Extensão - Análise de Dados

R Markdown

library(dplyr)
library(ggplot2)
library(readxl)
library(kableExtra)
library(lubridate)

dados <- read_excel("Vendas2024.xlsx")

#Garantindo os tipos corretos das colunas

dados$vendedor <- as.factor(dados$vendedor)
dados$dias <- as.Date(dados$dias)
dados$venda_diaria <- as.numeric(dados$venda_diaria)

#Verificar e remover NA em venda_diaria

sum(is.na(dados$venda_diaria))

## [1] 0

str(dados)       # estrutura

## tibble [113,202 × 5] (S3: tbl_df/tbl/data.frame)
##  $ vendedor    : Factor w/ 12 levels "101101","101102",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ dias        : Date[1:113202], format: "2018-01-01" "2018-01-01" ...
##  $ venda_diaria: num [1:113202] 372 139 354 241 123 ...
##  $ ano         : num [1:113202] 2018 2018 2018 2018 2018 ...
##  $ mes         : num [1:113202] 1 1 1 1 1 1 1 1 1 1 ...

head(dados)      # primeiras linhas

## # A tibble: 6 × 5
##   vendedor dias       venda_diaria   ano   mes
##   <fct>    <date>            <dbl> <dbl> <dbl>
## 1 101101   2018-01-01         372.  2018     1
## 2 101101   2018-01-01         139.  2018     1
## 3 101101   2018-01-01         354.  2018     1
## 4 101101   2018-01-01         241.  2018     1
## 5 101101   2018-01-01         123.  2018     1
## 6 101101   2018-01-01         165.  2018     1

summary(dados)   # resumo estatístico

##     vendedor          dias             venda_diaria         ano      
##  101102 :14604   Min.   :2018-01-01   Min.   :  9.79   Min.   :2018  
##  101101 :13742   1st Qu.:2020-09-19   1st Qu.:133.27   1st Qu.:2020  
##  101103 :12779   Median :2022-02-03   Median :254.06   Median :2022  
##  101104 :10493   Mean   :2021-11-21   Mean   :260.38   Mean   :2021  
##  101105 :10419   3rd Qu.:2023-03-15   3rd Qu.:373.85   3rd Qu.:2023  
##  101106 : 9997   Max.   :2024-03-30   Max.   :838.42   Max.   :2024  
##  (Other):41168                                                       
##       mes        
##  Min.   : 1.000  
##  1st Qu.: 3.000  
##  Median : 7.000  
##  Mean   : 6.548  
##  3rd Qu.:10.000  
##  Max.   :12.000  
##

# 1. Existe o objeto 'dados'?
if(!exists("dados")) stop("Objeto 'dados' não encontrado. Leia o Excel antes: dados <- read_excel('Vendas2024.xlsx')")

# 2. Mostrar número de linhas/colunas e nomes
cat("Dimensão: ", dim(dados), "\n")

## Dimensão:  113202 5

print(names(dados))

## [1] "vendedor"     "dias"         "venda_diaria" "ano"          "mes"

# 3. Verificar nomes vazios ou NA
which_empty <- which(is.na(names(dados)) | names(dados) == "")
if(length(which_empty) > 0) {
  cat("Colunas com nome vazio encontradas nas posições:", which_empty, "\n")
} else {
  cat("Nenhum nome de coluna vazio detectado.\n")
}

## Nenhum nome de coluna vazio detectado.

#Estatísticas por vendedor #Venda Total

# -- checagens rápidas (não elimina nada) --
if(!exists("dados")) stop("Objeto 'dados' não encontrado. Leia o Excel antes: dados <- readxl::read_excel('Vendas2024.xlsx')")

# Normaliza nomes (caso haja espaços/maiusculas)
names(dados) <- tolower(gsub("\\s+","_", names(dados)))

# Verifica se as colunas existem
if(!all(c("vendedor","venda_diaria") %in% names(dados))) {
  stop("As colunas 'vendedor' e/ou 'venda_diaria' não foram encontradas em dados. Verifique names(dados).")
}

# Força tipos minimamente seguros
dados$vendedor <- as.factor(dados$vendedor)
dados$venda_diaria <- as.numeric(dados$venda_diaria)

# Remove linhas onde venda_diaria é NA (opcional — conforme enunciado)
dados <- dados %>% filter(!is.na(venda_diaria))

# Calcula venda total por vendedor e ordena desc
venda_total_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(venda_total = sum(venda_diaria, na.rm = TRUE), .groups = "drop") %>%
  arrange(desc(venda_total))

# Exibe resultado
venda_total_vendedor

## # A tibble: 12 × 2
##    vendedor venda_total
##    <fct>          <dbl>
##  1 101102      4031176.
##  2 101101      3489519.
##  3 101103      3433923.
##  4 101105      2694622.
##  5 101104      2491491.
##  6 101108      2473665.
##  7 101106      2458056.
##  8 102111      2441308.
##  9 101107      2201441.
## 10 102112      1338667.
## 11 101109      1296194.
## 12 101110      1125154.

