Goals

My initial goal was to go through all the exercises in Dani’s tutorial videos and apply those lessons to other data, but I was unexpectedly overwhelmed with other work this week so I really only had time to do the exercises in the tutorial.

Exercises

Exercise 1

# Reading SWOW (inserted in global environment)
swow <- read_tsv(file = "data_swow.csv.zip")
swow <- swow %>% mutate(id = 1:n())

# Load Packages
library(tidyverse)

# Renaming variables
swow <- swow %>% 
  rename(R1 = n_response,
         N = n_total,
         R1.strength = strength)

I am not entirely sure if I did this right. I was under the impression that I was changing the variable names from the tutorial back to the original names, but I couldn’t do that by importing “data_swow.csv.zip” again. Maybe I just misunderstood the exercise, but regardless I was able to rename the variables!

Exercise 2

library(tidyverse)

# Filtering SWOW
swow %>% 
  filter(response == "woman")

# Plotting SWOW woman_bck
woman_bck <- swow %>% 
  filter(response == "woman", n_response > 1)

I was able to filter the responses quite easily.

Exercise 4


woman_fwd <- swow %>%
  filter(cue == "woman", n_response > 1) %>%
  select(cue, response, strength, id)

woman_bck <- swow %>%
  filter(response == "woman", n_response > 1) %>%
  arrange(desc(strength)) %>% 
  select(-n_response, -n_total)

man_fwd <- swow %>%
  filter(cue == "man", n_response > 1) %>% 
  select(cue, response, strength,)

man_bck <- swow %>%
  filter(response == "man", n_response > 1) %>%
  arrange(desc(strength)) %>% 
  select(-starts_with("n_"))

While I did try to figure out how to do the exercise another way, I ultimately could not figure it out and copied and pasted a lot of code.

Exercise 5

man_fwd <- swow %>%
  filter(cue == "man", n_response > 1)  %>%
  select(-n_response, -n_total) %>% 
  mutate(rank = rank(-strength),
         type = "forward",
         word = "man",
         associate = response)

man_bck <- swow %>%
  filter(response == "man", n_response > 1) %>%
  arrange(desc(strength)) %>%
  select(-starts_with("n_")) %>% 
  mutate(rank = rank(-strength),
         type = "backward",
         word = "man",
         associate = cue)

Again, this exercise was pretty straightforward.

Dani’s tutorial

For the other exercises where I just followed what Dani did, Everything was rather straight forward and I did not run into many issues. However, I did run into an issue toward the end, but I will talk about that in Challenges.


# Reading SWOW (inserted in global environment)

swow <- read_tsv(file = "data_swow.csv.zip")
swow <- swow %>% mutate(id = 1:n())

# Manual name cleaning

swow <- swow %>% 
  rename(n_response = R1,
         n_total = N,
         strength = R1.Strength)

# Filtering

swow %>% 
  filter(cue == "woman")

# Filter to keep a subset of the data

woman_fwd <- swow %>% 
  filter(cue == "woman") %>% 
  filter (n_response >1)

ggplot(woman_fwd) +
  geom_col(aes(x = response, y = strength)) +
  coord_flip()

# Creating woman_bck

woman_bck <- swow %>% 
  filter(response == "woman", n_response > 1)

# Data arranging

swow %>% 
  filter(response == "woman", n_response > 1) %>% 
  arrange(desc(strength))

# Forward associations for man

man_fwd <- swow %>% 
  filter(cue == "man", n_response >1)

# Backward associations for man

man_bck <- swow %>% 
  filter(response == "man", n_response >1)

# Select cue, response and strength

swow %>% 
  filter(response == "woman", n_response >1) %>% 
  arrange(desc(strength)) %>% 
  select(cue, response, strength)

# Mutate new variables

woman_fwd %>% 
  mutate(rank = rank(-strength))

woman_bck <- swow %>%
  filter(response == "woman", n_response > 1) %>%
  arrange(desc(strength)) %>% 
  select(-n_response, -n_total) %>% 
  mutate(rank = rank(-strength), type = "backwards")

woman_fwd <- swow %>%
  filter(cue == "woman", n_response > 1) %>%
  select(cue, response, strength, id) %>%
  mutate(
    rank = rank(-strength),  
    type = "forward",        
    word = "woman",          
    associate = response)

woman_bck <- swow %>%
  filter(response == "woman", n_response > 1)  %>%
  arrange(desc(strength)) %>%
  select(cue, response, strength, id) %>%
  mutate(rank = rank(-strength),
         type = "backward",
         word = "woman",
         associate = cue)


man_fwd <- swow %>%
  filter(cue == "man", n_response > 1)  %>%
  select(-n_response, -n_total) %>% 
  mutate(rank = rank(-strength),
         type = "forward",
         word = "man",
         associate = response)

man_bck <- swow %>%
  filter(response == "man", n_response > 1) %>%
  arrange(desc(strength)) %>%
  select(-starts_with("n_")) %>% 
  mutate(rank = rank(-strength),
         type = "backward",
         word = "man",
         associate = cue)

# Bind rows

gender <- bind_rows(woman_bck, woman_fwd,
                    man_fwd, man_bck) %>% 
  select(id:associate) %>% 
  filter(associate != "man", associate != "woman")

# Checking that bind worked

gender %>% 
  group_by(word, type) %>% 
  count()

# Pivot

gender_fwd <- gender %>% 
  filter(type == "forward") %>% 
  pivot_wider(
    id_cols = associate,
    names_from = word,
    values_from = rank)

# Reshape data

gender_fwd <- gender %>% 
  mutate(
    woman = replace_na(1/woman, 0),
    man = replace_na(1/man, 0),
    diff = woman - man) %>% 
  arrange(diff)

# Plotting gender_fwd

ggplot(gender_fwd) +
  geom_col(mapping = aes(y = associate, x = diff))

# Create gender_bck

gender_bck <- gender %>% 
  mutate(
    woman = replace_na(1/woman, 0),
    man = replace_na(1/man, 0),
    diff = woman - man) %>% 
  arrange(diff)

# Restoring data set

gender %>% 
  left_join(swow, by = "id")

While the exercises were very long, I managed to get through them. Dani’s tutorials were so easy to understand and her enthusiasm did not make it hard for me to watch all the videos.

Challenges

I ran into some errors when running these lines of code:


# Create gender_bck

gender_bck <- gender %>% 
  mutate(
    woman = replace_na(1/woman, 0),
    man = replace_na(1/man, 0),
    diff = woman - man) %>% 
  arrange(diff)

R kept saying that they could not find “object woman” even though it was there. I could not figure out why this happened, and Google wasn’t of much help either. I have been finding that when I get errors, I google the error but everyone who is asking/answering the question has really complicated code that I cannot follow.

However, for other errors I received, it was a lot of trial and error which worked about 75% of the time!

Next Steps

I would like to be able to do all of these functions and coding without consulting notes, and on a completely new set of data for practice. I will also look into the problems I encountered with the set of data.