MetaculR
+ + + + +
+
+Login
+Add the following lines to .Renviron using usethis::edit_r_environ():
Metaculus_username="yourUsername"
+Metaculus_password="yourPassword"Then, login:
+ +
+
+Download Your Predictions
+ +
+
+Analyze Your Predictions
+
+
+
+
+
+How different is my last prediction from current community?
+ +questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_me_q2_abs_odds) %>%
+ dplyr::mutate(diff_me_q2_abs_odds = round(diff_me_q2_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +my_prediction | +community_q2 | +community_q2_pre_me | +diff_me_q2_abs_odds | +
|---|---|---|---|---|---|
| 1634 | +Will US Income Inequality Increase by 2025? | +0.02 | +0.53 | +0.47 | +55.3 | +
| 9933 | +Will any NATO country invoke Article 5 by March 31, 2022? | +0.13 | +0.01 | +0.02 | +14.8 | +
| 5407 | +If Starlink offers an IPO before 2030, will it set a record for the largest IPO? | +0.03 | +0.24 | +0.30 | +10.2 | +
| 9937 | +Will more than 50,000 people be killed in the Russo-Ukrainian War in 2022? | +0.15 | +0.60 | +0.45 | +8.5 | +
| 2605 | +Will any country’s military expenditure exceed that of the United States before 2030? | +0.01 | +0.07 | +0.08 | +7.5 | +
| 7977 | +Will US core CPI inflation rise by more than 3% from December 2021 to December 2022? | +0.94 | +0.70 | +0.68 | +6.7 | +
+
+
+Plot those differences
+questions_myPredictions_byDiff %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ dplyr::slice_head(n = 10) %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = reorder(factor(id), -diff_me_q2_abs_odds),
+ y = diff_me_q2_abs_odds,
+ fill = reorder(factor(id), -diff_me_q2_abs_odds))
+ ) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "ID",
+ y = "Odds difference between me and community q2") +
+ ggplot2::guides(fill = FALSE) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+
+
+
+plot of chunk unnamed-chunk-8
+
+
+
+
+
+
+Where has community moved most since my last prediction?
+questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_comm_q2_pre_me_abs_odds) %>%
+ dplyr::mutate(diff_comm_q2_pre_me_abs_odds = round(diff_comm_q2_pre_me_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_comm_q2_pre_me_abs_odds)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +my_prediction | +community_q2 | +community_q2_pre_me | +diff_comm_q2_pre_me_abs_odds | +
|---|---|---|---|---|---|
| 10004 | +Will a major nuclear power plant in Germany be operational on June 1, 2023? | +0.10 | +0.20 | +0.56 | +5.1 | +
| 9939 | +Will Kyiv fall to Russian forces by April 2022? | +0.01 | +0.02 | +0.09 | +4.8 | +
| 6604 | +Will annual U.S. inflation reach 100% in any year before 2050? | +0.01 | +0.02 | +0.08 | +4.3 | +
| 8766 | +Will the Omicron variant be less lethal than Delta? | +0.98 | +0.98 | +0.94 | +3.1 | +
| 9933 | +Will any NATO country invoke Article 5 by March 31, 2022? | +0.13 | +0.01 | +0.02 | +2.0 | +
| 6725 | +Will a large American city fully abolish their police department before 2035? | +0.01 | +0.02 | +0.04 | +2.0 | +
+
+
+
+Score Predictions
+Let’s see some Brier statistics on resolved questions:
+ + +
+
+
+Plot scores
+brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 10 rows containing missing values (geom_col).
+#> Warning: Removed 10 rows containing missing values (geom_text).
+
+
+
+plot of chunk unnamed-chunk-14
+
+
+
+Histogram
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+
+
+
+plot of chunk unnamed-chunk-15
+
+
+
+Calibration
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df $obars) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-16
+
+
+
+
+Score Predictions (Equivalent Evidentiary Bins)
+What if question bins were not 5 percentage points each, but were based on logodds?
+brier_me <- MetaculR_brier(questions_myPredictions_resolved,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 10 rows containing missing values (geom_col).
+#> Warning: Removed 10 rows containing missing values (geom_text).
+
+
+
+plot of chunk unnamed-chunk-18
+
+
+
+Histogram (Equivalent Evidentiary Bins)
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.05), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+
+
+
+plot of chunk unnamed-chunk-19
+
+
+
+Calibration (Equivalent Evidentiary Bins)
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df$obars) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-20
+
+
+Analyze community predictions
+For questions you may not have predicted.
+
+
+
+
+Score, Community Predictions
+Let’s see some Brier statistics on resolved questions:
+questions_recent_resolved <-
+ MetaculR_questions(
+ order_by = "-resolve_time",
+ status= "resolved",
+ guessed_by = "",
+ pages = 32,
+ offset = 0)
+
+
+Plot scores, Community Predictions
+brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 17 rows containing missing values (geom_col).
+#> Warning: Removed 17 rows containing missing values (geom_text).
+
+
+
+plot of chunk unnamed-chunk-23
+
+
+
+Histogram, Community Predictions
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-24
+
+
+
+Calibration, Community Predictions
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-25
+
+
+
+
+Score Predictions, Community Predictions (Equivalent Evidentiary Bins)
+brier_recent_resolved <-
+ MetaculR_brier(
+ questions_recent_resolved,
+ me = FALSE,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 17 rows containing missing values (geom_col).
+#> Warning: Removed 17 rows containing missing values (geom_text).
