| Title: | Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table' |
|---|---|
| Description: | Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'. |
| Authors: | Gregory Demin [aut, cre] |
| Maintainer: | Gregory Demin <[email protected]> |
| License: | GPL-2 |
| Version: | 0.8.4 |
| Built: | 2024-08-09 05:33:09 UTC |
| Source: | https://github.com/gdemin/maditr |
Package provides pipe-style interface for data.table. It preserves
all data.table features without significant impact on performance. 'let'
and 'take' functions are simplified interfaces for most common data
manipulation tasks.
To select rows from data: rows(mtcars, am==0)
To select columns from data: columns(mtcars, mpg, vs:carb)
To aggregate data: take(mtcars, mean_mpg = mean(mpg), by = am)
To aggregate all non-grouping columns: take_all(mtcars, mean, by = am)
To aggregate several columns with one summary: take(mtcars, mpg, hp, fun = mean, by = am)
To get total summary skip by argument: take_all(mtcars, mean)
Use magrittr pipe '%>%' to chain several operations:
mtcars %>%
let(mpg_hp = mpg/hp) %>%
take(mean(mpg_hp), by = am)
To modify variables or add new variables:
mtcars %>%
let(new_var = 42,
new_var2 = new_var*hp) %>%
head()
To modify all non-grouping variables:
iris %>%
let_all(
scaled = (.x - mean(.x))/sd(.x),
by = Species) %>%
head()
To drop variable assign NULL: let(mtcars, am = NULL) %>% head()
To aggregate all variables conditionally on name:
iris %>%
take_all(
mean = if(startsWith(.name, "Sepal")) mean(.x),
median = if(startsWith(.name, "Petal")) median(.x),
by = Species
)
For parametric assignment use ':=':
new_var = "my_var"
old_var = "mpg"
mtcars %>%
let((new_var) := get(old_var)*2) %>%
head()
For more sophisticated operations see 'query'/'query_if': these
functions translates its arguments one-to-one to '[.data.table'
method. Additionally there are some conveniences such as automatic
'data.frame' conversion to 'data.table'.
Maintainer: Gregory Demin [email protected]
Useful links:
# examples form 'dplyr' package data(mtcars) # Newly created variables are available immediately mtcars %>% let( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # You can also use let() to remove variables and # modify existing variables mtcars %>% let( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped computations mtcars %>% let(rank = rank(-mpg, ties.method = "min"), by = cyl) %>% head() # You can drop variables by setting them to NULL mtcars %>% let(cyl = NULL) %>% head() # keeps all existing variables mtcars %>% let(displ_l = disp / 61.0237) %>% head() # keeps only the variables you create mtcars %>% take(displ_l = disp / 61.0237) # can refer to both contextual variables and variable names: var = 100 mtcars %>% let(cyl = cyl * var) %>% head() # select rows mtcars %>% rows(am==0) %>% head() # select rows with compound condition mtcars %>% rows(am==0 & mpg>mean(mpg)) # select columns mtcars %>% columns(vs:carb, cyl) mtcars %>% columns(-am, -cyl) # regular expression pattern columns(iris, "^Petal") # variables which start from 'Petal' columns(iris, "Width$") # variables which end with 'Width' # move Species variable to the front # pattern "^." matches all variables columns(iris, Species, "^.") # pattern "^.*al" means "contains 'al'" columns(iris, "^.*al") # numeric indexing - all variables except Species columns(iris, 1:4) # A 'take' with summary functions applied without 'by' argument returns an aggregated data mtcars %>% take(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% take(mean = mean(disp), n = .N, by = cyl) # You can group by expressions: mtcars %>% take_all(mean, by = list(vsam = vs + am)) # modify all non-grouping variables in-place mtcars %>% let_all((.x - mean(.x))/sd(.x), by = am) %>% head() # modify all non-grouping variables to new variables mtcars %>% let_all(scaled = (.x - mean(.x))/sd(.x), by = am) %>% head() # conditionally modify all variables iris %>% let_all(mean = if(is.numeric(.x)) mean(.x)) %>% head() # modify all variables conditionally on name iris %>% let_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) %>% head() # aggregation with 'take_all' mtcars %>% take_all(mean = mean(.x), sd = sd(.x), n = .N, by = am) # conditionally aggregate all variables iris %>% take_all(mean = if(is.numeric(.x)) mean(.x)) # aggregate all variables conditionally on name iris %>% take_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) # parametric evaluation: var = quote(mean(cyl)) mtcars %>% let(mean_cyl = eval(var)) %>% head() take(mtcars, eval(var)) # all together new_var = "mean_cyl" mtcars %>% let((new_var) := eval(var)) %>% head() take(mtcars, (new_var) := eval(var)) ######################################## # variable selection # range selection iris %>% let( avg = rowMeans(Sepal.Length %to% Petal.Width) ) %>% head() # multiassignment iris %>% let( # starts with Sepal or Petal multipled1 %to% multipled4 := cols("^(Sepal|Petal)")*2 ) %>% head() mtcars %>% let( # text expansion cols("scaled_{names(mtcars)}") := lapply(cols("{names(mtcars)}"), scale) ) %>% head() # range selection in 'by' # range selection + additional column mtcars %>% take( res = sum(cols(mpg, disp %to% drat)), by = vs %to% gear )# examples form 'dplyr' package data(mtcars) # Newly created variables are available immediately mtcars %>% let( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # You can also use let() to remove variables and # modify existing variables mtcars %>% let( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped computations mtcars %>% let(rank = rank(-mpg, ties.method = "min"), by = cyl) %>% head() # You can drop variables by setting them to NULL mtcars %>% let(cyl = NULL) %>% head() # keeps all existing variables mtcars %>% let(displ_l = disp / 61.0237) %>% head() # keeps only the variables you create mtcars %>% take(displ_l = disp / 61.0237) # can refer to both contextual variables and variable names: var = 100 mtcars %>% let(cyl = cyl * var) %>% head() # select rows mtcars %>% rows(am==0) %>% head() # select rows with compound condition mtcars %>% rows(am==0 & mpg>mean(mpg)) # select columns mtcars %>% columns(vs:carb, cyl) mtcars %>% columns(-am, -cyl) # regular expression pattern columns(iris, "^Petal") # variables which start from 'Petal' columns(iris, "Width$") # variables which end with 'Width' # move Species variable to the front # pattern "^." matches all variables columns(iris, Species, "^.") # pattern "^.*al" means "contains 'al'" columns(iris, "^.*al") # numeric indexing - all variables except Species columns(iris, 1:4) # A 'take' with summary functions applied without 'by' argument returns an aggregated data mtcars %>% take(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% take(mean = mean(disp), n = .N, by = cyl) # You can group by expressions: mtcars %>% take_all(mean, by = list(vsam = vs + am)) # modify all non-grouping variables in-place mtcars %>% let_all((.x - mean(.x))/sd(.x), by = am) %>% head() # modify all non-grouping variables to new variables mtcars %>% let_all(scaled = (.x - mean(.x))/sd(.x), by = am) %>% head() # conditionally modify all variables iris %>% let_all(mean = if(is.numeric(.x)) mean(.x)) %>% head() # modify all variables conditionally on name iris %>% let_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) %>% head() # aggregation with 'take_all' mtcars %>% take_all(mean = mean(.x), sd = sd(.x), n = .N, by = am) # conditionally aggregate all variables iris %>% take_all(mean = if(is.numeric(.x)) mean(.x)) # aggregate all variables conditionally on name iris %>% take_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) # parametric evaluation: var = quote(mean(cyl)) mtcars %>% let(mean_cyl = eval(var)) %>% head() take(mtcars, eval(var)) # all together new_var = "mean_cyl" mtcars %>% let((new_var) := eval(var)) %>% head() take(mtcars, (new_var) := eval(var)) ######################################## # variable selection # range selection iris %>% let( avg = rowMeans(Sepal.Length %to% Petal.Width) ) %>% head() # multiassignment iris %>% let( # starts with Sepal or Petal multipled1 %to% multipled4 := cols("^(Sepal|Petal)")*2 ) %>% head() mtcars %>% let( # text expansion cols("scaled_{names(mtcars)}") := lapply(cols("{names(mtcars)}"), scale) ) %>% head() # range selection in 'by' # range selection + additional column mtcars %>% take( res = sum(cols(mpg, disp %to% drat)), by = vs %to% gear )
It is an alias for data.table fcoalesce. For details see fcoalesce
coalesce(...)coalesce(...)
... |
vectors |
A vector the same length as the first ... argument with NA values replaced by the first non-missing value.
# examples from dplyr x = sample(c(1:5, NA, NA, NA)) coalesce(x, 0L) y = c(1, 2, NA, NA, 5) z = c(NA, NA, 3, 4, 5) coalesce(y, z)# examples from dplyr x = sample(c(1:5, NA, NA, NA)) coalesce(x, 0L) y = c(1, 2, NA, NA, 5) z = c(NA, NA, 3, 4, 5) coalesce(y, z)
columns: select columns from dataset. There are four ways of column selection:
Simply by column names
By variable ranges, e. g. vs:carb. Alternatively, you can use '%to%' instead of colon: 'vs %to% carb'.
With regular expressions. Characters which start with '^' or end with '$' considered as Perl-style regular expression patterns. For example, '^Petal' returns all variables started with 'Petal'. 'Width$' returns all variables which end with 'Width'. Pattern '^.' matches all variables and pattern '^.*my_str' is equivalent to contains "my_str"'.
