I'm trying to download the data file posted on the following website:
https://data.aclum.org/sjc-12926-tracker/
Originally I just called
download.file("https://data.aclum.org/sjc-12926-tracker/session/b6c83d85236fc1950a06c5eda2ed6746/download/downloadData?w=", destfile="MADoc.xlsx")
and it would save to my working directory. However, the URL download path keeps changing, since they update the file every day. I can see the html code for the webpage to download the link when I inspect the element, but I can't figure out how to extract it (sorry--very new to using html code). Does anyone have a solution? Thank you very much for the help!
This web is a Shiny app, so:
the link is changing every session and is dynamically created (in HTML it's empty, it needs to run some javascript), so its hard to webscrape, but
you can browse the source code and get a hack. The data is stored in Google Drive:
# you may need to install.packages("tidyverse")
library(httr)
library(readxl)
# get data from server
GET("https://docs.google.com/spreadsheets/d/1nmZ84rjOxQgdTL0PdV7SrbyDTbD7nROQ/export#gid=1419540291",
write_disk(tf <- tempfile(fileext = ".xlsx")))
#> Response [https://doc-10-3g-sheets.googleusercontent.com/export/l5l039s6ni5uumqbsj9o11lmdc/dcg0ddist9t9r6tkjmfpr6d3jk/1593757275000/102387500909510694712/*/1nmZ84rjOxQgdTL0PdV7SrbyDTbD7nROQ]
#> Date: 2020-07-03 06:21
#> Status: 200
#> Content-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
#> Size: 456 kB
data <- read_excel(tf)
head(data)
#> # A tibble: 6 x 19
#> Date County `Pre-Trial Popu~ `Sentenced Popu~ `Other Populati~
#> <dttm> <chr> <chr> <chr> <chr>
#> 1 2020-03-27 00:00:00 DOC NA NA NA
#> 2 2020-03-31 00:00:00 DOC NA NA NA
#> 3 2020-04-04 00:00:00 Brist~ 438 262 NA
#> 4 2020-04-05 00:00:00 Barns~ 89 115 NA
#> 5 2020-04-05 00:00:00 Berks~ 77 92 NA
#> 6 2020-04-05 00:00:00 Brist~ 436 259 NA
#> # ... with 14 more variables: `Total Population` <chr>, `N Tested -
#> # Detainees/Inmates` <chr>, `N Positive - Detainees/Inmates` <chr>, `N Tested
#> # - COs` <chr>, `N Positive - COs` <chr>, `N Tested - Staff` <chr>, `N
#> # Positive - Staff` <chr>, `N Tested - Contractors` <chr>, `N Positive -
#> # Contractor` <chr>, `Total Tested` <chr>, `Total Positive` <chr>, `N
#> # Released Pre-Trial` <chr>, `N Released Sentenced` <chr>, Notes <chr>
Created on 2020-07-03 by the reprex package (v0.3.0)
Related
Background
I'm using the R package {recipes} for data preprocessing. Assume that I would like to transform some variable and then declare the transformed variable as the outcome variable for modeling.
Problem and minimal example:
However, an error is thrown:
library(tidymodels)
rec <- recipe( ~ ., data = mtcars) |>
step_mutate(mpg2 = mpg * 2) |>
update_role(mpg2, new_role = "outcome")
#> Error in `update_role()`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `mpg2` doesn't exist.
Created on 2023-01-15 with reprex v2.0.2
What I've tried
The help pages of step_mutate() and update_role() do not mention the case of updating the role of an mutated variables. When I update the role of a variable without having mutated it, no error is thrown.
There are SO questions around with a similar error message (such as here, here, or here), but those questions seem to tap into different aspects.
