From b20f3f2a521a15a67261cdd39ac8f1ec978e04b6 Mon Sep 17 00:00:00 2001 From: Jim Hester Date: Mon, 6 May 2019 14:23:30 -0400 Subject: [PATCH 01/11] Add vroom blogpost --- .../articles/2019-05-vroom-1-0-0.Rmarkdown | 434 ++++++++++ content/articles/2019-05-vroom-1-0-0.markdown | 741 ++++++++++++++++++ content/articles/vroom-1-0-0-sq.jpg | Bin 0 -> 170738 bytes content/articles/vroom-1-0-0-wd.jpg | Bin 0 -> 206126 bytes .../figure-html/benchmark_plot-1.png | Bin 0 -> 107894 bytes 5 files changed, 1175 insertions(+) create mode 100644 content/articles/2019-05-vroom-1-0-0.Rmarkdown create mode 100644 content/articles/2019-05-vroom-1-0-0.markdown create mode 100644 content/articles/vroom-1-0-0-sq.jpg create mode 100644 content/articles/vroom-1-0-0-wd.jpg create mode 100644 static/articles/2019-05-vroom-1-0-0_files/figure-html/benchmark_plot-1.png diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown new file mode 100644 index 000000000..118063caa --- /dev/null +++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown @@ -0,0 +1,434 @@ +--- +title: vroom 1.0.0 +author: Jim Hester +date: '2019-05-06' +slug: vroom-1-0-0 +description: Introducing the vroom package, extremely fast data import in R. +categories: + - package +tags: [package, r-lib] +photo: + url: https://www.pexels.com/photo/12801/ + author: Chris Peeters +--- + +```{r include = FALSE} +knitr::opts_chunk$set(collapse = TRUE, comment = "#>") + +options(crayon.enabled = TRUE) + +old_hooks <- fansi::set_knit_hooks( + knitr::knit_hooks, + which = c("message", "output", "error"), + proc.fun = function(x, class) { + fansi::html_code_block(fansi::sgr_to_html(x), class = class) + } +) + +library(vroom) +library(nycflights13) +vroom_write(flights, "flights.tsv") + +# These are taken from the vroom vignette +pretty_sec <- function(x) { + x[!is.na(x)] <- prettyunits::pretty_sec(x[!is.na(x)]) + x +} + +library(forcats) +library(dplyr) +library(ggplot2) +library(tidyr) +read_benchmark <- function(file, desc) { + vroom::vroom(file, col_types = c("cccdddd")) %>% + filter(type == "real", op != "setup") %>% + mutate( + package = fct_reorder(sub("_", "\n", package), time, sum), + op = factor(op, desc) + ) +} + +generate_subtitle <- function(data) { + rows <- scales::comma(data$rows[[1]]) + cols <- scales::comma(data$cols[[1]]) + size <- fs::fs_bytes(data$size[[1]]) + glue::glue("{rows} x {cols} - {size}B") +} + +plot_benchmark <- function(data, title) { + + subtitle <- generate_subtitle(data) + + data %>% + ggplot() + + geom_bar(aes(x = package, y = time, fill = op, group = package), stat = "identity") + + scale_fill_brewer(type = "qual", palette = "Set2") + + scale_y_continuous(labels = function(x) format(bench::as_bench_time(x))) + + theme_grey(base_size = 18) + + theme(legend.position = "bottom") + + coord_flip() + + labs(title = title, subtitle = subtitle, x = NULL, y = NULL, fill = NULL) +} + +make_table <- function(data) { + times <- data %>% + group_by(package, op) %>% + filter(type == "real") %>% + tally(wt = time) %>% + spread(op, n) %>% + mutate(total = sum(read, print, head, tail, sample, filter, aggregate)) + + times %>% + arrange(desc(total)) %>% + mutate_if(is.numeric, pretty_sec) %>% + knitr::kable(digits = 2, align = "r", format = "markdown") +} + +desc <- c("setup", "read", "print", "head", "tail", "sample", "filter", "aggregate") +``` + + + + + +I'm excited to announce that [vroom 1.0.0](http://vroom.r-lib.org) is now +available on CRAN! + +vroom reads rectangular data, such as comma separated +(csv), tab separated (tsv) or fixed width files (fwf) into R. + +It performs the +same function as packages like [readr](http://readr.r-lib.org), +[data.table](http://r-datatable.com) or the `read.csv()` +function, but for many datasets vroom can read them much, much +faster (hence the name). + +The main reason vroom can be faster is because character data is read from the +file lazily; you only pay for the data you use. This lazy access is done +automatically, so no changes to your R data manipulation code are needed. + +vroom also provides efficient multi-threaded writing that is multiple times +faster on most inputs than the readr writer. + +Install vroom with: + +```{r, eval = FALSE} +install.packages("vroom") +``` + +The best way to get acquainted with the package is the [getting +started](http://vroom.r-lib.org/articles/vroom.html) vignette. + +## Reading delimited files + +Compared to readr, the first difference you may note is you use only one +function to read the files, `vroom()`. This is because `vroom()` guesses the +delimiter of the file automatically (based on the first few lines). This works +well most of the time, but may fail to guess properly in some cases. The +`delim` argument is used to specify the delimiter of the file explicitly. + +```{r} +library(vroom) + +data <- vroom("flights.tsv") +``` + +The summary message after reading also differs from readr. We hope this output +gives a more informative indication if the types of your columns are being guessed +properly. However you can still retrieve and print the full column specification +with `spec()`. + +```{r} +spec(data) +``` + +The message will be disabled if you supply a column specification to `col_types` when reading. + +```{r} +s <- spec(data) + +data <- vroom("flights.tsv", col_types = s) +``` + +## Reading multiple files + +One feature new to vroom is built-in support for reading sets of files with the +same columns into one table. Just pass the filenames to be read directly to +vroom. + +```{r} +# Split the flights data by carrier +purrr::iwalk( + split(flights, flights$carrier), + ~ vroom::vroom_write(.x, glue::glue("flights_{.y}.tsv"), delim = "\t") +) +``` + +Then we can efficiently read all of the files into one tibble by passing the filenames +directly to `vroom()`. + +```{r} +files <- fs::dir_ls(glob = "flights_*tsv") +files +vroom(files) +``` + +## Reading and writing compressed files + +Just like readr, vroom automatically reads and writes zip, gzip, bz2 and xz compressed +files with the standard file extensions. + +```{r} +vroom_write(flights, "flights.tsv.gz") + +# Check file sizes to show file is compressed +fs::file_size(c("flights.tsv", "flights.tsv.gz")) + +# Read the file back in +vroom("flights.tsv.gz") +``` + +# Reading remote files + +vroom can also read files from the internet as well by passing the URL of the file to `vroom()`. + +```{r} +file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcars.csv" +vroom(file) +``` + +It can even read gzipped files from the internet (although currently not the other compressed formats). + +## Reading and writing from pipe connections + +vroom provides efficient input and output from `pipe()` connections. + +This is useful for doing things like pre-filtering large inputs with command line tools like [grep](https://en.wikipedia.org/wiki/Grep). + +```{r} +# Return only flights on United Airlines +vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) +``` + +Or using multi-threaded compression programs like +[pigz](https://zlib.net/pigz/), which can greatly reduce the time to write compressed +files. + +```{r} +bench::workout({ + vroom_write(flights, "flights.tsv.gz") + vroom_write(flights, pipe("pigz > flights.tsv.gz")) +}) +``` + +## Column selection + +`vroom` introduces a new argument `col_select` which makes selecting columns to +keep (or omit) more straightforward. + +The parameter uses the same interface as `dplyr::select()`, so you can do flexible selection operations. + +- Select with the column names +```{r} +vroom("flights.tsv", col_select = c(year, flight, tailnum)) +``` +- Drop columns by name +```{r} +vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) +``` + +- Use numeric indices +```{r} +vroom("flights.tsv", col_select = c(1, 3, 11)) +``` +- Use the selection helpers +```{r} +vroom("flights.tsv", col_select = ends_with("time")) +``` +- Or rename columns +```{r} +vroom("flights.tsv", col_select = list(plane = tailnum, everything())) +``` + +## Column types + +Like readr, vroom guesses the data types of columns as they are read, however sometimes it +is necessary to change the type of one or more columns. + +The available specifications are: (with single letter abbreviations in quotes) + +* `col_logical()` 'l', containing only `T`, `F`, `TRUE`, `FALSE`, `1` or `0`. +* `col_integer()` 'i', integer values. +* `col_double()` 'd', floating point values. +* `col_number()` [n], numbers containing the `grouping_mark` +* `col_date(format = "")` [D]: with the locale's `date_format`. +* `col_time(format = "")` [t]: with the locale's `time_format`. +* `col_datetime(format = "")` [T]: ISO8601 date times. +* `col_factor(levels, ordered)` 'f', a fixed set of values. +* `col_character()` 'c', everything else. +* `col_skip()` '_, -', don't import this column. +* `col_guess()` '?', parse using the "best" type based on the input. + +You can tell vroom what columns to use with the `col_types()` argument in a number of ways. + +If you only need to override a single column the most concise way is to use a named vector. + +```{r} +# read the 'year' column as an integer +vroom("flights.tsv", col_types = c(year = "i")) + +# also skip reading the 'time_hour' column +vroom("flights.tsv", col_types = c(year = "i", time_hour = "_")) + +# also read the carrier as a factor +vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f")) +``` + +However you can also use the `col_*()` functions in a list. + +```{r} +vroom("flights.tsv", + col_types = list(year = col_integer(), time_hour = col_skip(), carrier = col_factor()) +) +``` + +This is most useful when a column type needs additional information, such as +for categorical data when you know all of the levels of a factor. + +```{r} +vroom("flights.tsv", + col_types = list(dest = col_factor(levels = c("EWR", "JFK", "LGA"))) +) +``` + +## Speed + +vroom is fast, but how fast? +We benchmarked vroom using a real world dataset of taxi trip data, with +14.7 million rows, 11 columns. It contains a mix of numeric and textual data and has a +total file size of 1.55 GB. + + #> Observations: 14,776,615 + #> Variables: 11 + #> $ medallion "89D227B655E5C82AECF13C3F540D4CF4", "0BD7C8F5B... + #> $ hack_license "BA96DE419E711691B9445D6A6307C170", "9FD8F69F0... + #> $ vendor_id "CMT", "CMT", "CMT", "CMT", "CMT", "CMT", "CMT... + #> $ pickup_datetime "2013-01-01 15:11:48", "2013-01-06 00:18:35", ... + #> $ payment_type "CSH", "CSH", "CSH", "CSH", "CSH", "CSH", "CSH... + #> $ fare_amount 6.5, 6.0, 5.5, 5.0, 9.5, 9.5, 6.0, 34.0, 5.5, ... + #> $ surcharge 0.0, 0.5, 1.0, 0.5, 0.5, 0.0, 0.0, 0.0, 1.0, 0... + #> $ mta_tax 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0... + #> $ tip_amount 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... + #> $ tolls_amount 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.8, 0.0, 0... + #> $ total_amount 7.0, 7.0, 7.0, 6.0, 10.5, 10.0, 6.5, 39.3, 7.0... + +We performed a series of simple manipulations with each approach. + + - Reading the data + - `print()` + - `head()` + - `tail()` + - Sampling 100 random rows + - Filtering for "UNK" payment, this is 6434 rows (0.0435% of total). + - Aggregation of mean fare amount per payment type. + + + +```{r benchmark_plot, fig.height = 8, fig.width=10, warning = FALSE, echo = FALSE, message = FALSE} +taxi <- read_benchmark(system.file("bench", "taxi-times.tsv", package = "vroom"), desc) + +# Keep only the vroom dplyr results +taxi <- taxi %>% + filter(package %in% c("readr", "data.table", "vroom\ndplyr", "read.delim")) %>% + mutate(package = recode(package, "vroom\ndplyr" = "vroom")) + +plot_benchmark(taxi, "Time to analyze taxi trip data") + +make_table(taxi) +``` + +```{r cleanup, include = FALSE} +unlink(c(files, "flights.tsv", "flights.tsv.gz")) +``` + +