By character variables with interpolated parts. Expression in the curly
brackets inside characters will be evaluated in the parent frame with
text_expand. For example, a{1:3} will be transformed to the names 'a1',
'a2', 'a3'. 'cols' is just a shortcut for 'columns'. See examples.
rows: select rows from dataset by logical conditions.
columns(data, ...) cols(data, ...) rows(data, ...)columns(data, ...) cols(data, ...) rows(data, ...)
data |
data.table/data.frame |
... |
unquoted or quoted column names, regex selectors or variable ranges for 'columns' and logical conditions for 'rows'. |
data.frame/data.table
## columns mtcars %>% columns(vs:carb, cyl) mtcars %>% columns(-am, -cyl) # regular expression pattern columns(iris, "^Petal") %>% head() # variables which start from 'Petal' columns(iris, "Width$") %>% head() # variables which end with 'Width' # move Species variable to the front. # pattern "^." matches all variables columns(iris, Species, "^.") %>% head() # pattern "^.*i" means "contains 'i'" columns(iris, "^.*i") %>% head() # numeric indexing - all variables except Species columns(iris, 1:4) %>% head() # variable expansion dims = c("Width", "Length") columns(iris, "Petal.{dims}") %>% head() # rows mtcars %>% rows(am==0) %>% head() # select rows with compound condition mtcars %>% rows(am==0 & mpg>mean(mpg))## columns mtcars %>% columns(vs:carb, cyl) mtcars %>% columns(-am, -cyl) # regular expression pattern columns(iris, "^Petal") %>% head() # variables which start from 'Petal' columns(iris, "Width$") %>% head() # variables which end with 'Width' # move Species variable to the front. # pattern "^." matches all variables columns(iris, Species, "^.") %>% head() # pattern "^.*i" means "contains 'i'" columns(iris, "^.*i") %>% head() # numeric indexing - all variables except Species columns(iris, 1:4) %>% head() # variable expansion dims = c("Width", "Length") columns(iris, "Petal.{dims}") %>% head() # rows mtcars %>% rows(am==0) %>% head() # select rows with compound condition mtcars %>% rows(am==0 & mpg>mean(mpg))
Mainly intended to copy data.table objects because by default they are modified by reference. See example.
copy(x)copy(x)
x |
object |
copy of the object 'x'
data(mtcars) dt_mtcars = as.data.table(mtcars) dt_mtcars2 = dt_mtcars dt_mtcars3 = copy(dt_mtcars) let(dt_mtcars, new = 1) head(dt_mtcars2) # we see 'new' column head(dt_mtcars3) # no 'new' columndata(mtcars) dt_mtcars = as.data.table(mtcars) dt_mtcars2 = dt_mtcars dt_mtcars3 = copy(dt_mtcars) let(dt_mtcars, new = 1) head(dt_mtcars2) # we see 'new' column head(dt_mtcars3) # no 'new' column
dt_count calculates number of cases by groups, possibly
weighted. dt_add_count adds number of cases to existing dataset.
dt_top_n returns top n rows from each group.
dt_count(data, ..., weight = NULL, sort = FALSE, name = "n") dt_add_count(data, ..., weight = NULL, sort = FALSE, name = "n") dt_top_n(data, n, by, order_by = NULL)dt_count(data, ..., weight = NULL, sort = FALSE, name = "n") dt_add_count(data, ..., weight = NULL, sort = FALSE, name = "n") dt_top_n(data, n, by, order_by = NULL)
data |
data.table/data.frame data.frame will be automatically converted to data.table. |
... |
variables to group by. |
weight |
optional. Unquoted variable name. If provided result will be the sum of this variable by groups. |
sort |
logical. If TRUE result will be sorted in desending order by resulting variable. |
name |
character. Name of resulting variable. |
n |
numeric. number of top cases. If n is negative then bottom values will be returned. |
by |
list or vector of grouping variables |
order_by |
unquoted variable name by which result will be sorted. If not specified, defaults to the last variable in the dataset. |
data.table
data(mtcars) # dt_count dt_count(mtcars, am, vs) dt_add_count(mtcars, am, vs, name = "am_vs")[] # [] for autoprinting # dt_top_n dt_top_n(mtcars, 2, by = list(am, vs)) dt_top_n(mtcars, 2, order_by = mpg, by = list(am, vs))data(mtcars) # dt_count dt_count(mtcars, am, vs) dt_add_count(mtcars, am, vs, name = "am_vs")[] # [] for autoprinting # dt_top_n dt_top_n(mtcars, 2, by = list(am, vs)) dt_top_n(mtcars, 2, order_by = mpg, by = list(am, vs))
Do different versions of SQL join operations. See examples.
dt_left_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_right_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_inner_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_full_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_semi_join(x, y, by = NULL) dt_anti_join(x, y, by = NULL)dt_left_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_right_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_inner_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_full_join(x, y, by = NULL, suffix = c(".x", ".y")) dt_semi_join(x, y, by = NULL) dt_anti_join(x, y, by = NULL)
x |
data.frame or data.table |
y |
data.frame or data.table |
by |
a character vector of variables to join by. If NULL, the default,
*_join() will do a natural join, using all variables with common names
across the two tables. A message lists the variables so that you can check
they're right (to suppress the message, simply explicitly list the
variables that you want to join). To join by different variables on x and y
use a named vector. For example, |
suffix |
If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2. |
data.table
workers = fread(" name company Nick Acme John Ajax Daniela Ajax ") positions = fread(" name position John designer Daniela engineer Cathie manager ") workers %>% dt_inner_join(positions) workers %>% dt_left_join(positions) workers %>% dt_right_join(positions) workers %>% dt_full_join(positions) # filtering joins workers %>% dt_anti_join(positions) workers %>% dt_semi_join(positions) # To suppress the message, supply 'by' argument workers %>% dt_left_join(positions, by = "name") # Use a named 'by' if the join variables have different names positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions' workers %>% dt_inner_join(positions2, by = c("name" = "worker"))workers = fread(" name company Nick Acme John Ajax Daniela Ajax ") positions = fread(" name position John designer Daniela engineer Cathie manager ") workers %>% dt_inner_join(positions) workers %>% dt_left_join(positions) workers %>% dt_right_join(positions) workers %>% dt_full_join(positions) # filtering joins workers %>% dt_anti_join(positions) workers %>% dt_semi_join(positions) # To suppress the message, supply 'by' argument workers %>% dt_left_join(positions, by = "name") # Use a named 'by' if the join variables have different names positions2 = setNames(positions, c("worker", "position")) # rename first column in 'positions' workers %>% dt_inner_join(positions2, by = c("name" = "worker"))
Subset of 'dplyr' verbs to work with data.table. Note that there is no
group_by verb - use by or keyby argument when needed.
dt_mutate adds new variables or modify existing variables. If
data is data.table then it modifies in-place.
dt_summarize computes summary statistics. Splits the data into
subsets, computes summary statistics for each, and returns the result in the
"data.table" form.
dt_summarize_all is the same as dt_summarize but work over all non-grouping variables.
dt_filter selects rows/cases where conditions are true. Rows
where the condition evaluates to NA are dropped.
dt_select selects column/variables from the data set. Range of
variables are supported, e. g. vs:carb. Characters which start with '^' or
end with '$' considered as Perl-style regular expression patterns. For
example, '^Petal' returns all variables started with 'Petal'. 'Width$'
returns all variables which end with 'Width'. Pattern '^.' matches all
variables and pattern '^.*my_str' is equivalent to contains "my_str". See
examples.
dt_arrange sorts dataset by variable(-s). Use '-' to sort in
descending order. If data is data.table then it modifies in-place.
dt_mutate(data, ..., by) dt_summarize(data, ..., by, keyby, fun = NULL) dt_summarize_all(data, fun, by, keyby) dt_summarise(data, ..., by, keyby, fun = NULL) dt_summarise_all(data, fun, by, keyby) dt_select(data, ...) dt_filter(data, ...) dt_arrange(data, ..., na.last = FALSE)dt_mutate(data, ..., by) dt_summarize(data, ..., by, keyby, fun = NULL) dt_summarize_all(data, fun, by, keyby) dt_summarise(data, ..., by, keyby, fun = NULL) dt_summarise_all(data, fun, by, keyby) dt_select(data, ...) dt_filter(data, ...) dt_arrange(data, ..., na.last = FALSE)
data |
data.table/data.frame data.frame will be automatically converted
to data.table. |
... |
List of variables or name-value pairs of summary/modifications
functions. The name will be the name of the variable in the result. In the
|
by |
unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table |
keyby |
Same as |
fun |
function which will be applied to all variables in
|
na.last |
logical. FALSE by default. If TRUE, missing values in the data are put last; if FALSE, they are put first. |
data.table
# examples from 'dplyr' # newly created variables are available immediately mtcars %>% dt_mutate( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # you can also use dt_mutate() to remove variables and # modify existing variables mtcars %>% dt_mutate( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped mutates mtcars %>% dt_mutate( rank = rank(-mpg, ties.method = "min"), keyby = cyl) %>% print() # You can drop variables by setting them to NULL mtcars %>% dt_mutate(cyl = NULL) %>% head() # A summary applied without by returns a single row mtcars %>% dt_summarise(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% dt_summarise(mean = mean(disp), n = .N, by = cyl) # Multiple 'by' - variables mtcars %>% dt_summarise(cyl_n = .N, by = list(cyl, vs)) # Newly created summaries immediately # doesn't overwrite existing variables mtcars %>% dt_summarise(disp = mean(disp), sd = sd(disp), by = cyl) # You can group by expressions: mtcars %>% dt_summarise_all(mean, by = list(vsam = vs + am)) # filter by condition mtcars %>% dt_filter(am==0) # filter by compound condition mtcars %>% dt_filter(am==0, mpg>mean(mpg)) # select mtcars %>% dt_select(vs:carb, cyl) mtcars %>% dt_select(-am, -cyl) # regular expression pattern dt_select(iris, "^Petal") # variables which start from 'Petal' dt_select(iris, "Width$") # variables which end with 'Width' # move Species variable to the front. # pattern "^." matches all variables dt_select(iris, Species, "^.") # pattern "^.*i" means "contains 'i'" dt_select(iris, "^.*i") dt_select(iris, 1:4) # numeric indexing - all variables except Species # sorting dt_arrange(mtcars, cyl, disp) dt_arrange(mtcars, -disp)# examples from 'dplyr' # newly created variables are available immediately mtcars %>% dt_mutate( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # you can also use dt_mutate() to remove variables and # modify existing variables mtcars %>% dt_mutate( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped mutates mtcars %>% dt_mutate( rank = rank(-mpg, ties.method = "min"), keyby = cyl) %>% print() # You can drop variables by setting them to NULL mtcars %>% dt_mutate(cyl = NULL) %>% head() # A summary applied without by returns a single row mtcars %>% dt_summarise(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% dt_summarise(mean = mean(disp), n = .N, by = cyl) # Multiple 'by' - variables mtcars %>% dt_summarise(cyl_n = .N, by = list(cyl, vs)) # Newly created summaries immediately # doesn't overwrite existing variables mtcars %>% dt_summarise(disp = mean(disp), sd = sd(disp), by = cyl) # You can group by expressions: mtcars %>% dt_summarise_all(mean, by = list(vsam = vs + am)) # filter by condition mtcars %>% dt_filter(am==0) # filter by compound condition mtcars %>% dt_filter(am==0, mpg>mean(mpg)) # select mtcars %>% dt_select(vs:carb, cyl) mtcars %>% dt_select(-am, -cyl) # regular expression pattern dt_select(iris, "^Petal") # variables which start from 'Petal' dt_select(iris, "Width$") # variables which end with 'Width' # move Species variable to the front. # pattern "^." matches all variables dt_select(iris, Species, "^.") # pattern "^.*i" means "contains 'i'" dt_select(iris, "^.*i") dt_select(iris, 1:4) # numeric indexing - all variables except Species # sorting dt_arrange(mtcars, cyl, disp) dt_arrange(mtcars, -disp)
let adds new variables or modify existing variables. 'let_if' make
the same thing on the subset of rows.
