Check working directory
getwd()
## [1] "C:/Users/libcl/OneDrive/Documents/DATA110"
Load library: I am loading Tidyverse and Steamgraph in order to use ggplot and steamgraph for making data visualizations
library(tidyverse)
## -- Attaching packages --------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.2
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## -- Conflicts ------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(streamgraph)

PROJECT 1:

FATAL POLICE SHOOTINGS IN U.S.

I began this project intending to see what I could learn about race and location by state as factors in police shootings. My main data set is fatal_police_shootings.csv which was created by The Washington Post and tracks years 2015 through spring of 2020. I used the variables Date, State, and Race from this data set. Because population proportions data by race will be important, I will attempt to use two other data sets which are csv format of proportions by race for each state in 2020. Two data sets were required because Hispanics are not included in the main set; this is because Hispanic is not considered a race. However, the shootings data set records it as race anyway. The source for these last 2 datasets is from a website called World Population Review with the following links:

https://worldpopulationreview.com/states/states-by-race https://worldpopulationreview.com/state-rankings/hispanic-population-by-state

The fatal_police_shootings set was already fairly clean. There were a number of missing data in the race column that I had to ignore because there was really no way to guess or estimate what those might be since these are individual events. I filtered the missing shooting data out when I was looking at race in the visualizations. I decided to change the Date variable’s class from character to date so that I could manipulate it from mm/dd/yyyy to just yyyy so that I could use it to view data by year by converting it to a numeric. For the two population proportion datasets, I needed to transform the state variable column of data from full names to abbreviations in order to use states as a key in joining the data sets to shootings. One difficulty here is that Washington DC is not a state but was included in all the datasets, and R-code expects 50 rows of data, so I had to drop it from the dataframe initially and try to add it back in afterwards manually. Also, there is no recording for Hispanics in the 3rd data set, so I used Maryland’s proportion, although it would be more correct to use Montgomery County and Prince George’s than the whole state. My solution could cause problems later if I want to add to this project later because of these manual changes being overlooked.

For the first visualization, I used a steamgraph to look at the overall number of shootings in the country by race each year from 2015 to 2020. The graph shows white have the highest number of fatalities, followed by nearly as large groups of blacks and Hispanics, and finally Asian and Native Americans in very small numbers. A question asked in the recent town hall was, “Is it true more whites are killed by police than these other groups? This visualization appear to depict in total number that whites are killed at more than double amount to say, blacks or Hispanics. In total numbers, the answer is yes according to this steamgraph. I have actually heard this used as an excuse about why people of color do not experience systemic racism because more whites are killed; however, that is an incomplete answer. One must consider proportionality of races for the whole population. I noticed that killings of whites appear to drop of slightly beginning in 2019 to present, while killings of blacks and Asians has increased.

To look at proportionality, I could look at the total population for the country, but that seems too general. When looking at the Tableau version of the fatal police shootings data, it seems Hispanics are killed in higher proportion in the southwest and west, while blacks appear to be killed more in in the south and northeast areas of the country. Therefore, my second visualization is a bar graph of total number of fatalities by state with proportion by race shown for each state. Again, this proportion is only within the fatal shooting data and does not consider actual population demographics for each state yet. Just looking at the demographics we have examples:

  1. although only 1.2% of Utah’s population is black and 12.86% are Hispanic, those races appear to represent approximately 50% of the deaths in that state.
  2. in Maryland approximate 56% of the population is white, and the bar graph shows the other races are overrepresented in the shootings population. Blacks represent about 30%, but in the shootings population, they represent equal number to white; this shows overrepresentation by blacks.
  3. Minnesota is 6% black, but again appear in this visualization to be disproportionally fatally shot by police.

This bar graph visualization surprised me by how clearly it shows with color (whites being displayed at the bottom in purple) that non-whites are being killed at much higher numbers and proportions within the shootings population than in the general population; because we know most of these states are white majority, thus higher proportion white in the general population, this is solid evidence that systemic racism exists in the area of policing.

Further exploration could be done to see what the picture is in each state. I ran into some problems with the second and third dataset in attempting to convert it to usable form. I wanted to compare the shooting proportions directly with population proportions for each state in a visualization, but just did not have time to get it right. I included the two data sets in my R code anyway because I used it to eyeball the 2nd visualization, and it shows my struggle with cleaning it and shaping it. The 3rd visualization is not completed for analysis yet because of DC being missing and other issues.

The two visualizations I did, together, are pretty clear in answering the question about racial factors in police shootings, but not for state yet. I would really like to go back at some point and produce visualizations for each state , because I think it would be vital in determining where more work needs to be done. I say “more work” because all of the states need work, but some need a harder look now.

