Learning Log 4
Jade Gurtala
25/06/2021
This week's coding goals
- Import data into RStudio
- Reproduce demographic statistics as shown in the report's Table 1 (Participant Characteristics)
- Create and reproduce Table 1
How did I go?
We had a group meeting this week where we aimed to import our data into RStudio and get started on reproducing the demographic statistics.
Goal 1: Import data into RStudio
Load the relevant packages
library(tidyverse)
library(janitor)
library(dplyr)Load the data:
library(remotes)
install_github("JanMarvin/readspss")library(remotes)
install_github("JanMarvin/readspss")I had issues with the read.sav and read_sav function as it kept coming up with the error: "function not found"
library(haven)
library(readspss)
replicationdata <- read.sav("Humiston & Wamsley 2019 data.sav")Goal 2: Reproduce demographic statistics
- All of the coding is here
- I encountered many challenges which I have outlined later in this learning log
Exclude participants from the data
cleandata <- replicationdata %>% #removing participants who were excluded
filter(exclude == "no")Calculate age average
ageaverage <- cleandata %>% #calculating average age including sd using cleaned data
select(General_1_Age) %>%
summarise(ageaverage = mean(General_1_Age),
agesd = sd(General_1_Age))
print(ageaverage)Calculate average ESS score
ESS = Epworth Sleepiness Scale
ESS <- cleandata %>%
select(Epworth_total) %>%
summarise(ESSaverage = mean(Epworth_total),
ESSsd = sd(Epworth_total))
print(ESS)Calculate SSS
SSS = Stanford Sleepiness Scale
Keeps coming up with error
SSSerror <- cleandata %>%
select(AlertTest_1_Feel) %>%
summarise(SSSaverage = mean(AlertTest_1_Feel),
SSSsd = sd(AlertTest_1_Feel))Gives non-matching values
SSStrial <- replicationdata %>%
select(AlertTest_1_Feel,
AlertTest_2_Feel,
AlertTest_3_Feel,
AlertTest_4_Feel) %>%
drop_na() %>%
summarise(SSStrialaverage = mean(rbind(AlertTest_1_Feel, AlertTest_2_Feel, AlertTest_3_Feel, AlertTest_4_Feel)),
SSStrialsd = sd(rbind(AlertTest_1_Feel, AlertTest_2_Feel, AlertTest_3_Feel, AlertTest_4_Feel)))
print(SSStrial)Another attempt: using mutate to create new variable (SUCCESS~~)
Create new variable SSSvalue
cleandata <- cleandata %>%
mutate(
SSSvalue = as.numeric(
x = AlertTest_1_Feel,
levels = 1:5,
labels = c("1 - Feeling active, vital alert, or wide awake",
"2 - Functioning at high levels, but not at peak; able to concentrate",
"3 - Awake, but relaxed; responsive but not fully alert",
"4 - Somewhat foggy, let down",
"5 - Foggy; losing interest in remaining awake; slowed down"),
exclude = NA
)
)SSS <- cleandata %>%
select(SSSvalue) %>%
summarise(SSSaverage = mean(SSSvalue),
SSSsd = sd(SSSvalue))Calculate baseline implicit bias
BIB <- cleandata %>%
select(
base_IAT_race,
base_IAT_gen) %>%
summarise(
BIBaverage = mean(rbind(base_IAT_race, base_IAT_gen)),
BIBsd = sd(rbind(base_IAT_race, base_IAT_gen))
)
print(BIB)Calculate Prenap implicit bias
PrenapIB <- cleandata %>%
select(
pre_IAT_race,
pre_IAT_gen) %>%
summarise(
PrenapIBaverage = mean(
rbind(
pre_IAT_race,
pre_IAT_gen)
),
PrenapIBsd = sd(
rbind(
pre_IAT_race,
pre_IAT_gen))
)
print(PrenapIB)Calculate Postnap implicit bias
PostnapIB <- cleandata %>%
select(
post_IAT_race,
post_IAT_gen) %>%
summarise(
PostnapIBaverage = mean(
rbind(
post_IAT_race,
post_IAT_gen
)),
PostnapIBsd = sd(
rbind(
post_IAT_race,
post_IAT_gen
))
)
print(PostnapIB)Calculate one-week delay implicit bias
OWDIB <- cleandata %>%
select(
week_IAT_race,
week_IAT_gen) %>%
summarise(
OWDIBaverage = mean(
rbind(
week_IAT_race,
week_IAT_gen
)
),
OWDIBsd = sd(
rbind(
week_IAT_race,
week_IAT_gen
)
)
)
print(OWDIB)Calculate average sex
Male <- cleandata %>%
select(General_1_Sex) %>%
tally(General_1_Sex == "Male")
Male_percentage <- Male/31 #31 as the clean data set has 31 participants
print(Male_percentage)Calculate average Cue played during nap (% racial cue)
Napcue <- cleandata %>%
select(Cue_condition) %>%
tally(Cue_condition == "race cue played")
racialcue_perentage <- Napcue/31
print(racialcue_perentage)Goal 3: Create and reproduce Table 1
After doing some research, there were three main packages for creating a table: - kableExtra() - magick() - gt()
I decided to go with gt() because it seemed to produce the cleanest table. If it wasn't what I wanted, then I would then try kableExtra(),and then magick().
