You do not need to have advanced knowledge of the R programming language to perform text analysis with quanteda because the package has a wide range of functions. However, you still need to understand a number of basic R commands.
R has three types of objects: vector, data frame and matrix. Since many of the quanteda objects behave similarly to these objects, you need to understand how to interact with them.
As a language for statistical analysis, R"s most basic objects are vectors. Vectors contain a set of values. In the examples below, vec_num is a numeric vector, while vec_char is a chracter vector. We use c() to combine elements of a vector and <- to assign a vector to a variable.
vec_num <- c(1, 5, 6, 3)
print(vec_num)
## [1] 1 5 6 3
vec_char <- c("apple", "banana", "mandarin", "melon")
print(vec_char)
## [1] "apple" "banana" "mandarin" "melon"
Once a vector is created, you can extract elements of vectors with the [] operator and index numbers of desired elements.
print(vec_num[1])
## [1] 1
print(vec_num[1:2])
## [1] 1 5
print(vec_char[c(1, 3)])
## [1] "apple" "mandarin"
You can apply arithmetical operations such as addition, subtraction, multiplication or division on numeric vectors. If only a single value is given for multiplication, for example, each element of the vector will be multiplied by the same value.
vec_num2 <- vec_num * 2
print(vec_num2)
## [1] 2 10 12 6
You can also compare elements of a vector by relational operators such as ==, >=, >, <=, <. The result of these operations will be a logical vector that contains either TRUE or FALSE.
vec_logi_gt5 <- vec_num >= 5
print(vec_logi_gt5)
## [1] FALSE TRUE TRUE FALSE
You cannot apply arithmetical operations on character vectors, but can apply the equality operator.
vec_logi_apple <- vec_char == "apple"
print(vec_logi_apple)
## [1] TRUE FALSE FALSE FALSE
You can also concatenate elements of character vectors using paste(). Since the two vectors in the example have the same length, elements in the same position of the vectors are concatenated.
vec_char2 <- paste(c("red", "yellow", "orange", "green"), vec_char)
print(vec_char2)
## [1] "red apple" "yellow banana" "orange mandarin" "green melon"
Finally, you can set names to elements of a numeric vector using names().
names(vec_num) <- vec_char
print(vec_num)
## apple banana mandarin melon
## 1 5 6 3
A data frame combines multiple vectors to construct a dataset. You can only combine vectors into a data frame if they have the same lengths. However, they can be different types. nrow() and ncol() show the number of rows (observations) and variables in a data frame.
dat_fruit <- data.frame(name = vec_char, count = vec_num)
print(dat_fruit)
## name count
## apple apple 1
## banana banana 5
## mandarin mandarin 6
## melon melon 3
print(nrow(dat_fruit))
## [1] 4
print(ncol(dat_fruit))
## [1] 2
You can use subset() to select records in the data frame.
dat_fruit_sub <- subset(dat_fruit, count >= 5)
print(dat_fruit_sub)
## name count
## banana banana 5
## mandarin mandarin 6
print(nrow(dat_fruit_sub))
## [1] 2
print(ncol(dat_fruit_sub))
## [1] 2
We use print() to show values and structures of objects in the examples, but you do not need to use the print() command in the console, because it is triggered automatically when objects are returned to the global environment.
Similar to a data frame, a matrix contains multi-dimensional data. In contrast to a data frame, its values must all be the same type.
mat <- matrix(c(1, 3, 6, 8, 3, 5, 2, 7), nrow = 2)
print(mat)
## [,1] [,2] [,3] [,4]
## [1,] 1 6 3 2
## [2,] 3 8 5 7
You can use colnames() or rownames() to set/retrieve names to rows or columns of a matrix.
colnames(mat) <- vec_char
print(mat)
## apple banana mandarin melon
## [1,] 1 6 3 2
## [2,] 3 8 5 7
rownames(mat) <- c("bag1", "bag2")
print(mat)
## apple banana mandarin melon
## bag1 1 6 3 2
## bag2 3 8 5 7
You can obtain the size of a matrix by dim() that returns a two-element numeric vector.
print(dim(mat))
## [1] 2 4
If a matrix has column and row names, you can extract rows or columns by their names.
print(mat["bag1", ])
## apple banana mandarin melon
## 1 6 3 2
print(mat[, "banana"])
## bag1 bag2
## 6 8
Finally, you can obtain marginals of matrix by colSums() or rowSums().
print(rowSums(mat))
## bag1 bag2
## 12 23
print(colSums(mat))
## apple banana mandarin melon
## 4 14 8 9
If you want to know the details of R commands, prepend ? to the command and execute. For example, ?subset() will show you how to use the subset function with different types of objects.