Spin it

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

2019-04-09/tidy_tuesday_4_9_19.R

@@ -1,7 +1,10 @@
-# Tidy Tuesday 4/9/2019
-# Allison Horst
-# Tennis Grand Slam Champions
+#' ---
+#' title: Tidy Tuesday 4/9/2019<br> Tennis Grand Slam Champions
+#' author: Allison Horst
+#' output: github_document
+#' ---
 
+#+ message = FALSE, warning = FALSE
 library(tidyverse)
 library(RColorBrewer)
 library(wesanderson)
@@ -11,14 +14,14 @@ library(LaCroixColoR)
 
 # font_import()
 
-# Get data:
-# player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")
+#' Get data:
 
-# grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")
+#+ message = FALSE
+player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")
+grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")
+grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")
 
-# grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")
-
-# This seems like excessive grouping and ungrouping, mer?
+#' This seems like excessive grouping and ungrouping, mer?
 
 past_qual <- c("Won","Finalist","Semi-finalist","Quarterfinalist", "4th Round", "3rd Round", "2nd Round", "1st Round")
 
@@ -30,13 +33,13 @@ sum_table <- grand_slam_timeline %>%
   group_by(gender) %>%
   group_split() # This is pretty cool! First time using group_split()
 
-# Access the separate tibbles (probably bad practice, but wanted to try using group_splitanyway...)
+#' Access the separate tibbles (probably bad practice, but wanted to try using group_splitanyway...)
 
 male_players <- sum_table[[2]]
 female_players <- sum_table[[1]]
 
 
-# Top 20 males by # appearances after qualification (doesn't include absence/retire data)
+#' Top 20 males by # appearances after qualification (doesn't include absence/retire data)
 top_male_appear <- male_players %>%
   group_by(player) %>%
   summarize(
@@ -45,7 +48,7 @@ top_male_appear <- male_players %>%
   arrange(-appearances) %>%
   head(20)
 
-# Top females by # appearances after qualification (doesn't include absence/retire data)
+#' Top females by # appearances after qualification (doesn't include absence/retire data)
 top_female_appear <- female_players %>%
   group_by(player) %>%
   summarize(
@@ -57,7 +60,7 @@ top_female_appear <- female_players %>%
 vec_m <- unique(top_male_appear$player)
 vec_f <- unique(top_female_appear$player)
 
-# Joins to keep top 10 by total appearances beyond qualifying round
+#' Joins to keep top 10 by total appearances beyond qualifying round
 
 m_appear <- top_male_appear %>%
   inner_join(male_players) %>%
@@ -69,36 +72,40 @@ f_appear <- top_female_appear %>%
   mutate(player = as.factor(player)) %>%
   mutate(outcome = as.factor(outcome))
 
-# Relevel by top players
+#' Relevel by top players
 f_appear$player <- fct_relevel(f_appear$player, vec_f)
 m_appear$player <- fct_relevel(m_appear$player, vec_m)
 
-# Relevel outcome
+#' Relevel outcome
 f_appear$outcome <- fct_relevel(f_appear$outcome,"Won", "Finalist","Semi-finalist","Quarterfinalist","4th Round", "3rd Round","2nd Round","1st Round")
 
 m_appear$outcome <- fct_relevel(m_appear$outcome,"Won", "Finalist","Semi-finalist","Quarterfinalist","4th Round", "3rd Round","2nd Round","1st Round")
 
-# Then make a graph that shows the level reached after qualifiers for each:
+#' Then make a graph that shows the level reached after qualifiers for each:
 
+#' Create palette:
 # pal <- wes_palette(8, name = "FantasticFox1", type = "continuous")
-# Create palette:
 pal <- lacroix_palette("PassionFruit", n = 8, type = "continuous")
 
-# Graph of female top appearances:
+#' Graph of female top appearances:
+#+ fig.retina = 3, fig.width = 8
 ggplot(f_appear, aes(x = reorder(player, desc(player)), y = n)) +
   geom_col(aes(fill = outcome)) +
   theme_pomological(base_family = "Courier New", base_size = 12) +
   scale_x_discrete(expand = c(0,0)) +
   scale_y_continuous(expand = c(0,0), limits = c(0,90)) +
   scale_fill_manual(values = pal, name = "Outcome:") +
   labs(x = "", y = "Number of appearances\n(beyond qualifiers)", title = "Top Grand Slam appearances & outcomes") +
-  theme(legend.position = "bottom", axis.text.x=element_text(size=11, face = "bold", hjust = 1), axis.text.y = element_text(size = 10, color = "slateblue4")) +
+  theme(legend.position = "bottom", 
+        legend.justification='left',
+        axis.text.x=element_text(size=11, face = "bold", hjust = 1), axis.text.y = element_text(size = 10, color = "slateblue4")) +
   coord_flip()
 
-# Save it:
+#' Save it:
 ggsave("my_tennis_plot.png", width = 8, height = 7)
 
-# Male version:
+#' Male version:
+#+ fig.retina = 3, fig.width = 8
 ggplot(m_appear, aes(x = reorder(player, desc(player)), y = n)) +
   geom_col(aes(fill = outcome)) +
   theme_pomological(base_family = "Courier New",
@@ -109,7 +116,8 @@ ggplot(m_appear, aes(x = reorder(player, desc(player)), y = n)) +
   scale_fill_manual(values = pal,
                     name = "Outcome:") +
   labs(x = "", y = "Number of appearances", title = "Grand Slam Appearances Colorblast") +
-  theme(legend.position = "bottom",
+  theme(legend.position="bottom",
+        legend.justification = "left",
         axis.text.x=element_text(size=10, face = "bold", angle = 50, hjust = 1)) +
   coord_flip()
 