Sessioninfo
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] yardstick_1.1.0 workflowsets_1.0.0 workflows_1.1.2 tune_1.0.1
#> [5] tidyr_1.2.1 tibble_3.1.8 rsample_1.1.1 recipes_1.0.4
#> [9] purrr_1.0.1 parsnip_1.0.3 modeldata_1.0.1 infer_1.0.4
#> [13] ggplot2_3.4.0 dplyr_1.0.10 dials_1.1.0 scales_1.2.1
#> [17] broom_1.0.2 tidymodels_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] foreach_1.5.2 splines_4.2.1 R.utils_2.12.2
#> [4] prodlim_2019.11.13 assertthat_0.2.1 highr_0.10
#> [7] GPfit_1.0-8 yaml_2.3.6 globals_0.16.2
#> [10] ipred_0.9-13 pillar_1.8.1 backports_1.4.1
#> [13] lattice_0.20-45 glue_1.6.2 digest_0.6.31
#> [16] hardhat_1.2.0 colorspace_2.0-3 htmltools_0.5.4
#> [19] Matrix_1.5-3 R.oo_1.25.0 timeDate_4022.108
#> [22] pkgconfig_2.0.3 lhs_1.1.6 DiceDesign_1.9
#> [25] listenv_0.9.0 gower_1.0.1 lava_1.7.1
#> [28] timechange_0.2.0 styler_1.8.1 generics_0.1.3
#> [31] ellipsis_0.3.2 furrr_0.3.1 withr_2.5.0
#> [34] nnet_7.3-18 cli_3.6.0 survival_3.5-0
#> [37] magrittr_2.0.3 evaluate_0.19 R.methodsS3_1.8.2
#> [40] fs_1.5.2 fansi_1.0.3 future_1.30.0
#> [43] parallelly_1.34.0 R.cache_0.16.0 MASS_7.3-58.1
#> [46] class_7.3-20 tools_4.2.1 lifecycle_1.0.3
#> [49] stringr_1.5.0 munsell_0.5.0 reprex_2.0.2
#> [52] compiler_4.2.1 rlang_1.0.6 grid_4.2.1
#> [55] iterators_1.0.14 rstudioapi_0.14 rmarkdown_2.19
#> [58] gtable_0.3.1 codetools_0.2-18 DBI_1.1.3
#> [61] R6_2.5.1 lubridate_1.9.0 knitr_1.41
#> [64] fastmap_1.1.0 future.apply_1.10.0 utf8_1.2.2
#> [67] stringi_1.7.12 parallel_4.2.1 Rcpp_1.0.9
#> [70] vctrs_0.5.1 rpart_4.1.19 tidyselect_1.2.0
#> [73] xfun_0.36
```
This behavior is currently not properly documented.
The reason why you are having problems is because add_role(), update_role() and remove_role() can only be applied to the variables passed to recipe(), and they are all executed before the step functions.
This means that the following two snippets of code returns the same result
recipe( ~ ., data = mtcars) |>
step_mutate(mpg2 = mpg * 2) |>
update_role(mpg2, new_role = "outcome")
recipe( ~ ., data = mtcars) |>
update_role(mpg2, new_role = "outcome") |>
step_mutate(mpg2 = mpg * 2)
Reference here https://github.com/tidymodels/recipes/blob/ab2405a0393bba06d9d7a52b4dbba6659a6dfcbd/R/roles.R#L132 :
Roles can only be changed on the original data supplied to recipe()
More talk here https://github.com/tidymodels/recipes/issues/437.
The role argument of step_mutate() allows you to specify the role of the variables it creates
library(recipes)
recipe( ~ ., data = mtcars) |>
step_mutate(mpg2 = mpg * 2, role = "outcome") |>
prep() |>
summary()
#> # A tibble: 12 × 4
#> variable type role source
#> <chr> <list> <chr> <chr>
#> 1 mpg <chr [2]> predictor original
#> 2 cyl <chr [2]> predictor original
#> 3 disp <chr [2]> predictor original
#> 4 hp <chr [2]> predictor original
#> 5 drat <chr [2]> predictor original
#> 6 wt <chr [2]> predictor original
#> 7 qsec <chr [2]> predictor original
#> 8 vs <chr [2]> predictor original
#> 9 am <chr [2]> predictor original
#> 10 gear <chr [2]> predictor original
#> 11 carb <chr [2]> predictor original
#> 12 mpg2 <chr [2]> outcome derived
Additionally, it is not recommended that you try to create/modify the outcome inside a recipe. Such modifications should happen before, preferable before data splitting.
