test-nursery.Rmd

---
title: "ACRO R Notebook"
output: html_notebook
---

```{r}
# install.packages("haven")
# install.packages("reticulate")
# install.packages("farff")
# install.packages("survival")
```

## Import Libraries

```{r}
source("../acro.R")  # ACRO
```

## Load Data

```{r}
data = farff::readARFF("../data/nursery.arff")
data = as.data.frame(data)

names(data)[names(data) == "class"] <- "recommend"
```

## Convert children to integers,

### replacing 'more' with random int from range 4-10

```{r}
unique(data$children)

#data$children <- sub("more","6",data$children)
unique(data$children)

data$children <-as.numeric(as.character(data$children))
unique(data$children)

data[is.na(data)] <-  round(runif(sum(is.na(data)), min = 4, max = 10),0)
unique(data$children)
```

```{r}
head(data)
#sapply(data)
```

## Tables

### ACRO Crosstab

```{r}
index = data[, c("recommend")]
columns = data[, c("parents")]
values = data[, c("children")]
aggfunc = "mean"

table = acro_crosstab(index, columns, values=values, aggfunc=aggfunc)
```

```{r}
table
```

```{r}
index = data[, c("recommend")]
columns = data[, c("parents")]

table = acro_table(index, columns, dnn= c("recommend", "parents"), deparse.level=0)
```

```{r}
table
```

### Add Comments to Output

```{r}
acro_add_comments("output_0", "This is a crosstab on the nursery dataset.")
```

### ACRO Pivot Table

```{r}
index = "parents"
values = "children"
aggfunc = list("mean", "std")

table = acro_pivot_table(data, values=values, index=index, aggfunc=aggfunc)
```

```{r}
table
```

### ACRO histogram

```{r}
acro_hist(data, "children")
```

### ACRO survival analysis

```{r}
data(package = "survival")

# Load the lung dataset
data(lung)
#head(lung)

acro_surv_func(time=lung$time, status=lung$status, output ="plot")
```
```

# Regression examples using ACRO

Again there is an industry-standard package in python, this time called **statsmodels**.

-   The examples below illustrate the use of the ACRO wrapper standard statsmodel functions

-   Note that statsmodels can be called using an 'R-like' format (using an 'r' suffix on the command names)

-   most statsmodels functiobns return a "results object" which has a "summary" function that produces printable/saveable outputs

### Start by manipulating the nursery data to get two numeric variables

-   The 'recommend' column is converted to an integer scale

```{r}
data$recommend <- as.character(data$recommend)
data$recommend[which(data$recommend=="not_recom")] <- "0"
data$recommend[which(data$recommend=="recommend")] <- "1"
data$recommend[which(data$recommend=="very_recom")] <- "2"
data$recommend[which(data$recommend=="priority")] <- "3"
data$recommend[which(data$recommend=="spec_prior")] <- "4"
data$recommend <- as.numeric(data$recommend)
```

##

```{r}
# extract relevant columns
df = data[, c("recommend", "children")]
# drop rows with missing values
df = df[complete.cases(df), ]
# formula to fit
formula = "recommend ~ children"
```

### Fit Linear Model

```{r}
model = lm(formula=formula, data=df)
summary(model)
```

### ACRO Linear Model

```{r}
acro_lm(formula=formula, data=df)
```

### ACRO Probit/ Logit regression

This is an example of logit/probit regression using ACRO\
We use a different combination of variables from the original dataset.

```{r}
# extract relevant columns
df = data[, c("finance", "children")]
# drop rows with missing values
df = df[complete.cases(df), ]
# convert finance to numeric
df = transform(df, finance = as.numeric(finance))
# subtract 1 to make 1s and 2S into 0a and 1s
df$finance <- df$finance -1
# formula to fit
formula = "finance ~ children"
```

### Fit Logit Model

```{r}
model = glm(formula=formula, data=df, family=binomial(link="logit"))
summary(model)
```

### ACRO Logit Model

```{r}
acro_glm(formula=formula, data=df, family="logit")
```

### Fit Probit Model

```{r}
model = glm(formula=formula, data=df, family=binomial(link="probit"))
summary(model)
```

### ACRO Probit Model

```{r}
acro_glm(formula=formula, data=df, family="probit")
```

### Add Custom Output

```{r}
acro_custom_output("XandY.jpeg", "This output is an image showing the relationship between X and Y")
```

### Rename Output

```{r}
acro_rename_output("output_5", "xy_plot")
```

### Remove Output

```{r}
acro_remove_output("output_3")
```

### Display Outputs

```{r}
acro_print_outputs()
```

## Finalise

```{r}
#acro_finalise("RTEST", "xlsx")
acro_finalise("RTEST", "json")
```

## Get the documentation for crosstab function

```{r}
acro_help("acro_crosstab")
```