Access Datastream content through <https://product.datastream.com/dswsclient/Docs/Default.aspx>., our historical financial database with over 35 million individual instruments or indicators across all major asset classes, including over 19 million active economic indicators. It features 120 years of data, across 175 countries – the information you need to interpret market trends, economic cycles, and the impact of world events.    Data spans bond indices, bonds, commodities, convertibles, credit default swaps, derivatives, economics, energy, equities, equity indices, ESG, estimates, exchange rates, fixed income, funds, fundamentals, interest rates, and investment trusts. Unique content includes I/B/E/S Estimates, Worldscope Fundamentals, point-in-time data, and Reuters Polls.    Alongside the content, sit a set of powerful analytical tools for exploring relationships between different asset types, with a library of customizable analytical functions.    In-house timeseries can also be uploaded using the package to comingle with Datastream maintained datasets, use with these analytical tools and displayed in Datastream’s flexible charting facilities in Microsoft Office.

Summarizing data frames by calculating various statistical measures, including measures of central tendency, dispersion, skewness(), kurtosis(), and normality tests. The package leverages the 'moments' package for calculating statistical moments and related measures, the 'dplyr' package for data manipulation, and the 'nortest' package for normality testing. 'DataSum' includes functions such as getmode() for finding the mode(s) of a data vector, shapiro_normality_test() for performing the Shapiro-Wilk test (Shapiro & Wilk 1965 <doi:10.1093/biomet/52.3-4.591>) (or the Anderson-Darling test when the data length is outside the valid range for the Shapiro-Wilk test) (Stephens 1974 <doi:10.1080/01621459.1974.10480196>), Datum() for generating a comprehensive summary of a data vector with various statistics (including data type, sample size, mean, mode, median, variance, standard deviation, maximum, minimum, range, skewness(), kurtosis(), and normality test result) (Joanes & Gill 1998 <doi:10.1111/1467-9884.00122>), and DataSumm() for applying the Datum() function to each column of a data frame. Emphasizing the importance of normality testing, the package provides robust tools to validate whether data follows a normal distribution, a fundamental assumption in many statistical analyses and models.