+ +Some things to note in the results. The initial reading is much faster in vroom +than any other method, and most of the manipulations, such as `print()`, +`head()`, `tail()` and `sample()` are equally fast. However because the +character data is read lazily operations such as `filter()` and `aggregrate()` +which need character values require additional time. +However this cost will only occur once, after the values have been read they +will be stored in memory and additional accesses will be equivalent to other +packages. + +For more details on how the benchmarks were performed and additional benchmarks +with other types of data see the [benchmark +vignette](http://vroom.r-lib.org/articles/benchmarks.html). + +## Feedback welcome! + +vroom is a new package and like any newborn may fall down a few times before +learning to run. If you do run into a bug or think of a new feature that +work well in vroom please [open an +issue](https://github.com/r-lib/vroom/issues) so we can discuss it! + +## Acknowledgements + +Even though this is a new release, a number of people have been testing out +pre-release versions on their datasets and opening issues, which has been a +huge help in making the package more robust. + +A big thanks to [@alex-gable](https://github.com/alex-gable), +[@andrie](https://github.com/andrie), +[@dan-reznik](https://github.com/dan-reznik), +[@Evgeniy-](https://github.com/Evgeniy-), +[@ginolhac](https://github.com/ginolhac), +[@ibarraespinosa](https://github.com/ibarraespinosa), +[@KasperSkytte](https://github.com/KasperSkytte), +[@ldecicco-USGS](https://github.com/ldecicco-USGS), +[@LuisQ95](https://github.com/LuisQ95), +[@matthieu-haudiquet](https://github.com/matthieu-haudiquet), +[@md0u80c9](https://github.com/md0u80c9), +[@mkiang](https://github.com/mkiang), +[@R3myG](https://github.com/R3myG), +[@randomgambit](https://github.com/randomgambit), +[@slowkow](https://github.com/slowkow), +[@telaroz](https://github.com/telaroz), +[@thierrygosselin](https://github.com/thierrygosselin), and +[@xiaodaigh](https://github.com/xiaodaigh) + +Also this package would not be possible without the following significant +contributions to the R ecosystem. + +- [Gabe Becker](https://twitter.com/groundwalkergmb), [Luke + Tierney](https://stat.uiowa.edu/~luke/) and [Tomas + Kalibera](https://github.com/kalibera) for conceiving, implementing + and maintaining the [Altrep + framework](https://svn.r-project.org/R/branches/ALTREP/ALTREP.html) used extensively in vroom. +- [Romain François](https://twitter.com/romain_francois), whose + [Altrepisode](https://purrple.cat/blog/2018/10/14/altrep-and-cpp/) + package and [related + blog-posts](https://purrple.cat/blog/2018/10/14/altrep-and-cpp/) + were a great guide for creating new Altrep objects in C++. +- [Matt Dowle](https://twitter.com/mattdowle) and the rest of the + [Rdatatable](https://github.com/Rdatatable) team, + `data.table::fread()` is blazing fast and a great motivator to think about + how we could read delimited files faster\! diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown new file mode 100644 index 000000000..1d0704109 --- /dev/null +++ b/content/articles/2019-05-vroom-1-0-0.markdown @@ -0,0 +1,741 @@ +--- +title: vroom 1.0.0 +author: Jim Hester +date: '2019-05-06' +slug: vroom-1-0-0 +description: Introducing the vroom package, extremely fast data import in R. +categories: + - package +tags: [package, r-lib] +photo: + url: https://www.pexels.com/photo/12801/ + author: Chris Peeters +--- + + + + + + + +I'm excited to announce that [vroom 1.0.0](http://vroom.r-lib.org) is now +available on CRAN! + +vroom reads rectangular data, such as comma separated +(csv), tab separated (tsv) or fixed width files (fwf) into R. + +It performs the +same function as packages like [readr](http://readr.r-lib.org), +[data.table](http://r-datatable.com) or the `read.csv()` +function, but for many datasets vroom can read them much, much +faster (hence the name). + +The main reason vroom can be faster is because character data is read from the +file lazily; you only pay for the data you use. This lazy access is done +automatically, so no changes to your R data manipulation code are needed. + +vroom also provides efficient multi-threaded writing that is multiple times +faster on most inputs than the readr writer. + +Install vroom with: + + +```r +install.packages("vroom") +``` + +The best way to get acquainted with the package is the [getting +started](http://vroom.r-lib.org/articles/vroom.html) vignette. + +## Reading delimited files + +Compared to readr, the first difference you may note is you use only one +function to read the files, `vroom()`. This is because `vroom()` guesses the +delimiter of the file automatically (based on the first few lines). This works +well most of the time, but may fail to guess properly in some cases. The +`delim` argument is used to specify the delimiter of the file explicitly. + + +```r +library(vroom) + +data <- vroom("flights.tsv") +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
+ +The summary message after reading also differs from readr. We hope this output +gives a more informative indication if the types of your columns are being guessed +properly. However you can still retrieve and print the full column specification +with `spec()`. + + +```r +spec(data) +``` + +
#> cols(
+#>   year = col_double(),
+#>   month = col_double(),
+#>   day = col_double(),
+#>   dep_time = col_double(),
+#>   sched_dep_time = col_double(),
+#>   dep_delay = col_double(),
+#>   arr_time = col_double(),
+#>   sched_arr_time = col_double(),
+#>   arr_delay = col_double(),
+#>   carrier = col_character(),
+#>   flight = col_double(),
+#>   tailnum = col_character(),
+#>   origin = col_character(),
+#>   dest = col_character(),
+#>   air_time = col_double(),
+#>   distance = col_double(),
+#>   hour = col_double(),
+#>   minute = col_double(),
+#>   time_hour = col_datetime(format = "")
+#> )
+
+ +The message will be disabled if you supply a column specification to `col_types` when reading. + + +```r +s <- spec(data) + +data <- vroom("flights.tsv", col_types = s) +``` + +## Reading multiple files + +One feature new to vroom is built-in support for reading sets of files with the +same columns into one table. Just pass the filenames to be read directly to +vroom. + + +```r +# Split the flights data by carrier +purrr::iwalk( + split(flights, flights$carrier), + ~ vroom::vroom_write(.x, glue::glue("flights_{.y}.tsv"), delim = "\t") +) +``` + +Then we can efficiently read all of the files into one tibble by passing the filenames +directly to `vroom()`. + + +```r +files <- fs::dir_ls(glob = "flights_*tsv") +files +#> flights_9E.tsv flights_AA.tsv flights_AS.tsv flights_B6.tsv flights_DL.tsv +#> flights_EV.tsv flights_F9.tsv flights_FL.tsv flights_HA.tsv flights_MQ.tsv +#> flights_OO.tsv flights_UA.tsv flights_US.tsv flights_VX.tsv flights_WN.tsv +#> flights_YV.tsv +vroom(files) +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      810            810         0     1048
+#>  2  2013     1     1     1451           1500        -9     1634
+#>  3  2013     1     1     1452           1455        -3     1637
+#>  4  2013     1     1     1454           1500        -6     1635
+#>  5  2013     1     1     1507           1515        -8     1651
+#>  6  2013     1     1     1530           1530         0     1650
+#>  7  2013     1     1     1546           1540         6     1753
+#>  8  2013     1     1     1550           1550         0     1844
+#>  9  2013     1     1     1552           1600        -8     1749
+#> 10  2013     1     1     1554           1600        -6     1701
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +## Reading and writing compressed files + +Just like readr, vroom automatically reads and writes zip, gzip, bz2 and xz compressed +files with the standard file extensions. + + +```r +vroom_write(flights, "flights.tsv.gz") + +# Check file sizes to show file is compressed +fs::file_size(c("flights.tsv", "flights.tsv.gz")) +#> 29.62M 7.87M + +# Read the file back in +vroom("flights.tsv.gz") +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +# Reading remote files + +vroom can also read files from the internet as well by passing the URL of the file to `vroom()`. + + +```r +file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcars.csv" +vroom(file) +``` + +
#> Observations: 32
+#> Variables: 12
+#> chr [ 1]: model
+#> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 32 x 12
+#>    model    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
+#>                
+#>  1 Mazda…  21       6  160    110  3.9   2.62  16.5     0     1     4     4
+#>  2 Mazda…  21       6  160    110  3.9   2.88  17.0     0     1     4     4
+#>  3 Datsu…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
+#>  4 Horne…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
+#>  5 Horne…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
+#>  6 Valia…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
+#>  7 Duste…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
+#>  8 Merc …  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
+#>  9 Merc …  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
+#> 10 Merc …  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
+#> # … with 22 more rows
+
+ +It can even read gzipped files from the internet (although currently not the other compressed formats). + +## Reading and writing from pipe connections + +vroom provides efficient input and output from `pipe()` connections. + +This is useful for doing things like pre-filtering large inputs with command line tools like [grep](https://en.wikipedia.org/wiki/Grep). + + +```r +# Return only flights on United Airlines +vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) +``` + +
#> Observations: 58,665
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 58,665 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      554            558        -4      740
+#>  4  2013     1     1      558            600        -2      924
+#>  5  2013     1     1      558            600        -2      923
+#>  6  2013     1     1      559            600        -1      854
+#>  7  2013     1     1      607            607         0      858
+#>  8  2013     1     1      611            600        11      945
+#>  9  2013     1     1      623            627        -4      933
+#> 10  2013     1     1      628            630        -2     1016
+#> # … with 58,655 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +Or using multi-threaded compression programs like +[pigz](https://zlib.net/pigz/), which can greatly reduce the time to write compressed +files. + + +```r +bench::workout({ + vroom_write(flights, "flights.tsv.gz") + vroom_write(flights, pipe("pigz > flights.tsv.gz")) +}) +``` + +
#> # A tibble: 2 x 3
+#>   exprs                                                process     real
+#>                                              
+#> 1 vroom_write(flights, "flights.tsv.gz")                 3.52s     2.7s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.52s  995.8ms
+
+ +## Column selection + +`vroom` introduces a new argument `col_select` which makes selecting columns to +keep (or omit) more straightforward. + +The parameter uses the same interface as `dplyr::select()`, so you can do flexible selection operations. + +- Select with the column names + +```r +vroom("flights.tsv", col_select = c(year, flight, tailnum)) +``` + +
#> Observations: 336,776
+#> Variables: 3
+#> chr [1]: tailnum
+#> dbl [2]: year, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 3
+#>     year flight tailnum
+#>         
+#>  1  2013   1545 N14228 
+#>  2  2013   1714 N24211 
+#>  3  2013   1141 N619AA 
+#>  4  2013    725 N804JB 
+#>  5  2013    461 N668DN 
+#>  6  2013   1696 N39463 
+#>  7  2013    507 N516JB 
+#>  8  2013   5708 N829AS 
+#>  9  2013     79 N593JB 
+#> 10  2013    301 N3ALAA 
+#> # … with 336,766 more rows
+
+- Drop columns by name + +```r +vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) +``` + +
#> Observations: 336,776
+#> Variables: 13
+#> chr [4]: carrier, tailnum, origin, dest
+#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 13
+#>     year month   day sched_dep_time dep_delay arr_time sched_arr_time
+#>                                   
+#>  1  2013     1     1            515         2      830            819
+#>  2  2013     1     1            529         4      850            830
+#>  3  2013     1     1            540         2      923            850
+#>  4  2013     1     1            545        -1     1004           1022
+#>  5  2013     1     1            600        -6      812            837
+#>  6  2013     1     1            558        -4      740            728
+#>  7  2013     1     1            600        -5      913            854
+#>  8  2013     1     1            600        -3      709            723
+#>  9  2013     1     1            600        -3      838            846
+#> 10  2013     1     1            600        -2      753            745
+#> # … with 336,766 more rows, and 6 more variables: arr_delay ,
+#> #   carrier , flight , tailnum , origin , dest 
+
+ +- Use numeric indices + +```r +vroom("flights.tsv", col_select = c(1, 3, 11)) +``` + +
#> Observations: 336,776
+#> Variables: 3
+#> dbl [3]: year, day, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 3
+#>     year   day flight
+#>       
+#>  1  2013     1   1545
+#>  2  2013     1   1714
+#>  3  2013     1   1141
+#>  4  2013     1    725
+#>  5  2013     1    461
+#>  6  2013     1   1696
+#>  7  2013     1    507
+#>  8  2013     1   5708
+#>  9  2013     1     79
+#> 10  2013     1    301
+#> # … with 336,766 more rows
+
+- Use the selection helpers + +```r +vroom("flights.tsv", col_select = ends_with("time")) +``` + +
#> Observations: 336,776
+#> Variables: 5
+#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 5
+#>    dep_time sched_dep_time arr_time sched_arr_time air_time
+#>                                   
+#>  1      517            515      830            819      227
+#>  2      533            529      850            830      227
+#>  3      542            540      923            850      160
+#>  4      544            545     1004           1022      183
+#>  5      554            600      812            837      116
+#>  6      554            558      740            728      150
+#>  7      555            600      913            854      158
+#>  8      557            600      709            723       53
