How to use gplot_na_imputations() or ggplot_na_distribution() from the package imputeTS-CodePudding

I have a dataframe (table with 100 rows/countries and 28 columns/months between 2020 and 2022). I used the package imputeTS and used the function na_kalman() to substitute my several NAs values by some estimated values. Everything goes fine till here. After, when I try to plot using gplot_na_imputations() or ggplot_na_distribution() an error is shown: "Input x_with_na is not numeric". I think the solution is to convert my dataframe into a time series 'ts'. Any suggestions?

This is what I have:

total_tests_imp <- na_kalman(total_tests_md)
ggplot_na_imputations(x_with_na = total_tests_md, x_with_imputations = total_tests_imp)
ggplot_na_distribution(total_tests_md)

(ps.) when I run: class(total_tests_md) it appears:[1] "tbl_df" "tbl" "data.frame"

When I run `head(total_tests_md)´

# A tibble: 6 x 29
  countries   jan_20 fev_20 mar_20 abr_20 mai_20 jun_20 jul_20 ago_20 set_20 out_20 nov_20 dez_20 jan_21 fev_21 mar_21 abr_21
  <chr>        <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Afghanistan NA     NA     NA      NA     NA     NA      NA     NA     NA     NA     NA     NA      NA     NA     NA     NA 
2 Albania     NA      0.009  0.54    2.83   5.08   8.19   12.9   20.3   29.1   42.0   61.7   86.2   119.   155.   187.   214.
3 Algeria     NA     NA     NA      NA     NA     NA      NA     NA     NA     NA     NA     NA      NA     NA     NA     NA 
4 Andorra     NA     NA     NA      NA     NA     NA      NA     NA    691.  1033.  1405.  1613.   1819.  2003.  2175.  2335.
5 Angola      NA     NA     NA      NA     NA     NA      NA     NA     NA     NA     NA     NA      NA     NA     NA     NA 
6 Argentina    0.013  0.015  0.162   1.55   4.44   9.91   19.7   34.3   52.3   74.3   92.3  112.    143.   172.   204.   257.
# ... with 12 more variables: mai_21 <dbl>, jun_21 <dbl>, jul_21 <dbl>, ago_21 <dbl>, set_21 <dbl>, out_21 <dbl>,
#   nov_21 <dbl>, dez_21 <dbl>, jan_22 <dbl>, fev_22 <dbl>, mar_22 <dbl>, abr_22 <dbl>´´´

dput(head(total_tests_md))
structure(list(countries = c("Afghanistan", "Albania", "Algeria", 
"Andorra", "Angola", "Argentina"), jan_20 = c(NA, NA, NA, NA, 
NA, 0.013), fev_20 = c(NA, 0.009, NA, NA, NA, 0.015), mar_20 = c(NA, 
0.54, NA, NA, NA, 0.162), abr_20 = c(NA, 2.831, NA, NA, NA, 1.546
), mai_20 = c(NA, 5.083, NA, NA, NA, 4.445), jun_20 = c(NA, 8.192, 
NA, NA, NA, 9.913), jul_20 = c(NA, 12.852, NA, NA, NA, 19.719
), ago_20 = c(NA, 20.317, NA, NA, NA, 34.32), set_20 = c(NA, 
29.089, NA, 691.095, NA, 52.255), out_20 = c(NA, 42.031, NA, 
1033.495, NA, 74.307), nov_20 = c(NA, 61.658, NA, 1404.711, NA, 
92.271), dez_20 = c(NA, 86.158, NA, 1613.414, NA, 112.404), jan_21 = c(NA, 
119.428, NA, 1819.053, NA, 143.415), fev_21 = c(NA, 154.702, 
NA, 2003.284, NA, 171.576), mar_21 = c(NA, 186.772, NA, 2174.988, 
NA, 203.784), abr_21 = c(NA, 214.329, NA, 2335.148, NA, 257.398
), mai_21 = c(NA, 243.676, NA, 2480.234, NA, 317.92), jun_21 = c(NA, 
271.086, NA, 2543.915, NA, 375.2), jul_21 = c(NA, 299.727, NA, 
2621.83, NA, 433.25), ago_21 = c(NA, 352.728, NA, 2709.918, NA, 
492.053), set_21 = c(NA, 404.621, NA, 2767.717, NA, 528.764), 
    out_21 = c(NA, 439.925, NA, 2850.247, NA, 556.29), nov_21 = c(NA, 
    467.614, NA, 3006.839, NA, 580.944), dez_21 = c(NA, 495.44, 
    NA, 3449.208, NA, 627.339), jan_22 = c(21.413, 543.967, NA, 
    3840.758, 40.321, 730.777), fev_22 = c(22.328, 552.997, NA, 
    3882.243, 41.965, 756.948), mar_22 = c(22.695, 556.666, 5.167, 
    NA, 43.944, 777.078), abr_22 = c(NA, 558.412, NA, NA, 44.198, 
    783.816)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", 
"data.frame"))

CodePudding user response：

When you use ggplot_na_imputations or ggplot_na_distribution, you should provide vector or ts object in one dimension as it is specified in the function description :

https://www.rdocumentation.org/packages/imputeTS/versions/3.2/topics/ggplot_na_imputations

So you must convert your data.frame with all countries into a vector by country. Moreover, to convert a vector to time series, see there :

https://stat.ethz.ch/R-manual/R-devel/library/stats/html/ts.html

Your data

total_tests_md <- structure(list(countries = c("Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Argentina"),
jan_20 = c(NA, NA, NA, NA, NA, 0.013),
fev_20 = c(NA, 0.009, NA, NA, NA, 0.015),
mar_20 = c(NA, 0.54, NA, NA, NA, 0.162),
abr_20 = c(NA, 2.831, NA, 0.3, NA, 1.546)),
row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"))

Import your libraries

library(zoo)
library(imputeTS)

Convert your data.frame into a vector

# remove country name
Albania <- total_tests_md[2,-1]
Albania <- as.numeric(Albania)

# create month vector
month <- seq(as.Date("2020-01-01"), as.Date("2020-04-01"), by = "month")

When you use time series

# reasonning with ts
Albaniats <- zoo(Albania, month)
AlbaniatsInput <- Albaniats
AlbaniatsInput[1] <- 0.5

ggplot_na_imputations(x_with_na = Albaniats,
                      x_with_imputations = AlbaniatsInput,
                      x_axis_labels = index(Albaniats))
ggplot_na_distribution(Albaniats,
                       x_axis_labels = index(Albaniats))

When you use only vector

#reasoning with numeric vector
AlbaniaInput <- Albania
AlbaniaInput[1] <- 0.5

ggplot_na_imputations(x_with_na = Albania,
                      x_with_imputations = AlbaniaInput,
                      x_axis_labels = month)
ggplot_na_distribution(Albania,
                       x_axis_labels = month)