My problem is very similar to this one. I want to identify all the HTML links in this website so I can then open the link and download the tables.
The problem is that when I create the extract_links functions as pointed out in that answer, I get a list of all the HTMLs, but this are not complete.
To make it more clear:
If you press "Junio" in year "2022" the real HTML is the following:
http://transparencia.uantof.cl/index.php?action=plantillas_generar_plantilla&ig=21&m=6&a=2022&ia=7658
but the HTML that I am recovering from the source of the website lacks the last bit (&ia=7658):
http://transparencia.uantof.cl/index.php?action=plantillas_generar_plantilla&ig=21&m=6&a=2022
Which does not direct me to the table I want.
The problem is that these numbers do not seem to follow any logic and change between year/month links. Any help on how to retrieve the full HTML links will be greatly appreciated. If you also happen to know how can I retrieve the year/month of the file to add as an extra column that would also be great.
Thanks to the help of #margusl I was able to realize that rvest redirects automatically and that solves my problem.
I am trying to use the following code to loop over different links to obtain the tables, store them in a data frame and then download them:
yr.list <- seq(2019,2020)
mes.list <- seq(1,12)
combined_df <- data.frame()
for (yr in yr.list){
for (mes in mes.list) {
root <- "http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21"
# Full link
url <- paste(root,"&m=",mes,"&a=",yr,sep="")
# Parse HTML File
file<-read_html(url, encoding = "latin1")
file<- rvest::html_table(file)
str(file)
# This is the relevant table
table <- as.data.frame(file[[1]])
# in your loop, add the files that you read to the combined_df
combined_df <- rbind(combined_df, table)
}
}
It does not work because the read_html code with the encoding works only for some years, but not for all. for example, when running:
url <- "http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=3&a=2015"
file<-read_html(url, encoding = "latin1")
It does not recover the tables with names/surnames that recovers in the previous months but something else. Why can't this work on all the sub-pages? Is this a encoding problem again?
If you open that last page you had issues with, you'll see that it serves a sort of a submenu with 2 more links - http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=3&a=2015 . Meaning that it's not enough to just generate links for each month & year and extract first table of each page, all those pages should be checked for content and exceptions should be handled.
Though I took somewhat opportunistic approach and it happened to work with URL range defined in question + those few odd samples, but there could be other surprises down the road. Switched to httr for making requests as it allows to collect and monitor response headers, separating content retrieval and parsing also seems to work around encoding issues, at least in this case. First collecting and then parsing also simplifies debugging, you can check if certain responses / headers were different from the rest (i.e. response length being 10x smaller than average or final, redirected, url differs from the rest). And it's easy to change content handling
/ parsing for a small subset of responses, if needed. If you are not sure what rvest has retrieved, you can always save the response to a html file and check it with browser or editor, something like
html <- read_html(url_or_text_content); write(as.character(html), "dump.html")
library(rvest)
library(httr)
library(purrr)
library(dplyr)
library(tidyr)
library(stringr)
yr.list <- seq(2019,2020)
mes.list <- seq(1,12)
# combine mes.list & yr.list
url.params <- expand.grid(mes = mes.list, yr = yr.list)
# few extra samples:
url.params <- rbind(url.params,
list(mes = 6, yr = 2022), # here rvest strugglest with correct encoding
list(mes = 3, yr = 2015) # returns page with sub-categories
)
url.list <- str_glue("http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m={url.params$mes}&a={url.params$yr}")
url.list
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=1&a=2019
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=2&a=2019
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=3&a=2019
#> ...