take/take_if aggregate data or aggregate subset of the data.
let_all applies expressions to all variables in the dataset. It is also
possible to modify the subset of the variables.
take_all aggregates all variables in the dataset. It is also possible
to aggregate the subset of the variables.
All functions return data.table. Expression in the 'take_all' and
'let_all' can use predefined variables: '.x' is a value of current variable ,
'.name' is a name of the variable and '.index' is sequential number of the
variable. '.value' is is an alias to '.x'.
Add new variables: let(mtcars, new_var = 42, new_var2 = new_var*hp)
Select variables: take(mtcars, am, vs, mpg)
Aggregate data: take(mtcars, mean_mpg = mean(mpg), by = am)
Aggregate all non-grouping columns: take_all(mtcars, mean = mean(.x), sd = sd(.x), n = .N, by = am)
Aggregate all numeric columns: take_all(iris, if(is.numeric(.x)) mean(.x))
To modify all non-grouping variables:
iris %>%
let_all(
scaled = (.x - mean(.x))/sd(.x),
by = Species) %>%
head()
Aggregate specific columns: take_all(iris, if(startsWith(.name, "Sepal")) mean(.x))
You can use 'columns' inside expression in the 'take'/'let'. 'columns' will be replaced with data.table with selected columns. In 'let' in the expressions with ':=', 'cols' or '%to%' can be placed in the left part of the expression. It is usefull for multiple assignment. There are four ways of column selection:
Simply by column names
By variable ranges, e. g. vs:carb. Alternatively, you can use '%to%' instead of colon: 'vs %to% carb'.
With regular expressions. Characters which start with '^' or end with '$' considered as Perl-style regular expression patterns. For example, '^Petal' returns all variables started with 'Petal'. 'Width$' returns all variables which end with 'Width'. Pattern '^.' matches all variables and pattern '^.*my_str' is equivalent to contains "my_str"'.
By character variables with interpolated parts. Expression in the curly
brackets inside characters will be evaluated in the parent frame with
text_expand. For example, a{1:3} will be transformed to the names 'a1',
'a2', 'a3'. 'cols' is just a shortcut for 'columns'. See examples.
let_if(data, i, ..., by, keyby) take_if(data, i, ..., by, keyby, .SDcols, autoname = TRUE, fun = NULL) take(data, ..., by, keyby, .SDcols, autoname = TRUE, fun = NULL) let(data, ..., by, keyby) ## S3 method for class 'data.frame' let(data, ..., by, keyby, i) ## S3 method for class 'etable' let(data, ..., by, keyby, i) sort_by(data, ..., na.last = FALSE) let_all(data, ..., by, keyby, .SDcols, suffix = TRUE, sep = "_", i) take_all(data, ..., by, keyby, .SDcols, suffix = TRUE, sep = "_", i)let_if(data, i, ..., by, keyby) take_if(data, i, ..., by, keyby, .SDcols, autoname = TRUE, fun = NULL) take(data, ..., by, keyby, .SDcols, autoname = TRUE, fun = NULL) let(data, ..., by, keyby) ## S3 method for class 'data.frame' let(data, ..., by, keyby, i) ## S3 method for class 'etable' let(data, ..., by, keyby, i) sort_by(data, ..., na.last = FALSE) let_all(data, ..., by, keyby, .SDcols, suffix = TRUE, sep = "_", i) take_all(data, ..., by, keyby, .SDcols, suffix = TRUE, sep = "_", i)
data |
data.table/data.frame data.frame will be automatically converted
to data.table. |
i |
integer/logical vector. Supposed to use to subset/conditional
modifications of |
... |
List of variables or name-value pairs of summary/modifications
functions. The name will be the name of the variable in the result. In the
|
by |
unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table |
keyby |
Same as |
.SDcols |
Specifies the columns of x to be included in the special symbol .SD which stands for Subset of data.table. May be character column names or numeric positions. For details see data.table. |
autoname |
logical. TRUE by default. Should we create names for unnamed expressions in |
fun |
Function which will be applied to all variables in |
na.last |
logical. FALSE by default. If TRUE, missing values in the data are put last; if FALSE, they are put first. |
suffix |
logical TRUE by default. For 'let_all'/'take_all'. If TRUE than we append summary name to the end of the variable name. If FALSE summary name will be added at the begining of the variable name. |
sep |
character. "_" by default. Separator between the old variables name and prefix or suffix for 'let_all' and 'take_all'. |
data.table. let returns its result invisibly.
# examples form 'dplyr' package data(mtcars) # Newly created variables are available immediately mtcars %>% let( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # You can also use let() to remove variables and # modify existing variables mtcars %>% let( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped computations mtcars %>% let(rank = rank(-mpg, ties.method = "min"), by = cyl) %>% head() # You can drop variables by setting them to NULL mtcars %>% let(cyl = NULL) %>% head() # keeps all existing variables mtcars %>% let(displ_l = disp / 61.0237) %>% head() # keeps only the variables you create mtcars %>% take(displ_l = disp / 61.0237) # can refer to both contextual variables and variable names: var = 100 mtcars %>% let(cyl = cyl * var) %>% head() # A 'take' with summary functions applied without 'by' argument returns an aggregated data mtcars %>% take(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% take(mean = mean(disp), n = .N, by = cyl) # You can group by expressions: mtcars %>% take_all(mean, by = list(vsam = vs + am)) # modify all non-grouping variables in-place mtcars %>% let_all((.x - mean(.x))/sd(.x), by = am) %>% head() # modify all non-grouping variables to new variables mtcars %>% let_all(scaled = (.x - mean(.x))/sd(.x), by = am) %>% head() # conditionally modify all variables iris %>% let_all(mean = if(is.numeric(.x)) mean(.x)) %>% head() # modify all variables conditionally on name iris %>% let_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) %>% head() # aggregation with 'take_all' mtcars %>% take_all(mean = mean(.x), sd = sd(.x), n = .N, by = am) # conditionally aggregate all variables iris %>% take_all(mean = if(is.numeric(.x)) mean(.x)) # aggregate all variables conditionally on name iris %>% take_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) # parametric evaluation: var = quote(mean(cyl)) mtcars %>% let(mean_cyl = eval(var)) %>% head() take(mtcars, eval(var)) # all together new_var = "mean_cyl" mtcars %>% let((new_var) := eval(var)) %>% head() take(mtcars, (new_var) := eval(var)) ######################################## # variable selection # range selection iris %>% let( avg = rowMeans(Sepal.Length %to% Petal.Width) ) %>% head() # multiassignment iris %>% let( # starts with Sepal or Petal multipled1 %to% multipled4 := cols("^(Sepal|Petal)")*2 ) %>% head() mtcars %>% let( # text expansion cols("scaled_{names(mtcars)}") := lapply(cols("{names(mtcars)}"), scale) ) %>% head() # range selection in 'by' # range selection + additional column mtcars %>% take( res = sum(cols(mpg, disp %to% drat)), by = vs %to% gear ) ######################################## # examples from data.table dat = data.table( x=rep(c("b","a","c"), each=3), y=c(1,3,6), v=1:9 ) # basic row subset operations take_if(dat, 2) # 2nd row take_if(dat, 3:2) # 3rd and 2nd row take_if(dat, order(x)) # no need for order(dat$x) take_if(dat, y>2) # all rows where dat$y > 2 take_if(dat, y>2 & v>5) # compound logical expressions take_if(dat, !2:4) # all rows other than 2:4 take_if(dat, -(2:4)) # same # select|compute columns take(dat, v) # v column (as data.table) take(dat, sum(v)) # return data.table with sum of v (column autonamed 'sum(v)') take(dat, sv = sum(v)) # same, but column named "sv" take(dat, v, v*2) # return two column data.table, v and v*2 # subset rows and select|compute take_if(dat, 2:3, sum(v)) # sum(v) over rows 2 and 3 take_if(dat, 2:3, sv = sum(v)) # same, but return data.table with column sv # grouping operations take(dat, sum(v), by = x) # ad hoc by, order of groups preserved in result take(dat, sum(v), keyby = x) # same, but order the result on by cols # all together now take_if(dat, x!