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I want to scrape the Sector Weightings Table from the following link:

http://portfolios.morningstar.com/fund/summary?t=SPY&region=usa&culture=en-US&ownerCountry=USA

The table i want is table 6 in the website's source code. I have the following script written in R:

 library(rvest)
 turl = 'http://portfolios.morningstar.com/fund/summary?t=SPY'
 turlr = read_html(turl) 
 df6<-html_table(html_nodes(turlr, 'table')[[6]], fill = TRUE)

However when i run the last line of the script i get the following error message

Error in out[j + k, ] : subscript out of bounds

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4
  • You should see How to create a Minimal, Complete, and Verifiable example Commented Nov 26, 2017 at 23:44
  • Precisely you didn't include the important code that had produced this error Commented Nov 26, 2017 at 23:47
  • 1
    There are embedded charts and groupings in your target table. You will need to alter the returned node before it will be accepted by html_table. See this question for some guidance. Commented Nov 27, 2017 at 0:10
  • There are nigh countless R + scraping + morningstar posts on SO. Which ones did not have info that could have helped you? I'm constantly mystified abt this since it take more energy to create a q than to do an actual search. Commented Nov 27, 2017 at 1:25

1 Answer 1

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3

Since the required table is designed in a different way rvest is not able to format it into proper table. But using XML package you can do it quite easily.

library(XML)
library(dplyr)

#read required table
turl = 'http://portfolios.morningstar.com/fund/summary?t=SPY'
temp_table <- readHTMLTable(turl)[[6]]

#process table to readable format
final_table <- temp_table %>%
  select(V2, V3, V4, V5) %>%
  na.omit() %>%
  `colnames<-` (c("","% Stocks","Benchmark","Category Avg")) %>%
  `rownames<-` (seq_len(nrow(.)))
final_table

Output is:

                          % Stocks Benchmark Category Avg
1                Cyclical                                
2         Basic Materials     2.79      3.16         3.22
3       Consumer Cyclical    11.06     11.42        11.15
4      Financial Services    16.39     16.50        17.22
5             Real Estate     2.24      3.18         2.00
6               Sensitive                                
7  Communication Services     3.56      3.37         3.50
8                  Energy     5.83      5.79         5.79
9             Industrials    10.37     10.89        11.70
10             Technology    22.16     21.41        19.72
11              Defensive                                
12     Consumer Defensive     8.20      7.60         8.56
13             Healthcare    14.24     13.57        14.57
14              Utilities     3.15      3.11         2.59

Hope it helps!

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1 Comment

This is great. Thanks a lot!

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