+#>  9      557            600      838            846      140
+#> 10      558            600      753            745      138
+#> # … with 336,766 more rows
+
+- Or rename columns + +```r +vroom("flights.tsv", col_select = list(plane = tailnum, everything())) +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
+#>                              
+#>  1 N142…  2013     1     1      517            515         2      830
+#>  2 N242…  2013     1     1      533            529         4      850
+#>  3 N619…  2013     1     1      542            540         2      923
+#>  4 N804…  2013     1     1      544            545        -1     1004
+#>  5 N668…  2013     1     1      554            600        -6      812
+#>  6 N394…  2013     1     1      554            558        -4      740
+#>  7 N516…  2013     1     1      555            600        -5      913
+#>  8 N829…  2013     1     1      557            600        -3      709
+#>  9 N593…  2013     1     1      557            600        -3      838
+#> 10 N3AL…  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , origin ,
+#> #   dest , air_time , distance , hour , minute ,
+#> #   time_hour 
+
+ +## Column types + +Like readr, vroom guesses the data types of columns as they are read, however sometimes it +is necessary to change the type of one or more columns. + +The available specifications are: (with single letter abbreviations in quotes) + +* `col_logical()` 'l', containing only `T`, `F`, `TRUE`, `FALSE`, `1` or `0`. +* `col_integer()` 'i', integer values. +* `col_double()` 'd', floating point values. +* `col_number()` [n], numbers containing the `grouping_mark` +* `col_date(format = "")` [D]: with the locale's `date_format`. +* `col_time(format = "")` [t]: with the locale's `time_format`. +* `col_datetime(format = "")` [T]: ISO8601 date times. +* `col_factor(levels, ordered)` 'f', a fixed set of values. +* `col_character()` 'c', everything else. +* `col_skip()` '_, -', don't import this column. +* `col_guess()` '?', parse using the "best" type based on the input. + +You can tell vroom what columns to use with the `col_types()` argument in a number of ways. + +If you only need to override a single column the most concise way is to use a named vector. + + +```r +# read the 'year' column as an integer +vroom("flights.tsv", col_types = c(year = "i")) +``` + +
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +```r + +# also skip reading the 'time_hour' column +vroom("flights.tsv", col_types = c(year = "i", time_hour = "_")) +``` + +
#> # A tibble: 336,776 x 18
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute 
+
+ +```r + +# also read the carrier as a factor +vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f")) +``` + +
#> # A tibble: 336,776 x 18
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute 
+
+ +However you can also use the `col_*()` functions in a list. + + +```r +vroom("flights.tsv", + col_types = list(year = col_integer(), time_hour = col_skip(), carrier = col_factor()) +) +``` + +
#> # A tibble: 336,776 x 18
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute 
+
+ +This is most useful when a column type needs additional information, such as +for categorical data when you know all of the levels of a factor. + + +```r +vroom("flights.tsv", + col_types = list(dest = col_factor(levels = c("EWR", "JFK", "LGA"))) +) +``` + +
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +## Speed + +vroom is fast, but how fast? +We benchmarked vroom using a real world dataset of taxi trip data, with +14.7 million rows, 11 columns. It contains a mix of numeric and textual data and has a +total file size of 1.55 GB. + + #> Observations: 14,776,615 + #> Variables: 11 + #> $ medallion "89D227B655E5C82AECF13C3F540D4CF4", "0BD7C8F5B... + #> $ hack_license "BA96DE419E711691B9445D6A6307C170", "9FD8F69F0... + #> $ vendor_id "CMT", "CMT", "CMT", "CMT", "CMT", "CMT", "CMT... + #> $ pickup_datetime "2013-01-01 15:11:48", "2013-01-06 00:18:35", ... + #> $ payment_type "CSH", "CSH", "CSH", "CSH", "CSH", "CSH", "CSH... + #> $ fare_amount 6.5, 6.0, 5.5, 5.0, 9.5, 9.5, 6.0, 34.0, 5.5, ... + #> $ surcharge 0.0, 0.5, 1.0, 0.5, 0.5, 0.0, 0.0, 0.0, 1.0, 0... + #> $ mta_tax 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0... + #> $ tip_amount 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... + #> $ tolls_amount 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.8, 0.0, 0... + #> $ total_amount 7.0, 7.0, 7.0, 6.0, 10.5, 10.0, 6.5, 39.3, 7.0... + +We performed a series of simple manipulations with each approach. + + - Reading the data + - `print()` + - `head()` + - `tail()` + - Sampling 100 random rows + - Filtering for "UNK" payment, this is 6434 rows (0.0435% of total). + - Aggregation of mean fare amount per payment type. + + + + + +| package| read| print| head| tail| sample| filter| aggregate| total| +|----------:|--------:|-----:|----:|----:|------:|------:|---------:|--------:| +| read.delim| 1m 21.5s| 6ms| 1ms| 1ms| 1ms| 315ms| 764ms| 1m 22.6s| +| readr| 33.1s| 90ms| 1ms| 1ms| 2ms| 202ms| 825ms| 34.2s| +| data.table| 15.7s| 13ms| 1ms| 1ms| 1ms| 129ms| 394ms| 16.3s| +| vroom| 3.6s| 86ms| 1ms| 1ms| 2ms| 1.4s| 1.9s| 7s| + + + +
+ +Some things to note in the results. The initial reading is much faster in vroom +than any other method, and most of the manipulations, such as `print()`, +`head()`, `tail()` and `sample()` are equally fast. However because the +character data is read lazily operations such as `filter()` and `aggregrate()` +which need character values require additional time. +However this cost will only occur once, after the values have been read they +will be stored in memory and additional accesses will be equivalent to other +packages. + +For more details on how the benchmarks were performed and additional benchmarks +with other types of data see the [benchmark +vignette](http://vroom.r-lib.org/articles/benchmarks.html). + +## Feedback welcome! + +vroom is a new package and like any newborn may fall down a few times before +learning to run. If you do run into a bug or think of a new feature that +work well in vroom please [open an +issue](https://github.com/r-lib/vroom/issues) so we can discuss it! + +## Acknowledgements + +Even though this is a new release, a number of people have been testing out +pre-release versions on their datasets and opening issues, which has been a +huge help in making the package more robust. + +A big thanks to [@alex-gable](https://github.com/alex-gable), +[@andrie](https://github.com/andrie), +[@dan-reznik](https://github.com/dan-reznik), +[@Evgeniy-](https://github.com/Evgeniy-), +[@ginolhac](https://github.com/ginolhac), +[@ibarraespinosa](https://github.com/ibarraespinosa), +[@KasperSkytte](https://github.com/KasperSkytte), +[@ldecicco-USGS](https://github.com/ldecicco-USGS), +[@LuisQ95](https://github.com/LuisQ95), +[@matthieu-haudiquet](https://github.com/matthieu-haudiquet), +[@md0u80c9](https://github.com/md0u80c9), +[@mkiang](https://github.com/mkiang), +[@R3myG](https://github.com/R3myG), +[@randomgambit](https://github.com/randomgambit), +[@slowkow](https://github.com/slowkow), +[@telaroz](https://github.com/telaroz), +[@thierrygosselin](https://github.com/thierrygosselin), and +[@xiaodaigh](https://github.com/xiaodaigh) + +Also this package would not be possible without the following significant +contributions to the R ecosystem. + +- [Gabe Becker](https://twitter.com/groundwalkergmb), [Luke + Tierney](https://stat.uiowa.edu/~luke/) and [Tomas + Kalibera](https://github.com/kalibera) for conceiving, implementing + and maintaining the [Altrep + framework](https://svn.r-project.org/R/branches/ALTREP/ALTREP.html) used extensively in vroom. +- [Romain François](https://twitter.com/romain_francois), whose + [Altrepisode](https://purrple.cat/blog/2018/10/14/altrep-and-cpp/) + package and [related + blog-posts](https://purrple.cat/blog/2018/10/14/altrep-and-cpp/) + were a great guide for creating new Altrep objects in C++. +- [Matt Dowle](https://twitter.com/mattdowle) and the rest of the + [Rdatatable](https://github.com/Rdatatable) team, + `data.table::fread()` is blazing fast and a great motivator to think about + how we could read delimited files faster\! 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z5X2T2P#*x2t%R<99TfrxG00rv>sG=ObnS&ilVw1O>)VMF)?xRYghD|pQ%)~<9$7T7 z{KYzo4?%WW=SBB3%6>tCW7`W+-dbWHbXh42?!z z+p+TWW}k6;thot209Qviz}cB^n?Q3=ej6(Epkt!FcR5^T!#vCX zG<^!Z)RWDHocmbGuOgB-ieA`-mj~m}SYK~P5Xk#}Pi?X55`^|ir#jcKM|TtHEnWoE zKLxU>pvSOEw2a7DmWmdnEg*pifuoM>)`CwDFD6j(=EYPvJh`PeRhCqW$mU48DfMqu z8rWzAXjWC1L$ULGQ6F3!Zt0=!_QP0{8Wqt;!Z%A1!yEUy8^U4pl(ZZf6r{GB#rY}E zqS!(ZjrZofNnE1Ig-p}j6=ckC3xUyFJCh5z?vxsAhck0;ndilu-9xB+3wiSmekOeH8&EY4`AiBMuE1!f!UU&7bTa6ay_CH@F zt@d=9=0F8xbi>;O)~7V@)yfvryzFJTAOW(;G_~s+s$X9{p(33|?`zJ#>4JqczT3?@ z3#>DKsZEVu^MVUUqMr7J{JHH&Jy=DQ1^KVnJP?_?a$xU-zgD!h#SL&S+sTeaC`^bp zb;Ns;CI3GY%lNiAc%Azl0pZK5j?YKl*VUK?Ty}nU%@oMW09e4mhAUt-tANBKU{UP+ zhh?Q4Qh|twe|SQe1-zgnFU$+Ft^&gpm~lLXz)YZ6*A&tZE7-AHd=_*dg0?DRUoDg1^8} ze`{Xuf{1Wz#)g1SzTjN#glHzii&`WG(cTnbSl1G|5Sv8^(=zr0mp%PHD?1T?1WpAG z;f5bt6@cIUTR=<3{yys?Ff{AM!qT9f7^;Cknr;Ex=XdIg-BqxJCnA+Or~*TF>ISVT z_}%|j9GIN Date: Mon, 6 May 2019 15:40:54 -0400 Subject: [PATCH 02/11] Do not auto-print data I think it is too cluttered --- .../articles/2019-05-vroom-1-0-0.Rmarkdown | 29 +- content/articles/2019-05-vroom-1-0-0.markdown | 263 ++---------------- 2 files changed, 38 insertions(+), 254 deletions(-) diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown index 118063caa..d582eda8c 100644 --- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown +++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown @@ -174,7 +174,7 @@ directly to `vroom()`. ```{r} files <- fs::dir_ls(glob = "flights_*tsv") files -vroom(files) +data <- vroom(files) ``` ## Reading and writing compressed files @@ -189,7 +189,7 @@ vroom_write(flights, "flights.tsv.gz") fs::file_size(c("flights.tsv", "flights.tsv.gz")) # Read the file back in -vroom("flights.tsv.gz") +data <- vroom("flights.tsv.gz") ``` # Reading remote files @@ -198,7 +198,7 @@ vroom can also read files from the internet as well by passing the URL of the fi ```{r} file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcars.csv" -vroom(file) +data <- vroom(file) ``` It can even read gzipped files from the internet (although currently not the other compressed formats). @@ -211,7 +211,7 @@ This is useful for doing things like pre-filtering large inputs with command lin ```{r} # Return only flights on United Airlines -vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) +data <- vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) ``` Or using multi-threaded compression programs like @@ -234,24 +234,25 @@ The parameter uses the same interface as `dplyr::select()`, so you can do flexib - Select with the column names ```{r} -vroom("flights.tsv", col_select = c(year, flight, tailnum)) +data <- vroom("flights.tsv", col_select = c(year, flight, tailnum)) ``` - Drop columns by name ```{r} -vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) +data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) ``` - Use numeric indices ```{r} -vroom("flights.tsv", col_select = c(1, 3, 11)) +data <- vroom("flights.tsv", col_select = c(1, 3, 11)) ``` - Use the selection helpers ```{r} -vroom("flights.tsv", col_select = ends_with("time")) +data <- vroom("flights.tsv", col_select = ends_with("time")) ``` - Or rename columns ```{r} -vroom("flights.tsv", col_select = list(plane = tailnum, everything())) +data <- vroom("flights.tsv", col_select = list(plane = tailnum, everything())) +data ``` ## Column types @@ -279,19 +280,19 @@ If you only need to override a single column the most concise way is to use a na ```{r} # read the 'year' column as an integer -vroom("flights.tsv", col_types = c(year = "i")) +data <- vroom("flights.tsv", col_types = c(year = "i")) # also skip reading the 'time_hour' column -vroom("flights.tsv", col_types = c(year = "i", time_hour = "_")) +data <- vroom("flights.tsv", col_types = c(year = "i", time_hour = "_")) # also read the carrier as a factor -vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f")) +data <- vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f")) ``` However you can also use the `col_*()` functions in a list. ```{r} -vroom("flights.tsv", +data <- vroom("flights.tsv", col_types = list(year = col_integer(), time_hour = col_skip(), carrier = col_factor()) ) ``` @@ -300,7 +301,7 @@ This is most useful when a column type needs additional information, such as for categorical data when you know all of the levels of a factor. ```{r} -vroom("flights.tsv", +data <- vroom("flights.tsv", col_types = list(dest = col_factor(levels = c("EWR", "JFK", "LGA"))) ) ``` diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown index 1d0704109..0522d160c 100644 --- a/content/articles/2019-05-vroom-1-0-0.markdown +++ b/content/articles/2019-05-vroom-1-0-0.markdown @@ -144,7 +144,7 @@ files #> flights_EV.tsv flights_F9.tsv flights_FL.tsv flights_HA.tsv flights_MQ.tsv #> flights_OO.tsv flights_UA.tsv flights_US.tsv flights_VX.tsv flights_WN.tsv #> flights_YV.tsv -vroom(files) +data <- vroom(files) ``` 

