datalab-vis01_v3.rmd

---
title: "datalab-vis01_v3"
output: html_document
---
#Lab 03
##Author:Ajinkya Shinde

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

```{r}
library(dplyr)
library(tidyverse)

anim <- read_csv(here::here("data", "animal_sounds_summary.csv"))
anim

```



* How many variables?
    * Which variables are continuous?
    * Which ones are categorical or ordinal?

```{r}
glimpse(anim)
```
*7 variables. 
The continuous variables are  age,kids_produce,kids_understand,kids_respond,prop_produce,prop_understand

The catergorical/ordinal variables are sound*


* How many total kids do we have data for?
```{r}
anim %>% tally(kids_respond) 
```



* How many ages (in months)?
```{r}
count(distinct(anim,age))
```


* How many kids per age?
```{r}
anim %>% select(age,kids_respond) %>% group_by(age) %>% summarize(grp_by_age=sum(kids_respond))
```


* How many types of animal sounds? What are they?
```{r}
count(distinct(anim,sound))#Types of animal sounds
distinct(anim,sound)#Animal sounds kids can produce/understand
```

```{r}
anim1<-anim %>% 
  group_by(sound) %>% 
    summarize(kids_produce_sum=sum(kids_produce))
ggplot(anim1,aes(x=sound,y=kids_produce_sum)) + geom_col() +
  labs(x="Sound",y="Total Children Producing")

```

```{r}

ggplot(anim,aes(x=age,y=prop_produce)) + geom_col() +
  labs(x="Age(mos)",y="Proportion of  Children Producing") + facet_wrap(~sound)
```



```{r}


ggplot(anim,aes(x=age,y=prop_produce)) + geom_point() +
  labs(x="Age(mos)",y="Proportion of  Children Producing") + facet_wrap(~sound)
```




```{r}

ggplot(anim,aes(x=age,y=prop_produce),size=2) + geom_point() +
  labs(x="Age(months)",y="Proportion of  Children Producing") 
```



*Challenge#2*
```{r}
ggplot(anim,aes(x=age,y=prop_produce,color=sound),size=2) + geom_point() +
  labs(x="Age(months)",y="Proportion of  Children Producing") 

```




*Challenge#3*
```{r}
ggplot(anim,aes(x=age,y=prop_produce))  +geom_line()+ geom_point(aes(color=sound),size=2)+
  labs(x="Age(months)",y="Proportion of  Children Producing") 

```



```{r}
ggplot(anim,aes(x=age,y=prop_produce),size=2)+ geom_point(aes(color=sound))+
  geom_line(aes(group=sound))+
  labs(x="Age(months)",y="Proportion of  Children Producing") 

```

*Challenge#4*
```{r}
ggplot(anim,aes(x=age,y=prop_produce,color=sound),size=2)+ geom_point()+
  geom_line(aes(group=sound))+
  labs(x="Age(months)",y="Proportion of  Children Producing") 
```





```{r}
ggplot(anim,aes(x=age,y=prop_produce,color=sound),size=2)+ 
    geom_smooth(aes(group=sound),se=FALSE,size=1)+
  geom_point(aes(color=sound))+
  labs(x="Age(months)",y="Proportion of  Children Producing") 

```

```{r}
sound_traj<-
ggplot(anim,aes(x=age,y=prop_produce,color=fct_reorder2(sound, age, prop_produce)),size=2)+ 
    geom_smooth(aes(group=sound),se=FALSE,size=1)+
  geom_point(aes(color=sound))+
  labs(x="Age(months)",y="Proportion of  Children Producing",color="sound") 
```


```{r}
sound_traj + scale_color_hue()
sound_traj +  scale_color_hue(h = c(0, 90), l = 55, c = 100)
```

```{r}
sound_traj +  scale_color_hue(h = c(0, 90), l = 45, c = 100)
```


*Challenge #5*


```{r}
ggplot(anim, aes(x = age, 
                         y = prop_produce, 
                         fill = fct_reorder2(sound, age, prop_produce))) + 
  geom_smooth(se = FALSE, lwd = .5) +
  geom_point(size = 2) +
  labs(x = "Age (months)", 
       y = "Proportion of Children Producing", 
       fill = "sound") +
  scale_fill_manual(values = c("cornflowerblue", 
                               "seagreen", "coral"))
```

*Challenge #6*
```{r}
my_colors <- c("cadetblue", "steelblue", "salmon") # quote color names
sound_traj +
  scale_color_manual(values = my_colors) # note: not in quotes
```

*Challenge #7*
```{r}
colors<-c("#aa6e28","#800000","#aaffc3","#808000","#008080","#000080")
sound_traj +
  scale_colour_manual(values = colors )
```


*Challenge #8*
```{r}
library(ggthemes)
```

```{r}
sound_traj +
  scale_color_economist()
```

*Challenge #9*
```{r}
library(viridis)
ggplot(anim, aes(x = age, 
                   y = prop_produce, 
                   fill = fct_reorder2(sound, age, prop_produce))) + 
  geom_smooth(aes(color = fct_reorder2(sound, age, prop_produce),
                  lty = fct_reorder2(sound, age, prop_produce)),
              se = FALSE, lwd = .5, show.legend = FALSE) +
  geom_point(size = 2, shape = 21) +
  labs(x = "Age (months)", 
       y = "Proportion of Children Producing", 
       fill = "sound") +
  scale_fill_grey(start = 0.3, end = .8) +
  scale_color_grey(start = 0.3, end = .8) 
```

*Challenge #10*
```{r}
#Dataset taken from first lab
library(babynames)
alison <- babynames %>%
  filter(name == "Alison" | name == "Allison") %>% 
  filter(sex == "F") %>% 
  filter(year > 1950) %>% 
  filter(year < 2000)

library(wesanderson)
first_plot <- ggplot(alison, aes(x = year,  y = prop ,color=fct_reorder2(name,year,prop))) +
  geom_point()+
  geom_line() +
  labs(x = "Year", y = "Proportion of female population",color="Category")

```

*1. First plot shows the trend of proportion for respective
categories of data. WES Anderson Paleete FantasticBox is been used for clarity.The color red has more luminance thus make the category standout.The color blue has lower luminance and is used for complememtory reasons*
```{r}

first_plot + scale_color_manual(values=wes_palette("FantasticFox1")[c(3,5)])
```

*2. Second plot shows the same plot in greyscale version*

```{r}
first_plot + scale_color_grey(start = 0.2, end = .8)
```


*3. In the third plot,t he worst color combination is apparent thus making it difficult to distinguish between the categories especially close to the 1960 value and making the data values seem to be the same.The color combinations used are"#672d2d","#47193b","#3d492b"*

```{r}
first_plot+scale_color_manual(values=c("#672d2d","#47193b","#3d492b"))
```