How to filter column with percent data?

[Tom_Dale]

Hello! I'm new to R and could use some help.

I'm running into a problem that I'm sure has an easy answer, but I feel like an idiot for not finding it in R's documentation.

I'm trying to filter two columns; one has cells with numeric data and the other one has cells that are expressed in percentages.

My filter function is filtering out the former correctly, but it's not filtering the column with percents. Also, I'm not getting any error messages for the latter, which is why I'm so confused.

My project is here:

Posit Cloud - Do, share, teach, and learn data science

And the lines of code should be 25-26, or

best_trimmed_flavors_df <- trimmed_flavors_df %>%
filter("Cocoa\nPercent" >=75, Rating >= 3.9)

[HanOostdijk]

Hello Tom,

shouldn't you just filter on 0.75 (independent of your formatting which I can't see) ?

[Tom_Dale]

Yeah, I've tried that and it still wouldn't work.

[HanOostdijk]

Tom,

maybe this helps (but if not provide a reprex for your question) :

library(dplyr) 
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

toms_data <- data.frame(
   "Cocoa\nPercent" = c(80,45) ,
   Rating = c(4.5, 2.1)
 )
 
toms_data
#>   Cocoa.Percent Rating
#> 1            80    4.5
#> 2            45    2.1
 
toms_data %>%
  filter(Cocoa.Percent >=75, Rating >= 3.9)
#>   Cocoa.Percent Rating
#> 1            80    4.5
Created on 2021-08-28 by the reprex package (v2.0.0)

[Tom_Dale]

OK, so really all I'm trying to do is take a csv with over 1700 rows of data
shrink it to just a handful of most important observations.

cacao_df <-read_csv("cacao_cleaned.csv")

trimmed_cacao_df <- cacao_df %>%
  select(Rating,Company,  "Company\nLocation" , "Cocoa\nPercent" )

best_trimmed_cacao_df <- trimmed_cacao_df %>% 
 filter("Cocoa\nPercent" >= 0.75 & Rating >= 3.9)

OK, so really all I'm trying to do is take a csv with over 1700 rows of data
shrink it to just a handful of most important observations.

cacao_df <-read_csv("cacao_cleaned.csv")
trimmed_cacao_df <- cacao_df %>%
  select(Rating,Company,  "Company\nLocation" , "Cocoa\nPercent" )

The following is the line of code that seems to be tripping up. The console is filtering out the ratings per instructed, BUT the percentage column is not being filtered!
The original csv had a general text format, so I changed the format of the column
to number, but both formats were not being filtered. So I'm stuck.

best_trimmed_cacao_df <- trimmed_cacao_df %>% 
 filter("Cocoa\nPercent" >= 0.75 & Rating >= 3.9)
view(best_trimmed_cacao_df)

[Tom_Dale]

Ope, I found the solution!

Thanks for your help

My problem was using quotation marks for the column name in the code instead of backticks. Here's the correct code:

best_trimmed_cacao_df <- trimmed_cacao_df %>% 
 filter(`Cocoa\nPercent` >= 0.75, Rating >= 3.9)