Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

This dataset is used to demo the combo chart functionality.

This data supports the assertions made in a paper (same name as this project) which surveyed 3 Second Language Acquisition journals (Modern Language Journal, Language Learning, and Studies in Second Language Acquisition) from the time of their inception to 2011/2012. The raw data used for calculations about the number of graphics and which type of graphics were published is included in the attached Excel file.

The Excel spreadsheet contains the quantitative questions (Questions 1, 3 and 4). Each question is analysed in the form of a frequency distribution table and a pie chart.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Using the User Manual as a guide and the Excel Graph Input Data Example file as a reference, the user enters the semantics of the graph model in this file.

Minority-Interest-Expense Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

This dataset contains Weekly U.S. Ending Stocks of Distillate Fuel Oil 2015-2022. Data from US Energy information administration.This series is available through the EIA open data API and can be downloaded to Excel or embedded as an interactive chart or map on your website.

Operating-Expenses Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

Working-Capital-Turnover Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition.

Mean monthly flow (ML/month) and Annual flow (ML/yr) data at key gauges in the Macalister Irrigation District (MID) as monitored by SRW. The data are provided in MS Excel format in worksheets and charts.

Data used to produce Time-series drainage volume data provided by a third party. Site information and monitoring drainage flow data provided by the Southern Rural Water are specific to the Macalister Irrigation District.

Time specific data in the range 23/07/1997 to 31/12/2013

Dataset History

This dialogue has been copied from a draft of the BA-GIP report.

A total of 197 river gauges were identified within the model area representing all of the major rivers. Daily gauge level data was sourced from the Victorian Department of Environment, Land, Water and Planning Water Measurement Information System (WMIS, 2015). A list of the river gauges is provided in the report for key river basins

Only main stems of the major rivers were included in the model. These river reaches were identified using the DEPI hydro25 spatial data set (DEPI, 2014). The river classification was used to vary river incision depth (depth below the ground surface as defined by the digital elevation model) and width attributes. In the absence of recorded stage height information, river classification was used to estimate river stage heights. A total of 22,573 river cells are included in the model. Fifty-one gauges were selected to calibrate the catchment modelling framework in unregulated catchments based on Base Flow Indexes and observed stream flows.

Drainage channels and man-made drainage features in the Macalister Irrigation District (MID) were included in the model based on available drainage network mapping. This information was sourced from Southern Rural Water (SRW) and the DEPI Corporate Spatial Data library. Drainage cells are assigned to the uppermost cells within the model to capture groundwater discharge processes. Drain cells in Modflow can only act as groundwater discharge points and as such those cells outside drainage channels will be characterised as having a bed elevation equivalent to ground surface elevation. A total of 410,504 drainage cells are incorporated in the model. Apart from 3 river gauges sourced from the WMIS, SRW also has 15 gauges monitored drainage from the MID. The measurements commenced between 1997 and 2005. Of the 15 gauges, six were selected to calibrate the catchment modelling framework based on observed discharge.

Dataset Citation

Victorian Department of Economic Development, Jobs, Transport and Resources (2015) Mean monthly flow & annual flow data - Macalister Irrigation District. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/6ba89d78-1e42-4e02-bd5c-a435ee15bef4.

Total-Other-Income-Expense-Net Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

Other-Cashflows-From-Financing-Activities Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Excel file that contains data underlying all graphs.

This MS Excel data has been processed into line graphs to create time series line graphs and data tables which give insight into changing physiochemical water quality characteristics and influences. The study sets out to determine if climate change has had an influence on physiochemical water quality characteristics both within and between the Breede and Olifants estuaries over a nine year monitoring period. The data represents changes and comparisons between salinity, temperature and rainfall within and between the Olifants and Breede river estuaries in the Wester Cape Province of South Africa.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-09-08 to 2025-09-05 about stock market, average, industry, and USA.