Exploring Baby Names.Rmd

---
title: "Baby Names"
author: "Lucia Walinchus"
date: "6/23/2020"
output: html_document
---

```{r}
library(dplyr)
library(ggplot2)
library(rio)
library(zip)
library(purrr)
library(tidyverse)
library(fs)
library(DT)

```

## Baby Names


Bringing in the government's list of baby names from the [Social Security Administration](https://www.ssa.gov/oact/babynames/limits.html)
NOTE: This is only the most popular 1,000 names per year. 
```{r}
download.file("https://www.ssa.gov/oact/babynames/names.zip", dest="dataset.zip", mode="wb") 
unzip ("dataset.zip", exdir = "./")
```
Putting all those files together


```{r}
list_of_files <- list.files(path = ".", recursive = TRUE,
                            pattern = "\\.txt$", 
                            full.names = TRUE)

df <- list_of_files %>%
  purrr::set_names(nm = (basename(.) %>% tools::file_path_sans_ext())) %>%
  purrr::map_df(read_csv, 
                col_names = FALSE,
                .id = "FileName",
                col_types = cols(X2 = col_character()))
```

And making it look nice


```{r}
US_Names <- df %>%
  mutate(year = str_sub(FileName,4,8)) %>% 
  select(-FileName) %>% 
  mutate(name = X1, gender = X2, number = X3) %>% 
  select(-X1, -X2, -X3) %>% 
  distinct()

US_Names$year <- as.numeric(US_Names$year)

```





```{r}
Lucias <- US_Names %>% 
  filter(name=="Lucia"& gender == "F")

ggplot(Lucias, aes(year, number))+
  geom_point()+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Lucia.jpg", plot=last_plot())
```


```{r}
Marys <- US_Names %>% 
  filter(name=="Mary" & gender == "F")

ggplot(Marys, aes(year, number))+
  geom_point()+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+
  labs(title = "Children Named 'Mary' in the United States per Year", subtitle = "Note: Data only includes top 1,000 names per year.")

ggsave("Mary.jpg", plot=last_plot())
```

```{r}
Leslies <- US_Names %>% 
  filter(name=="Leslie")

ggplot(Leslies, aes(year, number, fill = gender))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+
  labs(title = "Children Named 'Leslie' in the United States per Year", subtitle = "Note: Only includes top 1,000 names per year.")

ggsave("Leslies.jpg", plot=last_plot())
```


```{r}
Total_Names1990F <- US_Names %>% 
  filter(year == "1990" & gender == "F")
  
  
length(unique(Total_Names1990F[["name"]]))

length(unique(Total_Names1990F[["number"]]))


```
So they take the top 1,000 by rank. Unless there's fewer than 5 in each set. 

Now for the state data set. 
```{r}
download.file("https://www.ssa.gov/oact/babynames/state/namesbystate.zip", destfile = "state_names_dataset.zip", mode="wb") 
unzip ("state_names_dataset.zip", exdir = "State_Names/")
```



```{r}
setwd("~/Code/Red/BabyNames/State_Names")

list_of_files <- list.files(path = ".", recursive = TRUE,
                            pattern = "{6}",
                            #pattern = "\\.txt$",
                            full.names = TRUE)

list_of_files <-list_of_files[- 43]

df <- list_of_files %>%
  purrr::set_names(nm = (basename(.) %>% tools::file_path_sans_ext())) %>%
  purrr::map_df(read_csv, 
                col_names = FALSE,
                .id = "FileName",
                col_types = cols(
  X1 = col_character(),
  X2 = col_character(),
  X3 = col_double(),
  X4 = col_character(),
  X5 = col_double()
))
```



Looking just at *Ohio*
```{r}
OH_Names <- df %>%
  select(-FileName) %>% 
  mutate(state=X1, gender = X2, year = X3, name = X4, number = X5) %>% 
  select(-X1,-X2,-X3,-X4,-X5) %>% 
  filter(state=="OH") %>% 
  distinct()

```


```{r}
Most_Popular_OH <- OH_Names %>% 
  group_by(year, name, number) %>% 
  arrange(desc(number))


datatable(Most_Popular_OH)
```






Bonnie <- US_Names %>% 
     filter(name=="Bonnie" & gender == "F")

ggplot(Bonnie, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+

ggsave("Bonnie.jpg", plot=last_plot()
)

Jennifer <- US_Names %>% 
     filter(name=="Jennifer" & gender == "F")

ggplot(Jennifer, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+

ggsave("Jennifer.jpg", plot=last_plot()
)

Jennifer <- US_Names %>% 
     filter(name=="Jennifer" & gender == "F")
```{r}
Christian <- US_Names %>% 
     filter(name=="Christian" & gender == "M")

ggplot(Christian, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Christian.jpg", plot=last_plot()
)

```


```{r}
Michael <- US_Names %>% 
     filter(name=="Michael" & gender == "M")

ggplot(Michael, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Michael.jpg", plot=last_plot()
)

```



```{r}
Kyle <- US_Names %>% 
     filter(name=="Kyle" & gender == "M")

ggplot(Kyle, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Kyle.jpg", plot=last_plot()
)

```

```{r}
Mark <- US_Names %>% 
     filter(name=="Mark" & gender == "M")

ggplot(Mark, aes(year, number))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Mark.jpg", plot=last_plot()
)

```


```{r}
Sofia <- US_Names %>% 
     filter(name=="Sofia" | name== "Sophia" & gender == "F")

ggplot(Sofia, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Sofia.jpg", plot=last_plot()
)