#Média diária

# Cálculo da média diária
media_diaria_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    media_diaria = mean(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(media_diaria))

# Exibir resultado
media_diaria_vendedor

## # A tibble: 12 × 2
##    vendedor media_diaria
##    <fct>           <dbl>
##  1 101109           304.
##  2 102112           290.
##  3 101102           276.
##  4 101108           271.
##  5 101103           269.
##  6 101105           259.
##  7 101110           256.
##  8 101101           254.
##  9 101107           250.
## 10 101106           246.
## 11 102111           245.
## 12 101104           237.

#Mediana diária

mediana_diaria_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    mediana_diaria = median(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(mediana_diaria))

mediana_diaria_vendedor

## # A tibble: 12 × 2
##    vendedor mediana_diaria
##    <fct>             <dbl>
##  1 101109             298.
##  2 102112             283.
##  3 101102             268.
##  4 101108             265.
##  5 101103             265.
##  6 101110             252.
##  7 101105             251.
##  8 101101             249.
##  9 101107             243.
## 10 101106             241.
## 11 102111             241.
## 12 101104             232.

desvio_padrao_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    desvio_padrao = sd(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(desvio_padrao))

desvio_padrao_vendedor

## # A tibble: 12 × 2
##    vendedor desvio_padrao
##    <fct>            <dbl>
##  1 101109            174.
##  2 102112            167.
##  3 101102            161.
##  4 101108            157.
##  5 101103            155.
##  6 101105            150.
##  7 101107            147.
##  8 101101            146.
##  9 101110            146.
## 10 101106            143.
## 11 102111            141.
## 12 101104            138.

#Número total de registros

n_registros_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    n_registros = n(),
    .groups = "drop"
  ) %>%
  arrange(desc(n_registros))

n_registros_vendedor

## # A tibble: 12 × 2
##    vendedor n_registros
##    <fct>          <int>
##  1 101102         14604
##  2 101101         13742
##  3 101103         12779
##  4 101104         10493
##  5 101105         10419
##  6 101106          9997
##  7 102111          9978
##  8 101108          9112
##  9 101107          8796
## 10 102112          4624
## 11 101110          4395
## 12 101109          4263

#Ordenando o resultado pela venda total (decrescente)

venda_total_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),  # criado a partir de venda_diaria
    .groups = "drop"
  ) %>%
  arrange(desc(venda_total))  # ordena do maior para o menor

venda_total_vendedor

## # A tibble: 12 × 2
##    vendedor venda_total
##    <fct>          <dbl>
##  1 101102      4031176.
##  2 101101      3489519.
##  3 101103      3433923.
##  4 101105      2694622.
##  5 101104      2491491.
##  6 101108      2473665.
##  7 101106      2458056.
##  8 102111      2441308.
##  9 101107      2201441.
## 10 102112      1338667.
## 11 101109      1296194.
## 12 101110      1125154.

#Ordenando o resultado pela venda total de forma decrescente e apresentando em formato de tabela

estat_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    venda_total     = sum(venda_diaria, na.rm = TRUE),
    media_diaria    = mean(venda_diaria, na.rm = TRUE),
    mediana_diaria  = median(venda_diaria, na.rm = TRUE),
    desvio_padrao   = sd(venda_diaria, na.rm = TRUE),
    n_registros     = n(),
    .groups = "drop"
  ) %>%
  arrange(desc(venda_total))   # ordena pela venda total

# tabela formatada
estat_vendedor %>%
  mutate(
    venda_total    = round(venda_total, 2),
    media_diaria   = round(media_diaria, 2),
    mediana_diaria = round(mediana_diaria, 2),
    desvio_padrao  = round(desvio_padrao, 2)
  ) %>%
  kbl(caption = "Estatísticas por vendedor") %>%
  kable_classic(full_width = FALSE)

Estatísticas por vendedor
vendedor	venda_total	media_diaria	mediana_diaria	desvio_padrao	n_registros
101102	4031176	276.03	268.37	160.76	14604
101101	3489519	253.93	249.28	146.27	13742
101103	3433923	268.72	264.70	155.34	12779
101105	2694622	258.63	250.79	150.14	10419
101104	2491491	237.44	232.13	137.85	10493
101108	2473665	271.47	265.24	157.04	9112
101106	2458056	245.88	240.99	143.15	9997
102111	2441308	244.67	240.99	140.90	9978
101107	2201441	250.28	243.28	146.59	8796
102112	1338667	289.50	282.73	166.85	4624
101109	1296194	304.06	297.62	173.87	4263
101110	1125154	256.01	252.31	145.72	4395

#Venda mensal total

venda_mensal_geral <- dados %>%
  mutate(ano_mes = floor_date(dias, "month")) %>%   # cria coluna ano-mês
  group_by(ano_mes) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(ano_mes)

