6618_5.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="6618_5_unrooted.txt") |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/functions.r
\name{adMCMC}
\alias{adMCMC}
\title{Estimate a continuous posterior distribution via adaptive MCMC.}
\usage{
adMCMC(inits, logLik, logPrior, nIter, nThin, logScaleMCMC = TRUE,
blocks = NULL, ...)
}
\arguments{
\item{inits}{Vector of initial parameter values. The log likelihood function and prior need to have
finite values for these parameters.}
\item{logLik}{A function giving the log likelihood value for the model.
The function should take parameter values to be estimated via an argument called pars.}
\item{logPrior}{A function giving the log prior density for the parameters.
The function should take a single argument named pars.
For parameter values that are out of range the function should return -Inf.}
\item{nIter}{The size of the MCMC sample.}
\item{nThin}{The number of thining iterations in between each saved sample.}
\item{logScaleMCMC}{If true, the MCMC is run on the log scale of all parameters. Requires that all parameters are positive.}
\item{blocks}{May be used for blocking MCMC updates. In this case a vector of integers of
the same length as inits where each unique integer represents a block.
If NULL, no blocking is applied.}
\item{...}{Further arguments passed to \code{logLik}.}
}
\value{
An \code{\link[coda]{mcmc}} object.
}
\description{
The function is intended to be used with computationally expensive likelihoods,
and little attention has been giving to speed up the adaptive MCMC algorithm itself.
The underlying algorithm is an adaptive MCMC sampler with adaptive global scaling
and using the empirical covariance matrix to generate proposals.
}
\examples{
## Simulate a cohort with four stages, sampled at 15 occasions with 30 initial individuals in
## each sample.
cdata = simMNCohort(seq(1,50, length.out=15), N0 = 30, rGammaSD,
mu = c(5, 15, 8), cv = c(.5,.5,.5), mortRate = c(0.01, 0.01,0.01,0.01),
Narg = "N")
## Define a likelihood function using the function mnLogLik and rGammaSD.
## Note that it must have the argument named 'pars'.
exLogLik = function(pars, data, nSim) {
mnLogLik(data, rStageDur = rGammaSD, mu = pars[1:3], cv = rep(pars[4], 3),
mortRate = rep(pars[5], 4), N = nSim)
}
## Define the log-prior function using bounded uniform priors.
exLogPrior = function(pars) {
mu = pars[1:3]
cv = pars[4]
mr = pars[5]
sum(log(all(c(cv < 2,mu < 30,mr < 1,cv > 0,mu > 0,mr > 0))))
}
\dontrun{
## Fit model
inits = c(10,10,10,1,.001)
names(inits) = c(paste0("mu", 1:3), "cv", "mr")
mcmc = adMCMC(inits, exLogLik, exLogPrior, nIter = 1000, nThin = 10,
data = cdata, nSim = 1000)
plot(mcmc)
}
}
| /man/adMCMC.Rd | no_license | jknape/stageFreq | R | false | false | 2,733 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/functions.r
\name{adMCMC}
\alias{adMCMC}
\title{Estimate a continuous posterior distribution via adaptive MCMC.}
\usage{
adMCMC(inits, logLik, logPrior, nIter, nThin, logScaleMCMC = TRUE,
blocks = NULL, ...)
}
\arguments{
\item{inits}{Vector of initial parameter values. The log likelihood function and prior need to have
finite values for these parameters.}
\item{logLik}{A function giving the log likelihood value for the model.
The function should take parameter values to be estimated via an argument called pars.}
\item{logPrior}{A function giving the log prior density for the parameters.
The function should take a single argument named pars.
For parameter values that are out of range the function should return -Inf.}
\item{nIter}{The size of the MCMC sample.}
\item{nThin}{The number of thining iterations in between each saved sample.}
\item{logScaleMCMC}{If true, the MCMC is run on the log scale of all parameters. Requires that all parameters are positive.}
\item{blocks}{May be used for blocking MCMC updates. In this case a vector of integers of
the same length as inits where each unique integer represents a block.
If NULL, no blocking is applied.}
\item{...}{Further arguments passed to \code{logLik}.}
}
\value{
An \code{\link[coda]{mcmc}} object.
}
\description{
The function is intended to be used with computationally expensive likelihoods,
and little attention has been giving to speed up the adaptive MCMC algorithm itself.
The underlying algorithm is an adaptive MCMC sampler with adaptive global scaling
and using the empirical covariance matrix to generate proposals.
}
\examples{
## Simulate a cohort with four stages, sampled at 15 occasions with 30 initial individuals in
## each sample.
cdata = simMNCohort(seq(1,50, length.out=15), N0 = 30, rGammaSD,
mu = c(5, 15, 8), cv = c(.5,.5,.5), mortRate = c(0.01, 0.01,0.01,0.01),
Narg = "N")
## Define a likelihood function using the function mnLogLik and rGammaSD.
## Note that it must have the argument named 'pars'.
exLogLik = function(pars, data, nSim) {
mnLogLik(data, rStageDur = rGammaSD, mu = pars[1:3], cv = rep(pars[4], 3),
mortRate = rep(pars[5], 4), N = nSim)
}
## Define the log-prior function using bounded uniform priors.
exLogPrior = function(pars) {
mu = pars[1:3]
cv = pars[4]
mr = pars[5]
sum(log(all(c(cv < 2,mu < 30,mr < 1,cv > 0,mu > 0,mr > 0))))
}
\dontrun{
## Fit model
inits = c(10,10,10,1,.001)
names(inits) = c(paste0("mu", 1:3), "cv", "mr")
mcmc = adMCMC(inits, exLogLik, exLogPrior, nIter = 1000, nThin = 10,
data = cdata, nSim = 1000)
plot(mcmc)
}
}
|
#************************************
#
# (C) Copyright IBM Corp. 2015
#
# Author: Bradley J Eck
#
#************************************
#' Epanet's Net1 Example
#'
#' A dataset created by reading the Net1.inp file
#' distributed with Epanet using this package's
#' read.inp() function.
#'
#' @name Net1rpt
#' @docType data
#' @usage Net1rpt
#' @format An object of class \code{epanet.rpt} created by \link{read.inp}.
NULL | /epanetReader/R/Net1rpt-data.r | no_license | ingted/R-Examples | R | false | false | 445 | r | #************************************
#
# (C) Copyright IBM Corp. 2015
#
# Author: Bradley J Eck
#
#************************************
#' Epanet's Net1 Example
#'
#' A dataset created by reading the Net1.inp file
#' distributed with Epanet using this package's
#' read.inp() function.
#'
#' @name Net1rpt
#' @docType data
#' @usage Net1rpt
#' @format An object of class \code{epanet.rpt} created by \link{read.inp}.
NULL |
## Load libraries ================================
library(tidyverse)
library(lubridate)
library(forecast)
## Load preprocessed data ========================
powerData <- readRDS('./output/powerData.RDS')
## Create data per week per submeter ==========================
powerWeek <- powerData %>%
group_by(year, month, day, week, weekday, day) %>%
summarise('meter1' = mean(Sub_metering_1),
'meter2' = mean(Sub_metering_2),
'meter3' = mean(Sub_metering_3),
'meternon' = mean(Sub_unnumbered)) %>%
mutate(date = as_date(paste(year, month, day, sep="-"), "%Y-%m-%d"),
totalpower = meter1 + meter2 + meter3 + meternon)
ggplot(powerWeek, aes(x = date, y = meter1)) +
geom_line()
powerWeekTS <- powerData %>%
filter(year == 2008, week %in% c(2,3,4)) %>%
mutate(date = as_date(paste(year, month, day, sep="-"), "%Y-%m-%d"),
totalpower = Global_active_power * 1000 / 60)
powerWeekTS <- ts(powerWeek$totalpower, frequency = 365,
start = c(powerWeek$year[1], powerWeek$day[1]))
library(doParallel)
## Prepare clusters =================================
cl <- makeCluster(3)
registerDoParallel(cl)
taylor <- msts(powerWeek$totalpower, seasonal.periods=c(60, 1440, 10080))
taylor.fit <- tbats(taylor)
plot(forecast(taylor.fit, h = 1440))
test <- HoltWinters(powerWeekTS)
plot(test, ylim = c(0, 25))
forecasttest <- forecast(test, h = 90)
plot(forecasttest)
#
# y <- head(powerWeek, 1000)
# View(y)
decomp <- stl(powerWeekTS, s.window="periodic")
ap.sa <- seasadj(decomp)
autoplot(cbind(powerWeekTS, SeasonallyAdjusted=ap.sa)) +
xlab("Year") + ylab("Number of passengers (thousands)")
ap.sa %>% diff() %>% ggtsdisplay(main="")
fit <- auto.arima(ap.sa)
checkresiduals(fit)
autoplot(forecast(fit))
| /IoT/R/week_profile.R | no_license | Taros007/ubiqum_projects | R | false | false | 1,807 | r | ## Load libraries ================================
library(tidyverse)
library(lubridate)
library(forecast)
## Load preprocessed data ========================
powerData <- readRDS('./output/powerData.RDS')
## Create data per week per submeter ==========================
powerWeek <- powerData %>%
group_by(year, month, day, week, weekday, day) %>%
summarise('meter1' = mean(Sub_metering_1),
'meter2' = mean(Sub_metering_2),
'meter3' = mean(Sub_metering_3),
'meternon' = mean(Sub_unnumbered)) %>%
mutate(date = as_date(paste(year, month, day, sep="-"), "%Y-%m-%d"),
totalpower = meter1 + meter2 + meter3 + meternon)
ggplot(powerWeek, aes(x = date, y = meter1)) +
geom_line()
powerWeekTS <- powerData %>%
filter(year == 2008, week %in% c(2,3,4)) %>%
mutate(date = as_date(paste(year, month, day, sep="-"), "%Y-%m-%d"),
totalpower = Global_active_power * 1000 / 60)
powerWeekTS <- ts(powerWeek$totalpower, frequency = 365,
start = c(powerWeek$year[1], powerWeek$day[1]))
library(doParallel)
## Prepare clusters =================================
cl <- makeCluster(3)
registerDoParallel(cl)
taylor <- msts(powerWeek$totalpower, seasonal.periods=c(60, 1440, 10080))
taylor.fit <- tbats(taylor)
plot(forecast(taylor.fit, h = 1440))
test <- HoltWinters(powerWeekTS)
plot(test, ylim = c(0, 25))
forecasttest <- forecast(test, h = 90)
plot(forecasttest)
#
# y <- head(powerWeek, 1000)
# View(y)
decomp <- stl(powerWeekTS, s.window="periodic")
ap.sa <- seasadj(decomp)
autoplot(cbind(powerWeekTS, SeasonallyAdjusted=ap.sa)) +
xlab("Year") + ylab("Number of passengers (thousands)")
ap.sa %>% diff() %>% ggtsdisplay(main="")
fit <- auto.arima(ap.sa)
checkresiduals(fit)
autoplot(forecast(fit))
|
library(tm)
library(topicmodels)
dtm<-readRDS("dtm.rds")
idx<-unlist(lapply(which(grepl('clinton', dtm$dimnames$Terms)), function(x) which(dtm$j %in% x)))
dtm.clinton <- dtm[ sort(unique(dtm$i[idx])),]
dtm.clinton <- removeSparseTerms(dtm.clinton,0.995)
ui.clinton = unique(dtm.clinton$i)
dtm.clinton = dtm.clinton[ui.clinton,]
idx<-unlist(lapply(which(grepl('trump', dtm$dimnames$Terms)), function(x) which(dtm$j %in% x)))
dtm.trump <- dtm[ sort(unique(dtm$i[idx])),]
dtm.trump <- removeSparseTerms(dtm.trump,0.995)
ui.trump = unique(dtm.trump$i)
dtm.trump = dtm.trump[ui.trump,]
#Set parameters for Gibbs sampling
burnin <- 4000
iter <- 6000
thin <- 20
seed <-c(1:10)
nstart <- 10
best <- TRUE
#Number of topics
k <- 20
# Clinton
lda.clinton <-LDA(dtm.clinton,k, method="Gibbs",
control=list(nstart=nstart, seed = seed,
best=best, burnin = burnin, iter = iter, thin=thin))
clinton.topics <- as.matrix(topics(lda.clinton))
clinton.terms <- as.matrix(terms(lda.clinton,20))
# Trump
lda.trump <-LDA(dtm.trump,k, method="Gibbs",
control=list(nstart=nstart, seed = seed,
best=best, burnin = burnin, iter = iter, thin=thin))
trump.topics <- as.matrix(topics(lda.trump))
trump.terms <- as.matrix(terms(lda.trump,20))
int.hist = function(x,ylab="Frequency",...) {
barplot(table(factor(x,levels=min(x):max(x))),space=0,xaxt="n",ylab=ylab,...);axis(1)
}
| /clinton_trump_analysis2.R | no_license | milkha/FBElec16 | R | false | false | 1,452 | r | library(tm)
library(topicmodels)
dtm<-readRDS("dtm.rds")
idx<-unlist(lapply(which(grepl('clinton', dtm$dimnames$Terms)), function(x) which(dtm$j %in% x)))
dtm.clinton <- dtm[ sort(unique(dtm$i[idx])),]
dtm.clinton <- removeSparseTerms(dtm.clinton,0.995)
ui.clinton = unique(dtm.clinton$i)
dtm.clinton = dtm.clinton[ui.clinton,]
idx<-unlist(lapply(which(grepl('trump', dtm$dimnames$Terms)), function(x) which(dtm$j %in% x)))
dtm.trump <- dtm[ sort(unique(dtm$i[idx])),]
dtm.trump <- removeSparseTerms(dtm.trump,0.995)
ui.trump = unique(dtm.trump$i)
dtm.trump = dtm.trump[ui.trump,]
#Set parameters for Gibbs sampling
burnin <- 4000
iter <- 6000
thin <- 20
seed <-c(1:10)
nstart <- 10
best <- TRUE
#Number of topics
k <- 20
# Clinton
lda.clinton <-LDA(dtm.clinton,k, method="Gibbs",
control=list(nstart=nstart, seed = seed,
best=best, burnin = burnin, iter = iter, thin=thin))
clinton.topics <- as.matrix(topics(lda.clinton))
clinton.terms <- as.matrix(terms(lda.clinton,20))
# Trump
lda.trump <-LDA(dtm.trump,k, method="Gibbs",
control=list(nstart=nstart, seed = seed,
best=best, burnin = burnin, iter = iter, thin=thin))
trump.topics <- as.matrix(topics(lda.trump))
trump.terms <- as.matrix(terms(lda.trump,20))
int.hist = function(x,ylab="Frequency",...) {
barplot(table(factor(x,levels=min(x):max(x))),space=0,xaxt="n",ylab=ylab,...);axis(1)
}
|
#Tracks EON_ Figure 1B, Figure 2A, Fig S5A
##Read Data EONs
timewtmengod<- read.csv2("wtmengod_EONSkin.csv", header=TRUE)
View(timewtmengod)
subtabletEON = rbind( timewtmengod[grep( "EON", timewtmengod[, 1] ), ])
View(subtabletEON)
## Read data Control
WTEON_Dis = rbind( subtabletEON[grep( "wt", subtabletEON[, 1] ), ])
View(WTEON_Dis)
## Transform values for A, C & E, and store all values for col 3 in sym
sym = list();
for( n in c( "1wt_A1EON", "1wt_A2EON", "2wt_A1EON", "2wt_A2EON", "3wt_A1EON", "3wt_A2EON", "1wt_C1EON", "1wt_C2EON", "2wt_C1EON", "2wt_C2EON", "3wt_C1EON", "3wt_C2EON", "1wt_E1EON", "1wt_E2EON", "2wt_E1EON", "2wt_E2EON", "3wt_E1EON", "3wt_E2EON" ) ){
sym[[n]] <- WTEON_Dis [WTEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1wt_B1EON", "1wt_B2EON", "2wt_B1EON", "2wt_B2EON", "3wt_B1EON", "3wt_B2EON", "1wt_D1EON", "1wt_D2EON", "2wt_D1EON", "2wt_D2EON", "3wt_D1EON", "3wt_D2EON", "1wt_F1EON", "1wt_F2EON", "2wt_F1EON", "2wt_F2EON", "3wt_F1EON", "3wt_F2EON" ) ){
sym[[n]] <- WTEON_Dis [WTEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabToPlot, varToSelect = WTEON_Dis[WTEON_Dis$Track.name == "1wt_A1EON", 2], Thr = 37, col1 = 1, col2 = 8 ){
points( tabToPlot[varToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabToPlot[varToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot WT - Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_A1EON", "1wt_A2EON", "1wt_B1EON", "1wt_B2EON", "2wt_A1EON", "2wt_A2EON", "2wt_B1EON", "2wt_B2EON", "3wt_A1EON", "3wt_A2EON", "3wt_B1EON", "3wt_B2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel WT- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_C1EON", "1wt_C2EON", "1wt_D1EON", "1wt_D2EON", "2wt_C1EON", "2wt_C2EON", "2wt_D1EON", "2wt_D2EON", "3wt_C1EON", "3wt_C2EON", "3wt_D1EON", "3wt_D2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior WT- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_E1EON", "1wt_E2EON", "1wt_F1EON", "1wt_F2EON", "2wt_E1EON", "2wt_E2EON", "2wt_F1EON", "2wt_F2EON", "3wt_E1EON", "3wt_E2EON", "3wt_F1EON", "3wt_F2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
#### Read data neurog1mut
ngEON_Dis = rbind( subtabletEON[grep( "ng", subtabletEON[, 1] ), ])
View(ngEON_Dis)
## Transform values for A, C & E, and store all values for col 3 in ngsym
ngsym = list();
for( n in c( "1ng_A1EON", "2ng_A1EON", "2ng_A2EON", "3ng_A1EON", "3ng_A2EON", "1ng_C1EON", "2ng_C1EON", "2ng_C2EON", "3ng_C1EON", "3ng_C2EON", "1ng_E1EON", "2ng_E1EON", "2ng_E2EON","2ng_F1EON", "2ng_F2EON" ) ){
ngsym[[n]] <- ngEON_Dis [ngEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1ng_B1EON", "1ng_A2EON", "1ng_B2EON", "2ng_B1EON", "2ng_B2EON", "3ng_B1EON", "3ng_B2EON", "1ng_C2EON", "1ng_D1EON", "1ng_D2EON", "2ng_D1EON", "2ng_D2EON", "3ng_D1EON", "3ng_D2EON", "1ng_E2EON", "1ng_F1EON", "1ng_F2EON", "3ng_E1EON", "3ng_E2EON","3ng_F1EON", "3ng_F2EON" ) ){
ngsym[[n]] <- ngEON_Dis [ngEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabngToPlot, varngToSelect = ngEON_Dis[ngEON_Dis$Track.name == "1ng_A1EON", 2], Thr = 37, col1 = 6, col2 = 8 ){
points( tabngToPlot[varngToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabngToPlot[varngToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_A1EON", "1ng_A2EON", "1ng_B1EON", "1ng_B2EON", "2ng_A1EON", "2ng_A2EON", "2ng_B1EON", "2ng_B2EON", "3ng_A1EON", "3ng_A2EON", "3ng_B1EON", "3ng_B2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_C1EON", "1ng_C2EON", "1ng_D1EON", "1ng_D2EON", "2ng_C1EON", "2ng_C2EON", "2ng_D1EON", "2ng_D2EON", "3ng_C1EON", "3ng_C2EON", "3ng_D1EON", "3ng_D2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_E1EON", "1ng_E2EON", "1ng_F1EON", "1ng_F2EON", "2ng_E1EON", "2ng_E2EON", "2ng_F1EON", "2ng_F2EON", "3ng_E1EON", "3ng_E2EON", "3ng_F1EON", "3ng_F2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
###Figure 2A & Figure S5A
#### Read data ody mutant
odEON_Dis = rbind( subtabletEON[grep( "od", subtabletEON[, 1] ), ])
View(odEON_Dis)
## Transform values and store all values for col 3 in odsym
odsym = list();
for( n in c( "1od_A1EON", "1od_A2EON", "2od_A1EON", "2od_A2EON","2od_B1EON", "3od_A1EON", "3od_A2EON", "1od_C1EON","1od_C2EON", "1od_D1EON", "2od_C1EON", "2od_C2EON", "3od_C1EON", "3od_C2EON", "1od_E1EON","1od_E2EON", "2od_E1EON", "2od_E2EON","3od_E1EON", "3od_E2EON" ) ){
odsym[[n]] <- odEON_Dis [odEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1od_B1EON", "1od_B2EON", "2od_B2EON", "3od_B1EON", "3od_B2EON", "1od_D2EON", "2od_D1EON", "2od_D2EON", "3od_D1EON", "3od_D2EON", "1od_F1EON", "1od_F2EON", "2od_F1EON", "2od_F2EON", "3od_F1EON", "3od_F2EON" ) ){
odsym[[n]] <- odEON_Dis [odEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabodToPlot, varodToSelect = odEON_Dis[odEON_Dis$Track.name == "1od_A1EON", 2], Thr = 37, col1 = 4, col2 = 8 ){
points( tabodToPlot[varodToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabodToPlot[varodToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot od - Figure 2A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_A1EON", "1od_A2EON", "1od_B1EON", "1od_B2EON", "2od_A1EON", "2od_A2EON", "2od_B1EON", "2od_B2EON", "3od_A1EON", "3od_A2EON", "3od_B1EON", "3od_B2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel od - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_C1EON", "1od_C2EON", "1od_D1EON", "1od_D2EON", "2od_C1EON", "2od_C2EON", "2od_D1EON", "2od_D2EON", "3od_C1EON", "3od_C2EON", "3od_D1EON", "3od_D2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior od - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_E1EON", "1od_E2EON", "1od_F1EON", "1od_F2EON", "2od_E1EON", "2od_E2EON", "2od_F1EON", "2od_F2EON", "3od_E1EON", "3od_E2EON", "3od_F1EON", "3od_F2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
#### Read data medusa (me) mutant
meEON_Dis = rbind( subtabletEON[grep( "me", subtabletEON[, 1] ), ])
View(meEON_Dis)
## Transform values and store all values for col 3 in mesym
mesym = list();
for( n in c( "1me_B1EON", "1me_B2EON", "2me_B2EON", "2me_B1EON", "3me_B1EON", "3me_B2EON", "2me_D1EON", "2me_D2EON", "3me_D1EON", "3me_D2EON", "1me_E1EON","1me_E2EON","1me_F2EON","2me_F1EON", "2me_F2EON", "3me_F1EON", "3me_F2EON" ) ){
mesym[[n]] <- meEON_Dis [meEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1me_A1EON", "1me_A2EON", "2me_A1EON", "2me_A2EON","3me_A1EON", "3me_A2EON", "1me_C1EON","1me_C2EON", "1me_D1EON", "1me_D2EON", "2me_C1EON", "2me_C2EON", "3me_C1EON", "3me_C2EON", "1me_F1EON", "2me_E1EON", "2me_E2EON", "3me_E1EON", "3me_E2EON" ) ){
mesym[[n]] <- meEON_Dis [meEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabmeToPlot, varmeToSelect = meEON_Dis[meEON_Dis$Track.name == "1me_A1EON", 2], Thr = 37, col1 = 3, col2 = 8 ){
points( tabmeToPlot[varmeToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabmeToPlot[varmeToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot me- Figure 2A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_A1EON", "1me_A2EON", "1me_B1EON", "1me_B2EON", "2me_A1EON", "2me_A2EON", "2me_B1EON", "2me_B2EON", "3me_A1EON", "3me_A2EON", "3me_B1EON", "3me_B2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel me - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_C1EON", "1me_C2EON", "1me_D1EON", "1me_D2EON", "2me_C1EON", "2me_C2EON", "2me_D1EON", "2me_D2EON", "3me_C1EON", "3me_C2EON", "3me_D1EON", "3me_D2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior me - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_E1EON", "1me_E2EON", "1me_F1EON", "1me_F2EON", "2me_E1EON", "2me_E2EON", "2me_F1EON", "2me_F2EON", "3me_E1EON", "3me_E2EON", "3me_F1EON", "3me_F2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
######@### Figure 3C
## Define plot function WT Ant EON
addPoints = function( tabToPlot, varToSelect = WTEON_Dis[WTEON_Dis$Track.name == "1wt_A1EON", 2], Thr = 37, col1 = 1, col2 = 8 ){
points( tabToPlot[varToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabToPlot[varToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot WT - Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_A1EON", "1wt_A2EON", "1wt_B1EON", "1wt_B2EON", "2wt_A1EON", "2wt_A2EON", "2wt_B1EON", "2wt_B2EON", "3wt_A1EON", "3wt_A2EON", "3wt_B1EON", "3wt_B2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Define plot function neurog1 Ant EON
addPoints = function( tabngToPlot, varngToSelect = ngEON_Dis[ngEON_Dis$Track.name == "1ng_A1EON", 2], Thr = 37, col1 = 6, col2 = 8 ){
points( tabngToPlot[varngToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabngToPlot[varngToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot2 : Anterior Plot ng- Fig 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_A1EON", "1ng_A2EON", "1ng_B1EON", "1ng_B2EON", "2ng_A1EON", "2ng_A2EON", "2ng_B1EON", "2ng_B2EON", "3ng_A1EON", "3ng_A2EON", "3ng_B1EON", "3ng_B2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
# Read data neurog1 mutant + cxcr4b = rescue
resEON_Dis <-read.csv2("res_EON_Dis.csv", header=TRUE )
View(resEON_Dis)
## Transform values and store all values for col 3 in ressym
ressym = list();
for( n in c( "1res_B1EON", "1res_B3EON" ) ){
ressym[[n]] <- resEON_Dis [resEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1res_A1EON", "1res_A4EON", "1res_A5EON", "1res_B2EON", "2res_A1EON", "2res_A2EON","3res_B1EON", "4res_B1EON", "4res_B2EON", "4res_B3EON") ){
ressym[[n]] <- resEON_Dis [resEON_Dis$Track.name == n, 3];
}
## Define plot function rescue ANT EON
addPoints = function( tabresToPlot, varresToSelect = resEON_Dis[resEON_Dis$Track.name == "1res_A1EON", 2], Thr = 37, col1 = 5, col2 = 8 ){
points( tabresToPlot[varresToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabresToPlot[varresToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot3 : Anterior Plot Res - Figure 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1res_A1EON", "1res_A4EON", "1res_A5EON","1res_B1EON", "1res_B2EON", "1res_B3EON" , "2res_A1EON", "2res_A2EON","3res_B1EON", "4res_B1EON", "4res_B2EON", "4res_B3EON") ){
addPoints( tabresToPlot = cbind( ressym[[n]], resEON_Dis[resEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
######Read data cxcr4b surexpression
surEON_Dis <-read.csv2("sur_EON_Dis.csv", header=TRUE )
View(surEON_Dis)
## Transform value and store all values in sursym
sursym = list();
for( n in c( "1sur_B1EON", "1sur_B2EON", "1sur_B3EON", "3sur_A1EON","3sur_A2EON", "3sur_A3EON", "3sur_A4EON" ) ){
sursym[[n]] <- surEON_Dis [surEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1sur_A1EON", "2sur_A1EON", "2sur_A2EON", "2sur_A4EON", "4sur_B1EON", "4sur_B2EON" ) ){
sursym[[n]] <- surEON_Dis [surEON_Dis$Track.name == n, 3];
}
## Define plot function overexpression plot 4 EON
addPoints = function( tabsurToPlot, varsurToSelect = surEON_Dis[surEON_Dis$Track.name == "1sur_A1EON", 2], Thr = 37, col1 = "lightblue", col2 = 8 ){
points( tabsurToPlot[varsurToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabsurToPlot[varsurToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot4 : Anterior Plot sur - Figure 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1sur_A1EON", "1sur_B1EON", "1sur_B2EON", "1sur_B3EON", "2sur_A1EON", "2sur_A2EON", "2sur_A4EON", "3sur_A1EON", "3sur_A2EON", "3sur_A3EON", "3sur_A4EON", "4sur_B1EON", "4sur_B2EON" ) ){
addPoints( tabsurToPlot = cbind( sursym[[n]], surEON_Dis[surEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
####
| /Figure1B_2A_S5A_3C_EONs_control_neurog1_ody_me_res_sur_Track.R | no_license | BladerLab/Aguillon_2020 | R | false | false | 14,677 | r | #Tracks EON_ Figure 1B, Figure 2A, Fig S5A
##Read Data EONs
timewtmengod<- read.csv2("wtmengod_EONSkin.csv", header=TRUE)
View(timewtmengod)
subtabletEON = rbind( timewtmengod[grep( "EON", timewtmengod[, 1] ), ])
View(subtabletEON)
## Read data Control
WTEON_Dis = rbind( subtabletEON[grep( "wt", subtabletEON[, 1] ), ])
View(WTEON_Dis)
## Transform values for A, C & E, and store all values for col 3 in sym
sym = list();
for( n in c( "1wt_A1EON", "1wt_A2EON", "2wt_A1EON", "2wt_A2EON", "3wt_A1EON", "3wt_A2EON", "1wt_C1EON", "1wt_C2EON", "2wt_C1EON", "2wt_C2EON", "3wt_C1EON", "3wt_C2EON", "1wt_E1EON", "1wt_E2EON", "2wt_E1EON", "2wt_E2EON", "3wt_E1EON", "3wt_E2EON" ) ){
sym[[n]] <- WTEON_Dis [WTEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1wt_B1EON", "1wt_B2EON", "2wt_B1EON", "2wt_B2EON", "3wt_B1EON", "3wt_B2EON", "1wt_D1EON", "1wt_D2EON", "2wt_D1EON", "2wt_D2EON", "3wt_D1EON", "3wt_D2EON", "1wt_F1EON", "1wt_F2EON", "2wt_F1EON", "2wt_F2EON", "3wt_F1EON", "3wt_F2EON" ) ){
sym[[n]] <- WTEON_Dis [WTEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabToPlot, varToSelect = WTEON_Dis[WTEON_Dis$Track.name == "1wt_A1EON", 2], Thr = 37, col1 = 1, col2 = 8 ){
points( tabToPlot[varToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabToPlot[varToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot WT - Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_A1EON", "1wt_A2EON", "1wt_B1EON", "1wt_B2EON", "2wt_A1EON", "2wt_A2EON", "2wt_B1EON", "2wt_B2EON", "3wt_A1EON", "3wt_A2EON", "3wt_B1EON", "3wt_B2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel WT- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_C1EON", "1wt_C2EON", "1wt_D1EON", "1wt_D2EON", "2wt_C1EON", "2wt_C2EON", "2wt_D1EON", "2wt_D2EON", "3wt_C1EON", "3wt_C2EON", "3wt_D1EON", "3wt_D2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior WT- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_E1EON", "1wt_E2EON", "1wt_F1EON", "1wt_F2EON", "2wt_E1EON", "2wt_E2EON", "2wt_F1EON", "2wt_F2EON", "3wt_E1EON", "3wt_E2EON", "3wt_F1EON", "3wt_F2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
#### Read data neurog1mut
ngEON_Dis = rbind( subtabletEON[grep( "ng", subtabletEON[, 1] ), ])
View(ngEON_Dis)
## Transform values for A, C & E, and store all values for col 3 in ngsym
ngsym = list();
for( n in c( "1ng_A1EON", "2ng_A1EON", "2ng_A2EON", "3ng_A1EON", "3ng_A2EON", "1ng_C1EON", "2ng_C1EON", "2ng_C2EON", "3ng_C1EON", "3ng_C2EON", "1ng_E1EON", "2ng_E1EON", "2ng_E2EON","2ng_F1EON", "2ng_F2EON" ) ){
ngsym[[n]] <- ngEON_Dis [ngEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1ng_B1EON", "1ng_A2EON", "1ng_B2EON", "2ng_B1EON", "2ng_B2EON", "3ng_B1EON", "3ng_B2EON", "1ng_C2EON", "1ng_D1EON", "1ng_D2EON", "2ng_D1EON", "2ng_D2EON", "3ng_D1EON", "3ng_D2EON", "1ng_E2EON", "1ng_F1EON", "1ng_F2EON", "3ng_E1EON", "3ng_E2EON","3ng_F1EON", "3ng_F2EON" ) ){
ngsym[[n]] <- ngEON_Dis [ngEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabngToPlot, varngToSelect = ngEON_Dis[ngEON_Dis$Track.name == "1ng_A1EON", 2], Thr = 37, col1 = 6, col2 = 8 ){
points( tabngToPlot[varngToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabngToPlot[varngToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_A1EON", "1ng_A2EON", "1ng_B1EON", "1ng_B2EON", "2ng_A1EON", "2ng_A2EON", "2ng_B1EON", "2ng_B2EON", "3ng_A1EON", "3ng_A2EON", "3ng_B1EON", "3ng_B2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_C1EON", "1ng_C2EON", "1ng_D1EON", "1ng_D2EON", "2ng_C1EON", "2ng_C2EON", "2ng_D1EON", "2ng_D2EON", "3ng_C1EON", "3ng_C2EON", "3ng_D1EON", "3ng_D2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior ng- Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_E1EON", "1ng_E2EON", "1ng_F1EON", "1ng_F2EON", "2ng_E1EON", "2ng_E2EON", "2ng_F1EON", "2ng_F2EON", "3ng_E1EON", "3ng_E2EON", "3ng_F1EON", "3ng_F2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
###Figure 2A & Figure S5A
#### Read data ody mutant
odEON_Dis = rbind( subtabletEON[grep( "od", subtabletEON[, 1] ), ])
View(odEON_Dis)
## Transform values and store all values for col 3 in odsym
odsym = list();
for( n in c( "1od_A1EON", "1od_A2EON", "2od_A1EON", "2od_A2EON","2od_B1EON", "3od_A1EON", "3od_A2EON", "1od_C1EON","1od_C2EON", "1od_D1EON", "2od_C1EON", "2od_C2EON", "3od_C1EON", "3od_C2EON", "1od_E1EON","1od_E2EON", "2od_E1EON", "2od_E2EON","3od_E1EON", "3od_E2EON" ) ){
odsym[[n]] <- odEON_Dis [odEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1od_B1EON", "1od_B2EON", "2od_B2EON", "3od_B1EON", "3od_B2EON", "1od_D2EON", "2od_D1EON", "2od_D2EON", "3od_D1EON", "3od_D2EON", "1od_F1EON", "1od_F2EON", "2od_F1EON", "2od_F2EON", "3od_F1EON", "3od_F2EON" ) ){
odsym[[n]] <- odEON_Dis [odEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabodToPlot, varodToSelect = odEON_Dis[odEON_Dis$Track.name == "1od_A1EON", 2], Thr = 37, col1 = 4, col2 = 8 ){
points( tabodToPlot[varodToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabodToPlot[varodToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot od - Figure 2A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_A1EON", "1od_A2EON", "1od_B1EON", "1od_B2EON", "2od_A1EON", "2od_A2EON", "2od_B1EON", "2od_B2EON", "3od_A1EON", "3od_A2EON", "3od_B1EON", "3od_B2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel od - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_C1EON", "1od_C2EON", "1od_D1EON", "1od_D2EON", "2od_C1EON", "2od_C2EON", "2od_D1EON", "2od_D2EON", "3od_C1EON", "3od_C2EON", "3od_D1EON", "3od_D2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior od - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1od_E1EON", "1od_E2EON", "1od_F1EON", "1od_F2EON", "2od_E1EON", "2od_E2EON", "2od_F1EON", "2od_F2EON", "3od_E1EON", "3od_E2EON", "3od_F1EON", "3od_F2EON" ) ){
addPoints( tabodToPlot = cbind( odsym[[n]], odEON_Dis[odEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
#### Read data medusa (me) mutant
meEON_Dis = rbind( subtabletEON[grep( "me", subtabletEON[, 1] ), ])
View(meEON_Dis)
## Transform values and store all values for col 3 in mesym
mesym = list();
for( n in c( "1me_B1EON", "1me_B2EON", "2me_B2EON", "2me_B1EON", "3me_B1EON", "3me_B2EON", "2me_D1EON", "2me_D2EON", "3me_D1EON", "3me_D2EON", "1me_E1EON","1me_E2EON","1me_F2EON","2me_F1EON", "2me_F2EON", "3me_F1EON", "3me_F2EON" ) ){
mesym[[n]] <- meEON_Dis [meEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1me_A1EON", "1me_A2EON", "2me_A1EON", "2me_A2EON","3me_A1EON", "3me_A2EON", "1me_C1EON","1me_C2EON", "1me_D1EON", "1me_D2EON", "2me_C1EON", "2me_C2EON", "3me_C1EON", "3me_C2EON", "1me_F1EON", "2me_E1EON", "2me_E2EON", "3me_E1EON", "3me_E2EON" ) ){
mesym[[n]] <- meEON_Dis [meEON_Dis$Track.name == n, 3];
}
## Define plot function
addPoints = function( tabmeToPlot, varmeToSelect = meEON_Dis[meEON_Dis$Track.name == "1me_A1EON", 2], Thr = 37, col1 = 3, col2 = 8 ){
points( tabmeToPlot[varmeToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabmeToPlot[varmeToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot me- Figure 2A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_A1EON", "1me_A2EON", "1me_B1EON", "1me_B2EON", "2me_A1EON", "2me_A2EON", "2me_B1EON", "2me_B2EON", "3me_A1EON", "3me_A2EON", "3me_B1EON", "3me_B2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot2 : Middel me - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_C1EON", "1me_C2EON", "1me_D1EON", "1me_D2EON", "2me_C1EON", "2me_C2EON", "2me_D1EON", "2me_D2EON", "3me_C1EON", "3me_C2EON", "3me_D1EON", "3me_D2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Plot3 : Posterior me - Fig S5A
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1me_E1EON", "1me_E2EON", "1me_F1EON", "1me_F2EON", "2me_E1EON", "2me_E2EON", "2me_F1EON", "2me_F2EON", "3me_E1EON", "3me_E2EON", "3me_F1EON", "3me_F2EON" ) ){
addPoints( tabmeToPlot = cbind( mesym[[n]], meEON_Dis[meEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
######@### Figure 3C
## Define plot function WT Ant EON
addPoints = function( tabToPlot, varToSelect = WTEON_Dis[WTEON_Dis$Track.name == "1wt_A1EON", 2], Thr = 37, col1 = 1, col2 = 8 ){
points( tabToPlot[varToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabToPlot[varToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot1 : Anterior Plot WT - Fig 1B
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1wt_A1EON", "1wt_A2EON", "1wt_B1EON", "1wt_B2EON", "2wt_A1EON", "2wt_A2EON", "2wt_B1EON", "2wt_B2EON", "3wt_A1EON", "3wt_A2EON", "3wt_B1EON", "3wt_B2EON" ) ){
addPoints( tabToPlot = cbind( sym[[n]], WTEON_Dis[WTEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
## Define plot function neurog1 Ant EON
addPoints = function( tabngToPlot, varngToSelect = ngEON_Dis[ngEON_Dis$Track.name == "1ng_A1EON", 2], Thr = 37, col1 = 6, col2 = 8 ){
points( tabngToPlot[varngToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabngToPlot[varngToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot2 : Anterior Plot ng- Fig 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1ng_A1EON", "1ng_A2EON", "1ng_B1EON", "1ng_B2EON", "2ng_A1EON", "2ng_A2EON", "2ng_B1EON", "2ng_B2EON", "3ng_A1EON", "3ng_A2EON", "3ng_B1EON", "3ng_B2EON" ) ){
addPoints( tabngToPlot = cbind( ngsym[[n]], ngEON_Dis[ngEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
# Read data neurog1 mutant + cxcr4b = rescue
resEON_Dis <-read.csv2("res_EON_Dis.csv", header=TRUE )
View(resEON_Dis)
## Transform values and store all values for col 3 in ressym
ressym = list();
for( n in c( "1res_B1EON", "1res_B3EON" ) ){
ressym[[n]] <- resEON_Dis [resEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1res_A1EON", "1res_A4EON", "1res_A5EON", "1res_B2EON", "2res_A1EON", "2res_A2EON","3res_B1EON", "4res_B1EON", "4res_B2EON", "4res_B3EON") ){
ressym[[n]] <- resEON_Dis [resEON_Dis$Track.name == n, 3];
}
## Define plot function rescue ANT EON
addPoints = function( tabresToPlot, varresToSelect = resEON_Dis[resEON_Dis$Track.name == "1res_A1EON", 2], Thr = 37, col1 = 5, col2 = 8 ){
points( tabresToPlot[varresToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabresToPlot[varresToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot3 : Anterior Plot Res - Figure 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1res_A1EON", "1res_A4EON", "1res_A5EON","1res_B1EON", "1res_B2EON", "1res_B3EON" , "2res_A1EON", "2res_A2EON","3res_B1EON", "4res_B1EON", "4res_B2EON", "4res_B3EON") ){
addPoints( tabresToPlot = cbind( ressym[[n]], resEON_Dis[resEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
######Read data cxcr4b surexpression
surEON_Dis <-read.csv2("sur_EON_Dis.csv", header=TRUE )
View(surEON_Dis)
## Transform value and store all values in sursym
sursym = list();
for( n in c( "1sur_B1EON", "1sur_B2EON", "1sur_B3EON", "3sur_A1EON","3sur_A2EON", "3sur_A3EON", "3sur_A4EON" ) ){
sursym[[n]] <- surEON_Dis [surEON_Dis$Track.name == n, 3]*-1;
}
for( n in c( "1sur_A1EON", "2sur_A1EON", "2sur_A2EON", "2sur_A4EON", "4sur_B1EON", "4sur_B2EON" ) ){
sursym[[n]] <- surEON_Dis [surEON_Dis$Track.name == n, 3];
}
## Define plot function overexpression plot 4 EON
addPoints = function( tabsurToPlot, varsurToSelect = surEON_Dis[surEON_Dis$Track.name == "1sur_A1EON", 2], Thr = 37, col1 = "lightblue", col2 = 8 ){
points( tabsurToPlot[varsurToSelect < Thr, 1:2], col = col1, lwd = 3, type = 'l' );
points( tabsurToPlot[varsurToSelect > Thr - 1, 1:2], col = col2, lwd = 3, type = 'l' );
}
## Plot4 : Anterior Plot sur - Figure 3C
plot( 1, type = "n", xlim = c( -100, 100 ), ylim = c( -100, 120 ), main = "", xlab = "", ylab = "" );
for( n in c( "1sur_A1EON", "1sur_B1EON", "1sur_B2EON", "1sur_B3EON", "2sur_A1EON", "2sur_A2EON", "2sur_A4EON", "3sur_A1EON", "3sur_A2EON", "3sur_A3EON", "3sur_A4EON", "4sur_B1EON", "4sur_B2EON" ) ){
addPoints( tabsurToPlot = cbind( sursym[[n]], surEON_Dis[surEON_Dis$Track.name == n, 4] ) );
}
abline( h = 0, col = 1 );
abline( v = 0, col = 1 );
####
|
#' Pixel_shuffle
#'
#' Rearranges elements in a tensor of shape \eqn{(*, C \times r^2, H, W)} to a
#' tensor of shape \eqn{(*, C, H \times r, W \times r)}.
#'
#' @param input (Tensor) the input tensor
#' @param upscale_factor (int) factor to increase spatial resolution by
#'
#' @export
nnf_pixel_shuffle <- function(input, upscale_factor) {
torch_pixel_shuffle(input, upscale_factor)
}
| /R/nnf-pixelshuffle.R | permissive | mlverse/torch | R | false | false | 389 | r | #' Pixel_shuffle
#'
#' Rearranges elements in a tensor of shape \eqn{(*, C \times r^2, H, W)} to a
#' tensor of shape \eqn{(*, C, H \times r, W \times r)}.
#'
#' @param input (Tensor) the input tensor
#' @param upscale_factor (int) factor to increase spatial resolution by
#'
#' @export
nnf_pixel_shuffle <- function(input, upscale_factor) {
torch_pixel_shuffle(input, upscale_factor)
}
|
rm(list = ls())
data <- read.table("household_power_consumption.txt", header = T,
sep = ";", na.strings = "?")
# convert the date variable to Date class
data$Date <- as.Date(data$Date, format = "%d/%m/%Y")
# Subset the data
data <- subset(data, subset = (Date >= "2007-02-01" & Date <= "2007-02-02"))
# Convert dates and times
data$datetime <- strptime(paste(data$Date, data$Time), "%Y-%m-%d %H:%M:%S")
# Plot 2
data$datetime <- as.POSIXct(data$datetime)
attach(data)
plot(Global_active_power ~ datetime, type = "l",
ylab = "Global Active Power (kilowatts)", xlab = "")
dev.copy(png, file = "plot2.png", height = 480, width = 480)
dev.off()
detach(data) | /plot2.R | no_license | Joel11-N/ExploratoryDataAnalisisCourseProject | R | false | false | 681 | r | rm(list = ls())
data <- read.table("household_power_consumption.txt", header = T,
sep = ";", na.strings = "?")
# convert the date variable to Date class
data$Date <- as.Date(data$Date, format = "%d/%m/%Y")
# Subset the data
data <- subset(data, subset = (Date >= "2007-02-01" & Date <= "2007-02-02"))
# Convert dates and times
data$datetime <- strptime(paste(data$Date, data$Time), "%Y-%m-%d %H:%M:%S")
# Plot 2
data$datetime <- as.POSIXct(data$datetime)
attach(data)
plot(Global_active_power ~ datetime, type = "l",
ylab = "Global Active Power (kilowatts)", xlab = "")
dev.copy(png, file = "plot2.png", height = 480, width = 480)
dev.off()
detach(data) |
## Plot 3
## try to read the data file from the working directory
data <- 0
data <- read.table('household_power_consumption.txt', header = TRUE, sep=";")
## if the file is absent, download it from the internet
if (length(data)==1) {
code <- download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
"household_power_consumption.zip")
if (code==0) {
data <- read.table(unz("household_power_consumption.zip",
"household_power_consumption.txt"), header = TRUE, sep=";")
} else {
stop("The data file cannot be downloaded!")
}
}
## get data from two days of interest
day1 <- data[data$Date=='1/2/2007',]
day2 <- data[data$Date=='2/2/2007',]
days <- rbind(day1, day2)
## create a vector for the y axis
times <- c(1:length(days$Time))
## convert data into numeric format
days$Sub_metering_1 <- as.numeric(as.character(days$Sub_metering_1))
days$Sub_metering_2 <- as.numeric(as.character(days$Sub_metering_2))
days$Sub_metering_3 <- as.numeric(days$Sub_metering_3)
# set saving the plot into a png file
png('plot3.png', width = 480, height = 480)
# draw the plot
plot(times, days$Sub_metering_1,
type="l", xlab="", ylab="Energy sub metering", axes=FALSE,
ylim=c(0,max(days$Sub_metering_1)))
lines(times, days$Sub_metering_2, col="red")
lines(times, days$Sub_metering_3, col="blue")
axis(1, pos=-1.5, at=c(0,max(times)/2,max(times)), labels=c("Thu", "Fri", "Sat"))
axis(2)
# add legend
legend(1866, 40, c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty=c(1,1),lwd=c(2.5,2.5, 2.5),col=c("black", "red","blue"))
box()
# saving is finished
dev.off() | /plot3.R | no_license | dpol2000/ExData_Plotting1 | R | false | false | 1,722 | r | ## Plot 3
## try to read the data file from the working directory
data <- 0
data <- read.table('household_power_consumption.txt', header = TRUE, sep=";")
## if the file is absent, download it from the internet
if (length(data)==1) {
code <- download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
"household_power_consumption.zip")
if (code==0) {
data <- read.table(unz("household_power_consumption.zip",
"household_power_consumption.txt"), header = TRUE, sep=";")
} else {
stop("The data file cannot be downloaded!")
}
}
## get data from two days of interest
day1 <- data[data$Date=='1/2/2007',]
day2 <- data[data$Date=='2/2/2007',]
days <- rbind(day1, day2)
## create a vector for the y axis
times <- c(1:length(days$Time))
## convert data into numeric format
days$Sub_metering_1 <- as.numeric(as.character(days$Sub_metering_1))
days$Sub_metering_2 <- as.numeric(as.character(days$Sub_metering_2))
days$Sub_metering_3 <- as.numeric(days$Sub_metering_3)
# set saving the plot into a png file
png('plot3.png', width = 480, height = 480)
# draw the plot
plot(times, days$Sub_metering_1,
type="l", xlab="", ylab="Energy sub metering", axes=FALSE,
ylim=c(0,max(days$Sub_metering_1)))
lines(times, days$Sub_metering_2, col="red")
lines(times, days$Sub_metering_3, col="blue")
axis(1, pos=-1.5, at=c(0,max(times)/2,max(times)), labels=c("Thu", "Fri", "Sat"))
axis(2)
# add legend
legend(1866, 40, c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
lty=c(1,1),lwd=c(2.5,2.5, 2.5),col=c("black", "red","blue"))
box()
# saving is finished
dev.off() |
vecteur_1 <- 1:5
vecteur_2 <- c(1, 4, 9, -2, -1, 4:9)
vecteur_3 <- c(1, 4, 9, -2, 1)
vecteur_4 <- c(1, 4, 9, NA, -1)
n_1 <- length(vecteur_1)
prod(vecteur_1)^(1 / n_1)
# Exercices ---------------------------------------------------------------
# Dans ce document, on vous fournit le script pour calculer la moyenne
# géométrique. Tâches :
# 1. Attribuer les valeurs de la moyenne géométrique à valeur_vecteur_1
# et valeur_vecteur_2.
# 2. Extraire le script dans une fonction moyenne_geo.
# 3. Exécuter les tests, et les deux premiers passent.
# 4. On désire gérer les nombres négatifs. Coder la formule au tableau.
# Le test 3 passe.
# 5. On désire ignorer les NA. Utiliser un argument de la fonction mean
# pour ignorer les NA. Le test 4 passe.
valeur_vecteur_1 <- NA
valeur_vecteur_2 <- NA
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_1), valeur_vecteur_1)
if(!isTRUE(comparaison)) {
print('Le vecteur 1 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_2), valeur_vecteur_2)
if(!isTRUE(comparaison)) {
print('Le vecteur 2 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_3), -2.352158, tolerance = 1e-5)
if(!isTRUE(comparaison)) {
print('Le vecteur 3 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_4), -2.44949, tolerance = 1e-5)
if(!isTRUE(comparaison)) {
print('Le vecteur 4 a échoué')
}
}
)
| /Exercices/partie_1/exemple_1.R | no_license | vigou3/tests-automatises-en-r | R | false | false | 1,598 | r | vecteur_1 <- 1:5
vecteur_2 <- c(1, 4, 9, -2, -1, 4:9)
vecteur_3 <- c(1, 4, 9, -2, 1)
vecteur_4 <- c(1, 4, 9, NA, -1)
n_1 <- length(vecteur_1)
prod(vecteur_1)^(1 / n_1)
# Exercices ---------------------------------------------------------------
# Dans ce document, on vous fournit le script pour calculer la moyenne
# géométrique. Tâches :
# 1. Attribuer les valeurs de la moyenne géométrique à valeur_vecteur_1
# et valeur_vecteur_2.
# 2. Extraire le script dans une fonction moyenne_geo.
# 3. Exécuter les tests, et les deux premiers passent.
# 4. On désire gérer les nombres négatifs. Coder la formule au tableau.
# Le test 3 passe.
# 5. On désire ignorer les NA. Utiliser un argument de la fonction mean
# pour ignorer les NA. Le test 4 passe.
valeur_vecteur_1 <- NA
valeur_vecteur_2 <- NA
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_1), valeur_vecteur_1)
if(!isTRUE(comparaison)) {
print('Le vecteur 1 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_2), valeur_vecteur_2)
if(!isTRUE(comparaison)) {
print('Le vecteur 2 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_3), -2.352158, tolerance = 1e-5)
if(!isTRUE(comparaison)) {
print('Le vecteur 3 a échoué')
}
}
)
tryCatch(
{
comparaison <- all.equal(moyenne_geo(vecteur_4), -2.44949, tolerance = 1e-5)
if(!isTRUE(comparaison)) {
print('Le vecteur 4 a échoué')
}
}
)
|
#predicting photosythesis
#A = mmol CO2 m-2 s-1
#A=7.309*log(height) - 23.612 #note that log in R is ln
#equation for total leaf area:van pelt et al 2016?
#LA = x*height....blah
#y = -0.0819x2 + 10.867x - 176.95#stand in
####### calculate estimated productivity given different potential G fractions across all heights based on total LA and photosythesis (TSF-respiration penalty)
######Make a function and calculate a z matrix
productivity <- function(height, G_frac){
((-0.0819*height^2) + (10.867*height) - 176.95)*((7.309*log(height) - 23.612)*(1/G_frac))
} #the function "productivity" calculates the estimated range of photosynthetic output across the leaf investment space (all combos of height and G fraction)
photosynth <- outer(G_frac,height,productivity)#outer() function applies the function "productivity" at every combination of gs and VPD. Seconds is the z axis (a matrix)
rownames(photosynth) = height
colnames(photosynth) = G_frac
class(photosynth)
####### calculate uptake (hydraulic flux)given different potential G fractions across all heights
######Make a function and calculate a z matrix
#uptake for L leaves = 0.0017*height^2 - 0.1135*height + 5.8981
#uptake for G leaves = 22.85 g/m^2/min
up <- function(height, G_frac){
((0.0017*height^2 - 0.1135*height + 5.8981)*((-0.0819*height^2 + 10.867*height - 176.95)*(1/G_frac)))+(22.85*((-0.0819*height^2 + 10.867*height - 176.95)*G_frac))
} #the function "up" calculates the estimated range of foliar water uptake across the leaf investment space (all combos of height and G fraction)
uptake <- outer(G_frac,height,productivity)#outer() function applies the function "up" at every combination of gs and VPD. Seconds is the z axis (a matrix)
| /scripts/paper 2/photosyth and uptake functions.R | no_license | alanaroseo/fogdata | R | false | false | 1,733 | r |
#predicting photosythesis
#A = mmol CO2 m-2 s-1
#A=7.309*log(height) - 23.612 #note that log in R is ln
#equation for total leaf area:van pelt et al 2016?
#LA = x*height....blah
#y = -0.0819x2 + 10.867x - 176.95#stand in
####### calculate estimated productivity given different potential G fractions across all heights based on total LA and photosythesis (TSF-respiration penalty)
######Make a function and calculate a z matrix
productivity <- function(height, G_frac){
((-0.0819*height^2) + (10.867*height) - 176.95)*((7.309*log(height) - 23.612)*(1/G_frac))
} #the function "productivity" calculates the estimated range of photosynthetic output across the leaf investment space (all combos of height and G fraction)
photosynth <- outer(G_frac,height,productivity)#outer() function applies the function "productivity" at every combination of gs and VPD. Seconds is the z axis (a matrix)
rownames(photosynth) = height
colnames(photosynth) = G_frac
class(photosynth)
####### calculate uptake (hydraulic flux)given different potential G fractions across all heights
######Make a function and calculate a z matrix
#uptake for L leaves = 0.0017*height^2 - 0.1135*height + 5.8981
#uptake for G leaves = 22.85 g/m^2/min
up <- function(height, G_frac){
((0.0017*height^2 - 0.1135*height + 5.8981)*((-0.0819*height^2 + 10.867*height - 176.95)*(1/G_frac)))+(22.85*((-0.0819*height^2 + 10.867*height - 176.95)*G_frac))
} #the function "up" calculates the estimated range of foliar water uptake across the leaf investment space (all combos of height and G fraction)
uptake <- outer(G_frac,height,productivity)#outer() function applies the function "up" at every combination of gs and VPD. Seconds is the z axis (a matrix)
|
prepare_dumbell <- function(graph_type){
if(graph_type == 'top'){
data_for_graph <- data_for_analysis %>% arrange(`Budget Difference`) %>% tail(10)
} else {
data_for_graph <- data_for_analysis %>% arrange(`Budget Difference`) %>% head(10)
}
graph_for_analysis <- data_for_graph
graph_for_analysis$Particulars <- factor(graph_for_analysis$Particulars, levels = as.character(graph_for_analysis$Particulars))
grapher <- ggplot(graph_for_analysis, aes(x=`Actual 2018-2019 Total`, xend=`Budget 2020-2021 Total`, y=Particulars, group=Particulars)) +
geom_dumbbell(color="#0e668b",
size=0.75,
point.colour.l="#0e668b") +
scale_x_continuous() +
labs(x=NULL,
y=NULL,
title="Budget Allocation (In Crores): 2018/19 vs 2020/21",
subtitle=stringr::str_to_title(glue::glue("{graph_type} 20 departments in terms of increase in budget allocation",
caption="Source: OpenBudgetsIndia"))) +
theme(plot.title = element_text(hjust=0.5, face="bold"),
plot.background=element_rect(fill="#f7f7f7"),
panel.background=element_rect(fill="#f7f7f7"),
panel.grid.minor=element_blank(),
panel.grid.major.y=element_blank(),
panel.grid.major.x=element_line(),
axis.ticks=element_blank(),
legend.position="top",
panel.border=element_blank())
return(grapher)
}
| /utils/helpers.R | no_license | apoorv74/OBI-data-explorer | R | false | false | 1,436 | r | prepare_dumbell <- function(graph_type){
if(graph_type == 'top'){
data_for_graph <- data_for_analysis %>% arrange(`Budget Difference`) %>% tail(10)
} else {
data_for_graph <- data_for_analysis %>% arrange(`Budget Difference`) %>% head(10)
}
graph_for_analysis <- data_for_graph
graph_for_analysis$Particulars <- factor(graph_for_analysis$Particulars, levels = as.character(graph_for_analysis$Particulars))
grapher <- ggplot(graph_for_analysis, aes(x=`Actual 2018-2019 Total`, xend=`Budget 2020-2021 Total`, y=Particulars, group=Particulars)) +
geom_dumbbell(color="#0e668b",
size=0.75,
point.colour.l="#0e668b") +
scale_x_continuous() +
labs(x=NULL,
y=NULL,
title="Budget Allocation (In Crores): 2018/19 vs 2020/21",
subtitle=stringr::str_to_title(glue::glue("{graph_type} 20 departments in terms of increase in budget allocation",
caption="Source: OpenBudgetsIndia"))) +
theme(plot.title = element_text(hjust=0.5, face="bold"),
plot.background=element_rect(fill="#f7f7f7"),
panel.background=element_rect(fill="#f7f7f7"),
panel.grid.minor=element_blank(),
panel.grid.major.y=element_blank(),
panel.grid.major.x=element_line(),
axis.ticks=element_blank(),
legend.position="top",
panel.border=element_blank())
return(grapher)
}
|
#' Gets a table's sample
#'
#' @param con connection
#' @param tbl table name
#' @param column optional column name to apply filter
#' @param value optional column value to apply filter
#' @param n number of rows, defaults to 10.
#'
#' @return a data.frame sample according to specified arguements.
#' @export
#'
psql_stj_sample <- function(con, tbl = NULL, column = NULL, value = NULL, n = 10) {
if (is.null(column)) {
query <- glue::glue_sql("SElECT *
FROM {`tbl`}
ORDER BY random() LIMIT {n}",
.con = con
)
} else {
query <- glue::glue_sql("SElECT *
FROM {`tbl`}
WHERE {`tbl`}.{`column`} = {value}
ORDER BY random() LIMIT {n}",
.con = con
)
}
DBI::dbGetQuery(con, query)
}
| /R/psql_stj_sample.R | permissive | thgmoraes/stj | R | false | false | 866 | r | #' Gets a table's sample
#'
#' @param con connection
#' @param tbl table name
#' @param column optional column name to apply filter
#' @param value optional column value to apply filter
#' @param n number of rows, defaults to 10.
#'
#' @return a data.frame sample according to specified arguements.
#' @export
#'
psql_stj_sample <- function(con, tbl = NULL, column = NULL, value = NULL, n = 10) {
if (is.null(column)) {
query <- glue::glue_sql("SElECT *
FROM {`tbl`}
ORDER BY random() LIMIT {n}",
.con = con
)
} else {
query <- glue::glue_sql("SElECT *
FROM {`tbl`}
WHERE {`tbl`}.{`column`} = {value}
ORDER BY random() LIMIT {n}",
.con = con
)
}
DBI::dbGetQuery(con, query)
}
|
library(spectrolab)
### Name: max.spectra
### Title: Maximum reflectance
### Aliases: max.spectra
### ** Examples
library(spectrolab)
spec = as.spectra(spec_matrix_example, name_idx = 1)
max(spec)
| /data/genthat_extracted_code/spectrolab/examples/max.spectra.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 204 | r | library(spectrolab)
### Name: max.spectra
### Title: Maximum reflectance
### Aliases: max.spectra
### ** Examples
library(spectrolab)
spec = as.spectra(spec_matrix_example, name_idx = 1)
max(spec)
|
variable_assignment <- 'Data can be in double quotations'
# Single line comment
"Multi line"
assign('variable_assignment2',3)
# keep in quotes if not declared
# new environment
my.environment <- new.env()
assign('var1',10, my.environment)
my.environment$var2 = 1
my.environment1 <- new.env()
assign('var1',10, my.environment1)
my.environment1$var2 = 1
india <-new.env()
assign("temp",30,india)
india[["var_4"]]=10
# maths
abs()
log()
exp()
factorial()
## Special constants
pi
## Special Numbers
# Inf -Inf
## Infinity and -Infininty
# NaN
## Not a number
#
months <- c('Item1', 'Item2')
## Types of operations on vector
## Type 1: vector -> gives a single value
## Type 2: vector -> operation on each item of vector
## Type 3: multiple vector -> pairwise operation
### Eg: Addition, ==
## Complex Numbers
a <- 1 + 6i
b <- 6 + 2i
a + b
## Factors of a set
a <- c(1,2,3,4,5,1,2,3)
b <- factor(a)
b
c <- c(a,a,a,a)
c
c = data.frame(a,a,a,a)
c
BMI = data.frame(
gender = c('M','F'),
height = c(1,2)
)
BMI
| /Language/R/Suyog_R.R | permissive | ankschoubey/Personal-Developer-Notes | R | false | false | 1,040 | r |
variable_assignment <- 'Data can be in double quotations'
# Single line comment
"Multi line"
assign('variable_assignment2',3)
# keep in quotes if not declared
# new environment
my.environment <- new.env()
assign('var1',10, my.environment)
my.environment$var2 = 1
my.environment1 <- new.env()
assign('var1',10, my.environment1)
my.environment1$var2 = 1
india <-new.env()
assign("temp",30,india)
india[["var_4"]]=10
# maths
abs()
log()
exp()
factorial()
## Special constants
pi
## Special Numbers
# Inf -Inf
## Infinity and -Infininty
# NaN
## Not a number
#
months <- c('Item1', 'Item2')
## Types of operations on vector
## Type 1: vector -> gives a single value
## Type 2: vector -> operation on each item of vector
## Type 3: multiple vector -> pairwise operation
### Eg: Addition, ==
## Complex Numbers
a <- 1 + 6i
b <- 6 + 2i
a + b
## Factors of a set
a <- c(1,2,3,4,5,1,2,3)
b <- factor(a)
b
c <- c(a,a,a,a)
c
c = data.frame(a,a,a,a)
c
BMI = data.frame(
gender = c('M','F'),
height = c(1,2)
)
BMI
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Rfunctions_GC.R
\name{krig.mh.stx_carA_smple}
\alias{krig.mh.stx_carA_smple}
\title{Inverse-Gamma DLM MCMC Simulation}
\usage{
krig.mh.stx_carA_smple(i, datpred, parms, cov_names, linmod, nsize = 800)
}
\arguments{
\item{dat}{vector of length \emph{T} containing a time series of VI data}
}
\value{
List containing MCMC samples of \eqn{\sigma_e^2}, \eqn{\sigma_w^2}, and \eqn{\theta_{1:T}} (if \code{save.theta = TRUE})
\describe{
\item{sigma2s:}{mc x 2 matrix containing MCMC samples of \eqn{\sigma_e^2} (first column) and \eqn{\sigma_w^2} (second column)}
\item{thetas:}{if \code{save_value = TRUE}, a T x p x mc array containing MCMC samples of the latent states}
}
}
\description{
MCMC simulation of the reduced Fourier-form Bayesian DLM with conjugate Inverse-Gamma priors.
}
\details{
blah blah blah
}
| /man/krig.mh.stx_carA_smple.Rd | no_license | reich-group/Google-Car | R | false | true | 887 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Rfunctions_GC.R
\name{krig.mh.stx_carA_smple}
\alias{krig.mh.stx_carA_smple}
\title{Inverse-Gamma DLM MCMC Simulation}
\usage{
krig.mh.stx_carA_smple(i, datpred, parms, cov_names, linmod, nsize = 800)
}
\arguments{
\item{dat}{vector of length \emph{T} containing a time series of VI data}
}
\value{
List containing MCMC samples of \eqn{\sigma_e^2}, \eqn{\sigma_w^2}, and \eqn{\theta_{1:T}} (if \code{save.theta = TRUE})
\describe{
\item{sigma2s:}{mc x 2 matrix containing MCMC samples of \eqn{\sigma_e^2} (first column) and \eqn{\sigma_w^2} (second column)}
\item{thetas:}{if \code{save_value = TRUE}, a T x p x mc array containing MCMC samples of the latent states}
}
}
\description{
MCMC simulation of the reduced Fourier-form Bayesian DLM with conjugate Inverse-Gamma priors.
}
\details{
blah blah blah
}
|
nullirwsva = function (dat, n.sv, B = 5)
{
n <- ncol(dat)
m <- nrow(dat)
mod = as.matrix(rep(1, n), ncol = 1)
Id <- diag(n)
resid <- dat %*% (Id - mod %*% solve(t(mod) %*% mod) %*%
t(mod))
uu <- eigen(t(resid) %*% resid)
vv <- uu$vectors
ndf <- n - dim(mod)[2]
pprob <- rep(1, m)
one <- rep(1, n)
Id <- diag(n)
df1 <- dim(mod)[2] + n.sv
rm(resid)
cat(paste("Iteration (out of", B, "):"))
for (i in 1:B) {
mod.gam <- cbind(mod, uu$vectors[, 1:n.sv])
mod0.gam <- cbind(mod)
ptmp <- f.pvalue(dat, mod.gam, mod0.gam)
pprob.gam <- (1 - edge.lfdr(ptmp))
dats <- dat * pprob.gam
uu <- eigen(t(dats) %*% dats)
cat(paste(i, " "))
}
sv = svd(dats)$v[, 1:n.sv]
retval <- list(sv = sv, pprob.gam = pprob.gam,
n.sv = n.sv)
return(retval)
}
| /Mengjie/single_cell/R/nullirwsva.R | no_license | MeileiJiang/robust-against-heterogeneity | R | false | false | 837 | r | nullirwsva = function (dat, n.sv, B = 5)
{
n <- ncol(dat)
m <- nrow(dat)
mod = as.matrix(rep(1, n), ncol = 1)
Id <- diag(n)
resid <- dat %*% (Id - mod %*% solve(t(mod) %*% mod) %*%
t(mod))
uu <- eigen(t(resid) %*% resid)
vv <- uu$vectors
ndf <- n - dim(mod)[2]
pprob <- rep(1, m)
one <- rep(1, n)
Id <- diag(n)
df1 <- dim(mod)[2] + n.sv
rm(resid)
cat(paste("Iteration (out of", B, "):"))
for (i in 1:B) {
mod.gam <- cbind(mod, uu$vectors[, 1:n.sv])
mod0.gam <- cbind(mod)
ptmp <- f.pvalue(dat, mod.gam, mod0.gam)
pprob.gam <- (1 - edge.lfdr(ptmp))
dats <- dat * pprob.gam
uu <- eigen(t(dats) %*% dats)
cat(paste(i, " "))
}
sv = svd(dats)$v[, 1:n.sv]
retval <- list(sv = sv, pprob.gam = pprob.gam,
n.sv = n.sv)
return(retval)
}
|
library(shiny)
require(RJSONIO)
require(ggplot2)
require(scales)
# This function is used to access the SODA API and download the data from the NJ Open
# Data website. The funtion takes the year as an input and downloads the first 1000
# entries.
GetData<- function(n){
# Returns the dataframe from the download. The dataframe is 1000 rows by 1 column.
# The filters are applied by the string after the '?'. The 'master' record type
# was used because it had the YTD earnings. The alternative was to use the
# 'detail' record type which had current period payroll entries.
URL<-paste("http://data.nj.gov/resource/iqwc-r2w7.json?record_type=master&calendar_year=",n,sep="")
RawData<-fromJSON(URL)
Temp<- data.frame(salary= as.numeric(sapply(RawData,"[[","master_ytd_earnings")),
stringsAsFactors = F)
L<-list(mu=mean(Temp$salary),
SD=sd(Temp$salary),
values=Temp$salary)
L
}
shinyServer(
function(input,output){
# Use reactive functions so the data gets re-queried whenever the year input
# is changed by the user.
Data<- reactive({GetData({input$year})})
output$mu <- renderText(Data()$mu)
output$SD <- renderText((Data()[2]))
# Need to use reactive again to update these values when the data set
# is queried.
Norm<-reactive({
xval<- seq(0,150000,length=10000)
yval<- dnorm(xval,mean=as.numeric(Data()$mu), sd=as.numeric(Data()$SD))
data.frame(x=xval,y=yval)
})
# Calculate the theoretical percentile the user's salary would
# fall into. Use theoretical percentile as a statistical
# inference of where they user would fall.
Percentile<- reactive(pnorm(as.numeric({input$isalary}),
mean=as.numeric(unlist(Data()[1])),
sd=as.numeric(Data()[2])
))
# Draw the plot. The vertical line is the user's salary.
output$plot1<- renderPlot({
ggplot(Norm()) +
aes(x=x,y=y) +
geom_line(size=1.5) +
labs(title="Distribution of Salaries for New Jersey State Employees",
y= element_blank()) +
scale_x_continuous(labels=comma, limits=c(0,150000)) +
geom_vline(xintercept={input$isalary}, colour="blue", size=2) +
theme(plot.title=element_text(size=rel(2)),
axis.text.y=element_blank(),
panel.background=element_rect(fill="white",colour="black"),
panel.grid.major=element_blank(),
panel.grid.minor= element_blank())
})
# Craft the sentence at the bottom of the chart. The statement includes
# a binary value indicating if the user's salary is greater than or less
# than the mean. It also gives the mean salary during the year.
Bigger<-reactive({ifelse({input$isalary}< as.numeric(Data()[1]),"less","more")})
output$statement<- renderText(paste("Your salary is ",
Bigger()[1],
" than the average NJ state worker salary in ",
{input$year},
". The average pay that year was $",
format(round(as.numeric(Data()[1]),0),big.mark = ","),
" and you made more money than ",
round(Percentile(),3)*100,
"% of the state employees. Congrats!",
sep=""))
}
)
| /server.R | no_license | kuhnrl30/NJPayroll | R | false | false | 3,651 | r | library(shiny)
require(RJSONIO)
require(ggplot2)
require(scales)
# This function is used to access the SODA API and download the data from the NJ Open
# Data website. The funtion takes the year as an input and downloads the first 1000
# entries.
GetData<- function(n){
# Returns the dataframe from the download. The dataframe is 1000 rows by 1 column.
# The filters are applied by the string after the '?'. The 'master' record type
# was used because it had the YTD earnings. The alternative was to use the
# 'detail' record type which had current period payroll entries.
URL<-paste("http://data.nj.gov/resource/iqwc-r2w7.json?record_type=master&calendar_year=",n,sep="")
RawData<-fromJSON(URL)
Temp<- data.frame(salary= as.numeric(sapply(RawData,"[[","master_ytd_earnings")),
stringsAsFactors = F)
L<-list(mu=mean(Temp$salary),
SD=sd(Temp$salary),
values=Temp$salary)
L
}
shinyServer(
function(input,output){
# Use reactive functions so the data gets re-queried whenever the year input
# is changed by the user.
Data<- reactive({GetData({input$year})})
output$mu <- renderText(Data()$mu)
output$SD <- renderText((Data()[2]))
# Need to use reactive again to update these values when the data set
# is queried.
Norm<-reactive({
xval<- seq(0,150000,length=10000)
yval<- dnorm(xval,mean=as.numeric(Data()$mu), sd=as.numeric(Data()$SD))
data.frame(x=xval,y=yval)
})
# Calculate the theoretical percentile the user's salary would
# fall into. Use theoretical percentile as a statistical
# inference of where they user would fall.
Percentile<- reactive(pnorm(as.numeric({input$isalary}),
mean=as.numeric(unlist(Data()[1])),
sd=as.numeric(Data()[2])
))
# Draw the plot. The vertical line is the user's salary.
output$plot1<- renderPlot({
ggplot(Norm()) +
aes(x=x,y=y) +
geom_line(size=1.5) +
labs(title="Distribution of Salaries for New Jersey State Employees",
y= element_blank()) +
scale_x_continuous(labels=comma, limits=c(0,150000)) +
geom_vline(xintercept={input$isalary}, colour="blue", size=2) +
theme(plot.title=element_text(size=rel(2)),
axis.text.y=element_blank(),
panel.background=element_rect(fill="white",colour="black"),
panel.grid.major=element_blank(),
panel.grid.minor= element_blank())
})
# Craft the sentence at the bottom of the chart. The statement includes
# a binary value indicating if the user's salary is greater than or less
# than the mean. It also gives the mean salary during the year.
Bigger<-reactive({ifelse({input$isalary}< as.numeric(Data()[1]),"less","more")})
output$statement<- renderText(paste("Your salary is ",
Bigger()[1],
" than the average NJ state worker salary in ",
{input$year},
". The average pay that year was $",
format(round(as.numeric(Data()[1]),0),big.mark = ","),
" and you made more money than ",
round(Percentile(),3)*100,
"% of the state employees. Congrats!",
sep=""))
}
)
|
\name{psmelt}
\alias{psmelt}
\title{Melt phyloseq data object into large data.frame}
\usage{
psmelt(physeq)
}
\arguments{
\item{physeq}{(Required). An
\code{\link{otu_table-class}} or
\code{\link{phyloseq-class}}. Function most useful for
phyloseq-class.}
}
\value{
A \code{\link{data.frame}}-class table.
}
\description{
The psmelt function is a specialized melt function for
melting phyloseq objects (instances of the phyloseq
class), usually for the purpose of graphics production in
ggplot2-based phyloseq-generated graphics. It relies
heavily on the \code{\link[reshape]{melt}} and
\code{\link{merge}} functions. Note that ``melted''
phyloseq data is stored much less efficiently, and so RAM
storage issues could arise with a smaller dataset
(smaller number of samples/OTUs/variables) than one might
otherwise expect. For average-sized datasets, however,
this should not be a problem. Because the number of OTU
entries has a large effect on the RAM requirement,
methods to reduce the number of separate OTU entries, for
instance by agglomerating based on phylogenetic distance
using \code{\link{tipglom}}, can help alleviate RAM usage
problems. This function is made user-accessible for
flexibility, but is also used extensively by plot
functions in phyloseq.
}
\examples{
data("GlobalPatterns")
gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
mdf = psmelt(gp.ch)
nrow(mdf)
ncol(mdf)
colnames(mdf)
head(rownames(mdf))
# Create a ggplot similar to
library("ggplot2")
p = ggplot(mdf, aes(x=SampleType, y=Abundance, fill=Genus))
p = p + geom_bar(color="black", stat="identity", position="stack")
print(p)
}
\seealso{
\code{\link{plot_bar}}
\code{\link[reshape]{melt}}
\code{\link{merge}}
}
| /man/psmelt.Rd | no_license | BioinformaticsArchive/phyloseq | R | false | false | 1,761 | rd | \name{psmelt}
\alias{psmelt}
\title{Melt phyloseq data object into large data.frame}
\usage{
psmelt(physeq)
}
\arguments{
\item{physeq}{(Required). An
\code{\link{otu_table-class}} or
\code{\link{phyloseq-class}}. Function most useful for
phyloseq-class.}
}
\value{
A \code{\link{data.frame}}-class table.
}
\description{
The psmelt function is a specialized melt function for
melting phyloseq objects (instances of the phyloseq
class), usually for the purpose of graphics production in
ggplot2-based phyloseq-generated graphics. It relies
heavily on the \code{\link[reshape]{melt}} and
\code{\link{merge}} functions. Note that ``melted''
phyloseq data is stored much less efficiently, and so RAM
storage issues could arise with a smaller dataset
(smaller number of samples/OTUs/variables) than one might
otherwise expect. For average-sized datasets, however,
this should not be a problem. Because the number of OTU
entries has a large effect on the RAM requirement,
methods to reduce the number of separate OTU entries, for
instance by agglomerating based on phylogenetic distance
using \code{\link{tipglom}}, can help alleviate RAM usage
problems. This function is made user-accessible for
flexibility, but is also used extensively by plot
functions in phyloseq.
}
\examples{
data("GlobalPatterns")
gp.ch = subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
mdf = psmelt(gp.ch)
nrow(mdf)
ncol(mdf)
colnames(mdf)
head(rownames(mdf))
# Create a ggplot similar to
library("ggplot2")
p = ggplot(mdf, aes(x=SampleType, y=Abundance, fill=Genus))
p = p + geom_bar(color="black", stat="identity", position="stack")
print(p)
}
\seealso{
\code{\link{plot_bar}}
\code{\link[reshape]{melt}}
\code{\link{merge}}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pltltn.R
\name{pltltn}
\alias{pltltn}
\title{Correction for p>>n for an object of class \code{Bvs}}
\usage{
pltltn(object)
}
\arguments{
\item{object}{An object of class \code{Bvs} obtained with \code{GibbsBvs}}
}
\value{
\code{pltltn} returns a list with the following elements:
\item{pS }{An estimation of the probability that the true model is irregular (k>n)}
\item{postprobdim }{A corrected estimation of the posterior probabilities over the dimensions}
\item{inclprob }{A corrected estimation of the posterior inclusion probabilities}
}
\description{
In cases where p>>n and the true model is expected to be sparse, it is very unlikely that the Gibbs sampling
will sample models in the singular subset of the model space (models with k>n). Nevertheless, depending on
how large is p/n and the strenght of the signal, this part of the model space could be very influential in the
final response.
}
\details{
From an object created with GibbsBvs and prior probabilities specified as Scott-Berger,
this function provides an estimation of the posterior probability of models with k>n which is a measure of the
importance of these models. In summary, when this probability is large, the sample size is not large enough to beat
such large p.
Additionally, \code{pltltn} gives corrections of the posterior inclusion probabilities and posterior probabilities
of dimension of the true model.
}
\examples{
\dontrun{
data(riboflavin, package="hdi")
set.seed(16091956)
#the following sentence took 37.3 minutes in a single core
#(a trick to see the evolution of the algorithm is to monitor
#the files created by the function.
#you can see the working directory running
#tempdir()
#copy this path in the clipboard. Then open another R session
#and from there (once the simulating process is running and the burnin has completed)
#write
#system("wc (path from clipboard)/AllBF")
#the number here appearing is the number of completed iterations
#
testRB<- GibbsBvs(formula=y~.,
data=riboflavin,
prior.betas="Robust",
init.model="null",
time.test=F,
n.iter=10000,
n.burnin=1000)
set.seed(16091956)
system.time(
testRB<- GibbsBvs(formula=y~.,
data=riboflavin,
prior.betas="Robust",
init.model="null",
time.test=F,
n.iter=10000,
n.burnin=1000)
)
#notice the large sparsity of the result since
#the great majority of covariates are not influential:
boxplot(testRB$inclprob)
testRB$inclprob[testRB$inclprob>.5]
#xYOAB_at xYXLE_at
# 0.9661 0.6502
#we can discharge all covariates except xYOAB_at and xYXLE_at
#the method does not reach to inform about xYXLE_at and its posterior
#probability is only slightly bigger than its prior probability
#We see that dimensions of visited models are small:
plot(testRB, option="d", xlim=c(0,100))
#so the part of the model space with singular models (k>n)
#has not been explored.
#To correct this issue we run:
corrected.testRB<- pltltn(testRB)
#Estimate of the posterior probability of the
# model space with singular models is: 0
#Meaning that it is extremely unlikely that the true model is such that k>n
#The corrected inclusion probabilities can be accessed through
#corrected.testRB but, in this case, these are essentially the same as in the
#original object (due to the unimportance of the singular part of the model space)
}
}
\references{
Berger, J.O., Garcia-Donato, G., Martínez-Beneito M.A. and Peña, V. (2016)
Bayesian variable selection in high dimensional problems without assumptions on prior model probabilities.
arXiv:1607.02993
}
\seealso{
See
\code{\link[BayesVarSel]{GibbsBvs}} for creating objects of the class
\code{Bvs}.
}
\author{
Gonzalo Garcia-Donato
Maintainer: <gonzalo.garciadonato@uclm.es>
}
| /man/pltltn.Rd | no_license | comodin19/BayesVarSel | R | false | true | 3,959 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pltltn.R
\name{pltltn}
\alias{pltltn}
\title{Correction for p>>n for an object of class \code{Bvs}}
\usage{
pltltn(object)
}
\arguments{
\item{object}{An object of class \code{Bvs} obtained with \code{GibbsBvs}}
}
\value{
\code{pltltn} returns a list with the following elements:
\item{pS }{An estimation of the probability that the true model is irregular (k>n)}
\item{postprobdim }{A corrected estimation of the posterior probabilities over the dimensions}
\item{inclprob }{A corrected estimation of the posterior inclusion probabilities}
}
\description{
In cases where p>>n and the true model is expected to be sparse, it is very unlikely that the Gibbs sampling
will sample models in the singular subset of the model space (models with k>n). Nevertheless, depending on
how large is p/n and the strenght of the signal, this part of the model space could be very influential in the
final response.
}
\details{
From an object created with GibbsBvs and prior probabilities specified as Scott-Berger,
this function provides an estimation of the posterior probability of models with k>n which is a measure of the
importance of these models. In summary, when this probability is large, the sample size is not large enough to beat
such large p.
Additionally, \code{pltltn} gives corrections of the posterior inclusion probabilities and posterior probabilities
of dimension of the true model.
}
\examples{
\dontrun{
data(riboflavin, package="hdi")
set.seed(16091956)
#the following sentence took 37.3 minutes in a single core
#(a trick to see the evolution of the algorithm is to monitor
#the files created by the function.
#you can see the working directory running
#tempdir()
#copy this path in the clipboard. Then open another R session
#and from there (once the simulating process is running and the burnin has completed)
#write
#system("wc (path from clipboard)/AllBF")
#the number here appearing is the number of completed iterations
#
testRB<- GibbsBvs(formula=y~.,
data=riboflavin,
prior.betas="Robust",
init.model="null",
time.test=F,
n.iter=10000,
n.burnin=1000)
set.seed(16091956)
system.time(
testRB<- GibbsBvs(formula=y~.,
data=riboflavin,
prior.betas="Robust",
init.model="null",
time.test=F,
n.iter=10000,
n.burnin=1000)
)
#notice the large sparsity of the result since
#the great majority of covariates are not influential:
boxplot(testRB$inclprob)
testRB$inclprob[testRB$inclprob>.5]
#xYOAB_at xYXLE_at
# 0.9661 0.6502
#we can discharge all covariates except xYOAB_at and xYXLE_at
#the method does not reach to inform about xYXLE_at and its posterior
#probability is only slightly bigger than its prior probability
#We see that dimensions of visited models are small:
plot(testRB, option="d", xlim=c(0,100))
#so the part of the model space with singular models (k>n)
#has not been explored.
#To correct this issue we run:
corrected.testRB<- pltltn(testRB)
#Estimate of the posterior probability of the
# model space with singular models is: 0
#Meaning that it is extremely unlikely that the true model is such that k>n
#The corrected inclusion probabilities can be accessed through
#corrected.testRB but, in this case, these are essentially the same as in the
#original object (due to the unimportance of the singular part of the model space)
}
}
\references{
Berger, J.O., Garcia-Donato, G., Martínez-Beneito M.A. and Peña, V. (2016)
Bayesian variable selection in high dimensional problems without assumptions on prior model probabilities.
arXiv:1607.02993
}
\seealso{
See
\code{\link[BayesVarSel]{GibbsBvs}} for creating objects of the class
\code{Bvs}.