+
+
+
+plot of chunk unnamed-chunk-27
+
+
+
+Histogram, Community Predictions (Equivalent Evidentiary Bins)
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-28
+
+
+
+Calibration, Community Predictions (Equivalent Evidentiary Bins)
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+
+
+
+plot of chunk unnamed-chunk-29
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/man/MetaculR_brier.Rd b/man/MetaculR_brier.Rd
new file mode 100644
index 0000000..2a1616f
--- /dev/null
+++ b/man/MetaculR_brier.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_brier}
+\alias{MetaculR_brier}
+\title{Calculate Brier statistics on MetaculR_questions object}
+\usage{
+MetaculR_brier(MetaculR_questions, me = TRUE, thresholds = seq(0, 1, 0.1))
+}
+\arguments{
+\item{MetaculR_questions}{A MetaculR_questions object}
+
+\item{me}{Show my scores alongside Metaculus scores}
+
+\item{thresholds}{Thresholds to bin questions}
+}
+\value{
+A list of Brier statistics for you and Metaculus.
+\item{brier_me, brier_Metaculus}{}
+\item{baseline.tf}{Logical indicator of whether climatology was provided.}
+\item{bs}{Brier score}
+\item{bs.baseline}{Brier Score for climatology}
+\item{ss}{Skill score}
+\item{bs.reliability}{Reliability portion of Brier score.}
+\item{bs.resolution}{Resolution component of Brier score.}
+\item{bs.uncert}{Uncertainty component of Brier score.}
+\item{y.i}{Forecast bins -- described as the center value of the bins.}
+\item{obar.i}{Observation bins -- described as the center value of the bins.}
+\item{prob.y}{Proportion of time using each forecast.}
+\item{obar}{Forecast based on climatology or average sample observations.}
+\item{thresholds}{The thresholds for the forecast bins.}
+\item{check}{ Reliability - resolution + uncertainty should equal brier score.}
+\item{Other}{}
+\item{ss_me_Metaculus}{Skill score, me vs. Metaculus.}
+\item{count_questions}{Number of total questions included.}
+\item{brier_df: Used for plotting Brier score statistics}{}
+\item{ID}{Predictor.}
+\item{name}{Name of value, see above.}
+\item{value}{Value.}
+\item{brier_bins_df: Used for plotting histogram and calibration plots.}{}
+\item{ID}{Predictor.}
+\item{centers}{y.i, see above.}
+\item{freqs}{prob.y, see above.}
+\item{obars}{obar.i, see above.}
+\item{ideal}{Ideal calibration where centers equals obars.}
+\item{ci_low}{Low end of 95\% confidence interval for obar.i.}
+\item{ci_high}{High end of 95\% confidence interval for obar.i.}
+}
+\description{
+Calculate Brier statistics on MetaculR_questions object
+}
+\examples{
+\dontrun{
+brier_me <-
+ MetaculR_brier(
+ questions_myPredictions_resolved)
+}
+}
diff --git a/man/MetaculR_excitement.Rd b/man/MetaculR_excitement.Rd
new file mode 100644
index 0000000..8056747
--- /dev/null
+++ b/man/MetaculR_excitement.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_excitement}
+\alias{MetaculR_excitement}
+\title{Find exciting questions}
+\usage{
+MetaculR_excitement(MetaculR_questions)
+}
+\arguments{
+\item{MetaculR_questions}{A MetaculR_questions object}
+}
+\value{
+A dataframe of questions with excitement measures.
+\item{id}{Question ID.}
+\item{title}{Question title.}
+\item{Total_Change}{Cumulative delta in time period, by probability.}
+\item{Total_logodds_Change}{Cumulative delta in time period, by logodds.}
+\item{Total_Change_Even}{Cumulative delta toward even odds in time period, by probability.}
+\item{Total_logodds_Change_Even}{Cumulative delta toward even odds in time period, by logodds.}
+}
+\description{
+Find exciting questions
+}
+\examples{
+\dontrun{
+questions_myPredictions_byExcitement <-
+ MetaculR_excitement(
+ questions_myPredictions)
+}
+}
diff --git a/man/MetaculR_login.Rd b/man/MetaculR_login.Rd
new file mode 100644
index 0000000..81487a5
--- /dev/null
+++ b/man/MetaculR_login.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_login}
+\alias{MetaculR_login}
+\title{Login to Metaculus}
+\usage{
+MetaculR_login(api_domain = "www")
+}
+\arguments{
+\item{api_domain}{Use "www" unless you have a custom Metaculus domain}
+}
+\value{
+Your Metaculus_user_ID.
+}
+\description{
+Login to Metaculus
+}
+\examples{
+\dontrun{
+Metaculus_user_id <-
+ MetaculR_login()
+}
+}
diff --git a/man/MetaculR_myDiff.Rd b/man/MetaculR_myDiff.Rd
new file mode 100644
index 0000000..373397c
--- /dev/null
+++ b/man/MetaculR_myDiff.Rd
@@ -0,0 +1,43 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_myDiff}
+\alias{MetaculR_myDiff}
+\title{Find important changes within MetaculR_questions object}
+\usage{
+MetaculR_myDiff(MetaculR_questions)
+}
+\arguments{
+\item{MetaculR_questions}{A MetaculR_questions object}
+}
+\value{
+A dataframe of questions with difference measures (your most recent prediction vs. community's most recent prediction, etc.).
