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# Set working directory (optional; update the path as needed)
setwd("C:/Users/casti/Downloads/MASTER BRANCH/SamuelCastillo.com/github_pages/american_statistical_association_datafest_2025")
# Load the tidyverse (includes readr, dplyr, etc.)
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.4.3Warning: package 'ggplot2' was built under R version 4.4.3Warning: package 'tibble' was built under R version 4.4.3Warning: package 'tidyr' was built under R version 4.4.3Warning: package 'readr' was built under R version 4.4.3Warning: package 'purrr' was built under R version 4.4.3Warning: package 'dplyr' was built under R version 4.4.3Warning: package 'stringr' was built under R version 4.4.3Warning: package 'forcats' was built under R version 4.4.3Warning: package 'lubridate' was built under R version 4.4.3── Attaching core tidyverse packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.6.0
✔ ggplot2 4.0.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── 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 errors# 1. Load the data (similar to pd.read_csv)
data <- read_csv("Major Market Occupancy Data-revised.csv")Rows: 190 Columns: 6
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): quarter, market
dbl (4): year, ending_occupancy_proportion, starting_occupancy_proportion, a...
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.leases <- read_csv("Leases.csv")Rows: 194685 Columns: 35
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (17): quarter, monthsigned, market, building_name, building_id, address,...
dbl (18): year, zip, leasedSF, costarID, RBA, available_space, availability_...
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.price <- read_csv("Price and Availability Data.csv")Rows: 1680 Columns: 18
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): quarter, market, internal_class
dbl (15): year, RBA, available_space, availability_proportion, internal_clas...
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.# 2. Examine the price dataset:
colnames(price) # Similar to price.columns [1] "year" "quarter"
[3] "market" "internal_class"
[5] "RBA" "available_space"
[7] "availability_proportion" "internal_class_rent"
[9] "overall_rent" "direct_available_space"
[11] "direct_availability_proportion" "direct_internal_class_rent"
[13] "direct_overall_rent" "sublet_available_space"
[15] "sublet_availability_proportion" "sublet_internal_class_rent"
[17] "sublet_overall_rent" "leasing" year
quarter
market
internal_class
RBA
available_space
availability_proportion
internal_class_rent
overall_rent
direct_available_space
direct_availability_proportion
direct_internal_class_rent
direct_overall_rent
sublet_available_space
sublet_availability_proportion
sublet_internal_class_rent
sublet_overall_rent
leasingsummary(price$overall_rent) # Similar to price['overall_rent'].describe() Min. 1st Qu. Median Mean 3rd Qu. Max.
18.75 28.28 32.29 36.74 41.07 84.75 # 3. View the head of the main data
head(data) # Similar to data.head()# 4. Basic info about the data (similar to data.info())
glimpse(data) # Shows structure, types, etc.Rows: 190
Columns: 6
$ year <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020…
$ quarter <chr> "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "Q1"…
$ market <chr> "Washington D.C.", "Manhattan", "Chicago…
$ ending_occupancy_proportion <dbl> 0.19, 0.08, 0.14, 0.33, 0.20, 0.09, 0.29…
$ starting_occupancy_proportion <dbl> 0.98, 0.98, 0.99, 0.99, 0.99, 0.99, 0.99…
$ avg_occupancy_proportion <dbl> 0.78571429, 0.73285714, 0.78857143, 0.83…# 5. Inspect columns and unique values
colnames(data) # Similar to data.columns[1] "year" "quarter"
[3] "market" "ending_occupancy_proportion"
[5] "starting_occupancy_proportion" "avg_occupancy_proportion" year
quarter
market
ending_occupancy_proportion
starting_occupancy_proportion
avg_occupancy_proportionunique(data$market) # Similar to data['market'].unique() [1] "Washington D.C." "Manhattan" "Chicago"
[4] "Houston" "Philadelphia" "San Francisco"
[7] "Los Angeles" "Dallas/Ft Worth" "South Bay/San Jose"
[10] "Austin" Washington D.C.
