https://www.kaggle.com/jsphyg/weather-dataset-rattle-package https://data.cms.gov/provider-data/dataset/rs6n-9qwg
Source: https://apps.urban.org/features/wealth-inequality-charts/
Original Table
wealthbyrace <- read_excel("WealthbyRace.xlsx")## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5wealthbyrace## # A tibble: 13 x 5
## `Average Family Wealth by Race/Et… ...2 ...3 ...4 ...5
## <dbl> <chr> <chr> <chr> <chr>
## 1 NA Non-White White Black Hispa…
## 2 1963 19503.8372… 140632.660… <NA> <NA>
## 3 1983 73233.6157… 324057.599… 67269.6000… 62562…
## 4 1989 <NA> 424082.4 78092.2 84397…
## 5 1992 <NA> 373825.9 80779.48 90751…
## 6 1995 <NA> 394522.3 68908.6399… 96487…
## 7 1998 <NA> 497581.1 94972.45 12851…
## 8 2001 <NA> 662337.1 97930.09 11985…
## 9 2004 <NA> 715453.3 146127.9 15872…
## 10 2007 <NA> 802519.8 156285.1 215534
## 11 2010 <NA> 715067.3 110569.1 12803…
## 12 2013 <NA> 717069.1 102106 11116…
## 13 2016 <NA> 919336.1 139523.1 19172…Tidied Table
wealthbyrace <- read_excel("WealthbyRace.xlsx", skip = 1) #skips the first row that is just the title## New names:
## * `` -> ...1tidied_wealthbyrace <- wealthbyrace %>%
rename(year = 1) %>% #gives the nameless first column a name
pivot_longer(cols = c("Non-White":"Hispanic"), names_to = "race", values_to = "wealth_family") #breaks down by year and race
tidied_wealthbyrace## # A tibble: 48 x 3
## year race wealth_family
## <dbl> <chr> <dbl>
## 1 1963 Non-White 19504.
## 2 1963 White 140633.
## 3 1963 Black NA
## 4 1963 Hispanic NA
## 5 1983 Non-White 73234.
## 6 1983 White 324058.
## 7 1983 Black 67270.
## 8 1983 Hispanic 62562.
## 9 1989 Non-White NA
## 10 1989 White 424082.
## # … with 38 more rowsSource: https://catalog.data.gov/dataset/hate-crimes-by-county-and-bias-type-beginning-2010
Original Table
hatecrimes <- read_csv("hatecrimes.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## County = col_character(),
## `Crime Type` = col_character()
## )
## ℹ Use `spec()` for the full column specifications.hatecrimes## # A tibble: 605 x 44
## County Year `Crime Type` `Anti-Male` `Anti-Female` `Anti-Transgende…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Albany 2010 Crimes Against Pers… 0 0 0
## 2 Albany 2010 Property Crimes 0 0 0
## 3 Albany 2011 Crimes Against Pers… 0 0 0
## 4 Albany 2011 Property Crimes 0 0 0
## 5 Albany 2012 Crimes Against Pers… 0 0 0
## 6 Albany 2012 Property Crimes 0 0 0
## 7 Albany 2013 Crimes Against Pers… 0 0 0
## 8 Albany 2013 Property Crimes 0 0 0
## 9 Albany 2014 Crimes Against Pers… 0 0 0
## 10 Albany 2014 Property Crimes 0 0 0
## # … with 595 more rows, and 38 more variables:
## # Anti-Gender Identity Expression <dbl>, Anti-Age* <dbl>, Anti-White <dbl>,
## # Anti-Black <dbl>, Anti-American Indian/Alaskan Native <dbl>,
## # Anti-Asian <dbl>, Anti-Native Hawaiian/Pacific Islander <dbl>,
## # Anti-Multi-Racial Groups <dbl>, Anti-Other Race <dbl>, Anti-Jewish <dbl>,
## # Anti-Catholic <dbl>, Anti-Protestant <dbl>, Anti-Islamic (Muslim) <dbl>,
## # Anti-Multi-Religious Groups <dbl>, Anti-Atheism/Agnosticism <dbl>,
## # Anti-Religious Practice Generally <dbl>, Anti-Other Religion <dbl>,
## # Anti-Buddhist <dbl>, Anti-Eastern Orthodox (Greek, Russian, etc.) <dbl>,
## # Anti-Hindu <dbl>, Anti-Jehovahs Witness <dbl>, Anti-Mormon <dbl>,
## # Anti-Other Christian <dbl>, Anti-Sikh <dbl>, Anti-Hispanic <dbl>,
## # Anti-Arab <dbl>, Anti-Other Ethnicity/National Origin <dbl>,
## # Anti-Non-Hispanic* <dbl>, Anti-Gay Male <dbl>, Anti-Gay Female <dbl>,
## # Anti-Gay (Male and Female) <dbl>, Anti-Heterosexual <dbl>,
## # Anti-Bisexual <dbl>, Anti-Physical Disability <dbl>,
## # Anti-Mental Disability <dbl>, Total Incidents <dbl>, Total Victims <dbl>,
## # Total Offenders <dbl>Tidied Table
tidied_hatecrimes <- hatecrimes %>%
select('County', 'Year', 'Crime Type', 'Anti-Asian') %>% #gets rid of categories other than the one we are targeting
pivot_wider(names_from = 'Year', values_from = 'Anti-Asian') #shows trends by year of each type of crime for each county
tidied_hatecrimes## # A tibble: 116 x 12
## County `Crime Type` `2010` `2011` `2012` `2013` `2014` `2015` `2016` `2017`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Albany Crimes Again… 0 0 1 0 0 0 0 0
## 2 Albany Property Cri… 0 0 0 0 0 NA 0 0
## 3 Allega… Crimes Again… NA NA NA 0 NA NA NA NA
## 4 Allega… Property Cri… NA NA NA NA NA NA 0 NA
## 5 Bronx Crimes Again… 0 0 0 0 0 1 0 0
## 6 Bronx Property Cri… 0 0 0 0 0 0 0 0
## 7 Broome Crimes Again… 0 0 0 0 NA 0 1 0
## 8 Broome Property Cri… NA 0 0 0 0 NA NA NA
## 9 Cattar… Crimes Again… NA 0 0 0 NA 0 NA 0
## 10 Cayuga Crimes Again… 0 NA 0 NA NA 0 NA 0
## # … with 106 more rows, and 2 more variables: 2018 <dbl>, 2019 <dbl>