+\item{id}{Question ID.}
+\item{title}{Question title.}
+\item{my_prediction}{My most recent prediction.}
+\item{community_q2}{Community median.}
+\item{community_ave}{Community average.}
+\item{community_q2_pre_me}{Community median immediately prior to my_prediction.}
+\item{community_ave_pre_me}{Community average immediately prior to my_prediction.}
+\item{diff_me_q2}{Difference between me and the community median, by logodds.}
+\item{diff_me_ave}{Difference between me and the community average, by logodds.}
+\item{diff_comm_q2_pre_me}{Difference between community_q2_pre_me and the community average, by logodds.}
+\item{diff_comm_ave_pre_me}{Difference between community_ave_pre_me and the community average, by logodds.}
+\item{diff_me_q2_abs}{Absolute difference between me and the community median, by logodds.}
+\item{diff_me_ave_abs}{Absolute difference between me and the community average, by logodds.}
+\item{diff_comm_q2_pre_me_abs}{Absolute difference between community_q2_pre_me and the community average, by logodds.}
+\item{diff_comm_ave_pre_me_abs}{Absolute difference between community_ave_pre_me and the community average, by logodds.}
+\item{diff_me_q2_abs_odds}{Absolute difference between me and the community median, by odds.}
+\item{diff_me_ave_abs_odds}{Absolute difference between me and the community average, by odds.}
+\item{diff_comm_q2_pre_me_abs_odds}{Absolute difference between community_q2_pre_me and the community average, by odds.}
+\item{diff_comm_ave_pre_me_abs_odds}{Absolute difference between community_ave_pre_me and the community average, by odds.}
+}
+\description{
+Find important changes within MetaculR_questions object
+}
+\examples{
+\dontrun{
+questions_myPredictions_byDiff <-
+ MetaculR_myDiff(
+ questions_myPredictions)
+}
+}
diff --git a/man/MetaculR_myPredictions.Rd b/man/MetaculR_myPredictions.Rd
new file mode 100644
index 0000000..e2acca4
--- /dev/null
+++ b/man/MetaculR_myPredictions.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_myPredictions}
+\alias{MetaculR_myPredictions}
+\title{Retrieve questions from Metaculus API (A wrapper for MetaculR_questions())}
+\usage{
+MetaculR_myPredictions(
+ api_domain = "www",
+ order_by = "last_prediction_time",
+ status = "all",
+ search = "",
+ guessed_by = "",
+ offset = 0,
+ pages = 10
+)
+}
+\arguments{
+\item{api_domain}{Use "www" unless you have a custom Metaculus domain}
+
+\item{order_by}{Default is "last_prediction_time"}
+
+\item{status}{Choose "all", "upcoming", "open", "closed", "resolved"}
+
+\item{search}{Search term(s)}
+
+\item{guessed_by}{Generally your Metaculus_user_id}
+
+\item{offset}{Question offset}
+
+\item{pages}{Number of pages to request}
+}
+\value{
+A list of questions that I've predicted, ordered by last prediction time.
+}
+\description{
+Retrieve questions from Metaculus API (A wrapper for MetaculR_questions())
+}
+\examples{
+\dontrun{
+questions_myPredictions <-
+ MetaculR_myPredictions(
+ guessed_by = Metaculus_user_id)
+}
+}
+\seealso{
+Other Question Retrieval functions:
+\code{\link{MetaculR_myPredictions_Resolved}()},
+\code{\link{MetaculR_questions}()}
+}
+\concept{Question Retrieval functions}
diff --git a/man/MetaculR_myPredictions_Resolved.Rd b/man/MetaculR_myPredictions_Resolved.Rd
new file mode 100644
index 0000000..1258d96
--- /dev/null
+++ b/man/MetaculR_myPredictions_Resolved.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_myPredictions_Resolved}
+\alias{MetaculR_myPredictions_Resolved}
+\title{Retrieve questions from Metaculus API (A wrapper for MetaculR_questions())}
+\usage{
+MetaculR_myPredictions_Resolved(
+ api_domain = "www",
+ order_by = "-resolve_time",
+ status = "resolved",
+ search = "",
+ guessed_by = "",
+ offset = 0,
+ pages = 10
+)
+}
+\arguments{
+\item{api_domain}{Use "www" unless you have a custom Metaculus domain}
+
+\item{order_by}{Default is "-resolve_time"}
+
+\item{status}{Default is "resolved"}
+
+\item{search}{Search term(s)}
+
+\item{guessed_by}{Generally your Metaculus_user_id}
+
+\item{offset}{Question offset}
+
+\item{pages}{Number of pages to request}
+}
+\value{
+A list of questions that I've predicted, ordered by last prediction time, and resolved.
+}
+\description{
+Retrieve questions from Metaculus API (A wrapper for MetaculR_questions())
+}
+\examples{
+\dontrun{
+questions_myPredictions_resolved <-
+ MetaculR_myPredictions_Resolved(
+ guessed_by = Metaculus_user_id)
+}
+}
+\seealso{
+Other Question Retrieval functions:
+\code{\link{MetaculR_myPredictions}()},
+\code{\link{MetaculR_questions}()}
+}
+\concept{Question Retrieval functions}
diff --git a/man/MetaculR_plot.Rd b/man/MetaculR_plot.Rd
new file mode 100644
index 0000000..b84eca3
--- /dev/null
+++ b/man/MetaculR_plot.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_plot}
+\alias{MetaculR_plot}
+\title{Plot the history of a single question}
+\usage{
+MetaculR_plot(MetaculR_questions, Metacular_id, scale_binary = "prob")
+}
+\arguments{
+\item{MetaculR_questions}{A MetaculR_questions object}
+
+\item{Metacular_id}{The ID of the question to plot}
+
+\item{scale_binary}{Choose "prob", "odds", or "logodds"}
+}
+\value{
+A ggplot.
