content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2 values | repo_name large_stringlengths 5 125 | language large_stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.46M | extension large_stringclasses 75 values | text stringlengths 0 6.46M |
|---|---|---|---|---|---|---|---|---|---|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/em_functions.r
\name{beem2biomass}
\alias{beem2biomass}
\title{beem2biomass}
\usage{
beem2biomass(beem)
}
\arguments{
\item{beem}{a BEEM object}
}
\description{
extract biomass estimates from a BEEM object
}
\author{
Chenhao Li, Niranjan Nagarajan
}
| /man/beem2biomass.Rd | permissive | pythseq/BEEM-static | R | false | true | 328 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/em_functions.r
\name{beem2biomass}
\alias{beem2biomass}
\title{beem2biomass}
\usage{
beem2biomass(beem)
}
\arguments{
\item{beem}{a BEEM object}
}
\description{
extract biomass estimates from a BEEM object
}
\author{
Chenhao Li, Niranjan Nagarajan
}
|
#' Save list of expression matrices in a format suitable for runPEER.py
#'
#' @param expression_list list of expression matrices
#' @param outdir output directory
#' @param file_suffix suffix of the output file
#'
#' @export
#'
savePEERData <- function(expression_list, outdir, file_suffix = "exprs"){
#Save eQTL gene expression data to be used with PEER
sample_names = names(expression_list)
for (sample in sample_names){
matrix = expression_list[[sample]]
path = file.path(outdir, paste(sample, file_suffix ,"txt", sep = "."))
print(path)
write.table(t(matrix), path, row.names = FALSE, col.names = FALSE, sep = ",")
}
}
importPEERFactors <- function(file_path, design_matrix, remove_mean = TRUE){
#Import output from runPEER.py script back into R
peer_factors = read.table(file_path, sep =",")
if(remove_mean){ #Remove the first column that contains the mean
peer_factors = peer_factors[,2:ncol(peer_factors)]
}
colnames(peer_factors) = paste("PEER_factor_", c(1:ncol(peer_factors)), sep = "")
peer_factors = dplyr::mutate(peer_factors, sample_id = design_matrix$sample_id) %>%
dplyr::select(sample_id, everything())
return(peer_factors)
}
| /seqUtils/R/qtl_PEER.R | permissive | kauralasoo/macrophage-tuQTLs | R | false | false | 1,190 | r | #' Save list of expression matrices in a format suitable for runPEER.py
#'
#' @param expression_list list of expression matrices
#' @param outdir output directory
#' @param file_suffix suffix of the output file
#'
#' @export
#'
savePEERData <- function(expression_list, outdir, file_suffix = "exprs"){
#Save eQTL gene expression data to be used with PEER
sample_names = names(expression_list)
for (sample in sample_names){
matrix = expression_list[[sample]]
path = file.path(outdir, paste(sample, file_suffix ,"txt", sep = "."))
print(path)
write.table(t(matrix), path, row.names = FALSE, col.names = FALSE, sep = ",")
}
}
importPEERFactors <- function(file_path, design_matrix, remove_mean = TRUE){
#Import output from runPEER.py script back into R
peer_factors = read.table(file_path, sep =",")
if(remove_mean){ #Remove the first column that contains the mean
peer_factors = peer_factors[,2:ncol(peer_factors)]
}
colnames(peer_factors) = paste("PEER_factor_", c(1:ncol(peer_factors)), sep = "")
peer_factors = dplyr::mutate(peer_factors, sample_id = design_matrix$sample_id) %>%
dplyr::select(sample_id, everything())
return(peer_factors)
}
|
library(RRTCS)
### Name: ChaudhuriChristofidesDatapij
### Title: Matrix of the second-order inclusion probabilities
### Aliases: ChaudhuriChristofidesDatapij
### Keywords: datasets
### ** Examples
data(ChaudhuriChristofidesDatapij)
#Now, let select only the first-order inclusion probabilities
diag(ChaudhuriChristofidesDatapij)
| /data/genthat_extracted_code/RRTCS/examples/ChaudhuriChristofidesDatapij.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 336 | r | library(RRTCS)
### Name: ChaudhuriChristofidesDatapij
### Title: Matrix of the second-order inclusion probabilities
### Aliases: ChaudhuriChristofidesDatapij
### Keywords: datasets
### ** Examples
data(ChaudhuriChristofidesDatapij)
#Now, let select only the first-order inclusion probabilities
diag(ChaudhuriChristofidesDatapij)
|
pacotes <- c("tidyverse","knitr","kableExtra","car","rgl","gridExtra",
"PerformanceAnalytics","reshape2","rayshader","psych","pracma",
"polynom","rqPen","ggrepel")
if(sum(as.numeric(!pacotes %in% installed.packages())) != 0){
instalador <- pacotes[!pacotes %in% installed.packages()]
for(i in 1:length(instalador)) {
install.packages(instalador, dependencies = T)
break()}
sapply(pacotes, require, character = T)
} else {
sapply(pacotes, require, character = T)
}
#carregando arquivo
arquivo1 <- load("notasfatorial.RData")
#criando tabela
notasfatorial %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
#fatiando os valores
dados <- notasfatorial[1:9,2:5]
#calculando a correlação
rho <- cor(dados)
rho
# Observando as correlações entre variáveis
chart.Correlation(notasfatorial[,2:5])
# A estatística KMO
KMO(r = rho)
# O teste de efericidade de Bartlett --------------------------------------
cortest.bartlett(R = rho)
#Extraindo os autovalores
eigenvalues_rho <- eigen(rho)
eigenvalues_rho$values
sum(eigenvalues_rho$values)
#variancia compartilhada
var_compartilhada <- (eigenvalues_rho$values/sum(eigenvalues_rho$values))
var_compartilhada
sum(var_compartilhada)
principais_componentes <- 1:sum(eigenvalues_rho$values)
principais_componentes
# Juntando tudo o que temos até o momento:
data.frame(principais_componentes = paste0("PC", principais_componentes),
eigenvalue = eigenvalues_rho$values,
var_compartilhada = var_compartilhada) -> relatorio_eigen
# Overview dos resultados até o momento
relatorio_eigen %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
# A determinação dos autovetores a partir de seus respectivos eigenvalues
eigenvalues_rho$vectors
# Estabelecendo a matriz diagonal de eigenvalues (L2)
L2 <- diag(eigenvalues_rho$values)
L2
# Assim, com os eigenvectors calculados, podemos provar que V'.rho.V = L2
prova_01 <- t(eigenvalues_rho$vectors) %*% rho %*% eigenvalues_rho$vectors
round(x = prova_01,
digits = 14)
# Calculando os scores fatoriais
# Relembrando:
eigenvalues_rho$values
eigenvalues_rho$vectors
# De forma simplificada:
scores_fatoriais <- t(eigenvalues_rho$vectors)/sqrt(eigenvalues_rho$values)
(scores_fatoriais)
# Calculando os fatores
# O primeiro passo é o de padronização da base de dados pelo procedimento
# zscores, utilizando a função scale():
notasfatorial_std <- dados %>%
scale() %>%
data.frame()
# A seguir, vamos criar um objeto que servirá como um receptáculo para os
# k fatores (4, no caso estudado) a serem calculados:
fatores <- list()
# Agora, utilizaremos a função for
for(i in 1:nrow(scores_fatoriais)){
fatores[[i]] <- rowSums(x = sweep(x = notasfatorial_std,
MARGIN = 2,
STATS = scores_fatoriais[i,],
FUN = `*`))
,3
}
fatores_df <- data.frame((sapply(X = fatores, FUN = c)))
fatores_df
fatores_df %>%
rename(F1 = X1,
F2 = X2,
F3 = X3,
F4 = X4) %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
# Podemos verificar que os fatores calculados, de fato, são ortogonais entre
# si, isto é, possuem correlações iguais a 0:
round(x = cor(fatores_df),
digits = 14)
# Combinando a base original 'notasfatorial' com o objeto 'fatores_df':
notasfatorial_final <- cbind(dados,
fatores_df) %>%
rename(F1 = X1,
F2 = X2,
F3 = X3,
F4 = X4)
# Calculando as correlações entre as variáveis e originais e os fatores
correlacoes_entre_fatores <- cor(notasfatorial_final)
| /Análise Fatorial - PCA/Exemplo1.R | no_license | Felipe0042/Ciencia_dados_R | R | false | false | 4,083 | r | pacotes <- c("tidyverse","knitr","kableExtra","car","rgl","gridExtra",
"PerformanceAnalytics","reshape2","rayshader","psych","pracma",
"polynom","rqPen","ggrepel")
if(sum(as.numeric(!pacotes %in% installed.packages())) != 0){
instalador <- pacotes[!pacotes %in% installed.packages()]
for(i in 1:length(instalador)) {
install.packages(instalador, dependencies = T)
break()}
sapply(pacotes, require, character = T)
} else {
sapply(pacotes, require, character = T)
}
#carregando arquivo
arquivo1 <- load("notasfatorial.RData")
#criando tabela
notasfatorial %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
#fatiando os valores
dados <- notasfatorial[1:9,2:5]
#calculando a correlação
rho <- cor(dados)
rho
# Observando as correlações entre variáveis
chart.Correlation(notasfatorial[,2:5])
# A estatística KMO
KMO(r = rho)
# O teste de efericidade de Bartlett --------------------------------------
cortest.bartlett(R = rho)
#Extraindo os autovalores
eigenvalues_rho <- eigen(rho)
eigenvalues_rho$values
sum(eigenvalues_rho$values)
#variancia compartilhada
var_compartilhada <- (eigenvalues_rho$values/sum(eigenvalues_rho$values))
var_compartilhada
sum(var_compartilhada)
principais_componentes <- 1:sum(eigenvalues_rho$values)
principais_componentes
# Juntando tudo o que temos até o momento:
data.frame(principais_componentes = paste0("PC", principais_componentes),
eigenvalue = eigenvalues_rho$values,
var_compartilhada = var_compartilhada) -> relatorio_eigen
# Overview dos resultados até o momento
relatorio_eigen %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
# A determinação dos autovetores a partir de seus respectivos eigenvalues
eigenvalues_rho$vectors
# Estabelecendo a matriz diagonal de eigenvalues (L2)
L2 <- diag(eigenvalues_rho$values)
L2
# Assim, com os eigenvectors calculados, podemos provar que V'.rho.V = L2
prova_01 <- t(eigenvalues_rho$vectors) %*% rho %*% eigenvalues_rho$vectors
round(x = prova_01,
digits = 14)
# Calculando os scores fatoriais
# Relembrando:
eigenvalues_rho$values
eigenvalues_rho$vectors
# De forma simplificada:
scores_fatoriais <- t(eigenvalues_rho$vectors)/sqrt(eigenvalues_rho$values)
(scores_fatoriais)
# Calculando os fatores
# O primeiro passo é o de padronização da base de dados pelo procedimento
# zscores, utilizando a função scale():
notasfatorial_std <- dados %>%
scale() %>%
data.frame()
# A seguir, vamos criar um objeto que servirá como um receptáculo para os
# k fatores (4, no caso estudado) a serem calculados:
fatores <- list()
# Agora, utilizaremos a função for
for(i in 1:nrow(scores_fatoriais)){
fatores[[i]] <- rowSums(x = sweep(x = notasfatorial_std,
MARGIN = 2,
STATS = scores_fatoriais[i,],
FUN = `*`))
,3
}
fatores_df <- data.frame((sapply(X = fatores, FUN = c)))
fatores_df
fatores_df %>%
rename(F1 = X1,
F2 = X2,
F3 = X3,
F4 = X4) %>%
kable() %>%
kable_styling(bootstrap_options = "striped",
full_width = T,
font_size = 12)
# Podemos verificar que os fatores calculados, de fato, são ortogonais entre
# si, isto é, possuem correlações iguais a 0:
round(x = cor(fatores_df),
digits = 14)
# Combinando a base original 'notasfatorial' com o objeto 'fatores_df':
notasfatorial_final <- cbind(dados,
fatores_df) %>%
rename(F1 = X1,
F2 = X2,
F3 = X3,
F4 = X4)
# Calculando as correlações entre as variáveis e originais e os fatores
correlacoes_entre_fatores <- cor(notasfatorial_final)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dose_resp_ind.r
\name{dose_resp_ind}
\alias{dose_resp_ind}
\title{Extract the information from the simulation data frame to analyse the dose response effects}
\usage{
dose_resp_ind(allsim, dataset, dr = seq(0, 1, 0.1))
}
\arguments{
\item{allsim}{dataset with all simulations values}
\item{dataset}{dataset with all variables}
\item{dr}{a vector with dose response values}
}
\value{
a data frame with dose response values
}
\description{
Extract the information from the simulation data frame to analyse the dose response effects
}
| /man/dose_resp_ind.Rd | no_license | cran/expose | R | false | true | 633 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dose_resp_ind.r
\name{dose_resp_ind}
\alias{dose_resp_ind}
\title{Extract the information from the simulation data frame to analyse the dose response effects}
\usage{
dose_resp_ind(allsim, dataset, dr = seq(0, 1, 0.1))
}
\arguments{
\item{allsim}{dataset with all simulations values}
\item{dataset}{dataset with all variables}
\item{dr}{a vector with dose response values}
}
\value{
a data frame with dose response values
}
\description{
Extract the information from the simulation data frame to analyse the dose response effects
}
|
# https://ourcodingclub.github.io/2017/03/15/mixed-models.html
# https://rpsychologist.com/r-guide-longitudinal-lme-lmer
# https://stackoverflow.com/questions/49033016/plm-or-lme4-for-random-and-fixed-effects-model-on-panel-data
# https://www.princeton.edu/~otorres/Panel101R.pdf
# fredr_set_key('d0b9e64aba30b479343a06037a5a10c1')
library(rvest)
library(httr)
library(data.table)
library(tidyverse)
library(WDI)
library(countrycode)
library(lmtest)
library(tseries)
library(plm)
library(rvest)
library(httr)
library(quantmod)
# library(fredr)
library(scales)
library(quantreg)
refresh_downloads = T
setwd('~\\Public_Policy\\Projects\\Taxes vs. Deficits\\data')
##### Get political data #####
if (refresh_downloads) {
the_url = 'https://www.presidency.ucsb.edu/statistics/data/house-and-senate-concurrence-with-presidents'
concurrence_table = html_table(GET(the_url) %>% content(), fill = TRUE)
concurrence_table[[1]] %>% write.csv('concurrence_with_president.csv')
}
concurrence_with_president_clean = read_csv('concurrence_with_president_clean.csv')
# MIT data lab
house_elections = read.csv('1976-2018-house.csv') %>%
mutate(candidate_votes = as.numeric(candidatevotes)) %>%
filter(stage == 'gen') %>% data.table()
senate_elections = read.csv('1976-2018-senate.csv') %>% filter(stage == 'gen') %>% data.table()
presidential_elections = read.csv('1976-2016-president.csv') %>% data.table()
##### Combine historical top tax rate data from Brookings and Current OECD Data #####
## Brookings source:
# http://www.taxpolicycenter.org/sites/default/files/legacy/taxfacts/content/PDF/oecd_historical_toprate.pdf
## OECD source:
# https://www.oecd.org/tax/tax-policy/tax-database/tax-database-update-note.pdf
# get TOP marginal tax rates
brookings_oecd_toptax = read_csv('oecd_historical_toprate_raw.csv', na = '--') %>%
pivot_longer(cols = paste(c(1975:2013)), names_to = 'Year', values_to = 'TOP_TRATE')
current_oecd_toptax = read_csv('TABLE_I7_30102020214927846.csv') %>%
pivot_wider(id_cols = c('Country', 'Year'), names_from = 'TAX', values_from = 'Value')
# get tax revenues to GDP
all_oecd_downloads =
c("oecd_net_lending.csv" , "oecd_revenue.csv" , "oecd_spending.csv" ) %>%
map(read_csv) %>%
bind_rows() %>%
filter(LOCATION != 'OAVG', MEASURE == 'PC_GDP', SUBJECT == 'TOT')
# map country names
country_code_name_mappings = data.frame(
LOCATION = unique(all_oecd_downloads$LOCATION)
) %>%
mutate(
Country = map_chr(LOCATION, function(the_country){
countrycode(the_country, 'iso3c', 'country.name')
}),
Country = recode(Country, `South Korea` = 'Korea')
)
stacked_extra_oecd_stats = inner_join(all_oecd_downloads, country_code_name_mappings)
# check col classes
map_chr(brookings_oecd_toptax, class)
map_chr(current_oecd_toptax, class)
# check if any countries aren't found in either dataset, may need to change names
current_oecd_toptax$Country[!current_oecd_toptax$Country %in% brookings_oecd_toptax$Country] %>% unique()
brookings_oecd_toptax$Country[!brookings_oecd_toptax$Country %in% current_oecd_toptax$Country] %>% unique()
# check year ranges -- only keep non-overlapping
range(brookings_oecd_toptax$Year)
range(current_oecd_toptax$Year)
# recode columns
brookings_oecd_toptax_clean = mutate(
brookings_oecd_toptax,
Country = recode(Country, `Korea, Republic of` = 'Korea'),
Year = as.numeric(Year),
TOP_TRATE = TOP_TRATE * 100 # to match OECD
) %>%
filter(
Year < min(current_oecd_toptax$Year, na.rm = T)
)
range(brookings_oecd_toptax_clean$Year)
# clear memory
rm(brookings_oecd_toptax)
# outer join on country and year, keeping the year overlaps from oecd
joined_oecd_brookings_toptax = bind_rows(
brookings_oecd_toptax_clean,
current_oecd_toptax
) %>%
arrange(
Country, Year
)
# take a look at the data
ggplot(joined_oecd_brookings_toptax, aes(Year, TOP_TRATE, colour = Country)) +
geom_line()
# lots of missing values in the middle of the dataset, let's interpolate those
joined_oecd_brookings_toptax_dt = data.table(joined_oecd_brookings_toptax)
joined_oecd_brookings_toptax_filled = joined_oecd_brookings_toptax_dt[, {
# cat("\nCountry ==",.BY[[1]],"\n\n")
non_na_years = Year[!is.na(TOP_TRATE)]
na_years = Year[is.na(TOP_TRATE)]
# find the most recent existing value
filled_vals = map_dbl(na_years, function(this_year){
years_to_check = non_na_years[non_na_years < this_year]
if (length(years_to_check) == 0) {
return(NA)
} else {
closest_year = max(years_to_check)
return(TOP_TRATE[Year == closest_year])
}
})
filled_df = data.frame(
Year = na_years,
TOP_TRATE = filled_vals
)
full_df = data.frame(
Year, TOP_TRATE
) %>%
filter(Year %in% non_na_years)
stacked_df = bind_rows(filled_df, full_df) %>% arrange(Year)
list(
Year = stacked_df$Year,
TOP_TRATE = stacked_df$TOP_TRATE
)
}, by = Country]
# add back other variables
joined_oecd_brookings_toptax_fin = inner_join(
select(joined_oecd_brookings_toptax, -TOP_TRATE),
joined_oecd_brookings_toptax_filled
)
rm(joined_oecd_brookings_toptax, joined_oecd_brookings_toptax_dt)
# looks much better now
ggplot(joined_oecd_brookings_toptax_filled, aes(Year, TOP_TRATE, colour = Country)) +
geom_line()
##### Get fiscal data from the world bank #####
# WDIsearch('debt') %>% View()
start_year = min(joined_oecd_brookings_toptax_filled$Year, na.rm = T)
end_year = max(joined_oecd_brookings_toptax_filled$Year, na.rm = T)
wdi_indicators = c('NY.GDP.PCAP.KD.ZG', 'GC.TAX.TOTL.GD.ZS',
'DT.DOD.DECT.GN.ZS', 'GC.TAX.YPKG.RV.ZS', 'GC.XPN.INTP.RV.ZS',
'GC.REV.TOTL.CD', 'PAY.TAX.RK.DB19', 'NY.GDP.MKTP.KD.ZG', 'NY.GDP.PCAP.KD', 'GC.DOD.TOTL.GD.ZS')
wdi_names = map(wdi_indicators, function(ind){
WDIsearch(ind, field = 'indicator') %>% t() %>% as.data.frame()
}) %>%
bind_rows()
if (refresh_downloads | !file.exists('wdi_download_long.csv')) {
wdi_download = WDI(indicator = wdi_indicators,
start = start_year,
end = end_year, extra = T) %>%
filter(
income == 'High income'
)
wdi_download_long = pivot_longer(wdi_download, cols = wdi_indicators, names_to = 'indicator') %>%
inner_join(wdi_names) %>%
mutate(
country = recode(country, `Korea, Rep.` = 'Korea')
)
write.csv(wdi_download_long, 'wdi_download_long.csv', row.names = F)
} else {
wdi_download_long = read_csv('wdi_download_long.csv')
}
# check country names
# wdi_download_long$country[!wdi_download_long$country %in% joined_oecd_brookings_toptax_filled$Country] %>% unique()
# joined_oecd_brookings_toptax_filled$Country[!joined_oecd_brookings_toptax_filled$Country %in% wdi_download_long$country] %>% unique()
##### Combine all world bank and OECD data together #####
stacked_oecd_wdi_data = bind_rows(
joined_oecd_brookings_toptax_fin %>% mutate(Indicator = 'top_tax_rate') %>% select(Country, Year, Value = TOP_TRATE, Indicator),
stacked_extra_oecd_stats %>% select(Country, Year = TIME, Value, Indicator = INDICATOR)
) %>%
bind_rows(
wdi_download_long %>% select(Country = country, Year = year, Indicator = indicator, Value = value)
) %>%
arrange(
Country, Indicator, Year
) %>%
filter(!is.na(Country))
# test uniqueness of observations
unique_obs = select(stacked_oecd_wdi_data, Country, Year, Indicator) %>% unique()
stopifnot(nrow(unique_obs) == nrow(stacked_oecd_wdi_data))
stacked_oecd_wdi_data_dt = data.table(stacked_oecd_wdi_data)
duped_dt = stacked_oecd_wdi_data_dt[duplicated(stacked_oecd_wdi_data_dt, by = c('Country', 'Year', 'Indicator')),]
inner_join(stacked_oecd_wdi_data, duped_dt) %>% dim()
##### Compute new variables #####
# for each country and indicator, compute:
# Lags
# First differences
stacked_oecd_wdi_data_dt = data.table(stacked_oecd_wdi_data)
stacked_oecd_wdi_data_lags_diffs = stacked_oecd_wdi_data_dt[, {
last_value = dplyr::lag(Value, 1)
diff_value = Value - last_value
lag_diff_value = dplyr::lag(diff_value, 1)
pct_change = diff_value / last_value
lag_pct_change = dplyr::lag(pct_change, 1)
list(
Year = Year,
value = Value, # lowercase now
last_value = last_value,
diff_value = diff_value,
pct_change = pct_change,
lag_pct_change = lag_pct_change,
lag_diff_value = lag_diff_value
)
}, by = list(Country, Indicator)]
wide_oecd_wdi_data = pivot_wider(
stacked_oecd_wdi_data_lags_diffs,
id_cols = c('Country', 'Year'),
names_from = 'Indicator',
values_from = c('value', 'last_value', 'diff_value', 'pct_change', 'lag_pct_change', 'lag_diff_value')
) %>%
arrange(
Country, Year
)
write.csv(stacked_oecd_wdi_data_lags_diffs, 'stacked_oecd_wdi_data_lags_diffs.csv', row.names = F)
write.csv(wide_oecd_wdi_data, 'wide_oecd_wdi_data.csv', row.names = F)
| /Projects/Taxes vs. Deficits/(1)_get_political_fiscal_economic_data.r | permissive | vishalbelsare/Public_Policy | R | false | false | 8,936 | r |
# https://ourcodingclub.github.io/2017/03/15/mixed-models.html
# https://rpsychologist.com/r-guide-longitudinal-lme-lmer
# https://stackoverflow.com/questions/49033016/plm-or-lme4-for-random-and-fixed-effects-model-on-panel-data
# https://www.princeton.edu/~otorres/Panel101R.pdf
# fredr_set_key('d0b9e64aba30b479343a06037a5a10c1')
library(rvest)
library(httr)
library(data.table)
library(tidyverse)
library(WDI)
library(countrycode)
library(lmtest)
library(tseries)
library(plm)
library(rvest)
library(httr)
library(quantmod)
# library(fredr)
library(scales)
library(quantreg)
refresh_downloads = T
setwd('~\\Public_Policy\\Projects\\Taxes vs. Deficits\\data')
##### Get political data #####
if (refresh_downloads) {
the_url = 'https://www.presidency.ucsb.edu/statistics/data/house-and-senate-concurrence-with-presidents'
concurrence_table = html_table(GET(the_url) %>% content(), fill = TRUE)
concurrence_table[[1]] %>% write.csv('concurrence_with_president.csv')
}
concurrence_with_president_clean = read_csv('concurrence_with_president_clean.csv')
# MIT data lab
house_elections = read.csv('1976-2018-house.csv') %>%
mutate(candidate_votes = as.numeric(candidatevotes)) %>%
filter(stage == 'gen') %>% data.table()
senate_elections = read.csv('1976-2018-senate.csv') %>% filter(stage == 'gen') %>% data.table()
presidential_elections = read.csv('1976-2016-president.csv') %>% data.table()
##### Combine historical top tax rate data from Brookings and Current OECD Data #####
## Brookings source:
# http://www.taxpolicycenter.org/sites/default/files/legacy/taxfacts/content/PDF/oecd_historical_toprate.pdf
## OECD source:
# https://www.oecd.org/tax/tax-policy/tax-database/tax-database-update-note.pdf
# get TOP marginal tax rates
brookings_oecd_toptax = read_csv('oecd_historical_toprate_raw.csv', na = '--') %>%
pivot_longer(cols = paste(c(1975:2013)), names_to = 'Year', values_to = 'TOP_TRATE')
current_oecd_toptax = read_csv('TABLE_I7_30102020214927846.csv') %>%
pivot_wider(id_cols = c('Country', 'Year'), names_from = 'TAX', values_from = 'Value')
# get tax revenues to GDP
all_oecd_downloads =
c("oecd_net_lending.csv" , "oecd_revenue.csv" , "oecd_spending.csv" ) %>%
map(read_csv) %>%
bind_rows() %>%
filter(LOCATION != 'OAVG', MEASURE == 'PC_GDP', SUBJECT == 'TOT')
# map country names
country_code_name_mappings = data.frame(
LOCATION = unique(all_oecd_downloads$LOCATION)
) %>%
mutate(
Country = map_chr(LOCATION, function(the_country){
countrycode(the_country, 'iso3c', 'country.name')
}),
Country = recode(Country, `South Korea` = 'Korea')
)
stacked_extra_oecd_stats = inner_join(all_oecd_downloads, country_code_name_mappings)
# check col classes
map_chr(brookings_oecd_toptax, class)
map_chr(current_oecd_toptax, class)
# check if any countries aren't found in either dataset, may need to change names
current_oecd_toptax$Country[!current_oecd_toptax$Country %in% brookings_oecd_toptax$Country] %>% unique()
brookings_oecd_toptax$Country[!brookings_oecd_toptax$Country %in% current_oecd_toptax$Country] %>% unique()
# check year ranges -- only keep non-overlapping
range(brookings_oecd_toptax$Year)
range(current_oecd_toptax$Year)
# recode columns
brookings_oecd_toptax_clean = mutate(
brookings_oecd_toptax,
Country = recode(Country, `Korea, Republic of` = 'Korea'),
Year = as.numeric(Year),
TOP_TRATE = TOP_TRATE * 100 # to match OECD
) %>%
filter(
Year < min(current_oecd_toptax$Year, na.rm = T)
)
range(brookings_oecd_toptax_clean$Year)
# clear memory
rm(brookings_oecd_toptax)
# outer join on country and year, keeping the year overlaps from oecd
joined_oecd_brookings_toptax = bind_rows(
brookings_oecd_toptax_clean,
current_oecd_toptax
) %>%
arrange(
Country, Year
)
# take a look at the data
ggplot(joined_oecd_brookings_toptax, aes(Year, TOP_TRATE, colour = Country)) +
geom_line()
# lots of missing values in the middle of the dataset, let's interpolate those
joined_oecd_brookings_toptax_dt = data.table(joined_oecd_brookings_toptax)
joined_oecd_brookings_toptax_filled = joined_oecd_brookings_toptax_dt[, {
# cat("\nCountry ==",.BY[[1]],"\n\n")
non_na_years = Year[!is.na(TOP_TRATE)]
na_years = Year[is.na(TOP_TRATE)]
# find the most recent existing value
filled_vals = map_dbl(na_years, function(this_year){
years_to_check = non_na_years[non_na_years < this_year]
if (length(years_to_check) == 0) {
return(NA)
} else {
closest_year = max(years_to_check)
return(TOP_TRATE[Year == closest_year])
}
})
filled_df = data.frame(
Year = na_years,
TOP_TRATE = filled_vals
)
full_df = data.frame(
Year, TOP_TRATE
) %>%
filter(Year %in% non_na_years)
stacked_df = bind_rows(filled_df, full_df) %>% arrange(Year)
list(
Year = stacked_df$Year,
TOP_TRATE = stacked_df$TOP_TRATE
)
}, by = Country]
# add back other variables
joined_oecd_brookings_toptax_fin = inner_join(
select(joined_oecd_brookings_toptax, -TOP_TRATE),
joined_oecd_brookings_toptax_filled
)
rm(joined_oecd_brookings_toptax, joined_oecd_brookings_toptax_dt)
# looks much better now
ggplot(joined_oecd_brookings_toptax_filled, aes(Year, TOP_TRATE, colour = Country)) +
geom_line()
##### Get fiscal data from the world bank #####
# WDIsearch('debt') %>% View()
start_year = min(joined_oecd_brookings_toptax_filled$Year, na.rm = T)
end_year = max(joined_oecd_brookings_toptax_filled$Year, na.rm = T)
wdi_indicators = c('NY.GDP.PCAP.KD.ZG', 'GC.TAX.TOTL.GD.ZS',
'DT.DOD.DECT.GN.ZS', 'GC.TAX.YPKG.RV.ZS', 'GC.XPN.INTP.RV.ZS',
'GC.REV.TOTL.CD', 'PAY.TAX.RK.DB19', 'NY.GDP.MKTP.KD.ZG', 'NY.GDP.PCAP.KD', 'GC.DOD.TOTL.GD.ZS')
wdi_names = map(wdi_indicators, function(ind){
WDIsearch(ind, field = 'indicator') %>% t() %>% as.data.frame()
}) %>%
bind_rows()
if (refresh_downloads | !file.exists('wdi_download_long.csv')) {
wdi_download = WDI(indicator = wdi_indicators,
start = start_year,
end = end_year, extra = T) %>%
filter(
income == 'High income'
)
wdi_download_long = pivot_longer(wdi_download, cols = wdi_indicators, names_to = 'indicator') %>%
inner_join(wdi_names) %>%
mutate(
country = recode(country, `Korea, Rep.` = 'Korea')
)
write.csv(wdi_download_long, 'wdi_download_long.csv', row.names = F)
} else {
wdi_download_long = read_csv('wdi_download_long.csv')
}
# check country names
# wdi_download_long$country[!wdi_download_long$country %in% joined_oecd_brookings_toptax_filled$Country] %>% unique()
# joined_oecd_brookings_toptax_filled$Country[!joined_oecd_brookings_toptax_filled$Country %in% wdi_download_long$country] %>% unique()
##### Combine all world bank and OECD data together #####
stacked_oecd_wdi_data = bind_rows(
joined_oecd_brookings_toptax_fin %>% mutate(Indicator = 'top_tax_rate') %>% select(Country, Year, Value = TOP_TRATE, Indicator),
stacked_extra_oecd_stats %>% select(Country, Year = TIME, Value, Indicator = INDICATOR)
) %>%
bind_rows(
wdi_download_long %>% select(Country = country, Year = year, Indicator = indicator, Value = value)
) %>%
arrange(
Country, Indicator, Year
) %>%
filter(!is.na(Country))
# test uniqueness of observations
unique_obs = select(stacked_oecd_wdi_data, Country, Year, Indicator) %>% unique()
stopifnot(nrow(unique_obs) == nrow(stacked_oecd_wdi_data))
stacked_oecd_wdi_data_dt = data.table(stacked_oecd_wdi_data)
duped_dt = stacked_oecd_wdi_data_dt[duplicated(stacked_oecd_wdi_data_dt, by = c('Country', 'Year', 'Indicator')),]
inner_join(stacked_oecd_wdi_data, duped_dt) %>% dim()
##### Compute new variables #####
# for each country and indicator, compute:
# Lags
# First differences
stacked_oecd_wdi_data_dt = data.table(stacked_oecd_wdi_data)
stacked_oecd_wdi_data_lags_diffs = stacked_oecd_wdi_data_dt[, {
last_value = dplyr::lag(Value, 1)
diff_value = Value - last_value
lag_diff_value = dplyr::lag(diff_value, 1)
pct_change = diff_value / last_value
lag_pct_change = dplyr::lag(pct_change, 1)
list(
Year = Year,
value = Value, # lowercase now
last_value = last_value,
diff_value = diff_value,
pct_change = pct_change,
lag_pct_change = lag_pct_change,
lag_diff_value = lag_diff_value
)
}, by = list(Country, Indicator)]
wide_oecd_wdi_data = pivot_wider(
stacked_oecd_wdi_data_lags_diffs,
id_cols = c('Country', 'Year'),
names_from = 'Indicator',
values_from = c('value', 'last_value', 'diff_value', 'pct_change', 'lag_pct_change', 'lag_diff_value')
) %>%
arrange(
Country, Year
)
write.csv(stacked_oecd_wdi_data_lags_diffs, 'stacked_oecd_wdi_data_lags_diffs.csv', row.names = F)
write.csv(wide_oecd_wdi_data, 'wide_oecd_wdi_data.csv', row.names = F)
|
#Surya Aenuganti Ushasri
#Assignment: World Happiness Analysis
#Loading the data
worldh <- read.csv("WH_2017.csv")
#Loading packages required for the analysis
library(plyr)
library(plotly)
library(dplyr)
library(tidyverse)
library(lubridate)
library(caTools)
library(ggplot2)
library(ggthemes)
library(reshape2)
library(data.table)
library(tidyr)
library(corrgram)
library(corrplot)
library(formattable)
library(cowplot)
library(ggpubr)
library(plot3D)
#View the data
#View(worldh)
#Displays the first few rows of the dataset
head(worldh)
#Display the structure of the attributes
str(worldh)
# Changing the name of columns
colnames (worldh) <- c("Country", "Happiness.Rank", "Happiness.Score",
"Whisker.High", "Whisker.Low", "Economy", "Family",
"Life.Expectancy", "Freedom", "Generosity",
"Trust", "Dystopia.Residual")
# Deleting unnecessary columns (Whisker.high and Whisker.low)
worldh <- worldh[, -c(4,5)]
# Finding the correlation between numerical columns
Num.cols <- sapply(worldh, is.numeric)
Cor.data <- cor(worldh[, Num.cols])
corrplot(Cor.data, method = 'color')
#Analysis: We can see there is an inverse correlation between "Happiness Rank" and all the other numerical variables. In other words, the lower the happiness rank, the higher the happiness score, and the higher the other seven factors that contribute to happiness. So let's remove the happiness rank, and see the correlation again.
# Create a correlation plot
newdatacor = cor(worldh[c(3:10)])
corrplot(newdatacor, method = "number")
#Analysis: In the above cor plot, Economy, life expectancy, and family play the most significant role in contributing to happiness.
#Trust and generosity have the lowest impact on the happiness score.
#Plotting ScatterPLot
plot_ly(data = worldh,
x=~Economy, y=~Happiness.Score, type = "scatter",
text = ~paste("Country:", Country)) %>%
layout(title = "Happiness and GDP",
xaxis = list(title = "GDP per Capita"),
yaxis = list(title = "Happiness Score"))
#Analysis: This interactive scatterplot shows that there is a strong positive correlation between GDP and Happiness.
#Let's do multiple Regression
dat <- worldh[c("Happiness.Score","Economy","Generosity")]
head(dat)
plot(dat)
#It seems like there is a positive correlation between economy and happiness score but this is not true between happiness score
#and generosity.
#3D plot of same
scatter3D(dat$Generosity, dat$Economy, dat$Happiness.Score, phi = 0, bty = "g",
pch = 20, cex = 2, ticktype = "detailed",
main = "Happiness data", xlab = "Generosity",
ylab ="Economy", zlab = "Happiness.Score")
#From the scatter plot we cannot determine that combination of high economy and generosity leads to greater happiness score.
#This is something we have to conclude after analyzing the effect of these 2 taken together. | /MVA_WorldHappinessAnalysis.R | no_license | ausurya/Multivariate-Analysis | R | false | false | 3,022 | r | #Surya Aenuganti Ushasri
#Assignment: World Happiness Analysis
#Loading the data
worldh <- read.csv("WH_2017.csv")
#Loading packages required for the analysis
library(plyr)
library(plotly)
library(dplyr)
library(tidyverse)
library(lubridate)
library(caTools)
library(ggplot2)
library(ggthemes)
library(reshape2)
library(data.table)
library(tidyr)
library(corrgram)
library(corrplot)
library(formattable)
library(cowplot)
library(ggpubr)
library(plot3D)
#View the data
#View(worldh)
#Displays the first few rows of the dataset
head(worldh)
#Display the structure of the attributes
str(worldh)
# Changing the name of columns
colnames (worldh) <- c("Country", "Happiness.Rank", "Happiness.Score",
"Whisker.High", "Whisker.Low", "Economy", "Family",
"Life.Expectancy", "Freedom", "Generosity",
"Trust", "Dystopia.Residual")
# Deleting unnecessary columns (Whisker.high and Whisker.low)
worldh <- worldh[, -c(4,5)]
# Finding the correlation between numerical columns
Num.cols <- sapply(worldh, is.numeric)
Cor.data <- cor(worldh[, Num.cols])
corrplot(Cor.data, method = 'color')
#Analysis: We can see there is an inverse correlation between "Happiness Rank" and all the other numerical variables. In other words, the lower the happiness rank, the higher the happiness score, and the higher the other seven factors that contribute to happiness. So let's remove the happiness rank, and see the correlation again.
# Create a correlation plot
newdatacor = cor(worldh[c(3:10)])
corrplot(newdatacor, method = "number")
#Analysis: In the above cor plot, Economy, life expectancy, and family play the most significant role in contributing to happiness.
#Trust and generosity have the lowest impact on the happiness score.
#Plotting ScatterPLot
plot_ly(data = worldh,
x=~Economy, y=~Happiness.Score, type = "scatter",
text = ~paste("Country:", Country)) %>%
layout(title = "Happiness and GDP",
xaxis = list(title = "GDP per Capita"),
yaxis = list(title = "Happiness Score"))
#Analysis: This interactive scatterplot shows that there is a strong positive correlation between GDP and Happiness.
#Let's do multiple Regression
dat <- worldh[c("Happiness.Score","Economy","Generosity")]
head(dat)
plot(dat)
#It seems like there is a positive correlation between economy and happiness score but this is not true between happiness score
#and generosity.
#3D plot of same
scatter3D(dat$Generosity, dat$Economy, dat$Happiness.Score, phi = 0, bty = "g",
pch = 20, cex = 2, ticktype = "detailed",
main = "Happiness data", xlab = "Generosity",
ylab ="Economy", zlab = "Happiness.Score")
#From the scatter plot we cannot determine that combination of high economy and generosity leads to greater happiness score.
#This is something we have to conclude after analyzing the effect of these 2 taken together. |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/getHSMetrics.R
\name{getHSMetrics}
\alias{getHSMetrics}
\title{Get HS Metrics}
\usage{
getHSMetrics(reports, src = "PANEL_TUMOR_HSMETRICS")
}
\arguments{
\item{reports}{data frame with reports}
\item{src}{string with name of column name to use (from reports)}
}
\value{
A data table with hs metrics
}
\description{
Get HS Metrics
}
\examples{
#dat <- getHSMetrics(reports, src="PANEL_TUMOR_HSMETRICS")
#dat <- getHSMetrics(reports, src="PANEL_NORMAL_HSMETRICS")
#dat <- getHSMetrics(reports, src="RNASEQCAP_HSMETRICS")
}
| /man/getHSMetrics.Rd | permissive | dakl/clinseqr | R | false | false | 609 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/getHSMetrics.R
\name{getHSMetrics}
\alias{getHSMetrics}
\title{Get HS Metrics}
\usage{
getHSMetrics(reports, src = "PANEL_TUMOR_HSMETRICS")
}
\arguments{
\item{reports}{data frame with reports}
\item{src}{string with name of column name to use (from reports)}
}
\value{
A data table with hs metrics
}
\description{
Get HS Metrics
}
\examples{
#dat <- getHSMetrics(reports, src="PANEL_TUMOR_HSMETRICS")
#dat <- getHSMetrics(reports, src="PANEL_NORMAL_HSMETRICS")
#dat <- getHSMetrics(reports, src="RNASEQCAP_HSMETRICS")
}
|
#' Adjust p-values for multiple comparisons
#'
#' Depending upon the \code{pval_adjust} method selected, the supplied p_values are compared against an adjusted \code{pval_thresh} value or the provided
#' means are used to compute new statistics, p-values are computed and compared against the provided \code{pval_thresh}. A \code{data.frame} that indicates which
#' of the tests are significant, 1 if significant or 0 if insignificant. If \code{means} is also provided and the p-value is signficant then the direction
#' of the change is indicated by the sign on 1, i.e., means<0 and p_value<pval_thresh will return -1, similarly for means>0.
#'
#' @param p_values A matrix (or \code{data.frame}) of p-values to be adjusted.
#' @param diff_mean A matrix (or \code{data.frame}) of groups means that are to be compared
#' @param t_stats A matrix (or \code{data.frame}) of t-test statistics resulting from from standard procedures
#' @param sizes A matrix (or \code{data.frame}) of group sizes
#' @param pval_adjust character vector specifying the type of multiple comparisons adjustment to implement. A NULL value corresponds to no adjustment. Valid options include: holm, bonferroni, dunnett, tukey or none.
#'
#' @return a data frame with the following columns: group means, global G-test statistic and corresponding p-value
#'
#' @author Bryan Stanfill
#' @examples
#' dontrun{
#' library(pmartRdata)
#' library(pmartR)
#' mypepData <- edata_transform(omicsData = pep_object, data_scale = "log2")
#' mypepData <- group_designation(omicsData = mypepData, main_effects = c("Condition"))
#' group_df <- attr(mypepData, "group_DF")
#' imdanova_Filt <- imdanova_filter(omicsData = mypepData)
#' mypepData <- applyFilt(filter_object = imdanova_Filt, omicsData = mypepData, min_nonmiss_anova=2)
#' anova_res <- anova_test(omicsData = mypepData)
#'
#' adjusted_pvalues <- p_adjustment_anova(p_values = anova_res$Fold_change_pvalues, diff_mean = anova_res$Results[, grep("Mean", names(anova_res$Results))])
#' }
#' @export
p_adjustment_anova <- function(p_values = NULL, diff_mean = NULL, t_stats = NULL, sizes = NULL, pval_adjust = "None"){
#Match provided 'pval_adjust' to available options
pval_adjust <- try(match.arg(tolower(pval_adjust),c("bonferroni","none","tukey","dunnett","holm")),silent=TRUE)
if(class(pval_adjust)=='try-error')
stop("Provided 'pval_adjust' argument is invalid, please select 'holm', 'bonferroni', 'tukey', 'dunnett' or 'none'.")
if(pval_adjust=="tukey" | pval_adjust=="dunnett"){
#Tukey-Kramer statistics are t-statistic/sqrt(2)
if(is.null(t_stats) | is.null(sizes)){
stop("The standard t-tests and group sizes need to be supplied in order to apply Tukey-Kramer adjustment.")
}
n_compares <- ncol(t_stats)
if(n_compares==1){
#Tukey adjustment is not done if only one comparison is provided
pval_adjust <- 'none'
}else{
if(pval_adjust=="tukey"){
tukey_stats <- data.matrix(t_stats*sqrt(2))
#Tukey only needs sizes for the rows, not individual group sizes
if(is.data.frame(sizes)){
sizes <- rowSums(data.matrix(sizes))
}else if(is.matrix(sizes)){
sizes <- rowSums(sizes)
}
#Rcpp::sourceCpp('src/tukey_helper.cpp') #Use for debugging
adjusted_pvals <- ptukey_speed(tukey_stats,sizes)
}else{#Dunnett adjustment - Needs to be sped up
adjusted_pvals <- matrix(NA,nrow(t_stats), ncol(t_stats))
#colnames(adjusted_pvals) <- colnames(t_stats)
for(i in 1:nrow(adjusted_pvals)){
k <- length(which(!is.na(p_values[i,])))
if(k>0){
dfi <- sum(sizes[i,])-k
rm_nas <- which(is.na(p_values[i,]))
#Modified version of an example from "Additional multcomp Examples" viggnette of the multcomp package
#Until we add covariates the correlation matrix is assumed to be the identity
if(length(rm_nas)>0){
adjusted_pvals[i,-rm_nas] <- as.numeric(sapply(abs(t_stats[i,-rm_nas]), function(x,k,df) 1 - mvtnorm::pmvt(-rep(x, k), rep(x, k), df = df),k=k,df=dfi))
}else{
adjusted_pvals[i,] <- as.numeric(sapply(abs(t_stats[i,]), function(x,k,df) 1 - mvtnorm::pmvt(-rep(x, k), rep(x, k), df = df),k=k,df=dfi))
}
}
}
}
colnames(adjusted_pvals) <- colnames(t_stats)
colnames(adjusted_pvals) <- gsub("Test-Stat","p-value",colnames(adjusted_pvals)) #This is a band-aid right now, may need to make more general later
}
}
#Don't make this an "else if" because pval_adjust can be changed to 'none' if n_compares==1
if(pval_adjust%in%c('none',"bonferroni","holm")){
#p_values needs to be supplied to apply bonferroni adjustment, if it's NULL tell them that's a problem
if(is.null(p_values)){
stop("The `p_values` argument must be supplied to perform the selected `pval_adjust` method")
}
if(is.null(dim(p_values)) || ncol(p_values)==1){ # leaving this as "||" because only evaluating the 1st argument is fine in this case, as it returns TRUE when p_values is a vector (and thus ncol(p_values) does not compute and using a "|" gives an error)
pval_adjust='none'
}
if(pval_adjust !='holm'){
#For bonferroni adjustment, multiply p_values by number of comparisons (columns in p_values) else do no adjustment
multiplier <- ifelse(pval_adjust=="bonferroni",ncol(p_values),1)
adjusted_pvals <- multiplier*p_values
}else{
#Rcpp::sourceCpp('src/holm_adjustment.cpp') #For debugging
#source('~/pmartR/R/support_p_adjustment.R') #For debugging
adjusted_pvals <- t(apply(p_values,1,ranked_holm_cpp))
#NaN p-values should stay NaN
nan_pvals <- lapply(p_values,is.nan)
for(i in 1:ncol(adjusted_pvals)){
adjusted_pvals[,i][nan_pvals[[i]]] <- NaN
}
}
}
##############
#Return the adjusted p-values
return(pvalues=data.frame(adjusted_pvals))
}
#Can be used to replace "adjusted_pvals <- ptukey_speed(tukey_stats,sizes)" if necessary
#adjusted_pvals <- matrix(NA,nrow(p_values), ncol(p_values))
#for(i in 1:nrow(tukey_stats)){
# adjusted_pvals[i,] <- ptukey(abs(tukey_stats[i,]), nmeans = n_compares, df = sizes[i]-n_compares,lower.tail=FALSE)
#}
| /R/p_value_adjustment.R | permissive | rarichardson92/pmartR | R | false | false | 6,373 | r | #' Adjust p-values for multiple comparisons
#'
#' Depending upon the \code{pval_adjust} method selected, the supplied p_values are compared against an adjusted \code{pval_thresh} value or the provided
#' means are used to compute new statistics, p-values are computed and compared against the provided \code{pval_thresh}. A \code{data.frame} that indicates which
#' of the tests are significant, 1 if significant or 0 if insignificant. If \code{means} is also provided and the p-value is signficant then the direction
#' of the change is indicated by the sign on 1, i.e., means<0 and p_value<pval_thresh will return -1, similarly for means>0.
#'
#' @param p_values A matrix (or \code{data.frame}) of p-values to be adjusted.
#' @param diff_mean A matrix (or \code{data.frame}) of groups means that are to be compared
#' @param t_stats A matrix (or \code{data.frame}) of t-test statistics resulting from from standard procedures
#' @param sizes A matrix (or \code{data.frame}) of group sizes
#' @param pval_adjust character vector specifying the type of multiple comparisons adjustment to implement. A NULL value corresponds to no adjustment. Valid options include: holm, bonferroni, dunnett, tukey or none.
#'
#' @return a data frame with the following columns: group means, global G-test statistic and corresponding p-value
#'
#' @author Bryan Stanfill
#' @examples
#' dontrun{
#' library(pmartRdata)
#' library(pmartR)
#' mypepData <- edata_transform(omicsData = pep_object, data_scale = "log2")
#' mypepData <- group_designation(omicsData = mypepData, main_effects = c("Condition"))
#' group_df <- attr(mypepData, "group_DF")
#' imdanova_Filt <- imdanova_filter(omicsData = mypepData)
#' mypepData <- applyFilt(filter_object = imdanova_Filt, omicsData = mypepData, min_nonmiss_anova=2)
#' anova_res <- anova_test(omicsData = mypepData)
#'
#' adjusted_pvalues <- p_adjustment_anova(p_values = anova_res$Fold_change_pvalues, diff_mean = anova_res$Results[, grep("Mean", names(anova_res$Results))])
#' }
#' @export
p_adjustment_anova <- function(p_values = NULL, diff_mean = NULL, t_stats = NULL, sizes = NULL, pval_adjust = "None"){
#Match provided 'pval_adjust' to available options
pval_adjust <- try(match.arg(tolower(pval_adjust),c("bonferroni","none","tukey","dunnett","holm")),silent=TRUE)
if(class(pval_adjust)=='try-error')
stop("Provided 'pval_adjust' argument is invalid, please select 'holm', 'bonferroni', 'tukey', 'dunnett' or 'none'.")
if(pval_adjust=="tukey" | pval_adjust=="dunnett"){
#Tukey-Kramer statistics are t-statistic/sqrt(2)
if(is.null(t_stats) | is.null(sizes)){
stop("The standard t-tests and group sizes need to be supplied in order to apply Tukey-Kramer adjustment.")
}
n_compares <- ncol(t_stats)
if(n_compares==1){
#Tukey adjustment is not done if only one comparison is provided
pval_adjust <- 'none'
}else{
if(pval_adjust=="tukey"){
tukey_stats <- data.matrix(t_stats*sqrt(2))
#Tukey only needs sizes for the rows, not individual group sizes
if(is.data.frame(sizes)){
sizes <- rowSums(data.matrix(sizes))
}else if(is.matrix(sizes)){
sizes <- rowSums(sizes)
}
#Rcpp::sourceCpp('src/tukey_helper.cpp') #Use for debugging
adjusted_pvals <- ptukey_speed(tukey_stats,sizes)
}else{#Dunnett adjustment - Needs to be sped up
adjusted_pvals <- matrix(NA,nrow(t_stats), ncol(t_stats))
#colnames(adjusted_pvals) <- colnames(t_stats)
for(i in 1:nrow(adjusted_pvals)){
k <- length(which(!is.na(p_values[i,])))
if(k>0){
dfi <- sum(sizes[i,])-k
rm_nas <- which(is.na(p_values[i,]))
#Modified version of an example from "Additional multcomp Examples" viggnette of the multcomp package
#Until we add covariates the correlation matrix is assumed to be the identity
if(length(rm_nas)>0){
adjusted_pvals[i,-rm_nas] <- as.numeric(sapply(abs(t_stats[i,-rm_nas]), function(x,k,df) 1 - mvtnorm::pmvt(-rep(x, k), rep(x, k), df = df),k=k,df=dfi))
}else{
adjusted_pvals[i,] <- as.numeric(sapply(abs(t_stats[i,]), function(x,k,df) 1 - mvtnorm::pmvt(-rep(x, k), rep(x, k), df = df),k=k,df=dfi))
}
}
}
}
colnames(adjusted_pvals) <- colnames(t_stats)
colnames(adjusted_pvals) <- gsub("Test-Stat","p-value",colnames(adjusted_pvals)) #This is a band-aid right now, may need to make more general later
}
}
#Don't make this an "else if" because pval_adjust can be changed to 'none' if n_compares==1
if(pval_adjust%in%c('none',"bonferroni","holm")){
#p_values needs to be supplied to apply bonferroni adjustment, if it's NULL tell them that's a problem
if(is.null(p_values)){
stop("The `p_values` argument must be supplied to perform the selected `pval_adjust` method")
}
if(is.null(dim(p_values)) || ncol(p_values)==1){ # leaving this as "||" because only evaluating the 1st argument is fine in this case, as it returns TRUE when p_values is a vector (and thus ncol(p_values) does not compute and using a "|" gives an error)
pval_adjust='none'
}
if(pval_adjust !='holm'){
#For bonferroni adjustment, multiply p_values by number of comparisons (columns in p_values) else do no adjustment
multiplier <- ifelse(pval_adjust=="bonferroni",ncol(p_values),1)
adjusted_pvals <- multiplier*p_values
}else{
#Rcpp::sourceCpp('src/holm_adjustment.cpp') #For debugging
#source('~/pmartR/R/support_p_adjustment.R') #For debugging
adjusted_pvals <- t(apply(p_values,1,ranked_holm_cpp))
#NaN p-values should stay NaN
nan_pvals <- lapply(p_values,is.nan)
for(i in 1:ncol(adjusted_pvals)){
adjusted_pvals[,i][nan_pvals[[i]]] <- NaN
}
}
}
##############
#Return the adjusted p-values
return(pvalues=data.frame(adjusted_pvals))
}
#Can be used to replace "adjusted_pvals <- ptukey_speed(tukey_stats,sizes)" if necessary
#adjusted_pvals <- matrix(NA,nrow(p_values), ncol(p_values))
#for(i in 1:nrow(tukey_stats)){
# adjusted_pvals[i,] <- ptukey(abs(tukey_stats[i,]), nmeans = n_compares, df = sizes[i]-n_compares,lower.tail=FALSE)
#}
|
#' Small contact matrix
#'
#' Example of a contact matrix.
#'
#' @format A 20x20 symmetric nonnegative integer-valued matrix.
#'
"C"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 25kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 2498x2498 symmetric nonnegative integer-valued matrix.
#'
"IMR90_25kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 25kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 1878x1878 symmetric nonnegative integer-valued matrix.
#'
"IMR90_25kb_chr21"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 50kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 1249x1249 symmetric nonnegative integer-valued matrix.
#'
"IMR90_50kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 50kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 939x939 symmetric nonnegative integer-valued matrix.
#'
"IMR90_50kb_chr21"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 100kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 625x625 symmetric nonnegative integer-valued matrix.
#'
"IMR90_100kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 100kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 470x470 symmetric nonnegative integer-valued matrix.
#'
"IMR90_100kb_chr21"
| /R/data.R | no_license | ElenaTuzhilina/PoisMS | R | false | false | 1,597 | r | #' Small contact matrix
#'
#' Example of a contact matrix.
#'
#' @format A 20x20 symmetric nonnegative integer-valued matrix.
#'
"C"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 25kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 2498x2498 symmetric nonnegative integer-valued matrix.
#'
"IMR90_25kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 25kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 1878x1878 symmetric nonnegative integer-valued matrix.
#'
"IMR90_25kb_chr21"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 50kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 1249x1249 symmetric nonnegative integer-valued matrix.
#'
"IMR90_50kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 50kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 939x939 symmetric nonnegative integer-valued matrix.
#'
"IMR90_50kb_chr21"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 20 and resolutions 100kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 625x625 symmetric nonnegative integer-valued matrix.
#'
"IMR90_100kb_chr20"
#' Hi-C data for IMR90 cells
#'
#' Contact matrix calculated for chromosomes 21 and resolutions 100kb (obtained from the Gene Expression Omnibus).
#'
#' @format A 470x470 symmetric nonnegative integer-valued matrix.
#'
"IMR90_100kb_chr21"
|
##' @param codon_partitioning Do the partitions follow the codon positions?
##' @rdname beast
##' @export
##' @importFrom httr upload_file
##' @importFrom assertthat assert_that is.count
cipres_submit_beast1 <- function(input_file,
beast_version = c("1.8.2", "1.8.1", "1.8.0"),
use_beagle = TRUE,
max_runtime = 10,
codon_partitioning = FALSE,
use_seed = NULL,
job_name = NULL,
get_email = TRUE,
note = NULL, ...) {
## Documentation here: http://www.phylo.org/rest/beast_tg.html
beast_version <- match.arg(beast_version)
beast_version <- switch(beast_version,
"1.8.0" = "0",
"1.8.1" = "1",
"1.8.2" = "2"
)
input_file <- normalizePath(input_file)
check_file(input_file)
assertthat::assert_that(is_maxruntime(max_runtime))
assertthat::assert_that(assertthat::is.flag(use_beagle))
assertthat::assert_that(assertthat::is.flag(codon_partitioning))
bdy <- .cipres_submit_beast1(input_file = input_file,
beast_version = beast_version,
max_runtime = max_runtime,
codon_partitioning = codon_partitioning,
use_beagle = use_beagle,
use_seed = use_seed,
job_name = job_name,
get_email = get_email)
cipres_submit(bdy, tool = "BEAST_TG", job_name = job_name,
note = note, ...)
}
.cipres_submit_beast1 <- function(input_file,
beast_version,
use_beagle,
max_runtime,
codon_partitioning,
use_seed,
job_name,
get_email) {
bdy <- list(
`input.infile_` = httr::upload_file(input_file),
`vparam.which_beast_` = beast_version,
`vparam.no_beagle_` = as.numeric(!use_beagle), ## they use the opposite logic hence the negation
`vparam.runtime_` = max_runtime,
`vparam.codon_partitioning_` = as.numeric(codon_partitioning)
)
alg_info <- parse_beast1_xml(input_file)
n_patterns <- alg_info[["n_patterns"]]
assertthat::assert_that(assertthat::is.count(n_patterns))
bdy$`vparam.nu_patterns_` <- n_patterns
n_partitions <- alg_info[["n_partitions"]]
bdy <- beast_check_partitions(bdy, n_partitions, beast2 = FALSE)
bdy <- beast_use_seed(bdy, use_seed)
bdy <- add_meta_data(bdy, get_email, job_name)
bdy
}
beast_check_partitions <- function(bdy, n_partitions, beast2) {
assertthat::assert_that(assertthat::is.count(n_partitions))
if (n_partitions > 1) {
if (beast2) { bdy$`vparam.is_partitioned_` <- "1" }
bdy$`vparam.nu_partitions_` <- n_partitions
} else {
bdy$`vparam.is_partitioned_` <- "0"
}
bdy
}
beast_use_seed <- function(bdy, use_seed) {
if (!is.null(use_seed)) {
assertthat::assert_that(assertthat::is.count(use_seed))
bdy$`vparam.spec_seed_` <- "1"
bdy$`vparam.seed_val_` <- use_seed
} else {
bdy$`vparam.spec_seed_` <- "0"
}
bdy
}
##' @importFrom xml2 read_xml xml_find_all xml_contents
parse_beast1_xml <- function(input_file) {
bst <- xml2::read_xml(x = input_file)
## Get the number of partitions based on the number of elements
## with siteModel
n_partitions <- length(xml2::xml_find_all(bst, ".//siteModel[@id]"))
## Get length of sequences by calculating length of the first
## sequence for each partitions
seq_info <- xml2::xml_contents(
xml2::xml_find_all(bst, ".//alignment/sequence[1]")
)
clean_seq <- gsub("\\s|(^<.+)", "", seq_info)
clean_seq <- clean_seq[nzchar(clean_seq)]
n_patterns <- sum(sapply(clean_seq, nchar))
list(n_partitions = n_partitions,
n_patterns = n_patterns)
}
| /R/beast1.R | permissive | fmichonneau/cipresr | R | false | false | 4,326 | r | ##' @param codon_partitioning Do the partitions follow the codon positions?
##' @rdname beast
##' @export
##' @importFrom httr upload_file
##' @importFrom assertthat assert_that is.count
cipres_submit_beast1 <- function(input_file,
beast_version = c("1.8.2", "1.8.1", "1.8.0"),
use_beagle = TRUE,
max_runtime = 10,
codon_partitioning = FALSE,
use_seed = NULL,
job_name = NULL,
get_email = TRUE,
note = NULL, ...) {
## Documentation here: http://www.phylo.org/rest/beast_tg.html
beast_version <- match.arg(beast_version)
beast_version <- switch(beast_version,
"1.8.0" = "0",
"1.8.1" = "1",
"1.8.2" = "2"
)
input_file <- normalizePath(input_file)
check_file(input_file)
assertthat::assert_that(is_maxruntime(max_runtime))
assertthat::assert_that(assertthat::is.flag(use_beagle))
assertthat::assert_that(assertthat::is.flag(codon_partitioning))
bdy <- .cipres_submit_beast1(input_file = input_file,
beast_version = beast_version,
max_runtime = max_runtime,
codon_partitioning = codon_partitioning,
use_beagle = use_beagle,
use_seed = use_seed,
job_name = job_name,
get_email = get_email)
cipres_submit(bdy, tool = "BEAST_TG", job_name = job_name,
note = note, ...)
}
.cipres_submit_beast1 <- function(input_file,
beast_version,
use_beagle,
max_runtime,
codon_partitioning,
use_seed,
job_name,
get_email) {
bdy <- list(
`input.infile_` = httr::upload_file(input_file),
`vparam.which_beast_` = beast_version,
`vparam.no_beagle_` = as.numeric(!use_beagle), ## they use the opposite logic hence the negation
`vparam.runtime_` = max_runtime,
`vparam.codon_partitioning_` = as.numeric(codon_partitioning)
)
alg_info <- parse_beast1_xml(input_file)
n_patterns <- alg_info[["n_patterns"]]
assertthat::assert_that(assertthat::is.count(n_patterns))
bdy$`vparam.nu_patterns_` <- n_patterns
n_partitions <- alg_info[["n_partitions"]]
bdy <- beast_check_partitions(bdy, n_partitions, beast2 = FALSE)
bdy <- beast_use_seed(bdy, use_seed)
bdy <- add_meta_data(bdy, get_email, job_name)
bdy
}
beast_check_partitions <- function(bdy, n_partitions, beast2) {
assertthat::assert_that(assertthat::is.count(n_partitions))
if (n_partitions > 1) {
if (beast2) { bdy$`vparam.is_partitioned_` <- "1" }
bdy$`vparam.nu_partitions_` <- n_partitions
} else {
bdy$`vparam.is_partitioned_` <- "0"
}
bdy
}
beast_use_seed <- function(bdy, use_seed) {
if (!is.null(use_seed)) {
assertthat::assert_that(assertthat::is.count(use_seed))
bdy$`vparam.spec_seed_` <- "1"
bdy$`vparam.seed_val_` <- use_seed
} else {
bdy$`vparam.spec_seed_` <- "0"
}
bdy
}
##' @importFrom xml2 read_xml xml_find_all xml_contents
parse_beast1_xml <- function(input_file) {
bst <- xml2::read_xml(x = input_file)
## Get the number of partitions based on the number of elements
## with siteModel
n_partitions <- length(xml2::xml_find_all(bst, ".//siteModel[@id]"))
## Get length of sequences by calculating length of the first
## sequence for each partitions
seq_info <- xml2::xml_contents(
xml2::xml_find_all(bst, ".//alignment/sequence[1]")
)
clean_seq <- gsub("\\s|(^<.+)", "", seq_info)
clean_seq <- clean_seq[nzchar(clean_seq)]
n_patterns <- sum(sapply(clean_seq, nchar))
list(n_partitions = n_partitions,
n_patterns = n_patterns)
}
|
source('./take_input.R')
library(party)
library(caTools)
#library(rpart)
#library(rpart.plot)
data$game_event_id = NULL
data$game_id = NULL
data$time_remaining_in_period = (data$minutes_remaining*60 + data$seconds_remaining)
data$seconds_remaining = NULL
data$minutes_remaining = NULL
data$shot_distance = NULL #Banking on shot_zone_range for this
data$game_date = NULL
data$distance = (data$loc_x^2 + data$loc_y^2)^(1/2)
data$angle = atan(data$loc_y/data$loc_x) #angles in radians
data$loc_x = NULL
data$loc_y = NULL
train = subset(data, !is.na(data$shot_made_flag))
sample = sample.split(train$shot_made_flag, SplitRatio = 0.80)
train_train = subset(train, sample == TRUE)
train_test = subset(train, sample == FALSE)
test = subset(data, is.na(data$shot_made_flag))
train$shot_made_flag <- as.factor(train$shot_made_flag)
test$shot_made_flag = NULL
result= data.frame()
for(i in levels(train$season)){
train_season = subset(train_train, as.character(train_train$season) <= i)
test_season = subset(train_test, as.character(train_test$season) == i)
train_season$season = NULL
test_season$season = NULL
tree = ctree(shot_made_flag~.-shot_id, data = train_season)
pred_tree = predict(tree, type = "prob",newdata = test_season)
#for (j in 1:length(pred_tree)){
# result = rbind(result, list(test_season[j,]$shot_id, pred_tree[j][[1]][2]))
#}
for (j in 1:nrow(test_season)){
result = rbind(result, list(as.numeric(test_season[j,]$shot_made_flag), (as.numeric(pred_tree[j][[1]] > 0.5))))
#result = rbind(result, list(as.numeric(test_season[j,]$shot_made_flag), (as.numeric(pred_tree[j] > 0.5)+1)))
}
}
colnames(result) = c("shot_made_flag", "predicted shot flag")
print(nrow(subset(result, (result$shot_made_flag == 1 & result$`predicted shot flag`==1) | ((result$shot_made_flag == 0 & result$`predicted shot flag`==0))))/nrow(result))
#write.csv(result, "cTree_Train_Previous_Seasons_with_Distance_Angle_Time.csv") | /tree_forest.R | permissive | shubh24/kobe | R | false | false | 1,959 | r | source('./take_input.R')
library(party)
library(caTools)
#library(rpart)
#library(rpart.plot)
data$game_event_id = NULL
data$game_id = NULL
data$time_remaining_in_period = (data$minutes_remaining*60 + data$seconds_remaining)
data$seconds_remaining = NULL
data$minutes_remaining = NULL
data$shot_distance = NULL #Banking on shot_zone_range for this
data$game_date = NULL
data$distance = (data$loc_x^2 + data$loc_y^2)^(1/2)
data$angle = atan(data$loc_y/data$loc_x) #angles in radians
data$loc_x = NULL
data$loc_y = NULL
train = subset(data, !is.na(data$shot_made_flag))
sample = sample.split(train$shot_made_flag, SplitRatio = 0.80)
train_train = subset(train, sample == TRUE)
train_test = subset(train, sample == FALSE)
test = subset(data, is.na(data$shot_made_flag))
train$shot_made_flag <- as.factor(train$shot_made_flag)
test$shot_made_flag = NULL
result= data.frame()
for(i in levels(train$season)){
train_season = subset(train_train, as.character(train_train$season) <= i)
test_season = subset(train_test, as.character(train_test$season) == i)
train_season$season = NULL
test_season$season = NULL
tree = ctree(shot_made_flag~.-shot_id, data = train_season)
pred_tree = predict(tree, type = "prob",newdata = test_season)
#for (j in 1:length(pred_tree)){
# result = rbind(result, list(test_season[j,]$shot_id, pred_tree[j][[1]][2]))
#}
for (j in 1:nrow(test_season)){
result = rbind(result, list(as.numeric(test_season[j,]$shot_made_flag), (as.numeric(pred_tree[j][[1]] > 0.5))))
#result = rbind(result, list(as.numeric(test_season[j,]$shot_made_flag), (as.numeric(pred_tree[j] > 0.5)+1)))
}
}
colnames(result) = c("shot_made_flag", "predicted shot flag")
print(nrow(subset(result, (result$shot_made_flag == 1 & result$`predicted shot flag`==1) | ((result$shot_made_flag == 0 & result$`predicted shot flag`==0))))/nrow(result))
#write.csv(result, "cTree_Train_Previous_Seasons_with_Distance_Angle_Time.csv") |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stack.R
\name{stack_trim}
\alias{stack_trim}
\title{Trim top call layers from the evaluation stack}
\usage{
stack_trim(stack, n = 1)
}
\arguments{
\item{stack}{An evaluation stack.}
\item{n}{The number of call frames (not eval frames) to trim off
the top of the stack. In other words, the number of layers of
intervening frames to trim.}
}
\description{
\code{\link[=ctxt_stack]{ctxt_stack()}} can be tricky to use in real code because all
intervening frames are returned with the stack, including those at
\code{ctxt_stack()} own call site. \code{stack_trim()} makes it easy to
remove layers of intervening calls.
}
\section{Life cycle}{
These functions are in the questioning stage. We are no longer
convinced they belong in rlang as they are mostly for REPL
interaction and runtime inspection rather than function
development.
}
\examples{
# Intervening frames appear on the evaluation stack:
identity(identity(ctxt_stack()))
# stack_trim() will trim the first n layers of calls:
stack_trim(identity(identity(ctxt_stack())))
# Note that it also takes care of calls intervening at its own call
# site:
identity(identity(
stack_trim(identity(identity(ctxt_stack())))
))
# It is especially useful when used within a function that needs to
# inspect the evaluation stack but should nonetheless be callable
# within nested calls without side effects:
stack_util <- function() {
# n = 2 means that two layers of intervening calls should be
# removed: The layer at ctxt_stack()'s call site (including the
# stack_trim() call), and the layer at stack_util()'s call.
stack <- stack_trim(ctxt_stack(), n = 2)
stack
}
user_fn <- function() {
# A user calls your stack utility with intervening frames:
identity(identity(stack_util()))
}
# These intervening frames won't appear in the evaluation stack
identity(user_fn())
}
\keyword{internal}
| /man/stack_trim.Rd | no_license | mikmart/rlang | R | false | true | 1,934 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stack.R
\name{stack_trim}
\alias{stack_trim}
\title{Trim top call layers from the evaluation stack}
\usage{
stack_trim(stack, n = 1)
}
\arguments{
\item{stack}{An evaluation stack.}
\item{n}{The number of call frames (not eval frames) to trim off
the top of the stack. In other words, the number of layers of
intervening frames to trim.}
}
\description{
\code{\link[=ctxt_stack]{ctxt_stack()}} can be tricky to use in real code because all
intervening frames are returned with the stack, including those at
\code{ctxt_stack()} own call site. \code{stack_trim()} makes it easy to
remove layers of intervening calls.
}
\section{Life cycle}{
These functions are in the questioning stage. We are no longer
convinced they belong in rlang as they are mostly for REPL
interaction and runtime inspection rather than function
development.
}
\examples{
# Intervening frames appear on the evaluation stack:
identity(identity(ctxt_stack()))
# stack_trim() will trim the first n layers of calls:
stack_trim(identity(identity(ctxt_stack())))
# Note that it also takes care of calls intervening at its own call
# site:
identity(identity(
stack_trim(identity(identity(ctxt_stack())))
))
# It is especially useful when used within a function that needs to
# inspect the evaluation stack but should nonetheless be callable
# within nested calls without side effects:
stack_util <- function() {
# n = 2 means that two layers of intervening calls should be
# removed: The layer at ctxt_stack()'s call site (including the
# stack_trim() call), and the layer at stack_util()'s call.
stack <- stack_trim(ctxt_stack(), n = 2)
stack
}
user_fn <- function() {
# A user calls your stack utility with intervening frames:
identity(identity(stack_util()))
}
# These intervening frames won't appear in the evaluation stack
identity(user_fn())
}
\keyword{internal}
|
setwd("C:/Users/Kshiti Mehta/Desktop/Exploratory Data Analysis Coursera")
data_full <- read.csv("household_power_consumption.txt", header=T, sep=';', na.strings="?",
nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
data1 <- subset(data_full, Date %in% c("1/2/2007","2/2/2007"))
data1$Date <- as.Date(data1$Date, format="%d/%m/%Y")
hist(data1$Global_active_power, main="Global Active Power",
xlab="Global Active Power (kilowatts)", ylab="Frequency", col="Red")
| /Plot1.R | no_license | kshitimehta/Exploratory-Data-Analysis | R | false | false | 520 | r | setwd("C:/Users/Kshiti Mehta/Desktop/Exploratory Data Analysis Coursera")
data_full <- read.csv("household_power_consumption.txt", header=T, sep=';', na.strings="?",
nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
data1 <- subset(data_full, Date %in% c("1/2/2007","2/2/2007"))
data1$Date <- as.Date(data1$Date, format="%d/%m/%Y")
hist(data1$Global_active_power, main="Global Active Power",
xlab="Global Active Power (kilowatts)", ylab="Frequency", col="Red")
|
#use kNN to impute TMB
library(caret)
args <- commandArgs(trailingOnly=TRUE)
#data.expr<-read.csv("../FeatureGen/TMB-Syn/GRCh37ERCC_refseq105_genes_tpm.csv",header=T,row.names = 1)
#data.anno<-read.csv("../FeatureGen/TMB-Syn/clinical_data.csv",header=T,row.names = 1)
data.expr<-read.csv(args[1],header=T,row.names = 1)
data.anno<-read.csv(args[2],header=T,row.names = 1)
data.anno<-data.anno[colnames(data.expr),] #make sure these two data are the same
output<-args[3]
#filter data.expr
data.expr.sd<-apply(data.expr,1,sd)
data.expr.sel<-data.expr[order(data.expr.sd,decreasing = T)[1:2000],]
data.merged.tmb<-as.data.frame(cbind(t(data.expr.sel),data.anno$TMB))
colnames(data.merged.tmb)<-c(rownames(data.expr.sel),"TMB")
set.seed(300)
training <- data.merged.tmb[!is.na(data.merged.tmb$TMB),]
testing <- data.merged.tmb[is.na(data.merged.tmb$TMB),]
trainX <- training[,names(training) != "TMB"]
preProcValues <- preProcess(x = trainX,method = c("center", "scale"))
#10 times CV
ctrl <- trainControl(method="cv", number = 10) #,classProbs=TRUE,summaryFunction = twoClassSummary)
#fit KNN
knnFit <- train(TMB~ ., data = training, method = "knn", trControl = ctrl, preProcess = c("center","scale"), tuneLength = 20)
prediction<-as.integer(predict(knnFit,newdata = testing))
data.merged.tmb[is.na(data.merged.tmb$TMB),]<-prediction
tmb.results<-cbind(rownames(data.merged.tmb),data.merged.tmb$TMB)
colnames(tmb.results)<-c("Patient","TMB")
#write results
write.csv(file=output,tmb.results,quote = F, row.names = F)
| /Scripts/run_TMB.R | no_license | yinjun111/dream-pd1 | R | false | false | 1,593 | r |
#use kNN to impute TMB
library(caret)
args <- commandArgs(trailingOnly=TRUE)
#data.expr<-read.csv("../FeatureGen/TMB-Syn/GRCh37ERCC_refseq105_genes_tpm.csv",header=T,row.names = 1)
#data.anno<-read.csv("../FeatureGen/TMB-Syn/clinical_data.csv",header=T,row.names = 1)
data.expr<-read.csv(args[1],header=T,row.names = 1)
data.anno<-read.csv(args[2],header=T,row.names = 1)
data.anno<-data.anno[colnames(data.expr),] #make sure these two data are the same
output<-args[3]
#filter data.expr
data.expr.sd<-apply(data.expr,1,sd)
data.expr.sel<-data.expr[order(data.expr.sd,decreasing = T)[1:2000],]
data.merged.tmb<-as.data.frame(cbind(t(data.expr.sel),data.anno$TMB))
colnames(data.merged.tmb)<-c(rownames(data.expr.sel),"TMB")
set.seed(300)
training <- data.merged.tmb[!is.na(data.merged.tmb$TMB),]
testing <- data.merged.tmb[is.na(data.merged.tmb$TMB),]
trainX <- training[,names(training) != "TMB"]
preProcValues <- preProcess(x = trainX,method = c("center", "scale"))
#10 times CV
ctrl <- trainControl(method="cv", number = 10) #,classProbs=TRUE,summaryFunction = twoClassSummary)
#fit KNN
knnFit <- train(TMB~ ., data = training, method = "knn", trControl = ctrl, preProcess = c("center","scale"), tuneLength = 20)
prediction<-as.integer(predict(knnFit,newdata = testing))
data.merged.tmb[is.na(data.merged.tmb$TMB),]<-prediction
tmb.results<-cbind(rownames(data.merged.tmb),data.merged.tmb$TMB)
colnames(tmb.results)<-c("Patient","TMB")
#write results
write.csv(file=output,tmb.results,quote = F, row.names = F)
|
#!/usr/bin/env Rscript
# Helper script to create QC report
# Stephen Zhang, 2018
# Usage:
# create_qc.r [bamFilePath] [sampleName] [genomeBioStrings] [genomeTxDb]
# [Rmd_script] [outputDir] [nChr]
args <- commandArgs(trailingOnly = T)
bamFilePath <- args[1]
sampleName <- args[2]
genomeBioStrings <- args[3]
genomeTxDb <- args[4]
rmdPath <- args[5]
outputDir <- args[6]
nchr <- args[7]
library(rmarkdown)
render(rmdPath, output_format = "html_document",
output_dir = outputDir,
intermediates_dir = outputDir,
knit_root_dir = outputDir,
clean = T,
params = list(
bamFilePath = bamFilePath,
sampleName = sampleName,
genomeBioStrings = genomeBioStrings,
genomeTxDb = genomeTxDb,
nchr = nchr
))
| /qc/create_qc.r | permissive | zsteve/nf-ATAC | R | false | false | 791 | r | #!/usr/bin/env Rscript
# Helper script to create QC report
# Stephen Zhang, 2018
# Usage:
# create_qc.r [bamFilePath] [sampleName] [genomeBioStrings] [genomeTxDb]
# [Rmd_script] [outputDir] [nChr]
args <- commandArgs(trailingOnly = T)
bamFilePath <- args[1]
sampleName <- args[2]
genomeBioStrings <- args[3]
genomeTxDb <- args[4]
rmdPath <- args[5]
outputDir <- args[6]
nchr <- args[7]
library(rmarkdown)
render(rmdPath, output_format = "html_document",
output_dir = outputDir,
intermediates_dir = outputDir,
knit_root_dir = outputDir,
clean = T,
params = list(
bamFilePath = bamFilePath,
sampleName = sampleName,
genomeBioStrings = genomeBioStrings,
genomeTxDb = genomeTxDb,
nchr = nchr
))
|
## Investigation into the quality of articles created by newly created accounts
## (within the first 30 days after registration).
library(data.table);
library(ggplot2);
creations_30days = fread('datasets/article_creations_30days.tsv');
qual_preds = fread('datasets/article_quality_predictions_2009-2017.tsv');
creations_30days_recent = fread('datasets/article_creations_30days_recent.tsv');
qual_preds_recent = fread('datasets/article_quality_predictions_2017.tsv');
## Set keys to revision ID and do a left join.
setkey(creations_30days, revision_id);
setkey(qual_preds, rev_id);
creations_30days = creations_30days[qual_preds, nomatch = 0];
setkey(creations_30days_recent, revision_id);
setkey(qual_preds_recent, rev_id);
creations_30days_recent = creations_30days_recent[qual_preds_recent, nomatch = 0];
## Total creations: 1,353,535 (wc -l)
## Creations that match a quality prediction: 524,271
## That suggests that 829,264 articles (61.27%) were deleted in such a way that
## we no longer can retrieve the revision and make a prediction (copyright, attack)
## Total recent creations: 18662
## Creations that match a quality prediction: 8011
## That suggests that 57.1% were deleted in such a way that we no longer
## can retrieve them.
## That's concerning. I should plot that over time. Also, how does it develop
## since the start of ACTRIAL?
all_30day_creations = fread('datasets/article_creations_30days.tsv');
## Parse the timestamps and split into date/time.
all_30day_creations[, c("event_date", "event_time") := IDateTime(as.POSIXct(event_timestamp,
format='%Y-%m-%d %H:%M:%S.0',
tz='UTC'))];
creations_30days[, c("event_date", "event_time") := IDateTime(
as.POSIXct(rev_timestamp, format='%Y-%m-%d %H:%M:%S', tz='UTC'))];
surviving_article_data = merge(
all_30day_creations[, list(num_creations=sum(.N)), by=event_date],
creations_30days[, list(num_surviving_articles=sum(.N)), by=event_date],
by='event_date');
surviving_article_data[, prop_surviving := 100*num_surviving_articles/num_creations]
ggplot(surviving_article_data,
aes(x=event_date, y=prop_surviving)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Proportion in %') +
ggtitle("Proportion of created articles retrievable in late 2017") +
geom_smooth(method='loess', span=0.2) + ylim(0, 65)
all_recent_30day_creations = fread('datasets/article_creations_30days_recent.tsv');
all_recent_30day_creations[, c("event_date", "event_time") := IDateTime(as.POSIXct(event_timestamp,
format='%Y-%m-%d %H:%M:%S',
tz='UTC'))];
creations_30days_recent[, c("event_date", "event_time") := IDateTime(
as.POSIXct(event_timestamp, format='%Y-%m-%d %H:%M:%S', tz='UTC'))];
surviving_article_data_recent = merge(
all_recent_30day_creations[, list(num_creations=sum(.N)), by=event_date],
creations_30days_recent[, list(num_surviving_articles=sum(.N)), by=event_date],
by='event_date');
surviving_article_data_recent[, prop_surviving := 100*num_surviving_articles/num_creations]
ggplot(surviving_article_data_recent,
aes(x=event_date, y=prop_surviving)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Proportion in %') +
ggtitle("Proportion of articles created in 2017 retrievable in early 2018") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 100)
## Q1: What is the average quality score per day across time?
## We use Halfaker's measure: sum(I(c)*P(c)) where 'c' is the class,
## I(c) is 0 for "Stub", "1" for Start, and so on up to "5" for FA.
creations_30days[, qual_sum := start_prob + 2*c_prob + 3*b_prob +
4*ga_prob + 5*fa_prob];
ggplot(creations_30days[, list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, all retrievable revisions") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
creations_30days_recent[, qual_sum := start_prob + 2*c_prob + 3*b_prob +
4*ga_prob + 5*fa_prob];
ggplot(creations_30days_recent[, list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, all retrievable revisions") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
## Q2: What proportion of articles are flagged as not-OK by the draft quality model?
draft_pred_ok = merge(
creations_30days[, list(num_creations=sum(.N)), by=event_date],
creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664,
list(num_ok=sum(.N)), by=event_date],
by='event_date');
ggplot(draft_pred_ok,
aes(x=event_date, y=100*num_ok/num_creations)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of articles not flagged by the draft quality model") +
geom_smooth(method='loess', span=0.2) + ylim(0, 90);
draft_pred_ok_recent = merge(
creations_30days_recent[, list(num_creations=sum(.N)), by=event_date],
creations_30days_recent[draft_prediction == 'OK' & ok_prob >= 0.664,
list(num_ok=sum(.N)), by=event_date],
by='event_date');
ggplot(draft_pred_ok_recent,
aes(x=event_date, y=100*num_ok/num_creations)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of non-flagged articles created in 2017") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 100);
## Q3: What proportion of articles are flagged as spam/vandalism/attack.
draft_pred_split = merge(
creations_30days[, list(num_creations=sum(.N)), by=event_date],
merge(
creations_30days[draft_prediction == 'spam', list(num_spam=sum(.N)), by=event_date],
merge(
creations_30days[draft_prediction == 'vandalism', list(num_vandalism=sum(.N)), by=event_date],
creations_30days[draft_prediction == 'attack', list(num_attack=sum(.N)), by=event_date],
by='event_date'),
by='event_date'),
by='event_date');
ggplot() +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_spam/num_creations, colour='Spam')) +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_vandalism/num_creations, colour='Vandalism')) +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_attack/num_creations, colour='Attack')) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_spam/num_creations, colour='Spam'),
method='loess', span=0.2) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_vandalism/num_creations, colour='Vandalism'),
method='loess', span=0.2) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_attack/num_creations, colour='Attack'),
method='loess', span=0.2) +
ylim(0,45) + xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of new articles flagged by the draft quality model") +
scale_color_manual(values=c(
Attack=cbbPalette[1], Spam=cbbPalette[2], Vandalism=cbbPalette[3])) +
guides(colour=guide_legend(title='Flagged as'))
## There's a lot of variation in this data, can we do it by month instead?
## Revisiting Q1, isn't it more interesting to look at average quality for articles
## that were _not_ flagged by the draft quality model? Arguably, if the article
## is flagged, we suspect it'll get deleted or otherwise moved out of Main?
ggplot(creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664,
list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, non-flagged articles") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
ggplot(creations_30days_recent[draft_prediction == 'OK' & ok_prob >= 0.664,
list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Avg quality weighed sum per day, non-flagged 2017 articles") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
## Question: is the quality of content created by newcomers slowly increasing,
## or is the bar for not getting your article deleted increasing?
## Question: should we investigate average probability of an article not being
## flagged, given that the flags are not exclusive (e.g. an article can be 25%
## spam and 28% vandalism)?
## Q4: linear model for weighed sum of quality predicted by age and number of edits:
qualmodel = lm(qual_sum ~ event_user_age + event_user_revision_count,
data=creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664]);
summary(qualmodel);
qualmodel.log = lm(qual_sum ~ log2(1 + event_user_age)
+ log2(1 + event_user_revision_count),
data=creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664]);
summary(qualmodel.log);
## Let's redo the same analysis for more recent creations!
| /R/article_quality_predictions.R | no_license | nettrom/actrial | R | false | false | 10,035 | r | ## Investigation into the quality of articles created by newly created accounts
## (within the first 30 days after registration).
library(data.table);
library(ggplot2);
creations_30days = fread('datasets/article_creations_30days.tsv');
qual_preds = fread('datasets/article_quality_predictions_2009-2017.tsv');
creations_30days_recent = fread('datasets/article_creations_30days_recent.tsv');
qual_preds_recent = fread('datasets/article_quality_predictions_2017.tsv');
## Set keys to revision ID and do a left join.
setkey(creations_30days, revision_id);
setkey(qual_preds, rev_id);
creations_30days = creations_30days[qual_preds, nomatch = 0];
setkey(creations_30days_recent, revision_id);
setkey(qual_preds_recent, rev_id);
creations_30days_recent = creations_30days_recent[qual_preds_recent, nomatch = 0];
## Total creations: 1,353,535 (wc -l)
## Creations that match a quality prediction: 524,271
## That suggests that 829,264 articles (61.27%) were deleted in such a way that
## we no longer can retrieve the revision and make a prediction (copyright, attack)
## Total recent creations: 18662
## Creations that match a quality prediction: 8011
## That suggests that 57.1% were deleted in such a way that we no longer
## can retrieve them.
## That's concerning. I should plot that over time. Also, how does it develop
## since the start of ACTRIAL?
all_30day_creations = fread('datasets/article_creations_30days.tsv');
## Parse the timestamps and split into date/time.
all_30day_creations[, c("event_date", "event_time") := IDateTime(as.POSIXct(event_timestamp,
format='%Y-%m-%d %H:%M:%S.0',
tz='UTC'))];
creations_30days[, c("event_date", "event_time") := IDateTime(
as.POSIXct(rev_timestamp, format='%Y-%m-%d %H:%M:%S', tz='UTC'))];
surviving_article_data = merge(
all_30day_creations[, list(num_creations=sum(.N)), by=event_date],
creations_30days[, list(num_surviving_articles=sum(.N)), by=event_date],
by='event_date');
surviving_article_data[, prop_surviving := 100*num_surviving_articles/num_creations]
ggplot(surviving_article_data,
aes(x=event_date, y=prop_surviving)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Proportion in %') +
ggtitle("Proportion of created articles retrievable in late 2017") +
geom_smooth(method='loess', span=0.2) + ylim(0, 65)
all_recent_30day_creations = fread('datasets/article_creations_30days_recent.tsv');
all_recent_30day_creations[, c("event_date", "event_time") := IDateTime(as.POSIXct(event_timestamp,
format='%Y-%m-%d %H:%M:%S',
tz='UTC'))];
creations_30days_recent[, c("event_date", "event_time") := IDateTime(
as.POSIXct(event_timestamp, format='%Y-%m-%d %H:%M:%S', tz='UTC'))];
surviving_article_data_recent = merge(
all_recent_30day_creations[, list(num_creations=sum(.N)), by=event_date],
creations_30days_recent[, list(num_surviving_articles=sum(.N)), by=event_date],
by='event_date');
surviving_article_data_recent[, prop_surviving := 100*num_surviving_articles/num_creations]
ggplot(surviving_article_data_recent,
aes(x=event_date, y=prop_surviving)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Proportion in %') +
ggtitle("Proportion of articles created in 2017 retrievable in early 2018") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 100)
## Q1: What is the average quality score per day across time?
## We use Halfaker's measure: sum(I(c)*P(c)) where 'c' is the class,
## I(c) is 0 for "Stub", "1" for Start, and so on up to "5" for FA.
creations_30days[, qual_sum := start_prob + 2*c_prob + 3*b_prob +
4*ga_prob + 5*fa_prob];
ggplot(creations_30days[, list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, all retrievable revisions") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
creations_30days_recent[, qual_sum := start_prob + 2*c_prob + 3*b_prob +
4*ga_prob + 5*fa_prob];
ggplot(creations_30days_recent[, list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, all retrievable revisions") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
## Q2: What proportion of articles are flagged as not-OK by the draft quality model?
draft_pred_ok = merge(
creations_30days[, list(num_creations=sum(.N)), by=event_date],
creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664,
list(num_ok=sum(.N)), by=event_date],
by='event_date');
ggplot(draft_pred_ok,
aes(x=event_date, y=100*num_ok/num_creations)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of articles not flagged by the draft quality model") +
geom_smooth(method='loess', span=0.2) + ylim(0, 90);
draft_pred_ok_recent = merge(
creations_30days_recent[, list(num_creations=sum(.N)), by=event_date],
creations_30days_recent[draft_prediction == 'OK' & ok_prob >= 0.664,
list(num_ok=sum(.N)), by=event_date],
by='event_date');
ggplot(draft_pred_ok_recent,
aes(x=event_date, y=100*num_ok/num_creations)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of non-flagged articles created in 2017") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 100);
## Q3: What proportion of articles are flagged as spam/vandalism/attack.
draft_pred_split = merge(
creations_30days[, list(num_creations=sum(.N)), by=event_date],
merge(
creations_30days[draft_prediction == 'spam', list(num_spam=sum(.N)), by=event_date],
merge(
creations_30days[draft_prediction == 'vandalism', list(num_vandalism=sum(.N)), by=event_date],
creations_30days[draft_prediction == 'attack', list(num_attack=sum(.N)), by=event_date],
by='event_date'),
by='event_date'),
by='event_date');
ggplot() +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_spam/num_creations, colour='Spam')) +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_vandalism/num_creations, colour='Vandalism')) +
geom_line(data=draft_pred_split,
aes(x=event_date, y=100*num_attack/num_creations, colour='Attack')) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_spam/num_creations, colour='Spam'),
method='loess', span=0.2) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_vandalism/num_creations, colour='Vandalism'),
method='loess', span=0.2) +
geom_smooth(data=draft_pred_split,
aes(x=event_date, y=100*num_attack/num_creations, colour='Attack'),
method='loess', span=0.2) +
ylim(0,45) + xlab('Date') + ylab('Proportion (%)') +
ggtitle("Proportion of new articles flagged by the draft quality model") +
scale_color_manual(values=c(
Attack=cbbPalette[1], Spam=cbbPalette[2], Vandalism=cbbPalette[3])) +
guides(colour=guide_legend(title='Flagged as'))
## There's a lot of variation in this data, can we do it by month instead?
## Revisiting Q1, isn't it more interesting to look at average quality for articles
## that were _not_ flagged by the draft quality model? Arguably, if the article
## is flagged, we suspect it'll get deleted or otherwise moved out of Main?
ggplot(creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664,
list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 year', date_labels = '%Y') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Average quality weighed sum per day, non-flagged articles") +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
ggplot(creations_30days_recent[draft_prediction == 'OK' & ok_prob >= 0.664,
list(avg_weigh_sum=mean(qual_sum)), by=event_date],
aes(x=event_date, y=avg_weigh_sum)) +
geom_line() +
scale_x_date(date_breaks='1 month', date_labels = '%m') +
xlab('Date') + ylab('Weighed sum') +
ggtitle("Avg quality weighed sum per day, non-flagged 2017 articles") +
geom_vline(xintercept=as.IDate('2017-09-14'), linetype='dashed') +
geom_smooth(method='loess', span=0.2) + ylim(0, 1.5);
## Question: is the quality of content created by newcomers slowly increasing,
## or is the bar for not getting your article deleted increasing?
## Question: should we investigate average probability of an article not being
## flagged, given that the flags are not exclusive (e.g. an article can be 25%
## spam and 28% vandalism)?
## Q4: linear model for weighed sum of quality predicted by age and number of edits:
qualmodel = lm(qual_sum ~ event_user_age + event_user_revision_count,
data=creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664]);
summary(qualmodel);
qualmodel.log = lm(qual_sum ~ log2(1 + event_user_age)
+ log2(1 + event_user_revision_count),
data=creations_30days[draft_prediction == 'OK' & ok_prob >= 0.664]);
summary(qualmodel.log);
## Let's redo the same analysis for more recent creations!
|
library(shiny)
library(glasso)
library(d3heatmap)
library(igraph)
################### Function Definiton Begin ####################
# convert inverse covariance matrix to links
wi2link <- function(mat) {
mat <- abs(mat)
diag(mat) <- 0
min <- min(mat)
max <- max(mat)
links <- data.frame(from = NA, to = NA, weight = NA)
for (i in 1:nrow(mat)) {
for (j in 1:ncol(mat)) {
if (mat[i,j] != 0 & i != j) {
links <- rbind(links, c(i-1,j-1,range01(mat[i,j],min,max)))
}
}
}
return(links[-1,])
}
# scale to range [0,1]
range01 <- function(var, min, max) {
res <- (var-min)/(max-min)
return(res)
}
# for d3heatmap use
reorderfun <- function(d, w) {
return(d)
}
# get dendrogram for reorder
getDendrogram <- function(mat) {
distance <- dist(mat)
cluster <- hclust(distance, method = "ward.D")
dendrogram <- as.dendrogram(cluster)
Rowv <- rowMeans(mat)
dendrogram <- reorder(dendrogram, Rowv)
return(dendrogram)
}
################### Function Definiton END ####################
################### Logic Begin ###################
# Prepare Data
nodes <- read.csv(file = "AAL_Yan.csv")
measure = c("Assortivity", "Cliques", "Transitivity")
statsTable <- data.frame(measure, value = rep(NA, length(measure)),
meaning = c("The assortativity coefficient is positive is similar vertices (based on some external property) tend to connect to each, and negative otherwise.",
"The size of the largest clique",
"Transitivity measures the probability that the adjacent vertices of a vertex are connected. This is sometimes also called the clustering coefficient."))
