The built-in vector `letters`

contains the letters of the English alphabet. Use an indexing vector of integers to extract the letters that spell ‘cat’.

`cat <- letters[c(3, 1, 20)]`

The function `colors()`

returns all of the color names that R is aware of. What is the length of the vector returned by this function? (Use code to find the answer.)

`col_length <- length(colors())`

The function call `runif(1000, 0, 1)`

will draw 1000 numbers from a uniform distribution from 0 to 1, which simulates the p-values that you would get from 1000 experiments where the null hypothesis is true. Store the result of this call in `pvals`

. Create a logical vector called `is_sig`

that is `TRUE`

if the corresponding element of `pvals`

is less than .05, `FALSE`

otherwise (hint: vectorized operations from the last lession), then use this logical vector to pull out those p-values. Finally, calculate the proportion of those p-values that were significant.

```
pvals <- runif(1000, 0, 1)
is_sig <- pvals < .05
prop_sig <- length(pvals[is_sig]) / length(pvals)
# alternatively:
prop_sig <- mean(is_sig)
prop_sig <- mean(pvals < .05)
```

Create a tibble with the columns `name`

, `age`

, and `country`

of origin for 3 people you know.

```
# you can do this with the tibble function
people <- tibble(name = c("Lisa", "Ben", "Robbie"),
age = c(42, 43, 11),
country = c("US", "UK", "UK") )
# also note:
# you can type this in row by row, rather than column by column,
# using the 'tribble' function
people <- tribble(~name, ~age, ~country,
"Lisa", 42, "US",
"Ben", 43, "UK",
"Robbie", 11, "UK")
```

Convert the built-in base R `iris`

dataset to a tibble, and store it in the variable `iris2`

.

`iris2 <- as_tibble(iris)`

Create a tibble that has the structure of the table below, using the minimum typing possible. (Hint: `rep()`

). Store it in the variable `my_tbl`

.

ID | A | B | C |
---|---|---|---|

1 | A1 | B1 | C1 |

2 | A1 | B2 | C1 |

3 | A1 | B1 | C1 |

4 | A1 | B2 | C1 |

5 | A2 | B1 | C1 |

6 | A2 | B2 | C1 |

7 | A2 | B1 | C1 |

8 | A2 | B2 | C1 |

```
my_tbl <- tibble(ID = 1:8,
A = rep(c("A1", "A2"), each = 4),
B = rep(c("B1", "B2"), 4),
C = "C1")
```

Download the dataset disgust_scores.csv and read it into a table.

```
# change to the location to where you put your csv file
disgust <- read_csv("https://psyteachr.github.io/msc-data-skills/data/disgust_scores.csv")
```

Override the default column specifications to skip the `id`

column.

```
my_cols <- cols(
id = col_skip()
)
disgust_skip <- read_csv("https://psyteachr.github.io/msc-data-skills/data/disgust_scores.csv", col_types = my_cols)
```

How many rows and columns are in the dataset from question 7?

```
## dim() returns a vector c(rows, cols)
dimensions <- dim(disgust)
disgust_rows <- dimensions[1]
disgust_cols <- dimensions[2]
## nrow() returns the number of rows
disgust_rows <- nrow(disgust)
## ncol() returns the number of columns
disgust_cols <- ncol(disgust)
```

You’ve made it to the end. Make sure you are able to knit this document to HTML. You can check your answers below in the knit document.

Question | Answer |
---|---|

Question 1 | correct |

Question 2 | correct |

Question 3 | correct |

Question 4 | correct |

Question 5 | correct |

Question 6 | correct |

Question 7 | correct |

Question 8 | correct |

Question 9 | correct |