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Submission of my class-activity-2 assignment. #2

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178 changes: 124 additions & 54 deletions class-activity-2.Rmd
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Original file line number Diff line number Diff line change
@@ -1,23 +1,23 @@
---
title: "intro to viz"
author: "Charles Lang"
title: "class-activity-2"
author: "Minruo Wang (mw3399)"
date: "September 26, 2019"
output: html_document
---
#Input
```{r}
## Guide from Charles
### Input
```{r data input}
D1 <- read.csv("School_Demographics_and_Accountability_Snapshot_2006-2012.csv", header = TRUE, sep = ",")

#Create a data frame only contains the years 2011-2012
library(dplyr)
D2 <- filter(D1, schoolyear == 20112012)
```

#Histograms
```{r}
### Histograms
```{r histograms}
#Generate a histogramof the percentage of free/reduced lunch students (frl_percent) at each school

hist()
#hist()

#Change the number of breaks to 100, do you get the same impression?

Expand All @@ -31,12 +31,10 @@ hist(D2$frl_percent, breaks = 100, ylim = c(0,30))

hist(D2$frl_percent, breaks = c(0,10,20,80,100))



```

#Plots
```{r}
### Plots
```{r plots}
#Plot the number of English language learners (ell_num) by Computational Thinking Test scores (ctt_num)

plot(D2$ell_num, D2$ctt_num)
Expand All @@ -47,6 +45,7 @@ y <- c(2,4,2,3,2,4,3)

#Create a table from x & y
table1 <- table(x,y)
table1

#Display the table as a Barplot
barplot(table1)
Expand All @@ -64,89 +63,160 @@ D4 <- filter(D1, DBN == "31R075"|DBN == "01M015"| DBN == "01M345")
D4 <- droplevels(D4)
boxplot(D4$total_enrollment ~ D4$DBN)
```
#Pairs

### Pairs
```{r}
#Use matrix notation to select columns 5,6, 21, 22, 23, 24
D5 <- D2[,c(5,6, 21:24)]
#Draw a matrix of plots for every combination of variables
pairs(D5)
```
# Exercise

## Exercise
##### 1. Create a simulated data set containing 100 students, each with a score from 1-100 representing performance in an educational game. The scores should tend to cluster around 75. Also, each student should be given a classification that reflects one of four interest groups: sport, music, nature, literature.

1. Create a simulated data set containing 100 students, each with a score from 1-100 representing performance in an educational game. The scores should tend to cluster around 75. Also, each student should be given a classification that reflects one of four interest groups: sport, music, nature, literature.

```{r}
```{r data simulation}
set.seed(123)
#rnorm(100, 75, 15) creates a random sample with a mean of 75 and standard deviation of 20
StudentScores <- rnorm(100, 75, 20)
StudentScores <- runif(100, min = 1, max = 100)
#pmax sets a maximum value, pmin sets a minimum value
StudentScores <- pmax(1, StudentScores)
StudentScores <- pmin(100, StudentScores)
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There is an another way of setting maximum and minimum values with a single line of code. round(pmax(1, pmin(100, rnorm(100, 75, 20))))


#round rounds numbers to whole number values
#sample draws a random samples from the groups vector according to a uniform distribution
StudentScores <- round(StudentScores, digits = 0)

InterestGroups <- c("sport", "music", "nature", "literature")
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InterestGroups should be randomized as well. You can use sample()

stu_df <- data.frame(StudentScores, InterestGroups)
stu_df

#sample draws a random samples from the groups vector according to a uniform distribution
```

2. Using base R commands, draw a histogram of the scores. Change the breaks in your histogram until you think they best represent your data.

```{r}


##### 2. Using base R commands, draw a histogram of the scores. Change the breaks in your histogram until you think they best represent your data.

```{r score histogram}
score_hist <- hist(stu_df$StudentScores,
breaks = 10,
ylim = c(0,20),
main = "Frequency Distribution of Performance in an Educational Game",
xlab = "Scores",
labels = TRUE)
```


##### 3. Create a new variable that groups the scores according to the breaks in your histogram.

3. Create a new variable that groups the scores according to the breaks in your histogram.

```{r}
```{r score groups}
#cut() divides the range of scores into intervals and codes the values in scores according to which interval they fall. We use a vector called `letters` as the labels, `letters` is a vector made up of the letters of the alphabet.

letters <- c("F", "D", "C", "B", "A")
Grade <- cut(stu_df$StudentScores, breaks = c(0,60,70,80,90,100), labels = letters)
stu_df_ext <- cbind(stu_df, Grade)
stu_df_ext
```

##### 4. Now using the colorbrewer package [RColorBrewer] (http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) design a pallette and assign it to the groups in your data on the histogram.

