library(readr)
library(dplyr)
library(ggplot2)
library(readxl)
library(flextable)
moma <- read_delim("https://raw.githubusercontent.com/DATAUNIRIO/Base_de_dados/master/arte_MOMA.csv", delim = ";")
## New names:
## Rows: 2253 Columns: 24
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: ";" chr
## (6): title, artist, artist_bio, artist_gender, classification, department dbl
## (8): ...1, artist_birth_year, artist_death_year, num_artists, n_female_a... num
## (3): depth_cm, height_cm, width_cm lgl (7): circumference_cm, diameter_cm,
## length_cm, seat_height_cm, purchase,...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
head(moma) %>% data.frame() %>% flextable() %>% theme_zebra()
...1 | title | artist | artist_bio | artist_birth_year | artist_death_year | num_artists | n_female_artists | n_male_artists | artist_gender | year_acquired | year_created | circumference_cm | depth_cm | diameter_cm | height_cm | length_cm | width_cm | seat_height_cm | purchase | gift | exchange | classification | department |
1 | Rope and People, I | Joan Mir | (Spanish, 1893-1983) | 1,893 | 1,983 | 1 | 0 | 1 | Male | 1,936 | 1,935 | 1,048 | 746 | FALSE | TRUE | FALSE | Painting | Painting & Sculpture | |||||
2 | Fire in the Evening | Paul Klee | (German, born Switzerland. 1879-1940) | 1,879 | 1,940 | 1 | 0 | 1 | Male | 1,970 | 1,929 | 338 | 333 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture | |||||
3 | Portrait of an Equilibrist | Paul Klee | (German, born Switzerland. 1879-1940) | 1,879 | 1,940 | 1 | 0 | 1 | Male | 1,966 | 1,927 | 603 | 368 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture | |||||
4 | Guitar | Pablo Picasso | (Spanish, 1881-1973) | 1,881 | 1,973 | 1 | 0 | 1 | Male | 1,955 | 1,919 | 2,159 | 787 | FALSE | TRUE | FALSE | Painting | Painting & Sculpture | |||||
5 | Grandmother | Arthur Dove | (American, 1880-1946) | 1,880 | 1,946 | 1 | 0 | 1 | Male | 1,939 | 1,925 | 508 | 54 | FALSE | TRUE | TRUE | Painting | Painting & Sculpture | |||||
6 | "M'Amenez-y" | Francis Picabia | (French, 1879-1953) | 1,879 | 1,953 | 1 | 0 | 1 | Male | 1,968 | 1,919 | 1,292 | 899 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture |
head(moma) %>% data.frame() %>% flextable() %>% theme_zebra()
...1 | title | artist | artist_bio | artist_birth_year | artist_death_year | num_artists | n_female_artists | n_male_artists | artist_gender | year_acquired | year_created | circumference_cm | depth_cm | diameter_cm | height_cm | length_cm | width_cm | seat_height_cm | purchase | gift | exchange | classification | department |
