Why does lm generate NA for each independent variable?

Question

I tried to make a linear regression with the lm function, but the output is NA for every independent variable. The dataframe is numeric.

I have already tried to change the independent variable and only use one dependent variable, but the result was the same.

# Read csv file
gh_old_shorty <- read.csv(file.choose(), header=T, sep=";")

# make dataframe numeric
as.data.frame(lapply(gh_old_shorty, as.numeric))

# create linear regression
model1 <- lm(Year ~ Age +  OfficeOfPresidency + MembersOfParliament + 
     Assembly + GovernmentOfficials + LocalGovernmentOfficials + JudgesAndMagistrates + FightingCorruption, data=gh_old_shorty, na.action = na.omit)
summary(model1)

Call:
lm(formula = Year ~ Age + OfficeOfPresidency + MembersOfParliament + 
    Assembly + GovernmentOfficials + LocalGovernmentOfficials + 
    JudgesAndMagistrates + FightingCorruption, data = gh_old_shorty, 
    na.action = na.omit)

Residuals:
ALL 1 residuals are 0: no residual degrees of freedom!

Coefficients: (8 not defined because of singularities)
                         Estimate Std. Error t value
(Intercept)                  2007         NA      NA
Age                            NA         NA      NA
OfficeOfPresidency             NA         NA      NA
MembersOfParliament            NA         NA      NA
Assembly                       NA         NA      NA
GovernmentOfficials            NA         NA      NA
LocalGovernmentOfficials       NA         NA      NA
JudgesAndMagistrates           NA         NA      NA
FightingCorruption             NA         NA      NA
                         Pr(>|t|)
(Intercept)                    NA
Age                            NA
OfficeOfPresidency             NA
MembersOfParliament            NA
Assembly                       NA
GovernmentOfficials            NA
LocalGovernmentOfficials       NA
JudgesAndMagistrates           NA
FightingCorruption             NA

Residual standard error: NaN on 0 degrees of freedom