Module 1 Assignment

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library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   2.1.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

cel <- read_csv(url("https://www.dropbox.com/s/4ebgnkdhhxo5rac/cel_volden_wiseman%20_coursera.csv?raw=1"))

## Rows: 10262 Columns: 38

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): thomas_name, st_name
## dbl (36): thomas_num, icpsr, congress, year, cd, dem, elected, female, votep...

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

names(cel)

##  [1] "thomas_num"     "thomas_name"    "icpsr"          "congress"      
##  [5] "year"           "st_name"        "cd"             "dem"           
##  [9] "elected"        "female"         "votepct"        "dwnom1"        
## [13] "deleg_size"     "speaker"        "subchr"         "afam"          
## [17] "latino"         "votepct_sq"     "power"          "chair"         
## [21] "state_leg"      "state_leg_prof" "majority"       "maj_leader"    
## [25] "min_leader"     "meddist"        "majdist"        "all_bills"     
## [29] "all_aic"        "all_abc"        "all_pass"       "all_law"       
## [33] "les"            "seniority"      "benchmark"      "expectation"   
## [37] "TotalInParty"   "RankInParty"

cle_115 <- cel %>%
  filter(congress == 115)


cle_115 <- cle_115 %>%
  mutate(
    Gender = ifelse(female ==1, "Female","Male")
  )

ggplot(cle_115,
       aes(x= dwnom1,
           y = all_pass,
           color = Gender)) +
  
  geom_point() +
  labs(
    x= "Ideology",
    y= "Bills Passed",
    color = "Gender"
  ) +
  theme_minimal()

## Warning: Removed 6 rows containing missing values (geom_point).

cle_115 <- cle_115 %>%
  mutate(
    Majority = ifelse(majority ==1, "Majority", "Minority")
  )
  
  
ggplot(cle_115,
       aes(x= votepct,
           y= all_pass,
           color= Gender)) +
           
  geom_point() +
  facet_wrap(~Majority) +
  
  scale_color_manual(
    values = c("Male" = "green","Female" = "orange")
  ) +
  
  labs(
    x="Vote Percentage",
    y="Bills Passed",
    color = "Gender"
  ) +
  theme_minimal()

## Warning: Removed 6 rows containing missing values (geom_point).

ggplot(cle_115,
       aes(x= Majority,
           y=les)) +
  geom_boxplot() +
  labs(
    title= "LES in the 115th Congress",
    x="Majority or Minority",
    y="Legislative Effectiveness"
    ) +
  theme_minimal()