I am trying to scrape name/address information from yellowpages (https://www.yellowpages.ca/). I have a function (from :(R) Webscraping Error : arguments imply differing number of rows: 1, 0) that is able to retrieve this information:
library(rvest)
library(dplyr)
scraper <- function(url) {
page <- url %>%
read_html()
tibble(
name = page %>%
html_elements(".jsListingName") %>%
html_text2(),
address = page %>%
html_elements(".listing__address--full") %>%
html_text2()
)
}
However, sometimes the address information is not always present. For example : there are several barbers listed on this page https://www.yellowpages.ca/search/si/1/barber/Sudbury+ON and they all have addresses except one of them. As a result, when I run this function, I get the following error:
scraper("https://www.yellowpages.ca/search/si/1/barber/Sudbury+ON")
Error:
! Tibble columns must have compatible sizes.
* Size 14: Existing data.
* Size 12: Column `address`.
i Only values of size one are recycled.
Run `rlang::last_error()` to see where the error occurred.
My Question: Is there some way that I can modify the definition of the "scraper" function in such a way, such that when no address is listed, an NA appears in that line? For example:
barber address
1 barber111 address111
2 barber222 address222
3 barber333 NA
Is there some way I could add a statement similar to CASE WHEN that would grab the address or place an NA when the address is not there?
In order to match the businesses with their addresses, it is best to find a root node for each listing and get the text from the relevant child node. If the child node is empty, you can add an NA
library(rvest)
library(dplyr)
scraper <- function(url) {
nodes <- read_html(url) %>% html_elements(".listing_right_section")
tibble(name = nodes %>% sapply(function(x) {
x <- html_text2(html_elements(x, css = ".jsListingName"))
if(length(x)) x else NA}),
address = nodes %>% sapply(function(x) {
x <- html_text2(html_elements(x, css = ".listing__address--full"))
if(length(x)) x else NA}))
}
So now we can do:
scraper("https://www.yellowpages.ca/search/si/1/barber/Sudbury+ON")
#> # A tibble: 14 x 2
#> name address
#> <chr> <chr>
#> 1 Lords'n Ladies Hair Design 1560 Lasalle Blvd, Sudbury, ON P3A~
#> 2 Jo's The Lively Barber 611 Main St, Lively, ON P3Y 1M9
#> 3 Hairapy Studio 517 & Barber Shop 517 Notre Dame Ave, Sudbury, ON P3~
#> 4 Nickel Range Unisex Hairstyling 111 Larch St, Sudbury, ON P3E 4T5
#> 5 Ugo Barber & Hairstyling 911 Lorne St, Sudbury, ON P3C 4R7
#> 6 Gordon's Hairstyling 19 Durham St, Sudbury, ON P3C 5E2
#> 7 Valley Plaza Barber Shop 5085 Highway 69 N, Hanmer, ON P3P ~
#> 8 Rick's Hairstyling Shop 28 Young St, Capreol, ON P0M 1H0
#> 9 President Men's Hairstyling & Barber Shop 117 Elm St, Sudbury, ON P3C 1T3
#> 10 Pat's Hairstylists 33 Godfrey Dr, Copper Cliff, ON P0~
#> 11 WildRootz Hair Studio 911 Lorne St, Sudbury, ON P3C 4R7
#> 12 Sleek Barber Bar 324 Elm St, ON P3C 1V8
#> 13 Faiella Classic Hair <NA>
#> 14 Ben's Barbershop & Hairstyling <NA>
Created on 2022-09-16 with reprex v2.0.2
Perhaps even simpler solution
library(tidyverse)
library(rvest)
scraper <- function(url) {
page <- url %>%
read_html() %>%
html_elements(".listing_right_top_section")
tibble(
name = page %>%
html_element(".jsListingName") %>%
html_text2(),
address = page %>%
html_element(".listing__address--full") %>%
html_text2()
)
}
# A tibble: 14 x 2
name address
<chr> <chr>
1 Lords'n Ladies Hair Design 1560 Lasalle Blvd, Sudbury, ON P3A 1Z7
2 Jo's The Lively Barber 611 Main St, Lively, ON P3Y 1M9
3 Hairapy Studio 517 & Barber Shop 517 Notre Dame Ave, Sudbury, ON P3C 5L1
4 Nickel Range Unisex Hairstyling 111 Larch St, Sudbury, ON P3E 4T5
5 Ugo Barber & Hairstyling 911 Lorne St, Sudbury, ON P3C 4R7
6 Gordon's Hairstyling 19 Durham St, Sudbury, ON P3C 5E2
7 Valley Plaza Barber Shop 5085 Highway 69 N, Hanmer, ON P3P 1J6
8 Rick's Hairstyling Shop 28 Young St, Capreol, ON P0M 1H0
9 President Men's Hairstyling & Barber Shop 117 Elm St, Sudbury, ON P3C 1T3
10 Pat's Hairstylists 33 Godfrey Dr, Copper Cliff, ON P0M 1N0
11 WildRootz Hair Studio 911 Lorne St, Sudbury, ON P3C 4R7
12 Sleek Barber Bar 324 Elm St, ON P3C 1V8
13 Faiella Classic Hair NA
14 Ben's Barbershop & Hairstyling NA
I'm trying to parse some data to a sheet with VBA. My code work's fine when all data in the JSON are provided, but when there is no (team1)(name) or (team1)(id), I get an error of incompatible data.