="a", sum(v), by=x) # get sum(v) by "x" for each x != "a" # more on special symbols, see also ?"data.table::special-symbols" take_if(dat, .N) # last row take(dat, .N) # total number of rows in DT take(dat, .N, by=x) # number of rows in each group take(dat, .I[1], by=x) # row number in DT corresponding to each group # add/update/delete by reference # [] at the end of expression is for autoprinting let(dat, grp = .GRP, by=x)[] # add a group counter column let(dat, z = 42L)[] # add new column by reference let(dat, z = NULL)[] # remove column by reference let_if(dat, x=="a", v = 42L)[] # subassign to existing v column by reference let_if(dat, x=="b", v2 = 84L)[] # subassign to new column by reference (NA padded) let(dat, m = mean(v), by=x)[] # add new column by reference by group # advanced usage dat = data.table(x=rep(c("b","a","c"), each=3), v=c(1,1,1,2,2,1,1,2,2), y=c(1,3,6), a=1:9, b=9:1) take(dat, sum(v), by=list(y%%2)) # expressions in by take(dat, sum(v), by=list(bool = y%%2)) # same, using a named list to change by column name take_all(dat, sum, by=x) # sum of all (other) columns for each group take(dat, MySum=sum(v), MyMin=min(v), MyMax=max(v), by = list(x, y%%2) # by 2 expressions ) take(dat, seq = min(a):max(b), by=x) # j is not limited to just aggregations dat %>% take(V1 = sum(v), by=x) %>% take_if(V1<20) # compound query dat %>% take(V1 = sum(v), by=x) %>% sort_by(-V1) %>% # ordering results head()# examples form 'dplyr' package data(mtcars) # Newly created variables are available immediately mtcars %>% let( cyl2 = cyl * 2, cyl4 = cyl2 * 2 ) %>% head() # You can also use let() to remove variables and # modify existing variables mtcars %>% let( mpg = NULL, disp = disp * 0.0163871 # convert to litres ) %>% head() # window functions are useful for grouped computations mtcars %>% let(rank = rank(-mpg, ties.method = "min"), by = cyl) %>% head() # You can drop variables by setting them to NULL mtcars %>% let(cyl = NULL) %>% head() # keeps all existing variables mtcars %>% let(displ_l = disp / 61.0237) %>% head() # keeps only the variables you create mtcars %>% take(displ_l = disp / 61.0237) # can refer to both contextual variables and variable names: var = 100 mtcars %>% let(cyl = cyl * var) %>% head() # A 'take' with summary functions applied without 'by' argument returns an aggregated data mtcars %>% take(mean = mean(disp), n = .N) # Usually, you'll want to group first mtcars %>% take(mean = mean(disp), n = .N, by = cyl) # You can group by expressions: mtcars %>% take_all(mean, by = list(vsam = vs + am)) # modify all non-grouping variables in-place mtcars %>% let_all((.x - mean(.x))/sd(.x), by = am) %>% head() # modify all non-grouping variables to new variables mtcars %>% let_all(scaled = (.x - mean(.x))/sd(.x), by = am) %>% head() # conditionally modify all variables iris %>% let_all(mean = if(is.numeric(.x)) mean(.x)) %>% head() # modify all variables conditionally on name iris %>% let_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) %>% head() # aggregation with 'take_all' mtcars %>% take_all(mean = mean(.x), sd = sd(.x), n = .N, by = am) # conditionally aggregate all variables iris %>% take_all(mean = if(is.numeric(.x)) mean(.x)) # aggregate all variables conditionally on name iris %>% take_all( mean = if(startsWith(.name, "Sepal")) mean(.x), median = if(startsWith(.name, "Petal")) median(.x), by = Species ) # parametric evaluation: var = quote(mean(cyl)) mtcars %>% let(mean_cyl = eval(var)) %>% head() take(mtcars, eval(var)) # all together new_var = "mean_cyl" mtcars %>% let((new_var) := eval(var)) %>% head() take(mtcars, (new_var) := eval(var)) ######################################## # variable selection # range selection iris %>% let( avg = rowMeans(Sepal.Length %to% Petal.Width) ) %>% head() # multiassignment iris %>% let( # starts with Sepal or Petal multipled1 %to% multipled4 := cols("^(Sepal|Petal)")*2 ) %>% head() mtcars %>% let( # text expansion cols("scaled_{names(mtcars)}") := lapply(cols("{names(mtcars)}"), scale) ) %>% head() # range selection in 'by' # range selection + additional column mtcars %>% take( res = sum(cols(mpg, disp %to% drat)), by = vs %to% gear ) ######################################## # examples from data.table dat = data.table( x=rep(c("b","a","c"), each=3), y=c(1,3,6), v=1:9 ) # basic row subset operations take_if(dat, 2) # 2nd row take_if(dat, 3:2) # 3rd and 2nd row take_if(dat, order(x)) # no need for order(dat$x) take_if(dat, y>2) # all rows where dat$y > 2 take_if(dat, y>2 & v>5) # compound logical expressions take_if(dat, !2:4) # all rows other than 2:4 take_if(dat, -(2:4)) # same # select|compute columns take(dat, v) # v column (as data.table) take(dat, sum(v)) # return data.table with sum of v (column autonamed 'sum(v)') take(dat, sv = sum(v)) # same, but column named "sv" take(dat, v, v*2) # return two column data.table, v and v*2 # subset rows and select|compute take_if(dat, 2:3, sum(v)) # sum(v) over rows 2 and 3 take_if(dat, 2:3, sv = sum(v)) # same, but return data.table with column sv # grouping operations take(dat, sum(v), by = x) # ad hoc by, order of groups preserved in result take(dat, sum(v), keyby = x) # same, but order the result on by cols # all together now take_if(dat, x!="a", sum(v), by=x) # get sum(v) by "x" for each x != "a" # more on special symbols, see also ?"data.table::special-symbols" take_if(dat, .N) # last row take(dat, .N) # total number of rows in DT take(dat, .N, by=x) # number of rows in each group take(dat, .I[1], by=x) # row number in DT corresponding to each group # add/update/delete by reference # [] at the end of expression is for autoprinting let(dat, grp = .GRP, by=x)[] # add a group counter column let(dat, z = 42L)[] # add new column by reference let(dat, z = NULL)[] # remove column by reference let_if(dat, x=="a", v = 42L)[] # subassign to existing v column by reference let_if(dat, x=="b", v2 = 84L)[] # subassign to new column by reference (NA padded) let(dat, m = mean(v), by=x)[] # add new column by reference by group # advanced usage dat = data.table(x=rep(c("b","a","c"), each=3), v=c(1,1,1,2,2,1,1,2,2), y=c(1,3,6), a=1:9, b=9:1) take(dat, sum(v), by=list(y%%2)) # expressions in by take(dat, sum(v), by=list(bool = y%%2)) # same, using a named list to change by column name take_all(dat, sum, by=x) # sum of all (other) columns for each group take(dat, MySum=sum(v), MyMin=min(v), MyMax=max(v), by = list(x, y%%2) # by 2 expressions ) take(dat, seq = min(a):max(b), by=x) # j is not limited to just aggregations dat %>% take(V1 = sum(v), by=x) %>% take_if(V1<20) # compound query dat %>% take(V1 = sum(v), by=x) %>% sort_by(-V1) %>% # ordering results head()
Quote from data.table:
query(data, j, by) # + extra arguments
| |
| -------> grouped by what?
-------> what to do?
or,
query_if(data, i, j, by) # + extra arguments
| | |
| | -------> grouped by what?
| -------> what to do?
---> on which rows?
If you don't need 'i' argument, use 'query'. In this case you can
avoid printing leading comma inside brackets to denote empty 'i'.
query_if( data, i, j, by, keyby, with = TRUE, nomatch = getOption("datatable.nomatch"), mult = "all", roll = FALSE, rollends = if (roll == "nearest") c(TRUE, TRUE) else if (roll >= 0) c(FALSE, TRUE) else c(TRUE, FALSE), which = FALSE, .SDcols, verbose = getOption("datatable.verbose"), allow.cartesian = getOption("datatable.allow.cartesian"), drop = NULL, on = NULL ) query( data, j, by, keyby, with = TRUE, nomatch = getOption("datatable.nomatch"), mult = "all", roll = FALSE, rollends = if (roll == "nearest") c(TRUE, TRUE) else if (roll >= 0) c(FALSE, TRUE) else c(TRUE, FALSE), which = FALSE, .SDcols, verbose = getOption("datatable.verbose"), allow.cartesian = getOption("datatable.allow.cartesian"), drop = NULL, on = NULL )query_if( data, i, j, by, keyby, with = TRUE, nomatch = getOption("datatable.nomatch"), mult = "all", roll = FALSE, rollends = if (roll == "nearest") c(TRUE, TRUE) else if (roll >= 0) c(FALSE, TRUE) else c(TRUE, FALSE), which = FALSE, .SDcols, verbose = getOption("datatable.verbose"), allow.cartesian = getOption("datatable.allow.cartesian"), drop = NULL, on = NULL ) query( data, j, by, keyby, with = TRUE, nomatch = getOption("datatable.nomatch"), mult = "all", roll = FALSE, rollends = if (roll == "nearest") c(TRUE, TRUE) else if (roll >= 0) c(FALSE, TRUE) else c(TRUE, FALSE), which = FALSE, .SDcols, verbose = getOption("datatable.verbose"), allow.cartesian = getOption("datatable.allow.cartesian"), drop = NULL, on = NULL )
data |
data.table/data.frame data.frame will be automatically converted to data.table. |
i |
Integer, logical or character vector, single column numeric matrix, expression of column names, list, data.frame or data.table. integer and logical vectors work the same way they do in [.data.frame except logical NAs are treated as FALSE. expression is evaluated within the frame of the data.table (i.e. it sees column names as if they are variables) and can evaluate to any of the other types. For details see data.table |
j |
When with=TRUE (default), j is evaluated within the frame of the
data.table; i.e., it sees column names as if they are variables. This
allows to not just select columns in j, but also compute on them e.g., |
by |
unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table |
keyby |
Same as by, but with an additional |
with |
logical. For details see data.table. |
nomatch |
Same as nomatch in match. For details see data.table. |
mult |
For details see data.table. |
roll |
For details see data.table. |
rollends |
For details see data.table. |
which |
For details see data.table. |
.SDcols |
Specifies the columns of x to be included in the special symbol .SD which stands for Subset of data.table. May be character column names or numeric positions. For details see data.table. |
verbose |
logical. For details see data.table. |
allow.cartesian |
For details see data.table. |
drop |
For details see data.table. |
on |
For details see data.table. |
It depends. For details see data.table.
