library(readr)
reading <- read_csv("~/Zim Projects PHD/Fungai Mutema/reading.csv")
## Parsed with column specification:
## cols(
##   Grade = col_character(),
##   Reader = col_character(),
##   Gender = col_character(),
##   Total_words_read = col_double(),
##   Errors = col_double(),
##   Words_correct_per_minute = col_double()
## )
View(reading)
attach(reading)
names(reading)
## [1] "Grade"                    "Reader"                  
## [3] "Gender"                   "Total_words_read"        
## [5] "Errors"                   "Words_correct_per_minute"
Grade=as.factor(Grade)
Gender=as.factor(Gender)
Reader=as.factor(Reader)
library(ggplot2)
library(maps)
library(ggalt)
library(extrafontdb)
library(MASS)
library(pscl)
## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileis
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
library(gridExtra)
theme_set(theme_gray(base_size =12)) ###This sets the font sizes of anything writt
ggplot(reading, aes(x = Grade, y = Total_words_read))+geom_boxplot(size=1,outlier.colour = "red",outlier.shape = 1, shape=6) + geom_smooth(method=lm)+ ylab("Total words read") +xlab("Grade")

theme_set(theme_gray(base_size =12)) ###This sets the font sizes of anything writt
ggplot(reading, aes(x = Grade, y = Total_words_read))+geom_boxplot(size=1,outlier.colour = "red",outlier.shape = 1, shape=6) + geom_smooth(method=lm)+ ylab("Total words read") +xlab("Grade")+facet_wrap(.~Gender)

theme_set(theme_gray(base_size =12))
m <- ggplot(data=reading,aes(x=Grade, y=Words_correct_per_minute))
m + geom_violin(size=1.5,shape=8) + geom_boxplot(width=.1, outlier.size=0,fill=c("red","yellow","green","blue"))+ylab("Correct Words Per Minute") + xlab("Grade")
## Warning: Ignoring unknown parameters: shape

theme_set(theme_gray(base_size =12))
m <- ggplot(data=reading,aes(x=Grade, y=Words_correct_per_minute))
m + geom_violin(size=1.5,shape=8) + geom_boxplot(width=.1, outlier.size=0,fill=c("red","yellow","green","blue","red","yellow","green","blue"))+ylab("Correct Words Per Minute") + xlab("Grade")+facet_wrap(.~Gender)
## Warning: Ignoring unknown parameters: shape

theme_set(theme_gray(base_size =12))
ggplot(data=reading, aes(x = Grade, y = Errors)) + 
    geom_boxplot(size=1.2,varwidth = TRUE)+ylab("Errors Committed")+ xlab("Grade")
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

theme_set(theme_gray(base_size =12))
ggplot(data=reading, aes(x = Grade, y = Errors, colour = Gender)) + 
    geom_boxplot(size=1.2,varwidth = TRUE)+ylab("Errors Committed")+ xlab("Grade")+theme(legend.position = c(0.70, 0.85))
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

One to one plots

ggplot(reading, aes(x = Words_correct_per_minute, y = Total_words_read, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Total words read") + 
  xlab("Correct words per minute")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.2, 0.85))
## Warning: Ignoring unknown parameters: xintercept

ggplot(reading, aes(x = Words_correct_per_minute, y = Total_words_read, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Total words read") + 
  xlab("Correct words per minute")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.1, 0.95))+facet_wrap(.~Grade)
## Warning: Ignoring unknown parameters: xintercept

###############################

ggplot(reading, aes(x = Errors, y = Total_words_read, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Total words read") + 
  xlab("Errors Committed")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.35, 0.85))
## Warning: Ignoring unknown parameters: xintercept
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(reading, aes(x = Errors, y = Total_words_read, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Total words read") + 
  xlab("Errors Committed")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.35, 0.85))+facet_wrap(.~Grade)
## Warning: Ignoring unknown parameters: xintercept
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

########################################################

ggplot(reading, aes(x = Errors, y = Words_correct_per_minute, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Correct Words per minute") + 
  xlab("Errors Committed")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.35, 0.85))
## Warning: Ignoring unknown parameters: xintercept
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(reading, aes(x = Errors, y = Words_correct_per_minute, color = Gender)) +  
  geom_point(size=3, aes(shape=Gender)) + 
  geom_smooth(method=lm, position = "jitter", aes(fill=Gender), level = 0.95)+ylab("Correct Words per minute") + 
  xlab("Errors Committed")+
  geom_abline(xintercept = 0, linetype=2, color = "black", size=1)+theme(legend.position = c(0.35, 0.85))+facet_wrap(.~Grade)
## Warning: Ignoring unknown parameters: xintercept
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

library(readr)
rc_score <- read_csv("~/Zim Projects PHD/Fungai Mutema/rc_score.csv")
## Parsed with column specification:
## cols(
##   Grade = col_character(),
##   Score = col_double(),
##   Frequency = col_double()
## )
View(rc_score)
attach(rc_score)
## The following object is masked _by_ .GlobalEnv:
## 
##     Grade
## The following object is masked from reading:
## 
##     Grade
names(rc_score)
## [1] "Grade"     "Score"     "Frequency"
Grade=as.factor(Grade)
theme_set(theme_gray(base_size =12))
ggplot(data=rc_score, aes(x = Grade, y = Score)) + 
    geom_boxplot(size=1.2,varwidth = TRUE)+ylab("Score")+ xlab("Grade")

ggplot(data=rc_score, aes(x = Grade, y = Frequency)) + 
    geom_boxplot(size=1.2,varwidth = TRUE)+ylab("Frequency")+ xlab("Grade")

ggplot(rc_score, aes(x = Score, y = Frequency, color = Grade)) +
geom_point(size=3, aes(shape=Gender)) + geom_smooth(method=lm, position = “jitter”, aes(fill=Grade), level = 0.95)+ylab(“Frequency”) + xlab(“Score”)+ geom_abline(xintercept = 0, linetype=2, color = “black”, size=1)+theme(legend.position = c(0.35, 0.85))