Hi, and welcome to community.rstudio.com! Without knowing what your data and code look like, it's a little hard to answer your question. To help you get the right help for your question, can you please turn it into a reprex (reproducible example)? This will ensure we're all looking at the same data and code. A guide for creating a reprex can be found here.
From a high level, filter() and select() are different verbs in the tidyverse. filter() operates on rows, whereas select() operates on columns.
For example, in the reprex below, I'm using the built-in mtcars dataset to illustrate using filter() to retain certain rows by a certain criterion of interest, or using select() to retain certain columns based on column names.
library(tidyverse)
str(mtcars)
#> 'data.frame': 32 obs. of 11 variables:
#> $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#> $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
#> $ disp: num 160 160 108 258 360 ...
#> $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
#> $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
#> $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
#> $ qsec: num 16.5 17 18.6 19.4 17 ...
#> $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
#> $ am : num 1 1 1 0 0 0 0 0 0 0 ...
#> $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
#> $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Filtering for cars with 6 cylinders
cars_6cyl <- mtcars %>% filter(cyl == 6) %>% head()
cars_6cyl
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 4 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 5 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 6 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Subsetting to a few columns of interest (mpg, cyl, and disp)
cars_subset <- mtcars %>% select(mpg, cyl, disp) %>% head()
cars_subset
#> mpg cyl disp
#> Mazda RX4 21.0 6 160
#> Mazda RX4 Wag 21.0 6 160
#> Datsun 710 22.8 4 108
#> Hornet 4 Drive 21.4 6 258
#> Hornet Sportabout 18.7 8 360
#> Valiant 18.1 6 225
Created on 2018-10-06 by the reprex package (v0.2.0).
I hope this helps! If not, can you please add a minimal reprex with your data and code?