I am retrieving the monthly data on the US inflation from BLS.gov via library(blsR) and my issue is that the retrieved data is a list of lists.
library(blsR)
cpi <- bls_request(query_n_series(c("CUUR0000SA0", # CPI for All-Items
"CUUR0000SETA02", # CPI for Used Cars and Trucks
"CUUR0000SAF11211")), # CPI for Meats
api_key = "6b732f6c133d48669aadd2790cb71ccc")
map(cpi, as.data.table)
It returns (below), while 36 records per each V1,V2 and V3 variables remains to be organized as a data.frame.
$series
V1 V2 V3
1: CUUR0000SA0 CUUR0000SETA02 CUUR0000SAF11211
2: <list[36]> <list[36]> <list[36]>
And the expected format as a data.frame is:
V year period periodName value
CUUR0000SA0 2022 M12 December 100
CUUR0000SETA02 2022 M12 December 100.5
CUUR0000SAF11211 2022 M12 December 100.75
CodePudding user response：
You can try this
data_x <- lapply(1:length(cpi[["series"]]), \(x) as.data.frame(do.call(rbind, cpi[["series"]][[x]][["data"]]))[-5])
data_xx <- lapply(1:length(data_x), \(x) cbind(v = cpi[["series"]][[x]][["seriesID"]], data_x[[x]]))
do.call(rbind, data_xx)
Will give us
v year period periodName value
1 CUUR0000SA0 2022 M12 December 296.797
2 CUUR0000SA0 2022 M11 November 297.711
3 CUUR0000SA0 2022 M10 October 298.012
4 CUUR0000SA0 2022 M09 September 296.808
5 CUUR0000SA0 2022 M08 August 296.171
6 CUUR0000SA0 2022 M07 July 296.276
7 CUUR0000SA0 2022 M06 June 296.311
8 CUUR0000SA0 2022 M05 May 292.296
9 CUUR0000SA0 2022 M04 April 289.109
10 CUUR0000SA0 2022 M03 March 287.504
11 CUUR0000SA0 2022 M02 February 283.716
12 CUUR0000SA0 2022 M01 January 281.148
13 CUUR0000SA0 2021 M12 December 278.802
14 CUUR0000SA0 2021 M11 November 277.948
15 CUUR0000SA0 2021 M10 October 276.589
16 CUUR0000SA0 2021 M09 September 274.310
17 CUUR0000SA0 2021 M08 August 273.567
18 CUUR0000SA0 2021 M07 July 273.003
19 CUUR0000SA0 2021 M06 June 271.696
20 CUUR0000SA0 2021 M05 May 269.195
21 CUUR0000SA0 2021 M04 April 267.054
22 CUUR0000SA0 2021 M03 March 264.877
23 CUUR0000SA0 2021 M02 February 263.014
24 CUUR0000SA0 2021 M01 January 261.582
25 CUUR0000SA0 2020 M12 December 260.474
26 CUUR0000SA0 2020 M11 November 260.229
27 CUUR0000SA0 2020 M10 October 260.388
28 CUUR0000SA0 2020 M09 September 260.280
29 CUUR0000SA0 2020 M08 August 259.918
30 CUUR0000SA0 2020 M07 July 259.101
31 CUUR0000SA0 2020 M06 June 257.797
32 CUUR0000SA0 2020 M05 May 256.394
33 CUUR0000SA0 2020 M04 April 256.389
34 CUUR0000SA0 2020 M03 March 258.115
35 CUUR0000SA0 2020 M02 February 258.678
36 CUUR0000SA0 2020 M01 January 257.971
37 CUUR0000SETA02 2022 M12 December 188.864
38 CUUR0000SETA02 2022 M11 November 193.530
39 CUUR0000SETA02 2022 M10 October 199.176
40 CUUR0000SETA02 2022 M09 September 203.867
41 CUUR0000SETA02 2022 M08 August 212.895
42 CUUR0000SETA02 2022 M07 July 213.683
43 CUUR0000SETA02 2022 M06 June 212.980
44 CUUR0000SETA02 2022 M05 May 208.373
45 CUUR0000SETA02 2022 M04 April 206.981
46 CUUR0000SETA02 2022 M03 March 208.216
47 CUUR0000SETA02 2022 M02 February 212.040
48 CUUR0000SETA02 2022 M01 January 210.293
49 CUUR0000SETA02 2021 M12 December 207.164
50 CUUR0000SETA02 2021 M11 November 200.209
51 CUUR0000SETA02 2021 M10 October 195.213
52 CUUR0000SETA02 2021 M09 September 190.207
53 CUUR0000SETA02 2021 M08 August 197.535
54 CUUR0000SETA02 2021 M07 July 200.425
55 CUUR0000SETA02 2021 M06 June 198.855
56 CUUR0000SETA02 2021 M05 May 179.532
57 CUUR0000SETA02 2021 M04 April 168.647
58 CUUR0000SETA02 2021 M03 March 153.873
59 CUUR0000SETA02 2021 M02 February 150.221
60 CUUR0000SETA02 2021 M01 January 149.659
