Everything that exists is an object in R

According to John Chambers, everything that exists is an object in R and everything that happens is a function call. This summarizes the philosophy of R. Knowing this, we can understand R better.

data-science, r, vs-code

Published

14 May 2023

The author of R, John Chambers, said that everything that exists is an object in R and everything that happens is a function call. This summarizes the philosophy of R. Knowing this, we can understand R better.

I came across this statement when I notice the following code:

print(labels_str)
# keine Stutzung       Stutzung 
#              0              1 
typeof(labels_str)
# double
as.character(labels_str)
# "0" "1"

As a Python user, I was surprised by the output of typeof(labels_str). I thought it should be character instead of double. Then I found the following explanation from Stackoverflow.

Unlike Python, R is a functional programming language. This means that everything that happens is a function call. This is the reason why you will never see the following code in R:

df.head()

Instead, you will see:

head(df)

Unlike Python, everything that exists is an object in R. This means you can do the following:

obj <- 1:3
names(obj) <- c("a", "b", "c")
print(obj)
# a b c
# 1 2 3

You can never do this in Python. That’s why in R, you can do the following:

is.double(labels_str)
# TRUE
print(labels_str)
# keine Stutzung       Stutzung
#              0              1
as.data.frame(labels_str)
#                labels_str
# keine Stutzung          0
# Stutzung                1

The attributes of a variable can be queried with attributes and str.

Specially treated attributes (attribute names) are:

class (a character vector with the classes that an object inherits from). comment
dim (which is used to implement arrays)
dimnames
names (to label the elements of a vector or a list).
row.names
tsp (to store parameters of time series, start, stop and frequencies)
levels (for factors)

The class in R is very subtle. One could understand it as attributes of an object with flexible types. To learn more this, one could read Advanced R.

An example

stata_data <- haven::read_dta("../data/innovation_survey/extmidp21.dta",
                                encoding = "windows-1252")

str(stata_data)
# tibble [5,083 × 284] (S3: tbl_df/tbl/data.frame)
#  $ id          : chr [1:5083] "300127" "301003" "301078" "301084" ...
#   ..- attr(*, "label")= chr "Identifikation externe"
#   ..- attr(*, "format.stata")= chr "%10s"
#  $ branche     : dbl+lbl [1:5083]  9,  8, 10,  1,  1,  9,  1,  6,  9,  1,  2, 10,  9,  ...
#    ..@ label       : chr "Einteil. in 21 Wirtschaftszweige"
#    ..@ format.stata: chr "%38.0g"
#    ..@ labels      : Named num [1:21] 1 2 3 4 5 6 7 8 9 10 ...
sapply(stata_data, attributes) %>% 
    as.data.table() %>%
    # add one row with the variable names
    rbind(as.list(names(stata_data))) %>%
    # transpose the data.table
    t() %>%
    # convert to data.table
    as.data.table() %>%
    # select columns
    .[, c(5, 1, 3, 4)] %>%
    # set names
    setnames(c("variable", "labels", "type", "values")) %>%
    kable()

sapply(stata_data, attributes) %>%
    # save results as text
    capture.output(.) %>%
    # save as text file
    cat(file = "foo.txt", sep = "\n")