Data transformation with dplyr

Aims

This tutorial allows you to explore dplyr functionality based on the individual lectures. Every question can be answered with a combination of pipes (|>). Do not use temporary variables and any statements outside of the range of the tidyverse.

Part one - Base dplyr

Import the data from the website.

Assign to the name judgments

Use glimpse() to identify columns and column types in judgments.

Select all columns that refer to the STAI questionnaire.

Retrieve all subjects younger than 20 which are in the stress group.

The column for the group is condition.

Arrange all observations by STAI_pre so that the subject with the lowest stress level is on top.

What is the subject in question?

Abbreviate the gender column such that only the first character remains

Create a new STAI_pre_category column.

Use case_when() or case_match() to categorize values in STAI_pre as “low”, “normal” or “high”. For values < 25 in STAI_pre assign “low”, for values > 65 assign “high”, and for all other values assign “normal”.

To easily see the new column, use relocate() to move it to the first position of the dataframe.

Rescale values in the REI group

Divide all entries in the REI questionnaire by 5, the maximal value, so the values will be between 0 and 1.

across() allows modification of multiple columns in one go.

Part two - Grouping and summarizing

Use judgments to compute basic statistics for all moral dilemma columns considering the conditions.

1. Compute the mean, the median, the standard deviation as well as min and max values.
2. Find meaningful short names for the functions such as med for median().
3. Assign judgments_condition_stats to the results.

Sort by groups

1. Find the number of subjects by age, gender and condition, e.g. how many 20 years of age females are in the stress group.
2. Sort the resulting tibble such that the condition that contains the most populous group is sorted first (i.e. stress or control appear together).
3. Ensure that the resulting tibble does not contain groups.

Part three - joins and pivots

Judgements reprise

Pivoting for readability

Take the results from above that are available as judgments_condition_stats and bring the data into a more readable format.

1. Make the table longer. After this step, your tibble should contain three columns:

• The condition
• the name of the moral dilemma and stats (in one column)
• the values of the stats by moral dilemma.

2. Split the moral dilemma and stats column. You can use separate_wider_delim or mutate to create a column that contains the moral dilemma (dog, trolley, etc) and the stats (median, min, etc.).
3. Make the table wider again and move the stats to individual columns.

The final table should look something like this.

condition	moral_dilemma	mean	sd	…
control	dog	7.24	2.23	…
stress	wallet	7.4	2.01	…

Genetic variants

The tribble below contains changes of the sequence of a gene. The format in the input is the expected sequence (the reference allele), the position and the variant, commonly called alternative allele.

In T6G, T is the reference allele, 6 is the position (along the gene) and G is the variant allele.

variants <- tribble(
  ~sampleid, ~var1, ~var2, ~var3,
  "S1", "A3T", "T5G", "T6G",
  "S2", "A3G", "T5G", NA,
  "S3", "A3T", "T6C", "G10C",
  "S4", "A3T", "T6C", "G10C"
)

Clean the table of genetic variants such that all variants appear as a column labeled by their position.

The table should look something like this.

sampleid	3	5	6	…
S1	T	G	G	…
S2	G	G	NA	…
S3	…	…	…	…

Select relevant variants

In this table the variants are labeled according to their effect on stability of the gene product.

variant_significance <- tribble(
  ~variant, ~significance,
  "A3T", "unknown",
  "A3G", "damaging",
  "T5G", "benign",
  "T6G", "damaging",
  "T6C", "benign",
  "G10C", "unknown"
)

Identify the subjects in the table variants that carry variants labeled as damaging in variant_significance table.

The final output should be vector of sample ids.

You can use several join flavours and the %in% operator to achieve the same result.

Try using semi_join and the is.element() function!