README.Rmd

---
title: "Few functions"
output: rmarkdown::github_document
---
These are few functions I used at some point for something--for some of them, there are probably much better packages.

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

average_excluding <- function(G, n) {
  ######## AVERAGE EXCLUDING ###########
  # returns the mean of G variables
  # for cases with more than n missing
  # G is dataframe of desired vars
  apply(G, 1,
        function(x) {
          if (sum(is.na(x)) > n) mean(x)
          else mean(x, na.rm = TRUE)
          }
        )
}

imenuj <- function(x) {
  ifelse(x > 999999,
         paste(comma(x * 0.000001), "M"),
         ifelse(x > 999,
                paste(x * 0.001, "K"),
                paste(x)
                )
         )
}

```
# Mean on at least (average-excluding.R)
More details here: https://mdjeric.github.io/f-mean-on-at-least.html
Does the same thing as SPSS `mean.#`. Calculates mean of a case if more than n values are not NA, otherwise returns NA. Handy for making indexes of several variables when you can specify to calculate it if e.g. more than 75% or 90% of values are present in each case.

## Sample

Doing a simple sample, six variables, each case has different number of missing values.

```{r}
DF <- data.frame(var_1 = c(10, 20, 30, 40, 50),
                 var_2 = c(11, 21, 31, 41, NA),
                 var_3 = c(12, 22, 32, NA, NA),
                 var_4 = c(13, 23, NA, NA, NA),
                 var_5 = c(14, NA, NA, NA, NA),
                 var_6 = c(NA, NA, NA, NA, NA)
                 )

DF$av_miss_2 <- average_excluding(DF[, 1:6], 2)
DF$av_miss_5 <- average_excluding(DF[, 1:6], 5)

DF

```

And this is how it comes in the end. We have means for cases which have no more than two missing values, and no more than five missing values.

# Scaling thousands and millions (scaling-axis-ggplot2.R)

This one has probably even more unique/rare appliation. We don't want to mix different "units", and an axis that would include from 100s to 1,000,000s, well this wouldn't be a helpful way.

But, it solved a problem in faceting data for Belgrade Airport which included number of passengers, plane operations, cargo, and passengers per airplane to be presented in one plot with numbers formated in a clean way.

```{r}
library(ggplot2)
library(scales)

imenuj <- function(x) {
  ifelse(x > 999999,
         paste(comma(x * 0.000001), "M"),
         ifelse(x > 999,
                paste(x * 0.001, "K"),
                paste(x)
                )
         )
}
```

Once it is included in gglpot labels option,
```{r, results='hide'}  
scale_y_continuous(labels = imenuj, position = "right")
```
the first graph becomes the second one. Much better.

<img src="images/f-scale-1.png" alt="Faceted graph with units in numbers" style="width:50%; border:0px solid; " align="left"> 
<img src="images/f-scale-2.png" alt="Faceted graph with scaled units - M and K included where appropriate" style="width:50%; border:0px solid; "> 

# Summaries (various-summaries.R)

*Samples coming soon*

These are 6 different functions that summarize data .
Probably the most useful are `info.detail`, `out.tbls.wn`, and `out.stat`.

First returns min , max, mean, SD, and number NAs, along with type of variable and number of levels if factor in a data frame.

Second returns frequency, relative, and cumulative percent of for a variable values, along with min, max, mean, and SD (prints notification if it is a factor).

Third provides just min, max, mean, and SD for data frame.