# examples from data.table dat = data.table(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9) dat # basic row subset operations query_if(dat, 2) # 2nd row query_if(dat, 3:2) # 3rd and 2nd row query_if(dat, order(x)) # no need for order(dat$x) query_if(dat, y>2) # all rows where dat$y > 2 query_if(dat, y>2 & v>5) # compound logical expressions query_if(dat, !2:4) # all rows other than 2:4 query_if(dat, -(2:4)) # same # select|compute columns data.table way query(dat, v) # v column (as vector) query(dat, list(v)) # v column (as data.table) query(dat, sum(v)) # sum of column v, returned as vector query(dat, list(sum(v))) # same, but return data.table (column autonamed V1) query(dat, list(v, v*2)) # return two column data.table, v and v*2 # subset rows and select|compute data.table way query_if(dat, 2:3, sum(v)) # sum(v) over rows 2 and 3, return vector query_if(dat, 2:3, list(sum(v))) # same, but return data.table with column V1 query_if(dat, 2:3, list(sv=sum(v))) # same, but return data.table with column sv query_if(dat, 2:5, cat(v, "\n")) # just for j's side effect # select columns the data.frame way query(dat, 2, with=FALSE) # 2nd column, returns a data.table always colNum = 2 query(dat, colNum, with=FALSE) # same, equivalent to DT[, .SD, .SDcols=colNum] # grouping operations - j and by query(dat, sum(v), by=x) # ad hoc by, order of groups preserved in result query(dat, sum(v), keyby=x) # same, but order the result on by cols query(dat, sum(v), by=x) %>% query_if(order(x)) # same but by chaining expressions together # fast ad hoc row subsets (subsets as joins) # same as x == "a" but uses binary search (fast) query_if(dat, "a", on="x") # same, for convenience, no need to quote every column query_if(dat, "a", on=list(x)) query_if(dat, .("a"), on="x") # same # same, single "==" internally optimised to use binary search (fast) query_if(dat, x=="a") # not yet optimized, currently vector scan subset query_if(dat, x!="b" | y!=3) # join on columns x,y of 'dat'; uses binary search (fast) query_if(dat, .("b", 3), on=c("x", "y")) query_if(dat, .("b", 3), on=list(x, y)) # same, but using on=list() query_if(dat, .("b", 1:2), on=c("x", "y")) # no match returns NA query_if(dat, .("b", 1:2), on=.(x, y), nomatch=0) # no match row is not returned # locf, nomatch row gets rolled by previous row query_if(dat, .("b", 1:2), on=c("x", "y"), roll=Inf) query_if(dat, .("b", 1:2), on=.(x, y), roll=-Inf) # nocb, nomatch row gets rolled by next row # on rows where dat$x=="b", calculate sum(v*y) query_if(dat, "b", sum(v*y), on="x") # all together now query_if(dat, x!="a", sum(v), by=x) # get sum(v) by "x" for each i != "a" query_if(dat, !"a", sum(v), by=.EACHI, on="x") # same, but using subsets-as-joins query_if(dat, c("b","c"), sum(v), by=.EACHI, on="x") # same query_if(dat, c("b","c"), sum(v), by=.EACHI, on=.(x)) # same, using on=.() # joins as subsets X = data.table(x=c("c","b"), v=8:7, foo=c(4,2)) X query_if(dat, X, on="x") # right join query_if(X, dat, on="x") # left join query_if(dat, X, on="x", nomatch=0) # inner join query_if(dat, !X, on="x") # not join # join using column "y" of 'dat' with column "v" of X query_if(dat, X, on=c(y="v")) query_if(dat,X, on="y==v") # same as above (v1.9.8+) query_if(dat, X, on = .(y<=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, on="y<=foo") # same as above query_if(dat, X, on=c("y<=foo")) # same as above query_if(dat, X, on=.(y>=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, on=.(x, y<=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, .(x,y,x.y,v), on=.(x, y>=foo)) # Select x's join columns as well query_if(dat, X, on="x", mult="first") # first row of each group query_if(dat, X, on="x", mult="last") # last row of each group query_if(dat, X, sum(v), by=.EACHI, on="x") # join and eval j for each row in i query_if(dat, X, sum(v)*foo, by=.EACHI, on="x") # join inherited scope query_if(dat, X, sum(v)*i.v, by=.EACHI, on="x") # 'i,v' refers to X's v column query_if(dat, X, on=.(x, v>=v), sum(y)*foo, by=.EACHI) # NEW non-equi join with by=.EACHI (v1.9.8+) # more on special symbols, see also ?"special-symbols" query_if(dat, .N) # last row query(dat, .N) # total number of rows in DT query(dat, .N, by=x) # number of rows in each group query(dat, .SD, .SDcols=x:y) # select columns 'x' and 'y' query(dat, .SD[1]) # first row of all columns query(dat, .SD[1], by=x) # first row of 'y' and 'v' for each group in 'x' query(dat, c(.N, lapply(.SD, sum)), by=x) # get rows *and* sum columns 'v' and 'y' by group query(dat, .I[1], by=x) # row number in DT corresponding to each group query(dat, grp := .GRP, by=x) %>% head() # add a group counter column query(X, query_if(dat, .BY, y, on="x"), by=x) # join within each group # add/update/delete by reference (see ?assign) query(dat, z:=42L) %>% head() # add new column by reference query(dat, z:=NULL) %>% head() # remove column by reference query_if(dat, "a", v:=42L, on="x") %>% head() # subassign to existing v column by reference query_if(dat, "b", v2:=84L, on="x") %>% head() # subassign to new column by reference (NA padded) # NB: postfix [] is shortcut to print() query(dat, m:=mean(v), by=x)[] # add new column by reference by group # advanced usage dat = data.table(x=rep(c("b","a","c"),each=3), v=c(1,1,1,2,2,1,1,2,2), y=c(1,3,6), a=1:9, b=9:1) dat query(dat, sum(v), by=.(y%%2)) # expressions in by query(dat, sum(v), by=.(bool = y%%2)) # same, using a named list to change by column name query(dat, .SD[2], by=x) # get 2nd row of each group query(dat, tail(.SD,2), by=x) # last 2 rows of each group query(dat, lapply(.SD, sum), by=x) # sum of all (other) columns for each group query(dat, .SD[which.min(v)], by=x) # nested query by group query(dat, list(MySum=sum(v), MyMin=min(v), MyMax=max(v)), by=.(x, y%%2) ) # by 2 expressions query(dat, .(a = .(a), b = .(b)), by=x) # list columns query(dat, .(seq = min(a):max(b)), by=x) # j is not limited to just aggregations query(dat, sum(v), by=x) %>% query_if(V1<20) # compound query query(dat, sum(v), by=x) %>% setorder(-V1) %>% head() # ordering results query(dat, c(.N, lapply(.SD,sum)), by=x) # get number of observations and sum per group # anonymous lambda in 'j', j accepts any valid # expression. TO REMEMBER: every element of # the list becomes a column in result. query(dat, {tmp = mean(y); .(a = a-tmp, b = b-tmp) }, by=x) # using rleid, get max(y) and min of all cols in .SDcols for each consecutive run of 'v' query(dat, c(.(y=max(y)), lapply(.SD, min)), by=rleid(v), .SDcols=v:b ) ## Not run: pdf("new.pdf") query(dat, plot(a,b), by=x) # can also plot in 'j' dev.off() ## End(Not run)# examples from data.table dat = data.table(x=rep(c("b","a","c"),each=3), y=c(1,3,6), v=1:9) dat # basic row subset operations query_if(dat, 2) # 2nd row query_if(dat, 3:2) # 3rd and 2nd row query_if(dat, order(x)) # no need for order(dat$x) query_if(dat, y>2) # all rows where dat$y > 2 query_if(dat, y>2 & v>5) # compound logical expressions query_if(dat, !2:4) # all rows other than 2:4 query_if(dat, -(2:4)) # same # select|compute columns data.table way query(dat, v) # v column (as vector) query(dat, list(v)) # v column (as data.table) query(dat, sum(v)) # sum of column v, returned as vector query(dat, list(sum(v))) # same, but return data.table (column autonamed V1) query(dat, list(v, v*2)) # return two column data.table, v and v*2 # subset rows and select|compute data.table way query_if(dat, 2:3, sum(v)) # sum(v) over rows 2 and 3, return vector query_if(dat, 2:3, list(sum(v))) # same, but return data.table with column V1 query_if(dat, 2:3, list(sv=sum(v))) # same, but return data.table with column sv query_if(dat, 2:5, cat(v, "\n")) # just for j's side effect # select columns the data.frame way query(dat, 2, with=FALSE) # 2nd column, returns a data.table always colNum = 2 query(dat, colNum, with=FALSE) # same, equivalent to DT[, .SD, .SDcols=colNum] # grouping operations - j and by query(dat, sum(v), by=x) # ad hoc by, order of groups preserved in result query(dat, sum(v), keyby=x) # same, but order the result on by cols query(dat, sum(v), by=x) %>% query_if(order(x)) # same but by chaining expressions together # fast ad hoc row subsets (subsets as joins) # same as x == "a" but uses binary search (fast) query_if(dat, "a", on="x") # same, for convenience, no need to quote every column query_if(dat, "a", on=list(x)) query_if(dat, .("a"), on="x") # same # same, single "==" internally optimised to use binary search (fast) query_if(dat, x=="a") # not yet optimized, currently vector scan subset query_if(dat, x!="b" | y!=3) # join on columns x,y of 'dat'; uses binary search (fast) query_if(dat, .("b", 3), on=c("x", "y")) query_if(dat, .("b", 3), on=list(x, y)) # same, but using on=list() query_if(dat, .("b", 1:2), on=c("x", "y")) # no match returns NA query_if(dat, .("b", 1:2), on=.(x, y), nomatch=0) # no match row is not returned # locf, nomatch row gets rolled by previous row query_if(dat, .("b", 1:2), on=c("x", "y"), roll=Inf) query_if(dat, .("b", 1:2), on=.(x, y), roll=-Inf) # nocb, nomatch row gets rolled by next row # on rows where dat$x=="b", calculate sum(v*y) query_if(dat, "b", sum(v*y), on="x") # all together now query_if(dat, x!="a", sum(v), by=x) # get sum(v) by "x" for each i != "a" query_if(dat, !"a", sum(v), by=.EACHI, on="x") # same, but using subsets-as-joins query_if(dat, c("b","c"), sum(v), by=.EACHI, on="x") # same query_if(dat, c("b","c"), sum(v), by=.EACHI, on=.(x)) # same, using on=.() # joins as subsets X = data.table(x=c("c","b"), v=8:7, foo=c(4,2)) X query_if(dat, X, on="x") # right join query_if(X, dat, on="x") # left join query_if(dat, X, on="x", nomatch=0) # inner join query_if(dat, !X, on="x") # not join # join using column "y" of 'dat' with column "v" of X query_if(dat, X, on=c(y="v")) query_if(dat,X, on="y==v") # same as above (v1.9.8+) query_if(dat, X, on = .