── 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 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ 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
library (lubridate)library (scales)
Attaching package: 'scales'
The following object is masked from 'package:purrr':
discard
The following object is masked from 'package:readr':
col_factor
library (tidycensus) library (sf)
Warning: package 'sf' was built under R version 4.0.5
Linking to GEOS 3.9.1, GDAL 3.4.0, PROJ 8.1.1; sf_use_s2() is TRUE
library (leaflet) library (usdata) library (plotly)
Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':
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The following object is masked from 'package:stats':
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The following object is masked from 'package:graphics':
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<- read_csv ("~/Documents/untitled folder/Teen.csv" )
Rows: 450 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): STATE, URL
dbl (2): YEAR, RATE
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.
<- Teen %>% filter (YEAR == 2021 ) %>% mutate (NAME = abbr2state (STATE))
<- get_acs (geography = "state" , variables = "B01003_001" , geometry = TRUE , shift_geo = T)
Getting data from the 2018-2022 5-year ACS
Warning: The `shift_geo` argument is deprecated and will be removed in a future
release. We recommend using `tigris::shift_geometry()` instead.
Using feature geometry obtained from the albersusa package
Please note: Alaska and Hawaii are being shifted and are not to scale.
old-style crs object detected; please recreate object with a recent sf::st_crs()
%>% inner_join (state_pregnancy) %>% ggplot () + geom_sf (aes (fill = RATE)) + coord_sf (datum = NA ) + theme_minimal () + scale_fill_viridis_c (labels = label_comma ()) + labs (title = "Teen Pregnancy in 2021" )
Joining with `by = join_by(NAME)`
The map above shows the amount of teen pregnancy’s in each state during 2021.
%>% ggplot (aes (x = RATE)) + geom_histogram (binwidth = 1 ) + scale_x_continuous (labels = comma) + theme_minimal () + labs (title = "Teen Pregnancy in 2021" )
The histogram shows the amount of teen pregnancy in the states.
<- get_acs (geography = "state" , variables = "B19013_001" , geometry = TRUE , shift_geo = F)
Getting data from the 2018-2022 5-year ACS
Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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<- colorNumeric (palette = "viridis" , domain = state_pregnancy$ RATE)
%>% inner_join (state_pregnancy) %>% leaflet () %>% addTiles () %>% addPolygons (weight = 1 , fillColor = ~ teen_colors (RATE),label = ~ paste0 (NAME, ", Teen Pregnant = " , RATE), highlight = highlightOptions (weight = 2 )) %>% setView (- 95 , 40 , zoom = 4 ) %>% addLegend (pal = teen_colors, values = ~ RATE)
Joining with `by = join_by(NAME)`
Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'
The map above was created using leaflet and it shows the amount of teen pregnancies in each state during 2021.
%>% plot_ly (x = ~ RATE) %>% add_histogram ()
Above is a histogram showing the rate of teen pregnancy in the United States during 2021.
<- readRDS ("~/Documents/untitled folder/wb225bk3255_co_aurora_2023_01_26.rds" )
%>% filter (violation == "Failed to Obey Seat Belt Requirements Adult" )
# A tibble: 8,129 × 16
raw_row_number date time location lat lng geocode_source
<chr> <date> <time> <chr> <dbl> <dbl> <chr>
1 37 2012-01-02 09:25 13700 E 6TH AVE 39.7 -105. GM
2 87 2012-01-02 14:05 S PARKER RD AT E… 39.6 -105. GM
3 86 2012-01-02 14:05 S PARKER RD AT E… 39.6 -105. GM
4 134 2012-01-03 01:56 13600 BLOCK E AL… 39.7 -105. GM
5 163 2012-01-03 08:35 S AIRPORT BLVD A… 39.7 -105. GM
6 203 2012-01-03 10:25 2700 BLOCK S CHA… 39.7 -105. GM
7 230 2012-01-03 12:55 14900 BLOCK E AR… 39.6 -105. GM
8 346 2012-01-04 10:20 15500 BLOCK E I7… 39.7 -105. SU
9 451 2012-01-04 15:10 9500 BLOCK E JEF… 39.7 -105. GM
10 454 2012-01-04 15:15 2700 BLOCK N CHA… 39.8 -105. GM
# ℹ 8,119 more rows
# ℹ 9 more variables: subject_age <int>, subject_race <fct>, subject_sex <fct>,
# type <fct>, violation <chr>, citation_issued <lgl>, outcome <fct>,
# raw_ethnicity <chr>, raw_race <chr>
The code above filtered police stops to only include failure to obey seat-belt research for an adult.
%>% leaflet () %>% addTiles () %>% addCircleMarkers (stroke = F, fillOpacity = .6 , radius = 5 )
Assuming "lng" and "lat" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName): Data contains 7928 rows with
either missing or invalid lat/lon values and will be ignored
Above is a dotmap of traffic stops from the Aurora, Colorado Police Department that resulted in tickets for: Failure to Obey Seat Belt Requirements Adult.
%>% count (raw_race == "HISPANIC OR LATINO" , subject_sex == "female" )
# A tibble: 6 × 3
`raw_race == "HISPANIC OR LATINO"` `subject_sex == "female"` n
<lgl> <lgl> <int>
1 FALSE FALSE 146153
2 FALSE TRUE 93566
3 FALSE NA 2008
4 TRUE FALSE 9783
5 TRUE TRUE 6088
6 TRUE NA 8
This table shows that out of the 257,606 people pulled over in Aurora, Colorado 6,088 of them were Hispanic/Latina females.
