Learning Log Week 4

My Goals For Coading This Week

1. Try and fix all the software issues associated with both my laptop and desktop versions of Rstudio
2. Get the data set from Experiment 1 of the Haigh Journal Article into Rstudio
3. Discuss with the team on how to display this data set
4. If possible, to finally get a working code to produce the descriptive and plots for Experiment 1.

#Loading the packages

library("tidyverse")

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dplyr)

Data Wrangling

#Reading the data

HaighData1 <- read_csv("Study 8 data.csv") 

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_character()
## )
## i Use `spec()` for the full column specifications.

#Cleaning up the data

HaighData1 <- HaighData1 %>% slice(-1:-2)
#this is removes line 2 and 3 from the data set 

HaighData1 <- as.data.frame(HaighData1) 
#Converting the data into a data frame so that the data can actually be converted by the filter function 

HaighData1 <- HaighData1 %>% rename(Recall_score = SC0, Condition = FL_10_DO)
#This is  just too rename the incomprehensible variable names just like Dani told us to do last week! 

HaighData1 %>% count(duplicated(Prolific_PID)) #count duplicates

##   duplicated(Prolific_PID)   n
## 1                    FALSE 312
## 2                     TRUE  59

#Some of the participants doubled up, and this can be distinguished by their duplicate IDs which have to be filtered out

Working through the exclusions - both unplanned and planned

In the paper there was only 312 people that signed up for the study but a total of 371 responses. Therefore, it was realized that some of the participants must have doubled up on their survey responses.Huge shout-out to my team members for being able to work through this as I almost had no clue how to distinguish it!Hence why this is unplanned since the experimenters were not initially expecting it

#Unplanned Exclusions - Excluding all the double ups 

HaighData1 <- HaighData1 %>% 
  distinct(Prolific_PID, .keep_all = TRUE) 

Now working through the planned exclusions for the number of participants who failed to complete the task, weren’t seriously responding or failed the attention check that was set in place by the experimenters

#Planned Exclusions 
HaighData1 <- HaighData1 %>% 
  filter (Finished == 1, Serious_check == 1, Recall_score >= 4)

Descriptives

Now that I’ve filtered and fixed up the data, we can extract the following data to state the descriptives

#Number of participants 

count(HaighData1)

##     n
## 1 294

#Number of participants split by gender - once again another shoutout to Torunn for finding this! 

HaighData1 %>% group_by(Gender) %>% count(Gender)

## # A tibble: 2 x 2
## # Groups:   Gender [2]
##   Gender     n
##   <chr>  <int>
## 1 1        126
## 2 2        168

Age Demographics

HaighData1 %>%
  summarise(
    Mean_Age = mean(Age), 
    SD = sd(Age), 
    Max = max(Age), 
    Min = min(Age)
  )

## Warning in mean.default(Age): argument is not numeric or logical: returning NA

##   Mean_Age       SD Max Min
## 1       NA 12.96633  69  18

# For some reason the mean age will not appear 

write_csv(HaighData1, "Haigh_Data_Exp1_Tidied")

Independent and Dependent Variables

These are needed of course in order to make the plots!

#In order to do that, I have to create a new variable that sums the contradiction scores 

HaighData1$contradiction_1 <- as.numeric(HaighData1$contradiction_1)
HaighData1$contradiction_2 <- as.numeric(HaighData1$contradiction_2)
HaighData1$contradiction_3 <- as.numeric(HaighData1$contradiction_3)
HaighData1$contradiction_4 <- as.numeric(HaighData1$contradiction_4)
HaighData1$contradiction_5 <- as.numeric(HaighData1$contradiction_5)
HaighData1$contradiction_6 <- as.numeric(HaighData1$contradiction_6)

HaighData1 <- HaighData1 %>%
  rowwise() %>% 
  mutate(contradiction = sum(contradiction_1, contradiction_2, contradiction_3, contradiction_4, contradiction_5, contradiction_6))

# I now have to separate the IVs for condition into 4 separate columns to represent the separate levels of each IV 

HaighData1 <- separate(HaighData1, Condition, c("block", "number", "Format", "Conflict"))

#rename (to mimic the labels in the plots)
levels(HaighData1$Conflict)[levels(HaighData1$Conflict)=="Conflict"] <- "Conf."
levels(HaighData1$Conflict)[levels(HaighData1$Conflict)=="Consistent"] <- "Non-Conf."

