Trying to scrape the first 8 tables (very high, high, medium, low) from the human development index in Wikipedia.
Started with but getting a list of zero. What am I doing wrong? New to R :(
libray(rvest)
url <- "https://en.wikipedia.org/wiki/List_of_countries_by_Human_Development_Index#Complete_list_of_countries"
webpage <- read_html(url)
hdi_tables <- html_nodes(webpage, 'table')
head(hdi_tables, n = 10)
scrape <- url %>%
read_html() %>%
html_nodes(xpath = '//*[#id="mw-content-text"]/div/div[5]/table/tbody/tr/td[1]/table') %>%
html_table()
head(scrape, n=10)
I think it would be easier to work with the original data source:
Select "Human Development Index (HDI)" in both the drop-down select lists, then click the "Download Data" link to get a CSV file named Human Development Index (HDI).csv.
Read it into R:
library(tidyverse)
Human_Development_Index_HDI_ <- read_csv("path/to/Human Development Index (HDI).csv",
skip = 1)
You can reshape the data, get the values for 2015 and classify countries as low, medium, high or very high:
hdi <- Human_Development_Index_HDI_ %>%
gather(Year, HDI, -`HDI Rank (2015)`, -Country) %>%
filter(Year == "2015") %>%
na.omit() %>%
mutate(Year = as.numeric(Year),
classification = cut(HDI,
breaks = c(0, 0.549, 0.699, 0.799, 1),
labels = c("low", "medium", "high", "very_high")))
hdi
# A tibble: 188 x 5
`HDI Rank (2015)` Country Year HDI classification
<int> <chr> <dbl> <dbl> <fctr>
1 169 Afghanistan 2015 0.479 low
2 75 Albania 2015 0.764 high
3 83 Algeria 2015 0.745 high
4 32 Andorra 2015 0.858 very_high
5 150 Angola 2015 0.533 low
6 62 Antigua and Barbuda 2015 0.786 high
7 45 Argentina 2015 0.827 very_high
8 84 Armenia 2015 0.743 high
9 2 Australia 2015 0.939 very_high
10 24 Austria 2015 0.893 very_high
# ... with 178 more rows
You could change the filter to get values for 2014 too, if you want to replicate the "change from previous year" values in the Wikipedia table.
If you're okay with parsing the wikipedia markup language instead, you could try using WikipediR to grab the markup of the page (from skimming the documentation, try page_content with as_wikitext set to true). Then you'll get some lines that all look like this:
| 1 || {{steady}} ||style="text-align:left"| {{flag|Norway}} || 0.949 || {{increase}} 0.001
This should be parseable in R using strsplit or something.
Related
Recently, https://www.oddsportal.com/ changed their format. I can no longer use the html_table() to parse the game result table. It seems like the only option here is to use html_text2()and reconstruct the table manually.
library(RSelenium)
library(rvest)
library(dplyr)
library(stringr)
url_results <- "https://www.oddsportal.com/basketball/australia/nbl/results/"
rD <- rsDriver(port= sample(7600)[1], browser=c("firefox"), chromever = NULL)
remDr <- rD$client ; remDr$navigate(url_results)
try(remDr$findElement(using = "xpath", '//*[#id="onetrust-accept-btn-handler"]')$clickElement())
page <- remDr$getPageSource() ; remDr$close() ; rD$server$stop()
# R_table <- 0
# pop <- page[[1]] %>%
# read_html() %>%
# html_nodes(xpath='//*[#id="tournamentTable"]') %>%
# html_table()
# try(R_table <- pop[[1]])
# table <- R_table
R_table <- 0
pop <- page[[1]] %>%
read_html() %>%
html_nodes(xpath=paste0('//*[#id="app"]/div/div[1]/div/main/div[2]/div[7]')) %>%
html_text2()
try(R_table <- pop[[1]])
table <- R_table
Would anyone know good ways to reconstruct the table the way the website represents? This is the outcome I used to get before they changed the format by using html_table() :
V1 V2 V3 V4 V5
Today, 10 Jan 1 2 B's
21:30 Perth – New Zealand Breakers 93:90 1.98 1.79 16
19:30 Illawarra Hawks – Tasmania JackJumpers 89:92 3.95 1.24 16
08 Jan 2023 1 2 B's
16:00 Cairns Taipans – South East Melbourne 94:85 1.54 2.43 16
14:00 Adelaide – New Zealand Breakers 83:85 1.91 1.85 16
I have a CSV, extracted from an HTML site, many columns hold a lot of information in one cell. for example- this text is from one cell. It holds the name of 3 companies:
[{"company":"Orange","location":"","url":"https://www.xyz","positions":[{"title":"CEO","subtitle":"honelulu","description":"","duration":"Dec 2021 - Present 7 months"}] ,"industry":"Non-profit Organizations"},{"company":"Fig","location":"","url":"https://www.xyz2","positions":[{"title":"Business Development Manager","subtitle":"Fig","duration":"Feb 2019 Dec 2021 2 years 11 months",}],},
{"company":"Papaya","location":"","url":"https://www.xyz3","positions":[{"title":"Business Development Manager","subtitle":"Pragaya","description":"","duration":"Jan 2018 Oct 2018 10 months",}],"industry":"High Tech"},}]
I would like to extract each company into a different row, with the user name, position, duration and industry in different columns.
