Quiz 1

Classwork 5

Descriptive Statistics

The following provides the percentage of the correct answers for each question in Quiz 1:

Question 1. data.frame

In a data frame, what form does an observation take?

Answer: Row

• This slide in Lecture 7 explains what makes a data.frame tidy. In our course, all given data.frames are tidy.
  • It’s been a while since we discussed variable-column, observation-row, and value-cell terminologies in a tidy data.frame, but these concepts are crucial for conducting data analysis.
• In a data.frame:
  • A variable takes a coulmn
  • An observation takes a row
  • A value takes a cell

Question 2. nrow()

___(df)

• Fill in the blank (___) to calculate the number of observations in the data.frame, df.

Answer: nrow

nrow(df)

• This slide in Lecture 8 and a couple of class R scripts explain nrow().

• You might have difficulty in this question if you do not recall nrow().

  • Full credit is given to answers that imply counting observations.

Question 3. >= (Greater-than-or-equal-to operator)

df_filtered <- df |> filter(num __ 9)

• Fill in the blank (___) to keep observations, in which the value of the num variable is greater than or equal to 9.

Answer: >=

df_filtered <- df |> filter(num >= 9)

• This slide in Lecture 14 and a couple of class R scripts explain this inequality operator.

Question 4. ! (Negation operator)

non_dec <-  df |> filter( ___(month == 12) )

• Fill in the blank (___) to keep observations, in which the value of the month variable is not equal to 12 in the data.frame df.

Answer: !

non_dec <-  df |> filter( !(month == 12) )

• This slide in Lecture 14 and Line 56-57 in this class R script explain a negation operator, ! (not).

• A more natural way to write this line is using != (not-equal operator):

non_dec <-  df |> filter( month != 12 )

Question 5. & (And operator)

df_filtered <- df |> filter(subject == "DANL" ___ number == 101)

• Fill in the blank (___) to keep observations, in which the value of the subject variable is “DANL” and the value of the number variable is 101.

Answer: & (,)

df_filtered <- df |> filter(subject == "DANL" & number == 101)
df_filtered <- df |> filter(subject == "DANL", number == 101)

• This slide and this slide in Lecture 14 explain logical operation, & (and).

Discussion

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