# Tabela formatada
venda_mensal_geral %>%
  mutate(venda_total = round(venda_total, 2)) %>%
  kbl(caption = "Venda mensal total (geral)") %>%
  kable_classic(full_width = FALSE)

Venda mensal total (geral)
ano_mes	venda_total
2018-01-01	101567.93
2018-02-01	84566.85
2018-03-01	85530.13
2018-04-01	98811.06
2018-05-01	120225.68
2018-06-01	97619.83
2018-07-01	83419.18
2018-08-01	124339.31
2018-09-01	120982.09
2018-10-01	127807.90
2018-11-01	155285.26
2018-12-01	187369.81
2019-01-01	136127.00
2019-02-01	98931.27
2019-03-01	114280.20
2019-04-01	126960.42
2019-05-01	171233.06
2019-06-01	149914.75
2019-07-01	107252.77
2019-08-01	141441.62
2019-09-01	129484.14
2019-10-01	288625.81
2019-11-01	355359.75
2019-12-01	468261.28
2020-01-01	271472.65
2020-02-01	222533.09
2020-03-01	382382.08
2020-04-01	421068.32
2020-05-01	517596.70
2020-06-01	416373.11
2020-07-01	351208.06
2020-08-01	331284.75
2020-09-01	400772.74
2020-10-01	387258.35
2020-11-01	505659.62
2020-12-01	595835.98
2021-01-01	360726.31
2021-02-01	301422.03
2021-03-01	385502.04
2021-04-01	430170.60
2021-05-01	483334.28
2021-06-01	476022.42
2021-07-01	400116.30
2021-08-01	392945.07
2021-09-01	375571.56
2021-10-01	441425.94
2021-11-01	503923.84
2021-12-01	608599.31
2022-01-01	398929.02
2022-02-01	344108.08
2022-03-01	366832.24
2022-04-01	479610.28
2022-05-01	669782.26
2022-06-01	616457.44
2022-07-01	540985.75
2022-08-01	530524.87
2022-09-01	510590.58
2022-10-01	614907.36
2022-11-01	736200.89
2022-12-01	817665.03
2023-01-01	568067.46
2023-02-01	477297.07
2023-03-01	577768.37
2023-04-01	608208.92
2023-05-01	699119.04
2023-06-01	682889.59
2023-07-01	574676.55
2023-08-01	571171.72
2023-09-01	572114.63
2023-10-01	651203.73
2023-11-01	757312.16
2023-12-01	898480.62
2024-01-01	549534.22
2024-02-01	468779.94
2024-03-01	553397.54

#Venda total mensal por vendedor

venda_mensal_vendedor <- dados %>%
  mutate(ano_mes = floor_date(dias, "month")) %>%   # cria coluna ano-mês
  group_by(vendedor, ano_mes) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(vendedor, ano_mes)

# Tabela formatada
venda_mensal_vendedor %>%
  mutate(venda_total = round(venda_total, 2)) %>%
  kbl(caption = "Venda mensal total por vendedor") %>%
  kable_classic(full_width = FALSE)

