Oi, pessoal. Tudo bem?
Estou tentando ler e fazer análises de dados de um arquivo csv no R . A base de dados veio do site : SuperDataScience . Está na seção cinco como “Demographic Data”.
Eu tentei acrescentar uma nova coluna a esses dados apresentados como stats
e gostaria que repetissem entre de 1 a 5, eu tentei, depois vi na internet que o ideal era fazer uma nova coluna e depois acrescentar valores, mas também não adiantou.
Além disso, tentei fazer uma operação entre duas colunas e descobri que uma delas era tratado como character, tentei transformar em numeric e também não consegui.
Desde já agradeço.
stats <- read.csv(file.choose())
is.data.frame(stats)
getwd()
setwd("C:/Users/Maria Leite/Documents/R/Aula Seção 5 - DATAFRAME")
rm(stats)
getwd()
stats <- read.csv("P2-Demographic-Data.csv")
stats
#----------------------------------
# Dúvida
stats$xyz <- rep(1:5)
stats$xyz <- 1:5
stats$xyz <- 0
stats
stats$xyz <- rep(c(1:5),)
stats$xyz <- [1:5,]
stats$xyz <- 1,2,3,4,5
#operação
stats$Birth.rate + stats$Internet.users
is.character(stats$Birth.rate)
is.character(stats$Internet.users)
#---------------------------------
# Transformação dos dados
colunanumerica <- as.numeric(sub(",",".",stats$Birth.rate))
#transformação
n <- gsub(","," ",stats$Birth.rate)
n
as.numeric(n)
suppressWarnings(as.numeric(n))
as.numeric(n)
n
stats
stats
dataframe_novo <- transform(stats$Birth.rate=as.numeric(Birth.rate))
is.character(stats$Birth.rate)
is.character(stats$Internet.users)
clente
Novembro 14, 2022, 9:18pm
2
Maria, eu não sei se entendi. Os seus códigos funcionaram para mim, qual erro está aparecendo para você? A base foi lida corretamente?
Abaixo trago exemplos com as operações mencionadas:
stats <- read.csv("P2-Demographic-Data.csv")
stats$xyz <- 1:5
stats$xyz
#> [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2
#> [38] 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4
#> [75] 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1
#> [112] 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3
#> [149] 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
#> [186] 1 2 3 4 5 1 2 3 4 5
stats$Birth.rate + stats$Internet.users
#> [1] 89.14400 41.15300 65.08500 70.07700 99.04400 77.61600 55.20800
#> [8] 79.84700 96.20000 90.01880 77.00000 45.45100 93.37020 41.34000
#> [15] 49.65100 26.77200 62.26150 105.04004 87.33900 66.85200 66.67000
#> [22] 56.69200 105.70000 61.17600 65.97100 85.18800 80.90500 48.03400
#> [29] 40.26700 37.57600 96.70000 96.54000 79.88500 57.90000 45.72000
#> [36] 43.63600 43.61100 67.77600 40.82600 59.12500 60.98200 38.33000
#> [43] 86.60000 76.89080 84.31040 92.67000 34.98600 104.62970 67.09800
#> [50] 41.23800 61.42368 57.43200 35.70000 80.73500 89.70000 34.82500
#> [57] 102.21440 57.56300 94.21980 51.31100 39.75500 102.04410 56.63200
#> [64] 45.43100 38.93700 56.52500 40.60300 51.76200 68.36630 54.33400
#> [71] 80.30000 47.16500 82.78900 53.88500 82.10000 39.39300 76.14760