The code is below! It wrote 3 or 4 lines of data before the error.
The JSON data is more below.
IS there any way to avoid registers that don’t have all data or just write an “empty” value when the data is null?
Dim jsonText As String
Dim jsonObject As Object, item As Object
Dim i As Long
Dim ws As Worksheet
Set ws = Worksheets("Matchs")
jsonText = ws.Cells(1, 1)
Dim http As Object, JSON As Object
Set http = CreateObject("MSXML2.XMLHTTP")
http.Open "GET", "getMatches.json", False
http.Send
Set JSON = ParseJson(http.responseText)
i = 2
For Each item In JSON
ws.Cells(i, 1) = item("id")
ws.Cells(i, 2) = item("date")
ws.Cells(i, 3) = item("title")
ws.Cells(i, 5) = item("team1")("name")
ws.Cells(i, 6) = item("team1")("id")
i = i + 1
Next
The JSON =>
[{"id":2342835,"date":1594731600000,
"team1":{"name":"FATE","id":9863},
"team2":{"name":"Budapest Five","id":9802},
"format":"bo1",
"event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},
"stars":0,"live":false},
{"id":2342836,"date":1594731600000,
"team1":{"name":"PACT","id":8248},
"team2":{"name":"Singularity","id":6978},
"format":"bo1",
"event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},
"stars":0,"live":false},
{"id":2342843,"date":1594735200000,
"title":"Malta Vibes 3 - Group A Winners' Match",
"stars":0,"live":false},
{"id":2342862,"date":1594735200000,
"team1":{"name":"Nexus","id":7187},
"team2":{"name":"BIG Academy","id":10254},"format":"bo3","event":{"name":"Betano Masters Europe 2020","id":5427},"stars":0,"live":false},{"id":2342834,"date":1594746000000,"team1":{"name":"sAw","id":10567},"team2":{"name":"Nexus","id":7187},"format":"bo3","event":{"name":"ESEA Advanced Season 34 Europe","id":5415},"stars":1,"live":false},{"id":2342844,"date":1594746000000,"title":"Malta Vibes 3 - Group A Elimination Match","stars":0,"live":false},{"id":2342863,"date":1594750500000,"team1":{"name":"Unicorns of Love","id":9812},"team2":{"name":"Giants","id":4949},"format":"bo3","event":{"name":"Betano Masters Europe 2020","id":5427},"stars":0,"live":false},{"id":2342801,"date":1594751400000,"team1":{"name":"Secret","id":10488},"team2":{"name":"Tricked","id":4602},"format":"bo3","event":{"name":"ESEA Advanced Season 34 Europe","id":5415},"stars":0,"live":false},{"id":2342845,"date":1594756800000,"title":"Malta Vibes 3 - Group A Decider Match","stars":0,"live":false},{"id":2342803,"date":1594774800000,"team1":{"name":"Thunder Logic","id":9615},"team2":{"name":"RBG","id":10258},"format":"bo3","event":{"name":"ESEA Advanced Season 34 North America","id":5416},"stars":0,"live":false},{"id":2342864,"date":1594776600000,"team1":{"name":"Third Impact","id":10469},"team2":{"name":"Lethal Divide","id":10770},"format":"bo3","event":{"name":"ESEA Advanced Season 34 North America","id":5416},"stars":0,"live":false},{"id":2342816,"date":1594796400000,"team1":{"name":"Hard Legion","id":10421},"team2":{"name":"AGF","id":8704},"format":"bo3","event":{"name":"Nine to Five 1","id":5409},"stars":0,"live":false},{"id":2342817,"date":1594796400000,"team1":{"name":"Gambit Youngsters","id":9976},"team2":{"name":"ALTERNATE aTTaX","id":4501},"format":"bo3","event":{"name":"Nine to Five 1","id":5409},"stars":0,"live":false},{"id":2342818,"date":1594807200000,"title":"Nine to Five 1 Grand