CUNY SPS BRIDGE R Assignment 3 (Final Project)
Name: Chinedu Onyeka; Date: August 8th, 2021
Meaningful Question: I want to use the test data from California Schools to find out if students from higher income households do better in Math and Reading Tests
Problem 1: 1. Data Exploration: This should include summary statistics, means, medians, quartiles, or any other relevant information about the data set. Please include some conclusions in the R
#Read the data
url = "https://github.com/chinedu2301/CUNY-SPS-Bridge/raw/main/CASchools.csv"
data <- read.table(file = url, header = TRUE, sep = ",")
head(data)## X district school county grades students teachers
## 1 1 75119 Sunol Glen Unified Alameda KK-08 195 10.90
## 2 2 61499 Manzanita Elementary Butte KK-08 240 11.15
## 3 3 61549 Thermalito Union Elementary Butte KK-08 1550 82.90
## 4 4 61457 Golden Feather Union Elementary Butte KK-08 243 14.00
## 5 5 61523 Palermo Union Elementary Butte KK-08 1335 71.50
## 6 6 62042 Burrel Union Elementary Fresno KK-08 137 6.40
## calworks lunch computer expenditure income english read math
## 1 0.5102 2.0408 67 6384.911 22.690001 0.000000 691.6 690.0
## 2 15.4167 47.9167 101 5099.381 9.824000 4.583333 660.5 661.9
## 3 55.0323 76.3226 169 5501.955 8.978000 30.000002 636.3 650.9
## 4 36.4754 77.0492 85 7101.831 8.978000 0.000000 651.9 643.5
## 5 33.1086 78.4270 171 5235.988 9.080333 13.857677 641.8 639.9
## 6 12.3188 86.9565 25 5580.147 10.415000 12.408759 605.7 605.4summary(data) #summary of the data to view descriptive statistics of the data## X district school county
## Min. : 1.0 Min. :61382 Length:420 Length:420
## 1st Qu.:105.8 1st Qu.:64308 Class :character Class :character
## Median :210.5 Median :67761 Mode :character Mode :character
## Mean :210.5 Mean :67473
## 3rd Qu.:315.2 3rd Qu.:70419
## Max. :420.0 Max. :75440
## grades students teachers calworks
## Length:420 Min. : 81.0 Min. : 4.85 Min. : 0.000
## Class :character 1st Qu.: 379.0 1st Qu.: 19.66 1st Qu.: 4.395
## Mode :character Median : 950.5 Median : 48.56 Median :10.520
## Mean : 2628.8 Mean : 129.07 Mean :13.246
## 3rd Qu.: 3008.0 3rd Qu.: 146.35 3rd Qu.:18.981
## Max. :27176.0 Max. :1429.00 Max. :78.994
## lunch computer expenditure income
## Min. : 0.00 Min. : 0.0 Min. :3926 Min. : 5.335
## 1st Qu.: 23.28 1st Qu.: 46.0 1st Qu.:4906 1st Qu.:10.639
## Median : 41.75 Median : 117.5 Median :5215 Median :13.728
## Mean : 44.71 Mean : 303.4 Mean :5312 Mean :15.317
## 3rd Qu.: 66.86 3rd Qu.: 375.2 3rd Qu.:5601 3rd Qu.:17.629
## Max. :100.00 Max. :3324.0 Max. :7712 Max. :55.328
## english read math
## Min. : 0.000 Min. :604.5 Min. :605.4
## 1st Qu.: 1.941 1st Qu.:640.4 1st Qu.:639.4
## Median : 8.778 Median :655.8 Median :652.5
## Mean :15.768 Mean :655.0 Mean :653.3
## 3rd Qu.:22.970 3rd Qu.:668.7 3rd Qu.:665.9
## Max. :85.540 Max. :704.0 Max. :709.5str(data)## 'data.frame': 420 obs. of 15 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ district : int 75119 61499 61549 61457 61523 62042 68536 63834 62331 67306 ...
## $ school : chr "Sunol Glen Unified" "Manzanita Elementary" "Thermalito Union Elementary" "Golden Feather Union Elementary" ...