#load dataset from csv file
shootings <- read.csv("fatal-police-shootings.csv")
head(shootings)
##   id               name     date  manner_of_death      armed age gender race
## 1  3         Tim Elliot 1/2/2015             shot        gun  53      M    A
## 2  4   Lewis Lee Lembke 1/2/2015             shot        gun  47      M    W
## 3  5 John Paul Quintero 1/3/2015 shot and Tasered    unarmed  23      M    H
## 4  8    Matthew Hoffman 1/4/2015             shot toy weapon  32      M    W
## 5  9  Michael Rodriguez 1/4/2015             shot   nail gun  39      M    H
## 6 11  Kenneth Joe Brown 1/4/2015             shot        gun  18      M    W
##            city state signs_of_mental_illness threat_level        flee
## 1       Shelton    WA                    TRUE       attack Not fleeing
## 2         Aloha    OR                   FALSE       attack Not fleeing
## 3       Wichita    KS                   FALSE        other Not fleeing
## 4 San Francisco    CA                    TRUE       attack Not fleeing
## 5         Evans    CO                   FALSE       attack Not fleeing
## 6       Guthrie    OK                   FALSE       attack Not fleeing
##   body_camera longitude latitude is_geocoding_exact
## 1       FALSE  -123.122   47.247               TRUE
## 2       FALSE  -122.892   45.487               TRUE
## 3       FALSE   -97.281   37.695               TRUE
## 4       FALSE  -122.422   37.763               TRUE
## 5       FALSE  -104.692   40.384               TRUE
## 6       FALSE   -97.423   35.877               TRUE

First, I will clean the data for the first dataset for the columns I will need: Make all headers lowercase and remove spaces (already done in csv file). Also, the “date” column is a character vector, so I converted it to a date vector, and finally a numeric vector, showing year only. Look at the structure of the data and the summary.

#names(shootings) <- tolower(names(shootings)) not needed
#names(shootings) <- gsub(" #","",names(shootings)) not needed
# convert character date info to date format 'yyyy/mm/dd'
shootings$date <- as.Date(shootings$date, "%m/%d/%Y")
#now pull out the year and convert to a numeric class
format(shootings$date,'%Y')
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## [5621] "2020" "2020" "2020" "2020"
shootings$date <- as.numeric(format(shootings$date,'%Y'))
head(shootings)
##   id               name date  manner_of_death      armed age gender race
## 1  3         Tim Elliot 2015             shot        gun  53      M    A
## 2  4   Lewis Lee Lembke 2015             shot        gun  47      M    W
## 3  5 John Paul Quintero 2015 shot and Tasered    unarmed  23      M    H
## 4  8    Matthew Hoffman 2015             shot toy weapon  32      M    W
## 5  9  Michael Rodriguez 2015             shot   nail gun  39      M    H
## 6 11  Kenneth Joe Brown 2015             shot        gun  18      M    W
##            city state signs_of_mental_illness threat_level        flee
## 1       Shelton    WA                    TRUE       attack Not fleeing
## 2         Aloha    OR                   FALSE       attack Not fleeing
## 3       Wichita    KS                   FALSE        other Not fleeing
## 4 San Francisco    CA                    TRUE       attack Not fleeing
## 5         Evans    CO                   FALSE       attack Not fleeing
## 6       Guthrie    OK                   FALSE       attack Not fleeing
##   body_camera longitude latitude is_geocoding_exact
## 1       FALSE  -123.122   47.247               TRUE
## 2       FALSE  -122.892   45.487               TRUE
## 3       FALSE   -97.281   37.695               TRUE
## 4       FALSE  -122.422   37.763               TRUE
## 5       FALSE  -104.692   40.384               TRUE
## 6       FALSE   -97.423   35.877               TRUE
#look at structure and summary
str(shootings)
## 'data.frame':    5624 obs. of  17 variables:
##  $ id                     : int  3 4 5 8 9 11 13 15 16 17 ...
##  $ name                   : chr  "Tim Elliot" "Lewis Lee Lembke" "John Paul Quintero" "Matthew Hoffman" ...
##  $ date                   : num  2015 2015 2015 2015 2015 ...
##  $ manner_of_death        : chr  "shot" "shot" "shot and Tasered" "shot" ...
##  $ armed                  : chr  "gun" "gun" "unarmed" "toy weapon" ...
##  $ age                    : int  53 47 23 32 39 18 22 35 34 47 ...
##  $ gender                 : chr  "M" "M" "M" "M" ...
##  $ race                   : chr  "A" "W" "H" "W" ...
##  $ city                   : chr  "Shelton" "Aloha" "Wichita" "San Francisco" ...
##  $ state                  : chr  "WA" "OR" "KS" "CA" ...
##  $ signs_of_mental_illness: logi  TRUE FALSE FALSE TRUE FALSE FALSE ...
##  $ threat_level           : chr  "attack" "attack" "other" "attack" ...
##  $ flee                   : chr  "Not fleeing" "Not fleeing" "Not fleeing" "Not fleeing" ...
##  $ body_camera            : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ longitude              : num  -123.1 -122.9 -97.3 -122.4 -104.7 ...
##  $ latitude               : num  47.2 45.5 37.7 37.8 40.4 ...
##  $ is_geocoding_exact     : logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
summary(shootings)
##        id           name                date      manner_of_death   
##  Min.   :   3   Length:5624        Min.   :2015   Length:5624       
##  1st Qu.:1600   Class :character   1st Qu.:2016   Class :character  
##  Median :3128   Mode  :character   Median :2017   Mode  :character  
##  Mean   :3124                      Mean   :2017                     
##  3rd Qu.:4658                      3rd Qu.:2019                     
##  Max.   :6152                      Max.   :2020                     
##                                                                     
##     armed                age           gender              race          
##  Length:5624        Min.   : 6.00   Length:5624        Length:5624       
##  Class :character   1st Qu.:27.00   Class :character   Class :character  
##  Mode  :character   Median :35.00   Mode  :character   Mode  :character  
##                     Mean   :37.14                                        
##                     3rd Qu.:46.00                                        
##                     Max.   :91.00                                        
##                     NA's   :252                                          
##      city              state           signs_of_mental_illness
##  Length:5624        Length:5624        Mode :logical          
##  Class :character   Class :character   FALSE:4368             
##  Mode  :character   Mode  :character   TRUE :1256             
##                                                               
##                                                               
##                                                               
##                                                               
##  threat_level           flee           body_camera       longitude      
##  Length:5624        Length:5624        Mode :logical   Min.   :-158.14  
##  Class :character   Class :character   FALSE:4960      1st Qu.:-112.12  
##  Mode  :character   Mode  :character   TRUE :664       Median : -94.42  
##                                                        Mean   : -97.24  
##                                                        3rd Qu.: -83.05  
##                                                        Max.   : -68.01  
##                                                        NA's   :272      
##     latitude     is_geocoding_exact
##  Min.   :19.50   Mode :logical     
##  1st Qu.:33.48   FALSE:8           
##  Median :36.10   TRUE :5616        
##  Mean   :36.65                     
##  3rd Qu.:39.96                     
##  Max.   :71.30                     
##  NA's   :272

I notice the data has some missing data in the “race” column, so I am filtering out those rows so they aren’t distracting in the visualization to look at total shooting in the country by race from 2015 to 2020. Placing the cursor on the graph shows race and count by race(W = White, B = Black, H = Hispanic, N = Native American, A = Asian)

# separate into races by year in new df2
df2 <- shootings %>% 
  filter(race == "A" | race == "B" | race == "H" | race == "N" | race == "W") %>%
#place into yearly groups and count number of shootings by race in each year
  group_by(date)%>% 
  count(race) 
#create a steamgraph to better visualize racial counts in proportion to each other
df2 %>% 
  streamgraph(key = "race", value= "n", date = "date") %>% 
  sg_axis_x("year", date) %>%
  sg_fill_brewer("PuOr")
df2
## # A tibble: 30 x 3
## # Groups:   date [6]
##     date race      n
##    <dbl> <chr> <int>
##  1  2015 A        14
##  2  2015 B       258
##  3  2015 H       172
##  4  2015 N         9
##  5  2015 W       498
##  6  2016 A        15
##  7  2016 B       234
##  8  2016 H       160
##  9  2016 N        16
## 10  2016 W       470
## # ... with 20 more rows

Now, I’d like to look at the data broken down into individual states.

# separate into races in each state by year in new df3
df3 <- shootings %>%
  filter(race == "A" | race == "B" | race == "H" | race == "N" | race == "W") %>%
  group_by(state, date)  %>% 
  count(race)
df3
## # A tibble: 747 x 4
## # Groups:   state, date [295]
##    state  date race      n
##    <chr> <dbl> <chr> <int>
##  1 AK     2015 N         2
##  2 AK     2015 W         2
##  3 AK     2016 N         3
##  4 AK     2016 W         3
##  5 AK     2017 B         1
##  6 AK     2017 N         1
##  7 AK     2017 W         5
##  8 AK     2018 A         1
##  9 AK     2018 B         1
## 10 AK     2018 N         2
## # ... with 737 more rows
#create a bargraph to better visualize proportions by race for each state

plot2 <- df3 %>% 
  filter(race == "A" | race == "B" | race == "H" | race == "N" | race == "W") %>%
  ggplot(mapping = aes(x = state, fill = race), width = 10) +
  geom_bar() +
  xlab("Total Number by State") +
  ggtitle("Fatal Police Shooting") +
  theme(axis.text.x = element_text(angle = 90, hjust =1, vjust = 0.5))
plot2