# shiny server
shinyServer(function(input, output) {
### sharable data
inputdata <- reactive({
inFile <- input$file
if (is.null(inFile)) {
return(NULL)
}
tmp <- read.csv(inFile$datapath, header = TRUE)
tmp[,nodes$name]
})
# generate inverse covariance matrix given lambda
wi <- reactive({
tmp <- glasso(s = cov(inputdata()), rho = input$lambda)
tmp <- tmp$wi
})
# generate adjacancy matrix
adjMat <- reactive({
tmp <- wi()
tmp[which(tmp!=0)] <- 1
diag(tmp) <- 0
tmp
})
# update statistical table
statsTable2 <- reactive({
tmp.g <- graph.adjacency(adjMat(), mode = "undirected") # create igraph object given adjacency matrix
# update graph statistics
statsTable[statsTable$measure=="Assortivity","value"] <- assortativity_degree(tmp.g)
statsTable[statsTable$measure=="Cliques","value"] <- clique_num(tmp.g)
statsTable[statsTable$measure=="Transitivity","value"] <- transitivity(tmp.g, type = "undirected")
statsTable
})
### output
# output: data table
output$table <- renderTable({
tmp <- inputdata()
tmp[1:15, 1:6]
})
# output: interactive network plot
networkPlot <- eventReactive(input$updateNet, {
links <- wi2link(wi())
forceNetwork(Nodes = nodes, Links = links,
Source = "from", Target = "to",
Value = "weight", NodeID = "name",
Group = "region", zoom = TRUE)
})
output$networkPlot <- renderForceNetwork({
networkPlot()
})
# output: checkbox network plot
checkboxPlot <- eventReactive(input$updateNet, {
d3heatmap(adjMat(), Rowv = FALSE, Colv = "Rowv", colors = grey(c(1, 0)),
labRow = nodes$name, labCol = nodes$name)
})
output$checkboxPlot <- renderD3heatmap({
checkboxPlot()
})
# output: visulization for download usage
plotIgraph <- function() {
# create igraph object given adjacency matrix
tmp <- adjMat()
colnames(tmp) <- colnames(inputdata())
tmp.g <- graph.adjacency(tmp, mode = "undirected",
add.colnames = 'label')
plot(tmp.g, layout=layout.lgl)
}
igraphPlot <- eventReactive(input$updateNet, {
plotIgraph()
})
output$igraph <- renderPlot({
igraphPlot()
})
# output: statistics
stats <- eventReactive(input$updateNet, {
statsTable2()
})
output$stats <- renderTable({
stats()
})
## download
output$downloadGraph <- downloadHandler(
filename = c('brain_connectivity.png'),
content = function(file) {
png(file)
plotIgraph()
dev.off()
}
)
output$downloadStats <- downloadHandler(
filename = c('brain_connectivity.csv'),
content = function(file) {
write.csv(statsTable2(), file)
}
)
}) # shinyServer end
################### Logic END ################### | /main/server.R | permissive | jyfeather/BrainConn_APP | R | false | false | 4,578 | r | library(shiny)
library(glasso)
library(d3heatmap)
library(igraph)
################### Function Definiton Begin ####################
# convert inverse covariance matrix to links
wi2link <- function(mat) {
mat <- abs(mat)
diag(mat) <- 0
min <- min(mat)
max <- max(mat)
links <- data.frame(from = NA, to = NA, weight = NA)
for (i in 1:nrow(mat)) {
for (j in 1:ncol(mat)) {
if (mat[i,j] != 0 & i != j) {
links <- rbind(links, c(i-1,j-1,range01(mat[i,j],min,max)))
}
}
}
return(links[-1,])
}
# scale to range [0,1]
range01 <- function(var, min, max) {
res <- (var-min)/(max-min)
return(res)
}
# for d3heatmap use
reorderfun <- function(d, w) {
return(d)
}
# get dendrogram for reorder
getDendrogram <- function(mat) {
distance <- dist(mat)
cluster <- hclust(distance, method = "ward.D")
dendrogram <- as.dendrogram(cluster)
Rowv <- rowMeans(mat)
dendrogram <- reorder(dendrogram, Rowv)
return(dendrogram)
}
################### Function Definiton END ####################
################### Logic Begin ###################
# Prepare Data
nodes <- read.csv(file = "AAL_Yan.csv")
measure = c("Assortivity", "Cliques", "Transitivity")
statsTable <- data.frame(measure, value = rep(NA, length(measure)),
meaning = c("The assortativity coefficient is positive is similar vertices (based on some external property) tend to connect to each, and negative otherwise.",
"The size of the largest clique",
"Transitivity measures the probability that the adjacent vertices of a vertex are connected. This is sometimes also called the clustering coefficient."))
# shiny server
shinyServer(function(input, output) {
### sharable data
inputdata <- reactive({
inFile <- input$file
if (is.null(inFile)) {
return(NULL)
}
tmp <- read.csv(inFile$datapath, header = TRUE)
tmp[,nodes$name]
})
# generate inverse covariance matrix given lambda
wi <- reactive({
tmp <- glasso(s = cov(inputdata()), rho = input$lambda)
tmp <- tmp$wi
})
# generate adjacancy matrix
adjMat <- reactive({
tmp <- wi()
tmp[which(tmp!=0)] <- 1
diag(tmp) <- 0
tmp
})
# update statistical table
statsTable2 <- reactive({
tmp.g <- graph.adjacency(adjMat(), mode = "undirected") # create igraph object given adjacency matrix
# update graph statistics
statsTable[statsTable$measure=="Assortivity","value"] <- assortativity_degree(tmp.g)
statsTable[statsTable$measure=="Cliques","value"] <- clique_num(tmp.g)
statsTable[statsTable$measure=="Transitivity","value"] <- transitivity(tmp.g, type = "undirected")
statsTable
})
### output
# output: data table
output$table <- renderTable({
tmp <- inputdata()
tmp[1:15, 1:6]
})
# output: interactive network plot
networkPlot <- eventReactive(input$updateNet, {
links <- wi2link(wi())
forceNetwork(Nodes = nodes, Links = links,
Source = "from", Target = "to",
Value = "weight", NodeID = "name",
Group = "region", zoom = TRUE)
})
output$networkPlot <- renderForceNetwork({
networkPlot()
})
# output: checkbox network plot
checkboxPlot <- eventReactive(input$updateNet, {
d3heatmap(adjMat(), Rowv = FALSE, Colv = "Rowv", colors = grey(c(1, 0)),
labRow = nodes$name, labCol = nodes$name)
})
output$checkboxPlot <- renderD3heatmap({
checkboxPlot()
})
# output: visulization for download usage
plotIgraph <- function() {
# create igraph object given adjacency matrix
tmp <- adjMat()
colnames(tmp) <- colnames(inputdata())
tmp.g <- graph.adjacency(tmp, mode = "undirected",
add.colnames = 'label')
plot(tmp.g, layout=layout.lgl)
}
igraphPlot <- eventReactive(input$updateNet, {
plotIgraph()
})
output$igraph <- renderPlot({
igraphPlot()
})
# output: statistics
stats <- eventReactive(input$updateNet, {
statsTable2()
})
output$stats <- renderTable({
stats()
})
## download
output$downloadGraph <- downloadHandler(
filename = c('brain_connectivity.png'),
content = function(file) {
png(file)
plotIgraph()
dev.off()
}
)
output$downloadStats <- downloadHandler(
filename = c('brain_connectivity.csv'),
content = function(file) {
write.csv(statsTable2(), file)
}
)
}) # shinyServer end
################### Logic END ################### |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mpl_morphyex.R
\name{mpl_second_up_recon}
\alias{mpl_second_up_recon}
\title{Finalises the ancestral state reconstructions for characters with
inapplicable values.}
\usage{
mpl_second_up_recon(node_id, left_id, right_id, anc_id, morphyobj)
}
\arguments{
\item{node_id}{The index of the node being reconstructed.}
\item{left_id}{The index of the left descendant.}
\item{right_id}{The index of the right descendant.}
\item{anc_id}{The index of the immediate ancestor of the node.}
\item{morphyobj}{An instance of the Morphy object.}
}
\value{
The integral parsimony length (right now)
}
\description{
Finalises the nodal sets for any characters that may have involved
the inapplicable token and counts excess regions of applicability at nodes
having at least two descendant subtrees that possess any applicable characters.
Because this function needs to be fairly high-performance, it does not do much
checking for parameter validity, thus unsafe usage of this function might not
be caught. It is up to calling functions to ensure that the appropriate
parameters have been set before use.
}
\seealso{
Other Morphy API functions:
\code{\link{GapHandler}()},
\code{\link{MorphyErrorCheck}()},
\code{\link{MorphyWeights}()},
\code{\link{PhyDat2Morphy}()},
\code{\link{SingleCharMorphy}()},
\code{\link{UnloadMorphy}()},
\code{\link{is.morphyPtr}()},
\code{\link{mpl_apply_tipdata}()},
\code{\link{mpl_attach_rawdata}()},
\code{\link{mpl_attach_symbols}()},
\code{\link{mpl_delete_Morphy}()},
\code{\link{mpl_delete_rawdata}()},
\code{\link{mpl_first_down_recon}()},
\code{\link{mpl_first_up_recon}()},
\code{\link{mpl_get_charac_weight}()},
\code{\link{mpl_get_gaphandl}()},
\code{\link{mpl_get_num_charac}()},
\code{\link{mpl_get_num_internal_nodes}()},
\code{\link{mpl_get_numtaxa}()},
\code{\link{mpl_get_symbols}()},
\code{\link{mpl_init_Morphy}()},
\code{\link{mpl_new_Morphy}()},
\code{\link{mpl_second_down_recon}()},
\code{\link{mpl_set_charac_weight}()},
\code{\link{mpl_set_num_internal_nodes}()},
\code{\link{mpl_set_parsim_t}()},
\code{\link{mpl_translate_error}()},
\code{\link{mpl_update_lower_root}()},
\code{\link{mpl_update_tip}()},
\code{\link{summary.morphyPtr}()}
}
\author{
Thomas Guillerme
}
\concept{Morphy API functions}
\keyword{internal}
| /man/mpl_second_up_recon.Rd | no_license | cran/TreeSearch | R | false | true | 2,412 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mpl_morphyex.R
\name{mpl_second_up_recon}
\alias{mpl_second_up_recon}
\title{Finalises the ancestral state reconstructions for characters with
inapplicable values.}
\usage{
mpl_second_up_recon(node_id, left_id, right_id, anc_id, morphyobj)
}
\arguments{
\item{node_id}{The index of the node being reconstructed.}
\item{left_id}{The index of the left descendant.}
\item{right_id}{The index of the right descendant.}
\item{anc_id}{The index of the immediate ancestor of the node.}
\item{morphyobj}{An instance of the Morphy object.}
}
\value{
The integral parsimony length (right now)
}
\description{
Finalises the nodal sets for any characters that may have involved
the inapplicable token and counts excess regions of applicability at nodes
having at least two descendant subtrees that possess any applicable characters.
Because this function needs to be fairly high-performance, it does not do much
checking for parameter validity, thus unsafe usage of this function might not
be caught. It is up to calling functions to ensure that the appropriate
parameters have been set before use.
}
\seealso{
Other Morphy API functions:
\code{\link{GapHandler}()},
\code{\link{MorphyErrorCheck}()},
\code{\link{MorphyWeights}()},
\code{\link{PhyDat2Morphy}()},
\code{\link{SingleCharMorphy}()},
\code{\link{UnloadMorphy}()},
\code{\link{is.morphyPtr}()},
\code{\link{mpl_apply_tipdata}()},
\code{\link{mpl_attach_rawdata}()},
\code{\link{mpl_attach_symbols}()},
\code{\link{mpl_delete_Morphy}()},
\code{\link{mpl_delete_rawdata}()},
\code{\link{mpl_first_down_recon}()},
\code{\link{mpl_first_up_recon}()},
\code{\link{mpl_get_charac_weight}()},
\code{\link{mpl_get_gaphandl}()},
\code{\link{mpl_get_num_charac}()},
\code{\link{mpl_get_num_internal_nodes}()},
\code{\link{mpl_get_numtaxa}()},
\code{\link{mpl_get_symbols}()},
\code{\link{mpl_init_Morphy}()},
\code{\link{mpl_new_Morphy}()},
\code{\link{mpl_second_down_recon}()},
\code{\link{mpl_set_charac_weight}()},
\code{\link{mpl_set_num_internal_nodes}()},
\code{\link{mpl_set_parsim_t}()},
\code{\link{mpl_translate_error}()},
\code{\link{mpl_update_lower_root}()},
\code{\link{mpl_update_tip}()},
\code{\link{summary.morphyPtr}()}
}
\author{
Thomas Guillerme
}
\concept{Morphy API functions}
\keyword{internal}
|
ttestInheritedMethods() | /test.R | no_license | Yashimasan/new_git_test | R | false | false | 23 | r | ttestInheritedMethods() |
#######################################################################
# stream - Infrastructure for Data Stream Mining
# Copyright (C) 2013 Michael Hahsler, Matthew Bolanos, John Forrest
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
DSC_TwoStage <- function(micro, macro) {
state <- new.env()
state$newdata <- TRUE
l <- list(
description = paste(micro$description, " + ",
macro$description, sep=''),
micro_dsc = micro,
macro_dsc = macro,
macro = state
)
czs <- c("DSC_Macro", "DSC")
if(is(micro,"DSC_Outlier")) {
czs <- c("DSC_Outlier", czs)
l$recheck_outliers <- micro$recheck_outliers
}
if(is(micro,"DSC_SinglePass")) czs <- c("DSC_SinglePass", czs)
czs <- c("DSC_TwoStage", czs)
structure(l,class = czs)
}
get_outlier_positions.DSC_TwoStage <- function(x, ...) {
get_outlier_positions(x$micro_dsc)
}
recheck_outlier.DSC_TwoStage <- function(x, outlier_correlated_id, ...) {
recheck_outlier(x$micro_dsc, outlier_correlated_id)
}
### TwoStage has its own interface (does not use DSC_R)
update.DSC_TwoStage <- function(object, dsd, n=1, verbose=FALSE,
block=10000L, ...) {
### dsc contains an RObj which is a reference object with a cluster method
### some matrix to be processed in one go
if(!is(dsd, "DSD")) {
n <- nrow(dsd)
dsd <- DSD_Memory(dsd)
}
n <- as.integer(n)
if(n>0) {
if(!is(dsd, "DSD_data.frame"))
stop("Cannot cluster stream (need a DSD_data.frame.)")
### for DSC_TwoStage
if(is.environment(object$macro)) object$macro$newdata <- TRUE
### TODO: Check data
for(bl in .make_block(n, block)) {
update(object$micro_dsc, dsd, n=bl, ...)
if(verbose) cat("Processed", bl, "points -",
nclusters(object), "clusters\n")
}
}
# so cl <- cluster(cl, ...) also works
invisible(object)
}
### accessors
get_centers.DSC_TwoStage <- function(x, type=c("auto", "micro", "macro"), ...) {
type <- match.arg(type)
if(type=="micro") get_centers(x$micro_dsc)
else {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
get_centers(x$macro_dsc)
}
}
get_weights.DSC_TwoStage <- function(x, type=c("auto", "micro", "macro"), ...) {
type <- match.arg(type)
if(type=="micro") get_weights(x$micro_dsc, ...)
else {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
get_weights(x$macro_dsc, ...)
}
}
microToMacro.DSC_TwoStage <- function(x, micro=NULL, ...) {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
microToMacro(x$macro_dsc, micro, ...)
}
get_assignment.DSC_TwoStage <- function(dsc, points, type=c("auto", "micro", "macro"),
method="auto", ...) {
type <- match.arg(type)
if(type=="micro") {
if(is(dsc$micro_dsc, "DSC_SinglePass")) dsc$macro$newdata <- TRUE
get_assignment(dsc$micro_dsc, points, type, method, ...)
} else {
if(dsc$macro$newdata) {
recluster(dsc$macro_dsc, dsc$micro_dsc)
dsc$macro$newdata <- FALSE
}
get_assignment(dsc$macro_dsc, points, type, method, ...)
}
}
### make a deep copy
get_copy.DSC_TwoStage <- function(x) {
copy <- DSC_TwoStage(micro=get_copy(x$micro_dsc), macro=get_copy(x$macro_dsc))
copy$macro$newdata <- x$macro$newdata
copy
}
| /R/DSC_TwoStage.R | no_license | dinarior/stream | R | false | false | 4,079 | r | #######################################################################
# stream - Infrastructure for Data Stream Mining
# Copyright (C) 2013 Michael Hahsler, Matthew Bolanos, John Forrest
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
DSC_TwoStage <- function(micro, macro) {
state <- new.env()
state$newdata <- TRUE
l <- list(
description = paste(micro$description, " + ",
macro$description, sep=''),
micro_dsc = micro,
macro_dsc = macro,
macro = state
)
czs <- c("DSC_Macro", "DSC")
if(is(micro,"DSC_Outlier")) {
czs <- c("DSC_Outlier", czs)
l$recheck_outliers <- micro$recheck_outliers
}
if(is(micro,"DSC_SinglePass")) czs <- c("DSC_SinglePass", czs)
czs <- c("DSC_TwoStage", czs)
structure(l,class = czs)
}
get_outlier_positions.DSC_TwoStage <- function(x, ...) {
get_outlier_positions(x$micro_dsc)
}
recheck_outlier.DSC_TwoStage <- function(x, outlier_correlated_id, ...) {
recheck_outlier(x$micro_dsc, outlier_correlated_id)
}
### TwoStage has its own interface (does not use DSC_R)
update.DSC_TwoStage <- function(object, dsd, n=1, verbose=FALSE,
block=10000L, ...) {
### dsc contains an RObj which is a reference object with a cluster method
### some matrix to be processed in one go
if(!is(dsd, "DSD")) {
n <- nrow(dsd)
dsd <- DSD_Memory(dsd)
}
n <- as.integer(n)
if(n>0) {
if(!is(dsd, "DSD_data.frame"))
stop("Cannot cluster stream (need a DSD_data.frame.)")
### for DSC_TwoStage
if(is.environment(object$macro)) object$macro$newdata <- TRUE
### TODO: Check data
for(bl in .make_block(n, block)) {
update(object$micro_dsc, dsd, n=bl, ...)
if(verbose) cat("Processed", bl, "points -",
nclusters(object), "clusters\n")
}
}
# so cl <- cluster(cl, ...) also works
invisible(object)
}
### accessors
get_centers.DSC_TwoStage <- function(x, type=c("auto", "micro", "macro"), ...) {
type <- match.arg(type)
if(type=="micro") get_centers(x$micro_dsc)
else {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
get_centers(x$macro_dsc)
}
}
get_weights.DSC_TwoStage <- function(x, type=c("auto", "micro", "macro"), ...) {
type <- match.arg(type)
if(type=="micro") get_weights(x$micro_dsc, ...)
else {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
get_weights(x$macro_dsc, ...)
}
}
microToMacro.DSC_TwoStage <- function(x, micro=NULL, ...) {
if(x$macro$newdata) {
recluster(x$macro_dsc, x$micro_dsc)
x$macro$newdata <- FALSE
}
microToMacro(x$macro_dsc, micro, ...)
}
get_assignment.DSC_TwoStage <- function(dsc, points, type=c("auto", "micro", "macro"),
method="auto", ...) {
type <- match.arg(type)
if(type=="micro") {
if(is(dsc$micro_dsc, "DSC_SinglePass")) dsc$macro$newdata <- TRUE
get_assignment(dsc$micro_dsc, points, type, method, ...)
} else {
if(dsc$macro$newdata) {
recluster(dsc$macro_dsc, dsc$micro_dsc)
dsc$macro$newdata <- FALSE
}
get_assignment(dsc$macro_dsc, points, type, method, ...)
}
}
### make a deep copy
get_copy.DSC_TwoStage <- function(x) {
copy <- DSC_TwoStage(micro=get_copy(x$micro_dsc), macro=get_copy(x$macro_dsc))
copy$macro$newdata <- x$macro$newdata
copy
}
|
#' Slide along multiple inputs simultaneously relative to an index chunked by period
#'
#' `slide_period2()` and `pslide_period()` represent the combination
#' of [slide2()] and [pslide()] with [slide_period()], allowing you to slide
#' over multiple vectors at once, using indices defined by breaking up the
#' `.i`-ndex by `.period`.
#'
#' @inheritParams slide_period
#'
#' @template param-x-y
#' @template param-l
#'
#' @return
#' A vector fulfilling the following invariants:
#'
#' \subsection{`slide_period2()`}{
#'
#' * `vec_size(slide_period2(.x, .y)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_ptype(slide_period2(.x, .y)) == list()`
#'
#' }
#'
#' \subsection{`slide_period2_vec()` and `slide_period2_*()` variants}{
#'
#' * `vec_size(slide_period2_vec(.x, .y)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_size(slide_period2_vec(.x, .y)[[1]]) == 1L`
#'
#' * `vec_ptype(slide_period2_vec(.x, .y, .ptype = ptype)) == ptype`
#'
#' }
#'
#' \subsection{`pslide_period()`}{
#'
#' * `vec_size(pslide_period(.l)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_ptype(pslide_period(.l)) == list()`
#'
#' }
#'
#' \subsection{`pslide_period_vec()` and `pslide_period_*()` variants}{
#'
#' * `vec_size(pslide_period_vec(.l)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_size(pslide_period_vec(.l)[[1]]) == 1L`
#'
#' * `vec_ptype(pslide_period_vec(.l, .ptype = ptype)) == ptype`
#'
#' }
#'
#' @examples
#' i <- as.Date("2019-01-28") + 0:5
#'
#' slide_period2(
#' .x = 1:6,
#' .y = i,
#' .i = i,
#' .period = "month",
#' .f = ~data.frame(x = .x, i = .y)
#' )
#'
#' pslide_period(
#' .l = list(1:6, 7:12, i),
#' .i = i,
#' .period = "month",
#' .f = ~data.frame(x = .x, y = .y, i = ..3)
#' )
#'
#' @seealso [slide2()], [slide_index2()], [slide_period()]
#' @export
slide_period2 <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = list(),
.constrain = FALSE,
.atomic = FALSE
)
}
#' @rdname slide_period2
#' @export
slide_period2_vec <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.ptype = NULL) {
if (is.null(.ptype)) {
out <- slide_period2_vec_simplify(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
return(out)
}
slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = .ptype,
.constrain = TRUE,
.atomic = TRUE
)
}
slide_period2_vec_simplify <- function(.x,
.y,
.i,
.period,
.f,
...,
.every,
.origin,
.before,
.after,
.complete) {
out <- slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = list(),
.constrain = FALSE,
.atomic = TRUE
)
vec_simplify(out)
}
#' @rdname slide_period2
#' @export
slide_period2_dbl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = double()
)
}
#' @rdname slide_period2
#' @export
slide_period2_int <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = integer()
)
}
#' @rdname slide_period2
#' @export
slide_period2_lgl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = logical()
)
}
#' @rdname slide_period2
#' @export
slide_period2_chr <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = character()
)
}
#' @rdname slide_period2
#' @export
slide_period2_dfr <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.names_to = NULL,
.name_repair = c("unique", "universal", "check_unique")) {
out <- slide_period2(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
vec_rbind(!!!out, .names_to = .names_to, .name_repair = .name_repair)
}
#' @rdname slide_period2
#' @export
slide_period2_dfc <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.size = NULL,
.name_repair = c("unique", "universal", "check_unique", "minimal")) {
out <- slide_period2(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
vec_cbind(!!!out, .size = .size, .name_repair = .name_repair)
}
# ------------------------------------------------------------------------------
slide_period2_impl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every,
.origin,
.before,
.after,
.complete,
.ptype,
.constrain,
.atomic) {
vec_assert(.x)
vec_assert(.y)
# TODO - Do more efficiently internally by reusing rather than recycling
# https://github.com/tidyverse/purrr/blob/e4d553989e3d18692ebeeedb334b6223ae9ea294/src/map.c#L129
# But use `vec_size_common()` to check sizes and get `.size`
args <- vec_recycle_common(.x, .y)
.f <- as_function(.f)
f_call <- expr(.f(.x, .y, ...))
type <- -2L
slide_period_common(
x = args,
i = .i,
period = .period,
f_call = f_call,
every = .every,
origin = .origin,
before = .before,
after = .after,
complete = .complete,
ptype = .ptype,
constrain = .constrain,
atomic = .atomic,
env = environment(),
type = type
)
}
| /R/slide-period2.R | permissive | romainfrancois/slider | R | false | false | 10,215 | r | #' Slide along multiple inputs simultaneously relative to an index chunked by period
#'
#' `slide_period2()` and `pslide_period()` represent the combination
#' of [slide2()] and [pslide()] with [slide_period()], allowing you to slide
#' over multiple vectors at once, using indices defined by breaking up the
#' `.i`-ndex by `.period`.
#'
#' @inheritParams slide_period
#'
#' @template param-x-y
#' @template param-l
#'
#' @return
#' A vector fulfilling the following invariants:
#'
#' \subsection{`slide_period2()`}{
#'
#' * `vec_size(slide_period2(.x, .y)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_ptype(slide_period2(.x, .y)) == list()`
#'
#' }
#'
#' \subsection{`slide_period2_vec()` and `slide_period2_*()` variants}{
#'
#' * `vec_size(slide_period2_vec(.x, .y)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_size(slide_period2_vec(.x, .y)[[1]]) == 1L`
#'
#' * `vec_ptype(slide_period2_vec(.x, .y, .ptype = ptype)) == ptype`
#'
#' }
#'
#' \subsection{`pslide_period()`}{
#'
#' * `vec_size(pslide_period(.l)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_ptype(pslide_period(.l)) == list()`
#'
#' }
#'
#' \subsection{`pslide_period_vec()` and `pslide_period_*()` variants}{
#'
#' * `vec_size(pslide_period_vec(.l)) == vec_size(unique(warp::warp_distance(.i)))`
#'
#' * `vec_size(pslide_period_vec(.l)[[1]]) == 1L`
#'
#' * `vec_ptype(pslide_period_vec(.l, .ptype = ptype)) == ptype`
#'
#' }
#'
#' @examples
#' i <- as.Date("2019-01-28") + 0:5
#'
#' slide_period2(
#' .x = 1:6,
#' .y = i,
#' .i = i,
#' .period = "month",
#' .f = ~data.frame(x = .x, i = .y)
#' )
#'
#' pslide_period(
#' .l = list(1:6, 7:12, i),
#' .i = i,
#' .period = "month",
#' .f = ~data.frame(x = .x, y = .y, i = ..3)
#' )
#'
#' @seealso [slide2()], [slide_index2()], [slide_period()]
#' @export
slide_period2 <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = list(),
.constrain = FALSE,
.atomic = FALSE
)
}
#' @rdname slide_period2
#' @export
slide_period2_vec <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.ptype = NULL) {
if (is.null(.ptype)) {
out <- slide_period2_vec_simplify(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
return(out)
}
slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = .ptype,
.constrain = TRUE,
.atomic = TRUE
)
}
slide_period2_vec_simplify <- function(.x,
.y,
.i,
.period,
.f,
...,
.every,
.origin,
.before,
.after,
.complete) {
out <- slide_period2_impl(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = list(),
.constrain = FALSE,
.atomic = TRUE
)
vec_simplify(out)
}
#' @rdname slide_period2
#' @export
slide_period2_dbl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = double()
)
}
#' @rdname slide_period2
#' @export
slide_period2_int <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = integer()
)
}
#' @rdname slide_period2
#' @export
slide_period2_lgl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = logical()
)
}
#' @rdname slide_period2
#' @export
slide_period2_chr <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE) {
slide_period2_vec(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete,
.ptype = character()
)
}
#' @rdname slide_period2
#' @export
slide_period2_dfr <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.names_to = NULL,
.name_repair = c("unique", "universal", "check_unique")) {
out <- slide_period2(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
vec_rbind(!!!out, .names_to = .names_to, .name_repair = .name_repair)
}
#' @rdname slide_period2
#' @export
slide_period2_dfc <- function(.x,
.y,
.i,
.period,
.f,
...,
.every = 1L,
.origin = NULL,
.before = 0L,
.after = 0L,
.complete = FALSE,
.size = NULL,
.name_repair = c("unique", "universal", "check_unique", "minimal")) {
out <- slide_period2(
.x,
.y,
.i,
.period,
.f,
...,
.every = .every,
.origin = .origin,
.before = .before,
.after = .after,
.complete = .complete
)
vec_cbind(!!!out, .size = .size, .name_repair = .name_repair)
}
# ------------------------------------------------------------------------------
slide_period2_impl <- function(.x,
.y,
.i,
.period,
.f,
...,
.every,
.origin,
.before,
.after,
.complete,
.ptype,
.constrain,
.atomic) {
vec_assert(.x)
vec_assert(.y)
# TODO - Do more efficiently internally by reusing rather than recycling
# https://github.com/tidyverse/purrr/blob/e4d553989e3d18692ebeeedb334b6223ae9ea294/src/map.c#L129
# But use `vec_size_common()` to check sizes and get `.size`
args <- vec_recycle_common(.x, .y)
.f <- as_function(.f)
f_call <- expr(.f(.x, .y, ...))
type <- -2L
slide_period_common(
x = args,
i = .i,
period = .period,
f_call = f_call,
every = .every,
origin = .origin,
before = .before,
after = .after,
complete = .complete,
ptype = .ptype,
constrain = .constrain,
atomic = .atomic,
env = environment(),
type = type
)
}
|
library(ggplot2)
library(ggrepel)
library(grid)
library(gridExtra)
library(dplyr)
library(ggExtra)
library(lattice)
#install.packages("reshape2")
library(reshape2)
#histogram
lattice.options(axis.padding=list(factor=0.5))
df<-read.csv('birth-rate.csv')
head(df)
my.settings <- list(par.main.text = list(just = "left", col = 'dimgray',x = grid::unit(5, "mm")), axis.line = list(col='transparent'))
histogram(df$X2008,breaks=10, xlab = "Births per 1,000 population",ylab = "Percent of total(%)",border = 'white',par.settings=my.settings, main="DISTRIBUTION OF BIRTH RATES, 2008",col = c("orange"))
#boxplot
df1<-read.csv('crimeratesbystate-formatted.csv')
head(df1)
myvars <- c("state", "robbery", "burglary")
df1 <- df1[myvars]
head(df1)
df1<-melt(data = df1, id.vars = c("state"), measure.vars = c("burglary", "robbery"))
head(df1)
ggplot(df1, aes(x=variable, y=value, fill=variable)) +
geom_boxplot(outlier.size = 2.5)+
theme_classic()+
scale_fill_manual(values=c("gray", "orange"))+
ggtitle("\nBURGLARY AND ROBBERY DISTRIBUTIONS\n")+
ylab("Rates ( per 100,000 population)")+
theme_bw()+ removeGrid()+
theme(panel.border = element_blank(), title=element_text(color = 'dimgray'), axis.ticks.y = element_blank(), axis.ticks.x = element_blank(),axis.text.y = element_text(size = 10), axis.text.x = element_text(size = 11), axis.title.y=element_text(size=12, color = 'dimgray'))+
theme(axis.line = element_line(color = 'white'))+
theme(axis.title.x = element_blank())
#bulletplot
library(plotly)
library("readxl")
df4<-read_excel('sales_devices.xlsx')
head(df4)
df41<-df4[df4$devices == 'alarm clock', ]
head(df41)
df42<-df4[df4$devices == 'camera', ]
head(df42)
df43<-df4[df4$devices == 'printer', ]
head(df43)
t <- list(
family = "sans serif",
size = 20,
color = 'dimgray',
adj=0)
fig <- plot_ly()
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df43$actual_sales),
delta = list(reference = sum(df43$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.08, 0.29)),
title =list(text = "printer sales"),
gauge = list(
shape = "bullet",
axis = list(range = c(0, 500)),
threshold = list(
line= list(color = "black", width = 2),
thickness = 0.55,
value = sum(df43$expected_sales)),
steps = list(
list(range = c(0, sum(df43$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df43$actual_sales)*60*0.01, sum(df43$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
#radar chart
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df42$actual_sales),
delta = list(reference = sum(df42$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.4, 0.6)),
title = list(text = "camera sales"),
gauge = list(
shape = "bullet",
axis = list(range = list(NULL, 500)),
threshold = list(
line = list(color = "black", width= 2),
thickness = 0.55,
value = sum(df42$expected_sales)),
steps = list(
list(range = c(0, sum(df42$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df42$actual_sales)*60*0.01, sum(df42$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df41$actual_sales),
delta = list(reference = sum(df41$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.7, 0.9)),
title = list(text = "alarm clock sales"),
gauge = list(
shape = "bullet",
axis = list(range = list(NULL, 500)),
threshold = list(
line = list(color = "black", width = 2),
thickness = 0.55,
value = sum(df41$expected_sales)),
steps = list(
list(range = c(0, sum(df41$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df41$actual_sales)*60*0.01, sum(df41$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
fig<-fig %>%
layout(
title="\nACTUAL VS EXPECTED DEVICE SALES",
font = t
)
fig
# RADAR
#install.packages('fmsb')
library(fmsb)
df6<-read.csv('crimeratesbystate-formatted.csv')
head(df1)
myvars <- c( 'state','murder', 'robbery', 'aggravated_assault',
'burglary', 'larceny_theft', 'motor_vehicle_theft')
df6 <- df6[myvars]
df6<-df6 %>% filter(state == 'United States ')
df6
my<-c( 'murder', 'robbery', 'aggravated_assault',
'burglary', 'larceny_theft','motor_vehicle_theft')
df6 <- df6[my]
df6
df6 <- rbind(df6, '2' = c(0,0,0,0,0,0))
df6
df6 <- rbind(df6, '2' = c(2286.3,2286.3,2286.3,2286.3,2286.3, 2286.3))
df6
df6 <- rbind(df6, '2' = c(0,0,0,0,0))
df6 <- rbind(df6, '2' = c(5.6,140.7,291.1,726.7,2286.3,416.7))
df6
newdata <- df6[-c(1, 2), ]
names(newdata)[names(newdata) == "motor_vehicle_theft"] <- "motor vehicle theft"
names(newdata)[names(newdata) == "larceny_theft"] <- "larceny theft"
names(newdata)[names(newdata) == "aggravated_assault"] <- "aggravated asault"
head(newdata)
radarchart(newdata, pcol=rgb(0.2,0.5,0.5,0.9) , pfcol=rgb(0.2,0.5,0.5,0.5) ,
seg=5,axistype=1,cglcol="grey", caxislabels=c("0", "500", "1000", "1500","2000", ""),cglty=1,plwd = 2, axislabcol="grey", cglwd=0.5,
#custom labels
vlcex=0.8 )
mtext(side = 3, line = 2.5, at = 0, cex = 1.75, "CRIME IN THE UNITED STATES", font = 0.1, col = 'dimgray')
| /Casey_Natallia_histogram_boxplot_bulletchart_radarchart.R | no_license | natacasey/histogram_boxplot_bulletchart_radarchart | R | false | false | 5,521 | r | library(ggplot2)
library(ggrepel)
library(grid)
library(gridExtra)
library(dplyr)
library(ggExtra)
library(lattice)
#install.packages("reshape2")
library(reshape2)
#histogram
lattice.options(axis.padding=list(factor=0.5))
df<-read.csv('birth-rate.csv')
head(df)
my.settings <- list(par.main.text = list(just = "left", col = 'dimgray',x = grid::unit(5, "mm")), axis.line = list(col='transparent'))
histogram(df$X2008,breaks=10, xlab = "Births per 1,000 population",ylab = "Percent of total(%)",border = 'white',par.settings=my.settings, main="DISTRIBUTION OF BIRTH RATES, 2008",col = c("orange"))
#boxplot
df1<-read.csv('crimeratesbystate-formatted.csv')
head(df1)
myvars <- c("state", "robbery", "burglary")
df1 <- df1[myvars]
head(df1)
df1<-melt(data = df1, id.vars = c("state"), measure.vars = c("burglary", "robbery"))
head(df1)
ggplot(df1, aes(x=variable, y=value, fill=variable)) +
geom_boxplot(outlier.size = 2.5)+
theme_classic()+
scale_fill_manual(values=c("gray", "orange"))+
ggtitle("\nBURGLARY AND ROBBERY DISTRIBUTIONS\n")+
ylab("Rates ( per 100,000 population)")+
theme_bw()+ removeGrid()+
theme(panel.border = element_blank(), title=element_text(color = 'dimgray'), axis.ticks.y = element_blank(), axis.ticks.x = element_blank(),axis.text.y = element_text(size = 10), axis.text.x = element_text(size = 11), axis.title.y=element_text(size=12, color = 'dimgray'))+
theme(axis.line = element_line(color = 'white'))+
theme(axis.title.x = element_blank())
#bulletplot
library(plotly)
library("readxl")
df4<-read_excel('sales_devices.xlsx')
head(df4)
df41<-df4[df4$devices == 'alarm clock', ]
head(df41)
df42<-df4[df4$devices == 'camera', ]
head(df42)
df43<-df4[df4$devices == 'printer', ]
head(df43)
t <- list(
family = "sans serif",
size = 20,
color = 'dimgray',
adj=0)
fig <- plot_ly()
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df43$actual_sales),
delta = list(reference = sum(df43$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.08, 0.29)),
title =list(text = "printer sales"),
gauge = list(
shape = "bullet",
axis = list(range = c(0, 500)),
threshold = list(
line= list(color = "black", width = 2),
thickness = 0.55,
value = sum(df43$expected_sales)),
steps = list(
list(range = c(0, sum(df43$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df43$actual_sales)*60*0.01, sum(df43$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
#radar chart
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df42$actual_sales),
delta = list(reference = sum(df42$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.4, 0.6)),
title = list(text = "camera sales"),
gauge = list(
shape = "bullet",
axis = list(range = list(NULL, 500)),
threshold = list(
line = list(color = "black", width= 2),
thickness = 0.55,
value = sum(df42$expected_sales)),
steps = list(
list(range = c(0, sum(df42$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df42$actual_sales)*60*0.01, sum(df42$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
fig <- fig %>%
add_trace(
type = "indicator",
mode = "number+gauge+delta",
value = sum(df41$actual_sales),
delta = list(reference = sum(df41$expected_sales)),
domain = list(x = c(0.25, 1), y = c(0.7, 0.9)),
title = list(text = "alarm clock sales"),
gauge = list(
shape = "bullet",
axis = list(range = list(NULL, 500)),
threshold = list(
line = list(color = "black", width = 2),
thickness = 0.55,
value = sum(df41$expected_sales)),
steps = list(
list(range = c(0, sum(df41$actual_sales)*60*0.01), color = "gray"),
list(range = c(sum(df41$actual_sales)*60*0.01, sum(df41$actual_sales)*80*0.01), color = "lightgray")),
bar = list(color = "orange")))
fig<-fig %>%
layout(
title="\nACTUAL VS EXPECTED DEVICE SALES",
font = t
)
fig
# RADAR
#install.packages('fmsb')
library(fmsb)
df6<-read.csv('crimeratesbystate-formatted.csv')
head(df1)
myvars <- c( 'state','murder', 'robbery', 'aggravated_assault',
'burglary', 'larceny_theft', 'motor_vehicle_theft')
df6 <- df6[myvars]
df6<-df6 %>% filter(state == 'United States ')
df6
my<-c( 'murder', 'robbery', 'aggravated_assault',
'burglary', 'larceny_theft','motor_vehicle_theft')
df6 <- df6[my]
df6
df6 <- rbind(df6, '2' = c(0,0,0,0,0,0))
df6
df6 <- rbind(df6, '2' = c(2286.3,2286.3,2286.3,2286.3,2286.3, 2286.3))
df6
df6 <- rbind(df6, '2' = c(0,0,0,0,0))
df6 <- rbind(df6, '2' = c(5.6,140.7,291.1,726.7,2286.3,416.7))
df6
newdata <- df6[-c(1, 2), ]
names(newdata)[names(newdata) == "motor_vehicle_theft"] <- "motor vehicle theft"
names(newdata)[names(newdata) == "larceny_theft"] <- "larceny theft"
names(newdata)[names(newdata) == "aggravated_assault"] <- "aggravated asault"
head(newdata)
radarchart(newdata, pcol=rgb(0.2,0.5,0.5,0.9) , pfcol=rgb(0.2,0.5,0.5,0.5) ,
seg=5,axistype=1,cglcol="grey", caxislabels=c("0", "500", "1000", "1500","2000", ""),cglty=1,plwd = 2, axislabcol="grey", cglwd=0.5,
#custom labels
vlcex=0.8 )
mtext(side = 3, line = 2.5, at = 0, cex = 1.75, "CRIME IN THE UNITED STATES", font = 0.1, col = 'dimgray')
|
library(Rcpp)
library(dplyr)
#Tarea predecir Sepal.Length con las otras variables
# ESTIMATION OF A MEAN
data(iris)
Y <- iris$Sepal.Length
N <- dim(iris)[1]
X <- as.matrix(cbind(1,iris[,2:4]))
beta.hat<-solve(t(X)%*%X,t(X)%*%Y)
pred<-X%*%beta.hat
residuals<-Y-pred
#hist(residuals,breaks = 20)
SS<-sqrt(sum((Y-pred)^2)/(N-dim(iris)[2]+1))
cov.betas<-(SS^2)*solve(t(X)%*%X)
sqrt(diag(cov.betas))
# APPROACH 1) POINTWISE ESTIMATORS AND PIVOTAL QUANTITIES
lm <- lm(Sepal.Length ~ ., iris)
summary(lm)
# BAYESIAN APPROACH
# beta_j ~ N(0,10)
# Se puede jugar con los parámetros de la inicial aquí y en 1). Cuando es muy plana los coeficientes se parecen mucho a los de la regresión lineal
prior.beta <- function(x) dnorm(x, 0, .2)
prior.sd <- function(x) dgamma(x,5,100)
plot(prior.beta, col="darkblue", xlim=c(-50,50), lwd="2", main="Prior for mean", ylab="density")
plot(prior.sd, col="darkblue", xlim=c(-0,1), lwd="2", main="Prior for mean", ylab="density")
# 1) logposterior distribution (up to a constant)
cppFunction('
double objdens(NumericMatrix X, NumericVector y, NumericVector theta){
int i;
double lkh, logprior, yhat;
int m=X.nrow(), p=X.ncol();
NumericVector beta(m-1);
double sd;
for (i=0; i<p; i++){
beta[i] = theta[i];
}
sd = theta[p];
NumericVector aux(m);
// Compute loglikelihood
lkh=0;
for (int i=0; i<m; i++){
aux = X(i,_)*beta;
yhat = std::accumulate(aux.begin(), aux.end(), 0.0);
lkh += -.5*pow((y[i] - yhat)/sd,2) - log(sd);
}
// Compute logprior
logprior = 0.0;
for(int j=0; j<p; j++){
logprior += R::dnorm(beta[j], 0.0, 0.8, true); // Aquí la inicial!!
}
logprior += R::dgamma(sd, 5.0, 0.1, true);
// Log of target density
return lkh + logprior;
}')
# 2) Proposal: random walk in the same dimension as the number of parameters
cppFunction('
NumericVector proposal(NumericVector theta, NumericMatrix X){
int nparam = theta.size();
int m=X.nrow();
double jump = 0.25/sqrt(m); //checar paper
NumericVector newtheta(nparam);
for (int i=0; i<nparam; i++){
newtheta[i] = R::rnorm(theta[i], jump);
}
return newtheta;
}')
# 3) METROPOLIS
sourceCpp("MHBayesLinReg.cpp")
nsim <- 10000
init <- c(0,0,0,0.5)
proposal(init,X)
mh.samp <- MHBayesLinReg(nsim, init, objdens, proposal, X, Y)
estims <- mh.samp$theta
# SOME DIAGNOSTIC IMPORTANT STUFF
# Exploration graph:
library(calibrate)
pts <- seq(1,nrow(estims),by=5)
plot(estims[pts, ], type="l", asp=1)
###aceptacion
rejections <- mh.samp$rejections[-1]
trials <- rejections + 1
acc.rate<- 1-cumsum(rejections)/cumsum(trials)
plot(100*acc.rate,type = 'l',ylim = c(0,100), main = "Acceptance rate", ylab = "%")
### 2) AUTOCORRELATION
par(mfrow=c(2,3))
for(i in 1:ncol(estims)){
acf(estims[ , i],main=paste("theta",i))
}
# burnin and subsampling
burnin <- round(0.2*sim)
estims <- estims[-(1:burnin), ]
| /Tarea5/BayesianInference_regresion.R | no_license | montactuaria/CompuStat | R | false | false | 3,249 | r | library(Rcpp)
library(dplyr)
#Tarea predecir Sepal.Length con las otras variables
# ESTIMATION OF A MEAN
data(iris)
Y <- iris$Sepal.Length
N <- dim(iris)[1]
X <- as.matrix(cbind(1,iris[,2:4]))
beta.hat<-solve(t(X)%*%X,t(X)%*%Y)
pred<-X%*%beta.hat
residuals<-Y-pred
#hist(residuals,breaks = 20)
SS<-sqrt(sum((Y-pred)^2)/(N-dim(iris)[2]+1))
cov.betas<-(SS^2)*solve(t(X)%*%X)
sqrt(diag(cov.betas))
# APPROACH 1) POINTWISE ESTIMATORS AND PIVOTAL QUANTITIES
lm <- lm(Sepal.Length ~ ., iris)
summary(lm)
# BAYESIAN APPROACH
# beta_j ~ N(0,10)
# Se puede jugar con los parámetros de la inicial aquí y en 1). Cuando es muy plana los coeficientes se parecen mucho a los de la regresión lineal
prior.beta <- function(x) dnorm(x, 0, .2)
prior.sd <- function(x) dgamma(x,5,100)
plot(prior.beta, col="darkblue", xlim=c(-50,50), lwd="2", main="Prior for mean", ylab="density")
plot(prior.sd, col="darkblue", xlim=c(-0,1), lwd="2", main="Prior for mean", ylab="density")
# 1) logposterior distribution (up to a constant)
cppFunction('
double objdens(NumericMatrix X, NumericVector y, NumericVector theta){
int i;
double lkh, logprior, yhat;
int m=X.nrow(), p=X.ncol();
NumericVector beta(m-1);
double sd;
for (i=0; i<p; i++){
beta[i] = theta[i];
}
sd = theta[p];
NumericVector aux(m);
// Compute loglikelihood
lkh=0;
for (int i=0; i<m; i++){
aux = X(i,_)*beta;
yhat = std::accumulate(aux.begin(), aux.end(), 0.0);
lkh += -.5*pow((y[i] - yhat)/sd,2) - log(sd);
}
// Compute logprior
logprior = 0.0;
for(int j=0; j<p; j++){
logprior += R::dnorm(beta[j], 0.0, 0.8, true); // Aquí la inicial!!
}
logprior += R::dgamma(sd, 5.0, 0.1, true);
// Log of target density
return lkh + logprior;
}')
# 2) Proposal: random walk in the same dimension as the number of parameters
cppFunction('
NumericVector proposal(NumericVector theta, NumericMatrix X){
int nparam = theta.size();
int m=X.nrow();
double jump = 0.25/sqrt(m); //checar paper
NumericVector newtheta(nparam);
for (int i=0; i<nparam; i++){
newtheta[i] = R::rnorm(theta[i], jump);
}
return newtheta;
}')
# 3) METROPOLIS
sourceCpp("MHBayesLinReg.cpp")
nsim <- 10000
init <- c(0,0,0,0.5)
proposal(init,X)
mh.samp <- MHBayesLinReg(nsim, init, objdens, proposal, X, Y)
estims <- mh.samp$theta
# SOME DIAGNOSTIC IMPORTANT STUFF
# Exploration graph:
library(calibrate)
pts <- seq(1,nrow(estims),by=5)
plot(estims[pts, ], type="l", asp=1)
###aceptacion
rejections <- mh.samp$rejections[-1]
trials <- rejections + 1
acc.rate<- 1-cumsum(rejections)/cumsum(trials)
plot(100*acc.rate,type = 'l',ylim = c(0,100), main = "Acceptance rate", ylab = "%")
### 2) AUTOCORRELATION
par(mfrow=c(2,3))
for(i in 1:ncol(estims)){
acf(estims[ , i],main=paste("theta",i))
}
# burnin and subsampling
burnin <- round(0.2*sim)
estims <- estims[-(1:burnin), ]
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inspect_cor.R
\name{inspect_cor}
\alias{inspect_cor}
\title{Summarise and compare Pearson's correlation coefficients for numeric columns in one or two dataframes.}
\usage{
inspect_cor(df1, df2 = NULL, method = "pearson", with_col = NULL,
alpha = 0.05, show_plot = FALSE)
}
\arguments{
\item{df1}{A data frame.}
\item{df2}{An optional second data frame for comparing correlation
coefficients. Defaults to \code{NULL}.}
\item{method}{a character string indicating which type of correlation coefficient to use, one
of \code{"pearson"}, \code{"kendall"}, or \code{"spearman"}, which can be abbreviated.}
\item{with_col}{Character vector of column names to calculate correlations with all other numeric
features. The default \code{with_col = NULL} returns all pairs of correlations.}
\item{alpha}{Alpha level for correlation confidence intervals. Defaults to 0.05.}
\item{show_plot}{(Deprecated) Logical flag indicating whether a plot should be shown.
Superseded by the function \code{show_plot()} and will be dropped in a future version.}
}
\value{
A tibble summarising and comparing the correlations for each numeric column
in one or a pair of data frames.
}
\description{
Summarise and compare Pearson's correlation coefficients for numeric columns in one or two dataframes.
}
\details{
When \code{df2 = NULL}, a tibble containing correlation coefficients for \code{df1} is
returned:
\itemize{
\item \code{col_1}, \code{co1_2} character vectors containing names of numeric
columns in \code{df1}.
\item \code{corr} the calculated correlation coefficient.
\item \code{lower}, \code{upper} lower and upper values of the confidence interval
for the correlations.
\item \code{p_value} p-value associated with a test where the null hypothesis is that
the numeric pair have 0 correlation.
}
If \code{df1} has class \code{grouped_df}, then correlations will be calculated within the grouping levels
and the tibble returned will have an additional column corresponding to the group labels.
When both \code{df1} and \code{df2} are specified, the tibble returned contains
a comparison of the correlation coefficients across pairs of columns common to both
dataframes.
\itemize{
\item \code{col_1}, \code{co1_2} character vectors containing names of numeric columns
in either \code{df1} or \code{df2}.
\item \code{corr_1}, \code{corr_2} numeric columns containing correlation coefficients from
\code{df1} and \code{df2}, respectively.
\item \code{p_value} p-value associated with the null hypothesis that the two correlation
coefficients are the same. Small values indicate that the true correlation coefficients
differ between the two dataframes.
}
Note that confidence intervals for \code{kendall} and \code{spearman} assume a normal sampling
distribution for the Fisher z-transform of the correlation.
}
\examples{
# Load dplyr for starwars data & pipe
library(dplyr)
# Single dataframe summary
inspect_cor(starwars)
# Only show correlations with 'mass' column
inspect_cor(starwars, with_col = "mass")
# Paired dataframe summary
inspect_cor(starwars, starwars[1:10, ])
# NOT RUN - change in correlation over time
# library(dplyr)
# tech_grp <- tech \%>\%
# group_by(year) \%>\%
# inspect_cor()
# tech_grp \%>\% show_plot()
}
| /man/inspect_cor.Rd | no_license | wulixin/inspectdf | R | false | true | 3,377 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inspect_cor.R
\name{inspect_cor}
\alias{inspect_cor}
\title{Summarise and compare Pearson's correlation coefficients for numeric columns in one or two dataframes.}
\usage{
inspect_cor(df1, df2 = NULL, method = "pearson", with_col = NULL,
alpha = 0.05, show_plot = FALSE)
}
\arguments{
\item{df1}{A data frame.}
\item{df2}{An optional second data frame for comparing correlation
coefficients. Defaults to \code{NULL}.}
\item{method}{a character string indicating which type of correlation coefficient to use, one
of \code{"pearson"}, \code{"kendall"}, or \code{"spearman"}, which can be abbreviated.}
\item{with_col}{Character vector of column names to calculate correlations with all other numeric
features. The default \code{with_col = NULL} returns all pairs of correlations.}
\item{alpha}{Alpha level for correlation confidence intervals. Defaults to 0.05.}
\item{show_plot}{(Deprecated) Logical flag indicating whether a plot should be shown.
Superseded by the function \code{show_plot()} and will be dropped in a future version.}
}
\value{
A tibble summarising and comparing the correlations for each numeric column
in one or a pair of data frames.
}
\description{
Summarise and compare Pearson's correlation coefficients for numeric columns in one or two dataframes.
}
\details{
When \code{df2 = NULL}, a tibble containing correlation coefficients for \code{df1} is
returned:
\itemize{
\item \code{col_1}, \code{co1_2} character vectors containing names of numeric
columns in \code{df1}.
\item \code{corr} the calculated correlation coefficient.
\item \code{lower}, \code{upper} lower and upper values of the confidence interval
for the correlations.
\item \code{p_value} p-value associated with a test where the null hypothesis is that
the numeric pair have 0 correlation.
}
If \code{df1} has class \code{grouped_df}, then correlations will be calculated within the grouping levels
and the tibble returned will have an additional column corresponding to the group labels.
When both \code{df1} and \code{df2} are specified, the tibble returned contains
a comparison of the correlation coefficients across pairs of columns common to both
dataframes.
\itemize{
\item \code{col_1}, \code{co1_2} character vectors containing names of numeric columns
in either \code{df1} or \code{df2}.
\item \code{corr_1}, \code{corr_2} numeric columns containing correlation coefficients from
\code{df1} and \code{df2}, respectively.
\item \code{p_value} p-value associated with the null hypothesis that the two correlation
coefficients are the same. Small values indicate that the true correlation coefficients
differ between the two dataframes.
}
Note that confidence intervals for \code{kendall} and \code{spearman} assume a normal sampling
distribution for the Fisher z-transform of the correlation.
}
\examples{
# Load dplyr for starwars data & pipe
library(dplyr)
# Single dataframe summary
inspect_cor(starwars)
# Only show correlations with 'mass' column
inspect_cor(starwars, with_col = "mass")
# Paired dataframe summary
inspect_cor(starwars, starwars[1:10, ])
# NOT RUN - change in correlation over time
# library(dplyr)
# tech_grp <- tech \%>\%
# group_by(year) \%>\%
# inspect_cor()
# tech_grp \%>\% show_plot()
}
|
read_r24all <- function(ofile,sta=NULL) {
Nsta <- length(sta)
r24 <- scan(ofile,skip=11,quiet=TRUE,na.strings=c("****","*****","******"))
r24 <- matrix(r24,5,length(r24)/5)
r24 <- aperm(r24)
if(!is.null(sta)) {
r24all <- rep(0,Nsta)
r24 <- r24[order(r24[,1]),]
index <- diff(r24[,1])>0
index <- c(TRUE,index)
r24 <- r24[index,]
r24 <- r24[r24[,1] %in% sta,]
r24 <- matrix(r24,length(r24)/5,5)
r24all[sta %in% r24[,1]] <- r24[,5]
r24all[r24all<0] <- 0
} else {r24all <- r24}
r24all
} | /R/read_r24all.r | no_license | Quandony/RMicapsData | R | false | false | 524 | r | read_r24all <- function(ofile,sta=NULL) {
Nsta <- length(sta)
r24 <- scan(ofile,skip=11,quiet=TRUE,na.strings=c("****","*****","******"))
r24 <- matrix(r24,5,length(r24)/5)
r24 <- aperm(r24)
if(!is.null(sta)) {
r24all <- rep(0,Nsta)
r24 <- r24[order(r24[,1]),]
index <- diff(r24[,1])>0
index <- c(TRUE,index)
r24 <- r24[index,]
r24 <- r24[r24[,1] %in% sta,]
r24 <- matrix(r24,length(r24)/5,5)
r24all[sta %in% r24[,1]] <- r24[,5]
r24all[r24all<0] <- 0
} else {r24all <- r24}
r24all
} |
testlist <- list(G = numeric(0), Rn = numeric(0), atmp = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ra = numeric(0), relh = -1.72131968218895e+83, rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898424065867e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.57081998962836e+76, -1.18078903777423e-90, 1.86807199752012e+112, -5.58551357556946e+160, 2.00994342527714e-162, 1.81541609400943e-79, 7.89363005545926e+139, 2.3317908961407e-93, 2.16562581831091e+161))
result <- do.call(meteor:::ET0_PenmanMonteith,testlist)
str(result) | /meteor/inst/testfiles/ET0_PenmanMonteith/AFL_ET0_PenmanMonteith/ET0_PenmanMonteith_valgrind_files/1615839019-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 826 | r | testlist <- list(G = numeric(0), Rn = numeric(0), atmp = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ra = numeric(0), relh = -1.72131968218895e+83, rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898424065867e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.57081998962836e+76, -1.18078903777423e-90, 1.86807199752012e+112, -5.58551357556946e+160, 2.00994342527714e-162, 1.81541609400943e-79, 7.89363005545926e+139, 2.3317908961407e-93, 2.16562581831091e+161))
result <- do.call(meteor:::ET0_PenmanMonteith,testlist)
str(result) |
test_that("type of element translate in correct number of vertexes", {
expect_equal(getVertexFromTypeATLAS("102"),2)
expect_equal(getVertexFromTypeATLAS("203"),3)
expect_equal(getVertexFromTypeATLAS("204"),4)
expect_equal(getVertexFromTypeATLAS("304"),4)
expect_equal(getVertexFromTypeATLAS("308"),8)
})
| /tests/testthat/test-type_from_vertex.R | permissive | gabrielpreda/mesh2D | R | false | false | 325 | r | test_that("type of element translate in correct number of vertexes", {
expect_equal(getVertexFromTypeATLAS("102"),2)
expect_equal(getVertexFromTypeATLAS("203"),3)
expect_equal(getVertexFromTypeATLAS("204"),4)
expect_equal(getVertexFromTypeATLAS("304"),4)
expect_equal(getVertexFromTypeATLAS("308"),8)
})
|
\name{hist}
\alias{hist.gsaResult}
\title{
Null distribution histogram and statistic of the input set for enrichment analyses.
}
\description{
Plots the distribution of gene set statistic values obtained in different resampling settings of an enrichment analysis, and draws the statistic value of the input set as a vertical line.
}
\usage{
\method{hist}{gsaResult}(x,
signLevel = x$signLevel,
subset = NULL,
ask = FALSE,
addLegend = TRUE,
...)
}
\arguments{
\item{x}{
A result of a call to \code{geneSetAnalysis} (see also Details).
}
\item{signLevel}{
The significance level that should be applied for the plots. Default is the significance level used for the analysis in \code{x}.
}
\item{subset}{
Indices for the results that should be included in the diagram.
}
\item{ask}{
If set to true, the plot function will prompt for a user input for each new plot that is shown on an interactive device (see \code{\link[=par]{par("ask")}}).
}
\item{addLegend}{
If set to true (default), a \code{\link{legend}} is added to the plot.
}
\item{...}{
Other parameters which can be used for histograms (see \code{\link[graphics]{hist}}).
}
}
\details{
The function plots the distribution of gene set statistic values under the null hypothesis. It requires the
significance assessment step of the enrichment analysis configuration (parameter \code{significance} or \code{\link{gsAnalysis}}) to be a computer-intensive testing procedure that yields a distribution of gene set statistic p-values under the null hypothesis. Predefined configurations for which this plot works are \code{\link{analysis.gsea}}, \code{\link{analysis.averageCorrelation}} and \code{\link{analysis.averageTStatistic}}.
A histogram is plotted for the analysis in \code{x}. If \code{x} includes the analyses for several gene sets, one histogram is plotted for each of the gene sets.
The statistic value of the input set is depicted as a vertical line.
The most common graphical parameters can be supplied as vectors (one entry per analyzed gene set) to vary them between the different analyses. These parameters are: \code{main}, \code{xlab}, \code{ylab}.
}
\seealso{
\code{\link{geneSetAnalysis}}, \code{\link{predefinedAnalyses}}, \code{\link{gsAnalysis}}, \code{\link{evaluateGeneSetUncertainty}}, \code{\link{plot.uncertaintyResult}}
}
\examples{
# load data
require(GlobalAncova)
data(vantVeer)
data(phenodata)
data(pathways)
res <- geneSetAnalysis(
# global parameters
dat = vantVeer,
geneSets = pathways[3],
analysis = analysis.averageCorrelation(),
# additional parameters for analysis.averageCorrelation
labs = phenodata$metastases,
p = 1,
numSamples = 10)
# plot the histogram for the cell cycle control gene set
hist(res, main = names(pathways[3]))
}
%\keyword{}
| /man/hist.Rd | no_license | cran/GiANT | R | false | false | 2,770 | rd | \name{hist}
\alias{hist.gsaResult}
\title{
Null distribution histogram and statistic of the input set for enrichment analyses.
}
\description{
Plots the distribution of gene set statistic values obtained in different resampling settings of an enrichment analysis, and draws the statistic value of the input set as a vertical line.
}
\usage{
\method{hist}{gsaResult}(x,
signLevel = x$signLevel,
subset = NULL,
ask = FALSE,
addLegend = TRUE,
...)
}
\arguments{
\item{x}{
A result of a call to \code{geneSetAnalysis} (see also Details).
}
\item{signLevel}{
The significance level that should be applied for the plots. Default is the significance level used for the analysis in \code{x}.
}
\item{subset}{
Indices for the results that should be included in the diagram.
}
\item{ask}{
If set to true, the plot function will prompt for a user input for each new plot that is shown on an interactive device (see \code{\link[=par]{par("ask")}}).
}
\item{addLegend}{
If set to true (default), a \code{\link{legend}} is added to the plot.
}
\item{...}{
Other parameters which can be used for histograms (see \code{\link[graphics]{hist}}).
}
}
\details{
The function plots the distribution of gene set statistic values under the null hypothesis. It requires the
significance assessment step of the enrichment analysis configuration (parameter \code{significance} or \code{\link{gsAnalysis}}) to be a computer-intensive testing procedure that yields a distribution of gene set statistic p-values under the null hypothesis. Predefined configurations for which this plot works are \code{\link{analysis.gsea}}, \code{\link{analysis.averageCorrelation}} and \code{\link{analysis.averageTStatistic}}.
A histogram is plotted for the analysis in \code{x}. If \code{x} includes the analyses for several gene sets, one histogram is plotted for each of the gene sets.
The statistic value of the input set is depicted as a vertical line.
The most common graphical parameters can be supplied as vectors (one entry per analyzed gene set) to vary them between the different analyses. These parameters are: \code{main}, \code{xlab}, \code{ylab}.
}
\seealso{
\code{\link{geneSetAnalysis}}, \code{\link{predefinedAnalyses}}, \code{\link{gsAnalysis}}, \code{\link{evaluateGeneSetUncertainty}}, \code{\link{plot.uncertaintyResult}}
}
\examples{
# load data
require(GlobalAncova)
data(vantVeer)
data(phenodata)
data(pathways)
res <- geneSetAnalysis(
# global parameters
dat = vantVeer,
geneSets = pathways[3],
analysis = analysis.averageCorrelation(),
# additional parameters for analysis.averageCorrelation
labs = phenodata$metastases,
p = 1,
numSamples = 10)
# plot the histogram for the cell cycle control gene set
hist(res, main = names(pathways[3]))
}
%\keyword{}
|
library(car)
mydat<-Prestige
dim(mydat)
str(mydat)
summary(mydat)
head(mydat,5)##top 5 values
library(ggplot2)
result<-ggplot(data = mydat, mapping = aes(x = income, y = education)) +
geom_point()
##Average years of education is proportional to the income
ggplot(data = mydat, mapping = aes(x = type, y = women)) +
geom_point()
##ggplot(data = mydat, mapping = aes(x = education, y = women)) +
##geom_rug(sides = "tr")
##Type of job - Blue collar workers are higher compared to white collared workers.
ggplot(mydat) +
geom_bar(aes(type))
##Model building
##Linear model
##using rmse function
rmse_fun = function() { return(c(mean( (dtrain$pred - dtrain$income)^2,na.rm = TRUE) ^0.5,
mean( (dtest$pred - dtest$income)^2 ,na.rm = TRUE) ^0.5))
}
mydat$rowno <- 1:nrow(mydat)
dtrain<-subset(mydat,rowno <= nrow(mydat)*0.7)
dtest<-subset(mydat,rowno > nrow(mydat)*0.7)
model_lm <- lm(income ~ education, data=dtrain)
summary(model_lm)
dtrain$pred <- predict(model_lm, newdata = dtrain)
dtest$pred <- predict(model_lm, newdata = dtest)
rmse_fun()
cor(mydat$income,mydat$education) ##0.5 shows a moderate positive relationship
cor(dtest$income,dtest$education)##test data
cor(dtrain$income,dtrain$education)##train data
##visualising the train and test model fit lines
plot(dtrain$income ~ dtrain$education,data = dtrain)+
abline(model_lm)
plot(dtest$income ~ dtest$education,data = dtest)+
abline(model_lm)
| /Prestige_LR.R | no_license | RajashriN/PrestigeLinearRegression | R | false | false | 1,558 | r | library(car)
mydat<-Prestige
dim(mydat)
str(mydat)
summary(mydat)
head(mydat,5)##top 5 values
library(ggplot2)
result<-ggplot(data = mydat, mapping = aes(x = income, y = education)) +
geom_point()
##Average years of education is proportional to the income
ggplot(data = mydat, mapping = aes(x = type, y = women)) +
geom_point()
##ggplot(data = mydat, mapping = aes(x = education, y = women)) +
##geom_rug(sides = "tr")
##Type of job - Blue collar workers are higher compared to white collared workers.
ggplot(mydat) +
geom_bar(aes(type))
##Model building
##Linear model
##using rmse function
rmse_fun = function() { return(c(mean( (dtrain$pred - dtrain$income)^2,na.rm = TRUE) ^0.5,
mean( (dtest$pred - dtest$income)^2 ,na.rm = TRUE) ^0.5))
}
mydat$rowno <- 1:nrow(mydat)
dtrain<-subset(mydat,rowno <= nrow(mydat)*0.7)
dtest<-subset(mydat,rowno > nrow(mydat)*0.7)
model_lm <- lm(income ~ education, data=dtrain)
summary(model_lm)
dtrain$pred <- predict(model_lm, newdata = dtrain)
dtest$pred <- predict(model_lm, newdata = dtest)
rmse_fun()
cor(mydat$income,mydat$education) ##0.5 shows a moderate positive relationship
cor(dtest$income,dtest$education)##test data
cor(dtrain$income,dtrain$education)##train data
##visualising the train and test model fit lines
plot(dtrain$income ~ dtrain$education,data = dtrain)+
abline(model_lm)
plot(dtest$income ~ dtest$education,data = dtest)+
abline(model_lm)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/body.R
\name{mj_divider}
\alias{mj_divider}
\title{Divider}
\usage{
mj_divider(...)
}
\arguments{
\item{...}{Unnamed items that comprise this list of MJML tags.}
}
\description{
Displays a horizontal divider that can be customized like a HTML border.
}
\examples{
mj_ml(
mj_body(
mj_text("Some text"),
mj_divider(),
mj_text("The rest of the body")
)
)
}
\seealso{
\href{official documentation}{https://mjml.io/documentation/#mjml-divider}
}
| /man/mj_divider.Rd | no_license | jimsforks/mjml | R | false | true | 536 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/body.R
\name{mj_divider}
\alias{mj_divider}
\title{Divider}
\usage{
mj_divider(...)
}
\arguments{
\item{...}{Unnamed items that comprise this list of MJML tags.}
}
\description{
Displays a horizontal divider that can be customized like a HTML border.
}
\examples{
mj_ml(
mj_body(
mj_text("Some text"),
mj_divider(),
mj_text("The rest of the body")
)
)
}
\seealso{
\href{official documentation}{https://mjml.io/documentation/#mjml-divider}
}
|
#
# ddi.example()
#
# Return example of DDI-list-structure.
#
ddi.example = function() {
ddi = list(
doc_dscr = list(),
stdy_dscr = list(),
file_dscr = list(
file_plong = list(
name = "file1",
var_dscr = list(
var_age = list(
name = "age",
labl = "Age in 2011",
nature = "interval",
valid = c(12, 45, 22, 76, 52),
invalid = c(NA, NA, NA, NA, NA),
sumStat = list(
min = 12,
max = 76,
valid = 5,
invalid = 0
)
),
var_sex = list(
name = "sex",
labl = "Gender",
nature = "categorial",
valid = c(1, NA, 0, 1, 0),
invalid = c(NA, 1, NA, NA, NA),
sumStat = list(
min = 0,
max = 1,
valid = 4,
invalid = 1
),
catgry = list(
cat_1 = list(
catValu = 0,
labl = "male",
valid = TRUE,
catStat = list(
type = "freq",
value = 2
)
),
cat_2 = list(
catValu = 1,
labl = "female",
valid = TRUE,
catStat = list(
type = "freq",
value = 2
)
)
)
)
)
),
file_hlong = list()
)
)
class(ddi) = "ddi"
return(ddi)
}
#
# print.ddi(ddi)
#
# DDI-object-specific function for printing a DDI-object.
#
print.ddi = function(ddi) {
result =
lapply(
ddi$file_dscr,
function(x) {
x$name
} )
return(result)
}
#
# stata2ddi(filename)
#
# Returns file-element for given filename.
#
stata2ddi = function(filename) {
library("foreign")
# Read Stata file
stata_file =
read.dta(
filename,
convert.factors=FALSE,
convert.dates=FALSE,
missing.type=TRUE )
# Generate scaleton for file-object.
file = list()
class(file) = "ddi.file"
file$name = filename
file$var_dscr = list()
# Internal function: extract_metadata(stata_var)
extract_metadata = function(stata_var) {
var = list()
var$raw = stata_var
return(var)
}
file$var_dscr = lapply(stata_file, extract_metadata)
return(file)
}
| /examples/ddi.R | no_license | mhebing/r2ddi | R | false | false | 2,493 | r | #
# ddi.example()
#
# Return example of DDI-list-structure.
#
ddi.example = function() {
ddi = list(
doc_dscr = list(),
stdy_dscr = list(),
file_dscr = list(
file_plong = list(
name = "file1",
var_dscr = list(
var_age = list(
name = "age",
labl = "Age in 2011",
nature = "interval",
valid = c(12, 45, 22, 76, 52),
invalid = c(NA, NA, NA, NA, NA),
sumStat = list(
min = 12,
max = 76,
valid = 5,
invalid = 0
)
),
var_sex = list(
name = "sex",
labl = "Gender",
nature = "categorial",
valid = c(1, NA, 0, 1, 0),
invalid = c(NA, 1, NA, NA, NA),
sumStat = list(
min = 0,
max = 1,
valid = 4,
invalid = 1
),
catgry = list(
cat_1 = list(
catValu = 0,
labl = "male",
valid = TRUE,
catStat = list(
type = "freq",
value = 2
)
),
cat_2 = list(
catValu = 1,
labl = "female",
valid = TRUE,
catStat = list(
type = "freq",
value = 2
)
)
)
)
)
),
file_hlong = list()
)
)
class(ddi) = "ddi"
return(ddi)
}
#
# print.ddi(ddi)
#
# DDI-object-specific function for printing a DDI-object.
#
print.ddi = function(ddi) {
result =
lapply(
ddi$file_dscr,
function(x) {
x$name
} )
return(result)
}
#
# stata2ddi(filename)
#
# Returns file-element for given filename.
#
stata2ddi = function(filename) {
library("foreign")
# Read Stata file
stata_file =
read.dta(
filename,
convert.factors=FALSE,
convert.dates=FALSE,
missing.type=TRUE )
# Generate scaleton for file-object.
file = list()
class(file) = "ddi.file"
file$name = filename
file$var_dscr = list()
# Internal function: extract_metadata(stata_var)
extract_metadata = function(stata_var) {
var = list()
var$raw = stata_var
return(var)
}
file$var_dscr = lapply(stata_file, extract_metadata)
return(file)
}
|
#' Add weather data to leaflet map.
#'
#' @param map \code{\link[leaflet]{leaflet}} map object
#' @param data owm data
#' @param lng numeric vector of longitudes
#' (if \code{NULL} it will be taken from \code{data})
#' @param lat numeric vector of latitudes
#' (if \code{NULL} it will be taken from \code{data})
#' @param icon vector of owm icon names
#' (usually included in weather column of owm data)
#' @param template template in the form of \cr
#' \code{"<b>{{name}}</b>"} \cr
#' where variable names in brackets correspond to
#' column names of \code{data} (see also \code{\link{render}})
#' @param popup vector containing (HTML) content for popups,
#' skipped in case parameter \code{template} is given
#' @param ... see \code{\link[leaflet]{addMarkers}}
#'
#' @return updated map object
#' @export
#'
#' @examples \dontrun{
#' owm_data <- find_city("Malaga", units = "metric")$list %>% tidy_up_()
#' map <- leaflet() %>% addTiles() %>%
#' add_weather(owm_data,
#' template = "<b>{{name}}</b>, {{main_temp}}°C",
#' icon = owm_data$weather_icon)
#' }
add_weather <- function(map, data, lng = NULL, lat = NULL, icon = NULL, template = NULL, popup = NULL, ...){
if(is.null(lng) | is.null(lat)){
lng <- data[[grep("lon", names(data))]]
lat <- data[[grep("lat", names(data))]]
}
if(!is.null(icon)){
icon %<>% get_icon_url() %>%
leaflet::icons()
}
if(!is.null(template)){
popup <- template %$$% data
}
leaflet::addMarkers(map, lng, lat, data = data, icon = icon, popup = popup, ...)
}
| /R/leaflet.R | no_license | rubedawg/owmr | R | false | false | 1,575 | r | #' Add weather data to leaflet map.
#'
#' @param map \code{\link[leaflet]{leaflet}} map object
#' @param data owm data
#' @param lng numeric vector of longitudes
#' (if \code{NULL} it will be taken from \code{data})
#' @param lat numeric vector of latitudes
#' (if \code{NULL} it will be taken from \code{data})
#' @param icon vector of owm icon names
#' (usually included in weather column of owm data)
#' @param template template in the form of \cr
#' \code{"<b>{{name}}</b>"} \cr
#' where variable names in brackets correspond to
#' column names of \code{data} (see also \code{\link{render}})
#' @param popup vector containing (HTML) content for popups,
#' skipped in case parameter \code{template} is given
#' @param ... see \code{\link[leaflet]{addMarkers}}
#'
#' @return updated map object
#' @export
#'
#' @examples \dontrun{
#' owm_data <- find_city("Malaga", units = "metric")$list %>% tidy_up_()
#' map <- leaflet() %>% addTiles() %>%
#' add_weather(owm_data,
#' template = "<b>{{name}}</b>, {{main_temp}}°C",
#' icon = owm_data$weather_icon)
#' }
add_weather <- function(map, data, lng = NULL, lat = NULL, icon = NULL, template = NULL, popup = NULL, ...){
if(is.null(lng) | is.null(lat)){
lng <- data[[grep("lon", names(data))]]
lat <- data[[grep("lat", names(data))]]
}
if(!is.null(icon)){
icon %<>% get_icon_url() %>%
leaflet::icons()
}
if(!is.null(template)){
popup <- template %$$% data
}
leaflet::addMarkers(map, lng, lat, data = data, icon = icon, popup = popup, ...)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset_documentation.R
\name{GeneDropEx_ped}
\alias{GeneDropEx_ped}
\title{Dataset GeneDropEx_ped}
\description{
This dataframe contains a simulated pedigree for use in gene-dropping
}
\details{
This is simulated pedigree data for a population that
experiences density dependant selection and has modest migration.
It is loosely based on the dynamics seen in the data collected by the
Soay Sheep Project.
There is a small amount of missing data.
It spans 35 years and includes 10000 individuals. Migrants do not have a known cohort, but
the time that they arrived in the population can be found in the 'Arrived' column.
Please feel free to play around with this data and let me know how realistic, or otherwise,
you think it is.
}
| /man/GeneDropEx_ped.Rd | no_license | simplydch/GeneDrop | R | false | true | 815 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset_documentation.R
\name{GeneDropEx_ped}
\alias{GeneDropEx_ped}
\title{Dataset GeneDropEx_ped}
\description{
This dataframe contains a simulated pedigree for use in gene-dropping
}
\details{
This is simulated pedigree data for a population that
experiences density dependant selection and has modest migration.
It is loosely based on the dynamics seen in the data collected by the
Soay Sheep Project.
There is a small amount of missing data.
It spans 35 years and includes 10000 individuals. Migrants do not have a known cohort, but
the time that they arrived in the population can be found in the 'Arrived' column.
Please feel free to play around with this data and let me know how realistic, or otherwise,
you think it is.
}
|
#' Extract rank probability matrix
#'
#' A rank probability matrix is a matrix with all entries between 0 and 1. The
#' rows represent treatments while the columns represent ranks.
#'
#' @details
#' This function creats a rank probability matrix by reading information from a
#' model object. For example if \code{nma} is the result from a call to
#' \code{nma.ab.bin} from the \code{pcnetmeta} package, calling
#' \code{rank_probabilities(nma)} returns a matrix.
#'
#' @param x A model object holding rank probabilities.
#' @param ... currently not used
#'
#'
#' @family extractors
#'
#' @return A \code{matrix}
#' @export
rank_probabilities <- function(x,...){
UseMethod("rank_probabilities")
}
#' @rdname rank_probabilities
#'
#' @export
rank_probabilities.nma.ab <- function(x,...){
rp <- x$TrtRankProb
array(as.numeric(rp)
, dim = c(nrow(rp),ncol(rp))
, dimnames = list(treatment=rownames(rp), rank=seq_len(ncol(rp)))
)
}
#' Extract scra values
#'
#' @param x a \code{matrix} or a model object holding rank probabilities
#' @param ... currently not used
#'
#' @family extractors
#'
#' @export
sucra_values <- function(x,...){
UseMethod("sucra_values")
}
#' @rdname sucra_values
#' @export
sucra_values.matrix <- function(x,...){
t(apply(x,1,cumsum))
}
#' @rdname sucra_values
#' @export
sucra_values.default <- function(x,...){
x <- rank_probabilities(x)
sucra_values.matrix(x)
}
| /pkg/R/rankprobability.R | no_license | markvanderloo/metaplot | R | false | false | 1,428 | r |
#' Extract rank probability matrix
#'
#' A rank probability matrix is a matrix with all entries between 0 and 1. The
#' rows represent treatments while the columns represent ranks.
#'
#' @details
#' This function creats a rank probability matrix by reading information from a
#' model object. For example if \code{nma} is the result from a call to
#' \code{nma.ab.bin} from the \code{pcnetmeta} package, calling
#' \code{rank_probabilities(nma)} returns a matrix.
#'
#' @param x A model object holding rank probabilities.
#' @param ... currently not used
#'
#'
#' @family extractors
#'
#' @return A \code{matrix}
#' @export
rank_probabilities <- function(x,...){
UseMethod("rank_probabilities")
}
#' @rdname rank_probabilities
#'
#' @export
rank_probabilities.nma.ab <- function(x,...){
rp <- x$TrtRankProb
array(as.numeric(rp)
, dim = c(nrow(rp),ncol(rp))
, dimnames = list(treatment=rownames(rp), rank=seq_len(ncol(rp)))
)
}
#' Extract scra values
#'
#' @param x a \code{matrix} or a model object holding rank probabilities
#' @param ... currently not used
#'
#' @family extractors
#'
#' @export
sucra_values <- function(x,...){
UseMethod("sucra_values")
}
#' @rdname sucra_values
#' @export
sucra_values.matrix <- function(x,...){
t(apply(x,1,cumsum))
}
#' @rdname sucra_values
#' @export
sucra_values.default <- function(x,...){
x <- rank_probabilities(x)
sucra_values.matrix(x)
}
|
attach_trace_info <- function(ca, dc) {
if (missing(dc)) {
if (missing(ca)) {
abort("either 'ca' or 'dc' required")
}
dc_t <- compose_cells_raw(ca, trace_it_back = TRUE, silent = TRUE)
dc <- dc_t
} else {
if (!any(stringr::str_detect(colnames(dc), "cellAddress_"))) {
if (!missing(ca)) {
dc_t <- compose_cells_raw(ca, trace_it_back = TRUE, silent = TRUE)
dc_t <- dc_t[c("row", "col", colnames(dc_t)[stringr::str_detect(colnames(dc_t), "cellAddress_")])]
dc_t$chk_this <- "ok"
dc <- dc %>% left_join(dc_t, by = c("row", "col"))
if (any(is.na(dc$chk_this))) {
abort("at least one row could not be mapped properly")
}
} else {
abort("supplied composition does not contain trace information and 'ca' is not given")
}
}
}
dc
}
| /R/attach_trace_info.R | permissive | dondealban/tidycells | R | false | false | 846 | r |
attach_trace_info <- function(ca, dc) {
if (missing(dc)) {
if (missing(ca)) {
abort("either 'ca' or 'dc' required")
}
dc_t <- compose_cells_raw(ca, trace_it_back = TRUE, silent = TRUE)
dc <- dc_t
} else {
if (!any(stringr::str_detect(colnames(dc), "cellAddress_"))) {
if (!missing(ca)) {
dc_t <- compose_cells_raw(ca, trace_it_back = TRUE, silent = TRUE)
dc_t <- dc_t[c("row", "col", colnames(dc_t)[stringr::str_detect(colnames(dc_t), "cellAddress_")])]
dc_t$chk_this <- "ok"
dc <- dc %>% left_join(dc_t, by = c("row", "col"))
if (any(is.na(dc$chk_this))) {
abort("at least one row could not be mapped properly")
}
} else {
abort("supplied composition does not contain trace information and 'ca' is not given")
}
}
}
dc
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PerMallows.R
\name{read.perms}
\alias{read.perms}
\title{Read a text file with a collection of permtuations}
\usage{
read.perms(path)
}
\arguments{
\item{path}{string with a path}
}
\value{
A collection of permutations in matrix form
}
\description{
This function reads the text file in the specified path and
checks if each row is a proper permutation
}
\examples{
path = system.file("test.txt", package="PerMallows")
sample = read.perms(path)
}
| /fuzzedpackages/PerMallows/man/read.perms.Rd | no_license | akhikolla/testpackages | R | false | true | 526 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PerMallows.R
\name{read.perms}
\alias{read.perms}
\title{Read a text file with a collection of permtuations}
\usage{
read.perms(path)
}
\arguments{
\item{path}{string with a path}
}
\value{
A collection of permutations in matrix form
}
\description{
This function reads the text file in the specified path and
checks if each row is a proper permutation
}
\examples{
path = system.file("test.txt", package="PerMallows")
sample = read.perms(path)
}
|
# titanic is avaliable in your workspace
# 1 - Check the structure of titanic
str(titanic)
# 2 - Use ggplot() for the first instruction
ggplot(titanic, aes(x = factor(Pclass), fill = factor(Sex))) +
geom_bar(position = "dodge")
# 3 - Plot 2, add facet_grid() layer
ggplot(titanic, aes(x = factor(Pclass), fill = factor(Sex))) +
geom_bar(position = "dodge")
# 3 - Plot 2, add facet_grid() layer
ggplot(titanic, aes(x = Pclass, fill = Sex)) +
geom_bar(position = "dodge") +
facet_grid( . ~ Survived)
# 4 - Define an object for position jitterdodge, to use below
posn.jd <- position_jitterdodge(0.5, 0, 0.6)
# 5 - Plot 3, but use the position object from instruction 4
ggplot(titanic, aes(x = factor(Pclass), y =Age, col = factor(Sex))) +
geom_point(size = 3, alpha=0.5,position = posn.jd ) +
facet_grid( . ~ Survived)
| /R/Titanic.R | no_license | venkatkasarla/SB_DataWrangling | R | false | false | 839 | r | # titanic is avaliable in your workspace
# 1 - Check the structure of titanic
str(titanic)
# 2 - Use ggplot() for the first instruction
ggplot(titanic, aes(x = factor(Pclass), fill = factor(Sex))) +
geom_bar(position = "dodge")
# 3 - Plot 2, add facet_grid() layer
ggplot(titanic, aes(x = factor(Pclass), fill = factor(Sex))) +
geom_bar(position = "dodge")
# 3 - Plot 2, add facet_grid() layer
ggplot(titanic, aes(x = Pclass, fill = Sex)) +
geom_bar(position = "dodge") +
facet_grid( . ~ Survived)
# 4 - Define an object for position jitterdodge, to use below
posn.jd <- position_jitterdodge(0.5, 0, 0.6)
# 5 - Plot 3, but use the position object from instruction 4
ggplot(titanic, aes(x = factor(Pclass), y =Age, col = factor(Sex))) +
geom_point(size = 3, alpha=0.5,position = posn.jd ) +
facet_grid( . ~ Survived)
|
library(faraway)
data(gala)
gala <- gala[,-2]
modl <- lm(Species ~ . , gala)
plot(predict(modl),residuals(modl),xlab="Fitted",ylab="
Residuals")
modt <- lm(sqrt(Species) ~ . , gala)
plot(predict(modt),residuals(modt),xlab="Fitted",ylab="
Residuals")
summary(modt)
modp <- glm(Species ~ .,family=poisson, gala)
summary(modp)
halfnorm(residuals(modp))
plot(log(fitted(modp)),log((gala$Speciesfitted(modp))^2), xlab=expression(hat(mu)),ylab=expression((yhat(mu))^2))
abline(0,1)
(dp <-
sum(residuals(modp,type="pearson")^2)/modp$df.res)
summary (modp, dispersion=dp)
drop1(modp,test="F")
###########
options(contrasts=c("contr.treatment","contr.poly"))
#### Logistic regression
r=c(10,17,12,7,23,22,29,29,23)
n=c(31,30,31,27,26,30,31,30,30)
logconc=c(2.68,2.76,2.82,2.90,3.02,3.04,3.13,3.20,3.21)
counts=cbind(r,n-r)
result=glm(counts~logconc,family=binomial("logit"))
summary(result,correlation=TRUE,symbolic.cor = TRUE)
result$coefficients
#### plot residuals vs. linear predictor
plot(residuals(result, type="pearson"),result$linear.predictors)
#### plot logconc vs. empirical logits
emplogit=log((r+0.5)/(n-r+0.5))
plot(logconc,emplogit)
#### adjusting for overdispersion
#### This should give you the same model but with adjusted covariance
#### matirix, that is SE for your beta's and also changed z-values.
#### First estimate the dispersion parameter based on the MAXIMAL model;
#### in our example this is simple since we have only one model
#### X^2/df=4.08
#### Notice that this does not adjust overall fit statistics
summary(result, dispersion=4.08,correlation=TRUE,symbolic.cor = TRUE)
#### Notice you can also use new package DISPMOD
#### gives a bit different result because it uses G^2/df
#### It adjusts the overall fit statistis too
#install.packages("dispmod")
library(dispmod)
glm.binomial.disp(result)
#### For other diagonostic plots, see donner.R
#### Here is another way to get regression type plots
library()
#### This gives a series of plots such as;
#### residuals vs. fitted values
#### Q-Q plots
#### levarage, etc...
plot.lm(result)
#### The following is a function adapted from http//www.math.mcmaster.capeters4f03s4f03_0607index.html
#### roc.plot() will plot the ROC curve given two vectors of scores,
#### the first for the treatment group (y==1) and the second for the control group (y==0).
roc.plot <-
function (sd, sdc, newplot = TRUE, ...)