Venda mensal total por vendedor
vendedor	ano_mes	venda_total
101101	2018-01-01	40681.80
101101	2018-02-01	41947.67
101101	2018-03-01	39753.13
101101	2018-04-01	40270.40
101101	2018-05-01	60735.17
101101	2018-06-01	37626.92
101101	2018-07-01	52441.26
101101	2018-08-01	34252.90
101101	2018-09-01	30023.94
101101	2018-10-01	41341.30
101101	2018-11-01	51040.47
101101	2018-12-01	39680.91
101101	2019-01-01	41224.85
101101	2019-02-01	29976.22
101101	2019-03-01	39335.37
101101	2019-04-01	45675.40
101101	2019-05-01	62870.07
101101	2019-06-01	42763.94
101101	2019-07-01	22912.14
101101	2019-08-01	33704.01
101101	2019-09-01	43377.77
101101	2019-10-01	38542.64
101101	2019-11-01	45892.56
101101	2019-12-01	59826.48
101101	2020-01-01	41723.03
101101	2020-02-01	40801.88
101101	2020-03-01	36346.38
101101	2020-04-01	57174.21
101101	2020-05-01	46558.86
101101	2020-06-01	55376.25
101101	2020-07-01	31939.62
101101	2020-08-01	34637.89
101101	2020-09-01	46056.07
101101	2020-10-01	41511.59
101101	2020-11-01	55041.55
101101	2020-12-01	57423.68
101101	2021-01-01	47447.76
101101	2021-02-01	38324.18
101101	2021-03-01	44383.22
101101	2021-04-01	53238.65
101101	2021-05-01	53321.54
101101	2021-06-01	56447.64
101101	2021-07-01	55493.95
101101	2021-08-01	51480.23
101101	2021-09-01	31795.73
101101	2021-10-01	45357.13
101101	2021-11-01	65867.85
101101	2021-12-01	62318.68
101101	2022-01-01	44862.95
101101	2022-02-01	37567.03
101101	2022-03-01	38841.08
101101	2022-04-01	71495.72
101101	2022-05-01	53722.20
101101	2022-06-01	39531.22
101101	2022-07-01	48382.74
101101	2022-08-01	43965.06
101101	2022-09-01	33527.00
101101	2022-10-01	56199.89
101101	2022-11-01	72540.26
101101	2022-12-01	71716.98
101101	2023-01-01	45169.53
101101	2023-02-01	47677.66
101101	2023-03-01	35270.33
101101	2023-04-01	44725.80
101101	2023-05-01	55171.66
101101	2023-06-01	41878.86
101101	2023-07-01	33125.65
101101	2023-08-01	43020.87
101101	2023-09-01	50643.02
101101	2023-10-01	49500.21
101101	2023-11-01	63300.72
101101	2023-12-01	76105.38
101101	2024-01-01	43886.63
101101	2024-02-01	44546.65
101101	2024-03-01	37178.72
101102	2018-01-01	60886.13
101102	2018-02-01	42619.18
101102	2018-03-01	45777.00
101102	2018-04-01	58540.66
101102	2018-05-01	59490.51
101102	2018-06-01	59992.91
101102	2018-07-01	30977.92
101102	2018-08-01	49979.25
101102	2018-09-01	55287.71
101102	2018-10-01	42099.44
101102	2018-11-01	52017.64
101102	2018-12-01	67636.95
101102	2019-01-01	48626.39
101102	2019-02-01	40099.17
101102	2019-03-01	43901.94
101102	2019-04-01	40445.01
101102	2019-05-01	47833.10
101102	2019-06-01	61289.65
101102	2019-07-01	47540.56
101102	2019-08-01	62335.37
101102	2019-09-01	53614.69
101102	2019-10-01	61192.03
101102	2019-11-01	54301.42
101102	2019-12-01	94872.18
101102	2020-01-01	38565.85
101102	2020-02-01	33627.55
101102	2020-03-01	48784.70
101102	2020-04-01	53038.01
101102	2020-05-01	66041.61
101102	2020-06-01	49906.18
101102	2020-07-01	50975.88
101102	2020-08-01	33748.93
101102	2020-09-01	47629.52
101102	2020-10-01	42814.18
101102	2020-11-01	61986.54
101102	2020-12-01	90890.89
101102	2021-01-01	60906.04
101102	2021-02-01	23835.06
101102	2021-03-01	65606.24
101102	2021-04-01	45976.78
101102	2021-05-01	38404.23
101102	2021-06-01	49060.50
101102	2021-07-01	38758.11
101102	2021-08-01	49200.11
101102	2021-09-01	63058.72
101102	2021-10-01	56906.38
101102	2021-11-01	63786.96
101102	2021-12-01	78153.60
101102	2022-01-01	55255.92
101102	2022-02-01	34207.18
101102	2022-03-01	47499.21
101102	2022-04-01	62037.36
101102	2022-05-01	46808.94
101102	2022-06-01	55693.53
101102	2022-07-01	67518.71
101102	2022-08-01	44106.31
101102	2022-09-01	38518.99
101102	2022-10-01	49778.25
101102	2022-11-01	67588.72
101102	2022-12-01	85524.24
101102	2023-01-01	46544.32
101102	2023-02-01	50858.30
101102	2023-03-01	52274.98
101102	2023-04-01	56676.20
101102	2023-05-01	44685.25
101102	2023-06-01	75753.23
101102	2023-07-01	54960.13
101102	2023-08-01	54417.98
101102	2023-09-01	54924.12
101102	2023-10-01	75660.18
101102	2023-11-01	54897.82
101102	2023-12-01	95026.18
101102	2024-01-01	47541.22
101102	2024-02-01	33181.72