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=11&a=2020
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=12&a=2020
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=6&a=2022
#> http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=3&a=2015
# url list for input, output is a tibble with all responses (incl. "url", "date",
# "status_code", header details and response body)
fetch_urls <- function(url.list){
# collect all responses to a list with httr, enable verbose, parse responses later
# add progress bar - requests take a while
resp.list = vector(mode = "list", length = length(url.list))
pb <- txtProgressBar(max = length(url.list), style = 3)
for (i in seq_along(url.list)){
resp.list[[i]] <- GET(url.list[i])
setTxtProgressBar(pb,i)
}
close(pb)
# turn responses into tibble to check urls, response sizes and status codes
resp.tibble <- bind_cols(
map_df(resp.list, ~ .[c("url", "date", "status_code")], .id = "req_id"),
map_df(resp.list, headers) %>% rename_with(~ paste0("header_",.x)),
# map_df(resp_follow.list, "times"),
map_chr(resp.list, content, as = "text") %>% tibble(html_doc = .)
)
return(resp.tibble)
}
resp.tibble <- fetch_urls(url.list)
# check resulting table without html_doc column
# View(resp.tibble[-ncol(resp.tibble)])
resp.tibble %>%
select(req_id:status_code,`header_content-length`) %>%
arrange(`header_content-length`)
#> # A tibble: 26 × 5
#> req_id url date statu…¹ heade…²
#> <chr> <chr> <dttm> <int> <chr>
#> 1 14 http://transparencia.uantof.cl/in… 2022-10-31 17:29:12 200 21371
#> 2 26 http://transparencia.uantof.cl/in… 2022-10-31 17:31:45 200 2230
#> 3 24 http://transparencia.uantof.cl/in… 2022-10-31 17:31:21 200 24035
#> 4 21 http://transparencia.uantof.cl/in… 2022-10-31 17:30:42 200 24173
#> 5 20 http://transparencia.uantof.cl/in… 2022-10-31 17:30:29 200 24183
#> 6 23 http://transparencia.uantof.cl/in… 2022-10-31 17:31:08 200 24184
#> 7 22 http://transparencia.uantof.cl/in… 2022-10-31 17:30:55 200 24207
#> 8 18 http://transparencia.uantof.cl/in… 2022-10-31 17:30:04 200 24405
#> 9 16 http://transparencia.uantof.cl/in… 2022-10-31 17:29:38 200 24715
#> 10 7 http://transparencia.uantof.cl/in… 2022-10-31 17:27:32 200 24716
#> # … with 16 more rows, and abbreviated variable names ¹status_code,
#> # ²`header_content-length`
# 26. is kind of suspicious:
# 25 http://transparencia.uantof.cl/index.php?action=plantillas_generar_plantilla&ig=21&m=6&a=2022&ia=76…
# 26 http://transparencia.uantof.cl/index.php?action=plantillas_selec_archivo&ig=21&m=3&a=2015
# looks like there has been no redirection and its header_content-length is about 10x smaller than for other responses
# checking it more closely reveals that the page includes a "submenu" instead of table(s):
# <p class="subMenu_interiores">
# <b>2015 - Marzo</b>
# ABRIL 2015
# Marzo 2015
# </p>
# lets' collect urls that were not redirected from our tibble and harvest links from stored html:
suburl.list <- resp.tibble %>%
# urls that do NOT include "plantillas_generar_plantilla"
filter(!str_detect(url, "plantillas_generar_plantilla")) %>%
pull(html_doc) %>%
# rvest does not like lists, thus let's map()
map( ~ read_html(.x) %>% html_elements("#columna1_interiores a") %>% html_attr("href")) %>%
unlist() %>%
paste0("http://transparencia.uantof.cl/",.)
suburl.list
#> [1] "http://transparencia.uantof.cl/index.php?action=plantillas_generar_plantilla&ig=21&m=3&a=2015&ia=772"
#> [2] "http://transparencia.uantof.cl/index.php?action=plantillas_generar_plantilla&ig=21&m=3&a=2015&ia=648"
# fetch content from those submenu urls
subresp.tibble <- fetch_urls(suburl.list)
# sanity check:
subresp.tibble %>%
select(req_id:status_code,`header_content-length`)
