diff --git a/lab-05.html b/lab-05.html new file mode 100644 index 0000000..e589c5e --- /dev/null +++ b/lab-05.html @@ -0,0 +1,9338 @@ + + + + + + + + + + +lab-05 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+
+

lab-05

+
+ + + +
+ +
+
Author
+
+

Ellya Gholmieh

+
+
+ + + +
+ + + +
+ + +
+

1. Read in the Data

+
+
library(data.table)
+library(dtplyr)
+library(dplyr)
+
+

+Attaching package: 'dplyr'
+
+
+
The following objects are masked from 'package:data.table':
+
+    between, first, last
+
+
+
The following objects are masked from 'package:stats':
+
+    filter, lag
+
+
+
The following objects are masked from 'package:base':
+
+    intersect, setdiff, setequal, union
+
+
met <- data.table::fread("C:/Users/ellya/OneDrive/Desktop/PM566labs/met_all.gz")
+
+stations <- data.table::fread("https://noaa-isd-pds.s3.amazonaws.com/isd-history.csv")
+
+
+
+

2. Setup the Data

+
+
stations[, USAF := as.integer(USAF)]
+
+
Warning in eval(jsub, SDenv, parent.frame()): NAs introduced by coercion
+
+
stations[, USAF := fifelse(USAF == 999999, NA_integer_, USAF)]
+stations[, CTRY := fifelse(CTRY == "", NA_character_, CTRY)]
+stations[, STATE := fifelse(STATE == "", NA_character_, STATE)]
+
+stations <- unique(stations[, list(USAF, CTRY, STATE)])
+
+stations <- stations[!is.na(USAF)]
+
+stations[, n := 1:.N, by = .(USAF)]
+stations <- stations[n == 1,][, n := NULL]
+
+met <- met[, c('USAFID', 'WBAN', 'year', 'month', 'day', 'hour', 'min', 'lat', 'lon', 'elev', 'wind.sp', 'temp', 'atm.press')] 
+
+met <- merge(
+  x = met,
+  y = stations,
+  by.x = "USAFID",
+  by.y = "USAF",
+  all.x = TRUE,
+  all.y = FALSE
+) 
+
+nrow(met)
+
+
[1] 2377343
+
+
head(met[, c('USAFID', 'WBAN', 'STATE')], n = 4)
+
+
Key: <USAFID>
+   USAFID  WBAN  STATE
+    <int> <int> <char>
+1: 690150 93121     CA
+2: 690150 93121     CA
+3: 690150 93121     CA
+4: 690150 93121     CA
+
+
+
+
+

Question 1: Representative Station for the US

+
+

What is the median station in terms of temperature, wind speed, and atmospheric pressure?

+
+
#National Median Values
+quantile(met$temp, na.rm = TRUE)
+
+
   0%   25%   50%   75%  100% 
+-40.0  19.6  23.5  27.8  56.0
+
+
# median temp = 23.5
+quantile(met$wind.sp, na.rm = TRUE)
+
+
  0%  25%  50%  75% 100% 
+ 0.0  0.0  2.1  3.6 36.0
+
+
# median wind speed = 2.1
+quantile(met$atm.press, na.rm = TRUE)
+
+
    0%    25%    50%    75%   100% 
+ 960.5 1011.8 1014.1 1016.4 1059.9
+
+
# median atmospheric pressure = 1014.1
+
+
+med_met <- met[,.(
+  temp_med = median(temp, na.rm = TRUE),
+  wind.sp_med = median(wind.sp, na.rm = TRUE),
+  atm.press_med = median(atm.press, na.rm = TRUE)
+), by = USAFID]
+
+coincide <- med_met[temp_med == 23.5 & wind.sp_med == 2.1 & atm.press_med == 1014.1]
+coincide
+
+
Key: <USAFID>
+Empty data.table (0 rows and 4 cols): USAFID,temp_med,wind.sp_med,atm.press_med
+
+
mtempstation <- med_met[which.min(abs(med_met$temp_med - 23.5))]
+mtempstation
+
+
Key: <USAFID>
+   USAFID temp_med wind.sp_med atm.press_med
+    <int>    <num>       <num>         <num>
+1: 720501     23.5         1.5            NA
+
+
mwindstation <- med_met[which.min(abs(med_met$wind.sp_med - 2.1))]
+mwindstation
+
+
Key: <USAFID>
+   USAFID temp_med wind.sp_med atm.press_med
+    <int>    <num>       <num>         <num>
+1: 720110       31         2.1            NA
+
+
matmstation <- med_met[which.min(abs(med_met$atm.press_med - 1014.1))]
+matmstation
+
+
Key: <USAFID>
+   USAFID temp_med wind.sp_med atm.press_med
+    <int>    <num>       <num>         <num>
+1: 722420       30         4.6        1014.1
+
+
+

The station that represents the national median temperature is station 720501, the national median wind speed is station 720110, and the national median atmospheric pressure is station 722420. None of these stations overlap.

+
+
+
+

Question 2: Representative Station Per State

+

Identify the most representative, the median, station per state using the euclidean distance. If multiple stations show in the median, select the one located at the lowest latitude.

+
+
state_met1 <- met[,.(
+  temp_med = median(temp, na.rm = TRUE),
+  wind.sp_med = median(wind.sp, na.rm = TRUE),
+  atm.press_med = median(atm.press, na.rm = TRUE)
+), by = STATE]
+
+met1 <- merge(
+  x = met,
+  y = state_met1,
+  by.x = "STATE",
+  by.y = "STATE",
+  all.x = TRUE,
+  all.y = FALSE
+) 
+
+met1[, euc := sqrt(
+  (temp - temp_med)^2 +
+  (wind.sp - wind.sp_med)^2 +
+  (atm.press - atm.press_med)^2
+)]
+
+staterep <- met1[order(euc, lat), .SD[1], by = STATE]
+staterep[order(STATE)]
+
+
     STATE USAFID  WBAN  year month   day  hour   min    lat      lon  elev
+    <char>  <int> <int> <int> <int> <int> <int> <int>  <num>    <num> <int>
+ 1:     AL 722265 13821  2019     8    18     6    56 32.383  -86.350    52
+ 2:     AR 723419 93992  2019     8    19     6    53 33.221  -92.814    87
+ 3:     AZ 722730  3124  2019     8    31    22    55 31.583 -110.344  1438
+ 4:     CA 722903  3131  2019     8     3     3    53 32.816 -117.139   129
+ 5:     CO 724665 93010  2019     8    21     9    55 39.275 -103.666  1635
+ 6:     CT 725027 54788  2019     8    12    12    53 41.510  -72.828    32
+ 7:     DE 724180 13781  2019     8    16     0    51 39.674  -75.606    24
+ 8:     FL 722210 13858  2019     8    26     3    56 30.483  -86.517    27
+ 9:     GA 722175 13860  2019     8    27     1    22 32.633  -83.600    90
+10:     IA 725470 94908  2019     8    21    23    53 42.398  -90.704   329
+11:     ID 726810 24131  2019     8    28     5    53 43.567 -116.240   874
+12:     IL 725430 94822  2019     8    21    20    54 42.193  -89.093   226
+13:     IN 725335 94833  2019     8    22     9    56 40.650  -86.150   247
+14:     KS 724520  3957  2019     8    24     2    56 37.284  -98.553   469
+15:     KY 724240 13807  2019     8    26     2    50 37.900  -85.967   238
+16:     LA 722400  3937  2019     8     7     8    53 30.125  -93.228     3
+17:     MA 725065 94726  2019     8    11    13    53 41.676  -70.958    24
+18:     MD 724060 93721  2019     8    16     3    54 39.173  -76.684    47
+19:     ME 726083  4836  2019     8     1     0    53 47.286  -68.313   305
+20:     MI 725386 94898  2019     8    19    11    51 44.022  -82.793   180
+21:     MN 726440 14925  2019     8     5     6    54 43.904  -92.492   402
+22:     MO 724450  3945  2019     8    30     5    54 38.817  -92.218   274
+23:     MS 722358 93919  2019     8    15    12    53 31.183  -90.471   129
+24:     MT 726770 24033  2019     8    11     3     0 45.800 -108.533  1088
+25:     NC 723066 13713  2019     8    18     2    56 35.344  -77.965    34
+26:     ND 720491   150  2019     8     1     0    15 46.217  -97.633   386
+27:     NE 725555 94946  2019     8     9    10    53 41.433  -99.633   771
+28:     NH 726164 54728  2019     8    21    14    52 44.368  -71.545   327
+29:     NJ 724096 14706  2019     8    11    23    56 40.016  -74.592    43
+30:     NM 723600 23051  2019     8    21     3    55 36.449 -103.153  1515
+31:     NV 725825 24121  2019     8     6     3    56 40.829 -115.788  1547
+32:     NY 726228 94740  2019     8     4    21    51 44.385  -74.207   507
+33:     OH 724290 93815  2019     8    16     3     0 39.867  -84.117   306
+34:     OK 723544  3954  2019     8     6    10    53 35.534  -97.647   396
+35:     OR 720365 24267  2019     8     9    20    56 42.070 -124.290   140
+36:     PA 725116 54737  2019     8     5    10    53 40.120  -76.294   123
+37:     RI 725079 14787  2019     8    23    17    53 41.533  -71.283    52
+38:     SC 723106 13744  2019     8    18     5    53 34.188  -79.731    46
+39:     SD 726519 94993  2019     8     4    12    56 45.669  -96.991   354
+40:     TN 723340 13893  2019     8     3    12     0 35.050  -90.000    87
+41:     TX 722536 12911  2019     8    23    14    51 29.533  -98.262   232
+42:     UT 725720 24127  2019     8     5     6    54 40.778 -111.969  1288
+43:     VA 724035 13773  2019     8    28     0    56 38.504  -77.305     4
+44:     VT 725165 94737  2019     8    21    12    56 43.533  -72.950   240
+45:     WA 720254   119  2019     8     1     5    55 46.677 -122.983    54
+46:     WI 726424 94818  2019     8    19     9    53 42.761  -87.814   205
+47:     WV 724176 13736  2019     8    19     9    53 39.643  -79.916   382
+48:     WY 725690 24089  2019     8     4     4    53 42.898 -106.473  1612
+     STATE USAFID  WBAN  year month   day  hour   min    lat      lon  elev
+    wind.sp  temp atm.press   CTRY temp_med wind.sp_med atm.press_med       euc
+      <num> <num>     <num> <char>    <num>       <num>         <num>     <num>
+ 1:     1.5  25.3    1014.9     US     25.3         1.5        1014.8 0.1000000
+ 2:     2.1  25.6    1014.5     US     25.6         2.1        1014.5 0.0000000
+ 3:     3.1  29.0    1010.7     US     29.0         3.1        1010.8 0.1000000
+ 4:     2.6  21.1    1012.8     US     21.1         2.6        1012.8 0.0000000
+ 5:     2.6  18.9    1013.6     US     18.9         2.6        1013.7 0.1000000
+ 6:     2.1  22.2    1015.2     US     22.2         2.1        1015.2 0.0000000
+ 7:     2.6  24.4    1015.4     US     24.4         2.6        1015.4 0.0000000
+ 8:     2.6  26.9    1015.1     US     27.0         2.6        1015.1 0.1000000
+ 9:     1.5  26.0    1014.9     US     26.0         1.5        1014.9 0.0000000
+10:     2.6  21.1    1014.9     US     21.0         2.6        1014.9 0.1000000
+11:     2.6  20.0    1013.0     US     20.0         2.1        1013.1 0.5099020
+12:     2.1  22.2    1014.7     US     22.2         2.1        1014.6 0.1000000
+13:     2.1  22.0    1014.6     US     22.0         2.1        1014.8 0.2000000
+14:     3.6  23.3    1013.6     US     23.3         3.6        1013.5 0.1000000
+15:     1.5  23.0    1015.3     US     23.0         1.5        1015.3 0.0000000
+16:     1.5  27.8    1014.6     US     27.8         1.5        1014.6 0.0000000
+17:     2.6  21.7    1015.0     US     21.7         2.6        1014.9 0.1000000
+18:     2.1  24.4    1015.6     US     24.4         2.1        1015.3 0.3000000
+19:     2.1  18.9    1014.1     US     18.9         2.1        1014.1 0.0000000
+20:     2.1  20.6    1014.3     US     20.6         2.1        1014.4 0.1000000
+21:     2.1  18.9    1014.9     US     19.0         2.1        1014.7 0.2236068
+22:     2.1  23.3    1014.8     US     23.3         2.1        1014.8 0.0000000
+23:     1.5  26.1    1014.9     US     26.0         1.5        1014.9 0.1000000
+24:     3.1  18.3    1014.5     US     18.3         3.1        1014.3 0.2000000
+25:     1.5  24.0    1015.8     US     24.0         1.5        1015.7 0.1000000
+26:     5.1  27.0        NA     US     18.0         3.6            NA        NA
+27:     3.1  21.7    1014.3     US     21.7         3.1        1014.3 0.0000000
+28:     1.5  18.9    1014.8     US     18.9         1.5        1014.6 0.2000000
+29:     1.5  23.3    1015.1     US     23.3         1.5        1015.1 0.0000000
+30:     3.1  24.4    1012.0     US     24.4         3.1        1012.0 0.0000000
+31:     2.6  26.7    1011.8     US     27.0         2.6        1011.8 0.3000000
+32:     2.1  20.6    1014.9     US     20.6         2.1        1014.9 0.0000000
+33:     2.6  21.7    1015.0     US     21.7         2.6        1015.0 0.0000000
+34:     3.1  26.7    1012.8     US     26.7         3.1        1012.8 0.0000000
+35:     2.1  17.2    1015.5     US     17.2         2.1        1015.4 0.1000000
+36:     1.5  21.1    1015.6     US     21.1         1.5        1015.6 0.0000000
+37:     2.6  22.2    1014.1     US     22.2         2.6        1014.0 0.1000000
+38:     1.5  25.0    1015.3     US     25.0         1.5        1015.3 0.0000000
+39:     3.6  20.0    1014.1     US     20.0         3.6        1014.3 0.2000000
+40:     1.5  23.9    1014.9     US     24.0         1.5        1014.9 0.1000000
+41:     3.1  29.0    1012.6     US     29.0         3.1        1012.6 0.0000000
+42:     4.1  26.1    1012.2     US     26.1         4.1        1012.1 0.1000000
+43:     1.5  23.3    1015.2     US     23.4         1.5        1015.2 0.1000000
+44:     1.5  18.9    1014.5     US     18.9         1.5        1014.5 0.0000000
+45:     0.0  17.0        NA     US     18.0         0.0            NA        NA
+46:     2.1  18.9    1014.6     US     18.6         2.1        1014.6 0.3000000
+47:     1.5  21.1    1015.9     US     21.1         1.5        1015.7 0.2000000
+48:     3.6  18.3    1014.1     US     18.3         3.6        1014.0 0.1000000
+    wind.sp  temp atm.press   CTRY temp_med wind.sp_med atm.press_med       euc
+
+
+
+
+

3. In the Middle?

+

Identify what is the station that is closest to the mid-point of the state. Combining these with the stations you identified in the previous question, use leaflet() to visualize all ~100 points in the same figure, applying different colors for those identified in this question.

+
+
statecenters <- data.frame(
+  longitude = state.center[1],
+  latitude = state.center[2],
+  STATE = state.abb,
+  stringsAsFactors = FALSE
+)
+
+statecenters[statecenters$state == "AK", c("x", "y")] <- c(-152.00, 63.83)
+statecenters[statecenters$state == "HI", c("x", "y")] <- c(-156.33, 20.25)
+
+statecenters <- statecenters %>%
+  rename(
+    longitude = x,
+    latitude = y)
+
+met2 <- merge(
+  x = met1,
+  y = statecenters,
+  by.x = "STATE",
+  by.y = "STATE",
+  all.x = TRUE,
+  all.y = FALSE
+) 
+
+met2[, euc2 := sqrt(
+  (lat - latitude)^2 +
+  (lon - longitude)^2 
+)]
+
+centerstation <- met2[order(euc2), .SD[1], by = STATE]
+centerstation[order(STATE)]
+
+
     STATE USAFID  WBAN  year month   day  hour   min    lat      lon  elev
+    <char>  <int> <int> <int> <int> <int> <int> <int>  <num>    <num> <int>
+ 1:     AL 722265 13821  2019     8     1    10    56 32.383  -86.366    52
+ 2:     AR 723403 13963  2019     8     1     0    53 34.727  -92.239    78
+ 3:     AZ 723745   374  2019     8     1     0    15 34.257 -111.339  1572
+ 4:     CA 723890 93193  2019     8     1     0     0 36.767 -119.717   100
+ 5:     CO 726396   422  2019     8     1     0    10 39.050 -105.516  3438
+ 6:     CT 725084 54767  2019     8     1     0    52 41.742  -72.184    75
+ 7:     DE 724093 13764  2019     8    12     0    54 38.689  -75.359    15
+ 8:     FL 722123 12809  2019     8     1    18    20 27.943  -81.783    38
+ 9:     GA 720962   337  2019     8     1     0    15 32.214  -83.128    93
+10:     IA 725472 94989  2019     8     1     0    53 41.991  -93.619   291
+11:     ID 725865 94161  2019     8     1    12    50 43.504 -114.296  1622
+12:     IL 724390 93822  2019     8     1     0    52 39.845  -89.684   187
+13:     IN 720575   173  2019     8     1     0    15 40.031  -86.251   281
+14:     KS 724506 13986  2019     8    13     0    52 38.066  -97.861   470
+15:     KY 720448   144  2019     8     1     0    15 37.578  -84.770   312
+16:     LA 720468   466  2019     8     1     0    15 30.558  -92.099    23
+17:     MA 725107  4780  2019     8     1     0    52 42.552  -71.756   106
+18:     MD 724060 93721  2019     8     1     0     0 39.183  -76.667    47
+19:     ME 726196 14610  2019     8     1     0    36 45.648  -68.693   123
+20:     MI 725405 54816  2019     8     1     0    15 43.322  -84.688   230
+21:     MN 726555 94938  2019     8     1    13    53 46.398  -94.138   374
+22:     MO 724459 53931  2019     8     1     8    56 38.096  -92.549   265
+23:     MS 722350  3940  2019     8     1     0    38 32.321  -90.078   101
+24:     MT 726776 24036  2019     8     1     0    54 47.049 -109.457  1264
+25:     NC 722131  3713  2019     8     1     0    15 35.541  -78.390    50
+26:     ND 720871   296  2019     8     1     0    15 46.768 -100.894   593
+27:     NE 725555 94946  2019     8     1     0    53 41.433  -99.633   771
+28:     NH 726155 54736  2019     8     1     0    56 43.567  -71.433   166
+29:     NJ 720407   462  2019     8     1     0    56 39.928  -74.292    25
+30:     NM 722677  3027  2019     8    13     2    53 35.000 -105.670  2160
+31:     NV 724770  3170  2019     8     1    12    53 39.600 -116.010  1812
+32:     NY 725196 64775  2019     8     1    17    53 43.234  -75.407   154
+33:     OH 720414   139  2019     8     1     0    19 40.333  -82.517   363
+34:     OK 722187 93911  2019     8     1    19    55 35.358  -96.943   327
+35:     OR 720638   224  2019     8     1     0    15 44.095 -121.200  1055
+36:     PA 725128 54739  2019     8     1     5     0 40.849  -77.849   378
+37:     RI 725079 14787  2019     8    13     1    53 41.532  -71.282    52
+38:     SC 723107   371  2019     8     1     0    15 33.587  -80.209    31
+39:     SD 726560 24025  2019     8     1     0    53 44.381 -100.285   526
+40:     TN 723273 13827  2019     8     1     0     6 36.009  -86.520   166
+41:     TX 722666 93943  2019     8     1    12    55 31.794  -98.956   423
+42:     UT 724700 93141  2019     8     1     0    53 39.609 -110.754  1785
+43:     VA 724017  3707  2019     8     1     0    15 37.358  -78.438   127
+44:     VT 726145 94705  2019     8     1     0    34 44.204  -72.562   342
+45:     WA 720388   469  2019     8     1    10    55 47.100 -122.283   164
+46:     WI 726574 94985  2019     8     1     0    54 44.638  -90.188   389
+47:     WV 724127 53801  2019     8     1     0    10 37.867  -80.400   702
+48:     WY 725690 24089  2019     8     1     6    59 42.898 -106.474  1612
+     STATE USAFID  WBAN  year month   day  hour   min    lat      lon  elev
+    wind.sp  temp atm.press   CTRY temp_med wind.sp_med atm.press_med       euc
+      <num> <num>     <num> <char>    <num>       <num>         <num>     <num>
+ 1:     1.5  22.9    1016.7     US     25.3         1.5        1014.8  3.061046
+ 2:     3.1  29.4    1016.5     US     25.6         2.1        1014.5  4.409082
+ 3:     0.0  26.0        NA     US     29.0         3.1        1010.8        NA
+ 4:     3.1  36.7    1010.3     US     21.1         2.6        1012.8 15.806960
+ 5:     4.6  13.0        NA     US     18.9         2.6        1013.7        NA
+ 6:     0.0  21.1    1016.8     US     22.2         2.1        1015.2  2.860070
+ 7:     0.0  22.8    1017.0     US     24.4         2.6        1015.4  3.446738
+ 8:    11.3  30.0        NA     US     27.0         2.6        1015.1        NA
+ 9:     0.0  29.5        NA     US     26.0         1.5        1014.9        NA
+10:     1.5  20.6    1020.0     US     21.0         2.6        1014.9  5.232590
+11:     4.6  14.0        NA     US     20.0         2.1        1013.1        NA
+12:     2.1  22.8    1019.6     US     22.2         2.1        1014.6  5.035871
+13:     2.6  22.2        NA     US     22.0         2.1        1014.8        NA
+14:     4.1  31.7    1008.0     US     23.3         3.6        1013.5 10.052860
+15:     0.0  26.0        NA     US     23.0         1.5        1015.3        NA
+16:     2.6  27.8        NA     US     27.8         1.5        1014.6        NA
+17:     1.5  22.8    1016.4     US     21.7         2.6        1014.9  2.161018
+18:     2.1  27.2    1016.5     US     24.4         2.1        1015.3  3.046309
+19:     0.0  21.1        NA     US     18.9         2.1        1014.1        NA
+20:     1.5  22.7        NA     US     20.6         2.1        1014.4        NA
+21:     4.6  22.2    1021.6     US     19.0         2.1        1014.7  8.006248
+22:     1.5  20.0    1019.2     US     23.3         2.1        1014.8  5.532630
+23:     3.1  22.8        NA     US     26.0         1.5        1014.9        NA
+24:     4.6  25.0    1014.2     US     18.3         3.1        1014.3  6.866586
+25:     0.0  26.0        NA     US     24.0         1.5        1015.7        NA
+26:     4.6  27.0        NA     US     18.0         3.6            NA        NA
+27:     3.1  26.7    1014.6     US     21.7         3.1        1014.3  5.008992
+28:     2.1  21.7    1016.2     US     18.9         1.5        1014.6  3.280244
+29:     0.0    NA        NA     US     23.3         1.5        1015.1        NA
+30:     3.1  21.7    1015.7     US     24.4         3.1        1012.0  4.580393
+31:     1.5  12.2    1014.3     US     27.0         2.6        1011.8 15.049917
+32:     4.1  26.7    1020.6     US     20.6         2.1        1014.9  8.584870
+33:     2.1  24.0        NA     US     21.7         2.6        1015.0        NA
+34:     6.2  33.1        NA     US     26.7         3.1        1012.8        NA
+35:     4.6  31.0        NA     US     17.2         2.1        1015.4        NA
+36:     0.0  17.8        NA     US     21.1         1.5        1015.6        NA
+37:     3.1  22.2    1011.5     US     22.2         2.6        1014.0  2.549510
+38:     1.5  26.0        NA     US     25.0         1.5        1015.3        NA
+39:     6.2  26.1    1013.7     US     20.0         3.6        1014.3  6.658078
+40:     2.6  29.0        NA     US     24.0         1.5        1014.9        NA
+41:     2.1  24.0        NA     US     29.0         3.1        1012.6        NA
+42:     4.6  22.2    1014.7     US     26.1         4.1        1012.1  4.713809
+43:     0.0  19.9        NA     US     23.4         1.5        1015.2        NA
+44:     0.0  19.4        NA     US     18.9         1.5        1014.5        NA
+45:     0.0  15.0        NA     US     18.0         0.0            NA        NA
+46:     0.0  22.2    1021.4     US     18.6         2.1        1014.6  7.975588
+47:     1.5  22.3        NA     US     21.1         1.5        1015.7        NA
+48:      NA    NA        NA     US     18.3         3.6        1014.0        NA
+    wind.sp  temp atm.press   CTRY temp_med wind.sp_med atm.press_med       euc
+    longitude latitude       euc2
+        <num>    <num>      <num>
+ 1:  -86.7509  32.5901 0.43707942
+ 2:  -92.2992  34.7336 0.06056071
+ 3: -111.6250  34.2192 0.28848716
+ 4: -119.7730  36.5341 0.23953791
+ 5: -105.5130  38.6777 0.37231209
+ 6:  -72.3573  41.5928 0.22867779
+ 7:  -74.9841  38.6777 0.37507026
+ 8:  -81.6850  27.8744 0.11962425
+ 9:  -83.3736  32.3329 0.27286731
+10:  -93.3714  41.9358 0.25367854
+11: -113.9300  43.5648 0.37101569
+12:  -89.3776  40.0495 0.36837645
+13:  -86.0808  40.0495 0.17120248
+14:  -98.1156  38.4204 0.43637200
+15:  -84.7674  37.3915 0.18651812
+16:  -92.2724  30.6181 0.18351994
+17:  -71.5800  42.3645 0.25716191
+18:  -76.6459  39.2778 0.09711977
+19:  -68.9801  45.6226 0.28822139
+20:  -84.6870  43.1361 0.18590269
+21:  -94.6043  46.3943 0.46631468
+22:  -92.5137  38.3347 0.24129604
+23:  -89.8065  32.6758 0.44676089
+24: -109.3200  46.8230 0.26428205
+25:  -78.4686  35.4195 0.14470733
+26: -100.0990  47.2517 0.93058621
+27:  -99.5898  41.3356 0.10655046
+28:  -71.3924  43.3934 0.17828438
+29:  -74.2336  39.9637 0.06844743
+30: -105.9420  34.4764 0.59003471
+31: -116.8510  39.1063 0.97520290
+32:  -75.1449  43.1361 0.27978710
+33:  -82.5963  40.2210 0.13723152
+34:  -97.1239  35.5053 0.23328545
+35: -120.0680  43.9078 1.14737432
+36:  -77.4500  40.9069 0.40317913
+37:  -71.1244  41.5928 0.16892128
+38:  -80.5056  33.6190 0.29832124
+39:  -99.7238  44.3365 0.56296154
+40:  -86.4560  35.6767 0.33840699
+41:  -98.7857  31.3897 0.43870329
+42: -111.3300  39.1063 0.76451507
+43:  -78.2005  37.5630 0.31373755
+44:  -72.5450  44.2508 0.04979197
+45: -119.7460  47.4231 2.55749147
+46:  -89.9941  44.5937 0.19889620
+47:  -80.6665  38.4204 0.61422619
+48: -107.2560  43.0504 0.79671184
+    longitude latitude       euc2
+
+
library(leaflet)
+stationmap <-leaflet() |>
+  addProviderTiles('CartoDB.Positron') |>
+  addCircles(
+    data = staterep,
+    lat = ~lat, lng = ~lon,
+    color = "yellow",
+    fillColor = "yellow",
+    opacity = 1, fillOpacity = 1, radius = 500
+  ) |>
+  addCircles(
+    data = centerstation,
+    lat = ~lat, lng = ~lon,
+    color = "purple",
+    fillColor = "purple",
+    opacity = 1, fillOpacity = 1, radius = 500
+  ) |>
+  addLegend('bottomleft',
+            title = 'Monitoring Stations',
+            colors = c("yellow", "purple"),
+            labels = c("State Representative", "Closest to Center"),
+            opacity = 1)
+
+stationmap
+
+
+ +
+
+
+
+

4. Means of means

+

Using the quantile() function, generate a summary table that shows the number of states included, average temperature, wind-speed, and atmospheric pressure by the variable “average temperature level,” which you’ll need to create.

+
+
statetemplevel <- met[,.(
+  temp_mean = mean(temp, na.rm = TRUE),
+  wind.sp_mean = mean(wind.sp, na.rm = TRUE),
+  atm.press_mean = mean(atm.press, na.rm = TRUE),
+  temp, wind.sp, atm.press, USAFID
+  ), by = STATE]
+
+statetemplevel[, templevel := fifelse(temp_mean < 20, "Low",
+                                      fifelse(temp_mean >= 20 & temp_mean < 25, "Mid", "High"))]
+statetemplevel
+
+
          STATE temp_mean wind.sp_mean atm.press_mean  temp wind.sp atm.press
+         <char>     <num>        <num>          <num> <num>   <num>     <num>
+      1:     CA  22.36199     2.614120       1012.640  37.2     5.7    1009.9
+      2:     CA  22.36199     2.614120       1012.640  35.6     8.2    1010.3
+      3:     CA  22.36199     2.614120       1012.640  34.4     6.7    1010.6
+      4:     CA  22.36199     2.614120       1012.640  33.3     5.1    1011.6
+      5:     CA  22.36199     2.614120       1012.640  32.8     2.1    1012.7
+     ---                                                                     
+2377339:     MT  18.16680     3.426447       1014.095  30.6     3.1    1014.7
+2377340:     MT  18.16680     3.426447       1014.095  31.1     0.0    1013.8
+2377341:     MT  18.16680     3.426447       1014.095  31.1     5.1    1013.0
+2377342:     MT  18.16680     3.426447       1014.095  31.7     4.6    1012.6
+2377343:     MT  18.16680     3.426447       1014.095  31.1     5.1    1012.7
+         USAFID templevel
+          <int>    <char>
+      1: 690150       Mid
+      2: 690150       Mid
+      3: 690150       Mid
+      4: 690150       Mid
+      5: 690150       Mid
+     ---                 
+2377339: 726798       Low
+2377340: 726798       Low
+2377341: 726798       Low
+2377342: 726798       Low
+2377343: 726798       Low
+
+
summary <- statetemplevel %>%
+  group_by(templevel) %>%
+  summarise(
+  Entries = n(),
+  NA_entries = length(is.na(templevel)),
+  n_distinct(USAFID),
+  n_distinct(STATE),
+  temp_mean = mean(temp, na.rm = TRUE),
+  wind.sp_mean = mean(wind.sp, na.rm = TRUE),
+  atm.press_mean = mean(atm.press, na.rm = TRUE),
+  ) 
+
+summary
+
+
# A tibble: 3 × 8
+  templevel Entries NA_entries `n_distinct(USAFID)` `n_distinct(STATE)`
+  <chr>       <int>      <int>                <int>               <int>
+1 High       811126     811126                  555                  12
+2 Low        430794     430794                  259                  11
+3 Mid       1135423    1135423                  781                  25
+# ℹ 3 more variables: temp_mean <dbl>, wind.sp_mean <dbl>, atm.press_mean <dbl>
+
+
+
+ +
+ + +
+ + + + + \ No newline at end of file