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91134c9c434ee7ce2529efa478faa820dce61e0a
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/Programs/Method_eval/method_eval4_run2.R
|
c06923f641a0dc9e12a1cf80c0c21c93ec77f5d0
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[] |
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Adam-Brand/Pooled_Testing_HIV
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9855603f21eac6f479b035cadf446907d2a550e8
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| 2021-07-19T11:48:31
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r
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method_eval4_run2.R
|
#==============================================================================
# FILENAME: method_eval4_run2.R
# PROJECT: Pooled testing in HIV
# PURPOSE: This program calls the main evaluation function to evaluate the pooled testing methods
# in different scenarios, 4 different scenarios each evaluated in 13 different MEs using
# the SD=0 data; does not evaluate the hypred method, that is a separate program
#
#
# AUTHOR: Adam Brand
# INPUT datasets: Uganda_SimData_SD0.rds
# SD0_data_500_run2_perm.rds
# OUTPUT datasets: 52 results table located here: Pooled_Testing_HIV/Results/SimResults
# 13 file names beginning with Results_AGAIG_SD0
# 13 file names beginning with Results_reverse_SD0
# 13 file names beginning with Results_noassoc_SD0
# 13 file names beginning with Results_misspec_SD0
# R VERSION: 3.6.1
#==============================================================================
#Notes:
# =============================================================================
### Evaluating the methods (except for Hypred) in the AGAIG and reverse scenarios using the
### simulated data with SD=0
library(tidyverse)
library(dplyr)
library(here)
#### sourcing the main source file with all the needed functions
source("Programs/method_eval_source.R")
##############################################################################################
##### AGAIG scenarios using SD=0 and estimated betas
data <- readRDS("SimData/Uganda_SimData_SD0.rds")
#### these betas are the same that generated the data; scenario of perfect prediction accuracy
data <- predictVL(data, b0star=0.5, b1star=0.1, b2star=2, b3star=0.1)
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=0, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME0_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.025, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.025_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.05, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.05_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.075, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.075_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.1, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.1_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.125, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.125_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.15, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.15_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.175, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.175_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.2, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.2_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.225, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.225_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.25, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.25_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.5, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.5_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.75, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_AGAIG_SD0_ME.75_rand.rds"))
##### reverse scenarios using SD=0 and estimated betas from the reverse training set
data <- readRDS("SimData/Uganda_SimData_SD0.rds")
data <- predictVL(data, b0star=6, b1star=-0.1, b2star=-2, b3star=-0.1)
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=0, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME0_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.025, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.025_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.05, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.05_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.075, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.075_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.1, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.1_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.125, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.125_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.15, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.15_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.175, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.175_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.2, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.2_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.225, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.225_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.25, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.25_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.5, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.5_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.75, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_reverse_SD0_ME.75_rand.rds"))
##### no prediction association scenarios using SD=0 and estimated betas
data <- readRDS("SimData/Records_used_in_500_matrices_hypred/SD0_data_500_run2_perm.rds")
#### these betas are the same that generated the data to provide perfect prediction
#### the data used has had the VL permuted, so there is no association to the prediction model
data <- predictVL(data, b0star=0.5, b1star=0.1, b2star=2, b3star=0.1)
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=0, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME0_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.025, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.025_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.05, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.05_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.075, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.075_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.1, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.1_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.125, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.125_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.15, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.15_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.175, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.175_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.2, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.2_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.225, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.225_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.25, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.25_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.5, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.5_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.75, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_noassoc_SD0_ME.75_rand.rds"))
##### misspecified model scenarios using SD=0 and estimated betas
data <- readRDS("SimData/Uganda_SimData_SD0.rds")
#### these betas are set to reflect a misspecified model
data <- predictVL(data, b0star=2, b1star=0.3, b2star=0, b3star=0)
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=0, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME0_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.025, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.025_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.05, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.05_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.075, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.075_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.1, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.1_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.125, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.125_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.15, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.15_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.175, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.175_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.2, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.2_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.225, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.225_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.25, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.25_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.5, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.5_rand.rds"))
set.seed(18)
result1 <- pool.alg.cov(reps=500, data=data, matsize=10, prec=10, precrd=20,
cutoff=1000, SE=.75, tstperd=5, lowlimit=50, filltyp="rnd")
saveRDS(result1, file=here("Results","SimResults","Results_misspec_SD0_ME.75_rand.rds"))
|
e2c5d172f20474e30f0fcd404c2dbd54254c2309
|
49688b6a367dbfa82bfa1c96f00fd6291bcf2de3
|
/functions.R
|
6bb09a3537a9c1b7c8629e03e7e6aead72350ca5
|
[
"MIT",
"CC-BY-3.0"
] |
permissive
|
yanirmor/shiny-soccer-analysis
|
45ce69203c97ebe8e98b10a7c9cb49f3b655fdc6
|
64320404b99ca831cb2f7db2fc9df5fa6ce24c94
|
refs/heads/master
| 2021-06-28T11:32:27.961617
| 2021-01-18T14:40:39
| 2021-01-18T14:40:39
| 200,484,757
| 0
| 1
|
MIT
| 2021-01-18T14:40:40
| 2019-08-04T11:33:22
|
R
|
UTF-8
|
R
| false
| false
| 401
|
r
|
functions.R
|
nudge_small_nodes <- function(dimension, value, main_axis = T) {
max_value <- if (dimension == "x") 10500 else 6750
if (main_axis) {
case_when(
value < max_value * 0.06 ~ max_value * 0.03,
value > max_value * 0.94 ~ -1 * max_value * 0.03,
T ~ 0
)
} else {
case_when(
value < max_value / 2 ~ max_value * 0.06,
T ~ -1 * max_value * 0.05
)
}
}
|
935151e01e3764b882dc317ea97864fdda67420f
|
aa6b6bf70b316244ff92673456daac851f1c35ee
|
/ultraseq/man/mutect.Rd
|
55f6f39c06ada682805cc5134d51ecfb79b71024
|
[] |
no_license
|
flow-r/ultraseq
|
742035de86d55019d5c52b1b9a2c1795ec6ac5b9
|
58f72866c5b2504c18fd3e472ae187b5471826c8
|
refs/heads/master
| 2022-10-05T01:22:44.717599
| 2016-05-12T03:55:19
| 2016-05-12T03:55:19
| 42,262,784
| 0
| 1
| null | 2016-05-25T15:57:52
| 2015-09-10T18:28:31
|
R
|
UTF-8
|
R
| false
| true
| 1,935
|
rd
|
mutect.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mutect.R
\name{mutect}
\alias{mutect}
\title{A wrapper around somatic mutation caller MuTect}
\usage{
mutect(tumor_bam, normal_bam, samplename = opts_flow$get("samplename"),
out_prefix, is_merged = TRUE, split_by_chr = TRUE,
java_exe = opts_flow$get("java_exe"),
java_tmp = opts_flow$get("java_tmp"),
mutect_jar = opts_flow$get("mutect_jar"),
cpu_mutect = opts_flow$get("cpu_mutect"),
mem_mutect = opts_flow$get("java_mem"),
ref_fasta_path = opts_flow$get("ref_fasta_path"),
mutect_opts = opts_flow$get("mutect_opts"))
}
\arguments{
\item{tumor_bam}{path to a tumor bam file}
\item{normal_bam}{path to a normal bam file}
\item{samplename}{name of the sample, to be added to the flowmat}
\item{out_prefix}{output file name [optional].
By default, this is created using names of tumor and normal bam files.}
\item{is_merged}{specify if the input bam is already split by chromosomes (FALSE)
or is a merged file with all chromosome (TRUE). [FALSE]}
\item{split_by_chr}{fork running mutect by chromosome to make it faster [TRUE].
Turning is option OFF is not fully supported.}
\item{java_exe}{path to java's executable [java]}
\item{java_tmp}{path to a temp folder to be used by java}
\item{mutect_jar}{path to mutect's jar file}
\item{cpu_mutect}{integer specifying number of thread to be used per mutect fork}
\item{mem_mutect}{amount of memory to be used by mutect [-Xmx12g]}
\item{ref_fasta_path}{path to the reference genome in fasta format}
\item{mutect_opts}{additional arguments passed onto mutect}
}
\value{
The function returns a flowmat with all the commands.
The final file is called \code{'out_prefix'_merged.mutect.tsv}.
}
\description{
This generates a set of commandlines, per chromosome
}
\examples{
\dontrun{
x = "tumor.bam"
y = "normal.bam"
out = mutect(x, y, samplename="tumor_normal", is_merged = TRUE)
}
}
|
498e5c5d3d9aa4e7e40e10b21332e2ecf40f418f
|
45eac501e19509f6d23b582d5758295ac18318dd
|
/Section3Lecture24 - Section Recap.R
|
1faa26b0ad429ba4997c04543d7bbfc03106b745
|
[] |
no_license
|
ghettocounselor/R
|
2788bccfd72c69bb490fe47ca266c403e5f17c65
|
21755d048b15ae9ca85194c15b4893ddac194f67
|
refs/heads/master
| 2020-04-28T07:17:33.356620
| 2019-05-26T20:03:24
| 2019-05-26T20:03:24
| 175,086,650
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 40
|
r
|
Section3Lecture24 - Section Recap.R
|
#Kirill ran through the items we learned
|
7ddbff2a26a207281c6a4262c61159da389fc98b
|
ff6f6f2425660d2e2e0a68d8c30697cb04c30a82
|
/modules/module5.R
|
c2893c9ee1035c766b137e9c5b00be0b0f7c82eb
|
[] |
no_license
|
muschellij2/summerR_2015
|
0425bfa3d189788749d0a5c5607a6d9f107e5ba5
|
986f517782d756a034f74d7dbc7c55c8ede66397
|
refs/heads/gh-pages
| 2020-12-24T14:01:01.039281
| 2015-09-02T21:04:01
| 2015-09-02T21:04:01
| 41,681,611
| 1
| 1
| null | 2015-08-31T14:54:49
| 2015-08-31T14:54:49
| null |
UTF-8
|
R
| false
| false
| 2,464
|
r
|
module5.R
|
## ----writecsv------------------------------------------------------------
circ = read.csv("../data/Charm_City_Circulator_Ridership.csv", header=TRUE,as.is=TRUE)
circ2 = circ[,c("day","date", "orangeAverage","purpleAverage","greenAverage",
"bannerAverage","daily")]
write.csv(circ2, file="../data/charmcitycirc_reduced.csv", row.names=FALSE)
## ----xlsx1,eval=FALSE----------------------------------------------------
## install.packages("xlsx") # OR:
## install.packages("xlsx",
## repos="http://cran.us.r-project.org")
## library(xlsx) # or require(xlsx)
## ----save1---------------------------------------------------------------
save(circ,circ2,file="../data/charmcirc.rda")
## ----ls------------------------------------------------------------------
ls()
## ----loadData------------------------------------------------------------
tmp=load("../data/charmcirc.rda")
tmp
ls()
## ----negativeIndex-------------------------------------------------------
x = c(1,3,77,54,23,7,76,5)
x[1:3] # first 3
x[-2] # all but the second
## ----negativeIndex2------------------------------------------------------
x[-c(1,2,3)] # drop first 3
# x[-1:3] # shorthand. R sees as -1 to 3
x[-(1:3)] # needs parentheses
## ----andEx---------------------------------------------------------------
# which Mondays had more than 3000 average riders?
which(circ$day =="Monday" & circ$daily > 3000)[1:20]
## ----andEx2--------------------------------------------------------------
Index=which(circ$daily > 10000 & circ$purpleAverage > 3000)
length(Index) # the number of days
head(circ[Index,],2) # first 2 rows
## ----orEx1---------------------------------------------------------------
Index=which(circ$daily > 10000 | circ$purpleAverage > 3000)
length(Index) # the number of days
head(circ[Index,],2) # first 2 rows
## ----naEval--------------------------------------------------------------
circ$purpleAverage[1:10] > 0
which(circ$purpleAverage > 0)[1:10]
## ----inEx----------------------------------------------------------------
(circ$day %in% c("Monday","Tuesday"))[1:20] # select entries that are monday or tuesday
which(circ$day %in% c("Monday","Tuesday"))[1:20] # which indices are true?
## ----colSelect-----------------------------------------------------------
circ[1:3, c("purpleAverage","orangeAverage")]
circ[1:3, c(7,5)]
## ----colRemove-----------------------------------------------------------
tmp = circ2
tmp$daily=NULL
tmp[1:3,]
|
6b3300f699163a2b6b41552443e377b332169aa6
|
fd093d5729ebc5979eadd02bfe41167d5f7ced46
|
/man/smallexample.Rd
|
d5805e4927b5ee2cf5b813643ff63e97aea747d6
|
[] |
no_license
|
JohnlNguyen/freqparcoord.cd
|
6b42923b62d72c2a7a8c549757a11880c92445d8
|
2ebc98c9c358deb58825830338d353366e79e360
|
refs/heads/master
| 2021-01-22T03:22:59.717265
| 2017-05-25T07:26:39
| 2017-05-25T07:26:39
| 92,376,429
| 0
| 0
| null | 2017-05-25T07:18:00
| 2017-05-25T07:18:00
| null |
UTF-8
|
R
| false
| false
| 572
|
rd
|
smallexample.Rd
|
\name{smallexample}
\alias{smallexample}
\title{
A small example demonstrating freqparcoord.cd.
}
\description{
A small example showing how the freqparcoord.cd package creates
parallel coordinate plots.
}
\usage{
smallexample(n)
}
\arguments{
\item{n}{
The optional number of top frequencies you'd like to select.
}
}
\author{
Norm Matloff <matloff@cs.ucdavis.edu>, Harrison Nguyen <hhnguy@ucdavis.edu>, and Vincent Yang <vinyang@ucdavis.edu>
}
\examples{
# This will draw a plot for the smallexample.csv dataset
smallexample()
}
|
5139d6d6c53926104454b07fe3c95da76cc2cc0a
|
58a0cfa3891ea8fb2780cbd6a946b87825cd30a8
|
/calibration_joint_classic_phlo_MAR_90/compute.summary.statistics.classic.R
|
e9c36f8d1bd4ee1c4a0cfdf5dc578d46f01b06f3
|
[] |
no_license
|
niyukuri/benchmark_master_model
|
e32d563345ae465b355618e18a635366a9cacede
|
e0c117f85d60eabf714f0eebbb056fab1cf576f7
|
refs/heads/master
| 2020-04-06T14:49:12.282181
| 2019-06-01T20:14:50
| 2019-06-01T20:14:50
| 157,555,333
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,701
|
r
|
compute.summary.statistics.classic.R
|
#' Computing epidmiological, demographical, sexual behavioural, and interventions realted summary statistics
#'
#' @param datalist.agemix Data list of simpact output produced by \code{\link{readthedata()}}
#' @param timewindow Time window in which the experience is carried out
#' @export
# The outputs of the function are:
# (i) Demographic
# - Population growth
# (ii) Epidemic
# - Prevalence
# - Incidence
# (iii) Sexual behaviour
# - point prevalence of conurrent relationships
# - relationship per person per year
# - mean, median and standard deviation of age gap
# (iv) Interventions
# - ART coverage
# - Viral load suppression
# "Pop.growthrate",
#
# "prev.15.25.men", "prev.15.25.women",
# "prev.25.40.men", "prev.25.40.women",
# "prev.40.50.men", "prev.40.50.women",
#
# "incid.15.24.men", "incid.15.24.women",
# "incid.25.39.men", "incid.25.39.women",
# "incid.40.49.men", "incid.40.49.women",
#
# "pp.cp.6months.male.rels",
#
# "relsperpersonperyear",
# "agegap.mean", "agegap.med", "agegap.sd",
#
# "ART.cov.vector", "VL.suppression.fraction"
compute.summary.statistics.classic <- function(datalist = datalist.agemix,
timewindow = c(30, 40)){
source("/home/dniyukuri/lustre/calibration_joint_classic_phlo_MAR_90/needed.functions.RSimpactHelp.R")
# source("/home/david/benchmark_master_model/needed.functions.RSimpactHelp.R")
datalist.agemix <- datalist
########################################
# I. Behavioural and epidemic features #
########################################
# 1.2. Features from sexual and transmission network
#
# 1.2.1. Demographic feature:
# (i) Population growth rate (pop.growth.calculator function)
growthrate <- pop.growth.calculator(datalist = datalist.agemix,
timewindow = timewindow) # c(0, datalist.agemix$itable$population.simtime[1])
# 1.2.2. Prevalence
hiv.prev.lt25.women <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(15, 25),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(2) %>%
as.numeric()
hiv.prev.lt25.men <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(15, 25),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(1) %>%
as.numeric()
hiv.prev.25.40.women <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(25, 40),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(2) %>%
as.numeric()
hiv.prev.25.40.men <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(25, 40),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(1) %>%
as.numeric()
hiv.prev.40.50.women <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(40, 50),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(2) %>%
as.numeric()
hiv.prev.40.50.men <- prevalence.calculator(datalist = datalist.agemix,
agegroup = c(40, 50),
timepoint = timewindow[2]) %>%
dplyr::select(pointprevalence) %>%
dplyr::slice(1) %>%
as.numeric()
# Incidence
incid.15.24.men <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(15, 25),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(1) %>%
as.numeric()
incid.15.24.women <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(15, 25),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(2) %>%
as.numeric()
incid.25.39.men <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(25, 40),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(1) %>%
as.numeric()
incid.25.39.women <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(25, 40),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(2) %>%
as.numeric()
incid.40.49.men <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(40, 50),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(1) %>%
as.numeric()
incid.40.49.women <- incidence.calculator(datalist = datalist.agemix,
agegroup = c(40, 50),
timewindow = c(39, 40),
only.active = "No") %>%
dplyr::select(incidence) %>%
dplyr::slice(2) %>%
as.numeric()
#
# Sexual behaviour
# Point prevalence of concurrency in the adult population
# Concurrency point prevalence 6 months before a survey, among men
pp.cp.6months.male.rels <- concurr.pointprev.calculator(datalist = datalist.agemix,
timepoint = timewindow[2] - 0.5)
# pp.cp.6months.male.rels <- concurr.pointprev.calculator(datalist = datalist.agemix,
# timepoint = timewindow[2] - 0.5) %>%
# dplyr::select(concurr.pointprev) %>%
# dplyr::slice(1) %>%
# as.numeric()
#
# pp.cp.6months.female.rels <- concurr.pointprev.calculator(datalist = datalist.agemix,
# timepoint = timewindow[2] - 0.5) %>%
# dplyr::select(concurr.pointprev) %>%
# dplyr::slice(2) %>%
# as.numeric()
#
# (ii) Relationship per person per year ??
relsperpersonperyear <- nrow(datalist.agemix$rtable) / (nrow(datalist.agemix$ptable)/2) / (timewindow[2] - timewindow[1])
# (iv) SD age gap between couples
agegap.mean <- mean(datalist.agemix$rtable$AgeGap)
agegap.med <- median(datalist.agemix$rtable$AgeGap)
agegap.sd <- sd(datalist.agemix$rtable$AgeGap)
####
# ART coverage among adults 15+ years old from UNAIDS (2010 - 2017 estimates)
####
ART.cov.eval.timepoints <- seq(from = 33,
to = 40)
ART.cov.vector <- rep(NA, length(ART.cov.eval.timepoints))
for (art.cov.index in 1:length(ART.cov.vector)){
ART.cov.vector[art.cov.index] <- sum(ART.coverage.calculator(datalist = datalist.agemix,
agegroup = c(15, 50),
timepoint = ART.cov.eval.timepoints[art.cov.index])$sum.onART) /
sum(ART.coverage.calculator(datalist = datalist.agemix,
agegroup = c(15, 50),
timepoint = ART.cov.eval.timepoints[art.cov.index])$sum.cases)
}
names(ART.cov.vector) <- paste0("ART.", ART.cov.eval.timepoints)
####
# VL suppression fraction (all ages in 2017 ~ >= 15 yo) 0.74
####
VL.suppression.fraction <- VL.suppression.calculator(datalist = datalist.agemix,
agegroup = c(15, 50),
timepoint = timewindow[2],
vl.cutoff = 1000,
site="All") %>%
dplyr::select(vl.suppr.frac) %>%
dplyr::slice(3) %>%
as.numeric()
names(VL.suppression.fraction) <- "VL.suppr."
classic.features <- c(exp(growthrate),
hiv.prev.lt25.men, hiv.prev.lt25.women,
hiv.prev.25.40.men, hiv.prev.25.40.women,
hiv.prev.40.50.men, hiv.prev.40.50.women,
exp(incid.15.24.men), exp(incid.15.24.women),
exp(incid.25.39.men), exp(incid.25.39.women),
exp(incid.40.49.men), exp(incid.40.49.women),
pp.cp.6months.male.rels, # pp.cp.6months.female.rels,
relsperpersonperyear,
agegap.mean, agegap.med, agegap.sd,
ART.cov.vector, VL.suppression.fraction)
classic.features.names <- c("Pop.growthrate",
"prev.15.25.men", "prev.15.25.women",
"prev.25.40.men", "prev.25.40.women",
"prev.40.50.men", "prev.40.50.women",
"incid.15.24.men", "incid.15.24.women",
"incid.25.39.men", "incid.25.39.women",
"incid.40.49.men", "incid.40.49.women",
"pp.cp.6months.male.rels",
"relsperpersonperyear",
"agegap.mean", "agegap.med", "agegap.sd",
paste0(names(ART.cov.vector)), paste0(names(VL.suppression.fraction)))
names(classic.features) <- classic.features.names
# classic.features.num <- as.numeric(classic.features)
return(classic.features)
}
|
798c278c6e0dd5b83c7d99f877c533a5d39b0aec
|
c58bc4d218260a2f195aeb885e20f19dcdd95eda
|
/GPareto.R
|
2c82cc5414f5b02599800dc93760b44a4f93793b
|
[] |
no_license
|
misteliy/Rscripts
|
bdfca0452cbe220c6a23eea5d185c6b4bd00756c
|
a70029aff43d5d5625d729d956860acc4ec9c477
|
refs/heads/master
| 2020-03-21T17:32:41.993459
| 2018-09-11T21:26:47
| 2018-09-11T21:26:47
| 138,838,129
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,058
|
r
|
GPareto.R
|
library(GPareto)
design.init <- matrix(seq(0, 1, length.out = 6), ncol = 1)
response.init <- MOP2(design.init)
mf1 <- km(~1, design = design.init, response = response.init[, 1])
mf2 <- km(~1, design = design.init, response = response.init[, 2])
model <- list(mf1, mf2)
res <- GParetoptim(model = model, fn = MOP2, crit = "EHI", nsteps = 7,
lower = 0, upper = 1, critcontrol = list(refPoint = c(2, 2)))
plotParetoGrid(P1)
myobj <- function (x)
{
if (is.null(dim(x))) {
x <- matrix(x, nrow = 1)
}
#n <- ncol(x)
#g <- 1 + rowSums(x[, 2:n, drop = FALSE]) * 9/(n - 1)
#return(cbind(x[, 1], g * (1 - sqrt(x[, 1]/g))))
return(cbind(x^2,x[, 1]))
# f1 <- x1, f2 <- g(x)*(1-sqrt(x1/g(x)))
}
res <- easyGParetoptim(fn = myobj, budget = 50, lower = rep(0, 4),upper = rep(1, 4))
library(caret)
data("iris")
control <- trainControl(method="cv", number = 5, classProbs = TRUE, summaryFunction = mnLogLoss)
set.seed(7)
fit <- train(Species~. ,data = iris, method = "rf", metric="logLoss", trControl = control)
print(fit)
|
38a7a58e1586572aff389a395b9ecce561f5eb20
|
9ee0ab61b9d870fa5237a2869b60e9e8877f8403
|
/man/setFunctionData.Rd
|
0beac3764d8114d93d83fd05c5af51dfcee1cf9c
|
[] |
no_license
|
AustralianAntarcticDivision/EPOC
|
79390dc3456a9cbacfb64884f10cdcf5fedad393
|
778be35f146197c571bb1ebfb76eb2a629eaad70
|
refs/heads/master
| 2020-09-09T22:12:49.843987
| 2019-11-14T01:55:36
| 2019-11-14T01:55:36
| 221,583,790
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 998
|
rd
|
setFunctionData.Rd
|
\name{setFunctionData}
\alias{setFunctionData}
\title{
Element methods
}
\description{
Set additonal user function data for element object./cr
Set either the entire list or a single list item.
}
\usage{
setFunctionData(.Object, item = c("character", "missing"), value)
}
\arguments{
\item{.Object}{
Element object
}
\item{item}{
List item in functions list to set value at. Optional if value="list"
}
\item{value}{
Value to be set
}
}
\details{}
\value{
Returns modified element object invisibly
}
\references{}
\author{
Troy Robertson
}
\note{}
\seealso{
\code{\linkS4class{Element}, \linkS4class{EPOCObject}, \link{getFunctionData}}
}
\examples{
## Set function data item "printState" to TRUE
# setFunctionData(element, item="printState", value=TRUE)
## Set entire function data list
# setFunctionData(element, value=fdlist)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ~kwd1 }
\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
|
2a00dd1951709b1c8ab35a6f1694ceb392747c88
|
09e89406e888251996277b441feb5b84162bdaf5
|
/tests/run_tests.R
|
3f5092087a8e599ef4420c57b4983721b82b9375
|
[] |
no_license
|
HarlanH/futile.logger
|
6d3e137a2b3587ae341346806468c554c51c9c08
|
0a4a90b4c7c40473a5926548fe78d7c1e5019bff
|
refs/heads/master
| 2021-01-16T22:23:37.983736
| 2015-03-19T17:36:22
| 2015-03-19T17:36:22
| 31,793,920
| 0
| 0
| null | 2015-03-06T23:32:25
| 2015-03-06T23:32:25
|
R
|
UTF-8
|
R
| false
| false
| 84
|
r
|
run_tests.R
|
library(testthat)
library_if_available(futile.logger)
test_package('futile.logger')
|
c2b062fcad33ed527eda02f2054153074ec816f1
|
3b4f982be895984cc74638d36da8f3fd64148a19
|
/man/tomsTools.Rd
|
08638a496e19d2b445f6e06111573da948b16f2e
|
[] |
no_license
|
TomfromWales/tomsTools
|
f2a9de031c08dd6b7f995fa0aa041199dabd700e
|
8b779af57996a6e3e8867d635cb77dc8c5c40d3f
|
refs/heads/master
| 2021-09-19T21:26:21.779829
| 2017-12-24T16:39:46
| 2017-12-24T16:39:46
| 112,975,453
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 257
|
rd
|
tomsTools.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tomsTools-package.R
\docType{package}
\name{tomsTools}
\alias{tomsTools}
\alias{tomsTools-package}
\title{Describe the package. TEST.}
\description{
Describe the package. TEST.
}
|
126f08dbb78f728ad8185902f88cf72a2f3c45ee
|
81c0158c28d8bf0de94ef8676083dbbfbce06e73
|
/R/kaefa-package.r
|
0f668ad27ce73794d120c7fadc5ac2b4fbf3bf2b
|
[] |
no_license
|
seonghobae/kaefa
|
3daf1c294292b30b2589ee0d23bfb5788f20a217
|
266b7cfbc3d117cd8e85095970b4184a1cb315be
|
refs/heads/master
| 2022-05-16T09:50:53.236346
| 2022-05-03T05:12:20
| 2022-05-03T05:12:20
| 102,938,712
| 3
| 0
| null | 2018-03-06T14:37:56
| 2017-09-09T08:18:23
|
R
|
UTF-8
|
R
| false
| false
| 6,509
|
r
|
kaefa-package.r
|
#' kaefa: kwangwoon automated exploratory factor analysis
#'
#' This library stands for improving research capability to identify
#' unexplained factor structure with complexly cross-classified
#' multilevel structured data in R environment
#' in the automated exploratory factor analysis framework what imports
#' \code{mirt::mirt}, \code{mirt::mixedmirt} and \code{mirt::mdirt}
#' (Chalmers, 2012; Chalmers, 2015).
#'
#' In practice of applied psychological resarch, so much researcher ignoring
#' the impact of the MMMM (Multiple Membership Multilevel Model) and MM
#' in exploratory factor analysis from unfamiliar with statistical innovations
#' who noted in Sharpe (2013) and Foster, Min, and Zickar (2017).
#'
#' Moreover, A lot of researcher do not know what is the improper solution
#' in the exploratory factor analysis. That may lead wrong conclusion to
#' research society, The kaefa will filter possible improper solutions during
#' the automated exploratory factor analysis.
#' Filtering the Heywood cased models and fail to pass the second-order test
#' model will help this work. These model will not consider to model selection
#' that they are possible improper solutions.
#'
#' The kaefa may inspect this issues from the MMMM or MM
#' in statistical learning theory perspectives using model
#' selection criteria like the DIC (Kang, 2008; Kang, Cohen, & Sung, 2009;
#' Jiao, Kamata, Wang, & Jin, 2012; Jiao & Zhang, 2015) with maximising
#' generalisability of the number of factor decisions in every calibration
#' (Kang, 2008; Preacher, Zhang, Kim, & Mels, 2013).
#'
#' If researcher provide of demographical information in kaefa,
#' kaefa will inspect the optimal number of factor and optimal IRT model,
#' and possible error variances or latent differences from
#' demographic information of respondents.
#'
#' During the calibration, kaefa consider the these item response models:
#' Rasch, 2PL, 3PL, 3PLu, 4PL, ideal (for dichotomous)
#' nominal, gpcm, graded, grsm, ggum, pcm, rsm, monopoly (for polytomous).
#'
#' Moreover, factor rotation will decide automatically using \emph{Zh} for
#' minimizing potential outage of the item as actual criteria.
#' As the default, "bifactorQ","geominQ", "geominT", "bentlerQ", "bentlerT",
#' "oblimin", "oblimax", "simplimax", "tandemII", "tandemI", "entropy",
#' and "quartimax" will try to inspect the optimal structure of actual criteria
#' reflect to the conceptual criterion. It is make a way to increase interpretability
#' of the exploratory factor analysis without the human intervention
#' as objectivity and reproducibility what principles of the science.
#'
#' After the every \emph{n-th} calibration, kaefa do the item appropriateness test
#' for check which item contribute to explain conceptual criterion with
#' robustness of aberrant response using \emph{Zh, S-X2, PV-Q1}. If kaefa
#' find out the improper item, kaefa will exclude the worst one automatically
#' and recalibrating the models until all items are acceptable via statistcal
#' criteria.
#'
#' This software can be pallelise to multiple computers via LAN
#' even heterogeneous environment, so that applied researchers may expand
#' their research capability more easy with kaefa
#' even data has too complicated structure to calibrate in single machine.
#'
#' This project started in 2013, and restructured in 2017.
#' Hope to help exploring human behavioural mechanisms in complex contexts.
#'
#' @name kaefa
#' @title kwangwoon automated exploratory factor analysis.
#' @author Seongho Bae \email{seongho@kw.ac.kr}
#' @references
#' Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory
#' Package for the R Environment. \emph{Journal of Statistical Software, 48}(6), 1-29.
#' \doi{10.18637/jss.v048.i06}
#' @references
#' Chalmers, R. P. (2015). Extended Mixed-Effects Item Response Models
#' with the MH-RM algorithm. \emph{Journal of Educational Measurement, 52}(2), 200–222.
#' \doi{10.1111/jedm.12072}
#' @references
#' Foster, G. C., Min, H., & Zickar, M. J. (2017). Review of Item Response Theory
#' Practices in Organizational Research.
#' \emph{Organizational Research Methods, 20}(3), 465–486.
#' \doi{10.1177/1094428116689708}
#' @references
#' Jennrich, R. I., & Bentler, P. M. (2011). Exploratory Bi-Factor Analysis.
#' \emph{Psychometrika, 76}(4), 537–549.
#' \doi{10.1007/s11336-011-9218-4}
#' @references
#' Jiao, H., Kamata, A., Wang, S., & Jin, Y. (2012). A Multilevel Testlet Model
#' for Dual Local Dependence. \emph{Journal of Educational Measurement, 49}(1), 82-100.
#' \doi{10.1111/j.1745-3984.2011.00161.x}
#' @references
#' Jiao, H., & Zhang, Y. (2015). Polytomous multilevel testlet models for testlet-based
#' assessments with complex sampling designs.
#' \emph{British Journal of Mathematical and Statistical Psychology, 68}(1), 65–83.
#' \doi{10.1111/bmsp.12035}
#' @references
#' Kang, T. (2008). Application of Statistical Model Selection Methods to Assessing
#' Test Dimensionality. \emph{Journal of Educational Evaluation, 21}(4), 153–175.
#' Retrieved from \url{http://scholar.dkyobobook.co.kr/searchDetail.laf?barcode=4010022701731}
#' @references
#' Kang, T., Cohen, A. S., & Sung, H.-J. (2009). Model Selection Indices for
#' Polytomous Items. \emph{Applied Psychological Measurement, 33}(7), 499–518.
#' \doi{10.1007/s00330-011-2364-3}
#' @references
#' Mansolf, M., & Reise, S. P. (2016). Exploratory Bifactor Analysis:
#' The Schmid-Leiman Orthogonalization and Jennrich-Bentler Analytic Rotations.
#' \emph{Multivariate Behavioral Research, 51}(5), 698–717.
#' \doi{10.1080/00273171.2016.1215898}
#' @references
#' Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal
#' number of factors in exploratory factor analysis: A model selection perspective.
#' \emph{Multivariate Behavioral Research, 48}(1), 28–56.
#' \doi{10.1080/00273171.2012.710386}
#' @references
#' Reise, S. P., & Waller, N. G. (2009). Item Response Theory and Clinical Measurement.
#' \emph{Annual Review of Clinical Psychology, 5}(1), 27–48.
#' \doi{10.1146/annurev.clinpsy.032408.153553}
#' @references
#' Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models.
#' \emph{Multivariate Behavioral Research, 47}(5), 667–696.
#' \doi{10.1080/00273171.2012.715555}
#' @references
#' Sharpe, D. (2013). Why the resistance to statistical innovations?
#' Bridging the communication gap. \emph{Psychological Methods, 18}(4), 572–582.
#' \doi{10.1037/a0034177}
#' @docType package
NULL
|
258fe5a22f3819f6a6102cc23481a0adc84a2197
|
def7897686c993371f1848f0ba8c079be8d8b8bf
|
/evaluation.R
|
1440a3802ba291bea497ff3628596205ece54a81
|
[] |
no_license
|
kcf-jackson/ACEMS_2016_Advanced_Sampling
|
1fe54883c8a7bc0eb8dc59e598870e31cb6b5cf5
|
497b2ceb28e65852093e84cbab74198facca3d9d
|
refs/heads/master
| 2021-01-12T14:43:38.556758
| 2016-10-27T08:04:34
| 2016-10-27T08:04:34
| 72,070,871
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,397
|
r
|
evaluation.R
|
# evaluate_train_nn <- function(train_data, oracle) {
# oracle$FUN(train_data)$net.result %>% `>=`(0.5) %>% as.numeric()
# }
# evaluate_test_nn <- function(test_data, oracle) {
# map(
# seq_along(test_data),
# ~oracle(test_data[[.x]])$net.result %>% `>=`(0.5) %>% as.numeric()
# )
# }
#
evaluate_test <- function(test_data, oracle) {
map(seq_along(test_data), ~evaluate_df_by_row(test_data[[.x]], oracle))
}
evaluate_df_by_row <- function(df0, oracle) {
map_dbl(1:nrow(df0), ~( df0[.x, ] %>% oracle$FUN() ))
}
evaluate_train <- evaluate_df_by_row
# print_result_summary <- function(oracle_1, my_fun_1, test_data_1) {
# test_perf <- map2_dbl(
# evaluate_test(test_data_1, oracle_1),
# evaluate_test(test_data_1, my_fun_1),
# ~sum(.x == .y)
# )
#
# cat("Oracle\n")
# cat("z: \n")
# print(oracle_1$z)
# cat("threshold:", oracle_1$threshold, "\n\n")
#
# cat("My function\n")
# cat("z: \n")
# print(my_fun_1$z)
# cat("threshold:", my_fun_1$threshold, "\n\n")
#
# cat("Differences\n")
# cat("z:", sum(xor(oracle_1$z, my_fun_1$z)), "\n")
# cat("threshold:", abs(oracle_1$threshold - my_fun_1$threshold), "\n")
# cat("test data:", min(test_perf), "\n")
#
# return(data.frame(
# z = sum(xor(oracle_1$z, my_fun_1$z)),
# threshold = abs(oracle_1$threshold - my_fun_1$threshold),
# test_perf = min(test_perf)
# ))
# }
|
c32da64300fe303867793befcb2c672d14ae10db
|
77c937453ccca0a048a1bc1a0f80240698543aa0
|
/pkg/man/dist_data_cdf-methods.Rd
|
20335c85daa1b73847ccef4bb4615b4ee04fc1f6
|
[] |
no_license
|
way2joy/poweRlaw
|
17b3e8657cea144811a90fde02e1a4549c08a639
|
5f65a4408b6fe77fb8d828bd5c0fc6639d1bb107
|
refs/heads/master
| 2021-01-18T07:37:34.434193
| 2014-07-28T12:08:25
| 2014-07-28T12:08:25
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,383
|
rd
|
dist_data_cdf-methods.Rd
|
% Generated by roxygen2 (4.0.1): do not edit by hand
\docType{methods}
\name{dist_data_cdf}
\alias{dist_data_cdf}
\alias{dist_data_cdf,ctn_distribution-method}
\alias{dist_data_cdf,discrete_distribution-method}
\title{The data cumulative distribution function}
\usage{
dist_data_cdf(m, lower_tail = TRUE, all_values = FALSE)
\S4method{dist_data_cdf}{discrete_distribution}(m, lower_tail = TRUE,
all_values = FALSE)
\S4method{dist_data_cdf}{ctn_distribution}(m, lower_tail = TRUE)
}
\arguments{
\item{m}{a reference class distribution object.}
\item{lower_tail}{logical;
if \code{TRUE} (default), probabilities are \eqn{P[X \le x]},
otherwise, \eqn{P[X > x]}.}
\item{all_values}{logical, if \code{FALSE} (default), evaluate
at the data values. If \code{TRUE},
then the cdf is evaluated at points xmin, xmin+1, ..., xmax.}
}
\description{
This is generic function for distribution objects.
This function calculates the data cdf.
}
\note{
This method does *not* alter the internal state of
the distribubtion objects.
}
\examples{
##########################################
#Load data and create distribution object#
##########################################
data(moby_sample)
m = displ$new(moby_sample)
m$setXmin(7);m$setPars(2)
##########################################
# The data cdf #
##########################################
dist_data_cdf(m)
}
|
cb645236bf678b93e67a078c6780aca832a12d15
|
8e98acb706449542a9e4e40038da103f8c2704b6
|
/elicitation/experimental/thrombo-stopping.R
|
38a21506ac342e8feecdafa2251820e42b826827
|
[] |
no_license
|
tommite/pubs-code
|
f4e21dcafd999f8d7b995cefdffb3786c49b247e
|
b2b33e97adbef68787122519384000c55f956811
|
refs/heads/master
| 2016-09-05T09:01:44.793555
| 2014-05-09T12:08:03
| 2014-05-09T12:08:03
| 5,935,181
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,611
|
r
|
thrombo-stopping.R
|
set.seed(1911)
source('code.R')
source('problem.R')
source('ratio-problem.R')
cuts1 <- dget('cuts1')
#cuts2 <- dget('cuts2')
#cuts3 <- dget('cuts3')
ra <- function(w) {
smaa.ra(smaa.ranks(smaa.values(meas=meas, pref=w)))
}
entropy <- function(w) {
smaa.entropy.choice(ra=ra(w))
}
cu <- function(h, h.min) {
(h - h.min) / h
}
stopping.calc <- function(cut) {
wn <-
if (plane.side(rat.pt, cut$point, cut$normal)) {
cut$w1
} else {
cut$w2
}
hn <- entropy(wn)
i.min <- which.min(apply(wn, 1, entropy))
i.max <- which.max(apply(wn, 1, entropy))
list(h=hn,
ra=ra(wn),
w.min=wn[i.min,],
h.min=entropy(wn[i.min,]),
w.max=wn[i.max,],
h.max=entropy(wn[i.max,]))
}
w0 <- simplex.sample(n=3, N=1E4)$samples
h0 <- entropy(w0)
h0.min <- min(apply(w0, 1, entropy))
cu(h0, h0.min)
stopping1 <- lapply(cuts1, stopping.calc)
#stopping3 <- lapply(cuts3, stopping.calc)
source('simplex.heat.R')
max.entropy <- 1
data <- simplex.calc.raster(entropy, 0.01)
criteria <- c("p", "d", "b")
plot.raster.polytope(data$data[,,1]/max.entropy, data$xrng, data$yrng, criteria=criteria, bounds=0.15)
polytope.verts <- function(cuts) {
constr <- mergeConstraints(lapply(cuts, function(cut) { plane.constraint(cut$point, cut$normal, plane.side(rat.pt, cut$point, cut$normal)) }))
transform <- simplex.createTransform(3)
constr <- simplex.createConstraints(transform, constr)
verts <- findVertices(constr, TRUE)
centr <- apply(verts, 2, mean)
ord <- order(apply(verts, 1, function(x) { atan2(x[1]-centr[1],x[2]-centr[2]) }))
verts[ord,]
}
verts <- polytope.verts(cuts1)[,1:2]
polygon(verts)
w.point <- function(w) {
(simplex.createTransform(3, inverse=TRUE) %*% c(w, 1))[1:2]
}
### START PLOTTING
pdf()
lapply(1:10, function(i) {
plot.raster.polytope(pmin(data$data[,,1]/max.entropy*2, 1), data$xrng, data$yrng, criteria=criteria, bounds=0.15, main=paste("After Q", i))
polygon(polytope.verts(cuts1[1:i]))
points(t(w.point(rat.pt)), pch='*')
points(t(w.point(stopping1[[i]]$w.max)), pch='o')
points(t(w.point(stopping1[[i]]$w.min)), pch='x')
})
ra.est <- sapply(stopping1, function(s) { s$ra[2,1] })
ra.min <- sapply(stopping1, function(s) { ra(s$w.max)[2,1] })
ra.max <- sapply(stopping1, function(s) { ra(s$w.min)[2,1] })
plot(ra.est, ylim=c(0,1), type='l', lty=1, xlab="question", ylab="RAI(Enox, 1)")
lines(ra.min, lty=2)
lines(ra.max, lty=2)
abline(h=ra(rat.pt)[2,1], lty=3)
plot(sapply(stopping1, function(s) { s$h }), ylim=c(0,1), type='l', xlab="question", ylab="entropy")
lines(sapply(stopping1, function(s) { s$h.min }), lty=2)
lines(sapply(stopping1, function(s) { s$h.max }), lty=2)
abline(h=entropy(rat.pt), lty=3)
plot(sapply(stopping1, function(s) { cu(s$h, s$h.min) }), ylim=c(0,1), xlab="question", ylab="uncertainty coefficient")
dev.off()
### END PLOTTING
### What's wrong with question entropy??
h1 <- sapply(cuts1, function(cut) { entropy(cut$w1) })
h2 <- sapply(cuts1, function(cut) { entropy(cut$w2) })
p <- sapply(cuts1, function(cut) { cut$share / 100 })
qh <- p*h1 + (1-p)*h2
qh - sapply(cuts1, function(cut) { cut$h })
bla <- sapply(cuts1, function(cut) { cut$entropies[which.min(cut$entropies[,'h']),] })
h1 - bla['h1',]
### What would be the value of asking one of the "good" questions?
evaluate.cut.w <- function(constr, cut, w) {
w1 <- harSample(mergeConstraints(constr, plane.constraint(cut$point, cut$normal, TRUE)), nrW)
w2 <- harSample(mergeConstraints(constr, plane.constraint(cut$point, cut$normal, FALSE)), nrW)
h1 <- entropy(w1)
h2 <- entropy(w2)
p <- sum(partition.points(w, cut$point, cut$normal))/nrow(w)
qh <- p*h1 + (1-p)*h2
c(qh=qh, h1=h1, h2=h2, p=p)
}
evaluate.cut <- function(constr, cut) {
w <- harSample(constr, nrW)
evaluate.cut.w(constr, cut, w)
}
## skip question 9?
constr <- mergeConstraints(lapply(cuts1[1:8], function(cut) { plane.constraint(cut$point, cut$normal, plane.side(rat.pt, cut$point, cut$normal)) }))
hyp9 <- evaluate.cut(constr, cuts1[[10]])
hyp9['qh'] < qh[9]
# TRUE
## question 7 directly after 3?
constr <- mergeConstraints(lapply(cuts1[1:3], function(cut) { plane.constraint(cut$point, cut$normal, plane.side(rat.pt, cut$point, cut$normal)) }))
hyp4 <- evaluate.cut(constr, cuts1[[7]])
hyp4['qh'] < qh[4]
# TRUE
## question 7 directly after 2?
constr <- mergeConstraints(lapply(cuts1[1:2], function(cut) { plane.constraint(cut$point, cut$normal, plane.side(rat.pt, cut$point, cut$normal)) }))
hyp3 <- evaluate.cut(constr, cuts1[[7]])
hyp3['qh'] < qh[3]
# FALSE
## question 2 immediately?
hyp1 <- evaluate.cut(NULL, cuts1[[2]])
hyp1['qh'] < qh[1]
# FALSE
cu(c(h0, sapply(stopping1, function(s) { s$h }))[1:10], qh)
cu(c(h0, sapply(stopping1, function(s) { s$h }))[1:10], pmin(h1,h2))
## experiment: better hyperplane sampling?
source('hyperplane-sample.R')
sample.planes.test <- function(constr, N) {
state <- har.init(mergeConstraints(simplexConstraints(3), constr))
state.to.cut <- function(s) {
list(
point = as.vector(state$transform %*% s$point),
normal = as.vector(state$basis$basis %*% s$normal)
)
}
lapply(
hyperplane.sample(state$constr, N, homogeneous=TRUE),
state.to.cut)
}
sample.planes.old <- function(constr, N) {
W <- harSample(constr, N)
sample.planes.unrestricted()(W)
}
# evaluate resulting cuts
constr <- mergeConstraints(lapply(cuts1[1:3], function(cut) { plane.constraint(cut$point, cut$normal, plane.side(rat.pt, cut$point, cut$normal)) }))
plot.planes <- function(constr, planes) {
plot.raster.polytope(pmin(data$data[,,1]/max.entropy*2, 1), data$xrng, data$yrng, criteria=criteria, bounds=0.15)
polygon(polytope.verts(cuts1[1:3]))
lapply(planes, lines.cut)
}
testRuns <- function(constr, nrRuns, nrW, nrPlanes, planesFn) {
lapply(1:nrRuns, function(i) {
w <- harSample(constr, nrW)
planes <- planesFn(constr, nrPlanes)
eval <- sapply(planes, function(plane) { evaluate.cut.w(constr, plane, w) })
list(planes=planes, entropy=eval)
})
}
eval.old <- testRuns(constr, 20, 1E4, 1E4, sample.planes.old)
win.old <- sapply(eval.old, function(e) { which.min(e$entropy['qh',]) })
eval.new <- testRuns(constr, 20, 1E4, 1E4, sample.planes.test)
win.new <- sapply(eval.new, function(e) { which.min(e$entropy['qh',]) })
plane.old <- mapply(function(e, i) { e$planes[[i]] }, eval.old, win.old, SIMPLIFY=FALSE)
plane.new <- mapply(function(e, i) { e$planes[[i]] }, eval.new, win.new, SIMPLIFY=FALSE)
entr.old <- mapply(function(e, i) { e$entropy[,i] }, eval.old, win.old)
entr.new <- mapply(function(e, i) { e$entropy[,i] }, eval.new, win.new)
|
98a4e3286e552d886dee1a59174fc6f32aa6827d
|
06b6a2c2008c7f5e8400f8eb402d490ebb4bfd54
|
/man/isCountsOrZeros.Rd
|
24da44139055e9bba35e7a588884f456d3d34e0f
|
[
"MIT"
] |
permissive
|
BMEngineeR/midasHLA
|
55265be7baae2259c976bb5ea7f112737c0b7d1a
|
9ce02c8192852c16a296f63ecbd3e4791e5dbd83
|
refs/heads/master
| 2023-03-05T15:59:52.362313
| 2021-02-17T00:53:19
| 2021-02-17T00:53:19
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 590
|
rd
|
isCountsOrZeros.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/asserts.R
\name{isCountsOrZeros}
\alias{isCountsOrZeros}
\title{Check if vector contains only counts or zeros}
\usage{
isCountsOrZeros(x, na.rm = TRUE)
}
\arguments{
\item{x}{Numeric vector or object that can be \code{unlist} to numeric
vector.}
\item{na.rm}{Logical indicating if \code{NA} values should be accepted.}
}
\value{
Logical indicating if provided vector contains only positive integers
or zeros.
}
\description{
\code{isCountsOrZeros} checks if vector contains only positive integers or
zeros.