#> # A tibble: 2 × 5
#> req_id url date statu…¹ heade…²
#> <chr> <chr> <dttm> <int> <chr>
#> 1 1 http://transparencia.uantof.cl/ind… 2022-10-31 17:31:52 200 25385
#> 2 2 http://transparencia.uantof.cl/ind… 2022-10-31 17:31:59 200 25332
#> # … with abbreviated variable names ¹status_code, ²`header_content-length`
# better, sizes align with previous results.
# collect all relevant responses
table_1 <- resp.tibble %>%
filter(str_detect(url, "plantillas_generar_plantilla")) %>%
bind_rows(subresp.tibble) %>%
# extract html (as strings)
pull(html_doc) %>%
# rvest does not like lists, thus let's map(), pluck(1) extracts first table (from each page)
map(~ read_html(.x) %>% html_table() %>% pluck(1)) %>%
# first attempt to bind rows fails, aparently column types differ
# change all non-character columns to character
map (~ mutate(.x, across(!where(is.character),as.character))) %>%
# bind all tables by rows
bind_rows()
# columns vary across tables so number of NA fields in final result is rather high
Final result for 26 pages, a 10,987 × 30 tibble:
table_1
#> # A tibble: 10,987 × 30
#> Nº PLANTA PATERNO MATERNO NOMBRES G TITULO CARGO REGION ASIGN…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DIRECTIVO ABARZA CASTRO JESSIC… 6 ADMIN… JEFE… SEGUN… (1)(8)…
#> 2 2 PROFESIONAL ABASOLO QUINTE… NURY D… 12 EDUCA… PROF… SEGUN… (4)(8)…
#> 3 3 ACADEMICO ACOSTA PENA ROXANA… 11 EDUCA… PROF… SEGUN… (2)(8)…
#> 4 4 AUXILIARES ACOSTA PIZARRO ROBERT… 23 LICEN… AUXI… SEGUN… (7)(8)…
#> 5 5 DIRECTIVO AGENO SIERRA ROSELL… 4 MATRO… DIRE… SEGUN… (1)(8)…
#> 6 6 AUXILIARES AGUIRRE LAZO RENE G… 16 LICEN… AUXI… SEGUN… (7)(8)…
#> 7 7 TECNICOS ALAMOS MARIN SERGIO… 13 TECNI… TECN… SEGUN… (5)(8)…
#> 8 8 AUXILIARES ALAYANA CORTES CHRIST… 23 LICEN… AUXI… SEGUN… (7)(8)…
#> 9 9 ACADEMICO ALCOTA AGUIRRE PATRIC… 9 ING. … PROF… SEGUN… (2)(8)…
#> 10 10 ADMINISTRATI… ALFARO BARRAZA MARIA … 23 LICEN… ADMI… SEGUN… (6)(8)…
#> # … with 10,977 more rows, 20 more variables: `UNID MONETARIA` <chr>,
#> # `REMUNERACION MENSUAL BRUTA` <chr>, HORAS <chr>, `CANT. HORAS` <chr>,
#> # `MONTO HORAS EXTRAS` <chr>, `FECHA DE INGRESO` <chr>, `F. HASTA` <chr>,
#> # OBSERVACIONES <chr>, GRADO <chr>, ESTAMENTO <chr>,
#> # `Apellido Paterno` <chr>, `Apellido Materno` <chr>, Nombres <chr>,
#> # `Grado ERUA` <chr>, `CALIFICACION PROFESIONAL O FORMACION` <chr>,
#> # `CARGO O FUNCION` <chr>, `R BRUTA` <chr>, `Horas Extras` <chr>, …
Created on 2022-10-31 with reprex v2.0.2
Functions like last_fit() from the tune package produces last_fit objects which are large nested lists containing the fit results. I tried to transform them into data.frames using the tidy() function from the broom package but this resulted in the following error:
MRE :
library(tidymodels)
library(tidyverse)
data <- mtcars
model_default<-
parsnip::boost_tree(
mode = "regression"
) %>%
set_engine('xgboost',objective = 'reg:squarederror')
wf <- workflow() %>%
add_model(model_default) %>%
add_recipe(recipe(mpg~.,data))
lf <- last_fit(wf,split)
tidy_lf <- tidy(lf)
Error in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm) :
is.atomic(x) is not TRUE
In addition: Warning messages:
1: Data frame tidiers are deprecated and will be removed in an upcoming release of broom.
