Task 1:
This code tells R to read the data and the tidyverse package makes the data tidy, which is how we like it as data analysts.
if(!require(tidyverse))install.packages("tidyverse")## Loading required package: 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 3.4.4 ✔ 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 errorslibrary(tidyverse)
Inc2022 <- read.csv("https://drkblake.com/wp-content/uploads/2024/02/Inc2022.csv")
Inc2017 <- read.csv("https://drkblake.com/wp-content/uploads/2024/02/Inc2017.csv")Task 2:
This code joined the ’22(from) and ’17(to) data together with they key column variable GEOID.
Income <- left_join(Inc2017,
Inc2022,
by = join_by(GEOID == GEOID))Task 3: Using the mutate function, I was able to combine the data into one graph and find the change between 2022 and 2017.
Income <- Income %>%
mutate(Change = HHInc2022 - HHInc2017)
head(Income, 10)## GEOID HHInc2017 District County HHInc2022 Significance
## 1 4702190022 44091 District 1 Cheatham County 59741 Significant
## 2 4702190212 55247 District 2 Cheatham County 73634 Significant
## 3 4702190402 73802 District 3 Cheatham County 89861 Nonsignificant
## 4 4702190592 49731 District 4 Cheatham County 73293 Significant
## 5 4702190782 60793 District 5 Cheatham County 78380 Significant
## 6 4702190972 66750 District 6 Cheatham County 92305 Significant
## 7 4703790038 56258 District 1 Davidson County 75038 Significant
## 8 4703790228 32487 District 2 Davidson County 54346 Significant
## 9 4703790418 49402 District 3 Davidson County 67953 Significant
## 10 4703790608 79381 District 4 Davidson County 106260 Significant
## Change
## 1 15650
## 2 18387
## 3 16059
## 4 23562
## 5 17587
## 6 25555
## 7 18780
## 8 21859
## 9 18551
## 10 26879Task 4: This code added a column called level, then took my instructions regarding INC2022 and put either 100k+ if 22 income was greater than 100,000 and <100k when it was less.
Income <- Income %>%
mutate(Level = case_when(HHInc2022 < 100000 ~ "<100k+",
HHInc2022 == 100000 ~ "No change",
HHInc2022 > 100000 ~ "100k+",
.default = "Error"))
head(Income, 10)## GEOID HHInc2017 District County HHInc2022 Significance
## 1 4702190022 44091 District 1 Cheatham County 59741 Significant
## 2 4702190212 55247 District 2 Cheatham County 73634 Significant
## 3 4702190402 73802 District 3 Cheatham County 89861 Nonsignificant
## 4 4702190592 49731 District 4 Cheatham County 73293 Significant
## 5 4702190782 60793 District 5 Cheatham County 78380 Significant
## 6 4702190972 66750 District 6 Cheatham County 92305 Significant
## 7 4703790038 56258 District 1 Davidson County 75038 Significant
## 8 4703790228 32487 District 2 Davidson County 54346 Significant
## 9 4703790418 49402 District 3 Davidson County 67953 Significant
## 10 4703790608 79381 District 4 Davidson County 106260 Significant
## Change Level
## 1 15650 <100k+
## 2 18387 <100k+
## 3 16059 <100k+
## 4 23562 <100k+
## 5 17587 <100k+
## 6 25555 <100k+
## 7 18780 <100k+
## 8 21859 <100k+
## 9 18551 <100k+
## 10 26879 100k+Task 5: I used the code to create a new data set called LevelbyCounty by using the data from Income. I then used group_by code to tell r to group my new data set by County, Level, and 2022 income. Lastly, using the summarize function, I told r to tell me how many districts make over and under 100K. The head function let me see the first 10 rows to make sure the code worked correctly.