(y<=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, on="y<=foo") # same as above query_if(dat, X, on=c("y<=foo")) # same as above query_if(dat, X, on=.(y>=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, on=.(x, y<=foo)) # NEW non-equi join (v1.9.8+) query_if(dat, X, .(x,y,x.y,v), on=.(x, y>=foo)) # Select x's join columns as well query_if(dat, X, on="x", mult="first") # first row of each group query_if(dat, X, on="x", mult="last") # last row of each group query_if(dat, X, sum(v), by=.EACHI, on="x") # join and eval j for each row in i query_if(dat, X, sum(v)*foo, by=.EACHI, on="x") # join inherited scope query_if(dat, X, sum(v)*i.v, by=.EACHI, on="x") # 'i,v' refers to X's v column query_if(dat, X, on=.(x, v>=v), sum(y)*foo, by=.EACHI) # NEW non-equi join with by=.EACHI (v1.9.8+) # more on special symbols, see also ?"special-symbols" query_if(dat, .N) # last row query(dat, .N) # total number of rows in DT query(dat, .N, by=x) # number of rows in each group query(dat, .SD, .SDcols=x:y) # select columns 'x' and 'y' query(dat, .SD[1]) # first row of all columns query(dat, .SD[1], by=x) # first row of 'y' and 'v' for each group in 'x' query(dat, c(.N, lapply(.SD, sum)), by=x) # get rows *and* sum columns 'v' and 'y' by group query(dat, .I[1], by=x) # row number in DT corresponding to each group query(dat, grp := .GRP, by=x) %>% head() # add a group counter column query(X, query_if(dat, .BY, y, on="x"), by=x) # join within each group # add/update/delete by reference (see ?assign) query(dat, z:=42L) %>% head() # add new column by reference query(dat, z:=NULL) %>% head() # remove column by reference query_if(dat, "a", v:=42L, on="x") %>% head() # subassign to existing v column by reference query_if(dat, "b", v2:=84L, on="x") %>% head() # subassign to new column by reference (NA padded) # NB: postfix [] is shortcut to print() query(dat, m:=mean(v), by=x)[] # add new column by reference by group # advanced usage dat = data.table(x=rep(c("b","a","c"),each=3), v=c(1,1,1,2,2,1,1,2,2), y=c(1,3,6), a=1:9, b=9:1) dat query(dat, sum(v), by=.(y%%2)) # expressions in by query(dat, sum(v), by=.(bool = y%%2)) # same, using a named list to change by column name query(dat, .SD[2], by=x) # get 2nd row of each group query(dat, tail(.SD,2), by=x) # last 2 rows of each group query(dat, lapply(.SD, sum), by=x) # sum of all (other) columns for each group query(dat, .SD[which.min(v)], by=x) # nested query by group query(dat, list(MySum=sum(v), MyMin=min(v), MyMax=max(v)), by=.(x, y%%2) ) # by 2 expressions query(dat, .(a = .(a), b = .(b)), by=x) # list columns query(dat, .(seq = min(a):max(b)), by=x) # j is not limited to just aggregations query(dat, sum(v), by=x) %>% query_if(V1<20) # compound query query(dat, sum(v), by=x) %>% setorder(-V1) %>% head() # ordering results query(dat, c(.N, lapply(.SD,sum)), by=x) # get number of observations and sum per group # anonymous lambda in 'j', j accepts any valid # expression. TO REMEMBER: every element of # the list becomes a column in result. query(dat, {tmp = mean(y); .(a = a-tmp, b = b-tmp) }, by=x) # using rleid, get max(y) and min of all cols in .SDcols for each consecutive run of 'v' query(dat, c(.(y=max(y)), lapply(.SD, min)), by=rleid(v), .SDcols=v:b ) ## Not run: pdf("new.pdf") query(dat, plot(a,b), by=x) # can also plot in 'j' dev.off() ## End(Not run)
text_expand is simple string interpolation function. It searches in its
arguments expressions in curly brackets {expr}, evaluate them and substitute with
the result of evaluation. See examples.
text_expand(..., delim = c("\\{", "\\}"))text_expand(..., delim = c("\\{", "\\}"))
... |
character vectors |
delim |
character vector of length 2 - pair of opening and closing
delimiters for the templating tags. By default it is curly brackets. Note
that |
Vector of characters
i = 1:5 text_expand("q{i}") i = 1:3 j = 1:3 text_expand("q1_{i}_{j}") data(iris) text_expand("'iris' has {nrow(iris)} rows.")i = 1:5 text_expand("q{i}") i = 1:3 j = 1:3 text_expand("q1_{i}_{j}") data(iris) text_expand("'iris' has {nrow(iris)} rows.")
to_list always returns a list, each element of which is the
result of expression expr on the elements of data. By
default, NULL's will be removed from the result. You can change this behavior
with skip_null argument.
to_vec is the same as to_list but tries to convert its result
to vector via unlist.
to_df and to_dfr try to combine its results to data.table by rows.
to_dfc tries to combine its result to data.table by columns.
Expression can use predefined variables: '.x' is a value of current list element, '.name' is a name of the element and '.index' is sequential number of the element.
to_list( data, expr = NULL, ..., skip_null = TRUE, trace = FALSE, trace_step = 1L ) to_vec( data, expr = NULL, ..., skip_null = TRUE, trace = FALSE, trace_step = 1L, recursive = TRUE, use.names = TRUE ) to_df( data, expr = NULL, ..., trace = FALSE, trace_step = 1L, idvalue = NULL, idname = "item_id" ) to_dfr( data, expr = NULL, ..., trace = FALSE, trace_step = 1L, idvalue = NULL, idname = "item_id" ) to_dfc(data, expr = NULL, ..., trace = FALSE, trace_step = 1)to_list( data, expr = NULL, ..., skip_null = TRUE, trace = FALSE, trace_step = 1L ) to_vec( data, expr = NULL, ..., skip_null = TRUE, trace = FALSE, trace_step = 1L, recursive = TRUE, use.names = TRUE ) to_df( data, expr = NULL, ..., trace = FALSE, trace_step = 1L, idvalue = NULL, idname = "item_id" ) to_dfr( data, expr = NULL, ..., trace = FALSE, trace_step = 1L, idvalue = NULL, idname = "item_id" ) to_dfc(data, expr = NULL, ..., trace = FALSE, trace_step = 1)
data |
data.frame/list/vector |
expr |
expression or function. Expression can use predefined variables: '.x' is a value of current list element, '.name' is a name of the element and '.index' is sequential number of the element. |
... |
further arguments provided if 'expr' is function. |
skip_null |
logical Should we skip NULL's from result? Default is TRUE |
trace |
FALSE by default. Should we report progress during execution? Possible values are TRUE, FALSE, "pb" (progress bar) or custom expression in 'quote', e. g. 'quote(print(.x))'. Expression can contain '.x', '.name', and '.index' variables. |
trace_step |
integer. 1 by default. Step for reporting progress. Ignored if 'trace' argument is equal to FALSE. |
recursive |
logical. Should unlisting be applied to list components of x? For details see unlist. |
use.names |
logical. TRUE by default. Should names of source list be preserved? Setting it to FALSE in some cases can greatly increase performance. For details see unlist. |
idvalue |
expression for calculation id column. Usually it is just unquoted symbols: one of the '.name', '.index' or '.x'. |
idname |
character, 'item_id' by default. Name for the id column. |
'to_list' returns list, 'to_vec' tries to return vector and other functions return data.table
1:5 %>% to_list(rnorm(n = 3, .x)) # or in 'lapply' style 1:5 %>% to_list(rnorm, n = 3) %>% to_vec(mean) # or use an anonymous function 1:5 %>% to_list(function(x) rnorm(3, x)) # Use to_vec() to reduce output to a vector instead # of a list: # filtering - return only even numbers to_vec(1:10, if(.x %% 2 == 0) .x) # filtering - calculate mean only on the numeric columns to_vec(iris, if(is.numeric(.x)) mean(.x)) # mean for numerics, number of distincts for others to_vec(iris, if(is.numeric(.x)) mean(.x) else uniqueN(.x)) # means for Sepal to_vec(iris, if(startsWith(.name, "Sepal")) mean(.x)) # A more realistic example: split a data frame into pieces, fit a # model to each piece, summarise and extract R^2 mtcars %>% split(.$cyl) %>% to_list(summary(lm(mpg ~ wt, data = .x))) %>% to_vec(.x$r.squared) # If each element of the output is a data frame, use # to_df to row-bind them together: mtcars %>% split(.$cyl) %>% to_list(lm(mpg ~ wt, data = .x)) %>% to_df(c(cyl = .name, coef(.x))) ## Not run: # read all csv files in "data" to data.frame all_files = dir("data", pattern = "csv$", full.names = TRUE) %>% to_df(fread, idvalue = basename(.x), idname = "filename", trace = "pb" ) ## End(Not run)1:5 %>% to_list(rnorm(n = 3, .x)) # or in 'lapply' style 1:5 %>% to_list(rnorm, n = 3) %>% to_vec(mean) # or use an anonymous function 1:5 %>% to_list(function(x) rnorm(3, x)) # Use to_vec() to reduce output to a vector instead # of a list: # filtering - return only even numbers to_vec(1:10, if(.x %% 2 == 0) .x) # filtering - calculate mean only on the numeric columns to_vec(iris, if(is.numeric(.x)) mean(.x)) # mean for numerics, number of distincts for others to_vec(iris, if(is.numeric(.x)) mean(.x) else uniqueN(.x)) # means for Sepal to_vec(iris, if(startsWith(.name, "Sepal")) mean(.x)) # A more realistic example: split a data frame into pieces, fit a # model to each piece, summarise and extract R^2 mtcars %>% split(.$cyl) %>% to_list(summary(lm(mpg ~ wt, data = .x))) %>% to_vec(.x$r.squared) # If each element of the output is a data frame, use # to_df to row-bind them together: mtcars %>% split(.$cyl) %>% to_list(lm(mpg ~ wt, data = .x)) %>% to_df(c(cyl = .name, coef(.x))) ## Not run: # read all csv files in "data" to data.frame all_files = dir("data", pattern = "csv$", full.names = TRUE) %>% to_df(fread, idvalue = basename(.x), idname = "filename", trace = "pb" ) ## End(Not run)
to_long increases number of rows in the dataset and reduce number of
columns. to_wide makes invert transformation. You can use cols for
selecting variables in the arguments. See examples.