I wasn't sure how to input the information, but I decided to just create a new dataframe using the statistics that was reproduced from the earlier section.
Recreate Table 1: Participant characteristics
Install relevant packages
#install.packages("kableExtra")
#install.packages("magick")
#install.packages("gt")Load relevant packages
library(knitr)
library(kableExtra)
library(magick)
library(gt)
library(glue)Create a dataframe for Table 1 (Participant characteristics)
table1 <- tibble(
Characteristics = c("Age (yrs)", "ESS", "SSS", "Baseline implicit bias", "Prenap implicit bias", "Postnap implicit bias", "One-week delay implicit bias", "Sex (% male)", "Cue played during nap (% racial cue)"),
Mean = c(19.5, 15.3, 2.81, 0.557, 0.257, 0.278, 0.399, 0.484, 0.548),
SD = c(1.23, 2.83, 0.749, 0.406, 0.478, 0.459, 0.425, NA, NA)
)Create and format a table
table1 %>%
gt() %>%
tab_header(
title = "Participant characteristics")Challenges and successes
- I encountered many problems!
- I had issues with the read.sav and read_sav function as it kept coming up with an error
I started with the chunk below but it kept coming up with an error: "function not found".
library(remotes) install_github("JanMarvin/readspss") library(readspss) library(dplyr) replicationdata <- read.sav("Humiston & Wamsley 2019 data.sav")After consulting google, most people recommended using the [haven] package:
library(haven) library(readspss) replicationdata <- read.sav("Humiston & Wamsley 2019 data.sav")After solving and importing the SPSS date, I then had issues trying to exclude participants. I kept getting "0 observations" for my [cleandata] variable when I first tried doing this:
cleandata <- data %>%
filter(exclude==0)After trying various solutions, I was finally able to correctly exclude participants without getting "NA" or "O observations":
cleandata <- replicationdata %>% #removing participants who were excluded filter(exclude == "no")When calculating the SSS mean and SD, I had issues producing the relevant values. It kept coming up with the below error:
- I realised that this is because the SSS has been inputted as a categorical variable e.g. "1 - Feeling active, vital alert, or wide awake". I had to change this variable into numerical values in order to calculate means and standard deviations:
cleandata <- cleandata %>% #Create new variable SSSvalue
mutate(
SSSvalue = as.numeric(
x = AlertTest_1_Feel,
levels = 1:5,
labels = c("1 - Feeling active, vital alert, or wide awake",
"2 - Functioning at high levels, but not at peak; able to concentrate",
"3 - Awake, but relaxed; responsive but not fully alert",
"4 - Somewhat foggy, let down",
"5 - Foggy; losing interest in remaining awake; slowed down"),
exclude = NA
)
)SSS <- cleandata %>%
select(SSSvalue) %>%
summarise(SSSaverage = mean(SSSvalue), #Calculate mean and SD of new variable SSSvalue
SSSsd = sd(SSSvalue))Successes
After all the drama with reproducing the participant characteristics/statistics, creating the table was fairly simple. I created a dataframe including all the relevant values needed for Table1. I then created the table using gt()
table1 %>% gt() %>% tab_header( title = "Participant characteristics")
Next steps
- Find easier and better ways to format our data into a table, so that it looks more similar to what is seen our replication study
- Find out how to round up our figures into 2 decimal places, as is seen in our replication study
- Start reproducing our next figure!