1 | Rope and People, I | Joan Mir | (Spanish, 1893-1983) | 1,893 | 1,983 | 1 | 0 | 1 | Male | 1,936 | 1,935 | 1,048 | 746 | FALSE | TRUE | FALSE | Painting | Painting & Sculpture | |||||
2 | Fire in the Evening | Paul Klee | (German, born Switzerland. 1879-1940) | 1,879 | 1,940 | 1 | 0 | 1 | Male | 1,970 | 1,929 | 338 | 333 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture | |||||
3 | Portrait of an Equilibrist | Paul Klee | (German, born Switzerland. 1879-1940) | 1,879 | 1,940 | 1 | 0 | 1 | Male | 1,966 | 1,927 | 603 | 368 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture | |||||
4 | Guitar | Pablo Picasso | (Spanish, 1881-1973) | 1,881 | 1,973 | 1 | 0 | 1 | Male | 1,955 | 1,919 | 2,159 | 787 | FALSE | TRUE | FALSE | Painting | Painting & Sculpture | |||||
5 | Grandmother | Arthur Dove | (American, 1880-1946) | 1,880 | 1,946 | 1 | 0 | 1 | Male | 1,939 | 1,925 | 508 | 54 | FALSE | TRUE | TRUE | Painting | Painting & Sculpture | |||||
6 | "M'Amenez-y" | Francis Picabia | (French, 1879-1953) | 1,879 | 1,953 | 1 | 0 | 1 | Male | 1,968 | 1,919 | 1,292 | 899 | FALSE | FALSE | FALSE | Painting | Painting & Sculpture |
table(moma$n_female_artists)
##
## 0 1 2
## 1998 254 1
table(moma$year_acquired) %>% data.frame() %>% flextable() %>% theme_zebra()
Var1 | Freq |
1930 | 2 |
1931 | 2 |
1932 | 1 |
1934 | 14 |
1935 | 22 |
1936 | 18 |
1937 | 19 |
1938 | 8 |
1939 | 31 |
1940 | 18 |
1941 | 39 |
1942 | 71 |
1943 | 22 |
1944 | 17 |
1945 | 15 |
1946 | 5 |
1947 | 14 |
1948 | 11 |
1949 | 29 |
1950 | 23 |
1951 | 27 |
1952 | 20 |
1953 | 22 |
1954 | 30 |
1955 | 36 |
1956 | 42 |
1957 | 18 |
1958 | 39 |
1959 | 37 |
1960 | 34 |
1961 | 45 |
1962 | 21 |
1963 | 33 |
1964 | 36 |
1965 | 38 |
1966 | 23 |
1967 | 65 |
1968 | 20 |
1969 | 45 |
1970 | 37 |
1971 | 28 |
1972 | 7 |
1973 | 16 |
1974 | 11 |
1975 | 24 |
1976 | 19 |
1977 | 17 |
1978 | 21 |
1979 | 71 |
1980 | 20 |
1981 | 23 |
1982 | 20 |
1983 | 30 |
1984 | 15 |
1985 | 86 |
1986 | 15 |
1987 | 11 |
1988 | 8 |
1989 | 7 |
1990 | 26 |
1991 | 67 |
1992 | 30 |
1993 | 7 |
1994 | 41 |
1995 | 13 |
1996 | 33 |
1997 | 21 |
1998 | 26 |
1999 | 24 |
2000 | 29 |
2001 | 11 |
2002 | 25 |
2003 | 4 |
2004 | 27 |
2005 | 67 |
2006 | 21 |
2007 | 13 |
2008 | 55 |
2009 | 21 |
2010 | 18 |
2011 | 27 |
2012 | 27 |
2013 | 22 |
2014 | 20 |
2015 | 21 |
2016 | 33 |
2017 | 17 |
table(moma$year_created) %>% data.frame() %>% flextable() %>% theme_zebra()
Var1 | Freq |
1872 | 1 |
1875 | 6 |
1879 | 2 |
1882 | 1 |
1883 | 2 |
1885 | 2 |
1886 | 1 |
1887 | 2 |
1888 | 4 |
1889 | 5 |
1890 | 2 |
1891 | 1 |
1892 | 2 |
1893 | 3 |
1894 | 2 |
1895 | 3 |
