R Markdown
library(wooldridge)
data(wagepan)
library(rmarkdown)
paged_table(wagepan)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
wagepan2 <- wagepan %>% mutate(denbesbuyuk = ifelse(exper>=5,1,0))
wagepan2 <- wagepan2 %>% relocate(nr, year, exper, denbesbuyuk, lwage, married)
head(wagepan2, 10)
## nr year exper denbesbuyuk lwage married agric black bus construc ent
## 1 13 1980 1 0 1.1975402 0 0 0 1 0 0
## 2 13 1981 2 0 1.8530600 0 0 0 0 0 0
## 3 13 1982 3 0 1.3444617 0 0 0 1 0 0
## 4 13 1983 4 0 1.4332134 0 0 0 1 0 0
## 5 13 1984 5 1 1.5681251 0 0 0 0 0 0
## 6 13 1985 6 1 1.6998910 0 0 0 1 0 0
## 7 13 1986 7 1 -0.7202626 0 0 0 1 0 0
## 8 13 1987 8 1 1.6691879 0 0 0 1 0 0
## 9 17 1980 4 0 1.6759624 0 0 0 0 0 0
## 10 17 1981 5 1 1.5183982 0 0 0 0 0 0
## fin hisp poorhlth hours manuf min nrthcen nrtheast occ1 occ2 occ3 occ4 occ5
## 1 0 0 0 2672 0 0 0 1 0 0 0 0 0
## 2 0 0 0 2320 0 0 0 1 0 0 0 0 0
## 3 0 0 0 2940 0 0 0 1 0 0 0 0 0
## 4 0 0 0 2960 0 0 0 1 0 0 0 0 0
## 5 0 0 0 3071 0 0 0 1 0 0 0 0 1
## 6 0 0 0 2864 0 0 0 1 0 1 0 0 0
## 7 0 0 0 2994 0 0 0 1 0 1 0 0 0
## 8 0 0 0 2640 0 0 0 1 0 1 0 0 0
## 9 0 0 0 2484 0 0 0 1 0 1 0 0 0
## 10 0 0 0 2804 0 0 0 1 0 1 0 0 0
## occ6 occ7 occ8 occ9 per pro pub rur south educ tra trad union d81 d82 d83
## 1 0 0 0 1 0 0 0 0 0 14 0 0 0 0 0 0
## 2 0 0 0 1 1 0 0 0 0 14 0 0 1 1 0 0
## 3 0 0 0 1 0 0 0 0 0 14 0 0 0 0 1 0
## 4 0 0 0 1 0 0 0 0 0 14 0 0 0 0 0 1
## 5 0 0 0 0 1 0 0 0 0 14 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 13 0 1 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 13 0 1 0 1 0 0
## d84 d85 d86 d87 expersq
## 1 0 0 0 0 1
## 2 0 0 0 0 4
## 3 0 0 0 0 9
## 4 0 0 0 0 16
## 5 1 0 0 0 25
## 6 0 1 0 0 36
## 7 0 0 1 0 49
## 8 0 0 0 1 64
## 9 0 0 0 0 16
## 10 0 0 0 0 25
wagepan2 <- wagepan2 %>% mutate(bekar = ifelse(married==1,0,1))
wagepan2 <- wagepan2 %>% relocate(nr, year, d81, d82, d83, d84, d85, d86, d87)
head(wagepan2, 10)
## nr year d81 d82 d83 d84 d85 d86 d87 exper denbesbuyuk lwage married
## 1 13 1980 0 0 0 0 0 0 0 1 0 1.1975402 0
## 2 13 1981 1 0 0 0 0 0 0 2 0 1.8530600 0
## 3 13 1982 0 1 0 0 0 0 0 3 0 1.3444617 0
## 4 13 1983 0 0 1 0 0 0 0 4 0 1.4332134 0
## 5 13 1984 0 0 0 1 0 0 0 5 1 1.5681251 0
## 6 13 1985 0 0 0 0 1 0 0 6 1 1.6998910 0
## 7 13 1986 0 0 0 0 0 1 0 7 1 -0.7202626 0
## 8 13 1987 0 0 0 0 0 0 1 8 1 1.6691879 0
## 9 17 1980 0 0 0 0 0 0 0 4 0 1.6759624 0
## 10 17 1981 1 0 0 0 0 0 0 5 1 1.5183982 0
## agric black bus construc ent fin hisp poorhlth hours manuf min nrthcen
## 1 0 0 1 0 0 0 0 0 2672 0 0 0
## 2 0 0 0 0 0 0 0 0 2320 0 0 0
## 3 0 0 1 0 0 0 0 0 2940 0 0 0
## 4 0 0 1 0 0 0 0 0 2960 0 0 0
## 5 0 0 0 0 0 0 0 0 3071 0 0 0
## 6 0 0 1 0 0 0 0 0 2864 0 0 0
## 7 0 0 1 0 0 0 0 0 2994 0 0 0
## 8 0 0 1 0 0 0 0 0 2640 0 0 0
## 9 0 0 0 0 0 0 0 0 2484 0 0 0
## 10 0 0 0 0 0 0 0 0 2804 0 0 0
## nrtheast occ1 occ2 occ3 occ4 occ5 occ6 occ7 occ8 occ9 per pro pub rur south
## 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 5 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 6 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 8 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 9 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 10 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## educ tra trad union expersq bekar
## 1 14 0 0 0 1 1
## 2 14 0 0 1 4 1
## 3 14 0 0 0 9 1
## 4 14 0 0 0 16 1
## 5 14 0 0 0 25 1
## 6 14 0 0 0 36 1
## 7 14 0 0 0 49 1
## 8 14 0 0 0 64 1
## 9 13 0 1 0 16 1
## 10 13 0 1 0 25 1
wagepan2 <- wagepan2 %>% mutate(d80= ifelse(year==1980,1,0))