#set these new IV columns as factors 
HaighData1 <- HaighData1 %>%
  mutate(Format=as.factor(Format)) %>%
  mutate(Conflict=as.factor(Conflict)) 

sapply(HaighData1, class) #to check

##             StartDate               EndDate                Status 
##           "character"           "character"           "character" 
##             IPAddress              Progress Duration (in seconds) 
##           "character"           "character"           "character" 
##              Finished          RecordedDate            ResponseId 
##           "character"           "character"           "character" 
##     RecipientLastName    RecipientFirstName        RecipientEmail 
##           "character"           "character"           "character" 
##     ExternalReference      LocationLatitude     LocationLongitude 
##           "character"           "character"           "character" 
##   DistributionChannel          UserLanguage               Consent 
##           "character"           "character"           "character" 
##                   PID                Gender                   Age 
##           "character"           "character"           "character" 
##      QID2_First Click       QID2_Last Click      QID2_Page Submit 
##           "character"           "character"           "character" 
##      QID2_Click Count      QID4_First Click       QID4_Last Click 
##           "character"           "character"           "character" 
##      QID4_Page Submit      QID4_Click Count      QID6_First Click 
##           "character"           "character"           "character" 
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##     QID68_Click Count     QID70_First Click      QID70_Last Click 
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##           "character"           "character"           "character" 
##    QID152_Click Count       contradiction_1       contradiction_2 
##           "character"             "numeric"             "numeric" 
##       contradiction_3       contradiction_4       contradiction_5 
##             "numeric"             "numeric"             "numeric" 
##       contradiction_6             confusion           advancement 
##             "numeric"           "character"           "character" 
##           Memory_task        Memory_task_DO         Serious_check 
##           "character"           "character"           "character" 
##          Recall_score          Prolific_PID             Random_ID 
##           "character"           "character"           "character" 
##                 block                number                Format 
##           "character"           "character"              "factor" 
##              Conflict         contradiction 
##              "factor"             "numeric"

Creating the Plots

#Contradiction Plot 

ggplot(HaighData1) +
  geom_violin(aes(x = Conflict, y = contradiction)) + 
  facet_wrap(vars(Format), strip.position = "bottom")+
  ggtitle(label = "Contradiction")+
  scale_y_continuous(
    name = "Perceived Contradiction",
    limits = c(0,30))+
  scale_x_discrete(name = NULL)+
  theme(plot.title = element_text(hjust = 0.5))

#Advancement Plot 

HaighData1$advancement <- as.numeric(HaighData1$advancement) 
ggplot(HaighData1) +
  geom_violin(aes(x = Conflict, y = advancement)) + 
  facet_wrap(vars(Format), strip.position = "bottom")+
  ggtitle(label = "Advancement")+
  scale_y_continuous(name = "Perceived Scientific Advancement")+
  scale_x_discrete(name = NULL)+
  theme(plot.title = element_text(hjust = 0.5))

#Confusion Plot 

HaighData1$confusion <- as.numeric(HaighData1$confusion) #To change variable from Character to Numeric
ggplot(HaighData1) +
  geom_violin(aes(x = Conflict, y = confusion)) + 
  facet_wrap(vars(Format), strip.position = "bottom")+
  ggtitle(label = "Confusion")+
  scale_y_continuous(name = "Perceived Confusion")+
  scale_x_discrete(name = NULL)+
  theme(plot.title = element_text(hjust = 0.5)) 

Challenges and Successes

Challenges

1. One of the main issues that was pretty much consuming my entire week was a software issue regarding my one-drive. It’s an obsolete cloud that for some reason is still integrated into both my laptop and my desktop. This issue initially popped up on my laptop as an issue regarding the working directory and once that was solved I thought I was all good to go.However this issue persisted to the point that I needed to go to UNSW IT twice.It took a couple of hours but even then, all solutions wouldn’t work until we found out the root issue was still because a large majority of my files that go through folders, in part goes through the one drive and so essentially what I’ve had to do is do a factory reset of my desktop to be able to use a different (new) library to install packages. I have to do the same for my laptop which for some reason is harder to even work out so I’ll be doing that with UNSW IT at the nearest convenience.

2. Honestly it has just been finding the motivation to code, working through this issue wasn’t even coding error related and really made it difficult for me to even get started.I could not even get on to my goals for this and last week which was to improve my workflow as a result.

Successes

1. I honestly only have one success and that is regarding my team, they are full of wonderful and hardworking people that if I wasn’t apart of, would not have gotten even close to finishing for the data sets and graphs

2. Actually I guess it is also working out the one-drive error. Took so long I didn’t even realise it as a success in hindsight

The next steps

1. Once again, to improve work flow
2. Making sure I actually communicate with my team and join the collaborative effort
3. Experiment in my own time (but at this point it seems ridiculous since I’ve mentioned it for about 3 weeks in a row now but still really want to be able to for next week)
4. Proceed onto the descriptive plots for Experiment 2 of the Haigh Journal Article!