1896 | 1 |
1897 | 2 |
1898 | 1 |
1899 | 2 |
1900 | 6 |
1901 | 1 |
1902 | 2 |
1903 | 1 |
1904 | 1 |
1905 | 11 |
1906 | 6 |
1907 | 15 |
1908 | 14 |
1909 | 12 |
1910 | 18 |
1911 | 19 |
1912 | 33 |
1913 | 21 |
1914 | 37 |
1915 | 18 |
1916 | 11 |
1917 | 14 |
1918 | 13 |
1919 | 9 |
1920 | 11 |
1921 | 13 |
1922 | 13 |
1923 | 11 |
1924 | 14 |
1925 | 25 |
1926 | 23 |
1927 | 27 |
1928 | 32 |
1929 | 22 |
1930 | 21 |
1931 | 13 |
1932 | 21 |
1933 | 13 |
1934 | 13 |
1935 | 16 |
1936 | 15 |
1937 | 18 |
1938 | 24 |
1939 | 26 |
1940 | 56 |
1941 | 24 |
1942 | 31 |
1943 | 18 |
1944 | 28 |
1945 | 13 |
1946 | 22 |
1947 | 18 |
1948 | 25 |
1949 | 32 |
1950 | 37 |
1951 | 16 |
1952 | 27 |
1953 | 27 |
1954 | 23 |
1955 | 28 |
1956 | 34 |
1957 | 32 |
1958 | 26 |
1959 | 42 |
1960 | 39 |
1961 | 50 |
1962 | 49 |
1963 | 44 |
1964 | 56 |
1965 | 36 |
1966 | 36 |
1967 | 26 |
1968 | 40 |
1969 | 29 |
1970 | 17 |
1971 | 13 |
1972 | 19 |
1973 | 16 |
1974 | 26 |
1975 | 19 |
1976 | 26 |
1977 | 57 |
1978 | 13 |
1979 | 23 |
1980 | 10 |
1981 | 27 |
1982 | 13 |
1983 | 13 |
1984 | 31 |
1985 | 15 |
1986 | 12 |
1987 | 24 |
1988 | 11 |
1989 | 12 |
1990 | 5 |
1991 | 9 |
1992 | 9 |
1993 | 6 |
1994 | 9 |
1995 | 5 |
1996 | 6 |
1997 | 10 |
1998 | 4 |
1999 | 3 |
2000 | 10 |
2001 | 8 |
2002 | 7 |
2003 | 5 |
2004 | 6 |
2005 | 19 |
2006 | 6 |
2007 | 4 |
2008 | 22 |
2009 | 6 |
2010 | 4 |
2011 | 11 |
2012 | 5 |
2013 | 13 |
2014 | 7 |
2015 | 6 |
2016 | 2 |
2017 | 2 |
table(moma$year_created,moma$artist_gender) %>% data.frame() %>% flextable() %>% theme_zebra()
Var1 | Var2 | Freq |
1872 | Female | 0 |
1875 | Female | 0 |
1879 | Female | 0 |
1882 | Female | 0 |
1883 | Female | 0 |
1885 | Female | 0 |
1886 | Female | 0 |
1887 | Female | 0 |
1888 | Female | 0 |
1889 | Female | 0 |
1890 | Female | 0 |
1891 | Female | 0 |
1892 | Female | 0 |
1893 | Female | 0 |
1894 | Female | 0 |
1895 | Female | 0 |
1896 | Female | 0 |
1897 | Female | 0 |
1898 | Female | 0 |
1899 | Female | 0 |
1900 | Female | 0 |
1901 | Female | 0 |
1902 | Female | 0 |
1903 | Female | 0 |
1904 | Female | 0 |
1905 | Female | 0 |
1906 | Female | 0 |
1907 | Female | 1 |
1908 | Female | 0 |
1909 | Female | 1 |
1910 | Female | 0 |
1911 | Female | 1 |
1912 | Female | 2 |
1913 | Female | 2 |
1914 | Female | 1 |
1915 | Female | 2 |
1916 | Female | 0 |
1917 | Female | 1 |
1918 | Female | 1 |
1919 | Female | 1 |
1920 | Female | 0 |
1921 | Female | 1 |
1922 | Female | 2 |
1923 | Female | 0 |
1924 | Female | 0 |
1925 | Female | 2 |
1926 | Female | 0 |
1927 | Female | 1 |
1928 | Female | 1 |
1929 | Female | 1 |
1930 | Female | 1 |
1931 | Female | 0 |