{
sall <- sort(c(sd, sdc))
sens <- 0
specc <- 0
for (i in length(sall):1) {
sens <- c(sens, mean(sd >= sall[i], na.rm = T))
specc <- c(specc, mean(sdc >= sall[i], na.rm = T))
}
if (newplot) {
plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = "l",
xlab = "1-specificity", ylab = "sensitivity", ...)
abline(0, 1)
}
else lines(specc, sens, ...)
npoints <- length(sens)
area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] -
specc[-npoints]))
lift <- (sens - specc)[-1]
cutoff <- sall[lift == max(lift)][1]
sensopt <- sens[-1][lift == max(lift)][1]
specopt <- 1 - specc[-1][lift == max(lift)][1]
list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt)
}
#### Let us draw the ROC plot
roc.plot(r,n-r)
######
data(dicentric)
round(xtabs(ca/cells ~ doseamt+doserate,
dicentric),2)
with(dicentric,interaction.plot(doseamt,doserate,ca/cells))
interaction.plot(dicentric$doseamt,dicentric$doserate,dicentric$ca/dicentric$cells)
lmod <- lm(ca/cells ~ log(doserate)*factor(doseamt),
dicentric)
summary(lmod)$adj
plot(residuals(lmod) ~
fitted(lmod),xlab="Fitted",ylab="Residuals")
abline(h=0)
dicentric$dosef <- factor(dicentric$doseamt)
pmod <- glm(ca ~ log(cells)+log(doserate)*dosef,
family=poisson,dicentric)
summary(pmod)
rmod <- glm(ca ~ offset
(log(cells))+log(doserate)*dosef,
family=poisson,dicentric)
summary(rmod)
data(solder)
modp <- glm(skips ~ . , family=poisson, data=solder)
deviance(modp)
summary(modp)
df.residual(modp)
modp2 <- glm(skips ~ Opening +Solder + Mask +
PadType + Panel ,family=poisson, data=solder)
deviance(modp2)
pchisq(deviance(modp2),df.residual(modp2),lower=FALSE)
library(MASS)
modn <- glm(skips ~ . , negative.binomial(1),solder)
modn
modn <- glm.nb(skips ~ .,solder)
summary (modn)
| /621 - Data Mining/Logistic_regression_models/practice/count_regression_practice.R | no_license | bvshyam/cuny_data_science_repo | R | false | false | 4,446 | r | library(faraway)
data(gala)
gala <- gala[,-2]
modl <- lm(Species ~ . , gala)
plot(predict(modl),residuals(modl),xlab="Fitted",ylab="
Residuals")
modt <- lm(sqrt(Species) ~ . , gala)
plot(predict(modt),residuals(modt),xlab="Fitted",ylab="
Residuals")
summary(modt)
modp <- glm(Species ~ .,family=poisson, gala)
summary(modp)
halfnorm(residuals(modp))
plot(log(fitted(modp)),log((gala$Speciesfitted(modp))^2), xlab=expression(hat(mu)),ylab=expression((yhat(mu))^2))
abline(0,1)
(dp <-
sum(residuals(modp,type="pearson")^2)/modp$df.res)
summary (modp, dispersion=dp)
drop1(modp,test="F")
###########
options(contrasts=c("contr.treatment","contr.poly"))
#### Logistic regression
r=c(10,17,12,7,23,22,29,29,23)
n=c(31,30,31,27,26,30,31,30,30)
logconc=c(2.68,2.76,2.82,2.90,3.02,3.04,3.13,3.20,3.21)
counts=cbind(r,n-r)
result=glm(counts~logconc,family=binomial("logit"))
summary(result,correlation=TRUE,symbolic.cor = TRUE)
result$coefficients
#### plot residuals vs. linear predictor
plot(residuals(result, type="pearson"),result$linear.predictors)
#### plot logconc vs. empirical logits
emplogit=log((r+0.5)/(n-r+0.5))
plot(logconc,emplogit)
#### adjusting for overdispersion
#### This should give you the same model but with adjusted covariance
#### matirix, that is SE for your beta's and also changed z-values.
#### First estimate the dispersion parameter based on the MAXIMAL model;
#### in our example this is simple since we have only one model
#### X^2/df=4.08
#### Notice that this does not adjust overall fit statistics
summary(result, dispersion=4.08,correlation=TRUE,symbolic.cor = TRUE)
#### Notice you can also use new package DISPMOD
#### gives a bit different result because it uses G^2/df
#### It adjusts the overall fit statistis too
#install.packages("dispmod")
library(dispmod)
glm.binomial.disp(result)
#### For other diagonostic plots, see donner.R
#### Here is another way to get regression type plots
library()
#### This gives a series of plots such as;
#### residuals vs. fitted values
#### Q-Q plots
#### levarage, etc...
plot.lm(result)
#### The following is a function adapted from http//www.math.mcmaster.capeters4f03s4f03_0607index.html
#### roc.plot() will plot the ROC curve given two vectors of scores,
#### the first for the treatment group (y==1) and the second for the control group (y==0).
roc.plot <-
function (sd, sdc, newplot = TRUE, ...)
{
sall <- sort(c(sd, sdc))
sens <- 0
specc <- 0
for (i in length(sall):1) {
sens <- c(sens, mean(sd >= sall[i], na.rm = T))
specc <- c(specc, mean(sdc >= sall[i], na.rm = T))
}
if (newplot) {
plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = "l",
xlab = "1-specificity", ylab = "sensitivity", ...)
abline(0, 1)
}
else lines(specc, sens, ...)
npoints <- length(sens)
area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] -
specc[-npoints]))
lift <- (sens - specc)[-1]
cutoff <- sall[lift == max(lift)][1]
sensopt <- sens[-1][lift == max(lift)][1]
specopt <- 1 - specc[-1][lift == max(lift)][1]
list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt)
}
#### Let us draw the ROC plot
roc.plot(r,n-r)
######
data(dicentric)
round(xtabs(ca/cells ~ doseamt+doserate,
dicentric),2)
with(dicentric,interaction.plot(doseamt,doserate,ca/cells))
interaction.plot(dicentric$doseamt,dicentric$doserate,dicentric$ca/dicentric$cells)
lmod <- lm(ca/cells ~ log(doserate)*factor(doseamt),
dicentric)
summary(lmod)$adj
plot(residuals(lmod) ~
fitted(lmod),xlab="Fitted",ylab="Residuals")
abline(h=0)
dicentric$dosef <- factor(dicentric$doseamt)
pmod <- glm(ca ~ log(cells)+log(doserate)*dosef,
family=poisson,dicentric)
summary(pmod)
rmod <- glm(ca ~ offset
(log(cells))+log(doserate)*dosef,
family=poisson,dicentric)
summary(rmod)
data(solder)
modp <- glm(skips ~ . , family=poisson, data=solder)
deviance(modp)
summary(modp)
df.residual(modp)
modp2 <- glm(skips ~ Opening +Solder + Mask +
PadType + Panel ,family=poisson, data=solder)
deviance(modp2)
pchisq(deviance(modp2),df.residual(modp2),lower=FALSE)
library(MASS)
modn <- glm(skips ~ . , negative.binomial(1),solder)
modn
modn <- glm.nb(skips ~ .,solder)
summary (modn)
|
### Required packages ###
library(ggplot2); library(dplyr); library(tidyr);library(haven);library(readstata13);library(foreign);library(mice);library(MatchIt);library(stringdist);library(RecordLinkage);library(modelr)
### Parameters ###
#f_base10 = 0.95012 # baseline 10-year survival for females
#m_base10 = 0.88936 # baseline 10-year survival for males
f_rf <- list(log_age = 2.32888, log_hdl = -0.70833,log_totchol = 1.20904,log_SBP = 2.82263,Smoking = 0.52873,Diabetes = 0.69154) # log of hazard ratio for each risk factor for females
m_rf <- list(log_age = 3.06117, log_hdl = -0.93263,log_totchol = 1.12370,log_SBP = 1.99881,Smoking = 0.65451,Diabetes = 0.57367) # log of hazard ratio for each risk factor for males
m_log_age = 3.06117; m_log_hdl = -0.93263;m_log_totchol = 1.12370;m_log_SBP = 1.99881;m_Smoking = 0.65451;m_Diabetes = 0.57367 # log of hazard ratio for each risk factor for males
f_log_age = 2.32888; f_log_hdl = -0.70833;f_log_totchol = 1.20904;f_log_SBP = 2.82263;f_Smoking = 0.52873;f_Diabetes = 0.69154 # log of hazard ratio for each risk factor for females
betas <- list(f=f_rf,m=m_rf) # nested list of f_rf and m_rf
f_points=data.frame("points"=seq(-3,12),"age_low"=c(NA,NA,NA,30,NA,35,NA,40,45,NA,50,55,60,65,70,75),"age_high"=c(NA,NA,NA,34,NA,39,NA,44,49,NA,54,59,64,69,74,120),"HDL_low"=c(NA,60,50,45,35,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"HDL_high"=c(NA,1000,59,49,44,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_low"=c(NA,NA,NA,0,160,NA,200,240,280,NA,NA,NA,NA,NA,NA,NA),"TotChol_high"=c(NA,NA,NA,160,199,NA,239,279,1000,NA,NA,NA,NA,NA,NA,NA), "SBP_low"=c(NA,NA,0,NA,NA,NA,120,130,NA,140,150,160,NA,NA,NA,NA),"SBP_high"=c(NA,NA,120,NA,NA,NA,129,139,NA,149,159,1000,NA,NA,NA,NA), "Smoker"=c(NA,NA,NA,"No",NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA), "Diabetic"=c(NA,NA,NA,"No",NA,NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA))
m_points=data.frame("points"=seq(-2,15),"age_low"=c(NA,NA,30,NA,35,NA,NA,40,45,NA,50,NA,55,60,65,NA,70,75),"age_high"=c(NA,NA,34,NA,39,NA,NA,44,49,NA,54,NA,59,64,69,NA,74,120),"HDL_low"=c(60,50,45,35,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"HDL_high"=c(1000,59,49,44,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_low"=c(NA,NA,0,160,200,240,280,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_high"=c(NA,NA,160,199,239,279,1000,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "SBP_low"=c(NA,NA,0,NA,120,130,140,160,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"SBP_high"=c(NA,NA,120,NA,129,139,149,1000,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "Smoker"=c(NA,NA,"No",NA,NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "Diabetic"=c(NA,NA,"No",NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA))
################################################################################################################################################################
# This gets the row where the input (30 in this example) satisfies the condition that it is between the age_low and age_high columns of the data frame f_points
i=sapply(30, function(p) { which(f_points$age_low <= p & f_points$age_high >= p)})
# Delete all rows except row "i"
keep_df=f_points[-(setdiff(1:16,i)),]
################################################################################################################################################################
get_frs = function(riagendr,ridageyr,HDL,TotChol,sbp,smoker,diabetic) {
list_pts=c() # Initiate list of points
#### MALES ####
if (riagendr == 1) {
## GET POINTS FROM AGE ##
i=sapply(ridageyr, function(p) { which(m_points$age_low <= p & m_points$age_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_age <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM HDL ##
i=sapply(HDL, function(p) { which(m_points$HDL_low <= p & m_points$HDL_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_HDL <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM TOTAL CHOLESTEROL ##
i=sapply(TotChol, function(p) { which(m_points$TotChol_low <= p & m_points$TotChol_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_TotChol <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SBP ##
i=sapply(sbp, function(p) { which(m_points$SBP_low <= p & m_points$SBP_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_SBP <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SMOKER ##
if (isTRUE(smoker == 2)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,4)}
# m_smoke <<- 4
## GET POINTS FROM DIABETIC ##
if (isTRUE(diabetic < 6.5)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,3)}
#if (isTRUE(diabetic >= 126)) {list_pts=append(list_pts,3)} else {list_pts=append(list_pts,0)}
# m_diabetic <<- 3
}
#### FEMALES ####
else {
## GET POINTS FROM AGE ##
i=sapply(ridageyr, function(p) { which(f_points$age_low <= p & f_points$age_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_age <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM HDL ##
i=sapply(HDL, function(p) { which(f_points$HDL_low <= p & f_points$HDL_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_HDL <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM TOTAL CHOLESTEROL ##
i=sapply(TotChol, function(p) { which(f_points$TotChol_low <= p & f_points$TotChol_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_TotChol <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SBP ##
i=sapply(sbp, function(p) { which(f_points$SBP_low <= p & f_points$SBP_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_SBP <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SMOKER ##
if (isTRUE(smoker == 2)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,3)}
# f_smoke <<- 3
## GET POINTS FROM DIABETIC ##
if (isTRUE(diabetic < 6.5)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,4)}
#if (isTRUE(diabetic >= 126)) {list_pts=append(list_pts,4)} else {list_pts=append(list_pts,0)}
# f_diabetic <<- 4
}
return(sum(list_pts))
}
##############
#### ATP3 ####
##############
| /Code/NHANES_continuous/compute_frs.R | no_license | bryansashakim/Framingham-Risk-Scores | R | false | false | 6,336 | r | ### Required packages ###
library(ggplot2); library(dplyr); library(tidyr);library(haven);library(readstata13);library(foreign);library(mice);library(MatchIt);library(stringdist);library(RecordLinkage);library(modelr)
### Parameters ###
#f_base10 = 0.95012 # baseline 10-year survival for females
#m_base10 = 0.88936 # baseline 10-year survival for males
f_rf <- list(log_age = 2.32888, log_hdl = -0.70833,log_totchol = 1.20904,log_SBP = 2.82263,Smoking = 0.52873,Diabetes = 0.69154) # log of hazard ratio for each risk factor for females
m_rf <- list(log_age = 3.06117, log_hdl = -0.93263,log_totchol = 1.12370,log_SBP = 1.99881,Smoking = 0.65451,Diabetes = 0.57367) # log of hazard ratio for each risk factor for males
m_log_age = 3.06117; m_log_hdl = -0.93263;m_log_totchol = 1.12370;m_log_SBP = 1.99881;m_Smoking = 0.65451;m_Diabetes = 0.57367 # log of hazard ratio for each risk factor for males
f_log_age = 2.32888; f_log_hdl = -0.70833;f_log_totchol = 1.20904;f_log_SBP = 2.82263;f_Smoking = 0.52873;f_Diabetes = 0.69154 # log of hazard ratio for each risk factor for females
betas <- list(f=f_rf,m=m_rf) # nested list of f_rf and m_rf
f_points=data.frame("points"=seq(-3,12),"age_low"=c(NA,NA,NA,30,NA,35,NA,40,45,NA,50,55,60,65,70,75),"age_high"=c(NA,NA,NA,34,NA,39,NA,44,49,NA,54,59,64,69,74,120),"HDL_low"=c(NA,60,50,45,35,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"HDL_high"=c(NA,1000,59,49,44,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_low"=c(NA,NA,NA,0,160,NA,200,240,280,NA,NA,NA,NA,NA,NA,NA),"TotChol_high"=c(NA,NA,NA,160,199,NA,239,279,1000,NA,NA,NA,NA,NA,NA,NA), "SBP_low"=c(NA,NA,0,NA,NA,NA,120,130,NA,140,150,160,NA,NA,NA,NA),"SBP_high"=c(NA,NA,120,NA,NA,NA,129,139,NA,149,159,1000,NA,NA,NA,NA), "Smoker"=c(NA,NA,NA,"No",NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA), "Diabetic"=c(NA,NA,NA,"No",NA,NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA))
m_points=data.frame("points"=seq(-2,15),"age_low"=c(NA,NA,30,NA,35,NA,NA,40,45,NA,50,NA,55,60,65,NA,70,75),"age_high"=c(NA,NA,34,NA,39,NA,NA,44,49,NA,54,NA,59,64,69,NA,74,120),"HDL_low"=c(60,50,45,35,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"HDL_high"=c(1000,59,49,44,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_low"=c(NA,NA,0,160,200,240,280,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"TotChol_high"=c(NA,NA,160,199,239,279,1000,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "SBP_low"=c(NA,NA,0,NA,120,130,140,160,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),"SBP_high"=c(NA,NA,120,NA,129,139,149,1000,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "Smoker"=c(NA,NA,"No",NA,NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA), "Diabetic"=c(NA,NA,"No",NA,NA,"Yes",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA))
################################################################################################################################################################
# This gets the row where the input (30 in this example) satisfies the condition that it is between the age_low and age_high columns of the data frame f_points
i=sapply(30, function(p) { which(f_points$age_low <= p & f_points$age_high >= p)})
# Delete all rows except row "i"
keep_df=f_points[-(setdiff(1:16,i)),]
################################################################################################################################################################
get_frs = function(riagendr,ridageyr,HDL,TotChol,sbp,smoker,diabetic) {
list_pts=c() # Initiate list of points
#### MALES ####
if (riagendr == 1) {
## GET POINTS FROM AGE ##
i=sapply(ridageyr, function(p) { which(m_points$age_low <= p & m_points$age_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_age <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM HDL ##
i=sapply(HDL, function(p) { which(m_points$HDL_low <= p & m_points$HDL_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_HDL <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM TOTAL CHOLESTEROL ##
i=sapply(TotChol, function(p) { which(m_points$TotChol_low <= p & m_points$TotChol_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_TotChol <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SBP ##
i=sapply(sbp, function(p) { which(m_points$SBP_low <= p & m_points$SBP_high >= p)}); keep_df=m_points[-(setdiff(1:length(m_points$points),i)),];m_SBP <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SMOKER ##
if (isTRUE(smoker == 2)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,4)}
# m_smoke <<- 4
## GET POINTS FROM DIABETIC ##
if (isTRUE(diabetic < 6.5)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,3)}
#if (isTRUE(diabetic >= 126)) {list_pts=append(list_pts,3)} else {list_pts=append(list_pts,0)}
# m_diabetic <<- 3
}
#### FEMALES ####
else {
## GET POINTS FROM AGE ##
i=sapply(ridageyr, function(p) { which(f_points$age_low <= p & f_points$age_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_age <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM HDL ##
i=sapply(HDL, function(p) { which(f_points$HDL_low <= p & f_points$HDL_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_HDL <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM TOTAL CHOLESTEROL ##
i=sapply(TotChol, function(p) { which(f_points$TotChol_low <= p & f_points$TotChol_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_TotChol <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SBP ##
i=sapply(sbp, function(p) { which(f_points$SBP_low <= p & f_points$SBP_high >= p)}); keep_df=f_points[-(setdiff(1:length(f_points$points),i)),];f_SBP <<- keep_df[[1]]
list_pts=append(list_pts,keep_df[[1]])
## GET POINTS FROM SMOKER ##
if (isTRUE(smoker == 2)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,3)}
# f_smoke <<- 3
## GET POINTS FROM DIABETIC ##
if (isTRUE(diabetic < 6.5)) {list_pts=append(list_pts,0)} else {list_pts=append(list_pts,4)}
#if (isTRUE(diabetic >= 126)) {list_pts=append(list_pts,4)} else {list_pts=append(list_pts,0)}
# f_diabetic <<- 4
}
return(sum(list_pts))
}
##############
#### ATP3 ####
##############
|
# Implementación en R del Algoritmo Toom Cook
# Cargando librerías necesarias----
library(stringr) # creo que ni la usé
# Cargando funciones necesarias----
calcula_base <- function(num_1,num_2,b,k){
i <- max(floor(floor(log(x = as.numeric(num_1), base= b))/k),
floor(floor(log(x = as.numeric(num_2), base= b))/k))
i <- i+1
return(i)
}
separa_en_secciones <- function(cadena, tamanio_max_secciones){
"Esta función recibe el número como caracter y el tamaño máximo de las secciones
y lo separa en pedazos del tamaño correcto. Devuelve un vector con el número seccionado"
num_secciones <- ceiling(nchar(cadena)/tamanio_max_secciones)
numero_seccionado <- vector(mode="character")
for(i in 1:num_secciones){
numero_seccionado[num_secciones+1-i] <- substr(x = cadena,
start = max(1,(nchar(cadena)-i*tamanio_max_secciones+1)),
stop = nchar(cadena)-((i-1)*tamanio_max_secciones))
}# for
return(numero_seccionado)
}# function separa_en_secciones
define_numero_de_puntos_a_evaluar <- function(k_m,k_n){
num_puntos_a_evaluar <- k_m + k_n -1
return(num_puntos_a_evaluar)
}#function define_numero_de_puntos_a_evaluar
rellena_matriz_evaluada <- function(valores_a_evaluar, num_columnas, num_puntos_a_evaluar){
matriz_final <- matrix(ncol=num_columnas, nrow=num_puntos_a_evaluar)
for(i in 1:num_puntos_a_evaluar){
for(k in 1:num_columnas){
matriz_final[i,k] <- valores_a_evaluar[i]^(num_columnas-k)
}#for anidado
}#for
return(matriz_final)
}#function
# Declaración de variables----
# m_original <- "7"
# n_original <- "8"
# m_original <- "78979879"
m_original <- "1234567890"
n_original <- "1234567890"
# m_original <- "1234567890123456789012"
# n_original <- "987654321987654321098"
# m_original <-"931275469"
# n_original <-"897512436"
base_b <- 10
exponente_inicial_b <- 4
b <-base_b ^ exponente_inicial_b # esto es un ejemplo, 10^4 es un buen inicio
k <-3
# Calculamos la i y el tamaño máximo de las secciones----
# B=b^i si b es 10^4 entonces i*4 es el tamaño de máximo de los pedacitos
i <- calcula_base(num_1 = m_original,
num_2 = n_original,
b = base_b^exponente_inicial_b,
k = k)
tamanio_max_secciones <- i*exponente_inicial_b
tamanio_max_secciones <- 6
# Separamos los números originales en k secciones de tamaño máximo "tamanio_maximo_secciones"----
num_digitos<-18
ceros_m <- str_c(rep("0",num_digitos - nchar(m_original)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,m_original),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(n_original)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,n_original),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
# Definimos el número de puntos a evaluar ----
# esto es un cálculo super sencillo (lo especifico por si k_m y k_n llegaran a ser diferentes)
num_puntos_a_evaluar <- define_numero_de_puntos_a_evaluar(k_m = k,
k_n = k)
# Elegimos la matriz correcta ----
valores <- c(0,1,-1,2,-2)
matriz_evaluada_p_q <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 3,
num_puntos_a_evaluar = num_puntos_a_evaluar)
matriz_evaluada_r <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 5,
num_puntos_a_evaluar = num_puntos_a_evaluar)
# Encontramos el vector r(x) <- p(x)q(x) ----
vector_r <-(matriz_evaluada_p_q%*%as.numeric(m_vector))*(matriz_evaluada_p_q%*%as.numeric(n_vector))
# Resolvemos para encontrar los sumandos a utilizar----
inversa <- solve(a=matriz_evaluada_r)*24
sumandos <- solve(a=matriz_evaluada_r,b=vector_r)
# dividimos estos mismos en secciones del mismo tamaño----
sumandos_char <- as.character(trunc(sumandos))
num_columnas_matriz_final <- length(sumandos_char)+1
# creamos una matriz con las columnas necesarias y una fila extra para sumar los que llevamos----
matriz_suma_posicionada <- matrix(data=rep("0",(num_columnas_matriz_final*num_columnas_matriz_final)),
ncol = num_columnas_matriz_final,
nrow = num_columnas_matriz_final)
sumando_a_acomodar <- vector(mode="character")
# Relleno matriz sumandos ----
for(i in num_columnas_matriz_final:2){
sumando_a_acomodar <- separa_en_secciones(cadena = sumandos_char[i-1],
tamanio_max_secciones = tamanio_max_secciones)
for(j in length(sumando_a_acomodar):1){
matriz_suma_posicionada[i,i-length(sumando_a_acomodar)+j] <- sumando_a_acomodar[j]
}#for anidado1
}#for
# sumamos matriz y guardamos en el vector resultado_seccionado ----
resultado_seccionado <- vector(mode="character")
options(scipen=999)
for(i in ncol(matriz_suma_posicionada):1){
suma_auxiliar <- as.character(sum(as.numeric(matriz_suma_posicionada[,i])))
if(nchar(suma_auxiliar)>tamanio_max_secciones & i !=1){
matriz_suma_posicionada[1,i-1] <- substr(suma_auxiliar,
start = 1,
stop = nchar(suma_auxiliar)-tamanio_max_secciones)
resultado_seccionado[i] <- substr(suma_auxiliar,
start = nchar(suma_auxiliar)-tamanio_max_secciones+1,
stop = nchar(suma_auxiliar))
}else if(nchar(suma_auxiliar) < tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(suma_auxiliar)),collapse = "")
suma_auxiliar_completa <- str_c(c(ceros_sumando,suma_auxiliar),collapse="")
resultado_seccionado[i] <- suma_auxiliar_completa
} else {
resultado_seccionado[i] <- suma_auxiliar
}
}#for
# Juntamos las secciones en un solo número-caracter ----
resultado_final <- str_c(resultado_seccionado,collapse = "")
#Creamos las funciónes----
multiplica_numerotes <- function(numerote1, numerote2){
if(nchar(numerote1)<=18 & nchar(numerote2)<=18){
m_original <- numerote1
n_original <- numerote2
tamanio_max_secciones <- 6
# dividimos los números en secciones completando con ceros a la izquierda de ser necesario
num_digitos<-18
ceros_m <- str_c(rep("0",num_digitos - nchar(m_original)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,m_original),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(n_original)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,n_original),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
# definimos el número de puntos a evaluar
k <- 3
num_puntos_a_evaluar <- define_numero_de_puntos_a_evaluar(k_m = k,
k_n = k)
# definimos las matrices que utilizaremos para evaluar
valores <- c(0,1,-1,2,-2)
matriz_evaluada_p_q <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 3,
num_puntos_a_evaluar = num_puntos_a_evaluar)
matriz_evaluada_r <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 5,
num_puntos_a_evaluar = num_puntos_a_evaluar)
# encontramos el vector r para el cual buscaremos resolver
vector_r <-(matriz_evaluada_p_q%*%as.numeric(m_vector))*(matriz_evaluada_p_q%*%as.numeric(n_vector))
# resolvemos el sistemirri
inversa <- solve(a=matriz_evaluada_r)*24
sumandos <- solve(a=matriz_evaluada_r,b=vector_r)
# dividimos en pedacillos y los acomodamos en la matriz correcta
sumandos_char <- as.character(trunc(sumandos))
num_columnas_matriz_final <- length(sumandos_char)+1
matriz_suma_posicionada <- matrix(data=rep("0",(num_columnas_matriz_final*num_columnas_matriz_final)),
ncol = num_columnas_matriz_final,
nrow = num_columnas_matriz_final)
sumando_a_acomodar <- vector(mode="character")
#rellenamos la matriz-illa
for(i in num_columnas_matriz_final:2){
sumando_a_acomodar <- separa_en_secciones(cadena = sumandos_char[i-1],
tamanio_max_secciones = tamanio_max_secciones)
for(j in length(sumando_a_acomodar):1){
matriz_suma_posicionada[i,i-length(sumando_a_acomodar)+j] <- sumando_a_acomodar[j]
}#for anidado1
}#for
#sumamos posicionadamente
resultado_seccionado <- vector(mode="character")
options(scipen=999)
for(i in ncol(matriz_suma_posicionada):1){
suma_auxiliar <- as.character(sum(as.numeric(matriz_suma_posicionada[,i])))
if(nchar(suma_auxiliar)>tamanio_max_secciones & i !=1){
matriz_suma_posicionada[1,i-1] <- substr(suma_auxiliar,
start = 1,
stop = nchar(suma_auxiliar)-tamanio_max_secciones)
resultado_seccionado[i] <- substr(suma_auxiliar,
start = nchar(suma_auxiliar)-tamanio_max_secciones+1,
stop = nchar(suma_auxiliar))
}else if(nchar(suma_auxiliar) < tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(suma_auxiliar)),collapse = "")
suma_auxiliar_completa <- str_c(c(ceros_sumando,suma_auxiliar),collapse="")
resultado_seccionado[i] <- suma_auxiliar_completa
} else {
resultado_seccionado[i] <- suma_auxiliar
}
}#for
resultado_final <- str_c(resultado_seccionado,collapse = "")
}# ifsote
return(resultado_final)
}#function multiplica_numerotes
suma_numerotes <- function(numerote1, numerote2){
# los números pueden ser de hasta 48 dígitos
num_digitos<-48
ceros_m <- str_c(rep("0",num_digitos - nchar(numerote1)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,numerote1),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(numerote2)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,numerote2),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
num_columnas_vector_final <- max(length(m_vector), length(n_vector))+1
# creamos un vector que recibirá todos los pedacitos
vector_suma_seccionada <- vector(mode="character",length= num_columnas_vector_final)
aux_suma_extra <-"0"
for (seccion in num_columnas_vector_final:2){
aux_suma_seccionada <- 0
if(as.numeric(aux_suma_extra)!=0){
aux_suma_seccionada <- as.character(sum(as.numeric(m_vector[seccion-1]),
as.numeric(n_vector[seccion-1]),
as.numeric(aux_suma_extra)))
aux_suma_extra <-0
} else{
aux_suma_seccionada <- as.character(sum(as.numeric(m_vector[seccion-1]),
as.numeric(n_vector[seccion-1])))
}#if
# casos para completar la suma
if(nchar(aux_suma_seccionada)>tamanio_max_secciones){
aux_suma_extra <- substr(x = aux_suma_seccionada,
start = 1,
stop = (nchar(aux_suma_seccionada)-tamanio_max_secciones))
aux_suma_seccionada <- substr(x=aux_suma_seccionada,
start= (nchar(aux_suma_seccionada)-tamanio_max_secciones+1),
stop= nchar(aux_suma_seccionada))
} else if(nchar(aux_suma_seccionada)<tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(aux_suma_seccionada)),collapse = "")
aux_suma_seccionada <- str_c(c(ceros_sumando,aux_suma_seccionada),collapse="")
}
vector_suma_seccionada[seccion] <- aux_suma_seccionada
}
suma_colapsada <- str_c(vector_suma_seccionada,collapse = "")
return (suma_colapsada)
}# function suma_numerotes
## matrices a multiplicar ----
matriz1 <- matrix(data = c("1234567890","1234567890","78978978","7897879","78979879","34245322"), ncol = 2, nrow=3, byrow = TRUE)
matriz2 <- matrix(data = c("1234567890","1234567890","78978978","7897879","78979879","34245322"), ncol = 3, nrow=2, byrow = TRUE)
res_mult_1 <- multiplica_numerotes(matriz1[1,1], matriz2[1,1])
res_mult_2 <- multiplica_numerotes(matriz1[1,2], matriz2[2,1])
matriz_final <- matrix(data=rep("0",9), ncol=ncol(matriz2), nrow=nrow(matriz1))
for(rowfinal in 1:nrow(matriz_final)){
for(colfinal in 1:ncol(matriz_final)){
vectorsin1 <- matriz1[rowfinal,]
vectorsin2 <-matriz2[,colfinal]
sumandos <- vector(mode="character", length=length(vectorsin2))
suma_total<-"0"
for(i in 1:length(vectorsin2)){
sumandos[i]<- multiplica_numerotes(vectorsin1[i],vectorsin2[i])
suma_total <- suma_numerotes(suma_total,sumandos[i])
}#for i
matriz_final[rowfinal,colfinal]<- suma_total
}#for colfinal
}#for rowfinal
| /MNO/proyecto_final/MNO_2017/proyectos/equipos/equipo_4/avance_22_05_2017/codigoR/toom_cook.R | permissive | fernandatellezg/analisis-numerico-computo-cientifico | R | false | false | 13,820 | r | # Implementación en R del Algoritmo Toom Cook
# Cargando librerías necesarias----
library(stringr) # creo que ni la usé
# Cargando funciones necesarias----
calcula_base <- function(num_1,num_2,b,k){
i <- max(floor(floor(log(x = as.numeric(num_1), base= b))/k),
floor(floor(log(x = as.numeric(num_2), base= b))/k))
i <- i+1
return(i)
}
separa_en_secciones <- function(cadena, tamanio_max_secciones){
"Esta función recibe el número como caracter y el tamaño máximo de las secciones
y lo separa en pedazos del tamaño correcto. Devuelve un vector con el número seccionado"
num_secciones <- ceiling(nchar(cadena)/tamanio_max_secciones)
numero_seccionado <- vector(mode="character")
for(i in 1:num_secciones){
numero_seccionado[num_secciones+1-i] <- substr(x = cadena,
start = max(1,(nchar(cadena)-i*tamanio_max_secciones+1)),
stop = nchar(cadena)-((i-1)*tamanio_max_secciones))
}# for
return(numero_seccionado)
}# function separa_en_secciones
define_numero_de_puntos_a_evaluar <- function(k_m,k_n){
num_puntos_a_evaluar <- k_m + k_n -1
return(num_puntos_a_evaluar)
}#function define_numero_de_puntos_a_evaluar
rellena_matriz_evaluada <- function(valores_a_evaluar, num_columnas, num_puntos_a_evaluar){
matriz_final <- matrix(ncol=num_columnas, nrow=num_puntos_a_evaluar)
for(i in 1:num_puntos_a_evaluar){
for(k in 1:num_columnas){
matriz_final[i,k] <- valores_a_evaluar[i]^(num_columnas-k)
}#for anidado
}#for
return(matriz_final)
}#function
# Declaración de variables----
# m_original <- "7"
# n_original <- "8"
# m_original <- "78979879"
m_original <- "1234567890"
n_original <- "1234567890"
# m_original <- "1234567890123456789012"
# n_original <- "987654321987654321098"
# m_original <-"931275469"
# n_original <-"897512436"
base_b <- 10
exponente_inicial_b <- 4
b <-base_b ^ exponente_inicial_b # esto es un ejemplo, 10^4 es un buen inicio
k <-3
# Calculamos la i y el tamaño máximo de las secciones----
# B=b^i si b es 10^4 entonces i*4 es el tamaño de máximo de los pedacitos
i <- calcula_base(num_1 = m_original,
num_2 = n_original,
b = base_b^exponente_inicial_b,
k = k)
tamanio_max_secciones <- i*exponente_inicial_b
tamanio_max_secciones <- 6
# Separamos los números originales en k secciones de tamaño máximo "tamanio_maximo_secciones"----
num_digitos<-18
ceros_m <- str_c(rep("0",num_digitos - nchar(m_original)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,m_original),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(n_original)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,n_original),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
# Definimos el número de puntos a evaluar ----
# esto es un cálculo super sencillo (lo especifico por si k_m y k_n llegaran a ser diferentes)
num_puntos_a_evaluar <- define_numero_de_puntos_a_evaluar(k_m = k,
k_n = k)
# Elegimos la matriz correcta ----
valores <- c(0,1,-1,2,-2)
matriz_evaluada_p_q <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 3,
num_puntos_a_evaluar = num_puntos_a_evaluar)
matriz_evaluada_r <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 5,
num_puntos_a_evaluar = num_puntos_a_evaluar)
# Encontramos el vector r(x) <- p(x)q(x) ----
vector_r <-(matriz_evaluada_p_q%*%as.numeric(m_vector))*(matriz_evaluada_p_q%*%as.numeric(n_vector))
# Resolvemos para encontrar los sumandos a utilizar----
inversa <- solve(a=matriz_evaluada_r)*24
sumandos <- solve(a=matriz_evaluada_r,b=vector_r)
# dividimos estos mismos en secciones del mismo tamaño----
sumandos_char <- as.character(trunc(sumandos))
num_columnas_matriz_final <- length(sumandos_char)+1
# creamos una matriz con las columnas necesarias y una fila extra para sumar los que llevamos----
matriz_suma_posicionada <- matrix(data=rep("0",(num_columnas_matriz_final*num_columnas_matriz_final)),
ncol = num_columnas_matriz_final,
nrow = num_columnas_matriz_final)
sumando_a_acomodar <- vector(mode="character")
# Relleno matriz sumandos ----
for(i in num_columnas_matriz_final:2){
sumando_a_acomodar <- separa_en_secciones(cadena = sumandos_char[i-1],
tamanio_max_secciones = tamanio_max_secciones)
for(j in length(sumando_a_acomodar):1){
matriz_suma_posicionada[i,i-length(sumando_a_acomodar)+j] <- sumando_a_acomodar[j]
}#for anidado1
}#for
# sumamos matriz y guardamos en el vector resultado_seccionado ----
resultado_seccionado <- vector(mode="character")
options(scipen=999)
for(i in ncol(matriz_suma_posicionada):1){
suma_auxiliar <- as.character(sum(as.numeric(matriz_suma_posicionada[,i])))
if(nchar(suma_auxiliar)>tamanio_max_secciones & i !=1){
matriz_suma_posicionada[1,i-1] <- substr(suma_auxiliar,
start = 1,
stop = nchar(suma_auxiliar)-tamanio_max_secciones)
resultado_seccionado[i] <- substr(suma_auxiliar,
start = nchar(suma_auxiliar)-tamanio_max_secciones+1,
stop = nchar(suma_auxiliar))
}else if(nchar(suma_auxiliar) < tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(suma_auxiliar)),collapse = "")
suma_auxiliar_completa <- str_c(c(ceros_sumando,suma_auxiliar),collapse="")
resultado_seccionado[i] <- suma_auxiliar_completa
} else {
resultado_seccionado[i] <- suma_auxiliar
}
}#for
# Juntamos las secciones en un solo número-caracter ----
resultado_final <- str_c(resultado_seccionado,collapse = "")
#Creamos las funciónes----
multiplica_numerotes <- function(numerote1, numerote2){
if(nchar(numerote1)<=18 & nchar(numerote2)<=18){
m_original <- numerote1
n_original <- numerote2
tamanio_max_secciones <- 6
# dividimos los números en secciones completando con ceros a la izquierda de ser necesario
num_digitos<-18
ceros_m <- str_c(rep("0",num_digitos - nchar(m_original)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,m_original),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(n_original)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,n_original),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
# definimos el número de puntos a evaluar
k <- 3
num_puntos_a_evaluar <- define_numero_de_puntos_a_evaluar(k_m = k,
k_n = k)
# definimos las matrices que utilizaremos para evaluar
valores <- c(0,1,-1,2,-2)
matriz_evaluada_p_q <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 3,
num_puntos_a_evaluar = num_puntos_a_evaluar)
matriz_evaluada_r <- rellena_matriz_evaluada(valores_a_evaluar = valores,
num_columnas = 5,
num_puntos_a_evaluar = num_puntos_a_evaluar)
# encontramos el vector r para el cual buscaremos resolver
vector_r <-(matriz_evaluada_p_q%*%as.numeric(m_vector))*(matriz_evaluada_p_q%*%as.numeric(n_vector))
# resolvemos el sistemirri
inversa <- solve(a=matriz_evaluada_r)*24
sumandos <- solve(a=matriz_evaluada_r,b=vector_r)
# dividimos en pedacillos y los acomodamos en la matriz correcta
sumandos_char <- as.character(trunc(sumandos))
num_columnas_matriz_final <- length(sumandos_char)+1
matriz_suma_posicionada <- matrix(data=rep("0",(num_columnas_matriz_final*num_columnas_matriz_final)),
ncol = num_columnas_matriz_final,
nrow = num_columnas_matriz_final)
sumando_a_acomodar <- vector(mode="character")
#rellenamos la matriz-illa
for(i in num_columnas_matriz_final:2){
sumando_a_acomodar <- separa_en_secciones(cadena = sumandos_char[i-1],
tamanio_max_secciones = tamanio_max_secciones)
for(j in length(sumando_a_acomodar):1){
matriz_suma_posicionada[i,i-length(sumando_a_acomodar)+j] <- sumando_a_acomodar[j]
}#for anidado1
}#for
#sumamos posicionadamente
resultado_seccionado <- vector(mode="character")
options(scipen=999)
for(i in ncol(matriz_suma_posicionada):1){
suma_auxiliar <- as.character(sum(as.numeric(matriz_suma_posicionada[,i])))
if(nchar(suma_auxiliar)>tamanio_max_secciones & i !=1){
matriz_suma_posicionada[1,i-1] <- substr(suma_auxiliar,
start = 1,
stop = nchar(suma_auxiliar)-tamanio_max_secciones)
resultado_seccionado[i] <- substr(suma_auxiliar,
start = nchar(suma_auxiliar)-tamanio_max_secciones+1,
stop = nchar(suma_auxiliar))
}else if(nchar(suma_auxiliar) < tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(suma_auxiliar)),collapse = "")
suma_auxiliar_completa <- str_c(c(ceros_sumando,suma_auxiliar),collapse="")
resultado_seccionado[i] <- suma_auxiliar_completa
} else {
resultado_seccionado[i] <- suma_auxiliar
}
}#for
resultado_final <- str_c(resultado_seccionado,collapse = "")
}# ifsote
return(resultado_final)
}#function multiplica_numerotes
suma_numerotes <- function(numerote1, numerote2){
# los números pueden ser de hasta 48 dígitos
num_digitos<-48
ceros_m <- str_c(rep("0",num_digitos - nchar(numerote1)),collapse = "")
m_orig_rellenado <- str_c(c(ceros_m,numerote1),collapse="")
m_vector <- separa_en_secciones(cadena = m_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
ceros_n <- str_c(rep("0",num_digitos - nchar(numerote2)),collapse = "")
n_orig_rellenado <- str_c(c(ceros_n,numerote2),collapse="")
n_vector <- separa_en_secciones(cadena = n_orig_rellenado,
tamanio_max_secciones = tamanio_max_secciones)
num_columnas_vector_final <- max(length(m_vector), length(n_vector))+1
# creamos un vector que recibirá todos los pedacitos
vector_suma_seccionada <- vector(mode="character",length= num_columnas_vector_final)
aux_suma_extra <-"0"
for (seccion in num_columnas_vector_final:2){
aux_suma_seccionada <- 0
if(as.numeric(aux_suma_extra)!=0){
aux_suma_seccionada <- as.character(sum(as.numeric(m_vector[seccion-1]),
as.numeric(n_vector[seccion-1]),
as.numeric(aux_suma_extra)))
aux_suma_extra <-0
} else{
aux_suma_seccionada <- as.character(sum(as.numeric(m_vector[seccion-1]),
as.numeric(n_vector[seccion-1])))
}#if
# casos para completar la suma
if(nchar(aux_suma_seccionada)>tamanio_max_secciones){
aux_suma_extra <- substr(x = aux_suma_seccionada,
start = 1,
stop = (nchar(aux_suma_seccionada)-tamanio_max_secciones))
aux_suma_seccionada <- substr(x=aux_suma_seccionada,
start= (nchar(aux_suma_seccionada)-tamanio_max_secciones+1),
stop= nchar(aux_suma_seccionada))
} else if(nchar(aux_suma_seccionada)<tamanio_max_secciones){
ceros_sumando <- str_c(rep("0",tamanio_max_secciones - nchar(aux_suma_seccionada)),collapse = "")
aux_suma_seccionada <- str_c(c(ceros_sumando,aux_suma_seccionada),collapse="")
}
vector_suma_seccionada[seccion] <- aux_suma_seccionada
}
suma_colapsada <- str_c(vector_suma_seccionada,collapse = "")
return (suma_colapsada)
}# function suma_numerotes
## matrices a multiplicar ----
matriz1 <- matrix(data = c("1234567890","1234567890","78978978","7897879","78979879","34245322"), ncol = 2, nrow=3, byrow = TRUE)
matriz2 <- matrix(data = c("1234567890","1234567890","78978978","7897879","78979879","34245322"), ncol = 3, nrow=2, byrow = TRUE)
res_mult_1 <- multiplica_numerotes(matriz1[1,1], matriz2[1,1])
res_mult_2 <- multiplica_numerotes(matriz1[1,2], matriz2[2,1])
matriz_final <- matrix(data=rep("0",9), ncol=ncol(matriz2), nrow=nrow(matriz1))
for(rowfinal in 1:nrow(matriz_final)){
for(colfinal in 1:ncol(matriz_final)){
vectorsin1 <- matriz1[rowfinal,]
vectorsin2 <-matriz2[,colfinal]
sumandos <- vector(mode="character", length=length(vectorsin2))
suma_total<-"0"
for(i in 1:length(vectorsin2)){
sumandos[i]<- multiplica_numerotes(vectorsin1[i],vectorsin2[i])
suma_total <- suma_numerotes(suma_total,sumandos[i])
}#for i
matriz_final[rowfinal,colfinal]<- suma_total
}#for colfinal
}#for rowfinal
|
# Copyright (c) Microsoft Corporation. All rights reserved.
# Third Party Programs. This software enables you to obtain software applications from other sources.
# Those applications are offered and distributed by third parties under their own license terms.
# Microsoft is not developing, distributing or licensing those applications to you, but instead,
# as a convenience, enables you to use this software to obtain those applications directly from
# the application providers.
# By using the software, you acknowledge and agree that you are obtaining the applications directly
# from the third party providers and under separate license terms, and that it is your responsibility to locate,
# understand and comply with those license terms.
# Microsoft grants you no license rights for third-party software or applications that is obtained using this software.
##PBI_R_VISUAL: VIZGAL_CLUSTERING_WITH_OUTLIERS Graphical display of a clustering applied to point cloud
# Computes and visualizes a clustering performed with DBSCAN clustering algorithm.
# Allows user to control granularity of clusters or to find it automatically.
# Provides several options for scaling the data and for visualization of clusters.
# INPUT:
# The input dataset should include at least two numerical non-constant columns
#
#
# WARNINGS: Time consuming for large datasets
#
# CREATION DATE: 11/12/2016
#
# LAST UPDATE: 11/22/2016
#
# VERSION: 0.0.1
#
# R VERSION TESTED: 3.2.2
#
# AUTHOR: pbicvsupport@microsoft.com
#
# REFERENCES: https://cran.r-project.org/package=dbscan
# https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Density-Based_Clustering
############ User Parameters #########
#DEBUG
#save(list = ls(all.names = TRUE), file='C:/Users/boefraty/projects/PBI/R/tempData.Rda')
#load(file='C:/Users/boefraty/projects/PBI/R/tempData.Rda')
###############Library Declarations###############
libraryRequireInstall = function(packageName)
{
if(!require(packageName, character.only = TRUE))
warning( paste ("*** Failed to install '", packageName, "' ***", sep = ""))
}
libraryRequireInstall("scales")
libraryRequireInstall("fpc")
libraryRequireInstall("car")
libraryRequireInstall("dbscan")
###############Internal parameters definitions#################
##PBI_PARAM: the random number generator (RNG) state for random number generation
#Type: numeric, Default:42, Range:NA, PossibleValues:NA, Remarks: NA
randSeed = 42
##PBI_PARAM: transparency of points on plot, 0 is invisible, 1 is opaque
#Type: numeric, Default:0.25, Range:[0, 1], PossibleValues:NA, Remarks: NA
#pointOpacity = 0.5
##PBI_PARAM: minimum required samples (rows in data table)
#Type: positive integer, Default:10, Range:[5, 100], PossibleValues:NA, Remarks: NA
minSamplesToRun = 12
##PBI_PARAM: maximum samples to use inside autoNumClusters function
#Type: positive integer, Default:1200, Range:[100, 10000], PossibleValues:NA, Remarks: NA
maxSamples4autoGranularity = 1200
##PBI_PARAM: insignificant principle component threshold
# If PCA is applied all dimensions, that explain less than insigPC percentage of variance are removed
#Type: positive numeric, Default:0.05, Range:[0, 1], PossibleValues:NA, Remarks: NA
insigPC = 0.05
##PBI_PARAM: type for outlier marker
#Type: integer, Default:4, Range:[1:20], PossibleValues:NA, Remarks: NA
outLierPch = 4
##PBI_PARAM: size for legend text
#Type: float, Default:1, Range:[0:5], PossibleValues:NA, Remarks: NA
legendTextSize = 1
##PBI_PARAM: size for warning text
#Type: float, Default:0.8, Range:[0:2], PossibleValues:NA, Remarks: NA
warningCex = 0.8
###############Internal functions definitions#################
# if not enough space --> do not show the legend
validateIfToShowLegend = function(numClust, textSize)
{
ppp = par()$din
horFlag = (2.5*textSize < ppp[1])
verFlag = (0.35*numClust*textSize < ppp[2])
return(horFlag && verFlag)
}
# if not enough space replace "long text" by "long te..."
cutStr2Show = function(strText, strCex = 0.8, abbrTo = 100, isH = TRUE, maxChar = 0, partAvailable = 1)
{
# strText = text to modify
# strCex = font size
# abbrTo = very long string will be abbreviated to "abbrTo" characters
# isH = "is horizontal" ?
# maxChar = text smaller than maxChar is replaced by NULL
# partAvailable = which portion of window is available for text, in [0,1]
if(is.null(strText))
return (NULL)
SCL = 0.094*strCex
pardin = par()$din
gStand = partAvailable*(isH*pardin[1]+(1-isH)*pardin[2]) /SCL
# if very very long abbreviate
if(nchar(strText)>abbrTo && nchar(strText)> 1)
strText = abbreviate(strText, abbrTo)
# if looooooong convert to lo...
if(nchar(strText)>round(gStand) && nchar(strText)> 1)
strText = paste(substring(strText,1,floor(gStand)),"...",sep="")
# if shorter than maxChar remove
if(gStand<=maxChar)
strText = NULL
return(strText)
}
#function finds average distance of np1'th to np2'th neighbour, for example:
# if np1=np2=1, will find distance to the nearest neighbour
# in np1=np2=3, will find distance to the third nearest neighbour
# in np1=1, np2=3, will find average distance to the three nearest neighbours
avgNearestDist <- function(data, np1 = 1, np2 = np1 )
{
nn <- dbscan::kNN(data, k = np2)
distances = nn$dist[, c(np1:np2)]
if( np1<np2 )
{
res <- sort(apply (distances, 1, mean) )
}else{
res = sort(distances)
}
return(res)
}
#sum of square errors for linear fit
SSE = function(x, y) {sum( abs( lm( formula = y ~ x, data = data.frame(x = x, y = y) )$residuals )^2)}
# find knee point which corresponds to best cut-point of two linear fits
findKnee <- function( inX, inY )
{
orderX = order( inX )
inX = inX[orderX];inY = inY[orderX]
L = length(inX)
resV = rep(Inf, L)
first = 3
last = L-3
for (i in (first+2):(last-2))
{
x = inX[first:(i-1)]
y = inY[first:(i-1)]
resid = SSE(x, y)
x = inX[(i+1):last]
y = inY[(i+1):last]
resV[i]=resid+SSE(x, y)
}
mi = which.min(resV)-1
return( c(inX[mi], inY[mi]) )
}
#verify if the column is numeric and non-constant
correctColumn <- function(someColumn){ is.numeric(someColumn)&&length(unique(someColumn)) > 1 }
#euclidean distance between two points
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
#plot convex hull
plotCH = function(xcoord, ycoord, lcolor){
hpts <- chull(x = xcoord, y = ycoord)
hpts <- c(hpts, hpts[1])
lines( xcoord[hpts], ycoord[hpts], col = lcolor, lty = 3 )
}
# get datapoints closest to centers
getDelegates <- function(orig_data, clean_data, cluster_centers)
{
nc <- nrow(cluster_centers)
dc <- ncol(cluster_centers)
nr <- nrow(clean_data)
delegates <- NULL
for(clus in seq(1, length.out = nc))
{
B <- matrix(rep(cluster_centers[clus, ], times = nr), nrow = nr, ncol = dc, byrow = TRUE)
D <- clean_data[, c(1:dc)]-B
ed <- apply(D^2, 1, sum)
delegates <- rbind(delegates, orig_data[which.min(ed), ])
}
return(delegates)
}
# heuristic method for minPts parameter in dbscan
# larger number of points for high dimensionality
autoParamMinPtsFunc <- function(mydata, defaultMinPts = 5, extraPointPerRowCount = 250)
{
nc <- ncol( mydata )
nr <- nrow( mydata )
minPts <- max(defaultMinPts, (2*nc-1)) + floor( nr/extraPointPerRowCount )
return(minPts)
}
#autoGranularity
autoEpsFunc <- function(mydata, maxPoints, reachMinPts, alpha = 0.6)
{
if(nrow(mydata) > maxPoints)
mydata <- mydata[sample(nrow(mydata), maxPoints), ]
avgNNmp <- avgNearestDist(mydata, 1, reachMinPts) # to reachMinPts nearest
avgNN1 <- avgNearestDist(mydata, 1, 1) # to nearest
avgNN = alpha*avgNN1+(1-alpha)*avgNNmp
eps = findKnee(1:length(avgNNmp), avgNNmp)[2]
#eps = findKnee(1:length(avgNN1), avgNN1)[2]
return(eps)
}
#granularity2eps
granularity2epsMinMax <- function(mydata, g, maxPoints, reachMinPts)
{
#subsample if needed
if(ncol(mydata) > maxPoints)
mydata <- mydata[sample(ncol(mydata), maxPoints), ]
avgNN <- avgNearestDist(mydata, reachMinPts, reachMinPts)
resEps = as.numeric(quantile(avgNN, g))
return(resEps)
}
#compute eps for dbscan (not in use)
getEps <- function(mydata, frac = 0.04){euc.dist(sapply(mydata, max), sapply(mydata, min))*frac}
#get centers from clusters
centersFromClusters <- function(mydata, clusters)
{
NC <- max(clusters)
centers <- NULL
for (c in seq(1, length.out = NC))
{
centers = rbind(centers, apply(mydata[clusters == c, ], 2, mean))
}
return(centers)
}
myPallete = function(n=100,palleteType = "rainbow")
{
mp = rainbow(n)
if(palleteType == "heat")
mp = heat.colors(n)
if(palleteType == "terrain")
mp = terrain.colors(n)
if(palleteType == "topo")
mp = topo.colors(n)
if(palleteType == "cm")
mp = cm.colors(n+1)[-1] #remove white
if(palleteType == "gray")
mp = gray(0:n/ n)
return(mp)
}
# variables to come from group:show
addLabel2clusterDelegate = TRUE
addLabel2points = TRUE
addLegend = TRUE
#################################################################################
### The fuction to produce visual
#################################################################################
#********* PBI Parameters Block ***************
if(!exists("Values"))
Values = NULL
if(!exists("PointLabels"))
PointLabels = NULL
addLabel2points = TRUE #default
if (exists("settings_labeling_params_show"))
{
addLabel2points = settings_labeling_params_show
}
addLabel2clusterDelegate = FALSE #default
if (exists("settings_representative_params_show"))
{
addLabel2clusterDelegate = settings_representative_params_show
}
addLegend = TRUE #default
if (exists("settings_legend_params_show"))
{
addLegend = settings_legend_params_show
}
if(exists("settings_prepocessing_params_show") && settings_prepocessing_params_show == FALSE)
rm(list= ls(pattern = "settings_prepocessing_params_" ))
if(exists("settings_clusterNum_params_show") && settings_clusterNum_params_show == FALSE)
rm(list= ls(pattern = "settings_clusterNum_params_" ))
if(exists("settings_viz_params_show") && settings_viz_params_show == FALSE)
rm(list= ls(pattern = "settings_viz_params_" ))
if(exists("settings_labeling_params_show") && settings_labeling_params_show == FALSE)
rm(list= ls(pattern = "settings_labeling_params_" ))
if(exists("settings_representative_params_show") && settings_representative_params_show == FALSE)
rm(list= ls(pattern = "settings_representative_params_" ))
if(exists("settings_legend_params_show") && settings_legend_params_show == FALSE)
rm(list= ls(pattern = "settings_legend_params_" ))
if(exists("settings_additional_params_show") && settings_additional_params_show == FALSE)
rm(list= ls(pattern = "settings_additional_params_" ))
##PBI_PARAM: display_name: Scale data, tooltip:Used to standardize the range of features of data
# Type: bool, default:FALSE,
# Min: , Max:
scaleData = FALSE #default
if (exists("settings_prepocessing_params_scaleData"))
{
scaleData = settings_prepocessing_params_scaleData
}
##PBI_PARAM: display_name: Apply PCA, tooltip:Recommended for data with more than two dimensions
# Type: bool, default:FALSE,
# Min: , Max:
applyPCA = FALSE #default
if (exists("settings_prepocessing_params_applyPCA"))
{
applyPCA = settings_prepocessing_params_applyPCA
}
##PBI_PARAM: display_name: Granularity method, tooltip:Select preferable method to set granularity parameter(eps)
# Type: enumeration, default:'scale',
# Min: , Max:
# enumeration options: auto ,scale ,manual ,
granularityParameterType = 'auto' #default
if (exists("settings_clusterNum_params_granularityParameterType"))
{
granularityParameterType = settings_clusterNum_params_granularityParameterType
}
##PBI_PARAM: display_name: Granularity, tooltip:User-defined granularity level, used only if autoGranularity = FALSE. Smaller values correspond to more clusters
# Type: numeric, default:50,
# Min: 1, Max:100
granularityLevel = 50 #default
if (exists("settings_clusterNum_params_percentile"))
{
granularityLevel = settings_clusterNum_params_percentile
granularityLevel = max( min (granularityLevel, 100), 1)
}
##PBI_PARAM: display_name: Reachability distance, tooltip:How close points should be to each other to be considered a part of a cluster (eps)
# Type: numeric, default:0,
# Min: 0, Max:1e+08
eps = 0 #default
if (exists("settings_clusterNum_params_eps"))
{
eps = settings_clusterNum_params_eps
eps = max( min (eps, 1e+08), 0)
}
##PBI_PARAM: display_name: Find minimum points automatically, tooltip:
# Type: bool, default:TRUE,
# Min: , Max:
autoParamMinPts = TRUE #default
if (exists("settings_clusterNum_params_autoParamMinPts"))
{
autoParamMinPts = settings_clusterNum_params_autoParamMinPts
}
##PBI_PARAM: display_name: Minimum points per cluster, tooltip:User-defined minimum points parameter. Smaller values correspond to more clusters
# Type: numeric, default:5,
# Min: 1, Max:1000
minPtsParam = 5 #default
if (exists("settings_clusterNum_params_minPtsParam"))
{
minPtsParam = settings_clusterNum_params_minPtsParam
minPtsParam = max( min (minPtsParam, 1000), 1)
}
##PBI_PARAM: display_name: Draw ellipse, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawEllipse = FALSE #default
if (exists("settings_viz_params_drawEllipse"))
{
drawEllipse = settings_viz_params_drawEllipse
}
##PBI_PARAM: display_name: Draw convex hull, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawConvexHull = FALSE #default
if (exists("settings_viz_params_drawConvexHull"))
{
drawConvexHull = settings_viz_params_drawConvexHull
}
##PBI_PARAM: display_name: Draw centroid, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawCenters = FALSE #default
if (exists("settings_viz_params_drawCenters"))
{
drawCenters = settings_viz_params_drawCenters
}
##PBI_PARAM: display_name: Point opacity, tooltip:
# Type: numeric, default:30,
# Min: 0, Max:100
pointOpacity = 30 #default
if (exists("settings_viz_params_percentile"))
{
pointOpacity = settings_viz_params_percentile
pointOpacity = max( min (pointOpacity, 100), 0)
}
##PBI_PARAM: display_name: Point size, tooltip:
# Type: numeric, default:10,
# Min: 1, Max:50
pointSize = 10 #default
if (exists("settings_viz_params_weight"))
{
pointSize = settings_viz_params_weight
pointSize = max( min (pointSize, 50), 1)
}
##PBI_PARAM: display_name: Font size, tooltip:
# Type: numeric, default:8,
# Min: 8, Max:40
labelingFontSize = 8 #default
if (exists("settings_labeling_params_textSize"))
{
labelingFontSize = settings_labeling_params_textSize
labelingFontSize = max( min (labelingFontSize, 40), 8)
}
##PBI_PARAM: display_name: Label opacity, tooltip:
# Type: numeric, default:100,
# Min: 0, Max:100
labelOpacity = 100 #default
if (exists("settings_labeling_params_percentile"))
{
labelOpacity = settings_labeling_params_percentile
labelOpacity = max( min (labelOpacity, 100), 0)
}
##PBI_PARAM: display_name: Limit label length, tooltip:Abbreviate labels to a limited length
# Type: numeric, default:5,
# Min: 1, Max:100
maxLenPointLabel = 5 #default
if (exists("settings_labeling_params_maxLenPointLabel"))
{
maxLenPointLabel = settings_labeling_params_maxLenPointLabel
maxLenPointLabel = max( min (maxLenPointLabel, 100), 1)
}
##PBI_PARAM: display_name: Percentage of points labeled, tooltip:Avoids cluttering text
# Type: numeric, default:100,
# Min: 0, Max:100
percPointsLabeled = 100 #default
if (exists("settings_labeling_params_percentile1"))
{
percPointsLabeled = settings_labeling_params_percentile1
percPointsLabeled = max( min (percPointsLabeled, 100), 0)
}
##PBI_PARAM: display_name: Font size, tooltip:
# Type: numeric, default:8,
# Min: 1, Max:40
representativeFontSize = 8 #default
if (exists("settings_representative_params_textSize"))
{
representativeFontSize = settings_representative_params_textSize
representativeFontSize = max( min (representativeFontSize, 40), 1)
}
##PBI_PARAM: display_name: Limit label length, tooltip:Abbreviate labels to a limited length
# Type: numeric, default:30,
# Min: 1, Max:100
maxLenDelegate = 30 #default
if (exists("settings_representative_params_maxLenDelegate"))
{
maxLenDelegate = settings_representative_params_maxLenDelegate
maxLenDelegate = max( min (maxLenDelegate, 100), 1)
}
##PBI_PARAM: display_name: Pallete type, tooltip:Color pallete type
# Type: enumeration, default:'rainbow',
# Min: , Max:
# enumeration options: rainbow ,gray ,cm ,topo ,terrain ,heat ,
palleteType = 'rainbow' #default
if (exists("settings_legend_params_palleteType"))
{
palleteType = settings_legend_params_palleteType
}
##PBI_PARAM: display_name: Color of outliers, tooltip:
# Type: fill, default:'black',
# Min: , Max:
outLierCol = 'black' #default
if (exists("settings_legend_params_outLierCol"))
{
outLierCol = settings_legend_params_outLierCol
}
##PBI_PARAM: display_name: Show warnings, tooltip:
# Type: bool, default:TRUE,
# Min: , Max:
showWarnings = TRUE #default
if (exists("settings_additional_params_showWarnings"))
{
showWarnings = settings_additional_params_showWarnings
}
{
if(eps ==0) #not valid "eps" => "auto" mode
eps = NULL
#addLegend = TRUE
delegateCex = representativeFontSize/10
skipLabel2points = max(100/as.numeric(percPointsLabeled) , 1)
labelOpacity = labelOpacity/100
pointOpacity = pointOpacity/100
cexLabel2points = labelingFontSize/10
pointMarkerSize = pointSize/10
drawPoints = TRUE
if(!is.null(Values))
dataset = Values
if(!is.null(PointLabels))
PointLabels[,1] = as.character(PointLabels[,1])
if(!is.null(PointLabels) && !is.null(Values))
dataset = cbind(PointLabels, dataset)
if(!is.null(PointLabels) && is.null(Values))
dataset = PointLabels
if(addLabel2points && is.null(PointLabels))
addLabel2points = FALSE
###############Upfront input correctness validations (where possible)#################
minPtsParam = round(minPtsParam)
pbiWarning <- NULL
dataset <- na.omit(dataset) # deletion of missing
orig_dataset <- dataset #used later for delegates
# verify correctness of dataset
useColumns <- sapply(dataset, correctColumn)
if(showWarnings && sum(useColumns[-1])<ncol(dataset)-1)
pbiWarning <- "At least one of the columns was not numeric, or constant"
#exclude defect columns
dataset <- as.data.frame(dataset[, useColumns])
nc <- ncol(dataset)
nr <- nrow(dataset)
checkDimiensionality <- TRUE
if(nc < 2 || nr < minSamplesToRun || is.null(Values))
{
checkDimiensionality <- FALSE
if(showWarnings)
pbiWarning <- paste(pbiWarning, "\nNot enough input dimensions");
}
if(!autoParamMinPts && minPtsParam >= nr)
{
checkDimiensionality <- FALSE
if(showWarnings)
pbiWarning <- paste(pbiWarning, "\nParameter minPts is out of limits");
}
##############Main Visualization script###########
maxGLevel = 100 # for "scale" mode maxGLevel can be used to squeeze the range
set.seed(randSeed)
if(!checkDimiensionality)
{
plot.new()
}else
{
if(scaleData)
{
dataset <- as.data.frame(scale(dataset))
names(dataset) = paste(names(dataset), "scaled", sep = ".")
}
if(applyPCA)
{
dataset.pca <- prcomp(dataset, center = TRUE, scale = F)
pExplained <- dataset.pca$sdev^2/sum(dataset.pca$sdev^2)
flags <- (pExplained > insigPC); flags[1:2] = TRUE #at least 2 dimensions
dataset = as.data.frame(dataset.pca$x[, flags])#reduce dimensions with less than 5% variance
}
if(autoParamMinPts)
minPtsParam = autoParamMinPtsFunc(dataset, extraPointPerRowCount = 175)
#find eps
if(granularityParameterType == "auto")
eps = autoEpsFunc(dataset, maxSamples4autoGranularity, minPtsParam)
if(granularityParameterType == "scale")
eps <- granularity2epsMinMax(dataset, granularityLevel/maxGLevel, maxSamples4autoGranularity, minPtsParam)
if(is.null(eps))
eps = autoEpsFunc(dataset, maxSamples4autoGranularity, minPtsParam)
#DBSCAN call
cl <- dbscan::dbscan(dataset, eps, minPts = minPtsParam, scale = FALSE, borderPoints = TRUE)
numOfClusters = max(cl$cluster)
cl$centers <- centersFromClusters(dataset, cl$cluster)
drawColors <- c(outLierCol, myPallete(numOfClusters,palleteType = palleteType))
drawColorsLeg <- c(rainbow(numOfClusters), outLierCol)
drawPch <- c(outLierPch, rep(19, numOfClusters))
#visualize 2 first coordinates
if(drawPoints) colpoints = drawColors[cl$cluster+1] else colpoints = NULL
pchPoints = drawPch[cl$cluster+1]
#in case of legend extend xlim to the right by 20%
xrange = range(dataset[, 1])
drange = xrange[2]-xrange[1]
xlim = c(xrange[1]-0.01*drange, xrange[2]+0.01*drange+drange*0.20*addLegend)
plot(dataset[, 1], dataset[, 2], col = alpha(colpoints, pointOpacity), pch = pchPoints,
xlab = cutStr2Show(names(dataset)[1], strCex =1.1, isH = TRUE),
ylab = cutStr2Show(names(dataset)[2], strCex =1.1, isH = FALSE),
xlim = xlim, cex = pointMarkerSize)
leg <- NULL
if(!is.null(cl$centers))
leg <- paste("Cluster ", seq(1, length.out = numOfClusters)) #text
pc <- c(rep(19, numOfClusters)) # markers
colMarkers <- drawColors[-1]
if(drawCenters && !is.null(cl$centers))
{
points(cl$centers, pch = 7, col = drawColors[-1])
leg <- cbind(leg, paste("Cluster center " , seq(1, length.out = numOfClusters)))
pc <- cbind(pc, rep(7, numOfClusters))
colMarkers <- rep(colMarkers, 2)
}
leg <- c(leg, "Outlers")
pc = c(pc, outLierPch)
colMarkers <- c(colMarkers, outLierCol)
if(drawEllipse)
{
for(clus in seq(1, length.out = numOfClusters))
{
iii <- (cl$cluster == clus)
if(sum(iii) > 2)
dataEllipse(dataset[iii, 1], dataset[iii, 2], add = T, plot.points = F, levels = 0.85, col = drawColorsLeg[clus], lwd = 1,
fill = TRUE, fill.alpha = 0.075, center.pch = NULL)
}
}
if(drawConvexHull)
{
for(clus in seq(1, length.out = numOfClusters))
{
iii <- (cl$cluster == clus)
if(sum(iii) > 2)
plotCH(dataset[iii, 1], dataset[iii, 2], lcolor = drawColorsLeg[clus])
}
}
if(addLabel2clusterDelegate)
{
clean_data = dataset
cluster_centers = (cl$centers)
if(!is.null(cluster_centers))
{
deleg <- getDelegates(orig_dataset, dataset, cl$centers)
delegateText = abbreviate(apply(deleg, 1, toString),maxLenDelegate)
delegateText = sapply(delegateText, cutStr2Show, strCex = delegateCex, partAvailable = 0.75)
text(x = cl$centers[, 1], y = cl$centers[, 2],
delegateText,
col = "black",
cex = delegateCex)
}
}
if(addLabel2points)
{
iii=sample(1:nrow(dataset),max(1,floor(nrow(dataset)/skipLabel2points)))
text(x = dataset[iii, 1], y = dataset[iii, 2], labels = abbreviate(orig_dataset[iii,1],maxLenPointLabel),
col = alpha(colpoints[iii], labelOpacity), cex = cexLabel2points)
}
if(addLegend && validateIfToShowLegend(numClust = numOfClusters, textSize = legendTextSize ))
legend("topright", legend = leg, pch = pc, col = alpha(colMarkers, 1), cex = legendTextSize)
}
if(showWarnings && !is.null(pbiWarning))
{
pbiWarning = cutStr2Show(pbiWarning, strCex = warningCex)
title(main = NULL, sub = pbiWarning, outer = FALSE, col.sub = "gray50", cex.sub = warningCex)
}
}
| /old/PowerBI-visuals-dbscan/script.r | no_license | boefraty74/ProjectR2pbiviz | R | false | false | 23,868 | r | # Copyright (c) Microsoft Corporation. All rights reserved.
# Third Party Programs. This software enables you to obtain software applications from other sources.
# Those applications are offered and distributed by third parties under their own license terms.
# Microsoft is not developing, distributing or licensing those applications to you, but instead,
# as a convenience, enables you to use this software to obtain those applications directly from
# the application providers.
# By using the software, you acknowledge and agree that you are obtaining the applications directly
# from the third party providers and under separate license terms, and that it is your responsibility to locate,
# understand and comply with those license terms.
# Microsoft grants you no license rights for third-party software or applications that is obtained using this software.
##PBI_R_VISUAL: VIZGAL_CLUSTERING_WITH_OUTLIERS Graphical display of a clustering applied to point cloud
# Computes and visualizes a clustering performed with DBSCAN clustering algorithm.
# Allows user to control granularity of clusters or to find it automatically.
# Provides several options for scaling the data and for visualization of clusters.
# INPUT:
# The input dataset should include at least two numerical non-constant columns
#
#
# WARNINGS: Time consuming for large datasets
#
# CREATION DATE: 11/12/2016
#
# LAST UPDATE: 11/22/2016
#
# VERSION: 0.0.1
#
# R VERSION TESTED: 3.2.2
#
# AUTHOR: pbicvsupport@microsoft.com
#
# REFERENCES: https://cran.r-project.org/package=dbscan
# https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Density-Based_Clustering
############ User Parameters #########
#DEBUG
#save(list = ls(all.names = TRUE), file='C:/Users/boefraty/projects/PBI/R/tempData.Rda')
#load(file='C:/Users/boefraty/projects/PBI/R/tempData.Rda')
###############Library Declarations###############
libraryRequireInstall = function(packageName)
{
if(!require(packageName, character.only = TRUE))
warning( paste ("*** Failed to install '", packageName, "' ***", sep = ""))
}
libraryRequireInstall("scales")
libraryRequireInstall("fpc")
libraryRequireInstall("car")
libraryRequireInstall("dbscan")
###############Internal parameters definitions#################
##PBI_PARAM: the random number generator (RNG) state for random number generation
#Type: numeric, Default:42, Range:NA, PossibleValues:NA, Remarks: NA
randSeed = 42
##PBI_PARAM: transparency of points on plot, 0 is invisible, 1 is opaque
#Type: numeric, Default:0.25, Range:[0, 1], PossibleValues:NA, Remarks: NA
#pointOpacity = 0.5
##PBI_PARAM: minimum required samples (rows in data table)
#Type: positive integer, Default:10, Range:[5, 100], PossibleValues:NA, Remarks: NA
minSamplesToRun = 12
##PBI_PARAM: maximum samples to use inside autoNumClusters function
#Type: positive integer, Default:1200, Range:[100, 10000], PossibleValues:NA, Remarks: NA
maxSamples4autoGranularity = 1200
##PBI_PARAM: insignificant principle component threshold
# If PCA is applied all dimensions, that explain less than insigPC percentage of variance are removed
#Type: positive numeric, Default:0.05, Range:[0, 1], PossibleValues:NA, Remarks: NA
insigPC = 0.05
##PBI_PARAM: type for outlier marker
#Type: integer, Default:4, Range:[1:20], PossibleValues:NA, Remarks: NA
outLierPch = 4
##PBI_PARAM: size for legend text
#Type: float, Default:1, Range:[0:5], PossibleValues:NA, Remarks: NA
legendTextSize = 1
##PBI_PARAM: size for warning text
#Type: float, Default:0.8, Range:[0:2], PossibleValues:NA, Remarks: NA
warningCex = 0.8
###############Internal functions definitions#################
# if not enough space --> do not show the legend
validateIfToShowLegend = function(numClust, textSize)
{
ppp = par()$din
horFlag = (2.5*textSize < ppp[1])
verFlag = (0.35*numClust*textSize < ppp[2])
return(horFlag && verFlag)
}
# if not enough space replace "long text" by "long te..."
cutStr2Show = function(strText, strCex = 0.8, abbrTo = 100, isH = TRUE, maxChar = 0, partAvailable = 1)
{
# strText = text to modify
# strCex = font size
# abbrTo = very long string will be abbreviated to "abbrTo" characters
# isH = "is horizontal" ?
# maxChar = text smaller than maxChar is replaced by NULL
# partAvailable = which portion of window is available for text, in [0,1]
if(is.null(strText))
return (NULL)
SCL = 0.094*strCex
pardin = par()$din
gStand = partAvailable*(isH*pardin[1]+(1-isH)*pardin[2]) /SCL
# if very very long abbreviate
if(nchar(strText)>abbrTo && nchar(strText)> 1)
strText = abbreviate(strText, abbrTo)
# if looooooong convert to lo...
if(nchar(strText)>round(gStand) && nchar(strText)> 1)
strText = paste(substring(strText,1,floor(gStand)),"...",sep="")
# if shorter than maxChar remove
if(gStand<=maxChar)
strText = NULL
return(strText)
}
#function finds average distance of np1'th to np2'th neighbour, for example:
# if np1=np2=1, will find distance to the nearest neighbour
# in np1=np2=3, will find distance to the third nearest neighbour
# in np1=1, np2=3, will find average distance to the three nearest neighbours
avgNearestDist <- function(data, np1 = 1, np2 = np1 )
{
nn <- dbscan::kNN(data, k = np2)
distances = nn$dist[, c(np1:np2)]
if( np1<np2 )
{
res <- sort(apply (distances, 1, mean) )
}else{
res = sort(distances)
}
return(res)
}
#sum of square errors for linear fit
SSE = function(x, y) {sum( abs( lm( formula = y ~ x, data = data.frame(x = x, y = y) )$residuals )^2)}
# find knee point which corresponds to best cut-point of two linear fits
findKnee <- function( inX, inY )
{
orderX = order( inX )
inX = inX[orderX];inY = inY[orderX]
L = length(inX)
resV = rep(Inf, L)
first = 3
last = L-3
for (i in (first+2):(last-2))
{
x = inX[first:(i-1)]
y = inY[first:(i-1)]
resid = SSE(x, y)
x = inX[(i+1):last]
y = inY[(i+1):last]
resV[i]=resid+SSE(x, y)
}
mi = which.min(resV)-1
return( c(inX[mi], inY[mi]) )
}
#verify if the column is numeric and non-constant
correctColumn <- function(someColumn){ is.numeric(someColumn)&&length(unique(someColumn)) > 1 }
#euclidean distance between two points
euc.dist <- function(x1, x2) sqrt(sum((x1 - x2) ^ 2))
#plot convex hull
plotCH = function(xcoord, ycoord, lcolor){
hpts <- chull(x = xcoord, y = ycoord)
hpts <- c(hpts, hpts[1])
lines( xcoord[hpts], ycoord[hpts], col = lcolor, lty = 3 )
}
# get datapoints closest to centers
getDelegates <- function(orig_data, clean_data, cluster_centers)
{
nc <- nrow(cluster_centers)
dc <- ncol(cluster_centers)
nr <- nrow(clean_data)
delegates <- NULL
for(clus in seq(1, length.out = nc))
{
B <- matrix(rep(cluster_centers[clus, ], times = nr), nrow = nr, ncol = dc, byrow = TRUE)
D <- clean_data[, c(1:dc)]-B
ed <- apply(D^2, 1, sum)
delegates <- rbind(delegates, orig_data[which.min(ed), ])
}
return(delegates)
}
# heuristic method for minPts parameter in dbscan
# larger number of points for high dimensionality
autoParamMinPtsFunc <- function(mydata, defaultMinPts = 5, extraPointPerRowCount = 250)
{
nc <- ncol( mydata )
nr <- nrow( mydata )
minPts <- max(defaultMinPts, (2*nc-1)) + floor( nr/extraPointPerRowCount )
return(minPts)
}
#autoGranularity
autoEpsFunc <- function(mydata, maxPoints, reachMinPts, alpha = 0.6)
{
if(nrow(mydata) > maxPoints)
mydata <- mydata[sample(nrow(mydata), maxPoints), ]
avgNNmp <- avgNearestDist(mydata, 1, reachMinPts) # to reachMinPts nearest
avgNN1 <- avgNearestDist(mydata, 1, 1) # to nearest
avgNN = alpha*avgNN1+(1-alpha)*avgNNmp
eps = findKnee(1:length(avgNNmp), avgNNmp)[2]
#eps = findKnee(1:length(avgNN1), avgNN1)[2]
return(eps)
}
#granularity2eps
granularity2epsMinMax <- function(mydata, g, maxPoints, reachMinPts)
{
#subsample if needed
if(ncol(mydata) > maxPoints)
mydata <- mydata[sample(ncol(mydata), maxPoints), ]
avgNN <- avgNearestDist(mydata, reachMinPts, reachMinPts)
resEps = as.numeric(quantile(avgNN, g))
return(resEps)
}
#compute eps for dbscan (not in use)
getEps <- function(mydata, frac = 0.04){euc.dist(sapply(mydata, max), sapply(mydata, min))*frac}
#get centers from clusters
centersFromClusters <- function(mydata, clusters)
{
NC <- max(clusters)
centers <- NULL
for (c in seq(1, length.out = NC))
{
centers = rbind(centers, apply(mydata[clusters == c, ], 2, mean))
}
return(centers)
}
myPallete = function(n=100,palleteType = "rainbow")
{
mp = rainbow(n)
if(palleteType == "heat")
mp = heat.colors(n)
if(palleteType == "terrain")
mp = terrain.colors(n)
if(palleteType == "topo")
mp = topo.colors(n)
if(palleteType == "cm")
mp = cm.colors(n+1)[-1] #remove white
if(palleteType == "gray")
mp = gray(0:n/ n)
return(mp)
}
# variables to come from group:show
addLabel2clusterDelegate = TRUE
addLabel2points = TRUE
addLegend = TRUE
#################################################################################
### The fuction to produce visual
#################################################################################
#********* PBI Parameters Block ***************
if(!exists("Values"))
Values = NULL
if(!exists("PointLabels"))
PointLabels = NULL
addLabel2points = TRUE #default
if (exists("settings_labeling_params_show"))
{
addLabel2points = settings_labeling_params_show
}
addLabel2clusterDelegate = FALSE #default
if (exists("settings_representative_params_show"))
{
addLabel2clusterDelegate = settings_representative_params_show
}
addLegend = TRUE #default
if (exists("settings_legend_params_show"))
{
addLegend = settings_legend_params_show
}
if(exists("settings_prepocessing_params_show") && settings_prepocessing_params_show == FALSE)
rm(list= ls(pattern = "settings_prepocessing_params_" ))
if(exists("settings_clusterNum_params_show") && settings_clusterNum_params_show == FALSE)
rm(list= ls(pattern = "settings_clusterNum_params_" ))
if(exists("settings_viz_params_show") && settings_viz_params_show == FALSE)
rm(list= ls(pattern = "settings_viz_params_" ))
if(exists("settings_labeling_params_show") && settings_labeling_params_show == FALSE)
rm(list= ls(pattern = "settings_labeling_params_" ))
if(exists("settings_representative_params_show") && settings_representative_params_show == FALSE)
rm(list= ls(pattern = "settings_representative_params_" ))
if(exists("settings_legend_params_show") && settings_legend_params_show == FALSE)
rm(list= ls(pattern = "settings_legend_params_" ))
if(exists("settings_additional_params_show") && settings_additional_params_show == FALSE)
rm(list= ls(pattern = "settings_additional_params_" ))
##PBI_PARAM: display_name: Scale data, tooltip:Used to standardize the range of features of data
# Type: bool, default:FALSE,
# Min: , Max:
scaleData = FALSE #default
if (exists("settings_prepocessing_params_scaleData"))
{
scaleData = settings_prepocessing_params_scaleData
}
##PBI_PARAM: display_name: Apply PCA, tooltip:Recommended for data with more than two dimensions
# Type: bool, default:FALSE,
# Min: , Max:
applyPCA = FALSE #default
if (exists("settings_prepocessing_params_applyPCA"))
{
applyPCA = settings_prepocessing_params_applyPCA
}
##PBI_PARAM: display_name: Granularity method, tooltip:Select preferable method to set granularity parameter(eps)
# Type: enumeration, default:'scale',
# Min: , Max:
# enumeration options: auto ,scale ,manual ,
granularityParameterType = 'auto' #default
if (exists("settings_clusterNum_params_granularityParameterType"))
{
granularityParameterType = settings_clusterNum_params_granularityParameterType
}
##PBI_PARAM: display_name: Granularity, tooltip:User-defined granularity level, used only if autoGranularity = FALSE. Smaller values correspond to more clusters
# Type: numeric, default:50,
# Min: 1, Max:100
granularityLevel = 50 #default
if (exists("settings_clusterNum_params_percentile"))
{
granularityLevel = settings_clusterNum_params_percentile
granularityLevel = max( min (granularityLevel, 100), 1)
}
##PBI_PARAM: display_name: Reachability distance, tooltip:How close points should be to each other to be considered a part of a cluster (eps)
# Type: numeric, default:0,
# Min: 0, Max:1e+08
eps = 0 #default
if (exists("settings_clusterNum_params_eps"))
{
eps = settings_clusterNum_params_eps
eps = max( min (eps, 1e+08), 0)
}
##PBI_PARAM: display_name: Find minimum points automatically, tooltip:
# Type: bool, default:TRUE,
# Min: , Max:
autoParamMinPts = TRUE #default
if (exists("settings_clusterNum_params_autoParamMinPts"))
{
autoParamMinPts = settings_clusterNum_params_autoParamMinPts
}
##PBI_PARAM: display_name: Minimum points per cluster, tooltip:User-defined minimum points parameter. Smaller values correspond to more clusters
# Type: numeric, default:5,
# Min: 1, Max:1000
minPtsParam = 5 #default
if (exists("settings_clusterNum_params_minPtsParam"))
{
minPtsParam = settings_clusterNum_params_minPtsParam
minPtsParam = max( min (minPtsParam, 1000), 1)
}
##PBI_PARAM: display_name: Draw ellipse, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawEllipse = FALSE #default
if (exists("settings_viz_params_drawEllipse"))
{
drawEllipse = settings_viz_params_drawEllipse
}
##PBI_PARAM: display_name: Draw convex hull, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawConvexHull = FALSE #default
if (exists("settings_viz_params_drawConvexHull"))
{
drawConvexHull = settings_viz_params_drawConvexHull
}
##PBI_PARAM: display_name: Draw centroid, tooltip:
# Type: bool, default:FALSE,
# Min: , Max:
drawCenters = FALSE #default
if (exists("settings_viz_params_drawCenters"))
{
drawCenters = settings_viz_params_drawCenters
}
##PBI_PARAM: display_name: Point opacity, tooltip:
# Type: numeric, default:30,
# Min: 0, Max:100
pointOpacity = 30 #default
if (exists("settings_viz_params_percentile"))
{
pointOpacity = settings_viz_params_percentile
pointOpacity = max( min (pointOpacity, 100), 0)
}
##PBI_PARAM: display_name: Point size, tooltip:
# Type: numeric, default:10,
# Min: 1, Max:50
pointSize = 10 #default
if (exists("settings_viz_params_weight"))
{
pointSize = settings_viz_params_weight
pointSize = max( min (pointSize, 50), 1)
}
##PBI_PARAM: display_name: Font size, tooltip:
# Type: numeric, default:8,
# Min: 8, Max:40
labelingFontSize = 8 #default
if (exists("settings_labeling_params_textSize"))
{
labelingFontSize = settings_labeling_params_textSize
labelingFontSize = max( min (labelingFontSize, 40), 8)
}
##PBI_PARAM: display_name: Label opacity, tooltip:
# Type: numeric, default:100,
# Min: 0, Max:100
labelOpacity = 100 #default
if (exists("settings_labeling_params_percentile"))
{
labelOpacity = settings_labeling_params_percentile
labelOpacity = max( min (labelOpacity, 100), 0)
}
##PBI_PARAM: display_name: Limit label length, tooltip:Abbreviate labels to a limited length
# Type: numeric, default:5,
# Min: 1, Max:100
maxLenPointLabel = 5 #default
if (exists("settings_labeling_params_maxLenPointLabel"))
{
maxLenPointLabel = settings_labeling_params_maxLenPointLabel
maxLenPointLabel = max( min (maxLenPointLabel, 100), 1)
}
##PBI_PARAM: display_name: Percentage of points labeled, tooltip:Avoids cluttering text
# Type: numeric, default:100,
# Min: 0, Max:100
percPointsLabeled = 100 #default
if (exists("settings_labeling_params_percentile1"))
{
percPointsLabeled = settings_labeling_params_percentile1
percPointsLabeled = max( min (percPointsLabeled, 100), 0)
}
##PBI_PARAM: display_name: Font size, tooltip:
# Type: numeric, default:8,
# Min: 1, Max:40
representativeFontSize = 8 #default
if (exists("settings_representative_params_textSize"))
{
representativeFontSize = settings_representative_params_textSize
representativeFontSize = max( min (representativeFontSize, 40), 1)
}
##PBI_PARAM: display_name: Limit label length, tooltip:Abbreviate labels to a limited length
# Type: numeric, default:30,
# Min: 1, Max:100
maxLenDelegate = 30 #default
if (exists("settings_representative_params_maxLenDelegate"))
{
maxLenDelegate = settings_representative_params_maxLenDelegate
maxLenDelegate = max( min (maxLenDelegate, 100), 1)
}
##PBI_PARAM: display_name: Pallete type, tooltip:Color pallete type
# Type: enumeration, default:'rainbow',
# Min: , Max:
# enumeration options: rainbow ,gray ,cm ,topo ,terrain ,heat ,
palleteType = 'rainbow' #default
if (exists("settings_legend_params_palleteType"))
{
palleteType = settings_legend_params_palleteType
}
##PBI_PARAM: display_name: Color of outliers, tooltip:
# Type: fill, default:'black',
# Min: , Max:
outLierCol = 'black' #default
if (exists("settings_legend_params_outLierCol"))
{
outLierCol = settings_legend_params_outLierCol
}
##PBI_PARAM: display_name: Show warnings, tooltip:
# Type: bool, default:TRUE,
# Min: , Max:
showWarnings = TRUE #default
if (exists("settings_additional_params_showWarnings"))
{
showWarnings = settings_additional_params_showWarnings
}
{
if(eps ==0) #not valid "eps" => "auto" mode
eps = NULL
#addLegend = TRUE
delegateCex = representativeFontSize/10
skipLabel2points = max(100/as.numeric(percPointsLabeled) , 1)
labelOpacity = labelOpacity/100
pointOpacity = pointOpacity/100
cexLabel2points = labelingFontSize/10
pointMarkerSize = pointSize/10
drawPoints = TRUE
if(!is.null(Values))
dataset = Values
if(!is.null(PointLabels))
PointLabels[,1] = as.character(PointLabels[,1])
if(!is.null(PointLabels) && !is.null(Values))
dataset = cbind(PointLabels, dataset)
if(!is.null(PointLabels) && is.null(Values))
dataset = PointLabels
if(addLabel2points && is.null(PointLabels))
addLabel2points = FALSE
###############Upfront input correctness validations (where possible)#################
minPtsParam = round(minPtsParam)
pbiWarning <- NULL
dataset <- na.omit(dataset) # deletion of missing
orig_dataset <- dataset #used later for delegates
# verify correctness of dataset
useColumns <- sapply(dataset, correctColumn)
if(showWarnings && sum(useColumns[-1])<ncol(dataset)-1)
pbiWarning <- "At least one of the columns was not numeric, or constant"
#exclude defect columns
dataset <- as.data.frame(dataset[, useColumns])
nc <- ncol(dataset)
nr <- nrow(dataset)
checkDimiensionality <- TRUE
if(nc < 2 || nr < minSamplesToRun || is.null(Values))
{
checkDimiensionality <- FALSE
if(showWarnings)
pbiWarning <- paste(pbiWarning, "\nNot enough input dimensions");
}
if(!autoParamMinPts && minPtsParam >= nr)
{
checkDimiensionality <- FALSE
if(showWarnings)
pbiWarning <- paste(pbiWarning, "\nParameter minPts is out of limits");
}
##############Main Visualization script###########
maxGLevel = 100 # for "scale" mode maxGLevel can be used to squeeze the range
set.seed(randSeed)
if(!checkDimiensionality)
{
plot.new()
}else
{
if(scaleData)
{
dataset <- as.data.frame(scale(dataset))
names(dataset) = paste(names(dataset), "scaled", sep = ".")
