In mvazquezs/chmitools: Package to manage & obtain results of the CHMI Study

knitr::opts_chunk$set(comment = NA,
  fig.path = '00_figures_aim1/',
  fig.width = 20,
  fig.height = 12,
  results = 'markup',
  tidy = FALSE,
  message = FALSE,
  warning = FALSE,
  echo = FALSE)

alpha <- 0.05

r packages

### load required packages to `git` command
    pacman::p_load(
        prettydoc, rmarkdown, knitr,
        devtools, install = TRUE)

### enviroment
  setwd(Sys.getenv('HOME'))
  load_all('~/git/chmiddbb')

load data

### load data
    dat <- chmi.phen(
    data_type = 'ab_data',
    aim_data = 'aim_1',
    group_tr = 'ab_select')


### 'descriptive' and 'gaussian' distribution
    tab_1 <- chmi.stat.descriptive_tab(
    phen = dat,
    vars_x = c('log10_mfi', 'ppp_time'),
    vars_y = c('dataset', 't_igg', 'antigen'),
    shapiro_test = TRUE)

### 'descriptive' and 'sample' size distribution
  tab_2 <- chmi.stat.descriptive_tab(
    phen = dat,
    vars_x = c('log10_mfi'),
    vars_y = c('t_igg', 'antigen', 't2_point'),
    shapiro_test = TRUE)

### 'crosstab' for data in 'aim_1'
  dat_1 <- unique(
    dat[, c('original_id', 'dataset', 'gender', 'status', 'immune_status', 'mal_exposure')])

  tab_3 <- chmi.stat.crosstab_tab(
    phen = dat_1,
    vars_x = c('gender', 'status', 'immune_status', 'mal_exposure'),
    vars_y = c('dataset'),
    multi_tab = TRUE,
    sum_out = TRUE)

### Conclusions
## 'tab_1' shows 34 / 366 gaussian distribution
## 'tab_2' shows 'minimum' size sample in 19 and 'maximum' size sample in 60 (Dunn test)
## 'tab_3' shows 'status' and 'mal_exposure' are same variable
## 'tab_3' only shows differences in 8 naives ('status') are placebos ('mal_exposure') for 'T2'

subseting data

### vector to manage data
  l_vars1 <- c('t_igg', 'antigen')
  l_vars2 <- c('t_igg', 'antigen', 't2_point')


### subset data

mvazquezs/chmitools documentation built on May 1, 2020, 2:06 a.m.