I also have other date in other columns that I wish would stay the same.
Any ideas for a simple way to do this?
This tidyr approach with extract works for a start:
library(dplyr)
library(tidyr)
data.frame(dat) %>%
# simplify:
mutate(dat = gsub('["\\]\\[}{]', '', dat, perl = TRUE)) %>%
# separate:
separate_rows(dat, sep = '(?<!^)(?=company)') %>%
# extract:
extract(dat, "company", "company:([^,]+).*", remove = FALSE) %>%
extract(dat, "user_name", ".*url:([^,]+).*", remove = FALSE) %>%
extract(dat, "position", ".*\\btitle:([^,]+)", remove = FALSE)
# A tibble: 3 × 5
industry duration position user_name company
<chr> <chr> <chr> <chr> <chr>
1 Non-profit Organizations "Dec 2021 - Present 7 months " CEO https://www.xyz Orange
2 NA "Feb 2019 Dec 2021 2 years 11 months" Business Development Manager https://www.xyz2 Fig
3 High Tech "Jan 2018 Oct 2018 10 months" Business Development Manager https://www.xyz3 Papaya
Data:
dat <- '{"company":"Orange","location":"","url":"https://www.xyz","positions":[{"title":"CEO","subtitle":"honelulu","description":"","duration":"Dec 2021 - Present 7 months"}] ,"industry":"Non-profit Organizations"},{"company":"Fig","location":"","url":"https://www.xyz2","positions":[{"title":"Business Development Manager","subtitle":"Fig","duration":"Feb 2019 Dec 2021 2 years 11 months",}],},{"company":"Papaya","location":"","url":"https://www.xyz3","positions":[{"title":"Business Development Manager","subtitle":"Pragaya","description":"","duration":"Jan 2018 Oct 2018 10 months",}],"industry":"High Tech"},}]'
See also Use tidyr's function `extract` with optional capture group for a more elegant solution
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 am trying to scrape locations of Walmart in the State of Missouri using the link below:
https://www.walmart.com/store/finder?location=Missouri&distance=50
library(rvest)
library(xml2)
library(tidyverse)
url <- read_html("https://www.walmart.com/store/finder?location=Missouri&distance=50")
I used SelectorGadget to check what is in the NearbyStores and use it to extract store address.
Trying extracting the city first but I get nothing
url %>% html_elements(".city")
{xml_nodeset (0)}
Then I tried to extract address and store type but still get nothing.
url %>% html_elements(".result-element-address")
{xml_nodeset (0)}
url %>% html_elements(".result-element-store-type")
{xml_nodeset (0)}
I am trying to create a data frame with name of the city, and address
The tag you are looking for does not exist in the document you are requesting. It is built dynamically by javascript code after the page loads. Fortunately the actual data does exist on the page, in the form of a json string inside one of the script tags. This requires a bit of parsing, but contains all the info you need:
library(rvest)
library(xml2)
library(tidyverse)
url <- read_html("https://www.walmart.com/store/finder?location=Missouri&distance=50")
stores <- html_element(url, xpath = "//script[#id='storeFinder']") %>%
html_text() %>%
jsonlite::parse_json()
do.call(rbind, lapply(stores$storeFinder$storeFinderCarousel$stores,
function(x) as.data.frame(x$address)))
#> postalCode address city state country
#> 1 65401 500 S Bishop Ave Rolla MO US
#> 2 65584 185 Saint Robert Blvd Saint Robert MO US
#> 3 65453 100 Ozark Dr Cuba MO US
#> 4 65560 1101 W Highway 32 Salem MO US
#> 5 65066 1888 Highway 28 Owensville MO US
#> 6 63080 350 Park Ridge Rd Sullivan MO US
#> 7 65101 401 Supercenter Dr Jefferson City MO US
#> 8 65065 4252 Highway 54 Osage Beach MO US
#> 9 65483 1433 S Sam Houston Blvd Houston MO US
#> 10 65109 724 Stadium West Blvd Jefferson City MO US
#> 11 65026 1802 S Business 54 Eldon MO US
#> 12 65020 94 Cecil St Camdenton MO US
#> 13 65536 1800 S Jefferson Ave Lebanon MO US
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