Dataset description

Our fake study has 3 primary independent variables:

Load the data

1. The text file containing the data is called priming.txt. It is available at http://nathanieldphillips.com/wp-content/uploads/2016/10/priming.txt. Load the data into R by running the following:

priming <- read.table("http://nathanieldphillips.com/wp-content/uploads/2016/10/priming.txt")

Here is how the data should look:

Understanding and cleaning the data

2. Get to know the data using View(), summary(), head() and str().

3. Look at the names of the dataframe with names(). Those aren’t very informative are they? Change the names to the correct values (make sure to use the naming scheme I describe in the dataset description).

names(priming) <- c("id", "sex", "age", "attention", "prime", "prime.duration", "grandparents", "walk")

Applying functions to columns

4. What was the mean participant age?

mean(priming$age)

## [1] 21.996

4b. Create a new vector object called age.v that contains the age data. Calculate the mean age from this vector. Do you get the same result as you did in the previous question?

age.v <- priming$age
mean(age.v)

## [1] 21.996

5. What was the median walking time?

median(priming$walk)

## [1] 34.7

6. How many females were there? How many males?

sum(priming$sex == "m")

## [1] 248

7. What percent of participants passed the attention check (Hint: To calculate a percentage from a 0, 1 variable, use mean())

mean(priming$attention)

## [1] 0.886

8. Walking time is currently in seconds. Add a new column to the dataframe called walk.m That shows the walking time in minutes rather than seconds.

priming$walk.m <- priming$walk / 60

Indexing and subsettting dataframes

Try to split your answers to these problems into two steps

Step 1: Index or subset the original data and store as a new object with a new name.

Step 2: Calculate the appropriate summary statistic using the new, subsetted object you just created.

9. What were the sexes of the first 10 participants?

priming$sex[1:10]

##  [1] m m f m m m f m f m
## Levels: f m

10. What was the data for the 50th participant?

priming[50,]

##    id sex age attention   prime prime.duration grandparents walk    walk.m
## 50 50   m  21         1 elderly              1         none 34.3 0.5716667

11. What was the mean walking time for the elderly prime condition?

mean(priming$walk[priming$prime == "elderly"])

## [1] 39.17612

12. What was the mean walking time for the neutral prime condition?

mean(priming$walk[priming$prime == "neutral"])

## [1] 26.50167

13. What was the mean walking time for participants less than 23 years old?

mean(priming$walk[priming$age < 23])

## [1] 28.8359

14. What was the mean walking time for females with a close relationship with their grandparents?

mean(priming$walk[priming$sex == "f" & priming$grandparents == "yes"])

## [1] 34.22515

15. What was the mean walking time for males over 21 years old without a close relationship with their grandparents?

mean(priming$walk[priming$sex == "m" & priming$age > 21 & priming$grandparents == "none"])

## [1] 25.76126

Checkpoint! If you got this far you are doing great!

Creating new dataframe objects

16. Create a new dataframe called priming.simple that only contains the columns id, prime, and walk

priming.simple <- priming[c("id", "prime", "walk")]

17. Some of the data don’t make any sense. For example, some walking times are negative, some prime values aren’t correct, and some prime.duration values weren’t part of the original study plan. Create a new dataframe called priming.c (aka., priming clean) that only includes rows with valid values for each column – do this by looking for an few strange values in each column, and by looking at the original dataset description. Additionally, only include participants who passed the attention check. Here’s a skeleton of how your code should look

# Create priming.c, a subset of the original priming data
#  (replace __ with the appropriate values)
priming.c <- subset(priming,
                    subset = sex %in% c(_____) & 
                             age > ____ &
                             attention == ___ &
                             prime %in% c(___) &
                             prime.duration %in% c(___) &
                             grandparents %in% c(___) &
                             walk > ___ )

# Create priming.c, a subset of the original priming data
#  (replace __ with the appropriate values)
priming.c <- subset(priming,
                    subset = sex %in% c("m", "f") & 
                             age > 18 &
                             attention == 1 &
                             prime %in% c("elderly", "neutral") &
                             prime.duration %in% c(1, 5, 10, 30) &
                             grandparents %in% c("no", "none", "yes") &
                             walk > 0)

18. How many participants gave valid data and passed the attention check? (Hint: Use the result from your previous answer!)

nrow(priming.c)

## [1] 291

19. Of those participants who gave valid data and passed the attention check, what was the mean walking time of those given the elderly and neutral prime (calculate these separately).

with(subset(priming.c, prime == "elderly"), mean(walk))

## [1] 41.93209

with(subset(priming.c, prime == "neutral"), mean(walk))

## [1] 30.25669

Challenges

The following questions apply to your cleaned dataframe (priming.c)

20. Did the effect of priming condition on walking times differ between the first 50 and the last 50 participants. That is, what was the difference in the mean walking time between the two priming conditions for the first 50 participants? What about the last 50 participants? (Hint: Make sure to index the data using id!)?

mean(priming.c$walk[priming.c$id <= 50 & priming.c$prime == "elderly"]) - mean(priming.c$walk[priming.c$id <= 50 & priming.c$prime == "neutral"])

## [1] 11.04059

mean(priming.c$walk[priming.c$id >= 450 & priming.c$prime == "elderly"]) - mean(priming.c$walk[priming.c$id >= 450 & priming.c$prime == "neutral"])

## [1] 10.21579

21. Do you find evidence that a participant’s relationship with their grandparents affects how they responded to the primes?

# Strong relationship only
mean(priming.c$walk[priming.c$grandparents == "yes" & priming.c$prime == "elderly"]) - mean(priming.c$walk[priming.c$grandparents == "yes" & priming.c$prime == "neutral"])

## [1] 13.57851

# No relationship only
mean(priming.c$walk[priming.c$grandparents == "none" & priming.c$prime == "elderly"]) - mean(priming.c$walk[priming.c$grandparents == "none" & priming.c$prime == "neutral"])

## [1] 9.667544

22. Due to a computer error, the data from every participant with an even id number is invalid. Remove these data from your priming.c dataframe.

priming.c <- priming.c[priming.c$id %in% seq(1, 501, by = 2),]

Submit!

Save and email your wpa_X_LastFirst.R file to me at nathaniel.phillips@unibas.ch. Then, go to https://goo.gl/forms/8pKm39PMS29JoLjI2 to complete the WPA submission form.