to_long( data, columns = NULL, keep = NULL, names_in = "variable", values_in = "value", drop_na = FALSE, names_factor = TRUE, value_factor = FALSE, ... ) to_wide( data, keep = NULL, names_in = variable, values_in = value, fun = identity, sep = "_", fill = NA, missing_comb = c("none", "rows", "columns", "all"), ... )to_long( data, columns = NULL, keep = NULL, names_in = "variable", values_in = "value", drop_na = FALSE, names_factor = TRUE, value_factor = FALSE, ... ) to_wide( data, keep = NULL, names_in = variable, values_in = value, fun = identity, sep = "_", fill = NA, missing_comb = c("none", "rows", "columns", "all"), ... )
data |
A data.frame to convert |
columns |
unquoted names of variables for stacking. When missing, we
will stack all columns outside |
keep |
unquoted names of columns which will be kept as is, e. g. only
recycled or deduplicated. If missing, it is all columns except stacked or
unstacked. If |
names_in |
name of the stacked variable names column. The default name
is 'variable'. It is quoted in the |
values_in |
name(-s) of the stacked data values column(s). The default
name is 'value'. Multiple names can be provided here for the case when
|
drop_na |
If TRUE, NA values will be removed from the stacked data. |
names_factor |
If TRUE, the column with names will be converted to factor, else it will be a character column. TRUE by default. |
value_factor |
If TRUE, the value column will be converted to factor, else the stacked values type is left unchanged. FALSE by default. |
... |
other arguments passed to |
fun |
Should the data be aggregated before casting? By default, it is
|
sep |
Character vector of length 1, indicating the separating character in variable names generated during casting. Default is "_". |
fill |
Value with which to fill missing cells. |
missing_comb |
One of "none" (the default), "rows" - include missing combinations in rows, "columns" - include missing combinations in columns, and "all" include all missing combinations. |
data.table in the wide or long form.
data(iris) # 'to_long' long_iris = iris %>% to_long(keep = Species) long_iris iris_with_stat = long_iris %>% take(mean = mean(value), sd = sd(value), n = .N*1.0, by = .(Species, variable) ) %>% to_long(columns = c(mean, sd, n), names_in = "stat") # 'to_wide' - table with multiple stats iris_with_stat %>% to_wide() iris_with_stat %>% to_wide(names_in = c(variable, stat)) iris_with_stat %>% to_wide(names_in = c(variable, Species)) # 'to_wide' - aggregation function long_iris %>% to_wide(fun = list(Mean = mean, SD = sd, N = length)) # multiple variables iris %>% to_long(list(Sepal = cols("^Sepal"), Petal = cols("^Petal"))) %>% let( variable = factor(variable, levels = 1:2, labels = c("Length", "Width")) ) %>% to_wide(values_in = c(Sepal, Petal)) # '%to%' selector - example from tidyr::pivot_longer data(anscombe) anscombe %>% to_long( list(x = x1 %to% x4, y = y1 %to% y4), names_in = "set" ) ###################################### ## Examples from data.table melt/dcast ###################################### set.seed(45) DT = data.table( i_1 = c(1:5, NA)*1.0, i_2 = c(NA,6,7,8,9,10)*1.0, f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)), f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE), c_1 = sample(c(letters[1:3], NA), 6, TRUE), d_1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"), d_2 = as.Date(6:1, origin="2012-01-01") ) # id, values as character/integer/numeric vectors to_long(DT, f_1, keep = 1:2) to_long(DT, f_1, keep = c(i_1, i_2)) to_long(DT, f_1, keep = i_1 %to% i_2) to_long(DT, f_1, keep = cols(i_1:i_2), names_factor = FALSE) to_long(DT, f_1, keep = cols("i_{1:2}")) to_long(DT, f_1, keep = cols("^i_")) to_long(DT, f_1, keep = cols("^i_"), names_in = "var", values_in = "val") col_var = "^i_" to_long(DT, 3, keep = cols(col_var)) to_long(DT, cols("^f_"), keep = cols("^i_"), value_factor = TRUE) to_long(mtcars) to_long(mtcars, keep = am) to_long(mtcars, columns = c(am, vs, mpg)) to_long(mtcars, columns = c(am, vs, mpg), keep = FALSE) to_long(DT, keep = f_1, columns = c(i_1, i_2), drop_na = TRUE) to_long(DT, keep=1:2, columns = list(cols("^f_"), cols("^d_")), value_factor=TRUE) data("ChickWeight") names(ChickWeight) = tolower(names(ChickWeight)) DT = to_long(ChickWeight, keep=2:4) to_wide(DT, keep = time, fun = mean) to_wide(DT, keep = FALSE, fun = mean) to_wide(DT, keep = diet, fun = mean) to_wide(DT, keep = c(diet, chick), names_in = time, missing_comb = "all") to_wide(DT, keep = c(diet, chick), names_in = time, missing_comb = "all", fill = 0) to_wide(DT, chick, time, fun = mean) # using FALSE DT = data.table(v1 = rep(1:2, each = 6), v2 = rep(rep(1:3, 2), each = 2), v3 = rep(1:2, 6), v4 = rnorm(6)) ## for each combination of (v1, v2), add up all values of v4 to_wide(DT, cols("^v(1|2)"), names_in = FALSE, values_in = v4, fun = sum ) # multiple values_in and multiple fun DT = data.table(x=sample(5,20,TRUE), y=sample(2,20,TRUE), z=sample(letters[1:2], 20,TRUE), d1 = runif(20), d2=1L) # multiple values_in to_wide(DT, keep = c(x, y), names_in = z, values_in = c(d1, d2), fun = sum, fill = 0) # multiple funs to_wide(DT, keep = c(x, y), names_in = z, values_in = d1, fun = list(sum = sum, mean = mean), fill = NULL) # multiple fun and values_in (all combinations) to_wide(DT, keep = c(x, y), names_in = z, values_in = c(d1, d2), fun = list(sum = sum, mean = mean) ) # multiple fun and values_in (one-to-one) to_wide(DT, keep = c(x, y), names_in = z, values_in = list(d1, d2), fun = list(sum = sum, mean = mean) )data(iris) # 'to_long' long_iris = iris %>% to_long(keep = Species) long_iris iris_with_stat = long_iris %>% take(mean = mean(value), sd = sd(value), n = .N*1.0, by = .(Species, variable) ) %>% to_long(columns = c(mean, sd, n), names_in = "stat") # 'to_wide' - table with multiple stats iris_with_stat %>% to_wide() iris_with_stat %>% to_wide(names_in = c(variable, stat)) iris_with_stat %>% to_wide(names_in = c(variable, Species)) # 'to_wide' - aggregation function long_iris %>% to_wide(fun = list(Mean = mean, SD = sd, N = length)) # multiple variables iris %>% to_long(list(Sepal = cols("^Sepal"), Petal = cols("^Petal"))) %>% let( variable = factor(variable, levels = 1:2, labels = c("Length", "Width")) ) %>% to_wide(values_in = c(Sepal, Petal)) # '%to%' selector - example from tidyr::pivot_longer data(anscombe) anscombe %>% to_long( list(x = x1 %to% x4, y = y1 %to% y4), names_in = "set" ) ###################################### ## Examples from data.table melt/dcast ###################################### set.seed(45) DT = data.table( i_1 = c(1:5, NA)*1.0, i_2 = c(NA,6,7,8,9,10)*1.0, f_1 = factor(sample(c(letters[1:3], NA), 6, TRUE)), f_2 = factor(c("z", "a", "x", "c", "x", "x"), ordered=TRUE), c_1 = sample(c(letters[1:3], NA), 6, TRUE), d_1 = as.Date(c(1:3,NA,4:5), origin="2013-09-01"), d_2 = as.Date(6:1, origin="2012-01-01") ) # id, values as character/integer/numeric vectors to_long(DT, f_1, keep = 1:2) to_long(DT, f_1, keep = c(i_1, i_2)) to_long(DT, f_1, keep = i_1 %to% i_2) to_long(DT, f_1, keep = cols(i_1:i_2), names_factor = FALSE) to_long(DT, f_1, keep = cols("i_{1:2}")) to_long(DT, f_1, keep = cols("^i_")) to_long(DT, f_1, keep = cols("^i_"), names_in = "var", values_in = "val") col_var = "^i_" to_long(DT, 3, keep = cols(col_var)) to_long(DT, cols("^f_"), keep = cols("^i_"), value_factor = TRUE) to_long(mtcars) to_long(mtcars, keep = am) to_long(mtcars, columns = c(am, vs, mpg)) to_long(mtcars, columns = c(am, vs, mpg), keep = FALSE) to_long(DT, keep = f_1, columns = c(i_1, i_2), drop_na = TRUE) to_long(DT, keep=1:2, columns = list(cols("^f_"), cols("^d_")), value_factor=TRUE) data("ChickWeight") names(ChickWeight) = tolower(names(ChickWeight)) DT = to_long(ChickWeight, keep=2:4) to_wide(DT, keep = time, fun = mean) to_wide(DT, keep = FALSE, fun = mean) to_wide(DT, keep = diet, fun = mean) to_wide(DT, keep = c(diet, chick), names_in = time, missing_comb = "all") to_wide(DT, keep = c(diet, chick), names_in = time, missing_comb = "all", fill = 0) to_wide(DT, chick, time, fun = mean) # using FALSE DT = data.table(v1 = rep(1:2, each = 6), v2 = rep(rep(1:3, 2), each = 2), v3 = rep(1:2, 6), v4 = rnorm(6)) ## for each combination of (v1, v2), add up all values of v4 to_wide(DT, cols("^v(1|2)"), names_in = FALSE, values_in = v4, fun = sum ) # multiple values_in and multiple fun DT = data.table(x=sample(5,20,TRUE), y=sample(2,20,TRUE), z=sample(letters[1:2], 20,TRUE), d1 = runif(20), d2=1L) # multiple values_in to_wide(DT, keep = c(x, y), names_in = z, values_in = c(d1, d2), fun = sum, fill = 0) # multiple funs to_wide(DT, keep = c(x, y), names_in = z, values_in = d1, fun = list(sum = sum, mean = mean), fill = NULL) # multiple fun and values_in (all combinations) to_wide(DT, keep = c(x, y), names_in = z, values_in = c(d1, d2), fun = list(sum = sum, mean = mean) ) # multiple fun and values_in (one-to-one) to_wide(DT, keep = c(x, y), names_in = z, values_in = list(d1, d2), fun = list(sum = sum, mean = mean) )
vlookup function is inspired by VLOOKUP spreadsheet
function. It looks for a lookup_value in the lookup_column of
the dict, and then returns values in the same rows from
result_column.
