read.so is not on CRAN, but you can install it by running
# install.packages("remotes")
remotes::install_github("alistaire47/read.so")Sometimes you see a really interesting question on Stack Overflow (or a Github issue, or RStudio Community), but the asker only presents the data as a presentation-style table instead of as runnable R code, because they, unlike you, don’t use reprex. Fear no more! read.so will read even heinous tables into data frames and code in a trice.
For instance, should you want to return output copied from the R console
back into your own session, use read.so for a data.frame, and
read_so for a tibble. Pass in a filepath, a raw string of text, a
vector of lines, or if the data is on the clipboard, nothing at all, and
the functions will grab it for you:
library(read.so)
iris_lines <- capture.output(head(iris))
iris_lines
#> [1] " Sepal.Length Sepal.Width Petal.Length Petal.Width Species"
#> [2] "1 5.1 3.5 1.4 0.2 setosa"
#> [3] "2 4.9 3.0 1.4 0.2 setosa"
#> [4] "3 4.7 3.2 1.3 0.2 setosa"
#> [5] "4 4.6 3.1 1.5 0.2 setosa"
#> [6] "5 5.0 3.6 1.4 0.2 setosa"
#> [7] "6 5.4 3.9 1.7 0.4 setosa"
read.so(iris_lines)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
read_so(iris_lines)
#> # A tibble: 6 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
clipr::write_clip(head(iris))
read.so()
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosaFurther, read_so will attempt to read in the results of printing a
tibble:
mtcars_lines <- capture.output(tibble::as_tibble(mtcars))
mtcars_lines
#> [1] "# A tibble: 32 x 11"
#> [2] " mpg cyl disp hp drat wt qsec vs am gear carb"
#> [3] " <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>"
#> [4] " 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4"
#> [5] " 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4"
#> [6] " 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1"
#> [7] " 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1"
#> [8] " 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2"
#> [9] " 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1"
#> [10] " 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4"
#> [11] " 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2"
#> [12] " 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2"
#> [13] "10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4"
#> [14] "# … with 22 more rows"
read_so(mtcars_lines)
#> # A tibble: 10 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168 123 3.92 3.44 18.3 1 0 4 4When you need to read Markdown tables into R, read.so has you covered
with read.md and read_md:
chick_lines <- capture.output(
knitr::kable(head(ChickWeight), format = "markdown")
)
cat(chick_lines, sep = "\n")
#>
#>
#> | weight| Time|Chick |Diet |
#> |------:|----:|:-----|:----|
#> | 42| 0|1 |1 |
#> | 51| 2|1 |1 |
#> | 59| 4|1 |1 |
#> | 64| 6|1 |1 |
#> | 76| 8|1 |1 |
#> | 93| 10|1 |1 |
read.md(chick_lines)
#> weight Time Chick Diet
#> 1 42 0 1 1
#> 2 51 2 1 1
#> 3 59 4 1 1
#> 4 64 6 1 1
#> 5 76 8 1 1
#> 6 93 10 1 1
read_md(chick_lines)
#> # A tibble: 6 x 4
#> weight Time Chick Diet
#> <dbl> <dbl> <dbl> <dbl>
#> 1 42 0 1 1
#> 2 51 2 1 1
#> 3 59 4 1 1
#> 4 64 6 1 1
#> 5 76 8 1 1
#> 6 93 10 1 1They can handle a number of formats, including tables with delimiter rows composed of “-”, “=”, “+”, and whitespace.
If all you have is the results of calling str on a data frame,
read.str will read as many complete rows as possible into a new data
frame of the same class as the original:
warp_lines <- capture.output(str(warpbreaks))
warp_lines
#> [1] "'data.frame':\t54 obs. of 3 variables:"
#> [2] " $ breaks : num 26 30 54 25 70 52 51 26 67 18 ..."
#> [3] " $ wool : Factor w/ 2 levels \"A\",\"B\": 1 1 1 1 1 1 1 1 1 1 ..."
#> [4] " $ tension: Factor w/ 3 levels \"L\",\"M\",\"H\": 1 1 1 1 1 1 1 1 1 2 ..."
