Shulou(Shulou.com)06/01 Report--

This article mainly explains "what is the commonly used data visualization technology in R, Python and Julia". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what is the commonly used data visualization technology in R, Python and Julia".

1. The importance of data visualization

For users or researchers in the field of data science and business analysis, the use of various types of graphs, pie charts, bar charts, and other visual means to show underlying trends or patterns in the data is critical to understanding the data. at the same time, it can help researchers to better present the data to the audience or customers. There are several main reasons for doing so.

First, it is sometimes difficult for language to describe our findings, especially when there are several patterns or many influencing factors, complex relationships can be better understood and explained through several separate graphics and a connection diagram.

Second, we can use graphics or pictures to explain some algorithms.

Third, we can also use relative size to express different meanings. In the field of finance, a basic concept is called time value of money (Time Value of Money,TVM), which means "one bird in the hand is worth two in the bush". Today's $100 is more valuable than the same amount of future cash flow. Different circles of different sizes represent the present value of cash flow at different points in the future, which can help readers understand the concept more clearly.

Fourth, our data can be very confusing, so simply showing the data points may make the reader more confused. It would be helpful if we could use a simple graph to show its main features, attributes, or patterns.

2. R data visualization

First, let's look at the simplest figure in R. Using the following line of R code, we draw from the

To

CoSine function values in the range:

> plot (cos,-2*pi,2*pi)

The corresponding figure is shown in figure 4.1.

Fig. 4.1 CoSine function diagram

Histograms also help us to understand the distribution of data points. Figure 4.1 is a simple example. First, we generate a set of random numbers that obey the standard normal distribution. For illustration purposes, the set.seed () command on the first line is redundant, and its existence ensures that all users who use the same threshold (333 in this case) will get the same set of random numbers.

In other words, with the same input value, the histogram will look the same. In the next line, the rnorm (n) function draws n random numbers that follow the standard normal distribution. Next, the last line uses the hist () function to generate a histogram:

> set.seed (333) > data hist (data)

The relevant histogram is shown in figure 4.2.

Figure 4.2 sample histogram

Note that the code rnorm (5000) is the same as rnorm (5000), where the default value that means the mean is zero and the default value of SD is 1. The next R program will shadow the tail on the left side of the standard normal distribution:

X

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.