r training names and values

2.names.R

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+#--------------------------------------------
+# Advanced R technical session---------------
+# Chapter 2: Names & Values -----------------
+#--------------------------------------------
+
+# load packages
+library(lobstr)
+
+# 1. Exercises
+
+#  a. Difference between objects
+a <- 1:10
+b <- a
+c <- b
+d <- 1:10
+
+
+
+#  b. Do these functions point to the same function object?
+mean
+base::mean
+get("mean")
+evalq(mean)
+match.fun("mean")
+
+#  c. By default, base R data import functions,
+#      like read.csv(), will automatically convert
+#      non-syntactic names to syntactic ones.
+#      Why might this be problematic? What option
+#      allows you to suppress this behaviour?
+
+#  d. What rules does make.names() use to convert
+#      non-syntactic names into syntactic ones?
+
+#  e. I slightly simplified the rules that govern
+#     syntactic names. Why is .123e1 not a syntactic name?
+#     Read `?make.names` for the full details.
+
+
+# 2. Exercises
+
+# a. Why is `tracemem(1:10)` not useful?
+
+# b. Explain why tracemem() shows two copies when you run this code
+x <- c(1L, 2L, 3L)
+tracemem(x)
+x[[3]] <- 4
+
+# c. Sketch out the relationship between the following objects:
+a <- 1:10
+b <- list(a, a)
+c <- list(b, a, 1:10)
+
+# d. What happens when you run this code?
+x <- list(1:10)
+x[[2]] <- x
+x
+
+
+# 3. Exercises
+
+# a. In the following example, why are object.size(y) and obj_size(y)
+#   so radically different? Consult the documentation of object.size().
+y <- rep(list(runif(1e4)), 100)
+object.size(y)
+#> 8005648 bytes
+obj_size(y)
+#> 80,896 B
+
+# b. Take the following list. Why is its size somewhat misleading?
+funs <- list(mean, sd, var)
+obj_size(funs)
+#> 17,608 B
+obj_size(funs$mean)
+
+# c. Predict the output of the following code:
+a <- runif(1e6)
+obj_size(a)
+
+b <- list(a, a)
+obj_size(b)
+obj_size(a, b)
+
+b[[1]][[1]] <- 10
+obj_size(b)
+obj_size(a, b)
+
+b[[2]][[1]] <- 10
+obj_size(b)
+obj_size(a, b)
+
+
+# 4. Exercises
+
+# a. Explain why the following code doesn’t create a circular list.
+x <- list()
+x[[1]] <- x
+x
+
+# b. Wrap the two methods for subtracting medians into two functions,
+#    then use the ‘bench’ package17 to carefully compare their speeds.
+#    How does performance change as the number of columns increase?
+
+# c. What happens if you attempt to use tracemem() on an environment?
+
+
+