#> [78] 35.94500 81.84390 35.23700 35.39100 93.24770 47.85000 40.29300
#> [85] 109.94680 92.10000 66.95930 50.64000 68.04600 97.91000 76.73000
#> [92] 74.19400 50.20000 31.26200 40.54400 93.37000 96.03500 39.55100
#> [99] 83.92600 38.72100 37.92500 61.63000 103.00000 39.76300 33.73800
#> [106] 78.55290 105.07650 85.43440 77.05600 77.02300 57.14100 37.68600
#> [113] 65.54700 62.56400 76.46200 47.63800 78.41380 19.71900 71.92600
#> [120] 44.27500 45.10500 40.00100 49.90000 44.50900 83.77500 43.83700
#> [127] 83.00000 51.36100 78.04500 36.28800 104.15640 106.65340 34.22300
#> [134] 95.90000 86.86900 40.48200 63.71000 59.39800 60.79000 35.39900
#> [141] 72.44920 84.70000 69.99560 58.48800 73.19300 97.24000 58.56450
#> [148] 81.17000 41.68900 81.07600 56.17700 51.63300 90.30000 38.57800
#> [155] 38.42900 40.58530 45.39100 60.70000 51.22600 57.53700 55.85500
#> [162] 87.98260 82.87560 106.58360 54.79300 69.00000 50.24300 48.04500
#> [169] 40.58000 39.98100 46.79200 30.92200 36.85500 60.40900 78.39000
#> [176] 63.60000 63.08600 43.91800 59.67400 52.10000 72.06400 96.70000
#> [183] 60.70000 68.30600 74.74200 56.00000 59.43700 38.03900 76.99400
#> [190] 41.47200 52.94700 67.35000 44.59400 55.87100 54.21500
Created on 2022-11-14 with reprex v2.0.2
O que acaba saindo no prompt é isso
>
> #----------------------------------
> # Dúvida
>
> stats$xyz <- rep(1:5)
Error in `$<-.data.frame`(`*tmp*`, xyz, value = 1:5) :
replacement has 5 rows, data has 208
> stats$xyz <- 1:5
Error in `$<-.data.frame`(`*tmp*`, xyz, value = 1:5) :
replacement has 5 rows, data has 208
> stats$xyz <- 0
> stats$xyz <- rep(c(1:5),)
Error in `$<-.data.frame`(`*tmp*`, xyz, value = 1:5) :
replacement has 5 rows, data has 208
> stats$xyz <- [1:5,]
Error: unexpected '[' in "stats$xyz <- ["
> stats$xyz <- 1,2,3,4,5
Error: unexpected ',' in "stats$xyz <- 1,"
> stats$Birth.rate + stats$Internet.users
Error in stats$Birth.rate + stats$Internet.users :
non-numeric argument to binary operator
> is.character(stats$Birth.rate)
[1] TRUE
> is.character(stats$Internet.users)
[1] FALSE
> #---------------------------------
> # Transformação dos dados
> colunanumerica <- as.numeric(sub(",",".",stats$Birth.rate))
Warning message:
NAs introduced by coercion
> #transformação
> n <- gsub(","," ",stats$Birth.rate)
> n
[1] "10.244" "35.253" "45.985" "12.877" "11.044" "17.716" "13.308" "16.447" "13.2" "9.4"
[11] "18.3" "44.151" "11.2" "36.44" "40.551" "20.142" "9.2" "15.04" "BHS" ""
[21] "9.062" "12.5" "23.092" "10.4" "24.236" "14.931" "12.188" "16.405" "18.134" "25.267"
[31] "34.076" "10.9" "10.2" "13.385" "12.1" "37.32" "37.236" "COG" "" "16.076"
[41] "34.326" "21.625" "15.022" "10.4" "12.5" "11.436" "10.2" "8.5" "25.486" "10"
[51] "21.198" "24.738" "21.07" "EGY" "" "34.8" "9.1" "10.3" "32.925" "10.7"
[61] "20.463" "12.3" "FSM" "" "30.555" "12.2" "13.332" "33.131" "37.337" "GMB"
[71] "" "37.503" "35.362" "8.5" "19.334" "14.5" "27.465" "17.389" "18.885" "HKG"
