Last updated: 2020-05-24
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cd2754d | KaranSShakya | 2020-05-23 | web scrapping - base_r |
| html | cd2754d | KaranSShakya | 2020-05-23 | web scrapping - base_r |
| Rmd | 9c630f2 | KaranSShakya | 2020-05-23 | download online file |
| html | 9c630f2 | KaranSShakya | 2020-05-23 | download online file |
| html | e1a5784 | KaranSShakya | 2020-05-19 | link on all pages |
| html | 9097ee4 | KaranSShakya | 2020-05-19 | aes erros SOLVED + links |
| Rmd | 1a7c01c | KaranSShakya | 2020-05-19 | bug commit |
| Rmd | 6c2aa83 | KaranSShakya | 2020-05-17 | data manipulation updates |
| html | 6c2aa83 | KaranSShakya | 2020-05-17 | data manipulation updates |
| Rmd | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| html | 5adafbc | KaranSShakya | 2020-05-16 | panel regression + numeric/character |
| Rmd | d927485 | KaranSShakya | 2020-05-16 | GIT to R-Studio commits |
| html | d927485 | KaranSShakya | 2020-05-16 | GIT to R-Studio commits |
To use data readily online. You need the readr package which reads csv files.
url <- "https://raw.githubusercontent.com/rafalab/dslabs/master/inst/extdata/murders.csv"
dat <- read_csv(url)To download the file we can use: (or an alternative is to simply write.excel or write.csv).
download.file(url, tmp_filename)If there isnt a csv/excel file to download, we can still access the data online and convert it into a dataframe. This is done from the rvest package, but is inbuilt in tidyverse. First, save the url page into an object in R url.
url <- "https://en.wikipedia.org/wiki/Murder_in_the_United_States_by_state"
h <- read_html(url)If the HTML pages are divided into tables, you can extract it:
tab <- h %>%
html_nodes("table")
tab <- tab[[2]] #selecting the second node: which has the data.
tab <- tab %>%
html_table #finally a data with columns and rowsSet up coloumns names (makes sure it aligns with the data):
tab <- tab %>%
setNames(c("state", "population", "total", "murders", "gun_murders", "gun_ownership", "total_rate", "murder_rate", "gun_murder_rate"))
tab.data <- data.frame(tab) #data frame you can work withSometimes important data are only in the form of a PDF. We can still use R to extract data from this. This is the test pdf page we use - link. We need the package pdftools.
temp_file <- tempfile() #code to create a unique name.
url <- "http://www.pnas.org/content/suppl/2015/09/16/1510159112.DCSupplemental/pnas.201510159SI.pdf" #url from which to download the table
download.file(url, temp_file) #downloading the file and giving it a temp name
txt <- pdf_text(temp_file) #converting the url into text value in R
file.remove(temp_file)Now we look closely into the txt file to isolate our table. It is all about finding a pattern to isolate from.
raw.data <- txt[2] #our table was in the 2nd text.
raw.data1 <- str_split(raw.data, "\n") #each line in the table was seperated by "\n"
raw.data2 <- raw.data1[[1]] #isolating the section we need
table.1 <- raw.data2[3]
table.2 <- raw.data2[4]Focusing on table.1 (column names)
table.1 <- table.1 %>%
str_trim() %>%
str_replace_all(",\\s.", "") %>%
str_split("\\s{2,}", simplify = TRUE)
table.1 #we have the column names [,1] [,2] [,3]
[1,] "Applications" "Awards" "Success rates"Focusing on table.2 (more columns)
table.2 <- table.2 %>%
str_trim() %>%
str_split("\\s+", simplify = TRUE)
table.2 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] "Discipline" "Total" "Men" "Women" "Total" "Men" "Women" "Total" "Men"
[,10]
[1,] "Women"We can combine both these to form a column for our data:
tmp_names <- str_c(rep(table.1, each = 3), table.2[-1], sep = "_")
the_names <- c(table.2[1], tmp_names) %>%
str_to_lower() %>%
str_replace_all("\\s", "_")
the_names [1] "discipline" "applications_total" "applications_men"
[4] "applications_women" "awards_total" "awards_men"
[7] "awards_women" "success_rates_total" "success_rates_men"
[10] "success_rates_women"Finally combining all this with the data:
new_research_funding_rates <- raw.data2[6:14] %>%
str_trim %>%
str_split("\\s{2,}", simplify = TRUE) %>%
data.frame(stringsAsFactors = FALSE) %>%
setNames(the_names) %>%
mutate_at(-1, parse_number) #this is a new data.frame!Creating example data frame. Columns with commas are always chracter.
comma <- data.frame(c("1,233", "1,000,000"), c(1233, 1000000))
names(comma) <- c("Incorrect", "Correct")
comma Incorrect Correct
1 1,233 1233
2 1,000,000 1000000parse_number is a direct function that deals with commas. There are more parse functions.
comma$Incorrect <- parse_number(comma$Incorrect)
comma Incorrect Correct
1 1233 1233
2 1000000 1000000If x is character and y is numeric, and you want to reverse both these:
y <- as.numeric(x)
x <- as.character(y)This is mainly used to change WorldBank datasets. Example table:
# A tibble: 3 x 4
Country `2001` `2002` `2003`
<chr> <dbl> <dbl> <dbl>
1 Nepal 12 435 5524
2 India 31 355 2424
3 China 64 353 2244key The columns to turn into rows. value what to call the data.
long <- wide %>%
gather(key="Year", value="Population", 2:4)
long# A tibble: 9 x 3
Country Year Population
<chr> <chr> <dbl>
1 Nepal 2001 12
2 India 2001 31
3 China 2001 64
4 Nepal 2002 435
5 India 2002 355
6 China 2002 353
7 Nepal 2003 5524
8 India 2003 2424
9 China 2003 2244names(data)[1] <- "New_name"sum(is.na(tab.data$murders))
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pdftools_2.3.1 rvest_0.3.5 xml2_1.3.2 readxl_1.3.1
[5] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5 purrr_0.3.4
[9] readr_1.3.1 tidyr_1.0.3 tibble_3.0.1 ggplot2_3.3.0
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.8 lattice_0.20-41 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 utf8_1.1.4 R6_2.4.1
[9] cellranger_1.1.0 backports_1.1.6 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.4 rlang_0.4.6 curl_4.3
[17] rstudioapi_0.11 whisker_0.4 rmarkdown_2.1 qpdf_1.1
[21] selectr_0.4-2 munsell_0.5.0 broom_0.5.6 compiler_4.0.0
[25] httpuv_1.5.2 modelr_0.1.7 xfun_0.13 pkgconfig_2.0.3
[29] askpass_1.1 htmltools_0.4.0 tidyselect_1.1.0 fansi_0.4.1
[33] crayon_1.3.4 dbplyr_1.4.3 withr_2.2.0 later_1.0.0
[37] grid_4.0.0 nlme_3.1-147 jsonlite_1.6.1 gtable_0.3.0
[41] lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[45] scales_1.1.1 cli_2.0.2 stringi_1.4.6 fs_1.4.1
[49] promises_1.1.0 ellipsis_0.3.0 generics_0.0.2 vctrs_0.3.0
[53] tools_4.0.0 glue_1.4.1 hms_0.5.3 yaml_2.2.1
[57] colorspace_1.4-1 knitr_1.28 haven_2.2.0