getwd()[1] "/Users/ronaldohernandez/Desktop/Data 110 folder"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 3.5.1 ✔ 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 errorshatecrimes <- read_csv("hatecrimes2010correctforsure.csv")Rows: 423 Columns: 44
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): County, Crime Type
dbl (42): Year, Anti-Male, Anti-Female, Anti-Transgender, Anti-Gender Identi...
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.names(hatecrimes) <- tolower(names(hatecrimes))
names(hatecrimes) <- gsub(" ","",names(hatecrimes))
head(hatecrimes)# A tibble: 6 × 44
county year crimetype `anti-male` `anti-female` `anti-transgender`
<chr> <dbl> <chr> <dbl> <dbl> <dbl>
1 Albany 2016 Crimes Against Pe… 0 0 0
2 Albany 2016 Property Crimes 0 0 0
3 Allegany 2016 Property Crimes 0 0 0
4 Bronx 2016 Crimes Against Pe… 0 0 4
5 Bronx 2016 Property Crimes 0 0 0
6 Broome 2016 Crimes Against Pe… 0 0 0
# ℹ 38 more variables: `anti-genderidentityexpression` <dbl>,
# `anti-age*` <dbl>, `anti-white` <dbl>, `anti-black` <dbl>,
# `anti-americanindian/alaskannative` <dbl>, `anti-asian` <dbl>,
# `anti-nativehawaiian/pacificislander` <dbl>,
# `anti-multi-racialgroups` <dbl>, `anti-otherrace` <dbl>,
# `anti-jewish` <dbl>, `anti-catholic` <dbl>, `anti-protestant` <dbl>,
# `anti-islamic(muslim)` <dbl>, `anti-multi-religiousgroups` <dbl>, …summary(hatecrimes) county year crimetype anti-male
Length:423 Min. :2010 Length:423 Min. :0.000000
Class :character 1st Qu.:2011 Class :character 1st Qu.:0.000000
Mode :character Median :2013 Mode :character Median :0.000000
Mean :2013 Mean :0.007092
3rd Qu.:2015 3rd Qu.:0.000000
Max. :2016 Max. :1.000000
anti-female anti-transgender anti-genderidentityexpression
Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.01655 Mean :0.04728 Mean :0.05674
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :5.00000 Max. :3.00000
anti-age* anti-white anti-black
Min. :0.00000 Min. : 0.0000 Min. : 0.000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.000
Median :0.00000 Median : 0.0000 Median : 1.000
Mean :0.05201 Mean : 0.3357 Mean : 1.761
3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 2.000
Max. :9.00000 Max. :11.0000 Max. :18.000
anti-americanindian/alaskannative anti-asian
Min. :0.000000 Min. :0.0000
1st Qu.:0.000000 1st Qu.:0.0000
Median :0.000000 Median :0.0000
Mean :0.007092 Mean :0.1773
3rd Qu.:0.000000 3rd Qu.:0.0000
Max. :1.000000 Max. :8.0000
anti-nativehawaiian/pacificislander anti-multi-racialgroups anti-otherrace
Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0.00000 Median :0
Mean :0 Mean :0.08511 Mean :0
3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :3.00000 Max. :0
anti-jewish anti-catholic anti-protestant anti-islamic(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.02364 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :1.00000 Max. :10.0000
anti-multi-religiousgroups anti-atheism/agnosticism
Min. : 0.00000 Min. :0
1st Qu.: 0.00000 1st Qu.:0
Median : 0.00000 Median :0
Mean : 0.07565 Mean :0
3rd Qu.: 0.00000 3rd Qu.:0
Max. :10.00000 Max. :0
anti-religiouspracticegenerally anti-otherreligion anti-buddhist
Min. :0.000000 Min. :0.000 Min. :0
1st Qu.:0.000000 1st Qu.:0.000 1st Qu.:0
Median :0.000000 Median :0.000 Median :0
