Reshaping data to wide format with tidyr¶
The following makes use of the packages tidyr and readr. You may need to install them from CRAN using the code install.packages(c("tidyr","readr")) if you want to run this on your computer. (The packages are already installed on the notebook container, however.)
substr(readLines("inequality-oecd-downloaded.csv",n=5),
start=1,stop=40)
[1] "\"LOCATION\",\"INDICATOR\",\"SUBJECT\",\"MEASUR"
[2] "\"AUS\",\"INCOMEINEQ\",\"P50P10\",\"RT\",\"A\",\"20"
[3] "\"AUS\",\"INCOMEINEQ\",\"P50P10\",\"RT\",\"A\",\"20"
[4] "\"AUS\",\"INCOMEINEQ\",\"P50P10\",\"RT\",\"A\",\"20"
[5] "\"AUS\",\"INCOMEINEQ\",\"P90P10\",\"RT\",\"A\",\"20"
library(readr)
inequality.oecd.dld <- read_csv("inequality-oecd-downloaded.csv")
Parsed with column specification:
cols(
LOCATION = col_character(),
INDICATOR = col_character(),
SUBJECT = col_character(),
MEASURE = col_character(),
FREQUENCY = col_character(),
TIME = col_double(),
Value = col_double(),
`Flag Codes` = col_character()
)
inequality.oecd.dld
LOCATION INDICATOR SUBJECT MEASURE FREQUENCY TIME Value Flag Codes
1 AUS INCOMEINEQ P50P10 RT A 2012 2.200 NA
2 AUS INCOMEINEQ P50P10 RT A 2014 2.200 NA
3 AUS INCOMEINEQ P50P10 RT A 2016 2.100 NA
4 AUS INCOMEINEQ P90P10 RT A 2012 4.400 NA
5 AUS INCOMEINEQ P90P10 RT A 2014 4.300 NA
6 AUS INCOMEINEQ P90P10 RT A 2016 4.300 NA
7 AUS INCOMEINEQ P90P50 RT A 2012 2.000 NA
8 AUS INCOMEINEQ P90P50 RT A 2014 2.000 NA
9 AUS INCOMEINEQ P90P50 RT A 2016 2.100 NA
10 AUS INCOMEINEQ GINI INEQ A 2012 0.326 NA
11 AUS INCOMEINEQ GINI INEQ A 2014 0.337 NA
12 AUS INCOMEINEQ GINI INEQ A 2016 0.330 NA
13 AUT INCOMEINEQ P50P10 RT A 2007 2.000 NA
14 AUT INCOMEINEQ P50P10 RT A 2008 1.900 NA
15 AUT INCOMEINEQ P50P10 RT A 2009 2.000 NA
16 AUT INCOMEINEQ P50P10 RT A 2010 1.900 NA
17 AUT INCOMEINEQ P50P10 RT A 2011 1.900 NA
18 AUT INCOMEINEQ P50P10 RT A 2012 2.000 NA
19 AUT INCOMEINEQ P50P10 RT A 2013 1.900 NA
20 AUT INCOMEINEQ P50P10 RT A 2014 1.900 NA
21 AUT INCOMEINEQ P50P10 RT A 2015 1.900 NA
22 AUT INCOMEINEQ P50P10 RT A 2016 2.000 NA
23 AUT INCOMEINEQ P90P10 RT A 2007 3.600 NA
24 AUT INCOMEINEQ P90P10 RT A 2008 3.400 NA
25 AUT INCOMEINEQ P90P10 RT A 2009 3.600 NA
26 AUT INCOMEINEQ P90P10 RT A 2010 3.500 NA
27 AUT INCOMEINEQ P90P10 RT A 2011 3.500 NA
28 AUT INCOMEINEQ P90P10 RT A 2012 3.500 NA
29 AUT INCOMEINEQ P90P10 RT A 2013 3.400 NA
30 AUT INCOMEINEQ P90P10 RT A 2014 3.400 NA
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
