Filter data table by dynamic column name

Question

lets say I have a data.table with columns A, B and C

I\'d like to write a function that applies a filter (for example A>1) but \"A\" needs to be dynamic (the functio

Answer 1

Why write a function? You can do this...

Specifically:

d.new=d[d$A>1,]

where d is the dataframe d$A is the variable and d.new is a new dataframe.

More generally:

data=d #data frame
variable=d$A #variable 
minValue=1 #minimum value
d.new=data[variable>minValue,] #create new data frame (d.new) filtered by min value

To create a new column:

If you don't want to actually create a new dataframe but want to create an indicator variable you can use ifelse. This is most similar to coloring rows as shown in your example. Code below:

d$indicator1=ifelse(d$X1>0,1,0)

Answer 2

You can try

f1 <- function(dat, colName){dat[eval(as.name(colName))>1]}
setDT(df1)
f1(df1, 'A')
f1(df1, 'B')

If you need to make the value also dynamic

f2 <- function(dat, colName, value){dat[eval(as.name(colName))>value]}
f2(df1, 'A', 1)
f2(df1, 'A', 5)

data

set.seed(24)
df1 <- data.frame(A=sample(-5:10, 20, replace=TRUE), 
      B=rnorm(20), C=LETTERS[1:20], stringsAsFactors=FALSE) 

Answer 3

If your data is

a <- c(1:9)
b <- c(10:18)
# create a data.frame
df <- data.frame(a,b)
# or a data.table
dt <- data.table(a,b)

you can store your condition(s) in a variable x

x <- quote(a >= 3)

and filter the data.frame using dplyr (subsetting with [] won't work)

library(dplyr)
filter(df, x)

or using data.table as suggested by @Frank

library(data.table)
dt[eval(x),]

Answer 4

Try:

dt = data.table(A=c(1,1,2,3,1), B=c(4,5,1,1,1))

f=function(dt, colName) dt[dt[[colName]]>1,]
#> f(dt, 'A')
#   A B
#1: 2 1
#2: 3 1