2: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
3: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
4: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
5: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
6: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
7: In mean.default(X[[i]], ...) :
argument is not numeric or logical: returning NA
Question : How can I use tidy() with an last_fit() output?
The object that last_fit() creates is a tibble (containing metrics, predictions, etc), not a model that can be tidied. You can use extract_workflow() to extract out the fitted workflow from the object created by last_fit(), and this object can be tidied:
library(tidymodels)
car_split <- initial_split(mtcars)
wf <- workflow() %>%
add_model(linear_reg()) %>%
add_recipe(recipe(mpg ~ ., mtcars))
lf <- last_fit(wf, car_split)
lf
#> # Resampling results
#> # Manual resampling
#> # A tibble: 1 × 6
#> splits id .metrics .notes .predictions .workflow
#> <list> <chr> <list> <list> <list> <list>
#> 1 <split [24/8]> train/test split <tibble> <tibble> <tibble [8 × 4]> <workflow>
lf %>%
extract_workflow() %>%
tidy()
#> # A tibble: 11 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -33.6 36.0 -0.935 0.367
#> 2 cyl -0.0296 1.34 -0.0221 0.983
#> 3 disp 0.0252 0.0269 0.934 0.367
#> 4 hp -0.00539 0.0319 -0.169 0.868
#> 5 drat -0.167 2.54 -0.0659 0.948
#> 6 wt -5.69 2.79 -2.04 0.0623
#> 7 qsec 3.32 1.76 1.89 0.0820
#> 8 vs -4.40 3.80 -1.16 0.268
#> 9 am 2.54 2.67 0.950 0.360
#> 10 gear 2.69 2.28 1.18 0.259
#> 11 carb -0.0486 1.11 -0.0439 0.966
Created on 2022-03-23 by the reprex package (v2.0.1)
I am trying to write a R script that can download the csv file from the following website,"https://www.covidanalytics.io/projections There is a link for download data at the bottom of the page, which takes the form of "data:text/csv...". I was wondering if I can have a R script to download the file in csv format. Any help will be highly appreciated.
Thanks
IC
There are easier ways to get this data, but it is possible with a bit of low-level work using the httr package.
As #r2evans pointed out, this is a url-encoded csv built by Dash. To get the url, you need to request a json file containing the html page information using an xhr request. This needs all the correct headers as well as a json request in the body of the POST request:
library(httr)
page1 <- GET("https://www.covidanalytics.io/projections")
H <- add_headers( `Host` = "www.covidanalytics.io",
`User-Agent` = paste("Mozilla/5.0 (Windows NT 6.1; rv:77.0)",
"Gecko/20100101 Firefox/77.0"),
`Accept` = "application/json",
`Accept-Language` = "en-GB,en;q=0.5",
`Accept-Encoding` = "gzip, deflate",
`Referer` = "https://www.covidanalytics.io/projections",
`Content-Type` = "application/json",
`X-CSRFToken` = "undefined",
`Origin` = "https://www.covidanalytics.io",
`Connection` = "keep-alive")
post_data <- paste0('{"output":"page-content.children","outputs":{"id":',
'"page-content","property":"children"},"inputs":',
'[{"id":"url","property":"pathname","value":',
'"/projections"}],"changedPropIds":["url.pathname"]}')
res <- httr::POST("https://www.covidanalytics.io/_dash-update-component", H,
body = post_data, encode = "raw")
