Friendly & Fast Input-Output Analysis 
fio (Friendly Input-Output) is a R package designed for input-output analysis, emphasizing usability for Excel users and performance. It includes an RStudio Addin and a suite of functions for straightforward import of input-output tables from Excel, either programmatically or directly from the clipboard.
The package is optimized for speed and efficiency. It leverages the R6 class for clean, memory-efficient object-oriented programming. Furthermore, all linear algebra computations are implemented in Rust to achieve highly optimized performance.
Installation
R-universe version
You can install the latest version from the main branch, using the precompiled binaries available on R-universe:
install.packages("fio", repos = c("https://albersonmiranda.r-universe.dev", "https://cloud.r-project.org"))Development version
For the cutting-edge development branches from Github (other than the main branch), you’ll need to compile it from source. This requires Rust to be installed on your system. You can install Rust using the following commands:
- Debian/Ubuntu:
sudo apt install cargo - Fedora/CentOS:
sudo dnf install cargo - macOS:
brew install rust - Windows: https://www.rust-lang.org/tools/install
Getting Started
If you are just getting started with fio, we recommend you to read the vignettes for a comprehensive overview of the package.
Single-region input-output analysis
Calculate Leontief’s inverse from brazilian 2020 input-output matrix:
# load included dataset
iom_br <- fio::br_2020
# calculate technical coefficients matrix
iom_br$compute_tech_coeff()
# calculate Leontief's inverse
iom_br$compute_leontief_inverse()And pronto! 🎉, you’re all good to carry on with your analysis. You can evoke the Data Viewer to inspect the results with iom_br$technical_coefficients_matrix |> View() and iom_br$leontief_inverse_matrix |> View().
Leontief’s inverse from brazilian 2020 input-output matrix
Multi-region input-output analysis
Calculate multi-regional multipliers and interdependence from the 2000 World Input-Output Database (26 countries, 23 sectors):
# load included dataset
miom_world <- fio::world_2000
# calculate multi-regional multipliers
miom_world$compute_multiregional_multipliers()
# get regional interdependence
miom_world$get_regional_interdependence()
#> country self_reliance total_spillover_out total_spillover_in
#> 1 AUS 1.968515 0.3168527 0.0065116342
#> 2 AUT 1.614535 0.4900724 0.0040486980
#> 3 BEL 1.649908 0.7652207 0.0111482277
#> 4 BRA 1.918948 0.2328115 0.0040658244
#> 5 CAN 1.650380 0.4280919 0.0076344511
#> 6 CHN 2.342241 0.2867934 0.0167357151
#> 7 DEU 1.750123 0.4072660 0.0461095519
#> 8 DNK 1.588219 0.5336860 0.0045789821
#> 9 ESP 1.872652 0.4035966 0.0127873685
#> 10 FIN 1.814946 0.4446499 0.0051829261
#> 11 FRA 1.870104 0.3975701 0.0258689073
#> 12 GBR 1.880823 0.3553086 0.0323988530
#> 13 GRC 1.450459 0.4766389 0.0008403844
#> 14 HKG 1.456702 1.0297257 0.0050721423
#> 15 IND 1.930336 0.2933663 0.0030570998
#> 16 IRL 1.473624 0.7271773 0.0035162918
#> 17 ITA 1.975659 0.3413902 0.0210409652
#> 18 JPN 1.928339 0.1351515 0.0269893283
#> 19 KOR 1.988313 0.4362860 0.0102672586
#> 20 MEX 1.562506 0.3542938 0.0025632750
#> 21 NDL 1.579268 0.6539386 0.0170240366
#> 22 PRT 1.813322 0.5236525 0.0017732553
#> 23 SWE 1.725532 0.5043674 0.0103124186
#> 24 TWN 1.676048 0.5120606 0.0073320388
#> 25 USA 1.878020 0.1923963 0.0655620433
#> 26 ROW 1.756742 0.4076203 0.0956546757