1932 | Female | 0 |
1933 | Female | 2 |
1934 | Female | 1 |
1935 | Female | 2 |
1936 | Female | 2 |
1937 | Female | 1 |
1938 | Female | 2 |
1939 | Female | 0 |
1940 | Female | 4 |
1941 | Female | 0 |
1942 | Female | 4 |
1943 | Female | 3 |
1944 | Female | 1 |
1945 | Female | 3 |
1946 | Female | 2 |
1947 | Female | 0 |
1948 | Female | 4 |
1949 | Female | 2 |
1950 | Female | 1 |
1951 | Female | 3 |
1952 | Female | 2 |
1953 | Female | 3 |
1954 | Female | 3 |
1955 | Female | 1 |
1956 | Female | 2 |
1957 | Female | 6 |
1958 | Female | 2 |
1959 | Female | 4 |
1960 | Female | 6 |
1961 | Female | 3 |
1962 | Female | 1 |
1963 | Female | 4 |
1964 | Female | 9 |
1965 | Female | 4 |
1966 | Female | 6 |
1967 | Female | 2 |
1968 | Female | 1 |
1969 | Female | 2 |
1970 | Female | 2 |
1971 | Female | 2 |
1972 | Female | 1 |
1973 | Female | 3 |
1974 | Female | 7 |
1975 | Female | 3 |
1976 | Female | 8 |
1977 | Female | 3 |
1978 | Female | 4 |
1979 | Female | 2 |
1980 | Female | 5 |
1981 | Female | 6 |
1982 | Female | 3 |
1983 | Female | 3 |
1984 | Female | 1 |
1985 | Female | 2 |
1986 | Female | 0 |
1987 | Female | 15 |
1988 | Female | 1 |
1989 | Female | 3 |
1990 | Female | 1 |
1991 | Female | 3 |
1992 | Female | 2 |
1993 | Female | 1 |
1994 | Female | 2 |
1995 | Female | 2 |
1996 | Female | 2 |
1997 | Female | 3 |
1998 | Female | 1 |
1999 | Female | 0 |
2000 | Female | 2 |
2001 | Female | 3 |
2002 | Female | 2 |
2003 | Female | 1 |
2004 | Female | 1 |
2005 | Female | 4 |
2006 | Female | 3 |
2007 | Female | 0 |
2008 | Female | 7 |
2009 | Female | 3 |
2010 | Female | 2 |
2011 | Female | 2 |
2012 | Female | 2 |
2013 | Female | 3 |
2014 | Female | 3 |
2015 | Female | 2 |
2016 | Female | 0 |
2017 | Female | 1 |
1872 | Male | 1 |
1875 | Male | 6 |
1879 | Male | 2 |
1882 | Male | 1 |
1883 | Male | 2 |
1885 | Male | 2 |
1886 | Male | 1 |
1887 | Male | 2 |
1888 | Male | 4 |
1889 | Male | 5 |
1890 | Male | 2 |
1891 | Male | 1 |
1892 | Male | 2 |
1893 | Male | 3 |
1894 | Male | 2 |
1895 | Male | 3 |
1896 | Male | 1 |
1897 | Male | 2 |
1898 | Male | 1 |
1899 | Male | 2 |
1900 | Male | 6 |
1901 | Male | 1 |
1902 | Male | 2 |
1903 | Male | 1 |
1904 | Male | 1 |
1905 | Male | 11 |
1906 | Male | 6 |
1907 | Male | 14 |
1908 | Male | 14 |
1909 | Male | 11 |
1910 | Male | 18 |
1911 | Male | 18 |
1912 | Male | 31 |
1913 | Male | 19 |
1914 | Male | 36 |
1915 | Male | 16 |
1916 | Male | 11 |
1917 | Male | 13 |
1918 | Male | 12 |
1919 | Male | 8 |
1920 | Male | 11 |
1921 | Male | 12 |
1922 | Male | 11 |
1923 | Male | 11 |
1924 | Male | 14 |
1925 | Male | 23 |