}
if(applyPCA)
{
dataset.pca <- prcomp(dataset, center = TRUE, scale = F)
pExplained <- dataset.pca$sdev^2/sum(dataset.pca$sdev^2)
flags <- (pExplained > insigPC); flags[1:2] = TRUE #at least 2 dimensions
dataset = as.data.frame(dataset.pca$x[, flags])#reduce dimensions with less than 5% variance
}
if(autoParamMinPts)
minPtsParam = autoParamMinPtsFunc(dataset, extraPointPerRowCount = 175)
#find eps
if(granularityParameterType == "auto")
eps = autoEpsFunc(dataset, maxSamples4autoGranularity, minPtsParam)
if(granularityParameterType == "scale")
eps <- granularity2epsMinMax(dataset, granularityLevel/maxGLevel, maxSamples4autoGranularity, minPtsParam)
if(is.null(eps))
eps = autoEpsFunc(dataset, maxSamples4autoGranularity, minPtsParam)
#DBSCAN call
cl <- dbscan::dbscan(dataset, eps, minPts = minPtsParam, scale = FALSE, borderPoints = TRUE)
numOfClusters = max(cl$cluster)
cl$centers <- centersFromClusters(dataset, cl$cluster)
drawColors <- c(outLierCol, myPallete(numOfClusters,palleteType = palleteType))
drawColorsLeg <- c(rainbow(numOfClusters), outLierCol)
drawPch <- c(outLierPch, rep(19, numOfClusters))
#visualize 2 first coordinates
if(drawPoints) colpoints = drawColors[cl$cluster+1] else colpoints = NULL
pchPoints = drawPch[cl$cluster+1]
#in case of legend extend xlim to the right by 20%
xrange = range(dataset[, 1])
drange = xrange[2]-xrange[1]
xlim = c(xrange[1]-0.01*drange, xrange[2]+0.01*drange+drange*0.20*addLegend)
plot(dataset[, 1], dataset[, 2], col = alpha(colpoints, pointOpacity), pch = pchPoints,
xlab = cutStr2Show(names(dataset)[1], strCex =1.1, isH = TRUE),
ylab = cutStr2Show(names(dataset)[2], strCex =1.1, isH = FALSE),
xlim = xlim, cex = pointMarkerSize)
leg <- NULL
if(!is.null(cl$centers))
leg <- paste("Cluster ", seq(1, length.out = numOfClusters)) #text
pc <- c(rep(19, numOfClusters)) # markers
colMarkers <- drawColors[-1]
if(drawCenters && !is.null(cl$centers))
{
points(cl$centers, pch = 7, col = drawColors[-1])
leg <- cbind(leg, paste("Cluster center " , seq(1, length.out = numOfClusters)))
pc <- cbind(pc, rep(7, numOfClusters))
colMarkers <- rep(colMarkers, 2)
}
leg <- c(leg, "Outlers")
pc = c(pc, outLierPch)
colMarkers <- c(colMarkers, outLierCol)
if(drawEllipse)
{
for(clus in seq(1, length.out = numOfClusters))
{
iii <- (cl$cluster == clus)
if(sum(iii) > 2)
dataEllipse(dataset[iii, 1], dataset[iii, 2], add = T, plot.points = F, levels = 0.85, col = drawColorsLeg[clus], lwd = 1,
fill = TRUE, fill.alpha = 0.075, center.pch = NULL)
}
}
if(drawConvexHull)
{
for(clus in seq(1, length.out = numOfClusters))
{
iii <- (cl$cluster == clus)
if(sum(iii) > 2)
plotCH(dataset[iii, 1], dataset[iii, 2], lcolor = drawColorsLeg[clus])
}
}
if(addLabel2clusterDelegate)
{
clean_data = dataset
cluster_centers = (cl$centers)
if(!is.null(cluster_centers))
{
deleg <- getDelegates(orig_dataset, dataset, cl$centers)
delegateText = abbreviate(apply(deleg, 1, toString),maxLenDelegate)
delegateText = sapply(delegateText, cutStr2Show, strCex = delegateCex, partAvailable = 0.75)
text(x = cl$centers[, 1], y = cl$centers[, 2],
delegateText,
col = "black",
cex = delegateCex)
}
}
if(addLabel2points)
{
iii=sample(1:nrow(dataset),max(1,floor(nrow(dataset)/skipLabel2points)))
text(x = dataset[iii, 1], y = dataset[iii, 2], labels = abbreviate(orig_dataset[iii,1],maxLenPointLabel),
col = alpha(colpoints[iii], labelOpacity), cex = cexLabel2points)
}
if(addLegend && validateIfToShowLegend(numClust = numOfClusters, textSize = legendTextSize ))
legend("topright", legend = leg, pch = pc, col = alpha(colMarkers, 1), cex = legendTextSize)
}
if(showWarnings && !is.null(pbiWarning))
{
pbiWarning = cutStr2Show(pbiWarning, strCex = warningCex)
title(main = NULL, sub = pbiWarning, outer = FALSE, col.sub = "gray50", cex.sub = warningCex)
}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rotate3d.R
\name{flip3D}
\alias{flip3D}
\title{Flip a 3D array along an axis.}
\usage{
flip3D(volume, axis = 1L, how = "horizontally")
}
\arguments{
\item{volume}{a 3D image volume}
\item{axis}{positive integer in range 1L..3L or an axis name, the axis to use.}
\item{how}{character string, one of 'horizontally' / 'h' or 'vertically' / 'v'. How to flip the 2D slices. Note that flipping *horizontally* means that the image will be mirrored along the central *vertical* axis.}
}
\value{
a 3D image volume, flipped around the axis. The dimensions are identical to the dimensions of the input image.
}
\description{
Flip the slice of an 3D array horizontally or vertically along an axis. This leads to an output array with identical dimensions.
}
\seealso{
Other volume math:
\code{\link{rotate3D}()}
}
\concept{volume math}
| /man/flip3D.Rd | permissive | dfsp-spirit/freesurferformats | R | false | true | 904 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rotate3d.R
\name{flip3D}
\alias{flip3D}
\title{Flip a 3D array along an axis.}
\usage{
flip3D(volume, axis = 1L, how = "horizontally")
}
\arguments{
\item{volume}{a 3D image volume}
\item{axis}{positive integer in range 1L..3L or an axis name, the axis to use.}
\item{how}{character string, one of 'horizontally' / 'h' or 'vertically' / 'v'. How to flip the 2D slices. Note that flipping *horizontally* means that the image will be mirrored along the central *vertical* axis.}
}
\value{
a 3D image volume, flipped around the axis. The dimensions are identical to the dimensions of the input image.
}
\description{
Flip the slice of an 3D array horizontally or vertically along an axis. This leads to an output array with identical dimensions.
}
\seealso{
Other volume math:
\code{\link{rotate3D}()}
}
\concept{volume math}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{outliers}
\alias{outliers}
\title{Extracting the standard error}
\usage{
outliers(object, as.ts = FALSE)
}
\arguments{
\item{object}{a praislm or twoStepsBenchmark object.}
\item{as.ts}{a boolean of length 1. If \code{TRUE}, the returned
outliers are returned as a time series with (dim and colnames).
If \code{FALSE}, the returned outliers is the named list that was
submitted as a function argument.}
}
\value{
a named list or a time series, depending of the
argument \code{"as.ts"}.
}
\description{
The function \code{outliers} returns the outliers
from either a \link{praislm} or a \link{twoStepsBenchmark} object.
}
\keyword{internal}
| /man/outliers.Rd | permissive | InseeFr/disaggR | R | false | true | 735 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{outliers}
\alias{outliers}
\title{Extracting the standard error}
\usage{
outliers(object, as.ts = FALSE)
}
\arguments{
\item{object}{a praislm or twoStepsBenchmark object.}
\item{as.ts}{a boolean of length 1. If \code{TRUE}, the returned
outliers are returned as a time series with (dim and colnames).
If \code{FALSE}, the returned outliers is the named list that was
submitted as a function argument.}
}
\value{
a named list or a time series, depending of the
argument \code{"as.ts"}.
}
\description{
The function \code{outliers} returns the outliers
from either a \link{praislm} or a \link{twoStepsBenchmark} object.
}
\keyword{internal}
|
rm(list=ls())
library(dplyr)
library(tidyr)
setwd("~/RStats")
df <- read.csv('data/Churn.csv', stringsAsFactors = F)
names(df)
contract <- df %>%
select(Account.Length, VMail.Message, Day.Mins, Eve.Mins,
Night.Mins, Intl.Mins, CustServ.Calls, Churn, Int.l.Plan, VMail.Plan,
Area.Code, Phone)
usage <- df%>%
select(Area.Code, Phone, Day.Calls, Day.Charge,Eve.Calls, Eve.Charge,
Night.Calls, Night.Charge, Intl.Calls, Intl.Charge, State)
write.csv(usage, "Phone_Usage.csv", row.names = F)
write.csv(contract, "Contracts.csv", row.names = F)
df2 <- inner_join(usage, contract)
#quick check
table(df$Churn)
#split the dataset into Train and Test
split = as.integer(nrow(df)*0.8)
train = df[1:split,]
test = df[(split+1):nrow(df), ]
## here's an arguably better way to split
set.seed(4444)
train_rows = sample(1:nrow(df), 0.8*nrow(df))
str(train_rows)
train = df[train_rows, ]
test <- df[-train_rows,]
table(train$Churn)
table(test$Churn)
# Random Forest prediction of Churn data
names(df)
library(randomForest)
fit <- randomForest(Churn ~ CustServ.Calls + Day.Charge + Eve.Charge
+ Night.Charge + Intl.Charge, data=df)
print(fit) # view results
importance(fit) # importance of each predictor
text(fit, pretty=0)
#----------------------------------------
library(rpart)
# grow tree
tree <- rpart(Churn ~ CustServ.Calls + Day.Charge + Eve.Charge
+ Night.Charge + Intl.Charge,
method="class", data=train)
formula2 <- Churn ~ CustServ.Calls + Intl.Charge
fit2 <- rpart(formula2,
method="class", data=train)
printcp(fit2)
plotcp(fit)
summary(fit2)
# plot tree
plot(tree, uniform=TRUE,
main="Classification Tree for Churn")
text(tree, use.n=TRUE, all=TRUE, cex=.8)
# create attractive postscript plot of tree
post(fit, file = "churn_tree.ps",
title = "Classification Tree for Churn Data")
#Try some predictions
variablesToUse <- c("CustServ.Calls", "Day.Charge", "Eve.Charge",
"Night.Charge", "Intl.Charge")
treePredictions <- predict(tree, newdata=test,
type='class')
table(treePredictions, test$Churn)
#predictions using formula 2
pred_fit2 <- predict(fit2, newdata=test, type='class')
table(pred_fit2, test$Churn)
| /usecases/churn_prediction.R | no_license | Ram-N/analysis-class | R | false | false | 2,292 | r | rm(list=ls())
library(dplyr)
library(tidyr)
setwd("~/RStats")
df <- read.csv('data/Churn.csv', stringsAsFactors = F)
names(df)
contract <- df %>%
select(Account.Length, VMail.Message, Day.Mins, Eve.Mins,
Night.Mins, Intl.Mins, CustServ.Calls, Churn, Int.l.Plan, VMail.Plan,
Area.Code, Phone)
usage <- df%>%
select(Area.Code, Phone, Day.Calls, Day.Charge,Eve.Calls, Eve.Charge,
Night.Calls, Night.Charge, Intl.Calls, Intl.Charge, State)
write.csv(usage, "Phone_Usage.csv", row.names = F)
write.csv(contract, "Contracts.csv", row.names = F)
df2 <- inner_join(usage, contract)
#quick check
table(df$Churn)
#split the dataset into Train and Test
split = as.integer(nrow(df)*0.8)
train = df[1:split,]
test = df[(split+1):nrow(df), ]
## here's an arguably better way to split
set.seed(4444)
train_rows = sample(1:nrow(df), 0.8*nrow(df))
str(train_rows)
train = df[train_rows, ]
test <- df[-train_rows,]
table(train$Churn)
table(test$Churn)
# Random Forest prediction of Churn data
names(df)
library(randomForest)
fit <- randomForest(Churn ~ CustServ.Calls + Day.Charge + Eve.Charge
+ Night.Charge + Intl.Charge, data=df)
print(fit) # view results
importance(fit) # importance of each predictor
text(fit, pretty=0)
#----------------------------------------
library(rpart)
# grow tree
tree <- rpart(Churn ~ CustServ.Calls + Day.Charge + Eve.Charge
+ Night.Charge + Intl.Charge,
method="class", data=train)
formula2 <- Churn ~ CustServ.Calls + Intl.Charge
fit2 <- rpart(formula2,
method="class", data=train)
printcp(fit2)
plotcp(fit)
summary(fit2)
# plot tree
plot(tree, uniform=TRUE,
main="Classification Tree for Churn")
text(tree, use.n=TRUE, all=TRUE, cex=.8)
# create attractive postscript plot of tree
post(fit, file = "churn_tree.ps",
title = "Classification Tree for Churn Data")
#Try some predictions
variablesToUse <- c("CustServ.Calls", "Day.Charge", "Eve.Charge",
"Night.Charge", "Intl.Charge")
treePredictions <- predict(tree, newdata=test,
type='class')
table(treePredictions, test$Churn)
#predictions using formula 2
pred_fit2 <- predict(fit2, newdata=test, type='class')
table(pred_fit2, test$Churn)
|
# Set R terminal widht to actual terminal size
#options(width=Sys.getenv("COLUMNS"))
# Make all repositories available for package download:
setRepositories(ind = c(1,2,3,4,5,6,7,8,9))
# load libraries
# define Platform and initial path ----------------------------
if ( grepl(x = R.Version()["os"], pattern = "linux" ) ) {
pathPrefix <- "/data/SKDngs01/pg/"
} else {
pathPrefix <- "X:/pg/"
}
# Set your working directory where output files will be saved ---------
workingDir <- paste(pathPrefix , "collaborators/junke/eigenTrait_DEG/Raw", sep = "");
#
setwd(dir = workingDir)
# read in file names
# b1_tub_comps <- dir(pattern = "_T_*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# b2_tub_comps <- dir(pattern = "_T_*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# glom_comps <- dir(pattern = "*.txt", all.files = F,full.names = T,include.dirs = F, recursive = T)
#combined_comps <- c(tub_comps,glom_comps)
combined_comps <- dir(pattern = "deg_biopsy*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# collection of resulting outputs from analysis
res <- list()
colsToExtract <- c("adj.P.Val","rstat")
# isFirst <- TRUE
genesOrder <- character()
message("Preprocessing comparisons...")
for (i in combined_comps) {
message("Reading file: ",i)
if (grepl(pattern = "dge_Summary.txt", x = i, fixed = T)) {
message("Skipping")
next;
}
output <- read.table(file = i,header = T,sep = "\t",quote = "",as.is = T)
rownames(output) <- output[,"geneName"]
# parse file name
i <- gsub(pattern = ".txt",replacement = "", x = i, fixed = T)
i <- gsub(pattern = "./deg_",replacement = "", x = i, fixed = T)
i <- gsub(pattern = "biopsy_",replacement = "B_", x = i, fixed = T)
i <- gsub(pattern = "_X.",replacement = "_", x = i, fixed = T)
message("Name parsed as: ",i)
# if (isFirst) {
# genesOrder <- setNames(object = as.character(output[,"geneName"]), nm = as.character(row.names(output)))
# isFirst <- FALSE
# }
# res[[i]] <- output[names(genesOrder), colsToExtract]
genesOrder <- unique(c(genesOrder,as.character(output[,"geneName"])))
res[[i]] <- output[, colsToExtract]
}
for (i in names(res)) {
message("Resorting: ", i)
res[[i]] <- res[[i]][genesOrder,]
}
FirstHeader <- rep(names(res), each = 2)
SecondHeader <- rep(colsToExtract, length(res))
FinalHeader <- paste(FirstHeader,SecondHeader, sep = "_")
resDF <- as.data.frame(matrix(unlist(res), nrow = length(genesOrder), ncol = length(res)*length(colsToExtract)))
colnames(resDF) <- FinalHeader
FinalDF <- cbind(Symbol = genesOrder,
resDF)
write.table(x = FinalDF,
file = "DEGTableWithAllComps.txt",
sep = "\t",
quote = F,
row.names = F,
col.names = T)
saveRDS(object = FinalDF, compress = T, file = "DEGTableWithAllComps.rds")
| /compileDEGTableWithAllComps.R | no_license | Kris69w/BI_Summer | R | false | false | 2,877 | r | # Set R terminal widht to actual terminal size
#options(width=Sys.getenv("COLUMNS"))
# Make all repositories available for package download:
setRepositories(ind = c(1,2,3,4,5,6,7,8,9))
# load libraries
# define Platform and initial path ----------------------------
if ( grepl(x = R.Version()["os"], pattern = "linux" ) ) {
pathPrefix <- "/data/SKDngs01/pg/"
} else {
pathPrefix <- "X:/pg/"
}
# Set your working directory where output files will be saved ---------
workingDir <- paste(pathPrefix , "collaborators/junke/eigenTrait_DEG/Raw", sep = "");
#
setwd(dir = workingDir)
# read in file names
# b1_tub_comps <- dir(pattern = "_T_*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# b2_tub_comps <- dir(pattern = "_T_*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# glom_comps <- dir(pattern = "*.txt", all.files = F,full.names = T,include.dirs = F, recursive = T)
#combined_comps <- c(tub_comps,glom_comps)
combined_comps <- dir(pattern = "deg_biopsy*", all.files = F,full.names = T,include.dirs = F, recursive = F)
# collection of resulting outputs from analysis
res <- list()
colsToExtract <- c("adj.P.Val","rstat")
# isFirst <- TRUE
genesOrder <- character()
message("Preprocessing comparisons...")
for (i in combined_comps) {
message("Reading file: ",i)
if (grepl(pattern = "dge_Summary.txt", x = i, fixed = T)) {
message("Skipping")
next;
}
output <- read.table(file = i,header = T,sep = "\t",quote = "",as.is = T)
rownames(output) <- output[,"geneName"]
# parse file name
i <- gsub(pattern = ".txt",replacement = "", x = i, fixed = T)
i <- gsub(pattern = "./deg_",replacement = "", x = i, fixed = T)
i <- gsub(pattern = "biopsy_",replacement = "B_", x = i, fixed = T)
i <- gsub(pattern = "_X.",replacement = "_", x = i, fixed = T)
message("Name parsed as: ",i)
# if (isFirst) {
# genesOrder <- setNames(object = as.character(output[,"geneName"]), nm = as.character(row.names(output)))
# isFirst <- FALSE
# }
# res[[i]] <- output[names(genesOrder), colsToExtract]
genesOrder <- unique(c(genesOrder,as.character(output[,"geneName"])))
res[[i]] <- output[, colsToExtract]
}
for (i in names(res)) {
message("Resorting: ", i)
res[[i]] <- res[[i]][genesOrder,]
}
FirstHeader <- rep(names(res), each = 2)
SecondHeader <- rep(colsToExtract, length(res))
FinalHeader <- paste(FirstHeader,SecondHeader, sep = "_")
resDF <- as.data.frame(matrix(unlist(res), nrow = length(genesOrder), ncol = length(res)*length(colsToExtract)))
colnames(resDF) <- FinalHeader
FinalDF <- cbind(Symbol = genesOrder,
resDF)
write.table(x = FinalDF,
file = "DEGTableWithAllComps.txt",
sep = "\t",
quote = F,
row.names = F,
col.names = T)
saveRDS(object = FinalDF, compress = T, file = "DEGTableWithAllComps.rds")
|
library(tidyverse)
library(lubridate)
source('./data_preprocessing/processing_utils.R')
species_names = read_csv('raw_data/hjandrews/species_names.csv')
species_status_info = read_csv('raw_data/hjandrews/species_status_info.csv')
observations = read_csv('raw_data/hjandrews/TV07501_v1_without_encoding.csv') %>%
rename(species_code=SPECIES) %>%
left_join(species_names, by='species_code') %>%
select(year = YEAR, plot = PLOT, individual_id = TAG, date = SAMPLEDATE, species, vegatative = VEG_CODE, reproductive = RPRO_CODE) %>%
gather(obs_type, obs_code, vegatative, reproductive)
#Makes sites numerica to work in the python models
site_info = read_csv('non_npn_site_info.csv') %>%
filter(dataset=='hjandrews') %>%
select(Site_ID, plot=note)
observations = observations %>%
left_join(site_info, by='plot') %>%
select(-plot)
#Convert observations of leavs/flowers to status codes.
observations = observations %>%
left_join(species_status_info, by=c('species','obs_type')) %>%
mutate(status = 1*(obs_code == target_obs_code)) %>%
select(-obs_type, -obs_code, -target_obs_code)
#Add in doy and process
observations = observations %>%
mutate(doy = yday(date)) %>%
select(-date) %>%
process_phenology_observations() %>%
#group_sites_together() %>%
apply_minimum_observation_threshold(min_num_obs = 30)
write_csv(observations, './cleaned_data/hjandrews_observations.csv')
species_counts = observations %>%
group_by(species, Phenophase_ID) %>%
tally()
#Record the species and phenophase type to use in NPN data filter
species = observations %>%
select(species, Phenophase_ID) %>%
distinct() %>%
mutate(dataset='hjandrews')
append_species_file(species)
| /data_preprocessing/compile_hjandrews_data.R | no_license | sdtaylor/phenology_dataset_study | R | false | false | 1,701 | r | library(tidyverse)
library(lubridate)
source('./data_preprocessing/processing_utils.R')
species_names = read_csv('raw_data/hjandrews/species_names.csv')
species_status_info = read_csv('raw_data/hjandrews/species_status_info.csv')
observations = read_csv('raw_data/hjandrews/TV07501_v1_without_encoding.csv') %>%
rename(species_code=SPECIES) %>%
left_join(species_names, by='species_code') %>%
select(year = YEAR, plot = PLOT, individual_id = TAG, date = SAMPLEDATE, species, vegatative = VEG_CODE, reproductive = RPRO_CODE) %>%
gather(obs_type, obs_code, vegatative, reproductive)
#Makes sites numerica to work in the python models
site_info = read_csv('non_npn_site_info.csv') %>%
filter(dataset=='hjandrews') %>%
select(Site_ID, plot=note)
observations = observations %>%
left_join(site_info, by='plot') %>%
select(-plot)
#Convert observations of leavs/flowers to status codes.
observations = observations %>%
left_join(species_status_info, by=c('species','obs_type')) %>%
mutate(status = 1*(obs_code == target_obs_code)) %>%
select(-obs_type, -obs_code, -target_obs_code)
#Add in doy and process
observations = observations %>%
mutate(doy = yday(date)) %>%
select(-date) %>%
process_phenology_observations() %>%
#group_sites_together() %>%
apply_minimum_observation_threshold(min_num_obs = 30)
write_csv(observations, './cleaned_data/hjandrews_observations.csv')
species_counts = observations %>%
group_by(species, Phenophase_ID) %>%
tally()
#Record the species and phenophase type to use in NPN data filter
species = observations %>%
select(species, Phenophase_ID) %>%
distinct() %>%
mutate(dataset='hjandrews')
append_species_file(species)
|
require(ggplot2)
# Load data
b <- read.csv("clients.csv", stringsAsFactors = FALSE)
summary(b)
# Filter by latin american countries
b <- b[b$country %in% c("ar", "bo", "br", "cl", "cr", "do", "ec", "sv", "gt", "hn", "mx", "pa", "py", "pe", "sr", "uy", "ve", "gy", "gf", "ni", "cu"),]
summary(b)
# Plot per country
ggplot(b, aes(x = as.Date(date, "%Y-%m-%d"), y = clients, colour = country)) +
# geom_point() +
geom_line() +
ggtitle("Latin American Tor clients by Country") +
scale_x_date(name = "Date") +
# scale_y_log10() +
facet_grid(node~.,scales = "free_y")
# Aggregated data from all countries
d <- aggregate(list(clients = b$clients), by = list(date = b$date, node = b$node), FUN = sum)
summary(d)
# Plot aggregated data from all countries
ggplot(d, aes(x = as.Date(date, "%Y-%m-%d"), y = clients, colour = node)) +
geom_line() +
ggtitle("Latin American Tor clients") +
scale_x_date(name = "Date") +
# facet_grid(node~.,scales = "free_y") +
expand_limits(y = 0)
| /latin-america.r | no_license | arthuredelstein/metrics-playground | R | false | false | 983 | r | require(ggplot2)
# Load data
b <- read.csv("clients.csv", stringsAsFactors = FALSE)
summary(b)
# Filter by latin american countries
b <- b[b$country %in% c("ar", "bo", "br", "cl", "cr", "do", "ec", "sv", "gt", "hn", "mx", "pa", "py", "pe", "sr", "uy", "ve", "gy", "gf", "ni", "cu"),]
summary(b)
# Plot per country
ggplot(b, aes(x = as.Date(date, "%Y-%m-%d"), y = clients, colour = country)) +
# geom_point() +
geom_line() +
ggtitle("Latin American Tor clients by Country") +
scale_x_date(name = "Date") +
# scale_y_log10() +
facet_grid(node~.,scales = "free_y")
# Aggregated data from all countries
d <- aggregate(list(clients = b$clients), by = list(date = b$date, node = b$node), FUN = sum)
summary(d)
# Plot aggregated data from all countries
ggplot(d, aes(x = as.Date(date, "%Y-%m-%d"), y = clients, colour = node)) +
geom_line() +
ggtitle("Latin American Tor clients") +
scale_x_date(name = "Date") +
# facet_grid(node~.,scales = "free_y") +
expand_limits(y = 0)
|
## These functions can cache matrix inverses in order to reduce the time needed to compute
## the inverses of previously used matrices. This is especially useful when the inverses of
## these matrices are computed repeatedly.
## This function creates a matrix that allows the user to cache its inverse through a list;
## this list contains four different functions.
makeCacheMatrix <- function(x = matrix()) {
n <- NULL
set <- function(y) {
x <<- y
n <<- NULL
}
get <- function() x
setinverse <- function(solve) n <<- solve ## for storing inverse for specific matrix
getinverse <- function() n ## for retrieving cached inverse for specific matrix
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## This function checks if the inverse is calculated.
## If so, this function retrieves the inverse from the cache;
## otherwise, it calculates and stores the inverse.
cacheSolve <- function(x, ...) {
n <- x$getinverse()
if(!is.null(n)) {
message("getting cached data")
return(n)
}
data <- x$get() ## skipped if inverse is previously calculated
n <- solve(data, ...) ## solves inverse of specific matrix
x$setinverse(n) ## stores inverse to cache
n
}
| /cachematrix.R | no_license | rjl1719/Programming-Assignment-2 | R | false | false | 1,394 | r | ## These functions can cache matrix inverses in order to reduce the time needed to compute
## the inverses of previously used matrices. This is especially useful when the inverses of
## these matrices are computed repeatedly.
## This function creates a matrix that allows the user to cache its inverse through a list;
## this list contains four different functions.
makeCacheMatrix <- function(x = matrix()) {
n <- NULL
set <- function(y) {
x <<- y
n <<- NULL
}
get <- function() x
setinverse <- function(solve) n <<- solve ## for storing inverse for specific matrix
getinverse <- function() n ## for retrieving cached inverse for specific matrix
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## This function checks if the inverse is calculated.
## If so, this function retrieves the inverse from the cache;
## otherwise, it calculates and stores the inverse.
cacheSolve <- function(x, ...) {
n <- x$getinverse()
if(!is.null(n)) {
message("getting cached data")
return(n)
}
data <- x$get() ## skipped if inverse is previously calculated
n <- solve(data, ...) ## solves inverse of specific matrix
x$setinverse(n) ## stores inverse to cache
n
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_gt3x_utils.R
\name{check_packets}
\alias{check_packets}
\title{Check for packets with failed checksum}
\usage{
check_packets(packets)
}
\arguments{
\item{packets}{object containing data packets or subsequently parsed data}
}
\description{
Check for packets with failed checksum
}
\keyword{internal}
| /man/check_packets.Rd | permissive | muschellij2/AGread | R | false | true | 382 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_gt3x_utils.R
\name{check_packets}
\alias{check_packets}
\title{Check for packets with failed checksum}
\usage{
check_packets(packets)
}
\arguments{
\item{packets}{object containing data packets or subsequently parsed data}
}
\description{
Check for packets with failed checksum
}
\keyword{internal}
|
# read data
dt <- fread("https://raw.githubusercontent.com/SpehleHSGM/Datasets/main/datatable.csv",sep = ";",dec = ",",check.names=T)
# format
dt[,Wdh:=as.factor(Wdh)][,CO2_Stufe:=as.factor(CO2_Stufe)][,N_Form:=as.factor(N_Form)]
dt[,Knollenvolumen:=as.numeric(Knollenvolumen)][,Frischmasse.Knolle:=as.numeric(Frischmasse.Knolle)]
# define outlier function: Whisker by John W. Tukey
outlier.iqr <- function(dp){
qt <- quantile(dp,probs=c(0.25,0.75),na.rm = TRUE)
iqr <- IQR(dp,na.rm = TRUE)
out <- ifelse(dp>(qt[1]-1.5*iqr) & dp<(qt[2]+1.5*iqr),FALSE,TRUE)
return(out)
}
# define outlier function: median absolute deviation (MAD)
outlier.mad <- function(dp){
med <- median(dp)
mad <- mad(dp, constant=1)
out <- ifelse((dp > med-5.2*mad) & (dp < med + 5.2*mad),FALSE,TRUE)
return(out)
}
# add column Flag_outlier.iqr
dt[,Flag_outlier.iqr:=outlier.iqr(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
# add column Flag_outlier.mad
dt[,Flag_outlier.mad:=outlier.mad(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
# add column Flag_outlier.bonf (mean shift outlier)
dt[,Flag_outlier.bonf:=outlierTest(lm(eval(traitName)~CO2_Stufe*N_Form),cutoff=Inf, n.max=Inf,order=FALSE)$bonf.p < 0.05]
# data.table aggregate of trait means by Wdh*CO2_Stufe*N_Form
dt_means <- dt[ ,.(Knollenvolumen=mean(Knollenvolumen),
Frischmasse.Knolle=mean(Frischmasse.Knolle),
count=.N,
mainPlots=factor(Wdh:CO2_Stufe)),
by=list(Wdh,CO2_Stufe,N_Form)]
# add column with boolean "Flag_outlier" (TRUE/FALSE)
dt_means[,Flag_outlier.iqr:=outlier.iqr(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
| /dtRead_format.R | no_license | SpehleHSGM/Funktions | R | false | false | 1,666 | r | # read data
dt <- fread("https://raw.githubusercontent.com/SpehleHSGM/Datasets/main/datatable.csv",sep = ";",dec = ",",check.names=T)
# format
dt[,Wdh:=as.factor(Wdh)][,CO2_Stufe:=as.factor(CO2_Stufe)][,N_Form:=as.factor(N_Form)]
dt[,Knollenvolumen:=as.numeric(Knollenvolumen)][,Frischmasse.Knolle:=as.numeric(Frischmasse.Knolle)]
# define outlier function: Whisker by John W. Tukey
outlier.iqr <- function(dp){
qt <- quantile(dp,probs=c(0.25,0.75),na.rm = TRUE)
iqr <- IQR(dp,na.rm = TRUE)
out <- ifelse(dp>(qt[1]-1.5*iqr) & dp<(qt[2]+1.5*iqr),FALSE,TRUE)
return(out)
}
# define outlier function: median absolute deviation (MAD)
outlier.mad <- function(dp){
med <- median(dp)
mad <- mad(dp, constant=1)
out <- ifelse((dp > med-5.2*mad) & (dp < med + 5.2*mad),FALSE,TRUE)
return(out)
}
# add column Flag_outlier.iqr
dt[,Flag_outlier.iqr:=outlier.iqr(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
# add column Flag_outlier.mad
dt[,Flag_outlier.mad:=outlier.mad(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
# add column Flag_outlier.bonf (mean shift outlier)
dt[,Flag_outlier.bonf:=outlierTest(lm(eval(traitName)~CO2_Stufe*N_Form),cutoff=Inf, n.max=Inf,order=FALSE)$bonf.p < 0.05]
# data.table aggregate of trait means by Wdh*CO2_Stufe*N_Form
dt_means <- dt[ ,.(Knollenvolumen=mean(Knollenvolumen),
Frischmasse.Knolle=mean(Frischmasse.Knolle),
count=.N,
mainPlots=factor(Wdh:CO2_Stufe)),
by=list(Wdh,CO2_Stufe,N_Form)]
# add column with boolean "Flag_outlier" (TRUE/FALSE)
dt_means[,Flag_outlier.iqr:=outlier.iqr(eval(traitName)),by=interaction(CO2_Stufe,N_Form)]
|
# We first want to read our data so that R registers it as a dataset!
read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/hab.sta.csv")
# We can then input this read dataset into a vector so we can use it for processing!
dat_habitat <- read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/hab.sta.csv")
read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/bird.sta.csv")
dat_birds <- read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/bird.sta.csv")
# You can also use the 'here' package to download and read data files into R from your local drive
# here() works from your working directory, and you need to input subsequent coordinates within that working directory for it to identify it
# Your working directory is by default based off of the folder which holds your R-script file!
# getwd() - tells you what your current working directory is
# You can also manually set your working directory with setwd() - this is unsafe because your wd can always change
getwd()
require(here)
here("AoED Data", "AoED Birds", "bird.sta.csv")
here("AoED Data", "AoED Birds", "hab.sta.csv")
read.csv(here("AoED Data", "AoED Birds", "bird.sta.csv"))
read.csv(here("AoED Data", "AoED Birds", "hab.sta.csv"))
# file.exists() tells you if you're looking in the right spot for your file
file.exists(here("AoED Data", "AoED Birds", "bird.sta.csv"))
file.exists(here("AoED Data", "AoED Birds", "hab.sta.csv"))
# If the file truly exists, it will output : TRUE!
# Library() and require() allow you to load extra packages that you;ve installed
# Library() will re-load already loaded packages, while require() will check if it's already loaded and will decide whether or not it needs to activate it (does not reactivate)
# Using this at the beginning of your script is helpful!
library(name_of_package)
require(name_of_package)
# Require() is much more streamlined because some packages take a long time to load!
# ------------------------------------------------------------------------------------------------------------------- #
# In-Class R-Script! #
pairs(dat_habitat[, c("slope", "aspect")])
# pairs() creates multiple scatterplots for multiple variables - and most importantly for
# each combination of each variable! In this case we plotted just slope and aspect, but
# it is possible to add more variables which will all be plotted against each other.
# Next, for using the his() script, we first need to find the maximum data value.
# This is to allow the break argument to function properly.
max(dat_birds[,"AMRO"])
# Output: [1] 4
# This means that there is a maximum value of 4
# In the context of the dat_birds dataset, this means that the most amount of
# American Robins (AMRO) observed at a specific site is 4!
# This tells us our minimum break range.
# The break argument requires that the range be at least the
# total number of observations.
# Now to use the hist() function to plot a histogram in R!
hist(dat_birds[,"AMRO"],breaks = 0:5 - .5, xlab = "Number of Birds Counted", ylab = "Frequency", main = "Histogram of American Robin")
# In the dataset there is a max of [4] American Robins observations at a single site,
# which would initially make you want to have a break range of 0:4.
# Though this works, it doesn’t work well for discrete data where a break = 0:4
# will give you a histogram with x-values depicted as ranges from 0-1, 1-2, 2-3 etc..
# So in this case, we can fix this by adding 1 to the break range so you are able to more
# accurately describe the discrete nature of the data! By adding the 5th break range,
# you isolate each value as its own discrete (non-continuous) value!
# ------------------------------------------------------------------------------------------------------------------- #
# Individual Assignment Portion #
?par()
# par() allows us to view plots side by side
# First number is number of rows to arrange
# Second number is number of columns to arrange
dat_all = merge(dat_birds, dat_habitat)
?hist()
par(mfrow = c(1,3))
hist(dat_all$elev,
xlab = "Elevation (m)",
main = "Frequency of Elevation (m) Observed",
xlim = c(0, 1000))
hist(dat_all$slope,
main = "Frequency of Slope (%) Observed",
xlab = "Slope (%)",
xlim = c(0, 120),
ylim = c(0, 175))
hist(dat_habitat$aspect,
xlab = "Aspect (degrees)",
main = "Frequency of Aspect (degrees) Observed",
ylim = c(0, 175),
xlim = c(0, 400))
# dev.off() will clear the current plot being plotted!
dev.off()
# ---------------------------------------------------------------------------------- #
# Calculates the value of x for a linear function, given the coordinates
# of a known point (x1, y1) and the slope of the line.
line_point_slope = function(x, x1, y1, slope)
{
get_y_intercept =
function(x1, y1, slope)
return(-(x1 * slope) + y1)
linear =
function(x, yint, slope)
return(yint + x * slope)
return(linear(x, get_y_intercept(x1, y1, slope), slope))
}
# x1 and y1 are the coordinates which anchors your slope! It can be anywhere on the graph.
curve(line_point_slope(x, x1 = , y1 = , slope = ), add = TRUE)
# ---------------------------------------------------------------------------------- #
par(mfrow = c(1,3))
# You can combine this w/ the mtext() and line() to label!
plot(dat_all$elev, y = dat_all$ba.tot,
xlab = "Elevation (m)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Elevation")
curve(line_point_slope(x, x1 = 250, y1 = 30, slope = -0.01), add = TRUE)
plot(x = dat_all$slope, y = dat_all$ba.tot,
xlab = "Slope (%)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Slope")
curve(line_point_slope(x, x1 = 10, y1 = 20, slope = -0.06), add = TRUE)
# Slope is the steepness or the degree of incline of a surface
plot(dat_all$aspect, y = dat_all$ba.tot,
xlab = "Aspect (degrees)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Aspect")
curve(line_point_slope(x, x1 = 0, y1 = 20, slope = 0), add = TRUE)
# Aspect is the orientation of slope, measured clockwise in degrees from 0 to 360
# Where 0 is north-facing, 90 is east-facing, 180 is south-facing, and 270 is west-facing
# ------------------------------------------------------------------------------------------------------------------- #
# Lab 3 #
# Create pair plots using psych to plot slope, elevation and aspect!
# Use the pair plot function from psych to create a pair plot
# of the three terrain variables and basal area from the lecture questions.
# psych package helps make pretty pair plots
?pairs.panels
require(psych)
env_var = data.frame(dat_all$slope, dat_all$elev, dat_all$aspect, dat_all$ba.tot)
env_var
pairs.panels(env_var)
# ---------------------------------------------------------------------------------- #
# We want to merge dat_habitat and dat_birds
# How do we know if there are the same number of rows in both data frames?
# How can we be sure that we associate the correct row of dat+habitat/birds
dat_all = merge(dat_birds, dat_habitat)
plot(ba.tot ~ elev, data = dat_all)
# How to use R to to calculate the total number of waxwings
CEWA_sum = sum(dat_birds[, "CEWA"])
CEWA_sum
# Converting to Presence/Absence using as.numeric *********
CEWA_boolean = dat_birds[, "CEWA"] >= 1
CEWA_boolean
# You could also have inputted the CEWA_boolean vector script directly into the as.numeric() function
CEWA_present_absent = as.numeric(CEWA_boolean)
CEWA_present_absent
# This code creates a binary plot like in the McGarigal reading!
# You can transform these well with logistic models
plot(x = dat_all$elev, y = CEWA_present_absent)
# ---------------------------------------------------------------------------------- #
# Function to calculate the logistic parameter a given the slope and midpoint
get_logistic_param_a = function(slope, midpoint)
{
b = slope / 4
return (-midpoint * (slope / 4))
}
# Function to calculate the logistic parameter b given the slope
get_logistic_param_b = function(slope)
{
return (slope / 4)
}
# Calculate the value of the logistic function at x, given the parameters a and b.
logistic = function(x, a, b)
{
val = exp(a + b * x)
return(val / (1 + val))
}
# Calculate the value of the logistic function at x, given a slopoe and midpoint.
logistic_midpoint_slope = function(x, midpoint, slope)
{
b = get_logistic_param_b(slope)
a = get_logistic_param_a(slope, midpoint)
return(logistic(x, a, b))
}
plot(x = dat_all$elev, y = CEWA_present_absent)
curve(logistic_midpoint_slope(x, midpoint = 400, slope = -0.05), add = TRUE)
# ---------------------------------------------------------------------------------- #
# Question 1 & 2: Choose Two Bird Species and create plots of presence/absence (y) and basal area (x)
# I chose the American Goldfinch (AMGO) and Black-cap Chickadee (BCCH)
AMGO_boolean = dat_birds[, "AMGO"] >= 1
AMGO_present_absent = as.numeric(AMGO_boolean)
plot(x = dat_habitat$ba.tot, y = AMGO_present_absent,
main = "Total Basal Area versus American Goldfinch Observations",
xlab = "Total Basal Area (m^2 per ha)",
ylab = "Presence/Absence of American Goldfinch")
curve(logistic_midpoint_slope(x, midpoint = 12, slope = -0.8), add = TRUE)
BCCH_boolean = dat_birds[, "BCCH"] >= 1
BCCH_present_absent = as.numeric(BCCH_boolean)
plot(x = dat_habitat$ba.tot, y = BCCH_present_absent,
main = "Total Basal Area versus Black Cap Chickadee Observations",
xlab = "Total Basal Area (m^2 per ha)",
ylab = "Presence/Absence of Black-cap Chickadee")
curve(logistic_midpoint_slope(x, midpoint = 25, slope = -1), add = TRUE)
# Question 3: Sum of Observed Grey Jays
GRJA_sum = sum(dat_all[, "GRJA"])
GRJA_sum
# Question 4: Total number of sampling sites in which Gray Jay were observed
GRJA_sites = sum(dat_all[, "GRJA"] > 0)
GRJA_sites
| /individual_assignments/AoED Labs/AoED Lab 3/AoED - Lab 3.R | no_license | JamesMacCabeTsalah/ECo_602_2020 | R | false | false | 10,523 | r | # We first want to read our data so that R registers it as a dataset!
read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/hab.sta.csv")
# We can then input this read dataset into a vector so we can use it for processing!
dat_habitat <- read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/hab.sta.csv")
read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/bird.sta.csv")
dat_birds <- read.csv("https://michaelfrancenelson.github.io/eco_602_634_2020/data/bird.sta.csv")
# You can also use the 'here' package to download and read data files into R from your local drive
# here() works from your working directory, and you need to input subsequent coordinates within that working directory for it to identify it
# Your working directory is by default based off of the folder which holds your R-script file!
# getwd() - tells you what your current working directory is
# You can also manually set your working directory with setwd() - this is unsafe because your wd can always change
getwd()
require(here)
here("AoED Data", "AoED Birds", "bird.sta.csv")
here("AoED Data", "AoED Birds", "hab.sta.csv")
read.csv(here("AoED Data", "AoED Birds", "bird.sta.csv"))
read.csv(here("AoED Data", "AoED Birds", "hab.sta.csv"))
# file.exists() tells you if you're looking in the right spot for your file
file.exists(here("AoED Data", "AoED Birds", "bird.sta.csv"))
file.exists(here("AoED Data", "AoED Birds", "hab.sta.csv"))
# If the file truly exists, it will output : TRUE!
# Library() and require() allow you to load extra packages that you;ve installed
# Library() will re-load already loaded packages, while require() will check if it's already loaded and will decide whether or not it needs to activate it (does not reactivate)
# Using this at the beginning of your script is helpful!
library(name_of_package)
require(name_of_package)
# Require() is much more streamlined because some packages take a long time to load!
# ------------------------------------------------------------------------------------------------------------------- #
# In-Class R-Script! #
pairs(dat_habitat[, c("slope", "aspect")])
# pairs() creates multiple scatterplots for multiple variables - and most importantly for
# each combination of each variable! In this case we plotted just slope and aspect, but
# it is possible to add more variables which will all be plotted against each other.
# Next, for using the his() script, we first need to find the maximum data value.
# This is to allow the break argument to function properly.
max(dat_birds[,"AMRO"])
# Output: [1] 4
# This means that there is a maximum value of 4
# In the context of the dat_birds dataset, this means that the most amount of
# American Robins (AMRO) observed at a specific site is 4!
# This tells us our minimum break range.
# The break argument requires that the range be at least the
# total number of observations.
# Now to use the hist() function to plot a histogram in R!
hist(dat_birds[,"AMRO"],breaks = 0:5 - .5, xlab = "Number of Birds Counted", ylab = "Frequency", main = "Histogram of American Robin")
# In the dataset there is a max of [4] American Robins observations at a single site,
# which would initially make you want to have a break range of 0:4.
# Though this works, it doesn’t work well for discrete data where a break = 0:4
# will give you a histogram with x-values depicted as ranges from 0-1, 1-2, 2-3 etc..
# So in this case, we can fix this by adding 1 to the break range so you are able to more
# accurately describe the discrete nature of the data! By adding the 5th break range,
# you isolate each value as its own discrete (non-continuous) value!
# ------------------------------------------------------------------------------------------------------------------- #
# Individual Assignment Portion #
?par()
# par() allows us to view plots side by side
# First number is number of rows to arrange
# Second number is number of columns to arrange
dat_all = merge(dat_birds, dat_habitat)
?hist()
par(mfrow = c(1,3))
hist(dat_all$elev,
xlab = "Elevation (m)",
main = "Frequency of Elevation (m) Observed",
xlim = c(0, 1000))
hist(dat_all$slope,
main = "Frequency of Slope (%) Observed",
xlab = "Slope (%)",
xlim = c(0, 120),
ylim = c(0, 175))
hist(dat_habitat$aspect,
xlab = "Aspect (degrees)",
main = "Frequency of Aspect (degrees) Observed",
ylim = c(0, 175),
xlim = c(0, 400))
# dev.off() will clear the current plot being plotted!
dev.off()
# ---------------------------------------------------------------------------------- #
# Calculates the value of x for a linear function, given the coordinates
# of a known point (x1, y1) and the slope of the line.
line_point_slope = function(x, x1, y1, slope)
{
get_y_intercept =
function(x1, y1, slope)
return(-(x1 * slope) + y1)
linear =
function(x, yint, slope)
return(yint + x * slope)
return(linear(x, get_y_intercept(x1, y1, slope), slope))
}
# x1 and y1 are the coordinates which anchors your slope! It can be anywhere on the graph.
curve(line_point_slope(x, x1 = , y1 = , slope = ), add = TRUE)
# ---------------------------------------------------------------------------------- #
par(mfrow = c(1,3))
# You can combine this w/ the mtext() and line() to label!
plot(dat_all$elev, y = dat_all$ba.tot,
xlab = "Elevation (m)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Elevation")
curve(line_point_slope(x, x1 = 250, y1 = 30, slope = -0.01), add = TRUE)
plot(x = dat_all$slope, y = dat_all$ba.tot,
xlab = "Slope (%)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Slope")
curve(line_point_slope(x, x1 = 10, y1 = 20, slope = -0.06), add = TRUE)
# Slope is the steepness or the degree of incline of a surface
plot(dat_all$aspect, y = dat_all$ba.tot,
xlab = "Aspect (degrees)",
ylab = "Basal Area (m^2 per ha)",
main = "Basal Area versus Aspect")
curve(line_point_slope(x, x1 = 0, y1 = 20, slope = 0), add = TRUE)
# Aspect is the orientation of slope, measured clockwise in degrees from 0 to 360
# Where 0 is north-facing, 90 is east-facing, 180 is south-facing, and 270 is west-facing
# ------------------------------------------------------------------------------------------------------------------- #
# Lab 3 #
# Create pair plots using psych to plot slope, elevation and aspect!
# Use the pair plot function from psych to create a pair plot
# of the three terrain variables and basal area from the lecture questions.
# psych package helps make pretty pair plots
?pairs.panels
require(psych)
env_var = data.frame(dat_all$slope, dat_all$elev, dat_all$aspect, dat_all$ba.tot)
env_var
pairs.panels(env_var)
# ---------------------------------------------------------------------------------- #
# We want to merge dat_habitat and dat_birds
# How do we know if there are the same number of rows in both data frames?
# How can we be sure that we associate the correct row of dat+habitat/birds
dat_all = merge(dat_birds, dat_habitat)
plot(ba.tot ~ elev, data = dat_all)
# How to use R to to calculate the total number of waxwings
CEWA_sum = sum(dat_birds[, "CEWA"])
CEWA_sum
# Converting to Presence/Absence using as.numeric *********
CEWA_boolean = dat_birds[, "CEWA"] >= 1
CEWA_boolean
# You could also have inputted the CEWA_boolean vector script directly into the as.numeric() function
CEWA_present_absent = as.numeric(CEWA_boolean)
CEWA_present_absent
# This code creates a binary plot like in the McGarigal reading!
# You can transform these well with logistic models
plot(x = dat_all$elev, y = CEWA_present_absent)
# ---------------------------------------------------------------------------------- #
# Function to calculate the logistic parameter a given the slope and midpoint
get_logistic_param_a = function(slope, midpoint)
{
b = slope / 4
return (-midpoint * (slope / 4))
}
# Function to calculate the logistic parameter b given the slope
get_logistic_param_b = function(slope)
{
return (slope / 4)
}
# Calculate the value of the logistic function at x, given the parameters a and b.
logistic = function(x, a, b)
{
val = exp(a + b * x)
return(val / (1 + val))
}
# Calculate the value of the logistic function at x, given a slopoe and midpoint.
logistic_midpoint_slope = function(x, midpoint, slope)
{
b = get_logistic_param_b(slope)
a = get_logistic_param_a(slope, midpoint)
return(logistic(x, a, b))
}
plot(x = dat_all$elev, y = CEWA_present_absent)
curve(logistic_midpoint_slope(x, midpoint = 400, slope = -0.05), add = TRUE)
# ---------------------------------------------------------------------------------- #
# Question 1 & 2: Choose Two Bird Species and create plots of presence/absence (y) and basal area (x)
# I chose the American Goldfinch (AMGO) and Black-cap Chickadee (BCCH)
AMGO_boolean = dat_birds[, "AMGO"] >= 1
AMGO_present_absent = as.numeric(AMGO_boolean)
plot(x = dat_habitat$ba.tot, y = AMGO_present_absent,
main = "Total Basal Area versus American Goldfinch Observations",
xlab = "Total Basal Area (m^2 per ha)",
ylab = "Presence/Absence of American Goldfinch")
curve(logistic_midpoint_slope(x, midpoint = 12, slope = -0.8), add = TRUE)
BCCH_boolean = dat_birds[, "BCCH"] >= 1
BCCH_present_absent = as.numeric(BCCH_boolean)
plot(x = dat_habitat$ba.tot, y = BCCH_present_absent,
main = "Total Basal Area versus Black Cap Chickadee Observations",
xlab = "Total Basal Area (m^2 per ha)",
ylab = "Presence/Absence of Black-cap Chickadee")
curve(logistic_midpoint_slope(x, midpoint = 25, slope = -1), add = TRUE)
# Question 3: Sum of Observed Grey Jays
GRJA_sum = sum(dat_all[, "GRJA"])
GRJA_sum
# Question 4: Total number of sampling sites in which Gray Jay were observed
GRJA_sites = sum(dat_all[, "GRJA"] > 0)
GRJA_sites
|
library(XML)
library(jsonlite)
# This code snippet downloads a table of Sabah holidays from
# the web and creates inserts for the backend db
years <- c(2016, 2017)
calFun <- function(year){
# load the calendar days
url <- sprintf('http://www.officeholidays.com/countries/malaysia/regional.php?list_year=%i&list_region=sabah', year)
# extract the first table in the page
cal <- readHTMLTable(url, stringsAsFactors=FALSE)[[1]]
# drop unneeded columns
cal <- cal[,c('Date', 'Holiday')]
# format dates
cal$Date <- gsub('\n.*', paste('', year), cal$Date)
cal$Date <- strptime(cal$Date, '%B %d %Y')
return(cal)
}
# get the calendar blocks for each year and combine
calendars <- lapply(years, calFun)
calendars <- do.call('rbind', calendars)
# create SQL inserts for public_holidays table
inserts <- sprintf("insert into public_holidays (date,title) values ('%s','%s');\n", calendars$Date, calendars$Holiday)
# tidy out some non-essential ones
inserts <- inserts[! grepl('Mother', inserts)]
cat(inserts, file='public_holidays.sql')
| /static/public_holidays/create_public_holidays.R | permissive | ImperialCollegeLondon/safe_web | R | false | false | 1,046 | r | library(XML)
library(jsonlite)
# This code snippet downloads a table of Sabah holidays from
# the web and creates inserts for the backend db
years <- c(2016, 2017)
calFun <- function(year){
# load the calendar days
url <- sprintf('http://www.officeholidays.com/countries/malaysia/regional.php?list_year=%i&list_region=sabah', year)
# extract the first table in the page
cal <- readHTMLTable(url, stringsAsFactors=FALSE)[[1]]
# drop unneeded columns
cal <- cal[,c('Date', 'Holiday')]
# format dates
cal$Date <- gsub('\n.*', paste('', year), cal$Date)
cal$Date <- strptime(cal$Date, '%B %d %Y')
return(cal)
}
# get the calendar blocks for each year and combine
calendars <- lapply(years, calFun)
calendars <- do.call('rbind', calendars)
# create SQL inserts for public_holidays table
inserts <- sprintf("insert into public_holidays (date,title) values ('%s','%s');\n", calendars$Date, calendars$Holiday)
# tidy out some non-essential ones
inserts <- inserts[! grepl('Mother', inserts)]
cat(inserts, file='public_holidays.sql')
|
train_test_split <- function(df){
smp_size <- floor(0.75* nrow(df))
set.seed(123)
train_ind <- sample(seq_len(nrow(df)), size = smp_size)
train <- df[train_ind, ]
test <- df[-train_ind, ]
return (list(train,test))
} | /Aluminium data/train_test_split.R | no_license | onicolini/libs | R | false | false | 236 | r | train_test_split <- function(df){
smp_size <- floor(0.75* nrow(df))
set.seed(123)
train_ind <- sample(seq_len(nrow(df)), size = smp_size)
train <- df[train_ind, ]
test <- df[-train_ind, ]
return (list(train,test))
} |
# This script is a little bit different from the other createTables because
# we need to have a matrix with all samples to perform the downsampling.
# In the other script fpkm and tpm are first calculated for every sample
# and later the different data.frames are merged. Here first we have to merge
# all the counts and then perform the downsampling and fpkm/tpm.
log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
library(dplyr)
library(tibble)
source("workflow/scripts/custom_functions.R")
# Seed for downsampling
set.seed(snakemake@params[["seed"]])
#------------------------------------------------------------------------------------------
# Preapre count table
#------------------------------------------------------------------------------------------
sample_names <- unlist(snakemake@input) %>%
basename %>%
gsub(pattern = ".featureCounts", replacement = "")
# Merge all samples keeping geneid and length columns to calculate fpkm/tpm
counts <- snakemake@input %>%
purrr::map(read.delim, header = TRUE, skip = 1) %>% # Ignore first line of featureCounts output
purrr::map(select, 1,6,7) %>% # Select geneid, length and counts
purrr::map(setNames, c("Geneid", "Length", "Counts")) %>%
plyr::join_all(type='inner', by = c("Geneid", "Length")) %>%
setNames(c("Geneid", "Length", sample_names))
#------------------------------------------------------------------------------------------
# Remove excluded samples in case they are defined in config file prior to downsample
#------------------------------------------------------------------------------------------
if(!is.null(snakemake@params[["exclude"]])) {
counts <- counts %>% select(-one_of(snakemake@params[["exclude"]]))
}
#------------------------------------------------------------------------------------------
# Downsample counts and calculate fpkm / tpm
#------------------------------------------------------------------------------------------
# Downsample count matrix to the sample with less reads
counts_downsampled <- cbind(
select(counts, Geneid, Length),
Down_Sample_Matrix( select(counts, -Geneid, -Length) )
)
fpkm_downsampled <- counts_downsampled %>%
mutate_at( vars(sample_names), list(~ do_fpkm(., Length)) ) %>%
select(-Length)
tpm_downsampled <- counts_downsampled %>%
mutate_at( vars(sample_names), list(~ do_tpm(., Length)) ) %>%
select(-Length)
counts_downsampled <- select(counts_downsampled, - Length)
#------------------------------------------------------------------------------------------
# Write output
#------------------------------------------------------------------------------------------
write.table(counts_downsampled, snakemake@output[["raw_counts"]], sep = "\t", quote = F, row.names = FALSE)
write.table(fpkm_downsampled, snakemake@output[["fpkm"]], sep = "\t", quote = F, row.names = FALSE)
write.table(tpm_downsampled, snakemake@output[["tpm"]], sep = "\t", quote = F, row.names = FALSE) | /workflow/scripts/createTables_count_rpkm_tpm_downsampled.R | no_license | dfernandezperez/RNAseq-Snakemake | R | false | false | 2,977 | r | # This script is a little bit different from the other createTables because
# we need to have a matrix with all samples to perform the downsampling.
# In the other script fpkm and tpm are first calculated for every sample
# and later the different data.frames are merged. Here first we have to merge
# all the counts and then perform the downsampling and fpkm/tpm.
log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
library(dplyr)
library(tibble)
source("workflow/scripts/custom_functions.R")
# Seed for downsampling
set.seed(snakemake@params[["seed"]])
#------------------------------------------------------------------------------------------
# Preapre count table
#------------------------------------------------------------------------------------------
sample_names <- unlist(snakemake@input) %>%
basename %>%
gsub(pattern = ".featureCounts", replacement = "")
# Merge all samples keeping geneid and length columns to calculate fpkm/tpm
counts <- snakemake@input %>%
purrr::map(read.delim, header = TRUE, skip = 1) %>% # Ignore first line of featureCounts output
purrr::map(select, 1,6,7) %>% # Select geneid, length and counts
purrr::map(setNames, c("Geneid", "Length", "Counts")) %>%
plyr::join_all(type='inner', by = c("Geneid", "Length")) %>%
setNames(c("Geneid", "Length", sample_names))
#------------------------------------------------------------------------------------------
# Remove excluded samples in case they are defined in config file prior to downsample
#------------------------------------------------------------------------------------------
if(!is.null(snakemake@params[["exclude"]])) {
counts <- counts %>% select(-one_of(snakemake@params[["exclude"]]))
}
#------------------------------------------------------------------------------------------
# Downsample counts and calculate fpkm / tpm
#------------------------------------------------------------------------------------------
# Downsample count matrix to the sample with less reads
counts_downsampled <- cbind(
select(counts, Geneid, Length),
Down_Sample_Matrix( select(counts, -Geneid, -Length) )
)
fpkm_downsampled <- counts_downsampled %>%
mutate_at( vars(sample_names), list(~ do_fpkm(., Length)) ) %>%
select(-Length)
tpm_downsampled <- counts_downsampled %>%
mutate_at( vars(sample_names), list(~ do_tpm(., Length)) ) %>%
select(-Length)
counts_downsampled <- select(counts_downsampled, - Length)
#------------------------------------------------------------------------------------------
# Write output
#------------------------------------------------------------------------------------------
write.table(counts_downsampled, snakemake@output[["raw_counts"]], sep = "\t", quote = F, row.names = FALSE)
write.table(fpkm_downsampled, snakemake@output[["fpkm"]], sep = "\t", quote = F, row.names = FALSE)
write.table(tpm_downsampled, snakemake@output[["tpm"]], sep = "\t", quote = F, row.names = FALSE) |
utils::globalVariables(c("Pseudotime", "value", "ids", "prin_graph_dim_1", "prin_graph_dim_2", "State",
"value", "feature_label", "expectation", "colInd", "rowInd", "value",
"source_prin_graph_dim_1", "source_prin_graph_dim_2"))
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(size=0.25, color="black")) +
theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank())
}
#' Plots the minimum spanning tree on cells.
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param use_color_gradient Whether or not to use color gradient instead of cell size to show marker expression level
#' @param markers_linear a boolean used to indicate whether you want to scale the markers logarithimically or linearly
#' @param show_cell_names draw the name of each cell in the plot
#' @param show_state_number show state number
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param state_number_size the size of the state number
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param theta How many degrees you want to rotate the trajectory
#' @param ... Additional arguments passed into scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom igraph get.edgelist
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' lung <- load_lung()
#' plot_cell_trajectory(lung)
#' plot_cell_trajectory(lung, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(lung, markers="MYH3")
#' }
plot_cell_trajectory <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
use_color_gradient = FALSE,
markers_linear = FALSE,
show_cell_names=FALSE,
show_state_number = FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
state_number_size = 2.9,
show_branch_points=TRUE,
theta = 0,
...){
gene_short_name <- NA
sample_name <- NA
sample_state <- pData(cds)$State
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("simplePPT", "DDRTree") ){
reduced_dim_coords <- reducedDimK(cds)
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
ica_space_df$sample_state <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
dp_mst <- minSpanningTree(cds)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
S_matrix <- reducedDimS(cds)
data_df <- data.frame(t(S_matrix[c(x,y),]))
data_df <- cbind(data_df, sample_state)
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
return_rotation_mat <- function(theta) {
theta <- theta / 180 * pi
matrix(c(cos(theta), sin(theta), -sin(theta), cos(theta)), nrow = 2)
}
tmp <- return_rotation_mat(theta) %*% t(as.matrix(data_df[, c(2, 3)]))
data_df$data_dim_1 <- tmp[1, ]
data_df$data_dim_2 <- tmp[2, ]
tmp <- return_rotation_mat(theta = theta) %*% t(as.matrix(edge_df[, c('source_prin_graph_dim_1', 'source_prin_graph_dim_2')]))
edge_df$source_prin_graph_dim_1 <- tmp[1, ]
edge_df$source_prin_graph_dim_2 <- tmp[2, ]
tmp <- return_rotation_mat(theta) %*% t(as.matrix(edge_df[, c('target_prin_graph_dim_1', 'target_prin_graph_dim_2')]))
edge_df$target_prin_graph_dim_1 <- tmp[1, ]
edge_df$target_prin_graph_dim_2 <- tmp[2, ]
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
if(use_color_gradient) {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color= value), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("value"), ...) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...) + facet_wrap(~feature_label)
}
} else {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size= (value * 0.1))) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size=log10(value + 0.1))) + facet_wrap(~feature_label)
}
}
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
}
}else {
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=5, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=4, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
if (show_state_number){
g <- g + geom_text(aes(label = sample_state), size = state_number_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
g
}
#' @rdname package-deprecated
#' @title Plots the minimum spanning tree on cells.
#' This function is deprecated.
#' @description This function arranges all of the cells in the cds in a tree and
#' predicts their location based on their pseudotime value
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @export
#' @seealso plot_cell_trajectory
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_cell_trajectory(HSMM)
#' plot_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_spanning_tree <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE){
.Deprecated("plot_cell_trajectory") #include a package argument, too
plot_cell_trajectory(cds=cds,
x=x,
y=y,
color_by=color_by,
show_tree=show_tree,
show_backbone=show_backbone,
backbone_color=backbone_color,
markers=markers,
show_cell_names=show_cell_names,
cell_size=cell_size,
cell_link_size=cell_link_size,
cell_name_size=cell_name_size,
show_branch_points=show_branch_points)
}
#' @title Plots expression for one or more genes as a violin plot
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param log_scale a boolean that determines whether or not to scale data logarithmically
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("ACTA1", "ID1", "CCNB2"))),]
#' plot_genes_violin(my_genes, grouping="Hours", ncol=2, min_expr=0.1)
#' }
plot_genes_violin <- function (cds_subset, grouping = "State", min_expr = NULL, cell_size = 0.75,
nrow = NULL, ncol = 1, panel_order = NULL, color_by = NULL,
plot_trend = FALSE, label_by_short_name = TRUE, relative_expr = TRUE,
log_scale = TRUE)
{
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial",
"negbinomial.size")) {
integer_expression = TRUE
}
else {
integer_expression = FALSE
relative_expr = TRUE
}
if (integer_expression) {
cds_exprs = exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs = Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
#cds_exprs = reshape2::melt(round(as.matrix(cds_exprs)))
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
else {
cds_exprs = exprs(cds_subset)
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)) {
min_expr = cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) = c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] = min_expr
cds_pData = pData(cds_subset)
#
# # Custom bit for adding in a group for
# if(! is.null(show_combined)) {
# for(combine_gene in show_combined) {
# cds_pData_all = subset(cds_pData, gene == combine_gene)
# cds_pData_all[, grouping] = paste("All", combine_gene)
# cds_pData = rbind(cds_pData, cds_pData_all)
# }
# }
cds_fData = fData(cds_subset)
cds_exprs = merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs = merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs$adjusted_expression = log10(cds_exprs$expression)
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label = cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] = cds_exprs$f_id
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label = factor(cds_exprs$feature_label,
levels = panel_order)
}
q = ggplot(aes_string(x = grouping, y = "expression"), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q = q + geom_violin(aes_string(fill = color_by))
}
else {
q = q + geom_violin()
}
if (plot_trend == TRUE) {
q = q + stat_summary(fun.data = "mean_cl_boot",
size = 0.2)
q = q + stat_summary(aes_string(x = grouping, y = "expression",
group = color_by), fun.data = "mean_cl_boot",
size = 0.2, geom = "line")
}
q = q + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q = q + expand_limits(y = c(min_expr, 1))
}
q = q + ylab("Expression") + xlab(grouping)
if (log_scale == TRUE){
q = q + scale_y_log10()
}
q
}
#' Plots expression for one or more genes as a jittered, grouped points
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1", "CCNB2"))),]
#' plot_genes_jitter(my_genes, grouping="Media", ncol=2)
#' }
plot_genes_jitter <- function(cds_subset,
grouping = "State",
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by=NULL,
plot_trend=FALSE,
label_by_short_name=TRUE,
relative_expr=TRUE){
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}else{
cds_exprs <- exprs(cds_subset)
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
#print (head(cds_exprs))
if (is.null(panel_order) == FALSE)
{
cds_exprs$feature_label <- factor(cds_exprs$feature_label, levels=panel_order)
}
q <- ggplot(aes_string(x=grouping, y="expression"), data=cds_exprs)
if (is.null(color_by) == FALSE){
q <- q + geom_jitter(aes_string(color=color_by), size=I(cell_size))
}else{
q <- q + geom_jitter(size=I(cell_size))
}
if (plot_trend == TRUE){
q <- q + stat_summary(aes_string(color=color_by), fun.data = "mean_cl_boot", size=0.35)
q <- q + stat_summary(aes_string(x=grouping, y="expression", color=color_by, group=color_by), fun.data = "mean_cl_boot", size=0.35, geom="line")
}
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
# Need this to guard against plotting failures caused by non-expressed genes
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1))
}
q <- q + ylab("Expression") + xlab(grouping)
q <- q + monocle_theme_opts()
q
}
#' Plots the number of cells expressing one or more genes as a barplot
#'
#' @description Accetps a CellDataSet and a parameter,"grouping", used for dividing cells into groups.
#' Returns one or more bar graphs (one graph for each gene in the CellDataSet).
#' Each graph shows the percentage of cells that express a gene in the in the CellDataSet for
#' each sub-group of cells created by "grouping".
#'
#' Let's say the CellDataSet passed in included genes A, B, and C and the "grouping parameter divided
#' all of the cells into three groups called X, Y, and Z. Then three graphs would be produced called A,
#' B, and C. In the A graph there would be three bars one for X, one for Y, and one for Z. So X bar in the
#' A graph would show the percentage of cells in the X group that express gene A.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param plot_as_fraction whether to show the percent instead of the number of cells expressing each gene
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param plot_limits A pair of number specifying the limits of the y axis. If NULL, scale to the range of the data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' MYOG_ID1 <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1"))),]
#' plot_genes_positive_cells(MYOG_ID1, grouping="Media", ncol=2)
#' }
plot_genes_positive_cells <- function(cds_subset,
grouping = "State",
min_expr=0.1,
nrow=NULL,
ncol=1,
panel_order=NULL,
plot_as_fraction=TRUE,
label_by_short_name=TRUE,
relative_expr=TRUE,
plot_limits=c(0,100)){
percent <- NULL
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
marker_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
marker_exprs <- Matrix::t(Matrix::t(marker_exprs) / sizeFactors(cds_subset))
}
marker_exprs_melted <- reshape2::melt(round(as.matrix(marker_exprs)))
}else{
marker_exprs_melted <- reshape2::melt(exprs(marker_exprs))
}
colnames(marker_exprs_melted) <- c("f_id", "Cell", "expression")
marker_exprs_melted <- merge(marker_exprs_melted, pData(cds_subset), by.x="Cell", by.y="row.names")
marker_exprs_melted <- merge(marker_exprs_melted, fData(cds_subset), by.x="f_id", by.y="row.names")
if (label_by_short_name == TRUE){
if (is.null(marker_exprs_melted$gene_short_name) == FALSE){
marker_exprs_melted$feature_label <- marker_exprs_melted$gene_short_name
marker_exprs_melted$feature_label[is.na(marker_exprs_melted$feature_label)] <- marker_exprs_melted$f_id
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
if (is.null(panel_order) == FALSE)
{
marker_exprs_melted$feature_label <- factor(marker_exprs_melted$feature_label, levels=panel_order)
}
marker_counts <- plyr::ddply(marker_exprs_melted, c("feature_label", grouping), function(x) {
data.frame(target=sum(x$expression > min_expr),
target_fraction=sum(x$expression > min_expr)/nrow(x)) } )
#print (head(marker_counts))
if (plot_as_fraction){
marker_counts$target_fraction <- marker_counts$target_fraction * 100
qp <- ggplot(aes_string(x=grouping, y="target_fraction", fill=grouping), data=marker_counts) +
ylab("Cells (percent)")
if (is.null(plot_limits) == FALSE)
qp <- qp + scale_y_continuous(limits=plot_limits)
}else{
qp <- ggplot(aes_string(x=grouping, y="target", fill=grouping), data=marker_counts) +
ylab("Cells")
}
qp <- qp + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
qp <- qp + geom_bar(stat="identity") + monocle_theme_opts()
return(qp)
}
#' Plots expression for one or more genes as a function of pseudotime
#'
#' @description Plots expression for one or more genes as a function of pseudotime.
#' Plotting allows you determine if the ordering produced by orderCells() is correct
#' and it does not need to be flipped using the "reverse" flag in orderCells
#'
#' @param cds_subset CellDataSet for the experiment
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param vertical_jitter A value passed to ggplot to jitter the points in the vertical dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @param horizontal_jitter A value passed to ggplot to jitter the points in the horizontal dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply .
#' @importFrom reshape2 melt
#' @importFrom ggplot2 Position
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- row.names(subset(fData(HSMM), gene_short_name %in% c("CDK1", "MEF2C", "MYH3")))
#' cds_subset <- HSMM[my_genes,]
#' plot_genes_in_pseudotime(cds_subset, color_by="Time")
#' }
plot_genes_in_pseudotime <-function(cds_subset,
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by="State",
trend_formula="~ sm.ns(Pseudotime, df=3)",
label_by_short_name=TRUE,
relative_expr=TRUE,
vertical_jitter=NULL,
horizontal_jitter=NULL){
f_id <- NA
Cell <- NA
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
#cds_exprs$f_id <- as.character(cds_exprs$f_id)
#cds_exprs$Cell <- as.character(cds_exprs$Cell)
if (integer_expression) {
cds_exprs$adjusted_expression <- cds_exprs$expression
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$f_id <- as.character(cds_exprs$f_id)
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime)
model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(model_expectation) <- colnames(cds_subset)
expectation <- ddply(cds_exprs, .(f_id, Cell), function(x) data.frame("expectation"=model_expectation[x$f_id, x$Cell]))
cds_exprs <- merge(cds_exprs, expectation)
#cds_exprs$expectation <- expectation#apply(cds_exprs,1, function(x) model_expectation[x$f_id, x$Cell])
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$expectation[cds_exprs$expectation < min_expr] <- min_expr
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
else {
q <- q + geom_point(size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
q <- q + geom_line(aes(x = Pseudotime, y = expectation), data = cds_exprs)
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q <- q + expand_limits(y = c(min_expr, 1))
}
if (relative_expr) {
q <- q + ylab("Relative Expression")
}
else {
q <- q + ylab("Absolute Expression")
}
q <- q + xlab("Pseudo-time")
q <- q + monocle_theme_opts()
q
}
#' Plots kinetic clusters of genes.
#'
#' @description returns a ggplot2 object showing the shapes of the
#' expression patterns followed by a set of pre-selected genes.
#' The topographic lines highlight the distributions of the kinetic patterns
#' relative to overall trend lines.
#'
#' @param cds CellDataSet for the experiment
#' @param clustering a clustering object produced by clusterCells
#' @param drawSummary whether to draw the summary line for each cluster
#' @param sumFun whether the function used to generate the summary for each cluster
#' @param ncol number of columns used to layout the faceted cluster panels
#' @param nrow number of columns used to layout the faceted cluster panels
#' @param row_samples how many genes to randomly select from the data
#' @param callout_ids a vector of gene names or gene ids to manually render as part of the plot
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom stringr str_join
#' @importFrom ggplot2 Position
#' @import grid
#' @export
#' @examples
#' \dontrun{
#' full_model_fits <- fitModel(HSMM_filtered[sample(nrow(fData(HSMM_filtered)), 100),],
#' modelFormulaStr="~VGAM::bs(Pseudotime)")
#' expression_curve_matrix <- responseMatrix(full_model_fits)
#' clusters <- clusterGenes(expression_curve_matrix, k=4)
#' plot_clusters(HSMM_filtered[ordering_genes,], clusters)
#' }
plot_clusters<-function(cds,
clustering,
drawSummary=TRUE,
sumFun=mean_cl_boot,
ncol=NULL,
nrow=NULL,
row_samples=NULL,
callout_ids=NULL){
.Deprecated("plot_genes_heatmap")
m <- as.data.frame(clustering$exprs)
m$ids <- rownames(clustering$exprs)
if (is.null(clustering$labels) == FALSE)
{
m$cluster = factor(clustering$labels[clustering$clustering], levels = levels(clustering$labels))
}else{
m$cluster <- factor(clustering$clustering)
}
cluster_sizes <- as.data.frame(table(m$cluster))
cluster_sizes$Freq <- paste("(", cluster_sizes$Freq, ")")
facet_labels <- str_join(cluster_sizes$Var1, cluster_sizes$Freq, sep=" ") #update the function
m.melt <- melt(m, id.vars = c("ids", "cluster"))
m.melt <- merge(m.melt, pData(cds), by.x="variable", by.y="row.names")
if (is.null(row_samples) == FALSE){
m.melt <- m.melt[sample(nrow(m.melt), row_samples),]
}
c <- ggplot(m.melt) + facet_wrap("cluster", ncol=ncol, nrow=nrow, scales="free_y")
#c <- c + stat_density2d(aes(x = Pseudotime, y = value), geom="polygon", fill="white", color="black", size=I(0.1)) + facet_wrap("cluster", ncol=ncol, nrow=nrow)
if (drawSummary) {
c <- c + stat_summary(aes(x = Pseudotime, y = value, group = 1),
fun.data = sumFun, color = "red",
alpha = 0.2, size = 0.5, geom = "smooth")
}
#cluster_medians <- subset(m.melt, ids %in% clustering$medoids)
#c <- c + geom_line()
#c <- c + geom_line(aes(x=Pseudotime, y=value), data=cluster_medians, color=I("red"))
c <- c + scale_color_hue(l = 50, h.start = 200) + theme(axis.text.x = element_text(angle = 0,
hjust = 0)) + xlab("Pseudo-time") + ylab("Expression")
c <- c + theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(legend.position="none") +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank())
# if (draw_cluster_size){
# cluster_sizes <- as.data.frame(table(m$cluster))
# colnames(cluster_sizes) <- c("cluster", "Freq")
# cluster_sizes <- cbind (cluster_sizes, Pseudotime = cluster_label_text_x, value = cluster_label_text_y)
# c <- c + geom_text(aes(x=Pseudotime, y=value, label=Freq), data=cluster_sizes, size=cluster_label_text_size)
# }
if (is.null(callout_ids) == FALSE)
{
callout_melt <- subset(m.melt, ids %in% callout_ids)
c <- c + geom_line(aes(x=Pseudotime, y=value), data=callout_melt, color=I("steelblue"))
}
c <- c + monocle_theme_opts()
#c <- facet_wrap_labeller(c, facet_labels)
c
}
#
# #' Plots a pseudotime-ordered, row-centered heatmap
# #' @export
# plot_genes_heatmap <- function(cds,
# rescaling='row',
# clustering='row',
# labCol=FALSE,
# labRow=TRUE,
# logMode=TRUE,
# use_vst=TRUE,
# border=FALSE,
# heatscale=c(low='steelblue',mid='white',high='tomato'),
# heatMidpoint=0,
# method="none",
# scaleMax=2,
# scaleMin=-2,
# relative_expr=TRUE,
# ...){
#
# ## the function can be be viewed as a two step process
# ## 1. using the rehape package and other funcs the data is clustered, scaled, and reshaped
# ## using simple options or by a user supplied function
# ## 2. with the now resahped data the plot, the chosen labels and plot style are built
# FM <- exprs(cds)
#
# if (cds@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
# integer_expression <- TRUE
# }else{
# integer_expression <- FALSE
# relative_expr <- TRUE
# }
#
# if (integer_expression)
# {
# if (relative_expr){
# if (is.null(sizeFactors(cds)))
# {
# stop("Error: you must call estimateSizeFactors() first")
# }
# FM <- Matrix::t(Matrix::t(FM) / sizeFactors(cds))
# }
# FM <- round(FM)
# }
#
# m=FM
#
# if (is.null(fData(cds)$gene_short_name) == FALSE){
# feature_labels <- fData(cds)$gene_short_name
# feature_labels[is.na(feature_labels)] <- fData(cds)$f_id
# row.names(m) <- feature_labels
# }
#
# #remove genes with no expression in any condition
# m=m[!apply(m,1,sum)==0,]
#
# if (use_vst && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE){
# m = vstExprs(cds, expr_matrix=m)
# }else if(logMode){
# m = log10(m+pseudocount)
# }
#
# #remove genes with no sd
# #m=m[!apply(m,1,sd)==0,]
#
# ## you can either scale by row or column not both!
# ## if you wish to scale by both or use a different scale method then simply supply a scale
# ## function instead NB scale is a base funct
#
# ## I have supplied the default cluster and euclidean distance (JSdist) - and chose to cluster after scaling
# ## if you want a different distance/cluster method-- or to cluster and then scale
# ## then you can supply a custom function
#
# if(!is.function(method)){
# method = function(mat){as.dist((1 - cor(Matrix::t(mat)))/2)}
# }
#
# ## this is just reshaping into a ggplot format matrix and making a ggplot layer
#
# if(is.function(rescaling))
# {
# m=rescaling(m)
# } else {
# if(rescaling=='column'){
# m=m[!apply(m,2,sd)==0,]
# m=scale(m, center=TRUE)
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# if(rescaling=='row'){
# m=m[!apply(m,1,sd)==0,]
# m=Matrix::t(scale(Matrix::t(m),center=TRUE))
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# }
#
# # If we aren't going to re-ordering the columns, order them by Pseudotime
# if (clustering %in% c("row", "none"))
# m = m[,row.names(pData(cds)[order(-pData(cds)$Pseudotime),])]
#
# if(clustering=='row')
# m=m[hclust(method(m))$order, ]
# if(clustering=='column')
# m=m[,hclust(method(Matrix::t(m)))$order]
# if(clustering=='both')
# m=m[hclust(method(m))$order ,hclust(method(Matrix::t(m)))$order]
#
#
# rows=dim(m)[1]
# cols=dim(m)[2]
#
#
#
# # if(logMode) {
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt( log10(m+pseudocount)))
# # }else{
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
# # }
#
# melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
#
# g=ggplot(data=melt.m)
#
# ## add the heat tiles with or without a white border for clarity
#
# if(border==TRUE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd, fill=value),colour='grey')
# if(border==FALSE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd,ymax=rowInd, fill=value))
#
# ## add axis labels either supplied or from the colnames rownames of the matrix
#
# if(labCol==TRUE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=colnames(m))
# }
# if(labCol==FALSE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=rep('',cols))
# }
#
#
# if(labRow==TRUE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rownames(m))
# }
# if(labRow==FALSE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rep('',rows))
# }
#
# # Get rid of the ticks, they get way too dense with lots of rows
# g2 <- g2 + theme(axis.ticks = element_blank())
#
# ## get rid of grey panel background and gridlines
#
# g2=g2+theme(panel.grid.minor=element_line(colour=NA), panel.grid.major=element_line(colour=NA),
# panel.background=element_rect(fill=NA, colour=NA))
#
# ##adjust x-axis labels
# g2=g2+theme(axis.text.x=element_text(angle=-90, hjust=0))
#
# #write(paste(c("Length of heatscale is :", length(heatscale))), stderr())
#
# if(is.function(rescaling))
# {
#
# }else{
# if(rescaling=='row' || rescaling == 'column'){
# legendTitle <- "Relative\nexpression"
# }else{
# if (logMode)
# {
# legendTitle <- bquote(paste(log[10]," FPKM + ",.(pseudocount),sep=""))
# #legendTitle <- paste(expression(plain(log)[10])," FPKM + ",pseudocount,sep="")
# } else {
# legendTitle <- "FPKM"
# }
# }
# }
#
# if (length(heatscale) == 2){
# g2 <- g2 + scale_fill_gradient(low=heatscale[1], high=heatscale[2], name=legendTitle)
# } else if (length(heatscale) == 3) {
# if (is.null(heatMidpoint))
# {
# heatMidpoint = (max(m) + min(m)) / 2.0
# #write(heatMidpoint, stderr())
# }
# g2 <- g2 + theme(panel.border = element_blank())
# g2 <- g2 + scale_fill_gradient2(low=heatscale[1], mid=heatscale[2], high=heatscale[3], midpoint=heatMidpoint, name=legendTitle)
# }else {
# g2 <- g2 + scale_fill_gradientn(colours=heatscale, name=legendTitle)
# }
#
# #g2<-g2+scale_x_discrete("",breaks=tracking_ids,labels=gene_short_names)
#
# g2 <- g2 + theme(axis.title.x=element_blank(), axis.title.y=element_blank())
#
# ## finally add the fill colour ramp of your choice (default is blue to red)-- and return
# return (g2)
# }
plot_genes_heatmap <- function(...){
.Deprecated("plot_pseudotime_heatmap")
plot_pseudotime_heatmap(...)
}
#' Plots a pseudotime-ordered, row-centered heatmap
#'
#' @description The function plot_pseudotime_heatmap takes a CellDataSet object
#' (usually containing a only subset of significant genes) and generates smooth expression
#' curves much like plot_genes_in_pseudotime.
#' Then, it clusters these genes and plots them using the pheatmap package.
#' This allows you to visualize modules of genes that co-vary across pseudotime.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_pseudotime_heatmap <- function(cds_subset,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("log", "vstExprs"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
num_clusters <- min(num_clusters, nrow(cds_subset))
pseudocount <- 1
newdata <- data.frame(Pseudotime = seq(min(pData(cds_subset)$Pseudotime), max(pData(cds_subset)$Pseudotime),length.out = 100))
m <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds_subset@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds_subset, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=cluster_rows,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
border_color = NA,
color=hmcols)
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if(!is.null(add_annotation_col)) {
if(nrow(add_annotation_col) != 100) {
stop('add_annotation_col should have only 100 rows (check genSmoothCurves before you supply the annotation data)!')
}
annotation_col <- add_annotation_col
} else {
annotation_col <- NA
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Plot the branch genes in pseduotime with separate branch curves.
#'
#' @description Works similarly to plot_genes_in_psuedotime esceptit shows
#' one kinetic trend for each lineage.
#'
#' @details This plotting function is used to make the branching plots for a branch dependent gene goes through the progenitor state
#' and bifurcating into two distinct branchs (Similar to the pitch-fork bifurcation in dynamic systems). In order to make the
#' bifurcation plot, we first duplicated the progenitor states and by default stretch each branch into maturation level 0-100.
#' Then we fit two nature spline curves for each branchs using VGAM package.
#'
#' @param cds CellDataSet for the experiment
#' @param branch_states The states for two branching branchs
#' @param branch_point The ID of the branch point to analyze. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_labels The names for each branching branch
#' @param method The method to draw the curve for the gene expression branching pattern, either loess ('loess') or VGLM fitting ('fitting')
#' @param min_expr The minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size The size (in points) of each cell used in the plot
#' @param nrow Number of columns used to layout the faceted cluster panels
#' @param ncol Number of columns used to layout the faceted cluster panels
#' @param panel_order The a character vector of gene short names (or IDs, if that's what you're using), specifying order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by The cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param expression_curve_linetype_by The cell attribute (e.g. the column of pData(cds)) to be used for the linetype of each branch curve
#' @param trend_formula The model formula to be used for fitting the expression trend over pseudotime
#' @param reducedModelFormulaStr A formula specifying a null model. If used, the plot shows a p value from the likelihood ratio test that uses trend_formula as the full model
#' @param label_by_short_name Whether to label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether or not the plot should use relative expression values (only relevant for CellDataSets using transcript counts)
#' @param ... Additional arguments passed on to branchTest. Only used when reducedModelFormulaStr is not NULL.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
plot_genes_branched_pseudotime <- function (cds,
branch_states = NULL,
branch_point=1,
branch_labels = NULL,
method = "fitting",
min_expr = NULL,
cell_size = 0.75,
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "State",
expression_curve_linetype_by = "Branch",
trend_formula = "~ sm.ns(Pseudotime, df=3) * Branch",
reducedModelFormulaStr = NULL,
label_by_short_name = TRUE,
relative_expr = TRUE,
#gene_pairs = NULL,
...)