wagepan2 <- wagepan2 %>% relocate(nr, year,d80, d81, d82, d83, d84, d85, d86, d87)
head(wagepan2, 10)
## nr year d80 d81 d82 d83 d84 d85 d86 d87 exper denbesbuyuk lwage married
## 1 13 1980 1 0 0 0 0 0 0 0 1 0 1.1975402 0
## 2 13 1981 0 1 0 0 0 0 0 0 2 0 1.8530600 0
## 3 13 1982 0 0 1 0 0 0 0 0 3 0 1.3444617 0
## 4 13 1983 0 0 0 1 0 0 0 0 4 0 1.4332134 0
## 5 13 1984 0 0 0 0 1 0 0 0 5 1 1.5681251 0
## 6 13 1985 0 0 0 0 0 1 0 0 6 1 1.6998910 0
## 7 13 1986 0 0 0 0 0 0 1 0 7 1 -0.7202626 0
## 8 13 1987 0 0 0 0 0 0 0 1 8 1 1.6691879 0
## 9 17 1980 1 0 0 0 0 0 0 0 4 0 1.6759624 0
## 10 17 1981 0 1 0 0 0 0 0 0 5 1 1.5183982 0
## agric black bus construc ent fin hisp poorhlth hours manuf min nrthcen
## 1 0 0 1 0 0 0 0 0 2672 0 0 0
## 2 0 0 0 0 0 0 0 0 2320 0 0 0
## 3 0 0 1 0 0 0 0 0 2940 0 0 0
## 4 0 0 1 0 0 0 0 0 2960 0 0 0
## 5 0 0 0 0 0 0 0 0 3071 0 0 0
## 6 0 0 1 0 0 0 0 0 2864 0 0 0
## 7 0 0 1 0 0 0 0 0 2994 0 0 0
## 8 0 0 1 0 0 0 0 0 2640 0 0 0
## 9 0 0 0 0 0 0 0 0 2484 0 0 0
## 10 0 0 0 0 0 0 0 0 2804 0 0 0
## nrtheast occ1 occ2 occ3 occ4 occ5 occ6 occ7 occ8 occ9 per pro pub rur south
## 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 5 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
## 6 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 8 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 9 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 10 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## educ tra trad union expersq bekar
## 1 14 0 0 0 1 1
## 2 14 0 0 1 4 1
## 3 14 0 0 0 9 1
## 4 14 0 0 0 16 1
## 5 14 0 0 0 25 1
## 6 14 0 0 0 36 1
## 7 14 0 0 0 49 1
## 8 14 0 0 0 64 1
## 9 13 0 1 0 16 1
## 10 13 0 1 0 25 1
kesenli <- lm(lwage ~ black + exper + expersq + married, data = wagepan2)
kesensiz <- lm(lwage ~ black + exper + expersq + married -1, data = wagepan2)
wagepan2 <- wagepan2 %>% mutate(sabit= 1)
sabitli <- lm(lwage ~ black + exper + expersq + married + sabit - 1, data = wagepan2)
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
stargazer(kesensiz, kesenli, sabitli, type="text",single.row=TRUE, font.size = "tiny", column.labels = c("kesensiz", "kesenli", "sabitli"))
##
## ====================================================================================================
## Dependent variable:
## --------------------------------------------------------------------------------
## lwage
## kesensiz kesenli sabitli
## (1) (2) (3)
## ----------------------------------------------------------------------------------------------------
## black -0.089*** (0.027) -0.124*** (0.025) -0.124*** (0.025)
## exper 0.443*** (0.005) 0.121*** (0.011) 0.121*** (0.011)
## expersq -0.027*** (0.0005) -0.007*** (0.001) -0.007*** (0.001)
## married 0.133*** (0.018) 0.151*** (0.017) 0.151*** (0.017)
## sabit 1.150*** (0.035)
## Constant 1.150*** (0.035)
## ----------------------------------------------------------------------------------------------------
## Observations 4,360 4,360 4,360
## R2 0.892 0.081 0.913
## Adjusted R2 0.892 0.080 0.913
## Residual Std. Error 0.570 (df = 4356) 0.511 (df = 4355) 0.511 (df = 4355)
## F Statistic 8,984.575*** (df = 4; 4356) 96.114*** (df = 4; 4355) 9,166.794*** (df = 5; 4355)
## ====================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01