}
|
accbc0034ab20234750e3c7af05e4d6751a6c4d0
|
72186ca45fef2d8ffe88dcd9ab44265c293cb4c4
|
/Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022/MakeEMLChemistry.R
|
dcee82eeb9475f124a91c27b7ff3a2276d7b0478
|
[] |
no_license
|
CareyLabVT/Reservoirs
|
a4b3d025ade448cb54dd7a7c1d91fea5ff0368b6
|
2b6d62376d24e9e24d1841b83ebead8a21d57aa0
|
refs/heads/master
| 2023-08-17T10:28:24.450403
| 2023-08-10T18:23:14
| 2023-08-10T18:23:14
| 91,721,780
| 2
| 8
| null | 2023-09-11T16:08:55
| 2017-05-18T17:43:27
|
HTML
|
UTF-8
|
R
| false
| false
| 17,739
|
r
|
MakeEMLChemistry.R
|
##MakeEMLChemistry
##Author: Mary Lofton
##Modified by Whitney Woelmer
##Slight modification by Jacob Wynne
##29Mar2023 - 2022 nutrient aggregation
#NOTE - 11 samples need to be rerun with 2023 samples - will need to average/add these data when we publish 2022 field season samples (HLW)
#F100 5Jul22 0.1m NO3, F50 2May22 1.6m DOC, F50 23May22 5m SRP, B50 21Mar23 0.1, 6, and 9m NO3, B50 11Oct22 7m SRP, B50 19Sep22 6m SRP
#C50 30Jun22 21m SRP, C50 19Aug22 20m NH4, C50 17Nov22 21m NO3
#good site for step-by-step instructions
#https://ediorg.github.io/EMLassemblyline/articles/overview.html
#and links therein
#append this year's chemistry to last year's published data
library(tidyverse)
library(viridis)
library(plotly)
old <- read.csv("./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2021/chemistry_2013_2021.csv")
new <- read.csv("./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022/2022_chemistry_collation_final_nocommas.csv")
new <- new %>% select(-X)
#change column names to match new format
old <- old %>% rename(Flag_TP_ugL = Flag_TP,
Flag_TN_ugL = Flag_TN,
Flag_SRP_ugL = Flag_SRP,
Flag_NO3NO2_ugL = Flag_NO3NO2,
Flag_NH4_ugL = Flag_NH4,
Flag_DC_mgL = Flag_DC,
Flag_DIC_mgL = Flag_DIC,
Flag_DOC_mgL = Flag_DOC,
Flag_DN_mgL = Flag_DN)
#also drop ISCO samples bc being published in separate data product
old <- old[old$Site!=100.1,]
#add 8 flag for all DIC and DOC values run with NPOC
old$Flag_DN_mgL[old$Flag_DOC_mgL==8] <- 8
#manual manipulation - NA for 2 2014 SRP values and associated 5 demonic intrusion flag
old$SRP_ugL[as.Date(old$DateTime)=="2014-04-30" & old$Reservoir=="BVR" & old$Depth_m==0.1] <- NA
old$Flag_SRP_ugL[as.Date(old$DateTime)=="2014-04-30" & old$Reservoir=="BVR" & old$Depth_m==0.1] <- 5
old$SRP_ugL[as.Date(old$DateTime)=="2014-06-04" & old$Reservoir=="BVR" & old$Depth_m==0.1] <- NA
old$Flag_SRP_ugL[as.Date(old$DateTime)=="2014-06-04" & old$Reservoir=="BVR" & old$Depth_m==0.1] <- 5
#and suspect sample flag (9) for 2017 TP sample
old$Flag_TP_ugL[as.Date(old$DateTime)=="2017-07-20" & old$Reservoir=="BVR" & old$Depth_m==0.1 & old$TP_ugL==247.0 ] <- 9
old$Flag_TN_ugL[as.Date(old$DateTime)=="2017-07-20" & old$Reservoir=="BVR" & old$Depth_m==0.1 & old$TN_ugL==3680 ] <- 9
#get cols in same order
new <- new[,colnames(old)]
#manually remove 11 samples from 2022 data pub to be published at a later date after reruns
new$NO3NO2_ugL[new$Reservoir=="FCR" & as.Date(new$DateTime)=="2022-07-05" & new$Depth_m==0.1 & new$Site==100] <- NA
new$DOC_mgL[new$Reservoir=="FCR" & as.Date(new$DateTime)=="2022-05-02" & new$Depth_m==1.6 & new$Site==50] <- NA
new$SRP_ugL[new$Reservoir=="FCR" & as.Date(new$DateTime)=="2022-05-23" & new$Depth_m==5 & new$Site==50] <- NA
new$NO3NO2_ugL[new$Reservoir=="BVR" & as.Date(new$DateTime)=="2023-03-21" & new$Depth_m==0.1 & new$Site==50] <- NA
new$NO3NO2_ugL[new$Reservoir=="BVR" & as.Date(new$DateTime)=="2023-03-21" & new$Depth_m==6 & new$Site==50] <- NA
new$NO3NO2_ugL[new$Reservoir=="BVR" & as.Date(new$DateTime)=="2023-03-21" & new$Depth_m==9 & new$Site==50] <- NA
new$SRP_ugL[new$Reservoir=="BVR" & as.Date(new$DateTime)=="2022-10-11" & new$Depth_m==7 & new$Site==50] <- NA
new$SRP_ugL[new$Reservoir=="BVR" & as.Date(new$DateTime)=="2022-09-19" & new$Depth_m==6 & new$Site==50] <- NA
new$SRP_ugL[new$Reservoir=="CCR" & as.Date(new$DateTime)=="2022-06-30" & new$Depth_m==21 & new$Site==50] <- NA
new$NH4_ugL[new$Reservoir=="CCR" & as.Date(new$DateTime)=="2022-08-19" & new$Depth_m==20 & new$Site==50] <- NA
new$NO3NO2_ugL[new$Reservoir=="CCR" & as.Date(new$DateTime)=="2022-11-17" & new$Depth_m==21 & new$Site==50] <- NA
#merge old and new dfs
chem <- rbind(old, new)
#replace NA flags with 0
chem$Flag_DateTime[is.na(chem$Flag_DateTime)] <- 0
chem$Flag_TN_ugL[is.na(chem$Flag_TN_ugL)] <- 0
chem$Flag_TP_ugL[is.na(chem$Flag_TP_ugL)] <- 0
chem$Flag_NH4_ugL[is.na(chem$Flag_NH4_ugL)] <- 0
chem$Flag_NO3NO2_ugL[is.na(chem$Flag_NO3NO2_ugL)] <- 0
chem$Flag_SRP_ugL[is.na(chem$Flag_SRP_ugL)] <- 0
chem$Flag_DOC_mgL[is.na(chem$Flag_DOC_mgL)] <- 0
chem$Flag_DIC_mgL[is.na(chem$Flag_DIC_mgL)] <- 0
chem$Flag_DC_mgL[is.na(chem$Flag_DC_mgL)] <- 0
chem$Flag_DN_mgL[is.na(chem$Flag_DN_mgL)] <- 0
#change B1 to B40
chem$Site[chem$Site==1 & chem$Reservoir=="BVR"] <- 40
#add a 1 flag for all NA samples
chem$Flag_TN_ugL[is.na(chem$TN_ugL)] <- 1
chem$Flag_TP_ugL[is.na(chem$TP_ugL)] <- 1
chem$Flag_NH4_ugL[is.na(chem$NH4_ugL)] <- 1
chem$Flag_NO3NO2_ugL[is.na(chem$NO3NO2_ugL)] <- 1
chem$Flag_SRP_ugL[is.na(chem$SRP_ugL)] <- 1
chem$Flag_DOC_mgL[is.na(chem$DOC_mgL)] <- 1
chem$Flag_DIC_mgL[is.na(chem$DIC_mgL)] <- 1
chem$Flag_DC_mgL[is.na(chem$DC_mgL)] <- 1
chem$Flag_DN_mgL[is.na(chem$DN_mgL)] <- 1
write.csv(chem, "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022/chemistry_2013_2022.csv",row.names = FALSE)
#select columns for plotting
raw_chem <- chem [,(names(chem) %in% c("Reservoir","Site","DateTime",
"Depth_m","Rep","TP_ugL","TN_ugL","NH4_ugL","SRP_ugL","NO3NO2_ugL","DOC_mgL"))]
#### Chemistry diagnostic plots ####
chemistry_long <- raw_chem %>%
gather(metric, value, TN_ugL:DOC_mgL) %>%
mutate(month = strftime(DateTime, "%b")) %>%
mutate(DateTime = as.Date(DateTime))
# FCR deep hole data time series plot
ggplot(subset(chemistry_long, metric=="DOC_mgL" & Depth_m==0.1 & Reservoir=="FCR"), aes(x=DateTime, y=value )) +
geom_point(cex=2) + theme_bw()
#plots for each variable by reservoir
fcrplots <- chemistry_long %>%
filter(Reservoir == 'FCR') %>%
#filter(Site == 50) %>%
#filter(Depth_m %in% c(0.1, 1.6, 3.8, 5, 6.2, 8, 9)) %>% #If you want to just see the routine depths
ggplot(aes(x = DateTime, y = value, color = as.factor(Depth_m)))+
geom_point()+
ggtitle("FCR chem")+
theme_bw()+
facet_wrap(~metric, scales = 'free_y')
ggsave(file.path(getwd(),"./Data/DataNotYetUploadedToEDI/NutrientData/Figures/2022/FCR_allyears_andnutrients.jpg"), width=4, height=4)
ggplotly(fcrplots) #interactive plot
bvrplots <- chemistry_long %>%
filter(Reservoir == 'BVR') %>%
# filter(Site == 50) %>%
ggplot(aes(x = DateTime, y = value, color = as.factor(Depth_m)))+
geom_point()+
ggtitle("BVR chem")+
theme_bw()+
facet_wrap(~metric, scales = 'free_y')
ggplotly(bvrplots)
ggsave(file.path(getwd(),"./Data/DataNotYetUploadedToEDI/NutrientData/Figures/2022/BVR_allyears_andnutrients.jpg"), width=4, height=4)
ccrplots <- chemistry_long %>%
filter(Reservoir == 'CCR') %>%
# filter(Site == 50) %>%
ggplot(aes(x = DateTime, y = value, color = as.factor(Depth_m)))+
geom_point()+
ggtitle("CCR chem")+
theme_bw()+
facet_wrap(~metric, scales = 'free_y')
ccrplots
ggsave(file.path(getwd(),"./Data/DataNotYetUploadedToEDI/NutrientData/Figures/2022/CCR_allyears_andnutrients.jpg"), width=4, height=4)
# (install and) Load EMLassemblyline #####
# install.packages('devtools')
#devtools::install_github("EDIorg/EMLassemblyline")
#note that EMLassemblyline has an absurd number of dependencies and you
#may exceed your API rate limit; if this happens, you will have to wait an
#hour and try again or get a personal authentification token (?? I think)
#for github which allows you to submit more than 60 API requests in an hour
library(EMLassemblyline)
#Step 1: Create a directory for your dataset
#in this case, our directory is Reservoirs/Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2018
#Step 2: Move your dataset to the directory
#Step 3: Identify an intellectual rights license
#ours is CCBY
#Step 4: Identify the types of data in your dataset
#right now the only supported option is "table"; happily, this is what
#we have!
#Step 5: Import the core metadata templates
#for our application, we will need to generate all types of metadata
#files except for taxonomic coverage, as we have both continuous and
#categorical variables and want to report our geographic location
# View documentation for these functions
?template_core_metadata
?template_table_attributes
?template_categorical_variables #don't run this till later
?template_geographic_coverage
# Import templates for our dataset licensed under CCBY, with 1 table.
template_core_metadata(path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
license = "CCBY",
file.type = ".txt",
write.file = TRUE)
template_table_attributes(path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.table = c("chemistry_2013_2022.csv", "reservoir_site_descriptions.csv"),
write.file = TRUE)
#we want empty to be true for this because we don't include lat/long
#as columns within our dataset but would like to provide them
template_geographic_coverage(path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.table = c("chemistry_2013_2022.csv","reservoir_site_descriptions.csv"),
empty = TRUE,
write.file = TRUE)
#Step 6: Script your workflow
#that's what this is, silly!
#Step 7: Abstract
#copy-paste the abstract from your Microsoft Word document into abstract.txt
#if you want to check your abstract for non-allowed characters, go to:
#https://pteo.paranoiaworks.mobi/diacriticsremover/
#paste text and click remove diacritics
# updated for 2019 to include info on RC sites
#Step 8: Methods
#copy-paste the methods from your Microsoft Word document into abstract.txt
#if you want to check your abstract for non-allowed characters, go to:
#https://pteo.paranoiaworks.mobi/diacriticsremover/
#paste text and click remove diacritics
#Step 9: Additional information
#I put the notes about FCR manipulations and pubs documenting it in this file
#Step 10: Keywords
#DO NOT EDIT KEYWORDS FILE USING A TEXT EDITOR!! USE EXCEL!!
#not sure if this is still true...let's find out! :-)
#see the LabKeywords.txt file for keywords that are mandatory for all Carey Lab data products
#Step 11: Personnel
#copy-paste this information in from your metadata document
#Cayelan needs to be listed several times; she has to be listed separately for her roles as
#PI, creator, and contact, and also separately for each separate funding source (!!)
# Updated this for 2019 to include HLW and WMW
#Step 12: Attributes
#grab attribute names and definitions from your metadata word document
#for units....
# View and search the standard units dictionary
#view_unit_dictionary()
#put flag codes and site codes in the definitions cell
#force reservoir to categorical
#Step 13: Close files
#if all your files aren't closed, sometimes functions don't work
#Step 14: Categorical variables
# Run this function for your dataset
#THIS WILL ONLY WORK once you have filled out the attributes_chemistry.txt and
#identified which variables are categorical
template_categorical_variables(path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
write.file = TRUE)
#open the created value IN A SPREADSHEET EDITOR and add a definition for each category
#Step 15: Geographic coverage
#copy-paste the bounding_boxes.txt file (or geographic_coverage.txt file) that is Carey Lab specific into your working directory
## Step 16: Obtain a package.id. ####
# Go to the EDI staging environment (https://portal-s.edirepository.org/nis/home.jsp),
# then login using one of the Carey Lab usernames and passwords.
# Select Tools --> Data Package Identifier Reservations and click
# "Reserve Next Available Identifier"
# A new value will appear in the "Current data package identifier reservations"
# table (e.g., edi.123)
# Make note of this value, as it will be your package.id below
#Step 17: Make EML
# View documentation for this function
?make_eml
# Run this function
make_eml(
path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
eml.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
dataset.title = "Water chemistry time series for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2022",
temporal.coverage = c("2013-04-04", "2023-03-21"),
maintenance.description = 'ongoing',
data.table = c("chemistry_2013_2022.csv",
"reservoir_site_descriptions.csv"),
data.table.description = c("Reservoir water chemistry dataset",
"Description, latitude, and longitude of reservoir sampling sites"),
other.entity = "QAQC_chemistry_2015_2022.R",
other.entity.description = "Nutrient QAQC script",
user.id = 'ccarey',
user.domain = 'EDI',
package.id = 'edi.1025.6') #reserve new staging environment package id each year
## Step 8: Check your data product! ####
# Return to the EDI staging environment (https://portal-s.edirepository.org/nis/home.jsp),
# then login using one of the Carey Lab usernames and passwords.
# Select Tools --> Evaluate/Upload Data Packages, then under "EML Metadata File",
# choose your metadata (.xml) file (e.g., edi.270.1.xml), check "I want to
# manually upload the data by selecting files on my local system", then click Upload.
# Now, Choose File for each file within the data package (e.g., each zip folder),
# then click Upload. Files will upload and your EML metadata will be checked
# for errors. If there are no errors, your data product is now published!
# If there were errors, click the link to see what they were, then fix errors
# in the xml file.
# Note that each revision results in the xml file increasing one value
# (e.g., edi.270.1, edi.270.2, etc). Re-upload your fixed files to complete the
# evaluation check again, until you receive a message with no errors.
## Step 9: PUBLISH YOUR DATA! ####
# #DO NOT REQUEST A NEW PACKAGE ID FOR UPDATING THE CHEMISTRY DATASET
# SIMPLY INCREASE THE LAST DIGIT OF THE PREVIOUS PACKAGE ID BY 1 TO UPDATE THE CURRENT PUBLICATION
# DIRECTIONS ON HOW TO GET A NEW ID ARE HERE, BUT DO NOT USE THEM FOR ALREADY PUBLISHED DATASETS BEING UPDATED (E.G. CHEMISTRY, CATWALK, CTD, ETC.)
# NEVER ASSIGN this identifier to a staging environment package.
# Go to the EDI Production environment (https://portal.edirepository.org/nis/home.jsp)
# and login using the ccarey (permanent) credentials.
# Select Tools --> Data Package Identifier Reservations and click "Reserve Next
# Available Identifier". A new value will appear in the "Current data package
# identifier reservations" table (e.g., edi.518)
# This will be your PUBLISHED package.id
# In the make_eml command below, change the package.id to match your
# PUBLISHED package id. This id should end in .1 (e.g., edi.518.1)
# ALL OTHER entries in the make_eml() command should match what you ran above,
# in step 7
make_eml(
path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
data.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
eml.path = "./Data/DataAlreadyUploadedToEDI/EDIProductionFiles/MakeEMLChemistry/2022",
dataset.title = "Water chemistry time series for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2022",
temporal.coverage = c("2013-04-04", "2023-03-21"),
maintenance.description = 'ongoing',
data.table = c("chemistry_2013_2022.csv",
"reservoir_site_descriptions.csv"),
data.table.description = c("Reservoir water chemistry dataset",
"Description, latitude, and longitude of reservoir sampling sites"),
other.entity = "QAQC_chemistry_2015_2022.R",
other.entity.description = "Nutrient QAQC script",
user.id = 'ccarey',
user.domain = 'EDI',
package.id = 'edi.199.11') #DO NOT REQUEST A NEW PACKAGE ID, SIMPLY INCREASE THE LAST DIGIT HERE BY 1 TO UPDATE THE CURRENT PUBLICATION
# 2023 data pub = 199.10
# Once your xml file with your PUBLISHED package.id is Done, return to the
# EDI Production environment (https://portal.edirepository.org/nis/home.jsp)
# Select Tools --> Preview Your Metadata, then upload your metadata (.xml) file
# associated with your PUBLISHED package.id. Look through the rendered
# metadata one more time to check for mistakes (author order, bounding box, etc.)
# Select Tools --> Evaluate/Upload Data Packages, then under "EML Metadata File",
# choose your metadata (.xml) file associated with your PUBLISHED package.id
# (e.g., edi.518.1.xml), check "I want to manually upload the data by selecting
# files on my local system", then click Upload.
# Now, Choose File for each file within the data package (e.g., each zip folder),
# then click Upload. Files will upload and your EML metadata will be checked for
# errors. Since you checked for and fixed errors in the staging environment, this
# should run without errors, and your data product is now published!
# Click the package.id hyperlink to view your final product! HOORAY!
|
798821c90f1c83edf8b3201090dc0068cc0ecd0d
|
9d903aaeb20640d77ed94b682c5e026b9ee0735a
|
/code/26_myeloid_tcell_intersect_DEG.R
|
13d4332ec41f489933c79be7db279e8b5c794b79
|
[] |
no_license
|
Winnie09/GBM_t
|
c0a36a4140c47f8dfb124ca3c05620276d4effa7
|
c16f3df279e7b380871bbdbba3a525fba195fce3
|
refs/heads/master
| 2022-11-09T20:33:10.321563
| 2020-06-22T11:07:46
| 2020-06-22T11:07:46
| 274,114,440
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,534
|
r
|
26_myeloid_tcell_intersect_DEG.R
|
Mclu = list()
Mclu[['EMDSC_MMDSC_PMNMDSC']] = c(1,3)
Mclu[['EMDSC_MMDSC']] = c(1,2)
Mclu[['EMDSC_MMDSC_MAC1']] = c(1,2)
Mclu[['EMDSC_MAC1']] = c(4)
source('/home-4/whou10@jhu.edu/scratch/Wenpin/trajectory_variability/function/01_function.R')
for (path1 in names(Mclu)){
print(path1)
for (path2 in c('dm_CD8EM_CD8EX', 'dm_CD8RM_CD8EX')){
print(path2)
rdir1 <- paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/GBM_myeloid/result/testtime/M_MDSC/', path1, '/')
rdir2 = paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/GBM_t/result/testtime/M_MDSC/', path2, '/')
rdir = paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/GBM_t/result/testtime/M_MDSC/', path1,'_and_', path2, '/')
dir.create(rdir, recursive = T)
res1 <- read.csv(paste0(rdir1, 'fdr_foldchange_cluster.csv'), row.names = 1, stringsAsFactors = F)
res2 <- read.csv(paste0(rdir2, 'fdr_foldchange_cluster.csv'), row.names = 1, stringsAsFactors = F)
for (i in Mclu[[path1]]){
df <- sapply(seq(1, max(res2[,3])), function(j){
g = intersect(rownames(res1[res1[,'Cluster']==i, ]), rownames(res2[res2[,'Cluster']==j, ]))
})
mlen = max(sapply(df, length))
df <- sapply(seq(1, max(res2[,3])), function(j){
g = intersect(rownames(res1[res1[,'Cluster']==i, ]), rownames(res2[res2[,'Cluster']==j, ]))
c(g, rep("", mlen-length(g)))
})
colnames(df) <- paste0(path1, '_clu', i, '_and_', path2, '_clu', 1:ncol(df))
write.csv(df, paste0(rdir, 'intersect_DEG.csv'))
}
}
}
|
2b12a83975b85bd28e67ced2f611a79c49ddb61a
|
8f222104a54c6831982c03f592da5905d481e2a7
|
/Reddit Data Analysis.R
|
a97d668a63c4f743a5fbaf214147d46a04dfb058
|
[] |
no_license
|
sthobbs/Reddit-Analysis
|
df1c5c0b7e75eb9043ce049c0a277959728526c8
|
2d30f32341efcdd8c543b5361fa4af9a765256cf
|
refs/heads/master
| 2021-03-12T22:19:19.001142
| 2015-11-01T23:51:43
| 2015-11-01T23:51:43
| 42,701,494
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 15,269
|
r
|
Reddit Data Analysis.R
|
# Install and load packages
install.packages("ggplot2")
install.packages("RMySQL")
install.packages("scales")
install.packages("reshape2")
library(ggplot2)
library(RMySQL)
library(scales)
library("reshape2") # melt
# Open connection to MySQL database
con<-dbConnect(MySQL(),user="root",password="",db="db2",host="localhost")
# Read in the database
# lot is a list of data frames. Element i is post_ranki for the MySQL database
lot<-list()
for (i in 1:100){
suppressWarnings(lot[[i]]<-dbReadTable(con,paste0("post_rank",i)))
}
# Close connection
dbDisconnect(con)
# Create some data frames to work with
post_rank1<-lot[[1]] # Top post data frame
top5_post_rank<-post_rank1 # Top 5 posts data frame
for (i in 2:5){
top5_post_rank<-rbind(top5_post_rank,lot[[i]])
}
top10_post_rank<-top5_post_rank # Top 10 posts data frame
for (i in 6:10){
top10_post_rank<-rbind(top10_post_rank,lot[[i]])
}
p1_post_rank<-top10_post_rank # Top 25 posts data frame (front page/page 1)
for (i in 11:25){
p1_post_rank<-rbind(p1_post_rank,lot[[i]])
}
full_post_rank<-p1_post_rank # Top 100 posts data frame
for (i in 26:100){
full_post_rank<-rbind(full_post_rank,lot[[i]])
}
# The post url can be derived from the data as follows
# go to https://www.reddit.com/r/subreddit/xxxxxx
# where subreddit is the value in the 'subreddit' column
# and xxxxxx is the last 6 digits of the value in the 'name' column
# e.g. For the number 7 post, at time unit 6, go to
paste0("https://www.reddit.com/r/",lot[[7]]$subreddit[6],"/",substring(lot[[7]]$name[6],4,9))
##### Plots #####
## Distribution of how long a post is on the front page (given that it reaches
## the front page)
# We need to end the time window before any current front page posts reached
# the front page so that the lower (left) tail is not inflated. It is still
# possible that some of the posts that were included (which went off the
# front page a while ago) will come back to the front page and bias the result.
# However, this probably isn't the case, so the approximation is reasonable.
# EDIT: I forgot to remove the initial post, changing code now.
# Start collecting data ----- Start time ------------ end time ----- current time
# in other words we look at all posts from start time to end time, then from
# end time to current time we continue to track posts that were in the first
# interval but we don't look at any new ones. We also posts that were in the
# Top at the start of data collections. This elimiates almost all if not
# all of the bias from our density estimate.
# Picking the appropriate time window for data we should use
current_posts<-NULL # 'names' of all post currently on the front page
for (i in 1:25){
current_posts[i]<-lot[[i]]$name[nrow(lot[[1]])]
}
time_window_endpoint<-1
time_posts<-0
# Look forward from time 1 until I first encounter one of the current top posts
while (!any(time_posts%in%current_posts)){
for (i in 1:25){
time_posts[i]<-lot[[i]]$name[time_window_endpoint]
}
time_window_endpoint=time_window_endpoint+1
}
time_window_endpoint=time_window_endpoint-1
start_posts<-NULL # 'names' of all post currently on the front page
for (i in 1:25){
start_posts[i]<-lot[[i]]$name[1]
}
time_window_startpoint<-nrow(lot[[1]])
time_posts<-0
# Look backwards until I first encounter one of the starting top posts
while (!any(time_posts%in%start_posts)){
for (i in 1:25){
time_posts[i]<-lot[[i]]$name[time_window_startpoint]
}
time_window_startpoint=time_window_startpoint-1
}
time_window_startpoint=time_window_startpoint+1
# Now we have a time window that ends right before the first appearence of the
# current top posts.
# Determine the unique names in the time window #and look at the frequencies
p1_post_names_int<-NULL
for (i in 1:25){
p1_post_names_int<-unique(c(p1_post_names_int,lot[[i]]$name[time_window_startpoint:time_window_endpoint]))
}
# Collect names from all time which are in the time interval
p1_post_names_trunc<-NULL
for (i in 1:25){
p1_post_names_trunc<-c(p1_post_names_trunc,lot[[i]]$name[lot[[i]]$name%in%p1_post_names_int])
}
time_dist_df<-data.frame(hours=as.vector(sort(table(p1_post_names_trunc)))/6)
# Plot density approximation
ggplot(time_dist_df,aes(x=hours))+
geom_density(aes(fill="a"),size=2)+
geom_vline(xintercept=mean(time_dist_df$hours))+
ggtitle("Density Estimate of How Long a Post Lasts on the Front Page")+
theme(legend.position="none")+
annotate("text", label = "mean", x = mean(time_dist_df$hours)+0.83, y = 0.078, size = 6, colour = "black")
# Mean time a front page post spends on the front page
mean(time_dist_df$hours)
## Historical positions of the current top n posts
n<-25
# Formatting the data
post_tracking_df<-data.frame(time=(-nrow(lot[[1]])+1):0)
cur_top_post_names<-NULL # Current top n post names
for (i in 1:n){
cur_top_post_names[i]<-lot[[i]]$name[nrow(lot[[1]])]
rank<-rep(100,nrow(lot[[1]])) # Vector of rank positions over time
for (t in 1:nrow(lot[[1]])){ # Time int
for (pos in 1:100){ # Post rank position
if (cur_top_post_names[i]==lot[[pos]]$name[t]){
rank[t]<-pos
break
}
}
}
post_tracking_df<-cbind(post_tracking_df,rank)
}
names(post_tracking_df)<-c("time",cur_top_post_names)
post_tracking_df_long<-melt(post_tracking_df, id="time") # convert to long format
# Plot (warnings are okay)
ggplot(data=post_tracking_df_long,aes(x=time/6, y=value, colour=variable,alpha=0.8))+
geom_line()+
ylim(100,1)+
xlim((-sum(rowSums(post_tracking_df[,2:(n+1)]!=100)>0)-5)/6,0) +
# x-axis limits calculated to be based how much info we have on these posts
# + 5 extra time units
ggtitle("Historical Positions of the Current Top 25 Posts")+
xlab("Time (hours)")+
ylab("Relative Position")+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
## Historical Positions of the top n posts k hours ago
n<-10
k<-15 # Use an increment of 1/6
# Formatting the data
post_tracking_df<-data.frame(time=(-nrow(lot[[1]])+1):0)
top_post_names<-NULL # Top n post at specific time names
for (i in 1:n){
top_post_names[i]<-lot[[i]]$name[nrow(lot[[1]])-6*k]
rank<-rep(100,nrow(lot[[1]])) # Vector of rank positions over time
for (t in 1:nrow(lot[[1]])){ # Time int
for (pos in 1:100){ # Post rank position
if (top_post_names[i]==lot[[pos]]$name[t]){
rank[t]<-pos
break
}
}
}
post_tracking_df<-cbind(post_tracking_df,rank)
}
names(post_tracking_df)<-c("time",top_post_names)
post_tracking_df_long<-melt(post_tracking_df, id="time") # convert to long format
# Plot (warnings are okay)
ggplot(data=post_tracking_df_long,aes(x=time/6, y=value, colour=variable,alpha=0.8))+
geom_line()+
ylim(100,1)+
xlim((-sum(rowSums(post_tracking_df[,2:(n+1)]!=100)>0)-5)/6,0) +
# x-axis limits calculated to be how much info we have on these posts
# + 5 extra time units. doesn't work well for large k.
ggtitle(paste("Historical Positions of Top", n, "Posts", k, "Hours Ago"))+
xlab("Time (hours)")+
ylab("Relative Position")+
geom_vline(xintercept=-k)+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
## Bar plots of subreddit representation
# I couldn't find any built in option to sort the bar plot in descending order
# of frequency so I hacked this function together.
# Creates a bar plot, in descending order, of a factor (or character)
# variable var. Groups all frequencies less than x% into an "other"
# bar on the right.
# df is a data frame containing var
# percent is TRUE <=> use %s rather than counts for y-axis
sorted_bar_plot<-function(df,var,x=0,percent=TRUE){
dfa<-df # Defining duplicate data frame to alter
# So I can use the variable name as an argument
arguments <- as.list(match.call())
var = eval(arguments$var, dfa)
dfa<-within(dfa,var<-as.character(var)) # If var is a factor variable, this makes it a character variable
# Create a vector (sorted by frequencies) of all values with frequencies above the threshold
levels<-names(sort(table(dfa$var)[table(dfa$var)>nrow(dfa)*x/100],decreasing=T))
# If any values are below the threshold, then add an "other" bar
if (!all(table(dfa$var)>=nrow(dfa)*x/100)){
levels<-c(levels,"other")
# Changing the values with frequencies below the threshold to "other"
dfa$var[dfa$var%in%names(table(dfa$var)[table(dfa$var)<nrow(dfa)*x/100])]="other"
}
# Reordering levels based on frequencies, turning var into a factor (for the duplicate dataframe)
dfa<-within(dfa,var<-factor(var,levels=levels))
# Plot
if (percent==T){
p <- ggplot(dfa, aes(x = var)) +
geom_bar(aes(y = (..count..)/sum(..count..),fill = ..count../sum(..count..)),col="black")
scale_y_continuous(labels = percent_format())
}
else{
p <- ggplot(dfa, aes(x = var)) +
geom_bar(aes(y=..count..,fill=..count..),col="black")
}
return(p)
}
p<-sorted_bar_plot(post_rank1,subreddit,x=0)
p + ggtitle("Subreddit Representation of Top Post") +
xlab("Subreddit") +
ylab("Representation")+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
p<-sorted_bar_plot(top5_post_rank,subreddit,x=0)
p + ggtitle("Subreddit Representation of Top 5 Posts") +
xlab("Subreddit") +
ylab("Representation")+
theme(axis.text.x = element_text(angle=90, vjust=1))+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
p<-sorted_bar_plot(top10_post_rank,subreddit,x=0)
p + ggtitle("Subreddit Representation of Top 10 Posts") +
xlab("Subreddit") +
ylab("Representation")+
theme(axis.text.x = element_text(angle=90, vjust=1))+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
p<-sorted_bar_plot(p1_post_rank,subreddit,x=0)
p + ggtitle("Subreddit Representation of Top 25 Posts") +
xlab("Subreddit") +
ylab("Representation")+
theme(axis.text.x = element_text(angle=90, vjust=1))+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
p<-sorted_bar_plot(full_post_rank,subreddit,x=0)
p + ggtitle("Subreddit Representation of Top 100 Posts") +
xlab("Subreddit") +
ylab("Representation")+
theme(axis.text.x = element_text(angle=90, vjust=1))+
theme(legend.position="none",text = element_text(size=16,vjust=1),
axis.text.x = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="black",size=12,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="black",size=12,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="black",size=12,angle=90,hjust=.5,vjust=.5,face="plain"))
## Historical Positions of the all posts in a specific subreddit
sub<-"funny"
# Formatting the data
post_tracking_df<-data.frame(time=(-nrow(lot[[1]])+1):0)
post_names<-NULL # All post names of posts in sub in the top 100
for (i in 1:100){
post_names<-unique(c(post_names,lot[[i]]$name[lot[[i]]$subreddit==sub]))
}
for (i in 1:length(post_names)){
rank<-rep(100,nrow(lot[[1]])) # Vector of rank positions over time
for (t in 1:nrow(lot[[1]])){ # Time int
for (pos in 1:100){ # Post rank position
if (post_names[i]==lot[[pos]]$name[t]){
rank[t]<-pos
break
}
}
}
post_tracking_df<-cbind(post_tracking_df,rank)
}
names(post_tracking_df)<-c("time",post_names)
post_tracking_df_long<-melt(post_tracking_df, id="time") # Convert to long format
# Plot
ggplot(data=post_tracking_df_long,aes(x=time/6, y=value, colour=variable,alpha=0.8))+
geom_line()+
ylim(100,1)+
xlim((-sum(rowSums(post_tracking_df[,2:(n+1)]!=100)>0)-5)/6,0) +
# x-axis limits calculated to be how much info we have on these posts
# + 5 extra time units
ggtitle(paste0("Historical Positions of all r/", sub, " Posts in the Top 100"))+
xlab("Time (hours)")+
ylab("Relative Position")+
theme(legend.position="none")#+
#geom_hline(yintercept=43)
## Prints the % of time that all posts came from unique subreddits
for (k in 1:50){
count=0
for (i in 1:nrow(lot[[1]])){
fp_subs<-NULL
for (j in 1:k){
fp_subs<-c(fp_subs,lot[[j]]$subreddit[i])
}
if (length(unique(fp_subs))==k){
count=count+1
}
}
#if (count==nrow(lot[[1]])){
# print("at most one post from any subreddit on the front page at any time")
print(paste0("the top ", k," posts were in unique subreddits ",100*count/nrow(lot[[1]]),"% of the time"))
}
|
1b0354524db2e5c5bfdb7f797495cb4e85a9a4bc
|
820f53ee7d0f5b7038816bfbd13502b5bac81209
|
/google_maps.R
|
4b709bff5ce55b3e68c049fa3ae3290fb4169ed7
|
[] |
no_license
|
uw-ischool-info-201a-2019-autumn/json-example
|
481688d5c1add7fbc6f5f117dfd4da5d59d275e0
|
63b632e1f307e556a21dbd74cf500057072d4574
|
refs/heads/master
| 2020-09-03T23:51:32.140808
| 2019-11-13T17:47:30
| 2019-11-13T17:47:30
| 219,607,901
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 57
|
r
|
google_maps.R
|
install.packages("ggmap")
library(ggmap)
qmap("seattle")
|
594357ac274c585d347215a8498b132fd97edc3b
|
fbea782e161a7173ef98e7fa34d90743d75743e1
|
/R_analytics/mtc/mtc.R
|
695526e59271b29ce63f629df54f155c5ab9b148
|
[] |
no_license
|
edimaudo/R-projects
|
c3523a0bb551b0a945f12aa8d6c34107d7dd7ced
|
80af84e08fef61dfec4c87e7059f6dc50e24f868
|
refs/heads/master
| 2022-10-27T03:08:59.306013
| 2022-10-23T00:55:24
| 2022-10-23T00:55:24
| 94,769,351
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,466
|
r
|
mtc.R
|
#===================
## Load Libraries
#===================
rm(list = ls()) #clear environment
# libraries
packages <- c('ggplot2', 'corrplot','tidyverse',"caret","dummies","fastDummies"
,'FactoMineR','factoextra','readxl','scales','dplyr','mlbench','caTools',
'gridExtra','doParallel','readxl','grid','gridExtra')
# load packages
for (package in packages) {
if (!require(package, character.only=T, quietly=T)) {
install.packages(package)
library(package, character.only=T)
}
}
#===================
## Load Data
#===================
df <- read_excel("mtc.xlsx")
#===================
# Data Overview
#===================
# Summary
summary(df)
# Check for missing variables
missing_data <- apply(df, 2, function(x) any(is.na(x)))
print(missing_data)
# Check for for imbalance
table(df$label)
# drop missing values
df <- na.omit(df)
#=================
# Modeling
#=================
# Select columns
df <- df %>%
select(CANTIDAD,CANTIDAD_FISICA,CODIGO_CERTIFICADO,NOMBRE_PROVEEDOR,NOMBRE_EQUIPO,
MODELO, PARTIDA_ARANCELARIA,MARCA_EQUIPO,FACTURA,UM_FISICA_ID,label)
# Label Encoder
labelEncoder <-function(x){
as.numeric(factor(x))-1
}
# Normalize data
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
df_other <- df %>%
select(UM_FISICA_ID,label)
df_cts <- df %>%
select(CANTIDAD_FISICA)
df_cat<- df %>%
select(CODIGO_CERTIFICADO,NOMBRE_PROVEEDOR,NOMBRE_EQUIPO,
MODELO, PARTIDA_ARANCELARIA,MARCA_EQUIPO,FACTURA)
# Combine data
df_cts <- as.data.frame(lapply(df_cts, normalize))
df_cat <- as.data.frame(lapply(df_cat, labelEncoder))
df_new <- cbind(df_cts,df_cat,df_other)
df_new <- na.omit(df_new)
df_new$label <- as.factor(df_new$label)
# Create train and test data
set.seed(2020)
sample <- sample.split(df_new,SplitRatio = 0.75)
train <- subset(df_new,sample ==TRUE)
test <- subset(df_new, sample==FALSE)
# Weights
model_weights <- ifelse(train$label == 0,
(1/table(train$label)[1]) * 0.5,
(1/table(train$label)[2]) * 0.5)
# Cross fold validation
control <- trainControl(method="repeatedcv", number=10, repeats=5, classProbs = FALSE)
# Models
cl <- makePSOCKcluster(4)
registerDoParallel(cl)
# glm
fit.glm <- train(label~., data=train, method="glm",family=binomial(),
metric = "Accuracy", trControl = control,
weights = model_weights)
# random forest
fit.rf <- train(as.factor(label)~., data=train, method="rf",
metric = "Accuracy", trControl = control,
weights = model_weights)
# boosting algorithm - Stochastic Gradient Boosting
fit.gbm <- train(as.factor(label)~., data=train, method="gbm",
metric = "Accuracy", trControl = control,
weights = model_weights)
# svm
fit.svm <- train(as.factor(label)~., data=train, method="svmRadial",
metric = "Accuracy", trControl = control,
weights = model_weights)
# nnet
fit.nnet <- train(as.factor(label)~., data=train, method="nnet",
metric = "Accuracy", trControl = control,
weights = model_weights)
# naive
fit.naive <- train(as.factor(label)~., data=train,
method="naive_bayes", metric = "Accuracy",
trControl = control, weights = model_weights)
# extreme gradient boosting
fit.xgb <- train(as.factor(label)~., data=train,
method="xgbTree", metric = "Accuracy", trControl = control,
weights = model_weights)
# bagged cart
fit.bg <- train(as.factor(label)~., data=train,
method="treebag", metric = "Accuracy", trControl = control,
weights = model_weights)
# decision tree
fit.dtree <- train(as.factor(label)~., data=train,
method="C5.0", metric = "Accuracy", trControl = control,
weights = model_weights)
# knn
fit.knn <- train(as.factor(label)~., data=train,
method="kknn", metric = "Accuracy", trControl = control,
weights = model_weights)
stopCluster(cl)
#------------------
#compare models
#------------------
results <- resamples(list(randomforest = fit.rf,
`gradient boost` = fit.gbm,
`support vector machine` = fit.svm,
baggedCart = fit.bg,
neuralnetwork = fit.nnet,
xgboost = fit.xgb,
logisticregression = fit.glm,
`decision tree` = fit.dtree,
`naive bayes` = fit.naive,
`knn` = fit.knn))
summary(results)
# boxplot comparison
bwplot(results)
# Dot-plot comparison
dotplot(results)
# Model accuracy
#mean(predicted.classes == test$label)
# Test data accuracy
# Make predictions
predicted.classes <- fit.knn %>% predict(test)
output <- confusionMatrix(data = predicted.classes, reference = test$label, mode = "everything")
output
# Variable Importance
caret::varImp(fit.rf)
#plot confusion matrix of selected option
# plotting the matrix
output2 <- as.data.frame(output$table)
colnames(output2) <- c("Predicted",'Actual',"Freq")
cm_d_p <- ggplot(data =output2, aes(x = Predicted , y = Actual, fill = Freq))+
geom_tile() +
geom_text(aes(label = paste("",Freq)), color = 'white', size = 8) +
theme_light() +
guides(fill=FALSE)
cm_d_p
|
6b85c559e1100a39f44237563e42caa704845727
|
144e1b215a8546d820f929055138b06eb67eda74
|
/stock_download.R
|
e52d00715effa1bcaa0a6e32112d5b1b67a92a34
|
[] |
no_license
|
Mentalaborer/TradeNow
|
9acdb1bd5a9e0822fded0ec89aab9f80771845cb
|
7b82093f0324ab2a314216fa950f7a8e42c7a5e2
|
refs/heads/master
| 2020-11-26T01:04:15.277242
| 2020-07-05T20:52:12
| 2020-07-05T20:52:12
| 228,915,006
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,597
|
r
|
stock_download.R
|
# purpose - identify baskets of stocks want to analyze
mary_jane <- c('FB',
'ARNA',
'IAN',
'CARA',
'GWPH',
'HEXO',
'OGI',
'TGOD',
'ACRG',
'WEED',
'ACB',
'CURA',
'KSHB',
'NWKRF',
'GTII',
'IIPR',
'HARV',
'CRON',
'TLRY',
'LABS',
'SMG',
'CRBP',
'CVSI',
'APHA',
'CWEB',
'VFF')
restaurant <- c('EAT',
'DRI',
'DIN',
'MCD',
'QSR',
'SBUX',
'YUM',
'YUMC')
real_estate <- c('KRG',
'OHI',
'SNH',
'STAG',
'VER')
oil_gas <- c('EQM',
'BP',
'EP', # Energy Transfer LP (high yield)
'CVX', # chevron
'PTR', # petro china company limited (china's equivalent to chevon and exxon)
'CEQP',# Crestwood equity partners (high yield)
'OXY', # occidental petroleum
'BEP', # brookfield renewable
'PSXP', #
'EPD')
tech <- c('FB',
'MSFT',
'GOOGL',
'BABA',
'IBM',
'AMZN',
'LOGI',
'WORK',
'TWTR',
'TCEHY')
pharma <- c('NVAX',
'GILD',
'ABT',
'MRNA',
'RHHBY')
|
5c2eefc64d98273767ee3e706b02c091a93044ae
|
06aec25dadf0e5c54939fe87f845ee9007e144e0
|
/RStudioConf2018/examples/plumber/test.R
|
25fe3514b43d6ae1cb055cf6e2a620e331cb9ee3
|
[] |
no_license
|
rundel/Presentations
|
67beac96f767669d1c5cded0f122df007c37da12
|
bce5c7ba02eff9159c5252068aafe936e5bfd0aa
|
refs/heads/master
| 2023-08-17T00:01:37.099095
| 2023-08-07T18:03:03
| 2023-08-07T18:03:03
| 4,659,430
| 20
| 5
| null | null | null | null |
UTF-8
|
R
| false
| false
| 456
|
r
|
test.R
|
library(httr)
for(i in 1:10) {
x = suppressWarnings(
st_read("data/precincts.json", quiet = TRUE) %>% st_buffer(0.001 + (i-1)/1000)
)
st_write(x,"data/precincts2.geojson", quiet=TRUE, delete_dsn=TRUE)
file = base64enc::base64encode("data/precincts2.geojson")
body = list(
team="Team00",
key="0000000000000000",
file=file
)
POST("http://127.0.0.1:7887/prediction", body=body, encode = "json")
Sys.sleep(2)
}
|
4dee79a4d23ad4dc47a3e2f0bde0406b712e7676
|
93d1fcc7758e5e99927be0529fb9d681db71e70c
|
/R/correct_d.R
|
b0a05d1270416196f7db59c543e755e16086eb7d
|
[] |
no_license
|
psychmeta/psychmeta
|
ef4319169102b43fd87caacd9881014762939e33
|
b790fac3f2a4da43ee743d06de51b7005214e279
|
refs/heads/master
| 2023-08-17T20:42:48.778862
| 2023-08-14T01:22:19
| 2023-08-14T01:22:19
| 100,509,679
| 37
| 15
| null | 2023-08-14T01:06:53
| 2017-08-16T16:23:28
|
R
|
UTF-8
|
R
| false
| false
| 15,015
|
r
|
correct_d.R
|
#' Correct for small-sample bias in Cohen's \eqn{d} values
#'
#' Corrects a vector of Cohen's \eqn{d} values for small-sample bias, as Cohen's \eqn{d}
#' has a slight positive bias. The bias-corrected \eqn{d} value is often called
#' Hedges's \eqn{g}.
#'
#' The bias correction is:
#' \deqn{g = d_{c} = d_{obs} \times J}{g = d_c = d * J}
#'
#' where
#' \deqn{J = \frac{\Gamma(\frac{n - 2}{2})}{\sqrt{\frac{n - 2}{2}} \times \Gamma(\frac{n - 3}{2})}}{J = \Gamma((n - 2) / 2) / (sqrt(n - 2) * \Gamma((n - 2) / 2))}
#'
#' and \eqn{d_{obs}}{d} is the observed effect size, \eqn{g = d_{c}}{g = d_c} is the
#' corrected (unbiased) estimate, \eqn{n} is the total sample size, and
#' \eqn{\Gamma()}{\Gamma()} is the [gamma function][base::gamma()].
#'
#' Historically, using the gamma function was computationally intensive, so an
#' approximation for \eqn{J} was used (Borenstein et al., 2009):
#' \deqn{J = 1 - 3 / (4 * (n - 2) - 1)}{J = 1 - 3 / (4 * (n - 2) - 1}
#'
#' This approximation is no longer necessary with modern computers.
#'
#' @param d Vector of Cohen's d values.
#' @param n Vector of sample sizes.
#'
#' @return Vector of g values (d values corrected for small-sample bias).
#' @export
#'
#' @references
#' Hedges, L. V., & Olkin, I. (1985).
#' *Statistical methods for meta-analysis*.
#' Academic Press. p. 104
#'
#' Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009).
#' *Introduction to meta-analysis*.
#' Wiley. p. 27.
#'
#' @md
#'
#' @examples
#' correct_d_bias(d = .3, n = 30)
#' correct_d_bias(d = .3, n = 300)
#' correct_d_bias(d = .3, n = 3000)
correct_d_bias <- function(d, n){
df <- n
J <- exp(lgamma(df/2) - log(sqrt(df/2)) - lgamma((df - 1)/2))
out <- d
out[!is.na(n)] <- d[!is.na(n)] * J[!is.na(n)]
out
}
#' Correct for small-sample bias in Glass' \eqn{\Delta} values
#'
#' Correct for small-sample bias in Glass' \eqn{\Delta} values.
#'
#' @param delta Vector of Glass' \eqn{\Delta} values.
#' @param nc Vector of control-group sample sizes.
#' @param ne Vector of experimental-group sample sizes.
#' @param use_pooled_sd Logical vector determining whether the pooled standard deviation was used (`TRUE`) or not (`FALSE`; default).
#'
#' @return Vector of d values corrected for small-sample bias.
#' @export
#'
#' @references
#' Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect
#' size and related estimators. *Journal of Educational Statistics, 6*(2),
#' 107–128. \doi{10.2307/1164588}
#'
#' @details
#' The bias correction is estimated as:
#'
#' \deqn{\Delta_{c}=\Delta_{obs}\frac{\Gamma\left(\frac{n_{control}-1}{2}\right)}{\Gamma\left(\frac{n_{control}-1}{2}\right)\Gamma\left(\frac{n_{control}-2}{2}\right)}}{delta_c = delta * gamma((nc - 1) / 2) / (sqrt((nc - 1) / 2) * gamma((nc - 2) / 2))}
#'
#' where \eqn{\Delta} is the observed effect size, \eqn{\Delta_{c}}{\Delta_c} is the
#' corrected estimate of \eqn{\Delta},
#' \eqn{n_{control}}{nc} is the control-group sample size, and
#' \eqn{\Gamma()}{gamma()} is the [gamma function][base::gamma()].
#'
#' @encoding UTF-8
#' @md
#'
#' @examples
#' correct_glass_bias(delta = .3, nc = 30, ne = 30)
correct_glass_bias <- function(delta, nc, ne, use_pooled_sd = rep(FALSE, length(delta))){
n <- nc * ne / (nc + ne)
m <- nc - 1
m[use_pooled_sd] <- m[use_pooled_sd] + ne[use_pooled_sd] - 1
cm <- exp(lgamma(m/2) - log(sqrt(m/2)) - lgamma((m - 1)/2))
delta * cm
}
#' Correct \eqn{d} values for measurement error and/or range restriction
#'
#' @description
#' This function is a wrapper for the [correct_r()] function to correct \eqn{d} values
#' for statistical and psychometric artifacts.
#'
#' @param correction Type of correction to be applied. Options are "meas", "uvdrr_g", "uvdrr_y", "uvirr_g", "uvirr_y", "bvdrr", "bvirr"
#' @param d Vector of \eqn{d} values.
#' @param ryy Vector of reliability coefficients for Y (the continuous variable).
#' @param uy Vector of u ratios for Y (the continuous variable).
#' @param uy_observed Logical vector in which each entry specifies whether the corresponding uy value is an observed-score u ratio (`TRUE`) or a true-score u ratio. All entries are `TRUE` by default.
#' @param ryy_restricted Logical vector in which each entry specifies whether the corresponding rxx value is an incumbent reliability (`TRUE`) or an applicant reliability. All entries are `TRUE` by default.
#' @param ryy_type String vector identifying the types of reliability estimates supplied (e.g., "alpha", "retest", "interrater_r", "splithalf"). See the documentation for [ma_r()] for a full list of acceptable reliability types.
#' @param k_items_y Numeric vector identifying the number of items in each scale.
#' @param rGg Vector of reliabilities for the group variable (i.e., the correlations between observed group membership and latent group membership).
#' @param pi Proportion of cases in one of the groups in the observed data (not necessary if `n1` and `n2` reflect this proportionality).
#' @param pa Proportion of cases in one of the groups in the population.
#' @param sign_rgz Vector of signs of the relationships between grouping variables and the selection mechanism.
#' @param sign_ryz Vector of signs of the relationships between Y variables and the selection mechanism.
#' @param n1 Optional vector of sample sizes associated with group 1 (or the total sample size, if \code{n2} is \code{NULL}).
#' @param n2 Optional vector of sample sizes associated with group 2.
#' @param conf_level Confidence level to define the width of the confidence interval (default = .95).
#' @param correct_bias Logical argument that determines whether to correct error-variance estimates for small-sample bias in correlations (`TRUE`) or not (`FALSE`).
#' For sporadic corrections (e.g., in mixed artifact-distribution meta-analyses), this should be set to \code{FALSE} (the default).
#'
#' @return Data frame(s) of observed \eqn{d} values (`dgyi`), operational range-restricted \eqn{d} values corrected for measurement error in Y only (`dgpi`), operational range-restricted \eqn{d} values corrected for measurement error in the grouping only (`dGyi`), and range-restricted true-score \eqn{d} values (`dGpi`),
#' range-corrected observed-score \eqn{d} values (\code{dgya}), operational range-corrected \eqn{d} values corrected for measurement error in Y only (`dgpa`), operational range-corrected \eqn{d} values corrected for measurement error in the grouping only (`dGya`), and range-corrected true-score \eqn{d} values (`dGpa`).
#'
#' @export
#' @encoding UTF-8
#' @md
#'
#' @references
#' Alexander, R. A., Carson, K. P., Alliger, G. M., & Carr, L. (1987).
#' Correcting doubly truncated correlations: An improved approximation for
#' correcting the bivariate normal correlation when truncation has occurred on
#' both variables. *Educational and Psychological Measurement, 47*(2), 309–315.
#' \doi{10.1177/0013164487472002}
#'
#' Dahlke, J. A., & Wiernik, B. M. (2020). Not restricted to selection research:
#' Accounting for indirect range restriction in organizational research.
#' *Organizational Research Methods, 23*(4), 717–749. \doi{10.1177/1094428119859398}
#'
#' Hunter, J. E., Schmidt, F. L., & Le, H. (2006). Implications of direct and
#' indirect range restriction for meta-analysis methods and findings.
#' *Journal of Applied Psychology, 91*(3), 594–612.
#' \doi{10.1037/0021-9010.91.3.594}
#'
#' Le, H., Oh, I.-S., Schmidt, F. L., & Wooldridge, C. D. (2016).
#' Correction for range restriction in meta-analysis revisited:
#' Improvements and implications for organizational research.
#' *Personnel Psychology, 69*(4), 975–1008. \doi{10.1111/peps.12122}
#'
#' Schmidt, F. L., & Hunter, J. E. (2015).
#' *Methods of meta-analysis: Correcting error and bias in research findings* (3rd ed.).
#' Sage. \doi{10.4135/9781483398105}. pp. 43–44, 140–141.