2286 LTU INCOMEINEQ PALMA RT A 2006 1.26 NA
2287 LTU INCOMEINEQ PALMA RT A 2007 1.33 NA
2288 LTU INCOMEINEQ PALMA RT A 2008 1.48 NA
2289 LTU INCOMEINEQ PALMA RT A 2009 1.53 NA
2290 LTU INCOMEINEQ PALMA RT A 2010 1.24 NA
2291 LTU INCOMEINEQ PALMA RT A 2011 1.20 NA
2292 LTU INCOMEINEQ PALMA RT A 2012 1.42 NA
2293 LTU INCOMEINEQ PALMA RT A 2013 1.42 NA
2294 LTU INCOMEINEQ PALMA RT A 2014 1.67 NA
2295 LTU INCOMEINEQ PALMA RT A 2015 1.59 NA
2296 LTU INCOMEINEQ PALMA RT A 2016 1.65 NA
2297 RUS INCOMEINEQ PALMA RT A 2011 1.59 NA
2298 RUS INCOMEINEQ PALMA RT A 2016 1.28 NA
2299 SVN INCOMEINEQ PALMA RT A 2004 0.80 NA
2300 SVN INCOMEINEQ PALMA RT A 2005 0.80 NA
2301 SVN INCOMEINEQ PALMA RT A 2006 0.79 NA
2302 SVN INCOMEINEQ PALMA RT A 2007 0.79 NA
2303 SVN INCOMEINEQ PALMA RT A 2008 0.77 NA
2304 SVN INCOMEINEQ PALMA RT A 2009 0.82 NA
2305 SVN INCOMEINEQ PALMA RT A 2010 0.81 NA
2306 SVN INCOMEINEQ PALMA RT A 2011 0.81 NA
2307 SVN INCOMEINEQ PALMA RT A 2012 0.83 NA
2308 SVN INCOMEINEQ PALMA RT A 2013 0.86 NA
2309 SVN INCOMEINEQ PALMA RT A 2014 0.85 NA
2310 SVN INCOMEINEQ PALMA RT A 2015 0.84 NA
2311 SVN INCOMEINEQ PALMA RT A 2016 0.81 NA
2312 ZAF INCOMEINEQ PALMA RT A 2015 7.03 P
2313 KOR INCOMEINEQ PALMA RT A 2015 1.42 NA
2314 KOR INCOMEINEQ PALMA RT A 2016 1.45 NA
2315 KOR INCOMEINEQ PALMA RT A 2017 1.44 NA
library(tidyr)
inequality.oecd.dld %>% spread(key="SUBJECT",value="Value") ->
inequality.oecd
inequality.oecd[-c(2,4,6)]
LOCATION MEASURE TIME GINI P50P10 P90P10 P90P50 PALMA S80S20
1 AUS INEQ 2012 0.326 NA NA NA NA NA
2 AUS INEQ 2014 0.337 NA NA NA NA NA
3 AUS INEQ 2016 0.330 NA NA NA NA NA
4 AUS RT 2012 NA 2.2 4.4 2.0 1.24 5.5
5 AUS RT 2014 NA 2.2 4.3 2.0 1.34 5.7
6 AUS RT 2016 NA 2.1 4.3 2.1 1.26 5.5
7 AUT INEQ 2007 0.284 NA NA NA NA NA
8 AUT INEQ 2008 0.281 NA NA NA NA NA
9 AUT INEQ 2009 0.289 NA NA NA NA NA
10 AUT INEQ 2010 0.280 NA NA NA NA NA
11 AUT INEQ 2011 0.281 NA NA NA NA NA
12 AUT INEQ 2012 0.275 NA NA NA NA NA
13 AUT INEQ 2013 0.279 NA NA NA NA NA
14 AUT INEQ 2014 0.274 NA NA NA NA NA
15 AUT INEQ 2015 0.276 NA NA NA NA NA
16 AUT INEQ 2016 0.284 NA NA NA NA NA
17 AUT RT 2007 NA 2.0 3.6 1.8 1.00 4.4
18 AUT RT 2008 NA 1.9 3.4 1.8 1.00 4.3
19 AUT RT 2009 NA 2.0 3.6 1.8 1.03 4.5
20 AUT RT 2010 NA 1.9 3.5 1.8 0.98 4.3
21 AUT RT 2011 NA 1.9 3.5 1.8 0.99 4.4
22 AUT RT 2012 NA 2.0 3.5 1.8 0.96 4.2
23 AUT RT 2013 NA 1.9 3.4 1.8 0.99 4.2
24 AUT RT 2014 NA 1.9 3.4 1.7 0.96 4.1
25 AUT RT 2015 NA 1.9 3.3 1.7 0.96 4.2
26 AUT RT 2016 NA 2.0 3.5 1.8 1.00 4.5