[81] "" "21.593" "9.4" "25.345" "9.2" "20.297" "20.291" "15" "IRN" ""
[91] "31.093" "13.4" "21.3" "8.5" "13.54" "27.046" "8.2" "22.73" "35.194" "27.2"
[101] "24.462" "29.044" "KOR" "" "20.575" "27.051" "13.426" "35.521" "21.425" "15.43"
[111] "9.2" "17.863" "28.738" "10.1" "11.3" "10.2" "MAC" "" "21.023" "12.141"
[121] "34.686" "21.447" "19.104" "MKD" "" "44.138" "9.5" "18.119" "11.616" "24.275"
[131] "39.705" "33.801" "10.9" "39.459" "16.805" "29.937" "17" "49.661" "40.045" "20.788"
[141] "10.2" "11.6" "20.923" "13.12" "20.419" "29.582" "19.68" "20.198" "23.79" "28.899"
[151] "9.6" "10.8" "7.9" "21.588" "16.393" "11.94" "8.8" "13.2" "32.689" "20.576"
[161] "33.477" "38.533" "9.3" "30.578" "36.729" "17.476" "43.891" "9.2" "37.126" "34.537"
[171] "18.455" "10.1" "10.2" "11.8" "30.093" "18.6" "24.043" "45.745" "36.08" "11.041"
[181] "30.792" "21.322" "35.755" "25.409" "14.59" "19.8" "16.836" "39.518" "43.474" "11.1"
[191] "14.374" "12.5" "22.5" "16.306" "VEN" "" "10.7" "15.537" "26.739" "30.394"
[201] "26.172" "YEM" "" "20.85" "COD" "" "40.471" "35.715"
> as.numeric(n)
[1] 10.244 35.253 45.985 12.877 11.044 17.716 13.308 16.447 13.200 9.400 18.300 44.151 11.200
[14] 36.440 40.551 20.142 9.200 15.040 NA NA 9.062 12.500 23.092 10.400 24.236 14.931
[27] 12.188 16.405 18.134 25.267 34.076 10.900 10.200 13.385 12.100 37.320 37.236 NA NA
[40] 16.076 34.326 21.625 15.022 10.400 12.500 11.436 10.200 8.500 25.486 10.000 21.198 24.738
[53] 21.070 NA NA 34.800 9.100 10.300 32.925 10.700 20.463 12.300 NA NA 30.555
[66] 12.200 13.332 33.131 37.337 NA NA 37.503 35.362 8.500 19.334 14.500 27.465 17.389
[79] 18.885 NA NA 21.593 9.400 25.345 9.200 20.297 20.291 15.000 NA NA 31.093
[92] 13.400 21.300 8.500 13.540 27.046 8.200 22.730 35.194 27.200 24.462 29.044 NA NA
[105] 20.575 27.051 13.426 35.521 21.425 15.430 9.200 17.863 28.738 10.100 11.300 10.200 NA
[118] NA 21.023 12.141 34.686 21.447 19.104 NA NA 44.138 9.500 18.119 11.616 24.275
[131] 39.705 33.801 10.900 39.459 16.805 29.937 17.000 49.661 40.045 20.788 10.200 11.600 20.923
[144] 13.120 20.419 29.582 19.680 20.198 23.790 28.899 9.600 10.800 7.900 21.588 16.393 11.940
[157] 8.800 13.200 32.689 20.576 33.477 38.533 9.300 30.578 36.729 17.476 43.891 9.200 37.126
[170] 34.537 18.455 10.100 10.200 11.800 30.093 18.600 24.043 45.745 36.080 11.041 30.792 21.322
[183] 35.755 25.409 14.590 19.800 16.836 39.518 43.474 11.100 14.374 12.500 22.500 16.306 NA
[196] NA 10.700 15.537 26.739 30.394 26.172 NA NA 20.850 NA NA 40.471 35.715
Warning message:
NAs introduced by coercion
> suppressWarnings(as.numeric(n))
[1] 10.244 35.253 45.985 12.877 11.044 17.716 13.308 16.447 13.200 9.400 18.300 44.151 11.200
[14] 36.440 40.551 20.142 9.200 15.040 NA NA 9.062 12.500 23.092 10.400 24.236 14.931
[27] 12.188 16.405 18.134 25.267 34.076 10.900 10.200 13.385 12.100 37.320 37.236 NA NA