101102	2024-03-01	46217.34
101103	2018-08-01	40107.16
101103	2018-09-01	35670.44
101103	2018-10-01	44367.16
101103	2018-11-01	52227.15
101103	2018-12-01	80051.95
101103	2019-01-01	46275.76
101103	2019-02-01	28855.88
101103	2019-03-01	31042.89
101103	2019-04-01	40840.01
101103	2019-05-01	60529.89
101103	2019-06-01	45861.16
101103	2019-07-01	36800.07
101103	2019-08-01	45402.24
101103	2019-09-01	32491.68
101103	2019-10-01	29469.33
101103	2019-11-01	57024.57
101103	2019-12-01	69231.29
101103	2020-01-01	34040.09
101103	2020-02-01	34720.06
101103	2020-03-01	53394.72
101103	2020-04-01	56202.20
101103	2020-05-01	61665.25
101103	2020-06-01	42014.44
101103	2020-07-01	42022.52
101103	2020-08-01	45156.58
101103	2020-09-01	55491.47
101103	2020-10-01	50256.26
101103	2020-11-01	56058.54
101103	2020-12-01	67130.83
101103	2021-01-01	36376.50
101103	2021-02-01	33595.41
101103	2021-03-01	33227.95
101103	2021-04-01	59818.55
101103	2021-05-01	58715.66
101103	2021-06-01	62600.09
101103	2021-07-01	51214.27
101103	2021-08-01	53747.81
101103	2021-09-01	45131.78
101103	2021-10-01	59707.33
101103	2021-11-01	56102.67
101103	2021-12-01	63409.49
101103	2022-01-01	51045.78
101103	2022-02-01	34164.39
101103	2022-03-01	45948.09
101103	2022-04-01	45476.96
101103	2022-05-01	53996.74
101103	2022-06-01	46459.24
101103	2022-07-01	29727.23
101103	2022-08-01	45158.18
101103	2022-09-01	54360.77
101103	2022-10-01	67618.27
101103	2022-11-01	63546.87
101103	2022-12-01	49755.54
101103	2023-01-01	47465.35
101103	2023-02-01	49485.80
101103	2023-03-01	53620.82
101103	2023-04-01	62219.08
101103	2023-05-01	55499.90
101103	2023-06-01	56547.28
101103	2023-07-01	59279.52
101103	2023-08-01	60395.88
101103	2023-09-01	58152.23
101103	2023-10-01	44951.77
101103	2023-11-01	92007.39
101103	2023-12-01	86666.28
101103	2024-01-01	41747.12
101103	2024-02-01	46263.66
101103	2024-03-01	44313.81
101104	2019-10-01	33794.18
101104	2019-11-01	49385.73
101104	2019-12-01	65688.42
101104	2020-01-01	46715.12
101104	2020-02-01	29915.38
101104	2020-03-01	42158.82
101104	2020-04-01	34877.36
101104	2020-05-01	57734.07
101104	2020-06-01	39480.47
101104	2020-07-01	38777.38
101104	2020-08-01	30401.98
101104	2020-09-01	36096.87
101104	2020-10-01	40318.73
101104	2020-11-01	56019.02
101104	2020-12-01	62334.30
101104	2021-01-01	36774.92
101104	2021-02-01	25862.77
101104	2021-03-01	38957.57
101104	2021-04-01	43709.66
101104	2021-05-01	46277.30
101104	2021-06-01	52985.08
101104	2021-07-01	31522.06
101104	2021-08-01	37061.10
101104	2021-09-01	35948.57
101104	2021-10-01	58349.95
101104	2021-11-01	63357.07
101104	2021-12-01	72010.63
101104	2022-01-01	49932.40
101104	2022-02-01	27377.62
101104	2022-03-01	33783.94
101104	2022-04-01	34471.94
101104	2022-05-01	68517.78
101104	2022-06-01	52691.69
101104	2022-07-01	46350.99
101104	2022-08-01	37598.99
101104	2022-09-01	43736.90
101104	2022-10-01	47450.31
101104	2022-11-01	54349.11
101104	2022-12-01	68822.24
101104	2023-01-01	48155.44
101104	2023-02-01	31349.82
101104	2023-03-01	53884.72
101104	2023-04-01	41858.56
101104	2023-05-01	57249.61
101104	2023-06-01	65325.10
101104	2023-07-01	36866.69
101104	2023-08-01	39211.38
101104	2023-09-01	38574.54
101104	2023-10-01	50925.85
101104	2023-11-01	68969.34
101104	2023-12-01	57583.34
101104	2024-01-01	41084.28
101104	2024-02-01	40511.71
101104	2024-03-01	48342.67
101105	2019-10-01	42449.43
101105	2019-11-01	65144.64
101105	2019-12-01	57601.64
101105	2020-01-01	30683.57
101105	2020-02-01	28573.11
101105	2020-03-01	38557.74
101105	2020-04-01	46891.04
101105	2020-05-01	54064.64
101105	2020-06-01	55954.07
101105	2020-07-01	43596.58
101105	2020-08-01	39722.83
101105	2020-09-01	46191.38
101105	2020-10-01	42497.82
101105	2020-11-01	55015.43
101105	2020-12-01	62930.58
101105	2021-01-01	48910.62
101105	2021-02-01	32011.15
101105	2021-03-01	49272.21
101105	2021-04-01	43871.95
101105	2021-05-01	59294.86
101105	2021-06-01	50159.39
101105	2021-07-01	53520.67
101105	2021-08-01	52949.18
101105	2021-09-01	44501.40
101105	2021-10-01	41925.48
101105	2021-11-01	46053.34
101105	2021-12-01	76323.37
