Filter rows which contain a certain string
The answer to the question was already posted by the @latemail in the comments above. You can use regular expressions for the second and subsequent arguments of filter like this:
dplyr::filter(df, !grepl("RTB",TrackingPixel))
Since you have not provided the original data, I will add a toy example using the mtcars data set. Imagine you are only interested in cars produced by Mazda or Toyota.
mtcars$type <- rownames(mtcars)
dplyr::filter(mtcars, grepl('Toyota|Mazda', type))
mpg cyl disp hp drat wt qsec vs am gear carb type
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
3 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
4 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
If you would like to do it the other way round, namely excluding Toyota and Mazda cars, the filter command looks like this:
dplyr::filter(mtcars, !grepl('Toyota|Mazda', type))
Solution
It is possible to use str_detect of the stringr package included in the tidyverse package. str_detect returns True or False as to whether the specified vector contains some specific string. It is possible to filter using this boolean value. See Introduction to stringr for details about stringr package.
library(tidyverse)
# ─ Attaching packages ──────────────────── tidyverse 1.2.1 ─
# ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
# ✔ tibble 1.4.2 ✔ dplyr 0.7.4
# ✔ tidyr 0.7.2 ✔ stringr 1.2.0
# ✔ readr 1.1.1 ✔ forcats 0.3.0
# ─ Conflicts ───────────────────── tidyverse_conflicts() ─
# ✖ dplyr::filter() masks stats::filter()
# ✖ dplyr::lag() masks stats::lag()
mtcars$type <- rownames(mtcars)
mtcars %>%
filter(str_detect(type, 'Toyota|Mazda'))
# mpg cyl disp hp drat wt qsec vs am gear carb type
# 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
# 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
# 3 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
# 4 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
The good things about Stringr
We should use rather stringr::str_detect() than base::grepl(). This is because there are the following reasons.
- The functions provided by the
stringrpackage start with the prefixstr_, which makes the code easier to read. - The first argument of the functions of
stringrpackage is always the data.frame (or value), then comes the parameters.(Thank you Paolo)
object <- "stringr"
# The functions with the same prefix `str_`.
# The first argument is an object.
stringr::str_count(object) # -> 7
stringr::str_sub(object, 1, 3) # -> "str"
stringr::str_detect(object, "str") # -> TRUE
stringr::str_replace(object, "str", "") # -> "ingr"
# The function names without common points.
# The position of the argument of the object also does not match.
base::nchar(object) # -> 7
base::substr(object, 1, 3) # -> "str"
base::grepl("str", object) # -> TRUE
base::sub("str", "", object) # -> "ingr"
Benchmark
The results of the benchmark test are as follows. For large dataframe, str_detect is faster.
library(rbenchmark)
library(tidyverse)
# The data. Data expo 09. ASA Statistics Computing and Graphics
# http://stat-computing.org/dataexpo/2009/the-data.html
df <- read_csv("Downloads/2008.csv")
print(dim(df))
# [1] 7009728 29
benchmark(
"str_detect" = {df %>% filter(str_detect(Dest, 'MCO|BWI'))},
"grepl" = {df %>% filter(grepl('MCO|BWI', Dest))},
replications = 10,
columns = c("test", "replications", "elapsed", "relative", "user.self", "sys.self"))
# test replications elapsed relative user.self sys.self
# 2 grepl 10 16.480 1.513 16.195 0.248
# 1 str_detect 10 10.891 1.000 9.594 1.281
This answer similar to others, but using preferred stringr::str_detect and dplyr rownames_to_column.
library(tidyverse)
mtcars %>%
rownames_to_column("type") %>%
filter(stringr::str_detect(type, 'Toyota|Mazda') )
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 4 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Created on 2018-06-26 by the reprex package (v0.2.0).
edit included the newer across() syntax
Here's another tidyverse solution, using filter(across()) or previously filter_at. The advantage is that you can easily extend to more than one column.
Below also a solution with filter_all in order to find the string in any column,
using diamonds as example, looking for the string "V"
library(tidyverse)
String in only one column
# for only one column... extendable to more than one creating a column list in `across` or `vars`!
mtcars %>%
rownames_to_column("type") %>%
filter(across(type, ~ !grepl('Toyota|Mazda', .))) %>%
head()
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 2 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 3 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 4 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 5 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 6 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
The now superseded syntax for the same would be:
mtcars %>%
rownames_to_column("type") %>%
filter_at(.vars= vars(type), all_vars(!grepl('Toyota|Mazda',.)))
String in all columns:
# remove all rows where any column contains 'V'
diamonds %>%
filter(across(everything(), ~ !grepl('V', .))) %>%
head
#> # A tibble: 6 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 4 0.3 Good J SI1 64 55 339 4.25 4.28 2.73
#> 5 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
#> 6 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
The now superseded syntax for the same would be:
diamonds %>%
filter_all(all_vars(!grepl('V', .))) %>%
head
I tried to find an across alternative for the following, but I didn't immediately come up with a good solution:
#get all rows where any column contains 'V'
diamonds %>%
filter_all(any_vars(grepl('V',.))) %>%
head
#> # A tibble: 6 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 2 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
#> 3 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> 4 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
#> 5 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
#> 6 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
Update: Thanks to user Petr Kajzar in this answer, here also an approach for the above:
diamonds %>%
filter(rowSums(across(everything(), ~grepl("V", .x))) > 0)