}
\author{
Gonzalo Garcia-Donato
Maintainer: <gonzalo.garciadonato@uclm.es>
}
|
## Function to run joint models with simulated data
joint_model <- function(structured_data,
unstructured_data,
dat1,
biasfield,
plotting=FALSE,
mesh.edge = c(20,40), # added
mesh.offset = c(5,20), # added
resolution = c(10,10)){ # added
#packages
library(INLA)
library(reshape2)
library(rgeos)
library(fields)
max_x <- max(biasfield$x)
max_y <- max(biasfield$y)
#preparation - mesh construction - use the loc.domain argument
mesh <- inla.mesh.2d(loc.domain = biasfield[,c(1,2)],
max.edge=mesh.edge,
cutoff=2,
offset = mesh.offset)
#plot the mesh to see what it looks like
if(plotting == TRUE){
par(mfrow=c(1,1))
plot(mesh)}
##set the spde representation to be the mesh just created
spde <- inla.spde2.matern(mesh)
#make A matrix for structured data - should this be pulling the x and y coordinates for the location?
structured_data_A <- inla.spde.make.A(mesh = mesh,
loc = as.matrix(structured_data[,2:3]))
#make A matrix for unstructured data
unstructured_data_A <- inla.spde.make.A(mesh = mesh,
loc = as.matrix(unstructured_data[,1:2]))
# Joint model
# One spatial field
# Uses (as far as I can tell) Simpson approach for PP data
# Binomial model for PA data
# Using cloglog
# create integration stack
loc.d <- t(matrix(c(0,0,max_x,0,max_x,max_y,0,max_y,0,0), 2))
#make dual mesh
dd <- deldir::deldir(mesh$loc[, 1], mesh$loc[, 2])
tiles <- deldir::tile.list(dd)
#make domain into spatial polygon
domainSP <- SpatialPolygons(list(Polygons(
list(Polygon(loc.d)), '0')))
#intersection between domain and dual mesh
poly.gpc <- as(domainSP@polygons[[1]]@Polygons[[1]]@coords, "gpc.poly")
# w now contains area of voronoi polygons
w <- sapply(tiles, function(p) rgeos::area.poly(rgeos::intersect(as(cbind(p$x, p$y), "gpc.poly"), poly.gpc)))
#check some have 0 weight
table(w>0)
nv <- mesh$n
n <- nrow(unstructured_data)
#change data to include 0s for nodes and 1s for presences
y.pp <- rep(0:1, c(nv, n))
#add expectation vector (area for integration points/nodes and 0 for presences)
e.pp <- c(w, rep(0, n))
#diagonal matrix for integration point A matrix
imat <- Diagonal(nv, rep(1, nv))
A.pp <- rBind(imat, unstructured_data_A)
#get covariate for integration points
covariate = dat1$gridcov[Reduce('cbind',
nearest.pixel(mesh$loc[,1],
mesh$loc[,2],
im(dat1$gridcov)))]
#unstructured data stack with integration points
stk_unstructured_data <- inla.stack(data=list(y=cbind(y.pp, NA),
e = e.pp),
effects=list(list(data.frame(interceptB=rep(1,nv+n)), env = c(covariate, unstructured_data$env)),
list(uns_field=1:spde$n.spde)),
A=list(1,A.pp),
tag="unstructured_data")
#stack for structured data
#note intercept with different name
stk_structured_data <- inla.stack(data=list(y=cbind(NA,
structured_data$presence),
Ntrials = rep(1,
nrow(structured_data))),
effects=list(list(data.frame(interceptA=rep(1,length(structured_data$x)), env = structured_data$env)),
list(str_field=1:spde$n.spde)),
A=list(1,structured_data_A),
tag="structured_data")
##NOTE: doesn't use the copy function initially
stk <- inla.stack(stk_unstructured_data, stk_structured_data)
# join.stack <- stk
#
source("Create prediction stack.R")
join.stack <- create_prediction_stack(stk,
resolution=resolution,
biasfield = biasfield,
dat1 = dat1, mesh, spde)
formulaJ = y ~ interceptA + interceptB + env + f(uns_field, model = spde) +
f(str_field, copy = "uns_field", fixed = TRUE) -1
result <- inla(formulaJ,family=c("poisson", "binomial"),
data=inla.stack.data(join.stack),
control.predictor=list(A=inla.stack.A(join.stack),
compute=TRUE),
control.family = list(list(link = "log"),
list(link = "cloglog")),
E = inla.stack.data(join.stack)$e,
Ntrials = inla.stack.data(join.stack)$Ntrials,
control.compute = list(dic = FALSE,
cpo = FALSE,
waic = FALSE)
)
##project the mesh onto the initial simulated grid 100x100 cells in dimension
proj1<-inla.mesh.projector(mesh,
ylim=c(1,max_y),
xlim=c(1,max_x),
dims=c(max_x,max_y))
##pull out the mean of the random field for the NPMS model
xmean1 <- inla.mesh.project(proj1, result$summary.random$uns_field$mean)
##plot the estimated random field
# plot with the original
library(fields)
# some of the commands below were giving warnings as not graphical parameters - I have fixed what I can
# scales and col.region did nothing on my version
if(plotting == TRUE){
#png("joint_model.png") # option to save output
#, height = 1000, width = 2500, pointsize = 30)
par(mfrow=c(1,1))
image.plot(1:max_x,1:max_y,
xmean1,
col=tim.colors(),
xlab='', ylab='',
main="Joint-mean of r.f",
asp=1)
#zlim=c(-3,3))
# image.plot(list(x=dat1$Lam$xcol*100,
# y=dat1$Lam$yrow*100,
# z=t(dat1$rf.s)),
# main='Truth',
# asp=1,
# zlim=c(-3,3)) # make sure scale = same
# points(structured_data[structured_data[,4] %in% 0,2:3], pch=16, col='white')
#absences
# points(structured_data[structured_data[,4] %in% 1,2:3], pch=16, col='black')
##plot the standard deviation of random field
xsd1 <- inla.mesh.project(proj1, result$summary.random$uns_field$sd)
image.plot(1:max_x,1:max_y,
xsd1,
col=tim.colors(),
xlab='', ylab='',
main="Joint-sd of r.f",
asp=1)
#dev.off()
}
result$summary.fixed
return(list(join.stack = join.stack, result = result))
}
| /Run models joint.R | no_license | ssarahas/MScProject_ISDM | R | false | false | 7,076 | r | ## Function to run joint models with simulated data
joint_model <- function(structured_data,
unstructured_data,
dat1,
biasfield,
plotting=FALSE,
mesh.edge = c(20,40), # added
mesh.offset = c(5,20), # added
resolution = c(10,10)){ # added
#packages
library(INLA)
library(reshape2)
library(rgeos)
library(fields)
max_x <- max(biasfield$x)
max_y <- max(biasfield$y)
#preparation - mesh construction - use the loc.domain argument
mesh <- inla.mesh.2d(loc.domain = biasfield[,c(1,2)],
max.edge=mesh.edge,
cutoff=2,
offset = mesh.offset)
#plot the mesh to see what it looks like
if(plotting == TRUE){
par(mfrow=c(1,1))
plot(mesh)}
##set the spde representation to be the mesh just created
spde <- inla.spde2.matern(mesh)
#make A matrix for structured data - should this be pulling the x and y coordinates for the location?
structured_data_A <- inla.spde.make.A(mesh = mesh,
loc = as.matrix(structured_data[,2:3]))
#make A matrix for unstructured data
unstructured_data_A <- inla.spde.make.A(mesh = mesh,
loc = as.matrix(unstructured_data[,1:2]))
# Joint model
# One spatial field
# Uses (as far as I can tell) Simpson approach for PP data
# Binomial model for PA data
# Using cloglog
# create integration stack
loc.d <- t(matrix(c(0,0,max_x,0,max_x,max_y,0,max_y,0,0), 2))
#make dual mesh
dd <- deldir::deldir(mesh$loc[, 1], mesh$loc[, 2])
tiles <- deldir::tile.list(dd)
#make domain into spatial polygon
domainSP <- SpatialPolygons(list(Polygons(
list(Polygon(loc.d)), '0')))
#intersection between domain and dual mesh
poly.gpc <- as(domainSP@polygons[[1]]@Polygons[[1]]@coords, "gpc.poly")
# w now contains area of voronoi polygons
w <- sapply(tiles, function(p) rgeos::area.poly(rgeos::intersect(as(cbind(p$x, p$y), "gpc.poly"), poly.gpc)))
#check some have 0 weight
table(w>0)
nv <- mesh$n
n <- nrow(unstructured_data)
#change data to include 0s for nodes and 1s for presences
y.pp <- rep(0:1, c(nv, n))
#add expectation vector (area for integration points/nodes and 0 for presences)
e.pp <- c(w, rep(0, n))
#diagonal matrix for integration point A matrix
imat <- Diagonal(nv, rep(1, nv))
A.pp <- rBind(imat, unstructured_data_A)
#get covariate for integration points
covariate = dat1$gridcov[Reduce('cbind',
nearest.pixel(mesh$loc[,1],
mesh$loc[,2],
im(dat1$gridcov)))]
#unstructured data stack with integration points
stk_unstructured_data <- inla.stack(data=list(y=cbind(y.pp, NA),
e = e.pp),
effects=list(list(data.frame(interceptB=rep(1,nv+n)), env = c(covariate, unstructured_data$env)),
list(uns_field=1:spde$n.spde)),
A=list(1,A.pp),
tag="unstructured_data")
#stack for structured data
#note intercept with different name
stk_structured_data <- inla.stack(data=list(y=cbind(NA,
structured_data$presence),
Ntrials = rep(1,
nrow(structured_data))),
effects=list(list(data.frame(interceptA=rep(1,length(structured_data$x)), env = structured_data$env)),
list(str_field=1:spde$n.spde)),
A=list(1,structured_data_A),
tag="structured_data")
##NOTE: doesn't use the copy function initially
stk <- inla.stack(stk_unstructured_data, stk_structured_data)
# join.stack <- stk
#
source("Create prediction stack.R")
join.stack <- create_prediction_stack(stk,
resolution=resolution,
biasfield = biasfield,
dat1 = dat1, mesh, spde)
formulaJ = y ~ interceptA + interceptB + env + f(uns_field, model = spde) +
f(str_field, copy = "uns_field", fixed = TRUE) -1
result <- inla(formulaJ,family=c("poisson", "binomial"),
data=inla.stack.data(join.stack),
control.predictor=list(A=inla.stack.A(join.stack),
compute=TRUE),
control.family = list(list(link = "log"),
list(link = "cloglog")),
E = inla.stack.data(join.stack)$e,
Ntrials = inla.stack.data(join.stack)$Ntrials,
control.compute = list(dic = FALSE,
cpo = FALSE,
waic = FALSE)
)
##project the mesh onto the initial simulated grid 100x100 cells in dimension
proj1<-inla.mesh.projector(mesh,
ylim=c(1,max_y),
xlim=c(1,max_x),
dims=c(max_x,max_y))
##pull out the mean of the random field for the NPMS model
xmean1 <- inla.mesh.project(proj1, result$summary.random$uns_field$mean)
##plot the estimated random field
# plot with the original
library(fields)
# some of the commands below were giving warnings as not graphical parameters - I have fixed what I can
# scales and col.region did nothing on my version
if(plotting == TRUE){
#png("joint_model.png") # option to save output
#, height = 1000, width = 2500, pointsize = 30)
par(mfrow=c(1,1))
image.plot(1:max_x,1:max_y,
xmean1,
col=tim.colors(),
xlab='', ylab='',
main="Joint-mean of r.f",
asp=1)
#zlim=c(-3,3))
# image.plot(list(x=dat1$Lam$xcol*100,
# y=dat1$Lam$yrow*100,
# z=t(dat1$rf.s)),
# main='Truth',
# asp=1,
# zlim=c(-3,3)) # make sure scale = same
# points(structured_data[structured_data[,4] %in% 0,2:3], pch=16, col='white')
#absences
# points(structured_data[structured_data[,4] %in% 1,2:3], pch=16, col='black')
##plot the standard deviation of random field
xsd1 <- inla.mesh.project(proj1, result$summary.random$uns_field$sd)
image.plot(1:max_x,1:max_y,
xsd1,
col=tim.colors(),
xlab='', ylab='',
main="Joint-sd of r.f",
asp=1)
#dev.off()
}
result$summary.fixed
return(list(join.stack = join.stack, result = result))
}
|
##manual deisotope
deisotope <- function(data=...){
require(stringr)
str_which(data$isotopes, pattern = "\\+\\d")
data_1 <- data[-str_which(data$isotopes, pattern = "\\+\\d|\\d\\+"),]
return(data_1)}
| /deisotope.R | no_license | tuhulab/bfi-wholegrain | R | false | false | 213 | r | ##manual deisotope
deisotope <- function(data=...){
require(stringr)
str_which(data$isotopes, pattern = "\\+\\d")
data_1 <- data[-str_which(data$isotopes, pattern = "\\+\\d|\\d\\+"),]
return(data_1)}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s.SGD.R
\name{s.SGD}
\alias{s.SGD}
\title{Stochastic Gradient Descent (SGD) [C, R]}
\usage{
s.SGD(
x,
y = NULL,
x.test = NULL,
y.test = NULL,
x.name = NULL,
y.name = NULL,
model = NULL,
model.control = list(lambda1 = 0, lambda2 = 0),
sgd.control = list(method = "ai-sgd"),
upsample = FALSE,
downsample = FALSE,
resample.seed = NULL,
print.plot = TRUE,
plot.fitted = NULL,
plot.predicted = NULL,
plot.theme = getOption("rt.fit.theme", "lightgrid"),
question = NULL,
verbose = TRUE,
outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
...
)
}
\arguments{
\item{x}{Numeric vector or matrix / data frame of features i.e. independent variables}
\item{y}{Numeric vector of outcome, i.e. dependent variable}
\item{x.test}{Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in \code{x}}
\item{y.test}{Numeric vector of testing set outcome}
\item{x.name}{Character: Name for feature set}
\item{y.name}{Character: Name for outcome}
\item{model}{character specifying the model to be used: \code{"lm"} (linear
model), \code{"glm"} (generalized linear model), \code{"cox"} (Cox
proportional hazards model), \code{"gmm"} (generalized method of moments),
\code{"m"} (M-estimation). See \sQuote{Details}.}
\item{model.control}{a list of parameters for controlling the model.
\describe{
\item{\code{family} (\code{"glm"})}{a description of the error distribution and
link function to be used in the model. This can be a character string
naming a family function, a family function or the result of a call to
a family function. (See \code{\link[stats]{family}} for details of
family functions.)}
\item{\code{rank} (\code{"glm"})}{logical. Should the rank of the design matrix
be checked?}
\item{\code{fn} (\code{"gmm"})}{a function \eqn{g(\theta,x)} which returns a
\eqn{k}-vector corresponding to the \eqn{k} moment conditions. It is a
required argument if \code{gr} not specified.}
\item{\code{gr} (\code{"gmm"})}{a function to return the gradient. If
unspecified, a finite-difference approximation will be used.}
\item{\code{nparams} (\code{"gmm"})}{number of model parameters. This is
automatically determined for other models.}
\item{\code{type} (\code{"gmm"})}{character specifying the generalized method of
moments procedure: \code{"twostep"} (Hansen, 1982), \code{"iterative"}
(Hansen et al., 1996). Defaults to \code{"iterative"}.}
\item{\code{wmatrix} (\code{"gmm"})}{weighting matrix to be used in the loss
function. Defaults to the identity matrix.}
\item{\code{loss} (\code{"m"})}{character specifying the loss function to be
used in the estimating equation. Default is the Huber loss.}
\item{\code{lambda1}}{L1 regularization parameter. Default is 0.}
\item{\code{lambda2}}{L2 regularization parameter. Default is 0.}
}}
\item{sgd.control}{an optional list of parameters for controlling the estimation.
\describe{
\item{\code{method}}{character specifying the method to be used: \code{"sgd"},
\code{"implicit"}, \code{"asgd"}, \code{"ai-sgd"}, \code{"momentum"},
\code{"nesterov"}. Default is \code{"ai-sgd"}. See \sQuote{Details}.}
\item{\code{lr}}{character specifying the learning rate to be used:
\code{"one-dim"}, \code{"one-dim-eigen"}, \code{"d-dim"},
\code{"adagrad"}, \code{"rmsprop"}. Default is \code{"one-dim"}.
See \sQuote{Details}.}
\item{\code{lr.control}}{vector of scalar hyperparameters one can
set dependent on the learning rate. For hyperparameters aimed
to be left as default, specify \code{NA} in the corresponding
entries. See \sQuote{Details}.}
\item{\code{start}}{starting values for the parameter estimates. Default is
random initialization around zero.}
\item{\code{size}}{number of SGD estimates to store for diagnostic purposes
(distributed log-uniformly over total number of iterations)}
\item{\code{reltol}}{relative convergence tolerance. The algorithm stops
if it is unable to change the relative mean squared difference in the
parameters by more than the amount. Default is \code{1e-05}.}
\item{\code{npasses}}{the maximum number of passes over the data. Default
is 3.}
\item{\code{pass}}{logical. Should \code{tol} be ignored and run the
algorithm for all of \code{npasses}?}
\item{\code{shuffle}}{logical. Should the algorithm shuffle the data set
including for each pass?}
\item{\code{verbose}}{logical. Should the algorithm print progress?}
}}
\item{upsample}{Logical: If TRUE, upsample cases to balance outcome classes (for Classification only)
Caution: upsample will randomly sample with replacement if the length of the majority class is more than double
the length of the class you are upsampling, thereby introducing randomness}
\item{resample.seed}{Integer: If provided, will be used to set the seed during upsampling.
Default = NULL (random seed)}
\item{print.plot}{Logical: if TRUE, produce plot using \code{mplot3}
Takes precedence over \code{plot.fitted} and \code{plot.predicted}. Default = TRUE}
\item{plot.fitted}{Logical: if TRUE, plot True (y) vs Fitted}
\item{plot.predicted}{Logical: if TRUE, plot True (y.test) vs Predicted.
Requires \code{x.test} and \code{y.test}}
\item{plot.theme}{Character: "zero", "dark", "box", "darkbox"}
\item{question}{Character: the question you are attempting to answer with this model, in plain language.}
\item{verbose}{Logical: If TRUE, print summary to screen.}
\item{outdir}{Path to output directory.
If defined, will save Predicted vs. True plot, if available,
as well as full model output, if \code{save.mod} is TRUE}
\item{save.mod}{Logical: If TRUE, save all output to an RDS file in \code{outdir}
\code{save.mod} is TRUE by default if an \code{outdir} is defined. If set to TRUE, and no \code{outdir}
is defined, outdir defaults to \code{paste0("./s.", mod.name)}}
\item{...}{Additional arguments to be passed to \code{sgd.control}}
}
\value{
Object of class \pkg{rtemis}
}
\description{
Train a model by Stochastic Gradient Descent using \code{sgd::sgd}
}
\details{
From \code{sgd::sgd}:
"Models: The Cox model assumes that the survival data is ordered when passed in, i.e.,
such that the risk set of an observation i is all data points after it."
}
\seealso{
\link{elevate} for external cross-validation
Other Supervised Learning:
\code{\link{s.ADABOOST}()},
\code{\link{s.ADDTREE}()},
\code{\link{s.BART}()},
\code{\link{s.BAYESGLM}()},
\code{\link{s.BRUTO}()},
\code{\link{s.C50}()},
\code{\link{s.CART}()},
\code{\link{s.CTREE}()},
\code{\link{s.DA}()},
\code{\link{s.ET}()},
\code{\link{s.EVTREE}()},
\code{\link{s.GAM.default}()},
\code{\link{s.GAM.formula}()},
\code{\link{s.GAMSELX2}()},
\code{\link{s.GAMSELX}()},
\code{\link{s.GAMSEL}()},
\code{\link{s.GAM}()},
\code{\link{s.GBM3}()},
\code{\link{s.GBM}()},
\code{\link{s.GLMNET}()},
\code{\link{s.GLM}()},
\code{\link{s.GLS}()},
\code{\link{s.H2ODL}()},
\code{\link{s.H2OGBM}()},
\code{\link{s.H2ORF}()},
\code{\link{s.IRF}()},
\code{\link{s.KNN}()},
\code{\link{s.LDA}()},
\code{\link{s.LM}()},
\code{\link{s.MARS}()},
\code{\link{s.MLRF}()},
\code{\link{s.NBAYES}()},
\code{\link{s.NLA}()},
\code{\link{s.NLS}()},
\code{\link{s.NW}()},
\code{\link{s.POLYMARS}()},
\code{\link{s.PPR}()},
\code{\link{s.PPTREE}()},
\code{\link{s.QDA}()},
\code{\link{s.QRNN}()},
\code{\link{s.RANGER}()},
\code{\link{s.RFSRC}()},
\code{\link{s.RF}()},
\code{\link{s.SPLS}()},
\code{\link{s.SVM}()},
\code{\link{s.TFN}()},
\code{\link{s.XGBLIN}()},
\code{\link{s.XGB}()}
}
\author{
Efstathios D. Gennatas
}
\concept{Supervised Learning}
| /man/s.SGD.Rd | no_license | zeta1999/rtemis | R | false | true | 7,725 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s.SGD.R
\name{s.SGD}
\alias{s.SGD}
\title{Stochastic Gradient Descent (SGD) [C, R]}
\usage{
s.SGD(
x,
y = NULL,
x.test = NULL,
y.test = NULL,
x.name = NULL,
y.name = NULL,
model = NULL,
model.control = list(lambda1 = 0, lambda2 = 0),
sgd.control = list(method = "ai-sgd"),
upsample = FALSE,
downsample = FALSE,
resample.seed = NULL,
print.plot = TRUE,
plot.fitted = NULL,
plot.predicted = NULL,
plot.theme = getOption("rt.fit.theme", "lightgrid"),
question = NULL,
verbose = TRUE,
outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
...
)
}
\arguments{
\item{x}{Numeric vector or matrix / data frame of features i.e. independent variables}
\item{y}{Numeric vector of outcome, i.e. dependent variable}
\item{x.test}{Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in \code{x}}
\item{y.test}{Numeric vector of testing set outcome}
\item{x.name}{Character: Name for feature set}
\item{y.name}{Character: Name for outcome}
\item{model}{character specifying the model to be used: \code{"lm"} (linear
model), \code{"glm"} (generalized linear model), \code{"cox"} (Cox
proportional hazards model), \code{"gmm"} (generalized method of moments),
\code{"m"} (M-estimation). See \sQuote{Details}.}
\item{model.control}{a list of parameters for controlling the model.
\describe{
\item{\code{family} (\code{"glm"})}{a description of the error distribution and
link function to be used in the model. This can be a character string
naming a family function, a family function or the result of a call to
a family function. (See \code{\link[stats]{family}} for details of
family functions.)}
\item{\code{rank} (\code{"glm"})}{logical. Should the rank of the design matrix
be checked?}
\item{\code{fn} (\code{"gmm"})}{a function \eqn{g(\theta,x)} which returns a
\eqn{k}-vector corresponding to the \eqn{k} moment conditions. It is a
required argument if \code{gr} not specified.}
\item{\code{gr} (\code{"gmm"})}{a function to return the gradient. If
unspecified, a finite-difference approximation will be used.}
\item{\code{nparams} (\code{"gmm"})}{number of model parameters. This is
automatically determined for other models.}
\item{\code{type} (\code{"gmm"})}{character specifying the generalized method of
moments procedure: \code{"twostep"} (Hansen, 1982), \code{"iterative"}
(Hansen et al., 1996). Defaults to \code{"iterative"}.}
\item{\code{wmatrix} (\code{"gmm"})}{weighting matrix to be used in the loss
function. Defaults to the identity matrix.}
\item{\code{loss} (\code{"m"})}{character specifying the loss function to be
used in the estimating equation. Default is the Huber loss.}
\item{\code{lambda1}}{L1 regularization parameter. Default is 0.}
\item{\code{lambda2}}{L2 regularization parameter. Default is 0.}
}}
\item{sgd.control}{an optional list of parameters for controlling the estimation.
\describe{
\item{\code{method}}{character specifying the method to be used: \code{"sgd"},
\code{"implicit"}, \code{"asgd"}, \code{"ai-sgd"}, \code{"momentum"},
\code{"nesterov"}. Default is \code{"ai-sgd"}. See \sQuote{Details}.}
\item{\code{lr}}{character specifying the learning rate to be used:
\code{"one-dim"}, \code{"one-dim-eigen"}, \code{"d-dim"},
\code{"adagrad"}, \code{"rmsprop"}. Default is \code{"one-dim"}.
See \sQuote{Details}.}
\item{\code{lr.control}}{vector of scalar hyperparameters one can
set dependent on the learning rate. For hyperparameters aimed
to be left as default, specify \code{NA} in the corresponding
entries. See \sQuote{Details}.}
\item{\code{start}}{starting values for the parameter estimates. Default is
random initialization around zero.}
\item{\code{size}}{number of SGD estimates to store for diagnostic purposes
(distributed log-uniformly over total number of iterations)}
\item{\code{reltol}}{relative convergence tolerance. The algorithm stops
if it is unable to change the relative mean squared difference in the
parameters by more than the amount. Default is \code{1e-05}.}
\item{\code{npasses}}{the maximum number of passes over the data. Default
is 3.}
\item{\code{pass}}{logical. Should \code{tol} be ignored and run the
algorithm for all of \code{npasses}?}
\item{\code{shuffle}}{logical. Should the algorithm shuffle the data set
including for each pass?}
\item{\code{verbose}}{logical. Should the algorithm print progress?}
}}
\item{upsample}{Logical: If TRUE, upsample cases to balance outcome classes (for Classification only)
Caution: upsample will randomly sample with replacement if the length of the majority class is more than double
the length of the class you are upsampling, thereby introducing randomness}
\item{resample.seed}{Integer: If provided, will be used to set the seed during upsampling.
Default = NULL (random seed)}
\item{print.plot}{Logical: if TRUE, produce plot using \code{mplot3}
Takes precedence over \code{plot.fitted} and \code{plot.predicted}. Default = TRUE}
\item{plot.fitted}{Logical: if TRUE, plot True (y) vs Fitted}
\item{plot.predicted}{Logical: if TRUE, plot True (y.test) vs Predicted.
Requires \code{x.test} and \code{y.test}}
\item{plot.theme}{Character: "zero", "dark", "box", "darkbox"}
\item{question}{Character: the question you are attempting to answer with this model, in plain language.}
\item{verbose}{Logical: If TRUE, print summary to screen.}
\item{outdir}{Path to output directory.
If defined, will save Predicted vs. True plot, if available,
as well as full model output, if \code{save.mod} is TRUE}
\item{save.mod}{Logical: If TRUE, save all output to an RDS file in \code{outdir}
\code{save.mod} is TRUE by default if an \code{outdir} is defined. If set to TRUE, and no \code{outdir}
is defined, outdir defaults to \code{paste0("./s.", mod.name)}}
\item{...}{Additional arguments to be passed to \code{sgd.control}}
}
\value{
Object of class \pkg{rtemis}
}
\description{
Train a model by Stochastic Gradient Descent using \code{sgd::sgd}
}
\details{
From \code{sgd::sgd}:
"Models: The Cox model assumes that the survival data is ordered when passed in, i.e.,
such that the risk set of an observation i is all data points after it."
}
\seealso{
\link{elevate} for external cross-validation
Other Supervised Learning:
\code{\link{s.ADABOOST}()},
\code{\link{s.ADDTREE}()},
\code{\link{s.BART}()},
\code{\link{s.BAYESGLM}()},
\code{\link{s.BRUTO}()},
\code{\link{s.C50}()},
\code{\link{s.CART}()},
\code{\link{s.CTREE}()},
\code{\link{s.DA}()},
\code{\link{s.ET}()},
\code{\link{s.EVTREE}()},
\code{\link{s.GAM.default}()},
\code{\link{s.GAM.formula}()},
\code{\link{s.GAMSELX2}()},
\code{\link{s.GAMSELX}()},
\code{\link{s.GAMSEL}()},
\code{\link{s.GAM}()},
\code{\link{s.GBM3}()},
\code{\link{s.GBM}()},
\code{\link{s.GLMNET}()},
\code{\link{s.GLM}()},
\code{\link{s.GLS}()},
\code{\link{s.H2ODL}()},
\code{\link{s.H2OGBM}()},
\code{\link{s.H2ORF}()},
\code{\link{s.IRF}()},
\code{\link{s.KNN}()},
\code{\link{s.LDA}()},
\code{\link{s.LM}()},
\code{\link{s.MARS}()},
\code{\link{s.MLRF}()},
\code{\link{s.NBAYES}()},
\code{\link{s.NLA}()},
\code{\link{s.NLS}()},
\code{\link{s.NW}()},
\code{\link{s.POLYMARS}()},
\code{\link{s.PPR}()},
\code{\link{s.PPTREE}()},
\code{\link{s.QDA}()},
\code{\link{s.QRNN}()},
\code{\link{s.RANGER}()},
\code{\link{s.RFSRC}()},
\code{\link{s.RF}()},
\code{\link{s.SPLS}()},
\code{\link{s.SVM}()},
\code{\link{s.TFN}()},
\code{\link{s.XGBLIN}()},
\code{\link{s.XGB}()}
}
\author{
Efstathios D. Gennatas
}
\concept{Supervised Learning}
|
#' Prepare object for argument \code{design} of \code{spsurvey.analysis()}
#'
#' This function returns an object to feed the argument \code{design} when
#' creating an object of class \code{spsurvey.analysis}.
#'
#' The argument \code{design} used to create object of class
#' \code{spsurvey.analysis} requires a series of inputs. However, it can be fed
#' with data about site ID and coordinates. \code{coordenadas()} returns a data
#' frame that provides this information, assuming that all other design
#' variables are provided manually in the arguments list.
#'
#' @param x Object of class \code{SpatialPointsDataFrame} from
#' which site ID and XY coordinates are to be returned.
#' @return An object of class \code{data.frame} containing three columns with
#' names \code{siteID}, \code{xcoord}, and \code{ycoord}.
#' @author Alessandro Samuel-Rosa \email{alessandrosamuelrosa@@gmail.com}
#' @seealso \code{\link[pedometrics]{gcpDiff}},
#' \code{\link[spsurvey]{cont.analysis}}.
#' @references Kincaid, T. M. and Olsen, A. R. (2013). spsurvey: Spatial Survey
#' Design and Analysis. R package version 2.6. URL:
#' <\url{http://www.epa.gov/nheerl/arm/}>.
#' @keywords methods
#' @export
#' @examples
#'
#' \dontrun{
#' ## Create an spsurvey.analysis object
#' my.spsurvey <-
#' spsurvey.analysis(design = coordenadas(my.data),
#' data.cont = delta(ref.data, my.data),
#' popcorrect = TRUE, pcfsize = length(my.data$id),
#' support = rep(1, length(my.data$id)),
#' wgt = rep(1, length(my.data$id)), vartype = "SRS")
#' }
#'
# FUNCTION #####################################################################
coordenadas <-
function(x) {
# Check if suggested packages are installed
pkg <- c("sp")
id <- !sapply(pkg, requireNamespace, quietly = TRUE)
if (any(id)) {
pkg <- paste(pkg[which(id)], collapse = " ")
stop(paste("Package(s) needed for this function to work but not",
"installed: ", pkg, sep = ""), call. = FALSE)
}
coo <- data.frame(x$siteID, sp::coordinates(x))
coo <- coo[order(as.numeric(x$siteID)), ]
colnames(coo) <- c("siteID", "xcoord", "ycoord")
row.names(coo) <- NULL
return(coo)
}
| /pedometrics/R/coordenadas.R | no_license | ingted/R-Examples | R | false | false | 2,272 | r | #' Prepare object for argument \code{design} of \code{spsurvey.analysis()}
#'
#' This function returns an object to feed the argument \code{design} when
#' creating an object of class \code{spsurvey.analysis}.
#'
#' The argument \code{design} used to create object of class
#' \code{spsurvey.analysis} requires a series of inputs. However, it can be fed
#' with data about site ID and coordinates. \code{coordenadas()} returns a data
#' frame that provides this information, assuming that all other design
#' variables are provided manually in the arguments list.
#'
#' @param x Object of class \code{SpatialPointsDataFrame} from
#' which site ID and XY coordinates are to be returned.
#' @return An object of class \code{data.frame} containing three columns with
#' names \code{siteID}, \code{xcoord}, and \code{ycoord}.
#' @author Alessandro Samuel-Rosa \email{alessandrosamuelrosa@@gmail.com}
#' @seealso \code{\link[pedometrics]{gcpDiff}},
#' \code{\link[spsurvey]{cont.analysis}}.
#' @references Kincaid, T. M. and Olsen, A. R. (2013). spsurvey: Spatial Survey
#' Design and Analysis. R package version 2.6. URL:
#' <\url{http://www.epa.gov/nheerl/arm/}>.
#' @keywords methods
#' @export
#' @examples
#'
#' \dontrun{
#' ## Create an spsurvey.analysis object
#' my.spsurvey <-
#' spsurvey.analysis(design = coordenadas(my.data),
#' data.cont = delta(ref.data, my.data),
#' popcorrect = TRUE, pcfsize = length(my.data$id),
#' support = rep(1, length(my.data$id)),
#' wgt = rep(1, length(my.data$id)), vartype = "SRS")
#' }
#'
# FUNCTION #####################################################################
coordenadas <-
function(x) {
# Check if suggested packages are installed
pkg <- c("sp")
id <- !sapply(pkg, requireNamespace, quietly = TRUE)
if (any(id)) {
pkg <- paste(pkg[which(id)], collapse = " ")
stop(paste("Package(s) needed for this function to work but not",
"installed: ", pkg, sep = ""), call. = FALSE)
}
coo <- data.frame(x$siteID, sp::coordinates(x))
coo <- coo[order(as.numeric(x$siteID)), ]
colnames(coo) <- c("siteID", "xcoord", "ycoord")
row.names(coo) <- NULL
return(coo)
}
|
### discreteRoot.R ---
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: nov 22 2017 (13:39)
## Version:
## Last-Updated: Feb 10 2023 (09:19)
## By: Thomas Alexander Gerds
## Update #: 250
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
## * discreteRoot - Documentation
#' @title Dichotomic search for monotone function
#' @description Find the root of a monotone function on a discrete grid of value using dichotomic search
#' @name discreteRoot
#'
#' @param fn [function] objective function to minimize in absolute value.
#' @param grid [vector] possible minimizers.
#' @param increasing [logical] is the function fn increasing?
#' @param check [logical] should the program check that fn takes a different sign for the first vs. the last value of the grid?
#' @param tol [numeric] the absolute convergence tolerance.
## * discreteRoot
#' @rdname dicreteRoot
#' @export
discreteRoot <- function(fn, grid, increasing = TRUE, check = TRUE,
tol = .Machine$double.eps ^ 0.5) {
n.grid <- length(grid)
value.grid <- rep(NA, n.grid)
iter <- 1
ncv <- TRUE
iSet <- 1:n.grid
factor <- c(-1,1)[increasing+1]
### ** Check
if(check){
value.grid[1] <- fn(grid[1])
value.grid[n.grid] <- fn(grid[n.grid])
if(sign(value.grid[1])==value.grid[n.grid]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution because the function does not change sign \n"))
}
if(increasing[[1]] && value.grid[[1]] > value.grid[[n.grid]]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution - argument \'increasing\' does not match the variations of the functions \n"))
}
if(!increasing[[1]] && value.grid[[1]] < value.grid[[n.grid]]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution - argument \'increasing\' does not match the variations of the functions \n"))
}
}
### ** Expore the grid using dichotomic search
while(iter[[1]] <= n.grid[[1]] && ncv[[1]]==TRUE && length(iSet)>0){
iMiddle <- ceiling(length(iSet)/2)
iIndexInSet <- iSet[iMiddle]
if(check[[1]]==FALSE || iIndexInSet %in% c(1,n.grid) == FALSE){
## if the current index we are looking at has not already been computed,
## then evaluate the objective function.
## this is only the case when check is TRUE and we look at the borders
value.grid[iIndexInSet] <- fn(grid[iIndexInSet])
}
if(is.na(value.grid[iIndexInSet])){
## handle NA value by just removing the observation from the set of possibilities
iSet <- setdiff(iSet,iMiddle)
iter <- iter + 1
}else if(factor*value.grid[iIndexInSet] > tol){
## look in subgrid corresponding to the lowest values (left part)
iSet <- iSet[setdiff(1:iMiddle,iMiddle)]
iter <- iter + 1
}else if(factor*value.grid[iIndexInSet] < -tol){
## look in subgrid corresponding to the largest values (right part)
iN.set <- length(iSet)
iSet <- iSet[setdiff(iMiddle:iN.set,iMiddle)]
iter <- iter + 1
}else{
## convergence
ncv <- FALSE
solution <- grid[iIndexInSet]
value <- value.grid[iIndexInSet]
}
}
### ** If did not find a value whose image matched tol, give the closest solution
if(ncv){
iIndexInSet <- which.min(abs(value.grid))
ncv <- FALSE
solution <- grid[iIndexInSet]
value <- value.grid[iIndexInSet]
}
return(list(par = solution,
value = value,
## grid = setNames(value.grid,grid),
counts = iter,
cv = ncv,
message = NULL))
}
## * boot2pvalue - Documentation
#' @title Compute the p.value from the distribution under H1
#' @description Compute the p.value associated with the estimated statistic
#' using a bootstrap sample of its distribution under H1.
#'
#' @param x [numeric vector] a vector of bootstrap estimates of the statistic.
#' @param null [numeric] value of the statistic under the null hypothesis.
#' @param estimate [numeric] the estimated statistic.
#' @param FUN.ci [function] the function used to compute the confidence interval.
#' Must take \code{x}, \code{alternative}, \code{conf.level} and \code{sign.estimate} as arguments
#' and only return the relevant limit (either upper or lower) of the confidence interval.
#' @param alternative [character] a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
#' @param tol [numeric] the absolute convergence tolerance.
#' @details
#' For test statistic close to 0, this function returns 1. \cr \cr
#'
#' For positive test statistic, this function search the quantile alpha such that:
#'\itemize{
#' \item \code{quantile(x, probs = alpha)=0} when the argument alternative is set to \code{"greater"}.
#' \item \code{quantile(x, probs = 0.5*alpha)=0} when the argument alternative is set to \code{"two.sided"}.
#' }
#' If the argument alternative is set to \code{"less"}, it returns 1. \cr \cr
#'
#' For negative test statistic, this function search the quantile alpha such that:
#' \itemize{
#' \item \code{quantile(x, probs = 1-alpha=0} when the argument alternative is set to \code{"less"}.
#' \item \code{quantile(x, probs = 1-0.5*alpha=0} when the argument alternative is set to \code{"two.sided"}.
#' }
#' If the argument alternative is set to \code{"greater"}, it returns 1.
#'
#' @examples
#' set.seed(10)
#'
#' #### no effect ####
#' x <- rnorm(1e3)
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "two.sided")
#' ## expected value of 1
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "greater")
#' ## expected value of 0.5
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "less")
#' ## expected value of 0.5
#'
#' #### positive effect ####
#' x <- rnorm(1e3, mean = 1)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "two.sided")
#' ## expected value of 0.32 = 2*pnorm(q = 0, mean = -1) = 2*mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "greater")
#' ## expected value of 0.16 = pnorm(q = 0, mean = 1) = mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "less")
#' ## expected value of 0.84 = 1-pnorm(q = 0, mean = 1) = mean(x>=0)
#'
#' #### negative effect ####
#' x <- rnorm(1e3, mean = -1)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "two.sided")
#' ## expected value of 0.32 = 2*(1-pnorm(q = 0, mean = -1)) = 2*mean(x>=0)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "greater")
#' ## expected value of 0.84 = pnorm(q = 0, mean = -1) = mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "less") # pnorm(q = 0, mean = -1)
#' ## expected value of 0.16 = 1-pnorm(q = 0, mean = -1) = mean(x>=0)
## * boot2pvalue
#' @rdname boot2pvalue
#' @export
boot2pvalue <- function(x, null, estimate = NULL, alternative = "two.sided",
FUN.ci = quantileCI,
tol = .Machine$double.eps ^ 0.5){
x.boot <- na.omit(x)
n.boot <- length(x.boot)
statistic.boot <- mean(x.boot) - null
if(is.null(estimate)){
statistic <- statistic.boot
}else{
statistic <- estimate - null
if(sign(statistic.boot)!=sign(statistic)){
warning("the estimate and the average bootstrap estimate do not have same sign \n")
}
}
sign.statistic <- statistic>=0
if(abs(statistic) < tol){ ## too small test statistic
p.value <- 1
}else if(n.boot < 10){ ## too few bootstrap samples
p.value <- as.numeric(NA)
}else if(all(x.boot>null)){ ## clear p.value
p.value <- switch(alternative,
"two.sided" = 0,
"less" = 1,
"greater" = 0)
} else if(all(x.boot<null)){ ## clear p.value
p.value <- switch(alternative,
"two.sided" = 0,
"less" = 0,
"greater" = 1)
}else{ ## need search to obtain p.value
## when the p.value=1-coverage increases, does the quantile increases?
increasing <- switch(alternative,
"two.sided" = sign.statistic,
"less" = FALSE,
"greater" = TRUE)
## grid of confidence level
grid <- seq(0,by=1/n.boot,length.out=n.boot)
## search for critical confidence level
resSearch <- discreteRoot(fn = function(p.value){
CI <- FUN.ci(x = x.boot,
p.value = p.value,
alternative = alternative,
sign.estimate = sign.statistic)
return(CI[1]-null)
},
grid = grid,
increasing = increasing,
check = FALSE)
## check change sign
sign.before <- sign(FUN.ci(x = x.boot,
p.value = max(0,resSearch$par-1/n.boot),
alternative = alternative,
sign.estimate = sign.statistic)-null)
sign.after <- sign(FUN.ci(x = x.boot,
p.value = min(1,resSearch$par+1/n.boot),
alternative = alternative,
sign.estimate = sign.statistic)-null)
##
if (is.na(resSearch$value[[1]]) || is.na(sign.before[[1]])|| is.na(sign.after[[1]]) || length(resSearch$value)==0
|| resSearch$par[[1]]<0 || resSearch$par[[1]]>1 || sign.before[[1]]==sign.after[[1]]){
warning("incorrect convergence of the algorithm finding the critical quantile \n",
"p-value may not be reliable \n")
}
p.value <- resSearch$par
}
if(p.value %in% c(0,1)){
message("Estimated p-value of ",p.value," - consider increasing the number of bootstrap samples \n")
}
return(p.value)
}
## * quantileCI
quantileCI <- function(x, alternative, p.value, sign.estimate, ...){
probs <- switch(alternative,
"two.sided" = c(p.value/2,1-p.value/2)[2-sign.estimate], ## if positive p.value/2 otherwise 1-p.value/2
"less" = 1-p.value,
"greater" = p.value)
return(quantile(x, probs = probs)[1])
}
##----------------------------------------------------------------------
### discreteRoot.R ends here
| /R/discreteRoot.R | no_license | tagteam/riskRegression | R | false | false | 11,254 | r | ### discreteRoot.R ---
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: nov 22 2017 (13:39)
## Version:
## Last-Updated: Feb 10 2023 (09:19)
## By: Thomas Alexander Gerds
## Update #: 250
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
## * discreteRoot - Documentation
#' @title Dichotomic search for monotone function
#' @description Find the root of a monotone function on a discrete grid of value using dichotomic search
#' @name discreteRoot
#'
#' @param fn [function] objective function to minimize in absolute value.
#' @param grid [vector] possible minimizers.
#' @param increasing [logical] is the function fn increasing?
#' @param check [logical] should the program check that fn takes a different sign for the first vs. the last value of the grid?
#' @param tol [numeric] the absolute convergence tolerance.
## * discreteRoot
#' @rdname dicreteRoot
#' @export
discreteRoot <- function(fn, grid, increasing = TRUE, check = TRUE,
tol = .Machine$double.eps ^ 0.5) {
n.grid <- length(grid)
value.grid <- rep(NA, n.grid)
iter <- 1
ncv <- TRUE
iSet <- 1:n.grid
factor <- c(-1,1)[increasing+1]
### ** Check
if(check){
value.grid[1] <- fn(grid[1])
value.grid[n.grid] <- fn(grid[n.grid])
if(sign(value.grid[1])==value.grid[n.grid]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution because the function does not change sign \n"))
}
if(increasing[[1]] && value.grid[[1]] > value.grid[[n.grid]]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution - argument \'increasing\' does not match the variations of the functions \n"))
}
if(!increasing[[1]] && value.grid[[1]] < value.grid[[n.grid]]){
return(list(par = NA,
value = NA,
counts = 0,
cv = 1,
message = "Cannot find a solution - argument \'increasing\' does not match the variations of the functions \n"))
}
}
### ** Expore the grid using dichotomic search
while(iter[[1]] <= n.grid[[1]] && ncv[[1]]==TRUE && length(iSet)>0){
iMiddle <- ceiling(length(iSet)/2)
iIndexInSet <- iSet[iMiddle]
if(check[[1]]==FALSE || iIndexInSet %in% c(1,n.grid) == FALSE){
## if the current index we are looking at has not already been computed,
## then evaluate the objective function.
## this is only the case when check is TRUE and we look at the borders
value.grid[iIndexInSet] <- fn(grid[iIndexInSet])
}
if(is.na(value.grid[iIndexInSet])){
## handle NA value by just removing the observation from the set of possibilities
iSet <- setdiff(iSet,iMiddle)
iter <- iter + 1
}else if(factor*value.grid[iIndexInSet] > tol){
## look in subgrid corresponding to the lowest values (left part)
iSet <- iSet[setdiff(1:iMiddle,iMiddle)]
iter <- iter + 1
}else if(factor*value.grid[iIndexInSet] < -tol){
## look in subgrid corresponding to the largest values (right part)
iN.set <- length(iSet)
iSet <- iSet[setdiff(iMiddle:iN.set,iMiddle)]
iter <- iter + 1
}else{
## convergence
ncv <- FALSE
solution <- grid[iIndexInSet]
value <- value.grid[iIndexInSet]
}
}
### ** If did not find a value whose image matched tol, give the closest solution
if(ncv){
iIndexInSet <- which.min(abs(value.grid))
ncv <- FALSE
solution <- grid[iIndexInSet]
value <- value.grid[iIndexInSet]
}
return(list(par = solution,
value = value,
## grid = setNames(value.grid,grid),
counts = iter,
cv = ncv,
message = NULL))
}
## * boot2pvalue - Documentation
#' @title Compute the p.value from the distribution under H1
#' @description Compute the p.value associated with the estimated statistic
#' using a bootstrap sample of its distribution under H1.
#'
#' @param x [numeric vector] a vector of bootstrap estimates of the statistic.
#' @param null [numeric] value of the statistic under the null hypothesis.
#' @param estimate [numeric] the estimated statistic.
#' @param FUN.ci [function] the function used to compute the confidence interval.
#' Must take \code{x}, \code{alternative}, \code{conf.level} and \code{sign.estimate} as arguments
#' and only return the relevant limit (either upper or lower) of the confidence interval.
#' @param alternative [character] a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
#' @param tol [numeric] the absolute convergence tolerance.
#' @details
#' For test statistic close to 0, this function returns 1. \cr \cr
#'
#' For positive test statistic, this function search the quantile alpha such that:
#'\itemize{
#' \item \code{quantile(x, probs = alpha)=0} when the argument alternative is set to \code{"greater"}.
#' \item \code{quantile(x, probs = 0.5*alpha)=0} when the argument alternative is set to \code{"two.sided"}.
#' }
#' If the argument alternative is set to \code{"less"}, it returns 1. \cr \cr
#'
#' For negative test statistic, this function search the quantile alpha such that:
#' \itemize{
#' \item \code{quantile(x, probs = 1-alpha=0} when the argument alternative is set to \code{"less"}.
#' \item \code{quantile(x, probs = 1-0.5*alpha=0} when the argument alternative is set to \code{"two.sided"}.
#' }
#' If the argument alternative is set to \code{"greater"}, it returns 1.
#'
#' @examples
#' set.seed(10)
#'
#' #### no effect ####
#' x <- rnorm(1e3)
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "two.sided")
#' ## expected value of 1
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "greater")
#' ## expected value of 0.5
#' boot2pvalue(x, null = 0, estimate = mean(x), alternative = "less")
#' ## expected value of 0.5
#'
#' #### positive effect ####
#' x <- rnorm(1e3, mean = 1)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "two.sided")
#' ## expected value of 0.32 = 2*pnorm(q = 0, mean = -1) = 2*mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "greater")
#' ## expected value of 0.16 = pnorm(q = 0, mean = 1) = mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = 1, alternative = "less")
#' ## expected value of 0.84 = 1-pnorm(q = 0, mean = 1) = mean(x>=0)
#'
#' #### negative effect ####
#' x <- rnorm(1e3, mean = -1)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "two.sided")
#' ## expected value of 0.32 = 2*(1-pnorm(q = 0, mean = -1)) = 2*mean(x>=0)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "greater")
#' ## expected value of 0.84 = pnorm(q = 0, mean = -1) = mean(x<=0)
#' boot2pvalue(x, null = 0, estimate = -1, alternative = "less") # pnorm(q = 0, mean = -1)
#' ## expected value of 0.16 = 1-pnorm(q = 0, mean = -1) = mean(x>=0)
## * boot2pvalue
#' @rdname boot2pvalue
#' @export
boot2pvalue <- function(x, null, estimate = NULL, alternative = "two.sided",
FUN.ci = quantileCI,
tol = .Machine$double.eps ^ 0.5){
x.boot <- na.omit(x)
n.boot <- length(x.boot)
statistic.boot <- mean(x.boot) - null
if(is.null(estimate)){
statistic <- statistic.boot
}else{
statistic <- estimate - null
if(sign(statistic.boot)!=sign(statistic)){
warning("the estimate and the average bootstrap estimate do not have same sign \n")
}
}
sign.statistic <- statistic>=0
if(abs(statistic) < tol){ ## too small test statistic
p.value <- 1
}else if(n.boot < 10){ ## too few bootstrap samples
p.value <- as.numeric(NA)
}else if(all(x.boot>null)){ ## clear p.value
p.value <- switch(alternative,
"two.sided" = 0,
"less" = 1,
"greater" = 0)
} else if(all(x.boot<null)){ ## clear p.value
p.value <- switch(alternative,
"two.sided" = 0,
"less" = 0,
"greater" = 1)
}else{ ## need search to obtain p.value
## when the p.value=1-coverage increases, does the quantile increases?
increasing <- switch(alternative,
"two.sided" = sign.statistic,
"less" = FALSE,
"greater" = TRUE)
## grid of confidence level
grid <- seq(0,by=1/n.boot,length.out=n.boot)
## search for critical confidence level
resSearch <- discreteRoot(fn = function(p.value){
CI <- FUN.ci(x = x.boot,
p.value = p.value,
alternative = alternative,
sign.estimate = sign.statistic)
return(CI[1]-null)
},
grid = grid,
increasing = increasing,
check = FALSE)
## check change sign
sign.before <- sign(FUN.ci(x = x.boot,
p.value = max(0,resSearch$par-1/n.boot),
alternative = alternative,
sign.estimate = sign.statistic)-null)
sign.after <- sign(FUN.ci(x = x.boot,
p.value = min(1,resSearch$par+1/n.boot),
alternative = alternative,
sign.estimate = sign.statistic)-null)
##
if (is.na(resSearch$value[[1]]) || is.na(sign.before[[1]])|| is.na(sign.after[[1]]) || length(resSearch$value)==0
|| resSearch$par[[1]]<0 || resSearch$par[[1]]>1 || sign.before[[1]]==sign.after[[1]]){
warning("incorrect convergence of the algorithm finding the critical quantile \n",
"p-value may not be reliable \n")
}
p.value <- resSearch$par
}
if(p.value %in% c(0,1)){
message("Estimated p-value of ",p.value," - consider increasing the number of bootstrap samples \n")
}
return(p.value)
}
## * quantileCI
quantileCI <- function(x, alternative, p.value, sign.estimate, ...){
probs <- switch(alternative,
"two.sided" = c(p.value/2,1-p.value/2)[2-sign.estimate], ## if positive p.value/2 otherwise 1-p.value/2
"less" = 1-p.value,
"greater" = p.value)
return(quantile(x, probs = probs)[1])
}
##----------------------------------------------------------------------
### discreteRoot.R ends here
|
\name{get.model}
\alias{get.model}
\title{Get Model}
\description{
Get the model for a node.
}
\usage{
get.model(domain, node, model.nodes)
}
\arguments{
\item{domain}{an RHugin domain.}
\item{node}{a character string specifying the name of a node in \code{domain}.}
\item{model.nodes}{an optional character vector of \emph{model nodes}. If provided, an empty model suitable for \code{node} is returned. Use \code{model.nodes = character(0)} to create an empty model containing a single expression.}
}
\details{
This function has two uses. The first is simply to retrieve the model from a node (an error is generated if the node does not have a model). In this case, the \code{model.nodes} argument must be omitted. The second use is to create a template for a node's model. In this case, the \code{model.nodes} argument must be provided.
}
\value{
a \code{data.frame}-like object with class \code{RHugin.model}. There is one column for each model node and a final column named \code{Expression} containing the expression for each configuration of the model nodes. When there are no model nodes, the \code{Expression} column will have a single entry containing the model's expression.
}
\references{
HUGIN API Reference Manual \url{http://download.hugin.com/webdocs/manuals/api-manual.pdf}: \code{h_node_new_model} and \code{h_node_get_model}.
}
\author{Kjell Konis \email{kjell.konis@icloud.com}}
\examples{
# Create an RHugin domain
hd <- hugin.domain()
# Add node
add.node(hd, "Node", states = 0:10)
# Generate a template for Node's model
model <- get.model(hd, "Node", character(0))
# Enter an expression can call set.model
model$Expression <- "Poisson (2.25)"
set.model(hd, "Node", model)
# Retrieve the model from Note
get.model(hd, "Node")
}
\keyword{programming}
| /man/get.model.Rd | no_license | huginexpert/RHugin | R | false | false | 1,799 | rd | \name{get.model}
\alias{get.model}
\title{Get Model}
\description{
Get the model for a node.
}
\usage{
get.model(domain, node, model.nodes)
}
\arguments{
\item{domain}{an RHugin domain.}
\item{node}{a character string specifying the name of a node in \code{domain}.}
\item{model.nodes}{an optional character vector of \emph{model nodes}. If provided, an empty model suitable for \code{node} is returned. Use \code{model.nodes = character(0)} to create an empty model containing a single expression.}
}
\details{
This function has two uses. The first is simply to retrieve the model from a node (an error is generated if the node does not have a model). In this case, the \code{model.nodes} argument must be omitted. The second use is to create a template for a node's model. In this case, the \code{model.nodes} argument must be provided.
}
\value{
a \code{data.frame}-like object with class \code{RHugin.model}. There is one column for each model node and a final column named \code{Expression} containing the expression for each configuration of the model nodes. When there are no model nodes, the \code{Expression} column will have a single entry containing the model's expression.
}
\references{
HUGIN API Reference Manual \url{http://download.hugin.com/webdocs/manuals/api-manual.pdf}: \code{h_node_new_model} and \code{h_node_get_model}.
}
\author{Kjell Konis \email{kjell.konis@icloud.com}}
\examples{
# Create an RHugin domain
hd <- hugin.domain()
# Add node
add.node(hd, "Node", states = 0:10)
# Generate a template for Node's model
model <- get.model(hd, "Node", character(0))
# Enter an expression can call set.model
model$Expression <- "Poisson (2.25)"
set.model(hd, "Node", model)
# Retrieve the model from Note
get.model(hd, "Node")
}
\keyword{programming}
|
library(dplyr)
library(class)
library(e1071)
library(xgboost)
library(magrittr)
library(dplyr)
library(Matrix)
set.seed(123)
sample_split = function(data,size,n){
sample_data <- data[sample(1:nrow(data), size*3), ]
sample_split <-split(sample_data,
rep(1:3, length.out = nrow(sample_data),
each = ceiling(nrow(sample_data)/3)))
return(sample_split[[n]])
}
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x))) }
train_data = read.csv('train_data.csv')
test_data = read.csv('test_data.csv')
sample_size = c(5000, 10000, 20000)
dat_500_1 = sample_split(train_data, sample_size[1], 1)
SVM = function(data){
model = svm(as.factor(label) ~ ., data = data, kernel = "linear", scale = T)
pred = predict(model, test_data[,-1])
return(pred)
}
XGB = function(data){
train.label = as.integer(as.factor(data$label))-1
train_matrix = as.matrix(data[,-1])
test.label = as.integer(as.factor(test_data$label))-1
test_matrix = as.matrix(test_data[,-1])
xgb.train = xgb.DMatrix(data=train_matrix,label=train.label)
xgb.test = xgb.DMatrix(data=test_matrix,label=test.label)
model = xgboost(data = xgb.train, max.depth = 30, eta = 0.001,
nthread = 2, nrounds = 2, num_class = length(unique(data$label)), objective = "multi:softprob")
pred <- predict(model, newdata = xgb.test,reshape=T)
pred = as.data.frame(pred)
colnames(pred) = levels(as.factor(test_data$label))
pred$prediction = apply(pred,1,function(x) colnames(pred)[which.max(x)])
pred$label = levels(as.factor(test_data$label))[train.label+1]
return(pred$prediction)
}
models = c(SVM, XGB) ## add your models
scoreboard = data.frame()
for (k in 1:length(models)) {
for (i in 1:length(sample_size)) {
for (j in 1:3) {
train = sample_split(train_data, sample_size[i], j)
start_time <- Sys.time()
pred = models[[k]](train)
end_time <- Sys.time()
sys_time = end_time - start_time
Model = paste('Model',k)
Data = paste('dat_',sample_size[i],'_',j, sep = '')
A = sample_size[i]/60000
B = min(1,sys_time/60)
C = sum(test_data$label == pred)/NROW(test_data$label)
Points = 0.15 * A + 0.1 * B + 0.75 * C
score_row = data.frame(Model, 'Sample Size' = sample_size[i], Data, A, B, C, Points)
scoreboard = rbind(scoreboard,score_row)
}
}}
print(scoreboard)
| /Midterm Project - Heyden.R | no_license | drd4/Practical-Machine-Learning-Project | R | false | false | 2,410 | r | library(dplyr)
library(class)
library(e1071)
library(xgboost)
library(magrittr)
library(dplyr)
library(Matrix)
set.seed(123)
sample_split = function(data,size,n){
sample_data <- data[sample(1:nrow(data), size*3), ]
sample_split <-split(sample_data,
rep(1:3, length.out = nrow(sample_data),
each = ceiling(nrow(sample_data)/3)))
return(sample_split[[n]])
}
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x))) }
train_data = read.csv('train_data.csv')
test_data = read.csv('test_data.csv')
sample_size = c(5000, 10000, 20000)
dat_500_1 = sample_split(train_data, sample_size[1], 1)
SVM = function(data){
model = svm(as.factor(label) ~ ., data = data, kernel = "linear", scale = T)
pred = predict(model, test_data[,-1])
return(pred)
}
XGB = function(data){
train.label = as.integer(as.factor(data$label))-1
train_matrix = as.matrix(data[,-1])
test.label = as.integer(as.factor(test_data$label))-1
test_matrix = as.matrix(test_data[,-1])
xgb.train = xgb.DMatrix(data=train_matrix,label=train.label)
xgb.test = xgb.DMatrix(data=test_matrix,label=test.label)
model = xgboost(data = xgb.train, max.depth = 30, eta = 0.001,
nthread = 2, nrounds = 2, num_class = length(unique(data$label)), objective = "multi:softprob")
pred <- predict(model, newdata = xgb.test,reshape=T)
pred = as.data.frame(pred)
colnames(pred) = levels(as.factor(test_data$label))
pred$prediction = apply(pred,1,function(x) colnames(pred)[which.max(x)])
pred$label = levels(as.factor(test_data$label))[train.label+1]
return(pred$prediction)
}
models = c(SVM, XGB) ## add your models
scoreboard = data.frame()
for (k in 1:length(models)) {
for (i in 1:length(sample_size)) {
for (j in 1:3) {
train = sample_split(train_data, sample_size[i], j)
start_time <- Sys.time()
pred = models[[k]](train)
end_time <- Sys.time()
sys_time = end_time - start_time
Model = paste('Model',k)
Data = paste('dat_',sample_size[i],'_',j, sep = '')
A = sample_size[i]/60000
B = min(1,sys_time/60)
C = sum(test_data$label == pred)/NROW(test_data$label)
Points = 0.15 * A + 0.1 * B + 0.75 * C
score_row = data.frame(Model, 'Sample Size' = sample_size[i], Data, A, B, C, Points)
scoreboard = rbind(scoreboard,score_row)
}
}}
print(scoreboard)
|
testlist <- list(testX = c(191493125665849920, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), trainX = structure(c(1.78844646178735e+212, 1.93075223605916e+156, 121373.193669204, 1.26689771433298e+26, 2.46020195254853e+129, 8.54794497535107e-83, 2.61907806894971e-213, 1.5105425626729e+200, 6.51877713351675e+25, 4.40467528702727e-93, 7.6427933587945, 34208333744.1307, 1.6400690920442e-111, 3.9769673154778e-304, 4.76127371594362e-307, 8.63819952335095e+122, 1.18662128550178e-59, 1128.83285802937, 3.80478583615452e-72, 1.21321365773924e-195, 9.69744674150153e-268, 8.98899319496613e+272, 7.63669788330223e+285, 3.85830749537493e+266, 2.65348875902107e+136, 8.14965241967603e+92, 6.64773495141057e-171, 1.55228780425777e-91, 8.25550184376779e+105, 1.18572662524891e+134, 1.04113208597565e+183, 1.01971211553913e-259, 1.23680594512923e-165, 5.24757023065221e+62, 3.41816623041351e-96 ), .Dim = c(5L, 7L)))
result <- do.call(dann:::calc_distance_C,testlist)
str(result) | /dann/inst/testfiles/calc_distance_C/AFL_calc_distance_C/calc_distance_C_valgrind_files/1609867321-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 1,199 | r | testlist <- list(testX = c(191493125665849920, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), trainX = structure(c(1.78844646178735e+212, 1.93075223605916e+156, 121373.193669204, 1.26689771433298e+26, 2.46020195254853e+129, 8.54794497535107e-83, 2.61907806894971e-213, 1.5105425626729e+200, 6.51877713351675e+25, 4.40467528702727e-93, 7.6427933587945, 34208333744.1307, 1.6400690920442e-111, 3.9769673154778e-304, 4.76127371594362e-307, 8.63819952335095e+122, 1.18662128550178e-59, 1128.83285802937, 3.80478583615452e-72, 1.21321365773924e-195, 9.69744674150153e-268, 8.98899319496613e+272, 7.63669788330223e+285, 3.85830749537493e+266, 2.65348875902107e+136, 8.14965241967603e+92, 6.64773495141057e-171, 1.55228780425777e-91, 8.25550184376779e+105, 1.18572662524891e+134, 1.04113208597565e+183, 1.01971211553913e-259, 1.23680594512923e-165, 5.24757023065221e+62, 3.41816623041351e-96 ), .Dim = c(5L, 7L)))
result <- do.call(dann:::calc_distance_C,testlist)
str(result) |
ui<- fluidPage(
tags$head(
tags$style(
".h2,h2 {
font-size: 50px;
font-weight: bold;
font-family: Microsoft JhengHei;
color: white;
position: relative;
top: 15px;};"),
tags$style(
"#currentTime{
color: white;
font-size: 80px;
font-weight: bold;
font-family: Century Gothic;
position: relative;
top: 120px;
};"),
tags$style(
"#text1{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 20px;
};"),
tags$style(
"#text2{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 30px;
};"),
tags$style(
"#text3{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 30px;
};"),
tags$style(
"#projecttable{
position: relative;
top: 50px;
border: 0px;};"),
tags$style(
"#donut_plot1{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#donut_plot2{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#casemeantime_plot{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#day30bar{
position: relative;
top: -280px;
left: -25px};"),
tags$style(
"#myplot_dataplot{
position: relative;
top: 75px};"),
tags$style(
"#day180_done_area{
position: relative;
top: 120px};"),
tags$style(
"#projectplot{
position: relative;
left: -350px};"),
tags$style(
"#case_upper{
color: white;
font-size: 15px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 110px;
left: -25px
};"),
tags$style(
"#case_lower{
color: white;
font-size: 15px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 270px;
left: -25px
};")
),
titlePanel("2019-01-21 XX部案件Dashboard")
,
fluidRow(
column(2,
DTOutput("projecttable"),
textOutput("currentTime")
),
column(7,
plotOutput("projectplot",width = "160%" ,height = "700px"),
plotOutput("myplot_dataplot",width = "100%" ,height = "125px"),
plotOutput("day180_done_area",width = "102%" ,height = "150px")
),
column(1, textOutput("text1"),plotOutput("casemeantime_plot",width = "125%" ,height = "400px"),
textOutput("case_upper"),
textOutput("case_lower")),
column(1, textOutput("text2"),plotOutput("donut_plot1",width = "125%" ,height = "400px")),
column(1, textOutput("text3"),plotOutput("donut_plot2",width = "125%" ,height = "400px")),
fluidRow(
fixedRow(column(3,plotOutput("day30bar",width = "100%" ,height = "900px")))
)
),
setBackgroundColor(color = "black")
) | /dashboard_for_workload/ui.R | no_license | renardbao/shiny_project | R | false | false | 3,225 | r |
ui<- fluidPage(
tags$head(
tags$style(
".h2,h2 {
font-size: 50px;
font-weight: bold;
font-family: Microsoft JhengHei;
color: white;
position: relative;
top: 15px;};"),
tags$style(
"#currentTime{
color: white;
font-size: 80px;
font-weight: bold;
font-family: Century Gothic;
position: relative;
top: 120px;
};"),
tags$style(
"#text1{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 20px;
};"),
tags$style(
"#text2{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 30px;
};"),
tags$style(
"#text3{
color: white;
font-size: 22px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 160px;
left: 30px;
};"),
tags$style(
"#projecttable{
position: relative;
top: 50px;
border: 0px;};"),
tags$style(
"#donut_plot1{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#donut_plot2{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#casemeantime_plot{
position: relative;
top: -150px;
left: -10px};"),
tags$style(
"#day30bar{
position: relative;
top: -280px;
left: -25px};"),
tags$style(
"#myplot_dataplot{
position: relative;
top: 75px};"),
tags$style(
"#day180_done_area{
position: relative;
top: 120px};"),
tags$style(
"#projectplot{
position: relative;
left: -350px};"),
tags$style(
"#case_upper{
color: white;
font-size: 15px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 110px;
left: -25px
};"),
tags$style(
"#case_lower{
color: white;
font-size: 15px;
font-weight: bold;
font-family: Microsoft JhengHei;
position: relative;
top: 270px;
left: -25px
};")
),
titlePanel("2019-01-21 XX部案件Dashboard")
,
fluidRow(
column(2,
DTOutput("projecttable"),
textOutput("currentTime")
),
column(7,
plotOutput("projectplot",width = "160%" ,height = "700px"),
plotOutput("myplot_dataplot",width = "100%" ,height = "125px"),
plotOutput("day180_done_area",width = "102%" ,height = "150px")
),
column(1, textOutput("text1"),plotOutput("casemeantime_plot",width = "125%" ,height = "400px"),
textOutput("case_upper"),
textOutput("case_lower")),
column(1, textOutput("text2"),plotOutput("donut_plot1",width = "125%" ,height = "400px")),
column(1, textOutput("text3"),plotOutput("donut_plot2",width = "125%" ,height = "400px")),
fluidRow(
fixedRow(column(3,plotOutput("day30bar",width = "100%" ,height = "900px")))
)
),
setBackgroundColor(color = "black")
) |
#Plot 3
with(study_data, plot(datetime, Sub_metering_1, type = "l",
xlab = "", ylab = "Energy sub metering"))
with(study_data, lines(datetime, Sub_metering_2, type = "l", col = "red"))
with(study_data, lines(datetime, Sub_metering_3, type = "l", col = "blue"))
legend("topright", lty=1, col = c("black", "red", "blue"),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
dev.copy(png, file = "plot3.png", width = 480, height = 480)
dev.off()
| /plot3.R | no_license | yuanyuantulip/Exploratory-Data-Analysis-course-project-1 | R | false | false | 488 | r | #Plot 3
with(study_data, plot(datetime, Sub_metering_1, type = "l",
xlab = "", ylab = "Energy sub metering"))
with(study_data, lines(datetime, Sub_metering_2, type = "l", col = "red"))
with(study_data, lines(datetime, Sub_metering_3, type = "l", col = "blue"))
legend("topright", lty=1, col = c("black", "red", "blue"),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
dev.copy(png, file = "plot3.png", width = 480, height = 480)
dev.off()
|
# validateModel.R
# third piece of code for the clearScience Demo Project
# takes the output from buildModel(), and fits that model
# on held out validation data
validateModel <- function(returnTwo){
# SOURCE LIBRARIES
require(randomForest)
require(ggplot2)
require(ROCR)
# DEFINE VARIABLES
rfERFit <- returnTwo$rfERFit
validExpress <- returnTwo$returnOne$validExpress
validScore <- returnTwo$returnOne$validScore
# VALIDATE & VISUALIZE WITH HELD OUT VALIDATION COHORT
cat("[1] Evaluating predictions on held out validation set\n")
validScoreHat <- predict(rfERFit, t(validExpress), type = "prob")
validScoreHat <- validScoreHat[ , 2]
validScoreDF <- as.data.frame(cbind(validScore, validScoreHat))
colnames(validScoreDF) <- c("yValid", "yValidHat")
cat("[2] Producing diagnostic boxplot of predictions in the held out validation set\n")
validBoxPlot <- ggplot(validScoreDF, aes(factor(yValid), yValidHat)) +
geom_boxplot() +
geom_jitter(aes(colour = as.factor(yValid)), size = 4) +
opts(title = "ER Random Forest Model Indepenedent Validation Set") +
ylab("Validation Set ER Prediction") +
xlab("True ER Status") +
opts(plot.title = theme_text(size = 14))
# Alternative visualization (density plots)
cat("[3] Producing diagnostic density plot of predictions in the held out validation set\n")
validDensPlot <- ggplot(validScoreDF,
aes(yValidHat,
fill = factor(yValid))) +
geom_density(alpha = 0.3) +
ylab("Density") +
xlab("True ER Status") +
opts(plot.title = theme_text(size = 14))
# EVALUATE VALIDATION MODEL PERFORMANCE
erPred <- prediction(as.numeric(validScoreHat), as.numeric(validScore))
erPerf <- performance(erPred, "tpr", "fpr")
erAUC <- performance(erPred, "auc")
# FIND YOUDEN'S J POINT AND OPTIMAL SENSITIVITY AND SPECIFICITY
erRFPerf <- performance(erPred, "sens", "spec")
youdensJ <- erRFPerf@x.values[[1]] + erRFPerf@y.values[[1]] - 1
jMax <- which.max(youdensJ)
optCut <- erPerf@alpha.values[[1]][jMax]
optSens <- unlist(erRFPerf@x.values)[jMax]
optSpec <- unlist(erRFPerf@y.values)[jMax]
rankSum <- wilcox.test(validScoreHat[validScore == 0],
validScoreHat[validScore == 1])
## CREATE A ROC CURVE USING GGPLOT
cat("[4] Assessing model performance via ROC curve in held out validation set\n")
dfPerf <- as.data.frame(cbind(unlist(erPerf@x.values),
unlist(erPerf@y.values)))
colnames(dfPerf) <- c("FalsePositiveRate", "TruePositiveRate")
rocCurve <- ggplot(dfPerf, aes(FalsePositiveRate, TruePositiveRate)) +
geom_line() +
geom_abline(slope = 1, colour = "red") +
opts(title = "Validation Cohort ROC Curve") +
ylab("False Positive Rate") +
xlab("True Positive Rate") +
opts(plot.title = theme_text(size = 14))
## RETURN
return(list("validScoreDF" = validScoreDF,
"validBoxPlot" = validBoxPlot,
"validDensPlot" = validDensPlot,
"rankSum" = rankSum,
"rocCurve" = rocCurve,
"sensitivity" = optSens,
"specificity" = optSpec,
"auc" = erAUC@y.values))
}
| /analysisFunctions/validateModel.R | no_license | erichhuang/clearScience-demo | R | false | false | 3,340 | r | # validateModel.R
# third piece of code for the clearScience Demo Project
# takes the output from buildModel(), and fits that model
# on held out validation data
validateModel <- function(returnTwo){
# SOURCE LIBRARIES
require(randomForest)
require(ggplot2)
require(ROCR)
# DEFINE VARIABLES
rfERFit <- returnTwo$rfERFit
validExpress <- returnTwo$returnOne$validExpress
validScore <- returnTwo$returnOne$validScore
# VALIDATE & VISUALIZE WITH HELD OUT VALIDATION COHORT
cat("[1] Evaluating predictions on held out validation set\n")
validScoreHat <- predict(rfERFit, t(validExpress), type = "prob")
validScoreHat <- validScoreHat[ , 2]
validScoreDF <- as.data.frame(cbind(validScore, validScoreHat))
colnames(validScoreDF) <- c("yValid", "yValidHat")
cat("[2] Producing diagnostic boxplot of predictions in the held out validation set\n")
validBoxPlot <- ggplot(validScoreDF, aes(factor(yValid), yValidHat)) +
geom_boxplot() +
geom_jitter(aes(colour = as.factor(yValid)), size = 4) +
opts(title = "ER Random Forest Model Indepenedent Validation Set") +
ylab("Validation Set ER Prediction") +
xlab("True ER Status") +
opts(plot.title = theme_text(size = 14))
# Alternative visualization (density plots)
cat("[3] Producing diagnostic density plot of predictions in the held out validation set\n")
validDensPlot <- ggplot(validScoreDF,
aes(yValidHat,
fill = factor(yValid))) +
geom_density(alpha = 0.3) +
ylab("Density") +
xlab("True ER Status") +
opts(plot.title = theme_text(size = 14))
# EVALUATE VALIDATION MODEL PERFORMANCE
erPred <- prediction(as.numeric(validScoreHat), as.numeric(validScore))
erPerf <- performance(erPred, "tpr", "fpr")
erAUC <- performance(erPred, "auc")
# FIND YOUDEN'S J POINT AND OPTIMAL SENSITIVITY AND SPECIFICITY
erRFPerf <- performance(erPred, "sens", "spec")
youdensJ <- erRFPerf@x.values[[1]] + erRFPerf@y.values[[1]] - 1
jMax <- which.max(youdensJ)
optCut <- erPerf@alpha.values[[1]][jMax]
optSens <- unlist(erRFPerf@x.values)[jMax]
optSpec <- unlist(erRFPerf@y.values)[jMax]
rankSum <- wilcox.test(validScoreHat[validScore == 0],
validScoreHat[validScore == 1])
## CREATE A ROC CURVE USING GGPLOT
cat("[4] Assessing model performance via ROC curve in held out validation set\n")
dfPerf <- as.data.frame(cbind(unlist(erPerf@x.values),
unlist(erPerf@y.values)))
colnames(dfPerf) <- c("FalsePositiveRate", "TruePositiveRate")
rocCurve <- ggplot(dfPerf, aes(FalsePositiveRate, TruePositiveRate)) +
geom_line() +
geom_abline(slope = 1, colour = "red") +
opts(title = "Validation Cohort ROC Curve") +
ylab("False Positive Rate") +
xlab("True Positive Rate") +
opts(plot.title = theme_text(size = 14))
## RETURN
return(list("validScoreDF" = validScoreDF,
"validBoxPlot" = validBoxPlot,
"validDensPlot" = validDensPlot,
"rankSum" = rankSum,
"rocCurve" = rocCurve,
"sensitivity" = optSens,
"specificity" = optSpec,
"auc" = erAUC@y.values))
}
|
rm(list = ls())
library(Daniel)
library(dplyr)
library(nnet)
CalcCImultinom <- function(fit)
{
s <- summary(fit)
coef <- s$coefficients
ses <- s$standard.errors
ci.1 <- coef[1,2] + c(-1, 1)*1.96*ses[1, 2]
ci.2 <- coef[2,2] + c(-1, 1)*1.96*ses[2, 2]
return(rbind(ci.1,ci.2))
}
#key
# A, B,C,D,E,F - betaE[2] = 1.25, 1.5, 1.75, 2, 2.25, 2.5
# A,B,C, D, E,F - betaU = 2,3,4,5,6,7
patt <- "EE"
beta0 <- c(-6, -5)
betaE <- c(log(2.5), log(2.25))
betaU <- c(log(6), log(1/1.5))
sigmaU <- 1
n.sample <- 50000
n.sim <- 1000
AllY <- matrix(nr = n.sim, nc = 3)
sace.diff1 <- sace.diff2 <- ace.diff1 <- ace.diff2 <-
sace.or1 <- sace.or2 <- ace.or1 <- ace.or2 <-
or.approx1 <- or.approx2 <- or.approx.true1 <- or.approx.true2 <-
pop.never.s1 <- pop.never.s2 <- vector(length = n.sim)
ci1 <- ci2 <- matrix(nr = n.sim, nc = 2)
for (j in 1:n.sim)
{
CatIndex(j)
# Simulate genetic score
U <- rnorm(n.sample, 0, sd = sigmaU)
#### Calcualte probabilites for each subtype with and without the exposure ####
e1E0 <- exp(beta0[1] + betaU[1]*U)
e1E1 <- exp(beta0[1] + betaE[1] + betaU[1]*U)
e2E0 <- exp(beta0[2] + betaU[2]*U)
e2E1 <- exp(beta0[2] + betaE[2] + betaU[2]*U)
prE0Y1 <- e1E0/(1 + e1E0 + e2E0)
prE0Y2 <- e2E0/(1 + e1E0 + e2E0)
prE1Y1 <- e1E1/(1 + e1E1 + e2E1)
prE1Y2 <- e2E1/(1 + e1E1 + e2E1)
probsE0 <- cbind(prE0Y1, prE0Y2, 1 - prE0Y1 - prE0Y2)
probsE1 <- cbind(prE1Y1, prE1Y2, 1 - prE1Y1 - prE1Y2)
# Simulate subtypes #
Yctrl <- Ytrt <- vector(length = n.sample)
X <- rbinom(n = n.sample, 1, 0.5)
for (i in 1:n.sample)
{
Yctrl[i] <- sample(c(1,2,0), 1, replace = T, prob = probsE0[i, ])
Ytrt[i] <- sample(c(1,2,0), 1, replace = T, prob = probsE1[i, ])
}
Y <- (1-X)*Yctrl + X*Ytrt
AllY[j, ] <- table(Y)
Y1ctrl <- Yctrl==1
Y1trt <- Ytrt==1
Y2ctrl <- Yctrl==2
Y2trt <- Ytrt==2
pop.never.s1[j] <- mean(Y1ctrl==0 & Y1trt==0)
pop.never.s2[j] <- mean(Y2ctrl==0 & Y2trt==0)
# estimate causal parameters
sace.diff1[j] <- mean((Y1trt - Y1ctrl)[Y2ctrl==0 & Y2trt==0])
sace.diff2[j]<- mean((Y2trt - Y2ctrl)[Y1ctrl==0 & Y1trt==0])
ace.diff1[j] <- mean((Y1trt[Y2trt==0 & X==1]) - mean(Y1ctrl[Y2ctrl==0 & X==0]))
ace.diff2[j] <- mean((Y2trt[Y1trt==0 & X==1]) - mean(Y2ctrl[Y1ctrl==0 & X==0]))
# Ypo <- c(Yctrl, Ytrt)
# Upo <- rep(U,2)
# Xpo <- rep(x = c(0,1), each = n.sample)
# fit.full.po <- multinom(Ypo~ Xpo + Upo)
# fit.po <- multinom(Ypo~ Xpo)
fit <- multinom(Y~ X)
cis <- CalcCImultinom(fit)
ci1[j, ] <- cis[1, ]
ci2[j, ] <- cis[2, ]
Y1only <- Y[Y<2]
X1only <- X[Y<2]
U1only <-U[Y<2]
Y2only <- Y[Y!=1]
X2only <- X[Y!=1]
U2only <-U[Y!=1]
Y2only[Y2only>0] <- 1
vec.for.or.1only <- c(sum((1 - Y1only) * (1 - X1only)) , sum(Y1only * (1 - X1only)),
sum((1 - Y1only) * X1only), sum(Y1only*X1only))
vec.for.or.2only <- c(sum((1 - Y2only) * (1 - X2only)) , sum(Y2only * (1 - X2only)),
sum((1 - Y2only) * X2only), sum(Y2only*X2only))
ace.or1[j] <- CalcOR(vec.for.or.1only)
ace.or2[j] <- CalcOR(vec.for.or.2only)
Y1only.sace <- Y[Ytrt <2 & Yctrl < 2]
X1only.sace <- X[Ytrt <2 & Yctrl < 2]
U1only.sace <-U[Ytrt <2 & Yctrl < 2]
Y2only.sace <- Y[Ytrt!=1 & Y1ctrl!=1]
X2only.sace <- X[Ytrt!=1 & Y1ctrl!=1]
U2only.sace <-U[Ytrt!=1 & Y1ctrl!=1]
Y2only.sace[Y2only.sace>0] <- 1
vec.for.or.sace1 <- c(sum((1 - Y1only.sace) * (1 - X1only.sace)) , sum(Y1only.sace * (1 - X1only.sace)),
sum((1 - Y1only.sace) * X1only.sace), sum(Y1only.sace*X1only.sace))
vec.for.or.sace2 <- c(sum((1 - Y2only.sace) * (1 - X2only.sace)) , sum(Y2only.sace * (1 - X2only.sace)),
sum((1 - Y2only.sace) * X2only.sace), sum(Y2only.sace*X2only.sace))
sace.or1[j] <- CalcOR(vec.for.or.sace1)
sace.or2[j] <- CalcOR(vec.for.or.sace2)
Y1 <- Y==1
Y2 <- Y==2
fit.logistic.Y1 <- glm(Y1 ~ X, family = "binomial")
fit.logistic.true.Y1 <- glm(Y1 ~ X + U, family = "binomial")
fit.logistic.Y2 <- glm(Y2 ~ X, family = "binomial")
fit.logistic.true.Y2 <- glm(Y2 ~ X + U, family = "binomial")
or.approx1[j] <- exp(coef(fit.logistic.Y1)[2])
or.approx.true1[j] <- exp(coef(fit.logistic.true.Y1)[2])
or.approx2[j] <- exp(coef(fit.logistic.Y2)[2])
or.approx.true2[j] <- exp(coef(fit.logistic.true.Y2)[2])
}
save.image(paste0("CMPEn50krareScen19a",patt,".RData"))
| /Simulations/Scripts/R/Rare/Scenario 19a/CMPEn50KrareScen19aEE.R | no_license | yadevi/CausalMPE | R | false | false | 4,224 | r | rm(list = ls())
library(Daniel)
library(dplyr)
library(nnet)
CalcCImultinom <- function(fit)
{
s <- summary(fit)
coef <- s$coefficients
ses <- s$standard.errors
ci.1 <- coef[1,2] + c(-1, 1)*1.96*ses[1, 2]
ci.2 <- coef[2,2] + c(-1, 1)*1.96*ses[2, 2]
return(rbind(ci.1,ci.2))
}
#key
# A, B,C,D,E,F - betaE[2] = 1.25, 1.5, 1.75, 2, 2.25, 2.5
# A,B,C, D, E,F - betaU = 2,3,4,5,6,7
patt <- "EE"
beta0 <- c(-6, -5)
betaE <- c(log(2.5), log(2.25))
betaU <- c(log(6), log(1/1.5))
sigmaU <- 1
n.sample <- 50000
n.sim <- 1000
AllY <- matrix(nr = n.sim, nc = 3)
sace.diff1 <- sace.diff2 <- ace.diff1 <- ace.diff2 <-
sace.or1 <- sace.or2 <- ace.or1 <- ace.or2 <-
or.approx1 <- or.approx2 <- or.approx.true1 <- or.approx.true2 <-
pop.never.s1 <- pop.never.s2 <- vector(length = n.sim)
ci1 <- ci2 <- matrix(nr = n.sim, nc = 2)
for (j in 1:n.sim)
{
CatIndex(j)
# Simulate genetic score
U <- rnorm(n.sample, 0, sd = sigmaU)
#### Calcualte probabilites for each subtype with and without the exposure ####
e1E0 <- exp(beta0[1] + betaU[1]*U)
e1E1 <- exp(beta0[1] + betaE[1] + betaU[1]*U)
e2E0 <- exp(beta0[2] + betaU[2]*U)
e2E1 <- exp(beta0[2] + betaE[2] + betaU[2]*U)
prE0Y1 <- e1E0/(1 + e1E0 + e2E0)
prE0Y2 <- e2E0/(1 + e1E0 + e2E0)
prE1Y1 <- e1E1/(1 + e1E1 + e2E1)
prE1Y2 <- e2E1/(1 + e1E1 + e2E1)
probsE0 <- cbind(prE0Y1, prE0Y2, 1 - prE0Y1 - prE0Y2)
probsE1 <- cbind(prE1Y1, prE1Y2, 1 - prE1Y1 - prE1Y2)
# Simulate subtypes #
Yctrl <- Ytrt <- vector(length = n.sample)
X <- rbinom(n = n.sample, 1, 0.5)
for (i in 1:n.sample)
{
Yctrl[i] <- sample(c(1,2,0), 1, replace = T, prob = probsE0[i, ])
Ytrt[i] <- sample(c(1,2,0), 1, replace = T, prob = probsE1[i, ])
}
Y <- (1-X)*Yctrl + X*Ytrt
AllY[j, ] <- table(Y)
Y1ctrl <- Yctrl==1
Y1trt <- Ytrt==1
Y2ctrl <- Yctrl==2
Y2trt <- Ytrt==2
pop.never.s1[j] <- mean(Y1ctrl==0 & Y1trt==0)
pop.never.s2[j] <- mean(Y2ctrl==0 & Y2trt==0)
# estimate causal parameters
sace.diff1[j] <- mean((Y1trt - Y1ctrl)[Y2ctrl==0 & Y2trt==0])
sace.diff2[j]<- mean((Y2trt - Y2ctrl)[Y1ctrl==0 & Y1trt==0])
ace.diff1[j] <- mean((Y1trt[Y2trt==0 & X==1]) - mean(Y1ctrl[Y2ctrl==0 & X==0]))
ace.diff2[j] <- mean((Y2trt[Y1trt==0 & X==1]) - mean(Y2ctrl[Y1ctrl==0 & X==0]))
# Ypo <- c(Yctrl, Ytrt)
# Upo <- rep(U,2)
# Xpo <- rep(x = c(0,1), each = n.sample)
# fit.full.po <- multinom(Ypo~ Xpo + Upo)
# fit.po <- multinom(Ypo~ Xpo)
fit <- multinom(Y~ X)
cis <- CalcCImultinom(fit)
ci1[j, ] <- cis[1, ]
ci2[j, ] <- cis[2, ]
Y1only <- Y[Y<2]
X1only <- X[Y<2]
U1only <-U[Y<2]
Y2only <- Y[Y!=1]
X2only <- X[Y!=1]
U2only <-U[Y!=1]
Y2only[Y2only>0] <- 1
vec.for.or.1only <- c(sum((1 - Y1only) * (1 - X1only)) , sum(Y1only * (1 - X1only)),
sum((1 - Y1only) * X1only), sum(Y1only*X1only))
vec.for.or.2only <- c(sum((1 - Y2only) * (1 - X2only)) , sum(Y2only * (1 - X2only)),
sum((1 - Y2only) * X2only), sum(Y2only*X2only))
ace.or1[j] <- CalcOR(vec.for.or.1only)
ace.or2[j] <- CalcOR(vec.for.or.2only)
Y1only.sace <- Y[Ytrt <2 & Yctrl < 2]
X1only.sace <- X[Ytrt <2 & Yctrl < 2]
U1only.sace <-U[Ytrt <2 & Yctrl < 2]
Y2only.sace <- Y[Ytrt!=1 & Y1ctrl!=1]
X2only.sace <- X[Ytrt!=1 & Y1ctrl!=1]
U2only.sace <-U[Ytrt!=1 & Y1ctrl!=1]
Y2only.sace[Y2only.sace>0] <- 1
vec.for.or.sace1 <- c(sum((1 - Y1only.sace) * (1 - X1only.sace)) , sum(Y1only.sace * (1 - X1only.sace)),
sum((1 - Y1only.sace) * X1only.sace), sum(Y1only.sace*X1only.sace))
vec.for.or.sace2 <- c(sum((1 - Y2only.sace) * (1 - X2only.sace)) , sum(Y2only.sace * (1 - X2only.sace)),
sum((1 - Y2only.sace) * X2only.sace), sum(Y2only.sace*X2only.sace))
sace.or1[j] <- CalcOR(vec.for.or.sace1)
sace.or2[j] <- CalcOR(vec.for.or.sace2)
Y1 <- Y==1
Y2 <- Y==2
fit.logistic.Y1 <- glm(Y1 ~ X, family = "binomial")
fit.logistic.true.Y1 <- glm(Y1 ~ X + U, family = "binomial")
fit.logistic.Y2 <- glm(Y2 ~ X, family = "binomial")
fit.logistic.true.Y2 <- glm(Y2 ~ X + U, family = "binomial")
or.approx1[j] <- exp(coef(fit.logistic.Y1)[2])
or.approx.true1[j] <- exp(coef(fit.logistic.true.Y1)[2])
or.approx2[j] <- exp(coef(fit.logistic.Y2)[2])
or.approx.true2[j] <- exp(coef(fit.logistic.true.Y2)[2])
}
save.image(paste0("CMPEn50krareScen19a",patt,".RData"))
|
\name{interpolate.score}
\alias{interpolate.score}
\title{
Interpolate the \code{\link{score()}} of a (sparse) signal down to
base pair level.
}
\description{
Tiling arrays have a lower density of information per kbase.
To compare data measured with such techniques with basepair resolution
obtained from NGS, it can be useful to interpolate.score the sparser data.
}
\usage{
interpolate.score(granges, seqnames = NULL,
wanted.dist = function(dists){median(dists)},
max.dist = function(dists){3*median(dists)},
every = 1)
}
\arguments{
\item{granges}{
The \code{GRanges} object whose \code{score} is to be interpolated.
The \code{\link{seqlength}}'s of this object may not be NULL.
}
\item{seqnames}{
The subset of seqnames for which to do the interpolation. If
\code{NULL}, all \code{unique(seqnames(granges))} are taken.
}
\item{wanted.dist}{
Passed to \code{\link{uuutils::zeroterminate.islands}}
}
\item{max.dist}{
Passed to \code{\link{uuutils::zeroterminate.islands}}
}
\item{every}{
Interpolate every this many base pairs.
}
}
\details{
To avoid interpolation over too long distances, the 'islands' in the
data are first 'zero-terminated' on either side using
\code{\link{uuutils::zeroterminate.islands}}. This makes sure that,
for the simple linear interpolation done subsequently, the complete
gap is interpolated as having the value zero.
}
\value{
A \code{GRanges} object with, with 1-width ranges at the interpolate.scored
points, and \code{score} the interpolated values.
}
\author{
Philip Lijnzaad <plijnzaad@gmail.com>
}
\seealso{
\code{\link{uuutils::zeroterminate.islands}},\code{\link{granges.apply}}
}
\note{ Don't be seduced by the luscious curves of spline interpolation.
They are visually attractive but inappropriate as they
overshoot wildly. Better stick to the straight and narrow, as
in real life.
}
\examples{
gr <- GRanges(ranges=IRanges(start=seq(1, 1000, by=100),width=1),
score=rnorm(10),
seqnames=factor('foo'),strand='*', seqlengths=c(foo=1000))
interpolation <- interpolate.score(gr)
}
\keyword{misc}
| /ngs/R/ngsutils/man/interpolate.score.Rd | no_license | plijnzaad/phtools | R | false | false | 2,235 | rd | \name{interpolate.score}
\alias{interpolate.score}
\title{
Interpolate the \code{\link{score()}} of a (sparse) signal down to
base pair level.
}
\description{
Tiling arrays have a lower density of information per kbase.
To compare data measured with such techniques with basepair resolution
obtained from NGS, it can be useful to interpolate.score the sparser data.
}
\usage{
interpolate.score(granges, seqnames = NULL,
wanted.dist = function(dists){median(dists)},
max.dist = function(dists){3*median(dists)},
every = 1)
}
\arguments{
\item{granges}{
The \code{GRanges} object whose \code{score} is to be interpolated.
The \code{\link{seqlength}}'s of this object may not be NULL.
}
\item{seqnames}{
The subset of seqnames for which to do the interpolation. If
\code{NULL}, all \code{unique(seqnames(granges))} are taken.
}
\item{wanted.dist}{
Passed to \code{\link{uuutils::zeroterminate.islands}}
}
\item{max.dist}{
Passed to \code{\link{uuutils::zeroterminate.islands}}
}
\item{every}{
Interpolate every this many base pairs.
}
}
\details{
To avoid interpolation over too long distances, the 'islands' in the
data are first 'zero-terminated' on either side using
\code{\link{uuutils::zeroterminate.islands}}. This makes sure that,
for the simple linear interpolation done subsequently, the complete
gap is interpolated as having the value zero.
}
\value{
A \code{GRanges} object with, with 1-width ranges at the interpolate.scored
points, and \code{score} the interpolated values.
}
\author{
Philip Lijnzaad <plijnzaad@gmail.com>
}
\seealso{
\code{\link{uuutils::zeroterminate.islands}},\code{\link{granges.apply}}
}
\note{ Don't be seduced by the luscious curves of spline interpolation.
They are visually attractive but inappropriate as they
overshoot wildly. Better stick to the straight and narrow, as
in real life.
}
\examples{
gr <- GRanges(ranges=IRanges(start=seq(1, 1000, by=100),width=1),
score=rnorm(10),
seqnames=factor('foo'),strand='*', seqlengths=c(foo=1000))
interpolation <- interpolate.score(gr)
}
\keyword{misc}
|
fcsFile<-system.file("extdata/List-modeDataFiles","int-10_events_6_parameters.fcs",package="gatingMLData")
gateFile <- system.file("extdata/Gating-MLFiles","23TrLn.xml",package="gatingMLData")
csvFile<-paste(system.file("extdata/ExpectedResults/23TrLn",package="gatingMLData"))
flowEnv=new.env()
read.gatingML(gateFile,flowEnv)
fcs <- read.FCS(fcsFile,transformation=FALSE)
test.TrLnG1<- function() {
gateId<-"TrLnG1"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG2<- function() {
gateId<-"TrLnG2"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG3<- function() {
gateId<-"TrLnG3"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG4<- function() {
gateId<-"TrLnG4"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG5<- function() {
gateId<-"TrLnG5"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG6<- function() {
gateId<-"TrLnG6"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
} | /inst/RUnitScript_Files/runit.23TrLn.R | no_license | jspidlen/flowUtils | R | false | false | 1,689 | r | fcsFile<-system.file("extdata/List-modeDataFiles","int-10_events_6_parameters.fcs",package="gatingMLData")
gateFile <- system.file("extdata/Gating-MLFiles","23TrLn.xml",package="gatingMLData")
csvFile<-paste(system.file("extdata/ExpectedResults/23TrLn",package="gatingMLData"))
flowEnv=new.env()
read.gatingML(gateFile,flowEnv)
fcs <- read.FCS(fcsFile,transformation=FALSE)
test.TrLnG1<- function() {
gateId<-"TrLnG1"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG2<- function() {
gateId<-"TrLnG2"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG3<- function() {
gateId<-"TrLnG3"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG4<- function() {
gateId<-"TrLnG4"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG5<- function() {
gateId<-"TrLnG5"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
}
test.TrLnG6<- function() {
gateId<-"TrLnG6"
csvFile<-paste(csvFile,"/",gateId,".txt",sep="")
expectedResult<-read.csv(csvFile,header=TRUE)
flowUtils:::performGateTest(gateId,fcs,expectedResult,flowEnv)
} |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pdfAndProb.R
\name{getProbForDiscrete}
\alias{getProbForDiscrete}
\title{Get a probability of a discrete value.}
\usage{
getProbForDiscrete(data, value)
}
\arguments{
\item{data}{vector of observations that have the same type as the given value.}
\item{value}{a single observation of the same type as the data vector.}
}
\value{
the probability of value given data.
}
\description{
Similar to @seealso \code{estimatePdf}, this function returns
the probability for a discrete value, given some observations.
}
\note{
If no observations are given, then this function will warn and return
a probability of zero for the value given. While we could technically return
positive infinity, 0 is more suitable in the context of Bayesian inferencing.
}
\examples{
mmb::getProbForDiscrete(data = c(), value = iris[1,]$Species)
mmb::getProbForDiscrete(data = iris$Species, value = iris[1,]$Species)
}
\author{
Sebastian Hönel \href{mailto:sebastian.honel@lnu.se}{sebastian.honel@lnu.se}
}
\keyword{likelihood}
\keyword{probability}
| /man/getProbForDiscrete.Rd | no_license | cran/mmb | R | false | true | 1,134 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pdfAndProb.R
\name{getProbForDiscrete}
\alias{getProbForDiscrete}
\title{Get a probability of a discrete value.}
\usage{
getProbForDiscrete(data, value)
}
\arguments{
\item{data}{vector of observations that have the same type as the given value.}
\item{value}{a single observation of the same type as the data vector.}
}
\value{
the probability of value given data.