'res` now contains the json response, and our url-encoded csv is deep inside it. We get this parsed content and extract the string containing the url:
body <- parsed_content(res)$response$`page-content`$children$props$children[[2]]
div <- body$props$children[[10]]$props$children[[1]]
url <- div$props$children$props$children$props$href
Now we need to cut off the data:text/csv;charset=utf-8, part and unescape the url encoding. I actually found this was far quicker using nested gsubs, since my machine choked on URLdecode:
csv <- strsplit(url, ",")[[1]][2]
df <- read.csv(text = gsub("%0A", "\n", gsub("%20", " ", gsub("%2C", ",", csv))))
Your data is now in df. It's big, so I'll show it as a tibble here:
tidyr::as_tibble(df)
#> # A tibble: 7,106 x 10
#> Continent Country Province Day Total.Detected Active Active.Hospital~ Cumulative.Hosp~
#> <fct> <fct> <fct> <fct> <fct> <int> <int> <int>
#> 1 Africa Algeria None 2020~ 5651 1531 302 834
#> 2 Africa Algeria None 2020~ 5742 1514 300 848
#> 3 Africa Algeria None 2020~ 5831 1497 298 861
#> 4 Africa Algeria None 2020~ 5917 1477 296 874
#> 5 Africa Algeria None 2020~ 6000 1457 293 886
#> 6 Africa Algeria None 2020~ 6079 1435 291 898
#> 7 Africa Algeria None 2020~ 6156 1411 287 910
#> 8 Africa Algeria None 2020~ 6230 1387 284 921
#> 9 Africa Algeria None 2020~ 6300 1361 280 932
#> 10 Africa Algeria None 2020~ 6368 1335 277 942
#> # ... with 7,096 more rows, and 2 more variables: Total.Detected.Deaths <int>,
#> # Active.Ventilated <int>
I'm very new to R,and I'm currently stuck on this problem:
so I imported a JSON file and already***convert it to a dataframe***, now I need to return rows under condition:
As you can see in the picture, I have a column recording hours(payload.hours)
My GOAL is to find out the hours that meet: 1. sunday 2. time ealier than 10AM.
I tried several ways but somehow it even doesn't come close at all... I havent dealt with such nested form before... so I have to seek your idea&help...
e.g. one element in payload.hours column
payload.hours
...
[530] "{\"monday\":[[\"10:30\",\"16:00\"]],\"tuesday\":[[\"10:30\",\"16:00\"]],\"wednesday\":[[\"10:30\",\"16:00\"]],\"thursday\":[[\"10:30\",\"16:00\"]],\"friday\":[[\"10:30\",\"16:00\"]],\"saturday\":[[\"10:30\",\"16:00\"]],\"sunday\":[[\"10:30\",\"16:00\"]]}"
this is what I used for unpacking the nested lists in "hours" column...but it doesn't work...
library(ndjson)
json<- ndjson::stream_in("#localpath")
#successfully converted json to a dataframe...but each element in payload.hours column remains nested like above.
lapply(json$payload.hours, jsonlite::fromJSON)
#continue unwarp nested jason column BUT RESULT Error in if (is.character(txt) && length(txt) == 1 && nchar(txt, type = "bytes") < :missing value where TRUE/FALSE needed
Another approach I tried(FOR A LONG TIME) is RegEx
hrs<-json1$payload.hours #select column hours into hrs
tme<-"sunday{1}.{8}[0-9]{1}\"" # ???(not sure about this...seruously)...? match string with sunday and after 8characters..aka find preceding digit{1} when meet ":"
iftme<-grepl(tme,hrs) #set logical factor T/F if matches
checkhrs<-hrs[iftme] #check if open hours are correct
checkhrs
And this seems to work...but I am not sure why...(YES.IDK WHY)...so if anyone could explain to me that would be great!