Now we will read in the data for the population proportions by race for each state

#read in all but Hispanic Data
ABNWprops <- read.csv("StatePropsByRace.csv")
head(ABNWprops)
##     ï..State WhitePerc BlackPerc NativePerc AsianPerc IslanderPerc
## 1    Alabama    0.6819    0.2658     0.0053    0.0133       0.0004
## 2     Alaska    0.6484    0.0327     0.1444    0.0630       0.0120
## 3    Arizona    0.7722    0.0439     0.0446    0.0329       0.0020
## 4   Arkansas    0.7700    0.1541     0.0067    0.0147       0.0027
## 5 California    0.6010    0.0579     0.0076    0.1432       0.0039
## 6   Colorado    0.8417    0.0412     0.0099    0.0312       0.0015
##   OtherRacePerc TwoOrMoreRacesPerc
## 1        0.0144             0.0188
## 2        0.0149             0.0846
## 3        0.0679             0.0364
## 4        0.0264             0.0254
## 5        0.1383             0.0481
## 6        0.0388             0.0357
str(ABNWprops)
## 'data.frame':    52 obs. of  8 variables:
##  $ ï..State          : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
##  $ WhitePerc         : num  0.682 0.648 0.772 0.77 0.601 ...
##  $ BlackPerc         : num  0.2658 0.0327 0.0439 0.1541 0.0579 ...
##  $ NativePerc        : num  0.0053 0.1444 0.0446 0.0067 0.0076 ...
##  $ AsianPerc         : num  0.0133 0.063 0.0329 0.0147 0.1432 ...
##  $ IslanderPerc      : num  0.0004 0.012 0.002 0.0027 0.0039 0.0015 0.0003 0.0005 0.0005 0.0006 ...
##  $ OtherRacePerc     : num  0.0144 0.0149 0.0679 0.0264 0.1383 ...
##  $ TwoOrMoreRacesPerc: num  0.0188 0.0846 0.0364 0.0254 0.0481 0.0357 0.0317 0.0274 0.0294 0.0263 ...
#Read in Hispanic only data
Hprops <- read.csv("StatePropsHispanic.csv")
head(Hprops)
##      ï..State HispanicTotal HispanicPerc
## 1 Puerto Rico       3349340       1.1046
## 2  New Mexico       1015750       0.4845
## 3  California      15221600       0.3811
## 4       Texas      10921600       0.3706
## 5     Arizona       2163310       0.2932
## 6      Nevada        831597       0.2649
str(Hprops)
## 'data.frame':    52 obs. of  3 variables:
##  $ ï..State     : chr  "Puerto Rico" "New Mexico" "California" "Texas" ...
##  $ HispanicTotal: int  3349340 1015750 15221600 10921600 2163310 831597 5184720 1184790 1768020 3705590 ...
##  $ HispanicPerc : num  1.105 0.484 0.381 0.371 0.293 ...