+}
+\description{
+Plot the history of a single question
+}
+\examples{
+\dontrun{
+MetaculR_plot(
+ MetaculR_questions = questions_myPredictions,
+ Metacular_id = 10004)
+}
+}
diff --git a/man/MetaculR_questions.Rd b/man/MetaculR_questions.Rd
new file mode 100644
index 0000000..947b071
--- /dev/null
+++ b/man/MetaculR_questions.Rd
@@ -0,0 +1,52 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/basics.R
+\name{MetaculR_questions}
+\alias{MetaculR_questions}
+\title{Retrieve questions from Metaculus API}
+\usage{
+MetaculR_questions(
+ api_domain = "www",
+ order_by = "last_prediction_time",
+ status = "all",
+ search = "",
+ guessed_by = "",
+ offset = 0,
+ pages = 10
+)
+}
+\arguments{
+\item{api_domain}{Use "www" unless you have a custom Metaculus domain}
+
+\item{order_by}{Choose "last_prediction_time", "-activity", "-votes", "-publish_time", "close_time", "resolve_time", "last_prediction_time"}
+
+\item{status}{Choose "all", "upcoming", "open", "closed", "resolved"}
+
+\item{search}{Search term(s)}
+
+\item{guessed_by}{Generally your Metaculus_user_id}
+
+\item{offset}{Question offset}
+
+\item{pages}{Number of pages to request}
+}
+\value{
+A list of questions, ordered by last prediction time.
+}
+\description{
+Retrieve questions from Metaculus API
+}
+\examples{
+\dontrun{
+questions_recent_open <-
+ MetaculR_questions(
+ order_by = "close_time",
+ status = "open",
+ guessed_by = "")
+}
+}
+\seealso{
+Other Question Retrieval functions:
+\code{\link{MetaculR_myPredictions_Resolved}()},
+\code{\link{MetaculR_myPredictions}()}
+}
+\concept{Question Retrieval functions}
diff --git a/man/pipe.Rd b/man/pipe.Rd
new file mode 100644
index 0000000..0eec752
--- /dev/null
+++ b/man/pipe.Rd
@@ -0,0 +1,12 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-pipe.R
+\name{\%>\%}
+\alias{\%>\%}
+\title{Pipe operator}
+\usage{
+lhs \%>\% rhs
+}
+\description{
+See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+}
+\keyword{internal}
diff --git a/vignettes/MetaculR.Rmd b/vignettes/MetaculR.Rmd
new file mode 100644
index 0000000..ef7e62a
--- /dev/null
+++ b/vignettes/MetaculR.Rmd
@@ -0,0 +1,704 @@
+---
+title: "MetaculR"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{MetaculR}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+
+
+
+
+
+
+
+```r
+library(MetaculR)
+```
+
+# Login
+
+Add the following lines to `.Renviron` using `usethis::edit_r_environ()`:
+
+```
+Metaculus_username="yourUsername"
+Metaculus_password="yourPassword"
+```
+
+Then, login:
+
+
+```r
+Metaculus_user_id <- MetaculR_login()
+```
+
+# Download Your Predictions
+
+
+```r
+questions_myPredictions <- MetaculR_myPredictions(guessed_by = Metaculus_user_id)
+```
+
+# Analyze Your Predictions
+
+## How different is my last prediction from current community?
+
+
+```r
+questions_myPredictions_byDiff <- MetaculR_myDiff(questions_myPredictions)
+```
+
+
+
+```r
+questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_me_q2_abs_odds) %>%
+ dplyr::mutate(diff_me_q2_abs_odds = round(diff_me_q2_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | my_prediction| community_q2| community_q2_pre_me| diff_me_q2_abs_odds|
+|----:|:-------------------------------------------------------------------------------------|-------------:|------------:|-------------------:|-------------------:|
+| 1634|Will US Income Inequality Increase by 2025? | 0.02| 0.53| 0.47| 55.3|
+| 9933|Will any NATO country invoke Article 5 by March 31, 2022? | 0.13| 0.01| 0.02| 14.8|
+| 5407|If Starlink offers an IPO before 2030, will it set a record for the largest IPO? | 0.03| 0.24| 0.30| 10.2|
+| 9937|Will more than 50,000 people be killed in the Russo-Ukrainian War in 2022? | 0.15| 0.60| 0.45| 8.5|
+| 2605|Will any country's military expenditure exceed that of the United States before 2030? | 0.01| 0.07| 0.08| 7.5|
+| 7977|Will US core CPI inflation rise by more than 3% from December 2021 to December 2022? | 0.94| 0.70| 0.68| 6.7|
+
+### Plot those differences
+
+
+```r
+questions_myPredictions_byDiff %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ dplyr::slice_head(n = 10) %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = reorder(factor(id), -diff_me_q2_abs_odds),
+ y = diff_me_q2_abs_odds,
+ fill = reorder(factor(id), -diff_me_q2_abs_odds))
+ ) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "ID",
+ y = "Odds difference between me and community q2") +
+ ggplot2::guides(fill = FALSE) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+
+
+## Where has community moved most since my last prediction?
