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Editorial Team
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Asked: 2026-06-12T16:19:35+00:00

I’ve been searching for a while any answer to my question. I read this

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I’ve been searching for a while any answer to my question. I read this, this and this and some others related but I still get not answer.

My problem is quite simple (I hope it is) but the answer is not (at least for myself), I want to import some economic data from this web which is an indicator for Nicaraguan economic activity measured each month, so far I’ve tried this:

library(XML)
u <- "http://www.bcn.gob.ni/estadisticas/trimestrales_y_mensuales/siec/datos/4.IMAE.htm"
u <- htmlParse(u,encoding="UTF-8")
imae <- readHTMLTable(doc=u, header=T)
imae

library(httr)
u2 <- "http://www.bcn.gob.ni/estadisticas/trimestrales_y_mensuales/siec/datos/4.IMAE.htm"
page <- GET(u2, user_agent("httr"))
x <- readHTMLTable(text_content(page), as.data.frame=TRUE)

with no success as you can imagine. The first chunk of code gave me this output

   $`NULL`
                                  BANCO CENTRAL DE NICARAGUA    NA    NA    NA   NA   NA   NA   NA   NA   NA   NA    NA    NA       NA
1                                                             <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>
2 <U+633C><U+3E64>ndice Mensual de Actividad Económica(IMAE)  <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>
3                                           (Base: 1994=100)  <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>
4                                                             <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>
5                                                        Año   Ene   Feb   Mar  Abr  May  Jun  Jul  Ago  Sep  Oct   Nov   Dic Promedio
6                                                             <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>
7                                                       1994 101.6 107.6 100.1 95.7 94.7 92.8 92.1 96.8 98.5 97.4 101.7 121.1    100.0
8                                               Fuente: BCN.  <NA>  <NA>  <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>  <NA>  <NA>     <NA>

I tried using skip.rows=1:5 but it doesn’t really change the main result which is too much NA. Is there anybody who can shed some light on this question?

The expected result is a data.frame with the information shown in this web

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1 Answer

  1. Editorial Team

    As I mentioned in my comment, the problem is most likely arising because of the poorly coded table.

    You can try an approach something like the following (tested on Ubuntu using RStudio). It requires that you have wget and html tidy installed. If you don’t want to install these useful programs, jump to the updated part of this answer.

    1. Download the page and “tidy” it up. 

      system("wget http://www.bcn.gob.ni/estadisticas/trimestrales_y_mensuales/siec/datos/4.IMAE.htm")
system("tidy 4.IMAE.htm > new.html")

    2. Proceed with R as you normally would

      library(XML)
u <- htmlParse("new.html")
imae <- readHTMLTable(u)

    3. If we view the output of the above readHTMLTable, we would see that we need to skip a few rows. Let’s run it again:

      imae <- readHTMLTable(u, skip.rows=c(1:5, 7, 27, 28), header=TRUE)
imae
# $`NULL`
#     Año   Ene   Feb   Mar   Abr   May   Jun   Jul   Ago   Sep   Oct   Nov   Dic Promedio
# 1  1994 101.6 107.6 100.1  95.7  94.7  92.8  92.1  96.8  98.5  97.4 101.7 121.1    100.0
# 2  1995 113.2 105.0 113.6  98.0 100.9  95.4  99.8 101.5 108.3 107.1 107.6 133.2    107.0
# 3  1996 123.6 116.0 109.1 107.3  94.8 101.2 100.7 115.3 110.6 112.7 117.5 137.7    112.2
# 4  1997 133.4 115.9 117.4 118.8 120.4 108.2 107.4 111.1 120.3 117.7 119.5 142.3    119.4
# 5  1998 131.4 120.4 127.9 118.4 130.2 116.5 122.1 129.7 127.3 127.5 112.7 156.6    126.7
# 6  1999 146.0 139.6 146.9 134.8 140.6 131.8 130.6 128.3 128.9 131.8 142.7 172.6    139.5
# 7  2000 157.8 142.1 147.3 138.5 137.7 135.7 128.9 131.2 141.7 143.0 156.6 191.2    146.0
# 8  2001 163.3 143.8 154.8 141.5 147.6 134.0 135.7 143.3 138.2 138.8 145.3 187.3    147.8
# 9  2002 152.1 144.7 143.3 142.1 143.1 131.9 136.1 145.7 146.4 147.8 157.5 185.0    148.0
# 10 2003 159.3 151.4 149.1 142.7 139.7 139.1 145.6 147.8 154.9 158.4 157.8 195.7    153.5
# 11 2004 172.8 157.1 166.9 153.6 161.2 150.5 155.3 153.3 156.6 155.6 167.7 213.0    163.6
# 12 2005 183.1 170.6 173.6 158.7 160.8 158.5 158.8 168.7 165.8 165.4 178.4 218.8    171.8
# 13 2006 187.7 177.8 185.6 161.8 166.4 163.2 164.7 175.1 175.1 185.3 189.6 231.2    180.3
# 14 2007 200.1 184.1 196.5 180.1 169.7 171.4 181.6 180.9 173.0 182.8 202.0 236.7    188.2
# 15 2008 205.4 194.4 193.1 205.9 171.0 174.8 181.3 190.7 183.1 182.7 182.5 244.7    192.5
# 16 2009 195.7 191.0 190.8 177.0 168.1 172.6 179.2 185.6 178.9 181.4 191.3 241.4    187.7
# 17 2010 195.2 193.7 205.1 185.2 179.3 190.1 191.6 190.0 193.5 197.6 210.9 266.0    199.8
# 18 2011 213.9 207.4 217.3 198.7 196.1 198.8 191.9 210.0 203.7 207.9 217.3 274.5    211.5
# 19 2012 233.6                                                                      233.6

    Update: A little function to help out

    If you can live with having to do some text cleanup for the accented characters, the W3C offers an online implementation of html tidy. This allows you to write a basic function like the following:

    tidyHTML <- function(URL) {
  require(XML)
  URL = gsub("/", "%2F", URL)
  URL <- gsub(":", "%3A", URL)
  URL <- paste("http://services.w3.org/tidy/tidy?docAddr=", URL, sep = "")
  htmlParse(URL)
}

    Usage is simple:

    u <- tidyHTML("http://www.bcn.gob.ni/estadisticas/trimestrales_y_mensuales/siec/datos/4.IMAE.htm")
readHTMLTable(u)
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