In order to share some more tips and tricks for R, what is your single-most useful feature or trick? Clever vectorization? Data input/output? Visualization and graphics? Statistical analysis? Special functions? The interactive environment itself?
One item per post, and we will see if we get a winner by means of votes.
[Edit 25-Aug 2008]: So after one week, it seems that the simple str() won the poll. As I like to recommend that one myself, it is an easy answer to accept.
One very useful function I often use is dput(), which allows you to dump an object in the form of R code.
# Use the iris data set
R> data(iris)
# dput of a numeric vector
R> dput(iris$Petal.Length)
c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6,
1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1, 1.7, 1.9,
1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4,
1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7,
4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4, 4.7,
3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9, 4.7, 4.3, 4.4, 4.8,
5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4,
4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1, 6, 5.1, 5.9, 5.6,
5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5, 5.1, 5.3, 5.5,
6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8, 4.9, 5.6, 5.8,
6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1,
5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1)
# dput of a factor levels
R> dput(levels(iris$Species))
c("setosa", "versicolor", "virginica")
It can be very useful to post easily reproducible data chunks when you ask for help, or to edit or reorder the levels of a factor.
I do a lot of basic manipulation of data, so here are two built-in functions ( transform , subset ) and one library ( sqldf ) that I use daily.
sales <- expand.grid(country = c('USA', 'UK', 'FR'),
product = c(1, 2, 3))
sales$revenue <- rnorm(dim(sales)[1], mean=100, sd=10)
> sales
country product revenue
1 USA 1 108.45965
2 UK 1 97.07981
3 FR 1 99.66225
4 USA 2 100.34754
5 UK 2 87.12262
6 FR 2 112.86084
7 USA 3 95.87880
8 UK 3 96.43581
9 FR 3 94.59259
## transform currency to euros
usd2eur <- 1.434
transform(sales, euro = revenue * usd2eur)
>
country product revenue euro
1 USA 1 108.45965 155.5311
2 UK 1 97.07981 139.2125
3 FR 1 99.66225 142.9157
...
subset(sales,
country == 'USA' & product %in% c(1, 2),
select = c('product', 'revenue'))
>
product revenue
1 1 108.4597
4 2 100.3475
The sqldf package provides an SQL interface to R data frames
## recast the previous subset() expression in SQL
sqldf('SELECT product, revenue FROM sales \
WHERE country = "USA" \
AND product IN (1,2)')
>
product revenue
1 1 108.4597
2 2 100.3475
Perform an aggregation or GROUP BY
sqldf('select country, sum(revenue) revenue \
FROM sales \
GROUP BY country')
>
country revenue
1 FR 307.1157
2 UK 280.6382
3 USA 304.6860
For more sophisticated map-reduce-like functionality on data frames, check out the plyr package. And if find yourself wanting to pull your hair out, I recommend checking out Data Manipulation with R.
Although this question has been up for a while I recently discovered a great trick on the SAS and R blog for using the command cut. The command is used to divide data into categories and I will use the iris dataset as an example and divide it into 10 categories:
> irisSL <- iris$Sepal.Length
> str(irisSL)
num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
> cut(irisSL, 10)
[1] (5.02,5.38] (4.66,5.02] (4.66,5.02] (4.3,4.66] (4.66,5.02] (5.38,5.74] (4.3,4.66] (4.66,5.02] (4.3,4.66] (4.66,5.02]
[11] (5.38,5.74] (4.66,5.02] (4.66,5.02] (4.3,4.66] (5.74,6.1] (5.38,5.74] (5.38,5.74] (5.02,5.38] (5.38,5.74] (5.02,5.38]
[21] (5.38,5.74] (5.02,5.38] (4.3,4.66] (5.02,5.38] (4.66,5.02] (4.66,5.02] (4.66,5.02] (5.02,5.38] (5.02,5.38] (4.66,5.02]
[31] (4.66,5.02] (5.38,5.74] (5.02,5.38] (5.38,5.74] (4.66,5.02] (4.66,5.02] (5.38,5.74] (4.66,5.02] (4.3,4.66] (5.02,5.38]
[41] (4.66,5.02] (4.3,4.66] (4.3,4.66] (4.66,5.02] (5.02,5.38] (4.66,5.02] (5.02,5.38] (4.3,4.66] (5.02,5.38] (4.66,5.02]
[51] (6.82,7.18] (6.1,6.46] (6.82,7.18] (5.38,5.74] (6.46,6.82] (5.38,5.74] (6.1,6.46] (4.66,5.02] (6.46,6.82] (5.02,5.38]
[61] (4.66,5.02] (5.74,6.1] (5.74,6.1] (5.74,6.1] (5.38,5.74] (6.46,6.82] (5.38,5.74] (5.74,6.1] (6.1,6.46] (5.38,5.74]
[71] (5.74,6.1] (5.74,6.1] (6.1,6.46] (5.74,6.1] (6.1,6.46] (6.46,6.82] (6.46,6.82] (6.46,6.82] (5.74,6.1] (5.38,5.74]
[81] (5.38,5.74] (5.38,5.74] (5.74,6.1] (5.74,6.1] (5.38,5.74] (5.74,6.1] (6.46,6.82] (6.1,6.46] (5.38,5.74] (5.38,5.74]
[91] (5.38,5.74] (5.74,6.1] (5.74,6.1] (4.66,5.02] (5.38,5.74] (5.38,5.74] (5.38,5.74] (6.1,6.46] (5.02,5.38] (5.38,5.74]
[101] (6.1,6.46] (5.74,6.1] (6.82,7.18] (6.1,6.46] (6.46,6.82] (7.54,7.9] (4.66,5.02] (7.18,7.54] (6.46,6.82] (7.18,7.54]
[111] (6.46,6.82] (6.1,6.46] (6.46,6.82] (5.38,5.74] (5.74,6.1] (6.1,6.46] (6.46,6.82] (7.54,7.9] (7.54,7.9] (5.74,6.1]
[121] (6.82,7.18] (5.38,5.74] (7.54,7.9] (6.1,6.46] (6.46,6.82] (7.18,7.54] (6.1,6.46] (5.74,6.1] (6.1,6.46] (7.18,7.54]
[131] (7.18,7.54] (7.54,7.9] (6.1,6.46] (6.1,6.46] (5.74,6.1] (7.54,7.9] (6.1,6.46] (6.1,6.46] (5.74,6.1] (6.82,7.18]
[141] (6.46,6.82] (6.82,7.18] (5.74,6.1] (6.46,6.82] (6.46,6.82] (6.46,6.82] (6.1,6.46] (6.46,6.82] (6.1,6.46] (5.74,6.1]
10 Levels: (4.3,4.66] (4.66,5.02] (5.02,5.38] (5.38,5.74] (5.74,6.1] (6.1,6.46] (6.46,6.82] (6.82,7.18] ... (7.54,7.9]