27 BEL INEQ 2004 0.287 NA NA NA NA NA
28 BEL INEQ 2005 0.277 NA NA NA NA NA
29 BEL INEQ 2006 0.268 NA NA NA NA NA
30 BEL INEQ 2007 0.277 NA NA NA NA NA
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
742 SWE INEQ 2015 0.278 NA NA NA NA NA
743 SWE INEQ 2016 0.282 NA NA NA NA NA
744 SWE INEQ 2017 0.282 NA NA NA NA NA
745 SWE RT 2013 NA 1.9 3.2 1.7 0.94 4.0
746 SWE RT 2014 NA 1.9 3.3 1.7 0.97 4.1
747 SWE RT 2015 NA 2.0 3.3 1.7 0.99 4.2
748 SWE RT 2016 NA 1.9 3.3 1.7 1.02 4.2
749 SWE RT 2017 NA 2.0 3.3 1.7 1.02 4.2
750 TUR INEQ 2011 0.403 NA NA NA NA NA
751 TUR INEQ 2012 0.399 NA NA NA NA NA
752 TUR INEQ 2013 0.390 NA NA NA NA NA
753 TUR INEQ 2014 0.398 NA NA NA NA NA
754 TUR INEQ 2015 0.404 NA NA NA NA NA
755 TUR RT 2011 NA 2.5 6.1 2.5 1.89 8.0
756 TUR RT 2012 NA 2.4 6.0 2.5 1.86 7.7
757 TUR RT 2013 NA 2.4 5.9 2.5 1.76 7.5
758 TUR RT 2014 NA 2.4 5.9 2.5 1.84 7.7
759 TUR RT 2015 NA 2.3 5.7 2.5 1.91 7.8
760 USA INEQ 2013 0.396 NA NA NA NA NA
761 USA INEQ 2014 0.394 NA NA NA NA NA
762 USA INEQ 2015 0.390 NA NA NA NA NA
763 USA INEQ 2016 0.391 NA NA NA NA NA
764 USA INEQ 2017 0.390 NA NA NA NA NA
765 USA RT 2013 NA 2.7 6.4 2.3 1.82 8.6
766 USA RT 2014 NA 2.7 6.4 2.3 1.79 8.7
767 USA RT 2015 NA 2.7 6.1 2.3 1.75 8.3
768 USA RT 2016 NA 2.7 6.3 2.3 1.77 8.5
769 USA RT 2017 NA 2.7 6.2 2.3 1.76 8.4
770 ZAF INEQ 2015 0.620 NA NA NA NA NA
771 ZAF RT 2015 NA 4.8 25.6 5.3 7.03 37.6
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
inequality.oecd.sub <- select(inequality.oecd.dld,
LOCATION,SUBJECT,TIME,Value)
inequality.oecd.sub
LOCATION SUBJECT TIME Value
1 AUS P50P10 2012 2.200
2 AUS P50P10 2014 2.200
3 AUS P50P10 2016 2.100
4 AUS P90P10 2012 4.400
5 AUS P90P10 2014 4.300
6 AUS P90P10 2016 4.300
7 AUS P90P50 2012 2.000
8 AUS P90P50 2014 2.000
9 AUS P90P50 2016 2.100
10 AUS GINI 2012 0.326
11 AUS GINI 2014 0.337
12 AUS GINI 2016 0.330
13 AUT P50P10 2007 2.000
14 AUT P50P10 2008 1.900
15 AUT P50P10 2009 2.000
16 AUT P50P10 2010 1.900
17 AUT P50P10 2011 1.900
18 AUT P50P10 2012 2.000
19 AUT P50P10 2013 1.900
20 AUT P50P10 2014 1.900
21 AUT P50P10 2015 1.900
22 AUT P50P10 2016 2.000
23 AUT P90P10 2007 3.600
24 AUT P90P10 2008 3.400
25 AUT P90P10 2009 3.600
26 AUT P90P10 2010 3.500
27 AUT P90P10 2011 3.500
28 AUT P90P10 2012 3.500
29 AUT P90P10 2013 3.400
30 AUT P90P10 2014 3.400
⋮ ⋮ ⋮ ⋮ ⋮
2286 LTU PALMA 2006 1.26
2287 LTU PALMA 2007 1.33
2288 LTU PALMA 2008 1.48
2289 LTU PALMA 2009 1.53
2290 LTU PALMA 2010 1.24
2291 LTU PALMA 2011 1.20
