Fast Select, Replace or Add Data Frame Columns
Efficiently select and replace (or add) a subset of columns from (to) a data frame. This can be done by data type, or using expressions, column names, indices, logical vectors, selector functions or regular expressions matching column names.
## Select and replace variables, analgous to dplyr::select but significantly faster
fselect(x, ..., return = "data")
fselect(x, ...) <- value
slt(x, ..., return = "data") # Shortcut for fselect
slt(x, ...) <- value # Shortcut for fselect<-
## Select and replace columns by names, indices, logical vectors,
## regular expressions or using functions to identify columns
get_vars(x, vars, return = "data", regex = FALSE, ...)
gv(x, vars, return = "data", ...) # Shortcut for get_vars
gvr(x, vars, return = "data", ...) # Shortcut for get_vars(\dots, regex = TRUE)
get_vars(x, vars, regex = FALSE, ...) <- value
gv(x, vars, ...) <- value # Shortcut for get_vars<-
gvr(x, vars, ...) <- value # Shortcut for get_vars<-(\dots, regex = TRUE)
## Add columns at any position within a data.frame
add_vars(x, ..., pos = "end")
add_vars(x, pos = "end") <- value
av(x, ..., pos = "end") # Shortcut for add_vars
av(x, pos = "end") <- value # Shortcut for add_vars<-
## Select and replace columns by data type
num_vars(x, return = "data")
num_vars(x) <- value
nv(x, return = "data") # Shortcut for num_vars
nv(x) <- value # Shortcut for num_vars<-
cat_vars(x, return = "data") # Categorical variables, see is.categorical
cat_vars(x) <- value
char_vars(x, return = "data")
char_vars(x) <- value
fact_vars(x, return = "data")
fact_vars(x) <- value
logi_vars(x, return = "data")
logi_vars(x) <- value
Date_vars(x, return = "data") # See is.Date
Date_vars(x) <- valuex |
a data frame. |
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value |
a data frame or list of columns whose dimensions exactly match those of the extracted subset of |
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vars |
a vector of column names, indices (can be negative), a suitable logical vector, or a vector of regular expressions matching column names (if |
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return |
an integer or string specifying what the selector function should return. The options are:
Note: replacement functions only replace data, However column names are replaced together with the data (if available). |
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regex |
logical. |
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pos |
the position where columns are added in the data frame. |
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... |
for |
get_vars(<-) is around 2x faster than `[.data.frame` and 8x faster than `[<-.data.frame`, so the common operation data[cols] <- someFUN(data[cols]) can be made 10x more efficient (abstracting from computations performed by someFUN) using get_vars(data, cols) <- someFUN(get_vars(data, cols)) or the shorthand gv(data, cols) <- someFUN(gv(data, cols)).
Similarly type-wise operations like data[sapply(data, is.numeric)] or data[sapply(data, is.numeric)] <- value are facilitated and more efficient using num_vars(data) and num_vars(data) <- value or the shortcuts nv and nv<- etc.
fselect provides an efficient alternative to dplyr::select, allowing the selection of variables based on expressions evaluated within the data frame, see Examples. It is about 100x faster than dplyr::select but also more simple as it does not provide special methods for grouped tibbles.
Finally, add_vars(data1, data2, data3, ...) is a lot faster than cbind(data1, data2, data3, ...), and preserves the attributes of data1 (i.e. it is like adding columns to data1). The replacement function add_vars(data) <- someFUN(get_vars(data, cols)) efficiently appends data with computed columns. The pos argument allows adding columns at positions other than the end (right) of the data frame, see Examples.
All functions introduced here perform their operations class-independent. They all basically work like this: (1) save the attributes of x, (2) unclass x, (3) subset, replace or append x as a list, (4) modify the "names" component of the attributes of x accordingly and (5) efficiently attach the attributes again to the result from step (3).
Thus they can freely be applied to data.table's, grouped tibbles, panel data frames and other classes and will return an object of exactly the same class and the same attributes.
The functions here only check the length of the first column, which is one of the reasons why they are so fast. When lists of unequal-length columns are offered as replacements this yields a malformed data frame (which will also print a warning in the console i.e. you will notice that).
## Wold Development Data
head(fselect(wlddev, country, year, PCGDP)) # Fast dplyr-like selecting
head(fselect(wlddev, -country, -year, -PCGDP))
head(fselect(wlddev, country, year, PCGDP:ODA))
head(fselect(wlddev, -(PCGDP:ODA)))
fselect(wlddev, country, year, PCGDP:ODA) <- NULL # Efficient deleting
head(wlddev)
rm(wlddev)
head(num_vars(wlddev)) # Select numeric variables
head(cat_vars(wlddev)) # Select categorical (non-numeric) vars
head(get_vars(wlddev, is.categorical)) # Same thing
num_vars(wlddev) <- num_vars(wlddev) # Replace Numeric Variables by themselves
get_vars(wlddev,is.numeric) <- get_vars(wlddev,is.numeric) # Same thing
head(get_vars(wlddev, 9:12)) # Select columns 9 through 12, 2x faster
head(get_vars(wlddev, -(9:12))) # All except columns 9 through 12
head(get_vars(wlddev, c("PCGDP","LIFEEX","GINI","ODA"))) # Select using column names
head(get_vars(wlddev, "[[:upper:]]", regex = TRUE)) # Same thing: match upper-case var. names
head(gvr(wlddev, "[[:upper:]]")) # Same thing
get_vars(wlddev, 9:12) <- get_vars(wlddev, 9:12) # 9x faster wlddev[9:12] <- wlddev[9:12]
add_vars(wlddev) <- STD(gv(wlddev,9:12), wlddev$iso3c) # Add Standardized columns 9 through 12
head(wlddev) # gv and av are shortcuts
get_vars(wlddev, 13:16) <- NULL # Efficient Deleting added columns again
av(wlddev, "front") <- STD(gv(wlddev,9:12), wlddev$iso3c) # Again adding in Front
head(wlddev)
get_vars(wlddev, 1:4) <- NULL # Deleting
av(wlddev,c(10,12,14,16)) <- W(wlddev,~iso3c, cols = 9:12, # Adding next to original variables
keep.by = FALSE)
head(wlddev)
get_vars(wlddev, c(10,12,14,16)) <- NULL # DeletingPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.