examples.Rmd

---
title: "GG-Plot Exercises"
output:
  html_document:
    toc: true
    toc_float: true
    collapsed: false
    number_sections: false
    toc_depth: 1
    #code_folding: hide
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(message=FALSE,warning=FALSE, cache=TRUE)
```
##Examples for how to turn data into different graphs

###1. Example of line graph
Two conditions and Two different lines to indicate significance

```{r}
library(ggplot2)

# add "each" to keep both copies of the names together 
Names <- rep(c("Dara","Azalea","Barbi","Rowena","Fiona","Katie","Liza","Rita"),each=2)
MF <- rnorm(16,50,25) 
Condition <-rep(c("Social","Nonsocial"),8)
# "times=" to repeat how many times you want A and N_A to be repeated 
Aversity <- rep(c("A","N_A"),times=c(8,8))
plot_df<- data.frame(Names, MF, Condition) 

ggplot(plot_df, aes(x=Condition, y=MF, group=Names,
                    linetype=Aversity))+ 
  geom_line()+
  geom_text(label=Names)+
  theme_classic()

```



###2. ScatterPlot with a Regression Line
```{r}

AvNonRisk <- rnorm(8,40,2)
AvSocialRisk <- rnorm(8,40,2)
plot_df <- data.frame(AvNonRisk,AvSocialRisk)


ggplot(plot_df, aes(x=AvNonRisk,y=AvSocialRisk))+
  geom_point()+
  geom_point(size=2)+
  geom_smooth(method=lm)+
  
  xlab("Average Frequency Non Social Risk")+
  ylab("Average Frequency Social Risk")+
  ggtitle("Scatterplot with Regression")+
  theme_classic(base_size=12)+
  theme(plot.title = element_text(hjust = 0.5))

       
```

###2.Bar Graphs
```{r}
library(ggplot2)
#Create a dataframe
factor_one <- rep(as.factor(c("Fill","Empty")),2)
factor_two <- rep(as.factor(c("Wide Base","Wide Top")),2)
dv_means <- c(19.5,17.5,18.5,18)
plot_df <- data.frame(factor_one,
                      factor_two,
                      dv_means)
                      
ggplot(plot_df, aes(x=factor_one,y=dv_means,
                    group=factor_two,
                    color=factor_two))+
  geom_bar(stat="identity",position = "dodge")+
   xlab("First factor")+
  ylab("Estimate(xids)")+
  ggtitle("Two Factor Bar Graph")+
  theme_classic(base_size=12)+
  theme(plot.title = element_text(hjust = 0.5))


```

###Line Graph
```{r}
factor_one <-c(-25,-8,8,25)
dv_means <- c(4.7,4.9,5,5.3)
dv_SEs   <- c(.5,.4,.3,.3)
plot_df <- data.frame(factor_one,
                      dv_means,
                      dv_SEs)

                      

# basic line graph

ggplot(plot_df, aes(x=factor_one,y=dv_means, group=1))+
  geom_point()+
  geom_line()+
   geom_errorbar(aes(ymin=dv_means-dv_SEs,
                    ymax=dv_means+dv_SEs),
                width=.2)+
  coord_cartesian(ylim=c(4,6))+
  xlab("Manipulation (%)")+
  ylab("Mean rating")+
  ggtitle("Male Faces")+
  theme_classic(base_size=12)+
  theme(plot.title = element_text(hjust = 0.5))

```



###Make a Histogram
```{r}
score <- rnorm(152,0,1)
n <- 1:152
plot_df <- data.frame(score,n)

ggplot(plot_df, aes(x=score))+
  geom_histogram(bins=15,
                 color="white")+
  coord_cartesian(ylim=c(0,40))+
   xlab("Log Likleyhood ratio")+
  ylab("Participants in each condition")+
  ggtitle("Man Performed Surgery n=152")+
  theme_classic(base_size=12)+
  theme(plot.title = element_text(hjust = 0.5))
```