### install.packages("dplyr")

library(tidyverse)

## .dta , .sav, .xlsx, .csv



## alif1<-read.csv("docs/Alifailan.csv",header = TRUE) 


alif <- read_csv(here::here("data/Alifailan.csv"), col_names = TRUE)

names(alif)

alif |> filter(Province=="AJK")


## Modify column names 
library(janitor)

alif |> clean_names() -> alif

alif |> glimpse()

## select()

## filter()

## arrange()

## mutate()

## summarise()

## To have overview of data
 
## OR
glimpse(alif)


alif |> glimpse()

str(alif)

head(alif)

alif |> slice(4:8)

tail(alif)

View(alif)

names(alif)





## Select data for 4 main provinces (two ways either filter or select)
alif |> filter(province != "AJK",
               province != "GB",
               province != "ICT",
               province != "FATA")
## Select
alif |> select(district, province, drinking_water)

alif |> select(-district, -rank_2016)

View(alif)

## Or use filter as

alif |> filter (province %in% c("Punjab","Sind","Balochistan", "KP")) -> alif_small


## How many districts in each province

alif |>  count(province, name="District.count") |> 
  arrange(District.count)

alif |> count(province, name="dist_count") |> 
  arrange(dist_count)

alif |> count(province, name="dist_count") |> 
  arrange(-dist_count)

alif |> arrange(drinking_water,electricity) |> View()

library(gt)
library(gtsummary)
library(gtExtras)
library(svglite)

alif_small |> select(!c(rank_2016 , district, province)) |>     g_plt_summary() |> 

  tab_header(title = "Summary statistics of different school facilities indicators")


alif |> select_if(is.numeric) |>  gt_plt_summary() |> 
  tab_header(title = "Summary statistics of different school facilities indicators")


## If Punjab

alif |> filter(province=="Punjab") |> select_if (is.numeric) |> select(-rank_2016) |> 
  gt_plt_summary() |> gt_theme_guardian() |> tab_header(title = "School conditions in Punjab according to Alif Ailan Data")

alif |> count(province, name="dist_count") |> 
  arrange(desc(dist_count)) |> gt() |>
  gt_theme_()|> tab_header("Table of alif ailan")

alif |> select(province, infrastructure_score) |> 
  tbl_summary(by=province)



alif |>  count(province, name="district_count")|>
  arrange( desc(district_count)) |> as.data.frame()




## plots

library(ggplot2)

alif |> filter(province==Punjab) ## why an error

alif |> filter(province=="Punjab")

alif |> filter(province=="Punjab") |> 
  select(-rank_2016,-province) |> 
  slice(5:8)


ggplot(alif) 

ggplot(alif)+aes(x=drinking_water,y=electricity) 

ggplot(alif)+aes(x=drinking_water,y=electricity) +
  geom_point()


ggplot(alif)+aes(x=drinking_water,y=electricity) +
  geom_point() +
  aes(color=province)


ggplot(alif)+aes(x=drinking_water,y=electricity) +
  geom_point() +
  aes(color=province)+
  facet_grid(~province)


ggplot(alif) + aes(x = drinking_water, y = electricity) +
  geom_jitter(width = .25,
              height = .25) +
  aes(col = province)+
  labs(title="Relationship between drinking water and electricity among districts")

ggplot(alif) + aes(x = drinking_water, y = electricity) +
  geom_jitter(width = .25,
              height = .25) +
  aes(col = province)+scale_color_discrete(guide = F)+
  labs(title="Relationship between drinking water and electricity among districts")+
  labs(caption = "Data source: Alif Ailan: By :Zahid")


alif |> filter(province=="Balochistan") |> 
  ggplot() + aes(x = drinking_water, y = electricity) +
  geom_jitter(width = .25,
              height = .25) +
  aes(col = province)+scale_color_discrete(guide = F)+
  labs(title="Relationship between drinking water and electricity among districts")+
  labs(caption = "Data source: Alif Ailan: By :Zahid")
ggplot() + aes(x = drinking_water, y = electricity) +
  geom_jitter(width = .25,
              height = .25) +
  aes(col = province)+scale_color_discrete(guide = F)+
  labs(title="Relationship between drinking water and electricity among districts")+
  labs(caption = "Data source: Alif Ailan: By :Zahid")




## Only for Punjab

alif |> filter(province=="Punjab")

alif |> filter(province=="Punjab") |> 
  ggplot()+aes(x=drinking_water,y=electricity)




alif |> filter(province=="Punjab") |> 
  ggplot()+aes(x=drinking_water,y=electricity)+
  geom_point() 
## Concept of ecological correlations

alif |> filter(province=="Punjab") |> 
  ggplot()+aes(x=drinking_water,y=electricity)+
  geom_point()



















## Lets build scatter plot between drinking water and toilet facilty

p<-ggplot(alif,aes(x=drinking_water,y=toilet,color=province))+geom_point() ## Name plot as p
p
## Make comment on plot by observing where does each province districts lie
p+facet_wrap(~province,ncol = 4)+
  labs(x="Drinking Water",y="Toilet Facility",title = "Toilet facility and Drinking Water Facility situation province wise",
       caption = "Alif Ailan Data 2016")

ggplot(alif)+aes(x=province,y=drinking_water, fill=province)+geom_boxplot(method="restyle")


alif |> filter(province=="Punjab") |> 
  ggplot()+aes(x=reorder(district,toilet),y=toilet)+geom_bar(stat="identity")+
  coord_flip()+geom_col(fill = 'dodgerblue4') +
  theme_minimal() 

alif |> filter(province=="Balochistan") |> 
  ggplot()+aes(x=reorder(district,drinking_water),y=drinking_water)+geom_bar(stat="identity")+
  coord_flip()+geom_col(fill = 'dodgerblue4') +
  theme_minimal() 




alif |> filter(province=="Punjab") |> 
  ggplot()+aes(x=reorder(district,toilet),y=toilet)+geom_point(size=3,colour="darkgrey")+
  geom_segment(aes(x=district,y=0,xend=district,yend=toilet),size=0.6,colour="orange")+
  coord_flip()
  


alif |> filter(province=="Balochistan") |> 
  ggplot()+aes(x=reorder(district,toilet),y=toilet)+geom_point(size=3,colour="darkgrey")+
  geom_segment(aes(x=district,y=0,xend=district,yend=toilet),size=0.6,colour="orange")+
  coord_flip()+labs(x="",y="toilet facilty")

  
  geom_point()+
  coord_flip()+geom_col(fill = 'dodgerblue4') +
  theme_minimal() 










library(dataxray)
alif |> make_xray() |>
  view_xray()