This is the original json file:
https://drive.google.com/open?id=0B-jU6pp4pjS4Smg2RGpHSTlvN2c
This is RegEx output...seems right...but I am not sure about my expression..LOL
Unpacking JSON can be a lot of work, particularly if it is deeply nested. Most JSON reading packages (jsonlite, RJSONIO, etc.) can turn data into something close to a data.frame, but fixing the structure requires an understanding that the reader functions don't have. Since JSON most nearly corresponds to R's lists, cleaning up data coming from JSON typically involves a lot of lapply and its variants. Here I'll use purrr, which has many useful variants and helper functions and works neatly with dplyr.
library(tidyverse)
# Read data
json <- jsonlite::stream_in(file('~/Downloads/jsondata.json'))
# Initial cleanup to proper data.frame
json <- json$payload %>% map_df(simplify_all) %>% dmap(simplify) %>%
mutate(uuid = json$uuid, # re-add uuid subset out at beginning
# Convert hours to a list column of data.frames
hours = hours %>% map_if(negate(is.na), jsonlite::fromJSON) %>%
map(~map_df(.x, as_data_frame, .id = 'day')),
# Add Boolean variable for whether Sunday opening hours are before 10a. Subset,
open_sun_before_10 = hours %>% map(~.x %>% filter(day == 'sunday') %>% .[[2]]) %>%
map(as.POSIXct, format = '%H:%M') %>% # convert to datetime,
map(~.x < as.POSIXct('10:00', format = '%H:%M')) %>% # compare to 10a
map_lgl(~ifelse(length(.x) == 0, NA, .x))) # and cleanup.
Whereas stream_in returned a data.frame with two columns (one very deeply nested), the columns are now less nested. There are still JSON structures in some of the untouched columns, though, which will have to be addressed if you want to use the data.
json
#> # A tibble: 538 × 42
#> existence_full geo_virtual latitude
#> <dbl> <chr> <chr>
#> 1 1.000000 ["56.9459720|-2.1971226|20|within_50m|4"] 56.945972
#> 2 1.000000 ["56.237480|-5.073578|20|within_50m|4"] 56.237480
#> 3 1.000000 ["51.483872|-0.606820|100|rooftop|2"] 51.483872
#> 4 1.000000 ["57.343233|-2.191955|100|rooftop|4"] 57.343233
#> 5 1.000000 ["53.225815|-4.094775|20|within_50m|4"] 53.225815
#> 6 1.000000 ["58.9965740|-3.1882195|20|within_50m|4"] 58.996574
#> 7 1.000000 ["57.661419|-2.520144|100|rooftop|4"] 57.661419
#> 8 1.000000 ["51.642727|-3.934845|20|within_50m|4"] 51.642727
#> 9 0.908251 <NA> <NA>
#> 10 1.000000 ["56.510558|-5.401638|100|rooftop|2"] 56.510558
#> # ... with 528 more rows, and 39 more variables: locality <chr>,
#> # `_records_touched` <chr>, address <chr>, email <chr>,
#> # existence_ml <dbl>, domain_aggregate <chr>, name <chr>,
#> # search_tags <list>, admin_region <chr>, existence <dbl>,
#> # category_labels <list>, post_town <chr>, region <chr>,
#> # review_count <chr>, geocode_level <chr>, tel <chr>, placerank <int>,
#> # longitude <chr>, placerank_ml <dbl>, fax <chr>,
#> # category_ids_text_search <chr>, website <chr>, status <chr>,
#> # geocode_confidence <chr>, postcode <chr>, category_ids <list>,
#> # country <chr>, `_geocode_quality` <chr>, hours_display <chr>,
#> # hours <list>, neighborhood <list>, languages <chr>,
#> # address_extended <chr>, status_closed <chr>, po_box <chr>,
#> # name_variants <list>, yext_id <chr>, uuid <chr>,
#> # open_sun_before_10 <lgl>
And the columns created:
json %>% select(hours, open_sun_before_10)
#> # A tibble: 538 × 2
#> hours open_sun_before_10
#> <list> <lgl>
#> 1 <tibble [1 × 2]> NA
#> 2 <tibble [1 × 2]> NA
#> 3 <tibble [7 × 3]> FALSE
#> 4 <tibble [1 × 2]> NA
#> 5 <tibble [7 × 3]> FALSE
#> 6 <tibble [1 × 2]> NA
#> 7 <tibble [1 × 2]> NA
#> 8 <tibble [6 × 3]> NA
#> 9 <tibble [1 × 2]> NA
#> 10 <tibble [7 × 3]> TRUE
#> # ... with 528 more rows