61 CUUR0000SETA02 2020 M12 December 150.891
62 CUUR0000SETA02 2020 M11 November 152.324
63 CUUR0000SETA02 2020 M10 October 154.381
64 CUUR0000SETA02 2020 M09 September 152.887
65 CUUR0000SETA02 2020 M08 August 149.765
66 CUUR0000SETA02 2020 M07 July 141.493
67 CUUR0000SETA02 2020 M06 June 136.915
68 CUUR0000SETA02 2020 M05 May 138.383
69 CUUR0000SETA02 2020 M04 April 139.411
70 CUUR0000SETA02 2020 M03 March 140.689
71 CUUR0000SETA02 2020 M02 February 137.452
72 CUUR0000SETA02 2020 M01 January 136.064
73 CUUR0000SAF11211 2022 M12 December 318.440
74 CUUR0000SAF11211 2022 M11 November 320.049
75 CUUR0000SAF11211 2022 M10 October 323.412
76 CUUR0000SAF11211 2022 M09 September 322.857
77 CUUR0000SAF11211 2022 M08 August 321.534
78 CUUR0000SAF11211 2022 M07 July 322.115
79 CUUR0000SAF11211 2022 M06 June 320.989
80 CUUR0000SAF11211 2022 M05 May 321.060
81 CUUR0000SAF11211 2022 M04 April 319.884
82 CUUR0000SAF11211 2022 M03 March 317.642
83 CUUR0000SAF11211 2022 M02 February 313.597
84 CUUR0000SAF11211 2022 M01 January 311.191
85 CUUR0000SAF11211 2021 M12 December 312.139
86 CUUR0000SAF11211 2021 M11 November 316.520
87 CUUR0000SAF11211 2021 M10 October 314.292
88 CUUR0000SAF11211 2021 M09 September 308.306
89 CUUR0000SAF11211 2021 M08 August 301.283
90 CUUR0000SAF11211 2021 M07 July 300.549
91 CUUR0000SAF11211 2021 M06 June 296.597
92 CUUR0000SAF11211 2021 M05 May 285.991
93 CUUR0000SAF11211 2021 M04 April 280.945
94 CUUR0000SAF11211 2021 M03 March 276.764
95 CUUR0000SAF11211 2021 M02 February 275.065
96 CUUR0000SAF11211 2021 M01 January 273.843
97 CUUR0000SAF11211 2020 M12 December 272.013
98 CUUR0000SAF11211 2020 M11 November 272.939
99 CUUR0000SAF11211 2020 M10 October 274.473
100 CUUR0000SAF11211 2020 M09 September 273.730
101 CUUR0000SAF11211 2020 M08 August 277.383
102 CUUR0000SAF11211 2020 M07 July 283.826
103 CUUR0000SAF11211 2020 M06 June 298.033
104 CUUR0000SAF11211 2020 M05 May 286.185
105 CUUR0000SAF11211 2020 M04 April 270.149
106 CUUR0000SAF11211 2020 M03 March 261.489
107 CUUR0000SAF11211 2020 M02 February 260.417
108 CUUR0000SAF11211 2020 M01 January 259.503
CodePudding user response：
We could bind the data with rrapply and then reshape to 'long' with pivot_longer
library(rrapply)
library(dplyr)
library(tidyr)
rrapply(cpi, how = 'bind') %>%
pivot_longer(cols = everything(), names_to = ".value",
names_pattern = ".*\\.([^.] $)") %>%
fill(seriesID)%>%
type.convert(as.is = TRUE)
-output
# A tibble: 108 × 5
seriesID year period periodName value
<chr> <int> <chr> <chr> <dbl>
1 CUUR0000SA0 2022 M12 December 297.
2 CUUR0000SA0 2022 M11 November 298.
3 CUUR0000SA0 2022 M10 October 298.
4 CUUR0000SA0 2022 M09 September 297.
5 CUUR0000SA0 2022 M08 August 296.
6 CUUR0000SA0 2022 M07 July 296.
7 CUUR0000SA0 2022 M06 June 296.
8 CUUR0000SA0 2022 M05 May 292.
9 CUUR0000SA0 2022 M04 April 289.
10 CUUR0000SA0 2022 M03 March 288.
# … with 98 more rows
CodePudding user response：
You can parse the cpi data with jsonlite as well,
cpi <- do.call(rbind, cpi)
library(jsonlite)
out <- lapply(seq(cpi), function(x)
fromJSON(toJSON(cpi[[x]])))
out[[1]]
gives,
# $seriesID
# [1] "CUUR0000SA0"
# $data
# year period periodName value footnotes
# 1 2022 M12 December 296.797 NULL
# 2 2022 M11 November 297.711 NULL
# 3 2022 M10 October 298.012 NULL
# 4 2022 M09 September 296.808 NULL
# 5 2022 M08 August 296.171 NULL
Similarly you can reach other series with out[[2]] and out[[3]]. Also if you just want to get only the data, you can type i.e. out[[1]]$data.