richardsClassRepository/DataVis.Rmd at master · rtroise89/richardsClassRepository · GitHub

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---
title: "DataVis"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

#Figure 6

```{r}
  library(ggplot2)
  non_social <- c(0,1,3,5,25,46,52,58)
  social <- c(12,47,5,41,48,32,56,59)
  plot_df <- data.frame(non_social,social)

  ggplot(plot_df, aes(x=non_social,y=social)) +
    geom_point(size=2) +
    geom_smooth(method="lm", se = FALSE) +
    theme_classic() +
    xlab("Avg Frequency of Non Social Risk") +
    ylab("Avg Frequency of Social Risk") +
    coord_cartesian(xlim=c(0,70),ylim=c(0,70))

```

#Figure 1a

```{r}
  library(ggplot2)
  group <- c(rep("Doctor Study", 10), rep("Butcher Study", 10), rep("Firefighter Study", 10),
             rep("Construction Worker Study", 10))
  group_order <- factor(group, levels = c("Doctor Study", "Butcher Study", "Firefighter Study",                                                    "Construction Worker Study"))
  gender <- c(rep("Man More Likely",2), rep("Equally Likely",8),
              rep("Man More Likely",3), rep("Equally Likely",5), rep("Woman More Likely",2),
              rep("Man More Likely",4), rep("Equally Likely",5), rep("Woman More Likely",1),
              rep("Man More Likely",3), rep("Equally Likely",5), rep("Woman More Likely",2))
  gender_order <- factor(gender, levels = c("Woman More Likely", "Equally Likely", "Man More Likely"))

  plot_df2 <- data.frame(group_order,gender_order)

  ggplot(plot_df2, aes(group_order, fill = gender_order)) +
    geom_bar(position = "fill") +
    theme_classic() +
    xlab("") +
    ylab("Precentage of Participants") +
    scale_fill_discrete(name = "") +
    theme(legend.position = "top")
```

# Figure 1b

```{r}
  library(ggplot2)
  group <- c(doc_study = rep("Doctor Study", 6), buh_study = rep("Butcher Study", 6),
             fire_study = rep("Firefighter Study", 6),
             cons_study = rep("Construction Worker Study", 6))
  group_order <- factor(group, levels = c("Doctor Study", "Butcher Study", "Firefighter Study",                                            "Construction Worker Study"))
  eval_data <- c(1,1,1,2,3,4,5,6,1,1,2,2,2,2,3,4,1,2,3,4,4,5,6,7)
  plot_df3 <- data.frame(group_order,eval_data)

  ggplot(plot_df3, aes(group_order, eval_data)) +
    geom_violin(fill="gray80") +
    theme_classic() +
    ylab("Evaluation of Person X") +
    xlab("")

```

# Figure 6

```{r}
  library(ggplot2)
    Names <-rep(c("Dara", "Azalea", "Barbi", "Rowena", "Fiona"), each = 2)
    MF <- rnorm(10,45,25)
    Condition <- rep(c("Social","NonSocial"),5)
    Condition_ordered <- factor(Condition, c("Social", "NonSocial"))
    Aversity <- rep(c("Aversive","Not Aversive"), times=c(4,6))

    plot_df4 <- data.frame(Names, MF, Condition_ordered, Aversity)

  ggplot(plot_df4, aes(x = Condition_ordered, y = MF, group = Names)) +
    geom_line(aes(linetype=Aversity))+
    geom_text(label=Names) +
    theme_classic() +
    ylab("Mean Frequency of Risky Choice") +
    xlab("") +
    theme(legend.position="none")

```