Data World Blog

Visualizing data prior to any analysis is a basic and important step. Here we will cover boxplot , which is a type of univariate plot. Univariate plots are those that take into account one varible, these may include histograms, density plots, boxplots, etc. BOXPLOT: Boxplot is a standardized way of displaying the distribution of data based on five summary numbers from the data. Using boxplot we can see that the distribution of the data and its main characteristics are clearly observed. And it also allows us to compare different sets of data simultaneously. It is a powerfull visual tool that can be used to illustrate data, to study symmetry, to study queues, assumptions about distribution, and also can be used to compare different populations. The five numbers used as default in R are:  25th percentil : bottom of the box (Q1) 75th percentile : top of the box (Q3)  50th percentil : band near the middle of the box (Q2, median), and, the ends of the whiske...

Visualizing data prior to any analysis is a basic and important step. Univariate plots are those that take into account one varible, these may include histograms, density plots, boxplots, etc. Here we will cover pie chart and bar chart , which are types of univariate plots. PIE CHART Pie charts are a circular statistical graphic which is divided into slices to repesent a numerical proportion. We have to take into account that using pie charts is difficult to compare different sections of a given pie chart, or to compare data across different pie charts. pie(x, labels = names(x), edges = 200, radius = 0.8, clockwise = FALSE, init.angle = if(clockwise)       90 else 0, density = NULL, angle = 45, col = NULL, border = NULL, lty = NULL, main =             NULL, …) Using the dataset chickwts we can plot which percentatge of chickens have been feed with each feed type. TableFeed <- table(chickwts$feed) TableFeed ## ...

Visualizing data prior to any analysis is a basic and important step. Univariate plots are those that take into account one varible, these may include histograms, density plots, etc. Here there are som examples with the dataset airquality : HISTOGRAMS: Histogram is a very well-known univariate plot: hist(airquality$Wind) For the construction of the histogram a specific number of bins and their spacing in the horizontal axis is required. The number of bins can be set by the parameter breaks : par(mfrow = c(1,3)) hist(airquality$Wind, xlab = "Wind", col = "red", main=" Histogram Wind") hist(airquality$Wind, xlab = "Wind", col = "orange", main=" Histogram Wind", breaks= 20) hist(airquality$Wind, xlab = "Wind", col = "gold", main=" Histogram Wind", breaks= 40) For setting the axis limits: par(mfrow = c(1,2)) hist(airquality$Wind, xlab = "Wind", col = "red...