#'
#' @examples
#' ## Correction for measurement error only
#' correct_d(correction = "meas", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "meas", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for direct range restriction in the continuous variable
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for direct range restriction in the grouping variable
#' correct_d(correction = "uvdrr_g", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "uvdrr_g", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for indirect range restriction in the continuous variable
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for indirect range restriction in the grouping variable
#' correct_d(correction = "uvirr_g", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "uvirr_g", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for indirect range restriction in the continuous variable
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "uvdrr_y", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for direct range restriction in both variables
#' correct_d(correction = "bvdrr", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "bvdrr", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
#'
#' ## Correction for indirect range restriction in both variables
#' correct_d(correction = "bvirr", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = .7, pa = .5)
#' correct_d(correction = "bvirr", d = .5, ryy = .8, uy = .7,
#' rGg = .9, pi = NULL, pa = .5, n1 = 100, n2 = 200)
correct_d <- function(correction = c("meas", "uvdrr_g", "uvdrr_y", "uvirr_g", "uvirr_y", "bvdrr", "bvirr"),
d, ryy = 1, uy = 1,
rGg = 1, pi = NULL, pa = NULL,
uy_observed = TRUE, ryy_restricted = TRUE, ryy_type = "alpha", k_items_y = NA,
sign_rgz = 1, sign_ryz = 1,
n1 = NULL, n2 = NA, conf_level = .95, correct_bias = FALSE){
correction <- match.arg(correction)
correction <- gsub(x = correction, pattern = "_g", replacement = "_x")
n <- n1
if(!is.null(n)){
n[!is.na(n2)] <- n[!is.na(n2)] + n2[!is.na(n2)]
n1[is.na(n2)] <- n2[is.na(n2)] <- n[is.na(n2)] / 2
pi <- n1 / n
pi[pi == 1] <- .5
}
if(!is.null(pi)){
rxyi <- convert_es.q_d_to_r(d = d, p = pi)
}else{
rxyi <- convert_es.q_d_to_r(d = d, p = .5)
}
if(!is.null(rGg)){
rxx <- rGg^2
}else{
rxx <- NULL
}
if(!is.null(pi) & !is.null(pa)){
ux <- sqrt((pi * (1 - pi)) / (pa * (1 - pa)))
}else{
ux <- NULL
}
if(is.null(pa) & !is.null(pi)){
if(correction == "uvdrr_y" | correction == "uvirr_y"){
uy_temp <- uy
if(length(uy_observed) == 1) uy_observed <- rep(uy_observed, length(d))
if(length(ryy_restricted) == 1) ryy_restricted <- rep(ryy_restricted, length(d))
if(correction == "uvdrr_y"){
uy_temp[!uy_observed] <- estimate_ux(ut = uy_temp[!uy_observed], rxx = ryy[!uy_observed], rxx_restricted = ryy_restricted[!uy_observed])
rxpi <- rxyi
}
if(correction == "uvirr_y"){
ryyi_temp <- ryy
uy_temp[uy_observed] <- estimate_ut(ux = uy_temp[uy_observed], rxx = ryy[uy_observed], rxx_restricted = ryy_restricted[uy_observed])
ryyi_temp[ryy_restricted] <- estimate_rxxa(rxxi = ryy[ryy_restricted], ux = uy[ryy_restricted], ux_observed = uy_observed[ryy_restricted], indirect_rr = TRUE)
rxpi <- rxyi / ryyi_temp^.5
}
pqa <- pi * (1 - pi) * ((1 / uy_temp^2 - 1) * rxpi^2 + 1)
pqa[pqa > .25] <- .25
pa <- convert_pq_to_p(pq = pqa)
}else{
pa <- pi
}
}
if(is.null(pi)) pi <- .5
if(is.null(pa)) pa <- pi
out <- correct_r(correction = correction,
rxyi = rxyi, ux = ux, uy = uy,
rxx = rxx, ryy = ryy,
ux_observed = TRUE, uy_observed = uy_observed,
rxx_restricted = TRUE, rxx_type = "group_treatment",
ryy_restricted = ryy_restricted, ryy_type = ryy_type, k_items_y = k_items_y,
sign_rxz = sign_rgz, sign_ryz = sign_ryz,
n = n, conf_level = conf_level, correct_bias = correct_bias)
if(is.data.frame(out[["correlations"]])){
if(!is.null(pi)){
out[["correlations"]] <- convert_es.q_r_to_d(r = out[["correlations"]], p = matrix(pa, nrow(out[["correlations"]]), ncol(out[["correlations"]])))
}else{
out[["correlations"]] <- convert_es.q_r_to_d(r = out[["correlations"]], p = pa)
}
new_names <- colnames(out[["correlations"]])
new_names <- gsub(x = new_names, pattern = "r", replacement = "d")
new_names <- gsub(x = new_names, pattern = "x", replacement = "g", ignore.case = FALSE)
new_names <- gsub(x = new_names, pattern = "t", replacement = "G", ignore.case = FALSE)
colnames(out[["correlations"]]) <- new_names
}else{
out_names <- names(out[["correlations"]])
for(i in out_names){
if(!is.null(pi)){
out[["correlations"]][[i]][,1:3] <- convert_es.q_r_to_d(r = out[["correlations"]][[i]][,1:3], p = matrix(pa, nrow(out[["correlations"]][[i]]), 3))
}else{
out[["correlations"]][[i]][,1:3] <- convert_es.q_r_to_d(r = out[["correlations"]][[i]][,1:3], p = pa)
}
}
new_names <- gsub(x = out_names, pattern = "r", replacement = "d")
new_names <- gsub(x = new_names, pattern = "x", replacement = "g", ignore.case = FALSE)
new_names <- gsub(x = new_names, pattern = "t", replacement = "G", ignore.case = FALSE)
names(out[["correlations"]])[names(out[["correlations"]]) %in% out_names] <- new_names
}
names(out)[names(out) == "correlations"] <- "d_values"
class(out)[class(out) == "correct_r"] <- "correct_d"
out
}
|
f9a2744de35475b8c5ee0703773a2903be29c78f
|
51e22345b740b9ec1e2ddbf54a8cada4b0caf93e
|
/yams_pike_bootstrap.R
|
4eb8ead0ee7406cede1bfd850a19e036b5981e1c
|
[] |
no_license
|
kimwhoriskey/yams
|
9598673979de2a0f0df80dc13a75ae66cb5d3168
|
b61f0eafe0b7e875f3a7fae90a621468c813f32b
|
refs/heads/main
| 2023-04-16T19:13:34.745108
| 2022-01-28T15:33:26
| 2022-01-28T15:33:26
| 375,415,917
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,911
|
r
|
yams_pike_bootstrap.R
|
# bootstrap the 2 behaviour model
#### set up working directory
setwd("~/Desktop/yams")
rm(list=ls())
library(data.table)
library(ggplot2)
library(yaps)
library(TMB)
library(tidyverse)
library(gridExtra)
library(mgcv)
library(sp)
source('yapsfunctions.R')
source('issmfunctions.R')
source("simtrack.R")
fmf <- wesanderson::wes_palette("FantasticFox1", type = "discrete")
# TMB functions
compile("yaps_ssm.cpp")
dyn.load(dynlib("yaps_ssm"))
compile("yaps_hmm.cpp")
dyn.load(dynlib("yaps_hmm"))
#### load mods and data
#### load mods and data
# bring in sync data to extract hydro positions
sync <- readRDS('data/sync.RDS')
hydros <- data.table::data.table(sync$pl$TRUE_H)
colnames(hydros) <- c('x','y','z')
# bring in the lake
lake <- readRDS("data/lake.rds")
# bring in time of arrival data (see yams_prepdata.R)
toalist <- readRDS('output/toalist.RDS')
# extract toa and burst interval vector
toa <- toalist$toa
bivec <- toalist$seq
# bring in the model results
mods21 <- readRDS("output/m2svhmm1.RDS")
mods22 <- readRDS("output/m2svhmm60.RDS")
mods31 <- readRDS("output/m3svhmm1.RDS")
mods32 <- readRDS("output/m3svhmm60.RDS")
# need this too
rbi_min <- 10
rbi_max <- 30
####### pick the best mod for each group
# compare nlls
cbind(sapply(mods21, function(x)min(x$ssm_nll$nll)),
sapply(mods22, function(x)min(x$ssm_nll$nll)))
mods2winners <- apply(cbind(sapply(mods21, function(x)min(x$ssm_nll$nll)),
sapply(mods22, function(x)min(x$ssm_nll$nll))),
1,
which.min)
mods2winners
cbind(sapply(mods31, function(x)min(x$ssm_nll$nll)),
sapply(mods32, function(x)min(x$ssm_nll$nll)))
mods3winners <- apply(cbind(sapply(mods31, function(x)min(x$ssm_nll$nll)),
sapply(mods32, function(x)min(x$ssm_nll$nll))),
1,
which.min)
mods3winners
allmods2 <- list(mods21, mods22)
allmods3 <- list(mods31, mods32)
# get the best mods for 2 and 3
mods2 <- list()
for(i in 1:5) mods2[[i]] <- allmods2[[mods2winners[i]]][[i]]
mods3 <- list()
for(i in 1:5) mods3[[i]] <- allmods3[[mods3winners[i]]][[i]]
# pick a single mod to calculate the detection efficiency
# we'll pick the first group of the 2 state model
mod <- mods2[[1]]
modidx <- rep(1:5, each=5000)
toamats <- split(data.table(toa), factor(modidx)) %>% lapply(as.matrix)
bivecs <- split(bivec, factor(modidx))
#########################################################
########### estimate the detection efficiency ###########
#########################################################
# we need to estimate the detection efficiency for the simulation study
# i explored several options, this one worked by far the best
# to get the data in the right format, translate toa into 0s and 1s
toa01 <- ifelse(is.na(toamats[[1]]), 0, 1) # this is only for one subset
dets <- reshape2::melt(toa01 %>% t()) %>% rename(hydro=Var1, pingidx=Var2, det=value)
# var1 is row of original data frame / matrix (in our case hydro)
# var 2 is column of original data frame / matrix (in our case time)
# value is the value of the element
obj <- mod$ssm_results[[mod$winner]]$obj
# get the distances from the tmb object
dets$dist <- reshape2::melt(obj$report()$dist)$value
dets <- data.table(dets)
#######
# basic approach is to bin the data and calculating detection probabilities empirically
# then fit a binomial glm with weights
# use 5 m intervals
# assuming detection probability is constant through space and time
distidx <- seq(0, max(dets$dist), by=5)
dets$distidx <- findInterval(dets$dist, distidx)*5 # basically rounding to the nearest five
deteff <- dets %>% group_by(distidx) %>% arrange(distidx) %>% summarize(eff = mean(det), totdet = n())
head(deteff)
# find the largest distance without a 0 value
max(deteff[deteff$eff!=0,]$distidx)
# everything above 930 is = 0 (deteff that is)
# we'll assume everything over 1000m has a zero probability
# that is pretty standard for acoustic telemetry
which(deteff$eff==1)
# we also have two rows with efficiency = 1
deteff <- deteff[deteff$distidx < 1000,]
deteff %>%
ggplot(aes(x=distidx, y=eff, col=totdet)) +
geom_point() +
xlim(0, 1000) +
theme_bw()
# fit the binomial with weights
de <- gam(eff~ s(distidx), data = deteff, family=binomial, weights = totdet)
deteff %>% ggplot(aes(x=distidx, y=eff, col=totdet)) +
geom_point() +
geom_line(aes(x=distidx, y=fitted(de)), col='tomato') +
theme_bw()
# quick cross validation, sample 70% in inidx, the rest go into outidx
inidx <- sort(sample(1:nrow(deteff), size=round(.7*nrow(deteff))))
outidx <- (1:nrow(deteff))[!(1:nrow(deteff)) %in% inidx]
detefftrain <- deteff[inidx,]
detrain <- gam(eff~ s(distidx), data = detefftrain, family=binomial, weights = totdet)
outpreds <- predict(detrain, newdata=deteff[outidx,], type="response")
predpts <- rbind(
cbind(deteff[outidx,c('distidx', 'eff')], type=rep("Observed", length(outidx))),
cbind(deteff[outidx,'distidx'], eff=outpreds, type=rep("Predicted", length(outidx)))
)
# plot the training/testing results
detefftrain %>% ggplot(aes(x=distidx, y=eff, col=totdet)) +
geom_point() +
scale_color_gradient(low=fmf[3], high="navyblue", name="Total No. of\nDetections") +
geom_line(aes(x=distidx, y=fitted(detrain)), col=fmf[5]) +
ggnewscale::new_scale("col") + #whoa ggnewscale FOR THE WIN, man that was a b
geom_point(data=predpts, aes(x=distidx, y=eff, col=factor(type)), size=2, inherit.aes = FALSE) +
scale_color_manual(values=c(fmf[4], fmf[5]), name='Testing Data') +
xlab("Distance (m)") +
ylab("Detection Efficiency") +
theme_bw()
# efficiency at 250 and 500 m
predict(de, newdata=data.frame(distidx=c(100, 250, 500, 750)), type='response')
# really cool
# these are the same regardless of using mod2 or mod3
########################################
# run simulation studies
# need the lake data because we want to place the sim dat randomly within the lake
# have to input the centered hydros and lake to match the predictions from yaps
centeredlake <- lake[, c("X", "Y")] - hydros[, c("x", "y")][rep(1, nrow(lake))]
centeredhydros <- hydros[, c("x", "y")] - hydros[, c("x", "y")][rep(1, nrow(hydros))]
lakepoly <- Orcs::coords2Polygons(as.matrix(centeredlake), ID="lake")
# pick mod to base sim on
mod <- mods2[[1]]
# starting values: use second choice because it was better for the first set
boot <- bootstrap(mod,
startseed=42, # started at 42, so now do 72
nsims=60,
savepath='output/twostatesboot',
useSSMds = FALSE,
dstart=60,
simargs = list(
hydropos = centeredhydros,
poly=lakepoly,
nloc = 5000,
bi = c(10,30),
demod = de,
me="t",
sos=1465,
multipath=NULL,
move="Wiener",
case=2,
timescale=60),
issm.args = list(maxsteps=5,
fixsteps=TRUE,
allowfc=FALSE,
fcmax=3,
jiggle_fc=0.01,
initssmargs = list(mapping = list(working_A = factor(matrix(NA)))),
ssmargs = list(mapping = list(working_A = factor(matrix(NA, nrow=2, ncol=2))),
optimizer='nlminb'),
timescalehmm=60,
logdstart = c(log(60), log(60)),#c(1,3),
setinitstatdist=1)
)
mod <- mods3[[1]]
# starting values: use second choice because it was better for the first set
boot <- bootstrap(mod,
startseed=42,
nsims=60,
savepath='output/threestatesboot',
dstart=60,
simargs = list(
hydropos = centeredhydros,
poly=lakepoly,
nloc = 5000,
bi = c(10,30),
demod = de,
me="t",
sos=1465,
multipath=NULL,
move="Wiener",
case=2,
timescale=60),
issm.args = list(maxsteps=7,
m=3,
fixsteps=TRUE,
allowfc=FALSE,
fcmax=3,
jiggle_fc=0.03,
initssmargs = list(mapping = list(working_A = factor(matrix(NA)))),
ssmargs = list(mapping = list(working_A = factor(matrix(NA, nrow=3, ncol=3))),
inner_control = list(maxit=1500),
optimizer='nlminb'),
timescalehmm=60,
logdstart = c(log(60), log(60), log(60)),#c(1,3),
setinitstatdist=1)
)
##################################
# look at simulation results
boot2 <- readRDS('output/twostatesboot.RDS')
boot3 <- readRDS('output/threestatesboot.RDS')
# check if any didn't work
sapply(boot2$mods, length) %>% length
sapply(boot3$mods, length) %>% length
# only 57 worked
# check convergence
sapply(boot2$mods, function(x)x$ssm_results[[x$winner]]$mess)
sapply(boot2$mods, function(x)x$ssm_results[[x$winner]]$opt$convergence)
fcidx2 <- which(sapply(boot2$mods, function(x)x$ssm_results[[x$winner]]$opt$convergence)>0)
length(fcidx2)
length(boot2$mods)-length(fcidx2)
sapply(boot3$mods, function(x)x$ssm_results[[x$winner]]$mess)
sapply(boot3$mods, function(x)x$ssm_results[[x$winner]]$opt$convergence)
fcidx3 <- which(sapply(boot3$mods, function(x)x$ssm_results[[x$winner]]$opt$convergence)>0)
length(fcidx3)
length(boot3$mods)-length(fcidx3)
# summary stats
summary(1-boot2$err.rate[-fcidx2])
summary(boot2$lon.rmse[-fcidx2])
summary(boot2$lat.rmse[-fcidx2])
summary(boot2$rmse)
summary(1-boot3$err.rate[-fcidx3])
summary(boot3$lon.rmse[-fcidx3])
summary(boot3$lat.rmse[-fcidx3])
summary(boot3$rmse)
# get bootstrap results ready for plotting
bootdat <- data.frame(val=c(1-boot2$err.rate[-fcidx2], 1-boot3$err.rate[-fcidx3],
boot2$lon.rmse[-fcidx2], boot3$lon.rmse[-fcidx3],
boot2$lat.rmse[-fcidx2],boot3$lat.rmse[-fcidx3],
boot2$rmse[-fcidx2], boot3$rmse[-fcidx3]),
type=rep(c('bstateacc', 'Eastings', 'Northings', 'locstateacc'), each=length(boot2$err.rate[-fcidx2])+length(boot3$err.rate[-fcidx3])),
m=factor(rep(c('Two-State Model', 'Three-State Model'), times=c(length(boot2$err.rate[-fcidx2]),length(boot3$err.rate[-fcidx3]))),
levels=c('Two-State Model', 'Three-State Model')))
##########################
# look at state accuracy
bootdat %>%
filter(type=='bstateacc') %>%
ggplot(aes(x=m, y=val, group=m, fill=m)) +
geom_boxplot() +
ylim(0.6, 1) +
theme_bw() +
ylab('Proportion') +
ggtitle('Behavioural State Accuracy') +
scale_fill_manual(values=c('slategray1', 'cadetblue')) +
theme(legend.position = 'none',
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5))
# location state accuracy in each axis
bootdat %>%
filter(type %in% c('Eastings', 'Northings')) %>%
ggplot(aes(x=m, y=val, fill=m)) +
geom_boxplot() +
theme_bw() +
ylab('Root Mean Squared Error (m)') +
xlab('Location Axis') +
ggtitle('Location State Accuracy') +
facet_wrap(~type) +
scale_fill_manual(values=c('slategray1', 'cadetblue')) +
theme(legend.position = 'none',
strip.background = element_blank(),
panel.border = element_rect(colour = "black", fill = NA),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5))
# location state accuracy total
bootdat %>%
filter(type=='locstateacc') %>%
ggplot(aes(x=m, y=val, fill=m)) +
geom_boxplot() +
theme_bw() +
ylab('Root Mean Squared Error (m)') +
xlab('Location Axis') +
ggtitle('Location State Accuracy') +
scale_fill_manual(values=c('slategray1', 'cadetblue')) +
theme(legend.position = 'none',
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5))
# let's look at the winners
sapply(boot2$mods, function(x)x$winner)
sapply(boot3$mods, function(x)x$winner)
# don't need many iterations
##################################
# let's look at the parameters now
boot2$true.pars
boot2$pars
# lets do some tables
boot2$pars %>%
select(-par, -true) %>%
select(-fcidx2) %>%
mutate(mean=rowMeans(.),
sd=sqrt(apply(., 1, var)),
lower2.5=apply(., 1, quantile, 0.025),
upper97.5=apply(., 1, quantile, 0.975),
median=apply(., 1, median)) %>%
mutate(true=boot2$true.pars,
par=as.character(boot2$pars$par)) %>%
select(par, true, mean, median, sd, lower2.5, upper97.5) %>%
xtable::xtable(digits=3) %>%
print(include.rownames=FALSE)
boot3$pars %>%
select(-par, -true) %>%
select(-fcidx3) %>%
mutate(mean=rowMeans(.),
sd=sqrt(apply(., 1, var)),
lower2.5=apply(., 1, quantile, 0.025),
upper97.5=apply(., 1, quantile, 0.975),
median=apply(., 1, median)) %>%
mutate(true=boot3$true.pars,
par=as.character(boot3$pars$par)) %>%
select(par, true, mean, median, sd, lower2.5, upper97.5) %>%
xtable::xtable(digits=3) %>%
print(include.rownames=FALSE)
|
160216624fd0ac0580ba6bf70a7f0494ebe1cdd8
|
f804b28e0d1b4a12587f84c52cf9742c6bd845c6
|
/Plot2.R
|
13159fe498d770815a36620408b6d093ea59f21b
|
[] |
no_license
|
zzzz88/ExData_Plotting1
|
b56c943187f3fac2b6aa789fd14c02343c66316c
|
00488b832b8d10ba4336b67aff8be6220cacd3d9
|
refs/heads/master
| 2020-04-15T04:17:20.842313
| 2019-01-09T04:57:11
| 2019-01-09T04:57:11
| 164,377,202
| 0
| 0
| null | 2019-01-07T04:14:42
| 2019-01-07T04:14:42
| null |
UTF-8
|
R
| false
| false
| 533
|
r
|
Plot2.R
|
data <- read.table("household_power_consumption.txt",sep = ";", header = TRUE,stringsAsFactors = FALSE)
data[,"Date"]= as.Date(data[,"Date"], format = "%d/%m/%Y")
my_data <- subset (data, Date == "2007-02-01"| Date =="2007-02-02")
datatime <- strptime(paste(my_data[,"Date"], my_data[,"Time"], sep = " "), format = "%Y-%m-%d %H:%M:%S")
GlobalAP <-as.numeric(my_data$Global_active_power)
png("plot2.png", width = 480, height = 480)
plot(datatime, GlobalAP, type = "l", xlab = " ", ylab = "Global Active Power (kilowattes)")
dev.off()
|
e2338a535abe91d3e472872761f46c79984f7e56
|
7858fc8a03b2a14debcb4aadcc3ef2a221b911c3
|
/DSP/4.5.r
|
e7fe63e261ff0650dba89f490cf7f22a89f69b09
|
[] |
no_license
|
T-Oxy/free-depot
|
2cbf9bc8d13a648fc6175989753efee5c07fbb73
|
84396b989ada99a7e20f42b9bf73812cc434c530
|
refs/heads/master
| 2023-08-22T11:02:05.863276
| 2021-09-29T05:17:14
| 2021-09-29T05:17:14
| 241,281,261
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 144
|
r
|
4.5.r
|
x <- c(0.0,3.0,4.0 ,1.0 ,-2.0 ,-1.0 ,0.0 ,10 ,-1.0 ,0.0)
sum =0
N =length(x)
for(i in 1:N){
sum = sum +x[i]
}
mean <- (sum / N) #平均値
|
12f33f40a39af47f055eb52b4eeda35d870f88ef
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/datarobot/examples/UploadTransferrableModel.Rd.R
|
38a3e9d4265cf33660225f1757d9c1d7d79f4a18
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 255
|
r
|
UploadTransferrableModel.Rd.R
|
library(datarobot)
### Name: UploadTransferrableModel
### Title: Import a previously exported model for predictions.
### Aliases: UploadTransferrableModel
### ** Examples
## Not run:
##D UploadTransferrableModel("model.drmodel")
## End(Not run)
|
6196f6a95b67d8e8ba65dd7b28559e82719084d7
|
e256b7f59b8a9e59a025279f799cc1414b556bd6
|
/run_analysis.r
|
860dedf3caab940b91653f6e311fb2be3f1a07e3
|
[] |
no_license
|
nitinaggarwal1986/Getting_and_cleaning_data_Project
|
e44bcf3ccb75d7b39a030a0ee2efe14eb0e52e10
|
082c3ff838c30bd8c32909375d7f39474eea9885
|
refs/heads/master
| 2020-04-07T11:48:23.207903
| 2015-07-26T19:27:09
| 2015-07-26T19:27:09
| 39,737,573
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,747
|
r
|
run_analysis.r
|
#Load the data from the files with training set.
train<-read.table("UCI HAR Dataset/train/X_train.txt")
train_a<-read.table("UCI HAR Dataset/train/y_train.txt")
train_s<-read.table("UCI HAR Dataset/train/subject_train.txt")
#Load the data from the files with test set.
test<-read.table("UCI HAR Dataset/test/X_test.txt")
test_a<-read.table("UCI HAR Dataset/test/y_test.txt")
test_s<-read.table("UCI HAR Dataset/test/subject_test.txt")
#Load the list of features.
features<-read.table("UCI HAR Dataset/features.txt")
#Initializing the result with all the observations with the activity index.
Tidy<-rbind(cbind(subject=train_s,activity=train_a,train),cbind(subject=test_s,activity=test_a,test))
#Giving the columns proper names.
colnames(Tidy)<-c("subject","activity",as.character(features[,2]))
names<-as.character(colnames(Tidy))
#Identifying the indices with activity, mean and standard deviation of variables.
indices<-c("subject","activity",names[grep("-mean()",names)],names[grep("-std()",names)])
indices<-indices[order(indices)]
#Subsetting the Dataset to get the Tidy dataset required.
Tidy<-Tidy[,indices]
#Reading the activity lables from the activity_labels.txt file and then replacing the
#numbers with corresponding labels in the activity column.
activity<-read.table("UCI HAR Dataset/activity_labels.txt")
Tidy$activity<-activity$V2[Tidy$activity]
#Creating the second Tidy data set with the averages of each variable for each activity
#and each subject.
Tidy2<-NULL
for(i in 1:30){
for(j in 1:5){
ind<-which(Tidy[,"subject"]==i&Tidy[,"activity"]==as.character(activity[j,2]), arr.ind=TRUE)
Tidy2<-rbind(Tidy2, cbind(subject=i, activity=as.character(activity[j,2]), t(colMeans(Tidy[ind,3:81]))))
}
}
|
fbe3bb65dfb6e1efcf66690db84fab5bfd3acf8e
|
43b93fc1d6a858ee1eb09f037954b5a3c8bf3195
|
/R/utilities.R
|
d8e88b49dd41bc01175d7988a68bbc6319cd4a9a
|
[] |
no_license
|
abarbour/irisws
|
dfd7e7965d9c92c75fdc03cdaa58b9a23865f718
|
33c019c8550e319273859e1e558cd3230ba697f8
|
refs/heads/master
| 2021-01-17T07:26:00.671670
| 2016-06-10T22:15:21
| 2016-06-10T22:15:21
| 13,811,412
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 21,784
|
r
|
utilities.R
|
#' Constructs a query function
#'
#' @details
#' These functions form correctly formatted queries for IRIS WS.
#'
#' \code{\link{constructor}} is a basic constructor which simply
#' collapses the arguments into a single query.
#'
#' \code{\link{constructor2}} also does this, but enforces
#' parameter inputs so the
#' query at least has a chance for success.
#'
#' \code{\link{params2queryparams}} is a function
#' to collapse arguments into a single query string
#'
#' @param ... query parameters.
#' For \code{\link{constructor}} these are comma-separated character strings (\code{"a=1", "b=2"} etc)
#' For \code{\link{constructor2}} these are comma-separated arguments (\code{a=1, b=2} etc)
#' @param service character; the type of web service to construct for.
#' @param defaults list; the parameters to merge \code{...} with
#' @param list.fields.only logical; return names of default arguments
#' @param exclude.empty.options logical; should the result \emph{not} contain
#' optional arguments which were not specified?
#' @param exclude.null.fields logical; should \code{NULL} fields in \code{...}
#' or \code{defaults} be removed prior to operations?
#' @param ws.version character; corresponds to the documentation version (See [1]).
#' @param query.field character; the user should not need to change this (it only exists
#' to give the function wider applicability).
#' @return character string(s)
#'
#' @name irisws-queryconstruction
#' @author A.J. Barbour; and, see [2]
#'
#' @references
#' [1] \url{http://service.iris.edu/irisws/}
#' @references
#' [2] \url{http://pages.stern.nyu.edu/~dbackus/BCH/data/FRED/fredplot.R}
#' was the motivation for \code{\link{params2queryparams}}.
#'
#' @seealso
#' \code{\link{iris.query}} to query IRIS WS
#'
#' @seealso \code{\link{irisws-wadl}} for using .wadl files
#'
#' \code{\link{irisws-package}}
#'
#' @family Utilities
#'
#' @examples
#' \dontrun{
#' # Basic construction:
#' constructor()
#' constructor("net=1","sta=2")
#' #
#' # parameter flattening
#' params2queryparams(net=1,sta=2)
#' params2queryparams(net=1,sta=NULL)
#' # this only adds parameters
#' params2queryparams(a=1, b=2, defaults=c(a=1, b=1,c=2))
#' # this adds mandatory/optional (TRUE/FALSE) parameters:
#' params2queryparams(a=1, b=2, defaults=c(a=TRUE, b=FALSE))
#' # missing optionals are excluded by default
#' params2queryparams(a=1, defaults=c(a=TRUE, b=FALSE, c=TRUE))
#' # include them:
#' params2queryparams(a=1, defaults=c(a=TRUE, b=FALSE, c=TRUE), exclude.empty.options=FALSE)
#' #
#' # Constrained construction:
#' Q <- constructor2(net=1, sta=2)
#' print(Q) # note the 'MISSING.MANDATORY' field values
#' # check that it's valid:
#' try(check.query(Q)) # it's not.
#' #
#' # Another... Distaz
#' # What needs to be given though??
#' constructor2(service="distaz", list.fields.only=TRUE)
#' # fill them in...
#' Q <- constructor2(stalat=45, stalon=-120, evtlat=30.0, evtlon=-100.0, service="distaz")
#' print(Q)
#' # check that it's valid:
#' try(check.query(Q)) # it is.
#' #
#' # 'endtime' is an optional default, by default, and also
#' not recognized if it is NULL
#' all.equal(constructor2(), constructor2(endtime=NULL))
#' }
NULL
#' @rdname irisws-queryconstruction
#' @export
constructor <- function(...,
service=c("timeseries","distaz","traveltime","flinnengdahl","resp"),
ws.version=c("1","2"),
query.field="query?"){
#
# service here DOES need to match iris specification
service <- match.arg(service)
#
ver <- match.arg(ws.version)
#
service.iris.edu <- .iriswsBaseUrl
query <- query.field
irisquery <- paste(service.iris.edu, service, ver, query, sep="/")
query <- paste0(irisquery, paste(..., sep="&"))
#
return(query)
}
#' @rdname irisws-queryconstruction
#' @export
constructor2 <- function(...) UseMethod("constructor2")
#' @rdname irisws-queryconstruction
#' @export
constructor2.default <- function(...,
service=c("timeseries","distaz","tt.deg","tt.km","tt.llpairs","resp","flinnengdahl"),
list.fields.only=FALSE, ws.version="1"){
#
# service here does NOT need to match iris specification
service <- match.arg(service)
#
# minimum defaults
# FALSE fields are considered optional (was NULL)
# TRUE fields are considered mandatory
mandatory <- TRUE
optional <- FALSE
if (service=="timeseries"){
#http://service.iris.edu/irisws/timeseries/1/
#
opts <- list(...)
#
# default: endtime is optional
d.et <- optional
# and duration is mandatory
d.dur <- mandatory
#
if ("endtime" %in% names(opts)){
# endtime was given
if (!is.null(opts[["endtime"]])){
# and it was not null, so
# endtime is mandatory
d.et <- mandatory
# duration is optional
d.dur <- optional
}
}
mlst <- list(#rqd:
net=mandatory, sta=mandatory, loc=mandatory, cha=mandatory,
starttime=mandatory,
# these are either/or
endtime=d.et,
duration=d.dur,
# filter options: (order matters!)
taper=optional, envelope=optional,
lpfilter=optional, hpfilter=optional, bpfilter=optional,
demean=optional, differentiate=optional, integrate=optional,
scale=optional, divscale=optional, correct=optional,
freqlimits=optional, autolimits=optional, units=optional,
decimate=optional, antialiasplot=optional,
audiocompress=optional, audiosamplerate=optional,
# and rqd:
output=mandatory)
} else if (service=="flinnengdahl"){
#http://service.iris.edu/irisws/flinnengdahl/2/
mlst <- list(lat=mandatory, lon=mandatory, output=mandatory)
} else if (service=="distaz"){
#http://service.iris.edu/irisws/distaz/1/
mlst <- list(stalat=mandatory, stalon=mandatory, evtlat=mandatory, evtlon=mandatory)
} else if (service=="tt.deg"){
#http://service.iris.edu/irisws/traveltime/1/
# where distance is epicentral degrees
#/query? (distdeg=<degrees>) [evdepth=<km>] [model=<iasp91|prem|ak135>] [phases=<phaselist>] [output-params]
service <- "traveltime"
mlst <- list(distdeg=mandatory, evdepth=optional,
model=optional, phases=optional,
noheader=optional, traveltimeonly=optional,
rayparamonly=optional, mintimeonly=optional)
} else if (service=="tt.km"){
#http://service.iris.edu/irisws/traveltime/1/
# where distance is kilometers
#/query? (distdeg=<km>) [evdepth=<km>] [model=<iasp91|prem|ak135>] [phases=<phaselist>] [output-params]
service <- "traveltime"
mlst <- list(distkm=mandatory, evdepth=optional,
model=optional, phases=optional,
noheader=optional, traveltimeonly=optional,
rayparamonly=optional, mintimeonly=optional)
} else if (service=="tt.llpairs"){
#http://service.iris.edu/irisws/traveltime/1/
# where distance is lat/lon pairs
#/query? (evtloc=<LatLonPair>) [evdepth=<km>] (staloc=<LatLonList>) [model=<iasp91|prem|ak135>] [phases=<phaselist>] [output-params]
service <- "traveltime"
mlst <- list(evloc=mandatory, evdepth=optional, staloc=mandatory,
model=optional, phases=optional,
noheader=optional, traveltimeonly=optional,
rayparamonly=optional, mintimeonly=optional)
} else if (service=="resp"){
#http://service.iris.edu/irisws/resp/1/
#
opts <- list(...)
nopts <- names(opts)
#
mlst <- list(net=mandatory, sta=mandatory, loc=mandatory, cha=mandatory)
#
if ("time" %in% nopts){
# some time field was given
#[time=<time>] | [start=<time> & end=<time>] | [start = <time> ] | [end = <time>]
timepar <- opts[["time"]]
if ( !is.null(timepar) & (nchar(timepar)>0) ){
# and it was not null, so
mlst$time <- optional
} else {
mlst$starttime <- optional
mlst$endtime <- optional
}
}
}
if (list.fields.only){
## Return only the field names
defs <- unlist(mlst)
optional.fields <- !defs
defsn <- names(defs)
f.req <- defsn[!optional.fields]
f.opt <- defsn[optional.fields]
query <- list(required.fields=f.req, optional.fields=f.opt)
} else {
## or the actual query
qparams <- params2queryparams(..., defaults=mlst, exclude.empty.options=TRUE, exclude.null.fields=TRUE)
query <- constructor(qparams, service=service, ws.version=ws.version)
}
#print(query)
return(query)
}
#' @rdname irisws-queryconstruction
#' @export
params2queryparams <- function(..., defaults, exclude.empty.options=TRUE, exclude.null.fields=TRUE){
# creates a list of parameters: e.g., a, b
plist <- list(...)
#print(plist)
if (missing(defaults)){
defaults <- list()
} else {
if (!inherits(defaults, "list")){
defaults <- as.list(defaults)
warning("'defaults' was not of class 'list' and was therfore coerced to one: values may have been changed")
}
}
#print(defaults)
if (exclude.null.fields){
plist <- plist[!sapply(plist, is.null)]
defaults <- defaults[!sapply(defaults, is.null)]
}
if (exclude.empty.options){
NCFUN <- function(X){
#
# function returns logical depending if
# the string is empty, and fails
# if arg is of multiple-length
#
# nzchar??
#
if (length(X)>1){
stop(c("invalid multiple-length argument given. ",
"\nOffending values: ",
paste(X, collapse=" ")))
} else {
nchar(X) == 0
}
}
pEmpties <- sapply(plist, NCFUN)
plist <- plist[!pEmpties]
dEmpties <- sapply(defaults, NCFUN)
defaults <- defaults[!dEmpties]
}
params <- RCurl::merge.list(plist, defaults)
param.names <- names(params)
# flattens to strings with, e.g., "a=1", "b=1", etc
miss.opt <- .field.optional
miss.mand <- .field.mandatory
not.applic <- .field.na
Eparams <- sapply(param.names, function(Pname, Dat=params) {
val <- Dat[[Pname]]
##print(val)
if (!is.na(val)){
if (val=="TRUE"){
# mandatory, but missing
val <- miss.mand
} else if (val=="FALSE"){
# optional, and missing
val <- miss.opt
}
} else {
val <- not.applic
}
#print(c(Pname,val))
paste0(Pname, "=", val)
})
# collapse them into a single line
#print(Eparams)
if (exclude.empty.options){
optionals <- grepl(pattern=miss.opt, Eparams)
Eparams <- Eparams[!optionals]
#empties <- sapply(Eparams, function(X) nchar(X)==0)
#Eparams <- Eparams[!empties]
}
#print(Eparams)
Qparams <- paste(Eparams, collapse="&")
return(Qparams)
}
#' Perform a query to IRIS-WS
#' @details
#' This function uses \code{\link{curlPerform}} in the \strong{RCurl} package
#' to query the IRIS WS.
#' Firstly, it
#' checks \code{query}
#' for internal consistency, with \code{\link{check.query}};
#' then, \code{query} is checked externally with \code{\link{url.exists}}.
#' If \code{query} passes the checks, then it is sent to the IRIS WS
#' and successfull results are written to \code{filename} (which is instantiated
#' with \code{\link{CFILE}}).
#'
#' \code{\link{iris.query}} is simply a pointer to \code{\link{query.iris}}
#'
#' @author AJ Barbour
#' @export
#'
#' @param iquery character; the web service query
#' @param filename character; the file to save query results to.
#' If this is \code{NULL} a
#' temporary file from \code{\link{tempfile}} is used.
#' @param is.binary logical; will the output be binary? (e.g., \code{TRUE} for SAC binary, and \code{FALSE} for a plot)
#' @param check logical; should \code{\link{check.query}} be used to check the quality of \code{iquery}
#' @param verbose logical; should messages be given by this function, and \code{\link{curlPerform}}?
#' @param ... additional arguments to \code{\link{curlPerform}}
#'
#' @return A list (invisibly) with the filename, the query string, and a success flag
#'
#' @seealso
#' \code{\link{irisws-package}}
#'
#' @family Utilities
#'
#' @examples
#' \dontrun{
#' #
#' # irisws can be used to get timeseries, etc
#' # but there is also a fair amount invested in
#' # preventing failure because of bad args, etc
#' #
#' # This query will create ANMO.png (setting duration, but could also use end=2005.002T00:00:00)
#' # constructor2 assembles all the options for a given webservice into a query
#' Q <- constructor2(net="IU", sta="ANMO", loc="00", cha="BHZ", starttime="2005.001T00:00:00", duration="1000", output="plot")
#' # and this performs the query
#' query.iris(Q, "ANMO.png")
#' # again, but in a temporary file
#' query.iris(Q, NULL)
#' #... and in the default file
#' query.iris(Q)
#' #
#' # This query will fail, obviously (unless there's a seismic network named 'XXXTHISWILLFAILXXX')
#' Q <- constructor("net=XXXTHISWILLFAILXXX")
#' query.iris(Q)
#'
#' # Arbitrary query generation
#' q1 <- params2queryparams(a=1, defaults=list(a=TRUE))
#' q2 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=NA))
#' q3 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=FALSE))
#' q4 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=FALSE), exclude.empty.options=FALSE)
#' q5 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=TRUE))
#' q6 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=FALSE,d=NA,e=""))
#' q7 <- params2queryparams(a=1, defaults=list(a=TRUE,b=2,c=FALSE,d=NA,e=""), exclude.empty.options = FALSE, exclude.null.fields = FALSE)
#' q1;q2;q3;q4;q5;q6;q7
#'
#' # Arbitrary query validation
#' try(check.query(q1)) # succeeds
#' try(check.query(q2)) # fails
#' try(check.query(q3)) # succeeds
#' try(check.query(q4)) # fails
#' try(check.query(q5)) # fails
#' try(check.query(q6)) # fails
#' try(check.query(q7)) # fails
#' }
query.iris <- function(iquery, filename="iris.query.results", is.binary=FALSE, check=TRUE, verbose=TRUE, ...){
if (check) check.query(iquery)
ure <- RCurl::url.exists(iquery)
if (ure){
if (is.null(filename)){
filename <- tempfile('iris.query.results')
}
# binary mode for file init
md <- paste0("w", ifelse(is.binary, "b", ""))
lf = RCurl::CFILE(filename, mode=md)
# perform curl ops
RC <- RCurl::curlPerform(url = iquery, writedata = lf@ref, verbose=verbose, ...)
# close file
RCurl::close(lf)
if (verbose) message(sprintf("IRIS WS query complete: %s", filename))
} else {
if (verbose) warning(sprintf("IRIS WS query FAILED: %s", iquery))
}
toret <- list(file=filename, query=iquery, success=ure)
assign("last_irisquery", toret, envir=.iriswsEnv)
return(invisible(toret))
}
#' @export
#' @rdname query.iris
iris.query <- query.iris
#' @export
#' @rdname query.iris
check.query <- function(iquery){
QQ <- unlist(strsplit(as.character(iquery), "query?"))
nq <- length(QQ)
#[1] "http://service.iris.edu/irisws/timeseries/1/" "net=1&sta=2&loc=MISSING.MANDATORY&..."
Q <- QQ[nq]
Qs <- unlist(strsplit(Q,"&"))
grtests <- c(
gr1 <- grepl(pattern=.field.mandatory, Qs),
gr2 <- grepl(pattern=.field.optional, Qs),
gr3 <- grepl(pattern='=$', Qs), # empty field
gr4 <- grepl(pattern=' $', Qs), # white space at end
gr5 <- grepl(pattern=.field.na, Qs)
)
#[1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
not.ok <- any(grtests)
if (not.ok){
offenders <- unique(c(Qs[gr1], Qs[gr2], Qs[gr3], Qs[gr4], Qs[gr5]))
stop(paste("Invalid query:", paste(offenders, collapse=" ")))
}
return(invisible(list(Q=iquery, Qs=Qs, successful=!not.ok)))
}
#' Utilities to deal with time
#'
#' @description
#' \code{\link{timestring}} produces properly formatted
#' strings for the time-fields of, say, \code{\link{query.iris}},
#' acceptable to IRIS WS.
#'
#' \code{\link{ISOdatetime.j}} produces a \code{POSIXct} object
#' using the Julian date (day of year), or from an appropriately
#' formatted character string
#' in \code{tstr} (as produced by \code{\link{timestring}}).
#'
#' @details
#' An IRIS WS time-string can be formatted in two ways:
#' (1a) using year-month-day, (e.g., \code{'1997-01-31T12:04:32.123'}) or
#' (2) using year-day (e.g.,\code{'1997.031T12:04:32.123'}),
#' where the string after 'T' corresponds to hour:minute:fractional-seconds.
#' The string can also be of the form (1b)
#' \code{'1997-01-31'} (in this case \code{'00:00:00'} is assumed), but
#' we have found this format can lead to query failures.
#' \emph{\strong{
#' In this program the string is always returned in format (2):
#' \code{<year>.<day>T<hour>:<min>:<sec>}
#' }}
#'
#' \code{sec} may be fractional, but is formatted with \code{\link{sprintf}}
#' (\code{02.06f}) so
#' values less than 1 microsecond will be truncated (\emph{not} rounded).
#'
#' Note that IRIS WS accepts values for hour, minute, and second which are less
#' than 100. Return data will have times adjusted to account for values in excess of
#' the normal limits (i.e., 24, 60, 60).
#'
#' The functionality of \code{\link{ISOdatetime.j}} is roughly equivalent to
#' \code{\link{ISOdatetime}}, but is somewhat more flexible.
#'
#' @param year numeric; the \emph{full} year A.D. (e.g., 2012 \emph{not} 12)
#' @param day numeric; the day, either of-the-year, or of-the-month (see \code{month})
#' @param hour numeric; the hour of the day (less than 100)
#' @param min numeric; the minute (less than 100)
#' @param sec numeric; fractional seconds (less than 100; will be truncated to 6 decimal places)
#' @param month numeric; the month of the year.
#' If this is \code{NULL} then \code{day} is assumed
#' to be the Julian day of year.
#' @param tz character; the timezone
#' @param tstr character; an alternative wat of specifying a datetime,
#' e.g., \code{"2012.350T32:12:12.222000"}. See details.
#'
#' @name irisws-timeutilities
#' @author AJ Barbour
#' @references [1] \url{http://service.iris.edu/irisws/timeseries/1/}
#'
#' @seealso
#' \code{\link{irisws-package}}
#'
#' @family Utilities
#'
#' @examples
#' \dontrun{
#' #
#' # Specify the month
#' timestring(2012, 15, 32, 12, 12.222, month=12)
#' # [1] "2012.350T32:12:12.222000"
#' #
#' # or not
#' timestring(2012, 15, 32, 12, 12.222)
#' # [1] "2012.015T32:12:12.222000"
#' #
#' # some errors:
#' try(timestring(2012, 15, 32, 100, 12.222)) # min too large
#' try(timestring(2012, 75755, 32, 12, 12.222)) # day too large
#' try(timestring(2012, 15, 32, 100, 12.222, 13)) # month too large
#' # etc...
#' #
#' ISOdatetime.j(tstr=timestring(2010,111,sec=0.12123))
#' # or use the wrapper
#' ISOtimestring(timestring(2010,111,sec=0.12123))
#' }
NULL
#' @rdname irisws-timeutilities
#' @export
timestring <- function(year, day, hour=0, min=0, sec=0.0, tz = "UTC", month=NULL){
#
#stopifnot(length(c(year, day, hour, min, sec, month)) <= 6 )
hour <- as.numeric(hour)
min <- as.numeric(min)
sec <- as.numeric(sec)
irislim <- 100
if (any(c(sec, min, hour) >= irislim)){
stop(paste("IRISWS requires that 'sec', 'min', and 'hour all be less than", irislim))
}
#
if (is.null(month)){
# day is julian-day
nd <- as.numeric(strftime(sprintf("%04i-12-31",year),"%j", tz=tz))
stopifnot(day <= nd)
yjd <- sprintf("%04i.%03i", year, day)
} else {
# day is month-day
stopifnot(month <= 12 & day <= 31)
yjd <- strftime(sprintf("%04i-%02i-%02i", year, month, day), "%Y.%j", tz=tz)
}
isec <- floor(sec)
rsec <- signif(sec - isec, 5)
tstr <- sprintf("%sT%02i:%02i:%02i.%06i", yjd, hour, min, isec, 1e6*rsec)
return(tstr)
}
#' @rdname irisws-timeutilities
#' @export
ISOtimestring <- function(tstr, tz="UTC"){
ISOdatetime.j(tstr=tstr, tz=tz)
}
#' @rdname irisws-timeutilities
#' @export
ISOdatetime.j <- function(year, day, hour=0, min=0, sec=0.0, tz = "UTC", month=NULL, tstr=NULL) {
#
#op <- options(digits.secs=6)
#on.exit(options(op))
#
no.tstr <- is.null(tstr)
no.year <- missing(year)
no.day <- missing(day)
#
do.tstr <- no.year & no.day & !no.tstr
#
if (do.tstr){
#x <- gsub("T", "-", paste(tstr)) # not necessary (just replace - with T in fmt)
x <- paste(tstr)
fmt <- "%Y.%jT%H:%M:%OS"
} else {
no.month <- is.null(month)
if (no.month){
x <- paste(year, day, hour, min, sec, sep = "-")
fmt <- "%Y-%j-%H-%M-%OS"
} else {
x <- paste(year, month, day, hour, min, sec, sep = "-")
fmt <- "%Y-%m-%d-%H-%M-%OS"
}
}
stopifnot(exists('fmt') & exists('x'))
as.POSIXct(strptime(x, format=fmt, tz = tz), tz = tz)
}
|
0c396707e12a2e27571906350311830fad65aa0a
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/QCApro/examples/findTh.Rd.R
|
53cc3a37886e78cf34709ec8820f0a45e7145611
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 311
|
r
|
findTh.Rd.R
|
library(QCApro)
### Name: findTh
### Title: Find Calibration Thresholds
### Aliases: findTh
### Keywords: functions
### ** Examples
# 15 random values between 1 and 100
x <- sample(1:100, size = 15)
# split into two groups for csQCA
findTh(x)
# split into three groups for mvQCA
findTh(x, groups = 3)
|
20259811b7bb82b3d3b496d39277592826db18cd
|
3ed4722997fe6e01b014879d2646f451dbf8e4a5
|
/014-R/moritz.R
|
2936c3ce84660dbe33f448c3341fd5f83e0fe94a
|
[] |
no_license
|
ChristerNilsson/2020
|
7a83efd778dea37262174a4796cb9a2e5a0cd586
|
e6d22f9d7ce99f287ea374631eaf4bec1a36056c
|
refs/heads/master
| 2023-01-12T02:56:33.367492
| 2020-11-17T20:39:05
| 2020-11-17T20:39:05
| 298,788,667
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 670
|
r
|
moritz.R
|
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
setwd(file.path("/Lab/2020/014-R"))
# BiocManager::install("infercnv")
library(infercnv)
infercnv_obj = readRDS('run.final.infercnv_obj')
# ?plot_cnv
# apply median filtering adding on to the one before
infercnv_obj_medianfiltered = infercnv::apply_median_filtering(infercnv_obj)
infercnv::plot_cnv(infercnv_obj_medianfiltered,
#out_dir= o.dir,
output_filename='infercnv.median_filtered',
x.range="auto",
x.center=1,
title = "infercnv",
color_safe_pal = FALSE)
|
064db3e901e1a85c1185bd54fdfbf7af907070fa
|
6bef9d7657a42a76d01fd7b6e6ffac51dd3f4256
|
/man/iana-package.Rd
|
26faea2728faa8030fedeaa10f283e0fc2fcdd92
|
[] |
no_license
|
mihock/iana
|
131deacf848593ffe831bdaa7e245b6d38f0e4ed
|
ae6f881a48f250280cef99446bbe3cbebd14ea56
|
refs/heads/master
| 2022-09-04T00:26:09.067272
| 2022-08-29T13:58:31
| 2022-08-29T13:58:31
| 11,193,882
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,201
|
rd
|
iana-package.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/itemanalysis.R
\docType{package}
\name{iana-package}
\alias{iana-package}
\alias{iana}
\title{GUI for Item Analysis and Scale Construction}
\description{
Iana is a browser-based GUI for classical item and test analysis, factor analysis, and item response modeling with a focus on items with an ordered-category response format.
}
\details{
Iana is a browser-based graphical user interface to R functions for the psychometric analysis of questionnaires and tests with an ordinal response format. Iana tries to integrate the essential statistical analysis steps into a convenient interface. Iana covers classical item and test analysis (reliability, item discrimation), dimensionality tests (parallel analysis and MAP test), principal components and exploratory factor analysis (including factor analysis based on polychoric correlations), one-factor confirmatory analysis, and item response models (partial credit model). Graphical output includes histograms of item and test scores, empirical item characteric curves, and person-item maps, among others.
Iana is based on the \href{http://www.rstudio.com/shiny/}{Shiny Web Framework for R}, which allows a fast bidirectional communication between the browser and R. Iana is "reactive", meaning that its output is instantly updated if any of the inputs (e.g., the selected variables) are changed by the user. This makes it easy to compare the impact of item selection and/or modeling options on the results. Iana keeps track of the R commands it constructs as a response to user input, so the analysis steps are documented and can be replicated. Iana comes with some built-in data sets, with which the interface can be tested. However, other data can easily be used.
The basic usage is documented in \code{\link{runiana}}.
\tabular{ll}{
Package: \tab iana\cr
Type: \tab Package\cr
Version: \tab 0.1\cr
Date: \tab 2019-06-10\cr
License: \tab GPL (>= 2)\cr
LazyLoad: \tab yes\cr
}
}
\examples{
\dontrun{runiana()}
}
\references{
Shiny web framework for R: \url{http://www.rstudio.com/shiny/}
}
\author{
Michael Hock (\email{michael.hock@uni-bamberg.de})
}
\keyword{package}
|
6fa259ef1429da8dc64fadc7b443fc328dbf6e57
|
823cae650090d7ffda822d708b674286d7317228
|
/TCGA_Project/Mutation_Correction/Mutation-Correction.R
|
0f959bbb58911ec57243ef1c24ed1fc2b4062f75
|
[] |
no_license
|
JiFansen/JiFansen_CentOS
|
f1ff6f2b97d3d6629d558b497f137b5b87a43bac
|
4cdc22a7d28dfa24ca53a7540e4db7ea9c74ad45
|
refs/heads/master
| 2020-04-11T10:49:25.437139
| 2018-12-15T08:34:38
| 2018-12-15T08:34:38
| 161,727,352
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,422
|
r
|
Mutation-Correction.R
|
w <- 1
for(file in list.files('/Share/home/JiFansen/JiFansen/Mutation-Correction/Background/New-196-Mutation-Signature-Calculation/')){
tmp <- read.table(file = paste0('/Share/home/JiFansen/JiFansen/Mutation-Correction/Background/New-196-Mutation-Signature-Calculation/', file), header = T, sep = '\t', row.names = 1)
colnames(tmp) <- unlist(strsplit(file,'\\.'))[1]
if(w==1){
background <- tmp
w <- w+1
}else{
background <- cbind.data.frame(background, tmp)
}
}
sampleNumber <- c()
w <- 1
for(file in list.files('/Share/home/JiFansen/JiFansen/Mutation-Correction/MAF-summary/', pattern = '.barcode.txt')){
cancertype <- unlist(strsplit(file, '-'))[1]
print(cancertype)
tmp <- read.table(file = paste0('/Share/home/JiFansen/JiFansen/Mutation-Correction/MAF-summary/', file), header = T, sep = '\t')
tmp$cancertype <- cancertype
sample.barcode <- substr(as.character(tmp$Tumor_Sample_Barcode), 1, 16)
sampleNumber <- c(sampleNumber, length(unique(sample.barcode)))
if(w==1){
data.initial <- tmp
w <- w+1
}else{
data.initial <- rbind.data.frame(data.initial, tmp)
w <- w+1
}
}
total.background <- as.matrix(apply(background, 1, FUN = function(x){return(sum(x*sampleNumber))}))
write.table(total.background, file = '/Share/home/JiFansen/JiFansen/Mutation-Correction/Correction/total-cancertype-background.txt', sep = '\t', quote = FALSE, col.names = FALSE)
data.initial <- data.initial[which(data.initial$Variant_Classification=='Missense_Mutation'),]
match.result <- match(as.character(data.initial$change), rownames(total.background))
data.initial$true.value <- as.numeric(total.background[,1])[match.result]
data.initial$count <- 1
data.summary <- aggregate(x = data.initial[,13:14], by = list(data.initial$gene, data.initial$Protein_position), FUN = sum)
data.summary$absolute <- data.summary$true.value/data.summary$count
write.table(data.summary, file = '/Share/home/JiFansen/JiFansen/Mutation-Correction/Correction/Gene-Count-TrueValue.txt', sep = '\t', quote = FALSE, row.names = FALSE)
uniq.gene <- unique(as.character(data.summary$Group.1))
w <- 1
for(genename in uniq.gene){
tmp <- data.summary[which(data.summary$Group.1==genename),]
tmp$scale <- (tmp$absolute-min(tmp$absolute))/(max(tmp$absolute)-min(tmp$absolute))
if(w == 1){
score.correction <- tmp
w <- w+1
}else{
score.correction <- rbind.data.frame(score.correction, tmp)
}
}
sig.genes <- read.table(file = '/Share/home/JiFansen/JiFansen/Mutation-Correction/Background/Cancertype/New-Overlap.txt', header = F, sep = '\t')
sig.genes <- as.character(sig.genes[,1])
match.result <- match(as.character(score.correction$Group.1), sig.genes)
sig.index <- which(is.na(match.result)==FALSE)
score.correction.sig <- score.correction[sig.index, ]
write.table(score.correction.sig, file = '/Share/home/JiFansen/JiFansen/Mutation-Correction/Correction/Total-Codon-Score.txt', sep = '\t', quote = FALSE, row.names = FALSE)
gene.codon <- paste0(as.character(score.correction.sig$Group.1), '-', as.character(score.correction.sig$Group.2))
for(file in list.files('/Share/home/JiFansen/JiFansen/Mutation-Correction/MAF-summary/', pattern = '.barcode.txt')){
cancertype <- unlist(strsplit(file, '-'))[1]
tmp <- read.table(file = paste0('/Share/home/JiFansen/JiFansen/Mutation-Correction/MAF-summary/', file), header = T, sep = '\t')
tmp <- tmp[which(tmp$Variant_Classification=='Missense_Mutation'), ]
tmp$cancertype <- cancertype
sample.barcode <- substr(as.character(tmp$Tumor_Sample_Barcode), 1, 16)
tmp$Tumor_Sample_Barcode <- sample.barcode
tmp.gene.codon <- paste0(as.character(tmp$gene), '-',as.character(tmp$Protein_position))
match.result <- match(tmp.gene.codon, gene.codon)
big.index <- which(is.na(match.result)==FALSE)
small.index <- match.result[big.index]
tmp.sig <- tmp[big.index,]
tmp.sig$score <- score.correction.sig$scale[small.index]
tmp.sig <- tmp.sig[,c(1,10,13)]
frameshift <- read.table(file = paste0('/Share/home/JiFansen/JiFansen/Mutation-Correction/Frameshift/', cancertype, '-shift.txt-New'), header = T, sep = '\t', stringsAsFactors = FALSE)
frameshift <- t(frameshift)
colnames(frameshift) <- as.character(frameshift[1,])
frameshift <- frameshift[-c(1,2),]
frameshift <- frameshift[-nrow(frameshift),]
frameshift <- as.data.frame(frameshift)
for(m in 1:dim(frameshift)[1]){
index <- which(as.numeric(as.matrix(frameshift[m,]))>0)
frame.tmp <- data.frame(gene = rownames(frameshift)[m], Tumor_Sample_Barcode = colnames(frameshift)[index], score = 0)
tmp.sig <- rbind.data.frame(tmp.sig, frame.tmp)
}
Sample.uniq <- unique(as.character(tmp.sig$Tumor_Sample_Barcode))
p <- 1
for(i in Sample.uniq){
gene.score <- c()
test.1 <- tmp.sig[which(tmp.sig$Tumor_Sample_Barcode==i),]
Gene.uniq <- unique(as.character(test.1$gene))
for(j in Gene.uniq){
test.2 <- test.1[which(test.1$gene==j),]
gene.score <- c(gene.score,prod(as.numeric(test.2$score)))
}
result <- data.frame(sample = i, gene = Gene.uniq, score = gene.score)
if(p==1){
result.cancertype <- result
p <- p+1
}else{
result.cancertype <- rbind.data.frame(result.cancertype, result)
}
}
write.table(result.cancertype, file = paste0('/Share/home/JiFansen/JiFansen/Mutation-Correction/Correction/', cancertype, '-Score-Correction.txt'), sep = '\t', quote = FALSE, row.names = FALSE)
}
|
0813e191f612f6488f019fa4c474816ece730413
|
d26b1b446e18850cae39119828067d21a5079acd
|
/R/DEVarSelect.R
|
70ad75dbcb5eee8ce4b77d69266916a3ebbc2380
|
[] |
no_license
|
ziyili20/TOAST
|
c623dcb2c64a89f00f84fddfab759da62d6434a5
|
102b6b1fa801561976b070adf1e15378bde43f76
|
refs/heads/master
| 2022-08-31T11:28:40.598741
| 2022-08-24T19:35:31
| 2022-08-24T19:35:31
| 145,612,563
| 12
| 4
| null | 2021-07-20T06:07:43
| 2018-08-21T19:53:08
|
R
|
UTF-8
|
R
| false
| false
| 1,403
|
r
|
DEVarSelect.R
|
DEVarSelect <- function(Y_raw, Prop0, nMarker = 1000, bound_negative = FALSE){
if (is(Y_raw, "SummarizedExperiment")) {
se <- Y_raw
Y_raw <- assays(se)$counts
} else if (!is(Y_raw, "matrix")) {
stop("Y_raw should be a matrix or a SummarizedExperiment object!")
