Statistical computing plays a key role in many aspects of official statistics, e.g. statistical disclosure control, visualisation, imputation and time series analysis. The usage of open source software like R (R Development Core Team, 2014) is of growing importance due to budgetary restrictions in national statistical institutes (NSIs). In addition, software can be used by multiple organisations and users without license costs and therefore the use of R supports cooperations between NSIs, especially on an European level. NSIs collect a huge amount of confidential data, usually financed by public funds. Therefore it is of increasing importance to release anonymized micro data back to the public and to researchers.. By including sophisticated statistical disclosure control methods in R package sdcMicro (Templ et al., 2015, 2012b; Kowarik et al., 2012), NSIs have the possibility to check the disclosure risk of their data sets and afterwards protect the observations with high disclosure risk. Independently of the data source, it is almost always the case that missing values are included in a data set. These missing values have to be replaced by estimated values (=imputation) before it is possible to apply standard statistical methods. With the R package VIM (Templ et al., 2011a) it is easily possible to apply a wide range of imputation methods, such as an iterative stepwise regression imputation approach (see Templ et al., 2011b). An important step in understanding a specific data set and its quality is visual analysis. With the R package sparkTable (Kowarik et al., 2014a) tables presenting quantitative information can be enhanced by including sparklines and sparkbars (initially proposed by Tufte, 2001). Sparklines and sparkbars are simple, intense and illustrative graphs, small enough to fit in a single line. Therefore they can easily enrich tables and continuous texts with additional information in a comprehensive visual way. Seasonal adjustment, a special topic of time series analysis, is of great importance in official statistics to make time-dependent data comparable between different countries or just different points in time. The R package x12 (Kowarik and Meraner, 2014) provides an interface to the X12-ARIMA software (see e.g. Hood and Monsell, 2010). Moreover an easy to use graphical user interface is available through the R package x12GUI (Schopfhauser et al., 2014). A methodological and computational framework for solving all the mentioned aspects is given in this thesis.