This repository includes data and code to analyze donations made in support to the convoy that occupied Ottawa during the winter of 2022 using the Christian crowdsourcing platform GiveSendGo.
Load packages used for the analysis:
library(cartogram)
library(tidyverse)
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.5 v purrr 0.3.4
#> v tibble 3.1.6 v dplyr 1.0.7
#> v tidyr 1.1.4 v stringr 1.4.0
#> v readr 2.1.1 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(sf)
#> Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUERead data:
load(paste0(here::here(), "/data/da_2016_p1.rda"))
load(paste0(here::here(), "/data/da_2016_p2.rda"))
da_2016 <- rbind(da_2016_p1 %>% st_sf(),
da_2016_p2 %>% st_sf())Filter data to investigate only the Hamilton CMA (CMA_UID == “35537”):
hamilton_da_2016 <- da_2016 %>%
filter(CMA_UID == "35537")Project the geometry:
hamilton_da_2016 <- hamilton_da_2016 %>%
st_set_crs(4326) %>%
st_transform(crs = 26917)Plot geometry:
ggplot() +
geom_sf(data = hamilton_da_2016)Create population-based cartogram:
DA_pop_cartogram <- cartogram_cont(hamilton_da_2016,
weight = "Population")
#> Mean size error for iteration 1: 7.35195588132987
#> Mean size error for iteration 2: 8.76209419708295
#> Mean size error for iteration 3: 8.74813930519746
#> Mean size error for iteration 4: 5.03660601099965
#> Mean size error for iteration 5: 4.64712102055913
#> Mean size error for iteration 6: 4.18264531730831
#> Mean size error for iteration 7: 3.17410803100323
#> Mean size error for iteration 8: 3.28379198458139
#> Mean size error for iteration 9: 3.99789051463061
#> Mean size error for iteration 10: 3.37643601672692
#> Mean size error for iteration 11: 2.20579115832604
#> Mean size error for iteration 12: 2.06446574513358
#> Mean size error for iteration 13: 2.00249861128008
#> Mean size error for iteration 14: 1.92713967181393
#> Mean size error for iteration 15: 2.50835169181768Summary:
summary(hamilton_da_2016)
#> Population Households GeoUID Type
#> Min. : 0.0 Min. : 0.0 Length:1199 DA:1199
#> 1st Qu.: 438.0 1st Qu.: 165.0 Class :character
#> Median : 520.0 Median : 205.0 Mode :character
#> Mean : 623.5 Mean : 244.7
#> 3rd Qu.: 667.0 3rd Qu.: 268.0
#> Max. :4726.0 Max. :1607.0
#>
#> CD_UID Shape Area Dwellings CSD_UID
#> Length:1199 Min. : 0.00767 Min. : 0.0 Length:1199
#> Class :character 1st Qu.: 0.10047 1st Qu.: 171.0 Class :character
#> Mode :character Median : 0.16324 Median : 215.0 Mode :character
#> Mean : 1.17152 Mean : 255.2
#> 3rd Qu.: 0.31817 3rd Qu.: 283.0
#> Max. :32.23920 Max. :1729.0
#>
#> CT_UID CMA_UID Region Name Area (sq km)
#> Length:1199 Length:1199 Hamilton :882 Min. : 0.00767
#> Class :character Class :character Burlington :278 1st Qu.: 0.10047
#> Mode :character Mode :character Grimsby : 39 Median : 0.16324
#> 105 Mile Post 2: 0 Mean : 1.17152
#> Abbey : 0 3rd Qu.: 0.31817
#> Abbotsford : 0 Max. :32.23920
#> (Other) : 0
#> v_CA16_2398: Median after-tax income of households in 2015 ($)
#> Min. : 10771
#> 1st Qu.: 52112
#> Median : 71680
#> Mean : 72876
#> 3rd Qu.: 90368
#> Max. :181760
#> NA's :5
#> v_CA16_3957: Total visible minority population donation_n
#> Min. : 0 Min. : 1.000
#> 1st Qu.: 30 1st Qu.: 1.000
#> Median : 65 Median : 1.000
#> Mean : 109 Mean : 1.654
#> 3rd Qu.: 125 3rd Qu.: 2.000
#> Max. :1935 Max. :14.000
#> NA's :4 NA's :719
#> donation_amount geometry
#> Min. : 5.0 MULTIPOLYGON :1199
