tidylog

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)
}

ColeGC/tidylog