4. Now using the colorbrewer package (RColorBrewer; http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) design a pallette and assign it to the groups in your data on the histogram.

```{r}
```{r colorbrewer}
library(RColorBrewer)
#Let's look at the available palettes in RColorBrewer
display.brewer.all()

#The top section of palettes are sequential, the middle section are qualitative, and the lower section are diverging.
#Make RColorBrewer palette available to R and assign to your bins

#Use named palette in histogram

score_hist_color <- hist(stu_df_ext$StudentScores,
breaks = 5,
xlim = c(0,100),
ylim = c(0,30),
main = "Frequency Distribution of Performance in an Educational Game",
xlab = "Scores",
col = brewer.pal(5, "Greens"),
labels = TRUE)
```


##### 5. Create a boxplot that visualizes the scores for each interest group and color each interest group a different color.

5. Create a boxplot that visualizes the scores for each interest group and color each interest group a different color.

```{r}
```{r boxplot}
#Make a vector of the colors from RColorBrewer

library(ggplot2)
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Great job on using ggplot! You can also use boxplot()

boxplot_score <- ggplot(stu_df_ext, aes(x=stu_df_ext$InterestGroups, y=stu_df_ext$StudentScores)) +
geom_boxplot(fill=brewer.pal(4, "Blues")) +
xlab("Interest Groups") +
ylab("Student Scores") +
ggtitle("Boxplot of Scores for Each Interest Group")
boxplot_score
```


##### 6. Now simulate a new variable that describes the number of logins that students made to the educational game. They should vary from 1-25.

6. Now simulate a new variable that describes the number of logins that students made to the educational game. They should vary from 1-25.

```{r}

```{r logins}
set.seed(123)
# generate 100 random numbers betweem 1-25
Logins <- sample(1:25, 100, replace = TRUE)
# assign to students
stu_df_ext_2 <- cbind(stu_df_ext, Logins)
stu_df_ext_2
```

##### 7. Plot the relationships between logins and scores. Give the plot a title and color the dots according to interest group.

```{r scatter plot}
plot_log_score <- ggplot(stu_df_ext_2, aes(x = Logins, y = StudentScores, color = InterestGroups)) +
geom_point() +
ggtitle("Scatter Plot of Student Logins and Scores")
plot_log_score
```

7. Plot the relationships between logins and scores. Give the plot a title and color the dots according to interest group.

```{r}


##### 8. R contains several inbuilt data sets, one of these in called AirPassengers. Plot a line graph of the the airline passengers over time using this data set.

```{r AirPassengers}
# look at the dataset AirPassengers
AirPassengers
# line graph
air_plot <- plot(AirPassengers, type = "l", lty = "dashed")
```


##### 9. Using another inbuilt data set, iris, plot the relationships between all of the variables in the data set. Which of these relationships is it appropraiet to run a correlation on?
There are statistically significant correlation between those pairs of variables: Sepal.Length and Petal.Length, Sepal.Length and Petal.Width, Sepal.Width and Petal.Length, Sepal.Width and Petal.Width.

```{r pair plots}
library("ggpubr")

8. R contains several inbuilt data sets, one of these in called AirPassengers. Plot a line graph of the the airline passengers over time using this data set.

```{r}
# a brief look at the dataset iris
head(iris)
# show the relationships between each pair of variables in the data frame
pairs(iris)

# The plots suggest correlation between each pair of variables except Species. We will use correlatiion tests to examine the significance of correlation.
```

```{r correlation test}
# Correlation tests using Pearson
cor.test(iris$Sepal.Length, iris$Sepal.Width, method = "pearson", use = "complete.obs")
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Good job on using the cor.test()!

# Since p-value = 0.1519 > 0.05, there is no statistically significant correlation.

cor.test(iris$Sepal.Length, iris$Petal.Length, method = "pearson", use = "complete.obs")
# Since p-value < 2.2e-16 < 0.05, there is statistically significant correlation.

9. Using another inbuilt data set, iris, plot the relationships between all of the variables in the data set. Which of these relationships is it appropraiet to run a correlation on?
cor.test(iris$Sepal.Length, iris$Petal.Width, method = "pearson", use = "complete.obs")
# Since p-value < 2.2e-16 < 0.05, there is statistically significant correlation.

```{r}
cor.test(iris$Sepal.Width, iris$Petal.Length, method = "pearson", use = "complete.obs")
# Since p-value = 4.513e-08 < 0.05, there is statistically significant correlation.

```
cor.test(iris$Sepal.Width, iris$Petal.Width, method = "pearson", use = "complete.obs")
# Since p-value = 4.073e-06 < 0.05, there is statistically significant correlation.

10. Finally use the knitr function to generate an html document from your work. If you have time, try to change some of the output using different commands from the RMarkdown cheat sheet.
```

11. Commit, Push and Pull Request your work back to the main branch of the repository

##### 10. Finally use the knitr function to generate an html document from your work. If you have time, try to change some of the output using different commands from the RMarkdown cheat sheet.

##### 11. Commit, Push and Pull Request your work back to the main branch of the repository
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Excellent work overall! Try rendering your RMarkdown file into an HTML document.