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This document summarizes code to analyze strike data from different locations in China from 2011-2015. It reads in strike data from an Excel file, calculates the number of strikes by year and location, and exports this data to CSV files for plotting. It then creates plots including a time series plot with multiple locations, a bubble chart using the GoogleVis package, and individual and clustered bar graphs.
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![proj.txt
#install.packages("xlsx", dependencies=TRUE)
#install.packages("lubridate", dependencies=TRUE)
#install.packages("zoo")
library(xlsx)
library(lubridate)
library(zoo)
# 1a.
strikes = read.xlsx("proj01_strikes.xlsx", sheetName="Labour",
as.data.frame=TRUE,header=TRUE)
# calculate strikes by year by locations
# This is to make one plot with all four Locations on the time plot in different
colors.
# Also for dynamic bubble graph and stacked up bar graph.
year=rep(c("2011/12/31","2012/12/31","2013/12/31","2014/12/31", "2015/02/28"),4)
locations=rep(c("Beijing","Chongqing","Guangdong","Shanghai"),each=5)
num_strikes=numeric(length(year))
for (i in 1:length(year))
{
num_strikes[i]=sum(strikes$Strikes[which(year(strikes$Date)==year(year[i])
& (strikes$Locations==locations[i]))])
}
labour=data.frame(year, locations, num_strikes)
write.csv(labour, "labour.csv",row.names=FALSE )
# create another data set for individual bar graph and cluster bar graph
Year=c("2011/12/31","2012/12/31","2013/12/31","2014/12/31", "2015/02/28")
Beijing=num_strikes[1:5]
Chongqing=num_strikes[6:10]
Guangdong=num_strikes[11:15]
Shanghai=num_strikes[16:20]
labour.alt=data.frame(Year,Beijing,Chongqing,Guangdong,Shanghai)
write.csv(labour.alt, "labour.alt.csv",row.names=FALSE)
#2
#install.packages("googleVis")
library(googleVis)
labour=read.csv("labour.csv",header=TRUE,sep=",")
# create another data set for dynamic bubble graph using google Chart Tools
Year = year(labour$year)
locations=labour$locations
num_strikes=labour$num_strikes
labour.g=data.frame(locations, Year, num_strikes)
M1 <- gvisMotionChart(labour.g, idvar="locations", timevar="Year",
sizevar="num_strikes")
plot(M1)
Page 1](https://image.slidesharecdn.com/1f0b7fb5-89d4-4cb2-91ca-7c74a3d5ec1f-150316232529-conversion-gate01/85/proj-1-320.jpg)
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plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
Chapter3_Visualizations2.pdf
This document discusses different types of data visualizations that can be created using the matplotlib library in Python. It covers basic visualizations like line charts, bar charts, histograms, and scatterplots. Examples are provided for each type of visualization to illustrate how to construct them programmatically and customize aspects like titles, labels, legends. Guidelines are given for effective visualization practices, such as using consistent axis scales. The document demonstrates how to explore and communicate data through different matplotlib plotting functions.
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...The Statistical and Applied Mathematical Sciences Institute
R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions and packages, and is used through interfaces like RStudio. R represents data using objects like vectors, matrices, and data frames. Common operations include calculations, generating random variables, and visualizing data. R can be used to analyze a glass fragment dataset to visualize compositions and potentially classify an unknown fragment.Python lecture 05
This document provides an overview of dictionaries in Python. It discusses how dictionaries are defined using curly braces {}, how keys can be any immutable object like strings or numbers while values can be any object, and how to access values using keys. It also covers adding and deleting key-value pairs, checking for keys, shallow and deep copying, and creating dictionaries from keys or sequences.
ggtimeseries-->ggplot2 extensions
This document describes ggTimeSeries, an R package that provides extensions to ggplot2 for creating time series plots. It includes examples of using functions from ggTimeSeries to create calendar heatmaps, horizon graphs, steam graphs, and marimekko plots from time series data. The examples demonstrate how to generate sample time series data, create basic plots, and add formatting customizations.
第二讲 Python基礎
This document summarizes Python basics including its features, popularity in different fields and companies, data types, control flow, containers like lists and dictionaries, NumPy for numerical computing, and classes. Python is an interpreted, general-purpose language with rich library support. It is commonly used in computer science, data analysis, biology, and academic communities. Major companies like Google, Dropbox, and Instagram use Python.
第二讲 预备-Python基礎
This document summarizes Python basics including its features, popularity in different fields and companies, data types, control flow, containers like lists and dictionaries, NumPy for numerical computing, and classes. Python is an interpreted, general-purpose language with rich library support. It is commonly used in computer science, data analysis, biology, and academic communities. Major companies like Google, Dropbox, and Instagram use Python.
R language introduction
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
Python for data science by www.dmdiploma.com
https://www.dmdiploma.com/studymaterial?id=5/python-for-data-science
This Python course provides a beginner-friendly introduction to Python for Data Science.
Rcommands-for those who interested in R.
A short list of the most useful R commands
reference: http://www.personality-project.org/r/r.commands.html
R programı ile ilgilenen veya yeni öğrenmeye başlayan herkes için hazırlanmıştır.
JavaScript Editions ES7, ES8 and ES9 vs V8
ES7 introduced the exponentiation operator (**) and Array.prototype.includes() method. ES8 introduced async functions which allow asynchronous code to be written in a cleaner way using async and await keywords, shared memory and atomics to improve parallelism and concurrency between workers, and new methods like Object.entries() and Object.values(). Current proposals for ES9 include object destructuring with rest and spread properties, async iteration with for-await loops, and dynamic import() to load modules at runtime.