}
\description{
Similar to @seealso \code{estimatePdf}, this function returns
the probability for a discrete value, given some observations.
}
\note{
If no observations are given, then this function will warn and return
a probability of zero for the value given. While we could technically return
positive infinity, 0 is more suitable in the context of Bayesian inferencing.
}
\examples{
mmb::getProbForDiscrete(data = c(), value = iris[1,]$Species)
mmb::getProbForDiscrete(data = iris$Species, value = iris[1,]$Species)
}
\author{
Sebastian Hönel \href{mailto:sebastian.honel@lnu.se}{sebastian.honel@lnu.se}
}
\keyword{likelihood}
\keyword{probability}
|
# 1. Loading and Cleaning Data ------------------------------------------------------------
# This creates a clean data file to merge with pre-NOV 2020 abstractions
# This cleans data from final NOV 2020 abstraction (N=93 aggregate studies)
library(dplyr)
setwd("/Users/kristinandrejko/Box/01-Research/02-SP-AMR/LancetMicrobe-Revision/PneumoAMR-LMicrobe")
addt_93 <- read.csv("data/data_addt_93_final.csv") #Last pull of data from the 2007-2009 papers and 2020 update completed 12-2-20, note I manually removed Okade data from excel
length(unique(addt_93$studyID)) #93
#pcv_intro <- read_excel("data/PCV Vaccine Intro.xlsx") #found tiny errors on intro date, update to new sheet
pcv_intro <- read_excel("data/PCV Vaccine Intro Updated Nov2020.xlsx") #found tiny errors on intro date, update to new sheetincome_dta <- read_excel("data/income_status_worldbank.xlsx")
gbd_region_new <- read_excel("data/gbd_region_new_r.xlsx")
world_bank_gdp_new <- read_excel("data/world_bank_gdp_new2.xlsx")
addt_93 <- addt_93 %>% dplyr::select(-serotype.data.)
all_data4 <- addt_93
length(unique(all_data4$studyID)) #93
#levels(factor(extra_data3$country[!(extra_data3$country %in% all_data2$country)])) #this was used when merging names
levels(factor(all_data4$country[!(all_data4$country %in% world_bank_gdp_new$country)])) #this was used when merging names
#Countries to fix from addt_93:
# England -> United Kingdom
# United States -> United States of America
# Taiwan
all_data4$country <- as.character(all_data4$country)
all_data4$country[all_data4$country == "United States"] <- "United States of America"
all_data4$country[all_data4$country == "United States "] <- "United States of America"
all_data4$country[all_data4$country == "Taiwan"] <- "China"
all_data4$country[all_data4$country == "England"] <- "United Kingdom"
#1. Merge Data
newmerge <- merge(gbd_region_new, pcv_intro, by = "country", all = T) #replace gbd_region_new with who_region
newmerge <- merge(newmerge, income_dta, by = "country", all = T)
#2. Merge with wide_dta
fulldta <- left_join(all_data4, newmerge, by = "country") #added all_data instead of wide_dta
length(unique(all_data4$country))
length(unique(fulldta$country))
#3. Merge with GDP per capital - haven't done this yet!
#3.1 Items which are in fulldta which are not in world_bank_gdp_new
levels(factor(fulldta$country[!(fulldta$country %in% gbd_region_new$country)]))
levels(factor(fulldta$country[!(fulldta$country %in% pcv_intro$country)]))
levels(factor(fulldta$country[!(fulldta$country %in% world_bank_gdp_new$country)]))
#4. melt world_bank_gdp (need to convert world_bank_gdp into data frame for melt to work)
world_bank_gdp_new_v2 <- as.data.frame(world_bank_gdp_new)
world_bank_gdp_new_v2$'2020' <- world_bank_gdp_new_v2$"2019" #add 2020 data that is the same as 2019
world_bank_gdp_melt <- melt(data = world_bank_gdp_new_v2,
id.vars= "country",
measure.vars= c("1973", "1974", "1974", "1978", "1979", "1980", "1981", "1982",
"1983", "1984", "1985", "1986", "1987", "1989", "1990", "1991",
"1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999",
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015",
"2016", "2017", "2018", "2019", "2020"),
variable.name = "midpoint_yr",
value.name = "gdp")
colnames(world_bank_gdp_melt)[2] <- "midpoint_yr"
colnames(world_bank_gdp_melt)[3] <- "gdp"
#4.1. convert world_bank_gdp_melt from factor to numeric
world_bank_gdp_melt$midpoint_yr <- as.numeric(levels(world_bank_gdp_melt$midpoint_yr))[world_bank_gdp_melt$midpoint_yr]
#5 Create midpoint_yr value
fulldta <- fulldta %>%
mutate(midpoint_yr = ((sample_collection_endyear - sample_collection_startyear) / 2) + sample_collection_startyear)
#5.1 Round midpoint_year up
fulldta$midpoint_yr <- round(fulldta$midpoint_yr, digits = 0)
#6. Merge GDP with data set- a lot of these are NA's becuase midpoint year is not a whole number
fulldta <- left_join(fulldta, world_bank_gdp_melt, by = c("country", "midpoint_yr"))
#Check where there are not values of GDP and manually replace
na_df <- fulldta[is.na(fulldta$gdp),]
fulldta[293, "gdp"] <- 15068.982 #Turkey GDP in 2020
nrow(na_df)
#7. Add yr since vax (used start year of sample collection rather than midpoint yr bc otherwise 90 studies had pre = 0 and yr since vax >0)
fulldta <- fulldta %>%
mutate(yr_since_vax = case_when(as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) > 0 ~ sample_collection_startyear - as.numeric(pcv_intro_year),
as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) == 0 ~ 0,
as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) < 0 ~ 0,
pcv_intro_year == "NI" ~ 0)) #pre
length(which(is.na(fulldta$yr_since_vax)))
check <- fulldta %>%
dplyr::select(pcv_intro_year, sample_collection_endyear, sample_collection_startyear, yr_since_vax)
#View(check)
#8. Add prepost variable
fulldta <- fulldta %>%
mutate(prepost = case_when(as.numeric(pcv_intro_year) == sample_collection_startyear ~ "no_analysis",
as.numeric(pcv_intro_year) == sample_collection_endyear ~ "no_analysis",
sample_collection_startyear - as.numeric(pcv_intro_year) == 1 ~ "no_analysis",
as.numeric(pcv_intro_year) > sample_collection_endyear ~ "naive",
sample_collection_startyear - as.numeric(pcv_intro_year) >= 3 ~ "not_naive", #changed from 5
#sample_collection_startyear - as.numeric(pcv_intro_year) == 4 ~ "no_analysis",
#sample_collection_startyear - as.numeric(pcv_intro_year) == 3 ~ "no_analysis",
sample_collection_startyear - as.numeric(pcv_intro_year) == 2 ~ "no_analysis",
pcv_intro_year == "NI" ~ "naive"))
fulldta$prepost[is.na(fulldta$prepost)] <- "no_analysis"
length(which(is.na(fulldta$prepost)))
#8.1 Add prepost variable for meta-regression analysis
fulldta <- fulldta %>%
mutate(prepost_meta = case_when(yr_since_vax >= 1 ~ "post",
yr_since_vax == 0 ~ "pre"))
# check <- fulldta %>%
# dplyr::select(prepost_meta, yr_since_vax)
# View(check)
#9. Recode study ID to r_id
studyid_df <- fulldta %>%
group_by(studyID) %>%
summarize(var = mean(gdp))
studyid_df <- data.frame(studyid_df)
studyid_df <- studyid_df %>%
mutate(r_id = row_number())
length(unique(studyid_df$studyID))
length(unique(studyid_df$r_id)) #392
studyid_df <- studyid_df[,-c(2)]
fulldta <- merge(studyid_df, fulldta, by = "studyID")
nrow(fulldta)
names(fulldta)
fulldta$r_id <- fulldta$r_id + 5000 #add 450 to each of the r_id to not mess up the earlier stuff -> CHANGE TO 5000
unique(fulldta$r_id)
clsi_list <- c("National Committee for Clinical Laboratory Standards",
"National Committee for Clinical Laboratory Standards " ,
"NCCLS",
"CLSI were used for sus, int, res; EUCAST was used for benzylpenicillin")
for(i in 1:nrow(fulldta)){
fulldta$criteria_cl[i] <- ifelse(fulldta$criteria_specify[i] %in% clsi_list, 1, fulldta$criteria[i])
}
#Clean drug_class variable!!
fulldta$drug_class <- as.character(fulldta$drug)
#fulldta$drug_class <- as.character(fulldta$drug_class)
fulldta$drug_class[fulldta$drug_class == "Ampicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Amoxacillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Penicilin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "penicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Ceftriaxone"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Tetracycline"] <- "tetracycline"
fulldta$drug_class[fulldta$drug_class == "Cotrimoxazole"] <- "SXT"
fulldta$drug_class[fulldta$drug_class == "Cefotaxime"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Ceftoxamine"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Ceftiaxone"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Cefuroxime"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Cefuroxime "] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Amoxicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Clindamycin"] <- "tetracycline" #CHECK
fulldta$drug_class[fulldta$drug_class == "Cefaclor"] <- "3gen_cephalosporin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Imipenem"] <- "imipenem" #REMOVE
fulldta$drug_class[fulldta$drug_class == "Levofloxacin"] <- "fluoroquinolone" #CHECK
fulldta$drug_class[fulldta$drug_class == "Vancomycin"] <- "vancomycin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Chloramphenicol"] <- "chloramphenicol" #CHECK
fulldta$drug_class[fulldta$drug_class == "Ciprofloxacin"] <- "ciprofloxacin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Linezolid"] <- "linezolid"
fulldta$drug_class[fulldta$drug_class == "Penicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Erythromycin"] <- "macrolide"
fulldta$drug_class[fulldta$drug_class == "Azithromycn"] <- "macrolide"
table(fulldta$drug, fulldta$drug_class)
extra_data4 <- fulldta
length(unique(extra_data4$studyID)) #93
length(unique(extra_data4$r_id)) #93
range(extra_data4$studyID, na.rm = T); range(extra_data4$r_id, na.rm = T)
save(extra_data4, file = "data/data_120220.rda")
View(extra_data4)
#Take extra_data4 and select doi, country, sample_collection_startyear, sample_collection_endyear, prepost
finalTable <- extra_data4 %>%
dplyr::select(author, doi, pub_year, country, sample_collection_startyear, sample_collection_endyear, prepost, total_isolates_drug)
View(finalTable)
| /code/00-PneumoAMR-LMicrobe-DataCleaning-AddtStudies.R | no_license | joelewnard/amrPneumo | R | false | false | 10,224 | r | # 1. Loading and Cleaning Data ------------------------------------------------------------
# This creates a clean data file to merge with pre-NOV 2020 abstractions
# This cleans data from final NOV 2020 abstraction (N=93 aggregate studies)
library(dplyr)
setwd("/Users/kristinandrejko/Box/01-Research/02-SP-AMR/LancetMicrobe-Revision/PneumoAMR-LMicrobe")
addt_93 <- read.csv("data/data_addt_93_final.csv") #Last pull of data from the 2007-2009 papers and 2020 update completed 12-2-20, note I manually removed Okade data from excel
length(unique(addt_93$studyID)) #93
#pcv_intro <- read_excel("data/PCV Vaccine Intro.xlsx") #found tiny errors on intro date, update to new sheet
pcv_intro <- read_excel("data/PCV Vaccine Intro Updated Nov2020.xlsx") #found tiny errors on intro date, update to new sheetincome_dta <- read_excel("data/income_status_worldbank.xlsx")
gbd_region_new <- read_excel("data/gbd_region_new_r.xlsx")
world_bank_gdp_new <- read_excel("data/world_bank_gdp_new2.xlsx")
addt_93 <- addt_93 %>% dplyr::select(-serotype.data.)
all_data4 <- addt_93
length(unique(all_data4$studyID)) #93
#levels(factor(extra_data3$country[!(extra_data3$country %in% all_data2$country)])) #this was used when merging names
levels(factor(all_data4$country[!(all_data4$country %in% world_bank_gdp_new$country)])) #this was used when merging names
#Countries to fix from addt_93:
# England -> United Kingdom
# United States -> United States of America
# Taiwan
all_data4$country <- as.character(all_data4$country)
all_data4$country[all_data4$country == "United States"] <- "United States of America"
all_data4$country[all_data4$country == "United States "] <- "United States of America"
all_data4$country[all_data4$country == "Taiwan"] <- "China"
all_data4$country[all_data4$country == "England"] <- "United Kingdom"
#1. Merge Data
newmerge <- merge(gbd_region_new, pcv_intro, by = "country", all = T) #replace gbd_region_new with who_region
newmerge <- merge(newmerge, income_dta, by = "country", all = T)
#2. Merge with wide_dta
fulldta <- left_join(all_data4, newmerge, by = "country") #added all_data instead of wide_dta
length(unique(all_data4$country))
length(unique(fulldta$country))
#3. Merge with GDP per capital - haven't done this yet!
#3.1 Items which are in fulldta which are not in world_bank_gdp_new
levels(factor(fulldta$country[!(fulldta$country %in% gbd_region_new$country)]))
levels(factor(fulldta$country[!(fulldta$country %in% pcv_intro$country)]))
levels(factor(fulldta$country[!(fulldta$country %in% world_bank_gdp_new$country)]))
#4. melt world_bank_gdp (need to convert world_bank_gdp into data frame for melt to work)
world_bank_gdp_new_v2 <- as.data.frame(world_bank_gdp_new)
world_bank_gdp_new_v2$'2020' <- world_bank_gdp_new_v2$"2019" #add 2020 data that is the same as 2019
world_bank_gdp_melt <- melt(data = world_bank_gdp_new_v2,
id.vars= "country",
measure.vars= c("1973", "1974", "1974", "1978", "1979", "1980", "1981", "1982",
"1983", "1984", "1985", "1986", "1987", "1989", "1990", "1991",
"1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999",
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015",
"2016", "2017", "2018", "2019", "2020"),
variable.name = "midpoint_yr",
value.name = "gdp")
colnames(world_bank_gdp_melt)[2] <- "midpoint_yr"
colnames(world_bank_gdp_melt)[3] <- "gdp"
#4.1. convert world_bank_gdp_melt from factor to numeric
world_bank_gdp_melt$midpoint_yr <- as.numeric(levels(world_bank_gdp_melt$midpoint_yr))[world_bank_gdp_melt$midpoint_yr]
#5 Create midpoint_yr value
fulldta <- fulldta %>%
mutate(midpoint_yr = ((sample_collection_endyear - sample_collection_startyear) / 2) + sample_collection_startyear)
#5.1 Round midpoint_year up
fulldta$midpoint_yr <- round(fulldta$midpoint_yr, digits = 0)
#6. Merge GDP with data set- a lot of these are NA's becuase midpoint year is not a whole number
fulldta <- left_join(fulldta, world_bank_gdp_melt, by = c("country", "midpoint_yr"))
#Check where there are not values of GDP and manually replace
na_df <- fulldta[is.na(fulldta$gdp),]
fulldta[293, "gdp"] <- 15068.982 #Turkey GDP in 2020
nrow(na_df)
#7. Add yr since vax (used start year of sample collection rather than midpoint yr bc otherwise 90 studies had pre = 0 and yr since vax >0)
fulldta <- fulldta %>%
mutate(yr_since_vax = case_when(as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) > 0 ~ sample_collection_startyear - as.numeric(pcv_intro_year),
as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) == 0 ~ 0,
as.numeric(sample_collection_startyear) - as.numeric(pcv_intro_year) < 0 ~ 0,
pcv_intro_year == "NI" ~ 0)) #pre
length(which(is.na(fulldta$yr_since_vax)))
check <- fulldta %>%
dplyr::select(pcv_intro_year, sample_collection_endyear, sample_collection_startyear, yr_since_vax)
#View(check)
#8. Add prepost variable
fulldta <- fulldta %>%
mutate(prepost = case_when(as.numeric(pcv_intro_year) == sample_collection_startyear ~ "no_analysis",
as.numeric(pcv_intro_year) == sample_collection_endyear ~ "no_analysis",
sample_collection_startyear - as.numeric(pcv_intro_year) == 1 ~ "no_analysis",
as.numeric(pcv_intro_year) > sample_collection_endyear ~ "naive",
sample_collection_startyear - as.numeric(pcv_intro_year) >= 3 ~ "not_naive", #changed from 5
#sample_collection_startyear - as.numeric(pcv_intro_year) == 4 ~ "no_analysis",
#sample_collection_startyear - as.numeric(pcv_intro_year) == 3 ~ "no_analysis",
sample_collection_startyear - as.numeric(pcv_intro_year) == 2 ~ "no_analysis",
pcv_intro_year == "NI" ~ "naive"))
fulldta$prepost[is.na(fulldta$prepost)] <- "no_analysis"
length(which(is.na(fulldta$prepost)))
#8.1 Add prepost variable for meta-regression analysis
fulldta <- fulldta %>%
mutate(prepost_meta = case_when(yr_since_vax >= 1 ~ "post",
yr_since_vax == 0 ~ "pre"))
# check <- fulldta %>%
# dplyr::select(prepost_meta, yr_since_vax)
# View(check)
#9. Recode study ID to r_id
studyid_df <- fulldta %>%
group_by(studyID) %>%
summarize(var = mean(gdp))
studyid_df <- data.frame(studyid_df)
studyid_df <- studyid_df %>%
mutate(r_id = row_number())
length(unique(studyid_df$studyID))
length(unique(studyid_df$r_id)) #392
studyid_df <- studyid_df[,-c(2)]
fulldta <- merge(studyid_df, fulldta, by = "studyID")
nrow(fulldta)
names(fulldta)
fulldta$r_id <- fulldta$r_id + 5000 #add 450 to each of the r_id to not mess up the earlier stuff -> CHANGE TO 5000
unique(fulldta$r_id)
clsi_list <- c("National Committee for Clinical Laboratory Standards",
"National Committee for Clinical Laboratory Standards " ,
"NCCLS",
"CLSI were used for sus, int, res; EUCAST was used for benzylpenicillin")
for(i in 1:nrow(fulldta)){
fulldta$criteria_cl[i] <- ifelse(fulldta$criteria_specify[i] %in% clsi_list, 1, fulldta$criteria[i])
}
#Clean drug_class variable!!
fulldta$drug_class <- as.character(fulldta$drug)
#fulldta$drug_class <- as.character(fulldta$drug_class)
fulldta$drug_class[fulldta$drug_class == "Ampicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Amoxacillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Penicilin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "penicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Ceftriaxone"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Tetracycline"] <- "tetracycline"
fulldta$drug_class[fulldta$drug_class == "Cotrimoxazole"] <- "SXT"
fulldta$drug_class[fulldta$drug_class == "Cefotaxime"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Ceftoxamine"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Ceftiaxone"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Cefuroxime"] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Cefuroxime "] <- "3gen_cephalosporin"
fulldta$drug_class[fulldta$drug_class == "Amoxicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Clindamycin"] <- "tetracycline" #CHECK
fulldta$drug_class[fulldta$drug_class == "Cefaclor"] <- "3gen_cephalosporin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Imipenem"] <- "imipenem" #REMOVE
fulldta$drug_class[fulldta$drug_class == "Levofloxacin"] <- "fluoroquinolone" #CHECK
fulldta$drug_class[fulldta$drug_class == "Vancomycin"] <- "vancomycin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Chloramphenicol"] <- "chloramphenicol" #CHECK
fulldta$drug_class[fulldta$drug_class == "Ciprofloxacin"] <- "ciprofloxacin" #CHECK
fulldta$drug_class[fulldta$drug_class == "Linezolid"] <- "linezolid"
fulldta$drug_class[fulldta$drug_class == "Penicillin"] <- "penicillin"
fulldta$drug_class[fulldta$drug_class == "Erythromycin"] <- "macrolide"
fulldta$drug_class[fulldta$drug_class == "Azithromycn"] <- "macrolide"
table(fulldta$drug, fulldta$drug_class)
extra_data4 <- fulldta
length(unique(extra_data4$studyID)) #93
length(unique(extra_data4$r_id)) #93
range(extra_data4$studyID, na.rm = T); range(extra_data4$r_id, na.rm = T)
save(extra_data4, file = "data/data_120220.rda")
View(extra_data4)
#Take extra_data4 and select doi, country, sample_collection_startyear, sample_collection_endyear, prepost
finalTable <- extra_data4 %>%
dplyr::select(author, doi, pub_year, country, sample_collection_startyear, sample_collection_endyear, prepost, total_isolates_drug)
View(finalTable)
|
# Revision Date: 01-02-2016
outlyx <- function(x, iqr=TRUE, iqrP=2, pc=1,
mv=TRUE, mvP=0.15, plot=TRUE, ...) { # {{{
### Computes outliers within methylomic datasets
# Arguments:
# x : Methylumi/Minfi object or raw betas.
#
# pc : The desired principal component for outlier identification
#
# iqr : Logical, to indicate whether to determine outliers by
# interquartile ranges.
#
# iqrP : The number of interquartile ranges one wishes to
# discriminate from the upper and lower quartiles.
# Default = 2, Tukey's rule suggests 1.5
#
# mv : Logical, to indicate whether to determine outliers
# using distance measures using a modified version of pcout
# from mvoutlier.
#
# mvP : The threshold bywhich one wishes to screen
# data based on the final weight output from
# the pcout function
# Value between 0 and 1. Default is 0.15
# plot : Logical, to indicate if a graphical device to display
# sample outlyingness.
#
###
# Initialising objects to be used:
df <- list() # Converted into dataframe later on.
# Removing probes with NA values.
x <- na.omit(x)
if(iqr){ # {{{
pccompbetx <- prcomp(x, retx=FALSE)
out1 <- iqrFun(pccompbetx$rot, pc=pc, iqrP=iqrP)
v1 <- colnames(x) %in% out1[[1]]==TRUE
df[["iqr"]] <- v1
low <- min(c(min(pccompbetx$rot[,pc]),out1[["low"]]))-out1[[2]] # Used if Plot=T
upp <- max(c(max(pccompbetx$rot[,pc]),out1[["hi"]]))+out1[[2]] # Used if Plot=T
} # }}}
if(mv){ # {{{
tbetx <- t(x)
out2 <- mvFun(tbetx, mvP=mvP)
v2 <- colnames(x) %in% out2[[1]]==TRUE
df[["mv"]] <- v2
} # }}}
if(plot&mv&iqr){
plot(pccompbetx$rot[,pc],
out2[[2]],
xlim=c(low, upp),
xlab="Transformed Betas",
ylab="Final Weight", ...)
abline(v=c(out1[["low"]],out1[["hi"]]),
h=mvP,
lty=2)
rect(xleft=low,
xright=out1[["low"]],
ybottom=0,
ytop=mvP,
col="red",
density=10)
rect(xleft=out1[["hi"]],
xright=upp,
ybottom=0,
ytop=mvP,
col="red",
density=10)
}
# Possibly a better way to do the below.
df[["outliers"]] <- if(mv){df[["mv"]]==TRUE}&if(iqr){df[["iqr"]]==TRUE}
df <- data.frame(df)
rownames(df) <- colnames(x)
return(df)
} # }}}
iqrFun <- function(x, pc, iqrP){ # {{{
quantilesbx <- apply(x, 2, quantile) # fivenum also works
IQR <- quantilesbx[4, pc] - quantilesbx[2, pc]
thresh <- iqrP*IQR
hiOutlyx <- quantilesbx[4,pc] + thresh # Upper threshold
loOutlyx <- quantilesbx[2,pc] - thresh # Lower threshold
outhi <- x[, pc] > hiOutlyx # Upper Outliers
outlo <- x[, pc] < loOutlyx # Lower Outliers
return(list(c(rownames(x)[outhi], rownames(x)[outlo]),
IQR, "hi" = hiOutlyx, "low" = loOutlyx))
} # }}}
mvFun <- function(x, mvP, ...){ # {{{
pcoutbetx <- pcouted(x,...)
#pcout(x)
outmvbetx <- pcoutbetx < mvP
#pcoutbetx$wfinal < mvP
return(list(rownames(as.matrix(pcoutbetx[outmvbetx])),
pcoutbetx))
} # }}}
pcouted <- function(x,explvar=0.99,crit.M1=1/3,crit.c1=2.5,
crit.M2=1/4,crit.c2=0.99,cs=0.25,
outbound=0.25, ...){ # {{{
# Modified version of the pcout function from mvoutlier to
# calculated distance measures for methylomic data.
# Two minor things have been changed, mainly how the function
# copes with x.mad values = 0 and the output.
#
#
x.mad=apply(x,2,mad)
x <- x[,!x.mad==0] # Cheat to allow it to continue to function.
x.mad <- x.mad[!x.mad==0]
p = ncol(x)
n = nrow(x)
#
# PHASE 1:
# Step 1: robustly sphere the data:
x.sc <- scale(x,apply(x,2,median),x.mad)
# Step 2: PC decomposition; compute p*, robustly sphere:
x.svd <- svd(scale(x.sc,TRUE,FALSE))
a <- x.svd$d^2/(n-1)
p1 <- (1:p)[(cumsum(a)/sum(a)>explvar)][1]
x.pc <- x.sc%*%x.svd$v[,1:p1]
xpc.sc <- scale(x.pc,apply(x.pc,2,median),apply(x.pc,2,mad))
# Step 3: compute robust kurtosis weights, transform to distances:
wp <- abs(apply(xpc.sc^4,2,mean)-3)
xpcw.sc <- xpc.sc%*%diag(wp/sum(wp))
xpc.norm <- sqrt(apply(xpcw.sc^2,1,sum))
x.dist1 <- xpc.norm*sqrt(qchisq(0.5,p1))/median(xpc.norm)
# Step 4: determine weights according to translated biweight:
M1 <- quantile(x.dist1,crit.M1)
const1 <- median(x.dist1)+crit.c1*mad(x.dist1)
w1 <- (1-((x.dist1-M1)/(const1-M1))^2)^2
w1[x.dist1<M1] <- 1
w1[x.dist1>const1] <- 0
#
# PHASE 2:
# Step 5: compute Euclidean norms of PCs and their distances:
xpc.norm <- sqrt(apply(xpc.sc^2,1,sum))
x.dist2 <- xpc.norm*sqrt(qchisq(0.5,p1))/median(xpc.norm)
# Step 6: determine weight according to translated biweight:
M2 <- sqrt(qchisq(crit.M2,p1))
const2 <- sqrt(qchisq(crit.c2,p1))
w2 <- (1-((x.dist2-M2)/(const2-M2))^2)^2
w2[x.dist2<M2] <- 1
w2[x.dist2>const2] <- 0
#
# Combine PHASE1 and PHASE 2: compute final weights:
# Changed output slightly
wfinal <- (w1+cs)*(w2+cs)/((1+cs)^2)
return(wfinal)
} # }}}
| /R/outlyx.R | no_license | schalkwyk/wateRmelon | R | false | false | 5,248 | r | # Revision Date: 01-02-2016
outlyx <- function(x, iqr=TRUE, iqrP=2, pc=1,
mv=TRUE, mvP=0.15, plot=TRUE, ...) { # {{{
### Computes outliers within methylomic datasets
# Arguments:
# x : Methylumi/Minfi object or raw betas.
#
# pc : The desired principal component for outlier identification
#
# iqr : Logical, to indicate whether to determine outliers by
# interquartile ranges.
#
# iqrP : The number of interquartile ranges one wishes to
# discriminate from the upper and lower quartiles.
# Default = 2, Tukey's rule suggests 1.5
#
# mv : Logical, to indicate whether to determine outliers
# using distance measures using a modified version of pcout
# from mvoutlier.
#
# mvP : The threshold bywhich one wishes to screen
# data based on the final weight output from
# the pcout function
# Value between 0 and 1. Default is 0.15
# plot : Logical, to indicate if a graphical device to display
# sample outlyingness.
#
###
# Initialising objects to be used:
df <- list() # Converted into dataframe later on.
# Removing probes with NA values.
x <- na.omit(x)
if(iqr){ # {{{
pccompbetx <- prcomp(x, retx=FALSE)
out1 <- iqrFun(pccompbetx$rot, pc=pc, iqrP=iqrP)
v1 <- colnames(x) %in% out1[[1]]==TRUE
df[["iqr"]] <- v1
low <- min(c(min(pccompbetx$rot[,pc]),out1[["low"]]))-out1[[2]] # Used if Plot=T
upp <- max(c(max(pccompbetx$rot[,pc]),out1[["hi"]]))+out1[[2]] # Used if Plot=T
} # }}}
if(mv){ # {{{
tbetx <- t(x)
out2 <- mvFun(tbetx, mvP=mvP)
v2 <- colnames(x) %in% out2[[1]]==TRUE
df[["mv"]] <- v2
} # }}}
if(plot&mv&iqr){
plot(pccompbetx$rot[,pc],
out2[[2]],
xlim=c(low, upp),
xlab="Transformed Betas",
ylab="Final Weight", ...)
abline(v=c(out1[["low"]],out1[["hi"]]),
h=mvP,
lty=2)
rect(xleft=low,
xright=out1[["low"]],
ybottom=0,
ytop=mvP,
col="red",
density=10)
rect(xleft=out1[["hi"]],
xright=upp,
ybottom=0,
ytop=mvP,
col="red",
density=10)
}
# Possibly a better way to do the below.
df[["outliers"]] <- if(mv){df[["mv"]]==TRUE}&if(iqr){df[["iqr"]]==TRUE}
df <- data.frame(df)
rownames(df) <- colnames(x)
return(df)
} # }}}
iqrFun <- function(x, pc, iqrP){ # {{{
quantilesbx <- apply(x, 2, quantile) # fivenum also works
IQR <- quantilesbx[4, pc] - quantilesbx[2, pc]
thresh <- iqrP*IQR
hiOutlyx <- quantilesbx[4,pc] + thresh # Upper threshold
loOutlyx <- quantilesbx[2,pc] - thresh # Lower threshold
outhi <- x[, pc] > hiOutlyx # Upper Outliers
outlo <- x[, pc] < loOutlyx # Lower Outliers
return(list(c(rownames(x)[outhi], rownames(x)[outlo]),
IQR, "hi" = hiOutlyx, "low" = loOutlyx))
} # }}}
mvFun <- function(x, mvP, ...){ # {{{
pcoutbetx <- pcouted(x,...)
#pcout(x)
outmvbetx <- pcoutbetx < mvP
#pcoutbetx$wfinal < mvP
return(list(rownames(as.matrix(pcoutbetx[outmvbetx])),
pcoutbetx))
} # }}}
pcouted <- function(x,explvar=0.99,crit.M1=1/3,crit.c1=2.5,
crit.M2=1/4,crit.c2=0.99,cs=0.25,
outbound=0.25, ...){ # {{{
# Modified version of the pcout function from mvoutlier to
# calculated distance measures for methylomic data.
# Two minor things have been changed, mainly how the function
# copes with x.mad values = 0 and the output.
#
#
x.mad=apply(x,2,mad)
x <- x[,!x.mad==0] # Cheat to allow it to continue to function.
x.mad <- x.mad[!x.mad==0]
p = ncol(x)
n = nrow(x)
#
# PHASE 1:
# Step 1: robustly sphere the data:
x.sc <- scale(x,apply(x,2,median),x.mad)
# Step 2: PC decomposition; compute p*, robustly sphere:
x.svd <- svd(scale(x.sc,TRUE,FALSE))
a <- x.svd$d^2/(n-1)
p1 <- (1:p)[(cumsum(a)/sum(a)>explvar)][1]
x.pc <- x.sc%*%x.svd$v[,1:p1]
xpc.sc <- scale(x.pc,apply(x.pc,2,median),apply(x.pc,2,mad))
# Step 3: compute robust kurtosis weights, transform to distances:
wp <- abs(apply(xpc.sc^4,2,mean)-3)
xpcw.sc <- xpc.sc%*%diag(wp/sum(wp))
xpc.norm <- sqrt(apply(xpcw.sc^2,1,sum))
x.dist1 <- xpc.norm*sqrt(qchisq(0.5,p1))/median(xpc.norm)
# Step 4: determine weights according to translated biweight:
M1 <- quantile(x.dist1,crit.M1)
const1 <- median(x.dist1)+crit.c1*mad(x.dist1)
w1 <- (1-((x.dist1-M1)/(const1-M1))^2)^2
w1[x.dist1<M1] <- 1
w1[x.dist1>const1] <- 0
#
# PHASE 2:
# Step 5: compute Euclidean norms of PCs and their distances:
xpc.norm <- sqrt(apply(xpc.sc^2,1,sum))
x.dist2 <- xpc.norm*sqrt(qchisq(0.5,p1))/median(xpc.norm)
# Step 6: determine weight according to translated biweight:
M2 <- sqrt(qchisq(crit.M2,p1))
const2 <- sqrt(qchisq(crit.c2,p1))
w2 <- (1-((x.dist2-M2)/(const2-M2))^2)^2
w2[x.dist2<M2] <- 1
w2[x.dist2>const2] <- 0
#
# Combine PHASE1 and PHASE 2: compute final weights:
# Changed output slightly
wfinal <- (w1+cs)*(w2+cs)/((1+cs)^2)
return(wfinal)
} # }}}
|
c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 77330
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 75034
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 75034
c
c Input Parameter (command line, file):
c input filename QBFLIB/Gent-Rowley/Connect4/connect_7x6_3_W.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 97742
c no.of clauses 77330
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 75034
c
c QBFLIB/Gent-Rowley/Connect4/connect_7x6_3_W.qdimacs 97742 77330 E1 [10101 10102 10103 10104 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238 10239 10240 10241 10242 10301 10302 10303 10304 10305 10306 10307 10308 10309 10310 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320 10321 10322 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332 10333 10334 10335 10336 10337 10338 10339 10340 10341 10342 10401 10402 10403 10404 10405 10406 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416 10417 10418 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428 10429 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95327 95329 95331 95333 95335 95337 95339 95341 95401 95403 95405 95407 95409 95411 95413 95415 95417 95419 95421 95423 95425 95427 95429 95431 95433 95435 95437 95439 95441 95501 95503 95505 95507 95509 95511 95513 95515 95517 95519 95521 95523 95525 95527 95529 95531 95533 95535 95537 95539 95541 95601 95603 95605 95607 95609 95611 95613 95615 95617 95619 95621 95623 95625 95627 95629 95631 95633 95635 95637 95639 95641 95701 95703 95705 95707 95709 95711 95713 95715 95717 95719 95721 95723 95725 95727 95729 95731 95733 95735 95737 95739 95741 96101 96103 96105 96107 96109 96111 96113 96115 96117 96119 96121 96123 96125 96127 96129 96131 96133 96135 96137 96139 96141 96201 96203 96205 96207 96209 96211 96213 96215 96217 96219 96221 96223 96225 96227 96229 96231 96233 96235 96237 96239 96241 96301 96303 96305 96307 96309 96311 96313 96315 96317 96319 96321 96323 96325 96327 96329 96331 96333 96335 96337 96339 96341 96401 96403 96405 96407 96409 96411 96413 96415 96417 96419 96421 96423 96425 96427 96429 96431 96433 96435 96437 96439 96441 96501 96503 96505 96507 96509 96511 96513 96515 96517 96519 96521 96523 96525 96527 96529 96531 96533 96535 96537 96539 96541 96601 96603 96605 96607 96609 96611 96613 96615 96617 96619 96621 96623 96625 96627 96629 96631 96633 96635 96637 96639 96641 96701 96703 96705 96707 96709 96711 96713 96715 96717 96719 96721 96723 96725 96727 96729 96731 96733 96735 96737 96739 96741] 0 147 18092 75034 RED
| /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Gent-Rowley/Connect4/connect_7x6_3_W/connect_7x6_3_W.R | no_license | arey0pushpa/dcnf-autarky | R | false | false | 18,033 | r | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 77330
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 75034
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 75034
c
c Input Parameter (command line, file):
c input filename QBFLIB/Gent-Rowley/Connect4/connect_7x6_3_W.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 97742
c no.of clauses 77330
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 75034
c
c QBFLIB/Gent-Rowley/Connect4/connect_7x6_3_W.qdimacs 97742 77330 E1 [10101 10102 10103 10104 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238 10239 10240 10241 10242 10301 10302 10303 10304 10305 10306 10307 10308 10309 10310 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320 10321 10322 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332 10333 10334 10335 10336 10337 10338 10339 10340 10341 10342 10401 10402 10403 10404 10405 10406 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416 10417 10418 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428 10429 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|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/3_ComplexModels.R
\name{M_M_INF}
\alias{M_M_INF}
\title{Obtains the main characteristics of a M/M/\eqn{\infty} queueing model}
\usage{
M_M_INF(lambda = 3, mu = 6)
}
\arguments{
\item{lambda}{Mean arrival rate}
\item{mu}{Mean service rate}
}
\value{
Returns the next information of a M/M/\eqn{\infty} model:
\item{rho}{Constant coefficient: \eqn{\lambda/\mu}}
\item{barrho}{Traffic intensity: \ifelse{latex}{\eqn{\bar{\rho}}}{\out{<i>͞ρ</i>}}}
\item{p0}{Probability of empty system: \ifelse{latex}{\eqn{P_{0}}}{\out{<i>P<sub>0</sub></i>}}}
\item{l}{Number of customers in the system: \eqn{L}}
\item{lq}{Number of customers in the queue: \ifelse{latex}{\eqn{L_q} (\eqn{L_q} = 0 in this model)}{\out{<i>L<sub>q</sub> (L<sub>q</sub> = 0 in this model)</i>}}}
\item{w}{Waiting time in the system: \eqn{W}}
\item{wq}{Waiting time in the queue: \ifelse{latex}{\eqn{W_q} (\eqn{W_q} = 0 in this model)}{\out{<i>W<sub>q</sub> (W<sub>q</sub> = 0 in this model)</i>}}}
\item{eff}{System efficiency: \ifelse{latex}{\eqn{Eff = W/(W-W_q)}}{\out{<i>Eff = W/(W-W<sub>q</sub></i>)}}}
}
\description{
Obtains the main characteristics of a M/M/\eqn{\infty} queueing model
}
\examples{
#The number of people turning on their television sets
#on Saturday evening during prime time can be described
#rather well by a Poisson distribution with a mean of
#100000/hr.
#There are five major TV stations, and a given person
#choose among these essentially at random.
#Surveys have also shown that the average person tunes
#in for 90 min and that viewing times are approximately
#exponentially distributed.
M_M_INF(lambda=100000/5, mu=60/90)
}
\seealso{
Other AnaliticalModels: \code{\link{ClosedJacksonNetwork}};
\code{\link{M_M_1_INF_H}}; \code{\link{M_M_1_K}};
\code{\link{M_M_1}}; \code{\link{M_M_S_INF_H_Y}};
\code{\link{M_M_S_INF_H}}; \code{\link{M_M_S_K}};
\code{\link{M_M_S}}; \code{\link{OpenJacksonNetwork}}
}
| /man/M_M_INF.Rd | no_license | ghobbs9495/arqas | R | false | false | 2,043 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/3_ComplexModels.R
\name{M_M_INF}
\alias{M_M_INF}
\title{Obtains the main characteristics of a M/M/\eqn{\infty} queueing model}
\usage{
M_M_INF(lambda = 3, mu = 6)
}
\arguments{
\item{lambda}{Mean arrival rate}
\item{mu}{Mean service rate}
}
\value{
Returns the next information of a M/M/\eqn{\infty} model:
\item{rho}{Constant coefficient: \eqn{\lambda/\mu}}
\item{barrho}{Traffic intensity: \ifelse{latex}{\eqn{\bar{\rho}}}{\out{<i>͞ρ</i>}}}
\item{p0}{Probability of empty system: \ifelse{latex}{\eqn{P_{0}}}{\out{<i>P<sub>0</sub></i>}}}
\item{l}{Number of customers in the system: \eqn{L}}
\item{lq}{Number of customers in the queue: \ifelse{latex}{\eqn{L_q} (\eqn{L_q} = 0 in this model)}{\out{<i>L<sub>q</sub> (L<sub>q</sub> = 0 in this model)</i>}}}
\item{w}{Waiting time in the system: \eqn{W}}
\item{wq}{Waiting time in the queue: \ifelse{latex}{\eqn{W_q} (\eqn{W_q} = 0 in this model)}{\out{<i>W<sub>q</sub> (W<sub>q</sub> = 0 in this model)</i>}}}
\item{eff}{System efficiency: \ifelse{latex}{\eqn{Eff = W/(W-W_q)}}{\out{<i>Eff = W/(W-W<sub>q</sub></i>)}}}
}
\description{
Obtains the main characteristics of a M/M/\eqn{\infty} queueing model
}
\examples{
#The number of people turning on their television sets
#on Saturday evening during prime time can be described
#rather well by a Poisson distribution with a mean of
#100000/hr.
#There are five major TV stations, and a given person
#choose among these essentially at random.
#Surveys have also shown that the average person tunes
#in for 90 min and that viewing times are approximately
#exponentially distributed.
M_M_INF(lambda=100000/5, mu=60/90)
}
\seealso{
Other AnaliticalModels: \code{\link{ClosedJacksonNetwork}};
\code{\link{M_M_1_INF_H}}; \code{\link{M_M_1_K}};
\code{\link{M_M_1}}; \code{\link{M_M_S_INF_H_Y}};
\code{\link{M_M_S_INF_H}}; \code{\link{M_M_S_K}};
\code{\link{M_M_S}}; \code{\link{OpenJacksonNetwork}}
}
|
#' DSAIDE: A package to learn about Dynamical Systems Approaches to Infectious
#' Disease Epidemiology
#'
#' The DSAIDE package provides a number of shiny apps that simulate various
#' infectious disease dynamics models By manipulating the models and working
#' through the instructions provided within the shiny UI, you can learn about
#' some important concepts in infectious disease epidemiology. You will also
#' learn how models can be used to study such concepts.
#'
#' Start the main menu of the package by calling dsaidemenu().
#'
#' To learn more about how to use the package, see the vignette
#' or the short introduction on the package github repository.
#' https://github.com/ahgroup/DSAIDE
#'
#' @docType package
#' @name DSAIDE
NULL
| /R/DSAIDE.R | no_license | cgolden1993/DSAIDE | R | false | false | 750 | r | #' DSAIDE: A package to learn about Dynamical Systems Approaches to Infectious
#' Disease Epidemiology
#'
#' The DSAIDE package provides a number of shiny apps that simulate various
#' infectious disease dynamics models By manipulating the models and working
#' through the instructions provided within the shiny UI, you can learn about
#' some important concepts in infectious disease epidemiology. You will also
#' learn how models can be used to study such concepts.
#'
#' Start the main menu of the package by calling dsaidemenu().
#'
#' To learn more about how to use the package, see the vignette
#' or the short introduction on the package github repository.
#' https://github.com/ahgroup/DSAIDE
#'
#' @docType package
#' @name DSAIDE
NULL
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Sql.R
\name{lowLevelQuerySql.ffdf}
\alias{lowLevelQuerySql.ffdf}
\title{Low level function for retrieving data to an ffdf object}
\usage{
lowLevelQuerySql.ffdf(connection, query = "", batchSize = "auto",
datesAsString = FALSE)
}
\arguments{
\item{connection}{The connection to the database server.}
\item{query}{The SQL statement to retrieve the data}
\item{batchSize}{The number of rows that will be retrieved at a time from the server. A larger
batchSize means less calls to the server so better performance, but too large
a batchSize could lead to out-of-memory errors. The default is "auto", meaning
heuristics will determine the appropriate batch size.}
\item{datesAsString}{Should dates be imported as character vectors, our should they be converted
to R's date format?}
}
\value{
A ffdf object containing the data. If there are 0 rows, a regular data frame is returned instead
(ffdf cannot have 0 rows)
}
\description{
This is the equivalent of the \code{\link{querySql.ffdf}} function, except no error report is
written when an error occurs.
}
\details{
Retrieves data from the database server and stores it in an ffdf object. This allows very large
data sets to be retrieved without running out of memory.
}
| /man/lowLevelQuerySql.ffdf.Rd | permissive | TriNetX/DatabaseConnector | R | false | true | 1,300 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Sql.R
\name{lowLevelQuerySql.ffdf}
\alias{lowLevelQuerySql.ffdf}
\title{Low level function for retrieving data to an ffdf object}
\usage{
lowLevelQuerySql.ffdf(connection, query = "", batchSize = "auto",
datesAsString = FALSE)
}
\arguments{
\item{connection}{The connection to the database server.}
\item{query}{The SQL statement to retrieve the data}
\item{batchSize}{The number of rows that will be retrieved at a time from the server. A larger
batchSize means less calls to the server so better performance, but too large
a batchSize could lead to out-of-memory errors. The default is "auto", meaning
heuristics will determine the appropriate batch size.}
\item{datesAsString}{Should dates be imported as character vectors, our should they be converted
to R's date format?}
}
\value{
A ffdf object containing the data. If there are 0 rows, a regular data frame is returned instead
(ffdf cannot have 0 rows)
}
\description{
This is the equivalent of the \code{\link{querySql.ffdf}} function, except no error report is
written when an error occurs.
}
\details{
Retrieves data from the database server and stores it in an ffdf object. This allows very large
data sets to be retrieved without running out of memory.
}
|
# user options
my.runs<-
c('Single_species', # Single species
'Diri_Uniform_noNumber_noMesh_Sum_type2',
'Diri_Uniform_noNumber_noMesh_Sum_type3',
'Diri_Uniform_noNumber_noMesh_noSum_type2',
'Diri_Uniform_noNumber_noMesh_noSum_type3',
'Diri_Size_noNumber_noMesh_noSum_type2',
'Diri_Size_noNumber_noMesh_Sum_type2',
'Diri_Size_Number_noMesh_Sum_type2',
'Diri_Size_Number_noMesh_noSum_type2',
'Diri_Size_noNumber_noMesh_Sum_type2_ALK',
'lNorm_Size_noNumber_noMesh_Sum_type2_ALK',
'lNorm_Size_noNumber_noMesh_Sum_type2',
'lNorm_Size_Number_noMesh_Sum_type2',
'lNorm_Size_Number_noMesh_noSum_type2'
)
#my.runs<-c('lNorm_Size_noNumber_noMesh_Sum_type2_ALK')
area<-c('NorthSea','Baltic')[1]
do.run<- T # run the assessment (or just present results)
# make use of number model by species (if Number options has been chosen)
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
use.number.model<- c( F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, T, T, F, T)
use.size.model<-use.number.model
#use.size.model<- c( F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, T, T, F, T)
# Cod Whiting Haddock Saithe Herring Sandeel Nor. pout Sprat Plaice Sole
mesh.selction<-c( -1, -1, -1, -1, -1, -1, -1, -1, -1, -1)
file.copy(file.path(data.path,"SMS_org.dat"),file.path(data.path,"SMS.dat"),overwrite=TRUE)
###### end user options ###################
cleanup()
setwd(data.path)
oldDir<-data.path
copySMSfiles<-function(scenario.dir) {
if (file.exists(scenario.dir)) unlink(scenario.dir,recursive = T)
dir.create(scenario.dir,showWarnings = FALSE)
SMS.files.single<-c("area_names.in","natmor.in","canum.in","west.in","weca.in","propmat.in","fleet_catch.in",
"fleet_names.in","fleet_info.dat","just_one.in","sms.psv","species_names.in",
"SSB_R.in","Prediction_F.in","reference_points.in","predict_stock_N.in",
"proportion_M_and_F_before_spawning.in","proportion_landed.in",
"Exploitation_pattern.in","covariance_N.in",
"HCR_options.dat","sms.dat",
"SMS.exe")
for (from.file in SMS.files.single) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
SMS.files.multi<-c("alk_stom.in","ALK_all.in","consum.in","Length_weight_relations.in","lsea.in","N_haul_at_length.in",
"natmor1.in","other_food.in","season_overlap.in","stom_pred_length_at_sizecl.in","stom_struc_at_length.in",
"stomcon_at_length.in","stomlen_at_length.in","stomweight_at_length.in","stomnumber_at_length.in","stomtype_at_length.in","pred_prey_size_range_param.in",
"incl_stom.in","temperature.in")
for (from.file in SMS.files.multi) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
SMS.files.area<-c("stock_distribution.in","predator_area_presence.in")
if (control@no.areas > 1) for (from.file in SMS.files.area) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
if (area=='NorthSea') {
SMS.NS.files<-c("zero_catch_season_ages.in","zero_catch_year_season.in","F_q_ini.in","known_recruitment.in","other_pred_N.in")
for (from.file in SMS.NS.files) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
}
}
if (do.run) {
# read data and options into FLR objects
control<-read.FLSMS.control()
# write all the files in a number of directories
for (r in my.runs){
# retrospective runs are made in a separate directory
my.dir<-file.path(oldDir,r)
copySMSfiles(my.dir)
setwd(my.dir)
if (area=='NorthSea') {
#default settings
control@incl.stom.all<- 1
control@use.Nbar<- 0
control@M2.iterations<- 3
control@max.M2.sum2<- 3
control@stom.likelihood<- 1
control@stomach.variance<- 3
control@simple.ALK<- 0
control@consum<- 0
control@size.select.model<- 2 # 2=prey weights from stom_obs, 3=from l-W relation
control@L50.mesh[]<- -1
control@size.selection[]<- 0
control@sum.stom.like[]<- 0
control@stom.obs.var[]<- 1
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@stom.max.sumP[]<- c(100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 500, 100, 100, 100)
control@var.scale.stom[]<- 1
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@size.other.food.suit<- c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 , 0, 0, 0, 0, 0, 1, 1, 1)
control@min.stom.cont[]<- 1E-09
control@max.stom.sampl[]<- 1000
control@stom.type.include[]<- 2
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@prey.pred.size.fac<- c(5, 5, 5, 5, 5, 5, 5, 5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 50, 50, 0.5, 0.6, 0.5, 0.5)
control@use.overlap<- 0
control@phase.vulnera<- 2
control@phase.other.suit.slope<- 2
control@phase.pref.size.ratio<- -1
control@phase.pref.size.ratio.correction<- -1
control@phase.prey.size.adjustment<- -1
control@phase.var.size.ratio<- -1
control@phase.season.overlap<- 2
control@phase.stom.var<- 2
control@phase.mesh.adjust<- -1
# common settings for size selection
other.food.uniform<- paste("# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK\n",
" 1E6 1E5 1E7 1E7 1E7 1E7 1E5 1E5 1E6 1E6 1E6 1E7 1E6 1E5 1E6 1E6 1E6 1E6 1E7 1E6\n")
#Other food, size selection
other.food.size <- paste("# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK\n",
" 1E6 1E5 1E7 1E7 1E7 1E7 1E5 1E5 1E6 1E6 1E7 1E7 1E6 1E5 1E6 1E6 1E6 1E5 1E7 1E6\n")
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
var.scale.stom<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
var.scale.stom.uniform<-c( 10, 10, 10, 10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
var.scale.stom.uniform<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
sum.stom.like<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
no.sum.stom.like<- c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
}
if (area=='Baltic' & F) {
other.food.uniform<- paste("# COD\n",
" 1E6 \n")
other.food.size <- paste("# COD\n",
" 1E6 \n")
var.scale.stom<- c(0.1)
var.scale.stom.uniform<-c(0.1)
}
control@test.output <- 0
if (grepl('Single',r)) bio.interact<-F else bio.interact<-T
if (grepl('Diri_',r)) control@stomach.variance<- 3
if (grepl('lNorm_',r)) control@stomach.variance<- 1
if (grepl('_ALK',r)) control@simple.ALK<-1
if (grepl('_Uniform',r)) {
control@size.selection[]<-0
control@var.scale.stom<-var.scale.stom.uniform
control@phase.pref.size.ratio<- -2
control@phase.var.size.ratio<- -2
cat(other.food.uniform,file="other_food.in")
}
if (grepl('_Size',r)) {
do.size<-T
control@size.selection[]<-0;
control@size.selection[use.size.model]<-1
control@var.scale.stom<-var.scale.stom
control@phase.pref.size.ratio<-2
control@phase.var.size.ratio<-2
cat(other.food.size,file="other_food.in")
} else do.size<-F
if (grepl('_Number_',r)) {
control@stom.likelihood<-2
do.number<-T
} else {
control@stom.likelihood<-1
do.number<-F
}
if (grepl('noSum',r)) control@sum.stom.like[]<-no.sum.stom.like else if (grepl('_Sum',r)) control@sum.stom.like[]<-sum.stom.like
if (grepl('Single',r)) bio.interact<-F else bio.interact<-T
if (grepl('_type1',r)) {
control@stom.type.include[ ]<-1
if (do.number) control@stom.type.include[use.number.model]<-3
}
if (grepl('_type2',r)) {
control@stom.type.include[ ]<-2
if (do.number) control@stom.type.include[use.number.model]<-3
if (do.size) control@stom.type.include[use.size.model]<-3
}
if (grepl('_type3',r)) {
control@stom.type.include[ ]<-3
}
#if (grepl('74',r)) control@first.year.model<-1974
write.FLSMS.control(control,write.multi=bio.interact)
cat("\nDoing run:",r,"\n")
do.a.full.SMS.run(outdir=my.dir, rundir=my.dir,
label="run_", # label for output
cleanup=F, # delete files in the deleteFiles variable?
do.single=T, # run SMS in single species mode
do.multi.1=bio.interact, # Make preliminary estimate of "predation parameters"
do.multi.2=bio.interact, # Run the full model, with simultaneously estimation of all parameters except the stomach variance parameter
do.multi.2.redo=bio.interact, # bio.interact, # Run the full model, with simultaneously estimation of all parameters
do.multi.2.redo.Nbar=F, # Run the full model, with simultaneously estimation of all parameters, Use mean stock numbers (Nbar) for predation
do.hessian=F, # Make the Hessian matrix and estimate uncertainties
do.MCMC=F, # Prepare for MCMC analysis
mcmc=1000,mcsave=100, # Options for MCMS analysis
do.prediction=F, # Make a prediction
pause=F, # Make a pause between each stage
Screen.show=F, # show the output on screen, or save it in file
do.run=T, # Make the run immediately, or just make the batch file for the run
deleteFiles=NA , # clean up in files before the run is made
new.version=F) # copy a (new) version of the sms program from the program directory (default=FALSE)
# if (!file.copy("summary.out", paste("summary",y,".out",sep=""), overwrite = TRUE)) stop(paste("Retro stopped: something went wrong in copying summary.dat for year:",y))
# file.remove("summary.out")
}
}
dirs<-my.runs
labels<-my.runs
setwd(oldDir)
oldRoot<-root; root<-oldDir
cleanup()
source(file.path(prog.path,"compare_runs_objective_function.R"))
source(file.path(prog.path,"compare_runs.R"))
source(file.path(prog.path,"compare_runs_prey_size_selection.r"))
#if (bio.interact) source(file.path(prog.path,"compare_runs_M2.R"))
#if (bio.interact) source(file.path(prog.path,"compare_runs_N.R"))
# data.path<-file.path(data.path,'Size') ; setwd(data.path)
| /SMS_r_prog/r_prog_less_frequently_used/script-number_model.r | permissive | ices-eg/wg_WGSAM | R | false | false | 12,425 | r | # user options
my.runs<-
c('Single_species', # Single species
'Diri_Uniform_noNumber_noMesh_Sum_type2',
'Diri_Uniform_noNumber_noMesh_Sum_type3',
'Diri_Uniform_noNumber_noMesh_noSum_type2',
'Diri_Uniform_noNumber_noMesh_noSum_type3',
'Diri_Size_noNumber_noMesh_noSum_type2',
'Diri_Size_noNumber_noMesh_Sum_type2',
'Diri_Size_Number_noMesh_Sum_type2',
'Diri_Size_Number_noMesh_noSum_type2',
'Diri_Size_noNumber_noMesh_Sum_type2_ALK',
'lNorm_Size_noNumber_noMesh_Sum_type2_ALK',
'lNorm_Size_noNumber_noMesh_Sum_type2',
'lNorm_Size_Number_noMesh_Sum_type2',
'lNorm_Size_Number_noMesh_noSum_type2'
)
#my.runs<-c('lNorm_Size_noNumber_noMesh_Sum_type2_ALK')
area<-c('NorthSea','Baltic')[1]
do.run<- T # run the assessment (or just present results)
# make use of number model by species (if Number options has been chosen)
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
use.number.model<- c( F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, T, T, F, T)
use.size.model<-use.number.model
#use.size.model<- c( F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, T, T, F, T)
# Cod Whiting Haddock Saithe Herring Sandeel Nor. pout Sprat Plaice Sole
mesh.selction<-c( -1, -1, -1, -1, -1, -1, -1, -1, -1, -1)
file.copy(file.path(data.path,"SMS_org.dat"),file.path(data.path,"SMS.dat"),overwrite=TRUE)
###### end user options ###################
cleanup()
setwd(data.path)
oldDir<-data.path
copySMSfiles<-function(scenario.dir) {
if (file.exists(scenario.dir)) unlink(scenario.dir,recursive = T)
dir.create(scenario.dir,showWarnings = FALSE)
SMS.files.single<-c("area_names.in","natmor.in","canum.in","west.in","weca.in","propmat.in","fleet_catch.in",
"fleet_names.in","fleet_info.dat","just_one.in","sms.psv","species_names.in",
"SSB_R.in","Prediction_F.in","reference_points.in","predict_stock_N.in",
"proportion_M_and_F_before_spawning.in","proportion_landed.in",
"Exploitation_pattern.in","covariance_N.in",
"HCR_options.dat","sms.dat",
"SMS.exe")
for (from.file in SMS.files.single) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
SMS.files.multi<-c("alk_stom.in","ALK_all.in","consum.in","Length_weight_relations.in","lsea.in","N_haul_at_length.in",
"natmor1.in","other_food.in","season_overlap.in","stom_pred_length_at_sizecl.in","stom_struc_at_length.in",
"stomcon_at_length.in","stomlen_at_length.in","stomweight_at_length.in","stomnumber_at_length.in","stomtype_at_length.in","pred_prey_size_range_param.in",
"incl_stom.in","temperature.in")
for (from.file in SMS.files.multi) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
SMS.files.area<-c("stock_distribution.in","predator_area_presence.in")
if (control@no.areas > 1) for (from.file in SMS.files.area) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
if (area=='NorthSea') {
SMS.NS.files<-c("zero_catch_season_ages.in","zero_catch_year_season.in","F_q_ini.in","known_recruitment.in","other_pred_N.in")
for (from.file in SMS.NS.files) {
to.file<-file.path(scenario.dir,from.file)
file.copy(from.file, to.file, overwrite = TRUE)
}
}
}
if (do.run) {
# read data and options into FLR objects
control<-read.FLSMS.control()
# write all the files in a number of directories
for (r in my.runs){
# retrospective runs are made in a separate directory
my.dir<-file.path(oldDir,r)
copySMSfiles(my.dir)
setwd(my.dir)
if (area=='NorthSea') {
#default settings
control@incl.stom.all<- 1
control@use.Nbar<- 0
control@M2.iterations<- 3
control@max.M2.sum2<- 3
control@stom.likelihood<- 1
control@stomach.variance<- 3
control@simple.ALK<- 0
control@consum<- 0
control@size.select.model<- 2 # 2=prey weights from stom_obs, 3=from l-W relation
control@L50.mesh[]<- -1
control@size.selection[]<- 0
control@sum.stom.like[]<- 0
control@stom.obs.var[]<- 1
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@stom.max.sumP[]<- c(100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 500, 100, 100, 100)
control@var.scale.stom[]<- 1
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@size.other.food.suit<- c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 , 0, 0, 0, 0, 0, 1, 1, 1)
control@min.stom.cont[]<- 1E-09
control@max.stom.sampl[]<- 1000
control@stom.type.include[]<- 2
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
control@prey.pred.size.fac<- c(5, 5, 5, 5, 5, 5, 5, 5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 50, 50, 0.5, 0.6, 0.5, 0.5)
control@use.overlap<- 0
control@phase.vulnera<- 2
control@phase.other.suit.slope<- 2
control@phase.pref.size.ratio<- -1
control@phase.pref.size.ratio.correction<- -1
control@phase.prey.size.adjustment<- -1
control@phase.var.size.ratio<- -1
control@phase.season.overlap<- 2
control@phase.stom.var<- 2
control@phase.mesh.adjust<- -1
# common settings for size selection
other.food.uniform<- paste("# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK\n",
" 1E6 1E5 1E7 1E7 1E7 1E7 1E5 1E5 1E6 1E6 1E6 1E7 1E6 1E5 1E6 1E6 1E6 1E6 1E7 1E6\n")
#Other food, size selection
other.food.size <- paste("# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK\n",
" 1E6 1E5 1E7 1E7 1E7 1E7 1E5 1E5 1E6 1E6 1E7 1E7 1E6 1E5 1E6 1E6 1E6 1E5 1E7 1E6\n")
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
var.scale.stom<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
var.scale.stom.uniform<-c( 10, 10, 10, 10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
var.scale.stom.uniform<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
# FUL GLT HEG KTW GBG GNT PUF RAZ RAJ GUR W_M N_M W_H N_H GSE HBP COD WHG HAD POK
sum.stom.like<- c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
no.sum.stom.like<- c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
}
if (area=='Baltic' & F) {
other.food.uniform<- paste("# COD\n",
" 1E6 \n")
other.food.size <- paste("# COD\n",
" 1E6 \n")
var.scale.stom<- c(0.1)
var.scale.stom.uniform<-c(0.1)
}
control@test.output <- 0
if (grepl('Single',r)) bio.interact<-F else bio.interact<-T
if (grepl('Diri_',r)) control@stomach.variance<- 3
if (grepl('lNorm_',r)) control@stomach.variance<- 1
if (grepl('_ALK',r)) control@simple.ALK<-1
if (grepl('_Uniform',r)) {
control@size.selection[]<-0
control@var.scale.stom<-var.scale.stom.uniform
control@phase.pref.size.ratio<- -2
control@phase.var.size.ratio<- -2
cat(other.food.uniform,file="other_food.in")
}
if (grepl('_Size',r)) {
do.size<-T
control@size.selection[]<-0;
control@size.selection[use.size.model]<-1
control@var.scale.stom<-var.scale.stom
control@phase.pref.size.ratio<-2
control@phase.var.size.ratio<-2
cat(other.food.size,file="other_food.in")
} else do.size<-F
if (grepl('_Number_',r)) {
control@stom.likelihood<-2
do.number<-T
} else {
control@stom.likelihood<-1
do.number<-F
}
if (grepl('noSum',r)) control@sum.stom.like[]<-no.sum.stom.like else if (grepl('_Sum',r)) control@sum.stom.like[]<-sum.stom.like
if (grepl('Single',r)) bio.interact<-F else bio.interact<-T
if (grepl('_type1',r)) {
control@stom.type.include[ ]<-1
if (do.number) control@stom.type.include[use.number.model]<-3
}
if (grepl('_type2',r)) {
control@stom.type.include[ ]<-2
if (do.number) control@stom.type.include[use.number.model]<-3
if (do.size) control@stom.type.include[use.size.model]<-3
}
if (grepl('_type3',r)) {
control@stom.type.include[ ]<-3
}
#if (grepl('74',r)) control@first.year.model<-1974
write.FLSMS.control(control,write.multi=bio.interact)
cat("\nDoing run:",r,"\n")
do.a.full.SMS.run(outdir=my.dir, rundir=my.dir,
label="run_", # label for output
cleanup=F, # delete files in the deleteFiles variable?
do.single=T, # run SMS in single species mode
do.multi.1=bio.interact, # Make preliminary estimate of "predation parameters"
do.multi.2=bio.interact, # Run the full model, with simultaneously estimation of all parameters except the stomach variance parameter
do.multi.2.redo=bio.interact, # bio.interact, # Run the full model, with simultaneously estimation of all parameters
do.multi.2.redo.Nbar=F, # Run the full model, with simultaneously estimation of all parameters, Use mean stock numbers (Nbar) for predation
do.hessian=F, # Make the Hessian matrix and estimate uncertainties
do.MCMC=F, # Prepare for MCMC analysis
mcmc=1000,mcsave=100, # Options for MCMS analysis
do.prediction=F, # Make a prediction
pause=F, # Make a pause between each stage
Screen.show=F, # show the output on screen, or save it in file
do.run=T, # Make the run immediately, or just make the batch file for the run
deleteFiles=NA , # clean up in files before the run is made
new.version=F) # copy a (new) version of the sms program from the program directory (default=FALSE)
# if (!file.copy("summary.out", paste("summary",y,".out",sep=""), overwrite = TRUE)) stop(paste("Retro stopped: something went wrong in copying summary.dat for year:",y))
# file.remove("summary.out")
}
}
dirs<-my.runs
labels<-my.runs
setwd(oldDir)
oldRoot<-root; root<-oldDir
cleanup()
source(file.path(prog.path,"compare_runs_objective_function.R"))
source(file.path(prog.path,"compare_runs.R"))
source(file.path(prog.path,"compare_runs_prey_size_selection.r"))
#if (bio.interact) source(file.path(prog.path,"compare_runs_M2.R"))
#if (bio.interact) source(file.path(prog.path,"compare_runs_N.R"))
# data.path<-file.path(data.path,'Size') ; setwd(data.path)
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{CH19TA07}
\alias{CH19TA07}
\title{CH19TA07}
\format{\preformatted{'data.frame': 12 obs. of 4 variables:
$ V1: int 47 43 46 40 62 68 67 71 41 39 ...
$ V2: int 1 1 1 1 2 2 2 2 3 3 ...
$ V3: int 1 1 2 2 1 1 2 2 1 1 ...
$ V4: int 1 2 1 2 1 2 1 2 1 2 ...
}}
\usage{
CH19TA07
}
\description{
CH19TA07
}
\keyword{datasets}
| /man/CH19TA07.Rd | no_license | bryangoodrich/ALSM | R | false | false | 440 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{CH19TA07}
\alias{CH19TA07}
\title{CH19TA07}
\format{\preformatted{'data.frame': 12 obs. of 4 variables:
$ V1: int 47 43 46 40 62 68 67 71 41 39 ...
$ V2: int 1 1 1 1 2 2 2 2 3 3 ...
$ V3: int 1 1 2 2 1 1 2 2 1 1 ...
$ V4: int 1 2 1 2 1 2 1 2 1 2 ...
}}
\usage{
CH19TA07
}
\description{
CH19TA07
}
\keyword{datasets}
|
function(dataset,label_column = "Label"){
colnames(dataset)[which(colnames(dataset)==label_column)]='Label'
Boruta_models=Boruta(Label~.,data=dataset[,-1],doTrace=0)
Boruta_variable_importance <- TentativeRoughFix(Boruta_models)
Boruta_variable_importance <- attStats(Boruta_variable_importance)
Boruta_variable_importance[,7]=row.names(Boruta_variable_importance)
Boruta_variable_importance <- Boruta_variable_importance[order(Boruta_variable_importance$meanImp),]
#to view plot type print(Boruta_plots)
#Boruta_plots=plot(Boruta_models, cex.axis=1, las=2, xlab="", main="Variable Importance")
return(x = Boruta_variable_importance)
#assign(x = "Boruta_plots",value = Boruta_plots,envir = globalenv())
}
| /SKRYPTY/R_SCRIPTS/Scripts/Boruta_feature_selection.R | no_license | MMandziej/praca_magisterska | R | false | false | 725 | r | function(dataset,label_column = "Label"){
colnames(dataset)[which(colnames(dataset)==label_column)]='Label'
Boruta_models=Boruta(Label~.,data=dataset[,-1],doTrace=0)
Boruta_variable_importance <- TentativeRoughFix(Boruta_models)
Boruta_variable_importance <- attStats(Boruta_variable_importance)
Boruta_variable_importance[,7]=row.names(Boruta_variable_importance)
Boruta_variable_importance <- Boruta_variable_importance[order(Boruta_variable_importance$meanImp),]
#to view plot type print(Boruta_plots)
#Boruta_plots=plot(Boruta_models, cex.axis=1, las=2, xlab="", main="Variable Importance")
return(x = Boruta_variable_importance)
#assign(x = "Boruta_plots",value = Boruta_plots,envir = globalenv())
}
|
# Conclusion: use fipscounty. 3271 fipscounties, 410 CBSAs. To compare to Datawork we aggregate JOLTS data to CBSAs.
# Data aggregations needed: for JOLTS, Datawork, and Openjobs, a table with CBSA/County/Month-Yr/#jobs/#stem jobss
| /src/jobsCountTables/makeDataworkJobsTable.R | no_license | uva-bi-sdad/stem_edu | R | false | false | 233 | r | # Conclusion: use fipscounty. 3271 fipscounties, 410 CBSAs. To compare to Datawork we aggregate JOLTS data to CBSAs.
# Data aggregations needed: for JOLTS, Datawork, and Openjobs, a table with CBSA/County/Month-Yr/#jobs/#stem jobss
|
library(xgboost)
library(Matrix)
library(data.table)
#library(bit64, pos = .Machine$integer.max) #junk package that won't let use commercially and also won't let unmask functions even when you specify predescence
library(vtreat)
library(outliers)
library(R.utils)
remove(list = ls())
setwd("C:/Users/Laurae/Documents/Data Science/Santander/") ##set your own working directory
train <- read.csv("train.csv")
test <- read.csv("test.csv")
#train <- as.data.frame(fread("train.csv", header = TRUE, sep = ","))
#test <- as.data.frame(fread("test.csv", header = TRUE, sep = ","))
#unloadNamespace("bit64")
#detach("package:bit64", unload=TRUE, force=TRUE)
train_temp <- train
test_temp <- test
# removing ID
train_temp$ID <- NULL
test_temp$ID <- NULL
# extracting label
train_target <- train$TARGET
train_temp$TARGET <- NULL
##### 0 count per line
count0 <- function(x) {
return( sum(x == 0) )
}
train_temp$n0 <- apply(train_temp, 1, FUN=count0)
test_temp$n0 <- apply(test_temp, 1, FUN=count0)
##### Removing constant features
cat("\n## Removing the constants features.\n")
for (f in names(train_temp)) {
if (length(unique(train_temp[[f]])) == 1) {
cat(f, "is constant in train. We delete it.\n")
train_temp[[f]] <- NULL
test_temp[[f]] <- NULL
}
}
##### Removing identical features
features_pair <- combn(names(train_temp), 2, simplify = F)
toRemove <- c()
for(pair in features_pair) {
f1 <- pair[1]
f2 <- pair[2]
if (!(f1 %in% toRemove) & !(f2 %in% toRemove)) {
if (all(train_temp[[f1]] == train_temp[[f2]])) {
cat(f1, "and", f2, "are equals.\n")
toRemove <- c(toRemove, f2)
}
}
}
feature.names <- setdiff(names(train_temp), toRemove)
train_temp <- train_temp[, feature.names]
test_temp <- test_temp[, feature.names]
# ~~~~ GRADIENT DESCENT ASSOCIATION RULE TESTING
optimized_func <- function(target, scores, min_score, min_node, false_neg, cutoff) {
# cutoff has two values: the minimum and maximum. Everything between is dished out.
if (cutoff[2] < cutoff[1]) {
known <- integer(0)
} else {
known <- target[which(((scores >= cutoff[2]) == TRUE) | ((scores <= cutoff[1]) == TRUE))] #takes values in exterior to [cutoff1, cutoff2], else dishes out empty numeric
}
if (length(known) >= min_node) { #if cutoff not leading to empty variable
best <- ifelse(sum(known == 1) == 0, 0, ifelse(((sum(known == 0) / sum(known == 1)) >= min_score) & (sum(known == 1) <= false_neg), sum(known == 1) / sum(known == 0), 999)) #return 0 if pure rule, else return the probability if ratio over 25 (better than random), else return 999 (worse than random)
} else {
best <- 999 # node empty
}
return(best) # objective: return the purest node
}
# THIS IS FOR UNIVARIATE
prog_bar <- txtProgressBar(style = 3)
data_scores <- rbind(train_temp, test_temp)
for (i in colnames(train_temp)) {
data_scores[[i]] <- scores(data_scores[[i]])
#cat("Computed ", i, "'s scores. (", which(i == colnames(train_temp)), "/", length(colnames(train_temp)), ")\n", sep = "")
setTxtProgressBar(prog_bar, which(i == colnames(train_temp))/length(colnames(train_temp)))
}
close(prog_bar)
data_scores_parsed <- data.frame(matrix(ncol = ncol(data_scores)+1, nrow = nrow(data_scores)))
colnames(data_scores_parsed) <- c(colnames(data_scores), "Final")
data_scores_parsed$Final <- rep(1, nrow(data_scores))
minimal_score <- 25 #don't accept any node under the allowed score
minimal_node <- 5 #don't accept any node containing under that specific amount of samples
false_negatives <- 2 #allow at most 1 false negative | higher allows a more permissive algorithm, lower makes it very difficult to converge
for (i in colnames(train_temp)) {
data_scores_parsed[[i]] <- rep(1, nrow(data_scores))
scoring_input <- data_scores[[i]][1:nrow(train)] #get scores from train set
min_allowance <- min(scoring_input) #get the maximum allowed score
max_allowance <- max(scoring_input) #get the maximum allowed score
optimized_output <- optim(par = c(min_allowance, max_allowance), optimized_func, method = "L-BFGS-B", target = train_target, scores = scoring_input, min_score = minimal_score, min_node = minimal_node, false_neg = false_negatives, lower = min_allowance, upper = max_allowance, control = list(maxit = 1000, trace = 0))
cat("[", which(i == colnames(train_temp)), ": ", i, "] ", ifelse(optimized_output$value >= 999, "Failed to optimize with gradient descent (you should loose conditions!).", paste("Best node: ", ifelse(optimized_output$value == 0, "Inf", 1/optimized_output$value), ":1 (", round(optimized_output$value*100, digits = 3) , "%) [ ", sum((scoring_input >= optimized_output$par[2]) | (scoring_input <= optimized_output$par[1])), "(train) | ", sum((data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] >= optimized_output$par[2]) | (data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] <= optimized_output$par[1])), "(test) ] for params (", optimized_output$par[1], ", ", optimized_output$par[2], ").", sep = "")), sep = "")
if ((optimized_output$value >= 999) | (sum((data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] >= optimized_output$par[2]) | (data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] <= optimized_output$par[1]))) == 0) {
#do nothing
cat(" | was useless!\n", sep = "")
} else {
data_output <- ifelse(optimized_output$value == 0, 0, optimized_output$value)
data_scores_parsed[[i]][(scoring_input >= optimized_output$par[2]) | (scoring_input <= optimized_output$par[1])] <- data_output
data_scores_parsed$Final <- data_scores_parsed$Final * data_scores_parsed[[i]]
cat(" | was stored!\n", sep = "")
}
}
cat("\n-----\nSummary:\nTrain rows soft-ruled: ", nrow(train) - sum(data_scores_parsed$Final[1:nrow(train)] == 1), " (pure: ", sum(data_scores_parsed$Final[1:nrow(train)] == 0), ")\nTest rows soft-ruled: ", nrow(test) - sum(data_scores_parsed$Final[(nrow(train)+1):nrow(data_scores_parsed)] == 1), " (pure: ", sum(data_scores_parsed$Final[(nrow(train)+1):nrow(data_scores_parsed)] == 0), ")", sep = "")
# THIS IS FOR BIVARIATE
scored_rows <- rep(1, nrow(train_temp)+nrow(test_temp))
minimal_score <- 25 #don't accept any node under the allowed score
minimal_node <- 25 #don't accept any node containing under that specific amount of samples
false_negatives <- 0 #allow at most 1 false negative | higher allows a more permissive algorithm, lower makes it very difficult to converge
#for bivariate outliers = use Mahalonobis distance
Counter <- 0
MaxCounter <- ncol(train_temp)*(ncol(train_temp) - 1)
MaxChar <- 0
Paster <- paste("%0", nchar(MaxCounter, "width"), "d", sep = "")
TrainRows <- 1:nrow(train_temp)
TestRows <- (nrow(train_temp)+1):(nrow(train_temp)+nrow(test_temp))
StartTime <- System$currentTimeMillis()
for (i in colnames(train_temp)) {
for (j in colnames(train_temp)[-which(i == colnames(train_temp))]) {
#print text for default checking
Counter <- Counter + 1
#tempText <- paste("\r[", ( (which(i == colnames(train_temp)) - 1) * ncol(train_temp) ) + ( which(j == colnames(train_temp))) , "/", ncol(train_temp)*ncol(train_temp), "]: ", i, ":", j, " parsed!", sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
tempText <- paste("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2), "s]: ", i, ":", j, " parsed!", sep = "")
#cat(ifelse(nchar(tempText) < MaxChar, paste(tempText, rep(" ", MaxChar - nchar(tempText) + 1), sep = ""), tempText), sep = "")
cat(tempText, sep = "")
#merge columns
#tempCol <- as.data.frame(cbind(c(train_temp[[i]], test_temp[[i]]), c(train_temp[[j]], test_temp[[j]])))
tempCol <- data.frame(v1 = c(train_temp[[i]], test_temp[[i]]), v2 = c(train_temp[[j]], test_temp[[j]]), check.names = FALSE, stringsAsFactors = FALSE)
#compute Mahalonobis distance (df, m, sx) with near-zero tolerance to avoid unexpected interruptions
if (sum(cor(tempCol)) > 3.999999) {
#give up computing the inverse matrix and Mahalonobis distance due to singularity/ill-conditioned matrix
} else {
#try compute inverse matrix and Mahalonobis distance
tryCatch(tempCol <- mahalanobis(tempCol, colMeans(tempCol), cov(tempCol), tol=1e-30))
}
if (class(tempCol) == "data.frame") {
#computation failed, ignore what to do
MaxChar <- nchar(tempText)
cat("\r", rep(" ", MaxChar), sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
tempText <- paste("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2), "s]: ", i, ":", j, " is singular.\n", sep = "")
cat(tempText, sep = "")
} else {
#successful computation, continue
#score the data against outliers locally
data_scores <- scores(tempCol)
scoring_input <- data_scores[TrainRows] #get scores from train set
min_allowance <- min(scoring_input) #get the maximum allowed score
max_allowance <- max(scoring_input) #get the maximum allowed score
#gradient descent the outliers to find local isolated nodes
optimized_output <- optim(par = c(min_allowance, max_allowance), optimized_func, method = "L-BFGS-B", target = train_target, scores = scoring_input, min_score = minimal_score, min_node = minimal_node, false_neg = false_negatives, lower = min_allowance, upper = max_allowance, control = list(maxit = 1000, trace = 0))
if (!(optimized_output$value == 0)) {
#not pure node?
#has no value for us
#overwrite print
MaxChar <- nchar(tempText)
cat("\r", rep(" ", MaxChar), sep = "")
} else {
#pure node?
#has value for us
#MaxChar <- 0
#compute rows found
tempRows <- (data_scores >= optimized_output$par[2]) | (data_scores <= optimized_output$par[1])
tempRows_train <- sum(tempRows[TrainRows])
tempRows_test <- sum(tempRows[TestRows])
#update target rows
tempInt <- sum(scored_rows[TestRows] == 0)
scored_rows[tempRows] <- 0
tempInt <- sum(scored_rows[TestRows] == 0) - tempInt
#rewrite the current line
#cat("\r", rep(" ", nchar(tempText)), sep = "")
#cat("\r[", ( (which(i == colnames(train_temp)) - 1) * ncol(train_temp) ) + ( which(j == colnames(train_temp))) , "/", ncol(train_temp)*ncol(train_temp), "]: ", i, ":", j, " analysis led to: ", length(tempRows_train), "|", length(tempRows_test), " (", length(scored_rows[scored_rows[1:nrow(train_temp)] == 0]), "|", length(scored_rows[scored_rows[(nrow(train_temp)+1):(nrow(train_temp)+nrow(test_temp))] == 0]), ")", sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
cat("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2),"s]: ", i, ":", j, " analysis led to: ", tempRows_train, "|", tempRows_test, " (", sum(scored_rows[TrainRows] == 0), "|", sum(scored_rows[TestRows] == 0), ")", sep = "")
if (tempInt == 0) {
#if it added nothing to our test set
cat(" - No improvement.\n", sep = "")
} else {
#if it added something to our test set
cat(" | improved slightly! (+", tempInt, ")\n", sep = "")
}
}
}
}
}
# CHOOSE YOUR SUBMISSION!!!
submission <- read.csv("submission_rules.csv") #take the best script?
tempInt <- which(data_scores_parsed$Final[76021:151838] == 0)
submission$TARGET[tempInt]
#submission$TARGET <- submission$TARGET * data_scores_parsed$Final[76021:151838]
submission$TARGET[submission$TARGET == 0] <- 0.00000001 #because we know some are wrong
submission$TARGET[tempInt] <- 0 #because we know we are right there
write.csv(submission, file = "submission_rules_out.csv", row.names = FALSE)
| /scripts/Two-Way Outliers.R | no_license | Laurae2/Santander | R | false | false | 12,365 | r | library(xgboost)
library(Matrix)
library(data.table)
#library(bit64, pos = .Machine$integer.max) #junk package that won't let use commercially and also won't let unmask functions even when you specify predescence
library(vtreat)
library(outliers)
library(R.utils)
remove(list = ls())
setwd("C:/Users/Laurae/Documents/Data Science/Santander/") ##set your own working directory
train <- read.csv("train.csv")
test <- read.csv("test.csv")
#train <- as.data.frame(fread("train.csv", header = TRUE, sep = ","))
#test <- as.data.frame(fread("test.csv", header = TRUE, sep = ","))
#unloadNamespace("bit64")
#detach("package:bit64", unload=TRUE, force=TRUE)
train_temp <- train
test_temp <- test
# removing ID
train_temp$ID <- NULL
test_temp$ID <- NULL
# extracting label
train_target <- train$TARGET
train_temp$TARGET <- NULL
##### 0 count per line
count0 <- function(x) {
return( sum(x == 0) )
}
train_temp$n0 <- apply(train_temp, 1, FUN=count0)
test_temp$n0 <- apply(test_temp, 1, FUN=count0)
##### Removing constant features
cat("\n## Removing the constants features.\n")
for (f in names(train_temp)) {
if (length(unique(train_temp[[f]])) == 1) {
cat(f, "is constant in train. We delete it.\n")
train_temp[[f]] <- NULL
test_temp[[f]] <- NULL
}
}
##### Removing identical features
features_pair <- combn(names(train_temp), 2, simplify = F)
toRemove <- c()
for(pair in features_pair) {
f1 <- pair[1]
f2 <- pair[2]
if (!(f1 %in% toRemove) & !(f2 %in% toRemove)) {
if (all(train_temp[[f1]] == train_temp[[f2]])) {
cat(f1, "and", f2, "are equals.\n")
toRemove <- c(toRemove, f2)
}
}
}
feature.names <- setdiff(names(train_temp), toRemove)
train_temp <- train_temp[, feature.names]
test_temp <- test_temp[, feature.names]
# ~~~~ GRADIENT DESCENT ASSOCIATION RULE TESTING
optimized_func <- function(target, scores, min_score, min_node, false_neg, cutoff) {
# cutoff has two values: the minimum and maximum. Everything between is dished out.
if (cutoff[2] < cutoff[1]) {
known <- integer(0)
} else {
known <- target[which(((scores >= cutoff[2]) == TRUE) | ((scores <= cutoff[1]) == TRUE))] #takes values in exterior to [cutoff1, cutoff2], else dishes out empty numeric
}
if (length(known) >= min_node) { #if cutoff not leading to empty variable
best <- ifelse(sum(known == 1) == 0, 0, ifelse(((sum(known == 0) / sum(known == 1)) >= min_score) & (sum(known == 1) <= false_neg), sum(known == 1) / sum(known == 0), 999)) #return 0 if pure rule, else return the probability if ratio over 25 (better than random), else return 999 (worse than random)
} else {
best <- 999 # node empty
}
return(best) # objective: return the purest node
}
# THIS IS FOR UNIVARIATE
prog_bar <- txtProgressBar(style = 3)
data_scores <- rbind(train_temp, test_temp)
for (i in colnames(train_temp)) {
data_scores[[i]] <- scores(data_scores[[i]])
#cat("Computed ", i, "'s scores. (", which(i == colnames(train_temp)), "/", length(colnames(train_temp)), ")\n", sep = "")
setTxtProgressBar(prog_bar, which(i == colnames(train_temp))/length(colnames(train_temp)))
}
close(prog_bar)
data_scores_parsed <- data.frame(matrix(ncol = ncol(data_scores)+1, nrow = nrow(data_scores)))
colnames(data_scores_parsed) <- c(colnames(data_scores), "Final")
data_scores_parsed$Final <- rep(1, nrow(data_scores))
minimal_score <- 25 #don't accept any node under the allowed score
minimal_node <- 5 #don't accept any node containing under that specific amount of samples
false_negatives <- 2 #allow at most 1 false negative | higher allows a more permissive algorithm, lower makes it very difficult to converge
for (i in colnames(train_temp)) {
data_scores_parsed[[i]] <- rep(1, nrow(data_scores))
scoring_input <- data_scores[[i]][1:nrow(train)] #get scores from train set
min_allowance <- min(scoring_input) #get the maximum allowed score
max_allowance <- max(scoring_input) #get the maximum allowed score
optimized_output <- optim(par = c(min_allowance, max_allowance), optimized_func, method = "L-BFGS-B", target = train_target, scores = scoring_input, min_score = minimal_score, min_node = minimal_node, false_neg = false_negatives, lower = min_allowance, upper = max_allowance, control = list(maxit = 1000, trace = 0))
cat("[", which(i == colnames(train_temp)), ": ", i, "] ", ifelse(optimized_output$value >= 999, "Failed to optimize with gradient descent (you should loose conditions!).", paste("Best node: ", ifelse(optimized_output$value == 0, "Inf", 1/optimized_output$value), ":1 (", round(optimized_output$value*100, digits = 3) , "%) [ ", sum((scoring_input >= optimized_output$par[2]) | (scoring_input <= optimized_output$par[1])), "(train) | ", sum((data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] >= optimized_output$par[2]) | (data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] <= optimized_output$par[1])), "(test) ] for params (", optimized_output$par[1], ", ", optimized_output$par[2], ").", sep = "")), sep = "")
if ((optimized_output$value >= 999) | (sum((data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] >= optimized_output$par[2]) | (data_scores[[i]][(nrow(train)+1):NROW(data_scores_parsed[[i]])] <= optimized_output$par[1]))) == 0) {
#do nothing
cat(" | was useless!\n", sep = "")
} else {
data_output <- ifelse(optimized_output$value == 0, 0, optimized_output$value)
data_scores_parsed[[i]][(scoring_input >= optimized_output$par[2]) | (scoring_input <= optimized_output$par[1])] <- data_output
data_scores_parsed$Final <- data_scores_parsed$Final * data_scores_parsed[[i]]
cat(" | was stored!\n", sep = "")
}
}
cat("\n-----\nSummary:\nTrain rows soft-ruled: ", nrow(train) - sum(data_scores_parsed$Final[1:nrow(train)] == 1), " (pure: ", sum(data_scores_parsed$Final[1:nrow(train)] == 0), ")\nTest rows soft-ruled: ", nrow(test) - sum(data_scores_parsed$Final[(nrow(train)+1):nrow(data_scores_parsed)] == 1), " (pure: ", sum(data_scores_parsed$Final[(nrow(train)+1):nrow(data_scores_parsed)] == 0), ")", sep = "")
# THIS IS FOR BIVARIATE
scored_rows <- rep(1, nrow(train_temp)+nrow(test_temp))
minimal_score <- 25 #don't accept any node under the allowed score
minimal_node <- 25 #don't accept any node containing under that specific amount of samples
false_negatives <- 0 #allow at most 1 false negative | higher allows a more permissive algorithm, lower makes it very difficult to converge
#for bivariate outliers = use Mahalonobis distance
Counter <- 0
MaxCounter <- ncol(train_temp)*(ncol(train_temp) - 1)
MaxChar <- 0
Paster <- paste("%0", nchar(MaxCounter, "width"), "d", sep = "")
TrainRows <- 1:nrow(train_temp)
TestRows <- (nrow(train_temp)+1):(nrow(train_temp)+nrow(test_temp))
StartTime <- System$currentTimeMillis()
for (i in colnames(train_temp)) {
for (j in colnames(train_temp)[-which(i == colnames(train_temp))]) {
#print text for default checking
Counter <- Counter + 1
#tempText <- paste("\r[", ( (which(i == colnames(train_temp)) - 1) * ncol(train_temp) ) + ( which(j == colnames(train_temp))) , "/", ncol(train_temp)*ncol(train_temp), "]: ", i, ":", j, " parsed!", sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
tempText <- paste("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2), "s]: ", i, ":", j, " parsed!", sep = "")
#cat(ifelse(nchar(tempText) < MaxChar, paste(tempText, rep(" ", MaxChar - nchar(tempText) + 1), sep = ""), tempText), sep = "")
cat(tempText, sep = "")
#merge columns
#tempCol <- as.data.frame(cbind(c(train_temp[[i]], test_temp[[i]]), c(train_temp[[j]], test_temp[[j]])))
tempCol <- data.frame(v1 = c(train_temp[[i]], test_temp[[i]]), v2 = c(train_temp[[j]], test_temp[[j]]), check.names = FALSE, stringsAsFactors = FALSE)
#compute Mahalonobis distance (df, m, sx) with near-zero tolerance to avoid unexpected interruptions
if (sum(cor(tempCol)) > 3.999999) {
#give up computing the inverse matrix and Mahalonobis distance due to singularity/ill-conditioned matrix
} else {
#try compute inverse matrix and Mahalonobis distance
tryCatch(tempCol <- mahalanobis(tempCol, colMeans(tempCol), cov(tempCol), tol=1e-30))
}
if (class(tempCol) == "data.frame") {
#computation failed, ignore what to do
MaxChar <- nchar(tempText)
cat("\r", rep(" ", MaxChar), sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
tempText <- paste("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2), "s]: ", i, ":", j, " is singular.\n", sep = "")
cat(tempText, sep = "")
} else {
#successful computation, continue
#score the data against outliers locally
data_scores <- scores(tempCol)
scoring_input <- data_scores[TrainRows] #get scores from train set
min_allowance <- min(scoring_input) #get the maximum allowed score
max_allowance <- max(scoring_input) #get the maximum allowed score
#gradient descent the outliers to find local isolated nodes
optimized_output <- optim(par = c(min_allowance, max_allowance), optimized_func, method = "L-BFGS-B", target = train_target, scores = scoring_input, min_score = minimal_score, min_node = minimal_node, false_neg = false_negatives, lower = min_allowance, upper = max_allowance, control = list(maxit = 1000, trace = 0))
if (!(optimized_output$value == 0)) {
#not pure node?