#> Observations: 336,776
@@ -155,23 +155,6 @@ vroom(files)
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      810            810         0     1048
-#>  2  2013     1     1     1451           1500        -9     1634
-#>  3  2013     1     1     1452           1455        -3     1637
-#>  4  2013     1     1     1454           1500        -6     1635
-#>  5  2013     1     1     1507           1515        -8     1651
-#>  6  2013     1     1     1530           1530         0     1650
-#>  7  2013     1     1     1546           1540         6     1753
-#>  8  2013     1     1     1550           1550         0     1844
-#>  9  2013     1     1     1552           1600        -8     1749
-#> 10  2013     1     1     1554           1600        -6     1701
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour
## Reading and writing compressed files @@ -188,7 +171,7 @@ fs::file_size(c("flights.tsv", "flights.tsv.gz")) #> 29.62M 7.87M # Read the file back in -vroom("flights.tsv.gz") +data <- vroom("flights.tsv.gz") ``` 
#> Observations: 336,776
@@ -199,23 +182,6 @@ vroom("flights.tsv.gz")
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour
# Reading remote files @@ -225,7 +191,7 @@ vroom can also read files from the internet as well by passing the URL of the fi ```r file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcars.csv" -vroom(file) +data <- vroom(file) ``` 
#> Observations: 32
@@ -235,20 +201,6 @@ vroom(file)
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 32 x 12
-#>    model    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
-#>                
-#>  1 Mazda…  21       6  160    110  3.9   2.62  16.5     0     1     4     4
-#>  2 Mazda…  21       6  160    110  3.9   2.88  17.0     0     1     4     4
-#>  3 Datsu…  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
-#>  4 Horne…  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
-#>  5 Horne…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
-#>  6 Valia…  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
-#>  7 Duste…  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
-#>  8 Merc …  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
-#>  9 Merc …  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
-#> 10 Merc …  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
-#> # … with 22 more rows
It can even read gzipped files from the internet (although currently not the other compressed formats). @@ -262,7 +214,7 @@ This is useful for doing things like pre-filtering large inputs with command lin ```r # Return only flights on United Airlines -vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) +data <- vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights)) ``` 
#> Observations: 58,665
@@ -273,23 +225,6 @@ vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 58,665 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      554            558        -4      740
-#>  4  2013     1     1      558            600        -2      924
-#>  5  2013     1     1      558            600        -2      923
-#>  6  2013     1     1      559            600        -1      854
-#>  7  2013     1     1      607            607         0      858
-#>  8  2013     1     1      611            600        11      945
-#>  9  2013     1     1      623            627        -4      933
-#> 10  2013     1     1      628            630        -2     1016
-#> # … with 58,655 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour
Or using multi-threaded compression programs like @@ -307,8 +242,8 @@ bench::workout({ 
#> # A tibble: 2 x 3
 #>   exprs                                                process     real
 #>                                              
-#> 1 vroom_write(flights, "flights.tsv.gz")                 3.52s     2.7s
-#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.52s  995.8ms
+#> 1 vroom_write(flights, "flights.tsv.gz")                 3.46s    2.63s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.56s 992.69ms
## Column selection @@ -321,7 +256,7 @@ The parameter uses the same interface as `dplyr::select()`, so you can do flexib - Select with the column names ```r -vroom("flights.tsv", col_select = c(year, flight, tailnum)) +data <- vroom("flights.tsv", col_select = c(year, flight, tailnum)) ``` 
#> Observations: 336,776
@@ -331,25 +266,11 @@ vroom("flights.tsv", col_select = c(year, flight, tailnum))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 3
-#>     year flight tailnum
-#>         
-#>  1  2013   1545 N14228 
-#>  2  2013   1714 N24211 
-#>  3  2013   1141 N619AA 
-#>  4  2013    725 N804JB 
-#>  5  2013    461 N668DN 
-#>  6  2013   1696 N39463 
-#>  7  2013    507 N516JB 
-#>  8  2013   5708 N829AS 
-#>  9  2013     79 N593JB 
-#> 10  2013    301 N3ALAA 
-#> # … with 336,766 more rows
- Drop columns by name ```r -vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) +data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour)) ``` 
#> Observations: 336,776
@@ -359,27 +280,12 @@ vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 13
-#>     year month   day sched_dep_time dep_delay arr_time sched_arr_time
-#>                                   
-#>  1  2013     1     1            515         2      830            819
-#>  2  2013     1     1            529         4      850            830
-#>  3  2013     1     1            540         2      923            850
-#>  4  2013     1     1            545        -1     1004           1022
-#>  5  2013     1     1            600        -6      812            837
-#>  6  2013     1     1            558        -4      740            728
-#>  7  2013     1     1            600        -5      913            854
-#>  8  2013     1     1            600        -3      709            723
-#>  9  2013     1     1            600        -3      838            846
-#> 10  2013     1     1            600        -2      753            745
-#> # … with 336,766 more rows, and 6 more variables: arr_delay ,
-#> #   carrier , flight , tailnum , origin , dest
- Use numeric indices ```r -vroom("flights.tsv", col_select = c(1, 3, 11)) +data <- vroom("flights.tsv", col_select = c(1, 3, 11)) ``` 
#> Observations: 336,776
@@ -388,25 +294,11 @@ vroom("flights.tsv", col_select = c(1, 3, 11))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 3
-#>     year   day flight
-#>       
-#>  1  2013     1   1545
-#>  2  2013     1   1714
-#>  3  2013     1   1141
-#>  4  2013     1    725
-#>  5  2013     1    461
-#>  6  2013     1   1696
-#>  7  2013     1    507
-#>  8  2013     1   5708
-#>  9  2013     1     79
-#> 10  2013     1    301
-#> # … with 336,766 more rows
- Use the selection helpers ```r -vroom("flights.tsv", col_select = ends_with("time")) +data <- vroom("flights.tsv", col_select = ends_with("time")) ``` 
#> Observations: 336,776
@@ -415,25 +307,11 @@ vroom("flights.tsv", col_select = ends_with("time"))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 5
-#>    dep_time sched_dep_time arr_time sched_arr_time air_time
-#>                                   
-#>  1      517            515      830            819      227
-#>  2      533            529      850            830      227
-#>  3      542            540      923            850      160
-#>  4      544            545     1004           1022      183
-#>  5      554            600      812            837      116
-#>  6      554            558      740            728      150
-#>  7      555            600      913            854      158
-#>  8      557            600      709            723       53
-#>  9      557            600      838            846      140
-#> 10      558            600      753            745      138
-#> # … with 336,766 more rows
- Or rename columns ```r -vroom("flights.tsv", col_select = list(plane = tailnum, everything())) +data <- vroom("flights.tsv", col_select = list(plane = tailnum, everything())) ``` 
#> Observations: 336,776
@@ -444,7 +322,13 @@ vroom("flights.tsv", col_select = list(plane = tailnum, everything()))
 #> 
 #> Call `spec()` for a copy-pastable column specification
 #> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
+
+ +```r +data +``` + +
#> # A tibble: 336,776 x 19
 #>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
 #>                              
 #>  1 N142…  2013     1     1      517            515         2      830
@@ -489,135 +373,34 @@ If you only need to override a single column the most concise way is to use a na
 
 ```r
 # read the 'year' column as an integer
-vroom("flights.tsv", col_types = c(year = "i"))
-```
-
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour 
-

-
-```r
+data <- vroom("flights.tsv", col_types = c(year = "i"))
 
 # also skip reading the 'time_hour' column
-vroom("flights.tsv", col_types = c(year = "i", time_hour = "_"))
-```
-
-
#> # A tibble: 336,776 x 18
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute 
-

-
-```r
+data <- vroom("flights.tsv", col_types = c(year = "i", time_hour = "_"))
 
 # also read the carrier as a factor
-vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f"))
+data <- vroom("flights.tsv", col_types = c(year = "i", time_hour = "_", carrier = "f"))
 ```
 
-
#> # A tibble: 336,776 x 18
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute 
-

-
 However you can also use the `col_*()` functions in a list.
 
 
 ```r
-vroom("flights.tsv",
+data <- vroom("flights.tsv",
   col_types = list(year = col_integer(), time_hour = col_skip(), carrier = col_factor())
 )
 ```
 
-
#> # A tibble: 336,776 x 18
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute 
-

-
 This is most useful when a column type needs additional information, such as
 for categorical data when you know all of the levels of a factor.
 
 
 ```r
-vroom("flights.tsv",
+data <- vroom("flights.tsv",
   col_types = list(dest = col_factor(levels = c("EWR", "JFK", "LGA")))
 )
 ```
 
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour 
-

-
 ## Speed
 
 vroom is fast, but how fast?

From e33cda0b6a07cb4efa7f18374123e7ffd38cfa0d Mon Sep 17 00:00:00 2001
From: Jim Hester 
Date: Mon, 6 May 2019 15:48:22 -0400
Subject: [PATCH 03/11] Add name repair section

---
 .../articles/2019-05-vroom-1-0-0.Rmarkdown    | 15 ++++
 content/articles/2019-05-vroom-1-0-0.markdown | 77 ++++++++++++++++++-
 2 files changed, 90 insertions(+), 2 deletions(-)

diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
index d582eda8c..b6f1a089f 100644
--- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown
+++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
@@ -255,6 +255,21 @@ data <- vroom("flights.tsv", col_select = list(plane = tailnum, everything()))
 data
 ```
 
+## Name repair
+
+Often the names of columns in the original dataset are not ideal to work with.
+`vroom()` uses the same [.name_repair](https://www.tidyverse.org/articles/2019/01/tibble-2.0.1/#name-repair)
+argument as tibble, so you can use one of the default name repair strategies or
+provide a custom function. A great approach is to use the
+[janitor](http://sfirke.github.io/janitor/) `make_clean_names()` function as the input.
+
+```{r}
+vroom("flights.tsv", .name_repair = janitor::make_clean_names)
+
+vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel"))
+```
+
+
 ## Column types
 
 Like readr, vroom guesses the data types of columns as they are read, however sometimes it
diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown
index 0522d160c..8d9a479ea 100644
--- a/content/articles/2019-05-vroom-1-0-0.markdown
+++ b/content/articles/2019-05-vroom-1-0-0.markdown
@@ -242,8 +242,8 @@ bench::workout({
 
#> # A tibble: 2 x 3
 #>   exprs                                                process     real
 #>                                              
-#> 1 vroom_write(flights, "flights.tsv.gz")                 3.46s    2.63s
-#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.56s 992.69ms
+#> 1 vroom_write(flights, "flights.tsv.gz")                  3.5s    2.67s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.54s 968.81ms
 

 
 ## Column selection
@@ -347,6 +347,79 @@ data
 #> #   time_hour
+## Name repair + +Often the names of columns in the original dataset are not ideal to work with. +`vroom()` uses the same [.name_repair](https://www.tidyverse.org/articles/2019/01/tibble-2.0.1/#name-repair) +argument as tibble, so you can use one of the default name repair strategies or +provide a custom function. A great approach is to use the +[janitor](http://sfirke.github.io/janitor/) `make_clean_names()` function as the input. + + +```r +vroom("flights.tsv", .name_repair = janitor::make_clean_names) +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
+
+ +```r + +vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel")) +``` + +
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
+#>                                
+#>  1  2013     1     1     517          515        2     830          819
+#>  2  2013     1     1     533          529        4     850          830
+#>  3  2013     1     1     542          540        2     923          850
+#>  4  2013     1     1     544          545       -1    1004         1022
+#>  5  2013     1     1     554          600       -6     812          837
+#>  6  2013     1     1     554          558       -4     740          728
+#>  7  2013     1     1     555          600       -5     913          854
+#>  8  2013     1     1     557          600       -3     709          723
+#>  9  2013     1     1     557          600       -3     838          846
+#> 10  2013     1     1     558          600       -2     753          745
+#> # … with 336,766 more rows, and 11 more variables: arrDelay ,
+#> #   carrier , flight , tailnum , origin , dest ,
+#> #   airTime , distance , hour , minute ,
+#> #   timeHour 
+
+ + ## Column types Like readr, vroom guesses the data types of columns as they are read, however sometimes it From 9401a6e1c12bc3a83f10aa0437b64ded17948c64 Mon Sep 17 00:00:00 2001 From: Jim Hester Date: Tue, 7 May 2019 09:32:56 -0400 Subject: [PATCH 04/11] Need to escape html --- .../articles/2019-05-vroom-1-0-0.Rmarkdown | 2 +- content/articles/2019-05-vroom-1-0-0.markdown | 332 +++++++++--------- 2 files changed, 167 insertions(+), 167 deletions(-) diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown index b6f1a089f..16ec1a914 100644 --- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown +++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown @@ -21,7 +21,7 @@ old_hooks <- fansi::set_knit_hooks( knitr::knit_hooks, which = c("message", "output", "error"), proc.fun = function(x, class) { - fansi::html_code_block(fansi::sgr_to_html(x), class = class) + fansi::html_code_block(fansi::sgr_to_html(fansi::html_esc(x)), class = class) } ) diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown index 8d9a479ea..dddb75c5b 100644 --- a/content/articles/2019-05-vroom-1-0-0.markdown +++ b/content/articles/2019-05-vroom-1-0-0.markdown @@ -66,14 +66,14 @@ library(vroom) data <- vroom("flights.tsv") ``` -
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 The summary message after reading also differs from readr. We hope this output
@@ -86,27 +86,27 @@ with `spec()`.
 spec(data)
 ```
 
-
#> cols(
-#>   year = col_double(),
-#>   month = col_double(),
-#>   day = col_double(),
-#>   dep_time = col_double(),
-#>   sched_dep_time = col_double(),
-#>   dep_delay = col_double(),
-#>   arr_time = col_double(),
-#>   sched_arr_time = col_double(),
-#>   arr_delay = col_double(),
-#>   carrier = col_character(),
-#>   flight = col_double(),
-#>   tailnum = col_character(),
-#>   origin = col_character(),
-#>   dest = col_character(),
-#>   air_time = col_double(),
-#>   distance = col_double(),
-#>   hour = col_double(),
-#>   minute = col_double(),
-#>   time_hour = col_datetime(format = "")
-#> )
+
#> cols(
+#>   year = col_double(),
+#>   month = col_double(),
+#>   day = col_double(),
+#>   dep_time = col_double(),
+#>   sched_dep_time = col_double(),
+#>   dep_delay = col_double(),
+#>   arr_time = col_double(),
+#>   sched_arr_time = col_double(),
+#>   arr_delay = col_double(),
+#>   carrier = col_character(),
+#>   flight = col_double(),
+#>   tailnum = col_character(),
+#>   origin = col_character(),
+#>   dest = col_character(),
+#>   air_time = col_double(),
+#>   distance = col_double(),
+#>   hour = col_double(),
+#>   minute = col_double(),
+#>   time_hour = col_datetime(format = "")
+#> )
 

 
 The message will be disabled if you supply a column specification to `col_types` when reading.
@@ -147,14 +147,14 @@ files
 data <- vroom(files)
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 ## Reading and writing compressed files
@@ -174,14 +174,14 @@ fs::file_size(c("flights.tsv", "flights.tsv.gz"))
 data <- vroom("flights.tsv.gz")
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 # Reading remote files
@@ -194,13 +194,13 @@ file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcar
 data <- vroom(file)
 ```
 
-
#> Observations: 32
-#> Variables: 12
-#> chr [ 1]: model
-#> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 32
+#> Variables: 12
+#> chr [ 1]: model
+#> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 It can even read gzipped files from the internet (although currently not the other compressed formats).
@@ -217,14 +217,14 @@ This is useful for doing things like pre-filtering large inputs with command lin
 data <- vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights))
 ```
 
-
#> Observations: 58,665
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 58,665
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 Or using multi-threaded compression programs like
@@ -239,11 +239,11 @@ bench::workout({
 })
 ```
 
-
#> # A tibble: 2 x 3
-#>   exprs                                                process     real
-#>                                              
-#> 1 vroom_write(flights, "flights.tsv.gz")                  3.5s    2.67s
-#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.54s 968.81ms
+
#> # A tibble: 2 x 3
+#>   exprs                                                process     real
+#>   <bch:expr>                                          <bch:tm> <bch:tm>
+#> 1 vroom_write(flights, "flights.tsv.gz")                 3.44s    2.58s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.52s 963.08ms
 

 
 ## Column selection
@@ -259,13 +259,13 @@ The parameter uses the same interface as `dplyr::select()`, so you can do flexib
 data <- vroom("flights.tsv", col_select = c(year, flight, tailnum))
 ```
 
-
#> Observations: 336,776
-#> Variables: 3
-#> chr [1]: tailnum
-#> dbl [2]: year, flight
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 3
+#> chr [1]: tailnum
+#> dbl [2]: year, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 - Drop columns by name
 
@@ -273,13 +273,13 @@ data <- vroom("flights.tsv", col_select = c(year, flight, tailnum))
 data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))
 ```
 
-
#> Observations: 336,776
-#> Variables: 13
-#> chr [4]: carrier, tailnum, origin, dest
-#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 13
+#> chr [4]: carrier, tailnum, origin, dest
+#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 - Use numeric indices
@@ -288,12 +288,12 @@ data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))
 data <- vroom("flights.tsv", col_select = c(1, 3, 11))
 ```
 
-
#> Observations: 336,776
-#> Variables: 3
-#> dbl [3]: year, day, flight
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 3
+#> dbl [3]: year, day, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 - Use the selection helpers
 
@@ -301,12 +301,12 @@ data <- vroom("flights.tsv", col_select = c(1, 3, 11))
 data <- vroom("flights.tsv", col_select = ends_with("time"))
 ```
 
-
#> Observations: 336,776
-#> Variables: 5
-#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 5
+#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 - Or rename columns
 
@@ -314,37 +314,37 @@ data <- vroom("flights.tsv", col_select = ends_with("time"))
 data <- vroom("flights.tsv", col_select = list(plane = tailnum, everything()))
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 

 
 ```r
 data
 ```
 
-
#> # A tibble: 336,776 x 19
-#>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
-#>                              
-#>  1 N142…  2013     1     1      517            515         2      830
-#>  2 N242…  2013     1     1      533            529         4      850
-#>  3 N619…  2013     1     1      542            540         2      923
-#>  4 N804…  2013     1     1      544            545        -1     1004
-#>  5 N668…  2013     1     1      554            600        -6      812
-#>  6 N394…  2013     1     1      554            558        -4      740
-#>  7 N516…  2013     1     1      555            600        -5      913
-#>  8 N829…  2013     1     1      557            600        -3      709
-#>  9 N593…  2013     1     1      557            600        -3      838
-#> 10 N3AL…  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , origin ,
-#> #   dest , air_time , distance , hour , minute ,
-#> #   time_hour 
+
#> # A tibble: 336,776 x 19
+#>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
+#>    <chr> <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
+#>  1 N142…  2013     1     1      517            515         2      830
+#>  2 N242…  2013     1     1      533            529         4      850
+#>  3 N619…  2013     1     1      542            540         2      923
+#>  4 N804…  2013     1     1      544            545        -1     1004
+#>  5 N668…  2013     1     1      554            600        -6      812
+#>  6 N394…  2013     1     1      554            558        -4      740
+#>  7 N516…  2013     1     1      555            600        -5      913
+#>  8 N829…  2013     1     1      557            600        -3      709
+#>  9 N593…  2013     1     1      557            600        -3      838
+#> 10 N3AL…  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time <dbl>,
+#> #   arr_delay <dbl>, carrier <chr>, flight <dbl>, origin <chr>,
+#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
+#> #   time_hour <dttm>
 

 
 ## Name repair
@@ -360,31 +360,31 @@ provide a custom function. A great approach is to use the
 vroom("flights.tsv", .name_repair = janitor::make_clean_names)
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>                             
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
-#> #   arr_delay , carrier , flight , tailnum ,
-#> #   origin , dest , air_time , distance , hour ,
-#> #   minute , time_hour 
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>    <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time <dbl>,
+#> #   arr_delay <dbl>, carrier <chr>, flight <dbl>, tailnum <chr>,
+#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
+#> #   minute <dbl>, time_hour <dttm>
 

 
 ```r
@@ -392,31 +392,31 @@ vroom("flights.tsv", .name_repair = janitor::make_clean_names)
 vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel"))
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
-#>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
-#>                                
-#>  1  2013     1     1     517          515        2     830          819
-#>  2  2013     1     1     533          529        4     850          830
-#>  3  2013     1     1     542          540        2     923          850
-#>  4  2013     1     1     544          545       -1    1004         1022
-#>  5  2013     1     1     554          600       -6     812          837
-#>  6  2013     1     1     554          558       -4     740          728
-#>  7  2013     1     1     555          600       -5     913          854
-#>  8  2013     1     1     557          600       -3     709          723
-#>  9  2013     1     1     557          600       -3     838          846
-#> 10  2013     1     1     558          600       -2     753          745
-#> # … with 336,766 more rows, and 11 more variables: arrDelay ,
-#> #   carrier , flight , tailnum , origin , dest ,
-#> #   airTime , distance , hour , minute ,
-#> #   timeHour 
+
#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
+
#> # A tibble: 336,776 x 19
+#>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
+#>    <dbl> <dbl> <dbl>   <dbl>        <dbl>    <dbl>   <dbl>        <dbl>
+#>  1  2013     1     1     517          515        2     830          819
+#>  2  2013     1     1     533          529        4     850          830
+#>  3  2013     1     1     542          540        2     923          850
+#>  4  2013     1     1     544          545       -1    1004         1022
+#>  5  2013     1     1     554          600       -6     812          837
+#>  6  2013     1     1     554          558       -4     740          728
+#>  7  2013     1     1     555          600       -5     913          854
+#>  8  2013     1     1     557          600       -3     709          723
+#>  9  2013     1     1     557          600       -3     838          846
+#> 10  2013     1     1     558          600       -2     753          745
+#> # … with 336,766 more rows, and 11 more variables: arrDelay <dbl>,
+#> #   carrier <chr>, flight <dbl>, tailnum <chr>, origin <chr>, dest <chr>,
+#> #   airTime <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
+#> #   timeHour <dttm>
 

 
 

From a217ffb89317d34cece855ced8993832d98f9fa1 Mon Sep 17 00:00:00 2001
From: Jim Hester 
Date: Tue, 7 May 2019 09:42:00 -0400
Subject: [PATCH 05/11] Disable the spec message for the name repair block

They obscure the point I think
---
 .../articles/2019-05-vroom-1-0-0.Rmarkdown    |  2 +-
 content/articles/2019-05-vroom-1-0-0.markdown | 24 ++++---------------
 2 files changed, 5 insertions(+), 21 deletions(-)

diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
index 16ec1a914..93352927d 100644
--- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown
+++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
@@ -263,7 +263,7 @@ argument as tibble, so you can use one of the default name repair strategies or
 provide a custom function. A great approach is to use the
 [janitor](http://sfirke.github.io/janitor/) `make_clean_names()` function as the input.
 
-```{r}
+```{r, message = FALSE}
 vroom("flights.tsv", .name_repair = janitor::make_clean_names)
 
 vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel"))
diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown
index dddb75c5b..1e567997a 100644
--- a/content/articles/2019-05-vroom-1-0-0.markdown
+++ b/content/articles/2019-05-vroom-1-0-0.markdown
@@ -242,8 +242,8 @@ bench::workout({
 
#> # A tibble: 2 x 3
 #>   exprs                                                process     real
 #>   <bch:expr>                                          <bch:tm> <bch:tm>
-#> 1 vroom_write(flights, "flights.tsv.gz")                 3.44s    2.58s
-#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.52s 963.08ms
+#> 1 vroom_write(flights, "flights.tsv.gz")                 3.56s    2.72s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.59s    1.03s
 

 
 ## Column selection
@@ -360,15 +360,7 @@ provide a custom function. A great approach is to use the
 vroom("flights.tsv", .name_repair = janitor::make_clean_names)
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
+
#> # A tibble: 336,776 x 19
 #>     year month   day dep_time sched_dep_time dep_delay arr_time
 #>    <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
 #>  1  2013     1     1      517            515         2      830
@@ -392,15 +384,7 @@ vroom("flights.tsv", .name_repair = janitor::make_clean_names)
 vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel"))
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-
#> # A tibble: 336,776 x 19
+
#> # A tibble: 336,776 x 19
 #>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
 #>    <dbl> <dbl> <dbl>   <dbl>        <dbl>    <dbl>   <dbl>        <dbl>
 #>  1  2013     1     1     517          515        2     830          819

From 894c58b9068ab8d278e859f48ba2ee1e8a6f0d7c Mon Sep 17 00:00:00 2001
From: Jim Hester 
Date: Tue, 7 May 2019 09:42:54 -0400
Subject: [PATCH 06/11] Ditch color

The colored output is kind of beside the point for this, and it makes
some things worse, like the separate blocks and comment highlighting.
---
 .../articles/2019-05-vroom-1-0-0.Rmarkdown    |  10 -
 content/articles/2019-05-vroom-1-0-0.markdown | 336 ++++++++----------
 2 files changed, 150 insertions(+), 196 deletions(-)

diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
index 93352927d..24c0bfa0c 100644
--- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown
+++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
@@ -15,16 +15,6 @@ photo:
 ```{r include = FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 
-options(crayon.enabled = TRUE)
-
-old_hooks <- fansi::set_knit_hooks(
-  knitr::knit_hooks,
-  which = c("message", "output", "error"),
-  proc.fun = function(x, class) {
-    fansi::html_code_block(fansi::sgr_to_html(fansi::html_esc(x)), class = class)
-  }
-)
-
 library(vroom)
 library(nycflights13)
 vroom_write(flights, "flights.tsv")
diff --git a/content/articles/2019-05-vroom-1-0-0.markdown b/content/articles/2019-05-vroom-1-0-0.markdown
index 1e567997a..6fc267981 100644
--- a/content/articles/2019-05-vroom-1-0-0.markdown
+++ b/content/articles/2019-05-vroom-1-0-0.markdown
@@ -64,18 +64,16 @@ well most of the time, but may fail to guess properly in some cases. The
 library(vroom)
 
 data <- vroom("flights.tsv")
+#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 The summary message after reading also differs from readr. We hope this output
 gives a more informative indication if the types of your columns are being guessed
 properly. However you can still retrieve and print the full column specification
@@ -84,31 +82,29 @@ with `spec()`.
 
 ```r
 spec(data)
+#> cols(
+#>   year = col_double(),
+#>   month = col_double(),
+#>   day = col_double(),
+#>   dep_time = col_double(),
+#>   sched_dep_time = col_double(),
+#>   dep_delay = col_double(),
+#>   arr_time = col_double(),
+#>   sched_arr_time = col_double(),
+#>   arr_delay = col_double(),
+#>   carrier = col_character(),
+#>   flight = col_double(),
+#>   tailnum = col_character(),
+#>   origin = col_character(),
+#>   dest = col_character(),
+#>   air_time = col_double(),
+#>   distance = col_double(),
+#>   hour = col_double(),
+#>   minute = col_double(),
+#>   time_hour = col_datetime(format = "")
+#> )
 ```
 
-
#> cols(
-#>   year = col_double(),
-#>   month = col_double(),
-#>   day = col_double(),
-#>   dep_time = col_double(),
-#>   sched_dep_time = col_double(),
-#>   dep_delay = col_double(),
-#>   arr_time = col_double(),
-#>   sched_arr_time = col_double(),
-#>   arr_delay = col_double(),
-#>   carrier = col_character(),
-#>   flight = col_double(),
-#>   tailnum = col_character(),
-#>   origin = col_character(),
-#>   dest = col_character(),
-#>   air_time = col_double(),
-#>   distance = col_double(),
-#>   hour = col_double(),
-#>   minute = col_double(),
-#>   time_hour = col_datetime(format = "")
-#> )
-

-
 The message will be disabled if you supply a column specification to `col_types` when reading.
 
 
@@ -145,18 +141,16 @@ files
 #> flights_OO.tsv flights_UA.tsv flights_US.tsv flights_VX.tsv flights_WN.tsv 
 #> flights_YV.tsv
 data <- vroom(files)
+#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 ## Reading and writing compressed files
 
 Just like readr, vroom automatically reads and writes zip, gzip, bz2 and xz compressed
@@ -172,18 +166,16 @@ fs::file_size(c("flights.tsv", "flights.tsv.gz"))
 
 # Read the file back in
 data <- vroom("flights.tsv.gz")
+#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 # Reading remote files
 
 vroom can also read files from the internet as well by passing the URL of the file to `vroom()`.
@@ -192,17 +184,15 @@ vroom can also read files from the internet as well by passing the URL of the fi
 ```r
 file <- "https://raw.githubusercontent.com/r-lib/vroom/master/inst/extdata/mtcars.csv"
 data <- vroom(file)
+#> Observations: 32
+#> Variables: 12
+#> chr [ 1]: model
+#> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 32
-#> Variables: 12
-#> chr [ 1]: model
-#> dbl [11]: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 It can even read gzipped files from the internet (although currently not the other compressed formats).
 
 ## Reading and writing from pipe connections
@@ -215,18 +205,16 @@ This is useful for doing things like pre-filtering large inputs with command lin
 ```r
 # Return only flights on United Airlines
 data <- vroom(pipe("grep -w UA flights.tsv"), col_names = names(flights))
+#> Observations: 58,665
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 58,665
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 Or using multi-threaded compression programs like
 [pigz](https://zlib.net/pigz/), which can greatly reduce the time to write compressed
 files.
@@ -237,15 +225,13 @@ bench::workout({
   vroom_write(flights, "flights.tsv.gz")
   vroom_write(flights, pipe("pigz > flights.tsv.gz"))
 })
+#> # A tibble: 2 x 3
+#>   exprs                                                process     real
+#>                                              
+#> 1 vroom_write(flights, "flights.tsv.gz")                 3.45s    2.62s
+#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.53s 956.15ms
 ```
 
-
#> # A tibble: 2 x 3
-#>   exprs                                                process     real
-#>   <bch:expr>                                          <bch:tm> <bch:tm>
-#> 1 vroom_write(flights, "flights.tsv.gz")                 3.56s    2.72s
-#> 2 vroom_write(flights, pipe("pigz > flights.tsv.gz"))    1.59s    1.03s
-

-
 ## Column selection
 
 `vroom` introduces a new argument `col_select` which makes selecting columns to
@@ -257,96 +243,81 @@ The parameter uses the same interface as `dplyr::select()`, so you can do flexib
 
 ```r
 data <- vroom("flights.tsv", col_select = c(year, flight, tailnum))
+#> Observations: 336,776
+#> Variables: 3
+#> chr [1]: tailnum
+#> dbl [2]: year, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
-
-
#> Observations: 336,776
-#> Variables: 3
-#> chr [1]: tailnum
-#> dbl [2]: year, flight
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

 - Drop columns by name
 
 ```r
 data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))
+#> Observations: 336,776
+#> Variables: 13
+#> chr [4]: carrier, tailnum, origin, dest
+#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
 
-
#> Observations: 336,776
-#> Variables: 13
-#> chr [4]: carrier, tailnum, origin, dest
-#> dbl [9]: year, month, day, sched_dep_time, dep_delay, arr_time, sched_arr_time, arr...
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
 - Use numeric indices
 
 ```r
 data <- vroom("flights.tsv", col_select = c(1, 3, 11))
+#> Observations: 336,776
+#> Variables: 3
+#> dbl [3]: year, day, flight
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
-
-
#> Observations: 336,776
-#> Variables: 3
-#> dbl [3]: year, day, flight
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

 - Use the selection helpers
 
 ```r
 data <- vroom("flights.tsv", col_select = ends_with("time"))
+#> Observations: 336,776
+#> Variables: 5
+#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 ```
-
-
#> Observations: 336,776
-#> Variables: 5
-#> dbl [5]: dep_time, sched_dep_time, arr_time, sched_arr_time, air_time
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

 - Or rename columns
 
 ```r
 data <- vroom("flights.tsv", col_select = list(plane = tailnum, everything()))
-```
-
-
#> Observations: 336,776
-#> Variables: 19
-#> chr  [ 4]: carrier, tailnum, origin, dest
-#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
-#> dttm [ 1]: time_hour
-#> 
-#> Call `spec()` for a copy-pastable column specification
-#> Specify the column types with `col_types` to quiet this message
-

-
-```r
+#> Observations: 336,776
+#> Variables: 19
+#> chr  [ 4]: carrier, tailnum, origin, dest
+#> dbl  [14]: year, month, day, dep_time, sched_dep_time, dep_delay, arr_time, sched_arr...
+#> dttm [ 1]: time_hour
+#> 
+#> Call `spec()` for a copy-pastable column specification
+#> Specify the column types with `col_types` to quiet this message
 data
+#> # A tibble: 336,776 x 19
+#>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
+#>                              
+#>  1 N142…  2013     1     1      517            515         2      830
+#>  2 N242…  2013     1     1      533            529         4      850
+#>  3 N619…  2013     1     1      542            540         2      923
+#>  4 N804…  2013     1     1      544            545        -1     1004
+#>  5 N668…  2013     1     1      554            600        -6      812
+#>  6 N394…  2013     1     1      554            558        -4      740
+#>  7 N516…  2013     1     1      555            600        -5      913
+#>  8 N829…  2013     1     1      557            600        -3      709
+#>  9 N593…  2013     1     1      557            600        -3      838
+#> 10 N3AL…  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 11 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , origin ,
+#> #   dest , air_time , distance , hour , minute ,
+#> #   time_hour 
 ```
 
-
#> # A tibble: 336,776 x 19
-#>    plane  year month   day dep_time sched_dep_time dep_delay arr_time
-#>    <chr> <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
-#>  1 N142…  2013     1     1      517            515         2      830
-#>  2 N242…  2013     1     1      533            529         4      850
-#>  3 N619…  2013     1     1      542            540         2      923
-#>  4 N804…  2013     1     1      544            545        -1     1004
-#>  5 N668…  2013     1     1      554            600        -6      812
-#>  6 N394…  2013     1     1      554            558        -4      740
-#>  7 N516…  2013     1     1      555            600        -5      913
-#>  8 N829…  2013     1     1      557            600        -3      709
-#>  9 N593…  2013     1     1      557            600        -3      838
-#> 10 N3AL…  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 11 more variables: sched_arr_time <dbl>,
-#> #   arr_delay <dbl>, carrier <chr>, flight <dbl>, origin <chr>,
-#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
-#> #   time_hour <dttm>
-