Now we have to join the datasets using State Variable

state_probs <- full_join(ABNWprops, Hprops)
## Joining, by = "ï..State"
str(state_probs)
## 'data.frame':    53 obs. of  10 variables:
##  $ ï..State          : chr  "Alabama" "Alaska" "Arizona" "Arkansas" ...
##  $ WhitePerc         : num  0.682 0.648 0.772 0.77 0.601 ...
##  $ BlackPerc         : num  0.2658 0.0327 0.0439 0.1541 0.0579 ...
##  $ NativePerc        : num  0.0053 0.1444 0.0446 0.0067 0.0076 ...
##  $ AsianPerc         : num  0.0133 0.063 0.0329 0.0147 0.1432 ...
##  $ IslanderPerc      : num  0.0004 0.012 0.002 0.0027 0.0039 0.0015 0.0003 0.0005 0.0005 0.0006 ...
##  $ OtherRacePerc     : num  0.0144 0.0149 0.0679 0.0264 0.1383 ...
##  $ TwoOrMoreRacesPerc: num  0.0188 0.0846 0.0364 0.0254 0.0481 0.0357 0.0317 0.0274 0.0294 0.0263 ...
##  $ HispanicTotal     : int  203146 51186 2163310 219052 15221600 1184790 561791 86315 NA 5184720 ...
##  $ HispanicPerc      : num  0.0414 0.0697 0.2932 0.0721 0.3811 ...
head(state_probs)
##     ï..State WhitePerc BlackPerc NativePerc AsianPerc IslanderPerc
## 1    Alabama    0.6819    0.2658     0.0053    0.0133       0.0004
## 2     Alaska    0.6484    0.0327     0.1444    0.0630       0.0120
## 3    Arizona    0.7722    0.0439     0.0446    0.0329       0.0020
## 4   Arkansas    0.7700    0.1541     0.0067    0.0147       0.0027
## 5 California    0.6010    0.0579     0.0076    0.1432       0.0039
## 6   Colorado    0.8417    0.0412     0.0099    0.0312       0.0015
##   OtherRacePerc TwoOrMoreRacesPerc HispanicTotal HispanicPerc
## 1        0.0144             0.0188        203146       0.0414
## 2        0.0149             0.0846         51186       0.0697
## 3        0.0679             0.0364       2163310       0.2932
## 4        0.0264             0.0254        219052       0.0721
## 5        0.1383             0.0481      15221600       0.3811
## 6        0.0388             0.0357       1184790       0.2027
state_probs$ï..State
##  [1] "Alabama"              "Alaska"               "Arizona"             
##  [4] "Arkansas"             "California"           "Colorado"            
##  [7] "Connecticut"          "Delaware"             "District of Columbia"
## [10] "Florida"              "Georgia"              "Hawaii"              
## [13] "Idaho"                "Illinois"             "Indiana"             
## [16] "Iowa"                 "Kansas"               "Kentucky"            
## [19] "Louisiana"            "Maine"                "Maryland"            
## [22] "Massachusetts"        "Michigan"             "Minnesota"           
## [25] "Mississippi"          "Missouri"             "Montana"             
## [28] "Nebraska"             "Nevada"               "New Hampshire"       
## [31] "New Jersey"           "New Mexico"           "New York"            
## [34] "North Carolina"       "North Dakota"         "Ohio"                
## [37] "Oklahoma"             "Oregon"               "Pennsylvania"        
## [40] "Puerto Rico"          "Rhode Island"         "South Carolina"      
## [43] "South Dakota"         "Tennessee"            "Texas"               
## [46] "Utah"                 "Vermont"              "Virginia"            
## [49] "Washington"           "West Virginia"        "Wisconsin"           
## [52] "Wyoming"              "Washington DC"

Now we need to clean the data to use with shootings dataframe, i.e. change State names to State abbreviations and make column lables lowercase