Final","stars":0,"live":false},{"id":2342837,"date":1594818000000,"team1":{"name":"HellRaisers","id":5310},"team2":{"name":"HONORIS","id":10737},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342838,"date":1594818000000,"team1":{"name":"AGF","id":8704},"team2":{"name":"CR4ZY","id":10150},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342846,"date":1594821600000,"title":"Malta Vibes 3 - Group B Winners' Match","stars":0,"live":false},{"id":2342847,"date":1594832400000,"title":"Malta Vibes 3 - Group B Elimination Match","stars":0,"live":false},{"id":2342848,"date":1594843200000,"title":"Malta Vibes 3 - Group B Decider Match","stars":0,"live":false},{"id":2342532,"date":1594890000000,"team1":{"name":"Rooster","id":9881},"team2":{"name":"Paradox","id":7983},"format":"bo3","event":{"name":"LPL Pro League Season 5","id":5319},"stars":0,"live":false},{"id":2342839,"date":1594893600000,"team1":{"name":"Gambit Youngsters","id":9976},"team2":{"name":"Lyngby Vikings","id":8963},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342840,"date":1594897200000,"team1":{"name":"Illuminar","id":8813},"team2":{"name":"AVEZ","id":9797},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342800,"date":1594899000000,"team1":{"name":"Mako","id":10507},"team2":{"name":"TRUCKERS WITH ATTITUDE","id":10713},"format":"bo3","event":{"name":"LPL Pro League Season 5","id":5319},"stars":0,"live":false},{"id":2342849,"date":1594900800000,"title":"Malta Vibes 3 - Group C Winners' Match","stars":0,"live":false},{"id":2342850,"date":1594911600000,"title":"Malta Vibes 3 - Group C Elimination Match","stars":0,"live":false},{"id":2342851,"date":1594922400000,"title":"Malta Vibes 3 - Group C Decider Match","stars":0,"live":false},{"id":2342824,"date":1594962000000,"team1":{"name":"Invictus","id":7966},"team2":{"name":"D13","id":8607},"format":"bo3","event":{"name":"Perfect World Asia League Summer 2020","id":5376},"stars":0,"live":false},{"id":2342825,"date":1594971000000,"team1":{"name":"ViCi","id":7606},"team2":{"name":"Lucid Dream","id":8680},"format":"bo3","event":{"name":"Perfect World Asia League Summer 2020","id":5376},"stars":0,"live":false},{"id":2342826,"date":1594980000000,"team1":{"name":"TYLOO","id":4863},"team2":{"name":"Divine Vendetta","id":10396},"format":"bo3","event":{"name":"Perfect World Asia League Summer 2020","id":5376},"stars":0,"live":false},{"id":2342841,"date":1594980000000,"team1":{"name":"Hard Legion","id":10421},"team2":{"name":"SG.pro","id":10105},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342842,"date":1594983600000,"team1":{"name":"ALTERNATE aTTaX","id":4501},"team2":{"name":"Syman","id":8772},"format":"bo1","event":{"name":"Eden Arena Malta Vibes Cup 3","id":5426},"stars":0,"live":false},{"id":2342852,"date":1594987200000,"title":"Malta Vibes 3 - Group D Winners' Match","stars":0,"live":false},{"id":2342827,"date":1594989000000,"team1":{"name":"TIGER","id":10661},"team2":{"name":"Beyond","id":8262},"format":"bo3","event":{"name":"Perfect World Asia League Summer 2020","id":5376},"stars":0,"live":false},{"id":2342853,"date":1594998000000,"title":"Malta Vibes 3 - Group D Elimination Match","stars":0,"live":false},{"id":2342854,"date":1595008800000,"title":"Malta Vibes 3 - Group D Decider Match","stars":0,"live":false},{"id":2342855,"date":1595059200000,"title":"Malta Vibes 3 - Quarter-Final #1","stars":0,"live":false},{"id":2342828,"date":1595062800000,"title":"PAL Summer - Semi-final #1","stars":0,"live":false},{"id":2342856,"date":1595070000000,"title":"Malta Vibes 3 - Quarter-Final #2","stars":0,"live":false},{"id":2342829,"date":1595073600000,"title":"PAL Summer - Semi-final #2","stars":0,"live":false},{"id":2342857,"date":1595080800000,"title":"Malta Vibes 3 - Quarter-Final #3","stars":0,"live":false},{"id":2342858,"date":1595091600000,"title":"Malta Vibes 3 - Quarter-Final #4","stars":0,"live":false},{"id":2342830,"date":1595149200000,"title":"PAL Summer - 3rd Place Decider","stars":0,"live":false},{"id":2342859,"date":1595152800000,"title":"Malta Vibes 3 - Semi-Final #1","stars":0,"live":false},{"id":2342831,"date":1595160000000,"title":"PAL Summer - Grand Final","stars":0,"live":false},{"id":2342860,"date":1595163600000,"title":"Malta Vibes 3 - Semi-Final #2","stars":0,"live":false},{"id":2342861,"date":1595174400000,"title":"Malta Vibes 3 - Grand Final","stars":0,"live":false},{"id":2342643,"date":1597134600000,"team1":{"name":"Ground Zero","id":8536},"team2":{"name":"Paradox","id":7983},"format":"bo3","event":{"name":"ESL Australia & NZ Championship Season 11","id":5318},"stars":0,"live":false},{"id":2342520,"date":1597147200000,"team1":{"name":"Bantz","id":10712},"team2":{"name":"TRUCKERS WITH ATTITUDE","id":10713},"format":"bo3","event":{"name":"ESL Australia & NZ Championship Season 11","id":5318},"stars":0,"live":false}]
You can use the dictionary Exists method:
If item.Exists("team1") Then
If item("team1").Exists("name") Then
'record the name
End If
End If
I'm just learning how to use R to scrape data from webpages, and I'm running into a couple of issues.
For reference, the website that I am practicing on is here: http://www.rsssf.com/tables/34q.html
As far as I know, the website I am scraping data from is not a table so I can't directly scrape the information into a table, so here is the code I wrote to just have all of the text:
wcq_1934_html <- read_html("http://www.rsssf.com/tables/34q.html")
wcq_1934_node <- html_nodes(wcq_1934_html, "pre")
wcq_1934_text <- html_text(wcq_1934_node, trim = TRUE)
This results in a very long text file with all of the information that I need, just not formatted in an ideal way.
So I am next attempting to substring this text in order to get an output that looks something like this.
Country A - Country A Score - Country B - Country B Score
It doesn't have to be exactly like this, I just basically need for each game the country and how many goals they scored and ideally it should be comparable with the other country from the same game so I can know who won or lost! I do not need any of the other information like where the game was played, etc.
So I've tried three different ways to get this:
First test: split text by dashes:
test <- strsplit(wcq_1934_text, "-")
df_test <- data.frame(test)
This gives me the information I need in a table but the rows don't match the exact scores that I need (i.e. Lithuania 0, and Sweden 2 are in separate rows)
Second test: split text by spaces:
test2 <- strsplit(wcq_1934_text, " ")
df_test2 <- data.frame(test2)
This is helpful because it gives me the scores in one row (0-2 for the first game), but the countries are unevenly spaced out across rows.
Third test: split text by "tabs"
test3 <- strsplit(wcq_1934_text, " ")
df_test3 <- data.frame(test3)
This has a similar issue to the first test.
Any suggestions would be much appreciated. This is my first ever Stack Overflow post, although I've lurked around and this website has been helpful to me for a very long time. Thank you in advance!