#has no value for us
#overwrite print
MaxChar <- nchar(tempText)
cat("\r", rep(" ", MaxChar), sep = "")
} else {
#pure node?
#has value for us
#MaxChar <- 0
#compute rows found
tempRows <- (data_scores >= optimized_output$par[2]) | (data_scores <= optimized_output$par[1])
tempRows_train <- sum(tempRows[TrainRows])
tempRows_test <- sum(tempRows[TestRows])
#update target rows
tempInt <- sum(scored_rows[TestRows] == 0)
scored_rows[tempRows] <- 0
tempInt <- sum(scored_rows[TestRows] == 0) - tempInt
#rewrite the current line
#cat("\r", rep(" ", nchar(tempText)), sep = "")
#cat("\r[", ( (which(i == colnames(train_temp)) - 1) * ncol(train_temp) ) + ( which(j == colnames(train_temp))) , "/", ncol(train_temp)*ncol(train_temp), "]: ", i, ":", j, " analysis led to: ", length(tempRows_train), "|", length(tempRows_test), " (", length(scored_rows[scored_rows[1:nrow(train_temp)] == 0]), "|", length(scored_rows[scored_rows[(nrow(train_temp)+1):(nrow(train_temp)+nrow(test_temp))] == 0]), ")", sep = "")
CurrentTime <- System$currentTimeMillis()
SpentTime <- (CurrentTime - StartTime) / 1000
cat("\r[", sprintf(Paster, Counter) , "/", MaxCounter, " | CPU = ", round(SpentTime, digits = 2), "s | ETA = ", round((MaxCounter - Counter) * SpentTime / Counter, 2),"s]: ", i, ":", j, " analysis led to: ", tempRows_train, "|", tempRows_test, " (", sum(scored_rows[TrainRows] == 0), "|", sum(scored_rows[TestRows] == 0), ")", sep = "")
if (tempInt == 0) {
#if it added nothing to our test set
cat(" - No improvement.\n", sep = "")
} else {
#if it added something to our test set
cat(" | improved slightly! (+", tempInt, ")\n", sep = "")
}
}
}
}
}
# CHOOSE YOUR SUBMISSION!!!
submission <- read.csv("submission_rules.csv") #take the best script?
tempInt <- which(data_scores_parsed$Final[76021:151838] == 0)
submission$TARGET[tempInt]
#submission$TARGET <- submission$TARGET * data_scores_parsed$Final[76021:151838]
submission$TARGET[submission$TARGET == 0] <- 0.00000001 #because we know some are wrong
submission$TARGET[tempInt] <- 0 #because we know we are right there
write.csv(submission, file = "submission_rules_out.csv", row.names = FALSE)
|
# Definimos el espacio muestral
m <- factor(c(0,1), levels = c("cara","cruz"))
m <- c(0,1)
x <- sample(m,5,replace = TRUE)
sum(x)/length(x)
x <- sample(m,25,replace = TRUE)
sum(x)/length(x)
x <- sample(m,100,replace = TRUE)
sum(x)/length(x)
x <- sample(m,1000,replace = TRUE)
sum(x)/length(x)
x <- sample(m,1000000,replace = TRUE)
sum(x)/length(x)
| /probabilidad/probabilidad.R | no_license | jrlacalle/material_docente | R | false | false | 348 | r | # Definimos el espacio muestral
m <- factor(c(0,1), levels = c("cara","cruz"))
m <- c(0,1)
x <- sample(m,5,replace = TRUE)
sum(x)/length(x)
x <- sample(m,25,replace = TRUE)
sum(x)/length(x)
x <- sample(m,100,replace = TRUE)
sum(x)/length(x)
x <- sample(m,1000,replace = TRUE)
sum(x)/length(x)
x <- sample(m,1000000,replace = TRUE)
sum(x)/length(x)
|
# This script gets all documents (e.g. public comments) from regulations.gov
# UNDER CONSTRUCTION FOR V4
## load required packages
source("setup.R")
# I keep keep data I have already downloaded in ascending order in a directory called "ascending"
directory <- "ascending"
list.files(here::here(directory))
# The api call below loads data in ascending order.
# To start with the earliest comment, set page to 1
# To dowload more recent comments, either change order to "DESC" or specify a page of results to start at
page <- 1
# set defaults for regulations.gov api call
url <- "https://api.data.gov"
rpp <- 1000 # 1000 = max results per page
order <- "ASC" # DESC = Decending, ASC = Ascending
sortby <- "postedDate" #docketId (Docket ID) docId (Document ID) title (Title) postedDate (Posted Date) agency (Agency) documentType (Document Type) submitterName (Submitter Name) organization (Organization)
pages <- c(1, (seq(100000000)*rpp)+1) # up to 100,000,000 results
documenttype <- "PS" # "N%2BPR%2BFR%2BPS%2BSR%2BO"
## N: Notice,
## PR: Proposed Rule,
## FR: Rule,
## O: Other,
## SR: Supporting & Related Material,
## PS: Public Submission
## add your API Key
source("api-key.R")
api_key <- api_key
## initial API call (first page of 1000 results)
raw.result <- GET(
url = url,
path = paste0(
"/regulations/v4/documents?api_key=", api_key,
"&rpp=", rpp,
"&so=", order,
"&sb=", sortby,
"&dct=", "PS", # searching Public Submissions
"&po=", pages[page]
)
)
raw.result$status_code
# extract content to list
content <- fromJSON(rawToChar(raw.result$content))
# make a data frame
d <- as.data.frame(content[[1]])
unique(d$postedDate)
# to strings (to gaurentee consistent classes)
#FIXME mutate_at/if
if("organization" %in% names(d)){d$organization %<>% as.character()}
if("commentDueDate" %in% names(d)){d$commentDueDate %<>% as.character()}
if("commentStartDate" %in% names(d)){d$commentStartDate %<>% as.character()}
if("postedDate" %in% names(d)){d$postedDate %<>% as.character()}
# initialize
page <- page
error <- 0
skip <- NA
##################################################################################
# If adding to saved results, first run: load("data/comments.Rdata")
# loop until API fails for more than 1 hour
while (error < 61) {
# if returning errors for more than 1 hr
# API call
raw.result <- GET(
url = url,
path = paste0(
"/regulations/v3/documents?api_key=",
api_key,
"&rpp=",
rpp,
"&so=",
order,
"&sb=",
sortby,
"&dct=",
"PS",
"&po=",
pages[page]
)
)
# API call error counter
ifelse(raw.result$status_code != 200, error <-
error + 1, error <- 0)
# If call fails, wait a minute
if (error > 0) {
message(print(paste("Error", raw.result$status_code,"on page", page, "- waiting", error,"minutes" )))
Sys.sleep(60)
}
# If call works, merge in new data
if (raw.result$status_code == 200) {
# extract content to list
content <- fromJSON(rawToChar(raw.result$content))
# to data frame
temp <- as.data.frame(content[[1]])
if("organization" %in% names(temp)){temp$organization %<>% as.character()}
if("commentDueDate" %in% names(temp)){temp$commentDueDate %<>% as.character()}
if("commentStartDate" %in% names(temp)){temp$commentStartDate %<>% as.character()}
if("postedDate" %in% names(temp)){temp$postedDate %<>% as.character()}
# merge with previous pages silently
suppressMessages(
d %<>% full_join(temp)
)
message(paste("Page", page, "added", Sys.time()))
page <- page + 1
}
# If server error more than twice, skip
if (raw.result$status_code == 500 & error > 1) {
message(paste("Skipping page", page))
skip <- c(skip, page)
page <- page + 1
}
# save after each half million docs (it takes ~30 minutes to get 500k and you don't want to start over if you hit an error)
if (grepl("000$|500$", page)){
message("Saving", paste0(page, "comments.Rdata"))
save(d, page, skip, file = here::here(directory, paste0(page, "comments.Rdata")))
d <- temp
}
}# END LOOP
# Save last comments
save(d, "lastcomments.Rdata")
save(d, page, skip, file = here::here(directory, "lastcomments.Rdata") )
save.image()
# Save recent comments
save(d, file = "data/recentcomments.Rdata")
tail(d %>% drop_na(postedDate) %>% .$postedDate)
max(d$postedDate, na.rm = T)
min(d$postedDate, na.rm = T)
| /functions/regulations-gov-get-all-comments4.R | no_license | zoeang/rulemaking | R | false | false | 4,501 | r | # This script gets all documents (e.g. public comments) from regulations.gov
# UNDER CONSTRUCTION FOR V4
## load required packages
source("setup.R")
# I keep keep data I have already downloaded in ascending order in a directory called "ascending"
directory <- "ascending"
list.files(here::here(directory))
# The api call below loads data in ascending order.
# To start with the earliest comment, set page to 1
# To dowload more recent comments, either change order to "DESC" or specify a page of results to start at
page <- 1
# set defaults for regulations.gov api call
url <- "https://api.data.gov"
rpp <- 1000 # 1000 = max results per page
order <- "ASC" # DESC = Decending, ASC = Ascending
sortby <- "postedDate" #docketId (Docket ID) docId (Document ID) title (Title) postedDate (Posted Date) agency (Agency) documentType (Document Type) submitterName (Submitter Name) organization (Organization)
pages <- c(1, (seq(100000000)*rpp)+1) # up to 100,000,000 results
documenttype <- "PS" # "N%2BPR%2BFR%2BPS%2BSR%2BO"
## N: Notice,
## PR: Proposed Rule,
## FR: Rule,
## O: Other,
## SR: Supporting & Related Material,
## PS: Public Submission
## add your API Key
source("api-key.R")
api_key <- api_key
## initial API call (first page of 1000 results)
raw.result <- GET(
url = url,
path = paste0(
"/regulations/v4/documents?api_key=", api_key,
"&rpp=", rpp,
"&so=", order,
"&sb=", sortby,
"&dct=", "PS", # searching Public Submissions
"&po=", pages[page]
)
)
raw.result$status_code
# extract content to list
content <- fromJSON(rawToChar(raw.result$content))
# make a data frame
d <- as.data.frame(content[[1]])
unique(d$postedDate)
# to strings (to gaurentee consistent classes)
#FIXME mutate_at/if
if("organization" %in% names(d)){d$organization %<>% as.character()}
if("commentDueDate" %in% names(d)){d$commentDueDate %<>% as.character()}
if("commentStartDate" %in% names(d)){d$commentStartDate %<>% as.character()}
if("postedDate" %in% names(d)){d$postedDate %<>% as.character()}
# initialize
page <- page
error <- 0
skip <- NA
##################################################################################
# If adding to saved results, first run: load("data/comments.Rdata")
# loop until API fails for more than 1 hour
while (error < 61) {
# if returning errors for more than 1 hr
# API call
raw.result <- GET(
url = url,
path = paste0(
"/regulations/v3/documents?api_key=",
api_key,
"&rpp=",
rpp,
"&so=",
order,
"&sb=",
sortby,
"&dct=",
"PS",
"&po=",
pages[page]
)
)
# API call error counter
ifelse(raw.result$status_code != 200, error <-
error + 1, error <- 0)
# If call fails, wait a minute
if (error > 0) {
message(print(paste("Error", raw.result$status_code,"on page", page, "- waiting", error,"minutes" )))
Sys.sleep(60)
}
# If call works, merge in new data
if (raw.result$status_code == 200) {
# extract content to list
content <- fromJSON(rawToChar(raw.result$content))
# to data frame
temp <- as.data.frame(content[[1]])
if("organization" %in% names(temp)){temp$organization %<>% as.character()}
if("commentDueDate" %in% names(temp)){temp$commentDueDate %<>% as.character()}
if("commentStartDate" %in% names(temp)){temp$commentStartDate %<>% as.character()}
if("postedDate" %in% names(temp)){temp$postedDate %<>% as.character()}
# merge with previous pages silently
suppressMessages(
d %<>% full_join(temp)
)
message(paste("Page", page, "added", Sys.time()))
page <- page + 1
}
# If server error more than twice, skip
if (raw.result$status_code == 500 & error > 1) {
message(paste("Skipping page", page))
skip <- c(skip, page)
page <- page + 1
}
# save after each half million docs (it takes ~30 minutes to get 500k and you don't want to start over if you hit an error)
if (grepl("000$|500$", page)){
message("Saving", paste0(page, "comments.Rdata"))
save(d, page, skip, file = here::here(directory, paste0(page, "comments.Rdata")))
d <- temp
}
}# END LOOP
# Save last comments
save(d, "lastcomments.Rdata")
save(d, page, skip, file = here::here(directory, "lastcomments.Rdata") )
save.image()
# Save recent comments
save(d, file = "data/recentcomments.Rdata")
tail(d %>% drop_na(postedDate) %>% .$postedDate)
max(d$postedDate, na.rm = T)
min(d$postedDate, na.rm = T)
|
#!/usr/bin/env Rscript
suppressMessages(library(Matrix))
suppressMessages(library(parallel))
suppressMessages(library(optparse))
option_list <- list(make_option(c("-m", "--mutation_data"), action = "store", type = "character", help = "Mutation Matrix", default = NULL),
make_option(c("-e", '--expression_data'), action = "store", type = "character", help = "Expression Matrix", default = NULL),
make_option(c("-n", "--network_data"), action = "store", type = "character", help = "Network Data", default = NULL),
make_option(c("-t", "--num_threads"), action = "store", type = "integer", help = "Number of Threads", default = 2),
make_option(c("-o", "--output_prefix"), action = "store", type = "character", help = "Output Prefix", default = "NetworkProfile"))
opt <- parse_args(OptionParser(option_list = option_list))
if(is.null(opt$mutation_data) ||
is.null(opt$network_data) ||
is.null(opt$expression_data)){
warning("!Provide data files")
quit(save = "no")
}
message("...loading data")
mut.mat <- readRDS(opt$mutation_data)
expr.mat <- readRDS(opt$expression_data)
ppi <- readRDS(opt$network_data)
message("...processing data")
if(length(intersect(rownames(mut.mat),
intersect(rownames(expr.mat),
rownames(ppi)))) == 0){
warning("!Feature names do not match")
quit(save = "no")
}
## Filter Samples ##
samples.common <- intersect(colnames(mut.mat), colnames(expr.mat))
mut.mat <- mut.mat[,colnames(mut.mat) %in% samples.common]
expr.mat <- expr.mat[,colnames(expr.mat) %in% samples.common]
## Filter Features ##
idx <- colnames(ppi) %in% na.omit(intersect(rownames(mut.mat), rownames(expr.mat)))
ppi <- ppi[idx, idx]
idx <- colSums(ppi) == 0
ppi <- ppi[!idx,!idx]
mut.mat <- mut.mat[match(rownames(ppi),table=rownames(mut.mat)),]
mut.mat <- mut.mat[,colSums(mut.mat) > 8]
expr.mat <- expr.mat[match(rownames(ppi),table=rownames(expr.mat)),]
expr.mat <- expr.mat[,match(colnames(mut.mat),table=colnames(expr.mat))]
expr.mat <- t(scale(t(expr.mat)))
gc()
network.prof <- sapply(1:ncol(mut.mat), function(i){
cat(sprintf('\rProcessing Sample: %d',i))
## Weighted Network ##
edge.idx <- which(ppi, arr.ind = TRUE, useNames = FALSE)
edge.idx <- cbind(edge.idx, apply(edge.idx, 1, function(x){
sqrt(expr.mat[x[1],i]^2 + expr.mat[x[2],i]^2)
}))
edge.idx[is.na(edge.idx[,3]),3] <- 0.1 ## Small dysregulation
ppi.w <- sparseMatrix(i = edge.idx[,1],
j = edge.idx[,2],
x = edge.idx[,3],
dims = c(nrow(ppi), nrow(ppi)),
dimnames = list(rownames(ppi), rownames(ppi)),
use.last.ij = TRUE)
ppi.w <- ppi.w %*% Matrix::Diagonal(x = Matrix::colSums(ppi.w)^-1)
## Original ##
p0 <- Matrix(mut.mat[,i], ncol = 1, nrow = nrow(ppi.w))
p0 <- p0 / sum(p0)
pt <- Matrix(1/nrow(ppi.w), ncol = 1, nrow = nrow(ppi.w))
delta <- 1
count <- 1
r <- 0.75
while(delta > 1e-16 && count < 100){
px <- (1-r) * ppi.w %*% pt + r * p0
delta <- sum(abs(px - pt))
count <- count + 1
pt <- px
}
res.orig <- pt
## Random ##
nSeed <- sum(mut.mat[,i])
cl <- makeCluster(opt$num_threads)
clusterExport(cl, varlist = c('ppi.w', 'r', 'nSeed'), envir = environment())
res.random <- parSapply(cl, 1:1000, function(k){
require(Matrix)
p0 <- Matrix(0, ncol = 1, nrow = nrow(ppi.w))
p0[sample(1:nrow(ppi.w), size = nSeed, replace = FALSE), 1] <- 1/nSeed
pt <- Matrix(1/nrow(ppi.w), ncol = 1, nrow = nrow(ppi.w))
delta <- 1
count <- 1
while(delta > 1e-16 && count < 100){
px <- (1-r) * ppi.w %*% pt + r * p0
delta <- sum(abs(px - pt))
count <- count + 1
pt <- px
}
return(as.vector(pt))
})
stopCluster(cl)
## Significance ##
res.random <- log10(res.random)
mean.vals <- apply(res.random, 1, function(x){mean(x, na.rm = TRUE)})
sd.vals <- apply(res.random, 1, function(x){sd(x, na.rm = TRUE)})
p.vals <- pnorm(log10(as.vector(res.orig)),
mean = mean.vals,
sd = sd.vals,
lower.tail = FALSE)
p.vals <- p.adjust(p.vals, method = 'fdr')
return(setNames(p.vals, nm = rownames(ppi.w)))
})
colnames(network.prof) <- colnames(mut.mat)
out.file <- sprintf("%s/%s_NetworkProfile.rds", getwd(), opt$output_prefix)
message("...saving results to file ", out.file)
saveRDS(network.prof, file = out.file)
quit(save = "no") | /NetworkProfilesWeighted.R | no_license | ardadurmaz/pancancer-fsm | R | false | false | 4,543 | r | #!/usr/bin/env Rscript
suppressMessages(library(Matrix))
suppressMessages(library(parallel))
suppressMessages(library(optparse))
option_list <- list(make_option(c("-m", "--mutation_data"), action = "store", type = "character", help = "Mutation Matrix", default = NULL),
make_option(c("-e", '--expression_data'), action = "store", type = "character", help = "Expression Matrix", default = NULL),
make_option(c("-n", "--network_data"), action = "store", type = "character", help = "Network Data", default = NULL),
make_option(c("-t", "--num_threads"), action = "store", type = "integer", help = "Number of Threads", default = 2),
make_option(c("-o", "--output_prefix"), action = "store", type = "character", help = "Output Prefix", default = "NetworkProfile"))
opt <- parse_args(OptionParser(option_list = option_list))
if(is.null(opt$mutation_data) ||
is.null(opt$network_data) ||
is.null(opt$expression_data)){
warning("!Provide data files")
quit(save = "no")
}
message("...loading data")
mut.mat <- readRDS(opt$mutation_data)
expr.mat <- readRDS(opt$expression_data)
ppi <- readRDS(opt$network_data)
message("...processing data")
if(length(intersect(rownames(mut.mat),
intersect(rownames(expr.mat),
rownames(ppi)))) == 0){
warning("!Feature names do not match")
quit(save = "no")
}
## Filter Samples ##
samples.common <- intersect(colnames(mut.mat), colnames(expr.mat))
mut.mat <- mut.mat[,colnames(mut.mat) %in% samples.common]
expr.mat <- expr.mat[,colnames(expr.mat) %in% samples.common]
## Filter Features ##
idx <- colnames(ppi) %in% na.omit(intersect(rownames(mut.mat), rownames(expr.mat)))
ppi <- ppi[idx, idx]
idx <- colSums(ppi) == 0
ppi <- ppi[!idx,!idx]
mut.mat <- mut.mat[match(rownames(ppi),table=rownames(mut.mat)),]
mut.mat <- mut.mat[,colSums(mut.mat) > 8]
expr.mat <- expr.mat[match(rownames(ppi),table=rownames(expr.mat)),]
expr.mat <- expr.mat[,match(colnames(mut.mat),table=colnames(expr.mat))]
expr.mat <- t(scale(t(expr.mat)))
gc()
network.prof <- sapply(1:ncol(mut.mat), function(i){
cat(sprintf('\rProcessing Sample: %d',i))
## Weighted Network ##
edge.idx <- which(ppi, arr.ind = TRUE, useNames = FALSE)
edge.idx <- cbind(edge.idx, apply(edge.idx, 1, function(x){
sqrt(expr.mat[x[1],i]^2 + expr.mat[x[2],i]^2)
}))
edge.idx[is.na(edge.idx[,3]),3] <- 0.1 ## Small dysregulation
ppi.w <- sparseMatrix(i = edge.idx[,1],
j = edge.idx[,2],
x = edge.idx[,3],
dims = c(nrow(ppi), nrow(ppi)),
dimnames = list(rownames(ppi), rownames(ppi)),
use.last.ij = TRUE)
ppi.w <- ppi.w %*% Matrix::Diagonal(x = Matrix::colSums(ppi.w)^-1)
## Original ##
p0 <- Matrix(mut.mat[,i], ncol = 1, nrow = nrow(ppi.w))
p0 <- p0 / sum(p0)
pt <- Matrix(1/nrow(ppi.w), ncol = 1, nrow = nrow(ppi.w))
delta <- 1
count <- 1
r <- 0.75
while(delta > 1e-16 && count < 100){
px <- (1-r) * ppi.w %*% pt + r * p0
delta <- sum(abs(px - pt))
count <- count + 1
pt <- px
}
res.orig <- pt
## Random ##
nSeed <- sum(mut.mat[,i])
cl <- makeCluster(opt$num_threads)
clusterExport(cl, varlist = c('ppi.w', 'r', 'nSeed'), envir = environment())
res.random <- parSapply(cl, 1:1000, function(k){
require(Matrix)
p0 <- Matrix(0, ncol = 1, nrow = nrow(ppi.w))
p0[sample(1:nrow(ppi.w), size = nSeed, replace = FALSE), 1] <- 1/nSeed
pt <- Matrix(1/nrow(ppi.w), ncol = 1, nrow = nrow(ppi.w))
delta <- 1
count <- 1
while(delta > 1e-16 && count < 100){
px <- (1-r) * ppi.w %*% pt + r * p0
delta <- sum(abs(px - pt))
count <- count + 1
pt <- px
}
return(as.vector(pt))
})
stopCluster(cl)
## Significance ##
res.random <- log10(res.random)
mean.vals <- apply(res.random, 1, function(x){mean(x, na.rm = TRUE)})
sd.vals <- apply(res.random, 1, function(x){sd(x, na.rm = TRUE)})
p.vals <- pnorm(log10(as.vector(res.orig)),
mean = mean.vals,
sd = sd.vals,
lower.tail = FALSE)
p.vals <- p.adjust(p.vals, method = 'fdr')
return(setNames(p.vals, nm = rownames(ppi.w)))
})
colnames(network.prof) <- colnames(mut.mat)
out.file <- sprintf("%s/%s_NetworkProfile.rds", getwd(), opt$output_prefix)
message("...saving results to file ", out.file)
saveRDS(network.prof, file = out.file)
quit(save = "no") |
squirrels <- read.table('http://www.isi-stats.com/isi2/data/Squirrels.txt', header=T)
glimpse(squirrels)
squirrels <- squirrels %>% mutate(Location = as.factor(Location))
contrasts(squirrels$Location) <- contr.sum
squirrels.lm <- lm(Length~Location,data = squirrels)
summary(squirrels.lm)
anova(squirrels.lm)
R2 <- summary(squirrels.lm)$r.squared
M<-5000
stats.df<-data.frame(trial=seq(1,M),stat=NA)
for(j in 1:M){
squirrels$shuffled.cat<-sample(squirrels$Location)
shuff.lm<-lm(Length~shuffled.cat,data=squirrels)
stats.df[j,]$stat<-summary(shuff.lm)$r.squared
}
| /Lesson5 Code From Class.R | no_license | nick3703/MA376 | R | false | false | 579 | r | squirrels <- read.table('http://www.isi-stats.com/isi2/data/Squirrels.txt', header=T)
glimpse(squirrels)
squirrels <- squirrels %>% mutate(Location = as.factor(Location))
contrasts(squirrels$Location) <- contr.sum
squirrels.lm <- lm(Length~Location,data = squirrels)
summary(squirrels.lm)
anova(squirrels.lm)
R2 <- summary(squirrels.lm)$r.squared
M<-5000
stats.df<-data.frame(trial=seq(1,M),stat=NA)
for(j in 1:M){
squirrels$shuffled.cat<-sample(squirrels$Location)
shuff.lm<-lm(Length~shuffled.cat,data=squirrels)
stats.df[j,]$stat<-summary(shuff.lm)$r.squared
}
|
rm(list=ls())
setwd("~/Dropbox/enseignement/ENSAI/EvaluationDecisionsPubliques/TP")
require(FactoMineR)
# Importation des donnees
temperature <- read.table("data/temperatures.csv",header = TRUE, sep = ";", dec = ".", row.names = 1)
##Stat descriptives (on verifie l'importation des donnees)
summary(temperature)
# Graph bivariee avec la region en couleur
plot(temperature[,1:12], col=temperature[,17])
# Liste des villes
rownames(temperature)
# On regarde la matrice de correlation pour savoir si l'ACP est pertinente
correlations <- round(cor(temperature[,-17]), 2)
# Attention pour visualiser, il vaut mieux inverser les colonnes (plus c'est rouge, plus c'est corrélé)
image(correlations[,16:1])
# On met en individu actif les capitales et en variables actives les mesures au cours des 12 mois
res <- PCA(temperature, ind.sup = 24:35, quanti.sup = 13:16, quali.sup = 17, graph = FALSE)
# Pour retenir le nombre d'axes
barplot(res$eig[,1])
# Nuage des individus dans le premier plan factoriel avec la region en couleur
plot(res, choix = "ind", habillage = 17)
# Resume des sorties de l'ACP
summary(res)
# Resume pratique des axes en fonction des variables de départ
dimdesc(res)
# Cercle des correlations (quel bel effet taille!)
plot(res, choix = "var")
# On regarde ce qui se passe dans le plan 2/3 (mais en faite l'axe 3 n'explique rien car pas d'inertie)
plot(res, choix = "ind", axes = c(2,3), habillage = 17)
plot(res, choix = "var", axes = c(2,3), habillage = 17)
# Ellipses de confiance en supposant que les variables suivent une Gaussienne multivariée conditionnelle à la variable quali
plotellipses(res, cex=.8, level = .95)
| /enseignements/rcode/mspes/Exercice1.R | permissive | masedki/masedki.github.io | R | false | false | 1,644 | r | rm(list=ls())
setwd("~/Dropbox/enseignement/ENSAI/EvaluationDecisionsPubliques/TP")
require(FactoMineR)
# Importation des donnees
temperature <- read.table("data/temperatures.csv",header = TRUE, sep = ";", dec = ".", row.names = 1)
##Stat descriptives (on verifie l'importation des donnees)
summary(temperature)
# Graph bivariee avec la region en couleur
plot(temperature[,1:12], col=temperature[,17])
# Liste des villes
rownames(temperature)
# On regarde la matrice de correlation pour savoir si l'ACP est pertinente
correlations <- round(cor(temperature[,-17]), 2)
# Attention pour visualiser, il vaut mieux inverser les colonnes (plus c'est rouge, plus c'est corrélé)
image(correlations[,16:1])
# On met en individu actif les capitales et en variables actives les mesures au cours des 12 mois
res <- PCA(temperature, ind.sup = 24:35, quanti.sup = 13:16, quali.sup = 17, graph = FALSE)
# Pour retenir le nombre d'axes
barplot(res$eig[,1])
# Nuage des individus dans le premier plan factoriel avec la region en couleur
plot(res, choix = "ind", habillage = 17)
# Resume des sorties de l'ACP
summary(res)
# Resume pratique des axes en fonction des variables de départ
dimdesc(res)
# Cercle des correlations (quel bel effet taille!)
plot(res, choix = "var")
# On regarde ce qui se passe dans le plan 2/3 (mais en faite l'axe 3 n'explique rien car pas d'inertie)
plot(res, choix = "ind", axes = c(2,3), habillage = 17)
plot(res, choix = "var", axes = c(2,3), habillage = 17)
# Ellipses de confiance en supposant que les variables suivent une Gaussienne multivariée conditionnelle à la variable quali
plotellipses(res, cex=.8, level = .95)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/faraway-package.R
\docType{data}
\name{chicago}
\alias{chicago}
\title{Chicago insurance redlining}
\format{
This dataframe contains the following columns \describe{
\item{race}{ racial composition in percent minority }
\item{fire}{ fires per 100 housing units } \item{theft}{
theft per 1000 population } \item{age}{ percent of housing units
built before 1939 } \item{involact}{ new FAIR plan policies and
renewals per 100 housing units } \item{income}{ median family income
in thousands of dollars} \item{side}{ North or South side of
Chicago}
}
}
\source{
Adapted from "Data : A Collection of Problems from Many Fields for
the Student and Research Worker" by D. Andrews and A. Herzberg published by
Springer-Verlag, in 1985
}
\description{
Data from a 1970's study on the relationship between insurance redlining in
Chicago and racial composition, fire and theft rates, age of housing and
income in 47 zip codes.
}
\keyword{datasets}
| /man/chicago.Rd | no_license | cran/faraway | R | false | true | 1,016 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/faraway-package.R
\docType{data}
\name{chicago}
\alias{chicago}
\title{Chicago insurance redlining}
\format{
This dataframe contains the following columns \describe{
\item{race}{ racial composition in percent minority }
\item{fire}{ fires per 100 housing units } \item{theft}{
theft per 1000 population } \item{age}{ percent of housing units
built before 1939 } \item{involact}{ new FAIR plan policies and
renewals per 100 housing units } \item{income}{ median family income
in thousands of dollars} \item{side}{ North or South side of
Chicago}
}
}
\source{
Adapted from "Data : A Collection of Problems from Many Fields for
the Student and Research Worker" by D. Andrews and A. Herzberg published by
Springer-Verlag, in 1985
}
\description{
Data from a 1970's study on the relationship between insurance redlining in
Chicago and racial composition, fire and theft rates, age of housing and
income in 47 zip codes.
}
\keyword{datasets}
|
crtrate_UCB = rep(NA,itertimes)
exp_reg_UCB = rep(NA,itertimes)
post_reg_UCB = rep(NA,itertimes)
sum_reg_UCB = rep(NA,itertimes)
for(ldai in 1:itertimes){
usbcal = function(diff,N,N1,n){
ans = (8*N^2*log(N)/(sqrt(N*(4*sqrt(2*log(N)*(2*log(N)+N*diff^2))+8*log(N)+N*diff^2))-diff*N)^2-N1)/n
#ans=0.8
#print((sqrt(2*log(N)/(ans*n+N1))-sqrt(2*log(N)/(N-ans*n-N1))+diff))
return(ans)
}
correct_rpm = rep(0,num_iter)
for(iter in 1:num_iter){
#alpha1_store[iter,1] = control_size*p1[iter]
#beta1_store[iter,1] = control_size - alpha1_store[iter,1]
alpha1_store[iter,1] = floor(control_size*p1[iter]+0.5)
beta1_store[iter,1] = control_size-alpha1_store[iter,1]
#alpha1_store[iter,1] = 0
#beta1_store[iter,1] = 0
alpha2_store[iter,1] = 0
beta2_store[iter,1] = 0
idx_1 = 0
idx_2 = 0
stop = 0
for(i in 1:num_time){
if((alpha1_store[iter,i]==0)||(alpha2_store[iter,i]==0))
prob_1=0.5
else
prob_1 = usbcal(alpha1_store[iter,i]/(alpha1_store[iter,i]+beta1_store[iter,i])-alpha2_store[iter,i]/(alpha2_store[iter,i]+beta2_store[iter,i]),(alpha1_store[iter,i]+beta1_store[iter,i]+alpha2_store[iter,i]+beta2_store[iter,i]+num_batch),(alpha1_store[iter,i]+beta1_store[iter,i]),num_batch)
prob_1 = max(0,min(1,prob_1))
#if (stop==0)
#prob_1 = (prob_1-0.5)*(1-(num_time-i)/num_time*0.5)+0.5
n_p1 = rbinom(1,size=num_actual_batch[i], prob=prob_1)
n_p2 = num_actual_batch[i] - n_p1
s = 0
if(n_p1>0)
s = sum(data1[iter,idx_1:(idx_1+n_p1-1)])
alpha1_store[iter,i+1] = alpha1_store[iter,i] + s
beta1_store[iter,i+1] = beta1_store[iter,i] + n_p1 - s
idx_1 = idx_1 + n_p1
s = 0
if(n_p2>0)
s = sum(data2[iter,idx_2:(idx_2+n_p2-1)])
alpha2_store[iter,i+1] = alpha2_store[iter,i] + s
beta2_store[iter,i+1] = beta2_store[iter,i] + n_p2 - s
idx_2 = idx_2 + n_p2
if(p1[iter]>=p2[iter])
loss_rpm[iter,i] = n_p2*(p1[iter]-p2[iter])
else
loss_rpm[iter,i] = n_p1*(p2[iter]-p1[iter])
}
post_loss[iter] = pos_time * num_batch *abs(p1[iter]-p2[iter])
if(((alpha1_store[iter,num_time+1]/beta1_store[iter,num_time+1])>=(alpha2_store[iter,num_time+1]/beta2_store[iter,num_time+1]))==(p1[iter]>=p2[iter])){
correct_rpm[iter] = 1
post_loss[iter] = 0
}
cum_rew[iter] = sum(data1[iter,1:idx_1])+sum(data2[iter,1:idx_2])
if(((alpha1_store[iter,num_time+1]/beta1_store[iter,num_time+1])>=(alpha2_store[iter,num_time+1]/beta2_store[iter,num_time+1]))==(p1[iter]>=p2[iter]))
post_cum_rew[iter] = rbinom(1,size=pos_time * num_batch, prob=p1[iter])
else
post_cum_rew[iter] = rbinom(1,size=pos_time * num_batch, prob=p2[iter])
}
crtrate_UCB[ldai]=(sum(correct_rpm))
sum_reg_UCB[ldai]=(mean(rowSums(loss_rpm)+post_loss))
exp_reg_UCB[ldai]=(mean(rowSums(loss_rpm)))
post_reg_UCB[ldai]=(mean(post_loss))
}
print(mean(crtrate_UCB))
print(mean(sum_reg_UCB))
| /Experiment/UCB.R | no_license | liangdai/online-experiment | R | false | false | 2,766 | r | crtrate_UCB = rep(NA,itertimes)
exp_reg_UCB = rep(NA,itertimes)
post_reg_UCB = rep(NA,itertimes)
sum_reg_UCB = rep(NA,itertimes)
for(ldai in 1:itertimes){
usbcal = function(diff,N,N1,n){
ans = (8*N^2*log(N)/(sqrt(N*(4*sqrt(2*log(N)*(2*log(N)+N*diff^2))+8*log(N)+N*diff^2))-diff*N)^2-N1)/n
#ans=0.8
#print((sqrt(2*log(N)/(ans*n+N1))-sqrt(2*log(N)/(N-ans*n-N1))+diff))
return(ans)
}
correct_rpm = rep(0,num_iter)
for(iter in 1:num_iter){
#alpha1_store[iter,1] = control_size*p1[iter]
#beta1_store[iter,1] = control_size - alpha1_store[iter,1]
alpha1_store[iter,1] = floor(control_size*p1[iter]+0.5)
beta1_store[iter,1] = control_size-alpha1_store[iter,1]
#alpha1_store[iter,1] = 0
#beta1_store[iter,1] = 0
alpha2_store[iter,1] = 0
beta2_store[iter,1] = 0
idx_1 = 0
idx_2 = 0
stop = 0
for(i in 1:num_time){
if((alpha1_store[iter,i]==0)||(alpha2_store[iter,i]==0))
prob_1=0.5
else
prob_1 = usbcal(alpha1_store[iter,i]/(alpha1_store[iter,i]+beta1_store[iter,i])-alpha2_store[iter,i]/(alpha2_store[iter,i]+beta2_store[iter,i]),(alpha1_store[iter,i]+beta1_store[iter,i]+alpha2_store[iter,i]+beta2_store[iter,i]+num_batch),(alpha1_store[iter,i]+beta1_store[iter,i]),num_batch)
prob_1 = max(0,min(1,prob_1))
#if (stop==0)
#prob_1 = (prob_1-0.5)*(1-(num_time-i)/num_time*0.5)+0.5
n_p1 = rbinom(1,size=num_actual_batch[i], prob=prob_1)
n_p2 = num_actual_batch[i] - n_p1
s = 0
if(n_p1>0)
s = sum(data1[iter,idx_1:(idx_1+n_p1-1)])
alpha1_store[iter,i+1] = alpha1_store[iter,i] + s
beta1_store[iter,i+1] = beta1_store[iter,i] + n_p1 - s
idx_1 = idx_1 + n_p1
s = 0
if(n_p2>0)
s = sum(data2[iter,idx_2:(idx_2+n_p2-1)])
alpha2_store[iter,i+1] = alpha2_store[iter,i] + s
beta2_store[iter,i+1] = beta2_store[iter,i] + n_p2 - s
idx_2 = idx_2 + n_p2
if(p1[iter]>=p2[iter])
loss_rpm[iter,i] = n_p2*(p1[iter]-p2[iter])
else
loss_rpm[iter,i] = n_p1*(p2[iter]-p1[iter])
}
post_loss[iter] = pos_time * num_batch *abs(p1[iter]-p2[iter])
if(((alpha1_store[iter,num_time+1]/beta1_store[iter,num_time+1])>=(alpha2_store[iter,num_time+1]/beta2_store[iter,num_time+1]))==(p1[iter]>=p2[iter])){
correct_rpm[iter] = 1
post_loss[iter] = 0
}
cum_rew[iter] = sum(data1[iter,1:idx_1])+sum(data2[iter,1:idx_2])
if(((alpha1_store[iter,num_time+1]/beta1_store[iter,num_time+1])>=(alpha2_store[iter,num_time+1]/beta2_store[iter,num_time+1]))==(p1[iter]>=p2[iter]))
post_cum_rew[iter] = rbinom(1,size=pos_time * num_batch, prob=p1[iter])
else
post_cum_rew[iter] = rbinom(1,size=pos_time * num_batch, prob=p2[iter])
}
crtrate_UCB[ldai]=(sum(correct_rpm))
sum_reg_UCB[ldai]=(mean(rowSums(loss_rpm)+post_loss))
exp_reg_UCB[ldai]=(mean(rowSums(loss_rpm)))
post_reg_UCB[ldai]=(mean(post_loss))
}
print(mean(crtrate_UCB))
print(mean(sum_reg_UCB))
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EPFR.r
\name{sql.median}
\alias{sql.median}
\title{sql.median}
\usage{
sql.median(x, y, n, w = 0.5)
}
\arguments{
\item{x}{= column on which computation is run}
\item{y}{= column on which partitioning is performed}
\item{n}{= SQL statement}
\item{w}{= desired ptile break point}
}
\description{
median (or alternate ptile point) of <x> within <y>
}
\seealso{
Other sql: \code{\link{sql.1dActWtTrend.Alloc}},
\code{\link{sql.1dActWtTrend.Final}},
\code{\link{sql.1dActWtTrend.Flow}},
\code{\link{sql.1dActWtTrend.select}},
\code{\link{sql.1dActWtTrend.topline.from}},
\code{\link{sql.1dActWtTrend.topline}},
\code{\link{sql.1dActWtTrend.underlying.basic}},
\code{\link{sql.1dActWtTrend.underlying}},
\code{\link{sql.1dActWtTrend}},
\code{\link{sql.1dFloMo.CountryId.List}},
\code{\link{sql.1dFloMo.FI}},
\code{\link{sql.1dFloMo.Rgn}},
\code{\link{sql.1dFloMo.Sec.topline}},
\code{\link{sql.1dFloMo.filter}},
\code{\link{sql.1dFloMo.grp}},
\code{\link{sql.1dFloMo.select.wrapper}},
\code{\link{sql.1dFloMo.select}},
\code{\link{sql.1dFloMo.underlying}},
\code{\link{sql.1dFloMoAggr}}, \code{\link{sql.1dFloMo}},
\code{\link{sql.1dFloTrend.Alloc.data}},
\code{\link{sql.1dFloTrend.Alloc.fetch}},
\code{\link{sql.1dFloTrend.Alloc.final}},
\code{\link{sql.1dFloTrend.Alloc.from}},
\code{\link{sql.1dFloTrend.Alloc.purge}},
\code{\link{sql.1dFloTrend.Alloc}},
\code{\link{sql.1dFloTrend.select}},
\code{\link{sql.1dFloTrend.underlying}},
\code{\link{sql.1dFloTrend}}, \code{\link{sql.1dFundCt}},
\code{\link{sql.1dFundRet}}, \code{\link{sql.1dION}},
\code{\link{sql.1mActWt.underlying}},
\code{\link{sql.1mActWtIncrPct}},
\code{\link{sql.1mActWtTrend.underlying}},
\code{\link{sql.1mActWtTrend}},
\code{\link{sql.1mActWt}},
\code{\link{sql.1mAllocD.from}},
\code{\link{sql.1mAllocD.select}},
\code{\link{sql.1mAllocD.topline.from}},
\code{\link{sql.1mAllocD}},
\code{\link{sql.1mAllocMo.select}},
\code{\link{sql.1mAllocMo.underlying.from}},
\code{\link{sql.1mAllocMo.underlying.pre}},
\code{\link{sql.1mAllocMo}},
\code{\link{sql.1mAllocSkew.topline.from}},
\code{\link{sql.1mAllocSkew}},
\code{\link{sql.1mBullish.Alloc}},
\code{\link{sql.1mBullish.Final}},
\code{\link{sql.1mChActWt}}, \code{\link{sql.1mFloMo}},
\code{\link{sql.1mFloTrend.underlying}},
\code{\link{sql.1mFloTrend}}, \code{\link{sql.1mFundCt}},
\code{\link{sql.1mHoldAum}},
\code{\link{sql.1mSRIAdvisorPct}},
\code{\link{sql.1wFlow.Corp}},
\code{\link{sql.ActWtDiff2}},
\code{\link{sql.Allocation.Sec.FinsExREst}},
\code{\link{sql.Allocation.Sec}},
\code{\link{sql.Allocations.bulk.EqWtAvg}},
\code{\link{sql.Allocations.bulk.Single}},
\code{\link{sql.Allocation}},
\code{\link{sql.BenchIndex.duplication}},
\code{\link{sql.Bullish}}, \code{\link{sql.DailyFlo}},
\code{\link{sql.Diff}}, \code{\link{sql.Dispersion}},
\code{\link{sql.FloMo.Funds}}, \code{\link{sql.Flow}},
\code{\link{sql.Foreign}},
\code{\link{sql.FundHistory.macro}},
\code{\link{sql.FundHistory.sf}},
\code{\link{sql.FundHistory}}, \code{\link{sql.HSIdmap}},
\code{\link{sql.HerdingLSV}},
\code{\link{sql.Holdings.bulk.wrapper}},
\code{\link{sql.Holdings.bulk}},
\code{\link{sql.Holdings}}, \code{\link{sql.ION}},
\code{\link{sql.MonthlyAlloc}},
\code{\link{sql.MonthlyAssetsEnd}}, \code{\link{sql.Mo}},
\code{\link{sql.Overweight}}, \code{\link{sql.RDSuniv}},
\code{\link{sql.ReportDate}}, \code{\link{sql.SRI}},
\code{\link{sql.ShareClass}},
\code{\link{sql.TopDownAllocs.items}},
\code{\link{sql.TopDownAllocs.underlying}},
\code{\link{sql.TopDownAllocs}}, \code{\link{sql.Trend}},
\code{\link{sql.and}}, \code{\link{sql.arguments}},
\code{\link{sql.bcp}}, \code{\link{sql.breakdown}},
\code{\link{sql.case}}, \code{\link{sql.close}},
\code{\link{sql.connect.wrapper}},
\code{\link{sql.connect}},
\code{\link{sql.cross.border}},
\code{\link{sql.datediff}}, \code{\link{sql.declare}},
\code{\link{sql.delete}}, \code{\link{sql.drop}},
\code{\link{sql.exists}},
\code{\link{sql.extra.domicile}},
\code{\link{sql.index}}, \code{\link{sql.into}},
\code{\link{sql.in}}, \code{\link{sql.isin.old.to.new}},
\code{\link{sql.label}}, \code{\link{sql.map.classif}},
\code{\link{sql.mat.cofactor}},
\code{\link{sql.mat.crossprod.vector}},
\code{\link{sql.mat.crossprod}},
\code{\link{sql.mat.determinant}},
\code{\link{sql.mat.flip}},
\code{\link{sql.mat.multiply}}, \code{\link{sql.nonneg}},
\code{\link{sql.query.underlying}},
\code{\link{sql.query}}, \code{\link{sql.regr}},
\code{\link{sql.tbl}}, \code{\link{sql.ui}},
\code{\link{sql.unbracket}}, \code{\link{sql.update}},
\code{\link{sql.yield.curve.1dFloMo}},
\code{\link{sql.yield.curve}},
\code{\link{sql.yyyymmdd}}, \code{\link{sql.yyyymm}}
}
\keyword{sql.median}
| /man/sql.median.Rd | no_license | vsrimurthy/EPFR | R | false | true | 4,936 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EPFR.r
\name{sql.median}
\alias{sql.median}
\title{sql.median}
\usage{
sql.median(x, y, n, w = 0.5)
}
\arguments{
\item{x}{= column on which computation is run}
\item{y}{= column on which partitioning is performed}
\item{n}{= SQL statement}
\item{w}{= desired ptile break point}
}
\description{
median (or alternate ptile point) of <x> within <y>
}
\seealso{
Other sql: \code{\link{sql.1dActWtTrend.Alloc}},
\code{\link{sql.1dActWtTrend.Final}},
\code{\link{sql.1dActWtTrend.Flow}},
\code{\link{sql.1dActWtTrend.select}},
\code{\link{sql.1dActWtTrend.topline.from}},
\code{\link{sql.1dActWtTrend.topline}},
\code{\link{sql.1dActWtTrend.underlying.basic}},
\code{\link{sql.1dActWtTrend.underlying}},
\code{\link{sql.1dActWtTrend}},
\code{\link{sql.1dFloMo.CountryId.List}},
\code{\link{sql.1dFloMo.FI}},
\code{\link{sql.1dFloMo.Rgn}},
\code{\link{sql.1dFloMo.Sec.topline}},
\code{\link{sql.1dFloMo.filter}},
\code{\link{sql.1dFloMo.grp}},
\code{\link{sql.1dFloMo.select.wrapper}},
\code{\link{sql.1dFloMo.select}},
\code{\link{sql.1dFloMo.underlying}},
\code{\link{sql.1dFloMoAggr}}, \code{\link{sql.1dFloMo}},
\code{\link{sql.1dFloTrend.Alloc.data}},
\code{\link{sql.1dFloTrend.Alloc.fetch}},
\code{\link{sql.1dFloTrend.Alloc.final}},
\code{\link{sql.1dFloTrend.Alloc.from}},
\code{\link{sql.1dFloTrend.Alloc.purge}},
\code{\link{sql.1dFloTrend.Alloc}},
\code{\link{sql.1dFloTrend.select}},
\code{\link{sql.1dFloTrend.underlying}},
\code{\link{sql.1dFloTrend}}, \code{\link{sql.1dFundCt}},
\code{\link{sql.1dFundRet}}, \code{\link{sql.1dION}},
\code{\link{sql.1mActWt.underlying}},
\code{\link{sql.1mActWtIncrPct}},
\code{\link{sql.1mActWtTrend.underlying}},
\code{\link{sql.1mActWtTrend}},
\code{\link{sql.1mActWt}},
\code{\link{sql.1mAllocD.from}},
\code{\link{sql.1mAllocD.select}},
\code{\link{sql.1mAllocD.topline.from}},
\code{\link{sql.1mAllocD}},
\code{\link{sql.1mAllocMo.select}},
\code{\link{sql.1mAllocMo.underlying.from}},
\code{\link{sql.1mAllocMo.underlying.pre}},
\code{\link{sql.1mAllocMo}},
\code{\link{sql.1mAllocSkew.topline.from}},
\code{\link{sql.1mAllocSkew}},
\code{\link{sql.1mBullish.Alloc}},
\code{\link{sql.1mBullish.Final}},
\code{\link{sql.1mChActWt}}, \code{\link{sql.1mFloMo}},
\code{\link{sql.1mFloTrend.underlying}},
\code{\link{sql.1mFloTrend}}, \code{\link{sql.1mFundCt}},
\code{\link{sql.1mHoldAum}},
\code{\link{sql.1mSRIAdvisorPct}},
\code{\link{sql.1wFlow.Corp}},
\code{\link{sql.ActWtDiff2}},
\code{\link{sql.Allocation.Sec.FinsExREst}},
\code{\link{sql.Allocation.Sec}},
\code{\link{sql.Allocations.bulk.EqWtAvg}},
\code{\link{sql.Allocations.bulk.Single}},
\code{\link{sql.Allocation}},
\code{\link{sql.BenchIndex.duplication}},
\code{\link{sql.Bullish}}, \code{\link{sql.DailyFlo}},
\code{\link{sql.Diff}}, \code{\link{sql.Dispersion}},
\code{\link{sql.FloMo.Funds}}, \code{\link{sql.Flow}},
\code{\link{sql.Foreign}},
\code{\link{sql.FundHistory.macro}},
\code{\link{sql.FundHistory.sf}},
\code{\link{sql.FundHistory}}, \code{\link{sql.HSIdmap}},
\code{\link{sql.HerdingLSV}},
\code{\link{sql.Holdings.bulk.wrapper}},
\code{\link{sql.Holdings.bulk}},
\code{\link{sql.Holdings}}, \code{\link{sql.ION}},
\code{\link{sql.MonthlyAlloc}},
\code{\link{sql.MonthlyAssetsEnd}}, \code{\link{sql.Mo}},
\code{\link{sql.Overweight}}, \code{\link{sql.RDSuniv}},
\code{\link{sql.ReportDate}}, \code{\link{sql.SRI}},
\code{\link{sql.ShareClass}},
\code{\link{sql.TopDownAllocs.items}},
\code{\link{sql.TopDownAllocs.underlying}},
\code{\link{sql.TopDownAllocs}}, \code{\link{sql.Trend}},
\code{\link{sql.and}}, \code{\link{sql.arguments}},
\code{\link{sql.bcp}}, \code{\link{sql.breakdown}},
\code{\link{sql.case}}, \code{\link{sql.close}},
\code{\link{sql.connect.wrapper}},
\code{\link{sql.connect}},
\code{\link{sql.cross.border}},
\code{\link{sql.datediff}}, \code{\link{sql.declare}},
\code{\link{sql.delete}}, \code{\link{sql.drop}},
\code{\link{sql.exists}},
\code{\link{sql.extra.domicile}},
\code{\link{sql.index}}, \code{\link{sql.into}},
\code{\link{sql.in}}, \code{\link{sql.isin.old.to.new}},
\code{\link{sql.label}}, \code{\link{sql.map.classif}},
\code{\link{sql.mat.cofactor}},
\code{\link{sql.mat.crossprod.vector}},
\code{\link{sql.mat.crossprod}},
\code{\link{sql.mat.determinant}},
\code{\link{sql.mat.flip}},
\code{\link{sql.mat.multiply}}, \code{\link{sql.nonneg}},
\code{\link{sql.query.underlying}},
\code{\link{sql.query}}, \code{\link{sql.regr}},
\code{\link{sql.tbl}}, \code{\link{sql.ui}},
\code{\link{sql.unbracket}}, \code{\link{sql.update}},
\code{\link{sql.yield.curve.1dFloMo}},
\code{\link{sql.yield.curve}},
\code{\link{sql.yyyymmdd}}, \code{\link{sql.yyyymm}}
}
\keyword{sql.median}
|
library(raster)
# Package to handle raster-formatted spatial data
library(rasterVis)
# The rasterVis package complements the raster package, providing a set of methods for enhanced visualization and interaction
# Defines visualisation methods with 'levelplot'
library(dismo)
# Dismo has the SDM analyses for maxent and support vector machines used by R
library(rgeos)
# To define circles with a radius around the subsampled points
# geos is a geometry engine, need to install package to access these capabilities (such as defining circumfrances)
library(rJava)
library(rgdal)
# Provides access to projection/transformation operations from a different library
# Coordinate referancing system**
library(sp)
# Coordinate referancing system
library(ncdf4)
# Opens access to read and write on netCDF files
library(kernlab)
# Required for support vector machines
# installed and running BUT UNSURE of function
library(grDevices)
# For colouring maps
library(colorRamps)
#Allows easy construction of color palettes
#Loading data for project now
#Ensure WD is in correct place WILL BE IN NEW PLACE FOR EACH SPECIES
setwd("~/Documents/UoY/Dissertation/Pout")
locs = read.csv("Pout_Severn_UTM.csv", header=T, sep = ",")
#loading severn files
#had to add the file location for R to access the severn files, is this right?
dry_always<-raster("Severn_unaltered Pout/always_dry_masked.tif")
tidal_range<-raster("Severn_unaltered Pout/tidal_range_masked.tif")
subtidal<-raster("Severn_unaltered Pout/subtidal_masked.tif")
min_elev<-raster("Severn_unaltered Pout/min_elev_masked.tif")
max_velocity<-raster("Severn_unaltered Pout/max_vel_masked.tif")
max_elev<-raster("Severn_unaltered Pout/max_elev_masked.tif")
mask_2<-raster("Severn_unaltered Pout/mask2.tif")
intertidal<-raster("Severn_unaltered Pout/intertidal_masked.tif")
depth<-raster("Severn_unaltered Pout/bathy_masked.tif")
avg_velocity<-raster("Severn_unaltered Pout/av_vel_masked.tif")
#ALL raster data is uploaded here
mask<-depth
#DO NOT HAVE 'distance_to_coast' comparison in our data set as in MaxEnt Code
#DO NOT HAVE 'lat and lon' tifs as in MaxEnt Code
# Extract depth values to table of species co-ordinates
locs_ext=extract(depth, locs[,c("X","Y")])
#this has created a VALUE of depth for each single point as dictated by x&y coordinates from species data
#now each species seen has a depth based on its coordinates in the depth raster file we are given!!
# Build a data frame of species occurrence data and depth data
locs = data.frame(locs, locs_ext)
# added locs_ext to the final column in locs file so now coordinates for species can be coupled with their depth in teh same file
# Remove points with NA values for depth, i.e. on land
locs = subset(locs, !is.na(locs_ext))
e = extent(depth)
#subset extracted all values and rows with 'na' from the locs_ext column
# WHAT DOES EXTENT DO?!
# without using the 'mask' technique above will this still remove all 'land' data above?
#what is "e"?? - is it simply giving the 'extent' of the data set in a min and max of x and y?
# Create sequences of X and Y values to define a grid
# this a 1x1 km grid
xgrid = seq(e@xmin,e@xmax,1000)
ygrid = seq(e@ymin,e@ymax,1000)
#"seq()" works by 'from', 'to', 'by incremental step'
#generated a sequence from xmin value to xmax value in "e" that increase by 1000
# Identify occurrence points within each grid cell, then draw one at random
subs = c()
for(i in 1:(length(xgrid)-1)) {
for(j in 1:(length(ygrid)-1)) {
gridsq = subset(locs, Y > ygrid[j] & Y < ygrid[j+1] & X > xgrid[i] & X < xgrid[i+1])
if(dim(gridsq)[1]>0) {
subs = rbind(subs, gridsq[sample(1:dim(gridsq)[1],1 ), ])
}
}
}
dim(locs);dim(subs) # Confirm that you have a smaller dataset than you started with (1st number)
#for is an argument that will loop a desired action on a given value in a vector
#length will get value the legth of vectors and factors in a defined object
##this a loop going through x values (every 1000m) and at each new x square, looping through all the y's related to that x (and so on for all the x values)
#gridsq is a complex way of saying the square is greater than the start of one x/y value and less than the next one after it
#rbind & cbind combine/create a matrix by rows (rbind) or columns (cbind) of the two seperate vector sets
# Assign correct co-ordinate reference system to subset
coordinates <- cbind(subs$X, subs$Y)
subs_df <- SpatialPointsDataFrame(coordinates, subs, proj4string=CRS("+proj=utm +zone=30 ellps=WGS84"))
#cbind of subs$X and subs$Y created a new data set/matrix called coordinates that only has coordinate data in it!
# we create 20,000 random "background points". There are other ways to do this, but start with this.
#NOTE
psa <- randomPoints(mask, 20000, ext=e)
#need to make sure all is up-to-date: previous error due to 'dismo' not being updated
# Stack raster layers into one variable
#NOTE WITHOUT INTERTIDAL LAYER
env_uk<-stack(depth,max_elev,avg_velocity,subtidal,tidal_range)
# Pull environmental data for the sumbsampled-presence points from the raster stack
presence_uk= extract(env_uk, subs_df[,c("X","Y")])
#Warning messages: transforming SpatialPoints to the CRS of the Raster?
# Pull environmental data for the pseudo-absence points from the raster stack
pseudo_uk = extract(env_uk, psa)
# Build some useful dataframes, with two columns of coordinates followed by the environmental variables. For the presence points:
presence_uk = data.frame(X=subs_df$X, Y=subs_df$Y, presence_uk)
#HOW IS THIS DIFFERENT TO ABOVE FUCNTION WITH "EXTRACT"?
# Convert psa from atomic vector matrix to data.frame
psapoints=data.frame(psa)
# Bind co-ordinates
coordinates <- cbind(psapoints$x, psapoints$y)
# Create spatial data frame of pseudo absences
psadf <- SpatialPointsDataFrame(coordinates, psapoints, proj4string=CRS("+proj=utm +zone=30 ellps=WGS84"))
# Build dataframe, with two columns of coordinates followed by the 5 environmental variables. For the pseudo-absences:
psadfx = psadf@coords
colnames(psadfx) = c("X","Y")
pseudo_uk = data.frame(cbind(psadfx,pseudo_uk))
# Vector of group assignments splitting the subsampled presence points data fram with environmental data into 5 groups
group_p = kfold(presence_uk, 5)
#kfold partitions a data set k times (in this case 5 times) for model testing purposes
# Repeat above step for pseudo-absence points
group_a = kfold(pseudo_uk, 5)
# create output required for the loop
evaluations = list(5)
models = list(5)
# where it says maxent - you may need to swap this for other functions if you're exploring different models
# Note that some model may need different inputs etc. Read the docs to figure this out.
# This is our k-fold test. You will want to spend a bit of time making predictions on each of the 5 sub-models
# created here to check you can make decent predictions even with missing data
for (test in 1:5) {
# Then we use test and the kfold groupings to divide the presence and absence points:
train_p = presence_uk[group_p!=test, c("X","Y")]
train_a = pseudo_uk[group_a!=test, c("X","Y")]
test_p = presence_uk[group_p==test, c("X","Y")]
test_a = pseudo_uk[group_a==test, c("X","Y")]
# Now, estimate a maxent model using the "training" points and the environmental data. This may take a few moments to run:
models[test] = maxent(env_uk, p=train_p, a=train_a)
# To validate the model, we use the appropriately named function.
# Produces warning message about implicit list embedding being depreciated. May fail in future versions of R
evaluations[test] = evaluate(test_p, test_a, models[[test]], env_uk)
}
# print out the AUC for the k-fold tests
# ideally should be > 0.75 for all
cat("K-FOLD AUC: ")
for (test in 1:5) {
cat(paste0(evaluations[[test]]@auc,","))
}
#IF ONE WANTED to visualise the 1st model NEED TO FINISH WITH THE OTHER 4 KFOLDS
## pred <- predict(models[[1]], env_uk)
## plot(pred)
#-could visualise all k-fold models to show they are the same (strong statistical result)
#BUT DONT NEED TO
# Assess Spatial Sorting Bias (SSB)
pres_train_me <- train_p
pres_test_me <- test_p
back_train_me <- train_a
back_test_me <- test_a
sb <- ssb(pres_test_me, back_test_me, pres_train_me)
sb[,1] / sb[,2]
#creates a model of spacial biasing to compare to given preditions
# Adjust for SSB if present via distance based point-wise sampling
i <- pwdSample(pres_test_me, back_test_me, pres_train_me, n=1, tr=0.1)
pres_test_pwd_me <- pres_test_me[!is.na(i[,1]), ]
back_test_pwd_me <- back_test_me[na.omit(as.vector(i)), ]
sb2 <- ssb(pres_test_pwd_me, back_test_pwd_me, pres_train_me)
sb2[1]/ sb2[2]
#creates full model without any K fold statistics etc
pres_points = presence_uk[c("X","Y")]
abs_points = pseudo_uk[c("X","Y")]
# create full maxent with all points
model <- maxent(env_uk, p=pres_points, a=abs_points)
#turn model into prediction that can be plotted into a raster
pred_PredFull <- predict(model, env_uk)
#to see model and obtain jpeg
plot(pred_PredFull)
#Gives AUC for full model (pred_PredFull)
evaluate_full <- evaluate(presence_uk[c("X","Y")], pseudo_uk[c("X","Y")], model, env_uk)
#see what AUC is by typing it in
evaluate_full
#see what the specific sensitivity values is of species
#use value givenas a base level or higher that one would expect to see species (compare to evaluate_full)
message(threshold(evaluate_full)$spec_sens)
#check response curves to see if they change the FULL MODEL
response(model)
#creates a raster file in the WD
#will be useful when putting a file into qgis!
writeRaster(pred_PredFull, filename="pred5_me.tif", options="INTERLEAVE=BAND", overwrite=TRUE)
| /5th Code Pout -Inter-Max_vel-Min_fs-ADry.R | no_license | laxmack21/Severn-Estuary-SDMS | R | false | false | 9,623 | r | library(raster)
# Package to handle raster-formatted spatial data
library(rasterVis)
# The rasterVis package complements the raster package, providing a set of methods for enhanced visualization and interaction
# Defines visualisation methods with 'levelplot'
library(dismo)
# Dismo has the SDM analyses for maxent and support vector machines used by R
library(rgeos)
# To define circles with a radius around the subsampled points
# geos is a geometry engine, need to install package to access these capabilities (such as defining circumfrances)
library(rJava)
library(rgdal)
# Provides access to projection/transformation operations from a different library
# Coordinate referancing system**
library(sp)
# Coordinate referancing system
library(ncdf4)
# Opens access to read and write on netCDF files
library(kernlab)
# Required for support vector machines
# installed and running BUT UNSURE of function
library(grDevices)
# For colouring maps
library(colorRamps)
#Allows easy construction of color palettes
#Loading data for project now
#Ensure WD is in correct place WILL BE IN NEW PLACE FOR EACH SPECIES
setwd("~/Documents/UoY/Dissertation/Pout")
locs = read.csv("Pout_Severn_UTM.csv", header=T, sep = ",")
#loading severn files
#had to add the file location for R to access the severn files, is this right?
dry_always<-raster("Severn_unaltered Pout/always_dry_masked.tif")
tidal_range<-raster("Severn_unaltered Pout/tidal_range_masked.tif")
subtidal<-raster("Severn_unaltered Pout/subtidal_masked.tif")
min_elev<-raster("Severn_unaltered Pout/min_elev_masked.tif")
max_velocity<-raster("Severn_unaltered Pout/max_vel_masked.tif")
max_elev<-raster("Severn_unaltered Pout/max_elev_masked.tif")
mask_2<-raster("Severn_unaltered Pout/mask2.tif")
intertidal<-raster("Severn_unaltered Pout/intertidal_masked.tif")
depth<-raster("Severn_unaltered Pout/bathy_masked.tif")
avg_velocity<-raster("Severn_unaltered Pout/av_vel_masked.tif")
#ALL raster data is uploaded here
mask<-depth
#DO NOT HAVE 'distance_to_coast' comparison in our data set as in MaxEnt Code
#DO NOT HAVE 'lat and lon' tifs as in MaxEnt Code
# Extract depth values to table of species co-ordinates
locs_ext=extract(depth, locs[,c("X","Y")])
#this has created a VALUE of depth for each single point as dictated by x&y coordinates from species data
#now each species seen has a depth based on its coordinates in the depth raster file we are given!!
# Build a data frame of species occurrence data and depth data
locs = data.frame(locs, locs_ext)
# added locs_ext to the final column in locs file so now coordinates for species can be coupled with their depth in teh same file
# Remove points with NA values for depth, i.e. on land
locs = subset(locs, !is.na(locs_ext))
e = extent(depth)
#subset extracted all values and rows with 'na' from the locs_ext column
# WHAT DOES EXTENT DO?!
# without using the 'mask' technique above will this still remove all 'land' data above?
#what is "e"?? - is it simply giving the 'extent' of the data set in a min and max of x and y?
# Create sequences of X and Y values to define a grid
# this a 1x1 km grid
xgrid = seq(e@xmin,e@xmax,1000)
ygrid = seq(e@ymin,e@ymax,1000)
#"seq()" works by 'from', 'to', 'by incremental step'
#generated a sequence from xmin value to xmax value in "e" that increase by 1000
# Identify occurrence points within each grid cell, then draw one at random
subs = c()
for(i in 1:(length(xgrid)-1)) {
for(j in 1:(length(ygrid)-1)) {
gridsq = subset(locs, Y > ygrid[j] & Y < ygrid[j+1] & X > xgrid[i] & X < xgrid[i+1])
if(dim(gridsq)[1]>0) {
subs = rbind(subs, gridsq[sample(1:dim(gridsq)[1],1 ), ])
}
}
}
dim(locs);dim(subs) # Confirm that you have a smaller dataset than you started with (1st number)
#for is an argument that will loop a desired action on a given value in a vector
#length will get value the legth of vectors and factors in a defined object
##this a loop going through x values (every 1000m) and at each new x square, looping through all the y's related to that x (and so on for all the x values)
#gridsq is a complex way of saying the square is greater than the start of one x/y value and less than the next one after it
#rbind & cbind combine/create a matrix by rows (rbind) or columns (cbind) of the two seperate vector sets
# Assign correct co-ordinate reference system to subset
coordinates <- cbind(subs$X, subs$Y)
subs_df <- SpatialPointsDataFrame(coordinates, subs, proj4string=CRS("+proj=utm +zone=30 ellps=WGS84"))
#cbind of subs$X and subs$Y created a new data set/matrix called coordinates that only has coordinate data in it!
# we create 20,000 random "background points". There are other ways to do this, but start with this.
#NOTE
psa <- randomPoints(mask, 20000, ext=e)
#need to make sure all is up-to-date: previous error due to 'dismo' not being updated
# Stack raster layers into one variable
#NOTE WITHOUT INTERTIDAL LAYER
env_uk<-stack(depth,max_elev,avg_velocity,subtidal,tidal_range)
# Pull environmental data for the sumbsampled-presence points from the raster stack
presence_uk= extract(env_uk, subs_df[,c("X","Y")])
#Warning messages: transforming SpatialPoints to the CRS of the Raster?
# Pull environmental data for the pseudo-absence points from the raster stack
pseudo_uk = extract(env_uk, psa)
# Build some useful dataframes, with two columns of coordinates followed by the environmental variables. For the presence points:
presence_uk = data.frame(X=subs_df$X, Y=subs_df$Y, presence_uk)
#HOW IS THIS DIFFERENT TO ABOVE FUCNTION WITH "EXTRACT"?
# Convert psa from atomic vector matrix to data.frame
psapoints=data.frame(psa)
# Bind co-ordinates
coordinates <- cbind(psapoints$x, psapoints$y)
# Create spatial data frame of pseudo absences
psadf <- SpatialPointsDataFrame(coordinates, psapoints, proj4string=CRS("+proj=utm +zone=30 ellps=WGS84"))
# Build dataframe, with two columns of coordinates followed by the 5 environmental variables. For the pseudo-absences:
psadfx = psadf@coords
colnames(psadfx) = c("X","Y")
pseudo_uk = data.frame(cbind(psadfx,pseudo_uk))
# Vector of group assignments splitting the subsampled presence points data fram with environmental data into 5 groups
group_p = kfold(presence_uk, 5)
#kfold partitions a data set k times (in this case 5 times) for model testing purposes
# Repeat above step for pseudo-absence points
group_a = kfold(pseudo_uk, 5)
# create output required for the loop
evaluations = list(5)
models = list(5)
# where it says maxent - you may need to swap this for other functions if you're exploring different models
# Note that some model may need different inputs etc. Read the docs to figure this out.
# This is our k-fold test. You will want to spend a bit of time making predictions on each of the 5 sub-models
# created here to check you can make decent predictions even with missing data
for (test in 1:5) {
# Then we use test and the kfold groupings to divide the presence and absence points:
train_p = presence_uk[group_p!=test, c("X","Y")]
train_a = pseudo_uk[group_a!=test, c("X","Y")]
test_p = presence_uk[group_p==test, c("X","Y")]
test_a = pseudo_uk[group_a==test, c("X","Y")]
# Now, estimate a maxent model using the "training" points and the environmental data. This may take a few moments to run:
models[test] = maxent(env_uk, p=train_p, a=train_a)
# To validate the model, we use the appropriately named function.
# Produces warning message about implicit list embedding being depreciated. May fail in future versions of R
evaluations[test] = evaluate(test_p, test_a, models[[test]], env_uk)
}
# print out the AUC for the k-fold tests
# ideally should be > 0.75 for all
cat("K-FOLD AUC: ")
for (test in 1:5) {
cat(paste0(evaluations[[test]]@auc,","))
}
#IF ONE WANTED to visualise the 1st model NEED TO FINISH WITH THE OTHER 4 KFOLDS
## pred <- predict(models[[1]], env_uk)
## plot(pred)
#-could visualise all k-fold models to show they are the same (strong statistical result)
#BUT DONT NEED TO
# Assess Spatial Sorting Bias (SSB)
pres_train_me <- train_p
pres_test_me <- test_p
back_train_me <- train_a
back_test_me <- test_a
sb <- ssb(pres_test_me, back_test_me, pres_train_me)
sb[,1] / sb[,2]
#creates a model of spacial biasing to compare to given preditions
# Adjust for SSB if present via distance based point-wise sampling
i <- pwdSample(pres_test_me, back_test_me, pres_train_me, n=1, tr=0.1)
pres_test_pwd_me <- pres_test_me[!is.na(i[,1]), ]
back_test_pwd_me <- back_test_me[na.omit(as.vector(i)), ]
sb2 <- ssb(pres_test_pwd_me, back_test_pwd_me, pres_train_me)
sb2[1]/ sb2[2]
#creates full model without any K fold statistics etc
pres_points = presence_uk[c("X","Y")]
abs_points = pseudo_uk[c("X","Y")]
# create full maxent with all points
model <- maxent(env_uk, p=pres_points, a=abs_points)
#turn model into prediction that can be plotted into a raster
pred_PredFull <- predict(model, env_uk)
#to see model and obtain jpeg
plot(pred_PredFull)
#Gives AUC for full model (pred_PredFull)
evaluate_full <- evaluate(presence_uk[c("X","Y")], pseudo_uk[c("X","Y")], model, env_uk)
#see what AUC is by typing it in
evaluate_full
#see what the specific sensitivity values is of species
#use value givenas a base level or higher that one would expect to see species (compare to evaluate_full)
message(threshold(evaluate_full)$spec_sens)
#check response curves to see if they change the FULL MODEL
response(model)
#creates a raster file in the WD
#will be useful when putting a file into qgis!
writeRaster(pred_PredFull, filename="pred5_me.tif", options="INTERLEAVE=BAND", overwrite=TRUE)
|
d1 <- tibble::tibble(a = 1:5, b = letters[1:5])
d2 <- tibble::tibble(a = c(1,3:6), b = letters[1:5])
d3 <- tibble::tibble(c = 1:5)
d4 <- tibble::tibble(c = c(1:5,5))
d5 <- tibble::tibble(a = 1:5)
d6 <- tibble::tibble(c = 1:4)
check_cardinality_0_n(d1, a, d2, a)
check_cardinality_0_n(d1, a, d3, c)
check_cardinality_1_n(d1, a, d3, c)
check_cardinality_1_1(d1, a, d3, c)
check_cardinality_1_1(d1, a, d4, c)
check_cardinality_1_1(d4, c, d1, a)
check_set_equality(d1, a, d3, c)
check_cardinality_0_1(d1, a, d2, a)
check_cardinality_0_1(d1, a, d4, c)
check_cardinality_0_1(d4, c, d1, a)
check_cardinality_0_n(d4, c, d5, a)
check_cardinality_0_n(d5, a, d4, c)
check_cardinality_1_1(d5, a, d4, c)
check_cardinality_0_1(d5, a, d6, c)
| /scratch/other_test.R | permissive | philipp-baumann/dm | R | false | false | 730 | r | d1 <- tibble::tibble(a = 1:5, b = letters[1:5])
d2 <- tibble::tibble(a = c(1,3:6), b = letters[1:5])
d3 <- tibble::tibble(c = 1:5)
d4 <- tibble::tibble(c = c(1:5,5))
d5 <- tibble::tibble(a = 1:5)
d6 <- tibble::tibble(c = 1:4)
check_cardinality_0_n(d1, a, d2, a)
check_cardinality_0_n(d1, a, d3, c)
check_cardinality_1_n(d1, a, d3, c)
check_cardinality_1_1(d1, a, d3, c)
check_cardinality_1_1(d1, a, d4, c)
check_cardinality_1_1(d4, c, d1, a)
check_set_equality(d1, a, d3, c)
check_cardinality_0_1(d1, a, d2, a)
check_cardinality_0_1(d1, a, d4, c)
check_cardinality_0_1(d4, c, d1, a)
check_cardinality_0_n(d4, c, d5, a)
check_cardinality_0_n(d5, a, d4, c)
check_cardinality_1_1(d5, a, d4, c)
check_cardinality_0_1(d5, a, d6, c)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.bivden.R, R/plot.msden.R, R/plot.rrs.R,
% R/plot.rrst.R, R/plot.stden.R
\name{plot.bivden}
\alias{plot.bivden}
\alias{plot.rrs}
\alias{plot.msden}
\alias{plot.stden}
\alias{plot.rrst}
\title{Plotting sparr objects}
\usage{
\method{plot}{bivden}(x, what = c("z", "edge", "bw"), add.pts = FALSE,
auto.axes = TRUE, override.par = TRUE, ...)
\method{plot}{msden}(x, what = c("z", "edge", "bw"), sleep = 0.2,
override.par = TRUE, ...)
\method{plot}{rrs}(x, auto.axes = TRUE, tol.show = TRUE,
tol.type = c("upper", "lower", "two.sided"), tol.args = list(levels =
0.05, lty = 1, drawlabels = TRUE), ...)
\method{plot}{rrst}(x, tselect = NULL, type = c("joint",
"conditional"), fix.range = FALSE, tol.show = TRUE,
tol.type = c("upper", "lower", "two.sided"), tol.args = list(levels =
0.05, lty = 1, drawlabels = TRUE), sleep = 0.2, override.par = TRUE,
expscale = FALSE, ...)
\method{plot}{stden}(x, tselect = NULL, type = c("joint",
"conditional"), fix.range = FALSE, sleep = 0.2,
override.par = TRUE, ...)
}
\arguments{
\item{x}{An object of class \code{\link{bivden}}, \code{\link{stden}},
\code{\link{rrs}}, \code{\link{rrst}}, or \code{\link{msden}}.}
\item{what}{A character string to select plotting of result (\code{"z"};
default); edge-correction surface (\code{"edge"}); or variable bandwidth
surface (\code{"bw"}).}
\item{add.pts}{Logical value indicating whether to add the observations to
the image plot using default \code{\link{points}}.}
\item{auto.axes}{Logical value indicating whether to display the plot with
automatically added x-y axes and an `L' box in default styles.}
\item{override.par}{Logical value indicating whether to override the
existing graphics device parameters prior to plotting, resetting
\code{mfrow} and \code{mar}. See `Details' for when you might want to
disable this.}
\item{...}{Additional graphical parameters to be passed to
\code{\link[spatstat.geom]{plot.im}}, or in one instance, to
\code{\link[spatstat.geom]{plot.ppp}} (see `Details').}
\item{sleep}{Single positive numeric value giving the amount of time (in
seconds) to \code{\link[base]{Sys.sleep}} before drawing the next image in
the animation.}
\item{tol.show}{Logical value indicating whether to show pre-computed tolerance contours on the plot(s). The object \code{x} must already have the relevant \emph{p}-value surface(s) stored in order for this argument to have any effect.}
\item{tol.type}{A character string used to control the type of tolerance contour displayed; a test for elevated risk (\code{"upper"}), decreased risk (\code{"lower"}), or a two-tailed test (\code{two.sided}).}
\item{tol.args}{A named list of valid arguments to be passed directly to \code{\link[graphics]{contour}} to control the appearance of plotted contours. Commonly used items are \code{levels}, \code{lty}, \code{lwd} and \code{drawlabels}.}
\item{tselect}{Either a single numeric value giving the time at which to return the plot, or a vector of length 2 giving an interval of times over which to plot. This argument must respect the stored temporal bound in \code{x$tlim}, else an error will be thrown. By default, the full set of images (i.e. over the entire available time span) is plotted.}
\item{type}{A character string to select plotting of joint/unconditional spatiotemporal
estimate (default) or conditional spatial density given time.}
\item{fix.range}{Logical value indicating whether use the same color scale limits for each plot in the sequence. Ignored if the user supplies a pre-defined \code{\link[spatstat.geom]{colourmap}} to the \code{col} argument, which is matched to \code{...} above and passed to \code{\link[spatstat.geom]{plot.im}}. See `Examples'.}
\item{expscale}{Logical value indicating whether to force a raw-risk scale. Useful for users
wishing to plot a log-relative risk surface, but to have the raw-risk displayed on the colour ribbon.}
}
\value{
Plots to the relevant graphics device.
}
\description{
\code{plot} methods for classes \code{\link{bivden}}, \code{\link{stden}},
\code{\link{rrs}}, \code{\link{rrst}} and \code{\link{msden}}.
}
\details{
In all instances, visualisation is deferred to
\code{\link[spatstat.geom]{plot.im}}, for which there are a variety of
customisations available the user can access via \code{...}. The one
exception is when plotting observation-specific \code{"diggle"} edge
correction factors---in this instance, a plot of the spatial observations is
returned with size proportional to the influence of each correction weight.
When plotting a \code{\link{rrs}} object, a pre-computed \emph{p}-value
surface (see argument \code{tolerate} in \code{\link{risk}}) will
automatically be superimposed at a significance level of 0.05. Greater
flexibility in visualisation is gained by using \code{\link{tolerance}} in
conjunction with \code{\link{contour}}.
An \code{\link{msden}}, \code{\link{stden}}, or \code{\link{rrst}} object is plotted as an animation, one pixel image
after another, separated by \code{sleep} seconds. If instead you intend the
individual images to be plotted in an array of images, you should first set
up your plot device layout, and ensure \code{override.par = FALSE} so that
the function does not reset these device parameters itself. In such an
instance, one might also want to set \code{sleep = 0}.
}
\examples{
\donttest{
data(pbc)
data(fmd)
data(burk)
# 'bivden' object
pbcden <- bivariate.density(split(pbc)$case,h0=3,hp=2,adapt=TRUE,davies.baddeley=0.05,verbose=FALSE)
plot(pbcden)
plot(pbcden,what="bw",main="PBC cases\\n variable bandwidth surface",xlab="Easting",ylab="Northing")
# 'stden' object
burkden <- spattemp.density(burk$cases,tres=128) # observation times are stored in marks(burk$cases)
plot(burkden,fix.range=TRUE,sleep=0.1) # animation
plot(burkden,tselect=c(1000,3000),type="conditional") # spatial densities conditional on each time
# 'rrs' object
pbcrr <- risk(pbc,h0=4,hp=3,adapt=TRUE,tolerate=TRUE,davies.baddeley=0.025,edge="diggle")
plot(pbcrr) # default
plot(pbcrr,tol.args=list(levels=c(0.05,0.01),lty=2:1,col="seagreen4"),auto.axes=FALSE)
# 'rrst' object
f <- spattemp.density(fmd$cases,h=6,lambda=8)
g <- bivariate.density(fmd$controls,h0=6)
fmdrr <- spattemp.risk(f,g,tolerate=TRUE)
plot(fmdrr,sleep=0.1,fix.range=TRUE)
plot(fmdrr,type="conditional",sleep=0.1,tol.type="two.sided",
tol.args=list(levels=0.05,drawlabels=FALSE))
# 'msden' object
pbcmult <- multiscale.density(split(pbc)$case,h0=4,h0fac=c(0.25,2.5))
plot(pbcmult) # densities
plot(pbcmult,what="edge") # edge correction surfaces
plot(pbcmult,what="bw") # bandwidth surfaces
}
}
\author{
T.M. Davies
}
| /sparr/man/plotsparr.Rd | no_license | albrizre/spatstat.revdep | R | false | true | 6,711 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.bivden.R, R/plot.msden.R, R/plot.rrs.R,
% R/plot.rrst.R, R/plot.stden.R
\name{plot.bivden}
\alias{plot.bivden}
\alias{plot.rrs}
\alias{plot.msden}
\alias{plot.stden}
\alias{plot.rrst}
\title{Plotting sparr objects}
\usage{
\method{plot}{bivden}(x, what = c("z", "edge", "bw"), add.pts = FALSE,
auto.axes = TRUE, override.par = TRUE, ...)
\method{plot}{msden}(x, what = c("z", "edge", "bw"), sleep = 0.2,
override.par = TRUE, ...)
\method{plot}{rrs}(x, auto.axes = TRUE, tol.show = TRUE,
tol.type = c("upper", "lower", "two.sided"), tol.args = list(levels =
0.05, lty = 1, drawlabels = TRUE), ...)
\method{plot}{rrst}(x, tselect = NULL, type = c("joint",
"conditional"), fix.range = FALSE, tol.show = TRUE,
tol.type = c("upper", "lower", "two.sided"), tol.args = list(levels =
0.05, lty = 1, drawlabels = TRUE), sleep = 0.2, override.par = TRUE,
expscale = FALSE, ...)
\method{plot}{stden}(x, tselect = NULL, type = c("joint",
"conditional"), fix.range = FALSE, sleep = 0.2,
override.par = TRUE, ...)
}
\arguments{
\item{x}{An object of class \code{\link{bivden}}, \code{\link{stden}},
\code{\link{rrs}}, \code{\link{rrst}}, or \code{\link{msden}}.}
\item{what}{A character string to select plotting of result (\code{"z"};
default); edge-correction surface (\code{"edge"}); or variable bandwidth
surface (\code{"bw"}).}
\item{add.pts}{Logical value indicating whether to add the observations to
the image plot using default \code{\link{points}}.}
\item{auto.axes}{Logical value indicating whether to display the plot with
automatically added x-y axes and an `L' box in default styles.}
\item{override.par}{Logical value indicating whether to override the
existing graphics device parameters prior to plotting, resetting
\code{mfrow} and \code{mar}. See `Details' for when you might want to
disable this.}
\item{...}{Additional graphical parameters to be passed to
\code{\link[spatstat.geom]{plot.im}}, or in one instance, to
\code{\link[spatstat.geom]{plot.ppp}} (see `Details').}
\item{sleep}{Single positive numeric value giving the amount of time (in
seconds) to \code{\link[base]{Sys.sleep}} before drawing the next image in
the animation.}
\item{tol.show}{Logical value indicating whether to show pre-computed tolerance contours on the plot(s). The object \code{x} must already have the relevant \emph{p}-value surface(s) stored in order for this argument to have any effect.}
\item{tol.type}{A character string used to control the type of tolerance contour displayed; a test for elevated risk (\code{"upper"}), decreased risk (\code{"lower"}), or a two-tailed test (\code{two.sided}).}
\item{tol.args}{A named list of valid arguments to be passed directly to \code{\link[graphics]{contour}} to control the appearance of plotted contours. Commonly used items are \code{levels}, \code{lty}, \code{lwd} and \code{drawlabels}.}
\item{tselect}{Either a single numeric value giving the time at which to return the plot, or a vector of length 2 giving an interval of times over which to plot. This argument must respect the stored temporal bound in \code{x$tlim}, else an error will be thrown. By default, the full set of images (i.e. over the entire available time span) is plotted.}
\item{type}{A character string to select plotting of joint/unconditional spatiotemporal
estimate (default) or conditional spatial density given time.}
\item{fix.range}{Logical value indicating whether use the same color scale limits for each plot in the sequence. Ignored if the user supplies a pre-defined \code{\link[spatstat.geom]{colourmap}} to the \code{col} argument, which is matched to \code{...} above and passed to \code{\link[spatstat.geom]{plot.im}}. See `Examples'.}
\item{expscale}{Logical value indicating whether to force a raw-risk scale. Useful for users
wishing to plot a log-relative risk surface, but to have the raw-risk displayed on the colour ribbon.}
}
\value{
Plots to the relevant graphics device.
}
\description{
\code{plot} methods for classes \code{\link{bivden}}, \code{\link{stden}},
\code{\link{rrs}}, \code{\link{rrst}} and \code{\link{msden}}.
}
\details{
In all instances, visualisation is deferred to
\code{\link[spatstat.geom]{plot.im}}, for which there are a variety of
customisations available the user can access via \code{...}. The one
exception is when plotting observation-specific \code{"diggle"} edge
correction factors---in this instance, a plot of the spatial observations is
returned with size proportional to the influence of each correction weight.
When plotting a \code{\link{rrs}} object, a pre-computed \emph{p}-value
surface (see argument \code{tolerate} in \code{\link{risk}}) will
automatically be superimposed at a significance level of 0.05. Greater
flexibility in visualisation is gained by using \code{\link{tolerance}} in
conjunction with \code{\link{contour}}.
An \code{\link{msden}}, \code{\link{stden}}, or \code{\link{rrst}} object is plotted as an animation, one pixel image
after another, separated by \code{sleep} seconds. If instead you intend the
individual images to be plotted in an array of images, you should first set
up your plot device layout, and ensure \code{override.par = FALSE} so that
the function does not reset these device parameters itself. In such an
instance, one might also want to set \code{sleep = 0}.
}
\examples{
\donttest{
data(pbc)
data(fmd)
data(burk)
# 'bivden' object
pbcden <- bivariate.density(split(pbc)$case,h0=3,hp=2,adapt=TRUE,davies.baddeley=0.05,verbose=FALSE)
plot(pbcden)
plot(pbcden,what="bw",main="PBC cases\\n variable bandwidth surface",xlab="Easting",ylab="Northing")
# 'stden' object
burkden <- spattemp.density(burk$cases,tres=128) # observation times are stored in marks(burk$cases)
plot(burkden,fix.range=TRUE,sleep=0.1) # animation
plot(burkden,tselect=c(1000,3000),type="conditional") # spatial densities conditional on each time
# 'rrs' object
pbcrr <- risk(pbc,h0=4,hp=3,adapt=TRUE,tolerate=TRUE,davies.baddeley=0.025,edge="diggle")
plot(pbcrr) # default
plot(pbcrr,tol.args=list(levels=c(0.05,0.01),lty=2:1,col="seagreen4"),auto.axes=FALSE)
# 'rrst' object
f <- spattemp.density(fmd$cases,h=6,lambda=8)
g <- bivariate.density(fmd$controls,h0=6)
fmdrr <- spattemp.risk(f,g,tolerate=TRUE)
plot(fmdrr,sleep=0.1,fix.range=TRUE)
plot(fmdrr,type="conditional",sleep=0.1,tol.type="two.sided",
tol.args=list(levels=0.05,drawlabels=FALSE))
# 'msden' object
pbcmult <- multiscale.density(split(pbc)$case,h0=4,h0fac=c(0.25,2.5))
plot(pbcmult) # densities
plot(pbcmult,what="edge") # edge correction surfaces
plot(pbcmult,what="bw") # bandwidth surfaces
}
}
\author{
T.M. Davies
}
|
#define the file
thefile <- "./data/household_power_consumption.txt"
#read the file
thedata <- read.table(thefile, header=TRUE, sep=";", na.string="?")
#subset data to contain only the important period
subset_data <- subset(thedata, Date %in% c("1/2/2007","2/2/2007"))
rm(thedata)
#open a png device
png("plot3.png", width=480, height=480)
#get sub_metering variables, use as.numeric(), because R says it's not numeric
sub_metering1 <- as.numeric(subset_data$Sub_metering_1, na.rm = TRUE)
sub_metering2 <- as.numeric(subset_data$Sub_metering_2, na.rm = TRUE)
sub_metering3 <- as.numeric(subset_data$Sub_metering_3, na.rm = TRUE)
#get date and time columns into 1 variable with strptime
date_time <- strptime(paste(subset_data$Date, subset_data$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
#make the first plot
with(subset_data,{
plot(date_time, sub_metering1, type="l", xlab="", ylab="Energy sub metering")
#add data to the plot
lines(date_time, sub_metering2, type="l", col="red")
lines(date_time, sub_metering3, type="l", col="blue")
})
#add legend
legend("topright", c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), lty=1, col=c("black","red","blue"))
#close the device
dev.off() | /plot3.R | no_license | kurowska/ExData_Plotting1 | R | false | false | 1,213 | r | #define the file
thefile <- "./data/household_power_consumption.txt"
#read the file
thedata <- read.table(thefile, header=TRUE, sep=";", na.string="?")
