Problem Using Filter

[Elie_Tondreau]

I am trying to run this code:

     onlineta_city_hotels <- filter(hotel_bookings, 
                       (hotel=="City Hotel" & 
                         hotel_bookings$market_segment=="Online TA"))

but it gives this error:

       Error in filter(hotel_bookings, (hotel == "City Hotel" & hotel_bookings$market_segment ==  : 

object 'hotel' not found

I got the code from a coursera course and don't know how to make it work. Any help would be greatly appreciated.
N.B. It was not an exam question, just a practice activity, thanks!

[M_AcostaCH]

Hi @Elie_Tondreau , check this example for make a filter with dplyr:

library(dplyr)
onlineta_city_hotels %>% 
  select(hotel , market_segment)  %>%
  filter(hotel == "City Hotel" & market_segment == "Online TA")

The help could be better if you put a reproducible example of data:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[technocrat]

When getting an "object not found" error, the first thing to check if it exists. For a data frame, colnames does this

colnames(hotel_bookings)

and check for the variables hotel and market_segment. If they are, then the cause is probably that the dplyr library hasn't been loaded. Two ways to fix. Simplest

library(dplyr)

Alternatively,

dplyr::filter(hotel_bookings ...

There is a filter function in the {stats} library, which is loaded by default, so it's necessary to tell the script which to use. library(dplyr) does this by masking the {stats} function. dplyr::filter specifies the version to use.

When using {dplyr}, I prefer to write it left-to-right, starting with the data frame used as the input and ending up with the assignment to the new object

hotel_bookings %>% filter(hotel == "City Hotel" & market_segment == "Online TA")  -> onlineta_city_hotels

but usually you see

onlineta_city_hotels <- hotel_bookings %>% filter(hotel == "City Hotel" & market_segment == "Online TA")  

Two other ways to do the same are worth knowing about.

mtcars[mtcars$mpg > 20 & mtcars$cyl == 6,] 
#>                 mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4      21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag  21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1

library(data.table)
mt <- data.table(mtcars, keep.rownames = TRUE)
mt[mpg > 20 & cyl == 6]
#>                rn  mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> 1:      Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> 2:  Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> 3: Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1