README.md
In alistaire47/read.so: Read Tables from Stack Overflow Questions into R
read.so
Installation
read.so is not on CRAN, but you can install it by running
# install.packages("remotes")
remotes::install_github("alistaire47/read.so")
Read data from Stack Overflow questions into R
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.
Read data frame print output back into a data frame
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 setosa
Further, 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 4
Read Markdown tables into data frames
When 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 1
They can handle a number of formats, including tables with delimiter
rows composed of “-”, “=”, “+”, and whitespace.
Read str results back into a data frame
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 M
Read tibble::glimpse results back into a data frame
Similarly, 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>
Generate pretty code to reproduce a data frame
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)
#> )
Related packages
- 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 like tibble::tribble, you’ll
like this package.
alistaire47/read.so documentation built on April 23, 2020, 12:50 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
read.so
Installation
read.so is not on CRAN, but you can install it by running
# install.packages("remotes")
remotes::install_github("alistaire47/read.so")
Read data from Stack Overflow questions into R
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.
Read data frame print output back into a data frame
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 setosa
Further, 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 4
Read Markdown tables into data frames
When 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 1
They can handle a number of formats, including tables with delimiter rows composed of “-”, “=”, “+”, and whitespace.
Read str results back into a data frame
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 M
Read tibble::glimpse results back into a data frame
Similarly, 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>
Generate pretty code to reproduce a data frame
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)
#> )
Related packages
- 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.
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