How to Drop Columns by Passing Variable Name with Dplyr

how to drop columns by passing variable name with dplyr?

You can use one_of to find the column positions and then use - to drop it, select(-one_of(name)), if you check ?select, the usage is documented in the Drop variable section in the Examples:

name <- "drop"
a %>% select(-one_of(name))

# A tibble: 2 × 1
#   keep
#  <chr>
#1 hello
#2 world

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Or with select_, you need to paste - with the column names to drop them and pass the pasted column names to the .dots parameter if there are more than one column to be dropped:

name <- "drop"
a %>% select_(.dots = paste("-", name))

# A tibble: 2 × 1
#   keep
#  <chr>
#1 hello
#2 world

Remove columns with dplyr

Use modify_atand set columns to NULL which will remove them:

mtcars %>% modify_at(drop,~NULL)
#                    mpg cyl  hp    wt  qsec vs carb
# Mazda RX4         21.0   6 110 2.620 16.46  0    4
# Mazda RX4 Wag     21.0   6 110 2.875 17.02  0    4
# Datsun 710        22.8   4  93 2.320 18.61  1    1
# Hornet 4 Drive    21.4   6 110 3.215 19.44  1    1
# Hornet Sportabout 18.7   8 175 3.440 17.02  0    2
# Valiant           18.1   6 105 3.460 20.22  1    1
# ...

Closer to what you were trying, you could have tried magrittr::extract instead of dplyr::select

extract(mtcars,!names(mtcars) %in% drop) # same output

In dplyr, how to delete and rename columns that don't exist, manipulate all names, and name a new variable using a string?

1. I would use setNames for this:

iris %>% setNames(make.names(names(.)))

2. Include everything() as an argument for select:

iris %>% select(-matches("Width"), everything())
iris %>% select(-matches("X"), everything())

3. To my understanding there's no other shortcut than explicitly naming the string like you already do:

iris %>% mutate_("newcol" = 0)

R dplyr: Drop multiple columns

Check the help on select_vars. That gives you some extra ideas on how to work with this.

In your case:

iris %>% select(-one_of(drop.cols))

Drop data frame columns by name

There's also the subset command, useful if you know which columns you want:

df <- data.frame(a = 1:10, b = 2:11, c = 3:12)
df <- subset(df, select = c(a, c))

UPDATED after comment by @hadley: To drop columns a,c you could do:

df <- subset(df, select = -c(a, c))

Pass variable as column name to dplyr?

We can do this with data.table. We convert the 'data.frame' to 'data.table' (setDT(df)), grouped by the the row sequence, we get the value of the paste output, and assign (:=) it to a new column ('my.p').

library(data.table)
setDT(df)[, my.p:= get(paste0(max1, '.p')), 1:nrow(df)]
df
#    col1 col1.p col2 col2.p col3 col3.p max1 my.p
# 1:    a      1    a      6    c     11 col3   11
# 2:    b      2    c      7    d     12 col2    7
# 3:    c      3    l      8    e     13 col1    3
# 4:    d      4    c      9    f     14 col2    9
# 5:    c      5    l     10    g     15 col1    5
# 6:    a      1    a      6    c     16 col3   16
# 7:    b      2    c      7    d     17 col2    7
# 8:    c      3    l      8    e     18 col1    3
# 9:    d      4    c      9    f     19 col2    9
#10:    c      5    l     10    g     20 col1    5

Pass a string as variable name in dplyr::filter

!! or UQ evaluates the variable, so mtcars %>% filter(!!var == 4) is the same as mtcars %>% filter('cyl' == 4) where the condition always evaluates to false; You can prove this by printing !!var in the filter function:

mtcars %>% filter({ print(!!var); (!!var) == 4 })
# [1] "cyl"
#  [1] mpg  cyl  disp hp   drat wt   qsec vs   am   gear carb
# <0 rows> (or 0-length row.names)

To evaluate var to the cyl column, you need to convert var to a symbol of cyl first, then evaluate the symbol cyl to a column:

Using rlang:

library(rlang)
var <- 'cyl'
mtcars %>% filter((!!sym(var)) == 4)

#    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#1  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#2  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#3  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
# ...

Or use as.symbol/as.name from baseR:

mtcars %>% filter((!!as.symbol(var)) == 4)

mtcars %>% filter((!!as.name(var)) == 4)

Drop data frame columns by name

There's also the subset command, useful if you know which columns you want:

df <- data.frame(a = 1:10, b = 2:11, c = 3:12)
df <- subset(df, select = c(a, c))

UPDATED after comment by @hadley: To drop columns a,c you could do:

df <- subset(df, select = -c(a, c))

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