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File | Version | Author | Date | Message |
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html | e4c698e | Tina Lasisi | 2024-02-27 | Publish new pages + update plots |
Rmd | b6c047d | Tina Lasisi | 2024-01-26 | update extensions |
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Data based on Murphy & Tong
This document presents an analysis of the representation of various demographic groups in a database compared to their representation in the general population across different states.
We start by loading the necessary libraries and the dataset.
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.4.4 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.0
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Load the dataset
data <- read_csv("data/df_state-breakdown.csv")
Rows: 77 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): State, Demographic Group, Context, Value
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.
data_total <- read_csv("data/df_state_total.csv")
Rows: 14 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): Demographic, Region, Value
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.
The data contains demographic information with percentages in two contexts: ‘Database’ and ‘Population’. We need to clean and transform the data for analysis.
# Convert the 'Value' column to numeric
data <- data %>%
mutate(Value = as.numeric(str_remove(Value, "%")) / 100)
# Separate the data into database and population datasets
database_data <- data %>% filter(Context == "Database")
population_data <- data %>% filter(Context == "Population")
# Merge the two datasets
merged_data <- database_data %>%
inner_join(population_data, by = c("State", "Demographic Group")) %>%
rename(Value_Database = Value.x, Value_Population = Value.y) %>%
mutate(Difference = Value_Database - Value_Population)
We create a visualization to show the differences in representation for each demographic group by state.
# Plotting the differences
ggplot(merged_data, aes(x = State, y = Difference, fill = `Demographic Group`)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
theme_minimal() +
labs(title = "Difference in Demographic Group Representation: Database vs Population by State",
x = "State", y = "Difference in Representation") +
geom_hline(yintercept = 0, linetype = 1)
Warning: Removed 5 rows containing missing values (`geom_bar()`).
Version | Author | Date |
---|---|---|
9e71347 | Tina Lasisi | 2024-01-22 |
Finally, we create a summary table showing the difference in representation for each state and demographic group.
# Creating the summary table
state_group_summary <- merged_data %>%
group_by(State, `Demographic Group`) %>%
summarize(Difference = mean(Difference)) %>%
ungroup()
`summarise()` has grouped output by 'State'. You can override using the
`.groups` argument.
state_group_summary
# A tibble: 35 × 3
State `Demographic Group` Difference
<chr> <chr> <dbl>
1 California Asian -0.134
2 California Black 0.116
3 California Hispanic -0.045
4 California Native American NA
5 California White -0.0740
6 Florida Asian -0.0267
7 Florida Black 0.183
8 Florida Hispanic -0.232
9 Florida Native American -0.0044
10 Florida White 0.0730
# ℹ 25 more rows
sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.4.4 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.8 utf8_1.2.4 generics_0.1.3 stringi_1.8.3
[5] hms_1.1.3 digest_0.6.34 magrittr_2.0.3 timechange_0.2.0
[9] evaluate_0.23 grid_4.3.2 fastmap_1.1.1 rprojroot_2.0.4
[13] jsonlite_1.8.8 processx_3.8.3 whisker_0.4.1 ps_1.7.5
[17] promises_1.2.1 httr_1.4.7 fansi_1.0.6 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.2 crayon_1.5.2 rlang_1.1.3
[25] bit64_4.0.5 munsell_0.5.0 withr_2.5.2 cachem_1.0.8
[29] yaml_2.3.8 parallel_4.3.2 tools_4.3.2 tzdb_0.4.0
[33] colorspace_2.1-0 httpuv_1.6.13 vctrs_0.6.5 R6_2.5.1
[37] lifecycle_1.0.4 git2r_0.33.0 bit_4.0.5 fs_1.6.3
[41] vroom_1.6.5 pkgconfig_2.0.3 callr_3.7.3 pillar_1.9.0
[45] bslib_0.6.1 later_1.3.2 gtable_0.3.4 glue_1.7.0
[49] Rcpp_1.0.12 highr_0.10 xfun_0.41 tidyselect_1.2.0
[53] rstudioapi_0.15.0 knitr_1.45 farver_2.1.1 htmltools_0.5.7
[57] labeling_0.4.3 rmarkdown_2.25 compiler_4.3.2 getPass_0.2-4