我正在R中用水管工运行R脚本。我试图从R脚本取回JSON响应。但是似乎下面的语句输出不支持JSON响应。
summary(fm1 <- lm(log(dist) ~ log(speed), data = cars))
我得到以上陈述的以下内容。
Call:
lm(formula = log(dist) ~ log(speed), data = cars)
Residuals:
Min 1Q Median 3Q Max
-1.00215 -0.24578 -0.02898 0.20717 0.88289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.7297 0.3758 -1.941 0.0581 .
log(speed) 1.6024 0.1395 11.484 2.26e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4053 on 48 degrees of freedom
Multiple R-squared: 0.7331, Adjusted R-squared: 0.7276
F-statistic: 131.9 on 1 and 48 DF, p-value: 2.259e-15
我尝试使用jsonlite :: toJSON，但是我想我在某处缺少R的基本原理。
我应该怎么做才能将输出转换为JSON结构？
答案 0 :(得分：2)
Since you did not provide data:
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
s <- summary(lm.D9)
This is what ^^ looks like:
str(s)
## List of 11
## $ call : language lm(formula = weight ~ group)
## $ terms :Classes 'terms', 'formula' language weight ~ group
## .. ..- attr(*, "variables")= language list(weight, group)
## .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "weight" "group"
## .. .. .. ..$ : chr "group"
## .. ..- attr(*, "term.labels")= chr "group"
## .. ..- attr(*, "order")= int 1
## .. ..- attr(*, "intercept")= int 1
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(weight, group)
## .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
## .. .. ..- attr(*, "names")= chr [1:2] "weight" "group"
## $ residuals : Named num [1:20] -0.862 0.548 0.148 1.078 -0.532 ...
## ..- attr(*, "names")= chr [1:20] "1" "2" "3" "4" ...
## $ coefficients : num [1:2, 1:4] 5.032 -0.371 0.22 0.311 22.85 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "(Intercept)" "groupTrt"
## .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
## $ aliased : Named logi [1:2] FALSE FALSE
## ..- attr(*, "names")= chr [1:2] "(Intercept)" "groupTrt"
## $ sigma : num 0.696
## $ df : int [1:3] 2 18 2
## $ r.squared : num 0.0731
## $ adj.r.squared: num 0.0216
## $ fstatistic : Named num [1:3] 1.42 1 18
## ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
## $ cov.unscaled : num [1:2, 1:2] 0.1 -0.1 -0.1 0.2
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "(Intercept)" "groupTrt"
## .. ..$ : chr [1:2] "(Intercept)" "groupTrt"
## - attr(*, "class")= chr "summary.lm"
We can totally turn arbitrary objects to JSON if you don't mind a potential (in this case definite) loss in element resolution (you are likely fine at R fundamentals…you are lacking in reading docs tho ):
jsonlite::toJSON(s, force=TRUE, pretty=TRUE)
## {
## "call": {},
## "terms": {},
## "residuals": [-0.862, 0.548, 0.148, 1.078, -0.532, -0.422, 0.138, -0.502, 0.298, 0.108, 0.149, -0.491, -0.251, -1.071, 1.209, -0.831, 1.369, 0.229, -0.341, 0.029],
## "coefficients": [
## [5.032, 0.2202, 22.8501, 9.5471e-15],
## [-0.371, 0.3114, -1.1913, 0.249]
## ],
## "aliased": [false, false],
## "sigma": [0.6964],
## "df": [2, 18, 2],
## "r.squared": [0.0731],
## "adj.r.squared": [0.0216],
## "fstatistic": [1.4191, 1, 18],
## "cov.unscaled": [
## [0.1, -0.1],
## [-0.1, 0.2]
## ]
## }
If you want the missing values you'll need to convert them appropriately before doing the toJSON()
Further, this is what base:::print.summary.lm does so you could also adopt it and make your own JSON:
function (x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...) {
cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"),
"\n\n", sep = "")
resid <- x$residuals
df <- x$df
rdf <- df[2L]
cat(if (!is.null(x$weights) && diff(range(x$weights)))
"Weighted ", "Residuals:\n", sep = "")
if (rdf > 5L) {
nam <- c("Min", "1Q", "Median", "3Q", "Max")
rq <- if (length(dim(resid)) == 2L)
structure(apply(t(resid), 1L, quantile), dimnames = list(nam,
dimnames(resid)[[2L]]))
else {
zz <- zapsmall(quantile(resid), digits + 1L)
structure(zz, names = nam)
}
print(rq, digits = digits, ...)
}
else if (rdf > 0L) {
print(resid, digits = digits, ...)
}
else {
cat("ALL", df[1L], "residuals are 0: no residual degrees of freedom!")
cat("\n")
}
if (length(x$aliased) == 0L) {
cat("\nNo Coefficients\n")
}
else {
if (nsingular <- df[3L] - df[1L])
cat("\nCoefficients: (", nsingular, " not defined because of singularities)\n",
sep = "")
else cat("\nCoefficients:\n")
coefs <- x$coefficients
if (any(aliased <- x$aliased)) {
cn <- names(aliased)
coefs <- matrix(NA, length(aliased), 4, dimnames = list(cn,
colnames(coefs)))
coefs[!aliased, ] <- x$coefficients
}
printCoefmat(coefs, digits = digits, signif.stars = signif.stars,
na.print = "NA", ...)
}
cat("\nResidual standard error:", format(signif(x$sigma,
digits)), "on", rdf, "degrees of freedom")
cat("\n")
if (nzchar(mess <- naprint(x$na.action)))
cat(" (", mess, ")\n", sep = "")
if (!is.null(x$fstatistic)) {
cat("Multiple R-squared: ", formatC(x$r.squared, digits = digits))
cat(",\tAdjusted R-squared: ", formatC(x$adj.r.squared,
digits = digits), "\nF-statistic:", formatC(x$fstatistic[1L],
digits = digits), "on", x$fstatistic[2L], "and",
x$fstatistic[3L], "DF, p-value:", format.pval(pf(x$fstatistic[1L],
x$fstatistic[2L], x$fstatistic[3L], lower.tail = FALSE),
digits = digits))
cat("\n")
}
correl <- x$correlation
if (!is.null(correl)) {
p <- NCOL(correl)
if (p > 1L) {
cat("\nCorrelation of Coefficients:\n")
if (is.logical(symbolic.cor) && symbolic.cor) {
print(symnum(correl, abbr.colnames = NULL))
}
else {
correl <- format(round(correl, 2), nsmall = 2,
digits = digits)
correl[!lower.tri(correl)] <- ""
print(correl[-1, -p, drop = FALSE], quote = FALSE)
}
}
}
cat("\n")
invisible(x)
}