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tidylog's Introduction

tidylog

CRAN Version Build Status Coverage status

The goal of tidylog is to provide feedback about basic dplyr operations. It provides simple wrapper functions for the most common functions, such as filter, mutate, select, full_join, and group_by.

Example

Load tidylog after dplyr:

library("dplyr")
library("tidylog", warn.conflicts = FALSE)

Tidylog will give you feedback, for instance when filtering a data frame:

filtered <- filter(mtcars, cyl == 4)
#> filter: removed 21 rows (66%), 11 remaining

This can be especially helpful in longer pipes:

summary <- mtcars %>%
    select(mpg, cyl, hp, am) %>%
    filter(mpg > 15) %>%
    mutate(mpg_round = round(mpg)) %>%
    group_by(cyl, mpg_round, am) %>%
    tally() %>%
    filter(n >= 1)
#> select: dropped 7 variables (disp, drat, wt, qsec, vs, …)
#> filter: removed 6 rows (19%), 26 remaining
#> mutate: new variable 'mpg_round' with 15 unique values and 0% NA
#> group_by: 3 grouping variables (cyl, mpg_round, am)
#> tally: now 20 rows and 4 columns, 2 group variables remaining (cyl, mpg_round)
#> filter (grouped): no rows removed

Here, it might have been accidental that the last filter command had no effect.

Installation

Download from CRAN:

install.packages("tidylog")

Or install the development version:

devtools::install_github("elbersb/tidylog")

More examples

filter, distinct

a <- filter(mtcars, mpg > 20)
#> filter: removed 18 rows (56%), 14 remaining
b <- filter(mtcars, mpg > 100)
#> filter: removed all rows (100%)
c <- filter(mtcars, mpg > 0)
#> filter: no rows removed
d <- filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
#> filter_at: removed 19 rows (59%), 13 remaining
e <- distinct(mtcars)
#> distinct: no rows removed
f <- distinct_at(mtcars, vars(vs:carb))
#> distinct_at: removed 18 rows (56%), 14 remaining
g <- top_n(mtcars, 2, am)
#> top_n: removed 19 rows (59%), 13 remaining

mutate, transmute

a <- mutate(mtcars, new_var = 1)
#> mutate: new variable 'new_var' with one unique value and 0% NA
b <- mutate(mtcars, new_var = runif(n()))
#> mutate: new variable 'new_var' with 32 unique values and 0% NA
c <- mutate(mtcars, new_var = NA)
#> mutate: new variable 'new_var' with one unique value and 100% NA
d <- mutate_at(mtcars, vars(mpg, gear, drat), round)
#> mutate_at: changed 28 values (88%) of 'mpg' (0 new NA)
#> mutate_at: changed 31 values (97%) of 'drat' (0 new NA)
e <- mutate(mtcars, am_factor = as.factor(am))
#> mutate: new variable 'am_factor' with 2 unique values and 0% NA
f <- mutate(mtcars, am = as.factor(am))
#> mutate: converted 'am' from double to factor (0 new NA)
g <- mutate(mtcars, am = ifelse(am == 1, NA, am))
#> mutate: changed 13 values (41%) of 'am' (13 new NA)
h <- mutate(mtcars, am = recode(am, `0` = "zero", `1` = NA_character_))
#> mutate: converted 'am' from double to character (13 new NA)

i <- transmute(mtcars, mpg = mpg * 2, gear = gear + 1, new_var = vs + am)
#> transmute: dropped 9 variables (cyl, disp, hp, drat, wt, …)
#> transmute: changed 32 values (100%) of 'mpg' (0 new NA)
#> transmute: changed 32 values (100%) of 'gear' (0 new NA)
#> transmute: new variable 'new_var' with 3 unique values and 0% NA

select

a <- select(mtcars, mpg, wt)
#> select: dropped 9 variables (cyl, disp, hp, drat, qsec, …)
b <- select(mtcars, matches("a"))
#> select: dropped 7 variables (mpg, cyl, disp, hp, wt, …)
c <- select_if(mtcars, is.character)
#> select_if: dropped all variables

joins

a <- left_join(band_members, band_instruments, by = "name")
#> left_join: added 0 rows and added one column (plays)
b <- full_join(band_members, band_instruments, by = "name")
#> full_join: added one row and added one column (plays)
c <- anti_join(band_members, band_instruments, by = "name")
#> anti_join: removed 2 rows and added no new columns

summarize

a <- mtcars %>%
    group_by(cyl, carb) %>%
    summarize(total_weight = sum(wt))
#> group_by: 2 grouping variables (cyl, carb)
#> summarize: now 9 rows and 3 columns, one group variable remaining (cyl)

b <- iris %>%
    group_by(Species) %>%
    summarize_all(list(~min, ~max))
#> group_by: one grouping variable (Species)
#> summarize_all: now 3 rows and 9 columns, ungrouped

tally, count, add_tally, add_count

a <- mtcars %>% group_by(gear, carb) %>% tally
#> group_by: 2 grouping variables (gear, carb)
#> tally: now 11 rows and 3 columns, one group variable remaining (gear)
b <- mtcars %>% group_by(gear, carb) %>% add_tally()
#> group_by: 2 grouping variables (gear, carb)
#> add_tally (grouped): new variable 'n' with 5 unique values and 0% NA

c <- mtcars %>% count(gear, carb)
#> count: now 11 rows and 3 columns, ungrouped
d <- mtcars %>% add_count(gear, carb, name = "count")
#> add_count: new variable 'count' with 5 unique values and 0% NA

Turning logging off, registering additional loggers

To turn off the output for just a particular function call, you can simply call the dplyr functions directly, e.g. dplyr::filter.

To turn off the output more permanently, set the global option tidylog.display to an empty list:

options("tidylog.display" = list())  # turn off
a <- filter(mtcars, mpg > 20)

options("tidylog.display" = NULL)    # turn on
a <- filter(mtcars, mpg > 20)
#> filter: removed 18 rows (56%), 14 remaining

This option can also be used to register additional loggers. The option tidylog.display expects a list of functions. By default (when tidylog.display is set to NULL), tidylog will use the message function to display the output, but if you prefer a more colorful output, simply overwrite the option:

library("crayon")  # for terminal colors
crayon <- function(x) cat(red$bold(x), sep = "\n") 
options("tidylog.display" = list(crayon))
a <- filter(mtcars, mpg > 20)
#> filter: removed 18 rows (56%), 14 remaining

To print the output both to the screen and to a file, you could use:

log_to_file <- function(text) cat(text, file = "log.txt", sep = "\n", append = TRUE)
options("tidylog.display" = list(message, log_to_file))
a <- filter(mtcars, mpg > 20)
#> filter: removed 18 rows (56%), 14 remaining

Namespace conflicts

Tidylog redefines several of the functions exported by dplyr, so it should be loaded last, otherwise there will be no output. A more explicit way to resolve namespace conflicts is to use the conflicted package:

library(dplyr)
library(tidylog)
library(conflicted)
for (f in getNamespaceExports("tidylog")) {
    conflicted::conflict_prefer(f, 'tidylog', quiet = TRUE)
}

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