library(readr)
arte_MOMA <- read_delim("C:/Users/favor/Base_de_dados-master/arte_MOMA.csv",
";", escape_double = FALSE, locale = locale(encoding = "ISO-8859-1"),
trim_ws = TRUE)library(dplyr)Existem 2253 pinturas e 24 variáveis.
summary(arte_MOMA)## X1 title artist artist_bio
## Min. : 1 Length:2253 Length:2253 Length:2253
## 1st Qu.: 564 Class :character Class :character Class :character
## Median :1127 Mode :character Mode :character Mode :character
## Mean :1127
## 3rd Qu.:1690
## Max. :2253
##
## artist_birth_year artist_death_year num_artists n_female_artists
## Min. :1839 Min. :1890 Min. : 1.000 Min. :0.0000
## 1st Qu.:1890 1st Qu.:1956 1st Qu.: 1.000 1st Qu.:0.0000
## Median :1913 Median :1976 Median : 1.000 Median :0.0000
## Mean :1912 Mean :1975 Mean : 1.009 Mean :0.1136
## 3rd Qu.:1933 3rd Qu.:1996 3rd Qu.: 1.000 3rd Qu.:0.0000
## Max. :1987 Max. :2018 Max. :10.000 Max. :2.0000
## NA's :6 NA's :629 NA's :1
## n_male_artists artist_gender year_acquired year_created
## Min. :0.0000 Length:2253 Min. :1930 Min. :1872
## 1st Qu.:1.0000 Class :character 1st Qu.:1957 1st Qu.:1933
## Median :1.0000 Mode :character Median :1975 Median :1956
## Mean :0.8953 Mean :1976 Mean :1954
## 3rd Qu.:1.0000 3rd Qu.:1996 3rd Qu.:1972
## Max. :9.0000 Max. :2017 Max. :2017
## NA's :9 NA's :5
## circumference_cm depth_cm diameter_cm height_cm
## Mode:logical Min. :0.000e+00 Mode:logical Min. :7.000e+00
## NA's:2253 1st Qu.:0.000e+00 NA's:2253 1st Qu.:4.890e+02
## Median :0.000e+00 Median :1.067e+03
## Mean :3.938e+09 Mean :1.208e+11
## 3rd Qu.:7.500e+01 3rd Qu.:2.185e+03
## Max. :6.096e+11 Max. :3.626e+12
## NA's :1955
## length_cm width_cm seat_height_cm purchase
## Mode:logical Min. :1.000e+01 Mode:logical Mode :logical
## NA's:2253 1st Qu.:4.580e+02 NA's:2253 FALSE:2055
## Median :9.720e+02 TRUE :198
## Mean :1.317e+11
## 3rd Qu.:2.286e+03
## Max. :9.147e+12
##
## gift exchange classification department
## Mode :logical Mode :logical Length:2253 Length:2253
## FALSE:1086 FALSE:2108 Class :character Class :character
## TRUE :1167 TRUE :145 Mode :character Mode :character
##
##
##
## Existem duas pinturas datadas no ano de 1930, que no caso seriam as duas mais antigas. São elas, House by the Railroad de Edward Hopper e Seated Nude de Bernard Karfiol