LevelbyCounty <- Income
head(LevelbyCounty, 20)## GEOID HHInc2017 District County HHInc2022 Significance
## 1 4702190022 44091 District 1 Cheatham County 59741 Significant
## 2 4702190212 55247 District 2 Cheatham County 73634 Significant
## 3 4702190402 73802 District 3 Cheatham County 89861 Nonsignificant
## 4 4702190592 49731 District 4 Cheatham County 73293 Significant
## 5 4702190782 60793 District 5 Cheatham County 78380 Significant
## 6 4702190972 66750 District 6 Cheatham County 92305 Significant
## 7 4703790038 56258 District 1 Davidson County 75038 Significant
## 8 4703790228 32487 District 2 Davidson County 54346 Significant
## 9 4703790418 49402 District 3 Davidson County 67953 Significant
## 10 4703790608 79381 District 4 Davidson County 106260 Significant
## 11 4703790798 40220 District 5 Davidson County 71189 Significant
## 12 4703790988 50871 District 6 Davidson County 82996 Significant
## 13 4703791178 46594 District 7 Davidson County 72500 Significant
## 14 4703791368 39885 District 8 Davidson County 62232 Significant
## 15 4703791558 39769 District 9 Davidson County 54322 Significant
## 16 4703791748 47270 District 10 Davidson County 58338 Significant
## 17 4703791938 57086 District 11 Davidson County 73158 Significant
## 18 4703792128 61720 District 12 Davidson County 85559 Significant
## 19 4703792318 46435 District 13 Davidson County 63110 Significant
## 20 4703792508 53068 District 14 Davidson County 64380 Significant
## Change Level
## 1 15650 <100k+
## 2 18387 <100k+
## 3 16059 <100k+
## 4 23562 <100k+
## 5 17587 <100k+
## 6 25555 <100k+
## 7 18780 <100k+
## 8 21859 <100k+
## 9 18551 <100k+
## 10 26879 100k+
## 11 30969 <100k+
## 12 32125 <100k+
## 13 25906 <100k+
## 14 22347 <100k+
## 15 14553 <100k+
## 16 11068 <100k+
## 17 16072 <100k+
## 18 23839 <100k+
## 19 16675 <100k+
## 20 11312 <100k+LevelbyCounty <- group_by(LevelbyCounty, County, Level, HHInc2022)
LevelbyCounty <- summarize(LevelbyCounty, Count = n())## `summarise()` has grouped output by 'County', 'Level'. You can override using
## the `.groups` argument.head(LevelbyCounty, 10)## # A tibble: 10 × 4
## # Groups: County, Level [2]
## County Level HHInc2022 Count
## <chr> <chr> <int> <int>
## 1 Cheatham County <100k+ 59741 1
## 2 Cheatham County <100k+ 73293 1
## 3 Cheatham County <100k+ 73634 1
## 4 Cheatham County <100k+ 78380 1
## 5 Cheatham County <100k+ 89861 1
## 6 Cheatham County <100k+ 92305 1
## 7 Davidson County 100k+ 106260 1
## 8 Davidson County 100k+ 110739 1
## 9 Davidson County 100k+ 117294 1
## 10 Davidson County 100k+ 117474 1Task 6: This code created a new data set called RichDistricts and filtered the information by level and who made more than 100K(per household).
RichDistricts <- LevelbyCounty
RichDistricts <- filter(RichDistricts, Level == "100k+")
head(RichDistricts, 10)## # A tibble: 10 × 4
## # Groups: County, Level [3]
## County Level HHInc2022 Count
## <chr> <chr> <int> <int>
## 1 Davidson County 100k+ 106260 1
## 2 Davidson County 100k+ 110739 1
## 3 Davidson County 100k+ 117294 1
## 4 Davidson County 100k+ 117474 1
## 5 Davidson County 100k+ 120206 1
## 6 Davidson County 100k+ 161370 1
## 7 Robertson County 100k+ 106058 1
## 8 Rutherford County 100k+ 102862 1
## 9 Rutherford County 100k+ 103006 1
## 10 Rutherford County 100k+ 105750 1Task 7: Using the pipe operator, I asked r to sort RichDistricts by 2022 income information, specifically in descending order.
RichDistricts <- RichDistricts %>%
arrange(desc(HHInc2022))Task 8:
One thing I noticed while working through this information, is that wealth in Tennessee, much like the rest of the nation, is very concentrated. Only one county, Williamson, has more districts making over 100K per household than those making less than 100K. Even in places like Davidson county, the majority of districts are on the <100K end of the income spectrum.