#subset data to contain only the important period
subset_data <- subset(thedata, Date %in% c("1/2/2007","2/2/2007"))
rm(thedata)
#open a png device
png("plot3.png", width=480, height=480)
#get sub_metering variables, use as.numeric(), because R says it's not numeric
sub_metering1 <- as.numeric(subset_data$Sub_metering_1, na.rm = TRUE)
sub_metering2 <- as.numeric(subset_data$Sub_metering_2, na.rm = TRUE)
sub_metering3 <- as.numeric(subset_data$Sub_metering_3, na.rm = TRUE)
#get date and time columns into 1 variable with strptime
date_time <- strptime(paste(subset_data$Date, subset_data$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
#make the first plot
with(subset_data,{
plot(date_time, sub_metering1, type="l", xlab="", ylab="Energy sub metering")
#add data to the plot
lines(date_time, sub_metering2, type="l", col="red")
lines(date_time, sub_metering3, type="l", col="blue")
})
#add legend
legend("topright", c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), lty=1, col=c("black","red","blue"))
#close the device
dev.off() |
# *********************************************************************************************
# Title : Seeded KMeans
# Function : seeded_kmeans
# Created by: Owner
# Created on: 2021/01/26
# URL : https://dicits.ugr.es/software/SSLR/reference/seeded_kmeans.html
# *********************************************************************************************
# ＜概要＞
# - 初期化時にラベル付きデータの存在するクラスの数、つまり特定のクラスのラベル付きデータの平均と同じ数のクラスターが存在する
# ＜構文＞
# seeded_kmeans(max_iter = 10, method = "euclidean")
# ＜目次＞
# 0 準備
# 1 データ作成
# 2 モデリング
# 0 準備 -------------------------------------------------------------------------------
library(tidyverse)
library(caret)
library(SSLR)
library(tidymodels)
# データ準備
data <- iris
# 1 データ作成 --------------------------------------------------------------------------
# 列番号の取得
cls <- which(colnames(iris) == "Species")
# ラベルをNAに置換
set.seed(1)
labeled.index <- createDataPartition(data$Species, p = .2, list = FALSE)
data[-labeled.index,cls] <- NA
# 2 モデリング ----------------------------------------------------------------------------
# モデル構築
m <- seeded_kmeans() %>% fit(Species ~ ., data)
# ラベル分類の取得
# --- k-meansなので番号を出力
labels <- m %>% cluster_labels()
# 確認
data %>%
mutate(label = unlist(cluster_labels(m)))
| /library/SSLR/function/clustering/seeded_kmeans.R | no_license | delta0726/r-semi_supervised | R | false | false | 1,541 | r | # *********************************************************************************************
# Title : Seeded KMeans
# Function : seeded_kmeans
# Created by: Owner
# Created on: 2021/01/26
# URL : https://dicits.ugr.es/software/SSLR/reference/seeded_kmeans.html
# *********************************************************************************************
# ＜概要＞
# - 初期化時にラベル付きデータの存在するクラスの数、つまり特定のクラスのラベル付きデータの平均と同じ数のクラスターが存在する
# ＜構文＞
# seeded_kmeans(max_iter = 10, method = "euclidean")
# ＜目次＞
# 0 準備
# 1 データ作成
# 2 モデリング
# 0 準備 -------------------------------------------------------------------------------
library(tidyverse)
library(caret)
library(SSLR)
library(tidymodels)
# データ準備
data <- iris
# 1 データ作成 --------------------------------------------------------------------------
# 列番号の取得
cls <- which(colnames(iris) == "Species")
# ラベルをNAに置換
set.seed(1)
labeled.index <- createDataPartition(data$Species, p = .2, list = FALSE)
data[-labeled.index,cls] <- NA
# 2 モデリング ----------------------------------------------------------------------------
# モデル構築
m <- seeded_kmeans() %>% fit(Species ~ ., data)
# ラベル分類の取得
# --- k-meansなので番号を出力
labels <- m %>% cluster_labels()
# 確認
data %>%
mutate(label = unlist(cluster_labels(m)))
|
library(tidyverse)
library(data.table)
library(plotly)
#################################################
# ANALYSIS #
#################################################
data <- read.csv("Data/data.csv")
native_only <- data %>%
filter(AmIAKN == "Yes")
non_native <- data %>%
filter(AmIAKN == "No")
total_kids <- nrow(data)
native_kids <- nrow(native_only)
non_native_kids <- nrow(non_native)
# native placement settings in current FC setting
native_placement <- native_only %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# overall placement settings in current FC setting
overall_placement <- data %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# non-native placement settings in current FC setting
non_native_placement <- non_native %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# native average number of removals
native_avg_rems <- native_only %>%
select(TotalRem) %>%
mutate(avg = signif(sum(TotalRem) / native_kids, digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# overall average number of removals
overall_avg_rems <- data %>%
select(TotalRem) %>%
mutate(TotalRem = ifelse(is.na(TotalRem), 0, TotalRem)) %>%
mutate(avg = signif(sum(TotalRem) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# non-native average number of removals
non_native_avg_rems <- non_native %>%
select(TotalRem) %>%
mutate(TotalRem = ifelse(is.na(TotalRem), 0, TotalRem)) %>%
mutate(avg = signif(sum(TotalRem) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
avg_rems = data.table(
native = c("Native", "Non-Native", "Overall"),
num_rems = c(native_avg_rems,
non_native_avg_rems,
overall_avg_rems
)
)
# native average number of placements
native_avg_num_plep <- native_only %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / native_kids, digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# overall avg number of placements
overall_avg_num_plep <- data %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# non-native avg number of placements
non_native_avg_num_plep <- non_native %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
avg_num_plep = data.table(
native = c("Native", "Non-Native", "Overall"),
num_pleps = c(native_avg_num_plep,
non_native_avg_num_plep,
overall_avg_num_plep
)
)
# native repeaters vs non repeaters percentages
native_rep <- native_only %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
mutate(percent = signif((count * 100) / native_kids, digits = 4)) %>%
distinct(percent)
# overall avg repeater percentages
overall_rep <- data %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / n(), digits = 4)) %>%
distinct(percent, .keep_all = T) %>%
select(isRep, percent)
# non-native avg repeater percentages
non_native_rep <- non_native %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / n(), digits = 4)) %>%
distinct(percent, .keep_all = T) %>%
select(isRep, percent)
avg_repeater <- data.frame(
native = c(rep("native" , 2) , rep("non-native" , 2) , rep("overall" , 2)),
repeater = rep(c(TRUE , FALSE) , 3),
percent = c(native_rep$percent, non_native_rep$percent , overall_rep$percent)
)
# native average stay in FC
native_LifeLOS <- native_only %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / native_kids)) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
# overall average stay in FC
overall_LifeLOS <- data %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / n())) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
# non-native average stay in FC
non_native_LifeLOS <- non_native %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / n())) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
avg_LifeLOS <- data.frame(
native = c("Native", "Non-Native"),
LifeLOS = c(native_LifeLOS, overall_LifeLOS)
)
# Do native children placed with native caretakers have less LifeLOS and
# numPleps?
by_caretaker <- native_only %>%
select(NumPlep, LifeLOS, RF1AMAKN, RF1ASIAN, RF1BLKAA, RF1NHOPI, RF1WHITE, RF1UTOD) %>%
mutate(caretaker_native = ifelse(RF1AMAKN == "Yes", "Yes", "No")) %>%
mutate(caretaker_native = ifelse(is.na(caretaker_native), "No caretaker",
caretaker_native)) %>%
group_by(caretaker_native) %>%
mutate(Frequency = n(),
LifeLOS = sum(LifeLOS) / n(),
NumPlep = sum(NumPlep) / n()) %>%
distinct(caretaker_native, .keep_all = T) %>%
select(NumPlep, LifeLOS, caretaker_native, Frequency)
#################################################
# PLOTS #
#################################################
# average number of removals by native status
bubblefont <- list(size = 15,
color = '#ffefc1',
family = 'alte_haas_groteskregular')
titlefont <- list(family = 'alte_haas_groteskbold',
color = '#ff9770')
color_palette <- c('#610453', '#1b3496', '#026E5E', '#390561', '#116DAB')
ax <- list(
title = "",
zeroline = FALSE,
showline = FALSE,
showticklabels = FALSE,
showgrid = FALSE
)
num_rems_viz <- plot_ly(avg_rems,
x = ~native,
y = "",
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Number of <br>",
"Removals: <br>",
avg_rems$num_rems),
size = ~num_rems,
color = ~native,
sizes = c(117, 143),
marker = list(opacity = 0.55,
sizemode = 'radius'
),
colors = color_palette,
showlegend = F
) %>%
add_text(text = ~native,
textfont = bubblefont,
showlegend = F,
) %>%
layout(title = 'Native American Children Have a Higher Number of Removals
Than Non-Native Children',
font= titlefont,
xaxis = ax,
yaxis = ax) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
num_rems_viz
# average number of placements by native status
num_pleps_viz <- plot_ly(avg_num_plep,
x = ~native,
y = "",
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Number of <br>",
"Placements: <br>",
avg_num_plep$num_pleps),
size = ~num_pleps,
color = ~native,
sizes = c(166, 200),
marker = list(opacity = 0.55,
sizemode = 'radius'
),
colors = color_palette,
showlegend = F
) %>%
add_text(text = ~native,
textfont = bubblefont,
showlegend = F,
) %>%
layout(title = 'Native American Children Have a Higher Number of Placements
Than Non-Native Children',
xaxis = ax,
yaxis = ax,
font= titlefont) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
num_pleps_viz
# percentage of repeaters by native status
repeater_viz <- plot_ly(labels = c("Native", "Non-Native", "Overall"),
marker = list(colors = "Set1"))
repeater_viz <- repeater_viz %>% add_pie(data = native_rep,
labels = ~isRep,
values = ~percent,
name = "Native",
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Native American",
hoverinfo = none,
domain = list(x = c(0, 0.4), y = c(0.4, 1)))
repeater_viz <- repeater_viz %>% add_pie(data = non_native_rep,
labels = ~isRep,
values = ~percent,
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Non-Native American",
hoverinfo = none,
colors = color_palette,
name = "Non Native",
domain = list(x = c(0.6, 1), y = c(0.4, 1)))
repeater_viz <- repeater_viz %>% add_pie(data = overall_rep,
labels = ~isRep,
values = ~percent,
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Overall",
colors = color_palette,
hoverinfo = none,
name = "Overall",
domain = list(x = c(0.25, 0.75), y = c(0, 0.6)))
repeater_viz <- repeater_viz %>%
layout(
title = "Native American Children are More Likely To Go Back
Into Foster Care",
font = titlefont,
showlegend = F,
grid=list(rows=2, columns=2
),
xaxis = list(showgrid = FALSE,
zeroline = FALSE,
showticklabels = FALSE
),
yaxis = list(showgrid = FALSE,
zeroline = FALSE,
showticklabels = FALSE
)
) %>%
layout(
plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)"
)
repeater_viz
# Total stay in FC by native status
LifeLOS_viz <- plot_ly(avg_LifeLOS,
x = "",
y = "",
color = ~native,
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Stay in <br>",
"Foster Care: <br>",
avg_LifeLOS$LifeLOS,
"days"),
size = ~LifeLOS,
sizes = c(215, 247),
marker = list(
sizemode = 'radius'
),
colors = color_palette,
showlegend = T
) %>%
layout(title = "Native American Children Spend Longer in Foster Care\n than Non-Native Children",
xaxis = ax,
yaxis = ax,
font = titlefont,
plot_bgcolor='transparent',
paper_bgcolor='transparent'
) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
LifeLOS_viz
| /native.R | permissive | marinawooden/children-in-foster-care | R | false | false | 11,946 | r | library(tidyverse)
library(data.table)
library(plotly)
#################################################
# ANALYSIS #
#################################################
data <- read.csv("Data/data.csv")
native_only <- data %>%
filter(AmIAKN == "Yes")
non_native <- data %>%
filter(AmIAKN == "No")
total_kids <- nrow(data)
native_kids <- nrow(native_only)
non_native_kids <- nrow(non_native)
# native placement settings in current FC setting
native_placement <- native_only %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# overall placement settings in current FC setting
overall_placement <- data %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# non-native placement settings in current FC setting
non_native_placement <- non_native %>%
select(CurPlSet) %>%
group_by(CurPlSet) %>%
mutate(count = n()) %>%
distinct(CurPlSet, .keep_all = T) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / sum(count), digits = 4))
# native average number of removals
native_avg_rems <- native_only %>%
select(TotalRem) %>%
mutate(avg = signif(sum(TotalRem) / native_kids, digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# overall average number of removals
overall_avg_rems <- data %>%
select(TotalRem) %>%
mutate(TotalRem = ifelse(is.na(TotalRem), 0, TotalRem)) %>%
mutate(avg = signif(sum(TotalRem) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# non-native average number of removals
non_native_avg_rems <- non_native %>%
select(TotalRem) %>%
mutate(TotalRem = ifelse(is.na(TotalRem), 0, TotalRem)) %>%
mutate(avg = signif(sum(TotalRem) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
avg_rems = data.table(
native = c("Native", "Non-Native", "Overall"),
num_rems = c(native_avg_rems,
non_native_avg_rems,
overall_avg_rems
)
)
# native average number of placements
native_avg_num_plep <- native_only %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / native_kids, digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# overall avg number of placements
overall_avg_num_plep <- data %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
# non-native avg number of placements
non_native_avg_num_plep <- non_native %>%
select(NumPlep) %>%
mutate(avg = signif(sum(NumPlep) / n(), digits = 4)) %>%
distinct(avg) %>%
pull(avg)
avg_num_plep = data.table(
native = c("Native", "Non-Native", "Overall"),
num_pleps = c(native_avg_num_plep,
non_native_avg_num_plep,
overall_avg_num_plep
)
)
# native repeaters vs non repeaters percentages
native_rep <- native_only %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
mutate(percent = signif((count * 100) / native_kids, digits = 4)) %>%
distinct(percent)
# overall avg repeater percentages
overall_rep <- data %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / n(), digits = 4)) %>%
distinct(percent, .keep_all = T) %>%
select(isRep, percent)
# non-native avg repeater percentages
non_native_rep <- non_native %>%
select(TotalRem) %>%
mutate(isRep = ifelse(TotalRem > 1, TRUE, FALSE)) %>%
group_by(isRep) %>%
mutate(count = n()) %>%
ungroup() %>%
mutate(percent = signif((count * 100) / n(), digits = 4)) %>%
distinct(percent, .keep_all = T) %>%
select(isRep, percent)
avg_repeater <- data.frame(
native = c(rep("native" , 2) , rep("non-native" , 2) , rep("overall" , 2)),
repeater = rep(c(TRUE , FALSE) , 3),
percent = c(native_rep$percent, non_native_rep$percent , overall_rep$percent)
)
# native average stay in FC
native_LifeLOS <- native_only %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / native_kids)) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
# overall average stay in FC
overall_LifeLOS <- data %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / n())) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
# non-native average stay in FC
non_native_LifeLOS <- non_native %>%
select(LifeLOS) %>%
mutate(LifeLOS = ifelse(is.na(LifeLOS), 0, LifeLOS)) %>%
mutate(LifeLOS = round(sum(LifeLOS) / n())) %>%
distinct(LifeLOS) %>%
pull(LifeLOS)
avg_LifeLOS <- data.frame(
native = c("Native", "Non-Native"),
LifeLOS = c(native_LifeLOS, overall_LifeLOS)
)
# Do native children placed with native caretakers have less LifeLOS and
# numPleps?
by_caretaker <- native_only %>%
select(NumPlep, LifeLOS, RF1AMAKN, RF1ASIAN, RF1BLKAA, RF1NHOPI, RF1WHITE, RF1UTOD) %>%
mutate(caretaker_native = ifelse(RF1AMAKN == "Yes", "Yes", "No")) %>%
mutate(caretaker_native = ifelse(is.na(caretaker_native), "No caretaker",
caretaker_native)) %>%
group_by(caretaker_native) %>%
mutate(Frequency = n(),
LifeLOS = sum(LifeLOS) / n(),
NumPlep = sum(NumPlep) / n()) %>%
distinct(caretaker_native, .keep_all = T) %>%
select(NumPlep, LifeLOS, caretaker_native, Frequency)
#################################################
# PLOTS #
#################################################
# average number of removals by native status
bubblefont <- list(size = 15,
color = '#ffefc1',
family = 'alte_haas_groteskregular')
titlefont <- list(family = 'alte_haas_groteskbold',
color = '#ff9770')
color_palette <- c('#610453', '#1b3496', '#026E5E', '#390561', '#116DAB')
ax <- list(
title = "",
zeroline = FALSE,
showline = FALSE,
showticklabels = FALSE,
showgrid = FALSE
)
num_rems_viz <- plot_ly(avg_rems,
x = ~native,
y = "",
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Number of <br>",
"Removals: <br>",
avg_rems$num_rems),
size = ~num_rems,
color = ~native,
sizes = c(117, 143),
marker = list(opacity = 0.55,
sizemode = 'radius'
),
colors = color_palette,
showlegend = F
) %>%
add_text(text = ~native,
textfont = bubblefont,
showlegend = F,
) %>%
layout(title = 'Native American Children Have a Higher Number of Removals
Than Non-Native Children',
font= titlefont,
xaxis = ax,
yaxis = ax) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
num_rems_viz
# average number of placements by native status
num_pleps_viz <- plot_ly(avg_num_plep,
x = ~native,
y = "",
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Number of <br>",
"Placements: <br>",
avg_num_plep$num_pleps),
size = ~num_pleps,
color = ~native,
sizes = c(166, 200),
marker = list(opacity = 0.55,
sizemode = 'radius'
),
colors = color_palette,
showlegend = F
) %>%
add_text(text = ~native,
textfont = bubblefont,
showlegend = F,
) %>%
layout(title = 'Native American Children Have a Higher Number of Placements
Than Non-Native Children',
xaxis = ax,
yaxis = ax,
font= titlefont) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
num_pleps_viz
# percentage of repeaters by native status
repeater_viz <- plot_ly(labels = c("Native", "Non-Native", "Overall"),
marker = list(colors = "Set1"))
repeater_viz <- repeater_viz %>% add_pie(data = native_rep,
labels = ~isRep,
values = ~percent,
name = "Native",
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Native American",
hoverinfo = none,
domain = list(x = c(0, 0.4), y = c(0.4, 1)))
repeater_viz <- repeater_viz %>% add_pie(data = non_native_rep,
labels = ~isRep,
values = ~percent,
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Non-Native American",
hoverinfo = none,
colors = color_palette,
name = "Non Native",
domain = list(x = c(0.6, 1), y = c(0.4, 1)))
repeater_viz <- repeater_viz %>% add_pie(data = overall_rep,
labels = ~isRep,
values = ~percent,
text = c("Repeaters", "Non-Repeaters"),
textfont = bubblefont,
marker = list(colors = color_palette),
title = "Overall",
colors = color_palette,
hoverinfo = none,
name = "Overall",
domain = list(x = c(0.25, 0.75), y = c(0, 0.6)))
repeater_viz <- repeater_viz %>%
layout(
title = "Native American Children are More Likely To Go Back
Into Foster Care",
font = titlefont,
showlegend = F,
grid=list(rows=2, columns=2
),
xaxis = list(showgrid = FALSE,
zeroline = FALSE,
showticklabels = FALSE
),
yaxis = list(showgrid = FALSE,
zeroline = FALSE,
showticklabels = FALSE
)
) %>%
layout(
plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)"
)
repeater_viz
# Total stay in FC by native status
LifeLOS_viz <- plot_ly(avg_LifeLOS,
x = "",
y = "",
color = ~native,
type = 'scatter',
mode = 'markers',
hovertemplate = paste("Average <br>",
"Stay in <br>",
"Foster Care: <br>",
avg_LifeLOS$LifeLOS,
"days"),
size = ~LifeLOS,
sizes = c(215, 247),
marker = list(
sizemode = 'radius'
),
colors = color_palette,
showlegend = T
) %>%
layout(title = "Native American Children Spend Longer in Foster Care\n than Non-Native Children",
xaxis = ax,
yaxis = ax,
font = titlefont,
plot_bgcolor='transparent',
paper_bgcolor='transparent'
) %>%
layout(plot_bgcolor = "rgba(0, 0, 0, 0)",
paper_bgcolor = "rgba(0, 0, 0, 0)")
LifeLOS_viz
|
#' NMR spectra of urine sample of rats with bariatric surgery. A complete description of the dataset can be found here: https://www.frontiersin.org/articles/10.3389/fmicb.2011.00183
#'
#' The data set contains 59 nmr spectra of length 4582 It includes metadata (425 exprimental parameters)
#'
#' @format A data frame with 59 rows, X, metadata and ppm scale
#' \describe{
#' \item{data}{NMR spectra}
#' \item{binned.ppm}{a ppm scale}
#' }
#' @source {https://www.frontiersin.org/articles/10.3389/fmicb.2011.00183}
"bariatricRat.binned.5"
| /R/dataset_bariatricRat.binned.5.R | no_license | jwist/hastaLaVista | R | false | false | 542 | r | #' NMR spectra of urine sample of rats with bariatric surgery. A complete description of the dataset can be found here: https://www.frontiersin.org/articles/10.3389/fmicb.2011.00183
#'
#' The data set contains 59 nmr spectra of length 4582 It includes metadata (425 exprimental parameters)
#'
#' @format A data frame with 59 rows, X, metadata and ppm scale
#' \describe{
#' \item{data}{NMR spectra}
#' \item{binned.ppm}{a ppm scale}
#' }
#' @source {https://www.frontiersin.org/articles/10.3389/fmicb.2011.00183}
"bariatricRat.binned.5"
|
\name{compute.threshold.AROC.bnp}
\alias{compute.threshold.AROC.bnp}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
AROC-based threshold values.
}
\description{
Estimates AROC-based threshold values using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018).
}
\usage{
compute.threshold.AROC.bnp(object, newdata, FPF = 0.5)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{An object of class \code{AROC} as produced by \code{\link{AROC.bnp}}.}
\item{newdata}{Data frame with the covariate values at which threshold values are required.}
\item{FPF}{Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid.}
}
\details{
Estimation of the covariate-adjusted ROC curve (AROC) using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population
\deqn{F_{\bar{D}}(y|\mathbf{X}_{\bar{D}}) = Pr\{Y_{\bar{D}} \leq y | \mathbf{X}_{\bar{D}}\}.}
This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value \eqn{\mathbf{x}} and a FPF = t, the AROC-based threshold value at the \eqn{s}-th posterior sample (\eqn{s = 1,\ldots,S}) is calculated as follows
\deqn{c^{(s)}_{\mathbf{x}} = \hat{F}^{-1(s)}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).}
from which the posterior mean can be computed
\deqn{\hat{c}_{\mathbf{x}} = \frac{1}{S}\sum_{s = 1}^{S}c^{(s)}_{\mathbf{x}}.}
}
\value{As a result, the function provides a list with the following components:
\item{thresholds.est}{A matrix with the posterior mean of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
\item{thresholds.ql}{A matrix with the posterior 2.5\% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
\item{thresholds.qh}{A matrix with the posterior 97.5\% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
}
\references{
Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.
}
%\author{
%% ~~who you are~~
%}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{AROC.bnp}}
}
\examples{
library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
\donttest{
m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 5000, nburn = 1000)
# Compute the threshold values
FPF = c(0.1, 0.3)
newdata <- data.frame(age = seq(52, 80, l = 50))
th_bnp <- compute.threshold.AROC.bnp(m0, newdata, FPF)
names(th_bnp)
}
\dontshow{
m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 50, nburn = 10)
# Compute the threshold values
FPF = c(0.1, 0.3)
newdata <- data.frame(age = 52)
th_bnp <- compute.threshold.AROC.bnp(m0, newdata, FPF)
names(th_bnp)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
%\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS")
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
| /man/compute.threshold.AROC.bnp.Rd | no_license | cran/AROC | R | false | false | 3,909 | rd | \name{compute.threshold.AROC.bnp}
\alias{compute.threshold.AROC.bnp}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
AROC-based threshold values.
}
\description{
Estimates AROC-based threshold values using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018).
}
\usage{
compute.threshold.AROC.bnp(object, newdata, FPF = 0.5)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{An object of class \code{AROC} as produced by \code{\link{AROC.bnp}}.}
\item{newdata}{Data frame with the covariate values at which threshold values are required.}
\item{FPF}{Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid.}
}
\details{
Estimation of the covariate-adjusted ROC curve (AROC) using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population
\deqn{F_{\bar{D}}(y|\mathbf{X}_{\bar{D}}) = Pr\{Y_{\bar{D}} \leq y | \mathbf{X}_{\bar{D}}\}.}
This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value \eqn{\mathbf{x}} and a FPF = t, the AROC-based threshold value at the \eqn{s}-th posterior sample (\eqn{s = 1,\ldots,S}) is calculated as follows
\deqn{c^{(s)}_{\mathbf{x}} = \hat{F}^{-1(s)}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).}
from which the posterior mean can be computed
\deqn{\hat{c}_{\mathbf{x}} = \frac{1}{S}\sum_{s = 1}^{S}c^{(s)}_{\mathbf{x}}.}
}
\value{As a result, the function provides a list with the following components:
\item{thresholds.est}{A matrix with the posterior mean of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
\item{thresholds.ql}{A matrix with the posterior 2.5\% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
\item{thresholds.qh}{A matrix with the posterior 97.5\% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.}
}
\references{
Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.
}
%\author{
%% ~~who you are~~
%}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{AROC.bnp}}
}
\examples{
library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
\donttest{
m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 5000, nburn = 1000)
# Compute the threshold values
FPF = c(0.1, 0.3)
newdata <- data.frame(age = seq(52, 80, l = 50))
th_bnp <- compute.threshold.AROC.bnp(m0, newdata, FPF)
names(th_bnp)
}
\dontshow{
m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 50, nburn = 10)
# Compute the threshold values
FPF = c(0.1, 0.3)
newdata <- data.frame(age = 52)
th_bnp <- compute.threshold.AROC.bnp(m0, newdata, FPF)
names(th_bnp)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
%\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS")
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
|
##
## nsga2.R - Interface to nsga2.c
##
## Authors:
## Heike Trautmann <trautmann@statistik.uni-dortmund.de>
## Detlef Steuer <detlef.steuer@hsu-hamburg.de>
## Olaf Mersmann <olafm@statistik.uni-dortmund.de>
##
nsga2 <- function(fn, idim, odim, ...,
constraints=NULL, cdim=0,
lower.bounds=rep(-Inf, idim),
upper.bounds=rep(Inf, idim),
popsize=100, generations=100,
cprob=0.7, cdist=5,
mprob=0.2, mdist=10,
vectorized=FALSE) {
ff <- if (vectorized) {
function(x) fn(x, ...)
} else {
function(x) apply(x, 1, fn, ...)
}
cf <- if (vectorized) {
function(x) constraints(x, ...)
} else {
function(x) apply(x, 1, constraints, ...)
}
## Make sure popsize is a multiple of 4
if (popsize %% 4 != 0)
stop("Population size must be a multiple of 4")
## Check bounding box
if (any(is.infinite(lower.bounds)) || any(is.infinite(upper.bounds))) {
warning("While it is possible to optimize an unconstrained problem, it is not recommended. Please consider adding finite upper and lower bounds.")
sane_lower <- -.Machine$double.xmax / 4
sane_upper <- .Machine$double.xmax / 4
lower.bounds <- ifelse(lower.bounds == -Inf, sane_lower,
ifelse(lower.bounds == Inf, sane_upper,
lower.bounds))
upper.bounds <- ifelse(upper.bounds == -Inf, sane_lower,
ifelse(upper.bounds == Inf, sane_upper,
upper.bounds))
}
## Lag generations:
## C source expects each element of generations to be the number of
## generations to go forward before saving the next result set. This is
## unintuitive to specify, so we compute the lagged differences here.
if (length(generations) > 1)
generations <- c(generations[1], diff(generations))
if (!all(generations > 0))
stop("Cannot go back in time! Your generations argument must be sorted!")
## Set cdim = 0 if no cfn was given:
if (is.null(constraints)) cdim <- 0
res <- .Call(do_nsga2,
ff, cf, sys.frame(),
as.integer(odim),
as.integer(cdim),
as.integer(idim),
lower.bounds, upper.bounds,
as.integer(popsize), as.integer(generations),
cprob, as.integer(cdist),
mprob, as.integer(mdist))
if (1 == length(res)) {
res <- res[[1]]
names(res) <- c("par", "value", "pareto.optimal")
class(res) <- c("nsga2", "mco")
} else {
for (i in 1:length(res)) {
names(res[[i]]) <- c("par", "value", "pareto.optimal")
class(res[[i]]) <- c("nsga2", "mco")
}
class(res) <- "nsga2.collection"
}
return (res)
}
plot.nsga2 <- function(x, ...) {
v <- x$value
o <- x$pareto.optimal
d <- ncol(v)
col <- ifelse(o, "red", "blue")
pch <- ifelse(o, 4, 19)
if (d <= 2) {
plot(v, col=col, pch=pch, ...)
ov <- v[o,]
ov <- ov[order(ov[,1]),]
lines (ov, col="red", type="s")
} else if (d == 3) {
if (require("scatterplot3d")) {
scatterplot3d::scatterplot3d(v, color=ifelse(o, "red", "blue"))
} else {
pairs(v, col=col, pch=pch, ...)
}
} else {
pairs(v, col=col, pch=pch, ...)
}
}
plot.nsga2.collection <- function(x, ...) {
oask <- devAskNewPage(TRUE)
on.exit(devAskNewPage(oask))
sapply(x, plot)
return;
}
paretoSet.nsga2 <- function(x, ...)
x$par[x$pareto.optimal,]
paretoFront.nsga2 <- function(x, ...)
x$value[x$pareto.optimal,,drop=FALSE]
| /mco/R/nsga2.R | no_license | ingted/R-Examples | R | false | false | 3,611 | r | ##
## nsga2.R - Interface to nsga2.c
##
## Authors:
## Heike Trautmann <trautmann@statistik.uni-dortmund.de>
## Detlef Steuer <detlef.steuer@hsu-hamburg.de>
## Olaf Mersmann <olafm@statistik.uni-dortmund.de>
##
nsga2 <- function(fn, idim, odim, ...,
constraints=NULL, cdim=0,
lower.bounds=rep(-Inf, idim),
upper.bounds=rep(Inf, idim),
popsize=100, generations=100,
cprob=0.7, cdist=5,
mprob=0.2, mdist=10,
vectorized=FALSE) {
ff <- if (vectorized) {
function(x) fn(x, ...)
} else {
function(x) apply(x, 1, fn, ...)
}
cf <- if (vectorized) {
function(x) constraints(x, ...)
} else {
function(x) apply(x, 1, constraints, ...)
}
## Make sure popsize is a multiple of 4
if (popsize %% 4 != 0)
stop("Population size must be a multiple of 4")
## Check bounding box
if (any(is.infinite(lower.bounds)) || any(is.infinite(upper.bounds))) {
warning("While it is possible to optimize an unconstrained problem, it is not recommended. Please consider adding finite upper and lower bounds.")
sane_lower <- -.Machine$double.xmax / 4
sane_upper <- .Machine$double.xmax / 4
lower.bounds <- ifelse(lower.bounds == -Inf, sane_lower,
ifelse(lower.bounds == Inf, sane_upper,
lower.bounds))
upper.bounds <- ifelse(upper.bounds == -Inf, sane_lower,
ifelse(upper.bounds == Inf, sane_upper,
upper.bounds))
}
## Lag generations:
## C source expects each element of generations to be the number of
## generations to go forward before saving the next result set. This is
## unintuitive to specify, so we compute the lagged differences here.
if (length(generations) > 1)
generations <- c(generations[1], diff(generations))
if (!all(generations > 0))
stop("Cannot go back in time! Your generations argument must be sorted!")
## Set cdim = 0 if no cfn was given:
if (is.null(constraints)) cdim <- 0
res <- .Call(do_nsga2,
ff, cf, sys.frame(),
as.integer(odim),
as.integer(cdim),
as.integer(idim),
lower.bounds, upper.bounds,
as.integer(popsize), as.integer(generations),
cprob, as.integer(cdist),
mprob, as.integer(mdist))
if (1 == length(res)) {
res <- res[[1]]
names(res) <- c("par", "value", "pareto.optimal")
class(res) <- c("nsga2", "mco")
} else {
for (i in 1:length(res)) {
names(res[[i]]) <- c("par", "value", "pareto.optimal")
class(res[[i]]) <- c("nsga2", "mco")
}
class(res) <- "nsga2.collection"
}
return (res)
}
plot.nsga2 <- function(x, ...) {
v <- x$value
o <- x$pareto.optimal
d <- ncol(v)
col <- ifelse(o, "red", "blue")
pch <- ifelse(o, 4, 19)
if (d <= 2) {
plot(v, col=col, pch=pch, ...)
ov <- v[o,]
ov <- ov[order(ov[,1]),]
lines (ov, col="red", type="s")
} else if (d == 3) {
if (require("scatterplot3d")) {
scatterplot3d::scatterplot3d(v, color=ifelse(o, "red", "blue"))
} else {
pairs(v, col=col, pch=pch, ...)
}
} else {
pairs(v, col=col, pch=pch, ...)
}
}
plot.nsga2.collection <- function(x, ...) {
oask <- devAskNewPage(TRUE)
on.exit(devAskNewPage(oask))
sapply(x, plot)
return;
}
paretoSet.nsga2 <- function(x, ...)
x$par[x$pareto.optimal,]
paretoFront.nsga2 <- function(x, ...)
x$value[x$pareto.optimal,,drop=FALSE]
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat-summary.r
\name{stat_summary}
\alias{stat_summary}
\title{Summarise y values at every unique x.}
\usage{
stat_summary(mapping = NULL, data = NULL, geom = "pointrange",
position = "identity", ...)
}
\arguments{
\item{mapping}{The aesthetic mapping, usually constructed with
\code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set
at the layer level if you are overriding the plot defaults.}
\item{data}{A layer specific dataset - only needed if you want to override
the plot defaults.}
\item{geom}{The geometric object to use display the data}
\item{position}{The position adjustment to use for overlappling points
on this layer}
\item{...}{other arguments passed on to \code{\link{layer}}. This can
include aesthetics whose values you want to set, not map. See
\code{\link{layer}} for more details.}
}
\value{
a data.frame with additional columns:
\item{fun.data}{Complete summary function. Should take numeric vector as
input and return data frame as output}
\item{fun.ymin}{ymin summary function (should take numeric vector and
return single number)}
\item{fun.y}{y summary function (should take numeric vector and return
single number)}
\item{fun.ymax}{ymax summary function (should take numeric vector and
return single number)}
}
\description{
\code{stat_summary} allows for tremendous flexibilty in the specification
of summary functions. The summary function can either supply individual
summary functions for each of y, ymin and ymax (with \code{fun.y},
\code{fun.ymax}, \code{fun.ymin}), or return a data frame containing any
number of aesthetiics with with \code{fun.data}. All summary functions
are called with a single vector of values, \code{x}.
}
\details{
A simple vector function is easiest to work with as you can return a single
number, but is somewhat less flexible. If your summary function operates
on a data.frame it should return a data frame with variables that the geom
can use.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2.SparkR:::rd_aesthetics("stat", "summary")}
}
\examples{
\donttest{
# Basic operation on a small dataset
d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
d + stat_summary(fun.data = "mean_cl_boot", colour = "red")
p <- ggplot(mtcars, aes(cyl, mpg)) +
stat_summary(fun.y = "mean", geom = "point")
p
# Don't use ylim to zoom into a summary plot - this throws the
# data away
p + ylim(15, 30)
# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))
# You can supply individual functions to summarise the value at
# each x:
stat_sum_single <- function(fun, geom="point", ...) {
stat_summary(fun.y=fun, colour="red", geom=geom, size = 3, ...)
}
d + stat_sum_single(mean)
d + stat_sum_single(mean, geom="line")
d + stat_sum_single(median)
d + stat_sum_single(sd)
d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
colour = "red")
d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")
# Alternatively, you can supply a function that operates on a data.frame.
# A set of useful summary functions is provided from the Hmisc package:
stat_sum_df <- function(fun, geom="crossbar", ...) {
stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
}
# The crossbar geom needs grouping to be specified when used with
# a continuous x axis.
d + stat_sum_df("mean_cl_boot", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mult = 1, mapping = aes(group = cyl))
d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
# There are lots of different geoms you can use to display the summaries
d + stat_sum_df("mean_cl_normal", mapping = aes(group = cyl))
d + stat_sum_df("mean_cl_normal", geom = "errorbar")
d + stat_sum_df("mean_cl_normal", geom = "pointrange")
d + stat_sum_df("mean_cl_normal", geom = "smooth")
# Summaries are more useful with a bigger data set:
mpg2 <- subset(mpg, cyl != 5L)
m <- ggplot(mpg2, aes(x=cyl, y=hwy)) +
geom_point() +
stat_summary(fun.data = "mean_sdl", geom = "linerange",
colour = "red", size = 2, mult = 1) +
xlab("cyl")
m
# An example with highly skewed distributions:
set.seed(596)
mov <- movies[sample(nrow(movies), 1000), ]
m2 <- ggplot(mov, aes(x= factor(round(rating)), y=votes)) + geom_point()
m2 <- m2 + stat_summary(fun.data = "mean_cl_boot", geom = "crossbar",
colour = "red", width = 0.3) + xlab("rating")
m2
# Notice how the overplotting skews off visual perception of the mean
# supplementing the raw data with summary statistics is _very_ important
# Next, we'll look at votes on a log scale.
# Transforming the scale means the data are transformed
# first, after which statistics are computed:
m2 + scale_y_log10()
# Transforming the coordinate system occurs after the
# statistic has been computed. This means we're calculating the summary on the raw data
# and stretching the geoms onto the log scale. Compare the widths of the
# standard errors.
m2 + coord_trans(y="log10")
}
}
\seealso{
\code{\link{geom_errorbar}}, \code{\link{geom_pointrange}},
\code{\link{geom_linerange}}, \code{\link{geom_crossbar}} for geoms to
display summarised data
}
| /man/stat_summary.Rd | no_license | cattapre/ggplot2.SparkR | R | false | true | 5,290 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat-summary.r
\name{stat_summary}
\alias{stat_summary}
\title{Summarise y values at every unique x.}
\usage{
stat_summary(mapping = NULL, data = NULL, geom = "pointrange",
position = "identity", ...)
}
\arguments{
\item{mapping}{The aesthetic mapping, usually constructed with
\code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set
at the layer level if you are overriding the plot defaults.}
\item{data}{A layer specific dataset - only needed if you want to override
the plot defaults.}
\item{geom}{The geometric object to use display the data}
\item{position}{The position adjustment to use for overlappling points
on this layer}
\item{...}{other arguments passed on to \code{\link{layer}}. This can
include aesthetics whose values you want to set, not map. See
\code{\link{layer}} for more details.}
}
\value{
a data.frame with additional columns:
\item{fun.data}{Complete summary function. Should take numeric vector as
input and return data frame as output}
\item{fun.ymin}{ymin summary function (should take numeric vector and
return single number)}
\item{fun.y}{y summary function (should take numeric vector and return
single number)}
\item{fun.ymax}{ymax summary function (should take numeric vector and
return single number)}
}
\description{
\code{stat_summary} allows for tremendous flexibilty in the specification
of summary functions. The summary function can either supply individual
summary functions for each of y, ymin and ymax (with \code{fun.y},
\code{fun.ymax}, \code{fun.ymin}), or return a data frame containing any
number of aesthetiics with with \code{fun.data}. All summary functions
are called with a single vector of values, \code{x}.
}
\details{
A simple vector function is easiest to work with as you can return a single
number, but is somewhat less flexible. If your summary function operates
on a data.frame it should return a data frame with variables that the geom
can use.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2.SparkR:::rd_aesthetics("stat", "summary")}
}
\examples{
\donttest{
# Basic operation on a small dataset
d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
d + stat_summary(fun.data = "mean_cl_boot", colour = "red")
p <- ggplot(mtcars, aes(cyl, mpg)) +
stat_summary(fun.y = "mean", geom = "point")
p
# Don't use ylim to zoom into a summary plot - this throws the
# data away
p + ylim(15, 30)
# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))
# You can supply individual functions to summarise the value at
# each x:
stat_sum_single <- function(fun, geom="point", ...) {
stat_summary(fun.y=fun, colour="red", geom=geom, size = 3, ...)
}
d + stat_sum_single(mean)
d + stat_sum_single(mean, geom="line")
d + stat_sum_single(median)
d + stat_sum_single(sd)
d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
colour = "red")
d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")
# Alternatively, you can supply a function that operates on a data.frame.
# A set of useful summary functions is provided from the Hmisc package:
stat_sum_df <- function(fun, geom="crossbar", ...) {
stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
}
# The crossbar geom needs grouping to be specified when used with
# a continuous x axis.
d + stat_sum_df("mean_cl_boot", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mult = 1, mapping = aes(group = cyl))
d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
# There are lots of different geoms you can use to display the summaries
d + stat_sum_df("mean_cl_normal", mapping = aes(group = cyl))
d + stat_sum_df("mean_cl_normal", geom = "errorbar")
d + stat_sum_df("mean_cl_normal", geom = "pointrange")
d + stat_sum_df("mean_cl_normal", geom = "smooth")
# Summaries are more useful with a bigger data set:
mpg2 <- subset(mpg, cyl != 5L)
m <- ggplot(mpg2, aes(x=cyl, y=hwy)) +
geom_point() +
stat_summary(fun.data = "mean_sdl", geom = "linerange",
colour = "red", size = 2, mult = 1) +
xlab("cyl")
m
# An example with highly skewed distributions:
set.seed(596)
mov <- movies[sample(nrow(movies), 1000), ]
m2 <- ggplot(mov, aes(x= factor(round(rating)), y=votes)) + geom_point()
m2 <- m2 + stat_summary(fun.data = "mean_cl_boot", geom = "crossbar",
colour = "red", width = 0.3) + xlab("rating")
m2
# Notice how the overplotting skews off visual perception of the mean
# supplementing the raw data with summary statistics is _very_ important
# Next, we'll look at votes on a log scale.
# Transforming the scale means the data are transformed
# first, after which statistics are computed:
m2 + scale_y_log10()
# Transforming the coordinate system occurs after the
# statistic has been computed. This means we're calculating the summary on the raw data
# and stretching the geoms onto the log scale. Compare the widths of the
# standard errors.
m2 + coord_trans(y="log10")
}
}
\seealso{
\code{\link{geom_errorbar}}, \code{\link{geom_pointrange}},
\code{\link{geom_linerange}}, \code{\link{geom_crossbar}} for geoms to
display summarised data
}
|
library(variancePartition)
library(BiocParallel)
setwd("~/Documents/Batcave/GEO/ccdata/data-raw/")
pdata <- readRDS('cmap_es/pdata.rds')
all_exprs <- readRDS('cmap_es/all_exprs.rds')
# mixed effect model ---
pdata$platform <- factor(platform)
pdata$cell <- factor(pdata$cell)
pdata$batch <- factor(pdata$batch)
pdata$treatment <- paste(pdata$drug, paste0(pdata$molar, 'M'), paste0(pdata$hours, 'h'), sep='_')
# variance partition (treat all as random effects) ---
form <- ~(1|treatment) + (1|cell) + (1|batch) + (1|platform)
param <- SerialParam()
varPart <- fitExtractVarPartModel(all_exprs, form, pdata, BPPARAM = param)
saveRDS(varPart, 'cmap_es/varPart.rds')
plotVarPart(sortCols(varPart))
# remove effect of batch and platform then redo
varPart <- readRDS('cmap_es/varPart.rds')
param <- SerialParam()
residList <- fitVarPartModel(all_exprs, ~ (1|batch) + (1|platform), pdata, BPPARAM = param, fxn=residuals)
residMatrix <- do.call(rbind, residList)
# fit model on residuals
form <- ~ (1|treatment) + (1|cell)
varPartResid <- fitExtractVarPartModel(residMatrix, form, pdata, BPPARAM = param)
saveRDS(varPartResid, 'cmap_es/varPartResid.rds')
plotVarPart(sortCols(varPartResid))
# differential expression analysis (treatment has to be fixed effect) ----
param <- SerialParam()
form <- ~ 0 + treatment + (1|cell) + (1|batch) + (1|platform)
# exclude treatments with colinearity issues (see below)
keep <- row.names(pdata)[!pdata$treatment %in% maxs]
pdata <- pdata[keep, ]
all_exprs <- all_exprs[, keep]
# univariate contrasts (faster to supply)
Linit <- variancePartition:::.getAllUniContrasts(all_exprs, form, pdata, return.Linit = TRUE)
# interested contrasts
levels <- unique(pdata$treatment)
cons <- paste0('treatment', levels[!grepl('^ctl_', levels)])
L <- lapply(cons, function(con) {
ctrl <- ifelse(grepl('_6h$', con), 'treatmentctl_0M_6h', 'treatmentctl_0M_12h')
getContrast(all_exprs, form, pdata, c(con, ctrl), L = Linit)
})
L <- do.call(cbind, L)
colnames(L) <- paste0('L', seq_len(ncol(L)))
# initial fit used to speed up subsequent
system.time(fitInit <- dream(all_exprs, form, pdata, L = L, Linit = Linit, return.fitInit = TRUE, BPPARAM=param))
variancePartition:::checkModelStatus(fitInit, showWarnings=TRUE, dream=TRUE, colinearityCutoff=0.999)
# user system elapsed
# 252.463 0.390 230
# levels of treatment with very high correlation to ctrl/each other cause colinearity issues
# re-run above excluding maxs
vcor <- colinearityScore(fitInit)
vcor <- attributes(vcor)[[1]]
diag(vcor) <- 0
maxs <- apply(vcor, 2, max)
maxs <- names(maxs)[maxs > 0.999]
maxs <- setdiff(maxs, 'treatmentctl_0M_6h')
maxs <- gsub('^treatment', '', maxs)
Lret <- variancePartition:::format.contrasts(all_exprs, form, pdata, L, Linit = Linit)
# save arguments to run as parts
# see 2-run_dream.R and 2-process_dream.R
# ran on O2
dream_args <- list(form = form, pdata = pdata, L = L, Linit = Linit, fitInit = fitInit, Lret = Lret)
dir.create('cmap_es/dream')
dir.create('cmap_es/dream/resLists')
saveRDS(dream_args, 'cmap_es/dream/dream_args.rds')
saveRDS(all_exprs, 'cmap_es/all_exprs.rds')
# save expression values for each gene as seperate matrix
rpath <- '/n/scratch2/ap491/ccdata/data-raw/cmap_es/dream/resLists'
for (i in seq_len(nrow(all_exprs))) {
cat('Working on', i, 'of', nrow(all_exprs), '\n')
exprs_fpath <- file.path(rpath, paste(i, 'exprs.rds', sep = '_'))
saveRDS(all_exprs[i,,drop=FALSE], exprs_fpath)
}
| /data-raw/cmap_es/2b-setup_dream.R | no_license | alexvpickering/ccdata | R | false | false | 3,449 | r | library(variancePartition)
library(BiocParallel)
setwd("~/Documents/Batcave/GEO/ccdata/data-raw/")
pdata <- readRDS('cmap_es/pdata.rds')
all_exprs <- readRDS('cmap_es/all_exprs.rds')
# mixed effect model ---
pdata$platform <- factor(platform)
pdata$cell <- factor(pdata$cell)
pdata$batch <- factor(pdata$batch)
pdata$treatment <- paste(pdata$drug, paste0(pdata$molar, 'M'), paste0(pdata$hours, 'h'), sep='_')
# variance partition (treat all as random effects) ---
form <- ~(1|treatment) + (1|cell) + (1|batch) + (1|platform)
param <- SerialParam()
varPart <- fitExtractVarPartModel(all_exprs, form, pdata, BPPARAM = param)
saveRDS(varPart, 'cmap_es/varPart.rds')
plotVarPart(sortCols(varPart))
# remove effect of batch and platform then redo
varPart <- readRDS('cmap_es/varPart.rds')
param <- SerialParam()
residList <- fitVarPartModel(all_exprs, ~ (1|batch) + (1|platform), pdata, BPPARAM = param, fxn=residuals)
residMatrix <- do.call(rbind, residList)
# fit model on residuals
form <- ~ (1|treatment) + (1|cell)
varPartResid <- fitExtractVarPartModel(residMatrix, form, pdata, BPPARAM = param)
saveRDS(varPartResid, 'cmap_es/varPartResid.rds')
plotVarPart(sortCols(varPartResid))
# differential expression analysis (treatment has to be fixed effect) ----
param <- SerialParam()
form <- ~ 0 + treatment + (1|cell) + (1|batch) + (1|platform)
# exclude treatments with colinearity issues (see below)
keep <- row.names(pdata)[!pdata$treatment %in% maxs]
pdata <- pdata[keep, ]
all_exprs <- all_exprs[, keep]
# univariate contrasts (faster to supply)
Linit <- variancePartition:::.getAllUniContrasts(all_exprs, form, pdata, return.Linit = TRUE)
# interested contrasts
levels <- unique(pdata$treatment)
cons <- paste0('treatment', levels[!grepl('^ctl_', levels)])
L <- lapply(cons, function(con) {
ctrl <- ifelse(grepl('_6h$', con), 'treatmentctl_0M_6h', 'treatmentctl_0M_12h')
getContrast(all_exprs, form, pdata, c(con, ctrl), L = Linit)
})
L <- do.call(cbind, L)
colnames(L) <- paste0('L', seq_len(ncol(L)))
# initial fit used to speed up subsequent
system.time(fitInit <- dream(all_exprs, form, pdata, L = L, Linit = Linit, return.fitInit = TRUE, BPPARAM=param))
variancePartition:::checkModelStatus(fitInit, showWarnings=TRUE, dream=TRUE, colinearityCutoff=0.999)
# user system elapsed
# 252.463 0.390 230
# levels of treatment with very high correlation to ctrl/each other cause colinearity issues
# re-run above excluding maxs
vcor <- colinearityScore(fitInit)
vcor <- attributes(vcor)[[1]]
diag(vcor) <- 0
maxs <- apply(vcor, 2, max)
maxs <- names(maxs)[maxs > 0.999]
maxs <- setdiff(maxs, 'treatmentctl_0M_6h')
maxs <- gsub('^treatment', '', maxs)
Lret <- variancePartition:::format.contrasts(all_exprs, form, pdata, L, Linit = Linit)
# save arguments to run as parts
# see 2-run_dream.R and 2-process_dream.R
# ran on O2
dream_args <- list(form = form, pdata = pdata, L = L, Linit = Linit, fitInit = fitInit, Lret = Lret)
dir.create('cmap_es/dream')
dir.create('cmap_es/dream/resLists')
saveRDS(dream_args, 'cmap_es/dream/dream_args.rds')
saveRDS(all_exprs, 'cmap_es/all_exprs.rds')
# save expression values for each gene as seperate matrix
rpath <- '/n/scratch2/ap491/ccdata/data-raw/cmap_es/dream/resLists'
for (i in seq_len(nrow(all_exprs))) {
cat('Working on', i, 'of', nrow(all_exprs), '\n')
exprs_fpath <- file.path(rpath, paste(i, 'exprs.rds', sep = '_'))
saveRDS(all_exprs[i,,drop=FALSE], exprs_fpath)
}
|
.assign.synergy <-
function(all_data, nest_data = F, return_wide = F) {
result_types <-
all_data$typeResult %>% unique()
result_types %>%
walk(function(result_type){
table_name <-
glue::glue("dataSynergy{str_to_title(result_type)}") %>%
as.character()
df_result <-
all_data %>%
filter(typeResult == result_type) %>%
select(-typeResult) %>%
select(slugSeason, everything()) %>%
arrange(slugSeason) %>%
unnest() %>%
distinct()
if (result_type == "player") {
df_result <-
df_result %>%
filter(!is.na(idPlayer))
}
if (return_wide) {
df_long <-
df_result %>%
gather_data(
variable_name = 'item',
numeric_ids = c("^id", "gp", "numberJersey"),
use_logical_keys = TRUE,
use_factor_keys = TRUE,
unite_columns = NULL,
separate_columns = NULL,
use_date_keys = FALSE
)
df_result <-
df_long %>%
unite(item, item, categorySynergy, sep = "") %>%
return_wide()
}
if (nest_data) {
df_result <-
df_result %>%
nest(-c(slugSeason))
}
assign(table_name, df_result, envir = .GlobalEnv)
})
}
.number_to_pct <-
function(data) {
pct_cols <- data %>% select(dplyr::matches("pct[A-Z]")) %>% names()
data %>%
mutate_at(pct_cols,
funs(. %>% as.numeric() / 100))
}
.dictionary_synergy_categories <-
memoise::memoise(function() {
data_frame(
nameSynergy = c(
"Transition",
"Isolation",
"PRBallHandler",
"PRRollman",
"Postup",
"Spotup",
"Handoff",
"Cut",
"OffScreen",
"OffRebound",
"Misc"
),
nameTable = c(
"Transition",
"Isolation",
"Pick and Roll Ball Handler",
"Pick and Roll Rollman",
"Post Up",
"Spot Up",
"Handoff",
"Cut",
"OffScreen",
"Off Rebound",
"Misc"
)
)
})
.get_synergy_category_data <-
function(season = 2018,
result_type = "player",
season_type = "Regular Season",
category = "transition",
set_type = "offensive",
results = 500,
return_message = T
) {
if (season < 2016) {
stop("Synergy data starts for the 2015-16 season")
}
categories <-
c(
"Transition",
"Isolation",
"PRBallHandler",
"PRRollman",
"Postup",
"Spotup",
"Handoff",
"Cut",
"OffScreen",
"OffRebound",
"Misc"
)
cat_slug <- category %>% str_to_lower()
wrong_cat <-
cat_slug %>% str_detect(str_to_lower(categories)) %>% sum(na.rm = T) == 0
if (wrong_cat) {
stop(glue::glue(
"Synergy categories can only be:\n{str_c(categories, collapse = '\n')}"
))
}
slug_season_type <-
case_when(season_type %>% str_to_lower() %>% str_detect("regular") ~ "REG",
TRUE ~ "Post")
slug_season <- generate_season_slug(season = season)
season_synergy <- season - 1
json_url <-
glue::glue(
"https://stats-prod.nba.com/wp-json/statscms/v1/synergy/{result_type}/?category={category}&season={season_synergy}&seasonType={slug_season_type}&names={set_type}&limit={results}"
) %>%
as.character()
if (return_message) {
glue::glue(
"Acquiring {result_type} synergy data for {str_to_lower(set_type)} {str_to_lower(category)} in the {str_to_lower(season_type)} during the {slug_season}"
) %>%
cat(fill = T)
}
json <-
json_url %>%
curl_json_to_vector()
json_names <- json$results %>% names()
actual_names <-
json_names %>%
resolve_nba_names()
data <-
json$results %>%
as_data_frame() %>%
purrr::set_names(actual_names) %>%
mutate(
categorySynergy = category,
typeResult = result_type,
slugSeason = slug_season) %>%
select(-one_of("yearSeason")) %>%
dplyr::select(
typeResult,
typeSet,
categorySynergy,
slugSeason,
typeSeason,
everything()
) %>%
mutate_at("idTeam",
funs(. %>% as.numeric()))
if (result_type %>% str_to_lower() == "player") {
data <-
data %>%
unite(namePlayer, nameFirst, nameLast, sep = " ") %>%
mutate(idPlayer = idPlayer %>% as.numeric())
}
if (data %>% has_name("tov")){
data <-
data %>%
dplyr::rename(pctTOV = tov)
}
num_cols <- data %>% select(-dplyr::matches(char_words())) %>% names()
data <-
data %>%
mutate_at(num_cols,
funs(. %>% as.character() %>% readr::parse_number()))
ppp_names <- data %>% dplyr::select(dplyr::matches("PPP")) %>% names()
if (ppp_names %>% length() >0) {
data <- data %>%
mutate_at(ppp_names,
funs(. %>% as.numeric()))
}
data <-
data %>%
.number_to_pct()
data %>%
nest(-c(slugSeason, typeResult, categorySynergy, typeSet),
.key = dataSynergy)
}
#' Get Synergy data for specified season
#'
#' Get Synergy data for specified result type,
#' category, season type and set
#'
#' @param seasons vector of seasons from 2016 onward
#' @param result_types result type \itemize{
#' \item team
#' \item player
#' }
#' @param categories vector of synergy categories options include: \itemize{
#' \item Transition
#' \item Isolation
#' \item PRBallHandler
#' \item PRRollman
#' \item Postup
#' \item Spotup
#' \item Handoff
#' \item Cut
#' \item OffScreen
#' \item OffRebound
#' \item Misc
#' }
#' @param season_types type of season play \itemize{
#' \item Playoffs
#' \item Regular Season
#' }
#' @param set_types set type \itemize{
#' \item offensive
#' \item defensive
#' }
#' @param results number of results
#' @param assign_to_environment if \code{TRUE} assigns table to environment
#' @param return_wide if \code{return_wide} returns a spread \code{data_frame}
#' @param return_message
#'
#' @return a \code{data_frame}
#' @export
#' @import dplyr stringr magrittr curl jsonlite readr magrittr purrr tidyr rlang
#' @importFrom glue glue
#' @examples
#' synergy(seasons = 2019, result_types = c("player", "team"), season_types = c("Regular Season"), set_types = c("offensive", "defensive"), categories = c("Transition", "Isolation", "PRBallHandler", "PRRollman", "Postup", "Spotup", "Handoff", "Cut", "OffScreen", "OffRebound", "Misc"), results = 500, assign_to_environment = TRUE, return_wide = F, return_message = TRUE)
synergy <-
function(seasons = 2016:2018,
result_types = c("player", "team"),
season_types = c("Regular Season"),
set_types = c("offensive", "defensive"),
categories = c("Transition", "Isolation", "PRBallHandler", "PRRollman", "Postup",
"Spotup", "Handoff", "Cut", "OffScreen", "OffRebound", "Misc"
),
results = 500,
assign_to_environment = TRUE,
return_wide = F,
nest_data = F,
return_message = TRUE) {
if (seasons %>% purrr::is_null()) {
stop("please enter season")
}
if (types %>% str_to_lower() %in% c("player", "team") %>% sum(na.rm = T) == 0) {
stop("Result type can only be player and/or team")
}
input_df <-
expand.grid(
season = seasons,
result_type = result_types,
season_type = season_types,
set_type = set_types,
category = categories,
stringsAsFactors = F
) %>%
as_data_frame() %>%
distinct()
.get_synergy_category_data_safe <-
purrr::possibly(.get_synergy_category_data, data_frame())
all_data <-
1:nrow(input_df) %>%
future_map_dfr(function(x) {
df_row <- input_df %>% slice(x)
df_row %$%
.get_synergy_category_data_safe(
season = season,
result_type = result_type,
season_type = season_type,
category = category,
set_type = set_type,
results = results,
return_message = return_message
)
}) %>%
suppressWarnings()
if (assign_to_environment) {
all_data %>%
.assign.synergy(nest_data = nest_data,
return_wide = return_wide)
}
all_data
}
| /R/synergy.R | no_license | franckess/nbastatR | R | false | false | 8,668 | r | .assign.synergy <-
function(all_data, nest_data = F, return_wide = F) {
result_types <-
all_data$typeResult %>% unique()
result_types %>%
walk(function(result_type){
table_name <-
glue::glue("dataSynergy{str_to_title(result_type)}") %>%
as.character()
df_result <-
all_data %>%
filter(typeResult == result_type) %>%
select(-typeResult) %>%
select(slugSeason, everything()) %>%
arrange(slugSeason) %>%
unnest() %>%
distinct()
if (result_type == "player") {
df_result <-
df_result %>%
filter(!is.na(idPlayer))
}
if (return_wide) {
df_long <-
df_result %>%
gather_data(
variable_name = 'item',
numeric_ids = c("^id", "gp", "numberJersey"),
use_logical_keys = TRUE,
use_factor_keys = TRUE,
unite_columns = NULL,
separate_columns = NULL,
use_date_keys = FALSE
)
df_result <-
df_long %>%
unite(item, item, categorySynergy, sep = "") %>%
return_wide()
}
if (nest_data) {
df_result <-
df_result %>%
nest(-c(slugSeason))
}
assign(table_name, df_result, envir = .GlobalEnv)
})
}
.number_to_pct <-
function(data) {
pct_cols <- data %>% select(dplyr::matches("pct[A-Z]")) %>% names()
data %>%
mutate_at(pct_cols,
funs(. %>% as.numeric() / 100))
}
.dictionary_synergy_categories <-
memoise::memoise(function() {
data_frame(
nameSynergy = c(
"Transition",
"Isolation",
"PRBallHandler",
"PRRollman",
"Postup",
"Spotup",
"Handoff",
"Cut",
"OffScreen",
"OffRebound",
"Misc"
),
nameTable = c(
"Transition",
"Isolation",
"Pick and Roll Ball Handler",
"Pick and Roll Rollman",
"Post Up",
"Spot Up",
"Handoff",
"Cut",
"OffScreen",
"Off Rebound",
"Misc"
)
)
})
.get_synergy_category_data <-
function(season = 2018,
result_type = "player",
season_type = "Regular Season",
category = "transition",
set_type = "offensive",
results = 500,
return_message = T
) {
if (season < 2016) {
stop("Synergy data starts for the 2015-16 season")
}
categories <-
c(
"Transition",
"Isolation",
"PRBallHandler",
"PRRollman",
"Postup",
"Spotup",
"Handoff",
"Cut",
"OffScreen",
"OffRebound",
"Misc"
)
cat_slug <- category %>% str_to_lower()
wrong_cat <-
cat_slug %>% str_detect(str_to_lower(categories)) %>% sum(na.rm = T) == 0
if (wrong_cat) {
stop(glue::glue(
"Synergy categories can only be:\n{str_c(categories, collapse = '\n')}"
))
}
slug_season_type <-
case_when(season_type %>% str_to_lower() %>% str_detect("regular") ~ "REG",
TRUE ~ "Post")
slug_season <- generate_season_slug(season = season)
season_synergy <- season - 1
json_url <-
glue::glue(
"https://stats-prod.nba.com/wp-json/statscms/v1/synergy/{result_type}/?category={category}&season={season_synergy}&seasonType={slug_season_type}&names={set_type}&limit={results}"
) %>%
as.character()
if (return_message) {
glue::glue(
"Acquiring {result_type} synergy data for {str_to_lower(set_type)} {str_to_lower(category)} in the {str_to_lower(season_type)} during the {slug_season}"
) %>%
cat(fill = T)
}
json <-
json_url %>%
curl_json_to_vector()
json_names <- json$results %>% names()
actual_names <-
json_names %>%
resolve_nba_names()
data <-
json$results %>%
as_data_frame() %>%
purrr::set_names(actual_names) %>%
mutate(
categorySynergy = category,
typeResult = result_type,
slugSeason = slug_season) %>%
select(-one_of("yearSeason")) %>%
dplyr::select(
typeResult,
typeSet,
categorySynergy,
slugSeason,
typeSeason,
everything()
) %>%
mutate_at("idTeam",
funs(. %>% as.numeric()))
if (result_type %>% str_to_lower() == "player") {
data <-
data %>%
unite(namePlayer, nameFirst, nameLast, sep = " ") %>%
mutate(idPlayer = idPlayer %>% as.numeric())
}
if (data %>% has_name("tov")){
data <-
data %>%
dplyr::rename(pctTOV = tov)
}
num_cols <- data %>% select(-dplyr::matches(char_words())) %>% names()
data <-
data %>%
mutate_at(num_cols,
funs(. %>% as.character() %>% readr::parse_number()))
ppp_names <- data %>% dplyr::select(dplyr::matches("PPP")) %>% names()
if (ppp_names %>% length() >0) {
data <- data %>%
mutate_at(ppp_names,
funs(. %>% as.numeric()))
}
data <-
data %>%
.number_to_pct()
data %>%
nest(-c(slugSeason, typeResult, categorySynergy, typeSet),
.key = dataSynergy)
}
#' Get Synergy data for specified season
#'
#' Get Synergy data for specified result type,
#' category, season type and set
#'
#' @param seasons vector of seasons from 2016 onward
#' @param result_types result type \itemize{
#' \item team
#' \item player
#' }
#' @param categories vector of synergy categories options include: \itemize{
#' \item Transition
#' \item Isolation
#' \item PRBallHandler
#' \item PRRollman
#' \item Postup
#' \item Spotup
#' \item Handoff
#' \item Cut
#' \item OffScreen
#' \item OffRebound
#' \item Misc
#' }
#' @param season_types type of season play \itemize{
#' \item Playoffs
#' \item Regular Season
#' }
#' @param set_types set type \itemize{
#' \item offensive
#' \item defensive
#' }
#' @param results number of results
#' @param assign_to_environment if \code{TRUE} assigns table to environment
#' @param return_wide if \code{return_wide} returns a spread \code{data_frame}
#' @param return_message
#'
#' @return a \code{data_frame}
#' @export
#' @import dplyr stringr magrittr curl jsonlite readr magrittr purrr tidyr rlang
#' @importFrom glue glue
#' @examples
#' synergy(seasons = 2019, result_types = c("player", "team"), season_types = c("Regular Season"), set_types = c("offensive", "defensive"), categories = c("Transition", "Isolation", "PRBallHandler", "PRRollman", "Postup", "Spotup", "Handoff", "Cut", "OffScreen", "OffRebound", "Misc"), results = 500, assign_to_environment = TRUE, return_wide = F, return_message = TRUE)
synergy <-
function(seasons = 2016:2018,
result_types = c("player", "team"),
season_types = c("Regular Season"),
set_types = c("offensive", "defensive"),
categories = c("Transition", "Isolation", "PRBallHandler", "PRRollman", "Postup",
"Spotup", "Handoff", "Cut", "OffScreen", "OffRebound", "Misc"
),
results = 500,
assign_to_environment = TRUE,
return_wide = F,
nest_data = F,
return_message = TRUE) {
if (seasons %>% purrr::is_null()) {
stop("please enter season")
}
if (types %>% str_to_lower() %in% c("player", "team") %>% sum(na.rm = T) == 0) {
stop("Result type can only be player and/or team")
}
input_df <-
expand.grid(
season = seasons,
result_type = result_types,
season_type = season_types,
set_type = set_types,
category = categories,
stringsAsFactors = F
) %>%
as_data_frame() %>%
distinct()
.get_synergy_category_data_safe <-
purrr::possibly(.get_synergy_category_data, data_frame())
all_data <-
1:nrow(input_df) %>%
future_map_dfr(function(x) {
df_row <- input_df %>% slice(x)
df_row %$%
.get_synergy_category_data_safe(
season = season,
result_type = result_type,
season_type = season_type,
category = category,
set_type = set_type,
results = results,
return_message = return_message
)
}) %>%
suppressWarnings()
if (assign_to_environment) {
all_data %>%
.assign.synergy(nest_data = nest_data,
return_wide = return_wide)
}
all_data
}
|
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
data<-read.csv("outcome-of-care-measures.csv", colClasses = "character",na.strings="Not Available")
## Check that state and outcome are valid
validNames = c("heart attack","heart failure","pneumonia")
if (!outcome %in% validNames) { stop("invalid outcome")}
validState = unique(data[,7])
if (!state %in% validState) stop("invalid state")
## convert outcome name into column name
fullColName <- c("Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack", "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure", "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia")
colName <- fullColName[match(outcome,validNames)]
## Return hospital name in that state with the given rank 30-day death rate
data.state <- data[data$State==state,]
# order data by outcome
sorted.data.state <- data.state[order(as.numeric(data.state[[colName]]),data.state[["Hospital.Name"]],decreasing=FALSE,na.last=NA), ]
#handle num input
if (num=="best") num = 1
if (num=='worst') num = nrow(sorted.data.state)
#will automatically return NA if num > nrow, as well as if it's some other text value
# if someone passes num < 1, they'll get what's expected
#if (is.numeric(num) & num > nrwo(sorted.data.state) return(NA)
sorted.data.state[num,"Hospital.Name"]
## 30-day death rate
}
rankhospital("MN", "heart attack", 5000)
| /2. Programming R/4/rankhospital.R | no_license | gravialex/Coursera-Data-Scientist | R | false | false | 1,539 | r | rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
data<-read.csv("outcome-of-care-measures.csv", colClasses = "character",na.strings="Not Available")
## Check that state and outcome are valid
validNames = c("heart attack","heart failure","pneumonia")
if (!outcome %in% validNames) { stop("invalid outcome")}
validState = unique(data[,7])
if (!state %in% validState) stop("invalid state")
## convert outcome name into column name
fullColName <- c("Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack", "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure", "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia")
colName <- fullColName[match(outcome,validNames)]
## Return hospital name in that state with the given rank 30-day death rate
data.state <- data[data$State==state,]
# order data by outcome
sorted.data.state <- data.state[order(as.numeric(data.state[[colName]]),data.state[["Hospital.Name"]],decreasing=FALSE,na.last=NA), ]
#handle num input
if (num=="best") num = 1
if (num=='worst') num = nrow(sorted.data.state)
#will automatically return NA if num > nrow, as well as if it's some other text value
# if someone passes num < 1, they'll get what's expected
#if (is.numeric(num) & num > nrwo(sorted.data.state) return(NA)
sorted.data.state[num,"Hospital.Name"]
## 30-day death rate
}
rankhospital("MN", "heart attack", 5000)
|
#' Filter Corpus
#'
#' A function that removes all occurances from a given list of words from each (.txt) file in ipath.
#' Generally used after some analysis of the summary_corpus results. To remove the most and least frequent
#' terms. Handles each file in the ipath in parallel over the number of cores specified. Runs filter_file
#' on each file in the ipath.
#'
#' @param words A character vector of all the words to remove.
#' @param ipath A string specifying the path to all the text files to handle.
#' @param ncores A number specifying the number of cores to use.
#'
#' @examples
#' \dontrun{
#' filter_corpus(most_common_terms, "/path/to/corpus/", 10)
#' filter_corpus(stopwords, "/path/to/corpus/", 10)
#' }
filter_corpus = function (words, ipath, ncores)
{
# check if ipath exists
if (!dir.exists(ipath))
stop("no input directory")
# check if there are text files in input directory
filelist = list.files(path = ipath, pattern=".txt", full.names = TRUE)
if (length(filelist) < 1 )
stop ("no (.txt) files in directory")
# parlapply (clean_file)
cluster = makeCluster(ncores)
# list of results df
processed = parLapply (cluster, filelist, filter_file, words)
stopCluster(cluster)
}
#' Filter File
#'
#' A wrapper around the rcpp_filter function.
#' Given a file and a list of words. Remove each occurance of those words from that file.
#' Will modify the input file, so no output file is specified.
#'
#' @param words A character vector of all the words to remove.
#' @param ifilepath A string specifying the path to the input file.
#' @return ifilepath
#'
#' @examples
#' \dontrun{
#' filter_file(most_common_terms, "/path/to/file.txt")
#' filter_file(stopwords, "/path/to/file.txt")
#' }
filter_file = function (words, ifilepath)
{
res = rcpp_filter(ifilepath, words)
return (res)
}
| /R/filter_corpus.R | no_license | avkoehl/textprocessingDSI | R | false | false | 1,836 | r | #' Filter Corpus
#'
#' A function that removes all occurances from a given list of words from each (.txt) file in ipath.
#' Generally used after some analysis of the summary_corpus results. To remove the most and least frequent
#' terms. Handles each file in the ipath in parallel over the number of cores specified. Runs filter_file
#' on each file in the ipath.
#'
#' @param words A character vector of all the words to remove.
#' @param ipath A string specifying the path to all the text files to handle.
#' @param ncores A number specifying the number of cores to use.
#'
#' @examples
#' \dontrun{
#' filter_corpus(most_common_terms, "/path/to/corpus/", 10)
#' filter_corpus(stopwords, "/path/to/corpus/", 10)
#' }
filter_corpus = function (words, ipath, ncores)
{
# check if ipath exists
if (!dir.exists(ipath))
stop("no input directory")
# check if there are text files in input directory
filelist = list.files(path = ipath, pattern=".txt", full.names = TRUE)
if (length(filelist) < 1 )
stop ("no (.txt) files in directory")
# parlapply (clean_file)
cluster = makeCluster(ncores)
# list of results df
processed = parLapply (cluster, filelist, filter_file, words)
stopCluster(cluster)
}
#' Filter File
#'
#' A wrapper around the rcpp_filter function.
#' Given a file and a list of words. Remove each occurance of those words from that file.
#' Will modify the input file, so no output file is specified.
#'
#' @param words A character vector of all the words to remove.
#' @param ifilepath A string specifying the path to the input file.
#' @return ifilepath
#'
#' @examples
#' \dontrun{
#' filter_file(most_common_terms, "/path/to/file.txt")
#' filter_file(stopwords, "/path/to/file.txt")
#' }
filter_file = function (words, ifilepath)
{
res = rcpp_filter(ifilepath, words)
return (res)
}
|
## Course 3 Project
## Step 1: Download the data files
## Get required packages and set the Directory
install.packages("data.table")
install.packages("reshape2")
library(data.table)
library(reshape2)
setwd("C:/Giri/RProgramming/Courseera/Course3")
path <- getwd()
## Download the required data for analysis
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
f <- "Dataset.zip"
if (!file.exists(path)) {
dir.create(path)
}
download.file(url, file.path(path, f))
## Unzip the file
executable <- file.path("C:", "Program Files (x86)", "7-Zip", "7z.exe")
parameters <- "x"
cmd <- paste(paste0("\"", executable, "\""), parameters, paste0("\"", file.path(path,
f), "\""))
system(cmd)
## review files unzipped
pathIn <- file.path(path, "UCIHARDataset")
list.files(pathIn, recursive = TRUE)
## Step 2: Load the Files in R for analysis
dtSubTrain <- fread(file.path(pathIn, "train", "subject_train.txt"))
names(dtSubjectTrain)
dtSubTest <- fread(file.path(pathIn, "test", "subject_test.txt"))
names(dtSubjectTest)
dtActTrain <- fread(file.path(pathIn, "train", "Y_train.txt"))
names(dtActTrain)
dtActTest <- fread(file.path(pathIn, "test", "Y_test.txt"))
fileToDataTable <- function(f) {
df <- read.table(f)
dt <- data.table(df)
}
dtTrain <- fileToDataTable(file.path(pathIn, "train", "X_train.txt"))
dtTest <- fileToDataTable(file.path(pathIn, "test", "X_test.txt"))
## Step 3: Merging the data
dtSub <- rbind(dtSubTrain, dtSubTest)
setnames(dtSub, "V1", "subject")
names(dtSub)
dtAct <- rbind(dtActTrain, dtActTest)
setnames(dtAct, "V1", "activityNum")
dt <- rbind(dtTrain, dtTest)
names(dt)
dtSubAct <- cbind(dtSubject, dtActivity)
dt <- cbind(dtSubAct, dt)
setkey(dt, subject, activityNum)
names(dt)
## Step 4: Extract Mean and Standard Deviation
dtFeatures <- fread(file.path(pathIn, "features.txt"))
setnames(dtFeatures, names(dtFeatures), c("featureNum", "featureName"))
dtFeatures <- dtFeatures[grepl("mean\\(\\)|std\\(\\)", featureName)]
dtFeatures$featureCode <- dtFeatures[, paste0("V", featureNum)]
head(dtFeatures)
dtFeatures$featureCode
select <- c(key(dt), dtFeatures$featureCode)
dt <- dt[, select, with = FALSE]
## Step 5: Use descriptive names
dtActivityNames <- fread(file.path(pathIn, "activity_labels.txt"))
setnames(dtActivityNames, names(dtActivityNames), c("activityNum", "activityName"))
## Step 6: Label with descriptive activity names
dt <- merge(dt, dtActivityNames, by = "activityNum", all.x = TRUE)
setkey(dt, subject, activityNum, activityName)
dt <- data.table(melt(dt, key(dt), variable.name = "featureCode"))
head(dt)
dt <- merge(dt, dtFeatures[, list(featureNum, featureCode, featureName)], by = "featureCode",
all.x = TRUE)
head(dt)
dt$activity <- factor(dt$activityName)
dt$feature <- factor(dt$featureName)
head(dt)
grepthis <- function(regex) {
grepl(regex, dt$feature)
}
## Features with 2 categories
n <- 2
y <- matrix(seq(1, n), nrow = n)
x <- matrix(c(grepthis("^t"), grepthis("^f")), ncol = nrow(y))
dt$featDomain <- factor(x %*% y, labels = c("Time", "Freq"))
x <- matrix(c(grepthis("Acc"), grepthis("Gyro")), ncol = nrow(y))
dt$featInstrument <- factor(x %*% y, labels = c("Accelerometer", "Gyroscope"))
x <- matrix(c(grepthis("BodyAcc"), grepthis("GravityAcc")), ncol = nrow(y))
dt$featAcceleration <- factor(x %*% y, labels = c(NA, "Body", "Gravity"))
x <- matrix(c(grepthis("mean()"), grepthis("std()")), ncol = nrow(y))
dt$featVariable <- factor(x %*% y, labels = c("Mean", "SD"))
## Features with 1 category
dt$featJerk <- factor(grepthis("Jerk"), labels = c(NA, "Jerk"))
dt$featMagnitude <- factor(grepthis("Mag"), labels = c(NA, "Magnitude"))
## Features with 3 categories
n <- 3
y <- matrix(seq(1, n), nrow = n)
x <- matrix(c(grepthis("-X"), grepthis("-Y"), grepthis("-Z")), ncol = nrow(y))
dt$featAxis <- factor(x %*% y, labels = c(NA, "X", "Y", "Z"))
head(dt)
r1 <- nrow(dt[, .N, by = c("feature")])
r2 <- nrow(dt[, .N, by = c("featDomain", "featAcceleration", "featInstrument",
"featJerk", "featMagnitude", "featVariable", "featAxis")])
r1 == r2
## Step 7: Create a Tidy dataset
setkey(dt, subject, activity, featDomain, featAcceleration, featInstrument,
featJerk, featMagnitude, featVariable, featAxis)
dtTidy <- dt[, list(count = .N, average = mean(value)), by = key(dt)]
head(dtTidy) | /run_analysis.R | no_license | Giridash/GetCleanData | R | false | false | 4,549 | r | ## Course 3 Project
## Step 1: Download the data files
## Get required packages and set the Directory
install.packages("data.table")
install.packages("reshape2")
library(data.table)
library(reshape2)
setwd("C:/Giri/RProgramming/Courseera/Course3")
path <- getwd()
## Download the required data for analysis
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
f <- "Dataset.zip"
if (!file.exists(path)) {
dir.create(path)
}
download.file(url, file.path(path, f))
## Unzip the file
executable <- file.path("C:", "Program Files (x86)", "7-Zip", "7z.exe")
parameters <- "x"
cmd <- paste(paste0("\"", executable, "\""), parameters, paste0("\"", file.path(path,
f), "\""))
system(cmd)
## review files unzipped
pathIn <- file.path(path, "UCIHARDataset")
list.files(pathIn, recursive = TRUE)
## Step 2: Load the Files in R for analysis
dtSubTrain <- fread(file.path(pathIn, "train", "subject_train.txt"))
names(dtSubjectTrain)
dtSubTest <- fread(file.path(pathIn, "test", "subject_test.txt"))
names(dtSubjectTest)
dtActTrain <- fread(file.path(pathIn, "train", "Y_train.txt"))
names(dtActTrain)
dtActTest <- fread(file.path(pathIn, "test", "Y_test.txt"))
fileToDataTable <- function(f) {
df <- read.table(f)
dt <- data.table(df)
}
dtTrain <- fileToDataTable(file.path(pathIn, "train", "X_train.txt"))
dtTest <- fileToDataTable(file.path(pathIn, "test", "X_test.txt"))
## Step 3: Merging the data
dtSub <- rbind(dtSubTrain, dtSubTest)
setnames(dtSub, "V1", "subject")
names(dtSub)
dtAct <- rbind(dtActTrain, dtActTest)
setnames(dtAct, "V1", "activityNum")
dt <- rbind(dtTrain, dtTest)
names(dt)
dtSubAct <- cbind(dtSubject, dtActivity)
dt <- cbind(dtSubAct, dt)
setkey(dt, subject, activityNum)
names(dt)
## Step 4: Extract Mean and Standard Deviation
dtFeatures <- fread(file.path(pathIn, "features.txt"))
setnames(dtFeatures, names(dtFeatures), c("featureNum", "featureName"))
dtFeatures <- dtFeatures[grepl("mean\\(\\)|std\\(\\)", featureName)]
dtFeatures$featureCode <- dtFeatures[, paste0("V", featureNum)]
head(dtFeatures)
dtFeatures$featureCode
select <- c(key(dt), dtFeatures$featureCode)
dt <- dt[, select, with = FALSE]
## Step 5: Use descriptive names
dtActivityNames <- fread(file.path(pathIn, "activity_labels.txt"))
setnames(dtActivityNames, names(dtActivityNames), c("activityNum", "activityName"))
## Step 6: Label with descriptive activity names
dt <- merge(dt, dtActivityNames, by = "activityNum", all.x = TRUE)
setkey(dt, subject, activityNum, activityName)
dt <- data.table(melt(dt, key(dt), variable.name = "featureCode"))
head(dt)
dt <- merge(dt, dtFeatures[, list(featureNum, featureCode, featureName)], by = "featureCode",
all.x = TRUE)
head(dt)
dt$activity <- factor(dt$activityName)
dt$feature <- factor(dt$featureName)
head(dt)
grepthis <- function(regex) {
grepl(regex, dt$feature)
}
## Features with 2 categories
n <- 2
y <- matrix(seq(1, n), nrow = n)
x <- matrix(c(grepthis("^t"), grepthis("^f")), ncol = nrow(y))
dt$featDomain <- factor(x %*% y, labels = c("Time", "Freq"))
x <- matrix(c(grepthis("Acc"), grepthis("Gyro")), ncol = nrow(y))
dt$featInstrument <- factor(x %*% y, labels = c("Accelerometer", "Gyroscope"))
x <- matrix(c(grepthis("BodyAcc"), grepthis("GravityAcc")), ncol = nrow(y))
dt$featAcceleration <- factor(x %*% y, labels = c(NA, "Body", "Gravity"))
x <- matrix(c(grepthis("mean()"), grepthis("std()")), ncol = nrow(y))
dt$featVariable <- factor(x %*% y, labels = c("Mean", "SD"))
## Features with 1 category
dt$featJerk <- factor(grepthis("Jerk"), labels = c(NA, "Jerk"))
dt$featMagnitude <- factor(grepthis("Mag"), labels = c(NA, "Magnitude"))
## Features with 3 categories
n <- 3
y <- matrix(seq(1, n), nrow = n)
x <- matrix(c(grepthis("-X"), grepthis("-Y"), grepthis("-Z")), ncol = nrow(y))
dt$featAxis <- factor(x %*% y, labels = c(NA, "X", "Y", "Z"))
head(dt)
r1 <- nrow(dt[, .N, by = c("feature")])
r2 <- nrow(dt[, .N, by = c("featDomain", "featAcceleration", "featInstrument",
"featJerk", "featMagnitude", "featVariable", "featAxis")])
r1 == r2
## Step 7: Create a Tidy dataset
setkey(dt, subject, activity, featDomain, featAcceleration, featInstrument,
featJerk, featMagnitude, featVariable, featAxis)
dtTidy <- dt[, list(count = .N, average = mean(value)), by = key(dt)]
head(dtTidy) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rm.R
\name{step_rm}
\alias{step_rm}
\alias{tidy.step_rm}
\title{General Variable Filter}
\usage{
step_rm(recipe, ..., role = NA, trained = FALSE, removals = NULL,
skip = FALSE, id = rand_id("rm"))
\method{tidy}{step_rm}(x, ...)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose which
variables that will evaluated by the filtering bake. See
\code{\link[=selections]{selections()}} for more details. For the \code{tidy}
method, these are not currently used.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{removals}{A character string that contains the names of
columns that should be removed. These values are not determined
until \code{\link[=prep.recipe]{prep.recipe()}} is called.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{prep.recipe()}} is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using \code{skip = TRUE} as it may affect
the computations for subsequent operations}
\item{id}{A character string that is unique to this step to identify it.}
\item{x}{A \code{step_rm} object.}
}
\value{
An updated version of \code{recipe} with the new step
added to the sequence of existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} which
is the columns that will be removed.
}
\description{
\code{step_rm} creates a \emph{specification} of a recipe step
that will remove variables based on their name, type, or role.
}
\examples{
data(biomass)
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr)
library(dplyr)
smaller_set <- rec \%>\%
step_rm(contains("gen"))
smaller_set <- prep(smaller_set, training = biomass_tr)
filtered_te <- bake(smaller_set, biomass_te)
filtered_te
tidy(smaller_set, number = 1)
}
\concept{preprocessing}
\concept{variable_filters}
\keyword{datagen}
| /man/step_rm.Rd | no_license | ewv88/recipes | R | false | true | 2,431 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rm.R
\name{step_rm}
\alias{step_rm}
\alias{tidy.step_rm}
\title{General Variable Filter}
\usage{
step_rm(recipe, ..., role = NA, trained = FALSE, removals = NULL,
skip = FALSE, id = rand_id("rm"))
\method{tidy}{step_rm}(x, ...)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose which
variables that will evaluated by the filtering bake. See
\code{\link[=selections]{selections()}} for more details. For the \code{tidy}
method, these are not currently used.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{removals}{A character string that contains the names of
columns that should be removed. These values are not determined
until \code{\link[=prep.recipe]{prep.recipe()}} is called.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{prep.recipe()}} is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using \code{skip = TRUE} as it may affect
the computations for subsequent operations}
\item{id}{A character string that is unique to this step to identify it.}
\item{x}{A \code{step_rm} object.}
}
\value{
An updated version of \code{recipe} with the new step
added to the sequence of existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} which
is the columns that will be removed.
}
\description{
\code{step_rm} creates a \emph{specification} of a recipe step
that will remove variables based on their name, type, or role.
}
\examples{
data(biomass)
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr)
library(dplyr)
smaller_set <- rec \%>\%
step_rm(contains("gen"))
smaller_set <- prep(smaller_set, training = biomass_tr)
filtered_te <- bake(smaller_set, biomass_te)
filtered_te
tidy(smaller_set, number = 1)
}
\concept{preprocessing}
\concept{variable_filters}
\keyword{datagen}
|
#position of landmarks
land_x <- c(1,2,2.5)
land_y <- c(1.5,1.5,0.7)
#situation in t-1: position, landmarks, and distance between pos and landmarks
plot(0,0,
main = "state t-1",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main =3)
lines(c(0,1), c(0,1.5), col="red")
lines(c(0,2), c(0,1.5), col="red")
lines(c(0,2.5), c(0,0.7), col="red")
points(land_x,land_y,col="blue", pch=19, cex=3)
#prediction step, position changes, landmarks stay, no distances between pos and landmarks
plot(c(0,1),c(0,0.5),
main = "precition step",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main = 3)
arrows(0,0,1,0.5,
length=0.2,
adj=1,
col="black",
lwd=3)
points(land_x,land_y,col="blue", pch=19, cex=3)
#update step: measure distances between new pos and landmarks, use for updates in algorithm
plot(c(0,1),c(0,0.5),
main = "update step",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main = 3)
arrows(0,0,1,0.5,
length=0.2,
adj=1,
col="black",
lwd=3)
lines(c(1,1), c(0.5,1.5), col="red")
lines(c(1,2), c(0.5,1.5), col="red")
lines(c(1,2.5), c(0.5,0.7), col="red")
points(land_x,land_y,col="blue", pch=19, cex=3)
| /data-cleaning/plot-second-milestone.R | permissive | YutingYao/DBPro-EKF-SLAM | R | false | false | 1,473 | r | #position of landmarks
land_x <- c(1,2,2.5)
land_y <- c(1.5,1.5,0.7)
#situation in t-1: position, landmarks, and distance between pos and landmarks
plot(0,0,
main = "state t-1",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main =3)
lines(c(0,1), c(0,1.5), col="red")
lines(c(0,2), c(0,1.5), col="red")
lines(c(0,2.5), c(0,0.7), col="red")
points(land_x,land_y,col="blue", pch=19, cex=3)
#prediction step, position changes, landmarks stay, no distances between pos and landmarks
plot(c(0,1),c(0,0.5),
main = "precition step",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main = 3)
arrows(0,0,1,0.5,
length=0.2,
adj=1,
col="black",
lwd=3)
points(land_x,land_y,col="blue", pch=19, cex=3)
#update step: measure distances between new pos and landmarks, use for updates in algorithm
plot(c(0,1),c(0,0.5),
main = "update step",
col="red",
type = "p",
xlab="x",
ylab="y",
xlim = c(-0.5,3),
ylim = c(-0.5,2),
pch=19,
cex = 2,
cex.main = 3)
arrows(0,0,1,0.5,
length=0.2,
adj=1,
col="black",
lwd=3)
lines(c(1,1), c(0.5,1.5), col="red")
lines(c(1,2), c(0.5,1.5), col="red")
lines(c(1,2.5), c(0.5,0.7), col="red")
points(land_x,land_y,col="blue", pch=19, cex=3)
|
library(aroma.affymetrix)
### Name: SnpPlm
### Title: The SnpPlm interface class
### Aliases: SnpPlm
### Keywords: classes
### ** Examples
for (zzz in 0) {
# Setup verbose output
verbose <- Arguments$getVerbose(-2)
timestampOn(verbose)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Define an example dataset using this path
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Find any SNP dataset
path <- NULL
if (is.null(path))
break
if (!exists("ds")) {
ds <- AffymetrixCelSet$fromFiles(path)
}
print(ds)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Create a set of various PLMs for this dataset
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (!exists("models", mode="list")) {
mergeStrands <- TRUE
models <- list(
rma = RmaSnpPlm(ds, mergeStrands=mergeStrands),
mbei = MbeiSnpPlm(ds, mergeStrands=mergeStrands)
# affine = AffineSnpPlm(ds, background=FALSE, mergeStrands=mergeStrands)
)
}
print(models)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# For each model, fit a few units
#
# Note, by fitting the same set of units across models, the internal
# caching mechanisms of aroma.affymetrix makes sure that the data is
# only read into memory once. See log for reading speed.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
units <- 55+1:100
for (model in models) {
ruler(verbose)
fit(model, units=units, force=TRUE, verbose=verbose)
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# For each unit, plot the estimated (thetaB,thetaA) for all models
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Should we plot the on the log scale?
log <- TRUE
# Do only user to press ENTER if more than one unit is plotted
opar <- par(ask=(length(units) > 1))
Alab <- expression(theta[A])
Blab <- expression(theta[B])
if (log) {
lim <- c(6, 16)
} else {
lim <- c(0, 2^15)
}
# For each unit...
for (unit in units) {
# For all models...
for (kk in seq_along(models)) {
ces <- getChipEffects(models[[kk]])
ceUnit <- ces[unit]
snpName <- names(ceUnit)[1]
theta <- ceUnit[[1]]
thetaA <- theta[[1]]$theta
thetaB <- theta[[2]]$theta
if (log) {
thetaA <- log(thetaA, base=2)
thetaB <- log(thetaB, base=2)
}
# Create the plot?
if (kk == 1) {
plot(NA, xlim=lim, ylim=lim, xlab=Blab, ylab=Alab, main=snpName)
abline(a=0, b=1, lty=2)
}
# Plot the estimated parameters
points(thetaB, thetaA, col=kk, pch=19)
}
} # for (unit ...)
