How can I update R via RStudio?
R – Update R using RStudio
rrstudio
Related Solutions
By using the merge
function and its optional parameters:
Inner join: merge(df1, df2)
will work for these examples because R automatically joins the frames by common variable names, but you would most likely want to specify merge(df1, df2, by = "CustomerId")
to make sure that you were matching on only the fields you desired. You can also use the by.x
and by.y
parameters if the matching variables have different names in the different data frames.
Outer join: merge(x = df1, y = df2, by = "CustomerId", all = TRUE)
Left outer: merge(x = df1, y = df2, by = "CustomerId", all.x = TRUE)
Right outer: merge(x = df1, y = df2, by = "CustomerId", all.y = TRUE)
Cross join: merge(x = df1, y = df2, by = NULL)
Just as with the inner join, you would probably want to explicitly pass "CustomerId" to R as the matching variable. I think it's almost always best to explicitly state the identifiers on which you want to merge; it's safer if the input data.frames change unexpectedly and easier to read later on.
You can merge on multiple columns by giving by
a vector, e.g., by = c("CustomerId", "OrderId")
.
If the column names to merge on are not the same, you can specify, e.g., by.x = "CustomerId_in_df1", by.y = "CustomerId_in_df2"
where CustomerId_in_df1
is the name of the column in the first data frame and CustomerId_in_df2
is the name of the column in the second data frame. (These can also be vectors if you need to merge on multiple columns.)
Basically a minimal reproducible example (MRE) should enable others to exactly reproduce your issue on their machines.
A MRE consists of the following items:
- a minimal dataset, necessary to demonstrate the problem
- the minimal runnable code necessary to reproduce the error, which can be run on the given dataset
- all necessary information on the used packages, the R version, and the OS it is run on.
- in the case of random processes, a seed (set by
set.seed()
) for reproducibility
For examples of good MREs, see section "Examples" at the bottom of help files on the function you are using. Simply type e.g. help(mean)
, or short ?mean
into your R console.
Providing a minimal dataset
Usually, sharing huge data sets is not necessary and may rather discourage others from reading your question. Therefore, it is better to use built-in datasets or create a small "toy" example that resembles your original data, which is actually what is meant by minimal. If for some reason you really need to share your original data, you should use a method, such as dput()
, that allows others to get an exact copy of your data.
Built-in datasets
You can use one of the built-in datasets. A comprehensive list of built-in datasets can be seen with data()
. There is a short description of every data set, and more information can be obtained, e.g. with ?iris
, for the 'iris' data set that comes with R. Installed packages might contain additional datasets.
Creating example data sets
Preliminary note: Sometimes you may need special formats (i.e. classes), such as factors, dates, or time series. For these, make use of functions like: as.factor
, as.Date
, as.xts
, ... Example:
d <- as.Date("2020-12-30")
where
class(d)
# [1] "Date"
Vectors
x <- rnorm(10) ## random vector normal distributed
x <- runif(10) ## random vector uniformly distributed
x <- sample(1:100, 10) ## 10 random draws out of 1, 2, ..., 100
x <- sample(LETTERS, 10) ## 10 random draws out of built-in latin alphabet
Matrices
m <- matrix(1:12, 3, 4, dimnames=list(LETTERS[1:3], LETTERS[1:4]))
m
# A B C D
# A 1 4 7 10
# B 2 5 8 11
# C 3 6 9 12
Data frames
set.seed(42) ## for sake of reproducibility
n <- 6
dat <- data.frame(id=1:n,
date=seq.Date(as.Date("2020-12-26"), as.Date("2020-12-31"), "day"),
group=rep(LETTERS[1:2], n/2),
age=sample(18:30, n, replace=TRUE),
type=factor(paste("type", 1:n)),
x=rnorm(n))
dat
# id date group age type x
# 1 1 2020-12-26 A 27 type 1 0.0356312
# 2 2 2020-12-27 B 19 type 2 1.3149588
# 3 3 2020-12-28 A 20 type 3 0.9781675
# 4 4 2020-12-29 B 26 type 4 0.8817912
# 5 5 2020-12-30 A 26 type 5 0.4822047
# 6 6 2020-12-31 B 28 type 6 0.9657529
Note: Although it is widely used, better do not name your data frame df
, because df()
is an R function for the density (i.e. height of the curve at point x
) of the F distribution and you might get a clash with it.
Copying original data
If you have a specific reason, or data that would be too difficult to construct an example from, you could provide a small subset of your original data, best by using dput
.
Why use dput()
?
dput
throws all information needed to exactly reproduce your data on your console. You may simply copy the output and paste it into your question.
