Transformação de character em numeric e criação de colunas em dataframe

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)

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="

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.

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.

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.