}
if (nrow(Prop0) < ncol(Prop0)) {
stop("Prop0 should have dimension N (samples) by K (cell types)!")
}
if (!ncol(Y_raw) == nrow(Prop0)) {
stop("Y_raw should have dimension P (features) by N (samples)!")
}
K <- ncol(Prop0)
N_sample <- nrow(Prop0)
## find tissue specific genes
idx <- NULL
for(k in seq_len(K)) {
cvec <- rep(-1/(K-1),K)
cvec[k] <- 1
design <- rep(0,N_sample)
tmp <- DEKTissue(K, Y=Y_raw,
Prop=Prop0,
design=design,
contrast_vec=cvec,
bound_negative=bound_negative)
idx[[k]] <- sort(abs(tmp$t_stat),
decreasing=TRUE,
index=TRUE)$ix
}
nmarker <- nMarker
## number of markers per tissue. Consider overlaps
nmarker_tissue <- nmarker/K * 1.2
idxMarker <- NULL
for(k in seq_len(K)) {
idxMarker <- c(idxMarker,
idx[[k]][seq_len(nmarker_tissue)])
}
idxMarker <- unique(idxMarker)
return(idxMarker)
}
|
fb79ab3a1a101233a14bde322c9c55fb2511108a
|
046f9a3127441ccaa03e6f644996ac78f30f0f37
|
/NDE1snps-exp-finns.R
|
f205654ae5a68c9e1c9b75857481011c171104ea
|
[] |
no_license
|
amandazheutlin/misc_scripts
|
07bcdedbdb4391caf5968844ad92300a7efdbddc
|
0789d17ea48e0a345d45d7e645dcf785eb32014a
|
refs/heads/master
| 2021-01-15T11:03:49.421125
| 2017-08-07T20:48:38
| 2017-08-07T20:48:38
| 99,610,768
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,370
|
r
|
NDE1snps-exp-finns.R
|
# check 2 SNPs in NDE1 against genome-wide gene expression
# Finnish twins
# load data, libraries
libs <- c("nlme","dplyr")
invisible(lapply(libs, require, character.only = TRUE))
workDir <- "/data/swe_gwas/ABZ/NDE1/"
setwd(workDir)
load("finlandclean.rdata")
colnames(finlandclean)[2:18567] <- colnames(finlandclean)[1:18566]
finlandclean <- finlandclean[,2:18567]
demo <- read.table("demo_finns.txt",header=T,stringsAsFactors = F)
demo$age <- as.numeric(demo$age)
finlandclean$age2 <- demo[match(finlandclean$Persnb,demo$pid),6]
geno <- read.table("NDE1.ped",header=F,sep="\t")
geno <- geno[,c(1:2,7:8)]
names(geno) <- c("FID","IID","rs2242549","rs1050162")
# update finlandclean FIDs that are missing!
# USE FID NOT PAIRNB
finlandclean$FID <- geno[match(finlandclean$Persnb, geno$IID),1] %>% as.character
# snps = rs2242549 (snp1), rs1050162 (snp2)
# snps were coded 1 1, 1 2, 2 2; 1 = minor; 2 = major
# minor allele = G (snp1), C (snp2); major allele = T (both)
# additive = 1, 2, 3
# recessive = 1, 2, 2 (name = rs rec)
# dominant = 1, 1, 2 (name = rs dom)
NDE1 <- finlandclean
NDE1$rs2242549 <- geno[match(NDE1$Persnb,geno$IID),3]
NDE1$rs2242549rec <- NDE1$rs2242549
NDE1$rs2242549dom <- NDE1$rs2242549
NDE1$rs1050162 <- geno[match(NDE1$Persnb,geno$IID),4]
NDE1$rs1050162rec <- NDE1$rs1050162
NDE1$rs1050162dom <- NDE1$rs1050162
NDE1$rs2242549 <- ifelse(NDE1$rs2242549=="1 1", 1, ifelse(NDE1$rs2242549=="1 2",2,3)) # N(1,2,3) = 6,38,26
NDE1$rs2242549rec <- ifelse(NDE1$rs2242549rec=="1 1", 1, 2) # N(1,2) = 6,64
NDE1$rs2242549dom <- ifelse(NDE1$rs2242549dom=="2 2", 2, 1) # N(1,2) = 44,26
NDE1$rs1050162 <- ifelse(NDE1$rs1050162=="1 1", 1, ifelse(NDE1$rs1050162=="1 2",2,3)) # N(1,2,3) = 10,37,23
NDE1$rs1050162rec <- ifelse(NDE1$rs1050162rec=="1 1", 1, 2) # N(1,2) = 10,60
NDE1$rs1050162dom <- ifelse(NDE1$rs1050162dom=="2 2", 2, 1) # N(1,2) = 47,23
genes <- names(NDE1)[8:18566] # 18,559 markers
####################################### residual values for all genes
# effects of sex, diagnosis ('Analysis'), and age (use 'age2')
# Analysis = case/co-twin/control
df <- NDE1[complete.cases(NDE1$age2),]
df <- df[complete.cases(df$rs2242549),]
residuals <- lapply(genes, function(x) {
lm(eval(substitute(i ~ Sex + Analysis + age2, list(i = as.name(x)))),
data = df, na.action = na.omit)
})
res_vals <- lapply(residuals, function(x) residuals(x)) %>% as.data.frame()
resdf <- cbind(df[,c(1,18568,18569,18571,18572,18574)],res_vals)
names(resdf) <- c("IID","FID","rs2242549","rs2242549dom",
"rs1050162","rs1050162dom",genes)
####################################### snps x expression (residuals)
# effect of snp on all genes (N = 18,559 genes)
# residual values excluding effects of sex, diagnosis ('Analysis')
# random effect = family
# using lmeControl to deal with convergence issues
#########################################################
# snp1 - additive model
models_snp1 <- lapply(genes, function(x) {
lme(eval(substitute(i ~ rs2242549, list(i = as.name(x)))),
random = ~1|FID, data = resdf, na.action = na.omit,
control=lmeControl(returnObject=T, opt="optim"))
})
# stats
model_stats_snp1 <- lapply(models_snp1, function(x) summary(x)$tTable)
# naming genes
getResponse <- function(formula) {
tt <- summary(formula)$terms
vars <- as.character(attr(tt, "variables"))[-1] ## [1] is the list call
response <- attr(tt, "response") # index of response var
vars[response]
}
# list of overall effects
snp1 = NULL
for (i in 1:18559) {
temp <- as.data.frame(model_stats_snp1[[i]])
temp$marker <- getResponse(models_snp1[[i]])
snp1 <- rbind(snp1,temp[2,])
}
colnames(snp1)[4:5] <- c("tvalue","pvalue")
snp1 <- snp1[order(snp1$pvalue),]
write.table(snp1,"RNA-GWAS-snp1-add-res-agecov-june16.txt",
sep="\t",col.names=T,row.names=F,quote=F)
rm(models_snp1)
rm(model_stats_snp1)
rm(snp1)
#########################################################
# snp1 - dominant model
models_snp1dom <- lapply(genes, function(x) {
lme(eval(substitute(i ~ rs2242549dom, list(i = as.name(x)))),
random= ~1|FID, data = resdf, na.action = na.omit,
control=lmeControl(returnObject=T, opt="optim"))
})
# stats
model_stats_snp1dom <- lapply(models_snp1dom, function(x) summary(x)$tTable)
# list of overall effects
snp1_dom = NULL
for (i in 1:18559) {
temp <- as.data.frame(model_stats_snp1dom[[i]])
temp$marker <- getResponse(models_snp1dom[[i]])
snp1_dom <- rbind(snp1_dom,temp[2,])
}
colnames(snp1_dom)[4:5] <- c("tvalue","pvalue")
snp1_dom <- snp1_dom[order(snp1_dom$pvalue),]
write.table(snp1_dom,"RNA-GWAS-snp1-dom-res-agecov-june16.txt",
sep="\t",col.names=T,row.names=F,quote=F)
rm(models_snp1dom)
rm(model_stats_snp1dom)
rm(snp1_dom)
#########################################################
# snp2 - additive model
models_snp2 <- lapply(genes, function(x) {
lme(eval(substitute(i ~ rs1050162, list(i = as.name(x)))),
random= ~1|FID, data = resdf, na.action = na.omit,
control=lmeControl(returnObject=T, opt="optim"))
})
# stats
model_stats_snp2 <- lapply(models_snp2, function(x) summary(x)$tTable)
# list of overall effects
snp2 = NULL
for (i in 1:18559) {
temp <- as.data.frame(model_stats_snp2[[i]])
temp$marker <- getResponse(models_snp2[[i]])
snp2 <- rbind(snp2,temp[2,])
}
names(snp2)[4:5] <- c("tvalue","pvalue")
snp2 <- snp2[order(snp2$pvalue),]
write.table(snp2,"RNA-GWAS-snp2-add-res-agecov-june16.txt",
sep="\t",col.names=T,row.names=F,quote=F)
rm(models_snp2)
rm(model_stats_snp2)
rm(snp2)
#########################################################
# snp2 - dominant model
models_snp2dom <- lapply(genes, function(x) {
lme(eval(substitute(i ~ rs1050162dom, list(i = as.name(x)))),
random= ~1|FID, data = resdf, na.action = na.omit,
control=lmeControl(returnObject=T, opt="optim"))
})
# stats
model_stats_snp2dom <- lapply(models_snp2dom, function(x) summary(x)$tTable)
# list of overall effects
snp2_dom = NULL
for (i in 1:18559) {
temp <- as.data.frame(model_stats_snp2dom[[i]])
temp$marker <- getResponse(models_snp2dom[[i]])
snp2_dom <- rbind(snp2_dom,temp[2,])
}
names(snp2_dom)[4:5] <- c("tvalue","pvalue")
snp2_dom <- snp2_dom[order(snp2_dom$pvalue),]
write.table(snp2_dom,"RNA-GWAS-snp2-dom-res-agecov-june16.txt",
sep="\t",col.names=T,row.names=F,quote=F)
rm(models_snp2dom)
rm(model_stats_snp2dom)
rm(snp2_dom)
###########################################################
# tvab
famID <- unique(df$FID)
twin1 <- df[!duplicated(df$FID),]
twin2 <- df[!(df$Persnb %in% twin1$Persnb),]
###########################################################
###########################################################
####################################### snps x expression
# effect of snp on all genes (N = 18,559 genes)
# covar = sex, diagnosis ('Analysis')
# random effect = family
# Analysis = case/co-twin/control
# #########################################################
# # snp1 - additive model
# models_snp1 <- lapply(genes, function(x) {
# lme(eval(substitute(i ~ rs2242549 + Sex + Analysis, list(i = as.name(x)))),
# random = ~1|Pairnb, data = NDE1, na.action = na.omit)
# })
#
# # stats
# model_stats_snp1 <- lapply(models_snp1, function(x) summary(x)$tTable)
#
# # naming genes
# getResponse <- function(formula) {
# tt <- summary(formula)$terms
# vars <- as.character(attr(tt, "variables"))[-1] ## [1] is the list call
# response <- attr(tt, "response") # index of response var
# vars[response]
# }
#
# # list of overall effects
# snp1 = NULL
# for (i in 1:18559) {
# temp <- as.data.frame(model_stats_snp1[[i]])
# temp$marker <- getResponse(models_snp1[[i]])
# snp1 <- rbind(snp1,temp[2,])
# }
#
# colnames(snp1)[4:5] <- c("tvalue","pvalue")
# snp1 <- snp1[order(snp1$pvalue),]
#
# write.table(snp1,"RNA-GWAS-finns-snp1-add.txt",
# sep="\t",col.names=T,row.names=F,quote=F)
#
# #########################################################
# # snp1 - recessive model
# models_snp1rec <- lapply(genes, function(x) {
# lme(eval(substitute(i ~ rs2242549rec + Sex + Analysis, list(i = as.name(x)))),
# random= ~1|Pairnb, data = NDE1, na.action = na.omit)
# })
#
# # stats
# model_stats_snp1rec <- lapply(models_snp1rec, function(x) summary(x)$tTable)
#
# # list of overall effects
# snp1_rec = NULL
# for (i in 1:18559) {
# temp <- as.data.frame(model_stats_snp1rec[[i]])
# temp$marker <- getResponse(models_snp1rec[[i]])
# snp1_rec <- rbind(snp1_rec,temp[2,])
# }
#
# colnames(snp1_rec)[4:5] <- c("tvalue","pvalue")
# snp1_rec <- snp1_rec[order(snp1_rec$pvalue),]
#
# write.table(snp1_rec,"RNA-GWAS-finns-snp1-rec.txt",
# sep="\t",col.names=T,row.names=F,quote=F)
#
# #########################################################
# # snp2 - additive model
# models_snp2 <- lapply(genes, function(x) {
# lme(eval(substitute(i ~ rs1050162 + Sex + Analysis, list(i = as.name(x)))),
# random= ~1|Pairnb, data = NDE1, na.action = na.omit)
# })
#
# # stats
# model_stats_snp2 <- lapply(models_snp2, function(x) summary(x)$tTable)
#
# # list of overall effects
# snp2 = NULL
# for (i in 1:18559) {
# temp <- as.data.frame(model_stats_snp2[[i]])
# temp$marker <- getResponse(models_snp2[[i]])
# snp2 <- rbind(snp2,temp[2,])
# }
#
# names(snp2)[4:5] <- c("tvalue","pvalue")
# snp2 <- snp2[order(snp2$pvalue),]
#
# write.table(snp2,"RNA-GWAS-finns-snp2-add.txt",
# sep="\t",col.names=T,row.names=F,quote=F)
#
# #########################################################
# # snp2 - recessive model
# models_snp2rec <- lapply(genes, function(x) {
# lme(eval(substitute(i ~ rs1050162rec + Sex + Analysis, list(i = as.name(x)))),
# random= ~1|Pairnb, data = NDE1, na.action = na.omit)
# })
#
# # stats
# model_stats_snp2rec <- lapply(models_snp2rec, function(x) summary(x)$tTable)
#
# # list of overall effects
# snp2_rec = NULL
# for (i in 1:18559) {
# temp <- as.data.frame(model_stats_snp2rec[[i]])
# temp$marker <- getResponse(models_snp2rec[[i]])
# snp2_rec <- rbind(snp2_rec,temp[2,])
# }
#
# names(snp2_rec)[4:5] <- c("tvalue","pvalue")
# snp2_rec <- snp2_rec[order(snp2_rec$pvalue),]
#
# write.table(snp2_rec,"RNA-GWAS-finns-snp2-rec.txt",
# sep="\t",col.names=T,row.names=F,quote=F)
#
|
a40843550c5015c533820d5f8734add8d0fb3d6b
|
3ae1e8d5bccc46c43b26e01d66192aa271880e6f
|
/R/alignCurves.R
|
3f891b83095450ec7350b7e913a3d8c8c350ddd0
|
[] |
no_license
|
oliviertastet/eaeCurves
|
ff587b7b8a32b9c9db5a7d131e3a5571171d6348
|
ecddce643e5c7adca8c24d976d73783389fc90ad
|
refs/heads/master
| 2022-06-20T06:55:29.925997
| 2020-05-14T13:17:49
| 2020-05-14T13:17:49
| 263,908,916
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,441
|
r
|
alignCurves.R
|
#' Align the curves
#'
#' @param data Do you love cats? Defaults to TRUE.
#' @param days Do you love cats? Defaults to TRUE.
#' @param k Do you love cats? Defaults to TRUE.
#' @keywords alignCurves
#' @examples
#' alignCurves()
#' @export
alignCurves<-function(data, days = 1:263, k=3, n=5){
title='Mean clinical score'
m=melt(data[,c('condition','sex','souris',days)],id.vars=c('condition','sex','souris'))
m=na.omit(m)
m$key = paste(m$souris,m$sex,m$condition,m$variable,sep='.')
m$souris.id = paste(m$souris,m$sex,m$condition,sep='.')
vec=c()
ages = c()
vars = c()
sums=c()
m=m[!duplicated(m$key),]
m=m[order(m$souris.id,m$variable),]
for(mouse in unique(m$souris.id)){
sub = subset(m, souris.id==mouse)
sick = F
check = T
for(day in unique(as.character(sub$variable))){
if(check){
if(is.na(sub[which(sub$variable==day),'value'])){
if(!is.na(sub[which(sub$variable==as.character(as.numeric(day)+1)),'value'])){
sub[which(sub$variable==day),'value']=0
}
}
if(sub[which(sub$variable==day),'value']>0){
ages=c(ages,day)
sick=T
check=F
vec[(length(vec)-n):(length(vec))]=1
}
}
if(sick){
vec=c(vec,1)
}else{
vec=c(vec,0)
}
}
}
m$sick = vec
m.sick=subset(m, sick==1)
m.sick$variable=as.numeric(as.character(m.sick$variable))
df=data.frame(matrix(ncol=264))
m.sick$souris.id=paste(m.sick$souris,m.sick$sex,m.sick$condition,sep='.')
for(mouse in unique(m.sick$souris.id)){
vec=rep(NA,264)
names(vec)=1:263
colnames(df)=names(vec)
sub = subset(m.sick, souris.id==mouse)
sums=c(sums, sum(as.numeric(sub$value)))
vars=c(vars, var(as.numeric(sub$value)))
sub$variable=(sub$variable-min(sub$variable))+1
vec[sub$variable]=sub$value
df=rbind(df,vec)
}
df=df[2:nrow(df),]
df=data.frame(cbind(unique(m.sick$souris.id),df))
colnames(df)=gsub('X','',colnames(df))
df$souris = unlist(strsplit(as.character(df[,1]),'\\.'))[seq(1,nrow(df)*3,3)]
df$sex = unlist(strsplit(as.character(df[,1]),'\\.'))[seq(2,nrow(df)*3,3)]
df$condition = unlist(strsplit(as.character(df[,1]),'\\.'))[seq(3,nrow(df)*3,3)]
df=df[,c('souris','sex','condition',1:263)]
df$max_score = as.numeric(apply(df[,4:266],1,FUN=max,na.rm=T))
df$age.at.onset = ages
rownames(df)=paste(df[,1],df[,2],df[,3],sep='.')
hc=hclust(dist(df[,as.character(1:263)]))
df$cluster = cutree(hc,k=k)
df$var = vars
df$score_sum=sums
#df[is.na(df)]=5
df
}
|
6ab3926c9b04a3eb48aea62329581c0dbc12ee46
|
beaa721938b098c82ab768f01ad858487c32f59d
|
/report/miss_sent_report.R
|
2249c864e10fd490c81f31f3274b1e92d196d575
|
[] |
no_license
|
RanaivosonHerimanitra/Sentinel
|
3f354256fb26cc0d1b6ea77caf8395ab82539268
|
9393d2f69369110bd78e3cd080d4bcd72e664b76
|
refs/heads/master
| 2020-12-29T02:22:04.597292
| 2017-04-08T22:55:26
| 2017-04-08T22:55:26
| 50,108,349
| 10
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,390
|
r
|
miss_sent_report.R
|
#setwd("/media/herimanitra/Document/IPM_sentinelle/sentinel_hrmntr 291115/Sentinel")
###############################Missing sent report ############################
library(ReporteRs);
#require(Hmisc)
doc <- docx()
path1="/media/herimanitra/Document/IPM_sentinelle/sentinel_hrmntr 291115/Sentinel/report/logo.png"
path2="/srv/shiny-server/sentinel_hrmntr/Sentinel/report/logo.png"
mylogo=ifelse(file.exists(path1),path1,path2)
doc=addImage(doc, mylogo,
par.properties = parProperties(text.align = "left", padding = 5),
width=5,
height=1.5)
# Change the default font size and font family
options('ReporteRs-fontsize'=10, 'ReporteRs-default-font'='Arial')
# Add a formatted paragraph of texts:
doc = addTitle(doc, "Sms hebdomadaire du réseau sentinelle", level=1)
doc = addParagraph(doc , pot( Sys.Date(), textItalic() ) )
doc = addParagraph(doc, " ")
baseCellProp = cellProperties( padding = 2 )
vertical_text= cellProperties(text.direction = "btlr",
padding = 2)
horizontal_text= cellProperties(text.direction = "lrtb",
padding = 2)
cat("load data and preprocess missing sent...")
require(data.table)
path1="/media/herimanitra/Document/IPM_sentinelle/sentinel_hrmntr 291115/Sentinel/data/missing_sent.csv"
path2="/srv/shiny-server/sentinel_hrmntr/Sentinel/data/missing_sent.csv"
myfile=ifelse(file.exists(path1),path1,path2)
missing_sent= fread(myfile)
missing_sent[,code:=paste0(substr(Annee,3,4),"/",ifelse(nchar(Semaine)<2,paste0("0",Semaine),Semaine))]
cat("DONE\n")
cat("crosstabulation of weeks and Sites...")
#missing_sent[,Centre:=capitalize(tolower(Centre))]
missing_sent[,Centre:=(tolower(Centre))]
X=table(missing_sent$code,missing_sent$Centre)
cat("DONE\n")
cat("Add Semaine épidémiologique...")
X=cbind(Semaine=row.names(X),X)
cat("DONE\n")
cat("load sentinel lat/long define 34sites vs other...")
path1="/media/herimanitra/Document/IPM_sentinelle/sentinel_hrmntr 291115/Sentinel/data/sentinel.csv"
path2="/srv/shiny-server/sentinel_hrmntr/Sentinel/data/sentinel.csv"
myfile=ifelse(file.exists(path1),path1,path2)
sentinel_latlong = fread(myfile)
sites34 = which( tolower(colnames(X)) %in% tolower(c("CSBU MANANJARY MNJ",
"TSIMADILO",
sentinel_latlong$centre) ))
other_site =which( !( tolower(colnames(X)) %in% tolower(c("CSBU MANANJARY MNJ",
"TSIMADILO",
sentinel_latlong$centre)) ))
cat("DONE\n")
cat("divide data into 03 parts...")
#X=table(missing_sent$code,missing_sent$Centre)
X1=X[,c(1,sites34[1:15])]
X2=X[,c(1,sites34[16:33])]
#select only period where data collection has begun
X3=X[95:nrow(X),other_site[1:10]]
X4=X[95:nrow(X),c(1,other_site[11:length(other_site)])]
cat("DONE\n")
#vertical test for headers
mytable1 = vanilla.table(X1,text.direction = "btlr")
mytable2 = vanilla.table(X2,text.direction = "btlr")
mytable3 = vanilla.table(X3,text.direction = "btlr")
mytable4 = vanilla.table(X4,text.direction = "btlr")
#loop through columns and change into red those cells with value <4:
#First line in vertical order
for ( k in 2:ncol(X1) )
{
cat(k,"\n")
mytable1[as.numeric(X1[,k]) >= 4, k] = textProperties( font.size = 10)
mytable1[as.numeric(X1[,k]) < 4, k] = textProperties( color="#FF3333",font.size = 10)
}
for ( k in 2:ncol(X2) )
{
cat(k,"\n")
mytable2[as.numeric(X2[,k]) >= 4, k] = textProperties( font.size = 10)
mytable2[as.numeric(X2[,k]) < 4, k] = textProperties( color="#FF3333",font.size = 10)
}
for ( k in 2:ncol(X3) )
{
cat(k,"\n")
mytable3[as.numeric(X3[,k]) >= 4, k] = textProperties( font.size = 10)
mytable3[as.numeric(X3[,k]) < 4, k] = textProperties( color="#FF3333",font.size = 10)
}
for ( k in 2:ncol(X4) )
{
cat(k,"\n")
mytable4[as.numeric(X4[,k]) >= 4, k] = textProperties( font.size = 10)
mytable4[as.numeric(X4[,k]) < 4, k] = textProperties( color="#FF3333",font.size = 10)
}
cat("Writing document to a word document...")
mytable1 = addFooterRow( mytable1,
value = c("En rouge , les cas <4"),
cell.properties = horizontal_text
,colspan = ncol(X1))
mytable2 = addFooterRow( mytable2,
value = c("En rouge , les cas <4"),
cell.properties = horizontal_text
,colspan = ncol(X2))
mytable3 = addFooterRow( mytable3,
value = c("En rouge , les cas <4"),
cell.properties = horizontal_text
,colspan = ncol(X3))
mytable4 = addFooterRow( mytable4,
value = c("En rouge , les cas <4"),
cell.properties = horizontal_text
,colspan = ncol(X4))
#
doc = addFlexTable( doc, mytable1 )
doc = addParagraph(doc, " ")
doc = addFlexTable( doc, mytable2 )
doc = addParagraph(doc, " ")
doc = addFlexTable( doc, mytable3 )
doc = addParagraph(doc, " ")
doc = addFlexTable( doc, mytable4 )
writeDoc(doc, file = "missing_sent.docx")
#sudo apt-get install unoconv
Sys.sleep(5)
system("doc2pdf missing_sent.docx") #write in pdf using cli command
cat('DONE\n')
|
16ffe919344c99e998e4d6358a21341356ad638b
|
650af521421421f5e12f58b9ee9ce7e4b23db0bd
|
/app_ui.R
|
070354aabcf6a0e7ef66e4380a382c71513cc848
|
[
"MIT"
] |
permissive
|
info-201a-sp20/final-project-christiancruz-124
|
f15f752d17a217f03c7982246d5f95ae802938c2
|
aa8284437d081d24c92d7c13561e033eead74f57
|
refs/heads/master
| 2022-09-26T15:03:09.867197
| 2020-06-08T05:28:22
| 2020-06-08T05:28:22
| 262,849,108
| 0
| 0
|
MIT
| 2020-06-08T03:13:31
| 2020-05-10T18:22:06
|
HTML
|
UTF-8
|
R
| false
| false
| 6,278
|
r
|
app_ui.R
|
library(shiny)
source("maps/app_ui.R")
source("platformpage/app_ui.R")
source("Opinion/app_ui.R")
ui <- navbarPage(
"Effectiveness and influences of social media marketing",
# first panel: overview
tabPanel(
"Overview",
includeCSS("style.css"),
tags$h1(class = "header",
"Overview"),
sidebarLayout(
sidebarPanel(
tags$h3(class= "sub",
"Purpose"),
hr(),
tags$p("In an effort to understand the effects social media marketing has on users,
we have decided to explore social media and how marketing affects spending on
popular social media sites as well as it's over all reception by users. The
importance in such analysis lies in the ability to have a better understanding
of the strategies used to influence shoppers, understand the feelings of users,
identify social medias that have the most effect to then question why they are
most effective, and finally to use demographical knowledge in conjunction with
marketing data to evaluate their effectiveness in certain groups."),
br(),
tags$h3(class= "sub",
"Data Sources"),
hr(),
tags$p("In this project we have successfully found data on",
a("data world.", href= "https://data.world/"), "All of the datasets we chose from data.world was
collected via survey on an app known as Whatsgoodly. The datasets provided information about
demographics, location, and asked questions that showed opinions of different demographics about
social media marketing as well as questions realting to the effectiveness of different
social media platforms.")),
mainPanel(
tags$img(id = "img", "", src = "https://images.pexels.com/photos/927629/pexels-photo-927629.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=750&w=1260",
height = 500, width = 900)
)
)
),
# second panel: leaflet map on reactions
map_page,
# third panel: col chart on opinion by demographic
race_panel,
# fouth panel: col chart on platform effectiveness by demographic
ui_platform,
# fifth panel: conclusions page
tabPanel(
"Conclusions",
tags$h1(class = "header",
"Conclusions"),
br(),
tags$h4(class = "insight",
"Mapping Opinions: General Trends Show Negative Responses Throughout"),
tags$p("When comparing attitudes toward marketing in different locations,",
"the connection between user attitude and location is not abosolute.",
"However, we see a general trend of dislike across the country",
"followed by neutrality in second, and finally positivity as last.",
"This does not show whether that social media marketing is effective or not,",
"but consumers definitely have a generally negative attitude towards this kind of marketing.",
"Via this dataset, many of the ways effectiveness is measured is through",
"the perspective of the users and this leads us the question their objectivity",
"in the influence that social media holds in their own lives."),
DT::dataTableOutput("table"),
tags$p("The table above further highlights the points above, as we take percentage
of opinion from 5 different locations and compare them. We can clearly see
that almost all regions lean toward negative or neutral as opposed to positive."),
br(),
tags$h4(class = "insight",
"Demographics and Opinion: General Consensus"),
tags$p("Based on the summary of opinion of people who took the survey,",
"It could be found that among different group of people, opinions are varied.",
"The general trend, however, shows a negative/neutral attitute towards social media marketing",
"Whether social media marketing is effective cannot be told from the summary since the inluence",
"of the advertisements may be underestimated."),br(),
"According to",a("social media impact.",
href= "http://www.socialmediaimpact.com/impact-social-media-marketing-today/
#:~:text=Using%20social%20media%20platforms%20like,helps%20in%20developing%20business%20tactics."),
"Social Media Marketing is having a tremendous impact on business and market as business growth.",
"It helps in developing business tactics.",
"Social media marketing helps a brand/company to build a strong online presence",
"by innovative social media marketing techniques and customer satisfaction.",
br(),
tags$h4(class = "insight",
"Platform Effectiveness: Are They Really Effective?"),
tags$p("A quick look at each of the demographic categories on platform influence
will easily show that there are a couple platforms that dominate the ad
game. Both Facebook and instagram show clear signs as far as numbers that
they provide the most effective platform for social media marketting out
of the options provided by the Whatsgoodly survey. However, an even more
prevelant platform with bigger numbers than the two powerhouses seems to
be neither and none of the listed apps. \"None\" is among the listed
categories that also has a high relative count of answers in each
demographic category, which means a large amount of people feel they
haven't been influenced by social media marketing. This poses the
question of whether social media ads are effective at all."),
plotOutput("platforms_overall"),
tags$p("Something to consider is the objectivity of the data and thus the
insights from the data. Becuase all of the data for this report is
survey based, the data is all user reported. This means that a
survey taker may answer \"None\" when in reality, they have
purchased a product directly from a platform without knowing or
may have unknowingly bought a product advertised to them while on a
certain platform. There is no way to accurately determine this
without better, more direct data, so further investigation with
better data is needed to fully determine the effectiveness of social
media marketing.")
)
)
|
778410ae6e8994942cc5d31aeeea503d2b5abe55
|
9e6ca0e896acf5d7a02bed7e8d2d8e0a6bfd50e5
|
/man/sc_weights.Rd
|
c38fd484c1fccf82e3d9f9abee76122f20294003
|
[] |
no_license
|
cran/NonProbEst
|
986ea321fe9d213b63170a24fe2b9d2c015db455
|
978c6f6d645c01cb367ff0ad9100b9565de3190f
|
refs/heads/master
| 2020-07-21T19:38:17.932468
| 2020-06-03T11:10:03
| 2020-06-03T11:10:03
| 206,957,607
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,137
|
rd
|
sc_weights.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NonProbEst.R
\name{sc_weights}
\alias{sc_weights}
\title{Calculates Schonlau and Couper weights}
\usage{
sc_weights(propensities)
}
\arguments{
\item{propensities}{A vector with the propensities associated to the elements of the convenience sample.}
}
\value{
A vector with the corresponding weights.
}
\description{
Computes weights from propensity estimates using the (1 - pi_i)/pi_i formula introduced in Schonlau and Couper (2017).
}
\details{
The function takes the vector of propensities \eqn{\pi(x)} and calculates the weights to be applied in the Hajek estimator using the formula that can be found in Schonlau and Couper (2017). For an individual \emph{i}, weight is calculated as follows:
\deqn{w_i = \frac{1 - \pi_i (x)}{\pi_i (x)}}
}
\examples{
covariates = c("education_primaria", "education_secundaria")
data_propensities = propensities(sampleNP, sampleP, covariates)
sc_weights(data_propensities$convenience)
}
\references{
Schonlau, M., & Couper, M. P. (2017). \emph{Options for conducting web surveys.} Statistical Science, 32(2), 279-292.
}
|
9bfc6af00950a8c7e9f20339bff2c854a53251d2
|
86d11da7198eb29441a365f742e7e0c7f10a5632
|
/pipelineHead.R
|
4f2f099c3b4d6a62538862663200b13c431628fa
|
[
"MIT"
] |
permissive
|
mTEC-pipelines/tufts-pipelines
|
5d9fa8b3cdd2f3316b4eeb88c157a260ba675da5
|
f5cfa303550c90cc2027b06c3685b225ab4967c3
|
refs/heads/master
| 2021-09-10T14:40:30.727146
| 2018-03-27T23:41:55
| 2018-03-27T23:41:55
| 112,393,503
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,891
|
r
|
pipelineHead.R
|
###############################################################################
# Author: Aparna Rajpurkar
# Date: 2.15.17
#
# Required custom scripts:
# MainPipelineDriver.R
# funcs.R
# Required data files:
# Data files should be contained within a subdirectory named:
# tuft_analysis_datafiles
#
# Purpose
#
# These scripts perform analysis for single cell analysis of Tuft cells.
# No guarantees made. These scripts were written for personal use.
# Contact: arrajpur@stanford.edu with questions.
#
# Usage: Rscript MainPipelineDriver.R
# Modify the driver file for different plots
###############################################################################
#source("funcs.R", print.eval=TRUE)
library(scran) #needed for SingleCellExperiment
library(scater) #needed for calculateQCMetrics
ENSEMBL_MOUSE_CONVERSION_FILE<-"tuft_analysis_datafiles/mouse_ensembl_genesymbol_conversion.txt"
NORMALIZED_DATA_FILE<-"tuft_analysis_datafiles/POR_all_data.rda"
WT_COUNTS<-"tuft_analysis_datafiles/all_wt_counts.txt"
AIREKO_COUNTS<-"tuft_analysis_datafiles/aireko_counts.txt"
BATCH_INFO<-"tuft_analysis_datafiles/samples_all.txt"
conversion<-read.delim(ENSEMBL_MOUSE_CONVERSION_FILE, header=T)
convertNames<-function(gene_list, convert_from="mgi_symbol", convert_to="ensembl_gene_id") {
result<-conversion[match(gene_list, conversion[,convert_from]), convert_to]
result
}
# get normalized data
pos_control<-"kw_tuft_142"
vars<-load(NORMALIZED_DATA_FILE)
norm.exprs<-normalized
norm.exprs<-norm.exprs[,!(colnames(norm.exprs)==pos_control)]
norm.exprs<-norm.exprs[!(grepl("ERCC", rownames(norm.exprs))),,drop=FALSE]
# get raw counts for basic metadata
wt_file <- WT_COUNTS
aire_file <- AIREKO_COUNTS
controls<-c("kw_tuft_141", "kw_tuft_142", "kw_tuft_143", "kw_tuft_144", "June29_KW.96.featureCounts")
DATA_WT <- read.csv(wt_file, header = T, row.names = 1, sep="\t")
DATA_AIRE <- read.csv(aire_file, header = T, row.names = 1, sep="\t")
DATA <- cbind.data.frame(DATA_WT, DATA_AIRE[rownames(DATA_WT),,drop=FALSE])
raw.counts<-DATA[,!(colnames(DATA) %in% controls),drop=FALSE]
raw.counts<-raw.counts[!(grepl("ERCC", rownames(raw.counts))),,drop=FALSE]
# get total number of cells from raw counts
numCells <- ncol(raw.counts)
# set batches
batch_info<-read.delim(BATCH_INFO, header=T, row.names=1)
batch<-batch_info[colnames(raw.counts),,drop=FALSE]
##########################################################################
# deal with the ENSEMBL gene name issue
ensembl_mouse<-rownames(raw.counts)[grepl("ENSMUSG", rownames(raw.counts))]
rownames(raw.counts)<-sub("(ENSMUSG\\d+|ERCC.+)\\.*\\d*", "\\1", rownames(raw.counts), perl=TRUE)
geneNamesForMito<-convertNames(rownames(raw.counts), convert_from="ensembl_gene_id", convert_to="mgi_symbol")
mito_bool<-grepl("mt", geneNamesForMito)
# get ercc names from raw raw.counts
erccs_raw<-rownames(raw.counts[grepl("ERCC",rownames(raw.counts)),])
mito_raw<-rownames(raw.counts[mito_bool,])
# set phenotype data (describe cells)
pd<-data.frame(batch=batch)
rownames(pd)<-colnames(raw.counts)
colnames(pd)<-colnames(batch)
fd<-data.frame(rownames(raw.counts))
rownames(fd)<-rownames(raw.counts)
colnames(fd)<-"ensembl"
# create SingleCellExperiment to hold all data
mtecdev<-SingleCellExperiment(
list(counts=as.matrix(raw.counts)),
colData=pd,
rowData=fd
)
# calculate basic QC, setting controls as ERCCs
mtecdev<-calculateQCMetrics(
mtecdev,
feature_controls=list(ERCC=erccs_raw, MITO=mito_raw)
)
# get phenotype data / sample metadata
sampleData<-cbind.data.frame(colData(mtecdev))
rownames(sampleData)<-colnames(mtecdev)
norm.cells<-colnames(norm.exprs)
sampleData<-sampleData[norm.cells,]
|
e39d38caaa0d7921393c413bba62c81d5241feba
|
a388530e188ad749f79545cc06f7c341cf9472d9
|
/man/spatialHEAT.Rd
|
21559858906f30b066f21f4355e7e351c302dcc3
|
[] |
no_license
|
atfutures-labs/spatialHEAT
|
178a724a4e7e532833f0208d3dd24898e39ffd44
|
53b8d594b09bd97ed2162514de5b7e63781b75c5
|
refs/heads/master
| 2020-03-22T05:19:46.422409
| 2018-10-31T16:48:54
| 2018-10-31T16:48:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 233
|
rd
|
spatialHEAT.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spatialheat-package.R
\docType{package}
\name{spatialHEAT}
\alias{spatialHEAT}
\alias{spatialHEAT-package}
\title{spatialHEAT}
\description{
spatialHEAT
}
|
cab5459dbaf1a16328ba5c944a4eb6c6d30e2039
|
208358d9640a4280cb937011c91352403957b24a
|
/scripts/check/02-cor-top-pcs.R
|
60526bc68d38b450e2447077e0e7650cf4238eb3
|
[
"MIT"
] |
permissive
|
variani/paper-neff
|
9c91fe22d37ab04c902cb769004a00bb19cf1021
|
0e0bbc90c63ffb7c77a8331df6b07d99ca8e6452
|
refs/heads/master
| 2023-01-28T05:41:21.881878
| 2020-12-10T02:30:10
| 2020-12-10T02:30:10
| 278,204,788
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,019
|
r
|
02-cor-top-pcs.R
|
### inc
library(tidyverse)
library(glue)
library(data.table)
library(BEDMatrix)
library(bigsnpr)
library(devtools)
load_all("~/git/variani/biglmmz/")
## arguments
trait <- "hip"
ntop <- 500
## parameters
file_phen <- "out/phen.sync.tsv.gz"
file_pcs <- "out/pcs.sync.tsv.gz"
file_clump <- glue("out/clump/{trait}.top.5000.clump.gz")
## read top variants
cat(" - read clump\n")
clump <- read_tsv(file_clump) %>% arrange(P)
if(nrow(clump) < ntop) {
stop(glue("#rows in clump ({nrow(clump)}) < #top ({ntop})"))
}
clump <- head(clump, ntop)
snps_top <- clump$SNP
## load Z
cat(" - load Z\n")
Z <- glue("out/fbm/{trait}.rds") %>% big_attach
snps_z <- glue("out/fbm/{trait}.variants") %>% read_lines
## read phen.
phen <- file_phen %>% fread %>% as_tibble
stopifnot(trait %in% colnames(phen))
y <- phen[[trait]]
pcs <- file_pcs %>% fread %>% as_tibble
## compute corr.
G <- Z[, seq(ntop)]
cor(pcs$PC1, G) %>% as.numeric %>% sort %>% head %>% round(2)
cor(pcs$PC2, G) %>% as.numeric %>% sort %>% head %>% round(2)
|
9d30e267f058e578a3c715f523367a0579c2c8c3
|
d01ef90f1b72ae02ac5055c7c93c69fcf5a7be5c
|
/pedigree/plotRelDraft.R
|
9e0abb33f2cd2eb10131d6f6bce92fa14d0c695e
|
[] |
no_license
|
ngon/LgSm-DataProcessing
|
3b8dde9e4e75f1e2e6791ec342cda80b6d54070f
|
37c816f5257dcc87802124e87dbd9e445f6d146f
|
refs/heads/master
| 2020-07-04T15:07:14.876067
| 2015-11-21T02:52:33
| 2015-11-21T02:52:33
| 22,928,887
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,465
|
r
|
plotRelDraft.R
|
load("C:/Users/Administrator/Desktop/Scripts/LgSm-DataProcessing/pedigree/grm.geno.samples.RData")
load("C:/Users/Administrator/Desktop/Scripts/LgSm-DataProcessing/pedigree/genotypedRelatives.RData")
row.names(grm) <- as.numeric(row.names(grm))-0.1
colnames(grm) <- row.names(grm)
names(grm) <- colnames(grm)
asib <- c()
bsib <- c()
for (i in seq_along(sibPairs)) {
asib <- c(asib, as.character(sibPairs[[i]][1]))
bsib <- c(bsib, as.character(sibPairs[[i]][2]))
}
aidx <- c()
bidx <- c()
for (i in 1:length(asib)) {
aidx[[i]] <- which(row.names(grm) == asib[i])
bidx[[i]] <- which(row.names(grm) == bsib[i])
}
sibValues <- c()
for (i in seq_along(aidx)) {
sibValues[[i]] <- grm[aidx[i], bidx[i]]
}
ped.geno <- ped[ped$id %in% geno.samples$id,]
pedsib <- ped.geno[721:1830,]
pedsib$id <- pedsib$id - 0.1
sibgrm <- which(names(grm) %in% as.character(pedsib$id))
sibgrm <- grm[sibgrm,sibgrm]
plot(1:1229, sibgrm[1,])
library(lattice)
cols<- brewer.pal(11, 'PiYG')
test<-colorRampPalette(cols, alpha = TRUE)(50)
levelplot(sibgrm, col.regions=test, cuts=49, cex.axis=0.8,
scales=list(x=list(rot=45)))
# sibships > 2
# 46043 46166 46203 48335 50277 50291 51967 51999 52129
# 63 90 102 137 265 268 436 442 464
# sibPairs[[510]] <- c(48355, 48417)
# sibPairs[[511]] <- c(48358, 48417)
# sibPairs[[512]] <- c(50150, 49217)
# sibPairs[[513]] <- c(50150, 49218)
# sibPairs[[514]] <- c(50150, 50150)
# sibPairs[[515]] <- c(50151, 49217)
# sibPairs[[516]] <- c(50151, 49218)
# sibPairs[[517]] <- c(49255, 49258)
# sibPairs[[518]] <- c(49255, 49234)
# sibPairs[[519]] <- c(49255, 49233)
# sibPairs[[520]] <- c(49233, 49258)
# sibPairs[[521]] <- c(49234, 49258)
# sibPairs[[522]] <- c(50147, 50189)
# sibPairs[[523]] <- c(50148, 50189)
# sibPairs[[524]] <- c(51304, 51295)
# sibPairs[[525]] <- c(51304, 51275)
# sibPairs[[526]] <- c(51313, 51216)
# sibPairs[[527]] <- c(51313, 51209)
# sibPairs[[528]] <- c(54315, 54304)
# sibPairs[[529]] <- c(54315, 54253)
# sibPairs[[530]] <- c(54315, 54244)
# sibPairs[[531]] <- c(54304, 54253)
# sibPairs[[532]] <- c(54304, 54244)
# sibPairs[[533]] <- c(54207, 54190)
# sibPairs[[534]] <- c(54207, 54187)
# sibPairs[[535]] <- c(54872, 54871)
# sibPairs[[536]] <- c(54872, 54817)
# sibPairs[[537]] <- c(54872, 54816)
# sibPairs[[538]] <- c(54871, 54817)
# sibPairs[[539]] <- c(54871, 54816)
#
# which(lapply(sibPairs, length) < 2)
# sibPairs <- sibPairs[-c(162,166,323,328,333)]
|
a8e5ec194b28262117061cb857ce65fde5133c5c
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/fungible/examples/faAlign.Rd.R
|
d93ddf80d96a053dac9df5bf8cde96ebfa6a17a3
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,167
|
r
|
faAlign.Rd.R
|
library(fungible)
### Name: faAlign
### Title: Align the columns of two factor loading matrices
### Aliases: faAlign
### Keywords: fungible Statistics
### ** Examples
# This example demonstrates the computation of
# non-parametric bootstrap confidence intervals
# for rotated factor loadings.
library(GPArotation)
data(HS9Var)
HS9 <- HS9Var[HS9Var$school == "Grant-White",7:15]
# Compute an R matrix for the HSVar9 Mental Abilities Data
R.HS9 <- cor(HS9)
varnames <- c( "vis.per", "cubes",
"lozenges", "paragraph.comp",
"sentence.comp","word.mean",
"speed.add", "speed.count.dots",
"speed.discr")
# Extract and rotate a 3-factor solution
# via unweighted least squares factor extraction
# and oblimin rotation.
NFac <- 3
NVar <- 9
B <- 200 # Number of boostrap samples
NSubj <- nrow(HS9)
# Unrotated 3 factor uls solution
F3.uls <- fals(R = R.HS9, nfactors = NFac)
# Rotate via oblimin
F3.rot <- oblimin(F3.uls$loadings,
gam = 0,
normalize = FALSE)
F3.loadings <- F3.rot$loadings
F3.phi <- F3.rot$Phi
# Reflect factors so that salient loadings are positive
Dsgn <- diag(sign(colSums(F3.loadings^3)))
F3.loadings <- F3.loadings %*% Dsgn
F3.phi <- Dsgn %*% F3.phi %*% Dsgn
rownames(F3.loadings) <- varnames
colnames(F3.loadings) <- paste0("f", 1:3)
colnames(F3.phi) <- rownames(F3.phi) <- paste0("f", 1:3)
cat("\nOblimin rotated factor loadings for 9 Mental Abilities Variables")
print( round(F3.loadings, 2))
cat("\nFactor correlation matrix")
print( round( F3.phi, 2))
# Declare variables to hold bootstrap output
Flist <- Philist <- as.list(rep(0, B))
UniqueMatchVec <- rep(0, B)
rows <- 1:NSubj
# Analyze bootstrap samples and record results
for(i in 1:B){
cat("\nWorking on sample ", i)
set.seed(i)
# Create bootstrap sanples
bsRows <- sample(rows, NSubj, replace= TRUE)
Fuls <- fals(R = cor(HS9[bsRows, ]), nfactors = NFac)
# rotated loadings
Fboot <- oblimin(Fuls$loadings,
gam = 0,
normalize = FALSE)
out <- faAlign(F1 = F3.loadings,
F2 = Fboot$loadings,
MatchMethod = "LS")
Flist[[i]] <- out$F2 # aligned version of Fboot$loadings
UniqueMatchVec[i] <- out$UniqueMatch
}
cat("\nNumber of Unique Matches: ",
100*round(mean(UniqueMatchVec),2),"%\n")
# Make a 3D array from list of matrices
arr <- array( unlist(Flist) , c(NVar, NFac, B) )
# Get quantiles of factor elements over third dimension (samples)
F95 <- apply( arr , 1:2 , quantile, .975 )
F05 <- apply( arr , 1:2 , quantile, .025 )
Fse <- apply( arr , 1:2, sd )
cat("\nUpper Bound 95% CI\n")
print( round(F95,3))
cat("\n\nLower Bound 95% CI\n")
print( round(F05,3))
# plot distribution of bootstrap estimates
# for example element
hist(arr[5,1,], xlim=c(.4,1),
main = "Bootstrap Distribution for F[5,1]",
xlab = "F[5,1]")
print(round (F3.loadings, 2))
cat("\nStandard Errors")
print( round( Fse, 2))
|
186157efc60528b96683c7a0318173d59ccbfebf
|
0d66d47a81f3e54cc30e08f23dfcf0210800bf96
|
/R/buildtrigger_objects.R
|
d80232cee1ecf329b8f080538871f485b3b25b54
|
[] |
no_license
|
cran/googleCloudRunner
|
cda2c85a766dd811b03b3730f17475cb666b60fb
|
ae7eff0dcd74d28cec34bf7f9766fd8a9d5fe62a
|
refs/heads/master
| 2022-03-12T07:08:12.993736
| 2022-02-28T08:40:01
| 2022-02-28T08:40:01
| 238,888,139
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,504
|
r
|
buildtrigger_objects.R
|
#' BuildTrigger Object
#'
#' Configuration for an automated build in response to source repositorychanges.
#'
#' @param substitutions A named list of Build macro variables
#' @param filename Path, from the source root, to a file whose contents is used for the build
#' @param name User assigned name of the trigger
#' @param tags Tags for annotation of a `BuildTrigger`
#' @param build Contents of the build template
#' @param ignoredFiles ignored_files and included_files are file glob matches extended with support for "**".