{
Branch <- NA
if (is.null(reducedModelFormulaStr) == FALSE) {
pval_df <- branchTest(cds,
branch_states=branch_states,
branch_point=branch_point,
fullModelFormulaStr = trend_formula,
reducedModelFormulaStr = "~ sm.ns(Pseudotime, df=3)",
...)
fData(cds)[, "pval"] <- pval_df[row.names(cds), 'pval']
}
if("Branch" %in% all.vars(terms(as.formula(trend_formula)))) { #only when Branch is in the model formula we will duplicate the "progenitor" cells
cds_subset <- buildBranchCellDataSet(cds = cds,
branch_states = branch_states,
branch_point=branch_point,
branch_labels = branch_labels,
progenitor_method = 'duplicate',
...)
}
else {
cds_subset <- cds
pData(cds_subset)$Branch <- pData(cds_subset)$State
}
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
CM <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
CM <- Matrix::t(Matrix::t(CM)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(CM)))
}
else {
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
if (integer_expression) {
cds_exprs$adjusted_expression <- round(cds_exprs$expression)
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(full_model_expectation) <- colnames(cds_subset)
cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
if(!is.null(reducedModelFormulaStr)){
reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
relative_expr = T, new_data = new_data)
colnames(reduced_model_expectation) <- colnames(cds_subset)
cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
}
# FIXME: If you want to show the bifurcation time for each gene, this function
# should just compute it. Passing it in as a dataframe is just too complicated
# and will be hard on the user.
# if(!is.null(bifurcation_time)){
# cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# }
if (method == "loess")
cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
if(!is.null(reducedModelFormulaStr)){
cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
}
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size))
}
if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + scale_y_log10() + facet_wrap(~feature_label +
pval, nrow = nrow, ncol = ncol, scales = "free_y")
else q <- q + scale_y_log10() + facet_wrap(~feature_label,
nrow = nrow, ncol = ncol, scales = "free_y")
if (method == "loess")
q <- q + stat_smooth(aes(fill = Branch, color = Branch),
method = "loess")
else if (method == "fitting") {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
}
if(!is.null(reducedModelFormulaStr)) {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
color = 'black', linetype = 2, data = cds_exprs)
}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
#' Not sure we're ready to release this one quite yet:
#' Plot the branch genes in pseduotime with separate branch curves
#' @param cds CellDataSet for the experiment
#' @param rowgenes Gene ids or short names to be arrayed on the vertical axis.
#' @param colgenes Gene ids or short names to be arrayed on the horizontal axis
#' @param relative_expr Whether to transform expression into relative values
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param label_by_short_name a boolean that indicates whether cells should be labeled by their short name
#' @param show_density a boolean that indicates whether a 2D density estimation should be shown in the plot
#' @param round_expr a boolean that indicates whether cds_expr values should be rounded or not
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
plot_coexpression_matrix <- function(cds,
rowgenes,
colgenes,
relative_expr=TRUE,
min_expr=NULL,
cell_size=0.85,
label_by_short_name=TRUE,
show_density=TRUE,
round_expr=FALSE){
gene_short_name <- NA
f_id <- NA
adjusted_expression.x <- NULL
adjusted_expression.y <- NULL
..density.. <- NULL
row_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% rowgenes))
row_gene_ids <- union(row_gene_ids, intersect(rowgenes, row.names(fData(cds))))
col_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% colgenes))
col_gene_ids <- union(col_gene_ids, intersect(colgenes, row.names(fData(cds))))
cds_subset <- cds[union(row_gene_ids, col_gene_ids),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
if (round_expr){
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
} else {
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
}else{
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs$adjusted_expression <- cds_exprs$expression
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
row_cds_exprs <- subset(cds_exprs, f_id %in% row_gene_ids)
col_cds_exprs <- subset(cds_exprs, f_id %in% col_gene_ids)
joined_exprs <- merge(row_cds_exprs, col_cds_exprs, by="Cell")
cds_exprs <- joined_exprs
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs <- subset(cds_exprs, adjusted_expression.x > min_expr | adjusted_expression.y > min_expr)
q <- ggplot(aes(adjusted_expression.x, adjusted_expression.y), data=cds_exprs, size=I(1))
if (show_density){
q <- q + stat_density2d(geom="raster", aes(fill = ..density..), contour = FALSE) +
scale_fill_gradient(low="white", high="red")
}
q <- q + scale_x_log10() + scale_y_log10() +
geom_point(color=I("black"), size=I(cell_size * 1.50)) +
geom_point(color=I("white"), size=I(cell_size)) +
facet_grid(feature_label.x ~ feature_label.y, scales="free")
#scale_color_brewer(palette="Set1") +
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1), x=c(min_expr, 1))
}
#q <- q + monocle_theme_opts()
q
}
#The following code is swipped from colorRamps package which is used to make the pallette
table.ramp <- function(n, mid = 0.5, sill = 0.5, base = 1, height = 1)
{
x <- seq(0, 1, length.out = n)
y <- rep(0, length(x))
sill.min <- max(c(1, round((n - 1) * (mid - sill / 2)) + 1))
sill.max <- min(c(n, round((n - 1) * (mid + sill / 2)) + 1))
y[sill.min:sill.max] <- 1
base.min <- round((n - 1) * (mid - base / 2)) + 1
base.max <- round((n - 1) * (mid + base / 2)) + 1
xi <- base.min:sill.min
yi <- seq(0, 1, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
xi <- sill.max:base.max
yi <- seq(1, 0, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
height * y
}
#' @importFrom grDevices rgb
rgb.tables <- function(n,
red = c(0.75, 0.25, 1),
green = c(0.5, 0.25, 1),
blue = c(0.25, 0.25, 1))
{
rr <- do.call("table.ramp", as.list(c(n, red)))
gr <- do.call("table.ramp", as.list(c(n, green)))
br <- do.call("table.ramp", as.list(c(n, blue)))
rgb(rr, gr, br)
}
matlab.like <- function(n) rgb.tables(n)
matlab.like2 <- function(n)
rgb.tables(n,
red = c(0.8, 0.2, 1),
green = c(0.5, 0.4, 0.8),
blue = c(0.2, 0.2, 1))
blue2green2red <- matlab.like2
#' Create a heatmap to demonstrate the bifurcation of gene expression along two branchs
#'
#' @description returns a heatmap that shows changes in both lineages at the same time.
#' It also requires that you choose a branch point to inspect.
#' Columns are points in pseudotime, rows are genes, and the beginning of pseudotime is in the middle of the heatmap.
#' As you read from the middle of the heatmap to the right, you are following one lineage through pseudotime. As you read left, the other.
#' The genes are clustered hierarchically, so you can visualize modules of genes that have similar lineage-dependent expression patterns.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param branch_point The ID of the branch point to visualize. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_states The two states to compare in the heatmap. Mutually exclusive with branch_point.
#' @param branch_labels The labels for the branchs.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param branch_colors The colors used in the annotation strip indicating the pre- and post-branch cells.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @param ... Additional arguments passed to buildBranchCellDataSet
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_genes_branched_heatmap <- function(cds_subset,
branch_point=1,
branch_states=NULL,
branch_labels = c("Cell fate 1", "Cell fate 2"),
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
branch_colors = c('#979797', '#F05662', '#7990C8'),
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
scale_max=3,
scale_min=-3,
norm_method = c("log", "vstExprs"),
trend_formula = '~sm.ns(Pseudotime, df=3) * Branch',
return_heatmap=FALSE,
cores = 1, ...) {
cds <- NA
new_cds <- buildBranchCellDataSet(cds_subset,
branch_states=branch_states,
branch_point=branch_point,
progenitor_method = 'duplicate',
...)
new_cds@dispFitInfo <- cds_subset@dispFitInfo
if(is.null(branch_states)) {
progenitor_state <- subset(pData(cds_subset), Pseudotime == 0)[, 'State']
branch_states <- setdiff(pData(cds_subset)$State, progenitor_state)
}
col_gap_ind <- 101
# newdataA <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
# newdataB <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
newdataA <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[1]))
newdataB <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[2]))
BranchAB_exprs <- genSmoothCurves(new_cds[, ], cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = rbind(newdataA, newdataB))
BranchA_exprs <- BranchAB_exprs[, 1:100]
BranchB_exprs <- BranchAB_exprs[, 101:200]
#common_ancestor_cells <- row.names(pData(new_cds)[duplicated(pData(new_cds)$original_cell_id),])
common_ancestor_cells <- row.names(pData(new_cds)[pData(new_cds)$State == setdiff(pData(new_cds)$State, branch_states),])
BranchP_num <- (100 - floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime'])))
BranchA_num <- floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime']))
BranchB_num <- BranchA_num
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs') {
BranchA_exprs <- vstExprs(new_cds, expr_matrix=BranchA_exprs)
BranchB_exprs <- vstExprs(new_cds, expr_matrix=BranchB_exprs)
}
else if(norm_method == 'log') {
BranchA_exprs <- log10(BranchA_exprs + 1)
BranchB_exprs <- log10(BranchB_exprs + 1)
}
heatmap_matrix <- cBind(BranchA_exprs[, (col_gap_ind - 1):1], BranchB_exprs)
heatmap_matrix=heatmap_matrix[!apply(heatmap_matrix, 1, sd)==0,]
heatmap_matrix=Matrix::t(scale(Matrix::t(heatmap_matrix),center=TRUE))
heatmap_matrix=heatmap_matrix[is.na(row.names(heatmap_matrix)) == FALSE,]
heatmap_matrix[is.nan(heatmap_matrix)] = 0
heatmap_matrix[heatmap_matrix>scale_max] = scale_max
heatmap_matrix[heatmap_matrix<scale_min] = scale_min
heatmap_matrix_ori <- heatmap_matrix
heatmap_matrix <- heatmap_matrix[is.finite(heatmap_matrix[, 1]) & is.finite(heatmap_matrix[, col_gap_ind]), ] #remove the NA fitting failure genes for each branch
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
exp_rng <- range(heatmap_matrix) #bks is based on the expression range
bks <- seq(exp_rng[1] - 0.1, exp_rng[2] + 0.1, by=0.1)
if(is.null(hmcols)) {
hmcols <- blue2green2red(length(bks) - 1)
}
# prin t(hmcols)
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=F,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols
#color=hmcols#,
# filename="expression_pseudotime_pheatmap.pdf",
)
#save(heatmap_matrix, row_dist, num_clusters, hmcols, ph, branchTest_df, qval_lowest_thrsd, branch_labels, BranchA_num, BranchP_num, BranchB_num, file = 'heatmap_matrix')
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
annotation_col <- data.frame(row.names = c(1:ncol(heatmap_matrix)), "Cell Type" = c(rep(branch_labels[1], BranchA_num),
rep("Pre-branch", 2 * BranchP_num),
rep(branch_labels[2], BranchB_num)))
colnames(annotation_col) <- "Cell Type"
if(!is.null(add_annotation_col)) {
annotation_col <- cbind(annotation_col, add_annotation_col[fData(cds[row.names(annotation_col), ])$gene_short_name, 1])
}
names(branch_colors) <- c("Pre-branch", branch_labels[1], branch_labels[2])
annotation_colors=list("Cell Type"=branch_colors)
names(annotation_colors$`Cell Type`) = c('Pre-branch', branch_labels)
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
annotation_colors=annotation_colors,
gaps_col = col_gap_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(list(BranchA_exprs = BranchA_exprs, BranchB_exprs = BranchB_exprs, heatmap_matrix = heatmap_matrix,
heatmap_matrix_ori = heatmap_matrix_ori, ph = ph, col_gap_ind = col_gap_ind, row_dist = row_dist, hmcols = hmcols,
annotation_colors = annotation_colors, annotation_row = annotation_row, annotation_col = annotation_col,
ph_res = ph_res))
}
}
#' Plots genes by mean vs. dispersion, highlighting those selected for ordering
#'
#' Each gray point in the plot is a gene. The black dots are those that were included
#' in the last call to setOrderingFilter. The red curve shows the mean-variance
#' model learning by estimateDispersions().
#'
#' @param cds The CellDataSet to be used for the plot.
#' @export
plot_ordering_genes <- function(cds){
if(class(cds)[1] != "CellDataSet") {
stop("Error input object is not of type 'CellDataSet'")
}
disp_table <- dispersionTable(cds)
use_for_ordering <- NA
mean_expression <- NA
dispersion_empirical <- NA
dispersion_fit <- NA
gene_id <- NA
ordering_genes <- row.names(subset(fData(cds), use_for_ordering == TRUE))
g <- qplot(mean_expression, dispersion_empirical, data=disp_table, log="xy", color=I("darkgrey")) +
geom_line(aes(y=dispersion_fit), color="red")
if (length(ordering_genes) > 0){
g <- g + geom_point(aes(mean_expression, dispersion_empirical),
data=subset(disp_table, gene_id %in% ordering_genes), color="black")
}
g <- g + monocle_theme_opts()
g
}
#' Plots clusters of cells .
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_name_size the size of cell name labels
#' @param ... additional arguments passed into the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' HSMM <- reduceD
#' plot_cell_clusters(HSMM)
#' plot_cell_clusters(HSMM, color_by="Pseudotime")
#' plot_cell_clusters(HSMM, markers="MYH3")
#' }
plot_cell_clusters <- function(cds,
x=1,
y=2,
color_by="Cluster",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_name_size=2,
...){
if (is.null(cds@reducedDimA) | length(pData(cds)$Cluster) == 0){
stop("Error: Clustering is not performed yet. Please call clusterCells() before calling this function.")
}
gene_short_name <- NULL
sample_name <- NULL
data_dim_1 <- NULL
data_dim_2 <- NULL
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info <- pData(cds)
tSNE_dim_coords <- reducedDimA(cds)
data_df <- data.frame(t(tSNE_dim_coords[c(x,y),]))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- colnames(cds)
data_df <- merge(data_df, lib_info, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
g <- g + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...)
}else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(text = element_text(size = 15))
g
}
#' Plots the decision map of density clusters .
#'
#' @param cds CellDataSet for the experiment after running clusterCells_Density_Peak
#' @param rho_threshold The threshold of local density (rho) used to select the density peaks for plotting
#' @param delta_threshold The threshold of local distance (delta) used to select the density peaks for plotting
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_rho_delta(HSMM)
#' }
plot_rho_delta <- function(cds, rho_threshold = NULL, delta_threshold = NULL){
if(!is.null(cds@auxClusteringData[["tSNE"]]$densityPeak)
& !is.null(pData(cds)$Cluster)
& !is.null(pData(cds)$peaks)
& !is.null(pData(cds)$halo)
& !is.null(pData(cds)$delta)
& !is.null(pData(cds)$rho)) {
rho <- NULL
delta <- NULL
# df <- data.frame(rho = as.numeric(levels(pData(cds)$rho))[pData(cds)$rho],
# delta = as.numeric(levels(pData(cds)$delta))[pData(cds)$delta])
if(!is.null(rho_threshold) & !is.null(delta_threshold)){
peaks <- pData(cds)$rho >= rho_threshold & pData(cds)$delta >= delta_threshold
}
else
peaks <- pData(cds)$peaks
df <- data.frame(rho = pData(cds)$rho, delta = pData(cds)$delta, peaks = peaks)
g <- qplot(rho, delta, data = df, alpha = I(0.5), color = peaks) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
scale_color_manual(values=c("grey","black")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
}
else {
stop('Please run clusterCells_Density_Peak before using this plotting function')
}
g
}
#' Plots the percentage of variance explained by the each component based on PCA from the normalized expression
#' data using the same procedure used in reduceDimension function.
#'
#' @param cds CellDataSet for the experiment after running reduceDimension with reduction_method as tSNE
#' @param max_components Maximum number of components shown in the scree plot (variance explained by each component)
#' @param norm_method Determines how to transform expression values prior to reducing dimensionality
#' @param residualModelFormulaStr A model formula specifying the effects to subtract from the data before clustering.
#' @param pseudo_expr amount to increase expression values before dimensionality reduction
#' @param return_all A logical argument to determine whether or not the variance of each component is returned
#' @param use_existing_pc_variance Whether to plot existing results for variance explained by each PC
#' @param verbose Whether to emit verbose output during dimensionality reduction
#' @param ... additional arguments to pass to the dimensionality reduction function
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_pc_variance_explained(HSMM)
#' }
plot_pc_variance_explained <- function(cds,
max_components=100,
# reduction_method=c("DDRTree", "ICA", 'tSNE'),
norm_method = c("log", "vstExprs", "none"),
residualModelFormulaStr=NULL,
pseudo_expr=NULL,
return_all = F,
use_existing_pc_variance=FALSE,
verbose=FALSE,
...){
set.seed(2016)
if(!is.null(cds@auxClusteringData[["tSNE"]]$variance_explained) & use_existing_pc_variance == T){
prop_varex <- cds@auxClusteringData[["tSNE"]]$variance_explained
}
else{
FM <- normalize_expr_data(cds, norm_method, pseudo_expr)
#FM <- FM[unlist(sparseApply(FM, 1, sd, convert_to_dense=TRUE)) > 0, ]
xm <- Matrix::rowMeans(FM)
xsd <- sqrt(Matrix::rowMeans((FM - xm)^2))
FM <- FM[xsd > 0,]
if (is.null(residualModelFormulaStr) == FALSE) {
if (verbose)
message("Removing batch effects")
X.model_mat <- sparse.model.matrix(as.formula(residualModelFormulaStr),
data = pData(cds), drop.unused.levels = TRUE)
fit <- limma::lmFit(FM, X.model_mat, ...)
beta <- fit$coefficients[, -1, drop = FALSE]
beta[is.na(beta)] <- 0
FM <- as.matrix(FM) - beta %*% t(X.model_mat[, -1])
}else{
X.model_mat <- NULL
}
if (nrow(FM) == 0) {
stop("Error: all rows have standard deviation zero")
}
# FM <- convert2DRData(cds, norm_method = 'log')
# FM <- FM[rowSums(is.na(FM)) == 0, ]
irlba_res <- prcomp_irlba(t(FM), n = min(max_components, min(dim(FM)) - 1),
center = TRUE, scale. = TRUE)
prop_varex <- irlba_res$sdev^2 / sum(irlba_res$sdev^2)
#
# cell_means <- Matrix::rowMeans(FM_t)
# cell_vars <- Matrix::rowMeans((FM_t - cell_means)^2)
#
# irlba_res <- irlba(FM,
# nv= min(max_components, min(dim(FM)) - 1), #avoid calculating components in the tail
# nu=0,
# center=cell_means,
# scale=sqrt(cell_vars),
# right_only=TRUE)
# prop_varex <- irlba_res$d / sum(irlba_res$d)
#
# # pca_res <- prcomp(t(FM), center = T, scale = T)
# # std_dev <- pca_res$sdev
# # pr_var <- std_dev^2
# prop_varex <- pr_var/sum(pr_var)
}
p <- qplot(1:length(prop_varex), prop_varex, alpha = I(0.5)) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
theme(panel.background = element_rect(fill='white')) + xlab('components') +
ylab('Variance explained \n by each component')
cds@auxClusteringData[["tSNE"]]$variance_explained <- prop_varex # update CDS slot for variance_explained
if(return_all) {
return(list(variance_explained = prop_varex, p = p))
}
else
return(p)
}
#' @importFrom igraph shortest_paths degree shortest.paths
traverseTree <- function(g, starting_cell, end_cells){
distance <- shortest.paths(g, v=starting_cell, to=end_cells)
branchPoints <- which(degree(g) == 3)
path <- shortest_paths(g, from = starting_cell, end_cells)
return(list(shortest_path = path$vpath, distance = distance, branch_points = intersect(branchPoints, unlist(path$vpath))))
}
#' Plots the minimum spanning tree on cells.
#' @description Plots the minimum spanning tree on cells.
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param root_states the state used to set as the root of the graph
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param ... Additional arguments passed to the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom igraph V get.edgelist layout_as_tree
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_complex_cell_trajectory(HSMM)
#' plot_complex_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_complex_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_complex_cell_trajectory <- function(cds,
x=1,
y=2,
root_states = NULL,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE,
...){
gene_short_name <- NA
sample_name <- NA
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("SimplePPT", "DDRTree", "SGL-tree") ){
reduced_dim_coords <- reducedDimK(cds)
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
if (is.null(reduced_dim_coords)){
stop("You must first call reduceDimension() before using this function")
}
dp_mst <- minSpanningTree(cds)
if(is.null(root_states)) {
if(is.null(lib_info_with_pseudo$Pseudotime)){
root_cell <- row.names(lib_info_with_pseudo)[degree(dp_mst) == 1][1]
}
else
root_cell <- row.names(subset(lib_info_with_pseudo, Pseudotime == 0))
if(cds@dim_reduce_type != "ICA")
root_cell <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_cell, ]]
}
else {
candidate_root_cells <- row.names(subset(pData(cds), State %in% root_states))
if(cds@dim_reduce_type == "ICA") {
root_cell <- candidate_root_cells[which(degree(dp_mst, candidate_root_cells) == 1)]
} else {
Y_candidate_root_cells <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[candidate_root_cells, ]]
root_cell <- Y_candidate_root_cells[which(degree(dp_mst, Y_candidate_root_cells) == 1)]
}
}
# #root_cell <- cds@auxOrderingData[[cds@dim_reduce_type]]$root_cell
# root_state <- pData(cds)[root_cell,]$State
# #root_state <- V(pr_graph_cell_proj_mst)[root_cell,]$State
# pr_graph_root <- subset(pData(cds), State == root_state)
# closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
# root_cell_point_in_Y <- closest_vertex[row.names(pr_graph_root),]
tree_coords <- layout_as_tree(dp_mst, root=root_cell)
#ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
ica_space_df <- data.frame(tree_coords)
row.names(ica_space_df) <- colnames(reduced_dim_coords)
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
#S_matrix <- reducedDimS(cds)
#data_df <- data.frame(t(S_matrix[c(x,y),]))
if(cds@dim_reduce_type == "ICA"){
S_matrix <- tree_coords[,] #colnames(cds)
} else if(cds@dim_reduce_type %in% c("DDRTree", "SimplePPT", "SGL-tree")){
S_matrix <- tree_coords[closest_vertex,]
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}
data_df <- data.frame(S_matrix)
row.names(data_df) <- colnames(reducedDimS(cds))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
#print (head(edge_df))
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, I(cell_size))) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, ")"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}else {
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, " + 0.1)"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=2 * cell_size, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=1.5 * cell_size, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
# theme(axis.line.x = element_line(size=0.25, color="black")) +
# theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank()) +
xlab('') +
ylab('') +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
g
}
# Modified function: Plot heatmap of 3 branches with the same coloring. Each CDS subset has to have the same set of genes.
#' Create a heatmap to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @export
#'
plot_multiple_branches_heatmap <- function(cds,
branches,
branches_name = NULL,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("vstExprs", "log"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
pseudocount <- 1
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
cds_subset <- cds[, path_cells]
newdata <- data.frame(Pseudotime = seq(0, max(pData(cds_subset)$Pseudotime),length.out = 100))
tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=T,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols)
annotation_col <- data.frame(Branch=factor(rep(rep(branch_label, each = 100))))
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
col_gaps_ind <- c(1:(length(branches) - 1)) * 100
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
if(!(cluster_rows)) {
annotation_row <- NA
}
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
gaps_col = col_gaps_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 12,
color=hmcols,
silent=TRUE,
border_color = NA,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Create a kinetic curves to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param TPM Whether to convert the expression value into TPM values.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return a ggplot2 plot object
#'
#' @importFrom Biobase esApply
#' @importFrom stats lowess
#'
#' @export
#'
plot_multiple_branches_pseudotime <- function(cds,
branches,
branches_name = NULL,
min_expr = NULL,
cell_size = 0.75,
norm_method = c("vstExprs", "log"),
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "Branch",
trend_formula = '~sm.ns(Pseudotime, df=3)',
label_by_short_name = TRUE,
TPM = FALSE,
cores=1){
if(TPM) {
exprs(cds) <- esApply(cds, 2, function(x) x / sum(x) * 1e6)
}
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
cds_exprs <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
#if(is.null(pData(cds)$no_expression)) {
cds_subset <- cds[, path_cells]
# } else {
# cds_subset <- cds[, path_cells %in% colnames(cds)[!pData(cds)$no_expression]]
# }
newdata <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, row.names = colnames(cds_subset))
tmp <- t(esApply(cds_subset, 1, function(x) lowess(x[order(pData(cds_subset)$Pseudotime)])$y))
# tmp <- t(esApply(cds_subset, 1, function(x) {
# x <- x[order(pData(cds_subset)$Pseudotime)]
# c(smooth::sma(x, order = 100, h = 1, silent="all")$fitted)}) #, x[length(x)]
# )
colnames(tmp) <- colnames(cds_subset)[order(pData(cds_subset)$Pseudotime)]
# tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
# relative_expr = T, new_data = newdata)
cds_exprs_tmp <- reshape2::melt(log2(tmp + 1))
cds_exprs_tmp <- reshape2::melt(tmp)
colnames(cds_exprs_tmp) <- c("f_id", "Cell", "expression")
cds_exprs_tmp$Branch <- branch_label[which(branches == branch_in)]
if(is.null(cds_exprs))
cds_exprs <- cds_exprs_tmp
else
cds_exprs <- rbind(cds_exprs, cds_exprs_tmp)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
if (is.null(min_expr)) {
min_expr <- cds@lowerDetectionLimit
}
cds_pData <- pData(cds)
cds_fData <- fData(cds)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs <- plyr::ddply(cds_exprs, .(Branch), mutate, Pseudotime = (Pseudotime - min(Pseudotime)) * 100 / (max(Pseudotime) - min(Pseudotime)) )
# if (integer_expression) {
# cds_exprs$adjusted_expression <- round(cds_exprs$expression)
# }
# else {
# cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
# }
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
# new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
# full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
# relative_expr = T, new_data = new_data)
# colnames(full_model_expectation) <- colnames(cds_subset)
# cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
# if(!is.null(reducedModelFormulaStr)){
# reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
# relative_expr = T, new_data = new_data)
# colnames(reduced_model_expectation) <- colnames(cds_subset)
# cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
# }
# # FIXME: If you want to show the bifurcation time for each gene, this function
# # should just compute it. Passing it in as a dataframe is just too complicated
# # and will be hard on the user.
# # if(!is.null(bifurcation_time)){
# # cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# # }
# if (method == "loess")
# cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
# if (label_by_short_name == TRUE) {
# if (is.null(cds_exprs$gene_short_name) == FALSE) {
# cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
# cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# cds_exprs$feature_label <- factor(cds_exprs$feature_label)
# if (is.null(panel_order) == FALSE) {
# cds_exprs$feature_label <- factor(cds_exprs$feature_label,
# levels = panel_order)
# }
# cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
# cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
# cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
# cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
# if(!is.null(reducedModelFormulaStr)){
# cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
# cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
# }
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_line(aes_string(color = color_by), size = I(cell_size))
}
#if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + facet_wrap(~feature_label, nrow = nrow, ncol = ncol, scales = "free_y") #+ scale_y_log10()
#else q <- q + scale_y_log10() + facet_wrap(~feature_label,
# nrow = nrow, ncol = ncol, scales = "free_y")
#if (method == "loess")
# q <- q + stat_smooth(aes(fill = Branch, color = Branch),
# method = "loess")
#else if (method == "fitting") {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
# linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
#}
#if(!is.null(reducedModelFormulaStr)) {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
# color = 'black', linetype = 2, data = cds_exprs)
#}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
| /R/plotting.R | no_license | andrewwbutler/monocle-release | R | false | false | 127,507 | r | utils::globalVariables(c("Pseudotime", "value", "ids", "prin_graph_dim_1", "prin_graph_dim_2", "State",
"value", "feature_label", "expectation", "colInd", "rowInd", "value",
"source_prin_graph_dim_1", "source_prin_graph_dim_2"))
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(size=0.25, color="black")) +
theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank())
}
#' Plots the minimum spanning tree on cells.
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param use_color_gradient Whether or not to use color gradient instead of cell size to show marker expression level
#' @param markers_linear a boolean used to indicate whether you want to scale the markers logarithimically or linearly
#' @param show_cell_names draw the name of each cell in the plot
#' @param show_state_number show state number
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param state_number_size the size of the state number
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param theta How many degrees you want to rotate the trajectory
#' @param ... Additional arguments passed into scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom igraph get.edgelist
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' lung <- load_lung()
#' plot_cell_trajectory(lung)
#' plot_cell_trajectory(lung, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(lung, markers="MYH3")
#' }
plot_cell_trajectory <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
use_color_gradient = FALSE,
markers_linear = FALSE,
show_cell_names=FALSE,
show_state_number = FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
state_number_size = 2.9,
show_branch_points=TRUE,
theta = 0,
...){
gene_short_name <- NA
sample_name <- NA
sample_state <- pData(cds)$State
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("simplePPT", "DDRTree") ){
reduced_dim_coords <- reducedDimK(cds)
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
ica_space_df$sample_state <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
dp_mst <- minSpanningTree(cds)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
S_matrix <- reducedDimS(cds)
data_df <- data.frame(t(S_matrix[c(x,y),]))
data_df <- cbind(data_df, sample_state)
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
return_rotation_mat <- function(theta) {
theta <- theta / 180 * pi
matrix(c(cos(theta), sin(theta), -sin(theta), cos(theta)), nrow = 2)
}
tmp <- return_rotation_mat(theta) %*% t(as.matrix(data_df[, c(2, 3)]))
data_df$data_dim_1 <- tmp[1, ]
data_df$data_dim_2 <- tmp[2, ]
tmp <- return_rotation_mat(theta = theta) %*% t(as.matrix(edge_df[, c('source_prin_graph_dim_1', 'source_prin_graph_dim_2')]))
edge_df$source_prin_graph_dim_1 <- tmp[1, ]
edge_df$source_prin_graph_dim_2 <- tmp[2, ]
tmp <- return_rotation_mat(theta) %*% t(as.matrix(edge_df[, c('target_prin_graph_dim_1', 'target_prin_graph_dim_2')]))
edge_df$target_prin_graph_dim_1 <- tmp[1, ]
edge_df$target_prin_graph_dim_2 <- tmp[2, ]
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
if(use_color_gradient) {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color= value), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("value"), ...) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...) + facet_wrap(~feature_label)
}
} else {
if(markers_linear){
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size= (value * 0.1))) + facet_wrap(~feature_label)
} else {
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, size=log10(value + 0.1))) + facet_wrap(~feature_label)
}
}
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
}
}else {
if(use_color_gradient) {
# g <- g + geom_point(aes_string(color = color_by), na.rm = TRUE)
} else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=5, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=4, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
if (show_state_number){
g <- g + geom_text(aes(label = sample_state), size = state_number_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
g
}
#' @rdname package-deprecated
#' @title Plots the minimum spanning tree on cells.
#' This function is deprecated.
#' @description This function arranges all of the cells in the cds in a tree and
#' predicts their location based on their pseudotime value
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @export
#' @seealso plot_cell_trajectory
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_cell_trajectory(HSMM)
#' plot_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_spanning_tree <- function(cds,
x=1,
y=2,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE){
.Deprecated("plot_cell_trajectory") #include a package argument, too
plot_cell_trajectory(cds=cds,
x=x,
y=y,
color_by=color_by,
show_tree=show_tree,
show_backbone=show_backbone,
backbone_color=backbone_color,
markers=markers,
show_cell_names=show_cell_names,
cell_size=cell_size,
cell_link_size=cell_link_size,
cell_name_size=cell_name_size,
show_branch_points=show_branch_points)
}
#' @title Plots expression for one or more genes as a violin plot
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param log_scale a boolean that determines whether or not to scale data logarithmically
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("ACTA1", "ID1", "CCNB2"))),]
#' plot_genes_violin(my_genes, grouping="Hours", ncol=2, min_expr=0.1)
#' }
plot_genes_violin <- function (cds_subset, grouping = "State", min_expr = NULL, cell_size = 0.75,
nrow = NULL, ncol = 1, panel_order = NULL, color_by = NULL,
plot_trend = FALSE, label_by_short_name = TRUE, relative_expr = TRUE,
log_scale = TRUE)
{
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial",
"negbinomial.size")) {
integer_expression = TRUE
}
else {
integer_expression = FALSE
relative_expr = TRUE
}
if (integer_expression) {
cds_exprs = exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs = Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
#cds_exprs = reshape2::melt(round(as.matrix(cds_exprs)))
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
else {
cds_exprs = exprs(cds_subset)
cds_exprs = reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)) {
min_expr = cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) = c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] = min_expr
cds_pData = pData(cds_subset)
#
# # Custom bit for adding in a group for
# if(! is.null(show_combined)) {
# for(combine_gene in show_combined) {
# cds_pData_all = subset(cds_pData, gene == combine_gene)
# cds_pData_all[, grouping] = paste("All", combine_gene)
# cds_pData = rbind(cds_pData, cds_pData_all)
# }
# }
cds_fData = fData(cds_subset)
cds_exprs = merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs = merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs$adjusted_expression = log10(cds_exprs$expression)
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label = cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] = cds_exprs$f_id
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
}
else {
cds_exprs$feature_label = cds_exprs$f_id
}
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label = factor(cds_exprs$feature_label,
levels = panel_order)
}
q = ggplot(aes_string(x = grouping, y = "expression"), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q = q + geom_violin(aes_string(fill = color_by))
}
else {
q = q + geom_violin()
}
if (plot_trend == TRUE) {
q = q + stat_summary(fun.data = "mean_cl_boot",
size = 0.2)
q = q + stat_summary(aes_string(x = grouping, y = "expression",
group = color_by), fun.data = "mean_cl_boot",
size = 0.2, geom = "line")
}
q = q + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q = q + expand_limits(y = c(min_expr, 1))
}
q = q + ylab("Expression") + xlab(grouping)
if (log_scale == TRUE){
q = q + scale_y_log10()
}
q
}
#' Plots expression for one or more genes as a jittered, grouped points
#'
#' @description Accepts a subset of a CellDataSet and an attribute to group cells by,
#' and produces one or more ggplot2 objects that plots the level of expression for
#' each group of cells.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param plot_trend whether to plot a trendline tracking the average expression across the horizontal axis.
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1", "CCNB2"))),]
#' plot_genes_jitter(my_genes, grouping="Media", ncol=2)
#' }
plot_genes_jitter <- function(cds_subset,
grouping = "State",
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by=NULL,
plot_trend=FALSE,
label_by_short_name=TRUE,
relative_expr=TRUE){
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}else{
cds_exprs <- exprs(cds_subset)
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- cds_exprs$gene_short_name
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
#print (head(cds_exprs))
if (is.null(panel_order) == FALSE)
{
cds_exprs$feature_label <- factor(cds_exprs$feature_label, levels=panel_order)
}
q <- ggplot(aes_string(x=grouping, y="expression"), data=cds_exprs)
if (is.null(color_by) == FALSE){
q <- q + geom_jitter(aes_string(color=color_by), size=I(cell_size))
}else{
q <- q + geom_jitter(size=I(cell_size))
}
if (plot_trend == TRUE){
q <- q + stat_summary(aes_string(color=color_by), fun.data = "mean_cl_boot", size=0.35)
q <- q + stat_summary(aes_string(x=grouping, y="expression", color=color_by, group=color_by), fun.data = "mean_cl_boot", size=0.35, geom="line")
}
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
# Need this to guard against plotting failures caused by non-expressed genes
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1))
}
q <- q + ylab("Expression") + xlab(grouping)
q <- q + monocle_theme_opts()
q
}
#' Plots the number of cells expressing one or more genes as a barplot
#'
#' @description Accetps a CellDataSet and a parameter,"grouping", used for dividing cells into groups.
#' Returns one or more bar graphs (one graph for each gene in the CellDataSet).
#' Each graph shows the percentage of cells that express a gene in the in the CellDataSet for
#' each sub-group of cells created by "grouping".
#'
#' Let's say the CellDataSet passed in included genes A, B, and C and the "grouping parameter divided
#' all of the cells into three groups called X, Y, and Z. Then three graphs would be produced called A,
#' B, and C. In the A graph there would be three bars one for X, one for Y, and one for Z. So X bar in the
#' A graph would show the percentage of cells in the X group that express gene A.
#'
#' @param cds_subset CellDataSet for the experiment
#' @param grouping the cell attribute (e.g. the column of pData(cds)) to group cells by on the horizontal axis
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param plot_as_fraction whether to show the percent instead of the number of cells expressing each gene
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param plot_limits A pair of number specifying the limits of the y axis. If NULL, scale to the range of the data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' MYOG_ID1 <- HSMM[row.names(subset(fData(HSMM), gene_short_name %in% c("MYOG", "ID1"))),]
#' plot_genes_positive_cells(MYOG_ID1, grouping="Media", ncol=2)
#' }
plot_genes_positive_cells <- function(cds_subset,
grouping = "State",
min_expr=0.1,
nrow=NULL,
ncol=1,
panel_order=NULL,
plot_as_fraction=TRUE,
label_by_short_name=TRUE,
relative_expr=TRUE,
plot_limits=c(0,100)){
percent <- NULL
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
marker_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
marker_exprs <- Matrix::t(Matrix::t(marker_exprs) / sizeFactors(cds_subset))
}
marker_exprs_melted <- reshape2::melt(round(as.matrix(marker_exprs)))
}else{
marker_exprs_melted <- reshape2::melt(exprs(marker_exprs))
}
colnames(marker_exprs_melted) <- c("f_id", "Cell", "expression")
marker_exprs_melted <- merge(marker_exprs_melted, pData(cds_subset), by.x="Cell", by.y="row.names")
marker_exprs_melted <- merge(marker_exprs_melted, fData(cds_subset), by.x="f_id", by.y="row.names")
if (label_by_short_name == TRUE){
if (is.null(marker_exprs_melted$gene_short_name) == FALSE){
marker_exprs_melted$feature_label <- marker_exprs_melted$gene_short_name
marker_exprs_melted$feature_label[is.na(marker_exprs_melted$feature_label)] <- marker_exprs_melted$f_id
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
}else{
marker_exprs_melted$feature_label <- marker_exprs_melted$f_id
}
if (is.null(panel_order) == FALSE)
{
marker_exprs_melted$feature_label <- factor(marker_exprs_melted$feature_label, levels=panel_order)
}
marker_counts <- plyr::ddply(marker_exprs_melted, c("feature_label", grouping), function(x) {
data.frame(target=sum(x$expression > min_expr),
target_fraction=sum(x$expression > min_expr)/nrow(x)) } )
#print (head(marker_counts))
if (plot_as_fraction){
marker_counts$target_fraction <- marker_counts$target_fraction * 100
qp <- ggplot(aes_string(x=grouping, y="target_fraction", fill=grouping), data=marker_counts) +
ylab("Cells (percent)")
if (is.null(plot_limits) == FALSE)
qp <- qp + scale_y_continuous(limits=plot_limits)
}else{
qp <- ggplot(aes_string(x=grouping, y="target", fill=grouping), data=marker_counts) +
ylab("Cells")
}
qp <- qp + facet_wrap(~feature_label, nrow=nrow, ncol=ncol, scales="free_y")
qp <- qp + geom_bar(stat="identity") + monocle_theme_opts()
return(qp)
}
#' Plots expression for one or more genes as a function of pseudotime
#'
#' @description Plots expression for one or more genes as a function of pseudotime.
#' Plotting allows you determine if the ordering produced by orderCells() is correct
#' and it does not need to be flipped using the "reverse" flag in orderCells
#'
#' @param cds_subset CellDataSet for the experiment
#' @param min_expr the minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size the size (in points) of each cell used in the plot
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether to transform expression into relative values
#' @param vertical_jitter A value passed to ggplot to jitter the points in the vertical dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @param horizontal_jitter A value passed to ggplot to jitter the points in the horizontal dimension. Prevents overplotting, and is particularly helpful for rounded transcript count data.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply .
#' @importFrom reshape2 melt
#' @importFrom ggplot2 Position
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' my_genes <- row.names(subset(fData(HSMM), gene_short_name %in% c("CDK1", "MEF2C", "MYH3")))
#' cds_subset <- HSMM[my_genes,]
#' plot_genes_in_pseudotime(cds_subset, color_by="Time")
#' }
plot_genes_in_pseudotime <-function(cds_subset,
min_expr=NULL,
cell_size=0.75,
nrow=NULL,
ncol=1,
panel_order=NULL,
color_by="State",
trend_formula="~ sm.ns(Pseudotime, df=3)",
label_by_short_name=TRUE,
relative_expr=TRUE,
vertical_jitter=NULL,
horizontal_jitter=NULL){
f_id <- NA
Cell <- NA
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression) {
cds_exprs <- exprs(cds_subset)
if (relative_expr) {
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
}
else {
cds_exprs <- reshape2::melt(as.matrix(exprs(cds_subset)))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
#cds_exprs$f_id <- as.character(cds_exprs$f_id)
#cds_exprs$Cell <- as.character(cds_exprs$Cell)
if (integer_expression) {
cds_exprs$adjusted_expression <- cds_exprs$expression
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$f_id <- as.character(cds_exprs$f_id)
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime)
model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(model_expectation) <- colnames(cds_subset)
expectation <- ddply(cds_exprs, .(f_id, Cell), function(x) data.frame("expectation"=model_expectation[x$f_id, x$Cell]))
cds_exprs <- merge(cds_exprs, expectation)
#cds_exprs$expectation <- expectation#apply(cds_exprs,1, function(x) model_expectation[x$f_id, x$Cell])
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$expectation[cds_exprs$expectation < min_expr] <- min_expr
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
else {
q <- q + geom_point(size = I(cell_size), position=position_jitter(horizontal_jitter, vertical_jitter))
}
q <- q + geom_line(aes(x = Pseudotime, y = expectation), data = cds_exprs)
q <- q + scale_y_log10() + facet_wrap(~feature_label, nrow = nrow,
ncol = ncol, scales = "free_y")
if (min_expr < 1) {
q <- q + expand_limits(y = c(min_expr, 1))
}
if (relative_expr) {
q <- q + ylab("Relative Expression")
}
else {
q <- q + ylab("Absolute Expression")
}
q <- q + xlab("Pseudo-time")
q <- q + monocle_theme_opts()
q
}
#' Plots kinetic clusters of genes.
#'
#' @description returns a ggplot2 object showing the shapes of the
#' expression patterns followed by a set of pre-selected genes.
#' The topographic lines highlight the distributions of the kinetic patterns
#' relative to overall trend lines.
#'
#' @param cds CellDataSet for the experiment
#' @param clustering a clustering object produced by clusterCells
#' @param drawSummary whether to draw the summary line for each cluster
#' @param sumFun whether the function used to generate the summary for each cluster
#' @param ncol number of columns used to layout the faceted cluster panels
#' @param nrow number of columns used to layout the faceted cluster panels
#' @param row_samples how many genes to randomly select from the data
#' @param callout_ids a vector of gene names or gene ids to manually render as part of the plot
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom stringr str_join
#' @importFrom ggplot2 Position
#' @import grid
#' @export
#' @examples
#' \dontrun{
#' full_model_fits <- fitModel(HSMM_filtered[sample(nrow(fData(HSMM_filtered)), 100),],
#' modelFormulaStr="~VGAM::bs(Pseudotime)")
#' expression_curve_matrix <- responseMatrix(full_model_fits)
#' clusters <- clusterGenes(expression_curve_matrix, k=4)
#' plot_clusters(HSMM_filtered[ordering_genes,], clusters)
#' }
plot_clusters<-function(cds,
clustering,
drawSummary=TRUE,
sumFun=mean_cl_boot,
ncol=NULL,
nrow=NULL,
row_samples=NULL,
callout_ids=NULL){
.Deprecated("plot_genes_heatmap")
m <- as.data.frame(clustering$exprs)
m$ids <- rownames(clustering$exprs)
if (is.null(clustering$labels) == FALSE)
{
m$cluster = factor(clustering$labels[clustering$clustering], levels = levels(clustering$labels))
}else{
m$cluster <- factor(clustering$clustering)
}
cluster_sizes <- as.data.frame(table(m$cluster))
cluster_sizes$Freq <- paste("(", cluster_sizes$Freq, ")")
facet_labels <- str_join(cluster_sizes$Var1, cluster_sizes$Freq, sep=" ") #update the function
m.melt <- melt(m, id.vars = c("ids", "cluster"))
m.melt <- merge(m.melt, pData(cds), by.x="variable", by.y="row.names")
if (is.null(row_samples) == FALSE){
m.melt <- m.melt[sample(nrow(m.melt), row_samples),]
}
c <- ggplot(m.melt) + facet_wrap("cluster", ncol=ncol, nrow=nrow, scales="free_y")
#c <- c + stat_density2d(aes(x = Pseudotime, y = value), geom="polygon", fill="white", color="black", size=I(0.1)) + facet_wrap("cluster", ncol=ncol, nrow=nrow)
if (drawSummary) {
c <- c + stat_summary(aes(x = Pseudotime, y = value, group = 1),
fun.data = sumFun, color = "red",
alpha = 0.2, size = 0.5, geom = "smooth")
}
#cluster_medians <- subset(m.melt, ids %in% clustering$medoids)
#c <- c + geom_line()
#c <- c + geom_line(aes(x=Pseudotime, y=value), data=cluster_medians, color=I("red"))
c <- c + scale_color_hue(l = 50, h.start = 200) + theme(axis.text.x = element_text(angle = 0,
hjust = 0)) + xlab("Pseudo-time") + ylab("Expression")
c <- c + theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
theme(legend.position="none") +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank())
# if (draw_cluster_size){
# cluster_sizes <- as.data.frame(table(m$cluster))
# colnames(cluster_sizes) <- c("cluster", "Freq")
# cluster_sizes <- cbind (cluster_sizes, Pseudotime = cluster_label_text_x, value = cluster_label_text_y)
# c <- c + geom_text(aes(x=Pseudotime, y=value, label=Freq), data=cluster_sizes, size=cluster_label_text_size)
# }
if (is.null(callout_ids) == FALSE)
{
callout_melt <- subset(m.melt, ids %in% callout_ids)
c <- c + geom_line(aes(x=Pseudotime, y=value), data=callout_melt, color=I("steelblue"))
}
c <- c + monocle_theme_opts()
#c <- facet_wrap_labeller(c, facet_labels)
c
}
#
# #' Plots a pseudotime-ordered, row-centered heatmap
# #' @export
# plot_genes_heatmap <- function(cds,
# rescaling='row',
# clustering='row',
# labCol=FALSE,
# labRow=TRUE,
# logMode=TRUE,
# use_vst=TRUE,
# border=FALSE,
# heatscale=c(low='steelblue',mid='white',high='tomato'),
# heatMidpoint=0,
# method="none",
# scaleMax=2,
# scaleMin=-2,
# relative_expr=TRUE,
# ...){
#
# ## the function can be be viewed as a two step process
# ## 1. using the rehape package and other funcs the data is clustered, scaled, and reshaped
# ## using simple options or by a user supplied function
# ## 2. with the now resahped data the plot, the chosen labels and plot style are built
# FM <- exprs(cds)
#
# if (cds@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
# integer_expression <- TRUE
# }else{
# integer_expression <- FALSE
# relative_expr <- TRUE
# }
#
# if (integer_expression)
# {
# if (relative_expr){
# if (is.null(sizeFactors(cds)))
# {
# stop("Error: you must call estimateSizeFactors() first")
# }
# FM <- Matrix::t(Matrix::t(FM) / sizeFactors(cds))
# }
# FM <- round(FM)
# }
#
# m=FM
#
# if (is.null(fData(cds)$gene_short_name) == FALSE){
# feature_labels <- fData(cds)$gene_short_name
# feature_labels[is.na(feature_labels)] <- fData(cds)$f_id
# row.names(m) <- feature_labels
# }
#
# #remove genes with no expression in any condition
# m=m[!apply(m,1,sum)==0,]
#
# if (use_vst && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE){
# m = vstExprs(cds, expr_matrix=m)
# }else if(logMode){
# m = log10(m+pseudocount)
# }
#
# #remove genes with no sd
# #m=m[!apply(m,1,sd)==0,]
#
# ## you can either scale by row or column not both!
# ## if you wish to scale by both or use a different scale method then simply supply a scale
# ## function instead NB scale is a base funct
#
# ## I have supplied the default cluster and euclidean distance (JSdist) - and chose to cluster after scaling
# ## if you want a different distance/cluster method-- or to cluster and then scale
# ## then you can supply a custom function
#
# if(!is.function(method)){
# method = function(mat){as.dist((1 - cor(Matrix::t(mat)))/2)}
# }
#
# ## this is just reshaping into a ggplot format matrix and making a ggplot layer
#
# if(is.function(rescaling))
# {
# m=rescaling(m)
# } else {
# if(rescaling=='column'){
# m=m[!apply(m,2,sd)==0,]
# m=scale(m, center=TRUE)
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# if(rescaling=='row'){
# m=m[!apply(m,1,sd)==0,]
# m=Matrix::t(scale(Matrix::t(m),center=TRUE))
# m[is.nan(m)] = 0
# m[m>scaleMax] = scaleMax
# m[m<scaleMin] = scaleMin
# }
# }
#
# # If we aren't going to re-ordering the columns, order them by Pseudotime
# if (clustering %in% c("row", "none"))
# m = m[,row.names(pData(cds)[order(-pData(cds)$Pseudotime),])]
#
# if(clustering=='row')
# m=m[hclust(method(m))$order, ]
# if(clustering=='column')
# m=m[,hclust(method(Matrix::t(m)))$order]
# if(clustering=='both')
# m=m[hclust(method(m))$order ,hclust(method(Matrix::t(m)))$order]
#
#
# rows=dim(m)[1]
# cols=dim(m)[2]
#
#
#
# # if(logMode) {
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt( log10(m+pseudocount)))
# # }else{
# # melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
# # }
#
# melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows), reshape2::melt(m))
#
# g=ggplot(data=melt.m)
#
# ## add the heat tiles with or without a white border for clarity
#
# if(border==TRUE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd, fill=value),colour='grey')
# if(border==FALSE)
# g2=g+geom_raster(aes(x=colInd,y=rowInd,ymax=rowInd, fill=value))
#
# ## add axis labels either supplied or from the colnames rownames of the matrix
#
# if(labCol==TRUE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=colnames(m))
# }
# if(labCol==FALSE)
# {
# g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=rep('',cols))
# }
#
#
# if(labRow==TRUE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rownames(m))
# }
# if(labRow==FALSE)
# {
# g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rep('',rows))
# }
#
# # Get rid of the ticks, they get way too dense with lots of rows
# g2 <- g2 + theme(axis.ticks = element_blank())
#
# ## get rid of grey panel background and gridlines
#
# g2=g2+theme(panel.grid.minor=element_line(colour=NA), panel.grid.major=element_line(colour=NA),
# panel.background=element_rect(fill=NA, colour=NA))
#
# ##adjust x-axis labels
# g2=g2+theme(axis.text.x=element_text(angle=-90, hjust=0))
#
# #write(paste(c("Length of heatscale is :", length(heatscale))), stderr())
#
# if(is.function(rescaling))
# {
#
# }else{
# if(rescaling=='row' || rescaling == 'column'){
# legendTitle <- "Relative\nexpression"
# }else{
# if (logMode)
# {
# legendTitle <- bquote(paste(log[10]," FPKM + ",.(pseudocount),sep=""))
# #legendTitle <- paste(expression(plain(log)[10])," FPKM + ",pseudocount,sep="")
# } else {
# legendTitle <- "FPKM"
# }
# }
# }
#
# if (length(heatscale) == 2){
# g2 <- g2 + scale_fill_gradient(low=heatscale[1], high=heatscale[2], name=legendTitle)
# } else if (length(heatscale) == 3) {
# if (is.null(heatMidpoint))
# {
# heatMidpoint = (max(m) + min(m)) / 2.0
# #write(heatMidpoint, stderr())
# }
# g2 <- g2 + theme(panel.border = element_blank())
# g2 <- g2 + scale_fill_gradient2(low=heatscale[1], mid=heatscale[2], high=heatscale[3], midpoint=heatMidpoint, name=legendTitle)
# }else {
# g2 <- g2 + scale_fill_gradientn(colours=heatscale, name=legendTitle)
# }
#
# #g2<-g2+scale_x_discrete("",breaks=tracking_ids,labels=gene_short_names)
#
# g2 <- g2 + theme(axis.title.x=element_blank(), axis.title.y=element_blank())
#
# ## finally add the fill colour ramp of your choice (default is blue to red)-- and return
# return (g2)
# }
plot_genes_heatmap <- function(...){
.Deprecated("plot_pseudotime_heatmap")
plot_pseudotime_heatmap(...)
}
#' Plots a pseudotime-ordered, row-centered heatmap
#'
#' @description The function plot_pseudotime_heatmap takes a CellDataSet object
#' (usually containing a only subset of significant genes) and generates smooth expression
#' curves much like plot_genes_in_pseudotime.
#' Then, it clusters these genes and plots them using the pheatmap package.
#' This allows you to visualize modules of genes that co-vary across pseudotime.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_pseudotime_heatmap <- function(cds_subset,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("log", "vstExprs"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
num_clusters <- min(num_clusters, nrow(cds_subset))
pseudocount <- 1
newdata <- data.frame(Pseudotime = seq(min(pData(cds_subset)$Pseudotime), max(pData(cds_subset)$Pseudotime),length.out = 100))
m <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds_subset@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds_subset, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=cluster_rows,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
border_color = NA,
color=hmcols)
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if(!is.null(add_annotation_col)) {
if(nrow(add_annotation_col) != 100) {
stop('add_annotation_col should have only 100 rows (check genSmoothCurves before you supply the annotation data)!')
}
annotation_col <- add_annotation_col
} else {
annotation_col <- NA
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Plot the branch genes in pseduotime with separate branch curves.
#'
#' @description Works similarly to plot_genes_in_psuedotime esceptit shows
#' one kinetic trend for each lineage.
#'
#' @details This plotting function is used to make the branching plots for a branch dependent gene goes through the progenitor state
#' and bifurcating into two distinct branchs (Similar to the pitch-fork bifurcation in dynamic systems). In order to make the
#' bifurcation plot, we first duplicated the progenitor states and by default stretch each branch into maturation level 0-100.
#' Then we fit two nature spline curves for each branchs using VGAM package.
#'
#' @param cds CellDataSet for the experiment
#' @param branch_states The states for two branching branchs
#' @param branch_point The ID of the branch point to analyze. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_labels The names for each branching branch
#' @param method The method to draw the curve for the gene expression branching pattern, either loess ('loess') or VGLM fitting ('fitting')
#' @param min_expr The minimum (untransformed) expression level to use in plotted the genes.
#' @param cell_size The size (in points) of each cell used in the plot
#' @param nrow Number of columns used to layout the faceted cluster panels
#' @param ncol Number of columns used to layout the faceted cluster panels
#' @param panel_order The a character vector of gene short names (or IDs, if that's what you're using), specifying order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by The cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param expression_curve_linetype_by The cell attribute (e.g. the column of pData(cds)) to be used for the linetype of each branch curve
#' @param trend_formula The model formula to be used for fitting the expression trend over pseudotime
#' @param reducedModelFormulaStr A formula specifying a null model. If used, the plot shows a p value from the likelihood ratio test that uses trend_formula as the full model
#' @param label_by_short_name Whether to label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param relative_expr Whether or not the plot should use relative expression values (only relevant for CellDataSets using transcript counts)
#' @param ... Additional arguments passed on to branchTest. Only used when reducedModelFormulaStr is not NULL.
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom plyr ddply
#' @importFrom reshape2 melt
#' @importFrom BiocGenerics sizeFactors
#' @export
plot_genes_branched_pseudotime <- function (cds,
branch_states = NULL,
branch_point=1,
branch_labels = NULL,
method = "fitting",
min_expr = NULL,
cell_size = 0.75,
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "State",
expression_curve_linetype_by = "Branch",
trend_formula = "~ sm.ns(Pseudotime, df=3) * Branch",
reducedModelFormulaStr = NULL,
label_by_short_name = TRUE,
relative_expr = TRUE,
#gene_pairs = NULL,
...)
{
Branch <- NA
if (is.null(reducedModelFormulaStr) == FALSE) {
pval_df <- branchTest(cds,
branch_states=branch_states,
branch_point=branch_point,
fullModelFormulaStr = trend_formula,
reducedModelFormulaStr = "~ sm.ns(Pseudotime, df=3)",
...)
fData(cds)[, "pval"] <- pval_df[row.names(cds), 'pval']
}
if("Branch" %in% all.vars(terms(as.formula(trend_formula)))) { #only when Branch is in the model formula we will duplicate the "progenitor" cells
cds_subset <- buildBranchCellDataSet(cds = cds,
branch_states = branch_states,
branch_point=branch_point,
branch_labels = branch_labels,
progenitor_method = 'duplicate',
...)
}
else {
cds_subset <- cds
pData(cds_subset)$Branch <- pData(cds_subset)$State
}
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")) {
integer_expression <- TRUE
}
else {
integer_expression <- FALSE
}
if (integer_expression) {
CM <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset))) {
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
CM <- Matrix::t(Matrix::t(CM)/sizeFactors(cds_subset))
}
cds_exprs <- reshape2::melt(round(as.matrix(CM)))
}
else {
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)) {
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
if (integer_expression) {
cds_exprs$adjusted_expression <- round(cds_exprs$expression)
}
else {
cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
}
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
relative_expr = T, new_data = new_data)
colnames(full_model_expectation) <- colnames(cds_subset)
cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
if(!is.null(reducedModelFormulaStr)){
reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
relative_expr = T, new_data = new_data)
colnames(reduced_model_expectation) <- colnames(cds_subset)
cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
}
# FIXME: If you want to show the bifurcation time for each gene, this function
# should just compute it. Passing it in as a dataframe is just too complicated
# and will be hard on the user.
# if(!is.null(bifurcation_time)){
# cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# }
if (method == "loess")
cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
if (is.null(panel_order) == FALSE) {
cds_exprs$feature_label <- factor(cds_exprs$feature_label,
levels = panel_order)
}
cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
if(!is.null(reducedModelFormulaStr)){
cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
}
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_point(aes_string(color = color_by), size = I(cell_size))
}
if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + scale_y_log10() + facet_wrap(~feature_label +
pval, nrow = nrow, ncol = ncol, scales = "free_y")
else q <- q + scale_y_log10() + facet_wrap(~feature_label,
nrow = nrow, ncol = ncol, scales = "free_y")
if (method == "loess")
q <- q + stat_smooth(aes(fill = Branch, color = Branch),
method = "loess")
else if (method == "fitting") {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
}
if(!is.null(reducedModelFormulaStr)) {
q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
color = 'black', linetype = 2, data = cds_exprs)
}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
#' Not sure we're ready to release this one quite yet:
#' Plot the branch genes in pseduotime with separate branch curves
#' @param cds CellDataSet for the experiment
#' @param rowgenes Gene ids or short names to be arrayed on the vertical axis.
#' @param colgenes Gene ids or short names to be arrayed on the horizontal axis
#' @param relative_expr Whether to transform expression into relative values
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param label_by_short_name a boolean that indicates whether cells should be labeled by their short name
#' @param show_density a boolean that indicates whether a 2D density estimation should be shown in the plot
#' @param round_expr a boolean that indicates whether cds_expr values should be rounded or not
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
plot_coexpression_matrix <- function(cds,
rowgenes,
colgenes,
relative_expr=TRUE,
min_expr=NULL,
cell_size=0.85,
label_by_short_name=TRUE,
show_density=TRUE,
round_expr=FALSE){
gene_short_name <- NA
f_id <- NA
adjusted_expression.x <- NULL
adjusted_expression.y <- NULL
..density.. <- NULL
row_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% rowgenes))
row_gene_ids <- union(row_gene_ids, intersect(rowgenes, row.names(fData(cds))))
col_gene_ids <- row.names(subset(fData(cds), gene_short_name %in% colgenes))
col_gene_ids <- union(col_gene_ids, intersect(colgenes, row.names(fData(cds))))
cds_subset <- cds[union(row_gene_ids, col_gene_ids),]
if (cds_subset@expressionFamily@vfamily %in% c("negbinomial", "negbinomial.size")){
integer_expression <- TRUE
}else{
integer_expression <- FALSE
relative_expr <- TRUE
}
if (integer_expression)
{
cds_exprs <- exprs(cds_subset)
if (relative_expr){
if (is.null(sizeFactors(cds_subset)))
{
stop("Error: to call this function with relative_expr=TRUE, you must call estimateSizeFactors() first")
}
cds_exprs <- Matrix::t(Matrix::t(cds_exprs) / sizeFactors(cds_subset))
}
if (round_expr){
cds_exprs <- reshape2::melt(round(as.matrix(cds_exprs)))
} else {
cds_exprs <- reshape2::melt(as.matrix(cds_exprs))
}
}else{
cds_exprs <- reshape2::melt(exprs(cds_subset))
}
if (is.null(min_expr)){
min_expr <- cds_subset@lowerDetectionLimit
}
colnames(cds_exprs) <- c("f_id", "Cell", "expression")
cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
cds_pData <- pData(cds_subset)
cds_fData <- fData(cds_subset)
cds_exprs <- merge(cds_exprs, cds_fData, by.x="f_id", by.y="row.names")
cds_exprs$adjusted_expression <- cds_exprs$expression
#cds_exprs$adjusted_expression <- log10(cds_exprs$adjusted_expression + abs(rnorm(nrow(cds_exprs), min_expr, sqrt(min_expr))))
if (label_by_short_name == TRUE){
if (is.null(cds_exprs$gene_short_name) == FALSE){
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
}else{
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- factor(cds_exprs$feature_label)
row_cds_exprs <- subset(cds_exprs, f_id %in% row_gene_ids)
col_cds_exprs <- subset(cds_exprs, f_id %in% col_gene_ids)
joined_exprs <- merge(row_cds_exprs, col_cds_exprs, by="Cell")
cds_exprs <- joined_exprs
cds_exprs <- merge(cds_exprs, cds_pData, by.x="Cell", by.y="row.names")
cds_exprs <- subset(cds_exprs, adjusted_expression.x > min_expr | adjusted_expression.y > min_expr)
q <- ggplot(aes(adjusted_expression.x, adjusted_expression.y), data=cds_exprs, size=I(1))
if (show_density){
q <- q + stat_density2d(geom="raster", aes(fill = ..density..), contour = FALSE) +
scale_fill_gradient(low="white", high="red")
}
q <- q + scale_x_log10() + scale_y_log10() +
geom_point(color=I("black"), size=I(cell_size * 1.50)) +
geom_point(color=I("white"), size=I(cell_size)) +
facet_grid(feature_label.x ~ feature_label.y, scales="free")
#scale_color_brewer(palette="Set1") +
if (min_expr < 1)
{
q <- q + expand_limits(y=c(min_expr, 1), x=c(min_expr, 1))
}
#q <- q + monocle_theme_opts()
q
}
#The following code is swipped from colorRamps package which is used to make the pallette
table.ramp <- function(n, mid = 0.5, sill = 0.5, base = 1, height = 1)
{
x <- seq(0, 1, length.out = n)
y <- rep(0, length(x))
sill.min <- max(c(1, round((n - 1) * (mid - sill / 2)) + 1))
sill.max <- min(c(n, round((n - 1) * (mid + sill / 2)) + 1))
y[sill.min:sill.max] <- 1
base.min <- round((n - 1) * (mid - base / 2)) + 1
base.max <- round((n - 1) * (mid + base / 2)) + 1
xi <- base.min:sill.min
yi <- seq(0, 1, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
xi <- sill.max:base.max
yi <- seq(1, 0, length.out = length(xi))
i <- which(xi > 0 & xi <= n)
y[xi[i]] <- yi[i]
height * y
}
#' @importFrom grDevices rgb
rgb.tables <- function(n,
red = c(0.75, 0.25, 1),
green = c(0.5, 0.25, 1),
blue = c(0.25, 0.25, 1))
{
rr <- do.call("table.ramp", as.list(c(n, red)))
gr <- do.call("table.ramp", as.list(c(n, green)))
br <- do.call("table.ramp", as.list(c(n, blue)))
rgb(rr, gr, br)
}
matlab.like <- function(n) rgb.tables(n)
matlab.like2 <- function(n)
rgb.tables(n,
red = c(0.8, 0.2, 1),
green = c(0.5, 0.4, 0.8),
blue = c(0.2, 0.2, 1))
blue2green2red <- matlab.like2
#' Create a heatmap to demonstrate the bifurcation of gene expression along two branchs
#'
#' @description returns a heatmap that shows changes in both lineages at the same time.
#' It also requires that you choose a branch point to inspect.
#' Columns are points in pseudotime, rows are genes, and the beginning of pseudotime is in the middle of the heatmap.
#' As you read from the middle of the heatmap to the right, you are following one lineage through pseudotime. As you read left, the other.
#' The genes are clustered hierarchically, so you can visualize modules of genes that have similar lineage-dependent expression patterns.
#'
#' @param cds_subset CellDataSet for the experiment (normally only the branching genes detected with branchTest)
#' @param branch_point The ID of the branch point to visualize. Can only be used when reduceDimension is called with method = "DDRTree".
#' @param branch_states The two states to compare in the heatmap. Mutually exclusive with branch_point.
#' @param branch_labels The labels for the branchs.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param branch_colors The colors used in the annotation strip indicating the pre- and post-branch cells.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @param ... Additional arguments passed to buildBranchCellDataSet
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @importFrom stats sd as.dist cor cutree
#' @export
#'
plot_genes_branched_heatmap <- function(cds_subset,
branch_point=1,
branch_states=NULL,
branch_labels = c("Cell fate 1", "Cell fate 2"),
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
branch_colors = c('#979797', '#F05662', '#7990C8'),
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
scale_max=3,
scale_min=-3,
norm_method = c("log", "vstExprs"),
trend_formula = '~sm.ns(Pseudotime, df=3) * Branch',
return_heatmap=FALSE,
cores = 1, ...) {
cds <- NA
new_cds <- buildBranchCellDataSet(cds_subset,
branch_states=branch_states,
branch_point=branch_point,
progenitor_method = 'duplicate',
...)
new_cds@dispFitInfo <- cds_subset@dispFitInfo
if(is.null(branch_states)) {
progenitor_state <- subset(pData(cds_subset), Pseudotime == 0)[, 'State']
branch_states <- setdiff(pData(cds_subset)$State, progenitor_state)
}
col_gap_ind <- 101
# newdataA <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
# newdataB <- data.frame(Pseudotime = seq(0, 100, length.out = 100))
newdataA <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[1]))
newdataB <- data.frame(Pseudotime = seq(0, 100,
length.out = 100), Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[2]))
BranchAB_exprs <- genSmoothCurves(new_cds[, ], cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = rbind(newdataA, newdataB))
BranchA_exprs <- BranchAB_exprs[, 1:100]
BranchB_exprs <- BranchAB_exprs[, 101:200]
#common_ancestor_cells <- row.names(pData(new_cds)[duplicated(pData(new_cds)$original_cell_id),])
common_ancestor_cells <- row.names(pData(new_cds)[pData(new_cds)$State == setdiff(pData(new_cds)$State, branch_states),])
BranchP_num <- (100 - floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime'])))
BranchA_num <- floor(max(pData(new_cds)[common_ancestor_cells, 'Pseudotime']))
BranchB_num <- BranchA_num
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs') {
BranchA_exprs <- vstExprs(new_cds, expr_matrix=BranchA_exprs)
BranchB_exprs <- vstExprs(new_cds, expr_matrix=BranchB_exprs)
}
else if(norm_method == 'log') {
BranchA_exprs <- log10(BranchA_exprs + 1)
BranchB_exprs <- log10(BranchB_exprs + 1)
}
heatmap_matrix <- cBind(BranchA_exprs[, (col_gap_ind - 1):1], BranchB_exprs)
heatmap_matrix=heatmap_matrix[!apply(heatmap_matrix, 1, sd)==0,]
heatmap_matrix=Matrix::t(scale(Matrix::t(heatmap_matrix),center=TRUE))
heatmap_matrix=heatmap_matrix[is.na(row.names(heatmap_matrix)) == FALSE,]
heatmap_matrix[is.nan(heatmap_matrix)] = 0
heatmap_matrix[heatmap_matrix>scale_max] = scale_max
heatmap_matrix[heatmap_matrix<scale_min] = scale_min
heatmap_matrix_ori <- heatmap_matrix
heatmap_matrix <- heatmap_matrix[is.finite(heatmap_matrix[, 1]) & is.finite(heatmap_matrix[, col_gap_ind]), ] #remove the NA fitting failure genes for each branch
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
exp_rng <- range(heatmap_matrix) #bks is based on the expression range
bks <- seq(exp_rng[1] - 0.1, exp_rng[2] + 0.1, by=0.1)
if(is.null(hmcols)) {
hmcols <- blue2green2red(length(bks) - 1)
}
# prin t(hmcols)
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=F,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols
#color=hmcols#,
# filename="expression_pseudotime_pheatmap.pdf",
)
#save(heatmap_matrix, row_dist, num_clusters, hmcols, ph, branchTest_df, qval_lowest_thrsd, branch_labels, BranchA_num, BranchP_num, BranchB_num, file = 'heatmap_matrix')
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
if(!is.null(add_annotation_row)) {
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
annotation_col <- data.frame(row.names = c(1:ncol(heatmap_matrix)), "Cell Type" = c(rep(branch_labels[1], BranchA_num),
rep("Pre-branch", 2 * BranchP_num),
rep(branch_labels[2], BranchB_num)))
colnames(annotation_col) <- "Cell Type"
if(!is.null(add_annotation_col)) {
annotation_col <- cbind(annotation_col, add_annotation_col[fData(cds[row.names(annotation_col), ])$gene_short_name, 1])
}
names(branch_colors) <- c("Pre-branch", branch_labels[1], branch_labels[2])
annotation_colors=list("Cell Type"=branch_colors)
names(annotation_colors$`Cell Type`) = c('Pre-branch', branch_labels)
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols=FALSE,
cluster_rows=TRUE,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
annotation_colors=annotation_colors,
gaps_col = col_gap_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 6,
color=hmcols,
border_color = NA,
silent=TRUE)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(list(BranchA_exprs = BranchA_exprs, BranchB_exprs = BranchB_exprs, heatmap_matrix = heatmap_matrix,
heatmap_matrix_ori = heatmap_matrix_ori, ph = ph, col_gap_ind = col_gap_ind, row_dist = row_dist, hmcols = hmcols,
annotation_colors = annotation_colors, annotation_row = annotation_row, annotation_col = annotation_col,
ph_res = ph_res))
}
}
#' Plots genes by mean vs. dispersion, highlighting those selected for ordering
#'
#' Each gray point in the plot is a gene. The black dots are those that were included
#' in the last call to setOrderingFilter. The red curve shows the mean-variance
#' model learning by estimateDispersions().
#'
#' @param cds The CellDataSet to be used for the plot.
#' @export
plot_ordering_genes <- function(cds){
if(class(cds)[1] != "CellDataSet") {
stop("Error input object is not of type 'CellDataSet'")
}
disp_table <- dispersionTable(cds)
use_for_ordering <- NA
mean_expression <- NA
dispersion_empirical <- NA
dispersion_fit <- NA
gene_id <- NA
ordering_genes <- row.names(subset(fData(cds), use_for_ordering == TRUE))
g <- qplot(mean_expression, dispersion_empirical, data=disp_table, log="xy", color=I("darkgrey")) +
geom_line(aes(y=dispersion_fit), color="red")
if (length(ordering_genes) > 0){
g <- g + geom_point(aes(mean_expression, dispersion_empirical),
data=subset(disp_table, gene_id %in% ordering_genes), color="black")
}
g <- g + monocle_theme_opts()
g
}
#' Plots clusters of cells .
#'
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_name_size the size of cell name labels
#' @param ... additional arguments passed into the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' HSMM <- reduceD
#' plot_cell_clusters(HSMM)
#' plot_cell_clusters(HSMM, color_by="Pseudotime")
#' plot_cell_clusters(HSMM, markers="MYH3")
#' }
plot_cell_clusters <- function(cds,
x=1,
y=2,
color_by="Cluster",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_name_size=2,
...){
if (is.null(cds@reducedDimA) | length(pData(cds)$Cluster) == 0){
stop("Error: Clustering is not performed yet. Please call clusterCells() before calling this function.")
}
gene_short_name <- NULL
sample_name <- NULL
data_dim_1 <- NULL
data_dim_2 <- NULL
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info <- pData(cds)
tSNE_dim_coords <- reducedDimA(cds)
data_df <- data.frame(t(tSNE_dim_coords[c(x,y),]))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- colnames(cds)
data_df <- merge(data_df, lib_info, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2)) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
g <- g + geom_point(aes(color=log10(value + 0.1)), size=I(cell_size), na.rm = TRUE) +
scale_color_viridis(name = paste0("log10(value + 0.1)"), ...)
}else {
g <- g + geom_point(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE)
}
g <- g +
#scale_color_brewer(palette="Set1") +
monocle_theme_opts() +
xlab(paste("Component", x)) +
ylab(paste("Component", y)) +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(text = element_text(size = 15))
g
}
#' Plots the decision map of density clusters .
#'
#' @param cds CellDataSet for the experiment after running clusterCells_Density_Peak
#' @param rho_threshold The threshold of local density (rho) used to select the density peaks for plotting
#' @param delta_threshold The threshold of local distance (delta) used to select the density peaks for plotting
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_rho_delta(HSMM)
#' }
plot_rho_delta <- function(cds, rho_threshold = NULL, delta_threshold = NULL){
if(!is.null(cds@auxClusteringData[["tSNE"]]$densityPeak)
& !is.null(pData(cds)$Cluster)
& !is.null(pData(cds)$peaks)
& !is.null(pData(cds)$halo)
& !is.null(pData(cds)$delta)
& !is.null(pData(cds)$rho)) {
rho <- NULL
delta <- NULL
# df <- data.frame(rho = as.numeric(levels(pData(cds)$rho))[pData(cds)$rho],
# delta = as.numeric(levels(pData(cds)$delta))[pData(cds)$delta])
if(!is.null(rho_threshold) & !is.null(delta_threshold)){
peaks <- pData(cds)$rho >= rho_threshold & pData(cds)$delta >= delta_threshold
}
else
peaks <- pData(cds)$peaks
df <- data.frame(rho = pData(cds)$rho, delta = pData(cds)$delta, peaks = peaks)
g <- qplot(rho, delta, data = df, alpha = I(0.5), color = peaks) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
scale_color_manual(values=c("grey","black")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white'))
}
else {
stop('Please run clusterCells_Density_Peak before using this plotting function')
}
g
}
#' Plots the percentage of variance explained by the each component based on PCA from the normalized expression
#' data using the same procedure used in reduceDimension function.
#'
#' @param cds CellDataSet for the experiment after running reduceDimension with reduction_method as tSNE
#' @param max_components Maximum number of components shown in the scree plot (variance explained by each component)
#' @param norm_method Determines how to transform expression values prior to reducing dimensionality
#' @param residualModelFormulaStr A model formula specifying the effects to subtract from the data before clustering.
#' @param pseudo_expr amount to increase expression values before dimensionality reduction
#' @param return_all A logical argument to determine whether or not the variance of each component is returned
#' @param use_existing_pc_variance Whether to plot existing results for variance explained by each PC
#' @param verbose Whether to emit verbose output during dimensionality reduction
#' @param ... additional arguments to pass to the dimensionality reduction function
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_pc_variance_explained(HSMM)
#' }
plot_pc_variance_explained <- function(cds,
max_components=100,
# reduction_method=c("DDRTree", "ICA", 'tSNE'),
norm_method = c("log", "vstExprs", "none"),
residualModelFormulaStr=NULL,
pseudo_expr=NULL,
return_all = F,
use_existing_pc_variance=FALSE,
verbose=FALSE,
...){
set.seed(2016)
if(!is.null(cds@auxClusteringData[["tSNE"]]$variance_explained) & use_existing_pc_variance == T){
prop_varex <- cds@auxClusteringData[["tSNE"]]$variance_explained
}
else{
FM <- normalize_expr_data(cds, norm_method, pseudo_expr)
#FM <- FM[unlist(sparseApply(FM, 1, sd, convert_to_dense=TRUE)) > 0, ]
xm <- Matrix::rowMeans(FM)
xsd <- sqrt(Matrix::rowMeans((FM - xm)^2))
FM <- FM[xsd > 0,]
if (is.null(residualModelFormulaStr) == FALSE) {
if (verbose)
message("Removing batch effects")
X.model_mat <- sparse.model.matrix(as.formula(residualModelFormulaStr),
data = pData(cds), drop.unused.levels = TRUE)
fit <- limma::lmFit(FM, X.model_mat, ...)
beta <- fit$coefficients[, -1, drop = FALSE]
beta[is.na(beta)] <- 0
FM <- as.matrix(FM) - beta %*% t(X.model_mat[, -1])
}else{
X.model_mat <- NULL
}
if (nrow(FM) == 0) {
stop("Error: all rows have standard deviation zero")
}
# FM <- convert2DRData(cds, norm_method = 'log')
# FM <- FM[rowSums(is.na(FM)) == 0, ]
irlba_res <- prcomp_irlba(t(FM), n = min(max_components, min(dim(FM)) - 1),
center = TRUE, scale. = TRUE)
prop_varex <- irlba_res$sdev^2 / sum(irlba_res$sdev^2)
#
# cell_means <- Matrix::rowMeans(FM_t)
# cell_vars <- Matrix::rowMeans((FM_t - cell_means)^2)
#
# irlba_res <- irlba(FM,
# nv= min(max_components, min(dim(FM)) - 1), #avoid calculating components in the tail
# nu=0,
# center=cell_means,
# scale=sqrt(cell_vars),
# right_only=TRUE)
# prop_varex <- irlba_res$d / sum(irlba_res$d)
#
# # pca_res <- prcomp(t(FM), center = T, scale = T)
# # std_dev <- pca_res$sdev
# # pr_var <- std_dev^2
# prop_varex <- pr_var/sum(pr_var)
}
p <- qplot(1:length(prop_varex), prop_varex, alpha = I(0.5)) + monocle_theme_opts() +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
theme(panel.background = element_rect(fill='white')) + xlab('components') +
ylab('Variance explained \n by each component')
cds@auxClusteringData[["tSNE"]]$variance_explained <- prop_varex # update CDS slot for variance_explained
if(return_all) {
return(list(variance_explained = prop_varex, p = p))
}
else
return(p)
}
#' @importFrom igraph shortest_paths degree shortest.paths
traverseTree <- function(g, starting_cell, end_cells){
distance <- shortest.paths(g, v=starting_cell, to=end_cells)
branchPoints <- which(degree(g) == 3)
path <- shortest_paths(g, from = starting_cell, end_cells)
return(list(shortest_path = path$vpath, distance = distance, branch_points = intersect(branchPoints, unlist(path$vpath))))
}
#' Plots the minimum spanning tree on cells.
#' @description Plots the minimum spanning tree on cells.
#' @param cds CellDataSet for the experiment
#' @param x the column of reducedDimS(cds) to plot on the horizontal axis
#' @param y the column of reducedDimS(cds) to plot on the vertical axis
#' @param root_states the state used to set as the root of the graph
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to map to each cell's color
#' @param show_tree whether to show the links between cells connected in the minimum spanning tree
#' @param show_backbone whether to show the diameter path of the MST used to order the cells
#' @param backbone_color the color used to render the backbone.
#' @param markers a gene name or gene id to use for setting the size of each cell in the plot
#' @param show_cell_names draw the name of each cell in the plot
#' @param cell_size The size of the point for each cell
#' @param cell_link_size The size of the line segments connecting cells (when used with ICA) or the principal graph (when used with DDRTree)
#' @param cell_name_size the size of cell name labels
#' @param show_branch_points Whether to show icons for each branch point (only available when reduceDimension was called with DDRTree)
#' @param ... Additional arguments passed to the scale_color_viridis function
#' @return a ggplot2 plot object
#' @import ggplot2
#' @importFrom igraph V get.edgelist layout_as_tree
#' @importFrom reshape2 melt
#' @importFrom viridis scale_color_viridis
#' @export
#' @examples
#' \dontrun{
#' library(HSMMSingleCell)
#' HSMM <- load_HSMM()
#' plot_complex_cell_trajectory(HSMM)
#' plot_complex_cell_trajectory(HSMM, color_by="Pseudotime", show_backbone=FALSE)
#' plot_complex_cell_trajectory(HSMM, markers="MYH3")
#' }
plot_complex_cell_trajectory <- function(cds,
x=1,
y=2,
root_states = NULL,
color_by="State",
show_tree=TRUE,
show_backbone=TRUE,
backbone_color="black",
markers=NULL,
show_cell_names=FALSE,
cell_size=1.5,
cell_link_size=0.75,
cell_name_size=2,
show_branch_points=TRUE,
...){
gene_short_name <- NA
sample_name <- NA
data_dim_1 <- NA
data_dim_2 <- NA
#TODO: need to validate cds as ready for this plot (need mst, pseudotime, etc)
lib_info_with_pseudo <- pData(cds)
if (is.null(cds@dim_reduce_type)){
stop("Error: dimensionality not yet reduced. Please call reduceDimension() before calling this function.")
}
if (cds@dim_reduce_type == "ICA"){
reduced_dim_coords <- reducedDimS(cds)
}else if (cds@dim_reduce_type %in% c("SimplePPT", "DDRTree", "SGL-tree") ){
reduced_dim_coords <- reducedDimK(cds)
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}else {
stop("Error: unrecognized dimensionality reduction method.")
}
if (is.null(reduced_dim_coords)){
stop("You must first call reduceDimension() before using this function")
}
dp_mst <- minSpanningTree(cds)
if(is.null(root_states)) {
if(is.null(lib_info_with_pseudo$Pseudotime)){
root_cell <- row.names(lib_info_with_pseudo)[degree(dp_mst) == 1][1]
}
else
root_cell <- row.names(subset(lib_info_with_pseudo, Pseudotime == 0))
if(cds@dim_reduce_type != "ICA")
root_cell <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_cell, ]]
}
else {
candidate_root_cells <- row.names(subset(pData(cds), State %in% root_states))
if(cds@dim_reduce_type == "ICA") {
root_cell <- candidate_root_cells[which(degree(dp_mst, candidate_root_cells) == 1)]
} else {
Y_candidate_root_cells <- V(dp_mst)$name[cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[candidate_root_cells, ]]
root_cell <- Y_candidate_root_cells[which(degree(dp_mst, Y_candidate_root_cells) == 1)]
}
}
# #root_cell <- cds@auxOrderingData[[cds@dim_reduce_type]]$root_cell
# root_state <- pData(cds)[root_cell,]$State
# #root_state <- V(pr_graph_cell_proj_mst)[root_cell,]$State
# pr_graph_root <- subset(pData(cds), State == root_state)
# closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
# root_cell_point_in_Y <- closest_vertex[row.names(pr_graph_root),]
tree_coords <- layout_as_tree(dp_mst, root=root_cell)
#ica_space_df <- data.frame(Matrix::t(reduced_dim_coords[c(x,y),]))
ica_space_df <- data.frame(tree_coords)
row.names(ica_space_df) <- colnames(reduced_dim_coords)
colnames(ica_space_df) <- c("prin_graph_dim_1", "prin_graph_dim_2")
ica_space_df$sample_name <- row.names(ica_space_df)
#ica_space_with_state_df <- merge(ica_space_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
#print(ica_space_with_state_df)
if (is.null(dp_mst)){
stop("You must first call orderCells() before using this function")
}
edge_list <- as.data.frame(get.edgelist(dp_mst))
colnames(edge_list) <- c("source", "target")
edge_df <- merge(ica_space_df, edge_list, by.x="sample_name", by.y="source", all=TRUE)
#edge_df <- ica_space_df
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="source_prin_graph_dim_1", "prin_graph_dim_2"="source_prin_graph_dim_2"))
edge_df <- merge(edge_df, ica_space_df[,c("sample_name", "prin_graph_dim_1", "prin_graph_dim_2")], by.x="target", by.y="sample_name", all=TRUE)
edge_df <- plyr::rename(edge_df, c("prin_graph_dim_1"="target_prin_graph_dim_1", "prin_graph_dim_2"="target_prin_graph_dim_2"))
#S_matrix <- reducedDimS(cds)
#data_df <- data.frame(t(S_matrix[c(x,y),]))
if(cds@dim_reduce_type == "ICA"){
S_matrix <- tree_coords[,] #colnames(cds)
} else if(cds@dim_reduce_type %in% c("DDRTree", "SimplePPT", "SGL-tree")){
S_matrix <- tree_coords[closest_vertex,]
closest_vertex <- cds@auxOrderingData[["DDRTree"]]$pr_graph_cell_proj_closest_vertex
}
data_df <- data.frame(S_matrix)
row.names(data_df) <- colnames(reducedDimS(cds))
colnames(data_df) <- c("data_dim_1", "data_dim_2")
data_df$sample_name <- row.names(data_df)
data_df <- merge(data_df, lib_info_with_pseudo, by.x="sample_name", by.y="row.names")
markers_exprs <- NULL
if (is.null(markers) == FALSE){
markers_fData <- subset(fData(cds), gene_short_name %in% markers)
if (nrow(markers_fData) >= 1){
markers_exprs <- reshape2::melt(as.matrix(exprs(cds[row.names(markers_fData),])))
colnames(markers_exprs)[1:2] <- c('feature_id','cell_id')
markers_exprs <- merge(markers_exprs, markers_fData, by.x = "feature_id", by.y="row.names")
#print (head( markers_exprs[is.na(markers_exprs$gene_short_name) == FALSE,]))
markers_exprs$feature_label <- as.character(markers_exprs$gene_short_name)
markers_exprs$feature_label[is.na(markers_exprs$feature_label)] <- markers_exprs$Var1
}
}
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
data_df <- merge(data_df, markers_exprs, by.x="sample_name", by.y="cell_id")
#print (head(edge_df))
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2, I(cell_size))) + facet_wrap(~feature_label)
}else{
g <- ggplot(data=data_df, aes(x=data_dim_1, y=data_dim_2))
}
if (show_tree){
g <- g + geom_segment(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", xend="target_prin_graph_dim_1", yend="target_prin_graph_dim_2"), size=cell_link_size, linetype="solid", na.rm=TRUE, data=edge_df)
}
# FIXME: setting size here overrides the marker expression funtionality.
# Don't do it!
if (is.null(markers_exprs) == FALSE && nrow(markers_exprs) > 0){
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, ")"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}else {
if(class(data_df[, color_by]) == 'numeric') {
g <- g + geom_jitter(aes_string(color = paste0("log10(", color_by, " + 0.1)")), size=I(cell_size), na.rm = TRUE, height=5) +
scale_color_viridis(name = paste0("log10(", color_by, " + 0.1)"), ...)
} else {
g <- g + geom_jitter(aes_string(color = color_by), size=I(cell_size), na.rm = TRUE, height=5)
}
}
if (show_branch_points && cds@dim_reduce_type == 'DDRTree'){
mst_branch_nodes <- cds@auxOrderingData[[cds@dim_reduce_type]]$branch_points
branch_point_df <- subset(edge_df, sample_name %in% mst_branch_nodes)[,c("sample_name", "source_prin_graph_dim_1", "source_prin_graph_dim_2")]
branch_point_df$branch_point_idx <- match(branch_point_df$sample_name, mst_branch_nodes)
branch_point_df <- branch_point_df[!duplicated(branch_point_df$branch_point_idx), ]
g <- g + geom_point(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2"),
size=2 * cell_size, na.rm=TRUE, data=branch_point_df) +
geom_text(aes_string(x="source_prin_graph_dim_1", y="source_prin_graph_dim_2", label="branch_point_idx"),
size=1.5 * cell_size, color="white", na.rm=TRUE, data=branch_point_df)
}
if (show_cell_names){
g <- g +geom_text(aes(label=sample_name), size=cell_name_size)
}
g <- g +
#scale_color_brewer(palette="Set1") +
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
# theme(axis.line.x = element_line(size=0.25, color="black")) +
# theme(axis.line.y = element_line(size=0.25, color="black")) +
theme(panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank()) +
theme(panel.grid.major.x = element_blank(), panel.grid.major.y = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(legend.key=element_blank()) +
xlab('') +
ylab('') +
theme(legend.position="top", legend.key.height=grid::unit(0.35, "in")) +
#guides(color = guide_legend(label.position = "top")) +
theme(legend.key = element_blank()) +
theme(panel.background = element_rect(fill='white')) +
theme(line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
g
}
# Modified function: Plot heatmap of 3 branches with the same coloring. Each CDS subset has to have the same set of genes.
#' Create a heatmap to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param cluster_rows Whether to cluster the rows of the heatmap.
#' @param hclust_method The method used by pheatmap to perform hirearchical clustering of the rows.
#' @param num_clusters Number of clusters for the heatmap of branch genes
#' @param hmcols The color scheme for drawing the heatmap.
#' @param add_annotation_row Additional annotations to show for each row in the heatmap. Must be a dataframe with one row for each row in the fData table of cds_subset, with matching IDs.