Forecast stock prices python
This document contains Python code that imports libraries, loads stock price data, filters the data to select stocks with high returns and low volatility, performs time series decomposition and ARIMA modeling on the selected stocks, evaluates the model accuracy, and visualizes results through various plots. Key steps include filtering over 500 stocks down to 27, decomposing each stock's trend and seasonality, running ARIMA models to forecast prices 20 days ahead, and calculating the model accuracy score as 80% when comparing actual vs predicted direction of price movements.
Data Manipulation with Numpy and Pandas in PythonStarting with N
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
Using standard libraries like stdio and sdtlib.h and using stats.h a.pdf
Unlike the Federal income tax, most states do not require some form of state income tax.
True
False
Solution
Correct answer is True
Unlike Federal income tax, most states do not require some form of state income tax - This
statement is true as fililng of Fedral income tax return is mandatory for almost everybody but
some US states like Alaska, Florida, Navada, South Dakota, Texas, Washington and Wayoming
do not require filing of state income tax..
talk at Virginia Bioinformatics Institute, December 5, 2013
Extensible domain-specific programming for the sciences
The notion of scientists as programmers begs the question of what sort of programming language would be a good fit. The common answer seems to be both none of them and all of them. Many scientific applications are a combination of general-purpose and domain-specific languages: R for statistical elements, MATLAB for matrix-based computations, Perl-based regular expressions for string matching, C or FORTRAN for high performance parallel computations, and scripting languages such as Python to glue them all together. This clumsy situation demonstrates the need for different domain-specific language features.
Our hypothesis is that programming could be made easier, less error-prone and result in higher-quality code if languages could be easily extended, by the programmer, with the domain-specific features that a programmer or scientists needs for their particular task at hand. This talk demonstrates the meta-language processing tools that support this composition of programmer-selected language features, with several extensions chosen from the previously mentioned list of features.
Is your excel production code?
Mehar Singh, CEO of ProCogia, and Jason Grahn, Senior Business Analyst at Apptio, co-present on the journey from Excel to R at the second Bellevue chapter useR Group Meetup.
If we’re producing analysis that drives business decision making, that’s production-grade code! This talk will address this question, which in turn shows why R is the way to go – assumptions are built into the code and enables the analyst to automate & reproduce their efforts.
This presentation includes:
- Data importing (opening a CSV or connecting to a SQL in both tools)
- Filtering, grouping, summarizing (pivot tables in Excel vs. tidy code in R)
- Visualizations (charts in excel vs ggplot in R)
Short Reference Card for R users.
This document provides a concise reference card summarizing common functions and operations in R including getting help, input/output, data creation and manipulation, variable conversion and information, plotting, and dates/times. It covers essential functions for loading and saving data, accessing documentation, basic math operations, creating matrices and data frames, subsetting and selecting data, and creating basic plots and graphs.
Reference card for R
This document provides a concise reference card summarizing common functions and operations in R including getting help, input and output, data creation, slicing and extracting data, variable conversion and information, data selection and manipulation, math operations, matrices, strings, dates and times, and plotting. It covers essential functions for loading and saving data, accessing documentation, basic data manipulation and common statistical analyses in R.
C_Arrays.pptx
The slides present fundamental concepts about various types of arrays provided by the C programming language.
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Eag 201110-hrugregexpresentation-111006104128-phpapp02
Eag 201110-hrugregexpresentation-111006104128-phpapp02
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docx
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docx
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...
NCCU: Statistics in the Criminal Justice System, R basics and Simulation - Pr...
Data Manipulation with Numpy and Pandas in PythonStarting with N
Data Manipulation with Numpy and Pandas in PythonStarting with N
Using standard libraries like stdio and sdtlib.h and using stats.h a.pdf
Using standard libraries like stdio and sdtlib.h and using stats.h a.pdf
talk at Virginia Bioinformatics Institute, December 5, 2013
talk at Virginia Bioinformatics Institute, December 5, 2013
proj
- 1. proj.txt #install.packages("xlsx", dependencies=TRUE) #install.packages("lubridate", dependencies=TRUE) #install.packages("zoo") library(xlsx) library(lubridate) library(zoo) # 1a. strikes = read.xlsx("proj01_strikes.xlsx", sheetName="Labour", as.data.frame=TRUE,header=TRUE) # calculate strikes by year by locations # This is to make one plot with all four Locations on the time plot in different colors. # Also for dynamic bubble graph and stacked up bar graph. year=rep(c("2011/12/31","2012/12/31","2013/12/31","2014/12/31", "2015/02/28"),4) locations=rep(c("Beijing","Chongqing","Guangdong","Shanghai"),each=5) num_strikes=numeric(length(year)) for (i in 1:length(year)) { num_strikes[i]=sum(strikes$Strikes[which(year(strikes$Date)==year(year[i]) & (strikes$Locations==locations[i]))]) } labour=data.frame(year, locations, num_strikes) write.csv(labour, "labour.csv",row.names=FALSE ) # create another data set for individual bar graph and cluster bar graph Year=c("2011/12/31","2012/12/31","2013/12/31","2014/12/31", "2015/02/28") Beijing=num_strikes[1:5] Chongqing=num_strikes[6:10] Guangdong=num_strikes[11:15] Shanghai=num_strikes[16:20] labour.alt=data.frame(Year,Beijing,Chongqing,Guangdong,Shanghai) write.csv(labour.alt, "labour.alt.csv",row.names=FALSE) #2 #install.packages("googleVis") library(googleVis) labour=read.csv("labour.csv",header=TRUE,sep=",") # create another data set for dynamic bubble graph using google Chart Tools Year = year(labour$year) locations=labour$locations num_strikes=labour$num_strikes labour.g=data.frame(locations, Year, num_strikes) M1 <- gvisMotionChart(labour.g, idvar="locations", timevar="Year", sizevar="num_strikes") plot(M1) Page 1