101105	2022-01-01	53802.84
101105	2022-02-01	41266.12
101105	2022-03-01	37527.97
101105	2022-04-01	67326.98
101105	2022-05-01	38395.01
101105	2022-06-01	51755.83
101105	2022-07-01	47463.86
101105	2022-08-01	29691.43
101105	2022-09-01	50389.50
101105	2022-10-01	42623.25
101105	2022-11-01	60050.65
101105	2022-12-01	65712.66
101105	2023-01-01	54674.38
101105	2023-02-01	40549.65
101105	2023-03-01	50381.04
101105	2023-04-01	59397.07
101105	2023-05-01	58379.51
101105	2023-06-01	53443.95
101105	2023-07-01	53699.36
101105	2023-08-01	56296.74
101105	2023-09-01	41755.10
101105	2023-10-01	49514.58
101105	2023-11-01	55557.79
101105	2023-12-01	76852.62
101105	2024-01-01	55530.18
101105	2024-02-01	38729.36
101105	2024-03-01	55186.72
101106	2019-10-01	37548.02
101106	2019-11-01	49206.99
101106	2019-12-01	70671.43
101106	2020-01-01	42163.70
101106	2020-02-01	28291.89
101106	2020-03-01	44241.16
101106	2020-04-01	38296.63
101106	2020-05-01	52844.38
101106	2020-06-01	38589.21
101106	2020-07-01	29265.36
101106	2020-08-01	38487.96
101106	2020-09-01	42207.37
101106	2020-10-01	37963.25
101106	2020-11-01	59080.63
101106	2020-12-01	75311.16
101106	2021-01-01	30655.78
101106	2021-02-01	38480.01
101106	2021-03-01	39078.94
101106	2021-04-01	39281.75
101106	2021-05-01	54357.39
101106	2021-06-01	41095.05
101106	2021-07-01	33558.33
101106	2021-08-01	36138.42
101106	2021-09-01	35000.16
101106	2021-10-01	38592.88
101106	2021-11-01	49079.87
101106	2021-12-01	46704.00
101106	2022-01-01	38842.32
101106	2022-02-01	49873.47
101106	2022-03-01	32980.65
101106	2022-04-01	55487.60
101106	2022-05-01	60911.80
101106	2022-06-01	43988.99
101106	2022-07-01	33318.68
101106	2022-08-01	44194.64
101106	2022-09-01	52064.92
101106	2022-10-01	53858.19
101106	2022-11-01	64545.39
101106	2022-12-01	53160.32
101106	2023-01-01	40549.52
101106	2023-02-01	25910.63
101106	2023-03-01	52192.52
101106	2023-04-01	54543.35
101106	2023-05-01	80185.63
101106	2023-06-01	54460.64
101106	2023-07-01	42205.96
101106	2023-08-01	45039.40
101106	2023-09-01	48898.60
101106	2023-10-01	35861.74
101106	2023-11-01	48334.42
101106	2023-12-01	54848.14
101106	2024-01-01	38536.49
101106	2024-02-01	43693.53
101106	2024-03-01	43376.99
101107	2020-03-01	37411.23
101107	2020-04-01	56681.90
101107	2020-05-01	57528.10
101107	2020-06-01	47267.98
101107	2020-07-01	39980.54
101107	2020-08-01	42331.26
101107	2020-09-01	33294.96
101107	2020-10-01	50274.04
101107	2020-11-01	48352.69
101107	2020-12-01	51632.93
101107	2021-01-01	31529.34
101107	2021-02-01	38842.04
101107	2021-03-01	30994.21
101107	2021-04-01	46837.09
101107	2021-05-01	59902.15
101107	2021-06-01	56710.53
101107	2021-07-01	45639.08
101107	2021-08-01	37714.43
101107	2021-09-01	18645.64
101107	2021-10-01	52331.33
101107	2021-11-01	39344.72
101107	2021-12-01	71135.85
101107	2022-01-01	30047.33
101107	2022-02-01	48633.94
101107	2022-03-01	40480.66
101107	2022-04-01	36265.17
101107	2022-05-01	47390.38
101107	2022-06-01	39575.81
101107	2022-07-01	38074.92
101107	2022-08-01	53896.20
101107	2022-09-01	47856.28
101107	2022-10-01	38500.05
101107	2022-11-01	36167.03
101107	2022-12-01	64409.32
101107	2023-01-01	33212.71
101107	2023-02-01	33408.24
101107	2023-03-01	46666.91
101107	2023-04-01	47119.40
101107	2023-05-01	51355.69
101107	2023-06-01	49539.90
101107	2023-07-01	36181.39
101107	2023-08-01	40788.21
101107	2023-09-01	54367.96
101107	2023-10-01	48918.53
101107	2023-11-01	66891.38
101107	2023-12-01	62666.69
101107	2024-01-01	37202.57
101107	2024-02-01	27816.98
101107	2024-03-01	49625.06
101108	2020-03-01	40287.20
101108	2020-04-01	39834.30
101108	2020-05-01	59674.79
101108	2020-06-01	55556.24
101108	2020-07-01	42364.64
101108	2020-08-01	44083.46
101108	2020-09-01	52584.44
101108	2020-10-01	43293.87
101108	2020-11-01	61969.09
101108	2020-12-01	68630.77
101108	2021-01-01	34525.65
101108	2021-02-01	37742.07
101108	2021-03-01	48854.68
101108	2021-04-01	53487.21
101108	2021-05-01	50197.82
101108	2021-06-01	56985.85
101108	2021-07-01	45505.89
101108	2021-08-01	38917.57
101108	2021-09-01	56213.38
101108	2021-10-01	47959.59
101108	2021-11-01	63126.13