#' @param add_annotation_col Additional annotations to show for each column in the heatmap. Must be a dataframe with one row for each cell in the pData table of cds_subset, with matching IDs.
#' @param show_rownames Whether to show the names for each row in the table.
#' @param use_gene_short_name Whether to use the short names for each row. If FALSE, uses row IDs from the fData table.
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param scale_max The maximum value (in standard deviations) to show in the heatmap. Values larger than this are set to the max.
#' @param scale_min The minimum value (in standard deviations) to show in the heatmap. Values smaller than this are set to the min.
#' @param trend_formula A formula string specifying the model used in fitting the spline curve for each gene/feature.
#' @param return_heatmap Whether to return the pheatmap object to the user.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return A list of heatmap_matrix (expression matrix for the branch committment), ph (pheatmap heatmap object),
#' annotation_row (annotation data.frame for the row), annotation_col (annotation data.frame for the column).
#' @import pheatmap
#' @export
#'
plot_multiple_branches_heatmap <- function(cds,
branches,
branches_name = NULL,
cluster_rows = TRUE,
hclust_method = "ward.D2",
num_clusters = 6,
hmcols = NULL,
add_annotation_row = NULL,
add_annotation_col = NULL,
show_rownames = FALSE,
use_gene_short_name = TRUE,
norm_method = c("vstExprs", "log"),
scale_max=3,
scale_min=-3,
trend_formula = '~sm.ns(Pseudotime, df=3)',
return_heatmap=FALSE,
cores=1){
pseudocount <- 1
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
cds_subset <- cds[, path_cells]
newdata <- data.frame(Pseudotime = seq(0, max(pData(cds_subset)$Pseudotime),length.out = 100))
tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
relative_expr = T, new_data = newdata)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
# Row-center the data.
m=m[!apply(m,1,sd)==0,]
m=Matrix::t(scale(Matrix::t(m),center=TRUE))
m=m[is.na(row.names(m)) == FALSE,]
m[is.nan(m)] = 0
m[m>scale_max] = scale_max
m[m<scale_min] = scale_min
heatmap_matrix <- m
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
if(is.null(hmcols)) {
bks <- seq(-3.1,3.1, by = 0.1)
hmcols <- blue2green2red(length(bks) - 1)
}
else {
bks <- seq(-3.1,3.1, length.out = length(hmcols))
}
ph <- pheatmap(heatmap_matrix,
useRaster = T,
cluster_cols=FALSE,
cluster_rows=T,
show_rownames=F,
show_colnames=F,
clustering_distance_rows=row_dist,
clustering_method = hclust_method,
cutree_rows=num_clusters,
silent=TRUE,
filename=NA,
breaks=bks,
color=hmcols)
annotation_col <- data.frame(Branch=factor(rep(rep(branch_label, each = 100))))
annotation_row <- data.frame(Cluster=factor(cutree(ph$tree_row, num_clusters)))
col_gaps_ind <- c(1:(length(branches) - 1)) * 100
if(!is.null(add_annotation_row)) {
old_colnames_length <- ncol(annotation_row)
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row), ])
colnames(annotation_row)[(old_colnames_length+1):ncol(annotation_row)] <- colnames(add_annotation_row)
# annotation_row$bif_time <- add_annotation_row[as.character(fData(absolute_cds[row.names(annotation_row), ])$gene_short_name), 1]
}
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds)[row.names(heatmap_matrix), 'gene_short_name'])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds)[row.names(annotation_row), 'gene_short_name'])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
if(!(cluster_rows)) {
annotation_row <- NA
}
ph_res <- pheatmap(heatmap_matrix[, ], #ph$tree_row$order
useRaster = T,
cluster_cols = FALSE,
cluster_rows = cluster_rows,
show_rownames=show_rownames,
show_colnames=F,
#scale="row",
clustering_distance_rows=row_dist, #row_dist
clustering_method = hclust_method, #ward.D2
cutree_rows=num_clusters,
# cutree_cols = 2,
annotation_row=annotation_row,
annotation_col=annotation_col,
gaps_col = col_gaps_ind,
treeheight_row = 20,
breaks=bks,
fontsize = 12,
color=hmcols,
silent=TRUE,
border_color = NA,
filename=NA
)
grid::grid.rect(gp=grid::gpar("fill", col=NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap){
return(ph_res)
}
}
#' Create a kinetic curves to demonstrate the bifurcation of gene expression along multiple branches
#'
#' @param cds CellDataSet for the experiment (normally only the branching genes detected with BEAM)
#' @param branches The terminal branches (states) on the developmental tree you want to investigate.
#' @param branches_name Name (for example, cell type) of branches you believe the cells on the branches are associated with.
#' @param min_expr The minimum level of expression to show in the plot
#' @param cell_size A number how large the cells should be in the plot
#' @param norm_method Determines how to transform expression values prior to rendering
#' @param nrow the number of rows used when laying out the panels for each gene's expression
#' @param ncol the number of columns used when laying out the panels for each gene's expression
#' @param panel_order the order in which genes should be layed out (left-to-right, top-to-bottom)
#' @param color_by the cell attribute (e.g. the column of pData(cds)) to be used to color each cell
#' @param trend_formula the model formula to be used for fitting the expression trend over pseudotime
#' @param label_by_short_name label figure panels by gene_short_name (TRUE) or feature id (FALSE)
#' @param TPM Whether to convert the expression value into TPM values.
#' @param cores Number of cores to use when smoothing the expression curves shown in the heatmap.
#' @return a ggplot2 plot object
#'
#' @importFrom Biobase esApply
#' @importFrom stats lowess
#'
#' @export
#'
plot_multiple_branches_pseudotime <- function(cds,
branches,
branches_name = NULL,
min_expr = NULL,
cell_size = 0.75,
norm_method = c("vstExprs", "log"),
nrow = NULL,
ncol = 1,
panel_order = NULL,
color_by = "Branch",
trend_formula = '~sm.ns(Pseudotime, df=3)',
label_by_short_name = TRUE,
TPM = FALSE,
cores=1){
if(TPM) {
exprs(cds) <- esApply(cds, 2, function(x) x / sum(x) * 1e6)
}
if(!(all(branches %in% pData(cds)$State)) & length(branches) == 1){
stop('This function only allows to make multiple branch plots where branches is included in the pData')
}
branch_label <- branches
if(!is.null(branches_name)){
if(length(branches) != length(branches_name)){
stop('branches_name should have the same length as branches')
}
branch_label <- branches_name
}
#test whether or not the states passed to branches are true branches (not truncks) or there are terminal cells
g <- cds@minSpanningTree
m <- NULL
cds_exprs <- NULL
# branche_cell_num <- c()
for(branch_in in branches) {
branches_cells <- row.names(subset(pData(cds), State == branch_in))
root_state <- subset(pData(cds), Pseudotime == 0)[, 'State']
root_state_cells <- row.names(subset(pData(cds), State == root_state))
if(cds@dim_reduce_type != 'ICA') {
root_state_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[root_state_cells, ], sep = ''))
branches_cells <- unique(paste('Y_', cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex[branches_cells, ], sep = ''))
}
root_cell <- root_state_cells[which(degree(g, v = root_state_cells) == 1)]
tip_cell <- branches_cells[which(degree(g, v = branches_cells) == 1)]
traverse_res <- traverseTree(g, root_cell, tip_cell)
path_cells <- names(traverse_res$shortest_path[[1]])
if(cds@dim_reduce_type != 'ICA') {
pc_ind <- cds@auxOrderingData$DDRTree$pr_graph_cell_proj_closest_vertex
path_cells <- row.names(pc_ind)[paste('Y_', pc_ind[, 1], sep = '') %in% path_cells]
}
#if(is.null(pData(cds)$no_expression)) {
cds_subset <- cds[, path_cells]
# } else {
# cds_subset <- cds[, path_cells %in% colnames(cds)[!pData(cds)$no_expression]]
# }
newdata <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, row.names = colnames(cds_subset))
tmp <- t(esApply(cds_subset, 1, function(x) lowess(x[order(pData(cds_subset)$Pseudotime)])$y))
# tmp <- t(esApply(cds_subset, 1, function(x) {
# x <- x[order(pData(cds_subset)$Pseudotime)]
# c(smooth::sma(x, order = 100, h = 1, silent="all")$fitted)}) #, x[length(x)]
# )
colnames(tmp) <- colnames(cds_subset)[order(pData(cds_subset)$Pseudotime)]
# tmp <- genSmoothCurves(cds_subset, cores=cores, trend_formula = trend_formula,
# relative_expr = T, new_data = newdata)
cds_exprs_tmp <- reshape2::melt(log2(tmp + 1))
cds_exprs_tmp <- reshape2::melt(tmp)
colnames(cds_exprs_tmp) <- c("f_id", "Cell", "expression")
cds_exprs_tmp$Branch <- branch_label[which(branches == branch_in)]
if(is.null(cds_exprs))
cds_exprs <- cds_exprs_tmp
else
cds_exprs <- rbind(cds_exprs, cds_exprs_tmp)
if(is.null(m))
m <- tmp
else
m <- cbind(m, tmp)
}
#remove genes with no expression in any condition
m=m[!apply(m,1,sum)==0,]
norm_method <- match.arg(norm_method)
# FIXME: this needs to check that vst values can even be computed. (They can only be if we're using NB as the expressionFamily)
if(norm_method == 'vstExprs' && is.null(cds@dispFitInfo[["blind"]]$disp_func) == FALSE) {
m = vstExprs(cds, expr_matrix=m)
}
else if(norm_method == 'log') {
m = log10(m+pseudocount)
}
if (is.null(min_expr)) {
min_expr <- cds@lowerDetectionLimit
}
cds_pData <- pData(cds)
cds_fData <- fData(cds)
cds_exprs <- merge(cds_exprs, cds_fData, by.x = "f_id", by.y = "row.names")
cds_exprs <- merge(cds_exprs, cds_pData, by.x = "Cell", by.y = "row.names")
cds_exprs <- plyr::ddply(cds_exprs, .(Branch), mutate, Pseudotime = (Pseudotime - min(Pseudotime)) * 100 / (max(Pseudotime) - min(Pseudotime)) )
# if (integer_expression) {
# cds_exprs$adjusted_expression <- round(cds_exprs$expression)
# }
# else {
# cds_exprs$adjusted_expression <- log10(cds_exprs$expression)
# }
if (label_by_short_name == TRUE) {
if (is.null(cds_exprs$gene_short_name) == FALSE) {
cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
}
else {
cds_exprs$feature_label <- cds_exprs$f_id
}
cds_exprs$feature_label <- as.factor(cds_exprs$feature_label)
# trend_formula <- paste("adjusted_expression", trend_formula,
# sep = "")
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
# new_data <- data.frame(Pseudotime = pData(cds_subset)$Pseudotime, Branch = pData(cds_subset)$Branch)
# full_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = trend_formula,
# relative_expr = T, new_data = new_data)
# colnames(full_model_expectation) <- colnames(cds_subset)
# cds_exprs$full_model_expectation <- apply(cds_exprs,1, function(x) full_model_expectation[x[2], x[1]])
# if(!is.null(reducedModelFormulaStr)){
# reduced_model_expectation <- genSmoothCurves(cds_subset, cores=1, trend_formula = reducedModelFormulaStr,
# relative_expr = T, new_data = new_data)
# colnames(reduced_model_expectation) <- colnames(cds_subset)
# cds_exprs$reduced_model_expectation <- apply(cds_exprs,1, function(x) reduced_model_expectation[x[2], x[1]])
# }
# # FIXME: If you want to show the bifurcation time for each gene, this function
# # should just compute it. Passing it in as a dataframe is just too complicated
# # and will be hard on the user.
# # if(!is.null(bifurcation_time)){
# # cds_exprs$bifurcation_time <- bifurcation_time[as.vector(cds_exprs$gene_short_name)]
# # }
# if (method == "loess")
# cds_exprs$expression <- cds_exprs$expression + cds@lowerDetectionLimit
# if (label_by_short_name == TRUE) {
# if (is.null(cds_exprs$gene_short_name) == FALSE) {
# cds_exprs$feature_label <- as.character(cds_exprs$gene_short_name)
# cds_exprs$feature_label[is.na(cds_exprs$feature_label)] <- cds_exprs$f_id
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# }
# else {
# cds_exprs$feature_label <- cds_exprs$f_id
# }
# cds_exprs$feature_label <- factor(cds_exprs$feature_label)
# if (is.null(panel_order) == FALSE) {
# cds_exprs$feature_label <- factor(cds_exprs$feature_label,
# levels = panel_order)
# }
# cds_exprs$expression[is.na(cds_exprs$expression)] <- min_expr
# cds_exprs$expression[cds_exprs$expression < min_expr] <- min_expr
# cds_exprs$full_model_expectation[is.na(cds_exprs$full_model_expectation)] <- min_expr
# cds_exprs$full_model_expectation[cds_exprs$full_model_expectation < min_expr] <- min_expr
# if(!is.null(reducedModelFormulaStr)){
# cds_exprs$reduced_model_expectation[is.na(cds_exprs$reduced_model_expectation)] <- min_expr
# cds_exprs$reduced_model_expectation[cds_exprs$reduced_model_expectation < min_expr] <- min_expr
# }
cds_exprs$State <- as.factor(cds_exprs$State)
cds_exprs$Branch <- as.factor(cds_exprs$Branch)
q <- ggplot(aes(Pseudotime, expression), data = cds_exprs)
# if (!is.null(bifurcation_time)) {
# q <- q + geom_vline(aes(xintercept = bifurcation_time),
# color = "black", linetype = "longdash")
# }
if (is.null(color_by) == FALSE) {
q <- q + geom_line(aes_string(color = color_by), size = I(cell_size))
}
#if (is.null(reducedModelFormulaStr) == FALSE)
q <- q + facet_wrap(~feature_label, nrow = nrow, ncol = ncol, scales = "free_y") #+ scale_y_log10()
#else q <- q + scale_y_log10() + facet_wrap(~feature_label,
# nrow = nrow, ncol = ncol, scales = "free_y")
#if (method == "loess")
# q <- q + stat_smooth(aes(fill = Branch, color = Branch),
# method = "loess")
#else if (method == "fitting") {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "full_model_expectation",
# linetype = "Branch"), data = cds_exprs) #+ scale_color_manual(name = "Type", values = c(colour_cell, colour), labels = c("Pre-branch", "AT1", "AT2", "AT1", "AT2")
#}
#if(!is.null(reducedModelFormulaStr)) {
# q <- q + geom_line(aes_string(x = "Pseudotime", y = "reduced_model_expectation"),
# color = 'black', linetype = 2, data = cds_exprs)
#}
q <- q + ylab("Expression") + xlab("Pseudotime (stretched)")
q <- q + monocle_theme_opts()
q + expand_limits(y = min_expr)
}
|
## ********************
## Create the R package
## ********************
## To get started, install R from https://cran.r-project.org/
## and RStudio Desktop https://rstudio.com/products/rstudio/download/#download
## You can install both of them for free.
## Next, open RStudio as the code that will run benefits from running inside
## RStudio for interactivity purposes.
## Next, you might need to install several R packages that you can install with
## the following code:
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_cran(
c(
"available",
"BiocManager",
"devtools",
"knitcitations",
"knitr",
"pkgdown",
"rmarkdown",
"rstudioapi",
"sessioninfo",
"styler",
"usethis"
)
)
if (!requireNamespace("BiocStyle", quietly = TRUE)) {
BiocManager::install("BiocStyle")
}
remotes::install_github("lcolladotor/biocthis")
## Here's a very quick summary of why these packages are useful:
## * available: to check the name of your package
## * BiocManager: to install Bioconductor packages
## * BiocStyle: for styling your vignette and linking to other packages
## * devtools: to develop R packages
## * knitcitations: for citing utilities in your package vignette
## * knitr: for making your vignette
## * pkgdown: for creating beautiful documentation websites
## * rmarkdown: for making the README.md and processing your vignette
## * remotes: for installing R packages from several locations
## * rstudioapi: for navigating across files in RStudio
## * sessioninfo: for detailed R session information useful to you and your users
## * usethis: for creating templates that will jump start your R package work
## Package names have some properties. You can also use:
available::available("JunePackage")
## to check that your package name is not taken and that it doesn't have
## a meaning that you might not be aware of.
usethis::create_package("JunePackage")
## This opens a new window in RStudio
## Note: If you create packages frequently, check the help file for
## usethis::use_description() for more information on how to set some R author
## defaults.
## Add package development files from biocthis
biocthis::use_bioc_pkg_templates()
## Move to the next step: setting up Git and GitHub for your package
rstudioapi::navigateToFile(usethis::proj_path("dev", "02_git_github_setup.R"))
## This template was made using https://lcolladotor.github.io/biocthis/
| /dev/01_create_pkg.R | no_license | lahuuki/JunePackage | R | false | false | 2,514 | r | ## ********************
## Create the R package
## ********************
## To get started, install R from https://cran.r-project.org/
## and RStudio Desktop https://rstudio.com/products/rstudio/download/#download
## You can install both of them for free.
## Next, open RStudio as the code that will run benefits from running inside
## RStudio for interactivity purposes.
## Next, you might need to install several R packages that you can install with
## the following code:
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_cran(
c(
"available",
"BiocManager",
"devtools",
"knitcitations",
"knitr",
"pkgdown",
"rmarkdown",
"rstudioapi",
"sessioninfo",
"styler",
"usethis"
)
)
if (!requireNamespace("BiocStyle", quietly = TRUE)) {
BiocManager::install("BiocStyle")
}
remotes::install_github("lcolladotor/biocthis")
## Here's a very quick summary of why these packages are useful:
## * available: to check the name of your package
## * BiocManager: to install Bioconductor packages
## * BiocStyle: for styling your vignette and linking to other packages
## * devtools: to develop R packages
## * knitcitations: for citing utilities in your package vignette
## * knitr: for making your vignette
## * pkgdown: for creating beautiful documentation websites
## * rmarkdown: for making the README.md and processing your vignette
## * remotes: for installing R packages from several locations
## * rstudioapi: for navigating across files in RStudio
## * sessioninfo: for detailed R session information useful to you and your users
## * usethis: for creating templates that will jump start your R package work
## Package names have some properties. You can also use:
available::available("JunePackage")
## to check that your package name is not taken and that it doesn't have
## a meaning that you might not be aware of.
usethis::create_package("JunePackage")
## This opens a new window in RStudio
## Note: If you create packages frequently, check the help file for
## usethis::use_description() for more information on how to set some R author
## defaults.
## Add package development files from biocthis
biocthis::use_bioc_pkg_templates()
## Move to the next step: setting up Git and GitHub for your package
rstudioapi::navigateToFile(usethis::proj_path("dev", "02_git_github_setup.R"))
## This template was made using https://lcolladotor.github.io/biocthis/
|
#' Calibration for the simple linear regression model.
#'
#' The function \code{calibrate} computes the maximum likelihood estimate and a
#' condfidence interval for the unknown predictor value that corresponds to an
#' observed value of the response (or vector thereof) or specified value of the
#' mean response. See the reference listed below for more details.
#'
#' @param object A matrix, list, data frame, or object that inherits from class
#' \code{"lm"}.
#' @param formula A formula of the form \code{y ~ x}.
#' @param data an optional data frame, list or environment (or object coercible
#' by \code{as.data.frame} to a data frame) containing the variables in the
#' model. If not found in data, the variables are taken from
#' \code{environment(formula)}, typically the environment from which \code{lm}
#' is called.
#' @param subset An optional vector specifying a subset of observations to be
#' used in the fitting process.
#' @param na.action a function which indicates what should happen when the data
#' contain \code{NA}s.
#' @param y0 The value of the observed response(s) or specified value of the
#' mean response.
#' @param interval The method to use for forming a confidence interval.
#' @param level A numeric scalar between 0 and 1 giving the confidence level for
#' the interval to be calculated.
#' @param mean.response Logicial indicating whether confidence intervals should
#' correspond to an observed response(s) (\code{FALSE}) or a specified value
#' of the mean response (\code{TRUE}). Default is \code{FALSE}.
#' @param adjust A logical value indicating if an adjustment should be made to
#' the critical value used in constructing the confidence interval. This
#' useful when the calibration curve is to be used k > 0 times.
#' @param k The number of times the calibration curve is to be used for
#' computing a confidence interval. Only needed when \code{adjust = TRUE}.
#' @param ... Additional optional arguments. At present, no optional arguments
#' are used.
#'
#' @return An object of class \code{"invest"} containing the following
#' components:
#' \itemize{
#' \item \code{estimate} The estimate of x0.
#' \item \code{lwr} The lower confidence limit for x0.
#' \item \code{upr} The upper confidence limit for x0.
#' \item \code{se} An estimate of the standard error (Wald interval only).
#' \item \code{interval} The method used for calculating \code{lower} and
#' \code{upper} (only used by \code{print} method).
#' }
#'
#' @references
#' Graybill, F. A., and Iyer, H. K. (1994)
#' \emph{Regression analysis: Concepts and Applications}. Duxbury Press.
#'
#' Miller, R. G. (1981)
#' \emph{Simultaneous Statistical Inference}. Springer-Verlag.
#'
#' @rdname calibrate
#'
#' @aliases print.calibrate
#'
#' @export
#'
#' @note The function \code{invest} is more general, but based on numerical
#' techniques to find the solution. When the underlying model is that of the
#' simple linear regression model with normal errors, closed-form expressions
#' exist which are utilized by the function \code{calibrate}.
#'
#' @examples
#' #
#' # Arsenic example (simple linear regression with replication)
#' #
#'
#' # Inverting a prediction interval for an individual response
#' arsenic.lm <- stats::lm(measured ~ actual, data = arsenic)
#' plotFit(arsenic.lm, interval = "prediction", shade = TRUE,
#' col.pred = "lightblue")
#' (cal <- calibrate(arsenic.lm, y0 = 3, interval = "inversion"))
#' abline(h = 3)
#' segments(cal$estimate, 3, cal$estimate, par()$usr[3])
#' arrows(cal$lower, 3, cal$lower, par()$usr[3])
#' arrows(cal$upper, 3, cal$upper, par()$usr[3])
#'
#' #
#' # Crystal weight example (simple linear regression)
#' #
#'
#' # Inverting a confidence interval for the mean response
#' crystal.lm <- stats::lm(weight ~ time, data = crystal)
#' plotFit(crystal.lm, interval = "confidence", shade = TRUE,
#' col.conf = "lightblue")
#' (cal <- calibrate(crystal.lm, y0 = 8, interval = "inversion",
#' mean.response = TRUE))
#' abline(h = 8)
#' segments(cal$estimate, 8, cal$estimate, par()$usr[3])
#' arrows(cal$lower, 8, cal$lower, par()$usr[3])
#' arrows(cal$upper, 8, cal$upper, par()$usr[3])
#'
#' # Wald interval and approximate standard error based on the delta method
#' calibrate(crystal.lm, y0 = 8, interval = "Wald", mean.response = TRUE)
calibrate <- function(object, ...) {
UseMethod("calibrate")
}
#' @rdname calibrate
#' @export
#' @method calibrate default
calibrate.default <- function(object, y0,
interval = c("inversion", "Wald", "none"),
level = 0.95, mean.response = FALSE,
adjust = c("none", "Bonferroni", "Scheffe"), k,
...) {
# Extract needed components from fitted model
if (inherits(object, "matrix")) {
x <- object[, 1]
y <- object[, 2]
} else if (inherits(object, "data.frame")) {
object <- data.matrix(object)
x <- object[, 1]
y <- object[, 2]
} else if (inherits(object, "list")) {
x <- object[[1]]
y <- object[[2]]
if (length(x) != length(y)) {
stop(paste("Components of '", deparse(substitute(object)),
"' not of same length.", sep = ""), call. = FALSE)
}
} else {
stop("'", paste(deparse(substitute(object)),
"' is not a valid matrix, list, or data frame.", sep = ""),
call. = FALSE)
}
eta <- mean(y0) # mean of new observations
m <- length(y0) # number of new observations
if (mean.response && m > 1) stop("Only one mean response value allowed.")
# Fit a simple linear regression model and compute necessary components
z <- stats::lm.fit(cbind(1, x), y)
b <- unname(z$coefficients)
n <- length(r <- z$residuals) # sample size and residuals
DF <- (DF1 <- n - 2) + (DF2 <- m - 1) # degrees of freedom
var1 <- sum(r ^ 2) / z$df.residual # stage 1 variance estimate
var2 <- if (m == 1) 0 else stats::var(y0) # stage 2 variance estimate
var.pooled <- (DF1 * var1 + DF2 * var2) / DF # pooled estimate of variance
sigma.pooled <- sqrt(var.pooled) # sqrt of pooled variance estimate
ssx <- sum((x - mean(x))^2) # sum-of-squares for x, Sxx
x0.mle <- (eta - b[1L])/b[2L] # MLE of x0
# Return point estimate only
interval <- match.arg(interval)
if (interval == "none") return(x0.mle)
# Adjustment for simultaneous intervals
adjust <- match.arg(adjust) # FIXME: Does simultaneous work for m > 1?
crit <- if (m != 1 || adjust == "none") stats::qt((1 + level)/2, n+m-3) else {
switch(adjust,
"Bonferroni" = stats::qt((level + 2*k - 1) / (2*k), n+m-3),
"Scheffe" = sqrt(k * stats::qf(level, k, n+m-3)))
}
# Inversion interval --------------------------------------------------------
if (interval == "inversion") {
# Define constants; see Graybill and Iyer (1994)
c1 <- b[2L]^2 - (sigma.pooled^2 * crit^2)/ssx
c2 <- if (mean.response) {
c1/n + (eta - mean(y))^2/ssx
} else {
c1*(1/m + 1/n) + (eta - mean(y))^2/ssx
}
c3 <- b[2L] * (eta - mean(y))
c4 <- crit * sigma.pooled
# Calculate inversion interval
if (c1 < 0 && c2 <= 0) {
# Throw warning if interval is the real line
warning("The calibration line is not well determined.", call. = FALSE)
lwr <- -Inf
upr <- Inf
} else {
# Construct inversion interval
lwr <- mean(x) + (c3 - c4*sqrt(c2))/c1
upr <- mean(x) + (c3 + c4*sqrt(c2))/c1
# Throw error if result is not a single interval
if (c1 < 0 && c2 > 0) {
stop(paste("The calibration line is not well determined. The resulting \nconfidence region is the union of two semi-infinite intervals:\n(", -Inf, ",",
round(upr, 4), ") U (", round(lwr, 4), ",", Inf, ")"),
call. = FALSE)
}
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = lwr,
"upper" = upr,
"interval" = interval)
}
# Wald interval
if (interval == "Wald") {
# Compute standard error for Wald interval
se <- if (mean.response) {
abs((sigma.pooled/b[2]))*sqrt((1/n + (x0.mle - mean(x))^2/ssx))
} else {
abs((sigma.pooled/b[2]))*sqrt((1/m + 1/n + (x0.mle - mean(x))^2/ssx))
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = x0.mle - crit * se,
"upper" = x0.mle + crit * se,
"se" = se,
"interval" = interval)
}
# Assign class label and return results
class(res) <- "invest"
return(res)
}
#' @rdname calibrate
#' @export
#' @method calibrate formula
calibrate.formula <- function(formula, data = NULL, ..., subset,
na.action = stats::na.fail) {
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, sys.parent()))) {
m$data <- as.data.frame(data)
}
m$... <- NULL
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
Terms <- attr(m, "terms")
attr(Terms, "intercept") <- 0
y <- stats::model.extract(m, "response")
mm <- stats::model.matrix(Terms, m)
if (ncol(mm) > 1) stop("This function only works for the simple linear regression model (i.e., y ~ x).")
x <- as.numeric(mm)
calibrate(cbind(x, y), ...)
}
#' @rdname calibrate
#' @export
#' @method calibrate lm
calibrate.lm <- function(object, y0, interval = c("inversion", "Wald", "none"),
level = 0.95, mean.response = FALSE,
adjust = c("none", "Bonferroni", "Scheffe"), k, ...) {
# Check model formula for correctness
xname <- all.vars(stats::formula(object)[[3L]])
yname <- all.vars(stats::formula(object)[[2L]])
if (length(xname) != 1L) {
stop("Only one independent variable allowed.")
}
if (length(yname) != 1L) {
stop("Only one dependent variable allowed.")
}
# Check for intercept using terms object from model fit. Alternatively, this
# can also be checked by testing if the first column name in model.matrix is
# equal to "(Intercept)".
if (!attr(stats::terms(object), "intercept")) {
stop(paste(deparse(substitute(object)), "must contain an intercept."))
}
# Extract x values and y values from model frame
mf <- stats::model.frame(object)
if (ncol(mf) != 2) {
stop("calibrate only works for the simple linear regression model.")
}
x <- stats::model.matrix(object)[, 2]
y <- stats::model.response(mf)
# Eta - mean response or mean of observed respone values
eta <- mean(y0) # mean of new observations
m <- length(y0) # number of new observations
if (mean.response && m > 1) stop("Only one mean response value allowed.")
# Fit a simple linear regression model and compute necessary components
b <- unname(object$coefficients)
n <- length(r <- object$residuals) # sample size and residuals
DF <- (DF1 <- n - 2) + (DF2 <- m - 1) # degrees of freedom
var1 <- sum(r ^ 2) / object$df.residual # stage 1 variance estimate
var2 <- if (m == 1) 0 else stats::var(y0) # stage 2 variance estimate
var.pooled <- (DF1 * var1 + DF2 * var2) / DF # pooled estimate of variance
sigma.pooled <- sqrt(var.pooled) # sqrt of pooled variance estimate
ssx <- sum((x - mean(x))^2) # sum-of-squares for x, Sxx
x0.mle <- (eta - b[1L])/b[2L] # MLE of x0
# Return point estimate only
interval <- match.arg(interval)
if (interval == "none") return(x0.mle)
# Adjustment for simultaneous intervals
adjust <- match.arg(adjust) # FIXME: Does simultaneous work for m > 1?
crit <- if (m != 1 || adjust == "none") stats::qt((1 + level)/2, n+m-3) else {
switch(adjust,
"Bonferroni" = stats::qt((level + 2*k - 1) / (2*k), n+m-3),
"Scheffe" = sqrt(k * stats::qf(level, k, n+m-3)))
}
# Inversion interval --------------------------------------------------------
if (interval == "inversion") {
c1 <- b[2L]^2 - (sigma.pooled^2 * crit^2)/ssx
c2 <- if (mean.response) {
c1/n + (eta - mean(y))^2/ssx
} else {
c1*(1/m + 1/n) + (eta - mean(y))^2/ssx
}
c3 <- b[2L] * (eta - mean(y))
c4 <- crit * sigma.pooled
# FIXME: catch errors and throw an appropriate warning
if (c1 < 0 && c2 <= 0) {
warning("The calibration line is not well determined.", call. = FALSE)
lwr <- -Inf
upr <- Inf
} else {
lwr <- mean(x) + (c3 - c4*sqrt(c2))/c1
upr <- mean(x) + (c3 + c4*sqrt(c2))/c1
if (c1 < 0 && c2 > 0) {
stop(paste("The calibration line is not well determined. The resulting \nconfidence region is the union of two semi-infinite intervals:\n(", -Inf, ",",
round(upr, 4), ") U (", round(lwr, 4), ",", Inf, ")"),
call. = FALSE)
}
}
res <- list("estimate" = x0.mle,
"lower" = lwr,
"upper" = upr,
"interval" = interval)
}
# Wald interval
if (interval == "Wald") {
# Compute standard error for Wald interval
se <- if (mean.response) {
abs((sigma.pooled/b[2]))*sqrt((1/n + (x0.mle - mean(x))^2/ssx))
} else {
abs((sigma.pooled/b[2]))*sqrt((1/m + 1/n + (x0.mle - mean(x))^2/ssx))
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = x0.mle - crit * se,
"upper" = x0.mle + crit * se,
"se" = se,
"interval" = interval)
}
# Assign class label and return results
class(res) <- "invest"
return(res)
}
| /R/calibrate.R | no_license | PhDMeiwp/investr | R | false | false | 13,860 | r | #' Calibration for the simple linear regression model.
#'
#' The function \code{calibrate} computes the maximum likelihood estimate and a
#' condfidence interval for the unknown predictor value that corresponds to an
#' observed value of the response (or vector thereof) or specified value of the
#' mean response. See the reference listed below for more details.
#'
#' @param object A matrix, list, data frame, or object that inherits from class
#' \code{"lm"}.
#' @param formula A formula of the form \code{y ~ x}.
#' @param data an optional data frame, list or environment (or object coercible
#' by \code{as.data.frame} to a data frame) containing the variables in the
#' model. If not found in data, the variables are taken from
#' \code{environment(formula)}, typically the environment from which \code{lm}
#' is called.
#' @param subset An optional vector specifying a subset of observations to be
#' used in the fitting process.
#' @param na.action a function which indicates what should happen when the data
#' contain \code{NA}s.
#' @param y0 The value of the observed response(s) or specified value of the
#' mean response.
#' @param interval The method to use for forming a confidence interval.
#' @param level A numeric scalar between 0 and 1 giving the confidence level for
#' the interval to be calculated.
#' @param mean.response Logicial indicating whether confidence intervals should
#' correspond to an observed response(s) (\code{FALSE}) or a specified value
#' of the mean response (\code{TRUE}). Default is \code{FALSE}.
#' @param adjust A logical value indicating if an adjustment should be made to
#' the critical value used in constructing the confidence interval. This
#' useful when the calibration curve is to be used k > 0 times.
#' @param k The number of times the calibration curve is to be used for
#' computing a confidence interval. Only needed when \code{adjust = TRUE}.
#' @param ... Additional optional arguments. At present, no optional arguments
#' are used.
#'
#' @return An object of class \code{"invest"} containing the following
#' components:
#' \itemize{
#' \item \code{estimate} The estimate of x0.
#' \item \code{lwr} The lower confidence limit for x0.
#' \item \code{upr} The upper confidence limit for x0.
#' \item \code{se} An estimate of the standard error (Wald interval only).
#' \item \code{interval} The method used for calculating \code{lower} and
#' \code{upper} (only used by \code{print} method).
#' }
#'
#' @references
#' Graybill, F. A., and Iyer, H. K. (1994)
#' \emph{Regression analysis: Concepts and Applications}. Duxbury Press.
#'
#' Miller, R. G. (1981)
#' \emph{Simultaneous Statistical Inference}. Springer-Verlag.
#'
#' @rdname calibrate
#'
#' @aliases print.calibrate
#'
#' @export
#'
#' @note The function \code{invest} is more general, but based on numerical
#' techniques to find the solution. When the underlying model is that of the
#' simple linear regression model with normal errors, closed-form expressions
#' exist which are utilized by the function \code{calibrate}.
#'
#' @examples
#' #
#' # Arsenic example (simple linear regression with replication)
#' #
#'
#' # Inverting a prediction interval for an individual response
#' arsenic.lm <- stats::lm(measured ~ actual, data = arsenic)
#' plotFit(arsenic.lm, interval = "prediction", shade = TRUE,
#' col.pred = "lightblue")
#' (cal <- calibrate(arsenic.lm, y0 = 3, interval = "inversion"))
#' abline(h = 3)
#' segments(cal$estimate, 3, cal$estimate, par()$usr[3])
#' arrows(cal$lower, 3, cal$lower, par()$usr[3])
#' arrows(cal$upper, 3, cal$upper, par()$usr[3])
#'
#' #
#' # Crystal weight example (simple linear regression)
#' #
#'
#' # Inverting a confidence interval for the mean response
#' crystal.lm <- stats::lm(weight ~ time, data = crystal)
#' plotFit(crystal.lm, interval = "confidence", shade = TRUE,
#' col.conf = "lightblue")
#' (cal <- calibrate(crystal.lm, y0 = 8, interval = "inversion",
#' mean.response = TRUE))
#' abline(h = 8)
#' segments(cal$estimate, 8, cal$estimate, par()$usr[3])
#' arrows(cal$lower, 8, cal$lower, par()$usr[3])
#' arrows(cal$upper, 8, cal$upper, par()$usr[3])
#'
#' # Wald interval and approximate standard error based on the delta method
#' calibrate(crystal.lm, y0 = 8, interval = "Wald", mean.response = TRUE)
calibrate <- function(object, ...) {
UseMethod("calibrate")
}
#' @rdname calibrate
#' @export
#' @method calibrate default
calibrate.default <- function(object, y0,
interval = c("inversion", "Wald", "none"),
level = 0.95, mean.response = FALSE,
adjust = c("none", "Bonferroni", "Scheffe"), k,
...) {
# Extract needed components from fitted model
if (inherits(object, "matrix")) {
x <- object[, 1]
y <- object[, 2]
} else if (inherits(object, "data.frame")) {
object <- data.matrix(object)
x <- object[, 1]
y <- object[, 2]
} else if (inherits(object, "list")) {
x <- object[[1]]
y <- object[[2]]
if (length(x) != length(y)) {
stop(paste("Components of '", deparse(substitute(object)),
"' not of same length.", sep = ""), call. = FALSE)
}
} else {
stop("'", paste(deparse(substitute(object)),
"' is not a valid matrix, list, or data frame.", sep = ""),
call. = FALSE)
}
eta <- mean(y0) # mean of new observations
m <- length(y0) # number of new observations
if (mean.response && m > 1) stop("Only one mean response value allowed.")
# Fit a simple linear regression model and compute necessary components
z <- stats::lm.fit(cbind(1, x), y)
b <- unname(z$coefficients)
n <- length(r <- z$residuals) # sample size and residuals
DF <- (DF1 <- n - 2) + (DF2 <- m - 1) # degrees of freedom
var1 <- sum(r ^ 2) / z$df.residual # stage 1 variance estimate
var2 <- if (m == 1) 0 else stats::var(y0) # stage 2 variance estimate
var.pooled <- (DF1 * var1 + DF2 * var2) / DF # pooled estimate of variance
sigma.pooled <- sqrt(var.pooled) # sqrt of pooled variance estimate
ssx <- sum((x - mean(x))^2) # sum-of-squares for x, Sxx
x0.mle <- (eta - b[1L])/b[2L] # MLE of x0
# Return point estimate only
interval <- match.arg(interval)
if (interval == "none") return(x0.mle)
# Adjustment for simultaneous intervals
adjust <- match.arg(adjust) # FIXME: Does simultaneous work for m > 1?
crit <- if (m != 1 || adjust == "none") stats::qt((1 + level)/2, n+m-3) else {
switch(adjust,
"Bonferroni" = stats::qt((level + 2*k - 1) / (2*k), n+m-3),
"Scheffe" = sqrt(k * stats::qf(level, k, n+m-3)))
}
# Inversion interval --------------------------------------------------------
if (interval == "inversion") {
# Define constants; see Graybill and Iyer (1994)
c1 <- b[2L]^2 - (sigma.pooled^2 * crit^2)/ssx
c2 <- if (mean.response) {
c1/n + (eta - mean(y))^2/ssx
} else {
c1*(1/m + 1/n) + (eta - mean(y))^2/ssx
}
c3 <- b[2L] * (eta - mean(y))
c4 <- crit * sigma.pooled
# Calculate inversion interval
if (c1 < 0 && c2 <= 0) {
# Throw warning if interval is the real line
warning("The calibration line is not well determined.", call. = FALSE)
lwr <- -Inf
upr <- Inf
} else {
# Construct inversion interval
lwr <- mean(x) + (c3 - c4*sqrt(c2))/c1
upr <- mean(x) + (c3 + c4*sqrt(c2))/c1
# Throw error if result is not a single interval
if (c1 < 0 && c2 > 0) {
stop(paste("The calibration line is not well determined. The resulting \nconfidence region is the union of two semi-infinite intervals:\n(", -Inf, ",",
round(upr, 4), ") U (", round(lwr, 4), ",", Inf, ")"),
call. = FALSE)
}
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = lwr,
"upper" = upr,
"interval" = interval)
}
# Wald interval
if (interval == "Wald") {
# Compute standard error for Wald interval
se <- if (mean.response) {
abs((sigma.pooled/b[2]))*sqrt((1/n + (x0.mle - mean(x))^2/ssx))
} else {
abs((sigma.pooled/b[2]))*sqrt((1/m + 1/n + (x0.mle - mean(x))^2/ssx))
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = x0.mle - crit * se,
"upper" = x0.mle + crit * se,
"se" = se,
"interval" = interval)
}
# Assign class label and return results
class(res) <- "invest"
return(res)
}
#' @rdname calibrate
#' @export
#' @method calibrate formula
calibrate.formula <- function(formula, data = NULL, ..., subset,
na.action = stats::na.fail) {
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, sys.parent()))) {
m$data <- as.data.frame(data)
}
m$... <- NULL
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
Terms <- attr(m, "terms")
attr(Terms, "intercept") <- 0
y <- stats::model.extract(m, "response")
mm <- stats::model.matrix(Terms, m)
if (ncol(mm) > 1) stop("This function only works for the simple linear regression model (i.e., y ~ x).")
x <- as.numeric(mm)
calibrate(cbind(x, y), ...)
}
#' @rdname calibrate
#' @export
#' @method calibrate lm
calibrate.lm <- function(object, y0, interval = c("inversion", "Wald", "none"),
level = 0.95, mean.response = FALSE,
adjust = c("none", "Bonferroni", "Scheffe"), k, ...) {
# Check model formula for correctness
xname <- all.vars(stats::formula(object)[[3L]])
yname <- all.vars(stats::formula(object)[[2L]])
if (length(xname) != 1L) {
stop("Only one independent variable allowed.")
}
if (length(yname) != 1L) {
stop("Only one dependent variable allowed.")
}
# Check for intercept using terms object from model fit. Alternatively, this
# can also be checked by testing if the first column name in model.matrix is
# equal to "(Intercept)".
if (!attr(stats::terms(object), "intercept")) {
stop(paste(deparse(substitute(object)), "must contain an intercept."))
}
# Extract x values and y values from model frame
mf <- stats::model.frame(object)
if (ncol(mf) != 2) {
stop("calibrate only works for the simple linear regression model.")
}
x <- stats::model.matrix(object)[, 2]
y <- stats::model.response(mf)
# Eta - mean response or mean of observed respone values
eta <- mean(y0) # mean of new observations
m <- length(y0) # number of new observations
if (mean.response && m > 1) stop("Only one mean response value allowed.")
# Fit a simple linear regression model and compute necessary components
b <- unname(object$coefficients)
n <- length(r <- object$residuals) # sample size and residuals
DF <- (DF1 <- n - 2) + (DF2 <- m - 1) # degrees of freedom
var1 <- sum(r ^ 2) / object$df.residual # stage 1 variance estimate
var2 <- if (m == 1) 0 else stats::var(y0) # stage 2 variance estimate
var.pooled <- (DF1 * var1 + DF2 * var2) / DF # pooled estimate of variance
sigma.pooled <- sqrt(var.pooled) # sqrt of pooled variance estimate
ssx <- sum((x - mean(x))^2) # sum-of-squares for x, Sxx
x0.mle <- (eta - b[1L])/b[2L] # MLE of x0
# Return point estimate only
interval <- match.arg(interval)
if (interval == "none") return(x0.mle)
# Adjustment for simultaneous intervals
adjust <- match.arg(adjust) # FIXME: Does simultaneous work for m > 1?
crit <- if (m != 1 || adjust == "none") stats::qt((1 + level)/2, n+m-3) else {
switch(adjust,
"Bonferroni" = stats::qt((level + 2*k - 1) / (2*k), n+m-3),
"Scheffe" = sqrt(k * stats::qf(level, k, n+m-3)))
}
# Inversion interval --------------------------------------------------------
if (interval == "inversion") {
c1 <- b[2L]^2 - (sigma.pooled^2 * crit^2)/ssx
c2 <- if (mean.response) {
c1/n + (eta - mean(y))^2/ssx
} else {
c1*(1/m + 1/n) + (eta - mean(y))^2/ssx
}
c3 <- b[2L] * (eta - mean(y))
c4 <- crit * sigma.pooled
# FIXME: catch errors and throw an appropriate warning
if (c1 < 0 && c2 <= 0) {
warning("The calibration line is not well determined.", call. = FALSE)
lwr <- -Inf
upr <- Inf
} else {
lwr <- mean(x) + (c3 - c4*sqrt(c2))/c1
upr <- mean(x) + (c3 + c4*sqrt(c2))/c1
if (c1 < 0 && c2 > 0) {
stop(paste("The calibration line is not well determined. The resulting \nconfidence region is the union of two semi-infinite intervals:\n(", -Inf, ",",
round(upr, 4), ") U (", round(lwr, 4), ",", Inf, ")"),
call. = FALSE)
}
}
res <- list("estimate" = x0.mle,
"lower" = lwr,
"upper" = upr,
"interval" = interval)
}
# Wald interval
if (interval == "Wald") {
# Compute standard error for Wald interval
se <- if (mean.response) {
abs((sigma.pooled/b[2]))*sqrt((1/n + (x0.mle - mean(x))^2/ssx))
} else {
abs((sigma.pooled/b[2]))*sqrt((1/m + 1/n + (x0.mle - mean(x))^2/ssx))
}
# Store results in a list
res <- list("estimate" = x0.mle,
"lower" = x0.mle - crit * se,
"upper" = x0.mle + crit * se,
"se" = se,
"interval" = interval)
}
# Assign class label and return results
class(res) <- "invest"
return(res)
}
|
#Shire project
#Shire project
#main function for k-folds cross validation and grid search(weighted + lambda) for lasso and ridge regression
#the functions can do parallel on k-folds CV and meanwhile can do parallel on grid search for optimum parameter selection
#Developter - Jie Zhao
#Develop time - Dec2015-Jan2016
rm(list=ls())
library(xlsx)
library(ROCR)
library(plyr)
library(caret)
library(dplyr)
library(glmnet)
library(snow)
library(snowfall)
library(caTools)
library(e1071)
#set some constants for lasso grid search and optimum parameter selection
#rootPath <- "F:\\Jie\\Shire_project\\"
rootPath <- "D:\\Shire_project\\"
test_1fold <- T
k.folds= ifelse(test_1fold, 1, 5)
folder <- ifelse(test_1fold, 'test_1fold', 'CV')
n.iter=5
crit <- 0.3
result_list <- list()
type <- 'v2'
model <- 'svm'
ONE <- F
kernel <- 'lin'
#k.folds=1
wt_list <- c(190, 270)
if(ONE){
wt_list <- wt_list[1]
}
#wt_list <- ifelse(ONE, wt_list[1], wt_list)
cost_list <- c(0.00003, 0.00012,0.0008, 0.008, 0.003, 0.011, 0.013)
#cost_list <- (0.0005)
nu_list <- c(0.3, 0.5, 0.7) # a set of values for nu for one-class SVMs
if(ONE){
cost_list <- nu_list
}
#cost_list <- c(0.001, 0.01, 0.1, 1, 10, 100)
#gamma_list <-c(seq(0.001, 0.009, 0.003)
# , seq(0.01, 0.09, 0.03)
# , seq(0.1, 0.9, 0.1))
#gamma_list <- c(0.1, 0.3, 0.5)
gamma_list <- 0.5
DR <- 190
DR_onPrb0 <- F
iter <- 1
clsMethod <- ifelse(ONE, 'one-classification', 'C-classification')
path_sepData <- paste(rootPath, '04_result\\data_sep\\', type, '\\iter', iter, '\\norm', sep='')
wt_str <- ifelse(ONE, NA, paste(wt_list, collapse=','))
cost_str <- paste(cost_list, collapse=',')
gamma_str <- paste(gamma_list, collapse=',')
if(kernel=='lin'){
grid <- expand.grid(cost = cost_list, weights = wt_list)[,]
outPath <- paste(rootPath, '04_result\\', model, '\\', folder, '\\iter', iter, '\\cost(', cost_str, ')_wt(', wt_str, ')_ONE(', ONE, ')_rn_DR0(', DR_onPrb0, ')', sep='')
}else if(kernel=='rbf'){
grid <- expand.grid(cost = cost_list, gamma = gamma_list, weights = wt_list)[,]
outPath <- paste(rootPath, '04_result\\', model, '\\', folder, '\\iter', iter, '\\cost(', cost_str, ')_gm(', gamma_str, ')_wt(', wt_str, ')_ONE(', ONE, ')_rn_DR0(', DR_onPrb0, ')', sep='')
}
n.cpu <- nrow(grid)
if(!file.exists(outPath)){
dir.create(outPath, recursive=T, showWarnings=T)
setwd(outPath)
}else{
setwd(outPath)
}
#define the traceFile to trace how the paralle process
traceFile <- paste(outPath, '\\traceFile.csv', sep='')
#source("F:\\Jie\\Shire_project\\03_code\\svm\\DR_constrain\\svm_subFunc_svm_withFpConstrain.R")
source("D:\\Shire_project\\03_code\\Jie\\svm\\DR_constrain\\svm_subFunc_svm_withFpConstrain.R")
for(iter in 1:n.iter){
start <- proc.time()
cat(file=traceFile, append=T, 'for the model performance\n', 'cost-', cost_list, ' gamma-', gamma_list , ' wt list-', wt_list, ' k.folds-', k.folds, ' running....\n')
cat('simulation:', iter, ' start!\n')
#load in the separated data for iteration
result_list[[iter]] <- get_optimum_model_iter_withCV(traceFile, path_sepData, n.cpu)
save(file=paste('result_list_iter', iter, '.RData', sep=''), result_list)
cat(file=traceFile, append=TRUE, 'Iteration ', iter, ': grid iteration end!\n') #added by Jie
cat(append=TRUE, 'Iteration ', iter, ': grid iteration end!\n') #added by Jie
end <- proc.time()
execution_time<- (end-start)[3]/60
cat('iteration:', iter, ' end!\n time used:', execution_time, ' min!\n')
cat(file=traceFile, append=TRUE, 'iteration:', iter, ' end!\n time used:', execution_time, ' min!\n')
}
#summarize the results for 5 iterations;
pred_list <- lapply(result_list, function(X){
return(X[[1]])
})
#pred_df <- t(ldply(pred_list, quickdf))
pred_df <- pred_list[[iter]]
save(pred_df, file="resp_pred_df_forTs.RData")
write.csv(pred_df, paste('resp_pred_df_forTs_iter', iter, '.csv', sep=''), row.names = F)
optimum_pmt_list <- lapply(result_list, function(X){
return(X[[2]])
})
optimum_pmt_df <- ldply(optimum_pmt_list[iter], quickdf)
save(optimum_pmt_df, file="optimum_pmt_df.RData")
#outOfSmpCurve <- lapply(result_list, function(X){
# return(X[[3]])
#})
#save(outOfSmpCurve, file='outOfSmpCurve.RData')
#lasso grid search and pred on test completed
#generate the performance measure across all iterations
recall_tar_onTs <- sort(c(0.25, seq(0.5, 0.1, -0.1)), decreasing = T)
result_msOnTest <- msOnTest_sep_v2(pred_df[, 2], pred_df[, 1], recall_tar_onTs)
ms_onTest <- result_msOnTest$ms
write.csv(ms_onTest, 'lasso_result.csv', row.names = T)
write.csv(result_msOnTest$curve, 'curve_forTs.csv', quote=T, row.names = F)
#end2 <- proc.time()
#execution_time2<- (end2-start2)[3]/60
| /codess/SVM/DR_constrain/svm_sep_NormByRow_lin.R | no_license | jzhao0802/ShireHAE | R | false | false | 4,891 | r | #Shire project
#Shire project
#main function for k-folds cross validation and grid search(weighted + lambda) for lasso and ridge regression
#the functions can do parallel on k-folds CV and meanwhile can do parallel on grid search for optimum parameter selection
#Developter - Jie Zhao
#Develop time - Dec2015-Jan2016
rm(list=ls())
library(xlsx)
library(ROCR)
library(plyr)
library(caret)
library(dplyr)
library(glmnet)
library(snow)
library(snowfall)
library(caTools)
library(e1071)
#set some constants for lasso grid search and optimum parameter selection
#rootPath <- "F:\\Jie\\Shire_project\\"
rootPath <- "D:\\Shire_project\\"
test_1fold <- T
k.folds= ifelse(test_1fold, 1, 5)
folder <- ifelse(test_1fold, 'test_1fold', 'CV')
n.iter=5
crit <- 0.3
result_list <- list()
type <- 'v2'
model <- 'svm'
ONE <- F
kernel <- 'lin'
#k.folds=1
wt_list <- c(190, 270)
if(ONE){
wt_list <- wt_list[1]
}
#wt_list <- ifelse(ONE, wt_list[1], wt_list)
cost_list <- c(0.00003, 0.00012,0.0008, 0.008, 0.003, 0.011, 0.013)
#cost_list <- (0.0005)
nu_list <- c(0.3, 0.5, 0.7) # a set of values for nu for one-class SVMs
if(ONE){
cost_list <- nu_list
}
#cost_list <- c(0.001, 0.01, 0.1, 1, 10, 100)
#gamma_list <-c(seq(0.001, 0.009, 0.003)
# , seq(0.01, 0.09, 0.03)
# , seq(0.1, 0.9, 0.1))
#gamma_list <- c(0.1, 0.3, 0.5)
gamma_list <- 0.5
DR <- 190
DR_onPrb0 <- F
iter <- 1
clsMethod <- ifelse(ONE, 'one-classification', 'C-classification')
path_sepData <- paste(rootPath, '04_result\\data_sep\\', type, '\\iter', iter, '\\norm', sep='')
wt_str <- ifelse(ONE, NA, paste(wt_list, collapse=','))
cost_str <- paste(cost_list, collapse=',')
gamma_str <- paste(gamma_list, collapse=',')
if(kernel=='lin'){
grid <- expand.grid(cost = cost_list, weights = wt_list)[,]
outPath <- paste(rootPath, '04_result\\', model, '\\', folder, '\\iter', iter, '\\cost(', cost_str, ')_wt(', wt_str, ')_ONE(', ONE, ')_rn_DR0(', DR_onPrb0, ')', sep='')
}else if(kernel=='rbf'){
grid <- expand.grid(cost = cost_list, gamma = gamma_list, weights = wt_list)[,]
outPath <- paste(rootPath, '04_result\\', model, '\\', folder, '\\iter', iter, '\\cost(', cost_str, ')_gm(', gamma_str, ')_wt(', wt_str, ')_ONE(', ONE, ')_rn_DR0(', DR_onPrb0, ')', sep='')
}
n.cpu <- nrow(grid)
if(!file.exists(outPath)){
dir.create(outPath, recursive=T, showWarnings=T)
setwd(outPath)
}else{
setwd(outPath)
}
#define the traceFile to trace how the paralle process
traceFile <- paste(outPath, '\\traceFile.csv', sep='')
#source("F:\\Jie\\Shire_project\\03_code\\svm\\DR_constrain\\svm_subFunc_svm_withFpConstrain.R")
source("D:\\Shire_project\\03_code\\Jie\\svm\\DR_constrain\\svm_subFunc_svm_withFpConstrain.R")
for(iter in 1:n.iter){
start <- proc.time()
cat(file=traceFile, append=T, 'for the model performance\n', 'cost-', cost_list, ' gamma-', gamma_list , ' wt list-', wt_list, ' k.folds-', k.folds, ' running....\n')
cat('simulation:', iter, ' start!\n')
#load in the separated data for iteration
result_list[[iter]] <- get_optimum_model_iter_withCV(traceFile, path_sepData, n.cpu)
save(file=paste('result_list_iter', iter, '.RData', sep=''), result_list)
cat(file=traceFile, append=TRUE, 'Iteration ', iter, ': grid iteration end!\n') #added by Jie
cat(append=TRUE, 'Iteration ', iter, ': grid iteration end!\n') #added by Jie
end <- proc.time()
execution_time<- (end-start)[3]/60
cat('iteration:', iter, ' end!\n time used:', execution_time, ' min!\n')
cat(file=traceFile, append=TRUE, 'iteration:', iter, ' end!\n time used:', execution_time, ' min!\n')
}
#summarize the results for 5 iterations;
pred_list <- lapply(result_list, function(X){
return(X[[1]])
})
#pred_df <- t(ldply(pred_list, quickdf))
pred_df <- pred_list[[iter]]
save(pred_df, file="resp_pred_df_forTs.RData")
write.csv(pred_df, paste('resp_pred_df_forTs_iter', iter, '.csv', sep=''), row.names = F)
optimum_pmt_list <- lapply(result_list, function(X){
return(X[[2]])
})
optimum_pmt_df <- ldply(optimum_pmt_list[iter], quickdf)
save(optimum_pmt_df, file="optimum_pmt_df.RData")
#outOfSmpCurve <- lapply(result_list, function(X){
# return(X[[3]])
#})
#save(outOfSmpCurve, file='outOfSmpCurve.RData')
#lasso grid search and pred on test completed
#generate the performance measure across all iterations
recall_tar_onTs <- sort(c(0.25, seq(0.5, 0.1, -0.1)), decreasing = T)
result_msOnTest <- msOnTest_sep_v2(pred_df[, 2], pred_df[, 1], recall_tar_onTs)
ms_onTest <- result_msOnTest$ms
write.csv(ms_onTest, 'lasso_result.csv', row.names = T)
write.csv(result_msOnTest$curve, 'curve_forTs.csv', quote=T, row.names = F)
#end2 <- proc.time()
#execution_time2<- (end2-start2)[3]/60
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/footywire-calcs.R
\name{get_footywire_betting_odds}
\alias{get_footywire_betting_odds}
\title{Get AFL match betting odds from https://www.footywire.com}
\usage{
get_footywire_betting_odds(
start_season = "2010",
end_season = lubridate::year(Sys.Date())
)
}
\arguments{
\item{start_season}{First season to return, in yyyy format. Earliest season with data available is 2010.}
\item{end_season}{Last season to return, in yyyy format}
}
\value{
Returns a data frame containing betting odds and basic match info
}
\description{
\code{get_footywire_betting_odds} returns a data frame containing betting odds and basic match info for Men's AFL matches.
}
\details{
The data frame contains the home and away team as well as venue.
}
\examples{
\dontrun{
get_footywire_betting_odds(2012, 2018)
}
}
| /man/get_footywire_betting_odds.Rd | no_license | DTS098/fitzRoy | R | false | true | 873 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/footywire-calcs.R
\name{get_footywire_betting_odds}
\alias{get_footywire_betting_odds}
\title{Get AFL match betting odds from https://www.footywire.com}
\usage{
get_footywire_betting_odds(
start_season = "2010",
end_season = lubridate::year(Sys.Date())
)
}
\arguments{
\item{start_season}{First season to return, in yyyy format. Earliest season with data available is 2010.}
\item{end_season}{Last season to return, in yyyy format}
}
\value{
Returns a data frame containing betting odds and basic match info
}
\description{
\code{get_footywire_betting_odds} returns a data frame containing betting odds and basic match info for Men's AFL matches.
}
\details{
The data frame contains the home and away team as well as venue.
}
\examples{
\dontrun{
get_footywire_betting_odds(2012, 2018)
}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AutoGeneratedDefinitions.R
\name{postEstimationDefinition}
\alias{postEstimationDefinition}
\title{Post Estimation definition. \lifecycle{maturing}}
\usage{
postEstimationDefinition(name, estimationDefinition, baseUrl)
}
\arguments{
\item{name}{A valid name for the definition. WebApi will use this name (if valid)
as the name of the definition. WebApi checks for validity, such as
uniqueness, absence of unacceptable character etc. An error might be
thrown.}
\item{estimationDefinition}{An R list object containing the expression for the specification. This
will be converted to JSON expression by function and posted into the
WebApi. Note: only limited checks are performed in R to check the
validity of this expression.}
\item{baseUrl}{The base URL for the WebApi instance, for example:
"http://server.org:80/WebAPI". Note, there is no trailing '/'.
If trailing '/' is used, you may receive an error.}
}
\value{
This function will return a dataframe object with one row describing the posted WebApi expression
and its details. If unsuccessful a STOP message will be shown.
}
\description{
Post Estimation definition. \lifecycle{maturing}
}
\details{
Post Estimation definition to WebAPI
}
\examples{
\dontrun{
postEstimationDefinition(name = "new valid name",
estimationDefinition = definition,
baseUrl = "http://server.org:80/WebAPI")
}
}
| /man/postEstimationDefinition.Rd | permissive | George-Argyriou/ROhdsiWebApi | R | false | true | 1,473 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AutoGeneratedDefinitions.R
\name{postEstimationDefinition}
\alias{postEstimationDefinition}
\title{Post Estimation definition. \lifecycle{maturing}}
\usage{
postEstimationDefinition(name, estimationDefinition, baseUrl)
}
\arguments{
\item{name}{A valid name for the definition. WebApi will use this name (if valid)
as the name of the definition. WebApi checks for validity, such as
uniqueness, absence of unacceptable character etc. An error might be
thrown.}
\item{estimationDefinition}{An R list object containing the expression for the specification. This
will be converted to JSON expression by function and posted into the
WebApi. Note: only limited checks are performed in R to check the
validity of this expression.}
\item{baseUrl}{The base URL for the WebApi instance, for example:
"http://server.org:80/WebAPI". Note, there is no trailing '/'.
If trailing '/' is used, you may receive an error.}
}
\value{
This function will return a dataframe object with one row describing the posted WebApi expression
and its details. If unsuccessful a STOP message will be shown.
}
\description{
Post Estimation definition. \lifecycle{maturing}
}
\details{
Post Estimation definition to WebAPI
}
\examples{
\dontrun{
postEstimationDefinition(name = "new valid name",
estimationDefinition = definition,
baseUrl = "http://server.org:80/WebAPI")
}
}
|
setwd("C:\\Users\\ASUS\\Desktop\\Fiverr\\john\\New folder (3)")
library("rgdal")
library(raster)
library(adehabitatHR)
input<- read.csv("2020-10-north-wales-street.csv")
input<- input[,1:10] #We only need the first 10 columns
input<- input[complete.cases(input),] #This line of code removes rows with NA values in the data.
Crime.Spatial<- SpatialPointsDataFrame(input[,5:6], input, proj4string = CRS("+init=epsg:4326"))
Crime.Spatial<- spTransform(Crime.Spatial, CRS("+init=epsg:27700")) #We now project from WGS84 for to British National Grid
plot(Crime.Spatial) #Plot the data
Northwales<- readOGR(".", layer="North_Wales")
Extent<- extent(Northwales) #this is the geographic extent of the grid. It is based on the Northwales object.
#Here we specify the size of each grid cell in metres (since those are the units our data are projected in).
resolution<- 500
#This is some magic that creates the empty grid
x <- seq(Extent[1],Extent[2],by=resolution) # where resolution is the pixel size you desire
y <- seq(Extent[3],Extent[4],by=resolution)
xy <- expand.grid(x=x,y=y)
coordinates(xy) <- ~x+y
gridded(xy) <- TRUE
#You can see the grid here (this may appear solid black if the cells are small)
plot(xy)
plot(Northwales, border="black", add=T)
all <- raster(kernelUD(Crime.Spatial, h="href", grid = xy)) #Note we are running two functions here - first KernelUD then converting the result to a raster object.
#First results
writeRaster(all,filename="file1.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(all)
plot(Northwales, border="black", add=T)
plot(Crime.Spatial[Crime.Spatial$Crime.type=="Burglary",]) # quick plot of burglary points
Burglary<- raster(kernelUD(Crime.Spatial[Crime.Spatial$Crime.type=="Burglary",], h="href", grid = xy))
writeRaster(Burglary,filename="file2.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(Burglary)
plot(Northwales, border="black", add=T)
both<-Burglary/all
writeRaster(both,filename="file3.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(both)
plot(Northwales, border="black", add=T)
both2 <- both
both2[both <= 0] <- NA
both2[both >= 1] <- NA
writeRaster(both2,filename="file4.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
#Now we can see the hotspots much more clearly.
plot(both2)
plot(Northwales, add=T)
#writeRaster(both2,filename=pointpatternhotspot.asc,datatype='FLT4S',format="ascii",overwrite=TRUE) | /2020-10.R | no_license | ucfnwda/CASA0005 | R | false | false | 2,451 | r | setwd("C:\\Users\\ASUS\\Desktop\\Fiverr\\john\\New folder (3)")
library("rgdal")
library(raster)
library(adehabitatHR)
input<- read.csv("2020-10-north-wales-street.csv")
input<- input[,1:10] #We only need the first 10 columns
input<- input[complete.cases(input),] #This line of code removes rows with NA values in the data.
Crime.Spatial<- SpatialPointsDataFrame(input[,5:6], input, proj4string = CRS("+init=epsg:4326"))
Crime.Spatial<- spTransform(Crime.Spatial, CRS("+init=epsg:27700")) #We now project from WGS84 for to British National Grid
plot(Crime.Spatial) #Plot the data
Northwales<- readOGR(".", layer="North_Wales")
Extent<- extent(Northwales) #this is the geographic extent of the grid. It is based on the Northwales object.
#Here we specify the size of each grid cell in metres (since those are the units our data are projected in).
resolution<- 500
#This is some magic that creates the empty grid
x <- seq(Extent[1],Extent[2],by=resolution) # where resolution is the pixel size you desire
y <- seq(Extent[3],Extent[4],by=resolution)
xy <- expand.grid(x=x,y=y)
coordinates(xy) <- ~x+y
gridded(xy) <- TRUE
#You can see the grid here (this may appear solid black if the cells are small)
plot(xy)
plot(Northwales, border="black", add=T)
all <- raster(kernelUD(Crime.Spatial, h="href", grid = xy)) #Note we are running two functions here - first KernelUD then converting the result to a raster object.
#First results
writeRaster(all,filename="file1.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(all)
plot(Northwales, border="black", add=T)
plot(Crime.Spatial[Crime.Spatial$Crime.type=="Burglary",]) # quick plot of burglary points
Burglary<- raster(kernelUD(Crime.Spatial[Crime.Spatial$Crime.type=="Burglary",], h="href", grid = xy))
writeRaster(Burglary,filename="file2.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(Burglary)
plot(Northwales, border="black", add=T)
both<-Burglary/all
writeRaster(both,filename="file3.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
plot(both)
plot(Northwales, border="black", add=T)
both2 <- both
both2[both <= 0] <- NA
both2[both >= 1] <- NA
writeRaster(both2,filename="file4.asc",datatype='FLT4S',format="ascii",overwrite=TRUE)
#Now we can see the hotspots much more clearly.
plot(both2)
plot(Northwales, add=T)
#writeRaster(both2,filename=pointpatternhotspot.asc,datatype='FLT4S',format="ascii",overwrite=TRUE) |
rm(list=ls())
library(readr)
library(dplyr)
library(ggplot2)
library(lubridate)
fname = "yemen_dat.csv"
df = read_csv(fname)
df = df %>% select(-date_variable, adm2pcod, longitude, latitude)
df = df %>%
mutate(year = year(date_value))
min_year = min(df$year)
df = df %>%
mutate(diff_year = year - min_year,
add_weeks = diff_year * 52,
new_epiweek = add_weeks + epiweek) %>%
select(-year, -diff_year)
df = df %>% rename(group_name = adm1name)
df %>%
filter(date_value == ymd("2017-01-01") |
date_value == ymd("2017-01-02") |
date_value == ymd("2017-01-08") |
date_value == ymd("2016-09-29") |
date_value == ymd("2016-12-31") ) %>%
select(date_value, epiweek, add_weeks, new_epiweek) %>%
unique
week_df = df %>%
group_by(group_name, date_value) %>%
summarize(vweek = var(epiweek),
epiweek = unique(epiweek),
uweek = unique(lubridate::week(date_value)),
dow = unique(wday(date_value, label = TRUE))) %>%
ungroup
stopifnot(all(week_df$vweek == 0))
week_df = df %>%
group_by(group_name, new_epiweek) %>%
summarize(y = sum(incidence),
date_value = first(date_value)) %>%
ungroup
date_week_df = week_df %>%
mutate(group = group_name,
x = date_value)
date_week_df = date_week_df %>% select(x, y, group, group_name)
week_df = week_df %>%
mutate(group = group_name,
x = new_epiweek)
week_df = week_df %>% select(x, y, group, group_name)
df = df %>%
group_by(group_name, date_value) %>%
summarize(y = sum(incidence)) %>%
ungroup
df = df %>%
mutate(group = group_name,
x = date_value)
df = df %>% select(x, y, group, group_name)
regroup = function(x) {
factor(as.numeric(factor(x)))
}
# remove these when using real data
df = df %>%
mutate(group = regroup(group))
week_df = week_df %>%
mutate(group = regroup(group))
date_week_df = date_week_df %>%
mutate(group = regroup(group))
g = df %>% ggplot(aes(x = x,
y = y,
colour = group)) +
geom_line() +
ylab("Number of Cases") + xlab("Date")
pdf("incidence_plots_over_time.pdf", height = 5, width = 10)
print(g)
print(g + guides(color = FALSE))
print({g %+% week_df})
print({( g + guides(color = FALSE)) %+% week_df})
dev.off()
# saveRDS(df, file = "plot_data.rds")
saveRDS(week_df, file = "plot_data.rds")
saveRDS(date_week_df, file = "plot_data_date.rds")
| /summarize_data.R | no_license | muschellij2/epi_click | R | false | false | 2,454 | r | rm(list=ls())
library(readr)
library(dplyr)
library(ggplot2)
library(lubridate)
fname = "yemen_dat.csv"
df = read_csv(fname)
df = df %>% select(-date_variable, adm2pcod, longitude, latitude)
df = df %>%
mutate(year = year(date_value))
min_year = min(df$year)
df = df %>%
mutate(diff_year = year - min_year,
add_weeks = diff_year * 52,
new_epiweek = add_weeks + epiweek) %>%
select(-year, -diff_year)
df = df %>% rename(group_name = adm1name)
df %>%
filter(date_value == ymd("2017-01-01") |
date_value == ymd("2017-01-02") |
date_value == ymd("2017-01-08") |
date_value == ymd("2016-09-29") |
date_value == ymd("2016-12-31") ) %>%
select(date_value, epiweek, add_weeks, new_epiweek) %>%
unique
week_df = df %>%
group_by(group_name, date_value) %>%
summarize(vweek = var(epiweek),
epiweek = unique(epiweek),
uweek = unique(lubridate::week(date_value)),
dow = unique(wday(date_value, label = TRUE))) %>%
ungroup
stopifnot(all(week_df$vweek == 0))
week_df = df %>%
group_by(group_name, new_epiweek) %>%
summarize(y = sum(incidence),
date_value = first(date_value)) %>%
ungroup
date_week_df = week_df %>%
mutate(group = group_name,
x = date_value)
date_week_df = date_week_df %>% select(x, y, group, group_name)
week_df = week_df %>%
mutate(group = group_name,
x = new_epiweek)
week_df = week_df %>% select(x, y, group, group_name)
df = df %>%
group_by(group_name, date_value) %>%
summarize(y = sum(incidence)) %>%
ungroup
df = df %>%
mutate(group = group_name,
x = date_value)
df = df %>% select(x, y, group, group_name)
regroup = function(x) {
factor(as.numeric(factor(x)))
}
# remove these when using real data
df = df %>%
mutate(group = regroup(group))
week_df = week_df %>%
mutate(group = regroup(group))
date_week_df = date_week_df %>%
mutate(group = regroup(group))
g = df %>% ggplot(aes(x = x,
y = y,
colour = group)) +
geom_line() +
ylab("Number of Cases") + xlab("Date")
pdf("incidence_plots_over_time.pdf", height = 5, width = 10)
print(g)
print(g + guides(color = FALSE))
print({g %+% week_df})
print({( g + guides(color = FALSE)) %+% week_df})
dev.off()
# saveRDS(df, file = "plot_data.rds")
saveRDS(week_df, file = "plot_data.rds")
saveRDS(date_week_df, file = "plot_data_date.rds")
|
setwd("/home/andrzej/Dokumenty/Mownit/Lab4")
results = read.csv("csv.csv", sep=";")
avg_results = aggregate( TIME ~ N:TYPE, data=results, FUN=mean)
avg_results$sd = aggregate( TIME ~ N:TYPE, data=results, FUN=sd)$TIME
naive_r = avg_results[avg_results$TYPE == 'naive',]
better_r = avg_results[avg_results$TYPE == 'better',]
blas_r = avg_results[avg_results$TYPE == 'blas',]
fit1 = lm(naive_r$TIME ~ poly(naive_r$N, 3, raw=TRUE), data=data.frame(naive_r$N, naive_r$TIME))
fit2= lm(better_r $TIME ~ poly(better_r $N, 3, raw=TRUE), data=data.frame(better_r $N, better_r $TIME))
fit3= lm(blas_r$TIME ~ poly(blas_r$N, 3, raw=TRUE), data=data.frame(blas_r$N, blas_r$TIME))
ggplot(avg_results, aes(N,TIME,color=TYPE)) +
geom_point() +
geom_errorbar(aes(ymin = TIME - sd, ymax = TIME + sd, width=5)) +
geom_line(data=naive_r, aes(naive_r$N, predict(fit1, naive_r))) +
geom_line(data=better_r, aes(better_r$N, predict(fit2, better_r))) +
geom_line(data=blas_r, aes(blas_r$N, predict(fit3, blas_r))) +
xlab("N [size of matrix]") +
ylab("Time [ms]")
| /Lab4/zadanie4.r | no_license | BarkingBad/MOWNIT | R | false | false | 1,062 | r | setwd("/home/andrzej/Dokumenty/Mownit/Lab4")
results = read.csv("csv.csv", sep=";")
avg_results = aggregate( TIME ~ N:TYPE, data=results, FUN=mean)
avg_results$sd = aggregate( TIME ~ N:TYPE, data=results, FUN=sd)$TIME
naive_r = avg_results[avg_results$TYPE == 'naive',]
better_r = avg_results[avg_results$TYPE == 'better',]
blas_r = avg_results[avg_results$TYPE == 'blas',]
fit1 = lm(naive_r$TIME ~ poly(naive_r$N, 3, raw=TRUE), data=data.frame(naive_r$N, naive_r$TIME))
fit2= lm(better_r $TIME ~ poly(better_r $N, 3, raw=TRUE), data=data.frame(better_r $N, better_r $TIME))
fit3= lm(blas_r$TIME ~ poly(blas_r$N, 3, raw=TRUE), data=data.frame(blas_r$N, blas_r$TIME))
ggplot(avg_results, aes(N,TIME,color=TYPE)) +
geom_point() +
geom_errorbar(aes(ymin = TIME - sd, ymax = TIME + sd, width=5)) +
geom_line(data=naive_r, aes(naive_r$N, predict(fit1, naive_r))) +
geom_line(data=better_r, aes(better_r$N, predict(fit2, better_r))) +
geom_line(data=blas_r, aes(blas_r$N, predict(fit3, blas_r))) +
xlab("N [size of matrix]") +
ylab("Time [ms]")
|
#' @export
updateGrid = function (ep,grid,nGrid){
#first we again need our solutions as a matrix
matEP=lapply(ep,function(x){
return (x$solution)
})
matEP=do.call(rbind,matEP)
#we take each solution
assignments = getAssignmentC(grid, nGrid, matEP)
return (assignments)
}
| /PESAII/R/updateGrid.R | no_license | Malano1988/MOCK | R | false | false | 293 | r | #' @export
updateGrid = function (ep,grid,nGrid){
#first we again need our solutions as a matrix
matEP=lapply(ep,function(x){
return (x$solution)
})
matEP=do.call(rbind,matEP)
#we take each solution
assignments = getAssignmentC(grid, nGrid, matEP)
return (assignments)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/keyvalue.R
\name{is_keyvalue}
\alias{is_keyvalue}
\title{Is it a keyvalue?}
\usage{
is_keyvalue(x)
}
\arguments{
\item{x}{object to check}
}
\description{
Is it a keyvalue?
}
| /man/is_keyvalue.Rd | permissive | jameelalsalam/mapping | R | false | true | 253 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/keyvalue.R
\name{is_keyvalue}
\alias{is_keyvalue}
\title{Is it a keyvalue?}
\usage{
is_keyvalue(x)
}
\arguments{
\item{x}{object to check}
}
\description{
Is it a keyvalue?
}
|
#' Prior projected Polya tree distribution
#'
#' @description Simulates paths of prior projected Polya tree distributions centered
#' around a projected normal distribution.
#'
#' @usage dsimpriorppt(nsim = 5, mm = 4,mu = c(0, 0),
#' sig = 1, ll = 100, aa = 1, delta = 1.1, units = "radians")
#' @param nsim integer indicating the number of simulations.
#' @param mm integer indicating the number of finite levels of the Polya tree.
#' @param mu mean vector of the projected bivariate normal distribution.
#' @param sig standard deviation of the projected bivariate normal distribution. We advise to always use sig = 1.
#' @param ll number of equally spaced points at which the projected distribution will be evaluated.
#' @param aa alpha. Precision parameter of the Polya tree.
#' @param delta controls of the speed at which the variances of the branching probabilities move down in the tree, rho(m)=m^delta.
#' @param units units of the support: "radians", "degrees" or "hours".
#'
#' @examples z <- dsimpriorppt(mu = c(5,5), nsim = 5, units = "radians")
#' priorppt.plot(z, plot.type = "line")
#' summary(z$stats)
#'
#' @seealso \code{\link[PPTcirc]{priorppt.plot}}, \code{\link[PPTcirc]{priorppt.summary}}
#'
#' @return An object with class priorppt.circ whose underlying structure is a list containing the following components:
#' \item{x}{points where the density is evaluated.}
#' \item{ppt.sims}{simulated density paths of the prior projected Polya tree.}
#' \item{stats}{descriptive statistics: mean direction and concentration of each simulated density.}
#' @export
#'
#'
#' @references Nieto-Barajas, L.E. & Nunez-Antonio, G. (2019). Projected Polya tree. https://arxiv.org/pdf/1902.06020.pdf
dsimpriorppt <- function(nsim=5 , mm=4, mu = c(0,0), sig=1, ll = 100, aa=1,
delta=1.1, units= "radians"){
#Prog_bar <- txtProgressBar(min = 0, max = nsim, style = 3)
dt <- 2*pi/ll
t <- seq(from = 0, to= 2*pi*(1-1/ll), by = dt)
FT <- matrix(data = NA, nrow = ll, ncol = nsim, byrow = FALSE, dimnames = NULL)
vt <- mt <- vector("numeric", length = nsim)
for(i in 1:nsim){
#Generate simulations
FT[,i] <- dpptcirc(mm,mu = mu, sig=sig, ll = ll, aa=aa, delta=delta)
#Generate stats
alpha1 <- dt*sum(cos(t[1:ll])* FT[,i])
beta1 <- dt*sum(sin(t[1:ll])* FT[,i])
vt[i] <- sqrt(alpha1^2+ beta1^2)
mt[i] <- ppt.tan(alpha1, beta1)
#setTxtProgressBar(Prog_bar,i)
}
if (units == "degrees") {
mt.cc <- mt/pi *180
vt.cc <- vt/pi*180
t.cc <- t/pi*180
}
else if (units == "hours") {
mt.cc <- mt/pi*12
vt.cc <- vt/pi*12
t.cc <- t/pi*12
}
else if (units== "radians"){
mt.cc <- mt
vt.cc <- vt
t.cc <- t
}
FT <- as.data.frame(FT)
colnames(FT) <- paste0(rep("sim", nsim),1:nsim )
#close(Prog_bar)
return(structure(list(x = t.cc ,ppt.sim = FT, stats=data.frame(mean.direction =mt.cc, concentration=vt.cc)), class = "priorppt.circ"))
}
| /R/dsimpriorppt.R | no_license | Karlampm/PPTcirc | R | false | false | 2,983 | r | #' Prior projected Polya tree distribution
#'
#' @description Simulates paths of prior projected Polya tree distributions centered
#' around a projected normal distribution.
#'
#' @usage dsimpriorppt(nsim = 5, mm = 4,mu = c(0, 0),
#' sig = 1, ll = 100, aa = 1, delta = 1.1, units = "radians")
#' @param nsim integer indicating the number of simulations.
#' @param mm integer indicating the number of finite levels of the Polya tree.
#' @param mu mean vector of the projected bivariate normal distribution.
#' @param sig standard deviation of the projected bivariate normal distribution. We advise to always use sig = 1.
#' @param ll number of equally spaced points at which the projected distribution will be evaluated.
#' @param aa alpha. Precision parameter of the Polya tree.
#' @param delta controls of the speed at which the variances of the branching probabilities move down in the tree, rho(m)=m^delta.
#' @param units units of the support: "radians", "degrees" or "hours".
#'
#' @examples z <- dsimpriorppt(mu = c(5,5), nsim = 5, units = "radians")
#' priorppt.plot(z, plot.type = "line")
#' summary(z$stats)
#'
#' @seealso \code{\link[PPTcirc]{priorppt.plot}}, \code{\link[PPTcirc]{priorppt.summary}}
#'
#' @return An object with class priorppt.circ whose underlying structure is a list containing the following components:
#' \item{x}{points where the density is evaluated.}
#' \item{ppt.sims}{simulated density paths of the prior projected Polya tree.}
#' \item{stats}{descriptive statistics: mean direction and concentration of each simulated density.}
#' @export
#'
#'
#' @references Nieto-Barajas, L.E. & Nunez-Antonio, G. (2019). Projected Polya tree. https://arxiv.org/pdf/1902.06020.pdf
dsimpriorppt <- function(nsim=5 , mm=4, mu = c(0,0), sig=1, ll = 100, aa=1,
delta=1.1, units= "radians"){
#Prog_bar <- txtProgressBar(min = 0, max = nsim, style = 3)
dt <- 2*pi/ll
t <- seq(from = 0, to= 2*pi*(1-1/ll), by = dt)
FT <- matrix(data = NA, nrow = ll, ncol = nsim, byrow = FALSE, dimnames = NULL)
vt <- mt <- vector("numeric", length = nsim)
for(i in 1:nsim){
#Generate simulations
FT[,i] <- dpptcirc(mm,mu = mu, sig=sig, ll = ll, aa=aa, delta=delta)
#Generate stats
alpha1 <- dt*sum(cos(t[1:ll])* FT[,i])
beta1 <- dt*sum(sin(t[1:ll])* FT[,i])
vt[i] <- sqrt(alpha1^2+ beta1^2)
mt[i] <- ppt.tan(alpha1, beta1)
#setTxtProgressBar(Prog_bar,i)
}
if (units == "degrees") {
mt.cc <- mt/pi *180
vt.cc <- vt/pi*180
t.cc <- t/pi*180
}
else if (units == "hours") {
mt.cc <- mt/pi*12
vt.cc <- vt/pi*12
t.cc <- t/pi*12
}
else if (units== "radians"){
mt.cc <- mt
vt.cc <- vt
t.cc <- t
}
FT <- as.data.frame(FT)
colnames(FT) <- paste0(rep("sim", nsim),1:nsim )
#close(Prog_bar)
return(structure(list(x = t.cc ,ppt.sim = FT, stats=data.frame(mean.direction =mt.cc, concentration=vt.cc)), class = "priorppt.circ"))
}
|
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinverse <- function(inverse) inv <<- inverse
getinverse <- function() inv
list(set=set, get=get, setinverse=setinverse, getinverse=getinverse)
}
##"matrix" object that can cache its inverse | /makeCacheMatrix.R | no_license | questionedandmarked/R | R | false | false | 368 | r | makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinverse <- function(inverse) inv <<- inverse
getinverse <- function() inv
list(set=set, get=get, setinverse=setinverse, getinverse=getinverse)
}
##"matrix" object that can cache its inverse |
data<-NULL;
features <- c("result", as.vector(as.matrix(read.table("{{features}}", header=TRUE))));
{% for file in files -%}
tmp_data <- read.table("{{file}}", header=TRUE);
tmp_data <- tmp_data[, names(tmp_data) %in% features]
data <- rbind(data, tmp_data);
g<-gc();
{% endfor -%}
write.table(data, file="{{strip_data_file}}")
| /pygaga/model/r/generate_strip_data.r | no_license | qiaohui/pygaga | R | false | false | 328 | r | data<-NULL;
features <- c("result", as.vector(as.matrix(read.table("{{features}}", header=TRUE))));
{% for file in files -%}
tmp_data <- read.table("{{file}}", header=TRUE);
tmp_data <- tmp_data[, names(tmp_data) %in% features]
data <- rbind(data, tmp_data);
g<-gc();
{% endfor -%}
write.table(data, file="{{strip_data_file}}")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calculateWeatherVariables.R
\name{calculateDewPointDepression}
\alias{calculateDewPointDepression}
\title{calculateDewPointDepression.}
\usage{
calculateDewPointDepression(airTemp, RH, temp)
}
\arguments{
\item{-}{airTemp: in C - double}
\item{-}{RH: 1-100 - double}
\item{-}{temp: in C double}
}
\value{
double
}
\description{
\code{calculateDewPointDepression} function to calculate the dew point depression
}
\details{
function to calculate Dew Point Depression
definition from Kim, 2002 paper
}
\examples{
NEED TO INSERT
}
\references{
see aWhere API Documentation (insert HTTP address)
}
| /man/calculateDewPointDepression.Rd | no_license | aWhereAPI/CalculateWeatherVars_aWhere | R | false | true | 674 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calculateWeatherVariables.R
\name{calculateDewPointDepression}
\alias{calculateDewPointDepression}
\title{calculateDewPointDepression.}
\usage{
calculateDewPointDepression(airTemp, RH, temp)
}
\arguments{
\item{-}{airTemp: in C - double}
\item{-}{RH: 1-100 - double}
\item{-}{temp: in C double}
}
\value{
double
}
\description{
\code{calculateDewPointDepression} function to calculate the dew point depression
}
\details{
function to calculate Dew Point Depression
definition from Kim, 2002 paper
}
\examples{
NEED TO INSERT
}
\references{
see aWhere API Documentation (insert HTTP address)
}
|
#--------------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------------------------------------------
# R-3.4.1
# mordor
# Objective : Identify cluster specific peaks using scABC's function on GBM, PFA and HF
#--------------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------------------------------------------
#----------------------------------------------------------------
# load dependencies
#----------------------------------------------------------------
library(data.table)
source("scripts/scABC.cluster.R") #source("https://raw.githubusercontent.com/timydaley/scABC/master/R/cluster.R")
scalered <- colorRampPalette(c("white", "red"), space = "rgb")(256)
#----------------------------------------------------------------
# Define function to run scABC on different pairwise comparisons
#----------------------------------------------------------------
#--------------------------------
## Read in binary matrix
#--------------------------------
binarymatds="data/ConsensusSet/PCSC1/GBM.PFA.HF.Consensus.Catalogue.Binarymat.txt"
binarymat <- read.table(binarymatds, header=T, sep="\t", stringsAsFactors = F,check.names = F)
rownames(binarymat) <- paste(binarymat[,1], binarymat[,2], binarymat[,3], sep="_")
ncounts <- binarymat[,4:ncol(binarymat)]
rownames(ncounts) <- rownames(binarymat)
#--------------------------------
## Annotations
#--------------------------------
bg <- c(rep(1,33))
gbmpfa.hf.cl <- c(rep(1,23),rep(2,3), rep(1,7))
names(bg) <- colnames(ncounts)
names(gbmpfa.hf.cl) <- colnames(ncounts)
#--------------------------------
## Run scabc
#--------------------------------
gbmpfa.hf.scabc = getClusterSpecificPvalue(ForeGround=as.matrix(ncounts), cluster_assignments =gbmpfa.hf.cl, background_medians = bg)
save(gbmpfa.hf.scabc, file=paste0("results/PCSC1/Cluster/scABC/scABC.GBMPFAvsHF.peakselection.Rdata"))
#--------------------------------
## Extract P values
#--------------------------------
gbmpfa.hf.scabc.pval = as.data.frame(gbmpfa.hf.scabc$pvalue)
rownames(gbmpfa.hf.scabc.pval) <- rownames(ncounts)
colnames(gbmpfa.hf.scabc.pval) <- c("GBMPFA","HF")
gbmpfa.hf.scabc.pval$id <- rownames(gbmpfa.hf.scabc.pval)
gbmpfa.hf.scabc.pval <- gbmpfa.hf.scabc.pval[order(gbmpfa.hf.scabc.pval$id),]
#--------------------------------
## Combine dataset
#--------------------------------
opname="GBMPFAvsHF"
write.table(gbmpfa.hf.scabc.pval, file=paste0("results/PCSC1/Cluster/scABC/scABC.Combined.", opname,".txt"), row.names=F, col.names=T, sep="\t", quote=F)
#--------------------------------
## Plot images
#--------------------------------
gbmsp <- subset(gbmpfa.hf.scabc.pval$id, gbmpfa.hf.scabc.pval$GBMPFA < 0.05)
hfsp <- subset(gbmpfa.hf.scabc.pval$id, gbmpfa.hf.scabc.pval$HF < 0.05)
pdf(paste0("results/PCSC1/Cluster/scABC/scABC.P0.05.Image.", opname,".pdf"))
image(t(apply(as.matrix(ncounts[c(gbmsp,hfsp),]),2,rev)), col=scalered)
dev.off()
| /scripts/11_scabc.GBMPFAHF.R | no_license | aditiq/CSC-lupien | R | false | false | 3,208 | r | #--------------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------------------------------------------
# R-3.4.1
# mordor
# Objective : Identify cluster specific peaks using scABC's function on GBM, PFA and HF
#--------------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------------------------------------------------------------
#----------------------------------------------------------------
# load dependencies
#----------------------------------------------------------------
library(data.table)
source("scripts/scABC.cluster.R") #source("https://raw.githubusercontent.com/timydaley/scABC/master/R/cluster.R")
scalered <- colorRampPalette(c("white", "red"), space = "rgb")(256)
#----------------------------------------------------------------
# Define function to run scABC on different pairwise comparisons
#----------------------------------------------------------------
#--------------------------------
## Read in binary matrix
#--------------------------------
binarymatds="data/ConsensusSet/PCSC1/GBM.PFA.HF.Consensus.Catalogue.Binarymat.txt"
binarymat <- read.table(binarymatds, header=T, sep="\t", stringsAsFactors = F,check.names = F)
rownames(binarymat) <- paste(binarymat[,1], binarymat[,2], binarymat[,3], sep="_")
ncounts <- binarymat[,4:ncol(binarymat)]
rownames(ncounts) <- rownames(binarymat)
#--------------------------------
## Annotations
#--------------------------------
bg <- c(rep(1,33))
gbmpfa.hf.cl <- c(rep(1,23),rep(2,3), rep(1,7))
names(bg) <- colnames(ncounts)
names(gbmpfa.hf.cl) <- colnames(ncounts)
#--------------------------------
## Run scabc
#--------------------------------
gbmpfa.hf.scabc = getClusterSpecificPvalue(ForeGround=as.matrix(ncounts), cluster_assignments =gbmpfa.hf.cl, background_medians = bg)
save(gbmpfa.hf.scabc, file=paste0("results/PCSC1/Cluster/scABC/scABC.GBMPFAvsHF.peakselection.Rdata"))
#--------------------------------
## Extract P values
#--------------------------------
gbmpfa.hf.scabc.pval = as.data.frame(gbmpfa.hf.scabc$pvalue)
rownames(gbmpfa.hf.scabc.pval) <- rownames(ncounts)
colnames(gbmpfa.hf.scabc.pval) <- c("GBMPFA","HF")
gbmpfa.hf.scabc.pval$id <- rownames(gbmpfa.hf.scabc.pval)
gbmpfa.hf.scabc.pval <- gbmpfa.hf.scabc.pval[order(gbmpfa.hf.scabc.pval$id),]
#--------------------------------
## Combine dataset
#--------------------------------
opname="GBMPFAvsHF"
write.table(gbmpfa.hf.scabc.pval, file=paste0("results/PCSC1/Cluster/scABC/scABC.Combined.", opname,".txt"), row.names=F, col.names=T, sep="\t", quote=F)
#--------------------------------
## Plot images
#--------------------------------
gbmsp <- subset(gbmpfa.hf.scabc.pval$id, gbmpfa.hf.scabc.pval$GBMPFA < 0.05)
hfsp <- subset(gbmpfa.hf.scabc.pval$id, gbmpfa.hf.scabc.pval$HF < 0.05)
pdf(paste0("results/PCSC1/Cluster/scABC/scABC.P0.05.Image.", opname,".pdf"))
image(t(apply(as.matrix(ncounts[c(gbmsp,hfsp),]),2,rev)), col=scalered)
dev.off()
|
#' Get imputed data
#'
#' Get mean imputed data by running E-step once without GQ approximation using the LRT estimates.