Here's a solution that provides you most of what you need, though as MrFlick commented, it is a little fragile to this page. I'll stay with rvest, though as biomiha suggested, it isn't really buying you a lot here (though it does cleanly break out the <pre> block).
Starting with your wcq_1934_text, it's a single long string, let's break it up by newlines (CRLF in this case):
wcq_1934_text <- strsplit(wcq_1934_text, "[\r\n]+")[[1]]
str(wcq_1934_text)
# chr [1:51] "Hosts: Italy (not automatically qualified)" "Holders: Uruguay (did not enter)" "Group 1 [Sweden]" ...
I'll the magrittr package merely because it helps break out each step of the process using the %>% non-pipe; you can convert it non-magrittr by changing (say) func1() %>% func2() %>% func3() to func3(func2(func1())) (yuck) or intermediate assignment of return values, ret1 <- func1(); ret2 <- func2(ret1); ....
library(magrittr)
dat <- Filter(function(a) grepl("^[0-9][0-9]", a), wcq_1934_text) %>%
paste(., collapse = "\n") %>%
textConnection() %>%
read.fwf(file = ., widths = c(10, 16, 17, 4, 99), stringsAsFactors = FALSE) %>%
lapply(trimws) %>%
as.data.frame(stringsAsFactors = FALSE)
The widths are fragile and unique to this page. If other reporting pages have slightly different column layouts, you'll need to use a different function, perhaps one that can automatically determine the breaks.
head(dat)
# V1 V2 V3 V4 V5
# 1 11.06.33 Stockholm Sweden 6-2 Estonia
# 2 29.06.33 Kaunas Lithuania 0-2 Sweden
# 3 11.03.34 Madrid Spain 9-0 Portugal
# 4 18.03.34 Lisboa Portugal 1-2 Spain
# 5 25.03.34 Milano Italy 4-0 Greece
# 6 25.03.34 Sofia Bulgaria 1-4 Hungary
From here, it's up to you which columns you want to use.
For instance, handling of the date, you might want:
dat$V1 <- as.POSIXct(gsub("([0-9]+)$", "19\\1", dat$V1), format = "%d.%m.%Y")
dat$V1
# [1] "1933-06-11 PST" "1933-06-29 PST" "1934-03-11 PST" "1934-03-18 PST" "1934-03-25 PST" "1934-03-25 PST" "1934-04-25 PST" "1934-04-29 PST"
# [9] "1933-10-15 PST" "1934-03-15 PST" "1933-09-24 PST" "1933-10-29 PST" "1934-04-29 PST" "1934-02-25 PST" "1934-04-08 PST" "1934-04-29 PST"
# [17] "1934-03-11 PST" "1934-04-15 PST" "1934-01-28 PST" "1934-02-01 PST" "1934-02-04 PST" "1934-03-04 PST" "1934-03-11 PST" "1934-03-18 PST"
# [25] "1934-05-24 PST" "1934-03-16 PST" "1934-04-06 PST"
The gsub stuff is because as.POSIXct assumes 2-digit years less than 69 are in the 20th century, 19th for 69-99.
It's easy enough to use either strsplit on the scores, but you could also do:
library(tidyr)
dat %>%
separate(V4, c("score1", "score2"), sep="-") %>%
head()
# Warning: Too few values at 1 locations: 10
# V1 V2 V3 score1 score2 V5
# 1 1933-06-11 Stockholm Sweden 6 2 Estonia
# 2 1933-06-29 Kaunas Lithuania 0 2 Sweden
# 3 1934-03-11 Madrid Spain 9 0 Portugal
# 4 1934-03-18 Lisboa Portugal 1 2 Spain
# 5 1934-03-25 Milano Italy 4 0 Greece
# 6 1934-03-25 Sofia Bulgaria 1 4 Hungary
(The warning is expected, since one game was not played so has "n/p" for a score. You might want to handle non-score values in V4 before trying the split, perhaps replacing anything not numeric-dash-numeric with NA.)