#' @param github a \link{GitHubEventsConfig} object - mutually exclusive with \code{triggerTemplate}
#' @param includedFiles If any of the files altered in the commit pass the ignored_files
#' @param disabled If true, the trigger will never result in a build
#' @param sourceToBuild A \link{cr_buildtrigger_repo} object (but no regex allowed for branch or tag) This field is currently only used by Webhook, Pub/Sub, Manual, and Cron triggers and is the source of the build will execute upon.
#' @param triggerTemplate a \link{RepoSource} object - mutually exclusive with \code{github}
#' @param description Human-readable description of this trigger
#' @param pubsubConfig PubsubConfig describes the configuration of a trigger that creates a build whenever a Pub/Sub message is published.
#' @param webhookConfig WebhookConfig describes the configuration of a trigger that creates a build whenever a webhook is sent to a trigger's webhook URL.
#'
#' @seealso \url{https://cloud.google.com/build/docs/api/reference/rest/v1/projects.triggers}
#'
#' @return BuildTrigger object
#'
#' @family BuildTrigger functions
#' @export
BuildTrigger <- function(filename = NULL,
name = NULL,
tags = NULL,
build = NULL,
ignoredFiles = NULL,
github = NULL,
substitutions = NULL,
includedFiles = NULL,
disabled = NULL,
sourceToBuild = NULL,
triggerTemplate = NULL,
webhookConfig = NULL,
description = NULL,
pubsubConfig = NULL) {
assert_that(
xor(is.null(build), is.null(filename)),
!is.null(webhookConfig) ||
!is.null(pubsubConfig) ||
xor(is.null(github), is.null(triggerTemplate))
)
if (!is.null(github)) {
assert_that(is.gar_GitHubEventsConfig(github))
}
if (!is.null(triggerTemplate)) {
assert_that(is.gar_RepoSource(triggerTemplate))
}
if (!is.null(pubsubConfig)) {
assert_that(is.gar_pubsubConfig(pubsubConfig))
}
structure(rmNullObs(list(
filename = filename,
name = name,
tags = tags,
build = build,
ignoredFiles = ignoredFiles,
github = github,
substitutions = substitutions,
sourceToBuild = sourceToBuild,
includedFiles = includedFiles,
disabled = disabled,
triggerTemplate = triggerTemplate,
pubsubConfig = pubsubConfig,
webhookConfig = webhookConfig,
description = description
)),
class = c("BuildTrigger", "list")
)
}
is.gar_BuildTrigger <- function(x) {
inherits(x, "BuildTrigger")
}
as.BuildTrigger <- function(x) {
assert_that(is.buildTriggerResponse(x))
BuildTrigger(
filename = x$filename,
name = x$name,
tags = x$tags,
build = x$build,
ignoredFiles = x$ignoredFiles,
github = x$github,
substitutions = x$substitutions,
sourceToBuild = x$sourceToBuild,
includedFiles = x$includedFiles,
disabled = x$disabled,
triggerTemplate = x$triggerTemplate,
description = x$description
)
}
#' Pubsub Config (Build Trigger)
#'
#' PubsubConfig describes the configuration of a trigger that creates a build whenever a Pub/Sub message is published.
#'
#' @param subscription Output only. Name of the subscription.
#' @param topic The name of the topic from which this subscription is receiving messages.
#' @param serviceAccountEmail Service account that will make the push request.
#' @param state Potential issues with the underlying Pub/Sub subscription configuration. Only populated on get requests.
#'
#' @return A PubsubConfig object
#' @seealso `https://cloud.google.com/build/docs/api/reference/rest/v1/projects.locations.triggers#BuildTrigger.PubsubConfig`
#'
#' @export
PubsubConfig <- function(subscription = NULL,
topic = NULL,
serviceAccountEmail = NULL,
state = NULL) {
structure(rmNullObs(
list(
subscription = subscription,
topic = topic,
serviceAccountEmail = serviceAccountEmail,
state = state
)
),
class = c("gar_pubsubConfig", "list")
)
}
is.gar_pubsubConfig <- function(x) {
inherits(x, "gar_pubsubConfig")
}
as.gar_pubsubConfig <- function(x){
PubsubConfig(
subscription = x$subscription,
topic = x$topic,
serviceAccountEmail = x$serviceAccountEmail,
state = x$state
)
}
#' WebhookConfig (Build Triggers)
#'
#' WebhookConfig describes the configuration of a trigger that creates a build whenever a webhook is sent to a trigger's webhook URL.
#'
#' @param state Potential issues with the underlying Pub/Sub subscription configuration. Only populated on get requests.
#' @param secret Resource name for the secret required as a URL parameter.
#'
#' @return A WebhookConfig object
#'
#' @export
WebhookConfig <- function(secret, state = NULL) {
structure(rmNullObs(
list(
secret = secret,
state = state
)
),
class = c("gar_webhookConfig", "list")
)
}
is.gar_webhookConfig <- function(x) {
inherits(x, "gar_webhookConfig")
}
#' GitRepoSource
#' Used for PubSub triggers
#' @noRd
GitRepoSource <- function(uri,
ref,
repoType = c("GITHUB","CLOUD_SOURCE_REPOSITORIES")){
assert_that(
is.string(uri),
is.string(ref),
isTRUE(grepl("^refs/", ref)),
!grepl("[^A-Za-z0-9/.]", ref) # regex not allowed
)
repoType <- match.arg(repoType)
structure(
list(
uri = uri,
ref = ref,
repoType = repoType
),
class = c("gar_gitRepoSource","list")
)
}
is.gitRepoSource <- function(x){
inherits(x, "gar_gitRepoSource")
}
as.gitRepoSource <- function(x){
if(!is.buildtrigger_repo(x)){
stop("is not buildtrigger_repo")
}
if(is.gar_GitHubEventsConfig(x$repo)){
if(!is.null(x$repo$push$tag)){
ref <- paste0("refs/tags/", x$repo$push$tag)
} else if(!is.null(x$repo$push$branch)){
ref <- paste0("refs/heads/", x$repo$push$branch)
} else {
stop("No refs/ found", call. = FALSE)
}
return(
GitRepoSource(
uri = sprintf("https://github.com/%s/%s",
x$repo$owner, x$repo$name),
ref = ref,
repoType = "GITHUB"
)
)
}
if(is.gar_RepoSource(x$repo)){
if(!is.null(x$repo$tagName)){
ref <- paste0("refs/tags/", x$repo$tagName)
} else if(!is.null(x$repo$branchName)){
ref <- paste0("refs/heads/", x$repo$branchName)
} else {
stop("No refs/ found", call. = FALSE)
}
return(
GitRepoSource(
uri = sprintf("https://source.developers.google.com/p/%s/r/%s",
x$repo$projectId, x$repo$repoName),
ref = ref,
repoType = "CLOUD_SOURCE_REPOSITORIES"
)
)
}
stop("Could not convert object via as.gitRepoSource")
}
|
b04feb6caa8e9fb04be331233fa7779661d4214f
|
b88a25004b23282d65b5683c177eac9cc4df0638
|
/00_30diasDeGraficos/05_arco/05_arco.R
|
ba5ceb1a17e4699548a927edfd5cf6652af71cfe
|
[] |
no_license
|
AnguloB/datosdemiercoles
|
8eb140f3cf4e8bcd658063628c12647d367eb1e2
|
2ef5716544136faa0ab56a348fc44f6dec179171
|
refs/heads/master
| 2022-11-21T13:21:59.329486
| 2020-09-03T11:49:04
| 2020-09-03T11:49:04
| 241,910,703
| 14
| 4
| null | 2022-11-07T16:34:02
| 2020-02-20T14:52:29
|
HTML
|
UTF-8
|
R
| false
| false
| 4,063
|
r
|
05_arco.R
|
library(tidyverse)
# dia 4 facetas
#Datos del SIDC Cat (link directo en el script )
backcolor<-"white"
colortext<-"black"
#Defino paleta de colores
palette30<- c("#FD7FC4", "#FF8A33", "#EC4176", "#A13770" , "#33DDFF", "#FFEC33", "#5564eb", "#4c2882")
#Eligo la fuente que quiero utilizar
library(extrafont) # la primera vez ejecutar font_import()
loadfonts()
font<- "Trebuchet MS" #Fuente que voy a utlizar
library(readr)
Citaciones <- read_csv("Citaciones.csv")
#selecciono solo las variables que me interesan
Citaciones%>%
select(Authors, Title) ->data
data<-data%>%separate_rows(Authors, sep = ",") #Separo por coma los autores en cada linea
data<-data[seq(1,nrow(data),2) ,] #me quedo solo con los pares
data$Authors<-str_trim(data$Authors) #saco espacios en blanco
data<-data%>%
group_by(Title) %>%
mutate(titleid=factor(group_indices())) #cambio el titulo por un ID
data<-data[,c("titleid","Authors")]
library(stringi)
data$Authors<-stri_trans_general(data$Authors, "Latin-ASCII")
totals<-data%>% #Creo el total de articulos de cada
group_by(Authors)%>%
count()%>%
arrange(desc(n))
names(totals)<-c("from", "totalreal")
# transformo los datos de forma que haya la correspondencia entre autores
dta <- full_join(data, data, c('titleid' = 'titleid')) %>%
select(-titleid) %>%
filter(Authors.x != Authors.y) %>%
group_by(Authors.x, Authors.y) %>%
summarise(total = n())
names(dta)<- c("from", "to", "total")
dta<-dta%>%
left_join(totals)%>%
select(from, to, totalreal)
library(ggraph)
palette30 <- c("grey60","#FFEC33","#33DDFF","#EC4176","#FF8A33","#5564eb")
p1<-ggraph(dta, 'linear') +
geom_edge_arc(aes(color=factor(totalreal), alpha=factor(totalreal)), fold=FALSE)+theme_bw()+
geom_node_point(size=2,alpha=0.5) +
scale_edge_colour_manual(values=palette30)+
theme(text=element_text(family = font),
plot.background = element_rect(fill = "white", color=NA),
strip.background =element_blank(),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.ticks = element_blank(),
legend.background = element_rect(fill=backcolor,
size=0.5, linetype="solid"),
plot.title = element_text(size=20, hjust=0,face="bold", color="#9B77CF"),
plot.subtitle = element_text(face="italic", size=12),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
legend.position = "none")+
labs(title= "Día 5: arco",
fill="",
subtitle = "A journey around alpha and omega to estimate internal consistency reliability: \n
autores y autoras que han citado el articulo y relación entre ellos",
y = "",
x = "")+
expand_limits(x = c(-1.2, 1.2), y = c(-5.6, 1.2))
p2<-totals%>%
group_by(totalreal)%>%
count()%>%
ggplot(aes(x=factor(totalreal), y=n, fill=factor(totalreal)))+
geom_col(aes( alpha=factor(totalreal)))+
geom_text(aes(label=paste0("N = ",n), hjust=-.25))+
scale_fill_manual(values=palette30)+
coord_flip()+theme_bw()+
theme(text=element_text(family = font, color="#9B77CF"),
plot.background = element_rect(fill = "white", color=NA),
strip.background =element_blank(),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
legend.position = "none",
plot.caption = element_text( face="italic", size=10, hjust = 1, color="black"))+
labs(title ="",
subtitle="\n \n",
caption = "Viladrich, Angulo-Brunet y Doval (2017) \n Hecho por @AnguloBrunet \n #30díasdegráficos \n Fuente: Scopus 15 mayo 2020",
y="Autores",
x="Nº articulos")+
scale_y_continuous(position = "right", limits=c(0,180))
library(cowplot)
plot_grid(p1, p2, nrow=1, rel_widths = c(0.8, .2))
ggsave("05_arco.png", height = 5.89, width=8.58)
|
1061e4eb8e832915d9065fa1a5ece018e3760252
|
c4514cfc6f5e132de24709ccebf057007714ef5e
|
/dsx_project/all_data.r
|
1b0df0ce5d7b232d19a69c4ee9eb7eb6230c75a2
|
[] |
no_license
|
mmaccoun/dsx_project-
|
af53b58035226973ff5a439eac4dd538d0f31aa3
|
82e85ad4c1ff315bc371a95adae56508d4055f1f
|
refs/heads/master
| 2020-04-24T00:22:24.884499
| 2019-03-06T22:08:57
| 2019-03-06T22:08:57
| 171,564,285
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 886
|
r
|
all_data.r
|
setwd("~/Desktop/mmaccoun/dsx_project--master/master_files") #move all_M_data.csv and all_F_data.csv to directory to work in
male <- read.csv('all_M_data.csv')
male$sex <- "M"
female <- read.csv('all_F_data.csv')
female$sex <- "F"
dat <- rbind(male, female)
dat$sample<- as.character(dat$sample)
dat$rep <- gsub(".*_", "", dat$sample)
dat$strain <- gsub("_.*", "", dat$sample)
str(dat)
names(dat) <- c(' ', 'exonic region id', 'sample id', 'number of mapped reads', 'number of reads in the exonic region', 'coverage in exonic region (number of reads in exonic region / read length)', 'exonic region length','average per nucleotide coverage,','RPKM', 'sample','sex', 'rep', 'strain')
#write.csv(dat,'all_dat.csv')
hist(dat$RPKM) #hist of RPKM data
# hist(log(dat$RPKM)) #adj for bellc
dat$logRPKM = log(dat$RPKM)
# shapiro.test(log(dat$RPKM)) test to bellcurve
|
985c42c7df16c1fe37441b903c27095e9a2266a0
|
37a31c0ed3873b42397033cc218f381b887d91e9
|
/BNK48-Analysis.r
|
30ff629936241bad9dd900b3a43e125ee13dcee9
|
[] |
no_license
|
lebrancconvas/R-Analysis
|
ef48b443e8f740fb4311cf576b78251c1b897317
|
9358388c482ecbb17f2fe571a126ffa1d7847e3a
|
refs/heads/master
| 2020-03-11T10:27:27.225560
| 2018-04-18T17:45:50
| 2018-04-18T17:45:50
| 129,943,284
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 175
|
r
|
BNK48-Analysis.r
|
install.packages("tuber")
library(tuber)
yt_oauth(app id = "762981940110-r663ca3n6onh4vvlas4mtcnofrcubc0c.apps.googleusercontent.com", app_secret = "dDaYjoXgc6JRArETwQXq5e_2")
|
eba9edba64af61f2d8ca0344ec13f81111402bde
|
89326a15b8f6506ed5b75feda9d8857980efcd4d
|
/discreteRV.R
|
5fa6d75d2ee9d480773a55ccc9207f4d2ac5a163
|
[
"MIT"
] |
permissive
|
andrei-micuda/ShinyRV
|
f6bd8ff69f4caa4a9c971b955d502e5b576489e5
|
54707bdad4e1f1c064814baa873c87c47f1e1a47
|
refs/heads/main
| 2023-03-02T07:00:05.945757
| 2021-02-07T16:20:59
| 2021-02-07T16:20:59
| 328,680,559
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 807
|
r
|
discreteRV.R
|
validate_probability <- function(v, p) {
values <- array(v)
probs <- array(p)
print(sum(probs))
#if(!is.array(values) && !is.array(probs))
# stop("values are not arrays.")
if(length(values) != length(probs))
stop("values do not have the same length")
if(abs(1 - sum(probs)) > 0.000001)
stop("probabilites must add up to 1")
if(length(unique(values)) != length(values))
stop("all values must occur only once")
random_variable <- matrix(data=c(values,probs), nrow=2, byrow=TRUE)
rownames(random_variable) <- c('Outcomes', 'Probs')
random_variable
}
applyFunction <- function(m, f) {
m[1,] = f(m[1,])
m
}
getMean <- function(m) {
sum(m[2,] * m[1,])
}
getVariance <- function(m) {
getMean(applyFunction(m, function(x) x**2)) - getMean(m) ** 2
}
|
c30c92c14852dd2d5beddedfe5143cb99183a0b4
|
fff814356f63080b30cb2d47f3d6297c5b9841d5
|
/kmedoids.R
|
f6d6efcdcbe4107cfcd799b819d0f939d4ebe9e7
|
[] |
no_license
|
dasasathyan/datamining
|
117526e3416646ae6713cc52a10bae417b61c6bb
|
6c3878bf391235f501301d206ea7fb73cb6e0ab7
|
refs/heads/master
| 2021-05-10T13:13:38.098096
| 2018-01-22T14:30:51
| 2018-01-22T14:30:51
| 118,467,921
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 369
|
r
|
kmedoids.R
|
library(fpc)
pam.result <- pam(IrisDataset1,3)
table(pam.result$clustering, IrisDataset1$Class)
layout(matrix(c(1,2),1,2)) # 2 graphs per page
plot(pam.result)
layout(matrix(1)) # change back to one graph per page
plot(pam.result)
return()
return()
pam.result2 <- pam(IrisDataset1,4)
table(pam.result2$clustering, IrisDataset1$Class)
plot(pam.result2)
return()
return()
|
76c00cbade342ce7cc260cf55bcd95a696fc2925
|
d490fa1324d85e5e5c0a327358c09c9a5da969e8
|
/R/select_data.R
|
e9bae3e6bc788a4b45b569e17065afaab94eecde
|
[] |
no_license
|
HaikunXu/IATTCassessment
|
63e990425800bd0f3e2ae8b193e21cda597d0624
|
cb584cf47f2a22a616aaacd71a92542fe85e6dbd
|
refs/heads/master
| 2023-07-19T15:18:17.521122
| 2023-07-10T21:09:50
| 2023-07-10T21:09:50
| 191,084,703
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,330
|
r
|
select_data.R
|
#' Generate area code for longline
#'
#' \code{data_selection} This function filter the data for LL CPUE standardization
#'
#' @param c1 minimal sets by vessel
#' @param c2 minimal quarters by vessel
#' @param c3 minimal sets by grid
#' @param c4 minimal quarters by grid
#' @export
# Data selection criterion for JPN operational LL data in the EPO
# Inspired by http://www.iotc.org/sites/default/files/documents/2018/10/IOTC-2018-WPM09-12_Rev1.pdf
# Haikun Xu; 1/30/2019
select_data <- function(data,c1,c2,c3,c4) {
# Generate the quantities for data selection
Data <- data %>%
mutate(year=ceiling(Year/4)+1974) %>%
group_by(Vessel) %>% mutate(setsbyvessel=n(), # total number of sets per vessel
quartersbyvessel=length(unique(Year))) %>% # total number of quarters fished per vessel
group_by(Lat,Lon) %>% mutate(setsbygrid=n(), # total number of sets per grid cell
quartersbygrid=length(unique(Year))) # # total number of quarters per grid cell
# Data selection
data_selected <- Data %>% filter(setsbyvessel>=c1, # select vessel
quartersbyvessel>=c2, # select vessel
setsbygrid>=c3, # select grid
quartersbygrid>=c4) # select grid
return(data_selected)
}
|
cef5c81de6c8a296a57f3c26d9ca2571a7156479
|
320e9a7c8d2f6d0aa3b009b8ba599a59745bdd6b
|
/man/numUniqueValues.Rd
|
033fea99f4e21138225b192a97fd0edf27698d70
|
[] |
no_license
|
AlejandroKantor/akmisc
|
acaeeabd35bed31f6c8c9a033427ab6c5bb23a5f
|
5af7e9cd40edda6530f22aab5f3d87703041fa3d
|
refs/heads/master
| 2020-07-19T05:46:37.635593
| 2017-10-20T00:57:53
| 2017-10-20T00:57:53
| 73,770,909
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 523
|
rd
|
numUniqueValues.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/numUniqueValues.R
\name{numUniqueValues}
\alias{numUniqueValues}
\title{Gives the number of unique values of a vector}
\usage{
numUniqueValues(v_values)
}
\arguments{
\item{v_values}{a vector.}
}
\value{
An integer of the number of unique que values in v_values. If v_value has length zero returns 0.
}
\description{
Gives the number of unique values of a vector
}
\examples{
numUniqueValues(1:10)
numUniqueValues(c("a","a","b","c","c","c"))
}
|
13c9ed6f963fe7ee665464b16ebdab35150afe89
|
7e5dfa53a52b3aad29a22ebb12916a41a0e62e6b
|
/R/ic.icc.R
|
67e13c65b892eb9b2c9e49d94b40f0b8181cd758
|
[] |
no_license
|
cran/cccrm
|
3a12b3fd0f834c302006e691d37547d9d41b9f79
|
302e8dc1f85494f28ac3b39c52e5c2f3de9c0136
|
refs/heads/master
| 2022-09-14T18:01:05.004119
| 2022-08-31T16:30:02
| 2022-08-31T16:30:02
| 17,695,003
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 405
|
r
|
ic.icc.R
|
ic.icc <-
function(ccc,dev,S,alpha,m){
se.ccc <- as.numeric(sqrt(dev%*%S%*%t(dev)))
z<-ZF(ccc,m)
se.z<-dZF(ccc,m)*se.ccc
ic.z=z+c(-1,1)*qnorm(1-alpha/2)*se.z
ic.icc=(exp(2*ic.z)-1)/(exp(2*ic.z)+1)
result<-c(ccc,ic.icc,se.ccc,z,se.z)
conf.lab=paste((1-alpha)*100,"%",sep="")
names(result)<-c("ICC",paste("LL CI",conf.lab),paste("UL CI",conf.lab),"SE ICC","Z","SE Z")
return(result)
}
|
52cc64080d140d7461af4769e3d843a6d7b48c43
|
99ffac052ed1a630a9572b166d0b1f8f1f27fa63
|
/Scripts/StatFunctions/rN.R
|
47dfff39058beb76bee985815ad2d579bcb74dc1
|
[] |
no_license
|
rBatt/trawl
|
a253ed1cf4912c2dbf6c7599292283610f4b28a6
|
0d11503d900ddc5bf6777fbebcf01f585d6d264f
|
refs/heads/master
| 2021-06-04T03:50:35.595206
| 2016-07-13T02:29:42
| 2016-07-13T02:29:42
| 25,880,485
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 161
|
r
|
rN.R
|
# http://stackoverflow.com/questions/8664976/r-round-to-nearest-5-or-1/8665511#8665511
rN <- function(a,b){ # round a to the nearest decimal (b)
round(a/b)*b
}
|
991744a194662259f3e067a1d11f858015ea3f2d
|
a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3
|
/B_analysts_sources_github/topepo/R-Ladies-London/Scat.R
|
536e6515a81122bd8185bb5f8f12bd73513323c7
|
[] |
no_license
|
Irbis3/crantasticScrapper
|
6b6d7596344115343cfd934d3902b85fbfdd7295
|
7ec91721565ae7c9e2d0e098598ed86e29375567
|
refs/heads/master
| 2020-03-09T04:03:51.955742
| 2018-04-16T09:41:39
| 2018-04-16T09:41:39
| 128,578,890
| 5
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,199
|
r
|
Scat.R
|
#####################################################################
## Code for "Whose Scat Is That? An 'Easily Digestible' Introduction
## to Predictive Modeling in R and the caret Package" for the
## R-Ladies London RUG. Files are at
## https://github.com/topepo/R-Ladies-London
library(ggplot2)
theme_set(theme_bw())
plot_col <- scale_color_manual(values=c("#0072B2", "#D55E00", "#E69F00",
"#56B4E9", "#009E73", "#F0E442",
"#000000", "#CC79A7"))
opts_chunk$set(comment=NA,
digits = 3,
size = 'scriptsize',
prompt = TRUE,
cache = FALSE,
tidy = FALSE,
message = FALSE,
warning = FALSE,
fig.path = "Figures/",
background = 'white')
## A trick to make sure in-line code is formatted the same as in chunks
hook_inline = knit_hooks$get('inline')
knit_hooks$set(inline = function(x) {
if (is.character(x)) highr::hi_latex(x) else hook_inline(x)
})
###################################################################
## Slide 5: Load Data
library(caret)
data(scat)
str(scat)
###################################################################
## Slide 6: Missing Data Profile
pct_nonmissing <- function(x) mean(!is.na(x))
unlist(lapply(scat, pct_nonmissing))
###################################################################
## Slide 8: Split the Data
set.seed(11218)
in_train <- createDataPartition(scat$Species, p = 3/4, list = FALSE)
head(in_train)
train_data <- scat[ in_train,]
test_data <- scat[-in_train,]
## It isn't much data but it's better than nothing...
table(test_data$Species)
###################################################################
## Slide 9: Interaction Plot Code
int_plot <- function(dat, y, x, group) {
library(plyr)
if(!is.factor(dat[,group])) dat[,group] <- factor(dat[,group])
means <- ddply(dat, c(y, group),
function(obj) c(mean = mean(obj[,x], na.rm = TRUE)))
ggplot(dat,
aes_string(y = x, x = y, color = group, group = group)) +
geom_point(position = position_dodge(width = 0.2)) +
geom_line(data = means, aes_string(y = "mean")) +
theme(legend.position = "top")
}
###################################################################
## Slides 10-15
int_plot(train_data, y = "Species", x = "Mass", group = "ropey")
int_plot(train_data, y = "Species", x = "Mass", group = "Location")
int_plot(train_data, y = "Species", x = "Mass", group = "segmented")
int_plot(train_data, y = "Species", x = "Length", group = "Location")
int_plot(train_data, y = "Species", x = "Length", group = "ropey")
int_plot(train_data, y = "Species", x = "CN", group = "ropey")
###################################################################
## Slide 21: An Initial Model Specification
small_form <- paste("Species ~ Month + Year + Site + Age +",
"Number + Length*ropey + (Location + segmented)*Mass + ",
"flat + scrape")
small_form <- as.formula(small_form)
small_tr_dat <- train_data[, all.vars(small_form)]
small_tr_dat <- small_tr_dat[complete.cases(small_tr_dat),]
###################################################################
## Slide 29: Multinomial Model
ctrl <- trainControl(method = "repeatedcv", repeats = 5, classProbs = TRUE)
set.seed(2592) ## locks in the resamples
mnr_tune <- train(small_form, data = small_tr_dat,
method = "multinom",
preProc = c("center", "scale"),
## avoid regularization for now
tuneGrid = data.frame(decay = 0),
trControl = ctrl,
## this next argument is passed to `multinom`
trace = FALSE)
###################################################################
## Slide 30: Multinomial Model
mnr_tune
predict(mnr_tune, head(test_data)) ## or type = "prob"
###################################################################
## Slide 31: Multinomial Model Variable Importance
print(varImp(mnr_tune, scale = FALSE), top = 10)
###################################################################
## Slide 34: Multinomial Model -- All Data
full_form <- paste("Species ~ Month + Year + Site + Age + Number + ",
"Length*ropey + (Location + segmented)*Mass + ",
"flat + scrape + ",
"TI + d13C + d15N + CN + Diameter + Taper")
full_form <- as.formula(full_form)
set.seed(2592)
mnr_impute <- train(full_form, data = train_data,
method = "multinom",
## Add imputation to the list of pre-processing steps
preProc = c("center", "scale", "knnImpute", "zv"),
tuneGrid = data.frame(decay = 0),
trControl = ctrl,
## do not remove missing data before modeling
na.action = na.pass,
trace = FALSE)
mnr_impute
###################################################################
## Slide 36: Multinomial Model -- All Data Variable Importance
print(varImp(mnr_impute, scale = FALSE), top = 10)
###################################################################
## Slide 37: Resampled Confusion Matrix
confusionMatrix(mnr_tune)
###################################################################
## Slide 44: Model Tuning Grid
glmn_grid <- expand.grid(alpha = c(0.05, seq(.1, 1, by = 0.025)),
lambda = c(.001, .01, .1))
nrow(glmn_grid)
###################################################################
## Slide 45: Model Tuning
set.seed(2592) ## use the same resamples as mnr_impute
glmn_tune <- train(full_form, data = train_data,
method = "glmnet",
preProc = c("center", "scale", "knnImpute", "zv"),
## pass in the tuning grid
tuneGrid = glmn_grid,
## pick the sub-model with the best kappa value
metric = "Kappa",
na.action = na.pass,
trControl = ctrl)
## best sub-model results:
glmn_tune$bestTune
getTrainPerf(glmn_tune)
###################################################################
## Slide 46: Model Tuning Plot
theme_set(theme_bw())
ggplot(glmn_tune) + theme(legend.position = "top")
###################################################################
## Slide 47: Resampled Confusion Matrix
confusionMatrix(glmn_tune)
###################################################################
## Slide 49: Model Comparison
compare_models(glmn_tune, mnr_impute, metric = "Kappa")
###################################################################
## Slide 50: Fitting Other Models
set.seed(2592)
bagged_tree <- train(Species ~ ., data = train_data,
method = "treebag",
metric = "Kappa",
na.action = na.pass,
trControl = ctrl)
getTrainPerf(bagged_tree)
###################################################################
## Slide 51: Fitting Other Models
set.seed(2592)
knn_tune <- train(Species ~ ., data = train_data,
method = "knn",
preProc = c("center", "scale", "knnImpute", "zv"),
## pass in the tuning grid _size_
tuneLength = 20,
metric = "Kappa",
na.action = na.pass,
trControl = ctrl)
getTrainPerf(knn_tune)
###################################################################
## Slide 52: Preprocessing Too
set.seed(2592)
transformed <- train(full_form, data = train_data,
method = "glmnet",
## Also transform the predictors
preProc = c("center", "scale", "knnImpute",
"zv", "YeoJohnson"),
tuneGrid = glmn_grid,
metric = "Kappa",
na.action = na.pass,
trControl = ctrl)
getTrainPerf(transformed)
###################################################################
## Slide 53: Collecting the Results
rs <- resamples(list(knn = knn_tune, bagged = bagged_tree,
multinomial = mnr_impute, glmnet = glmn_tune,
"glmnet + trans" = transformed))
summary(rs, metric = "Kappa")
###################################################################
## Slide 54: Resampling Distributions
bwplot(rs, metric = "Kappa")
###################################################################
## Slide 55: Resampling Distributions
dotplot(rs, metric = "Kappa")
###################################################################
## Slide 56: Test Set Results
test_pred <- predict(glmn_tune, newdata = test_data, na.action = na.pass)
str(test_pred)
test_prob <- predict(glmn_tune, newdata = test_data,
na.action = na.pass, type = "prob")
str(test_prob)
###################################################################
## Slide 57: Test Set Results
confusionMatrix(test_pred, test_data$Species)
|
f5026524d3737b35f0e9e01ac444fa887a5450f6
|
13756dddacf2e8c3c3355ad32adcf94700576a8e
|
/R/processLandsat.R
|
c96cf59da1cf93486ea9258800161368db0c338f
|
[
"Apache-2.0"
] |
permissive
|
mengluchu/bfastspatial2
|
6e96b2b2af96448626b03cdffc584e28aa95936e
|
1254e661e33404d8b2ad3e5d830f864204b33a4f
|
refs/heads/master
| 2016-09-06T00:06:39.757295
| 2014-12-10T09:51:48
| 2014-12-10T09:51:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,554
|
r
|
processLandsat.R
|
#' @title Wrapper function to process Landsat data
#'
#' @description Processes a single Landsat scene, from tarball or zip archive (or hdf/tiff if untar is set to FALSE) to vegetation index. Easy to batch using sapply or mclapply for parallel implementation. Data obtained from espi may already contained pre-processed indices layers, in which case they are directly used.
#' @param x Character. filename of the tarball or zip archive of the hdf/tiff file.
#' @param vi Character. Vegetation index to be computed or extracted from the archive. Can be either 'ndvi', 'evi', 'savi', 'ndmi'*, 'nbr', 'nbr2'* or 'msavi'*. Indices with * need to be present in the archive.
#' @param srdir Character. Directory where the tarball should be uncompressed. Can be ommited if \code{untar} is set to \code{FALSE}
#' @param outdir Character. Directory where the vegetation index rasterLayer should be written.
#' @param untar Logical. Is there a need to untar data, or have they been previously unpacked.
#' @param delete Logical. Should surface reflectance files (hdf/tiff) be deleted after vegetation index calculated? (usefull for disk space management; surface reflectance files are very voluminous and a user may want to keep the Landsat archive in compressed format only)
#' @param mask Character or NULL. The name of the mask to be applied to the bands (e.g.: \code{mask = 'fmask'})
#' @param L Numeric. Soil-adjustment factor for SAVI (ignored if vi != 'savi'). L can take on values between 0 and 1, and a default of 0.5 is typically used.
#' @param ... Arguments to be passed to \link{sr2vi}. Do not specify \code{filename} since it is automatically generated
#' @author Loic Dutrieux
#' @return rasterLayer Object also written to file (in \code{outdir}) with an automatically generated filename
#' @seealso \link{sr2vi}
#'
#' @import stringr
#' @import raster
#' @import rgdal
#' @import gdalUtils
#'
#' @examples
#' # Get list of test data files
#' dir <- system.file('external', package='bfastSpatial')
#' list <- list.files(dir, full.names=TRUE)
#'
#' # Set the location of output and intermediary directories (everything in tmpdir in that case)
#' srdir <- dirout <- file.path(rasterOptions()$tmpdir, 'bfmspatial')
#' dir.create(dirout, showWarning=FALSE)
#' # Generate (or extract, depending on whether the layer is already in the archive or not) NDVI for the first archive file
#' processLandsat(x=list[1], vi='ndvi', outdir=dirout, srdir=srdir, delete=TRUE, mask='fmask', keep=0, overwrite=TRUE)
#'
#' # Visualize one of the layers produced
#' list <- list.files(dirout, pattern=glob2rx('*.grd'), full.names=TRUE)
#' plot(r <- raster(list[1]))
#'
#' @export
#'
processLandsat <- function(x, vi='ndvi', srdir, outdir, untar=TRUE, delete=FALSE, mask=NULL, L=0.5, ...) {
# x is the full path of a tarball containing the Landsat data or the path of a hdf file
# hdf dir is where the hdf files are extracted
# Output layers (NDVI for example) are generated in outdir
# ... arguments to be passed to hdf2ndvi (filename is automatically generated and therefore does not need to be passed)
# Although x can be a zip archive, Names are untar, tarlist, etc, since the function was first developped to deal with tar.gz compressed Landsat data
if(untar){
ex <- extension(x)
if(ex == '.gz') {
tarlist <- untar(x, list=TRUE)
} else if(ex == '.zip') {
tarlist <- unzip(x, list=TRUE)$Name
} else {
stop('The archive is neither tar.gz nor .zip; we don\'t know what to do with that.')
}
if(any(grepl(pattern="^.*\\.hdf$", x=tarlist))) { # are there any hdf files
x0 <- grep(pattern="^.*\\.hdf$", x=tarlist, value=TRUE)
} else if (any(grepl(pattern="^.*\\.tif$", x=tarlist))) { # Contains tiff
if(any(grepl(pattern=sprintf("^.*%s\\.tif$", vi), x=tarlist))) { # Contains pre-processed vi
x0 <- grep(pattern=sprintf("^.*%s\\.tif$", vi), x=tarlist, value=TRUE)
} else { # extract the bands needed to process vi
# Get viFormula object (in order to know which bands to extract)
if(vi == 'ndvi') {
viFormula <- .ndvi()
} else if(vi == 'evi') {
viFormula <- .evi()
} else if(vi == 'nbr') {
viFormula <- .nbr()
} else if(vi == 'savi') {
viFormula <- .savi(L=L)
} else {
stop("Unsupported vi")
}
x0 <- grep(pattern=sprintf("^.*(%s)\\.tif$", paste(viFormula$ind, collapse='|')), x=tarlist, value=TRUE)
}
} else {
stop("Did not find any .tif or .hdf files in the archive")
}
if (!is.null(mask)) {
x0 <- c(x0, grep(pattern=sprintf("^.*%s\\.tif$", mask), x=tarlist, value=TRUE))
}
if(ex == '.gz') {
untar(x, files=x0, exdir=srdir)
} else if(ex == '.zip') {
unzip(x, files=x0, exdir=srdir)
}
x <- file.path(srdir, x0)
}
name <- str_extract(string=basename(x[1]), '(LT4|LT5|LE7|LC8)\\d{13}')
# Filename generation (below) will have to be edited when dynamic indices will be implemented
# Also note that in case of geotiff length(x)>1
sr2vi(x=x, vi=vi, filename=sprintf('%s/%s.%s.grd', outdir, vi, name), datatype='INT2S', mask=mask, ...)
if(delete) {
file.remove(x)
}
}
|
510bf43f4fd4ca924d9ea3384c45ced5c4827e89
|
3bdea023069b55b7aa2c7172e124c6e02bfeacda
|
/200+ beamer 模板合集/awesome-beamers(18个主题合集)/proposal thesis presentation/code/code 1/gambia地图.R
|
52349f774fd6f43232588f59754c54c5527b672c
|
[
"MIT"
] |
permissive
|
is-xiaoyx/Awesome-Beamer-Collection
|
2df82009c18ba6c36f15ec6a58fbca82a9b0624d
|
3ab28a23fb60cb0a97fcec883847e2d8728b98c0
|
refs/heads/master
| 2022-02-13T08:10:09.167023
| 2019-07-22T10:00:38
| 2019-07-22T10:00:38
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,248
|
r
|
gambia地图.R
|
library(maps)
library(mapdata)
library(ggplot2)
ga <- ggplot2::map_data('worldHires', 'gambia')
class(ga)
ggplot(ga, aes(x = long, y = lat, group = group)) +
geom_polygon()
library(ggfortify)
ga <- map('worldHires', 'gambia', plot = FALSE, fill = TRUE)
autoplot(ga)
################
p <- autoplot(ga, geom = 'polygon', fill = 'subregion') +
theme(legend.position="none")
p
cities <- get('world.cities')
cities <- cities[cities$country.etc == 'Gambia', ]
head(cities)
p + geom_point(data = cities, aes(x = long, y = lat),
colour = 'blue', size = 0.1)
library(sp)
df <- data.frame(long = c(139.691704, 135.519711),
lat = c(35.689521, 34.686316),
label = c('Tokyo', 'Osaka'),
population = c(1335, 886))
coordinates(df) <- ~ long + lat
class(df)
library(ggmap)
bbox <- c(130.0, 30.0, 145.0, 45.0)
map <- get_openstreetmap(bbox = bbox, scale = 475000)
p <- ggmap(map)
autoplot(df, p = p, colour = 'red', size = 'population') +
scale_size_area() +
theme(legend.justification = c(1, 0), legend.position = c(1, 0))
##
library(ggmap)
bbox <- c(-17, 13, -13.5,14)
map <- get_openstreetmap(bbox = bbox, scale = 475000)
p <- ggmap(map)
|
966e67010fcd3cb436ca76da53bdc07edcd0ff1c
|
f0258ba2add23ceff662478182f91545a880f6f4
|
/DuguTogo/server.R
|
13f3600bbcef3bd8e9034815949ac5a7599f66b5
|
[] |
no_license
|
mcooper/village-names
|
cb3e46cfe4e55f24863cbca607e18441eac9e372
|
329a2ddeac54dd2c58c5d9244f9e273607212775
|
refs/heads/master
| 2021-06-27T17:02:44.609077
| 2021-01-26T18:39:06
| 2021-01-26T18:39:06
| 64,983,725
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 785
|
r
|
server.R
|
library(maps)
library(mapdata)
library(stringr)
vills <- readRDS('data/vills.Rda')
villsearchpoints <- function(str,norm){
map(database='world',regions=c('Senegal','Guinea-Bissau','Guinea','Liberia','Sierra Leone','Ivory Coast','Niger','Mauritania','Burkina Faso','Mali','Gambia','Ghana','Benin','Togo'),xlim=c(-18,16),ylim=c(4,27))
if (norm==FALSE){
points(vills$longitude[str_detect(vills$asciiname,str)],vills$latitude[str_detect(vills$asciiname,str)],pch=19,col="red",cex=.25)
}
if (norm==TRUE){
points(vills$longitude[str_detect(vills$new,str)],vills$latitude[str_detect(vills$new,str)],pch=19,col="red",cex=.25)
}
}
shinyServer(
function(input, output) {
output$map <- renderPlot({
villsearchpoints(toupper(input$str),input$norm)
})
}
)
|
8628452d26df6c27b7e58cc4abab2c6731cc5246
|
8cbc16b08945e437eab544222356c451aaf80d8f
|
/analysis/Fig5c-e-bulk-analyses.R
|
312a651faa681b63f098a4d01c045a182c802a39
|
[
"MIT"
] |
permissive
|
gladelephant/singlecellglioma-verhaaklab
|
f18b6b77596ba9adc5696f0d3224c470d6d0ddbe
|
b785e86484e826d94bd9c9ac1e88cead624f1762
|
refs/heads/master
| 2023-08-19T08:27:28.521716
| 2021-10-01T18:03:46
| 2021-10-01T18:03:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,849
|
r
|
Fig5c-e-bulk-analyses.R
|
##################################
# Investigate relationships between promoter DNAme and other variables (Klughammer et al. Nature Medicine).
# Updated: 2021.05.12
# Author: Kevin J.
###################################
# Working directory for this analysis.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
###################################
# Necessary packages:
library(tidyverse)
library(DBI)
library(ggpubr)
library(openxlsx)
library(survminer)
library(survival)
library(EnvStats)
###################################
## Generate plot theme.
plot_theme <- theme_bw(base_size = 12) + theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
panel.background = element_rect(fill = "transparent"),
axis.line = element_blank(),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line.x = element_line(size = 0.5, linetype = "solid", colour = "black"),
axis.line.y = element_line(size = 0.5, linetype = "solid", colour = "black"))
## Retrieve the chromosome arm-data for SCNA value estimates from database.
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB3")
chr_arms = dbReadTable(con, Id(schema="ref", table="chr_arms"))
## Load the metadata available from Klughammer (http://www.medical-epigenomics.org/papers/GBMatch/).
klughammer_dat_all <- read.delim("data/GBMatch_sampleAnnotation.tsv", sep="\t", header=T, stringsAsFactors = F)
klughammer_dict <- read.delim("data/GBMatch_columnAnnotation.tsv", sep="\t", header=T, stringsAsFactors = F)
# Restrict to tumor tissue by removing non-tumor tissue from epilepsy patients. Add some additional filtering criteria.
klughammer_dat = klughammer_dat_all %>%
# A few tumors do not have IDH status and a single sample appears to have an DNAme disorder of 0.
filter(tissue != "White matter", mean_pdr > 0, !is.na(IDH))
# "IDH" status separates based on IDHmut status.
klughammer_idh_mut = klughammer_dat %>%
filter(IDH == "mut")
klughammer_idh_wt = klughammer_dat %>%
filter(IDH == "wt")
# Create a new variable to account for the different data types as they might relate with the idh_codel_subtype project.
klughammer_dat$idh_codel_subtype <- c()
klughammer_dat$idh_codel_subtype[klughammer_dat$IDH=="mut"] <- "IDHmut_noncodel"
klughammer_dat$idh_codel_subtype[klughammer_dat$WHO2016_classification%in%c("Anaplastic Oligodendroglioma", "Oligo II")] <- "IDHmut_codel"
klughammer_dat$idh_codel_subtype[klughammer_dat$IDH=="wt"] <- "IDHwt"
## There are clear differences across the subtypes with IDHmut tumors having a higher promoter level, but likely also greater tumor purity.
kruskal.test(klughammer_dat$mean_pdr~klughammer_dat$idh_codel_subtype)
# Overall, what are some factors that are related with DNAme disorder? Promoter DNAme disorder is nearly normally distributed.
epi_mut_model <- glm(mean_pdr ~ Age + idh_codel_subtype + material + cohort, family = gaussian, data = klughammer_dat)
summary(epi_mut_model) # There's a slight association between PDR and 1) IDHmut, 2) FFPE, and 3) cohort.
##################################################
### Somatic Copy Number Alteration (SCNA) burden
##################################################
## Investigate the relationship between SCNAs and DNAme disorder,
## First, examine general SCNA burden.
klughammer_cnv = klughammer_dat %>%
select(patID, surgery, id, Age, MIB, idh_codel_subtype, mean_pdr, ends_with("_amplification"), ends_with("_deletion")) %>%
# Remove sex chromosomes.
select(-c(X_p_amplification,X_q_amplification, Y_p_amplification, Y_q_amplification,
X_p_deletion, X_q_deletion, Y_p_deletion, Y_q_deletion))
## Calculate the total bases affected by copy number alteration.
# FGA = fraction of genome with copy number alterations.
klughammer_fga = klughammer_cnv %>%
## Retrieve the amplification estimates for 22 autosomes.
mutate(amp_bp = apply(klughammer_cnv[, 8:49], 1, sum, na.rm=TRUE),
## Retrieve the deletion estimates for 22 autosomes.
del_bp = apply(klughammer_cnv[, 50:91], 1, sum, na.rm=TRUE)) %>%
select(patID, surgery, id, Age, MIB, idh_codel_subtype, mean_pdr, amp_bp, del_bp)
# What's the max number of bases altered for each chromosome. We can consider this the theoretical maximum MEASURED.
chrom_amp_max <- apply(klughammer_cnv[, 8:49], 2, max, na.rm=TRUE)
chrom_del_max <- apply(klughammer_cnv[, 50:91], 2, max, na.rm=TRUE)
## There were 46 samples that had no amplifications or deletions.
## This would be HIGH:Y unusual in glioma so filtering out as these are likely low purity or quality.
sum(klughammer_fga$del_bp==0 & klughammer_fga$amp_bp==0)
## Remove these samples from analysis, as they likely represent contaminating normal or low quality segmentations.
klughammer_fga_filtered = klughammer_fga %>%
# Calculate the fraction of the genome with amplifications and deletions.
mutate(amp_fga = amp_bp/sum(chrom_amp_max),
del_fga = del_bp/sum(chrom_del_max),
# To derive total fraction of the genome measured with alterations add both features together.
total_fga = amp_fga+del_fga) %>%
filter(total_fga!=0, surgery%in%c(1, 2)) %>%
mutate(idh_status = ifelse(idh_codel_subtype=="IDHwt", "IDHwt", "IDHmut"),
timepoint = ifelse(surgery=="1", "Initial", "Recurrence"))
## Restrict to IDHwt samples that have the highest sample size needed for bulk cohort validation analyses.
klughammer_fga_filtered_wt = klughammer_fga_filtered %>%
filter(idh_status=="IDHwt")
# Perform a statistical test to determine the association between genetic (SCNA) and epigenetic (DNAme disorder) instability.