Manhattan
Chicago
Houston
Philadelphia
San Francisco
Los Angeles
Dallas/Ft Worth
South Bay/San Jose
Austin# 6. Count how many times each market appears (similar to .value_counts())
data %>%
count(market, sort = TRUE)# 7. Calculate mean of avg_occupancy_proportion by market (similar to groupby, mean, sort)
data %>%
group_by(market) %>%
summarise(mean_avg_occupancy = mean(avg_occupancy_proportion, na.rm = TRUE)) %>%
arrange(desc(mean_avg_occupancy))# 8. Summary statistics (similar to data.describe())
summary(data) year quarter market
Min. :2020 Length:190 Length:190
1st Qu.:2021 Class :character Class :character
Median :2022 Mode :character Mode :character
Mean :2022
3rd Qu.:2023
Max. :2024
ending_occupancy_proportion starting_occupancy_proportion
Min. :0.0800 Min. :0.0500
1st Qu.:0.2200 1st Qu.:0.2400
Median :0.3550 Median :0.3550
Mean :0.3512 Mean :0.3779
3rd Qu.:0.4700 3rd Qu.:0.4500
Max. :0.6600 Max. :0.9900
avg_occupancy_proportion
Min. :0.05231
1st Qu.:0.28423
Median :0.41038
Mean :0.40140
3rd Qu.:0.48846
Max. :0.83857 # 9. Summary of avg_occupancy_proportion only (similar to data['avg_occupancy_proportion'].describe())
summary(data$avg_occupancy_proportion) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.05231 0.28423 0.41038 0.40140 0.48846 0.83857 # 10. Check missing values across all columns (similar to isnull().sum())
missing_values <- data %>%
summarise(across(everything(), ~ sum(is.na(.))))
missing_values# 11. Unique values of quarter (similar to data['quarter'].unique())
unique_quarters <- unique(data$quarter)
unique_quarters[1] "Q1" "Q2" "Q3" "Q4"Q1
Q2
Q3
Q4# 12. Mean of starting_occupancy_proportion (similar to data['starting_occupancy_proportion'].mean())
mean_starting_occupancy <- mean(data$starting_occupancy_proportion, na.rm = TRUE)
mean_starting_occupancy[1] 0.3778947# Merge datasets based on common columns (e.g., year, quarter, market)
# Ensure the column names match or rename them accordingly before merging
# Convert Python dataframes to R dataframes if needed
leases <- as.data.frame(leases)
price <- as.data.frame(price)
# Perform the merge
merged_data <- data %>%
left_join(leases, by = c("year", "quarter", "market")) %>%
left_join(price, by = c("year", "quarter", "market"))Warning in left_join(., price, by = c("year", "quarter", "market")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 173 of `x` matches multiple rows in `y`.
ℹ Row 507 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.glimpse(leases)Rows: 194,685
Columns: 35
$ year <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 201…
$ quarter <chr> "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "Q1…
$ monthsigned <chr> "01", "01", "01", "01", "01", "01", "01…
$ market <chr> "Atlanta", "Atlanta", "Atlanta", "Atlan…
$ building_name <chr> "10 Glenlake North Tower", "100 City Vi…
$ building_id <chr> "Atlanta_Central Perimeter_Atlanta_10 G…
$ address <chr> "10 Glenlake Pky NE", "3330 Cumberland …
$ region <chr> "South", "South", "South", "South", "So…
$ city <chr> "Atlanta", "Atlanta", "Atlanta", "Atlan…
$ state <chr> "GA", "GA", "GA", "GA", "GA", "GA", "GA…
$ zip <dbl> 30328, 30339, 30339, 30339, 30338, 3033…
$ internal_submarket <chr> "Central Perimeter", "Northwest", "Nort…
$ internal_class <chr> "A", "A", "A", "O", "A", "A", "A", "A",…
$ leasedSF <dbl> 24736, 965, 2215, 1925, 2404, 5091, 132…
$ company_name <chr> "Capital Investment Advisors", NA, "Efc…
$ internal_industry <chr> "Financial Services and Insurance", NA,…
$ transaction_type <chr> "Expansion", "New", "New", "New", "New"…
$ internal_market_cluster <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ costarID <dbl> 445509, 436994, 434890, 434720, 437562,…
$ space_type <chr> "Relet", "Relet", "Relet", "Relet", "Re…
$ CBD_suburban <chr> "Suburban", "Suburban", "Suburban", "Su…
$ RBA <dbl> 101140416, 101140416, 101140416, 658104…
$ available_space <dbl> 20239067, 20239067, 20239067, 12728989,…
$ availability_proportion <dbl> 0.2001086, 0.2001086, 0.2001086, 0.1934…
$ internal_class_rent <dbl> 27.65589, 27.65589, 27.65589, 18.56089,…
$ overall_rent <dbl> 24.34569, 24.34569, 24.34569, 24.34569,…
$ direct_available_space <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_availability_proportion <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_internal_class_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_overall_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_available_space <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_availability_proportion <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_internal_class_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_overall_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ leasing <dbl> 1205126, 1205126, 1205126, 715742, 1205…glimpse(price)Rows: 1,680
Columns: 18
$ year <dbl> 2018, 2018, 2018, 2018, 2018, 2018, 201…
$ quarter <chr> "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "Q1…