[40] 16.076 34.326 21.625 15.022 10.400 12.500 11.436 10.200 8.500 25.486 10.000 21.198 24.738
[53] 21.070 NA NA 34.800 9.100 10.300 32.925 10.700 20.463 12.300 NA NA 30.555
[66] 12.200 13.332 33.131 37.337 NA NA 37.503 35.362 8.500 19.334 14.500 27.465 17.389
[79] 18.885 NA NA 21.593 9.400 25.345 9.200 20.297 20.291 15.000 NA NA 31.093
[92] 13.400 21.300 8.500 13.540 27.046 8.200 22.730 35.194 27.200 24.462 29.044 NA NA
[105] 20.575 27.051 13.426 35.521 21.425 15.430 9.200 17.863 28.738 10.100 11.300 10.200 NA
[118] NA 21.023 12.141 34.686 21.447 19.104 NA NA 44.138 9.500 18.119 11.616 24.275
[131] 39.705 33.801 10.900 39.459 16.805 29.937 17.000 49.661 40.045 20.788 10.200 11.600 20.923
[144] 13.120 20.419 29.582 19.680 20.198 23.790 28.899 9.600 10.800 7.900 21.588 16.393 11.940
[157] 8.800 13.200 32.689 20.576 33.477 38.533 9.300 30.578 36.729 17.476 43.891 9.200 37.126
[170] 34.537 18.455 10.100 10.200 11.800 30.093 18.600 24.043 45.745 36.080 11.041 30.792 21.322
[183] 35.755 25.409 14.590 19.800 16.836 39.518 43.474 11.100 14.374 12.500 22.500 16.306 NA
[196] NA 10.700 15.537 26.739 30.394 26.172 NA NA 20.850 NA NA 40.471 35.715
> as.numeric(n)
[1] 10.244 35.253 45.985 12.877 11.044 17.716 13.308 16.447 13.200 9.400 18.300 44.151 11.200
[14] 36.440 40.551 20.142 9.200 15.040 NA NA 9.062 12.500 23.092 10.400 24.236 14.931
[27] 12.188 16.405 18.134 25.267 34.076 10.900 10.200 13.385 12.100 37.320 37.236 NA NA
[40] 16.076 34.326 21.625 15.022 10.400 12.500 11.436 10.200 8.500 25.486 10.000 21.198 24.738
[53] 21.070 NA NA 34.800 9.100 10.300 32.925 10.700 20.463 12.300 NA NA 30.555
[66] 12.200 13.332 33.131 37.337 NA NA 37.503 35.362 8.500 19.334 14.500 27.465 17.389
[79] 18.885 NA NA 21.593 9.400 25.345 9.200 20.297 20.291 15.000 NA NA 31.093
[92] 13.400 21.300 8.500 13.540 27.046 8.200 22.730 35.194 27.200 24.462 29.044 NA NA
[105] 20.575 27.051 13.426 35.521 21.425 15.430 9.200 17.863 28.738 10.100 11.300 10.200 NA
[118] NA 21.023 12.141 34.686 21.447 19.104 NA NA 44.138 9.500 18.119 11.616 24.275
[131] 39.705 33.801 10.900 39.459 16.805 29.937 17.000 49.661 40.045 20.788 10.200 11.600 20.923
[144] 13.120 20.419 29.582 19.680 20.198 23.790 28.899 9.600 10.800 7.900 21.588 16.393 11.940
[157] 8.800 13.200 32.689 20.576 33.477 38.533 9.300 30.578 36.729 17.476 43.891 9.200 37.126
[170] 34.537 18.455 10.100 10.200 11.800 30.093 18.600 24.043 45.745 36.080 11.041 30.792 21.322
[183] 35.755 25.409 14.590 19.800 16.836 39.518 43.474 11.100 14.374 12.500 22.500 16.306 NA
[196] NA 10.700 15.537 26.739 30.394 26.172 NA NA 20.850 NA NA 40.471 35.715
Warning message:
NAs introduced by coercion
> n
[1] "10.244" "35.253" "45.985" "12.877" "11.044" "17.716" "13.308" "16.447" "13.2" "9.4"
[11] "18.3" "44.151" "11.2" "36.44" "40.551" "20.142" "9.2" "15.04" "BHS" ""
[21] "9.062" "12.5" "23.092" "10.4" "24.236" "14.931" "12.188" "16.405" "18.134" "25.267"
[31] "34.076" "10.9" "10.2" "13.385" "12.1" "37.32" "37.236" "COG" "" "16.076"
[41] "34.326" "21.625" "15.022" "10.4" "12.5" "11.436" "10.2" "8.5" "25.486" "10"
[51] "21.198" "24.738" "21.07" "EGY" "" "34.8" "9.1" "10.3" "32.925" "10.7"
[61] "20.463" "12.3" "FSM" "" "30.555" "12.2" "13.332" "33.131" "37.337" "GMB"
[71] "" "37.503" "35.362" "8.5" "19.334" "14.5" "27.465" "17.389" "18.885" "HKG"
[81] "" "21.593" "9.4" "25.345" "9.2" "20.297" "20.291" "15" "IRN" ""
[91] "31.093" "13.4" "21.3" "8.5" "13.54" "27.046" "8.2" "22.73" "35.194" "27.2"
[101] "24.462" "29.044" "KOR" "" "20.575" "27.051" "13.426" "35.521" "21.425" "15.43"
[111] "9.2" "17.863" "28.738" "10.1" "11.3" "10.2" "MAC" "" "21.023" "12.141"
[121] "34.686" "21.447" "19.104" "MKD" "" "44.138" "9.5" "18.119" "11.616" "24.275"
[131] "39.705" "33.801" "10.9" "39.459" "16.805" "29.937" "17" "49.661" "40.045" "20.788"
[141] "10.2" "11.6" "20.923" "13.12" "20.419" "29.582" "19.68" "20.198" "23.79" "28.899"
[151] "9.6" "10.8" "7.9" "21.588" "16.393" "11.94" "8.8" "13.2" "32.689" "20.576"
[161] "33.477" "38.533" "9.3" "30.578" "36.729" "17.476" "43.891" "9.2" "37.126" "34.537"
[171] "18.455" "10.1" "10.2" "11.8" "30.093" "18.6" "24.043" "45.745" "36.08" "11.041"
[181] "30.792" "21.322" "35.755" "25.409" "14.59" "19.8" "16.836" "39.518" "43.474" "11.1"
[191] "14.374" "12.5" "22.5" "16.306" "VEN" "" "10.7" "15.537" "26.739" "30.394"
[201] "26.172" "YEM" "" "20.85" "COD" "" "40.471" "35.715"
> stats
Country.Name Country.Code Birth.rate Internet.users Income.Group xyz
1 Aruba ABW 10.244 78.90000 High income 0
2 Afghanistan AFG 35.253 5.90000 Low income 0
3 Angola AGO 45.985 19.10000 Upper middle income 0
4 Albania ALB 12.877 57.20000 Upper middle income 0
5 United Arab Emirates ARE 11.044 88.00000 High income 0
6 Argentina ARG 17.716 59.90000 High income 0
7 Armenia ARM 13.308 41.90000 Lower middle income 0
8 Antigua and Barbuda ATG 16.447 63.40000 High income 0
9 Australia AUS 13.2 83.00000 High income 0
10 Austria AUT 9.4 80.61880 High income 0
11 Azerbaijan AZE 18.3 58.70000 Upper middle income 0
12 Burundi BDI 44.151 1.30000 Low income 0
13 Belgium BEL 11.2 82.17020 High income 0
14 Benin BEN 36.44 4.90000 Low income 0
15 Burkina Faso BFA 40.551 9.10000 Low income 0
16 Bangladesh BGD 20.142 6.63000 Lower middle income 0
17 Bulgaria BGR 9.2 53.06150 Upper middle income 0
18 Bahrain BHR 15.04 90.00004 High income 0
19 Bahamas The BHS 15.33900 72 0
20 High income NA 0
21 Bosnia and Herzegovina BIH 9.062 57.79000 Upper middle income 0
22 Belarus BLR 12.5 54.17000 Upper middle income 0
23 Belize BLZ 23.092 33.60000 Upper middle income 0
24 Bermuda BMU 10.4 95.30000 High income 0
25 Bolivia BOL 24.236 36.94000 Lower middle income 0
26 Brazil BRA 14.931 51.04000 Upper middle income 0
27 Barbados BRB 12.188 73.00000 High income 0
28 Brunei Darussalam BRN 16.405 64.50000 High income 0
29 Bhutan BTN 18.134 29.90000 Lower middle income 0
30 Botswana BWA 25.267 15.00000 Upper middle income 0
