https://github.com/joyofdata/r-big-pivot
Pivoting and charting big tabular data sets in a browser UI based on R and Shiny.
https://github.com/joyofdata/r-big-pivot
Last synced: 6 months ago
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Pivoting and charting big tabular data sets in a browser UI based on R and Shiny.
- Host: GitHub
- URL: https://github.com/joyofdata/r-big-pivot
- Owner: joyofdata
- Created: 2013-12-09T22:10:08.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2014-01-02T14:08:54.000Z (over 11 years ago)
- Last Synced: 2024-08-13T07:14:14.115Z (10 months ago)
- Language: R
- Size: 1.36 MB
- Stars: 15
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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- jimsghstars - joyofdata/r-big-pivot - Pivoting and charting big tabular data sets in a browser UI based on R and Shiny. (R)
README
r-big-pivot
===========
Pivoting and charting big tabular data sets in a web UI based on R and Shiny.
Given that this works but lacks ease of use and necessary features, I consider this project so far rather a proof of concept to show that an interactive data explorer can be comparatively easily realized with R.
Further details on the project you may [find on my web-site](http://www.joyofdata.de/blog/pivoting-data-r-excel-style/).
##set up data to play with
1. Download `hlth_cd_acdr.sdmx.zip` from [EUROSTAT](http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?dir=data&filter=SDMX&sort=1&sort=2&start=h).
2. Convert containing SDMX/DSD to CSV using most recent version of [SDMX Converter](https://webgate.ec.europa.eu/fpfis/mwikis/sdmx/index.php/SDMX_Converter) with settings:
* Input File: ...\hlth_cd_acdr.sdmx.xml
* Output File: ...\hlth_cd_acdr.csv
* DSD File: ...\hlth_cd_acdr.dsd.xml
* Input Format: COMPACT_SDMX
* Output Format: CSV
3. The resulting CSV is lacking a header and needs further processing:
```
df <- read.table("path\\hlth_cd_acdr.csv", header=FALSE, sep=";")
df <- df[,1:(ncol(df)-1)]
names(df) <- c("freq","unit","sex","age","icd10","geo","T","V")
df$T <- paste(as.character(df$T),"-01-01",sep="")
dfx <- df[nchar(as.character(df$geo)) == 2, !(names(df) %in% c("unit","freq"))]
write.table(dfx,"path\\hlth_cd_acdr.tsv",sep="\t",row.names=FALSE)
```
The resulting TSV should look like this:
```
"sex" "age" "icd10" "geo" "T" "V"
"F" "TOTAL" "A-R_V-Y" "AL" "2000-01-01" 452.4
"F" "TOTAL" "A-R_V-Y" "AL" "2001-01-01" 417
"F" "TOTAL" "A-R_V-Y" "AL" "2002-01-01" 459.3
"F" "TOTAL" "A-R_V-Y" "AL" "2003-01-01" 509.7
```
##launch r-big-pivot:
Place server.r and ui.r in a folder named "r-big-pivot".
```
library(shiny)
runApp("path/r-big-pivot")
```
##load the data file and run some commands:
1. "upload" the TSV file (and wait until values for it are displayed)
2. adjust w1,w2 and h
3. t1:
```
sql(
select T, age, geo, sex, V
from o
where sex in ("M","F")
and age in ("Y_GE65","Y_LT65")
and geo in ("DE","FR")
# viral hepatitis
and icd10 = "B15-B19_B942"
);
```
4. t2:
```
sql(select T, (geo || "_" || sex || "_" || age) as x, V as V from t1);
```
5. t3:
```
wide([T],[x],V,t2,sum);
```
6. plot:
```
line(t2,T,V,x);
# also implemented are point(...) for scatter plots and box(...) for box plots
```
(Shiny checks if the content of a text box changed in short intervals. Only if command is ended with a semicolon, the command is parsed and executed.)
(In case you are curious about the sudden rise of death due to viral hepatitis, have a look [here](http://www.joyofdata.de/blog/increase-of-deaths-due-to-viral-hepatitis-in-the-year-1998-in-germany/))