2019-04-09/tidy_tuesday_4_9_19.md

@@ -0,0 +1,165 @@
+Tidy Tuesday 4/9/2019<br> Tennis Grand Slam Champions
+================
+Allison Horst
+2019-04-10
+
+``` r
+library(tidyverse)
+library(RColorBrewer)
+library(wesanderson)
+library(ggpomological)
+library(extrafont)
+library(LaCroixColoR)
+
+# font_import()
+```
+
+Get
+data:
+
+``` r
+player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")
+grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")
+grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")
+```
+
+This seems like excessive grouping and ungrouping,
+mer?
+
+``` r
+past_qual <- c("Won","Finalist","Semi-finalist","Quarterfinalist", "4th Round", "3rd Round", "2nd Round", "1st Round")
+
+sum_table <- grand_slam_timeline %>%
+  filter(!is.na(outcome), outcome %in% past_qual) %>%
+  group_by(player, gender, outcome) %>%
+    tally() %>%
+  ungroup() %>%
+  group_by(gender) %>%
+  group_split() # This is pretty cool! First time using group_split()
+```
+
+Access the separate tibbles (probably bad practice, but wanted to try
+using group\_splitanyway…)
+
+``` r
+male_players <- sum_table[[2]]
+female_players <- sum_table[[1]]
+```
+
+Top 20 males by \# appearances after qualification (doesn’t include
+absence/retire data)
+
+``` r
+top_male_appear <- male_players %>%
+  group_by(player) %>%
+  summarize(
+    appearances = sum(n)
+  ) %>%
+  arrange(-appearances) %>%
+  head(20)
+```
+
+Top females by \# appearances after qualification (doesn’t include
+absence/retire data)
+
+``` r
+top_female_appear <- female_players %>%
+  group_by(player) %>%
+  summarize(
+    appearances = sum(n)
+  ) %>%
+  arrange(-appearances) %>%
+  head(20)
+
+vec_m <- unique(top_male_appear$player)
+vec_f <- unique(top_female_appear$player)
+```
+
+Joins to keep top 10 by total appearances beyond qualifying round
+
+``` r
+m_appear <- top_male_appear %>%
+  inner_join(male_players) %>%
+  mutate(player = as.factor(player)) %>%
+  mutate(outcome = as.factor(outcome))
+```
+
+    ## Joining, by = "player"
+
+``` r
+f_appear <- top_female_appear %>%
+  inner_join(female_players) %>%
+  mutate(player = as.factor(player)) %>%
+  mutate(outcome = as.factor(outcome))
+```
+
+    ## Joining, by = "player"
+
+Relevel by top players
+
+``` r
+f_appear$player <- fct_relevel(f_appear$player, vec_f)
+m_appear$player <- fct_relevel(m_appear$player, vec_m)
+```
+
+Relevel
+outcome
+
+``` r
+f_appear$outcome <- fct_relevel(f_appear$outcome,"Won", "Finalist","Semi-finalist","Quarterfinalist","4th Round", "3rd Round","2nd Round","1st Round")
+
+m_appear$outcome <- fct_relevel(m_appear$outcome,"Won", "Finalist","Semi-finalist","Quarterfinalist","4th Round", "3rd Round","2nd Round","1st Round")
+```
+
+Then make a graph that shows the level reached after qualifiers for
+each: Create palette:
+
+``` r
+# pal <- wes_palette(8, name = "FantasticFox1", type = "continuous")
+pal <- lacroix_palette("PassionFruit", n = 8, type = "continuous")
+```
+
+Graph of female top appearances:
+
+``` r
+ggplot(f_appear, aes(x = reorder(player, desc(player)), y = n)) +
+  geom_col(aes(fill = outcome)) +
+  theme_pomological(base_family = "Courier New", base_size = 12) +
+  scale_x_discrete(expand = c(0,0)) +
+  scale_y_continuous(expand = c(0,0), limits = c(0,90)) +
+  scale_fill_manual(values = pal, name = "Outcome:") +
+  labs(x = "", y = "Number of appearances\n(beyond qualifiers)", title = "Top Grand Slam appearances & outcomes") +
+  theme(legend.position = "bottom", 
+        legend.justification='left',
+        axis.text.x=element_text(size=11, face = "bold", hjust = 1), axis.text.y = element_text(size = 10, color = "slateblue4")) +
+  coord_flip()
+```
+
+<img src="tidy_tuesday_4_9_19_files/figure-gfm/unnamed-chunk-11-1.png" width="768" />
+
+Save it:
+
+``` r
+ggsave("my_tennis_plot.png", width = 8, height = 7)
+```
+
+Male version:
+
+``` r
+ggplot(m_appear, aes(x = reorder(player, desc(player)), y = n)) +
+  geom_col(aes(fill = outcome)) +
+  theme_pomological(base_family = "Courier New",
+                    base_size = 12) +
+  scale_x_discrete(expand = c(0,0)) +
+  scale_y_continuous(expand = c(0,0),
+                     limits = c(0,80)) +
+  scale_fill_manual(values = pal,
+                    name = "Outcome:") +
+  labs(x = "", y = "Number of appearances", title = "Grand Slam Appearances Colorblast") +
+  theme(legend.position="bottom",
+        legend.justification = "left",
+        axis.text.x=element_text(size=10, face = "bold", angle = 50, hjust = 1)) +
+  coord_flip()
+```
+
+<img src="tidy_tuesday_4_9_19_files/figure-gfm/unnamed-chunk-13-1.png" width="768" />

allison-tidy-tuesdays.Rproj

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