![]() ![]() Use -starts_with() to ignore columns that start with a text. This function also takes a list of values to check contains. The following example removes the column chapters as it contains text apt. Use -contains() to ignore columns that contain text. The same function can also be used to remove variables by name range. This pipe can be used to write multiple operations that you can read left-to-right. Here I am using names() function which returns all column names and checks if a name is present in the list using %in% operator.įor example, x %>% f(y) converted into f(x, y) so the result from left-hand side is then “piped” into the right-hand side. You can also use the column names from the list to remove them from the R data frame. The following example removes multiple columns with indexes 2 and 3. Use vector to specify the column/vector indexes you want to remove from the R data frame. In the following example, removes all rows between 2 and 4 indexes, which ideally removes columns pages, names, and chapters. Mean_U1V2A2 = 7 mean_U2V2A2 = 8 mean_U3V2A2 = 10.5 ĭata9 = data.This notation also supports selecting columns by the range and using the negative operator to remove columns by range. S = 10 levels_U = 3 levels_V = 2 levels_A = 2 Long <- reshape(wide, direction="long") # reverses the effect completely (by using information stored within wide about the original reshaping)Īnd an example of a more complex long-to-wide transformation: creating a fictional data frame with one between-subject factor (A) and two within-subject factors (U, V), in long format, and then reshaping it whilst controlling the resulting column names carefully: Wide <- reshape(Indometh, v.names="conc", idvar="Subject", timevar="time", direction="wide") # Any gaps will be filled by "NA" values. # If v.names are not specified, all variables apart from idvar and timevar are assumed to vary, and are spread wide. VALUES of CONC are spread "wide" ( v.names). Columns are labelled with TIME ( timevar) Keep SUBJECT as the identifying variable, one per row ( idvar) ![]() We want to group them together by some variable that identifies an individual (group of observations). Indometh # one of the built-in R datasets. But let's glance at long-to-wide transformation: There are several methods reshape is powerful. one row per subject multiple observations/column per subject) and "long" format (one observation per row). Often, you need to transform data between "wide" format (e.g. In the R Commander, you can click the Data set button to select a data set, and then click the Edit data set button.įor more advanced data manipulation in R Commander, explore the Data menu, particularly the Data / Active data set and Data / Manage variables in active data set menus. Y <- edit(x) fix(x) # equivalent to x <- edit(x) X <- scan(" # the same, but from a URL (live)Įditing a variable, matrix, or data frame: X <- scan(filename) # do the same but reading from a file on disk X <- scan() # type in numbers, separated by spaces or newlines hit Enter twice to finish ![]() Typing stuff in note also that filenames and URLs are often interchangeable: Rm(x) # removes object "x" (if you know UNIX, this will be familiar) Other important object manipulation functions: ls() # list all objects (if you know UNIX, this will be familiar) # Another way, which has no residual effects: Search() # shows the current search path (will now include my.dataset)ĭetach(my.dataset) # when we've finished with it # By the way, get used to the R convention: my.dataset is just a variable name the dot doesn't mean anything special. # (otherwise a new variable called var is created that simply "overlies" the dataset. # Note that to change variables in the dataset, you still need to assign to dataset$var Making a data set visible on the main search path: attach(my.dataset) # we now don't need to use my.dataset$X, my.dataset$Y we can just use X and Y directly It's easy to sort data frames and to create new variables based on existing ones. there are lots of things you can do with this command see ?subset. X 3 # will make temp equal to the logical vector c(FALSE, FALSE, FALSE, TRUE, TRUE) by performing comparisons on each element of v ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |