![]() Use R! Albert:Bayesian Computation with R Cook/Swayne:Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Paradis: Analysis of Phylogenetics and Evolution with R Pfaff: Analysis of Integrated and Cointegrated Time Series with R Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. #> pkgconfig_2.0.1 htmltools_0.3.6 bindr_0.1.1 knitr_1.This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. #> loaded via a namespace (and not attached): ![]() #> stats graphics grDevices utils datasets methods base #> LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib #> BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib #> Running under: macOS High Sierra 10.13.5 #> group min_date min_int min_date_missing min_int_missing ![]() S2 % group_by(group) %>% summarise(min_date = min(date, na.rm = TRUE), min_int = min(int, na.rm = TRUE)) %>% mutate(min_date_missing = is.na(min_date), min_int_missing = is.na(min_int)) #> Warning: package 'bindrcpp' was built under R version 3.4.4 #> The following objects are masked from 'package:base':ĭf % group_by(group) %>% summarise(min_date = min(date), min_int = min(int)) %>% mutate(min_date_missing = is.na(min_date), min_int_missing = is.na(min_int)) #> The following objects are masked from 'package:stats': This is doubly confusing! Consider: library(dplyr) The question has been answered, but it is useful to point out that if the column in question is a Date or a datetime, then it will still appear to be an NA in the summary table, but actually isn't. What can I do to avoid any Inf and instead get NA so that I can further proceed with: Using pmin() instead of min returned the error message: Error in summarise_impl(.data, dots) :Ĭolumn 'in.age' must be length 1 (a summary value), not 3 With Inf I cannot call summary(df$min.age) in a meaningful way. No non-missing arguments to min returning Inf` Since there are NAs in the data frame I receive the warning: `Warning message: In min(age, na.rm = T) : Such a data frame could look like this: df % I recently 'discovered' the awesome plyr and dplyr packages and use those for analysing patient data that is available to me in a data frame.
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