31 Central African Republic CAF 34.076 3.50000 Low income 0
32 Canada CAN 10.9 85.80000 High income 0
33 Switzerland CHE 10.2 86.34000 High income 0
34 Chile CHL 13.385 66.50000 High income 0
35 China CHN 12.1 45.80000 Upper middle income 0
36 Cote d'Ivoire CIV 37.32 8.40000 Lower middle income 0
37 Cameroon CMR 37.236 6.40000 Lower middle income 0
38 Congo Rep. COG 37.01100 6.6 0
39 Lower middle income NA 0
40 Colombia COL 16.076 51.70000 Upper middle income 0
41 Comoros COM 34.326 6.50000 Low income 0
42 Cabo Verde CPV 21.625 37.50000 Lower middle income 0
43 Costa Rica CRI 15.022 45.96000 Upper middle income 0
44 Cuba CUB 10.4 27.93000 Upper middle income 0
45 Cayman Islands CYM 12.5 74.10000 High income 0
46 Cyprus CYP 11.436 65.45480 High income 0
47 Czech Republic CZE 10.2 74.11040 High income 0
48 Germany DEU 8.5 84.17000 High income 0
49 Djibouti DJI 25.486 9.50000 Lower middle income 0
50 Denmark DNK 10 94.62970 High income 0
51 Dominican Republic DOM 21.198 45.90000 Upper middle income 0
52 Algeria DZA 24.738 16.50000 Upper middle income 0
53 Ecuador ECU 21.07 40.35368 Upper middle income 0
54 Egypt Arab Rep. EGY 28.03200 29.4 0
55 Lower middle income NA 0
56 Eritrea ERI 34.8 0.90000 Low income 0
57 Spain ESP 9.1 71.63500 High income 0
58 Estonia EST 10.3 79.40000 High income 0
59 Ethiopia ETH 32.925 1.90000 Low income 0
60 Finland FIN 10.7 91.51440 High income 0
61 Fiji FJI 20.463 37.10000 Upper middle income 0
62 France FRA 12.3 81.91980 High income 0
63 Micronesia Fed. Sts. FSM 23.51100 27.8 0
64 Lower middle income NA 0
65 Gabon GAB 30.555 9.20000 Upper middle income 0
66 United Kingdom GBR 12.2 89.84410 High income 0
67 Georgia GEO 13.332 43.30000 Lower middle income 0
68 Ghana GHA 33.131 12.30000 Lower middle income 0
69 Guinea GIN 37.337 1.60000 Low income 0
70 Gambia The GMB 42.52500 14 0
71 Low income NA 0
72 Guinea-Bissau GNB 37.503 3.10000 Low income 0
73 Equatorial Guinea GNQ 35.362 16.40000 High income 0
74 Greece GRC 8.5 59.86630 High income 0
75 Grenada GRD 19.334 35.00000 Upper middle income 0
76 Greenland GRL 14.5 65.80000 High income 0
77 Guatemala GTM 27.465 19.70000 Lower middle income 0
78 Guam GUM 17.389 65.40000 High income 0
79 Guyana GUY 18.885 35.00000 Lower middle income 0
80 Hong Kong SAR China HKG 7.90000 74.2 0
81 High income NA 0
82 Honduras HND 21.593 17.80000 Lower middle income 0
83 Croatia HRV 9.4 66.74760 High income 0
84 Haiti HTI 25.345 10.60000 Low income 0
85 Hungary HUN 9.2 72.64390 High income 0
86 Indonesia IDN 20.297 14.94000 Lower middle income 0
87 India IND 20.291 15.10000 Lower middle income 0
88 Ireland IRL 15 78.24770 High income 0
89 Iran Islamic Rep. IRN 17.90000 29.95 0
90 Upper middle income NA 0
91 Iraq IRQ 31.093 9.20000 Upper middle income 0
92 Iceland ISL 13.4 96.54680 High income 0
93 Israel ISR 21.3 70.80000 High income 0
94 Italy ITA 8.5 58.45930 High income 0
95 Jamaica JAM 13.54 37.10000 Upper middle income 0
96 Jordan JOR 27.046 41.00000 Upper middle income 0
97 Japan JPN 8.2 89.71000 High income 0
98 Kazakhstan KAZ 22.73 54.00000 Upper middle income 0
99 Kenya KEN 35.194 39.00000 Lower middle income 0
100 Kyrgyz Republic KGZ 27.2 23.00000 Lower middle income 0
101 Cambodia KHM 24.462 6.80000 Low income 0
102 Kiribati KIR 29.044 11.50000 Lower middle income 0
103 Korea Rep. KOR 8.60000 84.77 0
104 High income NA 0
105 Kuwait KWT 20.575 75.46000 High income 0
106 Lao PDR LAO 27.051 12.50000 Lower middle income 0
107 Lebanon LBN 13.426 70.50000 Upper middle income 0
108 Liberia LBR 35.521 3.20000 Low income 0
109 Libya LBY 21.425 16.50000 Upper middle income 0
110 St. Lucia LCA 15.43 46.20000 Upper middle income 0
111 Liechtenstein LIE 9.2 93.80000 High income 0
112 Sri Lanka LKA 17.863 21.90000 Lower middle income 0
113 Lesotho LSO 28.738 5.00000 Lower middle income 0
114 Lithuania LTU 10.1 68.45290 High income 0
115 Luxembourg LUX 11.3 93.77650 High income 0
116 Latvia LVA 10.2 75.23440 High income 0
117 Macao SAR China MAC 11.25600 65.8 0
118 High income NA 0
119 Morocco MAR 21.023 56.00000 Lower middle income 0
120 Moldova MDA 12.141 45.00000 Lower middle income 0
121 Madagascar MDG 34.686 3.00000 Low income 0
122 Maldives MDV 21.447 44.10000 Upper middle income 0
123 Mexico MEX 19.104 43.46000 Upper middle income 0
124 Macedonia FYR MKD 11.22200 65.24 0
125 Upper middle income NA 0
126 Mali MLI 44.138 3.50000 Low income 0
127 Malta MLT 9.5 68.91380 High income 0
128 Myanmar MMR 18.119 1.60000 Lower middle income 0
129 Montenegro MNE 11.616 60.31000 Upper middle income 0
130 Mongolia MNG 24.275 20.00000 Upper middle income 0
131 Mozambique MOZ 39.705 5.40000 Low income 0
132 Mauritania MRT 33.801 6.20000 Lower middle income 0
133 Mauritius MUS 10.9 39.00000 Upper middle income 0
134 Malawi MWI 39.459 5.05000 Low income 0
135 Malaysia MYS 16.805 66.97000 Upper middle income 0
136 Namibia NAM 29.937 13.90000 Upper middle income 0
137 New Caledonia NCL 17 66.00000 High income 0
138 Niger NER 49.661 1.70000 Low income 0
139 Nigeria NGA 40.045 38.00000 Lower middle income 0
140 Nicaragua NIC 20.788 15.50000 Lower middle income 0
141 Netherlands NLD 10.2 93.95640 High income 0
142 Norway NOR 11.6 95.05340 High income 0
143 Nepal NPL 20.923 13.30000 Low income 0
144 New Zealand NZL 13.12 82.78000 High income 0
145 Oman OMN 20.419 66.45000 High income 0
146 Pakistan PAK 29.582 10.90000 Lower middle income 0
147 Panama PAN 19.68 44.03000 Upper middle income 0
148 Peru PER 20.198 39.20000 Upper middle income 0
149 Philippines PHL 23.79 37.00000 Lower middle income 0
150 Papua New Guinea PNG 28.899 6.50000 Lower middle income 0
151 Poland POL 9.6 62.84920 High income 0
152 Puerto Rico PRI 10.8 73.90000 High income 0
153 Portugal PRT 7.9 62.09560 High income 0
154 Paraguay PRY 21.588 36.90000 Upper middle income 0
155 French Polynesia PYF 16.393 56.80000 High income 0
156 Qatar QAT 11.94 85.30000 High income 0
157 Romania ROU 8.8 49.76450 Upper middle income 0
158 Russian Federation RUS 13.2 67.97000 High income 0
159 Rwanda RWA 32.689 9.00000 Low income 0
160 Saudi Arabia SAU 20.576 60.50000 High income 0
161 Sudan SDN 33.477 22.70000 Lower middle income 0
162 Senegal SEN 38.533 13.10000 Lower middle income 0
163 Singapore SGP 9.3 81.00000 High income 0
164 Solomon Islands SLB 30.578 8.00000 Lower middle income 0
165 Sierra Leone SLE 36.729 1.70000 Low income 0
166 El Salvador SLV 17.476 23.10930 Lower middle income 0
[ reached 'max' / getOption("max.print") -- omitted 42 rows ]
> dataframe_novo <- transform(stats$Birth.rate=as.numeric(Birth.rate))
Error: unexpected '=' in "dataframe_novo <- transform(stats$Birth.rate="
clente
Novembro 16, 2022, 6:42pm
4
Maria, qual versão do R você está usando? Certifique-se de que você está na mais atual: Download R-4.2.2 for Windows. The R-project for statistical computing.
Meu palpite é que não está dando certo porque você está usando uma versão antiga. Caso contrário, talvez a leitura da base não esteja funcionando.
Eu instalei essa versão, mas mesmo assim não funcionou.
clente
Novembro 17, 2022, 5:20pm
6
Maria, o que estou achando estranho é que o erro fala que a sua tabela tem 208 linhas, mas eu baixei o CSV e ele parece ter apenas 195. Eu também suspeito que o problema com a Birth.rate
venha de alguma formatação errada no arquivo original.
Você consegue baixar o arquivo que vem do link a seguir e compará-lo com o que você tem na sua máquina? Eles têm o mesmo número de linhas?
Link: https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P2-Demographic-Data.csv
Oi, Clente.
Em relação ao Birth.rate
ele lê como se character e não número. Tentei transformar e também não consegui.
Quando eu baixei esse link que me mandou o arquivo apareceu com 196 linhas, mas quando pedi para ler no R Studio, apareceu novamente 208.
clente
Novembro 17, 2022, 8:45pm
8
Maria, provavelmente está acontecendo alguma coisa com os caminhos dos arquivos. Se o arquivo veio com 196 linhas e no R ele aparece com 208, a única explicação (que eu consigo imaginar) é que o arquivo errado está sendo lido.
Minha sugestão é deletar o antigo, colocar o novo no lugar dele, reiniciar o RStudio e rodar tudo do começo. É importante garantir que os dois arquivos têm o mesmo nome também, senão a leitura não vai funcionar.
Clente, o que acabei fazendo foi resolver os dados “a mão”. Em uma linha nos dados csv havia vairas virgulas que criavam novas linhas e inclusive linhas vazias.
Porém, em relacão a criação de uma nova tabela e para colocar números de 1 a 5 ainda não consegui resolver.
stats$xyz <- 1:5
Error in `$<-.data.frame`(`*tmp*`, xyz, value = 1:5) :
replacement has 5 rows, data has 195
Nesse caso teu data frame tem 195 linhas, mas você está passando apenas 5 valores (1 a 5).
Uma das formas de fazer seria:
stats$xyz <- rep(1:5,39)
39, porque 195/5 = 39
Deu certo, muito obrigada.