1926 | Male | 23 |
1927 | Male | 25 |
1928 | Male | 31 |
1929 | Male | 21 |
1930 | Male | 20 |
1931 | Male | 13 |
1932 | Male | 21 |
1933 | Male | 11 |
1934 | Male | 12 |
1935 | Male | 14 |
1936 | Male | 13 |
1937 | Male | 17 |
1938 | Male | 22 |
1939 | Male | 26 |
1940 | Male | 52 |
1941 | Male | 24 |
1942 | Male | 27 |
1943 | Male | 15 |
1944 | Male | 27 |
1945 | Male | 10 |
1946 | Male | 20 |
1947 | Male | 18 |
1948 | Male | 21 |
1949 | Male | 30 |
1950 | Male | 36 |
1951 | Male | 13 |
1952 | Male | 25 |
1953 | Male | 24 |
1954 | Male | 20 |
1955 | Male | 27 |
1956 | Male | 32 |
1957 | Male | 26 |
1958 | Male | 24 |
1959 | Male | 38 |
1960 | Male | 33 |
1961 | Male | 47 |
1962 | Male | 48 |
1963 | Male | 40 |
1964 | Male | 47 |
1965 | Male | 32 |
1966 | Male | 30 |
1967 | Male | 24 |
1968 | Male | 39 |
1969 | Male | 27 |
1970 | Male | 15 |
1971 | Male | 11 |
1972 | Male | 18 |
1973 | Male | 13 |
1974 | Male | 19 |
1975 | Male | 16 |
1976 | Male | 18 |
1977 | Male | 54 |
1978 | Male | 9 |
1979 | Male | 21 |
1980 | Male | 5 |
1981 | Male | 19 |
1982 | Male | 10 |
1983 | Male | 10 |
1984 | Male | 29 |
1985 | Male | 13 |
1986 | Male | 11 |
1987 | Male | 9 |
1988 | Male | 10 |
1989 | Male | 8 |
1990 | Male | 4 |
1991 | Male | 6 |
1992 | Male | 7 |
1993 | Male | 5 |
1994 | Male | 7 |
1995 | Male | 3 |
1996 | Male | 4 |
1997 | Male | 7 |
1998 | Male | 3 |
1999 | Male | 3 |
2000 | Male | 8 |
2001 | Male | 5 |
2002 | Male | 5 |
2003 | Male | 4 |
2004 | Male | 5 |
2005 | Male | 15 |
2006 | Male | 3 |
2007 | Male | 4 |
2008 | Male | 14 |
2009 | Male | 2 |
2010 | Male | 1 |
2011 | Male | 9 |
2012 | Male | 3 |
2013 | Male | 10 |
2014 | Male | 3 |
2015 | Male | 4 |
2016 | Male | 2 |
2017 | Male | 1 |
moma_2 = moma %>% select(artist, artist_death_year, artist_birth_year, artist_gender) %>%
mutate(idade = artist_death_year - artist_birth_year)
mean(moma_2$idade, na.rm = TRUE)
## [1] 74.66379
boxplot(moma_2$idade ~ moma_2$artist_gender,
col=c("purple","blue"),
main="idade homens e mulheres")
moma_dim <- moma %>%
filter(height_cm < 600, width_cm < 760) %>%
mutate(hw_ratio = height_cm / width_cm,
hw_cat = case_when(
hw_ratio > 1 ~ "mais alto que largo",
hw_ratio < 1 ~ "mais largo que alto",
hw_ratio == 1 ~ "quadrado perfeito"
))
library(ggthemes)
ggplot(moma_dim, aes(x = width_cm, y = height_cm, colour = hw_cat)) +
geom_point(alpha = .5) +
ggtitle("Pinturas do MoMA, altas e largas") +
scale_colour_manual(name = "",
values = c("gray50", "#FF9900", "#B14CF0")) +
theme_fivethirtyeight() +
theme(axis.title = element_text()) +
labs(x = "Largura", y = "Altura")