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Mutating joins

Question 1A

Load data from disgust_scores.csv (disgust), personality_scores.csv (ocean) and users.csv (user). Each participant is identified by a unique user_id.

disgust <- read_csv("https://psyteachr.github.io/msc-data-skills/data/disgust_scores.csv")
ocean <- read_csv("https://psyteachr.github.io/msc-data-skills/data/personality_scores.csv")
user <- read_csv("https://psyteachr.github.io/msc-data-skills/data/users.csv")

Question 1B

Add participant data to the disgust table.

study1 <- left_join(disgust, user, by = "user_id")

Question 1C

Intermediate: Calculate the age of each participant on the date they did the disgust questionnaire and put this in a column called age_years in a new table called study1_ages. Round to the nearest tenth of a year.

study1_ages <- study1 %>%
  mutate(
    age = date - birthday,
    age_days = as.integer(age),
    age_years = round(age_days/365.25, 1)
  )

Question 2A

Add the participant data to the disgust data, but have the columns from the participant table first.

study2 <- right_join(user, disgust, by = "user_id")

Question 2B

Intermediate: How many times was the disgust questionnaire completed by each sex? Create a table called study2_by_sex that has two columns: sex and n.

study2_by_sex <- study2 %>%
  group_by(sex) %>%
  summarise(n = n())

Question 2C

Advanced: Make a graph of how many people completed the questionnaire each year.

study2 %>%
  mutate(year = substr(date, 1, 4)) %>%
  group_by(year) %>%
  summarise(times_completed = n()) %>%
  ggplot() +
  geom_col(aes(year, times_completed, fill = year)) +
  labs(
    x = "Year",
    y = "Times Completed"
  ) +
  guides(fill = FALSE)

Question 3A

Create a table with only disgust and personality data from the same user_id collected on the same date.

study3 <- inner_join(disgust, ocean, by = c("user_id", "date"))

Question 3B

Intermediate: Join data from the same user_id, regardless of date. Does this give you the same data table as above?

study3_nodate <- inner_join(disgust, ocean, by = c("user_id"))

Question 4

Create a table of the disgust and personality data with each user_id:date on a single row, containing all of the data from both tables.

study4 <- full_join(disgust, ocean, by = c("user_id", "date"))

Filtering joins

Question 5

Create a table of just the data from the disgust table for users who completed the personality questionnaire that same day.

study5 <- semi_join(disgust, ocean, by = c("user_id", "date"))

Question 6

Create a table of data from users who did not complete either the personality questionnaire or the disgust questionnaire. (Hint: this will require two steps; use pipes.)

study6 <- user %>%
  anti_join(ocean, by = "user_id") %>%
  anti_join(disgust, by = "user_id")

Binding and sets

Question 7

Load new user data from users2.csv. Bind them into a single table called users_all.

user2 <- read_csv("https://psyteachr.github.io/msc-data-skills/data/users2.csv")
users_all <- bind_rows(user, user2)

Question 8

How many users are in both the first and second user table?

both_n <- dplyr::intersect(user, user2) %>% nrow()

Question 9

How many unique users are there in total across the first and second user tables?

unique_users <- dplyr::union(user, user2) %>% nrow()

Question 10

How many users are in the first, but not the second, user table?

first_users <- dplyr::setdiff(user, user2) %>% nrow()

Question 11

How many users are in the second, but not the first, user table?

second_users <- dplyr::setdiff(user2, user) %>% nrow()

Answer Checks

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Question Answer
1A Question 1A correct
1B Question 1B correct
1C Question 1C correct
2A Question 2A correct
2B Question 2B correct
3A Question 3A correct
3B Question 3B correct
4 Question 4 correct
5 Question 5 correct
6 Question 6 correct
7 Question 7 correct
8 Question 8 correct
9 Question 9 correct
10 Question 10 correct
11 Question 11 correct