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Електронні заявки управління експериз ЗТПП
Аналіз тривалості виконання заявок проведено для налаштування системи контролю (SLA-планів). За даними аналызу виходить, що із впровадженням строк підготовки цінових довідок (для митниці, тендерного комітету, правоохоронних органів тощо) скоротився до кількох годин для 40-45% звернень, і для більшості заявок (до 80%) протягом останніх 9 місяців не перевищував трьох календарних днів.
par(mfrow = c(2,3)) beeswarm(distributions, col = 2:4, main = 'corral = "none" (default)') beeswarm(distributions, col = 2:4, corral = "gutter", main = 'corral = "gutter"') beeswarm(distributions, col = 2:4, corral = "wrap", main = 'corral = "wrap"') beeswarm(distributions, col = 2:4, corral = "random", main = 'corral = "random"') beeswarm(distributions, col = 2:4, corral = "omit", main = 'corral = "omit"')
> distributions <- list(runif = runif(100, min = -3, max = 3), + rnorm = rnorm(100), + rlnorm = rlnorm(100, sdlog = 0.5)) > beeswarm(distributions, xlab="prepared by VOLKAN OBAN using R-beeswarm", col = 2:4)
Make.Funny.Plot <- function(x){ unique.vals <- length(unique(x)) N <- length(x) N.val <- min(N/20,unique.vals) if(unique.vals>N.val){ x <- ave(x,cut(x,N.val),FUN=min) x <- signif(x,4) } # construct the outline of the plot outline <- as.vector(table(x)) outline <- outline/max(outline) # determine some correction to make the V shape, # based on the range y.corr <- diff(range(x))*0.05 # Get the unique values yval <- sort(unique(x)) plot(c(-1,1),c(min(yval),max(yval)), type="n",xaxt="n",xlab="") for(i in 1:length(yval)){ n <- sum(x==yval[i]) x.plot <- seq(-outline[i],outline[i],length=n) y.plot <- yval[i]+abs(x.plot)*y.corr points(x.plot,y.plot,pch=19,cex=0.5) } } x <- rnorm(1000) Make.Funny.Plot(x) boxplot(x, add = T, at = 0, col="#0000ff22") # my thanks goes to Greg Snow for the tip on the transparency colour (from 2007):
library(beeswarm) > data(breast) > beeswarm(time_survival ~ ER, data = breast, + pch = 16, pwcol = 1 + as.numeric(event_survival), + xlab = "beeswarm package", ylab = "Follow-up time (months)", + labels = c("ER neg", "ER pos")) > legend("topright", legend = c("Yes", "No"), + title = "Censored", pch = 16, col = 1:2
Machine Learning Income Prediction Using Census Data
The goal of this project is to data from the US Census to develop predictive models to predict if an individual has an income higher than $50,000/year. In this project, the following predictive methods were evaluated: logistic regression, CART tree models, random forest, C50 boosted tree models, GBM boosted tree models. A cross validation CART tree model was also constructed. Each model was evaluated and analyzed using several metrics such as accuracy and AUC. A C50 boosted tree model was found to have the best performance.
Volcano Plot using R
Data Librarian & Data Archive Salary Survey
Looking at data librarian salaries from public universities for the most recent years available.
Statistical Analysis of Nitrogen Soil Mineralization Rates
Based on statistical analyses of linear models of nitrogen mineralization versus temperature data, it was concluded (with a 98% confidence interval) that there is sufficient evidence to state there is an overall positive relationship (positive slope) between soil temperature and nitrogen mineralization rates. A linear model between N mineralization rate and both soil temperature and soil environment descriptors had a positive slope of 0.003326 micrograms/gram/day/'C and had an associated confidence 98% interval that ruled out a slope of 0. Based on linear models, a. Alfalfa, b. Mown Grassland, c. No Till Management, and d. Poplar soil environments had statistically significant positive relationships between N mineralization and soil temperature. The slopes ranged between 0.0133459 and 0.01750 micrograms/gram/day/'C. At the 90% confidence interval level, based on the data in this study,five soil environment descriptors are associated with data that indicate a slight negative relationship between N mineralization rates and soil temperature. These descriptor variables are a. Biologically Based Management b. Conventional Management c. Deciduous Forest d. Early Successional e. Reduced Input Management. The slopes ranged between -0.004896 and -0.0080018 micrograms/gram/day'C. These negative slopes indicate both that additional factors must be taken into account in future studies, and that the error of the N mineralization test should be studied, and if necessary a more accurate method should be used in future studies. I am also recommending that future work take into account moisture, climate, soil chemistry, and nitrogen speciation.