Category: Interview Questions

To parse a date from its string representation in R, we should use the lubridate package of the tidyverse collection. This package offers various functions for parsing a string and extracting the standard date from it based on the initial date pattern in that string. These functions are ymd(), ymd_hm(), ymd_hms(), dmy(), dmy_hm(), dmy_hms(), mdy(), mdy_hm(), mdy_hms(), etc., where y, m, d, h, m, and s correspond to year, month, day, hours, minutes, and seconds, respectively.

For example, if we run the dmy() function passing to it any of the strings “05-11-2023”, “05/11/2023” or “05.11.2023”, representing the same date, we’ll receive the same result: 2023-11-05. This is because in all three cases, despite having different dividing symbols, we actually have the same pattern: the day followed by the month followed by the year.

1. Using the transform() and ifelse() functions of the base R:

df <- data.frame(col_1 = c(1, 3, 5, 7),  col_2 = c(8, 6, 4, 2))
print(df)
​
# Adding the column col_3 to the data frame df
df <- transform(df, col_3 = ifelse(col_1 < col_2, col_1 + col_2, col_1 * col_2))
print(df)Powered By 

Output:

  col_1 col_2
1     1     8
2     3     6
3     5     4
4     7     2
  col_1 col_2 col_3
1     1     8     9
2     3     6     9
3     5     4    20
4     7     2    14Powered By 

2. Using the with() and ifelse() functions of the base R:

df <- data.frame(col_1 = c(1, 3, 5, 7),  col_2 = c(8, 6, 4, 2))
print(df)
​
# Adding the column col_3 to the data frame df
df["col_3"] <- with(df, ifelse(col_1 < col_2, col_1 + col_2, col_1 * col_2))
print(df)Powered By 

Output:

  col_1 col_2
1     1     8
2     3     6
3     5     4
4     7     2
  col_1 col_2 col_3
1     1     8     9
2     3     6     9
3     5     4    20
4     7     2    14Powered By 

3. Using the apply() function of the base R:

df <- data.frame(col_1 = c(1, 3, 5, 7),  col_2 = c(8, 6, 4, 2))
print(df)
​
# Adding the column col_3 to the data frame df
df["col_3"] <- apply(df, 1, FUN = function(x) if(x[1] < x[2]) x[1] + x[2] else x[1] * x[2])
print(df) Powered By 

Output:

  col_1 col_2
1     1     8
2     3     6
3     5     4
4     7     2
  col_1 col_2 col_3
1     1     8     9
2     3     6     9
3     5     4    20
4     7     2    14Powered By 

4. Using the mutate() function of the dplyr package and the ifelse() function of the base R:

df <- data.frame(col_1 = c(1, 3, 5, 7),  col_2 = c(8, 6, 4, 2))
print(df)
​
# Adding the column col_3 to the data frame df
df <- mutate(df, col_3 = ifelse(col_1 < col_2, col_1 + col_2, col_1 * col_2))
print(df)Powered By 

Output:

  col_1 col_2
1     1     8
2     3     6
3     5     4
4     7     2
  col_1 col_2 col_3
1     1     8     9
2     3     6     9
3     5     4    20
4     7     2    14

The subset() function in R is used for extracting rows and columns from a data frame or a matrix, or elements from a vector, based on certain conditions, e.g.: subset(my_vector, my_vector > 10).

Instead, the sample() function in R can be applied only to vectors. It extracts a random sample of the predefined size from the elements of a vector, with or without replacement. For example, sample(my_vector, size=5, replace=TRUE)

The str() function returns the structure of an R object and the overall information about it, the exact contents of which depend on the data structure of that object. For example, for a vector, it returns the data type of its items, the range of item indices, and the item values (or several first values, if the vector is too long). For a data frame, it returns its class (data.frame), the number of observations and variables, the column names, the data type of each column, and several first values of each column.

The summary() function returns the summary statistics for an R object. It’s mostly applied to data frames and matrices, for which it returns the minimum, maximum, mean, and median values, and the 1st and 3rd quartiles for each numeric column, while for the factor columns, it returns the count of each level.

The next statement is used to skip a particular iteration and jump to the next one if a certain condition is met. The break statement is used to stop and exit the loop at a particular iteration if a certain condition is met. When used in one of the inner loops of a nested loop, this statement exits only that inner loop.

Both next and break statements can be used in any type of loops in R: for loops, while loops, and repeat loops. They can also be used in the same loop, e.g.:

for(i in 1:10) {
if(i < 5)
    next
if(i == 8)
    break
print(i)}</code>Powered By </code></pre>


Output:



[1] 5
[1] 6
[1] 7