Mean :0.007092 Mean :0.104 Mean :0
3rd Qu.:0.000000 3rd Qu.:0.000 3rd Qu.:0
Max. :2.000000 Max. :4.000 Max. :0
anti-easternorthodox(greek,russian,etc.) anti-hindu
Min. :0.000000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.000000
Median :0.000000 Median :0.000000
Mean :0.002364 Mean :0.002364
3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.000000
anti-jehovahswitness anti-mormon anti-otherchristian anti-sikh
Min. :0 Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0 Median :0.00000 Median :0
Mean :0 Mean :0 Mean :0.01655 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :0 Max. :3.00000 Max. :0
anti-hispanic anti-arab anti-otherethnicity/nationalorigin
Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 0.3735 Mean :0.06619 Mean : 0.2837
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :17.0000 Max. :2.00000 Max. :19.0000
anti-non-hispanic* anti-gaymale anti-gayfemale anti-gay(maleandfemale)
Min. :0 Min. : 0.000 Min. :0.0000 Min. :0.0000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0 Median : 0.000 Median :0.0000 Median :0.0000
Mean :0 Mean : 1.499 Mean :0.2411 Mean :0.1017
3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :0 Max. :36.000 Max. :8.0000 Max. :4.0000
anti-heterosexual anti-bisexual anti-physicaldisability
Min. :0.000000 Min. :0.000000 Min. :0.00000
1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00000
Median :0.000000 Median :0.000000 Median :0.00000
Mean :0.002364 Mean :0.004728 Mean :0.01182
3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00000
Max. :1.000000 Max. :1.000000 Max. :1.00000
anti-mentaldisability totalincidents totalvictims totaloffenders
Min. :0.000000 Min. : 1.00 Min. : 1.00 Min. : 1.00
1st Qu.:0.000000 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :0.000000 Median : 3.00 Median : 3.00 Median : 3.00
Mean :0.009456 Mean : 10.09 Mean : 10.48 Mean : 11.77
3rd Qu.:0.000000 3rd Qu.: 10.00 3rd Qu.: 10.00 3rd Qu.: 11.00
Max. :1.000000 Max. :101.00 Max. :106.00 Max. :113.00 hatecrimes2 <- hatecrimes |>
select(county, year, 'anti-black', 'anti-white', 'anti-jewish', 'anti-catholic','anti-age*','anti-islamic(muslim)', `anti-multi-religiousgroups`, 'anti-gaymale', 'anti-hispanic', `anti-otherethnicity/nationalorigin`) |>
group_by(county, year)
head(hatecrimes2)# A tibble: 6 × 12
# Groups: county, year [4]
county year `anti-black` `anti-white` `anti-jewish` `anti-catholic`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Albany 2016 1 0 0 0
2 Albany 2016 2 0 0 0
3 Allegany 2016 1 0 0 0
4 Bronx 2016 0 1 0 0
5 Bronx 2016 0 1 1 0
6 Broome 2016 1 0 0 0
# ℹ 6 more variables: `anti-age*` <dbl>, `anti-islamic(muslim)` <dbl>,
# `anti-multi-religiousgroups` <dbl>, `anti-gaymale` <dbl>,
# `anti-hispanic` <dbl>, `anti-otherethnicity/nationalorigin` <dbl>dim(hatecrimes2)[1] 423 12# There are currently 12 variables with 423 rows.
summary(hatecrimes2) county year anti-black anti-white
Length:423 Min. :2010 Min. : 0.000 Min. : 0.0000
Class :character 1st Qu.:2011 1st Qu.: 0.000 1st Qu.: 0.0000
Mode :character Median :2013 Median : 1.000 Median : 0.0000
Mean :2013 Mean : 1.761 Mean : 0.3357
3rd Qu.:2015 3rd Qu.: 2.000 3rd Qu.: 0.0000
Max. :2016 Max. :18.000 Max. :11.0000
