Unique entries
Make vector entries unique with unique
length ( iris $ Sepal.Length )
## [1] 150
length ( unique ( iris $ Sepal.Length ))
## [1] 35
Count occurrences
Count occurrences of entries with table
table ( iris $ Species )
##
## setosa versicolor virginica
## 50 50 50
Aggregate data
Compute aggregate statistics with aggregate
aggregate ( iris [, 1 : 4 ], by = list ( iris $ Species ), FUN = mean , na.rm = TRUE )
## Group.1 Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026
Intersect data
Compute intersect between two vectors with %in%
month.name %in% c ( "May" , "July" )
## [1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
Merge data frames
Join two data frames by common field entries with merge
(here row names by.x=0
). To obtain only the common rows, change all=TRUE
to all=FALSE
. To merge on specific columns, refer to them by their position numbers or their column names.
frame1 <- iris [ sample ( 1 : length ( iris [, 1 ]), 30 ), ]
frame1 [ 1 : 2 ,]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 18 5.1 3.5 1.4 0.3 setosa
## 41 5.0 3.5 1.3 0.3 setosa
dim ( frame1 )
## [1] 30 5
my_result <- merge ( frame1 , iris , by.x = 0 , by.y = 0 , all = TRUE )
dim ( my_result )
## [1] 150 11