Just following on Matt and Dirk. If you want to recreate your existing data frame without changing the global option, you can recreate it with an apply statement:
bob <- data.frame(lapply(bob, as.character), stringsAsFactors=FALSE)
This will convert all variables to class "character", if you want to only convert factors, see Marek's solution below.
As @hadley points out, the following is more concise.
bob[] <- lapply(bob, as.character)
In both cases, lapply
outputs a list; however, owing to the magical properties of R, the use of []
in the second case keeps the data.frame class of the bob
object, thereby eliminating the need to convert back to a data.frame using as.data.frame
with the argument stringsAsFactors = FALSE
.
Another question asked specifically how to perform multiple left joins using dplyr in R . The question was marked as a duplicate of this one so I answer here, using the 3 sample data frames below:
x <- data.frame(i = c("a","b","c"), j = 1:3, stringsAsFactors=FALSE)
y <- data.frame(i = c("b","c","d"), k = 4:6, stringsAsFactors=FALSE)
z <- data.frame(i = c("c","d","a"), l = 7:9, stringsAsFactors=FALSE)
Update June 2018: I divided the answer in three sections representing three different ways to perform the merge. You probably want to use the purrr
way if you are already using the tidyverse packages. For comparison purposes below, you'll find a base R version using the same sample dataset.
1) Join them with reduce
from the purrr
package:
The purrr
package provides a reduce
function which has a concise syntax:
library(tidyverse)
list(x, y, z) %>% reduce(left_join, by = "i")
# A tibble: 3 x 4
# i j k l
# <chr> <int> <int> <int>
# 1 a 1 NA 9
# 2 b 2 4 NA
# 3 c 3 5 7
You can also perform other joins, such as a full_join
or inner_join
:
list(x, y, z) %>% reduce(full_join, by = "i")
# A tibble: 4 x 4
# i j k l
# <chr> <int> <int> <int>
# 1 a 1 NA 9
# 2 b 2 4 NA
# 3 c 3 5 7
# 4 d NA 6 8
list(x, y, z) %>% reduce(inner_join, by = "i")
# A tibble: 1 x 4
# i j k l
# <chr> <int> <int> <int>
# 1 c 3 5 7
2) dplyr::left_join()
with base R Reduce()
:
list(x,y,z) %>%
Reduce(function(dtf1,dtf2) left_join(dtf1,dtf2,by="i"), .)
# i j k l
# 1 a 1 NA 9
# 2 b 2 4 NA
# 3 c 3 5 7
3) Base R merge()
with base R Reduce()
:
And for comparison purposes, here is a base R version of the left join based on Charles's answer.
Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = "i", all.x = TRUE),
list(x,y,z))
# i j k l
# 1 a 1 NA 9
# 2 b 2 4 NA
# 3 c 3 5 7
Best Answer
I would probably do this like so:
(I use
%ni%
a lot, so I have it built into my .Rprofile already.)