In LarsenLab/immuntools: Larsen lab data analysis toolbox

knitr::opts_chunk$set(
  echo = FALSE
)

# load packages
# packages for data import
library(REDCapR)
library(ncdfFlow)

# packages for data manipulation
library(tidyverse)
library(dplyr)
library(purrr)
library(lubridate)
library(janitor)
library(skimr)

# packages for data export
library(writexl)

Import raw data

- csv or excel data

raw_data <- read_csv("/data/raw/yourcsvfile.csv")

- RDS

raw_data <- read_rds("/data/raw/yourrdsfile.rds")

- REDcap data

raw_data <- redcap_read(redcap_uri = yourredcapurl, token = apitoken, raw_or_label = 'label',verbose = FALSE)

- flow cytometry data

raw_data <- read.ncdfFlowSet("/data/raw/yourfcsfiles")

- other types of data

Check raw data

dplyr::glimpse(raw_data)
ggplot(data = raw_data, aes(x = x_var, y = y_var , color = col_var)) + geom_boxplot()

dplyr::glimpse(iris)  
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width , color = Species)) + geom_boxplot() 

Process raw data

code to clean the raw data

Check processed data

dplyr::glimpse(proc_data)
ggplot(data = proc_data, aes(x = x_var, y = y_var , color = col_var)) + geom_boxplot()

dplyr::glimpse(iris)  
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width , color = Species)) + geom_boxplot() 

Export processed data

- export to csv/excel/RDS

write.csv(proc_data, "/data/proc/processeddataname.csv")
write_xlsx(list(worksheet_name = proc_data), "/data/proc/processeddataname.xlsx")
write_rds(proc_data, "/data/proc/processeddataname.rds")

LarsenLab/immuntools documentation built on July 19, 2023, 9:43 a.m.