vincentarelbundock

vincentarelbundock

vincentarelbundock

vincentarelbundock

✔ | OK F W S | Context

⠏ |  0       | test-aaa-documentation-helper
⠋ |  0     1 | test-aaa-documentation-helper
✔ |  0     1 | test-aaa-documentation-helper
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test-aaa-documentation-helper.R:3: skip: (unknown)
documentation helper tests not yet written
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vincentarelbundock

library(tidyverse)

example <- tibble(id = 1:500,
                  A = sample(TRUE:FALSE, 500, replace = TRUE),
                  B = sample(TRUE:FALSE, 500, replace = TRUE),
                  C = sample(TRUE:FALSE, 500, replace = TRUE),
                  D = sample(TRUE:FALSE, 500, replace = TRUE),
                  E = sample(TRUE:FALSE, 500, replace = TRUE),
                  F = sample(TRUE:FALSE, 500, replace = TRUE))

vincentarelbundock

These packages were checked are are compatible with stats::nobs:

• aer
• plm
• betareg
• gam
• gamlss
• geefit
• glmnet
• lme4

vincentarelbundock

library(gghighlight)
library(tidyverse)

# load data
raw <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-03/game_goals.csv')

# 8 best scorers (there are 8 colors in scale_color_brewer Dark2)
best <- raw %>% 
        group_by(player) %>%
        summarize(goals = sum(goals)) %>%

vincentarelbundock

---
title: "COVID-19 Worldometer data"
output: html_notebook
---

This notebook pulls a table with useful information out of ![worldometer](https://www.worldometers.info/coronavirus/) and then makes a graph.

```{r}
library(WDI)
library(countrycode)

vincentarelbundock

library(lme4)
library(gt)
library(modelsummary)
library(tidyverse)

# y dependent variable
# x regressor varies at the county-year level
# z regressor varies at the county level

url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/plm/Crime.csv'

vincentarelbundock

library(lfe)
library(lme4)
library(MASS)
library(tidyverse)
library(modelsummary)

set.seed(290653)
sim <- function(Nobs = 100, Ngrp = 50, cor_x_u = .6, x_sd = .2, y_sd = 1, ...) {
    group <- mvrnorm(Ngrp, c(0, 0), matrix(c(1, cor_x_u, cor_x_u, 1), ncol = 2)) %>%
             data.frame %>% setNames(c('U', 'Ucor')) %>%