Calling dat
(from above) produces output that still lacks information about variable classes and other features if you share it in your question. Furthermore the spaces in the type
column make it difficult to do anything with it. Even when we set out to use the data, we won't manage to get important features of your data right.
id date group age type x
1 1 2020-12-26 A 27 type 1 0.0356312
2 2 2020-12-27 B 19 type 2 1.3149588
3 3 2020-12-28 A 20 type 3 0.9781675
Subset your data
Tho share a subset, use head()
, subset()
or the indices iris[1:4, ]
. Then wrap it into dput()
to give others something that can be put in R immediately. Example
dput(iris[1:4, ]) # first four rows of the iris data set
Console output to share in your question:
structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6), Sepal.Width = c(3.5,
3, 3.2, 3.1), Petal.Length = c(1.4, 1.4, 1.3, 1.5), Petal.Width = c(0.2,
0.2, 0.2, 0.2), Species = structure(c(1L, 1L, 1L, 1L), .Label = c("setosa",
"versicolor", "virginica"), class = "factor")), row.names = c(NA,
4L), class = "data.frame")
When using dput
, you may also want to include only relevant columns, e.g. dput(mtcars[1:3, c(2, 5, 6)])
Note: If your data frame has a factor with many levels, the dput
output can be unwieldy because it will still list all the possible factor levels even if they aren't present in the the subset of your data. To solve this issue, you can use the droplevels()
function. Notice below how species is a factor with only one level, e.g. dput(droplevels(iris[1:4, ]))
. One other caveat for dput
is that it will not work for keyed data.table
objects or for grouped tbl_df
(class grouped_df
) from the tidyverse
. In these cases you can convert back to a regular data frame before sharing, dput(as.data.frame(my_data))
.
Producing minimal code
Combined with the minimal data (see above), your code should exactly reproduce the problem on another machine by simply copying and pasting it.
This should be the easy part but often isn't. What you should not do:
- showing all kinds of data conversions; make sure the provided data is already in the correct format (unless that is the problem, of course)
- copy-paste a whole script that gives an error somewhere. Try to locate which lines exactly result in the error. More often than not, you'll find out what the problem is yourself.
What you should do:
- add which packages you use if you use any (using
library()
) - test run your code in a fresh R session to ensure the code is runnable. People should be able to copy-paste your data and your code in the console and get the same as you have.
- if you open connections or create files, add some code to close them or delete the files (using
unlink()
) - if you change options, make sure the code contains a statement to revert them back to the original ones. (eg
op <- par(mfrow=c(1,2)) ...some code... par(op)
)
Providing necessary information
In most cases, just the R version and the operating system will suffice. When conflicts arise with packages, giving the output of sessionInfo()
can really help. When talking about connections to other applications (be it through ODBC or anything else), one should also provide version numbers for those, and if possible, also the necessary information on the setup.
If you are running R in R Studio, using rstudioapi::versionInfo()
can help report your RStudio version.
If you have a problem with a specific package, you may want to provide the package version by giving the output of packageVersion("name of the package")
.
Seed
Using set.seed()
you may specify a seed1, i.e. the specific state, R's random number generator is fixed. This makes it possible for random functions, such as sample()
, rnorm()
, runif()
and lots of others, to always return the same result, Example:
set.seed(42)
rnorm(3)
# [1] 1.3709584 -0.5646982 0.3631284
set.seed(42)
rnorm(3)
# [1] 1.3709584 -0.5646982 0.3631284
1 Note: The output of set.seed()
differs between R >3.6.0 and previous versions. Specify which R version you used for the random process, and don't be surprised if you get slightly different results when following old questions. To get the same result in such cases, you can use the RNGversion()
-function before set.seed()
(e.g.: RNGversion("3.5.2")
).
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Best Answer
For completeness, the answer is: you can't do that from within RStudio. @agstudy has it right - you need to install the newer version of R, then restart RStudio and it will automagically use the new version, as @Brandon noted.
It would be great if there was an update.R() function, analogous to the install.packages() function or the update.packages(function).
So, in order to install R,
--wait - what about my beloved packages??--
ok, I use a Mac, so I can only provide accurate details for the Mac - perhaps someone else can provide the accurate paths for windows/linux; I believe the process will be the same.
To ensure that your packages work with your shiny new version of R, you need to:
move the packages from the old R installation into the new version; on Mac OSX, this means moving all folders from here:
to here:
[where you'll replace "2.15" and "3.0" with whatever versions you're upgrading from and to. And only copy whatever packages aren't already in the destination directory. i.e. don't overwrite your new 'base' package with your old one - if you did, don't worry, we'll fix it in the next step anyway. If those paths don't work for you, try using
installed.packages()
to find the proper pathnames.]now you can update your packages by typing
update.packages()
in your RStudio console, and answering 'y' to all of the prompts.finally, to reassure yourself that you have done everything, type these two commands in the RStudio console to see what you have got: