Last updated: 2021-03-24

Checks: 5 1

Knit directory: thesis/analysis/

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1 Loading the data

files = list.files("../data/vector/extraction/", pattern = "GDP", full.names = T)

data = lapply(files, function(x){
  filename = str_split(basename(x), "_")[[1]]
  if(length(filename) == 3){
    unit = filename[1]
    buffer = as.numeric(filename[2])
    var = str_remove(filename[3], ".gpkg")
  } else {
    unit = filename[1]
    buffer = 0
    var =  str_remove(filename[2], ".gpkg")
  }
  
  layers = ogrListLayers(x)
  layers = layers[grep("attr_", layers)]
  data = do.call(cbind, lapply(layers, function(l){
    tmp = st_read(x, layer = l, quiet = TRUE)
    names(tmp) = l
    tmp
  }))
  
  data$id = 1:nrow(data)
  data %>%
    as_tibble() %>%
    mutate(unit = unit, buffer = buffer, var = var) %>%
    gather("time", "value", -id, -unit, -buffer, -var) %>%
    mutate(time = str_remove(time, "attr_")) %>%
    mutate(time = str_sub(time, 1, 4)) %>%
    distinct(id, unit, buffer, time, value)
})

data = do.call(rbind, data)
str(data)
tibble [148,800 × 5] (S3: tbl_df/tbl/data.frame)
 $ id    : int [1:148800] 1 2 3 4 5 6 7 8 9 10 ...
 $ unit  : chr [1:148800] "basins" "basins" "basins" "basins" ...
 $ buffer: num [1:148800] 100 100 100 100 100 100 100 100 100 100 ...
 $ time  : chr [1:148800] "2000" "2000" "2000" "2000" ...
 $ value : num [1:148800] 7442 5459 4189 2447 2442 ...

2 Missing Values

data %>%
  group_by(unit, buffer) %>%
  summarise(N = n(), isna = sum(is.na(value)), isnotna = sum(!is.na(value)), perc = sum(is.na(value)) / n() * 100)
# A tibble: 8 x 6
# Groups:   unit [2]
  unit   buffer     N  isna isnotna  perc
  <chr>   <dbl> <int> <int>   <int> <dbl>
1 basins      0 20260    80   20180 0.395
2 basins     50 20260     0   20260 0    
3 basins    100 20260     0   20260 0    
4 basins    200 20260     0   20260 0    
5 states      0 16940   140   16800 0.826
6 states     50 16940     0   16940 0    
7 states    100 16940     0   16940 0    
8 states    200 16940     0   16940 0    

3 Time Series

data %>%
  filter(buffer==0) %>%
  mutate(time = as.Date(paste0(time, "-01-01"))) %>%
  group_by(time, unit) %>%
  summarise(value = median(value, na.rm = T)) %>%
  ggplot() +
  geom_point(aes(x=time, y=value, color = unit)) +
  theme_classic() +
  labs(y="GDP", x = "Time", color = "Unit of Analysis")

4 Scatter Plot

data %>%
  filter(buffer == 0) %>%
  mutate(time = as.Date(paste0(time, "-01-01"))) %>%
  ggplot()+
  geom_point(aes(y=value,x=time,color=unit), alpha = .5) +
  facet_wrap(~unit) +
  theme_classic() +
  labs(y="GDP", x = "Time", color = "Unit of Analysis") 

5 Spatial Pattern

poly_bas = st_read("../data/vector/basins_mask.gpkg", quiet = T)
crs <- st_crs("EPSG:3857")
poly_bas <- st_transform(poly_bas, crs)
poly_bas <- st_simplify(poly_bas, dTolerance = 1000, preserveTopology = T)
poly_bas$id = 1:nrow(poly_bas)
poly_adm = st_read("../data/vector/states_mask.gpkg", quiet = T)
poly_adm <- st_transform(poly_adm, crs)
poly_adm <- st_simplify(poly_adm, dTolerance = 1500, preserveTopology = T)
poly_adm$id = 1:nrow(poly_adm)


data %>%
  filter(buffer == 0) %>%
  mutate(time = as.Date(paste0(time, "-01-01"))) %>%
  group_by(unit, id) %>%
  summarise(obsv = sum(is.na(value))) -> obs_data


data %>%
  filter(buffer == 0) %>%
  group_by(unit, id, time) %>%
  summarise(value = mean(value, na.rm = T)) -> sum_data

poly_adm = left_join(poly_adm, filter(sum_data, unit == "states"))
poly_bas = left_join(poly_bas, filter(sum_data, unit == "basins"))
poly_adm = left_join(poly_adm, filter(obs_data, unit == "states"))
poly_bas = left_join(poly_bas, filter(obs_data, unit == "basins"))

5.1 Administrative Units by month

5.1.1 Number of missing observations

tm_shape(poly_adm) +
  tm_polygons("obsv", palette = "-RdBu", border.col = "white", lwd = .5) +
  tm_facets("time")