2292 LTU PALMA 2012 1.42
2293 LTU PALMA 2013 1.42
2294 LTU PALMA 2014 1.67
2295 LTU PALMA 2015 1.59
2296 LTU PALMA 2016 1.65
2297 RUS PALMA 2011 1.59
2298 RUS PALMA 2016 1.28
2299 SVN PALMA 2004 0.80
2300 SVN PALMA 2005 0.80
2301 SVN PALMA 2006 0.79
2302 SVN PALMA 2007 0.79
2303 SVN PALMA 2008 0.77
2304 SVN PALMA 2009 0.82
2305 SVN PALMA 2010 0.81
2306 SVN PALMA 2011 0.81
2307 SVN PALMA 2012 0.83
2308 SVN PALMA 2013 0.86
2309 SVN PALMA 2014 0.85
2310 SVN PALMA 2015 0.84
2311 SVN PALMA 2016 0.81
2312 ZAF PALMA 2015 7.03
2313 KOR PALMA 2015 1.42
2314 KOR PALMA 2016 1.45
2315 KOR PALMA 2017 1.44
inequality.oecd.sub %>% spread(key=SUBJECT,
value=Value) -> inequality.oecd
inequality.oecd
LOCATION TIME GINI P50P10 P90P10 P90P50 PALMA S80S20
1 AUS 2012 0.326 2.2 4.4 2.0 1.24 5.5
2 AUS 2014 0.337 2.2 4.3 2.0 1.34 5.7
3 AUS 2016 0.330 2.1 4.3 2.1 1.26 5.5
4 AUT 2007 0.284 2.0 3.6 1.8 1.00 4.4
5 AUT 2008 0.281 1.9 3.4 1.8 1.00 4.3
6 AUT 2009 0.289 2.0 3.6 1.8 1.03 4.5
7 AUT 2010 0.280 1.9 3.5 1.8 0.98 4.3
8 AUT 2011 0.281 1.9 3.5 1.8 0.99 4.4
9 AUT 2012 0.275 2.0 3.5 1.8 0.96 4.2
10 AUT 2013 0.279 1.9 3.4 1.8 0.99 4.2
11 AUT 2014 0.274 1.9 3.4 1.7 0.96 4.1
12 AUT 2015 0.276 1.9 3.3 1.7 0.96 4.2
13 AUT 2016 0.284 2.0 3.5 1.8 1.00 4.5
14 BEL 2004 0.287 1.9 3.3 1.7 1.05 4.2
15 BEL 2005 0.277 2.0 3.4 1.7 0.99 4.1
16 BEL 2006 0.268 2.0 3.4 1.7 0.93 4.0
17 BEL 2007 0.277 1.9 3.3 1.7 0.98 4.1
18 BEL 2008 0.266 2.0 3.4 1.7 0.91 3.9
19 BEL 2009 0.272 2.0 3.4 1.7 0.95 4.1
20 BEL 2010 0.267 2.0 3.4 1.7 0.92 4.0
21 BEL 2011 0.270 2.0 3.4 1.7 0.93 4.0
22 BEL 2012 0.265 2.0 3.4 1.7 0.91 3.9
23 BEL 2013 0.265 2.0 3.4 1.7 0.90 3.9
24 BEL 2014 0.266 1.9 3.4 1.7 0.92 3.9
25 BEL 2015 0.268 2.0 3.4 1.7 0.93 4.0
26 BEL 2016 0.266 2.0 3.3 1.7 0.92 3.9
27 BRA 2006 0.510 3.1 10.4 3.4 3.39 15.3
28 BRA 2009 0.485 2.8 8.9 3.1 2.96 13.1
29 BRA 2011 0.483 3.1 9.3 3.0 2.95 13.9
30 BRA 2013 0.470 3.0 8.7 2.9 2.71 12.5
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
357 SVK 2016 0.241 1.9 3.1 1.6 0.79 3.7
358 SVN 2004 0.241 1.8 3.0 1.7 0.80 3.5
359 SVN 2005 0.240 1.8 3.0 1.7 0.80 3.5
360 SVN 2006 0.237 1.8 3.0 1.7 0.79 3.4
361 SVN 2007 0.239 1.8 3.0 1.6 0.79 3.5
362 SVN 2008 0.234 1.8 3.0 1.6 0.77 3.4
363 SVN 2009 0.245 1.9 3.1 1.7 0.82 3.6
364 SVN 2010 0.244 1.9 3.2 1.6 0.81 3.6
365 SVN 2011 0.244 1.9 3.2 1.6 0.81 3.6
366 SVN 2012 0.249 2.0 3.2 1.7 0.83 3.7
367 SVN 2013 0.254 2.0 3.3 1.7 0.86 3.8
368 SVN 2014 0.251 2.0 3.3 1.7 0.85 3.7
369 SVN 2015 0.250 1.9 3.2 1.6 0.84 3.7