## $ county : chr "Alameda" "Butte" "Butte" "Butte" ...
## $ grades : chr "KK-08" "KK-08" "KK-08" "KK-08" ...
## $ students : int 195 240 1550 243 1335 137 195 888 379 2247 ...
## $ teachers : num 10.9 11.1 82.9 14 71.5 ...
## $ calworks : num 0.51 15.42 55.03 36.48 33.11 ...
## $ lunch : num 2.04 47.92 76.32 77.05 78.43 ...
## $ computer : int 67 101 169 85 171 25 28 66 35 0 ...
## $ expenditure: num 6385 5099 5502 7102 5236 ...
## $ income : num 22.69 9.82 8.98 8.98 9.08 ...
## $ english : num 0 4.58 30 0 13.86 ...
## $ read : num 692 660 636 652 642 ...
## $ math : num 690 662 651 644 640 ...The mean math score is 653.3 and the maximum math score is 709.5. The mean income is 15.317 while the maximum income is 55.328.
Problem 2: Data wrangling: Please perform some basic transformations. They will need to make sense but could include column renaming, creating a subset of the data, replacing values, or creating new columns with derived data (for example – if it makes sense you could sum two columns together)
#Install and or load relevant libraries for data wrangling
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.4 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggthemes)
var <- c("school","grades","income", "teachers", "students","read", "math")
data <- data %>% as_tibble() %>% select(all_of(var)) %>% arrange(desc(math, read, english)) %>% mutate(teacher_stu = teachers/students)
#I selected the relevant columns for my analysis
head(data)## # A tibble: 6 x 8
## school grades income teachers students read math teacher_stu
## <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Los Altos Elementary KK-08 41.7 208. 3724 704 710. 0.0560
## 2 Las Lomitas Elementary KK-08 28.7 59.7 984 701. 708. 0.0607
## 3 Cold Spring Elementary KK-06 43.2 12.3 220 693. 704. 0.0560
## 4 Saratoga Union Elemen~ KK-08 40.4 124. 2341 699. 702. 0.0530
## 5 Hillsborough City Ele~ KK-08 35.8 87.1 1318 695. 701. 0.0661
## 6 Portola Valley Elemen~ KK-08 50.7 44.6 687 698. 700. 0.0649The school with the highest Math Score is the Los Altos Elementary
any(is.na(data)) #Check for missing values## [1] FALSEThere are no missing values in the data set
Problem 3: Graphics: Please make sure to display at least one scatter plot, box plot and histogram. Don’t be limited to this. Please explore the many other options in R packages such as ggplot2.
#Plot a graph of Income vs Math Score
pl_Income_Math <- ggplot(data, aes(x = math, y = income)) + geom_point() + stat_smooth(se=FALSE) + labs(x="Math Score", y= "Income", title = "Income vs Math Score") + facet_grid(rows = "grades") + theme_economist()
print(pl_Income_Math)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#Plot a graph of Income vs Reading Score
pl_Income_Read <- ggplot(data, aes(x =read, y = income)) + geom_point() + stat_smooth(se=FALSE) + labs(x="Reading Score", y= "Income", title = "Income vs Reading Score") + facet_grid(rows = "grades") + theme_economist()
print(pl_Income_Read)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#Plot a graph of Math Score vs Teacher-Student Ratio
pl_Math_TSR <- ggplot(data, aes(x =teacher_stu, y = math)) + geom_point() + stat_smooth(se=FALSE)+ labs(x="Teachers-Students Ratio", y= "Math Score", title = "Math Score vs Teacher-Students Ratio")+theme_economist()
print(pl_Math_TSR)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#Correlation between Income and Math Score
cor_inc_Math <- cor.test(data$math, data$income)
print("Correlation between Income and Math Scores")## [1] "Correlation between Income and Math Scores"print(cor_inc_Math)##
## Pearson's product-moment correlation
##
## data: data$math and data$income
## t = 20.006, df = 418, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6470117 0.7452127
## sample estimates:
## cor
## 0.6993981cor_inc_Read <- cor_inc_Read <- cor.test(data$read, data$income)
print("Correlation between Income and Reading Scores")## [1] "Correlation between Income and Reading Scores"print(cor_inc_Read)##
## Pearson's product-moment correlation
##
## data: data$read and data$income
## t = 19.918, df = 418, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6452147 0.7438378
## sample estimates:
## cor
## 0.6978189