bryant_code.Rmd

```{r}
library(tidyverse)
library(here)
```

```{r}
# Get list of all files in this folder
all_files <- list.files(path = here("CSVs"), pattern = "*.csv", full.names = TRUE)
```

```{r}
library(tidyverse)

# Initialize an empty list to store the individual data frames
all_data_without_blanks <- data.frame()
all_data_with_blanks <- data.frame()
file = all_files[1]

#Remove filenames with "blank" in them
all_files_without_blanks <- all_files[!grepl("Blank", all_files)]

all_blank_files <- all_files[grepl("Blank", all_files)]

for (file in all_files_without_blanks) {
  # Extract metadata from filename
  filename <- str_extract(file, "(?<=/CSVs/)[^/]+(?=\\..+$)")
  split_filename <- str_split(filename, " ")[[1]]
  
  date <- split_filename[1]
  concentration <- split_filename[2]
  compound <- split_filename[4]
  replicate <- split_filename[5]
  
  # Read in the data
  data <- read_csv(file, col_names = c("Raman_Shift", "Intensity"), skip = 2) %>%
    mutate(Raman_Shift = as.character(Raman_Shift))  %>% 
    mutate(Date = date,
           Concentration = concentration,
           Compound = compound,
           Replicate = replicate)  %>% 
           select(-X3)
  
  # Append to data frame
    all_data_without_blanks <- bind_rows(all_data_without_blanks, data)
  
}

#Do the same for the blank files
for (file in all_blank_files) {
  # Extract metadata from filename
  filename <- str_extract(file, "(?<=/CSVs/)[^/]+(?=\\..+$)")
  split_filename <- str_split(filename, " ")[[1]]
  
  date <- split_filename[1]
  concentration <- "Blank"
  compound <- "Blank"
  replicate <- split_filename[3]
  
  # Read in the data
  data <- read_csv(file, col_names = c("Raman_Shift", "Intensity"), skip = 2) %>%
    mutate(Raman_Shift = as.character(Raman_Shift))  %>% 
    mutate(Date = date,
           Concentration = concentration,
           Compound = compound,
           Replicate = replicate)  %>% 
           select(-X3)
  
  # Append to data frame
    all_data_with_blanks <- bind_rows(all_data_with_blanks, data)
  
}
```

```{r}
# Bind all data frames together
all_data <- bind_rows(all_data_without_blanks, all_data_with_blanks)  %>% 
    drop_na()

# Group by technical replicates and compute average and standard deviation
summary_df <- all_data %>%
  group_by(Date, Concentration, Compound, Replicate, `Technical Replicate`) %>%
  summarise(Mean_Intensity = mean(Intensity, na.rm = TRUE),
            StdDev_Intensity = sd(Intensity, na.rm = TRUE))

print(summary_df)

```