370 SVN 2016 0.244 1.9 3.1 1.7 0.81 3.6
371 SWE 2013 0.268 1.9 3.2 1.7 0.94 4.0
372 SWE 2014 0.274 1.9 3.3 1.7 0.97 4.1
373 SWE 2015 0.278 2.0 3.3 1.7 0.99 4.2
374 SWE 2016 0.282 1.9 3.3 1.7 1.02 4.2
375 SWE 2017 0.282 2.0 3.3 1.7 1.02 4.2
376 TUR 2011 0.403 2.5 6.1 2.5 1.89 8.0
377 TUR 2012 0.399 2.4 6.0 2.5 1.86 7.7
378 TUR 2013 0.390 2.4 5.9 2.5 1.76 7.5
379 TUR 2014 0.398 2.4 5.9 2.5 1.84 7.7
380 TUR 2015 0.404 2.3 5.7 2.5 1.91 7.8
381 USA 2013 0.396 2.7 6.4 2.3 1.82 8.6
382 USA 2014 0.394 2.7 6.4 2.3 1.79 8.7
383 USA 2015 0.390 2.7 6.1 2.3 1.75 8.3
384 USA 2016 0.391 2.7 6.3 2.3 1.77 8.5
385 USA 2017 0.390 2.7 6.2 2.3 1.76 8.4
386 ZAF 2015 0.620 4.8 25.6 5.3 7.03 37.6
inequality.oecd.dld %>% pivot_wider(names_from=SUBJECT,
values_from=Value,
id_cols=c(LOCATION,TIME)) ->
inequality.oecd
inequality.oecd
LOCATION TIME P50P10 P90P10 P90P50 GINI S80S20 PALMA
1 AUS 2012 2.2 4.4 2.0 0.326 5.5 1.24
2 AUS 2014 2.2 4.3 2.0 0.337 5.7 1.34
3 AUS 2016 2.1 4.3 2.1 0.330 5.5 1.26
4 AUT 2007 2.0 3.6 1.8 0.284 4.4 1.00
5 AUT 2008 1.9 3.4 1.8 0.281 4.3 1.00
6 AUT 2009 2.0 3.6 1.8 0.289 4.5 1.03
7 AUT 2010 1.9 3.5 1.8 0.280 4.3 0.98
8 AUT 2011 1.9 3.5 1.8 0.281 4.4 0.99
9 AUT 2012 2.0 3.5 1.8 0.275 4.2 0.96
10 AUT 2013 1.9 3.4 1.8 0.279 4.2 0.99
11 AUT 2014 1.9 3.4 1.7 0.274 4.1 0.96
12 AUT 2015 1.9 3.3 1.7 0.276 4.2 0.96
13 AUT 2016 2.0 3.5 1.8 0.284 4.5 1.00
14 BEL 2004 1.9 3.3 1.7 0.287 4.2 1.05
15 BEL 2005 2.0 3.4 1.7 0.277 4.1 0.99
16 BEL 2006 2.0 3.4 1.7 0.268 4.0 0.93
17 BEL 2007 1.9 3.3 1.7 0.277 4.1 0.98
18 BEL 2008 2.0 3.4 1.7 0.266 3.9 0.91
19 BEL 2009 2.0 3.4 1.7 0.272 4.1 0.95
20 BEL 2010 2.0 3.4 1.7 0.267 4.0 0.92
21 BEL 2011 2.0 3.4 1.7 0.270 4.0 0.93
22 BEL 2012 2.0 3.4 1.7 0.265 3.9 0.91
23 BEL 2013 2.0 3.4 1.7 0.265 3.9 0.90
24 BEL 2014 1.9 3.4 1.7 0.266 3.9 0.92
25 BEL 2015 2.0 3.4 1.7 0.268 4.0 0.93
26 BEL 2016 2.0 3.3 1.7 0.266 3.9 0.92
27 CAN 1976 2.3 4.2 1.8 0.303 5.1 1.09
28 CAN 1977 2.4 4.3 1.8 0.289 4.9 0.99
29 CAN 1978 2.3 4.2 1.8 0.294 4.9 1.04
30 CAN 1979 2.3 4.1 1.8 0.289 4.8 1.00
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
357 LTU 2004 2.3 5.0 2.2 0.350 6.3 1.41
358 LTU 2005 2.3 5.2 2.2 0.352 6.4 1.43
359 LTU 2006 2.2 4.6 2.1 0.329 5.6 1.26
360 LTU 2007 2.3 4.6 2.0 0.338 5.8 1.33
361 LTU 2008 2.3 4.8 2.1 0.358 6.3 1.48
362 LTU 2009 2.4 5.5 2.3 0.366 7.2 1.53
363 LTU 2010 2.2 4.7 2.1 0.329 5.7 1.24
364 LTU 2011 2.1 4.5 2.1 0.322 5.3 1.20
365 LTU 2012 2.2 4.8 2.1 0.350 6.2 1.42
366 LTU 2013 2.2 4.9 2.3 0.352 6.1 1.42