#rename ï..State to state to match shootings and using state abbreviation instead of full name
#state <- state_probs$ï..State 
df4 <- state_probs %>%
   rename(state = ï..State)
#remove duplicate for DC and remove Puerto Rico
df5<-subset(df4, state != "Washington DC" & state != "Puerto Rico")
df5
##                   state WhitePerc BlackPerc NativePerc AsianPerc IslanderPerc
## 1               Alabama    0.6819    0.2658     0.0053    0.0133       0.0004
## 2                Alaska    0.6484    0.0327     0.1444    0.0630       0.0120
## 3               Arizona    0.7722    0.0439     0.0446    0.0329       0.0020
## 4              Arkansas    0.7700    0.1541     0.0067    0.0147       0.0027
## 5            California    0.6010    0.0579     0.0076    0.1432       0.0039
## 6              Colorado    0.8417    0.0412     0.0099    0.0312       0.0015
## 7           Connecticut    0.7636    0.1056     0.0027    0.0443       0.0003
## 8              Delaware    0.6897    0.2211     0.0036    0.0387       0.0005
## 9  District of Columbia    0.4097    0.4694     0.0029    0.0390       0.0005
## 10              Florida    0.7539    0.1610     0.0028    0.0271       0.0006
## 11              Georgia    0.5904    0.3146     0.0033    0.0391       0.0006
## 12               Hawaii    0.2501    0.0185     0.0021    0.3775       0.1008
## 13                Idaho    0.9049    0.0068     0.0135    0.0141       0.0016
## 14             Illinois    0.7167    0.1423     0.0025    0.0539       0.0004
## 15              Indiana    0.8359    0.0933     0.0022    0.0218       0.0004
## 16                 Iowa    0.9028    0.0351     0.0037    0.0240       0.0010
## 17               Kansas    0.8459    0.0584     0.0083    0.0287       0.0007
## 18             Kentucky    0.8708    0.0798     0.0022    0.0141       0.0006
## 19            Louisiana    0.6221    0.3223     0.0056    0.0171       0.0003
## 20                Maine    0.9448    0.0134     0.0062    0.0112       0.0002
## 21             Maryland    0.5619    0.2978     0.0026    0.0623       0.0005
## 22        Massachusetts    0.7848    0.0748     0.0021    0.0648       0.0003
## 23             Michigan    0.7852    0.1381     0.0053    0.0306       0.0003
## 24            Minnesota    0.8333    0.0619     0.0107    0.0475       0.0004
## 25          Mississippi    0.5859    0.3767     0.0046    0.0095       0.0002
## 26             Missouri    0.8224    0.1157     0.0044    0.0192       0.0011
## 27              Montana    0.8886    0.0044     0.0646    0.0076       0.0007
## 28             Nebraska    0.8749    0.0477     0.0091    0.0232       0.0007
## 29               Nevada    0.6621    0.0893     0.0123    0.0803       0.0066
## 30        New Hampshire    0.9303    0.0153     0.0016    0.0269       0.0003
## 31           New Jersey    0.6791    0.1347     0.0021    0.0937       0.0004
## 32           New Mexico    0.7450    0.0206     0.0955    0.0151       0.0007
## 33             New York    0.6379    0.1564     0.0041    0.0831       0.0004
## 34       North Carolina    0.6887    0.2146     0.0119    0.0278       0.0007
## 35         North Dakota    0.8711    0.0272     0.0525    0.0144       0.0005
## 36                 Ohio    0.8151    0.1235     0.0020    0.0215       0.0003
## 37             Oklahoma    0.7243    0.0735     0.0752    0.0213       0.0011
## 38               Oregon    0.8442    0.0191     0.0115    0.0428       0.0039
## 39         Pennsylvania    0.8085    0.1113     0.0019    0.0335       0.0003
## 41         Rhode Island    0.8087    0.0655     0.0052    0.0338       0.0008
## 42       South Carolina    0.6725    0.2703     0.0034    0.0152       0.0006
## 43         South Dakota    0.8447    0.0188     0.0872    0.0146       0.0003
## 44            Tennessee    0.7767    0.1680     0.0027    0.0170       0.0006
## 45                Texas    0.7431    0.1207     0.0049    0.0469       0.0008
## 46                 Utah    0.8643    0.0118     0.0107    0.0229       0.0089
## 47              Vermont    0.9433    0.0129     0.0034    0.0169       0.0003
## 48             Virginia    0.6802    0.1917     0.0027    0.0632       0.0007
## 49           Washington    0.7603    0.0370     0.0130    0.0833       0.0066
## 50        West Virginia    0.9318    0.0365     0.0020    0.0079       0.0002
## 51            Wisconsin    0.8559    0.0638     0.0087    0.0276       0.0003
## 52              Wyoming    0.9144    0.0095     0.0242    0.0082       0.0009
##    OtherRacePerc TwoOrMoreRacesPerc HispanicTotal HispanicPerc
## 1         0.0144             0.0188        203146       0.0414
## 2         0.0149             0.0846         51186       0.0697
## 3         0.0679             0.0364       2163310       0.2932
## 4         0.0264             0.0254        219052       0.0721
## 5         0.1383             0.0481      15221600       0.3811
## 6         0.0388             0.0357       1184790       0.2027
## 7         0.0517             0.0317        561791       0.1577
## 8         0.0190             0.0274         86315       0.0878
## 9         0.0490             0.0294            NA           NA
## 10        0.0282             0.0263       5184720       0.2357
## 11        0.0275             0.0245        968463       0.0902
## 12        0.0108             0.2403        147962       0.1047
## 13        0.0315             0.0276        209073       0.1145
## 14        0.0595             0.0248       2174840       0.1718
## 15        0.0218             0.0245        450267       0.0668
## 16        0.0125             0.0210        183296       0.0576
## 17        0.0232             0.0347        340616       0.1170
## 18        0.0099             0.0225        158744       0.0353
## 19        0.0130             0.0196        234920       0.0506
## 20        0.0022             0.0219         21421       0.0159
## 21        0.0416             0.0332        588912       0.0968
## 22        0.0417             0.0316        789127       0.1131
## 23        0.0120             0.0285        497897       0.0496
## 24        0.0177             0.0285        292764       0.0514
## 25        0.0096             0.0134         90493       0.0303
## 26        0.0117             0.0255        249105       0.0404
## 27        0.0058             0.0283         39019       0.0359
## 28        0.0189             0.0255        203281       0.1041
## 29        0.1014             0.0481        831597       0.2649
## 30        0.0049             0.0208         48356       0.0353
## 31        0.0639             0.0260       1768020       0.1978
## 32        0.0909             0.0323       1015750       0.4845
## 33        0.0876             0.0305       3705590       0.1906
## 34        0.0303             0.0259        935950       0.0882
## 35        0.0100             0.0243         26529       0.0348
## 36        0.0094             0.0281        431327       0.0367
## 37        0.0272             0.0774        407521       0.1030
## 38        0.0311             0.0474        523956       0.1218
## 39        0.0202             0.0243        905156       0.0706
## 41        0.0550             0.0310        158858       0.1504
## 42        0.0159             0.0222        275685       0.0529
## 43        0.0077             0.0267         31995       0.0354
## 44        0.0136             0.0214        352402       0.0511
## 45        0.0574             0.0262      10921600       0.3706
## 46        0.0519             0.0295        422123       0.1286
## 47        0.0039             0.0193         11677       0.0186
## 48        0.0248             0.0368        771177       0.0894
## 49        0.0427             0.0571        911573       0.1169
## 50        0.0040             0.0176         27522       0.0155
## 51        0.0201             0.0235        385779       0.0659
## 52        0.0161             0.0268         56966       0.1005
#change state to abbreviations; DC had to be dropped because only 50 states could be mutated this way.
