A Monitoria de Econometria

Monitoria 3 - Gráficos e Regressão Linear Simples

Gustavo Romero Cardoso e João Pedro Saldanha Corrêa

2022-08-29

library(tidyverse)

[pic 1]

• Warning: package ’tidyverse’ was built under R version 4.2.1

• -- Attaching packages --------------------------------------- tidyverse 1.3.2 --

## v ggplot2 3.3.6	v purrr	0.3.4
## v tibble	3.1.7	v dplyr	1.0.9
## v tidyr	1.2.0	v stringr 1.4.0
## v readr	2.1.2	v forcats 0.5.1

• Warning: package ’ggplot2’ was built under R version 4.2.1

• Warning: package ’readr’ was built under R version 4.2.1

• -- Conflicts ------------------------------------------ tidyverse_conflicts() --

• x dplyr::filter() masks stats::filter()

## x dplyr::lag()        masks stats::lag()

Parte 1 - Manipulação de bases

Contando observações em grupos

[pic 2]

data <- gss_cat

# Solução 1

data %>% count(partyid)

## # A tibble: 10 x 2
##		partyid	n
##		<fct>	<int>
##	1	No answer	154
##	2	Don’t know	1
##	3	Other party	393
##	4	Strong republican	2314

• 5 Not str republican  3032

##	6	Ind,near rep	1791
##	7	Independent	4119
##	8	Ind,near dem	2499
##	9	Not str democrat	3690
## 10	Strong democrat	3490

1

# Solução 2

[pic 3]

data %>% group_by(partyid) %>% tally()

## # A tibble: 10 x 2
##		partyid	n
##		<fct>	<int>
##	1	No answer	154
##	2	Don’t know	1
##	3	Other party	393
##	4	Strong republican	2314

• 5 Not str republican  3032

##	6	Ind,near rep	1791
##	7	Independent	4119
##	8	Ind,near dem	2499
##	9	Not str democrat	3690
## 10	Strong democrat	3490

• possível combinar grupos e aplicar o count(). No exemplo abaixo, juntamos 3 tipos de respostas em apenas um grupo.

gss_cat %>% mutate(partyid = fct_collapse(partyid, outros = c("No answer",

[pic 4]

"Don't know",

"Other party"))) %>%

count(partyid)

## # A tibble: 8 x 2
##	partyid	n
##	<fct>	<int>
## 1 outros	548
## 2 Strong republican	2314
## 3 Not str republican	3032
## 4 Ind,near rep	1791
## 5 Independent	4119
## 6 Ind,near dem	2499
## 7 Not str democrat	3690
## 8 Strong democrat	3490

data %>% group_by(relig) %>% count()

[pic 5]

• # A tibble: 15 x 2

• # Groups:   relig [15]

##		relig	n
##		<fct>	<int>
##	1	No answer	93
##	2	Don’t know	15

• 3 Inter-nondenominational   109

##	4	Native american	23
##	5	Christian	689
##	6	Orthodox-christian	95
##	7	Moslem/islam	104
##	8	Other eastern	32

...