367 LTU 2014 2.4 5.4 2.2 0.381 7.4 1.67
368 LTU 2015 2.5 5.5 2.3 0.372 7.1 1.59
369 LTU 2016 2.6 5.8 2.2 0.378 7.5 1.65
370 BRA 2006 3.1 10.4 3.4 0.510 15.3 3.39
371 BRA 2009 2.8 8.9 3.1 0.485 13.1 2.96
372 BRA 2011 3.1 9.3 3.0 0.483 13.9 2.95
373 BRA 2013 3.0 8.7 2.9 0.470 12.5 2.71
374 CHN 2011 7.8 23.0 2.9 0.514 28.3 3.86
375 CRI 2010 2.9 9.3 3.2 0.472 12.1 2.73
376 CRI 2011 3.0 10.2 3.4 0.480 13.1 2.87
377 CRI 2012 3.1 9.9 3.2 0.483 13.5 2.93
378 CRI 2013 3.2 10.8 3.4 0.494 14.3 3.09
379 CRI 2014 3.2 10.4 3.3 0.485 13.7 2.98
380 CRI 2015 3.1 10.3 3.3 0.479 13.4 2.85
381 CRI 2016 3.1 10.2 3.3 0.484 13.5 2.94
382 CRI 2017 3.0 10.0 3.4 0.480 13.0 2.88
383 CRI 2018 3.1 10.2 3.3 0.479 13.2 2.85
384 IND 2004 2.9 9.1 3.2 0.482 12.5 2.88
385 IND 2011 2.9 9.4 3.2 0.495 13.4 3.09
386 ZAF 2015 4.8 25.6 5.3 0.620 37.6 7.03
Downloadable R script and interactive version
- R Script: reshaping-to-wide-with-tidyr.R
- Interactive version (shuts down after 60s):
- Interactive version (sign in required):
Explanation
The link with the “jupyterhub” icon directs you to an interactive Jupyter1 notebook, which runs inside a Docker container2. There are two variants of the interative notebook. One shuts down after 60 seconds and does not require a sign it. The other requires sign in using your ORCID3 credentials, yet shuts down only after 24 hours. (There is no guarantee that such a container persists that long, it may be shut down earlier for maintenance purposes.) After shutdown all data within the container will be reset, i.e. all files created by the user will be deleted.4
Above you see a rendered version of the Jupyter notebook.5
- 1
-
For more information about Jupyter see
http://jupyter.org. The Jupyter notebooks make use of the IRKernel package. - 2
-
For more information about Docker see
https://docs.docker.com/. The container images were created with repo2docker, while containers are run with docker spawner. - 3
-
ORCID is a free service for the authentication of researchers. It also allows to showcase publications and contributions to the academic community such as peer review.. See
https://info.orcid.org/what-is-orcid/for more information. - 4
-
The Jupyter notebooks come with NO WARRANTY whatsoever. They are provided for educational and illustrative purposes only. Do not use them for production work.
- 5
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The notebook is rendered with the help of the nbsphinx extension.