```

```{r}
Jeanmarie <- US_Names %>% 
     filter(name=="Jeanmarie" & gender == "F")

ggplot(Jeanmarie, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Jeanmarie.jpg", plot=last_plot()
)

```


```{r}
Karen <- US_Names %>% 
     filter(name=="Karen" & gender == "F")

ggplot(Karen, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Karen.jpg", plot=last_plot()
)

datatable(Karen)
```


```{r}
Afi <- US_Names %>% 
     filter(name=="Afi" & gender == "F")

ggplot(Afi, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Afi.jpg", plot=last_plot()
)
```


```{r}
Odelia <- US_Names %>% 
     filter(name=="Odelia" & gender == "F")

ggplot(Odelia, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Odelia.jpg", plot=last_plot()
)
```


```{r}
Kaitlyn <- US_Names %>% 
     filter(name=="Kaitlyn" & gender == "F")

ggplot(Kaitlyn, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Kaitlyn.jpg", plot=last_plot()
)
```


```{r}
Amy <- US_Names %>% 
     filter(name=="Amy" & gender == "F")

ggplot(Amy, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Amy.jpg", plot=last_plot()
)
```


```{r}
Dorothy <- US_Names %>% 
     filter(name=="Dorothy" & gender == "F")

ggplot(Dorothy, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Dorothy.jpg", plot=last_plot()
)
```


```{r}
Carolyn <- US_Names %>% 
     filter(name=="Carolyn" & gender == "F")

ggplot(Carolyn, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Carolyn.jpg", plot=last_plot()
)
```


```{r}
Madison <- US_Names %>% 
     filter(name=="Madison" & gender == "F")

ggplot(Madison, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Madison.jpg", plot=last_plot()
)
```

```{r}
Tara <- US_Names %>% 
     filter(name=="Tara" & gender == "F")

ggplot(Tara, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Tara.jpg", plot=last_plot()
)
```


```{r}
Sadie <- US_Names %>% 
     filter(name=="Sadie" & gender == "F")

ggplot(Sadie, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Sadie.jpg", plot=last_plot()
)
```

```{r}
Nicholas <- US_Names %>% 
     filter(name=="Nicholas" & gender == "F")

ggplot(Nicholas, aes(year, number, color = name))+
     geom_point()+
     theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))


ggsave("Nicholas.jpg", plot=last_plot()
)
```


```{r}
Lia <- US_Names %>% 
  filter(name=="Lia" | name =="Leah")

ggplot(Lia, aes(year, number, fill = name))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Lia.jpg", plot=last_plot())
```

```{r}
LuciaLucy <- US_Names %>% 
  filter(name=="Lucia"| name=="Lucy"& gender=="F")

ggplot(LuciaLucy, aes(year, number, color = name))+
  geom_point(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+
  labs(title = "Children Named 'Lucy' and 'Lucia' in the United States per Year", subtitle = "Note: Data only includes top 1,000 names per year.")

ggsave("LuciaLucy.jpg", plot=last_plot())
```

```{r}
AnthonyAntonio <- US_Names %>% 
  filter(name=="Anthony"| name=="Antonio"& gender=="M")

ggplot(AnthonyAntonio, aes(year, number, color = name))+
  geom_point(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+
  labs(title = "Children Named 'Anthony' and 'Antonio' in the United States per Year", subtitle = "Note: Data only includes top 1,000 names per year.")

ggsave("LuciaLucy.jpg", plot=last_plot())
```



```{r}
Erin <- US_Names %>% 
  filter(name=="Erin" & gender =="F")

ggplot(Erin, aes(year, number))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Erin.jpg", plot=last_plot())
```

```{r}
Virginia <- US_Names %>% 
  filter(name=="Virginia" & gender =="F")

ggplot(Virginia, aes(year, number))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Virginia.jpg", plot=last_plot())
```

```{r}
Becky <- US_Names %>% 
  filter(name=="Rebecca" & gender =="F")

ggplot(Becky, aes(year, number))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Becky.jpg", plot=last_plot())
```


```{r}
Alise <- US_Names %>% 
  filter(name=="Alise" | name== "Elise" & gender =="F")

ggplot(Alise, aes(year, number, fill = name))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Alise.jpg", plot=last_plot())
```


```{r}
Elise <- US_Names %>% 
  filter(name=="Alise" | name== "Elise" & gender =="F")

ggplot(Elise, aes(year, number, color = name))+
  geom_line()+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Elise.jpg", plot=last_plot())
```



```{r}
Laura <- US_Names %>% 
  filter(name=="Laura" & gender =="F")

ggplot(Laura, aes(year, number))+
  geom_bar(stat = "identity")+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))

ggsave("Laura.jpg", plot=last_plot())
```



Lots

```{r}
ManyNames <- US_Names %>% 
  filter(name=="Edith" |
           name=="Rose"|
           name=="Gina"|
           name=="Tara"|
           name=="Kaitlyn"|
           name=="Tanisha"|
           name=="Maria"|
           name=="Yvonne"|
           name=="Lois"& gender =="F")

ggplot(ManyNames, aes(year, number, fill=name))+
  geom_bar(stat = "identity")+
  facet_wrap(~name)+
  theme(axis.text.x = element_text(angle = 45, vjust =1.2, hjust = 1.1))+
  labs(title = "US Baby Names",subtitle = "Sample of 9 popular baby names in the US per year")

```