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qualititative coding package | |
https://github.com/ropenscilabs/qcoder | |
Packages | |
lavaan, blavaan, blavaan.fimlm, lavaan.survey | |
panelView: Visualizing Panel Data with Dichotomous Treatments | |
ggstatsplot: 'ggplot2' Based Plots with Statistical Details | |
ggplotify: Convert Plot to 'grob' or 'ggplot' Object |
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https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3 | |
https://app.powerbi.com/Redirect?action=OpenApp&appId=0ebba6cb-7c99-4e6e-8dbf-a1aae3066680&ctid=3ce4c247-6019-4439-a614-97d7bdab77f3 |
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library(tidyverse) | |
humphries <- tribble( | |
~"Year", ~"G", ~"Rec", ~"Yards", ~"Avg", ~"TD", | |
2011, 14, 15, 130, 8.7, 0, | |
2012, 13, 41, 280, 6.8, 1, | |
2013, 12, 41, 483, 11.8, 2, | |
2014, 13, 30, 204, 6.8, 0) %>% | |
mutate(player = "Humphries") %>% | |
select(Year, Rec, Avg, TD, player) |
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--- | |
title: "Rmarkdown Exercises" | |
author: "Your Name" | |
date: "`r Sys.Date()`" | |
output: | |
html_document: | |
toc: true | |
toc_float: true | |
number_sections: true | |
df_print: paged |
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# Coefficient Plot Implemented in ggplot2 From Faraway's Linear Models with R | |
#'@description Building a Gelman-esque coefplot in ggplot based on the code | |
#' in Julian Faraway's Linear Models with R | |
#'@param fit a lm or glm object | |
#'@param ci the confidence levels to consider | |
#'@export | |
ggcoef_plot <- function(fit, ci = 0.95){ | |
stopifnot(class(fit) %in% c("lm", "glm")) | |
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# Parameters | |
set.seed(42) | |
n <- 100L | |
#true model | |
x <- rnorm(n, 5, 1) | |
treat <- rep(c(0,1), n/2) |
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date | approve | n | moe | pollster | ||
---|---|---|---|---|---|---|
2019-09-29 | 47 | 1009 | 3.5 | ssr | ||
2019-05-31 | 41 | 1009 | 3.5 | ssr | ||
2019-04-28 | 37 | 1009 | 3.5 | ssr | ||
2019-03-17 | 36 | 1009 | 3.5 | ssr | ||
2018-12-09 | 43 | 1009 | 3.5 | ssr | ||
2018-09-09 | 47 | 1009 | 3.5 | ssr | ||
2018-06-17 | 42 | 1009 | 3.5 | ssr | ||
2019-09-30 | 45 | 2234 | 2.4 | ipsos | ||
2019-09-30 | 47 | 1115 | 3.6 | Quinnipiac |
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library(Synth) | |
data(synth.data) | |
synth.data$dummy <- rnorm(168, 0, .00001) | |
# create matrices from panel data that provide inputs for synth() | |
dataprep.out<- | |
dataprep( | |
foo = synth.data, | |
predictors = c("dummy"), |
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library(dplyr) | |
library(glmnet) | |
library(furrr) | |
plan("multisession") | |
run_simulation <- function(n = 100, num_pred = 50, | |
rho = 0, true_beta= -.1, | |
error = 0.01){ |
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