+
+
+```r
+questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_comm_q2_pre_me_abs_odds) %>%
+ dplyr::mutate(diff_comm_q2_pre_me_abs_odds = round(diff_comm_q2_pre_me_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_comm_q2_pre_me_abs_odds)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | my_prediction| community_q2| community_q2_pre_me| diff_comm_q2_pre_me_abs_odds|
+|-----:|:-----------------------------------------------------------------------------|-------------:|------------:|-------------------:|----------------------------:|
+| 10004|Will a major nuclear power plant in Germany be operational on June 1, 2023? | 0.10| 0.20| 0.56| 5.1|
+| 9939|Will Kyiv fall to Russian forces by April 2022? | 0.01| 0.02| 0.09| 4.8|
+| 6604|Will annual U.S. inflation reach 100% in any year before 2050? | 0.01| 0.02| 0.08| 4.3|
+| 8766|Will the Omicron variant be less lethal than Delta? | 0.98| 0.98| 0.94| 3.1|
+| 9933|Will any NATO country invoke Article 5 by March 31, 2022? | 0.13| 0.01| 0.02| 2.0|
+| 6725|Will a large American city fully abolish their police department before 2035? | 0.01| 0.02| 0.04| 2.0|
+
+### Plot those differences
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 10004)
+```
+
+
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 6604,
+ scale_binary = "logodds")
+```
+
+
+
+## Score Predictions
+
+Let's see some Brier statistics on resolved questions:
+
+
+```r
+questions_myPredictions_resolved <- MetaculR_myPredictions_Resolved(guessed_by = Metaculus_user_id)
+```
+
+
+```r
+brier_me <- MetaculR_brier(questions_myPredictions_resolved)
+```
+
+### Plot scores
+
+
+```r
+brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 10 rows containing missing values (geom_col).
+#> Warning: Removed 10 rows containing missing values (geom_text).
+```
+
+
+
+#### Histogram
+
+
+```r
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+```
+
+
+
+#### Calibration
+
+
+```r
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df $obars) * 1.1))
+```
+
+
+
+## Score Predictions (Equivalent Evidentiary Bins)
+
+What if question bins were not 5 percentage points each, but were based on logodds?
+
+
+```r
+brier_me <- MetaculR_brier(questions_myPredictions_resolved,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))
+```
+
+
+```r
+brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 10 rows containing missing values (geom_col).
+#> Warning: Removed 10 rows containing missing values (geom_text).
+```
+
+
+
+#### Histogram (Equivalent Evidentiary Bins)
+
+
+```r
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.05), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+```
+
+
+
+#### Calibration (Equivalent Evidentiary Bins)
+
+
+```r
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df$obars) * 1.1))
+```
+
+
+
+# Analyze community predictions
+
+For questions you may not have predicted.
+
+## Score, Community Predictions
+
+Let's see some Brier statistics on resolved questions:
+
+
+```r
+questions_recent_resolved <-
+ MetaculR_questions(
+ order_by = "-resolve_time",
+ status= "resolved",
+ guessed_by = "",
+ pages = 32,
+ offset = 0)
+```
+
+
+```r
+brier_recent_resolved <-
+ MetaculR_brier(
+ questions_recent_resolved,
+ me = FALSE)
+```
+
+### Plot scores, Community Predictions
+
+
+```r
+brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 17 rows containing missing values (geom_col).
+#> Warning: Removed 17 rows containing missing values (geom_text).
+```
+
+
+
+#### Histogram, Community Predictions
+
+
+```r
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+```
+
+
+
+#### Calibration, Community Predictions
+
+
+```r
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+```
+
+
+
+## Score Predictions, Community Predictions (Equivalent Evidentiary Bins)
+
+
+```r
+brier_recent_resolved <-
+ MetaculR_brier(
+ questions_recent_resolved,
+ me = FALSE,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))
+```
+
+
+```r
+brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+#> Warning: Removed 17 rows containing missing values (geom_col).
+#> Warning: Removed 17 rows containing missing values (geom_text).
+```
+
+
+
+#### Histogram, Community Predictions (Equivalent Evidentiary Bins)
+
+
+```r
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+```
+
+
+
+#### Calibration, Community Predictions (Equivalent Evidentiary Bins)
+
+
+```r
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+```
+
+
+
+# Find Exciting Questions
+
+Various sports now have an "Excitement Index" to identify games that interest fans--maybe we can do the same for predictions?
+
+## Among your predictions
+
+
+```r
+questions_myPredictions_byExcitement <- MetaculR_excitement(questions_myPredictions)
+
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | Total_Change| Total_logodds_Change| Total_Change_Even| Total_logodds_Change_Even|
+|----:|:---------------------------------------------------------------------------------|------------:|--------------------:|-----------------:|-------------------------:|
+| 9790|Will Éric Zemmour be in the 2nd round of the 2022 French presidential election? | 1.12| 5.38| 0.50| 2.32|
+| 8944|Will Boris Johnson be Prime Minister of the UK on June 1, 2022? | 0.35| 2.00| 0.00| 0.00|
+| 8882|Will 2022 be the hottest year on record? | 0.22| 1.60| 0.11| 0.80|
+| 8554|Will women receive at least 70% of bachelor's degrees in the US in the year 2050? | 0.19| 1.49| 0.10| 0.79|
+| 2511|Will Emmanuel Macron be re-elected President of France in 2022? | 0.18| 1.40| 0.00| 0.00|
+| 6330|Will Donald J. Trump be a candidate for President in the 2024 cycle? | 0.15| 0.79| 0.05| 0.25|
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 9790)
+```
+
+
+
+
+```r
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | Total_Change| Total_logodds_Change| Total_Change_Even| Total_logodds_Change_Even|
+|----:|:---------------------------------------------------------------------------------|------------:|--------------------:|-----------------:|-------------------------:|
+| 9790|Will Éric Zemmour be in the 2nd round of the 2022 French presidential election? | 1.12| 5.38| 0.50| 2.32|
+| 8944|Will Boris Johnson be Prime Minister of the UK on June 1, 2022? | 0.35| 2.00| 0.00| 0.00|
+| 8882|Will 2022 be the hottest year on record? | 0.22| 1.60| 0.11| 0.80|
+| 8554|Will women receive at least 70% of bachelor's degrees in the US in the year 2050? | 0.19| 1.49| 0.10| 0.79|
+| 6604|Will annual U.S. inflation reach 100% in any year before 2050? | 0.06| 1.45| 0.00| 0.00|
+| 2511|Will Emmanuel Macron be re-elected President of France in 2022? | 0.18| 1.40| 0.00| 0.00|
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 6725)
+```
+
+
+
+## Among community predictions
+
+### Resolved questions
+
+What were the most exciting resolved questions?