df5 <- df5 %>% 
  filter(state != "District of Columbia") %>%
  mutate(state = state.abb)
df5
##    state WhitePerc BlackPerc NativePerc AsianPerc IslanderPerc OtherRacePerc
## 1     AL    0.6819    0.2658     0.0053    0.0133       0.0004        0.0144
## 2     AK    0.6484    0.0327     0.1444    0.0630       0.0120        0.0149
## 3     AZ    0.7722    0.0439     0.0446    0.0329       0.0020        0.0679
## 4     AR    0.7700    0.1541     0.0067    0.0147       0.0027        0.0264
## 5     CA    0.6010    0.0579     0.0076    0.1432       0.0039        0.1383
## 6     CO    0.8417    0.0412     0.0099    0.0312       0.0015        0.0388
## 7     CT    0.7636    0.1056     0.0027    0.0443       0.0003        0.0517
## 8     DE    0.6897    0.2211     0.0036    0.0387       0.0005        0.0190
## 9     FL    0.7539    0.1610     0.0028    0.0271       0.0006        0.0282
## 10    GA    0.5904    0.3146     0.0033    0.0391       0.0006        0.0275
## 11    HI    0.2501    0.0185     0.0021    0.3775       0.1008        0.0108
## 12    ID    0.9049    0.0068     0.0135    0.0141       0.0016        0.0315
## 13    IL    0.7167    0.1423     0.0025    0.0539       0.0004        0.0595
## 14    IN    0.8359    0.0933     0.0022    0.0218       0.0004        0.0218
## 15    IA    0.9028    0.0351     0.0037    0.0240       0.0010        0.0125
## 16    KS    0.8459    0.0584     0.0083    0.0287       0.0007        0.0232
## 17    KY    0.8708    0.0798     0.0022    0.0141       0.0006        0.0099
## 18    LA    0.6221    0.3223     0.0056    0.0171       0.0003        0.0130
## 19    ME    0.9448    0.0134     0.0062    0.0112       0.0002        0.0022
## 20    MD    0.5619    0.2978     0.0026    0.0623       0.0005        0.0416
## 21    MA    0.7848    0.0748     0.0021    0.0648       0.0003        0.0417
## 22    MI    0.7852    0.1381     0.0053    0.0306       0.0003        0.0120
## 23    MN    0.8333    0.0619     0.0107    0.0475       0.0004        0.0177
## 24    MS    0.5859    0.3767     0.0046    0.0095       0.0002        0.0096
## 25    MO    0.8224    0.1157     0.0044    0.0192       0.0011        0.0117
## 26    MT    0.8886    0.0044     0.0646    0.0076       0.0007        0.0058
## 27    NE    0.8749    0.0477     0.0091    0.0232       0.0007        0.0189
## 28    NV    0.6621    0.0893     0.0123    0.0803       0.0066        0.1014
## 29    NH    0.9303    0.0153     0.0016    0.0269       0.0003        0.0049
## 30    NJ    0.6791    0.1347     0.0021    0.0937       0.0004        0.0639
## 31    NM    0.7450    0.0206     0.0955    0.0151       0.0007        0.0909
## 32    NY    0.6379    0.1564     0.0041    0.0831       0.0004        0.0876
## 33    NC    0.6887    0.2146     0.0119    0.0278       0.0007        0.0303
## 34    ND    0.8711    0.0272     0.0525    0.0144       0.0005        0.0100
## 35    OH    0.8151    0.1235     0.0020    0.0215       0.0003        0.0094
## 36    OK    0.7243    0.0735     0.0752    0.0213       0.0011        0.0272
## 37    OR    0.8442    0.0191     0.0115    0.0428       0.0039        0.0311
## 38    PA    0.8085    0.1113     0.0019    0.0335       0.0003        0.0202
## 39    RI    0.8087    0.0655     0.0052    0.0338       0.0008        0.0550
## 40    SC    0.6725    0.2703     0.0034    0.0152       0.0006        0.0159
## 41    SD    0.8447    0.0188     0.0872    0.0146       0.0003        0.0077
## 42    TN    0.7767    0.1680     0.0027    0.0170       0.0006        0.0136
## 43    TX    0.7431    0.1207     0.0049    0.0469       0.0008        0.0574
## 44    UT    0.8643    0.0118     0.0107    0.0229       0.0089        0.0519
## 45    VT    0.9433    0.0129     0.0034    0.0169       0.0003        0.0039
## 46    VA    0.6802    0.1917     0.0027    0.0632       0.0007        0.0248
## 47    WA    0.7603    0.0370     0.0130    0.0833       0.0066        0.0427
## 48    WV    0.9318    0.0365     0.0020    0.0079       0.0002        0.0040
## 49    WI    0.8559    0.0638     0.0087    0.0276       0.0003        0.0201
## 50    WY    0.9144    0.0095     0.0242    0.0082       0.0009        0.0161
##    TwoOrMoreRacesPerc HispanicTotal HispanicPerc
## 1              0.0188        203146       0.0414
## 2              0.0846         51186       0.0697
## 3              0.0364       2163310       0.2932
## 4              0.0254        219052       0.0721
## 5              0.0481      15221600       0.3811
## 6              0.0357       1184790       0.2027
## 7              0.0317        561791       0.1577
## 8              0.0274         86315       0.0878
## 9              0.0263       5184720       0.2357
## 10             0.0245        968463       0.0902
## 11             0.2403        147962       0.1047
## 12             0.0276        209073       0.1145
## 13             0.0248       2174840       0.1718
## 14             0.0245        450267       0.0668
## 15             0.0210        183296       0.0576
## 16             0.0347        340616       0.1170
## 17             0.0225        158744       0.0353
## 18             0.0196        234920       0.0506
## 19             0.0219         21421       0.0159
## 20             0.0332        588912       0.0968
## 21             0.0316        789127       0.1131
## 22             0.0285        497897       0.0496
## 23             0.0285        292764       0.0514
## 24             0.0134         90493       0.0303
## 25             0.0255        249105       0.0404
## 26             0.0283         39019       0.0359
## 27             0.0255        203281       0.1041
## 28             0.0481        831597       0.2649
## 29             0.0208         48356       0.0353
## 30             0.0260       1768020       0.1978
## 31             0.0323       1015750       0.4845
## 32             0.0305       3705590       0.1906
## 33             0.0259        935950       0.0882
## 34             0.0243         26529       0.0348
## 35             0.0281        431327       0.0367
## 36             0.0774        407521       0.1030
## 37             0.0474        523956       0.1218
## 38             0.0243        905156       0.0706
## 39             0.0310        158858       0.1504
## 40             0.0222        275685       0.0529
## 41             0.0267         31995       0.0354
## 42             0.0214        352402       0.0511
## 43             0.0262      10921600       0.3706
## 44             0.0295        422123       0.1286
## 45             0.0193         11677       0.0186
## 46             0.0368        771177       0.0894
## 47             0.0571        911573       0.1169
## 48             0.0176         27522       0.0155
## 49             0.0235        385779       0.0659
## 50             0.0268         56966       0.1005

