ClimMobTools: API Client for the ‘ClimMob’ platform in R
Kauê de Sousa
Digital Inclusion, Bioversity International, Montpelier, FranceDepartment of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar, Norway
Jacob van Etten
Digital Inclusion, Bioversity International, Montpelier, FranceBrandon Madriz
MrBot Software Solutions, Cartago, Costa RicaSource:
vignettes/Overview.Rmd
Overview.RmdOverview
ClimMobTools the API Client for the ‘ClimMob’ platform in R. ClimMob is an open source software for for decentralized large-N trials with the ‘tricot’ approach1. The approach aims the rapid assessment of technologies in the target environment. Tricot turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information.
Usage
The breadwheat is a dataframe from crowdsourcing citizen
science trials of bread wheat (Triticum aestivum L.) varieties
in India. This is a sample data available at the ClimMob that can be fetched using the
function getDataCM() from ClimMobTools and
an API key from the ClimMob user’s account.
library("ClimMobTools")
library("PlackettLuce")
library("climatrends")
library("nasapower")
# the API key
key <- "d39a3c66-5822-4930-a9d4-50e7da041e77"
dat <- getDataCM(key = key,
project = "breadwheat",
userowner = "gosset",
pivot.wider = TRUE)
names(dat) <- gsub("firstassessment_|package_|lastassessment_|registration_", "",
names(dat))Tricot data with environmental covariates
We can add environmental covariates from package climatrends.
Here we use function temperature() to compute the
temperature indices for the first 80 days after planting.
dat$plantingdate <- as.Date(dat$plantingdate, format = "%Y-%m-%d")
dat$lon <- as.numeric(dat$farm_geo_longitude)
dat$lat <- as.numeric(dat$farm_geo_latitude)
temp <- temperature(dat[, c("lon","lat")],
day.one = dat[, "plantingdate"],
span = 80)
temp
#> maxDT minDT maxNT minNT DTR SU TR CFD WSDI CSDI T10p T90p
#> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <dbl> <dbl>
#> 1: 30.75 20.65 17.67 4.63 15 2 0 0 3 2 5.50 27.74
#> 2: 29.15 20.65 16.52 4.63 15 0 0 0 4 2 5.50 27.46
#> 3: 32.76 20.65 18.49 4.63 15 7 0 0 8 2 5.50 29.16
#> 4: 29.15 20.65 16.52 4.63 15 0 0 0 3 2 5.50 27.27
#> 5: 29.15 20.65 16.52 4.63 15 0 0 0 4 2 5.50 26.22
#> ---
#> 489: 29.15 20.65 16.52 4.63 15 0 0 0 3 2 5.50 27.27
#> 490: 29.15 20.65 16.52 4.63 15 0 0 0 3 2 5.50 27.27
#> 491: 32.76 20.65 17.67 4.63 15 5 0 0 6 2 5.50 28.41
#> 492: 29.15 20.65 16.52 4.63 15 0 0 0 3 2 5.50 27.27
#> 493: 29.15 20.65 16.52 4.63 15 0 0 0 3 2 5.50 27.27Tricot data into rankings
The Plackett-Luce model is one approach to analyse the ClimMob
data2. We build the farmers’
rankings as an object of class ‘grouped_rankings’. This allows the
rankings to be linked to the environmental covariates computed
previously and fit the model using pltree() from the
package PlackettLuce.
We build the rankings using the function
rankTricot().
R <- rankTricot(dat,
items = c("item_A","item_B","item_C"),
input = c("overallperf_pos","overallperf_neg"),
group = TRUE)
pld <- cbind(R, temp)
pl <- pltree(R ~ maxNT + maxDT,
data = pld)
summary(pl)
#> $`2`
#> Call: PlackettLuce(rankings = y, weights = weights, na.action = NULL,
#> start = start)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> CSW18 0.0000 NA NA NA
#> WR544 -3.3246 0.4380 -7.590 3.20e-14 ***
#> PBW343 -0.6595 0.4512 -1.462 0.143852
#> HP1633 -3.4306 0.4323 -7.936 2.09e-15 ***
#> HW2045 -3.5339 0.4396 -8.039 9.03e-16 ***
#> DBW17 -1.4614 0.4132 -3.537 0.000405 ***
#> HD2985 -2.0511 0.4413 -4.648 3.35e-06 ***
#> DPW621-50 -2.0924 0.4376 -4.782 1.74e-06 ***
#> HD2824 -3.1032 0.4347 -7.138 9.46e-13 ***
#> RAJ4120 -3.0018 0.4331 -6.932 4.16e-12 ***
#> PBW550 -3.2180 0.4269 -7.537 4.79e-14 ***
#> K0307 -3.4931 0.4458 -7.835 4.67e-15 ***
#> HI1563 -3.4131 0.4425 -7.713 1.23e-14 ***
#> PBW502 -3.1382 0.4539 -6.914 4.71e-12 ***
#> HD2932 -2.7048 0.4320 -6.261 3.83e-10 ***
#> K9107 0.2791 0.4794 0.582 0.560486
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual deviance: 766.05 on 855 degrees of freedom
#> AIC: 796.05
#> Number of iterations: 25
#>
#> $`3`
#> Call: PlackettLuce(rankings = y, weights = weights, na.action = NULL,
#> start = start)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> CSW18 0.0000 NA NA NA
#> WR544 -2.1327 0.4498 -4.741 2.13e-06 ***
#> PBW343 0.2557 0.4366 0.586 0.558045
#> HP1633 -2.1386 0.4676 -4.574 4.79e-06 ***
#> HW2045 -1.9125 0.4482 -4.267 1.98e-05 ***
#> DBW17 -0.7026 0.4481 -1.568 0.116904
#> HD2985 -1.1698 0.4286 -2.729 0.006343 **
#> DPW621-50 -1.5821 0.4475 -3.535 0.000408 ***
#> HD2824 -2.5380 0.4852 -5.231 1.69e-07 ***
#> RAJ4120 -2.5301 0.4974 -5.087 3.64e-07 ***
#> PBW550 -2.1052 0.4693 -4.486 7.26e-06 ***
#> K0307 -2.3455 0.4467 -5.251 1.51e-07 ***
#> HI1563 -2.3205 0.4502 -5.154 2.54e-07 ***
#> PBW502 -1.9471 0.4245 -4.586 4.51e-06 ***
#> HD2932 -1.5077 0.4469 -3.374 0.000741 ***
#> K9107 1.1110 0.5217 2.129 0.033215 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual deviance: 559.08 on 594 degrees of freedom
#> AIC: 589.08
#> Number of iterations: 15
plot(pl)