#'
#' @param data.obs Observed count matrix for endogeneous genes, rows represent genes, columns represent cells
#' @param par.est LRT estimates, using par.DE by default
#' @param sf A vector containing size factors for all cells
#' @param CE A vector containing capture efficiencies for all cells
#' @param XW A matrix containing information for X (cell-type) and W (other covariates)
#' @param tau cell-specific estimates (intcp,slope) that link Beta-Binomial dispersion parameter to the mean expression.
#' @param parallel If \code{TRUE}, run in parallel
#'
#' @return A matrix containing the mean imputed data.
#'
#' @export
#'
getImputed <- function(data.obs, par.est, sf, CE, XW, tau, parallel = T) {
ngene <- nrow(data.obs)
ncell <- ncol(data.obs)
DO.coef <- matrix(0, ncell, 2)
DO.coef[, 1] <- log(CE/(1-CE))
gq <- gauss.quad(16)
if (parallel) {
temp <- foreach (i = 1:ngene, .combine = 'rbind', .packages = c('VGAM', 'DECENT2')) %dopar% {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) )
est.disp<- exp(par.est[i,ncol(par.est)])
out <- Estep2ByGene(par = DO.coef, z = data.obs[i, ], sf = sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1], GQ.object=gq)
return(c(ifelse(is.na(out$EYZ0E1), data.obs[i, ]/CE, out$EYZ0E1), 1 - out$PE0Z0))
}
data.imp <- temp[, 1:ncell]
PE <- temp[, (ncell+1):(2*ncell)]
} else {
data.imp <- data.obs
PE <- matrix(0, ngene, ncell)
for (i in 1:ngene) {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) )
est.disp<- exp(par.est[i,ncol(par.est)])
out <- Estep2ByGene(par = DO.coef, z = data.obs[i, ], sf = sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1], GQ.object=gq)
data.imp[i, ] <- ifelse(is.na(out$EYZ0E1),data.obs[i, ]/CE,out$EYZ0E1)
PE[i, ]<- 1 - out$PE0Z0
}
}
data.imp = as.matrix(data.imp)
dimnames(data.imp) <- dimnames(data.obs)
return(data.imp*PE)
}
#' Get imputed data
#'
#' Get single imputed data by sampling from the posterior distribution of the complete data given the observed data and LRT estimates.
#'
#' @param data.obs Observed count matrix for endogeneous genes, rows represent genes, columns represent cells
#' @param par.est LRT/EM estimates, using par.DE by default
#' @param sf A vector containing size factors for all cells
#' @param CE A vector containing capture efficiencies for all cells
#' @param XW A matrix containing information for X (cell-type) and W (other covariates)
#' @param tau cell-specific estimates (intcp,slope) that link Beta-Binomial dispersion parameter to the mean expression.
#' @param M number of imputed values to be generated.
#' @param parallel If \code{TRUE}, run in parallel
#'
#' @return A matrix containing the single imputed data.
#'
#' @export
#'
getSImputed <- function(data.obs, par.est, sf, CE, XW, tau, parallel = T) {
ngene <- nrow(data.obs)
ncell <- ncol(data.obs)
DO.coef <- matrix(0, ncell, 2)
DO.coef[, 1] <- log(CE/(1-CE))
if (parallel) {
temp <- foreach (i = 1:ngene, .combine = 'rbind', .packages = c('VGAM', 'DECENT2')) %dopar% {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) * sf)
est.disp<- exp(par.est[i,ncol(par.est)])
out <- SImputeByGene(par = DO.coef, z = data.obs[i, ], sf=sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1],M=1)
return(out)
}
data.imp <- temp[,1:ncell]
} else {
data.imp <- matrix(0,nrow(data.obs),ncol(data.obs))
for (i in 1:ngene) {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) * sf)
est.disp<- exp(par.est[i,ncol(par.est)])
data.imp[i,] <- SImputeByGene(par = DO.coef, z = data.obs[i, ], sf=sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1],M=1)
}
}
dimnames(data.imp) <- dimnames(data.obs)
return(data.imp)
}
| /R/get_imputed.R | no_license | RudRho/DECENT | R | false | false | 4,545 | r | #' Get imputed data
#'
#' Get mean imputed data by running E-step once without GQ approximation using the LRT estimates.
#'
#' @param data.obs Observed count matrix for endogeneous genes, rows represent genes, columns represent cells
#' @param par.est LRT estimates, using par.DE by default
#' @param sf A vector containing size factors for all cells
#' @param CE A vector containing capture efficiencies for all cells
#' @param XW A matrix containing information for X (cell-type) and W (other covariates)
#' @param tau cell-specific estimates (intcp,slope) that link Beta-Binomial dispersion parameter to the mean expression.
#' @param parallel If \code{TRUE}, run in parallel
#'
#' @return A matrix containing the mean imputed data.
#'
#' @export
#'
getImputed <- function(data.obs, par.est, sf, CE, XW, tau, parallel = T) {
ngene <- nrow(data.obs)
ncell <- ncol(data.obs)
DO.coef <- matrix(0, ncell, 2)
DO.coef[, 1] <- log(CE/(1-CE))
gq <- gauss.quad(16)
if (parallel) {
temp <- foreach (i = 1:ngene, .combine = 'rbind', .packages = c('VGAM', 'DECENT2')) %dopar% {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) )
est.disp<- exp(par.est[i,ncol(par.est)])
out <- Estep2ByGene(par = DO.coef, z = data.obs[i, ], sf = sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1], GQ.object=gq)
return(c(ifelse(is.na(out$EYZ0E1), data.obs[i, ]/CE, out$EYZ0E1), 1 - out$PE0Z0))
}
data.imp <- temp[, 1:ncell]
PE <- temp[, (ncell+1):(2*ncell)]
} else {
data.imp <- data.obs
PE <- matrix(0, ngene, ncell)
for (i in 1:ngene) {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) )
est.disp<- exp(par.est[i,ncol(par.est)])
out <- Estep2ByGene(par = DO.coef, z = data.obs[i, ], sf = sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1], GQ.object=gq)
data.imp[i, ] <- ifelse(is.na(out$EYZ0E1),data.obs[i, ]/CE,out$EYZ0E1)
PE[i, ]<- 1 - out$PE0Z0
}
}
data.imp = as.matrix(data.imp)
dimnames(data.imp) <- dimnames(data.obs)
return(data.imp*PE)
}
#' Get imputed data
#'
#' Get single imputed data by sampling from the posterior distribution of the complete data given the observed data and LRT estimates.
#'
#' @param data.obs Observed count matrix for endogeneous genes, rows represent genes, columns represent cells
#' @param par.est LRT/EM estimates, using par.DE by default
#' @param sf A vector containing size factors for all cells
#' @param CE A vector containing capture efficiencies for all cells
#' @param XW A matrix containing information for X (cell-type) and W (other covariates)
#' @param tau cell-specific estimates (intcp,slope) that link Beta-Binomial dispersion parameter to the mean expression.
#' @param M number of imputed values to be generated.
#' @param parallel If \code{TRUE}, run in parallel
#'
#' @return A matrix containing the single imputed data.
#'
#' @export
#'
getSImputed <- function(data.obs, par.est, sf, CE, XW, tau, parallel = T) {
ngene <- nrow(data.obs)
ncell <- ncol(data.obs)
DO.coef <- matrix(0, ncell, 2)
DO.coef[, 1] <- log(CE/(1-CE))
if (parallel) {
temp <- foreach (i = 1:ngene, .combine = 'rbind', .packages = c('VGAM', 'DECENT2')) %dopar% {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) * sf)
est.disp<- exp(par.est[i,ncol(par.est)])
out <- SImputeByGene(par = DO.coef, z = data.obs[i, ], sf=sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1],M=1)
return(out)
}
data.imp <- temp[,1:ncell]
} else {
data.imp <- matrix(0,nrow(data.obs),ncol(data.obs))
for (i in 1:ngene) {
est.pi0 <- exp(par.est[i,1])/(1 + exp(par.est[i,1]))
est.mu <- c(exp( XW %*% as.matrix(par.est[i,-c(1,ncol(par.est))]) ) * sf)
est.disp<- exp(par.est[i,ncol(par.est)])
data.imp[i,] <- SImputeByGene(par = DO.coef, z = data.obs[i, ], sf=sf,
pi0 = rep(est.pi0,ncell), mu = est.mu, disp = est.disp,
k = tau[,2], b = tau[,1],M=1)
}
}
dimnames(data.imp) <- dimnames(data.obs)
return(data.imp)
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.mediastoredata_operations.R
\name{delete_object}
\alias{delete_object}
\title{Deletes an object at the specified path}
\usage{
delete_object(Path)
}
\arguments{
\item{Path}{[required] The path (including the file name) where the object is stored in the container. Format: <folder name>/<folder name>/<file name>}
}
\description{
Deletes an object at the specified path.
}
\section{Accepted Parameters}{
\preformatted{delete_object(
Path = "string"
)
}
}
| /service/paws.mediastoredata/man/delete_object.Rd | permissive | CR-Mercado/paws | R | false | true | 540 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.mediastoredata_operations.R
\name{delete_object}
\alias{delete_object}
\title{Deletes an object at the specified path}
\usage{
delete_object(Path)
}
\arguments{
\item{Path}{[required] The path (including the file name) where the object is stored in the container. Format: <folder name>/<folder name>/<file name>}
}
\description{
Deletes an object at the specified path.
}
\section{Accepted Parameters}{
\preformatted{delete_object(
Path = "string"
)
}
}
|
### 描画の基礎
## 描画の例として東京都の気候データを使う
mydata <- read.csv("tokyo-weather.csv",fileEncoding="utf8")
## 基本的な描画 (ベクトルの描画)
(temp <- subset(mydata,月==10,select=気温,drop=TRUE)) # 温度を取り出す
plot(temp) # 既定値は点(type="p")として描画
plot(temp, type="l", col="blue",ylim=c(0,30)) # y軸を制限，折れ線(type="l")
plot(temp, type="s", col="orange",
xlab="day",ylab="temperature",main="Tokyo, Oct") # タイトル付き，階段(type="s")
## 関数の描画
curve(sin, 0, 4*pi,
col="blue", # グラフの線の色
lwd=2, # グラフの線の太さ
ylab="sin/cos" # y軸のラベル
)
curve(cos,
add=TRUE, # グラフを上書き
col="red", lwd=2)
## データ点の描画
(x <- seq(0, 2*pi, by=0.1)) # 0から2πの点列
(y <- sin(x) + rep_len(c(-0.2, 0.1), length(x)))
plot(x, y, type="p", pch="x", ylim=c(-2,2)) # ylimで値域を指定
points(x, y, col="green", pch=19) # 点を追加. pchは点の形を指定
curve(sin, add=TRUE, col="orange", lwd=2)
lines(x, y, col="blue") # 折れ線を追加
## データフレームを用いた散布図
par(family="HiraginoSans-W4") # 日本語フォントの指定 (MacOSXの場合)
plot(気温 ~ 日, data=subset(mydata,月==8), # 8月の気温の推移
type="l", col="blue", lwd=2) # lwdは線の太さの倍率を指定
plot(気温 ~ 日射量, data=subset(mydata,月==8), # 8月の日射量と気温の関係
pch="*", col="red", cex=2) # cexは点の大きさの倍率を指定
abline(lm(気温 ~ 日射量, data=subset(mydata,月==8)),
col="blue",lwd=2) # 回帰直線を書く (詳しくは次回)
with(subset(mydata, 月==8), # withの中ではデータフレームの列名が使用可能
scatter.smooth(日射量,気温,col="red",pch="*",cex=2,
lpars=list(col="blue",lwd=2))) # 滑らかな曲線の例
| /docs/autumn/code/03-plot.r | no_license | noboru-murata/sda | R | false | false | 1,938 | r | ### 描画の基礎
## 描画の例として東京都の気候データを使う
mydata <- read.csv("tokyo-weather.csv",fileEncoding="utf8")
## 基本的な描画 (ベクトルの描画)
(temp <- subset(mydata,月==10,select=気温,drop=TRUE)) # 温度を取り出す
plot(temp) # 既定値は点(type="p")として描画
plot(temp, type="l", col="blue",ylim=c(0,30)) # y軸を制限，折れ線(type="l")
plot(temp, type="s", col="orange",
xlab="day",ylab="temperature",main="Tokyo, Oct") # タイトル付き，階段(type="s")
## 関数の描画
curve(sin, 0, 4*pi,
col="blue", # グラフの線の色
lwd=2, # グラフの線の太さ
ylab="sin/cos" # y軸のラベル
)
curve(cos,
add=TRUE, # グラフを上書き
col="red", lwd=2)
## データ点の描画
(x <- seq(0, 2*pi, by=0.1)) # 0から2πの点列
(y <- sin(x) + rep_len(c(-0.2, 0.1), length(x)))
plot(x, y, type="p", pch="x", ylim=c(-2,2)) # ylimで値域を指定
points(x, y, col="green", pch=19) # 点を追加. pchは点の形を指定
curve(sin, add=TRUE, col="orange", lwd=2)
lines(x, y, col="blue") # 折れ線を追加
## データフレームを用いた散布図
par(family="HiraginoSans-W4") # 日本語フォントの指定 (MacOSXの場合)
plot(気温 ~ 日, data=subset(mydata,月==8), # 8月の気温の推移
type="l", col="blue", lwd=2) # lwdは線の太さの倍率を指定
plot(気温 ~ 日射量, data=subset(mydata,月==8), # 8月の日射量と気温の関係
pch="*", col="red", cex=2) # cexは点の大きさの倍率を指定
abline(lm(気温 ~ 日射量, data=subset(mydata,月==8)),
col="blue",lwd=2) # 回帰直線を書く (詳しくは次回)
with(subset(mydata, 月==8), # withの中ではデータフレームの列名が使用可能
scatter.smooth(日射量,気温,col="red",pch="*",cex=2,
lpars=list(col="blue",lwd=2))) # 滑らかな曲線の例
|
plot(hc_result, hang=-1, labels = iris$Species[idx])
rect.hclust(hc_result, k=5) | /work/r데이터분석_예제파일/예제/9_06.R | no_license | bass4th/R | R | false | false | 81 | r | plot(hc_result, hang=-1, labels = iris$Species[idx])
rect.hclust(hc_result, k=5) |
## rnaseq barcode example
source("rnaseq_barcode.R")
load("geoMeans.rda")
load("barcode_params.rda")
load(file="e_test.rda")
## object should be a vector of unnormalized counts
## with the same length and names as geoMeans
e_test <- e_test[names(geoMeans),,drop=FALSE]
identical(names(geoMeans), rownames(e_test))
tst <- rnaseq_barcode(object=e_test, gmeans=geoMeans, mu=params$mu_r2, tau=sqrt(params$tau2_r2),
cutoff = 6.5, output = "binary")
| /functions/barcode_example.R | no_license | jaredcara/barcode | R | false | false | 470 | r | ## rnaseq barcode example
source("rnaseq_barcode.R")
load("geoMeans.rda")
load("barcode_params.rda")
load(file="e_test.rda")
## object should be a vector of unnormalized counts
## with the same length and names as geoMeans
e_test <- e_test[names(geoMeans),,drop=FALSE]
identical(names(geoMeans), rownames(e_test))
tst <- rnaseq_barcode(object=e_test, gmeans=geoMeans, mu=params$mu_r2, tau=sqrt(params$tau2_r2),
cutoff = 6.5, output = "binary")
|
library(DT)
library(shiny)
library(dplyr)
library(lubridate)
library(tidyverse)
library(leaflet)
library(patchwork)
library(ggpattern)
library(scales)
library(envreportutils)
library(plotly)
library(sf)
library(bcmaps)
library(readr)
library(ggplot2)
library(envair)
library(rcaaqs)
dirs_location <- 'https://raw.githubusercontent.com/bcgov/air-zone-reports/master/data/out'
if (0) {
dirs_location <- './data/out'
dirs_location <- '../data/out' #local location, two dots for final, one dot for debug
list.files(dirs_location)
test <- read_csv('./data/out/annual_results.csv') %>%
filter(parameter=='PM25')
}
#define functions-------
map_exceedance <- function(map_a = NULL,exceedances,az_mgmt,year,size = c('200px','400px'),
airzone = NULL) {
if (0) {
source('./level4_page/03_setup.R')
source('./level4_page/02_setup.R')
dirs_location <- './data/out'
year = 2017
map_a <- NULL
exceedances <- get_PM_exceedancesummary()
az_mgmt <- readr::read_rds(paste(dirs_location,'az_mgmt.Rds',sep='/')) %>%
left_join(df_colour)
size <- c('200px','400px')
}
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
airzone_select <- airzone
lst_airzones <- az_mgmt %>%
pull(airzone)
airzone_exceedance_season <- exceedances$season
airzone_exceedance <- exceedances$annual
station_exceedance <- exceedances$annual_stations
station_exceedance_season <- exceedances$season_stations
lst_stations <- exceedances$stations
year_select <- year
colfunc <- colorRampPalette(c("blue", "red"))
# station_exceedance$colorscaled <- color_scales[station_exceedance$days_exceed]
if (is.null(map_a)) {
#create map for annual station
a <- leaflet(width = size[1],height = size[2],
options = leafletOptions(attributionControl=FALSE, dragging = TRUE, minZoom = 4, maxZoom=10)) %>%
set_bc_view(zoom=3.5) %>%
# setView(zoom =5) %>%
setMaxBounds(lng1 = -110,lat1=45,lng2=-137,lat2=62) %>%
addProviderTiles(providers$Stamen.TonerLite,
options = providerTileOptions(opacity = 1)
) %>%
# addProviderTiles(providers$Stamen.TonerLabels) %>%
add_bc_home_button()
} else {
a <- map_a
}
#add colour for the station circles
max_days <- station_exceedance %>%
filter(year == year_select) %>%
pull(days_exceed) %>%
max()
#develop levels for the exceedance legends
color_scales <- colfunc(max_days)
for (airzone_ in lst_airzones) {
if (0) {
airzone_ <- lst_airzones[1]
}
liststations_ <- lst_stations %>%
filter(AIRZONE == airzone_, year == year_select)
station_exceedance_ <- station_exceedance %>%
filter(AIRZONE == airzone_, year == year_select)
lst_sites <- station_exceedance %>%
filter(AIRZONE == airzone_) %>%
pull(site) %>% unique()
a <- a %>%
addPolygons(data = az_mgmt %>% filter(airzone == airzone_),
layerId = airzone_,
color = 'black',
fillColor = ~colour_01,
weight = 1, opacity = 1, fillOpacity = 0.6,
label = paste(airzone_,'Air Zone'),
labelOptions = labelOptions(textsize = "15px"),
highlight = highlightOptions(weight = 3,
color = "blue",
bringToFront = FALSE))
#add stations
if (nrow(liststations_) >0) {
# print('trace inside map function')
# print(nrow(liststations_))
# print(year_select)
a <- a %>%
#remove all stations from that airzone
removeMarker( layerId = lst_sites) %>%
addCircleMarkers(lng=station_exceedance_$LONG,
lat = station_exceedance_$LAT,
layerId = station_exceedance_$site,
label = station_exceedance_$site,
color = color_scales[station_exceedance_$days_exceed+1],
radius=3
)
# addMarkers(lng=liststations_$LONG,
# lat=liststations_$LAT,
# # layerId = liststations$AIRZONE,
# # group = airzone_,
# label = liststations_$site,
# options=markerOptions())
}
}
if (is.null(map_a)) {
#add legend based on color_scales
tot_ <- length(color_scales)
scl <- c(color_scales[5],color_scales[tot_/2],color_scales[tot_])
lbl <- c('<5 days',paste(round(tot_/2),'days'),paste('>',round(tot_*0.8),' days',sep=''))
a <- a %>%
addLegend(position ="bottomleft", colors = scl,label = lbl)
}
#add for selected airzone
if (!is.null(airzone_select)) {
print(paste('updating,highlighting area',airzone_select))
a <- a %>%
addPolylines(data = az_mgmt %>% filter(airzone == airzone_select),
layerId = 'selectedairzone',
group = 'airzonehighlight',
color = 'blue',weight = 5)
} else {
a <- a %>%
clearGroup('airzonehighlight')
}
plot_a <- a
return(a)
}
#' Get a count of PM exceedances
#'
#'
get_PM_exceedancesummary <- function(dirs_location = './data/out') {
# dirs_location <- './data/out' #local location, two dots for final, one dot for debug
if (0) {
dirs_location <- './data/out'
}
# list.files(dirs_location)
print('get PM exceedance')
# df_stations <- readRDS(paste(dirs_location,'liststations_merged.Rds',sep='/'))
df_stations <- envair::listBC_stations(use_CAAQS = TRUE,merge_Stations = TRUE)
lst_remove <- df_stations %>%
filter(AQMS == 'N') %>%
pull(site)
#retrieve complete list of stations, to be included in resulting list for that year
df_stationdata <- readr::read_csv(paste(dirs_location,'annual_results.csv',sep='/')) %>%
filter(!is.na(value)) %>%
select(site,year) %>% distinct() %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations %>% select(site,LAT,LONG,AIRZONE))
#get TFEE list
tfee_list <- readr::read_csv(paste(dirs_location,'tfee.csv',sep='/')) %>%
left_join(df_stations %>% select(STATION_NAME,site)) %>%
select(site,DATE,PARAMETER) %>% distinct() %>%
filter(PARAMETER == 'PM25') %>%
mutate(tfee = TRUE,
DATE= as.Date(DATE))
# df_exceed_annual <- readRDS(paste(dirs_location,'exceed_annual.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceed_month<- readRDS(paste(dirs_location,'exceed_month.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceed_seasonal <- readRDS(paste(dirs_location,'exceed_seasonal.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceedances <- readRDS(paste(dirs_location,'exceedances.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
df_exceedances <- read_csv(paste(dirs_location,'exceedances.csv',sep='/')) %>%
rename(site = STATION_NAME)
# colnames(df_stations)
df_seasons <- tibble(
month = 1:12
)
df_seasons$seasons[df_seasons$month %in% c(12,1,2)] <- 'Winter'
df_seasons$seasons[df_seasons$month %in% c(3,4,5)] <- 'Spring'
df_seasons$seasons[df_seasons$month %in% c(6,7,8)] <- 'Summer'
df_seasons$seasons[df_seasons$month %in% c(9,10,11)] <- 'Fall'
#need to summarize exceedances to air zone and entire province
#do not add the number or will have spatial problems
#count for air zones
##count for an entire year----
#no wildfire assessment
df_exceedances_year <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
#wildfire (tfee) assessment
df_exceedances_year_tfee <-
df_exceedances %>%
filter((PARAMETER == 'PM25' & name == 'exceed_27')
)%>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_year_bc <- df_exceedances_year %>%
select(year,DATE) %>% distinct() %>%
group_by(year) %>%
summarise(days_exceed = n()) %>%
mutate(AIRZONE = 'BC')
df_exceedances_year_bc_tfee <- df_exceedances_year_tfee %>%
select(year,DATE) %>% distinct() %>%
group_by(year) %>%
summarise(days_exceed = n()) %>%
mutate(AIRZONE = 'BC')
df_exceedances_year <- df_exceedances_year %>%
bind_rows(df_exceedances_year_bc) %>%
select(year,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
df_exceedances_year_tfee <- df_exceedances_year_tfee %>%
bind_rows(df_exceedances_year_bc_tfee) %>%
select(year,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
##count for entire seasons-----
df_exceedances_season <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_season_tfee <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_season_bc <- df_exceedances_season %>%
select(year,seasons,DATE) %>% distinct() %>%
group_by(year,seasons) %>%
summarise(days_exceed = n())%>%
mutate(AIRZONE = 'BC')
df_exceedances_season_bc_tfee <- df_exceedances_season_tfee %>%
select(year,seasons,DATE) %>% distinct() %>%
group_by(year,seasons) %>%
summarise(days_exceed = n())%>%
mutate(AIRZONE = 'BC')
df_exceedances_season <- df_exceedances_season %>%
bind_rows(df_exceedances_season_bc)%>%
select(year,seasons,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
df_exceedances_season_tfee <- df_exceedances_season_tfee %>%
bind_rows(df_exceedances_season_bc_tfee)%>%
select(year,seasons,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
#create for each station
df_exceedances_station_year <- df_exceedances%>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,site,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup() %>%
select(year,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_year_tfee <- df_exceedances%>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,site,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup() %>%
select(year,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_seasonal <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,site,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()%>%
select(year,seasons,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_seasonal_tfee <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,site,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()%>%
select(year,seasons,site,AIRZONE,days_exceed) %>%
distinct()
#fix df_stationdata duplicates
df_stationdata <- df_stationdata %>%
group_by(site,AIRZONE,year) %>%
dplyr::mutate(index=1:n()) %>%
filter(index==1) %>%
select(-index) %>% ungroup()
#left join to display zero values
df_exceedances_year <- df_stationdata %>%
select(AIRZONE,year) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
)) %>%
left_join(df_exceedances_year) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_year_tfee <- df_stationdata %>%
select(AIRZONE,year) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',year = min(df_stationdata$year):max(df_stationdata$year)
)) %>%
filter(year>=2014) %>%
left_join(df_exceedances_year_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_season <- df_stationdata %>%
select(AIRZONE,year) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall')))
) %>%
left_join(df_exceedances_season) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_season_tfee <- df_stationdata %>%
select(AIRZONE,year) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall')))
) %>%
filter(year >=2014) %>%
left_join(df_exceedances_season_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_year <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
distinct() %>%
left_join(df_exceedances_station_year) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_year_tfee <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
distinct() %>%
filter(year>=2014) %>%
left_join(df_exceedances_station_year_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_seasonal <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
left_join(df_exceedances_station_seasonal) %>%
mutate(days_exceed= ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_seasonal_tfee <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
filter(year>=2014) %>%
left_join(df_exceedances_station_seasonal_tfee) %>%
mutate(days_exceed= ifelse(is.na(days_exceed),0,days_exceed))
df_exceedance <- list(annual = df_exceedances_year,
annual_tfee = df_exceedances_year_tfee,
season = df_exceedances_season,
season_tfee = df_exceedances_season_tfee,
annual_stations =df_exceedances_station_year,
annual_stations_tfee = df_exceedances_station_year_tfee,
season_stations = df_exceedances_station_seasonal,
season_stations_tfee = df_exceedances_station_seasonal_tfee,
stations = df_stationdata)
return(df_exceedance)
}
#' Determine the air zone based on lat longs
#'
#' @param lat is the latitude, vector OK
#' @param long is the longitude, vector OK
get_airzone <- function(lat,long) {
if (0) {
latlong <- c(57.68,-120.614)
}
az_mgmt_gitURL <- 'https://github.com/bcgov/air-zone-reports/blob/master/data/out/az_mgmt.Rds?raw=true'
az_mgmt <- readRDS(url(az_mgmt_gitURL))
#----------------
pnts <- data.frame(
"x" = long,
"y" = lat)
# create a points collection
pnts_sf <- do.call("st_sfc",c(lapply(1:nrow(pnts),
function(i) {st_point(as.numeric(pnts[i, ]))}), list("crs" = 4326)))
pnts_trans <- st_transform(pnts_sf, 2163) # apply transformation to pnts sf
tt1_trans <- st_transform(az_mgmt, 2163) # apply transformation to polygons sf
# intersect and extract state name
pnts$airzone <- apply(st_intersects(tt1_trans, pnts_trans, sparse = FALSE), 2,
function(col) {
tt1_trans[which(col), ]$airzone
})
return(pnts$airzone)
}
#' create graphs for days exceeding
#'
#' @param exceedances is the result of get_PM_exceedance() function
graph_exceedance <- function(exceedances,AIRZONE = NULL,year = NULL) {
if (0) {
source('./level4_page/02_setup.R')
dirs_location <- './data/out'
AIRZONE <- NULL
}
require(tidyr)
#set order for the seasons
df_seasons <- tribble(
~seasons,~order,
'Winter',4,
'Spring',3,
'Summer',2,
'Fall',1
)
year_select <- year
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
list_airzones <- df_colour$airzone
# View(exceedances$season_stations)
if (is.null(AIRZONE)) {
AIRZONE <- 'BC'
}
df_annual <- exceedances$annual
df_seasonal_tfee <- exceedances$season_tfee %>%
mutate(tfee = TRUE)
df_seasonal <- exceedances$season %>%
mutate(tfee = FALSE) %>%
bind_rows(df_seasonal_tfee) %>%
pivot_wider(names_from = tfee, values_from =days_exceed) %>%
dplyr::rename(`Wildfire Days` = `TRUE`,`Total` = `FALSE`) %>%
mutate(`Wildfire Days` = ifelse(is.na(`Wildfire Days`),0,`Wildfire Days`))%>%
mutate(`No Wildfire`= `Total` - `Wildfire Days`) %>%
select(-Total) %>%
pivot_longer(cols = c(`Wildfire Days`,`No Wildfire`)) %>%
mutate(seasons = paste(seasons,'(',name,')',sep=''))
vline <- function(x = 0, color = "red") {
list(
type = "line",
y0 = 0,
y1 = 1,
yref = "paper",
x0 = x,
x1 = x,
line = list(color = color,dash = 'dash')
)
}
p_annual <-
df_annual %>%
filter(!AIRZONE %in% c('BC',NA)) %>%
filter(year>=1997) %>%
plotly::plot_ly(x=~year,y=~days_exceed,color = ~AIRZONE,
type='scatter',mode='lines',showlegend =T,
hoverinfo ='y',
hovertemplate = paste('%{y:,.0f}',' days',sep='')
) %>%
layout(title = 'High PM<sub>2.5</sub> Levels in Air Zones',
legend = list(orientation = 'h'),
yaxis = list(title = 'Number of Days with High PM<sub>2.5</sub> Levels'),
xaxis = list(title = 'Year')
) %>%
plotly::layout(hovermode = 'x unified',
barmode = 'stack') %>%
layout(shapes = list(vline(year_select)))
# event_register("plotly_click")
#create results for BC, and all air zones
{
p_list <- NULL
i_list <- NULL
i <- 0
for (AIRZONE_ in c('BC',lst_airzones)) {
i <- i +1
p_seasonal <- df_seasonal %>%
left_join(df_seasons,by = 'seasons') %>%
filter(AIRZONE == AIRZONE_) %>%
filter(year >=2000) %>%
plotly::plot_ly(x=~year,y=~value,color = ~reorder(seasons,order),
type='bar',mode='bar',showlegend =T,
hoverinfo ='y',
hovertemplate = paste('%{y:,.0f}',' days',sep=''),
colors = c("navajowhite2",
"navajowhite3",
"seagreen3",
"seagreen4",
"red3",
"red4",
"slategray2",
'slategray4'
)
) %>%
layout(title = paste('<br>High PM<sub>2.5</sub> Levels for',
ifelse(AIRZONE == 'BC',AIRZONE_,
paste('the',AIRZONE_,'Air Zone')
)),
legend = list(orientation = 'h'),
yaxis = list(title = 'Number of Days with High PM<sub>2.5</sub> Levels'),
xaxis = list(title = 'Year')
) %>%
plotly::layout(hovermode = 'x unified',
barmode = 'stack',legend = list(x = 0.01, y = 0.9))%>%
layout(shapes = list(vline(year_select)))
# layout(annotations = list(x=2014,y=-1,yref='paper',xref='paper',text = 'wildfire counts only from 2014 onwards'))
p_list[[i]] <- p_seasonal
i_list <- c(i_list,AIRZONE_)
}
result <- list(plot_annual = p_annual,plot_seasonal = p_list,plot_definition = i_list, data = exceedances)
}
return(result)
}
#end of functions-----
#define colours-----
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
lst_airzones <- df_colour$airzone
#define external data------
exceedances <- get_PM_exceedancesummary(dirs_location)
az_mgmt <- readr::read_rds(paste(dirs_location,'az_mgmt.Rds',sep='/')) %>%
left_join(df_colour)
print('Load az_mgmt complete')
plots_list <- graph_exceedance(exceedances = exceedances,year = max(exceedances$annual$year))
print('Load Complete')
#---SHINY SECTION----------------
# Actual shiny part
##ui section----
ui <- {
fluidPage(
h4(HTML('Fine Particulate Matter (PM<sub>2.5</sub>) Pollution')),
tags$head(
tags$style(HTML("
body { background-color: #f2efe9; }
.container-fluid { background-color: #fff; width: 1200px; padding: 5px; }
.topimg { width: 0px; display: block; margin: 0px auto 0px auto; }
.title { text-align: center; }
.toprow { margin: 5px 0px; padding: 5px; background-color: #38598a; }
.filters { margin: 0px auto; }
.shiny-input-container { width:100% !important; }
.table { padding: 0px; margin-top: 0px; }
.leaflet-top { z-index:999 !important; }
"))
),
# h1("Fine Particulate Pollution", class = "title"),
fluidRow(class = "toprow",
fluidRow(class = 'filters',
column(6,
tags$style(type='text/css',
'.selectize-input { font-size: 15px; line-height: 10px;}
.selectize-dropdown { font-size: 16px; line-height: 20px; }
.control-label {font-size: 24px; color: white !important;}
.irs-min {font-size: 0px; color: white; !important}
.irs-max {font-size: 0px; color: white;}
.irs-single {font-size: 20px; color: white;}
.irs-grid-text {font-size: 10px; color: white;}'
),
sliderInput('year_slider',label ='Year',
min = 1997,
max = max(exceedances$annual$year),
value = max(exceedances$annual$year),
sep='')
))),
fluidRow(
column(4,h6(HTML("Number of Days with High PM<sub>2.5</sub> Levels</br>Click the map to select an air zone")),
# fluidRow(
leaflet::leafletOutput("map",height = '400px',width = '400px')),
column(8,h6(HTML("Scroll through the graph to view the number of days with high PM<sub>2.5</sub> Levels.")),
# div(style='height:400px;overflow-y: scroll;'),
plotlyOutput("plot1",height = '400px',width = '800px')
)
)
)
}
##server section----
server <- {shinyServer(function(input, output) {
if (0) {
map_exceedance(exceedances = exceedances, az_mgmt = az_mgmt, year = 2010)
}
#reactive_plot1 can carry over plots across events
reactive_plot1 <- reactiveVal(plots_list)
a <- map_exceedance(map_a = NULL,exceedances = exceedances ,az_mgmt = az_mgmt,year = max(exceedances$annual$year))
output$map <- renderLeaflet(a)
output$plot1 <- renderPlotly({plot_out <- plots_list$plot_annual
plot_out %>% ggplotly(source = 'plot1')
#%>% event_register("plotly_click")
})
observeEvent(input$year_slider,
{
print('Slider')
print(input$year_slider)
leafletProxy("map") %>%
map_exceedance(exceedances = exceedances ,az_mgmt = az_mgmt,year =input$year_slider)
plots_list <- graph_exceedance(exceedances = exceedances,year = input$year_slider)
reactive_plot1(plots_list) #pass on value to reactive_plot1
# output$plot1 <- renderPlotly(plots_list$plot_annual)
output$plot1 <- renderPlotly({
plot_out <- plots_list$plot_annual
p <- plot_out %>% ggplotly(source = 'plot1')
p
})
})
#clicking the map, an airzone is selected
observeEvent(input$map_shape_click, {
p <- input$map_shape_click
try({
airzone_select <- get_airzone(p$lat,p$lng)
print(p$lat)
print(p$lng)
print(airzone_select)
plots_list <- reactive_plot1()
if (airzone_select != 'Northwest') {
output$plot1 <- renderPlotly({
p <- plots_list$plot_seasonal[[which(plots_list$plot_definition == airzone_select)]]
# Add a footnote
footnote <- list(
font = list(size = 10),
x = 2000, y = 8,
# xref = "paper",
# yref = "paper",
text = "*Counts for wildfire days before 2014 are not included",
showarrow = FALSE
)
p <- layout(p, annotations = list(footnote))
print(p) # Check if the footnote is included in the annotations list
print('annotations inserted')
# Return the plotly graph
p
})
print('Plot Refreshed')
}
leafletProxy("map") %>%
map_exceedance(exceedances = exceedances ,az_mgmt = az_mgmt,year =input$year_slider,airzone = airzone_select)
})
})
#clicking on the graph
# observeEvent(event_data("plotly_click", source = "plot1"), {
# values$plot.click.results <- event_data("plotly_click", source = "plot1")
# print(values$plot.click.results)
# })
})
}
shinyApp(ui, server)
| /level_shiny/pm_exceedance/shiny_exceedances.R | permissive | bcgov/air-zone-reports | R | false | false | 28,935 | r | library(DT)
library(shiny)
library(dplyr)
library(lubridate)
library(tidyverse)
library(leaflet)
library(patchwork)
library(ggpattern)
library(scales)
library(envreportutils)
library(plotly)
library(sf)
library(bcmaps)
library(readr)
library(ggplot2)
library(envair)
library(rcaaqs)
dirs_location <- 'https://raw.githubusercontent.com/bcgov/air-zone-reports/master/data/out'
if (0) {
dirs_location <- './data/out'
dirs_location <- '../data/out' #local location, two dots for final, one dot for debug
list.files(dirs_location)
test <- read_csv('./data/out/annual_results.csv') %>%
filter(parameter=='PM25')
}
#define functions-------
map_exceedance <- function(map_a = NULL,exceedances,az_mgmt,year,size = c('200px','400px'),
airzone = NULL) {
if (0) {
source('./level4_page/03_setup.R')
source('./level4_page/02_setup.R')
dirs_location <- './data/out'
year = 2017
map_a <- NULL
exceedances <- get_PM_exceedancesummary()
az_mgmt <- readr::read_rds(paste(dirs_location,'az_mgmt.Rds',sep='/')) %>%
left_join(df_colour)
size <- c('200px','400px')
}
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
airzone_select <- airzone
lst_airzones <- az_mgmt %>%
pull(airzone)
airzone_exceedance_season <- exceedances$season
airzone_exceedance <- exceedances$annual
station_exceedance <- exceedances$annual_stations
station_exceedance_season <- exceedances$season_stations
lst_stations <- exceedances$stations
year_select <- year
colfunc <- colorRampPalette(c("blue", "red"))
# station_exceedance$colorscaled <- color_scales[station_exceedance$days_exceed]
if (is.null(map_a)) {
#create map for annual station
a <- leaflet(width = size[1],height = size[2],
options = leafletOptions(attributionControl=FALSE, dragging = TRUE, minZoom = 4, maxZoom=10)) %>%
set_bc_view(zoom=3.5) %>%
# setView(zoom =5) %>%
setMaxBounds(lng1 = -110,lat1=45,lng2=-137,lat2=62) %>%
addProviderTiles(providers$Stamen.TonerLite,
options = providerTileOptions(opacity = 1)
) %>%
# addProviderTiles(providers$Stamen.TonerLabels) %>%
add_bc_home_button()
} else {
a <- map_a
}
#add colour for the station circles
max_days <- station_exceedance %>%
filter(year == year_select) %>%
pull(days_exceed) %>%
max()
#develop levels for the exceedance legends
color_scales <- colfunc(max_days)
for (airzone_ in lst_airzones) {
if (0) {
airzone_ <- lst_airzones[1]
}
liststations_ <- lst_stations %>%
filter(AIRZONE == airzone_, year == year_select)
station_exceedance_ <- station_exceedance %>%
filter(AIRZONE == airzone_, year == year_select)
lst_sites <- station_exceedance %>%
filter(AIRZONE == airzone_) %>%
pull(site) %>% unique()
a <- a %>%
addPolygons(data = az_mgmt %>% filter(airzone == airzone_),
layerId = airzone_,
color = 'black',
fillColor = ~colour_01,
weight = 1, opacity = 1, fillOpacity = 0.6,
label = paste(airzone_,'Air Zone'),
labelOptions = labelOptions(textsize = "15px"),
highlight = highlightOptions(weight = 3,
color = "blue",
bringToFront = FALSE))
#add stations
if (nrow(liststations_) >0) {
# print('trace inside map function')
# print(nrow(liststations_))
# print(year_select)
a <- a %>%
#remove all stations from that airzone
removeMarker( layerId = lst_sites) %>%
addCircleMarkers(lng=station_exceedance_$LONG,
lat = station_exceedance_$LAT,
layerId = station_exceedance_$site,
label = station_exceedance_$site,
color = color_scales[station_exceedance_$days_exceed+1],
radius=3
)
# addMarkers(lng=liststations_$LONG,
# lat=liststations_$LAT,
# # layerId = liststations$AIRZONE,
# # group = airzone_,
# label = liststations_$site,
# options=markerOptions())
}
}
if (is.null(map_a)) {
#add legend based on color_scales
tot_ <- length(color_scales)
scl <- c(color_scales[5],color_scales[tot_/2],color_scales[tot_])
lbl <- c('<5 days',paste(round(tot_/2),'days'),paste('>',round(tot_*0.8),' days',sep=''))
a <- a %>%
addLegend(position ="bottomleft", colors = scl,label = lbl)
}
#add for selected airzone
if (!is.null(airzone_select)) {
print(paste('updating,highlighting area',airzone_select))
a <- a %>%
addPolylines(data = az_mgmt %>% filter(airzone == airzone_select),
layerId = 'selectedairzone',
group = 'airzonehighlight',
color = 'blue',weight = 5)
} else {
a <- a %>%
clearGroup('airzonehighlight')
}
plot_a <- a
return(a)
}
#' Get a count of PM exceedances
#'
#'
get_PM_exceedancesummary <- function(dirs_location = './data/out') {
# dirs_location <- './data/out' #local location, two dots for final, one dot for debug
if (0) {
dirs_location <- './data/out'
}
# list.files(dirs_location)
print('get PM exceedance')
# df_stations <- readRDS(paste(dirs_location,'liststations_merged.Rds',sep='/'))
df_stations <- envair::listBC_stations(use_CAAQS = TRUE,merge_Stations = TRUE)
lst_remove <- df_stations %>%
filter(AQMS == 'N') %>%
pull(site)
#retrieve complete list of stations, to be included in resulting list for that year
df_stationdata <- readr::read_csv(paste(dirs_location,'annual_results.csv',sep='/')) %>%
filter(!is.na(value)) %>%
select(site,year) %>% distinct() %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations %>% select(site,LAT,LONG,AIRZONE))
#get TFEE list
tfee_list <- readr::read_csv(paste(dirs_location,'tfee.csv',sep='/')) %>%
left_join(df_stations %>% select(STATION_NAME,site)) %>%
select(site,DATE,PARAMETER) %>% distinct() %>%
filter(PARAMETER == 'PM25') %>%
mutate(tfee = TRUE,
DATE= as.Date(DATE))
# df_exceed_annual <- readRDS(paste(dirs_location,'exceed_annual.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceed_month<- readRDS(paste(dirs_location,'exceed_month.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceed_seasonal <- readRDS(paste(dirs_location,'exceed_seasonal.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
# df_exceedances <- readRDS(paste(dirs_location,'exceedances.Rds',sep='/')) %>%
# rename(site = STATION_NAME)
df_exceedances <- read_csv(paste(dirs_location,'exceedances.csv',sep='/')) %>%
rename(site = STATION_NAME)
# colnames(df_stations)
df_seasons <- tibble(
month = 1:12
)
df_seasons$seasons[df_seasons$month %in% c(12,1,2)] <- 'Winter'
df_seasons$seasons[df_seasons$month %in% c(3,4,5)] <- 'Spring'
df_seasons$seasons[df_seasons$month %in% c(6,7,8)] <- 'Summer'
df_seasons$seasons[df_seasons$month %in% c(9,10,11)] <- 'Fall'
#need to summarize exceedances to air zone and entire province
#do not add the number or will have spatial problems
#count for air zones
##count for an entire year----
#no wildfire assessment
df_exceedances_year <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
#wildfire (tfee) assessment
df_exceedances_year_tfee <-
df_exceedances %>%
filter((PARAMETER == 'PM25' & name == 'exceed_27')
)%>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_year_bc <- df_exceedances_year %>%
select(year,DATE) %>% distinct() %>%
group_by(year) %>%
summarise(days_exceed = n()) %>%
mutate(AIRZONE = 'BC')
df_exceedances_year_bc_tfee <- df_exceedances_year_tfee %>%
select(year,DATE) %>% distinct() %>%
group_by(year) %>%
summarise(days_exceed = n()) %>%
mutate(AIRZONE = 'BC')
df_exceedances_year <- df_exceedances_year %>%
bind_rows(df_exceedances_year_bc) %>%
select(year,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
df_exceedances_year_tfee <- df_exceedances_year_tfee %>%
bind_rows(df_exceedances_year_bc_tfee) %>%
select(year,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
##count for entire seasons-----
df_exceedances_season <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_season_tfee <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()
df_exceedances_season_bc <- df_exceedances_season %>%
select(year,seasons,DATE) %>% distinct() %>%
group_by(year,seasons) %>%
summarise(days_exceed = n())%>%
mutate(AIRZONE = 'BC')
df_exceedances_season_bc_tfee <- df_exceedances_season_tfee %>%
select(year,seasons,DATE) %>% distinct() %>%
group_by(year,seasons) %>%
summarise(days_exceed = n())%>%
mutate(AIRZONE = 'BC')
df_exceedances_season <- df_exceedances_season %>%
bind_rows(df_exceedances_season_bc)%>%
select(year,seasons,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
df_exceedances_season_tfee <- df_exceedances_season_tfee %>%
bind_rows(df_exceedances_season_bc_tfee)%>%
select(year,seasons,AIRZONE,days_exceed) %>%
distinct() %>% arrange(year,AIRZONE)
#create for each station
df_exceedances_station_year <- df_exceedances%>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,site,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup() %>%
select(year,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_year_tfee <- df_exceedances%>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE)) %>%
ungroup() %>%
select(year,site,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup() %>%
select(year,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_seasonal <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,site,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()%>%
select(year,seasons,site,AIRZONE,days_exceed) %>%
distinct()
df_exceedances_station_seasonal_tfee <- df_exceedances %>%
filter(PARAMETER == 'PM25',name == 'exceed_27') %>%
filter(!site %in% lst_remove) %>%
left_join(df_stations) %>%
left_join(tfee_list) %>%
filter(tfee == TRUE) %>%
mutate(year = lubridate::year(DATE),
month = lubridate::month(DATE)) %>%
left_join(df_seasons) %>%
select(year,site,seasons,DATE,AIRZONE) %>% distinct() %>%
group_by(year,site,AIRZONE,seasons) %>%
dplyr::mutate(days_exceed = n()) %>%
ungroup()%>%
select(year,seasons,site,AIRZONE,days_exceed) %>%
distinct()
#fix df_stationdata duplicates
df_stationdata <- df_stationdata %>%
group_by(site,AIRZONE,year) %>%
dplyr::mutate(index=1:n()) %>%
filter(index==1) %>%
select(-index) %>% ungroup()
#left join to display zero values
df_exceedances_year <- df_stationdata %>%
select(AIRZONE,year) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
)) %>%
left_join(df_exceedances_year) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_year_tfee <- df_stationdata %>%
select(AIRZONE,year) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',year = min(df_stationdata$year):max(df_stationdata$year)
)) %>%
filter(year>=2014) %>%
left_join(df_exceedances_year_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_season <- df_stationdata %>%
select(AIRZONE,year) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall')))
) %>%
left_join(df_exceedances_season) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_season_tfee <- df_stationdata %>%
select(AIRZONE,year) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
bind_rows(tibble(
AIRZONE ='BC',
year = min(df_stationdata$year):max(df_stationdata$year)
) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall')))
) %>%
filter(year >=2014) %>%
left_join(df_exceedances_season_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_year <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
distinct() %>%
left_join(df_exceedances_station_year) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_year_tfee <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
distinct() %>%
filter(year>=2014) %>%
left_join(df_exceedances_station_year_tfee) %>%
mutate(days_exceed = ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_seasonal <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
left_join(df_exceedances_station_seasonal) %>%
mutate(days_exceed= ifelse(is.na(days_exceed),0,days_exceed))
df_exceedances_station_seasonal_tfee <- df_stationdata %>%
select(site,AIRZONE,year,LAT,LONG) %>%
merge(tibble(seasons = c('Winter','Spring','Summer','Fall'))) %>%
distinct() %>%
filter(year>=2014) %>%
left_join(df_exceedances_station_seasonal_tfee) %>%
mutate(days_exceed= ifelse(is.na(days_exceed),0,days_exceed))
df_exceedance <- list(annual = df_exceedances_year,
annual_tfee = df_exceedances_year_tfee,
season = df_exceedances_season,
season_tfee = df_exceedances_season_tfee,
annual_stations =df_exceedances_station_year,
annual_stations_tfee = df_exceedances_station_year_tfee,
season_stations = df_exceedances_station_seasonal,
season_stations_tfee = df_exceedances_station_seasonal_tfee,
stations = df_stationdata)
return(df_exceedance)
}
#' Determine the air zone based on lat longs
#'
#' @param lat is the latitude, vector OK
#' @param long is the longitude, vector OK
get_airzone <- function(lat,long) {
if (0) {
latlong <- c(57.68,-120.614)
}
az_mgmt_gitURL <- 'https://github.com/bcgov/air-zone-reports/blob/master/data/out/az_mgmt.Rds?raw=true'
az_mgmt <- readRDS(url(az_mgmt_gitURL))
#----------------
pnts <- data.frame(
"x" = long,
"y" = lat)
# create a points collection
pnts_sf <- do.call("st_sfc",c(lapply(1:nrow(pnts),
function(i) {st_point(as.numeric(pnts[i, ]))}), list("crs" = 4326)))
pnts_trans <- st_transform(pnts_sf, 2163) # apply transformation to pnts sf
tt1_trans <- st_transform(az_mgmt, 2163) # apply transformation to polygons sf
# intersect and extract state name
pnts$airzone <- apply(st_intersects(tt1_trans, pnts_trans, sparse = FALSE), 2,
function(col) {
tt1_trans[which(col), ]$airzone
})
return(pnts$airzone)
}
#' create graphs for days exceeding
#'
#' @param exceedances is the result of get_PM_exceedance() function
graph_exceedance <- function(exceedances,AIRZONE = NULL,year = NULL) {
if (0) {
source('./level4_page/02_setup.R')
dirs_location <- './data/out'
AIRZONE <- NULL
}
require(tidyr)
#set order for the seasons
df_seasons <- tribble(
~seasons,~order,
'Winter',4,
'Spring',3,
'Summer',2,
'Fall',1
)
year_select <- year
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
list_airzones <- df_colour$airzone
# View(exceedances$season_stations)
if (is.null(AIRZONE)) {
AIRZONE <- 'BC'
}
df_annual <- exceedances$annual
df_seasonal_tfee <- exceedances$season_tfee %>%
mutate(tfee = TRUE)
df_seasonal <- exceedances$season %>%
mutate(tfee = FALSE) %>%
bind_rows(df_seasonal_tfee) %>%
pivot_wider(names_from = tfee, values_from =days_exceed) %>%
dplyr::rename(`Wildfire Days` = `TRUE`,`Total` = `FALSE`) %>%
mutate(`Wildfire Days` = ifelse(is.na(`Wildfire Days`),0,`Wildfire Days`))%>%
mutate(`No Wildfire`= `Total` - `Wildfire Days`) %>%
select(-Total) %>%
pivot_longer(cols = c(`Wildfire Days`,`No Wildfire`)) %>%
mutate(seasons = paste(seasons,'(',name,')',sep=''))
vline <- function(x = 0, color = "red") {
list(
type = "line",
y0 = 0,
y1 = 1,
yref = "paper",
x0 = x,
x1 = x,
line = list(color = color,dash = 'dash')
)
}
p_annual <-
df_annual %>%
filter(!AIRZONE %in% c('BC',NA)) %>%
filter(year>=1997) %>%
plotly::plot_ly(x=~year,y=~days_exceed,color = ~AIRZONE,
type='scatter',mode='lines',showlegend =T,
hoverinfo ='y',
hovertemplate = paste('%{y:,.0f}',' days',sep='')
) %>%
layout(title = 'High PM<sub>2.5</sub> Levels in Air Zones',
legend = list(orientation = 'h'),
yaxis = list(title = 'Number of Days with High PM<sub>2.5</sub> Levels'),
xaxis = list(title = 'Year')
) %>%
plotly::layout(hovermode = 'x unified',
barmode = 'stack') %>%
layout(shapes = list(vline(year_select)))
# event_register("plotly_click")
#create results for BC, and all air zones
{
p_list <- NULL
i_list <- NULL
i <- 0
for (AIRZONE_ in c('BC',lst_airzones)) {
i <- i +1
p_seasonal <- df_seasonal %>%
left_join(df_seasons,by = 'seasons') %>%
filter(AIRZONE == AIRZONE_) %>%
filter(year >=2000) %>%
plotly::plot_ly(x=~year,y=~value,color = ~reorder(seasons,order),
type='bar',mode='bar',showlegend =T,
hoverinfo ='y',
hovertemplate = paste('%{y:,.0f}',' days',sep=''),
colors = c("navajowhite2",
"navajowhite3",
"seagreen3",
"seagreen4",
"red3",
"red4",
"slategray2",
'slategray4'
)
) %>%
layout(title = paste('<br>High PM<sub>2.5</sub> Levels for',
ifelse(AIRZONE == 'BC',AIRZONE_,
paste('the',AIRZONE_,'Air Zone')
)),
legend = list(orientation = 'h'),
yaxis = list(title = 'Number of Days with High PM<sub>2.5</sub> Levels'),
xaxis = list(title = 'Year')
) %>%
plotly::layout(hovermode = 'x unified',
barmode = 'stack',legend = list(x = 0.01, y = 0.9))%>%
layout(shapes = list(vline(year_select)))
# layout(annotations = list(x=2014,y=-1,yref='paper',xref='paper',text = 'wildfire counts only from 2014 onwards'))
p_list[[i]] <- p_seasonal
i_list <- c(i_list,AIRZONE_)
}
result <- list(plot_annual = p_annual,plot_seasonal = p_list,plot_definition = i_list, data = exceedances)
}
return(result)
}
#end of functions-----
#define colours-----
df_colour <- tribble(
~airzone,~colour_01,
"Northeast",'#CDC08C',
"Georgia Strait" ,'#F4B5BD',
"Southern Interior",'#9C964A',
"Lower Fraser Valley",'#85D4E3',
"Central Interior" ,'#FAD77B',
"Coastal",'#CEAB07',
"Northwest","#24281A"
)
lst_airzones <- df_colour$airzone
#define external data------
exceedances <- get_PM_exceedancesummary(dirs_location)
az_mgmt <- readr::read_rds(paste(dirs_location,'az_mgmt.Rds',sep='/')) %>%
left_join(df_colour)
print('Load az_mgmt complete')
plots_list <- graph_exceedance(exceedances = exceedances,year = max(exceedances$annual$year))
print('Load Complete')
#---SHINY SECTION----------------
# Actual shiny part
##ui section----
ui <- {
fluidPage(
h4(HTML('Fine Particulate Matter (PM<sub>2.5</sub>) Pollution')),
tags$head(
tags$style(HTML("
body { background-color: #f2efe9; }
.container-fluid { background-color: #fff; width: 1200px; padding: 5px; }
.topimg { width: 0px; display: block; margin: 0px auto 0px auto; }
.title { text-align: center; }
.toprow { margin: 5px 0px; padding: 5px; background-color: #38598a; }
.filters { margin: 0px auto; }
.shiny-input-container { width:100% !important; }
.table { padding: 0px; margin-top: 0px; }
.leaflet-top { z-index:999 !important; }
"))
),
# h1("Fine Particulate Pollution", class = "title"),
fluidRow(class = "toprow",
fluidRow(class = 'filters',
column(6,
tags$style(type='text/css',
'.selectize-input { font-size: 15px; line-height: 10px;}
.selectize-dropdown { font-size: 16px; line-height: 20px; }
.control-label {font-size: 24px; color: white !important;}
.irs-min {font-size: 0px; color: white; !important}
.irs-max {font-size: 0px; color: white;}
.irs-single {font-size: 20px; color: white;}
.irs-grid-text {font-size: 10px; color: white;}'
),
sliderInput('year_slider',label ='Year',
min = 1997,
max = max(exceedances$annual$year),
value = max(exceedances$annual$year),
sep='')
))),
fluidRow(
column(4,h6(HTML("Number of Days with High PM<sub>2.5</sub> Levels</br>Click the map to select an air zone")),
# fluidRow(
leaflet::leafletOutput("map",height = '400px',width = '400px')),
column(8,h6(HTML("Scroll through the graph to view the number of days with high PM<sub>2.5</sub> Levels.")),
# div(style='height:400px;overflow-y: scroll;'),
plotlyOutput("plot1",height = '400px',width = '800px')
)
)
)
}
##server section----
server <- {shinyServer(function(input, output) {
if (0) {
map_exceedance(exceedances = exceedances, az_mgmt = az_mgmt, year = 2010)
}
#reactive_plot1 can carry over plots across events
reactive_plot1 <- reactiveVal(plots_list)
a <- map_exceedance(map_a = NULL,exceedances = exceedances ,az_mgmt = az_mgmt,year = max(exceedances$annual$year))
output$map <- renderLeaflet(a)
output$plot1 <- renderPlotly({plot_out <- plots_list$plot_annual
plot_out %>% ggplotly(source = 'plot1')
#%>% event_register("plotly_click")
})
observeEvent(input$year_slider,
{
print('Slider')
print(input$year_slider)
leafletProxy("map") %>%
map_exceedance(exceedances = exceedances ,az_mgmt = az_mgmt,year =input$year_slider)
plots_list <- graph_exceedance(exceedances = exceedances,year = input$year_slider)
reactive_plot1(plots_list) #pass on value to reactive_plot1
# output$plot1 <- renderPlotly(plots_list$plot_annual)
output$plot1 <- renderPlotly({
plot_out <- plots_list$plot_annual
p <- plot_out %>% ggplotly(source = 'plot1')
p
})
})
#clicking the map, an airzone is selected
observeEvent(input$map_shape_click, {
p <- input$map_shape_click
try({
airzone_select <- get_airzone(p$lat,p$lng)
print(p$lat)
print(p$lng)
print(airzone_select)
plots_list <- reactive_plot1()
if (airzone_select != 'Northwest') {
output$plot1 <- renderPlotly({
p <- plots_list$plot_seasonal[[which(plots_list$plot_definition == airzone_select)]]
# Add a footnote
footnote <- list(
font = list(size = 10),
x = 2000, y = 8,
# xref = "paper",
# yref = "paper",
text = "*Counts for wildfire days before 2014 are not included",
showarrow = FALSE
)
p <- layout(p, annotations = list(footnote))
print(p) # Check if the footnote is included in the annotations list
print('annotations inserted')
# Return the plotly graph
p
})
print('Plot Refreshed')
}
leafletProxy("map") %>%
map_exceedance(exceedances = exceedances ,az_mgmt = az_mgmt,year =input$year_slider,airzone = airzone_select)
})
})
#clicking on the graph
# observeEvent(event_data("plotly_click", source = "plot1"), {
# values$plot.click.results <- event_data("plotly_click", source = "plot1")
# print(values$plot.click.results)
# })
})
}
shinyApp(ui, server)
|
## Exploratory data Analysis Programming Assignment 1
## This is a script that reads in electric power consumption data and generates the first
## exploratory plot for a 2 day period in Feb 2007
elecdata<-read.csv2("household_power_consumption.txt", header=TRUE, na.strings = "?",
stringsAsFactors=FALSE) # read in data AS CHARACTERS (stringsAsFactors)
elecdata<-subset(elecdata,Date == "1/2/2007" | Date == "2/2/2007") # select 2 days of interest
for (i in 3:9) { # loop thru' columns 3 to 9
elecdata[,i] <- as.numeric(as.character(elecdata[,i])) # convert data to numeric
}
temp=par(mar = c(5, 5, 4, 1)) # temporarily adjust the plot margins
hist(elecdata$Global_active_power,main = "Global Active Power",
xlab = "Global Active Power (kilowatts)", ylab = "Frequency",col="red") # plot histogram
dev.copy(png,file = "plot1.png",width=480,height=480) ## Copy plot to a PNG file
dev.off() # turn PNG device off
par(temp) # reset the window to original | /plot1.R | no_license | martinjhughes/ExData_Plotting1 | R | false | false | 990 | r |
## Exploratory data Analysis Programming Assignment 1
## This is a script that reads in electric power consumption data and generates the first
## exploratory plot for a 2 day period in Feb 2007
elecdata<-read.csv2("household_power_consumption.txt", header=TRUE, na.strings = "?",
stringsAsFactors=FALSE) # read in data AS CHARACTERS (stringsAsFactors)
elecdata<-subset(elecdata,Date == "1/2/2007" | Date == "2/2/2007") # select 2 days of interest
for (i in 3:9) { # loop thru' columns 3 to 9
elecdata[,i] <- as.numeric(as.character(elecdata[,i])) # convert data to numeric
}
temp=par(mar = c(5, 5, 4, 1)) # temporarily adjust the plot margins
hist(elecdata$Global_active_power,main = "Global Active Power",
xlab = "Global Active Power (kilowatts)", ylab = "Frequency",col="red") # plot histogram
dev.copy(png,file = "plot1.png",width=480,height=480) ## Copy plot to a PNG file
dev.off() # turn PNG device off
par(temp) # reset the window to original |
library(dplyr)
data(weather_mittadalen)
weather_2019 <- weather_mittadalen %>%
dplyr::filter(year == 2019)
weather_condition_2019 <-
with(weather_2019, analyze_weather(date, snow_depth, prec, temp_min, temp_max,
temp_avg, start = "first_permanent_snow",
plot_first_snow = T))
weather_condition_2019
weather_condition_2019 %>%
ggplot(aes(date)) +
geom_line(aes(y = snow_depth, color = "snow depth")) +
geom_line(aes(y = cumulative_precitation, color = "cum prec")) +
geom_line(aes(y = 100*prec_snow_ratio, color = "100*cum prec/snow depth")) +
geom_hline(yintercept = 100, linetype = 2) +
ylim(0, 250) +
labs(x = "Date", y = "Amount (mm)", color = "")
| /examples/winter_analyzer_example.R | no_license | bniebuhr/witch | R | false | false | 746 | r | library(dplyr)
data(weather_mittadalen)
weather_2019 <- weather_mittadalen %>%
dplyr::filter(year == 2019)
weather_condition_2019 <-
with(weather_2019, analyze_weather(date, snow_depth, prec, temp_min, temp_max,
temp_avg, start = "first_permanent_snow",
plot_first_snow = T))
weather_condition_2019
weather_condition_2019 %>%
ggplot(aes(date)) +
geom_line(aes(y = snow_depth, color = "snow depth")) +
geom_line(aes(y = cumulative_precitation, color = "cum prec")) +
geom_line(aes(y = 100*prec_snow_ratio, color = "100*cum prec/snow depth")) +
geom_hline(yintercept = 100, linetype = 2) +
ylim(0, 250) +
labs(x = "Date", y = "Amount (mm)", color = "")
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linter_tag_docs.R
\name{common_mistakes_linters}
\alias{common_mistakes_linters}
\title{Common mistake linters}
\description{
Linters highlighting common mistakes, such as duplicate arguments.
}
\seealso{
\link{linters} for a complete list of linters available in lintr.
}
\section{Linters}{
The following linters are tagged with 'common_mistakes':
\itemize{
\item{\code{\link{duplicate_argument_linter}}}
\item{\code{\link{equals_na_linter}}}
\item{\code{\link{missing_argument_linter}}}
\item{\code{\link{missing_package_linter}}}
\item{\code{\link{redundant_equals_linter}}}
\item{\code{\link{sprintf_linter}}}
\item{\code{\link{unused_import_linter}}}
}
}
| /man/common_mistakes_linters.Rd | permissive | cordis-dev/lintr | R | false | true | 738 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/linter_tag_docs.R
\name{common_mistakes_linters}
\alias{common_mistakes_linters}
\title{Common mistake linters}
\description{
Linters highlighting common mistakes, such as duplicate arguments.
}
\seealso{
\link{linters} for a complete list of linters available in lintr.
}
\section{Linters}{
The following linters are tagged with 'common_mistakes':
\itemize{
\item{\code{\link{duplicate_argument_linter}}}
\item{\code{\link{equals_na_linter}}}
\item{\code{\link{missing_argument_linter}}}
\item{\code{\link{missing_package_linter}}}
\item{\code{\link{redundant_equals_linter}}}
\item{\code{\link{sprintf_linter}}}
\item{\code{\link{unused_import_linter}}}
}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generated_client.R
\name{permission_sets_delete_resources}
\alias{permission_sets_delete_resources}
\title{Delete a resource in a permission set}
\usage{
permission_sets_delete_resources(id, name)
}
\arguments{
\item{id}{integer required. The ID for this permission set.}
\item{name}{string required. The name of this resource.}
}
\value{
An empty HTTP response
}
\description{
Delete a resource in a permission set
}
| /man/permission_sets_delete_resources.Rd | no_license | cran/civis | R | false | true | 497 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generated_client.R
\name{permission_sets_delete_resources}
\alias{permission_sets_delete_resources}
\title{Delete a resource in a permission set}
\usage{
permission_sets_delete_resources(id, name)
}
\arguments{
\item{id}{integer required. The ID for this permission set.}
\item{name}{string required. The name of this resource.}
}
\value{
An empty HTTP response
}
\description{
Delete a resource in a permission set
}
|
# Generation gene-pathway bipartite matrix
#' Generate the bipartite matrix from gene-pathway interaction networks
#' @description The function \code{genepathwayAdj} is to generate the adjacency
#' matrix from gene-pathway interaction network
#' @param drugAdj drug adjacency matrix (sparse matrix) generated from drug-drug
#' association network via \code{AdjMatrix_drugs}
#' @param pathwayadj pathway-pathway association adjacency matrix (sparse
#' matrix)
#' @param geneadj gene-gene association network adjacency matrix (sparse matrix)
#' @param gpweight the weight of gene-pathway edges
#' @param load_dir path to load or save modeling data files
#' @return \code{genepathwayAdj} gene-pathway adjacency matrix
#' @export
#'
#' @examples
#'
genepathwayAdj <- function(drugAdj,
pathwayadj,
geneadj,
gpweight,
load_dir){
# load(system.file("data", "KEGG_pw_gene.RData", package = "DComboNet"))
genepathwayAdj = matrix(0,nrow(pathwayadj),nrow(geneadj))
rownames(genepathwayAdj) = rownames(pathwayadj)
colnames(genepathwayAdj) = colnames(geneadj)
pathways <- rownames(pathwayadj)
N = nrow(pathwayadj)
# genepathway = read.table(paste0(load_dir,"pathway/KEGG_ID_gene.txt"),sep="\t",header = T,stringsAsFactors = F)
names(genepathway) = c("KEGG_ID","pathway_name","Gene")
for(i in 1:N){
pathway <- pathways[i]
gene <- genepathway[which(genepathway$KEGG_ID == pathway),]$Gene
if(length(gene) > 0){
for(j in 1:length(gene)){
if(!is.na(gene[j])){
# identify the drug position on the matrix.
index_gene <- which(colnames(genepathwayAdj) %in% gene[j])
if(length(index_gene) == 1){
genepathwayAdj[i,index_gene] <- gpweight
}
}
}
}
}
return(genepathwayAdj)
}
| /R/GenePathwayNet.R | no_license | FuChunjin/DComboNet | R | false | false | 1,943 | r | # Generation gene-pathway bipartite matrix
#' Generate the bipartite matrix from gene-pathway interaction networks
#' @description The function \code{genepathwayAdj} is to generate the adjacency
#' matrix from gene-pathway interaction network
#' @param drugAdj drug adjacency matrix (sparse matrix) generated from drug-drug
#' association network via \code{AdjMatrix_drugs}
#' @param pathwayadj pathway-pathway association adjacency matrix (sparse
#' matrix)
#' @param geneadj gene-gene association network adjacency matrix (sparse matrix)
#' @param gpweight the weight of gene-pathway edges
#' @param load_dir path to load or save modeling data files
#' @return \code{genepathwayAdj} gene-pathway adjacency matrix
#' @export
#'
#' @examples
#'
genepathwayAdj <- function(drugAdj,
pathwayadj,
geneadj,
gpweight,
load_dir){
# load(system.file("data", "KEGG_pw_gene.RData", package = "DComboNet"))
genepathwayAdj = matrix(0,nrow(pathwayadj),nrow(geneadj))
rownames(genepathwayAdj) = rownames(pathwayadj)
colnames(genepathwayAdj) = colnames(geneadj)
pathways <- rownames(pathwayadj)
N = nrow(pathwayadj)
# genepathway = read.table(paste0(load_dir,"pathway/KEGG_ID_gene.txt"),sep="\t",header = T,stringsAsFactors = F)
names(genepathway) = c("KEGG_ID","pathway_name","Gene")
for(i in 1:N){
pathway <- pathways[i]
gene <- genepathway[which(genepathway$KEGG_ID == pathway),]$Gene
if(length(gene) > 0){
for(j in 1:length(gene)){
if(!is.na(gene[j])){
# identify the drug position on the matrix.
index_gene <- which(colnames(genepathwayAdj) %in% gene[j])
if(length(index_gene) == 1){
genepathwayAdj[i,index_gene] <- gpweight
}
}
}
}
}
return(genepathwayAdj)
}
|
library(caret); library(lubridate) ; library(readr) ; library(xgboost)
train <- read_csv("D:/kaggle/Springleaf/DATA/CSV/train.csv")
test <- read_csv("D:/kaggle/Springleaf/DATA/CSV/test.csv")
##REMOVE RESPONSE AND ID VARIABLES
response <- train$target
train_ID <- train$ID
test_ID <- test$ID
##MODIFY train and test set
training <- subset(train, select = -c(ID, target))
testing <- subset(test, select = -c(ID))
dim(training); dim(testing)
########################################################################################
tmp <- rbind(training, testing)
tmp_num <- tmp[, sapply(tmp, is.numeric)]
dim(tmp_num)
tmp_char <- tmp[,sapply(tmp, is.character)]
dim(tmp_char)
numeric_ele <- (lapply(tmp_num, function(x) length(unique(x))))
char_ele <- (lapply(tmp_char, function(x) length(unique(x))))
numeric_one <- subset(numeric_ele , subset = c(numeric_ele == 1))
names(numeric_one)
ids <- c("VAR_0227", "VAR_0228")
remove_col <- c(names(numeric_one), ids)
tmp <- tmp[, !(names(tmp) %in% (remove_col))]
dim(tmp)
######################################################################################
tmp_num <- tmp[, sapply(tmp, is.numeric)]
tmp_char <- tmp[,sapply(tmp, is.character)]
#####################################################################################
tmp_date <- tmp_char[, grep("JAN1|FEB1|MAR1", tmp_char)]
dim(tmp_date)
tmp_dates <- data.frame(sapply(tmp_date, function(x) strptime(x, "%d%B%y :%H:%M:%S")))
tmp_year <- data.frame(sapply(tmp_dates, function(x) year(x)))
names(tmp_year) <- paste("YEAR_", names(tmp_year), sep = "")
tmp_wday <- data.frame(sapply(tmp_dates, function(x) wday(x)))
names(tmp_wday) <- paste("WDAY_", names(tmp_wday), sep = "")
tmp_month <- data.frame(sapply(tmp_dates, function(x) month(x)))
names(tmp_month) <- paste("MONTH_", names(tmp_month), sep = "")
tmp_yday <- data.frame(sapply(tmp_dates, function(x) yday(x)))
names(tmp_yday) <- paste("YDAY_", names(tmp_yday), sep = "")
tmp_mday <- data.frame(sapply(tmp_dates, function(x) mday(x)))
names(tmp_mday) <- paste("MDAY_", names(tmp_mday), sep = "")
tmp_weekdays <- data.frame(sapply(tmp_dates, function(x) weekdays(x)))
names(tmp_weekdays) <- paste("YEAR_", names(tmp_weekdays), sep = "")
tmp_hour <- data.frame(sapply(tmp_dates, function(x) hour(x)))
names(tmp_hour) <- paste("HOUR_", names(tmp_hour), sep = "")
tmp_minute <- data.frame(sapply(tmp_dates, function(x) minute(x)))
names(tmp_minute) <- paste("MINUTE_", names(tmp_minute), sep = "")
tmp_second <- data.frame(sapply(tmp_dates, function(x) second(x)))
names(tmp_second) <- paste("SECOND_", names(tmp_second), sep = "")
#################################################################################
tmp <- tmp[, !(names(tmp) %in% names(tmp_date))]
dim(tmp)
dates <- cbind( tmp_year, tmp_yday, tmp_weekdays, tmp_wday, tmp_second,
tmp_hour, tmp_minute)
##further modification when will people most likely take a loan
######################################################################################
training <- tmp[1:145231,]
testing <- tmp[(nrow(training)+1): nrow(tmp), ]
dim(training); dim(testing)
feature.names <- names(training)
for (f in feature.names) {
if (class(training[[f]])=="character") {
levels <- unique(c(training[[f]], testing[[f]]))
training[[f]] <- as.integer(factor(training[[f]], levels=levels))
testing[[f]] <- as.integer(factor(testing[[f]], levels=levels))
}
}
training[is.na(training)] <- -1
testing[is.na(testing)] <- -1
dates[is.na(dates)] <- -1
###############################################################################
tmp <- rbind(training, testing)
tmp <- cbind(tmp, dates)
training <- tmp[1:145231,]
testing <- tmp[(nrow(training)+1): nrow(tmp), ]
dim(training); dim(testing)
gc(); rm(train);rm(test)
#############################################################################
benchmark <- read_csv("D:/kaggle/Springleaf/SUBMISSION/second.csv")
first <- read_csv("D:/kaggle/Springleaf/SUBMISSION/third.csv")
second <- read_csv("D:/kaggle/Springleaf/SUBMISSION/third_first.csv")
third <- read_csv("D:/kaggle/Springleaf/SUBMISSION/fourth.csv")
fourth <- read_csv("D:/kaggle/Springleaf/sixth.csv")
fifth <- read_csv("D:/kaggle/Springleaf/eight.csv")
feature_1 <- benchmark$target[1:145231]
training$feature1 <- feature_1
testing$feature1 <- benchmark$target
feature_2 <- first$target[1:145231]
training$feature2 <- feature_2
testing$feature2 <- first$target
feature_3 <- second$target[1:145231]
training$feature3 <- feature_3
testing$feature3 <- second$target
feature_4 <- third$target[1:145231]
training$feature4 <- feature_4
testing$feature4 <- third$target
feature_5 <- fourth$target[1:145231]
training$feature5 <- feature_5
testing$feature5 <- fourth$target
dtraining <- xgb.DMatrix(data.matrix(training[,feature.names]), label= response)
param <- list( objective = "binary:logistic",
eta = 0.014,
max_depth = 10,
subsample = 0.7,
colsample_bytree = 0.7,
eval_metric = "auc"
)
#cv <- xgb.cv(params = param,data = dtraining,nrounds = 700, nfold = 5, showsd = T, metrics = "auc"
#, verbose = 2, maximize = TRUE)
clf_first <- xgb.train( params = param,
data = dtraining,
nrounds = 2000, # changed from 300
verbose = 2,
maximize = TRUE)
submission_second <- data.frame(ID=test_ID)
submission_second$target <- NA
submission_second[,"target"] <- predict(clf_first, data.matrix(testing[,feature.names]))
write_csv(submission_second, "ten.csv")
| /Springleaf/seventh_2.R | no_license | ronroc/kaggle | R | false | false | 5,985 | r | library(caret); library(lubridate) ; library(readr) ; library(xgboost)
train <- read_csv("D:/kaggle/Springleaf/DATA/CSV/train.csv")
test <- read_csv("D:/kaggle/Springleaf/DATA/CSV/test.csv")
##REMOVE RESPONSE AND ID VARIABLES
response <- train$target
train_ID <- train$ID
test_ID <- test$ID
##MODIFY train and test set
training <- subset(train, select = -c(ID, target))
testing <- subset(test, select = -c(ID))
dim(training); dim(testing)
########################################################################################
tmp <- rbind(training, testing)
tmp_num <- tmp[, sapply(tmp, is.numeric)]
dim(tmp_num)
tmp_char <- tmp[,sapply(tmp, is.character)]
dim(tmp_char)
numeric_ele <- (lapply(tmp_num, function(x) length(unique(x))))
char_ele <- (lapply(tmp_char, function(x) length(unique(x))))
numeric_one <- subset(numeric_ele , subset = c(numeric_ele == 1))
names(numeric_one)
ids <- c("VAR_0227", "VAR_0228")
remove_col <- c(names(numeric_one), ids)
tmp <- tmp[, !(names(tmp) %in% (remove_col))]
dim(tmp)
######################################################################################
tmp_num <- tmp[, sapply(tmp, is.numeric)]
tmp_char <- tmp[,sapply(tmp, is.character)]
#####################################################################################
tmp_date <- tmp_char[, grep("JAN1|FEB1|MAR1", tmp_char)]
dim(tmp_date)
tmp_dates <- data.frame(sapply(tmp_date, function(x) strptime(x, "%d%B%y :%H:%M:%S")))
tmp_year <- data.frame(sapply(tmp_dates, function(x) year(x)))
names(tmp_year) <- paste("YEAR_", names(tmp_year), sep = "")
tmp_wday <- data.frame(sapply(tmp_dates, function(x) wday(x)))
names(tmp_wday) <- paste("WDAY_", names(tmp_wday), sep = "")
tmp_month <- data.frame(sapply(tmp_dates, function(x) month(x)))
names(tmp_month) <- paste("MONTH_", names(tmp_month), sep = "")
tmp_yday <- data.frame(sapply(tmp_dates, function(x) yday(x)))
names(tmp_yday) <- paste("YDAY_", names(tmp_yday), sep = "")
tmp_mday <- data.frame(sapply(tmp_dates, function(x) mday(x)))
names(tmp_mday) <- paste("MDAY_", names(tmp_mday), sep = "")
tmp_weekdays <- data.frame(sapply(tmp_dates, function(x) weekdays(x)))
names(tmp_weekdays) <- paste("YEAR_", names(tmp_weekdays), sep = "")
tmp_hour <- data.frame(sapply(tmp_dates, function(x) hour(x)))
names(tmp_hour) <- paste("HOUR_", names(tmp_hour), sep = "")
tmp_minute <- data.frame(sapply(tmp_dates, function(x) minute(x)))
names(tmp_minute) <- paste("MINUTE_", names(tmp_minute), sep = "")
tmp_second <- data.frame(sapply(tmp_dates, function(x) second(x)))
names(tmp_second) <- paste("SECOND_", names(tmp_second), sep = "")
#################################################################################
tmp <- tmp[, !(names(tmp) %in% names(tmp_date))]
dim(tmp)
dates <- cbind( tmp_year, tmp_yday, tmp_weekdays, tmp_wday, tmp_second,
tmp_hour, tmp_minute)
##further modification when will people most likely take a loan
######################################################################################
training <- tmp[1:145231,]
testing <- tmp[(nrow(training)+1): nrow(tmp), ]
dim(training); dim(testing)
feature.names <- names(training)
for (f in feature.names) {
if (class(training[[f]])=="character") {
levels <- unique(c(training[[f]], testing[[f]]))
training[[f]] <- as.integer(factor(training[[f]], levels=levels))
testing[[f]] <- as.integer(factor(testing[[f]], levels=levels))
}
}
training[is.na(training)] <- -1
testing[is.na(testing)] <- -1
dates[is.na(dates)] <- -1
###############################################################################
tmp <- rbind(training, testing)
tmp <- cbind(tmp, dates)
training <- tmp[1:145231,]
testing <- tmp[(nrow(training)+1): nrow(tmp), ]
dim(training); dim(testing)
gc(); rm(train);rm(test)
#############################################################################
benchmark <- read_csv("D:/kaggle/Springleaf/SUBMISSION/second.csv")
first <- read_csv("D:/kaggle/Springleaf/SUBMISSION/third.csv")
second <- read_csv("D:/kaggle/Springleaf/SUBMISSION/third_first.csv")
third <- read_csv("D:/kaggle/Springleaf/SUBMISSION/fourth.csv")
fourth <- read_csv("D:/kaggle/Springleaf/sixth.csv")
fifth <- read_csv("D:/kaggle/Springleaf/eight.csv")
feature_1 <- benchmark$target[1:145231]
training$feature1 <- feature_1
testing$feature1 <- benchmark$target
feature_2 <- first$target[1:145231]
training$feature2 <- feature_2
testing$feature2 <- first$target
feature_3 <- second$target[1:145231]
training$feature3 <- feature_3
testing$feature3 <- second$target
feature_4 <- third$target[1:145231]
training$feature4 <- feature_4
testing$feature4 <- third$target
feature_5 <- fourth$target[1:145231]
training$feature5 <- feature_5
testing$feature5 <- fourth$target
dtraining <- xgb.DMatrix(data.matrix(training[,feature.names]), label= response)
param <- list( objective = "binary:logistic",
eta = 0.014,
max_depth = 10,
subsample = 0.7,
colsample_bytree = 0.7,
eval_metric = "auc"
)
#cv <- xgb.cv(params = param,data = dtraining,nrounds = 700, nfold = 5, showsd = T, metrics = "auc"
#, verbose = 2, maximize = TRUE)
clf_first <- xgb.train( params = param,
data = dtraining,
nrounds = 2000, # changed from 300
verbose = 2,
maximize = TRUE)
submission_second <- data.frame(ID=test_ID)
submission_second$target <- NA
submission_second[,"target"] <- predict(clf_first, data.matrix(testing[,feature.names]))
write_csv(submission_second, "ten.csv")
|
library(data.table)
install.packages('bit64')
library(bit64)
install.packages("ggplot2")
library(ggplot2)
library(ggrepel)
library(magrittr)
patient <- fread("PatientInfo.csv", na.strings = "")
region <- fread("Region.csv")
#Analysis of corrleation between the number of schools in a province and patients
##### 1. Group by the column of province from patient dataset and count the number
#of patients in each province. Also, group by the column of province from region
#dataset and count the number of schools, namely kindergardens, elementary schools,
#and universities, in each province.
province_patient <- patient[, .(patient_count = .N), by = province]
province_region <- region[province == city, .(province, elementary_school_count, kindergarten_count, university_count)]
###### 2. Merge the above transformed data tables into one table and melt it,
#creating a variable of school and a value of school count.
merge_province_patient <- merge(province_patient, province_region)
melt_province_patient <- melt(merge_province_patient, id.vars = c("province", "patient_count"), variable.name = "school", value.name = "school_count")
###### 3. Create a scatter plot and then it turns out a correlation between schools
#in each province and the number of patients exists.
ggplot(melt_province_patient, aes(school_count, patient_count, label = province))+
geom_point()+
facet_grid(~school, scales= c("free_x"))+
geom_text_repel()
###### 4. Confirm the correlation by cor.test. The correlation between the number
#of universites and the number of patients is approximately 0.83, which is stronger
#than all the others.
cor.test(merge_province_patient$elementary_school_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$kindergarten_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$university_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$elementary_school_count, merge_province_patient$patient_count, method = "spearman")
cor.test(merge_province_patient$kindergarten_count, merge_province_patient$patient_count, method = "spearman")
cor.test(merge_province_patient$university_count, merge_province_patient$patient_count, method = "spearman")
#Analysis of corrleation between the number of schools in a city and patients
##### 1. Group by the column of city from patient dataset and count the number of patients in each city. Also, group by the column of city from region dataset and count the number of schools, namely kindergardens, elementary schools, and universities, in each city.
city_patient <- patient[, .(patient_count = .N), by = city]
city_region <- region[, .(sum_elementary_school = sum(elementary_school_count), sum_kindergarden = sum(kindergarten_count), sum_university = sum(university_count)), by = city]
##### 2. Merge the above transformed data tables into one table and melt it, creating a variable of school and a value of school count.
merge_city_patient <- merge(city_patient, city_region)
melt_city_patient <- melt(merge_city_patient, id.vars = c("city", "patient_count"), variable.name = "school", value.name = "school_count")
##### 3. Create a scatter plot and then it seems that a correlation between schools in each city and the number of patients does not exist.
ggplot(melt_city_patient, aes(school_count, patient_count))+
geom_point()+
facet_grid(~school, scales= c("free_x"))
##### 4. Show the correlation by cor.test. The correlations between the numbers of schools and the number of patients are all below 0.4.
cor.test(merge_city_patient$elementary_school_count, merge_city_patient$patient_count)
cor.test(merge_city_patient$kindergarten_count, merge_city_patient$patient_count)
cor.test(merge_city_patient$university_count, merge_city_patient$patient_count)
| /association between schools and locations.R | no_license | cory1219/Statistical-Testing-on-correlation-of-COVID-19-in-South-Korea | R | false | false | 3,827 | r | library(data.table)
install.packages('bit64')
library(bit64)
install.packages("ggplot2")
library(ggplot2)
library(ggrepel)
library(magrittr)
patient <- fread("PatientInfo.csv", na.strings = "")
region <- fread("Region.csv")
#Analysis of corrleation between the number of schools in a province and patients
##### 1. Group by the column of province from patient dataset and count the number
#of patients in each province. Also, group by the column of province from region
#dataset and count the number of schools, namely kindergardens, elementary schools,
#and universities, in each province.
province_patient <- patient[, .(patient_count = .N), by = province]
province_region <- region[province == city, .(province, elementary_school_count, kindergarten_count, university_count)]
###### 2. Merge the above transformed data tables into one table and melt it,
#creating a variable of school and a value of school count.
merge_province_patient <- merge(province_patient, province_region)
melt_province_patient <- melt(merge_province_patient, id.vars = c("province", "patient_count"), variable.name = "school", value.name = "school_count")
###### 3. Create a scatter plot and then it turns out a correlation between schools
#in each province and the number of patients exists.
ggplot(melt_province_patient, aes(school_count, patient_count, label = province))+
geom_point()+
facet_grid(~school, scales= c("free_x"))+
geom_text_repel()
###### 4. Confirm the correlation by cor.test. The correlation between the number
#of universites and the number of patients is approximately 0.83, which is stronger
#than all the others.
cor.test(merge_province_patient$elementary_school_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$kindergarten_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$university_count, merge_province_patient$patient_count)
cor.test(merge_province_patient$elementary_school_count, merge_province_patient$patient_count, method = "spearman")
cor.test(merge_province_patient$kindergarten_count, merge_province_patient$patient_count, method = "spearman")
cor.test(merge_province_patient$university_count, merge_province_patient$patient_count, method = "spearman")
#Analysis of corrleation between the number of schools in a city and patients
##### 1. Group by the column of city from patient dataset and count the number of patients in each city. Also, group by the column of city from region dataset and count the number of schools, namely kindergardens, elementary schools, and universities, in each city.
city_patient <- patient[, .(patient_count = .N), by = city]
city_region <- region[, .(sum_elementary_school = sum(elementary_school_count), sum_kindergarden = sum(kindergarten_count), sum_university = sum(university_count)), by = city]
##### 2. Merge the above transformed data tables into one table and melt it, creating a variable of school and a value of school count.
merge_city_patient <- merge(city_patient, city_region)
melt_city_patient <- melt(merge_city_patient, id.vars = c("city", "patient_count"), variable.name = "school", value.name = "school_count")
##### 3. Create a scatter plot and then it seems that a correlation between schools in each city and the number of patients does not exist.
ggplot(melt_city_patient, aes(school_count, patient_count))+
geom_point()+
facet_grid(~school, scales= c("free_x"))
##### 4. Show the correlation by cor.test. The correlations between the numbers of schools and the number of patients are all below 0.4.
cor.test(merge_city_patient$elementary_school_count, merge_city_patient$patient_count)
cor.test(merge_city_patient$kindergarten_count, merge_city_patient$patient_count)
cor.test(merge_city_patient$university_count, merge_city_patient$patient_count)
|
nyt1<-read.csv(file.choose())
nyt1<-nyt1[which(nyt1$Impressions>0 & nyt1$Clicks>0 & nyt1$Age>0),]
nnyt1<-dim(nyt1)[1] # shrink it down!
sampling.rate=0.9
num.test.set.labels=nnyt1*(1.-sampling.rate)
training <-sample(1:nnyt1,sampling.rate*nnyt1, replace=FALSE)
train<-subset(nyt1[training,],select=c(Age,Impressions))
testing<-setdiff(1:nnyt1,training)
test<-subset(nyt1[testing,],select=c(Age,Impressions))
cg<-nyt1$Gender[training]
true.labels<-nyt1$Gender[testing]
library(class)
classif<-knn(train,test,cg,k=5) #
classif
attributes(.Last.value)
| /lab4/lab1_nyt.R | no_license | hangyenli/DataAnalytics_2021_Honghao_Li | R | false | false | 552 | r | nyt1<-read.csv(file.choose())
nyt1<-nyt1[which(nyt1$Impressions>0 & nyt1$Clicks>0 & nyt1$Age>0),]
nnyt1<-dim(nyt1)[1] # shrink it down!
sampling.rate=0.9
num.test.set.labels=nnyt1*(1.-sampling.rate)
training <-sample(1:nnyt1,sampling.rate*nnyt1, replace=FALSE)
train<-subset(nyt1[training,],select=c(Age,Impressions))
testing<-setdiff(1:nnyt1,training)
test<-subset(nyt1[testing,],select=c(Age,Impressions))
cg<-nyt1$Gender[training]
true.labels<-nyt1$Gender[testing]
library(class)
classif<-knn(train,test,cg,k=5) #
classif
attributes(.Last.value)
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/multipass.cppls.R
\name{multipass.cppls}
\alias{multipass.cppls}
\title{Wrapper for cppls function with multiple pass and outliers elmination}
\usage{
multipass.cppls(formula, ncomp, validation, data, nbpass, out = 2.5, Y.add)
}
\arguments{
\item{formula}{a model formula. Most of the lm formula constructs are supported. See below.}
\item{ncomp}{the number of components to include in the model (see below).}
\item{validation}{character. What kind of (internal) validation to use}
\item{data}{an optional data frame with the data to fit the model from.}
\item{nbpass}{Number of passes}
\item{out}{T value threshold for outlier elimination}
\item{Y.add}{a vector or matrix of additional responses containing relevant information about the observations. Only used for cppls.}
}
\value{
A list with the following components
outliers A vector with outliers names as deduced from data row.names
plsr the pls model object from the final pass
ncomp the number of components to be further used
pass a vector of same length as outliers indicating at which pass outliers were removed
}
\description{
Wrapper for cppls function with multiple pass and outliers elmination
}
\examples{
\dontrun{
}
}
\author{
J.-F. Rami \email{rami@cirad.fr}
}
| /man/multipass.cppls.Rd | no_license | jframi/multipass.pls | R | false | false | 1,329 | rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/multipass.cppls.R
\name{multipass.cppls}
\alias{multipass.cppls}
\title{Wrapper for cppls function with multiple pass and outliers elmination}
\usage{
multipass.cppls(formula, ncomp, validation, data, nbpass, out = 2.5, Y.add)
}
\arguments{
\item{formula}{a model formula. Most of the lm formula constructs are supported. See below.}
\item{ncomp}{the number of components to include in the model (see below).}
\item{validation}{character. What kind of (internal) validation to use}
\item{data}{an optional data frame with the data to fit the model from.}
\item{nbpass}{Number of passes}
\item{out}{T value threshold for outlier elimination}
\item{Y.add}{a vector or matrix of additional responses containing relevant information about the observations. Only used for cppls.}
}
\value{
A list with the following components
outliers A vector with outliers names as deduced from data row.names
plsr the pls model object from the final pass
ncomp the number of components to be further used
pass a vector of same length as outliers indicating at which pass outliers were removed
}
\description{
Wrapper for cppls function with multiple pass and outliers elmination
}
\examples{
\dontrun{
}
}
\author{
J.-F. Rami \email{rami@cirad.fr}
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DRaWR.R
\name{DRaWR}
\alias{DRaWR}
\title{DRaWR}
\usage{
DRaWR(possetfile = "data/sample_inputs/test.setlist",
unifile = "data/sample_inputs/test.uni",
networkfile = "data/sample_inputs/test.edge",
outdir = "data/sample_outputs/results_", restarts = c(0.7), nfolds = 1,
st2keep = 1, undirected = TRUE, unweighted = FALSE,
normalize = "type", maxiters = 50, thresh = 1e-04,
property_types = c("allen_brain_atlas", "chip_binding", "gene_ontology",
"motif_u5", "pfam_domain", "T1", "T2"), writepreds = 0)
}
\arguments{
\item{possetfile}{(string): location of file containing location of gene sets to test.}
\item{unifile}{(string): location of file listing gene universe.}
\item{networkfile}{(string): location of file containing network contents.}
\item{outdir}{(string): prefix of location of file to write performance results (optionally prediction results).}
\item{restarts}{(vector): vector of restart values to test. Default is c(0.7).}
\item{nfolds}{(int): number of folds for cross validation, 1 is no cross-validation. Default is 4.}
\item{st2keep}{(int): number of property nodes to keep in second stage for each property type. Default is 50.}
\item{undirected}{(bool): boolean to make network undirected.}
\item{unweighted}{(bool): boolean to make network unweighted.}
\item{normalize}{(string): "type" or "none". Default is 'type'.}
\item{maxiters}{(int): maximum number of allowable iterations. Default is 50.}
\item{thresh}{(float): threshold for L1 norm convergence. Default is 0.001.}
\item{property_types}{(vector): list of possible property types. Default is c("go_curated_evidence", "go_inferred_evidence", "pfam_domain").}
\item{writepreds}{(boolean): write predictions out to a file. Default is FALSE}
}
\description{
This function runs the DRaWR two stage random walk with restart method.