101108	2021-12-01	82214.15
101108	2022-01-01	34230.79
101108	2022-02-01	41191.62
101108	2022-03-01	58873.65
101108	2022-04-01	67481.57
101108	2022-05-01	76864.17
101108	2022-06-01	37195.79
101108	2022-07-01	51102.40
101108	2022-08-01	50987.90
101108	2022-09-01	39343.09
101108	2022-10-01	42726.22
101108	2022-11-01	59484.86
101108	2022-12-01	60823.14
101108	2023-01-01	39263.91
101108	2023-02-01	31232.69
101108	2023-03-01	39938.08
101108	2023-04-01	42656.60
101108	2023-05-01	70100.10
101108	2023-06-01	62499.65
101108	2023-07-01	52787.92
101108	2023-08-01	35130.81
101108	2023-09-01	46627.42
101108	2023-10-01	59917.05
101108	2023-11-01	56113.58
101108	2023-12-01	82224.56
101108	2024-01-01	36358.63
101108	2024-02-01	37009.96
101108	2024-03-01	37490.39
101109	2022-05-01	39008.32
101109	2022-06-01	58234.50
101109	2022-07-01	36848.68
101109	2022-08-01	51849.04
101109	2022-09-01	29144.07
101109	2022-10-01	52264.97
101109	2022-11-01	74003.36
101109	2022-12-01	67959.96
101109	2023-01-01	69639.75
101109	2023-02-01	55192.42
101109	2023-03-01	52421.49
101109	2023-04-01	56328.50
101109	2023-05-01	57419.20
101109	2023-06-01	75135.38
101109	2023-07-01	57240.51
101109	2023-08-01	49776.84
101109	2023-09-01	50912.83
101109	2023-10-01	79406.41
101109	2023-11-01	71912.10
101109	2023-12-01	83162.48
101109	2024-01-01	60652.90
101109	2024-02-01	33987.64
101109	2024-03-01	33693.06
101110	2022-05-01	45843.15
101110	2022-06-01	61213.07
101110	2022-07-01	47587.43
101110	2022-08-01	49755.38
101110	2022-09-01	38668.54
101110	2022-10-01	51085.62
101110	2022-11-01	50252.76
101110	2022-12-01	54660.01
101110	2023-01-01	42327.50
101110	2023-02-01	33983.23
101110	2023-03-01	36725.08
101110	2023-04-01	49210.58
101110	2023-05-01	57250.94
101110	2023-06-01	50591.92
101110	2023-07-01	50021.23
101110	2023-08-01	45758.96
101110	2023-09-01	43033.40
101110	2023-10-01	52427.85
101110	2023-11-01	70183.22
101110	2023-12-01	55249.31
101110	2024-01-01	59893.54
101110	2024-02-01	37693.37
101110	2024-03-01	41738.19
102111	2019-10-01	45630.18
102111	2019-11-01	34403.84
102111	2019-12-01	50369.84
102111	2020-01-01	37581.29
102111	2020-02-01	26603.22
102111	2020-03-01	41200.13
102111	2020-04-01	38072.67
102111	2020-05-01	61485.00
102111	2020-06-01	32228.27
102111	2020-07-01	32285.54
102111	2020-08-01	22713.86
102111	2020-09-01	41220.66
102111	2020-10-01	38328.61
102111	2020-11-01	52136.13
102111	2020-12-01	59550.84
102111	2021-01-01	33599.70
102111	2021-02-01	32729.34
102111	2021-03-01	35127.02
102111	2021-04-01	43948.96
102111	2021-05-01	62863.33
102111	2021-06-01	49978.29
102111	2021-07-01	44903.94
102111	2021-08-01	35736.22
102111	2021-09-01	45276.18
102111	2021-10-01	40295.87
102111	2021-11-01	57205.23
102111	2021-12-01	56329.54
102111	2022-01-01	40908.69
102111	2022-02-01	29826.71
102111	2022-03-01	30896.99
102111	2022-04-01	39566.98
102111	2022-05-01	63356.50
102111	2022-06-01	62146.51
102111	2022-07-01	43699.03
102111	2022-08-01	27354.80
102111	2022-09-01	37341.67
102111	2022-10-01	51251.84
102111	2022-11-01	54907.09
102111	2022-12-01	77431.22
102111	2023-01-01	50018.82
102111	2023-02-01	34667.70
102111	2023-03-01	47380.27
102111	2023-04-01	33408.09
102111	2023-05-01	49778.63
102111	2023-06-01	51324.63
102111	2023-07-01	45631.93
102111	2023-08-01	42452.77
102111	2023-09-01	39394.53
102111	2023-10-01	48633.20
102111	2023-11-01	63571.54
102111	2023-12-01	84882.05
102111	2024-01-01	52158.11
102111	2024-02-01	36653.85
102111	2024-03-01	50860.12
102112	2022-05-01	74967.27
102112	2022-06-01	67971.26
102112	2022-07-01	50911.08
102112	2022-08-01	51966.94
102112	2022-09-01	45638.85
102112	2022-10-01	61550.50
102112	2022-11-01	78764.79
102112	2022-12-01	97689.40
102112	2023-01-01	51046.23
102112	2023-02-01	42980.93
102112	2023-03-01	57012.13
102112	2023-04-01	60065.69
102112	2023-05-01	62042.92
102112	2023-06-01	46389.05
102112	2023-07-01	52676.26
102112	2023-08-01	58881.88
102112	2023-09-01	44830.88
102112	2023-10-01	55486.36
102112	2023-11-01	45572.86
102112	2023-12-01	83213.59
102112	2024-01-01	34942.55
102112	2024-02-01	48691.51
102112	2024-03-01	65374.47