#> interdependence_index
#> 1 0.16096024
#> 2 0.30353773
#> 3 0.46379603
#> 4 0.12132247
#> 5 0.25938982
#> 6 0.12244405
#> 7 0.23270710
#> 8 0.33602791
#> 9 0.21552141
#> 10 0.24499350
#> 11 0.21259247
#> 12 0.18891127
#> 13 0.32861241
#> 14 0.70688863
#> 15 0.15197676
#> 16 0.49346193
#> 17 0.17279815
#> 18 0.07008703
#> 19 0.21942515
#> 20 0.22674721
#> 21 0.41407711
#> 22 0.28878074
#> 23 0.29229676
#> 24 0.30551677
#> 25 0.10244637
#> 26 0.23203195
# get country summary
miom_world$get_country_summary()
#> country multiplier_simple_mean multiplier_simple_sum multiplier_simple_sd
#> 1 AUS 2.285368 52.56346 0.2943434
#> 2 AUT 2.104608 48.40598 0.2595025
#> 3 BEL 2.415129 55.54796 0.3495773
#> 4 BRA 2.151760 49.49047 0.4011159
#> 5 CAN 2.078472 47.80486 0.3334081
#> 6 CHN 2.629034 60.46779 0.4657446
#> 7 DEU 2.157389 49.61994 0.2843985
#> 8 DNK 2.121905 48.80382 0.3295126
#> 9 ESP 2.276249 52.35372 0.3673208
#> 10 FIN 2.259596 51.97070 0.3496083
#> 11 FRA 2.267674 52.15651 0.3314330
#> 12 GBR 2.236131 51.43102 0.2534105
#> 13 GRC 1.927098 44.32326 0.3743875
#> 14 HKG 2.486427 57.18783 0.6228530
#> 15 IND 2.223703 51.14516 0.5532397
#> 16 IRL 2.200801 50.61843 0.2742686
#> 17 ITA 2.317049 53.29214 0.3875865
#> 18 JPN 2.063490 47.46027 0.3292166
#> 19 KOR 2.424599 55.76579 0.4950520
#> 20 MEX 1.916799 44.08638 0.4216234
#> 21 NDL 2.233206 51.36375 0.3128269
#> 22 PRT 2.336974 53.75041 0.3771554
#> 23 ROW 2.164363 49.78034 0.3800173
#> 24 SWE 2.229899 51.28768 0.3076712
#> 25 TWN 2.188108 50.32649 0.4428694
#> 26 USA 2.070416 47.61957 0.2678378
#> multiplier_direct_mean multiplier_direct_sum multiplier_direct_sd
#> 1 0.5910851 13.59496 0.1243854
#> 2 0.5424757 12.47694 0.1079106
#> 3 0.6272375 14.42646 0.1371100
#> 4 0.5551566 12.76860 0.1760325
#> 5 0.5436337 12.50358 0.1541054
#> 6 0.6247874 14.37011 0.1544596
#> 7 0.5735571 13.19181 0.1257155
#> 8 0.5483842 12.61284 0.1627911
#> 9 0.5818832 13.38331 0.1514225
#> 10 0.5861276 13.48093 0.1504276
#> 11 0.6003713 13.80854 0.1365743
#> 12 0.5768597 13.26777 0.1178258
#> 13 0.4795222 11.02901 0.1730478
#> 14 0.6955571 15.99781 0.2574222
#> 15 0.5776381 13.28568 0.2488502
#> 16 0.5622517 12.93179 0.1102378
#> 17 0.5974856 13.74217 0.1545091
#> 18 0.5343955 12.29110 0.1212272
#> 19 0.5993085 13.78410 0.1704651
#> 20 0.4865362 11.19033 0.2019697
#> 21 0.5973635 13.73936 0.1482268
#> 22 0.6045684 13.90507 0.1527213
#> 23 0.5388760 12.39415 0.1530084
#> 24 0.5772032 13.27567 0.1396862
#> 25 0.5741787 13.20611 0.1716934
#> 26 0.5416578 12.45813 0.1136737
#> multiplier_indirect_mean multiplier_indirect_sum multiplier_indirect_sd
#> 1 1.694283 38.96850 0.1750524
#> 2 1.562132 35.92904 0.1570810
#> 3 1.787891 41.12149 0.2174743
#> 4 1.596603 36.72187 0.2283837
#> 5 1.534839 35.30129 0.1888710
#> 6 2.004247 46.09768 0.3222542
#> 7 1.583832 36.42813 0.1655095
#> 8 1.573521 36.19098 0.1887727
#> 9 1.694365 38.97040 0.2259730
#> 10 1.673468 38.48977 0.2052737
#> 11 1.667303 38.34797 0.2014659
#> 12 1.659271 38.16324 0.1512847
#> 13 1.447576 33.29425 0.2108174
#> 14 1.790870 41.19001 0.3712546
#> 15 1.646065 37.85949 0.3214000
#> 16 1.638549 37.68664 0.1695499
#> 17 1.719564 39.54997 0.2418168
#> 18 1.529095 35.16918 0.2148490
#> 19 1.825291 41.98169 0.3412776
#> 20 1.430263 32.89605 0.2357968
#> 21 1.635843 37.62439 0.1784893
#> 22 1.732406 39.84534 0.2372139
#> 23 1.625487 37.38619 0.2330413
#> 24 1.652696 38.01201 0.1776820
#> 25 1.613930 37.12038 0.2780501
#> 26 1.528758 35.16144 0.1606822
