library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── 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
library(readr)
Animalcare <- read_csv("Desktop/Applied Quantitative Methods/Animal_Care_and_Control_Division_Annual_Statistics-1.csv")
## Rows: 22 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (17): Year, Number of Employees, Number of Division Vehicles, Annual Bud...
##
## 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.
library(ggplot2)
names(Animalcare)
## [1] "Year"
## [2] "Number of Employees"
## [3] "Number of Division Vehicles"
## [4] "Annual Budget"
## [5] "Owner Surrenders"
## [6] "Strays"
## [7] "Impounds by ACO (Added in 2015)"
## [8] "Total Intake of Animals"
## [9] "Adoptions"
## [10] "Return to Owner"
## [11] "Euthanized"
## [12] "Transported to other shelters and rescues"
## [13] "Fostered Animals"
## [14] "Service Calls"
## [15] "Emergency Call-Outs"
## [16] "Grants Received"
## [17] "Annual Adoption Revenue"
ggplot(Animalcare, aes(x = `Annual Budget`, y = Adoptions)) +
geom_point()
library(janitor)
##
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
Animalcare <- clean_names(Animalcare)
names(Animalcare)
## [1] "year"
## [2] "number_of_employees"
## [3] "number_of_division_vehicles"
## [4] "annual_budget"
## [5] "owner_surrenders"
## [6] "strays"
## [7] "impounds_by_aco_added_in_2015"
## [8] "total_intake_of_animals"
## [9] "adoptions"
## [10] "return_to_owner"
## [11] "euthanized"
## [12] "transported_to_other_shelters_and_rescues"
## [13] "fostered_animals"
## [14] "service_calls"
## [15] "emergency_call_outs"
## [16] "grants_received"
## [17] "annual_adoption_revenue"
model <- lm(annual_budget ~ number_of_employees + total_intake_of_animals +
adoptions + annual_adoption_revenue,
data = Animalcare)
summary(model)
##
## Call:
## lm(formula = annual_budget ~ number_of_employees + total_intake_of_animals +
## adoptions + annual_adoption_revenue, data = Animalcare)
##
## Residuals:
## Min 1Q Median 3Q Max
## -146586 -71386 7507 35198 233012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.397e+06 7.254e+05 -3.304 0.00419 **
## number_of_employees 2.070e+05 2.589e+04 7.998 3.66e-07 ***
## total_intake_of_animals -1.259e+02 5.277e+01 -2.385 0.02897 *
## adoptions 4.290e+02 1.170e+02 3.668 0.00191 **
## annual_adoption_revenue -3.207e+00 1.238e+00 -2.592 0.01900 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 103700 on 17 degrees of freedom
## Multiple R-squared: 0.9488, Adjusted R-squared: 0.9368
## F-statistic: 78.78 on 4 and 17 DF, p-value: 9.662e-11
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(ggplot2)
library(gridExtra)
p1 <- ggplot(Animalcare, aes(x = number_of_employees, y = annual_budget)) +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
labs(title = "Employees vs Annual Budget")
p2 <- ggplot(Animalcare, aes(x = total_intake_of_animals, y = annual_budget)) +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
labs(title = "Total Intake vs Annual Budget")
p3 <- ggplot(Animalcare, aes(x = adoptions, y = annual_budget)) +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
labs(title = "Adoptions vs Annual Budget")
p4 <- ggplot(Animalcare, aes(x = annual_adoption_revenue, y = annual_budget)) +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
labs(title = "Annual Revenue vs Annual Budget")
grid.arrange(p1, p2, p3, p4, ncol = 2)
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