# Add back some additional covariates. SCNA seems generally positively associated with promoter DNAme disorder.
disorder_fga_model <- glm(mean_pdr~ Age + total_fga, family = gaussian, data = klughammer_fga_filtered_wt)
summary(disorder_fga_model)
disorder_amp_model <- glm(mean_pdr~ Age + as.factor(surgery) + MIB + amp_fga, family = gaussian, data = klughammer_fga_filtered_wt)
summary(disorder_amp_model)
disorder_del_model <- glm(mean_pdr~ Age + as.factor(surgery) + MIB + del_fga, family = gaussian, data = klughammer_fga_filtered_wt)
summary(disorder_del_model)
disorder_fga_surgery_model <- glm(mean_pdr~ Age + as.factor(surgery) + MIB + total_fga, family = gaussian, data = klughammer_fga_filtered_wt)
summary(disorder_fga_surgery_model)
## How many samples? (384 across all conditions)
dim(klughammer_fga_filtered_wt)
## Create a bubble chart for each of these variables. Color = direction of relationship, Height -log10(p-value).
variables <- c("Age", "Timepoint", "Proliferation\n(MIB staining)", "SCNA")
## P-values from disorder_fga_surgery_model results
pvalues <- c(summary(disorder_fga_surgery_model)$coefficients[2,4],
summary(disorder_fga_surgery_model)$coefficients[3,4],
summary(disorder_fga_surgery_model)$coefficients[4,4],
summary(disorder_fga_surgery_model)$coefficients[5,4])
sign <- c("-", "+", "-", "+")
disorder_assoc <- data.frame(pvalues, sign, variables)
disorder_assoc$variables <- factor(disorder_assoc$variables, levels = c("Age", "Timepoint", "Proliferation\n(MIB staining)", "SCNA"))
## Can demonstrate that copy number alterations are associated with DNAme disorder.
pdf(file = "results/Fig5/klughammer-disorder-vars.pdf", height = 4, width = 6, bg = "transparent", useDingbats = FALSE)
ggplot(disorder_assoc, aes(x=variables, y=-log10(pvalues), size = -log10(pvalues), color = sign)) +
geom_point() +
geom_hline(yintercept = -log10(0.05), alpha=0.8, linetype=2) +
labs(x="", y = "-log10(P-value)", size="-log10(P-value)", color = "Direction of assoc.") +
plot_theme + theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
## It appears that the relationship is stronger for the initial IDHwt samples. This could be a bias of sample size.
pdf(file = "github/results/Fig5/Fig5c-disorder-SCNA-nolegend.pdf", height = 4, width = 6, bg = "transparent", useDingbats = FALSE)
ggplot(klughammer_fga_filtered_wt, aes(x=total_fga, y=mean_pdr, color=idh_status)) +
geom_point() +
ylab("Promoter DNAme disorder") + xlab("SCNA burden") +
plot_theme +
theme(legend.position="bottom",
panel.spacing.x = unit(1.5, "lines")) +
geom_smooth(method = "lm") +
xlim(0, 1) +
scale_color_manual(name='Glioma subtype', values=c('IDHmut'='#AF8DC3', "IDHwt"="#7FBF7B")) +
facet_grid(. ~ timepoint, scales = "free_x", space = "free") +
stat_cor(method = "spearman", label.x = 0.4) +
guides(color=FALSE)
dev.off()
## What's the breakdown by sample type?
table(klughammer_fga_filtered_wt$timepoint)
##############################################################
### Analyze longitudinal changes in SCNA and DNAme disorder.
### How do they correlated?
##############################################################
## Create a longitudinal data frame for fraction of the genome with copy number alterations.
## Determine the longitudinal differences in FGA.
long_fga = klughammer_fga_filtered %>%
group_by(patID, surgery) %>%
summarise(avg_total_fga = mean(total_fga),
avg_mean_pdr = mean(mean_pdr)) %>%
select(-avg_mean_pdr, -surgery) %>%
mutate(grouped_id = row_number()) %>%
spread(grouped_id, avg_total_fga) %>%
filter(!is.na(`2`)) %>%
mutate(aneuploidy_diff = `2`-`1`) %>%
select(patID, fga_1 = `1`, fga_2 = `2`, aneuploidy_diff)
# Determine the longitudinal differences in epimutation rate.
long_epi_mut = klughammer_fga_filtered %>%
group_by(patID, surgery) %>%
summarise(avg_total_fga = mean(total_fga),
avg_mean_pdr = mean(mean_pdr)) %>%
select(-avg_total_fga, -surgery) %>%
mutate(grouped_id = row_number()) %>%
spread(grouped_id, avg_mean_pdr) %>%
filter(!is.na(`2`)) %>%
mutate(disorder_diff = `2`-`1`) %>%
select(patID, epi_mut_1 = `1`, epi_mut_2 = `2`, disorder_diff)
## Be able to stitch together the metadata.
# meta_data for all filtered samples.
long_meta_data = klughammer_fga_filtered %>%
select(patID, idh_codel_subtype) %>%
distinct()
## There should be distinct ind. for whose genomic data passed QC.
long_instability = long_fga %>%
inner_join(long_epi_mut, by="patID") %>%
inner_join(long_meta_data, by="patID")
n_distinct(long_instability$patID)
# Separate by subtype. IDHmut does not have much information.
long_instability_idh_wt = long_instability %>%
filter(idh_codel_subtype =="IDHwt")
## Restrict only to the IDHwt tumors to aid in clarity.
pdf(file = "results/Fig5/Fig5d-longitudinal-SCNA-disorder.pdf", height = 4, width = 4, bg = "transparent", useDingbats = FALSE)
ggplot(long_instability_idh_wt, aes(x=aneuploidy_diff, y=disorder_diff, color=idh_codel_subtype)) +
geom_point() + ylab("Delta DNAme disorder\n (Recurrence - Initial)") + xlab("Delta SCNA burden\n(Recurrence - Initial)") +
theme_bw() + geom_smooth(method = "lm") +
ylim(-0.1, 0.1) + xlim(-0.5, 0.5) +
guides(color=FALSE) +
scale_color_manual(name='Glioma subtype', values=c("IDHwt"="#7FBF7B")) +
plot_theme +
stat_cor(method = "spearman", label.x = -0.5, label.y = 0.08)
dev.off()
######################
### Survival
######################
## Retrieve survival information for these samples.
klughammer_clin = klughammer_dat %>%
select(patID, Age, IDH, timeToFirstProg, timeToSecSurg, VitalStatus, DateOfDeath_LastFollow.up,
Follow.up_years, treatment, Sex) %>%
mutate(patient_vital = ifelse(VitalStatus=="alive", 0, 1),
## All patients had a second surgery event.
patient_vital_surgical_int = 1)
## Only retain the first instance for a particular patient.
klughammer_clin_filt = klughammer_clin[match(unique(klughammer_clin$patID), klughammer_clin$patID), ]
## Create a set of data.frames on which to perform survival analyses.
long_instability_clin_wt = long_instability %>%
left_join(klughammer_clin_filt, by="patID") %>%
ungroup() %>%
## Restrict to IDHwt only.
filter(IDH=="wt") %>%
mutate(disorder_diff_group = ifelse(disorder_diff>median(disorder_diff), "1", "0"),
## Convert days to months.
timeToSecSurg.mo = timeToSecSurg/30.4167,
timeToFirstProg.mo = timeToFirstProg/30.4167,
primary_disorder_group = ifelse(epi_mut_1>median(epi_mut_1), "1", "0"),
recurrence_disorder_group = ifelse(epi_mut_2>median(epi_mut_2), "1", "0"))
## Perform Cox-proportional hazards models with epimutation rate + Age.
primary_pdr_cox <- coxph(Surv(Follow.up_years, patient_vital) ~ Age + Sex + primary_disorder_group, data = long_instability_clin_wt)
summary(primary_pdr_cox)
recurrent_pdr_cox <- coxph(Surv(Follow.up_years, patient_vital) ~ Age + Sex + recurrence_disorder_group, data = long_instability_clin_wt)
summary(recurrent_pdr_cox)
## Delta DNAme disorder with overall survival.
## Continuous variable.
diff_pdr_cox <- coxph(Surv(Follow.up_years, patient_vital) ~ Age + Sex + disorder_diff, data = long_instability_clin_wt)
summary(diff_pdr_cox)
## Treated as a discrete variable.
diff_pdr_cox <- coxph(Surv(Follow.up_years, patient_vital) ~ Age + Sex + disorder_diff_group, data = long_instability_clin_wt)
summary(diff_pdr_cox)
## Add in the delta SCNA value to account for potential shifts in tumor purity (i.e., loss of CNVs may represent decrease in purity).
diff_pdr_cox <- coxph(Surv(Follow.up_years, patient_vital) ~ Age + Sex + disorder_diff_group + aneuploidy_diff, data = long_instability_clin_wt)
summary(diff_pdr_cox)
## Generate visualizations based on groupings:
fit_wt_os <- survfit(Surv(Follow.up_years, patient_vital) ~ disorder_diff_group,
data = long_instability_clin_wt)
ggsurvplot(fit_wt_os, data = long_instability_clin_wt, risk.table = FALSE, pval= TRUE, pval.coord = c(6, 0.75),
palette = c("royalblue4", "tomato3"),
ylab = "Overall survival\n probability", xlab = "Time (years)")
## Examining only the primary tumor samples.
fit_wt_os_primary <- survfit(Surv(Follow.up_years, patient_vital) ~ primary_disorder_group,
data = long_instability_clin_wt)
ggsurvplot(fit_wt_os_primary, data = long_instability_clin_wt, risk.table = FALSE, pval= TRUE, pval.coord = c(6, 0.75),
palette = c("royalblue4", "tomato3"),
ylab = "Overall survival\n probability", xlab = "Time (years)")
## Examining only the recurrent tumor samples.
fit_wt_os_recurence <- survfit(Surv(Follow.up_years, patient_vital) ~ recurrence_disorder_group,
data = long_instability_clin_wt)
ggsurvplot(fit_wt_os_recurence, data = long_instability_clin_wt, risk.table = FALSE, pval= TRUE, pval.coord = c(6, 0.75),
palette = c("royalblue4", "tomato3"),
ylab = "Overall survival\n probability", xlab = "Time (years)")
##############################################
### Assess the "time to second surgery" ###
##############################################
## Make sure to use the separate patient vital variable for surgical interval.
table(long_instability_clin_wt$patient_vital_surgical_int)
pdr_surg_cox_con <- coxph(Surv(timeToSecSurg, patient_vital_surgical_int) ~ Age + Sex + disorder_diff , data = long_instability_clin_wt)
summary(pdr_surg_cox_con)
## Second surgery analysis treated as a group. Doesn't change much.
pdr_surg_cox_bi <- coxph(Surv(timeToSecSurg, patient_vital_surgical_int) ~ Age + Sex + disorder_diff_group, data = long_instability_clin_wt)
summary(pdr_surg_cox_bi)
## The relationship gets stronger when accounting for SCNA differences.
pdr_surg_cox_bi <- coxph(Surv(timeToSecSurg, patient_vital_surgical_int) ~ Age + Sex + disorder_diff_group + aneuploidy_diff, data = long_instability_clin_wt)
summary(pdr_surg_cox_bi)
## Generate visualizations based on groupings:
fit_wt_pdr <- survfit(Surv(timeToSecSurg.mo, patient_vital_surgical_int) ~ disorder_diff_group,
data = long_instability_clin_wt)
pdf(file = "results/Fig5/Fig5e-disorder-diff-secondsurg.pdf", height = 4, width = 6, bg = "transparent", useDingbats = FALSE)
ggsurvplot(fit_wt_pdr, data = long_instability_clin_wt, risk.table = FALSE, pval= TRUE, pval.coord = c(30, 0.75),
palette = c("royalblue4", "tomato3"),
ylab = "Time to Second Surgery\n probability", xlab = "Time (months)")
dev.off()
#### END #####
|
a7ecaf7291fdeb88b42ecad7e8b485d7ca889df4
|
1912eacc09bcf390de60316b48c3a0ee99783b87
|
/primarypackage/man/createRace.Rd
|
0c6a580355e204377941720670558a5d8e0b89cd
|
[] |
no_license
|
mau-vela/PS6
|
c1ffe09c321772f0dbb355f337fcd2c8aea70449
|
7ee1540852ce51266181c000118c4906382c1bf1
|
refs/heads/master
| 2021-01-17T20:29:04.747428
| 2016-03-21T08:05:31
| 2016-03-21T08:05:31
| 54,323,673
| 0
| 0
| null | 2016-03-20T15:01:15
| 2016-03-20T15:01:15
| null |
UTF-8
|
R
| false
| true
| 858
|
rd
|
createRace.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createRace.R
\name{createRace}
\alias{createRace}
\alias{createRace,ANY-method}
\title{Create an object of class 'Race'}
\usage{
createRace(Candidates, party)
}
\arguments{
\item{party}{Party}
\item{name}{List of canidates of object class 'Candidate'}
}
\value{
An object of class 'Race' containing
\itemize{
\item Candidates of class 'Candidate'
\item party Party
\item delegatesNeeded Number of delegates needed to win
\item delegatesRemaining Number of delegates remaining
}
}
\description{
Function creates an object of class 'Race'
}
\examples{
clinton <- createCandidate("Clinton", 1614, "Democrat")
sanders <- createCandidate("Sanders", 856, "Democrat")
Race <- createRace(Candidates = list(clinton, sanders), party = "Democrat")
plot(Race)
}
\author{
Mauricio Vela
}
|
a49bfb84cc0b89abc32da4270ae70adfff553209
|
6c0eb363f0da0bf2ba6c5d602f08bbdefb0ccc94
|
/Poster/Nigeria/Circular_Bar_Plot.R
|
f61b20e43a442eae9dee139bd3d621ba3c58d84a
|
[] |
no_license
|
bteinstein/COVID-19-Nigeria
|
4831ce5f82670899820d43e77fa08d95f4267472
|
f202a209fa4255d9f68bef80e59ad64b99924db2
|
refs/heads/master
| 2023-03-30T14:46:08.554494
| 2021-04-06T21:36:49
| 2021-04-06T21:36:49
| 272,509,194
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,689
|
r
|
Circular_Bar_Plot.R
|
---
title: "00. Learning"
author: "Babatunde Adebayo"
date: "8/21/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Basic Idea
()
The basic idea of this viz is circular barplot. I saw this a part of #TidyTuesday challenge contribution by Joseph Pope (Aug 18, 2020)
on endangered plant.
## Recreation Attempt
```{r setup}
library(tidyverse)
library(extrafont)
library(ggthemes)
library(kableExtra)
```
```{r data}
tuesdata <- tidytuesdayR::tt_load(2020, week = 34)
threats <- tuesdata$threats
# readr::write_csv(tuesdata$plants, path = here::here("data",'plant.csv'))
# readr::write_csv(tuesdata$actions, path = here::here("data",'actions.csv'))
# readr::write_csv(tuesdata$threats, path = here::here("data",'threats.csv'))
# plants <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-18/plants.csv')
# actions <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-18/actions.csv')
# threats <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-18/threats.csv')
rm(tuesdata)
```
### Explore Data
Attempt to use {skimr} package to get a sense of overview about the size of your data, missing values, mean rates, and a basic histogram to show the distribution of numerical data-type columns.
```{r exploration}
skimr::skim(threats)
```
- We have 500 unique plants in this dataset base on their Specie name (Genus+species) comprising of 6 taxonomic groups
- They type of threats for these plants are grouped into 12 different classes
- Data collected are from 72 countries in 6 continents
- All variable/column are complete except year_last_seen with 180 (3%) missing values
- threatened, which is the only numerical variable out of 8 variables in total is binary in nature. 1 indicates the plant is endangered (threatened with extinction) and 0 otherwise
#### Exploring futher
```{r type-of-threat-vs-threat-status}
threats %>% group_by(threat_type) %>% count(threatened)
table(threats$threat_type, threats$threatened)
```
The data shows that the top threat to this plants are related to Agriculture & Aquaculture, Biological Resources Use, Transportation Corridor and Natural System Modification. Interestingly, Pollution is found to be the least threat factor to threaten - As expected fellow plants are really protecting themeselves in detoxification.
### Data Processing
Getting data ready for visualization.
- Here the focus here are threatened plants by country and continent
- Saint Helena will be used as a truncted name for the Country Saint Helena, Ascension and Tristan da Cunha is too long,
Basic idea here is to filter just only threatened plant, group by continents then country and count.
I created two versions, one scaled count between 0-100 and unscaled count
```{r data_prep}
# data_scaled
data <- threats %>%
filter(threatened == 1) %>%
group_by(continent, country) %>%
count(name = "threatened_plants", sort = TRUE) %>%
ungroup() %>%
mutate(continent = factor(continent),
country = ifelse(str_detect(country, "Saint Helena"), "Saint Helena", country),
threatened_plants = scales::rescale(threatened_plants, to = c(0, 100))) %>%
filter(threatened_plants != 0)
data_unscaled <- threats %>%
filter(threatened == 1) %>%
group_by(continent, country) %>%
count(name = "threatened_plants", sort = TRUE) %>%
ungroup() %>%
mutate(continent = factor(continent),
country = ifelse(str_detect(country, "Saint Helena"), "Saint Helena", country))
data_unscaled
```
### Plotting Setup
The goal is to create circular barplot plot with extra-sauce of grouped format structure. The extra-sauce here include some extra row space to separate groups, arc line for group labelling and smooth grid for y-axis labelling.
Hence, there is a need for some data preps and geometry calculations (not to worry on this data-to-viz to the rescue).
The basic steps are as follow:
1. Create empty bar rows in the data to allow grouping
2. Build labels and add to data. In this case, we will label the country per bar and use continent for the grouping. More importantly for angular geometry for labels rotation.
3. Base Data - Dataframe handles continent labelling and line arc aesthetics.
4. Grid Data - A dataframe to structure y-axis scale (It basically acts as the y-axis label) - in the case of the scaled data it will handle bar of 0-20-40-60-80
```{r plot_setup}
# Step 1
# Set a number of 'empty bar' to add at the end of each group
empty_bar=3
to_add = data.frame( matrix(NA, empty_bar*nlevels(data$continent), ncol(data)) )
colnames(to_add) = colnames(data)
to_add$continent = rep(levels(data$continent), each=empty_bar)
data= full_join(data, to_add) %>% #rbind(data, to_add)
arrange(continent) %>%
mutate(id = row_number())
# Step 2
# Get the name and the y position of each label
label_data=data
number_of_bar=nrow(label_data)
angle= 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust<-ifelse( angle < -90, 1, 0)
label_data$angle<-ifelse(angle < -90, angle+180, angle)
# step 3
# prepare a data frame for base lines
base_data=data %>%
group_by(continent) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
# Step 4
# prepare a data frame for grid (scales)
grid_data = base_data
grid_data$end = grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start = grid_data$start - 1
grid_data=grid_data[-1,]
```
### Build Plot
Most of the code are patterned on that of sample provided by data-to-viz https://www.data-to-viz.com/graph/circularbarplot.html
with few modification - removing redundate code, margin correction and theme
#### Scaled Data
```{r build_plot, fig.height=9, fig.width=8}
p <- ggplot(data, aes(x=as.factor(id), y=threatened_plants, fill=continent)) + #NB id variable is a factor
geom_bar(stat = "identity", alpha =0.5) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 40, xend = start, yend = 40), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 20, xend = start, yend = 20), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines # Modify this accordingly for an unscaled data
annotate("text", x = rep(max(data$id),4), y = c(20, 40, 60, 80), label = c("20", "40", "60", "80") , color="white", size=3 , angle=0, fontface="bold", hjust=1) +
# geom_bar(aes(x=as.factor(id), y=threatened_plants, fill=continent), stat="identity", alpha=0.5) +
ylim(-100,120) +
labs(
title = "Number of Threatened Plants by Geo",
subtitle = "Scaled from 0-100",
caption = "Joseph Pope | @joepope44"
) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(), # Modify this to keep x or y axis text
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#a3b18a"),
plot.title = element_text(hjust = 0.5, size = 24, family = "Garamond"),
plot.subtitle = element_text(hjust = 0.5, family = "Garamond"),
plot.caption = element_text(hjust = 0.5, family = "Garamond")
# plot.margin = unit(rep(-1,4), "cm") <- this hides the plot title if not commented out
) +
coord_polar() + # Make it a circle
# add labels - save to remove inherit.aes = FALSE (soft-deprecated I believe)
geom_text(data=label_data, aes(x=id, y=threatened_plants+10, label=country, hjust=hjust), color="black", fontface="bold",alpha=0.7, size=3.5, angle= label_data$angle) +
# Add base line information - Arc (a line in a polar coordinate) Then Label ( gridText Can be use to enhance text placement - say for a two line display)
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
geom_text(data=base_data, aes(x = title, y = -18, label=continent),
hjust=c(1,1,.5,0,0,0), colour = "black", alpha=0.8, size=4,
fontface="bold", inherit.aes = FALSE)
```
### Unscaled Data
```{r}
empty_bar=3
to_add = data.frame( matrix(NA, empty_bar*nlevels(data_unscaled$continent), ncol(data_unscaled)) )
colnames(to_add) = colnames(data_unscaled)
to_add$continent = rep(levels(data_unscaled$continent), each=empty_bar)
data_uscl <- full_join(data_unscaled, to_add) %>% #rbind(data, to_add)
arrange(continent) %>%
mutate(id = row_number())
# Step 2
# Get the name and the y position of each label
label_data=data_uscl
number_of_bar=nrow(label_data)
angle= 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust<-ifelse( angle < -90, 1, 0)
label_data$angle<-ifelse(angle < -90, angle+180, angle)
# step 3
# prepare a data frame for base lines
base_data=data_uscl %>%
group_by(continent) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
# Step 4
# prepare a data frame for grid (scales)
grid_data = base_data
grid_data$end = grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start = grid_data$start - 1
grid_data=grid_data[-1,]
```
```{r build_plot, fig.height=8, fig.width=7}
ggplot(data_uscl, aes(x=as.factor(id), y=threatened_plants, fill=continent)) + #NB id variable is a factor
geom_bar(stat = "identity", alpha =0.5) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sure barplots are OVER it.
geom_segment(data=grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 150, xend = start, yend = 150), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 50, xend = start, yend = 50), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines # Modify this accordingly for an unscaled data
annotate("text", x = rep(max(data$id),4), y = c(50, 100, 150, 200), label = c("50", "100", "150", "200") , color="white", size=3 , angle=0, fontface="bold", hjust=1) +
# geom_bar(aes(x=as.factor(id), y=threatened_plants, fill=continent), stat="identity", alpha=0.5) +
ylim(-320,300) + # Modify Y Lim
labs(
title = "Number of Threatened Plants by Geo",
subtitle = "Scaled from 0-100",
caption = "Joseph Pope | @joepope44"
) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(), # Modify this to keep x or y axis text
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#a3b18a"),
plot.title = element_text(hjust = 0.5, size = 24, family = "Garamond"),
plot.subtitle = element_text(hjust = 0.5, family = "Garamond"),
plot.caption = element_text(hjust = 0.5, family = "Garamond")
# plot.margin = unit(rep(-1,4), "cm") <- this hides the plot title if not commented out
) +
coord_polar() + # Make it a circle
# add labels - save to remove inherit.aes = FALSE (soft-deprecated I believe)
geom_text(data=label_data, aes(x=id, y=threatened_plants+10, label=country, hjust=hjust), color="black", fontface="bold",alpha=0.7, size=3.5, angle= label_data$angle) +
# Add base line information - Arc (a line in a polar coordinate) Then Label ( gridText Can be use to enhance text placement - say for a two line display)
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
geom_text(data=base_data, aes(x = title, y = -18, label=continent),
hjust=c(1,1,.5,0,0,0), colour = "black", alpha=0.8, size=4,
fontface="bold", inherit.aes = FALSE)
```
|
8af9ab0cc4398cc0cd836d2990d6f6883e349446
|
b234deb3176fcfbf8c72628350cf12f4a0508349
|
/xCell/Dev_scripts/xCell_train_functions.R
|
232cd1e6ecc93561f31627475d0b06add32f3786
|
[] |
no_license
|
tsofiya/xCell2-old
|
23fc210c70b3484a1b8d4c0bf601304df1617dba
|
8b15fd7abd3686d5c157d749b6f68dcb54077871
|
refs/heads/master
| 2023-04-30T00:39:20.834379
| 2021-05-13T06:27:57
| 2021-05-13T06:27:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,871
|
r
|
xCell_train_functions.R
|
library(GSVA)
library(GSEABase)
library(pracma)
library(RColorBrewer)
library(pheatmap)
read.types.dependencies = function(file.name) {
con <- file(file.path(file.name), open = "r")
out <- list()
i=1
while (length(oneLine <- readLines(con, n = 1, warn = FALSE)) > 0) {
vec <- unlist(strsplit(oneLine, "\t"))
out$types[i] = vec[1]
n = max(which(!(vec=="")))
if (n<2)
out$dep[i] = list(types="")
else
out$dep[i] = list(types=vec[2:n])
i=i+1
}
close(con)
out
}
create.signatures = function(ref,dependencies){
probs = c(.1,.25,.33333333,.5,.6666666,.75,.9)
diff_vals = c(0, 0.1, 0.585, 1, 1.585, 2,3,4,5)
types=unique(colData(ref)$label.main)
#TODO: add dependencies
message('get quantiles...')
q = lapply(types,function(y){
q = apply(assays(ref)$logcounts,1,function(z) quantile(z,probs,na.rm=TRUE))
})
message('create all signatures...')
ntypes= length(types)
signature= data.frame(ref@colData)
listIn= list()
for (i in 1:ntypes) {
for (diff in diff_vals) {
for (j in 1:round(length(probs)/2+0.5)) {
diff_genes = lapply(q,function(x) q[[i]][j,]>x[length(probs)-j,]+diff)
output <- matrix(unlist(diff_genes), nrow = ntypes, byrow = TRUE)
for (n in (ntypes-1):(ntypes-3)) {
g = colSums(output)>=n
if (sum(g)>7 & sum(g)<201){
gs=GeneSet(rownames(ref)[g],
setName=sprintf("%%%s%%j%g%%d%g%%n%g",toupper(colnames(ref)[i]),round(probs[j]*100),diff,ntypes-n,
sep="\t",row.names=FALSE,quote =FALSE,col.names = FALSE))
listIn<-c(listIn, gs)
}
# write.table(rownames(ref)[g],file=file.path(working.dir,'signatures',
# sprintf("%%%s%%j%g%%d%g%%n%g.txt",toupper(colnames(ref)[i]),round(probs[j]*100),diff,ntypes-n)),sep="\t",row.names=FALSE,quote =FALSE,col.names = FALSE)
}
}
}
}
gsc= GeneSetCollection(listIn)
return gsc
}
score.ref.signatures = function(ref,working.dir) {
dir.create(file.path(working.dir, 'scores'), showWarnings = FALSE)
egc = read.signatures.dir(paste0(file.path(working.dir,'signatures')))
scores = gsva(as.matrix(assays(ref)$logcounts),egc,method='ssgsea',ssgsea.norm=FALSE)
write.table(scores,file=file.path(paste0(working.dir,"scores",paste(ref@colData$label.main,"_ssgsea.txt"))),sep="\t",row.names=TRUE,quote =FALSE,col.names = NA)
}
# TODO: delete this when replacing files with objects.
read.signatures.dir = function(path) {
sig = list()
files = list.files(path=path)
for (i in 1:length(files)) {
sig[i] = GeneSet(scan(paste0(path,'/',files[i]), what="", sep="\n"), setName=files[i])
}
signatures <- GeneSetCollection(sig)
#toGmt(signatures,'~/Documents/signatures/Mouse/signatures.txt')
signatures
}
|
e25ca5fb238ba2673157787634a8e2a0124e44d0
|
a1240975d6bcdbe14fe32c1f92139636d415ca8d
|
/man/read_statGenGroupOutFile.Rd
|
e9b683fa328f66d67b5de296499b36df80d069b3
|
[] |
no_license
|
pvrqualitasag/qgengroup
|
56eccd0cac9080643b8aec48e6d68caacd8b1497
|
6ecb06d1ca1b75f39281b4a57392675dc4538008
|
refs/heads/master
| 2020-12-10T14:43:29.105294
| 2020-01-16T12:43:30
| 2020-01-16T12:43:30
| 233,622,368
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 417
|
rd
|
read_statGenGroupOutFile.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ReadGG.R
\name{read_statGenGroupOutFile}
\alias{read_statGenGroupOutFile}
\title{Reading statistics on missing parents}
\usage{
read_statGenGroupOutFile(psInputFile, pbLog = FALSE)
}
\arguments{
\item{psInputFile}{file including number of missing parents per year and breed}
}
\description{
Read a table of statistics on missing parents
}
|
343fc46d3486e1d67bd50da71a0e23f0f89f724d
|
386cb146e762d1940f1e36d0b0d183072d6cc969
|
/NBA_Fan_Engagement_on_Twitter/Analysis.R
|
51347d71ee95b749c9f1bbea775dccf93dc4d30b
|
[] |
no_license
|
coderita/MSc_Business_Analytics_Portfolio
|
2e448c52e7bcfd6eb6f059b3ff1a7cc0c9b2f7d8
|
32bc59d465d8c34d055b04243e5d0ee26ff9370d
|
refs/heads/main
| 2023-08-01T14:44:48.376901
| 2021-09-18T00:47:13
| 2021-09-18T00:47:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 32,998
|
r
|
Analysis.R
|
#' ---
#' title: "Text Analysis: NBA Fan Engagement via Twitter"
#' author: "by Jack Daoud"
#' output:
#' html_notebook:
#' number_sections: yes
#' theme: readable
#' highlight: pygments
#' toc: yes
#' toc_float:
#' collapsed: no
#' html_document:
#' toc: yes
#' df_print: paged
#' ---
#'
## ----options & packages, message=FALSE, warning=FALSE, include=FALSE-------------------------------------------
# REMOVE # to install packages
#install.packages("plyr")
#install.packages("qdap")
#install.packages("tm")
#install.packages("docstring")
#install.packages("ggthemes")
#install.packages("lubridate")
#install.packages('dendextend')
#install.packages('circlize')
#install.packages("magrittr")
#install.packages('wordcloud')
#install.packages('wordcloud2')
# load Packages
lapply(c("lubridate", "plyr", "stringi", "ggdendro", 'dendextend',
'circlize', "qdap", "tm", "docstring", "ggthemes", 'magrittr',
'ggplot2', 'dplyr', 'readr', 'stringr', 'tidyr', 'ggalt',
'plotrix', 'wordcloud'),
library, character.only = TRUE)
# set options
options(stringsAsFactors = FALSE)
Sys.setlocale('LC_ALL', 'C')
set.seed(123)
# set working directory
#setwd('./personal/case_NBA')
#'
#' # Data Exploration
#'
#' ## Import
#'
## ----import & sample data, message=FALSE, warning=FALSE--------------------------------------------------------
# import and merge all data sets Oct 2019-20
data_directory <- "./_data/original"
csv_files <- list.files(path = data_directory,
pattern = "*.csv",
full.names = TRUE)
tweets <- ldply(csv_files, read_csv)
# take a sample of 1%
tweets <- slice_sample(tweets, prop = 0.01)
# export tweet sample
write.csv(tweets,'./_data/manipulated/tweets_sample.csv', row.names = F)
#'
#' ## Wrangle
#'
## ----data wrangling, message=FALSE, warning=FALSE--------------------------------------------------------------
# import tweet sample
tweets <- read_csv('./_data/manipulated/tweets_sample.csv')
################################################################################
# Create placeholder for Region column
tweets$region <- ''
# Loop through each row and match teams with their respective region
for (i in 1:nrow(tweets)) {
if (tweets[i, "team"] == "Boston Celtics" |
tweets[i, "team"] == "Brooklyn Nets" |
tweets[i, "team"] == "New York Knicks" |
tweets[i, "team"] == "Philadelphia Sixers" |
tweets[i, "team"] == "Toronto Raptors") {
tweets[i, "region"] <- "Atlantic"
}
else if (tweets[i, "team"] == "Chicago Bulls" |
tweets[i, "team"] == "Cleveland Cavaliers" |
tweets[i, "team"] == "Detroit Pistons" |
tweets[i, "team"] == "Indiana Pacers" |
tweets[i, "team"] == "Milwaukee Bucks") {
tweets[i, "region"] <- "Central"
}
else if (tweets[i, "team"] == "Atlanta Hawks" |
tweets[i, "team"] == "Charlotte Hornets" |
tweets[i, "team"] == "Miami Heat" |
tweets[i, "team"] == "Orlando Magic" |
tweets[i, "team"] == "Washington Wizards") {
tweets[i, "region"] <- "Southeast"
}
else if (tweets[i, "team"] == "Denver Nuggets" |
tweets[i, "team"] == "Minnesota Timberwolves" |
tweets[i, "team"] == "Oklahoma City Thunder" |
tweets[i, "team"] == "Portland Trail Blazers" |
tweets[i, "team"] == "Utah Jazz") {
tweets[i, "region"] <- "Northwest"
}
else if (tweets[i, "team"] == "Golden State Warriors" |
tweets[i, "team"] == "LA Clippers" |
tweets[i, "team"] == "LA Lakers" |
tweets[i, "team"] == "Phoenix Suns" |
tweets[i, "team"] == "Sacramento Kings") {
tweets[i, "region"] <- "Pacific"
}
else if (tweets[i, "team"] == "Dallas Mavericks" |
tweets[i, "team"] == "Houston Rockets" |
tweets[i, "team"] == "Memphis Grizzlies" |
tweets[i, "team"] == "New Orleans Pelicans" |
tweets[i, "team"] == "San Antonio Spurs") {
tweets[i, "region"] <- "Southwest"
}
}
################################################################################
# Feature engineering date & time values
tweets <- tweets %>% rename(timestamp = created)
tweets$year <- year(tweets$timestamp)
tweets$month <- month(tweets$timestamp, label = T)
tweets$day <- day(tweets$timestamp)
tweets$weekday <- wday(tweets$timestamp, label = T)
tweets$hour <- hour(tweets$timestamp)
# remove Sep 2019 tweets so upcoming tweets per month plot
tweets <- tweets %>% filter(year != 2019 | month != 'Sep')
################################################################################
# Export wrangled data
write.csv(tweets,'./_data/manipulated/tweets_sample_wrangled.csv', row.names = F)
#'
#' ## Data Overview Plots
#'
#' **Tweets per Region not used in presentation**
#'
## ----high level data exploration, message=FALSE, warning=FALSE-------------------------------------------------
# import wrangled tweets
tweets <- read_csv('./_data/manipulated/tweets_sample_wrangled.csv')
# Ordinal factors
tweets$month <- factor(tweets$month,
levels = c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'))
tweets$weekday <- factor(tweets$weekday,
levels = c('Mon', 'Tue', 'Wed', 'Thu', 'Fri',
'Sat', 'Sun'))
tweets$year <- factor(tweets$year,
levels = c('2019', '2020'))
################################################################################
# plot tweets per month
tweets_per_month <-
tweets %>%
group_by(month, year) %>%
summarize(count = n()) %>%
ggplot(., aes(x = month, y = count, fill = year)) +
geom_bar(stat = 'identity') +
scale_fill_manual(values=c("#2E86C1", "#201449")) +
geom_text(aes(label=count), position = "stack", vjust=-0.7, size=3) +
theme(legend.background = element_rect(fill="transparent", colour=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.line = element_line(colour = "black"),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16)) +
labs(title = "Tweets per Month",
x = "Month",
y = "Count",
fill = "Year") +
theme(plot.title = element_text(hjust = 0.5))
# save plot
ggsave("./_images/tweets_per_month.png", tweets_per_month, bg = "transparent")
################################################################################
# plot tweets per region
tweets_per_region <-
tweets %>%
group_by(region, team) %>%
summarize(count = n()) %>%
arrange(desc(count)) %>%
ggplot(., aes(x = reorder(region, -count), y = count), color="black") +
geom_bar(stat = 'identity', fill = '#FFFFFF', color = "black") +
#scale_fill_manual(values = c('#E03A3E', '#007A33', '#FFFFFF', '#1D1160',
# '#CE1141', '#860038', '#00538C', '#0E2240',
# '#C8102E', '#FFC72C', '#CE1141', '#002D62',
# '#C8102E', '#552583', '#5D76A9', '#98002E',
# '#00471B', '#0C2340', '#236192', '#F58426',
# '#007AC1', '#0077C0', '#006BB6', '#1D1160',
# '#E03A3E', '#5A2D81', '#E03A3E', '#5A2D81',
# '#C4CED4', '#CE1141', '#002B5C', '#002B5C')) +
geom_text(aes(label=count), position = "stack", vjust=-0.7, size=3) +
theme(legend.background = element_rect(fill="transparent", colour=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.line = element_line(colour = "black"),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16)) +
labs(title = "Tweets per Region",
x = "Region",
y = "Count",
fill = "Teams") +
theme(plot.title = element_text(hjust = 0.5))
# save plot
ggsave("./_images/tweets_per_region.png", tweets_per_region, bg = "transparent")
#'
#'
#' # Corpus Preprocessing
#'
#' ## Define Stop Words
#'
## ----stop words, message=FALSE, warning=FALSE------------------------------------------------------------------
nbaStopwords = c('nba','teams','fans','live','games','points','pacers', 'pts',
'year','back','coach','tonight','city','time','today',
'season','win','state','basketball','team','game','sports',
'play', 'player', 'players', 'played', 'playoff', 'playing',
'won', 'fan', 'lead', 'series', 'playoffs', 'star', 'home',
'center', 'championship', 'jersey', 'point', 'draft', 'loss',
'quarter', 'lost', 'highlights', 'winning', 'reb', 'tickets',
'allstar', 'national', 'arena', 'rankings', 'rookie', 'wins',
'round', 'rebounds', 'told', 'ball', 'swingman')
# print team names for nba_stopwords
#team_names <- as.character(unique(unlist(tweets$team)))
#team_names <- tryTolower(team_names)
#team_names <- scan(text = team_names, what = "")
#team_names <- paste0(team_names, sep = "'", collapse=",'")
#team_names
teamStopwords <- c('la','clippers','denver','nuggets','boston','celtics',
'brooklyn','nets','chicago','bulls','portland','trail',
'blazers','philadelphia','sixers','milwaukee','bucks','san',
'antonio','spurs','la','lakers','sacramento','kings',
'miami','heat','indiana','pacers','toronto','raptors',
'atlanta','hawks','golden','state','warriors','utah',
'jazz','dallas','mavericks','houston','rockets','orlando',
'magic','detroit','pistons','charlotte','hornets',
'minnesota','timberwolves','cleveland','cavaliers',
'new','york','knicks','memphis','grizzlies','phoenix',
'suns','new','orleans','pelicans','oklahoma','city',
'thunder','washington','wizards', 'los', 'angeles')
abbreviationStopwords = c('lol', 'smh', 'rofl', 'lmao', 'lmfao', 'wtf', 'btw',
'nbd','nvm', 'lmk', 'kk', 'obvi', 'obv', 'srsly',
'rly', 'tmi', 'ty', 'tyvm', 'yw', 'fomo', 'ftw',
'icymi', 'icyww', 'ntw', 'omg', 'omfg', 'idk', 'idc',
'jell', 'iirc', 'ffs', 'fml', 'idgaf', 'stfu', 'tf',
'omw', 'rn', 'ttyl', 'tyt', 'bball')
# from https://stylecaster.com/social-media-acronyms-abbreviations-what-they-mean/
# These are stopwords that I've identified after making visualizations
iterativeStopwords <- c('amp', 'years', 'beat', 'ufc', 'day', 'night',
'league', 'finals', 'watch', 'espn', 'guard', 'trade',
'pick', 'free', 'ago', 'head', 'big', 'center',
'love', 'ufa', 'make', 'itus', 'ers', 'theu', 'man',
'conference', 'forward', 'history', 'donut', 'ium',
'made', 'great', 'deal', 'harden', 'owner', 'full',
'news', 'ast', 'thu', 'left', 'breaking', 'bubble',
'uff', 'power', 'usa', 'assistant', 'contract',
'starting', 'video', 'sources', 'good', 'ufb', 'le',
'ad', 'de', 'uf', 'nbachallengefr', 'hu', 'makes', 'ua',
'ufd', 'gt', 'ufecu', 'check', 'nwt', 'blanku',
'grizzliesfr', 'stream', 'top', 'final', 'record',
'people', 'week', 'young', 'edition', 'gagnez',
'concours', 'maillot', 'ownsu', 'green', 'closet',
'closet', 'red', 'blue', 'yellow', 'white', 'size',
'medium', 'xl', 'xxl', 'xlarge', 'million', 'air',
'large', 'small', 'color', 'retweet', 'black')
customStopwords <- c(
stopwords('english'),
stopwords('SMART'),
nbaStopwords,
teamStopwords,
abbreviationStopwords,
iterativeStopwords)
#'
#' ## Preprocessing Functions
#'
## ----define text processing functions, message=FALSE, warning=FALSE--------------------------------------------
################################################################################
tryTolower <- function(text_column) {
#' Returns NA instead of tolower error
y = NA
# tryCatch error
try_error = tryCatch(tolower(text_column), error = function(e) e)
# if not an error
if (!inherits(try_error, 'error'))
y = tolower(text_column)
return(y)
}
################################################################################
basicSubs <- function(text_column) {
#' Run a variety of text cleaning functions
#'
#' Remove URLs, RTs, and non-ASCII text (i.e emojis)
#' Remove punctuation
#' Lower all text (case wise)
text_column <- gsub('http\\S+\\s*', '', text_column)
text_column <- gsub('(RT|via)((?:\\b\\W*@\\w+)+)', '', text_column)
text_column <- gsub("[^\x01-\x7F]", '', text_column)
text_column <- str_replace_all(text_column, "[[:punct:]]", " ")
text_column <- tryTolower(text_column)
return(text_column)
}
################################################################################
cleanCorpus <- function(corpus, customStopwords) {
#' Run a variety of corpus cleaning functions
#'
#' Functions that run:
#' - Remove numbers
#' - Remove punctuation
#' - Strip white space
#' - Remove stopwords
# corpus <- tm_map(corpus, content_transformer(replace_contraction))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, stripWhitespace)
corpus <- tm_map(corpus, removePunctuation)
# corpus <- tm_map(corpus, content_transformer(tryTolower))
corpus <- tm_map(corpus, removeWords, customStopwords)
return(corpus)
}
################################################################################
# correct spelling function
#correct_spelling <- function(text_column) {
# check <- check_spelling(text_column)
# splitted <- strsplit(text_column, split=' ')
# for (i in 1:length(check$row)) {
# splitted[[check$row[i]]][as.numeric(check$word.no[i])] = check$suggestion[i]
# }
# df <- unlist(lapply(splitted, function(x) paste(x, collapse = ' ')))
# return(df)
#}
# Did not include this function because the trade off in terms of accuracy is too high
################################################################################
cleanMatrix <- function(pth, columnName, collapse = F, customStopwords,
type, wgt){
print(type)
if(grepl('.csv', pth, ignore.case = T)==T){
print('reading in csv')
text <- read.csv(pth)
text <- text[,columnName]
}
if(grepl('.fst', pth)==T){
print('reading in fst')
text <- fst::read_fst(pth)
text <- text[,columnName]
}
if(grepl('csv|fst', pth, ignore.case = T)==F){
stop('the specified path is not a csv or fst')
}
if(collapse == T){
text <- paste(text, collapse = ' ')
}
print('cleaning text')
txtCorpus <- VCorpus(VectorSource(text))
txtCorpus <- cleanCorpus(txtCorpus, customStopwords)
if(type =='TDM'){
if(wgt == 'weightTfIdf'){
termMatrix <- TermDocumentMatrix(txtCorpus,
control = list(weighting = weightTfIdf))
} else {
termMatrix <- TermDocumentMatrix(txtCorpus)
}
response <- as.matrix(termMatrix)
}
if(type =='DTM'){
if(wgt == 'weightTfIdf'){
termMatrix <- DocumentTermMatrix(txtCorpus,
control = list(weighting = weightTfIdf))
} else {
termMatrix <- DocumentTermMatrix(txtCorpus)
}
response <- as.matrix(termMatrix)
if(grepl('dtm|tdm', type, ignore.case=T) == F){
stop('type needs to be either TDM or DTM')
}
}
print('complete!')
return(response)
}
################################################################################
bigramTokens <- function(x) {
unlist(lapply(NLP::ngrams(words(x), 2), paste, collapse = " "), use.names = F)
}
#'
#'
#' ## Corpus & TDM
#'
## ----create, clean & export corpus, message=FALSE, warning=FALSE-----------------------------------------------
# import wrangled tweets sample
tweets <- read_csv('./_data/manipulated/tweets_sample_wrangled.csv')
# Partially text column & save partially cleaned data
tweets$text <- basicSubs(tweets$text)
partially_cleaned_tweets <- tweets
# Export partially cleaned tweets as csv
write.csv(partially_cleaned_tweets,'./_data/manipulated/partially_cleaned_tweets.csv',
row.names = F)
# Create a volatile corpus
tweetCorpus <- VCorpus(DataframeSource(tweets))
# Clean the corpus
tweetCorpus <- cleanCorpus(tweetCorpus, customStopwords)
# Save corpus as dataframe
cleaned_corpora <-
data.frame(text = unlist(sapply(tweetCorpus, `[`,"content")),
stringsAsFactors=F)
# Export corpus as dataframe in csv format
write.csv(cleaned_corpora,'./_data/manipulated/cleaned_corpora.csv',
row.names = F)
# Convert corpus into Term Document Matrix
tweetTDM <- TermDocumentMatrix(tweetCorpus)
tweetTDMm <- as.matrix(tweetTDM)
#'
#' ## Bigram TDM
#'
## ----create bigram df, corpus, & TDM---------------------------------------------------------------------------
# Reduce size in order to process bigram TDM
bigram_tweets <- slice_sample(tweets, prop = 0.1)
# Create a volatile bigram corpus
bigramCorpus <- VCorpus(DataframeSource(bigram_tweets))
# Clean the bigram corpus
bigramCorpus <- cleanCorpus(bigramCorpus, customStopwords)
# Create a bigram TDM
bigramTDM <- TermDocumentMatrix(bigramCorpus, control=list(tokenize=bigramTokens))
bigramTDMm <- as.matrix(bigramTDM)
#'
#'
#' # Visualizations
#'
#' ## Share of Brand
#'
## ----share of brand, message=FALSE, warning=FALSE--------------------------------------------------------------
# Import cleaned corpora
#cleaned_corpora <- read_csv('./_data/manipulated/cleaned_corpora.csv')
# calculate frequency per brand
adidas <- sum(stri_count(cleaned_corpora$text, fixed ='adidas'))
nike <- sum(stri_count(cleaned_corpora$text, fixed ='nike'))
under_armour <- sum(stri_count(cleaned_corpora$text, fixed ='armour'))
reebok <- sum(stri_count(cleaned_corpora$text, fixed ='reebok'))
puma <- sum(stri_count(cleaned_corpora$text, fixed ='puma'))
# create data frame of brand & frequencies
brandFreq <- data.frame(terms = c('Adidas','Nike',
'Under Armour', 'Reebok', 'Puma'),
freq = c(adidas, nike, under_armour,
reebok, puma))
# plot share of brand
mentions_per_brand <-
ggplot(brandFreq, aes(x = reorder(terms, freq), y = freq, fill=freq)) +
geom_bar(stat = "identity") + coord_flip() +
theme_gdocs() +
geom_text(aes(label=freq), position = "stack", hjust = -0.1, size = 4) +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.line = element_line(colour = "black"),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16)) +
expand_limits(y = 200) +
labs(title = "Mentions per Brand",
x = "Brand",
y = "Frequency") +
theme(plot.title = element_text(hjust = 0.5))
# save image
ggsave("./_images/mentions_per_brand.png", mentions_per_brand, bg = "transparent")
#'
#' ## Word Frequency
#'
#' **Unigram & Bigram top names plots not used in presentation**
#'
#' ### Unigram
#'
## ----unigram frequencies, message=FALSE, warning=FALSE---------------------------------------------------------
# Frequency Data Frame
tweetSums <- rowSums(tweetTDMm)
tweetFreq <- data.frame(word=names(tweetSums),frequency=tweetSums)
rownames(tweetFreq) <- NULL
# Top Names
unigram_topNames <- subset(tweetFreq, tweetFreq$frequency > 500)
unigram_topNames$word <- stri_trans_totitle(unigram_topNames$word)
unigram_topNames <- unigram_topNames[order(unigram_topNames$frequency,
decreasing=F),]
unigram_topNames$word <- factor(unigram_topNames$word,
levels=unique(as.character(unigram_topNames$word)))
# Plot top names as bar plot
unigrams_top_names<-
ggplot(unigram_topNames, aes(x=word, y=frequency, fill=frequency)) +
geom_bar(stat="identity") +
coord_flip() + theme_gdocs() +
geom_text(aes(label=frequency), colour="black",hjust=-0.25, size=3.3) +
theme_gdocs() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.line = element_line(colour = "black"),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=13),
plot.title = element_text(face="bold", color="#000000", size=16)) +
expand_limits(y = 1200) +
labs(title = "Top Names of the 2019-20 NBA Season",
x = "Names",
y = "Frequency") +
theme(plot.title = element_text(hjust = 0.3, vjust = 1),
axis.title.y = element_text(hjust = 1, vjust = 1.2))
# save image
ggsave("./_images/top_names_unigrams.png", unigrams_top_names, bg = "transparent")
#'
#' ### Bigram
#'
## ----bigram frequencies, message=FALSE, warning=FALSE----------------------------------------------------------
# Frequency Data Frame
bigramSums <- rowSums(bigramTDMm)
bigramFreq <- data.frame(word=names(bigramSums),frequency=bigramSums)
rownames(bigramFreq) <- NULL
# Top Names
bigram_topNames <- subset(bigramFreq, bigramFreq$frequency > 18)
bigram_topNames$word <- stri_trans_totitle(bigram_topNames$word)
bigram_topNames <- bigram_topNames[order(bigram_topNames$frequency, decreasing=F),]
bigram_topNames$word <- factor(bigram_topNames$word,
levels=unique(as.character(bigram_topNames$word)))
# Plot top names as bar plot
bigrams_top_names <-
ggplot(bigram_topNames, aes(x=word, y=frequency, fill = frequency)) +
geom_bar(stat="identity") +
coord_flip() + theme_gdocs() +
geom_text(aes(label=frequency), colour="black",hjust=-0.25, size=3.3) +
theme_gdocs() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.line = element_line(colour = "black"),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=13),
plot.title = element_text(face="bold", color="#000000", size=16)) +
expand_limits(y = 60) +
labs(title = "Top Names of the 2019-20 NBA Season",
x = "Names",
y = "Frequency") +
theme(plot.title = element_text(hjust = -0.5, vjust = 1),
axis.title.y = element_text(hjust = 1, vjust = 1.2))
# save image
ggsave("./_images/top_names_bigrams.png", bigrams_top_names, bg = "transparent")
#'
#'
#' ## Word Clouds
#'
## ----word clouds, message=FALSE, warning=FALSE-----------------------------------------------------------------
#cleaned_corpora <- read_csv('./_data/manipulated/cleaned_corpora.csv')
# worldcloud of words associated with Nike
nike_wordcloud<-
word_associate(cleaned_corpora$text,
match.string = 'nike',
stopwords = c(customStopwords),
min.freq = 5,
wordcloud = T,
caps = T,
cloud.colors = c('black','darkblue'))
# wordcloud of words associated with Adidas
adidas_wordcloud<-
word_associate(cleaned_corpora$text,
match.string = 'adidas',
stopwords = c(customStopwords),
min.freq = 3,
wordcloud = T,
caps = T,
cloud.colors = c('black','darkred'))
# General word cloud of cleaned corpus
wordcloud(words = tweetFreq$word,
freq = tweetFreq$frequency,
min.freq = 7,
random.order = F,
rot.per = 0.35,
colors = c('black', 'darkred','darkblue'),
use.r.layout = T)
#'
#' ## Word Associations
#'
## ----word associations, message=FALSE, warning=FALSE-----------------------------------------------------------
################################################################################
# Nike associations
nikeAssociations <- findAssocs(tweetTDM, 'nike', 0.15)
# Organize the word associations
nikeAssocDF <- data.frame(terms=names(nikeAssociations[[1]]),
value=unlist(nikeAssociations))
nikeAssocDF$terms <- factor(nikeAssocDF$terms, levels=nikeAssocDF$terms)
rownames(nikeAssocDF) <- NULL
# Make a dot plot
nikeAssocPlot <-
ggplot(nikeAssocDF, aes(y=terms)) +
geom_point(aes(x=value), data=nikeAssocDF, col='#00008b') +
theme_gdocs() +
geom_text(aes(x=value,label=value), colour="darkblue",hjust=-0.5, vjust ="inward" , size=3) +
labs(title = "Nike Associations",
x = 'Association Level',
y = 'Terms') +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "none",
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16)) +
expand_limits(x = 0.3)
# save image
ggsave("./_images/associations_nike.png", nikeAssocPlot, bg = "transparent")
################################################################################
# Adidas associations
adidasAssociations <- findAssocs(tweetTDM, 'adidas', 0.15)
# Organize the word associations
adidasAssocDF <- data.frame(terms=names(adidasAssociations[[1]]),
value=unlist(adidasAssociations))
adidasAssocDF$terms <- factor(adidasAssocDF$terms, levels=adidasAssocDF$terms)
rownames(adidasAssocDF) <- NULL
# Make a dot plot
adidasAssocPlot <-
ggplot(adidasAssocDF, aes(y = terms)) +
geom_point(aes(x = value), data = adidasAssocDF, col = '#8b0000') +
theme_gdocs() +
geom_text(aes(x=value,label=value),
colour = "darkred", hjust = -0.5, vjust = "inward", size = 3) +
labs(title = "Adidas Associations",
x = 'Association Level',
y = 'Terms') +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "none",
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16)) +
expand_limits(x = 0.35)
# save image
ggsave("./_images/associations_adidas.png", adidasAssocPlot, bg = "transparent")
#'
#' ## Dendograms
#'
#' **Not used in Presentation**
#'
## ----NOT USED dendrogram, message=FALSE, warning=FALSE---------------------------------------------------------
# Reduce corpus by removing documents with more than 99.5% 0 terms
reducedTDM <- removeSparseTerms(tweetTDM, sparse=0.993)
reducedTDM <- as.data.frame(as.matrix(reducedTDM))
# Plot hierarchical dendogram
hierarchical_cluster <- hclust(dist(reducedTDM))
ggdendrogram(hierarchical_cluster, yaxt='n', rotate = F) +
labs(title = "NBA Dendogram",
x = 'Terms',
y = 'Frequency') +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill="transparent", colour=NA),
plot.background = element_rect(fill="transparent", colour=NA),
axis.text = element_text(face="bold", color="#000000", size=10),
axis.title = element_text(face="bold", color="#000000", size=12),
plot.title = element_text(face="bold", color="#000000", size=16))
# Create function to color clusters of dendogram
dend_change <- function(n) {
if (is.leaf(n)) {
a <- attributes(n)
labCol <- labelColors[clusMember[which(
names(clusMember) == a$label)]]
attr(n, 'nodePar') <- c(a$nodePar, lab.col =
labCol)
}
n
}
# Plot a Hierarchical dendogram with colored clusters
hierarchical_cluster_dendogram <- as.dendrogram(hierarchical_cluster)
clusMember <- cutree(hierarchical_cluster, 4)
labelColors <- c('darkgrey', 'darkred', 'black', '#bada55')
clusDendro <- dendrapply(hierarchical_cluster_dendogram, dend_change)
plot(clusDendro, main = "NBA Dendogram",type = "triangle",yaxt='n')
# Plot circular dendogram
hierarchical_cluster_dendogram <-
color_labels(hierarchical_cluster_dendogram, 4,
col = c('#bada55','darkgrey', "black", 'darkred'))
hierarchical_cluster_dendogram <-
color_branches(hierarchical_cluster_dendogram, 4,
col = c('#bada55','darkgrey', "black", 'darkred'))
circlize_dendrogram(hierarchical_cluster_dendogram,
labels_track_height = 0.4, dend_track_height = 0.3)
#'
#' ## Pyramid Plot
#'
## ----adidas vs nike, message=FALSE, warning=FALSE--------------------------------------------------------------
# Import cleaned tweets
#partially_cleaned_tweets <-
# read_csv('./_data/manipulated/partially_cleaned_tweets.csv')
# Create data frames containing 'nike' and 'adidas'
nike_df <- partially_cleaned_tweets[
str_detect(partially_cleaned_tweets$text, "nike"), ]
adidas_df <- partially_cleaned_tweets[
str_detect(partially_cleaned_tweets$text, "adidas"), ]
# Export above dataframes
write.csv(nike_df,'./_data/manipulated/nike.csv',
row.names = F)
write.csv(adidas_df,'./_data/manipulated/adidas.csv',
row.names = F)
# Read in data, clean & organize
textA <- cleanMatrix(pth = './_data/manipulated/nike.csv',
columnName = 'text',
collapse = T,
customStopwords = c(customStopwords, 'nike'),
type = 'TDM',
wgt = 'weightTf') # weightTfIdf or weightTf
textB <- cleanMatrix(pth = './_data/manipulated/adidas.csv',
columnName = 'text',
collapse = T,
customStopwords = c(customStopwords, 'adidas'),
type = 'TDM',
wgt = 'weightTf')
# Create merged data set
pyramid_df <- merge(textA, textB, by ='row.names')
names(pyramid_df) <- c('terms', 'nike', 'adidas')
# Calculate the absolute differences among in common terms
pyramid_df$diff <- abs(pyramid_df$nike - pyramid_df$adidas)
# Organize data frame for plotting
pyramid_df <- pyramid_df[order(pyramid_df$diff, decreasing=TRUE), ]
top15 <- pyramid_df[1:15, ]
# Pyarmid Plot
pyramid.plot(lx = top15$nike, #left
rx = top15$adidas, #right
labels = top15$terms, #terms
top.labels = c('', '', ''), #corpora
gap = 7, # space for terms to be read
main = 'Words in Common', # title
unit = 'Word Frequency',
lxcol = 'darkblue',
rxcol = 'darkred')
#'
|
93dd33da36028ca71a76dee6dd66f19a47c05b92
|
61c091c21d06b7c61f35a24d4fe3d8882e9fb254
|
/man/plot_cpue.Rd
|
0dcdd047e32c263522ec44b7455820307eeb045e
|
[] |
no_license
|
pfmc-assessments/nwfscSurvey
|
b3be76b410bdc5dae168e84d2ee1a2c64c98e098
|
423800ecb91137cba1587ac19226a3ebb8d50c2d
|
refs/heads/main
| 2023-07-28T08:35:55.810331
| 2023-07-20T17:10:25
| 2023-07-20T18:17:33
| 26,344,817
| 4
| 2
| null | 2023-07-20T17:31:58
| 2014-11-08T00:38:17
|
R
|
UTF-8
|
R
| false
| true
| 641
|
rd
|
plot_cpue.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_cpue.R
\name{plot_cpue}
\alias{plot_cpue}
\title{This function plots cpue and length by latitude and depth}
\usage{
plot_cpue(dir = NULL, catch, plot = 1:3, width = 7, height = 7)
}
\arguments{
\item{dir}{Directory to save files to}
\item{catch}{Data catch file}
\item{plot}{A vector of integers specifying the figures you want.}
\item{width}{Numeric figure width in inches, defaults to 7}
\item{height}{Numeric figure height in inches, defaults to 7}
}
\description{
This function plots cpue and length by latitude and depth
}
\author{
Chantel Wetzel
}
|
8bdf09c225065831da3c38aee0e18bd43f210ac5
|
23ced6ff5ba16d2bcf6701070dc840ca133ac22f
|
/Graficas_desempeño_votos.R
|
905601bc810c4851d0a11ac7551641a6aa6e53ad
|
[] |
no_license
|
dhumbertojs/random
|
2df79ec3c092e5de41c55ad8c100bd84d2442a77
|
63e54716249b4c383c57f8b108f4e8a5d61bbf47
|
refs/heads/master
| 2020-05-18T03:34:51.580014
| 2019-10-06T22:50:15
| 2019-10-06T22:50:15
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 11,751
|
r
|
Graficas_desempeño_votos.R
|
rm(list=ls())
setwd("~")
inp = "/Users/Emmanuel RB/Documents/tesis/Datos"
out = "/Users/Emmanuel RB/Documents/tesis/out"
####Paquetes####
library(dplyr)
library(ggplot2)
require(scales)
####Datos####
del <- read.csv(paste(inp, "delitos_b.csv", sep = "/"))
ser <- read.csv(paste(inp, "servicios_b.csv", sep = "/"))
del <- del %>%
group_by(muni) %>%
mutate(
PAN_lag = lag(PAN, n=1, order_by = muniYear),
PRI_lag = lag(PRI, n=1, order_by = muniYear),
PRD_lag = lag(PRD, n=1, order_by = muniYear),
PAN_PRD_lag = lag(PAN_PRD, n=1, order_by = muniYear),
PAN_perf = PAN - PAN_lag,
PRI_perf = PRI - PRI_lag,
PRD_perf = PRD - PRD_lag,
PAN_PRD_perf = PAN_PRD - PAN_PRD_lag
) %>%
ungroup(muni)
ser <- ser %>%
group_by(muni) %>%
mutate(
PAN_lag = lag(PAN, n=1, order_by = muniYear),
PRI_lag = lag(PRI, n=1, order_by = muniYear),
PRD_lag = lag(PRD, n=1, order_by = muniYear),
PAN_PRD_lag = lag(PAN_PRD, n=1, order_by = muniYear),
PAN_perf = PAN - PAN_lag,
PRI_perf = PRI - PRI_lag,
PRD_perf = PRD - PRD_lag,
PAN_PRD_perf = PAN_PRD - PAN_PRD_lag
) %>%
ungroup(muni)
####Gráficas nuevas####
#scatter delitos por partido####
delPAN1 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_total , 0.9, na.rm = T)), aes(x = PAN_perf, y = change_total, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Total de delitos", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
delPAN2 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_hom , 0.9, na.rm = T)), aes(x = PAN_perf, y = change_hom, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Homicidios", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(delPAN1, delPAN2)
annotate_figure(zscat_del, top = text_grob("Tipo de delito y desempeño electoral del PAN (1994-2010)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sdel_PAN.png", path = out, dpi = 320)
delPRI1 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_total , 0.9, na.rm = T)), aes(x = PRI_perf, y = change_total, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Total de delitos", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
delPRI2 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_hom , 0.9, na.rm = T)), aes(x = PRI_perf, y = change_hom, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Homicidios", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(delPRI1, delPRI2)
annotate_figure(zscat_del, top = text_grob("Tipo de delito y desempeño electoral del PRI (1994-2010)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sdel_PRI.png", path = out, dpi = 320)
delPRD1 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_total , 0.9, na.rm = T)), aes(x = PRD_perf, y = change_total, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Total de delitos", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
delPRD2 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_hom , 0.9, na.rm = T)), aes(x = PRD_perf, y = change_hom, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Homicidios", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(delPRD1, delPRD2)
annotate_figure(zscat_del, top = text_grob("Tipo de delito y desempeño electoral del PRD (1994-2010)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sdel_PRD.png", path = out, dpi = 320)
delPAN_PRD1 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_total , 0.9, na.rm = T)), aes(x = PAN_PRD_perf, y = change_total, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Total de delitos", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
delPAN_PRD2 <- ggplot(data = subset(del, !is.na(alt) & win_top != "Otros" & quantile(change_hom , 0.9, na.rm = T)), aes(x = PAN_PRD_perf, y = change_hom, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Homicidios", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(delPAN_PRD1, delPAN_PRD2)
annotate_figure(zscat_del, top = text_grob("Tipo de delito y desempeño electoral del PAN_PRD (1994-2010)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sdel_PAN_PRD.png", path = out, dpi = 320)
#scatter servicios por partido####
serPAN1 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_agua , 0.9, na.rm = T)), aes(x = PAN_perf, y = change_agua, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Agua", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPAN2 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_elec , 0.9, na.rm = T)), aes(x = PAN_perf, y = change_elec, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Electricidad", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPAN3 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_dren , 0.9, na.rm = T)), aes(x = PAN_perf, y = change_dren, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Drenaje", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(serPAN1, serPAN2, serPAN3)
annotate_figure(zscat_del, top = text_grob("Tipo de servicio y desempeño electoral del PAN (1994-2015)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sser_PAN.png", path = out, dpi = 320)
serPRI1 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_agua , 0.9, na.rm = T)), aes(x = PRI_perf, y = change_agua, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Agua", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPRI2 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_elec , 0.9, na.rm = T)), aes(x = PRI_perf, y = change_elec, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Electricidad", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPRI3 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_dren , 0.9, na.rm = T)), aes(x = PRI_perf, y = change_dren, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Drenaje", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(serPRI1, serPRI2, serPRI3)
annotate_figure(zscat_del, top = text_grob("Tipo de servicio y desempeño electoral del PRI (1994-2015)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sser_PRI.png", path = out, dpi = 320)
serPRD1 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_agua , 0.9, na.rm = T)), aes(x = PRD_perf, y = change_agua, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Agua", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPRD2 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_elec , 0.9, na.rm = T)), aes(x = PRD_perf, y = change_elec, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Electricidad", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPRD3 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_dren , 0.9, na.rm = T)), aes(x = PRD_perf, y = change_dren, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Drenaje", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(serPRD1, serPRD2, serPRD3)
annotate_figure(zscat_del, top = text_grob("Tipo de servicio y desempeño electoral del PRD (1994-2015)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sser_PRD.png", path = out, dpi = 320)
serPAN_PRD1 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_agua , 0.9, na.rm = T)), aes(x = PAN_PRD_perf, y = change_agua, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Agua", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPAN_PRD2 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_elec , 0.9, na.rm = T)), aes(x = PAN_PRD_perf, y = change_elec, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Electricidad", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
serPAN_PRD3 <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros" & quantile(change_dren , 0.9, na.rm = T)), aes(x = PAN_PRD_perf, y = change_dren, col = factor(alt))) +
geom_point(alpha = 0.4) + ylim(-200, 200) + scale_x_continuous(labels = comma) +
labs(y = "Drenaje", x = "Desempeño electoral", col = "Alt") +
geom_smooth(method = lm, se= F)
zscat_del <- ggarrange(serPAN_PRD1, serPAN_PRD2, serPAN_PRD3)
annotate_figure(zscat_del, top = text_grob("Tipo de servicio y desempeño electoral del PAN_PRD (1994-2015)", color = "black", face = "bold", size = 14), bottom = text_grob("Diferencial entre el año electoral t-1 y t", color = "blue", hjust = 1, x = 1, face = "italic", size = 10))
ggsave("sser_PAN_PRD.png", path = out, dpi = 320)
#este es otro tema####
try <- ggplot(data = subset(ser, !is.na(alt) & win_top != "Otros"), aes(x = PAN_perf, y = change_dren#, col = factor(alt)
)) +
geom_point(alpha = 0.4) + ylim(-200, 200) +
labs(y = "", x = "", col = "Alt") +
geom_smooth()#(method = lm, se= F)
|
663c05eb0890f97f733da3e5a30f242500dfe6ae
|
c900792007ef419990fb6ef194cfd9edf14f7e9c
|
/man/finn.Rd
|
98b547880b208b46dd28c467dfb80f45102d1091
|
[] |
no_license
|
ybkamaleri/boligfinn
|
1e6c01eb00a11ad15e9b5967ebd4ec58987e5cef
|
02833b372448b8268cf49a746508442bc0a4e9a9
|
refs/heads/master
| 2021-02-13T20:06:06.835470
| 2020-06-18T21:10:07
| 2020-06-18T21:10:07
| 244,728,060
| 6
| 0
| null | 2020-04-28T21:44:43
| 2020-03-03T19:52:01
|
R
|
UTF-8
|
R
| false
| true
| 642
|
rd
|
finn.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/finn.R
\name{finn}
\alias{finn}
\title{Create table from Finn search}
\usage{
finn(area = 1, pmin = 35, pmax = 5, add = NULL, ...)
