library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.4.2
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'readr' was built under R version 3.4.2
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
library(janitor)
library(car)
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
cces <- read_csv("https://raw.githubusercontent.com/ryanburge/cces/master/CCES%20for%20Methods/small_cces.csv")
## Warning: Missing column names filled in: 'X1' [1]
## Warning: Duplicated column names deduplicated: 'X1' => 'X1_1' [2]
## Parsed with column specification:
## cols(
## .default = col_integer()
## )
## See spec(...) for full column specifications.
cces %>% filter(state==41) %>% filter(vote16==2) %>% tabyl(vote16)
## vote16 n percent
## 1 2 409 1
cces %>% filter(state==41) %>% filter(vote16<2) %>% tabyl(vote16)
## vote16 n percent
## 1 1 296 1
cces %>% filter(state==41) %>% filter(vote16<3) %>% tabyl(vote16)
## vote16 n percent
## 1 1 296 0.4198582
## 2 2 409 0.5801418
cces %>% filter(sexuality==2|sexuality==3) %>% filter(pid7==4) %>% tabyl(pid7)
## pid7 n percent
## 1 4 259 1
cces %>% filter(sexuality==2|sexuality==3) %>% filter(pid7==5|pid7==6|pid7==7) %>% tabyl(pid7)
## pid7 n percent
## 1 5 61 0.286385
## 2 6 85 0.399061
## 3 7 67 0.314554
cces %>% crosstab(attend,gaym)
## attend 1 2 8 9
## 1 1 1227 3833 41 0
## 2 2 4907 6514 99 1
## 3 3 3262 2032 38 0
## 4 4 6388 2871 79 0
## 5 5 10547 4043 118 0
## 6 6 14997 2777 86 0
## 7 7 370 324 13 0
## 8 8 20 13 0 0
cces %>% crosstab(attend,religion)
## attend 1 2 3 4 5 6 7 8 9 10 11 12 98
## 1 1 3509 726 127 39 60 104 27 24 9 7 201 260 8
## 2 2 6054 3500 501 76 152 117 48 45 23 33 550 406 16
## 3 3 2485 1584 60 62 146 50 53 63 25 52 478 261 13
## 4 4 3671 2856 61 74 396 57 108 89 75 203 1232 508 8
## 5 5 4929 3353 88 67 401 65 178 47 320 905 3402 943 10
## 6 6 2017 1749 28 44 387 28 203 21 3562 2782 5849 1177 13
## 7 7 210 107 4 0 4 15 4 9 10 11 269 63 1
## 8 8 13 5 1 0 0 0 0 1 1 0 5 3 4
pew <- cces %>% group_by(race) %>% summarise(md=median(income))
pew %>% ggplot(.,aes(x=race, y=md)) + geom_col()
pew <- cces %>% filter(union==1|union==2) %>% group_by(race) %>% summarise(md=median(income))
pew <- cces %>% filter(union==3) %>% group_by(race) %>% summarise(md=median(income))
knitr::opts_chunk$set(echo = TRUE)
pew %>% ggplot(.,aes(x=race, y=md)) + geom_col()
pew %>% ggplot(.,aes(x=race, y=md)) + geom_col()
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
