CODEWITHSUNDEEP
raveragepanelpanel-data
LucaCoding
LucaCoding

Reputation: 65

Compute Average values in panel data R

I am quite new to R. I have unbalanced panel data. BvD_ID_Number is the identification number for each firm, TotalAsset is the value for Total Assets from Balance sheets, in each period of time (Year). Here an overview:

structure(list(BvD_ID_Number = c("FR810911719", "FR810911719", 
"GBFC024701", "GBFC024701", "GBFC024701", "GBFC32536", "GBFC32699", 
"GBFC32699", "GBFC032748", "GBFC032748"), Year = c(2017, 2016, 
2018, 2017, 2016, 2017, 2016, 2015, 2017, 2016), TotalAsset = c(2220, 
1174, 124726, 126010, 121837, 72912, 111298, 74457, 6579, 6056
)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
))

I want, for each BvD_ID_Number, to compute the average value of TotalAsset for the entire available time window.

I used this code, but it doesn't work well:

library(dplyr)
df <- 
p_TotalAsset1 %>% 
  group_by(p_TotalAsset1$BvD_ID_Number) %>% 
   mutate(TotalAsset_Avegage = round(mean(p_TotalAsset1$TotalAsset)))

Thanks for helping me.

Upvotes: 0

Views: 873

Answers (1)

Matt
Matt

Reputation: 7385

You can use summarize or mutate:

Using summarize will fully aggregate your data to provide only the grouping variable (ID number for each firm) and the mean.

df %>% 
  group_by(BvD_ID_Number) %>% 
  summarize(TotalAsset_Average = round(mean(TotalAsset),0))

This gives us:

  BvD_ID_Number TotalAsset_Average
  <chr>                      <dbl>
1 FR810911719                1697 
2 GBFC024701               124191 
3 GBFC032748                 6318.
4 GBFC32536                 72912 
5 GBFC32699                 92878.

Using mutate

df %>% 
  group_by(BvD_ID_Number) %>% 
  mutate(TotalAsset_Average = round(mean(TotalAsset),0))

This gives us:

# A tibble: 10 x 4
# Groups:   BvD_ID_Number [5]
   BvD_ID_Number  Year TotalAsset TotalAsset_Average
   <chr>         <dbl>      <dbl>              <dbl>
 1 FR810911719    2017       2220               1697
 2 FR810911719    2016       1174               1697
 3 GBFC024701     2018     124726             124191
 4 GBFC024701     2017     126010             124191
 5 GBFC024701     2016     121837             124191
 6 GBFC32536      2017      72912              72912
 7 GBFC32699      2016     111298              92878
 8 GBFC32699      2015      74457              92878
 9 GBFC032748     2017       6579               6318
10 GBFC032748     2016       6056               6318

Data:

structure(list(BvD_ID_Number = c("FR810911719", "FR810911719", 
"GBFC024701", "GBFC024701", "GBFC024701", "GBFC32536", "GBFC32699", 
"GBFC32699", "GBFC032748", "GBFC032748"), Year = c(2017, 2016, 
2018, 2017, 2016, 2017, 2016, 2015, 2017, 2016), TotalAsset = c(2220, 
1174, 124726, 126010, 121837, 72912, 111298, 74457, 6579, 6056
)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
))

Upvotes: 1

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