# Reset graphical parameter settings
par(opar)
} # for (zzz in 0)
rm(zzz)
| /data/genthat_extracted_code/aroma.affymetrix/examples/SnpPlm.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 2,707 | r | library(aroma.affymetrix)
### Name: SnpPlm
### Title: The SnpPlm interface class
### Aliases: SnpPlm
### Keywords: classes
### ** Examples
for (zzz in 0) {
# Setup verbose output
verbose <- Arguments$getVerbose(-2)
timestampOn(verbose)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Define an example dataset using this path
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Find any SNP dataset
path <- NULL
if (is.null(path))
break
if (!exists("ds")) {
ds <- AffymetrixCelSet$fromFiles(path)
}
print(ds)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Create a set of various PLMs for this dataset
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if (!exists("models", mode="list")) {
mergeStrands <- TRUE
models <- list(
rma = RmaSnpPlm(ds, mergeStrands=mergeStrands),
mbei = MbeiSnpPlm(ds, mergeStrands=mergeStrands)
# affine = AffineSnpPlm(ds, background=FALSE, mergeStrands=mergeStrands)
)
}
print(models)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# For each model, fit a few units
#
# Note, by fitting the same set of units across models, the internal
# caching mechanisms of aroma.affymetrix makes sure that the data is
# only read into memory once. See log for reading speed.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
units <- 55+1:100
for (model in models) {
ruler(verbose)
fit(model, units=units, force=TRUE, verbose=verbose)
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# For each unit, plot the estimated (thetaB,thetaA) for all models
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Should we plot the on the log scale?
log <- TRUE
# Do only user to press ENTER if more than one unit is plotted
opar <- par(ask=(length(units) > 1))
Alab <- expression(theta[A])
Blab <- expression(theta[B])
if (log) {
lim <- c(6, 16)
} else {
lim <- c(0, 2^15)
}
# For each unit...
for (unit in units) {
# For all models...
for (kk in seq_along(models)) {
ces <- getChipEffects(models[[kk]])
ceUnit <- ces[unit]
snpName <- names(ceUnit)[1]
theta <- ceUnit[[1]]
thetaA <- theta[[1]]$theta
thetaB <- theta[[2]]$theta
if (log) {
thetaA <- log(thetaA, base=2)
thetaB <- log(thetaB, base=2)
}
# Create the plot?
if (kk == 1) {
plot(NA, xlim=lim, ylim=lim, xlab=Blab, ylab=Alab, main=snpName)
abline(a=0, b=1, lty=2)
}
# Plot the estimated parameters
points(thetaB, thetaA, col=kk, pch=19)
}
} # for (unit ...)
# Reset graphical parameter settings
par(opar)
} # for (zzz in 0)
rm(zzz)
|
## First get data. Not using nrow and skip as it messes with dataframe column names
df1<-read.table("household_power_consumption.txt",header=T,sep=";",na.strings="?")
df1$Date<-strptime(paste(df1$Date,df1$Time),"%d/%m/%Y %H:%M:%S")
df<-df1[df1$Date<strptime("2007-02-03","%Y-%m-%d"),-2]
rm(df1) ## Remove memory intensive df1
df<-df[df$Date>=strptime("2007-02-01","%Y-%m-%d"),]
## Now start the plotting of the graphs
par(mfrow<-c(2,2)) ## Make 2X2 graphs on screen
## Chart 1: Line Graph between Global Active Power vs Day
plot(df$Date,df$Global_active_power,type="l",xlab="",ylab="Global Active Power")
## Chart 2: Line Graph between Voltage vs Day
plot(df$Date,df$Voltage,type="l",xlab="datetime",ylab="Voltage")
## Chart 3: Energy Sub Metering graph vs Day
plot(df$Date,df$Sub_metering_1,type="n",xlab="",ylab="Energy sub metering")
lines(df$Date,df$Sub_metering_1,col="black")
lines(df$Date,df$Sub_metering_2,col="red")
lines(df$Date,df$Sub_metering_3,col="blue")
legend("topright",lty=1,cex=0.8,bty="n",y.intersp=0.2,xjust=1,col=c("black","red","blue"),legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
## Chart 4: Line Graph between Global Reactive Power vs Day
plot(df$Date,df$Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power")
dev.copy(png,file="plot4.png")
dev.off() | /plot4.R | no_license | sharath333/ExData_Plotting1 | R | false | false | 1,314 | r | ## First get data. Not using nrow and skip as it messes with dataframe column names
df1<-read.table("household_power_consumption.txt",header=T,sep=";",na.strings="?")
df1$Date<-strptime(paste(df1$Date,df1$Time),"%d/%m/%Y %H:%M:%S")
df<-df1[df1$Date<strptime("2007-02-03","%Y-%m-%d"),-2]
rm(df1) ## Remove memory intensive df1
df<-df[df$Date>=strptime("2007-02-01","%Y-%m-%d"),]
## Now start the plotting of the graphs
par(mfrow<-c(2,2)) ## Make 2X2 graphs on screen
## Chart 1: Line Graph between Global Active Power vs Day
plot(df$Date,df$Global_active_power,type="l",xlab="",ylab="Global Active Power")
## Chart 2: Line Graph between Voltage vs Day
plot(df$Date,df$Voltage,type="l",xlab="datetime",ylab="Voltage")
## Chart 3: Energy Sub Metering graph vs Day
plot(df$Date,df$Sub_metering_1,type="n",xlab="",ylab="Energy sub metering")
lines(df$Date,df$Sub_metering_1,col="black")
lines(df$Date,df$Sub_metering_2,col="red")
lines(df$Date,df$Sub_metering_3,col="blue")
legend("topright",lty=1,cex=0.8,bty="n",y.intersp=0.2,xjust=1,col=c("black","red","blue"),legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
## Chart 4: Line Graph between Global Reactive Power vs Day
plot(df$Date,df$Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power")
dev.copy(png,file="plot4.png")
dev.off() |
# Exercise 1: writing and executing functions
# Write a function `AddThree` that adds 3 to an input value
AddThree <- function(num){
return(num + 3)
}
# Create a variable `ten` by passing 7 to your `AddThree` function
ten <- AddThree(7)
# Write a function `FeetToMeters` that converts from feet to meters
FeetToMeters <- function(feet){
return(feet * 0.3048)
}
# Create a variable `height.in.feet` that is your height in feet
height.in.feet <- 5.5
# Create a variable `height.in.meters` by passing `height.in.feet` to your `FeetToMeters` function
height.in.meters <- FeetToMeters(height.in.feet) | /exercise-1/exercise.R | permissive | KyleAvalani/ch6-functions | R | false | false | 604 | r | # Exercise 1: writing and executing functions
# Write a function `AddThree` that adds 3 to an input value
AddThree <- function(num){
return(num + 3)
}
# Create a variable `ten` by passing 7 to your `AddThree` function
ten <- AddThree(7)
# Write a function `FeetToMeters` that converts from feet to meters
FeetToMeters <- function(feet){
return(feet * 0.3048)
}
# Create a variable `height.in.feet` that is your height in feet
height.in.feet <- 5.5
# Create a variable `height.in.meters` by passing `height.in.feet` to your `FeetToMeters` function
height.in.meters <- FeetToMeters(height.in.feet) |
library(gstat)
### Name: ncp.grid
### Title: Grid for the NCP, the Dutch part of the North Sea
### Aliases: ncp.grid
### Keywords: datasets
### ** Examples
data(ncp.grid)
summary(ncp.grid)
| /data/genthat_extracted_code/gstat/examples/ncp.grid.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 196 | r | library(gstat)
### Name: ncp.grid
### Title: Grid for the NCP, the Dutch part of the North Sea
### Aliases: ncp.grid
### Keywords: datasets
### ** Examples
data(ncp.grid)
summary(ncp.grid)
|
### Numerical gradient====
grad <- function(mod, par, ..., Interval=1e-6) {
mat <- matrix(par, nrow=length(par), ncol=length(par))
for (i in 1:ncol(mat)) {
mat[i,i] <- par[i] + Interval
}
df <- vector("numeric", length=length(par))
f_x <- mod(par, ...)
for (i in 1:ncol(mat)) {
df[i] <- (mod(mat[,i], ...) - f_x) / Interval
}
df[which(!is.finite(df))] <- 0
return(df)
return(df)
}
steep <- function(mod, par, ..., est, df) {
mod(est - {par[1]*df*{(abs(df) > sd(df)) * 1}} -
{par[2]*df*{(abs(df) <= sd(df)) * 1}}, ...)
}
steepA <- function(mod, par, ..., est, df) {
mod(est - {par*df}, ...)
}
Gammas <- function(mod, par, ..., est, df, vi, si, iter, epsilon) {
v <- {{par[1] * vi} + {{1 - par[1]} * df }}/{1 - {par[1] ^ iter}}
s <- {{par[2] * si} + {{1 - par[2]} * {df*df}}}/{1 - {par[2] ^ iter}}
newV <- est - {{par[3]*v}/{epsilon+sqrt(s)}}
return(mod(newV, ...))
}
sHAR <- function(par, mod, ...) {
theta <- rnorm(length(par))
d <- theta / sqrt(sum(theta*theta))
u <- suppressWarnings(optim(.5, steepA, mod=mod, df=d, ..., est=par,
method="Nelder-Mead")$par)
prop <- par - {u * d}
return(list("prop"=prop, "u"=u, "d"=d))
}
reltol <- function(x, tol) tol * (abs(x) + tol)
### Adam====
ADAM <- function(fn, startvalue, gr, ..., Interval=1e-6,
maxit=100, tol=1e-8, verbose=T) {
# Opening message
if(verbose==T) {
cat("Steepest Adam will run for ", maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
}
# Initial settings
epsilon <- Interval
if(verbose==T) {
pb <- txtProgressBar(min=0, max=maxit, style=3)
}
par <- s <- v <- G <- matrix(NA, nrow=maxit, ncol=length(startvalue))
GG <- matrix(NA, nrow=maxit, ncol=3)
f <- vector("numeric", length=maxit)
convergence = F
# First step
G[1,] <- gr(fn, startvalue, ..., Interval=Interval)
alpha <- suppressWarnings(unlist(optim(0, fn=steepA, mod=fn, df=G[1,],
..., est=startvalue,
method="Nelder-Mead")$par))
GG[1,] <- c(.5, .5, alpha)
v[1,] <- G[1,]*GG[1,1]
s[1,] <- G[1,]*GG[1,2]
par[1,] <- startvalue - {alpha*G[1,]}
f[1] <- fn(par[1,], ...)
if(verbose==T) {
setTxtProgressBar(pb, 1)
}
# Run ADAM algorithm
for(i in 2:maxit) {
G[i,] <- gr(fn, par[i-1,], ..., Interval=Interval)
GG[i,] <- suppressWarnings(unlist(optim(par=c(0,0,0), fn=Gammas, mod=fn,
df=G[i,], ..., est=par[i-1,],
vi=v[i-1,], si=s[i-1,], iter=i,
epsilon=epsilon)$par))
if (sum(abs(GG[i,])) == 0) {
GG[i,] <- suppressWarnings(unlist(ucminf(par=c(0,0,0), fn=Gammas, mod=fn,
df=G[i,], ..., est=par[i-1,],
vi=v[i-1,], si=s[i-1,], iter=i,
epsilon=epsilon)$par))
}
gammav <- GG[i,1]
gammas <- GG[i,2]
alpha <- GG[i,3]
v[i,] <- {{gammav * v[i-1,]} + {{1 - gammav} * G[i,] }}/{1 - {gammav^i}}
s[i,] <- {{gammas * s[i-1,]} + {{1 - gammas} * {G[i,]*G[i,]}}}/{1 - {gammas^i}}
par[i,] <- par[i-1,] - {{alpha*v[i,]}/{epsilon+sqrt(s[i,])}}
f[i] <- fn(par[i,], ...)
# Check convergence
if (reltol(f[i], tol) > abs(f[i] - f[i-1])) {
convergence = T
if (verbose==T) {
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
}
break
} else {
if (verbose==T) {
setTxtProgressBar(pb, i)
}
}
}
if ({convergence==F} & {verbose==T}) {
cat("\nConvergence may not have been achieved!")
}
if (i < maxit) {
f <- f[-c({i+1}:maxit)]
par <- as.matrix(par[-c({i+1}:maxit),])
G <- G[-c({i+1}:maxit),]
s <- s[-c({i+1}:maxit),]
v <- v[-c({i+1}:maxit),]
GG <- GG[-c({i+1}:maxit),]
}
if (verbose==T) {
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2),
" secs for the run to finish. \n", sep="")
}
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=G,
"DSG"=s, "Momentum"=v, "par"=par[which.min(f),],
"value"=f[which.min(f)], "steps"=GG)
return(Results)
}
### Steepest 2-group Gradient Descent====
STDM <- function(fn, startvalue, gr, ..., Interval=1e-6, maxit=100, tol=1e-8,
verbose=T) {
# Opening message
if(verbose==T) {
cat("Steepest 2-group Gradient Descent will run for ",
maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
}
# Initial settings
if(verbose==T) {
pb <- txtProgressBar(min=0, max=maxit, style=3)
}
#par <- df <- array(dim = c(maxit,length(startvalue)))
par <- df <- matrix(NA, nrow=maxit, ncol=length(startvalue))
f <- vector("numeric", length=maxit)
step <- array(dim = c(maxit,2))
convergence = F
# First step
df[1,] <- gr(fn, startvalue, ..., Interval=Interval)
step[1,] <- suppressWarnings(unlist(optim(c(0,0), fn=steep, mod=fn,
..., df=df[1,], est=startvalue,
method="Nelder-Mead")$par))
par[1,] <- startvalue -
{step[1,1]*df[1,]*{(abs(df[1,]) > sd(df[1,])) * 1}} -
{step[1,2]*df[1,]*{(abs(df[1,]) <= sd(df[1,])) * 1}}
f[1] <- fn(par[1,], ...)
if(verbose==T) {
setTxtProgressBar(pb, 1)
}
# Start estimation
for(run in 2:maxit) {
# Calculate gradient and estimate step parameter
df[run,] <- gr(fn, par[run-1,], ..., Interval=Interval)
step[run,] <- suppressWarnings(unlist(optim(step[run-1,], fn=steep, mod=fn,
..., df=df[run,], est=par[run-1,],
method="Nelder-Mead")$par))
if (sum(abs(step[run,])) == 0) {
step[run,] <- suppressWarnings(unlist(ucminf(par=c(0,0), fn=steep, mod=fn,
..., df=df[run,],
est=par[run-1,])$par))
}
par[run,] <- par[run-1,] -
{step[run,1]*df[run,]*{(abs(df[run,]) > sd(df[run,])) * 1}} -
{step[run,2]*df[run,]*{(abs(df[run,]) <= sd(df[run,])) * 1}}
f[run] <- fn(par[run,], ...)
# Check convergence
if (reltol(f[run], tol) > abs(f[run] - f[run-1])) {
convergence = T
if(verbose==T) {
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
}
break
} else {
if(verbose==T) {
setTxtProgressBar(pb, run)
}
}
}
if ({convergence==F} && {verbose==T}) {
cat("\nConvergence may not have been achieved!")
}
if (run < maxit) {
f <- f[-c({run+1}:maxit)]
par <- par[-c({run+1}:maxit),]
df <- df[-c({run+1}:maxit),]
step <- step[-c({run+1}:maxit),]
}
if(verbose==T) {
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2)," secs for the run to finish. \n",
sep="")
}
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=df,
"par"=par[which.min(f),], "value"=f[which.min(f)],
"steps"=step)
return(Results)
}
### Steepest Hit-and-Run Gradient Descent====
HRGD <- function(fn, startvalue, gr, ..., Interval=1e-6, maxit=100) {
# Opening message
cat("Hit-and-Run Gradient Descent will run for ",
maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
# Initial settings
pb <- txtProgressBar(min=0, max=maxit, style=3)
par <- df <- array(dim = c(maxit,length(startvalue)))
f <- vector("numeric", length=maxit)
step <- vector("numeric", length=maxit)
convergence = F
# First step
df[1,] <- gr(fn, startvalue, ..., Interval=Interval)
SS <- suppressWarnings(unlist(optim(c(0,0), fn=steep, mod=fn,
..., df=df[1,], est=startvalue,
method="Nelder-Mead")$par))
step[1] <- mean(SS)
par[1,] <- startvalue -
{SS[1]*df[1,]*{(abs(df[1,]) > sd(df[1,])) * 1}} -
{SS[2]*df[1,]*{(abs(df[1,]) <= sd(df[1,])) * 1}}
f[1] <- fn(par[1,], ...)
setTxtProgressBar(pb, 1)
# Start estimation
for(run in 2:maxit) {
# Calculate gradient and estimate step parameter
temp <- sHAR(par=par[run-1,], mod=fn, ...)
df[run,] <- temp$d
step[run] <- temp$u
par[run,] <- temp$prop
f[run] <- fn(par[run,], ...)
# Check convergence
if ({step[run] - step[run-1]} == 0) {
convergence = T
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
break
} else { setTxtProgressBar(pb, run) }
}
if (convergence == F) {
cat("\nConvergence may not have been achieved!")
}
if (run < maxit) {
f <- f[-c({run+1}:maxit)]
par <- par[-c({run+1}:maxit),]
df <- df[-c({run+1}:maxit),]
step <- step[-c({run+1}:maxit)]
}
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2)," secs for the run to finish. \n", sep="")
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=df,
"par"=par[which.min(f),], "value"=f[which.min(f)], "steps"=step)
return(Results)
}
| /R/algorithms.R | no_license | cran/optimg | R | false | false | 10,029 | r | ### Numerical gradient====
grad <- function(mod, par, ..., Interval=1e-6) {
mat <- matrix(par, nrow=length(par), ncol=length(par))
for (i in 1:ncol(mat)) {
mat[i,i] <- par[i] + Interval
}
df <- vector("numeric", length=length(par))
f_x <- mod(par, ...)
for (i in 1:ncol(mat)) {
df[i] <- (mod(mat[,i], ...) - f_x) / Interval
}
df[which(!is.finite(df))] <- 0
return(df)
return(df)
}
steep <- function(mod, par, ..., est, df) {
mod(est - {par[1]*df*{(abs(df) > sd(df)) * 1}} -
{par[2]*df*{(abs(df) <= sd(df)) * 1}}, ...)
}
steepA <- function(mod, par, ..., est, df) {
mod(est - {par*df}, ...)
}
Gammas <- function(mod, par, ..., est, df, vi, si, iter, epsilon) {
v <- {{par[1] * vi} + {{1 - par[1]} * df }}/{1 - {par[1] ^ iter}}
s <- {{par[2] * si} + {{1 - par[2]} * {df*df}}}/{1 - {par[2] ^ iter}}
newV <- est - {{par[3]*v}/{epsilon+sqrt(s)}}
return(mod(newV, ...))
}
sHAR <- function(par, mod, ...) {
theta <- rnorm(length(par))
d <- theta / sqrt(sum(theta*theta))
u <- suppressWarnings(optim(.5, steepA, mod=mod, df=d, ..., est=par,
method="Nelder-Mead")$par)
prop <- par - {u * d}
return(list("prop"=prop, "u"=u, "d"=d))
}
reltol <- function(x, tol) tol * (abs(x) + tol)
### Adam====
ADAM <- function(fn, startvalue, gr, ..., Interval=1e-6,
maxit=100, tol=1e-8, verbose=T) {
# Opening message
if(verbose==T) {
cat("Steepest Adam will run for ", maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
}
# Initial settings
epsilon <- Interval
if(verbose==T) {
pb <- txtProgressBar(min=0, max=maxit, style=3)
}
par <- s <- v <- G <- matrix(NA, nrow=maxit, ncol=length(startvalue))
GG <- matrix(NA, nrow=maxit, ncol=3)
f <- vector("numeric", length=maxit)
convergence = F
# First step
G[1,] <- gr(fn, startvalue, ..., Interval=Interval)
alpha <- suppressWarnings(unlist(optim(0, fn=steepA, mod=fn, df=G[1,],
..., est=startvalue,
method="Nelder-Mead")$par))
GG[1,] <- c(.5, .5, alpha)
v[1,] <- G[1,]*GG[1,1]
s[1,] <- G[1,]*GG[1,2]
par[1,] <- startvalue - {alpha*G[1,]}
f[1] <- fn(par[1,], ...)
if(verbose==T) {
setTxtProgressBar(pb, 1)
}
# Run ADAM algorithm
for(i in 2:maxit) {
G[i,] <- gr(fn, par[i-1,], ..., Interval=Interval)
GG[i,] <- suppressWarnings(unlist(optim(par=c(0,0,0), fn=Gammas, mod=fn,
df=G[i,], ..., est=par[i-1,],
vi=v[i-1,], si=s[i-1,], iter=i,
epsilon=epsilon)$par))
if (sum(abs(GG[i,])) == 0) {
GG[i,] <- suppressWarnings(unlist(ucminf(par=c(0,0,0), fn=Gammas, mod=fn,
df=G[i,], ..., est=par[i-1,],
vi=v[i-1,], si=s[i-1,], iter=i,
epsilon=epsilon)$par))
}
gammav <- GG[i,1]
gammas <- GG[i,2]
alpha <- GG[i,3]
v[i,] <- {{gammav * v[i-1,]} + {{1 - gammav} * G[i,] }}/{1 - {gammav^i}}
s[i,] <- {{gammas * s[i-1,]} + {{1 - gammas} * {G[i,]*G[i,]}}}/{1 - {gammas^i}}
par[i,] <- par[i-1,] - {{alpha*v[i,]}/{epsilon+sqrt(s[i,])}}
f[i] <- fn(par[i,], ...)
# Check convergence
if (reltol(f[i], tol) > abs(f[i] - f[i-1])) {
convergence = T
if (verbose==T) {
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
}
break
} else {
if (verbose==T) {
setTxtProgressBar(pb, i)
}
}
}
if ({convergence==F} & {verbose==T}) {
cat("\nConvergence may not have been achieved!")
}
if (i < maxit) {
f <- f[-c({i+1}:maxit)]
par <- as.matrix(par[-c({i+1}:maxit),])
G <- G[-c({i+1}:maxit),]
s <- s[-c({i+1}:maxit),]
v <- v[-c({i+1}:maxit),]
GG <- GG[-c({i+1}:maxit),]
}
if (verbose==T) {
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2),
" secs for the run to finish. \n", sep="")
}
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=G,
"DSG"=s, "Momentum"=v, "par"=par[which.min(f),],
"value"=f[which.min(f)], "steps"=GG)
return(Results)
}
### Steepest 2-group Gradient Descent====
STDM <- function(fn, startvalue, gr, ..., Interval=1e-6, maxit=100, tol=1e-8,
verbose=T) {
# Opening message
if(verbose==T) {
cat("Steepest 2-group Gradient Descent will run for ",
maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
}
# Initial settings
if(verbose==T) {
pb <- txtProgressBar(min=0, max=maxit, style=3)
}
#par <- df <- array(dim = c(maxit,length(startvalue)))
par <- df <- matrix(NA, nrow=maxit, ncol=length(startvalue))
f <- vector("numeric", length=maxit)
step <- array(dim = c(maxit,2))
convergence = F
# First step
df[1,] <- gr(fn, startvalue, ..., Interval=Interval)
step[1,] <- suppressWarnings(unlist(optim(c(0,0), fn=steep, mod=fn,
..., df=df[1,], est=startvalue,
method="Nelder-Mead")$par))
par[1,] <- startvalue -
{step[1,1]*df[1,]*{(abs(df[1,]) > sd(df[1,])) * 1}} -
{step[1,2]*df[1,]*{(abs(df[1,]) <= sd(df[1,])) * 1}}
f[1] <- fn(par[1,], ...)
if(verbose==T) {
setTxtProgressBar(pb, 1)
}
# Start estimation
for(run in 2:maxit) {
# Calculate gradient and estimate step parameter
df[run,] <- gr(fn, par[run-1,], ..., Interval=Interval)
step[run,] <- suppressWarnings(unlist(optim(step[run-1,], fn=steep, mod=fn,
..., df=df[run,], est=par[run-1,],
method="Nelder-Mead")$par))
if (sum(abs(step[run,])) == 0) {
step[run,] <- suppressWarnings(unlist(ucminf(par=c(0,0), fn=steep, mod=fn,
..., df=df[run,],
est=par[run-1,])$par))
}
par[run,] <- par[run-1,] -
{step[run,1]*df[run,]*{(abs(df[run,]) > sd(df[run,])) * 1}} -
{step[run,2]*df[run,]*{(abs(df[run,]) <= sd(df[run,])) * 1}}
f[run] <- fn(par[run,], ...)
# Check convergence
if (reltol(f[run], tol) > abs(f[run] - f[run-1])) {
convergence = T
if(verbose==T) {
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
}
break
} else {
if(verbose==T) {
setTxtProgressBar(pb, run)
}
}
}
if ({convergence==F} && {verbose==T}) {
cat("\nConvergence may not have been achieved!")
}
if (run < maxit) {
f <- f[-c({run+1}:maxit)]
par <- par[-c({run+1}:maxit),]
df <- df[-c({run+1}:maxit),]
step <- step[-c({run+1}:maxit),]
}
if(verbose==T) {
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2)," secs for the run to finish. \n",
sep="")
}
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=df,
"par"=par[which.min(f),], "value"=f[which.min(f)],
"steps"=step)
return(Results)
}
### Steepest Hit-and-Run Gradient Descent====
HRGD <- function(fn, startvalue, gr, ..., Interval=1e-6, maxit=100) {
# Opening message
cat("Hit-and-Run Gradient Descent will run for ",
maxit, " iterations at most.\n\n", sep="")
startTime = proc.time()
# Initial settings
pb <- txtProgressBar(min=0, max=maxit, style=3)
par <- df <- array(dim = c(maxit,length(startvalue)))
f <- vector("numeric", length=maxit)
step <- vector("numeric", length=maxit)
convergence = F
# First step
df[1,] <- gr(fn, startvalue, ..., Interval=Interval)
SS <- suppressWarnings(unlist(optim(c(0,0), fn=steep, mod=fn,
..., df=df[1,], est=startvalue,
method="Nelder-Mead")$par))
step[1] <- mean(SS)
par[1,] <- startvalue -
{SS[1]*df[1,]*{(abs(df[1,]) > sd(df[1,])) * 1}} -
{SS[2]*df[1,]*{(abs(df[1,]) <= sd(df[1,])) * 1}}
f[1] <- fn(par[1,], ...)
setTxtProgressBar(pb, 1)
# Start estimation
for(run in 2:maxit) {
# Calculate gradient and estimate step parameter
temp <- sHAR(par=par[run-1,], mod=fn, ...)
df[run,] <- temp$d
step[run] <- temp$u
par[run,] <- temp$prop
f[run] <- fn(par[run,], ...)
# Check convergence
if ({step[run] - step[run-1]} == 0) {
convergence = T
setTxtProgressBar(pb, maxit)
cat("\nConvergence achieved!")
break
} else { setTxtProgressBar(pb, run) }
}
if (convergence == F) {
cat("\nConvergence may not have been achieved!")
}
if (run < maxit) {
f <- f[-c({run+1}:maxit)]
par <- par[-c({run+1}:maxit),]
df <- df[-c({run+1}:maxit),]
step <- step[-c({run+1}:maxit)]
}
close(pb)
cat("\n")
# Final messages
stopTime = proc.time()
elapsedTime = stopTime - startTime
cat("It took ",round(elapsedTime[3],2)," secs for the run to finish. \n", sep="")
# Return results
Results <- list("Cost"=f, "Estimates"=par, "Gradients"=df,
"par"=par[which.min(f),], "value"=f[which.min(f)], "steps"=step)
return(Results)
}
|
#' Gets starting state for scenario
#'
#' @param parameters base model parameters
#' @param starting_conditions a list specifying total_prevalence, total_population, cumulative_incidence, and proportion_vaccinated
#' @param scenario.specs a list which contains a strains.list which is a list of streans, each specifying
#' rel.transmissability. rel.severity, import.rate, frac.prevalence, and frac.cum.inc
#'
#' @return starting state vector
#' @export
get_starting_state_from_scenario = function(parameters,
starting_conditions,
scenario.specs){
with(as.list(c(starting_conditions, scenario.specs)),{
strain_prevalence_frac = get_attribute_from_specs(strains.list, "frac.prevalence")
immune_frac = create_immune_distribution(immune.list,
strains.list,
vaccination.list,
cumulative_incidence,
proportion_vaccinated)
get_starting_state(parameters,
starting_conditions,
immune_frac,
strain_prevalence_frac)
})
}
| /HutchCOVID/R/get_starting_state_from_scenario.R | no_license | FredHutch/COVID_modeling_schools | R | false | false | 1,287 | r | #' Gets starting state for scenario
#'
#' @param parameters base model parameters
#' @param starting_conditions a list specifying total_prevalence, total_population, cumulative_incidence, and proportion_vaccinated
#' @param scenario.specs a list which contains a strains.list which is a list of streans, each specifying
#' rel.transmissability. rel.severity, import.rate, frac.prevalence, and frac.cum.inc
#'
#' @return starting state vector
#' @export
get_starting_state_from_scenario = function(parameters,
starting_conditions,
scenario.specs){
with(as.list(c(starting_conditions, scenario.specs)),{
strain_prevalence_frac = get_attribute_from_specs(strains.list, "frac.prevalence")
immune_frac = create_immune_distribution(immune.list,
strains.list,
vaccination.list,
cumulative_incidence,
proportion_vaccinated)
get_starting_state(parameters,
starting_conditions,
immune_frac,
strain_prevalence_frac)
})
}
|
shinyUI(navbarPage("Exponential Distribution Simulation Navbar!",
tabPanel("Plot",
sidebarLayout(
sidebarPanel(
h2('Exponential Distribution Simulation Settings'),
sliderInput('lambda', 'Numeric input, labeled lambda', 0.05, min = 0.05, max = 1, step = 0.05),
numericInput('popSize', "Population Size",10,min=10,max=100,step=10),
numericInput('repTimes', "Repetition Times",100,min=100,max=2000,step=100),
checkboxInput("meanLine", "Show the Mean Line", FALSE)
),
mainPanel(
h2('Distribution of Average'),
plotOutput('figure')
)
)
),
tabPanel("Supporting Document",
h3("This application aims to do simulations on exponential distribution and Central Limit Theorem."),
p("1.Users can set the values of lambda,population size(step by 10) and repetition times of exponential simulation(step by 100). The generalized averages of each population are calculated and demonstrated in a histogram."),
p("2.Users can choose to add the mean line or not in order to make comparisons between theoretical mean and simulation result.") )
)
)
| /ui.r | no_license | richardsun-voyager/ExponentialDistributionApp | R | false | false | 1,108 | r | shinyUI(navbarPage("Exponential Distribution Simulation Navbar!",
tabPanel("Plot",
sidebarLayout(
sidebarPanel(
h2('Exponential Distribution Simulation Settings'),
sliderInput('lambda', 'Numeric input, labeled lambda', 0.05, min = 0.05, max = 1, step = 0.05),
numericInput('popSize', "Population Size",10,min=10,max=100,step=10),
numericInput('repTimes', "Repetition Times",100,min=100,max=2000,step=100),
checkboxInput("meanLine", "Show the Mean Line", FALSE)
),
mainPanel(
h2('Distribution of Average'),
plotOutput('figure')
)
)
),
tabPanel("Supporting Document",
h3("This application aims to do simulations on exponential distribution and Central Limit Theorem."),
p("1.Users can set the values of lambda,population size(step by 10) and repetition times of exponential simulation(step by 100). The generalized averages of each population are calculated and demonstrated in a histogram."),
p("2.Users can choose to add the mean line or not in order to make comparisons between theoretical mean and simulation result.") )
)
)
|
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
library(texblocks)
library(texPreview)
## ----include=FALSE-------------------------------------------------------
tex_opts$set(returnType = 'html')
tex_opts$append(list(cleanup='tex'))
## ------------------------------------------------------------------------
x <- as.tb('$\\alpha$')
y <- as.tb('aaa')
z <- as.tb('bbb')
## ------------------------------------------------------------------------
x1 <- x + y + z
x2 <- x1 / x1 # 2x3 object
x3 <- x2 / x2 # 4x3 object
k1 <- lapply(1:3,as.tb)
k2 <- lapply(4:6,as.tb)
k <- purrr::reduce(k1,`+`) / purrr::reduce(k2,`+`)
title <- c('param',sprintf('col%s',1:5))%>%
purrr::map(as.tb)%>%
purrr::reduce(`+`)
## ----hline---------------------------------------------------------------
(title / (x2 + x3))%>%
hline()%>%
tabular()%>%
texPreview::tex_preview()
## ----hline2--------------------------------------------------------------
(title / (x2 + x3))%>%
hline(lines = c(2,3))%>%
tabular()%>%
texPreview::tex_preview()
## ------------------------------------------------------------------------
l <- list(c(line=1,i=2,j=3),c(line=2,i=1,j=2),c(line=3,i=2,j=3))
d <- data.frame(line=1:3,i=c(1,2,3),j=c(1,2,3))
## ----cline---------------------------------------------------------------
purrr::reduce(rep(x1,4),`/`)%>%
cline(l)%>%
tabular()%>%
texPreview::tex_preview()
purrr::reduce(rep(x1,4),`/`)%>%
cline(d)%>%
tabular()%>%
texPreview::tex_preview()
## ----lines_pipe----------------------------------------------------------
purrr::reduce(rep(x1,4),`/`)%>%
hline(c(0,4))%>%
cline(d)%>%
tabular()%>%
texPreview::tex_preview()
## ----multirow,echo=TRUE,results='asis'-----------------------------------
title <- as.tb('param') + multicol('vals',3,'c')
(title / (multirow('$\\beta$',2) + k))%>%
tabular()%>%
texPreview::tex_preview()
| /vignettes/aesthetics.R | permissive | yonicd/texblocks | R | false | false | 2,051 | r | ## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
library(texblocks)
library(texPreview)
## ----include=FALSE-------------------------------------------------------
tex_opts$set(returnType = 'html')
tex_opts$append(list(cleanup='tex'))
## ------------------------------------------------------------------------
x <- as.tb('$\\alpha$')
y <- as.tb('aaa')
z <- as.tb('bbb')
## ------------------------------------------------------------------------
x1 <- x + y + z
x2 <- x1 / x1 # 2x3 object
x3 <- x2 / x2 # 4x3 object
k1 <- lapply(1:3,as.tb)
k2 <- lapply(4:6,as.tb)
k <- purrr::reduce(k1,`+`) / purrr::reduce(k2,`+`)
title <- c('param',sprintf('col%s',1:5))%>%
purrr::map(as.tb)%>%
purrr::reduce(`+`)
## ----hline---------------------------------------------------------------
(title / (x2 + x3))%>%
hline()%>%
tabular()%>%
texPreview::tex_preview()
## ----hline2--------------------------------------------------------------
(title / (x2 + x3))%>%
hline(lines = c(2,3))%>%
tabular()%>%
texPreview::tex_preview()
## ------------------------------------------------------------------------
l <- list(c(line=1,i=2,j=3),c(line=2,i=1,j=2),c(line=3,i=2,j=3))
d <- data.frame(line=1:3,i=c(1,2,3),j=c(1,2,3))
## ----cline---------------------------------------------------------------
purrr::reduce(rep(x1,4),`/`)%>%
cline(l)%>%
tabular()%>%
texPreview::tex_preview()
purrr::reduce(rep(x1,4),`/`)%>%
cline(d)%>%
tabular()%>%
texPreview::tex_preview()
## ----lines_pipe----------------------------------------------------------
purrr::reduce(rep(x1,4),`/`)%>%
hline(c(0,4))%>%
cline(d)%>%
tabular()%>%
texPreview::tex_preview()
## ----multirow,echo=TRUE,results='asis'-----------------------------------
title <- as.tb('param') + multicol('vals',3,'c')
(title / (multirow('$\\beta$',2) + k))%>%
tabular()%>%
texPreview::tex_preview()
|
####### task ############
## data cleaning and exploration
## wilcox testgg
library(readr)
library(ggplot2)
library(tidyverse)
library(rcompanion)
usertype_revenue <- read_csv("Downloads/R_shiny_app_GoogleTrends/Seleted dataset/Data_Usertype_Revenue.csv",
col_types = cols(Revenue = col_character()))
View(usertype_revenue)
######## clean the data and remove symbols from the rows and leave only numbers and characters
usertype_revenue$Revenue <- substring(usertype_revenue$Revenue, first = 2) # subset characters beginning from 1nd string to take out $ sign
View(usertype_revenue)
# Dataset$`Ecommerce Conversion Rate` <- substr(Dataset$`Ecommerce Conversion Rate`, 1, nchar(Dataset$`Ecommerce Conversion Rate`) - 1) # retains 1st to 4th characters and leaveout % sign
# Dataset$`Bounce Rate` <- substr(Dataset$`Bounce Rate`, 1, nchar(Dataset$`Bounce Rate`) - 1) # retains 1st to 4th characters and leaveout % sign
# View(Dataset)
########
#extract first 5 chars
char_first5 <- substr(usertype_revenue$`Day Index`, 1, 5) ## use for Day Index col chracters of length 7
# extarct first 6 chars
char_first6 <- substr(usertype_revenue$`Day Index`, 1, 6) ## use for Day index col characters of length 8
#extract last 2 chars
char_last2 <- substring(usertype_revenue$`Day Index`, first = nchar(usertype_revenue$`Day Index`) - 1) ## concatenate at the end of values in Day Index colume
char_last2
################ Formating Day Index col with 7 characters into Date format ####################
filter_char7 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 7) ## filter Day Index col with 7 strings
filter_char7_first5 <- substr(filter_char7$`Day Index`, 1, 5) ## Extract 1st 5 strings from col
filter_char7_last2 <- substring(filter_char7$`Day Index`, first = nchar(filter_char7$`Day Index`) - 1) #Extract last 2 strings of dataset in Day Index colume
filter_char7Re <- mutate(filter_char7, fill_char7Date = paste0("0", filter_char7_first5, "20", filter_char7_last2)) #create col and concatenate strings into it to form Date
colnames(filter_char7Re)[5] <- "Date" ## rename col as Date
View(filter_char7Re)
View(filter_char7)
View(filter_char7_last2)
################ Formating Day Index col with 8 characters into Date format ####################
filter_char8 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 8) ## filter Day Index col with 8 strings
filter_char8_first6 <- substr(filter_char8$`Day Index`, 1, 6) ## Extract 1st 6 strings from col
filter_char8_last2 <- substring(filter_char8$`Day Index`, first = nchar(filter_char8$`Day Index`) - 1) #Extract last 2 strings of dataset in Day Index colume
filter_char8Re <- mutate(filter_char8, fill_char8Date = paste0(filter_char8_first6, "20", filter_char8_last2)) #create col and concatenate strings into it to form Date
colnames(filter_char8Re)[5] <- "Date" ## rename col as Date
View(filter_char8)
View(filter_char8Re)
colnames(filter_char)
################ Formating Day Index col with 10 characters into Date format ####################
filter_char10 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 10) ## filter Day Index col with 10 strings
filter_char10["Date"] <- filter_char10$`Day Index` ## create Date col and assign the values of Day Index col to it
########binding rows of datasets ##############################################
GA_userType_Revenue_Dataset <- bind_rows(filter_char7Re, filter_char8Re, filter_char10) ## bind rows of all datasets filtered
View(GA_userType_Revenue_Dataset)
#### Exporting dataset as csv
write.csv(GA_userType_Revenue_Dataset, file = "Users\\lin\\Downloads\\R_shiny_app_GoogleTrends\\GA_userType_Revenue_Dataset.csv")
################## data exploration ####################
View(GA_userType_Revenue_Dataset)
GA_userType_Revenue_Dataset <- read_csv("GA_userType_Revenue_Dataset.csv",
col_types = cols(Date = col_date(format = "%m/%d/%Y")))
#View(GA_userType_Revenue_Dataset)
#ggplot(data = GA_userType_Revenue_Dataset, aes(x = Segment, y = "Revenue")) + geom_histogram()
table(GA_userType_Revenue_Dataset$Segment)
data_usertype <- GA_userType_Revenue_Dataset
ggplot(data = GA_userType_Revenue_Dataset, aes(x = Segment)) + geom_bar()
summary(data_usertype$Revenue)
quantile(data_usertype$Transactions)
summary(data_usertype$Transactions)
var(data_usertype$Transactions)
View(data_usertype)
ggplot(data_usertype, aes(x = Segment,y = Revenue)) + geom_boxplot()
ggplot(data_usertype, aes(x = Segment, y = Transactions)) + geom_boxplot() + coord_flip()
ggplot(data_usertype, aes(Transactions)) + geom_histogram()
ggplot(data_usertype, aes(Revenue)) + geom_histogram()
(transaction_ttest <- t.test(Transactions ~ Segment, data = data_usertype)) ## T test for mean of transactions between
(revenue_ttest <- t.test(Revenue ~ Segment, data = data_usertype))
## wilcon test for testing mean difference for non-normal distribution
(transaction_wilcox <- wilcox.test(Transactions ~ Segment, data = data_usertype))
(revenue_wilcox <- wilcox.test(Revenue ~ Segment, data = data_usertype))
plotNormalDensity(data_usertype$Revenue)
transformTukey(data_usertype$Revenue)
outliers::outlier(data_usertype$Transactions)
outliers::outlier(data_usertype$Revenue)
outliers::outlier(data_usertype$Transactions, opposite = TRUE)
# shapiro.test(data_usertype$Transactions)
# shapiro.test(transactions_transform)
# leveneTest(y = data_usertype$Transactions, group = data_usertype$Segment, data = data_usertype)
# fligner.test(x = data_usertype$Transactions, group = data_usertype$Segment, data = data_usertype )
plotNormalHistogram(data_usertype$Transactions)
(transactions_transform <- rcompanion::transformTukey(data_usertype$Transactions))
(transboxplot <- boxplot(data_usertype$Transactions))
(outlierrem <- rm.outlier(data_usertype[,5]))
View(outlierrem)
boxplot(outlierrem$Revenue)
plotDensityHistogram(outlierrem$Revenue)
| /Usertype_Revenue_data_wraggling.R | no_license | agbleze/R_shiny_app_GoogleTrends | R | false | false | 5,965 | r | ####### task ############
## data cleaning and exploration
## wilcox testgg
library(readr)
library(ggplot2)
library(tidyverse)
library(rcompanion)
usertype_revenue <- read_csv("Downloads/R_shiny_app_GoogleTrends/Seleted dataset/Data_Usertype_Revenue.csv",
col_types = cols(Revenue = col_character()))
View(usertype_revenue)
######## clean the data and remove symbols from the rows and leave only numbers and characters
usertype_revenue$Revenue <- substring(usertype_revenue$Revenue, first = 2) # subset characters beginning from 1nd string to take out $ sign
View(usertype_revenue)
# Dataset$`Ecommerce Conversion Rate` <- substr(Dataset$`Ecommerce Conversion Rate`, 1, nchar(Dataset$`Ecommerce Conversion Rate`) - 1) # retains 1st to 4th characters and leaveout % sign
# Dataset$`Bounce Rate` <- substr(Dataset$`Bounce Rate`, 1, nchar(Dataset$`Bounce Rate`) - 1) # retains 1st to 4th characters and leaveout % sign
# View(Dataset)
########
#extract first 5 chars
char_first5 <- substr(usertype_revenue$`Day Index`, 1, 5) ## use for Day Index col chracters of length 7
# extarct first 6 chars
char_first6 <- substr(usertype_revenue$`Day Index`, 1, 6) ## use for Day index col characters of length 8
#extract last 2 chars
char_last2 <- substring(usertype_revenue$`Day Index`, first = nchar(usertype_revenue$`Day Index`) - 1) ## concatenate at the end of values in Day Index colume
char_last2
################ Formating Day Index col with 7 characters into Date format ####################
filter_char7 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 7) ## filter Day Index col with 7 strings
filter_char7_first5 <- substr(filter_char7$`Day Index`, 1, 5) ## Extract 1st 5 strings from col
filter_char7_last2 <- substring(filter_char7$`Day Index`, first = nchar(filter_char7$`Day Index`) - 1) #Extract last 2 strings of dataset in Day Index colume
filter_char7Re <- mutate(filter_char7, fill_char7Date = paste0("0", filter_char7_first5, "20", filter_char7_last2)) #create col and concatenate strings into it to form Date
colnames(filter_char7Re)[5] <- "Date" ## rename col as Date
View(filter_char7Re)
View(filter_char7)
View(filter_char7_last2)
################ Formating Day Index col with 8 characters into Date format ####################
filter_char8 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 8) ## filter Day Index col with 8 strings
filter_char8_first6 <- substr(filter_char8$`Day Index`, 1, 6) ## Extract 1st 6 strings from col
filter_char8_last2 <- substring(filter_char8$`Day Index`, first = nchar(filter_char8$`Day Index`) - 1) #Extract last 2 strings of dataset in Day Index colume
filter_char8Re <- mutate(filter_char8, fill_char8Date = paste0(filter_char8_first6, "20", filter_char8_last2)) #create col and concatenate strings into it to form Date
colnames(filter_char8Re)[5] <- "Date" ## rename col as Date
View(filter_char8)
View(filter_char8Re)
colnames(filter_char)
################ Formating Day Index col with 10 characters into Date format ####################
filter_char10 <- filter(usertype_revenue, nchar(usertype_revenue$`Day Index`) == 10) ## filter Day Index col with 10 strings
filter_char10["Date"] <- filter_char10$`Day Index` ## create Date col and assign the values of Day Index col to it
########binding rows of datasets ##############################################
GA_userType_Revenue_Dataset <- bind_rows(filter_char7Re, filter_char8Re, filter_char10) ## bind rows of all datasets filtered
View(GA_userType_Revenue_Dataset)
#### Exporting dataset as csv
write.csv(GA_userType_Revenue_Dataset, file = "Users\\lin\\Downloads\\R_shiny_app_GoogleTrends\\GA_userType_Revenue_Dataset.csv")
################## data exploration ####################
View(GA_userType_Revenue_Dataset)
GA_userType_Revenue_Dataset <- read_csv("GA_userType_Revenue_Dataset.csv",
col_types = cols(Date = col_date(format = "%m/%d/%Y")))
#View(GA_userType_Revenue_Dataset)
#ggplot(data = GA_userType_Revenue_Dataset, aes(x = Segment, y = "Revenue")) + geom_histogram()
table(GA_userType_Revenue_Dataset$Segment)
data_usertype <- GA_userType_Revenue_Dataset
ggplot(data = GA_userType_Revenue_Dataset, aes(x = Segment)) + geom_bar()
summary(data_usertype$Revenue)
quantile(data_usertype$Transactions)
summary(data_usertype$Transactions)
var(data_usertype$Transactions)
View(data_usertype)
ggplot(data_usertype, aes(x = Segment,y = Revenue)) + geom_boxplot()
ggplot(data_usertype, aes(x = Segment, y = Transactions)) + geom_boxplot() + coord_flip()
ggplot(data_usertype, aes(Transactions)) + geom_histogram()
ggplot(data_usertype, aes(Revenue)) + geom_histogram()
(transaction_ttest <- t.test(Transactions ~ Segment, data = data_usertype)) ## T test for mean of transactions between
(revenue_ttest <- t.test(Revenue ~ Segment, data = data_usertype))
## wilcon test for testing mean difference for non-normal distribution
(transaction_wilcox <- wilcox.test(Transactions ~ Segment, data = data_usertype))
(revenue_wilcox <- wilcox.test(Revenue ~ Segment, data = data_usertype))
plotNormalDensity(data_usertype$Revenue)
transformTukey(data_usertype$Revenue)
outliers::outlier(data_usertype$Transactions)
outliers::outlier(data_usertype$Revenue)
outliers::outlier(data_usertype$Transactions, opposite = TRUE)
# shapiro.test(data_usertype$Transactions)
# shapiro.test(transactions_transform)
# leveneTest(y = data_usertype$Transactions, group = data_usertype$Segment, data = data_usertype)
# fligner.test(x = data_usertype$Transactions, group = data_usertype$Segment, data = data_usertype )
plotNormalHistogram(data_usertype$Transactions)
(transactions_transform <- rcompanion::transformTukey(data_usertype$Transactions))
(transboxplot <- boxplot(data_usertype$Transactions))
(outlierrem <- rm.outlier(data_usertype[,5]))
View(outlierrem)
boxplot(outlierrem$Revenue)
plotDensityHistogram(outlierrem$Revenue)
|
# Following two functions will calculate and cache the inverse of a matrix at its first use. When we need it again, it can be looked up in the cache rather than recomputed.
#################################################################################
# This function creates a special matrix object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
# set the value of the matrix
set <- function(y) {
x <<- y
m <<- NULL
}
# get the value of the matrix
get <- function() x
# set the value of the inverse
setinverse <- function(inverse) m <<- inverse
# get the value of the inverse
getinverse <- function() m
# return the results
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
#################################################################################
# This function retrieves the inverse of the matrix from the cache.
# If not in the cache, computes inverse of the special "matrix" returned by makeCacheMatrix.
cacheSolve <- function(x, ...) {
m <- x$getinverse()
# Return the inverse if available in the cashe
if(!is.null(m)) {
message("getting cached data")
return(m)
}
# calculate the inverse and cashe it when not there
data <- x$get()
m <- solve(data, ...)
message("getting calculated data")
x$setinverse(m)
# return the results
m
}
| /cachematrix.R | no_license | jairajsingh/ProgrammingAssignment2 | R | false | false | 1,611 | r | # Following two functions will calculate and cache the inverse of a matrix at its first use. When we need it again, it can be looked up in the cache rather than recomputed.
#################################################################################
# This function creates a special matrix object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
# set the value of the matrix
set <- function(y) {
x <<- y
m <<- NULL
}
# get the value of the matrix
get <- function() x
# set the value of the inverse
setinverse <- function(inverse) m <<- inverse
# get the value of the inverse
getinverse <- function() m
# return the results
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
#################################################################################
# This function retrieves the inverse of the matrix from the cache.
# If not in the cache, computes inverse of the special "matrix" returned by makeCacheMatrix.
cacheSolve <- function(x, ...) {
m <- x$getinverse()
# Return the inverse if available in the cashe
if(!is.null(m)) {
message("getting cached data")
return(m)
}
# calculate the inverse and cashe it when not there
data <- x$get()
m <- solve(data, ...)
message("getting calculated data")
x$setinverse(m)
# return the results
m
}
|
#' De-identify data through encrypting text columns, grouping rare values, and,
#' aggregating numeric or date columns.
#'
#'
#' @param data
#' A data.frame with the data you want to de-identify.
#' @param cols_to_encrypt
#' A string or vector of strings with the columns that you want to encrypt
#' using the `seed_cipher()` function from the `caesar` package.
#' @param group_rare_values_cols
#' A string or vector of strings with the columns that you want to convert
#' rare values (below a certain percent of all values as set in
#' `group_rare_values_limit`) into NA or a particular string (or NA) set in
#' `group_rare_values_text`.
#' @param group_rare_values_limit
#' A string or vector of strings (one for each col in `group_rare_values_cols`)
#' for what threshold (in percent of all non-NA values) to determine that a value
#' is rare enough to change to NA (or the string set in `group_rare_values_text`).
#' @param group_rare_values_text
#' A string or vector of strings (one for each col in `group_rare_values_cols`)
#' for what to rename the values that are determined to be rare enough (based on
#' threshold set in `group_rare_values_limit` to rename them. If NULL (default),
#' and the vector is strings, replaces them with a string that concatenates all
#' of the rare values together (separated by a comma). If NA, replaces them with NA.
#' @param date_cols
#' A vector of strings with the name of date columns that you want to be aggregated
#' to the unit set in the `date_aggreagtion` parameter. If NULL, will use
#' all date columns in the data.
#' @param date_aggregation
#' A string with the time unit to aggregate all Date variables to.
#' Can take one of the following: 'week', 'month', 'bimonth', 'quarter',
#' 'halfyear', 'year'.
#' @param quiet
#' A Boolean for whether you want to output a message that tells you which
#' columns that you are encrypting and the seed set for each column to
#' do the encryption. If you don't set the seed yourself, you need these
#' seeds to decrypt.
#'
#' @return
#' A data.frame with the selected columns de-identify based on user parameters.
#' @examples
#' @export
deidentify_data <- function(data,
date_cols = NULL,
date_aggregation = c("week",
"month",
"bimonth",
"quarter",
"halfyear",
"year"),
cols_to_encrypt = NULL,
group_rare_values_cols = NULL,
group_rare_values_limit = 5,
group_rare_values_text = NULL,
quiet = FALSE) {
# if (!is.null(cols_to_encrypt) & is.null(seeds_for_encryption)) {
# seeds_for_encryption <- sample(1e3:1e11, length(cols_to_encrypt))
# }
data <-
data %>%
dplyr::mutate_if(lubridate::is.Date, lubridate::floor_date, unit = date_aggregation)
# Looks in each selected columns and replaces all the values that are less
# than k% of the non-NA total with a selected replacement text (default
# is NA). This is to reduce privacy concerns with rare values (i.e. if
# person is only one with X offense, can identify through that).
group_rare_values_limit <- rep(group_rare_values_limit,
length.out = length(group_rare_values_cols))
if (!is.null(group_rare_values_cols)) {
for (i in 1:length(group_rare_values_cols)) {
col <- group_rare_values_cols[i]
rare_values <- get_rare_values(data[, col], group_rare_values_limit[i])
if (is.null(group_rare_values_text)){
data[, col][data[, col] %in% rare_values] <- paste0(rare_values,
collapse = ", ")
} else if (is.na(group_rare_values_text)) {
data[, col][data[, col] %in% rare_values] <- NA
} else {
data[, col][data[, col] %in% rare_values] <- group_rare_values_text[i]
}
}
}
return(data)
}
deidentify_date <- function(data, date_aggregation) {
data <- lubridate::floor_date(data, date_aggregation)
return(data)
}
deidentify_group <- function(data,
group_rare_values_limit,
group_rare_values_text) {
values_under_k_percent <-
get_rare_values(data,
group_rare_values_limit)
data[data %in% values_under_k_percent] <- group_rare_values_text
}
get_rare_values <- function(data,
k_percent = 5) {
numeric_data <- is.numeric(data)
values_by_percent <- table(data) / length(data[!is.na(data)]) * 100
values_under_k_percent <- names(values_by_percent[values_by_percent < k_percent])
if(numeric_data) {
values_under_k_percent <- as.numeric(values_under_k_percent)
}
# Sorts alphabetically (or smallest to largest if numeric) for easier testing.
values_under_k_percent <- sort(values_under_k_percent)
# Returns NULL if no responses
if (length(values_under_k_percent) == 0) {
values_under_k_percent <- NULL
}
return(values_under_k_percent)
}
bin_numbers <- function(data, percent_per_group) {
if (!is.numeric(percent_per_group) | length(percent_per_group) != 1 | !percent_per_group %in% 1:100) {
stop("percent_per_group must be a single integer from 1:100.")
}
if (!is.numeric(data)) {
stop("data must be a vector of numbers.")
}
# Convert to a proportion
percent_per_group <- percent_per_group / 100
}
| /R/deidentify.R | permissive | seathebass/deidentify | R | false | false | 5,648 | r | #' De-identify data through encrypting text columns, grouping rare values, and,
#' aggregating numeric or date columns.
#'
#'
#' @param data
#' A data.frame with the data you want to de-identify.
#' @param cols_to_encrypt
#' A string or vector of strings with the columns that you want to encrypt
#' using the `seed_cipher()` function from the `caesar` package.
#' @param group_rare_values_cols
#' A string or vector of strings with the columns that you want to convert
#' rare values (below a certain percent of all values as set in
#' `group_rare_values_limit`) into NA or a particular string (or NA) set in
#' `group_rare_values_text`.
#' @param group_rare_values_limit
#' A string or vector of strings (one for each col in `group_rare_values_cols`)
#' for what threshold (in percent of all non-NA values) to determine that a value
#' is rare enough to change to NA (or the string set in `group_rare_values_text`).
#' @param group_rare_values_text
#' A string or vector of strings (one for each col in `group_rare_values_cols`)
#' for what to rename the values that are determined to be rare enough (based on
#' threshold set in `group_rare_values_limit` to rename them. If NULL (default),
#' and the vector is strings, replaces them with a string that concatenates all
#' of the rare values together (separated by a comma). If NA, replaces them with NA.
#' @param date_cols
#' A vector of strings with the name of date columns that you want to be aggregated
#' to the unit set in the `date_aggreagtion` parameter. If NULL, will use
#' all date columns in the data.
#' @param date_aggregation
#' A string with the time unit to aggregate all Date variables to.
#' Can take one of the following: 'week', 'month', 'bimonth', 'quarter',
#' 'halfyear', 'year'.
#' @param quiet
#' A Boolean for whether you want to output a message that tells you which
#' columns that you are encrypting and the seed set for each column to
#' do the encryption. If you don't set the seed yourself, you need these
#' seeds to decrypt.
#'
#' @return
#' A data.frame with the selected columns de-identify based on user parameters.
#' @examples
#' @export
deidentify_data <- function(data,
date_cols = NULL,
date_aggregation = c("week",
"month",
"bimonth",
"quarter",
"halfyear",
"year"),
cols_to_encrypt = NULL,
group_rare_values_cols = NULL,
group_rare_values_limit = 5,
group_rare_values_text = NULL,
quiet = FALSE) {
# if (!is.null(cols_to_encrypt) & is.null(seeds_for_encryption)) {
# seeds_for_encryption <- sample(1e3:1e11, length(cols_to_encrypt))
# }
data <-
data %>%
dplyr::mutate_if(lubridate::is.Date, lubridate::floor_date, unit = date_aggregation)
# Looks in each selected columns and replaces all the values that are less
# than k% of the non-NA total with a selected replacement text (default
# is NA). This is to reduce privacy concerns with rare values (i.e. if
# person is only one with X offense, can identify through that).
group_rare_values_limit <- rep(group_rare_values_limit,
length.out = length(group_rare_values_cols))
if (!is.null(group_rare_values_cols)) {
for (i in 1:length(group_rare_values_cols)) {
col <- group_rare_values_cols[i]
rare_values <- get_rare_values(data[, col], group_rare_values_limit[i])
if (is.null(group_rare_values_text)){
data[, col][data[, col] %in% rare_values] <- paste0(rare_values,
collapse = ", ")
} else if (is.na(group_rare_values_text)) {
data[, col][data[, col] %in% rare_values] <- NA
} else {
data[, col][data[, col] %in% rare_values] <- group_rare_values_text[i]
}
}
}
return(data)
}
deidentify_date <- function(data, date_aggregation) {
data <- lubridate::floor_date(data, date_aggregation)
return(data)
}
deidentify_group <- function(data,
group_rare_values_limit,
group_rare_values_text) {
values_under_k_percent <-
get_rare_values(data,
group_rare_values_limit)
data[data %in% values_under_k_percent] <- group_rare_values_text
}
get_rare_values <- function(data,
k_percent = 5) {
numeric_data <- is.numeric(data)
values_by_percent <- table(data) / length(data[!is.na(data)]) * 100
values_under_k_percent <- names(values_by_percent[values_by_percent < k_percent])
if(numeric_data) {
values_under_k_percent <- as.numeric(values_under_k_percent)
}
# Sorts alphabetically (or smallest to largest if numeric) for easier testing.
values_under_k_percent <- sort(values_under_k_percent)
# Returns NULL if no responses
if (length(values_under_k_percent) == 0) {
values_under_k_percent <- NULL
}
return(values_under_k_percent)
}
bin_numbers <- function(data, percent_per_group) {
if (!is.numeric(percent_per_group) | length(percent_per_group) != 1 | !percent_per_group %in% 1:100) {
stop("percent_per_group must be a single integer from 1:100.")
}
if (!is.numeric(data)) {
stop("data must be a vector of numbers.")
}
# Convert to a proportion
percent_per_group <- percent_per_group / 100
}
|
# Plot4 : plot 4 different graphs on 2 rows x 2 column matrix
#setup working directory
setwd("~/GitHub/ExData_Plotting1/Plot2.R")
#Read the data in table format and store it in to dataset variable
dataset <- read.table("household_power_consumption.txt",header = TRUE, sep=";",na.strings = "?")
# Get the subset from dataset fro dates 1,2 feb 2007 and convert dates to R Date class and time to POSIXct
sub_dataset <- subset(dataset, Date %in% c("1/2/2007","2/2/2007"))
sub_dataset$Date <- as.Date(sub_dataset$Date, format="%d/%m/%Y")
datetime <- paste(as.Date(sub_dataset$Date), sub_dataset$Time)
sub_dataset$Datetime <- as.POSIXct(datetime)
#save the plot to png format
png("plot4.png", width=480, height=480)
#Plot 4 graphs in 2 rows and 2 columns
par(mfrow=c(2,2), mar=c(4,4,2,1), oma=c(0,0,2,0))
with(sub_dataset, {
#plot1 : Global_active_power vs Datetime
plot(Global_active_power~Datetime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
#plot2:Voltage vs Datetime
plot(Voltage~Datetime, type="l",
ylab="Voltage (volt)", xlab="")
#plot3: submetering vs Dtaetime
plot(Sub_metering_1~Datetime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
lines(Sub_metering_2~Datetime,col='Red')
lines(Sub_metering_3~Datetime,col='Blue')
legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2, bty="n",
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
#plot4: Global_reactive_power vs Datetime
plot(Global_reactive_power~Datetime, type="l",
ylab="Global Rective Power (kilowatts)",xlab="")
})
#close the png device
dev.off()
| /Plot4.R | no_license | Ruchipatel91/ExData_Plotting1 | R | false | false | 1,648 | r |
# Plot4 : plot 4 different graphs on 2 rows x 2 column matrix
#setup working directory
setwd("~/GitHub/ExData_Plotting1/Plot2.R")
#Read the data in table format and store it in to dataset variable
dataset <- read.table("household_power_consumption.txt",header = TRUE, sep=";",na.strings = "?")
# Get the subset from dataset fro dates 1,2 feb 2007 and convert dates to R Date class and time to POSIXct
sub_dataset <- subset(dataset, Date %in% c("1/2/2007","2/2/2007"))
sub_dataset$Date <- as.Date(sub_dataset$Date, format="%d/%m/%Y")
datetime <- paste(as.Date(sub_dataset$Date), sub_dataset$Time)
sub_dataset$Datetime <- as.POSIXct(datetime)
#save the plot to png format
png("plot4.png", width=480, height=480)
#Plot 4 graphs in 2 rows and 2 columns
par(mfrow=c(2,2), mar=c(4,4,2,1), oma=c(0,0,2,0))
with(sub_dataset, {
#plot1 : Global_active_power vs Datetime
plot(Global_active_power~Datetime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
#plot2:Voltage vs Datetime
plot(Voltage~Datetime, type="l",
ylab="Voltage (volt)", xlab="")
#plot3: submetering vs Dtaetime
plot(Sub_metering_1~Datetime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
lines(Sub_metering_2~Datetime,col='Red')
lines(Sub_metering_3~Datetime,col='Blue')
legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2, bty="n",
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
#plot4: Global_reactive_power vs Datetime
plot(Global_reactive_power~Datetime, type="l",
ylab="Global Rective Power (kilowatts)",xlab="")
})
#close the png device
dev.off()
|
\name{fast.geomorph.compare.multi.evol.rates}
\alias{fast.geomorph.compare.multi.evol.rates}
\title{
Fast covariance-based implementations of distance-based methods
}
\description{
The functions \code{fast.geomorph.compare.evol.rates}, \code{fast.geomorph.compare.multi.evol.rates}, \code{fast.geomorph.phylo.integration}, \code{fast.geomorph.procD.pgls}
, and \code{fast.geomorph.physignal} are covariance-baesd implementations of the geomorph functions \link[geomorph]{compare.evol.rates}, \link[geomorph]{compare.multi.evol.rates}, \link[geomorph]{phylo.integration}, \link[geomorph]{procD.pgls}, and \link[geomorph]{physignal} using a fast linear-time algorithm. Code is directly modified from the original geomorph code for direct comparison between distance-based and covariance-based methods.
}
\usage{
fast.geomorph.compare.multi.evol.rates(A, gp,
phy, Subset = TRUE, method = "ML", ShowPlot = TRUE, iter = 1000)
}
\arguments{
\item{A}{
From geomorph: A matrix (n x [p x k]) or 3D array (p x k x n) containing GPA-aligned coordinates for a set of specimens
}
\item{gp}{
From geomorph: A factor array designating group membership
}
\item{phy}{
From geomorph: A phylogenetic tree of class phylo
}
\item{Subset}{
From geomorph: A logical value indicating whether or not the traits are subsets from a single landmark configuration (default is TRUE)
}
\item{method}{
Maximum likelihood "ML" or restricted maximum likelihood "REML"
}
\item{ShowPlot}{
From geomorph: A logical value indicating whether or not the plot should be returned
}
\item{iter}{
From geomorph: Number of iterations for significance testing
}
}
\details{
See \link[geomorph]{compare.multi.evol.rates}
}
\value{
See \link[geomorph]{compare.multi.evol.rates}
}
\references{
Goolsby E.W. 2016. Likelihood-Based Parameter Estimation for High-Dimensional Phylogenetic Comparative Models: Overcoming the Limitations of 'Distance-Based' Methods. In review.
Adams, D.C. 2014. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Syst. Biol. 63:166-177.
Denton, J.S.S., and D.C. Adams. 2015. A new phylogenetic test for comparing multiple high-dimensional evolutionary rates suggests interplay of evolutionary rates and modularity in lanternfishes (Myctophiformes; Myctophidae). Evolution. 69: doi:10.1111/evo.12743
}
\author{
Eric W. Goolsby
}
\seealso{
\link[geomorph]{compare.multi.evol.rates}
}
\examples{
### NOTE: this example is identical
### to the example code for the
### analogous geomorph function
### for direct comparisons with
### 'fast.geomorph' phylocurve functions
require(geomorph)
data(plethspecies)
Y.gpa<-gpagen(plethspecies$land) #GPA-alignment
land.gp<-c("A","A","A","A","A","B","B","B","B","B","B") #mandible and cranium subsets
compare.multi.evol.rates(Y.gpa$coords,land.gp,phy=plethspecies$phy,iter=99)
fast.geomorph.compare.multi.evol.rates(Y.gpa$coords,land.gp,plethspecies$phy)
}
| /man/fast.geomorph.compare.multi.evol.rates.Rd | no_license | cran/phylocurve | R | false | false | 3,024 | rd | \name{fast.geomorph.compare.multi.evol.rates}
\alias{fast.geomorph.compare.multi.evol.rates}
\title{
Fast covariance-based implementations of distance-based methods
}
\description{
The functions \code{fast.geomorph.compare.evol.rates}, \code{fast.geomorph.compare.multi.evol.rates}, \code{fast.geomorph.phylo.integration}, \code{fast.geomorph.procD.pgls}
, and \code{fast.geomorph.physignal} are covariance-baesd implementations of the geomorph functions \link[geomorph]{compare.evol.rates}, \link[geomorph]{compare.multi.evol.rates}, \link[geomorph]{phylo.integration}, \link[geomorph]{procD.pgls}, and \link[geomorph]{physignal} using a fast linear-time algorithm. Code is directly modified from the original geomorph code for direct comparison between distance-based and covariance-based methods.
}
\usage{
fast.geomorph.compare.multi.evol.rates(A, gp,
phy, Subset = TRUE, method = "ML", ShowPlot = TRUE, iter = 1000)
}
\arguments{
\item{A}{
From geomorph: A matrix (n x [p x k]) or 3D array (p x k x n) containing GPA-aligned coordinates for a set of specimens
}
\item{gp}{
From geomorph: A factor array designating group membership
}
\item{phy}{
From geomorph: A phylogenetic tree of class phylo
}
\item{Subset}{
From geomorph: A logical value indicating whether or not the traits are subsets from a single landmark configuration (default is TRUE)
}
\item{method}{
Maximum likelihood "ML" or restricted maximum likelihood "REML"
}
\item{ShowPlot}{
From geomorph: A logical value indicating whether or not the plot should be returned
}
\item{iter}{
From geomorph: Number of iterations for significance testing
}
}
\details{
See \link[geomorph]{compare.multi.evol.rates}
}
\value{
See \link[geomorph]{compare.multi.evol.rates}
}
\references{
Goolsby E.W. 2016. Likelihood-Based Parameter Estimation for High-Dimensional Phylogenetic Comparative Models: Overcoming the Limitations of 'Distance-Based' Methods. In review.
Adams, D.C. 2014. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Syst. Biol. 63:166-177.
Denton, J.S.S., and D.C. Adams. 2015. A new phylogenetic test for comparing multiple high-dimensional evolutionary rates suggests interplay of evolutionary rates and modularity in lanternfishes (Myctophiformes; Myctophidae). Evolution. 69: doi:10.1111/evo.12743
}
\author{
Eric W. Goolsby
}
\seealso{
\link[geomorph]{compare.multi.evol.rates}
}
\examples{
### NOTE: this example is identical
### to the example code for the
### analogous geomorph function
### for direct comparisons with
### 'fast.geomorph' phylocurve functions
require(geomorph)
data(plethspecies)
Y.gpa<-gpagen(plethspecies$land) #GPA-alignment
land.gp<-c("A","A","A","A","A","B","B","B","B","B","B") #mandible and cranium subsets
compare.multi.evol.rates(Y.gpa$coords,land.gp,phy=plethspecies$phy,iter=99)
fast.geomorph.compare.multi.evol.rates(Y.gpa$coords,land.gp,plethspecies$phy)
}
|
\name{head}
\alias{head}
\title{Obtains the first rows of an H2O parsed data object
}
\description{
Obtains the first rows of an H2O parsed data object
}
\usage{
head(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{An H2O parsed data object
}
\item{\dots}{The number of rows to be returned (the default count is 6).
}
}
\value{ The first 6 rows of an H2O parsed data frame, unless the number of rows is otherwise specified to be N, in which case the first N rows are returned.
}
\examples{
library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, silentUpgrade = TRUE, promptUpgrade = FALSE)
ausPath = system.file("extdata", "australia.csv", package="h2oRClient")
australia.hex = h2o.importFile(localH2O, path = ausPath)
head(australia.hex, 10)
}
| /R/h2oRClient-package/man/head.Rd | permissive | cloudtrends/h2o | R | false | false | 820 | rd | \name{head}
\alias{head}
\title{Obtains the first rows of an H2O parsed data object
}
\description{
Obtains the first rows of an H2O parsed data object
}
\usage{
head(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{An H2O parsed data object
}
\item{\dots}{The number of rows to be returned (the default count is 6).
}
}
\value{ The first 6 rows of an H2O parsed data frame, unless the number of rows is otherwise specified to be N, in which case the first N rows are returned.
}
\examples{
library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, silentUpgrade = TRUE, promptUpgrade = FALSE)
ausPath = system.file("extdata", "australia.csv", package="h2oRClient")
australia.hex = h2o.importFile(localH2O, path = ausPath)
head(australia.hex, 10)
}
|
#' @include Gap_filling.R
NULL
#' @import progress
#' @import data.table
#' @importFrom tools file_path_sans_ext
#' @importFrom xcms xcmsRaw
#' @importFrom xcms rawEIC
NULL
#' @title Fill MS-gaps using SAX
#'
#' @description Function that fills gaps utilizing SAX (Symbol Aggregate approXimation)
#'
#' @param peak_table A mandatory character vector giving the path to an aligned peak table in csv format or a data.table that is the aligned peak table. See the Details section for more information on how this parameter and the filenames parameter interact.
#' @param mzML_filenames The names of the .mzML-mzML_filenames that are references in peak table. See the Details section for more information
#' @param SAXGAP_params A list containing the main parameters for the SAXGAP function. See the Details section for more information
#' @param output_file An optional charcter vector giving the filepath of where the output table should be stored.
#'
#' @details
#' \subsection{peak_table and mzML_filename:}{
#'
#' The peak_table and mzML_filenames parameter need to have the same underlying names for the storage of the relationship of the intensities and retention times to the files.
#' If, for example, one supplies the file \code{Sample_1.mzML} then the peak_table MUST contain a column named either \code{Intensity_Sample_1} or \code{Intensity_Sample_1.mzML}. The holds true for
#' retention time, i.e. the table needs to have a column named either \code{RT_Sample_1.mzML} or \code{RT_Sample_1}. The peak table also needs general \code{mz} and \code{RT} columns. All retention times must provided in seconds!
#'
#' }
#'
#' \subsection{SAXGAP_params:}{
#' The SAXGAP_params paarmeter must be a named list containing the parameters:
#' \describe{
#' \item{a}{The alphabet size of the SAX conversion and comparison}
#' \item{w}{The word length for peaks}
#' \item{t}{The length of the extracted EICs}
#' \item{pe}{The percentage cutoff for peak removal}
#' \item{dppm}{The instrument-specific allowed ppm error of the peaks}
#' \item{intensity_threshold}{The minimum intensity for peaks}
#' }
#' }
#'
#' @return The function returns the gap filled table with the gap filled peaks stored as a negative value and an added \code{Removal_Candidate} column that signifies whether a peak was likely picked in error.
#' If output_file is set it writes the same table to the path specified by output_file.
#'
#' @export
SAXGAP <- function(peak_table, mzML_filenames, SAXGAP_params = list(a=6,w=6,t=14,pe=0.9,intensity_threshold = 1e4,dppm=5), output_file){
# Parameter check and read section
# Check the peak_table parameter
# Is it a filepath? If it is, read the file
if(is.character(peak_table)){
if(file.exists(peak_table)){
peak_table <- fread(peak_table)
} else{
stop(paste("File",peak_table,"does not exist"))
}
} else{
if(!is.data.table(peak_table)){
stop("peak_table must be an object of class data.table")
}
}
# Check the mzML_filenames parameter
# Do all filenames exist?
if(is.character(mzML_filenames)){
mzML_filenames_exist <- file.exists(mzML_filenames)
if(!all(mzML_filenames_exist)){
not_existing_mzML_files <- mzML_filenames[!mzML_filenames_exist]
if(length(not_existing_mzML_files) == 1){
stop(paste0("File does not exist:\n", not_existing_mzML_files))
}
if(length(not_existing_mzML_files) > 1){
stop(paste0("Files do not exist:\n", paste(not_existing_mzML_files,collapse = "\n")))
}
}
} else{
stop("mzML_filenames must be a character vector containg filenames")
}
# Check the SAXGAP_params parameter
# Is SAXGAP_params a list with the proper named elements?
if(!is.list(SAXGAP_params) || !all(c("a","w","t","pe","intensity_threshold","dppm") %in% names(SAXGAP_params))){
stop("SAXGAP_params must be a list containing named elements \"a\",\"w\",\"t\",\"pe\",\"intensity_threshold\",\"dppm\"")
}
# Check the output_file parameter (if it is supplied)
# Does the folder exist where the table should go?
if(!missing(output_file)){
dirname_output_file <- dirname(output_file)
if(!dir.exists(dirname_output_file)){
stop(paste("The target directory of the output file", dirname_output_file, "does not exist"))
}
}
# Get intensity and rt column names
INT_column_names <- paste0("Intensity_",file_path_sans_ext(basename(mzML_filenames)))
RT_column_names <- paste0("RT_",file_path_sans_ext(basename(mzML_filenames)))
# If the column names are not found in the table check if they are found if we don't remove the file extension from the name
# It's easy to check this, so we allow this with file extensions and without, for user convenience.
if(!any(INT_column_names %in% colnames(peak_table))){
INT_column_names <- paste0("Intensity_",basename(mzML_filenames))
}
if(!any(RT_column_names %in% colnames(peak_table))){
RT_column_names <- paste0("RT_",basename(mzML_filenames))
}
# Get intensity table and retention time table
INT_Table <- peak_table[, INT_column_names,with=FALSE]
RT_Table <- peak_table[, RT_column_names,with=FALSE]
nPeaks <- nrow(INT_Table)
# Check if the intensity table has a column for every file
if(ncol(INT_Table) != length(INT_column_names)){
stop("The peak_table does not contain a properly named intensity column for every provided mzML_filename\nThe columns must have the same name as the mzML-files with \"Intensity_\" added in front")
}
# Check if the retention time table has a column for every file
if(ncol(RT_Table) != length(RT_column_names)){
stop("The peak_table does not contain a properly named retention time column for every provided mzML_filename\nThe columns must have the same name as the mzML-files with \"RT_\" added in front")
}
if(!all(c("mz","RT") %in% colnames(peak_table))){
stop("The peak_table does not contain mandatory columns with names \"mz\",\"RT\"")
}
if(all(peak_table$RT < 60)){
warning("All retention times are smaller than 60, please note that retention times must be supplied in seconds")
}
# Set up variables for EIC extraction
SAX <- list()
detected_peaks <- list() # Which peaks have already been found?
detected_peaks_l <- matrix(FALSE, nPeaks, length(mzML_filenames)) # Which peaks have already been found (but as a logical vector)
# Much faster with a matrix than with a data.table, so a one time conversion just for this purpose
INT_matrix <- as.matrix(INT_Table)
for(i in 1:nPeaks){
SAX[[i]] <- vector()
detected_peaks_l[i,] <- INT_matrix[i,] > 0
detected_peaks[[i]] <- which(detected_peaks_l[i,])
}
### Set up the EIC extraction and SAX conversion loop
# progress_bar stuff
length_progressbar <- length(mzML_filenames)*nPeaks
pb <- progress_bar$new(
format = " [NonTplus] Generating SAX sequences [:bar] :percent ETA: :eta Elapsed: :elapsed ",
total = length_progressbar, clear = FALSE, width = 80)
# Variable setup, create new data.tables and EIClengths
i <- 0
EIClengths <- matrix(0,nPeaks,length(mzML_filenames))
INT_dt_new <- copy(INT_Table)
RT_dt_new <- data.table(matrix(0,nrow(RT_Table),ncol(RT_Table)))
colnames(RT_dt_new) <- RT_column_names
# Calculate the ppmlimits of the mz of each peak and get the SAX string for empty EICs
mzranges <- t(sapply(peak_table$mz,.get_ppmlimits,SAXGAP_params$dppm))
empty_SAX <- paste0(rep("a",SAXGAP_params$w),collapse="")
# Go through each file
for(f in mzML_filenames){
# Read the file
suppressMessages(XR <- xcmsRaw(f))
# Go through each peak
for(p in 1:nPeaks){
# Only update the progress bar every 1000 peaks, or else the updating takes a noticable amount of time.
if(p %% 1000 == 0){
pb$update((i*nPeaks+p)/length_progressbar)
}
# Get the mzrange for each peak
current_mzrange <- mzranges[p,]
# Get the RT - either it is peak-specific or we take the "general" supplied RT
if(detected_peaks_l[p,i+1]){
RT <- RT_Table[[i+1]][p]
} else{
RT <- peak_table$RT[p]
}
# Retrieve the currently relevant EIC from the file
tempEIC <- rawEIC(XR,mzrange=current_mzrange,rtrange = c(RT-2*SAXGAP_params$t,RT+2*SAXGAP_params$t))
# Move the retention time window to center around the weighted means of intensities - i.e.
# "Center the middle of the peak if there is one"
intensities <- tempEIC$intensity
retention_t <- XR@scantime[tempEIC$scan]
ind <- .Internal(which(retention_t > RT - SAXGAP_params$t & retention_t < RT + SAXGAP_params$t))
new_RT <- weighted.mean(retention_t[ind],intensities[ind])
if(!is.nan(new_RT)){
ind2 <- which(retention_t > new_RT - SAXGAP_params$t & retention_t < new_RT + SAXGAP_params$t)
intensities <- intensities[ind2]
retention_t <- retention_t[ind2]
} else{
new_RT <- 0
ind <- .Internal(which(retention_t > RT - SAXGAP_params$t & retention_t < RT + SAXGAP_params$t))
intensities <- intensities[ind]
}
sumint <- sum(intensities)
# Save the new Intensities and RT in their tables if we assume a peak to be here
# If there is a peak, get the new RT with the weighted mean strategy
if(detected_peaks_l[p,i+1]){
set(RT_dt_new,p,RT_column_names[i+1], new_RT)
} else{
set(INT_dt_new,p,INT_column_names[i+1], sumint)
set(RT_dt_new,p,RT_column_names[i+1], new_RT)
}
# If the intensity is below the threshold, pretend that it is empty, otherwise create a SAX string
if(sumint < SAXGAP_params$intensity_threshold){
SAX[[p]][i+1] <- empty_SAX
} else{
SAX[[p]][i+1] <- paste0(.Func.SAX2(intensities, SAXGAP_params$w, SAXGAP_params$a, 0, T), collapse="")
}
# The EIC length is technically constant, but SAX is dependent on the number of data points and
# in MS experiments the same timerange can have a different number of datapoints at different RTs
# because scans are not exactly taken at a constant rate but at an only *somewhat* constant rate
EIClengths[p,i+1] <- length(intensities)
}
# Clear the memory
gc(reset = T,full = T)
i <- i + 1
}
# Finish up the progress bar in case it is not, because we only update it every 1000 steps
if(!pb$finished){
pb$update(1)
}
# Get a list of all SAX strings that belong to a "detected peak"
valid_SAX <- list()
for(p in 1:length(SAX)){
valid_SAX[[p]] <- SAX[[p]][detected_peaks[[p]]]
}
# Reduce the "valid" SAX strings by the accepted percentage
valid_SAX_table <- sort(table(do.call(c,valid_SAX)),decreasing = T)
valid_SAX_table_reduced <- valid_SAX_table[1:which(cumsum(valid_SAX_table)/sum(valid_SAX_table) > SAXGAP_params$p)[1]]
valid_SAX_sequences <- names(valid_SAX_table_reduced)
# Setup for finding the consensus sequences
consensus_mean <- list()
consensus_majority <- list()
length_progressbar <- nPeaks
pb <- progress_bar$new(
format = " [NonTplus] Finding consensus sequences [:bar] :percent ETA: :eta Elapsed: :elapsed",
total = length_progressbar, clear = FALSE, width = 80)
# Go through each peak
for(p in 1:nPeaks){
# If more than one peak has been detected, get the consensus
if(length(detected_peaks[[p]]) > 1){
SAX_correct <- do.call(rbind,strsplit(SAX[[p]][detected_peaks[[p]]],split = ""))
consensus_mean[[p]] <- pseudo_consensus(SAX_correct)
consensus_majority[[p]] <- consensus(SAX_correct)
} else{
# If only one was detected, it IS the consensus
consensus_mean[[p]] <- strsplit(SAX[[p]][detected_peaks[[p]]],split = "")
consensus_majority[[p]] <- strsplit(SAX[[p]][detected_peaks[[p]]],split = "")
}
if(p %% 100 == 0){
pb$update(p/length_progressbar)
}
}
# Finish progress bar
if(!pb$finished){
pb$update(1)
}
# Get distance matrix
dist_mat <- .Func.matrix2(SAXGAP_params$a)
# Set up variables for classification
classification_function <- consensus_distance_sax
consensus_fun <- consensus
mean_s <- vector()
sd_s <- vector()
cutoff <- vector()
A <- vector()
count <- 0
classFunc <- median
res <- list()
classes <- list()
single_score_mats <- list()
length_progressbar <- nrow(INT_Table)
pb <- progress_bar$new(
format = " [NonTplus] Adding new peaks [:bar] :percent ETA: :eta Elapsed: :elapsed",
total = length_progressbar, clear = FALSE, width = 80)
# Classification loop
rempeaks <- rep(FALSE,times=nPeaks)
Removed_Peaks <- logical(length = nPeaks)
for(p in 1:nPeaks){
# Get the index of the filex where the peak has been detected
training_index <- detected_peaks[[p]]
# In case something with the peak picking has gone horribly wrong (Sometimes the RT doesn't fit)
empty_index <- which(SAX[[p]][training_index] == empty_SAX)
if(length(empty_index) != 0){
training_index <- training_index[-empty_index]
}
test_index <- which(!detected_peaks_l[p,])
if(length(test_index) == 0){
next
}
# Convert the SAX sequences to a table
SAXtable <- do.call(rbind,strsplit(SAX[[p]], split=""))
# Differentiate method based on whether the current peak is low-coverage or not
if(length(training_index) < 0.05 * length(mzML_filenames)){
# Low-coverage: See if the low-coverage SAX sequences are plausibly peaks
current_SAX <- SAX[[p]][training_index]
current_SAX_test <- SAX[[p]][test_index]
if(!any(current_SAX %in% valid_SAX_sequences)){
# If not, remove the peak
rempeaks[p] <- TRUE
count <- count + 1
Removed_Peaks[p] <- TRUE
classes[[p]] <- rep(FALSE,length(mzML_filenames))
}else{
# If they are plausibly peaks, just look which other peaks match any peak of the valid ones (using the SAX distance function)
# See if the not yet classified peaks have the same sequence as one of the found peaks
SAX_matches <- which(sapply(current_SAX_test, function(SAX_test_sequence){
any(sapply(current_SAX, function(SAX_training_sequence){
.Func.dist2(strsplit(SAX_test_sequence, split="")[[1]], strsplit(SAX_training_sequence, split="")[[1]], dist_mat,n = mean(EIClengths[p,]))
}) == 0)
}))
classes[[p]] <- rep(FALSE,length(mzML_filenames))
if(length(SAX_matches) != 0){
classes[[p]][test_index][SAX_matches] <- TRUE
}
}
} else{
# High-coverage: Mintest with a consensus sequence
res[[p]] <- classification_function(SAXtable, consensus_fun, mean(EIClengths[p,]), training_index, dist_mat)
# Get mean distances and standard deviations
single_score_mats[[p]] <- res[[p]][[1]]
mean_s <- res[[p]][[2]]
sd_s <- res[[p]][[3]]
# If the mean is 0
if(is.nan(mean_s)){
mean_s <- 1
sd_s <- 1
}
if(is.na(sd_s)){
sd_s <- 0
}
# The cutoff is the maximum distance of one of training peaks
cutoff[p] <- max(single_score_mats[[p]][detected_peaks[[p]]])
A[p] <- all(single_score_mats[[p]][detected_peaks[[p]]] <= cutoff[p])
classes[[p]] <- (apply(single_score_mats[[p]],1, classFunc) <= cutoff[p]) & !detected_peaks_l[p,]
}
# Set the new intensities and retention times in the new tables
for(s in which(classes[[p]])){
set(peak_table,p,INT_column_names[s],-INT_dt_new[[s]][p])
set(peak_table,p,RT_column_names[s],RT_dt_new[[s]][p])
}
if(p %% 100){
pb$update(p/nPeaks)
}
}
if(!pb$finished){
pb$update(1)
}
# Clean up
peak_table[, Removal_Candidate := rempeaks]
if(!missing(output_file)){
fwrite(peak_table,file = output_file)
}
return(peak_table)
} | /R/Gap_filling.R | no_license | oolonek/SAX_Gap_Filling | R | false | false | 17,895 | r | #' @include Gap_filling.R
NULL
#' @import progress
#' @import data.table
#' @importFrom tools file_path_sans_ext
#' @importFrom xcms xcmsRaw
#' @importFrom xcms rawEIC
NULL
#' @title Fill MS-gaps using SAX
#'
#' @description Function that fills gaps utilizing SAX (Symbol Aggregate approXimation)
#'
#' @param peak_table A mandatory character vector giving the path to an aligned peak table in csv format or a data.table that is the aligned peak table. See the Details section for more information on how this parameter and the filenames parameter interact.
#' @param mzML_filenames The names of the .mzML-mzML_filenames that are references in peak table. See the Details section for more information
#' @param SAXGAP_params A list containing the main parameters for the SAXGAP function. See the Details section for more information
#' @param output_file An optional charcter vector giving the filepath of where the output table should be stored.
#'
#' @details
#' \subsection{peak_table and mzML_filename:}{
#'
#' The peak_table and mzML_filenames parameter need to have the same underlying names for the storage of the relationship of the intensities and retention times to the files.
#' If, for example, one supplies the file \code{Sample_1.mzML} then the peak_table MUST contain a column named either \code{Intensity_Sample_1} or \code{Intensity_Sample_1.mzML}. The holds true for
#' retention time, i.e. the table needs to have a column named either \code{RT_Sample_1.mzML} or \code{RT_Sample_1}. The peak table also needs general \code{mz} and \code{RT} columns. All retention times must provided in seconds!
#'
#' }
#'
#' \subsection{SAXGAP_params:}{
#' The SAXGAP_params paarmeter must be a named list containing the parameters:
#' \describe{
#' \item{a}{The alphabet size of the SAX conversion and comparison}
#' \item{w}{The word length for peaks}
#' \item{t}{The length of the extracted EICs}
#' \item{pe}{The percentage cutoff for peak removal}
#' \item{dppm}{The instrument-specific allowed ppm error of the peaks}
#' \item{intensity_threshold}{The minimum intensity for peaks}
#' }
#' }
#'
#' @return The function returns the gap filled table with the gap filled peaks stored as a negative value and an added \code{Removal_Candidate} column that signifies whether a peak was likely picked in error.
#' If output_file is set it writes the same table to the path specified by output_file.