anti-jewish anti-catholic anti-age* anti-islamic(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.05201 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :9.00000 Max. :10.0000
anti-multi-religiousgroups anti-gaymale anti-hispanic
Min. : 0.00000 Min. : 0.000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.000 Median : 0.0000
Mean : 0.07565 Mean : 1.499 Mean : 0.3735
3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :10.00000 Max. :36.000 Max. :17.0000
anti-otherethnicity/nationalorigin
Min. : 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean : 0.2837
3rd Qu.: 0.0000
Max. :19.0000 hatelong <- hatecrimes2|>
pivot_longer(cols = 3:12,names_to = "victim_cat", values_to = "crimecount" )hatecrimplot <-hatelong |>
ggplot(aes(year, crimecount))+
geom_point()+
aes(color = victim_cat)+
facet_wrap(~victim_cat)
hatecrimplot
hatenew <- hatelong |>
filter( victim_cat %in% c("anti-black", "anti-jewish", "anti-gaymale"))|>
group_by(year, county) |>
arrange(desc(crimecount))
hatenew# A tibble: 1,269 × 4
# Groups: year, county [277]
county year victim_cat crimecount
<chr> <dbl> <chr> <dbl>
1 Kings 2012 anti-jewish 82
2 Kings 2016 anti-jewish 51
3 Suffolk 2014 anti-jewish 48
4 Suffolk 2012 anti-jewish 48
5 Kings 2011 anti-jewish 44
6 Kings 2013 anti-jewish 41
7 Kings 2010 anti-jewish 39
8 Nassau 2011 anti-jewish 38
9 Suffolk 2013 anti-jewish 37
10 Nassau 2016 anti-jewish 36
# ℹ 1,259 more rowsplot2 <- hatenew |>
ggplot() +
geom_bar(aes(x=year, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(fill = "Hate Crime Type",
y = "Number of Hate Crime Incidents",
title = "Hate Crime Type in NY Counties Between 2010-2016",
caption = "Source: NY State Division of Criminal Justice Services")
plot2
plot3 <- hatenew |>
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(fill = "Hate Crime Type",
y = "Number of Hate Crime Incidents",
title = "Hate Crime Type in NY Counties Between 2010-2016",
caption = "Source: NY State Division of Criminal Justice Services")
plot3
counties <- hatenew |>
group_by(year, county)|>
summarize(sum = sum(crimecount)) |>
arrange(desc(sum))`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.counties# A tibble: 277 × 3
# Groups: year [7]
year county sum
<dbl> <chr> <dbl>
1 2012 Kings 136
2 2010 Kings 110
3 2016 Kings 101
4 2013 Kings 96
5 2014 Kings 94
6 2015 Kings 90
7 2011 Kings 86
8 2016 New York 86
9 2012 Suffolk 83
10 2013 New York 75
# ℹ 267 more rowscounties2 <- hatenew |>
group_by(county)|>
summarize(sum = sum(crimecount)) |>
slice_max(order_by = sum, n=5)
counties2# A tibble: 5 × 2
county sum
<chr> <dbl>
1 Kings 713
2 New York 459
3 Suffolk 360
4 Nassau 298
5 Queens 235plot4 <- hatenew |>
filter(county %in% c("Kings", "New York", "Suffolk", "Nassau", "Queens")) |>
ggplot() +
geom_bar(aes(x=county, y=crimecount, fill = victim_cat),
position = "dodge", stat = "identity") +
labs(y = "Number of Hate Crime Incidents",
title = "5 Counties in NY with Highest Incidents of Hate Crimes",
subtitle = "Between 2010-2016",
fill = "Hate Crime Type",
caption = "Source: NY State Division of Criminal Justice Services")
plot4
nypop <- read_csv("newyorkpopulation.csv")Rows: 62 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Geography
dbl (7): 2010, 2011, 2012, 2013, 2014, 2015, 2016