$ market <chr> "Atlanta", "Atlanta", "Austin", "Austin…
$ internal_class <chr> "A", "O", "A", "O", "A", "O", "A", "O",…
$ RBA <dbl> 101140416, 65810449, 36815073, 27947525…
$ available_space <dbl> 20239067, 12728989, 4281986, 3360936, 6…
$ availability_proportion <dbl> 0.2001086, 0.1934190, 0.1163107, 0.1210…
$ internal_class_rent <dbl> 27.65589, 18.56089, 40.38471, 30.11866,…
$ overall_rent <dbl> 24.34569, 24.34569, 36.59662, 36.59662,…
$ direct_available_space <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_availability_proportion <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_internal_class_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ direct_overall_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_available_space <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_availability_proportion <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_internal_class_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ sublet_overall_rent <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ leasing <dbl> 1205126, 715742, 1738905, 185674, 38075…glimpse(merged_data)Rows: 92,358
Columns: 53
$ year <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2…
$ quarter <chr> "Q1", "Q1", "Q1", "Q1", "Q1", "Q1", "…
$ market <chr> "Washington D.C.", "Washington D.C.",…
$ ending_occupancy_proportion <dbl> 0.19, 0.19, 0.19, 0.19, 0.19, 0.19, 0…
$ starting_occupancy_proportion <dbl> 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0…
$ avg_occupancy_proportion <dbl> 0.7857143, 0.7857143, 0.7857143, 0.78…
$ monthsigned <chr> "01", "01", "01", "01", "01", "01", "…
$ building_name <chr> "1105 15th St NW", "1225 New York Ave…
$ building_id <chr> "Washington D.C._East End_Washington_…
$ address <chr> "1101 15th St NW", "1201 New York Ave…
$ region <chr> "Northeast", "Northeast", "Northeast"…
$ city <chr> "Washington", "Washington", "Washingt…
$ state <chr> "DC", "DC", "DC", "DC", "DC", "DC", "…
$ zip <dbl> 20005, 20005, 20005, 20005, 20006, 20…
$ internal_submarket <chr> "East End", "East End", "East End", "…
$ internal_class.x <chr> "O", "A", "A", "A", "A", "A", "O", "A…
$ leasedSF <dbl> 1163, 27900, 24196, 15934, 29520, 204…
$ company_name <chr> NA, "Accenture", "Staas & Halsey", "B…
$ internal_industry <chr> NA, "Business, Professional, and Cons…
$ transaction_type <chr> "New", "Expansion", "Renewal", "New",…
$ internal_market_cluster <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ costarID <dbl> 129280, 129233, 129233, 129744, 13015…
$ space_type <chr> "Relet", "Relet", "Relet", "Relet", "…
$ CBD_suburban <chr> "CBD", "CBD", "CBD", "CBD", "CBD", "C…
$ RBA.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ available_space.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ availability_proportion.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ internal_class_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ overall_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_available_space.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_availability_proportion.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_internal_class_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_overall_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_available_space.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_availability_proportion.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_internal_class_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_overall_rent.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ leasing.x <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ internal_class.y <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ RBA.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ available_space.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ availability_proportion.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ internal_class_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ overall_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_available_space.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_availability_proportion.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_internal_class_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ direct_overall_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_available_space.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_availability_proportion.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_internal_class_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ sublet_overall_rent.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ leasing.y <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…library(ggplot2)
merged_data %>%
ggplot(aes(direct_available_space.x)) +
geom_histogram()`stat_bin()` using `bins = 30`. Pick better value `binwidth`.Warning: Removed 16798 rows containing non-finite outside the scale range
(`stat_bin()`).
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