#'
#' @export
SAXGAP <- function(peak_table, mzML_filenames, SAXGAP_params = list(a=6,w=6,t=14,pe=0.9,intensity_threshold = 1e4,dppm=5), output_file){
# Parameter check and read section
# Check the peak_table parameter
# Is it a filepath? If it is, read the file
if(is.character(peak_table)){
if(file.exists(peak_table)){
peak_table <- fread(peak_table)
} else{
stop(paste("File",peak_table,"does not exist"))
}
} else{
if(!is.data.table(peak_table)){
stop("peak_table must be an object of class data.table")
}
}
# Check the mzML_filenames parameter
# Do all filenames exist?
if(is.character(mzML_filenames)){
mzML_filenames_exist <- file.exists(mzML_filenames)
if(!all(mzML_filenames_exist)){
not_existing_mzML_files <- mzML_filenames[!mzML_filenames_exist]
if(length(not_existing_mzML_files) == 1){
stop(paste0("File does not exist:\n", not_existing_mzML_files))
}
if(length(not_existing_mzML_files) > 1){
stop(paste0("Files do not exist:\n", paste(not_existing_mzML_files,collapse = "\n")))
}
}
} else{
stop("mzML_filenames must be a character vector containg filenames")
}
# Check the SAXGAP_params parameter
# Is SAXGAP_params a list with the proper named elements?
if(!is.list(SAXGAP_params) || !all(c("a","w","t","pe","intensity_threshold","dppm") %in% names(SAXGAP_params))){
stop("SAXGAP_params must be a list containing named elements \"a\",\"w\",\"t\",\"pe\",\"intensity_threshold\",\"dppm\"")
}
# Check the output_file parameter (if it is supplied)
# Does the folder exist where the table should go?
if(!missing(output_file)){
dirname_output_file <- dirname(output_file)
if(!dir.exists(dirname_output_file)){
stop(paste("The target directory of the output file", dirname_output_file, "does not exist"))
}
}
# Get intensity and rt column names
INT_column_names <- paste0("Intensity_",file_path_sans_ext(basename(mzML_filenames)))
RT_column_names <- paste0("RT_",file_path_sans_ext(basename(mzML_filenames)))
# If the column names are not found in the table check if they are found if we don't remove the file extension from the name
# It's easy to check this, so we allow this with file extensions and without, for user convenience.
if(!any(INT_column_names %in% colnames(peak_table))){
INT_column_names <- paste0("Intensity_",basename(mzML_filenames))
}
if(!any(RT_column_names %in% colnames(peak_table))){
RT_column_names <- paste0("RT_",basename(mzML_filenames))
}
# Get intensity table and retention time table
INT_Table <- peak_table[, INT_column_names,with=FALSE]
RT_Table <- peak_table[, RT_column_names,with=FALSE]
nPeaks <- nrow(INT_Table)
# Check if the intensity table has a column for every file
if(ncol(INT_Table) != length(INT_column_names)){
stop("The peak_table does not contain a properly named intensity column for every provided mzML_filename\nThe columns must have the same name as the mzML-files with \"Intensity_\" added in front")
}
# Check if the retention time table has a column for every file
if(ncol(RT_Table) != length(RT_column_names)){
stop("The peak_table does not contain a properly named retention time column for every provided mzML_filename\nThe columns must have the same name as the mzML-files with \"RT_\" added in front")
}
if(!all(c("mz","RT") %in% colnames(peak_table))){
stop("The peak_table does not contain mandatory columns with names \"mz\",\"RT\"")
}
if(all(peak_table$RT < 60)){
warning("All retention times are smaller than 60, please note that retention times must be supplied in seconds")
}
# Set up variables for EIC extraction
SAX <- list()
detected_peaks <- list() # Which peaks have already been found?
detected_peaks_l <- matrix(FALSE, nPeaks, length(mzML_filenames)) # Which peaks have already been found (but as a logical vector)
# Much faster with a matrix than with a data.table, so a one time conversion just for this purpose
INT_matrix <- as.matrix(INT_Table)
for(i in 1:nPeaks){
SAX[[i]] <- vector()
detected_peaks_l[i,] <- INT_matrix[i,] > 0
detected_peaks[[i]] <- which(detected_peaks_l[i,])
}
### Set up the EIC extraction and SAX conversion loop
# progress_bar stuff
length_progressbar <- length(mzML_filenames)*nPeaks
pb <- progress_bar$new(
format = " [NonTplus] Generating SAX sequences [:bar] :percent ETA: :eta Elapsed: :elapsed ",
total = length_progressbar, clear = FALSE, width = 80)
# Variable setup, create new data.tables and EIClengths
i <- 0
EIClengths <- matrix(0,nPeaks,length(mzML_filenames))
INT_dt_new <- copy(INT_Table)
RT_dt_new <- data.table(matrix(0,nrow(RT_Table),ncol(RT_Table)))
colnames(RT_dt_new) <- RT_column_names
# Calculate the ppmlimits of the mz of each peak and get the SAX string for empty EICs
mzranges <- t(sapply(peak_table$mz,.get_ppmlimits,SAXGAP_params$dppm))
empty_SAX <- paste0(rep("a",SAXGAP_params$w),collapse="")
# Go through each file
for(f in mzML_filenames){
# Read the file
suppressMessages(XR <- xcmsRaw(f))
# Go through each peak
for(p in 1:nPeaks){
# Only update the progress bar every 1000 peaks, or else the updating takes a noticable amount of time.
if(p %% 1000 == 0){
pb$update((i*nPeaks+p)/length_progressbar)
}
# Get the mzrange for each peak
current_mzrange <- mzranges[p,]
# Get the RT - either it is peak-specific or we take the "general" supplied RT
if(detected_peaks_l[p,i+1]){
RT <- RT_Table[[i+1]][p]
} else{
RT <- peak_table$RT[p]
}
# Retrieve the currently relevant EIC from the file
tempEIC <- rawEIC(XR,mzrange=current_mzrange,rtrange = c(RT-2*SAXGAP_params$t,RT+2*SAXGAP_params$t))
# Move the retention time window to center around the weighted means of intensities - i.e.
# "Center the middle of the peak if there is one"
intensities <- tempEIC$intensity
retention_t <- XR@scantime[tempEIC$scan]
ind <- .Internal(which(retention_t > RT - SAXGAP_params$t & retention_t < RT + SAXGAP_params$t))
new_RT <- weighted.mean(retention_t[ind],intensities[ind])
if(!is.nan(new_RT)){
ind2 <- which(retention_t > new_RT - SAXGAP_params$t & retention_t < new_RT + SAXGAP_params$t)
intensities <- intensities[ind2]
retention_t <- retention_t[ind2]
} else{
new_RT <- 0
ind <- .Internal(which(retention_t > RT - SAXGAP_params$t & retention_t < RT + SAXGAP_params$t))
intensities <- intensities[ind]
}
sumint <- sum(intensities)
# Save the new Intensities and RT in their tables if we assume a peak to be here
# If there is a peak, get the new RT with the weighted mean strategy
if(detected_peaks_l[p,i+1]){
set(RT_dt_new,p,RT_column_names[i+1], new_RT)
} else{
set(INT_dt_new,p,INT_column_names[i+1], sumint)
set(RT_dt_new,p,RT_column_names[i+1], new_RT)
}
# If the intensity is below the threshold, pretend that it is empty, otherwise create a SAX string
if(sumint < SAXGAP_params$intensity_threshold){
SAX[[p]][i+1] <- empty_SAX
} else{
SAX[[p]][i+1] <- paste0(.Func.SAX2(intensities, SAXGAP_params$w, SAXGAP_params$a, 0, T), collapse="")
}
# The EIC length is technically constant, but SAX is dependent on the number of data points and
# in MS experiments the same timerange can have a different number of datapoints at different RTs
# because scans are not exactly taken at a constant rate but at an only *somewhat* constant rate
EIClengths[p,i+1] <- length(intensities)
}
# Clear the memory
gc(reset = T,full = T)
i <- i + 1
}
# Finish up the progress bar in case it is not, because we only update it every 1000 steps
if(!pb$finished){
pb$update(1)
}
# Get a list of all SAX strings that belong to a "detected peak"
valid_SAX <- list()
for(p in 1:length(SAX)){
valid_SAX[[p]] <- SAX[[p]][detected_peaks[[p]]]
}
# Reduce the "valid" SAX strings by the accepted percentage
valid_SAX_table <- sort(table(do.call(c,valid_SAX)),decreasing = T)
valid_SAX_table_reduced <- valid_SAX_table[1:which(cumsum(valid_SAX_table)/sum(valid_SAX_table) > SAXGAP_params$p)[1]]
valid_SAX_sequences <- names(valid_SAX_table_reduced)
# Setup for finding the consensus sequences
consensus_mean <- list()
consensus_majority <- list()
length_progressbar <- nPeaks
pb <- progress_bar$new(
format = " [NonTplus] Finding consensus sequences [:bar] :percent ETA: :eta Elapsed: :elapsed",
total = length_progressbar, clear = FALSE, width = 80)
# Go through each peak
for(p in 1:nPeaks){
# If more than one peak has been detected, get the consensus
if(length(detected_peaks[[p]]) > 1){
SAX_correct <- do.call(rbind,strsplit(SAX[[p]][detected_peaks[[p]]],split = ""))
consensus_mean[[p]] <- pseudo_consensus(SAX_correct)
consensus_majority[[p]] <- consensus(SAX_correct)
} else{
# If only one was detected, it IS the consensus
consensus_mean[[p]] <- strsplit(SAX[[p]][detected_peaks[[p]]],split = "")
consensus_majority[[p]] <- strsplit(SAX[[p]][detected_peaks[[p]]],split = "")
}
if(p %% 100 == 0){
pb$update(p/length_progressbar)
}
}
# Finish progress bar
if(!pb$finished){
pb$update(1)
}
# Get distance matrix
dist_mat <- .Func.matrix2(SAXGAP_params$a)
# Set up variables for classification
classification_function <- consensus_distance_sax
consensus_fun <- consensus
mean_s <- vector()
sd_s <- vector()
cutoff <- vector()
A <- vector()
count <- 0
classFunc <- median
res <- list()
classes <- list()
single_score_mats <- list()
length_progressbar <- nrow(INT_Table)
pb <- progress_bar$new(
format = " [NonTplus] Adding new peaks [:bar] :percent ETA: :eta Elapsed: :elapsed",
total = length_progressbar, clear = FALSE, width = 80)
# Classification loop
rempeaks <- rep(FALSE,times=nPeaks)
Removed_Peaks <- logical(length = nPeaks)
for(p in 1:nPeaks){
# Get the index of the filex where the peak has been detected
training_index <- detected_peaks[[p]]
# In case something with the peak picking has gone horribly wrong (Sometimes the RT doesn't fit)
empty_index <- which(SAX[[p]][training_index] == empty_SAX)
if(length(empty_index) != 0){
training_index <- training_index[-empty_index]
}
test_index <- which(!detected_peaks_l[p,])
if(length(test_index) == 0){
next
}
# Convert the SAX sequences to a table
SAXtable <- do.call(rbind,strsplit(SAX[[p]], split=""))
# Differentiate method based on whether the current peak is low-coverage or not
if(length(training_index) < 0.05 * length(mzML_filenames)){
# Low-coverage: See if the low-coverage SAX sequences are plausibly peaks
current_SAX <- SAX[[p]][training_index]
current_SAX_test <- SAX[[p]][test_index]
if(!any(current_SAX %in% valid_SAX_sequences)){
# If not, remove the peak
rempeaks[p] <- TRUE
count <- count + 1
Removed_Peaks[p] <- TRUE
classes[[p]] <- rep(FALSE,length(mzML_filenames))
}else{
# If they are plausibly peaks, just look which other peaks match any peak of the valid ones (using the SAX distance function)
# See if the not yet classified peaks have the same sequence as one of the found peaks
SAX_matches <- which(sapply(current_SAX_test, function(SAX_test_sequence){
any(sapply(current_SAX, function(SAX_training_sequence){
.Func.dist2(strsplit(SAX_test_sequence, split="")[[1]], strsplit(SAX_training_sequence, split="")[[1]], dist_mat,n = mean(EIClengths[p,]))
}) == 0)
}))
classes[[p]] <- rep(FALSE,length(mzML_filenames))
if(length(SAX_matches) != 0){
classes[[p]][test_index][SAX_matches] <- TRUE
}
}
} else{
# High-coverage: Mintest with a consensus sequence
res[[p]] <- classification_function(SAXtable, consensus_fun, mean(EIClengths[p,]), training_index, dist_mat)
# Get mean distances and standard deviations
single_score_mats[[p]] <- res[[p]][[1]]
mean_s <- res[[p]][[2]]
sd_s <- res[[p]][[3]]
# If the mean is 0
if(is.nan(mean_s)){
mean_s <- 1
sd_s <- 1
}
if(is.na(sd_s)){
sd_s <- 0
}
# The cutoff is the maximum distance of one of training peaks
cutoff[p] <- max(single_score_mats[[p]][detected_peaks[[p]]])
A[p] <- all(single_score_mats[[p]][detected_peaks[[p]]] <= cutoff[p])
classes[[p]] <- (apply(single_score_mats[[p]],1, classFunc) <= cutoff[p]) & !detected_peaks_l[p,]
}
# Set the new intensities and retention times in the new tables
for(s in which(classes[[p]])){
set(peak_table,p,INT_column_names[s],-INT_dt_new[[s]][p])
set(peak_table,p,RT_column_names[s],RT_dt_new[[s]][p])
}
if(p %% 100){
pb$update(p/nPeaks)
}
}
if(!pb$finished){
pb$update(1)
}
# Clean up
peak_table[, Removal_Candidate := rempeaks]
if(!missing(output_file)){
fwrite(peak_table,file = output_file)
}
return(peak_table)
} |
pbart = function(x.train, y.train, x.test=matrix(0.0,0,0),
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0.0,0,0), numcut=100L,
usequants=FALSE, cont=FALSE, rm.const=TRUE,
grp = NULL, xnames = NULL, categorical_idx = NULL,
k=2.0, power=2.0, base=-1.0, split.prob = "polynomial",
binaryOffset=NULL,
ntree=50L,
ndpost=1000L, nskip=1000L, keepevery=1L,
nkeeptrain=ndpost, nkeeptest=ndpost, nkeeptreedraws=ndpost,
printevery=100L, transposed=FALSE) {
#--------------------------------------------------
#data
n = length(y.train)
if(length(binaryOffset)==0) binaryOffset=qnorm(mean(y.train))
if(!transposed) {
if(is.null(dim(x.train))) {
xnames = "X"
} else {
xnames = dimnames(x.train)[[2]] # predictor names before dummification
}
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
cont=cont, xinfo=xinfo, rm.const=rm.const)
x.train = t(temp$X)
numcut = temp$numcut
xinfo = temp$xinfo
if(length(x.test)>0) {
x.test = bartModelMatrix(x.test)
x.test = t(x.test[ , temp$rm.const])
}
rm.const <- temp$rm.const
grp <- temp$grp
rm(temp)
if(length(grp)==0){
p0 = nrow(x.train)
grp <- 1:p0
} else {
p0 = length(unique(grp)) # number of predictors before dummification
}
categorical_idx = unique(grp)[which(sapply(unique(grp), function(s) sum(s==grp)) > 1)] # indices of categorical predictors in the original design matrix
} else {
if(any(length(rm.const)==0, length(grp)==0, length(xnames)==0))
stop('Did not provide rm.const, grp and xnames for x.train after transpose!')
if(is.logical(rm.const))
stop('Did not provide rm.const for x.train after transpose!')
if((length(grp) > length(unique(grp))) & (length(categorical_idx) <= 0))
stop('Did not provide categorical_idx for x.train that contains categorical predictors!')
p0 = length(unique(grp))
}
if(n!=ncol(x.train))
stop('The length of y.train and the number of rows in x.train must be identical')
p = nrow(x.train)
np = ncol(x.test)
if(length(rho)==0) rho <- p
if(!(split.prob %in% c("polynomial", "exponential"))) {
stop("split.prob is either polynomial or exponential.")
} else {
if(split.prob == "polynomial") {
if(base < 0)
base = 0.95
}
if(split.prob == "exponential") {
power = -1.0
if(base < 0)
base = 0.5
}
}
#--------------------------------------------------
#set nkeeps for thinning
if((nkeeptrain!=0) & ((ndpost %% nkeeptrain) != 0)) {
nkeeptrain=ndpost
cat('*****nkeeptrain set to ndpost\n')
}
if((nkeeptest!=0) & ((ndpost %% nkeeptest) != 0)) {
nkeeptest=ndpost
cat('*****nkeeptest set to ndpost\n')
}
if((nkeeptreedraws!=0) & ((ndpost %% nkeeptreedraws) != 0)) {
nkeeptreedraws=ndpost
cat('*****nkeeptreedraws set to ndpost\n')
}
#--------------------------------------------------
ptm <- proc.time()
#call
res = .Call("cpbart",
n, #number of observations in training data
p, #dimension of x
np, #number of observations in test data
x.train, #p*n training data x
y.train, #n*1 training data y
x.test, #p*np test data x
ntree,
numcut,
ndpost*keepevery,
nskip,
power,
base,
binaryOffset,
3/(k*sqrt(ntree)),
sparse,
theta,
omega,
grp,
a,
b,
rho,
augment,
nkeeptrain,
nkeeptest,
nkeeptreedraws,
printevery,
xinfo
)
res$proc.time <- proc.time()-ptm
#---------------------------------------------------------
#returns
if(nkeeptrain>0) {
res$yhat.train = res$yhat.train+binaryOffset
res$prob.train = pnorm(res$yhat.train)
res$prob.train.mean <- apply(res$prob.train, 2, mean)
} else {
res$yhat.train <- NULL
}
if(np>0) {
res$yhat.test = res$yhat.test+binaryOffset
res$prob.test = pnorm(res$yhat.test)
res$prob.test.mean <- apply(res$prob.test, 2, mean)
} else {
res$yhat.test <- NULL
}
if(nkeeptreedraws>0)
names(res$treedraws$cutpoints) = dimnames(x.train)[[1]]
#--------------------------------------------------
#importance
if(length(grp) == length(unique(grp))) {
## no dummy variables
dimnames(res$varcount)[[2]] = as.list(dimnames(x.train)[[1]])
dimnames(res$varprob)[[2]] = as.list(dimnames(x.train)[[1]])
## vip
res$vip = colMeans(t(apply(res$varcount, 1, function(s) s/sum(s))))
## (marginal) posterior variable inclusion probability
res$pvip = colMeans(res$varcount > 0)
## posterior s_j's (in DART)
res$varprob.mean <- colMeans(res$varprob)
## mi
mr_vecs = lapply(res$mr_vecs, function(s) lapply(s, function(v) v[-1])) # remove the meaningless first 0
res$mr_vecs = mr_vecs
mrmean = matrix(unlist(lapply(mr_vecs, function(s) lapply(s, function(v) ifelse(length(v)>0, mean(v), 0.0)))),
ncol = p, byrow = TRUE)
res$mrmean = mrmean
res$mi = colMeans(t(apply(mrmean, 1, function(s) s/sum(s))))
names(res$mi) = as.list(dimnames(x.train)[[1]])
dimnames(res$mrmean)[[2]] = as.list(xnames)
} else {
## merge importance scores for dummy variables
varcount = matrix(NA, nrow = nkeeptreedraws, ncol = p0)
varprob = matrix(NA, nrow = nkeeptreedraws, ncol = p0)
mr_vecs = lapply(res$mr_vecs, function(s) list(s[[1]][-1]))
varcount[, 1] = res$varcount[, 1]
varprob[, 1] = res$varprob[, 1]
j = 1
for (l in 2:p) {
if (grp[l] == grp[l-1]) {
varcount[, j] = varcount[, j] + res$varcount[, l]
varprob[, j] = varprob[, j] + res$varprob[, l]
for (i in 1:nkeeptreedraws) {
mr_vecs[[i]][[j]] = c(mr_vecs[[i]][[j]], res$mr_vecs[[i]][[l]][-1])
}
} else {
j = j + 1
varcount[, j] = res$varcount[, l]
varprob[, j] = res$varprob[, l]
for (i in 1:nkeeptreedraws) {
mr_vecs[[i]][[j]] = res$mr_vecs[[i]][[l]][-1]
}
}
}
dimnames(varcount)[[2]] = as.list(xnames)
dimnames(varprob)[[2]] = as.list(xnames)
res$varcount = varcount
res$varprob = varprob
res$mr_vecs = mr_vecs
## vip
res$vip = colMeans(t(apply(varcount, 1, function(s) s/sum(s))))
## within-type vip
within_type_vip = rep(0, p0)
for (i in 1:nkeeptreedraws) {
if (sum(varcount[i, categorical_idx]) != 0) {
within_type_vip[categorical_idx] = within_type_vip[categorical_idx] +
varcount[i, categorical_idx]/sum(varcount[i, categorical_idx])
}
if (sum(varcount[i, -categorical_idx]) != 0) {
within_type_vip[-categorical_idx] = within_type_vip[-categorical_idx] +
varcount[i, -categorical_idx]/sum(varcount[i, -categorical_idx])
}
}
res$within_type_vip = within_type_vip / nkeeptreedraws
names(res$within_type_vip) = xnames
## (marginal) posterior variable inclusion probability
res$pvip = colMeans(varcount > 0)
## posterior s_j's (in DART)
res$varprob.mean <- colMeans(varprob)
## mi
mrmean = matrix(unlist(lapply(mr_vecs, function(s) lapply(s, function(v) ifelse(length(v)>0, mean(v), 0.0)))),
ncol = p0, byrow = TRUE)
res$mrmean = mrmean
res$mi = colMeans(t(apply(mrmean, 1, function(s) s/sum(s))))
dimnames(res$mrmean)[[2]] = as.list(xnames)
names(res$mi) = as.list(xnames)
}
res$rm.const <- rm.const
res$binaryOffset=binaryOffset
attr(res, 'class') <- 'pbart'
return(res)
} | /R/pbart.R | no_license | chujiluo/BNPqte | R | false | false | 8,115 | r | pbart = function(x.train, y.train, x.test=matrix(0.0,0,0),
sparse=FALSE, theta=0, omega=1,
a=0.5, b=1, augment=FALSE, rho=NULL,
xinfo=matrix(0.0,0,0), numcut=100L,
usequants=FALSE, cont=FALSE, rm.const=TRUE,
grp = NULL, xnames = NULL, categorical_idx = NULL,
k=2.0, power=2.0, base=-1.0, split.prob = "polynomial",
binaryOffset=NULL,
ntree=50L,
ndpost=1000L, nskip=1000L, keepevery=1L,
nkeeptrain=ndpost, nkeeptest=ndpost, nkeeptreedraws=ndpost,
printevery=100L, transposed=FALSE) {
#--------------------------------------------------
#data
n = length(y.train)
if(length(binaryOffset)==0) binaryOffset=qnorm(mean(y.train))
if(!transposed) {
if(is.null(dim(x.train))) {
xnames = "X"
} else {
xnames = dimnames(x.train)[[2]] # predictor names before dummification
}
temp = bartModelMatrix(x.train, numcut, usequants=usequants,
cont=cont, xinfo=xinfo, rm.const=rm.const)
x.train = t(temp$X)
numcut = temp$numcut
xinfo = temp$xinfo
if(length(x.test)>0) {
x.test = bartModelMatrix(x.test)
x.test = t(x.test[ , temp$rm.const])
}
rm.const <- temp$rm.const
grp <- temp$grp
rm(temp)
if(length(grp)==0){
p0 = nrow(x.train)
grp <- 1:p0
} else {
p0 = length(unique(grp)) # number of predictors before dummification
}
categorical_idx = unique(grp)[which(sapply(unique(grp), function(s) sum(s==grp)) > 1)] # indices of categorical predictors in the original design matrix
} else {
if(any(length(rm.const)==0, length(grp)==0, length(xnames)==0))
stop('Did not provide rm.const, grp and xnames for x.train after transpose!')
if(is.logical(rm.const))
stop('Did not provide rm.const for x.train after transpose!')
if((length(grp) > length(unique(grp))) & (length(categorical_idx) <= 0))
stop('Did not provide categorical_idx for x.train that contains categorical predictors!')
p0 = length(unique(grp))
}
if(n!=ncol(x.train))
stop('The length of y.train and the number of rows in x.train must be identical')
p = nrow(x.train)
np = ncol(x.test)
if(length(rho)==0) rho <- p
if(!(split.prob %in% c("polynomial", "exponential"))) {
stop("split.prob is either polynomial or exponential.")
} else {
if(split.prob == "polynomial") {
if(base < 0)
base = 0.95
}
if(split.prob == "exponential") {
power = -1.0
if(base < 0)
base = 0.5
}
}
#--------------------------------------------------
#set nkeeps for thinning
if((nkeeptrain!=0) & ((ndpost %% nkeeptrain) != 0)) {
nkeeptrain=ndpost
cat('*****nkeeptrain set to ndpost\n')
}
if((nkeeptest!=0) & ((ndpost %% nkeeptest) != 0)) {
nkeeptest=ndpost
cat('*****nkeeptest set to ndpost\n')
}
if((nkeeptreedraws!=0) & ((ndpost %% nkeeptreedraws) != 0)) {
nkeeptreedraws=ndpost
cat('*****nkeeptreedraws set to ndpost\n')
}
#--------------------------------------------------
ptm <- proc.time()
#call
res = .Call("cpbart",
n, #number of observations in training data
p, #dimension of x
np, #number of observations in test data
x.train, #p*n training data x
y.train, #n*1 training data y
x.test, #p*np test data x
ntree,
numcut,
ndpost*keepevery,
nskip,
power,
base,
binaryOffset,
3/(k*sqrt(ntree)),
sparse,
theta,
omega,
grp,
a,
b,
rho,
augment,
nkeeptrain,
nkeeptest,
nkeeptreedraws,
printevery,
xinfo
)
res$proc.time <- proc.time()-ptm
#---------------------------------------------------------
#returns
if(nkeeptrain>0) {
res$yhat.train = res$yhat.train+binaryOffset
res$prob.train = pnorm(res$yhat.train)
res$prob.train.mean <- apply(res$prob.train, 2, mean)
} else {
res$yhat.train <- NULL
}
if(np>0) {
res$yhat.test = res$yhat.test+binaryOffset
res$prob.test = pnorm(res$yhat.test)
res$prob.test.mean <- apply(res$prob.test, 2, mean)
} else {
res$yhat.test <- NULL
}
if(nkeeptreedraws>0)
names(res$treedraws$cutpoints) = dimnames(x.train)[[1]]
#--------------------------------------------------
#importance
if(length(grp) == length(unique(grp))) {
## no dummy variables
dimnames(res$varcount)[[2]] = as.list(dimnames(x.train)[[1]])
dimnames(res$varprob)[[2]] = as.list(dimnames(x.train)[[1]])
## vip
res$vip = colMeans(t(apply(res$varcount, 1, function(s) s/sum(s))))
## (marginal) posterior variable inclusion probability
res$pvip = colMeans(res$varcount > 0)
## posterior s_j's (in DART)
res$varprob.mean <- colMeans(res$varprob)
## mi
mr_vecs = lapply(res$mr_vecs, function(s) lapply(s, function(v) v[-1])) # remove the meaningless first 0
res$mr_vecs = mr_vecs
mrmean = matrix(unlist(lapply(mr_vecs, function(s) lapply(s, function(v) ifelse(length(v)>0, mean(v), 0.0)))),
ncol = p, byrow = TRUE)
res$mrmean = mrmean
res$mi = colMeans(t(apply(mrmean, 1, function(s) s/sum(s))))
names(res$mi) = as.list(dimnames(x.train)[[1]])
dimnames(res$mrmean)[[2]] = as.list(xnames)
} else {
## merge importance scores for dummy variables
varcount = matrix(NA, nrow = nkeeptreedraws, ncol = p0)
varprob = matrix(NA, nrow = nkeeptreedraws, ncol = p0)
mr_vecs = lapply(res$mr_vecs, function(s) list(s[[1]][-1]))
varcount[, 1] = res$varcount[, 1]
varprob[, 1] = res$varprob[, 1]
j = 1
for (l in 2:p) {
if (grp[l] == grp[l-1]) {
varcount[, j] = varcount[, j] + res$varcount[, l]
varprob[, j] = varprob[, j] + res$varprob[, l]
for (i in 1:nkeeptreedraws) {
mr_vecs[[i]][[j]] = c(mr_vecs[[i]][[j]], res$mr_vecs[[i]][[l]][-1])
}
} else {
j = j + 1
varcount[, j] = res$varcount[, l]
varprob[, j] = res$varprob[, l]
for (i in 1:nkeeptreedraws) {
mr_vecs[[i]][[j]] = res$mr_vecs[[i]][[l]][-1]
}
}
}
dimnames(varcount)[[2]] = as.list(xnames)
dimnames(varprob)[[2]] = as.list(xnames)
res$varcount = varcount
res$varprob = varprob
res$mr_vecs = mr_vecs
## vip
res$vip = colMeans(t(apply(varcount, 1, function(s) s/sum(s))))
## within-type vip
within_type_vip = rep(0, p0)
for (i in 1:nkeeptreedraws) {
if (sum(varcount[i, categorical_idx]) != 0) {
within_type_vip[categorical_idx] = within_type_vip[categorical_idx] +
varcount[i, categorical_idx]/sum(varcount[i, categorical_idx])
}
if (sum(varcount[i, -categorical_idx]) != 0) {
within_type_vip[-categorical_idx] = within_type_vip[-categorical_idx] +
varcount[i, -categorical_idx]/sum(varcount[i, -categorical_idx])
}
}
res$within_type_vip = within_type_vip / nkeeptreedraws
names(res$within_type_vip) = xnames
## (marginal) posterior variable inclusion probability
res$pvip = colMeans(varcount > 0)
## posterior s_j's (in DART)
res$varprob.mean <- colMeans(varprob)
## mi
mrmean = matrix(unlist(lapply(mr_vecs, function(s) lapply(s, function(v) ifelse(length(v)>0, mean(v), 0.0)))),
ncol = p0, byrow = TRUE)
res$mrmean = mrmean
res$mi = colMeans(t(apply(mrmean, 1, function(s) s/sum(s))))
dimnames(res$mrmean)[[2]] = as.list(xnames)
names(res$mi) = as.list(xnames)
}
res$rm.const <- rm.const
res$binaryOffset=binaryOffset
attr(res, 'class') <- 'pbart'
return(res)
} |
\name{eVenn-package}
\alias{eVenn-package}
\alias{eVenn}
\docType{package}
\title{
A Powerful Tool to Quickly Compare Huge Lists and Draw Venn Diagrams
}
\description{
Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
}
\details{
\tabular{ll}{
Package: \tab eVenn\cr
Type: \tab Package\cr
Version: \tab 2.2.1\cr
Date: \tab 2015-08-04\cr
License: \tab GPL\cr
LazyLoad: \tab yes\cr
}
}
\author{
Author & Maintainer: Nicolas Cagnard <nicolas.cagnard@gmail.com>
}
\references{
\url{http://blog.mrbioinfo.com/}
}
\keyword{ package }
\examples{
library(eVenn)
YNdisplay = TRUE # Allows commentaries and display of the main steps of the process
# Matrix of binary data
data(Data_Binary_Matrix)
evenn(matLists=Data_Binary_Matrix, display=YNdisplay, CompName="Binary_Matrix")
# Matrix of folds
# data(Data_Matrix_Of_Folds)
# evenn(matLists=Data_Matrix_Of_Folds, display=YNdisplay, CompName="Matrix_Of_Folds")
# Matrix of ratios
# data(Data_Matrix_Of_Ratios)
# evenn(matLists=Data_Matrix_Of_Ratios, display=YNdisplay, CompName="Matrix_Of_Ratios")
# List of 2, 3 or 4 matrix w/wo modulations and w/wo profils data
data(Data_Lists)
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], annot=TRUE,
# display=YNdisplay, CompName="Lists_4")
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], ud=TRUE,
# annot=TRUE, display=YNdisplay, CompName="Lists_4_UD")
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4", "DataMoy")],
# ud=TRUE, annot=TRUE, Profils=TRUE, display=YNdisplay, CompName="Lists_4_UD_Profils")
}
| /man/eVenn-package.Rd | no_license | NicolasCagnard/eVenn_v2.2.1 | R | false | false | 1,896 | rd | \name{eVenn-package}
\alias{eVenn-package}
\alias{eVenn}
\docType{package}
\title{
A Powerful Tool to Quickly Compare Huge Lists and Draw Venn Diagrams
}
\description{
Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
}
\details{
\tabular{ll}{
Package: \tab eVenn\cr
Type: \tab Package\cr
Version: \tab 2.2.1\cr
Date: \tab 2015-08-04\cr
License: \tab GPL\cr
LazyLoad: \tab yes\cr
}
}
\author{
Author & Maintainer: Nicolas Cagnard <nicolas.cagnard@gmail.com>
}
\references{
\url{http://blog.mrbioinfo.com/}
}
\keyword{ package }
\examples{
library(eVenn)
YNdisplay = TRUE # Allows commentaries and display of the main steps of the process
# Matrix of binary data
data(Data_Binary_Matrix)
evenn(matLists=Data_Binary_Matrix, display=YNdisplay, CompName="Binary_Matrix")
# Matrix of folds
# data(Data_Matrix_Of_Folds)
# evenn(matLists=Data_Matrix_Of_Folds, display=YNdisplay, CompName="Matrix_Of_Folds")
# Matrix of ratios
# data(Data_Matrix_Of_Ratios)
# evenn(matLists=Data_Matrix_Of_Ratios, display=YNdisplay, CompName="Matrix_Of_Ratios")
# List of 2, 3 or 4 matrix w/wo modulations and w/wo profils data
data(Data_Lists)
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], annot=TRUE,
# display=YNdisplay, CompName="Lists_4")
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4")], ud=TRUE,
# annot=TRUE, display=YNdisplay, CompName="Lists_4_UD")
# evenn(matLists=Data_Lists[c("List_1", "List_2", "List_3", "List_4", "DataMoy")],
# ud=TRUE, annot=TRUE, Profils=TRUE, display=YNdisplay, CompName="Lists_4_UD_Profils")
}
|
### iUTAH GAMUT Aquatic Station
### Grab Sample download
### Version 1.1
### Written by: Erin Fleming Jones, contact at erinfjones3@gmail.com
### Last updated: 4/23/2017
setwd("~/Box Sync/BYU/metagenomics")
####NOTES: Check ARBR gage code
## Load packages ##
library("devtools")
library("plyr")
Sys.timezone()
library("WaterML")
## Data server connections ##
# services = GetServices() ##Lists URLs of all available datasets
Loganserver= 'http://data.iutahepscor.org/LoganRiverWOF/cuahsi_1_1.asmx?WSDL' ##Conects to each web database
RBserver= 'http://data.iutahepscor.org/RedButteCreekWOF/cuahsi_1_1.asmx?WSDL'
PRserver= 'http://data.iutahepscor.org/ProvoRiverWOF/cuahsi_1_1.asmx?WSDL'
variables=GetVariables(RBserver) ##Lists variable codes
Logansites= GetSites(Loganserver) ##Lists site codes
Provosites= GetSites(PRserver)
RBsites= GetSites(RBserver)
### Set Date and times ###
### Logan ctrl F: 24Nov
LRStartDate = "2014-11-24" ##yyyy-mm-dd format
LREndDate = "2014-11-25"
LR_FB_BADateTime= "2014-11-24 13:15:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
LR_TG_BADateTime = "2014-11-24 11:45:00"
LR_WaterLab_AADateTime = "2014-11-24 14:45:00"
LR_MainStreet_BADateTime = "2014-11-24 15:00:00"
LR_Mendon_AADateTime = "2014-11-24 15:30:00"
### Red Butte ctrl F: 18Nov
RBStartDate = "2014-11-18" ##yyyy-mm-dd format
RBEndDate = "2014-11-19"
RB_KF_BADateTime = "2014-11-18 12:00:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
RB_ARBR_AADateTime = "2014-11-18 15:00:00"
RB_RBG_BADateTime = "2014-11-18 15:15:00"
RB_CG_BADateTime = "2014-11-18 16:00:00"
RB_FD_AADateTime = "2014-11-18 16:45:00"
### Provo ctrl F: 12Nov
PRStartDate = "2014-11-12" ##yyyy-mm-dd format
PREndDate ="2014-11-13"
PR_ST_BADateTime = "2014-11-12 12:15:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
PR_BJ_AADateTime = "2014-11-12 14:00:00" ## must be rounded to on the hour e.g. 14:00:00
PR_LM_BADateTime = "2014-11-12 11:00:00" ## must be rounded to on the hour e.g. 14:00:00
PR_CH_AADateTime = "2014-11-12 13:00:00" ## must be rounded to on the hour e.g. 14:00:00
# Q- PR_BJ, PR_LM, PR_CH only available on the hour (for now)
##### Franklin Basin ####
##Franklin Basin Water Temp##
#exo_Temp
EXWTempFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" ) #Download 24 hrs
EXWTempFranklin = subset(EXWTempFranklin[ which(EXWTempFranklin$time == LR_FB_BADateTime),] ) ### Subset 24-hr for single time point
EXWTempFranklin$DataValue[EXWTempFranklin$DataValue==-9999.00] <- NA ### Substitute database NA for R NA
#Turbidity_Temp
TWTempFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempFranklin <- subset(TWTempFranklin[ which(TWTempFranklin$time == LR_FB_BADateTime),])
TWTempFranklin$DataValue[TWTempFranklin$DataValue==-9999.00] <- NA
##Franklin Basin pH##
pHFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHFranklin = subset(pHFranklin[ which(pHFranklin$time == LR_FB_BADateTime),] )
pHFranklin$DataValue[pHFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Specific Conductivity##
SpConFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConFranklin = subset(SpConFranklin[ which(SpConFranklin$time == LR_FB_BADateTime),] )
SpConFranklin$DataValue[SpConFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Dissolved Oxygen##
ODOFranklin<-GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOFranklin = subset(ODOFranklin[ which(ODOFranklin$time == LR_FB_BADateTime),] )
ODOFranklin$DataValue[ODOFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Specific Turbidity##
TurbFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbFranklin = subset(TurbFranklin[ which(TurbFranklin$time == LR_FB_BADateTime),] )
TurbFranklin$DataValue[TurbFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Gage##
GageFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageFranklin = subset(GageFranklin[ which(GageFranklin$time == LR_FB_BADateTime),] )
GageFranklin$DataValue[GageFranklin$DataValue==-9999.00] <- NA
##### Tony Grove ####
##Tony Grove Water Temp##
#exo_Temp
EXWTempTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempTonyGrove = subset(EXWTempTonyGrove[ which(EXWTempTonyGrove$time == LR_TG_BADateTime),] )
EXWTempTonyGrove$DataValue[EXWTempTonyGrove$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempTonyGrove = subset(TWTempTonyGrove[ which(TWTempTonyGrove$time == LR_TG_BADateTime),] )
TWTempTonyGrove$DataValue[TWTempTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove pH##
pHTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHTonyGrove = subset(pHTonyGrove[ which(pHTonyGrove$time == LR_TG_BADateTime),] )
pHTonyGrove$DataValue[pHTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Specific Conductivity##
SpConTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConTonyGrove = subset(SpConTonyGrove[ which(SpConTonyGrove$time == LR_TG_BADateTime),] )
SpConTonyGrove$DataValue[SpConTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Dissolved Oxygen##
ODOTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOTonyGrove = subset(ODOTonyGrove[ which(ODOTonyGrove$time == LR_TG_BADateTime),] )
ODOTonyGrove$DataValue[ODOTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Specific Turbidity##
TurbTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbTonyGrove = subset(TurbTonyGrove[ which(TurbTonyGrove$time == LR_TG_BADateTime),] )
TurbTonyGrove$DataValue[TurbTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Discharge (Gage)##
GageTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageTonyGrove = subset(GageTonyGrove[ which(GageTonyGrove$time == LR_TG_BADateTime),] )
GageTonyGrove$DataValue[GageTonyGrove$DataValue==-9999.00] <- NA
##### Water Lab advanced ####
##Water Lab Water Temperature##
#exo_Temp
EXWTempWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempWaterLab = subset(EXWTempWaterLab[ which(EXWTempWaterLab$time == LR_WaterLab_AADateTime),] )
EXWTempWaterLab$DataValue[EXWTempWaterLab$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempWaterLab = GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempWaterLab = subset(TWTempWaterLab[ which(TWTempWaterLab$time == LR_WaterLab_AADateTime),] )
TWTempWaterLab$DataValue[TWTempWaterLab$DataValue==-9999.00] <- NA
##Water Lab pH##
pHWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHWaterLab = subset(pHWaterLab[ which(pHWaterLab$time == LR_WaterLab_AADateTime),] )
pHWaterLab$DataValue[pHWaterLab$DataValue==-9999.00] <- NA
##Water Lab Specific Conductivity##
SpConWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConWaterLab = subset(SpConWaterLab[ which(SpConWaterLab$time == LR_WaterLab_AADateTime),] )
SpConWaterLab$DataValue[SpConWaterLab$DataValue==-9999.00] <- NA
##Water Lab Dissolved Oxygen##
ODOWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOWaterLab = subset(ODOWaterLab[ which(ODOWaterLab$time == LR_WaterLab_AADateTime),] )
ODOWaterLab$DataValue[ODOWaterLab$DataValue==-9999.00] <- NA
##Water Lab Specific Tubidity##
TurbWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbWaterLab = subset(TurbWaterLab[ which(TurbWaterLab$time == LR_WaterLab_AADateTime),] )
TurbWaterLab$DataValue[TurbWaterLab$DataValue==-9999.00] <- NA
##Water Lab fDOM##
fDOMWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:fDOM", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
fDOMWaterLab = subset(fDOMWaterLab[ which(fDOMWaterLab$time == LR_WaterLab_AADateTime),] )
fDOMWaterLab$DataValue[fDOMWaterLab$DataValue==-9999.00] <- NA
##Water Lab Chla##
chlaWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:chlorophyll", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
chlaWaterLab = subset(chlaWaterLab[ which(chlaWaterLab$time == LR_WaterLab_AADateTime),] )
chlaWaterLab$DataValue[chlaWaterLab$DataValue==-9999.00] <- NA
##Water Lab Blue Green Algae (BGA)##
BGAWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:BGA", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
BGAWaterLab = subset(BGAWaterLab[ which(BGAWaterLab$time == LR_WaterLab_AADateTime),] )
BGAWaterLab$DataValue[BGAWaterLab$DataValue==-9999.00] <- NA
# ##Water Lab Nitrate##
# NitrateWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:Nitrate-N", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
# NitrateWaterLab = subset(NitrateWaterLab[ which(NitrateWaterLab$time == LR_WaterLab_AADateTime),] )
# NitrateWaterLab$DataValue[NitrateWaterLab$DataValue==-9999.00] <- NA
## Water Lab Gage height ##
GageWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageWaterLab = subset(GageWaterLab[ which(GageWaterLab$time == LR_WaterLab_AADateTime),] )
GageWaterLab$DataValue[GageWaterLab$DataValue==-9999.00] <- NA
##### Main Street ####
##Main Street Water Temp##
#exo_Temp
EXWTempMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempMainSt = subset(EXWTempMainSt[ which(EXWTempMainSt$time == LR_MainStreet_BADateTime),] )
EXWTempMainSt$DataValue[EXWTempMainSt$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempMainSt = subset(TWTempMainSt[ which(TWTempMainSt$time == LR_MainStreet_BADateTime),] )
TWTempMainSt$DataValue[TWTempMainSt$DataValue==-9999.00] <- NA
##Main Street pH##
pHMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHMainSt = subset(pHMainSt[ which(pHMainSt$time == LR_MainStreet_BADateTime),] )
pHMainSt$DataValue[pHMainSt$DataValue==-9999.00] <- NA
##Main St Specific Conductivity##
SpConMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConMainSt = subset(SpConMainSt[ which(SpConMainSt$time == LR_MainStreet_BADateTime),] )
SpConMainSt$DataValue[SpConMainSt$DataValue==-9999.00] <- NA
##Main St Dissolved Oxygen##
ODOMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOMainSt = subset(ODOMainSt[ which(ODOMainSt$time == LR_MainStreet_BADateTime),] )
ODOMainSt$DataValue[ODOMainSt$DataValue==-9999.00] <- NA
##Main St Specific Turbidity##
TurbMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbMainSt = subset(TurbMainSt[ which(TurbMainSt$time == LR_MainStreet_BADateTime),] )
TurbMainSt$DataValue[TurbMainSt$DataValue==-9999.00] <- NA
##Main Street Gage height##
GageMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageMainSt = subset(GageMainSt[ which(GageMainSt$time == LR_MainStreet_BADateTime),] )
GageMainSt$DataValue[GageMainSt$DataValue==-9999.00] <- NA
##### Mendon Rd advanced ####
##Mendon Rd Water Temp##
#exo_Temp
EXWTempMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
EXWTempMendon = subset(EXWTempMendon[ which(EXWTempMendon$time == LR_Mendon_AADateTime),] )
EXWTempMendon$DataValue[EXWTempMendon$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
TWTempMendon = subset(TWTempMendon[ which(TWTempMendon$time == LR_Mendon_AADateTime),] )
TWTempMendon$DataValue[TWTempMendon$DataValue==-9999.00] <- NA
##Mendon Rd pH##
pHMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
pHMendon = subset(pHMendon[ which(pHMendon$time == LR_Mendon_AADateTime),] )
pHMendon$DataValue[pHMendon$DataValue==-9999.00] <- NA
##Mendon Rd Specific Conductivity##
SpConMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
SpConMendon = subset(SpConMendon[ which(SpConMendon$time == LR_Mendon_AADateTime),] )
SpConMendon$DataValue[SpConMendon$DataValue==-9999.00] <- NA
##Mendon Rd Dissolved Oxygen##
ODOMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
ODOMendon = subset(ODOMendon[ which(ODOMendon$time == LR_Mendon_AADateTime),] )
ODOMendon$DataValue[ODOMendon$DataValue==-9999.00] <- NA
##Mendon Rd Turbidity##
TurbMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
TurbMendon = subset(TurbMendon[ which(TurbMendon$time == LR_Mendon_AADateTime),] )
TurbMendon$DataValue[TurbMendon$DataValue==-9999.00] <- NA
##Mendon Rd fDOM##
fDOMMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:fDOM", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
fDOMMendon = subset(fDOMMendon[ which(fDOMMendon$time == LR_Mendon_AADateTime),] )
fDOMMendon$DataValue[fDOMMendon$DataValue==-9999.00] <- NA
##Mendon Rd Chla##
chlaMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:chlorophyll", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
chlaMendon = subset(chlaMendon[ which(chlaMendon$time == LR_Mendon_AADateTime),] )
chlaMendon$DataValue[chlaMendon$DataValue==-9999.00] <- NA
##Mendon Rd Blue Green Algea (BGA)##
BGAMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:BGA", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
BGAMendon = subset(BGAMendon[ which(BGAMendon$time == LR_Mendon_AADateTime),] )
BGAMendon$DataValue[BGAMendon$DataValue==-9999.00] <- NA
# ##Mendon Rd Nitrate##
# NitrateMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:Nitrate-N", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
# NitrateMendon = subset(NitrateMendon[ which(NitrateMendon$time == LR_Mendon_AADateTime),] )
# NitrateMendon$DataValue[NitrateMendon$DataValue==-9999.00] <- NA
##Mendon Rd Gage height##
GageMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageMendon = subset(GageMendon[ which(GageMendon$time == LR_Mendon_AADateTime),] )
GageMendon$DataValue[GageMendon$DataValue==-9999.00] <- NA
##### Logan Spreadsheet #####
Franklin24NovGrabSample <- data.frame(DateTime = EXWTempFranklin$time,
Site = "LR_FB_BA",
Temp = EXWTempFranklin$DataValue,
pH = pHFranklin$DataValue,
SpCon = SpConFranklin$DataValue,
ODO = ODOFranklin$DataValue,
Turbidity = TurbFranklin$DataValue,
Gage = GageFranklin$DataValue)
Franklin24NovGrabSample
TonyGrove24NovGrabSample <- data.frame(DateTime = EXWTempTonyGrove$time,
Site = "LR_TG_BA",
Temp = EXWTempTonyGrove$DataValue,
pH = pHTonyGrove$DataValue,
SpCon = SpConTonyGrove$DataValue,
ODO = ODOTonyGrove$DataValue,
Turbidity = TurbTonyGrove$DataValue,
Gage = GageTonyGrove$DataValue)
TonyGrove24NovGrabSample
WaterLab24NovGrabSample <- data.frame(DateTime = EXWTempWaterLab$time,
Site = "LR_WaterLab_AA",
Temp = EXWTempWaterLab$DataValue,
pH = pHWaterLab$DataValue,
SpCon = SpConWaterLab$DataValue,
ODO = ODOWaterLab$DataValue,
Turbidity = TurbWaterLab$DataValue,
Gage = GageWaterLab$DataValue,
BGA = BGAWaterLab$DataValue,
chla = chlaWaterLab$DataValue,
fDOM = fDOMWaterLab$DataValue)
#Nitrate = NitrateWaterLab$DataValue)
WaterLab24NovGrabSample
MainSt24NovGrabSample <- data.frame(DateTime = EXWTempMainSt$time,
Site = "LR_MainStreet_BA",
Temp = EXWTempMainSt$DataValue,
pH = pHMainSt$DataValue,
SpCon = SpConMainSt$DataValue,
ODO = ODOMainSt$DataValue,
Turbidity = TurbMainSt$DataValue,
Gage = GageMainSt$DataValue)
MainSt24NovGrabSample
Mendon24NovGrabSample <- data.frame(DateTime = EXWTempMendon$time,
Site = "LR_Mendon_AA",
Temp = EXWTempMendon$DataValue,
pH = pHMendon$DataValue,
SpCon = SpConMendon$DataValue,
ODO = ODOMendon$DataValue,
Turbidity = TurbMendon$DataValue,
Gage = GageMendon$DataValue,
BGA = BGAMendon$DataValue,
chla = chlaMendon$DataValue,
fDOM = fDOMMendon$DataValue)
#Nitrate = NitrateMendon$DataValue)
Mendon24NovGrabSample
Logan24Nov<-rbind.fill(Franklin24NovGrabSample, TonyGrove24NovGrabSample, WaterLab24NovGrabSample, MainSt24NovGrabSample, Mendon24NovGrabSample )
Logan24Nov
##### Knowlton Fork ####
##Knowlton Fork Water Temp##
#exo_Temp
EXWTempKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempKnowlton = subset(EXWTempKnowlton[ which(EXWTempKnowlton$time ==RB_KF_BADateTime),])
EXWTempKnowlton$DataValue[EXWTempKnowlton$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempKnowlton = subset(TWTempKnowlton[ which(TWTempKnowlton$time ==RB_KF_BADateTime),])
TWTempKnowlton$DataValue[TWTempKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork pH##
pHKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHKnowlton = subset(pHKnowlton[ which(pHKnowlton$time ==RB_KF_BADateTime),])
pHKnowlton$DataValue[pHKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Specific Conductivity##
SpConKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConKnowlton = subset(SpConKnowlton[ which(SpConKnowlton$time ==RB_KF_BADateTime),])
SpConKnowlton$DataValue[SpConKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Dissolved Oxygen##
ODOKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOKnowlton = subset(ODOKnowlton[ which(ODOKnowlton$time ==RB_KF_BADateTime),])
ODOKnowlton$DataValue[ODOKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Specific Turbidity##
TurbKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbKnowlton = subset(TurbKnowlton[ which(TurbKnowlton$time ==RB_KF_BADateTime),])
TurbKnowlton$DataValue[TurbKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Gage##
GageKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageKnowlton = subset(GageKnowlton[ which(GageKnowlton$time ==RB_KF_BADateTime),])
GageKnowlton$DataValue[GageKnowlton$DataValue==-9999.00] <- NA
##### Above Red Butte Reservoir advanced ####
##Above RB Res Water Temp##
#exo_Temp
EXWTempARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempARBR = subset(EXWTempARBR[ which(EXWTempARBR$time ==RB_ARBR_AADateTime),])
EXWTempARBR$DataValue[EXWTempARBR$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempARBR = subset(TWTempARBR[ which(TWTempARBR$time ==RB_ARBR_AADateTime),])
TWTempARBR$DataValue[TWTempARBR$DataValue==-9999.00] <- NA
##Above RB Res pH##
pHARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHARBR = subset(pHARBR[ which(pHARBR$time ==RB_ARBR_AADateTime),])
pHARBR$DataValue[pHARBR$DataValue==-9999.00] <- NA
##Above RB Res Specific Conductivity##
SpConARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConARBR = subset(SpConARBR[ which(SpConARBR$time ==RB_ARBR_AADateTime),])
SpConARBR$DataValue[SpConARBR$DataValue==-9999.00] <- NA
##Above RB Res Dissolved Oxygen##
ODOARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOARBR = subset(ODOARBR[ which(ODOARBR$time ==RB_ARBR_AADateTime),])
ODOARBR$DataValue[ODOARBR$DataValue==-9999.00] <- NA
##Above RB Res Turbidity##
TurbARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbARBR = subset(TurbARBR[ which(TurbARBR$time ==RB_ARBR_AADateTime),])
TurbARBR$DataValue[TurbARBR$DataValue==-9999.00] <- NA
##Above RB Res fDOM##
fDOMARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:fDOM", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
fDOMARBR = subset(fDOMARBR[ which(fDOMARBR$time ==RB_ARBR_AADateTime),])
fDOMARBR$DataValue[fDOMARBR$DataValue==-9999.00] <- NA
##Above Red Butte Res Chla##
chlaARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:chlorophyll", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
chlaARBR = subset(chlaARBR[ which(chlaARBR$time ==RB_ARBR_AADateTime),])
chlaARBR$DataValue[chlaARBR$DataValue==-9999.00] <- NA
##Above RB Res Blue Green Algea (BGA)##
BGAARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:BGA", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
BGAARBR = subset(BGAARBR[ which(BGAARBR$time ==RB_ARBR_AADateTime),])
BGAARBR$DataValue[BGAARBR$DataValue==-9999.00] <- NA
# ##Above RB Res Nitrate##
# NitrateARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:Nitrate-N", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
# NitrateARBR = subset(NitrateARBR[ which(NitrateARBR$time ==RB_ARBR_AADateTime),])
# NitrateARBR$DataValue[NitrateARBR$DataValue==-9999.00] <- NA
## Above Red Butte Res Gage height ##
GageARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_USGS", variableCode="iutah:USGSStage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageARBR = subset(GageARBR[ which(GageARBR$time ==RB_ARBR_AADateTime),])
GageARBR$DataValue[GageARBR$DataValue==-9999.00] <- NA
##### Red Butte Gate ####
##Red Butte Gate Water Temperature##
#exo_Temp
EXWTempRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempRBG = subset(EXWTempRBG[which(EXWTempRBG$time == RB_RBG_BADateTime),])
EXWTempRBG$DataValue[EXWTempRBG$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempRBG = subset(TWTempRBG[which(TWTempRBG$time == RB_RBG_BADateTime),])
TWTempRBG$DataValue[TWTempRBG$DataValue==-9999.00] <- NA
##Red Butte Gate pH##
pHRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHRBG = subset(pHRBG[which(pHRBG$time == RB_RBG_BADateTime),])
pHRBG$DataValue[pHRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Specific Conductivity##
SpConRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConRBG = subset(SpConRBG[which(SpConRBG$time == RB_RBG_BADateTime),])
SpConRBG$DataValue[SpConRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Dissolved Oxygen##
ODORBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODORBG = subset(ODORBG[which(ODORBG$time == RB_RBG_BADateTime),])
ODORBG$DataValue[ODORBG$DataValue==-9999.00] <- NA
##Red Butte Gate Specific Tubidity##
TurbRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbRBG = subset(TurbRBG[which(TurbRBG$time == RB_RBG_BADateTime),])
TurbRBG$DataValue[TurbRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Gage##
GageRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageRBG = subset(GageRBG[which(GageRBG$time == RB_RBG_BADateTime),])
GageRBG$DataValue[GageRBG$DataValue==-9999.00] <- NA
##### Cottams Grove ####
##Cottams Grove Water Temp##
#exo_Temp
EXWTempCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempCGrove= subset(EXWTempCGrove[which(EXWTempCGrove$time == RB_CG_BADateTime),])
EXWTempCGrove$DataValue[EXWTempCGrove$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempCGrove= subset(TWTempCGrove[which(TWTempCGrove$time == RB_CG_BADateTime),])
TWTempCGrove$DataValue[TWTempCGrove$DataValue==-9999.00] <- NA
##Cottams Grove pH##
pHCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHCGrove= subset(pHCGrove[which(pHCGrove$time == RB_CG_BADateTime),])
pHCGrove$DataValue[pHCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Specific Conductivity##
SpConCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConCGrove= subset(SpConCGrove[which(SpConCGrove$time == RB_CG_BADateTime),])
SpConCGrove$DataValue[SpConCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Dissolved Oxygen##
ODOCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOCGrove= subset(ODOCGrove[which(ODOCGrove$time == RB_CG_BADateTime),])
ODOCGrove$DataValue[ODOCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Specific Turbidity##
TurbCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbCGrove= subset(TurbCGrove[which(TurbCGrove$time == RB_CG_BADateTime),])
TurbCGrove$DataValue[TurbCGrove$DataValue==-9999.00] <- NA
##Cottam Grove Gage height##
GageCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageCGrove= subset(GageCGrove[which(GageCGrove$time == RB_CG_BADateTime),])
GageCGrove$DataValue[GageCGrove$DataValue==-9999.00] <- NA
##### Foothill Drive advanced ####
##Foothill Drive Water Temp##
#exo_Temp
EXWTempFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempFoothill= subset(EXWTempFoothill[which(EXWTempFoothill$time == RB_FD_AADateTime),])
EXWTempFoothill$DataValue[EXWTempFoothill$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempFoothill= subset(TWTempFoothill[which(TWTempFoothill$time == RB_FD_AADateTime),])
TWTempFoothill$DataValue[TWTempFoothill$DataValue==-9999.00] <- NA
##Foothill Drive pH##
pHFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHFoothill= subset(pHFoothill[which(pHFoothill$time == RB_FD_AADateTime),])
pHFoothill$DataValue[pHFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Specific Conductivity##
SpConFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConFoothill= subset(SpConFoothill[which(SpConFoothill$time == RB_FD_AADateTime),])
SpConFoothill$DataValue[SpConFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Dissolved Oxygen##
ODOFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOFoothill= subset(ODOFoothill[which(ODOFoothill$time == RB_FD_AADateTime),])
ODOFoothill$DataValue[ODOFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Specific Turbidity##
TurbFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbFoothill= subset(TurbFoothill[which(TurbFoothill$time == RB_FD_AADateTime),])
TurbFoothill$DataValue[TurbFoothill$DataValue==-9999.00] <- NA
##Foothill Drive fDOM##
fDOMFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:fDOM", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
fDOMFoothill= subset(fDOMFoothill[which(fDOMFoothill$time == RB_FD_AADateTime),])
fDOMFoothill$DataValue[fDOMFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Chla##
chlaFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:chlorophyll", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
chlaFoothill= subset(chlaFoothill[which(chlaFoothill$time == RB_FD_AADateTime),])
chlaFoothill$DataValue[chlaFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Blue Green Algae (BGA)##
BGAFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:BGA", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
BGAFoothill= subset(BGAFoothill[which(BGAFoothill$time == RB_FD_AADateTime),])
BGAFoothill$DataValue[BGAFoothill$DataValue==-9999.00] <- NA
# ##Foothill Drive Nitrate##
# NitrateFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:Nitrate-N", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
# NitrateFoothill= subset(NitrateFoothill[which(NitrateFoothill$time == RB_FD_AADateTime),])
# NitrateFoothill$DataValue[NitrateFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Gage height##
GageFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageFoothill= subset(GageFoothill[which(GageFoothill$time == RB_FD_AADateTime),])
GageFoothill$DataValue[GageFoothill$DataValue==-9999.00] <- NA
##### Red Butte Spreadsheet #####
Knowlton18NovGrabSample <- data.frame(DateTime = EXWTempKnowlton$time,
Site = "RB_KF_BA",
Temp = EXWTempKnowlton$DataValue,
pH = pHKnowlton$DataValue,
SpCon = SpConKnowlton$DataValue,
ODO = ODOKnowlton$DataValue,
Turbidity = TurbKnowlton$DataValue,
Gage = GageKnowlton$DataValue)
Knowlton18NovGrabSample
ARBR18NovGrabSample <- data.frame(DateTime = EXWTempARBR$time,
Site = "RB_ARBR_AA",
Temp = EXWTempARBR$DataValue,
pH = pHARBR$DataValue,
SpCon = SpConARBR$DataValue,
ODO = ODOARBR$DataValue,
Turbidity = TurbARBR$DataValue,
#Gage = GageARBR$DataValue,
BGA = BGAARBR$DataValue,
chla = chlaARBR$DataValue,
fDOM = fDOMARBR$DataValue)
#Nitrate = NitrateARBR$DataValue)
ARBR18NovGrabSample
RBG18NovGrabSample <- data.frame(DateTime = EXWTempRBG$time,
Site = "RB_RBG_BA",
Temp = EXWTempRBG$DataValue,
pH = pHRBG$DataValue,
SpCon = SpConRBG$DataValue,
ODO = ODORBG$DataValue,
Turbidity = TurbRBG$DataValue,
Gage = GageRBG$DataValue)
RBG18NovGrabSample
CGrove18NovGrabSample <- data.frame(DateTime = EXWTempCGrove$time,
Site = "RB_CG_BA",
Temp = EXWTempCGrove$DataValue,
pH = pHCGrove$DataValue,
SpCon = SpConCGrove$DataValue,
ODO = ODOCGrove$DataValue,
Turbidity = TurbCGrove$DataValue,
Gage = GageCGrove$DataValue)
CGrove18NovGrabSample
Foothill18NovGrabSample <- data.frame(DateTime = EXWTempFoothill$time,
Site = "RB_FD_AA",
Temp = EXWTempFoothill$DataValue,
pH = pHFoothill$DataValue,
SpCon = SpConFoothill$DataValue,
ODO = ODOFoothill$DataValue,
Turbidity = TurbFoothill$DataValue,
Gage = GageFoothill$DataValue,
BGA = BGAFoothill$DataValue,
chla = chlaFoothill$DataValue,
fDOM = fDOMFoothill$DataValue)
#Nitrate = NitrateFoothill$DataValue)
Foothill18NovGrabSample
RedButte18Nov<- rbind.fill(Knowlton18NovGrabSample, ARBR18NovGrabSample, RBG18NovGrabSample, CGrove18NovGrabSample, Foothill18NovGrabSample)
##### Soapstone #####
##Soapstone Water Temp
#exo_Temp
EXWTempST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempST= subset(EXWTempST[which(EXWTempST$time == PR_ST_BADateTime),])
EXWTempST$DataValue[EXWTempST$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempST= subset(TWTempST[which(TWTempST$time == PR_ST_BADateTime),])
TWTempST$DataValue[TWTempST$DataValue==-9999.00] <- NA
##Soapstone pH##
pHST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHST= subset(pHST[which(pHST$time == PR_ST_BADateTime),])
pHST$DataValue[pHST$DataValue==-9999.00] <- NA
##Soapstone Specific Conductivity##
SpConST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConST= subset(SpConST[which(SpConST$time == PR_ST_BADateTime),])
SpConST$DataValue[SpConST$DataValue==-9999.00] <- NA
##Soapstone Dissolved Oxygen##
ODOST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOST= subset(ODOST[which(ODOST$time == PR_ST_BADateTime),])
ODOST$DataValue[ODOST$DataValue==-9999.00] <- NA
##Soapstone Specific Turbidity##
TurbST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbST= subset(TurbST[which(TurbST$time == PR_ST_BADateTime),])
TurbST$DataValue[TurbST$DataValue==-9999.00] <- NA
##Soapstone Stage##
StageST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:Stage", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
StageST= subset(StageST[which(StageST$time == PR_ST_BADateTime),])
StageST$DataValue[StageST$DataValue==-9999.00] <- NA
##### Below Jordanelle advanced #########
##Below Jordanelle Temp
#exo_Temp
EXWTempBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, methodID= "99", qcID = "1" )
EXWTempBJ= subset(EXWTempBJ[which(EXWTempBJ$time == PR_BJ_AADateTime),])
EXWTempBJ$DataValue[EXWTempBJ$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempBJ= subset(TWTempBJ[which(TWTempBJ$time == PR_BJ_AADateTime),])
TWTempBJ$DataValue[TWTempBJ$DataValue==-9999.00] <- NA
## Below Jordanelle pH ##
pHBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHBJ= subset(pHBJ[which(pHBJ$time == PR_BJ_AADateTime),])
pHBJ$DataValue[pHBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Specific Conductivity##
SpConBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConBJ= subset(SpConBJ[which(SpConBJ$time == PR_BJ_AADateTime),])
SpConBJ$DataValue[SpConBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Dissolved Oxygen##
ODOBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOBJ= subset(ODOBJ[which(ODOBJ$time == PR_BJ_AADateTime),])
##Below Jordanelle Turbidity##
TurbBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbBJ= subset(TurbBJ[which(TurbBJ$time == PR_BJ_AADateTime),])
TurbBJ$DataValue[TurbBJ$DataValue==-9999.00] <- NA
##Below Jordanelle fDOM##
fDOMBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:fDOM", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
fDOMBJ= subset(fDOMBJ[which(fDOMBJ$time == PR_BJ_AADateTime),])
fDOMBJ$DataValue[fDOMBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Chla##
chlaBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:chlorophyll", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
chlaBJ= subset(chlaBJ[which(chlaBJ$time == PR_BJ_AADateTime),])
chlaBJ$DataValue[chlaBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Blue Green Algea (BGA)##
BGABJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:BGA", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
BGABJ= subset(BGABJ[which(BGABJ$time == PR_BJ_AADateTime),])
BGABJ$DataValue[BGABJ$DataValue==-9999.00] <- NA
# ##Below Jordanelle Nitrate##
# NitrateBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:Nitrate-N", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
# NitrateBJ= subset(NitrateBJ[which(NitrateBJ$time == PR_BJ_AADateTime),])
# NitrateBJ$DataValue[NitrateBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Discharge (Q)##
QBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QBJ= subset(QBJ[which(QBJ$time == PR_BJ_AADateTime),])
QBJ$DataValue[QBJ$DataValue==-9999.00] <- NA
##### Lower Midway #######
##Lower Midway Water Temp
#exo_Temp
EXWTempLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempLM= subset(EXWTempLM[which(EXWTempLM$time == PR_LM_BADateTime),])
EXWTempLM$DataValue[EXWTempLM$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempLM= subset(TWTempLM[which(TWTempLM$time == PR_LM_BADateTime),])
TWTempLM$DataValue[TWTempLM$DataValue==-9999.00] <- NA
##Lower Midway pH##
pHLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHLM= subset(pHLM[which(pHLM$time == PR_LM_BADateTime),])
pHLM$DataValue[pHLM$DataValue==-9999.00] <- NA
##Lower Midway Specific Conductivity##
SpConLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConLM= subset(SpConLM[which(SpConLM$time == PR_LM_BADateTime),])
SpConLM$DataValue[SpConLM$DataValue==-9999.00] <- NA
##Lower Midway Dissolved Oxygen##
ODOLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOLM= subset(ODOLM[which(ODOLM$time == PR_LM_BADateTime),])
ODOLM$DataValue[ODOLM$DataValue==-9999.00] <- NA
##Lower Midway Specific Tubidity##
TurbLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbLM= subset(TurbLM[which(TurbLM$time == PR_LM_BADateTime),])
TurbLM$DataValue[TurbLM$DataValue==-9999.00] <- NA
##Lower Midway Discharge (Q)##
QLM<- GetValues(PRserver, siteCode="iutah:PR_uM_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QLM= subset(QLM[which(QLM$time == PR_LM_BADateTime),])
QLM$DataValue[QLM$DataValue==-9999.00] <- NA
##### Charleston advanced ######
## Charleston Water Temp
#exo_Temp
EXWTempCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempCH= subset(EXWTempCH[which(EXWTempCH$time == PR_CH_AADateTime),])
EXWTempCH$DataValue[EXWTempCH$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempCH= subset(TWTempCH[which(TWTempCH$time == PR_CH_AADateTime),])
TWTempCH$DataValue[TWTempCH$DataValue==-9999.00] <- NA
##Charleston pH##
pHCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHCH= subset(pHCH[which(pHCH$time == PR_CH_AADateTime),])
pHCH$DataValue[pHCH$DataValue==-9999.00] <- NA
##Charleston Specific Conductivity##
SpConCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConCH= subset(SpConCH[which(SpConCH$time == PR_CH_AADateTime),])
SpConCH$DataValue[SpConCH$DataValue==-9999.00] <- NA
##Charleston Dissolved Oxygen##
ODOCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOCH= subset(ODOCH[which(ODOCH$time == PR_CH_AADateTime),])
ODOCH$DataValue[ODOCH$DataValue==-9999.00] <- NA
##Charleston Specific Turbidity##
TurbCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbCH= subset(TurbCH[which(TurbCH$time == PR_CH_AADateTime),])
TurbCH$DataValue[TurbCH$DataValue==-9999.00] <- NA
##Charleston fDOM##
fDOMCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:fDOM", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
fDOMCH= subset(fDOMCH[which(fDOMCH$time == PR_CH_AADateTime),])
fDOMCH$DataValue[fDOMCH$DataValue==-9999.00] <- NA
##Charleston Chla##
chlaCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:chlorophyll", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
chlaCH= subset(chlaCH[which(chlaCH$time == PR_CH_AADateTime),])
chlaCH$DataValue[chlaCH$DataValue==-9999.00] <- NA
##Charleston Blue Green Algae (BGA)##
BGACH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:BGA", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
BGACH= subset(BGACH[which(BGACH$time == PR_CH_AADateTime),])
BGACH$DataValue[BGACH$DataValue==-9999.00] <- NA
# ##Charleston Nitrate##
# NitrateCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:Nitrate-N", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
# NitrateCH= subset(NitrateCH[which(NitrateCH$time == PR_CH_AADateTime),])
# NitrateCH$DataValue[NitrateCH$DataValue==-9999.00] <- NA
##Charleston Discharge (Q)##
QCH<- GetValues(PRserver, siteCode="iutah:PR_CH_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QCH= subset(QCH[which(QCH$time == PR_CH_AADateTime),])
QCH$DataValue[QCH$DataValue==-9999.00] <- NA
##### Provo Spreadsheet #####
Soapstone12NovGrabSample <- data.frame(DateTime = EXWTempST$time,
Site = "PR_ST_BA",
Temp = EXWTempST$DataValue,
pH = pHST$DataValue,
SpCon = SpConST$DataValue,
ODO = ODOST$DataValue,
Turbidity = TurbST$DataValue,
Gage = StageST$DataValue)
Soapstone12NovGrabSample
BelowJordanelle12NovGrabSample <- data.frame(DateTime = EXWTempBJ$time,
Site = "PR_BJ_AA",
Temp = EXWTempBJ$DataValue,
pH = pHBJ$DataValue,
SpCon = SpConBJ$DataValue,
ODO = ODOBJ$DataValue,
Turbidity = TurbBJ$DataValue,
Gage = QBJ$DataValue,
BGA = BGABJ$DataValue,
chla = chlaBJ$DataValue,
fDOM = fDOMBJ$DataValue)
# Nitrate = NitrateBJ$DataValue)
BelowJordanelle12NovGrabSample
LowerMidway12NovGrabSample <- data.frame(DateTime = EXWTempLM$time,
Site = "PR_LM_BA",
Temp = EXWTempLM$DataValue,
pH = pHLM$DataValue,
SpCon = SpConLM$DataValue,
ODO = ODOLM$DataValue,
Turbidity = TurbLM$DataValue,
Gage = QLM$DataValue)
LowerMidway12NovGrabSample
Charleston12NovGrabSample <- data.frame(DateTime = EXWTempCH$time,
Site = "PR_CH_AA",
Temp = EXWTempCH$DataValue,
pH = pHCH$DataValue,
SpCon = SpConCH$DataValue,
ODO = ODOCH$DataValue,
Turbidity = TurbCH$DataValue,
Gage = QCH$DataValue,
BGA = BGACH$DataValue,
chla = chlaCH$DataValue,
fDOM = fDOMCH$DataValue)
#Nitrate = NitrateCH$DataValue)
Charleston12NovGrabSample
Provo12Nov <-rbind.fill(Soapstone12NovGrabSample, BelowJordanelle12NovGrabSample, LowerMidway12NovGrabSample, Charleston12NovGrabSample)
##### All combine and write ####
GrabSamples24Nov <- rbind.fill(RedButte18Nov, Logan24Nov, Provo12Nov)
write.csv(GrabSamples24Nov, file = "GrabSamples2" )
| /GAMUTNov14_nonitrate.R | no_license | erinfjones/GAMUTdownload | R | false | false | 51,741 | r | ### iUTAH GAMUT Aquatic Station
### Grab Sample download
### Version 1.1
### Written by: Erin Fleming Jones, contact at erinfjones3@gmail.com
### Last updated: 4/23/2017
setwd("~/Box Sync/BYU/metagenomics")
####NOTES: Check ARBR gage code
## Load packages ##
library("devtools")
library("plyr")
Sys.timezone()
library("WaterML")
## Data server connections ##
# services = GetServices() ##Lists URLs of all available datasets
Loganserver= 'http://data.iutahepscor.org/LoganRiverWOF/cuahsi_1_1.asmx?WSDL' ##Conects to each web database
RBserver= 'http://data.iutahepscor.org/RedButteCreekWOF/cuahsi_1_1.asmx?WSDL'
PRserver= 'http://data.iutahepscor.org/ProvoRiverWOF/cuahsi_1_1.asmx?WSDL'
variables=GetVariables(RBserver) ##Lists variable codes
Logansites= GetSites(Loganserver) ##Lists site codes
Provosites= GetSites(PRserver)
RBsites= GetSites(RBserver)
### Set Date and times ###
### Logan ctrl F: 24Nov
LRStartDate = "2014-11-24" ##yyyy-mm-dd format
LREndDate = "2014-11-25"
LR_FB_BADateTime= "2014-11-24 13:15:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
LR_TG_BADateTime = "2014-11-24 11:45:00"
LR_WaterLab_AADateTime = "2014-11-24 14:45:00"
LR_MainStreet_BADateTime = "2014-11-24 15:00:00"
LR_Mendon_AADateTime = "2014-11-24 15:30:00"
### Red Butte ctrl F: 18Nov
RBStartDate = "2014-11-18" ##yyyy-mm-dd format
RBEndDate = "2014-11-19"
RB_KF_BADateTime = "2014-11-18 12:00:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
RB_ARBR_AADateTime = "2014-11-18 15:00:00"
RB_RBG_BADateTime = "2014-11-18 15:15:00"
RB_CG_BADateTime = "2014-11-18 16:00:00"
RB_FD_AADateTime = "2014-11-18 16:45:00"
### Provo ctrl F: 12Nov
PRStartDate = "2014-11-12" ##yyyy-mm-dd format
PREndDate ="2014-11-13"
PR_ST_BADateTime = "2014-11-12 12:15:00" ##yyyy-mm-dd hh:mm:ss format, rounded to 0, 15, 30, or 45 minute mark
PR_BJ_AADateTime = "2014-11-12 14:00:00" ## must be rounded to on the hour e.g. 14:00:00
PR_LM_BADateTime = "2014-11-12 11:00:00" ## must be rounded to on the hour e.g. 14:00:00
PR_CH_AADateTime = "2014-11-12 13:00:00" ## must be rounded to on the hour e.g. 14:00:00
# Q- PR_BJ, PR_LM, PR_CH only available on the hour (for now)
##### Franklin Basin ####
##Franklin Basin Water Temp##
#exo_Temp
EXWTempFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" ) #Download 24 hrs
EXWTempFranklin = subset(EXWTempFranklin[ which(EXWTempFranklin$time == LR_FB_BADateTime),] ) ### Subset 24-hr for single time point
EXWTempFranklin$DataValue[EXWTempFranklin$DataValue==-9999.00] <- NA ### Substitute database NA for R NA
#Turbidity_Temp
TWTempFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempFranklin <- subset(TWTempFranklin[ which(TWTempFranklin$time == LR_FB_BADateTime),])
TWTempFranklin$DataValue[TWTempFranklin$DataValue==-9999.00] <- NA
##Franklin Basin pH##
pHFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHFranklin = subset(pHFranklin[ which(pHFranklin$time == LR_FB_BADateTime),] )
pHFranklin$DataValue[pHFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Specific Conductivity##
SpConFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConFranklin = subset(SpConFranklin[ which(SpConFranklin$time == LR_FB_BADateTime),] )
SpConFranklin$DataValue[SpConFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Dissolved Oxygen##
ODOFranklin<-GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOFranklin = subset(ODOFranklin[ which(ODOFranklin$time == LR_FB_BADateTime),] )
ODOFranklin$DataValue[ODOFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Specific Turbidity##
TurbFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbFranklin = subset(TurbFranklin[ which(TurbFranklin$time == LR_FB_BADateTime),] )
TurbFranklin$DataValue[TurbFranklin$DataValue==-9999.00] <- NA
##Franklin Basin Gage##
GageFranklin<- GetValues(Loganserver, siteCode="iutah:LR_FB_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageFranklin = subset(GageFranklin[ which(GageFranklin$time == LR_FB_BADateTime),] )
GageFranklin$DataValue[GageFranklin$DataValue==-9999.00] <- NA
##### Tony Grove ####
##Tony Grove Water Temp##
#exo_Temp
EXWTempTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempTonyGrove = subset(EXWTempTonyGrove[ which(EXWTempTonyGrove$time == LR_TG_BADateTime),] )
EXWTempTonyGrove$DataValue[EXWTempTonyGrove$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempTonyGrove = subset(TWTempTonyGrove[ which(TWTempTonyGrove$time == LR_TG_BADateTime),] )
TWTempTonyGrove$DataValue[TWTempTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove pH##
pHTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHTonyGrove = subset(pHTonyGrove[ which(pHTonyGrove$time == LR_TG_BADateTime),] )
pHTonyGrove$DataValue[pHTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Specific Conductivity##
SpConTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConTonyGrove = subset(SpConTonyGrove[ which(SpConTonyGrove$time == LR_TG_BADateTime),] )
SpConTonyGrove$DataValue[SpConTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Dissolved Oxygen##
ODOTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOTonyGrove = subset(ODOTonyGrove[ which(ODOTonyGrove$time == LR_TG_BADateTime),] )
ODOTonyGrove$DataValue[ODOTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Specific Turbidity##
TurbTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbTonyGrove = subset(TurbTonyGrove[ which(TurbTonyGrove$time == LR_TG_BADateTime),] )
TurbTonyGrove$DataValue[TurbTonyGrove$DataValue==-9999.00] <- NA
##Tony Grove Discharge (Gage)##
GageTonyGrove<- GetValues(Loganserver, siteCode="iutah:LR_TG_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageTonyGrove = subset(GageTonyGrove[ which(GageTonyGrove$time == LR_TG_BADateTime),] )
GageTonyGrove$DataValue[GageTonyGrove$DataValue==-9999.00] <- NA
##### Water Lab advanced ####
##Water Lab Water Temperature##
#exo_Temp
EXWTempWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempWaterLab = subset(EXWTempWaterLab[ which(EXWTempWaterLab$time == LR_WaterLab_AADateTime),] )
EXWTempWaterLab$DataValue[EXWTempWaterLab$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempWaterLab = GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempWaterLab = subset(TWTempWaterLab[ which(TWTempWaterLab$time == LR_WaterLab_AADateTime),] )
TWTempWaterLab$DataValue[TWTempWaterLab$DataValue==-9999.00] <- NA
##Water Lab pH##
pHWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHWaterLab = subset(pHWaterLab[ which(pHWaterLab$time == LR_WaterLab_AADateTime),] )
pHWaterLab$DataValue[pHWaterLab$DataValue==-9999.00] <- NA
##Water Lab Specific Conductivity##
SpConWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConWaterLab = subset(SpConWaterLab[ which(SpConWaterLab$time == LR_WaterLab_AADateTime),] )
SpConWaterLab$DataValue[SpConWaterLab$DataValue==-9999.00] <- NA
##Water Lab Dissolved Oxygen##
ODOWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOWaterLab = subset(ODOWaterLab[ which(ODOWaterLab$time == LR_WaterLab_AADateTime),] )
ODOWaterLab$DataValue[ODOWaterLab$DataValue==-9999.00] <- NA
##Water Lab Specific Tubidity##
TurbWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbWaterLab = subset(TurbWaterLab[ which(TurbWaterLab$time == LR_WaterLab_AADateTime),] )
TurbWaterLab$DataValue[TurbWaterLab$DataValue==-9999.00] <- NA
##Water Lab fDOM##
fDOMWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:fDOM", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
fDOMWaterLab = subset(fDOMWaterLab[ which(fDOMWaterLab$time == LR_WaterLab_AADateTime),] )
fDOMWaterLab$DataValue[fDOMWaterLab$DataValue==-9999.00] <- NA
##Water Lab Chla##
chlaWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:chlorophyll", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
chlaWaterLab = subset(chlaWaterLab[ which(chlaWaterLab$time == LR_WaterLab_AADateTime),] )
chlaWaterLab$DataValue[chlaWaterLab$DataValue==-9999.00] <- NA
##Water Lab Blue Green Algae (BGA)##
BGAWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:BGA", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
BGAWaterLab = subset(BGAWaterLab[ which(BGAWaterLab$time == LR_WaterLab_AADateTime),] )
BGAWaterLab$DataValue[BGAWaterLab$DataValue==-9999.00] <- NA
# ##Water Lab Nitrate##
# NitrateWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:Nitrate-N", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
# NitrateWaterLab = subset(NitrateWaterLab[ which(NitrateWaterLab$time == LR_WaterLab_AADateTime),] )
# NitrateWaterLab$DataValue[NitrateWaterLab$DataValue==-9999.00] <- NA
## Water Lab Gage height ##
GageWaterLab<- GetValues(Loganserver, siteCode="iutah:LR_WaterLab_AA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageWaterLab = subset(GageWaterLab[ which(GageWaterLab$time == LR_WaterLab_AADateTime),] )
GageWaterLab$DataValue[GageWaterLab$DataValue==-9999.00] <- NA
##### Main Street ####
##Main Street Water Temp##
#exo_Temp
EXWTempMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
EXWTempMainSt = subset(EXWTempMainSt[ which(EXWTempMainSt$time == LR_MainStreet_BADateTime),] )
EXWTempMainSt$DataValue[EXWTempMainSt$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TWTempMainSt = subset(TWTempMainSt[ which(TWTempMainSt$time == LR_MainStreet_BADateTime),] )
TWTempMainSt$DataValue[TWTempMainSt$DataValue==-9999.00] <- NA
##Main Street pH##
pHMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
pHMainSt = subset(pHMainSt[ which(pHMainSt$time == LR_MainStreet_BADateTime),] )
pHMainSt$DataValue[pHMainSt$DataValue==-9999.00] <- NA
##Main St Specific Conductivity##
SpConMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
SpConMainSt = subset(SpConMainSt[ which(SpConMainSt$time == LR_MainStreet_BADateTime),] )
SpConMainSt$DataValue[SpConMainSt$DataValue==-9999.00] <- NA
##Main St Dissolved Oxygen##
ODOMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
ODOMainSt = subset(ODOMainSt[ which(ODOMainSt$time == LR_MainStreet_BADateTime),] )
ODOMainSt$DataValue[ODOMainSt$DataValue==-9999.00] <- NA
##Main St Specific Turbidity##
TurbMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
TurbMainSt = subset(TurbMainSt[ which(TurbMainSt$time == LR_MainStreet_BADateTime),] )
TurbMainSt$DataValue[TurbMainSt$DataValue==-9999.00] <- NA
##Main Street Gage height##
GageMainSt<- GetValues(Loganserver, siteCode="iutah:LR_MainStreet_BA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageMainSt = subset(GageMainSt[ which(GageMainSt$time == LR_MainStreet_BADateTime),] )
GageMainSt$DataValue[GageMainSt$DataValue==-9999.00] <- NA
##### Mendon Rd advanced ####
##Mendon Rd Water Temp##
#exo_Temp
EXWTempMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:WaterTemp_EXO", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
EXWTempMendon = subset(EXWTempMendon[ which(EXWTempMendon$time == LR_Mendon_AADateTime),] )
EXWTempMendon$DataValue[EXWTempMendon$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:WaterTemp_Turb", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
TWTempMendon = subset(TWTempMendon[ which(TWTempMendon$time == LR_Mendon_AADateTime),] )
TWTempMendon$DataValue[TWTempMendon$DataValue==-9999.00] <- NA
##Mendon Rd pH##
pHMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:pH", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
pHMendon = subset(pHMendon[ which(pHMendon$time == LR_Mendon_AADateTime),] )
pHMendon$DataValue[pHMendon$DataValue==-9999.00] <- NA
##Mendon Rd Specific Conductivity##
SpConMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:SpCond", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
SpConMendon = subset(SpConMendon[ which(SpConMendon$time == LR_Mendon_AADateTime),] )
SpConMendon$DataValue[SpConMendon$DataValue==-9999.00] <- NA
##Mendon Rd Dissolved Oxygen##
ODOMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:ODO", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
ODOMendon = subset(ODOMendon[ which(ODOMendon$time == LR_Mendon_AADateTime),] )
ODOMendon$DataValue[ODOMendon$DataValue==-9999.00] <- NA
##Mendon Rd Turbidity##
TurbMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:TurbAvg", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
TurbMendon = subset(TurbMendon[ which(TurbMendon$time == LR_Mendon_AADateTime),] )
TurbMendon$DataValue[TurbMendon$DataValue==-9999.00] <- NA
##Mendon Rd fDOM##
fDOMMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:fDOM", startDate = LRStartDate, endDate = LREndDate, qcID = "0" )
fDOMMendon = subset(fDOMMendon[ which(fDOMMendon$time == LR_Mendon_AADateTime),] )
fDOMMendon$DataValue[fDOMMendon$DataValue==-9999.00] <- NA
##Mendon Rd Chla##
chlaMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:chlorophyll", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
chlaMendon = subset(chlaMendon[ which(chlaMendon$time == LR_Mendon_AADateTime),] )
chlaMendon$DataValue[chlaMendon$DataValue==-9999.00] <- NA
##Mendon Rd Blue Green Algea (BGA)##
BGAMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:BGA", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
BGAMendon = subset(BGAMendon[ which(BGAMendon$time == LR_Mendon_AADateTime),] )
BGAMendon$DataValue[BGAMendon$DataValue==-9999.00] <- NA
# ##Mendon Rd Nitrate##
# NitrateMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:Nitrate-N", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
# NitrateMendon = subset(NitrateMendon[ which(NitrateMendon$time == LR_Mendon_AADateTime),] )
# NitrateMendon$DataValue[NitrateMendon$DataValue==-9999.00] <- NA
##Mendon Rd Gage height##
GageMendon<- GetValues(Loganserver, siteCode="iutah:LR_Mendon_AA", variableCode="iutah:Stage", startDate = LRStartDate, endDate = LREndDate, qcID = "1" )
GageMendon = subset(GageMendon[ which(GageMendon$time == LR_Mendon_AADateTime),] )
GageMendon$DataValue[GageMendon$DataValue==-9999.00] <- NA
##### Logan Spreadsheet #####
Franklin24NovGrabSample <- data.frame(DateTime = EXWTempFranklin$time,
Site = "LR_FB_BA",
Temp = EXWTempFranklin$DataValue,
pH = pHFranklin$DataValue,
SpCon = SpConFranklin$DataValue,
ODO = ODOFranklin$DataValue,
Turbidity = TurbFranklin$DataValue,
Gage = GageFranklin$DataValue)
Franklin24NovGrabSample
TonyGrove24NovGrabSample <- data.frame(DateTime = EXWTempTonyGrove$time,
Site = "LR_TG_BA",
Temp = EXWTempTonyGrove$DataValue,
pH = pHTonyGrove$DataValue,
SpCon = SpConTonyGrove$DataValue,
ODO = ODOTonyGrove$DataValue,
Turbidity = TurbTonyGrove$DataValue,
Gage = GageTonyGrove$DataValue)
TonyGrove24NovGrabSample
WaterLab24NovGrabSample <- data.frame(DateTime = EXWTempWaterLab$time,
Site = "LR_WaterLab_AA",
Temp = EXWTempWaterLab$DataValue,
pH = pHWaterLab$DataValue,
SpCon = SpConWaterLab$DataValue,
ODO = ODOWaterLab$DataValue,
Turbidity = TurbWaterLab$DataValue,
Gage = GageWaterLab$DataValue,
BGA = BGAWaterLab$DataValue,
chla = chlaWaterLab$DataValue,
fDOM = fDOMWaterLab$DataValue)
#Nitrate = NitrateWaterLab$DataValue)
WaterLab24NovGrabSample
MainSt24NovGrabSample <- data.frame(DateTime = EXWTempMainSt$time,
Site = "LR_MainStreet_BA",
Temp = EXWTempMainSt$DataValue,
pH = pHMainSt$DataValue,
SpCon = SpConMainSt$DataValue,
ODO = ODOMainSt$DataValue,
Turbidity = TurbMainSt$DataValue,
Gage = GageMainSt$DataValue)
MainSt24NovGrabSample
Mendon24NovGrabSample <- data.frame(DateTime = EXWTempMendon$time,
Site = "LR_Mendon_AA",
Temp = EXWTempMendon$DataValue,
pH = pHMendon$DataValue,
SpCon = SpConMendon$DataValue,
ODO = ODOMendon$DataValue,
Turbidity = TurbMendon$DataValue,
Gage = GageMendon$DataValue,
BGA = BGAMendon$DataValue,
chla = chlaMendon$DataValue,
fDOM = fDOMMendon$DataValue)
#Nitrate = NitrateMendon$DataValue)
Mendon24NovGrabSample
Logan24Nov<-rbind.fill(Franklin24NovGrabSample, TonyGrove24NovGrabSample, WaterLab24NovGrabSample, MainSt24NovGrabSample, Mendon24NovGrabSample )
Logan24Nov
##### Knowlton Fork ####
##Knowlton Fork Water Temp##
#exo_Temp
EXWTempKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempKnowlton = subset(EXWTempKnowlton[ which(EXWTempKnowlton$time ==RB_KF_BADateTime),])
EXWTempKnowlton$DataValue[EXWTempKnowlton$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempKnowlton = subset(TWTempKnowlton[ which(TWTempKnowlton$time ==RB_KF_BADateTime),])
TWTempKnowlton$DataValue[TWTempKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork pH##
pHKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHKnowlton = subset(pHKnowlton[ which(pHKnowlton$time ==RB_KF_BADateTime),])
pHKnowlton$DataValue[pHKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Specific Conductivity##
SpConKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConKnowlton = subset(SpConKnowlton[ which(SpConKnowlton$time ==RB_KF_BADateTime),])
SpConKnowlton$DataValue[SpConKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Dissolved Oxygen##
ODOKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOKnowlton = subset(ODOKnowlton[ which(ODOKnowlton$time ==RB_KF_BADateTime),])
ODOKnowlton$DataValue[ODOKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Specific Turbidity##
TurbKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbKnowlton = subset(TurbKnowlton[ which(TurbKnowlton$time ==RB_KF_BADateTime),])
TurbKnowlton$DataValue[TurbKnowlton$DataValue==-9999.00] <- NA
##Knowlton Fork Gage##
GageKnowlton<- GetValues(RBserver, siteCode="iutah:RB_KF_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageKnowlton = subset(GageKnowlton[ which(GageKnowlton$time ==RB_KF_BADateTime),])
GageKnowlton$DataValue[GageKnowlton$DataValue==-9999.00] <- NA
##### Above Red Butte Reservoir advanced ####
##Above RB Res Water Temp##
