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# Install the necessary packages if you don't have them
#install.packages("readxl")  # For reading Excel files
#install.packages("dplyr")   # For data manipulation

# Load the libraries
library(readxl)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#Assignment 1: Load dataset
data <- read_excel("C:/my_folder/old_dummy_data_for_prospective_RAs.xlsx")

#Assignment 2: Remove first 2 rows from data
data <- data[-c(1, 2), ]

#Assignment 3:Remove all participants who took less than one minute on the survey
filtered_data <- data %>%
  filter(Duration_in_seconds > 60)

# View the filtered data
print(filtered_data)

## # A tibble: 178 × 16
##    ParticipantID Duration_in_seconds sex    CDS_1 CDS_2 SBQ_1 SBQ_2 SBQ_3 SBQ_4
##    <chr>                       <dbl> <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 fakedata_003                   73 male       3     3     1     3     1     4
##  2 fakedata_010                  179 male       0     2     1     3     2     2
##  3 fakedata_011                  277 male       2     2     2     2     2     1
##  4 fakedata_012                  255 male       2     2     1     4     1     4
##  5 fakedata_013                  358 male       2     0     1     3     2     3
##  6 fakedata_014                  469 female     0     1     3     2     1     0
##  7 fakedata_015                  253 male       2     2     2     3     2     5
##  8 fakedata_016                  491 female     2     3     3     2     1     2
##  9 fakedata_017                  399 male       1     0     1     4     2     2
## 10 fakedata_018                  563 female     0     0     3     3     1     5
## # ℹ 168 more rows
## # ℹ 7 more variables: GAD_1 <dbl>, GAD_2 <dbl>, GAD_3 <dbl>, GAD_4 <dbl>,
## #   GAD_5 <dbl>, GAD_6 <dbl>, GAD_7 <dbl>

# Assignment 4: Create a total score for the Cambridge Dissociation Scale (CDS) and the Suicide Behavior Questionnaire (SBQ). 
data <- data %>% mutate(CDS_TOTAL = CDS_1 + CDS_2)
data <- data %>% mutate(SBQ_TOTAL = SBQ_1 + SBQ_2 + SBQ_3 + SBQ_4)

data %>% select(ParticipantID, CDS_TOTAL, SBQ_TOTAL) 

# Assignment 5: Create a table that shows the average suicide risk total score by sex assigned at birth (i.e., each sex variable should have its own average total score)
data %>%
  group_by(sex) %>%
    summarise(average_value = mean(SBQ_TOTAL, na.rm = TRUE))

#data %>% select(ParticipantID, CDS_TOTAL,  SUICIDE_RISK_MALE) 

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