-
Notifications
You must be signed in to change notification settings - Fork 1
/
p12.proj.cnty.R
413 lines (322 loc) · 20.4 KB
/
p12.proj.cnty.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
## the code below projects populations using cohort change ratios (CCRs) by age/sex cohorts
## the study area is a 23 county region surrounding coastal Georgia extending into FL and SC
## county scale data from US Censuses 2000 and 2010 p12 tables
rm(list=ls())
## define rate of change (ie scenario) for cohort CCRs
popchg <- 1.0
data.out <- "tables/projections/outputs/middle/" ## define directory path for outputs dependent on scenario
data.in <- "tables/projections/inputs/" ## define directory path for data inputs
##############################################################################
## import data & limit to shared tracts/rows
p1200.un <- read.csv(paste(data.in, "p1200.10tr.raw.csv", sep = ""))
p1210 <- read.csv(paste(data.in, "p1210tr.raw.csv", sep = ""))
p1200 <- p1200.un[p1200.un$trtid10 %in% p1210$trtid10,] ## 2000 data normalized to 2010 boundaries
## sum raw data to 10-year age cohorts and rename columns accordingly; b=base year, l=launch year
p1200tr <- aggregate(cbind(VD03+VD04, VD05+VD06+VD07, VD08+VD09+VD10+VD11,
VD12+VD13, VD14+VD15, VD16+VD17, VD18+VD19+VD20+VD21, VD22+VD23+VD24+VD25, VD27+VD28,
VD29+VD30+VD31, VD32+VD33+VD34+VD35, VD36+VD37, VD38+VD39, VD40+VD41,
VD42+VD43+VD44+VD45, VD46+VD47+VD48+VD49)~trtid10, data=p1200,
sum, na.rm=TRUE)
p1200tr <- round (p1200tr, digits=0)
names(p1200tr) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
p1210tr <- aggregate(cbind(D003+D004, D005+D006+D007, D008+D009+D010+D011,
D012+D013, D014+D015, D016+D017, D018+D019+D020+D021, D022+D023+D024+D025, D027+D028,
D029+D030+D031, D032+D033+D034+D035, D036+D037, D038+D039, D040+D041,
D042+D043+D044+D045, D046+D047+D048+D049)~trtid10, data=p1210,
sum, na.rm=TRUE)
names(p1210tr) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
#################################################################################
## starting 2020 age/sex county projections here
#################################################################################
## copy base and launch year aggregated by age cohort tract data into new data frames
## create 5-digit county IDs & sum by them to create base and launch year county data frames
p1200co <- p1200tr
b.sub <- substring(p1200co$id10, 1, 5) ## submsaple tract id to 5 digits, being the county id
p1200co$id10 <- as.numeric(b.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
b <- aggregate(. ~ id10, data=p1200co, FUN=sum, na.rm=TRUE) ## sum data.frame by cntyid10
p1210co <- p1210tr
l.sub <- substring(p1210co$id10, 1, 5) ## subsample tract id to 5 digits, being the county id
p1210co$id10 <- as.numeric(l.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
l <- aggregate(. ~ id10, data=p1210co, FUN=sum, na.rm=TRUE) ## sum data.frame by cntyid10
## export both years at county level to use later
write.csv (b, paste(data.out, "p1200co.csv", sep = ""))
write.csv (l, paste(data.out, "p1210co.csv", sep = ""))
## merge data frames into one; .x=launch year and .y equals most recent census in output data.frame
merge <- merge(b, l, by="id10")
## Calculate cohort-change ratio
ccr <- aggregate (cbind(m1019.y/m09.x, m2029.y/m1019.x, m3039.y/m2029.x, m4049.y/m3039.x,
m5059.y/m4049.x, m6069.y/m5059.x, m70up.y/(m70up.x+m6069.x), f1019.y/f09.x, f2029.y/f1019.x, f3039.y/f2029.x,
f4049.y/f3039.x, f5059.y/f4049.x, f6069.y/f5059.x, f70up.y/(f70up.x+f6069.x))~id10, data=merge, sum,
na.rm=TRUE)
names(ccr) <- c("id10", "m00ccr", "m10ccr", "m20ccr", "m30ccr", "m40ccr", "m50ccr", "m60ccr",
"f00ccr", "f10ccr", "f20ccr", "f30ccr", "f40ccr", "f50ccr", "f60ccr")
id10 <- ccr$id10 ## save to reattach
ccr <- ccr*popchg ## change for each cohort
ccr$id10 <- id10 ## reattach id
## merge CCR into new data.frame with new projection launch year data
ccrmerge <- merge(ccr, l, by="id10")
## Hamilton-Perry Projection from launch year to target year
t.proj <- aggregate(cbind(m00ccr*m09, m10ccr*m1019, m20ccr*m2029, m30ccr*m3039, m40ccr*m4049,
m50ccr*m5059, m60ccr*(m6069+m70up), f00ccr*f09, f10ccr*f1019, f20ccr*f2029, f30ccr*f3039, f40ccr*f4049,
f50ccr*f5059, f60ccr*(f6069+f70up))~id10, data=ccrmerge, sum, na.rm=TRUE)
names(t.proj) <- c("id10", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
# Child-Woman Ratio (CWR) for launch population
l.cwr <- aggregate(cbind((m09+f09)/((f1019/2)+f2029+f3039+f4049))~id10, data=l,
sum, na.rm=TRUE)
names(l.cwr) <- c("id10", "cwr")
id10 <- l.cwr$id10 ## save to reattach
l.cwr$id10 <- id10 ## reattach id
## Hauer et al. (2013) iTFR, EQ(7)
tfr20 <- aggregate(cbind(35*(((m09+f09)/10)/((f1019/2)+f2029+f3039+f4049)))~id10,
data=l, sum, na.rm=TRUE)
names(tfr20) <- c("id10", "tfr")
## merge HP projected population with CWRs (male + female) for target year
t.cwrmerge <- merge(t.proj, l.cwr, by="id10")
## calculate male and female population for target year using CWRs
## final projected population for target year with unique file name according to table id (first 3),
## target year (characters 4-5), & geography level (last two)
p1220co <- aggregate(cbind((cwr/2)*((f1019/2)+f2029+f3039+f4049), m1019, m2029, m3039, m4049, m5059,
m6069, m70up, (cwr/2)*((f1019/2)+f2029+f3039+f4049), f1019, f2029, f3039, f4049, f5059, f6069,
f70up)~id10, data=t.cwrmerge, sum, na.rm=TRUE)
names(p1220co) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
p1220co <- round (p1220co, digits=0)
write.csv(p1220co, paste(data.out, "p1220co.csv", sep = ""))
#################################################################################
## starting 2030 age/sex county projections here
#################################################################################
## copy base and launch year aggregated by age cohort tract data into new data frames
## create 5-digit county IDs & sum by them to create base and launch year county data frames
# p1210co <- p1210tr
# b.sub <- substring(p1210co$id10, 1, 5) ## submsaple tract id to 5 digits, being the county id
# p1210co$id10 <- as.numeric(b.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
# b <- aggregate(. ~ id10, data=p1210co, FUN=sum) ## sum data.frame by cntyid10 for base year
## setup new base and launch years
b <- l
l <- p1220co
## merge data frames into one; .x=launch year and .y equals most recent census in output data.frame
merge <- merge(b, l, by="id10")
## Calculate cohort-change ratio between most target and launch years
ccr <- aggregate (cbind(m1019.y/m09.x, m2029.y/m1019.x, m3039.y/m2029.x, m4049.y/m3039.x,
m5059.y/m4049.x, m6069.y/m5059.x, m70up.y/(m70up.x+m6069.x), f1019.y/f09.x, f2029.y/f1019.x, f3039.y/f2029.x,
f4049.y/f3039.x, f5059.y/f4049.x, f6069.y/f5059.x, f70up.y/(f70up.x+f6069.x))~id10, data=merge, sum,
na.rm=TRUE)
names(ccr) <- c("id10", "m00ccr", "m10ccr", "m20ccr", "m30ccr", "m40ccr", "m50ccr", "m60ccr",
"f00ccr", "f10ccr", "f20ccr", "f30ccr", "f40ccr", "f50ccr", "f60ccr")
id10 <- ccr$id10 ## save to reattach
ccr <- ccr*popchg ## change for each cohort
ccr$id10 <- id10 ## reattach id
## merge CCR into new data.frame with new projection launch year data
ccrmerge <- merge(ccr, l, by="id10")
## Hamilton-Perry Projection from launch year to target year
t.proj <- aggregate(cbind(m00ccr*m09, m10ccr*m1019, m20ccr*m2029, m30ccr*m3039, m40ccr*m4049,
m50ccr*m5059, m60ccr*(m6069+m70up), f00ccr*f09, f10ccr*f1019, f20ccr*f2029, f30ccr*f3039, f40ccr*f4049,
f50ccr*f5059, f60ccr*(f6069+f70up))~id10, data=ccrmerge, sum, na.rm=TRUE)
names(t.proj) <- c("id10", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
# Child-Woman Ratio (CWR) for launch population
l.cwr <- aggregate(cbind((m09+f09)/((f1019/2)+f2029+f3039+f4049))~id10, data=l,
sum, na.rm=TRUE)
names(l.cwr) <- c("id10", "cwr")
id10 <- l.cwr$id10 ## save to reattach
l.cwr$id10 <- id10 ## reattach id
## Hauer et al. (2013) iTFR, EQ(7)
tfr30 <- aggregate(cbind(35*(((m09+f09)/10)/((f1019/2)+f2029+f3039+f4049)))~id10,
data=l, sum, na.rm=TRUE)
names(tfr30) <- c("id10", "tfr")
## merge HP projected population with CWRs (male + female) for target year
t.cwrmerge <- merge(t.proj, l.cwr, by="id10")
## calculate male and female population for target year using CWRs
## final projected population for target year with unique file name according to table id (first 3),
## target year (characters 4-5), & geography level (last two)
p1230co <- aggregate(cbind((cwr/2)*((f1019/2)+f2029+f3039+f4049), m1019, m2029, m3039, m4049, m5059,
m6069, m70up, (cwr/2)*((f1019/2)+f2029+f3039+f4049), f1019, f2029, f3039, f4049, f5059, f6069,
f70up)~id10, data=t.cwrmerge, sum, na.rm=TRUE)
names(p1230co) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
p1230co <- round (p1230co, digits=0)
write.csv(p1230co, paste(data.out, "p1230co.csv", sep = ""))
#################################################################################
## starting 2040 age/sex county projections here
#################################################################################
## copy base and launch year aggregated by age cohort tract data into new data frames
## create 5-digit county IDs & sum by them to create base and launch year county data frames
# b.sub <- substring(p1220co$id10, 1, 5) ## submsaple tract id to 5 digits, being the county id
# p1220co$id10 <- as.numeric(b.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
# b <- aggregate(. ~ id10, data=p1220co, FUN=sum) ## sum data.frame by cntyid10 for base year
#
# l.sub <- substring(p1230co$id10, 1, 5) ## subsample tract id to 5 digits, being the county id
# p1230co$id10 <- as.numeric(l.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
# l <- aggregate(. ~ id10, data=p1230co, FUN=sum, na.rm=TRUE) ## sum data.frame by cntyid10
## setup new base and launch years
b <- l
l <- p1230co
## merge data frames into one; .x=launch year and .y equals most recent census in output data.frame
merge <- merge(b, l, by="id10")
## Calculate cohort-change ratio between most target and launch years
ccr <- aggregate (cbind(m1019.y/m09.x, m2029.y/m1019.x, m3039.y/m2029.x, m4049.y/m3039.x,
m5059.y/m4049.x, m6069.y/m5059.x, m70up.y/(m70up.x+m6069.x), f1019.y/f09.x, f2029.y/f1019.x, f3039.y/f2029.x,
f4049.y/f3039.x, f5059.y/f4049.x, f6069.y/f5059.x, f70up.y/(f70up.x+f6069.x))~id10, data=merge, sum,
na.rm=TRUE)
names(ccr) <- c("id10", "m00ccr", "m10ccr", "m20ccr", "m30ccr", "m40ccr", "m50ccr", "m60ccr",
"f00ccr", "f10ccr", "f20ccr", "f30ccr", "f40ccr", "f50ccr", "f60ccr")
id10 <- ccr$id10 ## save to reattach
ccr <- ccr*popchg ## change for each cohort
ccr$id10 <- id10 ## reattach id
## merge CCR into new data.frame with new projection launch year data
ccrmerge <- merge(ccr, l, by="id10")
## Hamilton-Perry Projection from launch year to target year
t.proj <- aggregate(cbind(m00ccr*m09, m10ccr*m1019, m20ccr*m2029, m30ccr*m3039, m40ccr*m4049,
m50ccr*m5059, m60ccr*(m6069+m70up), f00ccr*f09, f10ccr*f1019, f20ccr*f2029, f30ccr*f3039, f40ccr*f4049,
f50ccr*f5059, f60ccr*(f6069+f70up))~id10, data=ccrmerge, sum, na.rm=TRUE)
names(t.proj) <- c("id10", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
# Child-Woman Ratio (CWR) for launch population
l.cwr <- aggregate(cbind((m09+f09)/((f1019/2)+f2029+f3039+f4049))~id10, data=l,
sum, na.rm=TRUE)
names(l.cwr) <- c("id10", "cwr")
id10 <- l.cwr$id10 ## save to reattach
l.cwr$id10 <- id10 ## reattach id
## Hauer et al. (2013) iTFR, EQ(7)
tfr40 <- aggregate(cbind(35*(((m09+f09)/10)/((f1019/2)+f2029+f3039+f4049)))~id10,
data=l, sum, na.rm=TRUE)
names(tfr40) <- c("id10", "tfr")
## merge HP projected population with CWRs (male + female) for target year
t.cwrmerge <- merge(t.proj, l.cwr, by="id10")
## calculate male and female population for target year using CWRs
## final projected population for target year with unique file name according to table id (first 3),
## target year (characters 4-5), & geography level (last two)
p1240co <- aggregate(cbind((cwr/2)*((f1019/2)+f2029+f3039+f4049), m1019, m2029, m3039, m4049, m5059,
m6069, m70up, (cwr/2)*((f1019/2)+f2029+f3039+f4049), f1019, f2029, f3039, f4049, f5059, f6069,
f70up)~id10, data=t.cwrmerge, sum, na.rm=TRUE)
names(p1240co) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
p1240co <- round (p1240co, digits=0)
write.csv(p1240co, paste(data.out, "p1240co.csv", sep = ""))
#################################################################################
## starting 2050 age/sex county projections here
#################################################################################
## copy base and launch year aggregated by age cohort tract data into new data frames
## create 5-digit county IDs & sum by them to create base and launch year county data frames
# b.sub <- substring(p1230co$id10, 1, 5) ## submsaple tract id to 5 digits, being the county id
# p1230co$id10 <- as.numeric(b.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
# b <- aggregate(. ~ id10, data=p1230co, FUN=sum) ## sum data.frame by cntyid10 for base year
#
# l.sub <- substring(p1240co$id10, 1, 5) ## subsample tract id to 5 digits, being the county id
# p1240co$id10 <- as.numeric(l.sub) ## attach new numeric "vector" to original data.frame as column with cntyid10 header
# l <- aggregate(. ~ id10, data=p1240co, FUN=sum, na.rm=TRUE) ## sum data.frame by cntyid10
## setup new base and launch years
b <- l
l <- p1240co
## merge data frames into one; .x=launch year and .y equals most recent census in output data.frame
merge <- merge(b, l, by="id10")
## Calculate cohort-change ratio between most target and launch years
ccr <- aggregate (cbind(m1019.y/m09.x, m2029.y/m1019.x, m3039.y/m2029.x, m4049.y/m3039.x,
m5059.y/m4049.x, m6069.y/m5059.x, m70up.y/(m70up.x+m6069.x), f1019.y/f09.x, f2029.y/f1019.x, f3039.y/f2029.x,
f4049.y/f3039.x, f5059.y/f4049.x, f6069.y/f5059.x, f70up.y/(f70up.x+f6069.x))~id10, data=merge, sum,
na.rm=TRUE)
names(ccr) <- c("id10", "m00ccr", "m10ccr", "m20ccr", "m30ccr", "m40ccr", "m50ccr", "m60ccr",
"f00ccr", "f10ccr", "f20ccr", "f30ccr", "f40ccr", "f50ccr", "f60ccr")
id10 <- ccr$id10 ## save to reattach
ccr <- ccr*popchg ## change for each cohort
ccr$id10 <- id10 ## reattach id
## merge CCR into new data.frame with new projection launch year data
ccrmerge <- merge(ccr, l, by="id10")
## Hamilton-Perry Projection from launch year to target year
t.proj <- aggregate(cbind(m00ccr*m09, m10ccr*m1019, m20ccr*m2029, m30ccr*m3039, m40ccr*m4049,
m50ccr*m5059, m60ccr*(m6069+m70up), f00ccr*f09, f10ccr*f1019, f20ccr*f2029, f30ccr*f3039, f40ccr*f4049,
f50ccr*f5059, f60ccr*(f6069+f70up))~id10, data=ccrmerge, sum, na.rm=TRUE)
names(t.proj) <- c("id10", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
# Child-Woman Ratio (CWR) for launch population
l.cwr <- aggregate(cbind((m09+f09)/((f1019/2)+f2029+f3039+f4049))~id10, data=l,
sum, na.rm=TRUE)
names(l.cwr) <- c("id10", "cwr")
id10 <- l.cwr$id10 ## save to reattach
l.cwr$id10 <- id10 ## reattach id
## Hauer et al. (2013) iTFR, EQ(7)
tfr50 <- aggregate(cbind(35*(((m09+f09)/10)/((f1019/2)+f2029+f3039+f4049)))~id10,
data=l, sum, na.rm=TRUE)
names(tfr50) <- c("id10", "tfr")
## merge HP projected population with CWRs (male + female) for target year
t.cwrmerge <- merge(t.proj, l.cwr, by="id10")
## calculate male and female population for target year using CWRs
## final projected population for target year with unique file name according to table id (first 3),
## target year (characters 4-5), & geography level (last two)
p1250co <- aggregate(cbind((cwr/2)*((f1019/2)+f2029+f3039+f4049), m1019, m2029, m3039, m4049, m5059,
m6069, m70up, (cwr/2)*((f1019/2)+f2029+f3039+f4049), f1019, f2029, f3039, f4049, f5059, f6069,
f70up)~id10, data=t.cwrmerge, sum, na.rm=TRUE)
names(p1250co) <- c("id10", "m09", "m1019", "m2029", "m3039", "m4049", "m5059", "m6069", "m70up",
"f09", "f1019", "f2029", "f3039", "f4049", "f5059", "f6069", "f70up")
p1250co <- round (p1250co, digits=0)
write.csv(p1250co, paste(data.out, "p1250co.csv", sep = ""))
#######################################################
## population (Age/Sex) sums by counties 2000 - 2050
#######################################################
pathname <- file.path(data.out, "p1250co.csv")
pop <- read.csv(pathname)
id10 <- pop$id10
pop <- pop[3:18]
pop50 <- rowSums(pop, na.rm=TRUE)
pop50 <- data.frame(cbind(id10, pop50))
pop <- read.csv(paste(data.out, "p1200co.csv", sep = ""))[,-1]
pop <- pop[pop$id10 %in% pop50$id10,]
id10 <- pop$id10
pop <- pop[,-1]
pop00 <- rowSums(pop, na.rm=TRUE)
pop00 <- data.frame(cbind(id10, pop00))
pop <- read.csv(paste(data.out, "p1210co.csv", sep = ""))[,-1]
pop <- pop[pop$id10 %in% pop50$id10,]
id10 <- pop$id10
pop <- pop[,-1]
pop10 <- rowSums(pop, na.rm=TRUE)
pop10 <- cbind(id10, pop10)
pathname <- file.path(data.out, "p1220co.csv")
pop <- read.csv(pathname)
id10 <- pop$id10
pop <- pop[3:18]
pop20 <- rowSums(pop, na.rm=TRUE)
pop20 <- cbind(id10, pop20)
pathname <- file.path(data.out, "p1230co.csv")
pop <- read.csv(pathname)
id10 <- pop$id10
pop <- pop[3:18]
pop30 <- rowSums(pop, na.rm=TRUE)
pop30 <- cbind(id10, pop30)
pathname <- file.path(data.out, "p1240co.csv")
pop <- read.csv(pathname)
id10 <- pop$id10
pop <- pop[3:18]
pop40 <- rowSums(pop, na.rm=TRUE)
pop40 <- cbind(id10, pop40)
pop0010 <- merge(pop00, pop10, by="id10")
pop2030 <- merge(pop20, pop30, by="id10")
pop4050 <- merge(pop40, pop50, by="id10")
pop0030 <- merge(pop0010, pop2030, by="id10")
pop0050 <- merge(pop0030, pop4050, by="id10")
write.csv(pop0050, paste(data.out, "sums/p12co.sums.csv", sep = ""))
library(ggplot2)
library(dplyr)
library(tidyr)
dat <- pop0050
dat <- dat %>%
group_by(id10) %>%
gather(popyear, estimate, pop00:pop50) %>%
mutate(year = substr(popyear, 4,5)) %>%
ungroup() %>%
group_by(year) %>%
summarise(estimate = sum(estimate))
ggplot(filter(dat), aes(year, estimate)) +
geom_point()
#######################################################
## population (Age/Sex) iTFR by counties 2020 - 2050
#######################################################
tfr2030 <- merge(tfr20, tfr30, by="id10")
tfr4050 <- merge(tfr40, tfr50, by="id10")
tfr2050 <- merge(tfr2030, tfr4050, by="id10")
names(tfr2050) <- c("id10", "tfr20", "tfr30", "tfr40", "tfr50")
tfr2050 <- round (tfr2050, digits=1)
write.csv(tfr2050, paste(data.out, "sums/p12co.tfr.sums.csv", sep = ""))