#exo_Temp
EXWTempARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempARBR = subset(EXWTempARBR[ which(EXWTempARBR$time ==RB_ARBR_AADateTime),])
EXWTempARBR$DataValue[EXWTempARBR$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempARBR = subset(TWTempARBR[ which(TWTempARBR$time ==RB_ARBR_AADateTime),])
TWTempARBR$DataValue[TWTempARBR$DataValue==-9999.00] <- NA
##Above RB Res pH##
pHARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHARBR = subset(pHARBR[ which(pHARBR$time ==RB_ARBR_AADateTime),])
pHARBR$DataValue[pHARBR$DataValue==-9999.00] <- NA
##Above RB Res Specific Conductivity##
SpConARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConARBR = subset(SpConARBR[ which(SpConARBR$time ==RB_ARBR_AADateTime),])
SpConARBR$DataValue[SpConARBR$DataValue==-9999.00] <- NA
##Above RB Res Dissolved Oxygen##
ODOARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOARBR = subset(ODOARBR[ which(ODOARBR$time ==RB_ARBR_AADateTime),])
ODOARBR$DataValue[ODOARBR$DataValue==-9999.00] <- NA
##Above RB Res Turbidity##
TurbARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbARBR = subset(TurbARBR[ which(TurbARBR$time ==RB_ARBR_AADateTime),])
TurbARBR$DataValue[TurbARBR$DataValue==-9999.00] <- NA
##Above RB Res fDOM##
fDOMARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:fDOM", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
fDOMARBR = subset(fDOMARBR[ which(fDOMARBR$time ==RB_ARBR_AADateTime),])
fDOMARBR$DataValue[fDOMARBR$DataValue==-9999.00] <- NA
##Above Red Butte Res Chla##
chlaARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:chlorophyll", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
chlaARBR = subset(chlaARBR[ which(chlaARBR$time ==RB_ARBR_AADateTime),])
chlaARBR$DataValue[chlaARBR$DataValue==-9999.00] <- NA
##Above RB Res Blue Green Algea (BGA)##
BGAARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:BGA", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
BGAARBR = subset(BGAARBR[ which(BGAARBR$time ==RB_ARBR_AADateTime),])
BGAARBR$DataValue[BGAARBR$DataValue==-9999.00] <- NA
# ##Above RB Res Nitrate##
# NitrateARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_AA", variableCode="iutah:Nitrate-N", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
# NitrateARBR = subset(NitrateARBR[ which(NitrateARBR$time ==RB_ARBR_AADateTime),])
# NitrateARBR$DataValue[NitrateARBR$DataValue==-9999.00] <- NA
## Above Red Butte Res Gage height ##
GageARBR<- GetValues(RBserver, siteCode="iutah:RB_ARBR_USGS", variableCode="iutah:USGSStage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageARBR = subset(GageARBR[ which(GageARBR$time ==RB_ARBR_AADateTime),])
GageARBR$DataValue[GageARBR$DataValue==-9999.00] <- NA
##### Red Butte Gate ####
##Red Butte Gate Water Temperature##
#exo_Temp
EXWTempRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempRBG = subset(EXWTempRBG[which(EXWTempRBG$time == RB_RBG_BADateTime),])
EXWTempRBG$DataValue[EXWTempRBG$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempRBG = subset(TWTempRBG[which(TWTempRBG$time == RB_RBG_BADateTime),])
TWTempRBG$DataValue[TWTempRBG$DataValue==-9999.00] <- NA
##Red Butte Gate pH##
pHRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHRBG = subset(pHRBG[which(pHRBG$time == RB_RBG_BADateTime),])
pHRBG$DataValue[pHRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Specific Conductivity##
SpConRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConRBG = subset(SpConRBG[which(SpConRBG$time == RB_RBG_BADateTime),])
SpConRBG$DataValue[SpConRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Dissolved Oxygen##
ODORBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODORBG = subset(ODORBG[which(ODORBG$time == RB_RBG_BADateTime),])
ODORBG$DataValue[ODORBG$DataValue==-9999.00] <- NA
##Red Butte Gate Specific Tubidity##
TurbRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbRBG = subset(TurbRBG[which(TurbRBG$time == RB_RBG_BADateTime),])
TurbRBG$DataValue[TurbRBG$DataValue==-9999.00] <- NA
##Red Butte Gate Gage##
GageRBG<- GetValues(RBserver, siteCode="iutah:RB_RBG_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageRBG = subset(GageRBG[which(GageRBG$time == RB_RBG_BADateTime),])
GageRBG$DataValue[GageRBG$DataValue==-9999.00] <- NA
##### Cottams Grove ####
##Cottams Grove Water Temp##
#exo_Temp
EXWTempCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempCGrove= subset(EXWTempCGrove[which(EXWTempCGrove$time == RB_CG_BADateTime),])
EXWTempCGrove$DataValue[EXWTempCGrove$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempCGrove= subset(TWTempCGrove[which(TWTempCGrove$time == RB_CG_BADateTime),])
TWTempCGrove$DataValue[TWTempCGrove$DataValue==-9999.00] <- NA
##Cottams Grove pH##
pHCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHCGrove= subset(pHCGrove[which(pHCGrove$time == RB_CG_BADateTime),])
pHCGrove$DataValue[pHCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Specific Conductivity##
SpConCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConCGrove= subset(SpConCGrove[which(SpConCGrove$time == RB_CG_BADateTime),])
SpConCGrove$DataValue[SpConCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Dissolved Oxygen##
ODOCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOCGrove= subset(ODOCGrove[which(ODOCGrove$time == RB_CG_BADateTime),])
ODOCGrove$DataValue[ODOCGrove$DataValue==-9999.00] <- NA
##Cottams Grove Specific Turbidity##
TurbCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbCGrove= subset(TurbCGrove[which(TurbCGrove$time == RB_CG_BADateTime),])
TurbCGrove$DataValue[TurbCGrove$DataValue==-9999.00] <- NA
##Cottam Grove Gage height##
GageCGrove<- GetValues(RBserver, siteCode="iutah:RB_CG_BA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageCGrove= subset(GageCGrove[which(GageCGrove$time == RB_CG_BADateTime),])
GageCGrove$DataValue[GageCGrove$DataValue==-9999.00] <- NA
##### Foothill Drive advanced ####
##Foothill Drive Water Temp##
#exo_Temp
EXWTempFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:WaterTemp_EXO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
EXWTempFoothill= subset(EXWTempFoothill[which(EXWTempFoothill$time == RB_FD_AADateTime),])
EXWTempFoothill$DataValue[EXWTempFoothill$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:WaterTemp_Turb", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TWTempFoothill= subset(TWTempFoothill[which(TWTempFoothill$time == RB_FD_AADateTime),])
TWTempFoothill$DataValue[TWTempFoothill$DataValue==-9999.00] <- NA
##Foothill Drive pH##
pHFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:pH", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
pHFoothill= subset(pHFoothill[which(pHFoothill$time == RB_FD_AADateTime),])
pHFoothill$DataValue[pHFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Specific Conductivity##
SpConFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:SpCond", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
SpConFoothill= subset(SpConFoothill[which(SpConFoothill$time == RB_FD_AADateTime),])
SpConFoothill$DataValue[SpConFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Dissolved Oxygen##
ODOFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:ODO", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
ODOFoothill= subset(ODOFoothill[which(ODOFoothill$time == RB_FD_AADateTime),])
ODOFoothill$DataValue[ODOFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Specific Turbidity##
TurbFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:TurbAvg", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
TurbFoothill= subset(TurbFoothill[which(TurbFoothill$time == RB_FD_AADateTime),])
TurbFoothill$DataValue[TurbFoothill$DataValue==-9999.00] <- NA
##Foothill Drive fDOM##
fDOMFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:fDOM", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
fDOMFoothill= subset(fDOMFoothill[which(fDOMFoothill$time == RB_FD_AADateTime),])
fDOMFoothill$DataValue[fDOMFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Chla##
chlaFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:chlorophyll", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
chlaFoothill= subset(chlaFoothill[which(chlaFoothill$time == RB_FD_AADateTime),])
chlaFoothill$DataValue[chlaFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Blue Green Algae (BGA)##
BGAFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:BGA", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
BGAFoothill= subset(BGAFoothill[which(BGAFoothill$time == RB_FD_AADateTime),])
BGAFoothill$DataValue[BGAFoothill$DataValue==-9999.00] <- NA
# ##Foothill Drive Nitrate##
# NitrateFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:Nitrate-N", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
# NitrateFoothill= subset(NitrateFoothill[which(NitrateFoothill$time == RB_FD_AADateTime),])
# NitrateFoothill$DataValue[NitrateFoothill$DataValue==-9999.00] <- NA
##Foothill Drive Gage height##
GageFoothill<- GetValues(RBserver, siteCode="iutah:RB_FD_AA", variableCode="iutah:Stage", startDate = RBStartDate, endDate = RBEndDate, qcID = "1" )
GageFoothill= subset(GageFoothill[which(GageFoothill$time == RB_FD_AADateTime),])
GageFoothill$DataValue[GageFoothill$DataValue==-9999.00] <- NA
##### Red Butte Spreadsheet #####
Knowlton18NovGrabSample <- data.frame(DateTime = EXWTempKnowlton$time,
Site = "RB_KF_BA",
Temp = EXWTempKnowlton$DataValue,
pH = pHKnowlton$DataValue,
SpCon = SpConKnowlton$DataValue,
ODO = ODOKnowlton$DataValue,
Turbidity = TurbKnowlton$DataValue,
Gage = GageKnowlton$DataValue)
Knowlton18NovGrabSample
ARBR18NovGrabSample <- data.frame(DateTime = EXWTempARBR$time,
Site = "RB_ARBR_AA",
Temp = EXWTempARBR$DataValue,
pH = pHARBR$DataValue,
SpCon = SpConARBR$DataValue,
ODO = ODOARBR$DataValue,
Turbidity = TurbARBR$DataValue,
#Gage = GageARBR$DataValue,
BGA = BGAARBR$DataValue,
chla = chlaARBR$DataValue,
fDOM = fDOMARBR$DataValue)
#Nitrate = NitrateARBR$DataValue)
ARBR18NovGrabSample
RBG18NovGrabSample <- data.frame(DateTime = EXWTempRBG$time,
Site = "RB_RBG_BA",
Temp = EXWTempRBG$DataValue,
pH = pHRBG$DataValue,
SpCon = SpConRBG$DataValue,
ODO = ODORBG$DataValue,
Turbidity = TurbRBG$DataValue,
Gage = GageRBG$DataValue)
RBG18NovGrabSample
CGrove18NovGrabSample <- data.frame(DateTime = EXWTempCGrove$time,
Site = "RB_CG_BA",
Temp = EXWTempCGrove$DataValue,
pH = pHCGrove$DataValue,
SpCon = SpConCGrove$DataValue,
ODO = ODOCGrove$DataValue,
Turbidity = TurbCGrove$DataValue,
Gage = GageCGrove$DataValue)
CGrove18NovGrabSample
Foothill18NovGrabSample <- data.frame(DateTime = EXWTempFoothill$time,
Site = "RB_FD_AA",
Temp = EXWTempFoothill$DataValue,
pH = pHFoothill$DataValue,
SpCon = SpConFoothill$DataValue,
ODO = ODOFoothill$DataValue,
Turbidity = TurbFoothill$DataValue,
Gage = GageFoothill$DataValue,
BGA = BGAFoothill$DataValue,
chla = chlaFoothill$DataValue,
fDOM = fDOMFoothill$DataValue)
#Nitrate = NitrateFoothill$DataValue)
Foothill18NovGrabSample
RedButte18Nov<- rbind.fill(Knowlton18NovGrabSample, ARBR18NovGrabSample, RBG18NovGrabSample, CGrove18NovGrabSample, Foothill18NovGrabSample)
##### Soapstone #####
##Soapstone Water Temp
#exo_Temp
EXWTempST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempST= subset(EXWTempST[which(EXWTempST$time == PR_ST_BADateTime),])
EXWTempST$DataValue[EXWTempST$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempST= subset(TWTempST[which(TWTempST$time == PR_ST_BADateTime),])
TWTempST$DataValue[TWTempST$DataValue==-9999.00] <- NA
##Soapstone pH##
pHST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHST= subset(pHST[which(pHST$time == PR_ST_BADateTime),])
pHST$DataValue[pHST$DataValue==-9999.00] <- NA
##Soapstone Specific Conductivity##
SpConST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConST= subset(SpConST[which(SpConST$time == PR_ST_BADateTime),])
SpConST$DataValue[SpConST$DataValue==-9999.00] <- NA
##Soapstone Dissolved Oxygen##
ODOST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOST= subset(ODOST[which(ODOST$time == PR_ST_BADateTime),])
ODOST$DataValue[ODOST$DataValue==-9999.00] <- NA
##Soapstone Specific Turbidity##
TurbST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbST= subset(TurbST[which(TurbST$time == PR_ST_BADateTime),])
TurbST$DataValue[TurbST$DataValue==-9999.00] <- NA
##Soapstone Stage##
StageST<- GetValues(PRserver, siteCode="iutah:PR_ST_BA", variableCode="iutah:Stage", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
StageST= subset(StageST[which(StageST$time == PR_ST_BADateTime),])
StageST$DataValue[StageST$DataValue==-9999.00] <- NA
##### Below Jordanelle advanced #########
##Below Jordanelle Temp
#exo_Temp
EXWTempBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, methodID= "99", qcID = "1" )
EXWTempBJ= subset(EXWTempBJ[which(EXWTempBJ$time == PR_BJ_AADateTime),])
EXWTempBJ$DataValue[EXWTempBJ$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempBJ= subset(TWTempBJ[which(TWTempBJ$time == PR_BJ_AADateTime),])
TWTempBJ$DataValue[TWTempBJ$DataValue==-9999.00] <- NA
## Below Jordanelle pH ##
pHBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHBJ= subset(pHBJ[which(pHBJ$time == PR_BJ_AADateTime),])
pHBJ$DataValue[pHBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Specific Conductivity##
SpConBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConBJ= subset(SpConBJ[which(SpConBJ$time == PR_BJ_AADateTime),])
SpConBJ$DataValue[SpConBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Dissolved Oxygen##
ODOBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOBJ= subset(ODOBJ[which(ODOBJ$time == PR_BJ_AADateTime),])
##Below Jordanelle Turbidity##
TurbBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbBJ= subset(TurbBJ[which(TurbBJ$time == PR_BJ_AADateTime),])
TurbBJ$DataValue[TurbBJ$DataValue==-9999.00] <- NA
##Below Jordanelle fDOM##
fDOMBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:fDOM", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
fDOMBJ= subset(fDOMBJ[which(fDOMBJ$time == PR_BJ_AADateTime),])
fDOMBJ$DataValue[fDOMBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Chla##
chlaBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:chlorophyll", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
chlaBJ= subset(chlaBJ[which(chlaBJ$time == PR_BJ_AADateTime),])
chlaBJ$DataValue[chlaBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Blue Green Algea (BGA)##
BGABJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:BGA", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
BGABJ= subset(BGABJ[which(BGABJ$time == PR_BJ_AADateTime),])
BGABJ$DataValue[BGABJ$DataValue==-9999.00] <- NA
# ##Below Jordanelle Nitrate##
# NitrateBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_AA", variableCode="iutah:Nitrate-N", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
# NitrateBJ= subset(NitrateBJ[which(NitrateBJ$time == PR_BJ_AADateTime),])
# NitrateBJ$DataValue[NitrateBJ$DataValue==-9999.00] <- NA
##Below Jordanelle Discharge (Q)##
QBJ<- GetValues(PRserver, siteCode="iutah:PR_BJ_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QBJ= subset(QBJ[which(QBJ$time == PR_BJ_AADateTime),])
QBJ$DataValue[QBJ$DataValue==-9999.00] <- NA
##### Lower Midway #######
##Lower Midway Water Temp
#exo_Temp
EXWTempLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempLM= subset(EXWTempLM[which(EXWTempLM$time == PR_LM_BADateTime),])
EXWTempLM$DataValue[EXWTempLM$DataValue==-9999.00] <- NA #Converts -9999.0 to NA
#Turbidity_Temp
TWTempLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempLM= subset(TWTempLM[which(TWTempLM$time == PR_LM_BADateTime),])
TWTempLM$DataValue[TWTempLM$DataValue==-9999.00] <- NA
##Lower Midway pH##
pHLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHLM= subset(pHLM[which(pHLM$time == PR_LM_BADateTime),])
pHLM$DataValue[pHLM$DataValue==-9999.00] <- NA
##Lower Midway Specific Conductivity##
SpConLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConLM= subset(SpConLM[which(SpConLM$time == PR_LM_BADateTime),])
SpConLM$DataValue[SpConLM$DataValue==-9999.00] <- NA
##Lower Midway Dissolved Oxygen##
ODOLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOLM= subset(ODOLM[which(ODOLM$time == PR_LM_BADateTime),])
ODOLM$DataValue[ODOLM$DataValue==-9999.00] <- NA
##Lower Midway Specific Tubidity##
TurbLM<- GetValues(PRserver, siteCode="iutah:PR_LM_BA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbLM= subset(TurbLM[which(TurbLM$time == PR_LM_BADateTime),])
TurbLM$DataValue[TurbLM$DataValue==-9999.00] <- NA
##Lower Midway Discharge (Q)##
QLM<- GetValues(PRserver, siteCode="iutah:PR_uM_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QLM= subset(QLM[which(QLM$time == PR_LM_BADateTime),])
QLM$DataValue[QLM$DataValue==-9999.00] <- NA
##### Charleston advanced ######
## Charleston Water Temp
#exo_Temp
EXWTempCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:WaterTemp_EXO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
EXWTempCH= subset(EXWTempCH[which(EXWTempCH$time == PR_CH_AADateTime),])
EXWTempCH$DataValue[EXWTempCH$DataValue==-9999.00] <- NA
#Turbidity_Temp
TWTempCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:WaterTemp_Turb", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TWTempCH= subset(TWTempCH[which(TWTempCH$time == PR_CH_AADateTime),])
TWTempCH$DataValue[TWTempCH$DataValue==-9999.00] <- NA
##Charleston pH##
pHCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:pH", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
pHCH= subset(pHCH[which(pHCH$time == PR_CH_AADateTime),])
pHCH$DataValue[pHCH$DataValue==-9999.00] <- NA
##Charleston Specific Conductivity##
SpConCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:SpCond", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
SpConCH= subset(SpConCH[which(SpConCH$time == PR_CH_AADateTime),])
SpConCH$DataValue[SpConCH$DataValue==-9999.00] <- NA
##Charleston Dissolved Oxygen##
ODOCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:ODO", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
ODOCH= subset(ODOCH[which(ODOCH$time == PR_CH_AADateTime),])
ODOCH$DataValue[ODOCH$DataValue==-9999.00] <- NA
##Charleston Specific Turbidity##
TurbCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:TurbAvg", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
TurbCH= subset(TurbCH[which(TurbCH$time == PR_CH_AADateTime),])
TurbCH$DataValue[TurbCH$DataValue==-9999.00] <- NA
##Charleston fDOM##
fDOMCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:fDOM", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
fDOMCH= subset(fDOMCH[which(fDOMCH$time == PR_CH_AADateTime),])
fDOMCH$DataValue[fDOMCH$DataValue==-9999.00] <- NA
##Charleston Chla##
chlaCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:chlorophyll", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
chlaCH= subset(chlaCH[which(chlaCH$time == PR_CH_AADateTime),])
chlaCH$DataValue[chlaCH$DataValue==-9999.00] <- NA
##Charleston Blue Green Algae (BGA)##
BGACH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:BGA", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
BGACH= subset(BGACH[which(BGACH$time == PR_CH_AADateTime),])
BGACH$DataValue[BGACH$DataValue==-9999.00] <- NA
# ##Charleston Nitrate##
# NitrateCH<- GetValues(PRserver, siteCode="iutah:PR_CH_AA", variableCode="iutah:Nitrate-N", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
# NitrateCH= subset(NitrateCH[which(NitrateCH$time == PR_CH_AADateTime),])
# NitrateCH$DataValue[NitrateCH$DataValue==-9999.00] <- NA
##Charleston Discharge (Q)##
QCH<- GetValues(PRserver, siteCode="iutah:PR_CH_CUWCD", variableCode="iutah:CUWCDDischarge", startDate = PRStartDate, endDate = PREndDate, qcID = "1" )
QCH= subset(QCH[which(QCH$time == PR_CH_AADateTime),])
QCH$DataValue[QCH$DataValue==-9999.00] <- NA
##### Provo Spreadsheet #####
Soapstone12NovGrabSample <- data.frame(DateTime = EXWTempST$time,
Site = "PR_ST_BA",
Temp = EXWTempST$DataValue,
pH = pHST$DataValue,
SpCon = SpConST$DataValue,
ODO = ODOST$DataValue,
Turbidity = TurbST$DataValue,
Gage = StageST$DataValue)
Soapstone12NovGrabSample
BelowJordanelle12NovGrabSample <- data.frame(DateTime = EXWTempBJ$time,
Site = "PR_BJ_AA",
Temp = EXWTempBJ$DataValue,
pH = pHBJ$DataValue,
SpCon = SpConBJ$DataValue,
ODO = ODOBJ$DataValue,
Turbidity = TurbBJ$DataValue,
Gage = QBJ$DataValue,
BGA = BGABJ$DataValue,
chla = chlaBJ$DataValue,
fDOM = fDOMBJ$DataValue)
# Nitrate = NitrateBJ$DataValue)
BelowJordanelle12NovGrabSample
LowerMidway12NovGrabSample <- data.frame(DateTime = EXWTempLM$time,
Site = "PR_LM_BA",
Temp = EXWTempLM$DataValue,
pH = pHLM$DataValue,
SpCon = SpConLM$DataValue,
ODO = ODOLM$DataValue,
Turbidity = TurbLM$DataValue,
Gage = QLM$DataValue)
LowerMidway12NovGrabSample
Charleston12NovGrabSample <- data.frame(DateTime = EXWTempCH$time,
Site = "PR_CH_AA",
Temp = EXWTempCH$DataValue,
pH = pHCH$DataValue,
SpCon = SpConCH$DataValue,
ODO = ODOCH$DataValue,
Turbidity = TurbCH$DataValue,
Gage = QCH$DataValue,
BGA = BGACH$DataValue,
chla = chlaCH$DataValue,
fDOM = fDOMCH$DataValue)
#Nitrate = NitrateCH$DataValue)
Charleston12NovGrabSample
Provo12Nov <-rbind.fill(Soapstone12NovGrabSample, BelowJordanelle12NovGrabSample, LowerMidway12NovGrabSample, Charleston12NovGrabSample)
##### All combine and write ####
GrabSamples24Nov <- rbind.fill(RedButte18Nov, Logan24Nov, Provo12Nov)
write.csv(GrabSamples24Nov, file = "GrabSamples2" )
|
library(readr)
library(dplyr)
library(tidyr)
library(usmap)
library(maptools)
library(ggplot2)
library(stringr)
seg_loc <- read_csv("SegregationLocation.csv")
hcrimes <- read_csv("HateCrimes.csv")
hcrimes$State <- as.factor(hcrimes$State)
hcrimes$Agency <- as.factor(hcrimes$Agency)
hcrimes$Agency_Type <- as.factor(hcrimes$Agency_Type)
hcrimes <- hcrimes %>%
mutate(City = as.character(Agency))
hcrimes <- hcrimes[,c(1:4,34,5:33)]
for (j in 1:length(hcrimes$Agency)) {
if(hcrimes$Agency_Type[j] == "University or College" | hcrimes$Agency_Type[j] == "State Police") {
hcrimes$City[j] = hcrimes$Unit[j]
}
else if(hcrimes$Agency_Type[j] == "Other" | hcrimes$Agency_Type[j] == "Federal" | hcrimes$Agency_Type[j] == "Other State Agency"){
hcrimes$City[j] = str_trim(str_c(as.character(hcrimes$Agency[j]), hcrimes$Unit[j], sep = " "))
}
}
hcnas <- hcrimes[which(is.na(hcrimes$City)),]
hcrimes$City <- as.factor(hcrimes$City)
sum_hc <- hcrimes[which(!is.na(hcrimes$City)),] %>%
group_by(St, City, Year) %>%
summarise(RaceCrimes = sum(RaceBias),
ReligionCrimes = sum(ReligionBias),
SexCrimes = sum(SexBias),
GenderCrimes = sum(GenderBias),
DisabilityCrimes = sum(DisBias),)
sum_hc <- sum_hc %>%
pivot_wider(names_from = Year, values_from = c(RaceCrimes, ReligionCrimes,
SexCrimes, GenderCrimes,
DisabilityCrimes), values_fill = 0)
sum_hc[is.na(sum_hc)] <- 0
sh <- seg_loc %>%
left_join(sum_hc, by = c("City" = "City", "State" = "St")) %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`))
write_csv(sh, "JoinedData.csv")
not_joined <- sum_hc %>%
anti_join(seg_loc, by = c("City" = "City", "St" = "State"))
loc <- read_csv("uscities.csv")
hc_loc <- not_joined %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`)) %>%
left_join(loc, by = c("City" = "city_ascii", "St" = "state_id")) %>%
select(c(1:152,157,158))
hc_loc_nas <- hc_loc[is.na(hc_loc$lat),]
hc_loc <- hc_loc[!is.na(hc_loc$lat),]
hc_map_points <- usmap_transform(data.frame(hc_loc$lng, hc_loc$lat))
hc_loc <- hc_loc %>%
left_join(hc_map_points, by = c("lng" = "hc_loc.lng", "lat" = "hc_loc.lat"))
write_csv(hc_loc, "NotJoinedData.csv")
colleges <- hcrimes[which(hcrimes$Agency_Type == "University or College"),]
colleges$College <- as.character(colleges$City)
for (c in 1:length(colleges$Agency)) {
if(!is.na(colleges$Unit[c])) {
colleges$College[c] = str_c(as.character(colleges$Agency[c]), as.character(colleges$Unit[c]), sep = " ")
}
else {
colleges$College[c] = as.character(colleges$Agency[c])
}
}
colleges$College <- as.factor(colleges$College)
colleges_sum <- colleges %>%
group_by(St, College, Year) %>%
summarise(RaceCrimes = sum(RaceBias),
ReligionCrimes = sum(ReligionBias),
SexCrimes = sum(SexBias),
GenderCrimes = sum(GenderBias),
DisabilityCrimes = sum(DisBias),) %>%
pivot_wider(names_from = Year, values_from = c(RaceCrimes, ReligionCrimes,
SexCrimes, GenderCrimes,
DisabilityCrimes), values_fill = 0) %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`))
write_csv(colleges_sum, "CollegeCrimes.csv")
| /Background_Code/ShinyData.R | no_license | mahatfield/HateCrimes | R | false | false | 17,000 | r | library(readr)
library(dplyr)
library(tidyr)
library(usmap)
library(maptools)
library(ggplot2)
library(stringr)
seg_loc <- read_csv("SegregationLocation.csv")
hcrimes <- read_csv("HateCrimes.csv")
hcrimes$State <- as.factor(hcrimes$State)
hcrimes$Agency <- as.factor(hcrimes$Agency)
hcrimes$Agency_Type <- as.factor(hcrimes$Agency_Type)
hcrimes <- hcrimes %>%
mutate(City = as.character(Agency))
hcrimes <- hcrimes[,c(1:4,34,5:33)]
for (j in 1:length(hcrimes$Agency)) {
if(hcrimes$Agency_Type[j] == "University or College" | hcrimes$Agency_Type[j] == "State Police") {
hcrimes$City[j] = hcrimes$Unit[j]
}
else if(hcrimes$Agency_Type[j] == "Other" | hcrimes$Agency_Type[j] == "Federal" | hcrimes$Agency_Type[j] == "Other State Agency"){
hcrimes$City[j] = str_trim(str_c(as.character(hcrimes$Agency[j]), hcrimes$Unit[j], sep = " "))
}
}
hcnas <- hcrimes[which(is.na(hcrimes$City)),]
hcrimes$City <- as.factor(hcrimes$City)
sum_hc <- hcrimes[which(!is.na(hcrimes$City)),] %>%
group_by(St, City, Year) %>%
summarise(RaceCrimes = sum(RaceBias),
ReligionCrimes = sum(ReligionBias),
SexCrimes = sum(SexBias),
GenderCrimes = sum(GenderBias),
DisabilityCrimes = sum(DisBias),)
sum_hc <- sum_hc %>%
pivot_wider(names_from = Year, values_from = c(RaceCrimes, ReligionCrimes,
SexCrimes, GenderCrimes,
DisabilityCrimes), values_fill = 0)
sum_hc[is.na(sum_hc)] <- 0
sh <- seg_loc %>%
left_join(sum_hc, by = c("City" = "City", "State" = "St")) %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`))
write_csv(sh, "JoinedData.csv")
not_joined <- sum_hc %>%
anti_join(seg_loc, by = c("City" = "City", "St" = "State"))
loc <- read_csv("uscities.csv")
hc_loc <- not_joined %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`)) %>%
left_join(loc, by = c("City" = "city_ascii", "St" = "state_id")) %>%
select(c(1:152,157,158))
hc_loc_nas <- hc_loc[is.na(hc_loc$lat),]
hc_loc <- hc_loc[!is.na(hc_loc$lat),]
hc_map_points <- usmap_transform(data.frame(hc_loc$lng, hc_loc$lat))
hc_loc <- hc_loc %>%
left_join(hc_map_points, by = c("lng" = "hc_loc.lng", "lat" = "hc_loc.lat"))
write_csv(hc_loc, "NotJoinedData.csv")
colleges <- hcrimes[which(hcrimes$Agency_Type == "University or College"),]
colleges$College <- as.character(colleges$City)
for (c in 1:length(colleges$Agency)) {
if(!is.na(colleges$Unit[c])) {
colleges$College[c] = str_c(as.character(colleges$Agency[c]), as.character(colleges$Unit[c]), sep = " ")
}
else {
colleges$College[c] = as.character(colleges$Agency[c])
}
}
colleges$College <- as.factor(colleges$College)
colleges_sum <- colleges %>%
group_by(St, College, Year) %>%
summarise(RaceCrimes = sum(RaceBias),
ReligionCrimes = sum(ReligionBias),
SexCrimes = sum(SexBias),
GenderCrimes = sum(GenderBias),
DisabilityCrimes = sum(DisBias),) %>%
pivot_wider(names_from = Year, values_from = c(RaceCrimes, ReligionCrimes,
SexCrimes, GenderCrimes,
DisabilityCrimes), values_fill = 0) %>%
mutate(DisCrimesTotal = (`DisabilityCrimes_1991`+`DisabilityCrimes_1992`+`DisabilityCrimes_1993`+
`DisabilityCrimes_1994`+`DisabilityCrimes_1995`+`DisabilityCrimes_1996`+
`DisabilityCrimes_1997`+`DisabilityCrimes_1998`+`DisabilityCrimes_1999`+
`DisabilityCrimes_2000`+`DisabilityCrimes_2001`+`DisabilityCrimes_2002`+
`DisabilityCrimes_2003`+`DisabilityCrimes_2004`+`DisabilityCrimes_2005`+
`DisabilityCrimes_2006`+`DisabilityCrimes_2007`+`DisabilityCrimes_2008`+
`DisabilityCrimes_2009`+`DisabilityCrimes_2010`+`DisabilityCrimes_2011`+
`DisabilityCrimes_2012`+`DisabilityCrimes_2013`+`DisabilityCrimes_2014`+
`DisabilityCrimes_2015`+`DisabilityCrimes_2016`+`DisabilityCrimes_2017`+
`DisabilityCrimes_2018`+`DisabilityCrimes_2019`),
GenderCrimesTotal = (`GenderCrimes_1991`+`GenderCrimes_1992`+`GenderCrimes_1993`+
`GenderCrimes_1994`+`GenderCrimes_1995`+`GenderCrimes_1996`+
`GenderCrimes_1997`+`GenderCrimes_1998`+`GenderCrimes_1999`+
`GenderCrimes_2000`+`GenderCrimes_2001`+`GenderCrimes_2002`+
`GenderCrimes_2003`+`GenderCrimes_2004`+`GenderCrimes_2005`+
`GenderCrimes_2006`+`GenderCrimes_2007`+`GenderCrimes_2008`+
`GenderCrimes_2009`+`GenderCrimes_2010`+`GenderCrimes_2011`+
`GenderCrimes_2012`+`GenderCrimes_2013`+`GenderCrimes_2014`+
`GenderCrimes_2015`+`GenderCrimes_2016`+`GenderCrimes_2017`+
`GenderCrimes_2018`+`GenderCrimes_2019`),
SexCrimesTotal = (`SexCrimes_1991`+`SexCrimes_1992`+`SexCrimes_1993`+
`SexCrimes_1994`+`SexCrimes_1995`+`SexCrimes_1996`+
`SexCrimes_1997`+`SexCrimes_1998`+`SexCrimes_1999`+
`SexCrimes_2000`+`SexCrimes_2001`+`SexCrimes_2002`+
`SexCrimes_2003`+`SexCrimes_2004`+`SexCrimes_2005`+
`SexCrimes_2006`+`SexCrimes_2007`+`SexCrimes_2008`+
`SexCrimes_2009`+`SexCrimes_2010`+`SexCrimes_2011`+
`SexCrimes_2012`+`SexCrimes_2013`+`SexCrimes_2014`+
`SexCrimes_2015`+`SexCrimes_2016`+`SexCrimes_2017`+
`SexCrimes_2018`+`SexCrimes_2019`),
ReligionCrimesTotal = (`ReligionCrimes_1991`+`ReligionCrimes_1992`+`ReligionCrimes_1993`+
`ReligionCrimes_1994`+`ReligionCrimes_1995`+`ReligionCrimes_1996`+
`ReligionCrimes_1997`+`ReligionCrimes_1998`+`ReligionCrimes_1999`+
`ReligionCrimes_2000`+`ReligionCrimes_2001`+`ReligionCrimes_2002`+
`ReligionCrimes_2003`+`ReligionCrimes_2004`+`ReligionCrimes_2005`+
`ReligionCrimes_2006`+`ReligionCrimes_2007`+`ReligionCrimes_2008`+
`ReligionCrimes_2009`+`ReligionCrimes_2010`+`ReligionCrimes_2011`+
`ReligionCrimes_2012`+`ReligionCrimes_2013`+`ReligionCrimes_2014`+
`ReligionCrimes_2015`+`ReligionCrimes_2016`+`ReligionCrimes_2017`+
`ReligionCrimes_2018`+`ReligionCrimes_2019`),
RaceCrimesTotal = (`RaceCrimes_1991`+`RaceCrimes_1992`+`RaceCrimes_1993`+
`RaceCrimes_1994`+`RaceCrimes_1995`+`RaceCrimes_1996`+
`RaceCrimes_1997`+`RaceCrimes_1998`+`RaceCrimes_1999`+
`RaceCrimes_2000`+`RaceCrimes_2001`+`RaceCrimes_2002`+
`RaceCrimes_2003`+`RaceCrimes_2004`+`RaceCrimes_2005`+
`RaceCrimes_2006`+`RaceCrimes_2007`+`RaceCrimes_2008`+
`RaceCrimes_2009`+`RaceCrimes_2010`+`RaceCrimes_2011`+
`RaceCrimes_2012`+`RaceCrimes_2013`+`RaceCrimes_2014`+
`RaceCrimes_2015`+`RaceCrimes_2016`+`RaceCrimes_2017`+
`RaceCrimes_2018`+`RaceCrimes_2019`))
write_csv(colleges_sum, "CollegeCrimes.csv")
|
# Make also private functions available
kwb.utils::assignPackageObjects("kwb.nextcloud")
# Are we the test user?
nextcloud_user()
# Ok, we seem to be the test user, let's mess around!
# List the files and folders on the main level
list_files()
# Get the file and folder properties
file_info <- list_files(full_info = TRUE)
file_info
# Show only the files
file_info <- exclude_directories(file_info)
file_info
# Relative paths to the files
file_info$file
# Download the files
download_files(file_info$href)
# Create a folder on the cloud
create_folder("testfolder-1")
# Did the folder arrive?
"testfolder-1/" %in% list_files()
# Path to a local temporary file
file <- file.path(tempdir(), "testfile.txt")
# Path to target folder on the cloud
target_path <- "testfolder-1"
# Upload first version of the file
writeLines("Hi folks version 1!", file)
readLines(file)
upload_file(file, target_path)
# Upload second version of the file
writeLines("Hi folks version 2!", file)
readLines(file)
upload_file(file, target_path)
# Upload third version of the file
writeLines("Hi folks version 3!", file)
readLines(file)
upload_file(file, target_path)
# Get the fileid of the uploaded file
file_info <- list_files(target_path, full_info = TRUE)
fileid <- file_info$fileid[file_info$file == "testfile.txt"]
fileid
# Show the versions of the file (identified by fileid)
version_info <- get_version_info(fileid)
# There are two versions (the current version is not shown!)
version_info
# Download all versions (unfortunately named by timestamp)
downloaded_files <- download_files(version_info$href)
downloaded_files
# Read the contents (should be version 1 and 2)
lapply(downloaded_files, readLines)
# Delete a specific version? This seems to be not working
nextcloud_request(version_info$href[1], "DELETE", really = really)
# Delete the file
# I am using the string constant instead of "href" to see what I do!
delete_file_or_folder("testfolder-1/testfile.txt", really = really)
# File deleted? Yes!
list_files("testfolder-1")
# All versions deleted? Yes!!!
get_version_info(fileid) # -> error
# Upload the testfile again
upload_file(file, target_path)
# Check for success
list_files(target_path)
# Now try to delete the whole directory
delete_file_or_folder("testfolder-1", really = really)
# Try to list again
list_files(target_path) # -> not found (any more)!
# List the upper level -> no more testfolder!
list_files()
| /R/.test_put-delete.R | permissive | KWB-R/kwb.nextcloud | R | false | false | 2,435 | r | # Make also private functions available
kwb.utils::assignPackageObjects("kwb.nextcloud")
# Are we the test user?
nextcloud_user()
# Ok, we seem to be the test user, let's mess around!
# List the files and folders on the main level
list_files()
# Get the file and folder properties
file_info <- list_files(full_info = TRUE)
file_info
# Show only the files
file_info <- exclude_directories(file_info)
file_info
# Relative paths to the files
file_info$file
# Download the files
download_files(file_info$href)
# Create a folder on the cloud
create_folder("testfolder-1")
# Did the folder arrive?
"testfolder-1/" %in% list_files()
# Path to a local temporary file
file <- file.path(tempdir(), "testfile.txt")
# Path to target folder on the cloud
target_path <- "testfolder-1"
# Upload first version of the file
writeLines("Hi folks version 1!", file)
readLines(file)
upload_file(file, target_path)
# Upload second version of the file
writeLines("Hi folks version 2!", file)
readLines(file)
upload_file(file, target_path)
# Upload third version of the file
writeLines("Hi folks version 3!", file)
readLines(file)
upload_file(file, target_path)
# Get the fileid of the uploaded file
file_info <- list_files(target_path, full_info = TRUE)
fileid <- file_info$fileid[file_info$file == "testfile.txt"]
fileid
# Show the versions of the file (identified by fileid)
version_info <- get_version_info(fileid)
# There are two versions (the current version is not shown!)
version_info
# Download all versions (unfortunately named by timestamp)
downloaded_files <- download_files(version_info$href)
downloaded_files
# Read the contents (should be version 1 and 2)
lapply(downloaded_files, readLines)
# Delete a specific version? This seems to be not working
nextcloud_request(version_info$href[1], "DELETE", really = really)
# Delete the file
# I am using the string constant instead of "href" to see what I do!
delete_file_or_folder("testfolder-1/testfile.txt", really = really)
# File deleted? Yes!
list_files("testfolder-1")
# All versions deleted? Yes!!!
get_version_info(fileid) # -> error
# Upload the testfile again
upload_file(file, target_path)
# Check for success
list_files(target_path)
# Now try to delete the whole directory
delete_file_or_folder("testfolder-1", really = really)
# Try to list again
list_files(target_path) # -> not found (any more)!
# List the upper level -> no more testfolder!
list_files()
|
library(mtk)
### Name: mtkMorrisAnalyser
### Title: The constructor of the class 'mtkMorrisAnalyser'
### Aliases: mtkMorrisAnalyser
### ** Examples
## Sensitivity analysis of the "Ishigami" model with the "Morris" method
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkMorrisDesigner( listParameters
= list(r=20, type="oat", levels=4, grid.jump=2))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process
exp1.analyser <- mtkMorrisAnalyser()
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp1.designer,
evaluate=exp1.evaluator, analyze=exp1.analyser))
# Run the workflow and report the results.
run(exp1)
print(exp1)
| /data/genthat_extracted_code/mtk/examples/mtkMorrisAnalyser.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 862 | r | library(mtk)
### Name: mtkMorrisAnalyser
### Title: The constructor of the class 'mtkMorrisAnalyser'
### Aliases: mtkMorrisAnalyser
### ** Examples
## Sensitivity analysis of the "Ishigami" model with the "Morris" method
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkMorrisDesigner( listParameters
= list(r=20, type="oat", levels=4, grid.jump=2))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process
exp1.analyser <- mtkMorrisAnalyser()
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp1.designer,
evaluate=exp1.evaluator, analyze=exp1.analyser))
# Run the workflow and report the results.
run(exp1)
print(exp1)
|
## Reading the data file downloaded and extracted from
## https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
power_cons <- read.table("./household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, na.strings="?")
plot_data <- subset(power_cons, power_cons$Date == "1/2/2007" | power_cons$Date == "2/2/2007")
# Joining Date and Time columns of data
plot_data$Date_Time <- paste(plot_data$Date, plot_data$Time, sep=" ")
# Deleting Date and Time columns as we created new joint column
plot_data <- subset(plot_data, select=c(3:10))
plot_data$Date_Time <- strptime(plot_data$Date_Time, format="%d/%m/%Y %H:%M:%S")
png("Plot3.png", width = 480, height = 480)
plot(plot_data$Date_Time, plot_data$Sub_metering_1, type = "l", xlab ="", ylab = "Energy sub metering")
lines(plot_data$Date_Time, plot_data$Sub_metering_2, col = "red")
lines(plot_data$Date_Time, plot_data$Sub_metering_3, col = "blue")
legend("topright",lty=c(1,1,1), col = c("black", "red", "blue"), legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
dev.off()
| /Plot3.R | no_license | Channabasavaraj/ExData_Plotting1 | R | false | false | 1,095 | r | ## Reading the data file downloaded and extracted from
## https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
power_cons <- read.table("./household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, na.strings="?")
plot_data <- subset(power_cons, power_cons$Date == "1/2/2007" | power_cons$Date == "2/2/2007")
# Joining Date and Time columns of data
plot_data$Date_Time <- paste(plot_data$Date, plot_data$Time, sep=" ")
# Deleting Date and Time columns as we created new joint column
plot_data <- subset(plot_data, select=c(3:10))
plot_data$Date_Time <- strptime(plot_data$Date_Time, format="%d/%m/%Y %H:%M:%S")
png("Plot3.png", width = 480, height = 480)
plot(plot_data$Date_Time, plot_data$Sub_metering_1, type = "l", xlab ="", ylab = "Energy sub metering")
lines(plot_data$Date_Time, plot_data$Sub_metering_2, col = "red")
lines(plot_data$Date_Time, plot_data$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()
|
#Set working directory
dirPath<-"/Users/MaxTan/Documents/CU_16spring/EDAV/assign1"
setwd(dirPath)
filename<-"Survey+Response.xlsx"
source("tidydata.R")
df<-tidydata(filename)
source("GenderProgramPlot.R")
GenderProgramPlot(df)
source("TextEditorProgramPlot.R")
TextEditorProgramPlot(df)
source("ToolScoreProgramPlot.R")
ToolScoreProgramPlot(df)
source("SkillScoreProgramPlot.R")
SkillScoreProgramPlot(df)
source("SkillScorePieChart.R")
SkillScorePieChart(df)
| /Main.R | no_license | MakarAl/EDAV_Project_Class | R | false | false | 468 | r | #Set working directory
dirPath<-"/Users/MaxTan/Documents/CU_16spring/EDAV/assign1"
setwd(dirPath)
filename<-"Survey+Response.xlsx"
source("tidydata.R")
df<-tidydata(filename)
source("GenderProgramPlot.R")
GenderProgramPlot(df)
source("TextEditorProgramPlot.R")
TextEditorProgramPlot(df)
source("ToolScoreProgramPlot.R")
ToolScoreProgramPlot(df)
source("SkillScoreProgramPlot.R")
SkillScoreProgramPlot(df)
source("SkillScorePieChart.R")
SkillScorePieChart(df)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/frauddetector_operations.R
\name{frauddetector_create_variable}
\alias{frauddetector_create_variable}
\title{Creates a variable}
\usage{
frauddetector_create_variable(
name,
dataType,
dataSource,
defaultValue,
description = NULL,
variableType = NULL,
tags = NULL
)
}
\arguments{
\item{name}{[required] The name of the variable.}
\item{dataType}{[required] The data type of the variable.}
\item{dataSource}{[required] The source of the data.}
\item{defaultValue}{[required] The default value for the variable when no value is received.}
\item{description}{The description.}
\item{variableType}{The variable type. For more information see \href{https://docs.aws.amazon.com/frauddetector/latest/ug/create-a-variable.html#variable-types}{Variable types}.
Valid Values:
\code{AUTH_CODE | AVS | BILLING_ADDRESS_L1 | BILLING_ADDRESS_L2 | BILLING_CITY | BILLING_COUNTRY | BILLING_NAME | BILLING_PHONE | BILLING_STATE | BILLING_ZIP | CARD_BIN | CATEGORICAL | CURRENCY_CODE | EMAIL_ADDRESS | FINGERPRINT | FRAUD_LABEL | FREE_FORM_TEXT | IP_ADDRESS | NUMERIC | ORDER_ID | PAYMENT_TYPE | PHONE_NUMBER | PRICE | PRODUCT_CATEGORY | SHIPPING_ADDRESS_L1 | SHIPPING_ADDRESS_L2 | SHIPPING_CITY | SHIPPING_COUNTRY | SHIPPING_NAME | SHIPPING_PHONE | SHIPPING_STATE | SHIPPING_ZIP | USERAGENT}}
\item{tags}{A collection of key and value pairs.}
}
\description{
Creates a variable.
See \url{https://www.paws-r-sdk.com/docs/frauddetector_create_variable/} for full documentation.
}
\keyword{internal}
| /cran/paws.machine.learning/man/frauddetector_create_variable.Rd | permissive | paws-r/paws | R | false | true | 1,578 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/frauddetector_operations.R
\name{frauddetector_create_variable}
\alias{frauddetector_create_variable}
\title{Creates a variable}
\usage{
frauddetector_create_variable(
name,
dataType,
dataSource,
defaultValue,
description = NULL,
variableType = NULL,
tags = NULL
)
}
\arguments{
\item{name}{[required] The name of the variable.}
\item{dataType}{[required] The data type of the variable.}
\item{dataSource}{[required] The source of the data.}
\item{defaultValue}{[required] The default value for the variable when no value is received.}
\item{description}{The description.}
\item{variableType}{The variable type. For more information see \href{https://docs.aws.amazon.com/frauddetector/latest/ug/create-a-variable.html#variable-types}{Variable types}.
Valid Values:
\code{AUTH_CODE | AVS | BILLING_ADDRESS_L1 | BILLING_ADDRESS_L2 | BILLING_CITY | BILLING_COUNTRY | BILLING_NAME | BILLING_PHONE | BILLING_STATE | BILLING_ZIP | CARD_BIN | CATEGORICAL | CURRENCY_CODE | EMAIL_ADDRESS | FINGERPRINT | FRAUD_LABEL | FREE_FORM_TEXT | IP_ADDRESS | NUMERIC | ORDER_ID | PAYMENT_TYPE | PHONE_NUMBER | PRICE | PRODUCT_CATEGORY | SHIPPING_ADDRESS_L1 | SHIPPING_ADDRESS_L2 | SHIPPING_CITY | SHIPPING_COUNTRY | SHIPPING_NAME | SHIPPING_PHONE | SHIPPING_STATE | SHIPPING_ZIP | USERAGENT}}
\item{tags}{A collection of key and value pairs.}
}
\description{
Creates a variable.
See \url{https://www.paws-r-sdk.com/docs/frauddetector_create_variable/} for full documentation.
}
\keyword{internal}
|
library(dplyr)
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(data.table)
library(reshape2)
library(knitr)
library(cowplot)
mutate_TB<-function(df){
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
df$TB<-gsub("LTBI", "QFT+", df$TB)
df<-filter(df, SM!="N", TB!="HC")
df$SM<-factor(df$SM, levels=c("X","SM"))
df$TB<-factor(df$TB, levels=c("QFT+","TB"))
df
}
filter_tb<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="TB")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$TB<-factor(df$TB, levels=c("TB"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_ltbi<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="LTBI")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("LTBI SM-", "LTBI SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_hc<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="HC")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC SM-", "HC SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
plot_filter<-function(datatable){
library(dplyr)
df<-filter(datatable, TB!="N")
df$TB<-factor(df$TB, levels=c("HC","LTBI","TB"))
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC SM-", "HC SM+",
"LTBI SM-", "LTBI SM+",
"TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_all<-function(datatable){
library(dplyr)
df<-datatable
df$TB<-factor(df$TB, levels=c("N","HC","LTBI","TB"))
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df$SM<-factor(df$SM, levels=c("N","SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-gsub("N N", "Naive", df$disease)
df$disease<-factor(df$disease, levels=c("Naive","HC SM-", "HC SM+",
"LTBI SM-", "LTBI SM+",
"TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
plot_compass<-function(DF, yval){
library(dplyr)
DF$disease<-paste(DF$TB, DF$SM, sep=" ")
DF$disease<-factor(DF$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
g<-ggplot(DF, aes(x=disease, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB, alpha=SM), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=20), axis.text.x = element_text(angle=90, vjust=0.6)) +
theme(plot.title = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_with_all_stats<-function(DF, yval){
DF<-dplyr::filter(DF, TB != "N")
DF$disease<-paste(DF$TB, DF$SM, sep=" ")
DF$disease<-factor(DF$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
my_comparisons <- list(c("HC SM", "HC X"),c("LTBI SM", "LTBI X"), c("TB SM", "TB X"),
c("TB SM", "LTBI SM"), c("TB X", "LTBI X"),
c("HC SM", "LTBI SM"), c("HC X", "LTBI X"))
g<-ggplot(DF, aes(x=disease, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB, alpha=SM), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_3<-function(DF, yval){
DF<-dplyr::filter(DF, TB != "N")
my_comparisons <- list(c("HC","LTBI"),
c("TB", "LTBI"),
c("HC", "TB"))
g<-ggplot(DF, aes(x=TB, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_3<-function(DF, yval, xval){
my_comparisons <- list(c("N", "HC"),
c("N", "LTBI"),
c("N", "TB"),
c("HC","LTBI"),
c("TB", "LTBI"),
c("HC", "TB"))
g<-ggplot(DF, aes(x=DF[,xval], y=DF[,yval]))
g+ geom_boxplot(aes(fill=DF[,xval]), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values =c("N" = "#da70d6","HC" = "#00AFBB", "LTBI" = "#E7B800","TB"="#FC4E07",
"X" = "#00AFBB", "SM" = "#E7B800"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons)
}
plot_cell<-function(DF, yval){
my_comparisons <- list(c("CD4","CD8"),
c("CD8", "GD"),
c("CD4", "GD"))
g<-ggplot(DF, aes(x=celltype, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("N" = "#da70d6","HC" = "#00AFBB", "LTBI" = "#E7B800","TB"="#FC4E07"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, label="p.signif")
}
triple_boolean_plot<-function(datatable){
library(dplyr)
DF<-datatable
Boolean<-dplyr::select(DF, Donor, TB, SM, Stim, G_4_13_T, G_4_x_T, G_x_13_T, G_4_13_x, x_4_13_T, G_4_x_x, G_x_13_x, x_4_x_T, x_x_13_T, G_x_x_T , G_x_x_x, x_x_x_T, x_4_13_x, x_4_x_x, x_x_13_x)%>%
dplyr::filter(TB!="N")
melt<-melt(Boolean,id.vars=c("Donor","TB","SM", "Stim"))
data1<-filter(melt, variable %in% c("G_4_13_T", "G_4_x_T", "G_x_13_T", "G_4_13_x", "x_4_13_T","G_4_x_x", "G_x_13_x","x_4_x_T","x_x_13_T"))
data2<-filter(melt, variable %in% c("G_x_x_T" , "G_x_x_x","x_x_x_T"))
data3<-filter(melt, variable %in% c("x_4_13_x","x_4_x_x", "x_x_13_x"))
g1<-ggplot(data1, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(strip.text.y = element_blank())+
theme(text = element_text(size=20), axis.text.x = element_blank())+
labs(y="Cytokine+ CD4+ Cells (%)", x="", title="TH1/2")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
g2<-ggplot(data2, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(strip.text.y = element_blank())+
theme(text = element_text(size=20), axis.text.x = element_blank()) +
labs(y="", x="", title="TH1")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
g3<-ggplot(data3, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16), axis.text.x = element_blank()) +
labs(y="", x="", title="TH2")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
plot<-grid.arrange(g1, g2, g3, ncol=3, widths=c(8,4,4),
top=text_grob("Cytokine Production", size=24))
}
plot_6<-function(data, xval, yval){
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
scale_x_discrete(labels=c("SM-", "SM+"))+
theme_classic()+ theme(legend.position="none")+
theme(axis.text.x = element_text(angle=90, vjust=0.6)) +
theme(text = element_text(size=12), axis.text.x = element_text(angle=90, vjust=0.6)) +
facet_grid(~TB, scale="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 4,
method="wilcox.test", paired = FALSE, label.y= (max(data[,yval])*1.1))
}
plot_2<-function(data, xval, yval){
my_comparisons <- list(c("LTBI SM", "LTBI X"), c("TB SM", "TB X"),
c("TB SM", "LTBI SM"), c("TB X", "LTBI X"))
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
scale_x_discrete(labels=c("QFT+SM-", "QFT+SM+", "TB SM-","TB SM+"))+
theme_classic()+ theme(legend.position="none")+
theme(plot.title = element_text(hjust = 0.5))+
theme(text = element_text(size=14)) +
stat_compare_means(comparisons=my_comparisons, aes(label=..p.adj..),
label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 5,
method="wilcox.test", paired = FALSE, na.rm=TRUE)+
labs(title="",
x="",
y=yval)}
plot_compare<-function(data, xval, yval){
##Compares Total responses to Mtb-specific responses not paired
##requires melted data where xval ==
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(plot.title = element_text(hjust = 0.5))+
theme(text = element_text(size=20), axis.text.x = element_text(angle=30, vjust=0.6)) +
theme(strip.text.x = element_blank())+
facet_grid(~TB+SM, scale="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 6,
method="wilcox.test", paired = FALSE,
label.y = 1.1*(max(data[,yval], na.rm = TRUE)))+
labs(y=paste(yval, "+ CD4+ (%)"),
x="")
}
plot_pbmc<-function(data, yval){
data[,yval]<-as.numeric(data[,yval])
ggplot(data, aes(x=SM, y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,0.5,1))+
scale_fill_manual(values = c("#e0e0e0", "#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=14)) +
facet_grid(~TB, scales="free")+
labs(x="",
y="")
}
plot_pbmc.pairs<-function(data){
ggplot(data, aes(x=variable, y=value, color=TB, alpha=SM))+
geom_point(shape=16,size=2)+
geom_line(aes(group=Donor))+
scale_alpha_manual(values=c(0.5,0.5,1))+
scale_color_manual(values = c("#e0e0e0", "#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+
theme(legend.position="none")+
scale_x_discrete(labels=c("Pre", "Post"))+
theme(text = element_text(size=20)) +
facet_grid(SM~TB, scales="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 6,
method="wilcox.test", paired = TRUE,
label.y = 1.1*(max(data$value, na.rm = TRUE)))+
labs(x="", y="")
}
plot_stim<-function(data, yval){
ggplot(data, aes(y=data[,yval], x=Stim, col=TB))+
geom_point(size=3, alpha=0.5)+geom_line(aes(group=Donor))+
scale_color_manual(values =c("#2166ac", "#b2182b"))+
theme_bw()+ theme(legend.position="none")+
theme(text = element_text(size=20)) +
facet_grid(SM~TB)+
stat_compare_means(comparisons = list(c("PMA", "PEP"), c("PMA","WCL"))
, na.rm=TRUE, size=4, label.y.npc = .9)+
labs(y= yval)
}
baseplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+ theme(legend.position="none")+
theme(panel.border = element_rect(fill = NA, colour = "black"))+
theme(text = element_text(size=20),
axis.text.x = element_text(size = 16),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
labs(x="", y="")
}
booleanplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none")+
theme(text = element_text(size=20),
axis.text.x = element_text(size=20),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
stat_compare_means(method="wilcox.test", label="p.signif", hide.ns = TRUE, size=10)+
labs(x="", y="")
}
booleanplot2<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=disease))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none")+
theme(text = element_text(size=20),
axis.text.x = element_text(size=20),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
labs(x="", y="")
}
allplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=disease))+
geom_boxplot(size=1, position=position_dodge2(width=0.2, padding = 0.2, preserve = "single"), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none") +
theme(panel.border = element_rect(fill = NA, colour = "black"))+
facet_wrap(~variable)+
theme(text = element_text(size=20),
axis.text.x = element_text(size = 16),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20))+
labs(x="", y="")
}
cyt_groups <- as_labeller(c("IFNg" = "IFNγ",
"TNFa" = "TNFα", "IL4" = "IL-4", "IL13" = "IL-13"))
all_comps<-list(c("HC SM+", "HC SM-"),c("LTBI SM+", "LTBI SM-"), c("TB SM+", "TB SM-"))
healthy.cols<- c("SM+" = "#1a9850", "SM-" ="#91cf60")
ltbi.cols<- c("SM+" = "#2166ac", "SM-" = "#85ABD1")
tb.cols<- c("SM+" = "#b2182b", "SM-"="#E59EA6")
all.cols<- c("Naive" = "#C8D0D8","HC SM+" = "#1a9850", "HC SM-" ="#91cf60",
"LTBI SM+" = "#2166ac", "LTBI SM-" = "#85ABD1",
"TB SM+" = "#b2182b", "TB SM-"="#E59EA6")
| /plot_functions.R | no_license | tarynam/random-code | R | false | false | 18,018 | r | library(dplyr)
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(data.table)
library(reshape2)
library(knitr)
library(cowplot)
mutate_TB<-function(df){
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
df$TB<-gsub("LTBI", "QFT+", df$TB)
df<-filter(df, SM!="N", TB!="HC")
df$SM<-factor(df$SM, levels=c("X","SM"))
df$TB<-factor(df$TB, levels=c("QFT+","TB"))
df
}
filter_tb<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="TB")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$TB<-factor(df$TB, levels=c("TB"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_ltbi<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="LTBI")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("LTBI SM-", "LTBI SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_hc<-function(df){
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df<-filter(df, SM!="N", TB=="HC")
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC SM-", "HC SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
plot_filter<-function(datatable){
library(dplyr)
df<-filter(datatable, TB!="N")
df$TB<-factor(df$TB, levels=c("HC","LTBI","TB"))
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df$SM<-factor(df$SM, levels=c("SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-factor(df$disease, levels=c("HC SM-", "HC SM+",
"LTBI SM-", "LTBI SM+",
"TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
filter_all<-function(datatable){
library(dplyr)
df<-datatable
df$TB<-factor(df$TB, levels=c("N","HC","LTBI","TB"))
df$SM<-gsub("SM", "SM+", df$SM)
df$SM<-gsub("X", "SM-", df$SM)
df$SM<-factor(df$SM, levels=c("N","SM-","SM+"))
df$disease<-paste(df$TB, df$SM, sep=" ")
df$disease<-gsub("N N", "Naive", df$disease)
df$disease<-factor(df$disease, levels=c("Naive","HC SM-", "HC SM+",
"LTBI SM-", "LTBI SM+",
"TB SM-", "TB SM+"))
names(df)<-gsub("Tbet", "T-bet", names(df))
df
}
plot_compass<-function(DF, yval){
library(dplyr)
DF$disease<-paste(DF$TB, DF$SM, sep=" ")
DF$disease<-factor(DF$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
g<-ggplot(DF, aes(x=disease, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB, alpha=SM), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=20), axis.text.x = element_text(angle=90, vjust=0.6)) +
theme(plot.title = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_with_all_stats<-function(DF, yval){
DF<-dplyr::filter(DF, TB != "N")
DF$disease<-paste(DF$TB, DF$SM, sep=" ")
DF$disease<-factor(DF$disease, levels=c("HC X", "HC SM", "LTBI X", "LTBI SM", "TB X", "TB SM"))
my_comparisons <- list(c("HC SM", "HC X"),c("LTBI SM", "LTBI X"), c("TB SM", "TB X"),
c("TB SM", "LTBI SM"), c("TB X", "LTBI X"),
c("HC SM", "LTBI SM"), c("HC X", "LTBI X"))
g<-ggplot(DF, aes(x=disease, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB, alpha=SM), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_3<-function(DF, yval){
DF<-dplyr::filter(DF, TB != "N")
my_comparisons <- list(c("HC","LTBI"),
c("TB", "LTBI"),
c("HC", "TB"))
g<-ggplot(DF, aes(x=TB, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values = c("#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, p.adjust="bonferroni")
}
plot_3<-function(DF, yval, xval){
my_comparisons <- list(c("N", "HC"),
c("N", "LTBI"),
c("N", "TB"),
c("HC","LTBI"),
c("TB", "LTBI"),
c("HC", "TB"))
g<-ggplot(DF, aes(x=DF[,xval], y=DF[,yval]))
g+ geom_boxplot(aes(fill=DF[,xval]), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,1))+
scale_fill_manual(values =c("N" = "#da70d6","HC" = "#00AFBB", "LTBI" = "#E7B800","TB"="#FC4E07",
"X" = "#00AFBB", "SM" = "#E7B800"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons)
}
plot_cell<-function(DF, yval){
my_comparisons <- list(c("CD4","CD8"),
c("CD8", "GD"),
c("CD4", "GD"))
g<-ggplot(DF, aes(x=celltype, y=DF[,yval]))
g+ geom_boxplot(aes(fill=TB), size=1, position=position_dodge(width = 1), outlier.shape=NA)+
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("N" = "#da70d6","HC" = "#00AFBB", "LTBI" = "#E7B800","TB"="#FC4E07"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16)) +
theme(plot.subtitle = element_text(hjust = 0.5)) +
stat_compare_means(comparisons = my_comparisons, label="p.signif")
}
triple_boolean_plot<-function(datatable){
library(dplyr)
DF<-datatable
Boolean<-dplyr::select(DF, Donor, TB, SM, Stim, G_4_13_T, G_4_x_T, G_x_13_T, G_4_13_x, x_4_13_T, G_4_x_x, G_x_13_x, x_4_x_T, x_x_13_T, G_x_x_T , G_x_x_x, x_x_x_T, x_4_13_x, x_4_x_x, x_x_13_x)%>%
dplyr::filter(TB!="N")
melt<-melt(Boolean,id.vars=c("Donor","TB","SM", "Stim"))
data1<-filter(melt, variable %in% c("G_4_13_T", "G_4_x_T", "G_x_13_T", "G_4_13_x", "x_4_13_T","G_4_x_x", "G_x_13_x","x_4_x_T","x_x_13_T"))
data2<-filter(melt, variable %in% c("G_x_x_T" , "G_x_x_x","x_x_x_T"))
data3<-filter(melt, variable %in% c("x_4_13_x","x_4_x_x", "x_x_13_x"))
g1<-ggplot(data1, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(strip.text.y = element_blank())+
theme(text = element_text(size=20), axis.text.x = element_blank())+
labs(y="Cytokine+ CD4+ Cells (%)", x="", title="TH1/2")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
g2<-ggplot(data2, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(strip.text.y = element_blank())+
theme(text = element_text(size=20), axis.text.x = element_blank()) +
labs(y="", x="", title="TH1")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
g3<-ggplot(data3, aes(x=variable, y=value, fill=TB, alpha=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1)) +
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=16), axis.text.x = element_blank()) +
labs(y="", x="", title="TH2")+
theme(plot.title = element_text(hjust = 0.5)) +
facet_grid(TB~., scale="fixed")+
stat_compare_means(label = "p.signif", p.adjust.method = "fdr", hide.ns = TRUE, size = 8,
method="wilcox.test", paired = FALSE)
plot<-grid.arrange(g1, g2, g3, ncol=3, widths=c(8,4,4),
top=text_grob("Cytokine Production", size=24))
}
plot_6<-function(data, xval, yval){
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#1a9850" , "#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
scale_x_discrete(labels=c("SM-", "SM+"))+
theme_classic()+ theme(legend.position="none")+
theme(axis.text.x = element_text(angle=90, vjust=0.6)) +
theme(text = element_text(size=12), axis.text.x = element_text(angle=90, vjust=0.6)) +
facet_grid(~TB, scale="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 4,
method="wilcox.test", paired = FALSE, label.y= (max(data[,yval])*1.1))
}
plot_2<-function(data, xval, yval){
my_comparisons <- list(c("LTBI SM", "LTBI X"), c("TB SM", "TB X"),
c("TB SM", "LTBI SM"), c("TB X", "LTBI X"))
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
scale_x_discrete(labels=c("QFT+SM-", "QFT+SM+", "TB SM-","TB SM+"))+
theme_classic()+ theme(legend.position="none")+
theme(plot.title = element_text(hjust = 0.5))+
theme(text = element_text(size=14)) +
stat_compare_means(comparisons=my_comparisons, aes(label=..p.adj..),
label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 5,
method="wilcox.test", paired = FALSE, na.rm=TRUE)+
labs(title="",
x="",
y=yval)}
plot_compare<-function(data, xval, yval){
##Compares Total responses to Mtb-specific responses not paired
##requires melted data where xval ==
ggplot(data, aes(x=data[,xval], y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_fill_manual(values =c("#2166ac", "#b2182b"))+
scale_alpha_manual(values=c(0.5,1))+
theme_classic()+ theme(legend.position="none")+
theme(plot.title = element_text(hjust = 0.5))+
theme(text = element_text(size=20), axis.text.x = element_text(angle=30, vjust=0.6)) +
theme(strip.text.x = element_blank())+
facet_grid(~TB+SM, scale="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 6,
method="wilcox.test", paired = FALSE,
label.y = 1.1*(max(data[,yval], na.rm = TRUE)))+
labs(y=paste(yval, "+ CD4+ (%)"),
x="")
}
plot_pbmc<-function(data, yval){
data[,yval]<-as.numeric(data[,yval])
ggplot(data, aes(x=SM, y=data[,yval]))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA, aes(fill=TB, alpha=SM)) +
geom_jitter(width=.1,height=0, shape=16,size=2)+
scale_alpha_manual(values=c(0.5,0.5,1))+
scale_fill_manual(values = c("#e0e0e0", "#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+ theme(legend.position="none")+
theme(text = element_text(size=14)) +
facet_grid(~TB, scales="free")+
labs(x="",
y="")
}
plot_pbmc.pairs<-function(data){
ggplot(data, aes(x=variable, y=value, color=TB, alpha=SM))+
geom_point(shape=16,size=2)+
geom_line(aes(group=Donor))+
scale_alpha_manual(values=c(0.5,0.5,1))+
scale_color_manual(values = c("#e0e0e0", "#1a9850" , "#2166ac", "#b2182b"))+
theme_classic()+
theme(legend.position="none")+
scale_x_discrete(labels=c("Pre", "Post"))+
theme(text = element_text(size=20)) +
facet_grid(SM~TB, scales="free")+
stat_compare_means(label = "p.format", p.adjust.method = "fdr", hide.ns = TRUE, size = 6,
method="wilcox.test", paired = TRUE,
label.y = 1.1*(max(data$value, na.rm = TRUE)))+
labs(x="", y="")
}
plot_stim<-function(data, yval){
ggplot(data, aes(y=data[,yval], x=Stim, col=TB))+
geom_point(size=3, alpha=0.5)+geom_line(aes(group=Donor))+
scale_color_manual(values =c("#2166ac", "#b2182b"))+
theme_bw()+ theme(legend.position="none")+
theme(text = element_text(size=20)) +
facet_grid(SM~TB)+
stat_compare_means(comparisons = list(c("PMA", "PEP"), c("PMA","WCL"))
, na.rm=TRUE, size=4, label.y.npc = .9)+
labs(y= yval)
}
baseplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+ theme(legend.position="none")+
theme(panel.border = element_rect(fill = NA, colour = "black"))+
theme(text = element_text(size=20),
axis.text.x = element_text(size = 16),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
labs(x="", y="")
}
booleanplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=SM))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none")+
theme(text = element_text(size=20),
axis.text.x = element_text(size=20),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
stat_compare_means(method="wilcox.test", label="p.signif", hide.ns = TRUE, size=10)+
labs(x="", y="")
}
booleanplot2<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=disease))+
geom_boxplot(size=1, position=position_dodge(width = 1), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none")+
theme(text = element_text(size=20),
axis.text.x = element_text(size=20),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20)) +
labs(x="", y="")
}
allplot<-function(data, xval, yval, colors){
ggplot(data, aes(data[,xval], data[,yval], fill=disease))+
geom_boxplot(size=1, position=position_dodge2(width=0.2, padding = 0.2, preserve = "single"), outlier.shape = NA)+
scale_fill_manual(values = colors)+
theme_classic()+
theme(legend.position="none") +
theme(panel.border = element_rect(fill = NA, colour = "black"))+
facet_wrap(~variable)+
theme(text = element_text(size=20),
axis.text.x = element_text(size = 16),
strip.text = element_text(size = 20))+
theme(plot.title = element_text(hjust = 0.5, size=20))+
labs(x="", y="")
}
cyt_groups <- as_labeller(c("IFNg" = "IFNγ",
"TNFa" = "TNFα", "IL4" = "IL-4", "IL13" = "IL-13"))
all_comps<-list(c("HC SM+", "HC SM-"),c("LTBI SM+", "LTBI SM-"), c("TB SM+", "TB SM-"))
healthy.cols<- c("SM+" = "#1a9850", "SM-" ="#91cf60")
ltbi.cols<- c("SM+" = "#2166ac", "SM-" = "#85ABD1")
tb.cols<- c("SM+" = "#b2182b", "SM-"="#E59EA6")
all.cols<- c("Naive" = "#C8D0D8","HC SM+" = "#1a9850", "HC SM-" ="#91cf60",
"LTBI SM+" = "#2166ac", "LTBI SM-" = "#85ABD1",
"TB SM+" = "#b2182b", "TB SM-"="#E59EA6")
|
# - source all the tabs -
tabpath <- "./Tabs_ui"
source(file.path(tabpath, "html_styles_ui.R"))
source(file.path(tabpath, "Tab01_ui.R"))
# --
ui <- fluidPage(
html_styles,
Tab01
) | /learn/Learn_ui.R | no_license | TBrach/shiny_apps | R | false | false | 198 | r | # - source all the tabs -
tabpath <- "./Tabs_ui"
source(file.path(tabpath, "html_styles_ui.R"))
source(file.path(tabpath, "Tab01_ui.R"))
# --
ui <- fluidPage(
html_styles,
Tab01
) |
#Pacchetti
library(bsts)
library(chron)
library(tidyverse)
library(data.table)
library(dplyr)
library(forecast)
library(tseries)
library(rjags)
library(coda)
library(plotrix)
#Dataset
load("../../Dataset_pronti/weather_temp.RData")
load("../../Dataset_pronti/train.RData")
key=read.table('../../Data/key.csv',sep=',',header=TRUE)
#Some useful function for selecting the time series
unit_series <- function(data,store,item,last_date='14/12/19',all.dates=FALSE) {
data=data[which(data$store_nbr==store & data$item_nbr==item & data$date<chron(date=last_date,format = c(date='d/m/y'))),c(1,4)]
if(all.dates)
{
#To be decided in the future
#I would like to add NAs for test dates
# and 0 for christmas
}
return(data)
}
weather_series <- function(data,station,features,last_date='14/12/19',all.dates=FALSE) {
data=data[which(data$station_nbr==station & data$date<chron(date=last_date,format = c(date='d/m/y'))),c('date',features)]
if(all.dates)
{
#To be decided in the future
}
return(data)
}
#Preliminary steps
#We try first with item 19 in store 32
last_date='01/01/13'
item=44
store=30#4
niter=2000
station=key$station_nbr[which(key$store_nbr==store)]
items_dataset=unit_series(train,item=item,last_date = last_date,store=store)
regressors_dataset=weather_series(data=weather,station=station,features =c('preciptotal','snowfall','resultspeed','rain','snow'),last_date=last_date)
working_dataset=merge(y=items_dataset,x=regressors_dataset,by='date',all.x=TRUE)
plot(working_dataset$date,working_dataset$units,col = 'green4', type='l')
hist(working_dataset$units)
boxplot(working_dataset$units)
#Fixing NAs in the regressors
#working_dataset$tavg[which(is.na(working_dataset$tavg))]=(working_dataset$tmax[which(is.na(working_dataset$tavg))]+working_dataset$tmin[which(is.na(working_dataset$tavg))])/2
working_dataset$units[which(working_dataset$date=='25/12/12')]=0
working_dataset$units[which(working_dataset$date=='25/12/13')]#=0
sum(is.na(working_dataset$units))
#working_dataset$tavg=c(0,diff(working_dataset$tavg)) #Taking the difference in avg(temp)
summary(working_dataset)
working_dataset$preciptotal[which(is.na(working_dataset$preciptotal))]=0 #because rain==0
working_dataset$snowfall[which(is.na(working_dataset$snowfall))]=0 #beacuse snow==0
summary(working_dataset)
working_dataset$snow_tomorrow=shift(working_dataset$snow,n=-1)
working_dataset$snowfall_tomorrow=shift(working_dataset$snowfall,n=-1)
working_dataset$snow_tomorrow[which(is.na(working_dataset$snow_tomorrow))]=0
working_dataset$snowfall_tomorrow[which(is.na(working_dataset$snowfall_tomorrow))]=0
#working_dataset$units=as.numeric(scale(working_dataset$units))
mean=mean(working_dataset$units)
working_dataset$units=working_dataset$units-mean(working_dataset$units)#detrend
#Holidays
july4 <- FixedDateHoliday("July4", "July", 4)
christmas <- FixedDateHoliday("Christmas", "Dec", 25)
#acf e pacf test
plot.ts(working_dataset$units)
adf.test(working_dataset$units, alternative="stationary", k=0) # stazionario
# let's try only with AR model
acf(working_dataset$units)
pacf(working_dataset$units) #AR(6)
# JAGS
data = list(y=working_dataset$units[7:dim(working_dataset)[1]],
x = working_dataset$units[1:(dim(working_dataset)[1])],
n = dim(working_dataset)[1],
m = 6)
modelRegress=jags.model("AR_model_long.bug",data=data,n.adapt=1000,n.chains=1)
update(modelRegress,n.iter=19000)
variable.names=c("a","b", "tau")#c("b","tau")#c("a","b", "tau")
n.iter=50000
thin=10
outputRegress=coda.samples(model=modelRegress,variable.names=variable.names,n.iter=n.iter,thin=thin)
data.out=as.matrix(outputRegress)
data.out=data.frame(data.out)
#summary(data.out)
head(data.out)
par(mfrow=c(1,1))
acf(data.out[,'a'],lwd=3,col="red3",main="autocorrelation of a0")
par(mfrow=c(1,2))
acf(data.out[,'b.1.'],lwd=3,col="red3",main="autocorrelation of b1")
acf(data.out[,'b.2.'],lwd=3,col="red3",main="autocorrelation of b2")
acf(data.out[,'b.3.'],lwd=3,col="red3",main="autocorrelation of b3")
acf(data.out[,'b.4.'],lwd=3,col="red3",main="autocorrelation of b4")
par(mfrow=c(1,1))
acf(data.out[,'tau'],lwd=3,col="red3",main="autocorrelation of tau")
#
# alfa.post <- data.out[,1]
# beta.post <- data.out[,2:5]
# sigma.post <- data.out[,6]
alfa.post <- data.out[,1]
beta.post <- data.out[,2:7]
sigma.post <- data.out[,8]
#posterior mean of the alfa and beta parameters
alfa.bayes <- mean(alfa.post)
alfa.bayes
beta.bayes <- colMeans(beta.post)
beta.bayes
sigma.bayes <- mean(sigma.post)
sigma.bayes
### ALFA
## Representation of the posterior chain of a0
chain <- alfa.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of a0")
acf(chain,lwd=3,col="red3",main="autocorrelation of a0")
hist(chain,nclass="fd",freq=F,main="Posterior of a0",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
### BETA
## Representation of the posterior chain of b1
chain <- beta.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b1")
acf(chain,lwd=3,col="red3",main="autocorrelation of b1")
hist(chain,nclass="fd",freq=F,main="Posterior of b1",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b2
chain <- beta.post[,2]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b2")
acf(chain,lwd=3,col="red3",main="autocorrelation of b2")
hist(chain,nclass="fd",freq=F,main="Posterior of b2",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b3
chain <- beta.post[,3]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b3")
acf(chain,lwd=3,col="red3",main="autocorrelation of b3")
hist(chain,nclass="fd",freq=F,main="Posterior of b3",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b4
chain <- beta.post[,4]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b4")
acf(chain,lwd=3,col="red3",main="autocorrelation of b4")
hist(chain,nclass="fd",freq=F,main="Posterior of b4",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of sigma
chain <- sigma.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of sigma")
acf(chain,lwd=3,col="red3",main="autocorrelation of sigma")
hist(chain,nclass="fd",freq=F,main="Posterior of sigma",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
par(mfrow=c(1,1))
### We first compare the observed data distribution with the posterior
### predictive distribution of Y
ypred <- rep(0, dim(working_dataset)[1])
ypred[1:6] <- working_dataset$units[1:6]
for ( i in 7:dim(working_dataset)[1] ){
#mu <- alfa.bayes + beta.bayes[1] * working_dataset$units[i-1]
mu <- alfa.bayes + beta.bayes[1] * working_dataset$units[i-1]
+ beta.bayes[2] * working_dataset$units[i-2]
+ beta.bayes[3] * working_dataset$units[i-3]
+ beta.bayes[4] * working_dataset$units[i-4]
+ beta.bayes[5] * working_dataset$units[i-5]
+ beta.bayes[6] * working_dataset$units[i-6]
ypred[i] <- rnorm(1, mu, 10)
}
par(mfrow=c(1,1))
plot(working_dataset$units, col="darkgreen", type="l", lty=1, lwd=1)
lines(ypred, col="red", type="l", lty=1, lwd=1)
error=working_dataset$units-ypred
hist(error,nclass="fd",freq=F,main="ERROR",col="gray", xlab = "mean(y)")
lines(density(error),col="blue",lwd=2)
abline(v=quantile(error,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(error,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(error,prob=c(0.975)),col="red",lty=2,lwd=2)
ypred[2:365] <- ypred[3:366]
ypred[366] <- working_dataset$units[366]
par(mfrow=c(1,1))
plot(working_dataset$units, col="darkgreen", type="l", lty=1, lwd=1)
lines(ypred, col="red", type="l", lty=1, lwd=1)
#
#
# error=working_dataset$units-ypred
# hist(error,nclass="fd",freq=F,main="ERROR",col="gray", xlab = "mean(y)")
# lines(density(error),col="blue",lwd=2)
# abline(v=quantile(error,prob=c(0.025)),col="red",lty=2,lwd=2)
# abline(v=quantile(error,prob=c(0.5)),col="red",lty=1,lwd=2)
# abline(v=quantile(error,prob=c(0.975)),col="red",lty=2,lwd=2)
| /03. Univariate models/AR with JAGS/AR_model_long.R | no_license | fmacca/bayesian-sales-in-stormy-weather | R | false | false | 9,287 | r |
#Pacchetti
library(bsts)
library(chron)
library(tidyverse)
library(data.table)
library(dplyr)
library(forecast)
library(tseries)
library(rjags)
library(coda)
library(plotrix)
#Dataset
load("../../Dataset_pronti/weather_temp.RData")
load("../../Dataset_pronti/train.RData")
key=read.table('../../Data/key.csv',sep=',',header=TRUE)
#Some useful function for selecting the time series
unit_series <- function(data,store,item,last_date='14/12/19',all.dates=FALSE) {
data=data[which(data$store_nbr==store & data$item_nbr==item & data$date<chron(date=last_date,format = c(date='d/m/y'))),c(1,4)]
if(all.dates)
{
#To be decided in the future
#I would like to add NAs for test dates
# and 0 for christmas
}
return(data)
}
weather_series <- function(data,station,features,last_date='14/12/19',all.dates=FALSE) {
data=data[which(data$station_nbr==station & data$date<chron(date=last_date,format = c(date='d/m/y'))),c('date',features)]
if(all.dates)
{
#To be decided in the future
}
return(data)
}
#Preliminary steps
#We try first with item 19 in store 32
last_date='01/01/13'
item=44
store=30#4
niter=2000
station=key$station_nbr[which(key$store_nbr==store)]
items_dataset=unit_series(train,item=item,last_date = last_date,store=store)
regressors_dataset=weather_series(data=weather,station=station,features =c('preciptotal','snowfall','resultspeed','rain','snow'),last_date=last_date)
working_dataset=merge(y=items_dataset,x=regressors_dataset,by='date',all.x=TRUE)
plot(working_dataset$date,working_dataset$units,col = 'green4', type='l')
hist(working_dataset$units)
boxplot(working_dataset$units)
#Fixing NAs in the regressors
#working_dataset$tavg[which(is.na(working_dataset$tavg))]=(working_dataset$tmax[which(is.na(working_dataset$tavg))]+working_dataset$tmin[which(is.na(working_dataset$tavg))])/2
working_dataset$units[which(working_dataset$date=='25/12/12')]=0
working_dataset$units[which(working_dataset$date=='25/12/13')]#=0
sum(is.na(working_dataset$units))
#working_dataset$tavg=c(0,diff(working_dataset$tavg)) #Taking the difference in avg(temp)
summary(working_dataset)
working_dataset$preciptotal[which(is.na(working_dataset$preciptotal))]=0 #because rain==0
working_dataset$snowfall[which(is.na(working_dataset$snowfall))]=0 #beacuse snow==0
summary(working_dataset)
working_dataset$snow_tomorrow=shift(working_dataset$snow,n=-1)
working_dataset$snowfall_tomorrow=shift(working_dataset$snowfall,n=-1)
working_dataset$snow_tomorrow[which(is.na(working_dataset$snow_tomorrow))]=0
working_dataset$snowfall_tomorrow[which(is.na(working_dataset$snowfall_tomorrow))]=0
#working_dataset$units=as.numeric(scale(working_dataset$units))
mean=mean(working_dataset$units)
working_dataset$units=working_dataset$units-mean(working_dataset$units)#detrend
#Holidays
july4 <- FixedDateHoliday("July4", "July", 4)
christmas <- FixedDateHoliday("Christmas", "Dec", 25)
#acf e pacf test
plot.ts(working_dataset$units)
adf.test(working_dataset$units, alternative="stationary", k=0) # stazionario
# let's try only with AR model
acf(working_dataset$units)
pacf(working_dataset$units) #AR(6)
# JAGS
data = list(y=working_dataset$units[7:dim(working_dataset)[1]],
x = working_dataset$units[1:(dim(working_dataset)[1])],
n = dim(working_dataset)[1],
m = 6)
modelRegress=jags.model("AR_model_long.bug",data=data,n.adapt=1000,n.chains=1)
update(modelRegress,n.iter=19000)
variable.names=c("a","b", "tau")#c("b","tau")#c("a","b", "tau")
n.iter=50000
thin=10
outputRegress=coda.samples(model=modelRegress,variable.names=variable.names,n.iter=n.iter,thin=thin)
data.out=as.matrix(outputRegress)
data.out=data.frame(data.out)
#summary(data.out)
head(data.out)
par(mfrow=c(1,1))
acf(data.out[,'a'],lwd=3,col="red3",main="autocorrelation of a0")
par(mfrow=c(1,2))
acf(data.out[,'b.1.'],lwd=3,col="red3",main="autocorrelation of b1")
acf(data.out[,'b.2.'],lwd=3,col="red3",main="autocorrelation of b2")
acf(data.out[,'b.3.'],lwd=3,col="red3",main="autocorrelation of b3")
acf(data.out[,'b.4.'],lwd=3,col="red3",main="autocorrelation of b4")
par(mfrow=c(1,1))
acf(data.out[,'tau'],lwd=3,col="red3",main="autocorrelation of tau")
#
# alfa.post <- data.out[,1]
# beta.post <- data.out[,2:5]
# sigma.post <- data.out[,6]
alfa.post <- data.out[,1]
beta.post <- data.out[,2:7]
sigma.post <- data.out[,8]
#posterior mean of the alfa and beta parameters
alfa.bayes <- mean(alfa.post)
alfa.bayes
beta.bayes <- colMeans(beta.post)
beta.bayes
sigma.bayes <- mean(sigma.post)
sigma.bayes
### ALFA
## Representation of the posterior chain of a0
chain <- alfa.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of a0")
acf(chain,lwd=3,col="red3",main="autocorrelation of a0")
hist(chain,nclass="fd",freq=F,main="Posterior of a0",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
### BETA
## Representation of the posterior chain of b1
chain <- beta.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b1")
acf(chain,lwd=3,col="red3",main="autocorrelation of b1")
hist(chain,nclass="fd",freq=F,main="Posterior of b1",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b2
chain <- beta.post[,2]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b2")
acf(chain,lwd=3,col="red3",main="autocorrelation of b2")
hist(chain,nclass="fd",freq=F,main="Posterior of b2",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b3
chain <- beta.post[,3]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b3")
acf(chain,lwd=3,col="red3",main="autocorrelation of b3")
hist(chain,nclass="fd",freq=F,main="Posterior of b3",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of b4
chain <- beta.post[,4]
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of b4")
acf(chain,lwd=3,col="red3",main="autocorrelation of b4")
hist(chain,nclass="fd",freq=F,main="Posterior of b4",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
##Representation of the posterior chain of sigma
chain <- sigma.post
layout(matrix(c(1,2,3,3),2,2,byrow=T))
plot(chain,type="l",main="Trace plot of sigma")
acf(chain,lwd=3,col="red3",main="autocorrelation of sigma")
hist(chain,nclass="fd",freq=F,main="Posterior of sigma",col="gray")
lines(density(chain),col="blue",lwd=2)
abline(v=quantile(chain,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(chain,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(chain,prob=c(0.975)),col="red",lty=2,lwd=2)
par(mfrow=c(1,1))
### We first compare the observed data distribution with the posterior
### predictive distribution of Y
ypred <- rep(0, dim(working_dataset)[1])
ypred[1:6] <- working_dataset$units[1:6]
for ( i in 7:dim(working_dataset)[1] ){
#mu <- alfa.bayes + beta.bayes[1] * working_dataset$units[i-1]
mu <- alfa.bayes + beta.bayes[1] * working_dataset$units[i-1]
+ beta.bayes[2] * working_dataset$units[i-2]
+ beta.bayes[3] * working_dataset$units[i-3]
+ beta.bayes[4] * working_dataset$units[i-4]
+ beta.bayes[5] * working_dataset$units[i-5]
+ beta.bayes[6] * working_dataset$units[i-6]
ypred[i] <- rnorm(1, mu, 10)
}
par(mfrow=c(1,1))
plot(working_dataset$units, col="darkgreen", type="l", lty=1, lwd=1)
lines(ypred, col="red", type="l", lty=1, lwd=1)
error=working_dataset$units-ypred
hist(error,nclass="fd",freq=F,main="ERROR",col="gray", xlab = "mean(y)")
lines(density(error),col="blue",lwd=2)
abline(v=quantile(error,prob=c(0.025)),col="red",lty=2,lwd=2)
abline(v=quantile(error,prob=c(0.5)),col="red",lty=1,lwd=2)
abline(v=quantile(error,prob=c(0.975)),col="red",lty=2,lwd=2)
ypred[2:365] <- ypred[3:366]
ypred[366] <- working_dataset$units[366]
par(mfrow=c(1,1))
plot(working_dataset$units, col="darkgreen", type="l", lty=1, lwd=1)
lines(ypred, col="red", type="l", lty=1, lwd=1)
#
#
# error=working_dataset$units-ypred
# hist(error,nclass="fd",freq=F,main="ERROR",col="gray", xlab = "mean(y)")
# lines(density(error),col="blue",lwd=2)
# abline(v=quantile(error,prob=c(0.025)),col="red",lty=2,lwd=2)
# abline(v=quantile(error,prob=c(0.5)),col="red",lty=1,lwd=2)
# abline(v=quantile(error,prob=c(0.975)),col="red",lty=2,lwd=2)
|
library(data.table)
plot2 <- function(){
powercon =read.table("./data/household_power_consumption.txt", header = TRUE, sep = ";",stringsAsFactors=F,na.strings=c("?","NA","N/A",""))
#powercon =fread("./data/household_power_consumption.txt", header = TRUE, sep = ";",
# stringsAsFactors=FALSE,
# verbose=FALSE,
# na.strings=c("?","NA","N/A",""))
powercon.sub <-subset(powercon, Date=="1/2/2007" | Date=="2/2/2007" )
datetime.v = paste(powercon.sub$Date,powercon.sub$Time);
powercon.sub$DateTime = datetime.v
powercon.sub$DateTime <- strptime(powercon.sub$DateTime,format="%d/%m/%Y %H:%M:%S")
powercon.sub$Global_active_power <- as.numeric(powercon.sub$Global_active_power)
with(powercon.sub, {
plot(DateTime, Global_active_power,xlab="Date", ylab="Global Active Power (kilowatts)",type='n')
})
with(subset(powercon.sub), lines(DateTime, Global_active_power))
dev.copy(png, file="plot2.png")
dev.off()
} | /plort2.R | no_license | kennethchung/HopkinsDataScience | R | false | false | 994 | r |
library(data.table)
plot2 <- function(){
powercon =read.table("./data/household_power_consumption.txt", header = TRUE, sep = ";",stringsAsFactors=F,na.strings=c("?","NA","N/A",""))
#powercon =fread("./data/household_power_consumption.txt", header = TRUE, sep = ";",
# stringsAsFactors=FALSE,
# verbose=FALSE,
# na.strings=c("?","NA","N/A",""))
powercon.sub <-subset(powercon, Date=="1/2/2007" | Date=="2/2/2007" )
datetime.v = paste(powercon.sub$Date,powercon.sub$Time);
powercon.sub$DateTime = datetime.v
powercon.sub$DateTime <- strptime(powercon.sub$DateTime,format="%d/%m/%Y %H:%M:%S")
powercon.sub$Global_active_power <- as.numeric(powercon.sub$Global_active_power)
with(powercon.sub, {
plot(DateTime, Global_active_power,xlab="Date", ylab="Global Active Power (kilowatts)",type='n')
})
with(subset(powercon.sub), lines(DateTime, Global_active_power))
dev.copy(png, file="plot2.png")
dev.off()
} |
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