LAB 4.2 - Collaboration Assignment (Group C)

Pamela Carta, Eli Kramer, Aaron Devine 
# A tibble: 10 × 28
     PID county  state  area poptotal popdensity popwhite popblack popamerindian
   <int> <chr>   <chr> <dbl>    <int>      <dbl>    <int>    <int>         <int>
 1   561 ADAMS   IL    0.052    66090      1271.    63917     1702            98
 2   562 ALEXAN… IL    0.014    10626       759      7054     3496            19
 3   563 BOND    IL    0.022    14991       681.    14477      429            35
 4   564 BOONE   IL    0.017    30806      1812.    29344      127            46
 5   565 BROWN   IL    0.018     5836       324.     5264      547            14
 6   566 BUREAU  IL    0.05     35688       714.    35157       50            65
 7   567 CALHOUN IL    0.017     5322       313.     5298        1             8
 8   568 CARROLL IL    0.027    16805       622.    16519      111            30
 9   569 CASS    IL    0.024    13437       560.    13384       16             8
10   570 CHAMPA… IL    0.058   173025      2983.   146506    16559           331
# ℹ 19 more variables: popasian <int>, popother <int>, percwhite <dbl>,
#   percblack <dbl>, percamerindan <dbl>, percasian <dbl>, percother <dbl>,
#   popadults <int>, perchsd <dbl>, percollege <dbl>, percprof <dbl>,
#   poppovertyknown <int>, percpovertyknown <dbl>, percbelowpoverty <dbl>,
#   percchildbelowpovert <dbl>, percadultpoverty <dbl>,
#   percelderlypoverty <dbl>, inmetro <int>, category <chr>
# A tibble: 10 × 28
     PID county  state  area poptotal popdensity popwhite popblack popamerindian
   <int> <chr>   <chr> <dbl>    <int>      <dbl>    <int>    <int>         <int>
 1  3043 VERNON  WI    0.048    25617       534.    25509       12            36
 2  3044 VILAS   WI    0.06     17707       295.    16116        9          1534
 3  3045 WALWOR… WI    0.032    75000      2344.    72747      454           201
 4  3046 WASHBU… WI    0.05     13772       275.    13585       25           122
 5  3047 WASHIN… WI    0.025    95328      3813.    94465      125           208
 6  3048 WAUKES… WI    0.034   304715      8962.   298313     1096           672
 7  3049 WAUPACA WI    0.045    46104      1025.    45695       22           125
 8  3050 WAUSHA… WI    0.037    19385       524.    19094       29            70
 9  3051 WINNEB… WI    0.035   140320      4009.   136822      697           685
10  3052 WOOD    WI    0.048    73605      1533.    72157       90           481
# ℹ 19 more variables: popasian <int>, popother <int>, percwhite <dbl>,
#   percblack <dbl>, percamerindan <dbl>, percasian <dbl>, percother <dbl>,
#   popadults <int>, perchsd <dbl>, percollege <dbl>, percprof <dbl>,
#   poppovertyknown <int>, percpovertyknown <dbl>, percbelowpoverty <dbl>,
#   percchildbelowpovert <dbl>, percadultpoverty <dbl>,
#   percelderlypoverty <dbl>, inmetro <int>, category <chr>

Histogram

Histogram

This histogram shows the frequency distribution of the percentage of individuals living below the poverty line in Midwestern U.S. counties, binned every 2 percentage points. The distribution appears unimodal and right-skewed, with most counties having a below poverty percentage between 5% and 15%.

Frequency Polygon

Frequency Polygon

This frequency polygon shows the frequency distribution of the percentage of individuals living below the poverty line in Midwestern U.S. counties, binned every 2 percentage points. The distribution appears unimodal and right-skewed, with most counties having a below poverty percentage between 5% and 15%.

Which one is better and why?

The frequency polygon stands out as a better choice due to its enhanced readability and seamless flow. Unlike histograms, where the width of bars can pose concerns, the frequency polygon represents data with a continuous line. This not only eliminates the need to fret over bar widths but also contributes to a more precise depiction of data changes. In contrast to histograms, which rely on rectangular blocks, the frequency polygon provides a nuanced and detailed representation, making it a more sophisticated tool for visualizing data variations.

Frequency Polygon (Child Poverty)

How do the results compare between the two variables?

While the overall trend remains similar, the percentage of children living below the poverty line displays greater variability compared to the overall rates.

Facet grid poverty by state

Facet grid % child poverty by state

How do the results compare between the two variables by state?

When comparing overall and child poverty rates, there appears to be a reversal in rankings between Illinois and Indiana, with Indiana having lower rates than Illinois across both measures. Michigan, Ohio and Wisconsin display relatively similar poverty rates to one another regardless of whether we look at overall or child rates. Overall, Indiana consistently has the lowest poverty percentages out of this group of Midwestern states, while Ohio tends to have the highest.

Feedback

We got feedback the we should account for the fact that each state could have a different number of counties. Additionally, it was recommended that we check into Wisconsin’s outliers.

Revisions

  • Created a bar chart of average percent of population below the poverty line for each state
  • Investigated Wisconsin's most impoverished county
  • Updated caption for accuracy
  • Revised earlier analysis and replaced the word "adult" with overall or individual for better accuracy (we never used percadultpoverty only the overall rate and child rate)

Count of Counties by State and Bar Chart of Average Poverty by State


 IL  IN  MI  OH  WI 
102  92  83  88  72

Bar Chart of Average Poverty by State

Here we see a slightly different story with Michigan having the highest average poverty rate and Indiana having the lowest average poverty rate.

Wisconsin’s Outlier

# A tibble: 6 × 28
    PID county   state  area poptotal popdensity popwhite popblack popamerindian
  <int> <chr>    <chr> <dbl>    <int>      <dbl>    <int>    <int>         <int>
1  3035 RUSK     WI    0.055    15079       274.    14821       31            82
2  2984 BAYFIELD WI    0.089    14008       157.    12707       29          1240
3  2997 DUNN     WI    0.052    35909       691.    34929      172            95
4  3038 SAWYER   WI    0.079    14181       180.    11962       18          2167
5  3001 FOREST   WI    0.06      8776       146.     7842      127           780
6  3020 MENOMIN… WI    0.021     3890       185.      416        0          3469
# ℹ 19 more variables: popasian <int>, popother <int>, percwhite <dbl>,
#   percblack <dbl>, percamerindan <dbl>, percasian <dbl>, percother <dbl>,
#   popadults <int>, perchsd <dbl>, percollege <dbl>, percprof <dbl>,
#   poppovertyknown <int>, percpovertyknown <dbl>, percbelowpoverty <dbl>,
#   percchildbelowpovert <dbl>, percadultpoverty <dbl>,
#   percelderlypoverty <dbl>, inmetro <int>, category <chr>

Wisconsin’s outlier is Menominee county which has 48.69% of its population below the poverty line. It is also much smaller than the next 4 counties with high poverty in Wisconsin. Additionally, the county is 89.18% American Indian with only 7.34% of the population having a degree.

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