Can I make a worldmap with the prompt function in R?-CodePudding

I'm new to programming and I've stumbled upon something I cannot resolve, and I was hoping you guys can.

I'm working with the FIFA 19 data set. See a section of the data set below:

               Name Nationality Overall                Club Balance
1          L. Messi   Argentina      94        FC Barcelona      95
2 Cristiano Ronaldo    Portugal      94            Juventus      70
3         Neymar Jr      Brazil      92 Paris Saint-Germain      84
4            De Gea       Spain      91   Manchester United      43
5      K. De Bruyne     Belgium      91     Manchester City      77
6         E. Hazard     Belgium      91             Chelsea      94

What I want is a worldmap where you can insert the club with the readline(prompt) function, and then gives you the map, with all the players of that club, and then displaying where in the world those players come from (so the count for each nationality per club, if that makes sense).

I saw on the following website how I could make a worldmap of this data set.

https://medium.com/@emrerencberoglu/visualization-of-fifa-2019-player-ratings-on-world-map-with-r-3c169a13347b

The code for making a world map of total player count (so for the whole data set), is the following. It gives me the right output.

overall_data <- fifa %>% 
  group_by(Nationality) %>% 
  summarise(Count = n(), 
            Avg_Overall = mean(Overall),
            Avg_Potential = mean(Potential),
            Avg_Pot_Diff = mean(Potential-Overall))

worldmap = map_data("world")

merged_data <- merge(x = worldmap, y = overall_data, by.x = "region", by.y = "Nationality", all.x = TRUE) %>% arrange(order)

ggplot(data = merged_data, aes(x = long, y = lat, group = group))  
  geom_polygon(aes(fill = Count))  
  labs(fill='Total Player Counts')

Namely, this worldmap: Worldmap Total Player Count

But this does not allow me to make different maps for each club. So I made a separate data file, that counts for each club, how many of each nationality there is. I made a crosstab using the crosstab() function from this source: ("http://pcwww.liv.ac.uk/~william/R/crosstab.r").This is the code I used:

nat_per_club <- crosstab(fifa, row.vars = "Nationality", col.vars = "Club", type ="f")
nat_per_club_crosstab <- nat_per_club$crosstab
nat_per_club_dataframe <- data.frame(nat_per_club_crosstab)
nat_per_club_dataframe <- nat_per_club_dataframe[!(nat_per_club_dataframe$Freq==0), ]

This gives me the following table:

    Nationality                 Club Freq
8       Armenia  SSV Jahn Regensburg    1
60      Germany  SSV Jahn Regensburg   19
87       Kosovo  SSV Jahn Regensburg    1
94    Lithuania  SSV Jahn Regensburg    1
104     Morocco  SSV Jahn Regensburg    1
121      Poland  SSV Jahn Regensburg    1

This is exactly what I want, as it shows per club and per nationality, the frequency. Now the question is, how can I implement it in the world map with the readline(prompt) function. So that if I insert e.g., SSV Jahn Regensburg, that it shows me the world map of where in the world their players come from?

This is what I tried but it unfortunately doesn't work.

worldmap2 = map_data("world")

merged_data2 <- merge(x = worldmap2, y = nat_per_club_dataframe, by.x = "region", by.y = "Nationality", all.x = TRUE) %>% arrange(order)

ggplot(data = merged_data2, aes(x = long, y = lat, group = group))  
  geom_polygon(aes(fill = Freq))  
  labs(fill='Total Player Count')

players_from_where <- function() {
  club_name <- as.character(readline(prompt="Club: "))
  for(i in 1:nrow(nat_per_club_dataframe)){
    if(nat_per_club_dataframe[i, "Club"] == club_name){
    ggplot(data = merged_data2, aes(x = long, y = lat, group = group))  
    geom_polygon(aes(fill = Freq))  
    labs(fill='Total Player Counts')
    }
  }
}

I hope someone can help!

CodePudding user response：

There is no need for a for loop. You could simply filter your aggregated dataset using dplyr::filter or subset for the inputted club name, merge it to the map data and plot.

library(tidyverse)

players_from_where <- function(club_name) {
  x <- filter(nat_per_club_dataframe, Club %in% club_name)

  worldmap <- map_data("world")

  merged_data2 <- merge(x = worldmap, y = x, by.x = "region", by.y = "Nationality", all.x = TRUE) %>%
    arrange(order)

  ggplot(data = merged_data2, aes(x = long, y = lat, group = group))  
    geom_polygon(aes(fill = n))  
    labs(fill = "Total Player Counts")
}

# club_name <- as.character(readline(prompt = "Club: "))
club_name <- "FC Barcelona"

players_from_where(club_name)

DATA

nat_per_club_dataframe <- structure(list(Nationality = c(
  "Argentina", "Belgium", "Brazil",
  "Chile", "Croatia", "France", "Germany", "Netherlands", "Portugal",
  "Senegal", "Spain", "Uruguay", "Austria", "Brazil", "Colombia",
  "France", "Germany", "Netherlands", "Poland", "Portugal", "Spain"
), Club = c(
  "FC Barcelona", "FC Barcelona", "FC Barcelona", "FC Barcelona",
  "FC Barcelona", "FC Barcelona", "FC Barcelona", "FC Barcelona",
  "FC Barcelona", "FC Barcelona", "FC Barcelona", "FC Barcelona",
  "FC Bayern München", "FC Bayern München", "FC Bayern München",
  "FC Bayern München", "FC Bayern München", "FC Bayern München",
  "FC Bayern München", "FC Bayern München", "FC Bayern München"
), n = c(
  1L, 1L, 4L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 17L, 1L, 1L,
  1L, 1L, 3L, 18L, 1L, 1L, 1L, 2L
)), class = "data.frame", row.names = c(
  NA,
  -21L
))