to combine with shootings data set, now df3, and state proportions by state for each state, now df5

comb_data <- left_join(df3, df5, key = state, )
## Joining, by = "state"
comb_data
## # A tibble: 747 x 13
## # Groups:   state, date [295]
##    state  date race      n WhitePerc BlackPerc NativePerc AsianPerc IslanderPerc
##    <chr> <dbl> <chr> <int>     <dbl>     <dbl>      <dbl>     <dbl>        <dbl>
##  1 AK     2015 N         2     0.648    0.0327      0.144     0.063        0.012
##  2 AK     2015 W         2     0.648    0.0327      0.144     0.063        0.012
##  3 AK     2016 N         3     0.648    0.0327      0.144     0.063        0.012
##  4 AK     2016 W         3     0.648    0.0327      0.144     0.063        0.012
##  5 AK     2017 B         1     0.648    0.0327      0.144     0.063        0.012
##  6 AK     2017 N         1     0.648    0.0327      0.144     0.063        0.012
##  7 AK     2017 W         5     0.648    0.0327      0.144     0.063        0.012
##  8 AK     2018 A         1     0.648    0.0327      0.144     0.063        0.012
##  9 AK     2018 B         1     0.648    0.0327      0.144     0.063        0.012
## 10 AK     2018 N         2     0.648    0.0327      0.144     0.063        0.012
## # ... with 737 more rows, and 4 more variables: OtherRacePerc <dbl>,
## #   TwoOrMoreRacesPerc <dbl>, HispanicTotal <int>, HispanicPerc <dbl>
Still need to combine shootings, proportion data and calculate and create bar graph comparing actual population proportion for the states, to the shooting group racial proportions to answer the question; however, from the 1st bar graph, we can pull out individual states and see that all groups, except whites, are over-represented in those who suffered these fatalities at the hands of law enforcement. White, on the other hand, were underrepresented.