+
+
+```r
+questions_recent_byExcitement <- MetaculR_excitement(questions_recent_resolved)
+
+questions_recent_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change_Even)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | Total_Change| Total_logodds_Change| Total_Change_Even| Total_logodds_Change_Even|
+|----:|:--------------------------------------------------------------------------------------------------------------------|------------:|--------------------:|-----------------:|-------------------------:|
+| 8859|Will Ukraine ban wheat export before April 2023? | 0.17| 1.37| 0.11| 0.92|
+| 9517|Will Russian troops enter Mariupol, Ukraine by December 31, 2022? | 0.52| 4.86| 0.09| 0.44|
+| 9590|Will there be a Pi variant of COVID by Pi Day (March 14, 2022)? | 0.41| 1.95| 0.07| 0.30|
+| 8898|Will Russia invade Ukrainian territory before 2023? | 0.38| 2.97| 0.06| 0.32|
+| 7211|Will any body of the US federal government conclude that COVID-19 originated in a lab in Hubei before June 1st 2022? | 0.02| 0.08| 0.02| 0.08|
+| 9693|Will the gray wolf be relisted as Threatened or Endangered by the US before 2030? | 0.00| 0.00| 0.00| 0.00|
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_recent_resolved,
+ Metacular_id = 8898)
+```
+
+
+
+### Open questions
+
+What are the most exciting questions that are still open?
+
+
+```r
+questions_recent_open <-
+ MetaculR_questions(
+ order_by = "close_time",
+ status = "open",
+ guessed_by = "")
+```
+
+
+```r
+questions_recent_open_byExcitement <- MetaculR_excitement(questions_recent_open)
+
+questions_recent_open_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change_Even)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+
+
+| id|title | Total_Change| Total_logodds_Change| Total_Change_Even| Total_logodds_Change_Even|
+|----:|:------------------------------------------------------------------------------------------------------|------------:|--------------------:|-----------------:|-------------------------:|
+| 9746|Will Ukraine fulfill its Minsk-II obligations in Donetsk and Luhansk Oblast by 2023? | 0.57| 11.73| 0.14| 4.72|
+| 9566|Will Russian troops enter Odessa, Ukraine before December 31, 2022? | 1.18| 10.03| 0.48| 4.56|
+| 9791|Will Éric Zemmour win the French presidential election in 2022? | 0.15| 9.40| 0.07| 4.35|
+| 9743|Will Volodymyr Zelensky remain President of Ukraine by 2023? | 1.66| 8.13| 0.74| 3.70|
+| 7237|Will Coinbase default on an obligation to hand over their users’ assets on request by the end of 2022? | 0.13| 5.05| 0.07| 2.67|
+| 9584|Will Ketanji Brown Jackson be confirmed as an Associate Justice of the Supreme Court before 2023? | 0.67| 8.62| 0.12| 2.46|
+
+
+```r
+MetaculR_plot(MetaculR_questions = questions_recent_open,
+ Metacular_id = 9566)
+```
+
+
+
+
diff --git a/vignettes/MetaculR.Rmd.orig b/vignettes/MetaculR.Rmd.orig
new file mode 100644
index 0000000..4d15f08
--- /dev/null
+++ b/vignettes/MetaculR.Rmd.orig
@@ -0,0 +1,569 @@
+---
+title: "MetaculR"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{MetaculR}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+```{r eval=FALSE, include=FALSE}
+# Use `knitr::knit("vignettes/MetaculR.Rmd.orig", output = "vignettes/MetaculR.Rmd")` in the Console to pre-build the .Rmd, move the `/figure` directory into `/vignettes`, and anyone can build the .Rmd without my credentials.
+```
+
+```{r eval=FALSE, include=FALSE}
+library(httptest)
+httptest::start_vignette("MetaculR")
+```
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+ collapse = TRUE,
+ comment = "#>",
+ fig.path = "MetaculRRMD_"
+)
+```
+
+```{r setup}
+library(MetaculR)
+```
+
+# Login
+
+Add the following lines to `.Renviron` using `usethis::edit_r_environ()`:
+
+```
+Metaculus_username="yourUsername"
+Metaculus_password="yourPassword"
+```
+
+Then, login:
+
+```{r message=FALSE}
+Metaculus_user_id <- MetaculR_login()
+```
+
+# Download Your Predictions
+
+```{r message=FALSE}
+questions_myPredictions <- MetaculR_myPredictions(guessed_by = Metaculus_user_id)
+```
+
+# Analyze Your Predictions
+
+## How different is my last prediction from current community?
+
+```{r message=FALSE, warning=FALSE}
+questions_myPredictions_byDiff <- MetaculR_myDiff(questions_myPredictions)
+```
+
+
+```{r}
+questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_me_q2_abs_odds) %>%
+ dplyr::mutate(diff_me_q2_abs_odds = round(diff_me_q2_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+### Plot those differences
+
+```{r}
+questions_myPredictions_byDiff %>%
+ dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
+ dplyr::slice_head(n = 10) %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = reorder(factor(id), -diff_me_q2_abs_odds),
+ y = diff_me_q2_abs_odds,
+ fill = reorder(factor(id), -diff_me_q2_abs_odds))
+ ) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "ID",
+ y = "Odds difference between me and community q2") +
+ ggplot2::guides(fill = FALSE) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+## Where has community moved most since my last prediction?
