MJOLNIR_XX_MAPQGIS.Rmd

---
title: "GPS coordinate participants"
output: html_document
date: '2022-06-07'
---

# Package

```{r}
library(tidyverse)
library(questionr)
```

# Data import

```{r setup, include=FALSE}
df <- read.csv("./data/mjolnir_clean_v6_Covmis.csv")
df$ID <- seq(1:nrow(df))
```


```{r}
map <- df[,c(1:80, 1152:1164,1562:1608)]
write_csv(map, file = "./data/dataformapqgis.csv")
```

```{r}
map2 <- map  %>%
  filter(EXPGRP_TEXT != "Non-Chinese Asian" &
           !(CONTINENT_BORN_TEXT_1 %in% c("4Tigers and Japan", "Africa", "Middle East",
                                        "North America", "Oceania", "South America")) &
           !(is.na(CONTINENT_BORN_TEXT_1)) &
           !(SEX_TEXT %in% c("Other", "Transgender")) &
           HH_INCOME_TEXT != "$500,000 or more") 

write_csv(map, file = "./data/dataformapqgis2.csv")
```



```{r}
df$CONTINENT_BORN_TEXT_1 %>% freq()
```

```{r}
df %>%
  filter(CONTINENT_BORN_TEXT_1=="Developping Asia") %>%
  select(HAS_LIVED_USA) %>%
  freq()
```


```{r}
df %>%
  filter(LocationLongitude<150,
         LocationLongitude>83) %>%
  dplyr::select(LocationLongitude)
```


## Get the city of the Location using Geopapify API

```{r}
library(rvest)
```



```{r}
nth2 <- function(x,j){x[j]}
l_City_scrap <- list()
scrapeur <- function(long, lat){
  res <- list()
  if(is.na(long)){
        res[["Loc_adresse_formated"]] <- NA
        
        res[["Loc_country"]] <- NA
        
        res[["Loc_state"]] <- NA
        
        res[["Loc_county"]] <- NA
        
        res[["Loc_city"]] <- NA
  
  }else{
    
    url <- paste0("https://api.geoapify.com/v1/geocode/reverse?lat=", lat, "&lon=", long, "&format=json&apiKey=273db677e751486db09fd2fedb9038ac")

      
    test <- tryCatch({read_html(url)}, error=function(e){return(T)})
    
    if(is.logical(test)){
        res[["Loc_adresse_formated"]] <- "APIERROR"
        
        res[["Loc_country"]] <- "APIERROR"
        
        res[["Loc_state"]] <- "APIERROR"
        
        res[["Loc_county"]] <- "APIERROR"
        
        res[["Loc_city"]] <- "APIERROR"
    }else{
      
      page_html <- read_html(url)
      infos <- html_nodes(page_html,'p') %>% html_text %>% jsonlite::fromJSON()
      
      if(length(infos$results) == 0){
        res[["Loc_adresse_formated"]] <- "NOTFOUND"
        
        res[["Loc_country"]] <- "NOTFOUND"
        
        res[["Loc_state"]] <- "NOTFOUND"
        
        res[["Loc_county"]] <- "NOTFOUND"
        
        res[["Loc_city"]] <- "NOTFOUND"
      }else{
        match <- infos$results$rank$match_type %>%
          as_vector()
        
        j <- c(1:length(match))
        
        if(any(str_detect(match, "full_match"))){
          j <- match %>%
            str_detect("full_match") %>%
            which()
        }
        
        res[["Loc_adresse_formated"]] <- infos$results$formatted
        
        res[["Loc_country"]] <- infos$results$country
        
        res[["Loc_state"]] <- infos$results$state
        
        res[["Loc_county"]] <- infos$results$county
        
        res[["Loc_city"]] <- infos$results$city
      }
      
      
    }
  }
  
  return(res)
}
scrapeur(df$LocationLongitude[3], df$LocationLatitude[3])
```




```{r}
for(i in c(558:length(df$LocationLatitude))){
  print(i)
  l_City_scrap[[i]] <- scrapeur(df$LocationLongitude[i], df$LocationLatitude[i])
  rt <- round(i/nrow(df)*100, digits=1) %>% as.character() %>% paste0(" %")
  print(rt)
  Sys.sleep(0.085)
  save(l_City_scrap, file = "./data/l_city_GPS_scrap.rdata")
}
```




```{r}
df_City <- do.call("rbind", l_City_scrap) %>% 
  as_tibble() %>%
  map_dfc(unlist)
df_City$ID <- df$ID

write.csv(df_City, file = "./data/coordoneesgpsscrap2.csv")
```




```{r}
d <- df %>%
  left_join(df_City, by='ID')

write.csv(df_City, file = "./data/mjolnir_clean_v7_GPS.csv")

```




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