arte_MOMA %>% filter(year_acquired=="1930") %>%
group_by(title, artist) %>%
summarise(min(year_acquired))## # A tibble: 2 x 3
## # Groups: title [2]
## title artist `min(year_acquired)`
## <chr> <chr> <dbl>
## 1 House by the Railroad Edward Hopper 1930
## 2 Seated Nude Bernard Karfiol 1930A pintura mais antiga é a Landscape at Daybreak de Odilon Redon de 1872.
arte_MOMA %>% filter(year_created=="1872") %>%
group_by(title,artist) %>%
summarise(min(year_created))## # A tibble: 1 x 3
## # Groups: title [1]
## title artist `min(year_created)`
## <chr> <chr> <dbl>
## 1 Landscape at Daybreak Odilon Redon 1872Existem 989 artistas distintos.
arte_MOMA %>% group_by(artist) %>%
summarise(all(artist, na.rm = FALSE))## # A tibble: 989 x 2
## artist `all(artist, na.rm = FALSE)`
## <chr> <lgl>
## 1 "\"Edward C. (\"\"Pa\"\") Hunt\"" NA
## 2 "A. E. Gallatin" NA
## 3 "A.R. Penck (Ralf Winkler)" NA
## 4 "Abraham Palatnik" NA
## 5 "Abraham Rattner" NA
## 6 "Abraham Walkowitz" NA
## 7 "Ad Dekkers" NA
## 8 "Ad Reinhardt" NA
## 9 "Adam Pendleton" NA
## 10 "Adolf Richard Fleischmann" NA
## # ... with 979 more rowsPablo Picasso
arte_MOMA %>% count(artist, sort = TRUE)## # A tibble: 989 x 2
## artist n
## <chr> <int>
## 1 Pablo Picasso 55
## 2 Henri Matisse 32
## 3 On Kawara 32
## 4 Jacob Lawrence 30
## 5 Batiste Madalena 25
## 6 Jean Dubuffet 25
## 7 Odilon Redon 25
## 8 Ben Vautier 24
## 9 Frank Stella 23
## 10 Philip Guston 23
## # ... with 979 more rows55
arte_MOMA %>% count(artist, sort = TRUE)## # A tibble: 989 x 2
## artist n
## <chr> <int>
## 1 Pablo Picasso 55
## 2 Henri Matisse 32
## 3 On Kawara 32
## 4 Jacob Lawrence 30
## 5 Batiste Madalena 25
## 6 Jean Dubuffet 25
## 7 Odilon Redon 25
## 8 Ben Vautier 24
## 9 Frank Stella 23
## 10 Philip Guston 23
## # ... with 979 more rows1991 pinturas de homens e 252 pinturas de mulheres.
table(arte_MOMA$artist_gender)##
## Female Male
## 252 1991São 143 artistas femininos e 837 artistas masculinos.
arte_MOMA %>% filter(artist_gender=="Female") %>%
group_by(artist) %>%
summarise(all(artist_gender, na.rm = FALSE))## # A tibble: 143 x 2
## artist `all(artist_gender, na.rm = FALSE)`
## <chr> <lgl>
## 1 Agnes Martin NA
## 2 Alexandra Exter NA
## 3 Alice Baber NA
## 4 Alice Neel NA
## 5 Alma Woodsey Thomas NA
## 6 Amelia Peláez Del Casal NA
## 7 Amy Sillman NA
## 8 Annette Lemieux NA
## 9 Antonieta Sosa NA
## 10 Atsuko Tanaka NA
## # ... with 133 more rowsarte_MOMA %>% filter(artist_gender=="Male") %>%
group_by(artist) %>%
summarise(all(artist_gender, na.rm = FALSE))## # A tibble: 837 x 2
## artist `all(artist_gender, na.rm = FALSE)`
## <chr> <lgl>
## 1 "\"Edward C. (\"\"Pa\"\") Hunt\"" NA
## 2 "A. E. Gallatin" NA
## 3 "A.R. Penck (Ralf Winkler)" NA
## 4 "Abraham Palatnik" NA
## 5 "Abraham Rattner" NA
## 6 "Abraham Walkowitz" NA
## 7 "Ad Dekkers" NA
## 8 "Ad Reinhardt" NA
## 9 "Adam Pendleton" NA
## 10 "Adolf Richard Fleischmann" NA
## # ... with 827 more rowsO ano com maior número de pinturas adquiridas foi 1985, com 86 pinturas.
arte_MOMA %>% count(year_acquired, sort = TRUE)## # A tibble: 88 x 2
## year_acquired n
## <dbl> <int>
## 1 1985 86
## 2 1942 71
## 3 1979 71
## 4 1991 67
## 5 2005 67
## 6 1967 65
## 7 2008 55
## 8 1961 45
## 9 1969 45
## 10 1956 42
## # ... with 78 more rowsO ano com maior criação de pinturas foi 1977, com 57 pinturas.
arte_MOMA %>% count(year_created, sort = TRUE)## # A tibble: 139 x 2
## year_created n
## <dbl> <int>
## 1 1977 57
## 2 1940 56
## 3 1964 56
## 4 1961 50
## 5 1962 49
## 6 1963 44
## 7 1959 42
## 8 1968 40
## 9 1960 39
## 10 1914 37
## # ... with 129 more rowsA primeira pintura feminina adquirida foi Landscape, 47 da artista Natalia Goncharova no ano de 1937.
arte_MOMA %>% filter(artist_gender=="Female") %>%
group_by(year_acquired, title, artist) %>%
summarise(first(artist_gender))## # A tibble: 249 x 4
## # Groups: year_acquired, title [248]
## year_acquired title artist `first(artist_gende~
## <dbl> <chr> <chr> <chr>
## 1 1937 Landscape, 47 Natalia Goncharo~ Female
## 2 1938 Shack Loren MacIver Female
## 3 1940 Hopscotch Loren MacIver Female
## 4 1941 Figure Varvara Stepanova Female
## 5 1941 Shadows with Painting Irene Rice Perei~ Female
## 6 1942 Desolation Raquel Forner Female
## 7 1942 Musical Squash Maud Morgan Female
## 8 1942 Still Life in Red Amelia Peláez De~ Female
## 9 1942 White Lines Irene Rice Perei~ Female
## 10 1943 Self-Portrait with Crop~ Frida Kahlo Female
## # ... with 239 more rowsDorothea Tanning com 102 anos
arte_MOMAidade <- arte_MOMA$artist_death_year - arte_MOMA$artist_birth_year
summary(arte_MOMAidade)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 27.00 67.00 77.00 74.66 85.00 102.00 629arte_MOMA %>%
filter(arte_MOMAidade=="102") %>%
pull(artist) %>%
first()## [1] "Dorothea Tanning"A idade média de um artista é de 74.66 anos
library(knitr)
arte_MOMA %>%
mutate(idade = artist_death_year - artist_birth_year) %>%
group_by(artist_gender) %>%
summarise(media = mean(idade, na.rm = T)) %>%
mutate(media = format(media, digits = 3)) %>%
kable()| artist_gender | media |
|---|---|
| Female | 74.0 |
| Male | 74.7 |
| NA | 72.0 |
A partir do que foi usado nas questões propostas, visitar os quadros do Pablo Picasso me parece muito interessante, visto que ele possui o maior acervo no MOMA, assim como visitar os quadros de Dorothea Tanning que é a pessoa mais velha dentre todas as mais de 900 artistas datados. Seguindo essa ideia, entender o quadro de Odilon Redon, o mais antigo da coleção, também seria interessante.