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.nypop$Geography <- gsub(" , New York", "", nypop$Geography)
nypop$Geography <- gsub("County", "", nypop$Geography)
nypoplong <- nypop |>
rename(county = Geography) |>
gather("year", "population", 2:8)
nypoplong$year <- as.double(nypoplong$year)
head(nypoplong)# A tibble: 6 × 3
county year population
<chr> <dbl> <dbl>
1 Albany , New York 2010 304078
2 Allegany , New York 2010 48949
3 Bronx , New York 2010 1388240
4 Broome , New York 2010 200469
5 Cattaraugus , New York 2010 80249
6 Cayuga , New York 2010 79844nypoplong12 <- nypoplong |>
filter(year == 2012) |>
arrange(desc(population)) |>
head(10)
nypoplong12$county<-gsub(" , New York","",nypoplong12$county)
nypoplong12# A tibble: 10 × 3
county year population
<chr> <dbl> <dbl>
1 Kings 2012 2572282
2 Queens 2012 2278024
3 New York 2012 1625121
4 Suffolk 2012 1499382
5 Bronx 2012 1414774
6 Nassau 2012 1350748
7 Westchester 2012 961073
8 Erie 2012 920792
9 Monroe 2012 748947
10 Richmond 2012 470978counties12 <- counties |>
filter(year == 2012) |>
arrange(desc(sum))
counties12# A tibble: 41 × 3
# Groups: year [1]
year county sum
<dbl> <chr> <dbl>
1 2012 Kings 136
2 2012 Suffolk 83
3 2012 New York 71
4 2012 Nassau 48
5 2012 Queens 48
6 2012 Erie 28
7 2012 Bronx 23
8 2012 Richmond 18
9 2012 Multiple 14
10 2012 Westchester 13
# ℹ 31 more rowsdatajoin <- counties12 |>
full_join(nypoplong12, by=c("county", "year"))
datajoin# A tibble: 41 × 4
# Groups: year [1]
year county sum population
<dbl> <chr> <dbl> <dbl>
1 2012 Kings 136 2572282
2 2012 Suffolk 83 1499382
3 2012 New York 71 1625121
4 2012 Nassau 48 1350748
5 2012 Queens 48 2278024
6 2012 Erie 28 920792
7 2012 Bronx 23 1414774
8 2012 Richmond 18 470978
9 2012 Multiple 14 NA
10 2012 Westchester 13 961073
# ℹ 31 more rowsdatajoinrate <- datajoin |>
mutate(rate = sum/population*100000) |>
arrange(desc(rate))
datajoinrate# A tibble: 41 × 5
# Groups: year [1]
year county sum population rate
<dbl> <chr> <dbl> <dbl> <dbl>
1 2012 Suffolk 83 1499382 5.54
2 2012 Kings 136 2572282 5.29
3 2012 New York 71 1625121 4.37
4 2012 Richmond 18 470978 3.82
5 2012 Nassau 48 1350748 3.55
6 2012 Erie 28 920792 3.04
7 2012 Queens 48 2278024 2.11
8 2012 Bronx 23 1414774 1.63
9 2012 Westchester 13 961073 1.35
10 2012 Monroe 5 748947 0.668
# ℹ 31 more rowsdt <- datajoinrate[,c("county","rate")]
dt# A tibble: 41 × 2
county rate
<chr> <dbl>
1 Suffolk 5.54
2 Kings 5.29
3 New York 4.37
4 Richmond 3.82
5 Nassau 3.55
6 Erie 3.04
7 Queens 2.11
8 Bronx 1.63
9 Westchester 1.35
10 Monroe 0.668
# ℹ 31 more rowsaggregategroups <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount"
)
unique(aggregategroups$victim_cat) [1] "anti-male"
[2] "anti-female"
[3] "anti-transgender"
[4] "anti-genderidentityexpression"
[5] "anti-age*"
[6] "anti-white"
[7] "anti-black"
[8] "anti-americanindian/alaskannative"
[9] "anti-asian"
[10] "anti-nativehawaiian/pacificislander"
[11] "anti-multi-racialgroups"
[12] "anti-otherrace"
[13] "anti-jewish"
[14] "anti-catholic"
[15] "anti-protestant"
[16] "anti-islamic(muslim)"
[17] "anti-multi-religiousgroups"
[18] "anti-atheism/agnosticism"
[19] "anti-religiouspracticegenerally"
[20] "anti-otherreligion"
[21] "anti-buddhist"
[22] "anti-easternorthodox(greek,russian,etc.)"