Equally specific to this particular site but may be easier to generalize:
library(rvest)
library(purrr)
library(dplyr)
library(stringi)
pg <- read_html("http://www.rsssf.com/tables/34q.html")
Target the <pre> and strip out some things that aren't part of "tables":
html_nodes(pg, "pre") %>%
html_text() %>%
stri_split_lines() %>%
flatten_chr() %>%
discard(stri_detect_regex, "^(NB| )") -> lines
Now, we get the start and end lines indexes of each "group":
starts <- which(grepl("^Group", lines))
ends <- c(starts[-1], length(lines))
We iterate over those starts and ends and:
extract the group info
clean up the table
discard any "empty" tables
turn the tabular data into a data frame, doing some munging along the way
I can annotate the following more if needed:
map2_df(starts, ends, ~{
grp_info <- stri_match_all_regex(lines[.x], "Group ([[:digit:]]+) \\[(.*)]")[[1]][,2:3]
lines[(.x+1):.y] %>%
discard(stri_detect_regex, "(^[^[:digit:]]| round)") %>%
discard(`==`, "") -> grp
if (length(grp) == 0) return(NULL)
stri_split_regex(grp, "\ \ +") %>%
map_df(~{
.x[1:4] %>%
as.list() %>%
set_names(c("date", "team_a", "team_b", "score_team")) %>%
flatten_df() %>%
separate(score_team, c("score", "team_c"), sep=" ") %>%
mutate(group_num = grp_info[1], group_info = grp_info[2]) %>%
separate(date, c("d", "m", "y")) %>%
mutate(date = as.Date(sprintf("19%s-%s-%s", y, m, d))) %>%
select(-d, -m, -y)
})
})
## # A tibble: 27 x 7
## team_a team_b score team_c group_num group_info date
## <chr> <chr> <chr> <chr> <chr> <chr> <date>
## 1 Stockholm Sweden 6-2 Estonia 1 Sweden 1933-06-11
## 2 Kaunas Lithuania 0-2 Sweden 1 Sweden 1933-06-29
## 3 Madrid Spain 9-0 Portugal 2 Spain 1934-03-11
## 4 Lisboa Portugal 1-2 Spain 2 Spain 1934-03-18
## 5 Milano Italy 4-0 Greece 3 Italy 1934-03-25
## 6 Sofia Bulgaria 1-4 Hungary 4 Hungary, Austria 1934-03-25
## 7 Wien Austria 6-1 Bulgaria 4 Hungary, Austria 1934-04-25
## 8 Budapest Hungary 4-1 Bulgaria 4 Hungary, Austria 1934-04-29
## 9 Warszawa Poland 1-2 Czechoslovakia 5 Czechoslovakia 1933-10-15
## 10 Praha Czechoslovakia n/p Poland 5 Czechoslovakia 1934-03-15
## 11 Beograd Yugoslavia 2-2 Switzerland 6 Romania, Switzerland 1933-09-24
## 12 Bern Switzerland 2-2 Romania 6 Romania, Switzerland 1933-10-29
## 13 Bucuresti Romania 2-1 Yugoslavia 6 Romania, Switzerland 1934-04-29
## 14 Dublin Ireland 4-4 Belgium 7 Netherlands, Belgium 1934-02-25
## 15 Amsterdam Netherlands 5-2 Ireland 7 Netherlands, Belgium 1934-04-08
## 16 Antwerpen Belgium 2-4 Netherlands 7 Netherlands, Belgium 1934-04-29
## 17 Luxembourg Luxembourg 1-9 Germany 8 Germany, France 1934-03-11
## 18 Luxembourg Luxembourg 1-6 France 8 Germany, France 1934-04-15
## 19 Port-au-Prince Haiti 1-3 Cuba 11 USA 1934-01-28
## 20 Port-au-Prince Haiti 1-1 Cuba 11 USA 1934-02-01
## 21 Port-au-Prince Haiti 0-6 Cuba 11 USA 1934-02-04
## 22 Cd. de Mexico Mexico 3-2 Cuba 11 USA 1934-03-04
## 23 Cd. de Mexico Mexico 5-0 Cuba 11 USA 1934-03-11
## 24 Cd. de Mexico Mexico 4-1 Cuba 11 USA 1934-03-18
## 25 Roma USA 4-2 Mexico 11 USA 1934-05-24
## 26 Cairo Egypt 7-1 Palestina 12 Egypt 1934-03-16
## 27 Tel Aviv Palestina 1-4 Egypt 12 Egypt 1934-04-06