1 + 1[1] 2ACS
ACS Housing Data
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[1] 4# keep adding your libraries to this cell block as you need them
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 4.0.1 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ 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 errorsACS_2022_Housing <- read_csv("ACS-2022-Housing.csv")Rows: 21008 Columns: 23
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): RT, SERIALNO
dbl (21): NP, BDSP, BLD, FS, HFL, MRGP, RNTP, VALP, VEH, YRBLT, CPLT, FINCP,...
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.#View(ACS_2022_Housing)head(ACS_2022_Housing)# A tibble: 6 × 23
RT SERIALNO NP BDSP BLD FS HFL MRGP RNTP VALP VEH YRBLT
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H 2022GQ00001… 1 NA NA 2 NA NA NA NA NA NA
2 H 2022GQ00002… 1 NA NA 2 NA NA NA NA NA NA
3 H 2022GQ00003… 1 NA NA 2 NA NA NA NA NA NA
4 H 2022GQ00004… 1 NA NA 1 NA NA NA NA NA NA
5 H 2022GQ00005… 1 NA NA 2 NA NA NA NA NA NA
6 H 2022GQ00005… 1 NA NA 2 NA NA NA NA NA NA
# ℹ 11 more variables: CPLT <dbl>, FINCP <dbl>, FPARC <dbl>, HHL <dbl>,
# HHLDRAGEP <dbl>, HHLDRRAC1P <dbl>, HHT2 <dbl>, KIT <dbl>, NPF <dbl>,
# PLM <dbl>, TAXAMT <dbl># this will find the number of rows in the data frame
nrow(ACS_2022_Housing)[1] 21008# this will find the count and percent of each race category
ACS_2022_Housing %>%
count(HHLDRRAC1P) %>%
mutate(percent = 100 * n / sum(n))# A tibble: 10 × 3
HHLDRRAC1P n percent
<dbl> <int> <dbl>
1 1 14567 69.3
2 2 204 0.971
3 3 205 0.976
4 4 5 0.0238
5 5 29 0.138
6 6 677 3.22
7 7 42 0.200
8 8 485 2.31
9 9 1432 6.82
10 NA 3362 16.0 5/21008[1] 0.0002380046summary(ACS_2022_Housing$FINCP) # Family income Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-6600 54200 93000 122126 149700 1479600 9987 summary(ACS_2022_Housing$VALP) # Property value Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1000 300000 450000 526143 650000 3797000 8668 summary(ACS_2022_Housing$RNTP) # Monthly rent Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
4 870 1200 1315 1600 4800 15749 summary(ACS_2022_Housing$TAXAMT) # Property tax Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0 2050 3450 4097 5250 23500 8732 # Sample standard deviation for property tax
sd(ACS_2022_Housing$TAXAMT, na.rm = TRUE)[1] 3278.774# Interquartile range for monthly rent
IQR(ACS_2022_Housing$RNTP, na.rm = TRUE)[1] 730ACS_2022_Housing %>% filter(FINCP > 80000)# A tibble: 6,337 × 23
RT SERIALNO NP BDSP BLD FS HFL MRGP RNTP VALP VEH YRBLT
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H 2022HU000… 5 4 2 1 1 4 NA 550000 6 1960
2 H 2022HU000… 3 4 2 2 6 2500 NA 750000 5 1950
3 H 2022HU000… 2 3 2 2 1 2500 NA 735000 2 1980
4 H 2022HU000… 2 2 2 2 8 600 NA 240000 2 1940
5 H 2022HU000… 4 3 2 2 1 NA NA 530000 3 1990
6 H 2022HU000… 3 2 4 2 3 4 3000 NA 1 1960
7 H 2022HU000… 2 2 2 2 3 1400 NA 320000 1 1970
8 H 2022HU000… 3 2 3 2 1 4 600 NA 2 1990
9 H 2022HU000… 4 4 2 2 3 2700 NA 700000 2 1940
10 H 2022HU000… 4 3 2 2 1 NA NA 400000 2 1990