[23] "anti-hindu"
[24] "anti-jehovahswitness"
[25] "anti-mormon"
[26] "anti-otherchristian"
[27] "anti-sikh"
[28] "anti-hispanic"
[29] "anti-arab"
[30] "anti-otherethnicity/nationalorigin"
[31] "anti-non-hispanic*"
[32] "anti-gaymale"
[33] "anti-gayfemale"
[34] "anti-gay(maleandfemale)"
[35] "anti-heterosexual"
[36] "anti-bisexual"
[37] "anti-physicaldisability"
[38] "anti-mentaldisability"
[39] "totalincidents"
[40] "totalvictims"
[41] "totaloffenders" aggregategroups <- aggregategroups |>
mutate(group = case_when(
victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual") ~ "anti-lgbtq",
victim_cat %in% c("anti-multi-racialgroups", "anti-jewish", "anti-protestant", "anti-multi-religousgroups", "anti-religiouspracticegenerally", "anti-buddhist", "anti-hindu", "anti-mormon", "anti-sikh", "anti-catholic", "anti-islamic(muslim)", "anti-atheism/agnosticism", "anti-otherreligion", "anti-easternorthodox(greek,russian,etc.)", "anti-jehovahswitness", "anti-otherchristian") ~ "anti-religion",
victim_cat %in% c("anti-asian", "anti-arab", "anti-non-hispanic", "anti-white", "anti-americanindian/alaskannative", "anti-nativehawaiian/pacificislander", "anti-otherrace", "anti-hispanic", "anti-otherethnicity/nationalorigin") ~ "anti-ethnicity",
victim_cat %in% c("anti-physicaldisability", "anti-mentaldisability") ~ "anti-disability",
victim_cat %in% c("anti-female", "anti-male") ~ "anti-gender",
TRUE ~ "others"))
aggregategroups# A tibble: 17,343 × 6
county year crimetype victim_cat crimecount group
<chr> <dbl> <chr> <chr> <dbl> <chr>
1 Albany 2016 Crimes Against Persons anti-male 0 anti…
2 Albany 2016 Crimes Against Persons anti-female 0 anti…
3 Albany 2016 Crimes Against Persons anti-transgender 0 anti…
4 Albany 2016 Crimes Against Persons anti-genderidentityexpr… 0 othe…
5 Albany 2016 Crimes Against Persons anti-age* 0 othe…
6 Albany 2016 Crimes Against Persons anti-white 0 anti…
7 Albany 2016 Crimes Against Persons anti-black 1 othe…
8 Albany 2016 Crimes Against Persons anti-americanindian/ala… 0 anti…
9 Albany 2016 Crimes Against Persons anti-asian 0 anti…
10 Albany 2016 Crimes Against Persons anti-nativehawaiian/pac… 0 anti…
# ℹ 17,333 more rowslgbtq <- hatecrimes |>
pivot_longer(
cols = 4:44,
names_to = "victim_cat",
values_to = "crimecount") |>
filter(victim_cat %in% c("anti-transgender", "anti-gayfemale", "anti-gendervictim_catendityexpression", "anti-gaymale", "anti-gay(maleandfemale", "anti-bisexual"))
lgbtq# A tibble: 1,692 × 5
county year crimetype victim_cat crimecount
<chr> <dbl> <chr> <chr> <dbl>
1 Albany 2016 Crimes Against Persons anti-transgender 0
2 Albany 2016 Crimes Against Persons anti-gaymale 1
3 Albany 2016 Crimes Against Persons anti-gayfemale 0
4 Albany 2016 Crimes Against Persons anti-bisexual 0
5 Albany 2016 Property Crimes anti-transgender 0
6 Albany 2016 Property Crimes anti-gaymale 0
7 Albany 2016 Property Crimes anti-gayfemale 0
8 Albany 2016 Property Crimes anti-bisexual 0
9 Allegany 2016 Property Crimes anti-transgender 0
10 Allegany 2016 Property Crimes anti-gaymale 0
# ℹ 1,682 more rows#At the end of your markdown/quarto file with the hatecrimes code, include an essay of approximately 200-400 words which that answers the following questions:
#1. Write about the positive and negative aspects of this hatecrimes dataset.
# 2. List 2 different paths you would like to (hypothetically) study about this dataset.
#3. Describe 2 things you would do to follow up after seeing the output from the hatecrimes tutorial.
#Publish your markdown file and submit the link in this Assignment Dropbox by 11:59 pm on Sunday, Sept.22nd
#We will present your submissions in class next week.# The hatecrimes data set which featured two different datasets, the first called " looked upon the
#in the tutorial hatecrimes done by my amazing dat 110 teacher, she looks at all types of crimes specifically in new york fron the time range 2010-2016. In her introduction she mentions that this dataset containts bias information which can be misleading, therefore the task is to clean up the data and decode it.
## A positive aspect about the hatecrimes tutorial is the simplicity of the tutorial meaning it was easy to follow.
## A negative aspect about this hatecrimeset is more of a personal issue, I had trouble understanding the code. One different research path I would of taken is figuring out where the misleading information was arriving. Another research path I would of taken is figuring out the standard deviation of the misleading information, and the correct information.
## 2 follow ups I would do is repractice this tutorial, and learn more of the code.