}
\examples{
DRaWR(possetfile = "data/sample_inputs/test.setlist", unifile = "data/sample_inputs/test.uni", networkfile = "data/sample_inputs/test.edge", outdir = "data/sample_outputs/results_", restarts = c(.7), nfolds = 1, st2keep = 1, undirected = TRUE, unweighted = FALSE, normalize = "type", maxiters = 50, thresh = 0.0001, property_types = c("T1", "T2"), writepreds = 0)
}
\keyword{random}
\keyword{restart}
\keyword{walk}
\keyword{with}
| /packages/DRaWR/man/DRaWR.Rd | permissive | minghao2016/DRaWR | R | false | true | 2,358 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DRaWR.R
\name{DRaWR}
\alias{DRaWR}
\title{DRaWR}
\usage{
DRaWR(possetfile = "data/sample_inputs/test.setlist",
unifile = "data/sample_inputs/test.uni",
networkfile = "data/sample_inputs/test.edge",
outdir = "data/sample_outputs/results_", restarts = c(0.7), nfolds = 1,
st2keep = 1, undirected = TRUE, unweighted = FALSE,
normalize = "type", maxiters = 50, thresh = 1e-04,
property_types = c("allen_brain_atlas", "chip_binding", "gene_ontology",
"motif_u5", "pfam_domain", "T1", "T2"), writepreds = 0)
}
\arguments{
\item{possetfile}{(string): location of file containing location of gene sets to test.}
\item{unifile}{(string): location of file listing gene universe.}
\item{networkfile}{(string): location of file containing network contents.}
\item{outdir}{(string): prefix of location of file to write performance results (optionally prediction results).}
\item{restarts}{(vector): vector of restart values to test. Default is c(0.7).}
\item{nfolds}{(int): number of folds for cross validation, 1 is no cross-validation. Default is 4.}
\item{st2keep}{(int): number of property nodes to keep in second stage for each property type. Default is 50.}
\item{undirected}{(bool): boolean to make network undirected.}
\item{unweighted}{(bool): boolean to make network unweighted.}
\item{normalize}{(string): "type" or "none". Default is 'type'.}
\item{maxiters}{(int): maximum number of allowable iterations. Default is 50.}
\item{thresh}{(float): threshold for L1 norm convergence. Default is 0.001.}
\item{property_types}{(vector): list of possible property types. Default is c("go_curated_evidence", "go_inferred_evidence", "pfam_domain").}
\item{writepreds}{(boolean): write predictions out to a file. Default is FALSE}
}
\description{
This function runs the DRaWR two stage random walk with restart method.
}
\examples{
DRaWR(possetfile = "data/sample_inputs/test.setlist", unifile = "data/sample_inputs/test.uni", networkfile = "data/sample_inputs/test.edge", outdir = "data/sample_outputs/results_", restarts = c(.7), nfolds = 1, st2keep = 1, undirected = TRUE, unweighted = FALSE, normalize = "type", maxiters = 50, thresh = 0.0001, property_types = c("T1", "T2"), writepreds = 0)
}
\keyword{random}
\keyword{restart}
\keyword{walk}
\keyword{with}
|
context("Projects")
with_mock_HTTP({
projects <- session()$projects
test_that("Getting projects catalog", {
expect_is(projects, "ProjectCatalog")
expect_length(projects, 2)
expect_identical(names(projects), c("Project One", "Project Two"))
})
aproject <- projects[["Project One"]]
test_that("Getting project from catalog", {
expect_is(projects[[1]], "CrunchProject")
expect_is(projects$`Project One`, "CrunchProject")
expect_is(projects[["Project One"]], "CrunchProject")
expect_null(projects$`Beta Project`)
})
test_that("Project attributes", {
expect_identical(name(aproject), "Project One")
})
test_that("Simple project creation by assignment", {
expect_error(projects[["A new project"]] <- list(),
'POST /api/projects.json {"name":"A new project"}',
fixed=TRUE)
expect_error(projects$`A new project` <- list(),
'POST /api/projects.json {"name":"A new project"}',
fixed=TRUE)
})
test_that("Project editing", {
expect_error(names(projects)[2] <- "New name",
paste('PATCH /api/projects.json',
'{"/api/projects/project2.json":{"name":"New name"}}'),
fixed=TRUE)
expect_error(name(projects[[2]]) <- "New name",
paste('PATCH /api/projects.json',
'{"/api/projects/project2.json":{"name":"New name"}}'),
fixed=TRUE)
})
test_that("Project deletion", {
expect_error(delete(projects[[1]], confirm=TRUE),
"Must confirm deleting project")
with(consent(), expect_error(delete(projects[[1]], confirm=TRUE),
"DELETE /api/projects/project1.json"))
})
m <- members(aproject)
test_that("Project members catalog", {
expect_is(m, "MemberCatalog")
expect_identical(names(m), c("Fake User", "Roger User"))
expect_identical(emails(m), c("fake.user@example.com", "roger.user@example.com"))
})
test_that("Add members by members<-", {
expect_error(members(aproject) <- c("new.user@crunch.io", "foo@example.co"),
'PATCH /api/projects/project1/members.json {"new.user@crunch.io":{},"foo@example.co":{}}',
fixed=TRUE)
})
test_that("Add members doesn't re-add if already a member", {
expect_error(members(aproject) <- c("new.user@crunch.io", "roger.user@example.com"),
'PATCH /api/projects/project1/members.json {"new.user@crunch.io":{}}',
fixed=TRUE)
})
test_that("Remove members by <- NULL", {
expect_error(members(aproject)[["roger.user@example.com"]] <- NULL,
'PATCH /api/projects/project1/members.json {"roger.user@example.com":null}',
fixed=TRUE)
})
test_that("is.editor on member catalog", {
expect_identical(is.editor(m), c(TRUE, FALSE))
})
test_that("is.editor<- on member catalog", {
expect_error(is.editor(m) <- c(TRUE, TRUE),
paste('PATCH /api/projects/project1/members.json',
'{"/api/users/user2.json":{"permissions":{"edit":true}}}'),
fixed=TRUE)
expect_error(is.editor(m[2]) <- TRUE,
paste('PATCH /api/projects/project1/members.json',
'{"/api/users/user2.json":{"permissions":{"edit":true}}}'),
fixed=TRUE)
expect_error(is.editor(m[2]) <- FALSE,
NA) ## No change, so no PATCH request made
})
test_that("Print method for MemberCatalog", {
expect_output(m,
paste(capture.output(print(data.frame(
name=c("Fake User", "Roger User"),
email=c("fake.user@example.com", "roger.user@example.com"),
is.editor=c(TRUE, FALSE)
))), collapse="\n")
)
})
d <- datasets(aproject)
test_that("Project datasets catalog", {
expect_is(d, "DatasetCatalog")
expect_identical(names(d), "ECON.sav")
})
do <- ordering(d)
test_that("Project datasets order", {
expect_is(do, "DatasetOrder")
expect_identical(do@graph, list(DatasetGroup("Group 1", "/api/datasets/dataset3.json")))
})
test_that("Add datasets to project by <- a dataset (which transfers ownership)", {
ds <- loadDataset("test ds")
expect_error(datasets(aproject) <- ds,
'PATCH /api/datasets/dataset1.json {"owner":"/api/projects/project1.json"}',
fixed=TRUE)
})
test_that("Organize datasets", {
expect_identical(DatasetOrder(DatasetGroup("new group", datasets(aproject))),
DatasetOrder(DatasetGroup("new group", "/api/datasets/dataset3.json")))
expect_error(ordering(datasets(aproject)) <- DatasetOrder(DatasetGroup("new group",
datasets(aproject))),
'PUT /api/projects/project1/datasets/order.json {"graph":[{"new group":["/api/datasets/dataset3.json"]}]}',
fixed=TRUE)
nested.ord <- DatasetOrder("/api/datasets/dataset3.json",
DatasetGroup("new group",
list(DatasetGroup("nested", "/api/datasets/dataset3.json"))))
expect_error(ordering(datasets(aproject)) <- nested.ord,
'PUT /api/projects/project1/datasets/order.json {"graph":["/api/datasets/dataset3.json",{"new group":[{"nested":["/api/datasets/dataset3.json"]}]}]}',
fixed=TRUE)
})
})
if (run.integration.tests) {
with_test_authentication({
projects <- session()$projects
ucat <- getAccountUserCatalog()
my.name <- names(ucat)[urls(ucat) == userURL()]
my.email <- emails(ucat)[urls(ucat) == userURL()]
nprojects.0 <- length(projects)
test_that("Can get project catalog", {
expect_is(projects, "ProjectCatalog")
})
name.of.project1 <- now()
test_that("Can create a project", {
expect_false(name.of.project1 %in% names(projects))
projects[[name.of.project1]] <- list()
expect_true(name.of.project1 %in% names(projects))
expect_true(length(projects) == nprojects.0 + 1L)
expect_is(projects[[name.of.project1]], "CrunchProject")
expect_length(members(projects[[name.of.project1]]), 1)
expect_identical(names(members(projects[[name.of.project1]])),
my.name)
})
projects <- refresh(projects)
pj <- projects[[name.of.project1]]
p_url <- self(pj)
name2 <- paste(name.of.project1, "revised")
test_that("Can rename a project by name<-", {
expect_identical(self(projects[[name.of.project1]]),
p_url)
expect_null(projects[[name2]])
name(projects[[name.of.project1]]) <- name2
expect_identical(projects[[name.of.project1]],
NULL)
expect_identical(self(projects[[name2]]), p_url)
})
name3 <- paste(name2, "FINAL")
test_that("Can rename a project with names<-", {
expect_false(name3 %in% names(projects))
names(projects)[urls(projects) == p_url] <- name3
expect_true(name3 %in% names(projects))
expect_identical(self(projects[[name3]]), p_url)
})
test_that("Get and set project icon", {
ico <- icon(pj)
# expect_true(nchar(ico) > 0) ## Unskip after #119305641 ships
icon(pj) <- "empty.png"
expect_false(icon(pj) == "empty.png")
expect_true(endsWith(icon(pj), ".png"))
expect_false(identical(icon(pj), ico))
})
test_that("Can delete a project by URL", {
projects <- refresh(projects)
expect_true(p_url %in% urls(projects))
try(crDELETE(p_url))
expect_false(p_url %in% urls(refresh(projects)))
})
test_that("Can create a project with members", {
skip("TODO")
skip_on_jenkins("Jenkins user needs more permissions")
projects <- refresh(projects)
nprojects.2 <- length(projects)
name.of.project2 <- now()
expect_false(name.of.project2 %in% names(projects))
with(cleanup(testUser()), as="u", {
projects[[name.of.project2]] <- list(members=email(u))
expect_true(name.of.project2 %in% names(projects))
with(cleanup(projects[[name.of.project2]]), as="tp", {
expect_true(length(projects) == nprojects.2 + 1L)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
})
})
})
test_that("Can add members to a project (and then set as an editor)", {
skip_on_jenkins("Jenkins user needs more permissions")
with(cleanup(testProject()), as="tp", {
with(cleanup(testUser()), as="u", {
expect_identical(names(members(tp)),
my.name)
members(tp) <- email(u)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
expect_identical(is.editor(members(tp)),
c(TRUE, FALSE))
is.editor(members(tp)[email(u)]) <- TRUE
expect_identical(is.editor(members(tp)),
c(TRUE, TRUE))
})
})
})
test_that("Can remove members from a project", {
skip_on_jenkins("Jenkins user needs more permissions")
with(cleanup(testProject()), as="tp", {
with(cleanup(testUser()), as="u", {
expect_identical(names(members(tp)),
my.name)
members(tp) <- email(u)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
try(members(tp)[[email(u)]] <- NULL)
expect_identical(names(members(tp)),
my.name)
})
})
})
with(test.dataset(), {
with(cleanup(testProject()), as="tp", {
test_that("Can add datasets to project", {
expect_is(tp, "CrunchProject")
expect_length(datasets(tp), 0)
datasets(tp) <- ds
expect_identical(names(datasets(tp)), name(ds))
expect_identical(owner(refresh(ds)), self(tp))
})
ds2 <- loadDataset(datasets(tp)[[1]])
test_that("Can load a dataset from a project", {
expect_true(is.dataset(ds2))
expect_identical(self(ds2), self(ds))
})
test_that("Can organize datasets", {
expect_identical(as.list(urls(datasets(tp))),
entities(ordering(datasets(tp))))
ordering(datasets(tp)) <- DatasetOrder(DatasetGroup("A group of one",
list(ds)))
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of one", entities=self(ds)))
})
with(test.dataset(), as="ds3", {
ord2 <- DatasetOrder(DatasetGroup("A group of two",
c(self(ds), self(ds3))))
test_that("Have to add dataset to project before organizing it", {
expect_error(ordering(datasets(tp)) <- ord2,
"Dataset URL referenced in Order not present in catalog")
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of one", entities=self(ds)))
})
owner(ds3) <- tp
tp <- refresh(tp)
test_that("Can reorganize datasets", {
ordering(datasets(tp)) <- ord2
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of two",
entities=c(self(ds), self(ds3))))
expect_output(ordering(datasets(tp)),
paste("[+] A group of two",
paste0(" ", name(ds)),
paste0(" ", name(ds3)),
sep="\n"),
fixed=TRUE)
})
ord3 <- DatasetOrder(DatasetGroup("G1", self(ds3)),
DatasetGroup("G2", self(ds)))
ord3.list <- list(DatasetGroup("G1", self(ds3)),
DatasetGroup("G2", self(ds)))
ord3.alt <- DatasetOrder(
DatasetGroup("G1", datasets(tp)[names(datasets(tp)) == name(ds3)]),
DatasetGroup("G2", datasets(tp)[names(datasets(tp)) == name(ds)]))
test_that("Can re-reorganize", {
expect_identical(ord3, ord3.alt)
ordering(datasets(tp)) <- ord3
expect_identical(ordering(datasets(tp))@graph,
ord3.list)
expect_identical(ordering(datasets(refresh(tp)))@graph,
ord3.list)
})
test_that("Can create a Group by assigning by name", {
ordering(datasets(tp))[["New group three"]] <- self(ds)
expect_output(ordering(datasets(tp)),
paste("[+] G1",
paste0(" ", name(ds3)),
"[+] G2",
" (Empty group)",
"[+] New group three",
paste0(" ", name(ds)),
sep="\n"),
fixed=TRUE)
})
})
test_that("Can rename a dataset in a project", {
newname <- paste(name(ds2), "edited")
name(ds2) <- newname
expect_identical(name(ds2), newname)
expect_identical(name(refresh(ds2)), newname)
expect_identical(name(datasets(refresh(tp))[[1]]),
newname)
})
test_that("Can privatize a dataset belonging to a project", {
expect_identical(owner(ds2), self(tp))
owner(ds2) <- me()
expect_identical(owner(ds2), self(me()))
})
})
})
})
}
| /tests/testthat/test-projects.R | no_license | digideskio/rcrunch | R | false | false | 14,927 | r | context("Projects")
with_mock_HTTP({
projects <- session()$projects
test_that("Getting projects catalog", {
expect_is(projects, "ProjectCatalog")
expect_length(projects, 2)
expect_identical(names(projects), c("Project One", "Project Two"))
})
aproject <- projects[["Project One"]]
test_that("Getting project from catalog", {
expect_is(projects[[1]], "CrunchProject")
expect_is(projects$`Project One`, "CrunchProject")
expect_is(projects[["Project One"]], "CrunchProject")
expect_null(projects$`Beta Project`)
})
test_that("Project attributes", {
expect_identical(name(aproject), "Project One")
})
test_that("Simple project creation by assignment", {
expect_error(projects[["A new project"]] <- list(),
'POST /api/projects.json {"name":"A new project"}',
fixed=TRUE)
expect_error(projects$`A new project` <- list(),
'POST /api/projects.json {"name":"A new project"}',
fixed=TRUE)
})
test_that("Project editing", {
expect_error(names(projects)[2] <- "New name",
paste('PATCH /api/projects.json',
'{"/api/projects/project2.json":{"name":"New name"}}'),
fixed=TRUE)
expect_error(name(projects[[2]]) <- "New name",
paste('PATCH /api/projects.json',
'{"/api/projects/project2.json":{"name":"New name"}}'),
fixed=TRUE)
})
test_that("Project deletion", {
expect_error(delete(projects[[1]], confirm=TRUE),
"Must confirm deleting project")
with(consent(), expect_error(delete(projects[[1]], confirm=TRUE),
"DELETE /api/projects/project1.json"))
})
m <- members(aproject)
test_that("Project members catalog", {
expect_is(m, "MemberCatalog")
expect_identical(names(m), c("Fake User", "Roger User"))
expect_identical(emails(m), c("fake.user@example.com", "roger.user@example.com"))
})
test_that("Add members by members<-", {
expect_error(members(aproject) <- c("new.user@crunch.io", "foo@example.co"),
'PATCH /api/projects/project1/members.json {"new.user@crunch.io":{},"foo@example.co":{}}',
fixed=TRUE)
})
test_that("Add members doesn't re-add if already a member", {
expect_error(members(aproject) <- c("new.user@crunch.io", "roger.user@example.com"),
'PATCH /api/projects/project1/members.json {"new.user@crunch.io":{}}',
fixed=TRUE)
})
test_that("Remove members by <- NULL", {
expect_error(members(aproject)[["roger.user@example.com"]] <- NULL,
'PATCH /api/projects/project1/members.json {"roger.user@example.com":null}',
fixed=TRUE)
})
test_that("is.editor on member catalog", {
expect_identical(is.editor(m), c(TRUE, FALSE))
})
test_that("is.editor<- on member catalog", {
expect_error(is.editor(m) <- c(TRUE, TRUE),
paste('PATCH /api/projects/project1/members.json',
'{"/api/users/user2.json":{"permissions":{"edit":true}}}'),
fixed=TRUE)
expect_error(is.editor(m[2]) <- TRUE,
paste('PATCH /api/projects/project1/members.json',
'{"/api/users/user2.json":{"permissions":{"edit":true}}}'),
fixed=TRUE)
expect_error(is.editor(m[2]) <- FALSE,
NA) ## No change, so no PATCH request made
})
test_that("Print method for MemberCatalog", {
expect_output(m,
paste(capture.output(print(data.frame(
name=c("Fake User", "Roger User"),
email=c("fake.user@example.com", "roger.user@example.com"),
is.editor=c(TRUE, FALSE)
))), collapse="\n")
)
})
d <- datasets(aproject)
test_that("Project datasets catalog", {
expect_is(d, "DatasetCatalog")
expect_identical(names(d), "ECON.sav")
})
do <- ordering(d)
test_that("Project datasets order", {
expect_is(do, "DatasetOrder")
expect_identical(do@graph, list(DatasetGroup("Group 1", "/api/datasets/dataset3.json")))
})
test_that("Add datasets to project by <- a dataset (which transfers ownership)", {
ds <- loadDataset("test ds")
expect_error(datasets(aproject) <- ds,
'PATCH /api/datasets/dataset1.json {"owner":"/api/projects/project1.json"}',
fixed=TRUE)
})
test_that("Organize datasets", {
expect_identical(DatasetOrder(DatasetGroup("new group", datasets(aproject))),
DatasetOrder(DatasetGroup("new group", "/api/datasets/dataset3.json")))
expect_error(ordering(datasets(aproject)) <- DatasetOrder(DatasetGroup("new group",
datasets(aproject))),
'PUT /api/projects/project1/datasets/order.json {"graph":[{"new group":["/api/datasets/dataset3.json"]}]}',
fixed=TRUE)
nested.ord <- DatasetOrder("/api/datasets/dataset3.json",
DatasetGroup("new group",
list(DatasetGroup("nested", "/api/datasets/dataset3.json"))))
expect_error(ordering(datasets(aproject)) <- nested.ord,
'PUT /api/projects/project1/datasets/order.json {"graph":["/api/datasets/dataset3.json",{"new group":[{"nested":["/api/datasets/dataset3.json"]}]}]}',
fixed=TRUE)
})
})
if (run.integration.tests) {
with_test_authentication({
projects <- session()$projects
ucat <- getAccountUserCatalog()
my.name <- names(ucat)[urls(ucat) == userURL()]
my.email <- emails(ucat)[urls(ucat) == userURL()]
nprojects.0 <- length(projects)
test_that("Can get project catalog", {
expect_is(projects, "ProjectCatalog")
})
name.of.project1 <- now()
test_that("Can create a project", {
expect_false(name.of.project1 %in% names(projects))
projects[[name.of.project1]] <- list()
expect_true(name.of.project1 %in% names(projects))
expect_true(length(projects) == nprojects.0 + 1L)
expect_is(projects[[name.of.project1]], "CrunchProject")
expect_length(members(projects[[name.of.project1]]), 1)
expect_identical(names(members(projects[[name.of.project1]])),
my.name)
})
projects <- refresh(projects)
pj <- projects[[name.of.project1]]
p_url <- self(pj)
name2 <- paste(name.of.project1, "revised")
test_that("Can rename a project by name<-", {
expect_identical(self(projects[[name.of.project1]]),
p_url)
expect_null(projects[[name2]])
name(projects[[name.of.project1]]) <- name2
expect_identical(projects[[name.of.project1]],
NULL)
expect_identical(self(projects[[name2]]), p_url)
})
name3 <- paste(name2, "FINAL")
test_that("Can rename a project with names<-", {
expect_false(name3 %in% names(projects))
names(projects)[urls(projects) == p_url] <- name3
expect_true(name3 %in% names(projects))
expect_identical(self(projects[[name3]]), p_url)
})
test_that("Get and set project icon", {
ico <- icon(pj)
# expect_true(nchar(ico) > 0) ## Unskip after #119305641 ships
icon(pj) <- "empty.png"
expect_false(icon(pj) == "empty.png")
expect_true(endsWith(icon(pj), ".png"))
expect_false(identical(icon(pj), ico))
})
test_that("Can delete a project by URL", {
projects <- refresh(projects)
expect_true(p_url %in% urls(projects))
try(crDELETE(p_url))
expect_false(p_url %in% urls(refresh(projects)))
})
test_that("Can create a project with members", {
skip("TODO")
skip_on_jenkins("Jenkins user needs more permissions")
projects <- refresh(projects)
nprojects.2 <- length(projects)
name.of.project2 <- now()
expect_false(name.of.project2 %in% names(projects))
with(cleanup(testUser()), as="u", {
projects[[name.of.project2]] <- list(members=email(u))
expect_true(name.of.project2 %in% names(projects))
with(cleanup(projects[[name.of.project2]]), as="tp", {
expect_true(length(projects) == nprojects.2 + 1L)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
})
})
})
test_that("Can add members to a project (and then set as an editor)", {
skip_on_jenkins("Jenkins user needs more permissions")
with(cleanup(testProject()), as="tp", {
with(cleanup(testUser()), as="u", {
expect_identical(names(members(tp)),
my.name)
members(tp) <- email(u)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
expect_identical(is.editor(members(tp)),
c(TRUE, FALSE))
is.editor(members(tp)[email(u)]) <- TRUE
expect_identical(is.editor(members(tp)),
c(TRUE, TRUE))
})
})
})
test_that("Can remove members from a project", {
skip_on_jenkins("Jenkins user needs more permissions")
with(cleanup(testProject()), as="tp", {
with(cleanup(testUser()), as="u", {
expect_identical(names(members(tp)),
my.name)
members(tp) <- email(u)
expect_true(setequal(names(members(tp)),
c(name(u), my.name)))
try(members(tp)[[email(u)]] <- NULL)
expect_identical(names(members(tp)),
my.name)
})
})
})
with(test.dataset(), {
with(cleanup(testProject()), as="tp", {
test_that("Can add datasets to project", {
expect_is(tp, "CrunchProject")
expect_length(datasets(tp), 0)
datasets(tp) <- ds
expect_identical(names(datasets(tp)), name(ds))
expect_identical(owner(refresh(ds)), self(tp))
})
ds2 <- loadDataset(datasets(tp)[[1]])
test_that("Can load a dataset from a project", {
expect_true(is.dataset(ds2))
expect_identical(self(ds2), self(ds))
})
test_that("Can organize datasets", {
expect_identical(as.list(urls(datasets(tp))),
entities(ordering(datasets(tp))))
ordering(datasets(tp)) <- DatasetOrder(DatasetGroup("A group of one",
list(ds)))
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of one", entities=self(ds)))
})
with(test.dataset(), as="ds3", {
ord2 <- DatasetOrder(DatasetGroup("A group of two",
c(self(ds), self(ds3))))
test_that("Have to add dataset to project before organizing it", {
expect_error(ordering(datasets(tp)) <- ord2,
"Dataset URL referenced in Order not present in catalog")
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of one", entities=self(ds)))
})
owner(ds3) <- tp
tp <- refresh(tp)
test_that("Can reorganize datasets", {
ordering(datasets(tp)) <- ord2
expect_identical(ordering(datasets(tp))@graph[[1]],
DatasetGroup(name="A group of two",
entities=c(self(ds), self(ds3))))
expect_output(ordering(datasets(tp)),
paste("[+] A group of two",
paste0(" ", name(ds)),
paste0(" ", name(ds3)),
sep="\n"),
fixed=TRUE)
})
ord3 <- DatasetOrder(DatasetGroup("G1", self(ds3)),
DatasetGroup("G2", self(ds)))
ord3.list <- list(DatasetGroup("G1", self(ds3)),
DatasetGroup("G2", self(ds)))
ord3.alt <- DatasetOrder(
DatasetGroup("G1", datasets(tp)[names(datasets(tp)) == name(ds3)]),
DatasetGroup("G2", datasets(tp)[names(datasets(tp)) == name(ds)]))
test_that("Can re-reorganize", {
expect_identical(ord3, ord3.alt)
ordering(datasets(tp)) <- ord3
expect_identical(ordering(datasets(tp))@graph,
ord3.list)
expect_identical(ordering(datasets(refresh(tp)))@graph,
ord3.list)
})
test_that("Can create a Group by assigning by name", {
ordering(datasets(tp))[["New group three"]] <- self(ds)
expect_output(ordering(datasets(tp)),
paste("[+] G1",
paste0(" ", name(ds3)),
"[+] G2",
" (Empty group)",
"[+] New group three",
paste0(" ", name(ds)),
sep="\n"),
fixed=TRUE)
})
})
test_that("Can rename a dataset in a project", {
newname <- paste(name(ds2), "edited")
name(ds2) <- newname
expect_identical(name(ds2), newname)
expect_identical(name(refresh(ds2)), newname)
expect_identical(name(datasets(refresh(tp))[[1]]),
newname)
})
test_that("Can privatize a dataset belonging to a project", {
expect_identical(owner(ds2), self(tp))
owner(ds2) <- me()
expect_identical(owner(ds2), self(me()))
})
})
})
})
}
|
library(mclust)
library(lubridate)
library(dplyr)
library(mvtnorm)
library(mixtools)
# Thanks to Chad Young for useful mclust discussions
#############
# UNIVARIATE
############
# read in our iPhone daily step counts
raw <- read.csv("datasets/iphone_health/stepsData.csv")
# day-level data
steps <- raw %>%
mutate(startDate = factor(as.Date(startDate))) %>%
dplyr::select(date = startDate, stepsWalked) %>%
group_by(date) %>%
summarize(stepsWalked = sum(stepsWalked))
## mixtools
# no mu supplied, data is binned, then K is picked
mixauto <- normalmixEM(steps$stepsWalked)
plot(mixauto, density=TRUE)
# K is supplied
mix4 <- normalmixEM(steps$stepsWalked, k=4)
plot(mix4, density=TRUE)
## mclust
# do density estimation, let mclust pick our distribution count for us
dens <- densityMclust(steps$stepsWalked)
# look at dianogstics
plot(dens, what = "BIC")
summary(dens$BIC)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data=steps$stepsWalked)
# extract parameters of the Gaussians
params <- dens$parameters
nDistros <- length(params$pro)
distros <- data.frame(matrix(0,4,4))
colnames(distros) <- c("id","n", "mean","sd")
distros$id <- 1:4
# re-sample the Gaussians
N <- nrow(steps)
for (i in 1:nDistros){
distros[distros$id==i,]$n <- floor(N * params$pro[i])
distros[distros$id==i,]$mean <- params$mean[i]
distros[distros$id==i,]$sd <- floor(sqrt(params$variance$sigmasq[i]))
}
set.seed(1)
densities <- apply(distros[, -1], 1, function(x) rnorm(n = x[1], mean = x[2], sd = x[2]))
# plot out the estimated densities
plot(0, type = "n", xlim = c(min(unlist(lapply(densities, min))),max(unlist(lapply(densities, max)))), ylim = c(0, 1.5e-4))
for (d in 1:length(densities)){
lines(density(densities[[d]]), lty = d)
}
legend("topright", legend=1:length(densities), lty=1:length(densities))
##############
# MULTIVARIATE
##############
# probably not a great idea since running and biking are temporally independent...
biking <- read.csv("cyclingData.csv", header=T)
biking <- biking %>%
group_by(startDate) %>%
summarize(kcalRun = sum(kcalBurned),milesCycled = sum(milesCycled)) %>%
mutate(startDate = as.Date(startDate)) %>%
dplyr::filter(milesCycled < 20) %>%
dplyr::select(c(startDate,milesCycled))
health <- merge(biking, steps, by="startDate", all=TRUE)
health[is.na(health$milesCycled),]$milesCycled <- 0
health <- dplyr::select(health, c(milesCycled, stepsWalked))
mdens <- densityMclust(health, classification=TRUE)
# retrieve model summary: model type, log-likelihood, BIC, etc.
summary(mdens)
# cluster membership
mdens$classification
# plot the multivariate density
plot(mdens, what="density", type="persp")
# retrieve the covariance matrix
covMat <- cov(health)
# resample
rmvnorm(n = nrow(health), mean=mdens$parameters$mean, sigma=mdens$parameters$variance$sigma[,,8])
| /modeling_gaussian_mixtures.R | no_license | ptvan/R-snippets | R | false | false | 2,843 | r | library(mclust)
library(lubridate)
library(dplyr)
library(mvtnorm)
library(mixtools)
# Thanks to Chad Young for useful mclust discussions
#############
# UNIVARIATE
############
# read in our iPhone daily step counts
raw <- read.csv("datasets/iphone_health/stepsData.csv")
# day-level data
steps <- raw %>%
mutate(startDate = factor(as.Date(startDate))) %>%
dplyr::select(date = startDate, stepsWalked) %>%
group_by(date) %>%
summarize(stepsWalked = sum(stepsWalked))
## mixtools
# no mu supplied, data is binned, then K is picked
mixauto <- normalmixEM(steps$stepsWalked)
plot(mixauto, density=TRUE)
# K is supplied
mix4 <- normalmixEM(steps$stepsWalked, k=4)
plot(mix4, density=TRUE)
## mclust
# do density estimation, let mclust pick our distribution count for us
dens <- densityMclust(steps$stepsWalked)
# look at dianogstics
plot(dens, what = "BIC")
summary(dens$BIC)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data=steps$stepsWalked)
# extract parameters of the Gaussians
params <- dens$parameters
nDistros <- length(params$pro)
distros <- data.frame(matrix(0,4,4))
colnames(distros) <- c("id","n", "mean","sd")
distros$id <- 1:4
# re-sample the Gaussians
N <- nrow(steps)
for (i in 1:nDistros){
distros[distros$id==i,]$n <- floor(N * params$pro[i])
distros[distros$id==i,]$mean <- params$mean[i]
distros[distros$id==i,]$sd <- floor(sqrt(params$variance$sigmasq[i]))
}
set.seed(1)
densities <- apply(distros[, -1], 1, function(x) rnorm(n = x[1], mean = x[2], sd = x[2]))
# plot out the estimated densities
plot(0, type = "n", xlim = c(min(unlist(lapply(densities, min))),max(unlist(lapply(densities, max)))), ylim = c(0, 1.5e-4))
for (d in 1:length(densities)){
lines(density(densities[[d]]), lty = d)
}
legend("topright", legend=1:length(densities), lty=1:length(densities))
##############
# MULTIVARIATE
##############
# probably not a great idea since running and biking are temporally independent...
biking <- read.csv("cyclingData.csv", header=T)
biking <- biking %>%
group_by(startDate) %>%
summarize(kcalRun = sum(kcalBurned),milesCycled = sum(milesCycled)) %>%
mutate(startDate = as.Date(startDate)) %>%
dplyr::filter(milesCycled < 20) %>%
dplyr::select(c(startDate,milesCycled))
health <- merge(biking, steps, by="startDate", all=TRUE)
health[is.na(health$milesCycled),]$milesCycled <- 0
health <- dplyr::select(health, c(milesCycled, stepsWalked))
mdens <- densityMclust(health, classification=TRUE)
# retrieve model summary: model type, log-likelihood, BIC, etc.
summary(mdens)
# cluster membership
mdens$classification
# plot the multivariate density
plot(mdens, what="density", type="persp")
# retrieve the covariance matrix
covMat <- cov(health)
# resample
rmvnorm(n = nrow(health), mean=mdens$parameters$mean, sigma=mdens$parameters$variance$sigma[,,8])
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simmr.R
\name{simmr}
\alias{simmr}
\alias{simmr-package}
\title{simmr: A package for fitting stable isotope mixing models via JAGS in R}
\description{
This package runs a
simple Stable Isotope Mixing Model (SIMM) and is meant as a longer term
replacement to the previous function SIAR.. These are used to infer dietary
proportions of organisms consuming various food sources from observations on
the stable isotope values taken from the organisms' tissue samples. However
SIMMs can also be used in other scenarios, such as in sediment mixing or the
composition of fatty acids. The main functions are \code{\link{simmr_load}}
and \code{\link{simmr_mcmc}}. The help files contain examples of the use of
this package. See also the vignette for a longer walkthrough.
}
\details{
An even longer term replacement for properly running SIMMs is MixSIAR, which
allows for more detailed random effects and the inclusion of covariates.
}
\examples{
\dontrun{
# A first example with 2 tracers (isotopes), 10 observations, and 4 food sources
data(geese_data_day1)
simmr_in <- with(
geese_data_day1,
simmr_load(
mixtures = mixtures,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# Plot
plot(simmr_in)
# MCMC run
simmr_out <- simmr_mcmc(simmr_in)
# Check convergence - values should all be close to 1
summary(simmr_out, type = "diagnostics")
# Look at output
summary(simmr_out, type = "statistics")
# Look at influence of priors
prior_viz(simmr_out)
# Plot output
plot(simmr_out, type = "histogram")
}
}
\references{
Andrew C. Parnell, Donald L. Phillips, Stuart Bearhop, Brice X.
Semmens, Eric J. Ward, Jonathan W. Moore, Andrew L. Jackson, Jonathan Grey,
David J. Kelly, and Richard Inger. Bayesian stable isotope mixing models.
Environmetrics, 24(6):387–399, 2013.
Andrew C Parnell, Richard Inger, Stuart Bearhop, and Andrew L Jackson.
Source partitioning using stable isotopes: coping with too much variation.
PLoS ONE, 5(3):5, 2010.
}
\author{
Andrew Parnell <andrew.parnell@mu.ie>
}
\keyword{multivariate}
| /man/simmr.Rd | no_license | andrewcparnell/simmr | R | false | true | 2,271 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simmr.R
\name{simmr}
\alias{simmr}
\alias{simmr-package}
\title{simmr: A package for fitting stable isotope mixing models via JAGS in R}
\description{
This package runs a
simple Stable Isotope Mixing Model (SIMM) and is meant as a longer term
replacement to the previous function SIAR.. These are used to infer dietary
proportions of organisms consuming various food sources from observations on
the stable isotope values taken from the organisms' tissue samples. However
SIMMs can also be used in other scenarios, such as in sediment mixing or the
composition of fatty acids. The main functions are \code{\link{simmr_load}}
and \code{\link{simmr_mcmc}}. The help files contain examples of the use of
this package. See also the vignette for a longer walkthrough.
}
\details{
An even longer term replacement for properly running SIMMs is MixSIAR, which
allows for more detailed random effects and the inclusion of covariates.
}
\examples{
\dontrun{
# A first example with 2 tracers (isotopes), 10 observations, and 4 food sources
data(geese_data_day1)
simmr_in <- with(
geese_data_day1,
simmr_load(
mixtures = mixtures,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# Plot
plot(simmr_in)
# MCMC run
simmr_out <- simmr_mcmc(simmr_in)
# Check convergence - values should all be close to 1
summary(simmr_out, type = "diagnostics")
# Look at output
summary(simmr_out, type = "statistics")
# Look at influence of priors
prior_viz(simmr_out)
# Plot output
plot(simmr_out, type = "histogram")
}
}
\references{
Andrew C. Parnell, Donald L. Phillips, Stuart Bearhop, Brice X.
Semmens, Eric J. Ward, Jonathan W. Moore, Andrew L. Jackson, Jonathan Grey,
David J. Kelly, and Richard Inger. Bayesian stable isotope mixing models.
Environmetrics, 24(6):387–399, 2013.
Andrew C Parnell, Richard Inger, Stuart Bearhop, and Andrew L Jackson.
Source partitioning using stable isotopes: coping with too much variation.
PLoS ONE, 5(3):5, 2010.
}
\author{
Andrew Parnell <andrew.parnell@mu.ie>
}
\keyword{multivariate}
|
library(devtools)
options(repos = c("CRAN" = "http://cran.us.r-project.org"))
options(Ncpus = parallel::detectCores())
# Set download method, to avoid the default behavior of using
# R's internal HTTP implementation, which doesn't support HTTPS connections.
# https://stackoverflow.com/questions/45061272/r-and-ssl-curl-on-ubuntu-linux-failed-ssl-connect-in-r-but-works-in-curl
options(download.file.method = "libcurl")
# Install the lightGBM installer package
install_github("Laurae2/lgbdl")
lgbdl::lgb.dl(compiler = "gcc", commit = "tags/v2.3.1")
install_github("dgrtwo/widyr")
install_github("ellisp/forecastxgb-r-package/pkg")
install_github("rstudio/leaflet")
# install_github fails for catboost.
# Following direct installation instructions instead: https://tech.yandex.com/catboost/doc/dg/installation/r-installation-binary-installation-docpage/
install_url('https://github.com/catboost/catboost/releases/download/v0.23.2/catboost-R-Linux-0.23.2.tgz', INSTALL_opts = c("--no-multiarch"))
install_github("sassalley/hexmapr")
install_github("hadley/multidplyr")
install_github("dselivanov/LSHR")
# install latest sparklyr and Spark (for local mode)
install_github("rstudio/sparklyr")
sparklyr::spark_install()
install.packages("genderdata", repos = "http://packages.ropensci.org")
install.packages("openNLPmodels.en",
repos = "http://datacube.wu.ac.at/",
type = "source")
install_github("davpinto/fastknn")
install_github("mukul13/rword2vec")
# These signal processing libraries are on CRAN, but they require apt-get dependences that are
# handled in this image's Dockerfile.
install.packages("fftw")
# https://github.com/Kaggle/docker-rstats/issues/74
install_github("thomasp85/patchwork")
# https://github.com/Kaggle/docker-rstats/issues/73
install.packages("topicmodels")
install.packages("tesseract")
# Try to reinstall igraph and imager her until fixed in rcran.
install.packages("igraph")
install.packages("imager")
# Torch: install the full package upfront otherwise it will be installed on loading the package which doesn't work for kernels
# without internet (competitions for example).
library(torch)
install_torch(reinstall = TRUE)
# The R Keras package must be reinstalled after installing it in the python virtualenv.
install_version("keras", version = "2.6.0", ask=FALSE) | /package_installs.R | permissive | carlsvelz/docker-rstats | R | false | false | 2,338 | r | library(devtools)
options(repos = c("CRAN" = "http://cran.us.r-project.org"))
options(Ncpus = parallel::detectCores())
# Set download method, to avoid the default behavior of using
# R's internal HTTP implementation, which doesn't support HTTPS connections.
# https://stackoverflow.com/questions/45061272/r-and-ssl-curl-on-ubuntu-linux-failed-ssl-connect-in-r-but-works-in-curl
options(download.file.method = "libcurl")
# Install the lightGBM installer package
install_github("Laurae2/lgbdl")
lgbdl::lgb.dl(compiler = "gcc", commit = "tags/v2.3.1")
install_github("dgrtwo/widyr")
install_github("ellisp/forecastxgb-r-package/pkg")
install_github("rstudio/leaflet")
# install_github fails for catboost.
# Following direct installation instructions instead: https://tech.yandex.com/catboost/doc/dg/installation/r-installation-binary-installation-docpage/
install_url('https://github.com/catboost/catboost/releases/download/v0.23.2/catboost-R-Linux-0.23.2.tgz', INSTALL_opts = c("--no-multiarch"))
install_github("sassalley/hexmapr")
install_github("hadley/multidplyr")
install_github("dselivanov/LSHR")
# install latest sparklyr and Spark (for local mode)
install_github("rstudio/sparklyr")
sparklyr::spark_install()
install.packages("genderdata", repos = "http://packages.ropensci.org")
install.packages("openNLPmodels.en",
repos = "http://datacube.wu.ac.at/",
type = "source")
install_github("davpinto/fastknn")
install_github("mukul13/rword2vec")
# These signal processing libraries are on CRAN, but they require apt-get dependences that are
# handled in this image's Dockerfile.
install.packages("fftw")
# https://github.com/Kaggle/docker-rstats/issues/74
install_github("thomasp85/patchwork")
# https://github.com/Kaggle/docker-rstats/issues/73
install.packages("topicmodels")
install.packages("tesseract")
# Try to reinstall igraph and imager her until fixed in rcran.
install.packages("igraph")
install.packages("imager")
# Torch: install the full package upfront otherwise it will be installed on loading the package which doesn't work for kernels
# without internet (competitions for example).
library(torch)
install_torch(reinstall = TRUE)
# The R Keras package must be reinstalled after installing it in the python virtualenv.
install_version("keras", version = "2.6.0", ask=FALSE) |
compileResults.OUTRIDER <- function(object, padjCutoff=0.05, zScoreCutoff=0,
round=2, all=FALSE, ...){
#
# input check and parsing
#
checkOutriderDataSet(object)
checkFullAnalysis(object)
if(is.null(rownames(object))){
rownames(object) <- paste('feature', seq_len(nrow(object)), sep='_')
}
if(is.null(colnames(object))){
colnames(object) <- paste('sample', seq_len(ncol(object)), sep='_')
}
if(isTRUE(round)){
round <- 2
}
if(isFALSE(all)){
abByGene <- aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="gene")
abBySample <- aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="sample")
object <- object[abByGene > 0, abBySample > 0]
}
if(nrow(object) == 0){
if(isFALSE(all)){
warning('No significant events: use all=TRUE to print all events.')
} else {
warning('Please provide an object with at least one feature.')
}
return(data.table(
geneID=NA_character_,
sampleID=NA_character_,
pValue=NA_real_,
padjust=NA_real_,
zScore=NA_real_,
l2fc=NA_real_,
rawcounts=NA_integer_,
normcounts=NA_real_,
meanCorrected=NA_real_,
theta=NA_real_,
aberrant=NA,
AberrantBySample=NA_integer_,
AberrantByGene=NA_integer_,
padj_rank=NA_real_)[0])
}
#
# extract data
#
ans <- data.table(
geneID = rownames(object),
sampleID = rep(colnames(object), each=nrow(object)),
pValue = c(pValue(object)),
padjust = c(padj(object)),
zScore = c(zScore(object)),
l2fc = c(assay(object, "l2fc")),
rawcounts = c(counts(object)),
normcounts = c(counts(object, normalized=TRUE)),
meanCorrected = rowMeans(counts(object, normalized=TRUE)),
theta = theta(object),
aberrant = c(aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff)),
AberrantBySample = rep(each=nrow(object), aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="sample")),
AberrantByGene = aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="gene"),
padj_rank = c(apply(padj(object), 2, rank)))
# round columns if requested
if(is.numeric(round)){
devNull <- lapply(
c("normcounts", "zScore", "l2fc", "theta", "meanCorrected"),
function(x) ans[,c(x):=round(get(x), as.integer(round))] )
}
#
# keep only aberrent events and sort by padj value
#
if(isFALSE(all)){
ans <- ans[aberrant == TRUE]
}
ans <- ans[order(padjust)]
return(ans)
}
#'
#' Accessor function for the 'results' object in an OutriderDataSet object.
#'
#' This function assembles a results table of significant outlier events based
#' on the given filter criteria. The table contains various information
#' accumulated over the analysis pipeline.
#'
#' @param object An OutriderDataSet object
#' @param padjCutoff The significant threshold to be applied
#' @param zScoreCutoff If provided additionally a z score threshold is applied
#' @param round Can be TRUE, defaults to 2, or an integer used for rounding
#' with \code{\link[base]{round}} to make the output
#' more user friendly
#' @param all By default FALSE, only significant read counts are listed in the
#' results. If TRUE all results are assembled resulting in a
#' data.table of length samples x genes
#' @param ... Additional arguments, currently not used
#'
#' @return A data.table where each row is an outlier event and the columns
#' contain additional information about this event. Eg padj, l2fc
#'
#' @examples
#'
#' ods <- makeExampleOutriderDataSet()
#' \dontshow{
#' ods <- ods[1:10,1:10]
#' }
#' ods <- OUTRIDER(ods)
#'
#' res <- results(ods, all=TRUE)
#' res
#'
#' @name results
#' @rdname results
#' @aliases results results,OutriderDataSet-method
#'
#' @export
setMethod("results", "OutriderDataSet", compileResults.OUTRIDER)
| /R/method-results.R | permissive | standardgalactic/OUTRIDER | R | false | false | 4,529 | r | compileResults.OUTRIDER <- function(object, padjCutoff=0.05, zScoreCutoff=0,
round=2, all=FALSE, ...){
#
# input check and parsing
#
checkOutriderDataSet(object)
checkFullAnalysis(object)
if(is.null(rownames(object))){
rownames(object) <- paste('feature', seq_len(nrow(object)), sep='_')
}
if(is.null(colnames(object))){
colnames(object) <- paste('sample', seq_len(ncol(object)), sep='_')
}
if(isTRUE(round)){
round <- 2
}
if(isFALSE(all)){
abByGene <- aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="gene")
abBySample <- aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="sample")
object <- object[abByGene > 0, abBySample > 0]
}
if(nrow(object) == 0){
if(isFALSE(all)){
warning('No significant events: use all=TRUE to print all events.')
} else {
warning('Please provide an object with at least one feature.')
}
return(data.table(
geneID=NA_character_,
sampleID=NA_character_,
pValue=NA_real_,
padjust=NA_real_,
zScore=NA_real_,
l2fc=NA_real_,
rawcounts=NA_integer_,
normcounts=NA_real_,
meanCorrected=NA_real_,
theta=NA_real_,
aberrant=NA,
AberrantBySample=NA_integer_,
AberrantByGene=NA_integer_,
padj_rank=NA_real_)[0])
}
#
# extract data
#
ans <- data.table(
geneID = rownames(object),
sampleID = rep(colnames(object), each=nrow(object)),
pValue = c(pValue(object)),
padjust = c(padj(object)),
zScore = c(zScore(object)),
l2fc = c(assay(object, "l2fc")),
rawcounts = c(counts(object)),
normcounts = c(counts(object, normalized=TRUE)),
meanCorrected = rowMeans(counts(object, normalized=TRUE)),
theta = theta(object),
aberrant = c(aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff)),
AberrantBySample = rep(each=nrow(object), aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="sample")),
AberrantByGene = aberrant(object,
padjCutoff=padjCutoff, zScoreCutoff=zScoreCutoff, by="gene"),
padj_rank = c(apply(padj(object), 2, rank)))
# round columns if requested
if(is.numeric(round)){
devNull <- lapply(
c("normcounts", "zScore", "l2fc", "theta", "meanCorrected"),
function(x) ans[,c(x):=round(get(x), as.integer(round))] )
}
#
# keep only aberrent events and sort by padj value
#
if(isFALSE(all)){
ans <- ans[aberrant == TRUE]
}
ans <- ans[order(padjust)]
return(ans)
}
#'
#' Accessor function for the 'results' object in an OutriderDataSet object.
#'
#' This function assembles a results table of significant outlier events based
#' on the given filter criteria. The table contains various information
#' accumulated over the analysis pipeline.
#'
#' @param object An OutriderDataSet object
#' @param padjCutoff The significant threshold to be applied
#' @param zScoreCutoff If provided additionally a z score threshold is applied
#' @param round Can be TRUE, defaults to 2, or an integer used for rounding
#' with \code{\link[base]{round}} to make the output
#' more user friendly
#' @param all By default FALSE, only significant read counts are listed in the
#' results. If TRUE all results are assembled resulting in a
#' data.table of length samples x genes
#' @param ... Additional arguments, currently not used
#'
#' @return A data.table where each row is an outlier event and the columns
#' contain additional information about this event. Eg padj, l2fc
#'
#' @examples
#'
#' ods <- makeExampleOutriderDataSet()
#' \dontshow{
#' ods <- ods[1:10,1:10]
#' }
#' ods <- OUTRIDER(ods)
#'
#' res <- results(ods, all=TRUE)
#' res
#'
#' @name results
#' @rdname results
#' @aliases results results,OutriderDataSet-method
#'
#' @export
setMethod("results", "OutriderDataSet", compileResults.OUTRIDER)
|
/Kassambara-Ggplot.R | no_license | christianchmejia/GgPlot | R | false | false | 66,285 | r | ||
### a first look at the basic features
### calc stats for all
train.mean.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
#train.q20.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
#train.q80.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### calc stats for humans
train.mean.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
train.q20.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
train.q80.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### calc stats for robots
train.mean.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
train.q20.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
train.q80.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### create dataframe
train.stats.numeric <- data.frame(train.mean.all, train.mean.humans, train.mean.robots, train.median.all, train.median.humans, train.median.robots,
train.q20.humans, train.q80.humans, train.q20.robots, train.q80.robots)
### shorten colnames of data frame
colnames(train.stats.numeric) <- sapply(colnames(train.stats.numeric), function(x) gsub("train.", "", x, fixed = TRUE))
### print data frame
print(round(train.stats.numeric, 2))
| /features_basic_analysis.R | permissive | PhilippMohr/UniHH-Project-BigData-Challenge | R | false | false | 2,072 | r | ### a first look at the basic features
### calc stats for all
train.mean.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
#train.q20.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
#train.q80.all <- apply(train[, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### calc stats for humans
train.mean.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
train.q20.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
train.q80.humans <- apply(train[train$outcome == 0, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### calc stats for robots
train.mean.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) mean(x, na.rm = TRUE))
train.median.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.5, na.rm = TRUE))
train.q20.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.2, na.rm = TRUE))
train.q80.robots <- apply(train[train$outcome == 1, train.colnames.stats.numeric], 2, function(x) quantile(x, 0.8, na.rm = TRUE))
### create dataframe
train.stats.numeric <- data.frame(train.mean.all, train.mean.humans, train.mean.robots, train.median.all, train.median.humans, train.median.robots,
train.q20.humans, train.q80.humans, train.q20.robots, train.q80.robots)
### shorten colnames of data frame
colnames(train.stats.numeric) <- sapply(colnames(train.stats.numeric), function(x) gsub("train.", "", x, fixed = TRUE))
### print data frame
print(round(train.stats.numeric, 2))
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Write_lt.R
\name{Write_lt}
\alias{Write_lt}
\title{\code{Write_lt} a function to write formatted .txt files for both period and cohort lifetables.}
\usage{
Write_lt(WORKING = getwd(), STATSFOLDER = "RSTATS", MPVERSION, XXX = NULL,
CountryLong = NULL)
}
\arguments{
\item{WORKING}{path to working directory, which typically ends with the HMD country abbreviation. Default \code{getwd()}.}
\item{STATSFOLDER}{the folder name where output is to be written to (not a full path). Default \code{"RSTATS"}.}
\item{MPVERSION}{5 or 6. Default 5. Here this only affects file headers.}
\item{XXX}{the HMD country abbreviation. If left \code{NULL}, this is extracted from \code{WORKING} as the last path part.}
\item{CountryLong}{the HMD country full name.}
}
\value{
function called for its side effect of creating the lifetable txt output files, e.g. \code{mltper_1x1.txt} and other time/sex configurations. No value returned.
}
\description{
This function requires that all pertinent \code{ltper_AxN.Rdata} and \code{ltcoh_AxN()} data objects be present in the folder \code{WORKING/Rbin/} for males, females and both-sex tables. Objects are selected by grepping, which works for the present HMD file naming scheme. \code{.Rdata} files are read in, rounded, formatted and written back out into \code{WORKING/RSTATS/} by default, although this can be changed with the \code{STATSFOLDER} argument. This function must be called after ltper_AxN() (for all time intervals and sexes), which must have been run with the argument \code{save.bin = TRUE}.
}
\author{
Tim Riffe \email{triffe@demog.berkeley.edu}
}
| /HMDLifeTables/HMDLifeTables/man/Write_lt.Rd | no_license | timriffe/HMDLifeTables | R | false | true | 1,677 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Write_lt.R
\name{Write_lt}
\alias{Write_lt}
\title{\code{Write_lt} a function to write formatted .txt files for both period and cohort lifetables.}
\usage{
Write_lt(WORKING = getwd(), STATSFOLDER = "RSTATS", MPVERSION, XXX = NULL,
CountryLong = NULL)
}
\arguments{
\item{WORKING}{path to working directory, which typically ends with the HMD country abbreviation. Default \code{getwd()}.}
\item{STATSFOLDER}{the folder name where output is to be written to (not a full path). Default \code{"RSTATS"}.}
\item{MPVERSION}{5 or 6. Default 5. Here this only affects file headers.}
\item{XXX}{the HMD country abbreviation. If left \code{NULL}, this is extracted from \code{WORKING} as the last path part.}
\item{CountryLong}{the HMD country full name.}
}
\value{
function called for its side effect of creating the lifetable txt output files, e.g. \code{mltper_1x1.txt} and other time/sex configurations. No value returned.
}
\description{
This function requires that all pertinent \code{ltper_AxN.Rdata} and \code{ltcoh_AxN()} data objects be present in the folder \code{WORKING/Rbin/} for males, females and both-sex tables. Objects are selected by grepping, which works for the present HMD file naming scheme. \code{.Rdata} files are read in, rounded, formatted and written back out into \code{WORKING/RSTATS/} by default, although this can be changed with the \code{STATSFOLDER} argument. This function must be called after ltper_AxN() (for all time intervals and sexes), which must have been run with the argument \code{save.bin = TRUE}.
}
\author{
Tim Riffe \email{triffe@demog.berkeley.edu}
}
|
\name{condens1}
\alias{condens1}
\title{Conditional density estimation}
\description{Estimates conditional density functions of the form f(y| x) = f(x,y)/f(x).
Kernel density estimators are used to estimate f(x,y) and f(x).
The conditional density function can be plotted as a three-dimensional surface or as a contour map.
Alternatively, the conditional density of y can be graphed for as many as five target values of x.
}
\usage{
condens1(form,window=.7,bandwidth=0,kern="tcub",
mingrid.x=NULL,maxgrid.x=NULL,mingrid.y=NULL,maxgrid.y=NULL,ngrid=50,
xlab="x",ylab="y",zlab="fxy/fx",contour=TRUE,level=TRUE,wire=TRUE,dens=TRUE,
targetx.dens=NULL,quantile.dens=c(.10,.25,.50,.75,.90),data=NULL)
}
\arguments{
\item{form}{Model formula}
\item{window }{Window size. Default: 0.25. }
\item{bandwidth }{Bandwidth. Default: not used. }
\item{kern}{Kernel weighting functions. Default is the tri-cube. Options include "rect", "tria", "epan", "bisq", "tcub", "trwt", and "gauss".}
\item{mingrid.x, maxgrid.x, mingrid.y, maxgrid.y,
ngrid}{The mingrid and maxgrid values are the boundaries for the \emph{ngrid x ngrid} lattice used in the graphs produced by \emph{condens1}.
By default, mingrid.x = min(x), maxgrid.x = max(x), mingrid.y = min(y), maxgrid.y = max(y), and ngrid=50.
}
\item{xlab}{Label for the \emph{x}-axis in graphs. Default: "x"}
\item{ylab}{Label for the \emph{y}-axis in graphs. Default: "y"}
\item{zlab}{Label for the \emph{z}-axis in graphs. Default: "fxy/fx"}
\item{contour}{If \emph{contour=T}, produces a two-dimensional contour plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{contour=T}.}
\item{level}{If \emph{level=T}, produces a two-dimensional level plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{level=F}.}
\item{wire}{If \emph{wire=T}, produces a three-dimensional plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{wire=T}.}
\item{dens}{If \emph{dens=T}, produces a plot showing how \emph{f(y|x)} varies over \emph{y} for given target values of \emph{x}.
Target values of \emph{x} are provided using the \emph{targetx.dens} or \emph{quantile.dens} options. Default is dens=F.}
\item{targetx.dens}{Target values for \emph{x} in the density plots, e.g, \emph{targetx.dens} = c(200,400,600). Maximum number of entries is 5.
If \emph{targetx.dens} has more than 5 entries, only the first 5 will be used. Default is \emph{targetx.dens = NULL},
meaning that the target values for \emph{x} are determined by the \emph{quantile.dens} option.}
\item{quantile.dens}{Quantiles for the target values for \emph{x} in the density plots, e.g, \emph{quantile.dens} = c(.25,.50,.75). Maximum number of entries is 5.
If quantile.dens has more than 5 entries, only the first 5 will be used. Default is \emph{quantile.dens} = c(.10,.25,.50,.75,.90).}
\item{data }{A data frame containing the data. Default: use data in the current working directory.}
}
\value{
\item{fx}{The values of \emph{f(x)}, one for each data point.}
\item{fy}{The values of \emph{f(y)}, one for each data point.}
\item{fxy}{The values of \emph{f(x,y)}, one for each data point. The conditional densities are fxy/fx for \emph{x} and fxy/fy for \emph{y}.}
\item{gridmat}{An (\emph{ngrid*ngrid})x3 matrix used to produce the contour, level, and wire maps.
The first column contains the lattice values for \emph{x}, the second column contains the lattice values for \emph{y},
and the third column has the estimated values of \emph{f(y|x)} at the target values for \emph{x} and \emph{y}.}
\item{densmat}{The estimated values of \emph{f(y|x)} for the two-dimensional density graphs produced when \emph{dens = TRUE}.
If the number of observations in the call to \emph{condens1} is \emph{n} and the number of entries in \emph{quantile.dens} is \emph{nq},
then densmat is an \emph{n} x \emph{nq} matrix.}
}
\references{
Li, Oi and Jeffrey Scott Racine. \emph{Nonparametric Econometrics: Theory and Practice.} Princeton, NJ: Princeton University Press, 2007.
Loader, Clive. \emph{Local Regression and Likelihood.} New York: Springer, 1999.
Pagan, Adrian and Aman Ullah. \emph{Nonparametric Econometrics.} New York: Cambridge University Press, 1999.
}
\author{
Daniel McMillen
}
\seealso{
\link{qreglwr1}
}
\examples{
data(dupage99)
dupage99$ratio <- dupage99$av/dupage99$price
dropobs1 <- nptrim_obs(dupage99$ratio,k=3)
dropobs2 <- (dupage99$price<quantile(dupage99$price,.01))|
(dupage99$price>quantile(dupage99$price,.99))
dupage99 <- dupage99[dropobs1==FALSE&dropobs2==FALSE,]
o <- order(dupage99$price)
dupage99 <- dupage99[o,]
dupage99$price <- dupage99$price/1000
par(ask=TRUE)
# show all plots
fit <- condens1(ratio~price, window=.7,
xlab="Price", ylab="Assessment Ratio", zlab="Density",
targetx.dens=c(100,200,300,400), ngrid=40, data=dupage99)
# skip plots in base command; add them afterward
fit <- condens1(ratio~price, window=.7, contour=FALSE, level=FALSE, wire=FALSE,
dens=FALSE, targetx.dens=c(100,200,300,400), ngrid=40, data=dupage99)
contourplot(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio")
levelplot(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio")
wireframe(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio", zlab="Density")
}
\details{
The \emph{locfit} package is used to find the target values of \emph{x} for \emph{f(x)} and \emph{y} for \emph{f(y)}.
The \emph{expand.grid} command is then used to determine the target values of \emph{x} and \emph{y} for \emph{f(x,y)}.
The \emph{akima} package is used to interpolate \emph{f(x)}, \emph{f(y)}, and \emph{f(x,y)} to the
full data set and to the grid of target values for the contour, level, and wire plots.
The density functions \emph{f(x)} and \emph{f(y)} are as follows:
\deqn{f(x) = \frac{1}{sd(x)*b*n} \sum_i K ( \frac{ x_i - x}{sd(x)*b} )}{(sd(x)*b)^{-1} mean((x_i - x)/(sd(x)*b)) }
\deqn{f(y) = \frac{1}{sd(y)*b*n} \sum_i K ( \frac{ y_i - y}{sd(y)*b} )}{(sd(y)*b)^{-1} mean((y_i - y)/(sd(y)*b)) }
A product kernel is used for \emph{f(x,y)}:
\deqn{f(x,y) = \frac{1}{sd(x)*b*sd(y)*b*n}\sum_i K ( \frac{ x_i - x}{sd(x)*b} ) K ( \frac{ y_i - y}{sd(y)*b} ) }{(sd(x)*b*sd(y)*b)^{-1} mean (K ( (x_i - x)/(sd(x)*b) ) K ( (y_i - y)/(sd(y)*b) ) }
where \emph{b} is the bandwidth and the target points are \emph{x} and \emph{y}.
The bandwidth, \emph{b}, can be set using the \emph{bandwidth} option.
If \emph{b} = 0 (the default), \emph{sd(x)*b} and \emph{sd(y)*b} are replaced by window values, \eqn{h = quantile(dist, window)},
where \eqn{dist = |x_i - x|} or \eqn{dist = |y_i - y|}.
The window size is set using the \emph{window} option. By default, \emph{window} = .7 and \emph{bandwidth} = 0.
Available kernel weighting functions include the following:
\tabular{lll}{
Kernel \tab Call abbreviation \tab Kernel function K(z) \cr
Rectangular \tab ``rect'' \tab \eqn{\frac{1}{2} I(|z| <1)}{1/2 * I(|z|<1)} \cr
Triangular \tab ``tria'' \tab \eqn{(1-|z|)I(|z|<1)}{(1-|z|) * I(|z|<1)}\cr
Epanechnikov \tab ``epan'' \tab \eqn{\frac{3}{4} (1-z^2) * I(|z| <1)}{3/4 * (1-z^2)*I(|z| < 1)} \cr
Bi-Square \tab ``bisq'' \tab \eqn{\frac{15}{16} (1-z^2)^2 * I(|z| <1)}{15/16 * (1-z^2)^2 * I(|z| < 1)} \cr
Tri-Cube \tab ``tcub'' \tab \eqn{\frac{70}{81} (1-|z|^3)^3 * I(|z| <1)}{70/81 * (1-|z|^3)^3 * I(|z| < 1)} \cr
Tri-Weight \tab ``trwt'' \tab \eqn{\frac{35}{32} (1-z^2)^3 * I(|z| <1)}{35/32 * (1-z^2)^3 * I(|z| < 1)} \cr
Gaussian \tab ``gauss'' \tab \eqn{(2\pi)^{-.5} e^{-z^2/2}}{2pi^{-.5} exp(-z^2/2)} \cr }
The contour, level, and wire plots are produced from the values in \emph{gridmat} using the \emph{lattice} package.
The two-dimensional density graphs produced when \emph{dens=TRUE} are plots of \emph{f(y,x)/f(x)} at given values of \emph{x}.
By default, the values for \emph{x} are the quantiles given in \emph{quantile.dens}.
Alternatively, the values of \emph{x} can be specified directly using the \emph{targetx.dens} option.
The values used to construct the density graphs are stored in \emph{densmat}.
Both \emph{gridmat} and \emph{densmat} are stored by \emph{condens1} even if the printing of the graphs is suppressed.
}
| /man/condens1.Rd | no_license | cran/aratio | R | false | false | 8,598 | rd | \name{condens1}
\alias{condens1}
\title{Conditional density estimation}
\description{Estimates conditional density functions of the form f(y| x) = f(x,y)/f(x).
Kernel density estimators are used to estimate f(x,y) and f(x).
The conditional density function can be plotted as a three-dimensional surface or as a contour map.
Alternatively, the conditional density of y can be graphed for as many as five target values of x.
}
\usage{
condens1(form,window=.7,bandwidth=0,kern="tcub",
mingrid.x=NULL,maxgrid.x=NULL,mingrid.y=NULL,maxgrid.y=NULL,ngrid=50,
xlab="x",ylab="y",zlab="fxy/fx",contour=TRUE,level=TRUE,wire=TRUE,dens=TRUE,
targetx.dens=NULL,quantile.dens=c(.10,.25,.50,.75,.90),data=NULL)
}
\arguments{
\item{form}{Model formula}
\item{window }{Window size. Default: 0.25. }
\item{bandwidth }{Bandwidth. Default: not used. }
\item{kern}{Kernel weighting functions. Default is the tri-cube. Options include "rect", "tria", "epan", "bisq", "tcub", "trwt", and "gauss".}
\item{mingrid.x, maxgrid.x, mingrid.y, maxgrid.y,
ngrid}{The mingrid and maxgrid values are the boundaries for the \emph{ngrid x ngrid} lattice used in the graphs produced by \emph{condens1}.
By default, mingrid.x = min(x), maxgrid.x = max(x), mingrid.y = min(y), maxgrid.y = max(y), and ngrid=50.
}
\item{xlab}{Label for the \emph{x}-axis in graphs. Default: "x"}
\item{ylab}{Label for the \emph{y}-axis in graphs. Default: "y"}
\item{zlab}{Label for the \emph{z}-axis in graphs. Default: "fxy/fx"}
\item{contour}{If \emph{contour=T}, produces a two-dimensional contour plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{contour=T}.}
\item{level}{If \emph{level=T}, produces a two-dimensional level plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{level=F}.}
\item{wire}{If \emph{wire=T}, produces a three-dimensional plot of the conditional density estimates.
Evaluated for an \emph{ngrid} x \emph{ngrid} lattice. Default is \emph{wire=T}.}
\item{dens}{If \emph{dens=T}, produces a plot showing how \emph{f(y|x)} varies over \emph{y} for given target values of \emph{x}.
Target values of \emph{x} are provided using the \emph{targetx.dens} or \emph{quantile.dens} options. Default is dens=F.}
\item{targetx.dens}{Target values for \emph{x} in the density plots, e.g, \emph{targetx.dens} = c(200,400,600). Maximum number of entries is 5.
If \emph{targetx.dens} has more than 5 entries, only the first 5 will be used. Default is \emph{targetx.dens = NULL},
meaning that the target values for \emph{x} are determined by the \emph{quantile.dens} option.}
\item{quantile.dens}{Quantiles for the target values for \emph{x} in the density plots, e.g, \emph{quantile.dens} = c(.25,.50,.75). Maximum number of entries is 5.
If quantile.dens has more than 5 entries, only the first 5 will be used. Default is \emph{quantile.dens} = c(.10,.25,.50,.75,.90).}
\item{data }{A data frame containing the data. Default: use data in the current working directory.}
}
\value{
\item{fx}{The values of \emph{f(x)}, one for each data point.}
\item{fy}{The values of \emph{f(y)}, one for each data point.}
\item{fxy}{The values of \emph{f(x,y)}, one for each data point. The conditional densities are fxy/fx for \emph{x} and fxy/fy for \emph{y}.}
\item{gridmat}{An (\emph{ngrid*ngrid})x3 matrix used to produce the contour, level, and wire maps.
The first column contains the lattice values for \emph{x}, the second column contains the lattice values for \emph{y},
and the third column has the estimated values of \emph{f(y|x)} at the target values for \emph{x} and \emph{y}.}
\item{densmat}{The estimated values of \emph{f(y|x)} for the two-dimensional density graphs produced when \emph{dens = TRUE}.
If the number of observations in the call to \emph{condens1} is \emph{n} and the number of entries in \emph{quantile.dens} is \emph{nq},
then densmat is an \emph{n} x \emph{nq} matrix.}
}
\references{
Li, Oi and Jeffrey Scott Racine. \emph{Nonparametric Econometrics: Theory and Practice.} Princeton, NJ: Princeton University Press, 2007.
Loader, Clive. \emph{Local Regression and Likelihood.} New York: Springer, 1999.
Pagan, Adrian and Aman Ullah. \emph{Nonparametric Econometrics.} New York: Cambridge University Press, 1999.
}
\author{
Daniel McMillen
}
\seealso{
\link{qreglwr1}
}
\examples{
data(dupage99)
dupage99$ratio <- dupage99$av/dupage99$price
dropobs1 <- nptrim_obs(dupage99$ratio,k=3)
dropobs2 <- (dupage99$price<quantile(dupage99$price,.01))|
(dupage99$price>quantile(dupage99$price,.99))
dupage99 <- dupage99[dropobs1==FALSE&dropobs2==FALSE,]
o <- order(dupage99$price)
dupage99 <- dupage99[o,]
dupage99$price <- dupage99$price/1000
par(ask=TRUE)
# show all plots
fit <- condens1(ratio~price, window=.7,
xlab="Price", ylab="Assessment Ratio", zlab="Density",
targetx.dens=c(100,200,300,400), ngrid=40, data=dupage99)
# skip plots in base command; add them afterward
fit <- condens1(ratio~price, window=.7, contour=FALSE, level=FALSE, wire=FALSE,
dens=FALSE, targetx.dens=c(100,200,300,400), ngrid=40, data=dupage99)
contourplot(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio")
levelplot(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio")
wireframe(fit$gridmat[,3]~fit$gridmat[,1]*fit$gridmat[,2], xlab="Price",
ylab="Assessment Ratio", zlab="Density")
}
\details{
The \emph{locfit} package is used to find the target values of \emph{x} for \emph{f(x)} and \emph{y} for \emph{f(y)}.
The \emph{expand.grid} command is then used to determine the target values of \emph{x} and \emph{y} for \emph{f(x,y)}.
The \emph{akima} package is used to interpolate \emph{f(x)}, \emph{f(y)}, and \emph{f(x,y)} to the
full data set and to the grid of target values for the contour, level, and wire plots.
The density functions \emph{f(x)} and \emph{f(y)} are as follows:
\deqn{f(x) = \frac{1}{sd(x)*b*n} \sum_i K ( \frac{ x_i - x}{sd(x)*b} )}{(sd(x)*b)^{-1} mean((x_i - x)/(sd(x)*b)) }
\deqn{f(y) = \frac{1}{sd(y)*b*n} \sum_i K ( \frac{ y_i - y}{sd(y)*b} )}{(sd(y)*b)^{-1} mean((y_i - y)/(sd(y)*b)) }
A product kernel is used for \emph{f(x,y)}:
\deqn{f(x,y) = \frac{1}{sd(x)*b*sd(y)*b*n}\sum_i K ( \frac{ x_i - x}{sd(x)*b} ) K ( \frac{ y_i - y}{sd(y)*b} ) }{(sd(x)*b*sd(y)*b)^{-1} mean (K ( (x_i - x)/(sd(x)*b) ) K ( (y_i - y)/(sd(y)*b) ) }
where \emph{b} is the bandwidth and the target points are \emph{x} and \emph{y}.
The bandwidth, \emph{b}, can be set using the \emph{bandwidth} option.
If \emph{b} = 0 (the default), \emph{sd(x)*b} and \emph{sd(y)*b} are replaced by window values, \eqn{h = quantile(dist, window)},
where \eqn{dist = |x_i - x|} or \eqn{dist = |y_i - y|}.
The window size is set using the \emph{window} option. By default, \emph{window} = .7 and \emph{bandwidth} = 0.
Available kernel weighting functions include the following:
\tabular{lll}{
Kernel \tab Call abbreviation \tab Kernel function K(z) \cr
Rectangular \tab ``rect'' \tab \eqn{\frac{1}{2} I(|z| <1)}{1/2 * I(|z|<1)} \cr
Triangular \tab ``tria'' \tab \eqn{(1-|z|)I(|z|<1)}{(1-|z|) * I(|z|<1)}\cr
Epanechnikov \tab ``epan'' \tab \eqn{\frac{3}{4} (1-z^2) * I(|z| <1)}{3/4 * (1-z^2)*I(|z| < 1)} \cr
Bi-Square \tab ``bisq'' \tab \eqn{\frac{15}{16} (1-z^2)^2 * I(|z| <1)}{15/16 * (1-z^2)^2 * I(|z| < 1)} \cr
Tri-Cube \tab ``tcub'' \tab \eqn{\frac{70}{81} (1-|z|^3)^3 * I(|z| <1)}{70/81 * (1-|z|^3)^3 * I(|z| < 1)} \cr
Tri-Weight \tab ``trwt'' \tab \eqn{\frac{35}{32} (1-z^2)^3 * I(|z| <1)}{35/32 * (1-z^2)^3 * I(|z| < 1)} \cr
Gaussian \tab ``gauss'' \tab \eqn{(2\pi)^{-.5} e^{-z^2/2}}{2pi^{-.5} exp(-z^2/2)} \cr }
The contour, level, and wire plots are produced from the values in \emph{gridmat} using the \emph{lattice} package.
The two-dimensional density graphs produced when \emph{dens=TRUE} are plots of \emph{f(y,x)/f(x)} at given values of \emph{x}.
By default, the values for \emph{x} are the quantiles given in \emph{quantile.dens}.
Alternatively, the values of \emph{x} can be specified directly using the \emph{targetx.dens} option.
The values used to construct the density graphs are stored in \emph{densmat}.
Both \emph{gridmat} and \emph{densmat} are stored by \emph{condens1} even if the printing of the graphs is suppressed.
}
|
#' @importFrom dplyr vars `%>%` tibble
| /R/utils.R | no_license | m-clark/tidyext | R | false | false | 39 | r | #' @importFrom dplyr vars `%>%` tibble
|
# 1.1 Imnport test data
X_test <- read.table("X_test.txt", sep = "", header = F)
y_test <- read.table("y_test.txt", sep = "", header = F)
subject_test <- read.table("subject_test.txt", sep = "", header = F)
# 1.2 Import train data
X_train <- read.table("X_train.txt", sep = "", header = F)
y_train <- read.table("y_train.txt", sep = "", header = F)
subject_train <- read.table("subject_train.txt", sep = "", header = F)
# 1.3 Import features and activity labels
features <- read.table("features.txt", col.names = c("ID", "Name"))
activity_labels <- read.table("activity_labels.txt", col.names = c("IdActivity", "Activity"))
# 1.4 Add colnames
colnames(X_test) <- features$Name
colnames(y_test) <- "IdActivity"
colnames(subject_test) <- "IdSubject"
colnames(X_train) <- features$Name
colnames(y_train) <- "IdActivity"
colnames(subject_train) <- "IdSubject"
# 1.5 Merge data
test.data <- cbind(subject_test, y_test, X_test)
train.data <- cbind(subject_train, y_train, X_train)
all.data <- rbind(test.data, train.data)
# 2. Extract only mean and std of each measurement
all.data_m_std <- all.data[, grepl("mean|std|IdSubject|IdActivity",colnames(all.data))]
# 3. Use descriptive activity names to name the activities in the data set
install.packages("plyr")
library(plyr)
all.data_m_std <- join(all.data_m_std, activity_labels, by = "IdActivity", match = "first")
all.data_m_std <- all.data_m_std[,-1] # delete first column
all.data_m_std <- all.data_m_std[, c(81, 1:80)] # change coloumn order: last order first
# 4. Label the data set with descriptive variable names.
names(all.data_m_std) <- gsub("Acc", "Accelerometer",names(all.data_m_std))
names(all.data_m_std) <- gsub("Gyro", "Gyroscope",names(all.data_m_std))
names(all.data_m_std) <- gsub("Mag", "Magnitude",names(all.data_m_std))
names(all.data_m_std) <- gsub("Freq", "Frequency",names(all.data_m_std))
names(all.data_m_std) <- gsub("angle", "Angle",names(all.data_m_std))
names(all.data_m_std) <- gsub("gravity", "Gravity",names(all.data_m_std))
names(all.data_m_std) <- gsub("^t", "Time",names(all.data_m_std))
names(all.data_m_std) <- gsub("^f", "Frequency",names(all.data_m_std))
names <- names(all.data_m_std)
# 5. From the data set in step 4, create a second, independent tidy data set with the average of each variable for each activity and each subject.
all.data_m_std_tidy <- aggregate(all.data_m_std[,3:81], by = list(all.data_m_std$Activity, all.data_m_std$IdSubject), mean) # get the means by the two groups
colnames(all.data_m_std_tidy)[1:2] <- c("Activity","IdSubject") # change first two column names
# final step: create text file of output 5
write.table(all.data_m_std_tidy,"./tidy_dataset.txt", row.names = F)
| /run_analysis.R | no_license | srh-k4/Getting_and_Cleaning_Data | R | false | false | 2,708 | r | # 1.1 Imnport test data
X_test <- read.table("X_test.txt", sep = "", header = F)
y_test <- read.table("y_test.txt", sep = "", header = F)
subject_test <- read.table("subject_test.txt", sep = "", header = F)
# 1.2 Import train data
X_train <- read.table("X_train.txt", sep = "", header = F)
y_train <- read.table("y_train.txt", sep = "", header = F)
subject_train <- read.table("subject_train.txt", sep = "", header = F)
# 1.3 Import features and activity labels
features <- read.table("features.txt", col.names = c("ID", "Name"))
activity_labels <- read.table("activity_labels.txt", col.names = c("IdActivity", "Activity"))
# 1.4 Add colnames
colnames(X_test) <- features$Name
colnames(y_test) <- "IdActivity"
colnames(subject_test) <- "IdSubject"
colnames(X_train) <- features$Name
colnames(y_train) <- "IdActivity"
colnames(subject_train) <- "IdSubject"
# 1.5 Merge data
test.data <- cbind(subject_test, y_test, X_test)
train.data <- cbind(subject_train, y_train, X_train)
all.data <- rbind(test.data, train.data)
# 2. Extract only mean and std of each measurement
all.data_m_std <- all.data[, grepl("mean|std|IdSubject|IdActivity",colnames(all.data))]
# 3. Use descriptive activity names to name the activities in the data set
install.packages("plyr")
library(plyr)
all.data_m_std <- join(all.data_m_std, activity_labels, by = "IdActivity", match = "first")
all.data_m_std <- all.data_m_std[,-1] # delete first column
all.data_m_std <- all.data_m_std[, c(81, 1:80)] # change coloumn order: last order first
# 4. Label the data set with descriptive variable names.
names(all.data_m_std) <- gsub("Acc", "Accelerometer",names(all.data_m_std))
names(all.data_m_std) <- gsub("Gyro", "Gyroscope",names(all.data_m_std))
names(all.data_m_std) <- gsub("Mag", "Magnitude",names(all.data_m_std))
names(all.data_m_std) <- gsub("Freq", "Frequency",names(all.data_m_std))
names(all.data_m_std) <- gsub("angle", "Angle",names(all.data_m_std))
names(all.data_m_std) <- gsub("gravity", "Gravity",names(all.data_m_std))
names(all.data_m_std) <- gsub("^t", "Time",names(all.data_m_std))
names(all.data_m_std) <- gsub("^f", "Frequency",names(all.data_m_std))
names <- names(all.data_m_std)
# 5. From the data set in step 4, create a second, independent tidy data set with the average of each variable for each activity and each subject.
all.data_m_std_tidy <- aggregate(all.data_m_std[,3:81], by = list(all.data_m_std$Activity, all.data_m_std$IdSubject), mean) # get the means by the two groups
colnames(all.data_m_std_tidy)[1:2] <- c("Activity","IdSubject") # change first two column names
# final step: create text file of output 5
write.table(all.data_m_std_tidy,"./tidy_dataset.txt", row.names = F)
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/pal-dichromat.r
\name{dichromat_pal}
\alias{dichromat_pal}
\title{Dichromat (colour-blind) palette (discrete).}
\usage{
dichromat_pal(name)
}
\arguments{
\item{name}{Name of colour palette. One of:
\Sexpr[results=rd,stage=build]{scales:::dichromat_schemes()}}
}
\description{
Dichromat (colour-blind) palette (discrete).
}
\examples{
show_col(dichromat_pal("BluetoOrange.10")(10))
show_col(dichromat_pal("BluetoOrange.10")(5))
# Can use with gradient_n to create a continous gradient
cols <- dichromat_pal("DarkRedtoBlue.12")(12)
show_col(gradient_n_pal(cols)(seq(0, 1, length = 30)))
}
| /man/dichromat_pal.Rd | no_license | LluisRamon/scales | R | false | false | 676 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/pal-dichromat.r
\name{dichromat_pal}
\alias{dichromat_pal}
\title{Dichromat (colour-blind) palette (discrete).}
\usage{
dichromat_pal(name)
}
\arguments{
\item{name}{Name of colour palette. One of:
\Sexpr[results=rd,stage=build]{scales:::dichromat_schemes()}}
}
\description{
Dichromat (colour-blind) palette (discrete).
}
\examples{
show_col(dichromat_pal("BluetoOrange.10")(10))
show_col(dichromat_pal("BluetoOrange.10")(5))
# Can use with gradient_n to create a continous gradient
cols <- dichromat_pal("DarkRedtoBlue.12")(12)
show_col(gradient_n_pal(cols)(seq(0, 1, length = 30)))
}
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ngchmshaidy.R
\name{ngchmGetDataFileShaid}
\alias{ngchmGetDataFileShaid}
\title{Compute shaid for a data file}
\usage{
ngchmGetDataFileShaid(format, filename)
}
\arguments{
\item{format}{The format of the data file}
\item{filename}{The filesystem path to the data file}
}
\value{
The shaid of the data file
}
\description{
Compute shaid for a data file
}
| /man/ngchmGetDataFileShaid.Rd | no_license | MD-Anderson-Bioinformatics/NGCHM-R | R | false | true | 434 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ngchmshaidy.R
\name{ngchmGetDataFileShaid}
\alias{ngchmGetDataFileShaid}
\title{Compute shaid for a data file}
\usage{
ngchmGetDataFileShaid(format, filename)
}
\arguments{
\item{format}{The format of the data file}
\item{filename}{The filesystem path to the data file}
}
\value{
The shaid of the data file
}
\description{
Compute shaid for a data file
}
|
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