#Trabalhando com ggplot #Gráfico de barras

# Primeiro, garantimos a tabela com a venda total por vendedor
venda_total_vendedor <- dados %>%
  group_by(vendedor) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(venda_total))

# Gráfico
ggplot(venda_total_vendedor, aes(x = reorder(vendedor, venda_total), 
                                 y = venda_total)) +
  geom_col(fill = "steelblue") +
  coord_flip() +   # deixa mais legível
  labs(title = "Venda Total por Vendedor",
       x = "Vendedor",
       y = "Venda Total") +
  theme_minimal()

#Gráfico de linhas

# Se ainda não existir a tabela mensal, calculamos aqui:
venda_mensal_geral <- dados %>%
  mutate(ano_mes = floor_date(dias, "month")) %>%
  group_by(ano_mes) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(ano_mes)

# Gráfico de linhas
ggplot(venda_mensal_geral, aes(x = ano_mes, y = venda_total)) +
  geom_line(linewidth = 1, color = "steelblue") +
  geom_point(color = "steelblue") +
  labs(title = "Tendencia Mensal das Vendas da Empresa",
       x = "Mes",
       y = "Venda Total") +
  scale_x_date(date_labels = "%b %Y", date_breaks = "3 month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

#Facet Grid

# Criar tabela mensal por vendedor
venda_mensal_vendedor <- dados %>%
  mutate(ano_mes = floor_date(dias, "month")) %>%
  group_by(vendedor, ano_mes) %>%
  summarise(
    venda_total = sum(venda_diaria, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(vendedor, ano_mes)

# Gráfico facetado
ggplot(venda_mensal_vendedor, aes(x = ano_mes, y = venda_total)) +
  geom_line(color = "steelblue") +
  geom_point(color = "steelblue") +
  facet_wrap(~ vendedor, scales = "free_y", ncol = 4)

  labs(
    title = "Tendencia Mensal das Vendas por Vendedor",
    x = "Mes",
    y = "Venda Total"
  ) +
  scale_x_date(date_labels = "%b %Y", date_breaks = "1 month") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## NULL

#Histograma

ggplot(dados, aes(x = venda_diaria)) +
  geom_histogram(color = "black", fill = "lightblue", bins = 30) +
  labs(
    title = "Distribuição dos Valores de Venda Diária",
    x = "Valor da Venda Diária",
    y = "Frequência"
  ) +
  theme_minimal()

#Boxplot por mês

# Criar coluna somente com o nome do mês
dados_mes <- dados %>%
  mutate(mes = factor(format(dias, "%b %Y"), 
                      levels = format(sort(unique(floor_date(dias, "month"))), "%b %Y")))

# Gráfico boxplot
ggplot(dados_mes, aes(x = mes, y = venda_diaria)) +
  geom_boxplot(fill = "lightblue", color = "black") +
  labs(
    title = "Distribuicao das Vendas Diarias por Mes",
    x = "Mes",
    y = "Venda Diaria"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

#Boxplot por Vendedor

# Garantir que vendedor é fator (caso ainda não tenha feito)
dados$vendedor <- as.factor(dados$vendedor)

ggplot(dados, aes(x = vendedor, y = venda_diaria)) +
  geom_boxplot(fill = "lightblue", color = "black") +
  labs(
    title = "Distribuicao das Vendas Diarias por Vendedor",
    x = "Vendedor",
    y = "Venda Diaria"
  ) +
  theme_minimal()

Extensão - Análise de Dados

Maria Eduarda Teles

2025-12-06

R Markdown