### Precipitation map

tm_shape(poly_adm) +
  tm_polygons(col = "value", palette = "-RdYlBu", breaks = c(0, 1000, 2000, 4000, 8000, 16000, 32000, +Inf), border.col = "white", lwd = .5) +
  tm_facets("time")

5.2 Sub-basin watersheds by month

5.2.1 Number of missing observations

tm_shape(poly_bas) +
  tm_polygons("obsv", palette = "-RdBu", border.col = "white", lwd = .5) +
  tm_facets("time")

5.2.2 Precipitation map

tm_shape(poly_bas) +
   tm_polygons(col = "value", palette = "-RdYlBu", breaks = c(0, 1000, 2000, 4000, 8000, 16000, 32000, +Inf), border.col = "white", lwd = .5) +
  tm_facets("time")


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.7.9.2  rgdal_1.5-18       countrycode_1.2.0  welchADF_0.3.2    
 [5] rstatix_0.6.0      ggpubr_0.4.0       scales_1.1.1       RColorBrewer_1.1-2
 [9] latex2exp_0.4.0    cubelyr_1.0.0      gridExtra_2.3      ggtext_0.1.1      
[13] magrittr_2.0.1     tmap_3.2           sf_0.9-7           raster_3.4-5      
[17] sp_1.4-4           forcats_0.5.0      stringr_1.4.0      purrr_0.3.4       
[21] readr_1.4.0        tidyr_1.1.2        tibble_3.0.6       tidyverse_1.3.0   
[25] huwiwidown_0.0.1   kableExtra_1.3.1   knitr_1.31         rmarkdown_2.7.3   
[29] bookdown_0.21      ggplot2_3.3.3      dplyr_1.0.2        devtools_2.3.2    
[33] usethis_2.0.0     

loaded via a namespace (and not attached):
  [1] readxl_1.3.1       backports_1.2.0    workflowr_1.6.2   
  [4] lwgeom_0.2-5       splines_3.6.3      crosstalk_1.1.0.1 
  [7] leaflet_2.0.3      digest_0.6.27      htmltools_0.5.1.1 
 [10] fansi_0.4.2        memoise_1.1.0      openxlsx_4.2.3    
 [13] remotes_2.2.0      modelr_0.1.8       prettyunits_1.1.1 
 [16] colorspace_2.0-0   rvest_0.3.6        haven_2.3.1       
 [19] xfun_0.21          leafem_0.1.3       callr_3.5.1       
 [22] crayon_1.4.0       jsonlite_1.7.2     lme4_1.1-26       
 [25] glue_1.4.2         stars_0.4-3        gtable_0.3.0      
 [28] webshot_0.5.2      car_3.0-10         pkgbuild_1.2.0    
 [31] abind_1.4-5        DBI_1.1.0          Rcpp_1.0.5        
 [34] viridisLite_0.3.0  gridtext_0.1.4     units_0.6-7       
 [37] foreign_0.8-71     htmlwidgets_1.5.3  httr_1.4.2        
 [40] ellipsis_0.3.1     farver_2.0.3       pkgconfig_2.0.3   
 [43] XML_3.99-0.3       dbplyr_2.0.0       utf8_1.1.4        
 [46] labeling_0.4.2     tidyselect_1.1.0   rlang_0.4.10      
 [49] later_1.1.0.1      tmaptools_3.1      munsell_0.5.0     
 [52] cellranger_1.1.0   tools_3.6.3        cli_2.3.0         
 [55] generics_0.1.0     broom_0.7.2        evaluate_0.14     
 [58] yaml_2.2.1         processx_3.4.5     leafsync_0.1.0    
 [61] fs_1.5.0           zip_2.1.1          nlme_3.1-150      
 [64] xml2_1.3.2         compiler_3.6.3     rstudioapi_0.13   
 [67] curl_4.3           png_0.1-7          e1071_1.7-4       
 [70] testthat_3.0.1     ggsignif_0.6.0     reprex_0.3.0      
 [73] statmod_1.4.35     stringi_1.5.3      highr_0.8         
 [76] ps_1.5.0           desc_1.2.0         lattice_0.20-41   
 [79] Matrix_1.2-18      nloptr_1.2.2.2     classInt_0.4-3    
 [82] vctrs_0.3.6        pillar_1.4.7       lifecycle_0.2.0   
 [85] data.table_1.13.2  httpuv_1.5.5       R6_2.5.0          
 [88] promises_1.1.1     KernSmooth_2.23-18 rio_0.5.16        
 [91] sessioninfo_1.1.1  codetools_0.2-16   dichromat_2.0-0   
 [94] boot_1.3-25        MASS_7.3-53        assertthat_0.2.1  
 [97] pkgload_1.1.0      rprojroot_2.0.2    withr_2.4.1       
[100] parallel_3.6.3     hms_1.0.0          grid_3.6.3        
[103] minqa_1.2.4        class_7.3-17       carData_3.0-4     
[106] git2r_0.27.1       base64enc_0.1-3