}
\arguments{
\item{area}{Areas in Oslo to choose from
\itemize{
\item 1:Bygdøy/Frogner
\item 2:Grünerløkka/Sofienberg
\item 3:Sagene/Torshov
\item 4:St.Hanshaugen/Ullevål
\item 5:Uranienborg/Majorstuen
}}
\item{pmin}{Minimum price}
\item{pmax}{Maximum price}
\item{add}{Other areas to choose from which isn't included in the default}
\item{...}{Extra arguments}
}
\description{
Create table from Finn search
}
|
54dbab51341236f4750065f3f316851cc18c4878
|
3b1c82ecb7622ce0030470c19732c17f6fda89ff
|
/SC2019Lab-7-王哲涵-16081043.R
|
1d0e8439525cdc021284e41b27fe3b5613343b00
|
[] |
no_license
|
anhnguyendepocen/SC2019-assignments
|
64bbb7a8d82afba4cc636122ed89268db8aca25e
|
a4cc348c40c4dc4cb373cbbde2cf92acb71cd69b
|
refs/heads/master
| 2020-09-02T03:41:35.656473
| 2019-04-12T12:48:48
| 2019-04-12T12:48:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 944
|
r
|
SC2019Lab-7-王哲涵-16081043.R
|
f<-function(X){
x1<-X[1]
x2<-X[2]
x1^2+x2^2
}
A=c(1,1)
B=c(1,2)
C=c(2,2)
x<-rbind(A,B,C)
y<-rbind(f(A),f(B),f(C))
r2=1
r3=2
while(sqrt((x[r2,1]-x[r3,1])^2+(x[r2,2]-x[r3,2])^2)>0.0001){
h<-rank(y)
r1<-which(h==3)
r2<-which(h==2)
r3<-which(h==1)
R<-c(as.numeric(x[r2,1]+x[r3,1]-x[r1,1]),as.numeric(x[r2,2]+x[r3,2]-x[r1,2]))
if (y[r3]<f(R)<y[r1]){
y[r1]<-f(R)
x[r1,1]<-R[1]
x[r1,2]<-R[2]
}
else if (y[r3]-f(R)>0){
Q<-c(as.numeric(x[r2,1]*1.5+x[r3,1]*1.5-x[r1,1]*2),as.numeric(x[r2,2]*1.5+x[r3,2]*1.5-x[r1,2]*2))
if (f(Q)<f(R)){
y[r1]<-f(Q)
x[r1,1]<-Q[1]
x[r1,2]<-Q[2]
}
else{
y[r1]<-f(R)
x[r1,1]<-R[1]
x[r1,2]<-R[2]
}
}
else{
R<-c(as.numeric(x[r2,1]*0.25+x[r3,1]*0.25+x[r1,1]*0.5),as.numeric(x[r2,2]*0.25+x[r3,2]*0.25+x[r1,2]*0.5))
y[r1]<-f(R)
x[r1,1]<-R[1]
x[r1,2]<-R[2]
}
}
print(y[1])
|
cbc29240e748059b3811079605d127fdb7796c7a
|
0216347b37fe7f78222e3e0729b105f4fb3e8b93
|
/Plot4.R
|
e5164d6f8be3805a30722524791ab3504f586654
|
[] |
no_license
|
serbrach/Eploratory-Data-Analysis
|
d2139950cec1529f4b8be7575314ffed4477cbdc
|
46da2ab9dd3c6d579dc36b61178420b4980e8a73
|
refs/heads/main
| 2023-03-08T00:14:09.561569
| 2021-02-20T17:31:13
| 2021-02-20T17:31:13
| 340,712,252
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 674
|
r
|
Plot4.R
|
setwd("E:/Descargas/exdata_data_NEI_data/")
library(dplyr)
library(ggplot2)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
CoalVect<-grepl("Coal",SCC$EI.Sector)
SCC.sub<-SCC[CoalVect,]
NEI.mod<-NEI%>% filter(SCC %in% SCC.sub$SCC)%>%
group_by(year)%>%summarise(Total.EM=sum(Emissions),)
png("Plot4.png", height=480 ,width =480)
ggplot(NEI.mod,aes(x=factor(year),y=Total.EM/1000,fill=year,label=round(Total.EM/1000)))+geom_bar(stat="identity")+
xlab("Year")+ylab("Total Emissions (*1000 Tons)")+ggtitle("Total coal-related emissions in the Country")+ theme(legend.position = "none")
dev.off()
|
2cea3ff243bbb6acbd932cab32cdb1441b6a29cf
|
33b6111a3ebc6a3c24e521bda012dafb3fa8c7e5
|
/task1/data_cleaning_scripts/decathlon.R
|
1332443f4d155b3f726383b4edf4b69ac77f7541
|
[] |
no_license
|
pjbateman/dirty_data_codeclan_project_paul
|
bb17b62f8f893e468c1852e98bdc8052270446b3
|
d527a03350757e5236e46dd70de72d87094a54f7
|
refs/heads/main
| 2023-06-24T05:54:48.751676
| 2021-07-15T16:17:39
| 2021-07-15T16:17:39
| 384,486,186
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,141
|
r
|
decathlon.R
|
# loading in the necessary libraries
library(tidyverse) # loads the tibble package to convert rownmes_to_col
library(janitor) # to use the clean names function
library(readr) # used to read in the raw data file type .rds
library(here) # to enable the file locations to be reproducible
# fetching the raw data file
decathlon <- read_rds(here("raw_data/decathlon.rds"))
# the first column contains row names and needs to be fixed
decathlon <- decathlon %>%
rownames_to_column(var = "athlete")
# cleaning the column names
decathlon_clean_names <- clean_names(decathlon)
# check the column data types make sense
glimpse(decathlon_clean_names)
# The athlete names should feature a consistent format
decathlon_clean_names <- decathlon_clean_names %>%
mutate(
athlete = str_to_title(athlete)
)
# we need to make the data "tidy", as rank 1 for example, appears twice
# use the pivot_wider() function
decathlon_wide <- decathlon_clean_names %>%
pivot_wider(names_from = competition,
values_from = rank)
# finally, output the clean data to a new file
write_csv(decathlon_wide, "clean_data/decathlon_clean.csv")
|
3a2a007bd8d552b52404b0b8ceab161ac8c2e5f0
|
7670b011e2504fc6213456b2f0f5992f056470bb
|
/Lectures/Lecture_2/ggplot2_example.R
|
fda9af14f1e8fd55003bde4598de3172dfe34a54
|
[
"MIT"
] |
permissive
|
ogencoglu/R_for_VTT
|
448f57387e4131c11d5f47f00b392ba2225e82da
|
a7f15e1feedc745cadbd7db7d43d192265c047fd
|
refs/heads/master
| 2021-01-01T03:53:28.362701
| 2016-05-15T06:31:06
| 2016-05-15T06:31:06
| 56,995,798
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,141
|
r
|
ggplot2_example.R
|
# R Lecture to VTT - Lecture 2
# Author : Oguzhan Gencoglu
# Latest Version : 03.05.2016
# Contact : oguzhan.gencoglu@tut.fi
# -------------- Yet another ggplot2 example --------------
# Read the data (temperature in chicago)
nmmaps <- read.csv("chicago-nmmaps.csv", as.is=T)
nmmaps$year <- substring(nmmaps$date, 1, 4) # year
nmmaps$date<-as.Date(nmmaps$date)
nmmaps <- nmmaps[nmmaps$date>as.Date("1996-12-31"),] # subset
nmmaps$season <- as.factor(nmmaps$season) # change to factor
# simple stuff
library(ggplot2)
g1 <- ggplot(nmmaps, aes(date, temp)) + geom_point(color = "firebrick")
g1
g2 <- ggplot(nmmaps, aes(date, temp, color = season)) + geom_point()
g2
# facet plot
g3 <- ggplot(nmmaps, aes(date,temp)) + geom_point(color="aquamarine4") + facet_wrap(~year, nrow=1)
g3
# facet plot
g4 <- ggplot(nmmaps, aes(date,temp)) + geom_point(color="chartreuse4") + facet_wrap(~year, ncol=2)
g4
# box plots
g5 <- ggplot(nmmaps, aes(x=season, y=o3)) + geom_boxplot(fill="chartreuse4") + coord_flip()
g5
g6 <- g5 + geom_jitter(alpha=0.5, aes(color=season), position = position_jitter(width = .2))
g6
|
f70a4e4e45ab4df535cc52e663bc677f70f45c1b
|
aee2c11aff6bd0874a03fbd16f852ad785efe5ba
|
/man/sirs.fct.Rd
|
0df5932233efbd88260cbc05d1e36de6d2ea9fbe
|
[
"MIT"
] |
permissive
|
maciejrosolowski/progressdatenbankderivate
|
bca72eadf47ba2dcffeed80cc120f25458f13581
|
1a2e31ed7e62970a0206883173d32e14d888563d
|
refs/heads/master
| 2021-02-18T18:34:28.100509
| 2020-08-02T13:07:09
| 2020-08-02T13:07:09
| 245,223,654
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 3,041
|
rd
|
sirs.fct.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sirs.fct.R
\name{sirs.fct}
\alias{sirs.fct}
\title{Compute the SIRS score.}
\usage{
sirs.fct(
DID_PROBAND,
FRM_BAS,
FRM_BEF,
FRM_B24,
FRM_DIL_LABORWERTE,
DID_CLIN,
FRM_RR,
FRM_KAT,
zp_fabian = "d0",
event2zeitpunkt_df = progressdatenbankderivate::event2zeitpunkt_table
)
}
\arguments{
\item{DID_PROBAND}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_BAS}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_BEF}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_B24}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_DIL_LABORWERTE}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{DID_CLIN}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_RR}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{FRM_KAT}{data.table containing the table with the same name from
the database of the PROGRESS study}
\item{zp_fabian}{vector of characters. They must be present in
event2zeitpunkt_table$zp_fabianref.}
\item{event2zeitpunkt_df}{data.table event2zeitpunkt_table (available with
the package).}
}
\value{
a named list with components: input, input2 and out. out is
a data.table with one row corresponding to a combination of PATSTUID
(patient) and "EVENT" (time point). infec.septic.servsept contains
the number of the met SIRS criteria (infected (1), sepsis (2),
severe sepsis(3)); septischer.schock indicates if criteria for septic shock
are met. If 50% or less of the 16 input parameters are NA then the score
is set to NA.
}
\description{
Compute the SIRS score.
}
\examples{
\dontrun{
excel_fn <- paste0("/net/ifs1/san_projekte/projekte/",
"PROGRESS/Datenmanagement/Data_freezes/",
"20190320/PROGRESS-freeze_201903_01.xlsx")
DID_PROBAND <- readxl::read_excel(excel_fn, 'DID_PROBAND')
FRM_BAS <- readxl::read_excel(excel_fn, 'FRM_BAS')
FRM_BEF <- readxl::read_excel(excel_fn, 'FRM_BEF')
FRM_B24 <- readxl::read_excel(excel_fn, 'FRM_B24')
FRM_DIL_LABORWERTE <- readxl::read_excel(excel_fn, "FRM_DIL_LABORWERTE")
DID_CLIN <- readxl::read_excel(excel_fn, 'DID_CLIN')
FRM_RR <- readxl::read_excel(excel_fn, 'FRM_RR', guess_max = 10e5)
FRM_KAT <- readxl::read_excel(excel_fn, 'FRM_KAT')
data.table::setDT(DID_PROBAND)
data.table::setDT(FRM_BAS)
data.table::setDT(FRM_BEF)
data.table::setDT(FRM_B24)
data.table::setDT(FRM_DIL_LABORWERTE)
data.table::setDT(DID_CLIN)
data.table::setDT(FRM_RR)
data.table::setDT(FRM_KAT)
erg_d0 <- sirs.fct(DID_PROBAND, FRM_BAS, FRM_BEF, FRM_B24,
FRM_DIL_LABORWERTE, DID_CLIN, FRM_RR, FRM_KAT, zp_fabian = "d0")
erg_d0
erg_d1 <- sirs.fct(DID_PROBAND, FRM_BAS, FRM_BEF, FRM_B24,
FRM_DIL_LABORWERTE, DID_CLIN, FRM_RR, FRM_KAT, zp_fabian = "d1")
erg_d1
}
}
|
f4185e4894c2e18bce23424c9d413cce1b3529cf
|
72d52da12b677aeebb7490c20c06738b179f2856
|
/man/cong1f.Rd
|
63f57eef93203f948a63613d73f173c89645db27
|
[] |
no_license
|
cran/Lambda4
|
94ac2cbd0f5213050a20a4c2920c939a531d109c
|
0ace2eaffe6b1f2c9d4afa19fe416c3bb5bc1ad6
|
refs/heads/master
| 2016-09-10T12:39:03.901179
| 2013-07-30T00:00:00
| 2013-07-30T00:00:00
| 17,680,297
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 899
|
rd
|
cong1f.Rd
|
\name{cong1f}
\alias{cong1f}
\docType{data}
\title{One-Factor Congeneric Covariance Matrix}
\description{
This Covariance matrix was used as the population model for one set of simulations. It was used to represent a congeneric data structure in which the factor loadings are set at .5, .6, .7, .8, .5, .6, .7, and .8. The error variances were set at .6^2, .7^2, .8^2, .9^2, .6^2, .7^2, .8^2, and .9^2.
}
\usage{data(cong1f)}
\format{
A covariance matrix of 8 theoretical items.
}
\examples{
###---Loadings
fx<-t(matrix(c(
.5,
.6,
.7,
.8,
.5,
.6,
.7,
.8), nrow=1))
###--Error Variances
err<-diag(c(.6^2,.7^2,.8^2,.9^2,
.6^2,.7^2,.8^2,.9^2))
###---matrix of factor covariances
phi<-matrix(1, nrow=1)
###---Reliability Calculation---###
t1<-matrix(c(rep(1,8)), nrow=1)
t1t<-matrix(c(rep(1,8)), ncol=1)
(fx\%*\%phi\%*\%t(fx)+err)
}
\keyword{datasets}
|
704a2bdb2df10b950fd6ae020fda760306a9ca6e
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/TMDb/examples/tv_season_external_ids.Rd.R
|
ea9c89be1bce24890e98b23ba8d2904a85b268d2
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 500
|
r
|
tv_season_external_ids.Rd.R
|
library(TMDb)
### Name: tv_season_external_ids
### Title: Retrieve external ids for a TV season.
### Aliases: tv_season_external_ids
### Keywords: tv_season_external_ids
### ** Examples
## Not run:
##D
##D ## An example of an authenticated request,
##D ## where api_key is fictitious.
##D ## You can obtain your own at https://www.themoviedb.org/documentation/api
##D
##D api_key <- "key"
##D
##D tv_season_external_ids(api_key = api_key, id = 1396, season_number = 3)
## End(Not run)
|
69f1c7585021984e485d28213f6b3156a57bffc5
|
f3f8a429bdfd404f8cbeb4e325b414a2bbfb0e61
|
/RProgram-Assigment2.R
|
9976156a615d32d2aa62d75ffdc298cfc7406ffd
|
[] |
no_license
|
karthickanand/ProgrammingAssignment2
|
a5d4ae146d148b073819c09be4420f9b1f03011b
|
f4d04d384a466d51ab6085e479eeee3facaa494e
|
refs/heads/master
| 2020-12-28T19:07:28.706214
| 2015-06-21T16:02:15
| 2015-06-21T16:02:15
| 37,814,467
| 0
| 0
| null | 2015-06-21T15:41:07
| 2015-06-21T15:41:07
| null |
UTF-8
|
R
| false
| false
| 497
|
r
|
RProgram-Assigment2.R
|
makeCacheMatrix <- function(x=matrix()) {
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinv <- function(solve)
m <<- solve
getinv <- function () m
list(set= set, get=get, setinv=setinv, getinv=getinv)
}
cacheSolve <- function(x=matrix()) {
m <- x$getinv()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data)
x$setinv(m)
m
}
a<-makeCacheMatrix()
a$set(matrix(c(1,2,2,1),2,2))
cacheSolve(a)
|
48fc5bfeba6b4c84f6431e47d366b4e9461cde63
|
0dc13ef62f4809459ae85579937ab857544658ab
|
/coursera-data-sci/02_GettingAndCleaningData/02_02_reading_from_hdf5.R
|
4325146121328da5c771e6d47509d28f2f259b13
|
[] |
no_license
|
dsk1/coursera-data-sci
|
aed19dd908ac5b1261f04c7edce92a97017e294a
|
d0eac7c8bc21f64a011f2c7fd85090ffce4e808d
|
refs/heads/master
| 2016-08-11T09:50:24.997982
| 2016-03-01T04:36:34
| 2016-03-01T04:36:34
| 46,252,940
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 906
|
r
|
02_02_reading_from_hdf5.R
|
source("http://bioconductor.org/biocLite.R")
biocLite("rhdf5")
library(rhdf5)
created = h5createFile("example.h5")
created
created = h5createGroup("example.h5", "foo")
created = h5createGroup("example.h5", "baa")
created = h5createGroup("example.h5", "foo/foobaa")
h5ls("example.h5")
# write a group INTO the hdf5 file
A = matrix(1:10, nr = 5, nc =2)
A
h5write(A, "example.h5", "foo/A")
B = array(seq(0.1, 2.0, by=0.1), dim = c(5,2,2))
B
attr(B, "scale") <- "liter"
B
h5write(B, "example.h5", "foo/foobaa/B")
h5ls("example.h5")
# writing a data frame into hdf5 file
df <- data.frame(1L:5L, seq(0,1,length.out = 5), c("abc", "cde", "fghi", "a", "s"), stringsAsFactors = FALSE)
df
h5write(df, "example.h5", "df")
h5ls("example.h5")
h5write(c(12, 13, 14), "example.h5", "foo/A", index = list(1:3, 1))
h5read("example.h5", "foo/A")
h5read("example.h5", "foo/foobaa")
h5read("example.h5", "df")
H5close()
|
5eef0a0354c88888ecfda82df05e2176f038e258
|
b150872f2739c4efc1b21c2b4b1edd2bcfcf51be
|
/man/predict.epplab.Rd
|
f6a5b93d25280de0daf49f7ef37924e42d5f70da
|
[] |
no_license
|
fischuu/REPPlab
|
51378051c9a88cbcb705b055975b75c3f735b4cc
|
4587d0534c23a64fc06ac5118c66e2f6f84e17bc
|
refs/heads/master
| 2023-04-30T01:57:20.872407
| 2023-04-14T06:00:23
| 2023-04-14T06:00:23
| 40,982,012
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,439
|
rd
|
predict.epplab.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.epplab.R
\docType{methods}
\name{predict.epplab}
\alias{predict,epplab-method}
\alias{predict-method}
\alias{predict.epplab}
\title{Calculates projections for a new Data Object}
\usage{
\method{predict}{epplab}(object, which = 1, data = NULL, ...)
}
\arguments{
\item{object}{Object of class \code{epplab}.}
\item{which}{Onto which direction should the new data be projected.}
\item{data}{The new data object}
\item{...}{Additional parameters}
}
\value{
A matrix having in each column the projection onto the direction of
a certain run and in each row the projected value.
}
\description{
Calculates the projections of a new data object onto the directions from an
existing \code{ebblab} object.
}
\details{
The default projection direction is the direction with the best objective
criterion. In case that no data is given to the function, the fitted scores
for the original data will be returned.
}
\examples{
library(tourr)
data(olive)
res <- EPPlab(olive[,3:10], PPalg="PSO", PPindex="KurtosisMin", n.simu=10, maxiter=20)
newData <- matrix(rnorm(80), ncol=8)
# Projection on the best direction
predict(res, data=newData)
# Projection on the best 3 directions
predict(res, which=1:3, data=newData)
# Similar with function fitted() when no data is given:
predict(res)
fitted(res)
}
\author{
Daniel Fischer
}
\keyword{methods}
\keyword{print}
|
a3a045759b816caaa6b07990a09d1bfaeb6ada97
|
95c0e1e6e4a69ac2cb20955b96692cabc7db6533
|
/ss_compare_fn.R
|
5ca850072c4250aac8a64b8c774eee76c9418ba5
|
[] |
no_license
|
mkapur/SS_compare
|
ac67effd102e100ba055be444aa96d0c1ec366cc
|
fb90d73e90c90a4f05ebec7ac053acf8338f061e
|
refs/heads/master
| 2018-07-19T19:19:03.699944
| 2018-06-02T00:14:57
| 2018-06-02T00:14:57
| 114,181,109
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,365
|
r
|
ss_compare_fn.R
|
## Function to generate and save plots from SSComparePlots and compute by-fleet likelihoods.
## M Kapur maia.kapur@noaa.gov
## Dec 2017
ss_compare <- function(rootdir,
pattern = "00_",
llabels = c('W 0.1','W 0.2','W 0.3','W 0.4','W 0.5','W 0.6','W 0.7','W 0.8','W 0.9','W 1','Survey05'),
likeCSV = T,
likeLabel = c('Surv_like','SizeFreq_like:_2'),
lambdaLabel = c('Surv_lambda','SizeFreq_lambda:_2'),
fishery = "Deep7",
fleetIdx = c(2,3)){
dir.create("plots") ## make storage for plots
## make summary object
mods = list.dirs(rootdir) %>%
.[grepl(pattern, .)]
summaryoutput = mods %>%
SSgetoutput(dirvec = .,
getcovar = F,
ncols = 1000) %>%
SSsummarize()
## dump plots to file
SSplotComparisons(summaryoutput, plot=T, pdf=F, png=F, print = T,plotdir = "plots",
legendloc='bottomright', legendlabels=llabels)
## dump special fleets to file
for (ii in 1:length(fleetIdx)) {
SSplotComparisons(summaryoutput, plot=F, pdf=F, png=T, plotdir="plots",
legendloc='bottomright', legendlabels=llabels, indexfleets=fleetIdx[ii])
}
## Not working - doxx says "needs to be generalized"
# SSplotSummaryF(summaryoutput,plot=T,print = T,plotdir="plots")
message("rendered plots")
if(likeCSV) {
dir.create("By-Fleet Likelihoods")
data.frame(
'model' = sub('.*/', '', mods),
'totlike' = as.numeric(summaryoutput$likelihoods[summaryoutput$likelihoods$Label == 'TOTAL', 1:length(mods)]),
summaryoutput$likelihoods_by_fleet %>%
filter(Label == lambdaLabel[2]) %>%
select(fishery) %>%
head(length(mods)),
summaryoutput$likelihoods_by_fleet %>%
filter(Label == likeLabel[2]) %>%
select(fishery) %>%
head(length(mods))
) %>%
plyr::rename(c(
"Deep7" = paste0(fishery, lambdaLabel[2]),
"Deep7.1" = paste0(fishery, likeLabel[2])
)) %>%
write.csv(
.,
file = paste(
'By-Fleet Likelihoods/LBF',
format(Sys.time(), "%Y%m%d_%H%M.csv"),
sep = '_'
),
row.names = F
)
}
message("created by-fleet likelihood CSV")
graphics.off()
}
|
34024dc159d52cdb175ea355b03e93c7fe562de9
|
d84acfead13e18553a2c04c8fa8bf51ed60d11a8
|
/Movies_da_App/system2_functions.R
|
67120425f9ba34b709432c88c9452f2119f0f67b
|
[] |
no_license
|
Z-Jarrah/PSL-FALL-2020
|
3d931f040092ad2a8b6611af3954c4d8fe8d40dd
|
59164ee31e51f5b2b6e18f76ed663038c2fde2ac
|
refs/heads/master
| 2023-01-30T00:12:37.501179
| 2020-12-15T06:01:38
| 2020-12-15T06:01:38
| 291,346,837
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,558
|
r
|
system2_functions.R
|
#system2_functions.R
library(dplyr)
library(data.table)
library(Matrix)
library(plyr)
library(recommenderlab)
# load rating data ----
# ratings = read.csv(paste0(data_url, 'ratings.dat?raw=true'),
# sep = ':',
# colClasses = c('integer', 'NULL'),
# header = FALSE)
ratings = readLines("data/ratings.dat")
ratings = strsplit(ratings, split = "::", fixed = TRUE, useBytes = TRUE) #seperated by ::
ratings = matrix(unlist(ratings), ncol = 4, byrow = TRUE) #is a list of lists
ratings = data.frame(ratings, stringsAsFactors = FALSE) # one line per movie unlike the ratings
colnames(ratings) = c('UserID', 'MovieID', 'Rating', 'Timestamp')
ratings = ratings[, -4] #dont need timestamp
sample_UserIDs = sample(ratings$UserID, size = 1000)
ratings = ratings[ratings$UserID %in% sample_UserIDs, ]
#assumes ratings and movies have already been loaded into a dataframe
#assumes new user ratings are being returned in a two column data frame of (movieID, Rating)
newuserID = 9999
#include the active user in the ratings dataframe
system2_recs = function(input, n = 10){
active_user_ratings = cbind(newuserID, input)
colnames(active_user_ratings) = c('UserID', 'MovieID', 'Rating')
ratings = rbind(ratings, active_user_ratings)
Rmat = create_rating_matrix(ratings)
active_user_ID = paste0('u', newuserID)
active_user_Rmat_row = which(rownames(Rmat) == active_user_ID)
rec_SVD = Recommender(Rmat,
method = 'SVD',
parameter = list(normalize = 'center', k = 25, maxiter = 50))
preds = predict(rec_SVD, Rmat[active_user_Rmat_row,], n = n)
preds_list = as(preds, "list")[[1]]
movie_ids = unlist(lapply(preds_list, left))
return(movie_ids)
}
#returns a movie ID without the m in front of the actual ID
left = function(string){
return(substr(string, 2, nchar(string)))
}
create_rating_matrix = function(ratings_df){
#create a sparse matrix with data x at location i,j
u = paste0('u', ratings_df$UserID) #user number ...
m = paste0('m', ratings_df$MovieID) #movie number ...
x = ratings_df$Rating
cat("in rating matrix")
#nessecary to prevent sparseMatrix freaking out over i + j being characters instead of integers
tmp = data.frame(u, m, x, stringsAsFactors = T)
Rmat = sparseMatrix(as.integer(tmp$u), as.integer(tmp$m), x = as.integer(tmp$x))
#the levels for each are the order in which the data is already entered into the matrix
rownames(Rmat) = levels(tmp$u)
colnames(Rmat) = levels(tmp$m)
Rmat = new('realRatingMatrix', data = Rmat)
return(Rmat)
}
|
3664eedfaf1979da0e64929fd6253da2127bdf83
|
873f2f21ba9477f77fbd63471f68fb74f0096fa7
|
/helpers.R
|
d110b37904bd8a99f5b5f5192b0c24a6529e32f8
|
[] |
no_license
|
cashoes/shinyDIABLO
|
9724036ecbc28bf0944873cec712e5cdce90b1aa
|
6849c1dffa10db61f497ed8b94aeba85dc2ce4dd
|
refs/heads/master
| 2021-09-06T00:15:44.371020
| 2018-01-31T22:21:46
| 2018-01-31T22:21:46
| 119,751,598
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,785
|
r
|
helpers.R
|
#' Get correlation matrix
#
#' @param model A DIABLO model object.
getCorMat <- function(model, selection, method = 'pearson') {
object <- model
keepA <- lapply(object$loadings, function(i) apply(abs(i), 1, sum) > 0)
cord <- mapply(function(x, y, keep){
cor(x[, keep], y, use = "pairwise", method = method)
}, x = object$X, y = object$variates[-length(object$variates)], keep = keepA[-length(keepA)])
simMatList <- vector("list", length(model$X))
for(i in 1:length(cord)){
for(j in 1:length(cord)){
simMatList[[i]][[j]] <- cord[[i]] %*% t(cord[[j]])
}
}
corMat <- do.call(rbind, lapply(simMatList, function(i) do.call(cbind, i)))
}
#' Gene set enrichment analysis
#
#' @param geneSet A list of gene symbols.
#' @param featureMapping A list of data frames containing 'Data.Names', 'Gene.Symbols', 'Display.Names' for each of the data blocks.
genesetEnrichment <- function(geneSet, featureMapping = NULL) {
# Get dataset
library(sear)
candidates <- geneSet
if (!is.null(featureMapping)){
for (name in candidates){
warning(sprintf("%s", name))
}
}
# Perform 'sear'
result <- sear::sear(candidates)
result <- dplyr::arrange(result, fdr)
result <- result[1:20, c("collection", "geneset", "fdr")]
result[, -c(1,2)] <- signif(result[, -c(1,2)], digits = 3)
result
}
#' Match with existing Protein-Protein Interaction data
#
#' If there is a match, returns the index of the match in the PPI data in a vector of length 'edges'
#' 'edges' and 'data' both two column data frames
#
#' @param edges A data frame consisting of two proteins that have an edge.
#' @param data A data frame consisting of two columns of proteins that interact
matchPPI <- function(edges, data){
matches <- prodlim::row.match(edges, data)
}
|
6c1574f30212ddf02e2c9e60f6276b8a4427a392
|
72db92ef4526cd72a71440244d5b4e9d9cd0568d
|
/lib/feature_3.R
|
3145b31b369e27b1e9f3b1b73b741bc7a7651707
|
[] |
no_license
|
TZstatsADS/Spr2016-Proj3-Grp5
|
e962ce0451b24332bad0bb17a6d9ae962cc47199
|
92406adeb942ff15c1317a247f11265552251ece
|
refs/heads/master
| 2021-01-18T06:58:58.255224
| 2016-03-23T20:32:07
| 2016-03-23T20:32:07
| 52,483,433
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,130
|
r
|
feature_3.R
|
#############################################################
### Construct features out of images for training/testing ###
#############################################################
# CONSTRUCTS HARMONIC COEFFICIENTS FROM ELLIPTICAL FOURIER OUTLINE ANALYSIS
source("http://bioconductor.org/biocLite.R")
biocLite("EBImage")
library(EBImage)
install.packages("devtools")
devtools::install_github("vbonhomme/Momocs")
library(Momocs)
feature <- function(img_dir, data_dir) {
### Constructs features out of images for training/testing
### img_dir: class "character", path to directory of images to be processed
### data_dir: class "character", path to directory to place feature data files in
### Output: .rds files, one for each image, containing the features for that image
##### CURRENT STATUS (2016/03/18 19:30):
##### This function constructs only harmonic coefficient features.
##### WARNING: This function also writes a new processed image file per image.
##### This will thus double the number of images in your image directory.
##### Maybe a separate directory for the processed files should be created.
##### Running time on Arnold's computer:
##### user: 2655.92 system: 43.69 elapsed 2824.62 (approx 47 minutes)
file_names <- list.files(img_dir, pattern = "[[:digit:]].jpg") # THIS IS NOT A GOOD SOLUTION
file_names <- sort(file_names)
file_paths <- rep(NA_character_, length(file_names))
for (i in 1:length(file_names)) {
file_paths[i] <- paste(img_dir, file_names[i], sep = "/")
}
file_paths <- sort(file_paths)
# Construct harmonic coefficient features from image outline
# Note: Some images may be invalid; features will not be constructed for those images
# Note: Some images may result in outlines with too little detail; features will not be
# constructed for those images
for (i in 1:length(file_paths)) {
tryCatch({
img <- readImage(file_paths[i])
img_bin <- channel(img, mode = "gray") # convert image to greyscale
img_bin <- gblur(img_bin, sigma = 5) # smooth image with a low-pass filter
threshold <- otsu(img_bin)
img_bin <- img_bin > threshold # create binary black/white image using Otsu threshold
writeImage(img_bin, paste(img_dir, "/", unlist(strsplit(file_names[i], split = "[.]"))[1],
"_bin.jpg", sep = ""), type = "jpeg")
momocs_out <- import_jpg1(jpg.path = paste(img_dir, "/",
unlist(strsplit(file_names[i], split = "[.]"))[1],
"_bin.jpg", sep = ""),
threshold = threshold)
momocs_out <- Out(list(momocs_out))
momocs_out <- coo_smooth(momocs_out, 5) %>% coo_scale() %>% coo_center()
momocs_ef <- efourier(momocs_out, nb.h = 10)
momocs_coeff <- momocs_ef$coe[1, ]
if(length(momocs_coeff) != 40) {
next
}
mat <- imageData(img)
nR <- 5
nG <- 5
nB <- 5
rBin <- seq(0, 1, length.out=nR)
gBin <- seq(0, 1, length.out=nG)
bBin <- seq(0, 1, length.out=nB)
freq_rgb <- as.data.frame(table(factor(findInterval(mat[,,1], rBin), levels=1:nR),
factor(findInterval(mat[,,2], gBin), levels=1:nG),
factor(findInterval(mat[,,3], bBin), levels=1:nB)))
rgb_feature <- as.numeric(freq_rgb$Freq)/(ncol(mat)*nrow(mat)) # normalization
final <- c(rgb_feature, momocs_coeff)
saveRDS(final,
file = paste(data_dir, "/", unlist(strsplit(file_names[i], "[.]"))[1], ".rds", sep = ""))
},
error = function(c) "invalid or corrupt JPEG file")
}
}
feature_mat <- function(data_dir) {
### Create a matrix with features for all observations
### data_dir: class "character", path to directory where feature data files are placed
### Output: matrix with features for all observations
feature_file_names <- Sys.glob(paste(data_dir, "/*.rds", sep = ""))
feature_file_names <- sort(feature_file_names)
# create feature matrix
catdog <- do.call('rbind', lapply(feature_file_names, readRDS))
return(catdog)
}
label_vec <- function(data_dir) {
### Create a vector with labels for all observations
### data_dir: class "character", path to directory where feature data files are placed
### Output: vector with labels for all observations
feature_names <- list.files(data_dir, pattern = "rds")
feature_names <- sort(feature_names)
# extract vector of labels (cat = 1, dog = 0)
breed_name <- rep(NA, length(feature_names))
for(i in 1:length(feature_names)){
tt <- unlist(strsplit(feature_names[i], "_"))
tt <- tt[-length(tt)]
breed_name[i] = paste(tt, collapse="_", sep="")
}
cat_breed <- c("Abyssinian", "Bengal", "Birman", "Bombay", "British_Shorthair", "Egyptian_Mau",
"Maine_Coon", "Persian", "Ragdoll", "Russian_Blue", "Siamese", "Sphynx")
iscat <- breed_name %in% cat_breed
y_cat <- as.numeric(iscat)
return(y_cat)
}
|
ec31fec634eeda657628452fa8ef582d1741a3c0
|
063ac42ba9d2dd563946f8a68936d89414bcbefc
|
/man/numverify.Rd
|
0372a87dc25de19a8d880c99c37f3db1ad27dd4c
|
[
"MIT"
] |
permissive
|
ashleylijie/rdevt
|
ed9e10668bac5e856543a8e09dfe7d81fd1f7178
|
54b0bfe22eae587ded3b869286a302e74c9968e7
|
refs/heads/master
| 2020-04-22T14:43:34.863678
| 2019-02-14T08:07:25
| 2019-02-14T08:07:25
| 170,453,586
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 468
|
rd
|
numverify.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{numverify}
\alias{numverify}
\title{Numverify : Validate Phone Number}
\usage{
numverify(country_code = "CN", number = NULL)
}
\arguments{
\item{country_code}{Country code, default China : "CN".}
\item{number}{Phone number}
}
\value{
the results from the validation
}
\description{
Numverify : Validate Phone Number
}
\examples{
\dontrun{
numverify(number = 14158586273)
}
}
|
5bd7112e78f791d93f57d5b1e17b0c6619a84c9d
|
c72b8531677736413ffdd39b157248445756b0ef
|
/man/compare_normalized_nearest_neighbor.Rd
|
8108ef541ea0ad06f9c034805ac57258febb946b
|
[
"MIT"
] |
permissive
|
gusef/IrisSpatialFeatures
|
731b7333ac3a54355c8a7b1058f4dcd079bef77f
|
c9f7eac1b7c13000aa1f04cbf0266c32c2b400ab
|
refs/heads/master
| 2021-03-27T19:22:00.769564
| 2019-04-04T02:55:41
| 2019-04-04T02:55:41
| 72,760,221
| 5
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,048
|
rd
|
compare_normalized_nearest_neighbor.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/normalized.nearest.neighbor.R
\docType{methods}
\name{compare_normalized_nearest_neighbor}
\alias{compare_normalized_nearest_neighbor}
\alias{compare_normalized_nearest_neighbor,NNN,character,character,character-method}
\title{Compare distances between two markers to a reference marker.}
\usage{
compare_normalized_nearest_neighbor(NNN, markerA, markerB, reference,
order = FALSE)
\S4method{compare_normalized_nearest_neighbor}{NNN,character,character,character}(NNN,
markerA, markerB, reference, order = FALSE)
}
\arguments{
\item{NNN}{Normalized nearest neighbor object}
\item{markerA}{Additional arguments}
\item{markerB}{Additional arguments}
\item{reference}{Additional arguments}
\item{order}{Optional character vector with sample names in an order for plotting}
}
\value{
Analysis data
}
\description{
Compare distances between two markers to a reference marker.
}
\examples{
dataset <- IrisSpatialFeatures_data
dataframe <- as.data.frame(dataset)
}
|
e51f555609d1dbacfd5d86ef33f9ac8081e48951
|
258290d6dd1fe844720553b340dd64768ef08f46
|
/cachematrix.R
|
2217379b683cf7ee61ecbd104c5b5e08057e199b
|
[] |
no_license
|
pvasilyev/ProgrammingAssignment2
|
cc7cd3d933d0c50123fa29b3d813bae38de09965
|
a902ca58795f8ff554d19e70c3560ce5385e3dc1
|
refs/heads/master
| 2020-12-31T02:31:44.860870
| 2014-10-27T10:07:17
| 2014-10-27T10:07:17
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,252
|
r
|
cachematrix.R
|
## Following functions can be used to create special wrapper over given matrix,
## which is able to cache invertion of this matrix, a.k.a. solve(matrix).
## So if the matrix is not changed - then the invertion computed only once and after that
## returned only cached result.
## When the data of the wrapper is changed - then cache is expired and invertion is re-computed.
## This function creates a caching-wrapper for a given matrix 'x'.
makeCacheMatrix <- function(x = matrix()) {
inverted <- NULL
set <- function(y) {
x <<- y
inverted <<- NULL
}
get <- function() x
setCachedInverted <- function(inv) inverted <<- inv
getCachedInverted <- function() inverted
list(set = set, get = get,
setCachedInverted = setCachedInverted,
getCachedInverted = getCachedInverted)
}
## This function computes the invertion for the wrapper if is hasn't computed so far.
## Otherwise it returns cached result for the 'x'.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inverted <- x$getCachedInverted()
if(!is.null(inverted)) {
message("getting cached data")
return(inverted)
}
data <- x$get()
inverted <- solve(data, ...)
