I have a data.frame with character data in one of the columns. I would like to filter multiple options in the data.frame from the same column. Is there an easy way to do this that I'm missing?
Example: data.frame name = dat
days name 88 Lynn 11 Tom 2 Chris 5 Lisa 22 Kyla 1 Tom 222 Lynn 2 Lynn
I'd like to filter out Tom and Lynn for example.
When I do:
target
I get this error:
longer object length is not a multiple of shorter object length
You need %in% instead of ==:
library(dplyr) target % filter(name %in% target)
Produces
days name 1 88 Lynn 2 11 Tom 3 1 Tom 4 222 Lynn 5 2 Lynn
To understand why, consider what happens here:
dat$name == target # [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
Basically, we're recycling the two length target vector four times to match the length of dat$name. In other words, we are doing:
Lynn == Tom Tom == Lynn Chris == Tom Lisa == Lynn ... continue repeating Tom and Lynn until end of data frame
In this case we don't get an error because I suspect your data frame actually has a different number of rows that don't allow recycling, but the sample you provide does (8 rows). If the sample had had an odd number of rows I would have gotten the same error as you. But even when recycling works, this is clearly not what you want. Basically, the statement dat$name == target is equivalent to saying:
return TRUE for every odd value that is equal to "Tom" or every even value that is equal to "Lynn".
It so happens that the last value in your sample data frame is even and equal to "Lynn", hence the one TRUE above.
To contrast, dat$name %in% target says:
for each value in dat$name, check that it exists in target.
Very different. Here is the result:
[1] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
Note your problem has nothing to do with dplyr, just the mis-use of ==.
Using the base package:
df
Output:
days name 1 88 Lynn 2 11 Tom 6 1 Tom 7 222 Lynn 8 2 Lynn
Using sqldf:
library(sqldf) # Two alternatives: sqldf('SELECT * FROM df WHERE name = "Tom" OR name = "Lynn"') sqldf('SELECT * FROM df WHERE name IN ("Tom", "Lynn")')
This can be achieved using dplyr package, which is available in CRAN. The simple way to achieve this:
Install dplyr package.
library(dplyr) df
Explanation:
So, once we’ve downloaded dplyr, we create a new data frame by using two different functions from this package:
filter: the first argument is the data frame; the second argument is the condition by which we want it subsetted. The result is the entire data frame with only the rows we wanted. select: the first argument is the data frame; the second argument is the names of the columns we want selected from it. We don’t have to use the names() function, and we don’t even have to use quotation marks. We simply list the column names as objects.