-
 ## Name repair
 
 Often the names of columns in the original dataset are not ideal to work with.
@@ -358,51 +329,44 @@ provide a custom function. A great approach is to use the
 
 ```r
 vroom("flights.tsv", .name_repair = janitor::make_clean_names)
-```
-
-
#> # A tibble: 336,776 x 19
-#>     year month   day dep_time sched_dep_time dep_delay arr_time
-#>    <dbl> <dbl> <dbl>    <dbl>          <dbl>     <dbl>    <dbl>
-#>  1  2013     1     1      517            515         2      830
-#>  2  2013     1     1      533            529         4      850
-#>  3  2013     1     1      542            540         2      923
-#>  4  2013     1     1      544            545        -1     1004
-#>  5  2013     1     1      554            600        -6      812
-#>  6  2013     1     1      554            558        -4      740
-#>  7  2013     1     1      555            600        -5      913
-#>  8  2013     1     1      557            600        -3      709
-#>  9  2013     1     1      557            600        -3      838
-#> 10  2013     1     1      558            600        -2      753
-#> # … with 336,766 more rows, and 12 more variables: sched_arr_time <dbl>,
-#> #   arr_delay <dbl>, carrier <chr>, flight <dbl>, tailnum <chr>,
-#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
-#> #   minute <dbl>, time_hour <dttm>
-

-
-```r
+#> # A tibble: 336,776 x 19
+#>     year month   day dep_time sched_dep_time dep_delay arr_time
+#>                             
+#>  1  2013     1     1      517            515         2      830
+#>  2  2013     1     1      533            529         4      850
+#>  3  2013     1     1      542            540         2      923
+#>  4  2013     1     1      544            545        -1     1004
+#>  5  2013     1     1      554            600        -6      812
+#>  6  2013     1     1      554            558        -4      740
+#>  7  2013     1     1      555            600        -5      913
+#>  8  2013     1     1      557            600        -3      709
+#>  9  2013     1     1      557            600        -3      838
+#> 10  2013     1     1      558            600        -2      753
+#> # … with 336,766 more rows, and 12 more variables: sched_arr_time ,
+#> #   arr_delay , carrier , flight , tailnum ,
+#> #   origin , dest , air_time , distance , hour ,
+#> #   minute , time_hour 
 
 vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower_camel"))
+#> # A tibble: 336,776 x 19
+#>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
+#>                                
+#>  1  2013     1     1     517          515        2     830          819
+#>  2  2013     1     1     533          529        4     850          830
+#>  3  2013     1     1     542          540        2     923          850
+#>  4  2013     1     1     544          545       -1    1004         1022
+#>  5  2013     1     1     554          600       -6     812          837
+#>  6  2013     1     1     554          558       -4     740          728
+#>  7  2013     1     1     555          600       -5     913          854
+#>  8  2013     1     1     557          600       -3     709          723
+#>  9  2013     1     1     557          600       -3     838          846
+#> 10  2013     1     1     558          600       -2     753          745
+#> # … with 336,766 more rows, and 11 more variables: arrDelay ,
+#> #   carrier , flight , tailnum , origin , dest ,
+#> #   airTime , distance , hour , minute ,
+#> #   timeHour 
 ```
 
-
#> # A tibble: 336,776 x 19
-#>     year month   day depTime schedDepTime depDelay arrTime schedArrTime
-#>    <dbl> <dbl> <dbl>   <dbl>        <dbl>    <dbl>   <dbl>        <dbl>
-#>  1  2013     1     1     517          515        2     830          819
-#>  2  2013     1     1     533          529        4     850          830
-#>  3  2013     1     1     542          540        2     923          850
-#>  4  2013     1     1     544          545       -1    1004         1022
-#>  5  2013     1     1     554          600       -6     812          837
-#>  6  2013     1     1     554          558       -4     740          728
-#>  7  2013     1     1     555          600       -5     913          854
-#>  8  2013     1     1     557          600       -3     709          723
-#>  9  2013     1     1     557          600       -3     838          846
-#> 10  2013     1     1     558          600       -2     753          745
-#> # … with 336,766 more rows, and 11 more variables: arrDelay <dbl>,
-#> #   carrier <chr>, flight <dbl>, tailnum <chr>, origin <chr>, dest <chr>,
-#> #   airTime <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
-#> #   timeHour <dttm>
-

-
 
 ## Column types
 

From 1c9c217aeda4cd2e731b30f44bf90b2d2f1921e2 Mon Sep 17 00:00:00 2001
From: Jim Hester 
Date: Tue, 7 May 2019 10:58:43 -0400
Subject: [PATCH 07/11] Changes based on Hadley's comments

---
 .../articles/2019-05-vroom-1-0-0.Rmarkdown    |  68 ++++++++-------
 content/articles/2019-05-vroom-1-0-0.markdown |  79 ++++++++----------
 .../figure-html/benchmark_plot-1.png          | Bin 107894 -> 107174 bytes
 3 files changed, 75 insertions(+), 72 deletions(-)

diff --git a/content/articles/2019-05-vroom-1-0-0.Rmarkdown b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
index 24c0bfa0c..d72186493 100644
--- a/content/articles/2019-05-vroom-1-0-0.Rmarkdown
+++ b/content/articles/2019-05-vroom-1-0-0.Rmarkdown
@@ -66,7 +66,7 @@ make_table <- function(data) {
     filter(type == "real") %>%
     tally(wt = time) %>%
     spread(op, n) %>%
-    mutate(total = sum(read, print, head, tail, sample, filter, aggregate))
+    mutate(total = sum(read, print, head, tail, sample, filter, summarise))
 
   times %>%
     arrange(desc(total)) %>%
@@ -89,13 +89,10 @@ I'm excited to announce that [vroom 1.0.0](http://vroom.r-lib.org) is now
 available on CRAN!
 
 vroom reads rectangular data, such as comma separated
-(csv), tab separated (tsv) or fixed width files (fwf) into R.
-
-It performs the
+(csv), tab separated (tsv) or fixed width files (fwf) into R. It performs the
 same function as packages like [readr](http://readr.r-lib.org),
-[data.table](http://r-datatable.com) or the `read.csv()`
-function, but for many datasets vroom can read them much, much
-faster (hence the name).
+[data.table](http://r-datatable.com) or the `read.csv()` function, but for many
+datasets vroom can read them much, much faster (hence the name).
 
 The main reason vroom can be faster is because character data is read from the
 file lazily; you only pay for the data you use. This lazy access is done
@@ -113,13 +110,22 @@ install.packages("vroom")
 The best way to get acquainted with the package is the [getting
 started](http://vroom.r-lib.org/articles/vroom.html) vignette.
 
+## vroom vs readr
+
+What does the release of vroom mean for readr? For now we plan
+to let the two packages evolve separately, but likely we will unite the
+packages in the future. One disadvantage to vroom's lazy reading is certain
+data problems can't be reported up front, so how best to unify them requires
+some thought.
+
 ## Reading delimited files
 
 Compared to readr, the first difference you may note is you use only one
 function to read the files, `vroom()`. This is because `vroom()` guesses the
-delimiter of the file automatically (based on the first few lines). This works
-well most of the time, but may fail to guess properly in some cases. The
-`delim` argument is used to specify the delimiter of the file explicitly.
+delimiter of the file automatically based on the first few lines (this feature
+is inspired by a similar feature in `data.table::fread()`). This works well
+most of the time, but may fail to guess properly in some cases. The `delim`
+argument can be used to specify the delimiter of the file explicitly.
 
 ```{r}
 library(vroom)
@@ -148,9 +154,10 @@ data <- vroom("flights.tsv", col_types = s)
 
 One feature new to vroom is built-in support for reading sets of files with the
 same columns into one table. Just pass the filenames to be read directly to
-vroom.
+vroom. Imagine we have a directory of files containing the flights data, where
+each file corresponds to a single airline.
 
-```{r}
+```{r, include = FALSE}
 # Split the flights data by carrier
 purrr::iwalk(
   split(flights, flights$carrier),
@@ -158,8 +165,8 @@ purrr::iwalk(
 )
 ```
 
-Then we can efficiently read all of the files into one tibble by passing the filenames
-directly to `vroom()`.
+Then we can efficiently read all of the files into one tibble by passing a
+vector of the filenames directly to `vroom()`.
 
 ```{r}
 files <- fs::dir_ls(glob = "flights_*tsv")
@@ -226,15 +233,12 @@ The parameter uses the same interface as `dplyr::select()`, so you can do flexib
 ```{r}
 data <- vroom("flights.tsv", col_select = c(year, flight, tailnum))
 ```
+
 - Drop columns by name
 ```{r}
 data <- vroom("flights.tsv", col_select = c(-dep_time, -air_time:-time_hour))
 ```
 
-- Use numeric indices
-```{r}
-data <- vroom("flights.tsv", col_select = c(1, 3, 11))
-```
 - Use the selection helpers
 ```{r}
 data <- vroom("flights.tsv", col_select = ends_with("time"))
@@ -262,8 +266,11 @@ vroom("flights.tsv", .name_repair = ~ janitor::make_clean_names(., case = "lower
 
 ## Column types
 
-Like readr, vroom guesses the data types of columns as they are read, however sometimes it
-is necessary to change the type of one or more columns.
+Like readr, vroom guesses the data types of columns as they are read. readr
+simply used the first `n` rows of data, vroom uses an improved heuristic of
+looking at data throughout the file, which should improve guessing accuracy.
+However if the guessing fails it can be necessary to change the type of one or
+more columns.
 
 The available specifications are: (with single letter abbreviations in quotes)
 
@@ -315,7 +322,7 @@ data <- vroom("flights.tsv",
 
 vroom is fast, but how fast?
 We benchmarked vroom using a real world dataset of taxi trip data, with
-14.7 million rows, 11 columns. It contains a mix of numeric and textual data and has a
+14.7 million rows, 11 columns. It contains a mix of numeric and text data and has a
 total file size of 1.55 GB.
 
     #> Observations: 14,776,615
@@ -340,7 +347,7 @@ We performed a series of simple manipulations with each approach.
   - `tail()`
   - Sampling 100 random rows
   - Filtering for "UNK" payment, this is 6434 rows (0.0435% of total).
-  - Aggregation of mean fare amount per payment type.
+  - Summarizing the mean fare amount per payment type.