x$setCachedInverted(inverted)
inverted
}
|
e65d019d722befa851612b2ae7476cbb9e0380c4
|
2709949bcb37fad772575f1516b07910ee1e0389
|
/man/addExposure2ExposomeSetDS.Rd
|
a809215a14b7d01742ab8ee95c0cce75c6155787
|
[
"MIT"
] |
permissive
|
isglobal-brge/dsExposome
|
fa87a72471535a9c1445383aff9dec8b458eb346
|
f6346fa576bcced74baafd0f0b27c416df2f5afd
|
refs/heads/master
| 2023-02-20T21:32:41.084519
| 2023-02-14T10:45:22
| 2023-02-14T10:45:22
| 238,680,883
| 0
| 6
|
MIT
| 2023-02-13T10:40:50
| 2020-02-06T12:11:54
|
R
|
UTF-8
|
R
| false
| true
| 1,133
|
rd
|
addExposure2ExposomeSetDS.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/addExposure2ExposomeSetDS.R
\name{addExposure2ExposomeSetDS}
\alias{addExposure2ExposomeSetDS}
\title{Add Phenotype data to ExposomeSet}
\usage{
addExposure2ExposomeSetDS(
x,
exposure,
identifier_ExposomeSet,
identifier_new_exposures,
families
)
}
\arguments{
\item{x}{\code{ExposomeSet} ExposomeSet to which add phenotype information}
\item{exposure}{\code{data.frame} Table with the new exposures}
\item{identifier_ExposomeSet}{\code{character} Name of the ID column on the already present
phenotypes data.frame on the ExposomeSet}
\item{identifier_new_exposures}{\code{character} Name of the ID column on the new exposure data.frame}
\item{complete_cases}{\code{bool} If \code{TRUE} only the matching individuals
between the ExpressionSet and the phenotypes table will be included on the resulting ExpressionSet. If
\code{FALSE} all the individuals on the input ExpressionSet will be on the output ExpressionSet}
}
\value{
\code{ExposomeSet} with updated phenotypes information
}
\description{
Add phenotype data to an ExpressionSet
}
|
2be4222f2ed3376b1f916aa1aa3a4bea881b0c9b
|
4682b24e96eb3deaef0be092d395c6a1fb8e6446
|
/from_rmd_to_R.R
|
f296053c717bc9b264f6f86a96dc8d441f838c9f
|
[] |
no_license
|
vjilmari/exproj
|
f7b76a4598bcd16ddcb51402c4a57aab9d65f9d4
|
88287513c2d60a9728dcd8569d54eff352c4696b
|
refs/heads/master
| 2022-04-22T17:23:45.057229
| 2020-04-23T07:59:14
| 2020-04-23T07:59:14
| 256,155,203
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 86
|
r
|
from_rmd_to_R.R
|
library(knitr)
purl("testmarkdown.Rmd", output = "testmarkdown.R", documentation = 2)
|
0fff4c7fc92bf2ec5135b2237ecfd2161a7df73f
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/RobExtremes/examples/WeibullFamily.Rd.R
|
bfb7a5cbedfd9b44fc91636a9788086f10365f3b
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 222
|
r
|
WeibullFamily.Rd.R
|
library(RobExtremes)
### Name: WeibullFamily
### Title: Generating function for Weibull family
### Aliases: WeibullFamily
### Keywords: models
### ** Examples
(G1 <- WeibullFamily())
FisherInfo(G1)
checkL2deriv(G1)
|
fd3e997b61045ee508977bb83485cbe8cd5a6af9
|
c9f45c5b10ecdf1b324cdc5a35179bbcc7b79d67
|
/plot3.R
|
a4256be2be4baa1a31fa896f62d48d8c34458aab
|
[] |
no_license
|
Jose230600/ExData_Plotting1
|
630e6acc8d530901e6c2461c61a329918ee85eb8
|
5bc5a77a4cb257da7672d27268e64e7ed8c27ec5
|
refs/heads/master
| 2022-11-29T23:31:05.815971
| 2020-08-09T23:38:39
| 2020-08-09T23:38:39
| 286,161,007
| 0
| 0
| null | 2020-08-09T03:39:07
| 2020-08-09T03:39:06
| null |
UTF-8
|
R
| false
| false
| 1,223
|
r
|
plot3.R
|
#Once the file is in your work directory
data <- read.table("household_power_consumption.txt",sep=";",header=TRUE)
data2 <- data[data$Date == "1/2/2007" | data$Date == "2/2/2007", ]
data2$Date <- as.Date(data2$Date, format="%d/%m/%Y")
data2$Time <- strptime(data2$Time, format="%H:%M:%S")
data2[1:1440,"Time"] <- format(data2[1:1440,"Time"],"2007-02-01 %H:%M:%S")
data2[1441:2880,"Time"] <- format(data2[1441:2880,"Time"],"2007-02-02 %H:%M:%S")
data2$Sub_metering_1 <- as.character(data2$Sub_metering_1)
data2$Sub_metering_2 <- as.character(data2$Sub_metering_2)
data2$Sub_metering_3 <- as.character(data2$Sub_metering_3)
data2$Sub_metering_1 <- as.numeric(data2$Sub_metering_1)
data2$Sub_metering_2 <- as.numeric(data2$Sub_metering_2)
data2$Sub_metering_3 <- as.numeric(data2$Sub_metering_3)
with(data2,plot(Time,Sub_metering_1, type="n", ylab="Energy sub metering",xlab=" "))
with(data2,points(Time,Sub_metering_1,col="black",type="l"))
with(data2,points(Time,Sub_metering_2,col="red",type="l"))
with(data2,points(Time,Sub_metering_3,col="blue",type="l"))
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")
dev.off()
|
c8c5b0b43cb3cb55afd7993a5670d413567789d9
|
9598c94fe076830bfece6e366b87cff83c4c66b6
|
/trade spillovers/Trade spillovers NL and DE v3 1.R
|
c365b7ed0e674f63627e31732ffaacad4b129fa4
|
[] |
no_license
|
mdg9709/spilloversNL
|
1b8d836ad4f5d1145f6cc15345745358d5f15b24
|
04388fe2bfcf764ab4a256b456db3de00ba824f9
|
refs/heads/master
| 2022-11-18T22:37:24.704113
| 2020-07-17T17:07:07
| 2020-07-17T17:07:07
| 280,177,983
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,248
|
r
|
Trade spillovers NL and DE v3 1.R
|
# --- Effect of Dutch govt spending shock on German exports
# Data period:1980q1-2018q4 and 1999q1-2018q4
# 95% and 68% confidence intervals
# h = 4, 8 and 12
# OLS with left-hand side in growth rates, 4 lags of x(t-1), and shock2 * (100)^3
source('~/Studie/MSc ECO/Period 5-6 MSc thesis/MSc thesis RStudio project/Scripts/Spillovers NL and DE v4 1.R')
# Load packages
library(ggplot2)
library(gridExtra)
library(dplyr)
library(data.table)
library(lmtest)
library(sandwich)
rmNL.l <- data.frame(shift(data2$rmNL, n = 1:12, type = "lead"))
names(rmNL.l) = c("rmNL.l1", "rmNL.l2", "rmNL.l3", "rmNL.l4", "rmNL.l5", "rmNL.l6",
"rmNL.l7", "rmNL.l8", "rmNL.l9", "rmNL.l10", "rmNL.l11", "rmNL.l12")
rxNL.l <- data.frame(shift(data2$rxNL, n = 1:12, type = "lead"))
names(rxNL.l) = c("rxNL.l1", "rxNL.l2", "rxNL.l3", "rxNL.l4", "rxNL.l5", "rxNL.l6",
"rxNL.l7", "rxNL.l8", "rxNL.l9", "rxNL.l10", "rxNL.l11", "rxNL.l12")
l.rmNL <- data.frame(shift(data2$rmNL, n = 1:4, type = "lag"))
names(l.rmNL) = c("l1.rmNL", "l2.rmNL", "l3.rmNL", "l4.rmNL")
l.rxNL <- data.frame(shift(data2$rxNL, n = 1:4, type = "lag"))
names(l.rxNL) = c("l1.rxNL", "l2.rxNL", "l3.rxNL", "l4.rxNL")
rmDE.l <- data.frame(shift(data2$rmDE, n = 1:12, type = "lead"))
names(rmDE.l) = c("rmDE.l1", "rmDE.l2", "rmDE.l3", "rmDE.l4", "rmDE.l5", "rmDE.l6",
"rmDE.l7", "rmDE.l8", "rmDE.l9", "rmDE.l10", "rmDE.l11", "rmDE.l12")
rxDE.l <- data.frame(shift(data2$rxDE, n = 1:12, type = "lead"))
names(rxDE.l) = c("rxDE.l1", "rxDE.l2", "rxDE.l3", "rxDE.l4", "rxDE.l5", "rxDE.l6",
"rxDE.l7", "rxDE.l8", "rxDE.l9", "rxDE.l10", "rxDE.l11", "rxDE.l12")
l.rmDE <- data.frame(shift(data2$rmDE, n = 1:4, type = "lag"))
names(l.rmDE) = c("l1.rmDE", "l2.rmDE", "l3.rmDE", "l4.rmDE")
l.rxDE <- data.frame(shift(data2$rxDE, n = 1:4, type = "lag"))
names(l.rxDE) = c("l1.rxDE", "l2.rxDE", "l3.rxDE", "l4.rxDE")
data3$shockNL2 <- (data3$shockNL / unlist(l.rxNL[1])) / sd((data3$shockNL / unlist(l.rxNL[1])), na.rm = TRUE)
data3$shockNL3 <- data3$shockNL2 / 100
data3$shockDE2 <- (data3$shockDE / unlist(l.rxDE[1])) / sd((data3$shockDE / unlist(l.rxDE[1])), na.rm = TRUE)
data3$shockDE3 <- data3$shockDE2 / 100
shockFR5 <- data.frame(data3$shockFR2 * 100); names(shockFR5) <- "shockFR5"
shockNL5 <- data.frame(data3$shockNL2 * 100); names(shockNL5) <- "shockNL5"
shockDE5 <- data.frame(data3$shockDE2 * 100); names(shockDE5) <- "shockDE5"
shockES5 <- data.frame(data3$shockES2 * 100); names(shockES5) <- "shockES5"
shockIT5 <- data.frame(data3$shockIT2 * 100); names(shockIT5) <- "shockIT5"
data4 <- cbind(data3, shockFR5, shockNL5, shockDE5, shockIT5, shockES5,
l.rmNL, l.rxNL, rmNL.l, rxNL.l, l.rmDE, l.rxDE, rmDE.l, rxDE.l)
data5 <- subset(data4, select = -c(30:32, 35:37, 152:203))
h <- 12
# -- OLS regressions
# -- Equation 5
lhsNLDE50 <- (data5$rxDE - data5$l1.rxDE) / data5$l1.rxDE
lhsNLDE5 <- lapply(1:h, function(x) (data5[, 202+x] - data5$l1.rxDE) / data5$l1.rxDE)
lhsNLDE5 <- data.frame(lhsNLDE5)
names(lhsNLDE5) = paste("lhsNLDE5", 1:h, sep = "")
data6 <- cbind(data5, lhsNLDE50, lhsNLDE5)
NLDE5 <- lapply(1:13, function(x) lm(data6[, 214+x] ~ shockNL2 + l1.debtDE + l1.intDE + l1.lrtrDE + l1.lrgDE + l1.lryDEc + l2.debtDE + l2.intDE + l2.lrtrDE + l2.lrgDE + l2.lryDEc + l3.debtDE + l3.intDE + l3.lrtrDE + l3.lrgDE + l3.lryDEc + l4.debtDE + l4.intDE + l4.lrtrDE + l4.lrgDE + l4.lryDEc + shockDE2 + shockFR2 + shockES2 + shockIT2, data = data6))
summariDENLDE5 <- lapply(NLDE5, summary)
NLDE5conf95 <- lapply(NLDE5, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.95)
NLDE5conf68 <- lapply(NLDE5, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.68)
NLDE5up95 <- lapply(1:13, function(x) NLDE5conf95[[x]][2,2])
NLDE5low95 <- lapply(1:13, function(x) NLDE5conf95[[x]][2,1])
NLDE5up68 <- lapply(1:13, function(x) NLDE5conf68[[x]][2,2])
NLDE5low68 <- lapply(1:13, function(x) NLDE5conf68[[x]][2,1])
betaNLDEt <- lapply(summariDENLDE5, function(x) x$coefficients[2,1])
names(betaNLDEt) <- paste("betaNLDEt", 0:h, sep = "")
# -- Equation 6
lhsNLDE60 <- (data6$rgNL - data6$l1.rgNL) / data6$l1.rxDE
lhsNLDE6 <- lapply(1:h, function(x) (data6[, 96+x] - data6$l1.rgNL) / data6$l1.rxDE)
lhsNLDE6 <- data.frame(lhsNLDE6)
names(lhsNLDE6) = paste("lhsNLDE6", 1:h, sep = "")
data6 <- cbind(data6, lhsNLDE60, lhsNLDE6)
NLDE6 <- lapply(1:13, function(x) lm(data6[, 227+x] ~ shockNL3 + l1.debtNL + l1.intNL + l1.lrtrNL + l1.lrgNL + l1.lryNLc + l2.debtNL + l2.intNL + l2.lrtrNL + l2.lrgNL + l2.lryNLc + l3.debtNL + l3.intNL + l3.lrtrNL + l3.lrgNL + l3.lryNLc + l4.debtNL + l4.intNL + l4.lrtrNL + l4.lrgNL + l4.lryNLc + shockDE3 + shockFR3 + shockES3 + shockIT3, data = data6))
summariesNLDE6 <- lapply(NLDE6, summary)
NLDE6conf95 <- lapply(NLDE6, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.95)
NLDE6conf68 <- lapply(NLDE6, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.68)
NLDE6up95 <- lapply(1:13, function(x) NLDE6conf95[[x]][2,2])
NLDE6low95 <- lapply(1:13, function(x) NLDE6conf95[[x]][2,1])
NLDE6up68 <- lapply(1:13, function(x) NLDE6conf68[[x]][2,2])
NLDE6low68 <- lapply(1:13, function(x) NLDE6conf68[[x]][2,1])
gammaNLDEt <- lapply(summariesNLDE6, function(x) x$coefficients[2,1])
names(gammaNLDEt) <- paste("gammaNLDEt", 0:h, sep = "")
# -- Cumulative multiplier
mNLDEtc <- cumsum(betaNLDEt) / cumsum(as.numeric(gammaNLDEt)); as.numeric(mNLDEtc)
# --- Effect of German govt spending shock on Dutch exports
# -- Equation 5
lhsDENL50 <- (data6$rxNL - data6$l1.rxNL) / data6$l1.rxNL
lhsDENL5 <- lapply(1:h, function(x) (data6[, 170+x] - data6$l1.rxNL) / data6$l1.rxNL)
lhsDENL5 <- data.frame(lhsDENL5)
names(lhsDENL5) = paste("lhsDENL5", 1:h, sep = "")
data6 <- cbind(data6, lhsDENL50, lhsDENL5)
DENL5 <- lapply(1:13, function(x) lm(data6[, 240+x] ~ shockDE2 + l1.debtNL + l1.intNL + l1.lrtrNL + l1.lrgNL + l1.lryNLc + l2.debtNL + l2.intNL + l2.lrtrNL + l2.lrgNL + l2.lryNLc + l3.debtNL + l3.intNL + l3.lrtrNL + l3.lrgNL + l3.lryNLc + l4.debtNL + l4.intNL + l4.lrtrNL + l4.lrgNL + l4.lryNLc + shockFR2 + shockES2 + shockIT2 + shockNL2, data = data6))
summariDEDENL5 <- lapply(DENL5, summary)
DENL5conf95 <- lapply(DENL5, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.95)
DENL5conf68 <- lapply(DENL5, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.68)
DENL5up95 <- lapply(1:13, function(x) DENL5conf95[[x]][2,2])
DENL5low95 <- lapply(1:13, function(x) DENL5conf95[[x]][2,1])
DENL5up68 <- lapply(1:13, function(x) DENL5conf68[[x]][2,2])
DENL5low68 <- lapply(1:13, function(x) DENL5conf68[[x]][2,1])
betaDENLt <- lapply(summariDEDENL5, function(x) x$coefficients[2,1])
names(betaDENLt) <- paste("betaDENLt", 0:h, sep = "")
# -- Equation 6
lhsDENL60 <- (data6$rgDE - data6$l1.rgDE) / data6$l1.rxNL
lhsDENL6 <- lapply(1:h, function(x) (data6[, 84+x] - data6$l1.rgDE) / data6$l1.rxNL)
lhsDENL6 <- data.frame(lhsDENL6)
names(lhsDENL6) = paste("lhsDENL6", 1:h, sep = "")
data6 <- cbind(data6, lhsDENL60, lhsDENL6)
DENL6 <- lapply(1:13, function(x) lm(data6[, 253+x] ~ shockDE3 + l1.debtDE + l1.intDE + l1.lrtrDE + l1.lrgDE + l1.lryDEc + l2.debtDE + l2.intDE + l2.lrtrDE + l2.lrgDE + l2.lryDEc + l3.debtDE + l3.intDE + l3.lrtrDE + l3.lrgDE + l3.lryDEc + l4.debtDE + l4.intDE + l4.lrtrDE + l4.lrgDE + l4.lryDEc + shockNL3 + shockFR3 + shockES3 + shockIT3, data = data6))
summariesDENL6 <- lapply(DENL6, summary)
DENL6conf95 <- lapply(DENL6, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.95)
DENL6conf68 <- lapply(DENL6, coefci, vcov = NeweyWest, lag = 0, prewhite = FALSE, level = 0.68)
DENL6up95 <- lapply(1:13, function(x) DENL6conf95[[x]][2,2])
DENL6low95 <- lapply(1:13, function(x) DENL6conf95[[x]][2,1])
DENL6up68 <- lapply(1:13, function(x) DENL6conf68[[x]][2,2])
DENL6low68 <- lapply(1:13, function(x) DENL6conf68[[x]][2,1])
gammaDENLt <- lapply(summariesDENL6, function(x) x$coefficients[2,1])
names(gammaDENLt) <- paste("gammaDENLt", 0:h, sep = "")
# -- Cumulative multiplier
mDENLtc <- cumsum(betaDENLt) / cumsum(as.numeric(gammaDENLt)); as.numeric(mDENLtc)
|
05131cde1e2784cf342ecaa29e7b756fecc45636
|
a42930bc351c40f425fc147763193990c267e0c2
|
/app.R
|
6c78102e258b7320934a3b2c876ad7b141961397
|
[] |
no_license
|
kggen/Feba-Sofia-Air
|
555677b83137ad5e059156c69db87e6c50908a54
|
1307a740a16e7d857cf4c1767b5b47b3cc670b6b
|
refs/heads/master
| 2020-04-25T00:41:27.147258
| 2019-02-24T20:41:40
| 2019-02-24T20:41:40
| 172,386,092
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,198
|
r
|
app.R
|
library(shiny)
library(leaflet)
library(RColorBrewer)
ui <- bootstrapPage(
titlePanel("FEBA - Sofia Air Project (Under construction)"),
tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
leafletOutput("map", width = "100%", height = "100%"),
absolutePanel(top = 70, right = 10,
sliderInput("date_range",
"Date and time of prediction: (NOT INTEGRATED YET)",
min = as.POSIXct("2019-02-25 00:00"),
max = as.POSIXct("2019-02-25 23:00"),
value = c(as.POSIXct("2019-02-25 01:00")),
timeFormat = "%a %H:%M", ticks = F, animate = T
)
)
)
server <- function(input, output, session) {
# Reactive expression for the data subsetted to what the user selected
sofia_summary <- read.csv("./data/sofia_summary.csv")
filteredData <- reactive({
sofia_summary[]
})
# This reactive expression represents the palette function,
# which changes as the user makes selections in UI.
output$map <- renderLeaflet({
# Use leaflet() here, and only include aspects of the map that
# won't need to change dynamically (at least, not unless the
# entire map is being torn down and recreated).
leaflet(sofia_summary) %>% addTiles() %>%
fitBounds(~min(lng), ~min(lat), ~max(lng), ~max(lat))
})
# Incremental changes to the map (in this case, replacing the
# circles when a new color is chosen) should be performed in
# an observer. Each independent set of things that can change
# should be managed in its own observer.
observe({
leafletProxy("map", data = filteredData()) %>%
clearShapes() %>%
addCircleMarkers(weight = 0.1, color = "red") %>%
addRectangles(lat1 = (min(sofia_summary$lat)-0.01), lng1 = (max(sofia_summary$lng)-0.18),
lat2 = (min(sofia_summary$lat)+0.13), lng2 = (min(sofia_summary$lng)+0.18),
fillColor = "transparent")
})
# Use a separate observer to recreate the legend as needed.
}
shinyApp(ui, server)
|
ae3a7708f6bbe574adab76ef7c0656696dbfa1f6
|
846d6f743403ba31d3e70c5f502a2fd90200fcb1
|
/impl/03_data_analysis/03_univariate/00_History/ARIMA_fitting_XBT_v08.R
|
ab6572086cc5c5f7426ba4f63d9618bc7f6a4c25
|
[] |
no_license
|
yangboyubyron/thesis-google-crypto-trading
|
4c5fb17721c62f97f7b75f1125110b575f6a948d
|
c920cbf3547d6fa670a389b4dcce9d7433c9e5e3
|
refs/heads/master
| 2022-01-05T15:15:17.005009
| 2019-02-03T16:31:44
| 2019-02-03T16:31:44
| null | 0
| 0
| null | null | null | null |
WINDOWS-1250
|
R
| false
| false
| 12,764
|
r
|
ARIMA_fitting_XBT_v08.R
|
setwd("C:/Users/Vojtěch Pulec/OneDrive/Dokumenty/HU/Thesis/master-thesis_Pulec") # thats for me to get to proprer level
wd <- getwd()
wd <- paste(wd, "/data/03_final_sample/Kraken", sep = "")
setwd(wd)
rm(list=ls())
library("tseries")
library("zoo")
library("forecast")
library("ggplot2")
library('stats')
library("gridExtra")
library("reshape2")
library("tcltk")
library("sarima")
# 1) *********** data prep ******************************************
load(file="XBT_5minute_returns.Rdata")
# 2) *********** preparing structure for fit ******************************
#setting up parametr of the window
window_start <- 81200 #any number from number of lags to nrow-1
window_end <- 82200 #any number from start to nrow
lookback <- 48
five_minute_data <- five_minute_data[c((window_start-lookback):window_end),]
# Progress bar
# before loop pb <- tkProgressBar(title = "progress bar", min = 0, max = nrow(OM_uni), width = 1000)
# into the loop setTkProgressBar(pb, i, label=paste( round(i/(nrow(OM_uni))*1000, 0),"‰ done"))
#preparing struncture
five_minute_data[,19:50] <- 1
colnames(five_minute_data)[c(19,22,25,28,31)] <- c("A_m5","A_m10","A_m15","A_m30", "A_m60") #mean for x minutes forward estimate
colnames(five_minute_data)[c(20,23,26,29,32)] <- c("A_u5(95%)", "A_u10(95%)", "A_u15(95%)", "A_u30(95%)","A_u60(95%)") #upper bound for x minutes forward estimate
colnames(five_minute_data)[c(21,24,27,30,33)] <- c("A_l5(95%)" ,"A_l10(95%)", "A_l15(95%)", "A_l30(95%)", "A_l60(95%)") # lower bound for x minutes forward estimate
colnames(five_minute_data)[c(34,35)] <- c("A_BIC", "A_AICC") # AIC a BIC criteria for choosing proper order
colnames(five_minute_data)[c(36:40)] <- c("R_5","R_10","R_15","R_30", "R_60") #real values
colnames(five_minute_data)[c(41:45)] <- c("A_SQe_5","A_SQe_10","A_SQe_15","A_SQe_30", "A_SQe_60") #squared errors of estimate
colnames(five_minute_data)[c(46:50)] <- c("A_DA_5","A_DA_10","A_DA_15","A_DA_30", "A_DA_60") #directional accuracy
# 4) *************** looping to fit ARIMA ******************************************************************************************
timeframe <- lookback #reference period
shift <- timeframe - 1 # last block of time frame it starts forecasting 5/10/15/30/60 mins
pb <- tkProgressBar(title = "progress bar", min = 0, max = (nrow(five_minute_data)- timeframe), width = 1000)
for(i in 1:(nrow(five_minute_data)-timeframe)){
fit <- auto.arima(five_minute_data[(i:(i+shift)),18], d = 0)
forecast60 <- forecast(fit, h=12)
# fitting 5 minutes ahead
five_minute_data[i+shift,19] <- forecast60$mean[1] + 1
five_minute_data[i+shift,20] <- forecast60$upper[1,2] + 1
five_minute_data[i+shift,21] <- forecast60$lower[1,2] + 1
# fitting 10 minute ahead
for(z in 1:2){
five_minute_data[i+shift,22] <- (forecast60$mean[z] + 1) * five_minute_data[i+shift,22]
five_minute_data[i+shift,23] <- (forecast60$upper[z,2] + 1) * five_minute_data[i+shift,23]
five_minute_data[i+shift,24] <- (forecast60$lower[z,2] + 1) * five_minute_data[i+shift,24]
}
# fitting 15 minute ahead
for(z in 1:3){
five_minute_data[i+shift,25] <- (forecast60$mean[z] + 1) * five_minute_data[i+shift,25]
five_minute_data[i+shift,26] <- (forecast60$upper[z,2] + 1) * five_minute_data[i+shift,26]
five_minute_data[i+shift,27] <- (forecast60$lower[z,2] + 1) * five_minute_data[i+shift,27]
}
# fitting 30 minute ahead
for(z in 1:6){
five_minute_data[i+shift,28] <- (forecast60$mean[z] + 1) * five_minute_data[i+shift,28]
five_minute_data[i+shift,29] <- (forecast60$upper[z,2] + 1) * five_minute_data[i+shift,29]
five_minute_data[i+shift,30] <- (forecast60$lower[z,2] + 1) * five_minute_data[i+shift,30]
}
# fitting 60 minute ahead
for(z in 1:12){
five_minute_data[i+shift,31] <- (forecast60$mean[z] + 1) * five_minute_data[i+shift,31]
five_minute_data[i+shift,32] <- (forecast60$upper[z,2] + 1) * five_minute_data[i+shift,32]
five_minute_data[i+shift,33] <- (forecast60$lower[z,2] + 1) * five_minute_data[i+shift,33]
}
five_minute_data[i+shift,34] <- forecast60$model$bic
five_minute_data[i+shift,35] <- forecast60$model$aicc
# gettting real returns
five_minute_data[i+shift,36] <- log(five_minute_data[i+shift+1,3]/five_minute_data[i+shift,3]) + 1
five_minute_data[i+shift,37] <- log(five_minute_data[i+shift+2,3]/five_minute_data[i+shift,3]) + 1
five_minute_data[i+shift,38] <- log(five_minute_data[i+shift+3,3]/five_minute_data[i+shift,3]) + 1
five_minute_data[i+shift,39] <- log(five_minute_data[i+shift+6,3]/five_minute_data[i+shift,3]) + 1
five_minute_data[i+shift,40] <- log(five_minute_data[i+shift+12,3]/five_minute_data[i+shift,3]) + 1
# gettting squred error
five_minute_data[i+shift,41] <- (five_minute_data[i+shift,36] - five_minute_data[i+shift,19])^2
five_minute_data[i+shift,42] <- (five_minute_data[i+shift,37] - five_minute_data[i+shift,22])^2
five_minute_data[i+shift,43] <- (five_minute_data[i+shift,38] - five_minute_data[i+shift,25])^2
five_minute_data[i+shift,44] <- (five_minute_data[i+shift,39] - five_minute_data[i+shift,28])^2
five_minute_data[i+shift,45] <- (five_minute_data[i+shift,40] - five_minute_data[i+shift,31])^2
# gettting direction accuracy
five_minute_data[i+shift,46] <- (sign(five_minute_data[i+shift,36]-1) == sign(five_minute_data[i+shift,19]-1))*1
five_minute_data[i+shift,47] <- (sign(five_minute_data[i+shift,37]-1) == sign(five_minute_data[i+shift,22]-1))*1
five_minute_data[i+shift,48] <- (sign(five_minute_data[i+shift,38]-1) == sign(five_minute_data[i+shift,25]-1))*1
five_minute_data[i+shift,49] <- (sign(five_minute_data[i+shift,39]-1) == sign(five_minute_data[i+shift,28]-1))*1
five_minute_data[i+shift,50] <- (sign(five_minute_data[i+shift,40]-1) == sign(five_minute_data[i+shift,31]-1))*1
setTkProgressBar(pb, i, label=paste( round(i/(nrow(five_minute_data)-timeframe)*1000, 0),"‰ done"))
}
save(five_minute_data,file=paste(window_start,"_", window_end,"_", lookback,"_five_minut_ARIMA_fit_XBT.Rdata", sep = ""))
forecasts <- five_minute_data[which(five_minute_data$A_m5 !=1),]
time_vector <- forecasts$time
save(time_vector,file=paste(window_start,"_", window_end,"_", lookback,"_time_vector_five_minut_ARIMA_fit_XBT.Rdata", sep = ""))
#actual returns share
returns_benchmark <- five_minute_data[which(five_minute_data$A_m5 !=1),]
returns_benchmark$five <- nrow(returns_benchmark[which(returns_benchmark$R_5 > 1),])/(nrow(returns_benchmark)-sum(is.na(five_minute_data$A_m5)))*100
returns_benchmark$ten <- nrow(returns_benchmark[which(returns_benchmark$R_10 > 1),])/(nrow(returns_benchmark)-sum(is.na(five_minute_data$A_m10)))*100
returns_benchmark$fifteen <- nrow(returns_benchmark[which(returns_benchmark$R_15 > 1),])/(nrow(returns_benchmark)-sum(is.na(five_minute_data$A_m15)))*100
returns_benchmark$thirty <- nrow(returns_benchmark[which(returns_benchmark$R_30 > 1),])/(nrow(returns_benchmark)-sum(is.na(five_minute_data$A_m30)))*100
returns_benchmark$sixty <- nrow(returns_benchmark[which(returns_benchmark$R_60 > 1),])/(nrow(returns_benchmark)-sum(is.na(five_minute_data$A_m60)))*100
returns_benchmark <- returns_benchmark[1,-c(1:50)]
#MDA
returns_benchmark[2,1] <- sum(forecasts$A_DA_5,na.rm = TRUE) / (nrow(forecasts) - sum(is.na(forecasts$A_DA_5)))*100
returns_benchmark[2,2] <- sum(forecasts$A_DA_10, na.rm = TRUE) / (nrow(forecasts) - sum(is.na(forecasts$A_DA_10)))*100
returns_benchmark[2,3] <- sum(forecasts$A_DA_15,na.rm = TRUE) / (nrow(forecasts) - sum(is.na(forecasts$A_DA_15)))*100
returns_benchmark[2,4] <- sum(forecasts$A_DA_30,na.rm = TRUE) / (nrow(forecasts) - sum(is.na(forecasts$A_DA_30)))*100
returns_benchmark[2,5] <- sum(forecasts$A_DA_60, na.rm = TRUE) / (nrow(forecasts) - sum(is.na(forecasts$A_DA_60)))*100
#actual change in %
temp <- as.data.frame(matrix(1, ncol = 5, nrow = nrow(forecasts)))
for(i in 1:nrow(forecasts)){
temp[i,1] <- (forecasts$R_5[i] - 1)*100
temp[i,2] <- (forecasts$R_10[i] -1)*100
temp[i,3] <- (forecasts$R_15[i] -1)*100
temp[i,4] <- (forecasts$R_30[i] -1)*100
temp[i,5] <- (forecasts$R_60[i] -1)*100
}
returns_benchmark[3,1] <- mean(temp[,1],na.rm = TRUE)
returns_benchmark[3,2] <- mean(temp[,2],na.rm = TRUE)
returns_benchmark[3,3] <- mean(temp[,3],na.rm = TRUE)
returns_benchmark[3,4] <- mean(temp[,4],na.rm = TRUE)
returns_benchmark[3,5] <- mean(temp[,5],na.rm = TRUE)
#Actual preditcion
temp <- as.data.frame(matrix(1, ncol = 5, nrow = nrow(forecasts)))
for(i in 1:nrow(forecasts)){
temp[i,1] <- ((forecasts$A_m5[i]) - 1)*100
temp[i,2] <- ((forecasts$A_m10[i]) -1)*100
temp[i,3] <- ((forecasts$A_m15[i]) -1)*100
temp[i,4] <- ((forecasts$A_m30[i]) -1)*100
temp[i,5] <- ((forecasts$A_m60[i]) -1)*100
}
returns_benchmark[4,1] <- mean(temp[,1],na.rm = TRUE)
returns_benchmark[4,2] <- mean(temp[,2],na.rm = TRUE)
returns_benchmark[4,3] <- mean(temp[,3],na.rm = TRUE)
returns_benchmark[4,4] <- mean(temp[,4],na.rm = TRUE)
returns_benchmark[4,5] <- mean(temp[,5],na.rm = TRUE)
# MSE of the prediction
temp <- as.data.frame(matrix(1, ncol = 5, nrow = nrow(forecasts)))
for(i in 1:nrow(forecasts)){
temp[i,1] <- ((forecasts$A_m5[i] - forecasts$R_5[i])^2)
temp[i,2] <- ((forecasts$A_m10[i] - forecasts$R_10[i])^2)
temp[i,3] <- ((forecasts$A_m15[i] - forecasts$R_15[i])^2)
temp[i,4] <- ((forecasts$A_m30[i] - forecasts$R_30[i])^2)
temp[i,5] <- ((forecasts$A_m60[i] - forecasts$R_60[i])^2)
}
returns_benchmark[5,1] <- mean(temp[,1],na.rm = TRUE)
returns_benchmark[5,2] <- mean(temp[,2],na.rm = TRUE)
returns_benchmark[5,3] <- mean(temp[,3],na.rm = TRUE)
returns_benchmark[5,4] <- mean(temp[,4],na.rm = TRUE)
returns_benchmark[5,5] <- mean(temp[,5],na.rm = TRUE)
save(returns_benchmark,file=paste(window_start,"_", window_end,"_", lookback,"_five_minut_ARIMA_benchmark_XBT.Rdata", sep = ""))
# ******************* plot "analysis"**********************
# ********** 5 minute*******************************************
ggplot(five_minute_data, aes(five_minute_data$time)) +
geom_line(aes(y=five_minute_data$A_m5), colour="red") + #estimates
geom_line(aes(y=five_minute_data$R_5), colour="green")+ #real
geom_line(aes(y=five_minute_data$`A_u5(95%)`), colour="black")+ #upper estimated bound
geom_line(aes(y=five_minute_data$`A_l5(95%)`), colour="black") #lower estimated bound
# ********** 10 minute*******************************************
ggplot(five_minute_data, aes(five_minute_data$time)) +
geom_line(aes(y=five_minute_data$A_m10), colour="red") + #estimates
geom_line(aes(y=five_minute_data$R_10), colour="green")+ #real
geom_line(aes(y=five_minute_data$`A_u10(95%)`), colour="black")+ #upper estimated bound
geom_line(aes(y=five_minute_data$`A_l10(95%)`), colour="black") #lower estimated bound
# ********** 15 minute*******************************************
ggplot(five_minute_data, aes(five_minute_data$time)) +
geom_line(aes(y=five_minute_data$A_m15), colour="red") + #estimates
geom_line(aes(y=five_minute_data$R_15), colour="green")+ #real
geom_line(aes(y=five_minute_data$`A_u15(95%)`), colour="black")+ #upper estimated bound
geom_line(aes(y=five_minute_data$`A_l15(95%)`), colour="black") #lower estimated bound
# ********** 30 minute*******************************************
ggplot(five_minute_data, aes(five_minute_data$time)) +
geom_line(aes(y=five_minute_data$A_m30), colour="red") + #estimates
geom_line(aes(y=five_minute_data$R_30), colour="green")+ #real
geom_line(aes(y=five_minute_data$`A_u30(95%)`), colour="black")+ #upper estimated bound
geom_line(aes(y=five_minute_data$`A_l30(95%)`), colour="black") #lower estimated bound
# ********** 60 minute*******************************************
ggplot(five_minute_data, aes(five_minute_data$time)) +
geom_line(aes(y=five_minute_data$A_m60), colour="red") + #estimates
geom_line(aes(y=five_minute_data$R_60), colour="green")+ #real
geom_line(aes(y=five_minute_data$`A_u60(95%)`), colour="black")+ #upper estimated bound
geom_line(aes(y=five_minute_data$`A_l60(95%)`), colour="black") #lower estimated bound
|
aefebcd7480041944d4930ad64e621ada2459f97
|
7f72ac13d08fa64bfd8ac00f44784fef6060fec3
|
/RGtk2/man/pangoGlyphItemIterInitStart.Rd
|
b3c2d88633380bacba9c597dc5b415875fc9e2c0
|
[] |
no_license
|
lawremi/RGtk2
|
d2412ccedf2d2bc12888618b42486f7e9cceee43
|
eb315232f75c3bed73bae9584510018293ba6b83
|
refs/heads/master
| 2023-03-05T01:13:14.484107
| 2023-02-25T15:19:06
| 2023-02-25T15:20:41
| 2,554,865
| 14
| 9
| null | 2023-02-06T21:28:56
| 2011-10-11T11:50:22
|
R
|
UTF-8
|
R
| false
| false
| 784
|
rd
|
pangoGlyphItemIterInitStart.Rd
|
\alias{pangoGlyphItemIterInitStart}
\name{pangoGlyphItemIterInitStart}
\title{pangoGlyphItemIterInitStart}
\description{Initializes a \code{\link{PangoGlyphItemIter}} structure to point to the
first cluster in a glyph item.
See \code{\link{PangoGlyphItemIter}} for details of cluster orders.}
\usage{pangoGlyphItemIterInitStart(object, glyph.item, text)}
\arguments{
\item{\verb{object}}{[\code{\link{PangoGlyphItemIter}}] a \code{\link{PangoGlyphItemIter}}}
\item{\verb{glyph.item}}{[\code{\link{PangoGlyphItem}}] the glyph item to iterate over}
\item{\verb{text}}{[char] text corresponding to the glyph item}
}
\details{ Since 1.22}
\value{[logical] \code{FALSE} if there are no clusters in the glyph item}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
afc030be36daade19166162d0c4e28beecf80d96
|
5588a0105e712197f67b459ad2101436005cf57c
|
/Assignments/Getting and Cleaning Data/UCI_HAR/run_analysis.R
|
4871323a178408dc1d4d1588ce76a39be4ba8d45
|
[] |
no_license
|
emersson126/Data-Science-Specialization-program
|
189ef3386a4b65315d95f1dc192a142539d17c8e
|
ae4b22e4265256278219d5a385927f79d71a4d4b
|
refs/heads/master
| 2020-04-12T04:57:55.375286
| 2016-04-29T19:21:54
| 2016-04-29T19:21:54
| 162,311,999
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,695
|
r
|
run_analysis.R
|
## run_analysis.R
# 0-a Load packages
#----------------------------------------------------------------------------------------
library(magrittr) # %>%
library(dplyr) # bind_cols(), bind_rows(), left_join()
library(stringr) # str_to_title()
#----------------------------------------------------------------------------------------
# 0-b. Download, unzip, and read the data
#----------------------------------------------------------------------------------------
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, "dataset.zip")
newwd <- paste(getwd(), "dataset", sep = "/")
unzip("dataset.zip", exdir = newwd)
files <- list.files(newwd, recursive = TRUE, full.names = TRUE)
raw_files <- files[c((1:2), (14:16), (26:28))] %>%
lapply(function(x) read.delim(
x,
header = F,
sep = " "
)
)
x_files <- lapply(raw_files[c(4,7)], function(x) apply(
x,
1,
function(y) y[!is.na(y)]
)
) %>%
lapply(function(x) data.frame(
matrix(
unlist(x),
byrow = T,
ncol = 561
),
stringsAsFactors = F
)
)
#----------------------------------------------------------------------------------------
# 1. Merge files and union the `test` and `training` datasets
#----------------------------------------------------------------------------------------
colnames(raw_files[[5]]) <- "V2"
colnames(raw_files[[8]]) <- "V2"
test <- bind_cols(raw_files[[3]], raw_files[[5]], x_files[[1]])
training <- bind_cols(raw_files[[6]], raw_files[[8]], x_files[[2]])
all_trials <- bind_rows(test, training)
#----------------------------------------------------------------------------------------
# 2. Extract only the mean and standard deviation measurements
#----------------------------------------------------------------------------------------
features <- raw_files[2]
col_vec1 <- c("mean()", "std()") %>%
paste(collapse = "|") %>%
grep(features[[1]]$V2)
col_vec2 <- col_vec1 + 2
col_vec2 <- c(c(1,2), col_vec2)
all_trials <- all_trials[ ,col_vec2]
#----------------------------------------------------------------------------------------
# 3. Give descriptive activity names
#----------------------------------------------------------------------------------------
activities <- raw_files[1]
activities[[1]]$V2 <- gsub("_", " ", activities[[1]]$V2) %>%
str_to_title()
analysis_table <- left_join(x = all_trials, y = activities[[1]], by = c("V2" = "V1")) %>%
select(1,-2,82,3:81)
#----------------------------------------------------------------------------------------
# 4. Give descriptive variable names
#----------------------------------------------------------------------------------------
features <- features[[1]]
features <- features[col_vec1, ]
cnames <- c("Subject", "Activity", as.character(features$V2))
colnames(analysis_table) <- cnames
write.table(analysis_table, "analysis_table.txt", row.names = TRUE)
#----------------------------------------------------------------------------------------
# 5. GROUP BY average(*), activity -- and -- GROUP BY average(*), subject
#----------------------------------------------------------------------------------------
summary_table <- aggregate(analysis_table[-(1:2)], by = list(analysis_table$Subject, analysis_table$Activity), FUN = mean)
write.table(summary_table, "summary_table.txt", row.names = TRUE)
#----------------------------------------------------------------------------------------
|
3f242dc62375a324fa6ad3529a4e3daba5f8d9b0
|
76f4e6ea643bad0c60ab2485cd40e5b87cdc8b51
|
/nanopore_example_run/scripts/basic_sequence_qc.r
|
9c2794b323dc6ba1558f1d3f3feda8641b1c300f
|
[] |
no_license
|
ngleadall/train_malta_nanopore
|
aeff1c240b4b871f75410098784e61aa9f0cd055
|
53e2d0cdb364e4ce494fef0202db0e2c80114fc0
|
refs/heads/master
| 2020-03-28T17:33:50.646166
| 2018-09-17T14:30:01
| 2018-09-17T14:30:01
| 148,801,909
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 798
|
r
|
basic_sequence_qc.r
|
# Plotting sequence run summary data
# Nick Gleadall - ng384@cam.ac.uk
#Pre-step - clear R envronment ( good practice )
rm( list=ls() )
# Step 1 - set the working directory
setwd( "~/data/" )
# Step 2 - read in the sequencing summary file
summary_data <- read.table( "nanopore_example_run/basecalled_reads/sequencing_summary.txt", sep="\t", header=TRUE)
# Step 3 - quickly view the data
View( summary_data )
# Step 4 - plot read length historgram
hist( summary_data$sequence_length_template , main="Read length distribution" , xlab = "Read Length" )
# Step 5 - plot quality against read length
plot( summary_data$mean_qscore_template ,
summary_data$sequence_length_template ,
main="Sequence length against quality score",
ylab="Read length" ,
xlab="Quality score")
|
df5d43cb2f9c0440ba5b26652f812a9a196c7709
|
7896e7eabb12e7a6d9417e83d0a3155603815a2a
|
/euler/R/tests/tests.R
|
64ad8c88ec5897ad129885f20555e0215d94d18b
|
[] |
no_license
|
ajdust/euler
|
7461df96a0926577a7c326e152b730f9c8e4dbb0
|
428d9f06bb7789741289e7642b764c5eb7220482
|
refs/heads/master
| 2023-08-24T16:26:51.233397
| 2021-10-28T23:52:37
| 2021-10-28T23:52:37
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 428
|
r
|
tests.R
|
library(euler)
library(RUnit)
library(jsonlite)
library(hash)
ans <- fromJSON("../answers.json")
ans <- lapply(ans, as.numeric)
problem01_is_correct <- function() {
checkEquals(ans$`1`, problem01())
}
problem02_is_correct <- function() {
checkEquals(ans$`2`, problem02())
}
problem03_is_correct <- function() {
checkEquals(ans$`3`, problem03())
}
problem04_is_correct <- function() {
checkEquals(ans$`4`, problem04())
}
|
4807d83da65f612ca496ff4780dfb0825c413ed8
|
50a3642a16fdc6e3e30141eb64066766a822d67f
|
/man/PC_model.Rd
|
77a8200a91a26d1c8212930fbfc0ea4783208fb1
|
[
"BSD-2-Clause"
] |
permissive
|
JGCRI/pridr
|
85687b997325ce2a3254f617d94fcc6d354f940c
|
5db4e202fc4e8606a74c080e0d7fcae8cb71703b
|
refs/heads/main
| 2023-04-27T03:49:03.966193
| 2023-04-07T18:10:57
| 2023-04-07T18:10:57
| 432,577,581
| 4
| 0
|
BSD-2-Clause
| 2023-04-07T18:10:59
| 2021-11-27T23:35:43
|
R
|
UTF-8
|
R
| false
| true
| 610
|
rd
|
PC_model.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pca_ineq.R
\name{PC_model}
\alias{PC_model}
\title{Function to calculate deciles using PC model}
\usage{
PC_model(
df,
grouping_variables = c("country", "year"),
value_col = "Income..net.",
id_col = c("country")
)
}
\arguments{
\item{df}{dataframe with all IV data. These are output by `compile_IV_data()`.}
\item{grouping_variables}{variables to group by}
\item{value_col}{Name of values to be output.}
\item{id_col}{column to split by for parallelization}
}
\description{
Function to calculate deciles using PC model
}
|
23421883df890b842d39c8a387b5e5e9d6e6a0f9
|
ac42c7b87b90f08c8ec9b12d5e17a534ffb84b02
|
/man/cr_buildstep_secret.Rd
|
760cc8c67793aaa1ccd7318ecfd142edb15c5a0c
|
[] |
no_license
|
alainlompo/googleCloudRunner
|
990c819c9f4f46ca5a39a720627da4003a6410f3
|
6622d22f1c19d58f6b5888ae929f32d86b57a1c6
|
refs/heads/master
| 2023-09-03T15:33:00.968050
| 2021-10-27T21:18:23
| 2021-10-27T21:18:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,022
|
rd
|
cr_buildstep_secret.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/buildsteps_templates.R
\name{cr_buildstep_secret}
\alias{cr_buildstep_secret}
\title{Create a buildstep for using Secret Manager}
\usage{
cr_buildstep_secret(secret, decrypted, version = "latest", ...)
}
\arguments{
\item{secret}{The secret data name in Secret Manager}
\item{decrypted}{The name of the file the secret will be decrypted into}
\item{version}{The version of the secret}
\item{...}{Other arguments sent to \link{cr_buildstep_bash}}
}
\description{
This is the preferred way to manage secrets for files, rather than
\link{cr_buildstep_decrypt}, as it stores the encrypted file in the cloud
rather than in your project workspace. For single environment values, \link{cr_build_yaml_secrets} may be more suitable.
}
\details{
This is for downloading encrypted files from Google Secret Manager. You will need to add the
Secret Accessor Cloud IAM role to the Cloud Build service account to use it.
Once you have uploaded your secret file and named it, it is available for Cloud
Build to use.
}
\examples{
cr_buildstep_secret("my_secret", decrypted = "/workspace/secret.json")
}
\seealso{
How to set up secrets using \href{https://cloud.google.com/cloud-build/docs/securing-builds/use-encrypted-secrets-credentials}{Secret Manager}
\link{cr_build_yaml_secrets} let you directly support environment arguments in the buildsteps
Other Cloud Buildsteps:
\code{\link{cr_buildstep_bash}()},
\code{\link{cr_buildstep_decrypt}()},
\code{\link{cr_buildstep_df}()},
\code{\link{cr_buildstep_docker}()},
\code{\link{cr_buildstep_edit}()},
\code{\link{cr_buildstep_extract}()},
\code{\link{cr_buildstep_gcloud}()},
\code{\link{cr_buildstep_gitsetup}()},
\code{\link{cr_buildstep_mailgun}()},
\code{\link{cr_buildstep_nginx_setup}()},
\code{\link{cr_buildstep_pkgdown}()},
\code{\link{cr_buildstep_run}()},
\code{\link{cr_buildstep_r}()},
\code{\link{cr_buildstep_slack}()},
\code{\link{cr_buildstep}()}
}
\concept{Cloud Buildsteps}
|
fc69950d25196f4ef02a5d0e1f8d796e8946de7a
|
80ccc4b25a784d6d93c4c5efdc88fa5174c64b94
|
/challenge.r
|
086724a67f8dc76ed8d661e958fcb11af1d1874c
|
[] |
no_license
|
zimmermankz/fullstory-challenge
|
0eba87c004fa129f403dfcd91cd6bb26d4241745
|
93a50cbb8758cb9cda6ef4bcc532782c1c7e73d7
|
refs/heads/master
| 2020-06-08T19:21:28.239192
| 2019-06-23T02:12:15
| 2019-06-23T02:12:15
| 193,291,692
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,348
|
r
|
challenge.r
|
library(dplyr)
#load data
trip.data <- read.csv("yellow_tripdata_2017-06.csv")
zone.lookup <- read.csv("taxi+_zone_lookup.csv")
######## create additional columns ########
#find length of trip (trip time will be in seconds)
trip.data$tpep_dropoff_datetime <- as.POSIXct(as.character(trip.data$tpep_dropoff_datetime), format="%Y-%m-%d %H:%M:%S")
trip.data$tpep_pickup_datetime <- as.POSIXct(as.character(trip.data$tpep_pickup_datetime), format="%Y-%m-%d %H:%M:%S")
trip.data$trip_time <- as.numeric(trip.data$tpep_dropoff_datetime - trip.data$tpep_pickup_datetime)
#compute income to driver as sum of fare and tips
trip.data$income <- trip.data$fare_amount + trip.data$tip_amount
#compute total income per hour, as well as tips/hour and fare/hour
trip.data$income_per_hour <- (trip.data$income / trip.data$trip_time) * 3600
trip.data$tips_per_hour <- (trip.data$tip_amount / trip.data$trip_time) * 3600
trip.data$fare_per_hour <- (trip.data$fare_amount / trip.data$trip_time) * 3600
#separate out hour as own column (to find best time of day)
trip.data$pickup_hour <- strftime(trip.data$tpep_pickup_datetime, format="%H")
trip.data$day_of_week <- weekdays(trip.data$tpep_pickup_datetime, F)
#remove trips with 0 length, those seem like errors
trip.data <- trip.data[which(trip.data$trip_distance > 0), ]
#some trips are 0 seconds (or other unreasonably low numbers), define minimum length to avoid what seem to be errors
min.trip.time = 30
min.at.location = 30
######## find the best pickup location ########
#by income
income.by.pickup.location <- trip.data[which(trip.data$trip_time >= min.trip.time), c("PULocationID", "income_per_hour", "trip_time")] %>%
group_by(PULocationID) %>%
filter(n() >= min.at.location) %>%
summarise(income_per_hour = weighted.mean(income_per_hour, trip_time))
income.by.pickup.location <- merge(income.by.pickup.location, zone.lookup[,c("LocationID", "Borough", "Zone")], by.x="PULocationID", by.y="LocationID")
income.by.pickup.location[order(-income.by.pickup.location$income_per_hour), ]
#by fare
# fare.by.pickup.location <- trip.data[which(trip.data$trip_time >= min.trip.time), c("PULocationID", "fare_per_hour", "trip_time")] %>%
# group_by(PULocationID) %>%
# filter(n() >= min.at.location) %>%
# summarise(fare_per_hour = weighted.mean(fare_per_hour, trip_time))
# fare.by.pickup.location <- merge(fare.by.pickup.location, zone.lookup[,c("LocationID", "Borough", "Zone")], by.x="PULocationID", by.y="LocationID")
# head(fare.by.pickup.location[order(-fare.by.pickup.location$fare_per_hour), ])
#by tips
# tips.by.pickup.location <- trip.data[which(trip.data$trip_time >= min.trip.time), c("PULocationID", "tips_per_hour", "trip_time")] %>%
# group_by(PULocationID) %>%
# filter(n() >= min.at.location) %>%
# summarise(tips_per_hour = weighted.mean(tips_per_hour, trip_time))
# tips.by.pickup.location <- merge(tips.by.pickup.location, zone.lookup[,c("LocationID", "Borough", "Zone")], by.x="PULocationID", by.y="LocationID")
# head(tips.by.pickup.location[order(-tips.by.pickup.location$tips_per_hour), ])
######## find best pickup time ########
#by income (time only)
income.by.pickup.time <- data.frame(trip.data[which(trip.data$trip_time >= min.trip.time), c("pickup_hour", "income_per_hour", "trip_time")] %>%
group_by(pickup_hour) %>%
summarise(income_per_hour = weighted.mean(income_per_hour, trip_time)))
income.by.pickup.time[order(-income.by.pickup.time$income_per_hour), ]
#by income (day of week only)
income.by.pickup.day <- data.frame(trip.data[which(trip.data$trip_time >= min.trip.time), c("day_of_week", "income_per_hour", "trip_time")] %>%
group_by(day_of_week) %>%
summarise(income_per_hour = weighted.mean(income_per_hour, trip_time)))
income.by.pickup.day[order(-income.by.pickup.day$income_per_hour), ]
#by income (time and day)
income.by.pickup.day.and.time <- data.frame(trip.data[which(trip.data$trip_time >= min.trip.time), c("pickup_hour", "day_of_week", "income_per_hour", "trip_time")] %>%
group_by(pickup_hour, day_of_week) %>%
summarise(income_per_hour = weighted.mean(income_per_hour, trip_time)))
income.by.pickup.day.and.time[order(-income.by.pickup.day.and.time$income_per_hour), ]
######## make some pretty pictures ########
#simple barplot of average income/hr by hour of the day
barplot(income.by.pickup.time$income_per_hour,
names.arg = income.by.pickup.time$pickup_hour,
xlab = "Hour of the Day",
ylab = "Avg Income/Hr with Passenger in Cab",
ylim = c(0, 80),
col = "darkgreen")
### shade map of NYC taxi zones with average income/hr
# NOTE - this will install any packages in the following list that are not installed
needed_packages <- c("rgdal", "leaflet", "RColorBrewer")
for(package in needed_packages) {
if(!require(package, character.only = TRUE)) {
install.packages(package)
library(package, character.only = TRUE)
}
}
#load zone map of NYC - source for map: https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc
new.york.zone.map <- readOGR("NYC Taxi Zones.kml")
#create color palette, bin cutoffs were chosen manually after inspecting the data
palette <- colorBin(brewer.pal(11, "RdYlGn"), bins=c(20, 40, 50, 55, 60, 65, 70, 80, 90, 100, 300, 500))
#prepare to fill out all zones, even ones without sufficient data
incomes.by.location.completed <- merge(income.by.pickup.location, zone.lookup[,c("LocationID", "Borough")], by.x="PULocationID", by.y="LocationID", all.y=T)[1:263,]
incomes.by.location.completed$income_per_hour[is.na(incomes.by.location.completed$income_per_hour)] <- 0
#shade the map and display it
shaded.map <- leaflet() %>%
addPolygons(data = new.york.zone.map,
fillColor = ~palette(incomes.by.location.completed$income_per_hour),
fillOpacity = 1,
weight = 1) %>%
addLegend(position = "bottomright",
colors = brewer.pal(11, "RdYlGn"),
labels = c("$20/hr - $40/hr", "$40/hr - $50/hr", "$50/hr - $55/hr", "$55/hr - $60/hr", "$60/hr - $65/hr", "$65/hr - $70/hr", "$70/hr - $80/hr", "$80/hr - $90/hr", "$90/hr - $100/hr", "$100/hr - $300/hr", "$300/hr - $500/hr"),
bins = 11,
opacity = 0.7)
print(shaded.map)
|
0071ee1838da18ff1f201b4683625a178d908103
|
ddcc526b8544b5be7bb9d6f744f318f2b871fbc7
|
/man/netherlands.Rd
|
b00ea197e40eca743c3ffe0193a8403200d6aa23
|
[] |
no_license
|
cran/mapmisc
|
9d21d865151541f607ae0818ce3cbe3ac7876390
|
3dab0a3c80ca64fc0709eeb1b5aaff632cb72e40
|
refs/heads/master
| 2021-05-16T02:36:00.435354
| 2021-03-10T22:50:02
| 2021-03-10T22:50:02
| 17,697,269
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,275
|
rd
|
netherlands.Rd
|
\name{netherlands}
\alias{netherlands}
\alias{nldElev}
\alias{nldTiles}
\alias{nldCities}
\alias{world}
\docType{data}
\title{
Data from the Netherlands
}
\description{
Elevation data and map tiles for the Netherlands
}
\usage{data("netherlands")}
\format{
\code{nldElev} is a raster of elevation
\code{nltTiles} is a background map
\code{nldCities} is a SpatialPointsDataFrame of city locations.
}
\details{
The inclusion of these datasets is intended to allow the package to build
when an internet connection is not present.
}
\source{
See examples.
}
\seealso{\link[sp]{meuse}, \link[raster]{getData}, \link{openmap}}
\examples{
# soil data
library("sp")
data("meuse")
coordinates(meuse) <- ~x+y
# proj4string(meuse) <- CRS("+init=epsg:28992")
proj4string(meuse) <- CRS(
paste("+proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889",
"+k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m",
"+no_defs +datum=WGS84")
)
meuse$soilFac = factor(meuse$soil, levels=c(1,2,3),
labels=c("Calcareous","Non-Calc's","Red Brick"))
soilCol = colourScale(meuse$soilFac)
data("netherlands")
map.new(meuse)
plot(nldTiles,add=TRUE)
points(nldCities)
text(nldCities,label=nldCities$name, pos=2)
points(meuse, pch=16, col=soilCol$plot)
legend('topleft', fill=soilCol$col,legend=soilCol$legend)
if(requireNamespace('rgdal', quietly=TRUE)) insetMap(meuse, "bottomright",map=world)
# location won't be marked on the inset map unless rgdal is available
\dontrun{
# this is how the data were obtained
# map tiles
nldTiles = openmap(meuse, zoom=12)
# cities
nldCities = GNcities(nldTiles, maxRows=25)
# world
world = openmap(extent(-10,30,40,60))
# elevation data
require('rgdal')
meuseLL = spTransform(meuse, mapmisc::crsLL)
getData("SRTM", lon=xmin(extent(meuseLL)),
lat=ymin(extent(meuseLL)),path=tempdir())
nldElev = raster(paste(tempdir(), "/", "srtm_38_02.tif", sep=""))
nldElev = crop(nldElev, extend(extent(meuseLL), 0.1))
nldElev = projectRaster(nldElev, crs=proj4string(meuse))
nldElev = crop(nldElev, extent(nldTiles))
# save the files where the package builder wants them
# save(nldElev, nldTiles, nldCities,world,
# file="~/research/diseasemapping/pkg/mapmisc/data/netherlands.RData",
# compress="xz")
}
}
\keyword{datasets}
|
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