#> 1st Qu.: 50.0 epsg:26917 : 0
#> Median : 100.0 +proj=utm ...: 0
#> Mean : 184.9
#> 3rd Qu.: 200.0
#> Max. :4180.0
#> NA's :719Replace NAs (donation_n and donation_amount):
hamilton_da_2016 <- hamilton_da_2016 %>%
mutate(donation_n = replace_na(donation_n, 0),
donation_amount = replace_na(donation_amount
8000
, 0))Total amount donated by residents of Hamilton CMA:
sum(hamilton_da_2016$donation_amount)
#> [1] 88730Total number of donations by residents of Hamilton CMA:
sum(hamilton_da_2016$donation_n)
#> [1] 794Number of individual donations divided by population:
sum(hamilton_da_2016$donation_n)/sum(hamilton_da_2016$Population) * 100
#> [1] 0.1062143Number of DAs with at least one donation:
hamilton_da_2016 %>%
filter(donation_n != 0) %>%
nrow()
#> [1] 480Donations:
ggplot(hamilton_da_2016,
aes(x = donation_amount)) +
geom_histogram(binwidth = 50) +
xlab("Donation amount by DA ($)") +
ylab("Count of Dissemination Areas") +
theme_minimal()Plot number of donations:
ggplot() +
geom_sf(data = hamilton_da_2016 %>%
mutate(donation_n = case_when(donation_n == 0 ~ "0",
donation_n > 0 & donation_n <= 5 ~ "(0, 5]",
donation_n > 5 & donation_n <= 10 ~ "(5, 10]",
donation_n > 10 ~ "(10, 15]"),
donation_n = factor(donation_n,
levels = c("0", "(0, 5]", "(5, 10]", "(10, 15]"),
ordered = TRUE)),
aes(fill = donation_n),
color = "black",
size = 0.01) +
scale_fill_manual(name = "Individual donations",
values = c("0" = "white",
"(0, 5]" = "darkgoldenrod1",
"(5, 10]" = "darkorange",
"(10, 15]" = "brown1")) +
theme_minimal()Plot number of donations in the cartogram:
ggplot() +
geom_sf(data = DA_pop_cartogram %>%
mutate(donation_n = case_when(donation_n == 0 ~ "0",
donation_n > 0 & donation_n <= 5 ~ "(0, 5]",
donation_n > 5 & donation_n <= 10 ~ "(5, 10]",
donation_n > 10 ~ "(10, 15]"),
donation_n = factor(donation_n,
levels = c("0", "(0, 5]", "(5, 10]", "(10, 15]"),
ordered = TRUE)),
aes(fill = donation_n),
color = "black",
size = 0.01) +
scale_fill_manual(name = "Individual donations",
values = c("0" = "white",
"(0, 5]" = "darkgoldenrod1",
"(5, 10]" = "darkorange",
"(10, 15]" = "brown1")) +
theme_minimal()Plot amount of donations:
ggplot() +
geom_sf(data = hamilton_da_2016 %>%
mutate(donation_amount = case_when(donation_amount == 0 ~ "0",
donation_amount > 0 & donation_amount <= 50 ~ "(0, 50]",
donation_amount > 50 & donation_amount <= 100 ~ "(50, 100]",
donation_amount > 100 ~ ">100"),
donation_amount = factor(donation_amount,
levels = c("0", "(0, 50]", "(50, 100]", ">100"),
ordered = TRUE)),
aes(fill = donation_amount),
color = "black",
size = 0.01) +
scale_fill_manual(name = "Total amounts by DA",
values = c("0" = "white",
"(0, 50]" = "darkgoldenrod1",
"(50, 100]" = "darkorange",
">100" = "brown1")) +
theme_minimal()Plot number of donations in the cartogram:
ggplot() +
geom_sf(data = DA_pop_cartogram %>%
mutate(donation_amount = case_when(donation_amount == 0 ~ "0",
donation_amount > 0 & donation_amount <= 50 ~ "(0, 50]",
donation_amount > 50 & donation_amount <= 100 ~ "(50, 100]",
donation_amount > 100 ~ ">100"),
donation_amount = factor(donation_amount,
levels = c("0", "(0, 50]", "(50, 100]", ">100"),
ordered = TRUE)),
aes(fill = donation_amount),
color = "black",
size = 0.01) +
scale_fill_manual(name = "Total amounts by DA",
values = c("0" = "white",
"(0, 50]" = "darkgoldenrod1",
"(50, 100]" = "darkorange",
">100" = "brown1")) +
theme_minimal()