read.str(warp_lines)
#> breaks wool tension
#> 1 26 A L
#> 2 30 A L
#> 3 54 A L
#> 4 25 A L
#> 5 70 A L
#> 6 52 A L
#> 7 51 A L
#> 8 26 A L
#> 9 67 A L
#> 10 18 A MSimilarly, if the data was printed by tibble::glimpse, try
read.glimpse or read_glimpse:
states <- data.frame(state.name, state.abb, state.region, state.division,
state.area, center = state.center, state.x77)
states_lines <- capture.output(tibble::glimpse(states))
states_lines
#> [1] "Rows: 50"
#> [2] "Columns: 15"
#> [3] "$ state.name <fct> Alabama, Alaska, Arizona, Arkansas, California, Colora…"
#> [4] "$ state.abb <fct> AL, AK, AZ, AR, CA, CO, CT, DE, FL, GA, HI, ID, IL, IN…"
#> [5] "$ state.region <fct> South, West, West, South, West, West, Northeast, South…"
#> [6] "$ state.division <fct> East South Central, Pacific, Mountain, West South Cent…"
#> [7] "$ state.area <dbl> 51609, 589757, 113909, 53104, 158693, 104247, 5009, 20…"
#> [8] "$ center.x <dbl> -86.7509, -127.2500, -111.6250, -92.2992, -119.7730, -…"
#> [9] "$ center.y <dbl> 32.5901, 49.2500, 34.2192, 34.7336, 36.5341, 38.6777, …"
#> [10] "$ Population <dbl> 3615, 365, 2212, 2110, 21198, 2541, 3100, 579, 8277, 4…"
#> [11] "$ Income <dbl> 3624, 6315, 4530, 3378, 5114, 4884, 5348, 4809, 4815, …"
#> [12] "$ Illiteracy <dbl> 2.1, 1.5, 1.8, 1.9, 1.1, 0.7, 1.1, 0.9, 1.3, 2.0, 1.9,…"
#> [13] "$ Life.Exp <dbl> 69.05, 69.31, 70.55, 70.66, 71.71, 72.06, 72.48, 70.06…"
#> [14] "$ Murder <dbl> 15.1, 11.3, 7.8, 10.1, 10.3, 6.8, 3.1, 6.2, 10.7, 13.9…"
#> [15] "$ HS.Grad <dbl> 41.3, 66.7, 58.1, 39.9, 62.6, 63.9, 56.0, 54.6, 52.6, …"
#> [16] "$ Frost <dbl> 20, 152, 15, 65, 20, 166, 139, 103, 11, 60, 0, 126, 12…"
#> [17] "$ Area <dbl> 50708, 566432, 113417, 51945, 156361, 103766, 4862, 19…"
read_glimpse(states_lines)
#> # A tibble: 4 x 15
#> state.name state.abb state.region state.division state.area center.x center.y
#> * <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 Alabama AL South East South Ce… 51609 -86.8 32.6
#> 2 Alaska AK West Pacific 589757 -127. 49.2
#> 3 Arizona AZ West Mountain 113909 -112. 34.2
#> 4 Arkansas AR South West South Ce… 53104 -92.3 34.7
#> # … with 8 more variables: Population <dbl>, Income <dbl>, Illiteracy <dbl>,
#> # Life.Exp <dbl>, Murder <dbl>, HS.Grad <dbl>, Frost <dbl>, Area <dbl>The reading functions make it easy to get a question’s data into R, but
to construct a reproducible response requires the data in code form.
dput will produce runnable code, but the result is not very readable.
write.so and its alias write_so take a data frame and restructure
the results of dput to make it an ordinary data.frame, tibble, or
data.table call, and when a name is detected, reassemble assignment to
that variable. The call is printed to the console and by default copied
to the clipboard. A language object version of the call is returned,
invisibly.
options(read.so.write_clip = FALSE) # don't write to clipboard by default
write.so(head(swiss))
#> swiss <- data.frame(
#> Fertility = c(80.2, 83.1, 92.5, 85.8, 76.9, 76.1),
#> Agriculture = c(17, 45.1, 39.7, 36.5, 43.5, 35.3),
#> Examination = c(15L, 6L, 5L, 12L, 17L, 9L),
#> Education = c(12L, 9L, 5L, 7L, 15L, 7L),
#> Catholic = c(9.96, 84.84, 93.4, 33.77, 5.16, 90.57),
#> Infant.Mortality = c(22.2, 22.2, 20.2, 20.3, 20.6, 26.6)
#> )
write_so(head(tibble::as_tibble(swiss)), indent = 2)
#> swiss <- tibble(
#> Fertility = c(80.2, 83.1, 92.5, 85.8, 76.9, 76.1),
#> Agriculture = c(17, 45.1, 39.7, 36.5, 43.5, 35.3),
#> Examination = c(15L, 6L, 5L, 12L, 17L, 9L),
#> Education = c(12L, 9L, 5L, 7L, 15L, 7L),
#> Catholic = c(9.96, 84.84, 93.4, 33.77, 5.16, 90.57),
#> Infant.Mortality = c(22.2, 22.2, 20.2, 20.3, 20.6, 26.6)
#> )- reprex: A great way to generate reproducible examples. read.so is [mostly] for when people don’t use reprex. You should use reprex.
- datapasta: A broader
approach to getting data from the clipboard into R code containing
alternatives to
write.so. If you liketibble::tribble, you’ll like this package.