Hi, and welcome!
A couple of preliminaries: please see the homework policy and the usefulness of a reproducible example, called a reprex.
The not found error. Pretty much everything in R is an object, including built-in and user created functions. When you invoke an object by name, such as Survey_number that name has to exist in either the global environment or the local environment. Most of the time, you can see what's in your local environment with the simple command ls.
# create a couple of objects
v <- seq(1,10,1)
string <- "This is a string"
# see if they appear in the local environment
ls()
#> [1] "string" "v"
In the case of subset, it has to be within the data argument.
Created on 2020-01-12 by the reprex package (v0.3.0)
You have the right syntax
mtcars
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
dat <- subset(mtcars, hp > 200)
dat
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
#> Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
#> Ford Pantera L 15.8 8 351 264 4.22 3.170 14.50 0 1 5 4
#> Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
Created on 2020-01-12 by the reprex package (v0.3.0)
It looks like you want
data_234 <- Scat_Hab_Data_1_, Survey_number > 1)
BTW, data() is a built in function in R, and to avoid potential confusion, using dat or some descriptive name is preferable. Another example is df, which can be substituted with df. or df_ruminants, for example.
Finally, a word on how to think of R. There's a lot of programming and computer science under the hood, but it's in service of making the user interface functional. That means just what you'd expect--school algebra writ large, f(x) = y.
When reading help() for any function, you're looking for the arguments, referred to as inputs and the result, referred to as output. Sometimes there's more to the output than meets the eye on the screen. For example, a linear regression model.
# create the model
fit <- lm(hp ~ wt, data = mtcars)
# display it
fit
#>
#> Call:
#> lm(formula = hp ~ wt, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt
#> -1.821 46.160
# display more
summary(fit)
#>
#> Call:
#> lm(formula = hp ~ wt, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -83.430 -33.596 -13.587 7.913 172.030
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -1.821 32.325 -0.056 0.955
#> wt 46.160 9.625 4.796 4.15e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 52.44 on 30 degrees of freedom
#> Multiple R-squared: 0.4339, Adjusted R-squared: 0.4151
#> F-statistic: 23 on 1 and 30 DF, p-value: 4.146e-05
# look deeper
str(fit)
#> List of 12
#> $ coefficients : Named num [1:2] -1.82 46.16
#> ..- attr(*, "names")= chr [1:2] "(Intercept)" "wt"
#> $ residuals : Named num [1:32] -9.12 -20.89 -12.27 -36.58 18.03 ...
#> ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
#> $ effects : Named num [1:32] -829.79 251.47 -8.14 -35.51 18.33 ...
#> ..- attr(*, "names")= chr [1:32] "(Intercept)" "wt" "" "" ...
#> $ rank : int 2
#> $ fitted.values: Named num [1:32] 119 131 105 147 157 ...
#> ..- attr(*, "names")= chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
#> $ assign : int [1:2] 0 1
#> $ qr :List of 5
#> ..$ qr : num [1:32, 1:2] -5.657 0.177 0.177 0.177 0.177 ...
#> .. ..- attr(*, "dimnames")=List of 2
#> .. .. ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
#> .. .. ..$ : chr [1:2] "(Intercept)" "wt"
#> .. ..- attr(*, "assign")= int [1:2] 0 1
#> ..$ qraux: num [1:2] 1.18 1.05
#> ..$ pivot: int [1:2] 1 2
#> ..$ tol : num 1e-07
#> ..$ rank : int 2
#> ..- attr(*, "class")= chr "qr"
#> $ df.residual : int 30
#> $ xlevels : Named list()
#> $ call : language lm(formula = hp ~ wt, data = mtcars)
#> $ terms :Classes 'terms', 'formula' language hp ~ wt
#> .. ..- attr(*, "variables")= language list(hp, wt)
#> .. ..- attr(*, "factors")= int [1:2, 1] 0 1
#> .. .. ..- attr(*, "dimnames")=List of 2
#> .. .. .. ..$ : chr [1:2] "hp" "wt"
#> .. .. .. ..$ : chr "wt"
#> .. ..- attr(*, "term.labels")= chr "wt"
#> .. ..- attr(*, "order")= int 1
#> .. ..- attr(*, "intercept")= int 1
#> .. ..- attr(*, "response")= int 1
#> .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
#> .. ..- attr(*, "predvars")= language list(hp, wt)
#> .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
#> .. .. ..- attr(*, "names")= chr [1:2] "hp" "wt"
#> $ model :'data.frame': 32 obs. of 2 variables:
#> ..$ hp: num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
#> ..$ wt: num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
#> ..- attr(*, "terms")=Classes 'terms', 'formula' language hp ~ wt
#> .. .. ..- attr(*, "variables")= language list(hp, wt)
#> .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
#> .. .. .. ..- attr(*, "dimnames")=List of 2
#> .. .. .. .. ..$ : chr [1:2] "hp" "wt"
#> .. .. .. .. ..$ : chr "wt"
#> .. .. ..- attr(*, "term.labels")= chr "wt"
#> .. .. ..- attr(*, "order")= int 1
#> .. .. ..- attr(*, "intercept")= int 1
#> .. .. ..- attr(*, "response")= int 1
#> .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
#> .. .. ..- attr(*, "predvars")= language list(hp, wt)
#> .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
#> .. .. .. ..- attr(*, "names")= chr [1:2] "hp" "wt"
#> - attr(*, "class")= chr "lm"
Created on 2020-01-12 by the reprex package (v0.3.0)