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Better integration with thetidyverseframework, especially for packagesdplyr,tibbleandbroom; Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables. Last but not least: A brand new edition AND online version ofFlexible Imputation of Missing Data. Second Editi...
This avoids potential conflicts with other packages using the more and more popular view() function (notably, tibble, part of the tidyverse family, defines view() as an alias for View()) Enforced adequate number formatting using format() internally, so that using the following optional arguments...
#loading the countrycode data data(countrycode) head(countrycode) #> # A tibble: 6 × 2 #> country country_name #> <chr> <chr> #> 1 AZE Azerbaijan #> 2 ARG Argentina #> 3 AUS Australia #> 4 AUT Austria #> 5 BEL Belgium #> 6 BRA Brazil...
df = R.tibble(x: R.c(1, R::NA, 3)) puts df## # A tibble: 3 x 1 ## x ## <int> ## 1 1 ## 2 ## 3 3 Now filtering by :x > 1 shows all lines that satisfy this condition, where the row with R:NA does not.puts df.filter(:x > 1)...
mclust","methods","minpack.lm","moments","msir","NBPSeq","nonnest2","parallel","penalized","plyr","pscl","reshape2","Rmagic","rsvd","Rtsne","scales","Seurat","snow","sctransform","stats","tibble","tidyr","truncnorm","VGAM","ZIM","zoo") ipak(cranpackages,repository="CRAN...