Impute Quarterly data rows - r

Question

First time doing TS and I want to calculate a time-series regression but first have to convert my data frame to a ts frame. The problem is, that I have missing quarters, so the ts conversion fails. How can I impute quarters and respective values to get a full ts frame?
Example with quarter 1981.3 missing:
1 1981.1 1.2 0.00000000 0 7.881275
2 1981.2 -0.2 1.17870604 0 7.676712
3 1981.4 -0.1 0.00000000 0 7.333129
4 1982.1 0.4 0.00000000 0 7.266816
This is the dput.
structure(list(Quarter.y = c(1981.1, 1981.2, 1981.4, 1982.1,
1982.4, 1983.4, 1984.1, 1984.3, 1984.4, 1985.2, 1985.4, 1986.1,
1986.4, 1987.2, 1987.4, 1988.2, 1988.4, 1989.2, 1989.4, 1990.1,
1990.2, 1990.4, 1991.2, 1991.4, 1992.2, 1992.4, 1993.2, 1993.3,
1993.4, 1994.1, 1994.2, 1994.3, 1995.1, 1995.2, 1995.3, 1995.4,
1996.1, 1996.2, 1996.4, 1997.1, 1997.2, 1997.4, 1998.2, 1998.4,
1999.1, 1999.2, 1999.4, 2000.1, 2000.2, 2000.3, 2000.4, 2001.1,
2001.2, 2001.3, 2001.4, 2002.1, 2002.3, 2002.4, 2003.1, 2003.3,
2003.4, 2004.1, 2004.2, 2004.4, 2005.1), GDPGrowth = c(1.2, -0.2,
-0.1, 0.4, 0.2, 1.4, 1.3, 2.7, 0.8, 1, 0.6, -0.6, 1, 2.2, 1.6,
1.8, 1.2, 0.4, 1.2, 2.1, 0.5, 1.8, -0.5, 1.3, -0.7, -0.3, 0,
0.6, -0.1, 1.4, 0.3, 0.7, -0.3, 0.8, 0.2, 0, -0.8, 1.4, 0.7,
-0.5, 1.3, 0.7, -0.4, -0.4, 1.2, 0, 0.8, 1.4, 0.8, 0, -0.3, 2,
0, -0.2, -0.2, -0.5, 0.5, -0.2, -1.5, 0.8, 0.3, -0.2, 0.5, -0.1,
0), AverageCONS = c(0, 1.17870603993396, 0, 0, 0, 0, 3.61936244127144,
0.416666666666667, 0, 4.32707915240231, 7.6364088578926, 0.257076257076257,
0, 22.4207759459411, 8.04871523114194, 14.6555459609091, 16.2250782932878,
1.17084307021898, 6.78706870557528, 0, 0, 9.7539118534683, 0,
0, 0, 0.0402095172505245, 0, 0, 0.0636265006342972, 0, 0.171974252305606,
0, 11.1524740312643, 2.68040672020172, 6.2111801242236, 3.24760735460988,
28.2976799963101, 0, 7.5732270977962, 0, 0, 1.49412278319752,
70.2064896755162, 34.0042105697558, 18.5823772614653, 18.0896275972026,
8.41449577357745, 10, 0, 0, 34.7491138493683, 8.36236933797909,
39.6563615833003, 74.4262295081967, 22.3611248302746, 10, 22.911760840126,
0, 0.0666722800439236, 0, 50.3843726943174, 0, 0, 1.72909969128655,
0), BRGOVMEHR = c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), ApprovalGOV = c(7.88127469343306, 7.67671239967451,
7.33312916270801, 7.26681550104175, 7.57774047678561, 7.44440610328638,
7.51950710910292, 7.43455364650782, 7.21735906430465, 6.93572597869877,
7.33281216290814, 7.34110311728404, 7.81953413874152, 7.78638539397364,
7.5580444419889, 6.70433786365357, 6.76698016084029, 6.9932335657093,
7.89148017350285, 8.24705859843768, 8.20161269191644, 6.13021112846596,
7.44587363057623, 7.48928443049876, 6.66476678973035, 6.48625864551226,
6.13914369458924, 6.05634679973895, 5.9702909369734, 6.19216550005443,
6.87967122943963, 7.49940214266322, 7.08991191311255, 7.3351806925688,
7.46762039999888, 7.22336518577119, 6.75192112299076, 6.61614229895973,
6.39396085192013, 6.09682321355397, 5.99711627005931, 5.95861508444216,
6.31451929735713, 6.07326509257996, 7.58677238161551, 7.24041796080827,
6.69547440053917, 7.72437292977251, 7.61985191697131, 7.85861327446016,
7.78974162557168, 8.00182694049075, 7.82019060836613, 7.58946475073855,
7.89751118735182, 7.14978411180804, 7.4578134112501, 6.24455242517448,
5.8823776113788, 6.08103241246385, 5.73879250743035, 5.68128370028589,
5.83312282222293, 6.49992542902688, 6.45920800878159)), row.names = c(NA,
-65L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

If the input is dat then convert that to a zoo object z having a yearqtr index. Converting z to a ts object will insert NAs for the missing entries and then we can use na.approx on that to fill in those NAs.
library(zoo)
z <- read.zoo(dat, FUN = function(x) as.yearqtr(paste(x), "%Y.%q"))
na.approx(as.ts(z))
EDIT
Simplified slightly.

Answer 2

The code below uses the tidyverse packages to create a new dataframe with 4 rows per each year in your dataset and then uses left_join to join your original dataset to that template. This will return NA values for missing quarters in your original data.
EDIT: removed assigning data. Data provided is in the df object.
library(tidyverse)
yrs <- df %>%
mutate(year = str_replace_all(Quarter.y, "\\.\\d{1}$", "")) %>%
distinct(year) %>%
as_vector()
impute <- tibble(yr = as.integer(rep(yrs, 4))) %>%
group_by(yr) %>%
mutate(qtr = row_number()) %>%
ungroup() %>%
arrange(yr, qtr) %>%
mutate(yr_qtr = paste0(yr, ".", qtr) %>%
as.numeric()) %>%
left_join(df, by = c("yr_qtr" = "Quarter.y"))
As far as imputing missing values goes, I'm not sure what the right approach here is -- it will depend on what patterns you expect to see/what a reasonable default value is.