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@abessudnov
abessudnov committed Feb 19, 2020
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Expand Up @@ -52,7 +52,7 @@ Plan for today:
## 8 6.80e7 2 72 1001. 2 73 1008. 2
## 9 6.80e7 2 73 1089. 2 74 1532. 2
## 10 6.80e7 2 36 491. 2 38 187. 2
## # … with 31,173 more rows, and 2 more variables: c_age_dv <dbl>,
## # ... with 31,173 more rows, and 2 more variables: c_age_dv <dbl>,
## # c_fimnnet_dv <dbl>

2. Calculate the average difference in monthly income between waves 1
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## 8 68006807 b 73 984. 2
## 9 68006807 c 74 1164. 2
## 10 68007487 a 57 400 2
## # … with 93,539 more rows
## # ... with 93,539 more rows

``` r
# Now you can use pivot_longer and pivot_wider

Wide %>%
Long <- Wide %>%
pivot_longer(a_sex_dv:c_fimnnet_dv, names_to = "variable", values_to = "value") %>%
separate(variable, into = c("wave", "variable"), sep = "_", extra = "merge") %>%
pivot_wider(names_from = variable, values_from = value)
```

## # A tibble: 93,549 x 5
## pidp wave sex_dv age_dv fimnnet_dv
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 68004087 a 1 59 802.
## 2 68004087 b 1 60 1277.
## 3 68004087 c 1 61 914.
## 4 68006127 a 2 39 1180.
## 5 68006127 b 2 40 1116.
## 6 68006127 c 2 41 1176.
## 7 68006807 a 2 72 673
## 8 68006807 b 2 73 984.
## 9 68006807 c 2 74 1164.
## 10 68007487 a 2 57 400
## # … with 93,539 more rows

5. Estimate a model showing how income depends on age and sex for all
three waves, adding dummy variables for wave. Remember that the
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## # A tibble: 93,549 x 7
## # Groups: pidp [31,183]
## pidp wave age_dv fimnnet_dv sex_dv lagIncome leadIncome
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 68004087 a 59 802. 1 NA 1277.
## 2 68004087 b 60 1277. 1 802. 914.
## 3 68004087 c 61 914. 1 1277. NA
## 4 68006127 a 39 1180. 2 NA 1116.
## 5 68006127 b 40 1116. 2 1180. 1176.
## 6 68006127 c 41 1176. 2 1116. NA
## 7 68006807 a 72 673 2 NA 984.
## 8 68006807 b 73 984. 2 673 1164.
## 9 68006807 c 74 1164. 2 984. NA
## 10 68007487 a 57 400 2 NA 16.7
## # … with 93,539 more rows
## pidp wave sex_dv age_dv fimnnet_dv lagIncome leadIncome
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 68004087 a 1 59 802. NA 1277.
## 2 68004087 b 1 60 1277. 802. 914.
## 3 68004087 c 1 61 914. 1277. NA
## 4 68006127 a 2 39 1180. NA 1116.
## 5 68006127 b 2 40 1116. 1180. 1176.
## 6 68006127 c 2 41 1176. 1116. NA
## 7 68006807 a 2 72 673 NA 984.
## 8 68006807 b 2 73 984. 673 1164.
## 9 68006807 c 2 74 1164. 984. NA
## 10 68007487 a 2 57 400 NA 16.7
## # ... with 93,539 more rows

7. Use the three original data tables for waves 1 to 3 and combine them
in the long format. (check *bind\_rows*). Now convert into the wide
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## 8 987365 2 20 692.
## 9 987365 2 21 497.
## 10 1114525 1 36 2247.
## # … with 155,245 more rows
## # ... with 155,245 more rows

``` r
# Note that this data frame is unbalanced and includes all observations (i.e. people who took part in at least one of the three waves.)
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