+
+```{r}
+questions_myPredictions_byDiff %>%
+ dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_comm_q2_pre_me_abs_odds) %>%
+ dplyr::mutate(diff_comm_q2_pre_me_abs_odds = round(diff_comm_q2_pre_me_abs_odds, 1)) %>%
+ dplyr::arrange(dplyr::desc(diff_comm_q2_pre_me_abs_odds)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+### Plot those differences
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 10004)
+```
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 6604,
+ scale_binary = "logodds")
+```
+
+## Score Predictions
+
+Let's see some Brier statistics on resolved questions:
+
+```{r message=FALSE}
+questions_myPredictions_resolved <- MetaculR_myPredictions_Resolved(guessed_by = Metaculus_user_id)
+```
+
+```{r}
+brier_me <- MetaculR_brier(questions_myPredictions_resolved)
+```
+
+### Plot scores
+
+```{r}
+brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+#### Histogram
+
+```{r}
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+```
+
+#### Calibration
+
+```{r}
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df $obars) * 1.1))
+```
+
+## Score Predictions (Equivalent Evidentiary Bins)
+
+What if question bins were not 5 percentage points each, but were based on logodds?
+
+```{r}
+brier_me <- MetaculR_brier(questions_myPredictions_resolved,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))
+```
+
+```{r}
+brier_me$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+#### Histogram (Equivalent Evidentiary Bins)
+
+```{r}
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ fill = ID),
+ position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.05), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, 1))
+```
+
+#### Calibration (Equivalent Evidentiary Bins)
+
+```{r}
+brier_me$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high,
+ color = ID),
+ position = ggplot2::position_dodge2(width = 0.02)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df$obars) * 1.1))
+```
+
+# Analyze community predictions
+
+For questions you may not have predicted.
+
+## Score, Community Predictions
+
+Let's see some Brier statistics on resolved questions:
+
+```{r message=FALSE}
+questions_recent_resolved <-
+ MetaculR_questions(
+ order_by = "-resolve_time",
+ status= "resolved",
+ guessed_by = "",
+ pages = 32,
+ offset = 0)
+```
+
+```{r}
+brier_recent_resolved <-
+ MetaculR_brier(
+ questions_recent_resolved,
+ me = FALSE)
+```
+
+### Plot scores, Community Predictions
+
+```{r}
+brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+#### Histogram, Community Predictions
+
+```{r}
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+```
+
+#### Calibration, Community Predictions
+
+```{r}
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+```
+
+## Score Predictions, Community Predictions (Equivalent Evidentiary Bins)
+
+```{r}
+brier_recent_resolved <-
+ MetaculR_brier(
+ questions_recent_resolved,
+ me = FALSE,
+ thresholds = c(0, exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) / (exp(seq(from = -log(99), to = log(99), by = log(99) / 5)) + 1), 1))
+```
+
+```{r}
+brier_recent_resolved$brier_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = name,
+ y = value,
+ fill = ID),
+ position = "dodge2"
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = name,
+ y = value,
+ label = round(value, 3)),
+ size = 2,
+ position = ggplot2::position_dodge2(width = 1), #"dodge2",
+ vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::labs(x = "Statistic",
+ y = "Value") +
+ ggplot2::coord_cartesian(ylim = c(0, 1)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
+```
+
+#### Histogram, Community Predictions (Equivalent Evidentiary Bins)
+
+```{r}
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_col(
+ ggplot2::aes(x = centers,
+ y = freqs)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = freqs,
+ label = round(freqs, 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ # vjust = 0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
+```
+
+#### Calibration, Community Predictions (Equivalent Evidentiary Bins)
+
+```{r}
+brier_recent_resolved$brier_bins_df %>%
+ ggplot2::ggplot() +
+ ggplot2::geom_pointrange(
+ ggplot2::aes(x = centers,
+ y = obars,
+ ymin = ci_low,
+ ymax = ci_high)
+ ) +
+ ggplot2::geom_line(
+ ggplot2::aes(x = centers,
+ y = ideal)
+ ) +
+ ggplot2::geom_text(
+ ggplot2::aes(x = centers,
+ y = obars,
+ label = format(round(obars, 3), nsmall = 3)),
+ size = 2,
+ # position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
+ vjust = -0.5,
+ hjust = -0.25,
+ angle = 90) +
+ ggplot2::theme_classic() +
+ ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
+```
+
+# Find Exciting Questions
+
+Various sports now have an "Excitement Index" to identify games that interest fans--maybe we can do the same for predictions?
+
+## Among your predictions
+
+```{r}
+questions_myPredictions_byExcitement <- MetaculR_excitement(questions_myPredictions)
+
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 9790)
+```
+
+```{r}
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_myPredictions,
+ Metacular_id = 6725)
+```
+
+## Among community predictions
+
+### Resolved questions
+
+What were the most exciting resolved questions?
+
+```{r}
+questions_recent_byExcitement <- MetaculR_excitement(questions_recent_resolved)
+
+questions_recent_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change_Even)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_recent_resolved,
+ Metacular_id = 8898)
+```
+
+### Open questions
+
+What are the most exciting questions that are still open?
+
+```{r message=FALSE}
+questions_recent_open <-
+ MetaculR_questions(
+ order_by = "close_time",
+ status = "open",
+ guessed_by = "")
+```
+
+```{r}
+questions_recent_open_byExcitement <- MetaculR_excitement(questions_recent_open)
+
+questions_recent_open_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change_Even)) %>%
+ head() %>%
+ knitr::kable()
+```
+
+```{r}
+MetaculR_plot(MetaculR_questions = questions_recent_open,
+ Metacular_id = 9566)
+```
+
+```{r eval=FALSE, include=FALSE}
+httptest::end_vignette()
+```
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Find Exciting Questions
+Various sports now have an “Excitement Index” to identify games that interest fans–maybe we can do the same for predictions?
