Chapter 9 Creating tables

In our final document we will want tables to describe our data. Below we create a table which contains demographic information on our sample.

# Method 1 using rstatix
tbl.demo  <- df.nirs %>%
  distinct(Subject, .keep_all = TRUE) %>%
  group_by(group, gender) %>%
  rstatix::get_summary_stats(age, type = "mean_sd") %>%
  select(-c("variable")) %>%
  dplyr::rename(
    "Group" = "group",
    "Sex" = "gender",
    "Mean Age" = "mean",
    "SD" = "sd"
  )

# Method 2 using RMisc

journey_time <- Rmisc::summarySE(data = df,
                                      measurevar = "journey_time_avg",
                                      groupvars = c("service","year"),
                                      conf.interval = 0.95,
                                      na.rm = TRUE,
                                      .drop = TRUE)

# Taking the mean and confidence interval =============
    # __Method #1 - Easy #########
      journey_time <- Rmisc::summarySE(data = df,
                                       measurevar = "journey_time_avg",
                                       groupvars = c("service","year"),
                                       conf.interval = 0.95,
                                       na.rm = TRUE,
                                       .drop = TRUE)
                                       
    # To get a descriptive table

  tbl.descFullACAP <- Rmisc::summarySE(data = df.mri %>%
                           filter(metric == "FA", mriloc == "CC_FMajor"),
                         measurevar = "age",
                         groupvars = c("group","gender"),
                         conf.interval = 0.95,
                         na.rm = TRUE,
                         .drop = TRUE) %>%
          janitor::clean_names("upper_camel") %>%
          rename(
            "CI" = "Ci",
            "SE" = "Se",
            "SD" = "Sd"
          ) %>%
            mutate_at(vars(Age,SD,SE,CI), funs(round(., 3)))
    
              cgwtools::resave(tbl.descFullACAP, file = "data/tables.RData") #resave a list of tables that I'll use in the .Rmd file.

    # __Method 2 - More options
    df %>%
        group_by(Channel) %>%
        summarise_each(funs(mean, sd))
        
        
    dt <- data.table(df)
    group_by(dt, Channel_46)
    # __Method 2b -  Not so Easy ##################

        # descriptives <- demo%>%dplyr::group_by(AthleteType)%>%
        #   dplyr::summarise(
        #     "Mean Height (cm)" = round(mean(Height_cm),2)
        #     , "Mean Weight (kg)" = round(mean(Weight_kg),2)
        #     , "Mean Age (Years)" = round(mean(AgeInYears),2)
        #     , "Number of Concussions" = round(mean(NumPriorConc),2)
        #     , "Number of Diagnosed Concussions" = round(mean(NumDiagConc),2)
        #     , "Number of Undiagnosed Concussions" = round(mean(NumUndiagConc),2)
        #   )

You may also choose to write these to an xlsx file

df <- iris
xlsx::write.xlsx2(df, "test.xlsx", row.names = FALSE, sheetName = "SheetNumber1", append = TRUE) # uses the xlsx package

9.1 Supplementary Material

Item Analysis