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https://github.com/elbersb/tidylog

Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
https://github.com/elbersb/tidylog

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Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# tidylog



[![CRAN Version](https://www.r-pkg.org/badges/version/tidylog)](https://CRAN.R-project.org/package=tidylog)

[![Downloads](http://cranlogs.r-pkg.org/badges/tidylog)](https://CRAN.R-project.org/package=tidylog)

[![R-CMD-check](https://github.com/elbersb/tidylog/workflows/R-CMD-check/badge.svg)](https://github.com/elbersb/tidylog/actions)

[![Coverage status](https://codecov.io/gh/elbersb/tidylog/branch/master/graph/badge.svg)](https://app.codecov.io/github/elbersb/tidylog?branch=master)



The goal of tidylog is to provide feedback about dplyr and tidyr operations. It provides simple wrapper functions for almost all dplyr and tidyr functions, such as `filter`, `mutate`, `select`, `full_join`, and `group_by`.



## Example



Load `tidylog` after `dplyr` and/or `tidyr`:



```{r message=FALSE, warning=FALSE}

library("dplyr")

library("tidyr")

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

```



Tidylog will give you feedback, for instance when filtering a data frame or adding a new variable:



```{r}

filtered <- filter(mtcars, cyl == 4)

mutated <- mutate(mtcars, new_var = wt ** 2)

```



Tidylog reports detailed information for joins:



```{r}

joined <- left_join(nycflights13::flights, nycflights13::weather,

    by = c("year", "month", "day", "origin", "hour", "time_hour"))

```



In this case, we see that 1,556 rows from the `flights` dataset do not have weather information.



Tidylog can be especially helpful in longer pipes:



```{r}

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)

```

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



## Installation



Download from CRAN:



``` r

install.packages("tidylog")

```



Or install the development version:



``` r

devtools::install_github("elbersb/tidylog")

```



## Benchmarks



Tidylog will add a small overhead to each function call. This can be relevant for very large datasets and especially for joins. If you want to switch off tidylog for a single long-running command, simply prefix `dplyr::` or `tidyr::`, such as in `dplyr::left_join`. See [this vignette](https://cran.r-project.org/package=tidylog/vignettes/benchmarks.html) for more information.



## More examples



### filter, distinct, drop_na



```{r}

a <- filter(mtcars, mpg > 20)

b <- filter(mtcars, mpg > 100)

c <- filter(mtcars, mpg > 0)

d <- filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))

e <- distinct(mtcars)

f <- distinct_at(mtcars, vars(vs:carb))

g <- top_n(mtcars, 2, am)

i <- sample_frac(mtcars, 0.5)



j <- drop_na(airquality)

k <- drop_na(airquality, Ozone)

```



### mutate, transmute, replace_na, fill



```{r}

a <- mutate(mtcars, new_var = 1)

b <- mutate(mtcars, new_var = runif(n()))

c <- mutate(mtcars, new_var = NA)

d <- mutate_at(mtcars, vars(mpg, gear, drat), round)

e <- mutate(mtcars, am_factor = as.factor(am))

f <- mutate(mtcars, am = as.ordered(am))

g <- mutate(mtcars, am = ifelse(am == 1, NA, am))

h <- mutate(mtcars, am = recode(am, `0` = "zero", `1` = NA_character_))



i <- transmute(mtcars, mpg = mpg * 2, gear = gear + 1, new_var = vs + am)



j <- replace_na(airquality, list(Solar.R = 1))

k <- fill(airquality, Ozone)

```



### joins



For joins, tidylog provides more detailed information. For any join,

tidylog will show the number of rows

that are only present in x (the first dataframe), only present in y (the second dataframe),

and rows that have been matched. Numbers in parentheses indicate that these rows are not

included in the result. Tidylog will also indicate whether any rows were duplicated

(which is often unintentional):



```{r}

x <- tibble(a = 1:2)

y <- tibble(a = c(1, 1, 2), b = 1:3) # 1 is duplicated

j <- left_join(x, y, by = "a")

```



More examples:



```{r}

a <- left_join(band_members, band_instruments, by = "name")

b <- full_join(band_members, band_instruments, by = "name")

c <- anti_join(band_members, band_instruments, by = "name")

```



Because tidylog needs to perform two additional joins behind the scenes to report this information,

the overhead will be larger than for the other tidylog functions (especially with large datasets).



### select, relocate, rename



```{r}

a <- select(mtcars, mpg, wt)

b <- select_if(mtcars, is.character)

c <- relocate(mtcars, hp)

d <- select(mtcars, a = wt, b = mpg)



e <- rename(mtcars, miles_per_gallon = mpg)

f <- rename_with(mtcars, toupper)

```



### summarize



```{r}

a <- mtcars %>%

    group_by(cyl, carb) %>%

    summarize(total_weight = sum(wt))



b <- iris %>%

    group_by(Species) %>%

    summarize_all(list(min, max))

```



### tally, count, add_tally, add_count



```{r}

a <- mtcars %>% group_by(gear, carb) %>% tally

b <- mtcars %>% group_by(gear, carb) %>% add_tally()



c <- mtcars %>% count(gear, carb)

d <- mtcars %>% add_count(gear, carb, name = "count")

```



### pivot_longer, pivot_wider



```{r}

longer <- mtcars %>%

    mutate(id = 1:n()) %>%

    pivot_longer(-id, names_to = "var", values_to = "value")

wider <- longer %>%

    pivot_wider(names_from = var, values_from = value)

```



Tidylog also supports `gather` and `spread`.



## Turning logging off, registering additional loggers



To turn off the output for just a particular function call, you can simply call the dplyr and tidyr functions

directly, e.g. `dplyr::filter` or `tidyr::drop_na`.



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



```{r}

options("tidylog.display" = list())  # turn off

a <- filter(mtcars, mpg > 20)



options("tidylog.display" = NULL)    # turn on

a <- filter(mtcars, mpg > 20)

```



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:



```{r}

library("crayon")  # for terminal colors

crayon <- function(x) cat(red$bold(x), sep = "\n")

options("tidylog.display" = list(crayon))

a <- filter(mtcars, mpg > 20)

```



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



```{r}

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)

```



## Namespace conflicts



Tidylog redefines several of the functions exported by dplyr and tidyr, 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](https://CRAN.R-project.org/package=conflicted) package:



``` r

library("dplyr")

library("tidyr")

library("tidylog")

library("conflicted")

for (f in getNamespaceExports("tidylog")) {

    conflicted::conflict_prefer(f, "tidylog", quiet = TRUE)

}

```