Sample_code_for_compare_columns_in_R.qmd

---
title: "Habib Ezatabadi Sample Code"
format: gfm
editor: visual
---


## sample code 


#### (a) Get khesarat --------------------


```{r}
#| warning: false
#| message: false

set.seed(1)
library(tidyverse)

n <- 1000
National_number <- sample(1:20, size = n, replace = TRUE)
khedmat <- sample(1:6, size = n, replace = TRUE)
sex <- sample(c("female", "male"), size = n, replace = TRUE, 
prob = c(.4, .6))
FR_CR <- sample(c("Ten", "Twenty"), size = n, replace = TRUE)

dat <- tibble(National_number, khedmat, sex, FR_CR) %>%
                mutate(khesarat = case_when(
                    khedmat == 1 ~ 1000, 
                    khedmat == 2 ~ 2000, 
                    khedmat == 3 ~ 3000, 
                    khedmat == 4 ~ 4000,
                    khedmat == 5 ~ 5000, 
                    TRUE ~ 6000 
                )) %>% mutate(across(c(2, 3, 4), as.factor))



head(dat)
```


```{r}
#| warning: false
#| message: false

get_khesarat <- function(x) {    # x is National_number
    if (! (x %in% dat$National_number)) {
        stop ("this value is not in data set")
    }
    result <- dat %>%
    filter (National_number == x) %>%
    group_by (khedmat) %>%
    summarise(Khesarat_kol = sum(khesarat))
    return (result)
}

get_khesarat(2)
```



## quantile regression 


```{r}
#| warning: false
#| message: false

dat2 <- dat %>% 
        group_by(khedmat, FR_CR, sex) %>%
            summarise(TOT_khesarat = sum(khesarat))
library(quantreg)

## q = 0.5 

Median_Model  <- rq(TOT_khesarat ~ ., data = dat2, tau = 0.5)

summary(Median_Model)


## q = 0.75

Model2  <- rq(TOT_khesarat ~ ., data = dat2, tau = 0.75)

summary(Model2)
```


***
***



## Update code for new data set ---------------------



## import data and get summary 
```{r}
#| warning: false
#| message: false
#| eval: false
#| echo: false

library(tidyverse)
library(readxl)
# dat <- read_xlsx(file.choose(), col_names = TRUE)
# saveRDS(dat, file = "b_a_k.RDS")
dat <- readRDS(file = "b_a_k.RDS")
head(dat)
tail(dat)
dim(dat)
names(dat)
glimpse(dat)

if (! require(summarytools)) {
    install.packages ("summarytools")
    library (summarytools)
}

dfSummary(dat)
dat$khedmat %>% table

dat2 <- dat %>%
    filter(khedmat %in% as.character(1:8))

# dat2 <- dat %>% mutate(
#     khedmat = case_when(
#         khedmat == "آمبولانس" ~ "Ambulance", 
#         khedmat == "خدمات توانبخشي" ~ "khedmat Tavanbakhshi", 
#         khedmat == "زايمان" ~ "zayeman", 
#         khedmat == "سمعک" ~ "samAk", 
#         khedmat == "لنز ،عينک" ~ "Lenz, Eynak", 
#         khedmat == "هزينه رفع عيوب انکساري ديددوچشم" ~ "hazineh enkesari ...",
#         khedmat == "هزينه هاي درمان نازایي و ناباروري" ~ "hazineh nazayi , ...", 
#         .default = khedmat 
#     )
# )
dat2 %>% .$khedmat %>% table


dat2 <- dat2 %>%
mutate(across(c(2, 5, 6), factor)) 
```




## Update new:1 ------------------


```{r}
#| warning: false
#| message: false


library(tidyverse)
library(readxl)
dat <- read_xlsx(file.choose(), col_names = TRUE)
head(dat)
tail(dat)
dim(dat)
names(dat)
glimpse(dat)

if (! require(summarytools)) {
    install.packages ("summarytools")
    library (summarytools)
}

dfSummary(dat)
dat$khedmat %>% table

dat2 <- dat %>%
    filter(khedmat %in% as.character(1:8))


dat2 %>% .$khedmat %>% table


dat2 <- dat2 %>%
mutate(across(c(2, 5, 6), factor)) 

```
***


```{r}
#| warning: false
#| message: false





get_khesarat <- function(x) {# x is National_number or vector of national number
    if (length(x) == 1) {
        if (! (x %in% dat2$National_number)) {
            stop ("this value is not in data set")
        } else {
            result <- dat2 %>%
            filter (National_number == x) %>%
            group_by (khedmat) %>%
            summarise(Khesarat_kol = sum(Khesarat))
        }
    } else {
        result <- list()
        for (i in x) {
            if (! (i %in% dat2$National_number)) {
            result[[as.character(i)]] = "this value is not in data set"
        } else {
            result[[as.character(i)]] <- dat2 %>%
            filter (National_number == i) %>%
            group_by (khedmat) %>%
            summarise(Khesarat_kol = sum(Khesarat))
            }
        }
    }
    return (result)
}

id_sample <- sample(unique(dat2$National_number), 10)
get_khesarat(id_sample)

```

***
***



## Quantile Regression ----------------------------


```{r}
#| warning: false
#| message: false

dat3 <- dat2 %>% 
        group_by(khedmat, FR_CR, sex) %>%
            summarise(TOT_khesarat = sum(Khesarat))
library(quantreg)

## q = 0.5 

Median_Model  <- rq(TOT_khesarat ~ ., data = dat3, tau = 0.5)

summary(Median_Model)


## q = 0.75

Model2  <- rq(TOT_khesarat ~ ., data = dat3, tau = 0.75)

summary(Model2)



```


***
***


## for get more details  ----------------


```{r}
#| warning: false
#| message: false
#| fig-height: 9
#| fig-width: 9
#| fig-align: center


dat3 %>%
ggplot(aes(y = TOT_khesarat, group = sex, fill = sex)) + 
geom_boxplot() + 
theme_bw()

```

***


```{r}
#| warning: false
#| message: false
#| fig-height: 9
#| fig-width: 9
#| fig-align: center


dat3 %>%
ggplot(aes(y = TOT_khesarat, group = FR_CR, fill = FR_CR)) + 
geom_boxplot() + 
theme_bw()

```


***


```{r}
#| warning: false
#| message: false
#| fig-height: 9
#| fig-width: 9
#| fig-align: center


dat3 %>%
ggplot(aes(y = TOT_khesarat, group = khedmat, fill = khedmat)) + 
geom_boxplot() + 
theme_bw()

```