+
+
+
+
+
+
+
+
+
+
+Among your predictions
+questions_myPredictions_byExcitement <- MetaculR_excitement(questions_myPredictions)
+
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +Total_Change | +Total_logodds_Change | +Total_Change_Even | +Total_logodds_Change_Even | +
|---|---|---|---|---|---|
| 9790 | +Will Éric Zemmour be in the 2nd round of the 2022 French presidential election? | +1.12 | +5.38 | +0.50 | +2.32 | +
| 8944 | +Will Boris Johnson be Prime Minister of the UK on June 1, 2022? | +0.35 | +2.00 | +0.00 | +0.00 | +
| 8882 | +Will 2022 be the hottest year on record? | +0.22 | +1.60 | +0.11 | +0.80 | +
| 8554 | +Will women receive at least 70% of bachelor’s degrees in the US in the year 2050? | +0.19 | +1.49 | +0.10 | +0.79 | +
| 2511 | +Will Emmanuel Macron be re-elected President of France in 2022? | +0.18 | +1.40 | +0.00 | +0.00 | +
| 6330 | +Will Donald J. Trump be a candidate for President in the 2024 cycle? | +0.15 | +0.79 | +0.05 | +0.25 | +
+
+
+
+plot of chunk unnamed-chunk-31
+questions_myPredictions_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +Total_Change | +Total_logodds_Change | +Total_Change_Even | +Total_logodds_Change_Even | +
|---|---|---|---|---|---|
| 9790 | +Will Éric Zemmour be in the 2nd round of the 2022 French presidential election? | +1.12 | +5.38 | +0.50 | +2.32 | +
| 8944 | +Will Boris Johnson be Prime Minister of the UK on June 1, 2022? | +0.35 | +2.00 | +0.00 | +0.00 | +
| 8882 | +Will 2022 be the hottest year on record? | +0.22 | +1.60 | +0.11 | +0.80 | +
| 8554 | +Will women receive at least 70% of bachelor’s degrees in the US in the year 2050? | +0.19 | +1.49 | +0.10 | +0.79 | +
| 6604 | +Will annual U.S. inflation reach 100% in any year before 2050? | +0.06 | +1.45 | +0.00 | +0.00 | +
| 2511 | +Will Emmanuel Macron be re-elected President of France in 2022? | +0.18 | +1.40 | +0.00 | +0.00 | +
+
+
+
+plot of chunk unnamed-chunk-33
+
+
+Among community predictions
+
+
+
+
+
+
+Resolved questions
+What were the most exciting resolved questions?
+questions_recent_byExcitement <- MetaculR_excitement(questions_recent_resolved)
+
+questions_recent_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_Change_Even)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +Total_Change | +Total_logodds_Change | +Total_Change_Even | +Total_logodds_Change_Even | +
|---|---|---|---|---|---|
| 8859 | +Will Ukraine ban wheat export before April 2023? | +0.17 | +1.37 | +0.11 | +0.92 | +
| 9517 | +Will Russian troops enter Mariupol, Ukraine by December 31, 2022? | +0.52 | +4.86 | +0.09 | +0.44 | +
| 9590 | +Will there be a Pi variant of COVID by Pi Day (March 14, 2022)? | +0.41 | +1.95 | +0.07 | +0.30 | +
| 8898 | +Will Russia invade Ukrainian territory before 2023? | +0.38 | +2.97 | +0.06 | +0.32 | +
| 7211 | +Will any body of the US federal government conclude that COVID-19 originated in a lab in Hubei before June 1st 2022? | +0.02 | +0.08 | +0.02 | +0.08 | +
| 9693 | +Will the gray wolf be relisted as Threatened or Endangered by the US before 2030? | +0.00 | +0.00 | +0.00 | +0.00 | +
+
+
+
+plot of chunk unnamed-chunk-35
+
+
+
+
+
+
+
+Open questions
+What are the most exciting questions that are still open?
+questions_recent_open <-
+ MetaculR_questions(
+ order_by = "close_time",
+ status = "open",
+ guessed_by = "")questions_recent_open_byExcitement <- MetaculR_excitement(questions_recent_open)
+
+questions_recent_open_byExcitement %>%
+ dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
+ Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
+ dplyr::arrange(dplyr::desc(Total_logodds_Change_Even)) %>%
+ head() %>%
+ knitr::kable()| id | +title | +Total_Change | +Total_logodds_Change | +Total_Change_Even | +Total_logodds_Change_Even | +
|---|---|---|---|---|---|
| 9746 | +Will Ukraine fulfill its Minsk-II obligations in Donetsk and Luhansk Oblast by 2023? | +0.57 | +11.73 | +0.14 | +4.72 | +
| 9566 | +Will Russian troops enter Odessa, Ukraine before December 31, 2022? | +1.18 | +10.03 | +0.48 | +4.56 | +
| 9791 | +Will Éric Zemmour win the French presidential election in 2022? | +0.15 | +9.40 | +0.07 | +4.35 | +
| 9743 | +Will Volodymyr Zelensky remain President of Ukraine by 2023? | +1.66 | +8.13 | +0.74 | +3.70 | +
| 7237 | +Will Coinbase default on an obligation to hand over their users’ assets on request by the end of 2022? | +0.13 | +5.05 | +0.07 | +2.67 | +
| 9584 | +Will Ketanji Brown Jackson be confirmed as an Associate Justice of the Supreme Court before 2023? | +0.67 | +8.62 | +0.12 | +2.46 | +
+
+
+
+plot of chunk unnamed-chunk-38
+