xlookup is simplified version of vlookup. It searches for a
lookup_value in the lookup_vector and return values in the same
position from the result_vector.
vlookup( lookup_value, dict, result_column = 2, lookup_column = 1, no_match = NA ) xlookup(lookup_value, lookup_vector, result_vector, no_match = NA)vlookup( lookup_value, dict, result_column = 2, lookup_column = 1, no_match = NA ) xlookup(lookup_value, lookup_vector, result_vector, no_match = NA)
lookup_value |
Vector of looked up values |
dict |
data.frame. Dictionary. |
result_column |
numeric or character. Resulting columns in the
|
lookup_column |
Column of |
no_match |
vector of length one. NA by default. Where a valid match is not found, return the 'no_match' value you supply. |
lookup_vector |
vector in which 'lookup_value' will be searched during 'xlookup'. |
result_vector |
vector with resulting values for 'xlookup'. |
xlookup always return vector, vlookup returns vector if
the result_column is single value. In the opposite case data.frame will
be returned.
# with data.frame dict = data.frame(num=1:26, small=letters, cap=LETTERS) vlookup(1:3, dict) vlookup(c(45,1:3,58), dict, result_column='cap') vlookup(c(45,1:3,58), dict, result_column='cap', no_match = "Not found") # the same with xlookup xlookup(1:3, dict$num, dict$small) xlookup(c(45,1:3,58), dict$num, dict$cap) xlookup(c(45,1:3,58), dict$num, dict$cap, no_match = "Not found") # example from base 'merge' authors = data.table( surname = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"), nationality = c("US", "Australia", "US", "UK", "Australia"), deceased = c("yes", rep("no", 4)) ) books = data.table( surname = c("Tukey", "Venables", "Tierney", "Ripley", "Ripley", "McNeil", "R Core"), title = c("Exploratory Data Analysis", "Modern Applied Statistics ...", "LISP-STAT", "Spatial Statistics", "Stochastic Simulation", "Interactive Data Analysis", "An Introduction to R") ) let(books, c("author_nationality", "author_deceased") := vlookup(surname, dict = authors, result_column = 2:3 ) )[] # Just for fun. Examples borrowed from Microsoft Excel. # It is not the R way of doing things. # Example 2 ex2 = fread(" Item_ID Item Cost Markup ST-340 Stroller 145.67 0.30 BI-567 Bib 3.56 0.40 DI-328 Diapers 21.45 0.35 WI-989 Wipes 5.12 0.40 AS-469 Aspirator 2.56 0.45 ") # Calculates the retail price of diapers by adding the markup percentage to the cost. vlookup("DI-328", ex2, 3) * (1 + vlookup("DI-328", ex2, 4)) # 28.9575 # Calculates the sale price of wipes by subtracting a specified discount from # the retail price. (vlookup("WI-989", ex2, "Cost") * (1 + vlookup("WI-989", ex2, "Markup"))) * (1 - 0.2) # 5.7344 A2 = ex2[["Item_ID"]][1] A3 = ex2[["Item_ID"]][2] # If the cost of an item is greater than or equal to $20.00, displays the string # "Markup is nn%"; otherwise, displays the string "Cost is under $20.00". ifelse(vlookup(A2, ex2, "Cost") >= 20, paste0("Markup is " , 100 * vlookup(A2, ex2, "Markup"),"%"), "Cost is under $20.00") # Markup is 30% # If the cost of an item is greater than or equal to $20.00, displays the string # Markup is nn%"; otherwise, displays the string "Cost is $n.nn". ifelse(vlookup(A3, ex2, "Cost") >= 20, paste0("Markup is: " , 100 * vlookup(A3, ex2, "Markup") , "%"), paste0("Cost is $", vlookup(A3, ex2, "Cost"))) #Cost is $3.56 # Example 3 ex3 = fread(' ID Last_name First_name Title Birth_date 1 Davis Sara "Sales Rep." 12/8/1968 2 Fontana Olivier "V.P. of Sales" 2/19/1952 3 Leal Karina "Sales Rep." 8/30/1963 4 Patten Michael "Sales Rep." 9/19/1958 5 Burke Brian "Sales Mgr." 3/4/1955 6 Sousa Luis "Sales Rep." 7/2/1963 ') # If there is an employee with an ID of 5, displays the employee's last name; # otherwise, displays the message "Employee not found". vlookup(5, ex3, "Last_name", no_match = "Employee not found") # Burke # Many employees vlookup(1:10, ex3, "Last_name", no_match = "Employee not found") # For the employee with an ID of 4, concatenates the values of three cells into # a complete sentence. paste0(vlookup(4, ex3, "First_name"), " ", vlookup(4, ex3, "Last_name"), " is a ", vlookup(4, ex3, "Title")) # Michael Patten is a Sales Rep.# with data.frame dict = data.frame(num=1:26, small=letters, cap=LETTERS) vlookup(1:3, dict) vlookup(c(45,1:3,58), dict, result_column='cap') vlookup(c(45,1:3,58), dict, result_column='cap', no_match = "Not found") # the same with xlookup xlookup(1:3, dict$num, dict$small) xlookup(c(45,1:3,58), dict$num, dict$cap) xlookup(c(45,1:3,58), dict$num, dict$cap, no_match = "Not found") # example from base 'merge' authors = data.table( surname = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"), nationality = c("US", "Australia", "US", "UK", "Australia"), deceased = c("yes", rep("no", 4)) ) books = data.table( surname = c("Tukey", "Venables", "Tierney", "Ripley", "Ripley", "McNeil", "R Core"), title = c("Exploratory Data Analysis", "Modern Applied Statistics ...", "LISP-STAT", "Spatial Statistics", "Stochastic Simulation", "Interactive Data Analysis", "An Introduction to R") ) let(books, c("author_nationality", "author_deceased") := vlookup(surname, dict = authors, result_column = 2:3 ) )[] # Just for fun. Examples borrowed from Microsoft Excel. # It is not the R way of doing things. # Example 2 ex2 = fread(" Item_ID Item Cost Markup ST-340 Stroller 145.67 0.30 BI-567 Bib 3.56 0.40 DI-328 Diapers 21.45 0.35 WI-989 Wipes 5.12 0.40 AS-469 Aspirator 2.56 0.45 ") # Calculates the retail price of diapers by adding the markup percentage to the cost. vlookup("DI-328", ex2, 3) * (1 + vlookup("DI-328", ex2, 4)) # 28.9575 # Calculates the sale price of wipes by subtracting a specified discount from # the retail price. (vlookup("WI-989", ex2, "Cost") * (1 + vlookup("WI-989", ex2, "Markup"))) * (1 - 0.2) # 5.7344 A2 = ex2[["Item_ID"]][1] A3 = ex2[["Item_ID"]][2] # If the cost of an item is greater than or equal to $20.00, displays the string # "Markup is nn%"; otherwise, displays the string "Cost is under $20.00". ifelse(vlookup(A2, ex2, "Cost") >= 20, paste0("Markup is " , 100 * vlookup(A2, ex2, "Markup"),"%"), "Cost is under $20.00") # Markup is 30% # If the cost of an item is greater than or equal to $20.00, displays the string # Markup is nn%"; otherwise, displays the string "Cost is $n.nn". ifelse(vlookup(A3, ex2, "Cost") >= 20, paste0("Markup is: " , 100 * vlookup(A3, ex2, "Markup") , "%"), paste0("Cost is $", vlookup(A3, ex2, "Cost"))) #Cost is $3.56 # Example 3 ex3 = fread(' ID Last_name First_name Title Birth_date 1 Davis Sara "Sales Rep." 12/8/1968 2 Fontana Olivier "V.P. of Sales" 2/19/1952 3 Leal Karina "Sales Rep." 8/30/1963 4 Patten Michael "Sales Rep." 9/19/1958 5 Burke Brian "Sales Mgr." 3/4/1955 6 Sousa Luis "Sales Rep." 7/2/1963 ') # If there is an employee with an ID of 5, displays the employee's last name; # otherwise, displays the message "Employee not found". vlookup(5, ex3, "Last_name", no_match = "Employee not found") # Burke # Many employees vlookup(1:10, ex3, "Last_name", no_match = "Employee not found") # For the employee with an ID of 4, concatenates the values of three cells into # a complete sentence. paste0(vlookup(4, ex3, "First_name"), " ", vlookup(4, ex3, "Last_name"), " is a ", vlookup(4, ex3, "Title")) # Michael Patten is a Sales Rep.