README.md

In masurp/pmmisc: Convenient functions for data transformations and reporting

pmmisc

This package includes some custom-made functions to facilitate some common data wrangling procedures as well as common computations in descriptive analyses. Please note that most functions are highly costumized to my own workflow. They may hence break in more general frameworks or when used in a different way…

Dependencies

Most functions require the following packages:

• For data wrangling procedure: tidyverse and magrittr
• For printing and typesetting: papaja
• For descriptive analyses: psych
• For some plots: gridExtra

These packages should be installed prior to using this package.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("masurp/pmmisc")

Some usage examples

library(pmmisc)
library(dplyr)

# Get example data
d <- psych::bfi

Simple data descriptions

# Describe numeric variables
d %>%
  select(A1:A5) %>%
  describe_vars(first_col = "items")
#> # A tibble: 5 x 7
#>   items  mean    sd   min   max   skew kurtosis
#>   <chr> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
#> 1 A1     2.41  1.41     1     6  0.825  -0.308 
#> 2 A2     4.80  1.17     1     6 -1.12    1.05  
#> 3 A3     4.60  1.30     1     6 -0.998   0.442 
#> 4 A4     4.70  1.48     1     6 -1.03    0.0405
#> 5 A5     4.56  1.26     1     6 -0.847   0.159

# Describe factor variable
describe_factor(d$gender,
                name = "gender",
                useNA = "ifany")
#> # A tibble: 3 x 3
#>   gender     n percent
#>   <chr>  <int>   <dbl>
#> 1 1        919    32.8
#> 2 2       1881    67.2
#> 3 sum     2800   100

Missing value analysis

# Count overall missings
count_na(d)
#> # A tibble: 3 x 3
#>   missings     n percent
#>   <chr>    <int>   <dbl>
#> 1 FALSE    77669  99.1  
#> 2 TRUE       731   0.932
#> 3 sum      78400 100

# Plot missingness patterns
missing_pattern_plot(d, var_labels = TRUE)

masurp/pmmisc documentation built on May 3, 2024, 7:13 p.m.