packages <- c("tidyverse", "fst", "modelsummary", "broom", "sjPlot", "ggplot2", "car", "Lock5Data", "mosaic")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
lapply(packages, library, character.only = TRUE)
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library(readr)
library(ggplot2)
towny = read_csv("C:/Users/tophe/Downloads/towny.csv")
## Rows: 414 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): name, website, status, csd_type, census_div
## dbl (21): rownames, latitude, longitude, land_area_km2, population_1996, pop...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
towny_filtered <- towny %>%
dplyr::select(pop_change_2016_2021_pct, pop_change_2011_2016_pct, pop_change_2006_2011_pct, population_2021, latitude, csd_type)
datasummary_skim(towny_filtered)
tinytable_jo9b4uq91r23ozo6pvqu
| |
Unique |
Missing Pct. |
Mean |
SD |
Min |
Median |
Max |
Histogram |
| pop_change_2016_2021_pct |
380 |
0 |
0.1 |
0.1 |
-0.2 |
0.0 |
0.8 |
 |
| pop_change_2011_2016_pct |
363 |
0 |
0.0 |
0.1 |
-0.4 |
0.0 |
0.5 |
 |
| pop_change_2006_2011_pct |
383 |
0 |
0.0 |
0.1 |
-0.4 |
0.0 |
0.6 |
 |
| population_2021 |
406 |
0 |
34141.7 |
160813.8 |
16.0 |
6489.0 |
2794356.0 |
 |
| latitude |
316 |
0 |
45.3 |
2.0 |
41.8 |
44.9 |
51.5 |
 |
| csd_type |
N |
% |
|
|
|
|
|
|
| city |
52 |
12.6 |
|
|
|
|
|
|
| municipality |
68 |
16.4 |
|
|
|
|
|
|
| town |
88 |
21.3 |
|
|
|
|
|
|
| township |
195 |
47.1 |
|
|
|
|
|
|
| village |
11 |
2.7 |
|
|
|
|
|
|
ggplot(towny, aes(x = reorder(csd_type, -pop_change_2016_2021_pct, FUN = median), y = pop_change_2016_2021_pct, fill = csd_type)) +
geom_boxplot() +
theme_minimal() +
theme(legend.position = "right") +
labs(
title = "Boxplots for Proportional Change in Population by Subdivision",
x = "Subdivision",
y = "Proportional Change in Population" )
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_boxplot()`).
Model1 <- lm(pop_change_2016_2021_pct ~ pop_change_2011_2016_pct + pop_change_2006_2011_pct + pop_change_2001_2006_pct, data = towny)
Model2 <- lm(pop_change_2016_2021_pct ~ latitude, data = towny)
Model3 <- lm(pop_change_2016_2021_pct ~ population_2021 + csd_type, data = towny)
models_world <- list(Model1, Model2, Model3)
tab_model(models_world)
|
|
pop_change_2016_2021_pct
|
pop_change_2016_2021_pct
|
pop_change_2016_2021_pct
|
|
Predictors
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
Estimates
|
CI
|
p
|
|
(Intercept)
|
0.05
|
0.04 – 0.06
|
<0.001
|
0.65
|
0.45 – 0.85
|
<0.001
|
0.06
|
0.03 – 0.08
|
<0.001
|
|
pop change 2011 2016 pct
|
0.06
|
-0.05 – 0.16
|
0.296
|
|
|
|
|
|
|
|
pop change 2006 2011 pct
|
0.14
|
0.04 – 0.23
|
0.007
|
|
|
|
|
|
|
|
pop change 2001 2006 pct
|
0.31
|
0.22 – 0.39
|
<0.001
|
|
|
|
|
|
|
|
latitude
|
|
|
|
-0.01
|
-0.02 – -0.01
|
<0.001
|
|
|
|
|
population 2021
|
|
|
|
|
|
|
0.00
|
-0.00 – 0.00
|
0.869
|
|
csd type [municipality]
|
|
|
|
|
|
|
0.00
|
-0.03 – 0.04
|
0.877
|
|
csd type [town]
|
|
|
|
|
|
|
0.01
|
-0.03 – 0.04
|
0.689
|
|
csd type [township]
|
|
|
|
|
|
|
0.00
|
-0.03 – 0.03
|
0.994
|
|
csd type [village]
|
|
|
|
|
|
|
-0.05
|
-0.12 – 0.01
|
0.090
|
|
Observations
|
410
|
413
|
413
|
|
R2 / R2 adjusted
|
0.151 / 0.145
|
0.076 / 0.073
|
0.010 / -0.002
|
ggplot(towny, aes(x = population_2021, y = pop_change_2016_2021_pct, color = csd_type)) +
geom_point(outlier.shape = NA, size = 0.5) +
labs(x = "Population in 2021", y = "Proportional change in Population", title = "Proportional Change in Population by Population Size, 2016-2021")
## Warning in geom_point(outlier.shape = NA, size = 0.5): Ignoring unknown
## parameters: `outlier.shape`
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