# ℹ 6,327 more rows
# ℹ 11 more variables: CPLT <dbl>, FINCP <dbl>, FPARC <dbl>, HHL <dbl>,
# HHLDRAGEP <dbl>, HHLDRRAC1P <dbl>, HHT2 <dbl>, KIT <dbl>, NPF <dbl>,
# PLM <dbl>, TAXAMT <dbl>ACS_2022_Housing %>% filter(CPLT == 2)# A tibble: 144 × 23
RT SERIALNO NP BDSP BLD FS HFL MRGP RNTP VALP VEH YRBLT
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H 2022HU00… 2 3 2 2 1 2500 NA 735000 2 1980
2 H 2022HU00… 2 2 1 2 3 4 NA 300000 2 2000
3 H 2022HU00… 2 3 2 2 1 1900 NA 360000 2 2000
4 H 2022HU00… 3 5 2 2 1 4100 NA 1000000 2 1939
5 H 2022HU00… 3 4 2 2 3 4 880 NA 1 1970
6 H 2022HU00… 2 7 2 2 1 5600 NA 950000 2 2000
7 H 2022HU00… 2 4 2 2 1 2000 NA 520000 2 2000
8 H 2022HU00… 2 3 2 2 1 4 NA 750000 2 2010
9 H 2022HU01… 2 3 2 1 3 790 NA 98000 1 1970
10 H 2022HU01… 5 4 2 1 3 980 NA 750000 1 1939
# ℹ 134 more rows
# ℹ 11 more variables: CPLT <dbl>, FINCP <dbl>, FPARC <dbl>, HHL <dbl>,
# HHLDRAGEP <dbl>, HHLDRRAC1P <dbl>, HHT2 <dbl>, KIT <dbl>, NPF <dbl>,
# PLM <dbl>, TAXAMT <dbl>ACS_2022_Housing %>% filter(FINCP > 80000 & CPLT == 2) #both# A tibble: 106 × 23
RT SERIALNO NP BDSP BLD FS HFL MRGP RNTP VALP VEH YRBLT
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H 2022HU000… 2 3 2 2 1 2500 NA 7.35e5 2 1980
2 H 2022HU002… 2 3 2 2 1 1900 NA 3.60e5 2 2000
3 H 2022HU003… 3 5 2 2 1 4100 NA 1 e6 2 1939
4 H 2022HU005… 2 7 2 2 1 5600 NA 9.5 e5 2 2000
5 H 2022HU006… 2 4 2 2 1 2000 NA 5.20e5 2 2000
6 H 2022HU008… 2 3 2 2 1 4 NA 7.5 e5 2 2010
7 H 2022HU010… 2 3 4 2 3 1600 NA 3.75e5 2 1980
8 H 2022HU011… 2 3 2 2 1 1800 NA 3.7 e5 2 1940
9 H 2022HU017… 2 3 2 2 3 1600 NA 3.25e5 2 2010
10 H 2022HU017… 2 3 2 2 3 1600 NA 5 e5 2 1939
# ℹ 96 more rows
# ℹ 11 more variables: CPLT <dbl>, FINCP <dbl>, FPARC <dbl>, HHL <dbl>,
# HHLDRAGEP <dbl>, HHLDRRAC1P <dbl>, HHT2 <dbl>, KIT <dbl>, NPF <dbl>,
# PLM <dbl>, TAXAMT <dbl>ACS_2022_Housing %>% filter(FINCP > 80000 | CPLT == 2) #either# A tibble: 6,375 × 23
RT SERIALNO NP BDSP BLD FS HFL MRGP RNTP VALP VEH YRBLT
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H 2022HU000… 5 4 2 1 1 4 NA 550000 6 1960
2 H 2022HU000… 3 4 2 2 6 2500 NA 750000 5 1950
3 H 2022HU000… 2 3 2 2 1 2500 NA 735000 2 1980
4 H 2022HU000… 2 2 2 2 8 600 NA 240000 2 1940
5 H 2022HU000… 4 3 2 2 1 NA NA 530000 3 1990
6 H 2022HU000… 3 2 4 2 3 4 3000 NA 1 1960
7 H 2022HU000… 2 2 2 2 3 1400 NA 320000 1 1970
8 H 2022HU000… 3 2 3 2 1 4 600 NA 2 1990
9 H 2022HU000… 4 4 2 2 3 2700 NA 700000 2 1940
10 H 2022HU000… 4 3 2 2 1 NA NA 400000 2 1990
# ℹ 6,365 more rows
# ℹ 11 more variables: CPLT <dbl>, FINCP <dbl>, FPARC <dbl>, HHL <dbl>,
# HHLDRAGEP <dbl>, HHLDRRAC1P <dbl>, HHT2 <dbl>, KIT <dbl>, NPF <dbl>,
# PLM <dbl>, TAXAMT <dbl>ACS_2022_Housing %>% group_by(CPLT) %>% summarize(n = n())# A tibble: 5 × 2
CPLT n
<dbl> <int>
1 1 8654
2 2 144
3 3 1364
4 4 94
5 NA 10752ACS_2022_Housing %>% group_by(CPLT) %>% summarize(avg_inc = mean(FINCP), na.rm = TRUE)# A tibble: 5 × 3
CPLT avg_inc na.rm
<dbl> <dbl> <lgl>
1 1 134963. TRUE
2 2 147825. TRUE
3 3 NA TRUE
4 4 NA TRUE
5 NA NA TRUE Lesson 3.5
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