Can anybody explain what is going on in regards to my box plot graph? Per_infest is the the count of infest bedbugs units per unit and inc_bracket is the income bracket displayed below(look at second pic) with ranges
ABC Knit Run - A | Import Dataset - | List . | C . Q per_invest All Replace Replace All R - Global Environment - Q Next Prev Data In selection Match case Whole word Regex Wrap b3 List of 6 Q 1.00- Bedbug_Report.. 126806 obs. of 21 variables db 35682 obs. of 7 variables . . . db_graph 569 obs. of 5 variables . db1 35682 obs. of 10 variables 0.75 - db2 35682 obs. of 13 variables db2 . heatmap Large gg (9 elements, 3.2 MB) a info 191 obs. of 4 variables . .. .. . $ nta_name: chr [1:191] "Allerton-Pelham Gardens... $ pop : num [1: 191] 32127 28814 26695 77568 ... 0.50 - per_infest Files Plots Packages Help Viewer + New Folder Delete Rename More . C Home > Downloads Name Size Modified 0.25 - project01 project02 R Studio Hw STA 9750 0.00 - Sta 9750- R Studio Lecture Notes Low Lower-middle Middle Upper inc_bracket Sta 9760 FileBrowser Folders 232:10 # Relationship between income brackets and infested units (6) * R Markdown STA 9760 Lecture NotesABC Knit +c + Run . A | Import Dataset . | List . | C Q per_invest Next Prev All Replace Replace All R - Global Environment . Q Data In selection Match case Whole word Regex Wrap yLuLu b3 List of 6 Q 133 group_by(nta_name) %% Bedbug_Report.. 126806 obs. of 21 variables 134 summarise(pop = sum(pop_total_est), db 35682 obs. of 7 variables 135 inc = mean(med_hhinc_est), 136 inpov = sum(pop_inpov_pct_est)) %>% db_graph 569 obs. of 5 variables 137 ungroup() db1 35682 obs. of 10 variables 138 db2 35682 obs. of 13 variables 139 #In this step, we complete the dataset toset by left joining our do with our info table and using a vector to db2. heatmap Large gg (9 elements, 3.2 MB) a #rename it to NTA. 140 db1 % info 191 obs. of 4 variables 141 left_join(info, CC"NTA" = "nta_name")) $ nta_name: chr [1:191] "Allerton-Pelham Gardens. 142 summary(db1) $ pop : num [1: 191] 32127 28814 26695 77568 143 144 #Based on the summary of income quartiles above, we created 4 groups of income defining them as low, Files Plots Packages Help Viewer #lower-middle, middle and upper. Since we need to take into consideration the size of the apartment units, + New Folder Delete Rename More . #we divide the number of infested units by the number of units to change it to a percentage. Moreover, we get the percentage of the population below the poverty line which implies inpay will mean more in terms of Home > Downloads the population within NTA. Name Size Modified 145 db2 % project01 146 mutate(inc_bracket = case_when(inc = 84001 ~ "Upper"), STA 9750 150 per_infest = infested. unitsum. units, Sta 9750- R Studio Lecture Notes 151 per_inpov = inpov/pop 152 Sta 9760 FileBrowser Folders 152:4 C Chunk 3: Cleanup $ R Markdown STA 9760 Lecture Notes
